mutual_ranks_to_modules.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate mutual ranks and call modules of co-expressed genes\n",
    "## Jen Wisecaver\n",
    "#### 2019-09-23\n",
    "This Bash jupyter notebook is a tutorial for \n",
    "1. Preparing a gene-level expression matrix \n",
    "2. Calculating pairwise Pearson's correlation coefficients (PCCs) and transforming PCCs to mutual ranks (MRs)\n",
    "3. Identify co-expressed gene modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Create expression matrices \n",
    "This step uses R to prepare a gene-level expression matrix based on transcript-level abundance estimates output from Kallisto. There are several input files to prepare for this step. I strongly recommend going through the [transform counts jupyter notebook tutorial](https://github.itap.purdue.edu/jwisecav/mr2mods/blob/master/tutorial/transform_counts.ipynb) BEFORE running `transform_counts.r`, because it walks you through preparing these input files and describes the different normalization/transformation sub-steps that are being performed under the hood. \n",
    "\n",
    "Here, we will use the example input files provided in the `tutorial/example` directory "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abundance_dir\t     sample_conditions.txt\n",
      "abundance_files.txt  transcripts2genes.txt\n"
     ]
    }
   ],
   "source": [
    "ls example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Purdue uses environment modules, so I first need to load R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "module load r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can execute `transform_counts.r` without any command line arguments to print a usage guide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usage: ../scripts/transform_counts.r [-[-help|h]] [-[-abundances|a] <character>] [-[-lookup|l] <character>] [-[-samples|s] <character>] [-[-seqtype|t] <character>] [-[-out|o] <character>]\n",
      "    -h|--help          \n",
      "    -a|--abundances    Enter list of abundance estimates from Kallisto (see jupyter notebook tutorial)\n",
      "    -l|--lookup        Enter transcript to gene lookup file (see jupyter notebook tutorial)\n",
      "    -s|--samples       Enter experimental design file (see jupyter notebook tutorial)\n",
      "    -t|--seqtype       Full-length or 3 prime tagged RNAseq? Enter [Full/full/F/f or Tag/tag/T/t]\n",
      "    -o|--out           Enter base name for all output files\n"
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "Rscript ../scripts/transform_counts.r "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pass `transform_counts.r` the necessary input files. Reference the [transform counts jupyter notebook tutorial](https://github.rcac.purdue.edu/jwisecav/coexp-pipe/blob/master/tutorial/transform_counts.ipynb) for more information about this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read 10 items\n",
      "[1] \"tximport options: type = kallisto, tx2gene = tx2gene, countsFromAbundance =  no\"\n",
      "Note: importing `abundance.h5` is typically faster than `abundance.tsv`\n",
      "reading in files with read.delim (install 'readr' package for speed up)\n",
      "1 2 3 4 5 6 7 8 9 10 \n",
      "summarizing abundance\n",
      "summarizing counts\n",
      "summarizing length\n",
      "Loading required package: S4Vectors\n",
      "Loading required package: stats4\n",
      "Loading required package: BiocGenerics\n",
      "Loading required package: parallel\n",
      "\n",
      "Attaching package: 'BiocGenerics'\n",
      "\n",
      "The following objects are masked from 'package:parallel':\n",
      "\n",
      "    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n",
      "    clusterExport, clusterMap, parApply, parCapply, parLapply,\n",
      "    parLapplyLB, parRapply, parSapply, parSapplyLB\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    IQR, mad, sd, var, xtabs\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    Filter, Find, Map, Position, Reduce, anyDuplicated, append,\n",
      "    as.data.frame, basename, cbind, colnames, dirname, do.call,\n",
      "    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,\n",
      "    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,\n",
      "    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,\n",
      "    tapply, union, unique, unsplit, which, which.max, which.min\n",
      "\n",
      "\n",
      "Attaching package: 'S4Vectors'\n",
      "\n",
      "The following object is masked from 'package:base':\n",
      "\n",
      "    expand.grid\n",
      "\n",
      "Loading required package: IRanges\n",
      "Loading required package: GenomicRanges\n",
      "Loading required package: GenomeInfoDb\n",
      "Loading required package: SummarizedExperiment\n",
      "Loading required package: Biobase\n",
      "Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
      "Loading required package: DelayedArray\n",
      "Loading required package: matrixStats\n",
      "\n",
      "Attaching package: 'matrixStats'\n",
      "\n",
      "The following objects are masked from 'package:Biobase':\n",
      "\n",
      "    anyMissing, rowMedians\n",
      "\n",
      "Loading required package: BiocParallel\n",
      "\n",
      "Attaching package: 'DelayedArray'\n",
      "\n",
      "The following objects are masked from 'package:matrixStats':\n",
      "\n",
      "    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    aperm, apply, rowsum\n",
      "\n",
      "using counts and average transcript lengths from tximport\n",
      "using 'avgTxLength' from assays(dds), correcting for library size\n"
     ]
    }
   ],
   "source": [
    "Rscript ../scripts/transform_counts.r -a example/abundance_files.txt -l example/transcripts2genes.txt -s example/sample_conditions.txt -t tag -o example/gene_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abundance_dir\t\t\t\t       gene_counts_statistics.txt\n",
      "abundance_files.txt\t\t\t       sample_conditions.txt\n",
      "gene_counts_normalized.matrix\t\t       transcripts2genes.txt\n",
      "gene_counts_normalized_vst_transformed.matrix\n"
     ]
    }
   ],
   "source": [
    "ls example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `transform_counts.r` script returned three files:\n",
    "* __\\*_normalized.matrix__ contains the gene counts matrix, normalized for sample library size\n",
    "* __\\*_normalized_vst_transformed.matrix__ contains the variance-stabilized gene counts matrix, normalized for sample library size\n",
    "* __\\*_statistics.txt__ contains information helpful for identifying genes with very low expression or variance. Your dataset (the type and number of conditions), will determine how stringent you need to be when deciding which of these low expression/variance genes to exclude from the co-expression network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Calculate PCC and MR\n",
    "This step uses python to calculate the Pearson's correlation coefficient (PCC) for every gene pair in a gene counts matrix file and uses these PCCs to calculate the mutual rank (MR) between gene pairs. \n",
    "\n",
    "The MR for two example genes A and B is given by the formula:\n",
    "\n",
    "$$MR_{AB} = \\sqrt{Rank_{AinB} \\times Rank_{BinA}}$$\n",
    "\n",
    "\n",
    "where $Rank_{AinB}$ is the rank of gene A in a PCC-ordered list of gene B against all other genes in the matrix; similarly, $Rank_{BinA}$ is the rank of gene B in a PCC-ordered list of gene A against all other genes. Smaller MR scores indicating stronger co-expression between gene pairs. See [Obayashi et al. (2017)](https://academic.oup.com/pcp/article/59/1/e3/4690683) for more information on MR and its statistical properties. \n",
    "\n",
    "Execute the python script without command line arguments to print a usage description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Usage: ../scripts/calc_mr_pcc.py -i <input.matrix> -o <output directory> [-t <threads> -k <skipfile>]\n",
      "    -i|--infile  <FILENAME>  INFILE: path to input file: matrix of gene counts\n",
      "    -o|--outdir  <DIRECTORY> OUTDIR: path to output directory\n",
      "\n",
      "    OPTIONAL:\n",
      "    -t|--threads <INTEGER>   THREADS: multithreaded using os.fork (default = 1)\n",
      "    -k|--skip    <FILENAME>  SKIPFILE: path to file containing list of gene to skip\n",
      "\n",
      "\n"
     ]
    },
    {
     "ename": "",
     "evalue": "2",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "python ../scripts/calc_mr_pcc.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of the input matrix will determine the runtime and amount of memory required. I haven't fully tested it to see how it scales, but an input matrix of 35,000 genes ran in 5.5 hours (using --threads 23) and required approximately 60 gigs of memory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "COMMAND: ../scripts/calc_mr_pcc.py -i example/gene_counts_normalized.matrix -o example/gene_counts_normalized_mr -t 20 \n",
      "\n",
      "Creating output directory: /tmp/COEXP1569546281.5440857 ...\n",
      "\n",
      "Creating output directory: example/gene_counts_normalized_mr ...\n",
      "\n",
      "Reading in gene expression values from example/gene_counts_normalized.matrix ...\n",
      "\n",
      "Skipping 0 genes...\n",
      "\n",
      "Creating gene lookup tables...\n",
      "\n",
      "Calculating PCCs...\n",
      "  thread 1 : Calculating PCCs for 193 genes...\n",
      "  thread 2 : Calculating PCCs for 193 genes...\n",
      "  thread 3 : Calculating PCCs for 193 genes...\n",
      "  thread 4 : Calculating PCCs for 193 genes...\n",
      "  thread 5 : Calculating PCCs for 193 genes...\n",
      "  thread 6 : Calculating PCCs for 193 genes...\n",
      "  thread 7 : Calculating PCCs for 193 genes...\n",
      "  thread 8 : Calculating PCCs for 193 genes...\n",
      "  thread 9 : Calculating PCCs for 193 genes...\n",
      "  thread 10 : Calculating PCCs for 193 genes...\n",
      "  thread 11 : Calculating PCCs for 193 genes...\n",
      "  thread 12 : Calculating PCCs for 193 genes...\n",
      "  thread 13 : Calculating PCCs for 193 genes...\n",
      "  thread 14 : Calculating PCCs for 193 genes...\n",
      "  thread 15 : Calculating PCCs for 193 genes...\n",
      "  thread 16 : Calculating PCCs for 193 genes...\n",
      "  thread 17 : Calculating PCCs for 193 genes...\n",
      "  thread 18 : Calculating PCCs for 193 genes...\n",
      "  thread 19 : Calculating PCCs for 193 genes...\n",
      "  thread 20 : Calculating PCCs for 175 genes...\n",
      "Finished calculating PCCs!\n",
      "\n",
      "\n",
      "Calculating ranks...\n",
      "\n",
      "Creating mutual ranks...\n",
      "\n",
      "Writing final correlations to file...\n",
      "  thread 1 : Writing correlations for 193 genes...\n",
      "  thread 2 : Writing correlations for 193 genes...\n",
      "  thread 3 : Writing correlations for 193 genes...\n",
      "  thread 4 : Writing correlations for 193 genes...\n",
      "  thread 5 : Writing correlations for 193 genes...\n",
      "  thread 6 : Writing correlations for 193 genes...\n",
      "  thread 7 : Writing correlations for 193 genes...\n",
      "  thread 8 : Writing correlations for 193 genes...\n",
      "  thread 9 : Writing correlations for 193 genes...\n",
      "  thread 10 : Writing correlations for 193 genes...\n",
      "  thread 11 : Writing correlations for 193 genes...\n",
      "  thread 12 : Writing correlations for 193 genes...\n",
      "  thread 13 : Writing correlations for 193 genes...\n",
      "  thread 14 : Writing correlations for 193 genes...\n",
      "  thread 15 : Writing correlations for 193 genes...\n",
      "  thread 16 : Writing correlations for 193 genes...\n",
      "  thread 17 : Writing correlations for 193 genes...\n",
      "  thread 18 : Writing correlations for 193 genes...\n",
      "  thread 19 : Writing correlations for 193 genes...\n",
      "  thread 20 : Writing correlations for 175 genes...\n",
      "Finished writing final correlations!\n",
      "\n",
      "\n",
      "\n",
      "real\t3m35.685s\n",
      "user\t17m18.654s\n",
      "sys\t0m9.028s\n"
     ]
    }
   ],
   "source": [
    "time python ../scripts/calc_mr_pcc.py -i example/gene_counts_normalized.matrix -o example/gene_counts_normalized_mr -t 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above example, we used --threads 20 to parallelize the PCC calculation and file I/O. The rank and MR calculations are not parallelized at this point. Given the example input matrix of 3842 genes, the runtime is less than 4 minutes. \n",
    "\n",
    "Let's take a look at the output. Inside the `gene_counts_normalized_mr` output directory, each gene gets its own file. Each gene file contains the PCC and MR for that gene against every other gene in the analysis (3841 genes in this example)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3841 example/gene_counts_normalized_mr/g0001\n"
     ]
    }
   ],
   "source": [
    "wc -l example/gene_counts_normalized_mr/g0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g1272\t1.0\t0.933\n",
      "g2381\t2.449\t0.894\n",
      "g3449\t2.449\t0.903\n",
      "g3468\t2.828\t0.918\n",
      "g3596\t2.828\t0.897\n",
      "g0549\t3.162\t0.896\n",
      "g1498\t7.483\t0.85\n",
      "g1090\t8.367\t0.879\n",
      "g0717\t8.485\t0.842\n",
      "g2992\t12.961\t0.827\n"
     ]
    }
   ],
   "source": [
    "head example/gene_counts_normalized_mr/g0001"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns are ordered: \\[gene2, MR, PCC\\]. Rows are ordered by ascending MR scores. In the example above, gene g0001 is most correlated to g1271, with a MR = 1 and PCC = 0.933."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Call co-expressed gene modules\n",
    "This step uses python to parse the `gene_counts_normalized_mr` output directory to create a network .abc file (tab delimited flat file with three columns: \\[node1, node2, edge weight\\]). In gene co-expression networks, genes are represented by nodes, and the strength of the co-expression between two genes is indicated by the edge weight. \n",
    "\n",
    "First, we need to apply some threshold. A network containing an edge between every gene in the analysis (even gene pairs with PCCs ≤ 0) would be needlessly complicated. So we need to decide what MR scores we consider biologically informative. \n",
    "\n",
    "But now, we have a slight complication, because MRs range for 1 to n-1 (where n is the number of genes in the analysis), but most network programs need edge weights that range from 0 to 1. An MR transformation is required, and an exponential decay function (see `MRtransformation.png` below) is the ideal solution. By imposing an edge weight cutoff of ≥ 0.01 and varying the decay denominator, we can vary the number of edges (ie gene correlations) retained in our network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "image/png": 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AAAIIIIAAAggggAACCCCAAAIIIIAAAggQzPEeQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEAgRzIUDmEggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQzPEeQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEAgRzIUDmEggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQzPEeQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEAgRzIUDmEggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQzPEeQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEAgRzIUDmEggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQzPEeQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEAgRzIUDmEggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQzPEeQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEAgRzIUDmEggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQzPEeQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEAgRzIUDmEggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQzPEeQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEAgRzIUDmEggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQzPEeQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEAgRzIUDmEggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQzPEeQACBiBcoKSmRSZMmyfr16yP+XrgBBBBAAAEEEEAAAQQQQAABBBCIHoGEhATp2bOn9OnTJ3puijtBAAEEEKiSAMFclfg4GQEE7CBw4MABeeCBB+zQFNqAAAIIIIAAAggggAACCCCAAAIIlBHQcO7f//53mX28QAABBBCIXQGCudjte+4cgagR2L9/v4wfPz5q7ocbQQABBBBAAAEEEEAAAQQQQACB6BIYN25cdN0Qd4MAAgggELAAwVzAdJyIAAJ2ESgdzDVq1EhGjBhhl6aVaYe2c9euXdKgQYMy+3mBQKQLHDx4UHRKWeeWmJjofMojAlEhsHPnTklJSZFq1apFxf1wEwg4BYqKipxPRX+TPy4uzvWaJwhEugCfvSO9B2m/JwE+e3uSsd/+3bt3m2U3nC0jmHNK8IgAAgggQDDHewABBCJeoHQw16RJExkzZowt76mwsFCys7MlKyvLlu2jUQgEKqBfDhQXF5vT9UtdgrlAJTnPrgI7duwwwVxGRoZdm0i7EAhIQKcDd276bzfBnFODx2gQ4LN3NPQi9+BOgM/e7lTsuS8nJ0eeeOIJV+MI5lwUPEEAAQRiXoBgLubfAgAgEPkCBHOR34fcQWQL8OVAZPcfrfcuQDDn3YgSkSlAMBeZ/UarfRMgmPPNiVKRJ8Bn78jpM4K5yOkrWooAAgiEWoBgLtTiXA8BBCwXIJiznJQKEfBLgC8H/OKicAQKEMxFYKfRZJ8ECOZ8YqJQhAoQzEVox9FsrwJ89vZKZJsCBHO26QoaggACCNhOgGDOdl1CgxBAwF8Bgjl/xSiPgLUCfDlgrSe12U+AYM5+fUKLrBEgmLPGkVrsKUAwZ89+oVVVF+Czd9UNQ1UDwVyopLkOAgggEHkCBHOR12e0GAG3Arq+03fffScJCQnSo0cPt2UC2blx40ZZuXKlpKamypFHHilJSUk+V6Nf9qxatUrWrl0r7dq1k5YtW/p8rj8FCeb80aIsAtYL8OWA9abUaC8Bgjl79QetsU6AYM46S2qynwDBnP36hBZZI8Bnb2scQ1ELwVwolLkGAgggEJkCBHOR2W+0GoEKAt98843069dPqlWrJnv37q1w3N8d8+fPl7Fjx4rW69zS09Ole/fucsUVV8iwYcOcuys8lpSUyHPPPScPPPCAbN682XW8cePG0rNnT3n44YelWbNmrv1VfUIwV1VBzkegagJ8OVA1P862vwDBnP37iBYGJkAwF5gbZ0WGAMFcZPQTrfRfgM/e/puF6wyCuXDJc10EEEDA/gIEc/bvI1qIgE8Co0aNksmTJ1sSzM2ZM0dOOukkyc/PN9eOi4szI+acr3VU3ttvv+0xnLv22mvl6aefdrVbR9vpD8Ya2OnWpk0bM7qvQYMGrjJVeUIwVxU9zkWg6gJ8OVB1Q2qwtwDBnL37h9YFLkAwF7gdZ9pfgGDO/n1ECwMT4LN3YG7hOItgLhzqXBMBBBCIDAGCucjoJ1qJgEeB3bt3y8SJE+Xee+81Zao6Yi43N1datWolO3fuNNNWPvPMMzJkyBCpWbOmzJs3T26//XaZO3euOfbJJ5/IySefXKZtb775ppx33nlmX5cuXeSxxx6T448/XrTeTz/9VC6//HIT+HXq1MmEc5mZmWXOD+QFwVwgapyDgHUCfDlgnSU12VOAYM6e/UKrqi5AMFd1Q2qwrwDBnH37hpZVTYDP3lXzC+XZBHOh1OZaCCCAQGQJEMxFVn/RWgSMwPLly+XRRx8VfdQpJwsKClwyVQ3m7rvvPrnrrrtMff/5z3/k4osvdtWtTzZt2iQauG3fvt2sObdgwQLXcf0BoUOHDmZNukaNGslPP/0kTZo0cR3XJy+//LJceumlZp+Oqrv66qvLHA/kBcFcIGqcg4B1Anw5YJ0lNdlTgGDOnv1Cq6ouQDBXdUNqsK8AwZx9+4aWVU2Az95V8wvl2QRzodTmWggggEBkCRDMRVZ/0VoEjICOVBs8eLBbjaoGc7oOnIZv7du3l6VLl7q9ho7Ou/vuu82xlStXSuvWrc3zL7/80kyBqS8eeughs0adOVDu/3RE3urVq81IOp02s6obwVxVBTkfgaoJ8OVA1fw42/4CBHP27yNaGJgAwVxgbpwVGQIEc5HRT7TSfwE+e/tvFq4zCObCJc91EUAAAfsLEMzZv49oIQIVBNauXSszZswos/+ll16ShQsXVmmNOQ3Z2rZta+q96aab5JFHHilzDeeLb7/9Vnr37m1ePvjgg3Lrrbea53fccYeMHz/ePF+8eLF07NjReUqZx4suukheffVV0bXrNmzYIFlZWWWO+/siGoK5P7ZslS178iQ9KVEObVBfaqSl+stAeQTCJsCXA2Gj58IhEiCYCxE0lwm5AMFcyMm5YAgFCOZCiM2lQirAZ++QclfpYgRzVeLjZAQQQCCqBQjmorp7ublYEjjzzDPl/fffr1IwN2nSJNfUlZ9//rlr9Ft5R/0hV0fm6Q8Eo0ePNiGblunVq5dZN06nsdRRd562V155RcaMGWMOz5o1yxXyeSrvbX+kBnM78vbK5J9XSmpShiOIS3bdZtHBYtm2e490bpwuJ7Q9xLWfJwjYVYAvB+zaM7TLKgGCOaskqcduAgRzdusR2mOlAMGclZrUZScBPnvbqTcqbwvBXOU+HEUAAQRiWYBgLpZ7n3uPKgErgjldW07XmNPt999/l06dOnk0qlevnugXlQMHDpTp06ebcs2aNZP169dL165d5eeff/Z4rpYfNGiQOf7ee+/JsGHDPJb15UAkBnPz166XXzYWSGZ6irnFkpISM4Kw9P3qvh17s+Wanl1L7+Y5ArYT4MsB23UJDbJYgGDOYlCqs40AwZxtuoKGBEGAYC4IqFRpCwE+e9uiG3xqBMGcT0wUQgABBGJSgGAuJrudm45GASuCuauvvlqeffZZw6NTTOp6c542nfJSp77s1q2bzJ8/3xSrXr265OXlyYknnii63pynbe7cuWZ9OT3+zDPPyFVXXeWpqE/7Iy2Yi3OMNpy2dLtkpP4zSq6yG92dv1MuPbZLZUU4hkBYBfhyIKz8XDwEAgRzIUDmEmERIJgLCzsXDZEAwVyIoLlMyAX47B1y8oAvSDAXMB0nIoAAAlEvQDAX9V3MDcaKgBXB3KhRo2Ty5MmGbN++fZKWluaRTwM5HRXXunVrE9BpOJaS8vforxEjRsg777zj8Vxdf845Gu/+++8XXZuuKlukBXPvrdgqDWvW8fmWCw8clEGH1pc6jkCPDQE7CvDlgB17hTZZKUAwZ6UmddlJgGDOTr1BW6wWIJizWpT67CLAZ2+79IT3dhDMeTeiBAIIIBCrAgRzsdrz3HfUCVgRzF100UWu9eIKCgpcQZs7rHbt2smKFStcI+aKi4slOTnZrDs3cuRIefvtt92dZvbNmzdPjjvuOPPc1xFz99xzjzz55JMe68zPz5fzzz9fjjrqKNf6dR4Lh+mA88uBaWv3lVlTzpfmHDyYK6OO9Dy1qC91UAaBYAnw5UCwZKnXLgIEc3bpCdphtQDBnNWi1GcnAedn76ysLDs1i7YgUGUBPntXmTBkFRDMhYyaCyGAAAIRJ0AwF3FdRoMRcC9gRTB32223yUMPPWQusHXrVqlfv777izn21q1bV7Kzs+WUU06RTz/91JRr1KiRbNmyRQYMGCAzZszweO60adPktNNOM8d1ZJ2OsPO2jR07ViZMmFBpsTFjxsgRRxwhQ4YMqbRcOA8eKDooc7OLK6wn561N23K2ylltPU8t6u18jiOAAAIIIIAAAggggAACCCCAQOgE9uzZU+aXlseNGxe6i3MlBBBAAAFbCxDM2bp7aBwCvgtYEczpiLR//etf5qLLli0THRXnbispKZHExETRUXLnnXeevP7666aYhmILFy6UY445RnRUnKdNy19wwQXm8MyZM6Vfv36eirr2+xrM6Yg5Z92uk23yRH8rfffu3fL5hgJJS070q1V5+dlybtdD/TqHwgiESkD/LdA/usXFxUlCQkKoLs11EAiJQG5uriQlJUl6enpIrsdFEAiVQFFRketS+m+3/hvOhkC0CDg/e9ep4/sU8tFy79xHdAvw2Tty+lc/Qz733HOuBhPMuSh4ggACCMS8AMFczL8FAIgWASuCuffee881eq2ykWybNm2Sxo3/Hr314IMPyq233moYBw4caEbPVa9eXXTKBk9fzo8fP96sK6df/mzcuFF0pJ23Ta+po/jcbfql0n/+8x8ziq9ly5a2n8ryneXbJatWLXe3UmGfhqAljr1ds9KkfQPPIxgrnMgOBEIowHQ6IcTmUmERYCrLsLBz0RAIMJVlCJC5RNgEmMoybPRcOMgCfPYOMrCF1TOVpYWYVIUAAghEmQDBXJR1KLcTuwJWBHP6xaOuwaBf0lxzzTXy1FNPuQXVdeH0uG6LFy+Wjh07muelR9z9/PPP0rVrV7O//P9169ZN9Lg+zp8/v/xhv1/v379fNOzTrUmTJrYP5tYfKJJVjuksE+LjK71XDeU0vNy0K1tu6HtEpWU5iEA4BfhyIJz6XDsUAgRzoVDmGuEQIJgLhzrXDJUAwVyopLlOqAX47B1q8cCvRzAXuB1nIoAAAtEuQDAX7T3M/cWMgBXBnGLp2m+6BpyOeluyZIk0a9asguFxxx1npqrU0Wl//fWX67iOaNORdPqDgtbz8ccfu445nyxfvlzat29vXt5zzz1y1113OQ8F/BhpwZyGn28tWCQSX8MRzsWJM4BzB7A5Z7eM6d5aMtJS3R1mHwK2EODLAVt0A40IogDBXBBxqTqsAgRzYeXn4kEWIJgLMjDVh02Az95ho/f7wgRzfpNxAgIIIBAzAgRzMdPV3Gi0C/gTzJ1//vmiYVYtx3SKzz//fBmaKVOmyLBhw8w+nZpSQ7rS643cdttt8tBDD5njEydOlBtuuKHM+YMGDZLp06ebfeWnw9y2bZv0799ffv/9d6lWrZosXbpUmjZtWub8QF5EYjCn9zlr1V/y28Z8aZCZUeG29+0vkvzCXLn02E6S5FjPjw0BOwvw5YCde4e2WSFAMGeFInXYUYBgzo69QpusEiCYs0qSeuwmwGdvu/WI5/YQzHm24QgCCCAQ6wIEc7H+DuD+o0bAn2AuLS1NCgoKzNpuunZb+e26665zTWOpI+CGDx8umZmZ8uWXX8r3339vinfp0sVMQ5lYLjTSUXM9e/aUlStXmkDv2GOPlcGDB8uaNWvkk08+MWvKaQWPPvqo3HjjjeUvHdDrSA3mnDf764aNsnDjDsnbXyxJjtktG2WmSd9DWjBKzgnEo+0F+HLA9l1EA6soQDBXRUBOt60AwZxtu4aGWSBAMGcBIlXYUoDP3rbsFreNIphzy8JOBBBAAAGHAMEcbwMEokTAymBOp1a8+OKL5dVXX3Wrc9RRR8mMGTOkbt26bo+vW7fOhHP66G67++67Zdy4ce4OBbQv0oO5gG6akxCwkQBfDtioM2hKUAQI5oLCSqU2ECCYs0En0ISgCRDMBY2WisMswGfvMHeAH5cnmPMDi6IIIIBAjAkQzMVYh3O70Sswd+5cMxotISFBhg4dWumNTp06VYqLiyU1NdWsBeeusB7X0XGzZs0yf3bu3CmtWrUy01zqVJd6nco2LT9z5kxzrrZN16w77LDD5Morr5ROnTpVdqrfxwjm/CbjBAQsFeDLAUs5qcyGAgRzNuwUmmSJAMGcJYxUYlMBgjmbdgzNqrIAn72rTBiyCgjmQkbNhRBAAIGIEyCYi7guo8EIIFBegGCuvAivEQitAF8OhNabq4VegGAu9OZcMTQCBHOhceYq4REgmAuPO1cNvgCfvYNvbNUVCOaskqQeBBBAIPoECOair0+5IwRiTiBag7kDxYWyYc9S2b5vreQX5YpOMZqamCF105pI04yOkpJYLeb6mhu2pwBfDtizX2iVdQIEc9ZZUpO9BKwO5sY//Ji9bpDWIIAAAggg4Ebg9rE3uNlr/S6COetNqREBBBCIFgGCuWjpSe4DgRgWiMZgbvPeVbJ4x0zRcM7dFh+XIO1rHy8tMru4O8w+BEIqQDAXUm4uFgYBgrkwoHPJkAgQzIWEmYsggAACCNhMgGDOZh1CcxBAAIEYFCCYi8FO55YRiDaBaAvmNu5dLr9t+9ynbmpT6xhpU+ton8pSCIFgCRDMBUuWeu0iQDBnl56gHVYLEMxZLUp9CCCAAAKRIEAwFwm9RBsRQACB6BYgmIvu/uXuEIgJgWgK5goP7pNZ616TgyUHfOy7OOnReKTUSKnnY3mKIWC9AMGc9abUaC8Bgjl79QetsU6AYM46S2pCAAEEEIgcAYK5yOkrWooAAghEqwDBXLT2LPeFQAwJRFMw91fOL7Js5xy/eq9ZjU5yWN2+fp1DYQSsFCCYs1KTuuwoQDBnx16hTVYIEMxZoUgdCCCAAAKRJkAwF2k9RnsRQACB6BMgmIu+PuWOEIg5gWgK5hZu+0I27V3mVx/WTGkoxzUe7tc5FEbASgGCOSs1qcuOAgRzduwV2mSFAMGcFYrUgQACCCAQaQIEc5HWY7QXAQQQiD4Bgrno61PuCIGYE4imYO43RzC30e9grpEjmBsWc/3ODdtHgGDOPn1BS4IjQDAXHFdqDb9AqIK5UH0BGn5RWoAAAgggYCeB8Q8/5rY5ofrvUk5OjjzxxBOuNowbN871nCcIIIAAArEtQDAX2/3P3SMQFQLRFMytyf1N/sie7Ve/NK/RWTrW7e3XORRGwEoBgjkrNanLjgIEc3bsFdpkhQDBnBWK1IEAAgggYFcBgjm79gztQgABBBAgmOM9gAACES8QTcHc/oMFMnv963KguMCnfomTeOnZ5Bypnlzbp/IUQiAYAgRzwVClTjsJEMzZqTdoi5UCBHNWalIXAggggIDdBAjm7NYjtAcBBBBAwClAMOeU4BEBBCJWIJqCOe2ErXl/yS9bp0uJ43/etg51eknLzC7einEcgaAKEMwFlZfKbSBAMGeDTqAJQREgmAsKK5UigAACCNhEgGDOJh1BMxBAAAEEKggQzFUgYQcCCESaQLQFc+q/fd86+X37l1J4MM9tdyTGJcuhjukrm2R0cHucnQiEUoBgLpTaXCscAgRz4VDnmqEQIJgLhTLXQAABBBAIlwDBXLjkuS4CCCCAgDcBgjlvQhxHAAHbC0RjMKfoB0uKZPPelY6Qbq3sK8qVkpISSUusLnXSmkrjjPaSFJ9i+76hgbEhQDAXG/0cy3dJMBfLvR/d904wF939y90hgAACsS5AMBfr7wDuHwEEELCvAMGcffuGliGAgI8C0RrM+Xj7FEMg7AIEc2HvAhoQZAGCuSADU33YBAjmwkbPhRFAAAEEQiBAMBcCZC6BAAIIIBCQAMFcQGychAACdhIgmLNTb9CWWBQgmIvFXo+teyaYi63+jqW7JZiLpd7mXhFAAIHYEyCYi70+544RQACBSBEgmIuUnqKdCCDgUYBgziMNBxAIiQDBXEiYuUgYBQjmwojPpYMqQDAXVF4qRwABBBAIswDBXJg7gMsjgAACCHgUIJjzSMMBBBCIFIFYDeYOFhdJdsEG2V24XYqKCyU5IU0yUxpIrdQsiY+Lj5Tuo51RIEAwFwWdyC1UKkAwVykPByNYgGAugjuPpiOAAAIIeBUgmPNKRAEEEEAAgTAJEMyFCZ7LIoCAdQKxFsyVlJTImtyFsipnvhwoLqgAmZaYIe1qHy9Z1dtWOMYOBIIhQDAXDFXqtJMAwZydeoO2WClAMGelJnUhgAACCNhNgGDObj1CexBAAAEEnAIEc04JHhFAIGIFYimYKykpll+3fSZb8lZ57a9DanZzBHTHeS1HAQSqKkAwV1VBzre7AMGc3XuI9gUqQDAXqBznIYAAAghEggDBXCT0Em1EAAEEYlOAYC42+527RiCqBGIpmFu16ydZsesHn/vviPoDpVH11j6XpyACgQgQzAWixjmRJEAwF0m9RVv9ESCY80eLsggggAACkSZAMBdpPUZ7EUAAgdgRIJiLnb7mThGIWoFYCeZ0TbmZa1+WopIDPvdlRnId6dlklM/lKYhAIAIEc4GocU4kCRDMRVJv0VZ/BAjm/NGiLAIIIIBApAkQzEVaj9FeBBBAIHYECOZip6+5UwSiViBWgrmd+Rvlh81T/e7HE5qPkZSEdL/P4wQEfBUgmPNVinKRKkAwF6k9R7u9CRDMeRPiOAIIIIBAJAsQzEVy79F2BBBAILoFCOaiu3+5OwRiQiBag7mSkhL5duU2me34s37nPtl3IE/ikzZJu6wC6dx8nyTE+9a9vZqcJ9WTa/lWmFIIBCBAMBcAGqdElADBXER1F431Q4Bgzg8siiKAAAIIRJwAwVzEdRkNRgABBGJGgGAuZrqaG0UgegWiMZhbtDFHbpryi6zYtsdtx9WsViTnHJ8tHZsWuD3u3Bkn8XJSi8scIV6ScxePCFguQDBnOSkV2kyAYM5mHUJzLBMgmLOMkooQQAABBGwoQDBnw06hSQgggAACRoBgjjcCAghEvEC0BXM/rN4ho/87TwqLir30TYlc0Dtbjm6d57Fcw2qtpWuDgR6PcwABKwQI5qxQpA47CxDM2bl3aFtVBAjmqqLHuaEQKCgokNTU1FBcKijXKN/+4uJi0Z9dIvmeggJFpQgESYBgLkiwVIsAAgggUGUBgrkqE1IBAgiEWyCagrm9hQfkhMe/lm17Kh8J5zRPSiiWO8/cLHUzipy7XI+J8SnSo/HZkp5Uw7WPJwgEQ4BgLhiq1GknAYI5O/UGbbFSgGDOSk3qslIgLy9PnnzyScnIyJBrrrnGVL1o0SKZMmWK6OcOnfJ9yJAhctRRR7m97LfffitfffWV6Hs8OTlZzjrrLGnatKlMmjRJ9N/0xMREc6yoqEjS0tLkkEMOkZ49e0qrVq3c1ufvzmXLlskrr7xiQrgnnnhC4uLiTBW7d++We+65R4455hgZNmyYv9VSHgEE/BQgmPMTjOIIIIAAAiETIJgLGTUXQgCBYAlEUzD39vw1cvuHv/lFdWKnXDnj6Jwy56QmVHeMlDtVaqY2KLOfFwgEQ4BgLhiq1GknAYI5O/UGbbFSgGDOSk3qskogNzdX/vWvf0l+fr5MmDBBmjVrZqrOycmRqVOnyi233CL6fNy4cfLvf/+7wmW3bNkiQ4cOlR9//FGqVasmDz30kIwYMUKqV68u33//vVx55ZWycuVK6d69uwnH/vzzT3nnnXekZs2a8tRTT8mAAQNcQVqFyr3s0La/+eab8vzzz8vSpUvlhBNOkC+++MJ1lo6Y09Dw4YcflksuucS003WQJwggYLkAwZzlpFSIAAIIIGCRAMGcRZBUgwAC4ROIpmDulvd/lf/9vM4vzEMaFMgNg7aac9ITMyWrejtpmXmEJCWkmH3FJQdF15pz/qauX5VTGAEfBAjmfECiSEQLEMxFdPfR+EoECOYqweFQWAT27dsn1113nfz6668ybdo0adiwYZl2/PDDD9K/f3/Zu3evXHrppfLiiy+WOa7B13333WdCOz0waNAg+eSTT1xlNOzr0aOHqV/DuOHDh4uOmtOg7M4775Rjjz3WhH+NGjVynePPE/3vxfLly00oqKFhnz59ZPr06RWq+Oabb0xA+NJLL5mRehUKsAMBBCwRIJizhJFKEEAAAQSCIEAwFwRUqkQAgdAKRFMw96/3fpaPftvgF2CnxjVk8pgukpyQ5vjz9xoc2/atkXW7F8uugo1yoLjQEczFSbWkWlK/WktpUaOzpCZW9+saFEagMgGCucp0OBYNAgRz0dCL3IM7AYI5dyrsC6fACy+8ILfddpuZBlJHvZXeNHTT4zfccIMJ00477TR5//33y/zy2ZdffikXXnih6Mg1De8mTpxoyjvr+f333+W4444zo+c+++wz6dKlizmkz3WkXOPGjUVDszZt2jhPCehRR8mNHDnSTFk5Y8YMt3Wcc845sm3bNjM9p47WY0MAAesFCOasN6VGBBBAAAFrBAjmrHGkFgQQCKNANAVzz89eIRO+WOqX5rAjm8mEoUeYczSE+23b56LBnKctIS5JDqvXTxo7RtaxIWCFAMGcFYrUYWcBgjk79w5tq4oAwVxV9DjXaoHs7Gw58cQTJT093Uz/qNNQlt527twpV199tezZs0cWLFhgprj8+uuvzXSVWu6vv/4yYVjr1q3lu+++Ey2vU1c6wzcto+u9XX/99dKrVy/RME7Xl9PAb+zYsfLoo4/KqaeeKq+//rrUrl1biwe8ffrppzJq1KhKgzkN784++2x58MEHzei/gC/GiQgg4FGAYM4jDQcQQAABBMIsQDAX5g7g8gggUHWBaArmNuXkS9/Hv5L9RcU+w0y5tKcc2by2lJQUyw+b33eMktvk07lH1B8ojaq39qkshRCoTIBgrjIdjkWDAMFcNPQi9+BOgGDOnQr7wiWgYVrfvn3NiDldPy4hIaFMUxYtWiSXXXaZjB492oRohYWFJqCrV6+eCdd0pJyGdoMHD5YxY8bIUUcdJRqQlR6NdsYZZ8iHH34oV1xxhRlNt3HjRhPEPfbYY6acrg/Xu3fvMqPwyjTCxxe+BHN6bb1WixYtTEiYmJjoY+0UQwABXwUI5nyVohwCCCCAQKgFCOZCLc71EEDAcoFoCuYU5/Uf/pK7P1nkk9OFx7aSuwZ1MmXX7v5dluyY5dN5Wig5PlX6NrtQEuKTfD6Hggi4EyCYc6fCvmgSIJiLpt7kXkoLEMyV1uB5uAV0mkoNzN544w0599xzKzRn0qRJJkR75plnZNiwYaLB1s8//2ymnXzuuefMaLh3331XdO24CRMmyC233CL333+/JCX9/Vl3zZo1ctJJJ8mff/4pHTp0kOTkZNm0aZMZoXfKKafI+eefb0a4VbhwADt8Cea0Wl3Tbu3atWb6zHbtmM0iAGpOQaBSAYK5Snk4iAACCCAQRgGCuTDic2kEELBGINqCOVV568fVcu+MxR5HzsU5ylzRu43c1L+D6zd6v9/4ruQWbvUL9Yj6Axyj5qq2hoZfF6RwVAoQzEVlt3JTpQQI5kph8DSqBAjmoqo7bXMz+tl83rx5smrVKvE2Ckw/Q/Tr189MS3nfffeJjpT74IMPZMiQIWXuR6ebvOSSS0zIpqFbnz59ZNmyZTJr1izJysoyr6+77joZMWKEDBw4UH777TeZOnWqGT3nrGjKlClm/TkdYffss8+a0G7u3Llyxx13mOfOciUlJWYk3uLFiyU+Pt652+1jUVGRdO/eXQ477LAyx30N5jQo1HBRA0WdxpMNAQSsFSCYs9aT2hBAAAEErBMgmLPOkpoQQCBMAtEYzCnl5tx8ecMR0M1esU3W78yTg44vCbIy0+W4Q+rKed1bSuv6GWXEP1/9vKPMgTL7vL1oU+sYaVPraG/FOI5ApQIEc5XycDAKBAjmoqATuQW3AgRzblnYWUWB3bt3y8033yyvvfaaWcOtsur0c/xbb70lgwYNkjvvvFMefvhht8Hc1q1bzWg3LXPmmWeKjnCbM2eOPPXUU/Ljjz+KvpdfeuklEwhqwKUj4t5//33R9eac21VXXSU6sk5H4+moPH1+0003SatWreTbb791rSunIeBdd91lRuA5R9s56yj/uG/fPnnkkUfk2muvLXPI12Bu6NCh8tVXX8nLL79sQsUylfACAQSqLEAwV2VCKkAAAQQQCJIAwVyQYKkWAQRCJxCtwZy/gl+tfVn2H8z367T2tXtIq5pd/TqHwgiUFyCYKy/C62gTIJiLth7lfpwCBHNOCR6tFNBga/369bJ9+3avI850dJqGZzVq1DAB19ixY+Xtt9+WkSNHlmnS559/Ltdff7188cUX0qRJEznnnHPkvffek0MOOURSU1PN9JUaxj300ENmjTodXff4449LtWrVTD269tyAAQPk+++/l+eff14uv/xyWblypZx++unmUdeW09F2zk2nuNy8ebNrZgrn/vKP2v6mTZtK/fr1yxzyNZjT9fA0YNTr60g/NgQQsFaAYM5aT2pDAAEEELBOgGDOOktqQgCBMAkQzP0N/8vWGbIlb5VfvXBc1nCpmdrQr3MojEB5AYK58iK8jjYBgrlo61HuxylAMOeU4NEOApMnT5ZRo0bJiy++KJdeemmZJukIPF0bTkfB6XbjjTfKY489JikpKWY9OR2xptNKavg2c+ZM+c9//iMXXXSRq44lS5aYEXe5ublmhF3Hjh3NsSuvvNIEdcOHDzeBoLepK10VenmiQaKGh8ccc4xMnz7dbWkN9Xr16mWm/NTAsVOnv9eNdluYnQggEJAAwVxAbJyEAAIIIBACAYK5ECBzCQQQCK4AwdzfvjkFW2Tupvd8xq6d2liOyTrT5/IURMCTAMGcJxn2R4sAwVy09CT3UV6AYK68CK/DKbBw4UITVJ133nlmtFtycrJpzq5du6R///5y1llnya233mr2Pfroo6Kj684++2wzhWV6erosXbpUjjvuODPK7euvv5YuXbq4bkfXcNOyuh6cjlBLSEgwx7TcaaedJrVq1RJdg06DNCu2jz76SM4//3w59thj5bPPPnNbpY7I02BOR9tpOzRkZEMAAWsFCOas9aQ2BBBAAAHrBAjmrLOkJgQQCJMAwdw/8H/l/CLLds75Z4eHZ6mJGXJs1lmS5nhkQ6CqAgRzVRXkfLsLEMzZvYdoX6ACBHOBynFeMAT0M72uH7d8+XKZO3eu1K1b11zmgQceMOvPHX/88eZR15fTqR+ffvppM4KuXr16ZirLJ598Un755RcTzOl6chdeeKHoyLhXX31VNMjTEXda9t577zUhXWZmpug1tb5vvvnGBHnPPvusCdPi4uICusW8vDwzQk6vN3/+fDNFp07BqVNztm/fvkydGt5pG/X+rrjiijLHeIEAAtYIEMxZ40gtCCCAAALWCxDMWW9KjQggEGKB0sFc48aNZfTo0SFugW+X03bq9Dn6hUAwt637/pTlOd9L4cE8t5epn9ZS2tfqJSkJ6W6PsxMBBBBAoKxATk6OJCUludYqKnuUVwgg4BR45LGnnU/LPN4+9oYyr3mBgCeBjz/+WC677DKz3ty5555rii1YsMCEarp2na4pd/TRR5v133QNu8MPP9yEa999953ounC63pxOEanBmk4N2bJlS5k1a5ZkZ2ebf8f1l4k0kOvRo4dkZPz9C2q///67CQP1s3rnzp3l0EMP9bo2nqf279u3z0yVqW3REX/aZm1Lt27dpFWrVmVO07Bu9erVouvR1a5du8wxXiCAgDUCnoK5m2+4xpoLeKlFf/5/5plnXKXGjRvnes4TBBBAAIHYFiCYi+3+5+4RiAoBgrmK3VhcclB2FKyTXQWbZH/xPomPS5RqiTWlXloLqZZUq+IJ7EEAAQQQ8ChAMOeRhgMIlBEgmCvDwYsABDTIevDBB0WnntQ/HTp0CKAW+53iDAudLdNpM++++2554YUXpGfPns7dPCKAgMUCBHMWg1IdAggggIBlAgRzllFSEQIIhEuAYC5c8lwXAQQQiA0BgrnY6GfusuoCBHNVN6QGkYKCAhNaLVu2zEzzeNhhh0UNi04fq1NY6pSZukaeTqPJhgACwRMgmAueLTUjgAACCFRNgGCuan6cjQACNhAoHcw1adJELr74Yhu0qmITCgsLZefOndKoUaOKB9mDQAQL6G+36x/ddLqmhISECL4bmo5ARQGdAk2nJHNOe1axBHsQiEyBoqIiV8P13+5A19VyVvLghMedT8s8MpVlGQ5e+CigAZaGdMOHD6/ye9PHSwa92O7du+Wtt96S7t27S9euXYN+PS6AQKwLeArmbrvl+pDQ6C936fqXzo2pLJ0SPCKAAAIIEMzxHkAAgYgXKB/MjRkzxpb3pMGcfrmblZVly/bRKAQCFdD1WkoHc4mJiYFWxXkI2FJgx44dkpKSQjBny96hUVUR0NE7zk3/7a5qMOfpC1CCOacyj/4K6Odn/fc3Wjb9vKSBuP6yBxsCCARfINz/XdJg7oknnnDdKMGci4InCCCAQMwLEMzF/FsAAAQiX4Bgzv8+3OlYe277vjWSdyBXShzr0aUmZkidtCZSP72FYz06Rjv5LxrbZxDMxXb/x8LdE8zFQi/H5j0SzPnf78UlJbJ9b6FsdfzJyd8vefsPygFH2BLvGDGenBAvGSmJUrdaijTKSJXqjudsCCCAAALhEyCYC589V0YAAQQQqFyAYK5yH44igEAECBDM+d5Je/fvlN+3fyU5hVvcnpSaUF0Oq9fXEdC1dHucnQi4EyCYc6fCvmgSIJiLpt7kXkoLEMyV1qj8eWHRQVmxY6+scvwpKPp7+ubKzxBHQJcsHerXkCaZad6KchwBBBBAIAgCBHNBQKVKBBBAAAFLBAjmLGGkEgQQCKcAwZxv+rmF2+THTe9LUcl+ryd0qnuCNK3R0Ws5CiCgAgRzvA+iXYBgLtp7OHbvj2DOt77XMO63zbmy/6BvgVz5WjWgO7ppbclMTSp/iNcIIIAAAkEUIJgLIi5VI4AAAghUSYBgrkp8nIwAAnYQIJjz3gsHS4pkzobJjqkrc7wXdpSIk3jp2WSUVE+u5VN5CsW2AMFcbPd/LNw9wVws9HJs3iPBXOX9XuSYonLe2p2yITe/8oI+HE1wTHV5dNNa0qJ2NR9KUwQBBHwR0J8DFy5cKKtWrZINGzaY9QibNWsm3bp1k6ZNm/pSRZXKrFu3Tn7//XfRR11LrHHjxtK+fXs58sgjxYo1l/Wevv76a+nXr580adKkSm2N1ZMJ5mK157lvBBBAwP4CBHP27yNaiAACXgQI5rwAOQ5v3rtSft32qfeCpUo0r3G4dKzbp9QeniLgXoBgzr0Le6NHgGAuevqSOykrQDBX1qP0qwOO0XHf/Lldsvd5n2mg9Hnenh+RVVPa18/wVizijhcVFcmuXbtEH9msF4iPj5fMzExJTU21vvIIrFHfZ998841MnDhRFixYYEIwtSksLJSCggKpVauWnHvuuXLJJZcEJaDTwOzxxx+X999/X/bs2WP6Rfto3759UuwI9Hv27CnXXHONnHDCCRLnCOX93bZs2SJTpkyR559/XrZv326u06NHD3+robxDgGCOtwECCCCAgF0FCObs2jO0CwEEfBYgmPNO9ceO2bJm92/eC5YqUSO5nvRocnapPTxFwL0AwZx7F/ZGjwDBXPT0JXdSVoBgrqyH81VxSYkJ5bbtLXTusvTxmGa1pWWUjZzTEUujRo2S5cuXWzJSyFLwCK+sxPF+zMjIkAkTJsjw4cODcjca/uifNm3aSFKSvadc1fBLQ7FHHnlEcnNzzWiyO++8U7p27SoaaL366qvy9NNPm5Ds+OOPl0mTJknbtm0tc/v+++/lpptukh9++EFq164tY8eOlXPOOUfS09PN6Lbx48fLr7/+ao499thjJiBMSEjw6fobN26Uzz//XJ588kkzEk9P0kB2+vTpovfC5r8AwZz/ZpyBAAIIIBAaAYK50DhzFQQQCKIAwZx33EXbv5b1exZ7L1iqRLWkWtK76Xml9vAUAfcCBHPuXdgbPQIEc9HTl9xJWQGCubIezle/bsyRZdv3OF9a/hjvGEBzctsGUjMt2fK6w1XhsmXL5OSTTzZT+oWrDdF8XQ12XnrpJbnooouCcpuffPKJ3HzzzXL66aeb8O+II44QHQFmt01DSg2+/v3vf4s+17DqrbfekubNm5dp6oMPPih33XWXGcF50kknmbAuKyurTJlAXixatMj46Pu9Ro0aosHbxRdfXKYqndpy5MiRsnTpUqlTp44899xzXgPVtWvXmvDtzTfflHnz5pWpr2bNmjJt2jSCuTIqvr8gmPPdipIIIIAAAqEVIJgLrTdXQwCBIAgQzHlHXZO7UP7I/tZ7wVIlGlY7RLo2OLXUHp4i4F6AYM69C3ujR4BgLnr6kjspK0AwV9ZDX+nUlV+s2FrxgMV7ajtCuZPa1g9omjuLm2JJdTqd3+zZs806W3YMdCy5yTBWoqPYjjrqKGnRokVQWvHRRx/JkCFDTN3169cXnTbxyiuvNKPRApmKMSiNdFT6xhtvyOWXX25Gw2kw9uGHH0rfvn0rXE5H1elUlh988IE5ps9feeUVSU4OPAzftGmTCdy+++47U+e1115rptJ0t5acTnGpo+h0ak31/Pjjj6V79+4V2qk7dG262267TXbv3i0aIup2xx13iI6e041gzjAE/H8EcwHTcSICCCCAQJAFCOaCDEz1CCAQfAGCOe/GhUX7ZNb6/8rBEt/X/ejWcLDUT2/hvXJKxLwAwVzMvwWiHoBgLuq7OGZvkGCuYtd/tXKbbM8LzhSW5a92bPPa0qJWtfK7eY1AyAWWLFliRn79/PPPrnUCdbRXnz59zDpt+piSkhLydpW+oAZjGh7Onz/f7O7du7cZSVa9evXSxVzPNQwbNmyY6M+Kuv6chmUDBgxwHff3yUMPPSQ6ZaZ+7tVQUOvXNrjbsrOzZcSIETJz5kxzWJ/raDh3IZ62T6fg1ABO69Xt1FNPlRkzZpjnBHOGIeD/I5gLmI4TEUAAAQSCLEAwF2RgqkcAgeALEMz5Zrxu92JZvONrnwpnVW8nXeqf7FNZCiFAMMd7INoFCOaivYdj9/4I5sr2ffa+QsdouW1ldwbxVa20JDmlXcMgXoGqEfBdQMOkqVOnymuvvWbWSMvPzzcnayCnI7lGjx5tRtBpUBSO7YknnpDbb79dtF06KvOWW24RnbLS07ZmzRoTzC1YsMAUGThwoBlhF8gaerr+3imnnCK//PKLqUun+tS14OrVq+f28jrNpq6Bp+vP6abrxGkw2K9fP7fly+88++yz5Z133jG7CebK6/j3mmDOPy9KI4AAAgiEToBgLnTWXAkBBIIkQDDnO6yGc39kz5bikoMeT2qWcZgcWrePxMfFeyzDAQRKCxDMldbgeTQKEMxFY69yTypAMFf2fbBgwy5ZuWNv2Z1BfjWgXXStNRdkLqoPgYAGdO+9957897//NUFUUdHfM27oOnc6Ym3UqFHSq1cvs35aCJpjLqE/751xxhmuUWR169Y1a+7pPk+bBng33nijPP/886ZIw4YNTTDnaUpJT/Xo/q+++sqsv6dTZOqmwdnkyZPNc0//p+foyDdtu04HetVVV8nTTz/tqXiZ/QRzZTiq9IJgrkp8nIwAAgggEEQBgrkg4lI1AgiERoBgzj/n/KI9sjb3N9mev1byDuSYhdNTE6tJndQm0qzG4VIztYHZp+WKigslKSFVUhOqR80aKP5pUdoXAYI5X5QoE8kCBHOR3Hu0vTIBgrmyOh//sUny9nv+5aWypa151blRphza4O/p66ypkVoQsEZAR4nNmTNH3n77bTNto66XpptOx9i1a1cZPHiwXHTRRdKoUSNrLlhJLUuXLpWTTz5Z1q9fb0q1b9/erB+nj5Vtzz33nAnEtIwGizqC7YEHHqjslArHNJjUc8aNG2eO6f3ffffdZlrLCoVL7dA26yi7devWmb2HH364cWzevHmpUu6fEsy5dwlkL8FcIGqcgwACCCAQCgGCuVAocw0EEAiqQDQHc1v3FMjmPfmyt7BIShyK6UkJ0iAjVbJqpMmGXfvksyWb5Y/NubKn4IDUSk+WLk1rySkds6Ru9cDWgNAw7s+cBbJ570o5UFzg6rfkhDTJqtZWWtXsJhrisSFQWoBgrrQGz6NRgGAuGnuVe1IBgrl/3gf5Bw7Kh0s2/bMjRM8a10iVXq3cT4cXoiZ4vIxOx6f/jddgQkMN/aNTCLLFlkBOTo7MnTtXXnnlFZk+fbqUDug0bDrrrLPk3HPPlSZNmgTtF/k+/fRTEwQ6R+/17NnTrC/nXJPNU49MmzZNzjzzTDNqTcvoCLYPP/zQ7VpvnurQzwAXX3yxCdW0jE5L+eKLL5o15Dydo/u3bdsmOn2mrtunm057OWnSJBk0aJB5Xdn/EcxVpuPfMYI5/7wojQACCCAQOgGCudBZcyUEEAiSQDQGc7vy98uP67IlJ/+AW7VCx5dHb81bLSu37qlwPDkxXi7v2Uau6dtWEhN8//Jka95fsnDb53KwxP019UJJ8SlyRP0BUje9WYXrsiN2BQjmYrfvY+XOCeZipadj7z4J5v7p8217C2Tmqu3/7AjRs4yURBnUIfgjjvy5nZ07d8rixYtNCPPXX3/J5s2bJS0tTTp27CjnnXeeHHnkkf5UR9koESgoKDDrqr300kvy/fffS25urrkzDWtbtWplwrnhw4dLu3btLA9wNQi7/PLLXZI6Wu+jjz5yvfb0REf8aRi3e/duU8Tb2nDu6lm1apUMHTpUFi1aZA7Xr19f/ve//5npPN2Vd+7TaS81hPvmm2/MruTkZBk/fryZXtNZxtMjwZwnGf/3E8z5b8YZCCCAAAKhESCYC40zV0EAgSAKRFswt93xxdCsP7c7AjIdI+d5099ifufHNfL7+hy3hU7p2EieHXmU4wfjOLfHS+/cVbBZftg01TEqr7j0brfP4+MS5LjGI6RGcl23x9kZewIEc7HX57F2xwRzsdbjsXO/BHP/9PX6nH0yZ032PztC9CzZ8UtUZ3ZqHKKrVX4ZHQk1c+ZMefLJJ+Xrr78WnSawT58+5qR33nlH9N/CTp06yeuvvy5dunSpvDKORq2Avk905Jy+D/R9smfPP78o2KxZM7n00kvltNNOEx1NZ8Wm/07deeedMmHCBFd1F154oRl95trh4YmOVtNgbuvWraaETiOpo+86dOjg4YyKu+fPn28CNh0Bp1vjxo3ls88+k8MOO6xi4VJ7iouLzbp4H3/8sWvvNddcI0899ZTrtacnBHOeZPzfTzDnvxlnIIAAAgiERoBgLjTOXAUBBIIoEE3B3IGDxTJj2WbR6ZR82fYXFcvjn/8hOfvcj3K7e1AnGX1sK69Vfb/xXckt/PsHVq+FHQXqpDWV7o08L7buSx2UiR4Bgrno6UvuxL0AwZx7F/ZGvgDB3D99uHZXnsxdu/OfHSF6luj4BaphhzcJ0dU8X2bZsmVm3Syd+s8ZhOgIJR0dpNsFF1xggpi4uDi59957va6v5flKHIkWAZ1Wcvbs2Wb02JQpUyQ7+59gOysrS/r16ycaMOl0jlXZ9u7dKxpo/fe//zXV6Hvw+uuvl4kTJ3qtVt/XAwYMkDVr1piyderUkRkzZsjRRx/t9VxngS+++EKGDBki+fn5ZpeODvzqq6+kZcuWziIeH3WE6Ztvvuk6fs4558hbb73leu3pCcGcJxn/9xPM+W/GGQgggAACoREgmAuNM1dBAIEgCkRTMPdn9l6Zv96/L4VmL9sqny1yvyZK45pp8t1N/Std72HfgVyZtf41v3voxOaXiK49x4YAwRzvgWgXIJiL9h6O3fsjmPun7zfm5su3q3f8syNEz1IdU5CfcVh4R8zpqCINPubNm2fu+tFHH5Vrr71WkpKSzGsd+aNTFE6dOtVMUXj33XfLXXfdFSKhwC+zcrtj7eTsPI8V1Hesydy1SS2Pxzngm4BO2bhw4UIT3L777ruia9I5N11X7cQTT5Sbb75ZdBrJQDadMlNH4b333nvmdJ06c+zYsWZaSG/16TSUp5xyivz555+mqK5Jp+GzrlHn66Yj3s444wzRvwe6tW3b1gRzTZs29VrFmDFjzNp8zoKnn366+Xuk6zVWthHMVabj3zGCOf+8KI0AAgggEDoBgrnQWXMlBBAIkkA0BXO6rtzqnZ6/QHBHuHr7Xnlp1kp3h8y+ebecJA0zPQdo2fnr5cfNH3g839OB4xuPlMyUv3+L2lMZ9seGAMFcbPRzLN8lwVws93503zvB3D/9q+v7frbc99kD/jmzas/qpCfLSW0bVK2SKpz9008/mdDjt99+M7WMGDHCjExKTU111frjjz/KyJEjzaijmjVriq73pUGd3bdfN+bI8m3/TLNYvr29WtWVrEo+I5cvz2vPArouoY4Me+SRR2TDhg2ugjq6Tdec0+kb+/fv79rvz5Ndu3bJ6NGjxTklpIZaOrXluHHjvFajayRqMLdy5d8/K+laiZ988omccMIJXs91FtD15Eq/33WtxS+//FIaNfK+NuRll10muiafczv55JPN2ngpKSnOXW4fCebcsgS0k2AuIDZOQgABBBAIgQDBXAiQuQQCCARXIJqCuXlrd8jaXfv8AlvvCPKem7nC4zmzbjhRmtep5vH47sLtMmfj2x6PezrQp+kFkp6U6ekw+2NIgGAuhjo7Rm+VYC5GOz4Gbptg7p9OPlhcIlMWbRDHQ0i3Qxyf0Y5uWjuk13ReTMOUoUOHmukIdZ+uKacjnkqvDabvEQ1FJk+ebE7TkEOf16pl/5FmC9bvkjWOz8mZqX+P/HPetyMrkpqTkYPsAABAAElEQVRpSdItTO7OdkTD4+rVq816a6+88oroyEvnlpiYKJ07d5azzjpLLr74YtGRc4FuOgJPR57piE3dNJi744475J577vFaZflgLj093QRzOs2mr9uHH35o/p7o+t66HXrooSaY0+k6vW3lgzn9+6P1Ecx5k7PuOMGcdZbUhAACCCBgrQDBnLWe1IYAAmEQiKZgbsnWXFm0OdcvxQWrs2XqgnVuz6mWnCC/3DFQkh3TJHnaSkqKZea6SbL/oO+BYHpipvRpdoGnKtkfYwIEczHW4TF4uwRzMdjpMXLLBHNlO/qrldtke15h2Z1BfnVs89rSopbnX6AK1uX187MGG+PHjzeX0CBlwoQJZu0u5zXz8vLkgQceEJ3aUt8rxxxzjEyaNEk6dOjgLGLrxx8dawbm7S+Sfm2Y4cHqjlqyZIm8/fbbZnpJ52g0vYZOM6nTPuqIrz59+oiu6VbVbc+ePXLVVVfJG2+8YarSUXg6NebDDz/stWptm4ZhGtDpplNZTp8+XXr06OH1XGeBzz77zKwxV1j4978Nbdq0McFc8+bNnUU8PmooqX9nnJsG4TolJ1NZOkWC/0gwF3xjroAAAgggEJgAwVxgbpyFAAI2EoimYE6/PJi+dJNPv62tv7WpP5i+8M0KWbvD/fSX53ZvIfcN7uy1t9buXiRLdnzjtZyzQOf6J0vj6u2cL3mMcQGCuRh/A8TA7RPMxUAnx+gtEsyV7fgVjjXJfnZMfxiqLd4xcmuoY325pATPv0AVrLboFJaDBg2S7du3m0t06tRJZs6c6RrZtGLFCnnwwQfN6Dj9rH3SSSeZ4E5HQUXKNndNtuwvKpY+rQMfrRUp9xqqdmrA9dprr5mRlcuXL3ddVoMmnary/PPPN+8VKwI5Z+UFBQVmTTmdDtO5XXfddfLEE084X3p8XLp0qQwYMEDWrl1ryujIvRkzZki3bt08nlP+wJw5c2Tw4MGiU2rq1qJFCxPMtW7dunzRCq/PPfdceeutt1z7zzvvPLMWn2uHhydMZekBJoDdBHMBoHEKAggggEBIBAjmQsLMRRBAIJgC0RTMqdPy7btF18TwZZu3art8/Os/6ziUPqdZ7XT5+MrekpmWXHq3x+eLHcHcOkdA521rldlV2tfx/bdMvdXH8cgXIJiL/D7kDioXIJir3IejkStAMFe27zTE+eiPTVIUovksW9WuJt2bhX4aS/3v9pVXXula+0p/0UtDuKuvvtqMLNK1wjRM2LZtm1lH64YbbhANGKwMW8rKB+fVd3/tEP1Ftl6H1JN9jl9+27v/oGMa9gTRGSX0ntl8EyguLpb169ebEXIvvPCCea77dMvIyJBevXqZdQr79u1rXvtWq++ltA8nTpxoRsk5z9L3o3MEnXOfu8cFCxbIqaeeat7LelzDNB0Bd8ghh7gr7nbfH3/8IaeffrqsWrXKHNcpLDXc8xZSq9GQIUPM1JnOinWkn45M9bYRzHkT8v04wZzvVpREAAEEEAitAMFcaL25GgIIBEEg2oI5JdLf2F64aZfHkXP6A+p3K7bJZ79vkr9XOygL26VpLXl25FGSVTOt7AEvrzbsWSords2TgqK9FUrq9JXtah8rjaq3rXCMHbEtQDAX2/0fC3dPMBcLvRyb90gwV7Hff9ucI39s3VPxgMV7dLTcwPYNJSOl7PpnFl/GbXUbNmwwU/k5RxFpSDVy5EgTXvz222+SlpYmrVq1ktNOO82srdWyZUu39dh95yzHL7DtP1gsiQ7sbXv/maI02TFC8fCsTNH1/QjoPPei/ryhIyf/97//yauvvuqaDlLPqF27thx77LEmzD3xxBNFp0L1tOmoTD1es2bNgL117UMNq7RNumnY9vHHH5upMz1dV/fPnj3bvI91OkzddKTcF1984dcaifr35ZxzzpHvvvvO1FG3bl0zYtDbOnU6FayOSp01a5Y5T9eV01Du2muvNa8r+z+Cucp0/DtGMOefF6URQAABBEInQDAXOmuuhAACQRKIxmBOqXRay5WOgG7zngLZW1jkCOBKHL/lmygNMlKlec10+XLpFpmxaKMscaxJp8drOUbGdW5aU4Z0aSoDO2Y5flAN7DeBix1rzuUUbpHcwm2O3xgvlKT4FMlMaSA1Uxo6WlAsW/NWy86CjSa8i49z/NZxUk2pX62l43iDIPUw1dpdgGDO7j1E+6oqQDBXVUHOt6sAwVzFnilyBDnTl22RfQcOVjxo4Z5D62dI56yaFtboe1UatGjQUFRUZE5q3LixtG3bVpo0aSIdO3aUnj17ik5tqaOhInmbqWsGOgK5BtVTpKUjhMtzjJhbsiXX9YtvTTLTpEerupF8i0Fru67NpmuhvfPOO7J48WLXdTSQ0/fHqFGjTDiWnp7uOlb+iYZhGma9/PLLJhwbPXq01yCtfB3O1z/88IOZkjIn5+9ZRTQU/OSTT7yO4vzwww9l2LBhrve6rn+n91VZkOi8pvNx3759cuONN4qOFtRN/148++yzotNSVrZt3rxZBg4cKAsXLjTFGjZsaKYB1WlhvW0Ec96EfD9OMOe7FSURQAABBEIrQDAXWm+uhgACQRCI1mAuCFRVqnJL3irHOnSzpfCg+/Xs6qQ2kU71TnSEhzWqdB1OjjwBgrnI6zNa7J8AwZx/XpSOHAGCOfd9tT2vUDTUcTcrgfsz/Ntb2/HLVP3b1pf4ME2nePvtt8tDDz3kGn30zDPPyMUXXyypqan+3YjNSy/ftsfxi2QH5bCGmZLw/7+wtsjxC21Ltux2tfzIJjWlTb3IDiBdN2PBk19++UUmTZpkpmpcvXq1q0adxlQDrosuushM4Zic7Hmq/OzsbDPKTqdD/fnnnyU/P99MlTp27NiAR8xt3brVBHO//vqraZOO4tSA+cgjj3S10d0THaGm19VNR6yNHz9edGpWf7fXX39dLrjgAnOajrK85ZZbzN+hyurR0acawumUsLodd9xxMm3aNJ9G6xHMVSbr3zGCOf+8KI0AAgggEDoBgrnQWXMlBBAIkgDBXJBgS1W7Jneh/JH9bak97p8mx6fKMVlnSfXk0K+X4r5F7A2FAMFcKJS5RjgFCObCqc+1gylAMOdZ96+defLjup2eCwR4RNc369+mgaQ51joL16YBgwYNulWvXt2EBb179w5Xc0J63WxH6PqlYzp455aZmiQDOjR0vozZxy1btsj9998vU6dOFX2umwZQjRo1krPOOsuEUoceemil4a3+t/Kjjz4yo8mWLl0qBQUFph6dGvWpp56SMWPGmNeB/J9OYXnJJZfIK6+8Yk7PzMwUDZR1rTlP2+7du+Waa65xvdd1bTgNxo444ghPp3jcr6PeBgwY4LLRteOmTJkiCQme/x7riD4doaefk9VSA8FHH33U4zVKHyCYK61RtecEc1Xz42wEEEAAgeAJEMwFz5aaEUAgRAIEc8GF3l24XeZsfMdxEd9+b7xGcj05vvEIxw+g8cFtGLXbRoBgzjZdQUOCJEAwFyRYqg27AMFc5V3wV/Ze+Wn9Lh8/AVVelx7NSEmUvofUk2rJntfj8l5L1UuU/tJfQxMNU/r3719pxevXrxd9v+jac5G86fTv0/7Y7LoFnfj9rM5NXCPqXAdi7IkGVqeffroUFxebO2/RooUMHz5czj//fDO9aWUc69atM+u96Ug754g2La/hma7ppuHYySefXGmoV1n9zmMzZswwAaH+N1m3K664Qp577jnn4QqPy5YtM1Norlq1yhzT6TfffPPNCuV0h47q06k3dapOd9Nc6ntfA0pd1063du3ayZdffilNmzY1r8v/n04Te+edd8rDDz9sDum6dLq2na+hoE41+/bbb5tzdW0+7Z/jjz++/GV47YMAwZwPSBRBAAEEEAiLAMFcWNi5KAIIWClAMGelZsW6Fm77QjbtXVbxQCV7jmp4utRLb15JCQ5FkwDBXDT1JvfiToBgzp0K+6JBgGDOey9u21sgc9fulPwqrjmn65l1b1pbkhPD/4tLOnLn8ccfd938Y489Jtdff73rdfknuk7YbbfdZkYM3XHHHeUPR9Rr7cePFm8q0+YzOmVJSqLnkU9lCkfpiw8++ECGDh0qbdq0MWvHaYiloVplm051qefpOnTz5893FdVATkMkrWPw4MFmVKbrYBWe6FpvGhTqqD7dunTpYsKuevXqua31P//5j1x66aVmytYaNWqYALpPnz4Vyv7555+ifwfWrFkjunbdZZddJu7q1KkzdcpXDfB02lcNznTknLtNw8pBgwbJokWLzGGdAlTboyPnfNk0BHTeJ8GcL2KeyxDMebbhCAIIIIBAeAUI5sLrz9URQMACAYI5CxArqWLWutdkX1FuJSUqHmpd82hpW/uYigfYE5UCBHNR2a3cVCkBgrlSGDyNKgGCOd+688DBYlm0JVdW7ciTg44p9fzZqjtGx3XJypSmNdP9OS2oZXXkkU6xp5+hdWvWrJkZSdSzZ88y19W1wp5//nkz/Z5O46fhhU5tGMnb7oIDMmPp31M16n2kO6YUHXxYViTfkiVt15FuP/30k5xwwgnSunXrSuvUtdNee+01mT59uqxYscJVVgMkfV9peKVrvwVjzUK93mmnnWauq9NI6hSZOnKufOCloeHAgQNFR83ppmvL3XrrrRXK6Xv8yiuvlPfee891H9ddd5088cQTrtfOJ/p5V+twTkd54okniq6jV79+fWcR1+Ndd90l9913n3mtFu+//775e+YqUMmTvLw8M4J13rx5ppSOatW/s+5CxUqq4dD/CxDM8VZAAAEEELCrAMGcXXuGdiGAgM8CBHM+UwVUcObaV6TwYJ5f57bM7Cod6vTw6xwKR64AwVzk9h0t902AYM43J0pFngDBnH99VuAYbaVrz63L2Se78g94PDkxPk4aZqRKy1rp0tgxUq58aODxxBAd0C/+R4wYYYIV5yWbN29u1uvq3r27pKSkmBFQOhpK1wrToEWDBp2OL1K2ldv3SI6jjzo1ypTUUuv5bdtTIF+v2u66jaY10+T4lpFzX66GW/xEP8tVtl6aHl+5cqVZ102nPt2wYYOrBfq+0EBu9OjR0rlzZ6lWrZrrWDCe6PWvuuoq2bhxozRs2FBefvllM5rT2f6tW7fKtddea8I2/bt34YUXysSJE0WDw/Kb3pMGfcuXL3cd6tGjh5mm0l2wqJ8HNAjU0Wy67t3ll18uDzzwgJkCUyvQKSzfffddE/bpGnc65eULL7zgNVTTunQaUT1fR+bpqD0dIejcxo4dK7fffruxjY+Pt92/Kc522vGRYM6OvUKbEEAAAQRUgGCO9wECCES8AMFccLvwx80fSHb+er8ucni9E6VJxqF+nUPhyBUgmIvcvqPlvgkQzPnmRKnIEyCYC7zP9hcVS07Bfsnbf1B0RF28IwDQaSp1HbnM1CTzOvDag3+mBhIabnzzzTcmDCh/xerVq5sRPv/6179MsOFu3a3y59jldU7+fvls2VbTnAYZKdLHsa6fMxz9fVOO/LF1jzmWnBAvJ7VrINUdfcbmXqCgoEAWL14sr7/+ukyZMkU2b/5nfb6srCw588wzzftDAzkNjEK1zZw5U/7973+bADk5OdmsPaeB2pYtW8xovt9//91MRzls2DB58MEHRaeydLfpf9/170HpEXOXXHKJvPTSS+6Km306yu7ee++VN954Q3bt2mWm7dR1G2vVqiWff/65TJ482YScOtXmM88843VKUK1U/x5+/fXX5n7mzJkjGp6X3jR07N27txx33HHSsmVL0dF6OtKVzbsAwZx3I0oggAACCIRHgGAuPO5cFQEELBQgmLMQ001Vm/Yul4XbPndzxP2uxPgU6dtstCQ5HtliQ4BgLjb6OZbvkmAulns/uu+dYC66+9fb3enIIh3NM3v2bNHRPbppINe4cWPp27evmQpQw5dI2wqLDsrHize7ph1tVaeatKuX4RhBt19+WrfL7NdRjT0cI+Ua1kiNtNsLSXv1s933339vwjgNrfS94txatWolJ598shlhqUFRuDYN4V599VWzzpyOntu7d68kJSVJnTp1zPpzI0eONCPVNLirbNP18XQ02s6dO6VTp05mPUUd6VbZpqPbPv74Y+OjwaWGdWqmAaAGZjoKT0NLX//+qLGu5agBso5YLR9y6oi6wsJCE6LryL9zzjlHOnbsWFkTOfb/AgRzvBUQQAABBOwqQDBn156hXQj4IaDTXKxfv170hyT9YSTWNoK54Pf4gi2fyLZ9q326UJf6J0tW9cp/mPWpIgpFjADBXMR0FQ0NUIBgLkA4TrO9AMGc7bsoJA3UUVF79uwxoUB6erron0jfsvMKZdHm3bLD8VhU/M+6gHGOGzukbjU5rGHZKS4j/X6tar+GP9OmTZO3335bvvrqK8nN/Wed6Q4dOpgpFnW9wbZt21p1ySrXk5+fL/rfaX3UkZ0ajvk77ar+PK2j1DTUKx+KeWvgtm3bTLCtYZ3+3dFru5sG01s9HA+OAMFccFypFQEEEECg6gIEc1U3pAYEwiagi0jrNBq6ALduujD0McccI7169ZKbb77Z7/n9dSHr5557zu/70ak3dF0K56bXL73ugHN/+UedVkTXt6jqRjBXVUHv5x8sLpJFO2aKjp7ztCXEJclhdftK44z2noqwP0oFCOaitGO5LZcAwZyLgidRJkAwF2Udyu1UENCRRrrW3D7H+oA6zahOW6nTjrKVFdCfpzSQmzRpksydO9dM0aglNKTSQE7XaRs8eLC0adOm7Im8QsDmAgRzNu8gmocAAgjEsADBXAx3Prce2QI6p/7999/v8Sb69+8vn3zyiZkKw2Ohcgd0Cg8Ny/zddI2K1q1bm9P0Cx4NCPWLem+bLtQ9ZswYb8W8HieY80pkWYGd+Rtl3Z7Foo8FB/McX2zES7WkmlI/vaU0r9FZUhOrOaYHKpKCor1SXHJQUhKqSXICUwRZ1gE2rYhgzqYdQ7MsEyCYs4ySimwmQDBnsw6hOQiESUDXbNOpF50j5HT6R1037txzz5Xhw4dLgwYNXOv0hamJXBaBgAQI5gJi4yQEEEAAgRAIEMyFAJlLIGC1wLvvvis6Z75uOo3IHXfcYdaB0OksP/roI5kwYYI5NmTIEDPvvS4W7cs2depUmT59ui9FzXV0Hn5dfFpH7GVkZJjzVqxYITonv86Pf+utt1b6A9wZZ5zh02LY3hpEMOdNKDjH9TeQtZ+dm4Z1f+b+LNn5600o59yfkVxHmmV0kqY1OjqCPN/ei85zeYwMAYK5yOgnWhm4AMFc4HacaW8Bgjl79w+tQyBUArpe2umnn26WRejatasJ40aPHi21a9cOVRO4DgJBESCYCworlSKAAAIIWCBAMGcBIlUgEEoB/QL80EMPFQ3AdA78H3/8UQ455JAyTbjhhhvk8ccfN/s++OAD0YDOyu2dd96Rs88+28zf/9NPP5kgzln/jBkz5NRTT5UmTZqYde+c+4P5SDAXTF3f6l6aPUdW5/5SaeHM5PrSreFgSUmM/HVLKr3RGDxIMBeDnR5jt0wwF2MdHkO3SzAXQ53NrSJQiYCuJ6ezmegyA3369CGQq8SKQ5ElQDAXWf1FaxFAAIFYEiCYi6Xe5l6jQkBHtA0aNMjcy8SJE0VDuPKbBlW66LQu4q4/XGmQZtW2ZMkS6d69u1kcWxcFd47cc9b/1FNPyXXXXScnnHCCWTDcuT+YjwRzwdT1XvfKXT/Jyl0/eC/oKJGRXFeObzyCkXM+aUVOIYK5yOkrWhqYAMFcYG6cZX8Bgjn79xEtRCAUAnv37hX996BWrVqhuBzXQCBkAgRzIaPmQggggAACfgoQzPkJRnEEwi1w0003iQZyuumoOU8LcA8cOFA+/fRTSU9Pl+3bt5vHqrZdA7AjjjhC/vjjDznvvPPk9ddfr1DltddeK08//bRcddVV8swzz1Q4HowdBHPBUPWtTl1L7pt1/5USKfbtBEepQ+v0khaZXXwuT0H7CxDM2b+PaGHVBAjmqubH2fYVIJizb9/QMgQQQACBqgsQzFXdkBoQQAABBIIjQDAXHFdqRSBoAkcddZQsWLBAWrVqJX/++afH6zz44INy++23m+OLFi2Sww47zGNZXw/ce++9cvfdd0v16tVNKNioUaMKpzoDQR05d80110hxcbGsWrXK/Aamrj2XmJhY4Zyq7iCYq6pg4OevyV0of2R/61cFNVMayXGNh/l1DoXtLUAwZ+/+oXVVFyCYq7ohNdhTgGDOnv1CqxBAAAEErBEgmLPGkVoQQAABBKwXIJiz3pQaEQiqQM2aNSU3N1d69+4ts2bN8ngtHc12wQUXmOMzZ86Ufv36eSzry4Hly5dL586dpbCwUO677z6588473Z6mI/g0iHvggQdErztv3jzJz883ZZOTk6Vbt25mxN8xxxzj9vxAdhLMBaJmzTkaymk458+WkpAuJzQf488plLW5AMGczTuI5lVZgGCuyoRUYFMBgjmbdgzNQgABBBCwRIBgzhJGKkEAAQQQCIIAwVwQUKkSgWAJ6JffSUlJUlJSIkOHDpWpU6d6vNS0adPktNNOM8fdrQXn8UQPBwYMGCCfffaZNG3aVDSkS0tLq1CyqKjI7NfHyrb4+Hi55ZZbREf1WbERzFmhGFgdqxzry63wcX055xWqJ9WWXk3Pdb7kMQoECOaioBO5hUoFCOYq5eFgBAsQzEVw59F0BBBAAAGvAgRzXokogAACCCAQJgGCuTDBc1kEAhHIzs6WunXrmlPHjBkjL7/8ssdqvvvuO+nVq5c5rmu96ZpvgW6//vqrdO3a1Zz+5ptvyqhRo9xWpVNrtm7d2hzTAFFH1Z100knSpUsX2bRpk3z44Ycybtw42bNnjynzzjvvyIgRI9zWVX5nQUGB6B93mwZzjz32mOiIvGbNmona2HHT0Ybah1lZWUFv3ubcfJn6yzqZvzZbtu0ulNSkBGlVr7qc2L6hnHRoI0mIj7OkDTkFW2Tupvf8qqtFjc5yaN3efp1DYXsLEMzZu39oXdUFCOaqbkgN9hQgmLNnv9AqBBBAAAFrBAjmrHGkFgQQQAAB6wUI5qw3pUYEgiawc+dOqVOnjqn/yiuvlGeffdbjtb744gs5+eSTzfHXXntNzj//fI9lvR0YOXKkvPvuu3L44YfLwoULJS7Ofaijx66++mrJy8uThx9+2IRy5etevHix6Dp5GrLptJwbNmyQatWqlS9W4fXYsWNlwoQJFfaX3qGB3NFHHy0XXnhh6d22ea4BYk5OjtSvXz9obSouLpFnZq+U579dJUWO5+621o6A7tEzu0jHRpnuDvu975dt02RHwTqfzkuIS5LjG50tqYnVfSpPocgQ0LUk9Y9u+u9DQkJCZDScViLgo8CuXbvML3/48t8rH6ukGAK2ECg9y4H+2+3pM56vjZ0w8Sm3RW8fe4Pb/exEAAEEEEAgmAKegrlbbrw2mJd11a0//+svSjs3/UVlNgQQQAABBFSAYI73AQIRJpCSkiIa8OioNR295mnT6SvPOeccc3jGjBmiU1EGsukouHbt2omOiKnqyDvn9XUkna5Bp9ucOXPk+OOPdx7y+OhrMHfEEUfIkCFDPNYT7Qce/maNfPzHdq+3mZYUL8+e0V7a1fMeinqrrKikUFbtny0FJbsrLRon8dIy6TipkdCw0nIcRAABBBBAAIHIFPjvG++4bTjBnFsWdiKAAAIIBFnAUzA3+ryRQb7y39XrbEH63YxzI5hzSvCIAAIIIEAwx3sAgQgT0Kka169fL6eeeqroOnKeNg3RrrnmGnP4p59+MqPUPJWtbP/ll18uL774oqSmpprpKGvVqlVZcZ+OaVCo7dft6aefNqPsvJ3oazAXyyPmZi7fKpdPXuCN0nVcR85Nu7KXJdNaFhUfkFW5P8qGvUukuOSg6xrOJ7VSGkv7Wj0kI/nvEZ/O/TxGhwAj5qKjH7kLzwKMmPNsw5HIFmDEXGT3H61HAAEEEKhcwFMwx4i5yt04igACCCAQfAGCueAbcwUELBXQ4Gn+/PnSokULWb16tce6naPS4uPjZdu2ba4pMD2e4ObAli1bzHV0bTQdfffWW2+5KeX/rgULFriCwueff140/PO26dSX2g53m44gnDhxYsyvMXfepLky50/vo+VKG75+4bHSs7V1U2seOOhYR69gveQdyDEBXUpCNamT1kSqJdWUwoP7ZGven5JTuFW0XFJCimQm15cG1VoxtWXpTonA56wxF4GdRpP9EmCNOb+4KBxBAqwxF0GdRVMRQAABBPwW8BTMhWokt05l+cQTT7jazYg5FwVPEEAAgZgXIJiL+bcAAJEmcPPNN8ujjz5qmr1p0yZp1KiR21vo0qWL/Pbbb3LsscfK3Llz3ZbxtlNHyjlDs5kzZ0q/fv0qPUXDQA3PjjzySNF16TxtkydPNlNx6vHZs2dLr169PBX1ab8Gc+PHjzdlmzRpIrrWnB03tcnOzpasrKygNK/L/TMkN/+AX3XfclIHuaJ3W7/O8bewjqBbuetHWZ37q9vRdDrFZYvMztK29rGSEJfob/WUt4EAwZwNOoEmBFWAYC6ovFQeRgGCuTDic2kEEEAAgaALEMwFnZgLIIAAAggEKEAwFyAcpyEQLgEN2zR00+3GG290hXSl2/PHH39Ix44dzS4NrG677bbSh31+PmzYMJkyZYrUrVvXjLqLi4ur9NzevXvLt99+K40bNzbTbXoqf+KJJ4oGfbpe3ubNm6Wq02MSzP3dLZ3vmyG7C/wL5m7u30Gu7BO8YK64pFh+2TpNtu1bU+l7Rw/WSW0i3RoNJpzzKmW/AgRz9usTWmStAMGctZ7UZh8Bgjn79AUtQQABBBCwXoBgznpTakQAAQQQsEaAYM4aR2pBIKQCnTp1ksWLF0tCQoL8+OOPZoSaswG5ubkycOBAM0ouLS1Nli1bJrouXent7rvvFv0iJiMjw2Nop2tG1atXT3bu3ClnnXWW/O9//ytdhdvn99xzjzinZrj//vvljjvuqFDuhRdekCuuuMLs13XjHnrooQpl/N1BMPe32DmvfC/z/trhF99/LzhGerdt4Nc5/hT+M2eBLN/p+4jNQ2oeJe0cI+fYIkuAYC6y+ovW+i9AMOe/GWdEhgDBXGT0E61EAAEEEAhMgGAuMDfOQgABBBAIvgDBXPCNuQIClgv8+uuv0rdvX9EQLjMzU4YOHSqnnnqqrFixQt58803REXO6PfDAA3L77bdXuL4Gdrpmm06DqdNhutt+/vln6datmzmkc6Jfd9117oqV2bd7924zdabz+oMGDZIBAwZIhw4dzHp4X3zxhbz77rvmnIYNG8ry5culRo0aZeoI5AXB3N9qny3ZJFdMnu8zYYs61eTL6/pJYkK8z+f4U1BHy3299hXZX5zv82mJcUlyQvNLJCGeKS19RrNBQYI5G3QCTQiqAMFcUHmpPIwCBHNhxOfSCCCAAAJBFyCYCzoxF0AAAQQQCFCAYC5AOE6LXoFVq1a5RpHpiC5nOOXrHS9ZssQ1akyDsbZtgzNNoK4b179/f9m3b5/bpl199dXy1FNPibvpJH0J5h5++GG59dZbTd0//PCDdO/e3e11yu9cvXr1/7F3HvBNVW8ff7r3AMosUFpW2QjI3kP2EBX4s0QElaUiKg4ciIqiIuIARFBEwAEKCgrI3kv23rtQRqF7981z+iakbca9SW5yk/yOnzb3nn2+J8Xc/M7zPMSCnFacK1zO9ywqLly4ULi8NFQuNw/C3ANiE5bup98PXHmQYeTK38eTFg9vQQ9VLG6khvXZyZl3acvVn2R31DyyP4X7KWfFJ3tCaGCWAIQ5s4hQwckJQJhz8g3E9I0SgDBnFA0KQAAEQAAEXIAAhDkX2EQsAQRAAARclACEORfdWCzLcgJ79uzRiVArVqygXr16yeps48aN1L59e9FmwYIFNHToUFnt5VRmEXD58uW0adMm4dKSrefq1atH48aNo86dOxvt6rPPPqPs7GwKCgoiFvAMJV77iRMnRNFLL71Evr6+hqoZzGM3mEuWLBHWcWzFx+4wq1evLiznmjZtSsOGDSNPT9tZaUGYe7ANObl59Om/x+nbrWdJc2kwVSweSDP6NaSHKignyvHA9zPiafu1nw3OwVRm07KPUfGASFNVUKYyAhDmVLYhmI7NCUCYszlSdKgSAhDmVLIRmAYIgAAIgIAiBCDMKYIVnYIACIAACNiAAIQ5G0BEF65FwBphjgWpSZMm0dSpUwUUtlhjkQxJWQIQ5oryvXw3hX7Zd4n2XrxD8UkZxBZylUuGUMcaZahHnUjyUch9pf5MsnIzaN3FuZRHufrZZq87RI0gP69As/VQQT0EIMypZy8wE2UIQJhThit6dTwBCHOO3wPMAARAAARAQDkCEOaUY4ueQQAEQAAErCMAYc46fmjt5ATY0uz9998vsAqOk7Z3b36crrp161LJkiULlJu6uXz5Mp05c0ZXZdmyZSL+my4DF4oQgDCnCFabdHrg5j8Ul/Lgb8Jcp6UCo6lRmZ7mqqFcZQQgzKlsQzAdmxOAMGdzpOhQJQQgzKlkIzANEAABEAABRQhAmFMEKzoFARAAARCwAQEIczaAiC6cl8Avv/xCAwYMUGQBfn5+dOrUKYqKilKkf3T6gACEuQcs1HaVnp0s3Flm5BiOhag/Xx9Pf2qhiS8X6BOmn41rJyAAYc4JNglTtIoAhDmr8KGxiglAmFPx5mBqIAACIAACVhOAMGc1QnQAAiAAAiCgEAEIcwqBRbfOQUApYS42NpbeeOMNGjJkiHOAcPJZQphT9wYmZybQ/purKDnrrtGJBvmE00Olu1Gob4TROihQLwEIc+rdG8zMNgQgzNmGI3pRHwEIc+rbE8wIBEAABEDAdgQgzNmOJXoCARAAARCwLQEIc7blid6cjMD58+dp48aNBWZ94cIF+uCDD0Te2LFjqX79+gXKTd0EBgZSq1atqHz58qaqoczGBCDM2RioAt3l5uXQtaSTdD35FN3PiKfsvEzy8vChML9SVDa4GpUPrkFent7EcekyslPIw8OT/L2CRZ4C00GXNiYAYc7GQNGd6ghAmFPdlmBCNiIAYc5GINENCIAACICAKglAmFPltmBSIAACIAACGgIQ5vA2AIFCBPbs2UNNmjQRuStWrKBevXoVqoFbtRGAMKe2HTE/n7y8XCG+cc28vDy6mnScLiUeocTMeF1jT/KkEgEVKCa8gXjVFeBCdQQgzKluSzAhGxOAMGdjoOhONQQgzKlmKzAREAABEAABBQhAmFMAKroEARAAARCwCQEIczbBiE5cicChQ4eod+/eYklz586lTp06udLyXHItEOacd1szc9Lpv5srKSH9uslFRIXWo5olWmvEPA+T9VDoGAIQ5hzDHaPajwCEOfuxxkj2JQBhzr68MRoIgAAIgIB9CUCYsy9vjAYCIAACICCdAIQ56axQEwRAQKUEIMypdGPMTIst5XZeX0r3MuLM1MwvjglvSLHFW0iqi0r2JQBhzr68MZr9CUCYsz9zjGgfAhDm7MMZo4AACIAACDiGAIQ5x3DHqCAAAiAAAuYJQJgzzwg1QAAEVE4AwpzKN8jI9K5o3FceubXOSKmhbA9qU2EIBfmEGypEngMJQJhzIHwMbRcCEObsghmDOIAAhDkHQMeQIAACIAACdiMAYc5uqDEQCIAACICATAIQ5mQCQ3X3IxAXF0fs3pK/lEtISBDxsKRSGDJkCBUrVkxqddSzkACEOQvBObjZnrjldDvtsqxZVC/enCqHN5LVBpWVJwBhTnnGGMGxBCDMOZY/RleOAIQ55diiZxAAARAAAccTgDDn+D3ADEAABEAABAwTgDBnmAtyQYDOnTtHTz75JO3YsUOWGKeP7sSJExQbG6ufhWsFCECYUwCqHbrcdHkBpWbflzVShZDaVKdke1ltUFl5AhDmlGeMERxLAMKcY/ljdOUIQJhTji16BgEQAAEQcDwBCHOO3wPMAARAAARAwDABCHOGuSDXzQmcOXOG2rVrR9euXbOKBIQ5q/BJbgxhTjIqVVXcfu1nup8RL2tOMWGaOHMlEGdOFjQ7VIYwZwfIGMKhBCDMORQ/BleQAIQ5BeGiaxAAARAAAYcTgDDn8C3ABEAABEAABIwQgDBnBAyy3ZdAdnY2RUVF0fXr13UQgoODKTo6Wril9PLy0uWbu1iwYAFVqFDBXDWUW0kAwpyVAB3U/NTdHXTu3j5Zozcu+yhFBOBvShY0O1SGMGcHyBjCoQQgzDkUPwZXkACEOQXhomsQAAEQAAGHE4Aw5/AtwARAAARAAASMEIAwZwQMst2XwMGDB+mhhx7SAZgxYwY999xz5Ofnp8vDhboIQJhT135InU1GTiptvrKQsnMzJDUp5l+OmpZ9jDw8PCTVRyX7EYAwZz/WGMkxBCDMOYY7RlWeAIQ55RljBBAAARAAAccRgDDnOPYYGQRAAARAwDQBCHOm+aDUDQnMnTuXnnnmGbHy8ePH0/Tp092QgnMtGcKcc+2X/mzvpF2lvTf+pNy8bP3sIteB3mHUtNzj5O8dVKQMGY4nAGHO8XuAGShLAMKcsnzRu+MIQJhzHHuMDAIgAAIgoDwBCHPKM8YIIAACIAAClhGAMGcZN7RyYQJsHTdnzhyxwtWrV1Pnzp1deLWusTQIc869j8mZCXT8zia6nXalyEI8yJPKh9QUceV8PGG1WgSQSjIgzKlkIzANxQhAmFMMLTp2MAEIcw7eAAwPAiAAAiCgKAEIc4riRecgAAIgAAJWEIAwZwU8NHVNAs8//zx9+eWXYnGnTp2iatWqueZCXWhV7irM3UnJoCv3UikhLZOycvLI19uTSgT6UlSxIAr19zG6w9k5ubTmeBxtOh1Pl+4kU1ZuHpUNDaBmMRHUu355k22NdmqDgpSse8QWdOnZyeTp4UmBPuEinpyvVwAlZd6hmynnKSUrgXLycsjfK5CK+UdSqaBK5OXhbYPR0YU1BCDMWUMPbZ2BAIQ5Z9glzNESAhDmLKGGNiAAAiAAAs5CAMKcs+wU5gkCIAAC7kcAwpz77TlWbIYAu66cMGGCqPXXX39Rjx49zLRAsaMJuJswl56VQ3uu3KXriWlG0VcuEUwPRYaTt6dngTr7L9+ll37bT5fuphTI194E+3nTpG61qX+jKG2WQ1/Ts1Po6O0NFJ96weA8/DQCXc0SralsMAR0g4DslAlhzk6gMYzDCECYcxh6DKwwAQhzCgNG9yAAAiAAAg4lAGHOofgxOAiAAAiAgAkCEOZMwEGRexK4ePEiValShfiL5meffZZmz57tniCcaNXuJMxlZOfQujM3KSnDdEw23r5SwX7UJqYUeXl6iN3cef4WDfthF2VqLObMpVcfqUmj2lQ1V03R8rSsRNp5fSml5ySbHSe2eAuKCW9oth4qKEMAwpwyXNGreghAmFPPXmAmtiUAYc62PNEbCIAACICAughAmFPXfmA2IAACIAACDwhAmHvAAlcgoCMwbNgwWrBgAXlqrI327t1LDRo00JXhQn0E3EmY23npNl1KSJW8CXXKhFEtzU9qZja1/3w93UxMl9SWtbw/R7ehWuXCJdVXohKLcgnp1yV33bxcPwr3LyO5PirajgCEOduxRE/qJABhTp37gllZTwDCnPUM0QMIgAAIgIB6CUCYU+/eYGYgAAIg4O4EIMy5+zsA6zdIIDU1lbp160abN2+mkJAQmjJlCo0dO5a8vLwM1kemYwm4izCXrrGWW3H0GuXJwO2viTvXu1Yk/X7gCr287ICMlkRPNKxI0/o+JKuNrSonpMdprOV+k9VdmaAq1KB0N1ltUNk2BCDM2YYjelEvAQhz6t0bzMw6AhDmrOOH1iAAAiAAAuomAGFO3fuD2YEACICAOxOAMOfOu+/Ga09ISKCPPvrIJIGMjAyaM2cOpafnWxiVLFmSKleuTNHR0VSuXDlJIt3LL79M3A5JWQLuIszFaWLKbda4o5SbetYsR9PWHKcfdxmO02asv9jSofTP8+2MFSuaf/7ef3Ty7nZZY/hq4s11jBohqw0q24YAhDnbcEQv6iUAYU69e4OZWUcAwpx1/NAaBEAABEBA3QQgzKl7fzA7EAABEHBnAhDm3Hn33XjtHEeOBTal08mTJ6l69epKD+P2/buLMHddI8xtsUCY61GjHH20+hgt2nNR1nulaqkQWvtCe1ltbFX5TMJu4h85ydvTlx6p9JycJqhrIwIQ5mwEEt2olgCEOdVuDSZmJQEIc1YCRHMQAAEQAAFVE4Awp+rtweRAAARAwK0JQJhz6+1338VDmHOtvXcXYY7jxP15XHrMNd5lXy9P6lM7kn7ee4neXHFI1sb3qhtJX/RvJKuNrSrHJZ+hA/H/yOou3K8MNY/sJ6sNKtuGAIQ523BEL+olAGFOvXuDmVlHAMKcdfzQGgRAAARAQN0EIMype38wOxAAARBwZwIQ5tx599147ezK8v3331ecwMSJE6lUqVKKj+PuA7iLMMf7zBZzbDknNcVqrN7qlytG99Myqc1n6zSvWVKb0i8jW1LjSiUk17dlxZzcLNp4+QfKzJW+1toR7aliaG1bTgN9SSQAYU4iKFRzWgIQ5px26zBxMwQgzJkBhGIQAAEQAAGnJgBhzqm3D5MHARAAAZcmAGHOpbcXiwMB9yDgTsJcisZq7t/TNyk9O8fs5oYH+FDHKqXJW2M1x2n1ses0evFeyjPbkmh488r0VnfHilw3Us7R/purJMyWqLh/eWpS9lHy8PCQVB+VbEsAwpxteaI39RGAMKe+PcGMbEMAwpxtOKIXEAABEAABdRKAMKfOfcGsQAAEQAAEiCDM4V0AAiDg9ATcSZjjzUrKyKIdF+9QgsYKzlgqF+pPTSqWID9vrwJV1h6Po4m/H6B7RiznvDTC1vPtq9O4dtVUIXJdTz5NR26tp5w845Z+pQNjqF6pR4hjzCE5hgCEOcdwx6j2IwBhzn6sMZJ9CUCYsy9vjAYCIAACIGBfAhDm7Msbo4EACIAACEgnAGFOOivUBAEQUCkBdxPmeBvy8vLo2v00unwvVSOyZVJmTp5GhPOkEoG+VKl4EJUK9je6W0npWfTb/su06dRNungnhbJzc6lMaAA1i4mg/z1cicoXCzTa1hEF6dkpdCnxEMWnXqDkzASNxV8u+XoGUPGAclQhpDaVDIwS08rISaWsnHQh0Pl5BalCWHQEL0eMCWHOEdQxpj0JQJizJ22MZU8CEObsSRtjgQAIgAAI2JsAhDl7E8d4IAACIAACUglAmJNKCvVAAARUS8AdhTnVboYdJpabl0ueHvnuOTM1QtyF+wfoWvJJjXvPJN3oLNyVCa5ClcMbUYB3iC4fF8oQgDCnDFf0qh4CEObUsxeYiW0JQJizLU/0BgIgAAIgoC4CEObUtR+YDQiAAAiAwAMCEOYesMAVCAgC6enp1LJlS6tocJyr8PBwKlmyJJUuXZratGlDnTt3poCAAKv6RWPDBCDMGebi6rkJ6XEiBh1byhlLXh4+ws1lmaDKxqog3wYEIMzZACK6UDUBCHOq3h5MzgoCEOasgIemIAACIAACqicAYU71W4QJggAIgIDbEoAw57Zbj4UbI5CamkpBQUHGii3O5z7HjBlDU6ZMIV9fxMKyGKSBhhDmDEBx8azUrPu07eoSys4zHmdPi8CDPKlJ2Uc1ri8jtVl4tTEBCHM2BoruVEcAwpzqtgQTshEBCHM2AoluFCPAhyb9/Y27aFdsYBt1XHj+uRoX8vzs4sxrshEadAMCdiEAYc4umDEICIAACICABQQgzFkADU1cm4BSwpyW2sMPP0wbN25URPzTjuFurxDm3G3Hif67sYpupp6TvPAQ3xLUqvwgyfVRUR4BCHPyeKG28xGAMOd8e4YZSyMAYU4aJ9SyP4GUlBT64osvKCQkhMaNGycmcOTIEVq6dCnx5w6Ot9ynTx/iZytDacuWLbRu3Tri9zgfinz88cepQoUKNH/+fOJ/0729vUVZdna28GpSuXJlatWqFcXExBjqTnbeyZMnad68eUKEmzFjhi72cWJiIk2ePJmaNm1KTzzxhOx+0QAEQEAeAQhz8nihNgiAAAiAgP0IQJizH2uM5EQEkpOTaefOndS7d29KS0sTD25Dhw6lLl26UMWKFcVPQkICHThwgA4ePEj//fefENt4iey+8scff6TAwEDiB8pjx47R1q1b6a+//hIPkFznqaeeEg+FfI1kPQEIc9YzdKYesnOz6N+LcyiPcmVNm4U5FuiQbE8AwpztmaJHdRGAMKeu/cBsbEcAwpztWKIn2xG4f/8+vfjii+I5bNq0aeLZi3u/d+8eLVu2jF599VVx/e6779Jbb71VZOAbN25Q3759affu3eIw5EcffUT9+/en4OBg2r59O40ePZrOnDlDTZo0EeLYuXPn6OeffxahCGbOnEldu3bVCWlFOjeTwXP/6aefaNasWXTixAnq0KEDrV27VteKLeZYNPz4449p5MiRYp66QlyAAAjYnACEOZsjRYcgAAIgAAI2IgBhzkYg0Y1rEWChjR+i+MGKT2EuWbKE+BSlqfTrr78KwY0t7ho1akQbNmwQJzy1bRYvXkzDhg0TJzM57/jx41SjRg1tMV6tIABhzgp4Ttg0OTOBtlxdKHvmDUv3pNJB0bLboYF5AhDmzDNCDecmAGHOufcPszdOAMKccTYocQwBfpZ64YUXxAHIlStXUpkyZQpMZNeuXdSpUyfig5TPPPMMzZkzp0A5C18cOoBFO049evQQByTFjeYXH7rkeOJ8wJLFuH79+hFbzbFQNmnSJGrWrJkQ/8qWLattIuuV/39x6tQpIQqyaNi2bVtatWpVkT7YgwoLhN9++62w1CtSARkgAAI2IQBhziYY0QkIgAAIgIACBCDMKQAVXTo/AX4g44e+iIgI8dBWvnx5SYvav38/tWvXjthFCZ/S5BOd+mnRokU0ePBgkfX2228LNyb65bi2jACEOcu4OWurjOxUWn/5O9nTb1rucSruX052OzQwTwDCnHlGqOHcBCDMOff+YfbGCUCYM84GJY4hMHv2bHr99deFG0h+ntJPLLpx+UsvvSTEtJ49e9Lvv/9ewLrt33//FYcl+YAli3efffaZqK/t5/Dhw9S8eXNhPbd69WqqX7++KOJrtpSLjIwUnlCqVq2qbWLRK1vJDRgwQLis/Pvvvw32MXDgQIqPjxfuOcPDww3WQSYIgIB1BCDMWccPrUEABEAABJQjAGFOObbo2UkJ7Nu3TxergE85vvfee7JWwrECOPYBxy2Ii4sT4p62A344DAsLI36oZIs8jnuAZD0BCHPWM3S2HrZeXURJmXckT9vTw4tiwhuSl4c3BXqHUYmA8uTrFSC5PSqaJgBhzjQflDo/AQhzzr+HWIFhAhDmDHNBrmMI3Llzhzp27ChCArCwFRQUVGAid+/epbFjx1JSUhLxMxuHGGAvJdp658+fF2JYlSpVRCgBrs+uK7XiG3fG8d7Gjx9PrVu3JhbjAgICxLPZxIkT6dNPP6Xu3buLsATFixcvMLbcm3/++YcGDRpkUpjjNf7vf/+jqVOnCus/uWOgPgiAgHkCEObMM0INEAABEAABxxCAMOcY7hhVxQS+++474e+fp/jnn38Sn8SUk9idynPPPSearF+/ntq3b1+geZ06dejo0aNUq1Yt8VqgEDcWEYAwZxE2p24Ul3yWDsQbPn0sZWEe5EnlQ2pQbPGW5OPlJ6UJ6pggAGHOBBwUuQQBCHMusY1YhAECEOYMQEGWwwhwXG72PsIWc+yK0svLq8Bcjhw5Qs8++6wID8AiWkZGhhDoOMY3H3zkON4s2vXq1YtGjBghDluyQKZvjfboo4/S8uXLadSoUcKa7tq1a0KImz59uqjH8eHatGlTwAqvwCQk3kgR5nhsHqtSpUpCJOSDnUggAAK2JQBhzrY80RsIgAAIgIDtCECYsx1L9OQiBCZPniweBHk5f/zxB/Xp00fWyjg+HceY48Sx6diFiX7SWtRx3ILr16/rF+HaQgKFhbmnn37awp6UbcZfHvDJXUtjVig7O+fr/fTdnXTu/j6rJh7gHUpNyvalAO8Qq/px98b8ZRj/cPLw8CjyRZq788H6nZ8AW3H4+voWiB3r/KvCCkCAhDtALQcWQfjfcGvS1GmfG2z+xsSXDOYjEwT0CbCbShbMFi5cqHP/r18+f/58IaJ99dVXxM9ULGzxsxe7nfzmm2+ENdwvv/wiYsdNmzaNXn31VXr//ffJx8dHdHPx4kV65JFH6Ny5cyLWN/+7zs9jgYGB1KVLFxo6dKiwcNMf09JrKcIc980hFC5duiTcZ1avXt3S4dAOBEDACAFjwtzrr4430sK22ffu3aMvvvhC1ykfOkACARAAARAAASYAYQ7vAxAoRICDgLNLEU5s+TZr1qxCNUzf6seRY9cqfOpTP7Vo0YJ27NhBDRo0EA+S+mW4toyAvjDHcSGGDRtmWUcKt+J5crwLPtWLZBsCN1PP0Zl7uygtJ9HiDkN9S1LjUo9Z/WWkxRNAQxAAAdUT4C9V+Itdrbs01U8YEwQBBxH4ZPqXBkeGMGcQi8tm8mfenTt30tmzZ4V7f1MLZat79jDCbimnTJkiDkgaOhzJB4BGjhwp/i1m0a1t27Z08uRJ2rRpE5UrV07cv/DCC9S/f3/q1q0bHTp0SMT7Zus5beJwA2xVx5/Fv/76ayHa8XPZm2++Ka619fLy8oQlHns58fT01GYbfM3OzqYmTZpQ7dq1C5RLFeZYKGRxkQVFduOJBAIgYFsCxoS5V14aZ9uBjPTGz/98mECbIMxpSeAVBEAABEAAwhzeAyBQiACfpIyOjha57Fbk9OnTulOWhaoavH388cfFQyAXsnVUsWLFdPXYtUqFChWEOMNuVDhYOZL1BCDMWc/QmXvIy8ule5k3KTEznrJy0ikx6zbdSb8sa0n1IrpQqYD8v3tZDVEZBEDALQhAmHOLbcYibUAAwpwNILpAF4mJifTKK6/QggULRAw3U0viz/F8sLFHjx40adIk+vjjjw16Lbl586awduM6jz32mLBw27ZtG82cOZN2795N7Jb122+/FYIgC1w1atQQz1ocb06bxowZIyzrBg8eLKzy2Mru5ZdfppiYGNqyZQtp48qxCPj2228LCzyttZ22j8Kvqamp9Mknn9Dzzz9foEiqMNe3b18Rd3zu3LlCVCzQCW5AAASsJgBhzmqE6AAEQAAEQEAhAhDmFAKLbp2bQLVq1ejMmTNiEexehK3o+BSnucQPZewyhRP3cerUKV0TPg3Kpzj5dCYnfujU1tVVwoVFBCDMWYTNZKOE1Ew6HpdISelZFBbgQzXLholXk41UUvhf/J90N+OarNmUD65NNYq1ktUGlUEABNyHAIQ599lrrNQ6AhDmrOPnKq1Z2Lpy5QrdunXLrMUZW6exeBYaGioErokTJxoMB7BmzRoaP348rV27lsqXL08DBw6kX3/9lSpXrkz+/v7ieY3FuI8++kjEqGPrus8//1xn6cwHJLt27Urbt28XHlHYMwo/7/Xu3Vu8cmw5trbTJnZxGRcXZ9ajAs+fD16WKlVK21S8ShXm2KKPBUYeny39kEAABGxLAMKcbXmiNxAAARAAAdsRgDBnO5boyYUIsDuRli1bUnp6ulgVn5586623hHuRWrVqFXhA4wfOXbt2CaGNH/S0aeXKldS9e3dx+/fff9PYsWPpwoUL4p7jGPDDqvZUprYNXi0joC/M8YM6B3tXY+IYcxyniN3tqDUduHKXpv97kradu1Vgihxypm210jShYyzVKhdeoExtN9uuLtFYzxWcv7k5lg2qRg+V7mKuGsqNEOCDB/ox5ry9vY3URDYIOCeB27dvk5+fH2LMOef2YdYmCLCVkTbxv93Wxpgz9gUosZmicQAAQABJREFUXFlqKePVFIHFixfToEGDaM6cOfTMM88UqMoWeBwbTutxZMKECTR9+nTxbzO7tmSLNXYryeLb+vXr6bvvvqPhw4fr+jh27JiwuGO3cmxhx890nEaPHi2Eun79+glB0JzrSl2HZi5YSGTxsGnTprRq1SqDtVnUa926tXD5yYJjnTp1DNZDJgiAgOUEHP3/JT7cNWPGDN0C4MpShwIXIAACIOD2BCDMuf1bAACMEZg3b55BgcfLy4vCw8MpODiY2KWKVrzT74cfKPnUozbx6U79D2PsGmXy5MnaYrxaSQDCnJUA/7/5DzvP05RVRyg3z3h/3p4e9GGfevREwyjjlRxc8t+NVcSx5+SkyuGNqHrx5nKaoK4eAQhzejBw6ZIEIMy55LZiURoCEObwNlATgYMHDwqhasiQIcLazdfXV0wvISGBOnXqRBwy4LXXXhN5n376KbF1HccGZxeWfPDxxIkT1Lx5cyEwc6zv+vXr65bHMdy4LseDYws1fqbjxPV69uwpwg9wDDoW0myRVqxYQUOHDiX2vrJ69WqDXbJFHgtzbG3H8+ADIEggAAK2JQBhzrY80RsIgAAIgIDtCECYsx1L9OSCBP7880/xwMeBxaUkjkHw0ksvCeu6oKAgXRN9Ye6dd94hnJLSobHJBYQ56zGuP3mDRizcLakjjfEcLX66BTWNiZBU396V4pJP04F4w1+AGJtLy8j/UahfSWPFyDdDAMKcGUAodnoCEOacfguxACMEIMwZAYNshxDgz/QcP47DAezYsYMiIvI/a37wwQci/lyLFi3Ea5cuXcQhyC+//FJY0JUsWVK4svziiy9o//79QpjjeHJPPfWUsIz7/vvviYU8trjjuu+9954Q6cLCwojH5P42btwohDwOO8BimqXWoykpKcJCjsfbu3evcNHJz4IDBgyg2NjYAlxZvOM58vpGjRpVoAw3IAACtiEAYc42HNELCIAACICA7QlAmLM9U/ToYgT4C2e2nuOTmGfPniV2f6Kf2N0JuyZs1KiRiGlQvXp1/WJxzQ+D7MKQH/pq165dpBwZ1hGAMGcdP27dfvo6unAnRXJHdSLD6c/RbSTXt2dFdgu0J+4PupN+VdKwFUNqU+2S7SXVRSXDBCDMGeaCXNchAGHOdfYSKylIAMJcQR64czwBPhj57LPPinhzgwcPFhPat2+fENXYbTbHlGvcuLGI/8YhBerWrSvEta1btxLHheN4c/xZkIU1dg0ZHR1NmzZtEs9ifIiSP7OwIMdhC0JCQkT/hw8fFmIgP1PUq1ePatasaTY2njFSqampwlUmz4Ut/njOPBd+VoyJiSnQjMU6DnXA8egQ4qAAGtyAgM0IQJizGUp0BAIgAAIgYGMCEOZsDBTduT4B/nKOT1uyr/BKlSqJH7gdcey+Q5izjv+5W0nUccYG2Z3smvgIlQ4NkN3OHg2ycjOIXVreNSPORQbHUp2SHcnTw9Me03LZMSDMuezWYmH/TwDCHN4KrkoAwpyr7qzzrouFrKlTpxK7nuSfGjVqOO9i9GauFQu1Wew2kz2pzJ49m1q1aqXNxisIgICNCUCYszFQdAcCIAACIGAzAhDmbIYSHYEACDiKAIQ568jvOHeLBs3fIbuTFaNaU93yxWS3s1cD/gLkavIJunT/ECVm3tIN60EeVMy/HEWHNaDSQdG6fFxYTgDCnOXs0NI5CECYc459wizlE4AwJ58ZWihPgGN4s2jF4QTYzaMreRzhvzl2YckuMzlGHntUQQIBEFCOAIQ55diiZxAAARAAAesIQJizjh9agwAIqIAAhDnrNuFMfCI98sVG2Z3sfPURKhOmTou5wovJykmntJxkjSTnQQHeIeTt6UucdynxCN1MPU/JmXcpJy+b/LwChWhXIaQmlQyMKtwN7o0QgDBnBAyyXYYAhDmX2UospBABCHOFgMi4zcjOoaPX7tP520l0NyVT87kil4L9vKl8sUCqUTaMIsMDZfSGqoYIsIDFIl2/fv0sjvlmqF9H5iUmJtKiRYuoSZMm1KBBA0dOBWODgFsQgDDnFtuMRYIACICAUxKAMOeU24ZJW0uAXaRwQHFt4tgDkZGR4jYjI0ME6taWWfPasGFDCghwDuHCmnU6ui2EOet3oO1n6+jSXekx5mpqvnBaNbat9QM7qIf41It0KH4tZeWmG51BqcBoqlfqEfLx9DNaBwX5BCDM4Z3g6gQgzLn6Drvv+iDMydv73Nw8Wnsijpbtv0xbz9yiTI0YZyxFFQ+iHnUjaVDjSk5zkMnYWhyZz89mrhQ2gJ9Ds7OzRfw5R3LF2CDgLgQgzLnLTmOdIAACIOB8BCDMOd+eYcY2IMBBuYOCgnQ9TZs2jV555RVxf/HiRREkXFdoxQW7X6levboVPaCpFAIQ5qRQMl1nzbHr9NzivaYr6ZUufKoZtaxSSi/HeS5vp12hvXErKI+Mf5mmXU0xv7LUpNxjiEGnBWLkFcKcETDIdhkCEOZcZiuxkEIEIMwVAmLidvPpmzRl1VGNhVyyiVpFi7w9PWhQk2ga3zGWQv19ilZADgiAAAiAgGIEIMwphhYdgwAIgAAIWEkAwpyVANHcOQlAmHPOfTM2awhzxsjIy5+77SxN/eeYRrAynrw8PGhyr7ri9LfxWuotyc3Lpc1XFlBadpLkSdYo0VoTj66+5PruWBHCnDvuunutGcKce+23O60Wwpz53c7IyqF3Vx6hX/ZdMl/ZRI2yYf40s//D1DCquIlaKAIBEAABELAlAQhztqSJvkAABEAABGxJAMKcLWmiL6chkJaWRjExMbr5vvXWWzR69Ghxf+XKFWrcuLGuzJqLrVu3UpUqVazpAm0lEIAwJwGSxCp7Lt6hz/49QfxaOLWoXJJe7lSD6lcoVrjIae5vpV6ivTdWyJpviG8JalV+kKw27lYZwpy77bj7rRfCnPvtubusGMKc6Z1OTM+ikT/uor2X7pquKLHU18uTPu/XkLrWLiexBaqBAAiAAAhYQwDCnDX00BYEQAAEQEBJAhDmlKSLvkEABOxCAMKc7THHJ6bT0ev3iL+QCg/wpdqR4RQR7Pyx1i7cO0An7m6VBcyDPKhL9Fjy0FgLIhkmAGHOMBfkug4BCHOus5dYSUECEOYK8tC/Y0u5oT/spL0GDivp15N77aVxbTl3cBNqW7203KaoDwIgUIgAPwcePHiQzp49S1evXhXxCCtWrEiNGjWiChUqFKpt+9vLly/T4cOHiV/v3bsn4tbHxsYSx5r39va2ekBe04YNG6h9+/ZUvnx5q/tzxw4gzLnjrmPNIAACIOAcBCDMOcc+YZYgAAImCECYMwEHRQUIXEo8TMdubyqQZ+7Gy8ObOkfnW9Saq+uu5RDm3HXn3WfdEObcZ6/dbaUQ5ozv+BvLD9LPe61zX2ms92A/b1o5ti1VLP4g5rWxus6Sn52dTQkJCcSvSLYn4OnpSWFhYeTv72/7zp2wR36fbdy4kT777DPat2+fEMGYTUZGBqWnp1OxYsVo8ODBNHLkSEUEOhbMPv/8c/r9998pKSlJ7AvvEYfMyM3NpVatWtG4ceOoQ4cOFh3uu3HjBi1dupRmzZpFt27dEuO0bNnSCXfK8VOGMOf4PcAMQAAEQAAEDBOAMGeYC3JBAASciACEOSfaLAdPNTHjFm27tkTWLEJ9S1GVYo3I28OXQnwjyM87UFZ7d6gMYc4ddtm91whhzr3335VXD2HO8O5uOR1PwxbsNFxoo9yHK5WgX0a6zhftbLE0aNAgOnXqlE0shWyE2SW6ycvLo5CQEJo2bRr169dPkTWx+MM/VatWJR8fH0XGsFWnLH6xKPbJJ5/Q/fv3hTXZpEmTqEGDBsSC1vfff09ffvmlEMlatGhB8+fPp2rVqtlqeNq+fTu9/PLLtGvXLipevDhNnDiRBg4cSIGBgcK67cMPP6QDBw6IsunTpwuB0MvLS9L4165dozVr1tAXX3whLPG4EQuyq1atIl4LknwCEObkM0MLEAABEAAB+xCAMGcfzhjFRQjwB312U5GSkiJOg3bq1MlFVubcy4Aw59z7Z+/Z7477g+6kXbF42GL+ZalqsaYUEaC8exyLJ2nnhhDm7Awcw9mdAIQ5uyPHgHYiAGGuKOjc3DzqPHMDnbuVXLTQxjnfDHyYutRyjXhzJ0+epM6dO4tnJRtjQncaAizsfPvttzR8+HBFePz111/0yiuvUO/evYX499BDDxFbgKktsUjJwhfHiOdrFqsWLVpEUVFRBaY6depUevvtt8Uz+yOPPCLEunLlrP9bO3LkiODD7/fQ0FBi4e3pp58uMDa7thwwYACdOHGCSpQoQd98841ZQfXSpUtCfPvpp59o586ChwLCw8Np5cqVEOYKUJZ+A2FOOivUBAEQAAEQsC8BCHP25Y3RnJDAlStXaMGCBfTDDz/QuXPndCvgE3Es0HF68cUXKT4+np566imCWKdDZLcLCHN2Q+0SA6VlJ9HO60spXfNqTaoS3piqFW9qTRcu0xbCnMtsJRZihACEOSNgkO30BCDMFd3Ctcfj6LlFe4oWKJBTVxPDd/noNgr0bP8u2Z3f5s2bRZwtNQo69idi2xHZiu3hhx+mSpUq2bbj/+9txYoV1KdPH3FXqlQpYreJo0ePFtZoaoqzvHDhQnruueeENRwLY8uXL6d27doVYcJWdezK8o8//hBlfD1v3jzy9fUtUldqxvXr14XgtnVrfrzq559/XrjSNBRLjl1cshUdu9Zknn/++Sc1adLE4FAcm+7111+nxMREYhGR05tvvklsPccJwpzAYPEvCHMWo0NDEAABEAABhQlAmFMYMLp3XgL8RcWoUaPE6Tr2E1846Qtz7Lv+u+++E1VGjBhBM2fOpICAgMJNcK8QAQhzCoF14W4zslPoyO0NFJ96wapV1onoQBVCa1nVhys0hjDnCruINZgiAGHOFB2UOTMBCHNFd++Zn3bTuhM3ihYolLP6+XZUrXSoQr2jWxCQRuDYsWPC8uu///7TxQlka6+2bduKOG386ufnJ60zhWqxMMbi4d69e8UIbdq0EZZkwcHBBkdkMeyJJ54gflbk+HMslnXt2tVgXSmZH330EbHLTP7cy6Ig989zMJTu3LlD/fv3p/Xr14tivmZrOEMiHs+PPfOwAMf9curevTv9/fff4hrCnMBg8S8IcxajQ0MQAAEQAAGFCUCYUxgwundeAo8++qg4gVd4BSzI8Qk8Y8Ic1+fTjHySztEPL4Xn7qr3EOZcdWeVX1dixm26mXqekjPvUE5eNvF9eo50SzpfzwBqHzWcPD2kxY1QfkWOGQHCnGO4Y1T7EYAwZz/WGMm+BCDMFeSdmZ1L9aasogzNq73Sq4/UpOfaVLXXcBgHBIwSYDFp2bJlwlsMx0hLS0sTdfmZli25hg0bJizoWChyRJoxYwa98cYbYl5slfnqq68Su6w0li5evCiEuX379okq3bp1E8/3lsTQ4/h7Xbp0of3794u+2NUnx4IrWbKkweHZzSbHwOP4c5w4ThwLg+3btzdYv3Dm//73P/r5559FNoS5wnTk3UOYk8cLtUEABEAABOxHAMKc/VhjJCciwG4r2S2lNvXs2ZPGjBlDzZo1I34geOeddwoIc3FxcfTuu+8K9xj8BTUndm3BQZuRlCcAYU55xu4wQm5eDq25MIvySN6XcU3K9qUSAeXdAZHRNUKYM4oGBS5CAMKci2wkllGEAIS5gkgOXrlLfWfnu6krWKLcXYfY0jR3CFxjK0cYPcslwALdr7/+KkI5sBCVnZ0tuuA4d2yxNmjQIGrdurWInya3b0vr8/MeH5zVWpFFRESImHucZyyxsDhhwgSaNWuWqFKmTBkhzBlzKWmsH85ft26diL/HB3Q5sXC2ePFicW3sF7dhyzeeO7sD5e8TvvzyS2PVC+RDmCuAw6obCHNW4UNjEAABEAABBQlAmFMQLrp2TgJ8uo1991++fFksgOPHff7557rFvPfee0WEOW3hpk2bqEOHDsSuL9lNBfuFZ5/ySMoSgDCnLF936T09O5k2XJ4ve7n1SnWmyODqstu5UgMIc660m1iLIQIQ5gxRQZ4rEIAwV3AXf99/mV5edqBgpsJ30RHBtH58B4VHQfcgIJ8AW4lt27aNlixZItw2crw0Tvyc26BBA+rVqxcNHz6cypYtK79zmS1OnDhBnTt3Jo7/zik2NlbEj+NXU+mbb74RghjXYWGRLdg++OADU02KlLEwyW34IC4nXj8f1GW3lqYSz5mt7LTfK9StW1dwjIqKMtVMlEGYM4tIcgUIc5JRoSIIgAAIgICdCUCYszNwDKd+Ahs3btS5mOjdu3cRd5amhDleHQdq/vDDD8VC2WKOLeeQlCUAYU5Zvu7SO7uyZIs50tjMyUmNyz5KEQEV5DRxuboQ5lxuS7GgQgQgzBUCgluXIQBhruBWfrftLH34z7GCmQrfhQX40IFJ3RQexbLu+cAi/z+ehQkWNfiHXQiqPWXn5FJaVg6F+PuofapOMb979+7Rjh07hHeYVas0rl71BDoWmx5//HEaPHgwlS9fXliGKbGof/75RwiBWuu9Vq1aifhy2phsxsZcuXIlPfbYY8JqjeuwBdvy5csNxnoz1gd/Bnj66aeFqMZ12C3lnDlzRAw5Y204Pz4+nth9Jsft48RuL+fPn089evQQ96Z+QZgzRUdeGYQ5ebxQGwRAAARAwH4EIMzZjzVGchICHNT59ddfF7P99ttvRbBr/ambE+bOnDlD1apVE034BOG8efP0m+NaAQIQ5oxDTdd8KcFfTHh5elCQr7d4NV5besmdlAy6mZhOft6eFBkeSP4+XtIbq7jm3rgVdCvtkuQZepAXlQmuTJ6a/wK8Q6lkYEUq5l9OcntXqQhhzlV2EuswRgDCnDEyyHd2AhDmCu7g7M1naNra4wUzFb4L9vOmw293V3gUed3fvXuXjh49SizCnD9/nthtf0BAANWqVYuGDBlCDRs2lNehnWonZ2TRot0Xad72c3Q7OYOOaLgGafgaS4lpWfTVplO09+JdOnUzkaqVCqGHK5Wgse2qUViAr7Fmbpufnp4u4qrxM/L27dvp/v37ggWLtTExMUKc69evH1WvXt3mAi4LYc8995yOPVvrrVixQndv7IIt/liMS0xMFFXMxYYz1M/Zs2epb9++dOTIEVHMHnF+++034c7TUH1tHru9ZBGOD/5y8vX1FQd42b2muQRhzhwh6eUQ5qSzQk0QAAEQAAH7EoAwZ1/eGM0JCLDvd3Z5wYk/fNeuXbvArM0Jc+zGkk/upaSkiCDZHBQaSVkCEOYK8s3JzaPTt5Po3O1kSs7MjwnBNTTaHJUJCaCapUMpIsivYCMJd5nZufTTngu0eM9FOncrWdfCx8uDmsWUpFGtq1LTmAhdvjNeJGbcou3XfpEdZ05/rWF+paluyY4U4ltCP9ulryHMufT2YnEaAhDm8DZwVQIQ5gru7MJdF+idvw4XzFT4rnSoP+2c2FnhUaR1z5ZQ69evF3GyN2zYINwFtm3bVjT++eefxb+FderUoR9//JHq168vrVOFa3FcwK83naHzms+9V+6mULbmc7A2mRLmriWk0lMLdtHZW0lUNsyf+jWMoqUaV6bX7qURuxedP7QpRZUI0naFVz0C/D5h0ZbfB/w+SUpK0pVWrFiRnnnmGeIY7WxNZ4vE/06x28hp06bpuuN48Gx9Zi6xtRoLczdv3hRV2Y0kW9/VqFHDXFNd+d69e4XAxhZwnCIjI2n16tVFvifQNfj/C/5egGPg/fnnn7qicePG0cyZM3X3xi4gzBkjIz8fwpx8ZmgBAiAAAiBgHwIQ5uzDGaM4EYH333+f3nrrLTHjY8eOUc2aNQvM3pwwxy5fWJhLTk6mNm3aEMedQ1KWAIS5B3yTM7Jpy/l4StS8mkq1NOJcnbLhpqoUKIvXWMc99eMuOh6XfzK2QKHezdMtKtOkbgXFbL1ip7iMSz5Dh26tpdy8HIvn6+nhTY3L9qHibmI9B2HO4rcKGjoJAQhzTrJRmKZsAhDmCiLbeOomPa35vGPP1LBicfrt2Vb2HNLgWCdPnhRxs9j1n1YIYQslbbzsJ598UggxHh4exM9D5uJrGRxEgczNp2/Sx2uOC2GOD5HpJ2PCXK5GvOs4Yz1dvJMiqn85oBF1rxNJG07eoBELd4u8yPAA2vhSR/L2Ur/rTv012/Oa3Upu3rxZWI8tXbqU7ty5oxu+XLlyIjwEC0zsztGaxM/VLGj98MMPoht+D44fP54+++wzs93y+7pr16508eJFUbdEiRL0999/U+PGjc221VZYu3Yt9enTh9LS0kQWWweuW7eOoqOjtVWMvrKF6U8//aQrHzhwIC1atEh3b+wCwpwxMvLzIczJZ4YWIAACIAAC9iEAYc4+nDGKExHgD+p8qo4TnwLkD9P6yZwwx64uqlatKppwfDmOM4ekLAEIc/l8OZ7G6lM3CljJmSL/ULlwql4q1FQVUZaRnUO9v9mscfHz4DSsqUYvdqhOL7Q3HYjdVHs1lCVl3qFTd3dQfOoFi6fj5xVErSsMJh9P+daJFg/qoIYQ5hwEHsPajQCEObuhxkB2JgBhriBwPojU9GP7ersY0iSaJveyjWVRwdVIv2OrIhY+du7cKRp9+umnIk62j09+jDa2/GEXhcuWLRMuCt955x16++23pQ9gh5rsirLrzHyXgdrhjAlzfx+9RmOX7NNWo92vdaaSIf6UpvE0UWvyKl3+p489RH0bVNTd48IwAXbZePDgQfHs/MsvvxDHpNMmjqvWsWNHeuWVV4jdSFqS2GUmW+H9+uuvojm7zpw4caIurrupPvnZvEuXLnTu3DlRjQ/QsvjMMeqkJrZ4Y8s3/jvgxGErWJirUKGC2S5GjBhRILQFx7DnvyOO12gqQZgzRUdeGYQ5ebxQGwRAAARAwH4EIMzZjzVGchICN27coLJly4rZ1qtXj3bv3k1+fg++WDcnzE2ZMkX3oMqn+vh0KZKyBCDM5fM9euM+8Y/U5KU5bdqrVjlNnDjTD4bztp+l9/8+JrVb8tb4zNz6cicqExYguY1aK2bmpNP9jBvEr7c1seeuJZ+SNdVaJdpSVJhjv2yTNWELK0OYsxAcmjkNAQhzTrNVmKhMAhDmigLrpLGk0nfZXbSGbXNmD2pMj9TMf/awbc/SetuzZ48QPQ4dOiQa9O/fX1gm+fv76zrg56EBAwYIq6Pw8HDieF8s1KkpsRVclbceuAzkuRkT5vrO3kIHrySI6Qdo4iQfe7eHbimNPviH7qZmivuqmphza15oryvDhXECHJeQLcM++eQTunr1qq4iW7dxzDl239ipUyddvpyLhIQEGjZsmM4lJItabLH57rvvmu2GYySyMMdx4DlxrMS//vqLOnToYLattgLHk9N/v3OsxX///Vf3nYG2nqHXZ599ljgmnzZ17txZxMbT/35BW6b/CmFOn4Z11xDmrOOH1iAAAiAAAsoRgDCnHFv07MQEWrZsKQJa8xKefvppEXOOgzVzMiXMsa99DkTNp+n4wza7zqhUqZJoh1/KEYAwl8/275NxlJieJQt0E437pOjiwSbbPDpL8+XF1fwvL0xW1Ct8t0cderJZjF6O81/ujVtBtzTinJxUNqgqPVS6q5wmTlkXwpxTbhsmLYMAhDkZsFDVqQhAmCu6XTM3nKQZ6+UdxCnai7ScED9v2vN6F/LTiEOOSCym9O3bV7gj5PFjY2OJLZ70Y4Pxe4RFkcWLF4spssjB18WKFXPElE2OWVUjzHGsZW0yJMxlaTxMxL7zF2miD4hURhPjb4dejL9HZmwQcee0fRx+uxsF++VbDmrz8PqAwIULF0S8tXnz5hFbXmqTt7c38SHXxx9/XDxPs+WcpYkt8NjyjC3NOLEw9+abb9LkyZPNdllYmAsMDBTCXPv20gXX5cuXi78TDlnBiUNdsDDH7jrNpcLCHP/9cH8Q5syRs105hDnbsURPIAACIAACtiUAYc62PNGbixC4fPmyCGjOp/M48UMqW8I1a9ZMuKJg9y38oT4lJUUEuz5w4ABNnz5dnOLTfmBnn/cvvfSSixBR9zIgzOXvz2+HrxT4MkLKrkmJNffwh6vpdkqGlO50dVwh1pxuMf9/sev6Mrqbfq1wtsn7UoHR1KhMT5N1XKEQwpwr7CLWYIoAhDlTdFDmzAQgzBXdvVvJ6dRq2r8aa/mC8cqK1rQ+Z2SrKvR6l1rWd2RBD/z5mYWNDz/8ULRmIWXatGkidpe2O37W+eCDD4hdW/J7pWnTpjR//nyqUaOGtoqqXqUIc9cSUqnVp//q5l2xeCBtmvDAkqv7VxvpRFyirnzNC+2oqgTX77oGbnLBsdiXLFki3EtqrdF46exmkt0+ssVX27ZtiWO6WZuSkpJozJgxtHDhQtEVW+Gxa8yPP/7YbNc8NxbDWKDjxK4s+TAtH8SVmlavXi1izGVk5D8PcdgKFuaioqLMdsGHfPlvRptYCGeXnHBlqSWi/CuEOeUZYwQQAAEQAAHLCECYs4wbWrkBAfYlzz7gCyeOtcAPpvxAULp0aWLXl4UT+9HnINFcB0l5AhDm8hn/eewapWblyALeILIYVSsZYrJNRwtcOr3cqQaNaVvNZL/OVnj09ka6nHhE1rRjwhpQbAnpD/6yOldRZQhzKtoMTEURAhDmFMGKTlVAAMKc4U348J+j9N22/JhUhmtYnxvi700bXupIJYIeuMy3vlfpPbALyx49etCtW7dEozp16tD69etJa9l0+vRpmjp1qrCO48/ajzzyiBDu2ApKrUmKMLfv0h3q9+023RKqaD4Hr33xgfXUo7M206GrD2Kkff9kU2pTrbSuvrtfsMC1YMECYVl56tQDy1IWmthV5dChQ8V7xRaCnJZ1enq6iCnH7jC16YUXXqAZM2Zob42+njhxgrp27UqXLuV7veD3N8eUb9SokdE2hQu2bdsmvOJoD+2yRxwW5qpUqVK4apH7wYMH06JFi3T5HL+e49ibS3BlaY6Q9HIIc9JZoSYIgAAIgIB9CUCYsy9vjOZkBPhD+4QJE4RLSilT5xOC7K6CT55y/AUk+xCAMJfPec/lO3T+boos6N1iy1Kov2n3PO+tPELf78w/ZSq18xWjWlPd8upzcSR1/obq3Uu/QTuu5wedN1RuKM/Lw4dy87LJ1yuQivmXpfIhNalUYCVDVZ06D8KcU28fJi+BAIQ5CZBQxSkJQJgzvG3JGVnU7ctNdFVjXaVU+qB3Pfpf40pKdW+yX/7/9ujRo3Wxr/gwIYtwY8eOFZZFHCuMxYT4+HgRR4u9gLDAYEuxxeQELSyUIsytPnadRi/eqxshtkwo/T2une6+37dbad+lu7r76U80oD71K+ju3fGCwzRcuXJFWMjNnj1bXHMep5CQEGrdurWIU9iuXTtxb2tG7JGGvdGwlZw28ftRa0GnzTP0um/fPurevbt4L3M5i2lsAVe5cmVD1Q3mHT9+XBzYPXv2rChnF5b8PYE5kZoZ9enTR7jO1HbMa2DLVHMJwpw5QtLLIcxJZ4WaIAACIAAC9iUAYc6+vDGaExLIzs6mWbNm0UcffUTXr183uAIW5Jo3b05ffPEFNWjQwGAdZCpHAMJcPtskzZdIq0/eoBxt0AwzyKPCA6lZpQgztYhu3E8jtppLyZRmjdchtjR9N6Sp2X6dscKx25voUuJhq6YeERBF9Us9ohHrAqzqR02NIcypaTcwFyUIQJhTgir6VAMBCHPGd+GQJr7ugLnbKCM7X4AwXlN+Sc+6kfRFf+kWO/JHMN3i6tWrwpWf1oqIhbkBAwYI8eLQoUMUEBBAMTEx1LNnTxFbKzo62nSHKimVIsxtOnWThv+4SzfjqqVCaM0LDyzmCgtzC55qRq2qlNLVd6cLFsTYcvK3336j77//XucOkhkUL15chHlgMZe9xbArVGOJrTK5nA+uWupRhmMfslilDRvBYht7uOHncFNp8+bN4n3M7jA5saUce7aREyOR/14GDhxIW7duFX1EREQIi0FzcerYFSxbpW7atEm047hyLMo9//zz4t7ULwhzpujIK4MwJ48XaoMACIAACNiPAIQ5+7HGSC5AgN1X8MMJ//ADBj+wVqtWTZy4MxfA2QWWr9olQJh7sDVX76fS9ou3dQHtH5QUvCoR6EttK5ciHy/TD7PaVlvPxtPIhbvNfjlVo2woLXm6BYUF+GqbutQrfxlw4u5Wunj/oFXrCvUtRc0iHycvD+NfYlg1gJ0bQ5izM3AMZ3cCEObsjhwD2okAhDnToNeduEFjluyhrJw80xVllLasXJLmDm1Cft5eMlrZtioLLSw08AFETpGRkeKZpnz58lSrVi1q1aoVsWtLtoZypiRFmPtPYw33hMYqTpsqlQgSLkW194/N3kIHruTHGee81c+3o2qlQ7XFbvPKsdk4FtrPP/9MR48e1a2bBTl+fwwaNEhYonHcdWOJxTAWs+bOnSvEsWHDhpkV0oz1tWvXLuGS8t69fDejHPv9r7/+MmvFuXz5cnriiSd073WOf8frMiUkFp5Damqq8KLD1oKc+O/i66+/JnZLaSrFxcVRt27d6ODB/OeGMmXKCDeg7BbWXIIwZ46Q9HIIc9JZoSYIgAAIgIB9CUCYsy9vjAYCIKAAAQhzBaEmpGXSfs0p71sp+QHK9Uu9PT1ETLlapcPIS3MtJ52NT6LJGreW287lxyLRbxvg40VDmkbT+A6x5K+5dvV0PyNeiHO3065QRk6++1AP8qA8zX9SU7ViTalKscZSq6u6HoQ5VW8PJmcDAhDmbAARXaiSAIQ589uyXfO5Z4zG9WFiepb5ymZq9NJYyk17rAH5eks7GGWmO4uL33jjDeENRGt99NVXX9HTTz9N/v7+FvephoZShDn2BNF82lrddMuFBdC2Vx8IJT2+2kTH4+7ryve/2ZXCNQfa3CXt37+f5s+fL1w1XrhwQbdsdmPKAtfw4cOFC0dfX+NM7ty5I6zs2B3qf//9R2lpacJV6sSJEy22mLt586YQ5g4cOCDmxFacLDA3bNhQN0dDF2yhxuNy4oO0HHKCXbPKTRwX7sknnxTN2Orv1VdfFX9Dpvph61MW4dglLCf2sLNy5UpJ1noQ5kyRlVcGYU4eL9QGARAAARCwHwEIc/ZjjZGchMDFixdp1apV4nSbs7htcRK0ik0TwpxhtOzaMj45g9KzcoQIF+rnQ6WC/chbopWc4V6JLmvi2O25cIduJKZpvljyouiIIGoWE0HBmv7dMeXm5VCCJv7c7rhlspYf4B1C7So+JauNWitDmFPrzmBetiIAYc5WJNGP2ghAmJO2I9fupdJrvx8kFuksSSH+3vR6l1o04OFKljS3eRsWGFho4BQcHCzEgjZt2th8HHt3KEWY4zk1/3iN5nNsupheoK8XHX2nh26q7aevo4t38g9dRWg+N+95vYuuzJUvbty4Qe+//z4tW7aM+JoTC1Bly5alxx9/XIhSNWvWNCne8v8rV6xYIazJTpw4Qenp+YzZNerMmTNpxIgRFiNkEXnkyJE0b9480UdYWBixoMyx5oylxMREGjdunO69zrHhWBh76KGHjDUxms9Wb127dtWx4dhxS5cuJS8v4wcS2aKPLfT4czKzZEHw008/NTqGfgGEOX0a1l1DmLOOH1qDAAiAAAgoRwDCnHJs0bOTEtizZw81adJEzL5GjRpCoGMf9i1btiQfH/UKD/zFilrnp/TcIMw56R+bC037cuJROnp7g+wVda40SiOaqvffFakLgjAnlRTqOSsBCHPOunOYtzkCEObMESpYzq4tZ285TfsvP3B1WLBGwTsW5AY2rkQjWlahEkF+BQsdeKf/pT+LJiymdOrUyeSMrly5Qvx+YVf+ak1Shblpa45r9vGMbhkH3+pGof75n8fqTVlFSen5Lj7HtatO4zvG6uq58gULVr1796bc3PyYipUqVaJ+/frR0KFDhXtTU2u/fPmyiPfGlnZaizauz+IZx3Rjcaxz584mRT1T/WvL/v77byEQ8v+TOY0aNYq++eYbbXGR15MnTwoXmmfPnhVl7H7zp59+KlKPM9iqj11vsqtOQ24u+b3PAiXHteNUvXp1+vfff6lChQrivvAvdhM7adIk+vjjj0URx6Xj2HZSRUF2NbtkyRLRlmPz8f60aNGi8DC4l0AAwpwESKgCAiAAAiDgEAIQ5hyCHYOqmYC+MKc/z9DQUBHYmkU6Pi3HpwcdnfjBZ8qUKbRz505i9x5Vq1YVPv9bt24t/P6bOsFnau78wPLDDz+YqiLKateuTTNmzDBY7/jx4/Tee++JuALsX5+tDzkeAf+wP35T7k8MdmgiE8KcCTgosguBa0kn6NCtf2WP1SV6LHl6ONadlexJG2gAYc4AFGS5FAEIcy61nViMHgEIc3owZFyev51M60/eoP2aeGV8fVfjPpzj0AX7eVNksUCqWTaMWlSOoFZVSzk0lpyxJbHlzueff64rnj59Oo0fP153X/iC44S9/vrr4hnozTffLFysivubGk8OzT5+4KKSJ7VufAeKiQguMr+M7Bzq+dVmOnsrSZQtHN5cs18liS0jW32S/3nOx8uDtr3yCJUMcW73nkUWbyTjjz/+oL59+4rnSX7eZRGLRTVTiV1dcjuOQ7d3715dVRbkWETiPnr16iWsMnWFVlxwrDcWCtmqj1P9+vWF2FWyZEmDvX733Xf0zDPPaGJv5xE/y7MA3bZt2yJ1z507R/w3wJ5zOHbds88+S4b6ZNeZ7PKVBTx2+8rCGVvOGUosVvbo0YOOHDkiitkFKM+HLeekJBYBteuEMCeFmPE6EOaMs0EJCIAACICAYwlAmHMsf4yuQgLXr1+nl19+mTZs2CDELkNT5A/UfNqNgznzD1vYeXra98t1/QcNQ3NkFzXff/+95A//+n3wAwefeDSX+IFr27ZtRapxQG0+ias9cVm4Aj/0cR1LhcPC/UGYK0wE9/YmkJJ1jzZf+VHWsEE+xah68ebk7elLIT7FNV/cBclqr6bKEObUtBuYixIEIMwpQRV9qoEAhDk17IL958CWR+xijz9Dc6pYsaKwJOIDdPqJY4XNmjVLuN/jg4ksXqjhcKL+HFMysunPQ1eFULrh1E39IuFqvWvtctS7XnkK+X+LOG2Fw5p4zI/N2Uo5uXnUsGJxerNbbZq3/SytOnJd8/xENLlnXRrcJFpb3eVf+cAnH1Dt0KEDValSxeR6OXbaggULRPiH06dP6+qygMTvK36W5NhvSsQs5PF69uxJ/MrPkuwiky3nCgteLBryczpbzXHi2HKvvfZakXr8Hh89erR4NtUu5IUXXjB4+JQ/73IfWneUHTt2JI6jV6pUKW1T3evbb78tDtByBrP4/fffxd+ZroKJi5SUFGHByodvObFVK//NGhIVTXSDov8nAGEObwUQAAEQAAG1EoAwp9adwbxUQeDYsWO0fv168bN582a6f/9BIHD9CXIw7C5duhCfLmQ3HewCQ8m0bt06MQ4LX/wgwA8j7dq1I3Yxw+41+DQfp7Fjx9KXX34peyocY2LLli3UuHFjk+5q2IXHu+++W6D/3bt3C6s4/qKnWLFiYm780MIWffxAsXDhQlGfTzuyVV7hh6gCnUm8gTAnERSqKUrgvxsr6WbqeYvHCPcrQ1WLNaGSgVEW9+GohhDmHEUe49qLAIQ5e5HGOPYmAGHO3sTVMR5/8d+/f38hrGhnFBUVJeJ18YFDPz8/YQHF1lAcK4yFFhYa2B2f2tIVTezjNp+tMzmt9RrLuWgDlnPsmvS9VUfoakKqrj3Hlfugdz3qVNPx3lF0k7LDBX+WM3VoksvPnDkj4rqx5dnVq1d1s+L3BQtyw4YNo3r16lFQkLKHzXj8MWPG0LVr16hMmTI0d+5cYc2pnT8/dz7//PNCbONnzaeeeoo+++wzYuGwcOI1sdB36tQpXRGHsGA3lYaERf48wM/ebM3GlnjPPfccffDBB7rnf3Zh+csvvwixj2Pc8fPy7NmzzYpq3Bc/23N7fpZnqz22ENSmiRMn0htvvCHY8oFgWzxDa/t29VcIc66+w1gfCIAACDgvAQhzzrt3mLmdCfDDyL59+3RC3Y4dO3QBrfWnwg8E/EDLIh0/EHBAdVunhx9+WMyFT8+xYMj3+mnAgAHigYDz+KGChTE5KTIykthy8J9//hGCo5y2HJ+ChUOOd7dmzRohGOq354eXOXPmiCx+6GA3HdYmCHPWEkR7WxDIyE6lndd/o9RswwK+1DGiwxpQjRItpVZXRT0Ic6rYBkxCQQIQ5hSEi64dSgDCnEPxO3RwFiRY3Ni4caMQAwpPhp9h2JLuxRdfFMKGobhbhds4431WTi6djU8S4ly10qEUVUJZUcnZGKWnp9PRo0fpxx9/pKVLlxKHKNCmcuXK0WOPPSbeHyzI2dODDB+efeutt4SAzCES2FsMC2o3btwQ1nyHDx8W7iifeOIJmjp1qnBlqZ23/iv//53/DtibizaNHDmSvv32W+1tkVe2suOQDXzgNCEhQbjtZG8xfCiVn38XL14sRE52tfnVV1+ZdQnKA/DfIXvsYZeg7JGGxXP9xN8x8OHZ5s2bixAR/HzPf59I5glAmDPPCDVAAARAAAQcQwDCnGO4Y1QXIMAPKSzOaS3qWLTjL6f1E58wjY21bcBwFuK0bizYWu2dd97RH1Jc8+m80qVLC+GQT7iy20upiU/maU85nj9/Xnzwl9qW3ZrwAwgnfojXj12h7YO5sfB39+5d8SDHD3jWJghz1hJEe1sRyMxJo6O3N9KNlPwg85b2W7NEa6oUlv+3ZGkf9mwHYc6etDGWIwhAmHMEdYxpDwIQ5uxBWb1jsGURW/Pw8wU/P3BiQY4/q7M3DnYFyOILkvsR4M9227dvF2Ici1b8XtGmmJgY4b1l8ODBQijS5tv7lUU4Dt2wdu1aYT2XnJwsDoeyNxt+JuXDqvzcbC62OYthbI3Gz6d16tQR8RTZ0s1UYus29lTDz7IsXLJYx8w4lh0LZmyFx6Kl1L8fZsyxHNkSji1WC4ucbFGXkZEhRHS2/Bs4cCDVqlXL1BRR9v8EIMzhrQACIAACIKBWAhDm1LozmJfTEGAXGps2bRLBpPmDOX9o1iYlhLlJkyYJdxk8xsGDB4W7EO14+q/8MM3z4pN7/CDFFmxSEgeorlu3rnDdwSf1Cj8UmOqD/e2/8sorogqPzaf6DCV2dbJ8+XIxRnx8PIWEhBiqJjkPwpxkVKhoJwJJmXfoZso54tecvCxKzkyQZUnn5eFDHaNGkJentL9bOy3L6DAQ5oyiQYGLEIAw5yIbiWUUIQBhrggSt8zgg3NJSUlCFAgMDCT+QXJPAiz+rFy5kpYsWSK8oOiHcqhRo4ZwscjxBqtVq6YaQGlpacT/n+ZXtuxkcUyu21U+nMrPvizqyXn+ZQj8PMvCNot1/LfDYxtyg6kaYG42EQhzbrbhWC4IgAAIOBEBCHNOtFmYqjoIaIU4Fp745+xZ45YxHJS6atWqNp04B2Vn9xZ8klXft3/hQdiabvLkySL7+PHjxA9SUhIHpubTfXxakF2AcGKxkd10mIudxycD+UEuLCxMPBwZc3kzY8YMGj9+vOibrQ6bNWsmri39BWHOUnLKtMvMzqUbiWnEr6VC/Ck0wDnEJWVo5Pe65sIsIdDJGaNh6Z5UOihaThOH1YUw5zD0GNhOBCDM2Qk0hrE7AQhzdkeOAUFAlQT4eYqf4+bPny+8wvCzHycWqfg5kuO09erVy+bPtqqEgUm5FAEIcy61nVgMCIAACLgUAQhzLrWdWIwSBDjWGgtw2h+Ox2AsVahQQcRz69ChA/EPB6O2deJTfOxmgwW6LVu2GO3+hx9+EA9QXIHnbsx6rXAH06ZNIw4u3adPH+HChoNXsxUdn6JlYY4FO3ZTyeWFU+XKlYndX+qLeoXr8D1by7HVnPa6d+/e4trSXxDmLCVn23b/XbpLs7ecoa1n4ylDI8ppU42yoTSocTT1b1iRvL08tdlu85qVm0H/XsyPqyhn0bUj2lHF0DpymjisLoQ5h6HHwHYiAGHOTqAxjN0JQJizO3IMCAKqJMDhGfhwptZCjt0/ctw4dlfZr18/ESaB3SwigYCzEYAw52w7hvmCAAiAgPsQgDDnPnuNlUokwG4sWDhiMYt/TAlxLFSxy0gOvsxCnK2t4wpPmd1jsEtKfmUxi+dpLK1YsUInnrHPeg58LSU988wzNHfuXHE6kscxlliY44DXHIdCm9ht5r1794QIyOyMJf04eRxYmwNsW5MgzFlDzzZtP15zXIhypnqrVz6cvhvSlCKC/UxVc7kytjhdc3EW5eZly1pbw9LdNRZzlWW1cVRlCHOOIo9x7UUAwpy9SGMcexOAMGdv4hgPBNRJgOOl8fMlP2s2aNBAiHHDhg0z6zFFnavBrEDgAQEIcw9Y4AoEQAAEQEBdBCDMqWs/MBsVENizZw81adLE4EwCAgKEpZrWIu6hhx6S7YPeYMcSM/XdSQ4fPpzmzZtntCVb02mt5L7++msaPXq00br6Be3bt6eNGzeKLA6azQ9k7GqS3Zgwmw8++IDYipATl3HAbU4s4rHrShYh+vbtS8uWLRP5hn6xi0w+gcmJ++Ng2+YSj8kBvg2l7OxswYL9+UdHR9OIESMMVXN4Hsds4MDgUoOAO3zCEifw9abT9Om/JyTVrlUujP54rjX5uJnl3P6bf9ONFONubwvD8yBPKhtclTw0/wV4a+JkBFak4v7lCldTzT2EOdVsBSaiEAEIcwqBRbcOJwBhzuFbgAmAgCoIrFu3ThzO7N+/P7Vt2xaCnCp2BZOwBQEIc7agiD5AAARAAASUIABhTgmq6NOpCRgS5rp160YTJkygFi1akJ+f46x9+IvBkiVLCr5jxoyhr776yihrfrjq1KmTKOdYARwXQEriNXJMOnaVyUG/g4KCCjTjwNZc5+jRoyKfYxF0796d+IsddnnCadCgQfTTTz+Ja0O/9u3bRw8//LAomj59ui7enKG62jx2r8luNk0lFuS43yeffNJUNYeVMSPmx+5IXSXdTEynDjM3U3ZunuQlTepSgwY3jpJc3xUqpmQl0J74ZZRLORYvJ8QngmLDW1Oob/6/ARZ3pEBDFua1Frbs5sjLy0uBUdAlCDiOALv2YiuCwMBAx00CI4OAAgT4cJM28b/d1rqq+2zG19ruCry+MfGlAve4AQEQUBeB5ORk8TzHHlCQQMCVCBgT5ia8OMYuy+TPkN98841urHfffVd3jQsQAAEQAAH3JgBhzr33H6s3QMCQMMfV2G0lW6Cx60r+qVWrltVfXhgY3mQWW6Ox+MVfogwZMoR+/PFHo/U5NtyAAQNE+V9//UU9evQwWldugb6wxpZ4bJHHSRv/rmfPnsTuUIylNWvWUJcuXUQxu8Pk2AXmklRhjq0YDcW/M9c/yi0jsPTwTfp862VZjWuXCaY5j9WQ1cYVKt/PuU4Xs3ZTnhXiHFvSxfi0oBCv0q6ABGsAARAAARBwMQI/LPzZ4IogzBnEgkwQAAEQAAGFCRgT5oYNyf+uROHhKSkpSRx41o4DYU5LAq8gAAIgAAIQ5vAeAIFCBNhd5Jw5c4jjoG3bto349KChxJZr7OaDXT+yUFe9enVD1WyeFxkZKVxJ9urViziOnLE0a9YsnfvKXbt2GXXPaay9ufzw8HARHJzdXO7YsUNUZ7GSre1at24t+Bnrgy3xBg4cKIpXr15NnTt3NlZVlz958mSaOXOm7r7wBccGHDp0qLCYgyvLwnSUu5+y6ijN33FO1gAlQ/xoz2v5wqyshi5QOTkzgU7d3U43U89bvBofT39qU2EI+XoFWNyHrRvClaWtiaI/tRGAK0u17QjmYysCcGVpK5LoBwRAAARAQI0EjAlz9jowcu/ePZoxY4YODYQ5HQpcgAAIgIDbE4Aw5/ZvAQAwRYAt0/bv30+bNm3SCXXsitBQKlu2rM6ajsW6mJgYQ9WszuNg3AcOHKDY2Fg6ccJ4XK/33nuP3nnnHTEex2fj+dky1a1bl44cOUJsocaMOGnj05UpU4bi4uKMDscC2wsvvCDKeQ28FmtSZmYmffjhh6KL8uXLI8acNTBltp254RR9vv6krFZVSobQvy+2l9XG1Spn5aRTQsYNysxJo7tp1+lq8jFZS6xevAVVDm8oq42SlSHMKUkXfauBAIQ5NewC5qAEAQhzSlBFnyAAAiAAAmohAGFOLTuBeYAACIAACBQmAGGuMBHcg4AJAvzlM4tiWqFu69atwmrMUJOKFSsKi7sKFSoYKrY4b9SoUTR79mzhRvPWrVtG45W1bNmStm/fTvXq1aODBw9KGm/Lli107NgxER9q5MiRRl11skvN0NBQYU2oH0/u9ddfp48++kiMde7cOaPiJLvVXLVqFUVFRdHFixclzc1UJQhzpugoW3bg8l3qO2errEGGNYuhd3rUkdXGlSsfuPkPxaWckbXEUoGVqFGZXrLaKFkZwpySdNG3GghAmFPDLmAOShCAMKcEVfQJAiAAAiCgFgIQ5tSyE5gHCIAACIBAYQIQ5goTwT0IyCCQm5srRC92e7lx40b6559/RPw3bRe2sAbT9qV9ZbGNRTdOLIJx7LXC6dKlSxQdHU0soE2aNImmTJlSuIrBew5KPGZMfhBkHqd58+YG6/E6u3XrJsqmTZtGr7zyirg+evQo1amTL7i89tprNHXq1CLt79y5I6z3+IugsWPH0pdfflmkjtwMCHNyidm2/pM/7KAtZ25J6jTQ14v+faEDlQtXjxtGSRNXsNK+G39RfOoFWSMU9y9PTcv1ldVGycoQ5pSki77VQADCnBp2AXNQggCEOSWook8QAAEQAAG1EIAwp5adwDxAAARAAAQKE4AwV5gI7kFAIgH+IvrQoUPCxSVbmrH1HItO+kkJYY7FNnaTyZZmAQEBdPjwYapSpYpuWP6CpX///vTHH38Iyzcur1mzpq6cL77//nvi+QcGBupivXH+yZMnqUaNGnxJ3bt3p2XLlpGfn5+41/7iNbZp00ZY1kVERNCZM2eI481pU/369QUXb29v2rdvn7DY05axkDl8+HBasGCBsMbbuXOnTWLfQZjTEnbM652UDPrfd9vpTHySyQn4eHnSt4MaU9vqpU3Wc7fCE3e20YX7+e5gpa69Ymgdqh3RTmp1xetBmFMcMQZwMAEIcw7eAAyvGAEIc4qhRccgAAIgAAIqIABhTgWbgCmAAAiAAAgYJABhziAWZIJAUQL8xcV///2nE+K2bdtGxuLNBQcHU8eOHWnu3LnE4pWt05o1a6hXr17EghRbxrHYxULa6dOnxZjr168XQ3IcN/1Aw9p5sKCXnp4uLNc4/px+GjZsmBDOOK9p06bCgq5Ro0ZiLHbj+fbbb9Ply5dFk6+++kpnYaftg7l07tyZUlNTieO98dx4riwk/vDDD7Ry5UpR9cknnxT32nbWvEKYs4aebdqmZGTTJ2uP05J9lygzO7dIp00qlaB3etahGmXCipS5e0Zi5m3adnWxLAzenr6Uk5tNvl4BVMy/DEUG16BSgdFG3c/K6tyCyhDmLICGJk5FAMKcU20XJiuDAIQ5GbBQFQRAAARAwOkIQJhzui3DhEEABEDAbQhAmHObrcZC5RLIyMig3bt364Q4tu5KSUkx2k1sbKxw78guHlu1akW+vr5G69qigK3Z2DKOvxA3lFgM++WXX8jf379IsSlhjkWunj170tq1a4u002awFR270WThz8PDQ5ute+W23Af3ZSi1b9+eli9fTiEhIYaKZedBmJONTLEG91Izafu5W3ThdjJl5uRS6dAAahpdgiqXtM1eKzZxB3d88u52On/vP6tmUULj3rJ+qS7k5x1oVT+WNIYwZwk1tHEmAhDmnGm3MFc5BCDMyXEBhSUAAEAASURBVKGFuiAAAiAAAs5GAMKcs+0Y5gsCIAAC7kMAwpz77DVWKpHAhQsXiK3GWJRjcc5YYnGLBaauXbsKQY4t1+ydWABj8W3Tpk10/vx5IZKxQDhu3DgaOXIksTtJQ4lju/EXMWFhYcQx4gwltmybPn06HTlyhPgLyaCgIOHmkt1iTpgwgerWrWuomS6P3XsuWrRIzI0t+VjAq1y5srCwGzVqVBEXmbqGFlxAmLMAGpqoigC7qD2dsJPO3dtn1byCfYpT88j+5O3pY1U/chtDmJNLDPWdjQCEOWfbMcxXKgEIc1JJoR4IgAAIgIAzEoAw54y7hjmDAAiAgHsQgDDnHvuMVcogsGfPHqNxz1hYYos4/mnbtq1BazQZQ9m06q1btyg0NNSmgpd2gklJScTuOQ1Zx2nrmHrluHQs7Bmy3jPVTmoZhDmppFBP7QQSM27RxfsH6VbaZcrIybfQ9SBPyqOi7kGNrSUmvCHFFm9hrFiRfAhzimBFpyoiAGFORZuBqdiUAIQ5m+JEZyAAAiAAAiojAGFOZRuC6YAACIAACOgIQJjTocAFCOQT0Bfm2GVj69atdWJctWrVgEmFBCDMqXBTMCWrCeTm5VBS5h3afu1nWX35egZQh6gRFgvpsgb7/8oQ5iyhhjbORADCnDPtFuYqhwCEOTm0UBcEQAAEQMDZCECYc7Ydw3xBAARAwH0IQJhzn73GSiUSOH78OM2cOVOIcR06dBCWXhKbopqDCECYcxB4DKs4gevJp+lg/GrZ47Aw5+dlv1hzEOZkbxEaOBkBCHNOtmGYrmQCEOYko0JFEAABEAABJyQAYc4JNw1TBgEQAAE3IQBhzk02GssEAVcmAGHOlXfXvdd2I+Uc7b+5SjaETpWeJR9PP9ntLG0AYc5ScmjnLAQgzDnLTmGecglAmJNLDPVBAARAAASciQCEOWfaLcwVBEAABNyLAIQ599pvrBYEXJIAhDnLtzU3TxO9TPPj7elpeScWtEzJyKZAXy+7ulu0YJoOb5KenUwbLs+XNQ9/rxCqFdGGvDx9KNinOPl7B8lqb0llCHOWUEMbZyIAYc6ZdgtzlUMAwpwcWqgLAiAAAiDgbAQgzDnbjmG+IAACIOA+BCDMuc9eY6Ug4LIEIMzJ29rE9Cw6fSuJriemUWpWjmjs6+VJpYL9qGpECJUO8ZfXoYTa6Zpxftl3if48fJWOXLtHWTksBnpQrXJh1LNuJP3v4Uoaoc5bQk/uV+Vg/Bq6nnzK4oWH+ZWmKuGNqXRQtMV9mGsIYc4cIZQ7OwEIc86+g5i/MQIQ5oyRQT4IgAAIgIArEIAw5wq7iDWAAAiAgGsSgDDnmvuKVYGAWxGAMCd9u0/GJ9Kh6/coz0STqGKB1LhCCY3FlYeJWtKLTt5IpFGL99DFOylGG0WGB9CsgY2pTmS40TruWpCZk067ri+l5Ky7ViGICq1LNUu0UcRKEcKcVVuDxk5AAMKcE2wSpmgRAQhzFmFDIxAAARAAASchAGHOSTYK0wQBEAABNyQAYc4NNx1LBgFXIwBhTtqOnrqVSAc01mpSUmRYALWKLimlqsk61+6lUq+vN9Pd1EyT9bgwxN+bVoxqQ9ERwWbruluFLI04d+zOJo3l3Gmrll6tWDOqUuxhq/ow1BjCnCEqyHMlAhDmXGk3sRZ9AhDm9GngGgRAAARAwNUIQJhztR3FekAABEDAdQhAmHOdvcRKQMBtCUCYM7/1qZnZtPLEdU08OfN1tTVaVoqg8uGB2luLXtlSbvWxuP9j7zzgo6q2Lr7Te4VACBB6B0EBpQlioYgFe0Hw2bA9VNBnL1ifXQTFgiIqiKJPFCxgwQII0ptU6Z1AEtI7312Hb8aZZMq9U5Ip6/CDufee/j8TMmfW2Xvrrtu/TQP58F+9dZcPtoIFZdlyqHC75JcdlYqqMs0V6XEprNAntoJVaEiYnJN5k0SEedZdKYW5YHsnBt98KcwF35oHy4wpzAXLSnOeJEACJBCcBCjMBee6c9YkQAIk4A8EKMz5wypxjCRAAg4JUJhziEdlbtFcWK7WXFgaSY0TNau5lq5bzeUVl8tpz30vlUbUQG2Ayx4cLGleiHNnZO7+Uvan3e9JWWWRoeF2azBYMuLbGarjrDCFOWeEmO/vBCjM+fsKcvz2CFCYs0eGz0mABEiABAKBAIW5QFhFzoEESIAEApMAhbnAXFfOigSCigCFOefLvXxvtmw/VuC8oEWJhKhwGdYhw+KJscv1mtvMiyb/ZqySVvrTm/vKGS3qG64XbBVOnKiS73e+YXja7VP7Ssvk7obrOapAYc4RHeYFAgEKc4GwipyDLQIU5mxR4TMSIAESIIFAIUBhLlBWkvMgARIggcAjQGEu8NaUMyKBoCNAYc75kq/anyNbs/KdF7QokRITIYPbNbJ4Yuxyu9bfuRMWGKuklZ5zxwDp0jjZcL1grPDjrnelvKrE0NS7pg2SxgntDdVxVpjCnDNCzPd3AhTm/H0FOX57BCjM2SPD5yRAAiRAAoFAgMJcIKwi50ACJEACgUmAwlxgritnRQJBRYDCnPPl3pdbJIt2HXVe0KJE27QEOa1xisUTY5dwYdnrhflytKBUd8XE6AhZ9tBgiQoP010nmAuuO/Kj7CvYZABBiObGso2ESJjEhCdI/ZimkhrT2EB920UpzNnmwqeBQ4DCXOCsJWdiTYDCnDUP3pEACZAACQQWAQpzgbWenA0JkAAJBBIBCnOBtJqcCwkEKQEKc84XvurECZm/5ZAcLyl3XlgrERYSIkPbN5J4zZ2lO+m9RX/Ls9//pbuJsee0l7vO9mz8M92d+2HBovI8WbhvulSeqHB59AmR9aRL/XMkOTrd5TYozLmMjhX9hACFOT9ZKA7TMAEKc4aRsQIJkAAJkIAfEaAw50eLxaGSAAmQQJARoDAXZAvO6bpOYOPGjfLHH3/Inj17pLCwUE5oQserr76qGty+fbtkZmZKRESE6x2wpssEKMzpQwdR7udth6WssspphT7N6klmSpzTcs4KwGpuzKcr5Pu/DjgrKgPbNZR3RpwuEWGhTsuywD8EjhbtkZWHv9XEOX2i6z81/7kKkVA5reEwaRjX4p+HBq4ozBmAxaJ+SYDCnF8uGwetgwCFOR2QWIQESIAESMBvCVCY89ul48BJgARIIOAJUJgL+CXmBN0hkJ+fLzNnzpSpU6fKn3/+adVUbGysEujw8JZbbpHZs2fLiBEj5JlnnpGEhASrsrzxLgEKc/r5FpZVyPK92XIo33ZcsgTNQq5n01RpEB+tv1EnJSHOvfXbVnnr921SVFZZo3R0RJjc0q+V3DWwnYRTlKvBR8+DwvJc2ZL9hxwq3K4VP6GnSo0y4SGR0r/pSIkONy7IUpirgZMPAowAhbkAW1BOx0yAwpwZBS9IgARIgAQCkACFuQBcVE6JBEiABAKEAIW5AFlITsPzBHbs2CFDhgyRbdu22Wy8ujD33nvvqXJt2rSRWbNmSbdu3WzW40PPE6AwZ5xpbnGZHMgrkYLSctF0M4mNDFNiXMP4KAnR3Fh6Ix3X+vx582FZvz9XcovKJDEmQrpkJMs5HdIlJTbSG10GXZvllaWSU3pQs4oskuOlR2R33jpDDFon95S2qb0N1UFhCnOGkbGCnxGgMOdnC8bh6iZAYU43KhYkARIgARLwQwIU5vxw0ThkEiABEggSAhTmgmShOU1jBOCqskuXLrJz505zRVjBnXrqqZKXlydr1qwRS2Hu4Ycflv/+97/mssnJyapMs2bNzM944T0CFOa8x5Yt+y+B9Vk/y958/fH9MNPU6MbSK+Myw5OmMGcYGSv4GQEKc362YByubgIU5nSjYkESIAESIAE/JEBhzg8XjUMmARIggSAhQGEuSBaa0zRGYNy4cfLaa6+pSlFRUTJx4kQZOXKkxMTEyFNPPSVPPPGElTCHgnPnzpX//Oc/smXLFlWvf//+8ttvv6lr/uNdAhTmvMuXrfsngTVH5smBgq2GBp8U2UD6NrnaUB0UpjBnGBkr+BkBCnN+tmAcrm4CFOZ0o2JBEiABEiABPyRAYc4PF41DJgESIIEgIUBhLkgWmtPUTwDWcunp6VJQUCCRkZHy5ZdfyrBhw8wN2BPmUODQoUPK0g5f4CGtW7dO3asb/uM1AhTmvIaWDfsxgW05fwr+GkkZ8e2kW4PBRqqoshTmDCNjBT8jQGHOzxaMw9VNgMKcblQsSAIkQAIk4IcEKMz54aJxyCRAAiQQJAQozAXJQnOa+gl89tlncvXVJy1Gxo4dK6+++qpVZUfCHArOmDFDrrvuOlXnscceUxZ2Vg3wxuMEKMx5HCkbDAACheW58vve6XJCqnTPJjw0Sos5WCGRoTGSFNVQGie0l4axLZ3GHaQwpxsxC/opAQpzfrpwHLZTAhTmnCJiARIgARIgAT8mQGHOjxePQycBEiCBACdAYS7AF5jTM04AbiohviF9/vnncvnll1s14kyYO3z4sLK4Q6Vrr71WCXVWDfDG4wQozHkcaa03WFV1QhZtz5IlO47KoePFEh4WKs3rxcnAtg2lY0ZSrY8nUDr8O2eZbM1Z6tZ0UqIbyakNhkp0eLzddijM2UXDjAAhQGEuQBaS06hBgMJcDSR8QAIkQAIkEEAEKMwF0GJyKiRAAiQQYAQozAXYgnI67hO48847ZfLkyaqhHTt2SIsWLawadSbMoXD9+vXl2LFjwjhzVui8dkNhzmtoa6XhPzRB7pGv18quY4U2++vXKk3+e0k3aZISazOfDx0TOOnScplW6ITjgg5y4yKSpU/GVRIRFmWzFIU5m1j4MIAIUJgLoMXkVKwIUJizwsEbEiABEiCBACNAYS7AFpTTIQESIIEAIkBhLoAWk1PxDIGJEyfK3XffrRr7448/pHfv3lYNOxPmSktLJT4+XioqKuT888+Xb7/91qo+bzxPgMKc55nWVotfr90nY2etdCoZpcRGyqc395W2DRNra2gB1U9+2THZeXy1ZBXtltLKkwJoiIQacnPZNKGztEruIWEh4RIZFmPl3pLCXEC9XTgZGwQozNmAwkcBQYDCXEAsIydBAiRAAiRghwCFOTtg+JgESIAESKDOCVCYq/Ml4AB8jcDixYulX79+algvv/yy3HvvvVZDdCbMrVq1Srp3767qMMacFTqv3VCY8xparza8W7OQGzRxgZRV6IuB1qZBgnz777M0q61Qr44r0BuvOlEpReV58vu+j12eaoQWiy4ttoUm1HWXhMh6QmHOZZSs6CcEKMz5yUJxmIYJUJgzjIwVSIAESIAE/IgAhTk/WiwOlQRIgASCjACFuSBbcE7XOYHCwkJJTEyUqqoqSU5OlrVr10pmZqa5oiNh7sSJE3LxxRfL3LlzVfnZs2fL8OHDzXV54R0CFOa8w9XbrY6fu04+XLrTUDfvjDhdBnVsZKgOC9ckcKRol6w4NKdmhsEnIRIi7VL7SrOErur/TFQPCQmR8PBwgy2xOAn4NgEKc769Phyd6wQozLnOjjVJgARIgAR8nwCFOd9fI46QBEiABIKVAIW5YF15ztshgTFjxsgbb7yhypxyyiny0UcfSdeuXdW9PWEuPz9fHnroIXnzzTdVOYh569evVyKfw86Y6TYBCnNuI6yTBoa98atsPHjcUN839W0lj57f2VAdFq5J4GjxXll2cHbNDBefdEw9SxrHdVC1Kcy5CJHVfJoAhTmfXh4Ozg0CFObcgMeqJEACJEACPk+AwpzPLxEHSAIkQAJBS4DCXNAuPSfuiACEnj59+sjKlStVMVh/jBgxQsWbW7p0qUybNk1iYmJk4cKFsnXrVlm9erW89957kpOTo8qHhobKggULZMCAAY66YZ6HCFCY8xDIWm7m7Fd/kp2aO0sj6drTm8uzF58UyY3UY1lrAuWVJfLT7vcMxZizbsH6LiwkQs7MGClwcUlhzpoN7wKDAIW5wFhHzqImAQpzNZnwCQmQAAmQQOAQoDAXOGvJmZAACZBAoBGgMBdoK8r5eIzA9u3bpVevXoIv44ymRx99VJ5++mmj1VjeRQIU5lwEV8fVRk//U37cdMjQKB4b1llu7NPKUB0Wtk1gfdYC2Zu/wXamC087p54jjeLaUphzgR2r+D4BCnO+v0YcoWsEKMy5xo21SIAESIAE/IMAhTn/WCeOkgRIgASCkQCFuWBcdc5ZNwFYwD355JMyefJksfziwl4DrVq1ktdff12GDRtmrwife4EAhTkvQK2FJn/YeFBunbFMd0/REaHy27jzpEFitO46LGifQHlVqfx54EvJK8uyX8hATsvEHtIqqSeFOQPMWNR/CFCY85+14kiNEbD8fAsPEbB6difV9Reg7oyddUmABEiABAKPQF3/XsrNzZUJEyaYwY4fP958zQsSIAESIIHgJkBhLrjXn7PXSQDuKl955RXZsGGDcl1psqKDy8pmzZpJ27ZtZeDAgXLPPfdIVFSUzlZZzFMEKMx5imTtt3ObJszN1wQ6PenJC7vIqF4t9RRlGZ0EIM5tPPq77C/YpLOG/WJtk/tIs4SuFObsI2KOHxOgMOfHi8ehOyRAYc4hHmaSAAmQAAn4OQEKc36+gBw+CZAACQQwAQpzAby4nJr3CMCSLisrS4lyFOK8x1lvyxTm9JLyvXKlFZXy0Ow1MnvNPruDiwgNkQeHdqILS7uE3M8oKMuRw0Xb5XhpllRoYl1hea4UV+QZarhZQjeJDI2RmPAESYtrKlHhcYbqszAJ+DIBCnO+vDocmzsEKMy5Q491SYAESIAEfJ0AhTlfXyGOjwRIgASClwCFueBde86cBAKGAIU5/1/KpTuPyvSlO2XJjqOSXVSmWV2JNE2JlbPaNpQb+rSU5vXi/X+SfjSD/LJjsmjfTDkhVS6POj2ulXSo118JdS43wook4CMEKMz5yEJwGB4nQGHO40jZoIcJlJaW+rVHkurjr6qqUiESeLjTw28UNkcCdghQmLMDho9JgARIgATqnACFuTpfAg6ABEjAXQIU5twl6Fv1yyqqJEyzksNfprojsD9/s6zN+lEbwAmXBxEeGimnpw+X5Oh0l9tgRRLwBQIU5nxhFTgGbxCgMOcNqmzTEwRKSkpk0qRJEhMTI//+979VkwgrMHv2bHPziOt92mmnme8tL5YsWSILFixQIhjiJw4fPlyaNGkiH330kWRnZ0tERITKwz4CfbRp00b69u0rTZs2tWzG5esdO3bI1KlTBfN46aWXzPEb8/Ly5JlnnlF9XXzxxS63z4okQAL6CFCY08eJpUiABEiABGqfAIW52mfOHn2cAE4xrlu3zq1RhmjmPsnJyZKWliaxsbFutcXKzglQmHPOiCVIwBUC2SUH5K+jv0p+2VFXqqs6cG85oOlIiQiLdrkNViSBuiZAYa6uV4D9e4sAhTlvkWW77hAoLCyU+++/Xw4ePCgvvPCCEs3Q3rFjx+TTTz+VJ554QnJzc+XJJ5+URx55pEZXCDlw5ZVXyqJFiyQ6OlrGjx8vI0eOlMTERCXWIS74zp07pWvXrnLJJZcIRLSvv/5aGjVqJK+//rqcffbZNdrU+wDC28yZM2Xy5Mny119/qTjkP/6Ig04nU2Vlpfzwww8yYcIEGTt2rAwZMsSUxVcSIAEvEKAw5wWobJIESIAESMAjBCjMeQQjGwkkAkVFRRIX57nYSPHx8TJgwAC57LLL5PLLL5eEhIRAwuUTc6Ew5xPLwEEEMIHcksOSXbJfSisLpbSiSA4UbjE02+SohhIXkaLEuSTtukFsc4kIjTLUBguTQF0SoDBXl/TZtzcJUJjzJl227QoBfK6HKPfLL7/InDlzVExvy3aWLVsm5557ruTn58ttt90mb731lmW2nDhxQl5++WXVBjIGDx4s3377rYSFhalysGA788wzZcWKFfLxxx/LNddcI+jz2WefVX/POussJf41bNjQql29N0eOHJH169fL0qVLVXsDBw5U/VevjzE9+OCDyqquZ8+e1bN5TwIk4CECFOY8BJLNkAAJkAAJeJwAhTmPI2WD/k7A08KcJY/mzZvL9OnTlesSy+e8do8AhTn3+FWvfaywVHbnFkmOFuutvLJKIsNDJTU2Upolx0mK9lrbqarqhPy46aD8tPmwbM/Kl5KKSmmYEC2nN68nl52aKQ0SaYlVm2uy+dhi2XF8pVtdhodESKuUntIyqbvZtZNbDbIyCXiZAIU5LwNm83VGgMJcnaFnx3YITJs2TcaNGydvvvmmEs0si0F0mzJlinJtCS8ncAX5xRdfWH2WWLhwoYwYMUJZ12FfB4s7CH2mBCu23r17K0u6efPmmV1hzp8/X1mvZWRkyK+//mq20jPVM/oKq7irr75aevXqJd99912N6pgLDm1CKPzss88EhzmZSIAEPE+AwpznmbJFEiABEiABzxCgMOcZjmwlgAggQPf111+vTlvOnTtXiouLzbOD+5PMzEz1NycnR7m8hKsVy5SUlCQQ4LARRB5csGDjZUqRkZHyxx9/SPfu3U2P+OomgerC3I033uhmi96pjvcW3jfp6b4Zb6tUE7yW78uRA3kldgE0TYqR7k1SJDIs1G4ZT2ZsOnhcxn6xSrYdKbDZbIQWh27MwLZyx4A2Vl/K2CzMhx4hsOHoAtlXsNEjbaXHtpauaYO5dh6hyUa8SQDu06KiovjFqTchs+06IQC3eqYUGhrq9v/Hz780wdSc1evDD4yzuucNCdgigM/JsHBD+vnnn2t4GkH+3XffLYcPH5a1a9dKixYtVDlT6IB9+/bJVVddpT5rL1++XODSEu4sLfddEPwQs65Pnz4CMc4kiD322GMq9tt5550nn3zyidSvX9/WEHU/+/7775VAaE+YQ0Owmhs1apS8+uqrav+pu3EWJAES0E3AnjD34H/u0d2GOwXhdnfixInmJuBal4kESIAESIAEQIDCHN8HJGCHwH333SevvPKKyoW7FMQwwAbOMuGk5tatW2XVqlXy6KOPqlgFCCSOoOQIRo6EDSQ2hIhlgPgFSK1bt5aNGzeqoOPqAf9xi4ClMNe4cWP517/+5VZ73qqMcR4/flzFHvRWH662W6ZZxv2285jklVY4bSIlOkL6t0iVcO0LPG+mvw4cl+umLZXCsn++NLTX3zU9MuXJCzrby+ZzDxLYcXyFbM9b7rEW2yb3lWYJp3isPTZEAt4ggC9V8Pvdk66uvTFOtkkCdU3gpVcn2RwChTmbWPiwGoE///xT+vfvr/ZNcC0ZHh5uVWLz5s1y8803q/hxkyZNkoqKCoEAZxLR7rrrLtm0aZNce+21cuutt0qXLl0E8d1SU1PN7UC4mzVrlowZM0Z9WQ6LaMSte/zxxwV7O7i3xD4OQrU7SY8wt3v3bhWDrmPHjsptp7t9ujNe1iWBQCVgT5j7z7gxtTJl7P/feOMNc18U5swoeEECJEACQU+AwlzQvwUIwBYBnFq89957VRaCi+v58ITT9HBHAtcnCDIO1yiILWdKhw4dkqFDh8qaNWvUo2+++cYs3pnK8NU1AhTmXONmWWuVJoLtzCmyfOTwuk29ODklPdFhGXcyKzX3lRe8tVBzXWnbUs5W2+9c20MGtm1gK4vPPEigoDxHlhz61GMtRoXGypkZo9y20vDYgNgQCdggQGHOBhQ+IgEbBCjM2YDCR7oJvP/++0p4gztLeDCpnmDJhphyr7/+urJGg2eSlStXSqtWrZTYhphtqLtgwQJ1qBJC3UsvvSTwWIJ04MABFZ8OAl+HDh2kSZMmAiu77Oxs6dGjh4wePVrOP/98czy66v0budcjzEEIhFvN/fv3y++//y4tW7Y00gXLkgAJ6CBAYU4HJBYhARIgARKoEwIU5uoEOzv1ZQJwXQl3lTg9CWENGzu9pxdxarNz586yZcsWZRW3bds2q6lu2LBBndzEQ2w2sXFkcp8AhTn3GCKO3DdbDoumhelO4ZoLyQvaNZQw7dUbadHfWXLjdGNWWf1a1ZepI0/3xnDYZjUCm3MWyd6C9dWeun7bJ/0aiYtIdr0B1iQBLxOgMOdlwGw+YAhQmAuYpXRrItgTwXINwpmzfRTEqa5duyqPErCSg+Xal19+KZdcconVGFAOLiixV3vttdfkrLPOUp5LIGjBpSX2bVdeeaXcdtttcumll8qSJUuUJdwVV1xhbgcHI6+77jrlvvK///2vciGJQ5MPP/ywoG/LBK8ou3btcjp+uINt166dCmVgWV+PMIfy8MyyevVqFStv4MCBlk3wmgRIwAMEKMx5ACKbIAESIAES8AoBCnNewcpG/ZnAnDlzVCBxzAG+wOHmxEiCy0qc4kSCENepUydzdWwok5OTJT8/X52ORKw5JvcJWApzOPkKFze+mBBjDpaVCCrvSymrsFR+3nbY8JCGtkuXpJiTJ5ANV3ZSYfKvW+WlHzc5KWWdnRIbKaseGWr9kHdeIXDiRJWsy/pJ9hds9kj7fRtfLUlRtHb0CEw24hUCOKyDGHMJCQleaZ+NkkBdESgvLzd3DbeBISHuHbix9wUoXVmaMQfFBVy3YU+EQ4gxMTEO54zPx3AtefHFF6vQAC+88IJ89dVX5v2YqXJeXp4MGjRIEEsafxELDq4vZ8yYoURAxIuDoLd3717p16+fEvoQLxyHJk0JAtzzzz8vF1xwgXId+dRTT8nTTz8tffv2VfHm8P88EvZsKIuwBnBj7ChBKES5ceOs4yjqFeaGDx+uDoK+9957Slh01BfzSIAEjBOo699LONw1YcI/8Vf1eGMyPkvWIAESIAES8EcCFOb8cdU4Zq8SwAlM08YKbikt3VHq6RgbwIsuukgVteWu8uyzz5ZffvlFnew0xZzT0y7L2CdAYc4+Gz05WQWaMPe3cWFuiCbMJXtJmHvjly3yyk/GRJ+kmAhZ8+j5eqbMMh4icLhwh+w4vlJySg663GKIhMi5zUdLROjJL8NcbogVScCLBCjMeREum65TAhTm6hR/wHYOi7n169er+NthYWEO53nixAnp1auXNGzYUGDF9sgjjyhLN8SCs0wLFy6UW265RbC/QrxuWMchrnf37t2loKBApk6dKqeffro6IAlREJZxiOuUlJSkmikqKlKCHPZhJiFt7dq1yroO7izRFtxYImFMsPiDFxRnFn+wmIPFH9xpWiYjwtxvv/0mH330kVx44YWWTfCaBEjAAwQozHkAIpsgARIgARLwCgEKc17Bykb9mQBOUT700ENqCggqDpcpRhKs4HDqEgmBxKtvKnEaFFZ5zZo1U+5RjLTNsrYJUJizzUXv07KKKpm9YZ8Y8GQpcGU5vHNj7TVUbzeGyv2iuda88aOlhur0aVlfZtx08mfPUEUWdptASXmRFJblal9kVcm240slp/SA7jbDQk6eRA8NCdPcWaZIg9jmkpnYWSLDHJ+w190BC5KABwhQmPMARDbhkwQozPnksgTtoGBhd8MNNwhizcEqzjLBsm3RokUCwQtiGeLHYa+G6/vuu09ZwsHSDa4rIbLB8wn2cSYrUIQYGDx4sIopt3jxYunZs6dAVBs1apQgdh08bkyZMsWyS7eu9QpzcF+5ceNGJThiTEwkQAKeJUBhzrM82RoJkAAJkIDnCFCY8xxLthQgBBBT7pxzzlGzgZsTWMAZSXBTMHbsWFUFpzBPOeUUq+q4xwnS/v37C05HMrlPgMKc+wyX7j4mu3IKdTfUpn68dG+Sqru80YIVWty7cycskN3Z+sc0+ZqeMrSzb7kJNTpvfy2PL7bwZRhSUUWu/Hnof1Jxoszl6YSHRkqX+udKo/jWLrfBiiTgSQIU5jxJk235EgEKc760GhzL0qVL5Swtdtzdd9+tYr7BvSoS3F3CjSXynnzySfUM1nVwN4kYbRDW0tLSlOgGcauwsFC5puzdu7cqi39gaYe4dXBtCWEuNjZW5cH95TXXXKNijOPwZIcOHcx13Ln49ttvldUexvDdd9/ZbCo7O1sd6IS7T8TKi4+Pt1mOD0mABFwnQGHOdXasSQIkQAIk4F0CFOa8y5et+yEBxH9DHDh8yRwXF6fcTuo9vYj4YSi7c+dOiYyMVG5VLOMSILB3jx49VNs4nfnhhx/6ISHfGzKFOffXpKSiUn7aelgKyiqcNpYUHSHntGmoWTR5x1rONICVu7NlxNTFUqpZ9DlLl3RrIq9e0d1ZMeZ7iYClMIeT6QUVR2XVke+kpKLArR5PazhM0uOsXUO51SArk4CLBCjMuQiO1XyeAIU5n1+ioBogYtPBqg3pp59+MgtVsKSDRdvIkSPlsccek5YtWwpiskGkmzlzpordDaszWMnhOfZhb7/9tgwdOlQJditXrpQnnnhC5s2bJ+3atZOPP/5YHZ5ETDnEf4J4tnnzZhkxYoSyvEPMalcTfqb+/vtvefHFFwXjzszMVG42u3XrJvXq1bNqFhaAEAthHYjYekwkQAKeJ0BhzvNM2SIJkAAJkIBnCFCY8wxHthJgBCCaYcOGhI0dNlY4uekoYTOIuAdwZYl06aWXyv/+9z9zlR9++EHFrvvrr7/UM1jiwSKPyX0CFObcZ4gWissrZIlmOXdEizlnLzVKiJYzmtWT6PAwe0U8+hzi3D2fr5R9OUU22w3Rno4+s7Xcd14HCfeyUGhzAHyoCFQX5nDCvaKqTPbkrZcDBdskryxLK2fEWepJsOEhUdK3ydUSGhKqXFuGhZw8OU/sJFDbBCjM1TZx9ldbBCjM1RZp9qOXANxYPvDAAzJjxgyzSAfrs+XLlwvi1SEe3Xnnnafiv23fvl3FhSspKZGvvvpKsM8yHYqE9RlihXfq1ElmzZqlxDJ8PkH8OIQUGD58uDqMiXHB7eSyZcvU4ckhQ4bIGWec4TS2nL35IN7d/PnzlYcUkxvNhIQEQbsdO3a0qnbnnXcqAfLHH39UAp5VJm9IgAQ8QoDCnEcwshESIAESIAEvEKAw5wWobNL/CSA4ODZ9cDlpSnB7AheXCO6dkpKiTnAeOnRI9u7dK3C7AqENGz0kWNxhY5iRcdKt3uOPPy6Ii2BKbdq0UZtJ02bN9JyvrhGgMOcaN3u1DuUXyx5NCMsuLpNyzaUkLONSY6OkWUqsNIiPtlfNa88RA2/Oun3y8+ZDsj2rQErKK6WBJhCe3qKeXNk9U5rXo9sfr8HX2bAtYc6yKv5vhGvL1Ye/l6PFeyyzdF+HSIgkR6dLZkIXyYhvZ44Zo7sBFiQBNwhQmHMDHqv6NAEKcz69PEE5OLihhGC1Z88eFSsuKSnJikNFRYWYXFyaMvA5A59FLJ/jHs8h5lXPQxuITYe/1RPyUMfVfZr6zKO1YRII0T5+ztCe5fjWrFmjrOXGjx8v119/ffVh8J4ESMBDBCjMeQgkmyEBEiABEvA4AQpzHkfKBgOFAFyQnH322Up4MzInbMI++OAD5QrFVA8x5xB7DgmiHk59WsY8MJXjq2sEKMy5xo21SMBTBJwJc6Z+ft79vpRW6o8baKpX/TU1urF019xcRoTVvlBcfSy8Dw4CFOaCY52DcZYU5oJx1X1/zgcOHJDRo0dL8+bNBbHkYHEWKAnCHQ5w3n///WqviX0ihEAmEiAB7xCgMOcdrmyVBEiABEjAfQIU5txnyBYCmADcoiBWATaEiD/gLEHImzRpUg03JdhwoR3En5syZYp06dLFWVPMN0CAwpwBWAFa9HBesazdlyvZhWWSEB0unTKSaElXi2utV5j7Zc80Ka7I88jIkqPSpXfG5doJ9Jqn3T3SARshAQsCFOYsYPAyoAhQmAuo5QyoySB2NzyOQJy75557AmZuiKOHOHmtW7eWMWPGuGyZFzBAOBES8DIBCnNeBszmSYAESIAEXCZAYc5ldKwYTASwMfz8889VbAJY0iGeAYQ6bBQRfBx/e/ToIcOGDbOJBcHE09LSagT8tlmYDw0ToDBnGFnAVFi9N1tenL9Jlu48WmNOHRolyn3ndpCz26fXyOMDzxLQK8ytOTJPizm31WOdt0w6TVJjmkhEaJQkRNaT8NBIj7XNhkjAkgCFOUsavA4kAhTm3FvNkooCKSjPkbLKYs1tYpX6PRQTniBxkSnCuKjusUVtvD8PHz4sjRs3DhgBC/uWrKwsNSf3CbEFEiABZwQozDkjxHwSIAESIIG6IkBhrq7Is18SIAGPEaAw5zGUftXQzOW75LGv10ml5hLIUbp9QBu5f1BHR0WY5yYBvcJcbslh+ePAZ272Zrt6iIRKg9jm0iblDEmMSrNdiE9JwEUCFOZcBMdqPk+AwpzxJTpeelj25m+SI0U7NCvwfJsNhIaESWp0hjSKayuNtbioYaERNsvxIQmQAAmQgHcJUJjzLl+2TgIkQAIk4DoBCnOus2NNEiABHyFAYc5HFqIWh/GnZiF3zXuLxbEk98+AXrikm1zZo9k/D3jlUQJ6hTl0uj13pWzJXuzR/i0bg0DXuf5AaZrYyfIxr0nALQIU5tzCx8o+TIDCnP7FySs7KpuOLZSjxXv0V9JKRoRGS+vkntI8qZuE0v2yIXYsTAIkQALuEqAw5y5B1icBEiABEvAWAQpz3iLLdkmABGqNAIW5WkPtMx1d+vbvsnpvju7x1I+PkqUPDNZOrIforsOC+gkYEebQ6v78zbLx2O9SXlWivxODJXumD5e02EyDtVicBGwToDBnmwuf+j8BCnPO1/CEZpm/PXe5bM1Zqh0I0nskqGa7iZFpclrDoRIXkVIzk09IgARIgAS8QoDCnFewslESIAESIAEPEKAw5wGIbML/CCBWwciRI70+8Pfff1+aNm3q9X6CvQMKc8H1DsgtKpNTn/3e8KRn39ZfujXll2GGwemoYFSYQ5OVVeVysPBvyS7ZLyUVhVJRVSa5pQd19KavSHxEqvRvep2+wixFAk4IUJhzAojZfkuAwpzjpas6USlrjszXfl9tc1xQZy5iovZIv0i5udRZhcVIgAScECgsLJTjx49LQUGBhIWFSUJCgorvHhLi/QN5EO4Rjz4/P19KS0slPj5eUlJSJC4uzsmo9WWj/X379kn9+vUlJiZGXyWWsiJAYc4KB29IgARIgAR8iACFOR9aDA6l9gjs2rVLWrRo4fUON2/eLO3atfN6P8HeAYW54HoHbM/Kl3MnLDA86SnXnSHndkg3XI8VnBNwRZiz1erSA/9TQp2tPFeeDWg6SrNMSHalKuuQgBUBCnNWOHgTQAQozNlfTHwhvurId3JIO0TiyRQWEiG9Mi6T5KiGnmyWbZFA0BGAGPfDDz/Ijz/+KFu2bJGDBw9KRESENG/eXHr37i0XXnihdO3a1Wtcli9fLvPmzRO87t69WwmD6enp0qlTJxk2bJicddZZkpSU5HL/27dvly+++EJ++eUXee655+S0005zua1grkhhLphXn3MnARIgAd8mQGHOt9eHo/MSAQpzXgJbR81SmKsj8HXUbV5JuXR75jvRvi8zlObeMUA6N6ZIYwiazsKeEuZgObf0wBdSVHFcZ8+Oi53R6BKpF0OrZceUmKuHAIU5PZRYxh8JUJizv2pbs5fIttxl9gu4kRMVFitnNhkheA2UBFHk5Zdflr1790p4eHigTMsn5lFVVaUssG644Qbp16+fV8aEz3KwNvOXtHjxYnn88cflt99+k6ioKLn11lulZ8+ecuDAAfn4449l7dq1kpGRIWD26KOPSnR0tMemBkHw+eefF3jHycrKkgEDBsjVV1+trOV++ukn+fDDDyUyMlI9nzhxorRv395Q3zt37pRJkybJ3Llz5e+//1ZjhwB55plnGmqHhU8SoDDHdwIJkAAJkICvEqAw56srw3F5lQDcTCxb5nijvWHDBhkzZoxgk4KEU2/4m5mZqf7iGTae+IuTcl9//bUmFJyQ5ORkmT59uvTv319toEJDQ1GUyYsEKMx5Ea6PNj3i/cXyx46jukfXODlGFt53ntSGSxvdgwqggp4S5oCkvLJUNmUv1OLQbXIrlg/awpeeCZH1cMlEAm4RoDDnFj5W9mECFOZsL05uySFZfOAz25keetowtpXm1vICD7VW983AU8jgwYNlz549dT+YABwBxM533nlHbrzxRq/MDgLXtGnTZNSoUUr8g+WZryZYyI0ePVpw2BYuHqdMmaL26aYxY39+5513KmELzyDMPfDAA0rAc3dOeXl5qu1PP/1UKioq5KabbpJnn31WGjY8aQFbVFQkM2bMkLvvvluKi4sVS6xbx44dHXaNz9Kwuvvggw9k5syZsmPHDvXdAirh+4VvvvlG+vbt67ANZtomQGHONhc+JQESIAESqHsCFObqfg04Ah8ksHXrViWsIRYdXFHgAzJO4DlKcJ9xxx13yIIFC5QLje+//17OOeccR1WY5yECFOY8BNKPmtmwP1cueft3LS6ZPrO5t67tKUM6ZfjRDP1rqJ4U5kwzL6kokKziPVJUniuI8bO/YIuUVRaZsp2+hmtxfNK1Lz1DQ8MkNjxJGsQ2l/jIVKf1WIAEbBGgMGeLCp8FAgEKc7ZXcfH+z7S4p4dsZ3rw6RmNLpX6AWLZDVFh7NixysKHFnMefJNoTeHwJ2KWPfTQQ0qA8mzrJ1vDIdNLLrlEWWddfPHFSgCEO0jETPOlBPeOl156qaxbt077jBcqL730knrfVT98h/38+eefLygPa7m3335brr/+eremgs+7Tz75pDzzzDNqTWDBBhGuekx5lMNaYWxIcKkJ0TM11fbnUBwaxvhg6YefHfzFIWLT/88U5txaNqEw5x4/1iYBEiABEvAeAQpz3mPLlv2YAPy3r169Wp1OW7FihbRq1UrXbBBwGq4sVq1apVxnbNu2TWJjA8dFjS4IdVCIwlwdQPeBLuf/dUDu+XyllJRX2R1NqBbz/ZHzO8uNffT9DNttiBkOCXhDmKve4ZGinbLi0Nzqjw3dQ5zrVH+gxIQnGKrHwiRAYY7vgUAlYPriF/PDl8HVv9w2Ou+6/gLU6HhtlT9avFf+PPilrSyPP6sX3VSLN3epx9utiwYhHuHzAF6ZvEMAria95Y0FLhghIJWUlKjBQ8waMWKEXHPNNXL66adLQkLdf3aChdptt92mXEhikNijw3rOVux4vBdfeOEFeeSRR9R8WrdurWK1NWnSRN278s/PP/8sV111lRw7dky5/YQlHCzmbKWNGzfKkCFDlHcd5L/++uty11132Soqubm5MnXqVBWb/rzzzlOx6oYOHWr28ENhziY23Q/r+vcS1nfChAnm8Y4fP958zQsSIAESIIHgJkBhLrjXn7O3QeDPP/+UXr16qRx8aHriiSdslLL/CG5AEOgZCYGaTdfqAf/xCgEKc17B6heN7jxaIK/+tFl+2HhQs6b6R6CDINevdQMZe0576dY0xS/m4s+DrA1hDnx25q5Wbi7dYRUZGqN9CXq5Zj3H94U7HIOtLoW5YFvx4Jkvhbmaa736yDw5oFlp11Ya2PQGiY1IrK3u2A8J2CSAA6bz58+Xjz76SOD5xfR/Aw6ZnnHGGUqgGz58uKSlpdmsXxsPv/vuO2X1ht/JSIgf995779kVK9evX68sDOHaEmncuHHyyiuvqGuj/8CqDaIcLAuRWrZsKYj7Zu8AL/an999/vxLkTOUhIqKenoS+Zs2apYpSmNNDzH4ZCnP22TCHBEiABEigbglQmKtb/uzdBwnAjcTtt9+uRobNyaBBgwyNEqcMk5KSxCQWwY0Fk3cJmFijF5yCvPnmm73boYutY0OHE5YIRO7vqaC0QorLKyRMU8DiIyMkMjy0TqdUUl4pmw4el+yiMkmIjpD2DRMlMeaf2BgHjxfLgdxibeMukpkaJ/Xioup0vIHWeW0Jc+B2rHifbMn+wy0XYwmR9aVv46slNKRu37eB9j4I5PlQmAvk1Q3uuZm+fAcFWszBZWCV/LDrHak4UVZrb4yO9fpLi6RTa60/dkQCjghkZWXJr7/+Ku+++67AQsxkAQmXll27dlUWaxDo6sLFJfZ4sCwzufaEyHbrrbfanU52drbK/+KLL1SZtm3bCiwDq7uetNuARcbatWtVDEWEukCCNRxEusjISItS1pcQ1q6++mo1Xlg7vvjii0octC5l++7aa69VseaQS2HONiO9TynM6SXFciRAAiRAArVNgMJcbRNnfz5PAFZy8B2PhE0JXFMaSXCxAVcfEOjgDmTOnDlGqrOsCwQozLkAzYUqFVVVsuVIvmw/VqDF/aq0aqG+JnR1bJAoGUkxVs/r8qZCs6D7+M+d8tHSnbLrWKHVUDpnJMkt/VrLRV1dd2dj1WCQ39SmMGdCXVyep4lzR6S8qkQOF+7Q4tHtMmXpek2IqKcJyxESFRYrqTGNJSO+nbrWVZmFgo4AhbmgW/KgmTCFOeulPq79Xlm0f6b1Qy/fpce1lu4Nh3m5FzZPAsYIHD9+XGbPnq0s0hCmobi4WDUQExMjCPswevRoJU41aNDAWMMuloYghphxGAtSZmamEq769Oljt0UIeE899ZSYXAdCTHzzzTdl1KhRduvYy/jwww+VhZ5JqBwzZoxMnDjRXnH1HCExcMg3JydH3cM95Zdffqli3jmsqGXCheinn36qilGYc0bLcT6FOcd8mEsCJEACJFB3BCjM1R179uyjBD755BPlTx/DQ8Dm++67z9BI16xZI6eeevLU60UXXWR2d2GoERY2RIDCnCFcLhUuLKuQX7cfkXzNUs5RalUvXno0SXE7Ro2jPvTk5WiWczd8uETW7st1WHxQx0Yy8aruEhUe5rAcMx0TqAthznJEi/bNlLyyLMtHhq9DQ8KkbUovzWrhtDp//xoePCt4nQCFOa8jZgd1RIDCnDX4/QWbZc2R+dYPvXwXH5EqA5qO9HIvbJ4EXCMAF5c4aPr+++8LhKa8vDxzQ927d1dx0wYOHOiSFZq5IR0XCDcxePBggWCIhP02xuUsZtz06dOVEGcS1OD+ElZ3RlJRUZHce++9As86SBAn8T3BnXfe6bCZXbt2CcS4zZs3q3LNmzeXzz//XHr06OGwHjIpzDlFpLsAhTndqFiQBEiABEiglglQmKtl4OzO9wns2LHD7Cu+c+fOgk0AfOvrTQgw/dxzz6nizz//vDzwwAN6q7KciwQozLkITmc1WMrN33LIqShnaq6DZjnXNSPZdFvrr1VVJ+TydxfK6r0nT6c6G8ClpzaVVy4/zVkx5jsgUNfC3G97P5LCcscirIPhW2U1T+wmHev3t3rGGxKgMMf3QKASoDBnvbI7cle5HcvUukXndxGh0TKouX13fM5b8F6JnTt3yqFDhyQ3N1eJEQ0bNpT27dvzAIv3kPtsyxDEYO31wQcfqP0x9l9IISEh0q9fPxk5cqScd955AvHJGwmC1pVXXmluGn3NnTtXoqKizM9sXcB1JQ7Lmiz+4A0HMfQgrulN+/fvVwd3EUseKTU1VcXiGzbMsaUrfm5g5bdkyRJVD+EuJk2apFipBw7+oTDnAI7BLApzBoGxOAmQAAmQQK0RoDBXa6jZkT8RgEsO+NdHQuDlGTNmCPzCO0vTpk1TLi5M5RYuXKg2KqZ7vnqHAIU573A1tbrpcJ6sPahf9AjRKl7QMUPiIsNNTdTq6+w1e2Xc5yfd3Ojt+Kvb+0tXzdKPyTUCdS3MrTg0V44U7XRt8DZq9Uy/WNJim9nI4aNgJUBhLlhXPvDnTWHOeo3/zl2u4phaP/XuXVhIhAxpcYd3OzHQOmIif/bZZ8rrx+rVq9UBRVgMYW9Ur149tTd69NFHlet+A82yaIAQgNi0aNEiwb73m2++EbxfTAmiLSzEEK+9TZs2psduv1ZphwQRn80ydjv26CZXj446WLp0qUBAQ7w5pHbt2gniyDdrpv9z3oYNG1SICljAITVq1EhZ6zmzfMP/rwhtgf6QIGI+/PDD8swzz6h7R/9QmHNEx1gehTljvFiaBEiABEig9ghQmKs91uzJjwjgJOCNN95oHnHr1q0FlnD4gFz9VB7cYiAw9htvvKFO7WHjgIRAzzNnzjS3wQvvEaAw5z22aPkHzVouu/jkqVi9PZ3aOFnapSXqLe7RcqOn/yk/bjpkqM1bz2wtDw7pZKgOC/9DoK6FuYMF22T1ke//GZCbV3BrGSKhEh4aKUlRaZIe10aLQddW8JwpOAlQmAvOdQ+GWVOYs17lXcfXyF/HTlrFWOd47w6xTs9tdov3OjDQ8sqVKwUeP+bNm6fiYMGlP4QFvE8gtsDyB7G04dLv2muvNdAyiwYaAQh0eD+88sorgsOoJgs6zBOi3GWXXaYOrGIfHRoa6tb0Ye2G9+LkyZPN7cCNJPbfztL69euV1dq+fftU0fT0dGUx161bN2dVzfmwlIPVncmNJ+Lb/fjjj9K2bVtzGXsXEBBnzZplzsZ3DHAL6ixRmHNGSH8+hTn9rFiSBEiABEigdglQmKtd3uzNjwggUPQTTzxhNWKccsOHeZywg3tLfMDfu3ev2TWGqTBcYOJ0XlxcnOkRX71IgMKcF+FqTc/esE9KK04Kznp7apeWIKc2rhsLtGFv/CobD56MP6F3vBd0aSyTrnYe70Fve8FWrq6FOfD2tNVc9TVEDKBTGw6VhMh61bN4HwQEKMwFwSIH6RQpzFkv/OHCHbLi8Fzrh16+S4pqKP0aX+3lXhw3X1FRIYizDWseuO3r1KmTEt/69u1rdlt50003mWNzPfbYY4K9EhMJIAYdRKp33nlHli9frtye4qAq9s0ZGRnqsCreO801F5dG3Edaks3Pz1fx3D7++GP1GG1DqIMVnbO0ZcsWZcUHt6xIKSkp8u2330rv3r2dVTXnw/Xl8OHDzeJjq1atBC4yMSdn6V//+pd8+OGH5mJwxwlLP8zBUaIw54iOsTwKc8Z4sTQJkAAJkEDtEaAwV3us2ZMfEoAw9/LLLwvct+hNCEqNjYkR9xh622Y52wQozNnm4qmn320+KHkl5YaaO6VRknRsmGSojqcKj5z6hyzaftIVrd42r+/VQsZfeIre4ixXjYAvCHOVVRWyNusHOVT4d7XRee42UosD1KfxVRIbUTfvbc/NhC0ZJUBhzigxlvcXAhTmrFeqqDxPftn7gfVDL981Tegkp6Sd6+Ve7DePz9Hvvvuu8g4Ci6C0tDT56quvpE+fPuZKEF9gMfTLL79IRESEPPvss/Kf//zHnM8LEgCBKVOmyPjx4+XAgQNWQBAmAq4v4ebSlQTrPIh7iHGHBAs8iMhPP/200+a2b98uQ4YMkb//Pvn5MD4+Xnm5Oeuss5zWNRWYPXu2XHrppaZbFWcRYmSTJk3Mz+xdjB49WnEx5V9wwQVqHvg5cpQozDmiYyyPwpwxXixNAiRAAiRQewQozNUea/bkpwTwZdyECROUqwwEvbaXcLIUIh4++DPVLgEKc97lvWZ/jmzOyjfUyeC26ZISG2mojqcKv7/4b3nmu78MNTd1VC8Z2K6hoTos/A8BXxDmTKPJKtotu/PWae5X90vFCWMuWE1tOHqtF50pXRucp046R4bGOD3x7Kgt5vkPAQpz/rNWHKkxAhTmavJasGeqFFcY+9xTsxX9T7o1GCyN49vrr+DhkrA6wmHEkpIS5abyueeek3//+99WveDQ4b333iuFhYXKSuiLL76Q7t27W5WpyxsIqtuPr1C/+4sq8iRMcz0dF5EsjeLaSrOkU7T78BrDW3X4Oyksz6nx3PSgdcoZWv3Wplu+OiCA+IN4T/zvf/+TZcuWCSzcTKlLly4yatQoueKKK1w+uJqTk6PcYn799deqWcR+h9Vmde82pj4tX3fs2KH259u2bVOPYbU3d+5cOeeccyyLObzG3DB+U8K+H8IcYs05S7feeqsSvk3lBg0apOLTVQ+PYco3vVKYM5Fw/5XCnPsM2QIJkAAJkIB3CFCY8w5XthqABLBZxYk70198SQf3FfChD//y+GDuzCVFAGLxiSlRmPPuMpSUV8q3mw9IeeUJXR01TY7gnjovAAAnCklEQVSVvs3r6yrrjUJFZRUy6PUFsj+3WFfzPZqlyqxb+vHnVxct24V8SZizHGF5VamI9rZdtP8Tr3zJGh4SKfVjm0qLpFMlJTrDsmteBxgBCnMBtqCcjpkAhTkzCvPFpmMLZcfxVeZ7b14gdul5zUarmKbe7Mde23/++aeyhDty5IgqAoumzz//3ModP1wAwlpuw4YNAkHkoYceUlZRuPaFZBlntmFcKyXC7S/Yog3t5OfW2PAkzdr9SkEsP8v0694PNWEu1/KR+RoxZs/OvFEiQqPMz3hRkwBiv3300Ucyffp0WbVqlZWXGeyPb7jhBuUCsn1794RnHI6FwPXZZ5+pQcBiDu/DZ555puagqj2pbjGHUBPffPONGLGYg5AHizm4fEXCfFy1mBs2bJiymIuMdHyAkcJctYV045bCnBvwWJUESIAESMCrBCjMeRUvGycBEqgNAhTmvE/5SEGJ/L4jSyqqHItz9TQrubNaNZCIMPeCvLs7oy2H82TE+4vlWKFji6lWafEy8+a+khYf7W6XQV3fV4U506IcK94nyw5+pX1FZyxWoqm+ntfmSd2kQ+qZFHj1wPLDMhTm/HDROGRdBCjM1cRUVH5cc2eJmFCOP/PUrGn8SWZCZ+mSpt9yx3gP9mtUFzsgWEBksXTZt3nzZhk3bpwgxlZ4eLiyfHrttdckMTHRfsO1mFNQliML983QfrtXyqkNhkpGfFvV+/bcFbI5e7F5JKnRjaV3xuXme1z8rFlGIkWHWccED5EQyUzsIk0SOqh8/lOTAFyefvfddyp8A+Kq4wArEt4jcO94++23y1VXXaWuPSHgwpXqXXfdJR988IHqB4dhYcH50ksvqXtH/2zdulVZzJlizCUnJ6sYc5auWh3VR94PP/ygBEYIkUitW7dWwhwO6TpLECfhxtOUYHmHGHMQFx0lCnOO6BjLozBnjBdLkwAJkAAJ1B4BCnO1x5o9kQAJeIkAhTkvga3WbEFphaw+kCP7j9e0RIsIC5F2aYlaXLlECdU2y76QsjQx8fl5G+XrtfukspqgGBMRJiNOby5jz22vxMadRwukWLMMTIuPkpb14ymuGFxAXxfmMB2Ic2uP/CAllQUGZ6e/eMuk7tK+Xl/9FVjSbwhQmPObpeJADRKgMGcb2NqsH2Vf/kbbmR56Cmu5AU1GaXFL60bkgttBfPlveg+cf/75SrDA9GBBN3/+fBVLbsuWLZKRkSG33HKL3HfffYIYXb6Sdh5fIxuP/aYNJ0SGNL9DwkJPuqyEi8pf935kNcxzM2+WqPB/RLgfd70rrZK7S0vtL5M+AtnZ2UqkQkzCX3/9VU6cOCleQ5Br166dXH311XLdddeJHsFKX48nS+E9+uijjwrcrprSHXfcIW+++abp1u4rLD3x3t67d68qAy83EBW7detmt071DLjnvPDCC9XPBfIyMzOVMAerQGfJUmBDWcTKe++995xVUz+bEPCQICbCyq9vX37GdArORgEKczag8BEJkAAJkIBPEKAw5xPLwEH4OgH4pMdJt8WLF8uePXtUfAWc/jMFtp4xY4aKyQD3L84COfv6XP1xfBTmanfV4Noyq7BUisorNHdBIZIQFSH146K0L0N8Q5CrTiO3qEyW7TomBzRBEePNTI2Vns3rydp9OfLGL1tl6c6jYqnbpWpWf1d0z5TbB7SRpBjHbmaq9xWs9/4gzGFtKqsqZH/BJjlctFMKtVP2FVXlUl5V4lFLuq5pgyQyLEa5zEqIrKeJvI5PRAfre8bf5k1hzt9WjOPVS8AkyqA8vlx31y17XX8BqnfezsqVVhbJ73unS1lVzcNIzurqzW+b0lvapJyut7hHy+Gz8+WXX65ibaFh7GsmTpwosCJavny5fPLJJ0p4SU9PV8/uvvtu6d+/v0fH4InGtuUsk605S1RTvRtdLqkxjc3N/rh7ipRp62hK1fPn7Zws7VP7CCzemRwTgCD3+++/K5eVX375pVmQQ61TTz1VLrnkEhkxYoS0bNnScUNu5L7xxhsyZswYcwuwyDMJV+aHNi7grhXuI48dO6Zy4YYSojPENb0JVnfDhw+XTZs2qSr4uYB7yx49ejhsAq4v4QYWFqdI+P/1kUcekaefftphPWRaCnoU5pzicligrn8v5ebmyoQJE8xjHD9+vPmaFyRAAiRAAsFNgMJccK8/Z++EgOmk6MKFC2uUjI2NVQIdMnCCFCff8CEdLmDOO++8GuX5wHsEKMx5j22gtvziDxvlrd9OBoG3N8f0xGiZen0v6ZCeZK8In/8/AX8R5mwt2I7clVburmyVcfUZYtQ0ie8grbUvXiHWMfkvAQpz/rt2HLljAhTm7PM5UrRLlh/62n4BN3LqRTeRMxpd6rYQ6uoQVq9eLQMHDhS4szQlxM3Oz89XLvb69eunhDiIcR06dFCiramcL70eK94vSw9+ISlRjaRT/bMkKaqBGh4suebtelM7eFVpHu6gZrdqrtb/cV3+3Y6J0rn+2dI0oaMWa+64JsIWSXxEKn9fm4mJHD58WGbNmqWE2hUrVphjrKEIYrSNHDlSiV4NGza0qFXzEq4g69evr0Q8V8X/b7/9Voljpjhv2G9DHIuKchwH8KefflLimMkNJd7b8+bNs4qjWHPE1k9gQQpLN1itIaWmpqo9PwQ/Rwk/XyiDw71ICQkJ8vrrr6vYe47qIY/CnDNC+vMpzOlnxZIkQAIkQAK1S4DCXO3yZm9+RGDq1KkyevRowRfOtpItYQ7l4C/+ySefVO42bNXjM88ToDDneaaB3OL7i/+WZ777S9cUYQk4/+6Bkqq9Mtkn4M/CXNWJKll5+BvJ0r6A9VaKDNOsNNMvMn9h6K1+2K73CFCY8x5btly3BCjMOea/J2+9rD+6wHEhg7kJkfVVvLOI0Lr7bDF58mRlfVRVVaVGf/bZZyvRA+4Im2txs3DY0FfiyDnDW1JRINHh1u41T8YJnGauCgv2/k2uM9+f0H73f7dzkjSKayu5pQeluCLfnJcYmaZEvtToDPOzYLtADLnZs2cLrNT++usvMYlaENXgTvHmm2+WwYMHq/eJPTYQR1etWqUOr8LK7v7775exY8c6ja1mrz24pESfJo81Xbt2VcJc06ZN7VVRz6dPn65iI5rcbkJgmzJliiFRHJ9zYeX0zDPPqDajo6NVfLt///vfDvvetWuXim8Hd7BIsCj8/PPP5bTTTnNYD5kU5pwi0l2AwpxuVCxIAiRAAiRQywQozNUycHbnHwTwwR8fmE1fVkCEu/LKK5Url0WLFqkTcpbC3Jw5c+S2226TgwcPmif4xRdfyGWXXWa+98bFoUOH5OWXX5YlS5Yov/mdOnWSM888U51wxWlAdxJOzMI9CNzZ4C/ceWKjDvcfOKEI0dLeiUfEAMDJQmdp1KhR4u440QeFOWekmW8ikFdSLr2en6/iyZmeOXu9oU9LeXxYF2fFgjrfn4U5LBxO1G/JXiK7tFg1J+Tkl5SeXtCI0Gjp3/Q65eLS022zPe8ToDDnfcbsoW4ImD7rone6srS9BvvyN8m6rJ888vsBYk+Phhdpllt1J8phlvfee6+89tpryiUhDhXCdSVcAwZK2qvFB1ynxQk0pe4NL5D0uFamW82VdZnM3/WWum+e2FVSoxvLoaLtcqDgpICCjC71z5HMxM7mOoF+AeEK7h4Rf+2dd94RWMhhj4WEfe8pp5yiLL3wPklKsu9NoqSkRNasWSM45ApBzuRC8vnnn1finL39ozO+cAd4wQUXmK3PIMjhfetoL4k54cAs/prmMWnSJLnxxhuddVcjHzEZMXfToV241YT7V0cJDLFvxtiRcP3VV18pno7qIY/CnDNC+vMpzOlnxZIkQAIkQAK1S4DCXO3yZm9+QgCnRn/55Rc12i5dusjPP/8saWlp6v6pp56SJ554Qn2gLiwsNM+oqKhIHnzwQcGHfaQGDRrIvn37vBZzDr7qIfyZTjCaB/L/F48//rh5E1I9z9k9Ng9DhgwR+OS3l+C+5OOPP5YmTZpYFcEGCJs3bMqcJZxWxIlLdxOFOXcJBk/9uev2y12frTA0Ybi0XPLAYEN1gq2wvwtzpvXCifmDBdvkeOlhLS5NsZRUFmrurXJM2W6/1otuKmmxzQQuLhM1i4mkqIZ2Dzi43Rkb8CgBCnMexcnGfIgAhTl9i4HfC2uOzJcCF38nhEiotErursWU6yWhPhB79Nprr5WZM2eqycMyDocMBwwYoA+GH5T6fd8MyS87qkbaJL6jdG1gHWagUosxu0WLTQfXlZbi26J9M+V42cnDhaEh4dKv8TWSEJnqBzN2f4iw6sJB019//dXcGFwvdu/eXbmsxL7TkSCHPSkEORzsRGx2WN2ZEtykvvjii8oq0/TMlVdY3b366qtKHMN+EwdUb7/9drtNQRREyAlY/yG1bt1a4FazRYsWduvYy9i+fbsMGjRIduzYoYrgGq40IyPtx6PGzxhi72F/jDiOzz77rDzwwAP2urB6TmHOCodbNxTm3MLHyiRAAiRAAl4kQGHOi3DZtH8SwIftVq1OnqiEOxfElzOJcpiRPWEOeXAHA1Hvt99+w606EXfxxRera0/+A7cgOB2IDVBcXJxceumlKk7E3r17le97WLghYbOCE7FGUk5Ojpx77rnK9QjqwWUIYlCABdhATNu8ebNqEnEnTHM19QEx0uRSpGfPng6/dH744YfFE3wozJno89UZgXd+3ybPz9/orFiN/O1PX6S53gmp8ZwPThIIFGGu+npCnPtt78dSXuX8oEH1unruo8MTtC9pz1DxbfSUZ5m6I0Bhru7Ys2fvEqAwp58vrKvh2nL78VVSYuH60HELIZq7xNbSVhPk4n1I4PnXv/4lH374oRo6LCXh6cPZZ3JYPyFh3+HLyTI2IA7A9M64XMI0kU1P2pL9h/yde3IfhfKtkntI+9S+eqr6fZmvv/7aLJxBrL3ooovk+uuvl969ezuMx4Y4aoj/Bus17JstBbkzzjhDrr76atUuPK+4m9avX6/aMoljV1xxheoX72FbaenSpcrKzmS15+jgKva3e/bskc6dO0tGhm03pnfccYe8/fbbSmhDGcSjR3lbCYd2IRoi/jwS9tLYNzuLx2dqi8KciYT7rxTm3GfIFkiABEiABLxDgMKcd7iyVT8mAF/6cE2B9N///ldZwVlOx5Ewh3LYAGADgwS/8yYLOvXAQ//ghN6PP/6ofPRjEwW3HqYEcRDCmsniD1Zvp59+uinb6evTTz8t2LQg/ec//1GnGy0rQQTDxh1Bs5GwCcPGwZRwyhJCHoJimzZBpjxvvVKY8xbZwGv3i5V75D9frjY0sZTYSFn1yFBDdYKtcKAKc1jHY8V7ZfmhOcrdpbfWtXF8ezkl7TyHBxm81Tfb1UeAwpw+TizlfwQozBlfsxNafLKj2u+GI0U7JbvkgGZZnSuVJ8rNDeHQRZIWp6xeTFNNlGujxT+LM+f5ygXiZWFPA0sepHHjxskLL7yg3JlWH2NFRYW88sor8vrrr8tjjz3m0EKpet3avocl3KL9M5VlY4y2Dn0yrqwRf87RmODS+q9jJw9YolxaTDM5vdFwR1UCJg9WZQgzAK8pN9xwg9rPpqSk2J0f3hfw4PLmm2/KsmXLBIc7TQluLyHqYc9oOvBqynPnFfvcRx55RMV3w2fPzMxM+emnnwQWedUTxof3OazUkDAOCGm2xoN9LazxMAeEbcDPgq04cDgcC2F69+7dah+OfT7EOlsJ1oNgefjwYZUNi0HsrfUkzHP48OHKIg/lk5OT1eFbxPdjMk6AwpxxZqxBAiRAAiRQOwQozNUOZ/biRwTuu+8+tfnEkCFuwWWjZXImzMGKLT4+XlnP4QO1yXWGZRvuXK9cuVJ69OihmkAAbbjzqJ4Q6w4bFWxI7rrrLrWRrl7G3j1O823dulU6dOggGzfatixCIGuTCxCcgjS5wkGbsKhD/DmIk3/88Ye9bjz6nMKcR3EGdGOH84qlz4s/aCKL/mle0q2JvHpFd/0VgrBkIAtzWM78smOy4egvkqN9Aeut1C61jzqZ76322a57BCjMucePtX2XAIU5z6xNZVWFikEXFhLhF4csFi9erESDgoICBQBf/E+ePFmJDnDNhzhg+HyNGNOIDYa4WPfcc486sAhvHb6aEAtwb/5fgriuEOXiI+0LS7bmsCdvg6w/+rM5KyWqkfRpfKX5PpAvsP/bv3+/EqQcuaxEKIcFCxaoOHSIvQ6LOSS4aoSrSIh7I0eOVOEOXI0n54gz4phfcskl5n0mxGL8jYiIsKqGPfOFF16oYsDjPY09KsZWPWHfDBERh15NCeOHRamt8U+YMEGJePi/E+IdDqli/2yZ8LODA7roEwmHaGE550jotKyfnZ2t4tFBCESKiooSHMaFJxsm4wQozBlnxhokQAIkQAK1Q4DCXO1wZi9+RACuH01iFwSoZs2aWY3emTCHwogvl5WVJX369DEHqLZqxI0bnPp79NFHVQsQvkzWedWbxHNY7zVq1EjFukNgd2eptLRUoqOjVTFsUBAfwF5KT09XJwA7duwof/31l7kY/ObjRCBc5HzwwQfm5968oDDnTbqB1/aT36yXaUtOxodwNrvI8FCZN2agtKgf76xoUOcHujBnWtw8LV4NLOhKKgqkQvsSdm/+elOW26+IPwTLudDQMIkJT9RO6WdKYtTJ2KZuN84G3CZAYc5thGzARwlQmPPRhfHysPDZGYf33nnnHXNPiNkFAQHuByFkwArqm2++UVZ0iK+NuNAQCHw1HSjYKquPfK/cVvZqdJkkR6cbHur23JWyOXuRuV6zxFOkc/2B5vtgvoBYBE8sEKZweNX0fwfeE7AyQyw1xC5s3Lix1zGtWLFCbr31VhV6Af0jfAP6h/CF9zb2pnfeeacsWbJE7W1hZYfDt6Z9ruUAIT7Dsm/Tpk3mx2eeeaaKRWerPOKow7sMrOVwff7556v+YbUHl5r4vACR+8knnzSHuYBHHhx6dZTAEwd8YbWHOO74mYPlnClBLMR3ANiDY84QIvXs7031g/mVwlwwrz7nTgIkQAK+TYDCnG+vD0dXBwRmzJgh1113neoZm9Fhw4ZZjcKZMIcP46aYdPgAbfIrb9WIGzcmN5b169dXwpi9D+QPPfSQOuGKrnAC0paLj+rDsIyvh80GTvrZS+gfrioRRw4bd1NCYHDEoLDlBtRUxtOvFOY8TdS99grLKiS/tFwqtb1kbESYJMVoG0ft5LWvpLKKKrn9k2WyYMtJ1zL2xgVR7rXLu0vDxGjJLiqV+KhwadcwUVLjfPdLKXtz8fbzYBHmqnPcdGyh7DxuzDVq9TYc3adGN5YuaedIXESyo2LMqwUCFOZqATK7qBMCpi/X0Tm+VLZlIWJkYHX9BaiRsQZ7WbjYe/DBB+Xzzz8XWEFVT3BLj8/5d999twwd6tsuvY8W7ZHlh+co15w90i+UBrHNq0/HfF9eWSLbcpdJZFiMtE7uaX6OC/xe36HFEDSlrmmDpEmCY0HFVDZQX7HfQ+w4iEXfffedEqMwV/xf0bVrVyWIwYNKkyZNahUB4s1hXw73lPn5+WZRGRZ1eE8fOnRICYawkoMLSXtx6GDxBzeW7777rnn8tsI5mDO1CxxmnThxooo3h/1zy5YtlRUfLE9hTQjhEj8/8LwD0dDkacayjerX2D8jVt/q1asFc7MU5VAW93AR2r17d+WOE/H12rZtW70Z3tsgUNe/l3Jzc5WgbRoaXKwykQAJkAAJkAAIUJjj+4AEqhHAaTlYgSHBv/7UqVOtSjgT5iwDZ7/22mvK7YtVA27emKzx4GMe7kPsJYz7pptuUtnYTPXr189eUfNzfODHST0knBCESxJbCacle/XqpbJuueUWq40MNmjr1q2T6dOnqwDacJWDe7jVRHBsWPLhxGJCQoKtpl16RmHOJWwer7Qru1A2HsmTvJJyq7YjwkKkVWq8dExP0r4EcW65aVXZSzdVmi/LD5fukLd+3yZZ+aU1eunZvJ4kaELcor+zpAwKo0U6o0U9ueec9tKrRX2Lp8F9GazCXJUWZ2iNdjr/UOF2r70B4BbtjEaXaif/G3qtDzbsnACFOeeMWMI/CVCY889189So8RkaIgK8cJhiYdWrV0+aN2+u3PR16dJFWc95qj9vtHOseJ8sO/SVigWbqMX2S7MjyqXHtlRWdH/nLJctOSfd7Xes119aJJ1qHtbv+2Zo7quPqvt60U2037+XaAKUb3x2NQ+yli4gyGE/ibAMsFAz/V+B/SEOiuIgK+KKwztLXSW8f7EfhhgGKzl4rImJiVHvX3iuwfjwXnaWDhw4oKxHIebBPeWVV16py+0kDr/i5wd7Y8Sdg2AHHtjzwu3kqaeeqtvKFIIcRD7whUVc9cO3iAeJ9rEO2KcjtAWs55icE6Aw55wRS5AACZAACdQNAQpzdcOdvfowAYhTEL+wGUHC6TX4sTclR8IcfNTjQzI+3CNho+DJIM34QA63FfgSHD7z58yZYxpWjVdsohCcGul///uf+bpGQYMP4MYEIh8ETGwcMEeTSIemEF8Pp27xaopbUb0LuDjBqUS4/vBEojDnCYqut1GlvS+X7D4me3OLHDYC67mzWjWQxGjrGBAOK3k5s0IT3dbtz5VtR/KluLxS0uKjpKyiUh6bs04Kyyod9j7u3PYyZmA7h2WCJTNYhTmsL/5fRjybv7XT93Bx6Y0UHR4v/ZuMlPBQ3/nZ8cY8fblNCnO+vDocmzsETF+2ow1azLlD0r/rWh7Ogyhgz7rI12aZU3JQ/jz4pVSeqHA6tNPTh2uiXTMxubxEhRDtT+f6Z0tqdIb6XW6ylouLSNFi1F2hrOqcNhxgBbDX++yzzwReZCDIQQxCglCEPR9iDQ4YMEDtl31l6vgcmpeXp9xYYn8Kt6z4ayThECnm6koMRex9i4qKlFUbRDPsgzEOJt8gQGHON9aBoyABEiABEqhJgMJcTSZ8QgIquPLw4cMVCQhhsPCCL3l80LYlzGEzO2vWLOULHifnkOCrHoHSPZngagMuMpCcxYD79ddf1SlBlH3rrbfktttuw6VbCadp4Z4Tp/mQ4PbjhRdeMLcJYTIjI8N8379/fxW4GqcFEUwcPL7//nuVj00/XGDCJYeeBJceW7ZssVkUGykIqHATApediIHhiwmbPQi+lox8cZxGx7RiX7b8fVSfIAFxbmj7RhLhI5Zz1ef6tybQDXvzV02cs7aSq17OdP/SZafK5adlmm6D9jWYhTnTokOgyyvLkoKybPUF4f78TZJTetCU7fYrviSMCI2SqLBY7cR/Iy0eXTuBYMdUOwQozNUOZ/ZS+wQozNU+c/boOQJbsv/QDsYs19XgOZk3a78341TZ3XnrNCFuo+SVHpET2h9TigzV3FumnC7NErtobtiDS1jBgUq4q3z//feVK0UcfESCUAWXpqNHj1ZWYHDRyEQC/kSAwpw/rRbHSgIkQALBRYDCXHCtN2drgMDYsWOtfIHj5Fu3bt2UNRhcTUCwQ1BpCHFw1bhv3z5z63ArASEJcdg8meAzv2HDk+7M0DcCSdtLcOlxzjnnqOwpU6a4JVbhC0kEm4aVG758RkJMimeffdbKzQZcZkKMQ0KQ7WeeeUZdW/5j6WKzU6dOsmbNGl2nch944AF58cUXLZuqcQ1BDhtHBP/2xQQBEScqk5KSfHF4Lo2pQLMq+2V3rqG67erFSNtUY6dYDXXgRuExn6+VX7Zl6W4hNTZCfh5zps8Kjbon4mZBHE4w/d+AmCPV3e+42bxfVi+uyJOV2bN1neJ3ZYIaZWka10Wax50WtG62XOHmah18YQkLEhzQYSKBQCKAgxWm5AkLj9ffeMfUnNXrww+Ms7rnDQnUBgF8Ntmas1QgxA1qfmuNLiuqyiW39BDM3yUuMkWiw+LdjrNYoxM/eTBv3jx1sNQkyCHsALyk4FAmDpwatUDzk2lzmEFAwJ4wd/e/a/6f4A0cOFxtGcOQMea8QZltkgAJkIB/EqAw55/rxlHXAgGcIIbQg5ODpi+c9XSLwNeffPKJnHnmmXqKGyqDL08iIyOVmwxnFnNffPGFICg0EizVsKEymiAkvfnmm4IPjwhajNS+fXuZPHmy2RqvepslJSVKfEJ8CnsJwhkYIa1du1aX1ZxeYQ5xAUwuPO31X1fP8T6CgOGJL77qag7V+91TUCHb8//5Uq96vq37hIgQ6VE/0lZWnT6DW8sBby2XSi3+nJE0+dKOclqTRCNVArKs6f9JCHNMJwnkV2bJ9pJFUiknT517g0tKWKa0jO7tjabZpgUB/P6l6GwBhJcBQ8D0fzcm5In/v9+fNsMmGwpzNrHwYS0QML3HPfH+roXh1lkXCJGA/SJitCEUA+KsXXPNNcotY50Nih2TgAcI2BPmbvpX7Rzmzc/Pl+nTp5tnQmHOjIIXJEACJBD0BCjMBf1bgACcEVi+fLnAem7x4sUOi0IwGzdunLIsc8U3vcPGLTJhMQfLObjaRBw5e+mdd94xu6/E2BEA20hCAOtrr71WBYNHPVh5wQru7rvvdjsIPNx+XnXVVWo406ZNU245nY1t0qRJ8tFHH9kshg034vohFiDcZtKVpU1MXnm4an+ObM3KN9R2dHioDO/cxFCd2ih86Hix9H7xB8NdTbiyu1zc1ffmY3giblSgK0v78EorCjU3Wyu0mDabpbzqZJwW+6Vdyzkl7TxpktDBtcqspYsAXVnqwsRCfkiAriz9cNE4ZBLwAgF4W4F4ADGud+/eFOS8wJhN1g0Be8JcbR0YwQHnCRMmmCdPYc6MghckQAIkEPQEKMwF/VuAAPQSgJvGDRs2KNeVcF+ZlZUlLVu2lLZt26q/OFmIGGfeTl27dlWuM/EKN5D20nPPPaeENOTv2rVLmjVrZq9ojeeITweRy2Qld8MNN8hLL70kjqzgajTi4MGqVauke/fuqsQrr7yiBE0HxZ1mweUK5osEi0UKc06ReazAxsN5su6gMVeWydERMkSLM+drqbSiUjqO/0YMGszJjBv7SJ9Wab42nVodD4U557hxgKCwPEfKKoulqPy4rDv6k/NKOkvArWVYaLiEhURIQmQ9SY9rpcWg66Ce6WyCxZwQoDDnBBCz/ZYAhTm/XToOnAQ8SgBeT/B5zpsHTD06YDZGAjoJUJjTCYrFSIAESIAEap0AhblaR84OScA9AqNGjVLuNeEOMScnR+D/31YaMmSIzJ8/X9q0aaPERFtlbD2D5Rli6UF4bNCggXzwwQdy/vnn2ypa49kPP/yg3H42bdpUOnbsWCPf9ODrr79WFn+4//bbb3W3b6pf/ZXCXHUitXd/vLhMvt+ixeYwkDo2TJRTGiUbqFF7Ra+ftkR+33ZEd4cJ0eGy/KEhEhUeprtOIBakMGd8Vddn/Sx78/8yXlFnjZjwBDm1wVBJjk7XWYPFHBGgMOeIDvP8mQCFOX9ePY6dBEiABEjAGQEKc84IMZ8ESIAESKCuCFCYqyvy7JcEXCSAwNxDhw5Vtd977z256aabarQEV5cZGRnq1CPca8IqTW8yxX+Lj4+X33//XbmG1Fu3S5cuyqoQwt7q1avtVrvtttsErjaR4DIzMzPTblk9GRTm9FDyXpmlu4/JrpxCXR1EaW4sh7XPkEjt1RfThv25Mvyt36VSs27Sk+4Y0EZ6NqunxeXRrDVTYqVVmm2hXE9b/lyGwpzx1Ttxoko2HP1VE+c2GK+sswYs6HplXCZJUQ101mAxewQozNkjw+f+ToDCnL+vIMdPAiRAAiTgiACFOUd0mEcCJEACJFCXBCjM1SV99k0CLhDAF+CNGzeWw4cPS2pqqmzcuFEQd84y3XnnnTJ58mT1aOnSpXLGGWdYZgtcVVZVVUlUVJQK7m2ZmZ6ertqGa8iHHnrIMsvpNeLPTZw4UZVDnIKBAwfWqAP3mz179pSKigqncfJqVLbzgMKcHTC19LhCey8t3HFUDheUOOwxKixUBrRqIKmxkQ7L1XXm3HX75N7PV2nxwByLczERYVJcXmk13IykGLmxb0u5vldLCdfmGyyJwpzrK51dvF925a2Vo8V7pKKqzPWG7NSMj6gnZ2RconIjQ2M0EVlTkZkME6AwZxgZK/gJAQpzfrJQHCYJkAAJkIBLBCjMuYSNlUiABEiABGqBAIW5WoDMLkjA0wSmTZsmN954o3IbefrppwsEMbib3LZtmxLkkI907bXXyowZM9S15T8xMTGCOAKNGjUSuK40JdRHzDykyy+/XAl/pjx7r3Bb+eijj6rsTZs2Sa9evSQvL0+Jfi+88IKy7mvdurXs27dP4Opy7NixUlBQIBEREUpURJ67icKcuwTdr4/4WVuP5svmI/k1xKpQTQdolhKn3FdCzPKHtOnQcXlh/kb5bWtNt5aw+iutqHI4jdMyU+SD63tLohZPLxgShTnPrHJ5VamEaH+WHvhS8spqvvfc7SUsJFzqxTSV5oldpX6se5bK7o7F3+pTmPO3FeN49RKgMKeXFMuRAAmQAAn4IwEKc/64ahwzCZAACQQHAQpzwbHOnGUAEpgwYYISuexNDVZpcHsJq7rqyZ4wN3XqVJuuMavXt7yv7rby+++/l4svvliqf9EDCzlTql+/vopdd8EFF5geufVKYc4tfB6tDIEup7hc8kvLpUq7jonQhADNQi7CT63HjhWWypq9OZKtvcZGhstbv22Tvw4e18XszNZp8tENfXSV9fdCFOY8u4J5pUc1ce4LqTjheQs600gbx7eXLmnnSGiIf4jlpnHX1SuFuboiz369TaD65zV3rWrr+gtQb/Ni+yRAAiRAAv5FoK5/L+Xm5gq+uzGl8ePHmy75SgIkQAIkEOQEKMwF+RuA0/dvAlOmTJFPPvlE4K4SFnBIiYmJMnr0aHniiScEceJspWHDhgnELIh2n332mbkI3FDOmTPHfK/nAhZvb7/9tlXRnTt3ylNPPSWzZs2SoqIilRcaGirNmjWT3r17y8svv6ys9awquXFDYc4NeKyqm8Dcdfvlrs9W6C6Pgh+M6iVntbN2NWuoAT8pTGHO8wt1vPSIrD78vRRV6BOCXRkBxLmuDQa5UjXo6lCYC7olD5oJU5gLmqXmREmABEggKAlQmAvKZeekSYAESMAvCFCY84tl4iBJwDGB0lLNqkeL3ZacnCwtWrSQyEjfiOEF66n9+/dLdna2tGnTRmCp541EYc4bVNlmdQJjPl0h36zfX/2xw/trejaT54Z3c1gmEDIpzHlnFatOVMqBgq1yqHC7FJQdUxZ0iEOH555KneqdJbERyRIVFiPxkam0oLMDlsKcHTB87PcEKMz5/RJyAiRAAiRAAg4IUJhzAIdZJEACJEACdUrg/wAAAP//V2zHTQAAQABJREFU7J0HnGRVlf9PV67qrs45TI7ECQwzDJKDIsFFgouKAV3xryjqusuuio457OqOCAuLiILgqohKXIKApBmGNDNMzqmnc+6qrlz9P+cO1dOhurtyvVf1u3x6quq9e88993teVxfvV/ecgmFuhAYCIAACOibg9/vpBz/4gVpBY2MjffrTn9bkanw+H3V3d1N9fb0m/YNTUxO49u6X6Y1DPVN3Gnf2vIU1dO/HVo07mnsvQ6EQhcNhtbCCggIymUy5t0iNrOjo4E7a3PlMWrwxFpipvmghzStbQXaTMy1z6NVoV1cXWa1WcjrBRa8xhN/RCQQCgZET8t4t7+HJtB/8+GdRh3/tlq9EPY6DIAACIAACIJBOAtn+u9TX10dr164dWeKaNWtGnuMJCIAACIBAfhMogDCX3xcAVg8CuUAAwlwuRFH7a/j8/75BT25ticvRa5fPoB9/cGlcY/TYGcJcZqO2ueMZOurambZJTQYrLat5P1Xam9I2h94MQ5jTW8Tgb6wEIMzFSgr9QAAEQAAE9EgAwpweowafQQAEQCA/CECYy484Y5UgkNMEIMzpK7yeQIj8oTBZjAaym426cf7htw/TVx/eGJe/P/yHU+m0mRVUUWSlMoclrrF66gxhLrPRkmQH+/reoL38Ex4OpWVyY4GJ3tP4YSo0l6bFvt6MQpjTW8Tgb6wEIMzFSgr9QAAEQAAE9EgAwpweowafQQAEQCA/CECYy484Y5UgkNMEIMxpP7xeFuN2dg7Qod4hEmEu0mwmA80oK6TF1U4W6bSd/jDIYuIVd75IO1oHIu5P+Wg0FFAofDxb9OyKQrqGd9B9/Iw55LBoe61TLizKSQhzUaBk4JAvNEStrt3U621lsdtD/rCXBv1dKZu5xFqjUluKSOe0lFOJtZYMBYaU2deTIQhzeooWfI2HAIS5eGihLwiAAAiAgN4IQJjTW8TgLwiAAAjkDwEIc/kTa6wUBHKWAIQ5bYe2fdBLrx7sUrvkJvPUbCyg1TMrqa7YPlkXTRxv7ffQh3/1Kh3sdifsz8zyQvrVx1bS3KrcqVUFYS7hyyGlA0PhAL3U/CB5grGJx/FObjU6aG7paTSz+NSk61DFO3e2+0OYy3YEMH+6CECYSxdZ2AUBEAABENACAQhzWogCfAABEAABEIhGAMJcNCo4BgIgoCsCEOa0G65+j5+e2d1OIU69N13jDWZ04fxaKtd4yke3L0i3vbCLHtxwgNz+47v/plvf6PPVTis99cXzcya9JYS50dHN7vMBXye91vpnCoZ9aXOk2jGLa9Bdyrvn9JOKNlkYEOaSJYjxWiUAYU6rkYFfIAACIAACqSAAYS4VFGEDBEAABEAgHQQgzKWDKmyCAAhklACEuYzijmuyF/Z2ULvLG/OYShblLlxQG3P/bHb0B8O0qbmXjvYNkeiO331iC/V5AjG79PFVs2nN5afE3F/LHSHMaSs6Q4EB2tb1AnV6DqXNsVm8a+6EynPSZl9rhiHMaS0i8CdVBCDMpYok7IAACIAACGiRAIQ5LUYFPoEACIAACAgBCHO4DkAABHRPAMKcNkPoC4boL1uPxu3c5SfUU6HOarC9tKeDPv6b9XGtVXYGvvX1S+Iao9XOEOa0GRmXv5e6WJzzBAd512qImge2UZgS2+UZbYUNRYvJaDCRzVhElfYmKrXVUiDkpY6hgzTAte5k157ZaKNSrk1XZZ+p+kazo4djEOb0ECX4mAgBCHOJUMMYEAABEAABvRCAMKeXSMFPEAABEMg/AhDm8i/mWDEI5BwBCHPaDGnPkE+lsYzXu/PmVlON0xbvsKz2f/D1g/SNRzbH7cPWb15KhVZT3OO0NgDCnNYiEt2f/X1v086eV6KfTMFRqUEXYDEuzCLg+GY22GhB+SquT6fPXaIQ5sZHFK9zhQCEuVyJJNYBAiAAAiAQjQCEuWhUcAwEQAAEQEALBCDMaSEK8AEEQCApAhDmksKXtsGDvgA9saM1bvvvXVhLZXZL3OOyOeCxd47SF//wZlwumLio3q5vX04GKa6n8wZhTh8BHOacq1s5xeWRwa1Zc3iG8yQ6qer8rM2f6MQQ5hIlh3FaJwBhTusRgn8gAAIgAALJEIAwlww9jAUBEAABEEgnAQhz6aQL2yAAAhkhAGEuI5jjnkREgMd3tJDbP3H3zGTGbCYDXXFiAxkK9CVWdbl8tPJHT/FOoclWNvF4GaeybCi1q7SdC2uL6b0n1NHquVUTO+rgCIQ5HQRplIstrl20p3cDuQN9o45m7ukpVRdRo3Nx5iZMwUwQ5lIAESY0SQDCnCbDAqdAAARAAARSRADCXIpAwgwIgAAIgEDKCUCYSzlSGAQBEMg0AQhzmSYe+3wHely04XBPzAOWN5TR/CpnzP211HHNY+/Qfa8dSMql02aW039ds5wayxxJ2cn0YAhzmSae/HwinA/6u/mHa8EN+6nDfYA6uR5dJpqxwEzF1koyFpio0FxO1Y7ZVOWYkYmpE54DwlzC6DBQ4wQgzGk8QHAPBEAABEAgKQIQ5pLCh8EgAAIgAAJpJABhLo1wYRoEQCAzBCDMZYZzorO83dxLu7sGpx0+p7yQVjSVU4HOdstFFuYLhOhTv91Ar+7rjBxK6LGa6+s9fONZuhLnIMwlFGpNDfKHPPTSkQfIH/Zkxa8SazWdWnUxFVnKszL/dJNCmJuOEM7rlQCEOb1GDn6DAAiAAAjEQgDCXCyU0AcEQAAEQCAbBCDMZYM65gQBEEgpAQhzKcWZFmMHe9z0TmsfDbF4Nb7ZzUY6qbaE5lYUjT+lu9fBUJjufGkP/fKVvTToDSbs/+o5lfTgp85MeHymB0KYyzTx9Mw34OuiN9seJW/IlZ4JprFqNlhpVf3V5LRUTNMz86chzGWeOWbMDAEIc5nhjFlAAARAAASyQwDCXHa4Y1YQAAEQAIHpCUCYm54ReoAACGicAIQ5jQfoXffCnDqvy+2jniE/+VnAMhsNVG63UFWRVXc15aYjPuQP0vr9XbS7fUDV2HvorcPUMeidbtiY88/cfB7Nry4ec0yrLyDMaTUy8fsVCHlpX99b1Dy4PSu755yWSnpPw3Wa2zkLYS7+awkj9EEAwpw+4gQvQQAEQAAEEiMAYS4xbhgFAiAAAiCQfgIQ5tLPGDOAAAikmQCEuTQDhvmkCPiCIVr0rcfjtvGfVy2lq5Zpu+5WZFEQ5iIkcudRatC5A30sog+Rj382djzFixvOyAJl5xxRAdlMRVRha6Qm50nkCnRTq3s39fs6KRD2ktlgo1JrDdUXLeAadXPSLuRBmMtI6DFJFghAmMsCdEwJAiAAAiCQMQIQ5jKGGhOBAAiAAAjESQDCXJzA0B0EQEB7BCDMaS8miXoUCg+Tl4UsA9eZs5oMObGTzsvpOxeviV+Y+/olJ9ClJzdSJe8olN2FWm4Q5rQcndT4tqtnPe+keyM1xlJspcxaR0tq3kd2kzPFlo+bgzB3nAWe5RYBCHO5FU+sBgRAAARAYCwBCHNjeeAVCIAACICAdghAmNNOLOAJCIBAggQgzCUITiPDZGfO4b4h2tvlUqkuI3tyTIYCqnXaaDGnc6wolB00+m0X/NdztJ/Xl0izsEC5clYFfXL1XDpvYU0iJtI+BsJc2hFrYoJdPetYnHtTE76Md8JhKqHVDdeSxWgffyolryHMpQQjjGiQAIQ5DQYFLoEACIAACKSMAIS5lKGEIRAAARAAgRQTgDCXYqAwBwIgkHkCEOYyzzxVMwa41tyrB7uobZr6ayLOnVpfmqppM27n3nX76LtPbE163stPaaD/vGoZiVinpQZhTkvRSK8v/b4OOtC/kTqHDqmUkjKbscBMFfZGaihaTIP+Lk45uYfTYPam15Eo1huLTqCF5at5p62Rd5mmVsyHMBcFOA7lBAEIczkRRiwCBEAABEBgEgIQ5iYBg8MgAAIgAAJZJwBhLushgAMgAALJEoAwlyzB7IyXnXIv7OugDpcvJgdOqi0h+dFjkxSd1/96Ha3f35W0+1ewOPfzD52WtJ1UGoAwl0qa+rEVCPPvLv8em7gmXAGnnx3dQsNBCob9SsB7p/PZ0acy8txisHPtuVk0u3QZOS0VSc8JYS5phDCgUQIQ5jQaGLgFAiAAAiCQEgIQ5lKCEUZAAARAAATSQADCXBqgwiQIgEBmCUCYyyzvVM12sMdNrx3ujtmc3Pa/dHE9FVlNMY/RUschf5BuffQd+vPGI0m79btPraYz5lQlbSdVBiDMpYpkbtp5u/0JanPvy8riCqhA7aKbU7o8qfkhzCWFD4M1TCBTwpyGEcA1EAABEACBPCTwtVu+kpFV9/X10dq1a0fmWrNmzchzPAEBEAABEMhvAhDm8jv+WD0I5AQBCHP6DOPfebfcdCksx6/s1LpSWlxTPP6wrl5vPdpHD719mN4+3ENdvFvQEwhRvycQ1xquXT6DfvzBpXGNSWdnCHPppKt/2+HhEG3vfokOD2zJ2mJOqDiHZpWcmvD8EOYSRoeBGicAYU7jAYJ7IAACIAACaSEAYS4tWGEUBEAABEAgDgIQ5uKAha4gAALaJABhTptxmc6rx7e3kIt3kcXT5lYU0Yqm8niGaL7vDfe/Ri/sao/Lz9NmltMd/7iCSh0WTdSbgzAXV/jytvOAr4uODG6jHu9R8oXcnAVzeKROXbqhFJCRltW8n9NumshqLKIiS9nIlJJ20+XvYV98ZOa0nEXmcjJyv9ENwtxoGnieSwQgzOVSNLEWEAABEACBWAlAmIuVFPqBAAiAAAikiwCEuXSRhV0QAIGMEYAwlzHUKZ3o6V1t1Ovxx2VzUbWTltQfv6Ee12CNdr75D2/So+8cTcg7Ket1Yl0JXbVsBl132kyymo0J2Ul2EIS5ZAnm5/hQOEgvNv+WvMHBjAOwGh1UWzif/KEhah86QGEW5yLNUGDk+nRzaH7Z6SP16SDMRejgMdcIQJjLtYhiPSAAAiAAArEQgDAXCyX0AQEQAAEQSCcBCHPppAvbIAACGSEAYS4jmFM+yaaWXtrZEd8N+XO4rlpdsT3lvmTT4B/fPES3/GVT0i7MrSqiuz+6kuZUFiVtK14DEObiJYb+EQK93hba0PrXMcJY5Fy2HwvIQKdUXUANzsUEYS7b0cD86SIAYS5dZGEXBEAABEBAywQgzGk5OvANBEAABPKDAIS5/IgzVgkCOU0Awpw+w+sJBOmJHa0UDA/HtIByTtt40fwaKpBtYjnUfFxj7uLbnqfDPUNJr6qm2EZP3nQulRdak7YVjwEIc/HQQt/xBPp9HbS54xlyBXrGn1KvRSAbpnDUc5k4uLLuShp228lqtZLT6czElJgDBDJGAMJcxlBjIhAAARAAAQ0RgDCnoWDAFRAAARDIUwIQ5vI08Fg2COQSAQhz+o1m64CHXj7Qybtlpl6Dg1M0XsCiXKFlbN2nqUfp5+zOtgG67p5XqM8TSNrpD58+i77/gVOTthOPAQhz8dBC32gEpN5cp+cQdQ4dJHegj+vPhclmKqIKeyPVccpJD6e7bHXvJRHxglwLzhMcUMei2Ur1MZvJSTWmhWQ2WajcWUNl1jpVgy48HKI+X7uqTyepMKV2XbmtnqwmR6pdgD0QSBuBVAtzaXMUhkEgAQI+n4+6u7upvr4+gdEYAgLaJYDP3tqNzXjP+vr6aO3atSOH16xZM/IcT0AABEAABPKbAIS5/I4/Vg8COUEAwpy+wyh15t5q7qEud/R6c7PKHLS0oYxvdmenflqm6Lb0eehbj71Df9vZltSURVYTbf7G+8lgyNzOQtwcSCpkGJwAARHvXm5+kEX9UAKjkxtiJDOV2mpo0N9N/rBngjGpT7e44j1UaC6dcA4HQEBrBCDMaS0i8CeVBCDMpZImbGmJAD57aykaU/sCYW5qPjgLAiAAAvlMAMJcPkcfaweBHCEAYS43AtnHAl27y0sef4gMnK5SBKZap40cvEtuwBugFt5dN+gLUijMO2l4B111oY1qOXWj9M2ldrjHTS/v7aTmXjftbh+k53e1x728951YR5L6c161ky5YVEszygvjthHPANwciIcW+qaKQItrN23qeJrNTbPlNlUTxmHHWGCi5bWXU6W9KY5R6AoCmScAYS7zzDFj5ghAmMsca8yUWQL47J1Z3snMBmEuGXoYCwIgAAK5TQDCXG7HF6sDgbwgAGEud8PsDYZ4N10vHemLXn9NUlwuayyjxpLcTB33HO+e+/RvNyQVYJEt/2FJI33rspOpxG5JytZkg3FzYDIyOJ5uAj3eFtrW9YLavZbuueK1byww09lNHyU7p8JEAwGtEoAwp9XIwK9UEIAwlwqKsKFFAvjsrcWoRPcJwlx0LjgKAiAAAiBABGEOVwEIgIDuCUCY030Ioy5gyB+k5/a2k5t30E3XlnGqywVVuXfzu2/IT6f/6CkKhJLfETSnsoj+8E/vocoi63Q44z6PmwNxI8OAFBPoZYGu23OUfCE3BbgGXYtrV4pnSMxcsaWKSqw1ZDZYqNhaTVX2mWQ2pv53MDHvMAoEiCDM4SrIZQIQ5nI5uvm9Nnz21k/8IczpJ1bwFARAAAQyTQDCXKaJYz4QAIGUE4Awl3KkmjD4PItyHS5fzL5cvKCG0zfm3g3v7zy+hX69fn/MHKbqeCGntfzl9Sun6pLQOdwcSAgbBqWRwMb2/6NW9540zpCYadlFN6d0Gc0tXcFpeA2JGcEoEEghAQhzKYQJU5ojAGFOcyGBQykigM/eKQKZATMQ5jIAGVOAAAiAgE4JQJjTaeDgNgiAwHECEOaOs8iVZ91uHz27J77aak2lDjpzVmWuIBhZhzcQok/ct542HOgeOZbMk2dvPl/VnkvGxvixuDkwngheZ5tAMOynN9oeoV5va7ZdiTq/7JxbXnsZi3PGqOdxEAQyRQDCXKZIY55sEIAwlw3qmDMTBPDZOxOUUzMHhLnUcIQVEAABEMhFAhDmcjGqWBMI5BkBCHO5F/AdHQO0uaUvroXZTAb6h5Ma4xqjl87+YJjWPr+Tfr1uP4lQl0w7sa6E6kvtVF9ip9Vzq+j8hTVkMia3cwc3B5KJCMami0B4OEz7+96iA/0bOb2ld9w0BVRuq1fHB/2pEb3HTTDtS9k1t7D8jGn7oQMIpJMAhLl00oXtbBOAMJftCGD+dBHAZ+90kU29XQhzqWcKiyAAAiCQKwQgzOVKJLEOEMhjAhDmci/4W9v6SX7iaSZDAV19SlM8Q3TXV2rOvbing3a09tOgN0D/+8YhSrb6XAOLdD+8cgmdNa86YR64OZAwOgzMAIHwcIj6fFKvspfCFCKbsYjKWJSzGG1qdnegj/r5vD/kVY9HXTsz4BUXeub/Sm11atecw1RCVY6ZVOOYrXb5tQ/tp0F/D4WGA2Q1OpSIWFe0gGQtra7d3KeN/GEPmTg1ZrG1imoL53Etu8R/hzOyYEyiSQIQ5jQZFjiVIgIQ5lIEEmY0RwCfvTUXkkkdgjA3KRqcAAEQAIG8JwBhLu8vAQAAAf0TgDCn/xiOX8GhXjetPxTfLpZyh4UuXlA73lROv77gv56j/V2upNdYwBZ+/qHldPkpie04xM2BpEMAAxohIMLXq0d/z6JYfO8/qXLfQEYlHka3J7+p0qLL8dUs6p1cdYES8o71w78gMD0BCHPTM0IP/RKAMKff2MHzqQngs/fUfLR0FsKclqIBX0AABEBAWwQgzGkrHvAGBEAgAQLjhbkbbrghASvpHyI3B3p7e6m2Nr/Eo0TIBsNhenwH7wgJhWMevqy+lOZVFsXcPxc63vXSHvqPZ1Ozu0dSgT5z83nUwLX64m1hjtfw8HGxwGhE3ax4GaK/dgh4gy56s/0xcgWyI84lQ0J23q2suwriXDIQ82ys3NyNNIPBQAUFEQE4chSPIKBfAvL/CD09Pfjsrd8QwvNJCOCz9yRgNHhYhLnbbrttxLM1a9aMPMcTEAABEACB/CYAYS6/44/Vg0BOEBgtzDU0NNAnPvEJTa5L/Ozv76eqqipN+qc1p5r7PbShObY6c+V2Ey1nYc4fHiYzp7R0Wkxk4Mdcb/5giK65Zz3taBtIyVKvWtJIVy9rpFK7hWZXFOYFw5SAg5GcIxAaDtLhwXeo2bWNvKGxu1KLzVWcWrKR02O2UZ+/VXNrr7HPpVMqL9acX3AIBEAABDJNAJ+9M00c84EACIwnIP//f/vtt48chjA3ggJPQAAEQCDvCUCYy/tLAABAQP8EIMzpP4aTreBQ3xBtbBngOkvHd2ON72vnnV4BFuSC/BNpRtbk6pw2WlxVRMU2c+RwTj72uP1080Nv04aDPSldn9NmostOqqf/d/Y8qi0+VosrpRPAGAjohMBQcIC6+9rIbDJTubOGa9Md31Ua4Lp0Q8F+fo8K0qHBzdTlPaSJVZkNNq5gZyC7yUkVtiaqL1xEPd5m6vDs552Ax2rXWQwOrktXQw18rtRapwm/4QQIgAAIpJIAhLlU0oQtEACBRAhAmEuEGsaAAAiAQH4QgDCXH3HGKkEgpwmMF+Y++clPanK94qeksqiurtakf1p1asgfoj3dLmod9JKLn0sz8W64YquJ+r3BKUU72TQnKS5nlR2/ka7VdSbjl6SRfGp7Gz309hF6+3APud/llIzNyNhiFuhuu3Y5nTm3MnJowuPoNJZyEqnQJiDCAZ0TkDTEFouFCgsLJ12JpMBc3/YQf1HAM2kfrZ6occylE8vP4/dWi1ZdhF9pIoD37zSBhVlNEMBnb02EAU6kgQDeu9MANU0m5f//sWMuTXBhFgRAAAR0TgDCnM4DCPdBAASIRgtzjY2N9OlPf1qTWFCAPvmwhFmAkh8Rnp7Z1TalKDd6tnPmVFFdsX30oZx+7vEH6d5X99F//i1F9efMBnrsc+fSvGpnVG4oQB8VCw7mEIGuri6yWq3kdEb/HYgsddDfTW+1Pa520UWO6eWxzFbH9ek+SIYC1IjUS8xS4WcgEBgxYzKZ8MWKERp4kgsE8Nk7F6KINUQjgM/e0aho85gIc2vXrh1xDqksR1DgCQiAAAjkPQEIc3l/CQAACOifAIQ5/ccw3hW8uK9D7aCLdZyTd9ddurg+1u450e8opwE9/7+eI38wnJL1nDm3ir52yYlkMxupodROVtPxm/e4OZASxDCiYQKxCnOyhFA4QIcHttJR104a8HdqeFUTXVtYtpoai08gU4GZjIaJaYAldackDTYbrBBwJuLT5REIc7oMG5yOkQCEuRhBoZvuCOCzt35CBmFOP7GCpyAAAiCQaQIQ5jJNHPOBAAiknACEuZQj1bTBQChMf97SrG4Ox+PoexfWUpk9v9K0/XrdPvrOE1vjwRRTXyvX9TtrXjV97pz5tHRGOeHmQEzY0EnHBOIR5kYvMzwcokDIx3XdemlD68OjT2n+ud1UTLWFc0l20rW4dlOX5zDX8vQrv40s3FXam2hWyRKqsDdqfi1wcHICEOYmZ4Mz+icAYU7/McQKohPAZ+/oXLR4FMKcFqMCn0AABEBAGwQgzGkjDvACBEAgCQIQ5pKAp8OhA94APbmzNW7Pz5pdSQ0luV1rLhqU36zfTz/4v20sDqRm59zoObiEH33hvIX0xfPmUzh8zL7Ul5N0aGggkEsEEhXmRjPY2f0K7e9/e/ShnHje6DyBTqo8n1NgGnJiPfm2CAhz+Rbx/FovhLn8inc+rRbCnH6iDWFOP7GCpyAAAiCQaQIQ5jJNHPOBAAiknACEuZQj1bRBXzBEf9l6NG4fL5xfQ5WF1rjH5cKA5t4h+u2GA/Ti7nY63DNEARbRgiFJSJea9vX3nUAfXzVLGYMwlxqmsKItAqkQ5mRFe3tfpz29G3jHb+p+/7RASsS5U6ou1IIr8CFOAhDm4gSG7roiAGFOV+GCs3EQgDAXB6wsd4Uwl+UAYHoQAAEQ0DABCHMaDg5cAwEQiI0AhLnYOOVSr6d3tVGv51hKtVjWZeRdXAuqinhHRwEVcb25WqeN7Ob83dUlu+dO/+FT1OcJxIJv2j5mQwF94ozZXJOqgOpK7HQmp7mcW+Wcdhw6gIBeCKRKmJP1DgX6VQ26bs8R8oQGqYAM5DAXU5V9JtUXLaJOzyFqd+/n9JfdXK8uSFajg9NJ1lOT80QKDfupeXAn9fnayB/y0PAwi+x8TAtNds0VmkvIwv4Wmcu4Bt2xHXTDw8Pk5lSevtAQGQtMVGgpUzXqIj77gm4aCvbzWobJZnIqFpFzeEw/AQhz6WeMGbJHAMJc9thj5vQSgDCXXr6ptA5hLpU0YQsEQAAEcosAhLnciidWAwJ5SQDCXP6FvXXAQy/u70xq4TPLHLS0oYxvBBuTsqPXwZLi8tuPb0mb+6s4deh3rjiZ5lcXp20OGAaBTBFIpTCXSp89wUF66chvWbALptJs0rZMBgvVcH06I//X6t7Lu3S9o2wWUIWtgUqsNUqEHPR3jTpHJLXtZhWfSjNLTuEvU+Tn+/MYIGl+AWEuzYBhPqsEIMxlFT8mTyMBCHNphJti0xDmUgwU5kAABEAghwhAmMuhYGIpIJCvBCDM5Wfkd3YM0KaWvqQWbzcb6YJ5NWoXXVKGdDr4649spt+9fjBt3ltNBrr3Y6to9dyqtM0BwyCQCQJaFeZk7S2uXbSp4+lMYMjoHCWWalpR9wHegWfP6Lz5NhmEuXyLeH6tF8JcfsU7n1YLYU4/0YYwp59YwVMQAAEQyDQBCHOZJo75QAAEUk4AwlzKkerGYNugl8W53qRSMpbazXTxglqV5lI3C0+ho3/ddIR+9reddITr0KWjOTl16K8+vpJMnNauxGGhmeWFKuVlOuaCTRBIFwEtC3Oy5o6hg7Sl8zlOF+mOikB2noWHQ1HPaflgqbWWzqi/htNiFmjZTV37BmFO1+GD89MQgDA3DSCc1i0BCHP6CR2EOf3ECp6CAAiAQKYJQJjLNHHMBwIgkHICEOZSjlR3Bvu9Aep2+7jmUpg6+fFovyeuNZw5q5KaSh1xjcmlzuHwMG3l3YfbWwdo0BegZ7e30huHetKyRKfNRJef3ECfO3cBNeQx87TAhdG0EdC6MCcLF+FN0kZ2Dx0mSXEpzcE13yrtMzit5Bzq93Vw7bp9NOjv5rp0AXL7e8kfju+9UhnN8D9SW89pqVQ750o5/aWsSZrUrOv1tpLUqDMYTFzXrpTTY8qXLAwqtWeft41r2/XRMIXJZiyicq7TZzbaMuy99qeDMKf9GMHDxAlAmEucHUZqmwCEOW3HZ7R3EOZG08BzEAABEACB0QQgzI2mgecgAAK6JABhTpdhS5vTz+9tpw6XLy778yqK6LSm8rjG5HLng90ueu9tL5A/GE7bMot4J91tHzqNzltYk7Y5YBgEUkVAD8JcvGvtZAHvjba/xjss6/1FpDMVWKjX1zLBF7PBRsWWKur3t1Mw7B9zvoAKqK5oIS0qP5NrixaOOZfPLyDM5XP0c3/tEOZyP8b5ukIIc/qJPIQ5/cQKnoIACIBApglAmMs0ccwHAiCQcgIQ5lKOVNcGn9rVGndqy1qnjU6qLeGbtca8rTc3PujP8K65m/73DQrwbrp0NbPRQHd/ZIVKcWk1Gqmp3EFOmzld08EuCCRMIBeFOYGxp/d1/nktYS56HCji3cq6K6nYitqXEj8Ic3q8iuFzrAQgzMVKCv30RgDCnH4iBmFOP7GCpyAAAiCQaQIQ5jJNHPOBAAiknACEuZQj1bXBVw50UnOcqSxHL9huNtIcroO2qLqY054ZRp/Ku+fbOL3ld57YSq8f7M7I2g1cRmrFrAr6f2cvoHMWVGdkTkwCArEQyFVhTtbe6tpDO3peJm/QFQVFAaeBLCRvKNq5KN11csjKazq76aNkNlh14nH63IQwlz62sJx9AhDmsh8DeJAeAhDm0sM1HVYhzKWDKmyCAAiAQG4QgDCXG3HEKkAgrwlAmMvr8E9Y/JG+IXr1YNeE4/EeKLQY6ew51VSCHVy0r3OQ3jjYQx2DXvIEQnT3y3u4nlW8ROPr/9GVs+g7l59CBQWs1qGBQJYJ5LIwJ2ilPl23p1nVbPNz7TajwcJ13SqoyjGTrEaHqkvXxakvh4IDqm/z4HZVuy3LYUlqeqk5V8RrFHGuhGvXVXEtPqPBzAxamMVRrmHHtesKjFTIteuq7DPJbi7m978B6vQcUrXrhJkIfBX2BipjW6FwgM8d5lp+7bzT2KfsFluqmeEMMjFPrTYIc1qNDPxKBQEIc6mgCBtaJABhTotRie4ThLnoXHAUBEAABECACMIcrgIQAAHdE4Awp/sQpnwBf9/XQW0sIiXbJLXlJYtqycqPaMcJ/OezO+iOv+8+fiBNz957Qh2d3FCqat1VF9to1ewKmlvlTNNsMAsCkxPIdWFu8pVHP7Ov7y3a1fNq9JM6PWrkunUmg4kFuaGoK7CbnOQJDkY9J+JlKByk4PDYunbSWUS5uaUraE7JMk1+0QDCXNSQ4mCOEIAwlyOBxDImEIAwNwGJZg9AmNNsaOAYCIAACGSdAIS5rIcADoAACCRLAMJcsgRzb3wgFKZ1vGuuNQXi3EIWgpY2lOUepCRWNDw8TN97chvdu25fElYSG7qSU12u4Z10i2qLEzOAUSCQAAEIc2OhyXvAlq7nSHbOocVGoLZwHi2tfh+Lc9pKkQxhLrb4oZc+CUCY02fc4PX0BCDMTc9IKz0gzGklEvADBEAABLRHAMKc9mICj0AABOIkAGEuTmB50l1uHB/mtJZ7ulzU7fZx2rXEmt1k4F1z9WQyFnBaM6RVHE1Ras/98uW99ArvUPQGwqNPpfW51P67+yOn09nzq8nlC1Kh1cQp6BCbtELPc+MQ5qJfAEcHd9Du3g28k2xgQodiS5VKBdnna5twzmJwqFSZci40HJhwPlcPLCw/k3fPLdfU8iDMaSoccCbFBCDMpRgozGmGAIQ5zYRiWkcgzE2LCB1AAARAIG8JQJjL29Bj4SCQOwQgzOVOLNO1kmA4rGqj7e92046OiTeQY523mOvNNZXYaQHvokN6y+PUvP4AtfV7KMg7FV/Z30XffmLb8ZNpehbR4aTWneilJ9SV0GUnN5DUpiuymtM0K8zmKwEIc5NHXr4EMeDv5Dp0XRQM+8nCaR1LrbXk4Jps0rxBl6pdJykijQUmrutWzudr1M4xSf8oNd1U7bpwiPb1vzFpKsnJPdDPGf56B9ejqzteu84xS9Wv6/E2U/vQAXL7e1moDJLNVMj9Gqi+cAEz9VGLew/1edu4dp1XpcYU0bO2aB4Vmkqpzb2XujxHmPOgYuowFavagNWO2THtzoMwp5/rB57GTwDCXPzMMEIfBCDM6SNO4iWEOf3ECp6CAAiAQKYJQJjLNHHMBwIgkHICEOZSjjRnDe7vdtHrR3qSXp+Fd2ydMbOC6ortSdvKBQPjbw788OmdWUlzKSzrWTi947oVtKQJ6Udz4drSyhogzGUmEu3u/fRW++OZmUwjsxhYrAyzGBetiZA3TJPvRjaQkc+Gog2lQnMpnVJ1kRICo3Z49yCEuano4JzeCUCY03sE4f9kBMZ/9jaZTJN1xfEsE4Awl+UAYHoQAAEQ0DABCHMaDg5cAwEQiI0AhLnYOKEX0ZA/SI9ub0kJCtmxdf68GqostKbEnp6NRLs58PiWo/TTZ3fQQd6lmOkmwunJDaXU7wmQ2VRAs8qL6NwF1fSBUxvJajZm2h3MlwMEIMxlLoiHB7bQtq4XpxSkMueNvmcSYW9F3Qeo0t406UIgzE2KBidygACEuRwIIpYQlUC0z95RO+Jg1glAmMt6COAACIAACGiWAIQ5zYYGjoEACMRKAMJcrKTQTwi8yTvm9vLOuVS0Ek5tecmiulSY0rWNyW4OhDnP5NaWPv7pVyIZ62V02/O7OF1b9B0e6YZQU2yjn169jM6cW5XuqWA/xwhAmMtsQAd8nbSHa9d1DB0cI9BJKswaxxxO52hVKRz9Yc8Yx8qsdbxDrJ5TOx5W6TXHnMzTF5Ja9Jym68nMzKI1CHPRqOBYrhCAMJcrkcQ6xhOY7LP3+H54nX0CEOayHwN4AAIgAAJaJQBhTquRgV8gAAIxE4AwFzMqdGQCIRaLXj7QSW2D3pTwKLKYyGQsoEJ+rHXaaFZZIZlFgcqjFs/Ngde4Bt2nfvsa717MjjgnOx1Xzq4kly9I3kCIqjlmp8+qoKuXzaD6UqQmzaPLNq6lQpiLC1fKOofCAa5d16PqrFmMdlWfzlBwbNer1LZzB3rJG3Kr2nWSulH6RJov6CZ3sJ+Gh0O0q2cd9fnaI6fy7tFssFEB/2c1OZRw2eQ8UdUDPOraSb2eFvJL7boCCzmtlVRXOE/1aR7cpmrXeYJSl7WApHZdpWMGNRadQF3eI9Tu3sex6ea/qQHFXWrnNTpPUGybB7eruoL+kIeMBjM5LRVUUziXGooWqvp6eRcALDhrBCDMZQ09Jk4zgXg+e6fZFZifhgCEuWkA4TQIgAAI5DEBCHN5HHwsHQRyhQCEuVyJZObWEeYburs6B2ln+wD5QpPX70nEI0mjuKKpnJpKHYkM1+WYeG8OHOQdi997cis9t1M7N8pNrNh97pwFdPP5fOOYnwf5ujDlmcCqy4svQ05DmMsQ6DRNIwLSuqN/pNBwIE0zwGwsBBymElpacwmVWKtj6Y4+IJA0AQhzSSOEAY0SiPezt0aXkRduQZjLizBjkSAAAiCQEAEIcwlhwyAQAAEtEYAwp6Vo6MsX2T3X5fbRgC9AvmCYtrb1p2wBq2ZUcG2zwpTZ07KhRG8OtPV76PWD3dTOuxdF0NxytI8e3ngkq0ut4R10bq5FKDvqbFyP7oS6Yrr0pAb6xxUzycG7ItHykwCEOf3HvcfbQhvbn+QvYwzpfzE6XoGxwExn1F9Dxbw7Dw0E0k0Awly6CcN+tggk+tk7W/7m87wQ5vI5+lg7CIAACExNAMLc1HxwFgRAQAcEIMzpIEg6cfGpXa3U50nNjgrObknLGss4bZekDzNShcOiHnWCIi43U3lz4MdPb6e7XtoT1/yZ6NzAaS7/65rlJLstW1hQNHJcZ7DwenJDKadq42Cj5TQBCHO5Ed5AyEsHBzZTi2u3SoMZWVWRuZxTOC4gu7mIjg5yekdvK4XpWLpdi8FO1Y5ZVF04hzrc+1Xdu0htOwMZOe1jHadoXKRSara4dpEr0BMxi8dJCBRbqmll/ZXEBQQ59fPE2neh4aBKkSl18QoKxqaGlhSmAU69KQKf0TDxyxLBsJ/Tl4bZrm3C7GFOayrnpUahYVK7JpV+c8JgHNAlAQhzugwbnI6BQCo/e8cwHbokQQDCXBLwMBQEQAAEcpwAhLkcDzCWBwL5QADCXD5EOTNrPNzrpnWHutM2WUOJnU6tK6Vimzltc2TDcKpvDrx1qEeJcy/v7VA7GbOxpljnLGPB9YbVc+gzZ80ni2nsDeRYbaCf9glAmNN+jOL1MBQOssDjIxF/xgs8Iuz4WcSTenbRhKNAyMcifYh3+nLttnECT8TugK+T3mx/NF638q6/yWChSnsTNRQuoj5/O7W597Jo2vcuhwIqtdZQPdemMxaYSNXEY9F0mGVTaXaTk2occ1TtOxFbO4cOKdFOzolwVyF2WTQd9Hcpu6NF0xIWB+vYrsTw6OCOMWKszVjEQuxsml2ylKR2IZp+CUCY02/s4PnUBFL92Xvq2XA2GQIQ5pKhh7EgAAIgkNsEIMzldnyxOhDICwIQ5vIizBlb5FvNPbSny5W2+WSn1epZlSQiXa60dN0c8HN60bYBD8ljpdNKtz6ymR7f0qJJbI1cU3BBjZMOdrvJEwhSVZGNls8sp2uXz6RFtcWa9BlOxU4AwlzsrNDzOIGtXS/Q4YEtxw/gma4IiDB7QsU5NKP4JF35DWePE4Awd5wFnuUWgXR99s4tStpYDYQ5bcQBXoAACICAFglAmNNiVOATCIBAXAQgzMWFC51jILCPhbl3Wvu4FtGxb+XHMCSuLpL4cGGVk4ifmAwGKuEddLVc28zMddb02DJ1cyDMNQHvfmUv3fH33aoGnF5YSX26S0+qp60t/dTDNQ2LrGYl1p05t4oKrRNTsellXfnkJ4S5fIp26tYqaRd39a6j/X1vpc4oLGWcwKlVF1GDc3HG58WEyROAMJc8Q1jQJoFMffbW5ur15RWEOX3FC96CAAiAQCYJQJjLJG3MBQIgkBYCEObSgjXvjQbDYWod8FL3kE+lU+zz+Kk3RfXnosE1cZ0yEetOrC1Rdemi9dHqsUzfHBj0BuiFXe20ubmP+jkuxXYzNfcO0bM72rSKKKpfTpuJbuQUmDeeNY92dwzS0b4h1moLqKmcd99VOzlFHmrXRQWXhYMQ5rIAPYemdPl76fDgFur2NJM36FL1zRycIrHKPlOlaWwf4tp1XL/OFejlFJlBshoLqdzeQE3OE/kLIkNcE28n9XnbSWrbSX20EksV1RbN58dqOjK4je0eIU9wkFM8DnMNNV8OkdPGUowFFlpeeykZ+D+bqYgc5pIRx6Qenpvjeywtqo2KLFJb1jhyXtJy+oJudUzGWYzHd8v7Qx4aCvSrtKhWU+GYtJmSKlWuG6mnJ+lWC9mupPOMtKHAAF9Lg8qu3VzM14wjcook1arMq66lCXbDqr6izC1pRKW+4uhUrnIdyY/8LXKYSrg27nG7IxPo6AmEOR0FC67GRSDTn73jcg6dxxCAMDcGB16AAAiAAAiMIgBhbhQMPAUBENAnAQhz+oyb3rwe9AXoyZ1c22Y4vZ5XF1lp5YxyvpF3bCKHxcg3Ao/f5Evv7IlZ18rNgXtf3Uc/fma7Sn2Z2EqyM8psLOAbqWMvrGreQSm16244cy7JTsFDPW6++RukykIrC3eF2XE0j2eFMJfHwdfZ0tcffYh6fa0681pf7tq4tl1t4Tz+O+2i9qEDSgCLrEDEs2queydCmdTL84bGpsYutdZx7btGJdL2jYuT1LYTuyKatQ/tIxH9Ik3EvhrHbP484FR2RTwb3Uqs1VRpm6Fi3+M9OvoUi4EOqmO7wXCA2t37KDjsHzkvdqvss5TYKOeGgv0j5+RJMYvAUmuvwblozHG9vIAwp5dIwc94CWjls3e8fudjfwhz+Rh1rBkEQAAEYiMAYS42TugFAiCgYQIQ5jQcnBxzbXfnIL19tDfjqyrlVJcLeDfdbBZktLiLSks3BzpdXvrTW0fotQOd1NrvJROLXqU2C715uHuC+JXxQCYwoaQ59QRCfKP2eFrVShZvr142gz579jwqsVvoMIt2XS6fSos5i68Rq1nbQm4CGLI+BMJc1kMAB2IkIDulRJyT3XVoIJAqAiI2Lq1535hde6mynU47EObSSRe2s0lAS5+9s8lBD3NDmNNDlOAjCIAACGSHAIS57HDHrCAAAikkAGEuhTBhaloCB3pc9FZzL3/zfOwOp2kHpqBDTZGNTmss4zRUQSUyWU0GTndmYfEpu7Xp9HBzYNORXvrSH99SO89SEApNmJDdlBaOfd+oFKsWvibOmV9NN527gE5uKFUpMiXNZ5i3ejaWOWhRTbEmxV1NAJ3CCQhzU8DBKc0RkPSImzufoV7vZDvnJE3u5H/DDGSkMIWirkt2WEmaxcnb1LYnH4czWidQX7SIllRfrHU3x/gHYW4MDrzIIQJ6+OydQ7iTWgqEuaTwYTAIgAAI5DQBCHM5HV4sDgTygwCEufyIs5ZW6Q+GSQS6tkEvi2R8g5JFj0FfcIrbnOnzXm6BNpU66OS6EnJazembaArLerk5EORdZ09sbaG/cS26fV2D5AuEqYp3n/UO+ZWANcUSdXdKrotCq4lcfF2ObpIK82NnzKaPr5pNm4/20c62AXJ5g1ReaKGlTWVKzNPirszRa8jGcwhz2aCOOZMlIDXt2lXtuh5OixhQtevKbHWqrp2Iay2uXdTjaeEduby7mOuNlVgrOZXifP5bUkFtrr3U5Tms6o2R1BvjOmaV9hl8fi71+9o5neJ+GvR3s10/WQwOOmZ3gXK5xbVbiYL+8JCqd+YK9CS7FIzXCIF5paerunNSB7GM03JGatBJas0+b9uxOohck8/J15LTXKG+CCIpNEUk9gQH1Cqk1p6Mldp2w/z5acDfyfX0ujnFZuDda6lW1fKTzl6uzydj5VoyFZi5hl+FSq8pf6dC4aBK3SlCtDQ7p/kss9XztWxWdgcD3dTjbqWBwT6qKq/l3fO1qo/0ldqNYtcXcvMuQLFbrmo2KrucQlTWInalbqOd6woes2uRoeq6H/B18he0+No32vj3plb9fsg5SUPa623hFKZDandhkbmMz1czB4MStMWum9OFDvPvn6Qlld8bqSGIBgLxEtDLZ+9415WL/SHM5WJUsSYQAAEQSA0BCHOp4QgrIAACWSQAYS6L8DH1CIFspbmMOCB75iSNoaQ89LJwaDIUUDGnQWwssVM177RLZ9P7zQGp4XbXS3vojhd305B/qp0g6aSYWdtGuakZpWDi/GonfYF323Vyasw3D/Xwo5dkZ95cTqV68Ql1tGp2pXJUbqb28049sVHGuzYNfL3lcoMwl8vRze+1BQKBEQAmkynlO2pFlHmp+QH+uzS2JtrIpHiiawJl1nq1u1LE2vHNYSpRteuk5t34XZYihoko5Q70viv+jh1dymKXCMLj6/BJL5vRyUJamRLWRHAe3WRHZzmLc0OBgQn18qRfiaWG/14ZlXg2epw8txoKlaAowtp4uwXEGQpsjSy4DSqfx4+VWnwibo+v7yf9LAY7FVurlKAdCPvGDBW79UULaFH5mSxyon7sGDh4MSUBvX/2nnJxOXYSwlyOBRTLAQEQAIEUEoAwl0KYMAUCIJAdAhDmssMds04ksKmll3Z2aO/mYxXvklpaX0YuToEpO6jkG+CFZhMLdlYWXUwTFxLnkVy5OdDv8dMz21tpU3Mf9fEuOqfNpNI/Pvz2ETrY7Y6TSm52X1RbTHUs9r55qJsGeaedNBvXtFs1u4J34c2hM+dV0VuHe2h7az+fD1CZw0JLOP2qpNXU8048CHO5eT1jVUTpFuaEcR+LNq+3/IV3RPmBHARAIAoBi8FGp9ddqQS8KKdxCAQmEMiVz94TFpaDByDM5WBQsSQQAAEQSBEBCHMpAgkzIJANAlI34de//jWtX7+ejhw5QieeeCKdddZZ6qeuri5pl9544w164IEHaM+ePWSz2UZsL126lIxG45T25UbPb3/7W3r55Zfp0KFDtHDhwpHxTU1NU46N9ySEuXiJoX86CbQOeGhbWz91sbCjh9bAIsvS+lK10653KECBcFjVLatgMa+Ed9zF0nL95oCX05XKjroHNhygbvfYuMpGsSyUG4wlLFnpY+Mad7Jjc3ybU1lE/3jaTNrf5aLXD3ZTB6eBtZlkJ14RXbi4lj7E54wM842DPVwH0EXB0DDVlvCNylmVVMkC8ugmOxwzvUMPwtzoCOB5LhHIhDAnvNyBPtra+Tx1e5uj4pPUiAFOqRmttp3sRJKdRYGwN8rYAt5BVcg7mVxRzvEuKLHL48bv2JLOJk65KDus/GFP1LE4CAKZJCDX6tlNH0Vqy0xC1/Fcuf7ZW8ehmeA6hLkJSHAABEAABEDgXQIQ5nApgIBOCWzcuJGuuOIKam6eeINDdiXce++99IlPfCKh1ckH/RtuuIHuv//+qOPPPPNMeuaZZ8jhcEQ9v2vXLrr00ktp3759Uc+vXbuWbr755qjnEjkIYS4RahiTbgIi5vTzjqEgC10G/p18/UgPeaQenY5amd1Mp9SVKv873T7lv4gnxVy7rK7YToXv7rbLl5sDkr5xV/sgtfRx7RjmMINThw5zPL/8x7foHa7XhpY4ARH0hKVvnKgnCTIlheYpvOPu5b2dtK2lT9VzlN2MpzaU0RWnNtAHTm1Uuxw3HOiitgEv34g30GwWAs9dUE1NHCNpA/y7eKTHTVIfsrrYRg1clzGeBmEuHlroqycCmRLmIkwG/F38xRWpXTegdm87TMVUYW9SdbikPlfn0EGuOdbFfzu53hjX7yrlWmSVjiaW5Qxc865ZpSCU+mBSn6zIXE7VjlmqHpnLz6l3PYdUCsNhlvek3lgFpx6UumIi+HXwuWN1wXxs187z1VCVfaYS5npYLOyRumBcz0z6tg3tjbiLRxDIKIFyWwM5uY6e1J2Ta1TqOsq1LvXwJE2mXKMiJheaS6nKMVNd51Lfr3PokBK/RYC2cUpMsSOpQqUOn9SKlFSjkkbTpOxWs90mlXpTdrP28O+VJ+hiuwZOPcp2+fdCakrKXPL76OJ0o2GuuyfCYbmd7fLvpAjoUsNSauaJ8C3iuaTzrHTMUL7L71qX5wjbGFT19ez8ey7+it/y+yv+yu/ssdqXUqOyXvksv7vKro/rFfL7gdh1WirZJ7bL7wdS11LVvgxwdgr+gCDvH8JIagQee/84RFJbMKTeP9gup0QttzdKV/5SwFHF0a/eP8yqBqL4JO8H4ovYHZI6iPxhxG52KrsSi0DIp95bBtX7ktQVtPP7Ui2/b4ldg4qLpD9V70sF/L7EY8Su1ehQMZG1qve74bCqKyjsJbWpxEPeC6XGotQrNPOuSXm/ErsG4nSrPo6556iqg2gQu1yvUOzauOahpGrtcB94N+Zit4iqCmewXzXqvfNYzDv4uS+nr6WM/nImMRmEuSTgYSgIgAAI5DgBCHM5HmAsLzcJ7N+/n1atWkWdnZ28a8BAK1eupPPOO0/tmnv22Wepra1N7Wj7/e9/T1dffXXcED796U/Tr371KzWuvr6eLr74YpIdeM899xy9+eabFGah4aKLLqLHHnuMrNaxOxlaW1uVP7KDTwTC5cuX04UXXkjt7e0kvomQKMfvueceJf7F7VyUARDmokDBIc0REGHg5QOdSlTQnHMJOjSzzME3F8zUwXXIBiVFJgtXdk5rWOO009yKIipiAc8XDFEv1yILcO07CwsmUo/MwiJMLjVZ90t7OuiZHW20l1OZigAru7xmlTvoqe1t1NqP3RjpjLdcV1JbcXyTG3ErZlXwjakwbToiNxaP92gotdNHTp9NC7im3v9tb6F3JH0ppzItsVnopPoSuuyUBjp/YQ1t5HHr93fRgfZesnL61/l1ZUrwm8XXtzQR+/Z1uvjmY4ivexudyLtPR1/fQfZLfveL+Pdk9PHjnuAZCGSXQKaFueyuNrbZ32h9RN2Ij603eoFA+ggYeVenyWBW4ky0WUTwEmFOqUnjOoiQJsKXiD7jm9T4E/Fvsp2mYldENUl9Pr6JXRHqxtfLk35GMikBbWq7LrY68W+21OKT+aLtjBWhympyvLvW8R6xkMb++lhIjLbjVtKEiooXbWesCGsiaolwFq1JPUN/eCjqjlsR0gpYzBShb2IreFc0ncxukVpniDmObyKeGlmI84WipVCPxa5vQo1EmSPXrqVqxxw6qfJcFb/xDLX2GsKc1iICf0AABEBAOwQgzGknFvAEBGIm8KEPfYj++Mc/qv533XUX3XjjjSNjPR4PnX766bR161Yym820efNmWrx48cj56Z5IWszVq1erbiKqvfTSS2N2xt1+++30hS98QZ2/6aab6Be/+MUYk+LL3XffrY796Ec/oltuuWXkvNz8Ofvss+m1115TgqLMJb4m2yDMJUsQ4zNFQASCvZzKbz/fzJeb9ZEmAsLEWx+Rs/p9LOIddS7/2JsOstY63rE0r8JJ3UP8LWTeiTfkD6kdaE4W8up5J97MskL1Wr8rP+65yxeg+9cfoIc3HlFpHCNnRKAMsaA3+jqInMOjNgiIyDzZLlepmSexPdA19saZjLmCRb0GFq2fZaF2K+/w4zCrNq/KSZeeXE+X83mpZbhuXxe1sGgrO2pnVjjonPk19MGlTVTIvwdoIJApAhDmJpL2866511v/onayTDyLIyAAAiAAAlogIGLuqvqr1I5JLfgzmQ8Q5iYjg+MgADMDNYAAAEAASURBVAIgAAIQ5nANgIDOCEiayBNOOEHtWvvYxz5G991334QV7NixQ9Wbk10cX//61+l73/vehD6THbjsssvoiSeeILvdTnv37iXZMTe+XXLJJfTUU09RWVmZ2p1nsVhUl6NHj9KcOXNIhDJJs/nII4+MHzpSb07q433+858nEfqSbRDmkiWI8dkgIDt8ZDeZiXe9hliwe35vB6em0Veqy3Rxc7C4sWpmBVUXybecc6cN8M7BHhYjRXip4rV5WLT8xd93q9p1g96xAqaZU2UGRm/xyh0MWMkUBMocFvrRlUtU+s4puuEUCKSMAIS56CglBeDevjfoUP8mCvKuo7GtQKXzk9R1Q8GJaYwLOeWc7NKRVHTjm+xaKbFWv5u+zjf+tLLr59SAbk4fOL45TKVq15CkNRz/dR7ZqVRiq1Gp/qLtODqW4i7IaQm7x5slO+8KsnHqPklLGG0n04QBOAACIAACGiEg77dnNX5YpXjViEsT3IAwNwEJDoAACIAACLxLAMIcLgUQ0BmBn/70p/TVr35VeS2pISVNZLS2ZMkStVtu7ty5SmCL1mf8MdltV1paqoQ1qRH3+OOPj++iXv/mN7+hT37yk+q5pLMUMU+apL+UNJjSHnrooUnTaJ577rn04osvUnV1NbW0tKi0m2pQgv9AmEsQHIZpioDUvtrc2kcHelxjUu5pyskMOsO6FJ0zp1qlB8zgtFmZStJ8buEadUe5dp2kW2ri3VaSTvGB1w/SHSzcdbkm3rzNiqOYNCMEZFfpbf94Gl12ckNG5sMk+U0AwtzU8ReBTupHuYP9vPtV6nc5uRZWvaozJSOlHp7U0xIxTFLbiegmdamkiXDX45HaU1K/y8T1tUq4hlSdSlMnKez6WGBzB/pVWkCrsYhrYdWrulQyVmpeSf2viF2pSVXMtbakSS2tYzXxpH4X1xszlagaXVKLTGqMicA2ui6Y1O+SumPSXP5eVW9M0vqpemNcF0xqeEmaeamvJ3YlNaLY2d2zPmpqQGUI/4AACICARggsqX4f1Rct0Ig3E92AMDeRCY6AAAiAAAgcIwBhDlcCCOiMwAc+8AF69NFHyel0UldXF0V2q41fxpe+9CX6+c9/rg5LTbrZs2eP7zLh9QsvvEDnn3++On7nnXfSZz/72Ql95MDBgwdH7H3xi18cmefjH/843X///SqFpvhWXFwcdfyaNWvo29/+tjr39ttv09KlS6P2i/UghLlYSaGfHgiIQNfh5m/Lc802aQ5OB9k+6KW93S49uJ9SH2Xn3KWL63MmrWUicES0kzpnu9sH1DVRwbXrls8oV/XQvv/kNnrrcM8Es0ZWNUPYbTeBi54OOG0meuErF1JFoVVPbsNXHRKAMKfDoGXI5f19b9POnlcyNBumAQEQAIHECDQ5T6KTq47dw0jMQnpHQZhLL19YBwEQAAE9E4Awp+fowfe8JCCpJVtbW2nVqlUkNdoma7/85S/pM5/5jDq9bt06OuOMMybrOnL8Jz/5yUhNOKkDt3LlypFz458UFhbS0NAQXXvttfSHP/xBnZYUm5JGc9GiRepx/JjIa9lNJ+OkPfnkkySpMZNpEOaSoYexeiFwlGtRbWnrY0FmfEotvawgMT9Xz6qkGaWOxAbnwagDXLNQxLlu3lUnKTIX1BTTCbXFdM+r++i+9fsnXC82s4ECwWFV3y4P8Oh6ibdeehLdsHqurtcA57VPAMKc9mOUTQ93dL9CB/rfzqYLmBsEQAAEpiRQV7iAlta8b8o+2TwJYS6b9DE3CIAACGibAIQ5bccH3oHABAKyQ05uorz//e9XteAmdHj3wMMPPzySSvKvf/0ryU676dq//uu/0n/8x3+obrt376b58+dPOqSpqYmam5tJ0lLKTjtpkpqys7OTVq9eTa+++uqkY5977rmRFJySFlN22iXTIMwlQw9j9UbAxTvp+rx+CoaGudaMgWvKmFmw6+cUmG69LSUmfxdXF9Op9aUx9UWnsQRk15ykyGzmFJlccpQaS+10ckMpHe4dojtf3EPPbG+lAe9xobe+hNOw2S20o21grCG8ygqBq5Y20X9evSwrc2PS/CEAYS5/Yp3oSruGDqt6ez3eo2NMSCrM2sK5nPYyTO3uffyFj+N/Two4LXOFrYmKLOXUPrSf02OO/btSaC6lKsdsGvR1Ube3me3yH6l3m4nr8IldaW1sNzjsf/eMPBRQubVepQzt8BzgVKBja/zZTcVU45jDqTz5CyueI2z1uF1jgZlqCueoWlTKbnhsmuhSax2VWmupy3NIjR81KacCLaJax1yuKdhPncxjdC0+SVNaw2sRHm3uvSr96OixYrOMbXd5j3ANwK7Rp/AcBEAgBQTmla6gBeXTfwk5BVMlZALCXELYMAgEQAAE8oIAhLm8CDMWmSsEXC6XSmEp67n++utV2sjJ1vb888/TBRdcoE7ffffd9E//9E+TdR05LvXhpE6ctO7ubiovLx85N/7JKaecQlu2bCHZJbdt2zZ12mw2UzAYpMsvv1yl2xw/JvJa0lcuX75cvZRdev/yL/8SOTXpo9Skk1180VooFCJZ76mnnkqLFy8eqXMXrW82j/l8PsVVdj2igUCqCfQM+Whft5u63D7yBkJkMhZw3RgT+Vmc6RkafVMr1TOn1x6EufTxDfO10cI7MUXsrSi0UJXTpiZ7p7mX/vDWYXrrUDd1DvrIbjHS/GonnT6rgt442E0v7O6I6hSXKFICYNSTOBg3gauWsTB3FYS5uMFhQFwEIMzFhSuvO/u5Bp2bBS+pu2flmnVF5nJVm06gSE06qYvn4/p3ZoOFCi1l/Hg8Fe8Q19KT2nXSHCye2c3H090HQj4lhAXDAVVjz8liXkGBQfUdZtFvUNkdYuHLrOY0G4/b9QQGWCw7JvrZuf6fg+v4RVow7Kcedxv19nVTXXWjEgkNXJNP2jB/W0XEO1/Qzemypf5fOdcNPPY3UM6Lr2JbBDipKyhCYqSJny5/NwtwfjVGagpGs2tQdktH6gbKeC/PN8RiYpj/29WzTtX7i9jFIwiAQCIECujsxo+o3+9ERmdiDIS5TFDGHCAAAiCgTwIQ5vQZN3idpwTa29uptrZWrf7zn/883X777ZOSECFLdrNJu+uuu+jGG29Uz6f657rrrqPf//73qovcqDGZTJN2l7pwmzZtGklbKaKTzXbsf2g/8pGP0AMPPDDp2M2bN9OSJUvU+R/96Ecj6TMnHcAnbrnlFhIRb6omwuJpp51Gsg4tNhEtvV4vFRUVadE9+JTDBNrdAU5F5aNeb3DUd8eJbCze+Xjn3fHvk2sPwtJqB9UVWbTnWB57dLBniNYd6OGdeB4Sca+22EYrZ5ZREafS/NVrh+hvuzp5F8SxGomCqZJFv3mVhfROC99AZdEYLTYC/3bhfLpueWNsndELBBIkIF9uijSDwTAitESO4REE9ExArm+Px6PJz97uYC9t7nuCJbrjfy/1zBq+g0A2CDTaT6JZhadlY+qY5xwYGKB77rlnpP+aNWtGnuMJCIAACIBAfhOAMJff8cfqdUZAxDKr1aq+5fnJT36S7r333klX8Oc//5muuuoqdV7SWn7wgx+ctG/kxE033UR33HGHetnf30/Fxce/zRrpE3mcMWMGHTlyhM466yx66aWX1GERnNxuN1155ZUk80/WRu/mkx16N9xww2RdR47HKszJTrxrrrlmZJyWnoTDYZWGVGKIBgLZIBBkEcUrQhwrcTZTAX+bvYC/MR6m/f1+6vSG+Bvvx72S75Sb+Lxv9MHjpzPyTITDM+vs/E103oqFphsCYb7AWlgIdvmDVGo3U63z2Hue7Mx7dlcXbTzazzs7+e8Zp2KdVW6ns+eUq1p49795lLa2Do4RihuKrVTN47fwcbl+xze5MiYeHd9Lf6+LWeT8yw3LObWoWX/Ow2NdEZDPJpFWwO+18oMGArlCQOufvQeCHbR36GUKDHtzBTnWAQIZI1BrWUQzbMs0/3dLhLn77rtvhAuEuREUeAICIAACeU8AwlzeXwIAoDcClZWVKh2iCG0iuE3W5FtZkfSVIpyJgDZd+973vke33nqr6nb48GGSOnKTtYgIN9qPefPm0b59++j8888nqSM3WfvTn/40Ip49+uijKvXlZH0jx3/3u9/RI488Enk55lH+p3v79u0qdaek2JSdc1psSGWpxajApwgBqUc26AtQIBQmi8lIThYG5PbsYa5Ptq/bpVJkRnQRu9lIFVyLrMfjT9sOKCPfHD53bhVVFR1PLRXxFY+5S6CP064e5nqJIsLVFtupnuviSWsf8NDTXBNvC4t6UhdPBL9TuF7eRSfU0tuHe+nBDQfpdU696Q8eExmqWMw7Z341C9BGemRz85gdfBF6dVxTr7VfezdDWQ+nOz98Ol18Ql3EVTyCQNoIIJVl2tDCsAYI6OGzt6QIPdi/iVrdu8fUzJNUoXWF81V9u6OunZz1oJW/iHLsb5zZYKNqxyyq5pp5He791DF0cKS2XQEZqMxWRw1Fi/jLWG5qde0eUzNP0nLWFS5QaT+VXU8LWw2paEmdvGrHTK6ZN486PQfZ7gHyc3pSaVI3sNRWS/VsN8jpR1vcuzjNaLc6J/84TCVUWzSPnOYKOuraRT1cO1BSnEozcXrTSvsMtR6pKdjOPvvYt2ON7Vpr2O5C1b+Fxw74O989RyQpSmsL56m6gnKu29PM9QyP7TKUuoGV9iY1tsfbomodekOukbEllmrlLxXwF4YGd1G//3gqbpuxiGsOzqVyWz2J3S6uSRipk2jkuoEV9kb2dyH70qFqB0ZSsYrxYksV211AMr8w7PO189FjXxOyGh2qzmEF+9Xq3sN2D/NnmmMp5SXlabmtkWOzkAYD3dTm2qvqFkYclrSoMqeF07Uqu942tnrMrsVg53jPpir7TK7buI9jc4jtHquTKDGXdTQ4F/Hn8oHJryVzER0dTMe1VKxi3jvuWhJfJXZSt1FqTabyWppdslRd5xF2Wn5EKkstRwe+gQAIgEB2CUCYyy5/zA4CcRM46aSTVE23008/nTZs2DDp+B//+Mf0b//2b+r87t27af78+ZP2jZwYLeatW7eOzjgjehHl0WkrP/e5z43ssnvPe95Dr776Ki1YsIB27doVMTvh8X/+53/os5/9rDr++uuv04oVKyb0ieeA3++nH/zgB2pIY2MjhLl44KEvCMRIQOqx+Fm0E8HMZDxW+0V2Rh3lGmVH+4dUnTJ57WDRroYFlSKub7e9fYA63cduGoyeRs65eTfVZDud5PyqmRWcAhG7S0dzw/OpCUhazX4W7Sx8fRaysBxpUnPxTRbtDnS5+RoOqevzdL6+qjkF526+Rh975yi9c7SP+lloLraZ6SQW/C47uYFFOw/9dsMB2nCgW9VtFHtSi+/cBTXUVO6gv2xspkMsIo5uMvdJDSW0r9PF9gKjT6nnZt4FKi3AO1ejNREU/+ODy+icBdXRTuMYCKScAIS5lCOFQQ0R0IMwNxqX1K8TsUXq8xm5pt7oJiKXiHgi7sj50btb5TNagMdJH6mVJ31GtxDblfMivEmtvtEtzHX8Aiy+SV0/EfxG25V+AZ5ThDCLUTIYjLcbVIKgCG/yM7pJfUDxV+xFt+tTQthUdoXB6FqFYl/ZDR/7Uo2IVRP85XXKes3MQQS20U3WIeuJbpc/54ZZhOQ/z8IwUucwMl74KbvMSOoSjm7HYuNRQt3oGojS51hsvMpvWet4uyLayY8wmtyuSa1n9JxSIsEX9LC7/IU6tmsxj+WfT9fSaC5afA5hTotRgU8gAAIgoA0CEOa0EQd4AQIxE5D0lJIm0mKxkKSbjNR1G2/g6quvVjvqGhoaVMrJ8f/TMr6/vH755Zfp7LPPVqd++tOf0le+8pVo3eiVV14Z2YH3xz/+cWT326c+9SmVXlPm6uzspIqKiqjjZUebpLAsKSlR/czmsf+DGHXQFAchzE0BB6dAIAMEpIZLJB2a/P6Prk/pYVGklwUP2clk4dSF5bzTzsbinRw/wKJGp8urdt2J4Oe0maieRb2mUgfSV2YgbpgiNgJyU21fcxsV2m1UV1k2ZtDejkHa3+VSwl2100anNpaSnYVlEQNf2tNBm5p7qdftV9f2ifWlLOodE9we3niE1u/rUqK2XPszywuVGHfFKY1k5d8PNBDIFAEIc5kijXmyQUBvwlw2GGFOfRKY6rO3PleUu15DmMvd2GJlIAACIJAsAQhzyRLEeBDIMIGHHnqIrr32WjWrCHRSz218kzzmNTU15PV66cYbb6S77rprfJeor+Xmo9SOa25upnPOOYf+/ve/R+33+c9/nv77v/+bRFDr6uoaqUX37LPP0sUXX6zGSP07qYM3vomIJr7JB9QPfehD9Pvf/358l7hfQ5iLGxkGgEBKCeDmQEpxwpgGCcjfOqkP6nQ6NegdXAKBxAlAmEucHUZqnwCEOe3HCB4mRgCfvRPjlo1REOayQR1zggAIgIA+CECY00ec4CUIjBAQsa22tlbtlhMRbdu2bST13ka3H/7wh/S1r31NHXr66adHxLJInyNHjqi0GkajkWRH3eh2yy230E9+8hN16P7776frr79+9Gm1+27p0qWqzt0ll1xCTz755Mh52TEjqSRbW1upqqqKduzYMWHX3J133kmS/lLaH/7whxGRccRIAk8gzCUADUNAIIUEcHMghTBhSpMEIMxpMixwKgUEIMylACJMaJYAhDnNhgaOJUkAn72TBJjB4RDmMggbU4EACICAzghAmNNZwOAuCAiB73//+/SNb3xDwfiHf/gH+vd//3eSmnM9PT1qJ9utt96qzl100UX0zDPPqOej/7Hb7Wo3XV1dHbW0tIw+RQcPHlQ13+QmpKSiXLt2LX3gAx8gh8Ohatp9+MMfpkOHDpGIem+//TadcsopY8bfcccddNNNN6ljsnvum9/8Jq1evVoJib/5zW/on//5n1XKO/F3/fr1ZDAcq1U1xkicLyDMxQkM3UEgxQRwcyDFQGFOcwQgzGkuJHAoRQQgzKUIJMxokgCEOU2GBU6lgAA+e6cAYoZMQJjLEGhMAwIgAAI6JABhTodBg8sgIARuvvlmuu2220ZgVFZWql1sko5S2syZM1UqylmzZqnXo/+ZSpiTfhs3bqTzzjtPiWnyWurYSdrKwcFBeanaz372M/ryl78ceTnmUcS47373uyPHRODr7e0dqUElO/6ef/55Wrx48UifZJ5AmEuGHsaCQPIEcHMgeYawoG0CEOa0HR94lzgBCHOJs8NI7ROAMKf9GMHDxAjgs3di3LIxCsJcNqhjThAAARDQBwEIc/qIE7wEgQkERICTnXIPPvigqgkX6SA70K644gq6/fbbJ6SpjPRZtGgRyf+oVldXq11wkeOjH9etW6d2t7355psUDAZHTs2ePVvt2LvuuutGjkV7IsKc1JmTHXiRVlBQQO973/uUb3PmzIkcTvoRwlzSCGEABJIigJsDSeHDYB0QgDCngyDBxYQIQJhLCBsG6YQAhDmdBApuxk0An73jRpa1ARDmsoYeE4MACICA5glAmNN8iOAgCExPYN++fUpgKykpoZNPPpmk9lyqmsvlIhHpuru7ScS00047TaWxjNW+pL2U8VIH78QTT1Q2Yh0baz8Ic7GSQj8QSA8B3BxID1dY1Q4BCHPaiQU8SS0BCHOp5Qlr2iIAYU5b8YA3qSOAz96pY5luSxDm0k0Y9kEABEBAvwQgzOk3dvAcBEDgXQIQ5nApgEB2CeDmQHb5Y/b0E4Awl37GmCE7BCDMZYc7Zs0MAQhzmeGMWTJPAJ+9M8880RkhzCVKDuNAAARAIPcJQJjL/RhjhSCQ8wQgzOV8iLFAjRPAzQGNBwjuJU0AwlzSCGFAowQgzGk0MHArJQQgzKUEI4xokAA+e2swKJO4BGFuEjA4DAIgAAIgQBDmcBGAAAjongCEOd2HEAvQOQHcHNB5AOH+tAQgzE2LCB10SgDCnE4DB7djIgBhLiZM6KRDAvjsrZ+gQZjTT6zgKQiAAAhkmgCEuUwTx3wgAAIpJwBhLuVIYRAE4iKAmwNx4UJnHRKAMKfDoMHlmAhAmIsJEzrplACEOZ0GDm5PSwCfvadFpJkOEOY0Ewo4AgIgAAKaIwBhTnMhgUMgAALxEoAwFy8x9AeB1BLAzYHU8oQ17RGAMKe9mMCj1BCAMJcajrCiTQIQ5rQZF3iVPAF89k6eYaYsQJjLFGnMAwIgAAL6IwBhTn8xg8cgAALjCIwW5oqLi2nJkiXjemjjpfwP1NDQEDmdTm04BC9AIEUEhoeHSX4izWAwRJ7iEQRygoC8d5tMJrJYLDmxHiwCBCIEwuFw5CnhvXsEBZ7kCAF89s6RQGIZEwjgs/cEJJo94PV66fXXXx/xb82aNSPP8QQEQAAEQCC/CUCYy+/4Y/UgkBMERgtzObEgLAIEQAAEQAAEQAAEQAAEQAAEQAAEQCCnCECYy6lwYjEgAAIgkBQBCHNJ4cNgEAABLRCQb8N+97vf1YIrk/rQ3t5Ov/rVr9R5m81GX/nKVybtixMgAAIgAALaIfDwww/Trl27lEMXXXQRrVixQjvOwRMQAAEQAIGoBDo6Ouiee+5R5/DZOyoiHAQBEMgwgZKSEvryl7+c4VkxHQiAAAiAgFYJQJjTamTgFwiAQFwEenp6yO12j0mnF5eBNHfevn07XXLJJWoWSbe5ZcuWNM8I8yAAAiAAAqkg8JnPfIaefvppZepb3/oW3XDDDakwCxsgAAIgAAJpJIDP3mmEC9MgAAJxEygoKKDKykqy2+1xj8UAEAABEACB3CQAYS4344pVgQAIaIzApk2baOnSpcqr0tJS6u3t1ZiHcAcEQAAEQCAagSuvvJL++te/qlNr166lm2++OVo3HAMBEAABENAQgc2bN4/UncZnbw0FBq6AAAiAAAiAAAiAAAgoAhDmcCGAAAiAQAYIQJjLAGRMAQIgAAJpIABhLg1QYRIEQAAE0kwAwlyaAcM8CIAACIAACIAACIBAUgQgzCWFD4NBAARAIDYCEOZi44ReIAACIKA1AhDmtBYR+AMCIAAC0xOAMDc9I/QAARAAARAAARAAARDIHgEIc9ljj5lBAATyiACEuTwKNpYKAiCQUwQgzOVUOLEYEACBPCEAYS5PAo1lggAIgAAIgAAIgIBOCUCY02ng4DYIgIC+CECY01e84C0IgAAIRAhAmIuQwCMIgAAI6IcAhDn9xAqeggAIgAAIgAAIgEA+EoAwl49Rx5pBAAQyTqCrq4t+97vfqXmtVivdeOONGfcBE4IACIAACMRP4IknnqB9+/apgeeccw6deuqp8RvBCBAAARAAgYwSwGfvjOLGZCAAAiAAAiAAAiAAAnESgDAXJzB0BwEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAIFECECYS4QaxoAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIBAnAQgzMUJDN1BAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAIBECEOYSoYYxIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIBAnAQhzcQJDdxAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARBIhACEuUSoYQwIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIxEkAwlycwNAdBEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABBIhAGEuEWoYAwIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAJxEoAwFycwdAcBEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEACBRAhAmEuEGsaAAAiAQIwEhoaG6De/+Q1t3LiROjo6aPny5XTWWWfRypUryeFwxGgF3UAABEAABFJFYOfOnfT8889Pa660tJQ+/OEPT9rvscceo+eee472799Ps2fPprPPPpve8573UE1NzaRjcAIEQAAEQCA+Ap/5zGeov7+f1q5dS3V1ddMOdrlc9Otf/5o2b95MnZ2dtGLFCvXZ+/TTTye73T7t+O3bt9ODDz5Ie/bsIavVqsbK+/uiRYumHYsOIAACIAACIAACIAACIBArAQhzsZJCPxAAARCIk4DcsP3oRz9KbW1tE0YWFRXRM888Q2ecccaEczgAAiAAAiCQPgLf//736Rvf+Ma0E8yfP5927949oZ98yeKKK66gDRs2TDgnB8T+1772tajncBAEQAAEQCB2Am+++aYS1mTErl27aMGCBVMOfuqpp+hjH/uYEuTGdywuLlZfpjjttNPGn1Kvh4eH6Qtf+ALdeeedFA6HJ/Q5//zz6YknniCbzTbhHA6AAAiAAAiAAAiAAAiAQLwEIMzFSwz9QQAEQCAGAnIj4dxzzyW3201Go5HkJsCZZ55J27Zto5deeok8Hg/JbowXXniBlixZEoNFdAEBEAABEEgFgU984hN03333qfdmeX+erM2bN0+9Z48+LzsxZNfzpk2b1OE5c+bQRRddRHL8b3/7G7W3t6vjv/jFL+imm24aPRTPQQAEQAAE4iAQDAbp8ssvJxHbpE0nzK1bt44uvPBC9Rlb3ttlh9zq1avVzrlXXnmFvF4vVVRU0IsvvkgnnnjiBE++9KUv0c9//nN1vKysjESIq6+vV2Ke7KKTdtlll9Gf//xnMpvN6jX+AQEQAAEQAAEQAAEQAIFECUCYS5QcxoEACIDAFARWrVqldlMYDAb6y1/+onZXRLofPnyYli5dSj09PVRdXU379u0j2UGHBgIgAAIgkH4Ckm7y1VdfpTvuuIM+97nPxTXhd7/7XfrmN7+pxnz2s59VOysiBmSHxcUXX6xu4hYUFKhd0XKTGA0EQAAEQCA2AqFQSKUHXr9+Pf3yl78kEdQibSphTna7yWdrSV8popzsbHvve98bGapsLlu2TKXEFLFNPnuP3vkmKeflvDQR7UTkkx12kfbTn/6UvvrVr6qXX/7yl+lnP/tZ5BQeQQAEQAAEQAAEQAAEQCAhAhDmEsKGQSAAAiAwOYFnn31W3ZyVHrfeeit95zvfmdD5T3/6E11zzTXquNSxmKqO0YTBOAACIAACIJAwgdraWrWzTXa4XXDBBTHbkR3QTU1N1Nvbq1KryY1bk8k0ZrykLpY6RFIP6brrrqPf/e53Y87jBQiAAAiAQHQC8v45Y8YMCgQCUTtMJcxJzU9JMSxtsnTCDzzwAF1//fWqz8MPP0wf/OAH1XP556qrrlI74SwWC0kdUqkbOrqJ8CdfvJC/G+Xl5SpNPXbNjSaE5yAAAiAAAiAAAiAAAvESgDAXLzH0BwEQAIFpCMgODKlPIU2+kSupzsY3uekgaXLkRq+k6Xn00UfHd8FrEAABEACBFBOQlJNOp1NZPXLkCDU2NsY8w5NPPkmXXnqp6i/pzr74xS9GHRu5wVtYWEj/n73zANeiONvwIE0QBEGaoIIEFcSKgKJSjCJRQMASjSW24K+JGjWoscSWEFti7xV7BVSKEbGiWFAUCxYsqGADREQQEL9/n4mzmW/O7lcOp5/7vS7c3ZnZ2Zl7dvc7zrPvO1qPrnHjxonlSIQABCAAgf8RmDdvXs53ci5h7ogjjjC33nqrrezzzz837du3/1/Fv+wplKXCyK9YscJ+HHf//ffbnJUrV5pmzZrZUJcKTaw1oJNM9es6Mnnk7bnnnknFSIMABCAAAQhAAAIQgEBBBBDmCsJEIQhAAAKFE1AIHK1F0bVrV7tNO1MhdvQ//w0bNrTrYSj0GQYBCEAAAuVHQGvDKdyZRLPvv//eFPPePfXUU81FF11kG5f20YUytb6cE+30jtdELwYBCEAAAsUTkPfbmWeeaU/MJcx17tzZhqvU+/21115LvVC/fv3sWs8KUynPZtlzzz1n+vbta/dzfXTx8ccfxx/b6R3v1qOzJ/IfCEAAAhCAAAQgAAEIFEkAYa5IYBSHAAQgkIuAvDH0P/sKeaNwObfffntqca1VoTUrZAsXLrShcVILkwEBCEAAAmtMwIUR1uStQp/peNasWbF3c/fu3c0BBxxgtAZRaLvuuqt56qmnrLez1ghNs6efftoMGDDAZo8ZM8YceuihaUVJhwAEIACBHAQKEeb0N/T6669va/nDH/5gbrjhhtQa//SnP9n1RVVAUSvk0az14k4++WR7jtYf7dOnT+r5WhNa5ykcvfO4Sy1MBgQgAAEIQAACEIAABHIQQJjLAYcsCEAAAsUSmDt3runYsaM97YQTTjCXXXZZahWjR482Z5xxhs2fPXu2XZcotTAZEIAABCCwxgQuuOAC89e//tW0a9fOLFmyxE6whpXq4wq9n4855hiz1lprxdlbbrmleeutt4w8M+bMmROnhzsS+rbeemubfPHFFxt9hIFBAAIQgEDxBAoR5uRJp7U9ZfJs1ns+zf72t7+Z888/32bLA05/s+s3wZ2T7+9xrYGnMMjyvNNHGBgEIAABCEAAAhCAAARKSwBhrrTkOA8CEIBAAoGZM2ea7bbbzuacd9555qyzzkoo9d8krUOn9ehkzzzzTBxG57+5/BcCEIAABMqawFFHHWVuvvlmW229evVMz549zY477mhDWyr8mdaRk8ezTBPCp59+ut3Xf7Rm0fz5802vXr3MSy+9FKeHO5q01eStbNSoUXH4y7AcxxCAAAQgkJtAIcLc9OnTYy83hRvWezfNLr30UnPSSSfZbL3H9T4/+uijYy87rQvaqlWrtNPtRxf6+CJfuPrUCsiAAAQgAAEIQAACEIDALwQQ5rgVIAABCJQhgULXqdAlb7rpJqOQO7KpU6cahUnDIAABCECg/Ai49YWaNWtmxo0bF4ecdFfUBO/w4cPNV199ZSTcvfDCC1a8U77OkZed1ozT2nFp9uWXX1qPPOWfeOKJNkxaWlnSIQABCEAgnUAhwtyUKVPMwIEDbSXXX3+9GTlyZGqFV111lTnuuONsvgtbedBBB5m7777bpq1YscI0aNAg9fztt9/evPrqq6ZLly7m/fffTy1HBgQgAAEIQAACEIAABPIRQJjLR4h8CEAAAkUQePvtt43WKJIpLI5C6qTZhRdeaE477TSbra9vFSYNgwAEIACB8iPwzjvvGHlEyKNtk002SbzQhAkTzJAhQ2yePOxuvPFGu9+pUyfzySefmJ133tnoI4w0838HFBJTYdIwCEAAAhAonkAhwpyEMglmMoWQVyj5NDv33HPNOeecY7MlrElgk1AnwU62ePFi+xGGPUj4j/sd0Dp0EvYwCEAAAhCAAAQgAAEIlJYAwlxpyXEeBCAAgQQC/gL0mozVpGyaSbRTyB3ZF198Ydq2bZtWlHQIQAACEKhAAnofy2uuR48eZsaMGfbKCnn54osv2o8o9DFFmvme0xL1JO5hEIAABCBQPIFChLl58+aZDh062MolvGkduTSTaHfFFVfY7G+//dY0b97chi0+88wzbZrWinahiJPqcJ7Te++9txk/fnxSEdIgAAEIQAACEIAABCBQEAGEuYIwUQgCEIBAYQS0NlHDhg3NqlWrzO9//3tz2223pZ7o1jqqW7eu+fHHH23YtNTCZEAAAhCAQIURGDBggHn66afN+uuvb7755ht7XYW41ERsy5YtzYIFC1LbojIqK3vkkUdi77vUE8iAAAQgAIFEAoUIc/qbW397629wrRd33XXXJdalxIMPPtjcddddtrz+9pZp3VH3AYXCGe+www42PfyPruPCXCpcpsJmYhCAAAQgAAEIQAACECgtAYS50pLjPAhAAAIpBLp162Zmz55tNt10U/Pee++llDJmu+22MzNnzswbFi21AjIgAAEIQKBgAkuXLjU//PCDLd+mTZuc52299dZGXnGaoNVEreyUU04xF198sd13IdDsQfAfeWucf/75Rh9dKGxmixYtghIcQgACEIBAIQQKEeZUj0ITf/zxx2arrbYyb7zxRmrV7m/03XbbzWhtOtmzzz5rtP6o7N///rddG9QeBP95+eWXTe/evW3qnXfeabQ2HQYBCEAAAhCAAAQgAIHSEkCYKy05zoMABCCQQsBfv0LCnAS60JS++eab22StNacJXwwCEIAABMqPwLXXXmuOPfZYe4FXXnklXpMovKI85DbccEOzYsUKc8QRR1hvCpXx1zHKNXmrNYvmzJljdtllFzvhG9bPMQQgAAEIFEagUGFO4eO1tvNaa61lPvzwQ9OxY8cSF5Bgt80229j0yy+/3Bx//PF2/+eff7bhKxUSc9dddzVTp04tca4STjzxRLuGnT660O/Eeuutl1iORAhAAAIQgAAEIAABCBRCAGGuEEqUgQAEIFAEAU3IamJW1r9/f/Pkk0+aOnXqZNVwzDHH2FA7Sn/33XcTxbusEziAAAQgAIE1IuB/EKF38DXXXJNY33HHHWeuuuoqm/fUU0/Z97gr2LVrV/vOXnfddc0777xj2rdv77LsduLEiWbw4MF2P5d4l3USBxCAAAQgkEigUGHu7bffNt27d7d1DBo0yEyePLlEfYcffrgNMZ8k3o0aNcpccskl9px77rnHHHDAAVnny/tZ3nhaezSXeJd1EgcQgAAEIAABCEAAAhDIQQBhLgccsiAAAQiUlsD+++9vHnjgAXv6ySefbL/K1WLyWmheIc4uvfRSm3fkkUeam266qbSX4TwIQAACECiCQJ8+feLQlPJ+OPvss02zZs1sDcuWLTPyurjiiivs8d57723XlPOrHzNmjDnssMNsktah++c//2lDm61cudKW1dqiWreoU6dORhPFjRo18k9nHwIQgAAEiiBQqDCnKocOHWoeffRRW7ve5fKQ7tChg1m0aJFRiOGrr77a5v3xj3+MP76wCdF/5GXXs2dP+3d669at7cdzv/nNb8zaa69twxrr73p93CFRT+GNe/Xq5U5lCwEIQAACEIAABCAAgVIRQJgrFTZOggAEIJCbgEKg7bXXXnE4HHnGaaL2k08+MQqZI9OXvc888wzrD+VGSS4EIACBMiMgbwdNqH766ae2Tr2bf/WrX9nJ1g8++CB+P/fo0cOMGzfOhrQML67ww6eddlqcLK85TfwuX77cpjVp0sTIc65v375xGXYgAAEIQKB4AsUIc3oHy1tOa8bJ3N/eWnsuk8nYtG233dbIE9p9kGETf/nPSy+9ZLT2nNYjlckzWh9X6HfDmcLVS+TDIAABCEAAAhCAAAQgsKYEEObWlCDnQwACEEgh8MMPP1jPCoXT0b4zrU2x7777Gq13xPoUjgpbCEAAAhVD4MsvvzSjR482N9xwg11Hzr9q48aNjUJZyrO5fv36flbWvjzlFO5y/vz5WekS9PRul+cFBgEIQAACa0ZAESb0vpa9+OKLpnPnzjkrXLJkiZHn8uOPP27kBe2sXr16NjylvOYkuKWZRDutPSePZyfmqWzbtm2NRLmRI0emnUo6BCAAAQhAAAIQgAAEiiKAMFcULgpDAAIQKJ7ATz/9ZGbMmGFmzpxp2rRpY7bffnu7yHzxNXEGBCAAAQiUFYHFixfbdeK0LmjLli1Nt27dTMeOHUusCZrrevKye+6550yDBg2M1p+TMIdBAAIQgEDlEli1apV55ZVXzBtvvGFFNX0sobCWhdqCBQus593ChQttxItddtnFNGzYsNDTKQcBCEAAAhCAAAQgAIG8BBDm8iKiAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAATWnADC3JozpAYIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI5CWAMJcXEQUgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgsOYEEObWnCE1QAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCAvAYS5vIgoAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIE1J4Awt+YMqQECEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACeQkgzOVFRAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIrDkBhLk1Z0gNEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEMhLAGEuLyIKQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGDNCSDMrTlDaoAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAXgIIc3kRUQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACa04AYW7NGVIDBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABPISQJjLi4gCEIAABCAAAQhAAAIQgEBlEfjqq6/Mjz/+aC+//vrrm3XWWaeymlIlrvvtt9+aJUuW2La0bNnSNGnSpMLbNWvWLHPXXXeZNm3amJNOOim+/k8//WTmzZsXH2unYcOGpm3btllphR7Mnz/frFq1Kqv4xhtvnHWsg6VLl5qFCxeWSE9LqF+/vhE7ta0Qu/zyy43acthhh5muXbsWcgplIAABCEAAAhCAAAQgAAEIpBJAmEtFQwYEIAABCEAAAhCAAAQgUNkEBg4caKZMmWKbceedd5qDDjqosptUqdc/8cQTzWWXXWbbcN1115mjjz66Qtvz888/m969e5sZM2aYiy66yIwaNSq+/kcffWQ6d+4cH2unefPmRuJqgwYNstLzHSxevNgKfytXrswqKvGvbt26WWm33nqrOeKII7LSCjlo2rSp6dWrlxk8eLA59NBDTYsWLRJPu+qqq8xxxx1n+vTpY6ZNm2bq1KmTWI5ECEAAAhCAAAQgAAEIQAAChRBAmCuEEmUgAAEIQAACEIAABCBQDQlIRLnmmmvilku8aNy4cXxcHXYQ5rJHqbKFOYmBxxxzjPU4++STT7I89pKEObX+kUceMUOGDMnuSJ6jMWPGWA+1sFhZCnN+3a1atTLXXnut2Wefffxku79ixQqzySabWK+566+/3owcObJEGRIgAAEIQAACEIAABCAAAQgUSgBhrlBSlIMABCAAAQhAAAIQgEA1IyBvIz9c3xdffFHqsIKV1XWEuWzylSnMLViwwGy66aZG4TT//ve/mzPOOCOrcWnCnLwc5e1YjO21115m0qRJJU4pRJjbaqutzFprrVXiXJewbNky8+GHH5rVq1e7JLuVJ97EiRPNHnvskZWugyuvvNIcf/zxZr311jPvvvuuad26dYkyJEAAAhCAAAQgAAEIQAACECiEAMJcIZQoAwEIQAACEIAABCAAgWpIAGGuGg5aniZXpjB35JFHmltuucWGp5w7d65Zd911s1obCnMK+ZjJZIxCRn799ddm7bXXziqfdiDhT+vXaX05V4crW4gwt3z58rzX0rqFb731lvn3v/9t7rnnHle92WijjczHH39cQthTeXnNSdxW2Et59GEQgAAEIAABCEAAAhCAAARKQwBhrjTUOAcCEIAABCAAAQhAAALVgADCXDUYpCKbWFnC3OzZs0337t2NwqP+5S9/MRdffHGJlofC3E477WSef/55W+6hhx4yI0aMKHFOUoLEP4mAsh122MG8+OKLcbGyEuZcherPYYcdZu644w6XZP7zn/8YeWqGds4555hzzz3XJr/wwgtmxx13DItwDAEIQAACEIAABCAAAQhAIC8BhLm8iCgAAQhAAAIQgAAEIACB6kkAYa56jluuVleWMKdwlHfffbf1YHvvvfdMly5dSjQzFOYuu+wy8+c//9mW23///c19991X4pykhEGDBllxTHn/+te/zMknnxwXK2thThXPmDHD9OzZM76GREeJj6HNmzfPdOzY0agNWjNPa+dhEIAABCAAAQhAAAIQgAAEiiWAMFcsMcpDAAIQgAAEIAABCNQKAp988ol55513bF87dOhgtG6V7PPPP7frZUmckPDVuXNnM3ToULP99tvbfPcfhdPThL88ht58802z2Wabmd69e9t/zZs3d8WytosWLYq9gxo0aGB22223rPzwQGtdSQyRKcze5ptvbvfff/99M2fOHBsKcNiwYTZN/5FXUIsWLeyxxA+3Dtfjjz9uxQZlyMupWbNmtkzSf/w2NmnSxPTt2zepWJwmMUPeUmqnmNavX9+0b9/eiKm4ac2yXFYWa8wtXbrUPPvss/YyWiPMeTopvKJCEr7xxht2HJ03VNieWbNmmcmTJ9v2f/bZZ5bPBhtsYJnvs88+OdcbUyhHnSvTen+//vWv7b7WN3v77bfNSy+9ZP8pzKPEIf3TOLqxsYW9/xQjzD333HPm+++/j88eMGCAadSoUXxc6I7u9W7dullvObX/iSeeSDw1FOa0jpv6opCUjRs3Nt98843dJp78S+LChQvtOogSv9Zff33z2GOPZT1b5SHMqX0Ky6lwlbKTTjrJCoK/NClrI6+/cePGWYFSz1+++zfrZA4gAAEIQAACEIAABCAAAQiIQPQ/ihgEIAABCEAAAhCAAAQgEBC4/PLLM/pzWf+iUHc296yzzspEglmc7vK1PfzwwzNRWDxbbvr06ZmWLVsmlosEgMwrr7wSXO2/h5F4FJ/TunXrxDJ+YuTVE5cfNWpUnKV2+m1L2o9EiLi82uTKvPzyy3F60s7TTz8dl+3atWtSEZsWrcWVOfDAAzOREBeXd9fwt/369cu8/vrrqfXsvvvu8fl33nlnarlcGZEAFtcRCY+26Pjx47PGqE+fPiWqePXVVzO77rprfK7fbrev/kXeYJlIdCpxvhJWrFgRnx+tm2bLRIJZJvI4i9NdXW47fPjwTCQWJdYXeaDF51133XWJZZQ4evTouJzqveCCC1LL5ss49dRT47puvPHG1OKREBeX0zXF5De/+U2cdu+996ae6zJUv+MwcuTIzGuvvRYfKz0S5lzReBuFvswqE4nicV4hO3puI+EwruO8885LPe2mm26Ky0UiaWo5MiAAAQhAAAIQgAAEIAABCKQRQJhLI0M6BCAAAQhAAAIQgECtJhAKc5EXTTwh74SDcBuFwMtMnDgxa5I/LKPjyGstE3lpleBbU4S5yEMts91225XgJbEy8sYrkd6qVatM5J1YgocSykOYmzBhQqZevXpZ7QiFucjjMBN5bGWVqVOnTkbimi/iuPHddtttM99++22JPoTC3NixY0tc29XhbyX2JYlQhQhzEpb8uqJwkCXaVUxC5C0a1yeRM81CYW7+/PmZyCMxPjfyNks7NU6PvETj8lOnTs1IKPb7ksRkTYW5jz/+OOsat99+e9yecCfyHozLRl6wYTbHEIAABCAAAQhAAAIQgAAE8hJAmMuLiAIQgAAEIAABCEAAArWRgC/MNW3a1E7Gy1vu9NNPz0ybNi0j4ebBBx/MRGtOxRP1Enuc4BOFuMtIMJg5c2Zm0qRJmaOPPjrj6pHQIAEitLIS5ubOnWvb+NRTT8Vt0zUffvhhm672O+8+taGsPeaOOOKI+LqqWxwWLFgQd/e7777L3HzzzVnC1x577BHn+ztlLczJy895M0bhIzO9evXKHHnkkZmLLrrIv2yme/fucR86deqUicJ9ZiQ4OotCdGb+/ve/x+MtvvJUDM0X5nT/OI9LeUSKiwRa3Ut33XVXZptttomvqfqSPMzyCXNnn312Vh1XXHFF2KSijtVPtUX/ohCsWfdNWFEozH366aeZxYsXZ6IQnvZ88V6yZEl4WnwchRbN1K1b15aVACoRTt6n7vralocwd9ppp8XXkOiaJLDGjYx21DbXpig8q5/FPgQgAAEIQAACEIAABCAAgbwEEObyIqIABCAAAQhAAAIQgEBtJOALc5qEj9ZTSwy5KG8bJya4yfo999wz44eKdPwUitGVkYAngcq3shLmXJ2+KKTrKrxkkpW1MLfhhhvG/YzWI0u6pE175pln4nJpok9ZC3OOf7QmYEZCUpKJkysnIS0tTKXOlaDnyiaJreEYqOzee++dkQgV2ldffZXZeOON4/oUCjS0XMLcGWecEZ8r775rr702PL3oY98bLVqXMOf5oTDnRCv11zHKFY5UoTlduWOPPdZeK1qjMU5TXlkKcxIJFeJTrNx1Cwn5ue+++8blr7/++pxMyIQABCAAAQhAAAIQgAAEIBASQJgLiXAMAQhAAAIQgAAEIACBiEAozJ1//vmpXCTyuIl9edw4QSI84fvvv4/Lqfybb76ZVaQmCHNffvll3McNNtggs3r16qw++gfy2vNFQXn6hVYewtxGG22UWbZsWXip+PiRRx6J+zB06NA4PWlHITjd2Ls15PxyoTDXvn37nNfWumWuPnnQhZYmzPnrwK211loZrYVWFnbUUUfF7TnllFNyVpkmzN19991xHUOGDEmtw1/PT2sZykojzEnUO+6441L/HXLIIZn+/ftnebBKXC9UZDv33HPj/uy3336p/SEDAhCAAAQgAAEIQAACEIBAEgGEuSQqpEEAAhCAAAQgAAEI1HoCvjAn77ZcQs7gwYPjifokrykf5nrrrReXffLJJ/2sTE0Q5hYuXJh56KGH7L8XX3wxq3/hgbyfFN7QCVEfffRRWKTM15jTtXJ5bakBH3zwQdwHrSmWyxSq1LVfa9KFFgpz11xzTVgk6/i2226L6+vQoUNWng6ShLm//OUv8TkS5bSuW1mZhDTXv3zeZGnCnATpRo0a2XrkgZgUKlKCrvM8bdeuXSzolkaYc+0tZivuhdpVV10VM+nZs2ehp1EOAhCAAAQgAAEIQAACEICAJYAwx40AAQhAAAIQgAAEIACBBAK+MKfwgrnst7/9bTxRP3LkyFxF4/XNJBpMnjw5q2xNEOayOpTj4LPPPsuMGDEi5iYeFSXMpYX0zNHcxKwZM2ZkevToEfehEGEu9JIMK37ppZfi+uRNGJovzClUpX8shqNHjw5PWaPj3r17x+1RqMlclibM6Rw//OOtt95aopqrr746vo683ZxVlDAnT8ZcYVdde7S955574rbmezf457EPAQhAAAIQgAAEIAABCEBABOroP9H/wGEQgAAEIAABCEAAAhCAgEfgiiuuMCeccIJN6du3r4nWQ/Nys3cPOOAAc99999nE8847z5x11lnZBbyjSLwxkVeZTYmEOROt2xXnPvfcc0bXkrVu3dpEa47FeUk7o0aNMpdcconN0n603llWsZUrV5qGDRvGaZEgZdq2bRsfu51mzZqZaL0te/jyyy+byAvIZZXYikMUBtCmd+3a1UShHEuU8RMijzMTCY7m/fffN1GIz/jfggUL/GJ2PxLmTKdOnbLSBw4caKZMmWLTIk83c9BBB2XlF3KgNm6xxRa2aCR2mWhtv0JOs2WiUJzmtddeMy+88IKJhKe4/epL5AmWVY/GNlqPLiut0DFwJ73++utm2223tYdNmzaNx8XlR6EuzWWXXWYPO3fubNvk8rSNwl+aV155xURenn5yqfd1DY2LTPf4/vvvn1qXyqm8MzGKhCt7+OCDD5oo7KPd1z2ve9833VPuGZs2bZrZaaedbLa4u30lRF6WJvKs8081kdBnjjjiiDgtCkVqIs+8+Djc+eGHH4zuP13v3nvvNVFIVVskWmvOqJ2RYByeknX8+OOPmz322MOmRZ6AJvKmzcrnAAIQgAAEIAABCEAAAhCAQE4CEuYwCEAAAhCAAAQgAAEIQCCbgO8xF4kR2ZnBke8x949//CPIzT5s2bJl7G1TUz3mtHacvKIUijH6n5HUfy1atMgo9KIrUxEec0lebdkj9N+j5cuXZyKBNROJlnH7XDv9batWreL8pLrDUJZJYRz96/uhMSNhzs+y+6EVnYNVAABAAElEQVSHnNqiUKs+x7L0mlMbXH8jQapEe/yEXB5zCgXbpEkTW1f9+vUzkTAWnzp//vy4/bpndP84K43HnMauUNP6d+Ln+rjjjjvmPVWekq68tpHQl/ccCkAAAhCAAAQgAAEIQAACEHAECGXpSLCFAAQgAAEIQAACEICAR8AX5iKPOC+n5G5lCXNHH310LBBonbHQQlEoLYSjQiY6oSHymAuryTp+9NFH47Kbb755Vp4OJKr465K5eiV+dOnSJRN5wGVOOumkTOR9lZGAss4668T1VRVhbtGiRZnNNtssbpfrg9ZJ6969e2bo0KFWtJNQpdCULr8QYW7x4sUlmPkJxQpzjRs3zkyYMCFzzDHHxO3Qun351sbzr5lr360Npz7qOrkslzCn8w488MC4jTfeeGNcVeSdGqdHHoFxunbKW5jTNYYNGxZfX/djPtPaiW7MtS5e5FWZ7xTyIQABCEAAAhCAAAQgAAEIxAQQ5mIU7EAAAhCAAAQgAAEIQOB/BKqDMCdPPicQVJQwd/vtt8fXTBLmotCacb7apjZGoQmtCPc/uv/bq4rCnM9VnminnHJKZtasWZkojOL/Gv7L3ttvvx33t6KFufXWW88KV2qKBL82bdrEbdlll12yPM9KNLzAhHbt2sV1jhkzJudZ+YS58ePHx3XttttucV0777xznD59+vQ4XTsVIcz5z7ruWd+bL6sxvxw89thjcXujkLNJRUiDAAQgAAEIQAACEIAABCCQSgBhLhUNGRCAAAQgAAEIQAACtZmAP1lfGR5zCpGYz3r06BELBGUlzL300ks5L3v22WfH10wS5tq3bx/ny6Mvl0VrtGXkceTExargMTdv3ry4PWrXzTffnKsLmWidsrh8RQtzV155ZVbbFJbRsdT2mmuuycovzUG3bt3iOi+99NKcVeQT5n788ceM887UuEdrKGY+//zzTLS2m71GtB5dCTGxIoQ5eT763KK18XL285577onLR2sX5ixLJgQgAAEIQAACEIAABCAAgZAAwlxIhGMIQAACEIAABCAAAQhEBCpDmJMo5gQCCRe51q7SWmX+umKjRo0qMW6FhrL010l76KGHStTjJ8gTy7Wxa9euflZGISBdnravv/56Vn54MHHixKzyVUGY872h5M23atWqsNlZx75QWdHC3HXXXZfVFh3IE82NgUSwzz77rESZYhJ878Ezzzwz56n5hDmdfOihh8btk3B42WWXxcdJ4nJFCHPy0nPMtJ09e3bOfl577bVx+QEDBuQsSyYEIAABCEAAAhCAAAQgAIGQAMJcSIRjCEAAAhCAAAQgAAEIRAQqQ5iTp44vEIRh/fyBOfbYY7PKJokahQpzWjfNXfeMM87wL5O1rzXGnHeTyocec++//35cj/K//PLLrPPDg5EjR2aVrwrC3J133hm3aaONNgqbnHWs0Jbbb799XL4qCHMag4YNG8ZtGjx4cFabiz24+uqr47o0XrmsEGHOF2P79euX2XHHHeP6X3nllRLVV4Qw568TqPv2tddeK9EOP+Hcc8+N23zIIYf4WexDAAIQgAAEIAABCEAAAhDISwBhLi8iCkAAAhCAAAQgAAEI1EYClSHMSUhr2rRpPOl/0EEHlQjtp7G444474jJOUEsS5lavXp1Ze+2147JPP/104lDus88+cZnOnTvbEINhQYlmWtPMXU/bUJjT9Ro3bhyXueGGG8Jq7LHKnXTSSXE5V2dVEObeeOONrHalef0tWbIkM2jQoKyyVUGYE+Bzzjknq10KvVha89fQ23bbbXNWU4gwt3LlykyLFi1s++Tx6YTeTTbZJLHuihDmQkFcXpO5TGKnu2dvu+22XEXJgwAEIAABCEAAAhCAAAQgUIIAwlwJJCRAAAIQgAAEIAABCECgcjzmxF0eOG7SX1uJZvfee29mxowZGa0ptvfee8ceURLGXNkkYU717b777nEZeXfdfvvtmfHjx2ckjjm777774jKqTx50CpMoDyZd+6ijjsq0bdvWlvGvGQpzqm/o0KFxXRLpTj/9dBvSUt5lr776akbrlPXt29eWkTDji3233HJLRuuQ+eEj/fbLm6005otLSeKZX6euveGGG8Z96NChQ+aSSy7JzJ07N7N8+fKMxM3zzz/fipJi1bx583idPPVHwp4fgjT0Wly8eLF/uRL7M2fOjK8tkTa0P//5z3F+UihLlRfDTTfdNC6nUKULFiwIqyro+Oeff864UKfq33fffZd6XiHCnE4+8sgj47a5+/e0005LrLcihLmFCxdmtefGG29MbItL1D2kdktU/OKLL1wyWwhAAAIQgAAEIAABCEAAAgURQJgrCBOFIAABCEAAAhCAAARqG4HK8JgTY4V/3HLLLbOEAide+Nu99trLimwuLU2Yu/DCCxPrknjjTCKdxDdXV9q2Xbt2mbfeeisulyTMff7551lim6urQYMG8XlKq1+/fmbMmDGZU045JStdeQot6KyihTld98knn4w9uVz7XZv9Y3l+vfzyy5n+/ftn9aFRo0au+ZnKEOZ08SeeeCKrTQcffHDcpmJ3fI/KSZMmpZ5eqDD3+OOPZ7VNTNPCR1aEMCfRWKKjG9t99903tY/vvfdeXC6fB2FqJWRAAAIQgAAEIAABCEAAArWaAMJcrR5+Og8BCEAAAhCAAAQgkEagsoQ5tUfeTT179owFACcYaCvRR15o8sqS95vLSxPm5s2bl5GA4Mq5rS/M6ZryjDr++ONLlHPlhw0blpkzZ05m0aJFcZkkYU51ydNuq622isu5Otx25513zsyaNUtFMxI6WrdunVW2soU5teuBBx6IvQRdu922bt26mQMPPDBeQ09r7/nCY1UQ5tQHhUJ1bdY2l6im8ml28803x/X89a9/TSuWKVSYkxDmvPDUri5duqTWWRHCnC6+xRZbxH1Um+S9mWQKz+qYnnnmmUlFSIMABCAAAQhAAAIQgAAEIJCTQB3lRv9jgUEAAhCAAAQgAAEIQAACHoFly5aZ77//3qZE67SZZs2aebnZu1F4PxMJXTaxSZMmZp111sku4B19/fXX+jjOpkRhHE0k6Hi52buffvqpicI/msibyEQeZmazzTYzu+yyi9lggw1sQV0zCo1o93XNKPRhdgW/HEXreplICDORsGai8HvmV7/6lenRo0diWdWn6+nfN998Y6KQiCYKgWm23nprW15t/+qrr+x+vXr1TBTWL7GeSHwxDz74oIlCO5oPPvjARCEfTadOnczw4cNNt27dss6ZP3++ibyobL0qE4XDNGIu+/bbb43aL9MYuHSbUOB/Io9AE4mdtrT6HwmBBZ0pFpFAZ6JQmOazzz4zbdq0MZEYafbbbz8TeQ9m1fHOO++YKMyl0X0TeTyaPfbYI853vJSga6sNaSZuUWhFm53UVt2TuoZs3XXXNZEIaPeT/hN561l+Lk9lc93Hrly41X0Whfe0DCMRzURiamIffM6qIxLfTOSJFlZnjzWuap9MY6r7I8mi0KImEoPjLI1BaFGIUROt+RcnJ5WJM1N29AyrHmdpz1OfPn3M9OnTTcOGDU0kRJr27du7U9hCAAIQgAAEIAABCEAAAhAoiADCXEGYKAQBCEAAAhCAAAQgAAEIQKD2Evjb3/5morX1LACJqFGI0VoHI/LkNJEnqO33sccea66++upax4AOQwACEIAABCAAAQhAAAJrTgBhbs0ZUgMEIAABCEAAAhCAAAQgAIEaTUDek/KWk2eZvB7Hjh1bo/ub1Lk//vGP5pprrrFervI+lRchBgEIQAACEIAABCAAAQhAoFgCCHPFEqM8BCAAAQhAAAIQgAAEIACBWkjgyiuvNNE6hCZaY8/Mnj3bCnW1BYPCkSrMqsJdnnrqqeaCCy6oLV2nnxCAAAQgAAEIQAACEIBAGRNAmCtjoFQHAQhAAAIQgAAEIAABCECgJhLQGnK9e/e26x4eeuihZsyYMTWxm4l9OuWUU8zFF19sveQkSuZaRzKxAhIhAAEIQAACEIAABCAAAQj8QgBhjlsBAhCAAAQgAAEIQAACEIAABAoiMHPmTNOzZ09btrZ4zS1cuNB07NjRLF261Dz00ENmxIgRBbGiEAQgAAEIQAACEIAABCAAgSQCCHNJVEiDAAQgAAEIQAACEIAABCAAgUQCo0aNMpdccompLV5zZ555pvnHP/5hBg0aZCZPnpzIhEQIQAACEIAABCAAAQhAAAKFEkCYK5QU5SAAAQhAAAIQgAAEIAABCEDArrP2/PPP27XmBgwYUOOJvPjii9ZbbquttjKtW7eu8f2lgxCAAAQgAAEIQAACEIBA+RJAmCtfvtQOAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAUsAYY4bAQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIVQABhrgIgcwkIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIIMxxD0AAAhCAAAQgAAEIQAACEIAABCAAgRpK4P777zd77bWXWWeddWpoD+kWBCAAAQhAAAIQqF4EEOaq13jRWghAAAIQgAAEIAABCEAAAhCAAAQgUDCB008/3TRu3NiKc9tuu23B51EQAhCAAAQgAAEIQKB8CCDMlQ9XaoUABCAAAQhAAAIQgAAEIAABCEAAApVOQMKcs86dO5vhw4ebFi1auCS2EIAABCAAAQhAAAIVTABhroKBczkIQAACEIAABCAAAQhAAAIQgAAEIFBRBHxhTtesV6+e2X333U2fPn1M3bp1K6oZXAcCEIAABCAAAQhA4BcCCHPcChCAAAQgAAEIQAACEIAABCAAAQhAoIYSCIU518127dpZ77kOHTq4JLYQgAAEIAABCEAAAhVAAGGuAiBzCQhAAAIQgAAEIAABCEAAAhCAAAQgUBkE0oQ5taVOnTrWc04edA0aNKiM5nFNCEAAAhCAAAQgUOsIIMzVuiGnwxCAAAQgAAEIQAACEIAABCAAAQjUFgK5hDnHoHnz5mbvvfc2m222mUtiCwEIQAACEIAABCBQTgQQ5soJLNVCAAIQgAAEIAABCEAAAhCAAAQgAIHKJlCIMOfauPXWW5s999zTNG3a1CWxhQAEIAABCEAAAhAoYwIIc2UMlOogAAEIQAACEIAABCAAAQhAAAIQgEBVIVCMMKc2r7322mavvfYyPXr0qCpdoB0QgAAEIAABCECgRhFAmKtRw0lnIAABCEAAAhCAAAQgAAEIQAACEIDA/wgUK8y5Mzt16mSGDx9u1l9/fZfEFgIQgAAEIAABCECgDAggzJUBRKqAAAQgAAEIQAACEIAABCAAAQhAAAJVkUBphTn1pV69embAgAGmb9++pm7dulWxe7QJAhCAAAQgAAEIVDsCCHPVbshoMAQgAAEIQAACEIAABCAAAQhAAAIQKIxAmjB32GGHmfHjx5vFixfnrahNmzbWe26jjTbKW5YCEIAABCAAAQhAAAK5CSDM5eZDLgQgAAEIQAACEIAABCAAAQhAAAIQqLYE0oS50aNHm5UrV5opU6aYF154wWQymZx9rFOnjtlhhx3MwIEDTcOGDXOWJRMCEIAABCAAAQhAIJ0Awlw6G3IgAAEIQAACEIAABCAAAQhAAAIQgEC1JpBLmHMdmzdvnhk3bpyZP3++S0rdrrvuumbvvfc2Xbt2TS1DBgQgAAEIQAACEIBAOgGEuXQ25EAAAhCAAAQgAAEIQAACEIAABCAAgWpNoBBhTh38+eefzbRp08zUqVPNqlWr8vZ5iy22MEOGDDES6jAIQAACEIAABCAAgcIJIMwVzoqSEIAABCAAAQhAAAIQgAAEIAABCECgWhEoVJhznfr222/t2nMffPCBS0rdKqTloEGDTK9evYxCXWIQgAAEIAABCEAAAvkJIMzlZ0QJCEAAAhCAAAQgAAEIQAACEIAABCBQLQkUK8y5Ts6cOdNMmjTJ/PDDDy4pdbvxxhubYcOGmTZt2qSWIQMCEIAABCAAAQhA4L8EEOa4EyAAAQhAAAIQgAAEIAABCEAAAhCAQA0lUFphTjiWLVtmJk6caCTS5bO6deua/v37m379+pl69erlK04+BCAAAQhAAAIQqLUEEOZq7dDTcQhAAAIQgAAEIAABCEAAAhCAAARqOoE1EeYcmw8//NCMGzfOLFq0yCWlblu1amW95zp16pRahgwIQAACEIAABCBQmwkgzNXm0afvEIAABCAAAQhAAAIQgAAEIAABCNRoAmUhzAnQqlWrzNSpU820adPMzz//nJeZ1p3bY489TKNGjfKWpQAEIAABCEAAAhCoTQQQ5mrTaNNXCEAAAhCAAAQgAAEIQAACEIAABGoVgbIS5hy0L7/80owdO9Z8/vnnLil126RJEzNkyBCz5ZZbppYhAwIQgAAEIAABCNQ2AghztW3E6S8EIAABCEAAAhCAAAQgAAEIQAACtYZAWQtzAiePuenTp5spU6aYlStX5mW5+eabm6FDh5rmzZvnLUsBCEAAAhCAAAQgUNMJIMzV9BGmfxCAAAQgAAEIQAACEIAABCAAAQjUWgLlIcw5mIsXLzaPPPKIeffdd11S6rZBgwY2tGXv3r3NWmutlVqODAhAAAIQgAAEIFDTCSDM1fQRpn8QgAAEIAABCEAAAhCAAAQgAAEI1FoC5SnMOaizZs0yEyZMMEuXLnVJqdsOHTqY4cOHm3bt2qWWIQMCEIAABCAAAQjUZAIIczV5dOkbBCAAAQhAAAIQgAAEIAABCEAAArWaQEUIcwK8fPlyM3nyZDNjxoy8vOUx17dvXzNgwABTv379vOUpAAEIQAACEIAABGoSAYS5mjSa9AUCEIAABCAAAQhAAAIQgAAEIAABCHgEKkqYc5f8+OOPzfjx480333zjklK3LVq0sN5znTt3Ti1DBgQgAAEIQAACEKhpBBDmatqI0h8IQAACEIAABCAAAQhAAAIQgAAEIPALgYoW5nTZn376yTz11FPm2WefNatXr847Ftttt53Zc889TePGjfOWpQAEIAABCEAAAhCo7gQQ5qr7CNJ+CEAAAhCAAAQgAAEIQAACEIAABCCQQqAyhDnXlK+//tqMGzfOzJ071yWlbtdZZx0zePBgs/XWW6eWIQMCEIAABCAAAQjUBAIIczVhFOkDBCAAAQhAAAIQgAAEIAABCEAAAhBIIFCZwpyak8lkzEsvvWT+85//mBUrViS0MDupS5cuZtiwYWa99dbLzuAIAhCAAAQgAAEI1BACCHM1ZCDpBgQgAAEIQAACEIAABCAAAQhAAAIQCAlUtjDn2rNkyRLzyCOPmHfeecclpW7r169vdt99d9OnTx+z1lprpZYjAwIQgAAEIAABCFRHAghz1XHUaDMEIAABCEAAAhCAAAQgAAEIQAACECiAQFUR5lxTJcxJoJNQl8822GADM3z4cNO+fft8RcmHAAQgAAEIQAAC1YYAwly1GSoaCgEIQAACEIAABCAAAQhAAAIQgAAEiiNQ1YQ5tV4hLR977DHz8ssv21CXuXpUp04ds9NOO5nddtvNNGjQIFdR8iAAAQhAAAIQgEC1IIAwVy2GiUZCAAIQgAAEIAABCEAAAhCAAAQgAIHiCVRFYc714tNPPzXjxo0zX331lUtK3TZv3tyuPbfpppumliEDAhCAAAQgAAEIVAcCCHPVYZRoIwQgAAEIQAACEIAABCAAAQhAAAIQKAWBqizMqTurV682zzzzjHn66afNTz/9lLeHW2+9tdlrr71MkyZN8palAAQgAAEIQAACEKiKBBDmquKo0CYIQAACEIAABCAAAQhAAAIQgAAEIFAGBKq6MOe6uGDBAus99/HHH7uk1G2jRo2sOLfddtulliEDAhCAAAQgAAEIVFUCCHNVdWRoFwQgAAEIQAACEIAABCAAAQhAAAIQWEMC1UWYc9185ZVXzOTJk82PP/7oklK3m2yyiRk+fLhp2bJlahkyIAABCEAAAhCAQFUjgDBX1UaE9kAAAhCAQJUlkDapUWUbTMMgAAEIQAACEIAABCCQQmD06NEpOZWfvHTpUvPoo4+aN998M29j6tWrZ3bddVezyy67mLp16+YtTwEIQAACEIAABCBQ2QQQ5ip7BLg+BCAAAQhUGwIIc9VmqGgoBCAAAQhAAAIQgEAeAlVZmHNNf++998zDDz9sFi9e7JJSt23atDEjRowwG264YWoZMiAAAQhAAAIQgEBVIIAwVxVGgTZAAAIQgEC1IIAwVy2GiUZCAAIQgAAEIAABCBRAoDoIc+rGypUrzeOPP26mT59uMplMzp7VqVPH7LDDDmbgwIGmYcOGOcuSCQEIQAACEIAABCqLAMJcZZHnuhCAAAQgUO0IIMxVuyGjwRCAAAQgAAEIQAACKQSqizDnmj9v3jwzduxY88UXX7ik1G2zZs3M0KFDTdeuXVPLkAEBCEAAAhCAAAQqiwDCXGWR57oQgAAEIFDtCCDMVbsho8EQgAAEIAABCEAAAikEqpswp26sXr3aTJs2zTz55JNm1apVKT37X3L37t3NkCFDTNOmTf+XyB4EIAABCEAAAhCoZAIIc5U8AFweAhCAAASqDwGEueozVrQUAhCAAAQgAAEIQCA3geoozLkeLVq0yIwfP97MmTPHJaVu1157bTNo0CDTs2dPo1CXGAQgAAEIQAACEKhsAghzlT0CXB8CEIAABKoNgTRhrjpPalQb+DQUAhCAAAQgAAEIQAACAYGZM2eaiRMnmmXLlgU5JQ87duxohg0bZlq3bl0ykxQIQAACEIAABCBQgQQQ5ioQNpeCAAQgAIHqTQBhrnqPH62HAAQgAAEIQAACEKh5BCTKTZgwwbz++ut5O1e3bl3Tv39/069fP1OvXr285SkAAQhAAAIQgAAEyoMAwlx5UKVOCEAAAhCokQQQ5mrksNIpCEAAAhCAAAQgAIEaQEBhLRXeUmEu81mrVq3MiBEjzMYbb5yvKPkQgAAEIAABCECgzAkgzJU5UiqEAAQgAIGaSgBhrqaOLP2CAAQgAAEIQAACEKgJBFatWmWmTp1qpk2bZn7++ee8XerVq5ddf07r0GEQgAAEIAABCECgogggzFUUaa4DAQhAAALVngDCXLUfQjoAAQhAAAIQgAAEIFALCHzxxRdm7NixZt68eXl727RpUzNkyBDTvXv3vGUpAAEIQAACEIAABMqCAMJcWVCkDghAAAIQqBUEEOZqxTDTSQhAAAIQgAAEIACBGkBAHnPTp083U6ZMMStXrszbo65du5qhQ4eaZs2a5S1LAQhAAAIQgAAEILAmBBDm1oQe50IAAhCAQK0igDBXq4abzkIAAhCAAAQgAAEI1AACixcvNg8//LB577338vamQYMGZo899jC9e/c2a621Vt7yFIAABCAAAQhAAAKlIYAwVxpqnAMBCEAAArWSAMJcrRx2Og0BCEAAAhCAAAQgUAMIzJo1y0yYMMEsXbo0b2823HBDM3z4cNO2bdu8ZSkAAQhAAAIQgAAEiiWAMFcsMcpDAAIQgECtJYAwV2uHno5DAAIQgAAEIAABCNQAAsuXLzeTJ082M2bMyNsbecz17dvXDBgwwNSvXz9veQpAAAIQgAAEIACBQgkgzBVKinIQgAAEIFDrCSDMVf1b4KuvvjLff/993oY2btzYtGzZ0jRs2DBvWQpUHQKfffaZmT9/ftygRo0ama222io+Zqd2EVi1apWZO3duVqfl4VBZz/Xq1atLTPR26dLFtGjRIquNHPyPgJjpvb1o0SKjUHM//fST6dy5s9E4YhCoCgSWLVtm3nzzzaymbLnllkZ/R2DVm8BHH31kxo0bZxYuXJi3I/qbUd5zm2yySd6yFIAABCAAAQhAAAKFEECYK4QSZSAAAQhAAAIRAYS5qn8b3HfffeaNN94ouKFNmjQxHTp0sF9CMxFcMLZKK/if//zHPPPMM/H1W7VqZU488cT4mJ3aRUBCzkUXXZTV6eOOO860a9cuK62iDlauXGnOOeecrMv97ne/M927d89K4+C/BL7++mtz//33Z4ntytl1113NbrvtBiYIVAkC+hjkqquuymrL8ccfT3jDLCLV90AfAzz11FP2b4uff/45b0d69Ohh9txzT6MPgzAIQAACEIAABCCwJgQQ5taEHudCAAIQgECtIoAwV/WHu1hhzu/RNttsY/bZZx9Tt25dP5n9KkQAYa4KDUYVaEpFCnO33HKL+fLLL+NeSzzaYYcd4mPtIMxl4ch5IE+5Sy65xHz33XclyiHMlUBCQiUSQJirRPgVeGl57sp77tNPP8171XXWWccMHjzYbL311nnLUgACEIAABCAAAQikEUCYSyNDOgQgAAEIQCAggDAXAKmCh2sizKk7Cou4//77G60pglU9AghzVW9MKrNFFSnMyWPGD6M6aNAgu+6Q33+EOZ9G7v133nnH3HnnnYmFEOYSsZBYSQQQ5ioJfCVcNpPJmJdeesnob40VK1bkbcGmm25q9t57b7PeeuvlLUsBCEAAAhCAAAQgEBJAmAuJcAwBCEAAAhBIIYAwlwKmCiWHwlyDBg1KfNH8448/2jWNFixYYJLCFo0YMcJsv/32VahXNMURePfdd80HH3zgDo1CkQ4YMCA+Zqd2Eahqwpy8wCZNmpQ1CHqXVFZozayGVLGD559/3kycODFulT6GOPjgg03Hjh3thxF6d2MQqAoE5NX57LPPZjWlX79+Zt11181K46DmENCYP/roo0YfEOSz+vXrm91339306dOHj7rywSIfAhCAAAQgAIEsAghzWTg4gAAEIAABCKQTQJhLZ1NVckJhrnnz5uaUU05JbN6qVavM2LFjS6xJ16ZNG3PCCScknkMiBCBQdQhUNWGu6pCp+i157LHHssSODTbYwPzpT3+q+g2nhRCAQK0h8Pbbb5tHHnnEfP/993n73L59ezN8+HCjdxkGAQhAAAIQgAAECiGAMFcIJcpAAAIQgAAEIgIIc1X/NihGmHO9uf/++83rr7/uDu32iCOOML/61a+y0tzBF198YeS5pfWm9K9x48ZGEzIbbrih2XjjjY3EwHwmz5rZs2cb1aV/3377rWnatKkNh6Q6FFKzXr16JapZtGiRefPNN+N0rXMijxyFX3rjjTfMW2+9ZRYuXGhDK8nzxDcJkeqnrqd2//DDD9aTp0OHDkb/Ntpoo7xfey9dutR+Qf7NN98YeRxKGFH/5Tmg/qvd8mLLZer7K6+8YubOnWvbqi/TXR3yLOrZs6dp2bJlYhUff/xx1vovOk/lfXvxxReNruFMa8CoTVo35uWXX7bt/umnn+w1NIGmdcIaNmzoiiduFdJK4/X555/bf/Luadu2rdluu+0sOzF9//3343PFY9ttt42Pi91x13NjpUnBZs2a2ftD92XXrl1NnTp1clYrb1DdK2qz1s5x66NJeFbbdc9qvNLCtn700Ufms88+i6+h8rq2wjXK2+mTTz6x43/88cfbdRl1rDF1prFUmC/ddy+88IJRfRrrY445pgTvJUuW2Laqjeqz2u7uS91XaR5nxQhzekbcM6d7V/ewGGqs1l9/fbPlllvaa7r2a6v75L333rNJkydPNnr+nGl8u3XrZg+7dOli5OGl+27atGmuiN1uscUWtv6sxF8OVL//TKr/8r7Q+Oif+q7z00xMxcuZOG2yySb22RZzjb3Yuj7qWVG9ZWEa7zlz5th7S23Q+6R169a2ft1jWrNTz2do4ql+v/rqq/Y96vL1zCs8qExh4Uo7ua36582bZ7noXah3pN7Jun/1vBbiiVfavrm+uK2eFb2XxUf/li1blvedq2dE73Hf9Iz26tWrxPqnqlvvZN90T7qweroH9Pw5U//1O6P3gcL1ff311zZLrN29lvY+0H3tv1d1L2l81S+9D/Q8de7c2QwcONBdLt7Ky1nvbt3fKqf26Xp6xvU71ahRo7hs0o6eXT0nut80pvq9XHvtte193apVK/sbWMj9oudFzFSH/qmv7r2q3wk9x0nvVTHW/epbjx49Un/rli9fbtur/uqfOOs+1LOnZ0Oc9JymmcQgvducqayebb1/NKaqU8+bOOqZ029YIX93uPrYFkdAURYU2lLPTD7TPbXTTjuZX//61wW9a/LVRz4EIAABCEAAAjWbAMJczR5fegcBCEAAAmVIAGGuDGGWU1WlEeY0eX3NNddktUjCxyGHHJKVpknJp556yjz99NOJITBVWJMy/fv3N1ojKW2CUxOCaqeum2aaSNcktSa3fdOk85gxY+IkTfL98Y9/NLfffrudtHQZhx9+uJ1kdMcSWB544AErSrm0cKtJ0n333ddO9IV5OpYY+dBDD9kJwaR8panPO+64o9lzzz0TJzg//PBDM378eDspmlaH0iUY7bfffiUmogtZY+6cc86x4pGrX3w00f3MM8+4pKytJkzV78022ywr3R1oglR8NRkamiZxNRGtiWX1y5lYHnvsse6wqK3ui3vvvTdLBAor0GT0kCFDUsVjTSRLcNZEdi7ThK/WVNT9Flro0aRx3Xnnnc0NN9xgBTZX/rzzzrMi8tSpU43+OZNgvNtuu9nyvqB11llnZU3Ez5o1yzz88MNGk9lpJtFM6/iEQk+hwpzEqQcffDAvD02c//73v7eT9WqLBNF//vOfac2K0//yl7+YFi1a2PtO959vv/vd70z37t39JLsvJnfffXfWunUlCkUJOlfhdSVEhCZPDgnRzjRBL/HlpptuynoGXL7uVwk8Ylla03twypQpWd5uSXVJ8Pjtb39rhRc/f/To0SXEJD9f+7p31OdiTPe8xtgXyMPz9Zwq9K3u4yRb0775depDAL1z9b5PM4lTevfoeXYmEerGG280Egd90/OuZ9CZ3unXXnutO7RbiY//93//F783zz///KznSu9C/YboOdV1QuvUqZN97yaJPOF79c9//rPR+3zChAlxXeG46ZnWs61nPM00Jupb+Fvnymt9t3Hjxlmx1aUlbfUu02920kcWeo7VjnxhCSUO6/mXUO9bMWvMaVzuueeeLGHNr8vtS7zRb3zdunVdUry95ZZbst5VgwcPtu8k1ZsUflsf8ehd27dv37gOdsqegD480b3oBO1cV5BoOmzYsKy/w3KVJw8CEIAABCAAgdpJAGGudo47vYYABCAAgVIQQJgrBbQKPqU0wpyaePHFF9uv8F1zNakyatQod2g9BSRI+B5EcWbCTu/evRMnvzWReccddyROmofVaBL9sMMOy5rYSRLmNCE5ffr0rNN9YU6eBpqwTprQyzopOpBIJUFJ/fdNX+lrArZQk0AwdOjQrOLylLj66qsL6rtO1EStxDnfg6E0wpw8O+Shl8s0OayJZnmT+KYJOIkcoVeKX0b7m2++eZb3T2mFOXllaOKvkLGS54+8zyTO+iZvIQmJhYTe0nka80MPPdR6sPj1hMKcBB2Jhpqk9i1NmJNAJM80CRS++cKc1hiTt00hJtFh5MiR1qPMlS9EmFOZK6+8MkugcOcnbXXvS9zQvVBewpw8iCTKyTOyEFOb9EyHgkEozOk+1Bjlu18Vbi30NC2kHfIcue2220qMadq5EuolKuh94Kw8hDmJ5hIz8vXbtSFpHdGy6Jur/7XXXrMfMSSJX66M28qbV+9cXwyTmHfFFVdkebvpHXXyySfH4vT111+f5aEqgUehQP33QSjM6RmW13AukwB83HHHlfgNCIU5iQ4Su/w++sKcPP/0wUuh76F99tnHyAvNN42nnt1C69BvoX4zfW9zvUv1Dg+FTv86/r7GQc+//7FCocKcfmfEpJD3t66p3wm1N/zgIBTm9Fuoj0vkaZrLjjrqqJyeeLnOJa8wAhoDrTeoj7R8D9K0szV2e+21l/2dTStDOgQgAAEIQAACtZcAwlztHXt6DgEIQAACRRJAmCsSWCUUL60wF06EaUJZgoPzelMYr0mTJmX1SIKGxAKFK5NgF07SHHTQQVlh6JR/+eWXJ3qtyWNCAoIfdkwX0ySp1shz3jKhMCdxRtcPzQlz8li45JJLskQJCV1qtyaEFYZME6i+KZTm0UcfHSepDgmXmrh2pklgeVdoQlHilf75k5Hi9re//S0rlJMmaX0vQYkNmshVO+TRJAEx9C7RBKlCbDorjTDnzlW/FQ5M4eTEOjR5MGgCzTcJXPIUDM3dF36f/TKlEebULo2Vz1l16lry5JCHVXiP6b458cQT40trklycJc75Ji8SjbnyNckcCkLyFNOkvuuXzg2FOdURnqdyacJcWnknzOleCD1VFcZR7DSxrvzQiy4co0KEOXl5hmHoXEg53Xd6BkLm8iiTuK4xueuuu9RNy9R/1nT/OjFFnmGayFe+BAzfQo85PeP//ve/Szx3epYUjk91qE3hu0BhQTWJ71sozPl58ljTO0qiVXifSuTROBRr4fOn8/UukBgkIVPtFlPflH/SSSfFQo+8QTWuetZ9T0rdL+5ZV1811oXazTffbL23wvJ6biTuhPet2iQhXvnOyqJvqivpOdZzpbHVu07PX8hI79I//OEPril2K8FawrVv8piTd5lC1Mp7yrc99tjD9OvXz08yoTDnZ6pN4X3h8uWlqfvWt1CYS3q+fWFO4xx6ysmrVKEX1f9Q4A/vE11bfQxDN0tc1HOncZUHXPiu07OosJTOwt9uXUd16J7Vs6bwluE7PnzPFCLMqU//+te/Sjy3ei/od0fiokKIhu/wPn36WPHatVfb8O8RP0/ivMZOH7r4oqjKlOZ3x6+b/cIJiL8+ovkk8GxNqkHvW/1toY9VMAhAAAIQgAAEIOATQJjzabAPAQhAAAIQyEEAYS4HnCqSVVphLmmdudNOO81OtmsCUBNu/uSu1vFRCEC3XpE8gxRi0heWFBpRYbGcaX0SfU3vmyYyd999dzuprQk7TcT6oelUVuG5FFpTFgpzNjH6j0QniQ0SXzQZr/WvNAEaTtxL+DjyyCPjCXBdMyksnSatdb5MX+rLu8eZriUPD13LmSZgNRHrm7ybOkbrB8k0Ea9JYt8UVs2vQ5OkEiz8SWuF51JYUGfh5HkoTKlcOIGsNIVClJeQEzi1hk8YFkzrp2ltQWdJk7GaZJVoI88MTY5qPDQ5FwpIpZkgTfIe0zo1miRWu3X/6f4OJ5ElzLlQeFo/SWV80yS1PIQ09jIJPhKrwklzhdTzJw5DYc7VqXo0bppsloAmQUAswlCWrrzKqLyECZ2rPintuuuuy/JA1YSzBGUJXjIxVShAv7+a4NR7WJPrskKEOQmHvvDmxA1bQfQf3XcSnrVmkzN57siDx7errroqS0xQGLowdJzqOiePMKeQqrqPfdMY/+Y3v4mFUYk7euYkGvimZ1frUzkLn2+l6/lXOb0PZHp/yWMoDL/m3m+2UAH/kYCv96DvtSNxQ+8nCS7OJCjpIwZfNNA9KMHEtyeeeMI8+eSTcVJpnhmdrNCVt912W1yPdvTu1XPqhFOFH3788cezysiTT6KIrCz7ppC2vleafiM0HgpbKdM7V+MfrkUobzhfKJRoppCW/rqNes707pVYrHXWnKlufUyhfN+ShDm9KxR+V+87tUXPl9rsj6vq8N/fOtZ9rfs7NAnAGjs9v3onKAyx1pNT231TCFG9z/X7IZP3uD588AVovRv0T6b7R+33n92kj130HvHFufDZDUUuiZd6Z/kWjpnu6xNOOCEukvRboLU13TOmguEHABoLPdO+wCwhWh7zEuic6V2md7j/DIVtVln9Huv9KN6yJG9u3Wvh+8cW5j/lQkD36IwZM4zWH/Xv07SL6d0tT1P/OU8rSzoEIAABCEAAArWDAMJc7RhnegkBCEAAAmVAAGGuDCCWcxWlFeYUplHhGn1z4tRzzz1nJ178PHmAhGHlwslITcydeuqpcXjEMFymJv80yeoEE9WvyVGFevMnefwv6pOEOU3sybvBiXeunZp0Pfvss7O8IhS+TgKVb5pc0lpFvjebP3kpLn64QXm1hJPsupYmBLV15oeLk8AgccA3TVDJc8E3CUv+JKsmIyVeOiuNMKdJME2i+pxVXzj5qUl8eSc6U/hPhaRzpvGUV4s8Cn0Lx115xYoMmvDWJLTPLxR2VW+SEKWQoS5U4KWXXmo9KVRWpkl43cduMvy/qcbeEyrrC8kSxPzwrUnCnCaFFS4taWIxSZiTF5VECSfyuusnrY+VFF5R4lzorelPzifxUBg+CQQyibwK+eebxtAJNi597NixdoLVHYdeo0ovC2FO4/v3v/89S+QPBWHXBgmxF154Yda7IPSsShLmQvFO9ckbVR8f+FZs2LtHH300K2Su7imJSb6g4OrXBwj6EMG38J1ZVsKc3iu+gCmvNIkqEih9u+yyy7LESX1ccfDBB9siZdU3iUx6D/qiZFJYY4lu8hb1vcYkXOkjDd/0wYdCWvqimUR6//dBIrfeb+HvkeoJhTm9A/Wbo98e35I88PzfHZVVv0JhTr8nEkBDQVCisj7ocKax0G+h2upb+Nuqe0nrNcokPOrZ983/XXLpCgurf870ztlll13cofnHP/6RJbrreTvssMOy2iyRSwKLbxIvXXvzCXN6D6mt/rgnhXNW/Xrn6gMUv2z4uxz+Nomvnp/wWQufIdVfrOCuc7A1IyBvSP0N6Xt3ptWoe0ris9a5dB+YpJUlHQIQgAAEIACBmk8AYa7mjzE9hAAEIACBMiKAMFdGIMuxmtIKc/JAkBeVby7knjyi/DXK5C2lSe3QNNEm7xzfs855ZWjCViKZb0mhx5Q/e/bsrBBvEkMUWkyWJMyl1aNJXU0A+qZwhfJcCi0UVXRNTaTmM03USkgTH03+++YLLQr7JCEoNHlSdenSxYbFlNeH82gLy7nj0ghz4aSnqyv0UFNoNn+MtB6eLxKmCSiqL1zvqVhhTuEGNQHvmy9A+ekSC30PPU2yq20aC02e+5Ykfrp8rUsoQcI3d88rLUmY05p2zvPHP0/74T2ktCSRSOkzZ8603nDal4n9X//619gD9b+p//2vvKHkFeXMD7OXT5hz5yRt9bzqvpwzZ471pvJFB/VRffWtLIQ5CQASiHyTSKDQjUkWjkHoERMKc5r01T0QCiXF3F9J7VBaeI9vscUWRvdokiW9ew444ADrTeXKh6JCsc+Mq+ef//xn1hpkEvt134cm8U6hNp3pnnOif1n1TZ5QClfsW+hZ5fLkqax1qpyFwrhLD0MxunS3VYg83yvLpWsbCnNpbPQsKIyu74Wn9Qq19qQz3Vf+M6LfEXk9h6K/yutdpnvOWZKgpryk58G9Y5JETp0jkV+/Gfod1kcioQCrMr6FbVGePJ/1zKkOifDOS9c/z9/PJ8zpN1uecL5JYAyFNJd/55132jCc7lgs9dvsLBTm1Gd9YBFa6M2ufInSofAansdx+RBI8oZOu1KSQJxWlnQIQAACEIAABGouAYS5mju29AwCEIAABMqYAMJcGQMth+pKK8xJwNJksjN5Fpx77rn2UCG55BXlTBO6aQKSvpz21+1x3hJJE5Dh2lOu/lzbJGHO9xDyz1WIMoUK882FwfLTtK8JV1/sUZr673uZqV8SMRSCTBOV6pP6m2a+MKdzxdhfUyo8TxO8mpxXKDT9k+dDaKUR5tImrsPwdqEwF05qhyEQ/baFHjfFigxaK0mTtb6lTej7Zfx9iYgSE30L1+jz85I8/fzyoSike17CXdJEvOoNhbk0kUhlQ1FGngPydEoy3Ze+IKBJZxdmrhhhTveePBp070o0kAjheyL51y4vYU7iv1uzzl0vl4dLkqebBHP3HIfCnDwF9T4ITc+pBCzf0oRfv4y/Hz4PfthBv5zb1xqTPt+wfHgPFPvM6Dq6LyQY+abwgb7HlJ+Xtl9WfUt6jt1YhddOeueqHaEXjd6dN9xwg/n000/DKkzHKFSwPhIJhVhXMOyX+1DE5fvb0Gs0DBMcCnP9+/e3ITH9Oty+PnDwQ1RKPHPeZ66M24ZrnPohdUNvSHeOv1U4SYUqVrjUJCEsfI/557p9jZEE/2222SYrvLLLzyfMhYKM//eDq8Pfhve+youve7eGwpz6d+CBB/pV2P2kdzjCXAlM5Z6g95zuAf1N4Xu9p11YIaP33HNPu0ZvWhnSIQABCEAAAhCoHQQQ5mrHONNLCEAAAhAoAwIIc2UAsZyrKI0wp0kVTST6oaUUqk9h2mQXXHBB1rpnxXRBE31aiy5JJEvzXstVf5IwJ0+jJBFL4SflFVZa0/3uxBKJlvJcyiWshdfxhTnlKXyhhEJ/La/wHHcskUwTV/J28600wlzoqePqyyXMKVScvB99U8g2Ca1JFk7MFisyJHnFaKLWrWGYdM0wLWl9OV/ECctrLOUl45s/KR5OaCtUnsKppVkozGmyO83rUusRhmvcpdUbpvshRwsR5iRsyOv11VdfDatKPS4vYS68TySo6D5LE1aSJt59L8RQmJMXkDzwQltTYU5r3ikEp29ag0/reaWZ7i3/fRGuMxeKE8U+M7puWXgClmXfwtCMaWzS0s8888zEyfok0V11KCyluKVZKMzl8s4Mn9/QOzMU5tJEPoWP1W9maW3IkCFGH0HIdP9InNNzns8kaEr81bqP/vMkgfDWW281n3zySb4qbL68KPXb4398k0+YC9eXC0XN8MLyMNc7yTf/XR0Kc2FYUXde0vsBYc7RqZit7iuNpbyv85mEY/1d5K8Tmu8c8iEAAQhAAAIQqNkEEOZq9vjSOwhAAAIQKEMCCHNlCLOcqiqNMKfwZldeeWVWi/wwXuHacJoA9Cf+sk4MDvSlu8SOpJBTZSXMSVSUkBXaiy++aDRx75vvAeenJ+0rFJcEP62JI69BTbiGJu8liRjyNnjggQeyRLdQmNO58n5S+DaJMkn1hfVL1JS46ayihDl99S6PH1+s1fpPWgcqycJ2FSsyhCKhrpE2rknXV1qSaBuu6+WfmxR2z/eiCoW5MNyaX5f2w4n9XJPTulcUztKZPEXSPGpcGbdVGDonmucT5iTKaa3AMMyq6pLorHFSSDFNqvpropWXMKdraP0130LPVD8vaUwVPlAhYGUVJcwlfbzgCyh+m91+vhCTZSHMJQlW/j3s2pJrW5Z903qcWmvKt2LeuVrjMik0YzjOrv5Q7HTpbhsKc1pfUesUJtnkyZONhEVnEhHcem9KC4U5fw1Rd462+vBC67r5pmfbeYP56Un78nh0a2YqX8Ka2qUQvr7Qm3Su0pLe03qPS5jX8+eHJ06rw//9V5l8wlwYFjnXRwmqL+mjGf99jzAnSlXb9PGOfiNffvnlvA3V34taU07CcTHvg7wVUwACEIAABCAAgWpPAGGu2g8hHYAABCAAgYoigDBXUaRLf53SCHNJk55+mEl5ivlrXIUh2QppbdLEXponV676kibq/Qk9/1yFnLz55pvjJIl3EpsKnSB1J95///1ZwoY8KeRR0LVr1yxPvXASOEmYc3VqolQedB988IENEzp37tzEEFCNGzc2Z5xxRtzmUABLEn/CCeQ0zqEYFoayDAVZf20z1w+3DT3AihXmJFSqDt+KFW4loP7rX//yqzBHHHGEFZ6yEn85SLqXtI6R1jOSlacwF7KXB4E8wYq1fMKc1hWTx41vCiMmjxwJje5ZmDRpkpHXorPyEubCZ1LXyyWeJgl5/vMevrvKy2NO7QyfB4WLlIiSZEnCdhhStiyEOQn9eu/4lrbmpl8m3C+rvul9Ju8sZ40aNbLhX91xabZJ94xfTy4hMnwn51pz8p577rGhXl3d4TMZvlfThDmdH143l6eeu14hW31Eo99ieYrpnx8u050vEUR/q+m3I8n0zpAHu87X+yHNg9v3TE36/fZDDYfPqd4r8oQNw5K69oRCnj6Akee7M4Q5R6JqbvWhlcJX5wrl7VquvwX0t5DCDGMQgAAEIAABCEAgJIAwFxLhGAIQgAAEIJBCAGEuBUwVSi5WmNOkpyZS5VnjTB4LWvfJTarJA0KeEM7Cr+ldeq7tihUr4jXrXLnddtvN7Lrrru4w3mqdMHkzOZPX3X777WcPk8QUf6LenaOt1u658MIL/aScIkBWQe8gDEk3dOjQLI8GFU0KB+cLc/JS0BfmzjQR6QQgpWm9JU1qP/744yVCQslrw60dVJHCnERN3R/O5GF14oknGk22+7Z06VIbEtJfB61YYS7J8ydJUJR3z0UXXZTFUl58+qd7WPeCv8ZNWgg0tT8UdTSZLO8t57lWnsJcuNZavvXrfN7+fj5hLhQA07z+FGJVk/XOykuYSwrzJ5Fb3hRJFrZL3oJ6NzkLx7A8hbnwAwU9vwqb58RN1yZtZ8+ebe644w4/yYTiTFkIc7pAKAIpFKFEo9DuvvvuEmMsDzJZWfVN6xZK5PPNf3/56YXs63fj8ssvzxnKUe8lCepJQlTIRqGB9V4OTe8uhZ/039Ehx2KEueuuuy5rTby037qwHf6xfgP1bnWm966eX2d6F0pc032kjzx8c6KafgP9tWMl2vkeg/pARO9eebApFLBvAwcONP2jdfRk+YS5pA8ADj30UKO/FULTNS+77LKs37lNNtnErhXoyiLMORJVa6v7SR7P/m9FWgv1AZO8N/URSKERFtLqIh0CEIAABCAAgZpLAGGu5o4tPYMABCAAgTImgDBXxkDLobpChTl5WmidF4XfC7+6Dz1Bwq/hJVyMHDmyxNo+mhzUWjO+KdybW08k9MrQJLu+uvcnVJPWTPJDxhUjzKkd4WSqvIUUWtM3TRTKW8sXAzUBqjCSSWutJXloKFSgPOt884W50KMsydNN5yb1z5/YrkhhLhR11L7NNtvMHHLIIfFEm/io3+FEXbHCnCbgFf5Nk83ONAbHHHNMLBArPSkk6uGHH266dOliT5MYIlHEmcJmSTxxwqZLl3edJvz964WiTnkKc19//bWdnHbt0TZJ8NVzKtFEfJzpHtY6UrJ8wpzEGDFzliSqawzlaeh7zhQizCWJDRI49Mz55nvfKv2qq66yE/2ujN4Dxx13XIkQhp9++qmRwOFbr169jLyenFWkMJcUGlcfDGy77bauOXar9+kNN9yQFTJQosqoUaOy1u0qK2FOHpESRpxpQlw8tU6oM304oNCavmgtMVSiqKys+qZ3qcbf/00JBS5dTyK63ol6DpzpnRG+m7V2lX6nfJPHtn63fNtqq62MhPzQQmFOIqrEyI4dO2YVnTJlig0x7CeGa2qqX/7HB7k85sI117QupNbDc2uWuuvMmDEjy1NV6UcddZQtF77rJd7rbzD34YCrI+ld4oS58AMAneN7urk69B7U/aH3jTPf8zKfMCcu+oDFFxI1nmIdhi5UCF+F8vUtFOcR5nw6lb+v51V/B+qe9J+BtJbp7wQ9P7rvMQhAAAIQgAAEIJCLAMJcLjrkQQACEIAABDwCCHMejCq6Gwpz+lJZ6734pokVfxLez5OAoRCCmgR0luS1oMl0eSlpAkbXePPNN+0Eo76odqbQiPJuceu/aS2S8ePHu2y71QSphEAJAVo/R955n3/+eVxG50qYcusOJQlXaR5zquSZZ56xk0lxhdGOvCa0bpsmDuVxoIlfTWD65iYKNdEsLyp/Mqpbt27m4IMPjotrUvzOO+/M8rZQpj+xqzCBChfom8LgyaPLeSZqMnvs2LFZngthKLhwsjZJ4AsnkJM8z9SOUHgLQ1mqzxJswnBVapPEVrVbfQ/zVXexwpzOCcMpKk3hMyXGtG3b1iiMm77W99dZkvggbxnHUJPUV1xxRZYHqIRfjafuNY2nwoYqlJo/Ca0Je4kZuo6z8hTmdI277ror677TcyQPUoVI1XMogVGeqv7zoPMkiqsvsnzCnMKNTZ8+3ZbVf8RJ63jJY1OmsZOI4IeqVXrS+IWCmtamkzCl+0Zigdqve0b3n2+hMJcUmlATuBojXVd1qIz4+wKPJvj1LnBt1zUqEAiFKAAAG2NJREFUUpiTqCVPH4m6znTf6D2od4LapXtUIk+4jlcYxlLnl5UwF4aPVN1a+1LtkneUxlieuP4Yq90SrJ3Xbln2TWtoioFveoa1HpzGV+8MvXPfeecdv4gZPHiwfR+6xKR+uQ8r5OWtfN+SPpgIhTmV1/tL73+Nmfot4VoCmW/6zdQ6jr4IFr5Xcwlzeg/pefGFf62LqN86fUQgz1GF79Xvgn+P+89d0kcIO+20k/VCkvgqU/0SKfU755tCNus3PMlrXB8gqO36DZdJdNHvn0J5+uaHAc4nzOm88AMepanPEvAVxlCincZc94few8707Cucrc8aYc7RqfytPpbS3yXh71BSyyQ86zmWUI5BAAIQgAAEIACBQgggzBVCiTIQgAAEIACBiADCXNW/DUJhrpgWa4JdX7j74bLc+ZpMDdeqcnlpW3k/aCLVmSZBL7300ixhxeWlbSVeaTLTWbHCnCYdr7/++hKhvlSfJqf9CUJ3jdBT68Ybb7QCnsvXVpOJmkRVmDBNXCWZP9GsyXEJRqEgKrHBfVWuusL2aCJWk/rOKlKY0zU1gS6vlXwmUcIX6PwJ5nznunyxkVelL4K6vLStBFJNsPsm8VcicDHWo0cPs88++2SdUt7CnCaq5bUX3hNqRNq9GXof5RPmXnvtNfPggw9m9UsT4BJWNakvkdIXD1xBTaRLqPQtSQxx+c6rsxBhTufIC9AXilw9ubZJa1tWpDCntiV5IOVqs/LWW289GwLWFx6UXlbCnOqSh94nn3yi3YJshx12sB6afuGy6pveuddee20JcVLXSruv9b44+uijY4FdXpx6NvwPPSRGSSzTuyZJgE8KaZkkzPl9TttPEt2KEeZU73PPPWcmT55c4hJpDCSay6vOrcWlZ0lhnb/55pusOnQfSTjUVnni7Vv44UjoNauyEtElzEmklNAcvnP1wYOefycAFiLM6fddv3Fhe/22Je3/9re/taKtn4cw59OonH0JxhJRn3322RL3WFKL9Nukv9d0T2EQgAAEIAABCECgUAIIc4WSohwEIAABCNR6AghzVf8WKK0wJzFKni1hyD+/x/oqX54Q4USgX0b7mmB03mBhnsJdylNIHgO5TJOXgwYNyhLlVL5YYU7nSPAaM2ZMlqeL0pNMHiYKYel7Gcr7RRPNufqtL8QlTMkDz1koosjDQ4JEKL658uFWXoTyjnLeYMqvaGFO13z11Vetp1qSgKN8eXjJM0JCg7PSCHM6V+KAhCQ/dKOr099qUlreWlp/MDRNKMrzMgyBF5ZzxxKPFUbSTUK79PIW5nQdiVN6Zn3vPXf9cKv7SWFd/dBw+YQ53bNpwrSrX/e6wjHKg9KZ+MrrRltnaUKD8osV5tRfeeqFXlPuWuFWoTu1XpH/LKhMRQtzuqZEX91fac+DyjjTcyGPVT+spMsrS2FO7zh57Uqwymfy0pV3osSZ0MqqbxJntD6g710YXssda30xvXOdB5fS9Q6QqOybH1pR6aE3qNLCkJahMNevXz/rQRoKUTrXWe/eve1zFvI5p4hQlqpLz57C/MozLp9JVNSHLPJm800ffei3x/eq8/PDfYnA8nj3xRGJnPLe8z2Nw/P8Y71fFELY9x4uRJhTHVpjUCFKwzXv/Prdvp5l3Ydagyw0hLmQSMUey2NZH7gU8vyuv/76dt1G/e2EQQACEIAABCAAgWIJIMwVS4zyEIAABCBQawkgzFX9oS9UmJN3nCbkNfkmYUJh6cKJyKTeauJXk9Jz5sxJyrb1aZLVn9QLC2q9I028huuSuXIKPSjvJQk+oZVGmFMd+pr/+eefN08++WQJ7wDla5JQIbfknZfEQW2VCCAhxDdxVPhBnReuUyRxUesF+RNWWsdOE/ISoNJM/Vd9mrAMxaLKEObUTk0Qi58maN1afBJxFaJOE9kKp6Yv652pz/K+LI1pAlmTu2mhszT5fOCBB1qPxVz1a0JcoS/TRC+FepMgp7B2SVYRwpyuq+dB94RCwSWJtgrjqnXVtthiixLNzCfM6QSJ4Lp3k0QwhaLVsyahQmtE+RYKy3qGJM5pnDXZ71uxwpw7V2E2w5CVLk9beUdJsHBrCPp52q8MYU7X1TOge9Q9C0rzTc++BDCJSb646ZcpS2FO9eYTpPUu0QcTel5zWVn0TfVLuNQ7Q143SUKYuEhs1Vp34uVM71qJer7pXaOQtT5LPTcKtRs+335Iy1CY03tD71d5AYdCVb73QbHCnGu/RA6JiGmiqdZ9lIdeuP6cO1/PuH63JFSmfRyisdXvhX431L/Q9LwqbKbGI+2jB42Bwo3q90xii2+FCnM6R+8J3dt6V6S1VyGY5SmX5J2vOhDmRKHiTc+U3sdhaNekluhvJn0woZC5/nOZVJY0CEAAAhCAAAQgkEYAYS6NDOkQgAAEIACBgADCXACkFh9qslATuPqnCT0JcVrXyPd6yIdHofxcHdpXHfK0yuW1l6/OfPmaINYEqa6rPsiTRdfVRGS+ySVNfCtcnLxBNOmsdsrDIRTP8rVB+ZoUlpeLPAwUrk11KKSlxFJ52ZSmzkKuWxZl1HeJl76AKe8nedY5kyebJsHXxDQ+bqw0mSwumsj1vRnz1S+hS6zdfaZj3acacwl8fh/y1VXe+ZoUlQCqPmu/rNup9c/0T/ebBK+O0Tp14QR8IX3UxLvapxCcuhf03GiiPfRmK6QuldHkvZ4FN0by2FHf9U9jVJVN7y2NmdouHuKgdmvttnzvk/Lql4Qqx1LPkNqiZ0djXcz9XlZ90zvXtUf3nnvnrsk9Uyi7JGFO7ybdw3qXyyNI91tp+BTaBpXTPa5riYOeb4ln7vn2vdty1SmBXee63wyNpfvNUPjLJEEurE/3hu5X3Rf6p+dX71PVo/vEhVUOzyvNserWb6XfZ/d3gt4/WNUi8MYbb9iPrpJCK4ct3WijjayXnO5hDAIQgAAEIAABCKwJAYS5NaHHuRCAAAQgUKsIIMzVquGms7WcgCaSQ88oCWO+d4tDpLXhNGHsTN5CWmMPgwAEIFBZBNKEucpqD9eFQFUjoN9tha1UqO18pggB8gKW123S3wH5zicfAhCAAAQgAAEIhAQQ5kIiHEMAAhCAAARSCCDMpYAhGQI1kIDCU+qfb1rXTeuR+Za01tOhhx5qFKINgwAEIFBZBBDmKos8163qBOTF+cILL9gQ3IWsYditWzcb+rmYqAhVnQHtgwAEIAABCECg8gkgzFX+GNACCEAAAhCoJgQQ5qrJQNFMCJQBgaR1hfSVvNYjVFg8hTPUOnDyrPNNodFGjRpVVNg8/3z2IQABCJQFAYS5sqBIHTWNgH7bx44da9eMzdc3CXFDhgxJXOM037nkQwACEIAABCAAgXwEEObyESIfAhCAAAQg8AsBhDluBQjULgJPPPGEefLJJwvutNbVOvzww02nTp0KPoeCEIAABMqDAMJceVClzupKQOs96vf8ueeeM1pvNZfpI5xevXrZ0JVrr712rqLkQQACEIAABCAAgVITQJgrNTpOhAAEIACB2kYAYa62jTj9hYCx4a6effZZs2TJkpw4NtxwQ/tlfYcOHXKWIxMCEIBARRBAmKsIylyjOhDQGnJaS85fCzat3a1btzYjRowwG220UVoR0iEAAQhAAAIQgECZEECYKxOMVAIBCEAAArWBAMJcbRhl+giBkgS0Hs17771n5s2bZwU6iXT169c3jRs3Nq1atTKdO3c27dq1K3kiKRCAAAQqicC0adOMv37WFltsYSQ6YBCoLQSWLl1qJk2aZF5//fW8Xa5bt64ZMGCA6devn9E+BgEIQAACEIAABMqbAMJceROmfghAAAIQqDEEEOZqzFDSEQhAAAIQgAAEIACBGkrgtddeMxMnTjTLly/P20OFnx4+fLhdPzZvYQpAAAIQgAAEIACBMiKAMFdGIKkGAhCAAARqPgGEuZo/xvQQAhCAAAQgAAEIQKB6Eli4cKENW/nhhx/m7YDWjxs0aJDp2bOn0bpyGAQgAAEIQAACEKhIAghzFUmba0EAAhCAQLUmgDBXrYePxkMAAhCAAAQgAAEI1EACq1evNgrfOnXqVPPTTz/l7eGWW25pBg8ebJo2bZq3LAUgAAEIQAACEIBAeRBAmCsPqtQJAQhAAAI1kgDCXI0cVjoFAQhAAAIQgAAEIFBNCXz22Wdm3Lhx5ssvv8zbg2bNmplhw4aZzTbbLG9ZCkAAAhCAAAQgAIHyJIAwV550qRsCEIAABGoUgTRhrkZ1ks5AAAIQgAAEIAABCNQKAqNHj662/VyxYoWZMmWKmT59uslkMjn7oVCVO+64oxk4cKBp0KBBzrJkQgACEIAABCAAgYoggDBXEZS5BgQgAAEI1AgCCHM1YhjpBAQgAAEIQAACEIBARKC6CnPvvvuuefjhh813332Xdxzbtm1rRowYYTp06JC3LAUgAAEIQAACEIBARRFAmKso0lwHAhCAAASqPQGEuWo/hHQAAhCAAAQgAAEIQOAXAtVNmPv+++/NhAkTzJtvvpl3DOvVq2d23XVXs8suu5i6devmLU8BCEAAAhCAAAQgUJEEEOYqkjbXggAEIACBak0AYa5aDx+NhwAEIAABCEAAAhDwCFQXYU6hKmfMmGEmT55sfvz/9u69JartjQP4o2kXM8qCkESKwiAICqMLhV2kG0rl9FJ6MedlOFlmREUUXQiEQCgIogsVUlEZ3Wuy3288nGMXdWfHtrP3fOaf9uz9jGs9n7X++7ZmPnz4poOJL9esWROFQiGWLl06cYG7BAgQIECAAIFZFhDMzfICGJ4AAQIEsiMgmMvOWpkpAQIECBAgQIDA1AJZCOaePXsWxWIx7t+/P3Uz/3/a0NAQXV1d0d7enlirgAABAgQIECAwmwKCudnUNzYBAgQIZEpAMJep5TJZAgQIECBAgACBKQQqOZgrlUpx6dKluHDhQnz58mWKLv5+tHHjxuju7o6FCxcm1iogQIAAAQIECMy2gGButlfA+AQIECBAgAABAgQIECBAgACBPyQw2X8uq9Rg7sGDB2On5J4+fZoo0tTUFD09PdHW1pZYq4AAAQIECBAgUCkCgrlKWQnzIECAAAECBAgQIECAAAECBAjMsEBWgrny78edOXMmrl+/nihQW1sb27dvj3379kV9fX1ivQICBAgQIECAQCUJCOYqaTXMhQABAgQIECBAgAABAgQIECAwgwJZCOZu3rwZJ06ciNevXyd23tLSEoVCIVasWJFYq4AAAQIECBAgUIkCgrlKXBVzIkCAAAECBAgQIECAAAECBAjMgEAlB3OvXr2KkydPxq1btxI7LZ+MK5+QK5+UK5+Y8yJAgAABAgQIZFVAMJfVlTNvAgQIECBAgAABAgQIECBAgECCQCUGc1+/fh37ysryV1d+/PgxoYOItWvXxpEjR6L8m3JeBAgQIECAAIGsCwjmsr6C5k+AAAECBAgQIECAAAECBAgQmESg0oK5J0+eRG9vbzx8+HCSGY/fbmxsjO7u7tiwYcP4TVcECBAgQIAAgYwLCOYyvoCmT4AAAQIECBAgQIAAAQIECBCYTKBSgrlSqRQXLlyIixcvxujo6GTT/fd+e3v7WCi3YMGCf++5IECAAAECBAjkQUAwl4dV1AMBAgQIECBAgAABAgQIECBAYAKBSgjm7t69G8ViMZ4/fz7BDL+/tWzZsigUCrF69ervH3hHgAABAgQIEMiJgGAuJwupDQIECBAgQIAAAQIECBAgQIDAjwKzGcy9f/8+Tp8+HYODgz9O66f3tbW1sXPnztizZ0/U19f/9NwNAgQIECBAgEBeBARzeVlJfRAgQIAAAQIECBAgQIAAAQIEfhCYrWBuaGgo+vv7482bNz/M6Oe3ra2tY6fkmpubf37oDgECBAgQIEAgZwKCuZwtqHYIECBAgAABAgQIECBAgAABAv8IpB3MjYyMRF9fX9y+ffufKUz679y5c+PAgQOxdevWKJ+Y8yJAgAABAgQIVIOAYK4aVlmPBAgQIECAAAECBAgQIECAQFUKpBXMjY6OxrVr1+Ls2bPx6dOnROt169bF4cOHY/HixYm1CggQIECAAAECeRIQzOVpNfVCgAABAgQIECBAgAABAgQIEPhGII1gbnh4OHp7e+Px48ffjDzx5aJFi+LQoUOxfv36iQvcJUCAAAECBAjkXEAwl/MF1h4BAgQIECBAgAABAgQIECBQvQJ/Mpj7/PlznD9/Pi5fvhzlE3NJry1btsTBgwdj/vz5SaWeEyBAgAABAgRyKyCYy+3SaowAAQIECBAgQIAAAQIECBCodoE/FczduXMnisVivHz5MpF4+fLlUSgUYuXKlYm1CggQIECAAAECeRcQzOV9hfVHgAABAgQIECBAgAABAgQIVK3ATAdzb9++jYGBgbhx40ai6Zw5c2L37t2xa9euqKurS6xXQIAAAQIECBCoBgHBXDWssh4JECBAgAABAgQIECBAgACBqhSYyWCuHMadOnUq3r17l2i5atWq6OnpifJpOS8CBAgQIECAAIFxAcHcuIUrAgQIECBAgAABAgQIECBAgECuBGYimHvx4kUcP348yl9fmfQq/35c+XfkNm/eHDU1NUnlnhMgQIAAAQIEqk5AMFd1S65hAgQIECBAgAABAgQIECBAoFoE/ksw9+XLl7hy5UqcO3cuSqVSItn69evj0KFDsWjRosRaBQQIECBAgACBahUQzFXryuubAAECBAgQIECAAAECBAgQyL3A7wZzjx49imKxGMPDw4lGixcvjsOHD8e6desSaxUQIECAAAECBKpdQDBX7TtA/wQIECBAgAABAgQIECBAgEBuBaYbzH369CnOnj0bV69eja9fv07pUv6qym3btsX+/ftj3rx5U9Z6SIAAAQIECBAg8LeAYM5OIECAAAECBAgQIECAAAECBAjkVGA6wdzt27ejr68vRkZGEjWam5ujUChEa2trYq0CAgQIECBAgACBcQHB3LiFKwIECBAgQIAAAQIECBAgQIBArgR+JZh78+ZN9Pf3x9DQUGLvdXV10dnZGR0dHTFnzpzEegUECBAgQIAAAQLfCwjmvvfwjgABAgQIECBAgAABAgQIECCQG4GkYG5wcDAGBgbiw4cPiT2vXr167JTcsmXLEmsVECBAgAABAgQITCwgmJvYxV0CBAgQIECAAAECBAgQIECAQOYFJgvmjh07FsViMe7du5fY44IFC6Krqys2bdqUWKuAAAECBAgQIEBgagHB3NQ+nhIgQIAAAQIECBAgQIAAAQIEMiswWTBX/krKUqmU2NeGDRuiu7s7GhsbE2sVECBAgAABAgQIJAsI5pKNVBAgQIAAAQIECBAgQIAAAQIEMikwWTCX1MySJUuip6cn1q5dm1TqOQECBAgQIECAwDQEBHPTwFJKgAABAgQIECBAgAABAgQIEMiSwHSDuZqamtixY0fs3bs35s6dm6VWzZUAAQIECBAgkAkBwVwmlskkCRAgQIAAAQIECBAgQIAAAQLTF5hOMLdixYooFArR0tIy/YF8ggABAgQIECBA4JcEBHO/xKSIAAECBAgQIECAAAECBAgQIJA9gV8J5urr68dOyJVPytXW1mavSTMmQIAAAQIECGRIQDCXocUyVQIECBAgQIAAAQIECBAgQIDAdASSgrm2trax35Jramqazp9VS4AAAQIECBAg8JsCgrnfhPMxAgQIECBAgAABAgQIECBAgEClC0wWzC1cuDC6u7tj48aNld6C+REgQIAAAQIEciUgmMvVcmqGAAECBAgQIECAAAECBAgQIDAuMFEw197eHl1dXdHQ0DBe6IoAAQIECBAgQCAVAcFcKswGIUCAAAECBAgQIECAAAECBAikL/BtMLd06dIoFAqxZs2a9CdiRAIECBAgQIAAgTEBwZyNQIAAAQIECBAgQIAAAQIECBDIqUA5mKutrY2Ojo7o7OyM+vr6nHaqLQIECBAgQIBANgQEc9lYJ7MkQIAAAQIECBAgQIAAAQIECExb4K+//oqjR49Gc3PztD/rAwQIECBAgAABAjMvIJibeVN/kQABAgQIECBAgAABAgQIECBQEQKjo6NjJ+YqYjImQYAAAQIECBAgEII5m4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBACgKCuRSQDUGAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAMGcPECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEhBQDCXArIhCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjm7AECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECKQgI5lJANgQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABwZw9QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAFAcFcCsiGIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICCYswcIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIpCAgmEsB2RAECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHP2AAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEUBARzKSAbggABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIBgzh4gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkIKAYC4FZEMQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEMzZAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRSEBDMpYBsCAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKCOXuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQAoCgrkUkA1BgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQDBnDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIQUAwlwKyIQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgI5uwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAikICOZSQDYEAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAcGcPUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgBQHBXArIhiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgmLMHCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKQgIJhLAdkQBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBARz9gABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBFAQEcykgG4IAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDA/wDLw8Gam9CEAgAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display < MRtransformation.png "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how using 5 as the decay constant creates the fastest rate of decay (light blue curve); MR scores ≤ approx 27 are included as edge weights. Using 25 as the decay constant creates the slowest rate of decay (light green curve); MR scores ≤ approx 130 are included. Using 10 as the decay constant puts you somewhere in between (dark blue curve). \n",
    "\n",
    "Different rates of decay result in different average module sizes. Fast rate of decay = small modules; Slow rate of decay = large modules. This makes different decays better for different metabolic pathways. If your pathway of interest consists of fewer genes, a faster rate of decay may work better for you, because the modules will be smaller and may contain less false positives. If your pathway of interest consists of more genes, or if you want to recover a large list of gene candidates, a slower rate of decay may work better for you, because the modules will be larger. \n",
    "\n",
    "That's why I recomment creating several networks with different rates of decay and comparing the results. We will do this now using the `create_network_and_modules.py` python script. This script calls the [ClusterONE](https://www.paccanarolab.org/cluster-one/) program by [Nepusz et al (2012)](https://www.nature.com/articles/nmeth.1938) to create modules of co-expressed genes, and parses the output into a tab deliminted file. \n",
    "\n",
    "Let's execute the script without command line arguements to print a usage description:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Usage: ../scripts/create_network_and_modules.py -i <mr directory> -c <cluster one jar> -d <decay> [-o <output base name> -w <min edge weight> -p <max pvalue> -q <min quality>\n",
      "    -i|--indir       <DIRECTORY> path to directory of gene mutual ranks\n",
      "    -c|--clusterone  <FILE> path to clusterone jar file\n",
      "    -d|--decay       <INTEGER> decay constant to use for calculating network edge weights\n",
      "\n",
      "    OPTIONAL:\n",
      "    -o|--out         <NAME> base name for all output files (default = same as input directory\n",
      "    -w|--minweight   <FLOAT> minimum network edge weight (default = 0.01)\n",
      "    -p|--maxpval     <FLOAT> retained modules must have p value less than or equal to this value (default = 1 to retain all modules)\n",
      "    -q|--minqual     <FLOAT> retained modules must have weight greater than or equal to this value (default = 0 to retain all modules)\n",
      "    -t|--threads     <INTEGER> number of forks to create when multithreading using os.fork (default = 1)\n",
      "    -x|--prefix      <NAME> prefix to append to all module names; helpful when comparing between different species/filtering options\n"
     ]
    },
    {
     "ename": "",
     "evalue": "2",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "python ../scripts/create_network_and_modules.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many optional command line arguments here. You can change the default edge weight cutoff from 0.01. You can also set minimum quality scores and p values for ClusterONE modules. \n",
    "\n",
    "Let's run the script with default settings, using 5 as our decay constant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "COMMAND: ../scripts/create_network_and_modules.py -i example/gene_counts_normalized_mr -c ../scripts/cluster_one-1.0.jar -d 5 -t 20 \n",
      "\n",
      "Creating output directory: /tmp/ABC1569546551.6862178 ...\n",
      "\n",
      "Writing network abc file to example/gene_counts_normalized_mr_005.abc\n",
      "Writing clusterone csv file to example/gene_counts_normalized_mr_005.modules.csv\n",
      "Writing parsed clusterone tab-delimited file to example/gene_counts_normalized_mr_005.modules.txt\n",
      "\n",
      "Parsing example/gene_counts_normalized_mr ...\n",
      "  thread 1 : Parsing 193 genes...\n",
      "  thread 2 : Parsing 193 genes...\n",
      "  thread 3 : Parsing 193 genes...\n",
      "  thread 4 : Parsing 193 genes...\n",
      "  thread 5 : Parsing 193 genes...\n",
      "  thread 6 : Parsing 193 genes...\n",
      "  thread 7 : Parsing 193 genes...\n",
      "  thread 8 : Parsing 193 genes...\n",
      "  thread 9 : Parsing 193 genes...\n",
      "  thread 10 : Parsing 193 genes...\n",
      "  thread 11 : Parsing 193 genes...\n",
      "  thread 12 : Parsing 193 genes...\n",
      "  thread 13 : Parsing 193 genes...\n",
      "  thread 14 : Parsing 193 genes...\n",
      "  thread 15 : Parsing 193 genes...\n",
      "  thread 16 : Parsing 193 genes...\n",
      "  thread 17 : Parsing 193 genes...\n",
      "  thread 18 : Parsing 193 genes...\n",
      "  thread 19 : Parsing 193 genes...\n",
      "  thread 20 : Parsing 175 genes...\n",
      "\tFinished!\n",
      "\n",
      "\n",
      "\n",
      "Combining temporary files:\n",
      " cat /tmp/ABC1569546551.6862178/*.abc > example/gene_counts_normalized_mr_005.abc \n",
      "\n",
      "\n",
      "Running clusterone:\n",
      " java -jar ../scripts/cluster_one-1.0.jar example/gene_counts_normalized_mr_005.abc --output-format csv > example/gene_counts_normalized_mr_005.modules.csv \n",
      "\n",
      "Loaded graph with 3842 nodes and 76764 edges\n",
      "[====================] 100% Growing clusters from seeds...\n",
      "[====================] 100% Finding highly overlapping clusters...\n",
      "[====================] 100% Merging highly overlapping clusters...\n",
      "Detected 490 complexes\n",
      "\n",
      "Parsing example/gene_counts_normalized_mr_005.modules.csv ...\n",
      "\tfinished!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "python ../scripts/create_network_and_modules.py -i example/gene_counts_normalized_mr -c ../scripts/cluster_one-1.0.jar -d 5 -t 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take a peak at the first line of the tab delimited output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N005M00001\t0.5577\t0.00000\tg0052 g0197 g0203 g0231 g0518 g0605 g0653 g0791 g0928 g0930 g0945 g0959 g0967 g1043 g1127 g1164 g1168 g1236 g1274 g1457 g1530 g2938 g2981 g3217 g3220 g3322 g3341 g3530 g3613 g3638 g3780 g3855 g3861 g3985 g4090 g4256 g4278 g4411 g4471 g4521 g4586 g4858 g4893 g0140 g0853 g1083 g1089 g1266 g2583 g2651 g3253 g4520 g4522\n"
     ]
    }
   ],
   "source": [
    "head -n 1 example/gene_counts_normalized_mr_005.modules.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns are ordered by \\[module ID, module quality, module p-value, list of genes in module\\]. Module IDs always start with the network decay constant. \n",
    "\n",
    "Let's run the script again, this time using a decay constant of 25. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "COMMAND: ../scripts/create_network_and_modules.py -i example/gene_counts_normalized_mr -c ../scripts/cluster_one-1.0.jar -d 25 -t 20 \n",
      "\n",
      "Creating output directory: /tmp/ABC1569546585.1479187 ...\n",
      "\n",
      "Writing network abc file to example/gene_counts_normalized_mr_025.abc\n",
      "Writing clusterone csv file to example/gene_counts_normalized_mr_025.modules.csv\n",
      "Writing parsed clusterone tab-delimited file to example/gene_counts_normalized_mr_025.modules.txt\n",
      "\n",
      "Parsing example/gene_counts_normalized_mr ...\n",
      "  thread 1 : Parsing 193 genes...\n",
      "  thread 2 : Parsing 193 genes...\n",
      "  thread 3 : Parsing 193 genes...\n",
      "  thread 4 : Parsing 193 genes...\n",
      "  thread 5 : Parsing 193 genes...\n",
      "  thread 6 : Parsing 193 genes...\n",
      "  thread 7 : Parsing 193 genes...\n",
      "  thread 8 : Parsing 193 genes...\n",
      "  thread 9 : Parsing 193 genes...\n",
      "  thread 10 : Parsing 193 genes...\n",
      "  thread 11 : Parsing 193 genes...\n",
      "  thread 12 : Parsing 193 genes...\n",
      "  thread 13 : Parsing 193 genes...\n",
      "  thread 14 : Parsing 193 genes...\n",
      "  thread 15 : Parsing 193 genes...\n",
      "  thread 16 : Parsing 193 genes...\n",
      "  thread 17 : Parsing 193 genes...\n",
      "  thread 18 : Parsing 193 genes...\n",
      "  thread 19 : Parsing 193 genes...\n",
      "  thread 20 : Parsing 175 genes...\n",
      "\tFinished!\n",
      "\n",
      "\n",
      "\n",
      "Combining temporary files:\n",
      " cat /tmp/ABC1569546585.1479187/*.abc > example/gene_counts_normalized_mr_025.abc \n",
      "\n",
      "\n",
      "Running clusterone:\n",
      " java -jar ../scripts/cluster_one-1.0.jar example/gene_counts_normalized_mr_025.abc --output-format csv > example/gene_counts_normalized_mr_025.modules.csv \n",
      "\n",
      "Loaded graph with 3842 nodes and 413058 edges\n",
      "[====================] 100% Growing clusters from seeds...\n",
      "[====================] 100% Finding highly overlapping clusters...\n",
      "[====================] 100% Merging highly overlapping clusters...\n",
      "Detected 128 complexes\n",
      "\n",
      "Parsing example/gene_counts_normalized_mr_025.modules.csv ...\n",
      "\tfinished!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "python ../scripts/create_network_and_modules.py -i example/gene_counts_normalized_mr -c ../scripts/cluster_one-1.0.jar -d 25 -t 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N025M00001\t0.3840\t1.51521e-08\tg1188 g2228 g1765 g0342 g1924 g1245 g0675 g0248 g0822 g2241 g3685 g2246 g4823 g4728 g3086 g2285 g3899 g0482 g1613 g2524 g0139 g1439 g4109 g2809 g3265 g4710 g1639 g2239 g4202 g3133 g2515 g4333 g1072 g0892 g3189 g2069 g4809 g1021 g0753 g4565 g2211 g4060 g2446 g3428 g2716 g4614 g0663 g1821 g3624 g3571 g2852 g3011 g2412 g2864 g3056 g2093 g0263 g0752 g4623 g3276 g3935 g3048 g2739 g2321 g0269 g1566 g3047 g1621 g1622 g3566 g4507 g3230 g1227 g0166 g3860 g4951 g1403 g2687 g4217 g4301 g4302 g2574 g1916 g1865 g1631 g4304 g0655 g2377 g2219 g3737 g3083 g2499 g0719 g2913 g4017 g2882 g0716 g0494 g4778 g4963 g4590 g0681 g3925\n"
     ]
    }
   ],
   "source": [
    "head -n 1 example/gene_counts_normalized_mr_025.modules.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the module IDs now start with 'N025' and the gene list is larger\n",
    "\n",
    "### That's it for now! I will be posting a second tutorial exploring the effects dataset size, variance stabilization, and gene filtering at a future date!"
   ]
  }
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