{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c45d7f6f",
   "metadata": {},
   "source": [
    "# CaSpER 실행용 튜토리얼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7b89272",
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressWarnings(suppressMessages(library(Seurat)))\n",
    "suppressWarnings(suppressMessages(library(CaSpER)))\n",
    "suppressWarnings(suppressMessages(data(\"hg19_cytoband\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cacc63e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#wget https://ftp.ncbi.nlm.nih.gov/geo/series/GSE110nnn/GSE110499/suppl/GSE110499_GEO_processed_MM_10X_raw_UMI_count_martix.txt.gz\n",
    "#gunzip GSE110499_GEO_processed_MM_10X_raw_UMI_count_martix.txt.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca7206d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# expression data is downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110499\n",
    "counts  <- read.delim(\"GSE110499_GEO_processed_MM_10X_raw_UMI_count_martix.txt\", stringsAsFactor=F, header=T)\n",
    "rownames(counts) <- counts[, 1] \n",
    "counts <- counts[, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e7d6f398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>AAACCTGTCAAACGGG</th><th scope=col>AAACCTGTCACTTATC</th><th scope=col>AAACCTGTCGAACGGA</th><th scope=col>AAAGATGAGAATTGTG</th><th scope=col>AAAGATGAGAGTACAT</th><th scope=col>AAAGATGGTAAGAGAG</th><th scope=col>AAAGATGGTGTGCGTC</th><th scope=col>AAAGATGTCGTGGGAA</th><th scope=col>AAAGCAAAGACGCTTT</th><th scope=col>AAAGCAATCCAGTATG</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>RP11-34P13.3</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>FAM138A</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>OR4F5</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>RP11-34P13.7</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>RP11-34P13.8</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>RP11-34P13.14</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>RP11-34P13.9</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>FO538757.3</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>FO538757.2</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>AP006222.2</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & AAACCTGTCAAACGGG & AAACCTGTCACTTATC & AAACCTGTCGAACGGA & AAAGATGAGAATTGTG & AAAGATGAGAGTACAT & AAAGATGGTAAGAGAG & AAAGATGGTGTGCGTC & AAAGATGTCGTGGGAA & AAAGCAAAGACGCTTT & AAAGCAATCCAGTATG\\\\\n",
       "  & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\tRP11-34P13.3 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tFAM138A & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tOR4F5 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tRP11-34P13.7 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tRP11-34P13.8 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tRP11-34P13.14 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tRP11-34P13.9 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tFO538757.3 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tFO538757.2 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tAP006222.2 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 10\n",
       "\n",
       "| <!--/--> | AAACCTGTCAAACGGG &lt;int&gt; | AAACCTGTCACTTATC &lt;int&gt; | AAACCTGTCGAACGGA &lt;int&gt; | AAAGATGAGAATTGTG &lt;int&gt; | AAAGATGAGAGTACAT &lt;int&gt; | AAAGATGGTAAGAGAG &lt;int&gt; | AAAGATGGTGTGCGTC &lt;int&gt; | AAAGATGTCGTGGGAA &lt;int&gt; | AAAGCAAAGACGCTTT &lt;int&gt; | AAAGCAATCCAGTATG &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| RP11-34P13.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| FAM138A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| OR4F5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| RP11-34P13.7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| RP11-34P13.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| RP11-34P13.14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| RP11-34P13.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| FO538757.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| FO538757.2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| AP006222.2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "              AAACCTGTCAAACGGG AAACCTGTCACTTATC AAACCTGTCGAACGGA\n",
       "RP11-34P13.3  0                0                0               \n",
       "FAM138A       0                0                0               \n",
       "OR4F5         0                0                0               \n",
       "RP11-34P13.7  0                0                0               \n",
       "RP11-34P13.8  0                0                0               \n",
       "RP11-34P13.14 0                0                0               \n",
       "RP11-34P13.9  0                0                0               \n",
       "FO538757.3    0                0                0               \n",
       "FO538757.2    0                0                0               \n",
       "AP006222.2    0                0                0               \n",
       "              AAAGATGAGAATTGTG AAAGATGAGAGTACAT AAAGATGGTAAGAGAG\n",
       "RP11-34P13.3  0                0                0               \n",
       "FAM138A       0                0                0               \n",
       "OR4F5         0                0                0               \n",
       "RP11-34P13.7  0                0                0               \n",
       "RP11-34P13.8  0                0                0               \n",
       "RP11-34P13.14 0                0                0               \n",
       "RP11-34P13.9  0                0                0               \n",
       "FO538757.3    0                0                0               \n",
       "FO538757.2    0                0                0               \n",
       "AP006222.2    0                0                0               \n",
       "              AAAGATGGTGTGCGTC AAAGATGTCGTGGGAA AAAGCAAAGACGCTTT\n",
       "RP11-34P13.3  0                0                0               \n",
       "FAM138A       0                0                0               \n",
       "OR4F5         0                0                0               \n",
       "RP11-34P13.7  0                0                0               \n",
       "RP11-34P13.8  0                0                0               \n",
       "RP11-34P13.14 0                0                0               \n",
       "RP11-34P13.9  0                0                0               \n",
       "FO538757.3    0                0                0               \n",
       "FO538757.2    0                0                0               \n",
       "AP006222.2    0                0                0               \n",
       "              AAAGCAATCCAGTATG\n",
       "RP11-34P13.3  0               \n",
       "FAM138A       0               \n",
       "OR4F5         0               \n",
       "RP11-34P13.7  0               \n",
       "RP11-34P13.8  0               \n",
       "RP11-34P13.14 0               \n",
       "RP11-34P13.9  0               \n",
       "FO538757.3    0               \n",
       "FO538757.2    0               \n",
       "AP006222.2    0               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts[1:10,1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "154e3b8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Feature names cannot have underscores ('_'), replacing with dashes ('-')”\n",
      "Warning message:\n",
      "“The following arguments are not used: do.plot”\n",
      "Centering and scaling data matrix\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mm135 <- CreateSeuratObject(counts = counts, project = \"mm135\", min.cells = 3, min.features = 200)\n",
    "mm135[[\"percent.mt\"]] <- PercentageFeatureSet(mm135, pattern = \"^MT-\")\n",
    "mm135 <- subset(mm135, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)\n",
    "mm135 <- NormalizeData(mm135 , scale.factor = 1e6, normalization.method = \"RC\")\n",
    "mm135 <- FindVariableFeatures(mm135, do.plot = T, nfeatures = 1000)\n",
    "mm135 <- ScaleData(mm135)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75091c97",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PC_ 1 \n",
      "Positive:  IGLC3, SSR4, IGHA1, IGHA2, SEC11C, MZB1, MANF, FKBP11, KRTCAP2, MTDH \n",
      "\t   MYDGF, PRDX4, FKBP2, ERLEC1, DNAJB9, JSRP1, ITM2C, HERPUD1, EDNRB, HLA-C \n",
      "\t   HSP90B1, AL928768.3, DERL3, SDC1, PDIA4, PHGDH, SCAPER, PAIP2B, TXNDC11, ARID5B \n",
      "Negative:  TYROBP, FTH1, FCER1G, ACTB, COTL1, TMSB4X, SH3BGRL3, S100A4, FCN1, EIF1 \n",
      "\t   TYMP, TSPO, AIF1, CSTA, ARPC1B, SPI1, LST1, FTL, S100A11, LCP1 \n",
      "\t   SAT1, ZEB2, CFP, CD68, TKT, NAMPT, STXBP2, SERPINA1, LINC00936, CLEC12A \n",
      "PC_ 2 \n",
      "Positive:  HBB, AHSP, HBA1, HBA2, HMBS, SLC4A1, HBD, EPB42, BLVRB, FECH \n",
      "\t   ALAS2, CA1, HBM, UBB, SNCA, SLC25A37, SELENBP1, PRDX2, HEMGN, TSPO2 \n",
      "\t   FAM210B, GYPA, NFE2, STRADB, BNIP3L, BPGM, TMCC2, SLC14A1, RHAG, RHCE \n",
      "Negative:  SSR4, IGLC3, HLA-C, SEC11C, IGHA1, IGHA2, MTDH, KRTCAP2, MZB1, MYDGF \n",
      "\t   FKBP2, MANF, PRDX4, HERPUD1, FKBP11, ERLEC1, MCL1, JSRP1, PABPC4, HSP90B1 \n",
      "\t   FTL, ITM2C, EDNRB, NEAT1, DNAJB9, PDIA4, TXNDC11, CCPG1, SCAPER, RHOB \n",
      "PC_ 3 \n",
      "Positive:  LTB, CD3E, IL32, TRAC, CD3D, CD3G, TRBC2, IL7R, EVL, MGAT4A \n",
      "\t   LCK, CD7, CXCR4, RORA, PIK3IP1, CLEC2D, IL2RG, CD69, CD2, ARHGAP15 \n",
      "\t   RNF125, TRBC1, ARL4C, PIK3R1, EIF1, AP3M2, FYB, TNFAIP3, KLRB1, FNBP1 \n",
      "Negative:  BLVRB, AHSP, HMBS, SLC4A1, HBD, HBB, HBA1, FECH, HBA2, CA1 \n",
      "\t   IGLC3, HBM, SLC25A37, ALAS2, EPB42, IGHA1, SSR4, SELENBP1, IGHA2, NFE2 \n",
      "\t   TSPO2, SNCA, MZB1, NEAT1, HEMGN, GYPA, SEC11C, CAT, RHAG, PRDX4 \n",
      "PC_ 4 \n",
      "Positive:  TUBA1B, H2AFZ, KIAA0101, HMGN2, HMGB2, HIST1H4C, HMGB1, STMN1, TUBB, BIRC5 \n",
      "\t   NUSAP1, MKI67, TYMS, IGLL1, HMGB3, MYBL2, CENPF, UBE2C, ZWINT, GTSE1 \n",
      "\t   AURKB, CENPW, VPREB3, CD24, TOP2A, PRC1, H2AFY, CD79B, ASPM, CENPU \n",
      "Negative:  ALAS2, HBB, TMCC2, BNIP3L, HBA1, BPGM, SELENBP1, HBA2, EPB42, HMBS \n",
      "\t   BCL2L1, SLC25A37, ARL4A, YPEL4, STRADB, TRIM58, IFIT1B, HBD, SNCA, SLC4A1 \n",
      "\t   BLVRB, PDZK1IP1, LPIN2, RSAD2, TSPAN5, SLC2A1, AHSP, FECH, TSPO2, LGALS3 \n",
      "PC_ 5 \n",
      "Positive:  FCGR3A, IFITM3, IFITM2, LST1, MS4A7, RHOC, LRRC25, CDKN1C, AIF1, LINC01272 \n",
      "\t   SERPINA1, TESC, ABI3, LILRB2, LYPD2, LY6E, HES4, STXBP2, PILRA, LILRA5 \n",
      "\t   CKB, SLC2A6, HMOX1, C10orf54, MT2A, CSF1R, LYN, MAPKAPK3, FTL, NEURL1 \n",
      "Negative:  GPX1, LYZ, VCAN, S100A9, S100A8, RP11-1143G9.4, LGALS2, RNASE6, S100A12, MS4A6A \n",
      "\t   CSF3R, CAPG, IER3, MGST1, CD14, PLBD1, MNDA, AP1S2, CCL3L3, VIM \n",
      "\t   CLEC4E, CSTA, JAML, TREM1, HLA-DMB, SELL, RNASE2, SOD2, HLA-DMA, PTPRE \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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KusrZEns7X7Hrb3e5JSX/yp2Lfb3lImTbPcehfpF1lMZ3uOUWfM1hcu8Hw4VNV4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8yc6fAWb8veGVFeqXnyjPvYy6ebQ2k1JA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CUhIcmTkHvzXW7u5YbcJMANxcQUgw3YYMAY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\nYBNvE6ezRH4eAIDFotx5r+01PnH8KP/q85Z90GEjaWwc8ffXPnjPWC9ys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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UiI4ioVQdPp9OLioiiKlNJcLjczM9PcfwIAdKehoSGGYdSnLMtWLiOJx+O5XE57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fCQaD99xzz/ve974///M/v+WWW/7+7//+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\nAAAAXOL/ARKOPOZs9xqfAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm135 <- RunPCA(mm135, features = VariableFeatures(object = mm135),npcs = 100)\n",
    "mm135 <- RunTSNE(mm135, dims.use = 1:10)\n",
    "DimPlot(mm135, reduction = \"tsne\")\n",
    "FeaturePlot(mm135, features = c(\"SDC1\", \"CD38\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08fea372",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 947\n",
      "Number of edges: 27262\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.8550\n",
      "Number of communities: 8\n",
      "Elapsed time: 0 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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gM2lOLOnebX/8vHqUb85iggqmVYkIIFKZf6cURupmRn26axggjJ+snxsj2IGMM9\n64joXBjsLguNevxsPYdmy9ntwiRY3X4FsKYILw3AT8X461GcbkJaIP4+2LxqMYCXjuJ4AwBg\n79qmNW9OfaN/n+joWC8cP3KwoKDAy8vr088+e6e0bbKenwK1OnjJ8VQS7rK7/3uxvjljdbij\nAmeazr8mCxEBMOpw+ue2ZcMlu/WAmitwag1ib3B+aUTUbXD20GXh1SwYrB+LDT27AogAqKxv\nCHrKcLgWN+/A7iqUtmBtEaZtN+/ZdbwBK8/OXcPQ6Rg706jT5u3eunnDekGuuP/++w8cOGDo\nO/67wrar1eqwZixqb+qcheIku5b216ojIiuaGhj15xmQqzjsrGqIqHviiN1lwX6huPvj2l7f\nG4M3j7UdhqjwxWmr3VdPNGBvNeK8ccVGlLc+F9F3LPq2bU3/4EDzBq8bc2HjTBNknXRz55Ze\nWHyy7TAtENEcriO6MC3lgHieP4aM+nOdJSLiiN1lwWafVrUM6SHQnv39/soAvDoI/krz4ZpC\n/OeEg4usKLBIdXZalz4Z5G97arBdS4ddHY5P09DPF8EeuLU3VnCiN9GFKdqO7OXnH+KWezml\nGiLqtjhi53qvZFrcPwUAKEREfw+VDH9JwfP9oBBxXQTmH2rrUKuDQoDu7I3PBG8keOPuXTiH\n1g3KRgfhkQS8dzYaPpyAK4I754uY3BuDe2M684JEbq/ikPT+m0u/O7DkVPlRo2SM8o8bGX/1\noxPftO/p4ev86oioO2Gwc7H/5eOFIwDMM6aDPKA1olYHAC0GzD+MYQGYEo7iFts33hiFvCbk\nNyItCIsG47Us5DVadfCSw1uGUg3kAp5Kxk1RbafeHYq5sThah/6+Ll0NmIgAnU532923bT68\n2l8dPKTPeFGUFdfkfbv7XYfBjqsqEtG5Mdi5WNvmWgIAVNrdS91QiinhSPWHp8xqn7F7YnCd\nxe4UO+1m6X05CleH42QjwlXWW1YAAIYEMNIRXRZefvnlzYdX3zj0wSemvKOUq0yNNU0VDjsH\n9nNiZUTUDfGvPxdTny9aB3oAQIgHloyAz9nOjyfiukg06jFvL4JXI+w7VFknQpmAbeXQS0j2\nMae64hbctRMRazBgHT7L6+yv4V4k4JszePEotpS5uhRyd9XV1YsWLUqI7P/M1PdaUx0Af7WD\nGRKiAuFjnFgcEXVDHLFzsVnRWHKy3bMBSsw8u7bcXX0wLQJZdeijRqQnADx1EP4JiaUAACAA\nSURBVB+efcS1TAOZ0LZmikHCW9mo0GLxcPMTrzN2YEcFAJS04L4MhHp0wnLEbqlKiwHrzPe+\nXwKujcD36Z25Iqy+CXk/oDobogJhwxCYAs8wbnHRc/3www/Nzc33/PHe2qbKLcdWVjQUh/r2\nuiJxerC3g59PpS9vxRLReTDYuVh6CNaNw9+zsaHMahW4ACVu7YWnkq1W9/VTYFRQ2+Fqi+Xo\nAMR7o06HEovZeEtPYU0h3h+GscHmVNfqmwIGO8ce3Gs1o/HHYqwr7sz/Vie/Q1UmABg0KNiK\ngq0AIPNA6p+guGx2yyWn2bt3L4Ambe1t/0luaDFv9ech9/y/az+YOmiWTefw0c4uj4i6Hf71\n52KrCrGiAKn+GOxn1f5IAj4c3rafhGOS1WulaLtsCoBKLe7LwJkm2/asOnx0EmXtL4/SY+2u\ntG056HBj3Q6RDKjJcdBu0ODAO532KdSNlJeXA/j7v14fGjf2q4eOrX+m5uUbv1DIlAu/n5td\nYrXxX9wNCB/poiqJqPtgsHOllzNx829YfBJ/z0Z2PeLPrlB1UxTmn3OK9KEa3PArqrQWTQLk\nIm7p5aBzswHHG5AeYtW4sxIP7kHSjzhce2nfwe30sltRuV8nLjAhtLuvgEEDbV3nfRB1E0aj\nEUB4ePjKH77uNzDZ28Pv6tQ7/zj9dYNR/+3ud1u7hQxB6HDXVUlE3QdvxbqMQbLaT6LFgDHB\n+Hk8vOUIU7X/NqC0BZO2OliLuFqDZ/ui2YD3T6BSa3UqUImvR+OZg9hUhipt264VtTosOIzv\nxtpeqid7dSCu2tI2W7G/r9XTx5dIEBE8EGX7HJ816jrtg6i78PPzAzBx4sSgGM+gh2DQQqZA\nVNF1r6546GTDvqABEOXwjUVwqqsLJaJugsHOeWp0MEgIUkICFmXjw1w0Wu8OVNyC+AuYZfVz\nieMdJvKb8OAefDgcTyVj0M84ffb2a4gHxofAS47PR0ICPL+1eldOfce+jdsaF4L9U/C3TBQ3\n45oIPN8XYic+OgHETIPcC5VZ0FjPehQVUAV25gdRt5CUlISz8a42FyU7oW+Gspc/AL2sOfF2\nF5dHRN0Og50z1Olw9y6sLQKAK0MxOQzzHe3kfUUwNpZidSFUMsyKwUA/B30AaNrfdOjjU6jS\n4usxGBrQFuzKNfjwJJ5MAgABGOCHvdVtbxnYefuJuY2Bfvi6y2api0r0mYI+U1B3ClmfQTIA\ngMwD/e8/z+7v5JYmTJgAIDMzs+4Usj41N2b+fhRATEyMq6oiou6Lwc4Znj9sTnUANpfhsKPJ\n+NdGIEiJSVvNh/8+jvXjbSfGmUwOg5fcdrSv1apCvHQUG0qtGlcWmIMdgH8OwbTt5s0tIj3x\n2sCL/TbUOXxjMfJFAJAMXO6k5xoyZMiIESM2bty4aun6ZEwGoDNoPtryAoBbbprh6uqIqPth\nsHOGTdYxq8Z6KpWHiCPXIMEbvde2NWqMeOOY42AX44WVY/DUQWTXI8UXR2rbJoSZLD1l2yKz\nGAq6Ihg512JTKRQirg6HN/8XcDWmuh5u8eLF6enpcxdeOzbxuiDv8D15m05XZo+InXTPnbNd\nXRoRdT/8V90ZAj0Ai6lsHjLoLcbbjBKiPGGQUGo9c66gud0LTgnH4XDz64f34j+5VmfrddBa\n365Ntb7fGuqBO/qAiC4HgwcPXr88Y/4zL+zN29yoqYv0j71//MsPzfg/Tz/+fiaii8ZfHM7w\ncLzV+sADfLHLYmtXnYTMOgwLQFogfrPoNsZiLeL2bC5DX1+MCMRuiwsODbTdC8vfbq9YIrp8\nhOj6LrzlG8uWuGtdVQsRdW8Mds4wMxq+CizLg0HCjN7IqbcKdgLM20t8NBw3/IoTDQAwOggL\nzzf77e5d+F+++XW8NwqbYZRwQxQeiccEy2AnIcpubTYiunyIdn96eTrYKpaI6PwY7Jzkusi2\n5dBKW/BhbtveXw8nINQDAFJ8cfQaHK6Fpwz9fM/ziOS28rZUByC3ARvHIz0EChEScG0Efiw2\nn4r3wR29O/fbEFFnCh6Mkp1th34JUHbisthE1JMw2LlAmAqHr8aSUyhuxvgQ3GSxXYRSxLCA\nC7rIMbv15148ijINgjzwdDJWX4FPTmFPNeK8MC8evrwVS3QZ8+6FfrNRvB26RvjGIWqCqwsi\nom6Lwc41gj3wXN9LukI/u21kt5vm59Xj5gpsnIAH4/HgJX0CETmJphoKLyTfA4G7PBLRpWGw\n667SQzArBsvyHJ/9Ih9XhTq1HiLqAKMOOV+g5gQAqAKRdCfU4ed7DxFR+/jnYTf2WRq2XYn3\nhmLFGNtT2vZ3pyCiy0fhZnOqA9BShdyVLq2GiLq/7hTsGhoavvrqqzvvvLNfv35qtdrPz2/s\n2LFLliwxGh2kmNzc3JkzZ4aHh6tUqsTExAULFjQ1Ndl36+7SQ/BwAm7uhRHW24ze3KudNxDR\n5aR8v9VhYwkMjnaCJiK6QN0p2C1ZsuSOO+5YuXKlv7//9OnTU1NTd+3adf/9999000022e7I\nkSPDhw9fvnx5WlravHnzfH19Fy5cOHHixObm9tf87ea+HYPpkVDLEK3Gv4fixihXF0RE51Of\nD22DVYsoh0zpomqIyC10pzl2vXv3fv/99++66y4/Pz9TS2Zm5pVXXrlmzRrTSF5rz7lz59bU\n1CxduvS+++4DYDQa77777uXLly9atGjBggUuKb6r9VFj7VhXF0FEFyNzqW2LOgznWeiIiOic\nutOI3S233PLQQw+1pjoAKSkpTzzxBICtW7e2Nu7bty8jIyM1NdWU6gCIovjWW2+Jovjhhx9K\nkvUuqkRELiIZbFu4LjERXaLuFOwcMuU8Dw+P1pZNmzYBmDp1qmW3qKioQYMGFRQU5OTkOLlC\nIqILFDrc1RUQUTfXnW7F2pMkadmyZQCuu+661sbs7GwAycnJNp2TkpIOHDiQk5Njc6q0tLSg\noKD1sKSkpAsrJiI6S+EHXa1Vi0+0i0ohInfRvYPdSy+9tHPnzptvvnnSpEmtjbW1tTg7kmfJ\n398fQE1NjU37559//swzz3RxpUREtob8Efv+AX0jAAgy9J/n6oKIqPvrxsHu3Xfffemll4YO\nHbp0qd0MZEdMs+sEwXZm8uDBgx944IHWw4yMjAMHDnRinUREDolKDH8ORj0gQeS+f0TUGbpr\nsFu0aNHTTz89bNiw9evX+/pabZdtGqszjdtZam8kb/LkyZMnT249fP755xnsiMhpxO76a5iI\nLkfd8uGJF1988emnnx49evTGjRsDAgJszpqm0Jlm2lk6fvw4gKSkJOcUSURERORk3S/YPfnk\nky+99NKECRN++eUX++E3AFdddRWAdevWWTYWFRUdPHgwKiqKwY6IiKhT5FVuXbn3vrUHH6lt\nPuPqWsisO90DMBqN8+bNW7x48dVXX71q1SpPT0+H3YYOHZqWlpaRkbFs2bJZs2aZ3vjss8+a\n3m4/x46IiIgu1qZjL27Oesn0evepD+4buz7cN3Vr9sLC6t0+qoixiU9HBYxwbYU9U3cKdosW\nLVq8eLEoioGBgQ899JDlqYEDBz711FOthx9//PHYsWNnz569cuXK2NjY7du37927d+TIkZZ9\niIiIqGMkybj12ELLwy923qzV10uSeYfP7JK1D07ICPMd4KICe67uFOwqKysBGI3G5cuX25y6\n+uqrLUPbgAED9u7d+8ILL2zYsOGnn37q1avX/Pnz58+f394gHxEREV24Jm2lUdJbtmisV2XU\nGZoPnF529YA3nVsXdatg9/rrr7/++usX2Dk+Pv6LL77o0nqIiIh6Ji+PEJmoNBi15+ij0dc5\nrR5q1f0eniAiIiKXm9j3pXN3SAy7xjmVkKXuNGJHREREl4n05OdiQyfsPPk+JENW8RqtvqH1\nlCjIJqb8rV/EjS4sr8fiiB0RERF1RK+AUTOGLYsOSrdMdQDuGLliXNLzrqqqh2OwIyIioo7T\nGVpcXQK1YbAjIiIiK7VNpy98zeGEsClymar1UC7zqGzIMRh1XVManQeDHREREZk1tJQs3nrF\n33+O/vu6Ph9vn9CkrbA8qzM0278l1Cfl1uFfBKhjTYd6g+bnI89+vft2Z5RLdhjsiIiICABy\nSn/45Ner8qt2mA7zKrb+dNi8RuzW7FdfWev3tzVeH20dU16fZfPGlMib0uKsNg7ILFpV0WC7\naTs5AYMdERFRTydJxnc3Dvx8x/Ty+izLnTezS74HcKTw6w2Zf9bo6yRIZ6p+X77rZgmSzRUa\nWkrO20JOwGBHRETU023Leb207oh9u97QDCCn5CfLxvL6Y9WNJ2169gkaa3noIfcJ9xvc2WXS\n+THYERER9XQnyzc5bJeJHgCUcm+bdqXMy6YlJfKmMQl/Mr32VAbeMnyZSuHf2WXS+XGBYiIi\nop4uyDsht3yjYNeeHD4dQGqfe/bmL9GfXdYkOXyatyrc/iJTB749Lun5upaiYO8khUzdtRVT\nOzhiR0RE1NONS3peLnrYNMaFTLx20NsAegWkzRrzU3L49F4BI8clPXfriC80+rr8yu32T1F4\neYRG+KUy1bkQR+yIiIh6On919BOTc9Znzq9sPN47cEx60v/JBLlaGdzaITZ4QmzwBNPrnNKf\nvt09s1lXDaBvxPV3pH0rExUuKZvsccSOiIiI4Kvu3T/q1sTQayL8UtWKQMtUZ8ko6VfsuceU\n6gAcK16z6+R7Nn2atVXZJd+fLN/IZYqdjyN2REREhBV7Zh0881/T610n3/3DuF/tb84CqGrM\nbdJWWrYUVO+yPMyr2PrFzptMyS/UJ2VO+hYvj5Auq5psccSOiIiopyupPdSa6gAUVu85UvCV\nw54+qkhBaAsPEuDn2bv1sElb8d2Bea3jeWX1mZuy/to1JZNjHLEjIiLq6aqbbNelq7Jbqc7E\nQ+5zRcJTvx5/y3TopQwaFf8YgFPlm1fum1PTlGfTv7h2fyfXSufEYEdERNTThfsOsmmJ8B8C\nwGDUNmhKfVSRoiBrPXX1gDf7BI05Vb7ZyyN0WPRcb1V4i65mecaMZm2V/ZUDveK7tHKywWBH\nRETU0wV4xU3u/9r6o8+bDgf3vrtvxPW/5/5z/dH5OkOTt0fYDUMX9w2/rrV/v4gb+0Xc2HpY\nWL3HJtVJgACoFH7jkxc45yuQCYMdERERYVzScwOiZhTXHgz0io/wSz1dtePHQ+adJBo0pd/s\nvuvJKbleHqEANPr6o4XfNOtq4kMmmvYNUyn8bK4W6pPir46OCU73VoU5+Yv0cAx2REREBACB\nXgmBXgmm16fKN1ue0uobzlTv6ht+XX1L8Qdb0uqaC0zt16d+MCL2wQj/ITJRaTBqW/uX12eW\n12ceL/3p99x/PTRhj69nlNO+RQ/Hp2KJiIjIlloZZNuiCATw6/G3WlMdgB8O/bGweo8AUYD9\nhmQAUN9SsuPEP7quTrLBYEdERES2UiJvsdwQtlfAyF6BIwFUNhy37GYwaj/YMuJYyZpegaMc\nXkcAqhpzu7RUssRgR0RERLa8PEIeHL9zZNwjsSEThkXPuWvUKlGQAwjzHWjfeVPWizcM+TDE\np6/DS5kesCXnYLAjIiIiB3xUEXXNhafKt+zN/+T9zUPzKrYBSE96Nsx3gE3PsrqjWn3joxMP\nP3zVAU+Fv+Upb4/wKxKecl7RPR6DHRERETnwe+6/sopXm143tJR8s2emBEml8O8bcZNNT6Ok\n/+/v03WGJh9VeLOuxvJUuO9ApdzbSRUTn4olIiIih85U/W55WNdc8P2BR8vqj5iG7mzUtxQX\nVO2KC53kqQy0XNMuxDelywslCxyxIyIiIgd8VJHWDULGqffzKrZJ7fQXRYUAYdqgf8lEpanF\nXx0zLvn5Li2SbHDEjoiIiBwYHf/HA6c/0+jrzzaYE53DdU2CvBN7BYxs0lYOiLo1KmDEyfKN\nnoqAvhHXK2Rq51RLJgx2RERE5ECQd+KD43d+tfv2svpMQJAkg30fT4W/pzIoKmD4sOg5i7dd\nUVyzXyYq0mIfnjrwH4LAu4IuwGBHREREju08+W5p3RGHp0bEPBgbMqF/1AzTMijvbhxo6mkw\n6n7P/WeIT98RsfOcWisBYLAjIiKi9mQWr7I8FARRkoxKuffV/d9Ii3u4tb2hpcQm/50o+4XB\nziUY7IiIiMiBvIptTZpKyxaFqP7jpKPeqgiZqLBsVyp8REFmtLhXq7JezY6chve/iYiIyIok\nGb/YeePH28cbJZ1lu1yu8lP3sUl1AJQyr8G972nrJlMNj3nAGYWSHY7YERERkZWjRSuyir+z\nb48JGtveW64f8kGob/8TZb+olYGj4h7r3c7WsdTVGOyIiIjISpmjByZUcr+0uEfae4tc9Bib\n+PTYxKebtJVy0aMrq6Nz4a1YIiIishLknWTf2KKv/e+O6fvyP2nvXbXNZ5ZsS3/th+BX1vp+\ns/suvaGlK2skxxjsiIiI3Epexbb1R+cv2Tb2zXVR76xP2nHi7Yu9woCo26Id3XXVGzXfH3xU\nZ2h2+K7V++/Pr/wVgATpUMHyLdl/u9jPpUvHW7FERETuY2v2qxsy/2zZ8tPhJz0UvsOi5174\nRWSiYk765qzi76obT50o/Tm3fEPrKZ2huarxRJjvQJu3GCV9XvkWy5YTZesnpSy86C9Al4Yj\ndkRERG5Ca2jclPVX+/YjBV9f7KVEQd4/8paxiU9HB6dbtstERYA61mF/pdzHssVTEXCxH0qX\njsGOiLqEVFmuX/+T/sc1xryTrq6FqKeobTpjlPT27fUtxZJk7Ng1R8U/FuSd2Ho4KeVVpdzb\nYc+0uIesDx922I26FG/FEtFFkBobpYJ8wctbiOoNweFW4AAgFZzW/uef0OsAGLZukM+4SzaC\nax8QdblArzgPua9GX2fTXlp3eOW+2bcM+6wD1/RUBDxy1cGjhd82astjg8dH+g9rr+dVfV/0\n9+yTVbxaLvMcFj03MeyaDnwcXSIGOyK6UMasI7ovl6GlBYAYl6CYMw8KpcOe+k2/mFKdiWH9\nDwx2RE4gE5U3DPlo1b7Z9s83HDi9bGK/l/zVMR24rELmmdrnnvN2EwRxWMwfhsX8oQMfQZ2F\nt2KJ6MJIkv6b/5lSHQDjyROG7Vva7VtTbXVYXw+Dob3ORNSJBva6/YkpJ+4cubJ/1AybU7XN\nBed4Y1bx6m/33L1q35y8iq1dWSB1LY7YEZFjUkO9YcsGqbxUCA2XjZ8IrVZqbLTsYCw4LQOM\np3INP35nLCsVwyNk024U+8QAEHtHGwrPtPYUIqIgkzm5fqIey0cVmRJ5k87QdLTw29ZGpczL\n/lHWVr/n/uvHQ4+bXu/LXzpz1Hd9I66376Y3tPx2YtGJsvWeCv9R8Y/FhUxsPVVefyyzaCUg\npUTeHOLTr/O+DV0cBjsickSr0f3nHamiHACOZRqzjigffhIymeXAmxAQKNXW6D790DSMZ8w7\naVz6gfKpPwvePrIp04xn8qXCMwAEPz/FLXe46GsQ9VyDet+VW7Z+/+nPAHjIfW4Yslil8Guv\n8+8n3rE83HHibYfBbs2BeaYLAsgq/m5O+ubY4AkATpZv/HzHNL1RA2DLsb/NHL0mIXRK530V\nuggMdkTkgDE7y5zqAABSeZkxL1c+aar+5+9NLYKXtyz9SuPxY603ZwGgqUk6kSOkDDT8tlVQ\nKIT4BGFgqnz4aChstwwnoq4mQLh52KcT+r5Q11wY5jfw3IuPNGrLrQ41ZfZ9dIbmg2f+Z9my\nN+9jU7DbmPkXU6oDoDdq1h99nsHOVRjsiMgBqbnJtqWxUXbVFCE6xng8W1B7icNGCl5eEOzm\n6QqC7stlxqOHzIf5+bLeMUKvPl1fMhEBQG3zmdLaw4HeCcHeSQACveIDveLP+67Y4PHZJT+0\nHsaETLDvozM02ayl0vr4bXVTnmV7cc3+o0Ur+kfectHV0yVjsCOiNsa8k4YtG6SGejEsHHJF\n25OtcoUYFw9AjE8S49s2kRST+kKtRpM5BQrePojsbfzi07Yr6nWGPbvkDHZETvHr8bc2ZC4w\nGLUARsY9On3wvy/wjdcNfr9BM6OwejeAhNApk1Nete+jVgZFBQwvrN7T2tK6oEmk/9DskqLW\ndgnSqr2zk8KuVcg8O/xdqGMY7IjITCou1C15DzodAMOZfCE0TKqvQ3MzPNXyG2YIQSH2bxF8\nfJVzH9av+14qKxbCo+RTr4PMbgyvhRuBEzlDRUPOz0eebT3cdfLdfhE3xIdOupD3+qn7zJuQ\nUdOUJxOVPqrI9rrdNmL5ij33nq7aIZepRsY9MiLmQVP71IH/OFWxRatvaO2p0deX12dF+g/t\n6LehDmKwI+qRNBrD0UPQasSEJCE41NRm2L/HlOpMpLJS2cRrZCNGCX7+EB0tjSRJUsFpqblZ\nMXM2PD1bG4WQUKm8bYKOEONg9yEi6nQltQdsWgprdl9gsDM57yp3gV4JE1P+9uOhxysasrOL\n13p7hA6NnqNWBgd5J46Of3xrdtvOsAIEfzWH6l2AwY6oJzEYDFs2GA7tR3mpZHq+VS5X3Ha3\nOHgoAGi1tt03rhPUatnYCQ4updXolrxvzD8FAGq1YuYcMSEJAARBPv0m3acfQZLMF9m9U5Y2\nBqIInba9BY2J6NL5q6PtWmI69yPqmgu+2HmjRl8P8wDh/2069vItQz/tHzVjdPwf9+UvrW8x\n35AdnfC4WhncuZ9OF4LBjqgH0f/4neHXLdZNev2aFcrBQwGIfVMMv2+3eYth327bYKfVGrZt\nMuzdJVVVmluamvRf/1c5/2XTkVRY0JrqAEgFpw3bNxt+3SLV1QpBwfIbbxOT+nbmtyIiAEBU\nQFrf8OuOlaw1HUb6D0uJvLlzP+Jk+SZTqmul0zeu2jcnKXyal0foYxMP783/pKGlOCZ4vMPV\nUsgJGOyIehDjvt3WDRIgSA31aGqCWi327S+/7mb996ssY5nlzmAmui8+NWYdsWmUamu0r78o\n+PrJJkySbEb+JOh//gEGPQCpskL334+Vz7wg+Ph21pciIhMBwp2jVh0p+Kqodl+wd1Jq71ly\n0aNzP0Lm6IIafX1F/bEI/yGeysCxiU9LkA4XfPntnnuUcq/hMQ9wmp2TMdgR9RiSJOn11k0C\nAMHHF2q16Vg2doJUWmzI+L21h5gyEJIEQTBfo7bWPtWZT1VXSdVVxs8Wy2681aIVEGBKdWYa\njXQqVxg0pBO+ERFZEwXZoN53Dep9VxddPz50krcqvKGlxLJRgOBnMZ1uc9aLm4+Zx+/35386\ne+zGPkFXdFE9ZI97xRL1GIIg9utv2yhXyG+6zaph2k2y4SOh9IDKUwiLMPy2TfPC0/ovPzc/\n3Npiu76dA+WlslFjz36oow7KTh5FICLnUCuDZl+xISlsqii0DQyNSXxSrQwyvZYk444Tb7ee\n0hs1v+f+y9lV9mwcsSPqQRQ33qYDjJmHIZOLsfFi/0FiYrLgb70evUolv3WmfMZd+nVrDVs2\nmNoM+3dDLpPPuEsICRP8A6Sa6tbuYkKy8US21RUMRiEktL0ahOAQMS6hE78UEV2KFl3trpPv\nVTbkhPkOTIt7SCFTn7t/qG//e8b82Kyt2pe/tEFTGhM8Ljl8eutZnaHJctETAA2a0i6pm9rB\nYEfUk6jVirvug9HoePkSS4Jgc8vVmHkYAERRcc9c3ZefS+WlkCtk4yfKJ0/V/vMNqbhtbVJx\nwCD7p1/Ffv2lhgYxIlI28Roo+Wws0WVBZ2havO2KsrqjpsOjRd/OTd8mE8+/B6CnMvCKxKfs\n25Vy78iAYZaLGMcGj++saulCMNgR9TznTXUmNuHs7KHQq4/y6T9LDfWCpxoyGQDFrPv1a1YY\nTx4XfHxlV04RE/sCkI2faNi6EQBkMvnka2VXTu7Mr0BEF6m8PuuXo88V1x4I8kqclPJK78BR\nAI4Vr21NdQDOVO3Mq9hqWvqupilvzYGHT5Vv8lQGjk18JiZ4/C9HnyuvzwzxSZnS/3XTIxEl\ntQf35C3RGZqSw6e1PoF789BPv8yYUV5/DEBK5E3jkp53wbftwRjsiHouqbzUWHBGCAgUY+Ls\nz8qGpekLTrceisNGWp4VvH3aXgcGKe57wObt8mtvkI0aK5WXCeERgp9/pxZORBenRVf76W9T\n6poLANQ2nf7st92PTTzsp+7ToCmx6dm6EN2XGbeaBt7qW4p/OvykUu6j1dcDqGsu/Kxm/x8n\nHa1sPLF4q/mpiH35n1zV78Ur+/4VQKhv/0cnHqluPKmUe/uoIpz2HcmEwY6ohzJsXq9fZ17v\nSkwZqLhnLgTBsG+3dDpP8PMXR14hG50OUTTu2y0ZjbJBQxwvU2yi0xm2bjRmZ0Klko1OF1MG\nmpqFwCAhMKjrvwoRnUd+5a+mVGei0ddll/6QFvtQn8AxNj17BY4CUN9SZHk7FYDWYvm6Jm3F\nlxm3GQxWuwVuOfa3sYnPmjaHFQVZkHdip38LZ9PpjIVn0NgAL28xqjcU579DfYFyc3P/8pe/\nbNy4saampnfv3rfffvv8+fPV6vPMbrxADHZEPZFUU92a6gAYMw8bD+03HDtq3G/+VS7s3K54\n/DnZqLFtz7e2T7/6a8OeXeZL5RxTzJknJqd0RdlE1DGSZLBpMRr1AKICRkzu/9qmrL8ajFqF\nzPOagYuCvZMAyMTzTITNq9hqe0HJkFW8alCvrlppxamamvS//GDYswu6s6tyKpSyEaPkk6/F\nJcevI0eOpKen19bWTp8+PS4ubvv27QsXLty4ceOmTZs8W/dmvAQMdkRuSyouNBzaD0GQDRoq\nhFvdEJFKi206G0/mtqY6mNar27NTNn7i+T9Grzfst/rL3rBnF4MddSMSpKLqPY2a8qiAEV4e\nIa4up0tEB41VewQ3aSpMhwpRnRQ+1fR6XNJzw2Pur248GeSdtCdv8Zs/RTbrquNDJsWGXHmq\nfHPrFZQyb62hwcGlLTSevX63JlVV6pa8J1Vafxed1rBjmzE7U/GHRy7xt4yT0gAAIABJREFU\nRsTcuXNramqWLl163333ATAajXfffffy5csXLVq0YMGCS7myCdexI3JPxqwj2nfeMGz6xbDx\nZ+2/3jTmZFmeFYLs/vWy+0tRqq25oE/S62E0WrVoNBdZLJHL6A0tS3+d+MGWtM9/n/aPX+Ky\nilc7vwZJMp6/06URBFEmtN1JDPZJClC3zaxVK4OiAkbklP7485Fn6luK9YaW7JLvtfqGUfGP\nhfqkxASPu2vU6pljvgv3HyIKsnN8iv2N3e7HYNB99pFtqjtLqqzQfbYYBtvhzwu3b9++jIyM\n1NRUU6oDIIriW2+9JYrihx9+KFnu+tNRHLEjck/69T+1HRgMhg3rxKR+AKS6WsNPa4z5pwRf\nP6mu1nRe6B0tT59g2LEVFruBidGxra81NTnVJ7+FXu9b10dZKReCgmVjxplvSahUYkyc8VRu\n2xstlkGWykv1a1dJBacRGCSfcq2pBqLLx+8n/9U6LqXVN6zcO/v5adMtV9/tUg0tJWsOPHS8\n9CeFTD0idt6klFcEoTMHXHSGZgAKmeeRwq/rW9rG6YtrDxTW7OkVkGbZ+Vjxd5aHhdW7x8T/\naVzScz6qyEMFy1fuvbdFV2tfnkxQGCSdTFRc1e+lqIDhnVi8Sxgydkgltjc0LEklRYaMHbLR\n6R27/qZNmwBMnTrVsjEqKmrQoEEHDhzIyclJTk7u2JVbMdgRuSmLNYQBSDVVACBJus8WS63P\nusrk8pGjhZh4ccBgyGSKW2fqViw37TAhSxstDhoinck3FhU0CqdyD/xBMmgAFEuy3uXX+jTF\nGPbtVv7xGahUAOR3zNJ/+4Ux9zjkCtmY9NZpecYDe3Rf/8/8121jg27ZEuVjTwthfEqOLiPF\nNfssD1t0NVWNucHel/qP6wVauW/28dJ1APRGzbac17xV4aPj/2hbYe2BDZl/Lq/LCvMdMKn/\nwjDfgRdy5RZd7ap9c0wDkP0ibrT/Rg0tJaV1R06U/uyh8OkfdaunIsB+H9hv9syUix5X9v3r\nluy/mTKizeBir4C0e8b8WNmQE+iV4B53sY0H9l5Inw4Hu+zsbAD26S0pKYnBjojORejdRzqW\n2XbYKxqAVFEmWaxgAoNekitkg81bdIuDhngk9TWWFAt+/kJAoP6H1YZtmwAUha+UVOa7q5Jg\n+H/2zjqwyWv9489rkUbapu6utKW4Q3EYbIMNGRNmd3bneqd3494x391vbmwDBgwYQ8dwH05L\nS9019aZp0sZe+/2RkKZp2qZUsPP56z3nfc55zzuW9JtzHmlwOyPThfJNDWxWOjFyLABgbu7U\nP54EhgGCaK8qW1VB/7a2w5pomsvJIpCwQ1wlWpWgygYMA48kcPGxdLq5hNra4BgpFwcOznoY\n1lBcf9C2J792l52wazXWrf57ps7UCADNutKq5nNPz8i21u/qhr1ZL+VUbzVf51RvjfGdZ3uX\nIsRqXdnGc4tZzgQAB3PefCzl7NCge9Ir1trNw3DGg7lv2cVehHhMdBF4RPnMHRX2GAC4KMY5\n+crXPlxtdc82Ncornr+lpQUAXF1d7frd3NwAQK12zgGmW5CPHQJxY0Leeifm4Wm+xjy9yVvv\nAABgO7ny2LnHicR4aDjmruAb6syqDgBoUmNrYrI2NR36gSStqg4A2I6FKyxoNVxxAeicKDgL\nwLe0gAm56yH6B1U2ZH0H1SdAeRwyv4WWy74D4yKekYp8rWYpsW8JCMngLAnHSbsaDyQusrMp\nqttvVnVmWo11dlqwK4rq9tk2SxuOxvrdZj5iFpLy24f9eDT/XbOqA4A2Y8OxvJWR3rPuGrMl\n2GOCROBpO7ZzRG1y8PK7x243q7obDZru2YZhoD+c4Wwxe9dhmMPq2r0D7dghEDcmmKeX4IXX\nuaoKAMCDQswlIjBvH8zbh69vL92ID0lyOJyva7cRG71psj2FlchkqQOLhTlIa9y+gM6uQhjO\nnDwGJ4+BSEQtuberRwMAX6Ok16/mG+oAw4ixE8nbF0F/fN8hbmaq2uM7gWdBeRRcIwAAZCL/\nZ6ZnX6xYqzM1hntNC/eaNmhLwjEyMXBZWvnP1p5hwffb2XA802OPQ4SUHPTtTRPbllezMzn4\nvvERz3tIozie3XLhXlv7quazpY1H4/xuH+J/5/nS73amP9HVzG4uoQkBi51Zw/UI5q7gGhu6\n+brhAXB3xRV/I5n36sz7drZ0tZN3BSBhh0BcbfQ6rrwUKAEeGm6WX/0GSdqXlMBx6v5HmZ1b\nuNJiTO5GTJ+Nh0c6HIp5+1ivfZon6IUN5n07kpP5qSYDQZDTZuFhjsdaHpUwFI4eaP/5SxDt\noWQGA715vfDfQ7p6X3rdL3xjPQAAz7OnT2C+fs6k00MgusHUcX/ZaHPkJRYoxkc+N8jrMTN/\n6FdykX9+7Z8CUjY6/IkhAYvsDCK8ZwhJmfFycmAR5eak9BwZ+ujWU8/mnYX8s1BfDq3N4CKH\nkIRf1375hF+SFABcXYJbdO2OGXWarJ9PTPWRJzw06cjQoHtPF3/RoM21mzNIMS7Ca/rYiGdE\n1A1bSwaPHcL/fbQbAwwAjx3SjUH3mF3ozJ52thQWFgJAdHT0Fc9sBeuX2Nobiddee+2DDz44\ncuRISkrK1V4L4saHKy2i164yH01iPn7UY09jEunVXpQFZucf7MljADwABlERptlDAAOJ+whM\n3Yq5eziTpZMrKmAP7uHVzVhwKMYybNYl27uC51/FfP07j+Jb1Kb3/m3bgycmU/c+1MfXQdzk\n5K0BdVF70yMRopZcvdV0AcvRJwo+yK7egmFEctB94yKexTC8tPHo3syXGrS5PvKEOYmfhng4\n9SOHB/6RZ27/6ctdQhfwjwShBBoqoEkJIrHw4IFDEyZMuFi5dnvqQ1ynY9Yx4U/OH/qViW3b\nnfGM7W4iADw08XCY19R+e9trEl7dbPpkZXte4s5QAsHLb15xmcS0tLQRI0YMGzYsLa09aqe6\nujooKMjPz6+ysrLvp7Foxw6BuJowf2y0OpzxdTXsgT3kAmfPOLjCfK6sGJPKiOGjQWgfzmYL\nr2lhjx3imxoxXz9y8vQeNRlfVcEpq/DYIXjSMF5ZiXl44TFxQuvXjcTdyRXikdF4pOUHKLPv\nTwAbYYdhmKvjeTCxGDDM1oUF66dKO4ibmdB5kPcrGFQAAGJvCJlztRfkiEO5b50o+NB8XaO+\nyPPchKgXwzxTnph6ofuBncEAmzxyUej/+ej8VhFmRz4ezu2Q7F/b9uSTT6anp58r/rqzqgOA\nanUaAAgIyfyhX1U1n63XZJv7QzwmhXhOvtI3u27A3NypxXfTG1Z3ZUAtvrsvxa+HDx8+evTo\nc+fOrV27dvny5QDAcdwrr7zCcdzjjz+OfOwQiOsc2sQ3Nth28MpKJ4cye3exRw6Yr9ljh6hn\nXsZcuvD41uvpbz7jm1UAALlZXF6O4KkXgezys8/s2cUetcyMR8dRDz4GeD9EWRHjJ3PnTvOt\nWvMOIDFpaueUyBYEQmLkGPb8GUuTpIgxE/q+AMRNjsgTkp4GTRnUnYO2ashbC77jwHvE1V5W\nRy5VbrBtZlStnxD1IgDoTE051Vsrmk5Wt6TRjC7ca/rMIe/1GBu7fPlynr93Wxp9sWINAEjF\nvt9+sm7igfsyMjKq6/Orms85HKWQWPw3KEL86JRT50q/a9Tm+bkOGxn6SPfZiW8Y8KHDKaGQ\n2bqR7+gJh7m6kXfe1feyOj/99NPEiRMffPDBrVu3hoWFnThxIjU1dcyYMS+++GIfZzaDhB0C\ncfWgBJhUxmttfH8Unl1b26DTsUfbI+P4ZhV3+gQxfQ4AAM/zmhZMIrVKNzY3y6LqzMY1Sq60\nCI+KtfZwjF6vyiQEcpFbLN/UYFV1AMAV5NKpJ2oNv2urj1Jib5/kl+XBHfJqOoXJyBzezxXm\ng48vETsExGI8IgqPS+hmBLlwKebrzxUVYC4uxIQpWEBQrx+KQHQCJ6H6KGjKAQBMLVCyHUgR\nKK7cXar/sYapmjGY1JvPL2vWldW1ZJhYvXUzR9VWrNaX3z9+b48TYhh+x4jVM+JXtpkavKSx\nNS0XKTGL4xgpcFwKVkS5Tolpr2olJOWTol7pZv46TZay+byrODDca3r/pla+uuCxQwQvv8Xl\nZHElRbyuDXOR4OGReHwiUFTPg3siISEhNTX1rbfeOnjw4J49ewIDA19//fXXX3+9XwrFAhJ2\nCMTVhZh7G7N5naUhEpPTZzsziler7ILteVUTAHAlRcymX3l1szm4gZgxFwBA12Y/vq29p63u\nbMm+O+k2JQDIA2eGhr9nZ9t85rsGciMAGCBbqzwSs/CkxLd3VYPo3zdwly6ar1kopB59Co/o\nyUGYIIiJKcTElF49CIHoCp6FpixozrOoOiuNmdeWsIvzX3C+9HuweLaCxlCZWVVqvmV3RFdU\nt09nanQROPVTUC4OkIsD9mf9a9OejypKICwJdly6L8wzpbTxqNVmWPD9Pq5JSYHLZCJnM00e\nL/jgQPZr5uswz5T7J+wjcMd68bqEEuBDh+OX03z2LxERERs2bOjZ7opAwg6BuJoQI0bjvn5c\nXg4IBHjyCEwm796+pWynpnIfQUhkYp1A3+55hvkHAW1i1v3Mt7UCALAsc2AP5h+Ixyfax71S\nFGYTKlt2eLlZ1QGApupAgzxJ0fGJWsNFsAnnUBWu752wMxq5zHTbDu7CuZ6FHQLRf3A0ZP8I\nbQ7LRF1j0YNzEj7heDZbuQXHCDdJaHVzd1UQHHrIOYQHPrX8p0OXPtr5JeAETLsPKppOzkv6\nQiGNLGk4LBF4Toh6qbcZTLSGGquqA4DSxqMXK9aMDH2kV5MgBgIk7BCIqwwWEEQ4d9RYc2FF\nzfl3zNf1fqKw2iUinSsA4FGxxNgJnLLKououwxUV4PGJmH8guXAJs3sHmIyYi4RcuARzs0Qt\nMEaVUV1gO0TTfI4cnsQV5En1ISQrgdAgDRR2mJPtXcZgnjbZby52E26GQAwADRe7UHUAHt15\nBFwFBKR0wbAfFwz7EQB2XHy0G2EX4jFRKvTp6q4tPPC/nbkjs2L77x9CYyXc8jj4RQAAtOgr\nzQ+6Mhpb7RN21Gsc5SRHDDpI2CEQ1wc8x9Rd/NDa5DiDehwXFPgISOV4UDBgWOfQUWsPMXYi\nMWocr2nBXN1sIyEIgStOSTm6XQ7q6s60cSfAEwhCEp68SjZqiej3X/VNmVYDt9Dbe7VsTCrD\nAoNt65j1JQUUAtEr2mqgtRKqjjq+iwvAw6myq1eHSO9ZF8o6CS8MFxDiKJ+585I+d3Ke0oYj\nWRXbf/8ASi/BjPth+CxLv4/cXtVmK7cczHmjWVcW4DZy/tCv/NyGdTOthyTKrsdTFuvQEjHI\n3DiujgjEdcqh5rxnCzf/q3hrrq62GzPW2Mwxetse2lSPxyfiwSHmHOiYhyceHdd+WyTGh49u\nbxIE5q6wi2/FMMJ32Ks2bZznLPmEWbattuZ7wPDw2VvlgTMxXEBJAoImfekaemtvX5C650E8\nIhpwHERicuZcYuSY3s6AQFwBlQch8xso3QW01rGB2LlQpavFkIBF0+LeoQgXAPCWDZmb+Oni\nURv+NUf51q2td43+XSZykAPSIbXNeZs/gOKLkHI3jL38uyzCe2ZS0N22ZjXqi1tS72tsLWA5\nU4Xq1K+n55uYVgfTXUYuDpgS84a1GeA+anjwg716QcQAgXbsEIirydfKo08VbDRff151+Oiw\nF8fKwxxakmIvoWuksaU9xaq9rxuGUfc9zJ44wpWXYq5uxOSpmKLnSuG+I94QeyZrKv4iBK6N\nOT8yhvaqlIbmXAAQukZG3rq/929msy6FB/XoU8Aw3eRY6QBtYs+e4hsbMF8/YuRYZ0chEDYY\nVKA81oONV3cbUtcEU2PfnhLzhpHRiilnk0faYTQa3/jnr8UXYfJSmHi5qsXchE9j/G/TGRtt\ni+Tm1+1mWIO1qTVUVzWf677KxYz4d2N851eqTru6BMf53W4uRIu46qB/BgTiavJu2V8AEKbH\npAzkSZgPy/dtS3y8K+PQ6WtL9i2i26oBQB402ye5UxoCgYCYPrv7TFM8xzTlr9Y1pAqkwV5D\nHieE7q4h81xD5gFAW91ZrfKw1VLUv+GCTuozhjF98398dZW5xaWnUo8+3S+J9BA3FfoGx/04\nAZQMcAq8h4Pv9bB3jGNkX1TdwoULjx46s/TRkVFzLCmOEwKXplb8vCfrRQCI81uweNQGihAD\nAInbJzl3JsQ1SDE2SDH2ypaHGCCQsEMgrhomjmk1tO5II2c34gBQ5sL/d3w9dO30I/EZN+Tu\nIn3TJULoJnKLuaJn8qX7F6tLt5sbjTk/xC2+SAgtWdQDxn1cuGsGa2wGAELoHjDu4yt6RJ/g\ncrOsqg4AuNJi+vMP8XGTiDETrrjqNuJmgGfBNntuV8esASkQkDIoC7oGeOutt/bs2ePm5iYy\nDsn/3ddAq0WU26Hmw23Ghqn3gEwBuTXbj+WvnBH/LgDE+S04krvCxFpyIXnJYgPcRl7V5SOu\nECTsEIirhgAnP6lyn91o8QAK1WEr03Qws7shOCmW+Fz5PoNelW1VdQBg0papCtd5+izmq5WY\nh6dL4PAhdxdoKvcBgDxoDinq+SS33+Fb1HY9XG0Nt20zV1pMLVoG1A2UJQvRT9BaKNkB6iLA\nSfAZDcEzATAQeYDfeKg5ZbGhpOAaDu6x11wY7ICiUqkAQK1Wr1mzxu7WuNtBpgAAsKay85BG\n3T9x/5HcFWpdWYD7qKmxK86UfJlXs4skRCNDH+ltMhTEVQQJOwRisOF5lm5TUmIfjBAu0SkA\n2l27fVW6oi1TA6d9dQXHoLxWA1ot5uXVjfoxH+PaQl0oNeWuNF/jySOpu+5TRN3T5SNUTXx9\nLebphXl693Z5ToIFO3Yx5NJTjZfSiKkzyVnzB+jRiOuU4q2gLgIAYFmoPgGUFPzGAwCEzAXF\nEGhTgtAd3GM6Zfi93tDolQdyXq9SnZWLA1Ni3wzzTOlxyKpVq1atWlWtTv32SPveGwY4B5z1\nP4ZU2P5ZDlaMv3/CPvP1nsznTxX9n/m6uP4AjN6MtN31AhJ2CMSg0lK+u+LoP2hdLU6K/Ub9\nR+HuzZW158LnMFpbf6x4z+3xy3Ix3OnaNTzPbNvMnj0JAJhEQi65D491XM3QxTMZJ8XW6FoB\n4ybOpdufnn6BSx7eVbEv9tA+Zv9u8zUxaSo5f6Gzy+sNeHAIMWMue3gfcJz9PY5nD+3HffwH\nKBc84nqEY6ClpEOPOt8i7ABAFgyy4MFfVP/D8cy60/NrWtIBoLE1v1J16rGUsz5ye78NA93C\nA2fnk5df+6dtk7dRdQAwLuLZzo/jgU8r/8W2J7X8JyTsrheQSzICMXgwhqayg3fTuloA4Bi9\n8vTLplhXINsFXJNrKo/xRk2xQZXd/VRccQHz5zZm3598Yz2bnmpWdQDAt7XRG9dAF0mASbF3\n8JQfcMpSSsLH9z47A75a6XAgX1djVXUAwJ44Qq/7iT19AmjaoX1fIGfOFby2Ao9zvGdJb/qV\n/ulbvqG+35+LuB7BcLArTO/8D6LriDpNllnVmaFZfbZyi62BkdGsP3P7yj/d3vtT8cvfM9qM\n7R8QHKPs6mtMjXsn2ndeQuCSR1POhHpO6fw4jmNsI2QBoPvUJ4hrCrRjh0AMHrqGC6xJY9uj\nodN8n32lZefHhtqLraJyjaTY3I+Rom7mYU+fYLb/bi4nyR4/QthpIL2er63BgkLae0wmNiMN\nWrVYWIQi+l7XkPn65myhNITUYqYLH9oOxTy9bJs88H81ZRXpGxJrWid0XAOXmcFlZrAXzgqe\neK7fM5JgclfqrvvpbZu49E6Z91mWLcjlvvxE8PgzmH9g/z4XcY3TVgvVx4GjwWc0uEUBa4Ta\n00BJwdjcbuPVf/u5DGs4UfhhXs0uASkdFfZYUuCyfpu6l7Cc/U81hutQA2Z/1qt5NTvN1yUN\nh/7MeHrp6E3mZrz/HcfyV9Ksztz0kETlVG+ta7kEABq98p6xO1wEFm9aHvhjee+eLv7CxGhF\nArdWQ511/hjfeQPwWtc0jKGpuei31urjjLGZFLpL/Se7R91NChU9j+yJrVu3HjlyJC0tLSMj\no62tbenSpRs3buz7tFaQsEMgBo/O4QiEyAPz9hEuvK/sj6+5yz+RJT7jRK7dVVNlD+4FuOwz\nxNBcfZ29hW3NWb3O9OWnfJMl/QM58xZixhyp7wQAACkQI0azqefMt/DQcDxhqHUcx/PzM7/e\n02QpE/RQAv5Nlv03Bl9VweVm4YnJ3az2ChGJqGX3szHxzOZ1dkXJMAAwGkzffCZ47lU7JYq4\ngVHlQMFvluvmPAiYAC3l0FrVwYaSgTSg3574V+Zz50u/N1+XNR7DMSIhYEm/ze4EHM+0GRuk\nIl9f16HuLmHNulLrrVi/22wtSxoO2TaLGw5ar71kscvH7zmS9x+1rjxQMVqjry67HDBR0XRy\nX9YrC4f/ZGJaj+a/e6nqtxadpUiM1lAnEXgY6BYCp4aHPDwx6uUBesdrE1X+2sqTz7LG9liu\n5uLfq8+9FTTxC0W0/UFHb3nvvfdSU1PlcnlAQEBBQUHPA3oJEnYIxOAh9hwm9ZvYWvO3uUmJ\nfdwjlwKA2GNoxPw9dWnvm9qUUr9J/qNWANa1mwTD2NWEBYLAJBK+zZKnAE8eaa0GCwCmo39a\nVR0AMIf2EhOmgFhsbpJL7sWHDuerKjFPLzwx2TZj3K6mS1ZVBwA/B3KPVPDJGvukI8zubXhJ\nETF9NiaV9eK/hXMQw0fhfgHMXzu4qnLQ6Trco2k27Rw566bbSLhpKd0N5l1qM8pTALy9Da2F\nzO/BMwn8JwEl6dPjOJ7NqFhn23OxYs1gCruThZ8cyHmT5YxigWLRiF/vHbdrV8aTlarTruKg\naXErghUd8pOLOvrVmZjWj/b4h3lNnZPwiYhyDfWc/OBEi9R7d5errWV50wkA2Jb2cJZys20/\nBqA3qV65pcZF4Ilj3SfHvNFozP6u4vgTnftZo7rs0HKO0XnGP9aX+T/55JPAwMCIiIjdu3ff\nemuva/n0CBJ2CMTggWFExC276y99pqs/L5CF+SS/RIktZbxl/iky/xSnZiFJzD+QV1ZaO/Cw\nCGLKDPbUcdC0YGERtjW7aH1dS/o6OYS2D+c4XtWIBQS1D4+JhxgHwRb5OvuNwOKZE0ZW8Oz5\n07adfHMze+o4V14qePIFIPr/DwDm5089/ARfozR9+SmwTId7dlIPcUPD6DpGttqpusuaj9ZC\nzUlQF0Di4+BEht0u4XiG5TscgNLM4P3/Vli/d2+WZZNMb1JtOLvw5TmVD0862pX9mPB/VqWe\ntTZZzqQ11Fyq3JBXs8PEtIkp95TYf4+PfA4AZCJfY2u7Q4hU6Gti23Kq/+g8J4bhAlJ6s6k6\nY0th5d8OAkqsVP79rCxgutA18oofkZKScsVjnQEFTyAQgwohkPuNfDvilj+DJn0pkIX0PMAR\n1KJl7Xty/j708ECQuZBzbyWX3kuMHme769Zc+JsOr+wwmCQwLx9nnhIv8bPrSYifSC5aRt5y\ne2cBxysrueJCZusm06cr6e+/4PJyevlOPYD5BVD3PYR1fC4edWVZmhHXJQJ5hyZGdIyT6LiT\nrG+AluI+PY7EhZHes2x7Yvz6f3OlK04WfmLbZDlTcf3BrowBIDl4+bIxW+P973B36fCtYmLa\nAEBPN+/JfL6wbi8A2B2qTop+hWWNPN8pCB1gSMAiAdG3bc/rkLqM//GdPBpt4VljXcang7ae\nKwAJOwTi+gPzDxS89Cbx6KM1yQXZ1Ju5u8bmbIx3GEjLGBqbZZk6kSV9HY+xdEoCCJzax5jn\nkXCnTTXNZwOnDZUGAgAxZbrg1XfwEaPtn7V7O3v2JF9fx5UU0b98x5WXQr+CxyWQjz1jKYBL\nksSMufiQpP59BOJaJmpJu4cChkHwLIhYCKS4S/vLWX2unAXDVsX53U7gAiEpnxj1ssPMIANE\n591BhjM4tLQS779w2Zg/IjqKUVsK6v4CgBGh/7h//N4Rof8YFfb4I1NOxvjOFwsUwR4dgqNc\nxYETo15eMGzVlS7/Osacob3vNlcRdBSLQFyfUFRt7SqVep95o8LYUlR+9B8xd5y2s5L6T6nF\nVpb6bpUY/ElWYpRqoyf/18knYIBtSXjsmLqgQFefLA0cJQ9tvyV3JcZO5FLPWV2eMImUr+2Q\n/ZhLT8VDHGcbvmLwkDDBv97mW1owqXQgjn0R1zLSABj5KlSfAs4EXiPAxRsAQBEPhmZgDdCY\nDnXnwbrxhJMgC+3zE0W+d4/dznI0Meg5VKJ85lao2j/OGIbH+y8yX7cZ63NrtnMcE+U7193F\n/iM2JGDRhbIfHc5p3X6L9Jkd6TPb9tbiURt2XHyspOGgkJSPi3h2Suyb2PWe0PlKoe3icRzb\nVHbw97zGQMIOgbheaa09ZdvU1Z/nORPW0atIHjjTb/R/ai+82yZSkmLvkKk/9TZcf4pb9BQ3\nByG6eHAouWgZu2cX39aK+foR0+cw6ztkNAWG6TyqX8BcXXs2QtyIEGIImt6hByMsZWGlgSAJ\nhPLdwBiAFEPofBD1Q2IKAIDBV3VaQ82Fsp+sTRwjb0v+TkTJAaBOk7nq+GQDrQYAMkt0z9gd\ndufFkd6zFo389UTBR62mBuA5a0I7AhckBd0DAM1tJedLv9eZmsK9pycG3mUWcK7ioOXj/+J4\n9mbzqOsMTklsg2Ed25CSa1bVARJ2CMRgUm5QGTk6QuxFYDgA6FVZtanvGjUlLp7D/Ea+TUn8\nezWbnT0p8sQc+Yr7jXjLJ+l5WlcjkIX2opqFExCjxhGjxgFtAkoAPM/6+vG1Nda76JwUMch4\nJYNnEtCtQEm7Cyu/9jma998WfXtBGonQe0Tow+brgzlvmlUdADCsYW/mS09Nv2Q3fGjQvUOD\n7gWA8qYTP5+YxvEMALCcKb1i7YjQh789MtKcbTi1/Cdl8/m5if+4u/CKAAAgAElEQVSzDkSq\nDgBcPIdplUd6sOnHZIkDABJ2CMRgoGUNi7J+2K/KAYAhEv+tCY+FcXTh9imMUQUAuvrzrbUn\nYxddwAkRAADDAIb1eNToM/SlltId7c3hr3VliVNSoWuUw1vqkj8asr/lTFp5yDyfYa9YFtAr\nzKVpMYxa/g9m++9ccSEmkxNTZ3ZV1gyBGDgw3D7G4nqkXtvBX1ZrqDbQahHlBgBNrR3SnjW1\nFvDAd3VserF8jVnVmTld/LmJabWtIXGm+IsZ8e9ShEt/rv46RxF9X4/CThG9fHAWc2UgYYdA\nDAZvl+7ar8oxu2Vkt1U/kr9uCyU0qzozBlV2W90ZmdtoessGLjcLMAxPGkbdcVc3gQ5Sv4lx\nSzKa8ldztM4tbIE8eE5vV9VSuqNk32XHnfpzdGtlcIpj7xxnwDy8qIf/ecXDEQiEGU9pTFnj\ncWtTKvI1qzoA8JYnNGjzrLe85UO6cYZrNXbIWMRyJmv+YTMcz7Ya6zo76t3MKGKWN+b80FZ3\npisDic84RUxfcxQPKNfzbjUCcf3wtyX1guUr+HRLCW3S2tmwpbnMrj+47EvAcUUi5gH2zJjj\nbz2ct7bc0K7/dPXnG3N+1FYdMmfxEnskBY7/X/CU73ql6gzNOdVn31CefqX+0v9s+1UF63iu\n/2u/IhCIXpES+5ZMZEk2ROLCGXH/tW68zRryvvWWiHKdl/RFN/OEeEy0bbq6BNW3dshDJBcH\nurmE9teybwwwjAifs1Xs6biajtgzOXzOVuzaPrNGO3YIxGDgQ3WoyqCgJG7Bc2rPvm7tIVmR\n4M+LHC4FgCYBP2M0UyvkAVrO1Zw6qi5IH/WmjBApT79cl27JbuUaMj9i7g7AcJ5nm3J/1lT8\nhZMSzyGPSf0mdb+S1urjhX/O4llj51s8R/OsqX/98BAIRG9xFQc9OzMvp3pbneZStvKP7Rcf\n2X3pmZTYtyZHv6aQRD47M7+obh/HM+Fe0yRC727mGR/5vLL5fHb1HwAgFwcZaY2BbrHelQh9\nlozacNNGv3YD5eIXs/BkXfrHjVnf0nrLricl9vFM+KdP8ks42deT661bt+7cuRMAqqqqAODs\n2bMPPPAAAHh6en7yySfdj3UGJOwQiMHghaAZfzZlWpsvB8908RwWOv1X5fHnabpRSCv8m1LO\nuApzpKYhrVAmhlphe1r9En3jkeaCWQRlVXUA0FL+p6pwg0AWqspf05hrSTelKlwfddthWcDU\nblZSm7bSoaoDAGnAVPwKyjDp9eyFM3yrFg8Nx+MSej0cgUB0QkjKEwKW7M9+tdVQCwA0qz+Q\n/bqPPCnGd56QlMX5L1A2n6/XZAcoJFWqs2pdub/bCF9X+3AlAhfcNWZLi77SSGvqNFmbz99l\ne3da3L9DPHr4HXjTgpMufiPf9hvxlqGlgNHVky7eItfo/grJSUtLW7NmjbVZVlZWVlYGACEh\nIUjYIRDXDVPdY86NePXn2lNGjrnNM2mBZzIAKKLvda3yNu3fjvPkg0nMb/6WY9Axzfa/obWs\nwaApsussP/Yo3ykHa0PW190LO1PHLE0YTmKEiGcNssCZIU442HFlJczOP/i6GszTm5y3AAsI\npD//iG9Rm3M6EZOmkvMX9jgJAtEneNA3AGsCFx+4gfeX6zVZZlVnpbh+f4zvvFZD7eqTM+s0\nWQBAEiKGtSQunhb3ztTYtzvP4yoOAjE0ttoXmydw4cAs/AYCw0VuseAW27+zvvvuu++++27/\nzmkLEnYIxCAxSh5qm+PXDObnj/PkcQX/m397SZ+z7ryQx4yYZdNOSginuEWJOu2ldVZ1AMAa\nmrpfhsRntKG53c9G4jsh+vYjPEc7TJVi/8RWLb36B9DrAICvraZ/XUWMHs+3qOGy8yD791Fi\n6ixMctOVIUIMGqwR8teBpgwAQOAKMXdDL9MEXTdYAybseg7kvF6ryTJ/4qyqDgAO574zLPh+\nO585I6PJrNqkN6kC3EfJRH5agyUhkZhyj/LudbgV4roACTsEon9geC5NW2HimZGyEJHT2wh4\n7BA8eWRe01m7/gcCJv3VlFlpbA4Xe34dvSxQ6A5Cd6+Epxqyvup+Qlng9O4N/Md+oGu4qG/K\nAACBPCx48rfmYt/OrJYvKTKrOgsmE19Z0dGCB7UKkLBDDBAGfcuaox5l1VLwridSTC2y4q2Q\n9NTVXtXAoJBGRnjPsJaIFZDS5OD7AEDZfKErt7g6TbatsNMaar47OkqjV5o31KfEvFbbcqmm\nJcNLFjczfqVcHKBsvlBUv09ASpMCl3Xvroe4jkDCDoHoB2pNmlnpn2e2KQEgWKTYnfRUgpPb\nCBhGLVs+pCAMlOttu+/2GfVdzN061uRCtEuuoElfukcu1TWkki4+ZQfuNgfG2s6liL7HZ9gr\n3T+QEvvELk7VN17kOcbFcxhG9OY4huz0jeHhAeUllmseQCjEvHx6MSEC4Twsa/rhK4my0txy\n4y/mk6/o6kWsEXr1f/H1AgbYsjFb/y78uKLppFwcODHqJYUkEgDcXELqNJkOh3jJYmybJ4s+\n1eiVcHlD/VTR52/MV1sLaaSW/7Q97R/m6yO5Kx6fes48P+J6Bwk7BOJKYIwqbdVBAEweOJMQ\nur1avM2s6gCgwqB6In/DieEvdT+DqnBDU+5PHKN3C1uQMvTZpXT+pvoL5lv3+Iye7BYFALaq\nzozUd4IEjwSWbRvyREP2N+ZOUuwVNX+/QB5OOJebFcMIF6+Rzr+sFTwsEnN141rUlvqwLhJy\n9nxGr+dyswAAhAJq0bJuEu8hEH2BKyniL6s6ABDyjXI+Uysc1elTcuMgJGXT4/5j1zkl5o3i\nhoO2h7Bmxkc+b6fMVK0dHHNpVqc1KM1bejzw+7P+Zb2lp5uP5b+3cPjP/bh4xNUCCTsEotfo\nGtKK/pzFGJrALKpuO3xeW2ZrcF5b1k06eABQFa4vO3iv+bqt7jStr904/n//8J+Q01aTIAmY\n5h7jcBTf1kr/8j1fWQ4APt5+kumr2rQXSbG3Z/yjlItvf72dAwx6vqEe3BWYVEY98iSz90++\ntgbz8iZnzcPc3KkHHuWrlby2BQsIwqSynmdDIK6Mtla7DopvDUi5lot2DghBirFPT8/MqFzP\nsMZYv9u1BmWzrgzHiNyaHZ8fiA32GD8jfqU5152Pa1JuTXtxGrFAgWPUb2fvLKrfLyCkOlMH\nf1xVW/FgvwliYEDCDoHoNVWnXmAuxygw+gbl6ZeDA2/JaWsvkxokdO8+O1RT3i8dm6sDx386\nwz1uhntcN6PYv3aaVR0A8PX1sgsKxT96cLnrO+yFs8yO38FkAhwnpkwn59xK3fewnQ3mH4BB\ngF0nr6zktRosMBipPUS/gIWEAUkB055D2/O2SPnYq7iiq4ZCEmkbAFvTkv7DsXHmPbzG1vza\nlkuPpZzFMWJi1Ev5tX/WqC8CAEWIb0/+/o/U+0saDgGAbWExM36uwwbxDRADCBJ2CEQvoHW1\nqvy1uvpztp36psw3Jn55RJ1v5CzZ4VeE3dr9PGyD0rbJ0zqeYzG8h88jV17asVnSlWV/wbe0\nMNs2W/6Uchx75AAeEY1HOd5QbIdl6TU/cPm5AACUgFp8Nz70mq6ZjbguwNwV5KK7mO1bwKAH\nSkDOvVU4NuiqrESjV54t+VprqAnxnDgs+AF80OsQsJwpo3J9c1uJr1tyvP8dWVWbbU9mq9Wp\nDdpcH3mCkJQ/nnKutOGIzqQK8ZgopGSbzi2xnYfAKZajAcBHnjg19t+D/BaIAQIJOwTCWYya\nkrwtI1ljs12/yC060TUyY9Rb6+rO0hy70Ct5jLzb2osMI61X6FzbO2TU0B5VHQBgcle+oc62\n2av1XwG8ssJ2gwQA+PIS6EnYsWdPWlQdAJhM9JYNwvhEoG7chGOIwYIYNopITOabVZibO1CD\n7VvXrCvV6qvFAo9VxyfpTI0AcLFidVnjsTtHrB2Ep3M8e7zg/fSKtSzPMKyh9XLikoSAJW4u\nwXbGLGcyX+AYGeE903xNszoMw3metZpF+cwdEfKwkJSFeE7CMaQHbhDQPyQC4Sz1GZ92VnU4\nJfUf+wEAxLj4/DfsNmfm4dvaPNXDGKxNLcvmgZPpQ/3FS50ZSEydwRW3Zxklps12eu1XiAPt\n6ISa5JU2OZAxAJOJb6jD/AP7dWmImxWSGvzIa57ntqU9fLFiNXTMCQwA6RW/zox/Xy62d0Xo\nd/4u/PhQzlud+7OUm29P/sG2RyGJ9JG314Dhga9uTjUyLf5uI+P8F2Yrt1hvDQ95KNbPqW8t\nxHUEEnYIhLOYtOV2PQFj3lfELKd6mSAVc3XF3Tz9VJP9VJN4jMd4nBw51JmBeFSs4JmX2bTz\nwLJ40jA8fMBzE2ABQXh0LFeQZ2l6eBJJPR+qYu6Kjm0Mc2vv4Wur2fRU4HgicSgWFNKv60Ug\nBoSMyvVmVQcdcwKb0RqqB0HYZVVt6urW4bx3Ir1nNrYWag3VIR4T5w/9iricmdLEtK45Naei\n6SQAuAg87hix2lUcWFi3V0i6jo14Os7v9oFeNmLw6R9hx3HcyZMn8/Pz5XJ5SkqKt7fjPIcf\nffSRTqd75513+uWhCMRgwnMmF68RLeW7rT2UxN9n+L96EZLHc00Fv2or9+MCuce8W6mdp3lN\nC8ZjeEISMTHFyTmwgCAyYBD9ijCMeuAx9twpXlmJKTyJcRNBJOpxED5uInbulLkiBQAQk6eD\ni6VsNldUQP/8LbAsALDHDlL3PIgnIZdtxLVOVbN9CnErYsrdWz5kENbAX85byXf60tEaqrWG\n6nj/hcvGlNqNOlHwoVnVAYDO1LQr/Z8vzamYm/jZgC/3RqHVUNtmapQIPKWi/sk80Nraunv3\n7u3bt6enp5eXl1MUlZiY+MADDzz00EM43j+1aPtB2BUWFt55552ZmZZ8iRRFPfHEE++//77L\n5a9yKx999FFTUxMSdojrC3XxlqrTL5m05SK3WLEiQa/KAgBC6B4y9ZdeJVpQnn6lLuNT87WK\nWB398BExGwBiF0zhMSDr7i8Ighg3CQC4Sxfpn7/j9Xo8MpqcPQ/E9h9wK5hEKnj+NTb1LN+q\nxcMi8ZjLob4Mw2xeZ1Z1lo5DewVI2CGuebrakHMReN45Yk29JrtOk+khjQrxmDRwaxgSsKi2\nJQO6/tLJq9lpYloFpNS2U6m+YNts0Ve2Gmr7S6PcwLCc6WzJN2dLvla1WXIBekijxoQ/OTrs\nCcK5Oj1dsWrVqueff14gEAwfPjwxMbGuru7UqVMnT57ctWvXtm3b+kXb9VXYabXaWbNmlZWV\nAYBYLAYAvV7/xRdfHDp0aPfu3SEh6JwFcX1jaM4pO3QfxxoAwKDOI4RukfP3AoDEezQhdHd+\nHp4z1dtUA+NYQ0POdyHTVvf3evsDk4k5dogvLQKpjJw8DQsMBgCuIJdeb0nRwjbU8c0q6sHH\nuptELLbfhmRZ08f/tW7jmeHV9j6LCMQ1yIiQh08Xf9FqqDU3hwXfPyN+Zaux1ksWdyD7tdPF\nX5j7EwKWLBm9sftUR1fM5OhXWc6UXvErxzMJAYuSgu7ZnfF0peqM1YDnOY5n7Ea5itvjKngA\nAeHiIvQciOXdSOhMTetP31ahOmXb2dRa+Nel57KUv98zdoeL4Mp/jQcFBX3zzTd33323q6vF\nXzknJ2fq1Kk7d+7ctGnTsmXL+rR0AADoqzb8+uuvy8rK3Nzctm7dqtVqtVrt5s2bg4KCsrOz\nJ0+eXFIy4OkYEIgBRVN5gLNxqWGNao5ukwfN7pWqAwDWpOFZo20Pra/v1Qy8upnLSOMK84Hj\nejWwt9AbVrMH93DFhVxGmum7z/maagDgLqba2nD5OZaisTzPFeaxF87yDT28Dpeb2VnG4aj+\nGOJ6QCL0fmpaxvS4/4wKe/zOEWsXDv9ZLg7wdxvRqM23qjoAyFJuLqzdM0BrwDFyetx/Xpxd\n+vKcyrmJnwW4jRwd9k9bgzCvaSLKzW7UxKiXRJRFQGAA0+JWoOjX7uF5buO5xXaqzkpF08lN\n55bw/JV/Cd95551PPPGEVdUBQHx8/PPPPw8Ax44du+JpbenrP/DWrVsB4OOPP164cKG5Z/Hi\nxdOmTbvzzjuPHTs2ZcqUI0eOREai8nOI6xWctD9w7NzjDKTIU+Qeb2jOsfbI/Kc4P5zLSKN/\nXw80DQBYQJDgsWdAOCDVMXlNi6U+mBmaZlPPkvMX8hzb0Y7nWRZjGHrV11xpMQAAQZDzFhAT\npvDVSq66CvPwxMMiOrxCTXXnx3GV5ezpE+ajXgTiWqao/sDFijUt+soGbY6f2zBz2Gm9NsfO\nLKt6S7TvLT3OVq/N2ZX+RKXqtFTkNy327eEhDzm/EprVF9TuNtDqMK+U2Qkfnyn+wkCro3zm\nzEv6orOxhzTqmRk5FyvW6E3N0T5zw7ymOv+gm5NM5abShiPdGJQ0HM5UbkoK7IetNStmnSfs\np2/1vgq7vLw8AFi8eLFtp4eHx/79+5cuXbp9+/aUlJSjR48ibYe4TnENnU+e9WQMjeam0DVS\n6udAhRiac+ovfcEYGmUBKZ7xj2G4g5xtYTPWl+xfYmwpBAD3qGXeQ19wdhEcR2/dZFZ1AMAr\nK9kTh4kZc6/gdXrGYB/xBwY9ABBDkrj09k07PDQck8rY0ycsqg4AWJbZvZ1vamRPWn504onJ\n1D0PAmY5liKi49iDezs/kPlrJzFmAvST1zACMRBUqk5vuWCpAVjWeHzd6VufnZFLEiJPqX1O\nx8zKDSkxbygkEZ3mAACo12Q3tRV5SmPWn77NXMKrRVexLe1hd5cwJyVXm7Hhh2PjzY5fJC5c\nMPynl+ZUdD9EJvKfHP2aM5MjACCt/BdnbPpR2PE8v3btWgC49dYeMts7SV+FnV6vF4vFtpuK\nZgQCwebNm++6666tW7dOmTLl6NGjUVFRfXwWAjFoqBndq8Xb9qqyXUnx45O+mlOy1dBS4OI5\nzG/k2zglsTM2NOfkbRnFMToAUJf80VZ/PnTams5zij2T45flmlqKCaEbKXYcOe4QXt1sVldW\nuBrlAKW6xzy9MHcF36yy9uDRcQCAJw0jNS3siSO8rg2PjiVvWwwAfG1Nh8Esa1V1AMBlpnM5\nmfiQJMvMIWF4UjJ3Kd3+kSYjr9Vinb5DEIhrh7yaXbZNta6spiU9SDE2wH1kmGdKaeNR6y2G\nMxbXH1CEORB2O9MfP1/6PQAQOMlyHZzh8mp3OSnsThR+aHXnZzjjnxlPJQUuwzD0u6jfqFan\n9mzTfKFHG+dZsWLFmTNn7rjjjhkzZvTLhH0Vdh4eHnV1dRqNRi6X292iKGrjxo1Lly7dtm2b\ned+uj89CIAaNB3PXbm+0SJB/tio3Jb+yxHtEV8aNOT+aVZ0ZVf6vgRM+I4UKOzPW2IxTMqFb\ndHsXx7FHDrBp54Hn8KRh5Iy5QDr4SGKubnYlMjGPXujC3oHj1PJ/0Jt+5WtrgKKIKTOs6UiI\niSl28RCYV8dlYBjwvG0HX1sNl4UdAFD3PMSOzmdWfd1hkECIdfr2QCCuKUjC/ozMGho5PvJ5\nW2EHACQh7jxDUf1+s6oDADtVBwAk7uwZXIM217ZpoNVaQ7VcjLJ/9xsmRtujjZHR8sD3S5TM\nV199tWLFiuHDh//yS887hU7SV5mfnJwMAKdOOXYzpChq06ZNt912W3V1dUpKik6nc2iGQFxT\n6FjTrqZLtj0b6893Y289qL0Mz+gbAIA1qMqPPFS4c3rV38/lbk7K+FmR8ZO85vw7Vjv26EFm\n/26usZ5vamSPHGD2/en4AQRBzm3fosfc3InJA+gog/kHCp5/TfD2+8L/fEzO7O7Alxg9HvNr\nT85MjplgP1Wn2Ai8Uxo8LCzcelyLQFybDPFfRNnINV/Xob6ull8sYV4pbi7t+R8kQu9on1uy\nlVt++XvGD8fGHctfaU5oXKO+yINjcJySiHxPF39hG+LaFV6yONumiHKViXqXIB3RPVKRX482\nMrF/v6i6Tz/99Omnnx4xYsTBgwc7745dMX3dsbv11lv37dv322+/zZkzx6EBRVG///77okWL\ndu3a5dAAgbjW4MD+G5jtNgZK5p+iKlhnbQqkQSLXKFpXm/VrKM8ZAUCrPGyZmdHXXFghUsS7\nRywBAPZSGvDtqobLSIN5Cxw+gpiYgoeGc8UF4CIhkoYPUOSELZiL/YmzAwQCwdMvc1kZvKYF\nCwnDg0N5jmPPWX7m4RFReIJ9RQ3My8du9xGPT+y/VSMQA4K3fMh94/86mveuRl8VpBg7I36l\nNbZUSMofnHjocO7btS0ZXrK4qXHvlDf9vfGcxe+8UnWmRV85f+hX7pIwOyGAYwTHswDAcfSe\nS8+ZO8dFPHNL0ufdrGRS1Cs51dua20oAgMAF85K+ROew/Uuk18zU8p96tOn7g955550VK1aM\nGzduz549nf3Z+gLG8139inCK6urqwMBAiqKKioqCgrpMiG8ymazaro9PHGhee+21Dz744MiR\nIykpKVd7LYirxvxLX+9uyrQ2f41/8F6fMV1a81zliacacr4HnhPIw8JmbJD4jC3Ze4e6dJtD\nc4+4h0NSVgGA6bP3udoa69c9JpML3ny3h5XxPJebxTc2YD5+7Yl/ux+ha2P/PsY3NeJ+/sT4\nySAY2NLpXFEBb46KjU90uBXHnj/DbN8MDAMAeHQs9cBjQAyQxyACcRVYf/q2vNr2jQwcIzCM\noAgXASnR6JXmTooQ06ze4fDFo9YnBd7dzfw0q8ur2amnm8O9pntKo7uxRFwB9Zrsb44MYzm6\nKwMCp/45Ld1bFt+Xp7zwwgufffZZSkrKrl27pFJpzwN6Q1937Pz9/XNzc2malslk3ZgJBIIt\nW7YcOHDgGld1CISZ1XH3v1i05a+mLDdS/FTg1O5UHQBgeNDkb/zHvMcYmwSyUAwjAMCo7TKJ\nIyGwbLnjCcm28Qc9F9fiefqX77l8S4YFPHkEtez+HoYYjfRX/+ObGgCASwcuJ5N6/NkBDUHF\nI6Mhsrs/NsSosXhUDF9VATI5HhI2cCtBIAaHnOqtB7JfV+vLA9xGzhv6pYnt4HTE8izGsyxn\nMtDq5ODlGGAXK9Z0peoAYF/mK90LO4pwSQy8q99Wj+iIt3zIjPiV+7Je6cpgRvx7fVF1HMc9\n/vjjP/744+zZs7dt22au7NC/9EOiwpgY+3hvhwgEgnnz5vX9cQjEIOBJSdfEPdCrIYTQjRC2\nZwd18Riub8zobIaTYo8Yy8zktFnAMtzFC8BxeNIwck4Pse5cXrZV1QEAl57KjZ9s0UZ6PXPi\nCF9dhXl6EZOnYXLLxj6blWFWdZYh5aVcWQkefpXTD2Fu7phb7zI8IxDXIEZGo2or+f3C3Qxr\nBIDypr/XnZ4/JvzJkoZDVhvbXes2Y4OtQ55DNAYlzeopRxEYiMFhYtTLFCHel/UvuqNGpwiX\n2QkfjQl/si+Tf/rppz/++COO4wqF4oknnrC9lZiY+OKLL/ZlcjNXMwP1K6+8smHDhqqqqqu4\nBgRigAia/M2xqgMJbVUAQAN+QeIv4vkUvwl+yS+JPS4HihIEOX02FzsEI0ksIKjHGAK+scG+\np6EeQsKAZU0/fsUrK82dXGa64Ll/WWq5trXaz6LV9PHVEIibB5aj24z1nZ3lSxoO77j4qKqt\nmMAFLGey9mv0ygC3UQHuI5WOMmJQhJjptFeHYbhtJQO5OBCpuqvOmPCn4vwWpFWsLms8rjc1\niQWKUM8pw4Mf7KpqsPM0NTUBAMdxv/32m92t2bNnX/fCTqVSKZXKq7gABGLgwEnRhqEv1yoP\n+9Gt2SKvSoGrlBCqJ31G2Hg688pKes0PfEsLmOtJ/ONJcOmurAXmax/+hvv5AwBXXmJVdQDA\nq5vZ7EvEyLEAgIWGdxhAUlhwaN/eDIG4WThZ+Omh3LdoVi8WKIYG3j0y7FEfeSIA6ExNG88u\n0tPNAGCr6ixgUKO+6HDCYSEPsKzxYkV7nkuZyP++8X+tOjHJRGsBgMAFcxI+GaDXQfQKuTgw\nJeZNcOpIshd88MEHH3zwQT9P2hFUMw6BGCiWew2/R5WTcfm393LfsUTH+DX6j41mVQcAvLKS\n2b+bXLAYugaPiiGGj2bTzlmblrCDtjZ708sbdXhwKHnLbcz+v4BhQCgkb1+Mudsn2EMgEJ0p\nbTy6N+sl87XepDpT8tWZkq/mD/1qTPiTVc3nzKquMxKhl6so0BzuakVEufm5Jo8JfzLW91YA\n8JRGN7YWmG9pDdU02/birJJs5RYj0xrjO88uoQkC0VuQsEMgukPDGH6oPlFiaBwmDXrAbxyF\nORe/yXOVfz8Tl/3du7LwbV4jOXnEAt+xrwTP6mDDsnx1Bz8ErrKc5xgM7+5TSS69Fx8zntm6\nia+r4QrzTZ99QM6ah48cC5QA6PadAzyiPXyBmDKDGD2Bb2nGFJ4DHRKLQNwwlDQc7ty5N/PF\n4SEPicguU45NiXndQxotE/lrDe3FkVNi35oQaSkhqDXUWFWdmYyKdbcmfzMq7PF+WjjiZgfl\nv0EguqSVNY5Kff/l4j++VR57NH/dgsxv+U4p7hzSkPN9Q9bXPM/O1RT+UPzburI//h06T2RX\nQJYgMFn7n4dGeVoetSL9R3HhzulGTbH9jDbwTY18XXssLbN/N/ActeQeS+Y5SkDetggLDO4w\nRizGfP2RqkMgnEdMOYjvYThjs640wH2Un2uyucf2G8FbNmRk6GMYhi8etV4qtGTnjvdfODb8\naZsZ7Msx17RknCv9Vq0r68/VI25i0I4dAtElm+ovFOjqrM2/mrJStRUjZT0EtQGAtuqQbdPQ\nnEu3KSmJvdctMXMu88dGAGiRFNYpTgIPwINWebhk36K4xY59dADAVtVZeupr8aRhgiFJvLoZ\nc3UDkgSWBQCUHw6BuGISA5cey39PZ+pQV4YixAqXcAIX3D9h37H8lUp1qlwcVNn0d4u+CgDa\nTHXV6tQQj4lhnikvzC6p02RKBF7ukg5+rgJSJiAlJqbdfeBIzOwAACAASURBVKJSdapSdYoi\nxPeM3RHh3Q+ZbxE3OWjHDoHokmqj2q6nyujYscYOUtjhtz6GEYTAQWJxYvR46vFnicnT2sKN\ntv36xnRTa0VXk9tXaLVW7iIIzMMTeJ7ZtM741kvGN1+kf10FqI4fAuEc1eq0PZkv/JnxdFnj\nMQCQifwfSzk9PPRBAWnJ0krggluS/o8kRAAgEXrfkvT5I5P/dhMHmlUdALQZG7elPWS+pgiX\nQPcxdqoOADacud1W1VmhWb3VpQ+B6Atoxw6B6JKx8g5fykKcHOHEdh0AeMY/qipYx7GWMxdF\n7AM45Ti3OB4WgYdFEMf+gpwjHfq7zndADBvFnTvNVZRZmlOm28ZDMPt3W6MruKxLDCUk77rP\nmTUjEDczRfX715ycbb4+W/LVHSNWDwu+X60rq2/JkQp8vTwnR3rPjvSZ5Sm1D5K0S2vS1Fpo\noNUiqj2lZUbluosVa3meHRKwKDFgaWXT6a7W0Nha0F+l5RE3M0jYIRBdMlMR92LQzE8rDwCA\nGKe+iF4aJHQqra6L96ioBccaMr9kjWpZ4AyvhCe6t1dE39eY86O16RoynxR7dWlNktQTz3HZ\nl3h1MxYUgndMaGKbwRgA2PzsHj/kXGEee+Qgr23BwyKJ2fMwST/Xt0Egrn2OF3TIQHE8/z0x\npdhw5nazW61KV6hqK00OXn6h7McGba6PPHFo0L0ETgGAq0sHf1YR5Sak2rfnU8tWbb/4iPm6\npOGwtaSYQ7xlQ5CqQ/QdJOwQiO74JPLO54KmlRma4lz8PCiJ8wMl3qMl03+1NhlDEyGQYbjj\n8AWp36TIeXvqM/7HGBplgdN9R7zZw+w4jicmO7yDCYW23tyYQNj9THxVBb36R2BoAGDra/m6\nGurxZ3tMlYxA3GBo9dW2TY1euTfrRdtgqQZtzvt/eXGXS4imV/76wIQDOEZMiv5XTvVWE2NJ\nMDQ97j+24uxC2Srbac+VfuvrOrSmJd3ag2OEOTeKiHKdN/SL/n4txM0IEnYIRA8ECt0Dnduo\n6wzPmfSqnPLDD+ibMjBc4J30bMC4D8HRj3J58Bx58Jy+rRQAAB81jqsotzaJ0eO7t2fTU3mG\nvrwgjCsr4VVNmIdn31eCQFxHBCnGNbbmtzc9xiubz9nZcDaF4UsbjpQ2HI7wnukti39mRk56\nxVojrQn1TFGqz/929g4PSdT4qBekQh8To7WdQW9SmRitjzzBnPHEzzV54YhfGjQ5HM+Ee02X\nCLvep0cgnAYJOwRiQGCN6vJjj7SUbgeONf/u5zlTXfrHIvdYj9iHnBjPAoYB3uvwJmL0eCAp\nLu0ccByemEyMmdDDAIbBABiirVmWRRM6F6O/N23sYQgCccMxO+HDem2W2WHOSxZ7W/I3m88v\nc1gWzIpaZ/kF5SoOmhLzBsuZfjw+wToko3Ld6PCn3V3C6rUdvSM4Wq0r//dtOvxyUsy+VJRH\nIDpzNYXdq6+++sADD1zFBSAQPdJWd7bmwgqTptTFa7j/mJUCWaiTA6tOvaAu3tK5X1O5vwdh\np9fTWzdy2ZcAw4jkEeSCxUD1Lv8cJpMDTfPqZq64EI+JxxQe3Rjj8YnG83uK/DayhB4AmmVZ\nxrx3gn1/7GYIAnHjIRF6PzblbL02m+MYH9dEHCMXDFv1/bGxDGufds5KgPso22ZF00lbIagx\nVB/MeQ0ARJSbkW6xPdU1MlqNvsrNxalILASit/RV2LW2tmIYJpE45Xt08uRJmqZTUlLMzcjI\nyMjIyD4uAIEYOIyakqJdM1laCwAGdV5bw4W4xek42WW8amvNiaa81TxrcAu/Q1Ox16ENIXBz\n2G+F+XMrd8mSxI69cBbELuT8hT0u1aQtrzj+uLbqsAuEhJTPxVgeAHh1M11bLXjuVSC7/KTj\n0bEtI3m2tr0weWPuKv8xK0mxfVIVBOLGBsNwcylYM76uQ1+cXXa25JsT+R+wfIeCsAROpcS8\n5es61NrDcMZaTYbDaQ20OsxzSmnjMWuPiHKViwP7e/mIfobm2UPNecfVhU10mwclmewWNcM9\njsT6miSOZdmVK1eePn06JyenoaFBJBKFhIQsWLDg6aefVij6p95jX4WdTCbz8PBobOyQwnHB\nggVyuXzt2rV2xrfffntTUxPPO5W7H4G46rSU7jCrOjNGdYGu/pzUf4pj4/LdxX/NN1+rCjc4\nDGvFCZFn3MPdP5TL63Bww+VmgxPCrnT/krb6cwDgonYzqzozfEM9p6zEQ8K6GUu7E1DbocfU\npuyFsOM4LvsSr2rE/ALx6FhnRyEQ1zxSoc/0uBXDgpenlq3S0+oonzl+rskN2lxv+RBXcZDV\nLLNq0670J/R0M3a5EAXf0ZFWLg70ksU2aPMAgMSF84d+jTtZnBBxlTimLng0f71tgvr3y/fG\nuPj8EHPvZLeovsxM0/Tbb7/t6+sbHR09evTo1tbW1NTUd95554cffjh16lRISD/s4w7IUeyO\nHTs8PLo7/UEgrgs4zt7bjGO79D9ruPS5bZOnO2QGFrrFuHgmeyc96+I9CrrHrvCXsIewVgCg\ndbVmVQcADtIl9PRTSuozrin3J2uTELqJ3J0uQ85x9I9fcSVFlrEjx5KL73Z2LAJxPaCQRMwc\n8r61aXeEqtFXbUt7kGb1YFNezO5D6O82YsHwnwpq/zLv3nXOWoy4ptjVeGlx9g9GjrHrz9fV\nzcr4fEvCY/M9Eh0OdAahUFhWVmYr4Ewm00MPPbR+/fqVK1f+8MMPVzyzFVR5AoHoEtfgW3BC\nZG1SLr4Sn7FdGdOGBtsmezm5vEAeGjL15yHLcsNmbpT4jOvxocSocR2bXT7RCk4IrX9KtC6l\nHMZabvDAilk8IKjLkQAA4BH7oCLqHvM1KVSETl9n+9bdw2WmW1UdALAXzvDV3WXq6jt8Qx2z\nayvz+wYuI61HzYpADDSVzWfNqs7KhMgX5yR8Ym0Guo8ZFfY4iQvj/RcOD3kQqbprnFqT5t7c\nnzurOjNGjrkn56c6k+aK58cwzG5bTiAQPPLIIwBQWFh4xdPagqJiEYguEXskhc5Yrzz7mklT\n6uI1ImjSl4RA3pWxzG+yvjG9c79JUyb1n+wwxYlDiJQZIBJxly4CjuPDRhEjx/Q8ROjuGjq/\npWwXAOgFDVXee71VYylWphfW1ilOxcJHOFDdjcfw0Bnr/Ef/l9bXihWJDotksMZmfVMmJfEX\nunbwi+WbGu0s+cZ6zN++Km5/wddUm77+FGgaANgLZ4hqJTn31gF6FgLhDBKBvdOFtzx+eMhD\n0b7zKlWnZSK/CO+Z6OD1OuKzyoMapsuIGQDQMIb/VR78MOKOfnzoH3/8AQBDhw7t0dIZkLBD\nILrDLfwOt3CnPsB+o/9raClwGDNhUOUI5RHOPhLHifGTifGTnV8kAIRO/7Xm/DvaqoO0rlYL\nJdqAEustjjPi0HN4k0AeJpA7dsVTF28pP/KQ2d1QEX1v6LQ1cNmDGPP1szPGfP17tfJewZ48\nalZ1AADAs38fIWfdAgT6q4m4agQpxgW6j666nPTOzSUk3v8OAPCSxXrJkMvp9ceuxks92uxs\nvNR3Yffcc88ZDIaWlpYLFy4UFRUlJSW98cYbfZzTDBJ2CESXsKaW1tqTGE5J/Sb1eDpJCOSR\n8/bQbdXNRRurTr1oe0vkPpDf7wYDc+wgX1HmKx8RMOUVVfNf5Uf/Yb0p9Z1ACruMtGKMKuB5\nUmTxiNVWHaw69aJBnS92jw+c8H9S/8kAwNLasiMPcrQlsb6qYJ08cKYiZrm5iccl4EnDrGG8\nxLRZmLfPQLylGV5rm+4VA4bhdW2YrMttVARioCFw6oGJB04XfV6tTvOURo+PfN62UCziuqPE\nYH8K0ZlSQ2Pfq/quWrWqrc3isTNnzpzVq1d7efVPhmok7BAIx+jqzxf9NY/RNwCAUB4edftR\ngbQHZzUAoCT+XolPq8t2tFYfN/d4D31B6NqnKKru4Hl67Y9ccSEAAA9sziXFs6+YRlbWZ37B\n0a2ywOnBU753OI4xqsoO3K2p3AcAssAZYTN/4+i24r0LzQJO13ixeM9t8ctyKRc/gyrHqurM\ntNWdsQo7wDDqnge5iSl8YwMeENR5A69/wUPDubxsaxPz9EKqDnHVEZLylNj/Z++8A5sq1z/+\nvOec7KZNd9N075a2jLJnGSIbUWSogAgqIqL+XNd59SLKvc6r1wEO3AsVlCFb9m4pLaWDTrr3\nTtLknPP+/khJk3SlSQoU3s8/97xP3nV6S/3mfZ/x8vXeBcExCBDdCp072BlhEG1/Vd/m5maM\ncUVFxeHDh5977rlBgwbt3LlzyJAhdk4LRNgR+g7+QjJ34ghu1VIRMcyU26GnoqWOWRTjF/P/\n2FhyRMvrZ3nEfxSxyFMgt22qoqNrDKoOAFob80pP/SNoyvfWDESUIHzOwcaCHa1NBTKvYTKf\nHop62QOuqmxTdQCAALRa/vw55eRXlcNexbweUV261pWceLqxaA/DydwbBgmqBPWVz1MjRpgK\nOE7X0FRy0C38XqGTZcItQQcLFRgM3WZUcRT0uIl8USGf3nZXgoQiXFvTfQZmAoFAsJ4BMt/T\njfnd94mVOcbhBCHk4+OzcOHC2NjY2NjY5cuXX7jQeTbEXuEAYdfS0rJq1SorjfYvR+gX8JfS\n9D98ZXjmykpxQ71g8dJuRziG94sPbChs83LbUpnUyuv/iFtt00xYXWP2D0xdfd76wQjRLsFz\nbVoXAADzLCCErHC4xlqNpUXTZulG1QFAU8lBmheHli5gOCcAgGzgG/ORCGHUHmeKKCEACGQq\n9+gVxnwoApmvR/SDvXgZx8IwVESUUdjxpcX6H74Srnmq+0EEAoFgJQu9hvYo7BZ6DXXsogMG\nDFAqlampqXV1da6uNpYmN+IAYafVajdutLzu6dRIuHXgks+aNvm08zB/MQi6jc10BBZ+r7tr\nL+l4VkjZ8HuORPJgbX2msd2L6Ac74FrrrxxZVZ+3FSHKNeJe/7EfdlPoAgAopS9ykuPmds8z\nK1MEMyI3l0avNlVnGFjeJAuKbIa2VxZIlXK/yYbnwAmbnP2mNJcdF8h8PWIeMvrkXRf4rAzT\nJi4qxOoWJLWq+A2BQCB0zyrV+I9LDuVoqrrqEC7xekTVu+C2HmlqaqqsrAQApusqQdZj7xQz\nZ860fxOEmxDW3EeB54HjroGwE5sfUwkQTXdRAeZ8c9FPFWcxwAKvhKHyTpJ9+458I3/vIszr\nAIASyJTDXu2D/VpSfPyJupyfAQAD1GR8wYhcVaPe6m6AQMgsWcH+8j2uqQKhkJkyjYqwKrew\nd+CDkqSLFsaAwf8uqftcW5cpcRvgO2J9e9QFolzDFrmGLbLhjRyPRcZmikJ9/3tFIBBuESSU\n4M/4R29Leb+ktb7jpyqR4o/41eJu70O659SpUxKJxDSzSU1NzcqVKzmOGz9+vFxuo++QKfYK\nux07dti/CcLNBxUZxWe06wYkElkWVOgblvqM3F3b7lx/n/fwToXdzpq0WakfGZ7furL399hV\n8zwHWfRRBM+Lvju5vuBPRAlcwxb2EDnBcfylNKxWU8GhNoeFYp6ty/vV1NJQuLMHYQdABYUI\nn30ZNzchqQwoa1OOy6tVHDavXYY4gVYaOv1Py64aDW5pRm7u1k/ep9DDRvLn20ut04OGguBa\n/GoRCIRbhGipz9mhzz+b8/uPlWc5zBmykNKIusd72H9C7/LpOpupNRw6dOj5558PCQkJDg52\ndXUtLy9PSkrSaDRKpdJR95wkeILQJyCJ2dUY1mr5vBwqLKKv113sPUyPuY2lR9WcbrZH/POB\n0zrt9nrBLtPmuoKdHYUdAIjdBvi4Deh5Va1G9/H7uKIMAICmmbl30yNsCZgoP/cvXm/mh0ox\nUivHIqdefs9rabLImVzletKrZrDZXwSM2R1buRNHgOeRi4JZtIQK6bPwXquhQiMEKx7hjh8G\nrZaKiKLHT77eOyIQCDcbSqHLtzHL3w9fcLIxr1LX5CWUj3IOcRc4wOVj7ty51dXVhw4dunDh\nQl1dnZOTU1xc3IwZM9auXWu/d50BIuwIfYKp11ebpbHh2iy91GfkUp8eynBZnLEXtdbZsyJ3\n7FCbqgMAjmO3/0YnDIfeu0rUXv7BwuIe9YA9G+sGFBQK504bmyytqZFf8HI1y7/FX0jijh0y\nPOOGevb7r4TPv2bDezkcKiLayhtnAoFAsBl3gcyesrCdEh0d/fbbb/fczw7s/RudmZnZcydz\noqJIMu6bH6ot+QVuOxZiGCog6HpuyJwRzsFFVe1ibmQXFReshK+sMGvr9biuBnl2fSGLMa6p\nAkQhN3dA7cdmrHm1WVqo8Ix9xJ6NdQM9dERr+iE6oxQAWLql2HOPBKvEY83Kc5kWgQUA3NyE\nK8uRr2WuEwKBQCDcONgr7KKje/29GZO63bcAyD+QmTab3bcLOA4EQmbWHcjDMTm1HcK74Xdn\nqMvTW0oBIErq80HEQntmo7y8edO2QIBcu4wbxXW1+m8+w6UlAEAFhTBLH0SytuN9zOlNezJi\nhfUVZq0B19Vyhw/g+jqk8mfGTZTe/4/qs+/VJX2mhisy16EBt2+2zDUo61A0tqPlGoMxn3ER\nl5UiL28qdqCpLCYQCAQCkKtYQt9BT7yNGjEaamuQhyeIu8vZce3xF7mmDHsppbkIYzxI7i+w\nr0Q3PSaRu5CMr57bMbPv7Oa+kv3jV4OqAwC+IE+75aPKoGyEaNewxRjM9CEt8rBnVxbg5ib9\n/95puyLPuIhzsgQPr/UY9qTHsCe7GkIPG8WdOALatnrYVPxg5HJdayVhrP/2C2MSOyoyWrB8\nFdF2BAKBYIq9wq6qqstcL0Zqa2s3bNjwzTffcBxn53KE/gWSysBxCcaaudYLzcWujDRG1ru6\nVfvrMv5bdLCOVU9xjX42YKqUFgIAg6hOs5zYgkQifPw5PuMibmmmgsOQt083ffmC3PYbagD2\ncnqN5hsAqL60SeI5WFPVngbZ2X+qY7ZnWDf1vKnjI1+Qh0uKkH93PwHk5i5YuYbbswM0anB2\nQc4u3Pmz9MCE6xUey+fnGFUdAPBZGXx2BhUZc102QyAQCDcm9go7D4/uDhVaWlref//9t956\nq6GhAQAmT568YcMGO1ck3Mj8VnV+W1WKmBIsV44a7eLIjL6H6rMXpn9WqWsCgGluA7bGrbIm\nk5CmOmVPzi/zNLWG5vGG3Cx1+Y8DVjpwY20wDBXXSVxtR5CT3FgcAgBYRt3+EdBOynHNZUcB\nkFv4Yp+hDq0+qbas+4JbWro/7MLFV/RffAwatamRT00RLF15Xc7JcI1lcW5cXQWR134jBAKB\ncOPSV1exOp3us88+W7duXUVFBQAMGzbszTffnDyZJCa4mXn7yr5ncn8zPH9edmz3wLW3uznm\nNAUDvuvi7lp2BCAacPHu2sx3i/a/EDi9+1H1eb/l7Zm/WZkILu3/8f+lKmkje58zI+5iJYxL\nirBWS/n599H1MT1hMvvrj8ZmtUuy8ZlrrYuaf5bV1lC0iBI40puttSGnWZKBJEUyjV/bYaFI\n1P1xHQCwO7ZaqDoA4C+l4eIrPY7tCyilysKCfC0tBAKBcIvjeGHH8/wPP/zwyiuv5OfnA0BU\nVNS6devmz5/v8IUINxrvFu03bb5XtN9Rwu7Tkiu1bEJbA3kBSE/1VMsPAErPvAwAavODPR7j\nFr7VGToTdrpW/Ref8AV5AABSqeDeBwyJ99jW2vYaDHZDDxuFXBR8Wgogqkz3W1NdnvEjJ9/x\nAODwgl3VlzYVHX0M8zrwBmmrb2D5HbSTKzN/sTFooytwRXnn9rra6yDsMOYuJANNw1WPDnrk\nWCo47Fpvg0AgEG5sHCzstm/f/uKLL6alpQGAn5/fP//5z+XLl9O0XZ7phH4Bh/ka1uyyz3Bt\n6hC2VltMFaoSdq45TNE1FQLAmOYr++UhRuNgJ3+l0KXT/uyhA22qDgDUavaX71ruji46/gSr\nqRQ4+QWM+8glaI6NL2COMQ2bd/Oolt256qpkAHBSjlWN+o9D5jeF0zcVHVtrKIwGAGpRaeMd\nKu/h/7DGTw55euHCTgQ0UnVbhKNv4E4c5Y4cNDYpXz9m3oJrvw0CgUC4wXGYE/SRI0fGjBkz\nZ86ctLQ0d3f3t99++/LlyytXriSq7haBRtRYc6e6Ca4OqzOh5S1S5NDRTj7HGnIwdJc6R+qZ\nAACzG7JWVCeLMYsAxrqE/Rz7YFf9cfEVs0XVeYV/P8BqKgFA31ycv/8eXXORHS/RCUKngKi7\nzg64J3vAvbkRdxxhxI4MgzXQWn8Zc62mFq0mp2dVx3G4qpKeMs2yEBxNM7PmIXfH77NH+Gzz\nAmhlJaDVdNWZQCAQblkccGKXkpLywgsv/PXXXwDg5OT0xBNPPPPMM87OdhVTI/RHNkXed9fF\njReaiwHgdreYfwXP7nGIlcxwdz3a0GhsUlD+ePYBAEhUROweuFZEdf5r7D/uo5xdM/TNxaur\nzz6uqw2cvd/ZOaibVZCr2X1ri6yc57TGJq9vaS474hZ+rz0v0tmqlMilD+t0iZxDEKIxbg9I\n73E5PusS+8v3uLkJaJoaOYbyVgJCyEcJGi1S+iLnzs8728AY19UCzSCXbrv1FrUal5uf0VIU\nMLbX4SYQCISbFXtP7BYvXjxkyJC//vpLKBSuWbMmNzd33bp1RNXdmoRKPJOHvpgzcl3R6Dd3\nD1wrp7sIUOg9T/urHlMpJRTFIERDKY9PGOyH6rM/KTnc1SiJe5x7xFLDs74xtznz824XwWxC\nsM5JC1dPAQUDhln0oLu4w72RoUUK5bBXjU2xIsozdnV3A7Ra/Y9ftyVG4Tj++BHk7EIPH00F\nBFOR0d2rOlxXq/vwbd2/X9O98bLuzVf5pLPgoGzk+t9/wvW1phZ6yLAbobgZgUC46Wnl+Z47\n2cr27dsRQgihl156yVFz2vuX8aeffgIAmqbvvvtuFxeXDz74oMchr7/+up2LEm5YKIRCJY6v\nMMEg9EF4yPthIUlNRcOTvjH9KKW52PCAuVZN7UVa6Gw8kVJXJZcnv2HsWZ603iVojsxreMf5\n9eryvN13tFScBg+Q+kUGejwvihmt8HMv++U7/dX5RYoIue9Eh7+aQ2jI/6O+4E+KFrlHLZd6\nWepRn4SX5H5TmsuOCqQ+ipD5FNNdtC9fVgIasytOPvcyFR1rzTbY33/CJW231bi+Vv/Lt3RJ\nITPH7sApnucz0k0NyM2dueNue6clEAiErtleU/txSfmxhsZmjnOi6XEuzo+qlDPdXR24RFVV\n1YMPPujk5NTc3OzAaR3zlZfjuO+//97KzkTYEWyDQhAudacQ4k3OgUIlHgCgrjqXt/suXfMV\nAHD2nxoybSvFSNXVyRYzqCvPdSrsio8/2VJxuq2PNqtctDM4dBkFEHnH0fKk9dr6TIn7QJ8h\nL1AChyVbdiCVF94tPvGU4bkq/ZPwOQfkqkkWfWTeI2XeI62ZDTlZpFnBHSxdgDGfn9veAkAA\n3ImjzJQZIJVaNUM3UGZp85C7B7mHJRAIfYSW55dnXv6psj1xZjPH/VVb91dt3WIvzy+jwsQO\nStL+0EMPURT15JNPrlu3ziETGrBX2M2cOdMh+yDcfOyrzTjekOsnVtzjNdxQ7MF+FIz0pcAZ\n/yrYaWgGiz0eVSUCQMGBpQZVBwCNRXvLk9/0Hb5O2KGwhKlFW3ep7Oxr2oZsqfvAptK/Tbs1\nlx292j8oIPEzh+y876hIecu0WZnyTkdhZz3Iw4uKjOGz2iIVkMyJGtKJFO5sJEIyJ1xf19Yy\n/A/Gug/+AyxLRUTRM+f1mGClcyiKHpjAnT3Zbhg01JZ5CAQCwQpWZOWYqjpTfqysohF8G+2A\n0MDNmzdv27Ztx44dly9ftn82U+wVdjt27HDIPgg3GU/n/PZO0T7D89tX9p1JeL7LnMC95LXg\n2ZNcIw/XX1YKXRZ7D3OiRVxrvbYuw7RPS/kJAJCrJslVE5tK2kSbk+94Z//bDc96TUX2H4ms\npgoANNUpFkswEm+HbNVIa2NeyclnWipOC538lUP/6RwwzVEzY55ltTWmFr2m50Qw3YGQYMkK\n7uRR/koBclHQYxN7iJYwgR6byO7YarnDuloA4JLO4OZmwQOrbNsUM/cukMr4S2kgFNIjxtBD\nR9g2D4FAIHTPntr6Hyq6q5X6XUXVfd5et7vZVTi7oKDg8ccfX758+cyZM99//317purITet9\nnJub+8orrxw4cKC+vt7f33/hwoUvvPCC1P4rIUJP1LFqLa83qjoAyFJXfF527P/8p1gzHPNs\nQ8GfuqZCqfdwJ58xnfaZoIiYoGj/wkQLnSmBE69v91EQyHwBACE6bNae2ss/aGpSJW5xbhH3\nIorR1mW01mdr6y4ZVF2neA9+1pqtWgnmdbk7Z2rrMwFA31KSt3te5F2nJO4DHTI5ohiZz6jm\n0iNGi5NyvL2TCgT0+Ek2pCmix01EClf9jq1gOLczSSYMAHx2Bmi1ILZJ3wuEzIw5MMMxeQQJ\nBAKhKz4t7fm78ael5fYIO57nly1bplAo3nvvPZsn6YabU9hdvHhx3LhxDQ0Ns2bNCgkJOXr0\n6Pr16w8cOHDw4EGJpE/qRBHytdVPXt6yt/aShtd7C+UWn+Z2raIMFGhr9tZekiIq+uRzqOI4\nAPztFLzRf1oxooc7B38QvjDeqeviUYjyGfyP0jNtIUUUI/Ee+NTVTwTukcuMHYuPP1mZ2sN3\nI7fwe9zC7+m+T6/QlJ0zqDoDPKetz/vdUcIOAAInfJa7505tbToAOPvdphz+LwAAjPnU81x6\nKmIYatjIa1ahgYobJIobhKsqcXMjd+Rv/lKaRQc+N5vPy0FyZ3rQUBtFHoFAIPQZx01Sa9nT\npxveeeedI0eO7N2718WxaaGu0rfC7tSpU8eOHWttbY2IiJgxY4bMNg+b3rNixYr6+vrNmzff\nf//9AMDz/H333ffjjz++8847DowoJhhJbykddm6D0GESVQAAIABJREFU5mp5g4oONScGy7ur\nVbCjJu3ui5u0vB4APOQhm2tTm2jhC6rJOswD5g/XZ89K/V/GiFdltKirGXwSXpS4xzcU7qRF\nLh5RK0SKThwgmsuO9ajqAMDKIANr4LMz2K2/0LU1kfSKcrejDbJsgx1jRwbPixQR0QsutDbk\nULRIKA8yGLnD+9m/trc9J50RLF9FRTmmvJs1IE8v5OkFTU2mwo6KjGGPHOQO7G7b1cG99LwF\ndFAISMg5OoFAuFGoY9ke+9SyLDZ6EveStLS0l19+edWqVbfddptNE/SMvZEdZWVlr7/++ttv\nv21hb21tvfPOO0eNGvXMM8+89NJLCxYsiIyMPHPmjJ3LWUNycvKZM2cGDRpkUHUAQFHUW2+9\nRVHUxo0bsYMSaxFMebdov1HVGRGgttu8Ge6x9/uM7mb42uyfDaoOAKoZ6UaPoUdlgTrUfhlY\n1Fp3rqmw+z24BM0OmPCpauS/O1V1AKCpPg8AakrwscewlYFz/qGakuUSAQCUoD3qU+Dk59rF\ncR1fmM+dOMJnZViZmw03Neq/+xLX1gAAw0l9q6eIde4AQNFiRfAd1sxgPQjRYkWkUdUBAHfM\nLL0fd/yQA5c7duzYbbfd5uLiIpfLExMT9+/f32k3Kn4wc9ci5O2DXBT0sFHMHfO5g3uMn+KG\nevarTa1v/JO/YBm8TCAQCNcLT0HPEfeeAoFtqg5jvGTJEl9f37feeqvn3rZi74ndzp07X375\n5Xvusfxv4bPPPrt161YAoChKoVDU1taWlJTMnj07Ozu7j84ejRw8eBAApk+fbmpUqVTx8fEp\nKSnZ2dmRkZF9uoFbkJLW+o7GL6OWSmmhr0gx0jm4m7GtPFtg7v6fL3IN1tVZdGOQvbXphM7B\nAPCs6raTsrbjw2POEadn742V+lSlf6quPCt0DvaMW8OI3TuOZf/8jTveJpWoyGjB/Q/3UJVL\no2G3/QKt7bW8KEzL2UikbFEOe9VQ66wP4XmsUZsasOOSJO3Zs2fmzJlSqXT+/PkMw2zZsmXq\n1Km//vrrnXfe2bEzPXw0PbxN0+Oy0k40sa5V/+sPoshoEBMfCQKBcP0Zr3D+uYuQWCOJChur\nMHAcd+HCBQCQyy0dltavX79+/foVK1Z8/nn3ufR7xl5hd+jQIQBYsMCsGnd5efknn3wCAEuW\nLPnoo4/kcvm5c+fmzJlTVlb22WefPf3003Yu2j1ZWVkA0FG9RUREEGHXR4xwDt5Ta1bKM0am\nvMtriITq+auPiGJCJB65mioB8BMaC9x5tTOrm9hU8JlHgha1/X5GSX0S5AF2btI5YHqTaopR\n1QGABnPfVib9J/ROr/jHuxmIK8qMqg4A+KwMPj2VihvU5QCe12/+lC/MtzD7jn+bHjbKxt33\nCoqigkP5y1nthlDHVC3T6XQPP/ywQCA4fvx4XFwcADzzzDMJCQmrV6+eNm1a98FJyNMTGAY6\nXnPodHx5GRUU4pAdEggEgj2sUSl7FHZrVErbJqcoasWKFRbG9PT0U6dODRo0KCEhYdy4cbbN\nbIq9wi41NRUhlJiYaGrcunWrXq9XqVSbNm0Si8UAMHTo0PXr1z/wwAO7du3qa2HX0NAAAB3P\nBRUKBQDU11ueLW3fvv3bb781NtPSLN29CT3yXMDtJxry9tdlAIAQMbM94z4MX2SNqjPwQfjC\npan/+yj/9/DWtqM717CFf4Yt2FCbXqCtGS4PeiP0DrHVs3UFQrTbxC/h3HpTYyPbcyF5XFlh\naakoh7iu+5eVdFR1yElOxXQ9xtEwdy7Sf/eloQ4EFR3LTJ3hkGn3799fWFi4cuVKg6oDgPDw\n8JUrV7733nvbt29fuHBht3sSUEEhfE52x0+Qi12JAzqH5wEhQLZdmBAIhFuUsS7Oa1TK/5WU\nddXhMZVyjIuNJ3YURXU8kHv//fdPnTo1c+ZMR5VvsFfYVVRUeHl5WaioY8eOAcAdd9whNol6\nW7Ro0YoVKzIyMiynuFYYvOtQhz/0WVlZW7ZsuR47unmQ0sJ9gx5PbyltZLUDnfx6m454hnvs\ncSdlS2v7hWx93m+TJmy8LWBqr+bRVKdcObJaXXVWIPPzHb7OLeI+iw7RTqpAsVuhtr3q6OAr\n26vZJvfolYjq8t8C8vKxtHhbWkzBWkuxSPn4MkseQDLrSjg4AuTmLlz7DK6rBYEAOVme+dvM\nkSNHAMDC53fq1Knvvffe4cOHexB2AMitk2tueugI5OrmqB0CAGg0+q0/8+mpgCh6yFBmzl2k\nTAWBQLCe98KCaYT+W1za8aMn/HzfCg265jvqHfYGT9TW1jIdSnEnJSUBgMUxnkQi8fDwqK2t\nhT7GoDIN53amdHWS9+CDD+aasGqVjTlUCQNkvqNcQmwrMiG+WjfCAObZ1oYcY1OvqSg4sCTt\na+Wln2JqMr7odAaeVef+Nael4iTmWV1TQcGBJS0Vpyz6MIj6PXaVIXOKlNc/WnVmWM7PV448\nUnzi/7rZG/L2occmGptUVAwV212yEkrlb1FEi557F/Lw6mZIH4Fc3Ryo6gAgJycHAMLCzJKn\nhIeHGz/qHirW/P7aSS5YvIyZ78jkMgDA/vkbfyEZWBb0Ou70CXbfX46dn0Ag3NwwCL0fFnxq\nSPwyH69gsdiZoYPF4mU+XqeHDHwvLJi54e8B7D2xk8lkFRUVPM9TV33JGxoasrOzAWDYMMti\n5AzDdDwwczgGFzqDp50phqodERGWIZMuLi6mas9wY0u4xojdzJJxIEoocmlzC9M1FebvW2io\n5apXlxceWklLPIT+0y0UpLr6vK65yNTSULijY+4SX5EiURH5QOGOMVVnKWjz5a9O3+g3+m1E\ndSlJmdl3UgMTcMkV5OFJhUX2cMEnlgjufYDd8j2urwOhiLl9JhXiGBe3605jYyMAODubXUMY\nmh2/SnWEioxm7r6HO7QftzRToRHMrHlI4ciK2gb4zHSzZkY6TCeZjQkEQu8Y4Swf4ezIL8Zd\n8cQTTzzxxBMOnNDeE7vIyEiWZXfv3m207N27F2OsUqkCA80qdep0uurqai+vPj+3mDRpEgCY\nbgkASktLL1y4oFKpOgo7wo2A54BHpJ7tBUD9Rr9NC515fUvOzukXvwsyqDoDX7kN8s/fJTuy\ndnjShrSWEqOdoi2z3aIOee/UnG7i+Xc/KD5IqcuNqg4AMK/jdF0nnMSYTznHnzuFmxqRl9Ia\nty0kc0IuCqAoJJXeTPGeXfkzdAfHcccO6b/8RP/9Zj4/hx46Uvj0S6J/bhDc90BfqDoAAKG5\nQLcieQGBQCDcNNgr7GbOnAkAzz33XHFxMQCUl5evW7cOAO64wzJTV1JSkl6vj46OtnPFHhky\nZMjw4cPPnz//zTffGCw8zz/77LM8z69ateoaHBkSbIBipJF3ngydsT0g8bOYRZc84x4DgNKz\nLzde2Q0ATbTwlMwvTeJ9QB7yodeIZkAAcLaxYF7ap9zVZL8S93iJxyDTCV1D77ZY5VB9dqa6\nHADOyMzqWEjc4xmxR1d7Y//4Vf/jN9zp49yBPbr33zQUP+0OVq//ehNfmA88j+vr2C3f8/m5\nVv8kbmg69XMwHON1lcaI/eNXdvvvfFYGn3pe/+kHfH7PN7Z2QiWYVZKlhzks4zSBQCDc+Nh7\nFbtmzZqPPvro4sWLwcHBKpWqpKSEZVmRSNTxXPH3338HgNGju0tU6yi++OKLsWPHLl++/Pff\nfw8ODj569GhSUtKIESOeeuqpa7A6wTYQxbgEzjI86zUVhQeWNRbtAYAaRnpP8F3VtBQAXDmz\nuIRcTVW2pjJa6gMAiBKEzdhZeubllopTQnmgMuElsSLKYol6ti2724+usWG62ln1WQhA7DYg\neMoPXW5Lo+ZOHWtvqtXcqWPM9Dlca52uqVDoHEILLcOjcFmpqfjDAHzGRSo4tFc/jRsTg3dd\nTk7OkCFDjEaDk4OF410bLMudM/N05E6f6Ov6ZsyUaUgi4VOTgaKphOHXKMUMgUAg3BjYe2Ln\n5ua2a9cuf39/lmULCwtZlhWLxV9++aXFX3mNRmM4P5sxwzFpF7onNjY2KSlp4cKFJ06c+Pjj\nj+vq6l544YUDBw6QQrH9hSuHHzaoOgBwZ9ULay8anutoy/8HnUzuWwUy38CJX8QsSg+buUvW\nWa2LsS5hBs88DlGv+iROi3pIdNe5mIUXxW4DutoJbmywTKvb0FB+fkPqVz4ZWwanfeNbndEh\nmaT53R8CuGmiMsePHw8AFqUm9u3bZ/zIEp0OOM7MYp45uU+gKHrcRMGjTwkeeYIePppkPCEQ\nCLcUDqgVm5CQkJWVtXfv3ry8PIVCMW3aNKXSMndfTU3N+vXraZruGFHRR4SGhv7wQ9fHMIQb\nGcw3Fe0zNYxuKfrIc7jhmQLEX3WPm+Y2wF/UCz+tALHbd9EPrM7+oVzX6C6QvRe+YIBXAgCw\n2pqio2saC3dSjNRjwCrlsH8aywAiDy8klWF1i3ESjaem9NTzhmde31J06BFZg6dkxByjgEBe\nPsgvABdfjfMVCOiBg3v5I7hBmTJlSkBAwHfffbd27drY2FgAyM3N/fzzz728vGbPnt3JAKkU\nqfwN6fQMUOGWx6gEAoFAcCAOEHYAIJFI5s6d200HPz+/lStXOmQtws0PoiiBE8+2H+20mMSr\nTnMfIEB0pb5pgiLi+YBpvZ17nuegOzwHVuqaPAVy6qoUu3JoZX3+NgDg9E1l515jxO4GJz8A\nAJpmFi9lf/oGt7QAADVwiMbdrAAaBrZx7ydCnZweP6nNRFGC+x/i9u3iC/KRwpWeNBV525im\n3F4w5g7s4ZJOA8dRsfHMtDmWgQW9RCgUbty4cdasWaNHj16wYAFN01u2bGlqatq8ebNMJut0\niGDxMv2PX+OSIqAoevhoenRnB3sEAoFAcBCOEXYGVq1aNWrUqGXLlnXV4d13383Ozv70008d\nuCjhpsQjekV58pvG5u+KtpgbF0byXtjdEVJveyZHgLxNHOMwr2so3GXaoT5/W7uwA6AiooX/\neJUvL0NyZ+Tqxly2PAkWcFLu+OF2YQeA5M7MnYs6WZvVcyeP8UWFyEVBj03sk4oLJnBHDrL7\n2l6NO34EV1dTkTHIR2lPhbFp06YdOnTo1Vdf/fnnnzHGCQkJL7/88pQpU7rqjzy9hGufwS3N\nSCS6aa6kCQQC4YbFkcJu48aNWq22G2H3559/Hj58mAg7Qo+4BN9hKuwW1KVHeAxS+U9d4jNC\nKew8+tJmWirOAPCmFtSxfJlQRAUEGR4VQXPFikhtfVuiRInWR6r1xfpG/defIRcFPS4RuXt2\nvhLG+m+/4DPbiuryyWcFTzyH5DaWprEGLi2FozV1sgyOUrs3DWayLvFZ6QCIHjKcWWhZlsN6\nxo4da+Fm1yPXsuoGgUAg3Mo4Utj1CM/zJNsIwSqwmdIaqCm/XSD27WWFMWtoLjuWvc2y6LJb\n5JJuhlACWeSdJ8t/Xa0tOy9u9XBrikdAAcfxl9IAgEtJEj7xXKcZ2nBVpVHVAQBubuKTz9IT\nJjviPTqD1bOashzfH1ha7aQJYOoNV6UIALjkM9TIMVRgcF8tTSAQCITrhL1Rsb2iuLhYLr8W\neZwJ/R2JxyChs5nsUARddeLkOD4znTt/tud8clZQnf6JaRMBFZC4yS383u5H0SJX1fyvAjyf\n8mgcQmHzb0caNZ90ptNRuKXJvA24uanTng5A16r74O0a7ghLqwFApLcUmriyvK+WJhAIBML1\nw94Tu7y8vLy8PGOzrKys0zsatVp94MCB/Pz8a5PHjtDfoWhx2IwdRcfWtpSfFMoDlcNek3oN\nAwDQaHSf/heXlwIAMALBgnupgUO6mQc3NLB//YEL8pCzCz3xNio6FnOtnK6BkbRVQGFbzdQh\nBt41ZL5VWxSJBMseBLWaKypgvzTzLsAtzZ2/lFIFYglor6biQ0CF9FVGN+7MSVxRpvNoE45a\ngaUIvm7xHAQCgUDoS+wVdt98881rr71mbO7du3fv3r3d9F+zZo2dKxJuEcSuMeGz9wOAtj6L\nVZez2hpG7M4eOdCm6gCA1eu3/iyKG8RjXW3mV9qGbKnHYNewxYi6+luNsf7rTYZcG7iulv9u\nc91oXFb8IeZZiXtc0OTvJe5xctUkQ3ELA1LPBLo3+VNAKqWDwzjzfChUWBdl68QSweKl7C/f\n45ZmoCh6wmQqOrYXa/UGXFsNAFKdVwNkAkCLuKhBlu3S0rYxevgoo8sggUAgEDrlcjMcq4LK\nVvASwThPCHOQq3BUVFTHcvbe3t7l5Y65SLFX2Hl5eQ0Y0JbcNT09XaFQqFQqiz4IIalUGh4e\nvmTJkttvv93OFQm3EJjPP3Bf3eUfAYASOAUmfu5Ubl7RVaPhayuyD81WVyUZDHWXfwqducPg\nSYarK3FJEUe11jhf0AnqRHo3SAWsYAFAU5OWv/fumMWZXvFP6ksza/J+4mi1xH1g0JTver1J\noZC5dzn7y7e4oQFomp4wmYqJ66ovFTVA+MK/cE0VclZAn6XLxo0NuLICAFyb4polV5okBYCg\nzPeEOHqRjA5DSpVt3nW4soLduxNXVSBvJTN1BvLo87rPBAKBcF3IboI1ybCvwsw41Qf+NwTC\nHSHvKIpassTMmburqow2YK+wW7169erVqw3PCKG5c+d+9dVX9m6KcJNSq295Jve3PbWX5LR4\njV/io6rE7vvXZH9rUHUAwOubCw+tGOD1pVkPobCh9pBR1QFAw5VdzeUnnHzGABiiddh85a+t\nVy8iGbY915q2PktXnAZb93gWy9zR0vqQKs7HV115VuQSjhDdq/eiwiKE/3gN19chJycQinro\nzTB9ew2q1eg/fs/ggIgwFVAxWyuuhTEJsgnLGJGbzbPilhb9pg9xUyMGQOVl+oJc4ZPPg0Tq\nuH0TCATCDcHZWrj9CNTpLO17y2HEftgzHobZ/qe0DYFA0HdiyZFRsZs3b+68XiSBAAAAyzO/\n/rM61fC8JvsnBSO913t4N/3VlWZRCLy+RRfrIUh3xfV1Bgsz8w6d+qTFKF1TIfiMAQDk6d2i\nbGw1cS9jmRaTjgh2/Y2Lr/CIzVdu0XI1kA6QDnV5v4VO22qsPGEtFIXc3Hs3pG/gMi5ahJU4\nzXmMThhh57R8dgZuaoSrPxfc0MDnZFNxg3o5Cw/UNQ3YIhAIhF7RqIc7jnei6gzU6WDecciY\nDvJrmlOkdzhya/fff78DZyPcZKg53c6ai6aWXyrPdS/sBE7+FhahV4TgqTFcagpo1FRYJFL6\nSktZiz5Sz4S2J4rCowfDue87ndwt4j58sBAAGmWXtcL2YhIN+X+oq5LbJ+l3qDsUY6V6dwDZ\nOazewoD1lpZu4E6f4Pbtws1NlH8Ac+cipLR02LAXjPlLabi8FHl4UXGDiHwkEAi28WEOlGq6\n61CigQ8uw4vRdq3C8/wbb7yRm5srkUji4+Pnz5/v5mb3MeBVbmDNSbjl8YheWXXxI31zsbEp\ncPIDAHpo+/mT3DfRa+CTlRfeAwBECX1HrBcrIo2fOoVMhXNmc7oEzsK8Xu43xSvuMf3JdbhJ\nr2Msg1h1TYX9V9hZRtoKBFRQiAOmDYsEoRB0V7/GikTWl6/gcy+zv//U9nylUP/1Z8KnXwLG\ncX98MNZ/9yV/8ULbVkMjBCtXE21HIBBs4Ldiq/rYKez0ev2LL75obD711FObNm1avHixXZNe\nhfztI1wjpLRwlrtZVMFCr6HdD2HEHtELLqhG/tsz7rHgqb8EJG7qtJvf6HcH3JsbNmt37H15\n3oOeNhgx4F8qk1ZVJh2PXAaMGAAoRhow/pPQGdvDZu32HvQ0okX02AkAIG01CwJAlFDq2V0K\nlRscpFQxdy4CkQgAkFQmuPte5OqAL4LI1U1w3wrk5Q0IIR+lYMlKq+qhYQwAfGY6YBNbXW17\naLMj4AvzjaoOAPjcbD4z3YHzEwiEW4esxp77ZFrRpxuWLVu2b9++srIytVp98eLFNWvWqNXq\nJUuWHD161K55r0JO7AjXji+jlj6Xu/Wv2ovOtHiN38R7ur2HNcCI3LwHP9tjN5FziMjZ7Fzq\n9YJdr+RvB4BvkFgWsnhP6F0jVeNpgVl+bHrCFKRwc05PdW9V17TuBwBECVWhTzG1GJww9Nsq\nKfSI0fTQEbixAbkoHHhwRUVGCyNf7NxPDmMu6Qx/OROJJfTIMUipwqXF7Naf+eIi5KIAXz9L\nl0VBF0VjeZ7PuoQbG6mAIKT0tXJjuKa6RwuBQCBYA+65C2AA3GtH7Haef/554/OAAQM+/PBD\nFxeX9evXv/nmm+PGWVZCsgEi7AjXDjeB7LMo20uUWg8G/G7RAWOzhRK+21z8m6BD1ROEqEEJ\n1KCEQFguq0s/d/zrEWfqpXnN+n3vUSFhghWP9OOi9TTtkIO6TuhMKbI7t3FH/zY8c+dOCx9c\nrf/ha9xQDwC4rhbqaoFhgGUBGyJ1g5GXTyczs3r9xg/5KwWGFjN9Dp04xaod+XZIsdTBQiAQ\nCNYQLofU+h76RMhtV3WdsmLFivXr158503nVot5CrmIJNxC6poKio2tyd80uO/cvTm97uS0t\nzzZyZu6vFbrujs6bOO2EjJ+HntRIW8UGC5+Xwx0/YvMGbi04jjtpcoPA6tmD+wyqzgg1cAgV\nHYtUKnrUeOb+hzo9DeXOnjKqOgBg9+zoqoaHBUipoke2f81FAgb1mHSGQCAQOmOeFd8KrenT\nKxQKBQC0trY6ZDZyYke4IdhenXqsJg1d/GhmdZKM1zUU7mgq3h8+9+/eppQzIKEEw+RBpxvz\njZbxiu48/X+qOMfXVMlZs/M5vqTIEdGkNye4uhKXlyF3T6T0xVotsOaxya1ai/6Uuye9oIfk\n5LjCPOs6z+OqSiSzKhkobm3X8VjP6rdtET72tDUDCQQCwZTHw+HjHKjqWmJ5ieBxayPHrOXw\n4cMAEBoa6pDZiLAjXH/WXv75w+K/AQDc4r+VB3+f/5sz39pcdlRTnWJzdOrm6GV3pX2aoS4H\ngFnucS8Fzuimc0lrfZEEWASMiXsFcvewbembHm7/X+y+vwzP9PBRzF2LkbePqSyjImOwrhWX\nlrS1hUIqfnCP0yJv8/tZikIenlZuCZeaRbLhshLA/dhLkkAgXC9chfDraJh5FJotU2kBADgx\n8OtocBXaPv/Zs2dFIlF8fLzRcu7cuUcffRQALGpR2AwRdoTrTJmuoU3VAQBAqUD+uyL6/toU\nAGC13bnAn28ueqPgr6LWumHOgS8HzvQSmrnQRUt90oa/kqupktJCv57Kv45wDmqh8Zuh3Ms5\nbYd0vLNcOGaCja90U4MrK4yqDgC4MyepuMGCRcv033+Jq6sAgBqUQE+YTA0dwe3dxV8pQK6u\n9KTbkWcP9cdwUSE0NSKFSfbpqTORkxwMQa+p5wEhekA8n5/L52SBVEaPTTSmceEL83FNjels\nyNWdqDoCgWAb4z3h+CR4OAlOmf1dgVHusHEoxNlX+uvw4cPPPPNMaGhocHCws7Nzfn5+SkoK\nxnjOnDlr1661a+qrEGFHuM4UamstLKVCOQDQQmepZ5f5UPI01ROS32nitABwujH/VEP+yYTn\nGGTmM0ojKkLqbc0eprvHrvWbtB4OHnXjJ9cxE/0SxiUuBHFf1XLt1+CyEgsLd/KoYNmDwqde\nxLXVIJYY1BhydmHmW5uTiTt9wpjoDoRCevwkesBAQwAEn3pe//1mAMAAxvgMAOAz0gUPP0YF\nBAEA+/O3ZvmTEWKmz7bx9QgEAgEgXgEnJkNyHRyrhgoteIthrAcMcXVAzMTkyZMffPDBU6dO\nJScnNzY2KhSKKVOmLF269N5770UO+jpKhB3hOhMjVUoogYZv/w9zjLZKIPUJSPycEXdZpOuX\nynMGVWfgXFNhSnPRUHmgzdv4b/iCp/ynFGhrYmRKD4EjijzfpHSsnMZfSuNzsqmwCOTRw7Fc\n52DM7v6zvaXTQX2dMayV/Xtv27oWo1g9f/YUFRCEW1oskpsgV3cu7QKfk02PHo+8rFL2BAKB\nYAECSHCFhB7ue3rN4MGDN23qPCeroyBRsYTrjDMj3hh5n4RqC1yY6x73jxk7Y5cWuwTO7GZU\nC29Zya+ZawWAFq61ytZw2gCx23hFOFF13YP8A1FYpIXRroTArVrTMmjIIgtdY5fhzFirAQAk\nkYDQxOEFA66t5lPOcSeP6j74j2MzIRMIBMKNDxF2hOvPEp8ReaPW74h/NGXYS9viH5W6hPQY\nDDvNbYBp01voPNDJb3nG1/IjT3gde2Z40oZcTZU1S2tqUqvSP6nP+x3zvah8eosjmH2n4aE9\n1EQktn06scTiFBCp2msEo4CgrsZREdEAABTFTJxqMtikh17PnTxm+8YIBAKhH0KEHeGGwEfo\nPNM9bqCTn5X9x7iEboq8z1MgB4A4mWpb3COflBz+qvwkBgwAZxsLlmZ81eMklRfey/hlYNGR\n1Xl77sr8dRivtyprGgF5+xgCF9pElFBID7artC4zfzFIpG2TK1XM5GntH825C/koOw6hYmKN\nJYPpSVMFy1fRY8ZTo8dbdOPSUkgVCgKBcEtBfOwI/ZUHfcc+6DtWzemktBAAXsjbZvrpqca8\nZq7Vie4yUS2nayw59Zyxqam5UHXxY2vKl11PMMb1dYAxcnW7BlGffGE+f+oYbm2lIqPpYaPa\na04gJFj2EHtwDy7MB4UrkzjFRu+6q1ChEaJnX+Gv5INQRAWFGBfirxRAfZ3gvhWg1/OXLrKH\n9oJeDwDUoATBoqWmPwEqKoaKigGMdZnpuNYkkq2lWf/9ZuHaZ+zZHoFAIPQjiLAj9G8Mqu50\nY/7phnxTu5gSGP32OqW1Mdfi+lVbd8nOzVRf2lievIHVVMp9E/3HfySUB9k5oSm4pYX99nM+\nPxcAkF+AYOlK5KLorn9VJW5qpJS+IBAC0+t/6Xx+rv7T/7Y9p6fiqkpm1rz2j6VSs6b9SKVU\nlMn1Os/rv/2Cv5QGAEDTzPQ59JTbqZGjcWlCZi6lAAAgAElEQVQxUrh1GRKBkOCe+3WbPgSd\nDqCtmiMuKcJNjUju7MjdEggEwo0KEXaE/geH+a/LTx2uz/YSylerJgSJ3Rekf6Y2D6d4xHcC\njbrzNBC5hCFKYKrtxOZ+e72loXDnlcOr2p6v7NLtnhd9d7IddaIt4XZtM6g6AMDFV9htWwTL\nHuy8K8/rf/yaTz0PAEAh4DFyc2fm3EVFx/ZiOdMqYQDcqWPMjLmdFortC7gLyW2qDgA4jv3r\nT2rgEOTsggx+dV2D/AMpX3++IBfg6s8eISTotzV/CQQCoZcQHztC/2NV1g8rMr/+pvzU21f2\nDTr7+pH6y1fMk+ElyAM2hM4DAB3fWe5wAACgBXLV6LeNTannEM8Bq+3ZVUO+2V2wpjqltTG/\nq842YFR1bc3sTO7o37ipk6BR7tzpNlUHADwGAFxbo//+K7M7yh4xiVQFAGBZ0DmmjqE1WGbL\n4zgr41u5pDNtqu4qVNwgkpKQQCDcOpATO0I/o1bf8kXZceNJWCOr3VqdQiHE4/YYzVEuIW8W\n/vVu0YEmTjteEf555JIQSSf1wbzi1jqrJjeXHxdIfZwDZiDKrn8OqMPNb0eLXfM7yc3iAFg9\nu2Mr7P9LuPpJ5N0WXoB5fdPlHcyZ850EFet1fE4WPXy0lctRYeH85cy2Bgbk538t5VHHem7I\n3aryYvz5s2ZtmhbMv8dRuyIQCIQbH3JiR+hnVOibsEmeDQCo0bWsVI41NqW00FeoeCV/ez2r\n5jD/d13WgvQus0GK3QZ4xDzkEjTHTlUHAK7hZoUW5L6JQif/rjp3hC/M5w7s5k4cUV85UnLq\nH6VnXtbWZ5l2oMdN7GSYVsse2GN45HQNxZ9NY77aQxfVg/mPyAAS9qLAIT1uEj1kuCFAAfmq\nBIscU8TQ2tWHDDfmKAYAetQ4a0v3suZntIiywb+QQCAQ+i/kTx6hnxEu8fIWOlfo2q8gxynC\nVijHDJUHHqjL9BA4PeqX+Fr+DtMhSU1XynQNSqF9Ff56wkk5VjVyQ23m1xxudVbd5jt8nfVj\nuWOH2O2/G54xra7x/ZmlmytS3gqfc8DJZ4zBTsUNEjz0GJ98lks6DSbHk8ZjvKqkt90Lw1sk\nRa2CGo+GwQibnRciZxcqIqYX70PTzML7mLnzsa4VOfftj64TBALho09xF5KhvhYFBFHhUVaO\no2LiTO+sqagYoHvIiUggEAg3E0TYEfoZDKJ+jFlx76Uvy3QNAHC/z6iVyrEUQobsJ4Y+hlBZ\nIwhQ9xGy9sNz2pwd05pLDwMAxUjkvhMYiVVXhwAAHMfu3m5sMZzUo2FIudsRzLWWJ70eNvMv\n40dUaDgVGs4XFeKKsnajb1vyP7bkcrlrar1TBgDUOF/wbEhwoybQCm+s1VA+vvSUaSCV9vrF\nxGIktiP5sPWwLJd8BldVIl8VPTABKAoYhk4Y3ttp6LGJuKWZO3MC9CwVE8vccXdfbJZAIBBu\nWIiwI/Q/JrpG5o9an62p8BA4dXoOt9Rn5OayE8bmPM9BCqb3mqY3VF38yKDqAIBnNYWHH1KE\n3Im6zqJnCm5qNKRnMyJk215K13SlY39m3gL9N58ZghuQpxc9dUbbBwoXg6oDAI7Slrsepz0G\neN+93pb3adsZxvV1QNN9fmLHcbpP/4uLCg0tPiVJcP/DNibqoyhm+hxm+hxHbo9AINx6aCqh\n4iw0FYJeDQIpyIPAeyhI7ErZacaBAwc+/PDDkydP1tfXe3l5DRky5Mknn0xMTLR/ZiLsCP0S\nEcXEyVQA0MhqTzTmMoga5xIuuuonl6iI2BW/5v3igzX65tvcYl4InNbtZA5AU51i2uT1zdqG\nyxI3q9KLIBcFkjnhlva6FxphWz00mXcnR1ZUcKjo2Vf4/FwQCKmQMONVo1PotMrLZt6Eeq9e\nONVZgOtq9d9+gUuKAICKjBbcsxz67OiOT081qjoA4DMv8YX5huIWBAKBcK3BUHQQSo8C5tqa\nugZoKYOKM6AaD34THZDG6vnnn9+wYYNIJBo5cqS3t3dVVdXx48fj4uKIsCPc6iQ3XZmR+j+D\nv12oxPPgoCcDxG6Gj6a7x05370XaNjsRyYNNmwjRQqeAbvo3lRzU1mVK3OOdlGMBIWb+Yv2P\nXxvS6mol9bXOyQAgcY/3Hbmh8/ESKRUTZ2Fz8hmDaBHm2pOSyHytjYHtCPv7TwZVBwB8Vga7\ndwczZ77Ns3UPrq+ztNTVABF2BALhenBlH5Sa5vG8KuMwB8V/A89BwG12zb958+YNGzaMGjVq\ny5YtKlVblBjP83V1ln8JbYMIO0I/5tHsH41RFLmaqn/kbf0hZsV12Yln3GM1WV/pmtuUkFvk\nEp5V08JOqx3gvL0L6nN/NTQ8olcGJH5GxcQJn/snLsgHqVQQoAqtepCixRLPIQj1wvGfkXj5\nj/2w+NhantMCgFv4Pa5hC218H4xNQxAwAJ+bY+NUVoD8LMOHKVV3sphAIBD6iOYSc1XXgdIj\n4B4DMlV3fbpBp9O98MILMpls69at3t7tRXQoinJ3d7dxUnOIsCP0VzLV5UnmLmjJnXmkXRsY\niWf0wou12d/V5fzUXHa0JvOr2uwfVCM3eA180thHW5/ZULC9tTHPqOoAoDrjc/folTLvEchJ\njmLjDUYn5TjbtuER86BL0Cx19XmRU6BdhTQQQmIxvur5hwCA1Xc7wC6okHB6bCJ37BAAAE0z\nt83osmgYgUAg9CXlJ3vuU3YSwmy9wDh48GB5efm9997r4uLy888/X7x4USKRjBgxYtKkSchB\nFcCJsCP0S74sO/FI9g/6Ng+INkKsD0TtA2ihs8Q9vujoo4Ym5nXFJ/7POXCGWBEJAA35f+Tt\nXYDN654Z0NZdknmPcNQ2BFKlS4DSARPJXaCpqb3Z1AQY2xjQYAXM7Dvp0eNwVRXyUeKCPP2n\n/8WtrVT0AGbiVCAFwQgEwrXCmoJBjXm2z3/27FkAcHd3j4+Pv3z5stE+atQoizM8myEJign9\nDz3m1l7+2aJcmJQWvhY063ptyYC68kwHS1shhOJTz3aq6gBA7Nqb9HKOhWVxSRGurur4iUU2\nY6xrtQjddTjI3ZOKisFFhfofv+bzc3FpMXdgD/vHlj5dlEAgEEzRN1vRp6XTHPBWUVlZCQAf\nffQRRVF///13U1NTamrqbbfddvLkyUWLFtk4qTnkxI7Q/yjS1rVwZnVLB8h8d8WvMUZOXB8w\nz3Rwu2grPoF5XRdfA92jVzjwuK5X4JIi/bdf4LpaAKDCIwVLHwQTMYcCgqCg/Wsp8lZCbwpX\n2AyXZCaOufNJzLyFJMkwgUC4NtBiYNU997E5MJbjOABACG3bti0qKgoA4uLitm7dGhERcejQ\noXPnzg0dOtTGqa9ChB2h/+EvdpXRIlNtN9sjvi9UXR2r/m/RwbSWkhiZ8gm/ye4CWafdeH1L\n0bHVdZd/BkCMyI1trTXY5X6ThU7+6qpksWu0xD1OXZVsHCJXTVKE3ClxH+hkUgztGqP/5TuD\nqgMA/nIWe3APM2228VNm8u34SgFfkAcAyEUhuLtPKq5yJ49yhw/glmYqNIK5426kcLWsCcZz\nwPNE2BEIhGuD3B/qsnrqY0dwl6urKwBERUUZVJ0BmUx22223ff3110TYEW5RBIj+b/iC1dk/\nGm9jf644e7fnkCH2/GvrgJbXT0h+J62lBAB+rzr/a2XyuaHPyzrmHMZ81tbRmppUQ4vntE4+\nYyUeg8RuA5qL91/8PhQABFKlcuhLpY0FBs0nlAcFTtrcfT6UPqe1FVeUmxpwofmZolgiWPU4\nLi/FOh3l69cXjm78pTR2W9tNK59xUd/UKHzsaSo6lr+caexDhUcRHzsCgXDN8Bras7DzSrB9\n/sjISABQKBQWdoNFq9XaPvVViI8doV+yQjlmrd9EYzNfW3PPpS8cu8T+ukyDqjOQqS7fXXvJ\nog8GfCH5DaOqM6BrLvQf9yHmtHV5vxksenVZWdL6Afdkh9z+W9jMXTGLLl1nVQcAQiGIzBMO\ndywvgRBSqqjA4D6SVvzFC6ZNXHwFN9TTo8fRk6aCRAoMQ8XGM31zUkggEAid4hoFbtHddXCL\nAVdri1d3wuTJkxFCmZmZenOv5bS0NAAIDg7uYlwvIMKO0F/JbDE7cMpSV5RfzWkHABzmczRV\nJa31Ns9fpWuysFSazA8ATZx2wvl3/r70uUU3QyWxlvITpkZ9Symrq1eE3OkcMJ1iJDbvCgAA\nY+7MCf2m/+k3/Y87cxKwTU68CDETp7Q3GQEzfpJdu+oteh0uL7U0UhQgxNw+S/TqBtG6twVL\nViJ5p7kACQQCoa8Im9+ldHONgrC77JpcpVLNmzevurp6/fr2eo87duw4ePCgh4fHlClTuhlr\nJeQqltBf8RLKTZtCinG9WhA2S10x/+LGiy2lADDTPe7nASs7uULtiTEuoWJKoOXbv1SNVYSZ\ndlhXsOtofc5U2jKkwD3qfgAQSM1yjiBECySOyc3GHTvE7thqeOZzs0HXSo9NtGEeOvE25K3k\nM9JBLKaHjUKedhRBZPV8bg7o9VRIGEitKsur/+0nvqTY1EKFR5rJOIp87SQQCNcBSgiR90B1\nGlSchqYiAAyAQO4P3iPAI84B9cQ+/PDD5OTk1157be/evUOGDCksLNy1a5dAIPj8889lss49\nuXsFEXaE/soav4k/VJw1Cq81qkRjrdgVmd8YVB0A7KxJ+1fBzn+H3tnb+SOk3v+LWPTE5V+a\nuVYZLXo79K4IVlOfv03kHCpxjwOA0435ALDVJXpGQ3suIiflGJ/BzwOAZ9yamszNnL7t2M8z\n7jFK4GT725rAnTVLoMmdPmGbsAMAKjqWira38BpuqNdv/ADXVAMASKSC+x/qucyrXsdfSDY1\nIA9P5p777dwJgUAgOAYEHvHgEQ+YA1YDjAR6UwaoB3x9fc+ePfuvf/3rzz//PHfunLOz89y5\nc1944QX7wyYMEGFH6K8MdvI/O/T5T0oO17Oa291i7ruaNETHs6fNc4scrm8TXizmTzfma3jd\nCOdgOd1zSfsVyjH3eA8v0NYEitwaU/6TfuZlg909ekVg4ucqkQIAkqXKhwJmL6hPd+XZ2fGP\necetBUQBgMglPHrBhapLG1lNlbPfFNdwxyQoAgBoNUv1gqsrsboFSR3wPc82uD072lQdAGjU\n7LZfhE/8o/shWK8Hnje1IC+f6/gKBAKB0CmIBgd9JTfDw8Pjgw8++OCDDxw/NRF2hH5NrMz3\no4jFAIABb6lMPtaQ4ydyXakco2Ck1SZZJg2XttX65skp76U2lwCAt9D5j7hHRjj37KYqoQTR\nUp8jpYc/LfgLe4+5vSEnXltRk/GFa8j8ZwKmbqtK0fD6JKlvktT3nbD5Pv5m7hFC52DVyA0O\nfmcAFBKOk02SvfE8f/oEPdG+qtR2wJeVGq4qDOCKcuD57i9SkVRGBQTyVwqNFirKjgJoBAKB\nQLgKEXaEm4GHMr//vOyY4fnD4r8f9h2/vnCX8dMn/SYDwCv52w2qDgAqdI0PZX13YdjL1ky+\ntSrlzqwfwS0OAH5yjf136b4pjXnq6uTBAdMuDH95c9mJFk430z12qts1KiDBDB2hSzbL4ovr\naq7N0p2C3D1wabu3HFK4WuMexyy+n93yA5+fAwIBPWYCPXxUX+6RQCAQbhWIsCP0e4pb64yq\nztB0ooVbYh/6s/qChBKuUI4Z7hwEAElNhaaj0ppLtbxeTPWcyOPNK7tNm5vdBk9pzBM5hwBA\nuMTrjZA7HPIW1oP8AkAgxHodarcEXuM9mMJMma67nAmG9Es0zcyYa80o5OYuePgx0OuBYfqu\nBC2BQCDcahBhR+j3FHfIaXKyMW+sIuyLqKUCE3/XQLH7mcYCY9NH6GyNqttfl3Gx2SwrR6VA\nJvMeqQjpdTSGNdRkflV27lV9S6nMZ3TA+I87LyMrEgnmL9Jt+R5YDgBwZAg99PoUJTOAfJTC\np1/iU8+DXk9FxyJvn14MJsmHCQQCwaEQYUfof/AYp6tLMYYBMiWNqAEypUWFsT+rU/+sTo2R\nKQ8MetJH2JZB48XA6Ttr0tScztBcH9LzwdKumoszU/9nYRwp84uY9DkyF4VH6y//XZ8tp8X3\neg+3yMNiPc2lhwv/Xm58zt01J2bxJUR1UqG1mjpWptwo0XmyTEsr2xxRNcqWgrNaDfvXdj7r\nEojE9Ojx9IjRtm0bAJDcmR4zwebhBAKBQHAURNgR+hklrfWzUj9KaS4CgHgn1fa4RwPEbl9E\nLVmZ+W0zZxYueqml7IW8bV9GLTU0Bzr5XRr+6vcVp1s43VyPgYb72e75oPighSVO5vtJ/BpT\nVZejqZp+4YMcTZWh+XrhrjMJ/wiVeNrwag0F202brY25l/+c7Bq2yCPmYUSZ/VMtT1rP0Zpm\nyRUAAA4qUt4Kuf3X3i6n//VHPi3F8Mz+/hMSiahBdhTKIRAIBMINAEkBSuhnPJmzxaDqACC1\nueTxy78AwEKvoYWj3jgw6EkGmf1KnzW5ewWAQLHbC4HT14fMtUbVAUCWusLC8mfcowFiN1PL\n3Rc3GlUdANTqW/59ZY9Vb4L5ygvvZf46LPPXhIrz/8GYQx2yKDeXHSs6uqbk5NOmRp7TGmrO\nGtG3FENvYfV8ulklNM48sRyBQCAQ+iNE2BH6GScb8kybJxpzDQ9uAtkk18hAsbvpp0Hmzd7i\n3iF/UYnOzJ+vtLU+pdkgqtrreuVpqsEKKlLeLj7xf+qqc+qq5JJTz5Unve4atoDqLLte9aVN\nmGeNTYoWSz0Gm3aQ+fT+FrVjITLzxHIEAoFA6I8QYUfoZ/iKzGrV+woVps11wXOMzxJK8ErQ\nTHvWWq40E0wCiomWmkUGSGkhakvg1h7XOUQeYM3ktdnfmjWzvpG4DwyduUvuN4VizFL18qyW\nZ9WmlsCJXwjlQYZnJ9/xyqGvWrOiGQIhFWkWmUHFDer1JAQCgUC4wSA+doR+xvOB0+alfWps\nvqQcpa5KErmE0UIXAFjsPSxS6r21OkVCCRZ7DwsWe9iz1kO+Y7dVpeyvywAAIcW8H3a3m8BM\ncikY6TzPQb9XnTdaIqTeLwZOt2ZynteZNblWAJCrJspVEyvO/6fk1HPGj2TeI2hhexFVbX1W\n45XdPqrlYuzLBAwQh420rXghc/e93I6tfGY6iMX06PHXN7SWQCAQCA6BCDtCP+MOj0EnE577\nseIsBnxf5WnBzmmZvJ4SyPzGvO8RvRIAhsgDrDwz6xEBovcOWnusPrdEVz/COahTmfh19P2R\nUu/dtZcogMVew57wn0wjqw7CFUFzKlLeNjZd/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k7xqqmpefnllxmGqaur27x5s91uX7p0qVzuFwIaPXbfot7nlS6vIPe6DWvA0EJERvNm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\nQLADAAAACBL/D3jWTI2CYCsJAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm135 <- FindNeighbors(mm135, dims = 1:10)\n",
    "mm135 <- FindClusters(mm135, resolution = 0.5)\n",
    "DimPlot(mm135, reduction = \"tsne\", label=T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ccf10537",
   "metadata": {},
   "outputs": [],
   "source": [
    "log.ge <- as.matrix(mm135@assays$RNA@data)\n",
    "control <- names(Idents(mm135) )[Idents(mm135) %in% c(2,7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22257dfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Batch submitting query [=======>-----------------------]  25% eta: 10s\n",
      "\n",
      "Batch submitting query [===============>---------------]  50% eta:  7s\n",
      "\n",
      "Batch submitting query [======================>--------]  75% eta:  4s\n",
      "                                                                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "genes <- rownames(log.ge)\n",
    "annotation <- generateAnnotation(id_type=\"hgnc_symbol\", genes=genes, centromere=centromere, ishg19 = T)\n",
    "log.ge <- log.ge[match( annotation$Gene,rownames(log.ge)) , ]\n",
    "rownames(log.ge) <- annotation$Gene\n",
    "log.ge <- log2(log.ge +1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "673414c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "load(\"maf.rda\") ## from https://github.com/akdess/CaSpER/blob/master/data/maf.rda\n",
    "loh<- list()\n",
    "loh[[1]] <- maf\n",
    "names(loh) <- \"MM135\"\n",
    "loh.name.mapping <- data.frame (loh.name= \"MM135\" , sample.name=colnames(log.ge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2755317f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chr</th><th scope=col>position</th><th scope=col>alt</th><th scope=col>ref</th><th scope=col>coverage</th><th scope=col>baf</th><th scope=col>dev</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td> 14677</td><td>  5</td><td>12</td><td> 17</td><td>0.2941176</td><td>0.2058824</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>268559</td><td> 17</td><td> 0</td><td> 17</td><td>1.0000000</td><td>0.5000000</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1</td><td>629218</td><td> 17</td><td>39</td><td> 56</td><td>0.3035714</td><td>0.1964286</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1</td><td>629885</td><td>168</td><td> 0</td><td>168</td><td>1.0000000</td><td>0.5000000</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1</td><td>629906</td><td>190</td><td> 2</td><td>192</td><td>0.9895833</td><td>0.4895833</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1</td><td>630490</td><td> 11</td><td>22</td><td> 33</td><td>0.3333333</td><td>0.1666667</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & chr & position & alt & ref & coverage & baf & dev\\\\\n",
       "  & <fct> & <int> & <int> & <int> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1 &  14677 &   5 & 12 &  17 & 0.2941176 & 0.2058824\\\\\n",
       "\t2 & 1 & 268559 &  17 &  0 &  17 & 1.0000000 & 0.5000000\\\\\n",
       "\t3 & 1 & 629218 &  17 & 39 &  56 & 0.3035714 & 0.1964286\\\\\n",
       "\t4 & 1 & 629885 & 168 &  0 & 168 & 1.0000000 & 0.5000000\\\\\n",
       "\t5 & 1 & 629906 & 190 &  2 & 192 & 0.9895833 & 0.4895833\\\\\n",
       "\t6 & 1 & 630490 &  11 & 22 &  33 & 0.3333333 & 0.1666667\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | chr &lt;fct&gt; | position &lt;int&gt; | alt &lt;int&gt; | ref &lt;int&gt; | coverage &lt;int&gt; | baf &lt;dbl&gt; | dev &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1 |  14677 |   5 | 12 |  17 | 0.2941176 | 0.2058824 |\n",
       "| 2 | 1 | 268559 |  17 |  0 |  17 | 1.0000000 | 0.5000000 |\n",
       "| 3 | 1 | 629218 |  17 | 39 |  56 | 0.3035714 | 0.1964286 |\n",
       "| 4 | 1 | 629885 | 168 |  0 | 168 | 1.0000000 | 0.5000000 |\n",
       "| 5 | 1 | 629906 | 190 |  2 | 192 | 0.9895833 | 0.4895833 |\n",
       "| 6 | 1 | 630490 |  11 | 22 |  33 | 0.3333333 | 0.1666667 |\n",
       "\n"
      ],
      "text/plain": [
       "  chr position alt ref coverage baf       dev      \n",
       "1 1    14677     5 12   17      0.2941176 0.2058824\n",
       "2 1   268559    17  0   17      1.0000000 0.5000000\n",
       "3 1   629218    17 39   56      0.3035714 0.1964286\n",
       "4 1   629885   168  0  168      1.0000000 0.5000000\n",
       "5 1   629906   190  2  192      0.9895833 0.4895833\n",
       "6 1   630490    11 22   33      0.3333333 0.1666667"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(maf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1af49536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>loh.name</th><th scope=col>sample.name</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>MM135</td><td>AAACCTGTCAAACGGG</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>MM135</td><td>AAACCTGTCACTTATC</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>MM135</td><td>AAACCTGTCGAACGGA</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>MM135</td><td>AAAGATGAGAGTACAT</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>MM135</td><td>AAAGATGGTAAGAGAG</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>MM135</td><td>AAAGATGGTGTGCGTC</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & loh.name & sample.name\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & MM135 & AAACCTGTCAAACGGG\\\\\n",
       "\t2 & MM135 & AAACCTGTCACTTATC\\\\\n",
       "\t3 & MM135 & AAACCTGTCGAACGGA\\\\\n",
       "\t4 & MM135 & AAAGATGAGAGTACAT\\\\\n",
       "\t5 & MM135 & AAAGATGGTAAGAGAG\\\\\n",
       "\t6 & MM135 & AAAGATGGTGTGCGTC\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | loh.name &lt;chr&gt; | sample.name &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 1 | MM135 | AAACCTGTCAAACGGG |\n",
       "| 2 | MM135 | AAACCTGTCACTTATC |\n",
       "| 3 | MM135 | AAACCTGTCGAACGGA |\n",
       "| 4 | MM135 | AAAGATGAGAGTACAT |\n",
       "| 5 | MM135 | AAAGATGGTAAGAGAG |\n",
       "| 6 | MM135 | AAAGATGGTGTGCGTC |\n",
       "\n"
      ],
      "text/plain": [
       "  loh.name sample.name     \n",
       "1 MM135    AAACCTGTCAAACGGG\n",
       "2 MM135    AAACCTGTCACTTATC\n",
       "3 MM135    AAACCTGTCGAACGGA\n",
       "4 MM135    AAAGATGAGAGTACAT\n",
       "5 MM135    AAAGATGGTAAGAGAG\n",
       "6 MM135    AAAGATGGTGTGCGTC"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(loh.name.mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f91d27e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n",
      "Warning message in runmed(x, filt$n):\n",
      "“'k' is bigger than 'n'!  Changing 'k' to 113”\n"
     ]
    }
   ],
   "source": [
    "object <- suppressMessages(CreateCasperObject(raw.data=log.ge,loh.name.mapping=loh.name.mapping, sequencing.type=\"single-cell\", \n",
    "cnv.scale=3, loh.scale=3, \n",
    "expr.cutoff=0.1, filter=\"median\", matrix.type=\"normalized\",\n",
    "annotation=annotation, method=\"iterative\", loh=loh, \n",
    "control.sample.ids=control, cytoband=cytoband))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "63d1a871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XMk+11wvYR373E/FJQu4qtyf/Kwwy41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\nJAAFUCQABVAkAAVQJAAFUCQABf4PlA0kD1bz+n4AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “density.default(x = as.vector(object@control.normalized[[3]]))”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pdf(\"MM135.Distrubution.pdf\")\n",
    "plot(density(as.vector(object@control.normalized[[3]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "296cdcd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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94jWhmAPQpdtNpnaKu7hcoCsC3/XLT63/NP+fQTCVLv4ZdNFa0MwJ78c9Hqzga1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\nQmCC",
      "text/plain": [
       "Plot with title “density.default(x = log2(object@control.normalized.noiseRemoved[[3]] + ”",
       "Plot with title “    1))”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(density(log2(object@control.normalized.noiseRemoved[[3]]+1)))\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9a0b471d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Performing recursive median filtering...\n",
      "\n",
      "Performing HMM segmentation...\n",
      "\n",
      "Processing cnv.scale:1 loh.scale:1...\n",
      "\n",
      "Processing cnv.scale:2 loh.scale:1...\n",
      "\n",
      "Processing cnv.scale:3 loh.scale:1...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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zk8qLBbpEcbvROF347SF6n3e5SjX1f2UQ4PKuwWqUlUjvuSvkhdlpAO3/pT0uEh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\n3zWHneMzOYs6QQxCkfR9qciURt/aJCfMU17PoEj6VqVSJzDna+U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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cnv.scale:1 loh.scale:2...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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av1VjWczZKwSKNNdtoQIHMhdQLQ5bZIdarlmmQJizSA/u3QwVOpE4Aut0X6cUd+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\nzcB1q4RQJE2LO9KO78TmlCLqCAkMRdI0ZTDt+E5c8u+gjpDAUCR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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cnv.scale:2 loh.scale:2...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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uoSUqScIe4ADLgNUqv7+fVSS8IgjbG2iFr9uerbS9wBGHAbpKPNn6qHPQ4Jg0R/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\nJC0XlUOk9R2oaOWxM18NDIKkZZ/PY0e/A556jbqDuIYgaVnVmbS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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cnv.scale:3 loh.scale:2...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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q0q60H6dueuQqpqqtgkSEJntfPB8W9VWQzS6X4/9X5C+rdBtKOgbRKk9Z2Ed4nj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\ntjRFdgXhS1Z+aolK0/SmGgggZSGfOqggSKENmxJ6G1VMGC67gvC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BEECQAAggSAAEECQAAggSAAEECYAAggRAAEECIIAgARBAkCLCcRZV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\nogAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cnv.scale:1 loh.scale:3...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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F/v3JVDVU0pdYN0WTsov9P9nivyO4Wo2AzSmVd+7P+E9NNetAs0qBukkxwTn56T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\nZfFlukV5uruX+hcvIkihdHmXtfuoLe7AXUFUPouPcta6/Z6jzlR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P9unBQfrT7QX+G494y38zVvzdf3NAdOOprjpAkGJIWZB2i/TA7ak7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\nAIIEQOD/AIi2Y7Hlu6V5AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cnv.scale:2 loh.scale:3...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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nWeI7lDLBLpB+6D9N2XW8g3Z1A5SBkrJHc4T/LZDXDCZZCOdf956BXSv/U+QViT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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cnv.scale:3 loh.scale:3...\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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TmgnfPelKdvMHz6onYozAsLxvIqCWRC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\nWCjnc5eTri+I3owwb0jet6RPhFYSAwSp2lntiPhOY3Fd6enJY3a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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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miQmj3GrpYgWYPEZxG6wa/zaBWESjRIxx8Yyv7DflmDVOPishzmy1hIyPkSDVLb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\n+Wxr6qBxDV6l6beoS2DuZOsRxifpTrQdJn2SECSfOf3F9pewyvQ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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1kQEEKQElZ/sa5Gs/+31tITB5RqkRUB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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## runCaSpER\n",
    "final.objects <- runCaSpER(object, removeCentromere=T, cytoband=cytoband, method=\"iterative\")\n",
    "\n",
    "## summarize large scale events \n",
    "finalChrMat <- extractLargeScaleEvents (final.objects, thr=0.75) \n",
    "\n",
    "obj <- final.objects[[9]]\n",
    "plotHeatmap10x(object=obj, fileName=\"heatmap.png\",cnv.scale= 3, cluster_cols = F, cluster_rows = T, show_rownames = T, only_soi = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "399f5086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 947 × 44 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>1p</th><th scope=col>1q</th><th scope=col>2p</th><th scope=col>2q</th><th scope=col>3p</th><th scope=col>3q</th><th scope=col>4p</th><th scope=col>4q</th><th scope=col>5p</th><th scope=col>5q</th><th scope=col>⋯</th><th scope=col>18p</th><th scope=col>18q</th><th scope=col>19p</th><th scope=col>19q</th><th scope=col>20p</th><th scope=col>20q</th><th scope=col>21p</th><th scope=col>21q</th><th scope=col>22p</th><th scope=col>22q</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>AAACCTGTCAAACGGG</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-1</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAACCTGTCACTTATC</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td> 1</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAACCTGTCGAACGGA</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAAGATGAGAGTACAT</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAAGATGGTAAGAGAG</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAAGATGGTGTGCGTC</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAAGATGTCGTGGGAA</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAAGCAAAGACGCTTT</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAAGCAATCCAGTATG</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>-1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-1</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAAGTAGCAGTATGCT</th><td> 0</td><td>0</td><td>0</td><td>0</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAAGTAGTCAGTTGAC</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td> 1</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAATGCCCAACACCCG</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACACGTAGTCTTGCA</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td> 1</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACACGTTCCACGACG</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACACGTTCCTTTCTC</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACACGTTCTAGCACA</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>-1</td><td>0</td><td>-1</td></tr>\n",
       "\t<tr><th scope=row>AACCATGAGGAGTACC</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACCATGCAAGCGAGT</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACCGCGGTATGAAAC</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACCGCGGTATTACCG</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACGTTGTCCACGCAG</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACTCTTTCGCACTCT</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACTGGTAGTTCGCGC</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>-1</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACTTTCGTTACGACT</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AACTTTCGTTTAAGCC</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAGACCTAGAAACCGC</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>⋯</td><td>-1</td><td>-1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td> 1</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAGACCTAGGTAGCCA</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>⋯</td><td>-1</td><td>-1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAGACCTCACAAGCCC</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAGCCGCAGGGATACC</th><td>-1</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>AAGCCGCAGTCTCCTC</th><td> 0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>0</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋱</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><th scope=row>TTAGTTCGTGCCTTGG</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTATGCTAGTACGACG</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-1</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td> 1</td><td> 0</td><td>0</td><td>-1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTCTACAGTAGCAAAT</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTCTCAATCGGAGGTA</th><td>0</td><td>0</td><td>0</td><td> 0</td><td>-1</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>-1</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTCTCCTAGGAATTAC</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTCTTAGTCATTTGGG</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>-1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTCTTAGTCCACGTGG</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 1</td><td>1</td><td>0</td><td>0</td><td>0</td><td> 1</td><td>⋯</td><td> 1</td><td> 0</td><td>0</td><td>0</td><td> 1</td><td> 0</td><td>0</td><td> 1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTGAACGCACCAGGCT</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td> 1</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTGACTTTCCACGCAG</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTGACTTTCGAATGGG</th><td>1</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>1</td><td>0</td><td>0</td><td>1</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 1</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTGCCGTAGAGTAAGG</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>1</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTGGAACCACTTAAGC</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTGGAACCATTCACTT</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>1</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 1</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTGGCAAAGGATGTAT</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTGGCAAAGTCTCAAC</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td> 1</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTGGCAAGTACAGCAG</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>1</td><td> 0</td><td>⋯</td><td> 1</td><td> 0</td><td>0</td><td>0</td><td> 1</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTGGCAATCAGCATGT</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-1</td><td>⋯</td><td>-1</td><td>-1</td><td>0</td><td>0</td><td> 1</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTGGCAATCGGAATCT</th><td>0</td><td>0</td><td>1</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTACTGAGATACACA</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTACTGAGCCGATTT</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td>-1</td><td>-1</td><td>0</td><td>0</td><td> 1</td><td>-1</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTACTGCAATGCCAT</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTACTGGTCTAAAGA</th><td>0</td><td>0</td><td>1</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>1</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 1</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTCCTCAGGAGTTGC</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTCCTCCAATCTGCA</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>1</td><td> 0</td><td>⋯</td><td> 1</td><td> 1</td><td>0</td><td>0</td><td> 1</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTCCTCCAATGAATG</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTCCTCGTAGGCATG</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td> 1</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTCCTCTCTTGTTTG</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>1</td><td> 0</td><td>⋯</td><td> 0</td><td> 1</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTGTCACATGATCCA</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTGTCATCCACGACG</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 0</td><td>⋯</td><td>-1</td><td> 0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TTTGTCATCCTCGCAT</th><td>0</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>1</td><td> 0</td><td>⋯</td><td> 1</td><td> 1</td><td>0</td><td>0</td><td> 0</td><td> 0</td><td>0</td><td> 0</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 947 × 44 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & 1p & 1q & 2p & 2q & 3p & 3q & 4p & 4q & 5p & 5q & ⋯ & 18p & 18q & 19p & 19q & 20p & 20q & 21p & 21q & 22p & 22q\\\\\n",
       "\\hline\n",
       "\tAAACCTGTCAAACGGG &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 0 & 0 & 0 & -1 & 0 &  0\\\\\n",
       "\tAAACCTGTCACTTATC &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 1 & 0 & ⋯ &  0 &  0 & 0 & 0 & 1 & 0 & 0 &  1 & 0 &  0\\\\\n",
       "\tAAACCTGTCGAACGGA &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAAAGATGAGAGTACAT &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAAAGATGGTAAGAGAG &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 1 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAAAGATGGTGTGCGTC &  0 & 0 & 0 & 0 &  0 &  1 & 0 & 1 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAAAGATGTCGTGGGAA &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAAAGCAAAGACGCTTT &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAAAGCAATCCAGTATG &  0 & 0 & 0 & 0 &  0 & -1 & 0 & 0 & 0 & 0 & ⋯ &  1 &  0 & 0 & 0 & 0 & 0 & 0 & -1 & 0 &  0\\\\\n",
       "\tAAAGTAGCAGTATGCT &  0 & 0 & 0 & 0 & -1 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 1 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAAAGTAGTCAGTTGAC &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 1 & 0 & 0 &  1 & 0 &  0\\\\\n",
       "\tAAATGCCCAACACCCG &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAACACGTAGTCTTGCA &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 1 & 0 & 0 &  1 & 0 &  0\\\\\n",
       "\tAACACGTTCCACGACG &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAACACGTTCCTTTCTC &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAACACGTTCTAGCACA &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 1 & 0 & 0 & -1 & 0 & -1\\\\\n",
       "\tAACCATGAGGAGTACC &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAACCATGCAAGCGAGT &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAACCGCGGTATGAAAC &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  1 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAACCGCGGTATTACCG &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 1 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAACGTTGTCCACGCAG &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAACTCTTTCGCACTCT &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  1 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAACTGGTAGTTCGCGC &  0 & 0 & 0 & 0 &  0 &  0 & 1 & 0 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 1 & 0 & 0 & -1 & 0 &  0\\\\\n",
       "\tAACTTTCGTTACGACT &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAACTTTCGTTTAAGCC &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAAGACCTAGAAACCGC &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 1 & 0 & ⋯ & -1 & -1 & 0 & 0 & 1 & 0 & 0 &  1 & 0 &  0\\\\\n",
       "\tAAGACCTAGGTAGCCA &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 1 & 0 & ⋯ & -1 & -1 & 0 & 0 & 1 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAAGACCTCACAAGCCC &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAAGCCGCAGGGATACC & -1 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ & -1 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\tAAGCCGCAGTCTCCTC &  0 & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & ⋯ &  0 &  0 & 0 & 0 & 0 & 0 & 0 &  0 & 0 &  0\\\\\n",
       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\tTTAGTTCGTGCCTTGG & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ &  0 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTATGCTAGTACGACG & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & -1 & ⋯ &  0 &  0 & 0 & 0 &  1 &  0 & 0 & -1 & 0 & 0\\\\\n",
       "\tTTCTACAGTAGCAAAT & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ & -1 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTCTCAATCGGAGGTA & 0 & 0 & 0 &  0 & -1 & 0 & 0 & 0 & 0 &  0 & ⋯ & -1 &  0 & 0 & 0 & -1 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTCTCCTAGGAATTAC & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ & -1 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTCTTAGTCATTTGGG & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ & -1 &  0 & 0 & 0 &  0 &  0 & 0 & -1 & 0 & 0\\\\\n",
       "\tTTCTTAGTCCACGTGG & 0 & 0 & 0 &  0 &  1 & 1 & 0 & 0 & 0 &  1 & ⋯ &  1 &  0 & 0 & 0 &  1 &  0 & 0 &  1 & 0 & 0\\\\\n",
       "\tTTGAACGCACCAGGCT & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ &  1 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTGACTTTCCACGCAG & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ &  0 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTGACTTTCGAATGGG & 1 & 0 & 0 &  0 &  0 & 1 & 0 & 0 & 1 &  0 & ⋯ & -1 &  0 & 0 & 0 &  1 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTGCCGTAGAGTAAGG & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 1 &  0 & ⋯ & -1 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTGGAACCACTTAAGC & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ & -1 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTGGAACCATTCACTT & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 1 &  0 & ⋯ & -1 &  0 & 0 & 0 &  1 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTGGCAAAGGATGTAT & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ &  0 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTGGCAAAGTCTCAAC & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ &  1 &  0 & 0 & 0 &  0 &  0 & 0 &  1 & 0 & 0\\\\\n",
       "\tTTGGCAAGTACAGCAG & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 1 &  0 & ⋯ &  1 &  0 & 0 & 0 &  1 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTGGCAATCAGCATGT & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 & -1 & ⋯ & -1 & -1 & 0 & 0 &  1 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTGGCAATCGGAATCT & 0 & 0 & 1 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ &  0 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTACTGAGATACACA & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ &  0 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTACTGAGCCGATTT & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ & -1 & -1 & 0 & 0 &  1 & -1 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTACTGCAATGCCAT & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ & -1 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTACTGGTCTAAAGA & 0 & 0 & 1 & -1 &  0 & 0 & 0 & 0 & 1 &  0 & ⋯ & -1 &  0 & 0 & 0 &  1 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTCCTCAGGAGTTGC & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ & -1 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTCCTCCAATCTGCA & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 1 &  0 & ⋯ &  1 &  1 & 0 & 0 &  1 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTCCTCCAATGAATG & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ & -1 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTCCTCGTAGGCATG & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ &  0 &  0 & 0 & 0 &  1 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTCCTCTCTTGTTTG & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 1 &  0 & ⋯ &  0 &  1 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTGTCACATGATCCA & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ &  0 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTGTCATCCACGACG & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 0 &  0 & ⋯ & -1 &  0 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\tTTTGTCATCCTCGCAT & 0 & 0 & 0 &  0 &  0 & 0 & 0 & 0 & 1 &  0 & ⋯ &  1 &  1 & 0 & 0 &  0 &  0 & 0 &  0 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 947 × 44 of type dbl\n",
       "\n",
       "| <!--/--> | 1p | 1q | 2p | 2q | 3p | 3q | 4p | 4q | 5p | 5q | ⋯ | 18p | 18q | 19p | 19q | 20p | 20q | 21p | 21q | 22p | 22q |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| AAACCTGTCAAACGGG |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 0 | 0 | 0 | -1 | 0 |  0 |\n",
       "| AAACCTGTCACTTATC |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 1 | 0 | ⋯ |  0 |  0 | 0 | 0 | 1 | 0 | 0 |  1 | 0 |  0 |\n",
       "| AAACCTGTCGAACGGA |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AAAGATGAGAGTACAT |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AAAGATGGTAAGAGAG |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 1 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AAAGATGGTGTGCGTC |  0 | 0 | 0 | 0 |  0 |  1 | 0 | 1 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AAAGATGTCGTGGGAA |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AAAGCAAAGACGCTTT |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AAAGCAATCCAGTATG |  0 | 0 | 0 | 0 |  0 | -1 | 0 | 0 | 0 | 0 | ⋯ |  1 |  0 | 0 | 0 | 0 | 0 | 0 | -1 | 0 |  0 |\n",
       "| AAAGTAGCAGTATGCT |  0 | 0 | 0 | 0 | -1 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 1 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AAAGTAGTCAGTTGAC |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 1 | 0 | 0 |  1 | 0 |  0 |\n",
       "| AAATGCCCAACACCCG |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AACACGTAGTCTTGCA |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 1 | 0 | 0 |  1 | 0 |  0 |\n",
       "| AACACGTTCCACGACG |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AACACGTTCCTTTCTC |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AACACGTTCTAGCACA |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 1 | 0 | 0 | -1 | 0 | -1 |\n",
       "| AACCATGAGGAGTACC |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AACCATGCAAGCGAGT |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AACCGCGGTATGAAAC |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  1 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AACCGCGGTATTACCG |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 1 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AACGTTGTCCACGCAG |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AACTCTTTCGCACTCT |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  1 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AACTGGTAGTTCGCGC |  0 | 0 | 0 | 0 |  0 |  0 | 1 | 0 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 1 | 0 | 0 | -1 | 0 |  0 |\n",
       "| AACTTTCGTTACGACT |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AACTTTCGTTTAAGCC |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AAGACCTAGAAACCGC |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 1 | 0 | ⋯ | -1 | -1 | 0 | 0 | 1 | 0 | 0 |  1 | 0 |  0 |\n",
       "| AAGACCTAGGTAGCCA |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 1 | 0 | ⋯ | -1 | -1 | 0 | 0 | 1 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AAGACCTCACAAGCCC |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AAGCCGCAGGGATACC | -1 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ | -1 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| AAGCCGCAGTCTCCTC |  0 | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | ⋯ |  0 |  0 | 0 | 0 | 0 | 0 | 0 |  0 | 0 |  0 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| TTAGTTCGTGCCTTGG | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ |  0 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTATGCTAGTACGACG | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | -1 | ⋯ |  0 |  0 | 0 | 0 |  1 |  0 | 0 | -1 | 0 | 0 |\n",
       "| TTCTACAGTAGCAAAT | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ | -1 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTCTCAATCGGAGGTA | 0 | 0 | 0 |  0 | -1 | 0 | 0 | 0 | 0 |  0 | ⋯ | -1 |  0 | 0 | 0 | -1 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTCTCCTAGGAATTAC | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ | -1 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTCTTAGTCATTTGGG | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ | -1 |  0 | 0 | 0 |  0 |  0 | 0 | -1 | 0 | 0 |\n",
       "| TTCTTAGTCCACGTGG | 0 | 0 | 0 |  0 |  1 | 1 | 0 | 0 | 0 |  1 | ⋯ |  1 |  0 | 0 | 0 |  1 |  0 | 0 |  1 | 0 | 0 |\n",
       "| TTGAACGCACCAGGCT | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ |  1 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTGACTTTCCACGCAG | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ |  0 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTGACTTTCGAATGGG | 1 | 0 | 0 |  0 |  0 | 1 | 0 | 0 | 1 |  0 | ⋯ | -1 |  0 | 0 | 0 |  1 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTGCCGTAGAGTAAGG | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 1 |  0 | ⋯ | -1 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTGGAACCACTTAAGC | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ | -1 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTGGAACCATTCACTT | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 1 |  0 | ⋯ | -1 |  0 | 0 | 0 |  1 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTGGCAAAGGATGTAT | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ |  0 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTGGCAAAGTCTCAAC | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ |  1 |  0 | 0 | 0 |  0 |  0 | 0 |  1 | 0 | 0 |\n",
       "| TTGGCAAGTACAGCAG | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 1 |  0 | ⋯ |  1 |  0 | 0 | 0 |  1 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTGGCAATCAGCATGT | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 | -1 | ⋯ | -1 | -1 | 0 | 0 |  1 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTGGCAATCGGAATCT | 0 | 0 | 1 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ |  0 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTTACTGAGATACACA | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ |  0 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTTACTGAGCCGATTT | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ | -1 | -1 | 0 | 0 |  1 | -1 | 0 |  0 | 0 | 0 |\n",
       "| TTTACTGCAATGCCAT | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ | -1 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTTACTGGTCTAAAGA | 0 | 0 | 1 | -1 |  0 | 0 | 0 | 0 | 1 |  0 | ⋯ | -1 |  0 | 0 | 0 |  1 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTTCCTCAGGAGTTGC | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ | -1 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTTCCTCCAATCTGCA | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 1 |  0 | ⋯ |  1 |  1 | 0 | 0 |  1 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTTCCTCCAATGAATG | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ | -1 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTTCCTCGTAGGCATG | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ |  0 |  0 | 0 | 0 |  1 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTTCCTCTCTTGTTTG | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 1 |  0 | ⋯ |  0 |  1 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTTGTCACATGATCCA | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ |  0 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTTGTCATCCACGACG | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 0 |  0 | ⋯ | -1 |  0 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "| TTTGTCATCCTCGCAT | 0 | 0 | 0 |  0 |  0 | 0 | 0 | 0 | 1 |  0 | ⋯ |  1 |  1 | 0 | 0 |  0 |  0 | 0 |  0 | 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "                 1p 1q 2p 2q 3p 3q 4p 4q 5p 5q ⋯ 18p 18q 19p 19q 20p 20q 21p\n",
       "AAACCTGTCAAACGGG  0 0  0  0   0  0 0  0  0  0  ⋯  0   0  0   0   0   0   0  \n",
       "AAACCTGTCACTTATC  0 0  0  0   0  0 0  0  1  0  ⋯  0   0  0   0   1   0   0  \n",
       "AAACCTGTCGAACGGA  0 0  0  0   0  0 0  0  0  0  ⋯ -1   0  0   0   0   0   0  \n",
       "AAAGATGAGAGTACAT  0 0  0  0   0  0 0  0  0  0  ⋯  0   0  0   0   0   0   0  \n",
       "AAAGATGGTAAGAGAG  0 0  0  0   0  0 0  0  0  0  ⋯ -1   0  0   0   1   0   0  \n",
       "AAAGATGGTGTGCGTC  0 0  0  0   0  1 0  1  0  0  ⋯  0   0  0   0   0   0   0  \n",
       "AAAGATGTCGTGGGAA  0 0  0  0   0  0 0  0  0  0  ⋯ -1   0  0   0   0   0   0  \n",
       "AAAGCAAAGACGCTTT  0 0  0  0   0  0 0  0  0  0  ⋯  0   0  0   0   0   0   0  \n",
       "AAAGCAATCCAGTATG  0 0  0  0   0 -1 0  0  0  0  ⋯  1   0  0   0   0   0   0  \n",
       "AAAGTAGCAGTATGCT  0 0  0  0  -1  0 0  0  0  0  ⋯ -1   0  0   0   1   0   0  \n",
       "AAAGTAGTCAGTTGAC  0 0  0  0   0  0 0  0  0  0  ⋯ -1   0  0   0   1   0   0  \n",
       "AAATGCCCAACACCCG  0 0  0  0   0  0 0  0  0  0  ⋯  0   0  0   0   0   0   0  \n",
       "AACACGTAGTCTTGCA  0 0  0  0   0  0 0  0  0  0  ⋯ -1   0  0   0   1   0   0  \n",
       "AACACGTTCCACGACG  0 0  0  0   0  0 0  0  0  0  ⋯  0   0  0   0   0   0   0  \n",
       "AACACGTTCCTTTCTC  0 0  0  0   0  0 0  0  0  0  ⋯  0   0  0   0   0   0   0  \n",
       "AACACGTTCTAGCACA  0 0  0  0   0  0 0  0  0  0  ⋯  0   0  0   0   1   0   0  \n",
       "AACCATGAGGAGTACC  0 0  0  0   0  0 0  0  0  0  ⋯ -1   0  0   0   0   0   0  \n",
       "AACCATGCAAGCGAGT  0 0  0  0   0  0 0  0  0  0  ⋯ -1   0  0   0   0   0   0  \n",
       "AACCGCGGTATGAAAC  0 0  0  0   0  0 0  0  0  0  ⋯  1   0  0   0   0   0   0  \n",
       "AACCGCGGTATTACCG  0 0  0  0   0  0 0  0  0  0  ⋯ -1   0  0   0   1   0   0  \n",
       "AACGTTGTCCACGCAG  0 0  0  0   0  0 0  0  0  0  ⋯ -1   0  0   0   0   0   0  \n",
       "AACTCTTTCGCACTCT  0 0  0  0   0  0 0  0  0  0  ⋯  1   0  0   0   0   0   0  \n",
       "AACTGGTAGTTCGCGC  0 0  0  0   0  0 1  0  0  0  ⋯  0   0  0   0   1   0   0  \n",
       "AACTTTCGTTACGACT  0 0  0  0   0  0 0  0  0  0  ⋯  0   0  0   0   0   0   0  \n",
       "AACTTTCGTTTAAGCC  0 0  0  0   0  0 0  0  0  0  ⋯ -1   0  0   0   0   0   0  \n",
       "AAGACCTAGAAACCGC  0 0  0  0   0  0 0  0  1  0  ⋯ -1  -1  0   0   1   0   0  \n",
       "AAGACCTAGGTAGCCA  0 0  0  0   0  0 0  0  1  0  ⋯ -1  -1  0   0   1   0   0  \n",
       "AAGACCTCACAAGCCC  0 0  0  0   0  0 0  0  0  0  ⋯  0   0  0   0   0   0   0  \n",
       "AAGCCGCAGGGATACC -1 0  0  0   0  0 0  0  0  0  ⋯ -1   0  0   0   0   0   0  \n",
       "AAGCCGCAGTCTCCTC  0 0  0  0   0  0 0  0  0  0  ⋯  0   0  0   0   0   0   0  \n",
       "⋮                ⋮  ⋮  ⋮  ⋮  ⋮  ⋮  ⋮  ⋮  ⋮  ⋮  ⋱ ⋮   ⋮   ⋮   ⋮   ⋮   ⋮   ⋮  \n",
       "TTAGTTCGTGCCTTGG 0  0  0   0  0 0  0  0  0   0 ⋯  0   0  0   0    0   0  0  \n",
       "TTATGCTAGTACGACG 0  0  0   0  0 0  0  0  0  -1 ⋯  0   0  0   0    1   0  0  \n",
       "TTCTACAGTAGCAAAT 0  0  0   0  0 0  0  0  0   0 ⋯ -1   0  0   0    0   0  0  \n",
       "TTCTCAATCGGAGGTA 0  0  0   0 -1 0  0  0  0   0 ⋯ -1   0  0   0   -1   0  0  \n",
       "TTCTCCTAGGAATTAC 0  0  0   0  0 0  0  0  0   0 ⋯ -1   0  0   0    0   0  0  \n",
       "TTCTTAGTCATTTGGG 0  0  0   0  0 0  0  0  0   0 ⋯ -1   0  0   0    0   0  0  \n",
       "TTCTTAGTCCACGTGG 0  0  0   0  1 1  0  0  0   1 ⋯  1   0  0   0    1   0  0  \n",
       "TTGAACGCACCAGGCT 0  0  0   0  0 0  0  0  0   0 ⋯  1   0  0   0    0   0  0  \n",
       "TTGACTTTCCACGCAG 0  0  0   0  0 0  0  0  0   0 ⋯  0   0  0   0    0   0  0  \n",
       "TTGACTTTCGAATGGG 1  0  0   0  0 1  0  0  1   0 ⋯ -1   0  0   0    1   0  0  \n",
       "TTGCCGTAGAGTAAGG 0  0  0   0  0 0  0  0  1   0 ⋯ -1   0  0   0    0   0  0  \n",
       "TTGGAACCACTTAAGC 0  0  0   0  0 0  0  0  0   0 ⋯ -1   0  0   0    0   0  0  \n",
       "TTGGAACCATTCACTT 0  0  0   0  0 0  0  0  1   0 ⋯ -1   0  0   0    1   0  0  \n",
       "TTGGCAAAGGATGTAT 0  0  0   0  0 0  0  0  0   0 ⋯  0   0  0   0    0   0  0  \n",
       "TTGGCAAAGTCTCAAC 0  0  0   0  0 0  0  0  0   0 ⋯  1   0  0   0    0   0  0  \n",
       "TTGGCAAGTACAGCAG 0  0  0   0  0 0  0  0  1   0 ⋯  1   0  0   0    1   0  0  \n",
       "TTGGCAATCAGCATGT 0  0  0   0  0 0  0  0  0  -1 ⋯ -1  -1  0   0    1   0  0  \n",
       "TTGGCAATCGGAATCT 0  0  1   0  0 0  0  0  0   0 ⋯  0   0  0   0    0   0  0  \n",
       "TTTACTGAGATACACA 0  0  0   0  0 0  0  0  0   0 ⋯  0   0  0   0    0   0  0  \n",
       "TTTACTGAGCCGATTT 0  0  0   0  0 0  0  0  0   0 ⋯ -1  -1  0   0    1  -1  0  \n",
       "TTTACTGCAATGCCAT 0  0  0   0  0 0  0  0  0   0 ⋯ -1   0  0   0    0   0  0  \n",
       "TTTACTGGTCTAAAGA 0  0  1  -1  0 0  0  0  1   0 ⋯ -1   0  0   0    1   0  0  \n",
       "TTTCCTCAGGAGTTGC 0  0  0   0  0 0  0  0  0   0 ⋯ -1   0  0   0    0   0  0  \n",
       "TTTCCTCCAATCTGCA 0  0  0   0  0 0  0  0  1   0 ⋯  1   1  0   0    1   0  0  \n",
       "TTTCCTCCAATGAATG 0  0  0   0  0 0  0  0  0   0 ⋯ -1   0  0   0    0   0  0  \n",
       "TTTCCTCGTAGGCATG 0  0  0   0  0 0  0  0  0   0 ⋯  0   0  0   0    1   0  0  \n",
       "TTTCCTCTCTTGTTTG 0  0  0   0  0 0  0  0  1   0 ⋯  0   1  0   0    0   0  0  \n",
       "TTTGTCACATGATCCA 0  0  0   0  0 0  0  0  0   0 ⋯  0   0  0   0    0   0  0  \n",
       "TTTGTCATCCACGACG 0  0  0   0  0 0  0  0  0   0 ⋯ -1   0  0   0    0   0  0  \n",
       "TTTGTCATCCTCGCAT 0  0  0   0  0 0  0  0  1   0 ⋯  1   1  0   0    0   0  0  \n",
       "                 21q 22p 22q\n",
       "AAACCTGTCAAACGGG -1  0    0 \n",
       "AAACCTGTCACTTATC  1  0    0 \n",
       "AAACCTGTCGAACGGA  0  0    0 \n",
       "AAAGATGAGAGTACAT  0  0    0 \n",
       "AAAGATGGTAAGAGAG  0  0    0 \n",
       "AAAGATGGTGTGCGTC  0  0    0 \n",
       "AAAGATGTCGTGGGAA  0  0    0 \n",
       "AAAGCAAAGACGCTTT  0  0    0 \n",
       "AAAGCAATCCAGTATG -1  0    0 \n",
       "AAAGTAGCAGTATGCT  0  0    0 \n",
       "AAAGTAGTCAGTTGAC  1  0    0 \n",
       "AAATGCCCAACACCCG  0  0    0 \n",
       "AACACGTAGTCTTGCA  1  0    0 \n",
       "AACACGTTCCACGACG  0  0    0 \n",
       "AACACGTTCCTTTCTC  0  0    0 \n",
       "AACACGTTCTAGCACA -1  0   -1 \n",
       "AACCATGAGGAGTACC  0  0    0 \n",
       "AACCATGCAAGCGAGT  0  0    0 \n",
       "AACCGCGGTATGAAAC  0  0    0 \n",
       "AACCGCGGTATTACCG  0  0    0 \n",
       "AACGTTGTCCACGCAG  0  0    0 \n",
       "AACTCTTTCGCACTCT  0  0    0 \n",
       "AACTGGTAGTTCGCGC -1  0    0 \n",
       "AACTTTCGTTACGACT  0  0    0 \n",
       "AACTTTCGTTTAAGCC  0  0    0 \n",
       "AAGACCTAGAAACCGC  1  0    0 \n",
       "AAGACCTAGGTAGCCA  0  0    0 \n",
       "AAGACCTCACAAGCCC  0  0    0 \n",
       "AAGCCGCAGGGATACC  0  0    0 \n",
       "AAGCCGCAGTCTCCTC  0  0    0 \n",
       "⋮                ⋮   ⋮   ⋮  \n",
       "TTAGTTCGTGCCTTGG  0  0   0  \n",
       "TTATGCTAGTACGACG -1  0   0  \n",
       "TTCTACAGTAGCAAAT  0  0   0  \n",
       "TTCTCAATCGGAGGTA  0  0   0  \n",
       "TTCTCCTAGGAATTAC  0  0   0  \n",
       "TTCTTAGTCATTTGGG -1  0   0  \n",
       "TTCTTAGTCCACGTGG  1  0   0  \n",
       "TTGAACGCACCAGGCT  0  0   0  \n",
       "TTGACTTTCCACGCAG  0  0   0  \n",
       "TTGACTTTCGAATGGG  0  0   0  \n",
       "TTGCCGTAGAGTAAGG  0  0   0  \n",
       "TTGGAACCACTTAAGC  0  0   0  \n",
       "TTGGAACCATTCACTT  0  0   0  \n",
       "TTGGCAAAGGATGTAT  0  0   0  \n",
       "TTGGCAAAGTCTCAAC  1  0   0  \n",
       "TTGGCAAGTACAGCAG  0  0   0  \n",
       "TTGGCAATCAGCATGT  0  0   0  \n",
       "TTGGCAATCGGAATCT  0  0   0  \n",
       "TTTACTGAGATACACA  0  0   0  \n",
       "TTTACTGAGCCGATTT  0  0   0  \n",
       "TTTACTGCAATGCCAT  0  0   0  \n",
       "TTTACTGGTCTAAAGA  0  0   0  \n",
       "TTTCCTCAGGAGTTGC  0  0   0  \n",
       "TTTCCTCCAATCTGCA  0  0   0  \n",
       "TTTCCTCCAATGAATG  0  0   0  \n",
       "TTTCCTCGTAGGCATG  0  0   0  \n",
       "TTTCCTCTCTTGTTTG  0  0   0  \n",
       "TTTGTCACATGATCCA  0  0   0  \n",
       "TTTGTCATCCACGACG  0  0   0  \n",
       "TTTGTCATCCTCGCAT  0  0   0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "finalChrMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b85549e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in type.convert.default(X[[i]], ...):\n",
      "“'as.is' should be specified by the caller; using TRUE”\n",
      "Warning message in type.convert.default(X[[i]], ...):\n",
      "“'as.is' should be specified by the caller; using TRUE”\n"
     ]
    }
   ],
   "source": [
    "#### VISUALIZATION \n",
    "chrMat <- finalChrMat\n",
    "plot.data <- melt(chrMat)\n",
    "plot.data$value2 <- \"neutral\"\n",
    "plot.data$value2[plot.data$value > 0] <- \"amplification\"\n",
    "plot.data$value2[plot.data$value < 0] <- \"deletion\"\n",
    "plot.data$value2 <- factor(plot.data$value2, levels = c(\"amplification\", \n",
    "    \"deletion\", \"neutral\"))\n",
    "plot.data$X2 <- factor(plot.data$X2, levels = colnames(chrMat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0f1b0f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "p <- ggplot(aes(x = X2, y = X1, fill = value2), data = plot.data) + \n",
    "    geom_tile(colour = \"white\", size = 0.01) + \n",
    "    labs(x = \"\", \n",
    "    y = \"\") + scale_fill_manual(values = c(amplification = muted(\"red\"), \n",
    "    deletion = muted(\"blue\"), neutral = \"white\")) + theme_grey(base_size = 6) + \n",
    "    theme(legend.position = \"right\", legend.direction = \"vertical\", \n",
    "        legend.title = element_blank(), strip.text.x = element_blank(), \n",
    "        legend.text = element_text(colour = \"black\", size = 7, \n",
    "            face = \"bold\"), legend.key.height = grid::unit(0.8, \n",
    "            \"cm\"), legend.key.width = grid::unit(0.5, \"cm\"), \n",
    "        axis.text.x = element_text(size = 5, colour = \"black\", \n",
    "            angle = -45, hjust = 0), axis.text.y = element_text(size = 6, \n",
    "            vjust = 0.2, colour = \"black\"), axis.ticks = element_line(size = 0.4), \n",
    "        plot.title = element_text(colour = \"black\", hjust = 0, \n",
    "            size = 6, face = \"bold\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9f71d374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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03TnE4nhsFxXEFBAcbDcRy8S6CKQHLWYrEwHSCEPPPMM+hC29LS8swzz9BmEgDP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WG2Ty75GO9EfsCCFjx47t6Oh46KGHnn/+\n+bq6Oq2HHCHkRz/6kcFg+O1vf4skJhaihA4U6uGHH162bBnCbzabTWtETDTxLahThw0bVltb\ni+ggy7JWqxUdWun6w4cP9/l84XBYURRIOqibXXd3t9lsRvCPEFJRUREOhxsaGmh5HM/zyPBS\nwzyq54CtMUxbOI7Ly8vr6OigJYCgpMg4az1QGIYZPXo0z/P79++nC+FsXFpaWl1dTQ/9/PPP\n33HHHYSQOXPmBAKBsWPHrl69Wq9X/abQf1B1nHYoijJz5qp/upo+D78pMrbzRDYAP7inexQ6\n/nf0SSj1tttuO3ny5OOPPx4KhWhcCryNZdl3330XVXQulysajSIqpjUf+eijjywWCyK9yNhS\nKxOr1frtb3/7hRdeoHnPpqYm5F4hjMUReZ4fMmTIl19+ybKsx+OBHTFJtSYLh8M8zyPHGgqF\nqAVxVVXV2rVrX3zxxZ07d+JwI0aMOH78OMbZ09MzePDgYDAYiUTA9lRVBauTZRk1c/Qs0NnW\n5XKdPHkSxy0qKho7duyuXbsuvPDCt956i6QCgbm5ue++++55550HAz96DefMmaO9pLS7mo5v\nBP2druO0g2EYfR72BXppUEyIfvHTD/33KJORzohdLBY766yzCCHBYBD9RkKhEM/zNpvNbDaD\nzCFNGQqFWJYdPHhwQ0MDwzClpaVoAkv7OlAjkp/+9KcbN24MBoOHDh1KJBKwgkMalBCCmJ/d\nbu/p6REEwWKxeL1elmU5jkNPMIvFctVVV/3xj39kGKagoCAQCPA87/f7qe0wIWTBggWrV6/G\nNKVhOQgyRowYcezYMUKIzWYLBoOwQYnH44j8EUKg7QVHxJXMycmhZzFo0KDW1lZFUaZNm+Z2\nu4PBoCAIHR0dO3bsgCGzoih5eXksy3Z2diL/iyHZ7Xafz4dDoKXYmDFjVq9erbdwOY3IWBNm\nHRkOvflStiEbauz0WZ3JSHMqFtwuGAzCvwMkjMo/cSyTyYQIHJVWaMkcIQQla4qimEymq6++\n+uOPP+7o6MD+ITXVrkxSjSsQqEMqE98OHjwYDnaI/OFw2IN2zC+//PL3v/99+q1WAEF3SEHb\nYHzlIjLMlClTPv/8c5L6OwbEEXujXcJYls3Nzc3NzT169Kh2tBaLBRYw2rBlbm7url27/rXO\nEzqyCnq6+Ruh//N3epl5HyFjxRO9QXrnYf+/BPRZncnokxq7pUuXLlq0iKQiajRmy3EcNX5z\nOBx+vx9hMy2xQ4vVSCQSi8XgSEebzFoslokTJ27evBljpn8uVFRUHDt2DMfKy8vr6uqipndY\nAbQSNXlaToaBoT8YVnM6nePGjfP5fFByIMQ4ZMgQs9lMu41Rx2CE9wCTyRQIBOgK1CQF5iyK\nojgcjmg0qiiKdgw0MIn4nJbj3nDDDW+88QYh5MILLwSxW7Fihf4gZRUytmOSjm8E/Sewj6DX\n2J1G6LM6k5G2G6PNw9Ke93a7PRKJcBwXDodBgJDKVFWVmrcVFhZ2dHSUlpYi6RmNRmOxGPjQ\nxx9/fOWVVxoMhtbW1ng8rijKRx99pKrquHHjQqHQueee+9prrxFCDh06JAgCuBTErYlEwmaz\nGQwGNKsAkYrFYhaLpaenx2azRSIRKt0Ad0SMsKur66OPPiKEGI1GnEUymYxEIvBMoQyVBu0w\nznnz5r3zzjuEEKvVGggEioqKmpubXS4Xju5yudrb2/EZbcRIitLhcywWM5lM4XBYez0nTJiA\nD4h66pWqfYc0/t1P0vor0kvGNqDjFtkAlmX1qOo3QnofSf3i9wX036NMRtqInSiKsBFZvnz5\nhg0bdu7cSQih7I1l2aFDh6IVLOiRoiiCIAwZMuT8889fs2YNWB0hxGg0OhyOtra2ZDI5ffp0\ntJSQJCkej3/yySdTp05lGObgwYMmk2nfvn1GozESifA8D3Uq9gCWlkgkqGeeIAgI4KH6TRRF\naCYMBgP6SeC/sDtmWXby5MnHjx+XZTmRSHAc19raqqV0PM9fccUVmzZtQkrXZrMdOXIE38I2\nua2tjRCCwjuWZdva2kwmE8dxkUjEZDL5/X6WZQ0GA12/rKwMIlm73e73+zFaXECSYpPI5Kbr\nZunQYqC/0wf6+M946E/uN0Ua84+931sa0f81dv1/RH1WZzLSHEqNxWKPP/54NBoFgUOpmSRJ\nDoejo6MDPCknJ+ess86C8rS2thbtVpE8tVgsBoOBsihYuImiiP/OmjULJiOIdZ04cQI1alAz\nEE2xHSGERg2tVuv06dPr6+tra2sRpSsqKvJ4PFTTivDb0KFD/X4/fIMVRUEzMVTFtbe3E0JA\nQ2tra0tLS+12O3xPFEU555xzJk+e/MQTT7AsC3KZTCZFUbz33nuffvppjAEBS5JiexaL5R9X\nn+dDodDBgwdxXjBewbnQxC41ixnoxaqnJbGYDW/YbMCAzkrLsqyT776AflUp+n/mD/TfozMb\nfdJ54q9//WtTU5Pdbj948CC4FxWBrlmz5sknn6xOzUJajqb1k8Oae/bsmTJlCtUuQDx74sSJ\nvXv3Tp48GctZlqUGclo5BcMwTqfT6/XSvKckSQsXLvzlL3/J8/yoUaMOHDhA1wSTQz0fmCiq\n+mibLxiyaJGXlxeNRmG/LElSMpmk44GxC2Jy0IjQMdBzpMbLGDackw0GA/YWDodVVR0zZgwG\nOW/evGAwWFFR8fzzz+s1DTq+jjRmmjKWP6VxYKdFPDF79pr+POJpgZ7xPI3o/78n9Rq7TEb6\nVbEFBQU9PT3aSjJarwYeAyI1ZMiQOXPmrFu3DlEu2m4LdEpr81tYWBgKhYLBINUcPP744z//\n+c9pKIsKDkDLtEvghHKKiLWkpIQQ4vF4wuEwdTbmOA6JUXjL8TyPjCpW0EpAtCSS4ziz2RwM\nBhEphNEdTQGfwjVx4tqFhBCe581mMzpkUBLJMMyMGTO2b99OCNFVsTp0DGjoP4F9hIxNxWYD\n9FmdyUjzjRFFcdGiRYsXL0Y2lmjsP5BMJCli1NjY+Morr1CKI0lSNBoFSeJ5HmxPVVWTydTW\n1gbOR1IyiFWrVkGmQONe0MyOGDFi7969DMM4HA6v18txnN/v/8EPfvD++++3tbUlEgms39ra\nirAcHTZMSWiZXVdXF/0Wm6CfmNlsbmhowH/tdns8HofRHbbFnlVVpQ0ntBRQS+a03C6ZTCJR\nq6oq4nnw6jv//POxQjwej8Vi1DlPh46BAj2EA+hPro4zD/qszmSkn3HzPH/77bfX1tZu3bqV\nEKIoSjgchpsdZU6SJJWVlbW0tFCDumg0yrJsKBSSZVmWZTieqKoaj8dhU5JMJseOHXvw4EFC\nSHNzM5UUIAwmiiLLsvv371cUBRbERqPRYDD09PSsWbNGFEWO46i6AvV/KMg766yz6urqUHtn\nMBjcbjdauyJQh9WQAvb5fN3d3YQQlmXj8Xg4HDYajfv370fiVVGUqVOnHjx4EClaGuHjOA5Z\nXWrjp6rqlClTmpubu7q6MKT8/HwwQqrSJYS89NJLt912m9vt7iPxRP8n3TI2zZex0K/YmYH0\nPrl6mEpHJkAXT2Qy+qTGrqmpCcQuEAjQojFCCMMwU6dOra6uHj9+/Pbt22nnVhCj+fPnv/LK\nK9iJJEnl5eVFRUVbtmyhMoJ/jFhDdEpKStC7AlxN60iHaB/LskiM4kAGgwHShCFDhrS3t4NX\nYYJq23kRQtC7QlGUkpKSmpqaWbNm7dixo7S0NBKJtLS0gAjyPD958uTPPvusrKzs+PHjkiTR\nQKPNZovH44MGDWpsbASnxIBRgUcIsVgsuCyKohw5cuRb3/rWoUOHaAZ52LBha9eunTlzJkl1\nnsjwXrG6YoBCvxQ6ToHuVZRtyIaXgD6rMxlpI3an9BNLJpNgNtdcc826des8Hg/DMMXFxW63\nu7KyEu1f0Rls6NCht9122yOPPEIFqgzDDB8+/Oyzz37vvfcoJ0P93NVXX019RkiqCA9mKBMm\nTOju7q6rq1MUZdWqVQsWLKCFepT53Xrrra+88grkPDk5OaFQKBaLsSxLd0gr6nJycgghqM9D\njG337t2EEJPJNGHChC+++AJVg3QASDTTvheCICQSiQkTJhgMhp07d8LBWJZl5IhP6bqxaNGi\nV1999eTJk/QKmEymiRMnosZONyjWoWNAQ69G6iPoIe3TCH1WZzLS3ysWBsXgLhzHTZs27csv\nv2xubgaJQUcvURRpYpRoVKJDhw49efIkXUizk18HtabDf3menzlzJsdxH3744T9OLHUso9Fo\nt9uhhPif02YYkqp7o8G8eDxO23zRIZ2CU6QP/+sSupBlWSpuJRqKecpWubm5S5Ysuf3227Wb\n33TTTa+//joh5JJLLgmFQuXl5bp4QoeOgQj9J7CPoBO70wh9Vmcy0nlj4FEMuxNCSFNTkyAI\nzc3N6CRBUswGnIykyI3ZbE4mk+B5lNWhcg5xL7p/beyNWt9hJ8lkctu2baIo4itRFI1GI0ri\nIpFINBoVBMFsNt9yyy0vvPACJYUwKIaooqSkpLa21mq1dnd3I0CII2r9VkiKCwqCIEkSauko\nO2QYZvTo0YcPH2ZZ1mKxhEIhs9l85MgR+J6gswVOAblaKv4QRXHx4sXaK6mqKlpoEEJisVg0\nGoViN403K0uQDTkRHRkO/cntI7yX/0BvVlvU1+P4GrLhtaPP6kxGHzJuVVWrq6thIwIuRcWn\nWtePSCRisVhQhUYTr/iKFuGJoggVRSQSwRJ8NWXKlC+++AKeJgzDJBIJCCwikQjWRBwOgwkE\nAr/73e8IIVpWh4wqwzAdHR0Oh8NkMsGjGGHFU1xOSKpBrSzLsDiBTwpV+4LVEULi8fiPfvSj\nl156ifJC2Cwbjcbi4uIjR47AqZhhGIvFsmHDhqlTp5KUdhB4EIsAACAASURBVBjrd3V14UMf\niSeyBJnpuJbeUWWD+LSXsZne/NL3/6XIkpZi2XCO2YBeqnMqKxf09Uh0/MvoK2I3fvz4ffv2\nIRmKJVar1WAw+P3+RCLB8zwkBeBqWgdgVLlp06BgNlarFext2rRpO3fulCRJluWamhpFUXp6\neqxWq81m6+zsHDFihCAIqIcDBURTMkKIyWSCDgN0beXKlffccw8hhGVZSZJuueWWTz/9FKEy\nhMcYhonFYuCULMsiUkgTqcjVQqKRSCRisRjP81iTEBKNRp999llqlQLLEqyJxmvz58//wx/+\nAGZ59OhRnGlOTk5XV5fRaCSETJs2DQvTrm7R0afofyqZDT+Wvbyq/R+b6SWy4R5lwzn2EgM6\nGtfL+6j/MGUy+orYcRzX3t4+ZMgQpBTBigKBQFFR0cmTJ6GcYBgGIlbkKEGDYP9G2ZiiKAUF\nBW1tbU6nE+t4PB5CCMzqhg8f/tlnn8FRJRKJxOPxt99+m6SIF8uyaAtbWFhYVlbW3t5eW1tL\n88I/+tGPotEowofhcHjlypUsy5pMJrPZjCAfgnn4fEq9ndFoHDRokMfj8fv96DyG7rRz5879\n8ssvw+FwS0sLNgEj5HleUZR4PI6zLi8vf/XVV2no8Y477gDXLC0t7erqwtW4+eabtUeEqEJv\n4ZL5yIYsTC+hXwpAf3KzDdnQyVCf1ZmM9NudLF26dNGiRYIgIEcJq16WZSEIJRpdwimNGdBZ\nS7srKmU4RaDwdb0CUqLagjwU87377ruXXnppMpkcNGhQa2sr3VCbYEW5G45F43C0/i8UCn19\nbDQ9SjTzW5Zl2Klox4aiupEjR1ZVVSEYSQi56KKLPvzwQ7oaTeZqwTDM9u3bZ8yYcdlll0EV\n++yzz/ayWFX/QdWhI3Ogl5nrOPOgz+pMRp8YFE+bNu2cc84pLi5uamqy2WyPPfaYoig+nw8r\nKIqSk5Pj8/moRkGWZUmSxo4du2fPHsS0rFZrPB5HZpNa+5IUTzpFspCTk+P3+wsKCmDzC4Y3\na9as7du3g9URQhA4RLkeDdrB+s5kMvX09KDwDgRLEIS8vLyWlpZIJILUqt1ut1gsfr9/6NCh\nTU1NtEMaGsVeeeWV27Zt8/l8yOFizJREKopSVVWlTeOeOHFCy2hNJlMwGDzlvH73u9/NmDGD\nEBJLofe3QCdtOnScqdCloBT6pdCh439F+ondfffdRwhBJRkhxGQyIRFJs5kMw6A1KiHkpptu\neuuttwghoigeOXIEGVsQJpvN1t7ebjKZ6MqEELvdDjEpDbmhxk6W5c7OTiQxweT27NmDCj+s\nSUUMaFPb1NSE8cTjcUVRrFbr0KFDDx8+TNOjLS0tWBm9zsALFUVBXwoaSoTedv369dg5bVZL\nCLnooos++OCDgoIChmFaWlqo9kJRlObmZvBFjEGSJIfD0draSgdJCGltbaUDhqhWF08MCKQx\nXKr/bp0ZSO+Tq99uioy9FNmQM9F/jzIZ6U/FUqAFxaBBgx5++GFCCOV2LMtCZ0AIsVgsubm5\nEKIi6YmglyRJV1111SeffNLT00M7T9DeEqIooqbN4XCIosjzfGtrK9ie2WxGOM1sNvf09GhD\nfdRmRZZlu93u9/slSbr44ovb2tr27NlDySLV0tIMqaIoTqcTjK28vPyzzz5DOFAroQAjdDgc\nfr8f/z0lNEgIUVUVYhGSSj1jhaNHjy5YsKCyslJ79RwOx759+9xu95w5cwKBwLhx41avXq0r\nzHXoGHDQiut16DgzoM/qTEaaI3ba/hOIZnEcV1RU1NnZiVb3hBCLxSIIAjQQwWBQURSIGGRZ\nLisrM5lMBw4ciEQi69atYximqKhIEAQ40tGOYbFYbPjw4eB8siwHAgGkdKEwAAMDq0ObL+o/\nDGY5Y8YMv99/4sSJaDSqqipYHUkJHdAKFjSUYRjE2Px+vyiKEyZMQLPaHTt24FzsdvvcuXP/\n+te/FhQU1NfXJxIJqupNJBI4KG0RC9qKC4Wj4Kuf/vSnFRUVWmLHMMyzzz7rdrtJSnx0Bogn\nTkv8KRv+dNZtJvoCaWzJqijKrFmr07KrgY70NrrV2+ZS9P9LYKD/Hp3Z6KuI3VNPPfXII4/g\ns7aeDCrUIUOGNDQ0kFR/CNpDgmXZ4uLi5uZmTBowJEEQ8N/7779/9+7dH330EQzk6M7pnw7o\n3woXuv/rnL8q2qAj5HlekiS73W42m5uamsLh8P+9E6LpXWGxWOBsp13HYDDAPFnbIYNGLrVK\nDqfT6fV6afyPZdmKigq0rJg7dy7EEwO984RO7HRkJ/Qy8z5CL18pvYH+Evim0Gd1JiOdN+aU\ncJ3BYBAE4bzzztuxY0coFBo2bFhdXR36ulJWh4I2QRCcTmd7e3tpaWl1dbUsy7TnBM/zPM9D\nWrt+/frq6mpCyIgRIyKRiNForK+vR3b1lIYQdEigShzH5efn8zzf2NhICDEYDLSvq6qqZWVl\nOKjT6UwkEihuQ7jOYDBIkmS1WpuamignA6fUtjtDjrWgoKCurg6NaKHkwCnQRC3q/8AIsROG\nYfLy8vLy8qqqqhCTww4R5qQ71341cHFaXp2Z+b7OkkiDHkrU0afoZecJfY7pyDakv6VYLBYr\nKCgghKDPxPbt26PRqM1mg9dJNBqdNm3ap59+yrIsLEgQ0+rs7GQYpqSkpKGhIRKJUJeTTZs2\nzZs3j2VZm82Wn59//PhxQkgwGDSbzbW1tTjuqFGjjh07RlJlfJQDwcSYLmlsbAQ5s9vtJSUl\nhw4dIoTE4/Hjx49DS9vR0aE9HdA1o9GI0B3MUOBXfEqvM0JId3c3LOhw4h0dHTzPU+UH+KXN\nZkO/MkEQBEHAbr1er8lkQjHfoEGDvF4vSvpQmEgISSQS8XgcspKMJQQZ+xPem7/p0/vz0Jsj\n7shIupl2DOgf1DQ+a718cgf05eolMvYNll5kQ6JAL7DLZKQtFXtKuA4L58+fv2/fvt27dy9Z\nsuTRRx9F0VtOTk5LS4vWSc5isaBvhNVq9Xq9RUVFbW1tCH0VFhY2NzcTQuAeDD5UVFRUX1+P\nejhFUcrKypqbmyORiMvlgpCWEBKJRO6///6//e1v1dXVDMMYjUZVVSORCLgd7UURi8U4jisu\nLi4oKEBDi0gkIgjC+PHjDx48iMAex3E5OTkej4fjOKfT6fF4qAMLz/MOh6OnpwcDo6nV3Nxc\np9NZV1eHNamTHwS8NpvN7/dDewHxB5pqmM1mo9HIMIzL5XK73e+++y4hhIon1qxZk5Y7pUOH\nDh1nALKEJvYG2UAldfQeaYvYIVxHCFm+fPmGDRt27txJCHnllVcgF1i0aBEhBNQKVInjuCFD\nhowZM2bDhg2JRKK9vZ0QgqheOBxGJlSW5dbWVqysKMr06dO3bNmiquqXX35JLeIIIUikMgzj\n8XhQsYcE7jPPPAOSN3bs2I6OjpEjR37yySeJRMLtdtfX1xNNb4mmpiZYkOTn5+MDy7IGg4G2\nIJMkiaRCcSRVLEgIQSMN6Gd5nnc6nbfffvsvfvELj8fT1dUF9+NkMgnOh7MjhPj9fpZlkWMF\nZSSEcByXl5dXX1+vqmpXV9evf/1rrKwVT+gK86yCbndCMaAvhf7kZhuygWbpszqT0SfFj+jo\nSgiRZbmystJqtVosFti5EUKQxJRlub6+3uPxqKoai8UcDsfGjRtnzpypqqrf7+c4jlqTMAyD\nNOjmzZuppzFJleihw4SiKGPHjkXTVXTuEgQBLSUIIQcPHuQ4DkZ3hBBU2p3SvgIOwCdPnszL\nywuFQrt27QKZI4QoigLOCtaolU2gWBDxPySg//CHPxBCYFwcj8cDgUBBQQFOmZrqIZMLYuf1\neumZCoIALhgMBj///PPLL7+cEMJxHM/ziE3qD1JWoZev/mxI8w3oX0FFUWbOXPVPVxvo90gH\nRf9P197Uk6S3k7L+e5TJ6CtVi6qqO3fuRCIyEAhQLzralYEQIkkSNR/2+/2zZ8/GZ22dHNGE\nrPABLIqazKmq6nK5QqFQXV1dIpEYOXJkTU0NuKDT6QwEAlR8AEbodDrRA0MrjEXaFyQyHA7D\ntWT69Onbtm0DJxME4eabb960aROia6focMHMQFsB2LJghba2NnruagoGgwFUMpFI2O12r9er\nquqJEycIIT09PYSQ55577rbbbnO73aIoJpNJURTTe4N0ZD56GabKkoq9AQ2dtGUV+j9ip08w\nHVr0FbGbMGHC/v37UTpGNLYg+NZoNObl5TU2NtK+sShEYxjGZrOB2ZBUUM1isRiNRo/HYzab\nCwsLIYyl7bmw53g8Di1CdXW1wWAwmUzd3d2BQABFbJFIhOM4OK0gH0ojZzCTw96g54BYQVGU\nDz/8UDuMN954g+M4eiKALMt5eXltbW2qqubm5v7nf/7n888/j9NBBvmiiy7avHkz5alUpYvI\nH5K8kUiEOu3RC/jaa6/Bxy4cDodCIeoCqCN7MKDDVDoo9Ce3j5CxbCYbnlx9Vmcy0hlKjcVi\nxcXFjz/+eGVl5QsvvIDQFLq7EkJEUfz+979P10TZHGJdJpMpNzcXX1FW53K5RowYIQjC5MmT\nEcMLBoM1NTUVFRUlJSVOp9NsNiMUjLYThBD4CRuNxu7ubpZlQ6EQy7KjRo0ihCSTSWRCqdsI\n7IiRyRUEgRBSUlLS0tLi8/kQRcvLyzOZTBzHjRgxQpblaDRaUVGBsB+Sv6CDJSUlsPM5//zz\n//KXv6DSDlqNRCLx3nvvIUJJrwNSzyB2DMNcdNFFK1asoN57IKBfd9rDgNN4s3To0NE/0J9c\nHWce9FmdyfgXI3ZaDSw6dBFCKisrn3vuuWuvvXbq1KkzZsxoaGjYsWOHJEl1dXU8z8disbVr\n1yKJCUYFU19CSDweh2Mcza4mk0mfz4cg3LZt2+666673338fviRVVVWiKJaVldXU1OTm5nZ0\ndHAch7SmwWAYNWoUx3G0mQTDMPv27SsvLz9x4oTWKFiSJKfTaTAYamtraZMJGOANHz68s7Mz\nEolMnDjx008/dTgcU6ZMqampYVkW/2KQaAjGMMyRI0ewz/Xr1+OroqKis88+W5Kk9evX4wQJ\nITzPW61Wt9tdXl6+fv16RBMVRUHIEDE8PCrI2F5xxRXYUJuJ1v9I+qbIhvozHRkO/cntI+iq\n2NMIfVZnMv5du5OlS5cuXrzYbreD4cmyHI/HCwsLJ06c+M4779x0003vvPNOLBb72c9+9tRT\nTyHKxXFcPB5nWVaSJFA3KoNgGGbIkCEej0cQBIZhoDxoampCp9d4PM7z/OWXX/7BBx/Ashib\nlJeX19TUEEKi0ajRaIxGozzPG43Gnp6exYsX/+xnP0P9nCRJkyZN2r9/vyiKXq+XYZgpU6bs\n3r1bEARZlidOnNja2nry5EmGYWbNmvXZZ5+JophIJHBSyJ+CfvE8f+mll37++edtbW1U06Ao\nCi22I4SwLFtYWHjBBRe89tpr2vYSLMuuXLnypptuogE/q9U6f/78F198EeSSrrxhw4arrrqK\nEHLxxRcHg8GKiooVK1boLVx0fB06eaXIzEshy7LeUqwvoBM7iv6f+fgpT+MOdaQR/y6xW758\neVNT09KlS5cvX15XV/fnP/95y5YtM2fOdDgc+fn5sizv2rWLKga0G9LqOnym3J9axDkcjquu\nuuro0aN79uxBSpdGrU7Z1deXEEIkSYKbCVSlsVhMS7zACB0OB7UgIZouYQsWLHjppZf+j7Om\n0g0tJS0qKmpvb8dyqvAwmUyRSEQrB6HyEYzZaDQibldYWNjR0UFHeP/99z/zzDOEkEsuuSQU\nCpWXlw/0lmI6sg2ZSbP6H8lkcvbsf25CmQ2XIr3QiR1F/z9rekuxTEY6b0xNTc28efNKS0sl\nSWpsbPzb3/52/vnn8zyPbl2dnZ2UllFaw7Isx3E//vGPN2zYUF9fjzI4BMb8fv8bb7yB6jRZ\nlh0OB9o2sCxrtVp7enrA8ywWCyEEGU+73Z5MJpFRpSbJkiShfM1qtQaDQShMkUpWFCUQCCQS\niauvvnr79u0+nw9jW7duHUkxMFEU8/LyrFYrml4QQjiOSyQSGMbDDz/8q1/9CjLbpqYmKqrA\nmrIsw7fPYDDA3wQCDqIho1SQ0draioI/jHbz5s0gdolEghYIpvFmpRf6T3hfoJeq2N44HZyW\ni39px9JerJWJXVJIWq9Yep/cAW3p18uL30vorxSKNF6KXt6j7dt/kK4j6kg/1H8Py5YtW7hw\nIT4MGzbs/fffV1V10qRJLperqKiIMnraGrWiooIQYjQa6XJwO7z7GIa5//77zzvvPPoq5Hme\nYRiDwbBs2bKSkhK6mslkohbBdGWGYXJycgghHMchCgheqD2WNjpIMXjw4HPPPRcSCror/HtK\niajBYPjWt76lXYKs8fXXX4+TwoYlJSWSJPE8v3DhQpZlcR04jps6dSr56ose/W3phaJfWa3W\nuro6VVXPP//8c84559Zbb5Vl+d+8WTp06Oh/6E+ujjMP+qzOZKQtYhcKhRobG3/wgx8wDNPc\n3Ay+QjUBdru9u7ub4ziYusViMZZlXS6X1+vleZ7afKiqajQab7/9drPZ/OGHH6qqCvVoIpG4\n4447Vq5caTabQ6GQqqqwCMEmVN9KUlldenogVaNGjTp48GBhYSGs5lA8B6878K3W1laIMGjl\nn5rK7UK9S09TVdW3334bp4bQIL7Nycm54oortmzZ4vF4GIaBoTHHcWPHjs3NzfX7/clkUlGU\nhoYGhOVQt6eqqsPhKCkp2bdvHwZGLwV6ZlBoA4E6sgQDOjajg0J/cnWcedBndSYjbcTuwIED\no0ePPnDggMfjGTRoUCQSoS4khBCe59HIoaurq7y8/OjRoxzHRaNRSBMIIVarNRqNJhKJpUuX\n5ufnwxkOU+fSSy/9+9//npOTg+xqQUGBx+MZPXr04cOHFUVpbW0FCbNarfgbAmE2kDZ8hY4U\n6PFlMBiKiorq6uokSbJarT6fD6SQjgSOcTzPg/yJoqiqqtvtDofDLS0tyKtGo1Gz2UwIkSQp\nFoupqrpq1Sqj0Qg2RvOtyWTy7rvvDgaDKDJVVbWzsxMRPpBRjuPsdvtzzz138cUXwxiZnjUy\ns/QUUKWXxmLVLCENaXQK7WWGojf5x14eUb/432hXvd9bP0NRFF080RfI2Bq7/jco7v8jpvf3\nSEd6kTZit3fv3vHjxxNCXnvtNfS8pxIB+NVFo1GwlmnTph05ciSZTNJ2FFardeHChb/85S9Z\nlp03b97GjRvBsQghqqq+++67RFMz197eft99923atEkrvyCEhMNhnuchd6WbGwwG9OMKBAKw\nRInH46j2o9V42I8gCAgugh2i0E1RlFAoxDBMbW0tdkjjfKFQCBQNmeJ4PB6JRNBwwmaz+f1+\ng8Fgs9m6urqwCS4F4n80LCfLck1NzRVXXEEPR1LmxgUFBbt27XK73QaDQRRFg8Gg/nsyl1OQ\nmT+BaUcaT7PXPw9n/s9zb39Q03fxB3R3tfQ+uRmLzLz45HQMrDfTNb2sNI0txXo5sG3bvt+7\n/ek4Dfh3VbEUkMcuWbLEbrdbLBbQJuRDc3JyVq1ade2115JUShFk32azQXbAMExeXl5XV1dO\nTg4ytlAnUIUsFBiEEKvVWl5evn//flAos9lsMBh8Ph/LskajkaZQ7XZ7IBAQRdFkMrlcrpqa\nGrDMyZMnHzlyJDc3t6GhgTrSUUNgGmmj/SFkWdZqac1ms6Io06dP/+ijj7S5WpfL1dHRQYkm\nRsswzNatW2fNmvWPC80wDMPcc889jY2NO3bsaG9vp8tzc3PRM5fGGq1Wa0lJCRzy5s2bB7uT\n559/Xlch6dAx4KDrB/sIGRuxywboszqT8e/emFOcitetW5ebm1tZWfmLX/zi97//fWFhodls\ndjgcL7/8Mi19I4QgyuV2u2nb1ng8brFYenp6aEqURt2o0QkOh8gcvvre9763evVqVK3NnDlz\nx44dgUCA47ji4uIjR44IguB0OmtqalBspyjKF198oapqNBql/nNOp9Pj8YDGmUymQCAAG5Tu\n7m4qX0Uwj5b0bdmyRcuGZVkGJcW5EEJoFvjCCy+klX+EEEVRNmzYEAwG4bECQ+YRI0bYbDaP\nx4PkLw4aj8cR/iSERKPRaDQaj8f1mgYdOgYi9Ce3j9A72TXJhiB6/0Of1ZmMf5fYiaIIoQA1\ntAPV6+joYBimra0NitejR49q3eYkSVIU5fDhw5RgPfvsszzPf+c736EcCApW0B015ZASi8Wc\nTufBgwex4bFjx9SUjVx1dXUkEgHnk2XZZDL19PRYrVaQQlqvhv2bzWb0sUXkD5KLUCiE0XZ3\nd5tMJpZlY7EYVA4YNlK3kiTRthNut7u1tTUcDvf09ED6IEkSwzBobksZHiWpDQ0NdDDYQ01N\nzcMPP7xr1y5ZlqkriiAIP/vZz7AJ1edmckFD/yc7+v+P9YwND6Tx4g/oUraMBcdxGZum7Gf0\nf/6R9DoFmUb0f8Vb/yOTf490pCcVG4vFCgoKoAaQZdlms7W1tVG3ueLi4s7OTpS4ochMVVWX\nywXfOHAp2kwM//I8j+I8mooFsTMYDMlkEgpT8DlJkkDOli5d+uijj4bD4QkTJhw6dIhlWagf\n6D5Jih6pqjphwoTdu3ezLCuKIvpV8DwfCAQgy41EIjSvKkmSIAjBYBCErLCwMB6P+/1+5G2L\ni4s5juvs7AwGg+PHj9+3bx8GDzkwONnUqVMPHDgQDAaNRmM8Hv/Od77z6quv4uxw9WhEkPoe\nE0KKiooqKyvdbvf5558fDAbHjh27atUq/VnSoWPAQffo7yP08u+Q3mCg06z+hz6rMxnpyZGL\novj44483NTUNGjRo8eLFkUiEYRiwOoZh/H4/WB0lcBaLhaYdIVYghNhstng8Ho1GQb9kWS4o\nKGhra3M4HPF4HCa9M2fO3Lp1K6JuV1555caNG3EsRVFOnjwJpxK/3+9yudrb22H2a7fbe3p6\nhg0bJopiVVWVzWbzer2wFyGEgBSGw2Gj0UizvdrgIvK2lB3CroUQoiiKyWQKh8MwXkEaF6vJ\nsgxWh3DjoUOHgsEgJBRjxoyprKxEOSAq6lRVtdvtHo9n2LBhzzzzzLXXXutyuXp6el5//XW3\n200IkSQJUtwsKcHWoeMMg/7k9hF0NnYaoc/qTEY6ix9lWX7iiSeQrySpG89xHNpCkFRVWSKR\ncDqdDQ0NWEhd6NCkARvefffdHo8HHSA4jkNLWULI1q1bEevief6dd94hhMATjmGYQCDg8/kY\nhvnyyy+xMqSmfr+fEFJXV4eFUGyAXyLsZ7FYgsEgjZ8pimIwGGbPnr1jx45wOMyyLI6OWKPD\n4aANKsLhsCiK55xzTmVlpaqq1V99yyAWSAUZJpMpFAqZzeYTJ05Eo1EsxLFA7Do6Om6//XZC\nCJzwXnjhhZkzZ5JUjzX9KfrXMKB9B/TEqA4d/wcytjpCh47Ti3QSO47jHn/88erq6pdeeok6\nd9B0KkmFxwghlNWRFLsimi6xhBB00/rHEHkekS2shlQpTVmCvXEc98c//vHr7IfjONgRYzVC\nSH19vXZvqqrCdQUxRWRg4/H4Rx99hFQv+JzdbhcEwev1Pvfcc9/73vdQCUcI6enp2bZtG6ri\n6JAsFguUsyzLjh49+uTJk4QQsMOdO3eSlJYC6VqO45xOZ1tbWzKZ7OzspCMvLS3Fh1gsFovF\nwBH/1TuTveh/0tP/R8zY+q1sqDTqDRiGydh71M/QqdgZA/33KJORZruT4uLid999NxgMVldX\nezyes88+u6WlJRwOU8cQzAZkWv/vQzOafrKgibTzGLWFw7d2u53nefgP0+XwliOE8Dw/ffp0\nCDVCoRByoCRlI4x8KBXAms3mcePG7dq1C0TQ4XBAlKodA7YSBAHLi4qKGhsbnU6nz+cTRVFR\nlIkTJ+7duzcejxsMBqvV6vF4cEbajrHaf2lUT4uCgoKdO3e63e45c+YEAoFx48ZB/5uWm0Wy\nJho0oCN2aUSW3O7MBP0zUkeWIBteO/qszmSkn9g988wzl19++caNG5ubm61WK02w8jxvsVi6\nu7u1W2lNhuks0fIzCo7jBEGIx+PgVW63u62tLR6P0zgZANs5URSvuuqqbdu2dXZ2WiwWQRD8\nfj/1qINmFrVxOTk5l1566dtvv01DYnS+0hwxlVMgMhePx6GxuPHGGz/44INQKARphaIogwcP\ntlqtzc3NM2bM+OCDDwRBQF8KnA66a1CZLaKJoig2NDQ4HA4ELKmFHqXCVDyxevXqUxrX6tCh\nI/MBjf/pHoUOHemEPqszGWkjdgCld4899lg4HB40aFB3dzdq7KiPCXjSPw6fInA0fAXmRHcI\neSwIFqJl4D0WiyUUCkmShJ1jc6PROGbMmF27dmFbkCSz2SzLMqJriBSyLPvYY489+eSTSI/C\n68RisQQCgWuuuWb9+vXUZ3jw4MEmk8lgMBw6dAhHwWr0iBj8jBkzKisrSUoBTtXBkMHSrDTE\nHCzL5uTkeDwep9MJW76Ghga3261lqLBoBr+88MILQ6FQRUXFypUrdRWSDh0DDrp+sI+gJ3ZP\nI/RZnclIW42dx+NZuHDh+vXreZ63Wq3hcFiW5ebmZkocaWSOxqKIRllDZadaVkdSPVKRrxwx\nYsQPf/jDBx54IJFI+P1+GtyifcAikQhldXSfVMxBa/hkWf7Nb34TDoe1jS7AwP7yl7/APw8r\n33DDDc8++ywdcH5+Pm1WoU2e5uTkUA0slmA1BBRpJBItyxRFgTwCNsWEkOHDh+MDpbmyLL/6\n6qvz588nhPA8D/+U3lPwzMwGnhbol0LHaYeufMo2ZMNrR5/VmYy0EbtvfetbkydP7urqgrnu\nb3/7W/j9wnNOEARFUUaOHFlVVQX2wzCMzWYLh8PUbQ7cKJlMSpJEG8tSqxFRFG+77bZHH30U\nJsBIfcJAWEueiCb4px0e3c/06dN37dqFLhGhUAjhwQ3U9wAAIABJREFUOo7j4HKCzXmej8fj\ns2bNQjdY7EEUxYULFz744IPkq33ACCEI1yEkmZ+f7/V6oYrQeqCQ1JNgs9lgBMOyLC7L9ddf\n//rrr5NUl1gEApcsWQJipygKaGvvCxoG+isjjdAvhY7TDr0UqY+QsZ0nsuG1o8/qTEZ6iN3u\n3bvr6+s3b94McuN0OuEAjGwpIUQQhFAo1NDQMGXKlN27dyMf6nA4XC5XXV0dDbkZDAZCCKzp\nCCGqqubl5XV2diaTyUgk8tBDDyF6l0gkpk2btmPHDjoAURTtdntHRwdNj2qZkLZ4rrKyklYG\nxONx9B+bM2fO2rVrqa8edLh79+6lNBQ72bp1q9lsDoVCsixTVkcIAU2Errajo4MGEWOxGHKy\nTqczJyenqakpFovde++9v/zlL1FpBzb55z//GfvRJqkbGhrq6+vdbjcM/KjdsY7TBV1/oONf\ng/7kZhsGdMSuly+6R44f7+uR6PiXkZ4au1deeWXjxo1vvvmmduF3v/vdV199lcojqPggmUwi\nxoY6OZiJwG3ObrdHo9GKiooDBw7AlTcWiy1fvvz111//4osvEP8zGo3JZPLNN998+umnCwsL\nN2zYALaHOF9eXl53dzfYmMFgYBgmGo1CeIGeXTCTQ35WFEWWZWl0kBBy7rnn7t27t6Cg4O23\n3548eTK9OCjvg2Hy/1w7hnG73SCmCPKRVJMMdJJgGGbcuHGHDh2aN2/ezp07vV4vrIyxcjQa\nJakAYSKRmDRp0iOPPHLttdeiu8aCBQtefPFFohFPrFmzRv+F0KFjwEHXD/YR9Bq70wh9Vmcy\n0paKpRxo4cKFmzZtUlX15ptvJoTk5ORYrdapU6euX78elmzUtQT0C2pZxLdKS0sPHDhw+PBh\nWBDH43Gr1RoKhWjlGbaSZfmaa67B4ViWRaArJyentbU1EomAYKEMThRFQgiNjYHP4d/8/Pwf\n//jHTzzxBPZjMBhyc3N37twpimJjY+OkSZPIV7O6tO2EIAgVFRX79+9XVbW+vh7rjBo1qqmp\nyev1grPS/mBffvllbm7uBx98gIwzbI1PSRwjAd3d3X3fffcxDIPx33PPPadcXhQUput+9X/8\nSY949RF0j7S+QBqnq6Ios2at/qerDfR+vr2kWb2BPl2/Kfr/JZDe3yMd6UV6iN3o0aN/8Ytf\nQP/8zDPPLF682O12I6vo9Xq7urrq6+up7RyVf9LmXT6fD+YjBw4cQKaVCh1oapKkuCOVMiAW\naDKZEPrq6uq64IILiouL/x97Xx4fVXm2/ZxlzpxZMpNJJjsJCVskYREFRARE2RTR+qpt7a91\n12rxtW9faf1sQStqbW3rWhequEBdqrZugKDFpYCKEjAQCDtZINtkJpPMvpzl++PqPD0G3xrb\nSZiQ5/qD33By5jnPec6ZOdfc931d98svvwxupKpqNBrNzc2NRqPYh6T8UDiO83g8d9xxh6Io\nc+bM+eCDDyBQRQDPOD6Oa5SsKopSV1dHE77YWFdXZzabwecEQUgkEjjTQCAQCASwD6KVcMvD\n3AghFovl6quvfvLJJw8fPmyMnv7973+vqqoihFitVsT5WLHqv4FBnRPpI4bCUzBj2UxfoOv6\nULhGQ+EcMxZ9KzdM5wViz6NMRtrsTubMmTN16tTly5dLknTs2LGJEycmk8lQKAT7EqgEqEMb\nQlZIj1JDE1VVS0tL29vbLRZLIBAA5zObzcaeEDgWFA8gW3l5eT6fj+d5RVFcLlcymYzH48lk\nsqCggOO4eDw+ffr0999/PxaLnXXWWVu3bsV8rr766lWrVmEEWhK3bNmyK664orq6GryQ5/lz\nzjnnww8/hB0dPVOE4ubPn3/FFVcsWbLE4/FwHIessXFBkL1FjSC2lJSUdHV1RaNR6ttCCBFF\nccmSJQ8//HCvt//oRz964okniMHuZMWKFaKYzk4hDBmOQc1mGCgURWGfXIaTDOyuzmSkjdh5\nvd5bb7313XffdTqdeXl5Ho+nra3NarVardajR4+SlN0JgrfUsxdBvkWLFq1duxavs7KyIIZF\nS1bjIRwOh9VqjUQiNAZGk6S0zwTIoqZpixYtKisr6+rqQgqYpAgZSJWu6wjdofKPzgfxNih5\nCSGiKEqShGiisV2E8ej0NdVt4FiI2yHvTGWt0G3QdC0hpKys7PHHH7/55puNbdY4jrvzzjvv\nuusukqqxGzdu3LPPPssMITMfA98rNo0YChxx4AuzNE2bOfPpgTziEEHG1tgNhUQBMyjOZKTZ\noBioqalZtGjRuHHjDh482NraChkEGBKCZKBTiJMtXLhw9erV+fn5mqaNGTPG4XDs2LHD6BIH\nOxJKwuh2lNaZTKacnBxVVREYO++88z777DO/3+/xeCZOnIg+Y4qiFBYWtre3S5JkNptjsRi1\n/zUyQtrvC2IOXddtNts999zzy1/+UhCEs846q66urrm5GV4np59++rvvvkupniRJ8EwxNtIA\nd8zLy/N6vYQQm80WCoVkWUYfC5L6bBQVFY0YMWLz5s301ARBePTRRxcvXkwI6aeWYgwnDViN\nXYYjvWXmLI5LkbHEbuAx8F8CTDyRyeiXUGp9fX1JScmhQ4eOHTuGy+92uz0eD+SrCNQRQiAm\n3bNnT3FxMRhbKBQ6dOgQRAbU1C0/P99kMkUiEahKJUkCO3Q6nTAlhqYBb1m2bNnNN9/s9/vz\n8/MJIUVFRX6/X1EUiDCgn0Du1el0mkwmSZJaW1vBKffu3auq6pQpU6ZPn/7II48QQmKx2M9+\n9jNMeMOGDXRinZ2dGzZsQEYYJ4h0M2iipmkOhyMcDkNFi2nrup6Tk7Np06ZJkyZB2EErT7ds\n2TJv3jxCiMVisdvtKD1EfwtisG5mxar/BobCT+c0YiiQhjTW+JO+PSzTK57I2MVn4gmKgf/a\nGfgVY8+jTEa/ROxWr179k5/8JBQKwfWDbqd5T/pfZCfpbuBJxt1o21abzYYMrCzLaMBaVlbW\n0tLSq1fs888/f9ttt3k8nn+cXipXe/bZZ+/bt6+zsxOcEjxPkiRobI0juN3u7u5uFAL2yrr2\nWiuTyeRwOHw+X1FRUVtbm/FP9ERw91Mua2yhRttXzJw585NPPuk1DY7jNm3aNGPGjPnz56PG\n7vHHHx/UNQ1DgTSkF2lcscG++IN6/qwaqZ8wqO+KwQ52V2cy+uXCcBzX3d0NMpSVlaVpWiQS\n0VMghAiCgM5jSJUadyOE0N5cgiCYzWY49MKFbtiwYdXV1WvWrCGEHD16lJIkKq249tprJUlC\nXhUjQEW7efNmI0sLhUKzZ8++/vrrr7/+eqhTRVEcOXLk/v374T9MDIWAlGjKsgzFLpLIiqL4\nfD5CiMfjQQgQTsWEELTfuPfee+GiR7tr2Gy2oqKiQ4cOITKHSX7xxRdUTULbctjt9mHDhvVa\n2DSmHgb+OzFjv14HPqHT1yP2bcX6lIXJ1MXvI/p482RsVjpjJ5ZGDHzEbn3+T/uy27L/bDKD\nAkPhBmPoO/qF2B05coSkBATBYBC6V2M+HiQMNKjXbpRF8TxPiRRkszab7ejRo+Xl5cYSPUSD\naT9ZXddjsRhtDobt6CRBZadgeH//+993796NLhewFPZ6vSUlJcbELqxJoGwNhUK9UsmUKULz\n6/f7aWAvkUhs2rRJkqRYLJZIJFAmiO0HDx4khNBMKyEkGo2KomjsG5aXl3fKKaeUl5cTQhKJ\nBCggx3Fp/GRmLM1KL/rCXwee9AyRb9iBf9icAPLdhyMOkVKkIXJX9wVDIRU7RO7qQYo0p2J9\nPt+SJUteeeUVyq5oUA0ubkhNFhYWqqrq9Xqp9Qn4WXZ2dmlpKbx/UZlXVVXl9/sbGxvBbzCg\nxWLJzs5ub2/HbojYud1uh8Nx+PBhqs+AxV1BQcENN9wAhaksy4lEorS0FDyys7MT03M6naFQ\nqKCg4KGHHrrhhhuo6hah5mHDhrW0tFCH4YqKio6OjpycnLa2tmQyyfM89XBZuHDhrl27Wlpa\ndF2vqKiQJGnfvn26roP50cgiz/NQbJBUPtpiscRiMZvNhg4cHMfdcccdy5cvJ0w8wcAwyMHK\nzBlOPrC7OpORZmI3b968KVOm7N279+2330ZcDQ0kjGlQavwxbNiwo0eP9toNRMdqtYZCIZ7n\nTSaTLMuBQGDMmDGapjU0NNBkJTBy5Mji4uK9e/eifo7G6nCUXupanuep4ci4ceMuu+yyO++8\n0+iQB0c69L2IRqNz586NRqOffvoplBxGzcf06dM/+OADztBkgsps4/H4GWec0djYmJube+DA\nAeqoTOvqaJmgKIo2my0nJ6epqQkSWsqDb7rppscff5wYWoqtXLmSycu/KViG4huByQz7A8zu\npJ+QsbfrUPjaYXYnmYx0Eruamprvfe97+/fvv+eeexBtop1bcZScnBy40yHFef3110N8it1o\nr9WbbrppxYoVYFdgTolEIjs7u6CgoKGhgboHGyv2SMocbsSIEYcPHxYE4fzzz29vb9+9ezc6\nSaCTLEn5Bhvr50RRdLlcLpfrwIEDGBMcSxCEpUuXdnR0PPXUUzk5OT6fj7JGzBM5VspTb7rp\npieeeEJPNdhApvj45Z06dWoikaitrcVQbrdbVVW/328ymYqLixsbG7F9zZo1F1xwAWEGxQwM\ngxyszLyfwMQTJxDsrs5kpOfCIAP75ptvRiIRWZbtdjsIDVWnAmazmaoZ4vH4qlWrwIpCoVBu\nbi6EpYqilJWVoSPFggUL1qxZU1ZW1tzc3NPTQ9OUxKBsIISoqopcqq7rIEaqqq5bty4rK8vp\ndCI2lp+fDws6kvKTw3s1TUskEh6PBzIIbLTb7d3d3aqq/upXvyorKyOEdHV1kVTMT1XVrKws\nSC4ob9M07Z133ikrK2tqaiKpIsL8/PyOjg7ME/WCqqrW1dWdccYZeJfJZOrp6UFxoaIoeC8m\nQEeGfARNbxkYGBgYACaeYGD4SqQnYocM7FtvvaVp2po1axYvXrxx40aag6ciBofDEY/HUX5n\nt9sjkQjq4XpV47lcLtp2gqoKyJe7itlstnHjxu3YsQM0kZ6FIAi33HLLww8//KWT5Di0JuM4\nLjc3t6ury0gKe52LKIoI6Rnb2mJkWA07HI5rrrnmgQceIIQUFBSMGDGirq4uFApRR2Kc72mn\nnXbXXXf913/9F/VzoQeFtTI9cZgnE0LMZjN1S6Yta5GKHT9+/MqVK5lvUOZj4OumBz7vw1Jg\n3wiqqqbRx46FqSgy9j4c1PaZfVzVTZuuZ8+jjEUaInY1NTWNjY2/+tWvXnjhBbPZPGLEiIqK\niqKiIlVVEa+ipW+SJFE1aCKRQIbe5XL5fD7KzFDcRsvRwOqw55gxY2i2NBKJfPHFF2ecccan\nn35KCNE0DU1gJUl69NFHYVBnLOwDYdI0DU0g0OnLqKXFWzRNO/PMM7ds2cLzfH5+/tSpU9et\nW6coitvtDgaDeIuiKCtX/uNruqOjA+eIqBu1NeE4bufOnSNHjjz11FN37NiBnRG3i8fjLper\noKCAZl0pi8XbVVWNx+PDhg3bsmULhLF4LytoGBTI2O/rNCJjy4P6MrGBZwPp/eRm7A2WRlad\nXhPpgcfAX6M0Ln4fd+sPB1yGdCENxK6+vn7SpEn79u2bNm2az+dbunRpQUFBIBDYvHnzaaed\nphuaboXD4eHDhzc2NnIcBw2Eka9QSqSqKnzgli37ZxCd47j9+/cTQrKysgKBAArvPv30U7jE\n6bo+f/789evXjxw5sq2t7c4773zooYdwIJIKGULlgPiZqqoFBQUul2vv3r00HEgIEQRh27Zt\ncFdpa2tbv349Svegyd2wYcNzzz1XW1t75MiRnp4et9vt8/mQJD3nnHPWr19PDLe7qqr33HPP\nihUrpkyZYjwLQsjEiRP/9re/YYsgCOeee+769esRKcS5C4Lw5JNPUlZHUjWFTIX0TTGofzr3\nERlLszITA79cQ+QROPCsus/NlNkH5Bugj9do8+Yb2PMoY5GGVOzq1avXrFlz4YUXrlmzJi8v\n78UXXwwGg0YWQnukEkIgfUV07R8zSHEvs9kcjUarqqoOHz6MtCnYTH5+vt/vhyEwfQs0ENRY\nzmKxnH322WBLNH+Kka1Wq8Ph6OjooFIJHNp44pMnT7ZYLJs3bzaZTHl5ebSNxOzZsz/88EMo\nIZBjtVgs8+fPf+utt3Rdz83NRXPYaDRqPF9RFAsLC48ePQq/FRqwBK1E+SACljiLOXPmfPjh\nh7quW61W1BESQvbt21dZWUkImTdvXigUgniChb4ZGAYdqN0mA8NJA3ZXZzLSQOwghn3xxRe/\n//3v79+/PxKJlJeXq6ra09ODUBk1b6M6Vo77V8elDMm4z//1FmynJXRgS4QQKlnF0WFioigK\n/ktVFCaTye/306K3rxy8l1zDarWCycHlhO6GdhrUBpmkigLp3P6vk3I4HMhQG//02GOP3Xzz\nzYSQBQsWhMPh6urqwd5SjIFhaILpBxlOPrC7OpORhgszefLksrKyN954Y9iwYUuXLv3hD3+o\nKIrD4QiFQrQ3F0gPcqAoayOGPCzto2q1WgkhJpPJarV2dHTg7WgsxnEc+pJBTgHKCF9fYmg7\nizo2OAxjfJvNxvM8nQwaf5nNZiRwwcNAvECz3G53JBIJh8M4O87Q3RUvIpEI5g+zZbPZDHEr\nz/PUYw+ROZwX9VWmsUkQPmyxWq0ulysYDMIAj67q9u3b8UJRFLpu//nFGmrIzGr6jAUrzO8P\ncBzH7sP+QMaKJ4YC2PMok5EeVazX67311lvXr18P0StSkKIohsNhjuNMJhMCZmgFSxkS2BhJ\nJU+tVqvP51u4cOEnn3wCHQNSrmgXgfyp0X9Y13WbzRYOh9HTorq6es+ePUYRhqIoRUVFnZ2d\niqJgT2z/9a9/fdttt1G6Vlxc7PP5YImMDCkImdPpDIfDs2bN+uCDD0gq/MbzfElJSUtLC/5L\nKanb7e7q6lJVdcSIEc3NzfgTJkanZLfbw+FwUVGRLMtHjhwBDTWZTB9//PHUqVONF4Ln+QUL\nFrzzzjsk1XmCGRQzMAxSMCvXfgIjdicQ7K7OZKQnlOp2u1evXk3/O27cuEQiYbVad+7cCa0o\nbR1GCIGAFBl6u91eVla2c+dOjuMURSkpKQkEAqIojhkzBlIJcL54PH7//ff//Oc/z8nJgcMc\nIUQQhEmTJm3ZsgXRr927d2P84cOHNzU1YR9Uy5lMJrPZDGKnKMqyZctAN3VdHzt2LN5YUVER\njUZ5nj/llFPQB6Knp0cQhDFjxoDYgcNpmtbe3k7zy1OnTvV6vY2NjT/96U/vvPNOVVXRJ9di\nseTk5ECEKwhCLBaz2+0XX3xxZ2dnTU1NZ2cnjVmqqvq3v/3NbDZTfxNMeN26dcYVpmHOwYuM\njQald2JDQa7B8I0w2D+5DN8UA/8lMPBHZHd1JiPNLcUIITU1NZdcconX662srOzo6AiHw0Yt\nBV5Qm2IYhbhcrng8HggEaE1bcXExGKHf708mk+i1aixoI4bKNmMnCboRh8NRzjvvvHfffdd4\npjk5OYqiBIPBwsJCj8eDRCfyvIgF0lAcKJfL5YpEIlT5gb4Udrvd6XQOGzaspqYmHo9DG0EI\nQUKWpNhYVlZWOByGb4skSTgvnuchFiGGzhnGZczLy/v888/Ly8vPPvvscDhcVVX13HPPpbFY\nlbEZhn8bGcvRMxOszLyfwO7DEwh2V2cy0l/8WF9fX1lZ6fV6ZVlGaIoyKkq8KInBFuoYrBs6\nfXk8nlgshlvHmNDsBeNQ9N9eJGnDhg3G/4qi+NBDD1111VWEkPb2dqN1MBXeUiCQ5vf7SUqN\nK0kSJLqxWKynpwe8UJKkRCIBxSvHcVOnTk0mk3V1dRCRYCiO4x544IH77ruvra1N13Uq15g+\nffrOnTtRF0j3DIVCeI3OExaLJb0UPL1fdn2xgM9Y//fB/nhIY/3WYF+KzISu60Ohxi6N5nN9\nXArWeeIEYoiY+AxSpJnY+Xy+p59++rPPPjObzTU1NZQnge7AgJfjuPLy8ubmZtAgQgh1Eiks\nLGxoaBg5ciRtvaCqKhqRRSIRkLyqqqrdu3fbbDYU3oGTmUymZDJ5xRVXvPrqq1SCSts88DxP\nxRAY85prrjEyzqysrNzc3IaGBkIIUrSEkG9961sbN24Mh8OKosBYmBACcxOjKqKXnFbTNJvN\nVlNTQ47jlxzHbd26FTzP+Km4/PLL9+3bx3Ec9BMQZIwfPx4+dqhEzPC496B+JmUsTekjzfo4\nU+efRgx2xjmoPyB9xFA4xz6CZTAYTizSmYr1+Xzjx4/v6emJx+N2u12SJJ/PR5uAAVlZWSUl\nJYcOHQKZk2XZ4XDouu71eu12O6JWgiDMnTt348aNVGcKCIJw+umnd3d3Hzhw4B+z5zhCiMlk\nuv7665988kljq4nhw4dHIhGPx0PFELIsL1269IEHHuju7iapvl7UBi8Wi0GNAec5nufR2QxZ\nWmq8x3FcTk6Oz+dzuVzgfBgcncp6WZlg8NzcXJ/PRwkoxuF53mq1JpPJeDw+b968np6ezz//\n3PjeGTNmbNq0iaRaik2YMOHpp59moW8GhkEHlrTqJzDxxAkEu6szGemM2C1cuDAWi3V1dc2c\nOXP37t1lZWXnn3/+q6++anQkicfjUEVAExqLxVRVXbRo0bp165B8hI/JzJkzA4HAtm3bMLLN\nZhs9enR9ff2MGTNWrFhBCLFarZFIpLCwEO3IDhw4IMtyNBotKSnp7OwsLCzs6uoqKirq7u5G\nVI8Qkkgkfvvb3xqr8QghYGlUuABHFUmSeJ5H8zFCiNvtdjqdhw4dQioWaVm/349TwJxHjhzZ\n3d3tcDhkWXa5XEeOHEFvDHBWkgrR0dK93NzcvLy8Y8eOcRy3bds2i8ViXEld1/fs2dPY2Ghs\nKcY+RQwMgxHsk9tPYIztBILd1ZmMtEXsampqFi1adMYZZ7hcrg0bNnR2diIAFgqFaMwML0wm\nU1lZWVdXF01KwotEFMURI0bs378fzbWo4Rzo0RlnnHHgwIHi4uL6+npJksrKyg4fPux0OqPR\n6PGewMbOFoDJZBJFcfz48Xv27EFaFtlbNO9avXr1p59+SiUUVqvVZrO53e6DBw8qijJhwoTW\n1lZCSF5eXkdHRzAYpB1sabKV+h5TJ5fs7OxEIhEKhWw2WyQSga5CluXs7GzoajECz/M33HDD\nU089ZbPZRo4c+cUXXwiCYLVazz333DfffJOwiB0DwyCHqqqzZq382t1YHeQ3RcZG7IZCKpZF\n7DIaepqwatWqqVOn5ufn//znP08kEpdddhm1qUMjL47jHnzwQWLwouN53u12y7JM41Vms1kU\nxeHDh+Ov2Iicps1m+/73v5+Xl4eNVqv1z3/+8+zZs51OJ9yP6cgjR47E4fBf6o5NpbJAXl4e\nttBj5eXljR07luf5/Px8QsjNN98siiLP806nc+TIkdiHJn95nrfb7XR8nudnzpxp9PWh+2dn\nZ9N3oZTQZrMZL0FZWVlWVhYxCMhxvg0NDbquL1iw4KyzzrrxxhvhqMzAwDC4wD65DCcf2F2d\nyUhbxG716tWPPfZYbW1tJBL5f//v/61bt665uTkWi8my7HQ6Ozs7VVVF/pQ6kui6jsSrflyX\nLULIRRddtGbNmq/8kxGUP9HgGayJe+0mimJpaWljY6OeSp5mZ2cHAoHj9Q2yLKPeDltAOuPx\nONrF0j0fe+yxn/3sZ9FoFPvzPC/L8re+9a3169cHAgEsLsifUe76lUCIDl3FKFwu144dO8rL\ny+fPnx8Oh8eNGzfYW4qxSAMFW4ohhfQ2X2I3D0XGLsVQiNixlmKZjDSnYnVdv/baa5cvX55I\nJIqKitAWgud5mi019lfNzs62WCzGEjdiYG9OpzMQCCDdaTSZo1QMncdAm5DT1HUdbWHnzZu3\ndu1a3eBjYhyfEIJsr9EYj7rrYYayLCeTSUSbQT2HDx/e0tKiKEp+fj5EIb3Gp1JcURRzcnI8\nHg+OAvKKP1mtVkVR8FuHTglWKSaTCftAVjJ16tStW7eSVCp2/PjxK1euZKHvE4iMzftkLIaC\nx0dfkN5ULAMF+0ieQLBUbCYjbYx78uTJubm5yWTy6NGjpaWl8Xg8FAqZzebS0tKrrrrqN7/5\nDdqLaZpWUlKCkjXY11H16KhRow4dOuR2u71er67rqMCj3WYJITk5Obm5uXv37gXZ0jQN1sfY\njUb+YrEYWB19I2WT2PLoo4++9tprBw8e9Hg8eqoQ0O12l5SUwHmOEILOGZqmTZs27aOPPtJ1\nvaWlBeQvPz//oYceWrJkCTzwSEoI8tZbb33nO9+JxWKKong8HrfbHQ6HY7GYxWIZOXLknj17\nVFWNxWKnnXZaXV0dmC7NR7e2tiqKcvvtt//617+2WCyJRGLSpEmYME1ns/4tJxbs8fBNwVYM\n4HmeLQXDSQb2PMpkpNPuZOPGjZdcconFYnE6nXl5eePHj3/uuedmzJhRW1sbjUahgRBFES22\nSCpsBss6GvoydpIoKSk5duzYP+fKcYIgYCP2kSTJbDaHQiFd14uKioxMS9M08Dn0sWhra7NY\nLDDDk2UZSVVVVVVVRROIcDhcUlIyZsyYDz/80HhEOqWysrLGxkZsXLNmzcUXX0wIOfPMMzdv\n3lxQUOD3+2mMTVEUk8mEoB0hpLS0tK2tDR7LxcXFubm5u3fvNi47nSdJMdHc3FzadoyKJ1iv\nWIb+RsbmtgY1WFfNfkLGRuyGQiqW3dWZjHTmyOfOnTtlypSpU6cuX75ckqQ///nPTz31VG1t\nLXzjUHZGeRshJJlM3nDDDXv37t2yZQvCVBzHgRWBciFIBkIG5qcoyrFjx2gKNS8vD3YngiBs\n3Lhx6dKlzc3Ne/bsicfjhYWFFRUVNTU1JpNp586dlZWVPp+PEMLzvMVi6ezsxJxB8pAy9nq9\nkE2QVBAOEbWysrIjR460traazWZkUa+44grmIL6WAAAgAElEQVTstnnzZpfLZbVas7OzDx06\nhGSxoiiKomBiOCOIMOLxeFZWVnd3NxzyjKJaQkhJSYnH48EiVFdXb9myZebMmZgJzdKm8WIx\nMByPwf6wyUywT24/gcVBTyDYXZ3JSHPx4yuvvHLzzTc7nc5kMonCskgkQggxmUxIPsbjcfAw\ncJo//elPCNdFo9Hp06dv3bpV13XqVKIbHH1JSh5bXFzc0tICmtjR0TF8+PAjR46oqnrWWWd1\nd3eXl5ejHi4SiXzxxRcoaJs7d+7IkSODwSDK5iirI4SgqA67EULgNkJL+gRByMvLO3LkCMdx\nZrO5qKioublZURRY2QF+v9/4XzpzmgtG3pkQIgiC1+tFwwwUC0I4PGHChNraWrA6vGXHjh0v\nvfQSiB1WgPbDYMhwDOof6xkbAhnU0NPaUmxQX6NBPXkGI9jzKJORNmLn8/mWLFny3nvv9fT0\n5ObmyrLc0NCApguaptEGEmB448eP37Vrl6Zp6L5ACFFV9eOPP6Z+H5Ik0bfIsgznOVEUFy1a\ntH//fljQEUJyc3MlScrKygoGg6jJa2pqQgOxUCh0zjnnoEvsrl27xowZAyoJr+Pt27fDIS8n\nJwelflSlAbnDzJkzx44d+8YbbzQ3N+OvwWAQwlVMEm+nnW1HjRoF42UAIlmO48LhcGVl5b59\n+3ieR2M0dEiDdR9Ceu3t7b3kRdFodOHChXgNkYeeclRm+EboY26xL8hMbR1J68TYA7U/wHFc\nGhd2UF+jQT35viMzvyvSOyv2PMpkpK3Gbt68eVOmTLnooouuuOKKBx544Pvf//5555332muv\nLVmy5Nlnn/X5fN/5znfeeustcJR/Ht5QWEYhSdLLL7986aWXchw3YsSI8ePHv/POO8lkEg0h\nYrGYyWSy2+3d3d1Op7Onp0cURaMdMVUbEEJo7RpihMcf2mazybKMxGgymbRardFo1G63h8Nh\najiM9DFyuKFQCDwPQ1mtVmyhClme52lMURTF/Pz8zs5ORVFcLpcoih6Pp6ioqK2tzWQyoeqO\npFhsr0UoKCjYunVreXn5OeecEwwGWY3dEASreDs5oGnazJlPf+1uQ4H0pDdix+J//YE+rurm\nzTew51HGIj0Ru5qamsbGxnffffeFF16YNGlSd3d3VVUV/lRaWqooCo3MUfoCbgQmRFKlbyBJ\niqJ873vfQ+K1oaGhoaEBJEyWZbQdUxSlu7sbe9JetHRAOibVYxsjXpgAdBsVFRVtbW205RfC\ndaIoIl9MCJEkSdO0/Pz81tZWVVV///vf33TTTZWVlTfeeOPtt9+eSCQQSqQTQPkgTfWqqrp+\n/fpJkybput7V1SWKotlsRmSRGubxPH/XXXe9+OKLe/bsMQ716quvop8YTemyX0hDDYyxnRxI\nb8RuUCO968BWtT/Qx1VlqdhMRnqIXX19/aRJk0CncL2zsrI2btx4yimnNDc3gyfR+wABMEmS\noI1FMEzTtFAolJubm0gkFEWJxWIofaOULh6PG62uMU4wGKR2xGazGUlYkmrJijfOmzdvw4YN\nsVgsJycHPTA6OzudTqfP5zt8+DBJNbEdM2aM3+/3er1ggdA3cBzndDq7u7txxMWLF3Mc19nZ\n+dOf/tSoe4A+Ny8vr6WlxW63R6NRkvLhO+uss2h4D0QTBJeG63ie7+zshN6WQtO0Bx98cNas\nWb02Mt+gbwpmpXYCMagXP43RIE3TmI9df4BF7E4g2PMok5GeVOzq1avXrFnz2muv1dTUzJs3\nz+l0tra2zpgxY+zYsU8++ST2QSCNUq4vTeLLNr9jx471+/3Uu4TuYLfbEbGjqU+SUlRQnSwh\nxGKxxONxxPNolA5WKYIgGBvLolaPHt2YHjVmV4+fLWoEs7KyBEFAxR72NLaLNQK1hiizg+We\ny+VSVTUQCHAcN27cuF4GKDiL+vr68vLyhQsXovPEI488wpy+GRgGHZhHP8PJB3ZXZzLSc2Gq\nqqruueceTdMmT5582mmnnXrqqX/4wx8mTJiwevVqSo8gFzDanZAUFZs8efK2bdtIKp3a3d3d\n09ND32ixWEwmUzAYjMfjVDZBCCksLIzFYt3d3SCLFouF47jzzz9/7dq1iPZh47BhwxobG5PJ\npKIoxlYThBDYiLhcLvgJT548+d1338XgNBWLwj6aP+V5/tRTT62treU4LpFIxOPxffv2jR07\n9vDhw/A0hjMLBtc0DapbOK3gJw5mC0qHA0UikX9Br+MppOVKMTAwMJwcYBE7BoavRHqKHydP\nnlxWVrZ06dJEIvHKK680NTUpivL444/DB0TX9VtuuQUpVyQ6bTYbIUSWZTC5HTt2II0rSRL8\nQWCSouu60+mUJIkmc2m3VkEQuru7u7u7jb1iq6qqDhw4AD8RBM9EUUwmkzRiTD2NCSE8z0ci\nkTPPPBNMMTs7+/PPP4dZMd3fbrf/4Ac/iEajoHSY/969e6HJoEnVhoYGsNX8/HxK10AlSYpx\nwkWFslJVVWlAEXZ9ZrOZVtHZbLb58+ejxg57MjdIBoZBCvbJZTj5wO7qTEbaQqmvvPLKrbfe\nWlpairYTY8eODQQC7e3tCJLBv40SFxj2IilJUrSMEAJPO2JoAhYKhYqKiiorK+vq6hRFcbvd\ngUAAsTdjiZ4gCGVlZbW1tYqiWCwWuOhJkhSNRhsaGnAIhNDGjh3b1NQEtjRs2LAdO3ag19nh\nw4cRNisqKjrzzDO3bdsGwcSKFStoOA3NxzBJEEez2RwIBERRxMRoqdyVV17Z2dn5wQcfoLWa\nzWZbsGDBu+++q6oqTOwIIeXl5eFwmOf5qqqq2tpav99PDxSJRIqKivDamHFO18ViYDgpkZlV\nfRzHZebEhggGfvEHtZllH8GeR5mMtBE7t9u9evVqvK6pqTn77LPPPffcAwcOgDC99tprkiRx\nHIdQXCAQQEUaIYRWzqHhBAhZQUFBa2srrEYQ7jrllFM6Ojo6OzsR9OJ5/sYbb3zqqacQydM0\nDTysoqKio6NDUZScnJxrrrkmGo0++eSTFoslGo2ifu7w4cPoNqHrOngncr7YR9f1Y8eOvf76\n6+BYDocjHA4nk0koWJ9//vmbb765vb0dp4mwXHl5+b333tvV1WVcDZj5UauUSCSyYcOGeDxe\nXFx87Ngx+Jt4PB4kYROJhN/vt9ls8E/G3KZPn06Pgn9ZsepQA7M7+abITG6kaVoaJ8buim+K\nzLwrBjvY8yiT0S/Fj+vWrUMd28GDBwkhuq6bzWZoRY3APmB1hBCaZtU07ejRo7T9Q1tbW0tL\ni8ViQagMPxQ0TXvuueeoNBUtyAgh1BsFVXpbt241luURQoqKiiZNmvTqq6+SL/eHMAppaQeI\nQCCA9yI6ePnll1933XUrVqygk9c0TdO0X//6172K5DZv3nz55Zcb3enAaOFvh3dRq5SLLrro\n+eefp3542Pj000+jcZnJZJIkSRAEJi8famDP5pMD6f3ksruCImMZ21C4Rux5lMnoF2KH4Fle\nXh5lKtFo1GjDi+1UrApKB9KmaRpCdyi/i0ajsCMpKipqaGjIy8vzeDyappnN5ksuueTPf/4z\nFaKCz1VWVqIDhK7rJpOprq4Ob0cWVZZlk8lUUVHB87zVaqXUymQy0eawFRUVXV1dHo9H13Wk\nSqlHSTKZ/OMf/xHVp47HHo8HMTxY06Egr6enZ+3atdSymFrAoHcFRqPtLuDtQv2TET684IIL\ncCAsCytoYGAYvGCpWIZ+BbvBGIzoL7lyIpFwu91G+zoaXTPGpYyuJTzPV1dX79y5s6qqavv2\n7QihUdvhjo4OVVXB6rDzunXr0KAMoyGoBnNg+Ko8/PDDcDPhOC4ejwuCkEgkjh49+sADD8A2\nj8720UcfffbZZ7dt22a32/fu3UsIEUVRVVWLxVJdXV1TU9PLxMTpdMbjcVEUTSZTKBSivA1z\nAD09duwYtiP9is4WdAQMCGe7jz76iNYaYk2i0eiOHTvoSsLDj9U0MDAMRqTXoJilYhmOxwmp\nHB3gIzL0Hf1C7Fwul67rDzzwgK7rp59+OsdxNTU1Rqs5SBmQ8aThK5LqALZ9+3ZCyOzZs5PJ\nZG1traZp0WgU0bXJkyePGDHi5ZdfjsViCHRRAxGwKOhwzWZzLBZTVRX2xTzPS5K0cOHCt956\nCx1aScon2el0xmKx2tpau93OcRwiana7HT7JJpOJ9swwmUyzZ8/+7LPPoJaAAUosFnO73ZIk\ntba2hsNh2m3WbDZPnTp1y5YthJC2tjZMb9KkSaWlpa+//rqqqtnZ2YFAADOHGxDP806nU1VV\ndDMbNmwYFtNIfPvjYp3cGApVzAwZjvR+ctntypAJYM+jTEa/ELu1a9fSxOuBAwfAySRJSiQS\nVCoBVgfihX81TauvrzebzTArqa+vF0UxFAqNGjXq2LFjyWSS5/nRo0e/9tpreCPGp91g6WiE\nENTz4U/UE/jNN98kKf7HcdzUqVObmpo6Ozs1TXv++eeh0kDGVtO0RCKBvrSffPIJBk8mk3/7\n298IIQ6Ho7Ky8pNPPhEEwWq19vT0gJ+hLSymFI/Hd+7cSZOwND43ceLEt99+m/qkOByOZDL5\n7W9/+5FHHtE0zeFwJBIJOLnA2I8YxBN6WruKDbwF1GCPNGTsiq3P/+nX7pPe5pt9xMBHEdKY\nkErj5dZ1fcaMp9IyFGHmbQZk7FIMhd+T6X0eMaQX6ek8YURNTc0ll1wSi8WSyWR3d7coirAd\nARXLysqCFJQmH3t1pHA6nRzHdXd3OxwOQRD8fr/FYoGiFqOZTCbacMztdsMl5PjRaOEd6t4s\nFouaAg4EOQIMkGOxmDEZCuc89JMFHxUEoaCgIJlMdnR04O04yrRp06g+Izs7e/Xq1RMnTiwv\nL9d1XZZlqFwJIfn5+R6PhxjqCDG9srIyv99/3XXX/fWvfz169Og/rwrH3XjjjWjaMWfOHHSe\nWLFiBXP6ZmAYdGAe/f2EjCV2QwHsrs5kpJ/YrV69eu3atbt27WppaaF5z95HNcgddF2HqgAb\ns7KyZFnu7OwsKCjo6OhA0Cs/Pz8cDhtHA6+iITHd0P6rVz3c8X3MkAhGjJAYyviO7wbG87zF\nYoHRHdK+tF+t2WwG+VMUBeSv1znSHhtGMkclvcY9s7KyaKEehSzLe/fuZS3FGBgGO9gjkOHk\nA7urMxn9kibXdd1ms1EnXhApktKN5ubm0hAudkBlGzaWlJT4/X6O4xAbQ1wNuVE6miRJ4EaS\nJE2ePDk3Nxd7EgOro2LS47vT8jxPdbhms3l0Kmw+a9asXsY8giDQHhiqqiaTSafTSX3vCCEo\n4KM9JGgbDJxIfn4+MXipgBGKomiMYOfk5NCsa3l5OZfCueeei84TkUgkHA7TTmUMDAyDC+yT\ny3Dygd3VmYz0M+6qqqply5aJopiVlQXbXjiPUEM42oLCGCGbOXPmxx9/zHGcx+PhDL1cwcCQ\nmfX5fPiT2WxWVdVqtebm5u7Zswcj0xwrYLSvw+FoBZ7NZjv99NPr6+s7OjoSiQQK4wghn332\nGYggjd6hsA9/xZ/wG4WKJwghF1544dq1azVNg8Ux9TpeuHBhY2Pjhg0bjLNyuVwbNmyYNm0a\nDeNFo9HnnnsO3ivNzc3YXlxcPHz4cOPkmXhiCGLga+wY+gM8zw8FN4qBP8eMTcUO6hq7Pq7q\nli0/7O+ZMPzbSH8qlhBSXV3d1NR05plnfv7554FAgBBiMpnsdruqqoFAgBa3gT+VlZU1NTUR\ng9kHKs8QxiMpRjV37lzjaEVFRT6fb9y4cRdccMFvf/tbuPtWVlaec845KE2DGR5JBQWdTqcg\nCF1dXYgmrlu37uKLL+7u7gaBEwShuLi4paXloosuevvtt0mqU5mu68OGDdM0DcpWTdNcLhe6\nRMTjcY7j4EKCQ+Tn56uq6vf7q6qqdu/e7XQ6Qf5kWcaeiqIIgpCfn4/R/nEBOO7cc89taGg4\ncuSILMsY1mq1XnjhhS+99BIhZPbs2aFQaMKECc888wz7kcTAMOjAyswZTj6wuzqT0S858kWL\nFj344IOff/459YpDWy0a+qKZUGIwsEZBWygUOnr0KE2JorItkUhs2bIFEThBEEpKShRFicVi\nRUVF77//PrKlhBCLxVJbWwumRQ+B+w/2KPn5+bIs+3y+c889lyoYUAwHbxQkQ42ZU1VVW1tb\n6anBTmX58uXvv//++vXr6SQ1TfP5fHixe/dujuM2btw4Y8aMRCJRXFycn5+/detWSZKg/CCG\naCWEw3iNdDOMkd9+++3GxkZkY8k3bCmWmb8XT4gqNjOXYuDRx8XvI4bCiqURrPnSUMNQ+Nph\nd3Umo1+IndVqlWX50Ucfveaaa+B7AsMR0BdjlIsQ0tzcjBfUNJjjOGN7MbiWOJ1Oj8cDM5Sm\npia8HRINh8OBBmJ1dXVGwkQMrBF0qqenx+PxlJaW3nHHHTfddBOEEQjstba2ms3mlStXZmdn\n9/T0KIoCOa3H4zm+UO8Xv/gF1BLUx4QQAhUtSfnSTZkyxWq1EkI8Hs+RI0dw4olEAgE8o1aj\nra2tVx4ZdBOvzWYz9Bl9j61mLGkbeGTmUgz8rAb7U2RQoz+yIgwMJxbsrs5k9Auxq6iosFqt\nK1euJITY7XZU2pWUlLS2tjqdTr/fD285nufdbncsFgsEAohU4e1G0QPlZ2gpYbFYkKxEwVlz\nc7OiKFRXkZWV5ff7e9md6LpeWVm5b98+lL4JgtDW1nbjjTciqmekmPF4PB6P04600WgU0tfh\nw4d3dHSAGgKYW15enizLPT09Vqu1vb1dVVWHw9HV1UWbg8FOr6Kior6+HryW53n4sxBC0E+M\npBghjR2SL1fUKYqiKIrRri8tSG/8LGN5QxppVsaS14xFZnLcgQfHcWwphhSGwqVkedhMRr/U\n2L3//vsXX3xxNBpFyyzQFzjJSZIUCoWgY+gVuCLH2aDgv4WFhe3t7cfv1sumhOf5wsLC1tbW\nadOmffbZZyBn8EPGBK666qpVq1ZBeEEjgoQQURQdDkdBQcGxY8fQ9YHn+VNOOaW+vr6wsBC8\nzefzYWeTyaTr+tSpUz/55BMcV5bl0aNH19XV9V7Z4yQguq673W6fz6enDPaSyaSmaR999NEz\nzzyzatUq9DHTdX3MmDH/8z//s3jxYkLIOeecEwwGJ0yYsHLlSqafYGAYdMBXyomexUmIjBVP\nDAWwuzqTkc6Inc/nW7JkyXvvvdfT06OqKjox/OY3v3niiScaGhpoN4jTTjtt165d1B/E6AxM\nPUpA+7DR5/Mhn0sIcbvdw4cPz8rK2r17Nw19we7E6/WiGO6zzz4DQ0IHCJrlfOGFF8xmM1K6\nxiNC8QD3Y+zJ83xjYyMhxOPxQHJBUnYqoJLNzc3G/GxPT8/o0aNvvfXW//3f/6W9a8HncnJy\nYrEYupMlEokLL7xw1apVoKHRaFSSJFVV77vvvjPPPBOaiUAggAZoe/bsMa4tXQ0GBobBBfbJ\n7ScwxnYCwe7qTEY6id3ll18+ZcqU119//YorrlixYsWFF16oKMptt92GqFs0GoVrya5du7C/\nzWaTJIlaouBfSZKQo0RvCUJIMpmsqKjwer3BYNDv9wcCAafTGQwGwec8Hg94JMrvMA7lhcba\nOFA9yup4nq+urm5vb+/q6lIURdd1p9Pp9XqxZyQSGTNmzKFDh9BqzGQylZWVHTp0CIO3trbS\nkdGOQhTFH/3oR8bVSCaT6EuB+FwymTSZTG+88Qbkupgh7FR+8IMfHD58GFSPEKJp2rFjx9xu\nN8ahK8OKVf8NZGYKrI+RhvM9v+/LbszuJMOhadqsWSu/djd2jb4pWMSuP9DHVd206Xr2PMpY\npC0VW1NT873vfW///v0vvPDC2rVr/X5/Xl7eq6++SlIOcwhxjRs37tChQ7quo1TuXxy9V6L2\nX+9MUubAqK4zbv/KdyFBjDEdDgcysL2I4PHzsdlsNpsNzsmghr1ED73maZw88sLRaJS662Vl\nZYXDYYvFMm/ePEEQXn/9dfpGm83205/+9K677iKEzJ8/H50nHn/8ceb0zcAw6MA8+hlOPrC7\nOpORtgtTX18/adIksKuuri4IV61WKxW6YrfGxkZVVe12O8rLOI5D2pS6B5MUOcNNg40VFRVO\np7O2tpYe7vg+YPhXEASbzRYMBrEDMq3kyxyLEGKz2SjHCgQCIF6lpaUdHR02m41W1NEDYfxk\nMunxeGRZ1jRt9OjRnZ2diP9JkoR+uBzHQcBRWlra2tpqs9kCgUBBQQFcl7OzsxGrw8TQRiwc\nDr/zzju5ubko14NUQpZljIwJZH4eNjMDYySDJ9YXnBCDmMwEWwqGQYRB/bXDcBIgnYwbBKiq\nqurTTz8lhDQ1Na1du/b888/XdV0URbCoUCgkCMLVV1/9hz/8AY0cwISSyWRWVlY8HsduYFEW\niwXeKA0NDb/4xS/QZAIUjbZ5pTE2HB0eyCaTKTc3t6urC6YkoigaNacgTxg/Ho9DkarrOnyS\njT5zGH/UqFFHjhyBrQn2dDqd7e3t1EIZ52WxWDRN6+np4TiutbVVVdUFCxZs3LgRrsiqqg4f\nPnz8+PHvvPOOIAi5ubnQ+RJCysvLjx07RgiBLwwhpLu7++WXX77tttvKy8vhwEfbdWQmMvZL\nKmMn1hcM6smnF4N6KTL5k8vA8O+B3dWZjPSnYlG71tXV1dXVVVVVhTCbKIovv/zysmXLmpqa\nYrEYbEQIITzPr169evHixaFQCIE6KjUF96LjW61WKCFo7C0nJycQCGCLpmnFxcUWi6WpqQnl\na7Isn3rqqbt27frWt7712WefVVdXv/3222h6kZubm5WV1dTU5HA4TCaT1+tFXhWpVbPZHIvF\nVFUtLCyElMHtdjc3N0uSRIURKARMJBJnn332hx9+SAjhOE4QBPSlGDVq1MGDBzVNQ4dcSCLo\nOh+/4CgZpE3JTCaTy+V6+umnL7roIpJSxU6cOPGZZ55Jy5XqDwyFjkkZC7b43wgnJPjHrlF/\nIGNr7AZ1xC5jV5Wh70in3cmcOXOmTp26fPnyTZs2LVq0CFV09K+yLLvd7mPHjulfbkUiy3JW\nVlYwGERyFgQO+covTTTF5xB443nearUWFRV1dnYGg0GkL5Hk7e7ulmVZFMVgMAjG5nQ6HQ4H\nvOggRAUtAxtzOBwQ2NrtdiUFWuEniqIsy8FgEM1nrVZrJBLheR65Y2q/R4N/qN4zm80Qc2A0\nnILT6SwqKjpw4AA1PUEEsaSkpLu7OxwOU2sYQkhOTs727dvLy8tpSzFmd8LAMGBII//TNO2+\nysq0DMVgBEvQn0Awu5NMRjpTsa+88sqtt95aWlrqdDoRbIONnN1uj0QisVisvb0dNAjbzWZz\nMplEZrOzs9NkMmmaFg6HyVeFtXRdhwUdOoxBUXvkyBHaOgwUCrGxefPmbdu2DZV2mqYhPodw\nHY5IrfVKSkqCwSAiiCBqsiyHw2GYlaAUD2WCmC1YnaZpxtwoON+CBQvee+89jCxJUjAYvO66\n6yZNmnT77bcjaRsIBED+uru7qcmLrusVFRWffPIJQoaiKGLk6upq9BMTBEEURRyUfZCGFNhz\ni2LglyKNQ2maNhSu0cCjL3pwQsiy/p7HcRjUEbs+gj2PMhnpJHZut3v16tWEkDfeeOPSSy8l\nhIDQRKPRCy+8cPPmzRMnTvzggw/odkVRsL20tPTgwYM0hIZwWq/Bkej0eDxgcsFgcNSoUVA5\ncIbmsA6HIxAIrFmzhioedF1vb2+vrq6ORqOHDx/G7Yi/RiKRg4ZPVzwehxcJqus4jotEIrQT\nGu17K8tyr6QwIWTy5MmJRIIKNXCCW7ZsueKKK0BVMRnqVGxkrp9++indQrPPw4YNoycOpOMS\nMQwmDPav/jQ+3gb7UgxqZOwPjD76AREy0ElDdrsynFikv/OEz+crLCxEuMtisXR1daF4DjEn\n5DehbDh+u9VqLS0t3bdvH6JrlO7YbDbUvRFCJEnSNO3000+vr683ii1EUTz77LNzc3PhsUII\n4TiuuLi4vb2dEHLBBReA7UmSNHPmzObmZlA62gNDFMXvfve7r7zyCqVWVVVVeXl54XC4pqbm\nn+vFcYjPoTvF/v37qZkLIYTa6WELwm+9VhiROVgWo4Xu9u3bzznnnEAgYDyK1WrdvXs3TcWO\nHz9+5cqVjN4xMAw6IM9womdxEoJVg51AsLs6o6GnD16v96qrroJiwGQyFRUVHT58eNq0afD4\nxeEQkPvK7Waz+YYbbhgxYoTFYjHOECna4uJi/Hfu3Ln5+fnIoppMJlBDmImYTCYQLNrBYvjw\n4ffccw8xSHhMJtOHH344e/Zs7AkZBM/zgiD4fL5KQykMjOtwCEIIXlssFpvNJgiC0+nEbpIk\ncRwnSRLucuxcUlJiNpunTZs2Y8YMOiAOZDab4eRCPxVOp7OXwkiSpL1792JVZ8+effrpp197\n7bVUvcvAwDCIwD65DCcf2F2dyUhnjvzyyy/nOM5isUBtGgqFJk6cuH//fhyJpDzhzGbzV263\n2+0vvfSS3++H4cgDDzxgsVjgIeJyudAuFknJqqqqefPmtbS0TJo0SRCEESNGOJ1OVKRBvuBy\nuRDYO3r06B133EFSfsJZWVk8zy9YsGDLli3UMAXtX9EDDWE8KCp0XR87dqwsy9iCfaLRaCQS\nQWsKLgWe5202m9GsuKWlJZFI7N+/f8uWLZTt6bquaRrtioEJk5TBCiHEarXSyN+sWbPQ1kw3\n5JTTeLEYGBgGBuyTy3Dygd3VmYz01Nj5fL6rr776o48+ysvLSyQS0CJMmjTp0KFDfr8f+5hM\nJvgVV1dXf+V2u92OsJmu63Awob5uoVCIpi+3b99eUFCwdetWi8VSV1fHcZzP57Pb7TABJoTo\nuh4IBMxms8ViQUsJQkhOTo4syxaLBY5xVKkai8VkWUbSNhQKhUIhsK5oNEq995CuRb8vNJC4\n++67lyxZgnnG43GLxTJ16tR3332XurTG7HoAACAASURBVCKDh6EsD6TQarWGw+GsrKwzzzxz\ny5Yt+fn5dGQIgQkhVqsVJE8UxUgkQtfHZDJhkLRcrAxHxhb0MJxADOq7Yoh8cgceGXtXDAXx\nBLurMxnpqbGbN2+eLMtms/ncc89dtmyZ3++nlWT/OAzH2Ww28LN/sR3CBWw/99xz33//fZ7n\nS0pK3G73F198wXFcYWFhe3s7YmyjR4/eu3evKIqapiEsLAiCyWQCN4IrCj07kC0wTsr/sBH2\nxbRHha7rkiRByUEnY4wskhQh+9dLJ8tyIpFAHSE9VnFxcXZ2dn19fa+dYYBH7U4kSXrwwQdv\nvvlmQsiCBQvC4XB1dTVrKcbAMBjBmi/1E1iN3QkEu6szGWm4MDU1NY2NjT/5yU9+97vfrVmz\nBnJRkmJOeMHzfDgc/trtNEim63pdXZ0kSbqujxw58vDhwyBGCMshK3rkyBHamoKkyFavuRmF\nq6IoCoJQWloqimJnZyftdWZ8FwaB+kFP2eaRVLtbBPMQSjSZTLIsl5eX7969+7rrrnv22Wc1\nTUO3sVNPPXX79u2Q+qJTGXXva2trKygoKCoqos4vmHwwGKSNMQghyWRy48aNIHaapoHvMqfv\nQYGh8GN9KCCN0aD0fnIzNkw18OijKvbe0V+/21BYrvSCPY8yGWkgdugS++yzz7a2tiIqJggC\nCtGgeAUZslgsX7sdA4KN+f1+p9Pp9/vPO++8pUuX4k8oUwMBgiMdLXRzu90dHR0IeiH4R2dI\n+4PF4/GjR4/S7QjyoUiOEJJIJERRxEGRxsXIyJYSQux2uyzLGCGZTCaTybq6OpPJVFVVhbs8\nFosJglBTU1NeXn7kyBFoMpqbm8HtLBZLJBKpra3NycmhEysrK+vs7IxEImazWVEUOm0kZwkh\n6MCByrz//GIx9DfYE+LkQBqvY3o/uewGo+ijjx2L2PUH2PMok5GeUGpHR8euXbsoV4OLB8rR\nwuEwZWNfu93hcMyePfuNN94ghKiqet5557366qurVq1CyRrEE1RGQDnQ7NmzLRbLhg0bCCHg\nUniRlZVVUVGxc+dOELhRo0bt37+fpMxEIpGILMuopbPZbGaz2ePxQBVBfZJhpAyBRTAYdDqd\nOTk5R48eRU72+eefv+qqq5LJ5J133knZKqZ05MgRvMC0W1paoIeNRCKovaOhxK6uLkwYtBIO\nKWazefHixcYVBsVMy8UaUmCtnBhOONgnd6hhKHztsLs6k5GGGruamprp06dzHPe73/3uwQcf\nDAaDfr8fDI8QwnFcZWUlrOko8/sX2430iKZoSYqohUIh/Bfq17POOmvz5s3UCY/WwBFCsrOz\nrVZrS0uLMRtLB0dOds2aNaeffjoSr3CVw8gkFeSz2+2xWIzGCE0mU25ubkdHB2aL4jyasX35\n5Zcvu+wyksreotoPsUDjGaHSzuv1oowPLTS6urpIygxP1/WSkhIaWTxpfOyGSAqJpWL7A4P6\n5mGOX0MNQ+FLgN3VmYz0ELtp06ahJWtPT48gCEZCQ1OllHt95favnpyB2FEIgpCbm9vZ2anr\nuizL1CsETiiJRAL0rpds4itH66WiIKnmYMY96Z8sFgvHcbFYjIYPdV13OBw2m62rqwvvAjU0\nm82SJIGDGnkq+XLNH0nF4X784x8//PDDxMA7oS/BNObPnx8Oh8eNG8fEEwwMgxGszHyoYSgQ\nO3ZXZzLSU2OHnCZJFYQRQnRdz8vLg9YhJycHJib/YjvHcXQQyn50XR82bFhnZyeafSWTSVEU\nrVYrOJau65C4QldbWlqam5tbV1dns9kgy6WKVKo2xX/heExbh+EsMJ+enh6Sono8z0uSNGvW\nrA8//BAOdlDg4k/QeQQCAXSMAAnDQeki9NpOJ1BSUuLz+WKxGOyRkbfleb6oqKilpWXs2LH7\n9++///77b7/9dkKImsJ/fqUYGBgYGPobg520MQx2pIHYNTQ0QDNhMpnAVBDWisfjSLNSazrE\nuv6v7aWlpQ0NDZBEkBQHQlEaSQX5FEUJBALU1A3/RqNRqGtbW1tlWYZDnjEzS0kVJgyCiDnQ\nP9lsNnSeRStY1PPFYrH33nsP75IkyWw2Iw4HVqfrusViwRF1XV+0aNGaNWtwOJvNhnZnxvQu\nenLg7dBGxONxnud37NiB47a1tRFC9u7dy3Hczp07cVxVVRVFYR2Xe6GPTgd90c1l7LfwoM4/\nnhD08a5II/pSKcXz/FAouuoL0mtQkrF2JwMfsRv4G4w9jzIZaUjFLl++/Pe///2oUaN2796N\nng2dnZ0cx+Xm5vp8PiRbzWbz124vKytramriOM5sNlNFBfStvTKYsIjLzs5OJpMQIhBCMCCt\n1aOgOlmQSLDPMWPG1NXVIbomCIIoiqWlpYcPH7700ktra2sPHTokSVIymUQ/iWg0WlhYGIlE\nwuEwRisoKGhtbTXOiuO4vLy8nJycgwcPYsJIDauqajKZFEWBtDaRSMiyDHbIpfztUN5ns9lo\nu1iO4x577DHoJ84555xgMDhx4sRnnnnmP7xSDAwMDAwMDCc30hCxq6iosFqt+fn5Tqezu7vb\n6/USQubMmfPxxx9T3pNMJr9yu6ZpYDNPPfXUM88809zcTAgpLy9va2tLJBLoSIGg1/jx4z0e\nj67riUTitNNOq6ur6+zsxI+GW2655emnn0aY7XjhBU1iCoJQUlIiCEJDQ8P27duRHqVVeog7\n/uUvf8EbkailxYIej2fcuHG7du0qLy+PRqMIrRkd8nie7+rqQuWfIAhwNikoKHC5XAcOHIBV\nXjQaLSsrAyOkrcNkWc7NzW1tbaUpXcQvly5dunDhwvLychqbZMWqDP2NjA2BDGqoqjpr1sqv\n3Y2t6jcFu11PINjzKJORBmJXVVWFKrdgMAjKoqoqmkZwHOdyuajk8/jtCMuZTKYf/ehHoihO\nmTKlra0NZAjtxXiez8vLi0Qib7755nnnnef3+7u7u+fNm7d161ae51GWt2LFiuOr0Gg+VxAE\nRVEsFsvw4cPR8gE1dpIkxWIxKoklqX6scEJB1G3YsGHXXnvtL3/5S03Tdu3aRQihVsnoRQvG\nRl8QQkRRtNvtSDd3dHTA22/KlCmyLG/btu3KK698+OGHjZE5Xdcff/zxSy65xDhtQkhXVxco\npizLiqLAq/k/v1j9hD4mDfuC9GYohkIVc18eb320cv14kKeH+oKBZwO6rjNuAQwRKjYUvnYy\n+XnEkJ6WYnPmzMnPz1+7di1t50AjT2azGWLSfx4y1d2L47h4PE6TkjabLZFIIDSFuJemaWVl\nZV6v12q18jwPnxHK2EpKSlpaWv71/IuLi5PJZGdnJ50V9UYxhvcEQYC/sSRJoHSo5xs+fLjP\n56MeKwiz2e12nueDwaCu60j+zp07d+PGjRgHDE8UxaVLl953331GFYWu63ffffcf/vAHzAcb\neZ7/4IMP5s2bB4pJsWrVqiuvvJL0myqW1W8xMAwMmH5wqGEoEDt2V2cy0kPsvF7vhAkT2tvb\niUEBqut6VlYWwnh0Ow4HRzqS6v2A7Var1W63e71eZDahbIDOAIIDbKFyV9peAoJZGhYWBIEq\nc81mM+0PtmjRoo0bN2I0q9UKBS72SSaTEyZM2LlzJ2QZsiyrqgoyaqSSCOaRL/9YMZlM2dnZ\nlKuRlMD229/+dl5e3j333EO38zzvcDi6u7tRfgcNB8/zEyZMqKmpoQQX5YBTp07dunUrSfnY\nTZgw4emnn2ah78zHUPhOZ/hGYEmrfkLGxv+GwpcAu6szGelh3I2NjaIout3uQCCQSCTwQpZl\nozm1pmnwN3E6nVQ3gLCZw+GA1hW6VLC3J5988qGHHtq9e/fixYufeOIJ+LrxPD9t2rRPPvnk\ntNNO27t3L2gWgmRcqusr8qokJaegJGzdunU44uOPP37//fc3NTXRuem6XltbS0eIxWJ05tSH\nBRMuKys7duwYjerpum6320ePHq2qand3N5gox3FdXV3vv/++1+ulwg5INCgTpfyysLDwiy++\nMA4Oxa7NZqMTIKnYXlouFkO/YrB/XzOkHeyT20/I2IztUPgSYHd1JiM9xK6+vr6oqCgcDkej\n0enTp+/bt4/jOLfb3dLSgh0QGAsGgyaT6fjt0WjUarX+6le/uuOOO4LBINw9/vu//zsWi8my\nDDWoKIqKovA8X1NTw3FcbW0tVcvSzCnHcRaLBSSMpBgeInxms9liscyYMWP9+vV33XVXZ2cn\nfm3Qnx00MieK4sKFCzdt2hQOh2E7THcIh8NoNUYIMZvN4H+hUOjgwYNwzkP5IES4siwjXQs+\np6qqxWIJBoOEEGScCSE9PT3RaHTu3Lnoh9bU1AQxh8VigaMeSUUHqStyWq4XGfyp2Iz9sZ5G\n9PEa9aVj5qBeh74jM6v6dF2fMeOpr91tKFwjZndCMdjtTtL7PGJIL9KTil29evVjjz0WDoeb\nm5tnz569du1a+qfCwsKsrKxDhw6dcsope/fuPX779ddf/8ILL8CLDsYfIEZGS2H6gv5LCNF1\nvaCgwOPxCIKA/l1WqxXJ32g0KknStGnTurq69u/fH4/HJUlCUwqO48aNG7dz507wLfgVI3XL\nfbkRBS3Io5YrJEWzBEFwOp2hUAiyhnvuuee2224zWp+YTCYQWUII6vY0TSsvL6dhQqg3OI67\n5ZZbNE178MEHwSAxJULIvn37KisrCSFz5sxBjd0f//hHFvpmYBh0YEmrfkLGEruhAHZXZzLS\nQ+xqamouueSSRCKB1KfD4WhsbOzlPALehn2M26GZcDqdcLOjqguE6Ho52NE3/nvTtlqtaAvx\nr98+fPhwRVFaW1vpll7kEiAp52GUCRJDT1h6doSQ7OxseCb36nVGQRtjUEdlXddLSkq2bNlS\nXl6+YMGCcDhcXV3NWoqdNEijuzLJ4KgqA6Aoytlnf70JJeMf3xQZS+yGQo0dE09kMtJD7Agh\nc+bM2bt3bygUSiaTlL1RcmOz2RRFQWGZcXtWVlZlZWVNTQ0hRNf1wsJCjuPa2toodcNvAp7n\nYResKIqu62BmX0nvrFZrLBbTNA01bdXV1Tt27Oi1J2gZGs6+9NJLN910UygUys7OxuSJgYkq\nimIymRCxoxFE2qmMDohAI1673e6uri5602MQurPZbIZZMf4LJnfZZZf95S9/6TXDN99886KL\nLiIp8cT48eNXrlzJfiExMAw6sNgGw8kHdldnMtJG7Lxe78033/zaa69hQJvNNnLkyH379vVy\nmPu/tiNJKggCRKzHUzFjRp+yQ2PGFn+SJAndukDsUK6HejjsUFlZeeDAARxRkiRBEKLRqKqq\naAtBKZckSaIoRiIRemhJkgghsMTLz8+HoTExWKXgNY3J5ebmer3eXl1ibTZbZWUleojRU3C7\n3Wiei/o8/On++++/7bbbSKrzxIQJE5555pmMrbHLzNomBorBXlI5qMFq7ChYjR0Fq7Fj6D+k\nLZTqdrtfeeWVWbNmLV26tKenRxCEuro60BpBEIqKio4dO0YI6bX9lFNOqa+vBz+jtW4lJSWi\nKDY1NaEMjprDkZRGNRKJZGVlhcNhlL6Zzebi4uJgMOh0OhVFaWxsdDgcTqdz5MiRW7ZskWX5\n8ssvf+655ziOy8/PP3z4MEikoihwPEHBXDQaRd1bIpEQBGH48OGEEK/X6/f7c3JyYrHYrFmz\n2tra9uzZE41Gjx49Ch4G+arD4fB6vWiDZjab0U+iu7sbATmjz0s0Gq2trUWTMXwwVFW12+3g\nhVDUosxu2bJl3/nOd2jnCYZ/D0MhJ9Kn7/S+nSPjfwwM/zkG/gPSt7KNk/+XAwOQHmLn8/mW\nLFny3nvvSZIUCoVycnKmTZv2zjvv0PAVmBkVBwCqqv7kJz+54YYbENOiKcu2tjZRFM1mM0yM\nR40atX//frgEC4JQUFDQ0NAQCoVQsYfMLByDq6urEY3TNG3kyJHJZBIBsFWrVhFCdF3v7Oz8\n4Q9/+MILLzidTihzkUVNJBImk2nMmDFer7ejo0PTtIqKihkzZvzyl78kqSYQmzZtisVi6LFh\njMzF4/GDqY8xtiNXi0PT3fLz8/Py8vbt26dpGlgpiKDT6cT4JpOJBiwJIXV1deXl5XSFMWzG\nhr4zNtgwFChIxi4+A6BpWmZeo4Hn8eldh8xcVXIifk8O/BddJj+PGNKTip09e3ZXV5fX63U4\nHA0NDYqi/OIXv3jooYdQV0cMqVKLxcJxXHZ2NngVz/N2ux1mxdRYmHy5CM/hcHR0dMB5BAQI\nfC4/Px8ZTF3Xr7zyyhdeeMGYyoRFnM/nQ2CPGv+SL9sL40CiKNJD0+QpffEVq5ZSyMKdBHV4\naFZrHN9utyPPS9+FKJ3Rk8VsNlutVoT3jK57e/bsqaqqIoQsXLgQqthHHnkkY4tV09jVirUU\n+6ZgeXCKzFwKJp44sRgKXwIDDyaeyGSk4cLU1NRs27btxz/+8d13320ymV588cUrr7zyvvvu\nM9Kj0aNH+/1+r9cLS2HkQIkhoIXAG6iPyWRyuVyKovT09ITD4UAgYDabeZ4vLy+32Wwgjqqq\nejwe5DQJIS+99BKiZSTFn4qLi1EGB9NgcCbQO1BDOgGO46hmAh1gERrMysry+/1gk5qmnXXW\nWV1dXQcPHlQUBf1bKW01lvpxBldkKiIRUoCrCz06ppqbm+v3+3FQGuf7+9//DmIXT+E/v1L9\nh749k9hzq1/ACAEFW4ohhb7W2A04aWNUkuHEIg0Ru+XLl//+97/v6emhVtQ5OTnxeBwcjhrI\ngWAJgnA8R3G5XFarFTE8FK5pmob2FUinOhyOcDiMwjiXy6Xrend3Nw2PCYJAxao0PMbzPHiS\nUbvwj3NOvTbWwAmCgP8aO8n2WhycCzHE3nqN2UveQc/dbrd/+umnS5cufeutt3Rdt9vt0A5P\nnz79888/h0ExNVIhhJhMpr/85S+LFi0iKVXsuHHjnn32WWb2zcAw6AAvzBM9CwaGdILd1ZmM\nNETsWltbHQ7Htdde+95778VisUgkgtIxSEqdTmcikYjH49Cl2u12pCytVms4HEYe9plnnvnu\nd79LB0TmtLOzs7CwsKOjQxCEQCDwxz/+ccWKFV988QUhhOpeQYaKioqys7M7OjrQT4IGz4YN\nG+ZyufAWyuTy8/MlSWpra0NJH6WPoijOnz//ww8/pHlhMEWbzYZuE7quJxIJWZYxAQg7KLe7\n55577rjjDpISuqLJxOjRo2OxWCwWa21tnTBhAu1gQVIFhR9//DEhBKyOGGoQFUVpbm7GamB/\nkNf//GL1E9gvVAaG/wuZ/Mll6A8Mhe9DdldnMtJA7IqLizs6OrKzs+fOnfv6668jTypJUm5u\nrs/nC4VCoiiCiqFPw8iRIw8fPgzaBxa1ePFicH9JkhBpC4fD8Ci2Wq2hUOiUU0658cYbf/7z\nn+/Zs+e88857+eWXSUqQYTKZjh071tbWZrFYiKG+Dc1bzWYzIWTs2LFer7erq0tV1WAwWFpa\nSivqOjs78eLUU0/9/PPPERc0mUyyLCcSCYvFMm3aNJ/Pt3v37nA47HK5NE1DbJI2qAVJXbly\nJS2So5V5DQ0NsVgMKWCO437zm98sWbIEeVu6T1FR0YUXXvjUU08Zf/0YLYJoqjqTi1UH+5dU\nZoJpVE8OZPInd1AjY+1OBv4jOfDVpeyuzmSkIRX7pz/96aqrriorK/vud79777333nvvvXff\nfbcx68px3JgxY8aOHfvmm2+KomgsMiMpBzujwqCwsLC7uxt1eJdeeulf//rXL834qyzu4FpH\ne7ByX+48ZtyCe5HSMlTp4R6lc0AsUBRFi8WCQjrsb7fbIfulvSsQ2Ou1hrIsT5ky5ZNPPnG5\nXF6vF+RP1/Vx48bt2bNH1/U//vGPP/zhD+mUFi9e/MQTT+i67nK5/H5/dnZ2T08PZX7z58+H\neIJ1nmBgGIxgZeb9hIz95TMUInb/n733jo+jOtfHz8zszu7ONm1Vl+XeG9iywQbcMNhgSggJ\n3OALpBAnud8UHAgJIfkYkhAguUkIhlxsAsiGQKhxgVAMLhg32ZabXCSrW221q9X2OvP748me\nO4jcHwKv5ZV1nj/0WY1mz5yZObP76H3f53nZqs5lZIHYVVZW/vCHPwyHw1arNS8vr62tLRKJ\nuFyu6dOnv/vuu5RXwTrY7XYnk8lgMIicI1UbqOmaxWIpLi4+fvy41Wo1mUxtbW2gbmqlBd1f\nFEUIL7RaLcdxJpNJo9F4PB4ExkaOHNnV1RUOh6nulbI9o9EoCEJZWVkikTh58uSYMWPa29vj\n8TiImsFgMBgM3d3diqLQUjn1hOk0RFFEkBKTp6FHZJwxSYhnkdWlbycZumkwGBDYU1+E1atX\nf/e73yWEXHnllWgp9tRTT7EH6cLAwNu0MlXBeQRTxVIMEYPigUcWPwT6eVW3bfsG+z7KWWSH\n2P3xj3/0+XzRaDQajcLmDUrYxsZGGkUjmUiY2WxOJBLJZFKdzaTJTWypqKjYt28frIntdrvX\n68WfaLtVtRcJ5UMgbQaDAboN8snwHiggpBihUAgVcp8eJC8vT5Zl2hgNniaJRAJ9LMAX0coC\nMo7CwsK6ujqaXUXkD6OBz9HxiYqS6nQ6MD+TyXTrrbeuWbOGTgYXZMWKFatXryYZ8cSUKVPW\nrFnDQt8MDIMOrPkSw4UHtqpzGVlg3IiuEUKGDRtmNpvRR0FRlJMnT4KjuN1uj8eDeJWiKL29\nvXij0WiEnZvNZhs+fPj+/fsRHkulUldeeeWoUaM2bdrU29sLVsfzvEajyc/PDwQCyFTCQATh\nOqvVGg6HQQ3Vqlu1ajWRSIBBInQH92ODwQCrEUIIbQgRDAZ1Oh2sgwkhw4cPP3XqFEyM6XE5\njvP7/Rj84osvbmpq6ujo6HPEhQsX7t271+PxLF26tKio6MyZM1VVVbAjxiQVRXnwwQePHz8O\nCgvOhxG+//3v03FIRix89jeLgWEwImeTbv0Be3KHGoZCKpat6lxGFojdb3/7W5vNFo1GZ86c\nuWHDBsSoKEdRFAUJTbVTMbgL1KY8z/f29nIch2zpyJEjjx8//sQTT9x9990HDhxQh9bS6fSZ\nM2ecTie1u4vH4xhq/Pjxe/fu5TjummuuOXjwYFdXF0ge1BjJZDIvL8/v94O3IZ6n1+sRYsRZ\n6HQ6u93u9/vBO2VZBm/jeR60FRtRlidJEnxYZFkuKSlpamqCTTEq8EaOHFlfX8/zPKz7CCFv\nvfWWIAhutxtaWpQV4jqsX79+9uzZNBlNr9uWLVvGjh1LPimeyFl5+VD4IMsisktThsLFH9Tz\nz+UndyhgKPSBGPhzZKs6l3G2qdiqqqpbb711586dZWVlsixrtdpoNGq1Wv1+vyAItbW1I0aM\noGlTsCu0C0PYDCoKmmBVj2y1WmlsT93pAYsJolr6FhxCXQBHIUlSPB7ned5isfj9fvAqg8Gg\nKApt/0AIgX62tLS0rq4OL+Crh3NRFKWgoGDz5s2XXnop2KHBYAiHwxBtGAyGyZMn79y5U30W\nBoPh7bffnj9/PhgkGCFUF1ROoSjK5MmTL7roosrKyj41dk899dSKFSsIIQsXLoR44i9/+Qur\naWBgGHRgZeYMFx7Yqs5lnO2NqampmT59enNzc1FRUSQSQdoUZXbpdHrGjBnqnWF9AhWqmsb1\n6aYFBAIBtVJVyfRyIBmh9ciRIz0eD+JqYHU0OKyunENMTpZlWqhHCInFYsOHD29oaKDjp9Pp\nVCrV0NCAX1taWiRJMplMHo8Hh+7o6Jg5cyaG0mq1CPvJshyPxyVJ6lNjp9VqdTrdL37xC47j\nYrEYGFteXl5RUVF1dTXlfxzHnTx5sqCgAGILtdJ24sSJmIlOp0ulUuCdDAyfxlCI2A3qVCxh\nApdzAyaeYGD4tzjbiF1lZeXGjRuXLVu2bt26rVu3WiyWvLy8OXPmvPDCC+qWD+Bzy5cv/9vf\n/oYk6addTvqErNQb1XE4aFT1en08HtfpdEihNjQ0oPSNECLL8qRJk/x+f2NjY3FxMSzuZFlO\npVK04SwhBGVtPM/TXG0gEIApXTAYRJwvPz+fZnWJqtyNWtMpqha0oHRIKMPZmOf5YDCorrqj\nQTu1+GPMmDGnT5+mlwLQ6/XHjx8vLy+HeGLy5Mlr165lZQ0MDIMOrMyc4cIDW9W5jLON2E2Y\nMOGhhx669tpr29ra9Hp9Q0NDfn7+9u3bQeZcLhckBcA///lPFMxR8xHySYZH2VIf8oQ9dTod\nFAypVCoWiwmCMGHChMOHDwcCAYhV6bAPPPDAbbfdBidkh8MRDofj8ThWIYbVarUmk6m3txc6\nWUJIIpEoKCiwWCx33HHHfffdh4BcZ2enmm+BX6KfLGStYGkGg8FsNqfTaa/XCw0EIWTSpEn7\n9u0jGcKHQ6PGDuFJMEJRFM+cOSPLst1u7+npIZnY5KpVq8rLy+mhsf9Z3iyGIYvzEvHKzVDi\nwF8KnudZxA44L3YnS7p+95n7DPaVP/BHZN9HuYyzJXYzZswoKyvbunVra2srgl5GozE/P7+p\nqUmr1aI6jRCi0WiolsJisej1+q6uLoxAo3GI6vWJIIKr0ZAYRBhIXMqyXF9fjz9RYzlFUaxW\n6759+2RZtlgs+JMsyy6XS5blsWPH7t69G9SK53mj0ZhMJgVBQCc0VAc+88wzJBOWQ4ZXr9eD\ngYFBQl1bVlY2duzYbdu2+Xw+vBc01GAwIPm7f/9+Wl03ZcqUtra2QCAQiUS0Wi0ll1BgYOY9\nPT0ul6urq8tkMkWj0a1bt9577730OlAXvbO8XwxDE+clR5mbidG33T/uz24/z94RFUUZCqSt\nPzgv12Hg1+FQOCL7PsplZMHHrru7++67737xxRdlWZ41a9bKlSt/8YtfHD9+XC2J4Hn+m9/8\n5htvvOHxeNTGdYIgaLVaCGnxHwC4FDrJIrYHPSw4UyQSQc8xROaMRqMkSbQtmDq/KYoiDQoS\nQkCkwNWwDwQccKRDLLCP6oJkqgA5mgAAIABJREFUgm19PPMwDXUo0W63Q2CrTs5aLJbrrrtu\n3bp1hBA44cmyrNFoLrnkkh07dtDRoOQYO3bs/v37//eucNyf//zn733ve4SQK6+8MhQKQTzB\nQt8MDIMOLGnFcOGBrepcRhaIHfD+++9fffXVoijSTq+EEI7jZs2atXv3bpJJpKpFo9RqhIL2\nZqC7SZJkNBphmEJ3U4sq+kBNwuieKMsjmSI5k8kUDAbV7yooKEAaVO2BB6KJ/0swZkFBQWdn\n57x58z788EPsA5IKWzuIQujbOY678cYbN27cSDtegPOhtk99OkajMRKJ9LkRkiQdO3asvLz8\nqquuQucJ1lJsqGHgk4Y5q1Ho58T6E40b+KAR6zxBwVKx5+iIAw+mis1lZO3GLFq06NJLLz14\n8GA0GjWZTKFQCJSora0NtW7wHCGZcjFZlqdMmYKeDSQjdwWr+3//7//V1dW9/fbbhBDUscEn\nxel0BoNBNGwghEyfPh0KU/U0+nQnw+A2m62zs5NmM8vLy+vq6mgXL0JINBoFpaO8UKPRFBYW\ntrS0EEI4jtPpdOl0uri4uLOzc9u2bdjTYDAsX75848aNnZ2doK1053g8rtFodu/ejeCixWIp\nKCjQaDQdHR2CIPT09Ki7bqxYseLxxx9HVzSdTheNRmVZnjx5Mmrs0KIDgoxs3SyGQYF+fvRn\nsX4ru182Az+xLOZPs4gh8uTmbB3hUEiMZhH9pMvbt3/zXM+E4QsjaxE7Qsj7779/ww03qDOb\ncAYhhNCsKFKroF86ne6yyy7bvn07IYQG8ziOu/TSS00m044dO8Dh0uk0upDJsoyerYQQRVGm\nTJly4sQJvFGSJJTf6fV6cCZUwqkDhwcPHgTfcrvd7e3ttPiPJlvVrnjIFHMch4ZmGMpkMtFg\npMvlQkdavIVmhxEjfOSRR372s5/p9Xp1KE6n0zmdzvb2dvSobWlpSSaTPM87nU6oLmjyWhAE\nvV5/9OjR8vLy+fPnB4PBKVOmrF27ltWrMjCcJQY+KsmsXBkuPLBVncvIJrGrrKxcvXq1wWA4\ncOAAwk6yLJtMJp7nw+GwKIrRaBRESlEUKlwAMwO1QvMGQgi0CG63u6ysrKamJhqNQmoA0paX\nl/fLX/7SYDDcd999siwHAgGNRlNUVGSxWFKpVH19vcPh6OjooAE5RVHWr1//wAMPNDY2UqUt\nGrkWFxfDMC8UChFC7Ha7x+P5zne+s27dOgQdwUTJJ2OBMC7Jz8+/6667HnvssWAwCDtlh8Ph\n9/vVSg51p43/veif9HahrcyQngbVW7NmzXXXXUcIAbGbOnXq2rVrh8i//gwMFxJYmfk5AvOx\nO49gqzqXkc0ceSgUqq+v93q90MCSjD+wJEmEEFTUod+rx+OJxWKyLNP6sxkzZvh8PmRsqW60\nu7vb6/UWFhaGQiEMixo1Qsivf/1rjuOCwSCymQaDwev1dnZ2FhYWOp3OtrY2g8FgNBq9Xu/k\nyZOPHj36+OOPNzQ0YCG63e6xY8fW1dV1dHS0tbWBVnIcN3fu3MOHDxNCnnnmmXg8jkBaUVFR\nNBoNh8PRaFQUxXQ6Df/hm2+++aWXXnrggQcwf+R84/F4Op22WCyhUAhSCcg+MGeO46iRHs/z\nUI1wHEeNV6j9cnd395133rl///7y8nIq+4UtcxbvFwMDwwBAluXLL1/7mbsx/nHBYCjU2LHv\no1xG1ohdbW3t3XffjUo1WZbBSJqbm9PpNFUqoF1ETU0N+WQQy263K4pSV1dHY2Pqtzc1NdE9\nx48f39bWFo/HQ6FQKpUyGAywqdNqteFwOJlMNjY2gr3BD0VRlCNHjhBC9u7dSzL/ZHR2dnZ2\ndqonD7UsiudIpuAvEAhgDnQ3tH8IBoOJROJvf/ubzWbz+XzgoAaDIRQK4UxxaEII4osYXK/X\np9Npq9UKy2LQOJC8e+6555FHHgkGg2q75okTJ6LGThRFnU4nimIWY6tZR86W1/QH7P9+hnOK\nXH5yGc4FBjtp6w/Yqs5lZC0Va7VaE4lEeXl5Y2Mjfn333XdnzpxJJQXU3XfEiBGJRKKlpQVW\nc4lEIhaLIbJlNpspC0Rekkb+8HatVjt16tTu7m4chWS0q2jqhcI4xPySyWRxcTH6vdI9aTMM\nolqXFosFZIvjOJvNZjabm5ub1ZcFb3Q4HMFgkEpfYcVCqZvJZEqn09TxhGTyrX0vt2qj1WoF\nd5QkCUlqkilG5DjuO9/5zurVqwkhV199NexOnnjiiUGtQspZxWXOYrBfsaEQt+gPmH5wqGEo\nGBSzVZ3LyM6NWbduXTgcvvHGG+fNm/fII4+0trZ2dnZOnToVFAqZWUmSQqFQIpE4efKkIAiS\nJMElBBVm6BthMBi0Wm1PT48sy9QMBYPQTg+HDx/GegILNJvNgUAgFouBY1F6xPP8mTNn1ESK\nFgSAUSGMDDaGfRRF8fv9VB7R511gdeB/cBJW++RRGzz6XuwJg2U1BaTDgtURQiD4gGaCjrl0\n6VK8iMVisVgM4b2s3KxzgX5SEIbPhcFOeoaCD0t/kMtPLsOFgYF/1n564kS2jsiQdWSH2H30\n0UdWq5UQMmvWLPAztORCpy+bzeb1eu12u9FoRA5Uo9FwHIe8rUajAWsRBOHrX//6008/fddd\ndz399NMQKxBCENAyGAzV1dXTpk2LRqOoVLvuuutefPFFWBxT95A5c+ZAZtuHZmEmeN3b24tq\nOaREsV0QhKlTp548eZL6nlxxxRWHDx9OpVLf+ta3/vu//xuC3BEjRjQ0NHR3d/M8X1BQ0NbW\nJoqiJEnLli175ZVXUqkUNUbGoeHiiMMRQrBzOp1GkBLCIoPBwPN8d3e3minefvvtVVVVtKsY\naF9Wbta5QG5+oTJcMBjUCyyXn1yGc4FBvVwH9eQZgKyFUgVBeOedd3bu3Ak5qkajoSExr9cr\nCAI84d57773nnnvuzTffRPaTEIL2XF6vF3qCQCDwl7/8BWNqtVokas1mM0x6nU4nz/NgjVu3\nblUUJRqNHj16dOzYsYQQRVHQphZvp5ZytAEGmOLw4cPr6+sx51GjRomi2N7e7vf7jxw5ArIl\nCIIsy7t27UqlUqlUau/eveBVPM+3traSTN0o2uAmEolUKrVhwwYwQpBU2jCDZNpLYAKJRAId\n0tCmDOzTYrE0NzdDAgL5hUajeeONN9S9YqHPyOI3xBAxvx3U2cAsxkFz9hyHAphH/zkCMyg+\nj2CrOpeRnRq7devW3X777WVlZVdcccXmzZvRO5UQotfr4/G4w+EwGo2hUCgSiVBb4KKiolAo\nFAqFYA4HszqDwXDzzTcfP358y5YthBAQLEEQjEbjCy+8cMstt0Sj0U/3nKCVeYIgDBs2zOv1\nut3u2tpaQsicOXP27NlDCLFYLGgd29jYaDKZRFHs6ekZN27cfffdd++99yKOWFhY6PP54vE4\n6CD8UHiez8/PB4dTFEWn06E08NixY4WFhVar1e/3U7u7/72sHKfVaiGhVW/U6/UajQZ2enCB\n0Wq169ev/+pXv0oIuf/++3/1q1+5XC6fz/etb33rqaeeIhdQjR0Dw9AE6zxxjsBkT+cRrMYu\nl5EdYldVVTVr1iybzRYIBGg+ccyYMTabbc+ePeqmsRqNZsKECUePHnU4HGgUxnFcYWFhV1eX\nTqdDKI628CopKeno6IC4lRq/uVwup9N57733fv3rX1cH5/7t6z5ArRttbgZNhnpwhPRAQz9x\nmTJ6CxijYGM6naY90GiXM2pNN2fOnLq6Oo/Hg0iey+UC+5w7d+4HH3wAh79oNCoIwrJlyzZs\n2KDOwxJCnnjiCfSKXbx4cTgcnjRpEmspxsAwGMG+As8RGLE7j2CrOpeRHefompoam82WTqep\nqFMUxc7OzqqqKjWrQ7axsbFRlmWj0SiKIrKWwWDQYDBAtaB2AI5EIvDAo+KGgoICUKUVK1aI\nojhq1CgqraBhYXXWled5jUZD88KI/1G/bJ7n4YdMuSAmADtlpFCxp06n02g0DocDvyIxajAY\nYrGYXq/vw+qGDx9uNpsPHDgQDAZRDqjRaGB3rNPpfvazn2E3aoD83nvvocsFvZ4cx+3fvx+v\n4/F4PB6H411WbhYDA8NAgj25DBce2KrOZWSHcTc0NMTj8S1btixZsqSnpwdyV4PBwHGc3++X\nJEmn0/n9fp7nUUOGNQGylUqlQqEQCuySyaQkSXq93u/36/X6oqKi1tZWURRhMiLLMvKe9fX1\nEBz4fD5CCAJmCIwRQiBfoEE4QRCgaYC9nCRJL7300s0335xIJNrb22fPnt3Y2Ag5KgxWwOcQ\nJoTklmS0C8FgEKFE0EHY1/E8H4vFSGahGwyGlpYWeCyjvA/niAGTyeT1119PA4qwaKZNbC0W\nC+S6er0eVskk4+eC1G1WbhbDBYZBbSI4FMDzPLtH5xEDf/GHwu1m30e5jGyGUo8fP+52u3t6\nesBpurq6sH3q1Kk9PT1+v1+9czwet1gs6KAFgSqCXti5p6cnEomcOnUK2yVJGjVq1KFDh6iQ\nAjV8+Kkoyte+9rWWlpZ33nmHkiT8FEXR7Xa3tbWBexFCIpHIDTfcYLVaR40aNWLEiEgkggmU\nlJQYDIb29naU34F7Ic1KCEmn02vXrr3jjjswSCqVWrp06bZt21auXLlq1SpEAVOplFarBTWk\nEbi8vLx4PI7TBNsrLy8/ffo0qF4qlZIkyWazIXoXDodxrHA43Nra2tjYSDtP0HxxFu8Xw4WB\nnP2GGAol5P2Boig5e48GNfqjiiDnY40NhdvNvo9yGdmpsausrFy5cqVerz9z5gwdUKvVIsxG\njYJ5npckCaIBp9PZ2dn5mTtLkuT1eqdMmXLgwAEqs8V6WrJkyVtvvfWv0+A4bCwpKWlpacGw\nPM/DIs5ms3V2dnIcV1xc3NHRcdVVV3Ect2vXLvSNwAjIvep0unA4DCPlVCqFwrhEIoGDgrGR\nTA6XkkXwyLFjx27atGnMmDEYUxAEdMKFFINkQnpmsxl2xIggajQap9Pp8XjoeeHtR48enThx\nIiFk4cKFqLH7n//5n/40JiJD42MlZwVx/UF2a4OGQnign8jNS5FOp1lLMWDgV37/R2MA+nlV\nt2//JlPF5i6UbGDfvn0Gg6GoqEg9cl5eHqK16u1arXbatGmiKNrt9n+7c3FxMd34wAMPdHd3\ni6L46agvx3GjR49Gzhdb3G43IUQURYPBAPcTQsidd97pdDr1er1Op6Pkj45gsViGDx+OX5E4\npn8imfq8r33tayUlJf1ZwePGjcO7YK3ndDo/PedLLrlEfS7Qyd50000Gg0G9keO43/72t7i2\nixcvnjNnzl133YX8MgMDw+ACe3IZLjywVZ3LONs0udfrveOOO5YsWYLcKxKORUVFHMdFIhFU\npLW3t2O7y+VSFOXQoUOJRKK3t1e9M+3c4Pf7ETwTBOE3v/mNy+VCohYCHMq9FEVpaGhATwvs\nX1JSQgiB710ymdRqtXq9/uOPP4bxLzqb2e12ygUROPT7/ZBWSJKEyjm9Xs/zvMlkKi0tTafT\nL774YmtrK9LEffglxsnPz58+fToh5MSJEyRjjKzVaktKSnAWhBDEKRVFaWtrW7RoER1BUZR4\nPO52uzE9cEGn08lxHG1cCz88hcW9GRgGJ9iTy3Dhga3qXMbZ1tjdcsstM2fOfPTRR59//vld\nu3bB4LetrY0QkkwmafIUPz0eD+SuoiiikRd2BgtEpArqCiVTcme1WqdMmbJz505FUdDUleYr\n0RyWEAJWRHO1hBAYqcRisfr6epfL5fV6FUUhhEBsoWTSr+l0uqenhxCi1WqNRqPX68UE8N7m\n5maSaVmBTDHJcDva1oLjuGAw2NbWBt0DHfZLX/oS/JPT6bQoioqiaLXaeDze0tKCOcCiDznc\nwsLCvLw8r9fb3d1NCPF4PBqNZtmyZRgtlUolk8k+slkGBorczD+eF+TmpeA4LjcnNvBgqdgL\nBuz7KJdxVsSuqqqqsbHxnXfeWb9+fSQSGT16dFNTk8ViaW9vR4wtmUwajUZ4gowZM6a6upoQ\ngs6wZrM5FovZ7XbsDIqD2jVZlq+44op33nmHEJJMJg8dOuR2uzs6OuADB6al1+sR5OvjWqfX\n66PR6PTp0/fv3x8KhVKpVGlpaSQSMRgMvb29tPMYGj/YbDYIXZPJZDweh5IXv9I2ZWhxC9cS\nRWWVB0s8RVGi0SgUGAaDYf78+f/85z9lWW5ubr788stfeeUVXAGIedEbjXJTaon361//2uVy\n2e12cD4AJ0tUVi/sQfoCGAr1++x7iyI3LwXHcbk5sYFHdq8Du6rnEez7KJdxVsSupqZm+vTp\nPM8XFxfTflzhcBhtGOBpB9ITjUarq6tLS0svu+yyDRs2BINBpGLb29vLysqamppAX2RZjkQi\nJpMJHcYEQYBlMfUc0Wg00DdAuIDImU6nKyoqamhowNENBsPJkyfBqCKRyMGDBxVFiUQi4EY3\n3XTTsGHD/vznPycSCYfDoSgKBu/u7kYbCRxCkiRZluPxOHUqNplMV199dXd398mTJ9vb23F2\nOHQsFmtvb4/H42+//TY436RJk+rr69F0JRAIoDXFwoULX3vtNcTw7Ha7x+MhhJSUlFx99dXr\n16+HAhdsWJblTZs23XvvveqrDRO+s7lfQxCDnbQxXABgT+45AovYnUewVZ3LOCtVbGVl5caN\nG1955ZUrr7xy165dcPpA3wjE0gghoigmEgmE2bgMCCHwdSMqc2BUvyFO9okpqkJlWq3WYDDQ\nPrP9BHzp6GvabaLPIKIo6nQ6GheEC/GwYcM6OjqoSR4dSt2pgqjSu4AgCKWlpY2NjerdnE7n\n6NGjP/744z5nDWoInkexYsUKtBRbunQpVLF/+tOfmNM3A8OgA/PoZ7jwwFZ1LuOsbsyECRMe\neuihvXv3NjY21tfXT5kyBa4iUDzAKAR5TxjOEUK+8pWvvPfee3l5ea2trWBLlCqhqE5N40aM\nGFFfX0/tgpEqRYCNMkiO4/7rv/7rz3/+c5+5qW2KHQ6H3++nZC4vL4861REVh0skErSvFyWC\nHR0dPM8bjUbE3pSMkcpll122Y8cO2Ndhfc+bN6+2ttbj8UQikXQ63dTUpJ6Poig+n2/Xrl0k\n0ycDwUhZln/2s599//vf73Pu1EslnsHZ3KlPo58N5rMY8Rr4I/YT2Z1YP0fLIlhUkmFoIovP\nGnuIGC4knK2P3cKFC9FY4tVXXz18+PBFF12E/CMK5sDVbr755q1bt1K/Yo1Gk5eXN3r06N27\ndyufbNIKkYHb7YaVHTbSAJv6uIsXLw6FQrt27YKowmq1Go3GEydOcJ9qFIstw4YNa29vh8wC\nPiYgnYSQlStX/v73vyefrGODmTDtkPbpq6TRaKhcFxHKgoICtTOf0+ns7u5GM1lsAdckhJhM\nJlpCRwh59dVXb775Zhq/xM+ZM2fu3r2bEDJv3rxQKDRp0qS//vWvzOybgWHQQZZl9uQyXGBg\nqzqXcbah1JdffvmGG26oqqoaM2aMy+UaOXJkbW2tRqNBvRryqsFgEDZyY8aMaWhoSCaTXq+3\nt7d39OjRp06dIoTYbDaNRtPT04NODBDVUqqnKIogCE6nE9RQr9cTQg4ePNjb2wvKheo0NJyN\nRqNoTQbZKcmQxY6ODjTvQlUfJoYivCeffJKoQoAul6unpwesVBTFdDqNJDLP81arNZFI2O32\nSCRClbb4q16v7+3tVZMzaGwTiQTsVBKJRCKRQHtZxC8RieR5HheBEPIf//EfH374oUaj6ejo\nMBqN2Mhl2q8N9mLVnI3Y5Sz6ecXedv/4M/cZ7GVGg3rxDPYnN4sYIqrYoaDZYqs6l3G2xM7p\ndP7xj3+89dZbT5w4wfN8ZWXlN7/5zUQiEQgEaFpzwYIFcGWrq6tDMA+mJ4sXL66trSWERKNR\n6mOHUBkhhOd5nU6Hrg+KopSUlEQikVAoBFZUUlLi8/nQAVZRFIhbQc5w3B//+MfHjx/fvHkz\n6uTq6urQTOLgwYMgW7IsQxgBca7f78cEvF4vtY5Dh1aLxXL55Zc3NjYeOnSIEILOtn2q62RZ\nHjt27NGjRwkheXl5kUgEGojS0tKGhga1WYmiKKCVNNv70EMPYZBXX30V81cUZceOHX1aig32\nYtWc/SDLWdLQzyP+/FzPg+HsMNif3CxisP+DwUDBVnUuIwvFjzNmzCgrK7v//vtXrVpF86o8\nz5eUlLS2thJCduzYYTAYUHlGJRQTJ048fvw4IQSmceA9o0aNWrZs2TPPPBMIBNLp9Je//OWX\nX34ZriL79++nR+R5vrGxkRBCq/SQ81UnTB999FFJkrC9s7MTHWZlWb766qsJITTHKghCKpXy\ner30jTTORwgBA+vt7d25cyctesM+6AxG/fYSiURNTQ1mgsljz4aGBofDAV89Qgj0Jep5Yig4\n1SFTDNCIHSQdlO/mJnK22CU3/3XO2ZZiORsCydn/CvoDRVGYj92QQm4WCmd3Vrn8fcSQnV6x\n3d3dd9999zvvvMNxnNfr1el0N95445YtW7xeLySxaqc6BJ8IIYIgoCDvRz/60e9+9zuLxRKP\nxyVJCgQCUEuIouhwODQaTUtLCyGE53mz2ZxIJBClU0tl0+m0y+UKBALRaBRxPliHUKqE4xJV\nIR3VXnAcZzKZYrFYKpWSZdlkMk2ZMgX1bU6ns6enByxQFMXx48d3dXV5vd68vDzkhTGUVqs1\nm82iKLa3txNCRo4cmUwmNRqN1+uF1wkV0o4fP761tVWt6uU47vrrr9+wYQP6xlJR8FNPPbVi\nxQpCyOLFi6GKXb16NVMhfV7kJrHrJ3KWLg8FZDGOy/SD5wg5G2sf1B87/QRb1bmM7BA7isrK\nyj/+8Y/Hjx+HXBRZRfzJaDQmEolUKoVKOJKh/AaDAYwqnU6jfI3GxjiOKyoq6unpoVvA9mAm\nTCkaxBCpVAoBMxSulZWV9fT09Pb20rmh0tNms3m9Xo1GI4oidKmEEKRc4V0nCEJvby+lYkVF\nRR6PhxJEjUZjsVgSiQT1t6NTBU9VFMVkMqG1RiwW63N5nU4nooOYPAjl6NGjjx8/Tt+OPX/x\ni1+sWrWKZMQTU6ZMWbNmTRZD3zn7mchwwYCFqYB0On355Ws/c7ehcCmyi5wNMA8FYoegzPme\nBcO/R5YZ94QJE5qamjQazaxZsyoqKgKBwFNPPYXoGiQOw4YNu+aaa1566aX77rvvpz/9aSqV\nQh52+vTpR48enTdvXk1NDcStGBAJXBrhu/POOy+66KKf//zn6OVAMmYl8DSmCVCUx0FdkZeX\nFw6HLRYLgnngVaCSRqMRHcwQJ0PO12QyWa3WcDicSCTAI8EUx44di4kFg0HoMBRFmTp1akVF\nxdq1a6mlC+Jz6FSmKIrBYKB2LbFY7JJLLtm8ebMsyw6HQ5blOXPmbNq0KRwOgwdD9oFBHn/8\n8TvvvLO8vJzGF7MrQcruxwr7Cv9cyNlUbHYx8AfNzaz0EBEP9vOK9QfssyL3MURW9SBFliN2\nhJARI0a0traWlZWdOnXq2LFjU6dOBVuKRqPUqc5ms8XjcQS0QMLS6TQ4UDAYlCSJEIJwGngb\niFFFRUVBQcGGDRusVuvChQuffvrpYcOGwUwEvIcW8IGKIbmJ98J2BLlgQoi6Fxn21Gq1ELEW\nFxdDlgusWLHiH//4R1tbm9vt9ng8oiiazWav14uGGYhFI9bo8/kwoCRJWq0Wpnfqijqe56dN\nmzZ79mzocLFFkqSCggK/39/T06PRaBCMtNvtzz77LNrFXnHFFUjFMrsThn+LnCV2DIAsy5dd\ntuYzd2P36PMiZyN2QwHM7iSXkf0c+cqVK3/0ox81NjaOHTuWmvS6XC6r1VpdXQ2i09PTo9Pp\nYD535swZBMA4jkPQi2ZICSEWiyUUCuFde/fuxcZgMPjhhx86nc5FixZt27YNMWGTyQQuRbW0\nJKN+IIRQMzmn09nb24ukJwAjFVA0juMCgQC6ZUiSFA6Hz5w5g8q5rq4u0Eekd+GTnEqlBEH4\nwx/+sHLlSp1Oh5QxbR1rNBrh4YJD63S6w4cPnzx5ksYjwS+9Xm8kEikuLoZ7H6yMx4wZ8687\nlAF7kC4YZDcPzr63chyyLLN7xHCBgX0f5TKyH7GrqqpaunSpwWAIhUK9vb1arRZ0h/qxgVTl\n5+en0+nu7u7/nQrHwXOOihUIIS6Xy2Aw1NfXf9qj+DNhs9mQEjUYDNDPqgN1aqUCIcTtdnd1\ndcFjhfbJIIRIkhSLxag1CQX1JaZbdDqduj8EDqTRaKZNm3bq1CkQwX8LOiVq3UcIyc/P3717\nd3l5OWspdjYY+GKXoVBew/C5wMrMGS48sFWdy8j+jZkxY0ZJScmxY8eQkYSFG0yGU6kUftVq\ntT6fT5IkjUZDHUOQS0UMDPnTVCrV1dVFo2vYmYpqKeAPrBZYYOOkSZM++ugjRVGoSR4hBNPg\nOM5isfh8PpfLVVxcXF1d7fF4kMOVJCmVSqGwD4FATFs9Aujanj17qNSX4zij0QjiCE0uMrwW\ni6WmpiYSiQiCYLFYYIYiCIKa75aUlLS1teE0qcx27dq15eXlhJBIJBIOhyORCDOE/ALIWfM5\nhqED9uQyXHhgqzqXkeVQqtfrveOOO44dO5ZKpZDxRP8GQRAkSaI6AEIINLNo7VBaWjp37txh\nw4bBvhgUB44hs2bNokQN7IoSLL1ej6E0Gk1RUZHZbFYUxWazFRYWotJu586d2BMldBgknU5D\n9+Dz+Xiej0aj1113nU6no6NJkjRt2jToPAghXV1dkMTa7XadTmcwGJ577jmO4/bs2UMIwT6i\nKLrdbr1eDzrIcVxhYSF6o4GTYQ6yLAeDQZ7nFyxYMGLECHop0MeWEAKREYxampub1ReWVgcy\nMDAMLrAnl+HCA1vVuYws35tbbrmF47ji4uKbb74ZrVFB1Ewm07Jly+AJAtKGgJzZbLZYLB0d\nHR999FFDQwMUDCQjhlD5vxSWAAAgAElEQVQUZdeuXTzPFxYWLly4UP0vAhpCGAwGQsjEiRML\nCgrgGNfT0+N0OiFHJZnoXV5eHqgexuR5HnleQkgoFHrwwQcVRTGbzeBVoigeOHAgnU63tLRg\nwhqN5vnnn58+fXoikYhGo9/85jcRghZFMRQKgZt2dHR4PJ7rr78erLS9vT0Wi4EUWq1WvV4/\natQoiGEVRZk2bRo1Z0YAEmwVcUpFUaLR6P333w8TZnq+zBCSgWEwgj25DBce2KrOZWQzFVtV\nVdXY2HjDDTe89NJLmzdvHjZsWEtLC7Kxvb29r732Gm0alkqlLBYLx3GdnZ0GgyGZTBYWFnZ1\ndRUVFbW2tiLOB4rDcRykDN3d3ep8qyzLHo8HO9TU1ICTQTbR0NBgs9kKCgpAngghqLSjRsGE\nkAULFrz77rtUZpFMJkOhUCqVGjlyZENDA611QxEex3GVlZVbtmxB+4p4PI5aOswKmWXwsw0b\nNqBKb8SIEd3d3R0dHTNnzqyurk4mk/Pnz5ck6Y033vB6vevXr6cVeISQe+6558EHHySEfO1r\nX/vb3/6GoR5//HGkYrVarVarBSHO4s3KLnLWSpdVvDGcd7DOE+cIOauKHfiPnYFfYLn8fcSQ\nTfFEZWXl66+//sEHH4RCodGjR8Nt+PDhw+p9eJ6Hn3AymaS2cxzHOZ3OSCQSiUTU80F6lPrD\nqf8EdkiztKico6NRgeonTvWTI1CtBsZR/xWqizlz5hw7dsxgMHR0dFBSiHI6nU6HBCsCgdRp\nWRAEJFtJRqgBaS09IrQj+fn5nZ2dNP1aXl7e0NAgy7Kauer1+uPHj5eXl1911VXhcHjixIms\n8wQDw2AEKzM/R2DE7jyCrepcRpZvzI4dO1KplNFovOaaa954443m5mZQGcp1SMY9OJlMItYF\nH2BoFyjXMRgMVqu1q6uLeluDq0HKCpolCEJeXl5PTw9SmQ6HgzZ1UFNGVLwlEonS0tKDBw8q\nipKXl0fL2jANgBCi1+sJIX6/n+f5qqqqZDKZTCZHjBhRX19fUFDg8Xh4nk8mkzA0QeaUWuih\ndaxWq+V5Ho3UZFlW62QBSZK6urooiVQUxe/3o50G3VkQhN///veI2EG6AYu+ft6F3IyfZdfj\nIzfPkQyNz3SGz4WcLTNn7WcYvjBydlUzkOwSO47jent7bTabz+d7/vnn4/G4LMvDhg1rbm5W\n61j1ej3P85WVlcuXL1f3chVFEV0oBEEIhUKTJ08uKirat28fdkAoCx3J8vPz29raeJ6HJzDS\ntRDeptNprVY7cuTIEydOkEwR25kzZ1B+ByfkMWPGVFVVgRoKggB3FcyfGp1cdNFFhw4dkmU5\nkUjAHq+jowOTR4SPnhF9YbPZent77Xa7LMter3f8+PHIEYOTUZ1vn8Choijbtm1btmxZY2Mj\nCCLJxPbo+FCN9P9Bys0P4uzOKjfPkeTwxBjOF3L2K3Cwr9WcTV4P9gvbH+TsqmYg2SV24CuB\nQIDn+Xg8jhRkZ2enTqejYTadThcOhzUazcMPPzxz5sy9e/eCkBFC4vE4jE7AtHw+H/U3Jpll\nxHHc1KlTjxw5AjKHdxFCli9ffurUKY/HU1NTo9Pp0LuCU/WTJYSkUilRFJFaNZvNaDhmNpvL\ny8tramrsdrvL5aquriaE8Dzv8XiKioqCwaDf70chnaIoDofD7Xa3t7eXl5dXV1fzPK/Vaisq\nKmpqanp6ejAlGo1zOByEEBA1KDZkWX7llVfa2tp+8IMf0Hw0x3F//etfv/KVrzz66KOUIxYV\nFdGgIz19juNyNvWQsxgKtU1D4RwHNfr55A58r7PBDnYpziMYsctlZLPGbtWqVQ8++CDy7siH\nCoJgNBqhS8A+oiiKojhmzJhkMtnQ0BAKhbAdsbTi4mKPxwOuxnHcyJEjfT5fIpFANzBBEObP\nnx8IBKqqqvLy8mbPnv3WW29BuHDjjTf+5S9/KS4uJoTE43GcFFQXNM2KoNf48ePHjRv3+uuv\n47g2m238+PG7du3CbjRgBl2t2qkYvC2VSqHMDi8wJs3JchxXUFDg8/ni8TiyzOSTtX0mkykW\ni+Fq4O0kQ9rUQU1BEMxm88GDB8vLy9FSbMKECc8++yxruvx5kcXE6MDnrVim7MIAa5fOcOGB\nrepcRjYjdm1tbaBfSJsiX9nb22uxWBAeg1dwMpmsrq5G81a8kXJ/UCK6sampiSobCCHpdFqS\npGPHjsmy3NPTs3PnTgS9CCEbNmzYtGlTH8EE5VVofgL7FY7jPvzwQ7pPMplEpzKLxZJKpcC6\ncETK6igzwxnRjrSoHhVFEXsiLOf1eqlmFm9XFEUURWRUicr+B/lWWZbffvvt66+/Xl2Np9Vq\nn3/+edTYId5pMBiyq0JipOHzIme9jlnE7nNh4MM8TD94jsAiducRbFXnMrKvasnLy7v88sv/\n8Y9/IHImSdK11177wgsvgC2h5gyEDHwfrSaQq1VHyKi7mzritW3bNtpJtre312g0EkKsVmtv\nby8Y3vXXX79hwwZCiNFoVBQlHA5jEFEUNRpNIpGoq6uLx+N0zHA4rNPpZFlGIR2lmDR5SjIt\nMQghtBqPEDJs2DDwTpvNhoYZhBAqa508ebLP52tra0NBYVFRUV1dHSEkFovxPI9AZjqdRk3h\nbbfdBhaIECN2u/POO/fv319eXo4A4eeKe7P6fYpBfZr9JN87B/M5DjzY1/wFg37eStZXkGGo\nIZvEDsZyCxYseOedd5CdJISkUqkXX3yREMJxnMViEQTB5/NNnDixpqaG5mfxguYu1Xj44YcP\nHDjwxhtvgPkFAgFoWidMmHDs2LFwOAzzEfjSEUI2bNiA45aUlPh8PhoUBIXSarVjx449fPgw\nl2kFhpI+MEsoLTBVOCq/+uqrSObSLmeEEKPRGIvFFixY8MorrwQCAY/Ho9VqZVmeO3cunI0J\nISdPnpRlWRRFl8vV3NxcX1+PI2IQWZZjsRgNKHq9Xsp6CSE8zxuNxoqKCkTsYrEYds5l8UTO\nalQZABagPY/IbjUSu5WfF+xSnAuwGrtcRpYjdhzHlZSUTJkyZdasWUuXLr3sssvQZIIQotPp\njEZjMBicOHFiS0sLZXUmkwmVdrCU69MH9sEHH0QMTKPRXHzxxXv37oVTycmTJ0VRrKioiMVi\njY2NYHXUuASRwlOnTuFXmOT95je/+fvf//7xxx8TQmhyNhaLoVaAUjqXy+XxeILB4EsvvYQS\nOpvN1tDQ8NRTT913333pdBpk8cUXX4xGoxqNBtRQo9FccsklbW1tjY2NhYWF7e3tgiDE4/HW\n1lYuY3eMn3q9PhKJJBIJkEWU7sXjcUmSJEmKRqMgoLfddpv6OlCJSW6CfXSeC2TxqrIbdB6R\n3SeX3crcx1C4R7n8fcSQTfHEt7/97eeee660tLSxsRF5T1jWIUal0+kURYnH49OnT29uboZT\nidvtvueee+69917yyc4QRKVppeZ2GBO8BxVp4EaItyGjOmPGDJ1Ot337dhyOhgA5jsvPzzca\njfX19bBEGTZsmEajgSsKtccjhMCfRa/XazSaYDCoKEp+fr6iKD09PUjFVlRU+Hy+hoYGFNtR\n2QRoKyGE53m9Xj979uxkMnnw4EHQVgxuNptRZah22psxY8ahQ4dMJpPP56OhxKuuuuqtt94i\nhMybNy8UCk2ZMmXNmjXsWfq8YDmR84hBffGzGBhjZebnCCx4eR7BVnUuI5sRu7lz5z777LP5\n+fmtra1lZWVarRZGbuBDkiT5/X5CyKlTp0aOHAkz4a6urp/85Cf/llzSEBcSptTsV1EUk8kE\nz97i4uJAINDb20sIkWXZbrcbjcYTJ07gvbQvGQZsb2/X6XRoGpFKpcLhcCwWg/SVxvkEQfD7\n/bIsRyIR9L1QFKWrq8vpdFKhBqz1eJ6/6KKLjEbjjh07MD6NQSKG9+GHH0qShIpASZLi8Xg6\nnY5EIhqNxmq1Iu4IXH755VVVVQgEYrayLB84cKCxsbG8vBzOeTDqy+LNGiIYCp/pOSueGNQX\n/233j/uz28/7sQ97cs8RsniPGD4v2KrOZWST2C1fvvwHP/hBV1fXkiVL3njjjbvuuqumpiaZ\nTCIO5/P5NBrNPffcs3r1ahrrUr+9z68WiyUQCNDt11577VtvvWWz2bq7u3t6ehAnCwQCF198\n8ZYtWwghyWSys7OT2sjFYjFFUcxmMyEkHA6DX8bjcYhS0UACXWJJxm2OJmdJJsAGTimKotPp\nBBOl80mn04cOHaJblExjWbwXw6JBhSAI8+bNQ7IYJilGo9Hv99NuY08++SSCi1qtluozaLcW\nlOWxp4jh/wJTA5wLsKvKwMAwSJHNVCwh5NSpUwsWLDhz5gyVlJpMpmg02kctAW0sfQ3ZKfZf\ntGjR0aNHkQOlItmLL744FosdO3YMPbtgemcymRBdo0fXaDTxeNxgMCQSCfBC9JbA4H3OFFE6\nq9Xa09ODLQ8//PD9999PBwS3s1qtbre7traW5oVtNltFRcW7776rHhBVeoqiTJo06dixYyTT\nkQJvAUvD6bvd7q6uLnXhHcdxY8eORVIY4Hn+8OHDEydOJJlU7OTJk9euXZvF0Hd2sxg5GzQa\nChgKF3/gk25ZtNJIp9OXX742K0MNdmTXoITZnZxHsFRsLiPL4okxY8a0trY6HI6RI0eePn3a\n5/PZ7fbW1lafzwei43A4RFFsb28nGdkEttOyudOnT2s0Grfb3dLSAnZYWFjI87zD4VAUBbqE\n8ePH19bWmkwmQgiidzh6Op2+//77S0tLT58+jdFAGdPpdFFRkcPhOHr0KGgWPFbS6XRZWVk4\nHEbp3qpVq5DnVdsmT5gwoaGhAeMYDIZIJLJo0aL333/farUmEgmkVkkmrjZs2LC6ujpIIjD5\nWbNmnTx5MhQK0VAc2tquWLHi0UcfxUZFUZBNppBl+dJLLz106FB5eTmNHVIDvKwgu2mynP3o\nHAplXszu5Fwgi0ua5/mcfUAGGNm9Duyqnkdk9/uIIctQsofu7u7bb7/d6XSKoqjVasFIiouL\nH3/88QkTJuBwhYWFNMlI4XA4vvvd76JFmCiKer0+Pz//pptuKiwsNBgMX/nKV7AdPwVBGDZs\n2B133EEIsdlshBCkO6nZm1arJYQIgjBu3Diz2Wy32wkhd955J9xDrFbrsWPH6KLEVB0OR0FB\nQVFREZVwS5I0derUlStX/va3v1XrujUaDf0VR+R5/pe//OXo0aOJSgEOhQfihXjNcRxOXBCE\niy++GONwHJeXl+d0OqGNHTVqFN2+YMECXNUrrrji4osvvvPOO2nJIAMDwyACe3IZLjywVZ3L\nyGYq9sorr5w5c+bo0aNffPFFRNRqamqsVmsgEKD5zeXLl7/44ou0xk6v18MumDbmQqOFq6++\nWhCE999/n1qKoCqOHgu2vTzPw3/O7XbDRc/hcNjt9traWpJJ+FL3k2g0Sk+WMjAE4RwORygU\nmj9//vvvvw95LPbkeV7JZEuVTKivsLDQ7/dDFYHtr7322g9/+MOOjg5FUWC/jOQvfbt6EJIx\ndqGTQdaY4zi9Xk8bVCxevBiq2AULFkSj0fHjxzNVLAPDYARLWg01DOpEQT/BVnUuI2up2Kqq\nqsbGxnfeeWf9+vW9vb3Tpk2DoQmUsCTTlaGoqEir1YLsE0Jo8zGSIVuyLGu12kgk8uGHH1JT\n3z6sjmR83ehGiEwJIaIooscDyUgi1NRK/Xa8QBmf1+vleX7Xrl0gVeBwtOyP+rDIslxQUFBd\nXV1aWqoe7ZZbbkkmk6BrRNWgQlEUtR6CZNQYBoMhHo/H43GEJy0WS5+aQo7jVqxYgdfwbUZL\n3C90Zxg+A6xSZ0jhvLQUGwp1kAOPnH1yB560ZXGB9fOqbtv2jf7sxnBekDViV1NTM336dJ7n\ni4uLjxw5sn//fihSadistLS0trb2kUceISqPOkKIkvGBkyQpFosJgpBIJKqqqpDBTCQS8MD7\nNLErLS2tr68nhIwePdrr9aKFA+J2hJDi4uLe3l5FUaA8paZ6l156qd/vv/baa3/zm9/wPF9W\nVhaPx+EYDBc6dIAwmUx6vb6rqwv0ked5rVY7fvz4o0ePFhYW0naxiqLY7fZ4PI4utJibXq8v\nLi6WZbm9vV0URRT50ZEVRQGNI4Qkk0m4E+ONLpcLJYM6ne7ZZ5+97rrrCCHoTquo2p0xZBeD\n/Qt14D/Tc/aK5SZ/Yk8uw7lGFld1P4ei8keGHEQ2xRMgKw899BAsTkKhEKx6w+GwoigtLS00\nfKXT6WisjpI8ZEvxXhiFOJ3OtrY2hLJoHpO+aGhowOtQKARrX5KxGiaEdHV1gRJhbtdcc82G\nDRskSTIajXl5eU8//TTicI2NjSTzyUsnk0ql/H7/pEmT/H4/5gmTvLq6OrPZjBjkvzLZHAcq\nxvO8TqeDH7IgCN/+9reffvppvV5vNpth2kIIqaioKC0t3bhxY2FhYUNDAyhvNBrNz8/3+Xzp\ndBoSCoy8efNm+NilUqlkMkn9kxkY+mDgP9NzFrk5f47jcnNigx3sqp5HsO+jXEbWiN2ECRMe\neuihvXv37tu3b+HChdu2bYvH44Ig2O32RCKRSqXi8biamSEgB92D3W7v6Ojo6urieX7+/Pkf\nfPABCFlHR4c6tsdxnMFgkGWZpiwhVu3o6CAZWkajX8jh4jXHcRs2bIC5ydatW9VOdSTjeMzz\nPCJ8hBBQtMWLFzc1NcViMdgLd3R08DyP0jpCiCAIw4cPP3PmDGWoCPgRQiKRCFyXeZ7HxvHj\nx584ceLUqVPHjh1LJpPgfzS929nZidcajQbjx2Ixt9tNp0fPLls3i+HcIYvlNcxY/8IAe3LP\nEXI2wDwUauzYqs5lZI3YzZgxo7Cw8LrrrotGo0eOHEG/BFmW0eGehuhQiAbBBO33NWfOnPb2\ndoTQDh8+bLfbYVCn0+kIIXq9PplMhsNhnU53zTXX7Nq1q6WlBR0jwP8QpUPMLJlM0swpQmLU\n2YQQgvifuupOFMVkMhkMBqlOFrpUs9m8adOmYDAIb7y8vLyenh6Hw9Hb24tpFxYWQucB6olx\n4GacTCZ1Oh0al0WjUb1ej2YYfr//6quvbm9vP3z4MCHEbrf39PTIsjx16tSqqqpEIoFTQJXh\nmjVrIOOlQPO0/tyLofCxkrMYCheWMc7Phf4/uQwMgwVsVecysqaK9Xq9EydOROMHcHlRFNHq\n/oc//OHvfvc72o/100dUS1bx3qKios7OznQ6jSZggUAAylb4233abRjgeZ72k4X6obS0tKmp\niWRUCxDYqj2NSSY+p+5UC8kt/VV9LNocrM+J3HvvvY899ph6C7VoVqO+vv706dNXXnkl3cJx\nXCqVwrC4Yqjeq6mpGTt2LCFk6dKl4XB40qRJf/rTnz7tFMPAwJDjSKVS7MlluMDAVnUu46w8\nBr1e7x133FFUVDRu3Ljy8vJwOFxeXi6K4okTJ/Lz85GKTaVSv//974mqlSohhOO4uXPniqII\nChWLxTiOMxqNhJBLLrlEo9EEg0HE2FBhptfr0aCio6MDSgL6v4LdbsfyQlbX4XDodDrUycmy\n3NTUBIu4srIyeKMsX76cEKLX60mGRNIWtNiCP2k0GsrSeJ53uVxwuXO73ZiYXq9HUJAQYjab\nn3vuOUVRRo8ePX78eKPRCF6IbrPUr47juCuuuGL58uXUc7iwsJDjOJfLhSggDWGOGjVq/fr1\neB3P4GzuFAMDAwMDA8NQwFlF7GBct2rVqkOHDi1ZsiQvLy8ej3d3d3//+9+vrq5GmR3Gp8lQ\n8DNkS/81A46jrVplWXa73e+///6SJUvOnDmDHaAVFQRhypQp1dXVkF9Qi7g33njj7rvvhjyW\nELJs2bJ//vOf0KjSqBs0HJFIhIokrFarRqOJRqPgf0ibJhIJ+I/AVY5qKTAypke9WujgiA5i\nVoIgiKIYjUYhE3G73cFgkCo/TCYTdCSYPEKYyPwitEmlHsgFo/MEWopNmTJl7dq1WTT7HiLZ\ntIHPSrM8OAUrNwRg2HS+Z8EwcBgKHwJsVecyvjixq6qquvXWW0+ePMnzfGVl5erVqxVFKS8v\nb2lpOXjwIO3oJQiC0+n0+/2oIUsmk5deeumePXvUjiFqyYJa/SpJUkVFxcKFCx944AFk9Hme\nF0Vx5MiRkUgkEolANqHX60GM4vG4w+GAfx7HcU6nMxaLQb5A1bKEEKPRiBBgMBg0mUwej4cQ\nAtvhMWPGzJkz5+WXXw4EAmCHmIxGo0Gx3eTJkw8fPjx16tSOjg6QNoPBgLZpsGtBPzFquYIQ\n47+uNcdRVodYplar/epXv1pXV7d79+7HHnvswQcfxGwFQfjHP/5xzTXXEELmz58fDAanTp36\nzDPPnM2dvsAwqL/p+4l+nuPb7h9/5j5Lun7Xn6Gy2BqYnI+i9Zx1fslNH5aBRz+vaj8xFK5Y\nP5H7C6yfn2afiUH9kT5g+OLErrKyctOmTX//+9/xurKy8ujRo3Pnzt2zZ097eztlM4IgUMkn\nauyQEkWgC9pVcDtYviH6hfgZISQ/Px/kjA6IxKvL5VIUJRKJxOPxb3zjG08++SSK6vqcDriU\nwWCAYwg2IgRIA2+CIFit1qlTpx45ciSRSKCej05ep9NRES7FPffc09DQ8Oabb9JAHWKNZrP5\n9OnTRqMR3WZnz57d3d19+vRpOqDRaISvnpq/0ksEiQm2OxyOqqoqdcSOdZ5gYBiMYB79DBce\nPu+qZsRuIHFWxY+Ur7z77rs7duzgef69996z2+0mk4l2tYdQNJFI0NZyCOHKstzHcxi1dG63\nu6mpCQE/juP8fr9Op7NarT6fjx7UaDR2dXXp9fqioqLa2tonn3ySfLKGD6DpVHVHB/iSUAqI\nLrH19fW7du3CbogjYs7Tpk07evSo2mwPGdjf/e53NMmLTC7HcV1dXWiAEQqFQNcOHz5M7VHo\n5Gk6mG4sKSlpampSd54gmSpAQohWq0WHXNZ5goFhMIJ1nvi8YIG9z4uBX2Ds+yiX8cWJHYzr\nwNLWr1/f2tq6Z88ejuPAUQghNpsN5r1Iws6ZM+fjjz9WFCWdTtvt9u7u7oKCAqPR2NjYSNWj\n0WgUcocnn3zy3nvvDQaD8Xgc0gpCyKhRo5qbmxHJQ0uJ06dP038a8A8EFcwigEcN8NRucBgQ\nG/1+f09PD7S0hBDUxnEcN27cuKNHj8ZiMRplxJnqdLpIJEJZHdxJaCkhbFzgjZJOpyENUdfq\nYU/qAITiv69+9auPPvqoIAharZYqOR5++GHYnWCqrJrhi2EoFLsw5D6GArfIIvp5ufodBLrw\nLz5bYAxqnJV4YuHChRUVFatWrRJF8fDhwxUVFYIgIEaFoBda3VNWBFanViSUlZWh9VYfBxO7\n3T537tzNmzert7tcru7ubuRq+8gaLr/88q1bt6o30sCYTqej8T8Ut2EO1HAuHo9DWvGJ68Jx\naG5L7e5oV4k77rjjr3/9K02k4kAWi6WoqKimpoaOAOPlSCQC3z5BECRJGj169KFDh7RaLVz9\nNBrNmDFjxo8f//rrr4MF0ozz3Llzt2/fTghhqVgGhkENlopluPDAUrG5jLNKxb788st33313\naWmp1Wp1uVxPPfXU448/Xl1dLYoiBKSzZs366KOPJEkKhUIIfSFHmU6nS0pKWlpampubsdFq\ntSJDCvrl8/k2bdq0aNEip9P58ssvQ7iAbCw1w8NQ4I41NTW33HLL9u3b29vb1cV86XT6lVde\nuf7669UFbdRIBcE2WZZhYkzZp6Iow4cPb2hooDQRXsTIulIRg1arveWWW9atW0cI8fv9yD7z\nPP+tb31rw4YNXq8XjcJIRggiSVJXV5c6T51Op2trazs7OylbxfZbb7311ltvxWsadGRBOwaG\nwQj25J4j5KyIZyiArepcRtYMigkhV155pV6v37ZtW15eXmdnJ3Vlo6oIROxoUIrneZ7n8/Pz\ne3t7w+Ew0qBGozGdTlut1q6uLlEUkdzUaDSSJAUCAbXlL8dxEydOrKmpUbcOczqdBoPB7Xaf\nPHkyHA7Lsmw2mzUaDarf9Ho9kqE8z1dUVOzZs0eSJEVR0K+CEOJ2uyVJQgNZl8vl9/vBt1Kp\n1K9//evHHnsMPSpw0aZOnerz+VpbW2F9DPZJda/0BJGupRFBi8WSl5fX3Ny8aNGi999/nxBS\nWlra2traxw/FZrP1EU9k1+6EgYFhYMCMIRguPHzeVc0idgOJrDlHV1VVNTY2Pvnkkx988EFL\nSwshBJ7DiqKgVYM6qYooVFlZWUtLy5kzZ7Rardlsdrlc7e3tcAzp7OwkhMA0zmAwBAIBShPp\n2wkhx44dU/erQI8Knuerq6sRhON5HipUkimJQ1pWluW9e/eiUI/nechmDQaDJEnt7e2gmLFY\nDN3MdDod5LEajUYQhB/84AdPPvlkMpmsrq52OBygcalUaunSpZs2bVIUhUp6CwsLW1tb1fHF\ndDodCAQwpe3bt2PaCDoaDAYa3uN5fufOnaixEwRBo9HQdHa27hfD5wULD+Q+clOjIMvyZZet\n+czdzosPS24iu+KJ/mBQXy5yPlY++z7KZWQtYgf3k66urr1790ajUbPZHAqF1I50YDOiKCJC\nRlOlhBCDwZBKpZCzR7wqmUyqQ18wQ1FzO5Jp/JVOp81mczwepyYp1I5YEASLxRIMBpGZtdvt\nXq+XuqJQvoWhKKlSs0/qQkwnhqa02OHTHcOIqraPbgG/BHsjGX3Gv72GavGHIAhbt26dO3cu\nayk2uMAskRn6gDVfGmoYCo/k513VLGI3kMjmx43P59u3b9/s2bM/+ugjGO0SQgoLCx0OR2dn\nZzgcRouF4uJidJWgFXXozaDX65ctW/b6669TAkeTkvn5+SdPnsRGtcS1oKCgvb09GAyChG3a\ntOmGG26IxWLIeyJMCHWqLMter5dkSvQIIShrkyQJ4oaCgoKOjg41q5MkieM4rVYLiSuOKAjC\nj3/84507d+7cuZZsaVsAACAASURBVJPOxOFw+P1+ug/J1B+YTKZAIIDsM7a4XK6JEyd++OGH\n6usmCAKCeUjmYsBnn3127ty5hJBIJBIOh2nbjGwhuwa//fl/Mbseuf3EwH/CDvznTn8Min8+\nAPPIAeRmxC67T+6gxsDbPvd/tCxiKJAPtqpzGVkjdhMmTDh48CDP852dncXFxRzH9fb2RqPR\njo6O9vZ27APS09nZOXbs2FOnTkF2ijK7QCBgNBo3b96MPY1GIyVnPT09Xq8XPbhkWb7xxhtf\nf/11SBx0Ot2oUaN8Ph+6TSxevNjhcMTjca1WK8syurX29vaipwVGoBoFMK2CgoLTp09rtVo0\nsbDZbAj1SZLU3d1ttVrV0TWwzz/96U+zZ892u91+vx88LBgMVlRU1NbW5ufn19bWgpii8A6B\nyUmTJjU0NASDQY/Hs3XrVo1GYzQag8Eg2lQgpj179uy9e/fiQD6fr6qqatmyZeSciSey+9HT\nH9I2FD7ssot+ku+d7MJmkJsJNZaxosjNG8TwBcBWdS4ja8RuxowZoigGAoGTJ0+iniyZTA4f\nPtzj8QQCAeoPh3I0BM8gjFAUxePxyLIcjUaRsiSqmjnU52m1WiRY0Q0MmVOO4zo6OhwOR3Fx\ncW9vL97o9Xo5jkNXWUIIavVAH3me/+lPf/rQQw8hkFZRUbF7927MqqCgoKWlheM4ZJDRAWLE\niBFOp7OqqooQUlhYCL3tFVdcMWbMmKeffhoNYUkmglhbWwtdBbXZczgcILKyLB85cgSqDnDB\n11577e9///sLL7xAMo3U0ul0S0uLOrdbVFSEF+fIBzK7/+zmLGnL2Yn1B4N68ucFuRmxY2Bg\nYBhIZFMVW15e3tzcjCo6RMug8fzXkTKQZfnRRx+95557CCEwk+N5HmSLZHjMypUr//CHP+BX\nOoLT6ZQkqbm5GTIIUCuLxUIIAVNcvHjxu+++i52prcm0adNaWlpglWKz2Xw+n8ViiUQi48aN\nq6+vHzFiRGNjYzQaxc5WqxVz1ul0S5Ysee2118gny+ZMJhN25jgO/E8UxVGjRjU0NNDGFdi5\nqKhIq9U2NTVhPnq9Pp1Oo9LO6XQiBsnzfFlZ2bx589atWwfrFnA7WpJIMj52kydPXrt2LXPD\nYmAYdGA+dgwXHpiPXS4ja8SuqqrqS1/6UmdnZ1lZWXl5+ZYtW+jIDocDgTTyqfjTqFGjuru7\ne3t7aWkamlLY7fZwOIz8KfbUarVUf/CJE1DJHeBgR19TkQQ9LhVMgOHRifE873A4PB6PoiiX\nXHLJrl271DsriuJyubxeL8z5UDCnZEAIkSQJMb9EIoG1nk6njUZjPB7HfCjLxGRmzpxJs67/\nF6h44qqrrgqHwxMnTly9ejUrwWZgGHTIrngiu9WxDAxfDEw8kcvI2sdNTU3NRRddtGPHjrq6\nuvr6elAiNJ8gn1KbKooiSVI0Gq2rq6P8SavVTp8+3ev1dnd3w3ZO/S7QqXQ6jcq822+/vbKy\nUt2eixCyZMkSGI4QQuC0gvZfZrOZFrS53e7Ozk5YBCPOB3e97u5uvBGsTpKkdDqNEjpRFHU6\nHfgfqvScTmdPTw/P83l5ed3d3aIo0sZoCxcu3LVrFwr1aGoVrC4vLy8SiSQSierqatqyghBy\n+vTpiy++mDbDJYRotdqDBw9OnDiREJJKpSAZzuVi1ZxVgeXsxLIIln/8XBj4inuO47J4jwb7\ncs0iclY8MfAY+A+6XP4+OkukUinUWaHCnr7WaDTNzc233Xbb3r174/H49773vdWrV990002v\nvvpqFo+YlX8CsxkB2rVr1+LFi1955RXkWxVFQcjN5/MZjUaj0ejxeJBwJIT0kXlyHJdMJvfs\n2SMIgl6vHz58+IkTJ8gn06Bjx46tra0F2aqsrATtQ0ITvGfjxo0YDZbIoVAIetje3l6Hw9HT\n0yPLcldXF0xSaNkfDgESRt3mUNuHQ48YMaK5uRmhOORbPR4PdvZ6vZB3UA66bds2NBALBAIw\nOkHjDUIIbTiBOjxJkiKRiKIoc+fOReI1Pz+/s7MTpX6bNm0CsUNjDLDSLN6s7CJnv2xydmIM\nOY4skobsPrk5y2bYPxjnEQP/QZfL30dZBMdxs2bNIpnzffjhh3fs2LFgwYJly5aZzeZZs2aN\nGTPmCww7adKkY8eO7du3b8aMGZ8+Shamna1U7Lp1677xjW9UVFRUVVXxPA8CdMkll/j9/hMn\nTqhthBGci8fjxcXF6KYVi8UEQZg1a9aBAwc0Gk00GjUajSBDFotFluVYLAYyRPmTxWKxWCzt\n7e2o5yMZkY7ValXTLECj0cDZDsclGZEp6CBkGaWlpXBUgadJaWlpW1sb7PSorTFook6nKy0t\nbWxspEFEpGKbm5tLS0tLS0u3b98+atSouro6vV6PZhjBYNDv90+fPn348OFvvvkmnRsijrNm\nzWpvb6fVeJieTqc7fvx4eXn5/Pnzg8Hg1KlT165dO0SeJQaGCwl9EgsMDBcAPu+qHkSp2P+f\n+NmiRYu2bNny+uuv33jjjWdziD7ELuvImmIZt7mzs3PJkiU/+clPkLusqalB4A0AwZJlGSyt\nra1Nq9Xed999MHKDyx2Sp6A+NpstFosFg0FEROFITDJ+wrCpk2UZgTG8kCQJZsJ2u91qtdLW\nDmgvBuM60D7a2Qw6jFOnTtGkcDqdbmxspBV+tK0Zz/PwST59+jTejp+RSKSpqSmVSjU2Nh45\nckQURShkY7FYKpXKy8tDrO7gwYOvv/46jUGKoohavX379jU3N/e5mJIk0df4+W/9kBkYGHIc\n7MlluPCQO6v6uuuuKyws1Ol0RUVF//mf/9nd3U0IicViSMetWbPG7XYXFxdv2LBh7dq1bre7\nqKho/fr1JGNqy3HcH/7wh5EjR+bn5//qV7/qMzjdJ5VKzZs3b8uWLYSQL33pS1QM+uUvfxl7\nNjY23nLLLcXFxZIkXXTRRXDk+LdzGzdu3LFjxwghM2fO5DjuzTffVB+FEFJXV3fDDTe43W6X\ny7Vs2bJTp06pZ/LEE0+MGjXK4XA89NBD/9c1yVoqtqGhQavVjh49euPGjRs3bgTjCQQCtJwO\nXAoUjUolYrHYr371K1A0WP4OGzasvb0dW2jZGerV0KAimUzKshwOh5EbRbxNkiSPx5NMJmF9\nTAhRl6wBSH326WyGOrxPXBGNBioNddgPLi3/17lTO+JoNNrT00MI6ezsBFWVZfno0aPq8KHJ\nZAqFQrSAj/x/7L1peBRl+j38VPW+L9n3QAwQAokJIIiAiAvIog6DcAGOuC+XOCLDiLg7jDP+\nVPyLC6jgBiIiKgMSRQXFECBIWBIJhCRkXzvp7qT3ter9cCbP24OOhrEJHVLnQ65O9VNVT1VX\ndZ2+7/ucu6ethVqtJoSgXQfLspmZmWgphgo/iURynnxPBAg4TxBycwDP88KpOB+I2Kz0QEDk\nPI8qKytnz56dkJCwffv2jRs3siz7/vvv03f/9re/ZWVlFRYWLly4UCaT5efnf/vtt/fff/9N\nN90kl8sxZvPmzXfcccerr7765JNPDhs27KabbvrFHc2ZM6e6urq5uXnmzJkZGRnl5eXo9k4I\ncTqdU6ZMqa2tzcnJmTFjxqFDh+x2e1xc3C/ObdGiRatWrTKbzfPnz4+Njc3IyAjdi8PhmDJl\nSmNj47XXXisSiXbu3Hns2LFTp04pFAoMeO2112bNmrVmzZqnnnpqzpw5WVlZP59qmFWWMTEx\n1EmY5/lrrrnm22+/pcwJl0JMTExnZyfT060L1WMSiSQ6Orq1tbW1tdXr9cLal+d5lUrldDqh\nbwUHR98wRDJBv9xut8/nA31UKpVisdjhcEDlQCvn6OoQrtIeYmKxWKvVQgmBqJhGowEpRBEe\nYoF08lRsGxUV5XQ6vV4vJkkIycjIqKyspAfLcZzRaExLS4PZCm2A4XK59Ho91LjYplwut9ls\ndrudRn1Zlj169GhdXV16ejrilGFP5QjfiRcNIpY09K7XiHCBCRDwexGxXwJ9gNLSUtjBzpw5\nMy8vr7CwMPTdzz77LD8/H5Xu77zzzty5cy+55JIzZ85UVVWNHDkSYzZs2DBs2DCdTvfggw9+\n+OGH/43YLV68+NNPP21ubr7zzjtvuumm9evXU2L35Zdf1tbWZmZmHjlyRCwW0wzbL85txYoV\nmzZtMpvNS5cuRSo2NGxUUFDQ2NiYl5cH77YxY8aUlJTs2LFj3rx5GLB58+b8/PyKiopdu3Yd\nP378/BK7mJgYt9u9efNmBNJAWfbu3QtnuEAgkJGRUVNTwzAMJBTU/gNVcYFAwGQyQW8hFovP\n0ljQdGRiYmJjYyMh5PTp0zExMXa7He6+aPPA87zX601MTERu12KxgEtRo2D8i160SqXS7/df\ne+21e/fuNRgMXV1d2Ag11AYFpPxPo9FYrdbx48fHxsYWFBSAmWGedrtdqVROmjQJrshgn3q9\nXq1Wm0wmKC1IT4c0mUxmtVqRqvZ4PIQQv9+v0+m6u7vpkbIs+/LLLyNi5/F4UGIYXm53sd7k\n/wMEjnue0K+VKxErnujX6O/3Wn+ffxgRIVe11+tdvHjxZ599hlwZ6ZE2UuTk5ODxbbFY8vLy\nCCEajYYQQp1iCSGpqan0L036nRPq6uoIIXl5eYjOgBv85tx+Eai2HzZsGP4dNmxYSUlJaLEW\nVJVw8A09ilCEjditWbOGEDJ69OijR4/iJCKsRXtC1NTUkJ6IF9gStKuojVMoFEhNqlQqj8ej\nUqkgXIiLi3O73Ujp8jwPRQUhRCQS4RyBqEkkEnSnHTduXGVlJarihg8fDj0E8r8ikUin09ls\nNuSq0Zi1uLjY7XYjU4yR8EBBLV1qamptbS0hJBAI4LMpLi7W6XSgaBzH/fWvf3333Xc7Ojp8\nPt+bb76JuaFKz2w2wxiPLkQ6FYlXRCVFIpFUKjWZTAaDgVb44SxhvyREcSJ4nJ4n9Pdv4f4+\n/8hEGM+qSCQSPiMgvOeh789qxH6OfT+xCHkeffjhh+vXr7/00ks3b97c3d09b968s3LElGmR\nHuOSnyeRy8vLx4wZg7q3pKSk/2EaiMIcP36c2vsFg8FfmRvO3i/WKaalpRFCqDgBL0A6Q9f9\ndWIdHmK3Z8+eM2fOEEJ++uknlUq1YMGCDz74oLu7G73CSA+fUygUocVqiEI98sgjzz//PKrf\nCCEul0utVi9ZsuSZZ57heR4tXP89V7H4nnvueeGFF5BXJYQolUqWZZ1O55gxY9AfbP/+/Tjy\n2NjYuLg4o9FYWVmJbmB5eXk6ne67777Dpux2O8MwSPVCzEF66uQQRwV31Gq10dHR9fX1Op3O\nYrHodDrSU5iJuBpyuKmpqU1NTWBvEonE6XSiiRnihUj+oqbQ4/HMmDGjpKTEYrHAoO7w4cPg\nmh999NHcuXMNBsPEiRMpEw8VTwjt+QQI6HcQ7tzzBCF+dgERIVc1gkR+v7+7uxuSiP8BN998\n88SJE2FHt3Dhwv9hC9OnTx80aFBlZeXo0aMvu+yyw4cPb9269VfmlpycXFZWtnz58tzc3Cee\neEKv19O3ZsyYkZycfOzYsalTp7IsW1JSkpiYiMbxvUd4iN1DDz1kMBhGjRrFcdzXX3/95ptv\nIo+JrmJUByoSiRBXIz2OHl6v97nnnkOXWEKIUqlUqVRmsxnsDY6+yFTabDa/3//yyy+D6Cxa\ntGj79u3JycmQJuzbt48Qotfr77rrrg0bNphMppaWltbWVrQsI4QEAoHDhw/TCWdlZen1+sLC\nwubmZjiYoN8Xta8Dy3Q4HBKJpKamJjk5OSMjo7Cw0Gw2r169+umnn0ZIEpZ1crkc8g6e5/1+\nPwgrjhcFfFFRUampqZMmTVq9enUwGCwoKAAlBV274447FAqFx+N55ZVXCCFer3f79u10qiKR\nCAniCLmR+hcGQumJcIwUkXmYHMdNnLjuN4dF5uQFCPhFRMjz6NZbb929e/eXX375+OOP3333\n3dTL9pzw7LPPPvbYYxqN5tFHH50zZw51zOg9VCrVd9999+ijjxYWFm7cuDErK0uj0fzK3B57\n7LGqqqrCwsK9e/cuXrw4lNip1eo9e/YsW7bswIEDhJDp06evWrVKq9X+inzz5wiDj11JScnM\nmTOzs7MbGhpOnz49aNAgi8UC1erEiRO///57OhJxO8S64FQSExOjVCpbWlpwarq6ujASWctL\nL73UbrcjnUoIEYlEEDTwPG8wGMxmMy4s6DBkMhlNcZ51CkCzHnzwwXXr1iHUh8wvx3EJCQk6\nna6yshJMLjs7++TJkz8/J2cZ4+FfTIb0BEVjY2NNJpNcLne73XQ8MsuI/8EdB0Z9zH+24hg2\nbFh1dTW2RghRq9UzZsz4+OOPCSHXXXed0+kcMWLEAGkpFt6OSeEyT+r9HsOIMJ6K/t6Hql/P\nP7wtxQREPvq+D0Tf7/EiaCkW9n4PkYMwHMzJkydHjhxZV1eXkpLy+OOPHzx4cOrUqSdOnAgG\ng6GsDkIExMYoh+M4Tq/X19fXsyzb1dUFzhcbG2u1Wj0eT3l5OTgQIUShUOh0uq6uLlBDVMLJ\nZDK/3y+VSpHixI6kUuntt9++bt06GikEhXK73ZSHYSMGg8FkMrW3t1PxbEdHxz333LNz586W\nlhbqeJKQkGC1WlG3RxWyUKpKJBKj0RgTE3PixAnU/MGZmfSQP7fbHR0dbTKZcBtAw0vtkTHy\nkUceOXjwYKjhn9PppNeZz+fzer1hF09ELML77ROZT/peIoyT79fngfTz+TNhbSkmgCJi47h9\nf7kKnScEhCIMxM7hcFRVVbW2tprN5uLi4hdeeAHLH3jggddff50Q8sorrzz++OMLFiz45ptv\nGhoaeJ6nzbXMZjMIU0pKSn19/fDhw0eOHPndd9+BNmVnZ5eUlMB2ePLkyTk5OZ988klbW5te\nr4dIBCRp/PjxEEzAEtDlcr377ruEEKR6GYaRSqXQSYDPQfoQCARcLlcwGNRqtQ6HgyoeNm3a\ndFb6OD09vbW1lRBCbUdoKzOPx2MymdCmDJwP/JVhGJTroQstIpQMw/j9fpFIJJPJ0G/D7/dL\nJJI5c+b8v//3/3ieR8xPqVR6vV6qbaaezJEQ9xYgQMC5gmVZgbSdDwhn9QJCeB5FMsJA7D74\n4AObzVZbW/voo49++eWXCoUCTb3efvttQgjDMEuXLuU4bteuXampqfX19Uql8qabbtq8eTPT\n01KWYRhIfP1+/9dffy0Wi+Pi4urq6qqqqliWnTBhglarValUx48fr6qq0mq1cDwBb0OZodls\nzszMNJlMpEdnADdj6h7Csizsm0HyQPjUarXP53M6ncnJyWCKCApS7zqwvVOnTimVSig2eJ5X\nKBRwWkb0keZPGYZBHhZM0Wazkf/M4cbHx3d2dvp8PpqKRb8y0lMBivnDCS+zJ3BNxRNCYyIB\nAvojhDv3PCFiI3YDARfBVQ2LtAs9i/OC30u6oe689NJLX3vttfXr13d2dmZnZ0ul0pSUFEqq\n8LepqQmSVa/X+9FHH8EZGO8mJiZia5WVlWKxePny5VKplGEYlMFBNFpfX3/JJZcwDIPeDKRH\nVCuTyeAnXFVVhQAbWjhcdtllRqMRm/X7/Tk5OSBPt956KyVV3d3dcrk8GAy2t7fjGoVTcXZ2\nNnV5JoR0d3fDSQ7NJJDSFYlEV1999fXXXx8VFZWQkICRbrdbJpPdcsstSUlJNLaHxCvDMO3t\n7R6PB9NAiZ5arbbb7a+//vrPf/1MmjQJ1jiQ2cIk5Xd+WAIECOh7CHeugIsPwlUdyfi94okN\nGzbs3LlzzZo1DzzwwI4dO9DsITMzs7m52eVy8Tw/dOjQ2tpan8+n1+u7urpSUlLa29upvQig\nVCoDgQA1BAaJobVxLMuq1Wqn06nRaGw2G7Ko/3EMzC8cRVJSUnNzs0wmA6mCgoH0ZDbhBEO3\nc1bRG134xz/+8cyZM9XV1VTMm5WVFRcXV1RUJJPJoKLFcolEkpGRUVFRMXjwYGpBh/AeLc6j\nk5w2bdqePXtoqC9UhBGqqCgsLJwwYcLUqVOdTmd2dvYbb7xx1jH+4rR/DrDG32ztR4W9vzms\nj/f4czXMz9H7U0Gvqz7bI+nbUxHJJ7+XE/vNTUXsye/lxCL55A+EL4HeTCxiT34vJ0arg87f\nHvu7eOIiRhhSsTzPR0dHWyyWnJycuro6q9Xa2dlJO0bU19ejSC4xMbGrqys6OrqpqYkQwjAM\nmoBxHOfz+TQaDXpI0JIyKlPgOM5ut/M8b7PZxGKx0Whsa2uTy+XQwGIOSMtCS0H98KCidbvd\nWVlZDQ0NyI0SQhITE9HTDBcrwzDR0dGTJ0/evn27z+cLFa6OGDGCNpPAZJqamiorK+GNQueJ\nu6iqqooQUlNTw7Ls0KFDq6ur0Tb4q6++oucK29m9e3fodwE6sEE5C/Esz/N5eXkTJkwghHAc\nhyZm/82juJd3V29KInrpORmxewyjsim8e7zgJ//3JK36/lT095N/1jD8WO3LPf43XPDrMLx7\n/D1X9UC4Ds/rHv8HTxABfYbf+yAcPnz4ypUrf/zxx7q6utOnT48YMcJsNi9evPill16CPpSK\nVU+ePMkwzOnTp/H7QCqVPvLII+vWrWttbQ0EAlROwfM8pXdngeM4tVoNy+JgMCgWi6n7CRqR\nKRSKqVOnfv/993a73Wq16vX67u5unudPnjxJeuJhiN7BGRg/RFA899lnn9HfNzKZDIxz5cqV\ntPUZ3lKpVAqFwul0QmBB5RShwTae50+dOsUwTH5+PnTBeFepVLpcLoh2d+zYAUEGCvWwHcoX\nCSF33303XgQCAb/fDwfj3/lhnT8Ior8LCOHknxP6vjArvHduv3Z+EXDRIJKfRwJ+L7EbPXp0\namrqypUrc3Nzjx49Cn7W2trq9/tzc3PXrVt32WWXUcYjl8uhDGAYRiwWP/300yzLjhgx4qef\nfkpLS2NZtq6ujnYbo9E4GvZHxpYQkp+fX1dXh63BNgV00G63f/HFF0qlkhCiUCio84hKpYqN\njWVZtqamJj4+vrm5WaPR2O32qKgoNKgNtU3Ozc09efIkEriU6kml0ujoaLgZ33bbbV999RWM\nkZVKpd1uj42NVSqVKIkDkPI7c+aMz+dTKpVgtzExMY2NjSCglMj6/X69Xm+1WuVyud/vpz+D\nIPWgoAwyMiHwhguI3px8gQ1cQITX7kT4jCIffe8q1/eI5OeRgDCkrrZs2XLTTTeVlJQUFRUF\nAgGDwbBx40a/3//3v//9wQcfhKAhGAyyLJuamlpZWYkiJ7TMkslk8CixWCyDBw/+95zE4jvu\nuGPt2rX4l3KdSZMm7dq1ixBSVlbGcdzChQs3b95Mp4EKPEguQINgHYfqvcWLF2/fvt1ms8EM\n2W63SySSFStWPPzww3K5XC6XI7ZHCGltbYVdcH5+fm1tbX19vV6vF4vFWNFkMr344oukR/2Q\nlpZWXl4OuxOWZZOTk0HdEAgE6URWeuTIkc3NzXC2e+211+hBcRxntVoRdJw5c+b27duNRqPb\n7T516hQGYFY0V/v7P68LhQvCLSLzG7bvT0V/f4qEEdebXurdwLD9VuE4Tvjlcz7Q9x+lAIr+\n/jy6uBGGzhOEkJKSkvnz5y9ZsuTFF1/0eDxdXV1gV1QTCh4jkUgCgQAKNmkwbNq0aaBroWBZ\nViaTaTSazs5OiUTi8/moXTAtwgsGg1R2ELriz6vjExMTp0yZsm/fPpiqUGi1WmpKQnoUrDEx\nMQcOHMjKygr1MTEajeghRggRi8UZGRlVVVXoV6ZQKBwOByFEo9E4nc6z6lV/Udjx8+U//zc2\nNra4uDg9PX2gdZ4ILyKT2Ak4V/TriKPQeeI8oV9fFeGF0HlCQCjCQ+wIIVdfffWpU6fcbvfM\nmTN37Nhhs9moxFUsFtNOX1AqxMTEnDp1iu6aZVlw/1BxaEpKCiGkqamJZVmdTud0OlGC9nMy\nR/5TpUVJklQqlUgkaG6Gt8C6QnVMGHz99defOHGiqamJYRh0M0tKSmpoaKDDYE1HSVsoD8vM\nzKypqUFIEtRQoVC4XC5wQcwNdFaj0aC8jwovVCqVUqlct27djTfeSE9FMBhUKpVbtmyZOXMm\nTiyI3Ztvvik8HgQI6HcQiN15guBjdwEhELtIRti+blasWDF79myHw7F582aWZdE+KxgMxsfH\ni8ViaGNBaGw2G5KPoatTCgV9KyGku7sbbnAcx1ksFsqlQJLOkuSg3A0GwhgGu2CfzwfhKl3I\nsmxSUlJjYyPK7LD63r174YrHcRz2XldXp9FoYmJiamtrIcslPQIinufvv//+wsLCn376iRBS\nVVWF5SqVKhAIuN1uZJnRylatVjscjpSUlMbGRlpXB2Czdrt9xIgRODlKpTIYDHIcFwgEwOpw\nZgKBADouC19kAgT0O/Tyzh0It63wDXbRQOg8EckIW8Ruw4YNGzdu3L9/f0ZGhslkstvtsC8Z\nNGgQjHlDhQhn+diR/8yKUhImk8mkUilideBnsAgGq0tLS2tpaQF1o+0ZkpOTbTabzWaTSqVQ\nm7IsS8eg4E8kEiHmN2jQIK1WW1paCqnE1VdfvWfPHhDHhIQEqFZhTg3ah4wtpqHVaru6uuhU\n6Wk0Go3d3d0sy6I1RSAQOMt+T6fT4V/S09lCp9Ohwo+Wo7Isq9frS0pK0tPTr7rqKrvdnpub\n+84774TlkxIgQIAAAQL6EkLEri8RzgRBS0uLSCQqLS198MEH3377bdCvpqYmiUQSqqAJ9eCl\nr0H74P1BLT/S09Pb29vhS5Kbm3vkyBHE3sDSQsvv6Hba29uxOuyOeZ4fO3bsvn37VCoVom60\nVo8QUlNTQ7tQMAxTUlISExPT2tpKreMwSfSZ4HleKpVKpVKHw4FDQ3ARqdtgMCiXyxUKxdKl\nS59++mm/38+y7BVXXFFcXGyz2TiOk8vlgUCAZVlafUhzytBY0GJE+NWtXLkyPT2dhLQU+0U3\nLAECBEQ4gBxn6wAAIABJREFUhDtXwMWHc72qv4pdFpb9PhGWrVzsCBuxGz58eFNTk16vZ1n2\n2LFj1NJaJpNBW0BBeVtosBDpS6RBYdjLMEx1dTW1kautrZ02bdpbb72VmZmJ+Bk2CxlsW1tb\nWloa9cADQ8K6RUVFhBCadaV+yC6XKxgMqlQqn88Hoziz2QxSxXFcbGxse3u7SCRKT0+vqalR\nKpXd3d0+n8/n84GDZmRk2Gw2mBXjEne73S6X64knnqCxt59++umBBx549dVXvV4vfF7UarVc\nLnc4HOhXi/nHxcVBV0tPi8/ne+KJJ66//vr09HQwQqlUGq7YqgABAvoSwp17niAkdi8ghKs6\nkhE2Yjd69GiDwdDY2BgTE2O1WtEEgtqahOIXL4jQhYiWsSxLg2F+vx/Z1YyMjGAwKJVK4SdC\nCGEYZu3atUuXLqW2IDKZDMwPmw0NEJKeiJ3dbk9NTYU8IrSnikgkgsq1tLT05ptvPnToEByV\ncSw8z2s0GkKI3W4vLS1FBA5pYpopBrPE4La2NpZlX375ZdIj77DZbC6X67nnnnvqqafoyNGj\nRxcUFJzVtgVEEEf0c52vAAECBAxwCIxNgIBfRNhq7Aghl112WUlJiUQigVnJxIkTd+7cSZuD\nweuE9HA4upwmRlHNRukOtimTycRicSg7RK1bcnKyVqutqKigbcGuuOIKBOfQbSw0pUv+000R\nIgkkUg0Gg1gs7ujoIISIRKK8vLzjx49DvUuJI2WEmCSSswqFwuPxqNVqFMyFHgut59NoNBzH\n0cljU1BIwLIYg0GFY2JiWlpa6IFXVFQMHTqUEDJ58mSHw5GTk7Nu3TohoXNxQIg0DCgIqdjz\nBOE+uoA416u6lx/Wb0L4NHuDsEXsjh49arFYrrjiirq6OpPJ5HK59uzZw7Ls119/fd1117Es\nazQaDQaDWq0+cuQIIQQyT4lEAgYGGQHP86B3YrFYqVRardbBgwe3trZGR0fHxcVVV1fTijqX\ny9XS0iKXy1NSUjo6OsRi8cmTJyl3ZBjGYDC4XC50BlOpVC6Xi3ImVMshE3rJJZfodLrdu3fL\n5XKPx1NaWhqqt0Xwj+f5CRMm7Nu3D7wN8/R4PHBpkUqlcrmcNqIViUQsy95000179uy55ppr\ntm7dSnpYbHx8fHt7O6YEGQfHcRkZGSzLejye1tZWWv+HbrPYIG1ZFl4VUngtoATR3zmhl6dC\nsOm6ODBAVLED4RgFUAiq2EhG2IjdiRMn8vPzn3vuuauuugqZ0EAgkJ2dPX36dPAV9Fd1u900\npkXXjYqKkkqlra2tLMtOnDjx8OHDCQkJXq/XarUiE8rzPByDwW84juvq6goEAg6HAzlflUq1\ndu3aTz/9dNOmTX6/H/46CQkJ1dXV2PXNN988c+bMxx57zOl0dnd3x8XFNTY2EkIOHz6sUqmg\njSCEgFMGAoHBgwfX1NTk5eUdOnSI5/mioiKGYbRaLVZEZhYtbrOzs9PS0nbt2iWTyWBQzDDM\nzp0733333ffffx9ETafTMQwzf/78DRs2oNkGxBYul8tqtSYkJOC0kJ64IMMw5eXl2dnZJEQ8\nESqb/f0ILxsQvq/PBwTGdnGA5/mBcIP0/TEOhLMasQjv80hAeBFO0s3z/H333WcwGGQymVwu\nF4vF5eXlYGM8z3u9XkSzCCGwFwkNodEeskVFRT6fz2QyNTc3Y+SgQYMIIfv27UOQLyYmhhAi\nEolUKhUhpKyszGazdXR0uFyu3bt3E0KioqJoSA8RNULIsWPHMjMzoUglhFitVhpGhnmyUqmU\nyWR6vR7cEe2/jhw5YjQaU1JSIGKVy+XgphDqIqVbUVHxzTffwMEuLS0NjNbtdi9YsODbb7/F\n1Y+utatXrzabzc3NzRKJhBCCpHNnZ2dlZaXP55NKpRMmTCCEqFSqqKionTt3YnpweD6LCgsQ\nIKC/QLhzBVx8EK7qSEbYiF12dvbBgweLi4tnzJhRX1+fl5cHhalcLofCIBAIeL1epGWRzYT8\nMxgMWiwWmPdCJ+Hz+eBdRwi55JJLXn/9db1en5qaSgjhOK6trU0kEl155ZWTJ0/W6/XR0dGg\ncbfccovJZNLpdCaTye/3O51O6F6Rcq2urh4/fnxnZye8kd1ut0KhwMyx0/T0dJ/PZ7FYMCXU\nwMnl8qysLNiRgG62tbWBktICPkwYccSGhgZE7AghPM9T3YPH47FYLJBZEEK8Xi8W0so8bH//\n/v2EEKfTaTKZ9u7di+2LxWKxWExjigIECOhfEO5cARcfhKs6khG2VOyoUaNgzPHMM8/I5fK7\n7rqrpqbmiSeeeOihhyjXkcvlpaWlN9544xdffAFiJBaLY2NjYR0HbnSWOLSiomLRokXr1q3b\nt2/f66+/LhaLfT5fIBD49ttvCSFbtmy57bbbRCJRZmbmmTNn/H4/CKJMJiOEeDwekDCYmwwd\nOhRVeoSQYDAItxSRSGQwGBA2Q2Uey7K5ubnFxcUOh8Nut+/fv59l2djYWLPZLJFIQPhITy2d\nQqF4//3358yZQ49RoVDAPCXU0Jj06CrAVnGkNGZJG6nBxBjL4+PjsSOv1+vxeKjOV8A5YSDU\n/QyEY+zXYBhmIHxGfX+MESueGAgtqoXnUSQjnKrYRYsWffrpp7BqGzFiRFdXl1gs3rdvX3p6\nen19PUibWq0eM2ZMYWEhAmksy6LHA+1LQQihzr30BUiSQqFwOp1YaDAYrFYrOJBMJpNIJA6H\ng2EYtLVA7hK7+PdxMgzadqlUqmAwmJ2dffz48aysrBMnTtDtI7qWkpISCATsdrvb7UYDidCN\n8Dyv1+tpo4ioqCiPx4NdZ2ZmVlZWYiTLslKp1OPxTJo06cCBA6B0VDxrNBqRCwaNk8vlyNWq\n1WoU23EcN2/evI8//pgQgs4TOTk577zzjnAvCRDQ7yBUIwm4+HCuV7Wgiu1LhLPGLiMjg2GY\n1tbW+vr6goKCHTt2oCtXXV2dXC7X6/XQEFRUVMydO5eKPbu6uhC+QuUZCBzepaE7VLOB/YR2\nawCo/S9WAVVNTEzEkqioqNDkqcfjgU1JXFxcbW0tdhQXF6fX65VKpVKpNJlMra2tDMOoVKp1\n69YplUqVSoUUKmQfIJfYptlsRuSP5/mqqip6ULA1IYQUFRUFAgGU040ZMyYrKwtiDkQ30SHN\n5XJFRUURQuimGIa58sorMX8h4i1AgAABAgQI6CXC2VJsxowZzz///IoVK1auXCmVSk+fPj14\n8OC2tja32+3xeFBg98wzzzzzzDMFBQUgRtHR0bShFhgbz/NJSUkNDQ0wKFGpVE6nMz4+vqWl\nhYSY3uEvYl20XQRiXdhUbW0tZoVV4Gai0+lYlrXb7T/99JPVamVZNjExsbm52WQyIZZGJdx2\nu10kEt11113UP4VuDXIQqVSq1+stFgs155PJZB6PBzMBXQudACHk8OHD4HywUCGEIDObnJxM\nG2PQThvLly9H5wksx6EJblgCBPQ7CHfueULEpmIHAoSrOpIRTmI3atSosWPHFhQUvPfeewqF\nIikpadCgQUhTwrbt8OHD48aNg6ICzKa9vZ1hGJlMhogaKFFVVRWN1cHdF6wuNF0LIIaHzhCE\nEIlEotFoLBaLSqWCXRwath48eNBqtRJC7HY7bO0gXwgGg6B3cXFxwWCws7OTuo3IZDKka7Ej\nGjaj/sler7ejowPRNUxMrVYHAgHMHFG60DkjzwuXvrOCcI2NjZdeemlZWRnqCxG8XLt2LVid\nUqnkeR5/w/hhDRAMhGKXiMVAKCzrDYQ7V8DFB+GqjmSEk9gRQj799NMlS5Z88803hBCDwdDQ\n0ACyAkLjdrvFYvFbb7314IMPgh5RecFll11WWVmJDhBUiICiNEq2JBKJz+ejwTnS06eLEDJ6\n9GiWZU+ePGmz2cRiMYJq2NT27dthXIwlRqOxu7ub/tRwuVwMw7S1tYVepgjL1dTUYIYMw9x8\n883bt28XiUROp5Pn+blz537yyScgbdRquLu7e8yYMQcPHsRGECOUSqUgnSKRiNYLSiQSkE4k\njouKiioqKkhPuI4Q4vf7//GPfyxcuJAQ4u1BeD8pAecJApWkGAikrZcQOO6AgvAlcKEwZ86c\nzz777L333rvtttt+cUAgEEDRF/W77Q3+t7UuIMI8xaioqI0bN+L10aNH586du379+kWLFlEJ\nQmJi4kMPPRR6asRicWJi4rRp00CJ0OwBHI7nebVa7XA4EOVSKpXwIqF8TiqVBoNBn8935MgR\nvqdTWWxsbEdHR6jsFOwKoTWLxcIwzJIlS1599VU0iiWEoKLO4XDo9Xq73R4MBoPBYFdXl0Kh\ncLlcCoXiyy+/pHpYQsj27dtJSFc0hPHEYnFpaSkGgERKJJIXX3xx2bJlYrFYIpF0d3crlUqH\nwyEWi71eL4KLhJC///3v9HhJzy+hurq6urq69PR0OMWgL214P6yBgL7/9hS+rwWcBZZlBdJ2\nPiCc1QuIi/t5NGLEiPLy8sOHD48ePZoQwjDM2LFjSf/RAp8v7mmxWP785z83Nzffc889VFLA\ncVxjYyN6cEkkEtTPSSSS9vb2Z599FtKE7OzsBQsWrFixIjExsbW1FalYhM3sdjteoDcXmnQF\ng0H0kCCEwOzNarXSSjX8zcjIKCsrS0tLa2pqQpBsw4YNqFojhLAs+/HHH2/atGn79u3gXijv\nA2QymUKhoEIHRP68Xi+ibqSHXEqlUljTYYnP59Pr9R0dHW+88UYwGFQqlV1dXVhdKpUGAgEo\nfDHtkpIS2k4XKWmlUklzuDgK7P08fVi/goFQxSJ07op89OvrcIDYnfQ9IvaqGAjfFf2F4oQF\nIpGouLj4Qs/iHBBOuxPAYrE8/PDDW7ZsEYlECoWC53mbzXZWA9Ybb7yxubm5tLQUGgWVSuX3\n+9VqtcVikUgkyHgiHiaTyUCkSM9PBJ7nz5w5M2PGjNOnT8NCBQE5GNQhx4rOEEuWLHnppZcI\nIQinIdR36aWXlpSUoNwNTb14ntdqtW63G43C7r77bo/Hs2XLFpA5GtUzGAw2mw3cUaFQ2Gw2\nTHv8+PGEkK6urrKyMtSTYg4Gg2HIkCEWi+XkyZOkJw7Hsqxardbr9WKxuKamBuWGEolErVab\nTCapVAphLMMwTz755LPPPksImTx5ssPhyMnJWbdunVCsKkBAv8O5tksXICDyca5X9fmzOzlw\n4MC9995bW1s7f/58k8m0Y8cOpGJramr++te/FhUV8Tx/3XXXvfLKK9HR0WclVX9xzLBhw06f\nPk23v23btpkzZ4auVV1dvWzZMhiZjRs3btWqVUOGDKFbfu2111555RWr1bpkyZInn3wyLEd9\nrgh/xG7BggUajYbjOL/f73K5RCIR7OJoYtTj8WzduhUdWhmGCQaDNpsNlWepqakul6u9vZ30\nyA58Pl9+fv7Ro0dD6+2GDh0KO19sAd5vlIRhTCAQWLVqFaYEGuf3+zmOA6vDALAo0iPRwPIt\nW7ZERUWhLhB6DtqdFnoLmUyGFT0eTzAYLCoqQgWhRCLxer0cx4HFogGGTqejW8YZsNlsdrud\nqmJBYW02G8dx4KYYn5CQgNdSqRRGfeGl4P09TBWx8xfKawScBaHM/Dyhl18CvUF4b8mB8CUQ\nIVe13W6fNWuWxWKZMmVKVVXVvn37sNzpdE6ZMqW+vh7WFu+//35TUxPt5/TrYxYtWrRq1Sqz\n2Tx//vzY2NiMjIzQtRwOx5QpUxobG6+99lqRSLRz585jx46dOnWK9rJ67bXXZs2atWbNmqee\nemrOnDlZWVl9cib+A2EjdgjUffnllzDvValUiEIFAoHy8nKZTMZxnMFgEIlEdrvd6XTC4EMk\nEj3wwAO7d+9ubGx0OBxutxuqUpZlQcUIIUeOHNHpdHa7nUbs3n///VtuuQXhMRj8Ig6XmZmp\nVqtLSkqUSqXRaLTb7aBQ1AYF1Eqr1drtdtTPEUJAQ6k7icvlcrlc4Gf02mUY5t13373rrrto\njzLSY3GiUCiio6OhtMBIpVIJTa7FYqFiDgiBMzIyKioqQAHFYjHHcRzHLV68+I033iCEpKSk\nNDU1YePohEt65LQ/VwT/TvT3r5WInX/ETkyAgIsMX8Uu680wIRV7EWPXrl0WiyU7O3vPnj08\nzw8ZMqS6upoQsnPnzvr6+gkTJvzzn/8khOzfv/+HH34oKysbPnw4Xfe/jVmxYsWmTZvMZvPS\npUtRYxfaDaugoKCxsTEvLw8i0TFjxpSUlOzYsWPevHkYsHnz5vz8/IqKil27dqEPQh+ej38j\nbMRuwYIFeXl5Tz/99LPPPtvZ2en1ei+//PI//OEP9913H8/ziEWZTCZCiFKpvPbaa7///nuk\nPrdu3Xrvvff+7W9/Q7AN3AsBP0gZ0FUCMTloCJ577jloTuFjx3HckSNHSI/vCdpONDU1SaXS\nkSNHlpWVQeVKfUZAthISEuRyeXl5OSgmrRigxnXgUgqFwuPx8DwPVodhUqk0NTW1qqoKetWu\nri6WZXmel0qlSChHRUWZzWa42cXFxVmtVvQ3a2xspIdJr5VXX30VLxobG3H4Uqn0zjvvnDhx\nYnp6OrLMdNcCBAjoXxDu3PMEoSrxAiJCrmpYoYGuMQyTlZUFYldfX08IKSoqiomJoYMrKytD\nid1/G5OTk/Mre8Raw4YNw7/Dhg0rKSlpaGigA7KzswkhWq2W9CQD+x7hIXZHjx6trKyMjY19\n6aWXOI6TSqVRUVFHjhyhlryUk/E8n5iY+N133yGEplAopk+fvnr1ap7nhw4devr0acrtRo4c\nieo0SmtwJaWmpppMJoxxuVwGg2H9+vUvv/wynI23bt3K87zT6cRpTUtLKy0tpSyNZdmhQ4ea\nTCaLxULdTBITE9va2kQiUUJCQn19PVQRiYmJFovF6XTCKg+ldQzDpKWlNTQ0cBx35swZpI9T\nUlKam5tjYmJaWlq0Wm1nZ2d+fn5OTs4HH3zAMExdXR1tkgFmCUpHS+4Yhrn77rvffPNNlmVZ\nlkUaNyMjY9u2bfCxoxKQSFYhCbXhFAMhC9NL9P2piMzrkGXZyJxYf0fEiicGwpdAhDyP0GKK\nCijpi7S0NELIuHHjVq5ciSV+vz83Nzd03V8Zg/LBX0yUYS04lNEXqampdADWvbDENzzE7sSJ\nE06n88CBA/REqNXqlpYWjUYDsoLoFKrW4AMMiqPRaGJiYtRqtdVqDS1XTExMTE1NbWlpsVqt\noR6/w4cPdzqdZrMZC2UymdPpfPzxx/1+f319PSVMeKuzs3PXrl2kp7ht0aJFn3/+eWNj49Sp\nU7/66is42FFtRzAYbGhoAAUMBoMzZ878/PPPMYb0VOn5fL62tjZQUvz1+XwNDQ1XXnnl8ePH\nJRIJ7EuOHTt29OhRQkhMTMyYMWNwpJdffvnBgweDwWBaWhooPyYmlUqHDBmCDU6ePHnv3r08\nz1dWVlK5CfVA4SO446TwTKLo79/XYUTfn4rIvA55no/MifV3ROxZHQhfAhHyPJo6darBYCgu\nLv7zn/8cCARoT4EZM2akpqYeOnRo8+bNgwYNqq2t/eKLL2pqakLX/ZUxycnJZWVly5cvz83N\nfeKJJ/R6fehaycnJx44dmzp1KsuyJSUliYmJs2bN6suj/k2Eh9jV1dWZzeYxY8bk5eWVl5ef\nOnXKZDLxPI/eDKSnvT3yoQ6H47rrrisuLrZarSaTac2aNVTEMG7cODA8t9v9zTff+Hw+hmGM\nRmNmZuahQ4f8fv+ZM2fkcjm9npDkPXXqFIQUQ4YMOX36NJzwOjs7wSnBmViWtVgs0Nju3r2b\n9gEjhIhEIkgT9Hr96NGjwQU3bNhw6aWXdnZ2arVapVLZ2tpKTY8JIcnJyc3NzaGOehaLhaZ6\n6WYdDgeljz/++CMGNDQ0IFCHAju32/3yyy9jzHfffUff2rx5M35GiEQiJJdxFGH5vARcTBgI\n0aBelsn3puiq708Fx3ETJ677zWH9/TPq++swYiN2fY8wnvxentUffrgzEp5HWq12+/bt999/\n/8aNG2fNmjV16tSvv/6aEKJWq/fs2fPoo48WFBTYbLaUlJS5c+fK5fLQdX9lzGOPPVZVVVVY\nWLh3797FixeHEjusBVUsIWT69OmrVq3SarWhdXgXHOGxO7nvvvvWrVv3zjvvFBQUtLe3FxUV\nEUJEIpFarQ4Gg+gqRgNvLMvCyy30RDA9HV1ZloWOAdbESIzSRl50ZOiKhBCqhKBgWfaSSy6p\nq6tDeR9s80aOHNnZ2Xnq1KnQ1SUSiVwut9lsRqPR7/fTtq1KpdLr9dJdw50Oq2g0GqfTSYkd\n2JhUKkXelvS0xKCTpwtRYCeXyyHRJYTAvQ+cDxFNDNZqtaWlpenp6dOnT3c6nSNGjFi9enW/\n8Lz+b7ggOta+z4mEcY8Rq/wVcE6AxP5Cz+IiRMQSu4GQij3Xq/r82Z0I+DnC83UD3rN3794D\nBw50dXXdcMMNxcXFHR0dUMgyDDNkyBCn09nU1AT6glatIEzUxwS8R6/XDxky5KeffkIYz+/3\nw/uXkjlK75KSkhwOByrYaFuIxMRE2li2urpaqVQi0ub3+yUSSXFx8VlEFjE/yG+RSKVwuVxy\nuZzSskAgIJfLvV6vwWBAdwrS0wSMZVm5XC4SiUDs4OGCVUKdfujgq6+++ssvvyQ9LTEWLlz4\n4YcfUsLKsqxMJsvMzESNncvlcjqdNCncf3FBvsj6deeJ/v7VLwDo73duxCJin/ED4c4VrupI\nRniIXVJSEjqfoiGE1+v94YcfFi5c2Nzc3N7ezrKs2+12u924FKiNCLjdZZdddujQITik2O12\nk8lktVoLCgrmzZtnt9vh7sb0tI598sknV65cyXHc4MGDGxsbQdp4nr/qqqv27NlDCGltbaVV\ncbS1Kxp2mc1mQgjaSBBC1Go1ta8LjZaBbCGNC0ED2CfLsgaDobW11WKxwGZlypQphw8fttls\nkydPLikpoSIJ0iO24DiORuAUCoVMJuvu7h49ejSE2YQQn8+nVqvb29tFIpFUKmUYxul0chzn\n8XjgTkyBOYTlwxIgoG/Q9+mhyHzSC3fueUK4gkAkUq+cSIZwVUcywpOKPXLkyMSJEx988MHW\n1tZPP/0UagmO44xGo0gk6urqgsoBJEwkEmk0GofDQdOUWP7II48olcoXXnjB5/MFAgE0HNPr\n9Q6HQ6PRwB84OTm5u7vb4XAkJib+61//Gjt2LBKaqEKjqVIclEqlgnL234fak+2lBXYKhcLr\n9aJL2IIFCzZt2kR6KBRyx08//fQ//vEPlAZCw3vNNdfs2LGDECKVSkN5GxPSlzYQCIwePbq+\nvh4tawkhSqXSYDAsW7Zs2bJlpMdLhbapzcnJqays9Hg8oINI/r7xxhv3338/Cek8sX79euFe\nEiCg30GojhVw8eFcr2ohFduXCE/EbtSoUWPHji0oKKitrXW73SKRCAqG2bNnv/vuu+iyhUCa\nRCKRSqXd3d3UMZgQotFobDZbQUFBS0sLeJhEIhk0aFB5ebnf7w8GgzSlO2zYsKKiIrlc3tbW\ndtlll9FM7uTJk3fv3k2Dw3iRkJBw5swZ0sO6kC++9tprv/jiC5Zl9Xq9VColhCDsd/nll2/a\ntClU/WA0GquqqhAvFIlEKSkpmZmZVqtVIpFgVrRXbFJSEvpM0LxtXV1dR0cHPErgfuxyuZYu\nXYr4n0KhiIqKgrUKIaSrq8vn84HSIb7IMExrays2JZFI0GYtLBRcgIABjr4P/vE8PxAELn2P\nfh3HDS/6PjpeWHhXb4YJuCAIW69Ys9m8ZMmSjz/+GME2tNtCry2ZTAaPX1pXh1XwLoYFAoHk\n5OTW1tbQrrISiYTyJ4q4uDjUnHEcp1QqQQQTEhJMJtOSJUu+/vprk8lE1biEEIZhMIHQjSiV\nSp7nMSuMoQ4mdDDkqLTPBCASieLi4lDGR7fPMExMTAw6oZEed7pgMAibYjpy4sSJ+/fvpz3K\n6DF++umnf/jDH7B3nCKGYU6ePDl06FBCyNSpU51OZ3Z29htvvCGUYF9A9PL77nrTS785ZiCY\nt5EInlgfIxAIXHnlO785LDJr/EkEV4xFLLETxBM/hxCx60uEjdgRQo4ePXr99dfTnqpwKiaE\nTJo0CbVoyMOCLdEQFMdxCO9FRUUxDNPR0aFQKKi8VC6Xy2Qy2pgLS4xGI4r5UDxHCGEYJjc3\nF9YnGAN9Bs/zeXl5J0+epIV6YE5oRyaVStHd9fbbb6+qqiouLobQVSKRuN1unU6HEr2kpCRq\newjSBlUE5aAymQx1hP8+pwyDor158+Zt2rQpVM9Lz1Uo79Tr9VarNfRMyuXyLVu23HDDDYSQ\nKVOmuFyu4cOHr1u3TmglLkDA70Tfs4FzbZcuoJeIWGI3EHCuV7VA7PoS4YwAnThxIiYmxmaz\nBQIBcCapVCoSiYqKisDJCCG0lRaUsKB0SIba7XawNMqQCCFer5dSNzAkj8cDl2BCCPQN2NTx\n48eR6MRaSqUSutrjx4/TBCsuRPwbDAZpxG7btm2PPvpoeXl5V1cXfFgYhvF4PBKJRCwWd3V1\noa8rfIwxmfHjx8OQGbpalmXRgpYauzAMc/z4cfSloBPQ6/WINUZHR9tsNpfLxfM84o6hoUGv\n13v77bcfOXIkPT2d53na5SyMH5YAAQMTff9gEO5cARcfhKs6khHm1B6koDKZbPbs2Zs2bUK9\nndFoRMEZIQQJR0KIWCyWSqVpaWknT54E74mLi6OBMYrt27c/8cQTZWVlVMHA8/y999779ttv\n4xcDQnGon8vJyTly5Ag1DQEbQzyMRgdRIafX6xFZxF68Xu+bb75ptVoZhkFeWKlUKpVKsVjc\n2tpKPVOwBHwR4T2WZdPT07/66qvc3FwkhQ0Gg9VqRcgQYUvsNBAISCQSg8EAKW5nZyd1yMvK\nyiopKUGZXVRUlMViSUpKGjlyJOxOAFqPKECAgP4F4c49TxCCNxR9n/wVrupIRjiJXXZ2dkdH\nh0j220ZuAAAgAElEQVQk8nq9DMOkpKQMHTr0wIEDgUAAbsNIUObk5JSUlAQCAb/fX1VVRQvO\naGQLgCHwzTffjFAW3lIoFB6PZ+3atVqtNhgM+nw+uVx+0003ffzxxz6fr7y8HBlS+BUj0EXd\n6RDPQ/zPbreLRCK9Xm82m9EZDG2+qCgVcgeIM2gEEdSNSjFAtq655pqTJ09ikmKx+K9//euT\nTz6ZmJhYU1NTX1/v8XiYnla5+fn5FRUVNAIHMAyDaeMAUZPX1NT02GOPYQClpEgfh/HzEiBA\nQB9AuHPPE/p7jWAY0ffHKFzVkYxw1tgRQsaNG3fs2DG/36/RaHw+34gRIz788MOHHnrIbrcf\nPnyYFsnJ5fJQ17d/T4Vh5HI5TY+SnzWZOAuhVW4UMpkMTiukp5yOboGOD9UuhAJUEhE7WpAX\nOj3wvM2bN8+bNw87QoRy8ODBzc3NCLnRNhL/bYah7SXO2njobJcuXbpq1SpCyHXXXYfOE/1d\nPDFAOk/0PQQ3r/OBMNZvRax4ou8RxmuVRPAZ6/uvnb4XKgniiUhGmIlCQUHBoEGDUDan1+v/\n8pe/rFmzprS01GazkR7lgV6vNxqNcDDBWnq93mazoV4NIgkY5IB+UR5GXX+xFrgXennRAjVk\nZlUqFbWvg4ADXE0ikWDLdDwMR/x+v1gsfuCBB1555ZVAIBBKzlA5B9YVGxvr9XoffPBB7IhK\nN2pra0M5mUwmQ8yS4zixWIy+F3QA7gfKHWmGmhCCY0fJXUFBAYgdTtpFEPceIJ0nwoheUmES\nkQ1S+zuEM3Y+MEB6xfbmaydiJy/gIkCYiV1UVNTzzz+/dOlSn8/X1tZ2xx13REVFORwOl8ul\nUCjQZdXhcHR3d4dakLjdbqPRaDabPR4Py7IqlWr27Nnbtm3zer20pyoh5Pbbb1epVKtXr8a/\ngUBg/PjxJpNp+vTpr776KgmJkKEMDsjMzPzXv/6VnZ0NEkYISU9P7+jocLlcIpHI7/cbjUa7\n3S4Wi7OysjArFALCbGX9+vUrVqyoqanhOK6zsxPxOZvNhrYWJCSsyDBMQkJCS0sLygGRfo2K\nikJSODRwCLKI+CUoI+lxLSaEoHtYUlISxns8Ho/HQ1W9As4J/bpXbC8Rxq/+iM1thXFifX+M\nDMMIj+cBhV5dY734PdZ79MZliZBwXoTC8yiSEeZULCHkyJEjf/zjHydMmLB161bUk4H6rFq1\nasWKFXCtCwaD6EWB7CcEDVh98ODBcIlDuVuonJbn+ZiYGK/XC/eTQCDwhz/8Ydu2bUqlUiQS\n2e12bEcmk3EchyAZ2NWECRMCgcChQ4ewEaPR6Pf7bTYbwzCZmZl1dXXIjYLnYXeXXHJJY2Mj\nWn5xHIe2GaRHWktr7DiOS05OttvtNpuNZVnsDsUH8fHxLS0tlK6RkGTuBx988OKLL5aVlTEM\nM2rUqJkzZz7zzDNisVgsFoPa8jw/bdo09JNF54mRI0e+99574f2kBAgQIECAgD6AkIrtS4S/\nZmvUqFFDhgxJTk4eP358Y2NjbW0tuOOzzz6rVqs1Gs2gQYOKiopQd0lTljTu1dnZqVQqMayw\nsBCdu2ilWkdHB16IRCKO4/bs2TNkyJDKykosxHb8fr9KpULYDEqIw4cPT5gwAbyws7MzIyOj\nra0NPc2qqqrA1SQSyfz587Oysh555BGGYaqrq6Ojo9euXTtnzpycnJyffvoJpA1iXrlc7nQ6\nx4wZc/DgwaamJnrsHMclJSU1NTUFg8GWlhaxWAwm2t3dTTWwPM8/99xzZrMZGduSkpLly5ej\n0y5tpEEIKSsrq6uro8JYOB4LxaoCBPQ7CHfuQMNAKO0VrupIRvgjdoSQ6urqyZMnNzc3sywr\nkUgQP0tMTLRYLH6/H+ZwDz/88IYNGxoaGsBm6LDa2tq5c+eWlZWBSE2cOPGHH35Ar61fFD2c\nVXgHJCcnBwKB7u5ur9e7bNmyl156iZbryWQypIa1Wi3P8xMmTPjxxx/hNnfHHXccPHiQ+t7l\n5+dPnjz5iy++qK6uDk2kIrBHA3hUJwGLk1DvYplMptVqrVbrWWqMoUOHnj59+t8fAMOo1WqX\ny0WZH4xaaBXgtGnTHA7HiBEjXn/99X4tnhAgYGDiXMvMBQiIfAhXdSTjvBC7adOm+f3+Q4cO\nneUP8u9dhogeQkvoaNBOpVJxHAeTEZZlNRoNldD+XOUa2k8iVFialpaGdqs+n08mk4nFYqfT\nybJsbGzsddddt3PnTmqDErq11NRUl8vV2dkJ57no6Gi73U5rAamIAclWsDeqcsVVDmKn0+mw\nfYTl6LoIvIXuF/liJKZDF6rV6rKysvT09FBV7PNZWb05/5H5W7C3UoDeITKPMbzo+2qwiK2x\n69cQHoEDDQMhYidc1ZGM8HwwFovl4Ycf/vbbb+VyeUJCQkNDQ3NzM9SpTqcTQTWlUgl5KRpO\nJCYmtrS0IHk6bdq0Xbt2Ievq9/sdDgfDMDKZTKPRuFwutHNA/RyoW6jEgfSwOihtz1Kb8jwv\nkUjQA0On09nt9vb29h07doS2eaBFoCzLNjY2ghei2I5hmJiYmMbGRsreGIbJyMhoamqinJUy\nPKlUCjIaDAbB6liWjYmJQa6WluUplUrki3E4Uqk0Pz8/Kytrw4YN1AaFYZinnnoKeViv1+v1\neuGH16+/C/r15AcIhM/ofIBhGKFt7vlAxApLB8J9JIgnIhnhIXYLFizIysq65pprdu/effLk\nSfiVgPQQQoLBYGxsLMQKhBCWZVmWbWtrA49hWTYzM/PAgQMOh0On06FLRHR0tMvlio2NVSgU\nZWVl8CImPUTqyiuv3L17N81dEkK0Wm1HR4dSqURdnUKhaG1tpVVrhBC73b5p06bbbrvN7/dD\nCUHJolKphD+LVCqlzdDA5FwuFyyXQ8NsVf9502q12s7OTkLI+vXrFyxYADnILbfc8vHHHweD\nQRBcQsg111yzd+/e3NzcyspK6p9CCPH5fPPmzXv++edDdyGRSPbs2bNs2TKcH0KIWCzGCwED\nBwPh8TAQwLLsQFAuhxG9PMb9/fkY+zuE51EkIwzE7ujRo9XV1T6fD8oGiUTidrs1Go3b7YbG\nMxgMmkwm0lOghk5c4GSot/v888/1en13dzdYnUgkWrt27Z/+9KfKykrwNplMBhoHIcWePXuQ\nwFUoFCqVqrOz0+PxJCQk0DgcooaxsbEZGRlHjhwBZ7rllltApxQKBfxQ9Hp9Z2enSqWKiYmp\nq6vz+/2xsbGYKiJ/fr9fp9NxHAdWimI+SFa//vprBOFw1AzDPPnkk4QQENDPP/9cpVLBvc9g\nMFgslhtuuKGwsNDpdCIAmZ6eXldXRwjhef7ZZ591OBxghFji8/muuuoqHAvf02eWckEBvcdA\nyIkI0aAIB8/zEya8/ZvDhM+Iope3ZMRG7AbC147wPIpo8L8bH3zwwTXXXKNUKpcvX97e3j5j\nxgzE1a6++urhw4djL1AtICUvl8vvv/9+DEMKVSQSKZVKmUyGNCXLsklJSUajkSZV09LS5HI5\nyvISExNp5O/vf/87IUSn061evXrSpElnXWcsy1511VUymYz0hAnhpRdaGRCatxWLxQgxYjLI\n3s6ePZv0JFVFIpFGo2FZNjo6GptFOV1mZib+jY6OppuVSCS0rg4LlUqlVCoFqY2KisK/IpEo\nLy8Pv37oYIZhJk6ciNM7ZcqUsWPH3nnnnQhAChAgoH9BuHMFXHwQrupIRhgidk6ns6SkxO12\nf/zxx6tXrwY1CQaDBw4coLIDQgg6buGt9957D0wrGAyiWs5oNLa0tNC2sBzHoYgNEb76+nqt\nVuvz+YYOHQohrV6v7+rqQpDM7XZv3779wIEDtBUE6fH7/f777/EvInzYPliUSCSCyQgdT18j\n88vz/NNPP/30009jSl1dXRKJBKE+5F4xsru72+l0Yl26nOd5hAmh7YWeF9E+iGrRExZRwMmT\nJzc2NmJdvsfrePr06dgU/O1oM1kBYcdAyG0JuIAQ7tzzBOHOvYAQrupIRhiI3caNG10uV1JS\nEgxN1qxZs3Pnzi1btrjdbgTqaNcEvqenllQqffPNNzEMjRa6u7ulUqnBYGhra+N5HoJWiUQi\nlUpREoe0ZkVFBc/zDMN0dXXRAjufz1dTUxPagJX6A8PslwpvExISurq6qNuwQqHwer10elqt\n1maz8Tzv8/lAyJ588km6LuQOmFUwGIR0IyEhwWQygSlCSEuVIgsWLHjrrbfOIo4sy2J3PM+P\nGjXKZDI1NjYqFArwPAqO44qLi+lruL0Ice/zBOF7X8B5hXDnnit6a2bbu+YNT/yuufwvGAip\nWOGqjmT8XmJ39OhRk8mUkpJSX1/v9XqnTp36+uuvp6SkFBcXX3nllVTOmZyc3NjYiMAVmNad\nd945ZsyYlJSUpqYmnuftdjvLstTihBAChSxcEFmWBakCo8KYQYMGnTlzBtNobGyk7iewFGlu\nbpZKpenp6ZWVlZRWms3mW2+99e233wZPCgQCNEZoMBhiY2O7u7uxnVCaSAiZNm2aQqH4/PPP\nUXin0WicTqdMJrPZbFD7EkICgQA4H8/zgUDgrbfeIj15ZJTT4S3SQ3ArKioQlVy1ahWWwMEO\nwcJvvvkGBsWBQMDv91O1r4CwI7yVOgOh/3fEIjIfqANEFTsQjjFi0fcnX3geRTJ+r4/dhg0b\ndu7ced9991177bWEENSQrVu3btWqVQaDIRAINDc32+12WIGoVKr6+npCCMuyKC9zu92gMoQQ\nVL8988wzjz/+OLURAclLTU1ta2tzu90KhYIqZAHajow6xlHLYhrSUygU2FEwGFQqldQxTqFQ\n+Hw+sEakhmnaFAPQ34Jl2WXLln3++ecQWBBCBg0aVF9fj2HYi1QqJYQkJibW1dWxLKtWq8H2\nMEMaFBw/fvyBAwdCz7lEInnggQdeeeUVqDrUarXVamUY5o477li7di0JaSn27rvvCveSAAH9\nDkK4XcDFB+GqjmSEQbHM8/yUKVOGDh0KnuTz+RYtWlRaWtrc3JyamtrU1NTZ2dnc3NzZ2dna\n2oqgFM/zfr/f4/HwPD948GCdTieRSBDoevzxxwkhSqVSp9Pl5OQQQoLBYG1tLcr1cCXJZLLY\n2FjsHUxLqVSq1WoIL4LB4K233kq7nWg0mrS0NEJIVFQUIcTr9UZFRcE9xO1263Q6KDYGDRpE\nhbf/PjUsq1QqxWKxTqd75ZVXqqqqsC+RSKRQKGQymUQiUalUYKipqalisbi1tVWtVmPOOEww\nVLFYjLnt37+fdgkjPY3C3nzzTbBDv99vMpkQ2KM1dhQ/77ohQICAyIdw5wq4+CBc1ZGM3xux\nO3LkyNy5c6uqqvbu3XvjjTdCW4C4HXKpiJ9BSYCYHOgR6SlKk0qlCLABDMNotdru7m69Xv/1\n11+PHTv2/59rTwSO2uMxPd0mwNU6OzupN55arYZHCZUvgPPxId0pQreMWkBYH/M9Hiu0hSsy\noVTZEMr/8K9MJuN53uv1Yi9isRi2L2ftK/QnDl2u1WrhbBc6Jb1ef+zYsfT09OnTp6PzxOrV\nqwWnbwEC+h0Ej/7zhIi1OxkIEK7qSMbvjdiNGjUqIyPjsccemzBhgkgkkkgkcrkc0bhAIJCY\nmLhu3TqRSBTa3QH0Ti6XgzP5fL7o6OihQ4fq9XqM6e7uJoTEx8fPnTtXJpPt2bMHyymrA3nC\nElxbLpdr2LBhdFYcx9FMKKgex3G08RfP83FxcUieIpZGCIE4F0V+hBCGYaKjo5GlpSuCw8XF\nxSmVSgwTiURomAHmhxo7QohOpwutivvLX/6C1wsWLKCCZHpCUlNTCSH08IHXX389tPNEaKsM\nAQIECBAgQICAX0QYesWazeYlS5Z8+eWXXV1diHgRQuRy+UMPPVRcXGyxWCorK1Fkhu5eoTEz\nhmEyMzOhaQ1Vzubl5aHhBDKYHMfJ5XK6bnR0NGyEKcRiMQJ+hBAoYdFAIioqihqLgA5SDzz6\nGnP+xTAe6fErCV2CCZjNZnA4UNW4uLj29vbrr7++oKAAqlhU76GOEObDCEbabDbMUKVSuVyu\nYDA4duzYH3/8kfyngFylUp04cSI9PR01diNGjHj33XcnTlzXm0+k73+h9lbF1gv095/XgniC\nou8nFsbrsJfozfw5juvNndvfr/zeILwxNiFidwGB6vMLPYuIAHSThBC/3x8hUcwwEDtgw4YN\nb7zxhkKhKC4uDgQCHMeJRCK1Wu33+xmGcblcuA5YljUYDBKJpKWlJTExsbOzE+wnKiqqpaUF\n6ct/z4xhqDyWikY5jhOLxVKpFPQRtnAsy77wwgsvvfRSU1MTy7IqleqKK65AZwhK1/R6vUQi\nsdlsaWlpIpHo1KlTpCeLGh8fz7JsU1MTwzByuVyr1cJLxefzQQMBouZ2u5VKpcfjob2/kPCN\niYnxer3Nzc2hiVq1Wn3HHXe89957iD6C1CYmJjocju7uboPB4Pf7fT4fDvaqq64qLCzEvkBw\nqaAE79rt9tzc3PXr1wvFqgIE9DsIZebnCQKxu4C4+K7qESNGlJeXHz58ePTo0ee0YgQSu3BO\nQqPR7Nu3Lyoqavz48W+99dasWbMOHTqkUCjQWIwQEhsb29nZabFYwI1MJlNMTIzRaDx58iSU\nqmq1mnYhg7CAECISiVAAh4BcMBh0u92UtAWDwWAwuHz5csTVeJ53Op1gdaQnxqbRaEaPHn3g\nwAGxWFxVVYW30A2W5/mWlhbSQ8iioqIefvjhl19+GTqG2NhYt9sdCATmzJmzbds2GJGExvzc\nbje1XMFCiHkdDsfq1avpHGBfB9MThmE6Ojq0Wi0MU5KSko4eParT6cxmM84SVBTl5eXZ2dkk\npKUYuHIYP68+xgVxEw1j/KzvnyK9PGNf9cLNq78/2/q1FS3HcZMmrf/NYf39M4rYyPFAQN8n\nCvr78+hc4ff7wd76B369MUXvUVJSkpSUJJVK09LSkpOTMzIyqAnwwoULf07tEWkjhOTn58vl\nckIIwzCoe6MA00Lkb8KECVgITcNZl1QvY8JoVha6fTox+gKC2bMW0jmjXpDmZGUyGY0sEkJw\nIIQQpVKp1WrxFnK1WG40GnU6HV2OFRMSEgYNGnTWVNVqdW1tLc/z11577fjx4++55x6/3x+u\nD0uAAAF9BuHOFXDxIUKuaqrFfO211zIyMoxG49/+9je8debMmdmzZ8fGxsbExCxcuLCjo4Pv\n6X0gl8sxZurUqYSQrVu3Dh06NPT5u23bNrrltWvXxsfHT5s2jef5WbNmxcfHS6XShISEP/3p\nT9gmHRkh54QPS0sxYNSoUQaDQSQSmc1mh8PBsmx8fHxXV9fgwYM/+eQTNMWSy+U2mw1VazSh\nWVpaCt4jl8vdbjeYExzsOI6zWq0cxzkcjgMHDoAP4SSGilLpvwzDaDQarVbb3NxMelS01BUZ\n40NL7viephRyuTwYDFIPZLplpVLpcrl4nr/88ssPHjyIU6ZWq+VyOT5RhUIB8S8hJDMzs6Gh\nASvqdLquri6MR4EddqfRaBobGyGqhUmeRCLxeDxtbW0Mw6SmptbX10OBkZ2dHWqMIkDAf8NA\niJT064gdGRifUd9DSMUKoHjttddmzZq1Zs2ap556as6cOampqVOmTKmvr7/rrrsCgcD777/f\n1NS0d+9eDP55pGnRokWrVq0ym83z58+PjY3NyMigby1fvnzatGkIvlRWVs6ePTshIWH79u0b\nN25kWfb999/voyM8F4QzFXv//ff/3//9H6rf4uPjrVar2+02m83333//J598YjabbTabWCzO\nz88/dOgQXYsSKaw4efLk4uJip9OZmJg4efLkrVu30nI6SvjS09Nramqw1p/+9KcPP/yQ53nk\nQD0eD5qPkZ4kZjAYNBqNhBCr1Uo7rqampjY2NmIYyungtBK6IiGEWhmjwRfHcePHjz98+DDy\n6FKptKuri26kuroaVI/n+ba2NixPT09HBhYM0u/3U4UHDtzv98ORmBACexTklCmr8/l8Xq8X\npYph+JAEXHQYCM+tiGVsvQHDMAPhMxIwoBBpz6PNmzfn5+dXVFTs2rXr+PHjZWVl9fX1EyZM\n+Oc//0kI2b9//w8//FBWVgYH3J+n+FasWLFp0yaz2bx06VLU2FHR5KZNm2bOnInXpaWlKAyb\nOXNmXl5eYWFh3x3huSCcxG7s2LFisXj+/PmfffaZz+cDcXE6nR999NHgwYPNZjNYS3FxMU1l\n8j2qiPj4+NbWVshLMzIyysrKOjs7t27dajAYurq6wHholM5ms8lkMpCkTZs2QUtBaRzGIEB4\nww03bNu2TSQSdXR0MAyjUCjgtNfa2sr3VNphy7m5uZWVlYmJiQaDoaSkZNy4cZWVlVarlQ9R\nYLAsi0YXVquVEAKh65VXXnno0CHqioKpbtu27b777jObzQaDob6+HsE5nucPHTqUkZFBa/UY\nhrnqqqtcLldFRYXVaqVSX7FYPGnSJLym50qQIAkQ0B8h3LnnCQJdvoCItKsaJelarZYQ4nQ6\nLRYLIaSoqCgmJoaOqayspK0NAEoYfgWTJ0/GC6/Xu3jx4s8++wwEgBDS0dERntmHG+EkdvC0\nO3bsGCEENnIsy+bm5hYVFYHVgaNIJJLBgwdXVlaG5kktFgu4zkcffSSTyQgh4GoOhwOnXiKR\ngOR5vd577733k08+gWoB7cIYhqFOyPSXhN/v//zzz/1+P3QJPM87nc5Q5xS0uwBpKy8v53m+\nvr6+traWEHLo0CGU8eEQjh8/jrxtYWFhqC2LWCzev38/mk/AzAWHc/PNN8PcuLu7G5YrGD9r\n1iyVSgVGyHGcVCo9dOjQ/PnzodI1GAxut9vr9cpkMqrJoJIRnucnTHi7Nx9EZH7fRWw2Lbwe\nGYJ44uJAGNN8vbxzB8JnNEDsTnpz5/bmtiURfFXwEaaKxfOaTgntpsaNG7dy5Uos8fv9ubm5\nKpWKEOLxeNA9oaKi4qwt/LyjBi2d//DDD9evX3/ppZdu3ry5u7t73rx5fJhMRcKOMEtzN2/e\nnJmZ6XK5xGLx8OHD6+vrvV7vH//4x/z8/FWrVqHszGg0GgwGnBGcRJ7nYWsMDmc0Gm02W2Zm\n5vHjx+Pj45ubm30+H2SqCLA9//zz4E8xMTHR0dGVlZVoFMvzPBrCorJNIpGEdnSgMltUOEql\nUohwOe7/Y+/Lw6Oo0vVPVXV19d6dztpJgGwiGCKbKDAsCiPLICqMo+OCOjNu47496qhXR/CZ\nO9dlUEcUFb2KCy4DiIALA8i+J2whhEACIYTsne6k966u+v3xTp/bE2d+xqETusl5/+BpKlWn\nTlWf6n77+773/RSbzdbU1ITdKN00GAwdHR2qqoLVUT6n1Wrff//9G264AfNXFAUJYofD4fV6\nOzs70X/ivPPOKy8vR0UdPTYYDAYCAY1Gk5GRcfr0aTgP/+///i/uQ2dnJ6QboVDor3/96333\n3ZeXl6eJQlGUhH3Iu4OEzaYl7F3t5h17uqfn0ScRx1WR7E9uHNFH7kN3ntxkf2wT3MduxowZ\n/fv337lz55IlS/Lz848fP75y5cqamhqTyYT/3njjjT6f79SpU/SQ3NzcAwcOPP7440OHDn36\n6ae7tAwg0fBeOBx2u90fffRRr17PT0SciV1tba3dbocw2OFwHDp0aO/evXv27Fm5ciUUBqqq\nNjc3I4AJNoYD3W43XSVerzcQCFRUVBBCjh07BjkF8q0DBw6sqqoym81FRUU7d+5saWlpbW3l\nOA5cjRBiMpngQkcIsdvtYF3gUjRlTt8eRGuLi4tfe+21KVOmUI0FDk9JSQEvpGFFu93udDpD\nodBNN92E/KzNZkM0TlGUkydPqtHmsMFgsLy8HGeJtT6urKzEa3iskBi6SQiRZRmTjEQiCCnj\nLsmyjChmN3+hTm9+6Uf36X1XkW4iYflfNxFH3wEWsaNI2HBvN5GY4onev6sJG2OLL3rffISh\nC0wm07p165544onVq1d3dHT069fv2muvRezt7bffvvPOO9etW3fllVdOmDCB1sk9+eSTR48e\n3bRp04YNG+69994fErubb7557dq1X3/99VNPPXX77bevXLmyt6+q24ibQTGwePHiZcuWnTp1\nqrS0FFu0Wi3s4kCPqBzVbDaDgQmCYLfbm5ubkVSlQyF2JcuyTqfz+XzZ2dkowsNQJCY7zvM8\nAnVIbkKCmpKSMmzYsE2bNhkMBpfLhaBXIBAAQ6KEEuE0QRDS09MzMjL27duHASVJMhqNONBi\nsXRpdIEzUqqKa8Sf1KjtHMgoThRbpQe7E3BK/Om//uu/3nvvvVOnTvXr16+urg6+x7fffvtL\nL71ECEHniZKSkkWLFvUp3yAGhnMDkUiEPbk9gWSn+0kNtqoTGfF3Sd6+fTvs6PLz81NTUwkh\ne/bsIYSA1YGB5efng9mgKq61tVWSJBh/gLfdfffd77zzDlgRSugaGhqefPLJ1157LbbqDtHg\nW265paOj44svvkDkrF+/foFAwOl0btiwIRKJoPcD8rOCINx7773vvvsuPEoo91IUZeDAgQcP\nHsQlKIoSCASCwaAgCD/72c/Wr19PCJEkCYG0jo4Oi8WC+QuCMGTIkKKioi+//LK4uLiyshId\nIziOS0lJcTqdgiBkZGQ0NTUJgqDRaMaPH3/8+PGGhgaaMqZqX6oOSUtLu+GGGxCwJEw8wcCQ\n5GBPbg+BMbazCLaqExlxjth9/PHHc+bMmT59emlp6a233jpu3Lj58+dv3bqV0h1CCOJw9BBR\nFEVR9Pl8HMdZLBb4xuXn5//P//zP7NmzSUy4y2w2ezwehMrQjsLv9/M8X1RUdOLEiWAwCOII\njYXJZHK73aCA6enpJpPJZDIdOXKERNWsYFS5ubl+vx/5XEKIJEkffvjhnj17XnrppUgkkpmZ\nCbkGKGAoFEKtG44tLCysrq6mco2MjIy2tjYIb2lYEYWAUFHwPF9QUBAOh2tqanDVqPDLzKed\nL0cAACAASURBVMykeWFAq9UaDIa9e/fSXrEXXnjhokWL2LPEwJB0iG+v2D6SzWRIcCR4jV0f\nR5wjdtXV1Wi0MGrUqOPHj7/44osQNEBbcPr0aZoqhTcvZAc+n0+n0wUCAbfbDcnqsWPHZs+e\njddpaWklJSV79uwBYQJX4zgOETWO48aMGXPkyBFBEC677LKqqqrGxkYMC2c7QRCGDh3q9XqR\nVw0EAjgvfJJPnTpFk6eEkEAg8OCDD7a2toKu0Va2iA5CunHbbbe9/fbbiqKcOHFi6NChZWVl\nCKd1dHTIsuzz+TA9k8kky7Lf729uboZPCiHE7XYjAQ2lCEJ0HR0d8OqjiGXboIDUgY+BoQ8i\nqZNuqqrGkWYxxpb46As1duz7KJERZ2JXU1Oj1Wq//fZbSr/sdju6R0B+Ags3+LohGYpgHo3h\nBQIBBMA0Gs20adO+/PLLlpYWJEMJITzPo0EFZXg8z2/evBl1e0ePHq2rq0O0zO/3jxo1atu2\nbZmZmWvXrqVaB1EU09PT29raYEVD1RKiKOr1erfbja4VALgXPRdO19jYSLvZ7tu3j9JTuBbT\nPeGTDNGGoig0rRwMBnU6nU6nwwQkSRIEwWKxoL4Qd0MQhI8++ggexWCWuMD4vlkMDMmCpP4W\nZE9uX0NSL9dugq3qREacid3WrVsDgcCjjz66adOm/fv3n3feeX/605/sdvs111zT3NyM6B31\n8qUmIHD9JdF2sVartbm52W63f/PNNxiWZmPR+0ur1aakpKANFyGkpqbGaDR6PB509CKEgI3t\n2LFDVVXoT2lArqioqKKiQq/XI28L2ayqqqFQCClaqp/leR5SXEKIRqMpKiqqq6vz+XxfffUV\nzgJbk7S0tPb29tjmEEjUIiopyzJe4JDOzk4QUxQOwgAlFAqZTCacnd7JHTt2zJgxg0SlsrH+\nfAwMsUhMxWWyI44ZT47j2HvUE0jqrHRST54kXucJhljEk9iVlZXRdl5XX331vn37ysvLr776\narvdPm3atM8//5y2o6DowvpBdCBBjW3DQHUVfr9fr9drtVrYzlF7YfSTQGYWHDEUCoFCKYpC\n9bbhcPjEiROKoqBXGOS6JCrdhbyDWuspipKbm3v06FGkj0Oh0O23375w4UKaNgUzc7lcsixb\nLBaoNAghFovFarXOmjVr6dKliP9ptVqaeKUUDblgq9V67bXX7t69G255GCEYDNKexGCHsR3P\nfhR9wXyk968xYbOBCfvRn9QJqTjeVdZSrIeQ1Hc1qSdPGLFLbMRTPLF48eIFCxYcP348IyMD\nMgVBEIYNG3bs2LFQKOT3+yORyOOPP/766697vV6QMBj8xs4h1vWNbtFoNKqqFhcX79+/v7Cw\nEG1nwZNQzYa4F23/QAj54x//+Oyzz9psNrfbjWI7ErUpQTI0GAxCEgu7E8wkNTU1HA6DnvI8\nb7PZUJmHwB7mQ88CtQQCdRBt0AYjSDcTQkRRhHkKqKdGowHFjL1MhPRA/jA9URTHjx+/bt06\nQshll13W2dk5dOjQRYsWsWfpp4JFShjOOhLNo/+cQbIHvZIabFUnMuKcilVVdcyYMX6/v6Ki\nAqVphw8f/v777zdt2vTUU0/5fL4PPvjA4/FwHId+vTTCB9jt9uLi4s2bN+O/tGcDQmv79+8n\nhNTW1oIR0rwnwngIB1IDkT//+c8XXXRRTU2NqqqyLINI5eTk1NfX8zwPQ2PE9nieHz169JYt\nWwghXq8XnCwtLU2v17tcLkIIKCMhRKPRwABZlmVBEBobG+nMIWtFGvfmm28+efJkXV3dyZMn\n09PTT506hVYcer1+0KBBe/bs4TjO4XBgJqIofvzxx7/61a9kWU5PT29qahJFsaioCH57JNqc\nA41xmW/QTwX7TGc462BPbg+BPd1nEWxVJzLiGbErLS296qqrJEnS6XT19fU+ny8Sidhsts7O\nTpr0RAys6yR+EKUjhEiSBDYjSVIoFKJmv7Ela7GA9Pruu+/eu3dvWVlZrNexIAgGg8Hn83WJ\nDnY5HG52NTU1tDAOyg9aMwfDOfz3h+PwPH///fe/8sor1NUF3sWhUIhOmA5CR+A4rqCgwOVy\ntbW1xY6WmZm5Y8eOvLy8KVOmeL3eIUOGLFiwAPqMuCBhE4sMDOcYZFmO45PLQJGwEbukLkLo\nJtiqTmTE840ZOXLkBRdccOjQodra2sLCwhkzZrz//vtut1ur1dJcJDhWOBzWaDSQC4BsoVcs\nKtswGvSh0IRC3IDtdAfqRWc2m1NSUmprawVBWL58eWFhIRKssf3BZs2a9fHHH9MgH2by6KOP\ntrS0vPfeezQE2NzcnJOTI8tyfX09FLuIGsJCDxlbSumysrLmzp17xx13IFCXkZHxwQcf0PGR\nug2FQgaD4fzzzz9w4AAukOM4o9Ho9XpppzI0w429kxzHTZ06lapiaeQyjkj2jxUGBoY+joSN\n2LFPV4azizgbDC5ZssRqtUYikaqqqldeecXj8WRkZIwfPz41NRXUpL29PRAIwOMtEolQP5FA\nIHDdddehO5tWqxVF0WAwgNLBGQ5syWg0oqCNEGIymdAZIhAIQGkRiUROnz69adMmRVGwZ2Zm\nJtxPPvnkE5iSXH/99STaofWll15aunQpBkTq0+Vy1dbWQvEgSZJGo8Ec4HUMrxZ6sY2NjXfe\neSf8ltGRggYjwUc5jisuLvZ4PGVlZeifO3PmTI1GA8YGhipJ0oQJExARRCc73CjquhIIBAKB\nQBfNLAMDQ7KAPbkM5x7Yqk5kxJnYpaamdnZ2ms3miRMnFhQURCKRtra2TZs2ud1uk8k0evRo\no9GoKEp7e3s4HDYYDGazmUT7Za1evdrpdPI8/7vf/W7YsGEQW6iqetFFF2VnZxNCNBqN1+sF\nJxNF0WKxqKq6bdu2cDjs9/vhHjxp0qSUlBRCCDpYNDU1cRyHbmCEEFmWP/vsM6gl0OgCcUSw\nTFyC0WhE6UAoFEL7MtrmFeV3HMcNGDAgIyNDq9VmZWUZDAZCiNfrpdbEJKrPRSc0EuWgiqKc\nd955Y8aMqaqqgnMeLvy7774TRRG1gDhWo9GcFw3m05ZirKCBgSEZwZ5chnMPbFUnMuKcI9+w\nYUNzc3M4HN65cyeYViQS0el0YEhlZWXYCKoUDodRNqeqaklJyfjx49944w1FUd555x2r1epw\nOJqammRZnjJlyp/+9CckLiVJMpvNbW1tCM4RQjiOGz9+/I4dO4xGY3t7e2lpKQ2bXXfddUuX\nLr3ggguOHz9uNpsh1FAUZfjw4fv27SOEYM/YzCkhBE4ohBCLxSKKYmFh4e7du6GZhUEJIeTE\niRPYh+on+vfvf+LECVVV9Xp9MBjMysoaMmTI2rVr4ULc2dmJ3hWzZ8/++uuvIdrAv36/v6Cg\nwGQyBQIBDA5lxpAhQzAybYzBmi73EHq/Uidha4MYegLsye0hsOfoLIKt6kRGnInd/fffn52d\n3dDQIEnSJZdcsn37do/H09HRgQiWTqebNGnSd999B1M6tFUFzzt48OChQ4dINIk5cuTIHTt2\nIFlZWloKdxJCCHyASbRXBJK5mzZtIlGeZLVaFUXp6OhwOBwrVqwghNTW1vr9/rFjx27ZskVV\n1YyMjLKyskceeeSvf/0r+pKRGKtkEo3MIf0qSRIoI6Jx1ILOYDB4vd7c3FxCCDpqoDUZOl4Q\nQurq6hoaGhDtM5vNnZ2dmN7EiRNjm1jgAtva2jo7O0mMiITn+ePHj2M3nU4ny7JWq2VO3z2E\n3v/cT9hvGvZN2RNQVZXZ7jCcY2DfR4mMeKpiy8rKpk+fPn78+IqKiqqqqlidwaBBg1RVPXbs\nWOzGvLy82tpaErNE0Hkitu2YqqpWq/Wiiy5av349FUOgdu3kyZPUE5jEaCkkSYKHMPaUJClW\nIQtGCNfffylupUPFSiUg+AA5czgchBCQV1p112Uo0FPqeEwFHwUFBfX19cFg0Gaz0TxvSkpK\nR0dHF+tmSZIqKyt7ThXbR9AX5GkMCQ6mH+whsN8hZxFsVScy4vnG7Ny5UxCEL7/80mw2w2vX\n4XAYjcYjR45UVlYiQAWNJ4jOiRMnwL1EUYQs1Gq1ms3muro6nuctFosgCG1tbW63e926dSgy\n69+/P0JZBw8eRPBMq9Wi2wRVtiLbS6J9KcDqNBrNXXfdhVQv4nxwLcawJSUltbW1Pp8P1iQm\nk8nj8QwePLiiogLjxPbMQCKYWp8gcKgoikajwT7Q9uIaabAaIgyz2Yx8q9vtpkRQluWioiJY\nOgMcx91zzz3QWIRCIdgvs2LV/wCMtDGcdbAnt4fAGNtZBFvViYx4iifQUgISB0LI/PnzP/30\nU4/HYzabaQ97EDs0Y6AHUg9rMEJCyMGDB/1+v9FoFEURWlEUmbW2tuIQi8WCnwuRSCQtLW3C\nhAnoTpGWlgZXEVEURVFMS0uDv52qqosWLYLqFqeASJYQ0q9fv7KysvT0dMTwCCHoJFtZWQkj\nOpBFm83GcVxGRgbc9SCG5TiusLCQNpkg0eU+ZcqUzMxMnCU2bnfkyBH8V1XVv//974MHD+Y4\nbvXq1dXV1SSqkMAI2EIH5Hkes2VgYEgusCeX4dwDW9WJjLi9N2VlZTU1NTzP7927F0Tn/vvv\nnzFjRlNTk6qqr776KvgZiM6wYcMQohMEAZEzUKj29nZIIi6++OJgMDhkyBDwOUpu0tPT8Xrx\n4sV2u50QAvO5gwcPYkCv14uQGEKGBoMBhBJBQZfLBUIJcomQXlFRESHE7/ejJSshpLm5ecqU\nKVThgTnAG89isaAVLG1o1tDQ8Nhjj2EEDKjX66dNm9bR0cHzvMlkItGkcCxJtVgsl19++eTJ\nkzUazauvvoprfOqppyKRyNChQ7EbQMUTrKaBgSEZwZ5chnMPbFUnMuJWYzd37twXXnjBYrE0\nNzcjmjV27Njdu3eHw2GtVgulZzgcHjVq1K5du+hRkiTB9YOqB9AydcCAAdXV1ShTo+3CYnu2\nIoomy/LPf/7ztWvXYjSQKrvdDp/h2tpaBAhlWTYajXPmzHnrrbd4nqcj0KPgV2cwGBRFQW9Z\nEqOTRYCtoKBgxIgR2dnZCxYsyM/PP3nyJDUctlgsOp1Oq9XW1dURQjIzMy+99NIvvviC53mr\n1RoIBJAOVhRl7NixM2fO/MMf/oAxEbk0Go1tbW2xbwRKAw8fPpyXlzdp0iS/3z948OB33nkn\njiqk+Hae6E6xy/Tml+J4xm4iqYvWu/kefZPx6I/uk7DX2Pvo/cKsSCQyYcKi3jxjH0HC1tj1\nhdJepopNZMSN2N15552LFy+eNWvWypUreZ7v6OjIysq65JJLVq1ahSYTJMrxhwwZUlhYuHr1\nalmWRVEUBCEYDKqqmp+fT6Wg/zc/jqM1ef8OkiQFg8G0tDQkaqFC1el0gUAgNTXV6XTGKiFi\nk8KxeoUfCimohEKSJKhiu3Se6NLcgs6TyjjoUIgXImiXk5MDYxT61y7DAjzPb9y4cdy4cVOn\nTvV6vcXFxUw8wdBnkdQd8GRZnjjx3R/djRG7nwpG7Ch6/xcsE08kMuL2xqAB15QpU2RZ/vzz\nz1NTU2VZXrlyJeVk6KOlqmplZeWRI0dAqnQ6ncPhOHr0KCEEClkSYz4iiqIkSZIkoY9qenq6\n1+tFxhNxso6ODlVVYVnidDqRYEW3LvyeoA1YKdOircyw5fzzz4dqgfYZI4SAZmGHSCTi8/nQ\nuNZqtUL0gBq71NRURNpi+SIXBa2lw50hURFGQ0MD3FKQg9br9UVFReXl5Xl5eaqq4iZwHDd1\n6tRx48ZhMrRBbbzeLAaG5EJiMrZuguM4Rtr6FHp/ufb+AmPfR4mMuBG77OxsjuOWLFlSWVmZ\nnZ0tCILL5QKrQ5UlpWiogcPG/Pz8iooKUCIqIzUYDJCCoiEEpAyqqra0tOBcPM8bjcbHHnvs\n5Zdfdjqd2AgiBcuV06dP47+EEI7jEMMjhOj1eliW0JK1yspKHK7VagVBCIVCPM9rNBqQRZvN\n5nK5KHd0u912u11RlLa2No7j0NwCrI5qY0H7wO3y8/NdLpfX66VueTabDZbFJJrq9fv9MMxr\nbW1FXBDTmz9/Pl7LshwOh6mwg4GBIbnAntweAqPLZxFsVScy4kbs8vLy7Hb7li1b/H4/OoCR\nqGkcuI4sy+jfBe2nzWZzOp0ZGRm1tbVIxQaDQTiGeDwenU5nt9vD4XBnZ2dLSwt1pAsEAiB5\nnZ2dTz/9tEajyczMbGpqgoewIAhjxowBPcJQWHwI0RmNRpQAEkKQqKWTR4CNxtVAPTmOs1qt\nLpdLq9VmZ2efOHEC1M3j8QiCMGzYsHHjxrW0tCxZsgREkCokBEEwGo2tra2nTp06//zzOY47\nfPiwLMuqqra3t2PCsbfu9OnTOGPsfAYOHNhlS/cfpL5Q4ZHUSOrEIsNPBfsK7GvoC5/AbFUn\nNNR4oK2tbcaMGYIgwJVDEISCgoIrr7xSkiTKSARBcDgcoihmZmZqtVpE7KCcoPsgOEdiXFEw\nSUrsBEGAzhQ72O12vV7vcDhoFSdM7DiOczgcMDeJxR133JGVlYVTSJJ06aWXarVaQojZbL7y\nyitJNF5otVozMjIyMzMhvI3Fvffeq9PpwE31en1KSsqAAQNiSw1iZ4LtON2gQYPwAg3ECCG3\n3nprSUmJKIrogaHX6+kgmZmZmzZtwr2dOHHiyJEjb731VlBDBgaG5AJ7chnOPbBVnciIj3hi\n2rRpw4cP//LLL2FW0tnZSZUE4GroIUY32u32nJyc8vJybNHpdHAGzs7ODofDTqcTacpZs2Yt\nX74cLIoKHbo/YSpuQDcLEnU5wVDI58LlBK4rsSbA6j+3kcDNwuvc3FxYKMdKJSCAgCFLU1MT\nrSyE1/G/mxhYIE6dkZHhdDoRujMYDDNnzvz0008JIb/4xS/QeeLVV19lxaoMDEkHVmbeQ2CR\n77MItqoTGXF4YzZs2LBt27YDBw60tbUpiiJJEs/zaJMVDodpeZmqqojPBQIBr9eLzrCDBw8+\ndOhQdnZ2c3Ozx+M5ffr0oEGDnE4nZArwMVFVVZZlu91eUFBQWlpKyD8pWEVRzM3Nra+vp9RN\nq9UqipKbm3vixAlCiCAIWIKyLOt0OrSdAKH0+/2ojcvKyqK1eog4qqqq0+lQRacoClq1QsQA\nZz5QNzXqYEybmPl8PqvV6vF4wuHw8OHDA4HA4cOHQShha4eUNC4hEomIopiSkuJ0OmkFISEk\nGAzeeOON9DVw5u8UQy+gL2RhEhYJ626TsBNLanTH6IcQ8nRPz+MHYB8CDGcXcYjYXXjhhYFA\nYODAgd988w3HcZmZmS6XKy8vD7E36FIR9DKZTIFAAO2/1KjMU1XV8847T1GU6upqytgMBoPP\n5+M4Li0tDYwnPz8ftWgcx+Xl5dXX10ciEWgdwBdBfbpcDnWks9vt7e3tYGyxBnUOh8Pr9SLP\n29zcrKqqJEmRSMRkMtFGYR0dHYgamkwmaCn+7/ZF+6F1cUsRBCE9Pb1fv3579uxR/1kzG7sb\nQn2ZmZnNzc3UXQ/ai40bN44fP54Qcumll3o8ngsvvHDRokXM7JuBIemAWP7ZngUDQzzBVnUi\n40wjdmVlZU1NTShrmz17ttvtbmpqam1trampCYVCGo1Gr9dDG6Gq6siRIzdv3kyivm5IXxJC\nxowZs3z5ckKIRqMJh8OIbAWDQVEUoZyAPwicShwOx8mTJxE8o0OhmI9yprS0tFAo1NHRAZmq\nIAhUdQGjY0Tj9Hr91VdfvX379n379lHiFQ6HjUYjjFTwAgeGw2HYtRBCzj///KqqKlVVbTab\nXq93Op3PP//8448/znFcenp6Q0MDIaS5udntdhNCTCYTDqTZW5PJhDYbmK3JZGpoaOhC+KZP\nnw4PFKrtTeSniP1Cpej9WxHHM7LcVk8gkZ9cBob/DGxVJzLONGK3ePHiBQsW7N279+233169\nevXChQuvvPLKbdu2gY48/fTTb7zxRjAYRE4W/A/py2AwWFRU1NjYiLwkYmngYWjMCrfh2OYN\nU6dO/e6772LFOAj7qaqq1+tB3UD+oEv1+/08zweDQYvFQgjp6OjAUVqtFk0jSHR1UlsWWjaH\nwVNSUmJDdEajEQncO++885133qFOdYQQWgUYyy+x0WQyhUKhUCgkiiIKTmN3E0VRr9ejKhFH\nYQ6LFy+eM2cOiYnYJXLnCQYGhn8H5tHfQ2AGxWcRbFUnMuJQY6eqqt1u37BhQ2lpaUpKyrhx\n47Zv347t8+bN4zguOzubVomBtSB3KQiCKIqEELQgIzGMCm5z2B+RLZ1Ot2HDBlA3/BXFcFAe\n+P1+JDHVqCUepXE4ncVi8Xq94F6oxqPjd2FUdA44RWyDCmSHOY5bsmQJdkbTC6zviy+++MCB\nA4FAABeVkpJiNBpPnTpFxRPwsXO73RzH0THD4fA999zz+uuvow9HOByGUcuSJUtA7ERRhMde\nXGQuDH0T8eXx7FfBTwJ7cnsI3exSSEhvE7u+sPLZqk5knCmxKy4uPnHixOjRoyORSH19vU6n\nQ1CK47jCwsKGhoZAIHD69GksArjH6XS6/v37Hzly5NixY+A36NlF43lo2Eo7g0mS5Ha7NRoN\nGJJer8do48aN27hxI84FKIqCTG6XqFggEEDADDqJzs7OQCBw2WWXrV27VhCEtLS0pqYmQRAg\nYqCCVo1GA6UqidFkoECQssZQKITQoFar3bdvHygj9mlvb4dnHoDJ0GQuvVhCSGlpKaQeIKyo\nI5wwYQI9sCfysH3ho4eBIr5vN1s8DIkAtg4ZGP4lzpTYjRw5sqCgYPPmzQ0NDbW1taWlpTTY\nVl1dDU+4YDAI02D8ye/3nzx5Eu3CwNWQpUUdHi28Q2ewYDCo1Wq1Wi31E0ZLsUgksmnTJiq2\nRb8vSZKo1Uis1wnP8yBbHMd5PJ5AIMDz/Pr16wkhkUikublZo9HAeZhmY/GnrKysI0eOIGhH\n5b2gpzQ0iEuggTpYq3AcRyNwPM8jsCeKIgKEcHgBVRVFMSMjY8uWLXq9HnFH3IcVK1Y88cQT\nhBA04cD8z/DN6jl0M4TTHST7h3VSZ2FYKK4nkMhPblIjYVOx3UGyP2tsVScy4pCKXb16dUlJ\nSUpKSjAYTE1NJYT4/f6MjIzW1lbUxlmtVlVV/X5///79XS5XZ2cnkq0wGQYzI4RYrdZjx44N\nGDAAZW2KoqByDuExm83m9XrD4XBaWlowGPR6vSBJI0eO3L59u0aj6ezsNJlM6enpbrc7GAwi\nsBcKhWRZHjRoUF1dHbqKIWZGRRUQYcAAhRbAmc3mcDgcCoW2bNkSm58lhAiC4Pf7kVHNzc0N\nhULXXHPNunXramtr/X4/iTYKAxNFpA3OKbm5uZMnTy4tLS0tLYU9ChK4Y8aMeeKJJ5YtWwYJ\niCRJqLfDPaGnTnDxRHc+feJI/rqP3qdZvf9BHEcrjYT9Fklq8DzP7E4YuiDZn7VE/j5iOFNi\n53Q6H374YUVRQJVcLhdymunp6X6/Hxyus7NTEASLxTJz5sylS5c6nU6E7iBQlWX5wQcfXLBg\nQVNTU0pKCqJZiJClpqa6XC6ExFwuF0Juo0ePXr58uV6vj0Qisizv2rXLZrP5fD5ZlhsaGjAN\nURTnzZu3YsWKTZs2kWhDWAwriiJifoSQ1NTUlpYWVLAZDAbINQghwWDwhhtuqK2t9Xq9u3fv\nRrYXwTwYoLjdblDVJ598ct68eQ6H45NPPrnmmmsikciECRPKy8vRoEySJKg0QqHQzJkzX375\n5Ztuumn37t0kGlC0WCwDBgxYsWJFQUEB3F46OjokSUpLS3vssccwGUwVYbxx497uzpuSmN8Q\nZ+WDLI4nTerwAMNZRB+pRurmA9IdxPchYqy6J0ANyxgSEGeqikXPiXnz5t1zzz1ffPGFIAjo\nZ6/X6+E2ArMSu93udDrh90tN7CwWi9FobGhooHoIQgjKyzZu3PhPs+Q4SZJSU1NbW1t5nqd9\nLH7/+9+//fbbKSkpra2t0HBAaSGK4jPPPPPCCy+AWSK9++yzzz733HOzZs1atmwZZgW2BD88\nElP3hkNwikgkYjAYnnvuuSeffJLneTjn0d4VJCq/iL0EOmeIf+HYTBUhiA5mZma2tbVBMqLR\naNRog1qKFStWoMvZ5MmT0Xli4cKFzOmbgSHpwDz6ewjdpJLd0Vgke/ys91MTbFUnMs6I2JWV\nlV177bVVVVU8z8+dO/dPf/rTzJkz6+rqdu3aNWrUqGXLlo0fP/748eOxhIlEzYcJIWCByGCq\nqopuE/92ojE2IvR1F7/f3NzcpqYmQghIkqqqU6ZM+fvf/x67DzSnXa6a47jBgwdXVFTQAVNS\nUhCWwzgI1+FYEvMTHD9Z8vPza2pqCCHFxcWHDx8WRRFVg3q9HqIQqrHFTcBolOCiuq7LlCwW\ny/79+/Py8lhLsZ4GC8X1EFikBJBleeLEd390t75wK+IL9uSeRTBil8g4I2K3ePHiVatWff75\n54SQ0tLSsWPH5ufnX3nllfPnz4deAQwGXnS0ZxfVH2g0GigJAPLPRA3HUu83nufNZnMX5odu\nEH6/n4bcRFEURTEnJ+fo0aN0qEGDBrnd7vb2dlglxx6O1G0XykVdTih9RFCNBuqsVmt7ezuN\nRaNrGY1LI7+M0rpLL710/fr1mJ4gCGazGdlqEq3GI4SkpqZiNAyOkF5BQcGxY8dI1MeupKRk\n0aJFzDeIgSHpwBy/egiM2J1FsFWdyDhTxk150siRI0tKSg4ePPj666+rqjpo0KD6+nqoHCKR\nCMzbqPATETUoPbVarU6nQ18vuBOD5OFf7AO+BVaH+B8IH90ImEwmjuPgmYfRdDqdwWBoaGhw\nu906nQ45ViqShT4jNkFMTw16h/nA6CSWFPp8Piq8wDiCIGg0GoPB0NnZGQ6HU1NT29rasrOz\n77///t27d6OvWv/+/b1eL4myNzptnU63ePHim266id5PvV4/ZcoU/DUpxBMJi6TulKb/WQAA\nIABJREFUA9FN9H5gjH2h/iTEVzyR1GrK+K6chF1g3XmPutnoNjHdlQkhT1VV9fRMGP5jnFHE\nrrS09Nprrz169Cg4R1tbW0lJSWtrqyzL0I2qqjp+/PjNmzdTbzkExnQ6ndVqhc0bqNX/TYjj\nEOBFpBeRsMGDB7e2tra0tNDAniiKBoOBNnjVaDRoGqvT6dLS0tra2qgSgud5tKPAf7VaLSEk\nHA7r9XpFURwOx4kTJ1RVlSQpNze3paXF4/HgLHq9XpblgQMHVlVVybIsSVIoFMJs0eKMRFkm\niVbaxYYbabSP+qfk5+e3trZ2dnZKkoTQHQjutGnTZs+efccdd8TeW6PRiJZitPPEu+++G8di\n1aT+eug+EtN8JL4C4WR/j855sDLzHkLCfogl5sdOfMFWdSLjjCJ2I0eOLCwsfPLJJ+fOnavV\nanfv3l1UVOR0OiGP5ThuypQpN954IxpRUG3BJZdc8t577w0dOpQQoqoqqtDwJ5oAhRaBihIq\nKipEUUTmVKPRaDSaQYMGVVRU0NI0UDqv1xsMBufMmfPKK6+QaKZVr9fzPJ+fn4/urnAqJlE/\nvJMnTxJCEEScNWtWQ0PDJ598gpmgI9nhw4dxCrSv0Ol0mZmZubm5W7duxVGQWdA2aBAFt7a2\narXatLQ09I0FPB5PMBiEOR9e4NT/9V//9fvf/578c+3gwoUL8/Ly6LGIJsYx9J3sHyvdBPtM\nZzjriO+Ty0DRzaDX0z09jz4JtqoTGWeqim1ra3vwwQfXrFmj0+n8fv+sWbNuu+228ePHy7JM\ne6eef/75jz/++G9/+1uoREm0fi4nJ6e+vp4QAiPf2Hq1LmdBWy2v12s0GkVRdLlcXXYYOHBg\nW1uby+VCgdoPZRYkmtPEWqSnSE9PhyTC6XTKsoxksSAI2dnZp0+fhh0dRpAkadCgQbBWRu8K\nGqvrek//zQSMRuOSJUvuuuuu06dPk5gcK2xfYmfOcdxll122bt06QsjUqVO9Xm9xcfGCBQtY\nsSoDQ9KBlZkznHtgqzqRcaZvTGpq6ocffkiiCtk333zzo48+UlUVyUpEa+vq6h566CESzVdC\nPOHz+WjHLUmSqNqU4zjoIQwGA+Sl8ITzer1d3OYwFHiVx+NxOp0cx6H0jU6vC22FqwitV0P8\nD7YjlJ9ZLJZgMFhXV4cgXGNjI84VDodramq8Xm9JScmQIUPWr1+vqqpWq0WIEYna2HZksiyP\nGTMmPT191apVaIYbCAR+/etfU2qrRntXZGRkYM4XXHBBdXU1KvzoZcqyTFkyAwNDMoIJhHsC\nrNaTgi0whljEjXGXl5ePGDGC5/nt27fLsrx58+Zrr722vr4ejAR+cuipFYlEaPqVxqgQqEMv\nCr/fbzKZcAj+hORmRkZGY2NjbH0bTfMjBvbDrH+s1YjVavX5fOj3VVhYWF1dTQhpbGyM3cfv\n98N+BWlWGhpUoz1nCSFHjhwBORMEAf7DGLylpYXujKsOBoPr1q2jZn6BQAA8Ndb8RRAE2i0t\nFAoFAoEhQ4aA4WGjoijgnUw80deQsCVEDD8JPM+z79SeQMLe1d6vx+j9W8G+jxIZ8Qylgqk0\nNzfzPF9QUODz+cBv0D4rEolYLBb8lRCiKEpKSkpLSwvP8w8//PCbb76JGBVkCuedd15DQ0Nj\nYyNtaMHzvMViaWpqKiwsrK2thcyW2pSQmA4NBoPBYDA4nU4UAYAvpqenZ2dn79u3D/ujtI4Q\nYjQaZVmGijbWXg6jBYPBCy+8sLq62uPxWK1WxA4zMjLq6uqQPs7KympqalJVta2tDZ580Pki\npRuJRN566605c+aglNDhcBgMhpqamlgpiV6vv+KKK1atWuXxeOBvUl5eXlVV9eqrr8beW6rS\nYOg76OZHf8L+WE/YifUy+siTy9TZFH2htLePrOokxZnW2FFAIbtz586xY8ceO3ZMEASr1RqJ\nRFwul9VqhVwACUpq9tGvXz9ZlhsbG7Ezx3FOp1Or1SIb29bWRu3rIFBwOBwVFRWEENjgxS4s\n5EC1Wi3Ur0ajEUE1iGfb2tqwBXnY2NghBkH93MGDB6G3AIsKhUJdDOcASZJoOpWywNhgISYM\n6gnvPdxkq9Vqs9lqa2tj+WhKSsrs2bPfe+893BwSZaiVlZXnn38+ifrYXXjhhe+88w4rVmVg\nSDowx68eQsISu74AtqoTGXEjdoSQKVOmVFVVhUIhKgWFrDUtLY0QErvRaDR6PB6LxYIer3R7\nWlqaXq/3er0ejycUCoEt5ebm1tXVxZ7IaDQqigJuF2tlAjO5YDCo1+shO6W+weCIKNojhFx0\n0UUnT55sbm7u0ruiC0wmUzgcDofDsWcxmUySJDmdzoKCgpqaGuqBJ4piUVFRbW1tIBAA0dRo\nNDNmzFi1alUXauhwOBobG+l5qXakuLi4vLzcbDZTyxXcVXSeYOIJBoZkBCszZzj3wFZ1IiNu\nbwy0C7W1tf8YV6N56qmnXnrpJUVRKKUTBOGBBx5466230tPTPR5PR0cHtnMc98wzz7z00kte\nr7e1tZXjuNTUVI7jLBbLqVOnqHyBCmYfe+yx5cuX79u3j5oDz549+8svvwyHw5FIBM1kBwwY\nYLPZDhw4gPo2UKVQKKTVaq1Wa2VlJWrpfvazn23fvh2cb+bMmV999RW2k6hgQqfThUIheLWA\nwPn9fhT50TI4ULRwOFxZWYmUbmZmZkNDg6Ioq1evRnkcOChyvk6nkza9IIQ89dRT8+bNi0Qi\n5eXlhBCPx0MIeeONN+6++24SjQsmeNybeXxQsFvBwNDHwT4EGM4u4haxmzZtms/n27NnD5w7\naAdYQogkSVqtFrJQGt/CbkajkYpnu+wcDAZNJlP//v3Ly8spDdLpdMFgcPDgwVqt9sCBA0ho\nnnfeeTzPt7S00MZchBCdTkei0gSe52+++eb33nsP21VVpXoFZHVRaZeRkQEBRF5eXmVlZXZ2\ntizLLS0tsbbDhBAIO/5x+zhOURStVgt3OkII4naVlZUIuaWnp2NMVBmCn9HGZdiSk5Pj9/vR\nnYLihwbFLBXLwJCMYEkrhnMPbFUnMuJD7OB1MmnSpPfff7+wsPCDDz64/vrrH3jggccffxyd\nuDiOGzp0aFVVlclkcrlckKbCLuThhx9+8cUX33777UceeQQqVLSRQLtV8s+WJaBH+HfYsGHl\n5eUo2kM2E8GtvLy8Y8eOSZKEXC3kFKFQiKZlTSZTYWHh/v37Y1t7SZIE8SlIJKbx61//evHi\nxSRaKJqTk9OvXz+tVrtp06ZYp7qKiorS0tKbb74ZB4qiGIlEQN2WL18+e/Zseha0wRAEAbdF\nVVWTyTR16tQVK1bwPC8IAo0XfvTRRzfeeCMh5LLLLuvs7Bw6dOi77/54H3EGhsQBE08w9ChY\njR0Dw79EfIjd4sWLV61aZbfb33777RkzZtjt9s8++0yWZZ1ON3ny5K+++grtImi7VUmScnJy\nampqJEnKz8+vrKycOHHisWPHkL4kMYob6EbhEoJAl0ajQZLUbDYjciaK4nXXXbd69er29nYc\nJYoiaBPifJBKKIpit9s9Hk84HKZNw4LBIGyT+ShgSky7SlDn5DFjxuzevZsQQhUYHMc98MAD\nr776KjXtQxgyto2sqqqDBg36/vvv8/PzUTWIqyCEIEBos9mefvrpRx55hBAiCAIOkWX5rrvu\nevPNN0mMeGLRokVMYc7AkHRgXkU9hDi25mOJ0Z8KtqoTGXGrsVNVNTs7m+O4b7/99sEHH1y1\natWMGTOCweDatWsJIQiGIWXp9/uDwWBTUxMhJBwOt7S0DBw4sKKiorW1lWY558yZ89///d8D\nBgxATpZ6+RJCwJkIISh043k+HA6DR5JoGRyifR999NE999zjdrthoRIIBJDupKxx0KBBnZ2d\nTU1NyBELgmCxWNDL1W63NzY2gsOJohgKhQ4cOEC7ohFC0Ovs888/J9GqOI7jMjIyTp8+TeUg\nlDSPGjUKyV/0mcAL3AGn0/nyyy+DXMa23LjnnnvwAooQmr2N1/vFwMDQO2BPbg+hmy3FWMSu\nJ8BWdSIjPhE7eJ0888wzDz30kMvl0uv1kiR5vV4wIerE26VrFm0dYTKZYHp36aWXbtiwobtT\nj8pd6RYEzBwOR0tLSzgc1uv1sVV9dLfs7OyGhoYuF/6rX/1q1apVoVAI8gtFUebMmbNq1Soa\nBaRROpPJBEc92sSCZlqzs7Obmpoikcjtt9++Zs2auro65HYlSYInM21optPpIpEICKjJZAqF\nQlR1C/A8v3HjxnHjxiWFKjZhk269/5u+9+umE/bm9z4S81b0Ef1gNxOj3UE336OETcX2hQ+B\nPrKqkxTxeWNGjhxZWFj4/fff8zyfk5PT3NxMe0sQQkRRNJlMXq+XBts4jkMPLtiIeL1eROOQ\n6+zSaxWxMagc6BlTU1Pb2tpoDzEcEolEdDrd3r17s7KyCCGU1WG3rKys9vZ2n893+vRpQRBi\n+8ASQpYtW6bT6UANMRm0SqMdMkDRIpEIGkigDRpldditoKAAPTDeeecdOn/kc0nUpSUSiej1\neo/HYzQakVNGiJFEU8O0uXJubi4hJBQKBYNBVCXG5c3qCSQ1b4jvJ2wcR+smK93KskhRxHEd\nxrHtRyI/uXFEUn8IJDumN7/Ujb3i+Qb1kVWdpIgb437jjTemTp0KsmWz2VDZBiGC1+tFGA9M\nKBwOG43G4uLinTt3BoPBkSNH1tbWou0EsqsQVQwZMgSGJujlYDabMzMz6+vrCSEajQa9KGiC\nMi8vb+DAgd99953f73c4HDRKx3GcXq/3+XyKojQ2NqakpAiCgP8Gg0FobE0mk8fjGTt27G9+\n85vbbrsNl2OxWBRF8Xg8GAETUxRlyJAhlZWV4XAYAbZIJKLRaGCe3NzcvGXLFo7jLr/88jVr\n1vTv3z8tLa2srAx7chwXCASsVuvzzz//0EMPqaqKBO6wYcPy8vJ27NjR0tKCQkCcqKKiIi8v\nj0SfH1xsvN4shqQAq/s5i4jjzY/vk8sazTH8EL3/drPvo0RGPO1Ohg8fPmnSJIhAA4EAIm2i\nKA4bNuzgwYOqqoZCISodsNvtJpOJ+t5RvPjii0888QSJVtQhhAYxxDXXXPP3v/89EAiAZmm1\nWlEUIXSYMmXKt99+S2UN/7g2jrv44ot37typ1WoFQQiFQtQ6DnwxNzfX7/e73e5wOAzlLOYs\ny7LBYKBNY8eNG3f48GF0P4O1MsaPHcpkMsHPRVGUoqIiNAeLnQxIpF6vz8/Pr6iogFbX7/en\npKQYjUa4AKKgkBDC8/z3338/fvx4wsQTDAxJDlaN1ENI2FRsXwBb1YmMeNqd7Nix45FHHvns\ns8+QWywpKSkvL0f4DeLTcDh84YUX1tfXt7S00Aq5/v3719XV2Ww2t9sN0SvN2BoMhlGjRsH3\nuKGhgeM4CCnMZjNUCLAOppcwefLkL7/80mw2E0JsNltHRwft30AIkSQJGVVZlpEdbm9vxxkl\nScrMzKytrTUYDIjnjRo1Ch3GqOkdEr6CINx3333r16+Hix5YHc/zWVlZjY2NmHn//v1bW1sV\nRcnNzR08ePCqVatiuWaXmj+dTrd69eopU6ZgOwioRqO57bbboIq9/PLLPR7PkCFDFi5cyHyD\nGBiSDszxi+HcA1vViYz4pGLLy8sHDhw4YMAAv98PMqQoytFocDgQCIDVYWMkEjGbzVBLSJKE\n5lpgdSRqJoIDjUbj1Vdf/dlnn9XW1lK7Eywmr9dLNaS0o+uRI0dycnKwkXZoHTRo0JEjR1RV\nhXGJyWRyu93oXYs9ZVm2Wq3Hjx9XVZU6D+/atStW50FfEELeeOMNHIvurr/+9a//9re/UVZH\nCDl58iQGsVqta9as6cLqIOkghPA8jySv0+lErzNU4OFPX3zxxeOPP56Xlwd/O7SXjcub1RNI\nWN8BZgHPcNahqmpiqjriCyaeoOgLHzuJ/H3EELcau7Vr1wqC4PF4AoHA0KFDT506RaKkftKk\nSVu2bME6gD0v8pKRSATKUGxHLZooisuWLbvmmmtCoZDT6XzllVdOnjyJir1nn3322WefxZiU\n/2k0mksuuWTLli2EEEmS0KZs0KBBlZWVaGtRVVWFTC6M9NxuN4nmTzEZaoPC8/yjjz762muv\nTZo06euvv45tOIFQHyKR4F5oVhGJRJYuXYqMs81ma29vdzgcTU1NcNErKytDsBoVcjgwEolQ\nVxe/38/z/PXXXw9SCHEGiZbu4exge4jnxevNijsS9kMqYScWR8TxWyRhvynjiN6/xkR+cuOI\n7tyxOJK/7qP3WXXvf+z0/jX2kVWdpDjTVKzT6XzooYeWL1/e2dmZmZm5fv36hx9+WFXVjRs3\nIkSHTCXP8zabra2tLTc3lwa0/m8S0SWi0Wi0Wq3BYLBYLDU1NXa73ev1olwvEonY7XatVtvc\n3Az2YzKZhg4dunXrVtorIjs7u76+vn///qdOnRJFEZ55JFrmCebE83woFJo7d+6zzz5Loi3F\nkCzW6/VDhw6tqKi4+OKLa2pqampqECdDNpl6C991111//etfc3JyoOQghOTm5tbX18feyZSU\nFFEUm5ubcXWpqamhUKijowN3Iy0trampiaZlCSFGo9Hv91PblFhjZHSeYDV2DAxJClaNxHDu\nga3qRMaZRuxuuOGG4cOHX3fddR988IEoirfccsvevXtBgMDhEJ/z+/1+v19RlPr6emROEUKj\n5m1gOYqiBAIBOBirqupyuW655ZYvv/wyPz+/tLQUCgOsJ41GM23atKlTp27dupUyKpfLJYqi\nXq9XFIW2lyDR+Jwsyw8++ODnn39+6tSpNWvWDBo0qLq6Gt0gMA1Zluvq6u67777XXnsNjFAQ\nhOzs7JMnT6LADhnY119/Ha85jkNNHsKThBCLxRIMBgVBoKJdEnVsQbc0eLIgGR0IBMDeVFX1\ner08z0NOgX0yMzN37doFYSy9hDN8sxgYGHof7MntIfSFAHPCgq3qRMYZReyoZmLs2LHV1dWg\nXHA5ycvLq6urg0Fd7ApQVRWiUQThSJTh0YZjeB2JREB6Yo3iLr/88qVLl9rtdp7ng8GgRqMx\nGo2EkEAgMGbMmJ07d8b60oEJ5efnV1dX/+EPf3j55Zdp81nUxiFaptVqMzIympubg8GgzWZz\nuVy0om78+PGHDh3yeDyKohgMhs7OTmy3WCx6vR59IwRBsFqtPp+PVuzRyxRFMS8vr6amhhAy\nYMCA06dP0xYaUIrE6idQdZeWluZ0OhEjNBqN5eXleXl5UMWWlJQsWrSIFasyMCQdWJk5w7kH\ntqoTGWdE7NAitqOjQ6vVfvPNN6IolpSUVFZWUmMRkDOr1Qp3YtoHtstJkZ1sbW2VJCk7O7u2\ntpYKI7ooGBADi9W6YrvZbIZhXuywIHCxe9KAH8gTLOgyMzPRqYJEf4UgMxs7T+rS0mVKoJ6x\nZ8HhJpMJ8UVCCKaNRDCc6rpclEajoQ52FDk5OVu2bMnLy0uKzhPdAfPfYuibkGV54sR3f3Q3\nFlj6qUjYiF1fEE+wzhOJjDN9Y9rb248fP15VVWW1WrVabU1NTWdnJ97v999//+abbyaEIAsJ\n+jJ58uSNGzdC+gDTOEIIsq5IiR4/fhwWJLIs2+32hQsXXnfddYjb0T4TlJlRhtTZ2QkWpdfr\nbTZba2srBA2UcvE8n56e7nK5gsFgeno6qt/ApRAk+8ft0Gio+TDkq5SMwi0lEAggZ4rsqqIo\noigipiiKIvVeoY3ISExATpZl/MShhA/zNxqNbrfbZDIJggBthyAIS5YsQR42EsUZvlNnHcn+\nQcbA8B+DkTYGBoZewxkRu+Li4v3790+cOJHn+WeeeebZZ5/1+/2oZhszZswNN9ywZcuWhQsX\nYmdwrO+//x7UBxEsbFRVNRgMoh8XcpqgceFweMyYMZIkgRghBBhr/EHjXqIowv0YITTwwlhT\nEkVRmpubwaVaW1upBd2NN9748ccfU9pE43bUYYSLNiujtaKRSASaVpwLZwFPReMvmvMFYrO0\nNGhH5y9JksFgcLvdo0ePPnr0aEdHB45dsGABDIohHO6S0WZgYEgWsCe3h5CwdLkv/IhlqzqR\ncaaq2JKSknA4fODAgYMHD/785z8vLi5esWJFYWGhx+PJzs62Wq3l5eWEEFEUYT4iyzIciUFu\nQMUikcj27dv/9re/zZ8/H051IEmqqo4ZM0ZV1R07dqSkpHg8HnAsnBozT01NdbvdCAubzWa3\n233JJZeUlpbSKBeYmSAIqJMjMfYlhBCTydSvX7+KigpCiE6nM5lM4XD4xhtvfOutt9DxAiNT\nwoehJk+evH37dmp6RwjJzc397W9/++c//zkUClHXErC9p59++rnnnlMUJS0t7fnnn//ss8+O\nHj2KfrWyLPM8379//xMnTtCjkLpNSUnZs2dPXl4eVLFDhw59990fz+acLSRs6qH3XQD6Qv/v\nhEXC3oqEnVgvI77JU5aKpYjjAkvYu8rQfZwpsVu/fv1VV11lMBh0Ol17e3taWlplZeWmTZuu\nuuoqURR9Ph/K7OAM7HA4jhw5Elu7VlRUFAqFTp48yXEcRBWQHWg0mssvv/yrr77KyMhoa2uL\nRCJpaWltbW1msxl0io5QU1MzduzYxsZGsChVVa1WazAYNBqN0DcA6FQbCoUkSRo+fHhWVtba\ntWtNJhNsR2i+NXZ8Ok+LxVJcXNzc3FxdXU0IKSwsjEQip0+fliSps7MT1YHvvvvuwoUL9+zZ\ng9Mh2ocwG404chyXkpLicDhqa2vRhRZnQaJZp9OFw2FwO61Wu3fv3gsuuIDEtBR75513WLEq\nA0PSgZWZM5x7YKs6kRGHlmJTpkwZMWLE3Llzt2zZcsUVV2i1WqvV6vF4TCZTXV0daBPHcbm5\nuaFQSJZluAEjGKbT6bq0cI3VJSCYR1+joA0HQtyQkZExb968u+++Gxa++/bte+GFFz755BPy\nr1QXKMuDDMJqtUYiEa/XG6uiTUlJmTFjxuTJk2+99VZCiMVi6ezstFqt/fv3d7vdra2tVJzB\n87xWq73kkks2btyISNsPFSEUqL3Da8QX4aWH7VqtduDAgYcOHcKY4HYajWbZsmVXXHEFIWTa\ntGloKfb666+zYlUGhqQDKzPvITA91lkEW9WJjDgQu7a2tgcffHDNmjU6nc7n882aNev111+f\nNm3avn372tvbkYmH5ZvH4wEN0uv1Go2GRq1+6hwoi0K7Laq3jf3TvzsklueRH/RF4ThuwIAB\nJ06cIIT85je/Wbt2bX19fRc/PEKITqfjed7j8WC7VqslhAwcOLCqqgpldhgWhXqxJXeIz+FG\nIVyn0Wi+++67yZMnq6pqsVg6OjowN5BFQsg5o4plYOibYF+BDOce2KpOZMThjUlNTf3www/x\nGiTP4XB0dHRkZmbC4E2r1SI8hn0EQUArrVhW14V4/X9Oh/Av3PI0Gg1kpISQAQMG1NbW0mMl\nSYIwFmJbeOOB0v3xj3/89NNPESSDtMJoNPp8PgQOweoIIaWlpS0tLZTMgYdNnDhx06ZNPp8P\n+2C2UNEeOnSIkkV47JnNZngXg4DCPDk3N3f//v04HHKQqVOnYij0Q1NVFV4nEE+EQqFgMEgb\nUTD8JCRsE9s4gtVv/ST0fpiH4zj2HgGsxo4iYT9Pugn2fZTIiEPE7oeAv11paemJEyfAcmRZ\nRh0eIaS9vR0bw+EwzWPGGhGrMX1RMT1oGmAmggo8sLRf/vKXaNVKCNFqtRAuyLKs1WqHDx++\nc+dOnPGiiy46efJkY2MjRLXUMKWkpOTgwYOxW2ItTggh+EWC8j6odM1mM+gXnWdsNR7oJoJ2\nkUgkKysLfWNj2SouFhMOBAKCIKSnpzc0NGAfXAWsVQ4fPhwrnli0aBF7lvoUWKaJIqlvhZrY\njZ6TFwlL7PoC2KpOZPRUKLW5ubm2thZVcdDDIrKFwBgK3WIzql3ypLHuHqhFg0gWHnJQLciy\n/M0334DhabXaoUOH7t+/X5Kkjo6OCy+8cOfOnYjtRSKRl19+efTo0TgFSBuo4alTp2w2W3t7\ne79+/axW66FDhwwGg6qq6GABn+GOjo6Wlhar1Qo+SlmdyWQqLi4uLS0Nh8M6nU6SJLfbrdPp\nYG6Ms7hcLqvVqijKY4899vzzz+MSOI5LS0tDrwtcUUtLCyHEYDAoioJWZoSQffv2wceORgET\n+UFiAYmeQGLSlLOCxOyqTrq3qlVVHTfu7bgMxcDwL9H7McJE/j5i6BFiV1xcvG3bNp7nQYzc\nbjcX7e7F87zRaER7LlVVBw8efPjwYepggoXSpTcDPEG0Wi2ooaIoLpdLp9MpihIKhRwOx6lT\np0Kh0K5duwRBgDNcVVUVxBbBYLCoqGjv3r20CwVOgdI3l8uVkpJCCMnIyDh8+DDP8xB24NSh\nUMhqtY4ePfrbb7/1er0omKMWxAMHDjx27Jjdbm9qagqFQv369QNRQ2yPRP3z3G738OHD58+f\nr9frLRYLjJFbWlpoAA9RRo7j0C4WFx4MBn/2s5/B7gQZ51j3vgREwn4ndfPruTtI2GvsDlhs\n46cijrcikZ/cvoC+8LOz93/5sFWdyIh/KtbpdN5yyy2rVq0ihGRmZsKshCZk6UZKs5CNjY3Y\nxSYukQxFpwq9Xg8apKoq9ezlOA6dJKhyFgE/SZLAtFBmB+6IRO2AAQP0en1lZSW1jgNi/0vn\nYLVaaRlfl/q/f6fDwHa9Xg8LGFioUELZr1+/2tpauj/P8/PmzfvLX/4CTkmxYMGCu+++mxAy\ndepUr9dbXFzMxBP/AXr/h2xSl9cw/kcRx+RvfMvMkzorzWrsKJL9Y4eJJxIZ8Sd206ZNI4Rs\n3boVoldCSE5Ojsfj0ev1bW1t0BmAjUEzi0o7CBfUaONUOiur1SpJUlNTEyiRRqOx2Wz79u3D\nPiBVmZmZoig2NjZiC4JqZrM5EomgEwbic5iM3W5XVXXSpEmrV69G3pPaDlMGmL1XAAAgAElE\nQVRWJwjCsWPHCgsLaUaYEkRCiMlkCgQCwWAQxXlgkBzHwYgOFX6CIKSkpBQUFGzbtk1RFIPB\nIIpiZ2cn9UlWFEWn06EphcFg8Pv91LEZ92H69Olff/01IeTyyy/3eDwlJSVvvvkm8w1i6JtI\najbDHL96CN0kdtObX/rRfZKdZvU+2KpOZMSZcZeVlVVVVbW3t0M3qtFo5s+f//7775eVlaFj\nLCGE47jHH398/vz51KaEdlyQJAmvwW8kSfL5fC6XC/1YvV7vzTffHAwG9+7dS+N8qampoihS\nUxK61GRZpu5xaDsLJ5GioqJdu3YtXbo0Vp9Bpa8IK6amph49elSNugeD3gmCEAwGRVGkF4Kp\ngpzxPO/3+wcMGFBfX19QUHDs2LGmpqaGhgbwVL/f7/f7RVEcMWLEzp07IZ4AAdVoNNOnT//b\n3/5GCLn00kt37NgRDocVRamqqoq9EEpMExNxTHYk9Vd4fNHNW/FNxqM/uk9fiLF1E70f5knk\nJzeOSNiyh77wWdH76COrOkkR54jd4sWLH3744VAopNPp/H4/VZhCi2CxWOhGVOBlZGRUV1fT\nBmImkwkkrKCgYPfu3ZCXEkJEUUSEDD1hCSEWiwVeJxC0IoBnNpv//Oc/33vvvYSQtLS0lpYW\nURRpLBCROYfD0dDQoNPpoJAg0cZfdHCMD0iSJEkS7Pd++9vfvvXWW5dccklTU1NqampZWRnN\nL4OG0k6ygiDo9Xo41dFYIMdxdrv9xRdf/N3vfofBMW1RFF999dW77747VmNrsVg+/PDDmTNn\nkpjOE++++y57lhgYkg6szJzh3ANb1YmMOEfsTpw44fF4srKyTp8+HQ6HkTkNhUJer9dkMrlc\nLrrR5XJ5PB4kJQkhHMcZjcZbbrll5cqV9fX1jY2NgiDAW0SSJJrAxQuNRnPZZZetXr1aluVY\nFnXVVVetX79eVdURI0YUFhbu378fpnQOh6O+vh6udW63G7pXOmee5y0WS1tbG2JvOC9c5YLB\nYDAY1Ov1wWDw/fffz8rKamtr69evX2NjI46lGl5CiMViQXIZJYBIzvp8PtiaQFdx33334Sjo\nZ6HtnTdvHrKxo0eP3rZtW3p6+qBBg2w2W+yNVVUVXXTj+34xMDD0NNiT20Ng0f2zCLaqExlx\njtjNnTv3ueeeM5lMoihCDWCz2RRF6ejo4DiOxtvQc0yNglrQ0S2EEAzS3t5OmdM/ZhwNa3HR\nHq86nU6W5Ugkkp2d3djYiPCYIAiff/75L3/5S1VVp0yZsmbNGhKNHne5ZIfDMXfu3Ntvv/3/\nd5v+2YgudhCYucTa76nRlhLIBVPXPUIIrSaMHTwlJcXn8+HOUEXF1KlTUWP3i1/8Ap0nXn31\nVVasysCQdGBl5gznHtiqTmTw8R0OaVaIQDMyMsxms9vtRiSMECKKos1mM5lMCJshd0kIEQRh\nypQpqamptKzNaDTOnDmzvb2d/DMPgyEIIYTjOKvVarfbTSYTrXLLzMxEo1XEzB544AEcu2bN\nGpvNptFotFot4nYYDUM1NjauWLGC53k6H+yGHWw22+DBgymVxCHjx4+HR4kgCEOGDKHbR40a\nRQcBq4uleoQQnU6HCxdFES7EhJBQKIR6O0r+BEGg/cqCUcT3nWJgYGBgYGA49xBnYvfRRx9x\nHJeZmUnt60hUXgCfYdpMjMT0YBAEYdOmTc3NzSA9FovFZrNVVlaSmPasHMelpKQg/odDQqGQ\n3+/X6/UQvUYikbKyMhLVNBBC0M4LcLlcsixjN5TlpaSkDB482Gg0qqq6evVq5DqxM9zy8DoQ\nCBw+fBgBuTvuuANtwaB1RVhu//799Nhdu3ZBJzt48GC0ETMajbIsg89NmzZtwIABhBCNRkPj\neRzHzZw5E17EtEpPluUnn3wSE8B/UckX3zeLgYGhF8CeXIZzD2xVJzLiGUotKytzu92SJF17\n7bVVVVXfffddrH2dzWYbOHDg1q1bYR1itVqDwaDP5zMajSQalkMMzOfzeTweq9UqCILVapVl\nORgMCoIwZ86cDz74AOfq16+fy+Vqb28PBAIajSY9Pb2jo8Pn89HuZF2Sp8jb0u0ajaajo8Pr\n9drtdgh4KamCEoJaJcdmSN99991IJAJ/FjAzKFiRYCUxzsYnTpzAFmRp4YeyefPmK6+8srKy\nEipXOr0lS5bMmjWrpqYmtpUZ1XBgJggQxvHNYuUpDAy9A1Zj3kNgPnZnEWxVJzLiSezKy8v7\n9et3/Pjxtra2HTt2qKqK3qxGozEUCuXk5OzduxcbBUEYMWJEamrqkiVL/H6/2WwGu1JVdfjw\n4f3791+2bFlFRYVGoxk1alR6evq3337LcVxFRYXb7Ubkr66uzuFweDwe5DEpDaJexKBNqG+b\nPn36hg0b/H4/Ng4ePLitra25uZnn+cbGRpPJBMs9OMwhJVpQUICWaLEXyPP86NGjt2zZotfr\n/X6/RqOBPwtaStBfMDBqgR4WI6uqmp6ePnny5K+++kpRFLS+BdsjhKxcufL777+PPZEsy+hd\nRmJaisW3WDXZP1YYKPqCsX5SQ1GUCRMW/ehuye7K2x0k9eS7j77w6crEE4mMOBc/ZmVl1dTU\n5OTk5Obmgu4Eg8FQKCTL8pAhQ44fP47gViQS2bBhA9KLNMCGUNmOHTt27tyJ0WRZ/u6772iZ\n2saNG+EGTAgJh8MNDQ3Uqc7pdP7L+SBYCBUCoKoqkrywMiaEoJpNo9F0dnaSKJGqrq6m+8eO\ntnXrVkIIiggFQfB6vYQQaGxp0wtCiMvlQtiP+h63tLR8/vnnNPVMG2MQQh599FGcOlaBcccd\nd4wbNy4vL0+r1UqSRO2RkxcsRthD6I7/KiHJ/U2Z1Ijvk5uwpIf9wOhTSPbvo3Mb8SR2xcXF\nx48fv/jii5cuXXr8+HGNRkO7Pthsti+++AIZVWhgFUUpKiqqra0F7SPRFrGxca/CwsJZs2a9\n/vrrCG6Fw2G9Xk9PR1kdIWTIkCHl5eWEEBgCS5IUCARoNpYuQUEQHA4HnFZQIffCCy88/PDD\n6IRBa/KQur3pppsWL15MjxVFkeO4YcOG7d69G63JoOfdsWPHiBEjOI6D68r1119fXl4eDoeP\nHDnCcVxaWlpTUxNGuPTSSx0OxyeffOLxeGJbmV111VVvvPGG1+ulsxVF8YILLkDh3TmDhGVs\nyR5F6I5B8dO9MI8fIKm/6ZN9VfQ+unMr2F1lYOgFxJPYjRw5srCwcPDgwaFQqK6uLhwOS5K0\ncuXKX/7yl+3t7YhFIUqXlZWl1+urqqpUVdXr9ddffz2K52KpGM/z+fn5L730klarpXGs2CZg\n9Lwcxx0+fBgvwBElSYJKQ1XVe+655+2330auNi0t7ec///lHH31ECFEURVGUxx57DINccMEF\nF1100Zo1a4LBIBxJPvnkExITRQOT27NnD/Y3m82qqloslhEjRiAMiWzykiVLQE/h7dLc3Ezn\nuWHDhpSUFBKVd9CrcDqdeXl5FRUV9KLC4fB1112H16FQKBgMhsNhVtPwHyCpuUWyo/dvbByb\nH3QvDkq6EwplTy5DT6P3P+jYqk5kxNnHrq2t7cEHH/zqq6/QwsFkMg0fPry5ufno0aO0Q4Mk\nSWazOTMz8+jRo5FI5KKLLnI4HCtWrKCUjuobRFH0+/1QwhoMhvb29lhbEEIIulm4XC40H6OH\nG41GJFhpMhQaDovFcsUVV3z66aegbjqdDnlhaCYMBoPX673gggvQjha6B4PBAIUENVJG4njC\nhAkbN278x02MdpvdsGHDxIkTaddXzDAUCun1eiRtaa6W4zj8CeV3ra2toijKsmwwGDDzSZMm\nrVu3jhBy2WWXdXZ2Dh06dNGiRexZYvghErZSO2En1stgHv09BFbdcRbBVnUiI87EDigtLZ0+\nffqYMWO2bdvW2tpqsVj69etXX1/vdru1Wi38O7CxqakJNXPoGIZ4W2yXMOruC4oGU8SMjAy/\n3482D7m5uQ0NDVTNGpt1tVgsHR0dONxgMID5TZo0SRTFdevW0e6rOMRgMEQiEUyDCl1VVR06\ndGhlZSXHcbNmzVq+fDlVxdJcampqqtPpjPVMBom0Wq1ZWVlHjhwxmUwcx8GQ+fzzz6+trY3t\ne4GpYjTafIwQMm3aNJQG0pZiixYtYgpzBoakA6pvz/YszkGwxO5ZBFvViYz4Ezun0/nQQw99\n8sknYF11dXVmsxmtw5CjRP8uGrsyGAyiKIJ1paWlNTQ06PV6s9k8ePDgzZs3xxp/AGaz2ev1\nggNR/WmsKUksT4LdHYkxCqbdYxVF+c1vfvPMM89MmzbtyJEjV1999ffff+92uwcMGFBfX19S\nUlJRUZGamgpHPSqPRe+vq6++ev369W63m+f5YcOGVVdXu91uQojJZEJkjmpyQR/pTV65cuVd\nd91VX1//j7vPcTzPP/TQQ3/5y1+6dNfYsGHDhAkTCCGTJ09G54mFCxcyp2+GHgULgfQEmEc/\nw7kHtqoTGXEmdk6ns6SkpKOjw2Qy/T/2vjw+qvJs+znnzDmzzySTPSHJBAgEQoIsgqxBRUBE\nrBtQtxasS2sr9PfW1+Kn1qL21fqqdV8atcUF3NBWK0pBBZQ9YUuAhATIvkyW2fc55/vj6jzv\nGGwb6xAm5Ln+4DecPOc5zzlzzsw1931f122z2cCf0OyV53mNRoOuqTQdiRAX+A34FjYi9hbb\ntoHneeRbtVotAnvhcBhaCpDCf5xPNDiMeRAAo2HCPqvleR59YJEmlmVZkqSCgoKampqioqKa\nmhoodnU6HcdxYJM5OTmNjY2xk1gsFkmSaPdYoKysbM+ePThZg8GAAF5HR4cgCAhMgmImJycH\ng8Fhw4ZB+RGLK6644sMPPySspdj3w6DOBvaTZvVHPNHPoMVQIHZxrMMj/buw4XC4rOyVuEw1\n2BHfGFvCRuwG9cdOP8GIXSIjzsRu+vTpR48e7ejocLvdmZmZCKqBPN15550tLS3vvPMORppM\nJrfbDVZHYkrrYtOjIFtoAgYDvKSkJJPJ5HA43G434sBIjIqiuGjRovfff1+SJEhoqSCXfLO7\nK8dxU6dOhaMKJBqSJEFCG0sotVptJBJBtG/ixIkVFRUkGgIELQPnI4SAtiqKMnXq1KqqKlTm\n5eXlNTc3Y4DRaIQKGDLePm7GHMdpNBqfz4eDGgyG3t7ecDi8ePFiEDukYktKSsrLy5lvEAPD\noEMkEmFP7plAwhK7oQB2Vycy4tx5or6+fu7cuZIkLVq0SKfTTZ48ecuWLWgIsW/fvra2Np1O\nN23atC1btsC5zWw2T5s2bdu2bTA9odyONn4A8YJ2AbbAdrtdkqQrr7xy8uTJq1evRszP7/eH\nw2GLxQKhw7Jlyz777DObzYYJ9Xq9Wq1OSkqqr6/Pz88HS6NsD3wrVrqh0+kCgYBerwdHRKey\nlJQUu91OC/5ASZ988sny8vKqqiqO4+68887ly5fDOYVEWSPtnAvHPuz1z8j0ypUr/+d//udH\nP/rRG2+8kZWVFfsnJaa1blwwFGIzDGcXQyFu0R+wUqQzhDgqlxm+K9hdnciIZ8Ru7dq1zz33\nXF5e3urVqy+99NLZs2f39PR8/fXXCGihuG38+PFJSUm7d+/2+/1QPBiNxnnz5h08eLC+vj6W\nXdGas29lQlqtFgbIRUVF1OsE5CkzM9NisfT29jY2Nup0OrVaXVJSwvN8S0tLXV0dVbAaDAa7\n3U7JFiEEqljum73IYqvlsMVgMKDdhSzLFosFdiSiKM6YMePzzz+nViYcx8HxBCE6WZZFURQE\nQaPRoIsuTlAUxaKioqqqqj4GLh9++OHixYsJE08wMAxysDJzhnMP7K5OZMQ5R240GisrK3ft\n2iXL8kcffRQMBi0WC8dx3d3d4Dow701LS7vwwgs3btyoKEogENi4cSPsedEujMSkKRVFufnm\nmz/++GPY/NIxgUAAKc62tjZCiMlkgj5DlmUE6np7ewkhsiyfd9552LGpqYkQgsRrOBy22+2C\nIGRkZHR3d4OZLV68eP369Ujm4nRAziKRiFqtRh0eIWTWrFmffPIJnJCdTid1V54xY8aWLVuw\nZkh0ZVlOS0vzeDxQVBBCIpHIhAkTYIYHnaxWq0VxYXFx8fHjx6kKBBFNQogoimCEzOmbgWEw\nQlGUoeCnOPDnmLCp2IGPVQ/8xWffR4mMuEXs6uvr58yZ09zcDAKXkpISDod9Pp9arUaAisQo\nG1AnhzI1Ko/9xrKiugq6F/pYhMPhwsLCmpqa2MHoZhGJRPR6PTxQaNUamkMEg8HCwkJ0CXv6\n6adfe+21AwcO0NAaOkaAtPWRpubm5jY3N/dZXmwtIP5Lf7vQF6jV+9ZTI4SALNKzMxqNTqdT\nkqRwOEzH6/X6qqoqq9U6f/58j8dTXFz83HPPsWLVM4GE/XroJxhp+E4Y+EvBxBNnCIP9yR3U\nYOKJREbciF1aWtrIkSP1en16evr7779PA29qtdpgMIwaNWrv3r3gUthYVlYmCMLmzZuzsrK8\nXm9XVxfP87ST7P+tL0b3QAjR6/UgYcOGDWtsbKSBPUmS0tLSWlpaYkN9SUlJdrv9G2fLcWgp\nhg4WiqKAulHZROxIjUYzffp0uAQDKpVKpVLNnDlz+/btiOoJgpCbm4vgX3d3N4bxPG82mxFj\nw0adThcMBvV6PXhbbGfY0tLSjIyMzZs3I29Lt9PFULuTl156iRWrMjAMOrAy8zMERuzOIthd\nnciID+N+6623HA7H9u3bHQ7H4sWLEW8TBAGNsHp7e3ft2tVn4+bNmwkharU6Jyfn0KFDJpMJ\nxWrkm0Z01BsFG71er6IokUikoaGBHj0tLa2npwedXtPT02GzEmuAR0N3Wq122LBhra2tJBoU\n7OzsjOVzWq1WpVL5fD5Qqy+++IIQwvN8YWFhXV2dXq+fPn36/v37MTOSwqdOnepzNaCozc/P\nP3LkCLZAFdunRaxWq01PTz906NAjjzyyb98+5I5JlMUOGzbsq6++slqtsiwjkscNcptvJtc4\nQ2AahQTHYH9yGRhOB7urExnxIXZbt25NS0u7+eab//73v4fDYYPBoNFoeJ5vamoKBoOiKOr1\neljZmc1muhGFcYcPH7711lufeuop0C9akyeKIs/ziPyhZE2j0UCyAJZGCCkoKGhra6uqqsrL\ny0Otm8Vi6erqUhQFpiSgg2q1Gh1gJ06cCIqZlpYG/hcOh/Py8jIzMysqKmRZ9vl8R48eLSsr\n6+7uVhRl+PDhWG1LS4tKpcrPzz98+PDFF1/81ltvYW0cx5lMJrQCs9lsoigSQkKhkN/vP378\n+PLly1977TUIfnGhwCbhYxwKhZqamjiOW7NmDe1FQeUafWyZqVJ48IJxizOE/vjY3TsA60gA\nJGZWmuO4xFxYfDEUzjFhwXrFMsQibjnynp6e7OzsxsbG7du3L1iwwOVygeWYzWaUx/X29lLv\nN2yEYRshZMOGDSTKXdrb26dPn97T0xMKhUwmE4rwMOzPf/7zj370I6psCIfDNTU1HMdNnjwZ\nWziOS09Pr62txX9peMzv98N/7quvvgJTlCRJEAS1Wu3xeBobGxHtUxQlJyfngQce6OzsJITI\nstzQ0MBxnFqtHjNmzIEDBw4fPqwoyl//+ldCCMzzRo0adfjwYcrGMLlarQZHfO211+iW5OTk\n0aNH7927l+M4j8cTiUR++MMfvvfeeyNHjgyFQm1tbU6nM7Zu7+qrr7ZarSQawIMuhIW+GU4H\n+7KkSMxLIctyYi4svhj4c2R2JxQDf/HZ91EiIz7ELjc3NxAI1NXVmUwmBJ80Gs0FF1yAAjWE\n0AghoihOnTr1888/j91ICFm0aNHevXv37t1LQ3GgOCNHjvT5fD6fr6mpKRKJLFu2DBRKFEVJ\nkkwmU1pa2tGjRxsbG6lkYdu2bZgThIxGwkDyaOQMTI4651HRQ2tr69tvv81xHMpCeZ4Hj9y3\nb5/RaHzsscfuuOOOQCAA9zue56urq00mE3Sy9LihUEitVo8bN87tdqPPbDAYtNvter0+IyMD\n50IIeeONNziOg7WewWBwOp30giiKMm3aNLyWJEmtVkuSxFRIDAyDEezJPUNgSYCzCHZXJzLi\nQ+zy8vI4jvvggw/UarUgCGlpaZ2dndu3byeE2O12eIVg41dffUU3RiKRzMzMpqam8vJymmqE\n4BQp1CNHjixYsIAqWKmkAAE/t9tNQ1y0BA2RORJVMCAhi7AfNsJhGOVxKpXKYrHYbLZ58+Y1\nNzcfOXIExXmjRo1CeRydH/4jd999d5++ZIqi7Nmzp7S0NJaTQbdx66233nbbbST6AAQCgYqK\niljbPJyp3+83Go3Hjh0jMUoRjuOef/75m266CWs+B/KwDEMQLDfHcEbBxBMMDN+K+BC7U6dO\nIfQFtanf77darTqdDrlLhM08Ho/VatVqtTDjxcbm5mae56+88soPPviAiiTS09M7OztRpYe2\nWoQQs9lcVFRUWVkJVoferEeOHKFpUJ1OB9NjQoher/f7/R0dHdQqBdpsdCeDjx0hJBwOd3Z2\nzpw5c8eOHVg5NlLRA3VLwWtobBELtFgsPT09fr+/qKiI/KOMZsb+/ftFUbTb7e3t7bfeeish\nJCMjo7e3F33P7HY7ZBBGoxF1eDjlvXv3YpFvvfXWkiVLkpOTYbOMNfj9fr/fHwwGBzu3Y+KJ\noQb2hQoM9ieX4btiKPjYsbs6kREfYtfa2irLcl5ensPhCIVCU6dOve+++y655BJCCExMeJ7P\nysq6+eabH3jgAboRsT2bzfbRRx8h46lWq3meh5sxWjUgxKXT6RRFqaysDIVCSM7GxtIIIVqt\nVqPRqFQqh8NBCKG9X0m0GRfHcb///e8rKyvXrVuH1mR08e3t7ZmZmSdPnqRRN0mS0IWCBv9k\nWf7FL37xzDPPYMJgMNjR0YGZU1NTOzs7RVGsqKjw+XwGg2HcuHG9vb29vb1er9dut0PtIQhC\nYWFhV1dXR0dHRkZGS0sLznfYsGGlpaUffPDB2LFj//CHPxBCnE5nSkrK6S3FBntBA2NsDGca\niRkjHOxPLkPig93VDLGID7EDJWpvbzcajW63u6mp6cknn4RmguO4SCQSCoVaWlq2bNnSZ2Nz\nczMSo8uWLVu/fj2Us1QwIcvykiVL1q9f7/P5sJ3jOJ/PJwjCmjVrVq9eTTt9BYNBv99PO5xg\nTp7nqS+J0Wi85557QqEQjothHMfxPO9yubKzsyH1QNVdKBSirI6e3YEDB3ieR1oZUgaQLSgt\nCCELFy7csGGD2+2urq6+8MIL9+zZg4VRc+aioqItW7Yg8gdmGQgETp48WVBQwPO80+lEQjYS\niXR3d7/wwguxVxhXjD1LDAz/AokZI4zvk8vyj4mPofAjln0fJTLiQ+yopy5kBz09Pfv370eX\nMFmWk5OTvV6v0Wisq6uL3Qi5QCQSCYfDH3/8MSFEpVIhl4ocZTgc/stf/kIIkSTpmmuueeut\nt0hUjHPZZZetXr2aci9wwdhCN3iRYE6oYqklnlarxTLgrlJSUrJ169ZwOJydne3xeGRZRoex\nUCgkSRIIHCFk+/btoijGhvrIN4vw3n//farhOHbsmNvtxmuVSoXT/Oyzz1AX6HQ6eZ4vLi4+\nfPgwISQ9PZ0Q0tzcjK5lfSyaNRpNOBxm4okzhzh2NSDsC5XhNAyRlmIMFEPBWpJ9HyUy4tN5\n4vbbb3/55Zfz8/OvueaaFStWTJgwgZqSCIKQmppaXl5+zTXXxG5MS0t7+eWXlyxZgmI7WipH\nXwCxagP8C3KWlZWFLrH/dyYchwAe3R3kjE6Yl5fX1NQEPzxZlqlRHArv+vjGXXXVVR9//LEk\nSRzHuVwuGhokhIwePbq2thZzIskbiUSSkpKcTidtCDZ37tzPP/8cK6frQTiT5o4JIaCJJSUl\n1dXVfZqPpaSk7Nu3z2q1zps3D50nWEsxBobBCNZ8ieFMY+CpJLurExnxIXa33Xbbn//854KC\nglOnTsENRKvVIq2Znp7e0dGB7CdtPpGZmdne3k5TooIgQMGQmZnZ0tJCKY5Wq73hhhteeeUV\nEuVz4Em0ByvHcWgyBmZGG7YKgqDVakVRpO0cyDc5IrVfgWtJ7Pz0KBqNJhgMInim0+kIIcgC\nU4kuiYYYkZOlEUGckSAI8GGmXBMMMhKJIKf8jzeA484777z9+/f3eSMyMjJ27dpltVrnzJnj\ndrtLSkrKy8tZ6JuBYdCBJa0Yzj2wuzqRER/GnZOTIwjCpEmTOjs7Q6FQcnKyTqcTRbGxsRHM\nhhCiUqnS0tJ4nu/o6IgNgBFCxo8fDy1Ca2sr5UCKohgMhs2bN8uyrFarEVHDC7SXwL6SJLnd\n7j6i0UgkgmQr3YJ4HtpzpaWldXV1qVQq2tAWI9GggkRJmyRJlI+CO/I8n5ubC9di8Eu9Xo/U\nsyiKNB5JCElPT/f5fCB2arWaUszx48cfPXoUcTu64M7OTurSQkE5IobxPE8rCAcpmCr2u6Kf\nV6w/nScGe5pvUN88PM/HMRWbsJcijiUN/bwUCVtuOBRUsYP9++jcRnyI3WWXXfbII4/k5OQ8\n9thjL730ksvlslgsa9asmTt3LrpmGY3GcDhss9lSU1PD4XBjYyPd6PV6u7q62tvbUWE2adKk\ntra2jo6OcDis0WhA4AKBgF6v93g8GzZsWLhw4cSJE7/88ktQvZ6eHrPZjLYNlC1RNikIAi2S\nCwaDSK3CtQRlebIsT548+ciRIx6PBzxMp9PJsuxyuSKRCNgVdseEJ0+eRNAOSy0tLV27di2J\n6nCTkpIcDoeiKO3t7ViM1+sFJ4POd9++fTRul5ycHA6HnU5neno60so0HEgIQYkePTrY58yZ\nL/fn7UjMb/HE/NKNO+L4md7PYRvjWiPYHwz8F2ocb56BX3x8q5GGyHPE8J0w8J/5sbEJhkRD\nfFKxhJALL7zQZrOhs2okEhk3blx5efmMGTN4nvd6vYQQjuNGjx795oiHrh8AACAASURBVJtv\nTps2jeM4xLegS92zZ8+iRYscDgdqznie12g0fr//qquu+uijj1CEh98HJpPJ6XTecccdzzzz\nDCxUbDYb+kMgz4usaEFBQV1dHbgUIeTpp59etWoV2Bg2Ug8USqQQ0uM4TpIkn88XiUQsFgvC\nbBqN5pJLLtm4cSN6SOBAGRkZgiC0tbUpUWASlA96vV6HwxFLK2mxHc7O5/NBuqtWq3G4BQsW\n3HrrrVdddRWelsWLF8PD7+KLL0aN3UsvvcRC3wwMgw6RSOR/ior+7TDG2L4rEjZ4ORTAUrGJ\njLgRu+7u7lWrVn388ce0NRbKzhCFQlUZlYh+60aDwaAoitvtlmUZtsCzZ8+uq6tD9EtRFJ1O\nh5QoHEn+cQJR6StehEIh+ktiypQpe/bsweFAsDiOmzx5MgyBAVEU09LS2traYJUHDoppDQYD\nVbZiPUgE0/X/30XkvuUyxmZyZVk2GAzwTxZFkfJaQkhZWVltbW1bW1tubi7sADHV0qVL169f\nTwiZP3++x+MpLi5m4gkGhsEIVmbOcO6B3dWJjLilyVNSUl5//fWpU6fm5eXl5eVBbQCKBgKn\nVqshBf0XG51Op9lsLigogJp1+/btbW1tlOuAdYXDYexLQ3QwNwmFQuFwWBCEpKQkEDuYifA8\nb7FYjEYjHEP27duHHRGx0+v1mB/9xwRBwJ+MRqPL5Ro5cqRWq+U4DnlYxPPw4rrrrostMsAR\nzzvvvOXLl2MLWpbhoBaLxe12Z2RkgJLGKnCrqqpSU1M5jmtpaYEOgxAiCMINN9yAAXA/YXFv\nBoZBCvbkMpx7YHd1IiOe9Y+VlZV1dXVPPvlka2sr0ouKoqByLj09PRwOI2RFN2ZkZMiyjPSr\noigop+vt7W1paUGQjCYx+xxIo9GIokiDZFRsC5LncDhMJhP4Jf5qs9kIIZFIRK1WE0Jef/31\ngoIC8DmHwwHtgslkoq3DELpTq9V1dXVer5fKXf1+v9lsxsLWrVuHjRdddFFOTo7BYCCEVFdX\nr1u3DpRRUZTCwkK86OnpIYTgvNauXVsUk5dxu93Nzc1gk2CNhJD58+cvWrQIr8PhMDgre5AY\nGAYj2JPLcO6B3dWJjHiGUquqqiZOnHj//fePGDHi8OHD999//yOPPBKJRCRJ6uzs5DguNTXV\nZDKdOHGCbiSEiKJosVi8Xq/L5br22mvfffddZDBpHhNdKHAI8KRAIDBjxozt27fHOhIbDIa0\ntLSMjIwdO3a43e7U1NQTJ05QU5KpU6du3boVIowVK1Zgr8mTJ584caK3t1dRlNraWkmS5s2b\nt2PHjkWLFr3++us0fgYbF+yCNXMcd+21127atMnhcGzfvl2j0aSnp7vd7nA4nJqaqtPp0KBs\nxIgRdXV1kADT4OLKlSuDwSBN5gaDQaSPU1NTLRbLkSNHJEkaO3YsvapUPBHfB2ngy1NYQcx3\nBVPFflckpg8wx3GJubCBR3yVKwmrih0KYMQuoaHED3/+85/nzp2bnp5+9dVXK4oycuRIURTP\nP/98pCw5jpsxY0ZeXp5KpSosLKR1l5MmTUJHL0mSMFIQhCVLlvS5bziOS0tLS01N7bN+jUZj\nNBpJVP2ALGp+fv6NN96IvCrHcStWrABBpFNxHJecnIyNgiCgRE8QhN27d5vNZoykWVeVSoVi\nAjpDRUXFtddeixXCgfnyyy/H0TUaDTXDQ4DwV7/6VVZWlslkIoSIopicnEy++VRkZWWdXoWa\nk5MDdlhWVjZp0qQf//jH8MNjYGAYXGBPLsO5B3ZXJzLiJp4A3bn00ktLS0tPnjz59ttvX3bZ\nZeA9nZ2dSox5ryRJZrMZniM0LKcoyvTp03fs2NFnzm/VJcRCkiRY2ZEoYwO3GzZsGGrsYgFH\nYhLlVUrUvo4QQlW62Phf//Vfjz/++OlHpw52uHypqamCIHR1dVFxBsoHUQVoMBicTmdycrLb\n7Y5VeyjRQjoE7bRabSAQ6NN5guf5rVu3zpw5c+HChVDFPvXUU6xY9UyAhRIZzihYmfkZAovY\nnUWwuzqREU9iRwgpKSkJhULDhg3TaDQdHR0nTpxwOBygLzzPGwwG9GlFBR42pqamqtXq5uZm\n6hhMCPn5z3/+yiuvZGdnnzx5kkTDZoIgeL1eUDfwKszw+9///q677pJlWaPRSJLk8XgwCXWh\nQ7NaWNbt3LmTEKLRaFJTU2GeF7t+tVrN83xsQ1i40MmyHNu1Am4sCNFBJGEymXp6epSoAhfp\nV/BIEEFRFEH4EB3EUwHnPEVR6NXARkVRLr300k8++YTE2J28+OKL7EFiYBh0YF+BDOce2F2d\nyIjzG/PUU09dccUV3d3dLpcLyla4x4HxeDwel8vF8zw1B1YUxWazIdYlCAIldu+9996CBQu2\nb99OO32VlJSoVKrdu3criiJJEsztBEEoLi6GQ4ogCH6/PxwO97GOg0swAnK7d+/G/IFAAGE2\n6mCi1Wr9fn9hYeHRo0dpUA2Mrays7K9//SuJFrpRsxL0seA4rrS0FD0zQNoikciYMWPq6ur8\nfj/mQWtalUpFqR6JRisVRQmHw3ByAfPDCu+44w68iEQiOCnm9M3AMBjBntwzBBZrP4tgd3Ui\nI84RO0LIvHnzxo4dW1tbu2nTJmRgUTyXnJyckpJy9OhRbFSpVBqNpqSkZPTo0X/605/Q7wv8\nDCRGo9FAkZqXl5eenr5mzZoVK1Z0dHQgxRkKhfx+f3Z2dkFBwY4dO2g4LSUlZeXKlffddx+J\nuhmvXLly6tSpK1asCAaDsX26kpOTe3t7IYxIS0vr6OjgeX7YsGHNzc2YjdK44cOHZ2ZmHjhw\ngMYCFUUxmUxutxudxKjX8fjx4w8ePEi+mepFGd/48eOrqqrA0khM41pZls1mM/qPxV5Gk8l0\n8OBBq9V64YUXulyu8ePHl5eXs3pVBoZBB4V5FTGcc2B3dSIj/sSuu7u7tLTUbrfDp4NEFQM9\nPT1opUXDcsnJyVar1e/39/T0FBUVffnllyQaZhMEYcKECYcOHUL4TaVSGQyG7Ozs+vr6WbNm\n7dmzBz0hSLTBK3gStiBAKIoixKerV69+9tlnA4EA4mcgYVarFU0ycESdTgcrFhqrwy374x//\n+M0339TpdHa7HTHF0aNH5+fnf/rppzQXzHHcjh075syZg5gc4pRYBq23Q4RSEARJktRqdVpa\nWlNTU6wZMkKG11xzzZtvvonXF1xwwZYtWwghc+bMcbvdpaWlf/zjH5nTN8MgApOCApFIZPbs\n8n87bLBfioF/uwe+Oy0DBes8kciIP7H78ssvFy9ebDAYgsEgys4IIWq1OhwOa7VanuddLhc2\narXatLS0xsbGG2644a9//avT6YydB3ZxlCeB7oDDWSwWJHC//ZRiFA/Tpk07dOiQx+Ohk1DW\nFUvjSDSulpycTA1NMBghQ4TxCCFQyPr9/tg6PxKj24BFHyEktiaPjsGLjIyMnp4e0EpKSVFH\nSLUdkiQdO3bMarXOmzcPNXbx7TzBshgM/zHYzfOdEA6Hy8pe+bfDGLf4rkhY8UQcG0YnLFiN\nXSIjzsSup6dnxIgRbrd72LBhoijW19fD9YO2RjUYDHa7HZnWcDgcCAQEQZg6derOnTtj1aaU\n5YAwkWiPr5/85CdPPPEEDZWlpqaiV6zJZILMluO4goICURRramr+cYYcB9FGb29vOBwuKysz\nmUyomYPRCdq/UqVqHzkFiWkOlp6eLstyV1dXbBQ6dtkkyvD0en2sszEGgE2SGD0s1MFWq7Wh\noYEQYjAYsJdWq3377bfhUXyGiB0DA8PAgH0FMpx7YHd1IiPOb8yiRYuCweCCBQssFsvbb78t\ny7LFYvF4PGBCkUjEbrcrimI2m81mc2NjIyFElmXqcpKcnNzd3U0I0Wq1yFTOnDnz66+/Bj1y\nu93PPfdcbIwNHR1ItNsYIYTjuMbGRkqnEDbz+/0ul4u61h05cgSVfJFIBCNpGpe2DjOZTIsX\nL163bh3dMmbMmNbWVipuAFGjJ05XBYbncrkgpACZw0YUGnZ3d2dlZaHbhMPh0Gg0aWlpYJYv\nvfTS9ddfD/sV2nkiGAwGAgEauWT4TujPT+f+GPySuP7uH3ib1oSNBsUxm9ZPMINiirNiFzzw\nRxwKEbuBv8HY91EiI54Ru8rKyksvvbSkpGTHjh133nlna2vrW2+9Ba0riEtGRsasWbM+/PDD\nUCg0adKkysrK2KNzHGexWEDsaAjNYDBYrdZjx44hxalWq6FdPX78+IQJE0Kh0OHDh5OTk51O\nZ1JSEirhrr/++jfffLNPswpRFGGoqNVqfT4fLWtTFCUQCKCMD7uA80mSBIMSv9+PmfV6PZKn\npaWllZWVOp1OkqTe3l6dTsdxnMfjMRgMLpfLZDKByMaeOAKQkUgEPTYQv8Ty9Hq9KIpOpxMj\nZVkWRfE3v/lNaWnp5ZdfTghh4gkGhkENVmZ+hsBKAs4i2F2dyIhzSzGr1Yo32+fzffjhh9Sk\nA8nHRYsWOZ3OUChkMpkOHTqEIBaVoCqKEgwGaVoTu4wcObKmpgbzQD2qKArM7ZKTk48fP15c\nXOxwOFwuFzqDSZLU1NTUp35OEASkgLEwbMQ6dTod9BkGg2HGjBmffvopuGMgEICnCSHE7/en\npqbyPA8b5La2NrS4Reko+CLHcS6XixCCfzmOw2o9Hg+VjGBJRqPR6/UWFBS43W6Hw6EoSkpK\nClSx1AbloYceMpvNu3btslqtiCYi7MceJAaGQQf25J4hMMZ2FsHu6kRGnK1ojEbjwYMHy8rK\nXn75ZYgkOI4bMWKETqebPn36rl273nnnHYxEeEyJNkIl0Qoz6kKHpOfJkyeXLFkiSZIgCHa7\nHTGzcDicnp7e0NDw4osvtra2trW1IanK8/x111134MCB2HI3eCAvXbpUq9UiPUqiCVOfz4dk\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ViZOcO5B3ZXJzLi9sasW7du1apVubm5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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24666bd9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "4.1.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}