code/Model-Analysis.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *O. polymorpha* as factory for small molecular weight biochemicals\n",
    "\n",
    "The notebook explores the metabolic capacities of *O. polymorpha* to produce the small molecular weight biochemicals malate, acetone, and isoprene. For the analysis a modified genome scale model based on iUL909 [[Liebal et al., 2021](https://doi.org/10.1186/s12896-021-00675-w)] is used, in which reactions for acetone and isoprene production are added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading pandas, a library for data manipulation\n",
    "import os\n",
    "import xlrd\n",
    "import pandas as pd\n",
    "from datetime import date\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from itertools import product\n",
    "\n",
    "# loading matplotlib, a library for visualization\n",
    "import matplotlib.pyplot as plt\n",
    "# from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.size'] = '14'\n",
    "# plt.rcParams['font.family'] = 'Calibri'\n",
    "\n",
    "# loading cobrapy, a library dedicated to the analysis of genome scale metabolic models\n",
    "from cobra.io import read_sbml_model, write_sbml_model\n",
    "from cobra import Reaction, Metabolite\n",
    "from iambcodes import countCinFormula, set_ObjGSMM, set_SubFlux, CreateTransReact\n",
    "\n",
    "Today = date.today().strftime('%y%m%d')\n",
    "SaveDir = os.path.join('..','data','MethanolBioconversion','SimulationResults')\n",
    "FigureType = 'svg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def set_SubiOpol(model, Sub, Flux):\n",
    "    '''\n",
    "    Setting the desired substrate in the model.\n",
    "    \n",
    "    Arguments:\n",
    "    model: cobra model, iOpol909\n",
    "    Sub: string/list, model metabolite ID\n",
    "    Flux: float, desired substrate uptake rate\n",
    "    \n",
    "    Return:\n",
    "    cobra model, updated with designed (co-) substrate uptake rates.\n",
    "    '''\n",
    "    model.reactions.get_by_id('Ex_glc_D').lower_bound = 0\n",
    "    model.reactions.get_by_id('Ex_{}'.format(Sub[0])).lower_bound = -Flux\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generating cobra variable from SBML/xml file\n",
    "# assuming the base path is in 'Simulation'\n",
    "GSMM_File = os.path.join('..','model', 'iOpol23.xml') # iUL909.xml for the original model\n",
    "model = read_sbml_model(GSMM_File)\n",
    "model_copy = model.copy()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optional: Adding reactions\n",
    "Applicable only if the original model w/o additional reactions is used. \n",
    "\n",
    "To activate set the `run` variable to `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using the adapted model iOpol23.xml\n"
     ]
    }
   ],
   "source": [
    "run = False # True if you want to adapt the original model\n",
    "if run:\n",
    "    # more realistic fluxes by forcing TCA activity\n",
    "    # model.reactions.get_by_id('MDH').bounds = 0,0\n",
    "    # model.reactions.get_by_id('AKGMALtm').bounds = 0,0\n",
    "\n",
    "    ID = 'IPSYN'\n",
    "    if ID not in model.reactions:\n",
    "        reaction = Reaction(ID)\n",
    "        reaction.name = 'Isoprene synthase'\n",
    "        reaction.lower_bound = 0.\n",
    "        reaction.upper_bound = 1000.\n",
    "        reaction.annotation = {'biocyc':'4.2.3.27-RXN' , 'brenda': '4.2.3.27'}\n",
    "        IP = Metabolite(\n",
    "            'isoprene[c]',\n",
    "            formula = 'C5H8',\n",
    "            name = 'Isoprene',\n",
    "            charge = 0,\n",
    "            compartment = 'c',\n",
    "        )\n",
    "        IP.annotation = {'biocyc':'CPD-9436', 'chebi':'35194', 'chemspider':'6309', 'inchi':' InChI=1S/C5H8/c1-4-5(2)3/h4H,1-2H2,3H3', 'kegg.compound':'C16521', 'pubchem':'6557'}    \n",
    "        IPP = model.metabolites.get_by_id('ipdp[c]')\n",
    "        DP = model.metabolites.get_by_id('ppi[c]')\n",
    "        reaction.add_metabolites({IPP:-1., IP:1., DP:1.})\n",
    "        model.add_reactions([reaction])\n",
    "\n",
    "        print(model.reactions.get_by_id(ID).id)\n",
    "        print(model.reactions.get_by_id(ID).reaction)\n",
    "        print(model.reactions.get_by_id(ID).check_mass_balance())\n",
    "    # Isoprene transport reaction\n",
    "    TransIso = CreateTransReact(model.metabolites.get_by_id('isoprene[c]'),'[]')\n",
    "    model.add_reactions([TransIso])\n",
    "    model.add_boundary(model.metabolites.get_by_id('isoprene[e]'), type=\"exchange\", lb=0)\n",
    "\n",
    "    # Acetone production\n",
    "    # Acetoacetyl-CoA hydrolase\n",
    "    # https://biocyc.org/META/NEW-IMAGE?type=REACTION&object=ACETOACETYL-COA-HYDROLASE-RXN\n",
    "    ID = 'AcAcCoAHYD'\n",
    "    if ID not in model.reactions:\n",
    "        reaction = Reaction(ID)\n",
    "        reaction.name = 'Acetoacetyl-CoA Hydrolase'\n",
    "        reaction.lower_bound = 0.\n",
    "        reaction.upper_bound = 1000.\n",
    "        reaction.annotation = {'biocyc': 'ACETOACETYL-COA-HYDROLASE-RXN', 'brenda': '3.1.2.11'}\n",
    "        model.add_reactions([reaction])\n",
    "        model.reactions.get_by_id(ID).build_reaction_from_string('aacoa[c] + h2o[c] --> acac[c] + coa[c] + h[c]')\n",
    "\n",
    "        print(reaction.id)\n",
    "        print(reaction.reaction)\n",
    "        print(model.reactions.get_by_id(ID).check_mass_balance())\n",
    "\n",
    "    # Acetoacetate decarboxylase\n",
    "    # https://biocyc.org/META/NEW-IMAGE?type=REACTION&object=ACETOACETATE-DECARBOXYLASE-RXN\n",
    "    ID = 'ACACDECARB'\n",
    "    if ID not in model.reactions:\n",
    "        reaction = Reaction(ID)\n",
    "        reaction.name = 'Acetoacetate decarboxylase'\n",
    "        reaction.lower_bound = 0.\n",
    "        reaction.upper_bound = 1000.\n",
    "        reaction.annotation = {'biocyc': 'ACETOACETATE-DECARBOXYLASE-RXN', 'brenda': '4.1.1.4'}\n",
    "        ACONE = Metabolite(\n",
    "            'acetone[c]',\n",
    "            formula = 'C3H6O',\n",
    "            name = 'Acetone',\n",
    "            charge = 0,\n",
    "            compartment = 'c',\n",
    "        )\n",
    "        ACONE.annotation = {'biocyc':'ACETONE', 'chebi':'15347', 'kegg.compound':'C00207', 'pubchem':'180'}    \n",
    "        CO2 = model.metabolites.get_by_id('co2[c]')\n",
    "        ACAC = model.metabolites.get_by_id('acac[c]')\n",
    "        H = model.metabolites.get_by_id('h[c]')\n",
    "        reaction.add_metabolites({ACAC:-1., H:-1., ACONE:1., CO2:1.})\n",
    "        model.add_reactions([reaction])\n",
    "\n",
    "        print(model.reactions.get_by_id(ID).id)\n",
    "        print(model.reactions.get_by_id(ID).reaction)\n",
    "        print(model.reactions.get_by_id(ID).check_mass_balance())\n",
    "\n",
    "    # Acetone transport reaction\n",
    "    TransAcetone = CreateTransReact(model.metabolites.get_by_id('acetone[c]'),'[]')\n",
    "    model.add_reactions([TransAcetone])\n",
    "    model.add_boundary(model.metabolites.get_by_id('acetone[e]'), type=\"exchange\", lb=0)\n",
    "\n",
    "    write_sbml_model(model, \"iOpol23.xml\")\n",
    "else:\n",
    "    print('Using the adapted model iOpol23.xml')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reaction definitions\n",
    "The dictionary contains reaction identifiers for whom the fluxes are stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HEX1: atp[c] + glc_D[c] --> adp[c] + g6p[c] + h[c]\n",
      "G6PDH2: g6p[c] + nadp[c] --> 6pgl[c] + h[c] + nadph[c]\n",
      "PYK: adp[c] + h[c] + pep[c] --> atp[c] + pyr[c]\n",
      "PC: atp[c] + hco3[c] + pyr[c] --> adp[c] + h[c] + oaa[c] + pi[c]\n",
      "FUM: fum[c] + h2o[c] <=> mal_L[c]\n",
      "MDH: mal_L[c] + nad[c] <=> h[c] + nadh[c] + oaa[c]\n",
      "MDHm: mal_L[m] + nad[m] <=> h[m] + nadh[m] + oaa[m]\n",
      "MDHp: mal_L[x] + nad[x] <=> h[x] + nadh[x] + oaa[x]\n",
      "PDHa1: h[m] + pyr[m] + thmpp[m] --> 2_Hydroxyethyl_ThPP[m] + co2[m]\n",
      "CSm: accoa[m] + h2o[m] + oaa[m] --> cit[m] + coa[m] + h[m]\n",
      "ICDOXSUCm: icit[m] + nad[m] --> h[m] + nadh[m] + oxasucc[m]\n",
      "AKGDH1: akg[m] + 5.0 h[m] + thmpp[m] --> 3_Carboxy_1_hydroxypropyl_ThPP[m] + co2[m]\n",
      "SUCOASm: atp[m] + coa[m] + succ[m] <=> adp[m] + pi[m] + succoa[m]\n",
      "SUCD2_u6m: q6[m] + succ[m] <=> fum[m] + q6h2[m]\n",
      "AKGMALtm: akg[m] + mal_L[c] --> akg[c] + mal_L[m]\n",
      "ICL: icit[c] --> glx[c] + succ[c]\n"
     ]
    }
   ],
   "source": [
    "RctFluxName = {\n",
    "               # 'Ex_meoh':1,\n",
    "               'HEX1':6, \n",
    "               'G6PDH2':6, \n",
    "               'PYK':3,\n",
    "               'PC':4,\n",
    "               'FUM':4,\n",
    "               'MDH':4, \n",
    "               'MDHm':4,\n",
    "               'MDHp':4,\n",
    "               'PDHa1':3,\n",
    "               'CSm':6, \n",
    "               'ICDOXSUCm':6,\n",
    "               'AKGDH1':6,\n",
    "               'SUCOASm':4,\n",
    "               'SUCD2_u6m':4,\n",
    "               'AKGMALtm':5, \n",
    "               'ICL':6, \n",
    "            #    'SUCDH_q6_m':4, \n",
    "            #    'MALSm':4, \n",
    "               # 'Ex_co2':1,\n",
    "               # 'DM_mal_L[e]':4,\n",
    "               }\n",
    "\n",
    "[print(model.reactions.get_by_id(Rct)) for Rct in RctFluxName.keys()];"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding metabolites in model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Reaction identifier</strong></td><td>ICL</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Name</strong></td><td>Isocitrate lyase</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Memory address</strong></td>\n",
       "                <td>0x7ff23a41ee20</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Stoichiometry</strong></td>\n",
       "                <td>\n",
       "                    <p style='text-align:right'>icit[c] --> glx[c] + succ[c]</p>\n",
       "                    <p style='text-align:right'>Isocitrate --> Glyoxylate + Succinate</p>\n",
       "                </td>\n",
       "            </tr><tr>\n",
       "                <td><strong>GPR</strong></td><td>OGAPODRAFT_17519</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Lower bound</strong></td><td>0.0</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Upper bound</strong></td><td>1000.0</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<Reaction ICL at 0x7ff23a41ee20>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returning the reaction details for reactions changed in the different scenarios\n",
    "model.reactions.get_by_id('ICL')\n",
    "# TCA-: PDHa1, PDHcm, ACLSm, AKGDH1+2, CITtam, CITtap, CSp+m\n",
    "# TCA+: AKGDam\n",
    "# Glx+Std: ICL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FBP26: f26bp[c] + h2o[c] --> f6p[c] + pi[c]\n",
      "MAN6PI: man6p[c] <=> f6p[c]\n",
      "GF6PTA: f6p[c] + gln_L[c] --> gam6p[c] + glu_L[c]\n",
      "FBP: fdp[c] + h2o[c] --> f6p[c] + pi[c]\n",
      "PGI: g6p[c] <=> f6p[c]\n",
      "G6PI3: g6p_B[c] <=> f6p[c]\n",
      "PFK26: atp[c] + f6p[c] --> adp[c] + f26bp[c] + h[c]\n",
      "HEX7: atp[c] + fru[c] --> adp[c] + f6p[c] + h[c]\n",
      "TALA: g3p[c] + s7p[c] <=> e4p[c] + f6p[c]\n",
      "ALS6PE: als6p_D[c] <=> f6p[c]\n",
      "PFK: atp[c] + f6p[c] --> adp[c] + fdp[c] + h[c]\n",
      "TKT2: e4p[c] + xu5p_D[c] <=> f6p[c] + g3p[c]\n",
      "SBTPD: nad[c] + sbt6p[c] <=> f6p[c] + h[c] + nadh[c]\n",
      "G6PDA: gam6p[c] + h2o[c] --> f6p[c] + nh4[c]\n"
     ]
    }
   ],
   "source": [
    "# Searching for metabolite IDs\n",
    "# CheckMetName = 'Acetone'\n",
    "# [print('{}:\\t{}:\\t{}:\\t{}\\n'.format(MetID.name, MetID.id, MetID.formula, MetID.annotation)) for MetID in model.metabolites.query(CheckMetName, 'name')];\n",
    "\n",
    "CheckMetID = 'f6p[c]'\n",
    "[print(f'{myRct.id}: {myRct.reaction}') for myRct in model.metabolites.get_by_id(CheckMetID).reactions];"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Production envelope Growth-rate versus Yield\n",
    "The following cell determines the production envelope of the production of malate, acetate, and isoprene with different growth rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# required minimum growth rate\n",
    "bio = np.linspace(.001,.12,5)\n",
    "Substrate = 'meoh'\n",
    "flux=10\n",
    "Product = ['mal_L[e]','acetone[e]','isoprene[e]']\n",
    "PCol = {'mal_L[e]':'m', 'acetone[e]':'b', 'isoprene[e]':'g'}\n",
    "PMark = {'mal_L[e]':'x', 'acetone[e]':'o', 'isoprene[e]':'d'}\n",
    "YieldProd = dict()\n",
    "\n",
    "for myProd in Product:\n",
    "    Cnum = countCinFormula(model.metabolites.get_by_id(myProd).formula)\n",
    "    Yield = list()\n",
    "    for bioi in bio:\n",
    "        with model as md:\n",
    "            md.reactions.get_by_id('BIOMASS').lower_bound = bioi\n",
    "            md = set_ObjGSMM(md, myProd)\n",
    "            md = set_SubFlux(md, [Substrate], 'Ex_glc_D', flux)\n",
    "            sol = md.optimize()\n",
    "            Yield.append(sol.fluxes['DM_{}'.format(myProd)]/flux) # *Cnum in the nominator for yield in Cmol/Cmol\n",
    "    YieldProd[myProd] = {'Yield':Yield, 'growth':list(bio), 'color':PCol[myProd], 'marker': PMark[myProd]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "funny-feelings",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure saved as ../data/MethanolBioconversion/231129_ProdYields-VS-Growth.svg\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FigureName = 'ProdYields-VS-Growth'\n",
    "for Target in YieldProd.keys():\n",
    "    m,n = np.polyfit(YieldProd[Target]['growth'], YieldProd[Target]['Yield'], 1)\n",
    "    plt.plot(YieldProd[Target]['growth'], YieldProd[Target]['Yield'], \n",
    "                c = PCol[Target], \n",
    "                marker = PMark[Target], markersize = 10, linewidth = 3,\n",
    "                label = f'{Target}, Slope:{round(m,1)}')\n",
    "plt.xlabel('Growth Rate, /h')\n",
    "plt.ylabel('Yield, mol/mol')\n",
    "plt.legend()\n",
    "PlotName = os.path.join(SaveDir, f'{Today}_{FigureName}.{FigureType}')\n",
    "plt.savefig(PlotName, dpi=300, bbox_inches='tight')\n",
    "print(f'Figure saved as {PlotName}')\n",
    "# plt.title('PYC Active') #+AKG/Mal mTran\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Malate Yield for Different Pathways\n",
    "\n",
    "Scenarios analysed:\n",
    "- Blocked TCA: deactivation of pyruvate dehydrogenase (PDH) and mitochondrial acetolactate synthase (ACLSm)\n",
    "- Forced TCA: minimum reaction flux through alpha-ketoglutarate dehydrogenase (AKGDam)\n",
    "- Forced standard Glyoxylate shunt: minimum reaction flux through isocitrate lyase (ICL)\n",
    "- Forced impaired Glyoxylate shunt: minimum reaction flux through isocitrate lyase (ICL), deactivated malate dehydrogenase (MDHm)\n",
    "\n",
    "### General simulation setup\n",
    "- Methanol is used as sole substrate with uptake rate of 10 mmol/gDW/h.\n",
    "- The objective function is set to malate.\n",
    "- All results are stored in a csv-file with the name `[Date]_MalPathFlux.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_mal = model.copy()\n",
    "\n",
    "# setting dictionary to store all flux results\n",
    "FluxAll = dict()\n",
    "\n",
    "def Model_Unconstrained(model, target):\n",
    "    '''\n",
    "    Setting the model to be unconstrained. With target as the desired product, the model will be optimized for production.\n",
    "    '''\n",
    "    # optimizing for production\n",
    "    set_ObjGSMM(model, target)\n",
    "    set_SubFlux(model, ['meoh'], 'Ex_glc_D', 10)\n",
    "    return model\n",
    "\n",
    "\n",
    "def Model_BlockedTCA(model, target):\n",
    "    '''\n",
    "    Setting the TCA cycle to be blocked. With target as the desired product, the model will be optimized for production.\n",
    "    '''\n",
    "    # optimizing for production\n",
    "    set_ObjGSMM(model, target)\n",
    "    set_SubFlux(model, ['meoh'], 'Ex_glc_D', 10)\n",
    "\n",
    "    model.reactions.get_by_id('PDHa1').upper_bound = 0\n",
    "    model.reactions.get_by_id('PDHcm').upper_bound = 0\n",
    "    model.reactions.get_by_id('ACLSm').upper_bound = 0\n",
    "    model.reactions.get_by_id('AKGDH1').upper_bound = 0\n",
    "    model.reactions.get_by_id('AKGDH2').upper_bound = 0\n",
    "    model.reactions.get_by_id('CITtam').upper_bound = 0\n",
    "    model.reactions.get_by_id('CITtap').upper_bound = 0\n",
    "    model.reactions.get_by_id('CSp').upper_bound = 0\n",
    "    model.reactions.get_by_id('CSm').upper_bound = 0\n",
    "    \n",
    "    return model\n",
    "\n",
    "def Model_ForceTCA(model, target):\n",
    "    '''\n",
    "    Setting the TCA cycle to be active. With target as the desired product, the model will be optimized for production.\n",
    "    '''\n",
    "    # optimizing for production\n",
    "    set_ObjGSMM(model, target)\n",
    "    set_SubFlux(model, ['meoh'], 'Ex_glc_D', 10)\n",
    "    model.reactions.get_by_id('AKGDam').lower_bound = 7.8\n",
    "    return model\n",
    "\n",
    "def Model_ForceStdGlx(model, target):\n",
    "    '''\n",
    "    Setting the Glx cycle to be active. With target as the desired product, the model will be optimized for production.\n",
    "    '''\n",
    "    # optimizing for production\n",
    "    set_ObjGSMM(model, target)\n",
    "    set_SubFlux(model, ['meoh'], 'Ex_glc_D', 10)\n",
    "\n",
    "    model.reactions.get_by_id('ICL').lower_bound = 10\n",
    "    return model\n",
    "\n",
    "def Model_ForceImpGlx(model, target):\n",
    "    '''\n",
    "    Setting the Glx cycle to be active but with mutations in MDHm. With target as the desired product, the model will be optimized for production.\n",
    "    '''\n",
    "    # optimizing for production\n",
    "    set_ObjGSMM(model, target)\n",
    "    set_SubFlux(model, ['meoh'], 'Ex_glc_D', 10)\n",
    "    model.reactions.get_by_id('ICL').lower_bound = 10\n",
    "    model.reactions.get_by_id('MDHm').bounds = -10, 2.85\n",
    "    return model\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unconstrained optimal product flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.499999999999998\n",
      "0.24999999999999978\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_mal_L[e] = 2.4999999999999987\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh   10         1 100.00%\n",
      "     o2[e]    Ex_o2  7.5         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      "Metabolite    Reaction  Flux  C-Number  C-Flux\n",
      "  mal_L[e] DM_mal_L[e]  -2.5         4 100.00%\n",
      "      h[e]        Ex_h    -5         0   0.00%\n",
      "    h2o[e]      Ex_h2o -12.5         0   0.00%\n",
      "\n",
      "1.9792307692307594\n",
      "0.19792307692307592\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_acetone[e] = 1.9792307692307631\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction  Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh    10         1 100.00%\n",
      "     o2[e]    Ex_o2 7.083         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      "Metabolite      Reaction   Flux  C-Number C-Flux\n",
      "acetone[e] DM_acetone[e] -1.979         3 59.38%\n",
      "    co2[e]        Ex_co2 -4.062         1 40.62%\n",
      "    h2o[e]        Ex_h2o -14.06         0  0.00%\n",
      "\n",
      "1.08933333333333\n",
      "0.108933333333333\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_isoprene[e] = 1.0893333333333288\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction  Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh    10         1 100.00%\n",
      "     o2[e]    Ex_o2 7.375         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      " Metabolite       Reaction   Flux  C-Number C-Flux\n",
      "isoprene[e] DM_isoprene[e] -1.089         5 54.47%\n",
      "     co2[e]         Ex_co2 -4.553         1 45.53%\n",
      "     h2o[e]         Ex_h2o -15.64         0  0.00%\n",
      "\n",
      "\n",
      "HEX1 \t atp[c] + glc_D[c] --> adp[c] + g6p[c] + h[c] \t 0.0\n",
      "G6PDH2 \t g6p[c] + nadp[c] --> 6pgl[c] + h[c] + nadph[c] \t 0.2\n",
      "PYK \t adp[c] + h[c] + pep[c] --> atp[c] + pyr[c] \t 3.27\n",
      "PC \t atp[c] + hco3[c] + pyr[c] --> adp[c] + h[c] + oaa[c] + pi[c] \t 0.54\n",
      "FUM \t fum[c] + h2o[c] <=> mal_L[c] \t 0.0\n",
      "MDH \t mal_L[c] + nad[c] <=> h[c] + nadh[c] + oaa[c] \t -1.48\n",
      "MDHm \t mal_L[m] + nad[m] <=> h[m] + nadh[m] + oaa[m] \t 0.94\n",
      "MDHp \t mal_L[x] + nad[x] <=> h[x] + nadh[x] + oaa[x] \t -0.0\n",
      "PDHa1 \t h[m] + pyr[m] + thmpp[m] --> 2_Hydroxyethyl_ThPP[m] + co2[m] \t 3.27\n",
      "CSm \t accoa[m] + h2o[m] + oaa[m] --> cit[m] + coa[m] + h[m] \t 0.0\n",
      "ICDOXSUCm \t icit[m] + nad[m] --> h[m] + nadh[m] + oxasucc[m] \t 0.0\n",
      "AKGDH1 \t akg[m] + 5.0 h[m] + thmpp[m] --> 3_Carboxy_1_hydroxypropyl_ThPP[m] + co2[m] \t -0.0\n",
      "SUCOASm \t atp[m] + coa[m] + succ[m] <=> adp[m] + pi[m] + succoa[m] \t -0.0\n",
      "SUCD2_u6m \t q6[m] + succ[m] <=> fum[m] + q6h2[m] \t 0.0\n",
      "AKGMALtm \t akg[m] + mal_L[c] --> akg[c] + mal_L[m] \t 1.48\n",
      "ICL \t icit[c] --> glx[c] + succ[c] \t 0.0\n"
     ]
    }
   ],
   "source": [
    "with model_mal as md:\n",
    "    FluxScenario = dict()\n",
    "    for target in Product:\n",
    "        # optimizing for production\n",
    "        md = Model_Unconstrained(md, target)\n",
    "        sol = md.optimize()\n",
    "        print(sol.fluxes[f'DM_{target}'])\n",
    "        # yield of malate from methanol\n",
    "        print(-sol.fluxes[f'DM_{target}']/sol.fluxes['Ex_meoh'])\n",
    "        # print exchange fluxes\n",
    "        print(md.summary())\n",
    "        FluxScenario[target] = {md.reactions.get_by_id(Rct).id: round(sol[Rct],2) for Rct in sol[np.abs(sol.fluxes)>1e-2].keys()}\n",
    "    FluxAll['UnconstrainedRef'] = FluxScenario\n",
    "\n",
    "print('')\n",
    "[print(f'{model_mal.reactions.get_by_id(Rct).id} \\t {model_mal.reactions.get_by_id(Rct).reaction} \\t {round(sol[Rct],2)}') for Rct in RctFluxName.keys()];"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Blocked TCA scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4999999999999933\n",
      "0.24999999999999933\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_mal_L[e] = 2.4999999999999987\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh   10         1 100.00%\n",
      "     o2[e]    Ex_o2  7.5         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      "Metabolite    Reaction  Flux  C-Number  C-Flux\n",
      "  mal_L[e] DM_mal_L[e]  -2.5         4 100.00%\n",
      "      h[e]        Ex_h    -5         0   0.00%\n",
      "    h2o[e]      Ex_h2o -12.5         0   0.00%\n",
      "\n",
      "1.92974999999999\n",
      "0.192974999999999\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_acetone[e] = 1.9297499999999936\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction  Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh    10         1 100.00%\n",
      "     o2[e]    Ex_o2 7.281         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      "Metabolite      Reaction   Flux  C-Number C-Flux\n",
      "acetone[e] DM_acetone[e]  -1.93         3 57.89%\n",
      "    co2[e]        Ex_co2 -4.211         1 42.11%\n",
      "    h2o[e]        Ex_h2o -14.21         0  0.00%\n",
      "\n",
      "1.0398181818181824\n",
      "0.10398181818181824\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_isoprene[e] = 1.0398181818181784\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction  Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh    10         1 100.00%\n",
      "     o2[e]    Ex_o2 7.721         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      " Metabolite       Reaction   Flux  C-Number C-Flux\n",
      "isoprene[e] DM_isoprene[e]  -1.04         5 51.99%\n",
      "     co2[e]         Ex_co2 -4.801         1 48.01%\n",
      "     h2o[e]         Ex_h2o -15.84         0  0.00%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "with model_mal as md:\n",
    "    FluxScenario = dict()\n",
    "    for target in Product:\n",
    "        # optimizing for production\n",
    "        md = Model_BlockedTCA(md, target)\n",
    "\n",
    "        sol = md.optimize()\n",
    "        print(sol.fluxes[f'DM_{target}'])\n",
    "        # yield of malate from methanol\n",
    "        print(-sol.fluxes[f'DM_{target}']/sol.fluxes['Ex_meoh'])\n",
    "        # print exchange fluxes\n",
    "        print(md.summary())\n",
    "        FluxScenario[target] = {md.reactions.get_by_id(Rct).id: round(sol[Rct],2) for Rct in sol[np.abs(sol.fluxes)>1e-2].keys()}\n",
    "    FluxAll['BlockedTCA'] = FluxScenario"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forced TCA scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.010545454545456035\n",
      "0.0010545454545456035\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_mal_L[e] = 0.010545454545559142\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction  Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh    10         1 100.00%\n",
      "     o2[e]    Ex_o2 14.97         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      "Metabolite    Reaction     Flux  C-Number C-Flux\n",
      "  mal_L[e] DM_mal_L[e] -0.01055         4  0.42%\n",
      "    co2[e]      Ex_co2   -9.958         1 99.58%\n",
      "      h[e]        Ex_h -0.02109         0  0.00%\n",
      "    h2o[e]      Ex_h2o   -19.97         0  0.00%\n",
      "\n",
      "0.007073170731641234\n",
      "0.0007073170731641234\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_acetone[e] = 0.007073170731631028\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction  Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh    10         1 100.00%\n",
      "     o2[e]    Ex_o2 14.97         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      "Metabolite      Reaction      Flux  C-Number C-Flux\n",
      "acetone[e] DM_acetone[e] -0.007073         3  0.21%\n",
      "    co2[e]        Ex_co2    -9.979         1 99.79%\n",
      "    h2o[e]        Ex_h2o    -19.98         0  0.00%\n",
      "\n",
      "0.003892617449644574\n",
      "0.0003892617449644574\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_isoprene[e] = 0.0038926174496554185\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction  Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh    10         1 100.00%\n",
      "     o2[e]    Ex_o2 14.97         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      " Metabolite       Reaction      Flux  C-Number C-Flux\n",
      "isoprene[e] DM_isoprene[e] -0.003893         5  0.19%\n",
      "     co2[e]         Ex_co2    -9.981         1 99.81%\n",
      "     h2o[e]         Ex_h2o    -19.98         0  0.00%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with model_mal as md:\n",
    "    FluxScenario = dict()\n",
    "    for target in Product:\n",
    "        # optimizing for production\n",
    "        md = Model_ForceTCA(md, target)\n",
    "\n",
    "        sol = md.optimize()\n",
    "        print(sol.fluxes[f'DM_{target}'])\n",
    "        # yield of malate from methanol\n",
    "        print(-sol.fluxes[f'DM_{target}']/sol.fluxes['Ex_meoh'])\n",
    "        # print exchange fluxes\n",
    "        print(md.summary())\n",
    "        FluxScenario[target] = {md.reactions.get_by_id(Rct).id: round(sol[Rct],2) for Rct in sol[np.abs(sol.fluxes)>1e-2].keys()}\n",
    "    FluxAll['ForcedTCA'] = FluxScenario"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forced standard Glyoxylate shunt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23966666666671216\n",
      "0.023966666666671216\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_mal_L[e] = 0.23966666666667155\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction  Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh    10         1 100.00%\n",
      "     o2[e]    Ex_o2 14.28         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      "Metabolite    Reaction    Flux  C-Number C-Flux\n",
      "  mal_L[e] DM_mal_L[e] -0.2397         4  9.59%\n",
      "    co2[e]      Ex_co2  -9.041         1 90.41%\n",
      "      h[e]        Ex_h -0.4793         0  0.00%\n",
      "    h2o[e]      Ex_h2o  -19.28         0  0.00%\n",
      "\n",
      "0.1563043478260904\n",
      "0.01563043478260904\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_acetone[e] = 0.15630434782609032\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction  Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh    10         1 100.00%\n",
      "     o2[e]    Ex_o2 14.37         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      "Metabolite      Reaction    Flux  C-Number C-Flux\n",
      "acetone[e] DM_acetone[e] -0.1563         3  4.69%\n",
      "    co2[e]        Ex_co2  -9.531         1 95.31%\n",
      "    h2o[e]        Ex_h2o  -19.53         0  0.00%\n",
      "\n",
      "0.08768292682933605\n",
      "0.008768292682933606\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_isoprene[e] = 0.08768292682928201\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction  Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh    10         1 100.00%\n",
      "     o2[e]    Ex_o2 14.39         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      " Metabolite       Reaction     Flux  C-Number C-Flux\n",
      "isoprene[e] DM_isoprene[e] -0.08768         5  4.38%\n",
      "     co2[e]         Ex_co2   -9.562         1 95.62%\n",
      "     h2o[e]         Ex_h2o   -19.65         0  0.00%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with model_mal as md:\n",
    "    FluxScenario = dict()\n",
    "    for target in Product:\n",
    "        # optimizing for production\n",
    "        md = Model_ForceStdGlx(md, target)\n",
    "        sol = md.optimize()\n",
    "        print(sol.fluxes[f'DM_{target}'])\n",
    "        # yield of malate from methanol\n",
    "        print(-sol.fluxes[f'DM_{target}']/sol.fluxes['Ex_meoh'])\n",
    "        # print exchange fluxes\n",
    "        print(md.summary())\n",
    "        FluxScenario[target] = {md.reactions.get_by_id(Rct).id: round(sol[Rct],2) for Rct in sol[np.abs(sol.fluxes)>1e-2].keys()}\n",
    "    FluxAll['ForcedStdGlx'] = FluxScenario"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forced impaired Glyoxylate shunt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0014285714285170315\n",
      "0.00014285714285170315\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_mal_L[e] = 0.001428571428474534\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh   10         1 100.00%\n",
      "     o2[e]    Ex_o2   15         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      "Metabolite    Reaction      Flux  C-Number C-Flux\n",
      "  mal_L[e] DM_mal_L[e] -0.001429         4  0.06%\n",
      "    co2[e]      Ex_co2    -9.994         1 99.94%\n",
      "      h[e]        Ex_h -0.002857         0  0.00%\n",
      "    h2o[e]      Ex_h2o       -20         0  0.00%\n",
      "\n",
      "0.0009356725145846641\n",
      "9.35672514584664e-05\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_acetone[e] = 0.0009356725146136245\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh   10         1 100.00%\n",
      "     o2[e]    Ex_o2   15         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      "Metabolite      Reaction       Flux  C-Number C-Flux\n",
      "acetone[e] DM_acetone[e] -0.0009357         3  0.03%\n",
      "    co2[e]        Ex_co2     -9.997         1 99.97%\n",
      "    h2o[e]        Ex_h2o        -20         0  0.00%\n",
      "\n",
      "0.0005263157894621051\n",
      "5.2631578946210516e-05\n",
      "Objective\n",
      "=========\n",
      "1.0 DM_isoprene[e] = 0.0005263157894350178\n",
      "\n",
      "Uptake\n",
      "------\n",
      "Metabolite Reaction Flux  C-Number  C-Flux\n",
      "   meoh[e]  Ex_meoh   10         1 100.00%\n",
      "     o2[e]    Ex_o2   15         0   0.00%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      " Metabolite       Reaction       Flux  C-Number C-Flux\n",
      "isoprene[e] DM_isoprene[e] -0.0005263         5  0.03%\n",
      "     co2[e]         Ex_co2     -9.997         1 99.97%\n",
      "     h2o[e]         Ex_h2o        -20         0  0.00%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with model_mal as md:\n",
    "    FluxScenario = dict()\n",
    "    for target in Product:\n",
    "        # optimizing for production\n",
    "        md = Model_ForceImpGlx(md, target)\n",
    "        sol = md.optimize()\n",
    "        print(sol.fluxes[f'DM_{target}'])\n",
    "        # yield of malate from methanol\n",
    "        print(-sol.fluxes[f'DM_{target}']/sol.fluxes['Ex_meoh'])\n",
    "        # print exchange fluxes\n",
    "        print(md.summary())\n",
    "        FluxScenario[target] = {md.reactions.get_by_id(Rct).id: round(sol[Rct],2) for Rct in sol[np.abs(sol.fluxes)>1e-2].keys()}\n",
    "    FluxAll['ForcedImpGlx'] = FluxScenario\n",
    "# print('')\n",
    "# [print(f'{model_mal.reactions.get_by_id(Rct).id} \\t {model_mal.reactions.get_by_id(Rct).reaction} \\t {round(sol[Rct],2)}') for Rct in RctFluxName.keys()];"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### File export of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mal_L[e]\n",
      "\n",
      "acetone[e]\n",
      "\n",
      "isoprene[e]\n",
      "\n",
      "Flux file saved in ../data/MethanolBioconversion/231128_ScenarioPathFlux.xlsx\n"
     ]
    }
   ],
   "source": [
    "# export fluxes from transposed myResults to xlsx file with multiple sheets\n",
    "File = os.path.join(SaveDir, f'{Today}_ScenarioPathFlux.xlsx')\n",
    "myResults = pd.DataFrame(FluxAll).transpose()\n",
    "with pd.ExcelWriter(File) as writer:\n",
    "    for target in list(myResults.columns):\n",
    "        print(target)\n",
    "        # print(myResults.to_dict()[target])\n",
    "        pd.DataFrame(myResults[target].to_dict()).to_excel(writer, sheet_name=target[:-3])\n",
    "        print('') \n",
    "        # print file location\n",
    "print(f'Flux file saved in {File}')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA of scenario fluxes\n",
    "A PCA of the fluxes of the scenarios shows how different the solutions are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                              mal_L  \\\n",
      "BlockedTCA        {'NADH2_u6mh': 5.0, 'PTE183x': nan, 'PIt2p': n...   \n",
      "ForcedImpGlx      {'NADH2_u6mh': 9.99, 'PTE183x': nan, 'PIt2p': ...   \n",
      "ForcedStdGlx      {'NADH2_u6mh': 8.56, 'PTE183x': nan, 'PIt2p': ...   \n",
      "ForcedTCA         {'NADH2_u6mh': 12.14, 'PTE183x': nan, 'PIt2p':...   \n",
      "UnconstrainedRef  {'NADH2_u6mh': 4.17, 'PTE183x': 1.29, 'PIt2p':...   \n",
      "\n",
      "                                                            acetone  \\\n",
      "BlockedTCA        {'NADH2_u6mh': 4.56, 'ASNt2m': 1.23, 'FORtrn':...   \n",
      "ForcedImpGlx      {'NADH2_u6mh': 9.99, 'ASNt2m': nan, 'FORtrn': ...   \n",
      "ForcedStdGlx      {'NADH2_u6mh': 8.75, 'ASNt2m': nan, 'FORtrn': ...   \n",
      "ForcedTCA         {'NADH2_u6mh': 12.14, 'ASNt2m': nan, 'FORtrn':...   \n",
      "UnconstrainedRef  {'NADH2_u6mh': 4.17, 'ASNt2m': 0.94, 'FORtrn':...   \n",
      "\n",
      "                                                           isoprene  \n",
      "BlockedTCA        {'NADH2_u6mh': 5.44, 'CYOR_u6m': 5.44, 'ACACT1...  \n",
      "ForcedImpGlx      {'NADH2_u6mh': 9.99, 'CYOR_u6m': 19.99, 'ACACT...  \n",
      "ForcedStdGlx      {'NADH2_u6mh': 8.77, 'CYOR_u6m': 18.77, 'ACACT...  \n",
      "ForcedTCA         {'NADH2_u6mh': 12.15, 'CYOR_u6m': 19.95, 'ACAC...  \n",
      "UnconstrainedRef  {'NADH2_u6mh': 4.75, 'CYOR_u6m': 4.75, 'ACACT1...  \n"
     ]
    }
   ],
   "source": [
    "# if you want to load an existing file set the file name and set LoadExisting to True\n",
    "File = os.path.join(SaveDir, 'ScenarioPathFlux_published.xlsx')\n",
    "LoadExisting = True\n",
    "\n",
    "if LoadExisting == True:\n",
    "    # Define the file path\n",
    "    file_path = File\n",
    "\n",
    "    # Load the Excel file\n",
    "    Data = pd.read_excel(file_path, sheet_name=None)\n",
    "\n",
    "    # Create an empty dataframe\n",
    "    myResults = pd.DataFrame()\n",
    "\n",
    "    # Iterate over each sheet in the loaded Excel file\n",
    "    for sheet_name, data in Data.items():\n",
    "        # Get the non-index columns as a single list\n",
    "        columns = data.columns[1:]\n",
    "        # Create a dictionary to store the values for each column\n",
    "        column_dict = {}\n",
    "        # Iterate over each column\n",
    "        for column in columns:\n",
    "            # Get the values for the current column\n",
    "            values = data[column].values\n",
    "            # Create a dictionary with the row index as the key and the value as the value\n",
    "            values_dict = {row: value for row, value in zip(data.iloc[:, 0].values.flatten(), values)}\n",
    "            # Add the column dictionary to the main dictionary\n",
    "            column_dict[column] = values_dict\n",
    "        # Create a dataframe with the column dictionary as the data\n",
    "        column_df = pd.DataFrame({sheet_name:column_dict}) #{sheet_name:column_dict}\n",
    "        # Set the row index of column_df to the values in columns\n",
    "        column_df.set_index(columns.sort_values(), inplace=True) #\n",
    "        # Add the column dataframe to the main dataframe\n",
    "        myResults = pd.concat([myResults, column_df], axis=1)\n",
    "    print(myResults)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ulf/.local/lib/python3.9/site-packages/sklearn/utils/validation.py:623: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/home/ulf/.local/lib/python3.9/site-packages/sklearn/utils/validation.py:623: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/home/ulf/.local/lib/python3.9/site-packages/sklearn/utils/validation.py:623: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/home/ulf/.local/lib/python3.9/site-packages/sklearn/utils/validation.py:623: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/home/ulf/.local/lib/python3.9/site-packages/sklearn/utils/validation.py:623: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/home/ulf/.local/lib/python3.9/site-packages/sklearn/utils/validation.py:623: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure saved as ../data/MethanolBioconversion/SimulationResults/231129_PCA-Flux-All.svg\n"
     ]
    }
   ],
   "source": [
    "IndxList = list()\n",
    "for target in myResults.columns:\n",
    "    IndxList.append(pd.DataFrame(myResults[target].to_dict()).index)\n",
    "# find intersection of all indices in IndxList\n",
    "Indx = list(set.intersection(*map(set, IndxList)))\n",
    "\n",
    "\n",
    "# Constructing a PCA plot of the fluxes in the dataframe myResults for all components but only with the index values in Indx\n",
    "myMark = {'mal_L[e]':'x', 'acetone[e]':'o', 'isoprene[e]':'d'}\n",
    "# Dictionary with labels for the different scenarios\n",
    "if LoadExisting == True:\n",
    "    myLabel = {'BlockedTCA':'TCA-', 'ForcedImpGlx':'GlxVar', 'ForcedStdGlx':'GlxStd', 'ForcedTCA':'TCA+', 'UnconstrainedRef':'Ref'}\n",
    "else: \n",
    "    myLabel = {'UnconstrainedRef':'Ref', 'BlockedTCA':'TCA-', 'ForcedTCA':'TCA+', 'ForcedStdGlx':'GlxStd', 'ForcedImpGlx':'GlxVar'}\n",
    "myLabelColor = {'Ref':'k', 'TCA-':'r', 'TCA+':'b', 'GlxStd':'g', 'GlxVar':'m'}\n",
    "AllDatSeq = list(product(myMark.keys(), myLabel.values()))\n",
    "PlotLabel = [[myMark[element[0]],myLabelColor[element[1]]] for element in AllDatSeq]\n",
    "PrintLabel = [f'{element[0]}-{element[1]}' for element in AllDatSeq]\n",
    "\n",
    "x = list()\n",
    "for target in list(myResults.columns):\n",
    "    # Replacing NaN values with 0\n",
    "    myDat = pd.DataFrame(myResults[target].to_dict()).fillna(0).loc[Indx]\n",
    "\n",
    "    # Standardizing the features\n",
    "    x.append(StandardScaler().fit_transform(myDat.T))\n",
    "x = np.array(x).reshape(-1, len(Indx)) # 134 is the number of reactions in the model \n",
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(x)\n",
    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2'], index = PrintLabel)\n",
    "# adding columns for target marker type and pathway color\n",
    "principalDf['Target'] = [mylist[0] for mylist in PlotLabel]\n",
    "principalDf['Path'] = [mylist[1] for mylist in PlotLabel]\n",
    "\n",
    "# Plotting the PCA\n",
    "# plt.figure(figsize=(10,10))\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "for idx, row in principalDf.iterrows():\n",
    "    plt.scatter(row['PC1'], row['PC2'], s=100, marker=row['Target'], c=row['Path'], label=idx)\n",
    "\n",
    "# adding manual legend for target marker and pathway color\n",
    "# https://matplotlib.org/stable/gallery/text_labels_and_annotations/custom_legends.html\n",
    "legend_elements = [Line2D([0], [0], marker='x', color='k', label='Malate', markerfacecolor='w', markersize=15),\n",
    "                   Line2D([0], [0], marker='o', color='k', label='Acetone', markerfacecolor='w', markersize=15),\n",
    "                   Line2D([0], [0], marker='d', color='k', label='Isoprene', markerfacecolor='w', markersize=15),\n",
    "                   Line2D([0], [0], color='k', lw=4, label='Ref'),\n",
    "                   Line2D([0], [0], color='r', lw=4, label='TCA-'),\n",
    "                   Line2D([0], [0], color='b', lw=4, label='TCA+'),\n",
    "                   Line2D([0], [0], color='g', lw=4, label='GlxStd'),\n",
    "                   Line2D([0], [0], color='m', lw=4, label='GlxVar')]\n",
    "# plt.legend(handles = legend_elements, bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n",
    "plt.legend(handles = legend_elements, loc='center')\n",
    "\n",
    "\n",
    "# saving figure\n",
    "FigureName = 'PCA-Flux-All'\n",
    "PlotName = os.path.join(SaveDir, f'{Today}_{FigureName}.{FigureType}')\n",
    "plt.savefig(PlotName, dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "print(f'Figure saved as {PlotName}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flux correlations to target production\n",
    "\n",
    "For the five different scenarios the fluxes for each reaction are correlated with the target flux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mal_L\n",
      "Top 5 reactions:\n",
      "aconitase\n",
      "succinate dehydrogenase (ubiquinone-6), mitochondrial\n",
      "fumarase, mitochondrial\n",
      "L-serine deaminase\n",
      "methylenetetrahydrofolate dehydrogenase (NADP)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ulf/anaconda3/envs/iambjudas/lib/python3.9/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/ulf/anaconda3/envs/iambjudas/lib/python3.9/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/tmp/ipykernel_7936/829677323.py:18: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  RctNames = CorrMat[np.where(np.logical_and(np.abs(CorrMat)>LimLow, np.abs(CorrMat)<LimHigh))[0]].index\n",
      "/tmp/ipykernel_7936/829677323.py:20: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  RctValues = CorrMat[np.where(np.logical_and(np.abs(CorrMat)>LimLow, np.abs(CorrMat)<LimHigh))[0]].values\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "acetone\n",
      "Top 5 reactions:\n",
      "L-asparagine transport, mitochondrial\n",
      "asparagine mitochondrial transport via proton transport\n",
      "succinate dehydrogenase (ubiquinone-6), mitochondrial\n",
      "succinate dehydrogenase\n",
      "aconitase\n",
      "\n",
      "\n",
      "isoprene\n",
      "Top 5 reactions:\n",
      "Hydroxymethylglutaryl-CoA reversible mitochondrial transport\n",
      "CoA transporter (mitochondrial), irreversible\n",
      "Hydroxymethylglutaryl CoA synthase, mitochondrial\n",
      "acetyl-CoA C-acetyltransferase, mitochondrial\n",
      "succinate dehydrogenase (ubiquinone-6), mitochondrial\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ulf/anaconda3/envs/iambjudas/lib/python3.9/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/ulf/anaconda3/envs/iambjudas/lib/python3.9/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/tmp/ipykernel_7936/829677323.py:18: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  RctNames = CorrMat[np.where(np.logical_and(np.abs(CorrMat)>LimLow, np.abs(CorrMat)<LimHigh))[0]].index\n",
      "/tmp/ipykernel_7936/829677323.py:20: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  RctValues = CorrMat[np.where(np.logical_and(np.abs(CorrMat)>LimLow, np.abs(CorrMat)<LimHigh))[0]].values\n",
      "/home/ulf/anaconda3/envs/iambjudas/lib/python3.9/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/ulf/anaconda3/envs/iambjudas/lib/python3.9/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/tmp/ipykernel_7936/829677323.py:18: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  RctNames = CorrMat[np.where(np.logical_and(np.abs(CorrMat)>LimLow, np.abs(CorrMat)<LimHigh))[0]].index\n",
      "/tmp/ipykernel_7936/829677323.py:20: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  RctValues = CorrMat[np.where(np.logical_and(np.abs(CorrMat)>LimLow, np.abs(CorrMat)<LimHigh))[0]].values\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IMeTp6w3g1iBwAbitNW7cWLVr11ZcXJw6duyYJHTt27dPHTt2dJ5OlJ21bdtWkjRixAgdPHjQuXzfvn167LHHUrzGJiEhQY8//rj++ecftW7dWgMHDpQk1a9fX6NGjXKuj4mJSVc9EyZMUM2aNXX69GnVq1dPEydOdM4Alyg2NlbTpk1T5cqVk3zQLV26tHr06CFJeuaZZ5KMKpw7d06hoaHat2+fAgICNGjQoHTV5a6uXbvqgQce0OnTp9W0aVOXH7J//fVXjRo1SosXL3arz8TgM2zYMOeIkiTt3LlTbdu2TXWUpnz58pKkVatWpWc3JEkvv/yyLMvSokWLFBERkeQ00cRrmiRp+PDhypUrV7r790Ra+2PXMZD4GowdO1Z79uxxLv/zzz/VunXrFANOlSpVnPV06NBBixYtSjLydv78eU2bNk3bt293LsvIMZT4+4mKikp1cg8AWcRNm4AeADxk942P9+7da4oWLeq82e29995rqlSpYhwOhylXrpx5/vnnjSTTu3fvJNtlxv2tUpKZNz5OrLVAgQJGksmVK5epXLmyqVSpkrEsy1SrVs0MGTLEZZ0RERFGkilatKg5efJkknVXr1511tmmTZt07Z8x124G27VrV+fv0MvLy9x5552mVq1apmLFisbb29t549wb76V09uxZU79+fee25cuXN/fff7/JkyePkWRy585tlixZkuw53Xk9UjtWrnf8+HFTt25dZw2FCxc2tWrVMtWrVzdBQUHO5TfeKyml+3Dt2rXL+Pv7O+8LV7VqVXPnnXcaSaZkyZJm7NixKb6+b775pvP57r77btOwYUPTqFGjJM+d2r2bJkyY4Lz5cWBgoKlZs6YpXry4s8/Q0NBkN7e28/ifP3++s++yZcuaBg0amEaNGiW5H5Wnx0Bqjhw5YgoXLmwkGW9vb1OpUiXne0H+/PnNe++9l+Kxcf78edOqVStnPQULFjQ1a9Y0ZcuWdR7LN947z9NjaPPmzc77kJUoUcLUr1/fNGrUyAwcODBd+wvg5sj+X9sCQAaVL19eP/zwg5566ikVLlxYv/76q2JjY/XMM89oy5Ytzm/1Ey+Mz45CQkK0ceNGPfbYY8qXL59+//13xcXFadiwYVq/fr3LyRA2bNig8PBwORwOzZo1S8HBwUnWOxwOffrppwoKCtI333yjiRMnpqsmf39/zZ49W9u2bdNzzz2nSpUq6cSJE/rhhx908uRJ1ahRQ//5z3/0yy+/JLuPUt68ebVq1SpNnjxZdevW1YkTJ7Rr1y4VLFhQffr00a5du9SqVav0/6LSoVChQoqOjtasWbP0f//3fzLGaMeOHfrrr79UqlQp9e7dW19//bW6dOniVn9VqlTRxo0b1b59e+XOnVu//fabrly5oueff147duxwzrrnyuDBg/Xmm2/q3nvv1cGDB7VmzRpFR0e7PdnC4MGDtWHDBnXu3Fl+fn7auXOnLl68qBYtWmj+/PmaNm3aTb1uqGPHjpo6dapq166tkydPat26dYqOjk4y6mTHMVCsWDFt3rxZ3bt3V2BgoPbu3aszZ86oZ8+e2rFjhypXrpzitnny5NE333yjOXPmOE9V3rlzpy5cuKDatWtr/Pjxya4N8/QYqlWrlhYuXKjGjRvr33//1YYNGxQdHa2dO3ema38B3ByWMYxFA0BqWrduraVLl+rdd9/V888/f6vLAQAA2QgjXACQioMHDzpvStqwYcNbXA0AAMhuCFwAbnv79u3ThAkTkk388OOPP6pNmza6fPmy6tevr2rVqt2aAgEAQLbFKYUAbns7d+5U9erVZVmWihUrpmLFiunkyZPO619Kliyp1atXq1y5cre2UAAAkO0QuADc9s6cOaMJEybo+++/14EDB3Tq1CnlypVL5cuXV5s2bTR48GAVKFDgVpcJAACyIQIXAAAAANiEa7gAAAAAwCbet7qA7CIhIUF///238ubNe1PvRQIAAAAgazHG6Ny5cypWrJgcjtTHsAhcbvr7779VsmTJW10GAAAAgCzi8OHDKlGiRKptCFxuyps3r6Rrv9SAgIBbXA0AAACAW+Xs2bMqWbKkMyOkhsDlpsTTCAMCAghcAAAAANy61IhJMwAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJlkycH366ad66qmndP/998vX11eWZWn69Onp7ichIUGTJk1S1apVlTt3bhUsWFCPPvqo9u7dm/lFAwAAAMANsmTgevnll/Xxxx/r4MGDKlq0qMf9PP300xowYICuXr2qAQMGqFWrVlq0aJFq1qyp3bt3Z2LFAAAAAJBclgxcU6ZM0YEDB3Ty5Ek9/fTTHvWxevVqffLJJ2rQoIF++OEHvf7665oxY4aWLFmis2fPqn///plcNQAAAAAklSUDV/PmzVW6dOkM9fHJJ59IksaMGSNfX1/n8mbNmqlly5Zas2aNfv/99ww9BwAAAACkJksGrswQFRUlf39/1atXL9m6li1bSpKio6NvdlkAAAAAbiPet7oAO5w/f15Hjx5V5cqV5eXllWx9hQoVJCnVyTPi4uIUFxfn/Pns2bOZXygAAACAHC1HjnDFxsZKkvLly+dyfUBAQJJ2rowbN0758uVzPkqWLJn5hQIAAADI0XJk4MoMw4YNU2xsrPNx+PDhW10SAAAAgGwmR55SmDiyldIIVuLpgSmNgEmSr69vksk2AAAAACC9cuQIl7+/v4oWLar9+/fr6tWrydYnXruVeC0XAAAAANghRwYuSWrUqJHOnz+v9evXJ1u3bNkyZxsAAAAAsEu2D1wxMTHas2ePYmJikizv16+fJOnll1/W5cuXnctXrlypZcuWqWHDhqpYseJNrRUAAADA7SVLXsM1ZcoUrVu3TpL0008/OZdFRUVJkh5++GE9/PDDkqRJkyZp9OjRCgsLU3h4uLOPJk2aqE+fPpoyZYqqV6+u1q1b6/jx45o7d64CAgL0wQcf3MxdAgAAAHAbypKBa926dZoxY0aSZevXr3eeHhgSEuIMXKn56KOPVLVqVX300UeaOHGi7rjjDrVt21avvvoqo1sAAAAAbGcZY8ytLiI7OHv2rPLly6fY2FjnfbwAAAAA3H7Skw2y/TVcAAAAAJBVEbgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAm2TJaeHhnpD/Lkn3Ngdea51p2wMAAABIHSNcAAAAAGATAhcAAAAA2IRTCuExT05JlDL3tMbMqAEAAACwCyNcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANjE+1YXANxqIf9dku5tDrzW2oZKAAAAkNMQuIAMyozARugDAADImTilEAAAAABsQuACAAAAAJtwSiGQA2SF0xqp4dZtnxNrAAAgpyBwAQCynJwQnqnB8+2pwXUfALInAhcAAEA2wMgxkD1xDRcAAAAA2IQRLgAAgNtATjhFNbP6AG4mRrgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAm3jf6gIAAACAmyXkv0vSvc2B11pneh+4fTDCBQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBPuwwUAAADcRNzH6/bCCBcAAAAA2ITABQAAAAA24ZRCAAAAIBvx5JREidMSbxUCFwAAAHCb4Tqym4dTCgEAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJt63ugAAAAAA2UvIf5d4tN2B11pnciVZHyNcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATbxvdQEAAAAAbj8h/12S7m0OvNbahkrsxQgXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADbJsoFr69atatWqlQIDA+Xv769atWpp9uzZ6erjzJkzGjVqlKpWraq8efMqODhYNWvW1KRJk3Tp0iWbKgcAAACAa7xvdQGuREVFqWXLlvLx8VGXLl2UL18+LViwQN27d9eBAwc0fPjwNPs4c+aMatSooT///FP169fXU089pbi4OH377bcaMGCAvvrqK33//fdyOLJs5gQAAACQzWW5wBUfH68+ffrIsiytWbNG1atXlySFhYWpTp06CgsLU+fOnVWhQoVU+/n444/1559/avDgwZowYYJz+eXLl1W/fn2tWrVK69atU8OGDW3dHwAAAAC3ryw3vLNq1Srt27dP3bp1c4YtScqbN69Gjhyp+Ph4TZs2Lc1+/vzzT0lSq1atkiz38fFRixYtJEknTpzIxMoBAAAAIKksF7iioqIkSQ8++GCydYnLoqOj0+znnnvukSR99913SZZfuXJFK1asUO7cuVWnTp0MVgsAAAAAKctypxTu3btXklyeMhgYGKjg4GBnm9T06dNHs2bN0ltvvaVt27apZs2aiouL03fffafTp09r9uzZKl68eIrbx8XFKS4uzvnz2bNnPdgbAAAAALezLBe4YmNjJUn58uVzuT4gIEB//fVXmv3kzp1bUVFReuqpp/Tpp586R8UcDoeee+451a9fP9Xtx40bp9GjR6ezegAAAAD4nyx3SmFmiYmJUYsWLbRp0yYtWbJEZ86c0bFjx/Thhx9q2rRpql27tk6fPp3i9sOGDVNsbKzzcfjw4ZtYPQAAAICcIMuNcCWObCWOdN3o7NmzKY5+XW/IkCHasGGDfvzxR1WtWtXZd9++fXX16lX1799f77zzToqjWL6+vvL19fVwLwAAAAAgC45wJV675eo6rdOnTysmJibNKeElacmSJQoKCnKGres1bdpUkrR9+/YMVgsAAAAAKctygatRo0aSpOXLlydbl7gssU1qLl++rLNnz+ry5cvJ1p08eVKSGMECAAAAYKssF7iaNWumsmXLavbs2dq5c6dz+blz5xQRESFvb2+FhoY6l8fExGjPnj2KiYlJ0k+9evUUHx+viIiIJMvj4uKcy5o0aWLbfgAAAABAlgtc3t7emjJlihISEtSgQQP169dPL774ou6991798ssvCg8PV8WKFZ3tJ02apLvvvluTJk1K0s9rr72mvHnzasyYMapdu7aGDBmiZ555RpUqVdKyZctUo0YN9enT52bvHgAAAIDbSJYLXNK1kad169apfv36mjdvnt5//30VKFBAn376qUaMGOFWH9WqVdP27dvVq1cvHTt2TJMmTdL06dPl7++v0aNHa82aNfLz87N5TwAAAADczrLcLIWJatWqpW+//TbNduHh4QoPD3e5rkKFCpo6dWomVwYAAAAA7smSI1wAAAAAkBMQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJh4Frt69e2vRokWptlm6dKl69+7tUVEAAAAAkBN4FLimT5+unTt3ptrmp59+0owZMzzpHgAAAAByBNtOKbx06ZK8vb3t6h4AAAAAsjyPE5FlWS6XG2P0119/aenSpSpWrJjHhQEAAABAduf2CJfD4ZCXl5e8vLwkSeHh4c6fr394e3srJCREW7duVZcuXWwrHAAAAACyOrdHuBo2bOgc1VqzZo1KlSqlkJCQZO28vLwUFBSkpk2bqm/fvplWKAAAAABkN24HrqioKOe/HQ6HevXqpVGjRtlREwAAAADkCB5dw5WQkJDZdQAAAABAjsONjwEAAADAJh7PUrh7925NmjRJW7du1ZkzZ3T16tVkbSzL0r59+zJUIAAAAABkVx4FrujoaD300EOKi4uTt7e3Chcu7PKeW8aYDBcIAAAAANmVR4Hrv//9r+Lj4zVlyhT17NnTOVU8AAAAAOB/PApcP/74o7p06aLevXtndj0AAAAAkGN4NGlG3rx5VahQocyuBQAAAAByFI8CV+vWrbV27drMrgUAAAAAchSPAtfrr7+u2NhYPf/887pw4UJm1wQAAAAAOYJH13A9+uij8vf31+TJkzV9+nRVqFBB+fLlS9bOsiytXLkyw0UCAAAAQHbkUeCKiopy/vvff//Vjh07XLazLMujogAAAAAgJ/AocCUkJGR2HQAAAACQ43h0DRcAAAAAIG0ejXBd799//9Xvv/+u8+fPq0GDBplREwAAAADkCB6PcB04cEDt27dXYGCgatasqSZNmjjXrV+/XpUqVUpyrRcAAAAA3G48ClyHDh3SAw88oKVLl6p9+/aqU6eOjDHO9bVr11ZMTIw+//zzTCsUAAAAALIbjwJXWFiYTp8+rejoaM2fP18tWrRIst7b21sNGjTQ+vXrM6VIAAAAAMiOPApcy5YtU4cOHVS3bt0U25QqVUpHjhzxuDAAAAAAyO48ClynTp1SSEhImu3i4uI86R4AAAAAcgSPAlfhwoX1xx9/pNrm559/VqlSpTwqCgAAAAByAo8CV4sWLbR48WL9/PPPLtevXbtWK1euVKtWrTJUHAAAAABkZx4Frpdfflm5c+dW/fr1NXbsWOdo17fffquRI0fqoYceUnBwsIYOHZqpxQIAAABAduLRjY9DQkK0bNkydenSRS+//LIsy5IxRm3atJExRqVKldL8+fNVtGjRzK4XAAAAALINjwKXdO1eW3v37tXixYu1efNmnTp1SgEBAapdu7bat28vHx+fzKwTAAAAALIdjwOXdO1+Wx06dFCHDh0yqx4AAAAAyDE8uoYLAAAAAJA2t0a4Zs6cKUnq0KGD8ubN6/zZHT169PCsMgAAAADI5twKXKGhobIsSw888IDy5s3r/Dk1xhhZlkXgAgAAAHDbcitwTZ06VZZlOWcdnDZtmq1FAQAAAEBO4PYI1/V69uxpRy0AAAAAkKNk2Ukztm7dqlatWikwMFD+/v6qVauWZs+ene5+zp07p7CwMFWuXFl58uRR/vz5dd9992n06NE2VA0AAAAA/+NR4Fq/fr2GDBmiY8eOuVx/7NgxDRkyRJs2bfKoqKioKNWvX19r165Vp06d1L9/f8XExKh79+4aO3as2/0cOnRI1atXV0REhIoVK6YBAwYoNDRUxYoV05dffulRbQAAAADgLo/uwzVhwgTt2rVLEyZMcLm+SJEi+uabb3TkyBHNnTs3XX3Hx8erT58+sixLa9asUfXq1SVJYWFhqlOnjsLCwtS5c2dVqFAh1X6uXr2qTp066e+//9bKlSvVpEmTZM8DAAAAAHbyaIRr69atql+/fqptGjZs6NEI16pVq7Rv3z5169bNGbYkKW/evBo5cqTi4+PdmrRj/vz52rp1q1588cVkYUu6dtNmAAAAALCTR6njxIkTKl68eKptihQpohMnTqS776ioKEnSgw8+mGxd4rLo6Og0+0kcWevcubMOHz6sJUuW6MyZMypXrpz+7//+T3fccUe6awMAAACA9PAocOXPn1+HDh1Ktc3Bgwc9CjV79+6VJJenDAYGBio4ONjZJjXbtm2TJK1bt06DBw9WXFycc13BggU1b948NW7cOMXt4+Likmxz9uxZd3cBAAAAACR5eEphnTp19NVXX+nw4cMu1x86dEgLFy5U3bp10913bGysJClfvnwu1wcEBDjbpCZxdG3AgAEaNGiQDh8+rJMnT2rixImKjY3Vww8/rKNHj6a4/bhx45QvXz7no2TJkuneFwAAAAC3N48C15AhQ3ThwgXVq1dPM2fOdAaXo0ePasaMGapXr54uXryoF154IVOLTY+EhARJUps2bfTaa6+pRIkSCg4O1oABAzR48GDFxsYqMjIyxe2HDRum2NhY5yOlcAkAAAAAKfHolMIGDRpo4sSJGjRokHr16iVJsixLxhhJksPh0LvvvquGDRumu+/Eka2URrHOnj2b4ujXjf3ExMSoXbt2yda1bdtW48ePd5526Iqvr698fX3drBoAAAAAkvN4qr5nn31WjRo10gcffKCtW7fqzJkzyp8/v2rVqqWnn35alStX9qjfxGu39u7dqxo1aiRZd/r0acXExLh1quKdd96pmJgY5c+fP9m6xGUXL170qEYAAAAAcIdHpxQmqly5siZPnqwtW7bo999/15YtWzRp0iSPw5YkNWrUSJK0fPnyZOsSlyW2SU3Tpk0lSbt37062LnFZSEiIp2UCAAAAQJoyFLjs0KxZM5UtW1azZ8/Wzp07ncvPnTuniIgIeXt7KzQ01Lk8JiZGe/bsUUxMTJJ+evXqJV9fX7333ns6cuRIkn7Gjh0rSXr00Udt3RcAAAAAtze3TilMnAK+ePHi8vLySnNK+OuVKlUqfQV5e2vKlClq2bKlGjRooK5duyogIEALFizQ/v37NWbMGFWsWNHZftKkSRo9erTCwsIUHh7uXF6mTBm98cYbev7553XvvfeqQ4cO8vX11ZIlS3TgwAH169dPzZo1S1dtAAAAAJAebgWukJAQWZalX3/9VRUrVnT+nBbLshQfH5/uopo0aaJ169YpLCxM8+bN0+XLl3XPPfcoIiJC3bt3d7ufAQMGKCQkRG+88YbmzJmj+Ph43XPPPRo+fLj69u2b7roAAAAAID3cClw9evSQZVnO2QETf7ZTrVq19O2336bZLjw8PMnI1o3atm2rtm3bZmJlAAAAAOAetwLX9OnTU/0ZAAAAAJCcW5NmlC1bVu+9957z55kzZ2rXrl22FQUAAAAAOYFbgevAgQM6ffq08+fQ0FAtXLjQrpoAAAAAIEdwK3AVKlQoydTqAAAAAIC0uXUNV+3atTVr1ix5e3urcOHCkqSoqKg0t7MsSyNHjsxQgQAAAACQXbkVuN544w09/PDD+uCDDyRdC1JRUVFphi4CFwAAAIDbmVuBq2LFivr555+1f/9+HTlyRI0bN1ZoaKh69uxpd30AAAAAkG25FbgkyeFwqFy5cipXrpxKly6tatWqqVGjRnbWBgAAAADZmluTZnh5eSkiIsL5c2hoqKpVq2ZXTQAAAACQI7g9wpWQkOD89yuvvCLLstSwYUNbigIAAACAnMCtEa6iRYtq3759zp+NMbYVBAAAAAA5hVsjXI0bN9bs2bP1zz//OKeFX7hwoQ4cOJDqdpZlKTIyMsNFAgAAAEB25Pa08CdOnNCyZcuUkJAgy7K0c+dO7dy5M9XtCFwAAAAAbmduBa6iRYtq+fLlunLlio4ePaqQkBANGjRIAwcOtLs+AAAAAMi23J40Q5Jy5cqlUqVKqVGjRqpWrZpKly5tV10AAAAAkO2lK3AlWr16dWbXAQAAAAA5jkeBK9GOHTv0+eefa8+ePbpw4YJWrFghSTp48KA2b96s5s2bKygoKFMKBQAAAIDsxuPA9dJLL+mtt95yThFvWZZznTFG3bp101tvvcV1XgAAAABuW27dh+tG06ZN05tvvqk2bdpo165dGjZsWJL1ISEhqlWrlhYtWpQpRQIAAABAduTRCNf777+vu+++W19++aW8vb3l4+OTrM1dd93lPMUQAAAAAG5HHo1w7d69Wy1atJC3d8p5rXDhwjpx4oTHhQEAAABAdudR4PL29tbly5dTbfP333/rjjvu8KgoAAAAAMgJPApcVapU0erVq5WQkOByfeKMhTVq1MhQcQAAAACQnXkUuHr37q3ffvtN/fv3TzbSdfbsWYWGhurYsWPq27dvphQJAAAAANmRR5Nm9O7dWytXrtQnn3yizz//XPnz55ck1apVS7/++qvOnz+v0NBQderUKTNrBQAAAIBsxaMRLkn67LPP9NFHH6lMmTI6cuSIjDHatm2bSpUqpQ8++EBTp07NzDoBAAAAINvx+MbHktS3b1/17dtXFy9e1OnTpxUQEMBEGQAAAADw/2UocCXKnTu3cufOnRldAQAAAECOkaHAdf78eX399dfauXOnYmNjFRAQoGrVqunhhx+Wv79/ZtUIAAAAANmSx4Fr4cKF6tOnj06fPi1jjHO5ZVnKnz+/PvnkEz3yyCOZUiQAAAAAZEceBa6NGzfq0UcflZeXl/r166fGjRurSJEiOn78uKKiojR9+nR16dJF0dHRqlOnTmbXDAAAAADZgkeB69VXX5Wvr682btyoypUrJ1n36KOP6plnnlGdOnU0duxYLV68OFMKBQAAAIDsxqNp4Tdu3KjHHnssWdhKVLlyZT366KPasGFDhooDAAAAgOzMo8B14cIFFSpUKNU2hQoV0oULFzwqCgAAAAByAo8CV0hIiL7//vtU26xcuVIhISGedA8AAAAAOYJHgeuxxx7T9u3b1bNnT/39999J1h09elShoaHavn27HnvssUwpEgAAAACyI48mzfjPf/6jZcuWadasWZo7d67Kly+vwoUL6/jx4/rjjz90+fJl1apVS//5z38yu14AAAAAyDY8GuHKnTu3oqOjNXr0aBUvXly7d+/W6tWrtXv3bpUoUUKjR49WdHS0cufOndn1AgAAAEC24fGNj318fDRy5EiNHDlS586d09mzZxUQEKC8efNmZn0AAAAAkG15HLiulzdvXoIWAAAAANzAo1MK169fryFDhujYsWMu1x87dkxDhgzRpk2bMlQcAAAAAGRnHgWuCRMmaPHixSpSpIjL9UWKFNE333yjt99+O0PFAQAAAEB25lHg2rp1q+rXr59qm4YNGzLCBQAAAOC25lHgOnHihIoXL55qmyJFiujEiRMeFQUAAAAAOYFHgSt//vw6dOhQqm0OHjyoO+64w6OiAAAAACAn8Chw1alTR1999ZUOHz7scv2hQ4e0cOFC1a1bN0PFAQAAAEB25lHgGjJkiC5cuKB69epp5syZOnr0qCTp6NGjmjFjhurVq6eLFy/qhRdeyNRiAQAAACA78eg+XA0aNNDEiRM1aNAg9erVS5JkWZaMMZIkh8Ohd999Vw0bNsy8SgEAAAAgm/H4xsfPPvusGjVqpA8++EBbt27VmTNnlD9/ftWqVUtPP/20KleunJl1AgAAAEC243HgkqTKlStr8uTJmVULAAAAAOQoHl3DBQAAAABIG4ELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJtk6D5c8fHx+u2333TmzBldvXrVZZuGDRtm5CkAAAAAINvyKHAZYzRq1Ci99957OnfuXKptUwpiAAAAAJDTeRS4IiIi9Oqrryp//vzq0aOHSpQoIW/vDA2WAQAAAECO41FKmjp1qkqXLq1t27apQIECmV0TAAAAAOQIHk2acfz4cT388MOELQAAAABIhUeBq0yZMjp79mxm1wIAAAAAOYpHgeu5557TN998oxMnTmR2PQAAAACQY3h0DVebNm0UFRWlunXratSoUapevbry5cvnsm2pUqUyVCAAAAAAZFceBa6QkBBZliVjjHr16pViO8uyFB8f73FxAAAAAJCdeRS4evToIcuyMrsWAAAAAMhRPApc06dPz+QyAAAAACDn8WjSDAAAAABA2jwa4brehg0btHPnTsXGxiogIEDVqlVTvXr1MqM2AAAAAMjWPA5cmzdvVs+ePbV3715JkjHGeV1XhQoVNG3aNNWpUydzqgQAAACAbMijwPXrr7+qefPmOn/+vFq2bKnGjRurSJEiOn78uKKiovTdd9+pZcuW2rRpkypVqpTZNQMAAABAtuBR4Bo9erQuX76sZcuWqUWLFknWvfTSS1qxYoVat26tV155RXPmzMmUQgEAAAAgu/Fo0ozVq1erU6dOycJWoubNm6tjx45avXp1hooDAAAAgOzMo8AVGxurkJCQVNuUKVNGsbGxnnQPAAAAADmCR4GrWLFi2rRpU6ptNm/erGLFinlUFAAAAADkBB4Frvbt2ysqKkojR47UpUuXkqy7dOmSwsLCtHr1arVv3z5TigQAAACA7MijSTNGjhypb775RmPHjtVHH32kWrVqqXDhwjp+/Li2bt2qkydPqmzZsho5cmRm1wsAAAAA2YZHgSsoKEibN2/W0KFDNWfOHC1dutS5zs/PT7169dL48eMVFBSUaYUCAAAAQHbj8Y2Pg4KCFBkZqQ8//FB79uzR2bNnFRAQoLvuuku5cuXKzBoBAAAAIFvyOHAlypUrl6pUqZIZtQAAAABAjuLRpBkAAAAAgLS5NcLVtGlTWZalGTNmqESJEmratKlbnVuWpZUrV2aoQAAAAADIrtwKXFFRUbIsSxcuXHD+7A7LsjwuDAAAAACyO7cCV0JCQqo/AwAAAACS4xouAAAAALCJR4Grd+/eWrRoUaptli5dqt69e3tUFAAAAADkBB4FrunTp2vnzp2ptvnpp580Y8YMT7oHAAAAgBzBtlMKL126JG/vDN/mCwAAAACyLY8TUUozEBpj9Ndff2np0qUqVqyYx4UBAAAAQHbn9giXw+GQl5eXvLy8JEnh4eHOn69/eHt7KyQkRFu3blWXLl1sKxwAAAAAsjq3R7gaNmzoHNVas2aNSpUqpZCQkGTtvLy8FBQUpKZNm6pv376ZVigAAAAAZDduB67rb3bscDjUq1cvjRo1yo6aAAAAACBH8OgaLm58DAAAAABp48bHAAAAAGATj2cpvHr1qubNm6cVK1bo77//VlxcXLI2lmVp5cqVGSoQAAAAALIrjwLX+fPn9eCDD2rTpk0yxsiyLBljnOsTf05p6ngAAAAAuB14dErhmDFjtHHjRo0ePVoxMTEyxig8PFxHjx7V3LlzVaZMGXXq1MnlqBcAAAAA3C48ClwLFizQAw88oJdffllBQUHO5YULF1bnzp0VFRWllStX6o033si0QgEAAAAgu/EocB06dEgPPPDA/zpxOJKMZpUoUUKtW7fWjBkzMl4hAAAAAGRTHgUuf39/ORz/2zRfvnw6evRokjZFihTRoUOHMlYdAAAAAGRjHgWu0qVLJwlTlStX1qpVq5yjXMYYrVy5UkWLFs2cKgEAAAAgG/IocDVr1kyrV69WfHy8JKlnz546dOiQ6tSpo6FDh6p+/frauXOnOnbsmKnFAgAAAEB24tG08H379lWBAgV08uRJFS1aVL1799aOHTv0/vvva+fOnZKkjh07Kjw8PBNLBQAAAIDsxaPAVaFCBf3nP/9Jsuy9997TqFGj9Oeff6p06dIqUqRIphQIAAAAANmVR4ErJQULFlTBggUzs0sAAAAAyLY8uoYLAAAAAJA2t0a4mjZt6lHnlmVp5cqVHm0LAAAAANmdW4ErKirKo84ty/JoOwAAAADICdwKXAkJCXbXAQAAAAA5DtdwAQAAAIBNMiVwnTp1SocPH86MrgAAAAAgx/A4cMXGxmrgwIEqXLiwChYsqDJlyjjXbd68Wa1atdL27dszpUgAAAAAyI48ClynTp1S7dq19d5776lkyZK6++67ZYxxrq9atarWr1+vzz77LNMKBQAAAIDsxqPAFR4ert9//12ff/65tm3bps6dOydZnzt3bjVq1EirVq3KlCIBAAAAIDvyKHAtWrRIbdq00WOPPZZim9KlS+uvv/7yuDAAAAAAyO48ClxHjx5VpUqVUm3j5+en8+fPe1QUAAAAAOQEHgWuAgUKpDkr4Z49e1S0aFGPigIAAACAnMCjwNWwYUMtWrRIR44ccbl+9+7d+u6779S8efMMFQcAAAAA2ZlHgWvEiBGKj49XvXr1NHv2bMXExEiSfv31V0VGRqpp06by9fXV0KFDM7VYAAAAAMhOvD3ZqEqVKpo7d6569OihJ554QpJkjFHlypVljFHevHk1b948VahQIVOLBQAAAIDsxKPAJUnt2rXTn3/+qRkzZmjz5s06deqUAgICVLt2bfXq1UvBwcGZWScAAAAAZDseBa6ZM2eqcOHCatmypQYPHpzZNQEAAABAjuDRNVxPPvmkli1bltm1AAAAAECO4lHgKlq0qC5fvpzZtQAAAABAjuJR4Hr44Yf1/fffKy4uLrPrAQAAAIAcw6PAFRERoTvuuEMdOnTQL7/8ktk1AQAAAECO4NGkGdWrV1dcXJx27typZcuWyc/PT4UKFZJlWUnaWZalffv2ZUqhAAAAAJDdeBS4EhIS5OPjo1KlSiVZboxJ9WcAAAAAuJ14FLgOHDiQyWUAAAAAQM7j0TVcvXv31jvvvJPJpQAAAABAzuJR4Jo9e7aOHz+e2bUAAAAAQI7iUeAqX768jh49mtm1AAAAAECO4lHgevLJJ7VkyRIdOXIks+sBAAAAgBzDo0kzOnTooJUrV6pu3bp66aWXVLNmTRUuXDjZtPCSks1kCAAAAAC3C48CV9myZWVZlowxev7551NsZ1mW4uPjPSps69atCgsL08aNG3X58mXdc889GjRokLp16+ZRf1euXFHNmjX1448/6s4779SePXs86gcAAAAA3OVR4OrRo4fL0azMEhUVpZYtW8rHx0ddunRRvnz5tGDBAnXv3l0HDhzQ8OHD091nRESE/vjjDxuqBQAAAADXPApc06dPz+Qy/ic+Pl59+vSRZVlas2aNqlevLkkKCwtTnTp1FBYWps6dO6tChQpu9/nDDz9o3LhxmjBhQqojcgAAAACQmTyaNMNOq1at0r59+9StWzdn2JKkvHnzauTIkYqPj9e0adPc7u/y5csKDQ3VAw88oOeee86OkgEAAADAJY9GuK63YcMG7dy5U7GxsQoICFC1atVUr149j/uLioqSJD344IPJ1iUui46Odru/8PBw7d27Vz/++GO6ToOMi4tTXFyc8+ezZ8+6vS0AAAAASBkIXJs3b1bPnj21d+9eSZIxxhloKlSooGnTpqlOnTrp7jexP1enDAYGBio4ONjZJi1bt27V66+/rrFjx6pixYrpqmPcuHEaPXp0urYBAAAAgOt5FLh+/fVXNW/eXOfPn1fLli3VuHFjFSlSRMePH1dUVJS+++47tWzZUps2bVKlSpXS1XdsbKwkKV++fC7XBwQE6K+//kqzn7i4OIWGhqp69ep64YUX0lWDJA0bNkxDhgxx/nz27FmVLFky3f0AAAAAuH15FLhGjx6ty5cva9myZWrRokWSdS+99JJWrFih1q1b65VXXtGcOXMypdD0GjlypPbu3avt27fLy8sr3dv7+vrK19fXhsoAAAAA3C48mjRj9erV6tSpU7Kwlah58+bq2LGjVq9ene6+E0e2Eke6bnT27NkUR78S/fDDD5owYYJGjBihKlWqpLsGAAAAAMgMHgWu2NhYhYSEpNqmTJkyKYam1CReu+XqOq3Tp08rJiYmzSnhd+3apatXryo8PFyWZSV5SNJvv/0my7KUP3/+dNcHAAAAAO7y6JTCYsWKadOmTam22bx5s4oVK5buvhs1aqRx48Zp+fLl6tKlS5J1y5cvd7ZJTcWKFfXkk0+6XBcZGal8+fKpU6dOypMnT7rrAwAAAAB3eRS42rdvr4kTJ2rkyJEaMWKE/Pz8nOsuXbqkcePGafXq1R7dZLhZs2YqW7asZs+ereeff17VqlWTJJ07d04RERHy9vZWaGios31MTIxiYmIUHBys4OBgSVLdunVVt25dl/1HRkaqSJEimjJlSrprAwAAAID08OiUwpEjR6ps2bIaO3asSpUqpTZt2ujJJ59UmzZtVLp0aUVERKhMmTIaOXJkuvv29vbWlClTlJCQoAYNGqhfv3568cUXde+99+qXX35ReHh4kineJ02apLvvvluTJk3yZFcAAAAAwDYejXAFBQVp8+bNGjp0qObMmaOlS5c61/n5+alXr14aP368goKCPCqqSZMmWrduncLCwjRv3jxdvnxZ99xzjyIiItS9e3eP+gQAAACAm83jGx8HBQUpMjJSH374ofbs2aOzZ88qICBAd911l3LlypXhwmrVqqVvv/02zXbh4eEKDw93u19jTAaqAgAAAAD3eRy4EuXKlYup1wEAAADAhXRdw/Xqq69q+PDhunLlSoptLl++rOHDh+u1117LcHEAAAAAkJ25HbhWrFihUaNGqUCBAqmeMujj46Pg4GCNGDFCq1atypQiAQAAACA7cjtwzZw5U4GBgXruuefSbPvss88qKChI06ZNy1BxAAAAAJCduR24NmzYoObNm8vX1zfNtr6+vmrevLk2bNiQoeIAAAAAIDtzO3D9/fffKlu2rNsdlylTRkePHvWoKAAAAADICdwOXA6HI9XJMm505coVORwe3VcZAAAAAHIEtxNRsWLF9PPPP7vd8c8//6zixYt7VBQAAAAA5ARuB64GDRpo1apVOnDgQJptDxw4oFWrVqlhw4YZqQ0AAAAAsjW3A9ezzz6rK1euqFOnToqJiUmx3T///KPOnTsrPj5e/fv3z5QiAQAAACA78na34X333adBgwbpnXfeUaVKlfT000+rSZMmKlGihCTpyJEjWrlypT7++GOdPHlSQ4YM0X333Wdb4QAAAACQ1bkduCTprbfekp+fn9544w29+uqrevXVV5OsN8bIy8tLw4YN05gxYzK1UAAAAADIbtIVuCzL0tixY/Xkk09q2rRp2rBhg44dOyZJKlKkiOrVq6fQ0FCVK1fOlmIBAAAAIDtJV+BKVK5cOUawAAAAACAN3CgLAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwSZYNXFu3blWrVq0UGBgof39/1apVS7Nnz3Z7+3Xr1umFF15QjRo1VKBAAfn5+emuu+7Sf/7zH505c8a+wgEAAADg//O+1QW4EhUVpZYtW8rHx0ddunRRvnz5tGDBAnXv3l0HDhzQ8OHD0+yjU6dOiomJUf369dWjRw9ZlqWoqCi9/vrr+vLLL7VhwwYVKlToJuwNAAAAgNtVlgtc8fHx6tOnjyzL0po1a1S9enVJUlhYmOrUqaOwsDB17txZFSpUSLWfwYMHq0ePHipatKhzmTFGzz77rD744AONHj1akydPtnVfAAAAANzestwphatWrdK+ffvUrVs3Z9iSpLx582rkyJGKj4/XtGnT0uznP//5T5KwJUmWZWnkyJGSpOjo6MwtHAAAAABukOUCV1RUlCTpwQcfTLYucVlGwlKuXLkkSd7eWW5wDwAAAEAOk+VSx969eyXJ5SmDgYGBCg4OdrbxxNSpUyW5DnTXi4uLU1xcnPPns2fPevycAAAAAG5PWW6EKzY2VpKUL18+l+sDAgKcbdJr586dGj16tAoVKqSXXnop1bbjxo1Tvnz5nI+SJUt69JwAAAAAbl9ZLnDZZf/+/WrTpo2uXr2qOXPmKDg4ONX2w4YNU2xsrPNx+PDhm1QpAAAAgJwiy51SmDiyldIo1tmzZ1Mc/UrJwYMH1aRJE508eVJffvmlmjRpkuY2vr6+8vX1TdfzAAAAAMD1stwIV+K1W66u0zp9+rRiYmLSnBL+egcOHFDjxo31999/a968eWrTpk2m1QoAAAAAqclygatRo0aSpOXLlydbl7gssU1aEsPWkSNHNHfuXLVv3z7zCgUAAACANGS5wNWsWTOVLVtWs2fP1s6dO53Lz507p4iICHl7eys0NNS5PCYmRnv27FFMTEySfq4PW3PmzFGHDh1u0h4AAAAAwDVZ7houb29vTZkyRS1btlSDBg3UtWtXBQQEaMGCBdq/f7/GjBmjihUrOttPmjRJo0ePVlhYmMLDw53LGzdurIMHD+qBBx7Qrl27tGvXrmTPdX17AAAAAMhsWS5wSVKTJk20bt06hYWFad68ebp8+bLuueceRUREqHv37m71cfDgQUnSpk2btGnTJpdtCFwAAAAA7JQlA5ck1apVS99++22a7cLDw10GJ2OMDVUBAAAAgPuy3DVcAAAAAJBTELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwSZYNXFu3blWrVq0UGBgof39/1apVS7Nnz05XHwkJCZo0aZKqVq2q3Llzq2DBgnr00Ue1d+9em6oGAAAAgP/JkoErKipK9evX19q1a9WpUyf1799fMTEx6t69u8aOHet2P08//bQGDBigq1evasCAAWrVqpUWLVqkmjVravfu3TbuAQAAAABI3re6gBvFx8erT58+sixLa9asUfXq1SVJYWFhqlOnjsLCwtS5c2dVqFAh1X5Wr16tTz75RA0aNND3338vX19fSVKPHj3UokUL9e/fX9HR0bbvDwAAAIDbV5Yb4Vq1apX27dunbt26OcOWJOXNm1cjR45UfHy8pk2blmY/n3zyiSRpzJgxzrAlSc2aNVPLli21Zs0a/f7775m/AwAAAADw/2W5wBUVFSVJevDBB5OtS1zmzshUVFSU/P39Va9evWTrWrZs6XY/AAAAAOCpLHdKYeKEFq5OGQwMDFRwcHCak16cP39eR48eVeXKleXl5ZVsfWLfqfUTFxenuLg458+xsbGSpLNnz6a9EzdJQtyFdG9zff23YntqSL49NVBDTjims0INWe21pAbPt6eGrFNDVjueqMHz7XNiDbdSYh3GmLQbmyymRYsWRpLZu3evy/Vly5Y1Pj4+qfZx5MgRI8nUq1fP5fo1a9YYSaZfv34p9hEWFmYk8eDBgwcPHjx48ODBg4fLx+HDh9PMN1luhCurGDZsmIYMGeL8OSEhQadOnVKBAgVkWdYtrCx1Z8+eVcmSJXX48GEFBATckj5u9fbUQA3UkPnbUwM1UEPmb08N1EANmb/9zWKM0blz51SsWLE022a5wJUvXz5J/zuF70Znz551tslIH9e3c8XX1zfJZBuSlD9//lSfNysJCAjI8EGa0T5u9fbUQA3UkPnbUwM1UEPmb08N1EANmb/9zZBWJkmU5SbNSO36qtOnTysmJibNKeH9/f1VtGhR7d+/X1evXk22PrXrxAAAAAAgs2S5wNWoUSNJ0vLly5OtS1yW2Catfs6fP6/169cnW7ds2TK3+wEAAAAAT2W5wNWsWTOVLVtWs2fP1s6dO53Lz507p4iICHl7eys0NNS5PCYmRnv27FFMTEySfvr16ydJevnll3X58mXn8pUrV2rZsmVq2LChKlasaOu+3Aq+vr4KCwtLdjrkzezjVm9PDdRADZm/PTVQAzVk/vbUQA3UkPnbZ0WWMe7MZXhzrV69Wi1btpSvr6+6du2qgIAALViwQPv379eYMWM0YsQIZ9vw8HCNHj1aYWFhCg8PT9JP3759NWXKFFWqVEmtW7fW8ePHNXfuXPn5+WnDhg2qVKnSTd4zAAAAALeTLDfCJUlNmjTRunXrVL9+fc2bN0/vv/++ChQooE8//TRJ2ErLRx99pIkTJ8qyLE2cOFFLlixR27ZttWXLFsIWAAAAANtlyREuAAAAAMgJsuQIFwAAAADkBAQuANlOfHz8rS4BAADALQQuZEszZ87Url27bnUZuMl2796tF154QSVKlLjVpQAAALiFa7iQLTkcDoWHh2vUqFG3upRbZt68eR5t9+ijj2ZyJfb6999/NWfOHEVGRmrLli0yxsjHx0eXLl261aVlCmOMLMvKtP4uXbokPz+/dG2zaNEitWvXLtNquNkuXryo3Llz3+oyACBLSUhI0JIlSxQZGamFCxfe6nJua963ugDceidPntSvv/6qI0eO6MqVKy7b9OjR4yZXlbPNnDnTo+2ufx26dOmSrg/qiR/sXQWuY8eOacOGDfL29laDBg0UGBjoso/o6GhFR0d7HHQjIyO1fv16TZ06Nc2269at09SpU/XFF1/owoULMsaoevXq6tWrl7p16+bR82ems2fP6syZMypVqpRz2fbt21WjRg23+0hISFCPHj306aefZlpdHTt21KJFi+Tl5eVW+8WLF+vRRx/NkgG2Y8eOioyMVP78+VNss2PHDnXv3l27d+92uf7w4cMqWbJkms+1cuVKNWvWzOU6d1/XqVOnqnfv3mm2Q1LGGP3xxx/KnTt3mqPXt/q1OHjwoLZv3y5Juv/++5P8/Wemq1evavHixXr44Ydt6T8tn3/+ubZu3aoJEyak2TYhIUGnTp2SJAUFBcnhuDknTzVt2lT169fXK6+84tH2586dU968edNst3v37mw3s/XevXs1depUzZgxQ8ePH0+1rTFGixYt0o8//pji50DLshQZGWlXubcFRriyuQ0bNuiNN95w/qG4urbFsiyXyy9evKhBgwZpxowZKQatxA/pV69eTbWOmx3a7Brh2rJli7Zu3aozZ8643GfLsjRy5MhU+/jxxx/TfOMKCwvzKCxdX1N4eLhHIyNhYWFJfp4wYYKGDx/urNXPz08jRozQsGHDkvU/evRovfLKK2keDynp1auXZs6cmeL2x48f14wZMzR16lTt3btXxhgVKVJEx44dU48ePTR9+nSPntcdQUFBGjZsmIYOHepcNnfuXM2dO1cLFixI1t7V76JQoUJau3at7rzzTree84knntDs2bM9/n264nA41LVrV3322Wdptl26dKkeeeQReXt7699//022/siRI1q4cKG2bt3qvLl8wYIFVbNmTXXo0EFFixZN8zmioqI0depUbdy4UUePHpVlWSpSpIjq1q2r3r17q1GjRqnuS4kSJTRr1iyX7SZMmOC8VcjFixdd9hEUFKSPPvpInTt3drk+Pj5ew4YN09tvv53itYF+fn6KiIhIcmxcLzY2Vn379tWXX36Z4mv5wgsv6LXXXlOuXLlcrpekv/76S0888YRWr17t3KejR48qODhYAQEBSdoeOHBAgwcP1qpVqyRJDRo00Jtvvqm77rpLZcuWTfE5UmJZlvbt25dkmScfZFN7j/z666/19ddf66233nJ+qXPgwAG1adNGv/76q6RrXyTNmjUrxQ/tmfFapMXVlymSNHDgQE2ePFmJH5ssy9Lzzz/vVihx12+//abIyEjNmjVLJ06cSHUfEhIS9Ndff6X6/27Dhg09qiOt9+qLFy9q8uTJmjt3rnbu3KmEhARJkpeXl6pVq6Zu3brp6aefTjbafv78eVWrVk2FChVSVFRUin8Ply9fVtOmTRUTE6MdO3a4HMH29/fXwIEDNXbsWI/2sWzZspo9e7YeeOCBFNtMnjxZL730ks6fP+92v19//bXz77J+/fopvve4kpCQoOPHj6f4eqYW8C9evKgvvvhCkZGRWrdunSQpV65catOmjXr16qXWrVsn22bfvn1q27atfvvtN6UWB9z5HOiJNWvWeLSdp8f1LWWQbc2aNcs4HA5jWZYpV66cqV+/vmncuLHLhyt9+/Y1lmWZe++910RERJgpU6aY6dOnu3yk5MKFC6Zfv37G19fXOBwOlw/LsozD4Uh1Xw4cOGAGDRpkmjRpYipWrGjKlCmT7FG2bFlne8uyzOjRoz37xbnwzz//mAYNGjjrTemR2n6cOHHCPPjgg0n2O6U+xo0bZ1577bUkj+bNmxuHw5Fs+fWPzLZs2TJjWZbJnz+/eeqpp8xzzz1nihQpYhwOh2ndurW5dOlSkvbh4eFpvpapCQ0NTbb91atXzaJFi0z79u1Nrly5jGVZJnfu3KZLly5m6dKlJj4+3liWZfr27Ztq33fffXe6H5UqVXJu7+qYSm1/Xa3z9vY2pUqVMocPH0611oSEBNOjRw9jWZZ54IEHnMv79++f7sczzzyTpO8OHToYh8ORbPmNli5danx9fU2ePHnMqlWrkq0fNWqU8fPzc3ksOxwOkzt3bhMREZFi/xcuXDBdunRJ82+hS5cu5sKFCy77mDZtmsmbN6/x8vIyw4YNM/Hx8cYYY44dO+b8W7vrrrvMjh07UqyjUKFCxuFwmN69e5t///03ybrff//d3H///cayLFOnTp0U+7j77ruNw+EwzZs3N0eOHEmybs2aNaZ06dLGsizTuXPnFPuwLMtUr17d7Nmzx+X6L7/80hQoUCDJMTV+/HjjcDjMpk2bkrQ9c+aMKVmyZLLfbaFChcyxY8eMr6+v8fPzS/Lw9vY2lmUlW379w1XNNz6HO+9vKXnooYdM5cqVkyxr3769sSzLNGvWzFSrVs04HA4zZcqUFPvw9LX47bffTJs2bUzevHlNYGCg6dKli/n9999dPoerv+3p06cby7JMnjx5zP/93/+Z//u//zN58uQxDofDzJo1K8V63XH+/HkzdepUU69ePefv1tfX13Tq1Mll+4SEBDN27FhTsGDBFP/fTXx4ytV7daJDhw6ZSpUqOWv18fExRYoUMYULF3a+hzscDlOlSpVkr9F7771nHA6HiY6OTrOGNWvWGMuyzKRJk1yur1GjhunatWv6d+7/y507t8mVK5d55ZVXTEJCQpJ1MTExpl27ds7PV9f7+uuvTYMGDVzuwxNPPJHk78PhcJh27dol6/9Gs2fPNtWqVTO5cuVK8bX08vJyue3mzZtNv379TL58+ZI894MPPmhiYmJSfd6WLVsay7LMM888Y9auXWv++OMPc+DAAZePtKxfv948/PDDpkyZMsbHx8etfbj+PSU9j+yIUwqzsYiICAUGBurbb79VzZo10739/Pnzdf/992vjxo1un350o4EDB2rKlCmqWrWqOnXqpKJFi8rbO32H1fLly9W+fXvFxcUpV65cKlSokMs+zA3fvhw4cCDd346k9K3IkCFDtG7dOjVu3Fg9e/ZUiRIl0r0fzz77rL7//nu1atVKXbp0SfV34erb+kuXLmnVqlX6z3/+k67nzYh33nlHefLk0ZYtW1ShQgVJ0rhx4/TUU0/p888/V9u2bbVo0aIUrwlK7zfgO3fuTLasRIkSzlMe6tWrpx49eujRRx9N9o1+Wvbs2ZOu9naYMWOGnnjiCTVv3lxr165VwYIFk7Uxxqh3796aNWuWatasqeXLlzvXffjhh7IsK9VvGm9kWZYmT57s/HnOnDlq1aqVPvzwQwUGBmrMmDHJtlm2bJkeeeQRORwOLVq0SE2aNEmyfsSIERo3bpx8fX31xBNPqFGjRipWrJiMMTp69KhWr16tL774QmFhYYqPj1d4eHiy5+jSpYsWL16s8uXL64UXXlCzZs2cp/YdPnxYK1as0IQJEzRv3jxdvHjR5fUFoaGhql+/vrp166bXXntNq1atUt++fTV8+HCdPHlS/fr109tvv53q9Vs///yzevbsqWnTpmndunWaPXu2atSoocjISA0aNEiXLl3SyJEjUx0t/+GHHzRo0CB9/PHHuvfeezVlyhS1bdtWo0aN0vjx4+Xn56fIyEj16tUrxT5GjRqlV199VTVq1NCECRPUr18/Sde+lR44cKAiIyMVHBysRYsWObdZs2aNSpUqpdq1ayfpa9KkSfrrr7/UqFEjTZs2Tf7+/ho7dqzeffddvf322y5PDw0PD1dERESKI4GufP7558mWzZ8/XwsWLHC5Li2//PKLWrRo4fw5NjZWS5cu1WOPPabPP/9cV65cUfXq1RUZGaknn3zSZR+evBZ///23GjRooJMnTypPnjy6evWq5s6dq8WLF+uTTz5R165d06x96tSpyp07t7Zs2aJ77rlHkvTTTz+pdu3amjJlih5//PF0/z42btyoyMhIzZs3zzmCUrJkSb3wwgvq3r27goKCXG43bNgwvf766ypUqJB69erl0f+7njLGqGvXrvr111/VrFkzjRgxQnXr1pWPj4+kayNT69at09ixY7Vq1Sp17dpV0dHRzu2//vpr3X333W6NUDRo0EBVqlTRggUL9OyzzyZbP2DAAD377LMen/K3fft2de3aVeHh4fr+++/12WefqWTJklqxYoV69uypo0eP6oknnkjy/pq4Dzt27Ej2d7lw4UJ9+umnyps3rwYPHix/f3998skn+uabbzRz5kz17NnTZR1vvfWWXnrpJeXKlUsNGzZ06/X8559/NGvWLEVGRmr37t0yxqhYsWJ66qmn1KNHD1WpUkWlS5dWgQIFUu1n7dq1ateuXbJ9TK9PP/1UPXv2lDFGZcuWVa1atdw6Jvv165fsTJpt27bphx9+cL5H5hi3MOwhg/z8/Myzzz7r8fYBAQFm6NChGaohMDDQ1KxZ0/nNsyeqVq1q8uTJY+bNm2euXr3q1jaZ/a1IgQIFTO3atdP8Fio1efPmNU2bNvV4e09GjzZu3GiaNm1q8ubNa/LmzWuaNWtmNm/e7Pb2wcHBpkePHi7XDRs2zPnN88WLF13W6Oob8LQeN+6jZVnGy8vLvPTSS+b06dMua3FnhCujMmOEyxhjJk+ebCzLMvfdd585e/ZssvW9e/c2lmWZ+++/35w5cyZZDT4+Pubhhx828+fPN1FRUW49bvTvv/+a2rVrG4fDYSZMmJBk3fLly50jGsuXL0+27b59+4y3t7cpW7ZsiiMAxlwbMQgJCTG5cuUyf/75Z5J1X375pbEsy7Rt29Z57Lhy6dIl065dO+NwOMyCBQtSbBcfH2+GDBniPH6CgoLM119/nWJ7V9555x3j5+dnfHx8TP369Y3D4TAhISFm3bp1bvexcOFCExwcbBwOhylTpoxxOBymZs2aZu/evW5tv27dOhMSEmIcDod55JFHzIoVK8xdd91lLMsyDz30kDl27FiS9mXLlnU5ylGjRg3jcDiS/d4rVqxoqlWr5vK5Mzo6nRn9+Pn5mREjRjh//uabb4xlWUley0GDBpmCBQum2Vd6XotnnnnGWJZlXn/9dXP16lWTkJBg5s6dawoXLmy8vLzMJ598kuY+BgYGmscffzxZ3926dTOBgYFp1pvoxIkT5s0333SO1FmWZYoVK2aGDh3q9vtc4cKFzV133WXOnTvn9vOmV0ojXN99952xLMt069Yt1f8vExISTNeuXY3D4TDff/+9c3nBggVN//793a6jf//+KR4P0dHRpnXr1iZv3rzmxRdfNHPnzjVRUVEmOjo62SMlcXFxZtCgQcbhcDhfYy8vL5M/f37z+eefu9ymcuXK5sEHH0y2vG3btsbhcCQ5no8fP27uuOMO06JFixRrKFWqlClZsmSaZ0Yk6ty5s/H19TWWZRl/f3/TvXt3s3z58iSvh7vHUqFChcwLL7zg1vOmpmLFiqZAgQJmy5YtGe4rs96rshoCVzZ21113ZehDaIsWLczDDz+coRoyI7T5+fmZPn36pGsby7JMtWrVTGhoaLoeKfH398/wfgQFBWWoj/S+yezatcvkzp07WaDJkyeP+fnnn93qw9fX1wwfPjzVmizLMk2bNjUXLlxIVqO/v7+pWLGimTNnjluPRo0aJdvHJ554wvj7+xuHw2H8/PxMp06dzNdff22uXLnibJOdApcxxowZM8ZYlmUaNWqU5LTMJ5980hnGXIXLd99911SvXj3JKUVLly716IuAU6dOmXvuucc4HA4zbdo0Y8y1sJU7d27j6+trvv32W5fbjRo1yjgcDreCyNq1a41lWSY8PDzJ8rZt25rg4GATGxubZh+xsbEmODjYtG3bNsU2P//8s6lcubIzcOXKlctERESk+/fy3nvvOf9OChYsmCzguOP1119P0seNp0ylJTY21vlBNPHUzHfffddl2zvuuCPZe8qlS5eMt7e3uffee5O1f/rpp01AQIDLvrJC4CpUqJB5+umnnT+/+OKLxsvLy5w6dcq5bOjQoSZPnjxu9efua1G2bFnTqFGjZMsPHTpkKleubLy8vMyHH37oXO5qHx0Ohxk1alSyPkaOHJniqV7XW7JkiXnkkUeMj4+P8326W7du5rvvvnN+0eju+5y/v3+mfEhOTUqB68knnzS5c+dO81Q1Y66Fyxv/f8+VK5d5+eWX3a5jxIgRxsfHx+W6G7/wy8gpaBMnTnT2UbBgQbN///4U2wYHB7sMjfnz5zeFCxdOtrxLly4ulyfKnTu3GTRoUJo1Jrr+S8obT5O+vo07x1KPHj1SPZ3aXRkdALheTg1cnFKYjT399NMaP368Tp06leKpB6mJiIhQ06ZN9c0336hNmzYe1VC7dm3t3bvXo20TFS1aNN3TWEtShw4dMm3SjOrVq+vAgQMZ6qNhw4YuT5mzy2uvvaZLly5pxIgRGjBggCzL0rvvvquxY8dq/Pjxbs2EWLx4cR05ciTF9YkTbIwePVqtWrXS/fffn2R9tWrV9Msvv+ixxx5zq+bvvvtOa9euTbJs5syZmjx5smbPnq3IyEh9+eWXWrBggQIDA9WlS5d0n6qzdu1abd26VZJUs2ZNNWjQIF3bZ4YRI0bo1KlTevvtt9WpUyd99dVXeuaZZzR16lTde++9WrFihcuZ955//nk9//zz2rFjhyIjI/X5559rwYIFKlq0qHr27KnQ0FDnqZ9pCQwM1PLly1W/fn3169dPe/fu1dtvv62EhAR9+eWXeuihh1xut379elWuXFn16tVL8znq16+vypUrJ3tNt27dqtatW7t1WmhAQIDatGmj7777zuX6999/X0OHDtWVK1c0ZswYdejQQd27d1dYWJiWL1/uPA0oNQkJCQoLC9Nrr70mf39/ValSRZs2bVLz5s01e/ZsValSJc06//33X/Xv31+zZ89W0aJF1aBBA82bN0/333+/pk+frgcffDDNPqRrpxDGxMQ4TxvNlStXiu/f8fHxunz5cpJlP/30k65evapatWola1+gQAHFxcW5VcetcNddd2nx4sV69dVX5eXlpTlz5ui+++5LMivqwYMHVbhw4VT7Se9rceTIEXXq1ClZPyVLltSaNWvUrFkzPfPMM0pISFD//v1dPqcxxuWp915eXm6dAtymTRs5HA41aNBATzzxhDp37uzWDHmuVKtWTX///bdH22bU9u3b1aBBgzRPVZOuTbDTsGFD56yO0rW/93/++cft5zt16lSKv6dRo0Zlyi01IiMjNXz4cHl5ealEiRI6ePCg+vbtq1mzZqlIkSLJ2sfGxipfvnxJlv3555+KjY3VI488kqx9SEiITp8+neLz33XXXamuv1Hp0qV18OBBvfnmm4qKilKPHj302GOPKTg42O0+Er3++ut64IEHNHToUL3yyise314jJCQk2XsVbnCLAx8y6LnnnjN33XWX+fTTT81PP/1kDh486PKRkvXr15ugoCDTvHlz88ILL5jRo0cne7zyyispbr9p0yaTJ08es3jxYo/3YeTIkaZcuXKpnnp0o8yeNGPFihXG19fXbNy40eM+9uzZY4KCgsx7773n0fbp/VanZMmSpkGDBsmW169f35QqVcqtPtq1a2dKliyZZruwsDDn6W7X1zhgwADjcDjMH3/84dbzpXYhdqKffvrJDBw40AQHByf51rJhw4apHstXrlwx7du3T/bNZocOHdw6VTUzR7gSJZ4+WKZMGWNZlqlatapb3wwniouLM7NnzzbNmzc3Xl5exuFwmPr165tp06a5/feyb98+U6xYMeNwOIyvr2+af6vFihVL14hznz59TPHixZMs8/HxSXLqWFpGjBhhfH19ky1PPN2wXLlySU6VvXz5shk8eLBxOBypnvpjjDH79+83derUMZZlmRo1ajhPkxw/frzx8fExfn5+KY4wJdq8ebMpV66csSzLtGvXzvkazps3z+TPn984HA4zePBgExcXl2o/33zzjSlUqJDz2+dvv/3WlChRwjgcDtOtW7dkI4IVKlRI9jc+YcIE43A4kp0GZ8y1069KlCjh8rmzwgjX/PnznRN35M2b1zgcDhMZGelcHx8fb4oVK5bqmReevBaFChUyAwcOTLHP06dPm+rVqxuHw2EmTZrkch8tyzIvvfSSOX78eJLHiy++aBwOhzlx4kSydcePH0+yvcPhMA888ICZPHmy+eeff5LV4e6oxLfffmvy5Mljtm/fnmbbROmdjOfOO+90+ToXKFAg1d/ljQYNGmQKFCjg/LlOnTrmnnvucXv7e+65x9StW9ft9ulx5swZ07lzZ+NwOEypUqXM2rVrzcWLF03//v2do6YLFy5Mtl2hQoVMly5dkiybM2eOsSzLjB07Nln7F1980QQFBaVYx6effmry58/v1sQUiVasWGG6dOli/Pz8nP83t2vXznzxxRfm0qVL6TorZM+ePSYwMNAEBASYGjVqmCZNmiR7pHW5xDvvvGOKFi3q8rhOr5w6wkXgyuamTJni/E8mvTPbxMTEmAYNGqT7mpsbZTS0XblyxbRt29Y0bNjQrFu3zq3z0jMauGbMmJHs0bFjR+Pj42N69eplJk6c6LLNjBkzUu33l19+MYGBgaZChQqmY8eOplevXskevXv3drltet9kfHx8XJ7C+OKLL7r88OrKRx99ZCzLMkuXLk2z7ahRo5IdD4sXLzaNGzd2+zqYhQsXJjv9LCWXL182c+fONS1btjReXl7O0yiaN29uZs+enaz922+/bSzLMoULF3Z+aChcuLBxOBxm4sSJaT5f4n+w189imDgLmKsZDhPXpebq1aumY8eOxrIsU6VKlXSFrRsdOnTIjB492hQtWjTZdQLXGz9+fLLH448/7jw11NX68ePHO7fPkydPqqeZ3mj48OHG398/ybKCBQum6xTQvn37urxOw7Is88QTT6T4nrBs2TLnrJopSZy5a+jQoeby5ctJ1m3bts1UrFjROBwO89BDD6XYR65cuUzu3LnN5MmTk607ePCg833U1Wl+iZ5//nnn9Wdffvmlc/mpU6fMI4884gzm1/8t9e/f3zgcDvPZZ58ZY65dD1K+fHnj7e3t8vS5WrVqmXr16rl8/qwQuIwx5v333zc1atQwNWrUSHLcGXPt9cyfP3+S0/tu5Mlr8cADDySZDdSVU6dOOUNX4gx810vtlLWU1l3/f++qVatM9+7dnTMb+vr6mg4dOpgFCxY4j8v0fEj+4osvTKFChUyfPn3Me++9l+b/V+m5zja1//tz5cplwsLC3KrRmGv/b1x/SmBERIRxOBypfkmSaO7cucayLPPqq6+6/Xzpcf2MljdeT/v11187rxF86qmnkqxr1aqVueOOO5Kckvzwww8bh8Ph8kvbNm3amCpVqqRay5tvvmmKFStmxowZYxYvXuzyOjRX16KdPn3aTJw40VSrVs35miV+JnzsscfS/B388MMPpkCBAhn+HGhMxgcAEhG4kOVMnDjR+cbdrFkz8/jjj6fr2qWuXbsay7JM69atzaxZs8yKFSvcvig/UWaFtmXLlpnAwEC3g2NGA5er/yBd1ezqP9WU/Pnnn6Z8+fJu/S5cfeBNnBb+9ddfT/NDcWq/g/S8Wf3zzz/mww8/NKtXr3ar/bRp09wOTJnp8OHDZvTo0c6RIlf7V7NmTRMYGGiOHj3qXHbkyBGTP39+U7t27TSfIzM+kLgKZuXKlTMOh8OUKFEizanpU3LlyhUzf/5806pVK2f4/O6771LcD1fHs7v7kd6/LVfHW4sWLUzx4sWT3VbAlUuXLpnixYub5s2bJ1v36aefprn9iRMnUr3+q2jRombFihUprj9//rzp3bt3qn8zVapUSfW6yISEBPPKK6+YXLlypdjGsq5d05fShfEff/yxyZMnT5I+Dh065PzwFBQU5Lz+x9V7+uHDh43D4UgxLGeVwJVRnrwWL7/8svHy8ko2yciNTp06leSD6/UaNWqU4m1XUnvc6MyZM2by5Mnmvvvucz5PgQIFzFNPPeV24Lp06ZJ5/PHHU71+6cZ9cHcCnrT+78/o+8Pp06dNUFCQ8ff3d15b6sr06dPNHXfcYYKDg1OcTCmj7rjjjiQjrDc6evSoadGiRbJjIXGyl5CQEDN48GDTtm1bY1lWslseGHPttQoICDA9e/ZMtZaXX37ZeS2zp9eibd++3fTv398EBgY6X/9y5cqZMWPGpPi+07BhQ+Pl5WXGjx9vDh8+nKEJ0DIyAHC9W/0eYxeu4crG3n77bRUvXlwbNmxQiRIl0r39d999p8aNG+ubb77xuIYBAwZo3bp1bk2FnpK5c+eqe/fuSkhIUNmyZd3qo2fPnqpWrZrHdU+bNs3jbVMyYMAA7du3T08//bS6deuW6n6UKVMmxem/U5oW3rIsvfTSS5la87///quuXbu6PQV7aGhokp8PHTqk/Pnzp3sK9/QqUaKERo0apVGjRmnFihWaOnVqsja//fabOnfunOSc+2LFiqlDhw4ub1x8o8Qbd2ZEalPTp3atXEp++uknTZ06VZ999pliYmJUsGBBDRw4UE8++WSK0yDbcWynV9euXfXkk0/q+eef10cffZRq24EDB+ro0aOKiIhItq579+5pPlfBggWTTKV+o127dqV6bUOePHkUGRmpVq1apdhm69at8vX1TXG99f9v9pvadVwREREaPnx4itec9O3bVw0bNkyyzyVLllRUVJSGDBmiTZs2qVChQurcubPLG71OmzZNAQEBatWqlfLkyZNsfeJNnV2tS9yHG2/u+swzzyRrt23bthTXJfaTkSmmn332Wc2ZMyfF63w8eS06deqkFStW6Ntvv02xbunadY+rVq1Shw4ddPDgwSTroqKi0rcjKciXL5+eeeYZPfPMM/rxxx81ZcoUzZ49Wx9//LEk6ZtvvlFERIR69uyZ4k1uhwwZos8++yxdt2NJ7Qbj6XX+/HmdOHHCrbY33lA9f/78mjdvntq1a6cnn3xSYWFhaty4sfMzzJEjRxQVFaXDhw/Lz89P8+bNc3m965dffqno6Gh5e3urZcuWatmypcvnnzFjhmbMmOG8EfH1tm/frooVK6ZYe5EiRbR8+XK99dZbSZa3bt1aYWFhioiI0DvvvCPp2v9Rrq6bnjdvns6dO5difdK1a9HGjh2rggULuvU5as2aNQoJCUl2fNx33316//339fbbb2v+/PmKjIxUdHS0Ro4cqdGjR7u8xmr79u167LHHMvzZ4r333tOgQYOUK1cuNWnSxO3PgvPmzUu27Oeff5YkffHFFyleH/noo49mqN5bwTIp7Q2yvDx58uipp57S22+/7dH2gYGB6tu3r15//XWPawgKClK1atVcvpm565577tGxY8f07bffurwQPLsICAhQkyZN9PXXX6fZdsaMGR49x/X38XA4HCpfvrzKly+fpM0ff/yhffv2uXyDtyxLS5Yscf7s5eWl8PBwjRw50qN6vLy8FBYWlmmTl7hjxYoV2r17t55//vkkyx0Oh0aNGpXsnlCJ9x+6evXqTasxI2JjY/XZZ59p6tSp2rFjh7y8vPTQQw+pd+/eatOmje3320npuEpJ4vF2/e83ISFB9erV05YtW9SwYUONGDFCDRs2THKvnujoaI0bN07R0dGqVauW1q9fL4fDkaTvsmXLatCgQcle6+t9+OGHeuONN7Rv3z6X61P6cHK9v/76S3/++WeK9waaOXOmqlWrpqpVq6bYxy+//KLt27erR48ezmXGmHRf1L927doMT/QSEhLi0WQC+/fvT/Lzja+HOyzLytDfWq9evTRz5swU+/D0tcjKLl++rAULFmjq1KlauXKlpGu/x2bNmmnZsmXJ2hcqVEilS5fWxo0bb9r9t6RrkyeFh4d7dFzc+Hr++OOPev7555NNuJOoYcOGmjhxYrLXOSEhQY888ogWL17s/DCe+LuaNWtWsglXRo8erVdeecWW9/+DBw9qy5YtCgoK0gMPPCB/f/9kbbZu3aq///5bzZs3d7leuhbW8ubNq61bt+qOO+5I83nT8//u/v37FRkZqZkzZ+rQoUPJ1hcrVkxdunTRhAkT0uwrNWXLllV8fHy6BwAcDkey96rrX9cbJb6nZpf/z6/HCFc2Vr58eZ05c8bj7evVq6cff/wxQzUYY5LNXJde+/fvV69evdIVtjwNiZk9QnQ9X1/fVL8tu15KN0BMrz/++EN//PGHy3WuZn1z9caWke9cbsX3NZ999plmzpzp8kO4qw8Cnnzg9XSWQ08+YF+vW7duWrhwoeLi4lSxYkWNGzdOPXv2THPWtsyW2nHlyo377Pj/N1Ru3769oqOjtWbNGnl7eztHmmJiYhQfHy9jjGrXrq2vv/7a5Wt34MCBNN/jYmNjU51htEmTJml+OPnss880fPjwFP8TDw0NVXh4eKof8r/55hsNHz48yYf8vn37asqUKanWf70tW7aoXbt26Zqx7HqJN0PN6IyriVavXp0p/WQmT1+LrMzHx0ddunRRly5ddPDgQU2dOlXTp0/XihUrXLa/dOmSmjRpclPD1vXcuWlxWu69915FR0dr3759Wr9+vY4dOybp2qhSvXr1VK5cOZfbffTRR1q0aJHuv/9+DRkyRLly5VJkZKS+++471a1bV6tWrVLp0qU9rmvnzp3O475+/fqqWbNmim1Lly6d5nOltn2i06dPq0uXLm6FLSl9/++WKVNGY8aMcXkGgSS1b99eq1atUkJCgkdBOtGxY8f01FNPpftsq8yaaTI7IHBlY4MHD9YLL7yggwcPevQG8+abb6pu3bqaNGmSnnvuOY9qyIzQVrJkyXR/W/Hf//43xVPybnT9H3NagSshIUF//fWXjhw5oitXrrhsk9J/Ni1atNCGDRvSrCezjBw5UlOnTtW6detu2nNmdX/99Ze2bNmSbJl07ZtGV8dLYtCPj49Xp06dtHjx4iTr27dvr/nz57v1n1G9evU0a9asFD8spGXOnDnKlSuX2rZtq7p160pybzTUnS8SLly4kOz0Hkm64447kpxmduMoh6cKFiyotWvXavr06Zo6daq2bdumo0ePSro2FXqdOnUUGhqq0NDQDH1wjI2NTfUUM3feIxISEjL8n/7Vq1eTHSNTp05VUFCQW18Qbdu2TS1btkx2Wp87li1bpsjISC1atEhXrlzRoEGD0t3HjXbt2qWDBw9mm9ByPVevRWY4f/685s+fr5MnT+q+++5T06ZNJV27lUJYWJh++OEHJSQkqGHDhnrttddSPO03NaVLl3aOJH3//fcu29SoUSNdX4hkJsf/a+++45pI/v+Bv3bpNsQKKoK9Yy8flY6K6FlRsdDtp2f3FDgFVFTEXu5UQlUUUUQFRQUBGzbselbE3mg2pGZ+f/BNfoRNICGhefN8PPJ46GR2MxuSzc7OzPvNsgrtiLdq1Uqm82VgYCCaNm2KhIQEYQjzsWPHYs+ePZg7dy6MjY0RFxeHFi1aiN3+/Pnz8PX1xezZs9GvXz+R59zc3LB27VqRstmzZ2P79u0yHpVsunTpIjw3lhdJ57f169dj0KBBmDx5MjZs2FCm5SlA2QcAis9I+ZXRDlc11qpVKxgbG6NXr16YN28eunXrJnEtjbhOgre3NwwMDDBv3jxs3boVXbt2Fbs9wzDg8Xhi96uITtu0adOwefNmeHl5SZ1PTNp1Kt++fcPWrVvx/PnzEi+oCCFYt24dNm/eXGqOEEmdQx8fHwwcOFDufBbSYlkW7969k+tu3q+Gx+OJ/awSQjg/rgKCv+eOHTtw/PhxNGrUSJhLJTw8HMeOHcPOnTsxd+7cUl//ypUr6Nq1K7y9vUtcK1KSvLw8HD9+XGTKTEnEre0TrAMKDg4WXnhu2LABnp6enO27d+8uXJcDQKGfJyUlJTg7O8PZ2RkFBQXC71b9+vXF5jQCCi+IikpJSeGUAYV/tzdv3iA4OFjqkWVJnj59ysmrI6tbt25xzl//+9//sHHjRmhpaWH58uUSt7158yYGDRqEb9++SZU/Dyh8X/z9/REQEIA3b96AEILGjRvLnLdOkqNHj8Ld3R2ZmZklTumsior/LVq2bCnzPhiGEZmmmpqaiv79++P58+fCkez58+djwoQJsLCwEMl/FhkZiUuXLuHWrVslTmUt7fUlrQn08vKCubm5XDk0K4s0U3xL8vDhQ0yZMoXz+zp9+nQ0bdoU1tbWMDExQVxcnNi/e2hoKMLCwrBjxw6R8ri4OHh5eUFZWRmTJk1CzZo1cfjwYezatQvm5uYYNWqUsK6iZ9i4urrCxsYGN2/eRI8ePcq077Lq1q0bcnNzcePGDeF6OXHnwuLfh+LkHQD4L6AdrmrMxMREOMpT2rCsuE5CQECA8N/Pnz+X+GUqqcOliE6btbU1Ll26hP79+8PNza3EjqPgJF3alLz8/Hzs3r0bq1evxqdPn6CnpydxSB0Ali9fDm9vbzRq1AiOjo5lCv4xZcoUaGpqYtOmTdizZw/atGkj8b0QzNOXh4eHh9z7AGSfcqfo7RVF3mmaISEhqFu3Lm7fvi0MvOHm5oZOnTph//79UnW4zp49CycnJ8ydOxcRERHw9/dH06ZNpW6DIgJeREZG4uDBg/D19eXc5SeEiCye//HjB5KSknDy5ElO0IiXL18KE5b26tWrzBdIAkpKSmjUqFGp9QTnNaDwsyVY9C6O4MK3eBAJJycnkf9HRESInWYn6LSdP3+ekwhaMHohEBAQIDZwgmAfKSkpnIXcJ0+ehLGxMdzc3FC/fn1Mnz6ds/2tW7dgYWGBb9++wd/fH5MmTRJ7rEDhWp8jR46Ax+MhPj5eGOilc+fOWLNmDaysrCR2ZMtKnmnriiLv3+Ldu3ec81R+fj4KCgpKHB0tat26dXj27BmGDRuGIUOG4MyZM9ixYweSkpLQtGlT7N27F3369BEmPN+yZQvWrVuHXbt2CfehqBGys2fPwsTEBCNHjoSpqanE30xBABFFIoTg1atXZT4fiJviGxoaitDQUKmDG0m6OTJs2DBERERg9OjRMDY2Fru2PDExEX379uW8X7t37wbDMPjnn3+E54958+bBwMAAAQEBIh0uRc+wycjIwKBBg9C/f39MmTKlxGsgwYhzZmam2DVZJRH3N+Pz+VBRURF5TtxxlXas8g4ACDx48ADXr19HamoqgMKZEr179y7TaHFVQ4NmVGPu7u5SX+yuXLmSU1Y8ClNJJN2xkHbaRkmLHAWLJktb/8IwjDDSVkkOHjyIv/76C8nJyahXrx5cXV0xe/Zs4YJ9cbS1taGlpSX1olVJxyENRS34ZBgGDMPIFV1P3IJVaV5X8HeQd/uyKG1RfVlpampi3LhxnDU3Tk5OCA8Pl/rC8/v375g/fz78/PygqamJ7du3K2zUQRr29vY4efIk3r17BxUVFWG5uMXjeXl50NXVxeDBg0VGVubNm4edO3eKLF7+448/5F5YLQ3BeY0QAk9PTxgbG8PExIRTT0lJCfXq1YOpqSk6dOgg8lzR72JpF0YMw6B3797Yt2+fSKAQaffBsizq1asHMzMzbN26lbPe7tOnTxgwYABSUlIQEhKCcePGCZ+7c+cOzMzMkJmZCT8/P4k3DW7fvg0ej4eQkBBkZmaCEILevXvD3t4ec+bMwdSpU4VR7hRFMK3Nw8OjQoPiiPt+K+pvUZSswXTat28PNTU1kSn0Xbt2xf379xEZGYmhQ4eK1O/bty/S0tKEU/9KGiEzNjYWGSEDCoNaSRohq+jfGgFJnwlZOkwsy8Ld3V1ke1kCW3Ts2BHt27cv8bWio6MxevRo1KtXDyYmJjh48KBw39ra2hgxYgTn+9K4cWPk5uYiLS1N5P0dM2YMrl27JpyaDkgf9Kr4DBtproEExK23FuyjMn53S1P8GGQdAIiJicHChQvx4MEDsdsYGBhg06ZNMDU1VUyDKwEd4arG5J37qohhX0Ws97Czs1PIKElMTAyWLVuGW7duQUNDA8uXL8eff/6J2rVrl7rt9+/fMWXKlDJ3tgDFhBWvDHXq1BEbdreitq8sL168gIeHh3Ck99u3b2Lnrzdv3hzfvn2Ter+1atWCr68vrK2tMXXqVNjb2+Po0aPw8vKSOM1U3tGjoq5evQoTExORzpYkKioqsLCwEFn3FhgYiO3bt0NDQ0M4GpaQkICtW7eiR48eUnUey3I3kmEYPHjwQOS8lpCQAEdHR5nXEQnOS4QQYaTDefPmceopKSlBS0tLbPSwot9ncReJ0mrUqBFiYmIwYMAA2Nraok6dOhgyZAju3LkDc3NzZGZmYu/evWI7W7t27QKPx8Pt27dBCIGuri5mzpwJOzs7tGvXDgDKPJW7okgKQy+JuLWzivpbFCXrb86rV68wbdo0kTJTU1Pcv38f//vf/zj1//e//4mkRFDECJlAVQtm8ujRI6mi8ypCnz59EBERgezsbKirq4utY2lpiaNHj2L06NE4ePCgyHMZGRmcNBFv3rzB58+fMWzYME5ntnXr1jh58qRImaJn2Pj5+cn8eWzevDn09fVl2qY8yRP84vDhw5g0aRLy8/Ohr68PQ0NDNGnSBIQQvH//HufPn8edO3cwZMgQHDp0SGS0sTqhHa5qTBHhjuWliE5b0amNZXHr1i38+eefiI2NhZKSEqZNmwZ3d3eRfEyl6datG969eydXO6qrBQsWyHXxIu/2ss6Hv3fvXplfCyi8cFq1ahWCgoKQn58v8vlTRJRDAUtLSzx48AAODg6IiIhARESE2HrS3HmUNuAFUPidHzlyJKdu7dq10aRJE065jo6OSI4wPz8/aGho4Nq1a+jUqROAwve8b9++8PX1larDVVI+MknEvc9HjhyReT+A6HnJ398f3bt3l+tcVdIifGnbc/bsWRgaGmLs2LHYvHkzXFxckJGRgb///pszBVJgzpw5YFkWkydPhoODA0xNTavMFF5pNWrUSKFtjouLq5QLzezsbNSvX1+kTEtLCwDE3nDS0tISyXsUGRmJLl26CIPyzJkzB127dsWFCxcQGRkpvHNfs2ZNbNq0CZcuXcKZM2fEtkWRObWqm99++w1BQUEIDAzEjBkzJNYTdLpGjRol0omvXbs257deMHW6Z8+enP0wDCOxYydO8Rk2mzZtKnWGTfH8ltJwdHRUyMiztKk3vL29kZycLLFOWQcAMjIyMGPGDGG0ySlTpogd3QsKCsLs2bMxffp0mJqayr3mtjLQDlc1pohwx6X5+vUrMjMzFXoHXlFevHgBV1dXHDp0SJibw8vLq0wL6N3c3DB27NhKWbT6XyfLfHgBSRdwFy9exF9//YWkpCQoKyvD0NAQ3t7eaNeuHbKysuDm5oZdu3YhNzcXTZo04QQykCfKoTinTp3C+fPnQQhBnz59pAqkIk/AC6Dw7qq4dTwLFy7EwoULOeXKysoiFyT37t3DmDFjhJ0toDCK1ujRo3Hq1KlS2w9IP9p76NAhuLm54dmzZyJBDsLDw7FkyRLhuqsWLVrAx8dH5jubFy5cQFpaGmJiYvDlyxeZ81vl5+fjwIEDImkCJk6cWKaoih06dEB0dDTMzc0xc+ZMAMDOnTvFrusqis/n49y5c9DR0UHjxo1F/i4VQdJ6KUmKr1FVVIh6AUFng8/nIz09HUBhPsjyiEqoSPKOkBXl6ekJExOTEm+kXrp0CbGxsRU6HbQijBw5Ev/++69UM1csLS1x584dYch5oHBqWmRkJH78+CEc2T569CgYhhH7fj5//lzsjari5JlhU5m55aRNvSHLEhRZ7N+/HxkZGeDxeLC1tRVbh2EY2Nvbo6CgAFOnTkVISAhmzZpVLu0pT7TDVY2VNdzxkydPsGjRIpEs7Z6enmjTpg1n+82bN0s9t/r+/fvYtm0bbty4gZ8/f0JfXx8TJkyAvb29wu/K/vHHH9izZw/y8vJgbGyM9evXS5XvQhJLS0sEBgZi6NChGDFihMTgHwAknvDS09OhrKwscTug8Ec3JSVFYSOOgilT0iot0lBlUESgCKDwLqWFhYXIXeUTJ07g+vXrOH/+PEaNGoWHDx+iSZMm+PPPPzF9+nTOgnl5ohwWlZ6ejpkzZ+LIkSNo0KABwsPDpeosKCLgRYMGDWT6cXz58qXInfsvX76I/Uy1atUKX79+lXq/JYmLi8Off/6JpKQkqKurY9myZVi2bBmAwk7SuHHjQAhBzZo1QQhBcnIyxo0bh4SEBGG4/JIoIsR/VlYWTExMkJSUJLIuYefOnYiLi5Oq83zo0CFO2aRJk7B7924MGjQI9erVE1tHEPDh+fPn4PF4CAwMxIYNG+Dj4wMDAwPY2dlh0qRJFZKfLSUlRaZOU3mOwP38+RM7d+5EaGgobt++LezYKykpoVu3bpg0aRJmzpwp04hERZF3hKwod3d3uLu7l/g7cvHiRYWvv1PUkn95PiMhISHo1q2bcEptadq1aydS18nJCXZ2djA2NoadnR2ePXuGffv2QVdXl7NWtKCgAOfPny9x3ZAiZthU9dxypaXeAAo7+EeOHMHSpUvFHvuHDx/g7e2N8ePHi/yWxsTEoGnTplKN8jk4OGDlypU4ffo07XBRVU/xcMfv3r2DoaEhPn/+jBo1aqCgoAChoaE4ceIE9u7di4kTJ5a6zzlz5uDQoUP48OGD8MLl0KFDsLOzE/mBePz4Mc6cOYPw8HBERETIdQdy/fr1OH36tDDq0I4dO8AwDFq1aoX27dvD39+/1At3wcWSODk5OTh27BhSU1OFF9ySFq0WP+FdunQJM2fOxMOHDwEUjnh4e3uLvZvu7++v8Kz3ir6DXJrly5dzcpXIs72ikkB7e3sjNzcXa9euhbOzM4DCqRArVqwQfubd3Nzg4uIi9oJMUe04fvw4ZsyYgY8fP2LUqFHYvXs3GjZsKNW2YWFhqFevnthpewzDiKzdEAS8OHjwoEiHq2fPnoiNjUVubm6J01iAws99bGysyCgdIUTsCJmSkpLcF1x37tzBn3/+ibNnz4JlWTg7O8PDwwM6OjrCOps2bQIhBL6+vnB0dARQ2BGePn06Nm3aJFWHSxEh/jds2IAbN26gffv2wosBf39/XL9+HRs3boSbm1up+7CxsZF4cXn27FlOYlvBOUbQ4RIkLfX09ER0dDR4PB4iIyOxaNEiLF26FObm5qW2QV6S1r9VtNevX8PS0hKPHj0CIQQqKipo0KABCCFIT0/HjRs3kJSUBD8/P0RHR0s1KiGrHTt2iKwJEkRSE7dmUfBcZcnNzVX4qN+CBQvg6ekpd6d61apVIr8BginVktb7MQwjzE8nTeekJFOmTEFsbCwCAwNx69YtEEJQu3Zt7N27l/N+RUVFITU1FUOGDOHsR5EzbKShyNxy5ZF6Y9OmTbh7967EwEra2tqIjIzE27dvERoaKiy/e/cuDA0NpfpMsSwLIyMjXLlypdS6VRHtcFUz8oY7XrNmDT5//oz169dj0aJFYBgGYWFh+OOPP2Bra4sfP35g6tSpJbYhISEBxsbGwi9/eno6pk6dCnV1dWzduhW//fYb6tatiwcPHmDlypWIiorC5s2bsWjRojIf96NHj5CQkCBSRgjBs2fPpE4AWVKHa+HChdi/fz8MDAxgbW0tdVj4x48fY/Dgwfj58yfatm0LFRUVXL16FWZmZli9ejX+/PNPqdomD3mCdTAMU+pFeVEuLi7w9vYW/ljKu31ZpKWlITg4mJPc9dKlSzAzMxN5z93c3BAbG4vz589jw4YNYqfUCShipM3BwQHBwcGoU6cOAgICZL4bKW/ACwDCkZ1Vq1aVuFAbKJyalJqaygln/uPHD3z69EmkTLCG7PPnz2I7XiWFfE9JSYGbmxsOHjwIPp+PUaNGYe3atWLvUl+9ehWWlpYi57qpU6fiyJEjUicWV0SI//DwcOjo6OD69evCqUezZs1Cu3btcOTIEak6XPIsJC+KZVlYWVnBysoKqampCAwMhJ+fn3CNz4EDB1BQUABHR0cMHDhQ7tcrqm7dunKtf5M2r1hxRb87hBBMnDgR//77L8zNzeHq6or+/fsLzz25ubm4ePEivLy8cO7cOUycOJHze6EIqampYjtSktYsludoX0n7zs3NxYULFySOgN64cQOhoaFiw2/b2NhInFYvuHkrT4epefPmlb4O0d/fH87OzkhMTES9evUwZMgQsQGT1NTUsHnzZs6aWEXPsJFG0dxyxsbGcq1jVETqjeKuX79e6g0gIyMjTjLvtLQ0mZItN2vWrNJvZpQZoaoVhmGED5ZlRf5f/MGyLOnbty95+vSpcPuWLVsSY2Njzn5fvXpFOnfuTJSUlMg///wjLHd3dycsy4rUrV27NlmyZInw//v27SMMw5CQkBDOfnNzc0nnzp1Jly5d5DpuBwcHkXbEx8eX6SFJw4YNSa9evUheXp5M7bKzsyMMw5DQ0FBh2dWrV0n79u0Jy7LEzc1NpL6497OsGIYhjo6Ocu/DwMCAZGRklFrXzc2NMAxD2rRpo7DtZREdHU3GjRtH1NTUxL6HKioqZOnSpZzyJUuWEJZlSWpqapleV5Lk5GRib28vUsYwDLGwsCBv3rwp0z5r1qwp9hg2btxImjVrxilfvHgxqVWrlkhZfn4+6d69O2FZlkybNo28ePFCbNudnZ0Jy7KkW7duJD8/X+QYWJYV+5D0nJKSktjjSU1NJfPmzSPq6uqEYRhiZGREEhMTS3wPlJWVybJlyzjly5YtIyoqKiVuK1CnTh3i7OzMKXd0dCSamppS7aNmzZpk5syZnPKZM2dy3vPKkpiYSKZOnUrq1Kkj/Nu0bdtWIfsOCAggAIiHh4dc+ynp81TSZ6yo6OhowjAMmTRpEuHz+RJfi8/nk4kTJxKWZcnZs2eF5RoaGpyHiooKYVlW7HMaGhqkRo0ach23uPehYcOGpEOHDsJHw4YNCcuyImXFnxNo0aKF8MEwDNHS0hIpEzyaN29OatSoQViWJTNmzBBpQ05ODrGzsyvx2oFlWeLs7Czxt1BPT4/o6+vL/FDk+yjvZ1IRbWBZlrRp04bMmjVLqsfs2bNF9mFqaip8MAxDWrRoIVImeBgZGZGWLVsSlmWJjY2NcPu8vDwSFBRE5s6dS+bOnUuCgoKkvn5ZuXIlcXd3JytXriQMwxATExPi7u7OeaxatYrs3LmTPHz4sNR9qqmpEVdX1xLruLq6EnV1dZEylmVl+nu6u7tL/L2p6ugIVzUjb7jjt2/fwtramlNfV1cX58+fh7m5OWbPng0+ny/1HNnXr1+DYRgMGzaM85zgLvw///wjUi4pKpckFy9eFPm/oqM0ZWdnw9TUVObF8HFxcbCyshIZIejTpw+uXr2K0aNHw8vLCwUFBaXeHQIKR6revHmDt2/fig2NDHATBsobrWvdunVYtmwZhg0bhrNnz0q8Q7ly5UqsWbMGrVq1EpnWJu/2pUlJSYG/vz8CAgLw5s0bEELQuHFjsVPu8vPzxYb3FpQVXz9RViVFOdy2bRt69uwJOzs7YaCFPn36wMvLq8TgGkWPQZ6AF0Dhdz8iIgKDBw+Gr68veDwemjdvLpxi9fbtW7x+/RqEELRu3RoREREir2lkZCT3HeifP39i48aN8PHxwdevX9G5c2d4eXlh+PDhpW5bUFAgdn2Uurq61FNxFRHiPysrS2Sqo4C2tjaysrKk2oesCgoKcOLECamDg/Tr1w/9+vXD1q1bERoaCh6PV+IoYH5+Pp48eYIvX75AU1MTbdu2lXjOs7e3F07plIeXlxfn8xQTE4Nz585JdV4ECqfaqqurY9u2baXmaty6dSuOHj2K0NBQWFhYAFB8pMSykmeErOh6bEGQISJmpFlFRQWdOnWCmZkZJ+nx7NmzERwcjEaNGmHmzJkwNjYWCb8dFxeHPXv2wN/fH8rKypzfbaDip7BXVUTOGTZFA9EwDCNxraQgt9y4ceOwZcsWAPKvL1VE6o3i6tatW2oi5pcvX3JS74j7DJemLNtUBbTDVc0UD3fcrVs3maZ7aGpqchIsCmhpaeHcuXMwMzPDnDlzJE5Va9euHRITE4X/F0wlysjIEBswIiMjg3MhHhAQoLDIdIrQs2dPqU+cRX38+BFdunThlNepUwcnT57EqFGjsH79evD5fKxbt07sPgghWLduHTZv3oy0tLQSX0/RyX6XLl2KjIwMrF+/HmPGjEFkZCTnAszDwwOrVq1CixYtEBcXh6ZNmypse3Fyc3Nx5MgR8Hg8xMfHCz+HnTt3xpo1a2BlZSW2U6II8kY5NDY2Rt++fZGdnS0sO3fuHExNTUXCrEsib8ALgebNm+P69evw9vaGv78/Xr58KbLfJk2awMHBAUuXLuV8Z2WJSCdJq1at8PHjRzRv3hzbtm2rlIXeigzxX94eP34MHo+H4OBgfPr0Sez3/O3bt4iIiOBMA+vTpw9GjRoFR0dHODo64smTJ5xtP3/+DBcXFxw4cAA/f/4UlmtoaGDSpElYs2aN1OsMZSUIhlJUdnY2zp07J/WU66SkJBgaGkp106Rhw4YwMjIShvoGFNNJUFJSgru7u0gn5urVq7h69WqJIbUF5M3TWPQYWJaVOR3H3bt34efnh+7du+P06dOcXFTt27eHqakp/vjjDwwaNAh79+7FnDlz0LlzZ7naXR4krTkqieBmpbhor0WxLAtNTU0YGBjA0NBQ7HlEEXnQ5Mktp4j1pQLSHMv27dsRFxdXYrLp//3vfzh69Chev34NXV1dzvOvXr1CREQEzMzMOM9dvHhR6vQwxW++VycMqa5dRapMBOFni3aYisvIyIC5uTnu3LmD9u3b49GjRyIXAJs2bcKSJUsQEhKCCRMmIC0tDa1bt8bo0aPh5+cnsq/79++jX79+MDY2RlRUlLC8bt26aNq0Kae+JKtWrcKpU6fEXoh8+/YNYWFhSExMxPv378EwDLS1tfG///0P1tbWJUYNFEhMTIS5uTkOHTok1V14gaZNm2LUqFES14bl5uZixIgROHv2LBYtWoSaNWtygmYsW7YM3t7eaNSoEYYPH17i+rGVK1cK/62oBKAAMHPmTOzZswfW1tYIDQ0VXph6enrC3d0d+vr6iI+Pl5geQN7tAeD27dvg8XgICQlBZmYmCCHo3bs37O3tMWfOHEydOhV79uyRuD3LsmjdujVat24tUv7s2TM8f/5c7MJnhmGEn8ukpCQMGDCAExlMW1tb6iiHkydPxoEDB+Dq6oq5c+cK77h7eXlhypQppa5nGTlyJK5evYpXr15JFfCiefPm6NOnDycaX3GvX7/Ghw8fhCOEisifV9Tnz59FLthZlgXDMFBRUZF6obdgnQfLsmjQoAHngjA1NRVpaWli130JkiYXff2pU6dy1qPu3bsXfn5+uHLlSqkh/lmWhbW1NWdGQFhYGMLDw3Hw4EGx+yi+Hk6SrKws4ahUYmIiCCFQVVXFb7/9hrCwMJG6K1euFAaFKf6aDMNATU0NLi4uYi+03r59iwEDBuDVq1do2LAhevbsicaNG+Pjx49ISkrC58+foaenh4sXL3Juhrx8+RJaWlpSnUNl4eHhIVPwoAYNGmDKlCnCO/ylWbBgAYKDgxW61kPc+VbW41CUly9fom7dujLlIlqyZIkwsEFpN37u378PAwMDLF26VOKNwrKSd02f4NwiK8HfSLB9SZe+gv23aNECwcHBYsP2K1JCQgL09fWlPi937doVqampePLkiXAGx7dv39CuXTs0btwYt27dUmj7HB0dERQUVOLn/MKFCzAxMUHTpk2xevVqDBo0CDo6Onj//j3OnDkDNzc3vH//HufOnROZqVOWQCAMw1T4d04RaIermim+QF5agguJv/76C2vXrsXTp09LTOSZkZEBMzMz3Llzh/PhzsvLw8CBA5GUlARbW1tYW1vj6dOnWLJkCbp27Yrhw4ejbt26ePjwIfbv34+8vDwkJCSInLTMzMxw/fp1fP36VaqTp6Qv/D///IPly5fj69evYi9EateujfXr15eYIBEo7BhcuXIFp0+fhqmpKbp16yb2IoNhGJE7nMbGxsjMzMSdO3ck7jsnJwcjR47E2bNnoa2tjQ8fPogch7a2NrS0tHD9+nXOcHtFIf+3KD0sLAzOzs7Ys2cPVq1ahZUrV6J58+ZISEgo8cdAnu137doFHo+H27dvgxACXV1dTJkyBXZ2dsKLa8EFdGkdLlkV/WxPmDABYWFhYqMcNm7cWDhKICnKIVA4sqSvr8+5+2poaIhXr16VOnoVHBwMe3t7uLq6lhrwwtXVFevWrUNAQAAnf8mVK1fg6upapmmN0iKE4NSpU+DxeIiKihIZ1dPX1y/TRdGLFy/KHImr+B1jSa9P/m8RuDhFv5eS9lF0Co+4/ZZ2IZCYmAgej4dDhw4JAwno6upi0aJFmDx5skg+MqDw77x27VqoqalhwoQJYqeBhYWFITc3F3/99RcnAangJoCHhweWLFki8tnNzs6Gt7c33N3dMWnSJOzbt4/TXldXV/z8+RM+Pj7Cv82aNWuwZs0aTl0jIyNER0eXePyA7B0VVVVVuLi4SJ1cdeXKlVi3bp3E2RyA7Hm85O1wyTtCJi9DQ0Pk5ubi6tWrUtXv27cv1NTUOOeyiu4wFf9esSyLrl27olu3bjK9viAokqTgEAJ8Ph+pqam4fPkyIiMjUbNmTdy8eVOm9CvFrVy5EpGRkSKjruIQQpCWlgaGYVCvXj2J71OtWrVga2uLv//+W6R81qxZ2Ldvn9TTpqUlTYcLKMwrOH/+fOG5uGjHlmVZbNmyBb///rvINqX9PSRRVFThikSnFFYz/fr1k/lChmEYYRQha2trxMTE4NSpU5g9e7bEbQTTC0ePHs25SFRRUcHp06fh5OSEwMBA4QmYEIJbt24J764QQtCgQQPs3buXc4eoR48eSEhIwKNHj9ChQweZjkdg1apVcHd3h7KyMsaPHw9zc3Po6uqCEII3b94gJiYGERERmD17tjAkuCRFf8jPnTsnDD9fXPEO15AhQ/DXX3/h3r17YqcWAoWRjo4dOyYc6Sr+9/v+/TumTJlSaZ0toPC49u3bh69fv4LH4+Hhw4e4fPkymjVrhnPnzpV6502e7efMmQOWZTF58mQ4ODjA1NS0zBfr8pA3yiFQOMXUxsaGU96vXz9h56ckkyZNwubNm+Hl5YWPHz/CxcWFs07vxYsXWLNmDfz9/WFgYIBJkyaJPH/v3j2YmZmVeVpjaZKTk+Hn54fAwEC8e/dOOGpWlDxTuOSdegUo5se46GiyvD5//oygoCDweDw8fvwYhBDo6Ohg5syZ8PHxweDBg8VGTkxOToa3tzdatGiB6OhosbkSHR0d4ebmhiFDhsDLywv29vYiN9Oio6NhaWnJWc8DFK6LW7FiBS5fviw2qXVCQgLWrVuHDRs2iHRI8vPzkZ2dLfK9zs3NxdmzZ4VRbBUpPz9fpo44y7LC37yiKjOPl7g1V9HR0fD09JSpw5WXl4d79+5BWVkZXbp0kXiuvHv3Lm7fvi3s6Dx79kymxOE9e/bE0aNHOeUODg5l6jAJ2qGINX2jR48u88wOWc4NMTExsLS0hI+PD3bt2lWm1wMKp9Pdvn1b7HN8Ph9BQUHw8/PD9evXhTMsVFVV0adPHzg5OcHW1lbk818Z60ul8fvvv8PY2Bh///03rl+/jszMTNStWxd9+vTBzJkzxU5PrY4dp7KiHa5qxs7OTuqT3cWLFzlJbrt27VridMKitLS0JK7nqFu3LsLDw3Ht2jUcPHgQSUlJ+PTpE/h8PjQ1NdGuXTuYmZnB2tpabKb1kSNHIi0tTWQ9QUmmTp0qkpTw1q1bcHd3R6tWrRARESE2D8rUqVPx77//YuTIkXB3d8ewYcPQvXt3sfsv65zs8ePH4969e7h165bEDhdQ2OkS5Gcq3oHt1q0b3r17V6bXVyRlZWUcOXIEgwcPxqVLl9C0aVPEx8dLfWdPnu35fD7OnTsHHR0dNG7cuEydAnmnyX369AmTJ0/mlPfu3Rvnz5+X6ochLy9PbMe5Vq1aEgOhFCVvwAugMJBJdna22GmN69evL9Md6uzsbBw+fBh+fn7CcNvKysrCtUNDhw6VeZ/lSREh/hXR4Tp58qQwd1ZeXh40NDRgY2MDOzs7DBo0CCzLwsfHR+L2gYGBwgsycZ0tgbZt2yI4OBhGRkYICgoSaXtubq7EMN8CPXv2xKVLlzjlBw4cQK1atTBz5kzOcwzDiNzk+PnzJ3R0dBASEqLwDhcgPlWBJIIUBkVVhTxe8jp8+DBmzZolHJnT0dGBt7c356YLABw9ehSenp7Cjs6XL18403RLUr9+fXz58oVTLm+HSRFr+iqKhYUFLC0tOaHMFSU1NRWjR4/G5cuXQQiBsrIyGjVqJPxMXrhwARcvXgSPx0N4eLhMf7/K0rlzZ4lLLP7raIermikaEU2Su3fvYtmyZXj+/DmUlJQkZvA+f/489PX1S1xX8+bNGyQnJ0vMaN+nT58yTVMyNDQUmxhYkgEDBmDAgAHC/2/duhWqqqqIiooq8UKkQ4cOiIqKQpcuXbB9+3aJa8bKeoHQunVrHDhwQKq6ampqYv9+bm5uGDt2LG7evFnqhZGiFU2YKyCIhlW3bl3MmTOH83zRdU/ybg8Az58/B4/HQ2BgIDZs2AAfHx8YGBjAzs4OkyZNkphLRtEqKsphaeQJeAEUzqUfOHCgyJTE1atXIyEhQebcRElJSeDxeDhw4IBw2m6vXr1w48YN2NvbS5zi2aNHD8ycORPTp08Xlp0+fRqnT58WmxhTEFhF3KhEdSEuwuDw4cPBsiwMDQ1ha2uLcePGib0BJcmlS5fQuXNnkXOfJAMHDkTnzp1x4cIFkfKePXtKjIIn8OjRI/Ts2ZNTfvnyZRgbG0uMPlqUhoYGhgwZInWuNFn5+PiU2DktiWDKc2Xn8ZLHtWvXYGNjAyUlJQwaNAgqKiqIiYmBra0tLly4wJleVlx2drZU+f0ElJWVxU7JrE4dJkUwMDCQOONFHnw+HyNGjMCVK1cwcOBAuLq6wtjYWDi6mp2djfj4eKxduxYXLlzAyJEjceHCBeFI1/3793Ho0CGRfd6/fx9A4TpTedaXViRxN8tLU3zNbnVBO1y/kFevXsHNzQ0hISHCL/PatWslTtkzNTXFypUrSxya379/P1xcXKrcAkVBQueSOlsCbdq0wdChQxUSWagkQUFB6NatGwwMDCTWefDgAZKSkkSitllaWiIwMBBDhw7FiBEj0LVrV4mL1BUd7a2k9RYPHjwQe1IrendT3u2BwoXJq1evhqenJ6Kjo4UjAosWLcLSpUtLTaZY1ezbtw9XrlwRKRNEwBTXQS3eAQWA2rVrCxMXyxrwQt5pjRkZGdi3bx94PB7u3bsHQgiaNWuGmTNnwt7eHu3bty91etft27fx4cMHkbIrV65g69atYjtcgHShfr98+YLr168jKysL+vr6JX7XKkppEQYJIcjOzkZ2drZUo5xF/fvvv2I/M5L07duXMzVw1apVGDRoEAICAsTefPPz88PJkyfF3sVPSUnB4MGDOeWSQpI3bdqUc04QF31MEGlsw4YNEv/uS5cuFf5b3lQFZ86cweXLlzFx4kTs27ePsy9VVVWYmZnB1NQUkydPRmhoKGJiYoRh5QWKf7dL+l4D4r/bZeXt7Q2WZXHu3DlhB/zVq1ewtbXFnj178PPnT/j7+1fZSJzVVUFBQZnXlZYkMDAQV65cwbRp07B7927O8+rq6rC0tISlpSVmzpyJvXv3IigoSPgdPnLkCI4cOSKyjeC7VPz8L5jWqegOV2lh4CUpepP/0aNHMkesrq5oh+sXkJ6ejtWrV+Pvv/9GTk4OBgwYgPXr16N///4lbifNB7xo7o/yJssi5vfv34u9qJSkQ4cOUi3mBgovWHk8Hm7cuIGfP39CX18f48ePh6mpaYnbOTg4wN3dvcSLwMjISLi4uIh0nHJycnDs2DGkpqaCx+MBkLwgX9EdLnnXPcm7fVEsy8LKygpWVlZITU1FYGAg/Pz8cObMGQCF05sKCgrg6OiIgQMHKux1i1JEZ6mk3CziPoPF/9byBryQd1pjkyZNkJubixo1amDy5Mmws7ODubl5hZ0HDh06hM6dO3PufLq6umLTpk0iUSQ7deqE4OBgdO3aVey+Tp06hWfPnqFz587C7+/379+xZs0axMfHQ1lZGVZWVliwYIFM63YkRRgcM2aMSL3Y2FjweDwcPXoUc+fOxcKFC2FlZQVbW1sMHz681BGHzMxMYdoNaTRq1AiZmZkiZXFxcTA1NYWzszO8vb0xYMAANGrUCJ8+fcKlS5fw+PFjDB48mLN2lWEY5OTkiH1fVq5cKXZNmIaGhsjaQaBwRETSBZWkERGGYUQ6XPKmKpA3j5eApO+2pN8WRX5nLl26hFGjRomMdjZv3hyxsbEiQQ2CgoIkvu6OHTtw8OBBqV5PkREeFcne3l7mgBnyuHbtmsKjugJASEgImjZtiu3bt5dad9u2bYiKisL+/fvh4OAg93RnWW7iAIXrgsUpS3CkojEFBATnYUdHR+GsgF8R7XBVYz9//sTmzZuxYcMGfPnyBR07doSXlxdGjBihsNd4+vRpiaFnpY04JO5LBpR9EbO6urpMi0OzsrI4+/D09ER4eDhu3rwp/IInJCRg9OjR+PLli8gFwp49ezBjxgy5Fs4C4u+WLVy4EPv374eBgQGsra1LDAuvaPL+kJTHDxFQGAZ60aJFWLRoEa5evQpfX18cOnRImAS5devWePz4scJfV97Okrwd0PIOeCGNnJwcsCwLe3t7zJ49u0xTPuRhY2PDiQa3ePFibNq0CfXr14eVlRUaNGiAq1ev4vLly7CwsMCdO3dE1tzk5+djxIgROH36tPBmhaOjI/bs2YOhQ4eKrFe6fPkyoqOjERcXV+oPvawRBk1NTWFqaoovX75g//794PF4iIiIwLFjx6ClpSU2CX1RP3/+5KQeKImqqipnXWzRgECPHj0SO71QMN2zKIZh0KhRI7x9+5ZTn2EYsbnw3r59Cy0tLZEyPz+/Ch91uX37Nl69eiX8LZQ3jxegmJtL8oyQpaeni53RoaysjKCgIKiqqsLf3x98Ph/BwcFi9ycp8bIkVXG0TBHrM6UVGBiI8+fPlxosqSzu3LmD0aNHl5r+Ayj8XltZWQmDmMjb4ZL25nNR4j4LgwcP5pSXlIZFnLt378LX1xchISE4fvw4GjVqBDs7Ozg5OYlNAVKd0Q5XNcTn8+Hr6wsPDw+8f/8ezZo1w8aNG+Hg4FDqBYOTk5PI/yMiIsRGFCsoKMCbN2+EU/ckkTTV48uXL3j69Cl+/PiBrl27om7dupw68ixibtOmjUxTBOPj4zn5mY4dO4bmzZsL37MfP35g/PjxyMrKwtKlS/Hbb7+hbt26ePDgAby8vLB792706tWL8x7K4tatW5yLsrCwMPTs2ROJiYkV1tESEBequCK3l2Yfffv2Rd++fbF161bhqEJ5rBNRxAWVvB1QRQW8kGekzs3NDUFBQdi1axf+/vtv9OjRA7a2trCxsZFptEVR3r59i23btqFjx46IjY0VWdO3adMmLF68GBs3bsTGjRuF5UFBQYiOjoapqSlGjRqFU6dOwd/fH/Xr18eDBw9w6NAhDB48GG/fvsXChQtx5swZ8Hg8TJs2jfP6ZY0wWJSmpiZmz56N2bNn486dO8ILDMEauMjISKxatQr29vYlrqktC3mmUnt5eSEuLq7EUPoCfD4fcXFxnOBBktYQl6etW7eKhLF+/fq1TOt0O3bsyOm0KOLmkjwjZNra2hKDhjAMAx6PB0IIAgICwOfzOb93ioj+WRVImyC3OMGIaWmJj/l8PtLS0pCYmIhbt26hUaNGWLx4sUgdWW9CvX//nlP25csXmZKNN2zYEF+/fpXpdSVR1MwUcZ9bQaoEcVFPxencuTO2bNmCDRs24NixY/Dz88OmTZvg4+ODvn37wsnJCTY2NpUaxVlhCFWtHDlyhLRv356wLEvq1atHvL29SXZ2ttTbMwwjfLAsK/L/4g+WZUnfvn3J06dPy9TWHz9+kLlz55I2bdqQT58+iTzH5/PJgAEDCMMwxMLCgsTFxZGcnBzh8zk5OSQ2NpaYm5sThmGIkZGRyPaenp6EZVmyZ8+eUtvh6+tLWJYlnp6eIuV169YlCxYsEP4/LCyMMAxDduzYwdnH169fSYsWLUjv3r1Fyk1NTYUPhmFIixYtRMoEDyMjI9KyZUvCsiyxsbER2Uft2rXJkiVLSj2O8sAwDPHw8Ki07cu6j8ePH8v1mlWVrq4uMTQ05JQPHDiQNG/eXKp9lPSdLum7XhSfzyfR0dFk3LhxRE1NjTAMQ1RUVMiwYcPIgQMHCMMwZNq0aSW2ofjf1N3dnfM64p4rvm1gYCBhWZZERkaK3bZHjx6kQ4cOImX9+/cnLVu2JAUFBcLj6dChA1FSUiK7d+8WqZuVlUUaNGhATE1NRcqjoqLImDFjiKqqKmEYhtSoUYNMmjSJREdHC/db2vtQkpycHHLgwAEyaNAgwrIsYVmWKCkpkcGDBwvrMAxD2rRpQ4YOHSrVo02bNhLf47LYuXMnYVmWbN++vdS627ZtIyzLcs6fpqamJDAwUGFtkoaDg4PI+6CiokJWrlwp9fYrVqwgqqqqCm1TSkpKmR4CgwcPJm3atCnxNfh8PnF0dCQMw5A6deoo9LNQkpK+28WtX7+e87CwsCAsyxJvb2+xz69fv164veB8Vdr1i6TzmzTXPoKHoaGh2N8aRZxjdXR0iJ2dndTvsZ2dHdHR0ZGq7q1bt8imTZvIpk2byLVr16R+DUWQ5bMgydu3b8nq1atJmzZtCMMwpGbNmsTe3p68fv1aQa2sHHSEq5qxtrYGwzBo1aoVFi1aBC0tLRw7dqzU7QSLJQV3NgghaNmyJebPn4958+Zx6ispKUFLS0ts1DZp1ahRA9u2bUPv3r2xdOlSkakA8i5injdvHnbt2oXZs2fjzZs3WLRoESfQxLdv37Bx40Z4eXmhUaNGnFwnubm5ItN1nj9/DoZhMHHiRM6x1K5dG8OGDeNEOSy6toBhGKSkpIgdMWRZFvXq1cO4ceOwZcsWked69uwpcRobJV7btm0ruwnlQt6AF4Bi7l4yDIMhQ4ZgyJAhSE9PF+aJOXnyJE6dOgWGYZCUlIQLFy7IFG20LAQpE/r16yf2+f/973+caUbPnz/H2LFjhaPXDMPA3Nwcjx8/xvDhw0XqamhoYOjQoZy7tfJGGCyNqqoqbGxsYGNjg5cvX8LPzw8BAQGIiYkRqVfSNFdxip9LPT09YWJiIjHSLFC4Pig2NpYTQMnJyQk+Pj5YuHAhvn79ivnz53MiFv748QObN2+Gp6cn9PT0hEnDBeLj40VSelQGReXxkoe8I2RDhw7FwoULcenSJYlRK4uOdAUGBso9JVDc307eICiKWNMnWPMzfPhwmWeFlDYlkWVZaGpqokuXLiL57IpSxGhhr169EBUVha9fv0oMkiXw9etXREVFCdflnz9/Hr6+vpg9ezbnvOjm5oa1a9eKlM2ePVuqtWLi8Pl8REVFCadDV4QmTZrA1dUVrq6uOHPmDJydnREcHIwxY8agWbNmFdKG8kA7XNUQIQTPnj0rMXFx0bpFo9MUPen7+/ujW7du5bYOR8DQ0BD79u0TKZN3EXOdOnVw/PhxWFpaYvXq1fDx8UHPnj2hq6sLoHAKSVJSErKzs1G3bl0cO3aMsxatZcuWwiTNAITPF1/0LZCTk8M5uRc98bIsy1l7Ig0vLy+Ym5sjMjKSczFI/bfIG/ACUPy6unr16mH+/PmYP38+rl+/Dh6Ph4MHD+LWrVswMTFBy5Yt4eTkhOXLl4tsJ8t6lZI6FILpyJKCWmhoaHDKvnz5wllLJMhhIy6/UtOmTcXmHCJyRBiUhZ6eHjw8PODu7i4SLVARnWd3d3e4u7uX2OG6ePEiPDw8OOcudXV1HD16FIMHD8Zff/2FtWvXolevXiJ54ZKSkpCVlYX69esjPDy8XJIGK4K8ebwq2/jx4/Hx40d8/vy5xHoMw8DPzw96enqcnI/SunTpElasWIH4+HhO1E15O0zyrumbP38+9u/fj2PHjiExMRG2trZwdHSUepqfrIl2V65cicjISM6aPnk5OjoiMjIStra2OHTokMS1mrm5ubC1tUVGRoZwem5oaCjCwsKwY8cOkbpxcXHw8vKCsrIyJk2ahJo1a+Lw4cPYtWsXzM3NZUp8/fTpU2GC+48fP5b1MMvs1q1b8PPzw4EDB5Ceng5tbW00bdq0wtuhSLTDVc0oIhmnQEVl+P78+TPnB0wRi5h79+6NO3fuYPny5Th06JDwLpuAiooKJk6cCC8vL7HrIsaMGYM1a9bg/PnzMDIywvDhw7FgwQLs3LkTa9asEan77t07HD16VGLiZKDwZKevr1/q8RR39uxZmJiYYOTIkTA1NUW3bt3E3vFiGEautVKSyHsXVBELq6vi4myKq3fv3ujduzc2b96MsLAw8Hg8XLhwAW5ubpwOlzzrVYquLRWMcCUnJ4tNLv769WtOQtB69epxLkyJhFDmQGEHrfjolbwRBsuCYRiRMOzlfTNMIDc3V+IIUNeuXXHz5k0sW7YMhw4d4uSnKu08W1XIk8erKmjSpAln5EIShmFEgqUI5OXlISQkBElJSVBWVsbAgQNFImvevn0by5Ytw9mzZ4U594qTt8Mk75q+TZs2wdvbG8ePH4efnx+2bNmCTZs2oU+fPnBycsKECRNKHTGSxatXr3D79m1OeXp6OrKzs6GjoyN8Py5duiQ2gbiBgQFnLfzo0aPx22+/4cSJE+jWrRsWLVoEMzMzkZvGsbGx2LRpE548eQIrKyvh3yoxMRF9+/blHOfu3bvBMAz++ecf4VrzefPmwcDAAAEBAaV2uH7+/Ck8rwuup1RUVDB69Gg4OjqW/mbJKT09Hfv374efnx/u3r0rHMl0cnKClZWV2EA91QlDJP0CUb+ka9eulWm7siQ35vP52L9/P5ydndGrVy+RQAcNGjTAlClTONPrJFmwYAGCg4MlRljKysrCjRs3hLl/tLW10atXrxITdn7//h3dunXDhw8fsGTJElhbW+PChQv4448/8NtvvwmDZjx8+BDbt2/Hp0+fcPz4cQwbNkz6N0EK0k51YRhG4fnQWJZF3bp1xQY1Kakdz58/V8j2itrHr4JlWbRu3Zqz4L2kyE9FA15UBkHiai8vL2FZWe+s6+npSfw+iBs9zs/Ph66uLnr06CHyHpiamqKgoADnz5+X6nUtLS3x4cMHsRdWRSMM3rp1CwzDCCMM7tmzB1OnTpWYALq0BfriKPrGCsuy8PDwkLjP3NxcDB8+HA8fPsSbN29K3NfPnz+F51nyf3nhevfuXeJ5tqwj//IoGiYdAExMTMrUSSjv3I0V6du3bzAyMsLdu3eFNx4YhsGoUaNw5MgRrFixAl5eXuDz+ejRowfc3d3LZcaFmZkZHBwcFJbi5P379wgICEBgYCCePHkCDQ0NjBkzBk5OTqWmcpFG8c8SUNgxaNmyJXr27InY2FhhuSBgRPHLak1NTTx//pwTMCsrKwsODg44fPiwxM8nIQRjxoxBYGCgcImHtrY2RowYwTnvNG7cGLm5uUhLSxM5j44ZMwbXrl2T+P2+du0aeDweQkND8e3bN2H7Bw0ahJCQEKlujBd/D6S9ViGE4PTp0/Dz88OJEyeQk5ODTp06wdHREba2tjIFFqnq6AjXf0y/fv0UkjdBoGXLlmLL8/Pz8enTJ+Tl5UFZWVnkYgwonJMsywV2nTp18O3bN4nP16hRo8QpM+LUqlULcXFxGDt2rPAkIRARESGcr0wIgZqaGrZu3crpbBXP9C6togkIK/tHPTMzk5O7pyK3V9Q+fhXyhqZXBHF3by9fvswZRQYK794W/37LMzIjaRqduIv6W7duoX379hg7dqxIea9evbBjxw7k5uaWGnb58+fPiI+Px9SpU8U+L0+EQXd3d870K8H7KelepyI6XMXPy5s3bxa7dqWgoACpqanIzs4WG6GxOA0NjTKt2ZMUDVcSwVokAVnPs8U/Q/Lm8apqkpOTERAQgMTERLx//x4Mw0BbWxv9+/eHnZ0dWrVqxdlm/fr1uHPnDrp27YrJkyeDEILg4GBERERg3LhxOHLkCFq3bg0fH58SU8vI22FS9Jo+HR0dLF++HMuXL8eFCxfg7++PsLAwhISEICIiAr/99pvCXksgODgY3759kxgxseh3LSMjAwsXLsS+ffs468hr1KiBQ4cO4dy5c8KcfkVvGvfv3x8ODg6cfHAZGRmcUf03b97g8+fPGDZsGOemVevWrXHy5EmRsrS0NAQHB4PH4+Hhw4cghKBJkyaYMWMG7Ozs0KVLF+jp6ZXY2RJ3A1/Qqbt+/brEc1zRG/jNmzfHu3fvoKmpCQcHBzg5OaF3794SX7M6ox2uX8zt27eFF/ADBw7kfHDt7OykvkC7ePFiqaMIkhIjq6iooHPnzujVqxfmzJmDzp07izyviEXM+/fvR2ZmJqZPny5xek9ubi727t0LLS0tTJo0ifO8rq4urly5grCwMISGhiIpKQmfPn0Cn8+HpqYm2rVrBzMzMzg6OoqdLmhjYyPTBW/xNXUAZApXXB7kvfusiLvXFX0HvKpSZCLpspJ09/bs2bMy3b0tK1k6a7179xZ7w2LDhg1Yt26dVFNQ0tPTsX79es5FjThdu3bF9u3bsXHjRoSHh8PPzw+xsbFwd3eHh4cHzM3NRfJZHThwgLOPw4cPIzw8XOxzilL0vCzo8Im7+FFRUUGnTp1gZmYmtpMXFBSEbt26lZjMXRq3b98WO3ooSfEOV1nPs2VVPI9XVcHn8+Hi4oKNGzeCz+eL/E0fPHiA2NhYeHl5YfHixVizZo3Ib+yxY8egp6eHq1evCm9CzJkzB+3atUN4eDiGDh2K8PDwUvO+VYUgKJLUqFEDGhoaUFZWlnixrwgnT55Ehw4d0LNnT7HPF1+uERISgqioKE6HS8DMzAxmZmZSv37t2rWFU60FBEsuxLWJYRiRtZXjx4/H8ePHhQnuJ06cCHt7e1hYWMj0vZF0A58QIjHIEQCR0a+3b99CRUUFXbt2xcuXL6VaNlPZszrKina4qhl5o9MEBASU+hp3797FsmXL8Pz5cygpKZU451qWu5bFybOI+dq1a7Czs8PSpUtLXEuhqqqKt2/f4o8//kC7du3EnoxYlsWECRMwYcIE2Q4AwIoVK+QeYZAnihj1a6moNTslUdTd28om7Xz/du3ayZxgU9oIg+LOKY8ePUJ4eHiZzjfSKnpeZlkWCxYsKNO5w8HBAe7u7iIdrt27d2P37t24efOmTPuRZ82wIs6zsiiex6uqmD17Nvbs2YN69eph2rRpMDc3h66uLgghePPmDWJiYsDj8eDt7Y3MzEz8/fffwm2Tk5Nhb28vMuKroaGB4cOHY/fu3fDx8ZEpyXZVkZaWhn379sHPzw/379+HsrIyhg8fDicnJwwdOrRcXvPevXsyTbfs1q0bIiMjFfb6BgYGiIyMxI8fP4TTDI8ePQqGYcReRzx//lwkWNDhw4fBsiyWLFmCFStWlDkatSw38EuSl5fHWRtakmq75ru8485TijV79myirq5Ovnz5IlJ+7tw5Ya4ce3t7Mnv2bNKoUSPCsiw5evSoVPt++fIlsbW1JUpKSoRhGDJy5Ejy8OHDcjgKIpKbQpaHwIwZM0jt2rU574M4X79+JXXq1CHTp08vl2ORlzR5qNatW1cuOVXKkgNLkdsrah+U4gwePJh06tSJUy4pv0rv3r1FckdVFWlpaeTt27eEz+cLyy5duiQ2x8+pU6fkfj0+n09Onz5daj1F5KmRRUpKilTnSXFkzacm7T6quuJ5vKoCwW983759ycePHyXW+/z5M+nXrx9hWZbExcUJyyX9HQR/T0FuudJUhd8MPp9PoqKiyNixY4V5Ag0MDMjmzZvJ58+f5dp3ceI+C6qqqsTV1ZVT99y5c8TNzY1T7urqStTU1BTWpuDgYMIwDOnZsyfZunUrmTt3LlFSUiJ6enqcv2N+fj5p2LAhGT9+vLBMX19feP3Vp08fsmPHDs77Jk+OQVnIm5+uOqEjXNVMeUSnSU9Px+rVq/H3338jJycHAwYMwPr164U5H8pD8+bN8fLlyzJPpzt//jzMzc2likZUu3ZtmJuby3QHRRZXrlyBq6urME9Snz594OXlVaZAI5KUFEWMohSpsu/eljYyxTAMNDU10bVrV9jZ2Ykdga+MaZHFIwxWtoyMDOzYsUPkvDRnzhyZ1s5WVwUFBThx4oRMYbCrun/++Qe1atVCREQEGjVqJLFegwYNcPToUbRt2xb//POP1NP/KvL3RZ41fS4uLggKCsL79++hqakJJycnODk5iY2oKI604eMF3r9/zylTV1dHVlYWp9zU1FRsoI6srCyxa0lzcnKwY8cOJCQkQFlZGUOGDIGTk5PYWTtbt27F1q1bkZycjClTpiA2NhaBgYG4desWCCGoXbs29u7dy/k7RkVFITU1VSTg0osXLxAbGwtfX19EREQIo7BaWlrC1ta2XNa9SVIVZnVUFNrhqmbevXsn9sQSFxeHOnXqiFx8tG3bFlZWVhIjE/78+RObN2/Ghg0b8OXLF3Ts2BFeXl4VMm/d0dERnp6eZQ4Y8erVK5lOCm3atBFZW1GcpOAfxRWPjnfv3j2YmpoiJydHWHbu3DmYmpri2rVr6NSpk9T7lSQ3NxcXLlxA48aNpdoXRckjLS1N7AWdkZERXFxcOOWNGjVCenq6wl5fV1e3xO8Dn89Heno64uPjkZCQgKioKISFhYnUUcS0yKoQYbCs0tLS0KdPH6SkpAg7l1FRUQgMDMTVq1cVtt6uqnn8+DF4PB6Cg4Px6dOnKjclUB6XL1/GsGHDoK2tXWpdbW1tDBs2jBPk5v79+5wAJPfv3wdQmBuz+I0IQDTAk4C8QVDkWdO3bt06qKio4LfffsOYMWOgrq6O5ORkJCcnl7gPwXE8evRI6tct+vpFNWvWDPfu3ZN6+7t373JySOXk5MDY2FgkuMSxY8ewY8cOhIWFoX379iL1MzMzRaK/+vv7w9nZGYmJiahXrx6GDBkiNimwmpoaNm/ezLleMjc3h7m5OTIzMxEcHCyMEhgZGYk6deqAYRh8/fpV6mMECoOh3bx5UxhNumHDhujRo4dCE8ZXa5U6vkbJTFVVlSxfvlyk7PXr14RhGDJ8+HBO/SVLlnCGsgsKCsju3btJkyZNCMMwRFdXl/B4PKmnFCiCvNNqNDQ0yNKlS6Wu/+eff5IaNWpIfF5PT4/o6+tzHlpaWsLpj02aNCH6+voi202aNIkwDEPc3NzIx48fyadPn4irqythGIbY2tpKfL0WLVoIHwzDEC0tLZEywaN58+akRo0ahGVZMmPGDKmPl6LKqk6dOmTBggVS11+wYAGpXbt2ObZIvNu3b5PBgwcTlmWJv7+/yHOKmBYpmHIj+P4XnQJdtKz486WpiCmFCxcuJAzDEDMzMxIaGkpCQ0OJiYkJYRiGLFmyROr9VIcphT9+/CB+fn5kwIABwr+Nmpoasba2LvM+q+KUQjU1NeLi4iJ1fRcXF5HffklT+CVN75f0eZb02S/pUXQ/DMMQR0dHEh8fL9Oj+OtLuwxB2u+lLGbMmEFUVVXJixcvSq37/PlzoqyszFnS4OXlRRiGISNGjCCJiYnkxo0bZNasWURJSYk0aNCAJCUlidQv63njwYMHZMGCBaRx48al1k1KSiKzZs0SXvewLEtatWpFVq9eTV6/fi1xu3v37pFhw4YRZWVlzvuvoqJCRo4cSR48eCBz2381dISrmpE3Ok14eDhcXV3x5MkT1K1bF+vXr8cff/xRKYtlCSE4ceIELC0tS4wyePr0adStW1ckHHHjxo3x5MkTqV/ryZMnJY4QlXS3LiUlBQsXLsTHjx9x9uxZkecuXLiAgQMHYtWqVcKy1atXIyEhocQpjIqKIkZRiqaIu7cVoWvXrjhx4gTatm2LwMBAkdF9RUyLrKwIg4pw8uRJtG7dGmfOnBFO0RwzZgw6dOiAyMhIiSN/4hQPbiQIYPT582eJkeCKjpDGxcWhRYsWZTmMEiUmJoLH4+HQoUP48eMHgMLR0UWLFmHy5Mm/3CherVq1ZEqd8eXLF5FgCNJEf5OWvEFQ9PX1y7ycQJHHUVazZs3Cnj17YGNjg1OnTkFLS0tsvfT0dNjY2IDP52PmzJkiz4WGhqJt27YIDw8Xfkd79uyJESNGYMKECRg0aBBOnz4t9VTJor5//46DBw+Cx+Ph2rVrIISUmh4DAHr06IFdu3Zh8+bNOHz4MHg8HhISEvDXX3/Bw8MDubm5nG0SEhLw22+/4fv376hRowZ69uyJJk2agBCC9+/f48aNGzh+/Dji4+MRFRWFAQMGyHw8vwra4apm5I1OY21tDYZh0KpVKyxatAhaWlo4duxYqa8rblqBPAQ/1seOHStxaqCqqipOnDiBgIAA3Lt3TxhN7H//+x9OnDiB1NRUTj4Kca915syZMs/n19fXR2hoKLp27QpXV1ds3rxZ+NzHjx9hY2PD2aZfv37CtRPiKCqKGEUpmqGhIfz9/ZGSkiI2FUJRycnJSEhIEK4brWiqqqqwsrJCaGioSLkipkUqIsLg7NmzOWU3btyQ+BxQeANm586dUu1fkpcvX8LZ2VlkPZyysjIsLS1FpnZJw8fHBz4+PiJlhBCJU9uK5200NjbGnj178PXrVyxcuFC4xmTnzp1ij9PQ0BC7d+8Wu+/Pnz8jKCgIPB4Pjx8/BiEEOjo6mDlzJnx8fDB48GDMnTuXs528ebyqgo4dO+LMmTNShbzn8/k4ffq0yHolRXZU5OkwyUsRxyEuz+ClS5dw6dIlTl0DAwNYWlqKlHXt2hULFy7Epk2b0LFjR8ycORMmJibCa623b98iLi4Oe/bswcePHzF//nx0795dZB9Pnz7F1KlTOWtWLS0tERsbi8GDB2Pw4MGIjo6Wej34xYsX4efnh7CwMGRlZYEQgu7du8PR0VFsWhxJ1NTUMHnyZEyePBkvXrwAj8dDUFAQp15WVhamTJmCnz9/wsPDAwsXLuREPPzx4wd8fHywatUqTJkyBf/++6/IIMB/SiWNrFFlJG90GnHTYyp6OJ4QQvr160cAkA8fPpRa98OHD0RDQ0NkmlNMTAxhGIZYWlqSnz9/Stw2OzubDB06lLAsS2JjY+Vq8x9//EGaNGkiUlZa5CdpyBNFjKIU7fbt28JoaOnp6RLrpaWlkd69exOWZcnNmzcrsIWi3NzciKqqqkhZeU2LLMt0OnmmX5WVIs5LhBBibGxMTExMZH4UlZSURFiW5UR1c3d3l3j8xT9PUVFRZMyYMURVVZUwDENq1KhBJk2aRKKjo4W/eyVFVZM1Im55/e7JY8uWLYRhGOLu7l5qXU9PT8KyLNmyZYvC21EVohTKIy0tjWhqahIzMzORcsF3o/jnsW7duiQtLY2zHz6fT5YtWyZ2Gp1gP0pKSmTJkiVil2toamqWuCzi5s2bpF69eqRu3brk8uXLEr+7Hz58IOvXryft2rUTvq6Ojg5hGIbY29vL/gZJUDTaq0BAQABhGIasX7++1O3Xr19PWJYlQUFBCmtTdUNHuKoZeaPTVIXheOD/30GUJhBE48aNYWJiIhJtzNzcHBMmTEBoaCh69OiBxYsXw9TUVLho9O3bt4iNjcXGjRvx+PFj2NjYyJRYUJysrCyFBgf4L0cRo6ouRdy9rUgfPnzgfGeqyrTIsgYFqiri4+Pl3kdwcDDU1NSwcOFCznMMw4gEtvjy5Qt0dHQQFBQk8pkaPnw4WJaFoaEhbG1tMW7cOJkW4ld0Hq/yMHPmTOzatQuenp5ITk6Gm5sb2rRpI1Ln6dOnWLNmDYKDg9G6dWuRaWx3796FtrZ2iREOi7pz5w7u3LkDOzs7hR5HZVNUnkGGYbB27Vo4OzvD398fV65cwYcPH0AIQePGjdG/f3/Y29ujbdu2Yl9HT0+vxHNU9+7dcfbsWVhYWMDS0hIDBw4UPsfn8xEVFQUej4eTJ08iPz8f6urqGD9+POzs7DB48GCoqKhINY1QWuK+P1FRUWjQoAEWLFhQ6vbz58/Hhg0bEBkZCVtbW4W1qzqhHa5qSJ7oNFWlw5WRkSFT/Y4dO+LChQsiZf7+/mAYBgcPHsS0adPEbkcIwcSJE2WeRlPc+fPnceDAAbEJUvft24crV66IlD179gwAYGVlxanPMAyCgoL+k1HEqOphw4YNUFFRgY+PDzw9PTkR+wghYFkWixcvxrp16yqplYUX6MePH+esX60q0yIra9oVUDi9qPhFpSBq3YYNG8Suv1q6dKlMr5Gfny+8aOzcubPYtbgXLlzAgAEDpDqnaWpqYvDgwZxzPVD4mcvOzkZ2djby8vJkaqe7u7tM9asiNTU1REVFwdLSEsHBwdi3bx+aNm0KXV1dAMDr16/x9u1bEELQokULREVFiazN7t69O1auXCkydX39+vXw9vZGWloa5/UiIiLg6emp8A6XvGv6ynLjlGEY4Q3bkydPokOHDmLXvAPgrE0LCQlBVFSUxMTurVu3xpo1a2RuU//+/REYGIivX79KTG/To0cPxMTEYNCgQYiOjhaWN2vWDB8/fgQADBgwAHZ2dhg/frxUaXIA2UPjA4Xv4YMHD0TK7ty5AyMjI4lr8ItSVVWFsbGxTNEpfzW0w1VNDRw4UOSOhzi6uro4ffo0unTpgg8fPlRQy6Qja7heVVVVkXUBQGEujJCQEEyfPh2+vr64fPmy8Di1tbUxYMAAODs7C/OQ5OfnQ1lZ/Ede0kk8Pz8fb9++FXaM3NzcOHWePXsm7GAVV/QkKcAwDLy8vPDixQuYmppixowZAIC///4bCQkJWLdunUyL2ilK0eS9e1ve0tLSkJiYiBUrViA1NRXTp08XeV4Ri9oV7e3bt4iIiMD169dFwib37t0bo0ePho6OjkJfLyYmBjExMWKf+/PPPzllDMNwOlzJycmIj4/HwIEDOX/ryMhIODs7C49FS0sLu3bt4qz3ffbsGRwdHTmvRyQECtLT0+MEHIqNjQWPx8PRo0eFOYOsrKxga2uL4cOHS3XBJ4uqmserdevWuHXrFry9vREQEIA3b97gzZs3wuebNGkCBwcHLFmyBJqamiLbinuvs7OzZQrEAcjfYZo1a5bM2xS92Jc06ioIPiXpOYHKzjMoMGLECOzevRu7du3CsmXLJNbr0aOHcKTry5cvAApH9VmWxaJFi7B8+XKZZ8U8evSoxPdLWp8+fSr1hlZRLVq0EJmp9F9DO1y/mLJGpxG4ffu2cBrMwIED0bt373JpZ7169aSe2gAUTkGUFBzDxMSkxOSODx8+BI/Hw/79+yV2PEs6iWtpaWHQoEFYsGCByPRMQbvKwtLSUmFRxCiqvJT17q08Skt8XBQhBPPmzeNcGFe1aZErV66Et7c3cnNzORc5QUFBWLx4MVxcXMTe0CmLotOi5OHr64v169dzchw9e/YM48ePR3Z2NvT09FCjRg08evQIkydPRps2bUTey6ysLNSqVYuz73nz5omdWlS7dm1OUllBQtkvX75g//794PF4iIiIwLFjx6ClpQVra2uFHG91yONVu3ZtrFq1CqtWrcKrV69EbjI2b9683F9f3g6TvBf74hIRb9iwAZs3bxb7XHGVnWdQYOjQofj586dU57sePXrg+fPnwrxYU6ZMQXh4OHx8fLBt2zYMHz4ctra2sLKyknhTuThlZWVYWVnB0dFROGVXVt++fZN6VA0ojLQpiHL6X0Q7XL8IaaPTnD9/Hr6+vpg9ezb69esnsg83NzesXbtWpGz27NnYvn27wts7cOBAJCQk4Pv372J/jIv6/v07zpw5U2KnStw2snQ8+Xy+1PsuqqxZ0hUZRYyifiWlJT5mWRaampro0qUL7O3tJY5OyzstUlERBl1dXbF27VqoqanB1tYWxsbGImGT4+LiEBYWhpUrVyI/P18h09/kCdld1MWLF9G1a1fOeW7r1q3Izs7G77//Lvx9CA8Ph7W1NXbs2CFyDqtbt65w+lNRWlpaYkceP336JPEiTlNTE7Nnz8bs2bNx584d+Pr6IiQkBHv27AFQOOq2atUq2NvbS935yMrKQmhoKHg8HhITE4W/FWPGjJFq+4q0Zs0a/PjxAx4eHlBRUUHz5s05x5mbmwt3d3fUqVOnxJGTslLE6Ig8F/vi1n0L1vNJsyZcXV2d06EH/n+nvrisrCyFroUqSpZ0PEW/L0FBQdi5cydCQkLA4/Fw5MgRhIeHQ0tLCzY2NpgyZUqJ+7p7967wu3P8+HE0atQIdnZ2cHJyErtsQpKyXDeV9Vrrl1Bh4TkohStLdJrZs2cTdXV1TlS8c+fOEYZhiIqKCrG3tyezZ88mjRo1IizLkqNHj3L28/PnT7Jr1y6ycOFC4u/vT/Lz8wkhhLx9+5bY2NgQbW1t0qxZMzJ9+nTy6dMnzvZHjhyROoqOIAlleHh4qXUvXLhAHB0dSa1atYTvSY8ePcj27dvFRhqSV3x8fJkiLikqihhFUSV7+vQpcXFxIWZmZqRjx46kQ4cOxMTEhLi4uJDHjx9L3E4REQYFSU9btmxJnjx5IvG1Hj9+TPT19YmKigpJTk6W+5hNTU1JYGCg3Ptp1qwZmTJlCqe8devWRE1NjXz9+lWk3MjIiLRp04ZT1r59e6lfs127dsTQ0FDq+jk5OeTAgQNk0KBBwghxSkpKnETWxV2+fJk4OzuT2rVrC7fT09Mj27ZtK5ffCnmdPXuWsCxLfHx8Sq27ceNGTmReWZNYS3qOYRiiqqpKRo0aRY4dOyY2Al9J7t27R+bNm0caNmxIWJYl2traZOnSpeTRo0cy7UeatorTsWNHYmFhIfW+zc3NZfr8SsvR0VHmh5OTk9h9Cd7TBg0aiETkNDIyIi9fvpTYhtzcXBIWFkaGDh0qjLb4v//9j+zdu5d8+/at1GNgGIaMGzdOmFy9tIe1tfV/+vqGdriqmYKCAnL8+HEycuRIoqKiQhiGIRoaGsTGxoacPHmS5Ofnlxget3v37sTY2JhTPmHCBMKyLOHxeMKyx48fEzU1NTJy5EiRullZWaR79+4i4XOHDh1Kfvz4QTp16kQYhiH16tUjSkpKhGEY0rFjR5KVlcV5TTMzM8KyLDE1NSWxsbEkJydH+Fxubi6JiYkR1inpBFlRYVHFKWsHiXa4KEq8169fy7yNv7+/wtsRHx9fpkdRK1asICzLkosXL5b6ehcuXJA67HdpFBV6W11dnSxfvlykLCMjgzAMQ4yMjDj1//jjD1KjRg2RsnXr1hGWZUloaGipr3fw4EHCMAxZu3atSDnLssTT01Ok7MqVK2Tr1q0iZSkpKWTFihWkefPmYs+jnz59Ij4+PqRDhw7C34omTZqQJUuWlPi7WRXY2tqS+vXrk+zs7FLrZmdnkwYNGoh0lhXV4VJUh0nei31pj6O4GTNmEFVVVfLixYtS6wpumEyfPl2m9kijpJs2ZU0ZkZubS0JDQ8mQIUOE119KSkrEwsKChISElLjt27dvyerVq0mbNm0IwzCkZs2axN7evsTz8a+QbqEi0SmF1Yy80WnevXsnNnN5XFwc6tSpAwcHB2FZ27ZtYWVlhWvXronU/eeff3D79m3Y2trC2toakZGR8PX1xaxZs/D161dcu3YNvXr1wo8fPzBv3jz4+/tj165dWLRokch+Dh8+jFGjRiE+Ph4JCQlQVlYWrtNKS0tDXl4eCCEwNDREWFiYyLaVERZV0SoiihhFVTedO3fGli1bRM5Fkrx79w7Tp0/HqVOnpKovC0VEGLx06RI6d+6MAQMGlFp34MCB6Ny5s9gIfZVFWVmZE1Th1q1bACD2d0Tc9HBBUuKpU6ciPz9fYgLWffv2YdasWahfvz5nnRARE2AjOjoanp6eItHj9PT04OHhAXd3d5w9e1ZYfvLkSfB4PERGRiIvLw8aGhqwsbGBnZ0dBg0aBJZlOcmdq5rLly/DwsJCqmloampqsLCwwOXLl0XKf/z4gU+fPgn/L1hP8/nzZ877K2mtjeD7uWHDBhw7dgx+fn7YtGkTfHx80LdvXzg5OcHGxqbUpQIqKiqwtraGtbU13r17B39/fwQGBmL69OmYP38+rK2tsXr1arERmOVRVQLqJCYmcsp8fX3h5+cn9jlpqKioYPz48Rg/fjzevHkDPz8/BAQEIDY2FufOncPEiRMlbtukSRO4urrC1dUVZ86cgbOzM4KDgzFmzBiJf4OqEvW62qjU7h4lM8Edi6VLl5KMjAyJdSTdqVNVVeXcsXz9+jVhGIYMHz6cU3/JkiVETU1NpKxXr16kS5cunDKWZcnBgwdFyvPy8kjz5s1J//79xbYnPz+f+Pn5kQEDBgiTWgqmLAwcOFBkumJROjo6wrsmhoaGZO/evZxpkhVxx1KeEa7KSIhKUVWdYCrz8OHDyfv37yXWCwgIIFpaWoRhGDJ27NhybdObN2/Ijh07iL29PRk2bBgZNmwYcXBwIDt37iTv3r2TuF2TJk3I1KlTpX6dqVOnkqZNm8rdXkWNcPXo0YO0bdtWpGzZsmWEZVly+PBhTn0HBwfSqlUrTnlsbCypUaMGYVmWtGjRgjg4OBAXFxfi4uJC7O3tib6+PmFZlmhoaJCYmBipjkeWc6/gd9PExITweDzOVEhBnao8wqWhocH57S7J8uXLiYaGhvD/kkYjShulkEZZRkckOX36NGnWrBlhWZYcO3ZMqm1k/R1etGgRYRiGaGtrE3d3dxIfH0+ePHlCnjx5QuLi4siKFSuItrY2YRhGpgTq8iqvGS5nz54lEydOLLXezZs3yZw5c0j9+vWFM4Vu3Lih8Pb8V9ERrmpG3ug0tWvXxrt370TKkpKSAEBsXgqGYaCuri5S9uLFC85dyoEDB+LmzZucRafKysqwsLDA0aNHxbZHSUkJjo6OcHR0REFBgTAfSP369UuM3iNvWNTKpqgoYhT1q3nw4AFmzpyJ8PBwdO7cGdu3bxe5M/v+/XtMmzYNp06dQr169RASEgIbG5tya488EQYzMzNlisbaqFEjmcN0l6exY8fCzc0NM2bMwO+//45nz57h77//Rq1atWBpacmpf+nSJbRu3ZpTbmZmhkuXLuGPP/7AxYsXkZKSwqkzYMAAbN26FT169CiPQ5Erj1dVwLKsTO3Oy8sTCUZhZGRUbsmfyzI6UtytW7fg5+eHAwcOID09Hdra2uWSjByoPnkGFcXCwgIWFhZin0tPT8f+/fvh5+eHu3fvCgOaODk5wcrKqtQoileuXIGrqyuuX78OAOjTpw+8vLzQp08fhR9HdUc7XNWMvNFpDAwMEBkZiR8/fqBmzZoAgKNHj4JhGBgZGXHqP3/+XBhKWUBcmF9Bh0fcxUXjxo2lCgWqpKQk9cWJIsKiViZFRRGjqF9NgwYNcPjwYezfvx9z584Vftf//vtvREVFYcGCBcjMzMTIkSOxe/dumTo0spI3wuDPnz9likSmqqqKnz9/KqTtERERYjs2kjAMw4mQumDBAoSGhmLv3r3w9fUFUHgxumHDBuHvh8CNGzfw7NkzYV7B4rp164bz58/j2bNnSExM5OR1a9OmjWwHKIOKzuNVHpo0aYL79+9LXf/+/fsiHRZJqU8UpSwdprJc7ItL2ivIBScpoW/xpL1VPc9geSOE4PTp0/Dz88OJEyeQk5ODTp06YcOGDbC1tUXDhg2l2s+9e/dgamqKnJwcYdm5c+dgamqKa9euoVOnTuV1CNVT5Q2uUYoga3Sa4OBgwjAM6dmzJ9m6dSuZO3cuUVJSInp6epxoQ/n5+aRhw4Zk/PjxIuW6urrE2dlZpGzz5s1EX19fbBunTZtGGjZsKOeRcn39+pX8888/pHfv3sJjr1+/Pvn9999JYmJilZ5SqKgoYhT1K3v37h2xsrISBgcSfMf3799f7q+tiAiDsk7tU9SUIkVOWf727Rvx9PQkVlZWZMqUKRKnee3evZuMGjWqxPdKGrdu3eK8hrxTCgUyMzPJzp07SY8ePUR+M2bMmFHlpxQ6OTlJHezhxYsXRFVVVabprHl5eeTmzZvk5s2bJDc3V6pt0tLSyLZt20i3bt0Iy7LC6IXHjx8XuxSAEEL4fD45deoUGTduHFFXVycMw5DOnTuTjRs3io1oXFxZPtsMw0j9PlSmigqaJZiyqaWlRWbOnEmuXbtWpv1MmjSJMAxD3NzcyMePH8mnT5+Iq6srYRiG2NraKrjV1R9DiJyppqkqIS8vD0ePHoWfnx9iYmLA5/PBsixMTU3h5OQkMiXH0dERgYGBwlwatWvXxuHDhzFo0CCRfR4/fhyjRo2Cr68vnJychOVDhgxBZmYmrl69KlXbTE1N8ePHD5HgG6UNUzMMA01NTXTt2hV2dnalLoi/f/8+fH19sX//fqSlpQmnTgwcOBDBwcHllhDSw8MDnp6eMifIZFkW7u7uWLFiRbm0i6J+FYcPH4atrS1ycnLAMAy8vb05AXjKw8qVK7F69WqcP3++1KAXFy9ehJGREVauXCmykJxlWbRu3VrsNDtxnj17hufPn8udcJdlWTg4OMg8kq6IQCHycnR0RFBQkMh7IO59FLxXxZPRCzAMg6ioKImvUzSPV0ZGBoDC5MGzZs2SKY9XRbl58yZ69eqFHj16IDo6Whhgqri0tDRYWlri5s2buH79unCKZnJyMuLj4zFw4EDOyE1kZCScnZ2FI0VaWlrYtWsXxo8fz9k/kTA64ujoKNXoiK6uLt69ewdNTU1MmDABTk5O6N27d1nekl9OWa8nZMWyLFRUVNC/f39oaGhItY2471Pz5s2hr6+P8+fPi5QbGhri1atXePnypcLa/Euo3P4eVR5ev35NPDw8SIsWLSTeubxw4QLx9vYmvr6+Ehe2RkdHky1btnDuOq1du5a0aNFCJIy7JCkpKURJSYksWrRIpFxPT4/o6+tLfDRv3pzUqlVL2H5ra2upjl2esKhloeiw8BRFFcrIyCCTJk0iLMsSTU1Nsnz5ctKgQQPCsiwZM2aMVHfD5WFubk4MDAykrt+lSxdibm4uUlZZwXGq8/lFkHexqPJ8H8uax6syLFiwgDAMQxo2bEj++usvcu7cOZFgD25ubqRRo0aEYRjOb+7y5csJy7IkJSVFpPzp06dEQ0ODMAxD9PX1SceOHQnLskRZWZncvHmT0wZ5R0cEQbFMTEzI0KFDpXpYWVnJ/mZVQxU1wqWo75OqqipZsmQJp3zx4sWcYGsUDZrxS2rWrBlWrFiBFStWICYmBn5+fpw6AwcOxMCBA0vcj66uLk6fPo0uXbrgw4cPwvJly5ZJncGeYRgcOXKEsxBa2rUFd+7cwdKlSxEeHo6AgIBSR7rkCYtKUVTVcPLkSUybNg3v37+HhYUF/Pz80KxZM8yfPx/Tpk3D0aNHcfHiRezcuRPW1tbl0oZ///0XVlZWUtfv27cvTp06JVL24sULRTer3OTn51fZ9a/yvo9KSkpwd3fHX3/9JSy7evUqrl69ij/++AM2NjawsbHBy5cvhb8ZMTEx8jZb4TZu3Ah1dXVs2LABa9aswZo1a0SeJ4RASUkJy5cvx+rVq0Weu3jxIrp27Qo9PT2R8q1btyI7Oxu///47tm/fDgAIDw+HtbU1duzYwVnX9/btW6ioqKBr1654+fKlVKHBi4+O5OXlISEhQerjLh7sIz09HdnZ2dDR0RE+d+nSJVy6dImzrYGBgdgAL5VN3Lnl2bNnEp8DSh+1lZaizkt5eXliw//XqlWrWgamKXeV3eOjqpZv376RvXv3kn79+glDxlb2nYqcnByip6dHTExMyrwPacOiyioiIoI4ODjIvF11vgNNUeXJ2dmZsCxLatWqRXbt2iW2TkBAAKlbty5hWZZMmDCBpKWlKbwdNWrUIC4uLlLXd3FxITVr1lR4O8pClvPLgwcPyMKFC0njxo3LuVXSETfCJS9Z14Dx+Xxy+vRphbZBkZ49e0ZcXV2Jqakp6dChA+nQoQMxNTUlbm5u5NmzZ2K3adasmUgiZIHWrVsTNTU1Tqh8IyMj0qZNG059eUdHUlJSyvQQSEtLI5qamsTMzEykXYK/Z/HXrlu3brmcH+RVWaPfiiTpPFNRI3XVTdW8nUVVuIsXL8LPzw9hYWHIysoCIQTdu3eHo6OjxESVRfH5fKSnpwMA6tWrJxKOVl6qqqqwsrJCaGhomfdRUlhUeYwcORIjR44s07aKiCJGUb8aPz8/GBoaIiAgAC1atBBbx97eHhYWFnBycsKhQ4dw/vx5TroLeVVmhMHy9v37dxw8eBA8Hg/Xrl0DIaRKJ4mvaAzDYPDgwZXdDIlatWrFGcEqTWpqKnR1dUXKMjMz8fz5cxgaGqJ27doiz3Xr1g03btzg7Efe0ZHiI2yyCg4Oxrdv3+Dt7S32+aIpVzIyMrBw4ULs27dPJEF2VVCdRr9Lsm/fPly5ckWkrKSROkWN0lVHtMP1H/bx40cEBgbCz88PT58+BSEE2tra+PHjB+zs7BAQEFDi9j9//sTOnTsRGhqK27dvg8/nAyicvtGtWzdMmjQJM2fO5OTxKov69etLFVq+LD58+IDLly9DWVkZhoaGEjPPJyQkICEhQWGBLm7fvo3bt29LXZ92uKj/go0bN2L+/Pml5gxq2rQpTp8+jX/++QdLly6toNZVD3FxcdDX1+eUy3tjjaq+lJWVOTnebt26BQDo1asXp764qWKA/B0meZ08eRIdOnQQmzcU4KZcCQkJQVRUVJXrcFX2+yjOw4cPxaYeaNOmDbp37y52m2fPngk7WMVFR0dzyko7r//KaIfrP4bP5yMqKgo8Hg8nT55Efn4+1NXVMX78eNjZ2WHw4MFQUVEp9W7n69evYWlpiUePHoEQAhUVFTRo0ACEEKSnp+PGjRtISkqCn58foqOjObm8ZPXhw4dySW68adMmuLi4COcbq6urw9XVFcuXL+ecGOLj4+Hp6amwDldZoohR1K9uwYIFMtWfOXNmua3REHf3VhJJFx2VoWi0QXlvrMnr0KFDMtX/Ve78VzVt27ZFbGysSNmZM2fAMAz69+/Pqf/u3Tvo6OhUVPOkdu/ePQwfPlzq+t26dUNkZGQ5tqj6ycrKQps2bdCkSRNcvXpVOCMpLCyMkwQaKIze+fTpU9SoUUOknH5XZUM7XP8xzZo1w8ePHwEAAwYMgJ2dHcaPH486depIvQ9CCCZOnIh///0X5ubmcHV1Rf/+/YWdtNzcXFy8eBFeXl7CQBWyLJAt7suXLzh+/LjEO1pldebMGSxevBiamppwcHCAiooKDh8+jL/++guXL1/GkSNHZJpSJCt9ff0qEYaZoqo7caM5ilDS3VtxqsrdW0XdWFMEGxsbmd4XQkiVeR9/JWPHjoWbmxtmzJiB33//Hc+ePcPff/+NWrVqib1hcenSJanTGVSktLQ0scnOjYyM4OLiwilv1KiRcLkDVSgkJATv379HcHAwZ/kHIUQkEEpmZia2bt2KAwcOwNnZWaRuVRylq8poh+s/5sOHD2BZFosWLcLy5cvLNGp05swZXL58GRMnTsS+ffs4P46qqqowMzODqakpJk+ejNDQUMTExMi8hiotLQ2JiYlYsWIFUlNTMX36dJnbWpItW7agRo0auHbtGtq0aQMAWLt2LWbMmIEDBw7gt99+w/HjxxUyJVIRqnIUMYr61VTnu7eKuLGmKCtWrKgSHajio5UVFRGuqliwYAFCQ0Oxd+9e+Pr6Aii8uN6wYQNq1qwpUvfGjRt49uwZZsyYURlNLZG6ujqysrI45aampjA1NeWUZ2Vl0fWJxRw/fhwtW7aEmZkZ5zmGYTiRJ8+cOYNjx45xOlyUbOjV23/MlClTEB4eDh8fH2zbtg3Dhw+Hra0trKyspL6YDwsLg7q6OrZt21biDynDMNi6dSuOHj2K0NBQkQ5XaYmPiyKEYN68eRg1apTU20jj+vXrGDt2rLCzBRTOW9+/fz/09PSwbt06DB8+HJGRkZXa6Xr48CF4PB72798vEp6foqjyU53v3irixpqiuLu7V9prFyVptFLcOhOg6oxWKoqGhgYuXbqEzZs348qVK6hXrx7GjRuHESNGcOrevHkTI0eOFPtcZWvWrBnu3bsndf27d++iadOm5dii6uf27dswNzeXun6/fv1w9uzZcmzRfwPtcP3HBAUFYefOnQgJCQGPx8ORI0cQHh4OLS0t2NjYYMqUKaXuIykpCYaGhqhfv36pdRs2bAgjIyMkJSWJlOvq6pb4g8ayLDQ1NdGlSxfY29uLvRMjr2/fvqFZs2Zin/Py8oKamho8PDwwbNiwCp8DTqOIURRVVoq4sVZZCgoKcOLECYXeYKvOo5WKVKtWLZFcZJJMnz5d4TNKFMXQ0BD+/v5ISUkpdSpxcnIyEhIS4OTkVDGNqyY+f/4sdl29gYGB2OA5jRs3RmpqakU07ddWKcHoqSrj3r17ZN68eaRBgwbCPA8syxIjIyPy8uVLsdvUr1+fzJs3T+rXmD9/Pqlfv76CWqw4LVu2JPb29iXWcXd3JwzDEBMTE7J48WKF5ZaIj48XyS0icOHCBeLo6Ehq1aolzCnSo0cPsn379iqZS4SiqKrp69ev5J9//iG9e/cWntvr169Pfv/9d5KYmEgYhiHTpk2r7GYKPXr0iCxZsoRoa2vTHD6URLdv3yYMw5C+ffuS9PR0ifXS0tJI7969Ccuy5ObNmxXYwqqvVq1aZPHixVLXX7RoUZXJMVid0Q4XRQghJDc3l4SGhpIhQ4YQJSUlwjAMUVJSIhYWFiQkJESkroqKClm5cqXU+16xYgVRVVWVqT15eXnk5s2b5ObNmyQ3N1embaU1YsQIoqurW2q9lStXEoZhiKqqarlcCHz48IGsX7+etGvXTtjJ0tHRIQzDlNohpCiKKk1ZbqxVhB8/fhA/Pz8yYMAA4blPTU2NWFtbV1qbqKpv0aJFhGEYoq2tTdzd3Ul8fDx58uQJefLkCYmLiyMrVqwg2trahGEYsmDBgspubpXTunVrMnz4cKnrDxs2jLRq1aocW/TfQDtcFMfr16+Jh4cHadGihdjs5pKyi0siLuv48+fPCY/HI48fP+bUP3HiBGnUqJHwoqB+/fokNDS0bAdTgt27dxOGYcjJkydLrbtixQqFZnovKCggx48fJyNHjiQqKiqEYRiioaFBbGxsyMmTJ0l+fn6VuwNNUVT1JsuNtfJ0+fJl4uzsTGrXri08z+vp6ZFt27bRkXyqVHw+nyxbtowoKysLPz9FH4LP9ZIlS0hBQUFlN7fKsbW1JTVr1iSfP38ute7Hjx+JhoYGsbW1rYCW/dpoh4sq0dmzZ8nEiRNFyhiGIUuXLiUfP36U6iFuKt7y5csJy7KcaXVPnz4lGhoahGEYoq+vTzp27EhYliXKysoKnxaQlpZG/vnnHxIXFydVfX9/f+Lu7q6Q19bR0RH+OBgaGpK9e/eSL1++iNShHS6KospLaTfWFO3Tp0/Ex8eHdOjQQXhR3KRJE7JkyRJ6rqPK5OnTp8TFxYWYmZmRjh07kg4dOhATExPi4uIi9mYuVSghIYEwDEOGDRtGcnJyJNbLyckhVlZWhGVZqa+TKMloh4uSWdEpKbI8ijI0NCTdu3fn7HvOnDmEYRgyZ84cYdmRI0cIwzDEycmp3I+togjuwC1dupRkZGRIrEMvQiiKKm/ibqwpSlRUFBkzZgxRVVUlDMOQGjVqkEmTJpHo6Gjh6AM911FUxZo8eTJhGIZ06dKFBAQEkJSUFJKbm0tycnLIixcviJ+fH+nUqRNhWbbczg3/NQwhhFR24A6qejExMSlTyNy4uDjhv3V1dWFiYoLg4GCROm3atMHr16/x+fNn1K5dW1hubGyM9+/f48mTJ2VveBViZ2eH8PBw/Pz5E6qqqmKjiLEsi6lTp2LPnj2V3FqKoqiyYVkWLMvC0NAQtra2GDdunMi5XVCHnusoquJkZ2fD0dERoaGhEq/nCCEYN24cAgMDq0w+0uqsaseHpaqk+Ph4ufeRmpoKXV1dkbLMzEw8f/4choaGnB/kbt264caNG3K/blHnz58v03ZGRkZyv7YiwvNTFEVVB4QQZGdnIzs7G3l5eZXdHIr6z1NXV8eBAwcwdepU+Pn54cqVK/jw4QMIIWjcuDH69+8PR0dHkfyplHxoh4uqFMrKysjMzBQpu3XrFgCgV69enPq1atVSeBvKOlJXUFCgkNevXbs2ZsyYgRkzZuD+/fvw9fXF/v37sWvXLvz9999gGAaPHz/Gq1ev0Lx5c4W8JkVRVEWKjY0Fj8fD0aNHMXfuXCxcuBBWVlawtbXF8OHDoaKiUtlNpKj/LHNzc6mSIP/8+RMaGhoV0KJfF+1wUWVy/vx5fPnyBZaWlhJ/MHNzc3H69GnUrVsXhoaGIs+1bdsWsbGxImVnzpwBwzDo378/Z1/v3r2Djo6O4g4Ahckdi3e4bty4gZs3b1Z40sfOnTtjy5Yt2LBhA44ePQo/Pz/ExMTgwoULaNmyJUxNTeHk5ISJEydWaLsoiqLkYWpqClNTU3z58gX79+8Hj8dDREQEjh07Bi0tLVhbW1d2EynqP+XUqVMYOnSo1PV//vyJ4cOHc67ZKNnQNVyUzP79918YGBjA3t4evr6+JdadPn06AgICcO/ePbRr105Y7uXlBTc3N0ybNg2///47nj17BicnJxBC8O7dO9SsWVNkP23btkXLli0RHR1dLsck4OHhAU9PT4WNYsnjzZs38PPzQ0BAAFJSUsAwTJVoF0VRlDzu3LkDX19fhISEICMjAwCgra2NWbNmwd7eno7oU1Q5qlWrFs6cOSP25nZxOTk5GDZsGOLi4uj1h5zYym4AVf3s3bsXysrKWLNmTal1V61aBWVlZezevVukfMGCBejSpQv27t2L7t27Y9y4cfj69StWrFjB6WzduHEDz549w6BBgxR6HFVds2bNsGLFCiQnJ+PMmTOYMGFCZTeJoihKbl27dsX27dvx/v17hISEwMLCAh8/foS7uztatmyJIUOGVHYTKeqXxTAMfvvtN9y9e7fEetnZ2fjtt99w7tw5DB8+vIJa9+uiHS5KZufOnYOJiQkaN25cat3GjRvDxMSEMxStoaGBS5cuwcPDA5aWlpg0aRIiIiKwaNEizj5u3ryJkSNHYsSIEQo7hurGwsICISEhld0MiqIomSgpKWHVqlUiZVevXsW2bdugqqoKGxsbnDlzBsnJyXBzc0PTpk0RExNTSa2lqF/fsWPHkJWVBUtLSzx//lxsnZycHIwaNQoxMTEYNmwYjhw5UsGt/PXQDhcls+TkZHTq1Enq+h07dkRycjKnvFatWvjrr78QFRWF4OBgiR2q6dOn4+jRo2jTpk2Z20xRFEVVPFKY71OkLDo6GgsWLBAp09PTg4eHB1JSUnDq1KmKbCJF/aeYmZnh4MGD+Pz5MwYNGoT379+LPC/obJ05cwZDhw5FeHi4MF0NVXa0w0XJLDc3F6qqqlLXV1VVRX5+fjm2iKIoivoVMAyDwYMHV3YzKOqXNnLkSPj6+uLly5cYNGgQ0tPTARRe340ZMwanT5/GkCFDcPToURpJVEFoh4uSWcOGDcWOWEny4sULNGjQoBxbRFEURVEURUnL3t4emzZtwsOHD2FlZYX09HSMHj0ap06dgoWFBSIiImS6uU6VjI4RUjLr3bs3zp49i+/fv5eaH+v79+84c+YMTExMKqZxMjh06BCn7P79+wCAsLAwzjQYgfHjx5druyiKoiiKosrbvHnzkJGRAU9PT7Rs2RJfv36FmZkZjh8/DjU1tcpu3i+FdrgomU2ZMgURERGYM2cOAgICSqw7d+5cZGZmYsqUKRXTOBnY2Nhw8nAJOlk2Njac+oQQMAxDO1wURVEURf0S3N3dkZGRge3bt8PExASRkZFQV1ev7Gb9cmgeLqpMzM3NER8fD2NjY7i5uWHgwIHCoee8vDycP38eXl5eiI+Ph5mZGc6ePVvJLeZyd3fndLiksXLlynJoDUVR1K+HZVm0bt0arVu3FpY9e/YMz58/lxj+nWEYREVFVVQTKeo/pUaNGmLLc3JyoKKiApblrjZiGAY/fvwo76b90miHiyqTjIwMjBo1ChcuXADDMFBWVhau00pLS0NeXh4IITA0NMSxY8dQt27dym0wRVEUVeHEXbyVhiZ5p6jyo6+vX6abzS9evCiH1vx30A4XVWYFBQUICgoCj8fD9evXkZeXBwBQUVFBnz594OzsDFtbWygpKVVySymKoqjK8PLlyzJtp6enp+CWUBRVVvn5+TQ0vJxoh4tSiIKCAqSlpQEA6tevXy07WQ8ePMD169eRmpoKoDAaY+/evdGxY8dKbhlFURRFUVTFevjwIXg8Hvbv348PHz5UdnOqNdrhov7zYmJisHDhQjx48EDs8wYGBti0aRNMTU0ruGUURVEURVEV5/v37zh48CB4PB6uXbsGQghUVVWRnZ1d2U2r1miHi/pPO3z4MCZNmoT8/Hzo6+vD0NAQTZo0ASEE79+/x/nz5/Hy5UsoKyvj0KFDGDVqVGU3maIoiqIoSqEuXrwIPz8/hIWFISsrC4QQdO/eHY6Ojpg0aRLq1atX2U2s1miHi5JZadMFGYaBpqYmunbtCjs7Ozg4OFRMw2SUkZGB1q1bIzs7G//88w+mTJkiNkx8UFAQZs+ejZo1a+Lp06fQ1NSspBZTFEVRFEUpxsePHxEYGAg/Pz88ffoUhBBoa2vjw4cPsLOzKzX1DyU9ugKOkpmurm6JEW74fD7S09MRHx+PhIQEREVFISwsrAJbKJ39+/cjIyMDPB4Ptra2YuswDAN7e3sUFBRg6tSpCAkJwaxZsyq4pRRFURRFUfLj8/mIiooCj8fDyZMnkZ+fD3V1dYwfPx52dnYYPHgwVFRUhKl+KMWgI1xUublz5w6WLl2KmJgY8Hi8KjfSNWrUKCQlJeHVq1elhkjl8/nQ09NDz549ERERUTENpCiKoiiKUqAmTZrg48ePAIABAwbAzs4O48ePR506dYR1WJbF1KlTsWfPnspq5i9H9gQZFCWlrl274sSJE9DV1UVgYGBlN4fj7t27MDQ0lCofBcuyMDIywr179yqgZRRFURRFUYr34cMHMAyDxYsX4/jx45g6dapIZ4sqH7TDRZUrVVVVWFlZ4e7du5XdFI60tDQ0a9ZM6vrNmjUThoynKIqiKIqqbqZMmQJ1dXX4+PhAR0cH48aNw/Hjx5Gfn1/ZTful0Q4XVe7q16+P79+/V3YzOL5//45atWpJXb9GjRr48eNHObaIoiiKoiiq/AQFBeH9+/fYtWsXunTpgiNHjmD06NHQ1tbGnDlzcOXKlcpu4i+JdriocvfhwwfUrVu3spvBUZbli3TJI0VRFEVR1Vnt2rUxY8YMXLt2DXfv3sXcuXPBMAx27dqFAQMGgGEYPH78GK9evarspv4yaNAMqlx9+fIFbdu2Rc+ePXHy5MnKbo4IlmVhYWEBCwsLqeqfPXsW586dQ0FBQTm3jKIoiqIoquLk5eXh6NGj8PPzQ0xMDPh8PliWhampKZycnDBx4sTKbmK1RjtcVLlIS0tDYmIiVqxYgTt37uDIkSNVLmkwy8o+wMswDO1wURRFURT1y3rz5g38/PwQEBCAlJQUeu2jALTDRcmstMTHRRFCMG/ePGzevLkcW1Q2ZY2caG9vr+CWUBRFURRFVT0xMTHw8/NDSEhIZTelWqMdLkpm+vr6JYZSZ1kWmpqa6NKlC+zt7WFmZlaBraMoiqIoiqKoqoN2uChKSmlpaQgODsb8+fMruykURVEURVFUNUE7XJTC5efnCxMEd+7cGSoqKpXcIvmcPn0aPB4Px48fR15eHp3HTFEURVEURUmNhoWnZJacnAw/Pz88efKE81xkZCSaNm2KXr16oVevXtDR0cGhQ4cqoZXySUlJwcqVK6GnpwcrKyscPnwYWlpaWLhwYWU3jaIoiqIoiqpG6AgXJTMXFxesX78eycnJ0NPTE5Y/e/YMBgYGyM7Ohp6eHmrUqIFHjx6BZVlcu3YN3bt3r8RWly43NxdHjhwBj8dDfHw8+Hw+gMJRujVr1sDKykqmgCEURVEURVEURUe4KJldvHgRXbt2FelsAcDWrVuRnZ2N33//HS9evMCDBw8QFhaGgoIC7Nixo5JaW7rbt29j7ty50NHRwZQpU3Du3Dn07NlT2OZ+/frht99+o50tiqIoiqIoSmZ0hIuSma6uLkxMTBAcHCxS3qZNG7x+/RqfP39G7dq1heXGxsZ4//692CmIlWnXrl3g8Xi4ffs2CCHQ1dXFlClTYGdnh3bt2gEojLg4depU7Nmzp5JbS1EURVEURVVHypXdAKr6SU1Nha6urkhZZmYmnj9/DkNDQ5HOFgB069YNN27cqMgmSmXOnDlgWRaTJ0+Gg4MDTE1NSwx3T1EURVEURVGyoh0uSmbKysrIzMwUKbt16xYAoFevXpz6tWrVqohmlQmfz8e5c+ego6ODxo0bo1OnTpXdJIqiKIqiKOoXQtdwUTJr27YtYmNjRcrOnDkDhmHQv39/Tv13795BR0enopontefPn8PFxQUMw2DDhg0wMDBA9+7dsXnzZnz8+LGym0dRFEVRFEX9AmiHi5LZ2LFj8fTpU8yYMQN3795FeHg4/v77b9SqVQuWlpac+pcuXULr1q0roaUla9GiBVavXo2XL18iMjISo0aNwsOHD7Fo0SI0a9ZM7LFQFEVRFEVRlCxo0AxKZj9//kS/fv1w79494ZonQgg2bNiARYsWidS9ceMG+vTpI/a5qig1NRWBgYHw8/PDv//+CwCoWbMmxo8fD0dHRwwcOLCSW0hRFEVRFEVVJ7TDRZXJ9+/fsXnzZly5cgX16tXDuHHjMGLECE69PXv24NSpU/D29kabNm0qoaVld/XqVfj6+uLQoUP49u0bGIZB69at8fjx48puGkVRFEVRFFVN0A4XRZUiKysLoaGh4PF4uHz5sjAhMkVRFEVRFEWVhna4KEoGT548Qdu2bSu7GRRFURRFUVQ1QcPCU/9Zs2fPlnkbhmGwc+fOcmgNRVEURVEU9SuiI1zUfxbLSh+kk2EYEELAMAwKCgrKsVUURVEURVHUr4SOcFH/WXFxcVLVe/XqFTw9PfH8+fNybhFFURRFURT1q6EjXBQlQUZGBry8vLBz505kZ2fjf//7H9avX09Dw1MURVEURVFSoyNcFFVMdnY2tmzZAm9vb2RmZqJ9+/bw8vLCqFGjKrtpFEVRFEVRVDUj/SIWivrFEULg6+uLNm3awMXFBTVq1MCePXtw//592tmiKIqiKIqiyoROKaQoABEREXBxccHjx49Rp04d/Pnnn5g/fz7U1dUru2kURVEURVFUNUY7XNR/2sWLF/Hnn3/iypUrUFVVxe+//w5XV1doaWlVdtMoiqIoiqKoXwDtcFH/WSNGjEBUVBRYloWtrS08PT3RrFmzym4WRVEURVEU9QuhHS7qP4tlWTAMA319fbRr106qbRiGQVRUVDm3jKIoiqIoivpV0A4X9Z8lS+JjAZr4mKIoiqIoipIFDQtP/We9ePGisptAURRFURRF/eLoCBdFURRFURRFUVQ5oXm4KIqiKIqiKIqiygntcFEURVEURVEURZUT2uGiKIqiKIqiKIoqJ7TDRVEURVEURVEUVU5oh4uiKIqiKkB8fDwYhoG7u3tlN4WiKIqqQLTDRVEURVUpKSkpYBhG5KGiooKmTZti/PjxuHHjRmU3USKGYWBiYlLZzaAoiqKqEJqHi6IoiqqSWrVqhSlTpgAAfvz4gaSkJISFhSEiIgIxMTEwMjKq5BbKpk+fPvj333/RoEGDym4KRVEUVYFoh4uiKIqqklq3bs2Zfrdu3TosX74cf/31FxISEiqnYWVUo0YNtG/fvrKbQVEURVUwOqWQoiiKqjacnZ0BAElJSSLlubm52LRpE3r06IGaNWuidu3aMDQ0xPHjxzn7ePLkCZYuXYoePXqgfv36UFdXR9u2bbFs2TJ8//5d7Ot++/YNnp6eMDAwQM2aNaGpqYnu3bvjr7/+Ql5ennB9FgAkJCSITIcMCAgAUPIargcPHmDChAlo1KgR1NTU0KJFCyxYsADp6emcuvr6+tDX18ePHz+wcOFCNG3aFGpqajAwMMDhw4dleTspiqKoCkBHuCiKoqhqR1n5//985eTkwNLSEvHx8ejevTucnZ2Rl5eHqKgojBw5Etu3b8ecOXOE9cPDw8Hj8WBqagoTExPw+XxcuXIF69evR0JCAs6fPw8VFRVh/dTUVBgbG+Phw4fo1q0bZs6cCT6fj0ePHmH9+vVYtGgR9PX1sXLlSnh4eEBPTw8ODg7C7bt161bisVy+fBmDBw9GTk4OrK2toa+vjytXrmDLli2IiopCYmIi6tevL7JNXl4eBg8ejPT0dIwZMwZZWVk4ePAgxo8fj+joaAwePFi+N5iiKIpSHEJRFEVRVciLFy8IADJkyBDOc6tWrSIAyLBhw4RlLi4uBABxd3cnfD5fWP7161fSq1cvoqqqSt6+fSssf/PmDcnJyeHs28PDgwAg+/btEykfN24cAUBcXFw423z48IHk5eUJ/w+AGBsbiz2uuLg4AoCsXLlSWFZQUEDatGlDAJDo6GiR+suXLycAiLOzs0i5np4eAUBGjhwpchwxMTES3zeKoiiq8jCEEFJJfT2KoiiK4khJSUGLFi04QTOuX7+OhIQENGrUCPHx8ejQoQP4fD4aNGiA+vXr48mTJ8JpfQInTpzAiBEjOKNc4qSnp6N+/fpwcHCAv78/AODjx4/Q0dFBy5Yt8e+//4qMfInDMAyMjY0RHx/PeS4+Ph6mpqZYuXKlcFrhhQsXYGRkhKFDh+LkyZMi9X/8+AE9PT1kZWUhMzMTqqqqAAqnFL58+RLJyclo0aKFyDb6+vr49u0b0tLSSmwnRVEUVXHolEKKoiiqSnr+/Dk8PDxERoZ12AAABJFJREFUyho1aoQLFy6gbdu2AIDHjx8jIyMDTZo04dQFgM+fPwMAHj16JCwjhMDf3x8BAQG4f/8+vnz5Aj6fL3z+3bt3wn/fuHEDhBCYmpqW2tkqi1u3bgGA2FDyNWvWRK9evXD69Gk8efIEnTt3Fj5Xt25dTmcLAJo1a4bExESFt5OiKIoqO9rhoiiKoqqkIUOGIDo6GkBhxykwMBB//vknRo0ahWvXrqFWrVrCoBIPHjzAgwcPJO7rx48fwn//8ccf2LFjB3R1dTFixAjo6OhATU0NAODh4YGcnBxh3czMTABA06ZNFX14AICvX78CABo3biz2eW1tbQDAly9fRMo1NTXF1ldWVhbpPFIURVGVj3a4KIqiqCqvYcOGWLx4Mb58+YLVq1fDzc0NW7ZsQZ06dQAAY8eOlSpC36dPn7Bz504YGBggMTERNWrUED734cMHzihZ3bp1AQBv375V3MEUIWj/x48fxT4vKBfUoyiKoqofGhaeoiiKqjZcXFzQpEkT7Nq1CykpKejQoQPq1KmDGzduIC8vr9Ttk5OTQQiBhYWFSGcLKFxPVVyvXr3Asizi4uKk2j/LsigoKJD6eLp37w4AYtd8ZWVl4caNG9DQ0EC7du2k3idFURRVtdAOF0VRFFVtaGho4M8//0ReXh5WrVoFZWVlzJo1Cy9fvsTixYvFdoru37+PT58+AQD09PQAFIZiLzr17s2bN1i2bBln28aNG2Ps2LFi15MBhSNm+fn5wv/Xq1cPb968kfp4BgwYgFatWuHUqVOIiYkReW7t2rVITU3FxIkThQEzKIqiqOqHTimkKIqiqpXp06dj/fr1CAoKgouLCzw8PHDz5k1s27YNUVFRMDY2RsOGDfH27Vvcu3cPd+7cQWJiIho1agQdHR2MHTsWR44cQa9evWBubo6PHz8iMjISZmZmSE5O5rzerl27cP/+faxZswYnT56EmZkZCCF48uQJzpw5g48fPwqnHpqZmeHQoUOwtrZG9+7doaSkhGHDhqFLly5ij4VlWQQEBGDIkCGwsrLCuHHjoKenh6tXr+LcuXNo1aoV1q1bV55vJ0VRFFXOaIeLoiiKqlbU1dWxfPlyzJ07Fx4eHggKCsKpU6fA4/EQFBSEw4cPIycnB40bN0bHjh0xc+ZMkQ5PQEAA9PX1ceTIEWzfvh3NmzfHwoUL8eeff4odSWrQoAGuXLkCHx8fhIWFYceOHVBXV0eLFi2wbNky1KxZU1h369atAIBz587h6NGj4PP50NbWltjhAoCBAwfiypUr8PT0xJkzZ/Dlyxc0adIEf/zxB/766y80aNBAge8eRVEUVdFoHi6KoiiKoiiKoqhyQtdwURRFURRFURRFlRPa4aIoiqIoiqIoiiontMNFURRFURRFURRVTmiHi6IoiqIoiqIoqpzQDhdFURRFURRFUVQ5oR0uiqIoiqIoiqKockI7XBRFURRFURRFUeWEdrgoiqIoiqIoiqLKCe1wURRFURRFURRFlRPa4aIoiqIoiqIoiiontMNFURRFURRFURRVTmiHi6IoiqIoiqIoqpz8P85ywqhMKoPqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rtwDkfgQuADeUVq1aqWHDhkpMTFSvXr1cQte+ffvUq1cv53UoeVnXrl0lSePHj9fBgwedy/ft26e+ffume41NSkqK/vOf/+ivv/5S586d9fDDD0uSmjVrpokTJzrXx8XFZaue6dOnq379+jp16pSaNm2qV199VfHx8S5t4uPjNXfuXNWoUUPz5893Li9fvrwGDhwoSXrggQdcRn7OnDmjwYMHa9++fQoODk53lrec6tevnxo1aqRTp06pTZs2bkP5zz//rIkTJ2rlypVZ6jP1A/rYsWOdIx/S5ZnounbtmuEozS233CIp+zMiStJTTz0ly7K0YsUKTZ482eU00cWLF+u5556TJI0bN04FChTIdv+eyGx/rvUx8O6772rixInas2ePy/JLly7phRde0OnTp+Xr66s6deo413njdd2wYYMmTZrk3NYYo3feecd5Pd+4ceNyvG8AroNrPxM9AGTM7hsf792715QqVcp5w9DatWubmjVrGofDYSpVqmRGjRplJJmhQ4e6bOeN+1ulx5s3Pk6ttWjRos6bqdaoUcNUq1bNWJZl6tSpY8aMGeO2zsmTJxtJplSpUubkyZMu65KTk511dunSJVv7Z4wx//zzj+nXr5/zNfTx8TFVq1Y1DRo0MFWqVDG+vr7OG+defS+lhIQE06xZM+e2t9xyi7njjjtMoUKFjCRTsGBBs2rVqjTPmZXfR0bHypWOHz9umjRp4qyhRIkSpkGDBqZu3bomNDTUufzq+16ldx+u77//3gQGBjrvC1erVi1TtWpVI8mULVvWTJ06Nd3f74svvuh8vttuu820aNHCtGzZ0uW507sPlzHGTJ8+3XmT3pCQEFO/fn1z8803O/scPHhwmptb23n8f/jhh86+K1asaJo3b25atmzpck8zT4+BzKT2d+V9uGbMmOFcXrRoUXP77bebunXrmiJFijiXT58+PU1fOX1dX3nlFSPJhIaGmvr165uSJUs61z3wwANpni+rxy6A6yvvf40LANl0yy236Ntvv9V9992nEiVK6Oeff1Z8fLweeOABffXVV85vn4ODg69zpZ4LDw/Xtm3b1LdvXxUuXFi//vqrEhMTNXbsWG3ZssXthfdbt25VVFSUHA6HFi5cqLCwMJf1DodD7777rkJDQ/XJJ5/o1VdfzVZNgYGBWrRokb7++ms99NBDqlatmk6cOKFvv/1WJ0+eVL169fTkk0/qp59+cjmlULo8UcCGDRs0a9YsNWnSRCdOnND333+vYsWKadiwYfr+++/VqVOn7L9Q2VC8eHHFxsZq4cKF+te//iVjjHbu3Kk//vhD5cqV09ChQ7V8+XJFRERkqb+aNWtq27Zt6t69uwoWLKhffvlFly5d0qhRo7Rz584MTx175JFH9OKLL6p27do6ePCgNm3apNjY2CxP2PHII49o69at6tOnjwICArRr1y6dP39e7du314cffqi5c+dm+5YLOdGrVy/NmTNHDRs21MmTJ7V582bFxsa6jDBdy2OgV69eevHFF9W5c2cFBwfrl19+0U8//aSgoCD17dtXmzZt0iOPPJJmu5y+rqNGjdLy5ctVs2ZN/frrr0pISFCDBg20YMECzZo1yyv7BuDas4xJ5+pTALhBde7cWatXr9Yrr7yiUaNGXe9yAORjBw4cUIUKFSSlP6sogLyNES4AuMLBgwedN7Zt0aLFda4GAADkdQQuADecffv2afr06Wkmfvjuu+/UpUsXXbx4Uc2aNXO5IB4AAMATnFII4Iaza9cu1a1bV5ZlqXTp0ipdurROnjzpvP6lbNmy2rhxoypVqnR9CwWQ73FKIZD/McIF4IYTHh6uCRMmqGHDhkpOTtZ3332nkydPqnbt2ho/frx27txJ2AIAAF7BCBcAAAAA2IQRLgAAAACwie/1LiCvSElJ0Z9//qmgoKBrem8SAAAAALmLMUZnzpxR6dKl5XBkPIZF4MqiP//8U2XLlr3eZQAAAADIJQ4fPqwyZcpk2IbAlUVBQUGSLr+owcHB17kaAAAAANdLQkKCypYt68wIGSFwZVHqaYTBwcEELgAAAABZutSISTMAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmuTJwvfvuu7rvvvt0xx13yN/fX5Zlad68ednuJyUlRTNnzlStWrVUsGBBFStWTHfddZf27t3r/aIBAAAA4Cq5MnA99dRTevvtt3Xw4EGVKlXK437uv/9+jRw5UsnJyRo5cqQ6deqkFStWqH79+tq9e7cXKwYAAACAtHJl4Jo9e7YOHDigkydP6v777/eoj40bN+qdd95R8+bN9e233+r555/X/PnztWrVKiUkJGjEiBFerhoAAAAAXOXKwNWuXTuVL18+R3288847kqQpU6bI39/fubxt27bq2LGjNm3apF9//TVHzwEAAAAAGcmVgcsbYmJiFBgYqKZNm6ZZ17FjR0lSbGxsutsnJiYqISHB5QEAAAAA2ZEvA9fZs2d19OhRVahQQT4+PmnWV65cWZIynDxj2rRpKly4sPNRtmxZ2+oFAAAAkD/ly8AVHx8vSSpcuLDb9cHBwS7t3Bk7dqzi4+Odj8OHD3u/UAAAAAD5mu/1LiC38vf3d7n2CwAAAACyK1+OcKWObKU3gpV6PVZ6I2AAAAAA4A35MnAFBgaqVKlS2r9/v5KTk9OsT712K/VaLgAAAACwQ74MXJLUsmVLnT17Vlu2bEmzbs2aNc42AAAAAGCXPB+44uLitGfPHsXFxbksv/feeyVJTz31lC5evOhcvn79eq1Zs0YtWrRQlSpVrmmtAAAAAG4suXLSjNmzZ2vz5s2SpB9++MG5LCYmRpLUo0cP9ejRQ5I0c+ZMTZo0SZGRkYqKinL20bp1aw0bNkyzZ89W3bp11blzZx0/flxLlixRcHCw3njjjWu5SwAAAABuQLkycG3evFnz5893WbZlyxbn6YHh4eHOwJWRt956S7Vq1dJbb72lV199VTfddJO6du2qZ555htEtAAAAALazjDHmeheRFyQkJKhw4cKKj4933scLAAAAwI0nO9kgz1/DBQAAAAC5Va48pRBZE/7fVdne5sCznb22PQAAAICMEbjgMU8Cm0ToAwAAwI2DUwoBAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmzFKIPC03zJTITIsAAABIDyNcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2MT3ehcAQAr/76psb3Pg2c42VAIAAABvYoQLAAAAAGzCCBeQD3hjhCynfVDD9dve2zUAAADvYYQLAAAAAGzCCBcAwEVuGGXLD6OV1OD59lf3AQB5GYELAADkarkh9OWGGgDkTZxSCAAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhBsfAwAA5AE5vfkygOuDES4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAm/he7wIAAABgv/D/rsr2Ngee7ez1PoAbDSNcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgE2YpBAAAwDXBTIm4ETHCBQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE18r3cBAAAAwLUS/t9V2d7mwLOdbagENwpGuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCbMUggAAABkAzMdIjsY4QIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmvte7AAAAAOBGEv7fVdne5sCznW2oBNcCI1wAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATZikEAAAA8hBPZjmUXGc6zOlMid6o4UaRa0e4duzYoU6dOikkJESBgYFq0KCBFi1alK0+Tp8+rYkTJ6pWrVoKCgpSWFiY6tevr5kzZ+rChQs2VQ4AAAAAl+XKEa6YmBh17NhRfn5+ioiIUOHChbVs2TINGDBABw4c0Lhx4zLt4/Tp06pXr55+//13NWvWTPfdd58SExP16aefauTIkfrf//6nzz//XA5Hrs2cAAAAQL51o9yPLNcFrqSkJA0bNkyWZWnTpk2qW7euJCkyMlKNGzdWZGSk+vTpo8qVK2fYz9tvv63ff/9djzzyiKZPn+5cfvHiRTVr1kwbNmzQ5s2b1aJFC1v3BwAAAMCNK9cN72zYsEH79u1T//79nWFLkoKCgjRhwgQlJSVp7ty5mfbz+++/S5I6derkstzPz0/t27eXJJ04ccKLlQMAAACAq1wXuGJiYiRJHTp0SLMudVlsbGym/VSvXl2S9Nlnn7ksv3TpktatW6eCBQuqcePG6W6fmJiohIQElwcAAAAAZEeuO6Vw7969kuT2lMGQkBCFhYU522Rk2LBhWrhwoV566SV9/fXXql+/vhITE/XZZ5/p1KlTWrRokW6++eZ0t582bZomTZrk+Y4AAAAAuOHlusAVHx8vSSpcuLDb9cHBwfrjjz8y7adgwYKKiYnRfffdp3fffdc5KuZwOPTQQw+pWbNmGW4/duxYjRkzxvlzQkKCypYtm9XdAAAAAIDcd0qht8TFxal9+/bavn27Vq1apdOnT+vYsWN68803NXfuXDVs2FCnTp1Kd3t/f38FBwe7PAAAAAAgO3LdCFfqyFbqSNfVEhIS0h39utKYMWO0detWfffdd6pVq5az7+HDhys5OVkjRozQyy+/zGmDAAAAAGyT60a4Uq/dcned1qlTpxQXF5fplPCStGrVKoWGhjrD1pXatGkjSfrmm29yWC0AAAAApC/XBa6WLVtKktauXZtmXeqy1DYZuXjxohISEnTx4sU0606ePCnp8mmDAAAAAGCXXBe42rZtq4oVK2rRokXatWuXc/mZM2c0efJk+fr6avDgwc7lcXFx2rNnj+Li4lz6adq0qZKSkjR58mSX5YmJic5lrVu3tm0/AAAAACDXBS5fX1/Nnj1bKSkpat68ue6991499thjql27tn766SdFRUWpSpUqzvYzZ87UbbfdppkzZ7r08+yzzyooKEhTpkxRw4YNNWbMGD3wwAOqVq2a1qxZo3r16mnYsGHXevcAAAAA3EByXeCSLo88bd68Wc2aNdPSpUv1+uuvq2jRonr33Xc1fvz4LPVRp04dffPNNxoyZIiOHTummTNnat68eQoMDNSkSZO0adMmBQQE2LwnAAAAAG5kuW6WwlQNGjTQp59+mmm7qKgoRUVFuV1XuXJlzZkzx8uVAQAAAEDW5MoRLgAAAADIDwhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBOPAtfQoUO1YsWKDNusXr1aQ4cO9agoAAAAAMgPPApc8+bN065duzJs88MPP2j+/PmedA8AAAAA+YJtpxReuHBBvr6+dnUPAAAAALmex4nIsiy3y40x+uOPP7R69WqVLl3a48IAAAAAIK/L8giXw+GQj4+PfHx8JElRUVHOn698+Pr6Kjw8XDt27FBERIRthQMAAABAbpflEa4WLVo4R7U2bdqkcuXKKTw8PE07Hx8fhYaGqk2bNho+fLjXCgUAAACAvCbLgSsmJsb5b4fDoSFDhmjixIl21AQAAAAA+YJH13ClpKR4uw4AAAAAyHe48TEAAAAA2MTjWQp3796tmTNnaseOHTp9+rSSk5PTtLEsS/v27ctRgQAAAACQV3kUuGJjY3XnnXcqMTFRvr6+KlGihNt7bhljclwgAAAAAORVHgWu//73v0pKStLs2bM1aNAg51TxAAAAAID/41Hg+u677xQREaGhQ4d6ux4AAAAAyDc8mjQjKChIxYsX93YtAAAAAJCveBS4OnfurC+++MLbtQAAAABAvuJR4Hr++ecVHx+vUaNG6dy5c96uCQAAAADyBY+u4brrrrsUGBioWbNmad68eapcubIKFy6cpp1lWVq/fn2OiwQAAACAvMijwBUTE+P89z///KOdO3e6bWdZlkdFAQAAAEB+4FHgSklJ8XYdAAAAAJDveHQNFwAAAAAgcx6NcF3pn3/+0a+//qqzZ8+qefPm3qgJAAAAAPIFj0e4Dhw4oO7duyskJET169dX69atneu2bNmiatWquVzrBQAAAAA3Go8C16FDh9SoUSOtXr1a3bt3V+PGjWWMca5v2LCh4uLi9P7773utUAAAAADIazwKXJGRkTp16pRiY2P14Ycfqn379i7rfX191bx5c23ZssUrRQIAAABAXuRR4FqzZo169uypJk2apNumXLlyOnLkiMeFAQAAAEBe51Hg+vvvvxUeHp5pu8TERE+6BwAAAIB8waPAVaJECf32228Ztvnxxx9Vrlw5j4oCAAAAgPzAo8DVvn17rVy5Uj/++KPb9V988YXWr1+vTp065ag4AAAAAMjLPApcTz31lAoWLKhmzZpp6tSpztGuTz/9VBMmTNCdd96psLAwPf74414tFgAAAADyEo9ufBweHq41a9YoIiJCTz31lCzLkjFGXbp0kTFG5cqV04cffqhSpUp5u14AAAAAyDM8ClzS5Xtt7d27VytXrtSXX36pv//+W8HBwWrYsKG6d+8uPz8/b9YJAAAAAHmOx4FLuny/rZ49e6pnz57eqgcAAAAA8g2PruECAAAAAGQuSyNcCxYskCT17NlTQUFBzp+zYuDAgZ5VBgAAAAB5XJYC1+DBg2VZlho1aqSgoCDnzxkxxsiyLAIXAAAAgBtWlgLXnDlzZFmWc9bBuXPn2loUAAAAAOQHWR7hutKgQYPsqAUAAAAA8hUmzQAAAAAAm3gUuLZs2aIxY8bo2LFjbtcfO3ZMY8aM0fbt23NUHAAAAADkZR4FrunTp2vlypUqWbKk2/UlS5bUJ598ohkzZnhc2I4dO9SpUyeFhIQoMDBQDRo00KJFi7Ldz5kzZxQZGakaNWqoUKFCKlKkiG6//XZNmjTJ49oAAAAAICs8uvHxjh071LZt2wzbtGjRQp9//rlHRcXExKhjx47y8/NTRESEChcurGXLlmnAgAE6cOCAxo0bl6V+Dh06pDZt2uj3339Xu3bt1LlzZyUmJuq3337TRx99pMjISI/qAwAAAICs8ChwnThxQjfffHOGbUqWLKkTJ05ku++kpCQNGzZMlmVp06ZNqlu3riQpMjJSjRs3VmRkpPr06aPKlStn2E9ycrJ69+6tP//8U+vXr1fr1q3TPA8AAAAA2MmjUwqLFCmiQ4cOZdjm4MGDuummm7Ld94YNG7Rv3z7179/fGbYkKSgoSBMmTFBSUlKWpqX/8MMPtWPHDj322GNpwpYk+fp6lDUBAAAAIMs8Sh2NGzfW//73Px0+fFhly5ZNs/7QoUP6+OOP1aZNm2z3HRMTI0nq0KFDmnWpy2JjYzPtZ8mSJZKkPn366PDhw1q1apVOnz6tSpUq6V//+pdHYRAAAAAAssOjEa4xY8bo3Llzatq0qRYsWKCjR49Kko4ePar58+eradOmOn/+vB599NFs9713715JcnvKYEhIiMLCwpxtMvL1119LkjZv3qzKlStrxIgRGjt2rO666y5VrFjRGezSk5iYqISEBJcHAAAAAGSHR4GrefPmevXVV3X06FENGTJEZcqUka+vr8qUKaOhQ4fq2LFjeuWVV9SiRYts9x0fHy9JKly4sNv1wcHBzjYZSb1+bOTIkRo9erQOHz6skydP6tVXX1V8fLx69OjhDIruTJs2TYULF3Y+3I3kAQAAAEBGPL7x8YMPPqidO3fq/vvvV7169VSxYkXVq1dPI0aM0M6dO/Xggw96s85sS0lJkSR16dJFzz77rMqUKaOwsDCNHDlSjzzyiOLj4xUdHZ3u9mPHjlV8fLzzcfjw4WtVOgAAAIB8IkczR9SoUUOzZs3yVi2S/m9kK71RrISEhHRHv67uJy4uTt26dUuzrmvXrnruueecpx264+/vL39//yxWDQAAAABpeTzCZZfUa7fcXad16tQpxcXFZTolvCRVrVpV0uUZFa+Wuuz8+fOeFwoAAAAAmchS4Dp06JAOHTqk5ORkl5+z8siuli1bSpLWrl2bZl3qstQ2GUmdIXH37t1p1qUuCw8Pz3Z9AAAAAJBVWQpc4eHhqlChgvbt2+fyc2aPihUrZrugtm3bqmLFilq0aJF27drlXH7mzBlNnjxZvr6+Gjx4sHN5XFyc9uzZo7i4OJd+hgwZIn9/f7322ms6cuSISz9Tp06VJN11113Zrg8AAAAAsipL13ANHDhQlmU5r51K/dmWgnx9NXv2bHXs2FHNmzdXv379FBwcrGXLlmn//v2aMmWKqlSp4mw/c+ZMTZo0SZGRkYqKinIur1Chgl544QWNGjVKtWvXVs+ePeXv769Vq1bpwIEDuvfee9W2bVtb9gEAAAAApCwGrnnz5mX4s7e1bt1amzdvVmRkpJYuXaqLFy+qevXqmjx5sgYMGJDlfkaOHKnw8HC98MILWrx4sZKSklS9enWNGzdOw4cPt3EPAAAAACCLgatixYp65JFHNHLkSEnSggULVKdOHdWqVcu2who0aKBPP/0003ZRUVEuI1tX69q1q7p27erFygAAAAAga7J0DdeBAwd06tQp58+DBw/Wxx9/bFdNAAAAAJAvZClwFS9e3GXiCQAAAABA5rJ0SmHDhg21cOFC+fr6qkSJEpKkmJiYTLezLEsTJkzIUYEAAAAAkFdlKXC98MIL6tGjh9544w1Jl4NUTExMpqGLwAUAAADgRpalwFWlShX9+OOP2r9/v44cOaJWrVpp8ODBGjRokN31AQAAAECelaXAJUkOh0OVKlVSpUqVVL58edWpU0ctW7a0szYAAAAAyNOyNGmGj4+PJk+e7Px58ODBqlOnjl01AQAAAEC+kOURrpSUFOe/n376aVmWpRYtWthSFAAAAADkB1ka4SpVqpT27dvn/NkYY1tBAAAAAJBfZGmEq1WrVlq0aJH++usv57TwH3/8sQ4cOJDhdpZlKTo6OsdFAgAAAEBelOVp4U+cOKE1a9YoJSVFlmVp165d2rVrV4bbEbgAAAAA3MiyFLhKlSqltWvX6tKlSzp69KjCw8M1evRoPfzww3bXBwAAAAB5VpYnzZCkAgUKqFy5cmrZsqXq1Kmj8uXL21UXAAAAAOR52QpcqTZu3OjtOgAAAAAg3/EocKXauXOn3n//fe3Zs0fnzp3TunXrJEkHDx7Ul19+qXbt2ik0NNQrhQIAAABAXuNx4HriiSf00ksvOaeItyzLuc4Yo/79++ull17iOi8AAAAAN6ws3YfranPnztWLL76oLl266Pvvv9fYsWNd1oeHh6tBgwZasWKFV4oEAAAAgLzIoxGu119/Xbfddps++ugj+fr6ys/PL02bW2+91XmKIQAAAADciDwa4dq9e7fat28vX9/081qJEiV04sQJjwsDAAAAgLzOo8Dl6+urixcvZtjmzz//1E033eRRUQAAAACQH3gUuGrWrKmNGzcqJSXF7frUGQvr1auXo+IAAAAAIC/zKHANHTpUv/zyi0aMGJFmpCshIUGDBw/WsWPHNHz4cK8UCQAAAAB5kUeTZgwdOlTr16/XO++8o/fff19FihSRJDVo0EA///yzzp49q8GDB6t3797erBUAAAAA8hSPRrgk6b333tNbb72lChUq6MiRIzLG6Ouvv1a5cuX0xhtvaM6cOd6sEwAAAADyHI9vfCxJw4cP1/Dhw3X+/HmdOnVKwcHBTJQBAAAAAP9fjgJXqoIFC6pgwYLe6AoAAAAA8o0cBa6zZ89q+fLl2rVrl+Lj4xUcHKw6deqoR48eCgwM9FaNAAAAAJAneRy4Pv74Yw0bNkynTp2SMca53LIsFSlSRO+8847+/e9/e6VIAAAAAMiLPApc27Zt01133SUfHx/de++9atWqlUqWLKnjx48rJiZG8+bNU0REhGJjY9W4cWNv1wwAAAAAeYJHgeuZZ56Rv7+/tm3bpho1arisu+uuu/TAAw+ocePGmjp1qlauXOmVQgEAAAAgr/FoWvht27apb9++acJWqho1auiuu+7S1q1bc1QcAAAAAORlHgWuc+fOqXjx4hm2KV68uM6dO+dRUQAAAACQH3gUuMLDw/X5559n2Gb9+vUKDw/3pHsAAAAAyBc8Clx9+/bVN998o0GDBunPP/90WXf06FENHjxY33zzjfr27euVIgEAAAAgL/Jo0ownn3xSa9as0cKFC7VkyRLdcsstKlGihI4fP67ffvtNFy9eVIMGDfTkk096u14AAAAAyDM8GuEqWLCgYmNjNWnSJN18883avXu3Nm7cqN27d6tMmTKaNGmSYmNjVbBgQW/XCwAAAAB5hsc3Pvbz89OECRM0YcIEnTlzRgkJCQoODlZQUJA36wMAAACAPMvjwHWloKAgghYAAAAAXMWjUwq3bNmiMWPG6NixY27XHzt2TGPGjNH27dtzVBwAAAAA5GUeBa7p06dr5cqVKlmypNv1JUuW1CeffKIZM2bkqDgAAAAAyMs8Clw7duxQs2bNMmzTokULRrgAAAAA3NA8ClwnTpzQzTffnGGbkiVL6sSJEx4VBQAAAAD5gUeBq0iRIjp06FCGbQ4ePKibbrrJo6IAAAAAID/wKHA1btxY//vf/3T48GG36w8dOqSPP/5YTZo0yVFxAAAAAJCXeRS4xowZo3Pnzqlp06ZasGCBjh49Kkk6evSo5s+fr6ZNm+r8+fN69NFHvVosAAAAAOQlHt2Hq3nz5nr11Vc1evRoDRkyRJJkWZaMMZIkh8OhV155RS1atPBepQAAAACQx3h84+MHH3xQLVu21BtvvKEdO3bo9OnTKlKkiBo0aKD7779fNWrU8GadAAAAAJDneBy4JKlGjRqaNWuWt2oBAAAAgHzFo2u4AAAAAACZI3ABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYJMc3YcrKSlJv/zyi06fPq3k5GS3bVq0aJGTpwAAAACAPMujwGWM0cSJE/Xaa6/pzJkzGbZNL4gBAAAAQH7nUeCaPHmynnnmGRUpUkQDBw5UmTJl5Oubo8EyAAAAAMh3PEpJc+bMUfny5fX111+raNGi3q4JAAAAAPIFjybNOH78uHr06EHYAgAAAIAMeBS4KlSooISEBG/XAgAAAAD5ikeB66GHHtInn3yiEydOeLseAAAAAMg3PLqGq0uXLoqJiVGTJk00ceJE1a1bV4ULF3bbtly5cjkqEAAAAADyKo8CV3h4uCzLkjFGQ4YMSbedZVlKSkryuDgAAAAAyMs8ClwDBw6UZVnergUAAAAA8hWPAte8efO8XAYAAAAA5D8eTZoBAAAAAMicRyNcV9q6dat27dql+Ph4BQcHq06dOmratKk3agMAAACAPM3jwPXll19q0KBB2rt3ryTJGOO8rqty5cqaO3euGjdu7J0qAQAAACAP8ihw/fzzz2rXrp3Onj2rjh07qlWrVipZsqSOHz+umJgYffbZZ+rYsaO2b9+uatWqebtmAAAAAMgTPApckyZN0sWLF7VmzRq1b9/eZd0TTzyhdevWqXPnznr66ae1ePFirxQKAAAAAHmNR5NmbNy4Ub17904TtlK1a9dOvXr10saNG3NUHAAAAADkZR4Frvj4eIWHh2fYpkKFCoqPj/ekewAAAADIFzwKXKVLl9b27dszbPPll1+qdOnSHhUFAAAAAPmBR4Gre/fuiomJ0YQJE3ThwgWXdRcuXFBkZKQ2btyo7t27e6VIAAAAAMiLPJo0Y8KECfrkk080depUvfXWW2rQoIFKlCih48ePa8eOHTp58qQqVqyoCRMmeLteAAAAAMgzPApcoaGh+vLLL/X4449r8eLFWr16tXNdQECAhgwZoueee06hoaFeKxQAAAAA8hqPb3wcGhqq6Ohovfnmm9qzZ48SEhIUHBysW2+9VQUKFPBmjQAAAACQJ3kcuFIVKFBANWvW9EYtAAAAAJCveDRpBgAAAAAgc1ka4WrTpo0sy9L8+fNVpkwZtWnTJkudW5al9evX56hAAAAAAMirshS4YmJiZFmWzp075/w5KyzL8rgwAAAAAMjrshS4UlJSMvwZAAAAAJAW13ABAAAAgE08ClxDhw7VihUrMmyzevVqDR061KOiAAAAACA/8ChwzZs3T7t27cqwzQ8//KD58+d70j0AAAAA5Au2nVJ44cIF+frm+DZfAAAAAJBneZyI0puB0BijP/74Q6tXr1bp0qU9LgwAAAAA8rosj3A5HA75+PjIx8dHkhQVFeX8+cqHr6+vwsPDtWPHDkVERNhWOAAAAADkdlke4WrRooVzVGvTpk0qV66cwsPD07Tz8fFRaGio2rRpo+HDh3utUAAAAADIa7IcuK682bHD4dCQIUM0ceJEO2oCAAAAgHzBo2u4uPExAAAAAGSOGx8DAAAAgE08nqUwOTlZS5cu1bp16/Tnn38qMTExTRvLsrR+/focFQgAAAAAeZVHgevs2bPq0KGDtm/fLmOMLMuSMca5PvXn9KaOBwAAAIAbgUenFE6ZMkXbtm3TpEmTFBcXJ2OMoqKidPToUS1ZskQVKlRQ79693Y56AQAAAMCNwqPAtWzZMjVq1EhPPfWUQkNDnctLlCihPn36KCYmRuvXr9cLL7zgtUIBAAAAIK/xKHAdOnRIjRo1+r9OHA6X0awyZcqoc+fOmj9/fs4rBAAAAIA8yqPAFRgYKIfj/zYtXLiwjh496tKmZMmSOnToUM6qAwAAAIA8zKPAVb58eZcwVaNGDW3YsME5ymWM0fr161WqVCnvVAkAAAAAeZBHgatt27bauHGjkpKSJEmDBg3SoUOH1LhxYz3++ONq1qyZdu3apV69enm1WAAAAADISzyaFn748OEqWrSoTp48qVKlSmno0KHauXOnXn/9de3atUuS1KtXL0VFRXmxVAAAAADIWzwKXJUrV9aTTz7psuy1117TxIkT9fvvv6t8+fIqWbKkVwoEAAAAgLzKo8CVnmLFiqlYsWLe7BIAAAAA8iyPruECAAAAAGQuSyNcbdq08ahzy7K0fv16j7YFAAAAgLwuS4ErJibGo84ty/JoOwAAAADID7IUuFJSUuyuAwAAAADyHa7hAgAAAACbeCVw/f333zp8+LA3ugIAAACAfMPjwBUfH6+HH35YJUqUULFixVShQgXnui+//FKdOnXSN99845UiAQAAACAv8ihw/f3332rYsKFee+01lS1bVrfddpuMMc71tWrV0pYtW/Tee+95rVAAAAAAyGs8ClxRUVH69ddf9f777+vrr79Wnz59XNYXLFhQLVu21IYNG7xSJAAAAADkRR4FrhUrVqhLly7q27dvum3Kly+vP/74w+PCAAAAACCv8yhwHT16VNWqVcuwTUBAgM6ePetRUQAAAACQH3gUuIoWLZrprIR79uxRqVKlPCoKAAAAAPIDjwJXixYttGLFCh05csTt+t27d+uzzz5Tu3btclQcAAAAAORlHgWu8ePHKykpSU2bNtWiRYsUFxcnSfr5558VHR2tNm3ayN/fX48//rhXiwUAAACAvMTXk41q1qypJUuWaODAgbr77rslScYY1ahRQ8YYBQUFaenSpapcubJXiwUAAACAvMSjwCVJ3bp10++//6758+fryy+/1N9//63g4GA1bNhQQ4YMUVhYmDfrBAAAAIA8x6PAtWDBApUoUUIdO3bUI4884u2aAAAAACBf8OgarnvuuUdr1qzxdi0AAAAAkK94FLhKlSqlixcversWAAAAAMhXPApcPXr00Oeff67ExERv1wMAAAAA+YZHgWvy5Mm66aab1LNnT/3000/ergkAAAAA8gWPJs2oW7euEhMTtWvXLq1Zs0YBAQEqXry4LMtyaWdZlvbt2+eVQgEAAAAgr/EocKWkpMjPz0/lypVzWW6MyfBnAAAAALiReBS4Dhw44OUyAAAAACD/8egarqFDh+rll1/2cikAAAAAkL94FLgWLVqk48ePe7sWAAAAAMhXPApct9xyi44ePertWgAAAAAgX/EocN1zzz1atWqVjhw54u16AAAAACDf8GjSjJ49e2r9+vVq0qSJnnjiCdWvX18lSpRIMy28pDQzGQIAAADAjcKjwFWxYkVZliVjjEaNGpVuO8uylJSU5HFxAAAAAJCXeRS4Bg4c6HY0y5t27NihyMhIbdu2TRcvXlT16tU1evRo9e/f36P+Ll26pPr16+u7775T1apVtWfPHi9XDAAAAACuPApc8+bN83IZrmJiYtSxY0f5+fkpIiJChQsX1rJlyzRgwAAdOHBA48aNy3afkydP1m+//WZDtQAAAADgnkeTZtgpKSlJw4YNk2VZ2rRpk9555x29+OKL+u6771S9enVFRkZq79692erz22+/1bRp0zRt2jSbqgYAAACAtHIcuLZu3arXX39d06ZN06xZs7Rly5Yc9bdhwwbt27dP/fv3V926dZ3Lg4KCNGHCBCUlJWnu3LlZ7u/ixYsaPHiwGjVqpIceeihHtQEAAABAdnh0SqEkffnllxo0aJBztMkY47yuq3Llypo7d64aN26c7X5jYmIkSR06dEizLnVZbGxslvuLiorS3r179d1339l+3RkAAAAAXMmjwPXzzz+rXbt2Onv2rDp27KhWrVqpZMmSOn78uGJiYvTZZ5+pY8eO2r59u6pVq5atvlMDXOXKldOsCwkJUVhYWJZPKdyxY4eef/55TZ06VVWqVMlWHYmJiUpMTHT+nJCQkK3tAQAAAMCjwDVp0iRdvHhRa9asUfv27V3WPfHEE1q3bp06d+6sp59+WosXL85W3/Hx8ZKkwoULu10fHBysP/74I9N+EhMTNXjwYNWtW1ePPvpotmqQpGnTpmnSpEnZ3g4AAAAAUnl0DdfGjRvVu3fvNGErVbt27dSrVy9t3LgxR8XlxIQJE7R3717NmTNHPj4+2d5+7Nixio+Pdz4OHz5sQ5UAAAAA8jOPRrji4+MVHh6eYZsKFSo4R6uyI3VkK71tExIS0h39SvXtt99q+vTpmjBhgmrWrJntGiTJ399f/v7+Hm0LAAAAAJKHI1ylS5fW9u3bM2zz5ZdfqnTp0tnuO/XaLXfXaZ06dUpxcXFur++60vfff6/k5GRFRUXJsiyXhyT98ssvsixLRYoUyXZ9AAAAAJBVHo1wde/eXa+++qomTJig8ePHKyAgwLnuwoULmjZtmjZu3KhRo0Zlu++WLVtq2rRpWrt2rSIiIlzWrV271tkmI1WqVNE999zjdl10dLQKFy6s3r17q1ChQtmuDwAAAACyyqPANWHCBH3yySeaOnWq3nrrLTVo0EAlSpTQ8ePHtWPHDp08eVIVK1bUhAkTst1327ZtVbFiRS1atEijRo1SnTp1JElnzpzR5MmT5evrq8GDBzvbx8XFKS4uTmFhYQoLC5MkNWnSRE2aNHHbf3R0tEqWLKnZs2dnuzYAAAAAyA6PTikMDQ3Vl19+qcGDB+vs2bNavXq15s6dq9WrV+vMmTMaMmSItm/frtDQ0Gz37evrq9mzZyslJUXNmzfXvffeq8cee0y1a9fWTz/9pKioKJcp3mfOnKnbbrtNM2fO9GRXAAAAAMA2Ht/4ODQ0VNHR0XrzzTe1Z88eJSQkKDg4WLfeeqsKFCiQo6Jat26tzZs3KzIyUkuXLtXFixdVvXp1TZ48WQMGDMhR3wAAAABwrXgcuFIVKFDA45kAM9KgQQN9+umnmbaLiopSVFRUlvs1xuSgKgAAAADIumydUvjMM89o3LhxunTpUrptLl68qHHjxunZZ5/NcXEAAAAAkJdlOXCtW7dOEydOVNGiRTM8ZdDPz09hYWEaP368NmzY4JUiAQAAACAvynLgWrBggUJCQvTQQw9l2vbBBx9UaGio5s6dm6PiAAAAACAvy3Lg2rp1q9q1ayd/f/9M2/r7+6tdu3baunVrjooDAAAAgLwsy4Hrzz//VMWKFbPccYUKFXT06FGPigIAAACA/CDLgcvhcGQ4WcbVLl26JIfDo9t8AQAAAEC+kOVEVLp0af34449Z7vjHH3/UzTff7FFRAAAAAJAfZDlwNW/eXBs2bNCBAwcybXvgwAFt2LBBLVq0yEltAAAAAJCnZTlwPfjgg7p06ZJ69+6tuLi4dNv99ddf6tOnj5KSkjRixAivFAkAAAAAeZFvVhvefvvtGj16tF5++WVVq1ZN999/v1q3bq0yZcpIko4cOaL169fr7bff1smTJzVmzBjdfvvtthUOAAAAALldlgOXJL300ksKCAjQCy+8oGeeeUbPPPOMy3pjjHx8fDR27FhNmTLFq4UCAAAAQF6TrcBlWZamTp2qe+65R3PnztXWrVt17NgxSVLJkiXVtGlTDR48WJUqVbKlWAAAAADIS7IVuFJVqlSJESwAAAAAyAQ3ygIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAmxC4AAAAAMAmBC4AAAAAsAmBCwAAAABsQuACAAAAAJsQuAAAAADAJgQuAAAAALAJgQsAAAAAbELgAgAAAACbELgAAAAAwCYELgAAAACwCYELAAAAAGxC4AIAAAAAm+TawLVjxw516tRJISEhCgwMVIMGDbRo0aIsb79582Y9+uijqlevnooWLaqAgADdeuutevLJJ3X69Gn7CgcAAACA/8/3ehfgTkxMjDp27Cg/Pz9FRESocOHCWrZsmQYMGKADBw5o3LhxmfbRu3dvxcXFqVmzZho4cKAsy1JMTIyef/55ffTRR9q6dauKFy9+DfYGAAAAwI0q1wWupKQkDRs2TJZladOmTapbt64kKTIyUo0bN1ZkZKT69OmjypUrZ9jPI488ooEDB6pUqVLOZcYYPfjgg3rjjTc0adIkzZo1y9Z9AQAAAHBjy3WnFG7YsEH79u1T//79nWFLkoKCgjRhwgQlJSVp7ty5mfbz5JNPuoQtSbIsSxMmTJAkxcbGerdwAAAAALhKrgtcMTExkqQOHTqkWZe6LCdhqUCBApIkX99cN7gHAAAAIJ/Jdalj7969kuT2lMGQkBCFhYU523hizpw5ktwHuislJiYqMTHR+XNCQoLHzwkAAADgxpTrRrji4+MlSYULF3a7Pjg42Nkmu3bt2qVJkyapePHieuKJJzJsO23aNBUuXNj5KFu2rEfPCQAAAODGlesCl13279+vLl26KDk5WYsXL1ZYWFiG7ceOHav4+Hjn4/Dhw9eoUgAAAAD5Ra47pTB1ZCu9UayEhIR0R7/Sc/DgQbVu3VonT57URx99pNatW2e6jb+/v/z9/bP1PAAAAABwpVw3wpV67Za767ROnTqluLi4TKeEv9KBAwfUqlUr/fnnn1q6dKm6dOnitVoBAAAAICO5LnC1bNlSkrR27do061KXpbbJTGrYOnLkiJYsWaLu3bt7r1AAAAAAyESuC1xt27ZVxYoVtWjRIu3atcu5/MyZM5o8ebJ8fX01ePBg5/K4uDjt2bNHcXFxLv1cGbYWL16snj17XqM9AAAAAIDLct01XL6+vpo9e7Y6duyo5s2bq1+/fgoODtayZcu0f/9+TZkyRVWqVHG2nzlzpiZNmqTIyEhFRUU5l7dq1UoHDx5Uo0aN9P333+v7779P81xXtgcAAAAAb8t1gUuSWrdurc2bNysyMlJLly7VxYsXVb16dU2ePFkDBgzIUh8HDx6UJG3fvl3bt29324bABQAAAMBOuTJwSVKDBg306aefZtouKirKbXAyxthQFQAAAABkXa67hgsAAAAA8gsCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYhMAFAAAAADYhcAEAAACATQhcAAAAAGATAhcAAAAA2ITABQAAAAA2IXABAAAAgE0IXAAAAABgEwIXAAAAANiEwAUAAAAANiFwAQAAAIBNCFwAAAAAYBMCFwAAAADYJNcGrh07dqhTp04KCQlRYGCgGjRooEWLFmWrj5SUFM2cOVO1atVSwYIFVaxYMd11113au3evTVUDAAAAwP/JlYErJiZGzZo10xdffKHevXtrxIgRiouL04ABAzR16tQs93P//fdr5MiRSk5O1siRI9WpUyetWLFC9evX1+7du23cAwAAAACQfK93AVdLSkrSsGHDZFmWNm3apLp160qSIiMj1bhxY0VGRqpPnz6qXLlyhv1s3LhR77zzjpo3b67PP/9c/v7+kqSBAweqffv2GjFihGJjY23fHwAAAAA3rlw3wrVhwwbt27dP/fv3d4YtSQoKCtKECROUlJSkuXPnZtrPO++8I0maMmWKM2xJUtu2bdWxY0dt2rRJv/76q/d3AAAAAAD+v1wXuGJiYiRJHTp0SLMudVlWRqZiYmIUGBiopk2bplnXsWPHLPcDAAAAAJ7KdacUpk5o4e6UwZCQEIWFhWU66cXZs2d19OhR1ahRQz4+PmnWp/adUT+JiYlKTEx0/hwfHy9JSkhIyHwnrpGUxHPZ3ubK+q/H9tSQdntqoIb8cEznhhpy2++SGjzfnhpyTw257XiiBs+3z481XE+pdRhjMm9scpn27dsbSWbv3r1u11esWNH4+fll2MeRI0eMJNO0aVO36zdt2mQkmXvvvTfdPiIjI40kHjx48ODBgwcPHjx48HD7OHz4cKb5JteNcOUWY8eO1ZgxY5w/p6Sk6O+//1bRokVlWdZ1rCxjCQkJKlu2rA4fPqzg4ODr0sf13p4aqIEavL89NVADNXh/e2qgBmrw/vbXijFGZ86cUenSpTNtm+sCV+HChSX93yl8V0tISHC2yUkfV7Zzx9/f32WyDUkqUqRIhs+bmwQHB+f4IM1pH9d7e2qgBmrw/vbUQA3U4P3tqYEaqMH7218LmWWSVLlu0oyMrq86deqU4uLiMp0SPjAwUKVKldL+/fuVnJycZn1G14kBAAAAgLfkusDVsmVLSdLatWvTrEtdltoms37Onj2rLVu2pFm3Zs2aLPcDAAAAAJ7KdYGrbdu2qlixohYtWqRdu3Y5l585c0aTJ0+Wr6+vBg8e7FweFxenPXv2KC4uzqWfe++9V5L01FNP6eLFi87l69ev15o1a9SiRQtVqVLF1n25Hvz9/RUZGZnmdMhr2cf13p4aqIEavL89NVADNXh/e2qgBmrw/va5kWVMVuYyvLY2btyojh07yt/fX/369VNwcLCWLVum/fv3a8qUKRo/fryzbVRUlCZNmqTIyEhFRUW59DN8+HDNnj1b1apVU+fOnXX8+HEtWbJEAQEB2rp1q6pVq3aN9wwAAADAjSTXjXBJUuvWrbV582Y1a9ZMS5cu1euvv66iRYvq3XffdQlbmXnrrbf06quvyrIsvfrqq1q1apW6du2qr776irAFAAAAwHa5coQLAAAAAPKDXDnCBQAAAAD5AYELAHBDS0pKut4lAADyMQIX4KEFCxbo+++/v95lAPDQ7t279eijj6pMmTLXuxQAQD7GNVyAhxwOh6KiojRx4sTrXUqec/78eRUsWPB6l4ErLF261KPt7rrrLi9XYq9//vlHixcvVnR0tL766isZY+Tn56cLFy5c79IkScYYWZbltf4uXLiggICAbG2zYsUKdevWzWs1AICnUlJStGrVKkVHR+vjjz++3uV4zPd6FwAYY7RixQp99913OnLkiC5dupSmjWVZio6Ovg7V5T4JCQk6ffq0ypUr57L84MGD+uabbyRJd9xxR5r13pScnKyVK1eqR48eLst79eql6OhoFSlSJN1td+7cqQEDBmj37t1u1x8+fFhly5bNtIb169erbdu22Sk7VzDG6LffflPBggUzHFn55ptvVK9evUz7mzNnjoYOHZrjuiIiIrL1QT81GLgLXMeOHdPWrVvl6+ur5s2bKyQkxG0fsbGxio2N9fhLi+joaG3ZskVz5szJtO3mzZs1Z84cffDBBzp37pyMMapbt66GDBmi/v37e/T8mcnq7zBVSkqKBg4cqHfffddrNfTq1UsrVqyQj49PltqvXLlSd911V64JoNnx/vvva8eOHZo+fXqmbVNSUvT3339LkkJDQ+VwXLsTftq0aaNmzZrp6aefvmbPmRMLFizwaLuBAwemu+7MmTMKCgrKtI/du3czq7QXrFu3Trt379aoUaOudylZtnfvXs2ZM0fz58/X8ePHr3c5OcYIVz7wzz//KDo6OtPAsn79+utQXcb27dunrl276pdfflFGh6JlWUpOTs6wr5MnT+rnn39O9zWQMv4PILu8NcIVGhqqsWPH6vHHH3cuW7JkiZYsWaJly5alaT9p0iQ9/fTTLq/Hww8/rFmzZjlfQ8uyNGrUqCx98MiOX375RdHR0Vq4cKFOnDiR5nficDhUpkwZLVy4UC1btkyz/fTp0523djh//rzb5wgNDdVbb72lPn36uF2flJSksWPHasaMGS7X3pw/f15Hjx5VWFiYgoODXbY5cOCAHnnkEW3YsEGS1Lx5c7344ou69dZbnW08+fBjWZYmTJjgdt3y5cu1fPlyvfTSS87AceDAAXXp0kU///yzpMshZ+HChW4/7AUEBGjy5Mkux8WV4uPjNXz4cH300UeZ/m1kJDXAz5kzx6ORlcjISJefp0+frnHjxjn/BgMCAjR+/HiNHTs2Tf/ujuXsGDJkiBYsWJDu9sePH9f8+fM1Z84c7d27V8YYlSxZUseOHdPAgQM1b948j543q4oXL64vvvhCVatWzVL7u+++W4sWLcrR7/NqDodD/fr103vvvZdp29WrV+vf//63fH199c8//7isO3LkiD7++GPt2LFDcXFxkqRixYqpfv366tmzp0qVKpVp/zExMZozZ462bdumo0ePyrIslSxZUk2aNNHQoUPdvmdkR2bHw/nz5zVr1iwtWbJEu3btUkpKiiTJx8dHderUUf/+/XX//fe7HRE8e/as6tSpo+LFiysmJkYFChRw+xwXL15UmzZtFBcXp507d7odyQ8MDNTDDz+sqVOn5mBvpa+++ko7duzQ6dOn3e5zRu9Pqb777rtMPztERkZ69GVMRsdxxYoVtWjRIjVq1CjdNrNmzdITTzyhs2fPZvm5ly9f7nyfb9asWbr/j7iTkpKi48ePp/v5IaMvMVNSUvTHH39k+PmjRYsWWa7F2zL720hISNC3337r8rd9++23ZykUe9P58+f1wQcfKDo6Wps3b5YkFShQQF26dNGQIUPUuXPna1qPVxnkaV9//bUJCwszDofDWJaV7sPhcKTbx4EDB8zo0aNN69atTZUqVUyFChXSPCpWrJhhHZ720bFjR2NZlnnggQfMF198YX777Tdz4MABt4/0nDt3ztx7773G39/fOBwOt4/MXgNP9sGyLDNp0qQM+8wKd/1ERUWlW+/V6+bNm2csyzKFChUy//rXv8y//vUvU6hQIeNwOMzChQtzXN/Zs2fNnDlzTNOmTZ2vpb+/v+ndu3eatnPnzjVBQUHGx8fHjB071iQlJRljjDl27Jjp0KGDcTgc5tZbbzU7d+5M9/mKFy9uHA6HGTp0qPnnn39c1v3666/mjjvuMJZlmcaNG7use+6554zD4TDbt293WX769GlTtmzZNH8jxYsXN8eOHXO2Sz1Grv67yehvK6Nj6s477zQ1atRwWda9e3djWZZp27atqVOnjnE4HGb27Nlut7/tttuMw+Ew7dq1M0eOHHFZt2nTJlO+fHljWZbp06dPmm1/+eUX06VLFxMUFGRCQkJMRESE+fXXX90+T0bHWnatWbPGWJZlihQpYu677z7z0EMPmZIlSxqHw2E6d+5sLly44NXnHjx4cJrtk5OTzYoVK0z37t1NgQIFjGVZpmDBgiYiIsKsXr3aJCUlGcuyzPDhwzPs+7bbbsv2o1q1ai59+Pr6mnLlypnDhw9n+FwpKSlm4MCBxrIs06hRI+fyESNGZPvxwAMPuPTds2dP43A40iy/2urVq42/v78pVKiQ2bBhg8u6iRMnmoCAALd/Cw6HwxQsWNBMnjw53b7PnTtnIiIiMv1bioiIMOfOncuwzoy4Ox5SHTp0yFSrVs1Zg5+fnylZsqQpUaKE8zhxOBymZs2aaf7ejDHmtddeMw6Hw8TGxmZax6ZNm4xlWWbmzJlu19erV8/069cvezt3hb/++ss0b948R//vnzhxwvmenNnvZdq0aebZZ591ebRr1844HI40y698ZKRgwYKmQIEC5umnnzYpKSku6+Li4ky3bt2MZVmmUqVKLuuWL19umjdv7vb3cPfdd7vsi8PhMN26dUvT/9UWLVpk6tSpYwoUKJDuZwgfHx+326akpJipU6eaYsWKpbtt6iMjW7ZsMT169DAVKlQwfn5+2aohK9L72/jhhx9M586dja+vb5rnK1CggOnevbv56aef0u33woUL5sUXXzRdu3Y1PXv2NG+++aa5ePGi27Yvv/yyqVChgtt1X375pbn33ntN4cKFXX6HHTp0MHFxcZ7tdC7DKYV53MiRI3Xq1Ck9++yz6tevn0qVKpXlU0ckae3aterevbsSExNVoEABFS9eXL6+aQ8Lk8HoU076+OKLL9StWzfNmjUryzVf7eGHH9bs2bNVq1Yt9e7dW6VKlXL7/BnxdB8OHDigTZs2Zeu5vP0t15w5c1SwYEF99dVXql69uiTphx9+UMOGDTV79mz95z//8ajfbdu2KTo6WkuXLnV+w1i2bFk9+uijGjBggEJDQ9NsM3jwYDVr1kz9+/fXs88+qw0bNmj48OEaN26cTp48qXvvvVczZszI8PqtH3/8UYMGDdLcuXO1efNmLVq0SPXq1VN0dLRGjx6tCxcuaMKECWlGFjdt2qRy5cqpYcOGLstnzpypP/74Qy1bttTcuXMVGBioqVOn6pVXXtGMGTP07LPPSrp8OtLVPvzwQy1btsztusz89NNPat++vfPn+Ph4rV69Wn379tX777+vS5cuqW7duoqOjtY999yTZvtvv/1Wo0eP1ttvv63atWtr9uzZ6tq1qyZOnKjnnntOAQEBio6O1pAhQ1y2+/PPP9W8eXOdPHlShQoVUnJyspYsWaKVK1fqnXfeUb9+/bK9L1n18ssvq1ChQvrqq69UuXJlSdK0adN033336f3331fXrl21YsWKdK8pyu4o465du9IsK1OmjPP0k6ZNm2rgwIG666670ox6ZmbPnj3Zau/O/Pnzdffdd6tdu3b64osvVKxYsTRtjDEaOnSoFi5cqPr162vt2rXOdW+++aYsy8rw/fdqlmW5vJ8uXrxYnTp10ptvvqmQkBBNmTIlzTZr1qzRv//9bzkcDq1YsUKtW7d2rhs/frymTZsmf39/3X333WrZsqVKly4tY4yOHj2qjRs36oMPPlBkZKSSkpIUFRWVpv+IiAitXLlSt9xyix599FG1bdvWedrw4cOHtW7dOk2fPl1Lly7V+fPnvX6dhjFG/fr1088//6y2bdtq/PjxatKkifz8/CRdHpXavHmzpk6dqg0bNqhfv36KjY116WP58uW67bbbsvT+3bx5c9WsWVPLli3Tgw8+mGb9yJEj9eCDD3p8utyYMWO0efNmtWrVSoMGDVKZMmWy/X/egw8+qM8//1ydOnVSREREhv9vuht5vHDhgjZs2KAnn3wy2/VLl0+37devn6KiovT555/rvffeU9myZbVu3ToNGjRIR48e1d13353ms8Hy5cu1c+fONO/zH3/8sd59910FBQXpkUceUWBgoN555x198sknWrBggQYNGuS2jpdeeklPPPGEChQooBYtWmT788PYsWP1/PPPq3jx4hoyZIhHnz/effddDRo0SMYYVaxYUQ0aNMh2H56IjY1V165d9c8//6hQoUKqV6+ey9/2119/rRUrVigmJkarVq1S06ZNXbZPTExUy5YttWPHDud71PLlyzVz5kx98MEHLmeQSNLp06d18OBB589//fWXFi5cqOjoaO3evVvGGJUuXVr33XefBg4cqJo1a6p8+fIqWrSo7a/FNXGdgh68JCAgwAwYMMDj7WvVqmUKFSpkli5dapKTk695H8WLFzePPvqoR8+bKiQkxNSvX985muIJT/bhyhGQ7Dzc9ZOTEa6QkBDzn//8J027/v37m5CQkCztS6oTJ06YF1980Tm6YlmWKV26tHn88cezNCqQKikpyYwZM8b5GoWGhprly5dnq5aXX37ZBAQEGD8/P9OsWTPjcDhMeHi42bx5s9v2FStWdDvqVq9ePeNwOMzvv//usrxKlSqmTp06GdaQkxGYgIAAM378eOfPn3zyibEsy+V1GD16tClWrFiG/Xz88cfOUewKFSoYh8Nh6tevb/bu3eu2/QMPPGAsyzLPP/+8SU5ONikpKWbJkiWmRIkSxsfHx7zzzjvp7uO2bdtMmzZtTFBQkAkKCjJt27Y1X375ZZb3OSwszAwcONDturFjxzpH986fP5/muY1xP8qY2ePq349lWcbHx8c88cQT5tSpU25ryc6xnFOzZs0ylmWZ22+/3SQkJKRZP3ToUGNZlrnjjjvM6dOn09Tp5+dnevToYT788EMTExOTpcfV/vnnH9OwYUPjcDjM9OnTXdatXbvWBAQEmICAALN27VqXdfv27TO+vr6mYsWK6Y6QGnN5RDU8PNwUKFAgzd/ZRx99ZCzLMl27dnX+3t25cOGC6datm3E4HGbZsmXptstIet/if/bZZ8ayLNO/f/8MRztSUlJMv379jMPhMJ9//rnLumLFipkRI0ZkuZYRI0ak+7cdGxtrOnfubIKCgsxjjz1mlixZYmJiYkxsbGyahztFixY1DRs2zHTkJiNBQUGmTZs2Hm/vjZHxxMREM3r0aONwOJz/j/n4+JgiRYqY999/3+02NWrUMB06dEizvGvXrsbhcLi8vx4/ftzcdNNNpn379unWUK5cOVO2bNlMR6HTU6JECXPrrbeaM2fOeLS9MZf/LypatKj56quvPO4jM1f/bZw9e9aUKVPG+Pr6mqeffjrN2STGXH7fiIqKMj4+PiY8PDzN3+/UqVONZVmmW7duZtu2bebrr782I0aMMD4+PiYsLMx88803Lu2vPGb69Olj/P39jWVZJjAw0AwYMMCsXbvW5Zi+lu/T1wKBK4+7+eabzejRoz3ePiAgwAwbNixHNeSkj4EDB6Y5NSy7goODzeOPP56jPjzZB8uyTJ06dczgwYOz9XDXT04Cl8PhMBMnTkzTbsKECVk+BWHVqlXm3//+t/Hz83Oenti/f3/z2WefOQNodt78fvzxR1OjRg3nB+ICBQqYyZMnZ/sDwmuvveb8YF2sWDGXUwCvdtNNN6U5Di5cuGB8fX1N7dq107S///77TXBwcIbPn5MPFcWLFzf333+/8+fHHnvM+Pj4mL///tu57PHHHzeFChXKtK/nn3/e5XVwd8pTqooVK5qWLVumWX7o0CFTo0YN4+PjY958803n8tR9/P77703BggXTBJpChQqZH3/8MUv77O/vb8aNG5fu+qioKGNZlmnTpo05d+5cmtc3MDDQVKlSxSxevDhLj5YtW6b5/dx9990mMDDQOBwOExAQYHr37m2WL19uLl265Gxzrf8jnzJlirEsy7Rs2dLltMp77rnHGcbchcNXXnnF1K1b1+U03tWrV3v0Qfvvv/821atXNw6Hw8ydO9cYczlsFSxY0Pj7+5tPP/00zTYTJ040Docj3S85rvTFF18Yy7JMVFSUy/KuXbuasLAwEx8fn2kf8fHxJiwszHTt2jVrO3WV9ALXPffcYwoWLJilU5NOnDjh9v+DAgUKmKeeeirLtYwfP974+fm5XXf1FwvZPQ0tMDAwx//nhYaG5qgPb56K/Oqrrzpfh2LFipn9+/en2zYsLMxt8C1SpIgpUaJEmuURERFul6cqWLBgjj5DBQYG5vhL44CAAPPggw/mqI/MXP23kXopwnPPPZfptqmn6y9YsMBlee3atU3VqlXTfNn96aefmuDgYBMaGmp27NjhXH7lMXPlF2Puwl5qm/wUuDilMI/797//rQ0bNiglJcWjWZZKlSqV7SmDvdnH888/r0aNGunxxx/X008/7dFU4Q0bNtTevXs9ev5Unu5Dz549r/u08MYYt6eR+vj4ZPlUpC5dusjhcKh58+a6++671adPH48vln399df1+OOP69KlS5oyZYp69uypAQMGKDIyUmvXrnWeOpKRlJQURUZG6tlnn1VgYKBq1qyp7du3q127dlq0aJFq1qyZZpukpCRdvHjRZdkPP/yg5ORkNWjQIE37okWLKjEx0aN9zIpbb71VK1eu1DPPPCMfHx8tXrxYt99+u8uMfQcPHlSJEiXS7eOff/7RiBEjtGjRIpUqVUrNmzfX0qVLdccdd2jevHnq0KFDmm2OHDmi3r17p1letmxZbdq0SW3bttUDDzyglJQUjRgxwrn+2Wef1YULFzR+/HiNHDlSlmXplVde0dSpU/Xcc89laaaym2++WUeOHEl3feoEG5MmTVKnTp10xx13uKyvU6eOfvrpJ/Xt2zfT55Kkzz77TF988YXLsgULFmjWrFlatGiRoqOj9dFHH2nZsmUKCQlRREREtk+x/eKLL7Rjxw5JUv369dW8efNsbS9dPi3v77//1owZM9S7d2/973//0wMPPKA5c+aodu3aWrdunduZPUeNGqVRo0Zp586dio6O1vvvv69ly5apVKlSGjRokAYPHuw8dTMzISEhWrt2rZo1a6Z7771Xe/fu1YwZM5SSkqKPPvpId955Z5pttmzZoho1aqQ5lcidZs2aqUaNGml+Hzt27FDnzp2zdEpncHCwunTpos8++yxL+5RV33zzjZo3b56lU5OKFSumFi1aOGd8vbK2v/76K8vP+ffff6f7Hjpx4sQcTf1ft25dHThwwOPtpcuntrs7Jfdai46O1rhx4+Tj46MyZcro4MGDGj58uBYuXKiSJUumaR8fH6/ChQu7LPv9998VHx+vf//732nah4eH69SpU+k+/6233prh+szUqVNHf/75p8fbS5drvPr/LrutWrVKYWFheuSRRzJtO3r0aL3wwgv65JNPdPfddzuX7927V8OGDUvz+ePOO+/U+vXr1aFDB3Xo0EGfffZZmv+Dy5cvr4MHD+rFF19UTEyMBg4cqL59+yosLMw7O5gbXe/Eh5w5c+aMqV+/vomIiDB//PFHtrefMGGCqVSpUoanetjdx549e0xISIgJDg429erVM61bt07zyOjUh+3bt5tChQqZlStXeroLHu1Dbpk0w7Is88QTT5jjx4+7PB577DHjcDjMiRMn0qw7fvx4mhocDodp1KiRmTVrlvnrr7/c1pnZt02ppwRVqlTJ5VS0ixcvmkceecQ4HI4MTxcxxpj9+/ebxo0bG8uyTL169ZynMj333HPGz8/PBAQEmFdeeSXNdpUrVzbNmzd3WTZ9+nTjcDjSnEZnzOVTfsqUKZPh/uTkW9wPP/zQWJZlAgICTFBQkHE4HCY6Otq5PikpyZQuXdr06NHD7fZffvmlqVSpkvOUjdRv55cuXWqKFCliHA6HeeSRR0xiYqLLdsWLFzcPP/xwunWdOnXK1K1b1zgcDjNz5kznPpYtWzbN62eMMc2aNTPlypXL0j5369bNlC1bNtN2kZGRztPlrnx9R44caRwOh/ntt9+y9HwZTZKQ6ocffjAPP/ywCQsLcxlNaNGihTl48GC62126dMl07949zYhDz549PT79OvX0wQoVKhjLskytWrWydUF4YmKiWbRokWnXrp3x8fExDofDNGvWzMydOzfL71379u0zpUuXNg6Hw/j7+2f4vlm6dOlsjfwPGzbM3HzzzS7L/Pz8XE6tzcz48eONv7+/MSb7k4ZUrVrV7fFQtGjRDP8mrjZ69GhTtGhRl2WNGzc21atXz3If1atXN02aNMly++xYt26d8ff3N9u2bfO4jz179pjQ0FDz2muvebR9Tke4Tp8+bfr06WMcDocpV66c+eKLL8z58+fNiBEjnCP5H3/8cZrtihcvbiIiIlyWLV682FiWZaZOnZqm/WOPPWZCQ0PTrePdd981RYoUyXBirox8+umnplChQmlOn8uOl19+2ZQqVcrt/7vpee6557L1SD21PlWVKlVMr169svx8vXr1MlWqVHFZVrhwYfPEE0+ku823335rQkNDTZEiRczWrVvTHDPr1q0zERERJiAgwPn/Qbdu3cwHH3xgLly4kO9GuAhc+cC3335rQkJCnNfKZGeWwUuXLpmuXbuaFi1amM2bN3t0HnJO+vj2229N0aJFs32dxtW2bNliQkNDTbt27cyjjz5qJk2alObx9NNPe3UfvBm4ihUr5jLbWeqMR+5mQktdd+X26Z2Okt66q0813LBhgxkwYIBzdkN/f3/Ts2dPs2zZMueMQ1l587Msy9x9993pvn5r1qxxzlqXntRZih5//PE0sx19/fXXpkqVKsbhcJg777zTZd2IESOMw+Ew7733njHm8vn7t9xyi/H19XV7Cl6DBg1M06ZNM9yfnH6oeP311029evVMvXr10py6sWbNGlOkSBGX0/uuVKBAAVOwYEEza9asNOsOHjxomjdvbizLSnO6ZKNGjVxmunPn77//doau1Jnb/Pz83J5i9Nhjjzk/AGfmrbfeMpZlmdWrV2faduLEiWn+tleuXGlatWqVpVPYjLl8fdvVp7Cl5+LFi2bJkiWmY8eOxsfHx3lKS7t27cyiRYvStJ8xY4axLMuUKFHC+YG+RIkSxuFwmFdffTVLz3m15ORk06tXL2NZlqlZs2aOZt86dOiQmTRpkilVqlSaa1eu5O7D13/+8x/nqZ3pfUAzxphChQpleIro1caNG2cCAwNdlhUrVixbH5qGDx/uvPYpO9fyZfR/RYECBUxkZGSWa5g4cWKa0wEnT55sHA5Hhl8WpVqyZImxLMs888wzWX7OjMyfPz/No1evXsbPz88MGTLEvPrqq27bzJ8/P8N+f/rpJxMSEmIqV65sevXqZYYMGZLmMXToULfb5vS98cpZVq++dnH58uXO61bvu+8+l3WdOnUyN910k8vp5T169DAOh8NtAO3SpYupWbNmhrW8+OKLpnTp0mbKlClm5cqVbq+ly2h2yg8++MAUL17cDBs2zLz22mse/S4eeughc+utt5p3333X/PDDD+bgwYNuH6lyer1rkSJFsnUq5GOPPWaKFCnisqxWrVrmX//6V4bbffPNN84v1Dt16uT2mDl16pR59dVXTZ06dZx1pn6p2Ldv3yzXmNtxH648bv369eratasuXLiQ4ex6krR//363y9euXauIiAjFx8en+zyWZbnc88hbfbRs2VJbtmzR1KlT1b9//2zPsihdnummZ8+ezns2ZPT8Gd0XJLv74PDSfbg8ORX0yn1p1aqVR6enbNy4Mc2y+Ph4vffee4qOjtbOnTtlWZZCQkLUu3dvvf322xo2bJjefvvtdPt87733NGDAgAyf9+TJk7rnnnu0YsUKt+tLly6thQsXpntT43PnzmnkyJGaN2+ey+/z8OHDqlWrlhISElSkSBH9888/unTpknPGwyv98ccfKl++vP773//qmWeeSbfWnN4nKidq1aql999/3znz5NWMMZoyZYomT57scjrKhAkTNG3aNO3du1cVKlRIt/9Tp06pTZs2+u6775wz4bk7nrPzGvz999/64IMPVLVqVbVq1SrT9vPmzdPBgwfT3MvLbn/88YfmzJmjefPm6cCBA27fGxo0aKDffvtNu3fvdp7a9Oeff6p69eqqWrWqtm/fnunzuJuB7uLFi9q/f79Kly7t9pQzy7L0008/ZdhvUlKSli9frjlz5mjNmjVKSUnRp59+qo4dO6Zp63A4PJrpMDk5Odvvce6OlQ4dOmj37t3at2+f/P39M9w+MTFRlSpV0m233abPP/88zUyBWXX1rHre2I/Tp0+rUqVKSkxM1MyZMzV48GC3286fP18PPfSQAgICtHfv3gxvAp9Vqb/DK139+3S3PqP/8/bv368OHTpo3759GT63ZVmaNm1amuWff/65NmzYoGeffTbdY+uJJ55It9+goCC98sor6d60PfVeeevXr3fZh1WrVqlr164qX768evbsqd9++02ffPKJqlevrh9++MGlj8TERBUvXlw9e/bM8J57EyZM0IwZM9K9N2Qqd69lYmKihg0bpkWLFrncB/NKmf0upMunVj722GNKSEhIt82Vnz/mz5+fYa3pSZ2t0dfXVxMnTszW38SUKVNc7jE2YsQIzZ8/X8eOHcvwlOFvv/1W7du31+nTpyW5fx2vbDt79mwtXrxYp0+flmVZqlChgoYMGeKckTOv4hquPO7JJ5+UMUaLFy9W7969s/3hfcmSJRowYIBSUlJUsWJFj6Y0zUkf33zzjfr27ZvhG3NmRo4cqc2bN2dpetv0eLIPgwYNUp06dTyuO1XqzTc9FRMTk+MaUhUuXFgPPPCAHnjgAX333XeaPXu2Fi1a5AxZn3zyiSZPnqxBgwa5vQlkZmFLunyNRHphS5K+//77DM/jLlSokKKjo9WpUyeX5WXLllVMTIzGjBmj7du3q3jx4urTp4/bm4vOnTtXwcHBLn088MADadp9/fXX6a6T0k7BnV0PPvigFi9e7PbakB07dmT4AdX6/zc1vfo6rt69e2vdunX69NNP061bunxNz4YNG9SzZ08dPHjQZbpeT/3zzz/q169flqdgv/pD66FDh1SkSJFsT+GeXWXKlHF+2Fi3bp3mzJmTps0vv/yiPn36uFxHUrp0afXs2dPtDcndyWhq+YyudUvPDz/8oDlz5ui9995TXFycihUrpocfflj33HNPutOLX/1lw7XWr18/3XPPPRo1apTeeuutDNs+/PDDOnr0qCZPnizJ/XTknjp79qxOnDiRpbZX3/RZkooUKaKlS5eqW7duuueeexQZGalWrVo5PwAeOXJEMTExOnz4sAICArR06VK3Yeujjz5SbGysfH191bFjR7chWbr8gXr+/PnasGGDLb/DkSNHat++fbr//vudX3am939ehQoV0g3t6U0Lb1lWhv+vf/PNN6pSpUq660uWLKm1a9fqpZdeclneuXNnRUZGavLkyXr55ZclXf57dneN6dKlS3XmzJl0X2Pp8vV0U6dOVbFixTz6/DBmzBi99957ObotzWuvvabRo0erQIECat26dZY/f+SEJ587rt6mW7dueuutt/T666/rv//9b7rb3X777fr888/Vrl075xfamzZtUnh4eJrPEbfffrtef/11zZgxQx9++KGio6MVGxurCRMmaNKkSdf8WjdvYoQrjytUqJAGDBigd955x6Ptq1evrmPHjunTTz91O7GA3X2ULl1aERERmj59ukfPLUmhoaGqU6eO8+7ynvDG65BfXbx4UcuWLdOcOXO0fv16SZf/M23btq3WrFnj0rZixYoaPXq0Ro0alW5/b775pl544YV0v1lN7434Sn/88Yd+//13r97TLKcjjZ4YMmSIFixY4LaPBQsWqE6dOqpVq1a62//000/65ptvNHDgQI9rSOVwOHTLLbfolltucVn+22+/ad++fW4/tFiWpVWrVjl/9vHxUVRUlCZMmOBRDT4+PoqMjLymE9GsW7dOu3fvTnPMOhwOTZw4Mc19paKiojR58uRrNuqZOuo8Z84c7dy5Uz4+Prrzzjs1dOhQdenSxdb79aR3TKQn9Vi58rVJSUlR06ZN9dVXX6lFixYaP368WrRo4XIPrNjYWE2bNk2xsbFq0KCBtmzZ4tHfozuTJk1SVFSUR/25+x1/9913GjVqVJrJQVK1aNFCr776apq/25SUFP373//WypUrXUZC2rZtq4ULF6aZPMfu0fXg4GC1bt1ay5cvz7RtTkdT7HDw4EF99dVXCg0NVaNGjRQYGJimzY4dO/Tnn3+qXbt2btdLl8NaUFCQduzYoZtuuinbdRQvXlzly5fXtm3bPP5brFixopKSkrR169ZrNoLjcDjUu3dvtxMsufPBBx9o2bJlaY7HxMRE+fj4ZGnfT506pYSEBJUvXz5b7/X79+9XdHS0FixYoEOHDmWp3tyIEa48rlixYh7N7Jdq//79GjJkSI5CRk766N69e45mWZQuD9dfPdtZdnmyD88//7xHz5Xet37emA3NDn5+foqIiFBERIQOHjzoPBVr3bp1adoeOHDAedpAeuLj4zOcYat169aZvhG/9957GjduXI4+jKTedHH06NGS3J9ieT0NHjxYUVFRGQauTz75ROPGjfNK4JIuf2D+7bff3K5zN3Ocu1NncvId3vX4/u+9997TggUL3H5J4O49KTun76aeSuSp/v376+OPP1ZiYqKqVKmiadOmadCgQRnObOltGR0T7ly9v47/fzPl7t27KzY2Vps2bZKvr69zFDsuLk5JSUkyxqhhw4Zavny518LWlbz15Uzt2rUVGxurffv2acuWLTp27JikyyMyTZs2VaVKldxu99Zbb2nFihW64447NGbMGBUoUEDR0dH67LPP1KRJE23YsEHly5f3So1Z4e/vn+EI05XsDE5X2rVrl/N9uFmzZqpfv366bcuXL5/p65XR9qlOnTqliIgIj8KWdPkG0K1bt87RFx/Hjh3Tfffd55Wwde7cObcjtDfddJMKFSrksuyjjz7SRx99lKV+03svy+w04SuFhIQ4Z+nNznt9hQoVnKfP52UErjxuwIAB+uCDD3T+/HmPglfZsmVz/A1aTvp47rnn1L59ew0YMEAvvPCCR284TZs21XfffefR86fyZB/++9//ZvnaiCvfqK4OXElJSerdu7dWrlzpsrx79+768MMPs/Th4+zZs/rw/7V33lFRJN/bf3oIYgQRswgq4poQcwTBgIg5Z6IBXSOGVWEVUTFhDt9VGaKygoqooBiQZBbTGnZVQMwJEAyIhKn3D3/TL0P3DDPMkLQ+58w5Wl1dVT30dNetuve5R47gw4cP6NChA/r06QPgh6zzypUrcevWLYhEIpibm2P9+vVS3Y+KwsDAgF0xPnfuXLHayMzMlPmQluf7FIlExZ7InjlzBkKhECdOnEBubi5rcKnSfam0EMfZqAJpMZ6/Mi9fvsT169c5ZcCP1XO+e7Xgok3Pnj0RGBgodRJeFIcOHYKGhgaGDBmCHj16AJBvt0EeF215Jmaquidq166N+Ph4+Pn5wcfHBwkJCXjz5g0AQENDA927d4e9vT3s7e1LZMdOIBCofEGlWbNmCv1d/f390bBhQ8TGxrLv6lGjRmHfvn2YM2cOevfujejoaJlxl4URiUR4+fIlXr16JRFbUxBphmb//v1x+fJluftSlu3bt2P9+vXo378/Zs2ahW7dukkcd3Nz48SKzZo1Czt37izRcbVt25a9F4tDx44dFVqQ4MPIyKjIhUo+xC78gYGB7Htg06ZN8PDw4NRt37496yIPoNTjZlWBMotX5QFqcFVw3N3d8e+//2LAgAHw9PSEqampQis106ZNw9atW+Hp6QldXd1ijUGZNkxNTZGTk4OEhATW571wjg3gxw9Nmgual5cXevTogV27dmH27Nmldg3y+tV//vwZ27dvR1JSEu8DY9euXThx4gTq1KnD5hEJDQ3F8ePHsXv3bsyZM0dm+6mpqejRoweSkpLYVaj58+dj3Lhx6Nevn0SuqfDwcFy6dAm3b9+W6bJXFAzDsLFDcXFxEsdSUlI4ZcAP4+Dly5cIDAyUe2VVGk+ePOG9T6SRkpICX19f+Pn54eXLlyCEoG7duhI5meRxhyxv3L59m3O/Nm3aVOF2ZP2+fmWEQiGEQiGnnBDCmTCKKbhwc/XqVbRr1w4bN26UGU8ni9zcXJw4cULCFU0WfLEzxZ2YqXLHRU1NDU5OTnByckJ+fj4bt1irVi2FhZLKAnlcnWXx8OFDTJ48mbMwOn36dDRs2BCjR4+GhYUFoqOji/wNE0Kwfv16bN26tcjcYNIWEr28vNCrVy+lcmAqQkZGBt6+fYvDhw9j165dEseio6Ph6ekJdXV1TJw4EVWrVsWRI0ewZ88e9O3bF8OHD2frqtqzxNXVFePHj8etW7fQoUMHhdv19PRE3759ER4ejsGDBxdrbAsWLMDChQvx7NkzuX9z4eHhOHToELy9vTmLboQQiQXEr1+/4ubNmzh16hQbt6wKg0ua4IksGIbhfab+ClCDq4IjfkgW/oEVRprK4OjRo3Hp0iX06NEDbm5uMDU1lRqwLu1Fo0wbIpEIGhoaEuV8kwpZE42NGzfCxMQE8+bNw/bt29GuXTve/mX90ItzDUW5WeTl5WHv3r1Ys2YN3r9/DwMDA94t8aCgIOjo6ODOnTtsgL6bmxtat26NgwcPFmlwrV+/HomJiRg0aBAGDBiAs2fPYteuXbh58yYaNmyI/fv3o0uXLmzi1W3btmH9+vXYs2ePRDvF3SUrqJLIMAwb7M2H2CAsLGRR+MEdFhbG63YoNtri4uJ4E7UWJCcnB0ePHoVQKERMTAwb8NumTRusXbsWNjY2EhM9edwhSxrxdy7Gz8+PVxRF/D2kpKRg7NixEsdev37NMezz8vKQn58vc2dR2fgrMcquQpaXVUxVuFGdO3cOjo6OmDNnDsLCwuDr64uGDRvKfb4qxBKUnZgBP+JlxImAO3XqpNRiDfDjXqtTp45SbSgCIQTPnz9Xatx8rs7BwcEIDg6WS0RFJBJJXSQaNGgQwsLCMGLECPTu3bvIeORly5Zh48aNqFOnDhwcHIol1DB58mRoa2tjy5Yt2LdvH5o3by71vSmO3VUFXbt25fSzd+9eMAyDv/76i30XzJs3DyYmJvDz85MwuFTlWSLm48eP6N+/P3r06IHJkyfLfPfzuW6fO3cOFhYWGDZsGCwtLaWeLxY54qNZs2bo3bs3OnXqhHnz5skcg3jH8vDhw9DV1eVN5M4wjMSObm5uLvT19XHo0CGO2JQySFN+lPX3KTgPy8jIUDgmS9lnT5lScorzlNKgd+/exMLCQq4PHwVzOUjL5cSXt0nVbSiDMvlZSuoa/v77b2JkZEQEAgHR09MjW7du5SSoFVOjRg3i5OTEKXdwcCDa2tpF9tWiRQtiYmIiUWZiYkIEAgFvPqQuXbqQZs2aSZR9+PCBNG/eXOI7cHFxIdeuXWOTEhb86OrqsjlBVq5cSdzd3dlkthYWFsTd3Z3zWb16Ndm9ezd5+PAhZ0yF/05F/R27du1Knjx5wvt93L59m8yePZvo6uqybXXp0oXs3r1bZi4xRkV51RShcOJeeb8HNTU1Urt2bTJu3DiJfDTSWLlyZZE5c1Rx/UX9for6TSl7fnGQJ3myMnz+/Jk4OTkRhmGIjo4OCQwMLLG++LC1tSV6enqcnHZ8eZRycnJI3bp1yZQpU9iyuXPnskmWxd/3ggULSmXsqsDd3Z0A4Nzbhw4dIiNGjJC7Hb7fhyK5qFq2bFlkf6dPnyZaWlqkQYMGZOLEiVLbrlu3Lvntt9+KlTdTjCrem4og/jvwPX/r1KlDdHR0OAnFR4wYwUmk7efnJ9dn586dxMjISKF3f8Fr5stnKe18Vc4/inoGimnRogUZPXo07/fM19ekSZNIixYtpI6hOFy9epXzmTp1KhEIBLzHxJ+C11yaz/qyhu5wVXCUlQS3tbVVekVZFW0ogyriDFR1DefPn8fSpUtx+/ZtVK5cGcuWLcMff/zBm29HzOfPn3lj1xo3bozPnz8X2efz588xbdo0iTJLS0vcv38f3bt359Tv3r07R55ZmV2ygipusbGxcHBwUFjEQfw3JISwrn3z5s3j1FNTU0PNmjV5Faf27NkDoVCIO3fugBACfX19ODs7w9bWFi1atACAYrucykvhoOSiKBx3UVB2V1V53oDS3TWqUaOGUrmHlD2/rHj69ClWrVrFWfWtVq0avL29MXr0aEydOhV2dnY4duwYPD09pbpxqXIV99q1a7CwsICGhkaRdTU0NNCvXz82bs3f3x87d+5E5cqV2Z2w2NhYbN++HR06dOBdXS9MceJFGTlykSnLf//9J5dCn6ro0qULwsLCkJ2dDS0tLd461tbWOHbsGEaMGIFDhw5JbevLly+YPHlysYUeAOXTkRSXwik/Xr58iQ8fPmDQoEGcHVgjIyOcOnVKokxVniVifHx8lHo+qiI2cMWKFQqP4eXLlxg2bBinvHr16mjQoAGnvH79+hKpKIqz01VYlbZr166cOmJxJb5jhWncuDEMDQ0VHkdFhRpcvziykgGWZhvKoIo4A2Wv4fbt2/jjjz8QFRUFNTU1TJs2De7u7hI5fGShjBpadnY2atWqJVEmVgLim7jWrFmTk8siPDwcbdu2ZYU7Zs+ejXbt2iE+Ph7h4eGwtLQEAFStWhVbtmzBpUuXcPbsWU7b8ioeFabg39DX1xft27dX+O86e/ZsCAQCTJo0Cfb29rC0tCz1hYA6deqorM/o6OgK+TJasGCBUkaisucrGuNROFmqojx//hyrV69GQEAA8vLypD5LrK2t8eDBA9jb2yMsLAxhYWG89aS5fxdEESUyZSZmPj4+qFy5Mq5fv84m4L537x66du0Kb29vuQwuWbnIpFFe3EpVyZAhQxAQEAB/f3/MmDFDaj2x0TV8+HCpQhimpqZ4/fp1SQ21RCk8brGraseOHTl1GYaRapzycejQIfz5559ITk6Grq4utmzZglmzZrEpCPiQlsBaXlQhtlQ49YQ85OXl8cY+uri4wMXFhVOurq4ucT/xqc4Whap/lw4ODqWaAqSsoQZXBUeeuIsNGzYoLaFdlhw/fhx3794tlz/Mp0+fwtXVFSEhIWyeFU9PT4VFIZRVQ1MWZXfJQkNDsXjxYjbuqkmTJvDy8pLwvZeH+Ph4pKWl4fz588jMzFRYGl8kEuHChQuoX78+6taty04S5UVazJQ0Csc3yJK7VxTxi1wkEiE9PR3Aj5xzJSGZLeZnmOgqEuMhRtp1X7x4EX/++Sdu3rwJdXV1mJmZYePGjWjRogWysrLg5uaGPXv2ICcnBw0aNMCyZctk9nP69GnExcWBEIIuXbrIJVSgjBIZoNzE7N69exg5cqTE76ht27YYMWIETp8+XeTYAfl3UkJCQuDm5obExMRiCziVZ4YNG4Z///1XpreDGGtra9y9e5eVnC+Mm5sbRo0aVWyhh7IkPDwcX79+Zb0Ujh07BoZheNUUk5KSeBcFClMczxIxyuY79PDwgIWFhcy0A5cuXUJUVJRK5zB6enoKJat/9uyZxMKsvJ5B165dg6urK5KSkuTaJadIhxpcFRyiZN6bkkKRfBpFERYWhoCAALkeVvfv38eOHTuQkJCAb9++wdDQEOPGjYOdnZ3KJ5Nz587Fvn37kJubi969e2PDhg3Fvk5l1dCURZldsvj4eIwZMwaEEFStWhWEECQnJ2PMmDGIjY1l5axloQpp/KSkJAiFQvj7+2PTpk3w8vKCiYkJbG1tMXHiRLlyF6WkpChkNJWUgfLt2zfs3r0bwcHBuHPnDjtpVVNTg6mpKSZOnAhnZ2eFVn/lYevWrQoJNZRHdUNVCE0AP1be+/XrJ7EbfPLkSdy4cQNxcXEYPnw4Hj58iAYNGuCPP/7A9OnTpYqSpKenw9nZGUePHoWenh5CQ0PlWoxQheCFMhOzzMxMXsW8Zs2a4dOnT3K3KYvo6Gj88ccfuHnzJrS0tLB06VIsXbpUJW0Dqs3tpszvPSgoCKampqx7c1G0aNFCal1ra2v4+/tj4MCBGDp0qFShKIBf6AH4cU+qq6tLPQ/4sRCXkpKishxmDMMgPT0dvXv3hq2tLRITE3HgwAHo6+vDwsJCom5+fj7i4uJY7wo+lPUsAZTPd+ju7g53d3eZ39HFixexatUqqXOYS5cu4ejRo1iyZAnv2N++fYuNGzdi7Nix7HygY8eOiIqKQk5OjswdPOBHcuKoqCiJRdqiPEiePHmC5cuXs4Iw48ePx9q1a2WeQ5ENNbh+AT58+MCupPbp04dVkmvUqBFHFU0ahVfy4+Li4O3tXWb5NGbPno2QkBC8ffuWnYiEhITA1tZWYoL06NEjnD17FqGhoQgLC1Nqd2DDhg04c+YMqyC1a9cuMAyDZs2a4bfffoOvr2+Rkz2GYbB7926JMlWooe3atUvC5z81NRUAf/yE+Jiq2LJlCwgh8Pb2hoODA4AfBuT06dOxZcsWuQwuZaXxgf+fHNHDwwORkZEQCoUIDw/HwoULsWTJEvTt27fINqTFjpUmL168gLW1Nf777z8QQqChoQE9PT0QQpCeno6EhATcvHkTPj4+iIyMlGsFWF4yMjJKXalx2bJlnOeFMuerKknrxo0bkZOTg3Xr1sHJyQkA8Ndff2HFihUwMzPDhw8f4ObmhuXLl8s0fE+cOIEZM2bg3bt3GD58OPbu3YvatWvLNQZVKJEpMzEjhPDujqmpqSltyNy9exd//PEHzp07B4FAACcnJ6xatQr169dXqt3CLFiwAB4eHipZHFm9erXEvSZ2/ZQWu8kwDL5+/QpAvom9vHz//h3Hjx9Hamoqu1BX+PrI/ynCFjYSLl26BGdnZzx8+BDAD0+JjRs38noT+Pr6wsPDQ2WLe+Lx+Pv74/bt2yCEoHr16ti/fz/n3RwREYHU1FQMGDCA046qPEvkRdl8hzk5OTLP37JlC/755x9s2bKF93i9evUQHh6OV69eITg4GADYBcrVq1cXmRDYw8MDqampHEVbPt6+fQt3d3f4+PggLy8P/fr1w4YNG9C+ffsiz6XIhhpcFZCAgACJ/9+5c4dTBvx/6WhfX1+0adMGwA+RDYZhkJWVxf5fHgo/zIODg4udT4NvrLLgSyoYGxuL3r17sw+x9PR0TJ06FVpaWti+fTuGDBkCHR0dPHjwACtXrkRERAS2bt2KhQsXKtR3Qf777z/ExsZKlBFCkJiYKHfiQz6DSxUr8qmpqbyGlLT4CVXuzFy7dg3W1tYS0u5Tp07F0aNH5U6sqaw0fkEEAgFsbGxgY2OD1NRU+Pv7w8fHh405+/vvv5Gfnw8HBwf06tVL4lwdHR2lYgIVvbfFiCdFhBBMmDAB//77L/r27QtXV1f06NGDnSjn5OTg4sWL8PT0xIULFzBhwgTOPakMyop0MAxT5KS+IMuXL8fGjRvZSayy5xeHtLQ0BAYGsgmwxVy6dAl9+vTBH3/8wZa5ubkhKioKcXFx2LRpE69LXkHs7e0RGBiIGjVqwM/PT2ExGWUEL8QoOzH7+vUr3r9/L1FPHD/24cMHXsNLluR7SkoK3NzccOjQIYhEIgwfPhzr1q0rcucnISEBwcHBuHHjBvusq127Njp37ozx48dLda0TS7ErYywBPwL8y4vLrYuLCw4ePAgTExOMHj1abln4R48ewcrKCt++fYOxsTE0NDRw7do19OnTB2vWrJG411WN2BvH19cXTk5OuHLlCnR1dTFgwABe0ahKlSph69atnPhDVXqWyAtfvsOCyLovcnJyEB8fL9PD4saNG0UuCJqbm+PcuXPs/ydOnMjmDn337h2WL1/Oifl9+vQp1q5dC19fX5iYmGDixIlS2//8+TM2bNiA7du34+vXr+jQoQPWr1+Pfv36yRxXcendu3eFjFFWitITRKSoCnnlNMUSo1WqVCGnT59W6Rjat29PevfuzSkfN24cEQgERCgUsmWPHj0ilSpVIsOGDVNo/LIkWatXr04WL17M/v/AgQOEYRgSFBTEGVNOTg5p06YNadu2rVLXXFg+OiYmplgfZUlOTiZ2dnZKt1MQhmFI7dq1ScuWLdlP7dq1iUAgkCgrfIwQQtTV1cnSpUs5bS5dupRoaGjI1b+y0vjycOXKFTJ16lRSo0YN9p4yNjZmj5eFLHrhezsyMpIwDEMmTpxIRCKR1H5EIhGZMGECEQgE5Ny5cxLHKleuzPloaGgQgUDAe6xy5cqkSpUqKrt+ExMT8vHjxyLrurm5EYZhSPPmzVV2viJERkaSMWPGkEqVKvHKKGtoaJAlS5ZwyhcvXkwEAgFJTU0tsg+GYUi/fv3Iy5cvizXGqlWr8o5h8+bNpFGjRpzyRYsWkWrVqkmU5eXlkfbt2xOBQECmTZtGnj59yjkvOTmZODk5EYFAQExNTUleXh47/qLuXXmlm1NTU8m8efPYNBPm5ubkypUrRX4H379/J7a2tjLTJAgEAuLk5ERyc3N52zAwMCCGhoYKf1SJKtNO1K5dm3Tq1Enq9UrD1taWMAxDgoOD2bJr166R3377jQgEAuLm5iZRXxHZ+6JISUlRyftP/Pdu3rw5mTlzplyfWbNmSbRhaWnJfhiGIU2aNJEoE3/Mzc1J06ZNiUAgIOPHj2fPb9KkCfthGIbUrFlTokz8ady4MalSpQoRCARkxowZUq+pUqVKxNXVVeZ1u7q6Ei0tLYmyZ8+ekRYtWrDfiaGhIenRowfp0aMHMTAwYH8zzZs3JykpKbzt5uTkkK1bt5LatWsThmGIkZER+fvvv4v6MxBCCBk4cCDnI04vw3ds4MCBxMbGhj0/NzeXBAQEkDlz5pA5c+aQgIAAhe/pigTd4aqAiHdECCFwdHTE8OHDeVWo1NTUoKuri+7du7PxOKri9evX6NSpE6c8OjoaNWrUkFD+MTY2ho2NDbvyqqmpiQYNGshUairI4cOHcfv2bZl1Xrx4AYZhMGjQIM4x8crvX3/9JVGuaJb0ixcvSvxfFepEiiCvGlpxKe4uWX5+Pm/wv5aWltyuKMpK48tDt27d0K1bN2zfvh3BwcEQCoVy78DJi6enJ2e18/z587hw4QIn2TMfhw8fhpaWFnbs2CFz1ZRhGGzfvh3Hjh1DcHCwxCpkcZUSFYnzkcb69euxdOlSDBo0COfOnZO6e7By5UqsXbsWzZo1k3CLU/b8okhJSYGvry/8/Pzw8uVLEEJQt25dXpe9vLw83vQD4rLCMY987NixAx07doStrS1u3LgB4IcLl6enp1yiN8oqkQE/3gNhYWGwsrKCt7c3hEIhGjduzLqivnr1Ci9evAAhBEZGRggLC2P7NDc3V3pX59u3b9i8eTO8vLzw6dMntGnTBp6enhg8eLBc58+aNQuBgYGoU6cOnJ2d0bt3bzRo0ACEELx58wbR0dHYt28ffH19oa6uznnOA6oVsykPZGdnw9LSUuFkx9HR0bCxsZHYwezSpQuuXbuGESNGwNPTE/n5+XI9q0QiEV6+fIlXr15JVVMsGNdkYGCgEkVhQHnPkoKePQzDSI3dFQgE0NXVxZgxY7Bt2za2XCQSsb8LsUAP4dnp1dDQQOvWrdGnTx+ZwmY6OjpFJgB+9uwZJw1A48aNcePGDWzcuBG+vr549uyZxHO8QYMGsLe3x5IlS3hj9Q4cOIAVK1YgJSUFderUwY4dO+Ds7Cz3fSVL6VDaMfH3lpWVBQsLC9y8eZP97sR/p+joaLkEhSoa1OCqgBSMUYiNjcWIESMwdOhQmefk5eXx/ojEBpus80+dOoUjR47Ax8eHLfv48WOx82m0bdsWz58/l9t94b///uMYXC1atMCVK1fY/4tdWD5+/Mj7YPn48SNn8ubn56cyNTNlUUYNjU+p8tq1a7h27Rrmzp0rV/9llZOlIMpI4xfm1atXCAsL47gfdenSBcOHD4eDgwMcHBzw+PFjpcZcGL5g/+zsbFy4cEGu+/3mzZswMzOTazJfu3ZtmJubs7LKYoo7uVSF+uGSJUvw8eNHbNiwASNHjkR4eDjnubNq1SqsXr0aTZo0QXR0NBo2bKiy8/nIycnB0aNHIRQKERMTw97rbdq0wdq1a2FjY8Nr1KiC3r17o2vXrsjOzmbLLly4AEtLSwmpdWkoq0QmprgTM2XzPAI/BDbevXuHxo0bY8eOHQq5Vf7zzz/w8fFB+/btcebMGc4757fffoOlpSXmzp2L/v37Y//+/Zg9ezbrQl/eSElJQVxcnELn8IkxdOzYUW5joyDv3r1D27ZtOeU1atTAqVOnMHz4cGzYsAEikQjr16/nbYMQgvXr12Pr1q1IS0uT2V/hBTc+Zc2CCAQCaGtrw8TEBGZmZrzPJFXkvVI232HBZ6xAIFA6lUX37t1x7NgxvHjxAvr6+pzjz58/R1hYGG/MffXq1Vl34RcvXuDt27fsQpIsA9fU1BT37t1DtWrV4O7ujkWLFimcR1KZHKibNm1CQkICfvvtN3aB3tfXFzdu3MDmzZvh5uZW7LbLK9TgquAUFf/z8OFDCIVCHDx4kFdi1s/PD4aGhjINrnv37rFxMGKqV69e7HwaHTt2xK1bt6Q+XORhwoQJWLx4MYKDgzFu3DgMGzYMCxcuxKpVqyTGCfxQLjxy5AhnR6pGjRpo2LAhp740Vq9eLVUK+fPnzzh8+DCuXLmCN2/egGEY1KtXD927d8fo0aNlKkEpq4bGt7oWGRkJDw8PuQ0uZSks2gHIFu7gS2yqKmn8lStXsoIHhc8JCAjAwoULsXz5cri5uUkEWT99+lTlO8GK8uLFC4V2Tlu1aoXAwMASHJHirFu3Dh8/fsS+ffswceJEBAcHs4azh4cHVq1aBUNDQ0RHR/Puaip7vpg7d+5AKBQiKCgIGRkZIISgc+fOsLOzw+zZs9GtWzcMGTJE5rUcOHAAV69elSgTT3T5EocWTgy6fv16ZGdnw9XVFXPmzGF3Jj09PbFhw4YiY/6UVSIrSHEnZory4cMHCVGQt2/fgmEYvHnzBs7OznB2di6yDXH8VGBgIBiGQUBAAMfYKoienh4CAwNhYmKCAwcOSDUWlEHZ+EzgRyJpf39/hc7n8xLw9PRE3759ER4eLvdOIfDje5KmLlmpUiUcP34cQ4cOxaZNmyASiXh3eJctW4aNGzeiTp06cHBwkDt+DPgRI1rUIqf4t96kSRMEBgZy0pKo2rNE2XyHT58+VTpRu4uLC06cOIGePXtizZo16N+/P+rXr483b97g7NmzcHNzw7dv3zgx6FevXoWrq2uxds//+ecfMAyDKlWq4NChQzITbYsp/N5W5rkRGhqK+vXr48aNG+x9NnPmTLRo0QJHjx6lBhelYvDlyxccOnQIQqEQ169fByFEoUD0wmRnZ3MeqCYmJsXOp2FmZoYzZ87gyZMnchlchYUNAGDOnDkIDg7GpEmTEBkZidGjR2PlypVYvHgx/vnnHwwePBg6Ojp4+PAhDh48iNzcXM4PuEOHDrhx4wa6dOki106KNGWxv/76C8uWLcOnT584LxIfHx+4uLhgw4YNUl0oVaWGpgzK7pJJc0cE5E98qgppfFdXV6xbtw6VKlXClClTeN2PDh8+jJUrVyIvL08i4aSBgQFcXV3x7ds3eHl5saura9eu5ZXDNTc3L1bySFl8+vRJoZd3jRo15HK5lCeXlyp3Of/3v/8hIyMDhw8fxowZM7Bv3z6sXr0a7u7uaNy4MaKjo9G4ceMSOX/Pnj0QCoW4c+cOCCHQ19eHs7MzbG1tWWGG2bNny3UdstyW+P72hZ8j8fHx6NWrl4RYxZo1axAbGyuX2ImqlMiUmZjJAyEEp0+fhlAoREREhMSOnjJiE1evXkWnTp3kyqfXpk0bdO7cmddNWBXGkr29vULXQXhUAk1MTGBqalqssRTk3LlzsLCwwLBhw2BpaQlTU1PeRT2GYSSe6UZGRhzX+IJoamri+PHjGDZsGDZv3swrUe7n54cWLVrgxo0bHBe3oihqgVgkEiE1NRWXL19GeHg4Bg4ciFu3bvGmJpCXlStXIjw8nOMJIEZswBFCkJaWBoZhoKurK/ffWhWLFWZmZtixYwfmz5/PKv0WNEwFAgG2b98uMbe6d+8e+vTpU+zdc+DHNb979w7v3r1T+hoUJSkpCVOmTJEw6qtXr45hw4bhwIEDpT6eUqF0QsUopUF8fDxxcHAg1apVY4MlO3ToQHbu3EnS0tJ4z2EYhnh4ePAeE4lE5Pnz56RLly6cAOLAwEDCMAzp2LEj2b59O5kzZw5RU1MjBgYGJD8/X6JuXl4eqV27Nhk7dqxqLvT/+PjxIxkxYoRE8HbhQG6xGERYWBjn/IULFxKBQEAePnwoV3+FRTMIIcTDw4MIBAKiqalJxo8fT/bv308iIyPJ6dOnyf79+8m4cePYoPzVq1fzttuwYUPSt29fTrmFhQURCARk8+bNMsfFF4ytaLCzKtpQBnt7+2J9CpKUlETU1dVJ06ZNyePHj6X29ejRI2JoaEg0NDRIcnIyWx4TE8P7fbu7uxOGYSSC6Rs0aEAEAoFcQeCKfI+KBtbLajsrK4ts2rSJdOrUiairq7O/CQ0NDdK5c2eydetW8u3bN7n7UpTc3FwycOBAIhAISM+ePQnDMERfX58kJSWV6PkMwxA1NTUyZcoUEhUVxSs+wjAMmTZtmsx2UlJSivUpiKampoS4j5hFixaRSpUqFfkdKCN4Ieaff/4hlStX5ghNVKlShdy/f7/IMcgiKSmJuLq6kkaNGrHP23r16inVZkHq1atHnJ2d5a4/c+ZM3v5VIdS0bt06sn79eolPv379iEAg4JQX/BQcg6pEM6SJh/CJiRRk7dq1RCAQkH/++Udm+9nZ2cTKyoq3japVq5KFCxeq5Dpkce7cOaKmpkZmzpypVDt8720x+fn5xNfXl5iZmREtLS3276+lpUXMzc2Jn58fZz5TkJycHHLz5k1y9+5dmSJHd+/eJf7+/kWO9d69e2TWrFmkc+fOpHnz5qRz587k999/J/fu3ePUnThxImEYhri5uZF3796R9+/fE1dXV8IwDJkyZUqRfamaGzdukEWLFpHevXuT1q1bk9atWxMLCwuyePFicvPmTU59ab+H0px3lDZ0h6uC8+7dO9bd78mTJyCEoF69evj69StsbW15hRUEAoHE6o04cZ80CCGc+JPJkycjKipK6XwayqCjo4PQ0FBcv34dhw4dws2bN/H+/XuIRCJoa2ujRYsW6NOnD0aPHs2bcX7YsGFIS0vDt2/f5Opv6tSpEskZb9++DXd3dzRr1gxhYWG8rnNTp07Fv//+i2HDhsHd3R2DBg3i5LN4//49Jk2axDm3c+fOiIuLU1leofKMKqTx/f39IRKJEBAQgObNm0utZ2xsjMDAQJibmyMgIAArV64E8EMuvlq1arwuTwzDSPirf/v2DfXr10dQUJDKXVz4ZLilIZbnLkxZ5vISo66ujqNHj8LKygqXLl1Cw4YNERMTI/dqtTLni0QiXLhwAfXr10fdunXlWu0tjCpWrnNzc3l3AapVqyZVaKAgygheiFHWrbEw2dnZbEyveJdOXV2djY0cOHCgQu3JIjMzU6YrYWFq1aqFzMxMTrmyYjaA8vGZqqS4cUxjx47FvXv3cPv2bd5YLjGVKlVi88cVjiE0NTXlhBOUBP369YO1tbWEFLoqSU1NxYgRI3D58mUQQqCuro46deqwz8j4+HhcvHgRQqEQoaGhnPvwyJEjmDlzJus5UL9+fWzcuJFXev3YsWPw8PAoMn6xTZs2nLQx0lB291xV5OTkYNq0aThw4ACvq2hsbCw2b94MBwcH/PXXXwoLvfxM/LpXXoERiUSIiIiAUCjEqVOnkJeXBy0tLYwdOxa2trawsrKChoaGVDfCgspTcXFxaNy4Ma8Ps1jlsE+fPpg2bRrnuKL5NMRxYnzua8rQpUuXYrnGmJmZ8SZ7lEbPnj3Rs2dP9v/bt2+HpqYmIiIiZE7wW7ZsiYiICLRt2xY7d+7kxIypQg2N8iNvUps2bST+RtLo1asX2rRpg/j4eLbs8uXL6N27t1yBw5UrV8aAAQNUrnIIAF5eXvDy8ir2+aQMc3nxxTWJFb10dHR4XfkKxj0pez7ww1VFKBTC398fmzZtgpeXF0xMTGBra4uJEyfKzIdTHlFWiUxVE7ObN29CKBTi77//Zt2nO3XqhISEBNjZ2WHfvn2853Xo0AHOzs6YPn06W3bmzBmcOXOGN9GrWBQlLy8P2dnZcuUgE6Ouro7v379zysuTsaQKirvIY2RkhL///luuupUqVeJdsHVzc8OoUaNw69YtqbnPVIWJiQkuXLig8nZFIhGGDh2Kq1evolevXnB1dUXv3r1Zl/3s7GzExMRg3bp1iI+Px7BhwxAfH88uJl+/fh3jx4+Hmpoa+vfvDw0NDZw/fx5TpkxBfHw8/ve//6l8zIV59+4dxo8fzynv1q0b6zpcFOnp6cjOzkb9+vXZOeGlS5dw6dIlTl0TExNYW1tzypVREb1//z5CQkIk2rt//z6AH4q9fAacPMmbyyvU4KqANGrUiPW57dmzJ2xtbTF27FiZwgwFKag8JRAI4ODgUGyFnV69evHGWBVkwIABGDBgAJtokkiRUK1oxMXFwdraWqaxJaZ58+YYOHCgShSW+Cgc3C8rsB/gTlJLiszMTNy4cQNZWVkwNDSEiYlJifX177//Sr1ePrp27SohgpKSkgIrKytOPWn3a8OGDTlxPBs3buTUE8dMbNq0Sep9v2TJEgCqkeE+e/YsLl++jAkTJuDAgQOc9jQ1NdGnTx9YWlpi0qRJCA4Oxvnz51WS4FJWTNuDBw84QimAZNyTsucDP4Lt16xZAw8PD0RGRkIoFCI8PBwLFy7EkiVLikwwqmqUFd4AlBO8UGZi9vHjRxw4cABCoRD37t0DIQSNGjWCs7Mz7Ozs8NtvvxWpbnnnzh2OYNPVq1exfft2XoMLwE/xfijvBAQEwNTUVOYz+cGDB7h586bEzoy1tTX8/f0xcOBADB06FO3atZM691A00Xdh8vPzVaKeWhh/f39cvXoV06ZNw969eznHtbS0YG1tDWtrazg7O2P//v0ICAhg1fQ2btwIgUCACxcusAt8z58/x5QpU7Bv3z58+/YNvr6+Mp/lRcnAS0Mcu6rs7nl6ejqaNm3KCvOIOX/+PDw8PDi/QW1tbSQlJUkkgFZWRfTo0aM4evSoxDnifgs/s8j/xURSg4tSqrx9+xYCgQALFy7EsmXLlFLIkSdQ/t69exAKhRJ5KBShKKXE8oQ84gJi3rx5wzuRkUbLli2lTiiVnZRJC+4vKheGKggJCUGbNm04LpWurq7YsmWLhPpi69atERgYiHbt2vG2dfr0aSQmJqJNmzawtLQE8MNtbu3atYiJiYG6ujpsbGywYMECjoBIRkYGmx5AHurUqYOMjAz2/9+/f+cVJVm5ciXvbmzlypUlApaBHyvp0lS4pK2kMwzDGlyqkOFWRS6v4qKMTLAqzi+IQCCAjY0NbGxskJqayrpenz17FsAPF9L8/Hw4ODgUuWikDMoKbygreKHMxKxBgwbIyclBlSpVMGnSJNja2qJv374llh6DDz4FVGlIE+4pD9jZ2alEMKMw7969g1AoREJCAr59+wZDQ0OMHTuWfX5Kw97eHu7u7jINrvDwcCxfvlzCcPr+/TuOHz+O1NRUVuSo8P1AeARDisP169dVqqApJigoCA0bNsTOnTuLrLtjxw5ERETg4MGDrMF16dIlDB8+XMKbonHjxoiKioKDgwMCAgKQn5+PgIAAqb8VQ0NDhX9HDMOwC9fKEhgYiM+fP/MuEgKSbv4fP36Ei4sLDhw4ICGipYyKqNiV/1eCGlwVkMmTJyM0NBReXl7YsWMHBg8ejClTpsDGxkZl/rGfP39GUFAQhEIhq+6jiMGlaqVEWRSOSZOGtIfVt2/fsHv3bgQHB+POnTusEaqmpgZTU1NMnDgRzs7OnMm4lpYWsrKy5B5nVlaWVJVBZSZlqpqkFneXbPz48Zw8JosWLcKWLVtQq1Yt2NjYQE9PD9euXcPly5fRr18/3L17VyJuKC8vD0OHDsWZM2fYl7WDgwP27duHgQMHSrg4XL58GZGRkYiOjpYwiL99+yYhl18UmpqaEvF7urq6ePXqFe918uVpevXqFUdG3sfHp1Qno3fu3MHz588l0jqoIpdXcVF2clQSkyvgxwt/4cKFWLhwIa5duwZvb2+EhISwSZCNjIzw6NEjlfer7G9TFUpkyvD9+3cIBALY2dlh1qxZvHGqJY0sBVQ+SvP3pwjKxql6eHggNDQUt27dYp974jycmZmZEos8+/btw4wZM7Bnzx6l+uTbYXJxccHBgwdhYmKC0aNHKyQLrwj+/v6Ii4vjTfCtLHfv3sWIESPkmpNoamrCxsYGx44dY8vS09N5PVvU1dUREBAATU1N+Pr6QiQSSU3bYWVlxblXExMTkZSUJHe8uzILtadOnULLli150/gA4MSOBwUFISIiQsLgUkZFlBpclApBQEAAdu/ezRpER48eRWhoKGrWrInx48dj8uTJxW47Li6ObfPbt28ghKBDhw6sVGlRXLx4ET4+Pjh8+DCysrJACEH79u3h4OAgEUyqypeiNDeszMxMPHnyBF+/fkW7du14dwKVERdo3ry5Qi6CMTExMDIy4pQrOylT1SRVVbtkr169wo4dO9CqVStERUVJxMxs2bIFixYtwubNm7F582a2PCAgAJGRkbC0tMTw4cNx+vRp+Pr6olatWnjw4AFCQkJgZWWFV69ewcXFBWfPnoVQKOSNLSwuJiYmiI6OZg0+WYhEIkRHR3MCz8UroKXF9u3b2dVUMWWZy0vZ+ExVxHcW1UbXrl3RtWtXbN++HcHBwRAKhSUSiwco/9tUleBFcSdmbm5uCAgIwJ49e/C///0PHTp0wJQpUzB+/HiFdpOLS3lIyK4qpO0kFIV49/v48eNo3LgxawB9/foVY8eORVZWFpYsWYIhQ4ZAR0cHDx48gKenJ/bu3YtOnTrB0dGx2GO+ffu2hAsZ8GMHvWPHjrhy5YrChlZRiY9FIhHS0tJw5coV3L59G3Xq1MGiRYsk6ihq9L9584ZTlpmZKTXNCx+1a9eWyF9Wr149qcJGDMNAKBSCEAI/Pz+IRCLe9z7fe3XVqlXw8PCQmu+zMMos1N67d0+hHG6mpqYIDw/n9D98+HC52+jYsaOE4fqrQQ2uCkr16tUxY8YMzJgxA/fv34e3tzcOHjzIvhgZhsGjR4/w/PlzmflugB8PJD8/P/j4+CA5OZldKevWrRv27t0rU80IKJ5S4tatWxVa8WMYBklJSbzHZLlhZWVlYenSpYiMjGRdicQoKy4wZMgQuLu7Y//+/UVO/MUxEHxqkCW1qq8IqnTlioqKQn5+PjZs2MARKBCvjp4+fVrC4BIKhWjSpAnOnTsHgUCA2bNno3Xr1ti8eTP27NmD0aNHA/iRdyo0NBSNGzfG33//zfne+SaW0ij8oho2bBjmzJmD3bt3F5mnaffu3Xj58iU7GRLTp08f2NvbK+1KowwllctLHpSNz1RFfKe8bVSpUgUODg5wcHDA48ePleqzpFCV4EVxJ2biRNPiBY4TJ05g/vz5WLRoEaysrJRa3KtoKBufKXY3BuSPUSvobpycnCyxkHL69Gl8+PABO3fuxO+//86Wt2rVCtbW1mjXrh3++usvCYOrT58+Eu37+fnxvj/z8/Px8uVLpKSkcGJmsrOzYWlpWaxdLXkSH4vp1asXvL29Oe8QefM6FqTwAlqtWrXw4sULuc9/8eKFhOH522+/Ffn7E7tb+vn58aokK4uy7+y0tDTeRRNzc3MsX76cU16nTh023EKMqlRE+bhz5w67oN2rVy907txZ7n7KK9Tg+glo06YNtm3bhk2bNuHYsWPw8fHB+fPnER8fj6ZNm8LS0hKOjo6YMGECe05+fj5OnjwJoVCIM2fOsEqHo0ePhp2dHQYPHow2bdpINbaUVUrMyMiQiJ8pKapUqYIdO3agc+fOWLJkiYSRp6y4wLx587Bnzx7MmjULL1++xMKFCznBw58/f8bmzZvh6emJOnXqyJVEuCxQpdEnlgyWlqy4e/fuHGM7KSkJo0aNYldvGYZB37598ejRI84qXOXKlTFw4EDeiaKsiSUfBf/mjo6O8PLygouLCz59+oT58+dzFAu/fv2KrVu3wsPDAwYGBmyiajExMTESqQPKgry8PIUCzQUCgcriAioqxsbGZT0EXlShRKbsxIxhGFb4KD09HQEBAfDx8cGpU6dw+vRpMAyDmzdvIj4+XiHV15KC7zeorLEEKB+fCYCNQR08eLDCBktOTo6Ey3RSUhIYhpF4r4upXr06Bg0axFHELWhcMQyDlJQUpKSkcM4XCATQ1dXFmDFjOKEEHTt2VOgZW5CiFlkFAgG0tbXRtm1bNGnShLeOKnY9O3XqhIiICHz69KlIsbFPnz4hIiICPXr0YMsGDhwIFxcXXLp0SaoqbsGdLn9/f5W7uir7zpYWEmFpackb/5eVlcWZzymjIhoXFwdvb2/MmjWLM1dwc3PDunXrJMpmzZolV8xdeYYaXD8RGhoaGDt2LMaOHYuXL1/Cx8cHfn5+iIqKYndogB8vkYCAAHz48AGEEPTo0QN2dnYYO3YstLW15epLWaXEwjE/JY2ZmRkne7my4gI1atTAiRMnYG1tjTVr1sDLywsdO3aEvr4+gB+rYjdv3kR2djZ0dHRw/Phxub/fiox4d0VavFrlypU5ZZmZmZx4KPHKGV+OqIYNG3JWypSdWGppaeHYsWOwsrLCn3/+iXXr1qFTp04SOY9u3ryJrKws1KpVC6GhoVKvsaxRRS4vStmjrBIZoNrFFF1dXcyfPx/z58/HjRs3IBQKcejQIdy+fRsWFhZo2rQpHB0dsWzZMonzFIkPLe5k/tKlS1ixYgViYmIkXGwB1RhLysZnzp8/HwcPHsTx48dx5coVTJkyBQ4ODnK7yDVt2hS3b99m/y9+lxQW7hHz/ft3jlFX0FgRCATFeg97enqib9++CA8PV8glDeDGBRXFypUrER4errIYUzEODg4IDw/HlClTEBISIjX2NycnB1OmTMHHjx8l3MXHjh2Ld+/e4cOHDzL7YRgGPj4+MDAw4OQzK2saNWqEe/fuyV3/n3/+QcOGDVXWf3BwMA4fPoxdu3ZJlEdHR8PT0xPq6uqYOHEiqlatiiNHjmDPnj3o27evQi6M5Q1qcP2kNGrUCCtWrMCKFStw/vx5iZUuLy8vCAQCzJkzB/PmzZO6kiQLVSollgYfPnzgTCxVIS7QuXNn3L17F8uWLUNISAi7aipGQ0MDEyZMgKenZ5GunRWZsLAwdqVUvMOVnJzMu0P64sULjhuCrq4u5+UlyzUsMzOT46ahiollu3btcOvWLSxduhQhISEct5GK8vdUNpeXMii7kquKleDyKpzwM9G5c2d07twZW7duxeHDhyEUChEfHw83NzeOwaVMfGhubi6CgoJw8+ZNqKuro1evXhg5ciR7/M6dO1i6dCnOnTvH5gYrjCrEbJSNz9yyZQs2btyIEydOwMfHB9u2bcOWLVvQpUsXODo6Yty4cTIXLEeOHIm1a9ciLi4O5ubmGDx4MBYsWIDdu3dj7dq1EnVfv36NY8eOoX379lLbi46O5s2/WRTnzp2DhYUFhg0bBktLS5iamvKOm2EYpXNtPn/+HHfu3OGUK5s/asSIERgyZAhOnjwJU1NTLFy4EH369JFYLI2KisKWLVvw+PFj2NjYSNxzDRo04OzASINhGN5QgrLGzMwMvr6+SElJKfI+SE5ORmxsLG88YHFVRK9cuYKuXbty7p29e/eCYRgJd9h58+bBxMQEfn5+FdrgYghNePFT8OrVK7x58wYMw6BevXoyVyKqVavGKuYNGTIEtra2sLa2llBiEwgEmDp1qtRklra2tggNDcW3b9+gqanJq5QorY3irqwVB5FIhIMHD8LJyQmdOnWSCI7X09PD5MmT5VZfXLBgAQIDA6UqZmVlZSEhIYGVvq9Xrx46deokVyLdiow09zW+v3FeXh709fXRoUMHCVl7S0tL5OfnIy4uTq4+ra2t8fbtW96Xsar49u0b+/cU5zzq3LmzzL9nad7bACQkiMVYWFgUa3KpihxxAoEAOjo6Ci3AFIzPVPZ8VbVRXhAIBDAyMuIE3ctSMyutHHt8iJNOe3p6smXFXdk3MDDA58+fYW5ujn/++YddfGEYBsOHD8fRo0exYsUKeHp6QiQSoUOHDnB3d1d410VeVB2fKY6d9vf3x+PHj1G5cmWMHDkSjo6OvC5dX758gampKd6+fYvFixdj9OjRiI+Px9y5czFkyBBWNOPhw4fYuXMn3r9/jxMnTmDQoEEqGa8Yed2VGYbh7DQqCt/zTVr+KLHghDz5o4Af72t7e3scOXJE6vOSEIKRI0fC398fVatWVepa5EF8Dcp+b/Jw9+5dtG/fHl26dMHp06c5HiZi0tPTYW1tjZs3byIhIUHCiC9OjjTxfVGvXj0MHTqUMz+sW7cucnJykJaWJtH+yJEjcf36dbx8+VLhPssLdIerAvPlyxd4eXnBx8eHI2fdsGFDODk5YeHChRyXlLdv37KS7YcPH8aRI0dQq1YtTJgwAVOmTOFdISxMSSolKkrTpk15y/Py8vD+/Xvk5uZCXV1dYhIAqF5coEqVKjA3N5e7vZ8Faa58fIbJ7du38dtvv2HUqFES5Z06dcKuXbuQk5NTpFTvhw8fEBMTg6lTpxZ/0HJQuXLlYsWkFNztkwexrz/wI6eZIvB996rI5aUMysZnqiK+s7RiREsDZfN4KQvfbsLly5c5u/nAj92Ews9ZZXaeN2zYgLt376Jdu3aYNGkSCCEIDAxEWFgYxowZg6NHj8LIyAheXl4SqREKowpjSdXxmfXr18eyZcuwbNkyxMfHw9fXF4cPH0ZQUBDCwsIwZMgQifrVqlVDdHQ0Ro0axU7MxYSFhSEsLAzADyOhUqVK2L59O8fYUvT5IqagcIYqFmaUQRX5o4Af76eQkBBcuHABQqEQV65ckVgs7dGjB+zt7YvMT5icnAw/Pz9cuXJFYtG7R48esLW1RbNmzXjPu379OqdMbEzcuHFDqneHvPn3iqJdu3ZwcXHBli1b0KpVKzg7O8PCwkLChT46Ohr79u3Du3fvMH/+fM6OqTLxdB8/fuR4urx8+RIfPnzAoEGDOMackZERTp06Vez+ygWEUiFJTEwkxsbGRCAQEIZhSMOGDUm3bt1I165dScOGDQnDMEQgEBBjY2OSnJwstZ2HDx8SFxcXUqdOHfacFi1aEIFAQJycnOQez71798i8efOInp4e245AICDm5ubk2bNnEnUZhiGrVq0q9rUXxsDAgBgaGnI+TZs2JR07diQzZswg9+7d45yn6Djc3d2JQCCQKDtw4ADZtWsXycnJkXre9+/fya5du8jBgwflv6hfkLy8PLnq/ffff2Tbtm3k/v37Ku3f39+f3L17V6k2GIZR+FPwnir425HnU/j84nD79m1y/PhxpdooOH5lftuqeDao+vlSlqSkpBTroyrS0tKItrY26dOnj0S5+FlY+F7W0dEhaWlpKuu/TZs2xNDQkHz//p0ty8rKIvr6+kQgEBAbGxuSnZ1dZDvl/b5KSEggs2bNIjo6OoRhGHLixAmpdfPz88mhQ4fIiBEjSOPGjYmWlhbR1NQktWvXJr169SIrVqwgT58+lXoNpf18UQZ7e3tO/1ZWVqR169acunzvZ0II6dy5M7GyslL52PLz88kff/xB1NXVeX8LDMMQdXV1snTpUpKfn885X9rfoqi/kSoRiURk6dKl7DXwjUVNTY0sXryY9xqUoVatWsTOzk6iLCwsjDAMQ1auXMmpv2TJEqKtra3SMZQ2dIerAvL9+3cMGjQIT548wcSJE/Hnn3+iRYsWEnUePXqENWvW4ODBg7CxscGdO3d4A0NbtmyJzZs3Y8OGDTh+/DiEQiHrC+/n54enT5/C0dERo0aNkikQoIhSoqpzqyiym1AYZcQFrl+/DltbWyxZskSmUo+mpiZevXqFuXPnokWLFlITDf7q8CUX5qNFixac+10V2Nvbw93dHSYmJmzZ3r17sXfvXty6dUuhdhQNDhezYsWKUo8/4svlRSkflHXKCFXtJhSX5ORk2NnZSex6V65cGYMHD8bevXvh5eWlULLz8kRaWhoOHDgAHx8f3L9/H+rq6hg8eDAcHR0xcOBAqecJBAKMGzcO48aNU7hPVTxfPDw8YGFhIdOb49KlS4iKiioR12pV5I9SBbNmzcK+ffugq6uLadOmoW/fvtDX1wchBC9fvsT58+chFAqxceNGZGRk4H//+5/E+ba2tmUea8owDNatWwcnJyf4+vri6tWrEi70YkG1olRc4+PjWdXUzp07y+UZYmJigvDwcHz9+pV11zx27BgYhuG9t5KSkngFtCoUZWzwUYrB1q1bCcMwxN3dvci6Hh4ehGEYsm3bNrnbf/XqFVmzZg0xMjJiV1t0dHQUHueLFy/IqlWrSJMmTSRWyr58+UKMjIxIjx49itwZ6tmzJ2nRogXJyspSuP+iKLjDoMhHzIwZM0j16tVJZmZmkX19+vSJ1KhRg0yfPl3l1/GzkJaWRl69ekVEIhFbdunSJbJhwwbO5/Tp0yrvn28FW9qqqSJtlHf4VpGLC93h+rko690EaX9Lcf/yrrqXl/tKJBKRiIgIMmrUKFKpUiXCMAwxMTEhW7duJR8+fFCq7dJCnu9h/fr1Knmm8D2bNDU1iaurK6fuhQsXiJubG6fc1dWVVKpUSemxFO6LYRjStWtX8u7dO6n1Pnz4QLp160YEAgGJjo5W6RjKA7m5uWTYsGGcOdKIESOK/G0GBgYShmFIx44dyfbt28mcOXOImpoaMTAw4Jybl5dHateuTcaOHVuSl1Pi0B2uCojYb12e1SM3NzccOHAAhw8fxrx58+Rqv0GDBnB1dYWrqytiYmLg7e1drOzg0pQSfX192eDqonaG1q1bh969e8PHx0ciuaMqaNy4MZ49eyaRTFIR4uLi0LdvX7mk8KtXr46+ffsqlKy0olDUzhTDMNDW1ka7du1ga2vLq/YlLRD63LlzCgVC/2qI8+lVZOUmSvmlvOwmSKM4QfvKoEx85vLlyxEQEIA3b95AW1sbjo6OcHR0lCtmWpVcvXoVrq6u7I5Ely5d4OnpqbLYIOCHnDrf30ZeCXwxb9684ZSpIn8U8MNTaNeuXYiNjYW6ujoGDBgAR0dH3jnJ9u3bsX37diQnJwMA/vrrL1SrVg1hYWG8yYPF6Onp4dixYzA2NsZff/1V5jkaVc2uXbtw4sQJ1KlTh1VxDA0NxfHjx7F7927MmTNH6rmTJ09GVFQU/P39cfv2bRBCUL16dezfv59z70RERCA1NZVXJKgiQQ2uCsjDhw8xYcIEubajGYaBlZUVgoKCitWXhYUFLCws8OnTpyLrylJK7NevHxt8evz4cbRq1UougQkzMzO0bdsWoaGhKje4HBwc4OHhUewg4OfPn3MCm2XRvHlznDlzplh9lWf09fVl3osikQjp6emIiYlBbGwsIiIicPjwYYk6Ze26VNF49OgRhEIhAgMD8f79e+oOSCkR0tLSeCeU5ubmWL58Oae8Tp06SE9PV+kY7t+/zxF7uH//PoAfuRQLL8YAkiIPYpQxlsTcuXNHIWXUgm2sX78eGhoaGDJkCEaOHAktLS0kJyezk3hp8F2LNKEovv4Lqm/eu3cPlpaWbPJZALhw4QIsLS1x/fp1tG7dWu52pZGTk4P4+HjUrVuXc+y///6Tq31Zfakif9T379/Ru3dvCXGK48ePY9euXTh8+DB+++03ifoZGRkSapuXL1/GoEGDUK9evSL7r1evHgYNGsQrMlOQT58+4datW6wKcu3atdGhQwdO+pPyRFBQEHR0dHDnzh32u3Bzc0Pr1q1x8OBBmQYX8OO97uTkhCtXrkBXVxcDBgxAo0aNOPUqVaqErVu3KjTfKo9Qg6sC8vXrV4US6NaoUQNfv37lPSavwo60XZziKCXevXsXo0ePlnv8PXv2xJEjR+SuX1ooGotW1v7aJYW8k5i7d+9iyZIlCA0NhZ+fn8RO16lTp9CyZUup8W2FY6KCgoIQERHxSxlcWVlZCA4OZhW1CCHQ1NSUyA9T1igbn6mK+E5Vx4j+yqhqN0EZjh49iqNHj0qUiSfJ48eP55QzDMNrpChjLIlRJj4T+JFT7OTJkzh58mSRdWVdi0gk4n2fZGZmsuqc9evX5/wt1q9fj+/fv8PV1RVz5swBwzDYvn07PD09sWHDBgQEBPCOpbCBt3XrVolFMDH5+flITU1FdnY2pk2bxjtuZVFF/qgtW7bg+vXrGDJkCJYtWwYNDQ0IhULs27cPZmZmOHPmDDp06CC13Q8fPsht9AI/vj9pXkL379/H0qVLcebMGc73o6amBhsbG3h6eiq8O1gaPHr0CGPGjJEwPBs0aIARI0YgNDRUrjZ69eqFXr16yayjr6+PM2fOoG3btqySZEWEGlwVkDp16kiVCeYjKSkJtWvXligTiURYvnw5Nm/eDJFIJLFK+ODBA0RFRcHT0xOLFi3C2rVred0DkpKSYGNjg8TERBBC0KBBA4mg0ZcvX8LDwwNBQUGIjIxkEyxnZGTIlWxYjK6uLjIzM+WurwiEEJw8eRLW1tZS3RtzcnJw5swZ6OjoSASD1q1bF48fP5a7r8ePH/Ou+v0qtGvXDidPnoSxsTH8/f0lDK7y4rpUWERFLJTy4cMHqTK9BXcAoqOji5VIXBZXrlyBUChESEgIu3Cir6+PhQsXYtKkSb+8WyWl5FDFboIyrFy5UmVtKWssAYChoWGxXdBVeS2yFrlSUlLg4uKCd+/e4dy5cxLH4uPj0atXL6xevZotW7NmDWJjY2W6uxc08BiGkZqUXkNDA61bt0afPn2UTnosjZkzZ2Lfvn0YP358kfmjxo8fD5FIBGdnZ4ljwcHBMDY2RmhoKOsS37FjRwwdOhTjxo1D//79cebMGanuntWqVVMo7URmZiZvHq/Y2FgMGTIEX758QZUqVdCxY0c0aNAAhBC8efMGCQkJOHHiBGJiYhAREYGePXvK3Wdp8PnzZ94dqcaNG8tMoSMPX758YdMXXb9+nV1grMhQg6sC0r17d5w+fRpv374tckv77du3iIiI4OTjUFZhRxmlxBo1aiAtLU3u601PTy+RbXXxJPr48eMyt6o1NTVx8uRJ+Pn54d69e+x1du/eHSdPnkRqaionnwRfX2fPnv3lY200NTVhY2OD4OBgifLy4LoEAF5eXvDy8pIoI4RI/Z0xDIO8vDz2/71798a+ffvw6dMnuLi4sAsVu3fvxu7duznnm5mZYe/evZzyDx8+ICAgAEKhEI8ePQIhBPXr14ezszO8vLxgZWUl1V1DFbm8KBRANbsJyqBKI0UZY0kVqPJaZGFoaIjg4GC0a9cOrq6u2Lp1K3vs3bt3nF1BAOjWrRsb08VHQQNPIBBgwYIFxVYg5MvrdunSJVy6dIlT18TEBNbW1hJlqsgf9eTJE0ydOpUTf2xtbY2oqChYWVnBysoKkZGRvLFtrVq1wtmzZ9ldSFmIRCKcOXOGs0OVlZWFyZMn49u3b1i1ahVcXFw4RtnXr1/h5eWF1atXY/Lkyfj3339lqkWXBXyL8cp481y8eBE+Pj44fPgwsrKyQAhB+/bt4eDggIkTJyoz1LKnFAU6KCpCrJDTrVs3mcpGqamprEJOVFQU53xlFHaUUUrs3r07r/KVNFq3bk169Oghd3156datGwFA3r59W2Tdt2/fksqVK5MFCxawZefPnycMwxBra2vy7ds3qedmZ2eTgQMHcv4Ovypubm5EU1NToqxGjRoS321RLFiwgFSvXl2l4+rduzexsLBQ+FOQmzdvEoFAwFHRcnd3l5qD69atW2y9iIgIMnLkSKKpqUkYhiFVqlQhEydOJJGRkaxyE8MwZNq0aVKvo6Ll2qGUX+7cucO+K9LT06XWS0tLI507d+bcz+WF8qJSWJrMnTuXNGjQQKKsKNVHeUhJSZFLmZcPVeV1UzZ/lLa2NlmyZInUcd66dYvo6uoSHR0dcvnyZc73s23bNoXmPwKBgKMU7efnRxiGIRs2bCiyjQ0bNhCBQEACAgKKrFuaiN9F165dk/hMnTqVCAQCcv36dc6xa9eucdp5+/Yt2bBhA5sDlmEYUr9+fcIwDCdXV0WG7nBVQCwtLTFt2jTs378fLVu2xIwZM9CnTx/o6+sDAF68eIGoqCjs378fqampcHJyQp8+fdjzVaGwo4xSoo2NDVauXIlDhw7xrrYVJCQkBA8fPsSaNWuK7EdRxCv78rj51a1bFxYWFhIKen379sW4ceMQHByMDh06YNGiRbC0tGS32F+9eoWoqChs3rwZjx49wvjx4yX+Dr8qb9++hY6OjkRZWbsuAUBMTIzSbQQGBqJSpUpwcXHhHGMYRkLcIjMzE/Xr10dAQAC7Ajt48GAIBAKYmZlhypQpGDNmjMK7u2WRy4vyc6KK3QRl+Oeff1CvXj2Z76mC3L17F3fv3oWtra3KxlBRycrKUqkXwMePH7Fr1y4JdcPZs2dznuWyUJU4krL5owwMDGS+b9q3b49z586hX79+sLa25sQYOTs7Y8+ePfDw8EBycjLc3NzQvHlziTpPnjzB2rVrERgYCCMjI45bY0REBPT09LBgwQKp4xAzf/58bNq0CeHh4ZgyZUqR9UsToVDIiXUEfniGdOvWjfec/Px8iEQiREREQCgU4tSpU8jLy4OWlhbGjh0LW1tbWFlZQUNDo8K7ERaEGlwVlD179qBGjRrYunUr1q1bh3Xr1kkcJ4SwW/+FH26qUNhRRilx9uzZ2Lp1K6ZOnYrs7GxemXAA8Pf3x+zZs1GrVi3MmjWryH4U5ePHjwrVb9WqFeLj4yXKfH19wTAMDh06xBskDPz4W0yYMIH3ofSrkZmZiRMnTnDEMcradUke8vLy2Jd0mzZteGP+4uPj0bNnT7niqrS1tWFlZcW5pwghyM7ORnZ2NnJzcxUep7u7u8LnUCjS2LRpEzQ0NODl5QUPDw94eHhIHBe/axYtWoT169ertO/27dtj5cqVEgt7GzZswMaNG3nd0sPCwuDh4VEiBpey8ZnFWWxjGEZikU9e4uLi8Pfff/MmiD9w4ACuXr0qUSaOCbexseEdQ0BAALp06YKUlBQ2disiIgL+/v64du2a3HGkqhZHMjIywtq1a+XquyA9evSAv78/Pn36JFUQrEOHDjh//jz69++PyMhIiWOVKlVCREQErK2tERgYiAMHDqBhw4YSi96vXr0CIQRNmjRBREQEJ0H33bt3YW5uLjM1jhhNTU307t1bIdGX0kCZmMhGjRrh3bt3AH4Io9na2mLs2LFypdmpqFCDq4KipqaGTZs2YcaMGfD19cWVK1dY9RaxyqCdnR1n1QVQjcKOMkqJOjo6CAkJwdChQ+Hk5ISVK1fCwsJCYmcoJiYGL168gJaWFkJCQhRaRZMXRaW0NTU1JeJ1gB8qXkFBQZg+fTq8vb1x+fJlib9Dz5494eTkxO4O5uXlQV391/vZpaWl4cqVK1ixYgVSU1Mxffp0ieOqCIRWluTkZMTExKBXr16cldHw8HA4OTmxkr01a9bEnj17OApiiYmJcHBw4LRNpASZGxgYSASrR0VFQSgU4tixY5gzZw5cXFxgY2ODKVOmYPDgwXK9nBWF5vKiyELZ3QRl4PvNZGdnKyRYAKhGzGbmzJkKn8MwDB48eABA+g66WIRC2jE+pBlveXl5ePXqFWsYubm5ceokJiZKFd0qbFiIx+Dp6YmnT5/C0tISM2bMAAD873//Q2xsLNavXy91x6ow5UUcaejQodi7dy/27NmDpUuXSq3XoUMHdqersHCXkZERbt++jY0bN8LPz48VChPToEED2NvbY/Hixbxzpffv3xe5uFiQJk2aFMv4Lkn4lCrl5e3btxAIBFi4cCGWLVtWInO88savN/P7ySjOCo8qFHaUVUrs27cvLl++jLlz5yI+Ph6BgYGcc8zNzbFjxw6YmJjI3Y8i6Orqyu2qAvxwQZQmjiHOVyaNhw8fQigU4uDBgxVa1pSPohIfF4QQgnnz5nEm92XtugQA3t7e2LBhAycvTmJiIsaOHYvs7GwYGBigSpUq+O+//zBp0iQ0b95cYhxZWVkSKRDEzJs3j9cVpHr16hKy22K57czMTBw8eBBCoRBhYWE4fvw4atasqVA6haKgubwoilDc3YTygLLGEvAjh5Qs46go+JL4btq0CVu3buU9JgtZxlvNmjXRv39/LFiwgJMotrgCOdbW1jAyMsLZs2fZ5/3IkSPRsmVLhIeHy21wlRdxpIEDB+Lbt29yvbs6dOiApKQk3lyk1atXx+rVq7F69Wo8f/5cYrG1cePGMtv9/PmzQrs51apVY1VzfwYmT56M0NBQeHl5YceOHRg8eDCmTJkCGxubn3ZR+ue8KopMVKGwowqlxHbt2iE2NhZJSUm4dOkSZ2dInAOspOjVqxdiY2Px5csX3klyQb58+YKzZ88qlCn+Z5Q15aOoxMcCgQDa2tpo27Yt7OzspK7OlqXrEvBDHaldu3YwMDCQKN++fTuys7Px+++/Y+fOnQCA0NBQjB49Grt27ZJwFdXR0WHdJApSs2ZN3l279+/f8750tbW1MWvWLMyaNQt3796Ft7c3goKCsG/fPgA/dtxWr14NOzu7Il/sBakIubwoFFWjrLEkRl1dHTY2NnBwcGDjLeWFL1ZYHJ+paLqQ4uazKvxsk5dnz57ByclJwkBRV1eHtbW1Qq7y5SGvm5jCLn6y4Ht+r127Fl+/fsWqVaugoaGBxo0bc57FOTk5cHd3R40aNTg7acX5G1akHIO5ubn4999/8e3bNxgaGnLu8YCAAOzevRtBQUEQCoU4evQoQkNDUbNmTYwfPx6TJ08uo5GXHNTgqoB8/foVpqamqFOnDmJiYmTmj+rTpw9SU1Nx+/ZtVK5cGQAwatQoLFiwAB4eHkVK1a5duxbJycmYPXu2RLmzszMOHz6MESNG4OTJk1J3ftLS0jBixAhkZWVx3MjENGvWTKZxlZaWhsDAQMyfP1+iPDs7G76+vkhMTETbtm0xZcoUqKmp4fXr11i4cCFiYmLYF+SaNWs4ucgmT56MsLAwzJ49G35+fjK/hzlz5iAjI0Ouh8BPLWvKg7yJj4uiLF2XgB+rv3wGdWRkJDQ1NeHp6cmWjRw5EmZmZpz4q5YtW3LKZBEXF4eWLVvKrNOuXTvs3LkTmzdvRmhoKHx8fBAVFQV3d3esWrUKffv2xZkzZ2S2QXN5UX51lDGWgB8CHuKFjxMnTqBOnTqwtbWFo6Mjb6xUeUScb0tRSffs7GzO+xP4Iaz1/ft3udspD+JIAIoV/1swEfb58+exYsUKbNy4Uaabt6amJvT09LB48WJ06dKFs9h4//59udN43L9/X+ExlzT37t3DvXv3MH78ePb3lJeXhz///BO7du2SMK7FKVAK/laqV6+OGTNmYMaMGbh//z68vb1x8OBB7NmzB//73//AMAwePXqE58+fK7SwWG4pRUVEiorYuXMnEQgEJDY2tsi6cXFxhGEYsmvXLrYsOzubGBsbE4FAQGxtbcnjx4855z1+/JjY2dkRgUBAjI2NSXZ2NqfO9OnTCcMwRE9Pj7i6upKoqCjy+PFj8vjxYxIVFUWWL19OateuTRiGIVOnTlX4OiMjI8mYMWNIpUqVOJK1WVlZpH379hKy1gMHDiRfv34lrVu3JgzDEF1dXaKmpkYYhiGtWrUiWVlZnD769OlDBAIBsbS0JFFRUeT79+/ssZycHHL+/Hm2Tr9+/aSO9VeRNeXjxYsXCp/j6+ur+oEoiZaWFlm2bJlE2cePHwnDMMTc3JxTf+7cuaRKlSoSZevXrycCgYAEBwcX2d+hQ4cIwzBk3bp1bJlAICAeHh4S9a5evUq2b98uUZaSkkJWrFhBGjduLFXO+f3798TLy4u0bNmSvScbNGhAFi9eXKS0PIVSHuCTMZclYS7t2L1798i8efNI7dq1iUAgIPXq1SNLliwh//33n8JjysnJIYcPHyYDBw5kZcm7d+9O9u/fTz5//qxQW4rIsauC4vanCjl5QgiZMWMG0dTUJE+fPi2yblJSElFXVyfTp09XZKhywZeiQzyPkHVMzJQpU0itWrV450WFyc7OJnp6emTy5MmcMVT09B0jRowgLVu2lCibPHkyEQgEpEaNGqR///5kzJgxpE2bNuxcsai/fU5ODgkODiYDBgxg529qamqkX79+JCgoqASvpuShBlcFpF+/fgrlsTIxMeHkvXjy5Alp1qwZ+yPW19cnPXr0ID169CD6+vrsD7xp06bkyZMnvO3m5eWRRYsWETU1NZm5MFxcXEheXp5cY3369KnERJJhGFKvXj2yaNEiiXpbtmwhDMMQW1tbcuLECTJ9+nTWgNTX1yc3btwghBDy5csX4uTkRAQCAfHy8uL0l56eTszNzdnvQVNTkzRo0IA0aNCANfTEE+6PHz9KnJufn09OnDhBhg0bRjQ0NAjDMKRy5cpk/Pjx5NSpUyQvL++XmNhqa2vLbUC9evWKDBo0qNy9OAghpFq1amTmzJkSZeKcdS4uLpz6y5cv5xhcGRkZRE9Pj1SvXp0cPHhQal+BgYGkWrVqRE9Pj2RkZLDlik4wRSIROXPmjESZKnJ5USjlAYZhyJIlS8i7d+/Yz6JFi4hAICDv37+XKC94TBqqNJYI+fE8W7NmDWnevDlhGIZUrVqV2NnZyb0IVZEMrv79+5MNGzZIfPr160cEAgHZuHEj5xhffqnyktft6tWrnI84dxTfMfFHTLNmzci4cePk7m/8+PGkadOmEmXu7u7F+pQn9PX1JRbT7969SxiGIT179iTv37+XqBsQEEAEAgGxt7eXu/0XL16QVatWkSZNmpRLg1NRqMFVAalduzZnYiiLmTNnktq1a3PKP336RNzc3EijRo04qzkNGzYkrq6uEpNBaTx58oQsX76cWFpakpYtW5KWLVsSS0tL4urqyrt7Vpjv37+ToKAg0rdvX3ZFg2EY0rZtW3LixAleY61Tp06kbdu2nDKBQEAOHTokUZ6bm0saN24sNXlyXl4e8fHxIT179mQnqQzDEE1NTdKrVy/i6+vLO4b69euzxqWZmRnZv38/JyHkrzCxrVOnDhEIBGTw4MHkzZs3Uuv5+fmRmjVrEoZhyKhRo0pxhPLRoUMHYmxsLFG2dOlSIhAIyJEjRzj17e3tSbNmzTjlUVFRpEqVKkQgEJAmTZoQe3t7snz5crJ8+XJiZ2dHDA0NiUAgIJUrVybnz5+XOFdRg4sP8UKHhYUFEQqF5NOnT7x1fvb7klLxkbYLUNTugDwoaywV5syZM6RRo0ZEIBCQ48ePy3VORTK4FP1I62fhwoXsQqq7uzuJiYlhPWOio6PJihUrSL169QjDMGTBggXKXrLcKPLdVK5cmeMNIYtly5aRypUrF3do5ZZKlSoRV1dX9v9i76s7d+7w1h86dCipX79+sfo6d+4cmTBhQrHOLS/QGK4KSEZGBmrVqiV3fV1dXY6kKaCcwk5BiqtedefOHQiFQgQFBSEjIwOEEHTu3Bl2dnaYPXs2unXrhiFDhvCe+/TpU048VK9evXDr1i1O8K26ujr69evHkbYXo6amBgcHBzg4OCA/P5/N71KrVi2ZKka/oqwpHw8ePICzszNCQ0PRpk0b7Ny5ExMmTGCPv3nzBtOmTcPp06ehq6uLoKCgIhNelwWjRo2Cm5sbZsyYgd9//x2JiYn43//+h2rVqsHa2ppT/9KlSzAyMuKU9+nTB5cuXcLcuXNx8eJF3hi3nj17Yvv27ejQoUNJXIrSubwolPKAubl5iSXxbtCgAVxdXeHq6oqzZ8/CyckJgYGBGDlyJJuiRB5u374NHx8f/P3330hPT0e9evVKJO6oLFFG/rswZS2OpAoEAoFCz9Xc3FzemMGrV6/C1dVVIpm0p6cnunTporKxliTa2tpsqhQAbMwWXzoiADA2Ni4y3lga/fr1Q79+/Yp1bnmBGlwVkBo1avAmfZRGeno6q4YkRlmFHWXYs2cPhEIh7ty5A0II9PX14ezsDFtbWzagsrBIR2H45LfFBg+f7GzdunXlklRVU1OTWyr+V5Q15UNPTw9HjhzBwYMHMWfOHPZ7+d///oeIiAgsWLAAGRkZGDZsGPbu3auQFH9psmDBAgQHB2P//v3w9vYG8OPlv2nTJk5ahISEBCQmJrI5aQpjamqKuLg4JCYmsjnySAHxD2kvJFVQFrm8KJSSQJr8uaoorrGUnp6OgwcPwsfHB//88w8ryOHo6AgbGxvehbrCSr8A2Mkq3zGAK01fViiT4LYwZS2OpAoaNGigkIjF/fv3OffVvXv3YGlpKSE6cuHCBVhaWuL69eto3bq1ysZbUnTr1g0RERH4/v07KlWqhLZt24IQgoSEBJibm3PqJyQkFKlq/VNTdptrlOLSvXt3hWK4WrduLeFOd+7cOakxTYXZvHkzEQgEJCoqSqL8y5cvxMjIiPTo0YPk5ORIPf/79++kZ8+epEWLFqxohdjlacqUKSQqKoqIRCLOeUW5POnr6xMnJyeJsq1btxJDQ0Pe+tOmTeN1q1SWT58+kb/++ot07tyZdaOoVasW+f3338mVK1d+Odet169fExsbGzaeTfx9yIpnKk98/vyZeHh4EBsbGzJ58mSprkF79+4lw4cPl8tlVha3b9+W6EMVLoViMjIyyO7du0mHDh0k7s0ZM2b8cvcl5eckNzeX3Lp1i9y6dUvme6ggaWlpZMeOHcTU1JSN2x0+fLhU93UxIpGInD59mowZM4ZoaWkRhmFImzZtyObNmznxKnwUxy2PYRi5vwt5KO6zxNLSkvj7+6t0LOUNRb4bR0dHucU/nj59SjQ1NTnCYRMnTiQMwxA3Nzfy7t078v79e+Lq6koYhiFTpkwpziWUOtHR0WyIwOfPn0l+fj7p3r07MTY2Jnfv3mXrff/+nfz5559EIBCQ+fPnl+GIyxZqcFVAVq9eTQQCAfn777+LrBscHEwYhiFr165ly1ShsKOMUmLBOLElS5aQ+/fvc84pakJoZWVFunTpUmTfYiwsLEjnzp0lyopSBVJTUyO6urrE0tJSLlEIsRKWnp6eRIyBubk5efbsmdxjregcPnyYnZDIa9j/qtjb20u85BmGIc2bNycDBw5kP82bN2dVOPk+NjY2RfZz584dMnv2bKKrq8v+/urXr088PDx+qXuTUrFISkoiQqGQPHr0iHPs5MmTbPyoeDFBmjqossYSIYSNz6pZsyZxdnYm169fV+raygJVqxT+TCjy3dy8eZMwDEM6duxIPnz4ILVeamoqG1t+8+ZNiWP6+vrEzMyMc06vXr1I48aNFRt8GbJmzRrCMAzR1tYmY8aMITNmzCDq6upETU2NGBgYkHbt2pHq1asTgUBAWrRowREf+5WgBlcF5OPHj0RXV5dUrVpVpiHg5+fHKqEVvMlVobCjjFJicnIycXV1ZcU6BAIBMTU1JVu2bCFv374lhBRtcK1bt440adJEQsZdGikpKURNTY0sXLhQotzAwIAYGhpK/TRu3JhUq1aNHePo0aPlutafWdZUFh8/fiQTJ04kAoGAaGtrk2XLlhE9PT0iEAjIyJEj5Z7Y/ErwGVyqCk7n4/v37+Tvv/8m/fv3l1hYsLKyKonLo1CUYtmyZUQgEJCUlBSJ8idPnpDKlSsThmGIoaEhadWqFREIBERdXZ1X1U4VxpJYSMnCwkLq4kdxFkNKE2pwSUfR72bBggWEYRhSu3Zt8ueff5ILFy5IiH+4ubmROnXqEIZhOHMPQgjR1NQkixcv5pQvWrSIVKpUSalrKW2OHz9OWrZsKfUdVaVKFTJjxgySlpZW1kMtUxhClEy9TikToqKiMHToUGRnZ6NRo0awsLBgA31fvXqFmJgYvHjxAlpaWggPD5cQkqhSpQrmz58vkchVFsuXL8e2bdskktjVqVMHo0ePxp49e+RqY9asWThy5Ajev3/PlolEIkRGRkIoFCI8PBy5ublQU1ND3759cfbsWUydOhX79u2Tq31ZPH/+HLdv30aHDh2gr6+v8Pl3797FkiVLcP78eQiFQtjb28t97suXL+Hj4wM/Pz+kpKSAYRjk5+crPIbyzqlTpzBt2jS8efMG/fr1g4+PDxo1aoT3799j2rRpOHnyJGrXro3du3dj9OjRZT3ccoODgwMCAgLYe+LZs2fFasfAwID9t5qaGtzd3fHnn3+yZdeuXcO1a9cwd+5ctuzZs2fsvfny5cuf8r6kVGzMzc3x5csX3Lp1S6J8zpw52L17N37//Xfs3LkTABAaGorRo0fDwcGBTVArRiAQQENDAz169EDlypXl6pthGEREREi0oSiFn/fp6enIzs5G/fr1WTGQS5cu4dKlS5xzTUxMeMV6lGHVqlXw8PBQ+LcuEAjg7u6ucMLk8oqNjQ2nLDExEUlJSRgwYADvOYXvB0IIXF1dsWnTJohEIk59QgjU1NSwZMkSrFmzhiP+Iu07Le7fqDxw7949JCQk4P379xCJRNDW1kaLFi3QrVs3Thz0L0nZ2nsUZbhz5w6bQ4rv07t3bwk/WjFVq1bl5LWSxaJFi0jVqlUlyjQ0NIibm5vcbbi6uhJNTU2pxz98+EC8vLxIq1at2PFXq1aNODo6kvj4eLn7KSm+f/9ODAwMiIWFRbHb+BlkTfkQ5zmrVq0a2bNnD28dPz8/oqOjQwQCARk3btwvv9IlpvAOlypQRS4vCqU80KhRI447OyGEGBkZkUqVKnFSHpibm5PmzZtz6qti5zglJaVYHzFpaWlEW1ubkxNT/Nss3L+Ojo7Kn5NhYWEK5UES87PtcKnSkyAxMZG4urpy0uK4ubmRxMREmWNQRTJpSsXh15FS+wlp164dYmNjkZSUhEuXLknIuvfs2RPNmjXjPU8VCjuqUEosiJ6eHhYuXIiFCxfi2rVr8Pb2RkhICHx9feHn5wcjIyM8evRIZh8ikQjp6ekAfkjhF2dFUhqampqwsbFBcHBwsdv4GWRN+fDx8YGZmRn8/PzQpEkT3jp2dnbo168fHB0dERISgri4OLx+/bqUR0rhg2EYWFlZlfUwKBQOqampHK+EjIwMJCUlwczMjPNOMTU1RUJCAqedp0+fKj2WgrvIxSEwMBCfP3/Gxo0beY8XlF7/+PEjXFxccODAAYldaWUZNmwYhg0bVqxzw8LCeFNcSINhGM5OY3lBFfeDmGbNmmHNmjXFOvfAgQO4evWqRFliYiIA/l24wrts5QG+XdvLly/j4sWLnLolsWtbkaAG109As2bNpBpXfJiZmeHAgQNISUmBoaGhzLopKSm4cOECbG1tJcqNjY0RFxcnd59xcXGs5HtRdO3aFV27dsX27dsRHBwMoVCIy5cv89b99u0bdu/ejeDgYNy5c4fd2ldTU4OpqSkmTpwIZ2dnaGlpyT1WadSqVUsuaflfjc2bN2P+/PlF5stp2LAhzpw5g7/++gtLliwppdFRKJSKirq6OjIyMiTKbt++DQDo1KkTp37hVCFilDWWVMGpU6fQsmVLdOzYkfd4Yen1oKAgREREcAyut2/f4vLly1BXV4eZmRlq1qzJ215sbCxiY2NV5gZ4584d3LlzR+765dngKg/3A/DDuBIbWIWJjIzklJVUTrrikp6ejqZNm6Jjx46Iiopiy8+dOwcPDw+QQhFL2traSEpKgq6ubmkPtVxADa5fkN9//x2+vr4YPXo0IiMjoaenx1svLS0NY8aMQV5eHmbOnClxzMbGBitXrsShQ4eKTGIbEhKChw8fKrwKVKVKFTYh8ePHjznHX7x4AWtra/z3338ghEBDQwN6enoghCA9PR0JCQm4efMmfHx8EBkZiQYNGijUf2Hevn37yyY3lsWCBQsUqu/s7PzTrnKFhIQoVF+VK60Uys+GsbGxxEQOAM6ePQuGYdCjRw9O/devX6N+/fqlNTyFuHfvHgYPHix3fVNTU4SHh0uUbdmyBcuXL2eT7mppacHV1RXLli3jTMZjYmLg4eGhMoPL3t5epfm4fnV+hmd/edi1rUhQg6uCEhkZiYyMDIwdO5Z1nTt48CAOHjzIqduzZ0+4urqy/+/QoQPmz5+Pbdu2oVWrVnB2doalpaWE6EZUVBT27duHDx8+wMXFBR06dJBoc/bs2di6dSumTp2K7OxsqUIS/v7+mD17NmrVqoVZs2ax5QcPHkRGRgamT58uNRFrTk4O9u/fD11dXUyYMEHiGCEEEyZMwL///ou+ffvC1dUVPXr0gKamJnvuxYsX4enpiQsXLmDChAmIjY0t4luVTmZmJk6cOCF1dZKiGEXtrFZUxo8fr9AqJCGk3K1aUijlhVGjRsHNzQ0zZszA77//jsTERPzvf/9DtWrVeBdtLl26BCMjozIYadGkpaXxJn03NzfH8uXLOeV16tRhXeSBH4bmokWLoK2tDXt7e2hoaODIkSP4888/cfnyZRw9ehSVKlUqsfEbGhqid+/eJdb+r0Z52WVTBlXt2v4qUIOrAvL48WMMGTIEU6dOldhdSkxM5N2GPnfuHMaOHYvmzZuzZZs3b4aWlhY2bdqEtWvXYu3atRLnkP9T2Fm2bBnvzpSOjg5CQkIwdOhQODk5YeXKlTKVEkNCQtjdoevXr8PW1hZLliyRamwBP+KmXr16hblz58LY2FjiR3327FlcvnwZEyZMwIEDBziTVk1NTfTp0weWlpaYNGkSgoODcf78eYVjqNLS0nDlyhWsWLECqampmD59ukLnU34tVqxYUS4MqMKxAbLiAoDyGRtAoSxYsADBwcHYv38/vL29Afx4N23atImjepaQkIDExETMmDGjLIZaJFpaWhJKv2IsLS0lVITFZGVlsQuIALBt2zZUqVIF169fZ9/l69atw4wZM/D3339jyJAhOHHihErc51VBXl4e1NXpFPNnRhW7tr8UZSjYQSkmy5cvJxoaGuTly5cS5WJ1m4IKSbdv3yYaGhpk+fLlvG0VV2FHTHGUEmfMmEGqV69OMjMzi2z/06dPpEaNGmT69OkS5U5OTqRy5cokNTW1yDbev39PtLS0OJnei0p8XPDDMMwvnSGdUnEo6VxeFEpp8vnzZ+Lh4UFsbGzI5MmTyfHjx3nr7d27lwwfPpw8fvy4lEcoH61atSL9+vWTu37fvn3Jb7/9xv5fT0+P2Nra8tZdtmwZYRiG9O3bl3z79o0Qolq1O0VUCh88eEBcXFxI3bp1VdI3pfyiqalJXF1dOeUXLlzgVbF2dXWtcDnGVAldfqiAREdHo2vXrhzlQDEFt6oNDAzQt29fREdH89ZVRmEHKJ5SYlxcHPr27YsaNWoU2X716tXRt29fjjvgzZs3YWZmhlq1ahXZRu3atWFubo6bN29KlOvr68vcjRAIBNDW1kbbtm1hZ2eHPn36FNkXhaII+fn5OHnyJIYPH66yNn+G2AAKRUy1atUkcspJY/r06eXaA8HMzAy+vr5yiVUlJycjNjYWjo6ObNnnz59ZD5LCeHp6olKlSli1ahUGDRpU6rsIX758waFDhyAUCnH9+nUQQiR25yg/J8ru2v5qUIOrAvLo0SNMnjyZU04I4ajCAD+MqkOHDpXomBRRSnz+/DmGDBkid9vNmzfHmTNnJMpevHihkD95q1atEBgYKFGmiMQthaJKHj16BKFQiMDAQLx//16lSS5/htgACuVnY+bMmdi3bx/Gjx+P06dPS1UXTE9Px/jx4yESieDs7MyWN2zYEK9evZLa/sqVKwH8SJxrY2PDq+JYXKKjo3mNxIsXL8LHxweHDx9GVlYWCCFo3749HBwcMHHiRJX1TymfNGrUCPfu3ZO7/j///CN1o+BXgBpcFZDPnz/z7g7Z2dnBzMyMU16zZk18/vy5NIYmF3xZ2WXBtwv16dMnhRQDa9SoofB3kJeXxz5M2rRpIzPejEIpiqysLDbNwZUrV9hV4JEjR5b10CgUSgnTrl07uLi4YMuWLaxYlYWFBaue++rVK0RHR2Pfvn149+4d5s+fj/bt27Pnt2nTBhcuXJDZx8qVK0EIgYeHh9RUKsWh4OLmu3fv4O/vDx8fHzx58gSEENSrVw9fv36Fra0t/Pz8VNYvpXyj7K7trwY1uCogNWrUkFAvEtOkSRPexLPp6elS85MoQ3GVEuvWrcsr8y6Nx48fo27duhJleXl5CiU2FggEyMvLkyhLTk5GTEwMevXqBWNjY4lj4eHhcHJyQmpqKoAfRuuePXswduxYufukUADgypUrEAqFCAkJwdevXwH8cGdduHAhJk2a9MvmJKFQfjU2bdoEDQ0NeHl5wcPDAx4eHhLHCSEQCARYtGgR1q9fL3Fs0KBBOHnyJE6fPo2BAwdK7cPd3R2EEKxevVplAj4ikQgREREQCoU4deoU8vLyoKWlhbFjx8LW1hZWVlbQ0ND4pd3FfkWU3bX91aAGVwXEyMiIk51cFleuXFEoMbI8KKOU2L17d5w8eRKpqalSc4CJ+fDhA86ePcsb4/L161e8f/9ervHyJSz29vbGhg0bkJycLFGemJiIsWPHIjs7GwYGBqhSpQr+++8/TJo0Cc2bN5dYdaRQ+Pjw4QMCAgIgFArx6NEjEEJQv359ODs7w8vLC1ZWVpgzZ05ZD5NCoZQiDMNg3bp1cHJygq+vL65evYq3b9+CEIK6deuiR48esLOz4ywAAsDo0aNBCEHlypWL7GfVqlVo0qQJnj17ppJxN2rUCO/evQPwY/HU1tYWY8eOlSsOm/Lzouyu7S9H2Wh1UJTBzc2NCAQCcuHChSLrnj9/njAMw6sYowzKKCWKx2Rtbc0qKvGRnZ1NBg4cSAQCAYmKipI4JlZWU/RTEDMzM9K+fXtOv7NnzyYMw5DZs2ezZUePHiUMwxBHR0eFvyvKr0NERAQZOXIk0dTUJAzDkCpVqpCJEyeSyMhIkp+fTwj5ce9OmzatjEdKoVAo8sEwDFFTUyNLliwhHz9+lFqHPtd+PUQiEVm6dClRV1eXqvCspqZGFi9ezL4Df1UYQnhUFijlmrdv38LIyAjVq1fH0aNH0aNHD956Fy9exKhRo/D161c8efIE9evXV9kYevToATU1NcTHx0uUr1q1Ch4eHhwRgIEDByIzM5P1K58wYQKCg4Px22+/YdGiRbyJlzdv3oxHjx5h/PjxHDdFCwuLYrlLFFRr1NfXh4WFBUdMo3nz5njx4gU+fPiA6tWrs+W9e/fGmzdvFHKHpPxaCAQCCAQCmJmZYcqUKRgzZozEPSSuM3XqVOzbt6+MRkmhUCjyY2tri9DQUHz79g2ampoYPHgwpkyZAhsbGzbXFn2u/dokJiYqvGv7q0FdCisg9erVg7e3NyZPngwzMzOYm5vzbuNevHgRABAYGKhSYwtQXinR19cXDMPg0KFDmDZtGm8fhBBMmDABQqGQcywmJqb4g/8/UlNToa+vL1GWkZGBpKQkmJmZcSbKpqamSEhIULpfys8NIQTZ2dnIzs5Gbm5uWQ+HQqH8BMTFxRXrPHNzc6X7DggIwO7duxEUFAShUIijR48iNDQUNWvWxPjx43nnApRfCyMjI6xdu7ash1GuoQZXBWX8+PHQ1tbG77//jtjYWMTGxrI7PmKDx8DAALt27cKgQYNU3r+ySolaWloICgrC9OnT4e3tjcuXL3NyeDk5OcHCwgJAyWStV1dXR0ZGhkTZ7du3AYBXUrckhEcoPxdRUVEQCoU4duwY5syZAxcXF9jY2GDKlCkYPHgwVbqkUCjForheHapKOVG9enXMmDEDM2bMwP379+Ht7Y2DBw9iz549+N///geGYfDo0SM8f/4cjRs3VkmfFMrPBDW4KjADBw7EkydPEB0djStXrnC2cfv06QM1NbUS6VtVSokWFhasUcXHw4cPIRQKcfDgQdYgExMXF4fMzExYW1tLncjm5OTgzJkz0NHR4RiCxsbGiIqKkig7e/YsGIbhddN8/fq1yncKKT8X4oSPmZmZOHjwIIRCIcLCwnD8+HHUrFkTo0ePLushUiiUCsj06dM5BldCQgJu3bpV6gmf27Rpg23btmHTpk04duwYfHx8cP78ecTHx6Np06awtLSEo6MjJkyYUKrjolDKMzSGi1IsunXrhtzcXNy8eVOu+h06dIC6ujquX79eZF1pWeuzs7PZOv/++y9MTExgZ2cHb29vme1Nnz4dfn5+uHfvHlq0aMGWe3p6ws3NDdOmTcPvv/+OxMREODo6ghCC169fo2rVqhLtGBsbo2nTprwqjBSKNO7evQtvb28EBQXh48ePAH7s4s6cORN2dnZ0NZhCoRQLaTHTZcHLly/h4+MDPz8/pKSkgGGYcjEuCqW8IH8iIwqlAP3798edO3ckRCikERUVhTt37mDAgAEy6128eBGOjo6oX78+ZsyYgWvXrsHU1BQ7duzA69evJeru378f6urqcvkMr169Gurq6ti7d69E+YIFC9C2bVvs378f7du3x5gxY/Dp0yesWLGCY2wlJCQgMTER/fv3L7I/CqUg7dq1w86dO/HmzRsEBQWhX79+ePfuHdzd3dG0adMifxcUCoVS3mnUqBFWrFiB5ORknD17FuPGjSvrIVEo5Qq6w1UBCQkJKdZ5qkzaqyqlRGlZ69++fSsza72pqSnq16+P06dPyzVeGxsbvHr1Cnfv3pUo//LlC7Zu3YqrV69CV1cXY8aMwdChQznn79u3D6dPn8bGjRvRvHlzufqk/HqoqanB3d0df/75J1t27do1XLt2DXPnzmXLnj17xq4Gv3z5kq4EUygUhSlPO1wUCkU2NIarAjJ+/HiFgmcJIWAYRqUGlzJKiarIWp+cnIx+/frJPd5WrVpxJOyBH0IYBSfH0pg+fXqp+8lTKh58Kp2RkZHw8PCQMLgMDAywatUquLu749y5c6U9TAqFQqFQKKUINbgqKOrq6rCxsYGpqWmZjaG4SomqyFqfk5Mj0yArjKamJvLy8uSuT6GUBgzDwMrKqqyHQaFQKBQKpQShBlcFZPjw4YiIiMCJEyfw7NkzODo6YtKkSahZs2apj6U4Solv376FQCDAwoULsWzZMujo6Cjcb+3atZGcnCx3/adPn0JPT0/hfigUCoVCoVAoFGWgBlcFJDQ0FKmpqQgICICfnx/mzp2LxYsXY/jw4XB0dCx1YQc1NTX069dPbhe/yZMnIzQ0FF5eXtixYwdv1vqi6Ny5M86dO4cvX74UmR/ry5cvOHv2rEz5eQqFQqFQyit8sdv3798HABw+fJjjyixGlaEEFAql+FCDq4Kip6cHFxcXuLi44Pr16xAKhQgJCUFISAgaNmwIe3t72Nvbo2nTpmU9VA6qyFo/efJkhIWFYfbs2VKFNcTMmTMHGRkZcrVLoVAoFEp5gy92W2xkjR8/nlO/JGK3KRRK8aEqhT8R2dnZOHz4MHx9fdl4qsjISIXEJeRFlUqJBbPWp6WlsS+VXr16ITAwUGqeor59+yImJga9e/eGm5sbevXqxcZ15ebmIi4uDp6enoiJiUGfPn2oOAGlxBEIBDAyMoKRkRFblpiYiKSkJKny7wzDICIiorSGSKFQKiDu7u4KiWWJWblyZQmMhkKhKAo1uH4yxDLr4txVYWFhvDLnyiIQCIqllChLvjY3N1cia71IJIJAIJCatf7jx48YPnw44uPjwTAM1NXV2TittLQ05ObmghACMzMzHD9+vFixYhSKIggEiqc2pAlCKRQKhUL5uaEG109Afn4+wsPD4ePjg9OnTyM/Px+mpqZwdHSEvb09J4mvKhAIBNDQ0FBYKVHe1TZ5s9bn5+cjICAAQqEQN27cQG5uLgBAQ0MDXbp0gZOTE6ZMmcIR7qBQSoJnz54V6zwDAwMVj4RCoVAoFEp5gRpcFZiHDx/Cx8cHBw4cwPv371GrVi1MmjQJjo6OMDExKdG+R44ciYiICOTl5aFdu3YlqpR4/vx5+Pj4ICgoSGa9/Px8pKWlAQBq1apFjSwKhUKh/JQ8ePAAN27cQGpqKoAfyr2dO3dGq1atynhkFAqFD2pwVUD27dsHHx8f3Lhxg83j4+joiGHDhkFDQ6PUxlFQKfH+/fuoVKlSmSklUigUCoXys3P+/Hm4uLjgwYMHvMdNTEywZcsWWFpalvLIKBSKLKjBVQERu/MNHDgQdnZ2aNiwoVzndenSpcTGVFAp8dOnT+VeKZFCoVAolIrEkSNHMHHiROTl5cHQ0BBmZmZo0KABCCF48+YN4uLi8OzZM6irqyMkJATDhw8v6yFTKJT/gxpcFRBxYL6iikWlEZhfWkqJRbkLMgwDbW1ttGvXDra2trC3t1dp/xQKhUKhlBYfP36EkZERsrOz8ddff2Hy5Mm8MvEBAQGYNWsWqlatiidPnkBbW7uMRkyhUApC83BVQOzs7Mp6CFLR0tKClZUV3rx5g8ePH+P169fIyspSeT/6+voyDU6RSIT09HTExMQgNjYWEREROHz4sMrHQaFQKBRKSXPw4EF8/PgRQqEQU6ZM4a3DMAzs7OyQn5+PqVOnIigoCDNnzizlkVIoFD7oDhdFJZSFUqI83L17F0uWLMH58+chFArpTheFQqFQKhzDhw/HzZs38fz58yK9W0QiEQwMDNCxY0eEhYWVzgApFIpMqMFFUYqyVEqUl5ycHBgbG6NJkyaIjo4u6+FQKBQKhaIQTZs2Rbdu3YpU6xUzadIkXL16FUlJSSU8MgqFIg/UpbAC4ujoqPA5DMNAKBSqbAzlRSlRHjQ1NWFjY4Pg4OCyHgqFQqFQKAqTlpaGRo0ayV2/UaNGrGQ8hUIpe6jBVQHx8/PjLWcYBtI2LFVtcDk7O0NDQwNDhgyRUEq8ffu2zPNKUilRFrVq1cKXL1/KpG8KhUKhUJThy5cvqFatmtz1q1Spgq9fv5bgiCgUiiJQg6sCcuXKFU6Zt7c3fHx8eI+VFLm5uTh58iROnjwp9zmloZTIx9u3b6Gjo1MmfVMoFAqFogzFif6gESMUSvmBGlwVkK5du3LKIiMjpR4rCcqzUmJhMjMzceLECXTs2LGsh0KhUCgUSrG4ePEiNm7cKHddCoVSfqAGF6VY+Pr6lvUQiiQtLQ1XrlzBihUrkJqaiunTp5f1kCgUCoVCKRbnz5/H+fPn5a6vaK5OCoVSclCDi1IhKSrxcUEIIZg3bx6GDx9ecgOiUCgUCqWEqAiLnBQKRTrU4KIUi7JWSiwq8bFAIIC2tjbatm0LOzs79OnTRyX9UigUCoVS2lQkN34KhcKFGlyUYlHWSokpKSkqaYdCoVAolJ+NtLQ0BAYGYv78+WU9FAqFAmpwUYpJeVFKlEZeXh7u3bsHAGjTpk25yw1GoVAoFIqqOXPmDIRCIU6cOIHc3FxqcFEo5QRqcFVAbGxsOGWJiYlSjwE/dpciIiJUNoayVkpMTk5GTEwMevXqBWNjY4lj4eHhcHJyYpM+1qxZE3v27MHYsWNLfFwUCoVCoZQmKSkp8PX1hZ+fH16+fAlCCOrWrYvJkyeX9dAoFMr/QQ2uCojYsFHk2M+mVuTt7Y0NGzYgOTlZojwxMRFjx45FdnY2DAwMUKVKFfz333+YNGkSmjdvjvbt25fRiCkUCoVCUQ05OTk4evQohEIhYmJiIBKJAPzw6Fi7di1sbGwUEpeiUCglCzW4KiBPnz4t6yGUORcvXkS7du1gYGAgUb59+3ZkZ2fj999/x86dOwEAoaGhGD16NHbt2qWyGDIKhUKhUEqbO3fuQCgUIigoCBkZGSCEoHPnzrCzs8Ps2bPRrVs3DBkypKyHSaFQCkENrgpIYSPjV+Tp06ewsLDglEdGRkJTUxOenp5s2ciRI2FmZob4+PhSHCGFQqFQKKphz549EAqFuHPnDggh0NfXh7OzM2xtbdGiRQsAwOzZs8t4lBQKRRrU4KJUSFJTU6Gvry9RlpGRgaSkJJiZmaF69eoSx0xNTZGQkFCaQ6RQKBQKRSXMnj0bAoEAkyZNgr29PSwtLX+6UAEK5WeGGlyUCom6ujoyMjIkym7fvg0A6NSpE6d+tWrVSmNYFAqFQqGUCCKRCBcuXED9+vVRt25dtG7duqyHRKFQ5IQaXJRiUdZKicbGxoiKipIoO3v2LBiGQY8ePTj1X79+jfr166ukbwqFQqFQSpOkpCQIhUL4+/tj06ZN8PLygomJCWxtbTFx4kTUrVu3rIdIoVBkwBBpWWopFBkIBAKFz2EYBvn5+Srp39PTE25ubpg2bRp+//13JCYmwtHREYQQvH79GlWrVpWob2xsjKZNm8pUeKRQKBQKpTwjEokQGRkJoVCI8PBw5ObmQk1NDX379sXZs2cxdepU7Nu3r6yHSaFQCkENLkqxePbsWbHOU5Xgx7dv39CtWzfcu3eP9WMnhGDTpk1YuHChRN2EhAR06dKF9xiFQqFQKBWR1NRU+Pv7w8fHB//++y8AoGrVqhg7diwcHBzQq1evMh4hhUIRQw0uSoXly5cv2Lp1K65evQpdXV2MGTMGQ4cO5dTbt28fTp8+jY0bN6J58+ZlMFIKhUKhUEqOa9euwdvbGyEhIfj8+TMYhoGRkREePXpU1kOjUCigBheFQqFQKBTKT0FWVhaCg4MhFApx+fJlNiEyhUIpW6jBRaFQKBQKhfKT8fjxYxgbG5f1MCgUCqhKIYVCoVAoFEq5ZtasWQqfwzAMdu/eXQKjoVAoikJ3uCgUCoVCoVDKMYooAzMMA0KISpWBKRSKctAdLgqFQqFQKJRyTHR0tFz1nj9/Dg8PDyQlJZXwiCgUiiLQHS4KhUKhUCiUCszHjx/h6emJ3bt3Izs7G927d8eGDRuoNDyFUk6gO1wUCoVCoVAoFZDs7Gxs27YNGzduREZGBn777Td4enpi+PDhZT00CoVSAPmdgikUCoVCoVAoZQ4hBN7e3mjevDmWL1+OKlWqYN++fbh//z41tiiUcgh1KaRQKBQKhUKpIISFhWH58uV49OgRatSogT/++APz58+HlpZWWQ+NQqFIgRpcFAqFQqFQKOWcixcv4o8//sDVq1ehqamJ33//Ha6urqhZs2ZZD41CoRQBNbgoFAqFQqFQyjFDhw5FREQEBAIBpkyZAg8PDzRq1Kish0WhUOSEGlwUCoVCoVAo5RiBQACGYWBoaIgWLVrIdQ7DMIiIiCjhkVEoFHmgBheFQqFQKBRKOUaRxMdiaOJjCqX8QGXhKRQKhUKhUMoxT58+LeshUCgUJaA7XBQKhUKhUCgUCoVSQtA8XBQKhUKhUCgUCoVSQlCDi0KhUCgUCoVCoVBKCGpwUSgUCoVCoVAoFEoJQQ0uCoVCoVAoFAqFQikhqMFFoVAoFEopEBMTA4Zh4O7uXtZDoVAoFEopQg0uCoVCoZQrUlJSwDCMxEdDQwMNGzbE2LFjkZCQUNZDlArDMLCwsCjrYVAoFAqlHEHzcFEoFAqlXNKsWTNMnjwZAPD161fcvHkThw8fRlhYGM6fPw9zc/MyHqFidOnSBf/++y/09PTKeigUCoVCKUWowUWhUCiUcomRkRHH/W79+vVYtmwZ/vzzT8TGxpbNwIpJlSpV8Ntvv5X1MCgUCoVSylCXQgqFQqFUGJycnAAAN2/elCjPycnBli1b0KFDB1StWhXVq1eHmZkZTpw4wWnj8ePHWLJkCTp06IBatWpBS0sLxsbGWLp0Kb58+cLb7+fPn+Hh4QETExNUrVoV2traaN++Pf7880/k5uay8VkAEBsbK+EO6efnB0B2DNeDBw8wbtw41KlTB5UqVUKTJk2wYMECpKenc+oaGhrC0NAQX79+hYuLCxo2bIhKlSrBxMQER44cUeTrpFAoFEopQHe4KBQKhVLhUFf//6+v79+/w9raGjExMWjfvj2cnJyQm5uLiIgIDBs2DDt37sTs2bPZ+qGhoRAKhbC0tISFhQVEIhGuXr2KDRs2IDY2FnFxcdDQ0GDrp6amonfv3nj48CFMTU3h7OwMkUiE//77Dxs2bMDChQthaGiIlStXYtWqVTAwMIC9vT17vqmpqcxruXz5MqysrPD9+3eMHj0ahoaGuHr1KrZt24aIiAhcuXIFtWrVkjgnNzcXVlZWSE9Px8iRI5GVlYVDhw5h7NixiIyMhJWVlXJfMIVCoVBUB6FQKBQKpRzx9OlTAoAMGDCAc2z16tUEABk0aBBbtnz5cgKAuLu7E5FIxJZ/+vSJdOrUiWhqapJXr16x5S9fviTfv3/ntL1q1SoCgBw4cECifMyYMQQAWb58Oeect2/fktzcXPb/AEjv3r15rys6OpoAICtXrmTL8vPzSfPmzQkAEhkZKVF/2bJlBABxcnKSKDcwMCAAyLBhwySu4/z581K/NwqFQqGUHQwhhJSRrUehUCgUCoeUlBQ0adKEI5px48YNxMbGok6dOoiJiUHLli0hEomgp6eHWrVq4fHjx6xbn5iTJ09i6NChnF0uPtLT01GrVi3Y29vD19cXAPDu3TvUr18fTZs2xb///iux88UHwzDo3bs3YmJiOMdiYmJgaWmJlStXsm6F8fHxMDc3x8CBA3Hq1CmJ+l+/foWBgQGysrKQkZEBTU1NAD9cCp89e4bk5GQ0adJE4hxDQ0N8/vwZaWlpMsdJoVAolNKDuhRSKBQKpVySlJSEVatWSZTVqVMH8fHxMDY2BgA8evQIHz9+RIMGDTh1AeDDhw8AgP/++48tI4TA19cXfn5+uH//PjIzMyESidjjr1+/Zv+dkJAAQggsLS2LNLaKw+3btwGAV0q+atWq6NSpE86cOYPHjx+jTZs27DEdHR2OsQUAjRo1wpUrV1Q+TgqFQqEUH2pwUSgUCqVcMmDAAERGRgL4YTj5+/vjjz/+wPDhw3H9+nVUq1aNFZV48OABHjx4ILWtr1+/sv+ednrY+wAAA/RJREFUO3cudu3aBX19fQwdOhT169dHpUqVAACrVq3C9+/f2boZGRkAgIYNG6r68gAAnz59AgDUrVuX93i9evUAAJmZmRLl2travPXV1dUljEcKhUKhlD3U4KJQKBRKuad27dpYtGgRMjMzsWbNGri5uWHbtm2oUaMGAGDUqFFyKfS9f/8eu3fvhomJCa5cuYIqVaqwx96+fcvZJdPR0QEAvHr1SnUXUwDx+N+9e8d7XFwurkehUCiUigeVhadQKBRKhWH58uVo0KAB9uzZg5SUFLRs2RI1atRAQkICcnNzizw/OTkZhBD069dPwtgCfsRTFaZTp04QCASIjo6Wq32BQID8/Hy5r6d9+/YAwBvzlZWVhYSEBFSuXBktWrSQu00KhUKhlC+owUWhUCiUCkPlypXxxx9/IDc3F6tXr4a6ujpmzpyJZ8+eYdGiRbxG0f379/H+/XsAgIGBAYAfUuwFXe9evnyJpUuXcs6tW7cuRo0axRtPBvzYMcvLy2P/r6uri5cvX8p9PT179kSzZs1w+vRpnD9/XuLYunXrkJqaigkTJrCCGRQKhUKpeFCXQgqFQqFUKKZPn44NGzYgICAAy5cvx6pVq3Dr1i3s2LEDERER6N27N2rXro1Xr17h3r17uHv3Lq5cuYI6deqgfv36GDVqFI4ePYpOnTqhb9++ePfuHcLDw9GnTx8kJydz+tuzZw/u37+PtWvX4tSpU+jTpw8IIXj8+DHOnj2Ld+/esa6Hffr0QUhICEaPHo327dtDTU0NgwYNQtu2bXmvRSAQwM/PDwMGDICNjQ3GjBkDAwMDXLt2DRcuXECzZs2wfv36kvw6KRQKhVLCUIOLQqFQKBUKLS0tLFu2DHPmzMGqVasQEBCA06dPQygUIiAgAEeOHMH3799Rt25dtGrVCs7OzhIGj5+fHwwNDXH06FHs3LkTjRs3houLC/744w/enSQ9PT1cvXoVXl5eOHz4MHbt2gUtLS00adIES5cuRdWqVdm627dvBwBcuHABx44dg0gkQr169aQaXADQq1cvXL16FR4eHjh79iwyMzPRoEEDzJ07F3/++Sf09PRU+O1RKBQKpbShebgoFAqFQqFQKBQKpYSgMVwUCoVCoVAoFAqFUkJQg4tCoVAoFAqFQqFQSghqcFEoFAqFQqFQKBRKCUENLgqFQqFQKBQKhUIpIajBRaFQKBQKhUKhUCglBDW4KBQKhUKhUCgUCqWEoAYXhUKhUCgUCoVCoZQQ1OCiUCgUCoVCoVAolBKCGlwUCoVCoVAoFAqFUkJQg4tCoVAoFAqFQqFQSghqcFEoFAqFQqFQKBRKCUENLgqFQqFQKBQKhUIpIf4fetN+meoWk84AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RctTarget_dict = dict()\n",
    "\n",
    "for target in list(myResults.columns):\n",
    "    print(target)\n",
    "    FluxColumns = pd.DataFrame(myResults[target].to_dict()).fillna(0).transpose()\n",
    "\n",
    "    # calculate the correlation matrix of FluxColumns with respect to the malate flux Y\n",
    "    if LoadExisting == True:\n",
    "        CorrMat = FluxColumns.corrwith(FluxColumns[f'DM_{target}[e]'])\n",
    "    else:\n",
    "        CorrMat = FluxColumns.corrwith(FluxColumns[f'DM_{target}'])\n",
    "\n",
    "    # generate a bar plot of the absolute correlation coefficients with values in the range [LimLow,LimHigh] and with the name of the reaction on x-axis\n",
    "    plt.figure(figsize=(10,10))\n",
    "    LimLow = 0.8\n",
    "    LimHigh = 0.99\n",
    "    # names of reactions within the correlation range\n",
    "    RctNames = CorrMat[np.where(np.logical_and(np.abs(CorrMat)>LimLow, np.abs(CorrMat)<LimHigh))[0]].index\n",
    "    # values of reactions within the correlation range\n",
    "    RctValues = CorrMat[np.where(np.logical_and(np.abs(CorrMat)>LimLow, np.abs(CorrMat)<LimHigh))[0]].values\n",
    "    # print original values and names\n",
    "    RctTarget_dict[target] = {RctName: round(RctValue,2) for RctName, RctValue in zip(RctNames, RctValues)}\n",
    "    # sort values and names\n",
    "    RctNames = [x for _,x in sorted(zip(np.abs(RctValues),RctNames), reverse=True)]\n",
    "    RctValues = sorted(np.abs(RctValues), reverse=True)\n",
    "    # print the reaction names for the first five reactions\n",
    "    print('Top 5 reactions:')\n",
    "    [print(model.reactions.get_by_id(Rct).name) for Rct in RctNames[:5]]\n",
    "    print('')\n",
    "\n",
    "    plt.bar(RctNames, RctValues)#, color='k')\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.xlabel('Reaction')\n",
    "    plt.ylabel('Correlation coefficient')\n",
    "    plt.title(f'High Flux Correlation to {target[:-3]}')\n",
    "    # saving figure\n",
    "    # FigureName = f'CorrFlux-{target[:-3]}'\n",
    "    # PlotName = os.path.join(SaveDir, f'{Today}_{FigureName}.{FigureType}')\n",
    "    # plt.savefig(PlotName, dpi=300, bbox_inches='tight')\n",
    "    # print(f'Figure saved as {PlotName}')\n",
    "    print('')\n",
    "plt.show()\n",
    "\n",
    "# saving correlation coefficients to xlsx file\n",
    "File = os.path.join(SaveDir, f'{Today}_CorrFlux.xlsx')\n",
    "with pd.ExcelWriter(File) as writer:\n",
    "    pd.DataFrame(RctTarget_dict).to_excel(writer, sheet_name='CorrFlux')\n",
    "    # print file location\n",
    "print(f'Correlation file saved in {File}')\n",
    "# pd.DataFrame(RctTarget_dict)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "iambjudas",
   "language": "python",
   "name": "iambjudas"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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 },
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}