{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Standardized Uptake Value Ratio\n",
"\n",
"To illustrate standardized uptake value ratio (SUVR) calculation,\n",
"we will download an 18F-AV45 amyloid\n",
"PET scan from The Dallas Lifespan Brain Study via OpenNeuro.\n",
"This PET scan is reconstructed as a single time frame, so it is a 3-D image.\n",
"(We can still use _Dynamic PET_ to read it, but most of the functions\n",
"implemented in _Dynamic PET_ will not be relevant.)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"import requests\n",
"\n",
"\n",
"outdir = Path.cwd() / \"nb_data\"\n",
"outdir.mkdir(exist_ok=True)\n",
"\n",
"petjson_fname = outdir / \"pet_av45.json\"\n",
"pet_fname = outdir / \"pet_av45.nii.gz\"\n",
"\n",
"baseurl = \"https://s3.amazonaws.com/openneuro.org/ds004856/sub-1003/ses-wave1/pet/\"\n",
"\n",
"peturl = (\n",
" baseurl\n",
" + \"sub-1003_ses-wave1_trc-18FAV45_run-1_pet.nii.gz\"\n",
" + \"?versionId=qL.9p.hInakWrNSF1LeefT4VOIuBy6Xm\"\n",
")\n",
"\n",
"if not petjson_fname.exists():\n",
" r = requests.get(\n",
" baseurl\n",
" + \"sub-1003_ses-wave1_trc-18FAV45_run-1_pet.json\"\n",
" + \"?versionId=HvaYMcTWZjYwq6GVwjfeePZ9dKAtJlFM\",\n",
" timeout=10,\n",
" )\n",
" r.raise_for_status()\n",
" with open(petjson_fname, \"wb\") as f:\n",
" f.write(r.content)\n",
"\n",
"if not pet_fname.exists():\n",
" with requests.get(peturl, timeout=10, stream=True) as r:\n",
" r.raise_for_status()\n",
" with open(pet_fname, \"wb\") as f:\n",
" for chunk in r.iter_content(chunk_size=8192):\n",
" f.write(chunk)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At the time of writing of this notebook, this dataset is not PET-BIDS valid.\n",
"Trying to read it with the `load` function from\n",
"`dynamicpet.petbids.petbidsimage` will fail.\n",
"Because of this, we need to fix the json first.\n",
"\n",
"We can first read the json using the `read_json` from the same module.\n",
"`read_json` does not perform any validity checks (and does not look at the\n",
"corresponding imaging data at all)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from dynamicpet.petbids.petbidsjson import read_json\n",
"\n",
"\n",
"json = read_json(petjson_fname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The problem with this dataset is that the image contains only a single time\n",
"frame, but the json indicates two time frames, with the second one having a\n",
"duration of 0."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FrameDuration: [600000]\n",
"FrameTimesStart: [0]\n"
]
}
],
"source": [
"print(f\"FrameDuration: {json[\"FrameDuration\"]}\")\n",
"print(f\"FrameTimesStart: {json[\"FrameTimesStart\"]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We modify these tags by keeping their first element only."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FrameDuration: [600000]\n",
"FrameTimesStart: [0]\n"
]
}
],
"source": [
"json.update(\n",
" {\n",
" \"FrameDuration\": json[\"FrameDuration\"][:1],\n",
" \"FrameTimesStart\": json[\"FrameTimesStart\"][:1],\n",
" }\n",
")\n",
"\n",
"print(f\"FrameDuration: {json[\"FrameDuration\"]}\")\n",
"print(f\"FrameTimesStart: {json[\"FrameTimesStart\"]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that the frame timing information is fixed, we update the json file using\n",
"the `write_json` function:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from dynamicpet.petbids.petbidsjson import write_json\n",
"\n",
"\n",
"write_json(json, petjson_fname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, reading in this dataset will work:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from dynamicpet.petbids.petbidsimage import load\n",
"\n",
"\n",
"pet = load(pet_fname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To calculate SUVR, we need to specify a reference region.\n",
"Usually, some type of cerebellar reference would be used for\n",
"18F-AV45.\n",
"In this notebook, however, we use an (approximate) whole brain reference region\n",
"for simplicity."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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2iuF+9dVXO8tf03ohdXn8+PER2/3a6R07dnh11c8wlg5uvPnX5l4bU/2u0iKEMcZT/eM1qv0M99LSUvP111+b008/3QBVIxX2M2h38l1/Z511VmCeuo7HCw13/vEvef92SzhIv/jRv/vd7/D3v/89Yt+XX36JTZs2YciQIWjTpk3UOVlZWejbty/efvtt/Pjjj+jRo0fMfCsrK/Hhhx+iSZMmOOWUU3yPOe644/Dmm29i/vz5nu+hcMYZZ0SVva5pnnzyyVHHt2rVCi1btsTPP//sbSsvL/fif19++eUxrxOA57s5bNgwhMPRUxXE533+/PmBadmcdNJJaNKkiTM/7WMrHHfccXjssccwf/58nH322SgrK8Pnn3+OUCjkhUjT+bRs2RJbtmyJq1yzZs0CAIwaNSpq37777ouTTz4Zb7zxBv7zn/8gPz8/Yn+8v0EQMj+jRYsWMY/T+QNAWloaRowYgT//+c+YNWsWBgwYELHfdd+FHTt2YNq0aVi0aBG2bNnirYnw888/wxiD5cuXo0+fPnFdR+fOnX3r0gEHHOCl6UfLli0BABs3bowrH+GII46IumfNmjVDy5YtsXnzZt/fRyae+5Vl7dq1mDZtGpYsWYLCwkLPvzo9PR0//vhjxLF9+/bF4sWLcdFFF+H2229H3759feuLzc6dO3HGGWfggw8+wJgxY/DUU08hJSUl7uudPXs2AODUU0/17pnNRRddhEceecR7poGqUIJnnXUWXnzxRbz99tsR9eyf//wnAODCCy/0ttW17TzzzDPjvh6bRYsW4f3338eqVauwa9cuGGOwfft2AIi6934sWLAARUVFOOqoo3wnoF9wwQWYOHFi3OWpzb0W0tLScMIJJ0RtD6oHLlatWuX77mvTpg1ee+01NGvWLGpfrHCQ8dZnQsjexW6N415cXIyvvvoKS5YswZNPPoljjjkmYhKpTEr84IMPAheL2bRpU6DhvmnTJm+yWXp6euCxmk6dOtV7mh06dPA9Njc3N8Jo3bx5M4qKitCyZctAwxCovne33XYbbrvtNudxxcXFgWnZ+N0DO7/27dvHPF/uwebNm1FaWop99tkHmZmZzrziNdzXrVsHAM5Jq7J97dq1Ufvi/Q2C2LZtm3deLDp37hyzjHItNq77DgAfffQR8vPzYxrMYjzFQ6z7AcA5Ma9p06YA4JwE6sL1zOTk5GDz5s2++2VNBl2WRx55BDfffHPci7ndd999+OabbzBt2jRMmzYNLVq0wIABA3DmmWfiwgsv9H02//znP6O8vBynnXYannnmmRovZFXbZ3XUqFF48cUX8c9//tMz3Dds2IAPP/wQHTp0wPHHH+8dW9e2M9bz5kdpaSnGjBmDl156yXlMPM+g3JugOhIvdWkX8vLyfDtkQfXAhR3HPSUlBc2bN0efPn1w9tlnOzvll112WVRgBUKSgZtvvhnDhw/HgQceiKKiInz22WeYMGGCM2ACAJx99tm49dZb0a1bN6SlpeHHH3/Eww8/jClTpuzWsl511VW48cYbkZeXh6+++grXXnstPv/8c99j33nnHQwdOhTDhg1L2EhQu8Vw16u2Pfjgg7jppptw9dVX44QTTvAabVHKunXrhmOPPTZmmq1atQrMV9LLycnBOeecE/NYiXJj4/cSr2uaQepebZFyDRgwoF5DJ7qMbMnPb7EPm379+tVbWWpCLOOlvn4DUcxqYiTHi+u+79ixA+eddx62bNmCO+64A/n5+ejcuTOysrIQCoUwcuRIvPTSSzWKxlHb+yEdl6AIJDXNL97yzJ07F9dffz2aNWuGSZMmYdCgQcjLy0NGRgYAoF27dlEqaceOHfHFF1/go48+wltvvYWZM2d6RvwDDzyAOXPmRLUtQ4cOxaeffor3338fr732mu+IUV1wPasnn3wy9tlnH7z99tvYtm0bmjVrhpdffhkVFRW44IILIu5TXdtO1/Pm4pFHHsFLL72EQw89FA888AD69OmDFi1aIC0tDUuXLkWPHj1qHRFmd9IQ7YLQunVrrlhK9hoGDhyIxx9/HJ9//jlSU1Nx33334f3338fBBx/sG9EKqIomde+992LJkiUoLS3FGWecgWeffRa//PKLMwJUEKNHj/bCrvpx3nnn4ZFHHsEVV1yBefPm4brrrsN7772HHj16RIlh1113XUK2Y5rdYrhrbrzxRsyYMQPvv/8+7r77bjzzzDMAqpW/Aw88sF4avNatWyMzMxPhcBjPPvtsvSz5vjvSdOWTlZWFLVu2YOvWrYHGkdy7YcOG4frrr98tZdL5LV++HA8//HBcnahWrVohLS0NmzZtQnFxsa+hUJPwaO3atUNBQQFWrVqFgw8+OGp/vCMCdWHfffcFgECVftWqVTG3t2vXLu48Z82ahc2bN2PEiBG4++67o/avWLEi7rTqyq+//goA2GeffRosT5t///vfAIB77703qgNZVFSE9evX+56XmpqKk08+2XPJWbVqFcaOHYuPPvoIEydOxAMPPBBxfJ8+fXDbbbfh5JNP9gxml4uYH/L7up4D17OampqK888/H3/961/x2muvYezYsb5uMkD9t51ByL1/6aWXogSKmjyDbdu2BRBcR+KltveaEFI3hg4dGvF9zJgx2LhxI/r27evrmgYAM2fOjPj+2GOPYfTo0RgwYIBnuKenp+Pee+/FBRdcgObNm+Pbb7/FhAkTos6Nl/Hjx+PJJ5/02skrrrgCp59+OsaOHRvhlnf44Yfj+uuvxxFHHOF8lyQKDRbHXZZ7njx5stfIHnnkkWjWrBlmzpxZI5cFF6mpqRg0aBAKCwvx4Ycf1jm93ZWmHykpKRg0aBCA+GJlDxkyBED1C3V3U9P80tLScNRRR8EYg9dffz1q/0cffYTNmzfHnf9xxx0HAL5D9Rs3bsR7772HUCgUqD7WhcMPPxwA8MMPP8Q87l//+lfUtvLycm+Jcu3fHgsxlv3cW5YtW4Yvv/wy7rTqyuLFiwFULcHeGMS6F6+88krcSknnzp0xYcIEAMC3337re0y/fv0wffp0ZGVlIT8/v0ZDpvL7Tp8+3detSIaF5Zm2kTkcL774In788Ud8/vnnOOSQQ3DYYYdFHFffbWcQse693/Puom/fvsjKysKCBQt8Df6XX365RuWqy70mhNQfMiJdk/boxBNPRI8ePfDpp5962/7617+if//+yM/Px2GHHYZXXnkF06dPd84FiUVaWhr69u2LGTNmeNuMMZgxYwb69+/vbcvKysKLL76Iq6++Ghs2bKhxPg1NgxnuvXv3xrBhw1BeXu4pXBkZGbjpppuwfft2DB8+3LchX7t2LSZPnhx3PrfddhvC4TAuueQSb7KnzY4dO/DMM8+gqKioUdP0Y8KECQiFQrj33nvx8ccfR+wrLy/HO++8433v168fhgwZgv/85z+4+uqrUVhYGJXeV199henTp9epTML111+PrKws3HDDDb6GeElJCV599VVvVVugqmcLAHfccUeEj+mWLVtw44031ij/q6++GuFwGI899hi++OILb3tpaSmuvfZaFBUVYfjw4ejYsWNNLy1u5OXv8o0TZs+e7Y0qCXfeeSdWr16Nww47rEZGhEyUe/311yOG9bZu3YpLL700bl/vulJcXIxvvvkGHTt2RJcuXRokT43ci6effjriur///nvPENc8+uijvuqJ1KVYz0v//v0xffp0ZGRk4LzzzsO0adPiKuf++++P008/Hdu3b8e4ceMiyjpnzhw88cQTSElJwdVXXx117tFHH42uXbvi448/xkMPPQTAf0L27mg7YyH3/m9/+1vE9ldffRUvvPBC3Onk5OTgoosuQkVFhVdvhS+++AJ//etfa1SuutxrQkj9EAqF8Oc//xmzZ892LsYoNG3aFNu3b0dpaSnefvttXHvttZ5h3bFjR1xyySU499xzMXv2bKxYsQIPP/wwZs+ejUsuuaTG5WrdujVSU1OjjPENGzYgLy/P+/7oo4/is88+w5tvvlnjPISMjAzk5ubG/ScunrUlrvAzdY3jbowxixYtMqFQyGRmZnqxcysqKsxFF11kAJj09HRvkZThw4ebnj17mlAoZA4//PCIdGKFgzTGmCeeeMJbfOOQQw4xw4cP9xZ+kQWEfv31V+94CRsYK/xXfafpF67PGGMefPBBL0zeEUccYS644AIzZMgQ5wJMEtarefPmZtCgQWbkyJHm9NNP9xZhGTdunPOabILuqTHGTJ061VtwpVu3bua//uu/TH5+vjnuuONMdna2ARARL9legCknJ8ecddZZZvjw4aZFixbmiCOOMEcffbQBYNauXRuRD+AfDu7ee++tCoWUmmpOOukkk5+f711n9+7dnQsw1fQ3cLF9+3aTk5NjunXr5rtfQrxdeeWVJhQKmaOOOspccMEFpmfPngaoCotohww1Jr77PmTIEO83HjZsmBk2bJhp3ry56datmxeqVIdNjBUOcuDAgb75xLpfM2bMMADMFVdc4SynRsJB+i3O4ypjrLJs2rTJ5OXlGaBqbQBZuCctLc2ce+65vuk1a9bMhMNh07t3b3PeeeeZc8891xxwwAEGgGnZsqVZunSpd6zrt5g1a5bJzs426enp3qJYQfz000+mS5cu3rOcn59vBg8e7LUhDz/8sPPcO+64w2tLQ6GQWbVqle9xtWk7JRykDsFq41f/Zs6c6ZW9b9++5oILLvDit99www2+z5Xrfm7evNn06NHDADBt27b1FhxLTU01V199tbP+u6jNvY6VRzx10iZoASY/pK049thjvUUE/f5cvz3DQfIvkf7+93//1xQUFJj27dsHHhsKhUzXrl3N4YcfbsaPH29+/fVXr1067bTTjDFV71r7r7S01Lz88ssGgOnYsWPEvqKiIlNeXh6xTRbNa9u2rTHGeLaG/E2cONHMnTvXADD/9V//ZZYuXerZMEBVnZJ3azx/GRkZNa6D69at82zHWvzFd2B9GO7GGDN8+HADwNx4440R29944w1z+umnm3333dekpaWZfffd1/Tt29fcdNNNZsGCBRHHxtOwLly40IwePdp07tzZpKenm+bNm5uePXuasWPHmrfeeisiXns8hnt9pxnLaPn000/N2Wef7d2Ltm3bmsGDB/vGuy4qKjKPPfaYOeaYY0yzZs1Menq66dixoxk4cKB58MEHIxY+ikW8L6tly5aZq666ynTv3t1kZmaa3Nxc06NHD5Ofn2/+9a9/RSyAYkxVPOP777/fdO/e3aSnp5v27duba6+91hQWFppu3bqZUCgUtboj4H4JvvXWW2bw4MHetXbr1s3cdNNNUXHCjal/w92Y6lUQ58+fH7VPXsYff/yxmTZtmunfv79p0qSJadasmTnrrLPMd999F3VOPPd9165d5rbbbjPdu3c3GRkZpmPHjuaKK64wmzZtisgz6NrqYriPHTvWAIiqi7Gob8PdGGPWrFljRo4cadq3b28yMzPNQQcdZP70pz+Z8vJy3/ReeOEFM3LkSNOjRw+Tm5trcnNzzcEHH2zGjx9vfvrpp4hjY/0WM2fONNnZ2SYjIyPuRc02bdpkrr/+etO1a1evvTj55JPNe++9F/O8H374wWtLjz/++MB8atJ21tZwN6ZqIbATTzzRtGjRwuTm5ppjjjnGvPbaa87nKtb93LRpk7nyyitNXl6eycjIMAceeKB56KGHTGVlZcz676Km9zpWHg1puAf96YWjdJ41bb8EGu78q6+/v/zlL2b16tXeQmM1/XvyySfN9OnTDQBz3nnnmbKyMnPAAQeYrl27Rvy1adPGADApKSkR22+88UYzd+7ciG0tWrQwAExaWpopKyuLMsKfe+45M3XqVAPAPProo6aiosKUlZV5f8YYU15eHrGIYKy/3NxcY0yVWBnvnzHGWyW8Fn/xHRiP4U5IvKxZs8akpqaaAw88sLGLUiNk4atrrrmmsYvSYOzatcs0b97cHHXUUY1dFEJIPUDDnX/18feXv/zF/PTTT6Zbt261TuPpp5/2DOTu3bsbY4wZMGBA3OePHj06poE9d+5c89hjj3nfQ6GQWbNmjbfYW5s2bUzPnj0j/owx5tprr427MyKGe0VFRdx/xtTecG+QqDJk7+Xrr7/GQQcdhLS0NG/bhg0bMGbMGJSXl0dFy0h0evXqhXPPPRfPPPMMbr/9di/SzJ7M3/72N2zduhX3339/YxeFEEJIAvD4449j5MiROOuss7B9+3ZvIbht27Z568c8//zzWLt2LW699VYAVbHfv/jiCyxfvhwZGRk47bTTcNFFF+HKK68EULWI25QpU/DCCy/g+uuvx8KFC7HPPvtg8ODB+PrrryPm+cXLI488gueffx5ffPEF5s+fj+uuuw7Z2dl49tlnAVTZI34TUlevXu1FpYoX00ChJGm4k93KTTfdhPnz56NXr15o06YNfv75ZyxYsAA7duzAkUce2SChLOub+++/H1OnTsUDDzzgTSDcUykqKsIDDzyAoUOH4sQTT2zs4hBCCEkArrrqKgDRIR7HjBmD559/HkDVQm+y5gRQtUjZ//7v/6JDhw4oKirCkiVLcOGFF0ZEprrkkkvwP//zP3j44YfRvn17bNq0CXPnzsVbb71Vq3L+61//wj777IM//OEPyMvLw6JFi3Dqqad6K6HXJw1luIdQJb0H0rt37wYNPUf2DF5++WU8++yz+Oabb7BlyxakpKTggAMOwIgRI/D//t//c64oSAghZPfQp08fLFy4sLGLQcgeQW5uLgoLC1FeXh73OampqV6EnZpCxZ3sVvLz85Gfn9/YxSCEEEII2W3QVYYQQgghhJAkgIY7IYQQQgghSQANd0IIIYQQQpKAhjLcww2SCyGEEEIIIaROUHEnhBBCCCGkDiScq8ymTZtQVFSErKys3VkeQgghhOwmioqKsGnTpsYuBiF7HAlnuK9ZswY9evRA69atd2d5CCGEELKb2LRpE9asWdPYxSBkjyPhDHegynhnhSeEEEIIIaQaTk4lhBBCCCGEeHByKiGEEEIIIXUgIV1lCCGEEEIIIZHQcCeEEEIIISQJoOFOCCGEEEJIEsDJqYQQQgghhBAPKu6EEEIIIYTUAbrKEEIIIYQQkgTQcCeEEEIIISRJiMd4D4VCdcqDhjshhBBCCCF1wBjTIKo7DXdCCCGEEELqQEMZ7owqQwghhBBCSBJAxZ0QQgghhJA6QFcZQgghhBBCkgAa7oQQQgghhCQBNNwJIYQQQghJAjg5lRBCCCGEEOJBxZ0QQgghhJA6QFcZQgghhBBCkgAa7oQQQgghhCQBNNwJIYQQQghJAjg5lRBCCCGEEOJBxZ0QQgghJAEJh6v01crKykYuCQmCrjKEEEIIIYQkATTcCSGEEEL2Yqi0Jw803AkhhBBCCEkCODmVEEIIIYQQ4kHFnRBCCCGEkDpAVxlCCCGEkAYkFAoBQIMYYMlISkoKAKCioqKRS5J40HAnhBBCCCEkCaDhTgghhBDSgFBpj41LaWe8+SrieX5kVKe20HAnhBBCyB4NXWB2L9pg3xvvd7yKe13vCaPKEEIIIYQQkgRQcSeEEELIHs3epPwmAnvj/abiTgghhJCEYPTo0TDGoG/fvo1dFELqFXm25a+srAw//fQTnn32WbRr1y7udOw0gv7qAhV3QgghhJAEYG/0DU8Ubr/9dhQUFCAzMxNHH300xowZgwEDBuCQQw5BSUlJ4PkNpbjTcCeEEEIIIXs17777LhYsWAAAePrpp7Fp0ybcfPPNOPPMM/HKK68Enk9XGUIIIYSQvYiGigVOgpk1axYAoGvXro1ckkiouBNCCCGEEGKx3377AQB+/fXXuI6nqwwhhBBCCCENQLNmzdCqVStkZmaiX79+uPPOO1FcXIy33norrvNpuBNCCCGEENIAfPjhhxHfCwoKcOGFF2Lt2rVxnU/DnRBCCCGE7NU0VKSdq666CkuXLkWzZs0wduxYHH/88XFFkxFouBNCCCGEENIAzJ8/34sqM3XqVMyePRsvvvgievTogZ07dzZy6aphVBlCCCGEEJKQNEakncrKStxyyy1o3749rrnmmrjOaagFmGi4E0IIIYQQYjFz5kzMmzcP1113HTIyMgKP58qphBBCCEkoxo4di1NPPTVq+6RJk7Bjx45GKBFpTPb0lV4ffPBBvPrqqxgzZgz+/ve/xzyWPu6EEEIISSiuuuoq3+3PPfccDXeyx/H6669j2bJluOGGG/Dkk0+isrLSeWxDGe4hAHtmN4kQQgghex17ugrsh1wzsHddN1B17Y15zbm5uSgsLMSiRYtiGvZCOBxGr1690LRpU2zfvr3G+VFxJ4QQQgghpA7QVYYQQgghex11VcxjnbenqvF72vXUhES5dhruhBBCCNnr2J2GWKIYeQ2B7T4D7F3X3hjQcCeEEEIIISQJoOFOCCGEEEJqBRX2hqch7jkXYCKEEEIIISQJoOJOCCGEkHplT50ESogLusoQQgghhBCSBNBwJ4QQQkhSQqW9bjAiTPJBw50QQgghhJAkgIY7IYQQQsheRH3ODaBqv2fCqDKEEEIIISRujjvuOLz55ptYu3YtjDE466yzYh7/7LPPeoq0/fftt9/u1nKOGDECixcvRlFREb7++msMHTrUeewTTzwBYwzGjRtXq7z8rs/1VxdouBNCCCGkVoTDYYTDNCVqSygUilDGtWEn+/Vxru0NRXZ2Nr766itcffXVcR0/btw45OXleX8dOnTA5s2b8corr9S6DAMHDkRBQYFzf//+/fHSSy/h6aefRu/evTF16lRMnToVPXv2jDp22LBhOProo7F27dpal4eGOyGEEEIISTimT5+O22+/HVOnTo3r+MLCQmzYsMH7O+KII9CiRQs8++yz3jGhUAg333wzVqxYgV27dmHRokU455xzal3GcePGYfr06XjooYewZMkS3HHHHfjyyy9xzTXXRBzXrl07/OUvf8GoUaNQVlZW6/xouBNCCCEkoamsrERlZWVjFyNpCTLkXAZfPIZgfRqL9c2ll16KGTNmYPXq1d62W265BRdffDGuuOIK9OzZE48++iimTJmC448/vlZ59O/fHzNmzIjY9t5776F///7e91AohMmTJ+PBBx/E999/X7uL+Y2GMtw5OZUQQgghhDQIbdu2xdChQzFy5EhvW3p6Om699VacdNJJmDt3LgCgoKAAAwYMwH//93/j008/rXE+eXl52LBhQ8S2DRs2IC8vz/s+YcIElJeX47HHHqvl1TQ8NNwJIYQQQpII8WvXUWgSTVn3Y/To0di6dWuEm023bt2QnZ2NDz74IOLY9PR0LFy40Pu+fft27/+UlBRkZGREbJsyZQquvPLKuMrRp08fjBs3Dn369KnllUTCcJCEEEIIIWSPYuzYsZg8eXKEP3lOTg4A4PTTT4+aIFpSUuL936tXL+//fv36YeLEiRg0aJC3rbCw0Pt//fr1aNOmTURabdq0wfr16wFURcbZd999I9x1UlNT8fDDD+O6665Dly5danRdNNwJIYQQQvYi6hohJhQKJbTqPnDgQHTv3h1PP/10xPbvv/8excXF6NSpU0y3mOXLl3v/d+jQAeXl5RHbbObMmYPBgwdj0qRJ3rYhQ4Zgzpw5AIDJkyf7+sBPnjw5YtJsvNBwJ4QQQgghCUd2dja6devmfe/SpQsOP/xwbNmyBWvWrMF9992H9u3bY/To0RHnXXrppZg7dy6+++67iO07duzAQw89hEcffRThcBizZ89Gs2bNcOyxx6KwsBAvvPBCjcs4adIkzJw5E+PHj8fbb7+N/Px8HHHEEbj88ssBAFu2bMGWLVsizikrK8P69euxdOnSGudHw50QQgghZA8llroepLxrn3Yd493eF5RGbTjiiCPwySefeN8fffRRAMBzzz2HSy65BG3btkWnTp0izmnatCnOOecc5wJHt99+OzZu3IhbbrkF+++/P7Zu3Yovv/wS9913X63KOGfOHIwcORL33HMP7rvvPvz4448YNmxYVKch2QgBSNwxFUIIIYSQPZD6MNxjHb87DXdSTW5uLgoLC/Hxxx+joqIi8PiUlBSccMIJaNq0acTE2nih4k4IIYQQJ/EquCQ2fsa1rDor+/R3fZwrLb+oMvK/GJNB8fb5+9YNusoQQgghpNGhQVc7tHEt31NSUgAAaWlpSE9PB1AVzUS22ZSXl/ueK59ijPsZ57JN0pB98inb5Tj5nbmgVu2g4U4IIYQQQkgSQMOdEEIIIaSWNHRoRO3uotVxUdVFZc/MzIxS2OVcUb0lTa2Wy6dfXtqtRvtdy7mlpaUAquOk23HV7eNIfDSU4R4OPoQQQgghhBDS2FBxJ4QQQsgeR0Op7aJwaz91+RSFPSMjI+J4v/KJ6i3+56KKax92OVfnnZKSEvG/vU9PfJVySTmLioqiyqD930lsGuI+0XAnhBBCCCGkDtDHnRBCCCENBsM+1gztsy7fteItn34hHEVZ1xFe9KcrMoykKSp5OByOKodW/gUpl2yXtOwyif+7KP/0e3dDw50QQgghhJAkgIY7IYQQQhoMKu3xIQp2ZmYmALfCLp+CKNlyn0tLS6Nip7t82QW/hZY0Oi3XYk5aPZftto++qPG7du0CABQXF0ekTRoeGu6EEEIIIYTUASruhBBCCCEJgijrorRr33CtYLv8wbW/OhCspOuVU3VkGju6jFbY41Xx5Tz7U/LLzs6O2CfKu1wLoeFOCCGEEEJIUkDDnRBCCCEkAUhJSYlS2kWN1kq2RHjRKrr+1Mo4gKhVT3XarjTs7VqNl0+tvLtWYbWj0WgffL3SK5X3arhyKiGEEEIIIcSDijshhBBCiA+iQmdmZiIrKwtAtWKqfdVdirb2HbfRvuvaX97lP6+VcDttV35ByrvfdlHYdZpyL+T+7Ny5MyKNvZGEdZUJWqAhJSXF+cPVZXGHui4MIeenpqZ6w1i7E1d547kOGabSQ09yrl5OWSqQHJ+bmxt1vvwvlUuOkcUVtm/fDiA6fFTTpk0BAN27dwcALF26FEBVGKsWLVoAAA444AAAwNq1ayOuTe6zlHPr1q0AgB07dkTsB6KH6lyTeupzgZA9cbER1xApqT166FqeZ5msJcuYyzMrQ8c5OTno2LEjgOrJbF26dAEAFBQUAAC++eYbAECPHj0AVNeNX3/9NSLNvLw8AMCyZcu8uqqHymX4vlWrVgCAX375JSINvfy5tBuVlZXe/9IubN68GUB1+6DvhSzGIvdAyrdq1aqoJdLlfgRNuqtLm9jYz7kr3N7ebMQQsreRsIY7IYQQQsiejO4QS+cbiF7NVMdn12nIpxYBbFw+7C6fdy3S2HkF+cXr8mnBzF7FVa5Vx6bXAoAIcUEd9T2ZhDXcXRnGeiCDzq1LvoKoYC41XcrXUBMoXOUNuo5QKBSl0uihNGlAmjVrBqBa9ZJrk3sg27dt2+Yp7aIOCrLdNaRXVFQEAFiyZAmAyKE02bdmzRoAwD777BPxXdRDqeAtW7aMOG7jxo1emqIiNuRyyntiw0Klr+7IPdT1Tl7grsVXBKmHpaWlWLZsWURauh5JXV29ejWA6OFmyVMU+qKioqhl1iUNyVfqlaCH9f1e9KKsS52W766l2uUe6Jd4mzZtvHZn06ZNEZ9STr+h/SBc7Xai1GFdDtY/QvY+EtZwJ4QQQgjZk9GuqKmpqVFuYPq7IJ1/rVK3La5ylVuXscI7T6vcumOtlXXd8dVx3duXdvXSF1zx27XgKmna16XLpUcCRAzULrsN4ZK8t1Jvhnt9KKV18VcMUjjs/fXtBxwKheolDY1Ws8T/NCcnB0B1uV1LEUvDs3PnTs8nVYepkmP1Use6XPYcATlfVLmff/4ZQLVfrP4t9DCclFPStCu4azKN/q6P8wuJtacRT/1wvUjqO5+GTEvqQUOomOFwOOpFKPVNXky6HPrFJkr89u3bvXonaeqXmeQhI066Dkvdtg0B25Cwz5F7LedIXZbj9PwVuy3Q5ZBrkXO1kaBdBCTtrKws7xxpH5o0aQKg2i9ePnUbVBMSxbfdRdBcHULInkfCKu51mXQZRF3OrUkDWZuh2ljY5XalGWRw2kgPVoac9VC9vPjkpSkuKXpGuG34y0tc97x1r167Ccjx8hK2e9naCNF+f1oh0MaAGCDZ2dnexFUdE1b/rq4Z9kKivsjrg9o0CLUxIOLJJ976Xh+/R0MY7LYbml4NUeqjfG7btg1AtVuJq7NklztIJdMTuWN1wKT+S3kkLT2RVI6TPKQ9ke1yvL06op4EqmM364687BeDf+fOnV4HXvsIy6fOQ8qjr9mvXU2WjjkN9uRFv5/syedaTdadYZcfuCjtEoC7Q1k3AMD6rJVRkV30u7ldyf4R5fspbVnEd8mrfWlXLw/5X451uTK7Vkz1q2e6o63bFymHnqy/N7mNJazhTgghhBCyJ6FdP+RTjNnS0tKoTqYeidYLGInRbeAvqLQr2R8/ZxZEbMsr2i/i2FBKpFHdqaIqips3SbUi0nAOh8OeYdixvHvEsdqFxjUB1h5Z00am7mhIZ0bPvZF7JALB3tCZTVjD3TX5rb4UkNGjR+O5557DEUccgQULFtRLmi52h2oTrwKpe7h2qMfmzZsDcE+SkzSkQmh1TK/qlpKSEhXGTZDKJp8uFV271JSVlUX1yiUNcRXQilphYWFEGnJ8kyZNvBB2cg1a1dTXmGwKHFD9bAvl5eXYsGEDPvjgA9x2221Yt25dvecZq7FMdHeD+sQVrk+2yzObk5PjPZficiL3UEaFZLvUFV0vddQHv/xdcaBjxXsGquqhVvu04iXnStqyX1+PXQ91vqLOSxujr9V2mbNJS0uLUPL9rk0QFxq599I+SLnt4/eGZ5QQktwkrOFOCKkbt99+OwoKCpCZmYmjjz4aY8aMwYABA3DIIYdEuTsQQgjZfej1UbSrnK2y606v7iyLQdZmV+eq7+HYLsXGmOpjRf1OiRQVXJ14LT5I+cPhsNPVTkYARHnX7jjy/Zfs1V6arpCX2hXGNeogx+moUnsiCWu4703+ShqXslYTdIW3K5t8l8ojRpw0HFpJEyQNOU7KZU8s02loxUyHd3P5ndph6vQkM+3zptV63QDJ9VVUVHjlEeVdFD/xeZfFZEQ1rM2D35ATxmKp2e+++643mvT0009j06ZNuPnmm3HmmWfilVdeqdf8Bb9y1GWeSrIroK6XdXZ2dtQQr4z6aN9sSUOUY6k7MufEfoHqcLV6HoquM67JoPZEUnvEyj7GFYVCyiXXar9Q5RwZ7dOT2F0jhH4rLUo55Jp1XGd9z/VInRxvGwaNOTq0N41MEUISHyruhDQys2bNws0334yuXbs2dlEIIWSvQkdUk86y32JEOpiD7sh6E0mVSB4rkp1rwrruMOpOsmtRp8rKSu8cPSIgaXgTWVUZhH12VK34/HNmQZTQpQVMO1+7PK5PKu50lWlQ6mO0QYdX05EXcnJyohZBEfSQlWsmu98Qlky00Sq4ngkueYviphV6exEa3Rhoxd81S96v4ZH8xI9YQvDpRaeCJrzEqhCJurjTfvvtB6B6mfuGzr8u5yQL+jm2fdqByJefLILkimykn+dYKrp+fvVomY4MI/gZDYJcg4xKybHah12nKfhdl15wTUfU0cPderRNsOfQ6PZIGyCirMv16Htltyf1FW6X9YIQsrug4b6HoY1We2geqH5p7dq1y3sBa+NacPVk9YvS/i6hrVpuaweg+sHZlPUTALf7jbxEJZyVqfjtgSuqDjXlCiGnOxaxfPV03GoxEKTc4g4geUn4OTE0kqkX36xZM7Rq1QqZmZno168f7rzzThQXF+Ott95q7KLtcehnUJ4fra7ZbjE6zKOrw6k7s7puh8Nhz38VVV4q2JXyQ0S+us6KEiZ5rEn9EUD1Gg72ysraNU3HT5fjpO64FluxjxV07HwdfcLl5xoKhaJWqdaGu+4EaBcfvfCN7X4j1PTFR+ObaPTzpt1HtcJtn+OMHhP2dy3T5wvGGKfibh+jy2Hj1ynVqznrtsslrul6kle0n/eedy3iJOj2UoegtvfvyfWxIa6NhjshDcyHH34Y8b2goAAXXngh1q5d20glIoQQQkhdSFjFnRN14rsH2g9MD9HrRUnshR2khyrD1DoN7e7iCstoh3gUnzVRBKR8EudVetU6JqvsD6VFXnNlZSW64CAAwKrQDxH3RX+6Zt7baWn3H60qyn2TXrwokHKvXJNpE5GrrroKS5cuRbNmzTB27Fgcf/zxjCazm9Cqmjw3oubKfRe3jZKSkqhnSCvqgq7TWqnPK9oPGZmRriZdTc+qtCt/q4dlkcq2dqWThVq2NKsKFbrPjo4owGIA1aNgkv/O9lULH0lYUT0SJ+iFmMrLy6NiVgtSriCXGSEzMzNqITYXuj3Qip6UMy0tzXOhC0qTRMN3diT6vaTfLa5J++FwOEpNljqo65jrnuvjjDFRo286Da2Ou1z3pG7Y4Z/1Mfpd7MrDLoO0QTL6p6PLuK5Z3yvbbtmbg5zUB1TcCWlg5s+f70WVmTp1KmbPno0XX3wRPXr08PyrCSGEEJI8JKzizl57zcLl6VXYtBouip+9mJLLP06rstrvVPu1iRrWoawbUjMj9+kYq7Ja26pwlXouvrYVKZHh7Gx1QNKXc1enLI1IWyuXru2hUChqaXXtL6f9DsVvV7ZLqDtRTisqKpLiWa2srMQtt9yCTz75BNdccw0mTpxYr+nvrWqbPKeilsvzIt/lOfELexikRrnqp+fnKs97WmqU+uSKRqHVZn1cq8L2AIAKU4GOpmoUTOI9iwKW+XPzqs/MLRH3QC+epBWzUCjk1WEdx1mrZvp+6bbHL0ysVv9cc3dcNGnSxEtT/27JNLelJtTn4lN1OX9PbD/0HBU92qSfT3u7veJp1c7YeehnX7Drhlb6g9oC3WbohdjC4XCgP7rLpz2e6DeuUMNaRdf32Z4LF1S+ZKWhDHf/p4oQ0mDMnDkT8+bNw3XXXee9RAghhBCSPIjhHs9fXUhYV5mxY8fi1FNPjdo+adIkT11NZLQSpRdaEgVJlCP5tM/x8zez0X7q+M3ddG36cgDVqnlqWmpUr1fQ/qg6vqvOy6/XLNfUubIHAODn9IKI8mql0s/nTdA+s67V2QRRUJs2bRqxvaioKNCPLpHUpAcffBCvvvoqxowZg7///e/1lm4iXFtD4ooUIR0iec51yEd7QTQd6UVHktA0/zWvan9KpFplp+vy59Zp6/rop/pJGlpJl7og6vnmpmsj9rfe3iEib7uMcr8EmROzocmqiPJIHvo67LbJ1W7p9kDKL2nKtct2+Z6dnR3VjrpUycbwnd0dUTISpd4mSjnqA62Cy7OklXY938r2iRelXc9FEYJCxwZt90sjXnXab+Q9yIZwldevrRNfdxlZ1+e42gS/6HcuP/lkJ2FdZRqKq666ynf7c889lxSGOyE14fXXX8eyZctwww034Mknn9xjXQAIIYQQUntCAPasLk8joXvn0uu14y8D7igofouhSA91/9DBEdt1b921wJGQlpbmVP4FHT9dcCmYfn5qWh13xb0WZJa6HUnH1VvXfnwSFUQiT4hqV1hYCADYtm1blHq/p/Xu64tEGnmoLfo51XH/Zbte3MzPL1OeJf28amVOFChX/OVwOOylpet7kPKlr8uua7qe6fxd7UGsdkOXXauOG3PWRHzX0V3s7drH3bXSolbdXOtRpKWlRS1UJfVcfN5dPrv1wZ5QP0j18yV1Ut4h+vmMNTdD6rxrldWgeuyXh44fr58zvbCh4IpYk56eHri6qW5DXGna8890fZWR/aB5PPo+lpSUePV2T4kSlZubi8LCQkyePDnKhvIjLS0NF110EZo2beqtSVMTElZxTwT0yzVWA64bBW1QimGsVz/V5xtjvEZhP3NgxD5dDr+FDfzKWVlZGXWOzt+1AItriN82alzD+kFhrCSU1prUH52LOOlG1NW4SUMgpKamRt0fvnj9Seb7okO7icHesmVLANUGunZN08+kbfy6VkyVeinD5aEU/5lpse6nq0PqcifTixnFant0Wq6VS3U57e0u1x1ZuC2ehWXWZa7wvVbXNQQ9f/bKqa4Qndq9Jh7iNcgbq36ww0BIcrHXu8oQQgghhNQF3YG0Vza20SM+enSpbXEXhFIjO656REyn5RdRxT6+srIyar6LpK0jOAkuX3J7uytik6u8LtHPvh+6My+jD+syIjvqetVlv3lEfvdhT4CGeyPiUjpc6q99jgzpiuKuw5dp9dd+mAVR2iXUlK44eolwvd9eeEnK5grfJq4DWpVzqeiCn+IuuJY61pVUytI13NM7t9RUKaOrflsaXtBuCnoEQ0+ua9GihTcBUYaidudwuqYh1DIqctXPoR7h0mqzKO/6Zeg3WVSHTdSTqQXXpFX7xRvkGuMXmtEuryYcDjvDxOlyBi1nrq/DL02/yXN2Wn4LushImkyODar/QeUrKyuLUtrjveZYJHq9SfTyEUIioeFOCCGEEFIP6PkuLj9wLYjYczZc8zRcyrHLl9zuILvU+6D1JFzltjv3Oi0dHS4o6oydh2t+nLgOivIuxFL9XSMQyQ4N90YkHp9Lm9TUVE/xk+WTtQotabr80aUytC3uAvwmHuulkPWEMa1YuSbZpKWleb6/erhLH+vydfdTF/Wk01h+wwBiLsyi09ivomrUYX3WyohzdR6iTMp3e6EraWhcIeR2J3tKHolMOBz2JpjpiWYyeVFPQnVNQLNHvnQdkee8Cw6qyjg9shx2PdPbtbrsF4rRTsM1p8T+rXVddS3AptN0GQLhcNjZLuihfj1S4BcOUs7V4SdlMptraXk9+mcbGa4Jb/pTIo5xSXUCRE681nXN9awLMjon4VVNyEQ9/y5jW+MaebPRo92C3wi/vkb7OHsBpqCQtq567zfi5upQyDky0ibvbI19Hdq24Ty0mkHDnRBCCCGEkDpAxT0JsP0sRdEWxLfd1RPXPpqdKg6oOj6lMqo3KkiP1VaVgWqlSh9nq4qu0I0u/BZ+sdO2VQEdacYVKk726951eXl5lCIh+cuCUNKLd12HPs8YEzX64VquPVHUOfqs1x55tuT319FjXIqO/l5RUeH5UetFyUIZkZPV9EiXDgNmR0PR805cQ9TxKGBa5XOpgBpXuFg/FVDn4SqPVjHLysqcIxZyH12+73pegj3HQIfRlXZT2kKtKO6pS6qTmqOfDf3u0PPM5LhWhe0BAOWh6JCFrlCHrjwFPzVdjyYFvZv18XoUsby8PHCibVC0Nr95Ly53oHjDVAvGmKj2I5aLTjJBwz0JsA1sl8uJ/oH0sLa3UmlqdZraYNcvf/3C03np2fP2yojSoRAXAt2QaB84QYextGPDu4bf9HdXQ2Nfi6tBkZUcd4V/8L1m+S4v9nA47L3UxY1C9ukh+UQhaCg0WRuz3YWfi4c8n/Jb61j+rpeK3ZGWIV/P8AxHvqT18K4gRrmfO1mQL6x+0ceK8qBfeq40XW2Pn0tdUFx5V13xi4PveoEL4kIjrjMuNxi7LdDGg9xPqePakHf5FhNCyO6ChjshhBBC6o09VQRwXZc9EVILTa7ISPvu7AQAKAuXRWy3z3GJZULQSJ9LkQeqO6E6LKRrroq+Di262ehyuEI92p1oPcruWtBNl1/naQsMusyu/JPtOaXhngTYyrZL2ZGHXfZLpepc2aPqgN+eeXlQ09PToyqCHl7Xqr2efOk3Sc2l+AXNgvcLV+l3/To/u9yuRsougx4mdH3XaqHeL9dZVFQUFUJOXGekXFqNbSyCGqlka7x2N/IMZGZmeqMp8mzpESWXy4RWaO1npaLEf4K2/u56idsvPdmnXc1cQ+yusIZ+Kr6rXEEveNfENb9jg4awg8JG2ufo++W6Rr8IGPpY7Z4kbZ7Ude0+SAghewo03AkhhBCStLg6lLabqBa09Nwr+dzaYj0AoMmGqtWXJcyhhD0EYnd+7fLoeSe6LPb8M5cvue7oCtr1TYSpcDgcNd/GpbQHLYDkJ/q5hKSfMwuq0q7wV/5t4cAVHcolgNZEgW9MtZ6KewLjVwn1w+0KqyiLK4VT/BU3299UK+uu8IuxhgnlOD1ZVsey1RM4dRpaZUxNTfXUrVih6+ztUm5pYOQeFRcXOxU+3SDKam1rUn/0zcs+Xzd4WpVzrUzX0DR2/smKHfJTfmvXQmd6BMdvTgRQVQ9cCwT5DSP7pe2KX2yfI7iGnV3quX2s4Mpfuwa4DAK/8rnmALgWdYo1eVaXU86R+T3i6+4KmWfPw9H3Vo/quUYhWccIIbsbGu6EEEIIqTf2lA5MkAuXreS6Osuujrm42W1O3Vy1v7Jq//qsldXBJFS+rmhVLl9uP1wrDMdyQ9PXKnlpAUPQowsiXMRal0Gjy6cXXhJcLnv2PdD3R0e9STZouCcwouaIcmyHK3Mpfp5qXhnsd6qHsbQKF6vy+6Vtoyuu9sHXKqLGHm3Qw4FBDYzGVsNcIbpcCrwo76scUWaMMVEh+HQaNS0vSQzsF5R+Bl2TxvRLwxVqtV3J/jApkS/doHkXLp9zG9eL3OW77Vot0S98nK7vui67Qr3Z211D/RqXem/fk1gvar88XMq7rdjrti/IbUCPqiVqdJlknYRHCPGnIeoyDXdCCCEkyXGFKd2TCQrvGgqFnJ1kQb6L0q5DydoCz+qUpQCqhSPXZHMt2Lnc1uIJkODqoOvtdnl1QAvtXqtXIheCIsbYx/yUtqzq2Ar/yDq6M23n5RIsXL7uQSFm/Y5NBmKJPbFIesO9IRULl/Ltl7fe17G8e9W5aZGKmp8/uPa7dS0RrheKcS2VHA6HnYubyKdfbGcb7X9aUlISdY7gigCjGylbHQ1S53TaGr8GR6uvch+1f//e9KJLZuzIS0DVs6JVcY0ewZFzXf7WSHHXTV03Ys0p0fnrkYGgcsdyBXCVz9U+udZRsNsolzrvGl1w1VdjTNQIl8vw0C9p/aK3R1F0eVxLzmtf96BJb37lakiSycgghMSGrjJx0hgNnzao7bikerjae4Eg7Huuy7VG0rW3uV6e+qXlN9TsMjr8Fjuxv7teiLZh4SqXX0/bTtveH7SaY7z3wn4Ju1Zm1LPwtX8fSSxcIQft6AlaYQrq4Mkz4EWKCFWn6crfNdFVf9rHudxaXNem66FfDGXd5uk66apvOsqDPUnVNdnU1TnX5bfPd6ljQaqZVgdtwcK1mrTLPUiX3xWZI9a1JRv1KUAkmwuPq0NpLxToCnFsi1FAdOfafhfKseLXLcq76xkXdLtkh4l11ROXiKWP14KdX76ud5vuLMdqRyU/UdorKyLbO5f7mt97WLdVeuXroMXkEpWaGu61rbNJb7gTQgghhBDSmFBxT0C0CiafpaWlgYpU0ERIv3BqLvcVHcZQ98z9ZqcHDe8HDd37Teh0DaO7yqGv1R7O1qpBkIIh32Uhq5WpS6LOD1IuXH6HpHEIUvm0UpaZmRkV5tQ1IqMVW5dC5/fc1CQUov1p/69Vca206HJoVxA7TVc7pMuty+uKFx3LD1jyjTfqhO2+5Lo/rjZI3JU2pK6KOk4r6q7J9dodTo9skvhIdIUzaPK0fGZkZDjbAl0Xtdrs95zKcycjdZWI/Q50vVftd6PLP971zhZc5QyFQjX+/XT7aJ8v/8vkca2062t0RcXR/v72ubpe64XrkoWGMtxjOxQTQgghhBBCEgIq7rXAT7ESdA/Sm/SWGrlfK0C2+uunAtrfBUlD9/Z1GETbh1z7lwsuVVwrCDLB1PbNk3K5Jv65FED7erT6rRV4XT59/zpVHACgeta/rY5IeeQcUeEEPRmONA4uFUIrt/aiSy5VOZYKDlQrZrIQWiylyeW7Kbj8wW2fWMGlQmllSfuH2nVN110dIUL7dbtGEGzl0bWCoVbpNbF83IN82gV939oWdwFQvRKjjSvOs74nerE1fY8SXVGuDcnmlx6L2l5LLPVZP/eC+LbrkWzddqSlpVX7tMN/YrhrNEyP2vkFZggaEQhaVdQ+3m+bjatN8xvFk/shbab49+v2Uftty/3UC8LZ5XL9Nq4Rdtf5iQJdZQghhBBCCEkCaLgnIDragd2b1gqV9gsTX2zXzHG/EHf6GK0UaL9Tl796eXl5lPom52ql2xWxxrWqm52GK03Bb5Ek+9PvHMHl7+8ql9/qbIIrbF6i9uL3dlyKtt1IaiXW9dvqKDJ+irGuR0HqT5Da78rH/tRxlmPVaR2JSl970HwawW5XXIq1K86wDstoK3rxRHKxcS3glle0H4DqUTT7GJfvvfbVlXZV2uxY0XlI8uKqo7YPueud5qo/+lnqWN7dcy52LY7oUoxd0V1CoeoFy4LmpsQ7jy4lJSUwmowuv2uhOr9RQ91W6VE6/el3312j70GLyyU6NNwTENeQc1pamjcU63p5ul7u2vDIyMjwztGVyFUOjV8oJVdFdg2FBzU8toGjCZo4pNO0w9Hphk+n5Roi94uPLelr1xg5Vrv2kMbFVWdcE0ljhRENCi+qsQ1PnYar/rmMcbuO6zUY9Ln6hRV0PfY1u8JQxlpfIgh9DUEGid999Qttax+rX/RBk4FDoVDg0L7rXsj9d7nMEEJIfUHDnRBCCCF7HbU1bPzmarg6hK5P3UmVeWrh1HBgNCjXgl+uvOw0XKPdQWKgvnZ7zoormo1r/pn2ACgrK4sSAmTUUrwJdLn1XAEdIcvvmlzKf7wC3t4GDfdaIA+5TGqxh7t05Qqa5BXPwy0EqXSxJsS4wi3GCinld7xd4XR+8U5O1UNpdt7xKI5+5fWWXy6NdqMQXOr8nuQqsydcg+Bq1O3J1/olEe/wsmuEyX6uXfcyqM7YKnWsieyA251Eu6L4jST5ld3vGl3H22m6Xoyu+xTruoJCRrpcEmK9nINcZLTrnF5sRxZbkza7oqIi6ULNxcue1AbUFD+DT4cSlfsibYcr1KkEPAil+E/wtNMKmpDtMsbt+uUX+tXvnCC7wHaV8avrsfCr1y7XHVe74hpR8+usuPJ3BYxI9GeaijshhBBCCCFJAA33BCIoVJH9Y7l8WH9bp8GpKklvtLS01DlkprfrJcx1eW0lXCt4rh65q9fvN4lEK6CCa9liHerRVhNd/vwu5SJqcmo4Oi/XCISePLcnLcSU6IpELILK7jeBKt4JW95zU+k/18RO2/XcupRk15C1/b8rlKur3RDiCQepcS2AEmsSbbzh4fR1+I1OuO69EDTsrduaThUHYFX4BwDR902fo9s5v/lIQJXyvqcp0zWdnNxYxHPfa/rb+KnO9qd9jGvBJX1cCPFPkNTqssvVw+9dpBcLi6XK+5ZTvdfC4XDcc9dco4n26LhLOZfJ4xK21fV+1ddTXl4eNe9Mj2a42rig8jc2NNwJIYQQQghJAmi4JyA6xJytlrn80nQvVHD1yMvLy51KleucrKysiDz04kT2BB1Rl1097SC1TLBDyLlUOa1+uRQOuzyuCTmu8opvO37rsNvquU5Devk6AlCi9dpJFbruyO8nkZdKS0udPuLyDMo5Hcu7/5Zo1YfrebLzdfmlCn6jUPo8v7kr9jW55m5opT3WYkmuEJJ6Tkk8CqceVQwaffLz+w1SfF1+wa4wnLYPfpDPsF9ELRs7zKWrzXHNQwpqkxKFRC2XsDsMG7/5JbLdVZ9do25euOfy6HoW72iR63r0iEBaWlrUiLXrHegKs6zTTk1NDXyfuvDznw8a+ZdFqTY0WRWRht+CdEBVmyHz4fRika42NVne1Q1luPuPwRBCCCGEEEISChruNUAiOkg0C+ldhUKhiP91hIXKykqsy1iBdRkrvONke3l5ecSfLE4gvXu/P/Hflj85V8ol28U/zT5Hl8svSoWNzsvvfL2ttLQUpaWlXnlTU1ORmpqKtLQ0pKWlRV0PUN2zd90/V0+2fWlXtC/t6p0n11xaWurdFym7lMNvwRmSuOhnr6SkBCUlJRF1RT+L+hy9Xf7082Yj+/Szp9O265k+Tqfvyk/OdaUl5/ldU1A55bmXP797E1ROXT5dx/X+eNoxV12PB1d74bqP+tr97pcuh6vtiVdVIw2H63kAohcY0+9vXU86lHVDh7JuUW2EjOL6/WmC2pdY7085R8pt5y/+6/afX92Vd638CUHl1vtj1ccjj+uLx1/9Mz5a9i6+3fkFRg4ejbyi/bw89fv2rLPOwttvv42VK1eioKAA7777Lk488cSIa9TXGqt9jpcRI0Zg8eLFKCoqwtdff42hQ4c6j33iiSdgjMG4ceNqlZfr+YjnmakJjWq418eP0hC4XjD2xA1dyeTHEeOxqKgIRUVFWJ2yFKtTlka9vPR5tnGvGxhBGiAxZORlql9a9otMX1OQwWy/iO0/O005VsrnekG7jCa/Y3Wl1+WTNGS/q4MBRL9o9f2UNDIyMpCRkeE0+mrzrJAqanJPXM+L/D7ye9kvLVc9cr0Q9HMjxGpYddq6fLqu2Z3V9PR0pKenO+ubrku6PK566Ff/XNfqqit+ddJVPt22+J0vuAx3V16uNuintGVxG89aELFDhgLwfg/bkKMhTkjNyWqShaXfLsV91z8Y1/EDBgzAhx9+iOHDh2Pw4MGYPXs2nnvuOfTs2bPWZRg4cCAKCgqc+/v374+XXnoJTz/9NHr37o2pU6di6tSpvnkOGzYMRx99NNauXVvr8jSU4U4fd0IIIYQkPfYcBkH7ZrvmrGhkv/60/9fzWrRAoc/1i8Kk98mnnj+iy6vVcHs+jU5L7ouO6KbT1vM39CimvW/W+//BrPf/Uy18/NbxblvcBUD1Ak1yHTfeeKOXR2ZmJv7whz9g6NChGDx4MBYuXOjt+/3vf4+LL74Y++67L3788Uf86U9/wr/+9a+Y1+5XdgAYN24cpk+fjoceeggAcMcdd2DIkCG45pprcOWVV3rHtWvXDn/5y19wyimn4O23345KO17iNcqT2nBPNpVDTyKR8osiDADFxcUAoiuhNCQykUMmyemJMXYoJyFWZbLTlvLJ0JicZ0941WXXaeryCn5D63pyqqCXeZdGQk+480tfT46TtPTCKnY5AKBTedViGWvSfvSuR/KXc+S3EQVON2oyabUuJNsz3VCI+i3/+xGkyvtNrNYvOUlDjpX6lprmH/JTP7uiJvuVU7/cdFp6v9/Lxa8eAdXPnsuosMurJ50KrrCmum755aHLrnHdE318KBSKMpRcabiMHbvd0nnqeyvf9YItelKw1Hm7bXJds8uIIYTUH6FQCDk5Ofj111+9bddddx3OPfdc3HDDDVixYgX69OmDZ555BuvXr8enn35a4zz69++PRx55JGLbe++9h2HDhkWUY/LkyXjwwQfx/fff1/p6gL3EcCeEEEIIqQu6AyeICxkQHV1Jd9Ckky8rpcZS3HWn00+Ak/z9ymd/6o6jK9KUKwqOFqDERddvnysPv/smeQUJLd4oAiI7waK8yxoMNsYYXHvttcjOzsbUqVMBVK03c9111+Gcc87BF198AQBYunQpBgwYgN/97neYNWtWjQ3evLw8bNiwIaK8GzZsQF5ennfMhAkTUF5ejscee6xGadcXOu59PNTYcE9UFcJVrvoor6vS2RXLpXbJdglHl5IaOyykfY7gKrsuj87bPt91X1yNgz7ebwhSNxyuYTmXomWXRafhKp++N3rIzy+sn1b+NbEU0iAStT4kIvEOTWt0yDC/e+43r8H+dDWKfi9g/by4FGE9dKzzstsF/dy6hsNdoQljPcOua3d91/XUb5QhqE64jAl7JM6Vv8vocY0o2n7/ui32u+f2Neo8tcHiV76g7azzhNSNESNGYMKECbjooouwadMmhEIhdOnSBdnZ2Xj11Vcjjk1PT8eiRYu879u2bfP+l7lp27dv97ZNmTIlwg0mFn369MG4cePQp0+ful3QbxgTe0KvfVxdoOJOCCGEJDl7c4dCi1h+bmCu1U29Tt5v9pa+j/Z5rk5xrM67X15+irtrVXOXIq/3+7nTBYlqghYI/PILQiaX69+gU2mVG+vy0HcAgHPPPRePPfYYxowZ47m/pKSkIDc3FwAwcuRI/PTTTxF5FxYWetfZp08fb3u/fv0wceJEDBo0yCtHYWGh9//69evRpk2biPK0adMG69evBwAcd9xx2HfffbF69WrvnNTUVDz88MO47rrr0KVLl7iuXaiNq0xNlHavjDU9IVEbhZqqJjXBVYFqcqyr4gp2dBqtnOtFZrQq7fKntyunq5Jrn2BdHkEPqdnHy7GyTdLM/G2hnOLfjtMLQ9mqmJyjfZWjGld1fFRYRxN9jM5PrkXuj/arrwmNUR/29Be0S5HVLzgdKQmo/o1lhCtI3fXLM8iPO15fbbsR13VXpxG0TLirrPYxuo66RtN0+2EvRuSa+6LR5bbLpOuZ61qCvstS6iiLzsdVXv2b6PP0fBs/9vT6RUhjcd555+Fvf/sbLr30Urz//vsRdsTSpUtRXFyM9u3bY9asWQCi56cBwPLly702pUOHDigvL8fy5ct985szZw4GDx6MSZMmeduGDBmCOXPmAAAmT56MGTNmRJzz3nvvYfLkyXj22WdrfH30cU8gtPEoD5P9AtS9Zf3DuNxa/F76QX5yujzaTSdWWVwdB5dLgd9KdPLpMli8l6OaeKfvm22Mu+6bnujqmrgmaUnvflXqD4EGl8vVpyYGfGO85Pd0gyLINcyeyBk0qdL1DLh8PP06k0FuZa56ICEigWg3n3jqqn2837oDrnxdK6W63OFsY9vVuXYpcn733TUJXV+7/g3kc13Giqp0yqOvw3WOzsP1u9srNNZW7NnT619taYy2UPKU94Tt/iXod1vQMxOrTQlSwV3H6/bFfq9qIcnVJrg603732/Vu0+f6vYvtNG03On3NWdlZ6LR/R6SkVJ3bvnM79Di0O3Zs24n1azfgmjuuwL5t98HNl94OoMpof+qpp3D99ddj7ty5aNmyJdLS0lBcXIzi4mLs2LEDjz/+OO655x4AVUZ3bm4ujj76aGzZsgVTpkyJus4gJk2ahJkzZ2L8+PF4++23kZ+fjyOOOAKXX345AGDLli3YsmVLxDllZWVYv349li5dWuP8Gspw5wJMhBBCCCEkbnr2OQivzvkn/m/2ZADADfdfh/+bPRlX3HIZAKB1m1bI69DGO/7SSy9FWloaHnvsMaxYsQIrVqzADz/8gPvvv9875r777sNDDz2E//f//h/mz5+P119/HaeccgpWrlxZqzLOmTMHI0eOxOWXX46vvvoKI0aMwLBhw/Ddd9/V/sITgBAinAuIH+np6QCAnJwcANFDzunp6V5vvqSkBIDl31VRpQC7FHfd07VVRD3kLeheu5RPHy/KpJ/LjO69ixLlCjXppxjoiXdaBde+bvEo71pt0OhRjyDVzi6fa2RA0pTfToblXJNZScMgKprUu4zfXK+KiooARA6fClqB61zZI2K74HrObZVcp6nPDYoHrRf5sdPSrjH6U7uwyadf+5CZmRlRHu0OotP0U6uDRplcric6bfte+Pne2p+6fD+lLYvI03YVDAqTKUj55PfX36UN2rlzZ618SxOZvdHFR35f8Y9u0qQJgMjRMKkP0r7LM6ADHkhb4Te6LGm6XN1cvux6VFx/GmO8d6/Ljcs1UuYaOZbFC+18tG3gCtGsR6ZkNXc7fd02udxvdV4rzPfedjlX2i5p212jDTt37owoV20CSQi7o57k5uaisLAQf/jDH7znLBYZGRm444470LRp04iJtfFCVxlCCCGEEELqQML6uO+NvXqtNvlNGnX1vF3+sLEmf+mevijqLnUpyO/TngCiQ9xJ2oJrURc/n/hYoRjlGPtcrcDbPXdddtfkWMHllyiqnakwUQtRuXxudXn3JJKxvrpUK786Fisagr3dNcLlmvjtl4ZLVdNp2Gq06/5rP3RXuEM/hU4rYLqc+rtrxMBWuGUyryZW5Aq/tP3y0T7G+tq9OhvDv9+ltAepgK4oH3bkjWSqG7HYU66jJuj6I2q6PfKm24igye+CrufGRIf6c81BcY2YyftW0iwuLo5SmYOeS1fQBcFv7o/2/Q+yV+xPVyz4eG0cwS/ijx7dcC3qKNv1HJzasDvrScIa7oQQQgghhJBqEtZw35t69a5eqFa6/SKsuPzRXOHr7B6mK5qMS+XSPV6thPuFuHMp/q7INZqUlBRnqDV9rS5lw68MQcvI+6kg9nZZ/e6ntGVeGuI/5xeVJNY1+pU32Z7/ZCsvEBzNxX7+XXVEngOZfh9vpKDKysqofHVeQqwoLUHXpH8XPW9FP9929BtdPv08u/w/pX6uSf3xtwuo+khLS8PmJmsBAK0K20fkGxS1RxNPtBZpF6QclaWRv6EeKbPPDaqzcpweWdTRg1JSUpzRd0jyot85aWlpznZEv0+DorbYintNR3H1e96OIOOKVuV65mP54Mt5ch/sKEp+16znqvhF3nFFlnOtSCvoNizWgm96rp0r5DWpgoo7IYQQQpIW1+rhFRUVURMaXWJPKOxvwNsGqKsDG29ISTHY7cARLpHPZdwKLlHLLmeQUOhyo7MNfVcoSVcnxuXKI/d5Rej7qIAVQcKmK8BEXdgdbqQJq7iT6N6r7QcmapFU0HUpVdFNXAvC+PnG+fms2flpguK/hsNhp5Kne8MaP780OV77o+vKFxTT3o5GExRtR3ApF/oedSzvjvVZK+O6Zr1dU5cZ7LFIRv/zhsA1OuT3HLleekLQPXaN4Ej69rag5yAeX1OXQqe3az92O061VpUF14tePkXh1sp2Zmamd62/ZFetINh6ewcAwNr0qoVN2pd2jTjXZVz4zTtwlUPfA409sumKXOW6b7qN1s+JPWIYVP9d+I0IEEL2Xmi4NyBBvWRBv0RtY1EPJ7kWe9BpxZpgFjShzhU6UfeE7f/9XmBAZLi5WOWy3XFck1PjVQzs69QvUb2olGvVU0Eb4eXl5cgr2g8AsDFlTcQ+l9HfkPi5LxF/XCqR36RwQQzOeCdd6u1+abpWJI1ljLvqQpCrh54UbitOuj65QkrKcatTqhYSqSx3T5DX91YM+JTKqvxlFVMx4AU/kcDVDsgk1DBiu/z5tb926F37WJdCpyex+d1/3ebU1HWG9Tdx0K5Q8gyVlZVFKe5aNEtBZB1zTRy3zw0SmHS9dSncfu2NS+GOV0CwXXfjRV+H3Ua4wlO6QkXrNAS/8Kv2ivF+1KfCrqlJ/Y1XZKPhTgghhBBCSBJAw72G1MXtQCs9utcuvWbxx/KbPCXHaKXHG2JO9Z9gqhXu1NRU58JL2iXGNRnEbwJKkJ9akGqvXWlsxVuXx6Uuuiaz2G43Qb32mvy+ohqGEDs0nM7LpcbWty8ciU1QiFB7n6v+6+fapZDZz1/QIiuu50Zj17ugsJWC1LOsrCzfcvspYFoFlProKe2OyW72hDC9mJMg5ZA8N6ZXjV7tu7NTRN6C7TYoiFqfavzDyely+4WI07+vXujONXHOz5XHvh77XGFvmLSaqG56NS2XdhvT7+ZYoZr1BHZXvfIbBdfH6Heu7dpmp63LG6t8NQ0LGau+uN7FGj1CUFlZGXcAi6D3pV0WV7lc7Xey1MmGMtw5VZcQQgghhJAkYI9R3OuzJ6aVd9071D3zlJSUKEVaQhCGSvzVuFihHl1Ku1bKXCMEfhO4ZCQgqJesy6d9x+1Jc66QjC61K5baqdU2l4+9y79XK/a/ZK9Gakqqbxr6u1/4TDutZF4aPVGVtZrgugZ7YZ6gcIuuURS//a575fIxjWeBKJ2PS2HS5dPtSmVlZVR9d42adSo9AIAVdtHhM2v7e+sQqi7lcFPuTwCqJ7HKRHA/BTElHD0Z3a88rrYGcC9C52on9P31q8P63uvIFUHzlJKZZG4P/HDNdfDzT3f5qcejcPvND7HRirXgeg791PyajgT7HecaZZNj9MKLLpsiHA5H2Q5B73mdtw7/CsspweWBoOugy8ugoajJCBBdZQghhBBCCElwaLg3Ii4Vz0V5eTmKi4sjzpUeovh3ij+dK227F+tSeFxKgX4IRC2zY8ZKD1oUNJ2mjoYj6B6xn6+Z7nHriBha0fbrqeuFIqScOlKGLrf+beyoM7r37vLbd12bSzlobGqioiejsub6TfUIiP17BPldBqlUOi87DZePvR4d0hESwuGw81nS16TL64qaZCt5rgVkZC6OlKdTRZXyvqHJqog0bUVZR23R6rhe0Eg+JU1TWf2c6WsIGnXQ+I14uUbWXHMcXP6/fn62un2QtifeKB6k/qhtexVrkSJdf719iD3vzG9Oi2vExxXC2TWqax+nn0M9ahTkAeCXpr5m1zswaITSnrPiam9co+BR9yJcfZ2ua9Yk2rs3CBrujYirYrgmaaSlpXkP9a5duwBELywgE8XEgHeFeLTz0UPiGtekUP2S9TtGN3Da2NYNoSteup2PKzydy0XF/q4bJZd7kOs3kPJ4IefC4aiY+oKOke1y+UlUktEYjwfX5Cb9u9guFbrDF8/L1+94+wWiX0RBk7P8wq+6cJXD9ULVx9mx610dUZcLQJtdnQEA6zJWROy3X6R+bYe93fVS9jNMgiagBbmo+YkZQW53rt/VL0xkkNGg3ZSSzYgghDQsNNwJIYQQEhcNOZ8l1lyQhsTVMbeFKO0n7XVKTWQaugOsO8i2UaY7x0GRVfzSks9YowX2p4yG6d/ZT+Ryde7tSFL2tboivvmN8Ak6Pr4rT8EvWp5LnBFc4sneDg13C9fQj67Igq1G68rmGj5aGV4CAOhUXjV8LaqwrQi5Ko9rOC6eYS9Xg6KHs11Klr7mlJSUqAku4i7kmkgSTxgp3bjq+6rvzZZm6yLOKysu887TowhSXmlwJC3dAAWNGNjlacyXVyKUoT4JUsU19gtK3wv57WQERq9cLPi5WAQNReuXoKAXJPF7KbvqoX7etcuKH3pkTq5ZRvtcL1AJUSvKu22Q6Gdev2x13fab8Or6HYuKigC4F5iJNQrpMpS0u5sup8sV0O+Z0r+FuBxqlz/9fOwp9Y8QUncaoj2g4U4IIYQkOQ3ZgUi0zopL2bYjO7k6XEEqul8UJN0R1yKWIKKQfLp83W2kM6o7pS6XTi0s2u5gLsEgaA6Nff/0OgcuN1aXS5wggsGq8A/eNv27aZfZIJfHRIOuMo2IS3l3qerl5eXOkFIu1UsmrYoSaPvqarWwXcn+EefqITXdwGiVyT5Gq966EkoDJMenOUYb7JEBXR77GPte6HtgV3DdCASFq5TQUmZH5G9lT2LT1yLoSuOaAKuPs9NJhKG7RG/EgnCNGLgabT2ca7+UXaNRsl2eF8lTXiJ+z5vLT15Gx7QK7Xp52yNxgmtxNa0Uy/ecnBwA1tC/NeE1qF7pNkmHVJR7sLnpWi8NUetdo2Z6MTqtYKempjrVeJexoNskPT/IPlYvECVtjB6h0L+dnpxvh9V0jQzKsfY5QPTIYllZWdLXRUJI3aHhTgghhBASgBhC0umzO6/xTgh3Ra+yO6CudUYEHY1Ji0J+7mFaUXfFgncJHfo4e30LlxjlivTm57OvRT7tTudyP9R52p+6s+4XSQoIjq2faNBwb0BcFUJXZF3hbSVGlB+95LKco/2tJU3xM5WID6FQyFPaJU0ZWvIe/N9Cr3Uu7xEzbftTq2/yXRQs7R8btLCJn++tK8qGKwqDXKeBQaX5TXH/7TM1VJW+LCIj+YtyWlEeqfbrCUh29A19jL5G3Xjohsl+LpKlAUkGgu6lfsn4DYfr59E14iXo6EPym9vRniTEoSwupNPSdcP1EvQrn36RuwwAUZClPfGG2q1ztBLsaq9c/v8y6pcRynAOSbvCVurj7OhUUr90fXLNR3CNmvhF+nGNNsj9cYXQE+xRUz1a4GrnvcX0VDtRUlLiHW+7JxBCyO6EhjshhBBCkpZY0VKC/MrjnYweDoejJqDrTqh2NXOFPBVSUlKcIZldrrquNQzsNVqC3D51J17KKZ1U+3iXiBcUCUbjubdWRMeGd7nGxgoQkYhQcW9AXDfRpcD7oVUbrYLpB1ArNKKqp6WlobzMv9LrckmEmv0rDwZQrZ5rn3cbvU++S4XVEVb0csd+DaJW70WJcql4K0NV5UZR9fW4IuSsTV8eUd7Kisj76or0YE+qkc+dO3f63gvX/AQ/dmckl8aOEtPQ+Qflp58F/Xva57oWCHKpzvplJM9kajgVqeVVz7i8aMQX3JV2PPdLj/q4Fm/SL1wxBOxRItdwvR510PdNjt+Ys6bqeFN9vNRzbTSIP7ffC91O027Xgn5PwaXA67QrKiq8fboO65EMV+x1PwVetx3aeND3JDs7OyIPu3yNvSQ7IaTxoeHeiOjG3vUilMY6NTXVe7G5JqO6jFi/oX5XOEJtPMtw+vrUlQCADqWRizvZ5Y3Xd0yMb70SrF+HxL5+v3x12tI5ETcXv4dX+9PpDpGoCjrknV8nS19rLBcYv3I39Eu4sV/6iXa9WnHSLk4ZGRnegmeuSc2u594VK7m0tDRqUrUY8PuHDo4on6vjZ+/Xrhp6UbAg1Uq7ndj7XKFcXe4tUrc7lVW5n3n1saLCq8PaNcfPLcTOw6/euTo4rsgRrhCUdjvoStMv4offtQs6HV12G10+3UnJysryyqDvT2PS2AJAY+K3wrZuC+x99qegn0P7fP2+lnewdsMUXJPRhbS0tKiQ0H4ugUB0HdQdcr+RAVcQCpePuV/wCi3mud6PrrkDfse79uk0km2xMxruhBBCCKkRe6PBLgRN3LQJMmIF+7vLaNVimxb5dCfQdm/R0Z60m402mF3ltDuYIiTqjoQ2hPUopu4slJeXO9eVCRopc41y2pNTdUQ617ymZIGGewKgb672RbMVeZd7iO75xlrECaiqWC6lXaeh/elWplYN93euqJq0KpXXTks3ZNKblrSaNGkScZyr91xZWekpBbqR0BXXm4RaFuyC4ppl7rqOWOqSTsu1WItLUdCEw+GohjdRG5bdXb7dkb4rTdeEznA4HKUQ62P8lFWg+lkVFVqe+127dkU9e1K/VqcuBVA9kdU1Eibf7fCQ+tlyrToo2JOs7U/7enQd1ffAtdiQfG9V2B5AVf3UoSxd9d41kTOWgu1S5lxtkl8d0y5GgmuBNkGeD7+2XNotfY7rt5JRSH2vsrKyPBcebSgRQkh9Q8OdEEIIIfVCY4oaWtnOzMyMEmrkGJk/JR1xfZzfHBLtyqHnqrnUcFcEM7+OuC5HkFuddtcJh8NOAdHlSugnDLjSitVZt/e7vodCoah85bteH6Kx3FZrCxX3RiRI7fULd6jDQAqucHWuldTsYSSXj6jfzHS/8tvDdbFmoNt5iM+wVr3kfNsXVs7ZsWNHxLXIsRLqsrIkMm+XT7Bfefx8fP3OlXLZjYprcRbXcKFrDoN+GdjbEpX6Kp/rJbw7rt+Vh2teRkpKivccu45xKdqu0StjTNSz5PLrdrUTdixn10JL8c550ZNC/SZ/6jZEv/QkL6nbcrzUzzCqY0nruSWueSCaWCNeut657qtut+S3TU1Ndd43Pz/kWOWy09H5+flH+5VTsJ+TWC4ZhJC9AxruhBBCCEkqGlPU8POR1p0qPfFRXDllFXOXQGZ3kKXTpidxuzpwLl9uO6KTq4Po6uAGTU630aKUyyXVT5SoaTQo3TH3wkBWVosTuhxyD7SgmegCmYaGex2py3CdSzXXCqCtCLlmSfstDGR/+qlQQeqma+a1pwBVRjZe9tCUXk5cK3yume1aAbfDQerKtjqlyhe4otQ/lqzf9biGGF1KqeBacMVvtr6OTOOKnKMn6iTbcF19kgjXrIeMZQQlFAo5h3i12qtfBHp42c5D/pdjJA8vLGSKv+qsX6gVFRUxR5XsfPVxekE3v2dQ1385RwyRTiZy8TJ53iWaTEpF9UidHrLW/vKu+Taxhv6DJsIFhWPURo7fsa6oE9qIcV2fTt/OX1+zlFfuY05OjndcItQTQkjjskcZ7o3h81YfeWnj1WXcGmOier86DddEV8GeCOcKP+lCjhPDAr8d7jc5LdZEPxtXXGy/8Gvyv/SsJQ693u9yNfC7lqBnxjUbXk+Y1WUFojtT+pr00s7JTKJPoq0LZWVlUfXONfFYG3x64qEQCoWizpVnTFYa1cqcnjjpp5wFub256rie7FhZWensDMiKrymVVWltyKj63ra4C4Bqg1461La7iZ4s65oM6jKy7fusz3G5C/nFQ7fTtNsLl2uMLpe+z1pUsPNyBQ4I6kz5xeAPWuSH7F5cgllpaWnUO0531l1iVTz56DrvUvdd2MEoXC5brrbBJXLZebpcyVzldoVhjpWva7++z/Z+l5uaKxR2srBHGe7JePOFoIeUELJ70S9Uu6Plejn4jZawLhNCCEl29lhXmbqgDXfXBCe/IVzBFb9Vp+Hnl6ZVOJfvmu4Vr02tmiXfqeKAiONlaNdGu8hodxu9AqFWqcvKyjwFskluVSg97IpMO2gI2t6ufRP1ObrcrnB/cn5aWlpUuDftJqR7/a4RgWTreNokc9k1EnJVPmuDLKKiV/nVI1BAtIIliuuq9CpXE3GZaVeyf8RxdmdB11nXSJtW8nQ9kJEkWx0XfsleXXUNxZFtjFybNxJW4r9YkT3KoBU4uU+ucJWuCBz2tbpUSpe7oF/I3Hgnf8Zyx7PzstucoHbANQIjISCLiooClVXSsNjvK7vu2PuCXCD9niHdJsg7xrVir+BSmMvLy52jbq5wqi413L6uICVbhzTVafsJHq4gGLq8kodE7aksd7c3gh79Ttb3VkMp7kkxFf6yyy7DJ598gvXr16O4uBgrVqzAM888g86dO8c879hjj/VuZKtWrSL2nX322Xj55ZexfPly7Ny5E0uWLMFDDz2EZs2a7c5LIWSvJt663KFDB9xxxx2YN28etmzZgo0bN+Ljjz/G4MGDo9LMy8vD/fffj48++giFhYUwxmDgwIENdUmEEEKIZ2/G81cXkkJx7927NwoKCvDmm2/i119/RZcuXfC73/0OZ5xxBg4//HD8/PPPUeeEQiH85S9/wY4dO7xJRDb/+Mc/sG7dOkyZMgWrV6/GoYceimuuuQannXYa+vTp46nU8fhhxxPrVH/qEIW28qcnUWr/M8GeiGmzJvRjxHfbL1Zi1mo1XwiaXOst9hRaggxELrwk52ilW/LS/sZ+vu5BE3N1eV3+i/a90tfq8jN2jRRoJYHUnnjr8llnnYUJEyZg6tSpeP7555GamoqLL74YM2bMwCWXXILnnnvOS7NHjx64+eabsXTpUnzzzTc45phjnPnLbylqqVaY7brsCgup/UR/SV0d8b319g5R+WolXT9rcq4o3C71uby83Nu3MlS14FpoV+REXN3WFBUVRXyXPAR78rprkr2+dj3pV7DLK+1A0EqQ2sfXLwRt0KR53S7oCbB+PrOu6B26nuvt8m6Qdq+kpCRpFcI9FbvNdgUp0PMffkr/bVL3byPWfs+j/p21QqxHrPWzpJVvO03X3K+gEQG/ESLXCIBuh3RdE2y3RFcdc3kRePce7vl0Ojx2rHqaTOxRPu515eqrr47aNnXqVCxYsAAXX3wxJk6cGLX/8ssvR8eOHfHUU0/huuuui9o/YsQIzJw5M2LbggUL8MILL2DUqFF4+umn6638hJAq4q3LH3/8MTp16oTNmzd7x/3tb3/DokWL8Ic//CHCcF+wYAFatmyJX3/9Feecc05Mw50QQgjZHSSk4T5o0CB8/PHHOPvsszF16tSIfRdccAFefPFF9O/fH3Pnzq1ToeJh5cqVAIDmzZtH7WvRogXuuece3HHHHdh33319z9dGOwD8+9//BgAcdNBB3jbbx0v7c4pylZaW5qlauver473qNGLN2tZKQXZ2NoBqxScoMozkvX37dm/bptyfAERHxtBRWfSkP/ku/rLhyuq8XL7jWiWTUQatFNgqp0sF02ilw29BKT+/ZftadO9ep6F/s2TE5aefmZmJhQsXAqhSweWZatGiBb777jsUFBTguOOOa5Br96vL33//fdRxpaWleOedd3D99dcjJyfHW/hLPuNBfEr1s2GrazoSg8vXWY/YaJ/yjuXdo549CZUqz29qSlUd3c8cGJGXKzqFrdClpUcu+iYji1ppd8WatlV+raDrc6Xuunzb7bqvRxN1ZByNTltTUVERFdVK0KN7rkWSBNvv3i/SjJ2G/t21OhhPFJKa4ldf94S5No2JruMutVme9TXp1fUXiIzMokdv9acenXE9G37z41yR3PzOsb/7vTv95onE+tTn+fnXa1tAv+9lv0Svkjk1+riUlJSoUfhkV9obmhoZ7p988glWr16NUaNGRRnuo0aNwrJlyzB37lykp6cjNzc3rjRtRS2Ili1bIiUlBZ06dcIdd9wBAPjwww+jjvvjH/+I9evX4+9//ztuv/32uNPPy8sDAGzatCnucwhJRoqLizF69Gj85z//wb333ovrr78eAPD444+jWbNmGDNmDCorKxu9Lmvy8vKwc+dObxVQQgghJBFISMUdAKZMmYLx48ejadOmKCwsBAC0bt0aJ598Mu69914AVeq7PZQdi5qEaFu7dq2ncm/atAnXXnstZsyYEXHMoYceiv/+7//GaaedVmMlZMKECSgvL8err74asd0VC9VPcdG+lS4/aq0cieqUlpYW5actSpookpKm3H9RHPW99Ju5Lqrg8tB3AKpVBZcvuShWMkM8vbxaHZNztKLnWglO7o2f77BW54Jm5btUUDuCjOQnqpyeFa/vs/ZP9FMKk039ilXO+fPn44EHHsCECRPw73//G23atMEFF1yAcePG4ccfq1SnxqzLmq5du2L48OF45ZVXaq1y6slBMsoi6cWK+6xH2iQNvZCXPD/rs1Yir2g/ANUqfEW5/2hPQepiAED38kMj8tCLQQHV8drTTbpXZgDYsmVLxLG67dGLOdmRpLKysgD4r9NgpyHl0nVGvpeUlDijM8n9k/JJueVTr8lg+726Im1p/3jdNruiY/ndF/nUbYnLD3h3tAF+aSZLW5MI6JEge10Uv9E1v3OD3t2AewVS18ixK2KRvTJpLAU9Fn7zUXTZXXkErbrqN3/PNRLg8rn3q7uuY/eEZ70hrqHGhvsLL7yAW2+9FSNGjMAzzzwDADj//PORlpaGKVOmAADee+89nHTSSfVbUgBDhw5FZmYmDjroIFx44YWe64jNY489hnfffRcffPBBjdK+4IILcNlll2HixIlYtmyZt91eLEAMQPslL58udws9gSNoJceysrKoH14PK8l1i8HuGrKyK6ekIeqplGtLeF1EGvvu7BSRlxdqTk00LS4udroF6c6LdvnRL0Y7DZdxLcQKP2eXwT5POit6qC/oZe/3wt4dlTLWUtW7m7vuugtnnHEGnn/+eeTk5OCTTz7BY4895u1vzLpsk5WVhVdeeQVFRUW4+eab65S3HQrQL1Sq/P5NmlSFOdWuOPI8uQxTu65IyNTU35paqQsyOVb/5iXttlWltT7SnUXq0qrwDwhV+NdzVwhaV4dK10ugum6KWCC/i05Th1iVPJo0aeKNhuhzdP3XBrsIE+LaZ4sb+hqlLdaigXY7cLm9pKSkRN0/iSim3YT2JKOCELL7SFjF/YcffsD8+fMxatQoz3AfNWoU5syZg+XLq1TZ9evXY/369TVKNzs7OyL6S0VFRZTLyieffAIAmD59Ot544w18++232LFjBx5//HEAwHnnnYdjjjkGhxxySI3yHjBgAJ5++mlMnz4dt912W43OJSSZKSsrw9ixY/HFF1+gqKgIl1xyScT+xqrLNuFwGC+//DIOPvhgDB061DeKFCEkNn5rkuzplJeXex1ELaq5OrTaV7tzZQ8AkR1cfR9daxHoESKt1FdUVDhHl12RnlzYees0XL7scm90lBc7zaDRA/lcFa5a30KvnK7vgR3FqqajC3Vhd46W22knpOEOVKnukyZNQvv27ZGRkYH+/ftHRIvIzMyMOx76hg0bAAA33HAD7rrrLm/7ypUr0aVLF+d5K1aswMKFCzFq1CjvZf/ggw/ilVdeQWlpqRcXWlScjh07Ij09Peqlf9hhh+HNN9/Et99+ixEjRkQpvPZDJog6LttLSkqiKqZW1F3qk3YvKS4u9s6VtMQlRiqZDufmUqHkuMzMzIjFQux8ZYhcyiXD8N5EGRMZvtJuBLWqJZ+SR9BkGvueSVldk3r0pD1X6EYpp98ERLlPftdib9cLrbgWwqgvghqtoAW46lquU045BUDVs9C9e3dvsijQeHXZ5sknn8QZZ5yBUaNG4eOPP46rLC7S0tIQDoej1FR7SFmem23btvkeI+j77jcC5XKJ06NTUndFzc/cr6oOlS6vOl7qpSmN/q31wjJ6FEHquOtlabuEBbnKyKcr3F1GRoanuGt3GlHvXZPbtNJuh8WVa9CjnvJd2hztziRp6/C+xhiv3us0dDvqmjTb0CSbmx4hZPdQK8P95ZdfxiOPPIILLrgAWVlZKC0txf/93/95+88///wa+8W+8MILmD17trddGtFYZGVleY0sAHTq1AmjRo3CqFGjoo5duHAhFi1ahN69e3vb9t9/f0yfPh2//PILTjvtNM+4JWRv4dBDD8Udd9yBZ555Br169cJTTz2FQw891OssNlZdFh544AGMHTsW48aNw8svvxxXOQgh0TS2wd+QHQ9bMHO5tOnyBPmrG2OcfvIudyrXegO28BQkDOg8tbuaxl5pOMgXX89T02Wxy+fyZZeRCVkh1XU/pQNvi34NuVJqQ+WRsIr75s2b8e677+LCCy9EZmYmpk+fHhFRojZ+sQUFBSgoKIjanpKSgtzcXGzdujVi+5FHHolDDz0UL774ordt2LBhUefn5+cjPz8fF110EX766Sdve5s2bfD++++jsrISp5xySsxIMnrJZD2pyfaX1JVM1Ce9tLr4msuIgBhKoVAoapKkuB3Ijy33QnxvXZVA8igrK/NUNSmnKFjSWZG8pPx6QqfkZatzejKPbngkTa2m+yluklaLFi0AVCuT8rtIvrJdPvUEU/v6dJg5SUOuTX4/18I1TZs2BVD92/jNP2gI4gkpBkQOhcfzkkxNTcVzzz2HdevWYdy4cejSpQs+//xzPProo7j00ksBNF5dBqqU+xtvvBH33ntvhN99XcjIyIiYsCjPjz1J1eUjrl9Y+nnRL9LS0lLvWRPFV4/m6HOlPsr3raGtAIBwSVW9bNKkCVq2bAkAXnvmCocp5ZWOkx6ps9sLaWPkGD1qJmq4a6Eze+RA5gZI/ZORC93WuEbX5LeQdEpKSrz2U9d/ua+ShtRZQc6T8tmjaK7Jc660G3IBNj+3lsY2vAkhsUlowx2oUtVee+01AIgKuVgbv1gXOTk5WLNmDf7v//4P3333HXbu3IlDDz0Ul1xyCbZt24Y//vGP3rFvvPFG1Pm9evUCALz77rsRnYvp06eja9eumDhxIgYMGIABAwZ4+zZs2BAY4YKQZOd//ud/0KtXLwwePBg7duzAN998gz/84Q+499578eqrr+Ldd99ttLo8bNgwPPjgg1i6dCkWL14cNYr2wQcf4JdffvG+y9yUnj17AgAuuugir05LtCtC9nYa092mIfO0O9nSudRuaq6JzFGRTlDdkde+64LuxLsiEfmp/rozrAmK1uUX9UYLiIJr9MEV3aU2UY70PdDR5EKhkNehbkjFvTbUdH5BQxnuIQC1SiEtLQ3r169HOBxGXl6e90PUN2lpaXjggQdwwgknYL/99kNWVhbWrVuHGTNm4J577sGqVatinn/nnXfirrvuQuvWrSMM91g37pNPPsEJJ5wAoCrSwD777AOgWj3XSozEuwaiFSlRrly+rgcccEDE9p9++inKX1MefFGM9XCb7csOVCtwdqXUode0eijlkVj2oshLWr/++iuAagUrNTXVUw916Ei5dlEbRUXX5ZXfo6KiIqpy2+HvgGrVW0eIEf9r3SDl5uZ6x+hnU0fCkLxErdOjI6I2bt68OWH8XePF1fD07t0b8+bNwxNPPIFx48Z528PhMObMmYP27dujZ8+enlpaH9SkLku9dTFo0KCIRdRi1Wf75SU+7q6FVDIzM71r1nVD+z7rqCh65MsePdO/g16MTI6TZ1DaAGlr5Pjc3Nyo59K1uJufymzn5VfXJD/9nOtQkroO2RFq5Fy5XzLC4lowTtR+KZ+0s9IGlZSUROUjdVTPT9GjaHKPJG17REGHuNTGix5lrEvdb8zIUXs7eo6FPFd6JEXqkXwK8kzZC6q5opq5DF5txEqadtvkipimo7C5wlT6ubfod7Nr1DAW+j7Jp4SI1u2eIHlKO2CPkksb6wr/nGzk5uaisLAQo0ePjts19Pnnn0fTpk0jFsiMl1ob7ikpKVi3bh2mTZuGyy67rDZJJAUdOnRAu3btAFQP+cokPPtFKMO68pDaRilQ/XKQRkPUQl35bINYI64veiKbHu7XISdzc3O9Mmv1QU9k071lyVOub926dV6e2qCRa5DKKG5Ahx9+OAB4K3XKfbSvWRsw2rVAl093Dmy3ICmv7JMh+tatWwOobiTkN5DGtEOHDgCqDQf5FMV5586dtW5g6kPp8lulUqeZ7A3g7iYzMzNCLZP7JfVzv/32w+rVVSFQXfG85VOec9dKqvYEyLZt2wIANm7cGHGMpC31S9AGnr2mgzZWJS3pXOtVnHWseqkrrVq1AlDVjkgMeCmH1EOpO3YYWLs8ujPesmXLqHql3X9c8dN1B9uu+1IuqaMSwUzugdxfuUZ5GUodlrRsA0bfH+3SJ+XR5feDE0cTFxruNNx3Nw1tuNfaVWbYsGHYd9998cILL9Q2CUIIIYQ0MHtTR0N3xLQhqQ1O7TIjn+syVqBDWTcA7ohpgstFRbuGpKamRrnduCaB+i2OBES77fgtOqWvTaftmjxrzz+Tsstk1Ioy/0my2mDXoSZ37twZNRl2TyFhfdyPOuooHHbYYbj99tvx5Zdf4tNPP61TARKdwsJCr4LLkK/cdHEB2bVrV1QoM61ki/qsh439FlHRipgoWKL8SHm6d69a9fSrr74CUN1YyCTdOXPmAAA6d+7sqXEy/Gu7vNjllgor6nT//v0BAAsWLIi49szMzCjFXa5ZT/KSNObOnevdL5v09HQvX0lD98S1z6AOgSef++67LwCgffv2XlqicmrkHoh7kLhEtWnTBgCwaNEiAIjwpa4tQaEE4yEoRGZaWprTT5ND9JFoFUnq1rp16zzFRJ4PrdDpCdyul1AoFPL2SRha/fvoxZ50+yB5tG/fHgBwyCGHeG2u5HfkkUcCgBcqU7uxiEotZZC2QFSvioqKKFcOOfeoo44CAG+ysXadknKKu0tZWZnXxugXuijaUs+kzdRtp67rfvdRG0BSR+VajznmGADAmjVrAABLly4FUD1ampGRETXBXf9+0r5LOxJLcdfuUq79DTnBlRDSsCSs4X7llVfiwgsvxKJFizBmzJg6ZU4IIYSQhmVPUzpjoUMeSsdcz/UQtOuMrTiL2tyuZH8A0YKMK8Sk3q/zirXP9Vu5VH57tXfBz43P71MfZ0/uXZP6I4DoBZb8BCSgWvDQ0a1KS0v3WCEp4SenEkIIIYQkAzLqIaNbMrItBqceLdcjaSkpKZ6RH2S46++xYsm7Yq1LeVxhU7X/un2ea+VWwTUyq33xjTHeCN7qlKUR5dJp6fkuMqdAG+7bt293zuNLVsTHfdSoUXH7uP/zn/9seB93QgghhJBkQK8sLMapGJjilqaNb3tyqBihoj6Lz7sQtDCT9ke39+ngA9pwF7Qfup8LpZ+vup22ztulyAPVk1ArSt0LPgHVrrFiuEuHQgd7SLbIbDWhoRR3/6nRhBBCCCGEkISCijshhBBCakSyRqYRxVcHSRDXGb2GiO0jr9XileElAKpV547l3SP2r8tYAaDatcZvQSbtY6/dabSrjFboBVuZ12s3aJcZ14JLunw/ZxagorwiIg2dv71qMlCttIvLiEz831Mjydgk7ORUQgghhBBCSDU03AkhhBBC6hHtQ64X2NI+70JRUZFzwqh8rgr/AMCKylJatX1laEnEdvszJfzbAoDFVf7yerLpL9mRC8JJXuJf7zchVkekcfnPu0IMe7HaS6pVfO03r+PhS1p61We9UOSehF+YZxruhBBCCCGEJDg03AkhhBCSkCS7r7JWn0Up1iq6REkxxniqsZxjR5yxzwnK0/YXlzQKsBgAEK5UoRt3RvvFA9GRbew5B6447fIp5RVl3fNxx2/XXladp45MI3HaZWRA0pL7J9cjYSSTLYqMHQc/3t+zoaHhTgghhBBCSB2g4k4IIYQQshvQxpOO864XJUpNTfXUZtmnF2vSvuMuA81PydUjAEGIEi5+9fZCTq448hWVv/m+h34rVzl8jxNSUlI8X3a5dvkURGkXX3bti59sxGt81+VcGu6EEEIIIYQ0IjTcCSGEEEIaAO3/LWq6KMdZWVmev7uo0BKBRtRm8evWMct1nHQ/w80VW90vcolfmlLulJSUKMU9aARAENVePtPS0ryoMeLTLiMCcs3atz3evPZUGuK6uXIqIYQQQgghSQAVd0IIIYQQC1GORVkuLy/3VHhR3LOysgBE+327opL4qeVa6deKrV/sd1dako7rGL2CqhxnK+z6u6Qr1y4Ku4wu6HLvrUo7QFcZQgghhBBCkgIa7oQQQgghjYitiIv6LP7cokKLUq0jsNiRXmzsePBBqrwgPuY6+oyOIW+MiVLU5buUS5dHr9Zqx2Z3xWV3lXtvhoY7IYQQQkgC4LewkRivYsyKcSuGsDbkZbt8ahcbO21Bu8gIeqEoezEoMdT1p6Qh5dXXoxejKisrcxrq8S5StDfRUIY7J6cSQgghhBCSBNBwJ4QQQgiJE1FWKysrUVlZifLycpSXl6O0tBSlpaUoLi5GcXExduzYgR07dqCwsBCFhYXYtm0btm3b5n3fvn27d6ycK38lJSUoKSnx0q6oqIj4sxdashF1HYB3rKS5c+dO3z9dLrtMkoZcs/zdfvvtUdsXL14c876NGzcOS5Yswa5du7B69Wo88sgj3qjE7mLEiBFYvHgxioqK8PXXX2Po0KER+++8804sXrwYO3bswJYtW/DBBx/gqKOOqlVe+h7F+qsLNNwJIYQQQkiN+Pbbb5GXl+f9DRgwwHnsBRdcgD/96U+4++67cdBBB+HSSy/F+eefj/vuu6/W+Q8cOBAFBQXO/f3798dLL72Ep59+Gr1798bUqVMxdepU9OzZ0ztm6dKluOaaa3DooYdiwIABWLlyJd5//320bt26xuVpKMOdPu6EEEIIIXVEh3bU/ud+oR31BFatost+nYfL+AuFQlH+766FmFxpxWtYlpeXY8OGDXEde8wxx+A///kPXnrpJQDAqlWr8NJLL6Ffv34RZZ8wYQIuv/xy5OXlYenSpfjjH/+I1157La48NOPGjcP06dPx0EMPAQDuuOMODBkyBNdccw2uvPJKAPDKI4wfPx6XXXYZDjvsMHz00Uc1yo8+7oQQQgghJCHp3r071q5di+XLl2PKlCno2LGj89jPPvsMffv2xZFHHgkA6NKlC0477TS888473jG33HILLr74YlxxxRXo2bMnHn30UUyZMgXHH398rcrXv39/zJgxI2Lbe++9h/79+/sen5aWhssvvxxbt27FV199VeP8qLgTQgghJCERX2pGFXETj5It6rzgWnApVhqufGurpMfDvHnzMGbMGPzwww9o27Yt7rzzTsyaNQuHHHIIduzY4R0n5X/ppZfQunVrzJ49G6FQCGlpaXjiiSdw//33AwDS09Nx66234qSTTsLcuXMBAAUFBRgwYAD++7//G59++mmNy5iXlxc1IrBhwwbk5eVFbDv99NPx8ssvo0mTJvj5558xZMgQbN68ucb5MRwkIYQQQghJOKZPn+79/80332DevHlYtWoVzjvvPDzzzDNRxw8cOBC33norrrrqKsybNw/dunXDpEmT8D//8z+455570K1bN2RnZ+ODDz6IOC89PR0LFy70vm/fvt37PyUlBRkZGRHbpkyZ4rnBxMvHH3+MXr16oXXr1vjd736Hf/3rX+jXrx82btxYo3QaChruhBBCCKkRVNp3D0E+7PGe39Bs27YNS5cuRbdu3XzL88c//hGTJ0/G008/DaBqYmt2djb+8Y9/4N5770VOTg6AKvV77dq1EWmUlJR4//fq1cv7v1+/fpg4cSIGDRrkbSssLPT+X79+Pdq0aRORVps2bbB+/fqIbbt27cLy5cuxfPlyzJs3D0uXLsWll16KP/3pTzW6B1TcCSGEEEJIwpOdnY2uXbti8uTJvvubNGkS1dkTN6FQKITvv/8excXF6NSpU0y3mOXLl3v/d+jQAeXl5RHbbObMmYPBgwdj0qRJ3rYhQ4Zgzpw5Ma8lHA7XKkwlDXdCCCGEkD2Y2hpxtfF9r08efPBBTJs2DatWrUK7du1w9913o6KiwovS8vzzz2Pt2rW49dZbAQDTpk3D+PHjsXDhQs9V5o9//COmTZuGyspK7NixAw899BAeffRRhMNhzJ49G82aNcOxxx6LwsJCvPDCCzUu46RJkzBz5kyMHz8eb7/9NvLz83HEEUfg8ssvB1DVmbjtttvw5ptv4ueff0br1q1x9dVXo3379njllVdqnB8Nd0IIIYQQknB06NABL730Elq1aoWNGzdi9uzZOProo7Fp0yYAQKdOnSIU9nvuuQfGGNxzzz1o3749Nm7ciGnTpuG2227zjrn99tuxceNG3HLLLdh///2xdetWfPnll7WO9T5nzhyMHDkS99xzD+677z78+OOPGDZsGL777jsAVYr/gQceiNGjR6N169bYvHkzPv/8cxx33HH4/vvva5xfQxnuIQCN4xBFCCGEEJJkiNrdWP7kJLHIzc1FYWEhTj75ZOzatSvw+CZNmuD9999H06ZNIybWxgsVd0IIIYQQQuoAXWUIIYQQQhoRP3U9yPCiIr93QsOdEEIIIYSQJICGOyGEEEJII1IbI8t1jl4NNV5DjyQHDWW4h+t0NiGEEEIIIaRBoOJOCCGEEOJDffqr13VVVBv60ScmDfF70HAnhBBCSNKzO4zZRDWME7VcezP0cSeEEEIIISQJoOFOCCGEEBInu1uFDoerpgXaK4ISInByKiGEEEIIIcSDijshhBBCSABU2kks6CpDCCGEEEJIEkDDnRBCCCGEkCSAhjshhBBCCCFJAA13QgghhBBCkgBGlSGEEEIIIYR4UHEnhBBCCCERhEIhrtBaA+gqQwghhBBCSBJAw50QQgghhDQKjaW2h0KhRs2/ttBwJ4QQQgghJAng5FRCCCGEJBSdO3f2DBS/v3/84x/esQMHDnQe169fv0a8ivolFAp5KjGpO/EawHsrVNwJIYQQEhcbN27EhRdeGLX91FNPxYUXXoj3338/at+kSZPw+eefR2xbtmzZbisjIY0BXWUIIYQQklDs2rUL//znP6O2jxkzBtu2bcO0adOi9s2aNQuvvfZaQxSvUaA6TAC6yhBCCCEkgCDXlYYgLy8PJ5xwAl5//XWUlJT4HpOTk4OUlJQGKQ8hjUGselif9ZKKOyGEEJKk+LmupKWl4dFHH0VpaSkAICsrC02aNAlMq6KiAlu3bq1xGfLz85GSkuKrxAPAs88+i9zcXJSXl2PWrFm48cYbsWDBghrnQ0giQ1cZQgghhMTEz3Xlr3/9K3JycjBkyBAAwE033YS77rorMK2VK1eiS5cuNS7DqFGjsG7dOnz00UcR20tLS/Hqq6/inXfewaZNm3DwwQfjhhtuwKxZs3DMMcdg0aJFNc6LkL2dEAA6ZxFCCCF7ABdddBFeeOEFjB8/Ho8++igAoEuXLth///0Dzy0qKsJnn31Wo/y6d++OpUuX4pFHHsH1118feHzXrl3x9ddf49NPP8XQoUNrlBchiUhubi4KCwvRu3dv7Ny5M/D47OxsLFy4EE2bNsX27dtrnB8Vd0IIIWQP4PDDD8ff/vY3vPjii57RDgAFBQUoKCioUVpNmzZFVlaW9720tBS//vpr1HGjRo0CAKebjGb58uV44403MHz4cITDYVRWVtaoXIQkMg0xr4SGOyGEEJLkNG/eHK+99hqWLl2Kyy67LGJfdnY2cnJyAtOoqKjApk2bAFSFcBwzZoy375NPPsEJJ5wQdc7IkSOxZMkSfPnll3GXdc2aNcjIyEB2dnatFEdCEhH6uBNCCCEkkFAohH/+859o3rw5TjrpJBQVFUXsv+GGG2rs4/7AAw9gypQp3j4/tf2oo45C9+7dcfvtt9eovPvvvz+KioqwY8eOGp1HSCJDw50QQgghgdx555045ZRTMHToUKxcuTJq/wsvvIDZs2cHpmMb/IsXL8bixYtjHj9y5EgAwIsvvui7v3Xr1p6CLxx22GE488wz8e677zL+OSG1gJNTCSGEkCTlkEMOwVdffYVPP/0UTz31VNT+eH3Pa0o4HMbatWtRUFCAY445xveYDz/80Jvw+ssvv+Dggw/G5ZdfjrKyMvTv3x9LlizZLWUjpCGRyamHHXZYXKNIOTk5+Prrrzk5lRBCCNnbaNWqFcLhMAYNGoRBgwZF7d9dhvtJJ52EvLw83Hvvvc5jpk6dilGjRmH8+PFo2rQpNm7ciNdffx133303li9fvlvKRUhj0VCuMlTcCSGEEEIIqQWiuB9yyCFxK+7ffvstFXdCCCGEEEIaA05OJYQQQgjZiwmFQgAaJj44qRsNZbiH63Q2IYQQQgghpEGg4k4IIYQQkoBQaU8e6CpDCCGEEEJIEkDDnRBCCCGEkCSAhjshhBBCCCFJACenEkIIIYQQQjyouBNCCCGEEFIH6CpDCCGEEEJIEkDDnRBCCCGEkCSAhjshhBBCCCFJACenEkIIIYQQQjyouBNCCCGEEFIH6CpDCCGEEEJIEkDDnRBCCCGEkCShrkZ5PNBwJ4QQQgghpA5wciohhBBCCCHEg4o7IYQQQgghdYA+7oQQQgghhCQBNNwJIYQQQghJAmi4E0IIIYQQkgTQcCeEEEIIISQJYFQZQgghhBBCiAcVd0IIIYQQQuoAXWUIIYQQQghJAmi4E0IIIYQQkgTQcCeEEEIIISQJ4ORUQgghhBBCiAcVd0IIIYQQQuoAXWUIIYQQQghJAmi4E0IIIYQQkgTQcCeEEEIIISQJ4ORUQgghhBBCiAcNd0IIIYQQstdy1VVXoaCgAEVFRZg7dy6OPPLIGqchins8f3WBhjshhBBCCNkrOe+88/DII4/g7rvvRp8+ffDVV1/hvffewz777FOjdGi4E0IIIYQQshsZP348nnzySTz33HNYvHgxrrjiCuzatQtjx46tUTo03AkhhBBCCNlNpKWloW/fvpgxY4a3zRiDGTNmoH///jVKq6EMd0aVIYQQQgghex2tW7dGamoqNmzYELF9w4YNOPDAA2uUVtOmTev1OBc03AkhhBBCCKkFpaWl+Pnnn/HTTz/Ffc7PP/+M0tLSWuVHw50QQgghhOx1bNq0CeXl5WjTpk3E9jZt2mD9+vVxpVFSUoIuXbogPT097nxLS0tRUlJSo7IKNNwJIYQQQsheR1lZGRYsWIDBgwfjjTfeAACEQiEMHjwYf/3rX+NOp6SkpNaGeE2h4U4IIYQQQvZKHnnkETz//PP44osvMH/+fFx33XXIzs7Gs88+29hF84WGOyGEEEII2Sv517/+hX322Qd/+MMfkJeXh0WLFuHUU0/FL7/80thF8yUEoG5xaQghhBBCCCG7HcZxJ4QQQgghJAmg4U4IIYQQQkgSQMOdEEIIIYSQJICGOyGEEEIIIUkADXdCCCGEEEKSABruhBBCCCGEJAE03AkhhBBCCEkCaLgTQgghhBCSBNBwJ4QQQgghJAmg4U4IIYQQQkgSQMOdEEIIIYSQJICGOyGEEEIIIUkADXdCCCGEEEKSABruhBBCCCGEJAE03AkhhBBCCEkCaLgTQgghhBCSBNBwJ4QQQgghJAmg4U4IIYQQQkgSQMOdEEIIIYSQJICGOyGEEEIIIUkADXdCCCGEEEKSgP8P2Cus3HrHZt8AAAAASUVORK5CYII=",
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