{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DNA Assemblies, gene expression, transcription, and translation\n",
    "The central Dogma - namely that DNA is transcribed to mRNA which is translated to proteins - is a key part of modeling many biochemical processes, especially in synthetic biology. Towards enabling easy modeling of transcription and translation in diverse context, BioCRNpyler has a number of Mixtures, Components and Mechanisms to produce models which include the central dogma."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: Creating a DNAassembly\n",
    "DNAassembly is a Component consisting of 2 sub Components: Promoter and RBS (ribosome binding site). DNAassembly automatically produces transcription and translation reactions from simple specifications: a DNAassembly X will produce 3 species dna_X, rna_X, and protein_X. These species can also be renamed manually if desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "repr(Mixture) gives a printout of what is in a mixture and what it's Mechanisms are:\n",
      " Mixture: Catalysis Mixture\n",
      "Components = [\n",
      "\tDNAassembly: X ]\n",
      "Mechanisms = {\n",
      "\ttranscription:simple_transcription\n",
      "\ttranslation:simple_translation } \n",
      "\n",
      "Pretty_print representation of the CRN:\n",
      " Species(N = 3) = {\n",
      "protein[X] (@ 0),  rna[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (2) = [\n",
      "0. dna[X] --> dna[X]+rna[X]\n",
      " Kf=k_forward * dna_X\n",
      "  k_forward=0.5\n",
      "  found_key=(mech=None, partid=None, name=ktx).\n",
      "  search_key=(mech=simple_transcription, partid=P, name=ktx).\n",
      "\n",
      "1. rna[X] --> rna[X]+protein[X]\n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=2\n",
      "  found_key=(mech=None, partid=None, name=ktl).\n",
      "  search_key=(mech=simple_translation, partid=RBS, name=ktl).\n",
      "\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler.core import Mixture\n",
    "from biocrnpyler.components.dna import DNAassembly\n",
    "from biocrnpyler.mechanisms import SimpleTranscription, SimpleTranslation\n",
    "\n",
    "#promoter is the name of the promoter or a Promoter component\n",
    "#RBS is the name of the RBS or an RBS Component\n",
    "#   RBS = None means there will be no translation\n",
    "#By default (if transcript and protein are None), the transcript and protein will have the same name as an assembly\n",
    "#   Users can also assign Species or String names to transcript and protein\n",
    "G = DNAassembly(\"X\", promoter = \"P\", rbs = \"RBS\", transcript = None, protein = None)\n",
    "\n",
    "#create a transcription and translation Mechanisms. \n",
    "mech_tx = SimpleTranscription()\n",
    "mech_tl = SimpleTranslation()\n",
    "\n",
    "#place that mechanism in a dictionary: {\"transcription\":mech_tx, \"translation\":mech_tl}\n",
    "default_mechanisms = {mech_tx.mechanism_type:mech_tx, mech_tl.mechanism_type:mech_tl}\n",
    "\n",
    "#Use default parameters for convenience\n",
    "default_parameters = {\"kb\":100, \"ku\":10, \"ktx\":.5, \"ktl\":2}\n",
    "#Create a mixture.\n",
    "M = Mixture(\"Catalysis Mixture\", components = [G], parameters = default_parameters, mechanisms = default_mechanisms)\n",
    "    \n",
    "print(\"repr(Mixture) gives a printout of what is in a mixture and what it's Mechanisms are:\\n\", repr(M),\"\\n\")\n",
    "\n",
    "#Compile the CRN with Mixture.compile_crn\n",
    "CRN = M.compile_crn()\n",
    "\n",
    "print(\"Pretty_print representation of the CRN:\\n\",\n",
    "      CRN.pretty_print(show_rates = True, show_attributes = True, show_materials = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Placing a DNAassembly in different Cell-like Mixtures for Transcription and Translation\n",
    "BioCRNpyler contains many pre-built Mixtures to model Transcription and Translation at different levels of detail in different contexts. Cell-like Mixtures are designed to more accurately model the internal environment of a cell.\n",
    "\n",
    "## Cell-Like Mixtures:\n",
    "__ExpressionDilutionMixture__ uses the Mechanisms: OneStepGeneExpression\n",
    "* Genes express in a single step $G \\to G + P_G$. \n",
    "* Proteins are degraded: $P_G \\to \\emptyset$.\n",
    "\n",
    "__SimpleTxTlDilutionMixture__ uses the Mechanisms: SimpleTranscription, SimpleTranslation, and Dilution (A global Mechanisms)\n",
    "* Genes transcribe via simple catalysis $G_X \\to G_X + T_X$\n",
    "* mRNAs translate via simple catalysis $T_X \\to T_X + P_X$. \n",
    "* Proteins and mRNA are degraded/diluted at different rates: $T_X \\to \\emptyset$, $P_X \\to \\emptyset$.\n",
    "\n",
    "__TxTlDilutionMixture__ uses the Mechanisms: Transcription_MM, Translation_MM, Degradation_mRNA_MM and Dilution (A global Mechanisms)\n",
    "* Genes transcribe via RNApolymerase (RNAP) $G_X + RNAP \\rightleftarrows G_X:RNAP \\to G_X + RNAP + T_X$\n",
    "* mRNAs translate via Ribosomes (Ribo) $T_X + Ribo \\rightleftarrows T_X:Ribo \\to T_X + Ribo + P_X$\n",
    "* mRNAs are degraded via endonucleases (Endo): $T_X + Endo \\rightleftarrows T_X:Endo \\to Endo$\n",
    "* Proteins & mRNA are Diluted: $P_X \\to \\emptyset$, $T_X \\to \\emptyset$\n",
    "* A Background DNAassembly $G_{\\text{cellular\\_processes}}$ puts loading on all the cellular resources.\n",
    "\n",
    "In the following examples, the parameter file default_parameters.txt will be used to quickly produce more complex models with realistic parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExpressionDilutionMixture: ExpressionDilution\n",
      "Components = [\n",
      "\tDNAassembly: X ]\n",
      "Mechanisms = {\n",
      "\ttranscription:gene_expression\n",
      "\ttranslation:dummy_translation\n",
      "\tcatalysis:basic_catalysis\n",
      "\tbinding:one_step_binding }\n",
      "Global Mechanisms = {\n",
      "\tdilution:dilution } \n",
      " Species(N = 2) = {\n",
      "protein[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (2) = [\n",
      "0. dna[X] --> dna[X]+protein[X]\n",
      " Kf=k_forward * dna_X\n",
      "  k_forward=0.28125\n",
      "  found_key=(mech=gene_expression, partid=None, name=kexpress).\n",
      "  search_key=(mech=gene_expression, partid=strong, name=kexpress).\n",
      "\n",
      "1. protein[X] --> \n",
      " Kf=k_forward * protein_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=dilution, partid=protein_X, name=kdil).\n",
      "\n",
      "] \n",
      "\n",
      "\n",
      "SimpleTxTlDilutionMixture: SimpleTxTl\n",
      "Components = [\n",
      "\tDNAassembly: X ]\n",
      "Mechanisms = {\n",
      "\ttranscription:simple_transcription\n",
      "\ttranslation:simple_translation\n",
      "\tcatalysis:basic_catalysis\n",
      "\tbinding:one_step_binding }\n",
      "Global Mechanisms = {\n",
      "\tdilution:global_degradation_via_dilution\n",
      "\trna_degradation:rna_degradation } \n",
      " Species(N = 3) = {\n",
      "protein[X] (@ 0),  rna[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (5) = [\n",
      "0. dna[X] --> dna[X]+rna[X]\n",
      " Kf=k_forward * dna_X\n",
      "  k_forward=0.4775625\n",
      "  found_key=(mech=simple_transcription, partid=strong, name=ktx).\n",
      "  search_key=(mech=simple_transcription, partid=strong, name=ktx).\n",
      "\n",
      "1. rna[X] --> rna[X]+protein[X]\n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.06\n",
      "  found_key=(mech=simple_translation, partid=weak, name=ktl).\n",
      "  search_key=(mech=simple_translation, partid=weak, name=ktl).\n",
      "\n",
      "2. rna[X] --> \n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degradation_via_dilution, partid=rna_X, name=kdil).\n",
      "\n",
      "3. protein[X] --> \n",
      " Kf=k_forward * protein_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degradation_via_dilution, partid=protein_X, name=kdil).\n",
      "\n",
      "4. rna[X] --> \n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degradation, partid=None, name=kdil).\n",
      "  search_key=(mech=rna_degradation, partid=rna_X, name=kdil).\n",
      "\n",
      "] \n",
      "\n",
      "\n",
      "TxTlDilutionMixture: e coli\n",
      "Components = [\n",
      "\tDNAassembly: X\n",
      "\tProtein: RNAP\n",
      "\tProtein: Ribo\n",
      "\tProtein: RNAase\n",
      "\tDNAassembly: cellular_processes ]\n",
      "Mechanisms = {\n",
      "\ttranscription:transcription_mm\n",
      "\ttranslation:translation_mm\n",
      "\tcatalysis:michalis_menten\n",
      "\tbinding:one_step_binding }\n",
      "Global Mechanisms = {\n",
      "\trna_degradation:rna_degradation_mm\n",
      "\tdilution:global_degradation_via_dilution } \n",
      " Species(N = 17) = {\n",
      "dna[cellular_processes] (@ 5.0),  \n",
      "   found_key=(mech=None, partid=e coli, name=cellular_processes).\n",
      "   search_key=(mech=initial concentration, partid=e coli, name=cellular_processes).\n",
      "complex[protein[Ribo]:rna[cellular_processes]] (@ 0),  complex[protein[Ribo]:rna[X]] (@ 0),  complex[protein[RNAase]:rna[cellular_processes]] (@ 0),  complex[protein[RNAase]:rna[X]] (@ 0),  complex[dna[cellular_processes]:protein[RNAP]] (@ 0),  complex[dna[X]:protein[RNAP]] (@ 0),  complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] (@ 0),  complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]] (@ 0),  protein[cellular_processes] (@ 0),  rna[cellular_processes] (@ 0),  protein[X] (@ 0),  rna[X] (@ 0),  dna[X] (@ 0),  protein[Ribo(machinery)] (@ 0),  protein[RNAase(machinery)] (@ 0),  protein[RNAP(machinery)] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (20) = [\n",
      "0. dna[X]+protein[RNAP(machinery)] <--> complex[dna[X]:protein[RNAP]]\n",
      " Kf=k_forward * dna_X * protein_RNAP_machinery\n",
      " Kr=k_reverse * complex_dna_X_protein_RNAP_machinery_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=kb).\n",
      "  k_reverse=0.5\n",
      "  found_key=(mech=None, partid=strong, name=ku).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=ku).\n",
      "\n",
      "1. complex[dna[X]:protein[RNAP]] --> dna[X]+rna[X]+protein[RNAP(machinery)]\n",
      " Kf=k_forward * complex_dna_X_protein_RNAP_machinery_\n",
      "  k_forward=3.926187672\n",
      "  found_key=(mech=None, partid=strong, name=ktx).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=ktx).\n",
      "\n",
      "2. rna[X]+protein[Ribo(machinery)] <--> complex[protein[Ribo]:rna[X]]\n",
      " Kf=k_forward * rna_X * protein_Ribo_machinery\n",
      " Kr=k_reverse * complex_protein_Ribo_machinery_rna_X_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=kb).\n",
      "  k_reverse=5.0\n",
      "  found_key=(mech=None, partid=weak, name=ku).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=ku).\n",
      "\n",
      "3. complex[protein[Ribo]:rna[X]] --> rna[X]+protein[X]+protein[Ribo(machinery)]\n",
      " Kf=k_forward * complex_protein_Ribo_machinery_rna_X_\n",
      "  k_forward=0.05\n",
      "  found_key=(mech=None, partid=None, name=ktl).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=ktl).\n",
      "\n",
      "4. dna[cellular_processes]+protein[RNAP(machinery)] <--> complex[dna[cellular_processes]:protein[RNAP]]\n",
      " Kf=k_forward * dna_cellular_processes * protein_RNAP_machinery\n",
      " Kr=k_reverse * complex_dna_cellular_processes_protein_RNAP_machinery_\n",
      "  k_forward=500\n",
      "  found_key=(mech=transcription, partid=None, name=kb).\n",
      "  search_key=(mech=transcription_mm, partid=average_promoter, name=kb).\n",
      "  k_reverse=50\n",
      "  found_key=(mech=transcription, partid=None, name=ku).\n",
      "  search_key=(mech=transcription_mm, partid=average_promoter, name=ku).\n",
      "\n",
      "5. complex[dna[cellular_processes]:protein[RNAP]] --> dna[cellular_processes]+rna[cellular_processes]+protein[RNAP(machinery)]\n",
      " Kf=k_forward * complex_dna_cellular_processes_protein_RNAP_machinery_\n",
      "  k_forward=0.1\n",
      "  found_key=(mech=transcription, partid=None, name=ktx).\n",
      "  search_key=(mech=transcription_mm, partid=average_promoter, name=ktx).\n",
      "\n",
      "6. rna[cellular_processes]+protein[Ribo(machinery)] <--> complex[protein[Ribo]:rna[cellular_processes]]\n",
      " Kf=k_forward * rna_cellular_processes * protein_Ribo_machinery\n",
      " Kr=k_reverse * complex_protein_Ribo_machinery_rna_cellular_processes_\n",
      "  k_forward=500\n",
      "  found_key=(mech=translation, partid=None, name=kb).\n",
      "  search_key=(mech=translation_mm, partid=average_rbs, name=kb).\n",
      "  k_reverse=5\n",
      "  found_key=(mech=translation, partid=None, name=ku).\n",
      "  search_key=(mech=translation_mm, partid=average_rbs, name=ku).\n",
      "\n",
      "7. complex[protein[Ribo]:rna[cellular_processes]] --> rna[cellular_processes]+protein[cellular_processes]+protein[Ribo(machinery)]\n",
      " Kf=k_forward * complex_protein_Ribo_machinery_rna_cellular_processes_\n",
      "  k_forward=0.1\n",
      "  found_key=(mech=translation, partid=None, name=ktl).\n",
      "  search_key=(mech=translation_mm, partid=average_rbs, name=ktl).\n",
      "\n",
      "8. rna[X]+protein[RNAase(machinery)] <--> complex[protein[RNAase]:rna[X]]\n",
      " Kf=k_forward * rna_X * protein_RNAase_machinery\n",
      " Kr=k_reverse * complex_protein_RNAase_machinery_rna_X_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degradation_mm, partid=rna_X, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degradation_mm, partid=rna_X, name=ku).\n",
      "\n",
      "9. complex[protein[RNAase]:rna[X]] --> protein[RNAase(machinery)]\n",
      " Kf=k_forward * complex_protein_RNAase_machinery_rna_X_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=rna_X, name=kdeg).\n",
      "\n",
      "10. complex[protein[Ribo]:rna[X]]+protein[RNAase(machinery)] <--> complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_machinery_rna_X_ * protein_RNAase_machinery\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_machinery_rna_X__protein_RNAase_machinery_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_machinery_rna_X_, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_machinery_rna_X_, name=ku).\n",
      "\n",
      "11. complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]] --> protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_machinery_rna_X__protein_RNAase_machinery_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_machinery_rna_X_, name=kdeg).\n",
      "\n",
      "12. rna[cellular_processes]+protein[RNAase(machinery)] <--> complex[protein[RNAase]:rna[cellular_processes]]\n",
      " Kf=k_forward * rna_cellular_processes * protein_RNAase_machinery\n",
      " Kr=k_reverse * complex_protein_RNAase_machinery_rna_cellular_processes_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degradation_mm, partid=rna_cellular_processes, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degradation_mm, partid=rna_cellular_processes, name=ku).\n",
      "\n",
      "13. complex[protein[RNAase]:rna[cellular_processes]] --> protein[RNAase(machinery)]\n",
      " Kf=k_forward * complex_protein_RNAase_machinery_rna_cellular_processes_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=rna_cellular_processes, name=kdeg).\n",
      "\n",
      "14. complex[protein[Ribo]:rna[cellular_processes]]+protein[RNAase(machinery)] <--> complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_machinery_rna_cellular_processes_ * protein_RNAase_machinery\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_machinery_rna_cellular_processes__protein_RNAase_machinery_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_machinery_rna_cellular_processes_, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_machinery_rna_cellular_processes_, name=ku).\n",
      "\n",
      "15. complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] --> protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_machinery_rna_cellular_processes__protein_RNAase_machinery_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_machinery_rna_cellular_processes_, name=kdeg).\n",
      "\n",
      "16. rna[X] --> \n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degradation_via_dilution, partid=rna_X, name=kdil).\n",
      "\n",
      "17. protein[X] --> \n",
      " Kf=k_forward * protein_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degradation_via_dilution, partid=protein_X, name=kdil).\n",
      "\n",
      "18. rna[cellular_processes] --> \n",
      " Kf=k_forward * rna_cellular_processes\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degradation_via_dilution, partid=rna_cellular_processes, name=kdil).\n",
      "\n",
      "19. protein[cellular_processes] --> \n",
      " Kf=k_forward * protein_cellular_processes\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degradation_via_dilution, partid=protein_cellular_processes, name=kdil).\n",
      "\n",
      "] \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler.mixtures import ExpressionDilutionMixture, SimpleTxTlDilutionMixture, TxTlDilutionMixture\n",
    "#Changing the promoter and RBS name will result in different parameters\n",
    "#Here are some parameters loaded into default_parameters.txt\n",
    "#Promoter Names: strong, medium, weak correspond to bicistronic RBSs: BCD2, BCD8 and BCD12\n",
    "#RBS Names: strong, medium, Weak correspond to Anderson Promoters: J23100, J23106, and J23103\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "#Also notice that the names of transcript and protein can be changed, or set to Species.\n",
    "\n",
    "#Compare the Following Mixtures and Resulting CRNs\n",
    "M1 = ExpressionDilutionMixture(\"ExpressionDilution\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "CRN1 = M1.compile_crn()\n",
    "print(repr(M1),\"\\n\", CRN1.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n",
    "\n",
    "#Components should be re-instantiated before passing them into a new Mixture\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "M2 = SimpleTxTlDilutionMixture(\"SimpleTxTl\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "CRN2 = M2.compile_crn()\n",
    "print(repr(M2),\"\\n\", CRN2.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n",
    "\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "M3 = TxTlDilutionMixture(\"e coli\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "\n",
    "CRN3 = M3.compile_crn()\n",
    "print(repr(M3),\"\\n\", CRN3.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3: Placing a DNAassembly in different Extract-like Mixtures for Transcription and Translation\n",
    "BioCRNpyler contains many pre-built Mixtures to model Transcription and Translation at different levels of detail in different contexts. Cell-like Mixtures are designed to more accurately model the internal environment of a cell.\n",
    "\n",
    "## Extract-Like Mixtures:\n",
    "__ExpressionDilutionMixture__ uses the Mechanisms: OneStepGeneExpression\n",
    "* Genes express in a single step $G \\to G + P_G$. \n",
    "__SimpleTxTlExtract__ uses the Mechanisms: SimpleTranscription, SimpleTranslation, and Dilution (A global Mechanisms)\n",
    "* Genes transcribe via simple catalysis $G_X \\to G_X + T_X$\n",
    "* mRNAs translate via simple catalysis $T_X \\to T_X + P_X$. \n",
    "* mRNA are degraded: $T_X \\to \\emptyset$\n",
    "\n",
    "__TxTlExtract__ uses the Mechanisms: Transcription_MM, Translation_MM, Degradation_mRNA_MM and Dilution (A global Mechanisms)\n",
    "* Genes transcribe via RNApolymerase (RNAP) $G_X + RNAP \\rightleftarrows G_X:RNAP \\to G_X + RNAP + T_X$\n",
    "* mRNAs translate via Ribosomes (Ribo) $T_X + Ribo \\rightleftarrows T_X:Ribo \\to T_X + Ribo + P_X$\n",
    "* mRNAs are degraded via endonucleases (Endo): $T_X + Endo \\rightleftarrows T_X:Endo \\to Endo$\n",
    "\n",
    "In the following examples, the parameter file default_parameters.txt will be used to quickly produce more complex models with realistic parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExpressionExtract: ExpressionExtract\n",
      "Components = [\n",
      "\tDNAassembly: X ]\n",
      "Mechanisms = {\n",
      "\ttranscription:gene_expression\n",
      "\ttranslation:dummy_translation\n",
      "\tcatalysis:basic_catalysis\n",
      "\tbinding:one_step_binding } \n",
      " Species(N = 2) = {\n",
      "protein[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (1) = [\n",
      "0. dna[X] --> dna[X]+protein[X]\n",
      " Kf=k_forward * dna_X\n",
      "  k_forward=0.28125\n",
      "  found_key=(mech=gene_expression, partid=None, name=kexpress).\n",
      "  search_key=(mech=gene_expression, partid=strong, name=kexpress).\n",
      "\n",
      "] \n",
      "\n",
      "\n",
      "SimpleTxTlExtract: SimpleTxTlExtract\n",
      "Components = [\n",
      "\tDNAassembly: X ]\n",
      "Mechanisms = {\n",
      "\ttranscription:simple_transcription\n",
      "\ttranslation:simple_translation\n",
      "\tcatalysis:basic_catalysis\n",
      "\tbinding:one_step_binding }\n",
      "Global Mechanisms = {\n",
      "\trna_degradation:rna_degradation } \n",
      " Species(N = 3) = {\n",
      "protein[X] (@ 0),  rna[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (3) = [\n",
      "0. dna[X] --> dna[X]+rna[X]\n",
      " Kf=k_forward * dna_X\n",
      "  k_forward=0.4775625\n",
      "  found_key=(mech=simple_transcription, partid=strong, name=ktx).\n",
      "  search_key=(mech=simple_transcription, partid=strong, name=ktx).\n",
      "\n",
      "1. rna[X] --> rna[X]+protein[X]\n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.06\n",
      "  found_key=(mech=simple_translation, partid=weak, name=ktl).\n",
      "  search_key=(mech=simple_translation, partid=weak, name=ktl).\n",
      "\n",
      "2. rna[X] --> \n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degradation, partid=None, name=kdil).\n",
      "  search_key=(mech=rna_degradation, partid=rna_X, name=kdil).\n",
      "\n",
      "] \n",
      "\n",
      "\n",
      "TxTlExtract: e coli extract\n",
      "Components = [\n",
      "\tDNAassembly: X\n",
      "\tProtein: RNAP\n",
      "\tProtein: Ribo\n",
      "\tProtein: RNAase ]\n",
      "Mechanisms = {\n",
      "\ttranscription:transcription_mm\n",
      "\ttranslation:translation_mm\n",
      "\tcatalysis:michalis_menten\n",
      "\tbinding:one_step_binding }\n",
      "Global Mechanisms = {\n",
      "\trna_degradation:rna_degradation_mm } \n",
      " Species(N = 10) = {\n",
      "protein[Ribo] (@ 24.0),  \n",
      "   found_key=(mech=None, partid=e coli extract, name=protein_Ribo).\n",
      "   search_key=(mech=initial concentration, partid=e coli extract, name=protein_Ribo).\n",
      "protein[RNAase] (@ 6.0),  \n",
      "   found_key=(mech=None, partid=e coli extract, name=protein_RNAase).\n",
      "   search_key=(mech=initial concentration, partid=e coli extract, name=protein_RNAase).\n",
      "protein[RNAP] (@ 3.0),  \n",
      "   found_key=(mech=None, partid=e coli extract, name=protein_RNAP).\n",
      "   search_key=(mech=initial concentration, partid=e coli extract, name=protein_RNAP).\n",
      "complex[protein[Ribo]:rna[X]] (@ 0),  complex[protein[RNAase]:rna[X]] (@ 0),  complex[dna[X]:protein[RNAP]] (@ 0),  complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]] (@ 0),  protein[X] (@ 0),  rna[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (8) = [\n",
      "0. dna[X]+protein[RNAP] <--> complex[dna[X]:protein[RNAP]]\n",
      " Kf=k_forward * dna_X * protein_RNAP\n",
      " Kr=k_reverse * complex_dna_X_protein_RNAP_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=kb).\n",
      "  k_reverse=0.5\n",
      "  found_key=(mech=None, partid=strong, name=ku).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=ku).\n",
      "\n",
      "1. complex[dna[X]:protein[RNAP]] --> dna[X]+rna[X]+protein[RNAP]\n",
      " Kf=k_forward * complex_dna_X_protein_RNAP_\n",
      "  k_forward=3.926187672\n",
      "  found_key=(mech=None, partid=strong, name=ktx).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=ktx).\n",
      "\n",
      "2. rna[X]+protein[Ribo] <--> complex[protein[Ribo]:rna[X]]\n",
      " Kf=k_forward * rna_X * protein_Ribo\n",
      " Kr=k_reverse * complex_protein_Ribo_rna_X_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=kb).\n",
      "  k_reverse=5.0\n",
      "  found_key=(mech=None, partid=weak, name=ku).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=ku).\n",
      "\n",
      "3. complex[protein[Ribo]:rna[X]] --> rna[X]+protein[X]+protein[Ribo]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_X_\n",
      "  k_forward=0.05\n",
      "  found_key=(mech=None, partid=None, name=ktl).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=ktl).\n",
      "\n",
      "4. rna[X]+protein[RNAase] <--> complex[protein[RNAase]:rna[X]]\n",
      " Kf=k_forward * rna_X * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_X_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degradation_mm, partid=rna_X, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degradation_mm, partid=rna_X, name=ku).\n",
      "\n",
      "5. complex[protein[RNAase]:rna[X]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_X_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=rna_X, name=kdeg).\n",
      "\n",
      "6. complex[protein[Ribo]:rna[X]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_X_ * protein_RNAase\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_rna_X__protein_RNAase_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_rna_X_, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_rna_X_, name=ku).\n",
      "\n",
      "7. complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_rna_X__protein_RNAase_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_rna_X_, name=kdeg).\n",
      "\n",
      "] \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler.mixtures import ExpressionExtract, SimpleTxTlExtract, TxTlExtract\n",
    "#Compare the Following Mixtures and Resulting CRNs\n",
    "\n",
    "#Components should be re-instantiated before passing them into a new Mixture\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "M1 = ExpressionExtract(\"ExpressionExtract\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "CRN1 = M1.compile_crn()\n",
    "print(repr(M1),\"\\n\", CRN1.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n",
    "\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "M2 = SimpleTxTlExtract(\"SimpleTxTlExtract\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "CRN2 = M2.compile_crn()\n",
    "print(repr(M2),\"\\n\", CRN2.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n",
    "\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "M3 = TxTlExtract(\"e coli extract\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "CRN3 = M3.compile_crn()\n",
    "print(repr(M3),\"\\n\", CRN3.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 4: Retroactivity and Loading Using a Custom Promoter Object\n",
    "Most of the default Mixtures in BioCRNpyler include transcription, translation, and degradation machinery such as RNAP (RNA Polymerase), Ribosomes, and RNAases. In the following example, we will illustrate loading effects due to competition over this machinery. For this example, we will use the following model of constitutive transcription and translation:\n",
    "\n",
    "$G_i + \\textrm{RNAP} \\leftrightarrow G_i:\\textrm{RNAP} \\rightarrow G_i + \\textrm{RNAP} + T_i$\n",
    "\n",
    "$T_i + \\textrm{Ribosome} \\leftrightarrow T_i:\\textrm{Ribosome} \\rightarrow T_i + \\textrm{Ribosome} + P_i$\n",
    "\n",
    "$T_i + \\textrm{RNAase} \\leftrightarrow T_i:\\textrm{RNAase} \\rightarrow \\textrm{RNAase}$\n",
    "\n",
    "Here $G_i$, $T_i$ and $P_i$ are gene, transcript, and protein $i$, respectively. In the example that follows, we will allow different RNA polymerases for different genes. Also, some genes may not be translated at all. By using orthogonal polymerases and/or loads without translation, we will see different kinds of loading effects.\n",
    "\n",
    "We will create 4 different DNA assemblies. The reference assembly, \"ref\", will have a RNAP promoter and an RBS. We will examine the output of this reporter as a function of the amount of various load assemblies.\n",
    "* The \"Load\" assembly will be identical to the \"ref\" assembly; this assembly will put load on all parts of transcription, RNA degradation, and translation for the ref assembly.\n",
    "* The \"TxLoad\" assembly will have an RNAP promoter, but no RBS, ensuring that only RNAP and RNAases experience loading, not ribosomes.\n",
    "* The \"T7Load\" assembly will have a T7 promoter and an RBS so there will be no loading on polymerases.\n",
    "* The \"T7TxLoad\" assembly will have a T7 promoter and no RBS, so there will only be load on the RNAases.\n",
    "\n",
    "The creation of these assemblies highlights the flexible, object oriented nature of bioCRNpyler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pretty_print gives a nicely formatted representation of the CRNS, reactions, and species. The names of species are formatted for clarity, but are not the actual species representations. Additionally a number of printing options are available. \n",
      " Species(N = 33) = {\n",
      "protein[ref] (@ 0),  rna[ref] (@ 0),  dna[ref] (@ 0),  complex[protein[Ribo]:rna[ref]] (@ 0),  complex[protein[Ribo]:rna[T7Load]] (@ 0),  complex[protein[Ribo]:rna[Load]] (@ 0),  complex[protein[RNAase]:rna[ref]] (@ 0),  complex[protein[RNAase]:rna[TxLoad]] (@ 0),  complex[protein[RNAase]:rna[T7TxLoad]] (@ 0),  complex[protein[RNAase]:rna[T7Load]] (@ 0),  complex[protein[RNAase]:rna[Load]] (@ 0),  complex[dna[ref]:protein[RNAP]] (@ 0),  complex[dna[TxLoad]:protein[RNAP]] (@ 0),  complex[dna[T7TxLoad]:protein[T7]] (@ 0),  complex[dna[T7Load]:protein[T7]] (@ 0),  complex[dna[Load]:protein[RNAP]] (@ 0),  complex[complex[protein[Ribo]:rna[ref]]:protein[RNAase]] (@ 0),  complex[complex[protein[Ribo]:rna[T7Load]]:protein[RNAase]] (@ 0),  complex[complex[protein[Ribo]:rna[Load]]:protein[RNAase]] (@ 0),  rna[TxLoad] (@ 0),  dna[TxLoad] (@ 0),  rna[T7TxLoad] (@ 0),  dna[T7TxLoad] (@ 0),  protein[T7Load] (@ 0),  rna[T7Load] (@ 0),  dna[T7Load] (@ 0),  protein[T7] (@ 0),  protein[Ribo] (@ 0),  protein[RNAase] (@ 0),  protein[RNAP] (@ 0),  protein[Load] (@ 0),  rna[Load] (@ 0),  dna[Load] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (32) = [\n",
      "0. dna[ref]+protein[RNAP] <--> complex[dna[ref]:protein[RNAP]]\n",
      " Kf=k_forward * dna_ref * protein_RNAP\n",
      " Kr=k_reverse * complex_dna_ref_protein_RNAP_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "1. complex[dna[ref]:protein[RNAP]] --> dna[ref]+rna[ref]+protein[RNAP]\n",
      " Kf=k_forward * complex_dna_ref_protein_RNAP_\n",
      "  k_forward=3.0\n",
      "\n",
      "2. rna[ref]+protein[Ribo] <--> complex[protein[Ribo]:rna[ref]]\n",
      " Kf=k_forward * rna_ref * protein_Ribo\n",
      " Kr=k_reverse * complex_protein_Ribo_rna_ref_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "3. complex[protein[Ribo]:rna[ref]] --> rna[ref]+protein[ref]+protein[Ribo]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_ref_\n",
      "  k_forward=5.0\n",
      "\n",
      "4. dna[Load]+protein[RNAP] <--> complex[dna[Load]:protein[RNAP]]\n",
      " Kf=k_forward * dna_Load * protein_RNAP\n",
      " Kr=k_reverse * complex_dna_Load_protein_RNAP_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "5. complex[dna[Load]:protein[RNAP]] --> dna[Load]+rna[Load]+protein[RNAP]\n",
      " Kf=k_forward * complex_dna_Load_protein_RNAP_\n",
      "  k_forward=3.0\n",
      "\n",
      "6. rna[Load]+protein[Ribo] <--> complex[protein[Ribo]:rna[Load]]\n",
      " Kf=k_forward * rna_Load * protein_Ribo\n",
      " Kr=k_reverse * complex_protein_Ribo_rna_Load_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "7. complex[protein[Ribo]:rna[Load]] --> rna[Load]+protein[Load]+protein[Ribo]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_Load_\n",
      "  k_forward=5.0\n",
      "\n",
      "8. dna[T7Load]+protein[T7] <--> complex[dna[T7Load]:protein[T7]]\n",
      " Kf=k_forward * dna_T7Load * protein_T7\n",
      " Kr=k_reverse * complex_dna_T7Load_protein_T7_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "9. complex[dna[T7Load]:protein[T7]] --> dna[T7Load]+rna[T7Load]+protein[T7]\n",
      " Kf=k_forward * complex_dna_T7Load_protein_T7_\n",
      "  k_forward=3.0\n",
      "\n",
      "10. rna[T7Load]+protein[Ribo] <--> complex[protein[Ribo]:rna[T7Load]]\n",
      " Kf=k_forward * rna_T7Load * protein_Ribo\n",
      " Kr=k_reverse * complex_protein_Ribo_rna_T7Load_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "11. complex[protein[Ribo]:rna[T7Load]] --> rna[T7Load]+protein[T7Load]+protein[Ribo]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_T7Load_\n",
      "  k_forward=5.0\n",
      "\n",
      "12. dna[TxLoad]+protein[RNAP] <--> complex[dna[TxLoad]:protein[RNAP]]\n",
      " Kf=k_forward * dna_TxLoad * protein_RNAP\n",
      " Kr=k_reverse * complex_dna_TxLoad_protein_RNAP_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "13. complex[dna[TxLoad]:protein[RNAP]] --> dna[TxLoad]+rna[TxLoad]+protein[RNAP]\n",
      " Kf=k_forward * complex_dna_TxLoad_protein_RNAP_\n",
      "  k_forward=3.0\n",
      "\n",
      "14. dna[T7TxLoad]+protein[T7] <--> complex[dna[T7TxLoad]:protein[T7]]\n",
      " Kf=k_forward * dna_T7TxLoad * protein_T7\n",
      " Kr=k_reverse * complex_dna_T7TxLoad_protein_T7_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "15. complex[dna[T7TxLoad]:protein[T7]] --> dna[T7TxLoad]+rna[T7TxLoad]+protein[T7]\n",
      " Kf=k_forward * complex_dna_T7TxLoad_protein_T7_\n",
      "  k_forward=3.0\n",
      "\n",
      "16. rna[ref]+protein[RNAase] <--> complex[protein[RNAase]:rna[ref]]\n",
      " Kf=k_forward * rna_ref * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_ref_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "17. complex[protein[RNAase]:rna[ref]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_ref_\n",
      "  k_forward=2\n",
      "\n",
      "18. complex[protein[Ribo]:rna[ref]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[ref]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_ref_ * protein_RNAase\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_rna_ref__protein_RNAase_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "19. complex[complex[protein[Ribo]:rna[ref]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_rna_ref__protein_RNAase_\n",
      "  k_forward=2\n",
      "\n",
      "20. rna[Load]+protein[RNAase] <--> complex[protein[RNAase]:rna[Load]]\n",
      " Kf=k_forward * rna_Load * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_Load_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "21. complex[protein[RNAase]:rna[Load]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_Load_\n",
      "  k_forward=2\n",
      "\n",
      "22. complex[protein[Ribo]:rna[Load]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[Load]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_Load_ * protein_RNAase\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_rna_Load__protein_RNAase_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "23. complex[complex[protein[Ribo]:rna[Load]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_rna_Load__protein_RNAase_\n",
      "  k_forward=2\n",
      "\n",
      "24. rna[T7Load]+protein[RNAase] <--> complex[protein[RNAase]:rna[T7Load]]\n",
      " Kf=k_forward * rna_T7Load * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_T7Load_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "25. complex[protein[RNAase]:rna[T7Load]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_T7Load_\n",
      "  k_forward=2\n",
      "\n",
      "26. complex[protein[Ribo]:rna[T7Load]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[T7Load]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_T7Load_ * protein_RNAase\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_rna_T7Load__protein_RNAase_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "27. complex[complex[protein[Ribo]:rna[T7Load]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_rna_T7Load__protein_RNAase_\n",
      "  k_forward=2\n",
      "\n",
      "28. rna[TxLoad]+protein[RNAase] <--> complex[protein[RNAase]:rna[TxLoad]]\n",
      " Kf=k_forward * rna_TxLoad * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_TxLoad_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "29. complex[protein[RNAase]:rna[TxLoad]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_TxLoad_\n",
      "  k_forward=2\n",
      "\n",
      "30. rna[T7TxLoad]+protein[RNAase] <--> complex[protein[RNAase]:rna[T7TxLoad]]\n",
      " Kf=k_forward * rna_T7TxLoad * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_T7TxLoad_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "31. complex[protein[RNAase]:rna[T7TxLoad]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_T7TxLoad_\n",
      "  k_forward=2\n",
      "\n",
      "]\n",
      "Simulating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n",
      "  result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\chemical_reaction_network.py:363: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n",
      "  result = py_simulate_model(timepoints, Model = m, stochastic = stochastic, safe = safe,\n"
     ]
    },
    {
     "data": {
      "image/png": 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O+YrncYWGhtK3b19mzZqlt/3WrVu6Jxbz4uDgoFs1u2nTpjRt2hRvb2+GDh1KeHg4JiYPfhh99uzZeHl55Tg/gFar1c2H+O85cf5t9erVOeY5qlu3ri6egqpUqVK+jv1vUnc/6X7QZ2aoz+v+uR7nu3F//8SJEx/4e61du7bee1mxWwghhDAeyVUfL1ctqJCQEFxcXHI8pZgXKyurR8ojC/L5Pi4nJyd+/fVXlFJ6cdy8eZPMzMx8nSe/n33FihWxtrZ+4Kin/55L8k0hHk6GeAtRBtja2tK0aVO++uorvTuxWq2W0NBQqlSpolvwRaPR6O743nfq1Kkcw1jatm3LvXv3+Oabb/S2b9y4MV8xtW/fnp9//ln3H/r71q1bh42NjW5l6seV2/V89913XL9+/ZH7rFmzJuPGjeP06dO5Tur9b3Xq1GHAgAEsWbKEqKgovX0//PAD0dHRvPPOO+zZsyfHq379+qxbt47MzMxHjtVQmjVrhrW1NaGhoXrbo6OjdcNgDCW/340HPZlQu3ZtatasyW+//Ubjxo1zfdnZ2RksXiGEEEI8HslVDZurPsyxY8c4deoU/fr1w8ysaJ5XKsjn+7jat29PYmIi27Zt09u+bt063f77LC0tc31KNb+fvZ+fH5cvX8bJySnXfPOJJ54wyDUJUVbIE5RClCI///wzV65cybG9S5cuzJ49m44dO9K2bVvGjBmDhYUFy5cv58yZM2zatEl3R8/Pz4/33nuPoKAgWrduzYULF5gxYwbVqlXTK5T17duXhQsX0rdvX95//31q1qzJjh07+OGHH/IVa1BQEN9++y1t27Zl2rRpODo6smHDBr777jvmzp2Lg4ODQX4nfn5+rFmzhjp16vDUU09x/Phx5s2bR5UqVR6r3zFjxrBixQqmT59Or169ch0mfV9wcDAbNmxgz5492Nra6raHhIRgZmbGpEmTdENG/u2tt95ixIgRfPfdd7z44ouPFe/jKl++PFOnTmXSpEn07duXV199lbi4OKZPn46VlRVBQUEGO1d+vxs1atTA2tqaDRs2ULduXcqVK4e7uzvu7u6sXLkSX19fOnfuTEBAAJUrV+b27dtERERw4sQJvvzyS4PFK4QQQoj8kVw1p8LKVR8kJCQEgIEDBxZK/w+S38/3cfXt25dly5bRr18/rly5QoMGDTh48CCzZs2iS5cudOjQQde2QYMG7N27l+3bt+Pm5oadnR21a9fO92c/cuRItmzZQqtWrRg1ahRPPfUUWq2WqKgofvzxR0aPHk3Tpk0Ncl1ClAlGXaJHCGEQ91e7e9ArMjJSKaXUgQMHVLt27ZStra2ytrZWzz77rNq+fbteX2lpaWrMmDGqcuXKysrKSj399NNq27Ztql+/fqpq1ap6baOjo1XPnj1VuXLllJ2dnerZs6cKCwvL18qISil1+vRp5e/vrxwcHJSFhYXy9vbOcdx/V8a+7/5KhXmd586dO2rgwIHK2dlZ2djYqJYtW6oDBw6o1q1bq9atW+cZY9WqVdULL7yQ675ly5YpQK1du/ahsSql1KRJkxSgW8X7r7/+UhYWFqpbt24Pjd3a2lr5+/srpfK3AubDAOqdd955aJuHXYNSSn366afqqaeeUhYWFsrBwUG9+OKL6uzZs3ptHrRa+f3VH/8rt99xfr4bSim1adMmVadOHWVubq4AFRQUpNv322+/qV69eilnZ2dlbm6uXF1dVbt27dSKFSt0bR73dyqEEEKIvEmu+mCPm6v+93wPW8U7OTlZOTg4qFatWuW73/selMf918Pyzfx8vg/KzYKCghSg/vrrL73tueWdcXFxasiQIcrNzU2ZmZmpqlWrqokTJ6rU1FS9duHh4apFixbKxsZGAXq/7/zmoomJiWrKlCmqdu3auvy4QYMGatSoUSo2NjZfvxchRDaNUkoVdhFUCCGEEEIIIYQQQgghciNzUAohhBBCCCGEEEIIIYxGCpRCCCGEEEIIIYQQQgijkQKlEEIIIYQQQgghhBDCaKRAKYQQQgghhBBCCCGEMBopUAohhBBCCCGEEEIIIYxGCpRCCCGEEEIIIYQQQgijMTN2AMWBVqvlxo0b2NnZodFojB2OEEIIIUSBKaW4d+8e7u7umJjIPeiSRvJRIYQQQpR0j5OPSoESuHHjBh4eHsYOQwghhBDisV27do0qVaoYOwxRQJKPCiGEEKK0eJR8VAqUgJ2dHZD9C7S3tzdyNEIIIYQQBZeQkICHh4curxEli+SjQgghhCjpHicflQIl6IbR2NvbS0IohBBCiBJNhgeXTJKPCiGEEKK0eJR8VCYoEkIIIYQQQgghhBBCGI0UKIUQQgghhBBCCCGEEEYjBUohhBBCCCGEEEIIIYTRyByUQgghRDGglCIzM5OsrCxjhyKKKVNTU8zMzGSOSSGEEEIUCslHRV4KMx81aoFy//79zJs3j+PHjxMTE8PWrVvp1q2bXpuIiAjGjx/Pvn370Gq11K9fny+++AJPT08A0tLSGDNmDJs2bSIlJYX27duzfPnyAi9nLoQQQhhLeno6MTExJCcnGzsUUczZ2Njg5uaGhYWFsUMRQgghRCki+ajIr8LKR41aoExKSsLb25v+/fvTs2fPHPsvX75My5YtGThwINOnT8fBwYGIiAisrKx0bUaOHMn27dvZvHkzTk5OjB49Gj8/P44fP46pqWlRXo4QQghRYFqtlsjISExNTXF3d8fCwkKekBM5KKVIT0/nr7/+IjIykpo1a2JiIjP1CCGEEOLxST4q8qOw81GjFih9fX3x9fV94P7JkyfTpUsX5s6dq9tWvXp13c/x8fGEhISwfv16OnToAEBoaCgeHh7s3r2bzp07F17wQgghhAGkp6ej1Wrx8PDAxsbG2OGIYsza2hpzc3OuXr1Kenq63g1bIYQQQohHJfmoyK/CzEeL7a13rVbLd999R61atejcuTPOzs40bdqUbdu26docP36cjIwMOnXqpNvm7u6Ol5cXYWFhD+w7LS2NhIQEvZcQQghhTPI0nMgP+Z4IIYQQuVNKGTuEEk/yDJEfhfU9Kbbfvps3b5KYmMgHH3zA888/z48//kj37t3p0aMH+/btAyA2NhYLCwsqVKigd6yLiwuxsbEP7Hv27Nk4ODjoXh4eHoV6LUIIUZpNPjiZtWfXkqWVybSFEEIIIUTR2x+9nxE/jyAuJc7YoQghHlGxLVBqtVoAXnzxRUaNGkXDhg2ZMGECfn5+rFix4qHHKqUeOl/CxIkTiY+P172uXbtm0NiFEKKs2HV1F99c/oZFxxdxJeGKscMRQgghhBBlzK2UW0z9ZSp7o/eyIWKDscMRQjyiYlugrFixImZmZtSrV09ve926dYmKigLA1dWV9PR07ty5o9fm5s2buLi4PLBvS0tL7O3t9V5CCCEKJj4tnvcPvw9Af6/+1Chfw8gRibLgypUraDQawsPDjR2KEEIIIYxMKcW0X6ZxO/U2NSvU5C3vt4wdkigDJB8tHMW2QGlhYcEzzzzDhQsX9Lb//vvvVK1aFYBGjRphbm7Orl27dPtjYmI4c+YMzZs3L9J4hRCirJl7dC5xqXFUc6gmyWAZFRAQQLdu3YwdRqFYvnw51apVw8rKikaNGnHgwAFjhySEEEKI/9h4fiMHrh/AwsSCOc/NwdLU0tghiSIm+WjpYdRVvBMTE7l06ZLufWRkJOHh4Tg6OuLp6cnYsWPp3bs3rVq1om3btuzcuZPt27ezd+9eABwcHBg4cCCjR4/GyckJR0dHxowZQ4MGDXSregshhDC8/dH7+ebyN2jQMKP5DEkGRany+eefM3LkSJYvX06LFi1YuXIlvr6+nDt3Dk9PT2OHJ4QQQgjg4p2LLDi2AIDRjUdTs0JNI0ckhOGUxXzUqE9QHjt2DB8fH3x8fAAIDAzEx8eHadOmAdC9e3dWrFjB3LlzadCgAZ9++ilbtmyhZcuWuj4WLlxIt27d6NWrFy1atMDGxobt27djampqlGsSQojS7l76PWYcmgHA6/Vep6FzQ+MGVAoppUhOzyzyl6FXv9y3bx9NmjTB0tISNzc3JkyYQGZmpm7/zp07admyJeXLl8fJyQk/Pz8uX76s18eRI0fw8fHBysqKxo0bc/LkSYPGmJsFCxYwcOBA3nzzTerWrcuiRYvw8PDg448/LvRzi6K3f/9+/P39cXd3R6PRsG3bNr39Go0m19e8efN0bdq0aZNj/yuvvFLEVyKEEGVHWlYa4/aPI12bznOVn+PVOq8aO6RSx1j5qKFzUslHSw6jPkHZpk2bPL94AwYMYMCAAQ/cb2VlxZIlS1iyZImhwxNCCJGLD499yJ/Jf+Jh58Fwn+HGDqdUSsnIot60H4r8vOdmdMbGwjCpwfXr1+nSpQsBAQGsW7eO8+fPM2jQIKysrAgODgYgKSmJwMBAGjRoQFJSEtOmTaN79+6Eh4djYmJCUlISfn5+tGvXjtDQUCIjI3n33XfzPPeQIUMIDQ19aJsH3X1OT0/n+PHjTJgwQW97p06dCAsLy/8vQJQYSUlJeHt7079/f3r27Jljf0xMjN7777//noEDB+ZoO2jQIGbMmKF7b21tXTgBCyGEYOHxhVy6ewlHK0dmtJjx0EVyxaMxVj4KhstJJR8tWYxaoBRCCFGyHLpxiC0XtwAwvfl0rM3kH+Aid8uXL8fDw4OlS5ei0WioU6cON27cYPz48UybNg0TE5McBZ6QkBCcnZ05d+4cXl5ebNiwgaysLFavXo2NjQ3169cnOjqat99++6HnnjFjBmPGjHloG3d391y337p1i6ysrByL7bm4uBAbG5uPKxclja+vL76+vg/c7+rqqvf+66+/pm3btlSvXl1vu42NTY62QgghDO9A9AHdat3vtXiPitYVjRyRKK4kHy1ZpEAphBAiX5Izkpl+aDoAvWv35hnXZ4wcUellbW7KuRmdjXJeQ4mIiKBZs2Z6TzS0aNGCxMREoqOj8fT05PLly0ydOpXDhw9z69YttFotAFFRUXh5eREREYG3tzc2Nja6Ppo1a5bnuZ2dnXF2dn6s+P/7JIZSSp7OEPz555989913rF27Nse+DRs2EBoaiouLC76+vgQFBWFnZ/fAvtLS0khLS9O9T0hIKJSYhRCiNIlLiWPqL1MB6FOnD62qtDJyRKWXsfLR++c2BMlHSxYpUAohhMiXxScWcz3xOu627oxqNMrY4ZRqGo3GYEOtjSW3BOr+tC73t/v7++Ph4cEnn3yCu7s7Wq0WLy8v0tPT9doX1OMMqalYsSKmpqY57k7fvHkzx11sUfasXbsWOzs7evToobf9tddeo1q1ari6unLmzBkmTpzIb7/9xq5dux7Y1+zZs5k+fXphhyyEEKWGUoqpv0wlLjWOJ8s/SWDjQGOHVKpJPir5aFEr2d82IYQQReL4n8fZeH4jAEHNg7A1tzVyRKK4q1evHlu2bNFLDMPCwrCzs6Ny5crExcURERHBypUree655wA4ePBgjj7Wr19PSkqKbj6/w4cP53nuxxlSY2FhQaNGjdi1axfdu3fXbd+1axcvvvhinucWpdvq1at57bXXsLKy0ts+aNAg3c9eXl7UrFmTxo0bc+LECZ5++ulc+5o4cSKBgf/84zohIQEPD4/CCVwIIUqBzRc2c+D6ASxMLJjTag6WppbGDkkUc5KPlixSoBRCCPFQKZkpTPtlGgA9avaguXtzI0ckipP4+HjCw8P1tjk6OjJ06FAWLVrE8OHDGTZsGBcuXCAoKIjAwEBMTEyoUKECTk5OrFq1Cjc3N6KionJMBN6nTx8mT57MwIEDmTJlCleuXGH+/Pl5xvS4Q2oCAwN54403aNy4Mc2aNWPVqlVERUUxZMiQR+5TlHwHDhzgwoULfP7553m2ffrppzE3N+fixYsPLFBaWlpiaSn/uBZCiPy4dOcSHx77EIDAxoHUqlDLyBGJ4kTy0dJBCpRCCCEeann4cqLuReFs7czoxqONHY4oZvbu3YuPj4/etn79+rFmzRp27NjB2LFj8fb2xtHRUZfYAZiYmLB582ZGjBiBl5cXtWvX5qOPPqJNmza6fsqVK8f27dsZMmQIPj4+1KtXjzlz5uS60rIh9e7dm7i4OGbMmEFMTAxeXl7s2LGDqlWrFup5RfEWEhJCo0aN8Pb2zrPt2bNnycjIwM3NrQgiE0KI0i0tK41xB8aRlpVGi8ot6FOnT442Wffukbh3Lw7+/kaIUBib5KOlg0Y96oD6UiQhIQEHBwfi4+Oxt7c3djhCCFFsnPrrFG98/wZapWVpu6W09mht7JBKndTUVCIjI6lWrVqOYaNC/NfDvi+SzzyaxMRELl26BICPjw8LFiygbdu2ODo66uaFSkhIwM3NjQ8//DDHkwuXL19mw4YNdOnShYoVK3Lu3DlGjx6NtbU1R48exdQ0fxP9y+cnhBC5m3NkDqERoThaObKl65Ycq3Zrk5OJenMQKSdO4DxhPE4BAcYJtASTfFQURGHlo/IEpRBCiFylZ6Uz7ZdpaJUWv+p+UpwUQpRKx44do23btrr39+eFvP/kBcDmzZtRSvHqq6/mON7CwoKffvqJxYsXk5iYiIeHBy+88AJBQUH5Lk4KIYTI3cHrBwmNyF5o5L0W7+UsTqalce2dd0g5cQITOztsmzQxRphCCAOQAqUQQohcrfhtBZfjL+Nk5cT4Z8YbOxwhhCgUbdq0yXOFzsGDBzN48OBc93l4eLBv377CCE0IIcq026m3mXIweyjuK7VfoVWVVnr7VXo610e8S/Khw5jY2OD5ySqs6tUzRqhCCAMwMXYAQgghip+IuAhWn1kNwJRnp1DeqrxxAxJCCCGEEGWGUoppv0wjLjWOGg41csyDrjIzuT5mLIn79qGxsqLKio+xbtjQOMEKIQxCCpRCCCH0ZGgzmPrLVLJUFp2qdqJD1Q7GDkkIIYQQQpQhn1/4nH3R+zA3MWdOqzlYmf0zz53SarkxaRL3fvwRjbk5VZYulaHdQpQCUqAUQgihZ/Xp1Vy4c4HyluWZ2HSiscMRQgghhBBlyOW7l5l/bD4AoxqNorZjbd0+pRSxQcEkfLMdzMyovHgR5Vq2MFaoQggDkgKlEEIInYt3LrLi1AoAJjSZkGMiciGEEEIIIQpLelY64/ePJy0rjRbuLXit7mu6fUop/pw1m7tffgkmJlSeOwe7du2MGK0QwpCkQCmEEAKATG0m036ZRqY2kzYebehSrYuxQxJCCCGEEGXIohOLuHDnAhUsK/Bei/cw0fxTsvhr4SLurF8PgNv772Pf5Z9c9V5cIjuXbs1z0TMhRPElBUohhBAArDu3jjNxZ7Azt2Pqs1PRaDTGDkkIIYQQQpQRYdfDWH8uuwA5o8UMKtlU0u27tWIFcatWAeAaNI3y3bvp9t29eZdNH67kyF+n2PbB+iKNWQhhOGbGDkAIIYTxXb57mWUnlwEw9pmxONs4GzkiIYQQQghRVtxOvc3kXyYD0Lt2b9p4tNHti/tsDX8tWgyA8/jxVHj11X/2Xb/Nl8s+I9biHhoFto62RRq3EMJw5AlKIYQo4zK1mUw+OJl0bTrPVX6Obk92M3ZIQjzQlStX0Gg0hIeHGzsUIYQQQhiAUoqgX4K4lXKL6g7VGd14tG7fnc2buTlnDgAVRwzHqX+Abt/Nqzf5YtlqYi3uYaI0PFfVh05v9Szq8EUZJPlo4ZACpRBClHGfnfmMs3FnsbOwI6hZkAztFvkWEBBAt27djB2Gwe3fvx9/f3/c3d3RaDRs27YtX8ft27ePRo0aYWVlRfXq1VmxYkXhBiqEEEKUApvOb2Jv9F7MTcyZ22ou1mbWANzduo3Y4OkAOA0aRMW339YdE3Mphv+tWsufFomYKg2tajSm3YAXjRK/MC7JR/WV5HxUCpRCCFGG/X7nd5b/thyAiU0m4mLrYuSIhDC+pKQkvL29Wbp0ab6PiYyMpEuXLjz33HOcPHmSSZMmMWLECLZs2VKIkQohhBAl24XbF/jw2IcABDYKpLZjbQASvv+emMnZQ74rvPEGlQJH6W6iR5+/xpbPQrlpnoSpMqFtnWdp0/cF41yAEIWkLOajUqAUQogyKkObwZSDU8jUZtLWoy1+1f2MHZK4TylITyr6l4FXvty3bx9NmjTB0tISNzc3JkyYQGZmpm7/zp07admyJeXLl8fJyQk/Pz8uX76s18eRI0fw8fHBysqKxo0bc/LkSYPGmBtfX19mzpxJjx498n3MihUr8PT0ZNGiRdStW5c333yTAQMGMH/+/EKMVAghhCi5UjNTGb9/vG6aodfqvgbAvZ9/5vrYcaDVUv7ll3CZNFFXnLz621W2hm7ilnkSZsqETk+1oOWrnY15GaWXsfJRA+ekko+WnHxUFskRQogy6tNTnxJxOwIHSwemNZsmQ7uLk4xkmOVe9OeddAMsDDO5/PXr1+nSpQsBAQGsW7eO8+fPM2jQIKysrAgODgay7wwHBgbSoEEDkpKSmDZtGt27dyc8PBwTExOSkpLw8/OjXbt2hIaGEhkZybvvvpvnuYcMGUJoaOhD25w7dw5PT09DXCoAhw4dolOnTnrbOnfuTEhICBkZGZibmxvsXEIIIURpMP/YfC7HX8bJyon3WryHRqMh8cBBrr87EjIzsff3xzU4WJej/nHsIt9u28pts2TMlSmdG7WmcddWxr2I0sxY+SgYLCeVfLRk5aNGLVDu37+fefPmcfz4cWJiYti6desD5w546623WLVqFQsXLmTkyJG67WlpaYwZM4ZNmzaRkpJC+/btWb58OVWqVCmaixBCiBIoIi6CVadWATC56WQqWlc0ckSitFm+fDkeHh4sXboUjUZDnTp1uHHjBuPHj2fatGmYmJjQs6f+RPYhISE4Oztz7tw5vLy82LBhA1lZWaxevRobGxvq169PdHQ0b/9rDqrczJgxgzFjxjy0jbu7YRPu2NhYXFz0p0hwcXEhMzOTW7du4ebmZtDzCSGEECXZT1E/8fmFzwGY1XIWTtZOJB3+lehhw1AZGdh17oz77FloTE0B+P1QBDt2fMNdsxQslCldnu1AQ99mxrwEUQJIPlqy8lGjFijvj6nv379/ji/Fv23bto1ff/011w9v5MiRbN++nc2bN+Pk5MTo0aPx8/Pj+PHjmP79x0wIIcQ/MrIymPzLZDJVJh2rduT5J543dkjiv8xtsu8cG+O8BhIREUGzZs30nsxt0aIFiYmJREdH4+npyeXLl5k6dSqHDx/m1q1baLVaAKKiovDy8iIiIgJvb29sbP6Jq1mzvP8x4uzsjLOzs8GuJb/++xSy+nt4kjydLIQQQvwjNimWoLAgAALqB9C8cnOST5zg2tChqLQ0yrVtS+V5c9GYZZcrIvaf5odd33HXNBVLZYZ/q854tX/GmJdQNhgrH71/bgOQfLRk5aNGLVD6+vri6+v70DbXr19n2LBh/PDDD7zwgv7Et/Hx8YSEhLB+/Xo6dOgAQGhoKB4eHuzevZvOnWUuCiGE+K8Vp1Zw8c5FKlhWYHLTySXiP1ZljkZjsKHWxqKUyjNB8vf3x8PDg08++QR3d3e0Wi1eXl6kp6frtS8oYwypcXV1JTY2Vm/bzZs3MTMzw8nJyWDnEUIIIUqyLG0Wkw5OIj4tnrqOdRnhM4KU02e4NvgtVHIyts2bU3nRQjQWFgCc2nWc3ft3kWCaipXWnK4dfKnX6mkjX0UZIfmoXvuCkny04Ir1HJRarZY33niDsWPHUr9+/Rz7jx8/TkZGht4Ye3d3d7y8vAgLC3tggTItLY20tDTd+4SEBMMHL4QQxdCZW2cIOR0CwORnJ+NkXfz/QyVKpnr16rFlyxa9xDAsLAw7OzsqV65MXFwcERERrFy5kueeew6AgwcP5uhj/fr1pKSkYG1tDcDhw4fzPLcxhtQ0a9aM7du362378ccfady4cbGf70cIIYQoKqvPrOZo7FGszayZ22ouWRcvE/Xmm2gTE7F55hmqLFuKiaUlACe/P8LPh37inmka1lpzenTpSs1nGxj5CkRJIvloycpHi3WBcs6cOZiZmTFixIhc98fGxmJhYUGFChX0tru4uOSoGv/b7NmzmT59ukFjFUKI4i4tK40pB6eQpbJ4/onn6fyEPGUuHl98fDzh4eF62xwdHRk6dCiLFi1i+PDhDBs2jAsXLhAUFERgYCAmJiZUqFABJycnVq1ahZubG1FRUUyYMEGvnz59+jB58mQGDhzIlClTuHLlSr5WIXzcITWJiYlcunRJ9z4yMpLw8HAcHR11d7knTpzI9evXWbduHZB9l3zp0qUEBgYyaNAgDh06REhICJs2bXrkOIQQQojS5NRfp1gWvgyAiU0m4vZXJlcHDEQbH491w4ZU+fhjTP4uAB39Ooy9x/eRZJKGjdaCl1/sQbVGdYwZvijGJB8tHflosS1QHj9+nMWLF3PixIkCDz/M7THef5s4cSKBgYG69wkJCXh4eDxyrEIIURIsD1+uWylxctPJxg5HlBJ79+7Fx8dHb1u/fv1Ys2YNO3bsYOzYsXh7e+Po6KhL7ABMTEzYvHkzI0aMwMvLi9q1a/PRRx/Rpk0bXT/lypVj+/btDBkyBB8fH+rVq8ecOXMeOm+1IRw7doy2bdvq3t/PGe5fF0BMTAxRUVG6NtWqVWPHjh2MGjWKZcuW4e7uzkcffVTosQohhBAlQWJ6IuP2j9PdKO9i7kPUG33Jun0bq3r18Fi1EtNy2cOJD/1vHwdO/UKySTq2WkteefllPBo8aeQrEMWZ5KPZSno+qlGPOqDewDQajd4q3osWLdJVte/LysrCxMQEDw8Prly5ws8//0z79u25ffu23lOU3t7edOvWLd9PSSYkJODg4EB8fDz29vYGvS4hhCgOfvvrN/p+3xet0rK47WLaebYzdkjib6mpqURGRlKtWjWsrKyMHY4o5h72fZF8pmSTz08IUZpNODCB7/74DndbdzY1WkzcgKFkxsRgWasWnmvXYPb3v+cPbtzNL+d/JcUkAzutJa/0eZXKdZ4wbvBlgOSjoiAKKx81ybuJcbzxxhucOnWK8PBw3cvd3Z2xY8fyww8/ANCoUSPMzc3ZtWuX7riYmBjOnDlD8+bNjRW6EEIUK6mZqUw5OAWt0uJX3U+Kk0IIIYQQoshsv7yd7/74DlONKXPqjuf24BFkxsRgUb06nqtDdMXJfWt/4ODfxUn7LCte7/u6FCeFKEOMOsQ7rzH1/11lyNzcHFdXV2rXrg2Ag4MDAwcOZPTo0Tg5OeHo6MiYMWNo0KCBblVvIYQo65acXMKVhCtUsq7EhCYT8j5ACCGEEEIIA4hKiGLm4ZkADK/6BnZj5pN+7RrmHh54frYas4oVAfjp0285ci2cNJNMHLKseWNQXyp6uhkzdCFEETNqgTI/Y+rzsnDhQszMzOjVqxcpKSm0b9+eNWvWYGpqWhghCyFEiXLizxOsP7cegKBmQThYOhg5IiGEEEIIURZkZGUwfv94kjOTaWHTgDbz95EeGYmZuxtV13yGuYsLAD98vI3jsadJ12RRIcuGN94OwNH90RcXEUKUTEYtULZp04aCTIF55cqVHNusrKxYsmQJS5YsMWBkQghR8iVnJDPllykoFC/WeJHWHq2NHZIQQgghhCgjloUv40zcGZy1tgSG3iP94iXMKlWi6mefYV65MgDfL/kfJ25FkKHJwjHLhn7DBuHgUiGPnoUQpVGxXcVbCCHE4/nw2Idcu3cNFxsXxjUZZ+xwhBBCCCFEGfFrzK+sPrMaqzTFhzvKk3XhEqaOjniu+QyLqlUB+Hbh54TfvUCmRkvFLFv6jRyEnVN54wYuhDCaYrtIjhBCiEd3IPoAX/z+BQAzW87E3kJWhBVCiNzs378ff39/3N3d0Wg0bNu2TW9/QEAAGo1G7/Xss8/qtUlLS2P48OFUrFgRW1tbunbtSnR0dBFehRBCFB93Uu8w8cBELNK1zP/WEcvzVzF1cMDzs9VY1qgBwNdzN3Dy7+Kkc1Y5BoweIsVJIco4KVAKIUQpczf1LtPCpgHwet3Xedbt2TyOEEKIsispKQlvb2+WLl36wDbPP/88MTExuteOHTv09o8cOZKtW7eyefNmDh48SGJiIn5+fmRlZRV2+EIIUawopZj2yzTu3rvJtK8tcP79L0zs7PAICcHq78Vuv5q9llNJl8jSaHHJsmPA+KHYlLczcuRCCGOTId5CCFGKKKV47/B73Eq5RTWHarz79LvGDkkIIYo1X19ffH19H9rG0tISV1fXXPfFx8cTEhLC+vXr6dChAwChoaF4eHiwe/duOnfubPCYhRCiuNp8YTMHru5h3FZFzUspaGxs8Fi1Emuv+iil2PL+Gs5lRKHVKFyz7Bk46R3MrS2NHbYQohiQJyiFEKIU+S7yO368+iNmGjNmt5yNlZmVsUMSQogSb+/evTg7O1OrVi0GDRrEzZs3dfuOHz9ORkYGnTp10m1zd3fHy8uLsLCwB/aZlpZGQkKC3ksIIUqy3+/8zoJf5zHyay0+l7RorKzwWPExNj4+KKX4csZqzv5dnHTPcmDg5GFSnBRC6EiBUgghSonYpFhmHZ4FwGDvwdSvWN/IEQlheFeuXEGj0RAeHm7sUEQZ4evry4YNG/j555/58MMPOXr0KO3atSMtLQ2A2NhYLCwsqFBBf9VZFxcXYmNjH9jv7NmzcXBw0L08PDwK9TqEEKIwpWamMmHvOAZ/nUrTCwqNuTlVli7FtkkTlFJ8Pv0TIrTXUBpFFW0FBkwbjrmVhbHDFuKRSD5aOKRAKYQQpYBWaZnyyxTuZdyjQcUGDGowyNghiTIgICCAbt26GTsMgwsODs6xKMqDhvf+2759+2jUqBFWVlZUr16dFStWFEG0orD17t2bF154AS8vL/z9/fn+++/5/fff+e677x56nFIKjUbzwP0TJ04kPj5e97p27ZqhQxdCiCIz/8g8Omz6nZbnFJiZUnnxYsq1bIHSKjZNW8F5dQOlAU/lSMDUdzAzl9nmhGFIPqqvJOej8ldBCCFKgU3nN/FrzK9YmVoxq+UszEzkz7sQj6N+/frs3r1b997U1PSh7SMjI+nSpQuDBg0iNDSUX375haFDh1KpUiV69uxZ2OGKIuTm5kbVqlW5ePEiAK6urqSnp3Pnzh29pyhv3rxJ8+bNH9iPpaUllpYytFEIUfL9fPUnyi3ZRNtTCmWiocr8D7Fr15asLC2bgj7mktlfADxBRfoGDcXERJ6TEiI/ylo+Kn8ZhBCihPsj/g8WHl8IQGDjQJ5weMK4AYnHppQiOSO5yF9KKYNex759+2jSpAmWlpa4ubkxYcIEMjMzdft37txJy5YtKV++PE5OTvj5+XH58mW9Po4cOYKPjw9WVlY0btyYkydPGjTGBzEzM8PV1VX3qlSp0kPbr1ixAk9PTxYtWkTdunV58803GTBgAPPnzy+SeEXRiYuL49q1a7i5uQHQqFEjzM3N2bVrl65NTEwMZ86ceWiBUgghSoOYxBhOB42h80mF0kDlOXOwf74zWZlZbJy2XFecrK5xpu80KU6WJMbKRw2dk0o+WnLyUXnERgghSrAMbQaTDkwiLSuN5u7NeaX2K8YOSRhASmYKTTc2LfLz/trnV2zMbQzS1/Xr1+nSpQsBAQGsW7eO8+fPM2jQIKysrAgODgYgKSmJwMBAGjRoQFJSEtOmTaN79+6Eh4djYmJCUlISfn5+tGvXjtDQUCIjI3n33bxXph8yZAihoaEPbXPu3Dk8PT0fuP/ixYu4u7tjaWlJ06ZNmTVrFtWrV39g+0OHDuktkgLQuXNnQkJCyMjIwNzcPM+4hXEkJiZy6dIl3fvIyEjCw8NxdHTE0dGR4OBgevbsiZubG1euXGHSpElUrFiR7t27A+Dg4MDAgQMZPXo0Tk5OODo6MmbMGBo0aKBb1VsIIUqjTG0m3018nU6HUwFwmR6Mg78/mZlZbJi2jEiL2wDUNHXl1cmDpThZwhgrHwXD5aSSj5asfFQKlEIIUYJ9cuoTzsadxd7CnhnNZzx0vjMhitLy5cvx8PBg6dKlaDQa6tSpw40bNxg/fjzTpk3DxMQkx1CTkJAQnJ2dOXfuHF5eXmzYsIGsrCxWr16NjY0N9evXJzo6mrfffvuh554xYwZjxox5aBt3d/cH7mvatCnr1q2jVq1a/Pnnn8ycOZPmzZtz9uxZnJyccj0mNjYWFxcXvW0uLi5kZmZy69Yt3dN2ovg5duwYbdu21b0PDAwEoF+/fnz88cecPn2adevWcffuXdzc3Gjbti2ff/45dnZ2umMWLlyImZkZvXr1IiUlhfbt27NmzZo8h2IJIURJtjP4TVrsugGAxdhhOPXqTXpqOhtnfMwVizsA1LGozCuTZG50YRySj5asfFQKlEIIUUKd/us0q06tAmDKs1NwsXXJ4whRUlibWfNrn1+Ncl5DiYiIoFmzZnpF8xYtWpCYmEh0dDSenp5cvnyZqVOncvjwYW7duoVWqwUgKioKLy8vIiIi8Pb2xsbmnzvozZo1y/Pczs7OODs7P3Lsvr6+up8bNGhAs2bNqFGjBmvXrtUVr3Lz3xsE94cnyY2D4q1NmzYPHUr2ww8/5NmHlZUVS5YsYcmSJYYMTQghiq2Ti2dQ44vsXOX2QH9aDHyH1JRUNs5YQZTlXQDqW3ny8oQBRoxSPA5j5aP3z20Iko+WrHxUCpRCCFECpWSmMOngJLJUFr5P+OJbzTfvg0SJodFoDDbU2lhyW8H4vwmSv78/Hh4efPLJJ7i7u6PVavHy8iI9PV2vfUEZYkjNv9na2tKgQQPdoii5cXV1JTY2Vm/bzZs3MTMze+BdbiGEEKIkil77KVYfbwLgdLf69Bo7l5SkVDbO/JhrlvEANLB9gp5jA4wYpXhcko9KPlrUpEAphBAl0KLji7iScAVna2cmPzvZ2OEIkUO9evXYsmWLXmIYFhaGnZ0dlStXJi4ujoiICFauXMlzzz0HwMGDB3P0sX79elJSUrC2zr6Tfvjw4TzP/bhDav4rLS2NiIgIXZy5adasGdu3b9fb9uOPP9K4ceNiP9+PEEIIkV93/vc/7s3+EIA9bRzpN2MtSQlJbJy9kuuWCQD42NfgxcA3jBmmEIDko1Cy8lEpUAohRAlz8PpBNp7fCMB7Ld7DwdLByBGJsiw+Pp7w8HC9bY6OjgwdOpRFixYxfPhwhg0bxoULFwgKCiIwMBATExMqVKiAk5MTq1atws3NjaioKCZMmKDXT58+fZg8eTIDBw5kypQpXLlyJV+rED7ukJoxY8bg7++Pp6cnN2/eZObMmSQkJNCvXz9dm4kTJ3L9+nXWrVsHZN8lX7p0KYGBgQwaNIhDhw4REhLCpk2bHjkOIYQQojiJ3/4tMVOnogG+b2LGC7NWoxIVG+ev5IblPVDQ2LE2fu++auxQRRkj+WjpyEelQCmEECXI7dTbTDk4BYBX67xK88rNjRyRKOv27t2Lj4+P3rZ+/fqxZs0aduzYwdixY/H29sbR0VGX2AGYmJiwefNmRowYgZeXF7Vr1+ajjz6iTZs2un7KlSvH9u3bGTJkCD4+PtSrV485c+bkmMzc0KKjo3n11Ve5desWlSpV4tlnn+Xw4cNUrVpV1yYmJoaoqCjd+2rVqrFjxw5GjRrFsmXLcHd356OPPir0WIUQQoiikPDDj1yfMB6Ngh99NFSZNAWXDBc2LF5JjMU9NEpDE+d6+L7zsrFDFWWQ5KPZSno+qlGPOqC+FElISMDBwYH4+Hjs7e2NHY4QQuRKKcWIPSPYe20vT5Z/kk0vbMLKzMrYYYnHlJqaSmRkJNWqVcPKSj5P8XAP+75IPlOyyecnhCiu7u3dS/Sw4ZCZyZ4GGi693ZlJtSfx5fK1/GmeiEZpaFa5AZ0G9zB2qOIRST4qCqKw8lF5glIIIUqIL3//kr3X9mJuYs4Hz30gxUkhhBBCCFGoksLCuD7iXcjM5Je6Gr55uQofewzn8+Vr+Ms8CROl4bmqPrQd0NXYoQohSjgpUAohRAnwR/wfzDs6D4CRT4+ktmNtI0ckhBBCCCFKs+SjR7k29B1Uejq/1tLwcVdzFlWbwndrt3DLPAlTZULrWo1p9VoXY4cqhCgFpEAphBDFXEZWBhP2TyA1K5Vmbs14vd7rxg5JCCGEEEKUYinh4Vx7awgqNZXfapiy+EV4p9JQTn59mNtmyZgpU9rVe5bmvTsaO1QhRCkhBUohhCjmloQvIeJ2BOUtyzOz5UxMNCbGDkkIIYQQQpRSKWfPEjVoMNrkZC49acPcbml0NHmepF/vcscsBXNlSseGz9GkextjhyqEKEWM+q/c/fv34+/vj7u7OxqNhm3btun2ZWRkMH78eBo0aICtrS3u7u707duXGzdu6PWRlpbG8OHDqVixIra2tnTt2pXo6OgivhIhhCgcR2KOsObMGgCCmwfjbONs3ICEEEIIIUSplXrhd64NGIj23j3+qlWJ6S+m0TDpadz+qMgd0xQslRldmraT4qQQwuCMWqBMSkrC29ubpUuX5tiXnJzMiRMnmDp1KidOnOCrr77i999/p2tX/cl3R44cydatW9m8eTMHDx4kMTERPz8/srKyiuoyhBCiUMSnxTPx4EQUip41e9Les72xQ8pVVmIiKiPD2GEIIYQQQojHkPbHH0QNGEBWfDxpdaoyxu82dW7Xx+tWPeJNU7HSmuPXqjM+XVoYO1QhRClk1CHevr6++Pr65rrPwcGBXbt26W1bsmQJTZo0ISoqCk9PT+Lj4wkJCWH9+vV06NABgNDQUDw8PNi9ezedO3cu9GsQQojCoJRi+qHp3Ey+yRP2TzDumXHGDilX2uRkrg0ajGn58lRetBATS0tjhySEEEIIIQooPSqKqID+ZMXFYVKrBmP8b+F105t6KV4kmKZio7XgxU5dqN2yobFDFUKUUiVqIrP4+Hg0Gg3ly5cH4Pjx42RkZNCpUyddG3d3d7y8vAgLC3tgP2lpaSQkJOi9hBCiOPn68tfsuroLM40ZHzz3ATbmNsYOKQdtejrRw4aTcvIkycePk3H9urFDEkIIIYQQBZQefZ2rAQFk3ryJxZM1mP96OWrE1qFOSn0STdKw1VrQ06+7FCeFEIWqxBQoU1NTmTBhAn369MHe3h6A2NhYLCwsqFChgl5bFxcXYmNjH9jX7NmzcXBw0L08PDwKNXYhhCiIqIQoZv86G4B3fN6hfsX6Ro4oJ5WZyY3Ro0kKC0NjY4PHyhVYVq9u7LCEEEIIIUQBZMTEEBUQQOaNGCyqVePHwBZYn7enelpNkk3SsdNa0uull6nRpK6xQxVClHIlokCZkZHBK6+8glarZfny5Xm2V0qh0WgeuH/ixInEx8frXteuXTNkuEII8cgytBlMPDCR5MxkGrk0on/9/sYOKQel1RIzeTL3du1GY26Ox7Kl2Pj4GDssUUZcuXIFjUZDeHi4sUMRQgghSrSMmzeJCuhPRnQ05p6e/Dl7KBf3X8Mty5MUkwzss6x4tc+rVH2qprFDFaJYkXy0cBT7AmVGRga9evUiMjKSXbt26Z6eBHB1dSU9PZ07d+7oHXPz5k1cXFwe2KelpSX29vZ6LyGEKA6WnVzGqVunsDO3Y3bL2ZiamBo7JD1KKf6c+T7xX38DpqZUXrwI22bNjB2WMJKAgAC6detm7DAMbv/+/fj7++Pu7o5Go2Hbtm052iilCA4Oxt3dHWtra9q0acPZs2fz7HvLli3Uq1cPS0tL6tWrx9atWwvhCoQQQoiHy7x1i6iA/qRfvYp55crYLJ/Hzq/3UlG5k6bJpHyWNa/1fwP3Ok8YO1QhHkry0dKTjxbrAuX94uTFixfZvXs3Tk5OevsbNWqEubm53mI6MTExnDlzhubNmxd1uEII8VgO3TjE6jOrAQhuHoxbOTcjR5TTXwsXcWfjRtBocP/gA+zatTN2SEIYXFJSEt7e3ixduvSBbebOncuCBQtYunQpR48exdXVlY4dO3Lv3r0HHnPo0CF69+7NG2+8wW+//cYbb7xBr169+PXXXwvjMoQQQohcZd65Q1T/AaT/8Qdmrq5U/uxTNm7ahqXWgXRNJo5ZNrwxJACX6pWNHaoQZVZZzEeNWqBMTEwkPDxc91hsZGQk4eHhREVFkZmZyUsvvcSxY8fYsGEDWVlZxMbGEhsbS3p6OpC90vfAgQMZPXo0P/30EydPnuT111+nQYMGulW9hRCiJIhLiWPSwUkoFC/VeolOT3TK+6AiduuTT4hbtQoA16AgHPz9jBxR6aWUQpucXOQvpZRBr2Pfvn00adIES0tL3NzcmDBhApmZmbr9O3fupGXLlpQvXx4nJyf8/Py4fPmyXh9HjhzBx8cHKysrGjduzMmTJw0aY258fX2ZOXMmPXr0yHW/UopFixYxefJkevTogZeXF2vXriU5OZmNGzc+sN9FixbRsWNHJk6cSJ06dZg4cSLt27dn0aJFhXQlQgghhL6s+HiiBgwk7eJFzCpVouqaz9gQ+jWpmZChycIpy5Z+w9/EqfKDRySKssFY+aihc1LJR/UV53zU7FEPTE9P5+bNm2i1Wr3tnp6e+e7j2LFjtG3bVvc+MDAQgH79+hEcHMw333wDQMOGDfWO27NnD23atAFg4cKFmJmZ0atXL1JSUmjfvj1r1qzB1LR4DYsUQogH0Sotk3+ZzK2UWzxZ/knGPTPO2CHlcHvjRv76cAEAzmPHUOGV3kaOqHRTKSlceLpRkZ+39onjaGwMs2L89evX6dKlCwEBAaxbt47z588zaNAgrKysCA4OBrLvDAcGBtKgQQOSkpKYNm0a3bt3Jzw8HBMTE5KSkvDz86Ndu3aEhoYSGRnJu+++m+e5hwwZQmho6EPbnDt3rkA5y79FRkYSGxtLp07/3EiwtLSkdevWhIWF8dZbb+V63KFDhxg1apTets6dOxeLhLCkMkQ+KoQQZUXWvXtEvTmItIgITJ2c8Fy7hi2bfiQmLZ4sjZaKmbYEBL5FOUeZAk0YLx8Fw+Wkko/mVJzz0QIXKC9evMiAAQMICwvT235/YZqsrKx899WmTZuHVsbzUzW3srJiyZIlLFmyJN/nFUKI4mT9ufX8cv0XLE0tmdtqLtZm1sYOSU/8N9/w54z3AHAa8hZOAwcaOSJREixfvhwPDw+WLl2KRqOhTp063Lhxg/HjxzNt2jRMTEzo2bOn3jEhISE4Oztz7tw5vLy8dCMoVq9ejY2NDfXr1yc6Opq33377oeeeMWMGY8aMeWgbd3f3R7622NhYgBzzXbu4uHD16tWHHpfbMff7E/lnyHxUCCHKgqzEJK4NfovU06cxLV8ez89W883ne7mYGoNWo3DOtKX/uKFY29saO1QhDEby0dyPK675aIELlAEBAZiZmfHtt9/i5ub20NWyhRBCPNzZW2dZdGIRAOOeGUfNCsVrlcR7u3dzY+IkACq8/jqV8nG3UDw+jbU1tU8cN8p5DSUiIoJmzZrp5QktWrQgMTGR6OhoPD09uXz5MlOnTuXw4cPcunVL9xRcVFQUXl5eRERE4O3tjc2/7qA3y8eiTM7Ozjg7OxvsWh7kvznQ/eKYoY8ROUk+KoQQ+adNTiZ6yBBSTp7ExN4ez9UhfPvlQc6lX0NpFM4Ztrw+cTDW5aQ4Kf5hrHz0/rkNQfJRwx1TFApcoAwPD+f48ePUqVOnMOIRQogyIzE9kbH7x5KpzaRj1Y68XOtlY4ekJ/GXX7g+KhCysnDo3h2XSROLxX+4ygKNRmOwodbGkluic39kxP3t/v7+eHh48Mknn+Du7o5Wq8XLy0s31/Sjzj9U2ENqXF1dgew70G5u/yxmdfPmzRx3pP973H/vTud1jMid5KNCCJE/2tRUrr3zDsnHjmFSrhwen37C11/+wnntdZQGnNNteH5Ud+zLORg7VFHMSD4q+WhRK/AiOfXq1ePWrVuFEYsQQpQZSineO/we1+5dw83WjaBmQcWq+Jd84iTRw4ajMjKw69QJt/dmoDEx6rpqooSpV68eYWFhekldWFgYdnZ2VK5cmbi4OCIiIpgyZQrt27enbt263LlzJ0cfv/32GykpKbpthw8fzvPcM2bM0C3C96DX4wypqVatGq6uruzatUu3LT09nX379tG8efMHHtesWTO9YwB+/PHHhx4jcif5qBBC5E2bnk708BEkHzqMiY0NVVatZNuWMCJUdnGyUro1Twz0onqlJ40dqhCFQvLRnIpzPlrgJyjnzJnDuHHjmDVrFg0aNMDc3Fxvv729TKgrhBB5+fry1+yI3IGpxpS5rebiYFl87lqnRkRw7a23UCkp2LZsifv8eWjMHnlNNVHKxcfHEx4errfN0dGRoUOHsmjRIoYPH86wYcO4cOECQUFBBAYGYmJiQoUKFXBycmLVqlW4ubkRFRXFhAkT9Prp06cPkydPZuDAgUyZMoUrV64wf/78PGN63CE1iYmJXLp0Sfc+MjKS8PBwHB0d8fT0RKPRMHLkSGbNmkXNmjWpWbMms2bNwsbGhj59+uiO69u3L5UrV2b27NkAvPvuu7Rq1Yo5c+bw4osv8vXXX7N7924OHjz4yLGWVZKPCiHEw6n0dK6/O5KkAwfQWFtT+ePlbN16iEumfwHgnG5N3AtpvFOji5EjFeLxST5aSvJRVUAajUZpNBplYmKi97q/rSSKj49XgIqPjzd2KEKIMuCPu3+oZ0KfUV5rvNTK31YaOxw9qZcvqwvNmqtzteuoyD6vqazkZGOHVOqlpKSoc+fOqZSUFGOHUmD9+vVTQI5Xv379lFJK7d27Vz3zzDPKwsJCubq6qvHjx6uMjAzd8bt27VJ169ZVlpaW6qmnnlJ79+5VgNq6dauuzaFDh5S3t7eysLBQDRs2VFu2bFGAOnnyZKFd1549ex56XUoppdVqVVBQkHJ1dVWWlpaqVatW6vTp03r9tG7dWu8YpZT68ssvVe3atZW5ubmqU6eO2rJlS4Fie9j3pSzlM5KPCiHEg2kzMtS14SPUudp1VMRT3ir+wEG1dvJHKigoSAUFBaklE2epl7e9rFIzU40dqigmJB+VfLQgCisf1ShVsAH1+/bte+j+1q1bF6S7YiEhIQEHBwfi4+PljrsQolClZ6Xz2o7XOH/7PE1cm7Cq4ypMTUyNHRYA6dHXufr662TGxmJVrx6ea9dgamdn7LBKvdTUVCIjI6lWrRpWVlbGDkcUcw/7vpSlfMaQ+ej+/fuZN28ex48fJyYmhq1bt9KtWzcAMjIymDJlCjt27OCPP/7AwcGBDh068MEHH+gNy2rTpk2OmHr37s3mzZvzHUdZ+vyEEIVHZWVxY+w4EnbsQGNujsvij/j6p9+4Yp49bNUp3ZwN9bfzhf8XeNo/2tx3ovSRfFQURGHlowUes1cSC5BCCFFcLDi+gPO3z1PBsgKzn5tdbIqTGTdvEjVgAJmxsVjUqIHHp59IcVIIUWwZMh9NSkrC29ub/v3707NnT719ycnJnDhxgqlTp+Lt7c2dO3cYOXIkXbt25dixY3ptBw0axIwZM3TvrQ20AqkQQuSX0mqJmTSZhB07wMyMinM/ZOvucKIs7gJQPsOUVbU3M6/FPClOCiGKnXwVKE+dOoWXlxcmJiacOnXqoW2feuopgwQmhBClzU9Xf2JDxAYAZracibPNo89JYkhZd+9ybeCbZERFYV6lCp6rQzBzdDR2WEIIoaew8lFfX198fX1z3efg4JBjIvklS5bQpEkToqKi9FbetLGx0a2oKYQQRU1ptcQGBRH/9ddgakrF2XPZduAM0RbxAJTPNCWk1hf0rt2b55943sjRCiFETvkqUDZs2JDY2FicnZ1p2LAhGo0m16XWNRoNWVlZBg9SCCFKumv3rjH1l6kA9KvXj1ZVWhk5omxZiUlEDRpM2sWLmFWqhOdnqzF3cSlYJ7cugW1FsC5fKDEKIQQUn3w0Pj4ejUZD+fLl9bZv2LCB0NBQXFxc8PX1JSgoCLuHPImelpZGWlqa7n1CQkJhhSyEKOWUUvw5cyZ3v/wfmJhQfvr7bD0UwQ2LBFBQUWPBypqbqOtYl7HPjDV2uEIIkat8FSgjIyOpVKmS7mchhBD5l56Vzph9Y7iXcQ/vSt682+hdY4cEgDY1leihQ0k9fRrT8uXx/Gw1Fh4eBeskIQbWvQiW5eD1LeBQpXCCFUKUecUhH01NTWXChAn06dNHb16l1157jWrVquHq6sqZM2eYOHEiv/32W46nL/9t9uzZTJ8+vSjCFkKUYkopbn4whzsbN4FGg93UYL45cYkYi3tolAZ3S3s+qrwaW3Nb5reej6WppbFDFkKIXOWrQFm1atVcfxZCCJG3+cfmcy7uHA6WDsxvPR9zE3Njh4Q2PZ3o4SNIPnIEE1tbPD79FMsnnyxYJ2mJsKk3JESDU00wtymcYIUQAuPnoxkZGbzyyitotVqWL1+ut2/QoEG6n728vKhZsyaNGzfmxIkTPP3007n2N3HiRAIDA3XvExIS8CjoTSIhRJmmlOKvBQu4vXYtADbjp7D9zFX+tEjERGmoWd6dWY4fATCj+QyZd1IIUayZPMpB69evp0WLFri7u3P16lUAFi1axNdff23Q4IQQoqT74coPbDq/CYBZLWfhamv8+clUZiY3Ro8m6cABNFZWeKxaibVX/YJ1kpUJ/xsAMb+BjRO89gXYyLyVQoiiU5T5aEZGBr169SIyMpJdu3bluSrl008/jbm5ORcvXnxgG0tLS+zt7fVeQghRELeWLCXuk08BsAycwLcXr/OneSKmSoOPW20+clkNwBv13qDTE52MGaoQQuSpwAXKjz/+mMDAQLp06cLdu3d1c/yUL1+eRYsWGTo+IYQosaISoggKCwJggNeAYjHvpMrK4sbESdzbtRuNuTlVli3FplGjAnaiYOd4uPgDmFnBq5vBsXrhBCyEELkoynz0fnHy4sWL7N69GycnpzyPOXv2LBkZGbi5uRk0FiGEuO/WipXc+vtpbtN3RrMj6iZ/mSdhpkxoUf1p1rh/SWJGIj7OPoxqNMrI0QohRN4KXKBcsmQJn3zyCZMnT8bU1FS3vXHjxpw+fdqgwQkhREmVlpXG6H2jScpI4mnnpxnuM9zYIaGUIjZ4Ognbt4OZGZUXL6ZcixYF7+jQMjj6KaCBHqvAo4nBYxVCiIcxZD6amJhIeHg44eHhQPb8luHh4URFRZGZmclLL73EsWPH2LBhA1lZWcTGxhIbG0t6ejoAly9fZsaMGRw7dowrV66wY8cOXn75ZXx8fGjxKH9jhRAiD3Ehq/nr75sx2kHvsvPP29wyS8JcmdK2bjN+rvErv9/5HUcrx2IzvZAQQuQlX3NQ/ltkZCQ+Pj45tltaWpKUlGSQoIQQoqSbc2QO52+fp4JlBea2mouZSYH/3BpU9gTqH3D3yy/BxITKc+dg165twTs69zX8OCX7507vQb0XDRuoEELkgyHz0WPHjtG27T9/D+/PC9mvXz+Cg4P55ptvgOxVxP9tz549tGnTBgsLC3766ScWL15MYmIiHh4evPDCCwQFBekVT4UQwhBur1vPzXnzAEjr9w5778Zz1ywFC2VKB59WXKt3i68PfY2JxoR5rebhbONs5IiFECJ/Cvwv5mrVqhEeHp5jcvLvv/+eevXqGSwwIYQoqXb8sYMvf/8SDRpmPzcbF1sXY4fEX4sXc3vtOgDcZs7EvkuXgncSfQy+GgwoeOZNaDbMsEEKkQ9XrlyhWrVqnDx5MkfBSJQdhsxH27Rpg1Lqgfsftg/Aw8ODffv2FeicQgjxKO5s/pw/Z80CIOnVwRxITiTeNBVLZcbzz7bDskl5Zu3Ivsky3Gc4TdxklIsQhUHy0cJR4CHeY8eO5Z133uHzzz9HKcWRI0d4//33mTRpEmPHji2MGIUQosSIjI9k+qHpALzZ4E1aVDb+8L5bK1cRt2IlAC5Tp1C+R/eCd3I7Ejb2hsxUqNkZnp8DGo2BIxUlTUBAAN26dTN2GAYXHByMRqPRe7m66i9wpZQiODgYd3d3rK2tadOmDWfPnjVSxGWP5KNCiLLm7paviA0OBiD+5TfZl5lKvGkqVlpz/Fs9T/V29QncG0i6Np3WVVozwGuAcQMWoohIPlp68tECP0HZv39/MjMzGTduHMnJyfTp04fKlSuzePFiXnnllcKIUQghSoSUzBRG7xtNcmYyjV0aM7ThUGOHxO116/lr4UIAnMeMxvG11wreSfJt2PAyJN8C16fgpdVgatwh60IUtvr167N7927d+/8O1Z07dy4LFixgzZo11KpVi5kzZ9KxY0cuXLiAnZ1dUYdb5kg+KoQoS+K/+YaYKdlT7NzuPoAwTTpJJmnYaC3o2smPWi28GPHzCK4nXqdyucq83/J9TDQFfhZJCFHMlLV89JH+ag0aNIirV69y8+ZNYmNjuXbtGgMHDjR0bEIIUaJ8cOQDLt65iKOVY7GYd/Lu//6nGwZUcehQnN58s+CdZKTC569D3EWwrwJ9vgDLcgaOVPyXUoqMtKwif+U1lLWg9u3bR5MmTbC0tMTNzY0JEyaQmZmp279z505atmxJ+fLlcXJyws/Pj8uXL+v1ceTIEXx8fLCysqJx48acPHnSoDE+iJmZGa6urrpXpUqVdPuUUixatIjJkyfTo0cPvLy8WLt2LcnJyWzcuLFI4hOSjwohyoaE77/nxoSJoBR/+gXwi3kGSSZp2Got6dm1O3VaPsXqM6vZF70PCxMLFrRZgIOlg7HDFqWAsfJRQ+ekko+WHAX+13O7du346quvKF++PBUrVtRtT0hIoFu3bvz8888GDVAIIUqCrRe38tXFr9CgYU6rOVSyqZT3QYUo/tvviJk6DQDHgAAqDn+E+SK1Wtg6GK7+Apb28NoXYO9m4EhFbjLTtax6t+jntBu8uDXmloZZ1OP69et06dKFgIAA1q1bx/nz5xk0aBBWVlYE/z1ELSkpicDAQBo0aEBSUhLTpk2je/fuhIeHY2JiQlJSEn5+frRr147Q0FAiIyN599138zz3kCFDCA0NfWibc+fO4enp+cD9Fy9exN3dHUtLS5o2bcqsWbOoXr06kL1AS2xsLJ06ddK1t7S0pHXr1oSFhfHWW2/l4zckHofko0KIsuDe7t1cHzMWtFqiu/TjuG0mqZoM7LOseKnXy3g2qMGRmCMsObkEgElNJ1HPSdaFEIZhrHwUDJeTSj5ashS4QLl3717S09NzbE9NTeXAgQMGCUoIIUqSc3HnmHl4JgBvN3ybZ92eNWo893bv5sb48aAU5V/pjfP4cWgKOl+kUvDDxOxVu03MoXcouNQvnIBFqbR8+XI8PDxYunQpGo2GOnXqcOPGDcaPH8+0adMwMTGhZ8+eeseEhITg7OzMuXPn8PLyYsOGDWRlZbF69WpsbGyoX78+0dHRvP322w8994wZMxgzZsxD27i7uz9wX9OmTVm3bh21atXizz//ZObMmTRv3pyzZ8/i5OREbGwsAC4u+gtgubi4cPXq1YeeVxiG5KNCiNLu3t69RI8KhKwsrjzfl5N2maRrMimfZU2vN/rgXsuDP5P+ZOz+sWiVlhdrvEiPmj2MHbYQxYrkoyVLvguUp06d0v187tw53S8DICsri507d1K5cuUCnXz//v3MmzeP48ePExMTw9atW/UmN1VKMX36dFatWsWdO3do2rQpy5Yto379f/6RnJaWxpgxY9i0aRMpKSm0b9+e5cuXU6VKlQLFIoQQjyI+LV43IXmrKq146ynj3qlKPHCQ638nsw4vdsV12rSCFycBwj6CX1dk/9x9BVRvbdhAxUOZWZgweHHR/87NLAw3X1VERATNmjXT+/61aNGCxMREoqOj8fT05PLly0ydOpXDhw9z69YttFotAFFRUXh5eREREYG3tzc2Nja6Ppo1a5bnuZ2dnXF2dn7k2H19fXU/N2jQgGbNmlGjRg3Wrl1LYGCgbt9//7+llHq0/7+JfCuMfFQIIYqbxIO/cH34CMjI4NLzfTnlkEmGJgvHLBteHdSXSp6uZGgzGLt/LLdTb1OrQi0mPztZ/hskDMpY+ej9cxuC5KMlS74LlA0bNtStHNSuXbsc+62trVmyZEmBTp6UlIS3tzf9+/fPUbWG/E34OXLkSLZv387mzZtxcnJi9OjR+Pn5cfz48RwTiAohhCFplZYJByZwPfE6VcpVYVbLWUadkDz56FGihw9HZWRg17kzbu+/j8bkEeL57XPYlT08nE7vQ4OXDBuoyJNGozHYUGtjyS05uj+f0P3t/v7+eHh48Mknn+Du7o5Wq8XLy0v3ZNyjzj9kiCE1/2Zra0uDBg24ePEigG4FxdjYWNzc/pn24ObNmznuYgvDKox8VAghipOkw78S/c47qIwMzj/flzMOGWRptFTMtOWNYQNxcHUE4MNjH3Ly5knKmZdjYZuFWJtZGzlyUdpIPir5aFHLd4EyMjISpRTVq1fnyJEjepNzWlhY4OzsXOCCoK+vr15V+N/+O+EnwNq1a3FxcWHjxo289dZbxMfHExISwvr16+nQoQMAoaGheHh4sHv3bjp37lygeIQQoiBW/raSg9cPYmlqycK2C406IXnKqVNce2sIKjUV29atqDxvLhqzR1ik5/LP8PXfq483GwbNH2HuSiGAevXqsWXLFr3EMCwsDDs7OypXrkxcXBwRERGsXLmS5557DoCDBw/m6GP9+vWkpKRgbZ39D6/Dhw/nee7HHVLzX2lpaUREROjirFatGq6uruzatQsfHx8A0tPT2bdvH3PmzMl3v6LgCiMfFUKI4iL56FGuvf02Ki2Ns537cc4hDa1G4ZxZjr6BgynnaA/A9svb2RCxAYD3W76Pp33+ChxClDWSj5Ys+f7Xa9WqVQF0j7sWtvxM+Hn8+HEyMjL02ri7u+Pl5UVYWNgDC5RpaWmkpaXp3ickJBTehQghSqX90fv5+LePAZjWbBp1HOsYLZbU8+eJenMQ2uRkbJ59liqLF6OxsCh4RzG/wedvgDYTvHpCx/cMH6wodeLj4wkPD9fb5ujoyNChQ1m0aBHDhw9n2LBhXLhwgaCgIAIDAzExMaFChQo4OTmxatUq3NzciIqKYsKECXr99OnTh8mTJzNw4ECmTJnClStXmD9/fp4xPe6QmjFjxuDv74+npyc3b95k5syZJCQk0K9fPyD7jvvIkSOZNWsWNWvWpGbNmsyaNQsbGxv69OnzyOcVeSvqfFQIIYpK8okTRL01BJWSwqnOAZwvn4rSKFwz7QkYPwQru+zhpRFxEUw/NB2AwU8Npp1nzqfJhShrJB8tHfnoIzxeA5cvX2bRokVERESg0WioW7cu7777LjVq1DBYYPmZ8DM2NhYLCwsqVKiQo82/5yT6r9mzZzN9+nSDxSqEKFui70Uz8cBEFIpetXrRtUZXo8WSdvkyUQMGok1IwLphQzyWLcXEyqrgHd25AhtehvREeOI56PYxPMrwcFHm7N27V3fX9r5+/fqxZs0aduzYwdixY/H29sbR0VGX2AGYmJiwefNmRowYgZeXF7Vr1+ajjz6iTZs2un7KlSvH9u3bGTJkCD4+PtSrV485c+bkOi2MIUVHR/Pqq69y69YtKlWqxLPPPsvhw4d1xTGAcePGkZKSwtChQ3XzZP/444+6KWhE4SuKfFQIIYpC8smTXHtzECo5mXDf/lywTwYNVM50oN+UoVhYWQJwN/Uuo/aOIi0rjZaVWzLUe6iRIxeieJB8tHTkoxpVwAH1P/zwA127dqVhw4a0aNECpRRhYWH89ttvbN++nY4dOz5aIBqN3iI5YWFhtGjRghs3buiNpx80aBDXrl1j586dbNy4kf79++s9DQnQsWNHatSowYoVK3I9V25PUHp4eBAfH4+9vf0jxS+EKBtSM1Pp+31fIm5H0KBiA9Y8vwYL00d4WtEA0q9d4+prr5N58yZW9erhueYzTB/lb1hSHKzuBHGXwMUL+u8AK+MNVy9rUlNTiYyMpFq1alg9SnFZlCkP+74kJCTg4OBQJvKZwspHjaksfX5CiH+knDpF1ICBZCUmcqLLAC7ZJwHgmVWBvtPewcw8+5miLG0WQ38aStiNMDzsPNj0wiajTi9U7CgF92LAPv9DZsU/JB8VBVFY+WiBn6CcMGECo0aN4oMPPsixffz48QZLCPMz4aerqyvp6encuXNH7ynKmzdv0rx58wf2bWlpiaWlpUHiFEKUHUop3v/1fSJuR1DBsgIL2iwwWnEyIyaGqID+ZN68icWTNfAI+fTRipPpybCpd3Zx0sEDXvufFCeFEMVeUeWjQghRmFLOnCVq4JtkJiZy7IUBRNplFyerqYq8Hvy23py6S04uIexGGNZm1ixqu0iKk/+19wP49WN4fStUaWTsaIQQj6DA4/ciIiIYOHBgju0DBgzg3LlzBgkK9Cf8vO/+hJ/3i4+NGjXC3Nxcr01MTAxnzpx5aIFSCCEexZaLW9h2aRsmGhPmtp6Lq62rUeLI/OsvovoPIOP6dcyreuK5ejVm/5nqIl+yMuF/AyD6KFiVh9e3gL1bnocJIYSxFVU+KoQQhSX13DmiBg4k414iR/z/KU7W1LjwRtBQveLkrqu7CDkTAsD05tOpVaGWUWIutg58CPs+gNR4uH7c2NEIIR5RgZ+grFSpEuHh4dSsWVNve3h4eIEnAE1MTOTSpUu695GRkYSHh+Po6Iinp2eeE346ODgwcOBARo8ejZOTE46OjowZM4YGDRroVvUWQghDOHPrDLN+nQXAcJ/hPOv2rFHiyLx9m6v9+5N+5Qpm7m5U/ewzzB9l8mWtFr4ZDr9/D2ZW0OdzqFTb8AELIUQhMGQ+KoQQRS31/Hmi+g8gPSGRI10HcM0mEYA6Zu68MmWwXtvLdy8z5WD2fHl96/XFt5pvkcdbrB1aBj/NyP65QzA0HfzQ5kKI4qvABcpBgwYxePBg/vjjD5o3b45Go+HgwYPMmTOH0aNHF6ivY8eO0bZtW937wMBA4J/JTPMz4efChQsxMzOjV69epKSk0L59e9asWaN3x0kIIR7H7dTbBO4NJEObQTuPdgz0yvnUTlHIvHMnO5m9dBkzFxeqrlmDufsjzLOjFOyaCr9tBI0pvPQZeBqn4CqEEI/CkPmoEEIUpdTffyeq/wBS7yXz64sBXLfKLk56WVXlpQn99dreS7/Hu3veJTkzmSauTRjVaJQxQi6+jnwCP0zK/rnNRGgpvx8hSrICL5KjlGLRokV8+OGH3LhxAwB3d3fGjh3LiBEj0Gg0hRJoYZJJyYUQD5KhzeCtXW9xNPYoVe2rsumFTdhZFP2qaFkJCUQF9Cf13DlMK1ak6rp1WFav9midHVgAP03P/rnbCmj4quECFQUmk5KLgpBFcrJJPiqEKInSLl3iat9+JN9L5le/14ixTESjoKH9k7w4+nW9tlql5d2f32Vv9F5cbV353O9zHK0cjRR5MXRiXfZoIICWgdB+GpTAv/3FheSjoiCKxSI5mZmZbNiwgVdffZVRo0Zx7949gBK7hLkQQuRl/tH5HI09iq25LR+1/cg4xcnERKIGDcouTlaoQNXPVj96cfL4mn+Kk51nSXFSCFHiSD4qhCiJ0v6I5GpAf+4lZ3LEvw9/WiRiojQ0rliHLsN752i/8tRK9kbvxcLEgkVtFklx8t9++xy+GZH987PvSHFSiFKiQIvkmJmZ8fbbb5OWlgZkJ4KSDAohSqutF7ey8fxGAGa1nEX18tWLPAZtUhLX3hpC6m+nMHVwwPOz1Vj+Z861fDu7Db79e+jLc6Oh2TsGi1MIIYqK5KNCiJIm/coVovr1426q4tALPfnTIgkTpaFZ5adyLU7uu7aPj8M/BmDKs1OoX7F+UYdcfJ35CrYNARQ88yZ0fl+Kk0KUEgVexbtp06acPHmyMGIRQohi49Rfp3jv8HsADPUeSjvPdkUegzYlhWtD3yHl+HFM7OzwWB2CVZ06j9bZ5T3w1SBQWmgUAO2mGjRWIYQoSpKPCiFKivSoKK72CyAu3ZRfn+/KLfNkzJQJras3puPg7jnaX757mfEHxqNQ9K7dm+41c7YpsyK+hS1vZuezT/cF33lSnBSiFCnwIjlDhw5l9OjRREdH06hRI2xtbfX2P/XUUwYLTgghjOFWyi1G7RlFhjaDth5tecv7rSKPQZuWRvQ7w0j+9VdMbG3x/PQTrOs/4t3z6OOw+TXISoe6XeGFBZLMCSFKNMlHhRAlQXp0NFf7BXAz05KjHdtx1ywZc2VK23rP0rx3xxzt49PiGf7zcJIykmjs0pjxTcYbIepi6vcf4csAUFnw1CvgtwhMCvy8lRCiGCtwgbJ37+xH0EeMGKHbptFoUEqh0WjIysoyXHRCCFHE0rPSGbVnFDdTblLdoTqzWs7CRFO0yY9KT+f6iHdJCgtDY2ODx6qVWHt7P1pnf12ADS9BRhJUaw09PwUTU8MGLEQRunLlCtWqVePkyZM0bNjQ2OEII5F8VAhR3GVcv05UvwBilC3H2rUgwTQFS2VGx0atadz1uRztM7WZjNk3hmv3ruFu686HbT7E3MTcCJEXQ5f3wOevgzYD6neHF5dJPiuMSvLRwlHgf3VHRkbmeP3xxx+6/xVCiJJs9pHZhP8Vjp25HR+1+4hyFuWK9PwqI4PowEAS9+1DY2WFx8cfY9Oo0aN1dvcarO8OKbfB/Wl4ZQOYWRo2YFGmBQQE0K1bN2OHYXD79+/H398fd3d3NBoN27Zty9FGKUVwcDDu7u5YW1vTpk0bzp49q9cmLS2N4cOHU7FiRWxtbenatSvR0dFFdBWlm+SjQojiLCMmhqsB/YnCgSOtmpNgmoq11pwXWnTKtTgJ8OGxDzkccxhrM2s+aveRLIpz3x97YdMrkJUGdfygxydgWuDnrEQpJvlo6clHC1SgvHfvHr///jtnz57F1taWqlWr5ngJIURJ9cWFL/jf7/9Dg4Y5reZQ1b5o/6apzEyujxtH4u6f0FhYUGXZUmybNnm0zhL/yi5OJlyHirXgtf+BpSwiIUR+JCUl4e3tzdKlSx/YZu7cuSxYsIClS5dy9OhRXF1d6dixo25FaYCRI0eydetWNm/ezMGDB0lMTMTPz0+e7ntMko8KIYqzjD//5GpAAJFmFTneohGJJmnYai14sZMfT3XKPa/benEroRGhQPbCjLUdaxdlyMXXH3thY2/ITIVaz8NLq8FUnioVZUNZzEfzXaA8deoUderU4fnnn8fPz48nn3yS3bt3F2ZsQghRZE78eYLZv84GYMTTI3iuSu53twuLysrixqRJ3Pt+J5ibU2XJR5Rr0eLROku+Deu7QdxFsK8Cb2wFWyeDxisKl1KKjNTUIn8ppQx6Hfv27aNJkyZYWlri5ubGhAkTyMzM1O3fuXMnLVu2pHz58jg5OeHn58fly5f1+jhy5Ag+Pj5YWVnRuHHjIlkYxdfXl5kzZ9KjR49c9yulWLRoEZMnT6ZHjx54eXmxdu1akpOT2bhxIwDx8fGEhITw4Ycf0qFDB3x8fAgNDeX06dOSPz0GyUeFEMVZxs2bRPUL4JKlOyeeqU+SSTrltJb07NqdOi1zn64n/GY4Mw7PALIXZuxQtUNRhlx8/bc42WudjAQqYsbKRw2dk0o+WnLy0Xw/Gz1hwgQ8PT358ssvsbKyYvr06QwbNozz588XZnxCCFHoYhJjGLV3FJkqk85PdGag18AiPb/SaomZNo2Eb7aDmRlVFi6gXOvWj9ZZ2r3sOSf/PAO2ztDvG3CoYtiARaHLTEvjo34vFfl5R6z9H+ZWVgbp6/r163Tp0oWAgADWrVvH+fPnGTRoEFZWVgQHBwPZd4YDAwNp0KABSUlJTJs2je7duxMeHo6JiQlJSUn4+fnRrl07QkNDiYyM5N13383z3EOGDCE0NPShbc6dO4enp+cjXVtkZCSxsbF06tRJt83S0pLWrVsTFhbGW2+9xfHjx8nIyNBr4+7ujpeXF2FhYXTu3PmRzl3WFUY+un//fubNm8fx48eJiYlh69atekPFlFJMnz6dVatWcefOHZo2bcqyZcuo/6+Fy9LS0hgzZgybNm0iJSWF9u3bs3z5cqpUkb+/QpQVmbduERXQn/M2VTnVoAppmgzss6x4qXdvPL2q5XpMbFIsI/eMJFObSceqHY2yMGOxJMXJYsFY+SgYLieVfLRk5aP5LlAeO3aMHTt20LhxYwBWr16Ns7MziYmJlCtXtHO0CSGEoSRnJDP85+HcTr1N7Qq1mdF8BpoiXOFaKUXsjBnEb/kKTEyoPH8edh0e8c55ejJsfAWuHwfrCtD3a3CqYdiAhcin5cuX4+HhwdKlS9FoNNSpU4cbN24wfvx4pk2bhomJCT179tQ7JiQkBGdnZ86dO4eXlxcbNmwgKyuL1atXY2NjQ/369YmOjubtt99+6LlnzJjBmDFjHtrG3d39ka8tNjYWABcXF73tLi4uXL16VdfGwsKCChUq5Ghz/3hRcIWRj94fQtW/f/8c30n4Z/jUmjVrqFWrFjNnzqRjx45cuHABO7vsqTNGjhzJ9u3b2bx5M05OTowePRo/Pz+OHz+Oqaks5CBEaZcZF8fVgADO2lXndB1nMjSZVMiypne/Prg+6ZHrMSmZKYz4eQRxqXHUqlCLmS1mFvnCjMXS5T3Zc05KcVIYgOSjJSsfzXeB8tatW3qVXScnJ2xsbPjrr7+kQCmEKJG0SsvEAxO5cOcCjlaOLGm3BBtzmyI7v1KKP9+fxd3Nn4NGg/ucOdg///yjdZaZBl+8AVcPgoUdvP4VuNQzbMCiyJhZWjJi7f+Mcl5DiYiIoFmzZnoF/xYtWpCYmEh0dDSenp5cvnyZqVOncvjwYW7duoVWqwUgKioKLy8vIiIi8Pb2xsbmn/9fNmvWLM9zOzs74+zsbLBreZD/3sy4v4L0w+SnjXiwwshHfX198fX1zXXff4dPAaxduxYXFxc2btzIW2+9pRs+tX79ejr8fYMpNDQUDw8Pdu/eXeyeThBCGFbmnTtE9R/Abw61OfekA5maLJwybXhtSACOVXL/b5FSiqBfgoi4HUEFywp81O6jIs1Biy0pThYrxspH75/bECQfzV1xzUfzXaDUaDTcu3cPq78fs71/Qffu3SMhIUHXzt7e3vBRCiFEIVh6cik/X/sZcxNzFrddjFs5tyI7t1KKP2fN5k5oKGg0uM2ciYO/36N1lpUJWwbCpd1gZg2vfQmVnzZswKJIaTQagw21NpbcEp/78wnd3+7v74+HhweffPIJ7u7uaLVavLy8SE9P12tfUIU9pMbV1RXIvivt5vbP342bN2/q7mK7urqSnp7OnTt39O5a37x5k+bNmz/SeUXR56OFOXwqLS2NtLQ03ft/xy+EKBmy7t4lasBATjjWJaKqDVkaLZUyy/HGiAHYOz94Fe6QMyF8f+V7zDRmfNjmQyqXq1yEURdTUpwsdiQflXy0qOW7QKmUolatWjm2+fj46H7WaDTFciUgIYT4r2//+JZPTn8CwPTm02no3LDIzq2U4s/Zs7mzfj0AbjPfo3zP3Cc/zpNWC1+/AxHbwdQCXt0IVfO+oydEYatXrx5btmzRSwzDwsKws7OjcuXKxMXFERERwcqVK3nuuexFqQ4ePJijj/Xr15OSkoK1tTUAhw8fzvPchT2kplq1ari6urJr1y5dHpSens6+ffuYM2cOAI0aNcLc3Jxdu3bRq1cvAGJiYjhz5gxz58595HOXdUWdjxbm8KnZs2czffp0g8QphCh6WfHxRA0YyBGn+vxe2QytRotLph39xgzGprzdA4/bE7WHj058BMDEphN5xvWZogq5+JLipCgkko+WrHw03wXKPXv2FGYcQghRZE79dYqgX4IAGOg1EP8a/kV2bqUUNz/4gDvr/l2czDnnWT47gx2j4dRm0JjCy2uhRjsDRitE3uLj4wkPD9fb5ujoyNChQ1m0aBHDhw9n2LBhXLhwgaCgIAIDAzExMaFChQo4OTmxatUq3NzciIqKYsKECXr99OnTh8mTJzNw4ECmTJnClStXmD9/fp4xPe6QmsTERC5duqR7HxkZSXh4OI6Ojnh6eqLRaBg5ciSzZs2iZs2a1KxZk1mzZmFjY0OfPn0AcHBwYODAgYwePRonJyccHR0ZM2YMDRo00A0DFgVnrHy0MIZPTZw4kcDAQN37hIQEPDxyn6tOCFG8ZN27R9SbgzhcqQG/u4LSKNwz7ek38W0sba0feFxEXATjD4xHoehVqxe9avcqwqiLKSlOCgOQfLSU5KNKqPj4eAWo+Ph4Y4cihChkMYkxqvXm1sprjZca/tNwlaXNKrJza7VaFTtrtjpXu446V7uOuv3FF4/TmVI7JykVZK9UkINSp740WJyiaKWkpKhz586plJQUY4dSYP369VNAjle/fv2UUkrt3btXPfPMM8rCwkK5urqq8ePHq4yMDN3xu3btUnXr1lWWlpbqqaeeUnv37lWA2rp1q67NoUOHlLe3t7KwsFANGzZUW7ZsUYA6efJkoV3Xnj17HnpdSmX//zkoKEi5uroqS0tL1apVK3X69Gm9flJSUtSwYcOUo6Ojsra2Vn5+fioqKuqxYnvY90Xymcf33+/f5cuXFaBOnDih165r166qb9++SimlfvrpJwWo27dv67V56qmn1LRp0/J9bvn8hCgZMu/dU5G9eqv177yngoKCVFBQkPpk8kKVnpr20OP+TPpTtfuinfJa46UG/TBIpWelF1HExdiFH5SaUSk7n93QS6mMVGNHVGZJPir5aEEUVj6qUeoRB9SXIgkJCTg4OBAfHy9zaApRiiVnJNNvZz/O3z5PrQq1WO+7vsgmJFdKcXPOXG6vWQOA64zpVOj1GHfN934Ae2dn/9x1CTzd9/GDFEaRmppKZGQk1apV082rJ8SDPOz7IvnM49NoNGzdupVu3boB2X+73d3dGTVqFOPGjQOyh085OzszZ84c3SI5lSpVIjQ0VG/4VJUqVdixY0e+F8mRz0+I4i8rMYmrb77JAdeniCyfPYfsE1lOvD7tbczMHzw4MTkjmf4/9Odc3DmqO1RnfZf12FuU8f+fR2yHL/uDNgNqvwAvfyZPThqR5KOiIAorH833EG8hhCjJtErLpIOTOH/7fJGv2K2U4ubcef8UJ6c/ZnFy//x/ipPPfyDFSSGEeAx5DaEqdcOnhBCPRJuczJW3BrOvckOi7FIAqIEzfYLfwtTU9MHHKS2TD07mXNw5KlhWYGn7pVKcPLMFtgwClQX1u0OPT8DU3NhRCSGMTAqUQogyYenJpfwU9ZNuxW73co8+IXFBKKW4OW8+tz/7DADX4GAq9H6M4uTBRfDze9k/tw+CZ99+/CCFEKIMO3bsGG3bttW9vz8vZL9+/VizZg3jxo0jJSWFoUOHcufOHZo2bcqPP/6Ind0/i2AsXLgQMzMzevXqRUpKCu3bt2fNmjUPLVoIIUoObXIyVwYPYU/lhkTbJAFQ28SNV6e9leexi08sZnfU7uwctN1iPOzK+Fyz4RuzF3hUWnjqFXhxGZhKWUIIIQVKIUQZ8PWlr3Urdgc3Dy6yFbuVUtycP5/bq1cD4BocRIVXej96h2FLYXf24j60mwLPBT68vRBCiDy1adOGh814pNFoCA4OJjg4+IFtrKysWLJkCUuWLCmECIUQxqRNTubyW++wp0oDblglgQIvK09emjggz2O3XtzK6jPZeeD05tPxcfYp7HCLt2OfwbejAJU9AshvMZiYGDsqIUQx8ch/DS5dusQPP/xASkr24+0ylaUQojg6EnOE4EPBAAxqMIiuNboWyXmVUvy1YAG3Q/4uTgZNo8Irrzx6h4c/hh8nZ//cZiK0GmuAKIUQomSTfFQIUZi0KSn8PmQEuz3qccMqCY3S8LR9zXwVJ4/GHmXGoRkADH5qMP41/As73OLt15Xw7UhAQZPBUpwUQuRQ4L8IcXFxdOjQgVq1atGlSxdiYmIAePPNNxk9erTBAxRCiEf1x90/GLl3JJnaTHyf8GWYz7AiOW92cXIhcZ98CoDLtKlUePXVR+/w11Wwc0L2z63GQuvxBohSCCFKLslHhRCFTZuSwrm3R7HboxZ/WiRhqjQ0rVSPrqNfy/PYK/FXGLlnJJkqk85PdOadhu8UQcTF2C+L4fvshcZoPhx850pxUgiRQ4H/KowaNQozMzOioqKwsflngYnevXuzc+dOgwaXmZnJlClTqFatGtbW1lSvXp0ZM2ag1Wp1bZRSBAcH4+7ujrW1NW3atOHs2bMGjUMIUfLEpcQx9Keh3Eu/R8NKDXmv5XuYaAo/EfqnOJk9pNxl6hQc/15I4ZEc/RS+//tpyZajoO1k0GgMEKkQQpRcRZmPCiHKHm1KCr+9M4afq1TjlnkyZsqEFp5P8/ywl/M8Nj4tnmE/DyMhPYGnKj7FzBYziyQHLZaUgn1zYde07PetxkHH9ySXFULkqsBzUP7444/88MMPVKlSRW97zZo1uXr1qsECA5gzZw4rVqxg7dq11K9fn2PHjtG/f38cHBx49913AZg7dy4LFixgzZo11KpVi5kzZ9KxY0cuXLigN3m5EKLsSM1MZcTPI7ieeB0POw8Wt1uMpalloZ9Xt1r33wviuEyZguNred9lf6Dja+C7v58Eaj4ie1EcSeiEEKJI81EhRNmiTUnh+PAJHKzsQbxpChbKlNZ1nqXFqx3zPDYjK4NRe0dxNeEqbrZuLG63GCszqyKIuhhSCn6aDgcXZr9vN0WmKBJCPFSBC5RJSUl6d6rvu3XrFpaWhi0AHDp0iBdffJEXXngBgCeeeIJNmzZx7NgxILsYsGjRIiZPnkyPHj0AWLt2LS4uLmzcuJG33sp7VTUhROmiVVomHZzEqVunsLewZ1n7ZThaORb6eZVS/Pn+LO6EhgLZw7of68nJE+the/aNGJ59BzrOkOKkEEL8rSjzUSFE2aFNTeXXEVMJc3PlnmkqVlpzOjRuTeOuLfM8VilF8KFgjsYexdbclqXtl1LRumIRRF0MabWwYwwcC8l+32lm9tBuIYR4iAI/a96qVSvWrVune6/RaNBqtcybN4+2bdsaNLiWLVvy008/8fvvvwPw22+/cfDgQbp06QJAZGQksbGxdOrUSXeMpaUlrVu3Jiws7IH9pqWlkZCQoPcSQpQOi04sYtfVXZiZmLG47WKqOVQr9HMqrZbY6dOzi5MaDa7vzXi84mT4Rvjm7ySu6RDo/L4UJ4X425UrV9BoNISHhxs7FGFERZmPCiHKBm1qKgffDeKgmyP3TNOw0Vrg17pTvoqTAMvCl/HN5W8w1Zgyr9U8alWoVcgRF1NZGbB18N/FSQ34LZTipCh1JB8tHAUuUM6bN4+VK1fi6+tLeno648aNw8vLi/379zNnzhyDBjd+/HheffVV6tSpg7m5OT4+PowcOZJX/15sIjY2FgAXFxe941xcXHT7cjN79mwcHBx0Lw8PD4PGLYQwji9//5LPzmQPr57RfAaNXRsX+jlVVhYxU6dyd/PnoNHgNmsWFV7Oe36iBzoZCtuGAgqeeROe/0CKk6LYCggIoFu3bsYOw+CCg4PRaDR6L1dXV702+ZkDOy0tjeHDh1OxYkVsbW3p2rUr0dHRRXkppVZR5qNCiNJPm5rK3pHvEeZiT5JJOuW0lnTzfRGv9s/k6/ivLn7FylMrAZj67FSeq/JcYYZbfGWkwOdvwOkvwcQMen4KjfNe8VyIxyH5aOnJRwtcoKxXrx6nTp2iSZMmdOzYkaSkJHr06MHJkyepUaOGQYP7/PPPCQ0NZePGjZw4cYK1a9cyf/581q5dq9dO859/vCulcmz7t4kTJxIfH697Xbt2zaBxCyGKXtj1MN4//D4AQ72H4l/Dv9DPqbKyiJk0ifgtX4GJCe5z51C+e7dH7/DYZ/D1O4DKTuZ85xX74qRSihX7LrNq/2VjhyKEQdWvX5+YmBjd6/Tp03r778+BvXTpUo4ePYqrqysdO3bk3r17ujYjR45k69atbN68mYMHD5KYmIifnx9ZWVlFfTmlTlHmo0KI0k2bmsqPgbM57GJNqkkGDllW9Hr5ZWo1q5+v4w9eP8iMQzMAGPzUYHrW6lmY4RZfafdgw8vw+/dgZgWvbIQGLxk7KiFKtLKWjz7ScmKurq5Mnz6db7/9lh07djBz5kzc3NwMHRtjx45lwoQJvPLKKzRo0IA33niDUaNGMXv2bF0cQI6nJW/evJnjqcp/s7S0xN7eXu8lhCi5IuIiGLV3FFkqC//q/gzxHlLo51QZGdwYO474r78BU1MqfzgfB//HKIoe+QS+HZn9c5O34IUFYFK8V3zMyNIyYctpPvj+PLO/P8+5GzJdhtC3b98+mjRpgqWlJW5ubkyYMIHMzEzd/p07d9KyZUvKly+Pk5MTfn5+XL6sX+w+cuQIPj4+WFlZ0bhxY06ePFkksZuZmeHq6qp7VapUSbfvv3Nge3l5sXbtWpKTk9m4cSMA8fHxhISE8OGHH9KhQwd8fHwIDQ3l9OnT7N69u0iuobQrqnxUCFF6adPS2DFmLscqmZGuycQx04Y+AW/g2eDJfB0fERfB6L2jdTnosIbDCjniYiopDtb6w5UDYGEHr38FtTobOyohAMlHS1I+WuB//X722Wd8+eWXObZ/+eWXOZ5sfFzJycmY/Ocf6Kampmi1WgCqVauGq6sru3bt0u1PT09n3759NG/e3KCxCCGKp+h70by9+22SM5Np4tqE4ObBD32C2hBUejrXR48hYccOMDen8qKF2Pv6PnqHhz/OnkgcoNkw8J1T7J+cjE/OoN/qI3x+7BomGgj2r089d7nZYyhKKbTpWUX+UkoZ7BquX79Oly5deOaZZ/jtt9/4+OOPCQkJYebMmbo2SUlJBAYGcvToUX766SdMTEzo3r277r/zSUlJ+Pn5Ubt2bY4fP05wcDBjxozJ89xDhgyhXLlyD31FRUU9tI+LFy/i7u5OtWrVeOWVV/jjjz90+/IzB/bx48fJyMjQa+Pu7o6Xl9dD58kW+VOU+agQonTSpqXx9Zj5nHSCTE0WFTNteWPoAFxqVM7X8TcSbzD0p6EkZybT1K0p05tPL/QctFhKuAFrusCNk2DtCAHb4YkWxo5KGICx8lFD5qSSj5asfLTAq3h/8MEHrFixIsd2Z2dnBg8eTL9+/QwSGIC/vz/vv/8+np6e1K9fn5MnT7JgwQIGDMiex0Kj0TBy5EhmzZpFzZo1qVmzJrNmzcLGxoY+j7NAhRCiRLiTeoe3d79NXGoctSrUYlHbRViYWhTqObXp6VwfOYrEn39GY25O5cWLsWv3GAsy/PIR7Jqa/XOLkdAhuNgXJ6Pikum/5giX/0rC1sKUJX18aFfnwU+ti4JTGVpuTCv6pMF9RnM0FqYG6Wv58uV4eHiwdOlSNBoNderU4caNG4wfP55p06ZhYmJCz576w+BCQkJwdnbm3LlzeHl5sWHDBrKysli9ejU2NjbUr1+f6Oho3n77/+zdd3hUxdfA8e/uJtn03kMSWqih9yZIe2kq2MWC2AWpIohKU7qoSBFBELCCioiodAi9Sa8hgfTee9ky7x8XgvnRsiGd+TxPHrh3786eFVzOnjtz5u27vvbHH398z8TR29v7jo916NCB7777jgYNGpCQkMDMmTPp3LkzFy5cwMXF5a49sCMiIgBldYeFhQVOTk63XHO3PtlSyVRkPipJUs1jLCjg90mfc8FZj1AJPHS2vDj+DWxdSnazNaMggxE7R5Ccl0x9x/p80eMLzDXm5Rx1FZQaBt89BukRYOcNL24E90aVHZVURiorH4Wyy0llPlq98lGTC5QRERHUqXPrrrj+/v73rP6aavHixUyZMoURI0aQmJiIt7c3b775JlOnTi26ZuLEieTl5TFixAjS0tLo0KED27dvx87OrkxjkSSpasnT5/HO7ncIzwzHy8aLZb2XYWdRvv/fGwsKiB41ipx9+1FZWFBr6RJsu91HE/R9C2D3J8rvH5oID39Q5YuTR66l8PYPJ0jL1eHlYMmqYe3kzEnpti5dukSnTp2KzSbp0qUL2dnZREdH4+fnx9WrV5kyZQpHjhwhOTm56E51ZGQkgYGBXLp0iRYtWmBtbV00RqdOne752u7u7ri7u5c69v7/mRHdrFkzOnXqRL169Vi7di3jx48veszUHtglvUa6t4rMRyVJqlmMBQWsf38hwQ4FoAJvnR0vTX4bS1vrez8ZKDQUMi5oHFczruJu7V4hOWiVlHARvh8C2fHgVAde2gRO/pUdlSQVI/PR26uq+ajJBUp3d3fOnj1L7dq1i50/c+YMLi4uZRUXAHZ2dixcuJCFCxfe8RqVSsX06dOZPn16mb62JElVl96oZ+K+iZxNOou9hT1f9/4ad+vSf/iXhDE3l+h3RpFz6BAqS0t8v1qKzf20kgiaB0Gzld/3+AB6TCqbQMvR+uORfLjxPHqjoEUtB1a81BYPe8vKDqtGUpmr8f644luVqMzLru/p7RKfG8t1bpx/5JFH8PX15ZtvvsHb2xuj0UhgYCCFhYXFrjfVW2+9xQ8//HDXay5evIifn1+JxrOxsaFZs2aEhIQAxXtg/7fn4X97YHt6elJYWEhaWlqxu9aJiYmyDU0ZqMh8VJKkmsNYWMhPHywi1CEPAN9CB16aOhJzy5KtwDEKI1MOTuF4/HFszG34qtdXeNp43vuJNU3kEfjpacjPAPem8OLvYPcA/neo4SorH73x2mVB5qPVKx81uUD57LPPMnr0aOzs7HjooYcApenomDFjePbZZ8s8QEmSpP8SQjDr6CyCooLQarQs7rmYuo51y/U1DZmZRL31NnknT6KytsZ32TJsOrQv3WBCwJ5ZsO9T5bjXVOj2btkFWw4MRsHsfy6x6kAYAIOae7HgqRZYmpfNUmDpViqVqsyWWleWJk2asGHDhmKJ4aFDh7Czs8PHx4eUlBQuXbrE8uXL6XZ9JvKBAwduGeP7778nLy8PKysrAI4cOXLP177fJTX/q6CggEuXLhXF+d8e2K1atQJu9sCeN28eAG3atMHc3JwdO3bw9NNPAxAXF8f58+eZP39+iV9buj2Zj0qSZKrC3Fx+nvoVYXY5ANTWOfHCxyMxMyv5V+IvTnzBP2H/YKYy4/Men9PQuWF5hVt1Xf4HfhsO+nyo1R6Grgdr58qOSioHMh+9OYbMRyuGyQXKmTNnEhERQa9evYo+zI1GIy+99BKzZ88u8wAlSZL+a/nZ5fx25TdUqJjXbR6tPVqX6+vpU1OJfO01Ci5eQm1nh++K5Vhf/wfAZELAzmlw8EvluO9M6Dyq7IItB1n5Okb/fIo9wUkAjOvdgNG96lfJJQFS5cjIyOD06dPFzjk7OzNixAgWLlzIqFGjeOeddwgODmbatGmMHz8etVqNk5MTLi4urFixAi8vLyIjI3n//feLjTN06FA+/PBDXn31VT766CPCw8NZsGDBPWO63yU1EyZM4JFHHsHPz4/ExERmzpxJZmZmUV/DkvTAdnBw4NVXX+Xdd9/FxcUFZ2dnJkyYQLNmzejdu3epY5MUMh+VJMkUhdk5/PDJMiJtswGor3dh6Ccjb9mQ9W7WnF/DmgtrAJjeeTqdvave7KNyd/I72DwGhBEa9IMnV4NFyZbGS1J5kvloDclHRSkFBweLX375RWzevFmEh4eXdpgqISMjQwAiIyOjskORJOkufr/yuwhcEygC1wSKny79VO6vVxgfL0IHDBQXGzYSwZ06i7yLF0s/mMEgxF/jhZhmr/wc/qrsAi0nEck5ovdnQcJ/0l+i4Uf/iL/OxFZ2SDVSXl6euHjxosjLy6vsUEw2bNgwAdzyM2zYMCGEEEFBQaJdu3bCwsJCeHp6ikmTJgmdTlf0/B07dojGjRsLrVYrmjdvLoKCggQgNm7cWHTN4cOHRYsWLYSFhYVo2bKl2LBhgwDEqVOnyu19PfPMM8LLy0uYm5sLb29v8fjjj4sLFy4Uu8ZoNIpp06YJT09PodVqxUMPPSTOnTtX7Jq8vDzxzjvvCGdnZ2FlZSUGDRokIiMj7yu2u/19eRDzGZmPSpJ0L7npmWLFpE/FtGnTxLRp08TPU5aYPMam0E1FOei3574thyirOKNRiL3zb+axG0cIodfd+3lStSHzUZmPmqK88lGVEGW0f3s1lpmZiYODAxkZGdjby80eJKkq2he9j9G7R2MQBl5r9hpjWo8p19crjI4m8uXh6KKjMfP0xO/bVWjrlnIpuUEPm0bC2XWACgZ+Bu1eLdN4y9rRaym8dX0zHA97LStfakezWg6VHVaNlJ+fT1hYGHXq1MHSUvb0lO7ubn9fZD5Tvck/P0kqe9lJqfz4+bfEabNRCWiq8uTJ6W+ZNMZ/c9BhTYYxod3dl2zWOEYjbJ0Ex1Yox13HKy2K5GqaGkXmo5IpyisfNXmJt8FgYM2aNezatYvExMSiHY5u2L17t6lDSpIk3dXJhJOMDxqPQRh4tN6jjG41ulxfr+DqVSKHv4I+MRFzX1/8Vq/GopZP6QbTF8Bvr8Dlv0ClgSFfQ/OnyzbgMvbL8Sg+/OMcOoOgeS0HvpGb4UiSVMXIfFSSpHtJi07k52VrSdTmoBYqWph78dhHb5g0xunE07wb9C4GYWBQ3UGMbzv+3k+qSfQFsPFNuLBROe43Fzq+XbkxSZJUY5lcoBwzZgxr1qxh4MCBBAYGyj5kkiSVq+DUYN7Z9Q4FhgK6+XRjeufp5fq5k3/xIpGvvoYhLQ2L+vXwW/Ut5h6l7B1SmAPrX4Cru0FjAU+tgUYDyzTesmQwCuZuucQ3+5XNcAY292LBky2wqubNsSVJqnlkPipJ0t0khkSxfu3PpJjnohFq2lrVov/7r5g0xtX0q4zcNZJ8Qz5dfbrycZePUavKZmfhaiE/E9Y/D2H7QG2u3GRv9mRlRyVJUg1mcoFy3bp1/PLLLwwYMKA84pEkSSoSmRnJmzveJEuXRWv31nzW4zPM1ebl9nq5J08S9eZbGLOysGzaFN+V32Dm5FS6wfLS4adnIOoImFvDsz9BvYfLNN6ylJGrY8z6UwRd3wxnbO8AxvQKkF/6JUmqkmQ+KknSnUSdC+X39RtIM8vDXGjoYOdP7wkvmTRGfE48b+54k8zCTJq7Nuez7uWbg1Y5WQnw45MQfxYsbOGZH6p0HitJUs1gcoHSwsKC+vXrl0cskiRJRRJzE3ljxxuk5KfQ0Kkhi3stxsrMqtxeL+fQIaJGvoPIy8OqTRt8v16Gxs6ulIMlw/dDlKRO6wDP/wp+Hco24DJ0JSGLN777l/CUXCzN1Xz6ZAseaeFd2WFJkiTdkcxHJUm6nZBD59i85W8yzfLRCjO6utSh2+jnTRojPT+dN3e8SUJuAnUc6rC011KszR+gnaqTQ+CHJyA9AmzclDzWu1VlRyVJ0gPA5Dnq7777Ll9++SVybx1JkspLRkEGb+54k5jsGHztfPm6z9fYW5TfhgFZu3YR9eZbiLw8bLp0wW/lN6UvTmbGwur+SnHS2hVe/qtKFye3XYhnyNKDhKfk4uNoxYa3O8vipCRJVZ7MRyVJ+l9ntx3mj62bydTkY2U0p5d3A5OLk7m6XEbuHsm1jGt4WHuwvPdyHC0dyyfgqijiEKzsrRQnnWrDK9uqVXFS/psgSdWbyTMoDxw4wJ49e9iyZQtNmzbF3Lz4VPfff/+9zIKTJOnBk6vLZeSukYSmh+Jm5caKPitwtXItt9dL3/gHcR99BAYDdn364P3ZAtQWFqUbLDUMvntMSersfeClTeAaULYBlxGjUbBwVwiLdoUA0KmuC0ufb42zTSnfuyRJUgWS+agkSf917Lfd7D57mHy1Dlujlj71GtNi2GCTxig0FDJ2z1jOJp3F3sKer3t/jZetV/kEXBWd3wAb3wJDIfi0haHrwab8cvCydig0mS92XmHVy+2wt3yAluNLUg1icoHS0dGRIUOGlEcskiQ94HQGHeOCxnEm6Qz2FvYs77OcWna1yu31UlZ9S+KnnwLg8NhjeM2aicrM5I9FRfx5ZTlMdjw41YFhf4KjXxlGW3ay8nWMW3+GnZcSAHilSx0+GNAIM80D1PhdkqRqTeajkiTdsHf13xwIP4lObcDBYMnAZs1o8LRpmxLqjXom7pvI4bjDWJlZ8VXvr6jv9IC0kRACDn4JO6cpx40GwePfgEX1WNZuMAoW7Qph0e4QhIClu0OZPKBxZYclSVIpmPxNfPXq1eURhyRJDziD0cDkA5M5FHsIKzMrlvZaSoBT+cw+FEKQuGABqau+BcB5+HDc35uASl3KAl3EIfjpWSjIAPcm8OJGsPMsw4jLztWkbN747l+uJuVgYaZmzpBmPNGm/IrAkiRJ5aGi89HatWsTERFxy/kRI0awdOlSXn75ZdauXVvssQ4dOnDkyJGKClGSHkhbl/7K8cRLGFRGXPTWDOrQmjqP9DZpDKMwMvXgVHZF7sJCbcGinoto4dainCKuYgx62PIe/KvkxHQcAX1nglpTuXGVUGJmPmPWnebwtRQAnmnry9jeDSo5KkmSSqtUU4X0ej1BQUFcvXqVoUOHYmdnR2xsLPb29tja2pZ1jJIk1XBCCD458gnbwrdhpjZjYY+FtHRvWT6vpdcTN2UqGRs3AuD+3gRcXn219ANe/ht+ewX0+eDbEYauA6tS7vxdznZfTmDMz6fJKtDj5WDJ8hfb0LyWY2WHJUmSVCoVmY8eP34cg8FQdHz+/Hn69OnDU089VXSuX79+xQqnFqVtFyJJUon8ueAHTmddxagSuBVa80TPDnj27m7SGEIIZh+dzeZrm9GoNCzovoCOXh3LKeIqpiBbyWFDtgEq6DcHOr5d2VGV2P6QJMatP01ydiHWFhpmD2nG4FY+lR2WJEn3weQCZUREBP369SMyMpKCggL69OmDnZ0d8+fPJz8/n6+//ro84pQkqYYSQjDv+Dw2hGxArVIzp9scOvt0LpfXMubnEzNuPNl79oBGg9fHH+P4xOOlH/DEWvhrLAgjNBwAT34L5uW303hpCSFYuieUz3ZcQQhoV9uJr55vg5udtrJDkySThYeHU6dOHU6dOkXLli0rOxypklR0Purm5lbseO7cudSrV4/u3W8WQ7RaLZ6eVXP2vCTVNL/OWsmFwmhQgVeBDU8M6Ixrty4mj7Po1CLWB69HhYpZXWfxsN/D5RBtFZSVAD89BXFnwMwSnlgJjR+p7KhKRG8wsnBnCEuDQhECGnnasfT51tRzkxOlpIoj89HyYfJ6xjFjxtC2bVvS0tKwsrr5RXzIkCHs2rWrTIOTJKlmE0Lw5ckv+fHSjwDM6DyDfrX7lctrGTIziXz1NbL37EGl1VJr8aLSFyeFgH0LYPNopTjZ6gV4+vsqWZzMzNfx1g8nWLBdKU6+2NGfH1/rKIuTUpl4+eWXGTx4cGWHUeb27dvHI488gre3NyqVij/++OOWa4QQTJ8+HW9vb6ysrOjRowcXLlwodk1BQQGjRo3C1dUVGxsbHn30UaKjo4tdk5aWxosvvoiDgwMODg68+OKLpKenl+O7qxkqMx8tLCzkhx9+4JVXXkGlUhWdDwoKwt3dnQYNGvD666+TmJh413EKCgrIzMws9iNJ0r39OH0ZF3RKcbJWrg1PP9a9VMXJledWsvLcSgA+6vgRA+ua1rey2kq8rOzUHXcGrF1g2F/VpjgZl5HH0G+OsmSPUpx8voMff4zsIouTDziZj9acfNTkAuWBAwf46KOPblm24u/vT0xMTJkFJklSzbfi7ApWnV8FwEcdPmJw/cHl8jq6xEQiXniRvBMnUNvZ4bdqJXY9e5ZuMKMRtr4Puz9RjruOh0eXgKaUm+uUo8vxmTy25CDbLiRgoVEz9/FmfDI4EAszuRmOJN1NTk4OLVq0YMmSJXe8Zv78+Xz++ecsWbKE48eP4+npSZ8+fcjKyiq6ZuzYsWzcuJF169Zx4MABsrOzGTRoULGlwkOHDuX06dNs3bqVrVu3cvr0aV588cVyfX81QWXmo3/88Qfp6em8/PLLRef69+/Pjz/+yO7du/nss884fvw4PXv2pKCg4I7jzJkzp+iLgIODA76+vuUatyRVd3q9njVTFhOCsslf7Swrnnn6YZw6tjd5rHWX1/HlyS8BGN9mPE83fLpMY62yru6GVX0hIxKc68GrO8C3XWVHVSJ7Licy4Mv9HAtPxVZrxuLnWjFrSDMszatHv0xJMtUDmY8KEzk5OYkLFy4IIYSwtbUVV69eFUIIsX//fuHu7m7qcFVCRkaGAERGRkZlhyJJD4w159eIwDWBInBNoFhzfk25vU5BWJgI6dlLXGzYSAR37SryLl8u/WC6AiF+HS7ENHvl5/BXZRdoGfv9ZJRo+NE/wn/SX6LznF3idGRaZYck3UFeXp64ePGiyMvLq+xQTDZs2DDx2GOP3fHxoKAg0a5dO2FhYSE8PT3FpEmThE6nK3p8y5YtokuXLsLBwUE4OzuLgQMHitDQ0GJjHD16VLRs2VJotVrRpk0b8fvvvwtAnDp1qpzeVXGA2LhxY7FzRqNReHp6irlz5xady8/PFw4ODuLrr78WQgiRnp4uzM3Nxbp164quiYmJEWq1WmzdulUIIcTFixcFII4cOVJ0zeHDhwUgLt/hs+puf18epHymMvPRvn37ikGDBt31mtjYWGFubi42bNhwx2vy8/NFRkZG0U9UVNQD8+cnSabKz8sX33z4hZg2bZqYNm2a+G70bJF75kypxvoz9M+iHPTLE1+WcaRV2NEVQkx3UnLYlX2FyE6u7IhKpEBnELP+vij8J/0l/Cf9JQYu2ifCkrIrO6waR+ajMh+tCvmoydNo+vTpw8KFC4uOVSoV2dnZTJs2jQEDBtxvvVSSpAfAusvrWPDvAgBGtRrFsKbDyuV18s6dI/z5F9DFxGDu70ftn3/GsmHD0g1WkAU/PQ3nN4DaDB5fWSUbiRfoDXz0xznGrT9Dvs5ItwBXNo/qSgtfx8oOTTKBEILCwsIK/1Hyn7IRExPDgAEDaNeuHWfOnGHZsmWsWrWKmTNnFl2Tk5PD+PHjOX78OLt27UKtVjNkyBCMRmPR44MGDaJhw4acOHGC6dOnM2HChHu+9ltvvYWtre1dfyIjI0v93sLCwoiPj6dv375F57RaLd27d+fQoUMAnDhxAp1OV+wab29vAgMDi645fPgwDg4OdOjQoeiajh074uDgUHSNdHuVlY9GRESwc+dOXnvttbte5+Xlhb+/PyEhIXe8RqvVYm9vX+xHkqRb5aRnseaTJUSbpaMS0DBJw5OvPYJV8+Ymj7UtfBtTDk4BYGijoYxqNaqsw616DHr4ZyL8MwGEAVo8B8P+BBuXyo7snq4lZfPEskOs2HcNgJc712bD252p7WpTyZE9GCorHy3LnFTmo9UrHzV5TeLnn39Oz549adKkCfn5+QwdOpSQkBBcXV35+eefyyNGSZJqkD9C/2DW0VkAvN7sdd5o/ka5vE7Wnj3EjH8XkZeHtklj/FaswMzVtXSDZScqxcnYU2BuA898D/V7lW3AZSAmPY8RP57kTFQ6AKN7BTCmVwAateruT5SqHJ1Ox+zZsyv8dT/44IMy23n4q6++wtfXlyVLlqBSqWjUqBGxsbFMmjSJqVOnolareeKJJ4o9Z9WqVbi7u3Px4kUCAwP58ccfMRgMfPvtt1hbW9O0aVOio6N5++273xz4+OOP75k4ent7l/q9xcfHA+Dh4VHsvIeHBxEREUXXWFhY4OTkdMs1N54fHx+Pu7v7LeO7u7sXXSPdXmXlo6tXr8bd3Z2BA+/eqy4lJYWoqCi8vLzKLRZJehCkxafy05JvSTLPRi1UNI7TM2jMU1g1bWryWLsid/H+vvcxCAOD6w9mUvtJxfrI1kj5GfDrcLh6vTdvr2nQdRxU8fcthODXE9FM//MCuYUGHK3NmfdEc/6vqdyIrCJVVj4KZZeTyny0euWjJhcofXx8OH36NOvWrePEiRMYjUZeffVVnn/++WJNyiVJkv7XlrAtTDs0DYAXGr9Qbnet09atJ/7jj8FoxKZrV3wWLkRjW8o7rUlX4McnID0SrJzh+d+gVpuyDbgM7LuSxJh1p0jL1eFgZc7CZ1rycKNb/6GRpIpy6dIlOnXqVOzLX5cuXcjOziY6Oho/Pz+uXr3KlClTOHLkCMnJyUV3qiMjIwkMDOTSpUu0aNECa2vrojE6dep0z9d2d3e/baJV1v73i60Q4p5fdv/3mttdX5JxHnSVkY8ajUZWr17NsGHDMDO7mUJnZ2czffp0nnjiCby8vAgPD+eDDz7A1dWVIUOGlEsskvQgSLgaw/o1P5JqlouZUBMYkUO/icOwbNLE5LH2Ru1lwt4J6IWeQXUHMb3TdNSqGt6TOy0cfnoGki6DmRU8vgKaPFrZUd1TRp6ODzae4++zcQB0quvCF8+0xNPBspIjk6ojmY/eXlXNR00qUOp0Oho2bMhff/3F8OHDGT58eHnFJUlSDbMrYheT90/GKIw81eApJrabWOYfeEIIkr78kpSvlwPg8PjjeM2YjsrcvHQDhh+EdUMhPx2c6sALG8ClXtkFXAaMRsGSPaF8sVPZpbuZjwNfPd8aX2frez9ZqrLMzc354IMPKuV1y8rtkpoby3VunH/kkUfw9fXlm2++wdvbG6PRSGBgIIWFhcWuN9Vbb73FDz/8cNdrLl68iJ+fX6nG9/RUZnDEx8cXmyGXmJhYdBfb09OTwsJC0tLSit21TkxMpHPnzkXXJCQk3DJ+UlLSLXfDpZsqKx/duXMnkZGRvPLKK8XOazQazp07x3fffUd6ejpeXl48/PDDrF+/Hjs7uwqJTZJqmvDToWzc8BsZmnwshBktQpPpNeUtLBs1MnmsAzEHGBc0Dr1RT//a/fmkyydo1DV8Y5WIw7D+echNATsveG4deLes7Kju6Xh4KmPXnSYmPQ8ztYrxfRvw5kP15GqgSlJZ+eiN1y4LMh+tXvmoSQVKc3NzCgoKKrSKGhMTw6RJk9iyZQt5eXk0aNCAVatW0aaNMoNJCMGMGTNYsWIFaWlpdOjQgaVLl9K0FNP+JUkqH7sidjFh7wQMwsAjdR/ho44flX1xsrCQuClTydi0CQDXkSNxfWdk6V/n3G/wx9tgKIRa7ZTEzqaUS8TLSXpuIePWn2ZPcBIAz7X3Y9ojTeRuhjWASqUqs6XWlaVJkyZs2LChWGJ46NAh7Ozs8PHxISUlhUuXLrF8+XK6desGKDsz/+8Y33//PXl5eUWz4o4cOXLP1y7vJTV16tTB09OTHTt20KpVKwAKCwvZu3cv8+bNA6BNmzaYm5uzY8cOnn5a2R02Li6O8+fPM3/+fEC5+56RkcGxY8do317Zhfbo0aNkZGQUJY3SrSojHwXo27fvbb+kWFlZsW3btgqNRZJqskv7T/P3ji1kawqwNJrT+lIUD80cV6o+4odiDzFm9xh0Rh19/Pswu9tszNQmLyKsXs6sgz9HKTmsV0t47mewL/2/eRVBbzCyeHcoi3eHYBTg72LNl8+2oqXsoV6pZD56cwyZj1YMkz+dR40axbx581i5cmWx5S3lIS0tjS5duvDwww+zZcsW3N3duXr1Ko6OjkXX3NhWfc2aNTRo0ICZM2fSp08fgoOD5V1rSaoCbhQn9ULPgDoD+KTLJ2W+pMaQlUXMmDHkHDoMGg1eM6bj+OSTpRtMCDi4EHZOV44bPwKPfwPmVauFxanINN756RQx6XlozdTMHBzIU219Kzss6QGUkZHB6dOni51zdnZmxIgRLFy4kFGjRvHOO+8QHBzMtGnTGD9+PGq1GicnJ1xcXFixYgVeXl5ERkby/vvvFxtn6NChfPjhh7z66qt89NFHhIeHs2DBgnvGdL9LarKzswkNDS06DgsL4/Tp0zg7O+Pn54dKpWLs2LHMnj2bgIAAAgICmD17NtbW1gwdOhQABwcHXn31Vd59911cXFxwdnZmwoQJNGvWjN69ewPQuHFj+vXrx+uvv87y5crM7zfeeKOoEbt0ZxWZj0qSVHFO/nOIHUf2kKfWYWO0oO25YDrN+xDLBg1MHutY3DFG7x5NobGQh30fZt5D82p2cdJogN0z4cDnynHjR2HIcrCo2qtqotNyGbvuNP9GpAHweGsfPn4sEFttDf6zksqczEdrSD5q6rbfgwcPFnZ2dsLLy0v07dtXDBkypNhPWZo0aZLo2rXrHR8vybbqJXE/26BLknRnO8N3ipZrW4rANYFi4t6JQm/Ql/lrFMbHi6uPPiYuNmwkLrVqLbL27Sv9YHqdEH+OEWKavfKzZbIQ5RDz/TAYjGLF3qui3uS/hf+kv0S3ebvF+Zj0yg5Lug95eXni4sWLIi8vr7JDMdmwYcMEcMvPsGHDhBBCBAUFiXbt2gkLCwvh6ekpJk2aJHQ6XdHzd+zYIRo3biy0Wq1o3ry5CAoKEoDYuHFj0TWHDx8WLVq0EBYWFqJly5Ziw4YNAhCnTp0qt/e1Z8+eu74vIZQcZNq0acLT01NotVrx0EMPiXPnzhUbJy8vT7zzzjvC2dlZWFlZiUGDBonIyMhi16SkpIjnn39e2NnZCTs7O/H888+LtLS0O8Z2t78vD1I+U5H5aEV5kP78JOl2Dvy8Xcya+omYNm2a+OyjOWL/wKEiPySkVGMdjzsu2v3QTgSuCRQjdo4QBfqCMo62islLF+KHp27msDtnCGEwVHZU97TpdIwInLZV+E/6SwRO3Sr+OBVd2SE9sGQ+KvPRqpCPqoQwbUH9vfr8rF692pTh7qpJkyb83//9H9HR0ezduxcfHx9GjBjB66+/DsC1a9eoV68eJ0+eLJrSCvDYY4/h6OjI2rVrbztuQUEBBQUFRceZmZn4+vqSkZGBvb19mcUvSQ+y/505Obvr7DLv95N/5QpRb7yJPj4ejasrvsu/LtWujgAUZMNvwyFkO6CCfnOh41tlGu/9Sssp5N1fz7D7ciIAA5t5MeeJZthbll3fQKni5efnExYWRp06dbC0lA3gpbu729+XzMxMHBwcHoh8piLz0YryIP35SdL/2rXyTw5HnUavMuKkt6LTv8dosXQ+2nqm9/4+nXiaN3a8QZ4+j87enVnUcxFajbYcoq4ikkPg5+cgJQTMLOHRJdD8qcqO6q4ycnVM2XSeP8/EAtDKz5FFz7aSPdQrkcxHJVOUVz5q8rzpikz4rl27xrJlyxg/fjwffPABx44dY/To0Wi1Wl566aUSbat+O3PmzGHGjBnlGrskPcgqojiZc+gQ0WPGYszKwqJOHXy/+QaLWj6lGywrHn56GuLOKIndEyuVpd1VyPHwVEb/fIq4jHwszNRMHdSE5zv4yZ1+JUl6IFXHAqQkSbf396J1nEgJxqgSuOqs6fLvAZouX4RF7domj3Uq8RRv73ybPH0eHbw68OXDX9bs4uSVbbDhNSjIBPta8OwP4N3q3s+rRAdCkpnw6xniM/PRqFWM7FGPUb0CMNfU8F3VJUm6p1I1dtDr9QQFBXH16lWGDh2KnZ0dsbGx2NvbY2trW2bBGY1G2rZty+zZswFo1aoVFy5cYNmyZbz00ktF15m6rfrkyZMZP3580fGNGZSSJN2/iihOpq1bT/wnn4DBgFWbNvguXYLmP71pTZJwAX56BjKiwNoFnlsPvu3KNN77YTQKlu29yuc7rmAwCuq62rBkaGuaeMvZNZIkPdgqKh+VJKn8/D5vDedywxEq8CywocuJvTT89mssatUyeazj8ccZuWskefo82nm2Y3HPxVia1dCZYELA/s+UnpMI8OsMT38Htm6VHdkd5esMzNt6mdUHwwGo7WLN58+0pLWf092fKEnSA8PkAmVERAT9+vUjMjKSgoIC+vTpg52dHfPnzyc/P5+vv/66zILz8vKiSZMmxc41btyYDRs2ACXbVv12tFotWm0NvpMmSZWkvIuTwmAgcf6npF5v32D/6CN4zZyJurS7ywVvhQ2vQmE2ONeDF34D57plFu/9Ss4uYNz60+wPSQZgcEtvZg5pJpuGS5L0wKvIfFSSpLJnNBpZ98k3XBFxoAKfPBu6nN5DwJqVmJdiV9ujcUd5Z9c75Bvy6ejVkUU9F2FlVrU2OCwzhTmwaSRc2Kgct31VaU1kVnV3Wz4XncG4X04TmpgNwAsd/fhgQGOsLWROK0nSTSbPox4zZgxt27YlLS2taIt1gCFDhrBr164yDa5Lly4EBwcXO3flyhX8/f2B4tuq33BjW/WK3g5dkh5028O3l2tx0piTQ/Q7o4qKk66jR+E9b17pipNCwKHF8POzSnGydjd4bWeVKk4euppM/y/3sz8kGUtzNfOfaM4Xz7SUxUlJkiQqNh+VJKls6XV6vpu2VClOAv5ZVnQ/F0TAd2tKVZw8FHOIkbtGkm/Ip4tPF5b0WlJzi5NpEbDq/5TipNocBi2EQZ9X2eKk3mBkye4Qhnx1kNDEbNzstKwe3o6Zg5vJ4qQkSbcw+VPhwIEDHDx4EIv/KQr4+/sTExNTZoEBjBs3js6dOzN79myefvppjh07xooVK1ixYgVAibZVlySp/P159U+mHJyCURjLpTipi48n6u0RFFy6hMrCAu+5c7AfMKB0g+kL4e/xcOp75bjNyzBgAWiqxkYzeoORxbtDWbw7BKOAAHdblj7fmgYedpUdmiRJUpVRkfmoJEllJz8nj+/nfE2MWQYA9VIt6Bx+CP/vv8PMxcXk8fZF72PsnrHojDq61+rO5z0+x0JTNYt19y1sH/wyDPJSwcYNnv4e/DtVdlR3FJ6cw/hfTnMyMh2A/oGezBrSDGebGvrnI0nSfTO5QGk0GjEYDLecj46Oxs6ubL9At2vXjo0bNzJ58mQ+/vhj6tSpw8KFC3n++eeLrpk4cSJ5eXmMGDGCtLQ0OnTowPbt28s8FkmSbu+X4F/45MgnADwe8DhTO04t0+Jk3vkLRL/9NvqkJDQuLvguXYJVy5alGyw3Fda/CBEHQKWG/5sNHd6CKrLRTFRqLuPWn+bfiDQAnmpTixmPNZV3mB8QRqOxskOQqgH590RRkfmoJEllIzMpjR++XEWiWTYqoaJBvKBD0r/4rV2DmZPpfQj3RO5h/N7x6I16evr2ZEH3BZhXkRvOZUoIOPgl7JoBwgheLeHZH8HB9D6dFUEIwU/HIpn19yVyCw3Yac2Y8VhThrTykZs7VgMyz5BKorz+nqiEEMKUJzzzzDM4ODiwYsUK7OzsOHv2LG5ubjz22GP4+flVy10V72cbdEl6kH134Ts+/fdTAIY2Gsqk9pNQq8puB77M7duJnTgJkZ+PNiCAWsuWlX6n7qQryk7daWFgYQdPrYaAPmUW6/3adDqGjzaeJ6tAj63WjE8GN2VIq6qZeEply2g0EhISgkajwc3NDQsLC5nAS7cQQlBYWEhSUhIGg4GAgADU6uKftw9SPiPzUUmqXhKvxrBuzY+kanLRCDWNI3JokxuO3zcr0Dg4mDzejogdTNw7Eb3Q09e/L3Mfmou5ugYWJ/MzYdMIuLRZOW7+LDyyEMyr5hL2mPQ83t9wtqh/ese6znz2dEt8HKtmvNJNMh+VSqK881GTC5SxsbE8/PDDaDQaQkJCaNu2LSEhIbi6urJv3z7c3d1NCqAqkAmhJJluxdkVLD61GIBXAl9hbOuxZfaPmBCC1FWrSFzwGQA23brh88XnaEq7K2voLvh1OBRkgKMfDP0F3BuXSaz3Kytfx9RNF9h4SlmS2NrPkS+fbYWvs3UlRyZVpMLCQuLi4sjNza3sUKQqztraGi8vr1uWNsODlc/IfFSSqo9r/wbzx6aNZGrysRAaAoMTaa5OxXfF8lLldlvDtvL+/vcxCAP96/RndtfZmKlr4GqTxEuw/gVICVX6TfafB21fqTIrf/5LCMG641HM+vsS2QV6tGZq3vu/hrzSpQ5qddWLV7o9mY9KJVVe+ajJBUqAvLw81q1bx4kTJzAajbRu3Zrnn3++WJPy6kQmhJJUckIIFp1axMpzKwEY2XIkbzZ/s8yKk8b8fOKmTiXzT+VOsdPzz+Mx+X1UZqVIPIWA4ythyyQQBvDrBM/8ADauZRLr/ToRkcbY9aeISs1DrYJRPQMY1bM+Zpqym4UqVR9CCPR6/W2XrUoSgEajwczM7I6ftw9aPiPzUUmq+s7vOs6WvTvIURdiZTSnxemrNHYy4vvVUtQ2NiaP93vI78w4PAOjMPJI3Uf4pMsnZdpaqMo49xv8OQp0uWDvA09/B7XaVnZUt/W/sybb+Dvx6ZPNqetWyokFUqWS+ah0L+WZj5aoQNm6dWt27dqFk5MTH3/8MRMmTMDauubM7pEJoSSVjBCCecfn8eOlHwGY0HYCw5oOK7PxdQkJRI98h/zz50GjwWPyZJxfeP7eT7wdfQH8/e7NzXBaDFWWxJhpyyze0tIbjCzdc5VFu0MwGAW1nKxY+ExL2tZ2ruzQJEmqxmp6PiPzUUmqXo78tps95w5RoNJja9TS+shJ6vs6UGvJYtSluJGw9sJaFvy7AIAnAp5gSscpNa84qS+EHVPg6NfKcZ3u8OS3Vebm+n/dadbk8C510MhZk5L0wCr3AqWVlRUhISHUqlULjUZDXFxctVw6cycyIZSkezMYDXxy5BM2hGwA4MMOH/Jso2fLbPzcU6eIHj0aQ1IyGkdHfBYuxKZjh9INlhkHv7wI0ccBFfSeDl3GVIklMf+7Ec7glt58PDgQe8sa2DdJkqQKVdPzGZmPSlL1sXvVnxyKPINeZcDRYEXboCD8m9bG58uFqLWm3SwWQvDVma/4+oxStHu56cuMbzO+5vXHy4yDX4dB1FHluOt46PkRVMEi7P/Ommzt58inT7Wgnpw1KUkPvPvJZ0q0ZrJly5YMHz6crl27IoRgwYIF2N6hX8jUqVNNCkCSpKqv0FDIBwc+YFv4NtQqNTM6z2Bw/cFlNn76hg3ET5+B0OnQNmhAra+WYlGrlBvERB1T+vVkJ4Clg3LXuX7vMov1fvzvRjgzBwcyuFUpN/2RJEl6wMh8VJKqh7+/XMeJ1GCMKoGrzpp2Ozbj06EVPp8tQHWbfmV3YxRG5h+fX7R6Z3Sr0bzW7LWaV5wMP6D0S89JBK09DPkaGg2s7KhuIYRg/fEoZv5n1uSEvg15paucNSlJ0v0r0QzK4OBgpk2bxtWrVzl58iRNmjTB7Db94FQqFSdPniyXQMuTvGMtSXeWq8tlzJ4xHIk7gpnajDnd5tCvdr8yGVvo9STMm0/a98oybLs+ffCeO6dUPYkAOLEG/p4ARh24N1H6TbrUK5NY70daTiFTNp3nr7NxgNwIR5Kk8lHT8xmZj0pS1ffbnG85nx8JKvAssKH9P+vx7N8Hr5mfmNxPXG/UM/3QdDZd3QTA5PaTGdp4aHmEXXmMBtj/OQTNBmEE96bwzPdVIn/9X9FpuXyw8Tz7riQBctakJEm3V6Gb5KjVauLj4+WSGkl6AKTlpzFy10jOJZ/DysyKhQ8vpLN35zIZW5+WRsy48eQeOQKA6+hRuL71Fip1KTaI0RfClolwYrVy3OQxeOwr0FZ+wrQnOJFJv50lMasAjVrFOw/XlxvhSJJULh6kfEbmo5JUtRgMBtbNXEGISADAJ9eaDn99h/tzz+Lx4Qcm53eFhkIm7ZvEzsidaFQaPu7yMY/We7Q8Qq882Ynw++twLUg5bvEcDPwMLEp5o76cGIyCtYfCWbA9mNxCg5w1KUnSXZX7Eu//MhqNpj5FkqRqKD4nnjd2vEFYRhiOWke+6vUVzdyalcnY+cFXiB45El10NGpra7znz8OudymXYWclwC8vQdQRQAW9pig9eyp56U9OgZ6Zf1/i52ORANRzs+Hzp1vSwtexUuOSJEmqCWQ+KklVh75Qx/efLCNCkwqAf6YV7f5Zg/ubb+A2dozJy7FzdbmMCxrHodhDmKvN+bT7p/Ty61UeoVeesH2w4TWlJZGZlVKYbFXKjSHL0eX4TCZtOMeZqHQA2tdxZs7jzeSsSUmSykWppvB8//33dOnSBW9vbyIiIgD44osv2LRpU5kGJ0lS5biWcY0Xt7xIWEYYHtYerO23tsyKk5lbthD+3HPooqMxr1UL/3U/l744Gf0vrOiuFCe1DvD8r9Dt3UovTh4PT6X/l/uLipOvdq3D36O7yeKkJElSGZL5qCRVvrzMHL79eHFRcbJ+ijkd/lmD57vjcB831uTiZHp+Om/seINDsYewMrNiaa+lNas4aTRA0Fz47jGlOOnWCN4IqnLFyXydgc+2BzNo0QHORKVjpzVj9pBmrHu9oyxOSpJUbkwuUC5btozx48czYMAA0tPTMRgMADg5ObFw4cKyjk+SpAp2Pvk8w7YMIz4nntr2tfm+//fUdax73+MKnY6EOXOJGTcekZuLdaeO1P71FywbNCjFYAKOr4LV/SEr7npytwcC+tx3nPcjX2dgzj+XeHr5YSJTc/FxtOKn1zswZVATLM2r3g6MkiRJ1ZXMRyWp8qXEJLJq/lfEmmWiEioaxRpps+MHPKdOwfX1100eLzY7lhe3vMiZpDPYWdixos8KOnl3KofIK0lWAnw/GILmKP0mW70Ar+8B90aVHVkxx8JSGbBoP4t3h6I3Cvo28WDH+O4M7eCHWi7pliSpHJm8xHvx4sV88803DB48mLlz5xadb9u2LRMmTCjT4CRJqliHYw8zZs8Y8vR5BLoE8lXvr3CydLrvcXWJicSMH0/evycAcHn9ddzGjDa5WToAhTnw1zg4u145bjRI2elQa3ffcd6P8zEZjP/lNFcSsgF4qk0tpj7SBDtL80qNS5IkqSaS+agkVa7IM6H8/tsG0s3yMBNqGodl0eTEFrznzcXhscdMHi84NZi3d75NUl4SHtYefN37a+o71S+HyCvJ1T1Kv8mcJDC3gUGfQ4tnKzuqYjLzdczbcpkfjyorgNzstHzyWFP6BXpVcmSSJD0oTK4OhIWF0apVq1vOa7VacnJyyiQoSZIq3l/X/mLKwSnojXo6enVk4cMLsTG//ybduSdOED12LIakZNS2tnjPnVP6Jd3JIbD+RUi6BCoN9J4OnUdV6pLuQr2RpXtCWbpHucvsamvBnMeb06eJR6XFJEmSVNPJfFSSKs+5ncfZum8HOZpCLIU5geciqR9yGO+FX2Dfx/TVLMfjjzN692iyddnUd6zPst7L8LTxLIfIK4FBpyzp3v8ZIJRdup9aA26lWEFUjnZcTGDKH+eJz8wH4Nl2vkzu3xgHa3mjXZKkimNygbJOnTqcPn0af3//Yue3bNlCkyZNyiwwSZIqhhCCledWsujUIgD+r/b/MbvrbCw0Fvc9btr335Mw/1PQ69EG1Mdn0SK0deqUbsALf8CmkVCYDbYe8ORqqN3lvmK8X+eiM3jvtzNcjs8CoF9TT2YNCcTFVlupcUmSJNV0Mh+VpMpxaP1O9l48QoFaj61RS8ujZ6mVGEytr5dh28X0vGxb+DYm75+MzqijtXtrFvVchIPWoRwirwSp12DD6xDzr3Lcehj0nwfmVpUb13/EpOcx488LbL+o7L5e28Wa2Y83o3M910qOTJKkB5HJBcr33nuPkSNHkp+fjxCCY8eO8fPPPzNnzhxWrlxZHjFKklRO9EY9s47O4rcrvwHwctOXGddmHGpVqfbPKmLMySFuyhQy/9kCgP3AgXh98jFqa2vTBzPoYMdUOPKVcuzfFZ78Fuwqb4Zivs7Aol0hLN93DYNR4GxjwcePNWVgMy+Tm8FLkiRJppP5qCRVvG3LNnAs/gIGlREnvRWt9+3FPTcJ31UrsW7d2uTxfrz0I/OOzUMg6O3Xm7kPzUWrqQE3eYWAM+vgnwnKjXWtAzzyBQQ+UdmRFdEZjKw+GMbCnSHkFhowU6t4rVtdxvYOkH3TJUmqNCYXKIcPH45er2fixInk5uYydOhQfHx8+PLLL3n22arVR0OSpDvL1eUyYe8E9sfsR4WK99u/z9DGQ+973IJrYUSPHkVh6FUwM8Nj4kScXnyhdIW7jBj4bThEHVWOu4yFnlNAU4relWXkZGQaE387S2ii0mtyUHMvZjzaVM6alCRJqkAyH5WkivX7/LWcywlHqARuOhvab9uEg6UKv+/WYmnirGUhBItOLWLlOeVmwjMNn2Fy+8lo1DWgMJaXDn+Ph/MblGO/TvD4CnD0q9Sw/uvf8FQ+3Hie4ARlBVC72k7MHNyMhp6V289dkiRJJYQQpX1ycnIyRqMRd3d3AGJiYvDx8Smz4CpKZmYmDg4OZGRkYG9vX9nhSFK5S85LZsTOEVxKvYSlxpJ5D82jp1/P+x43859/iJsyFWNODmZubvh8ubBUd9QBCNkJG9+A3BTlzvOQr6HRgPuOsbTyCg18viOYVQfCMApwtdUyc3Ag/QJrSI8kSZKqvQc1n6mIfHT69OnMmDGj2DkPDw/i4+MBpeAyY8YMVqxYQVpaGh06dGDp0qU0bdq0xK/xoP75SVWbwWBg3cwVhAhlCbB3vg3t//4RW093/FatxMLPtMJboaGQ6Yems/naZgBGtRrF681erxkrUCIOw+9vQEak0iu9x2ToNh6qSOE1LaeQuVsus/7fKACcrM2ZPKAxT7auJXfnliSpzNxPPnNf05BcXZXeFPHx8cyaNYuVK1eSl5d3P0NKklTOrqVf4+2dbxObE4uzpTOLey6muVvz+xrTmJ9Pwpy5pK9Xdta2btsWny8+x8zNzfTBDDrY9TEcUnpi4tkMnv4OnOveV4z341hYKhN/O0N4Si4Aj7fyYeojTXC0vr8+nZIkSdL9q6h8tGnTpuzcubPoWKO5WXSYP38+n3/+OWvWrKFBgwbMnDmTPn36EBwcjJ2dnJUkVU+F+QV8P+trojRpAPhnWdPu79VYN26E34rlJud56fnpjA0ay4mEE2hUGqZ2msrjAY+XR+gVy6CHffNh36cgjODoD0+sAt92lR0ZAEaj4LeT0cz55xJpuToAnmnry/v9G+FkI3NZSZKqjhI3mktPT+f555/Hzc0Nb29vFi1ahNFoZOrUqdStW5cjR47w7bfflmeskiTdp3/j/+XFLS8SmxOLv70/P/T/4b6LkwXXwgh/5lmlOKlS4fLWm/itWV264mRaOHzb72Zxsv0b8OrOSitOZuTp+HDjOZ5efpjwlFw87LV8+3JbPn+mpSxOSpIkVYLKzEfNzMzw9PQs+nG7/u+cEIKFCxfy4Ycf8vjjjxMYGMjatWvJzc3lp59+uuN4BQUFZGZmFvuRpKoiKyWDVTOXKMVJAfWTzenw92rs2rfD/7u1Jud5EZkRvLDlBU4knMDW3Javen1VM4qTaeGwuj/snacUJ5s/C28dqDLFyeD4LJ5ZcZiJv50lLVdHQw87fnurE/OebC6Lk5IkVTklnkH5wQcfsG/fPoYNG8bWrVsZN24cW7duJT8/ny1bttC9e/fyjFOSpPu0KXQTMw7PQGfU0cKtBYt7LsbJ0um+xszYvJm4adMRublonJ3x/nR+qXZwBODiJtg0CgoywNIBHl0CTR69r/hKSwjB1vPxTPvzAolZBYByp/mDgY1xsDKvlJgkSZKkys1HQ0JC8Pb2RqvV0qFDB2bPnk3dunUJCwsjPj6evn37Fl2r1Wrp3r07hw4d4s0337zteHPmzLll2bgkVQXxoVH8suZnUs1y0QgVDWN0NDuwHrs+ffBe8ClqrWl9t08knGDMnjFkFGTgZePF0l5LCXAKKKfoK4gQcPI72PbB9Y1w7GHQF9DsycqODICsfB1f7gxhzaFw9EaBtYWGsb0DGN6lDuaa+9sMU5IkqbyUuED5999/s3r1anr37s2IESOoX78+DRo0YOHCheUYniRJ98tgNPDlqS9ZfX41AH38+zC762wszSxLPaYxL4/4WbPI+E1pAG7dvj3eCz7F/Hr/L5Po8pXk7t9VynGtdsqyGCf/Usd3P2LT85i66QI7Lym9luq42jBrSCCd67lWSjySJEnSTZWVj3bo0IHvvvuOBg0akJCQwMyZM+ncuTMXLlwo6kPp4eFR7DkeHh5ERETccczJkyczfvz4ouPMzEx8fX3L5w1IUgld3neav3ZuIdusAAuhocmVFBqe2obj00/jOW0qKo1p/RQ3X93MtEPT0Bl1BLoEsrjXYlytqnlOlRUPf46GkG3KsV8nGLK80nLX/zIaBRtORjNvazDJ2cpN9r5NPJj2aFN8HK0qOTpJkqS7K3GBMjY2libXd2irW7culpaWvPbaa+UWmCRJ9y9Xl8uk/ZMIigoC4I3mbzCy5UjUqtLfOS24epWYseMoCAkBlQrXESNwHfG2yQkrAElXlF26E84rx13GQs+PQFPxsxQNRsH3h8P5dFswOYUGzDUq3u5ejxEP18fSvGo0N5ckSXrQVVY+2r9//6LfN2vWjE6dOlGvXj3Wrl1Lx44dAW7Z5EMIcdeNP7RaLVoTZ6JJUnk6uH4H+y4epUCtx9poQbPTodS+chjXEW/jOmqUSRvZCCH4+szXfHXmKwB6+/VmdrfZWJlV8yLZ+Q3w97uQlwYaC+g5BTqNrBIb4ZyJSmfanxc4HZUOQF1XG6Y+0oQeDUsxgUCSJKkSlLhKYTQaMTe/WTTQaDTY2NiUS1B3MmfOHFQqFWPHji06J4Rg+vTpeHt7Y2VlRY8ePbhw4UKFxiVJVVFsdiwvbnmRoKggLNQWzO02l1GtRpW6OCmEIO2XXwh78ikKQkLQuLri9+0q3Ea9Y3pxUgj491tY0V0pTlq7wgsboM+MSilOXorL5PFlh5i++SI5hQba+Dvx9+hujO/bUBYnJUmSqpCqkI8C2NjY0KxZM0JCQvD09AQomkl5Q2Ji4i2zKiWpqvpn0Xp2XzxMgUqPo8GK9vuOUDv0KB5TPsJt9GiTipOFhkI+OPBBUXFyeNPhfNbjs+pdnMxNhV+Hw2+vKMVJrxbw5j7oMrrSi5PJ2QVM+u0sg786yOmodGwsNEzu34itYx+SxUlJkqqVEs+gFELw8ssvF93pzc/P56233rolKfz999/LNsLrjh8/zooVK2jevPiGHnLXREm61enE04zZM4bU/FRcLF1Y1HPRfW2Go09LI37qVLJ2KLuXWnfqiM/8+aXbCCcnGf4cBcH/KMd1usPjK8DOs9TxlVZeoYFFu0P4Zt819EaBndaMSf0bMbS9H2p1yRNxSZIkqWJUdj56Q0FBAZcuXaJbt27UqVMHT09PduzYQatWrQAoLCxk7969zJs3r1zjkKT7ZTQa+XX2Ki7pYkAF7oU2tNu+CTt9Dj5LFmPXs6dJ4yXnJTNuzzhOJ51Go9LwUcePeLJB1ejLWGpXtsOf70B2Aqg08NB78NCESrmp/l86g5HvD0fwxc4rZOXrAXi8lQ/v92+Eu33pWzlJkiRVlhIXKIcNG1bs+IUXXijzYO4kOzub559/nm+++YaZM2cWnf/fXRMB1q5di4eHBz/99NMdm5JLUk32314/DZ0asrjnYrxsvUo9Xs6RI8ROeh99QgKYm+M+dgzOw4ejUpdiJmbIDvhjBOQkKstiek2FjiOhNGPdByEEOy4mMGPzRWLS8wDoH+jJ9Eeb4iETOkmSpCqrsvLRCRMm8Mgjj+Dn50diYiIzZ84kMzOTYcOGFa3umT17NgEBAQQEBDB79mysra0ZOnRohcQnSaVRmFfAj7OXE6FJBRXUyrWh/d8/YOloh++3a7FqbtrN7QvJFxizZwwJuQnYmduxoPsCOvt0LqfoK0BBltIn/eR3yrFrQxjyNfi0rty4gEOhyUzffIErCdkABPrYM+PRprTxd67kyCRJkkqvxAXK1atXl2ccdzVy5EgGDhxI7969ixUoS7trYkFBAQUFBUXHmZmZ5Re8JFUQg9HAktNLWHluJQAP+z7M3G5zsTa3LtV4orCQpMWLSVm5CoTAonZtvBcswCqwqemD6fJgx1Q4tkI5dmsET6wEz2aliu1+RKbkMn3zBXZfTgTAx9GKaY80oW/Tip/BKUmSJJmmsvLR6OhonnvuOZKTk3Fzc6Njx44cOXIEf39lU4yJEyeSl5fHiBEjSEtLo0OHDmzfvl2u5pGqrIz4VH5aspoEsywQUDddS+tt32JVpw6+K5ZjYeKGTX9f+5tph6ZRYCigjkMdFj28iNoOtcsn+IoQugs2j4WMSECl9Jns+RGYV+4y9bDkHOZuucS2C8pmjk7W5rz3f414pp0vGrn6R5Kkaq7EBcrKsm7dOk6ePMnx48dveay0uybOmTOHGTNmlG2gklSJMgoymLR/EgdjDgLwauCrjG49utT9JgvCwoid8B751/u5Oj71FB6T30dtXYpiZ/w52PAaJF1Wjtu/qfSarOAEL19nYPnea3wVFEqB3oi5RsXr3eryTs/6WFtU+Y9CSZIkqRKtW7furo+rVCqmT5/O9OnTKyYgSboPkWdD+f3XDaSb5aERahrE6mi+/zus2rbBd8kSNI6OJR7LYDTw5akvWX1euXnwUK2HmNttLnYW1bQ4n5cG2z6E0z8qx45+MHgZ1O5aqWFl5OpYtDuE7w6HozMI1Cp4saM/4/s0xMG6cpeaS5IklZUq/a08KiqKMWPGsH37diwt77zs0tRdEydPnsz48eOLjjMzM/E18S6hJFUVwanBjN0zlujsaCw1lkzrPI1BdQeVaiwhBBkbNhA/azYiLw+1gwNen3yM/X9mKZeY0QCHl8LuT8BQCDbuSoIX0LtUsd2PoOBEpv15gYiUXAC61HdhxqOB1He3rfBYJEmSJEmSKsupfw6x40gQuZpCtMKMppcTCDizE/sBA/CaMxu1CTvLZxZmMmnfJA7EHADgtWav8U7Ld9BUgR2tS+XSZmWH7uwEQAUd3lJmTWorL18s1Bv54UgEi3aHkJ6rA6BHQzc+GNCYBh7VtAgsSZJ0B1W6QHnixAkSExNp06ZN0TmDwcC+fftYsmQJwcHBgDKT0svrZo+9e+2aqNVqi5qrS1J1tjVsK1MPTSVPn4ePrQ8LH15II+dGpRpLn5RE3JSpZAcFAWDdoQPe8+Zi7lmKpc8pV5Vek1FHlOOGA+DRxWDjWqrYSis2PY+PN19k64Xrs63ttXw0sAmDmnuZtBulJEmSJElSdbdt2QaOx19ErzZga9TS4vg5aoWdwuX113AbN86k/uJhGWGM3j2a8MxwLDWWfNzlY/rX6V+O0Zej7ET45z24+Idy7NoAHl0Cfh0qLaQb/dLnbLlMWHIOAA097PhgYGO6NyjFJpWSJEnVQJUuUPbq1Ytz584VOzd8+HAaNWrEpEmTqFu3rtw1UXog6Y16vjz5JWsurAGgk1cn5j80H0dLx1KNl7l1G/HTp2NIT0dlbo7bmNHKRjgaE++AG41wfKXSb1KfBxZ28H+zoPVLUIEFwXydgZX7r7F0z1XydAY0ahXDO9dmbJ8G2Gqr9MeeJEmSJElSmTIYDPw6exWX9bGgAledNa2CduGcHoPn9Ok4PfuMSePtjdrL5P2TydJl4WHtwaKei2ji0qScoi9HQsDZX2DrJGVpt0oDXcfCQxPBvPI2TTwfk8HMvy9y5FoqAK62FrzbtyFPtamFmaZiN5aUJEmqSFX6m7qdnR2BgYHFztnY2ODi4lJ0Xu6aKD1o0vLTeG/fexyNOwrAK4GvMLrV6FItpzFkZBD/yUwy//oLAG3jxnjPnYtlwwalCCwCNo2E8P3KcZ2H4LGlSu+eCiKEYOv5eGb9c4noNGV37na1nfhkcCCNPO0rLA5JkiRJkqSqIDcjm58+XUm0WbqyU3eeLW3/+REray21Vq3EpmPHEo9lMBpYenop35z7BoBW7q34vMfnuFpV7AqZMpERDX+Ng5DtyrFnMyVv9WpRaSHFZ+SzYHswG05GIwRYmKl5vVsd3u5RX95glyTpgVDtP+nkronSg+RiykXG7RlHbE4sVmZWzOwyk761S9EfEsjef4C4jz5Cn5AAajUub7yO24gRqCwsTBtICDi5VmkoXpgN5tbQ52No+yqYsFTofl2Ky+TjzRc5fC0FAC8HS97v34hHW3jL5dySJEmSJD1woi+F8fuPv5JqlotKQL00C1puX4VV7dr4fr0Mi9q1SzxWan4qk/ZN4kic0r5naKOhTGg7AXNNNdugxaCHYytgzywlb9VYQPdJ0GUMVNJ7ycjV8dXeUNYcDKdAbwTgsZbevPd/DanlVIoNKiVJkqoplRBCVHYQlS0zMxMHBwcyMjKwt5ezrKSqRwjBr1d+Ze6xueiMOvzt/VnYYyH1neqbPJYxJ4eETz8lfd16ACxq18Z73lysWpTijnFGDGweDaE7lWO/TsrdZ5d6po9VSqk5hXy+I5ifjkZiFKA1U/Nm93q81b2u3J1bkqQHisxnqjf55yeVpVP/HGLnkSBy1IWYCw2NwrJocmwzNp074fPFF2gcHEo81rmkc4zfO574nHiszKyY3mk6A+oOKMfoy0nMCdg8FuLPKse+HZQe6W4NKyWcvEIDaw6FsywolMx8PaCs/PlgQGNa+TlVSkySJEn3637yGfntXZKquBxdDjMOz2BL2BYAevj2YFbXWdhbmP7lJefIEeKmTEUXFQWA0wsv4P7ueNRWVqYNZDTCyTWwYxoUZIJGC72mQse3oYJ2btQZlF0Nv9hxpSipG9jci8n9G8m7zZIkSZIkPbD+dzOcZicv4xd6DKehQ/H4YDIqs5J9BRRC8EvwL8w9Phe9UU9t+9p83uNzApwCyvkdlLH8DNj1idInHQGWDspqn1YvVehqnxv0BiO/nohm4c4rJGQWAMoGOBP7NaRnI3e58keSpAeWLFBKUhV2Je0K7wa9S3hmOBqVhnFtxvFSk5dMTlwMWVkkfrqA9F9+AcDMywvv2bOw6dTJ9KCSQ5VZkxEHlWOftjD4qwq7+yyEYE9wIrP/uUxoYjYAjb3smfZIEzrWdamQGKTiDDojGcl5OHvZVHYokiRJkvTA0uv1rJ+9khBD/H82w9mJc3osHlM+wvn550s8Vq4ul5lHZrL52mYAevv15pMun2BrYVte4Zc9IeDCRtg6GbLjlXPNn4G+s8C24nfCFkKw5Xw8C7YFc+36ztw+jlaM79OAwa180KhlYVKSpAebLFBKUhW1MWQjs4/OJt+Qj4e1Bwu6L6Cle0uTx8kKCiJ+2nSl1yTgNPQ53Ma/i8bWxGKSQQeHFkPQXDAUKL0me02F9m9U2KzJ8zEZzP7nEoeuKn0mnW0smNC3Ic+085VJXQXLTisg8kIK4eeSib6chrmlhpfndpF3/SVJkiSpEqTFJLN+2VrizbKUzXBybWi75Sesba3wWbUKm44dSjxWcGow7+17j7CMMDQqDWNbj2VY02HV69/41DD4Z8LNNkTO9WDQ51C3R6WEcyg0mXlbL3MmOkMJx8aCkQ/X54WOfmjNKiaPliRJqupkgVKSqphcXS6zj85m09VNAHTx6cKcrnNwsjStF40+LY2E2XPI3Kzc+Tb398Prk0+wad/e9KDizsCmd2727KnXEwYtBCd/08cqhZj0PBZsC2bjqRhA2dVweOfajHi4Pg5W1aw5ezVlNAoSwzMJP5dMxPkUkqOyiz1urtWQnVaAnbNlJUUoSZIkSQ+m87tPsH3PDjLN8lELFfWS1LTc/S3WTZpQa8lizL29SzTOjZ7n84/Pp8BQgLuVO3Mfmks7z3bl/A7KkC4fDi+GfQtAn69sgtPtXegyFswrPkc5GZnGFzuusD8kGQBrCw2vdavL693qYGcpc1hJkqT/kgVKSapCLqdeZuK+iYRlhKFWqRnVahSvBL6CWlXy/jhCCLK2bSP+k5kYUlJArcZ52DDcRo8yvdekLg/2zoODi0AYwNIR+s2BFs9BBdxFz8zX8dWeq3x7MIzC67saDm7pzQS5q2GFyMsqJPJiKhHnU4i8mEJBjv7mgyrwqG2Pf6ALtZu54lrLFpWcxSpJkiRJFWrbsg38G38RncaAldGcxpfjCDi7C4fHHsNzxnTUliUrymUVZjHj8Ay2hW8DoKtPV2Z1nYWzpXN5hl92hIArW5Xl3Glhyrk63WHg5+Bq+qaS9+tsdDpf7LjCnuAkAMw1Koa29+OdngG42WkrPB5JkqTqQBYoJakKMAojP1z8gYUnF6Iz6kp9x1oXF0f8rFlk79wFgDagPl6zZmHVvLnpQYXshH/ehbRw5bjpEOg/H2zdTR/LRIV6Iz8ejWDRrhDScnUAdKzrzAcDGtO8lmO5v/6DShgFiZFZSkHyQgoJ4Zkgbj6utTbDt4kztQNd8GvqgpWdReUFK0mSJEkPML1Oz7rZ3xAqEkAFLnprWuzfj1tyGB4ffojTC8+XeEn2heQLTNg7gejsaMxUZoxpPYaXmr5k0g3ySpUcClvfh9AdyrGtJ/SdCc2erJAb6v91ITaDhTtD2HFRaa2kUat4orUPo3oG4Ossb65LkiTdjSxQSlIlS85L5qODH3EwRtl05mHfh/m488c4WjqWeAyh15P6ww8kL1qMMTcXzMxwfeMNXN56E7WFiUWkzFglybuoLDHHzhsGLoBGA00bpxSMRsHf5+L4bHsw4Sm5ANRzs2Fy/8b0aix3NSwP+Tk6oi6lFhUl87J0xR539bXFv6kL/oEueNSxR62pJl9WJEmSJKmGSgyLYcOqdSSYZQHgm2tDmy0/Y21nhc/qb0vczufGDfIvTn6B3qjH28ab+d3n08KtRXmGX3YKsmDfp3D4KzDqQG0OnUbCQxNAa1ehoQTHZ7Fw5xW2nFc241GrYHBLH0b1CqCOq9xEUJIkqSRkgVKSKtGBmAN8eOBDUvNT0Wq0vNf2PZ5u+LRJhbi8c+eImzaNgouXALBq3RrP6dOwbNDAtGAMeji2AvbMgsJsUGmg49vQ4/1yT/KEEAQFJ/HptmAuxmUC4GprwdjeDXi2nS9msihWZoQQJEdnKwXJ8ynEX8tA/GeWpLmlBr/GzvgFuuDf1AUbR7kMSZIkSZKqiiMbdrP/zBFyzArRCBX1EqBl0LdYt2iBz8IvMPfyKtE4ibmJfHTgIw7HHQagl18vZnSegYPWoTzDLxtCwLlfYfuUm7tzB/SFfnPBpV6FhhKamM3CnVf4+1wcQigTNgc192ZMrwDqu1ejHc8lSZKqAFmglKRKUGAoYOGJhfxw6QcA6jvWZ/5D8wlwCijxGIasLJIWfknaTz+BEKgdHHCf8C6OTzyBSm1iQS/qOPw9DuLPKce12sGgL8CzmWnjlMKxsFQ+3XaZ4+FpANhpzXj9obq80rUOtlr5EVUWCvL0RF9KJeJCChHnU8jNKCz2uLO3TdEsSc96DmjMZEFYkiRJkqoSg8HA7/PWcKkgGqNaYGPU0uR8BHUv7sV52Eu4v/suqhKumtkZsZPph6eTUZCBVqNlQtsJPNPwmeqxUiXuDPwzEaKOKMdOdZTCZMN+FRpGSEIWS/eE8ueZWIzXb/QOaObJmF4NaOhZsbM3JUmSagr57V+SKtjFlIt8eOBDQtNDAXiu0XOMbzMeS7OSNTG/sQlOwqzZ6JOUxtv2jz6Cx6RJmLm4mBZMbirs+hhOrAGEsglOnxnQ6iUwtchpovMxGXy6LZi9V5T3oDVT83Ln2rzVvR5ONrK34f0QQpAal1M0SzIuNAOj8eY0STMLNbUaOeMfqBQl5c7bkiRJklR1pcYk8uuyH4gzy1Q2qSuwodXuf3AyZOO16Evs+/Yt0Tg5uhzmHZvHxtCNADR2bszcbnOp61i3PMMvGxkxsHsmnPkZEGBurSzl7vQOmFXcao/zMRks3RPK1gvxRStQ+jTxYFzvBjTxtq+wOCRJkmoiWaCUpAqiN+pZeW4ly88sRy/0OFs6M6PzDHr49ijxGAXXwkiYPZucAwcAsPD3x3P6NGw6dTItGKMBTqxWEr08ZeYiLYZCn4/B1s20sUx0NSmbz7crS2EAzNQqnmnny+heAXjYy0JZaRXm64kJTiPifAoRF1LITi0o9rijh3XRLEnvAEc05nKWpCRJkiRVdSf+OsieY/vINitAJVTUTTWn1Y7VWDdpRK2Fa7Hw8yvROGeSzjB5/2SisqJQoeKVwFcY2XIk5hrzcn4H96kgCw5+CYeWgD5PORf4pJKzOvhUWBgnIlJZsju0aFdugP9r6sE7DwfQrFY1WBYvSZJUDcgCpSRVgLCMMD488CHnkpUl1L39ejOl0xScLZ1L9HxDdg7Jy74i9bvvQadDZW6Oyxtv4PLG66i1Jt41Dj8AWyZBwnnl2L0JDPgUanc1bRwTXUvKZsnuUP44HYPxeo+ex1p4M65PA/xdZPNwUwkhSE/IJfJCKhHnk4kJSceovzlLUmOuxqeB0/VZks44uMmdIyVJkiSpujAYDGxa8D0XciMxqI1YGy1odDmOgLM7cXz2GTwmTy5RDqgz6FhxbgXfnP0GgzDgZePFrK6zaOfZrgLexX0w6OHU97BnNuQkKuf8Oim7c9dqWyEhCCE4dDWFJbtDOXwtBVA2v3mkhTcjetSXS7klSZLKmCxQSlI5MgojP1/+mS9OfEGBoQA7czsmd5jMoLqDStTnRwhB5ubNJH66oGg5t22PHnhMfh8Lf3/TgkmPgh1T4IKyrAdLR3j4Q2j7CmjK76Pg6vXC5KbrhUmA3o09mPB/DWjkKZfCmEJfaCDmSroyS/J8MpnJ+cUet3e1xD/QFb+mzvg0dMLcQlNJkUqSJEmSVFoJ12LY+O164q8v6XYvtKHl3p245Kfg+emnODwyqETjXE69zEcHPiI4LRiAAXUG8GHHD7G3qML5lxAQulPZACdJ2QAS57rQewY0fkS5w13uIQh2X05kyZ5QTkWmA2CuUfF4q1q83aMeteWu3JIkSeVCFiglqZxEZUUx49AMjsYfBaCTVyc+7vIxnjaeJXp+/qVLxH8yk7yTJwEw9/fDY/Jk7Hr0MC0QXR4cXAQHvlCWxqjU0Ga4Upy0MbFnpQluX5h0Z0yvBnIpTAn9d5Zk5MUUYq6kY9AZix5Xa1R4BzgW9ZJ09LCuHg3uJUmSJEm6rb3fb+FoyClyzQpRCRV10i1otX0tts2b4vPpNyVa0q0z6lh5diUrzq5AL/Q4ah35sOOH9KtdsRvJmCz+HGz/CK4FKcdWTtD9feVmuln59yfXGYz8fTaO5fuucSkuE1B6pD/X3o83HqqLt6NVuccgSZL0IJMFSkkqYwajgZ8u/8TiU4vJ0+dhqbHk3bbvlnh3RH1aGkmLFpG+/hcwGlFZWeH61ls4D38ZdQl3ZwTAaIQLv8POGZARqZzz7wL955Xr7tyhidks2R1SbFfD3o09GNNL9ugpicJ8PdGX04i8mErkhRSyUorPkrR10uIf6IJfUxdqNXLCwlJ+jEuSJElSdVeQm88v81ZxjSSEGmyNWhpejKT+hb24vPkGbiNHojK/d7/I4NRgPjr4EZdTLwNKW6EPO36Iq5Vreb+F0ku5qizlPr8BEKCxgA5vQrd3lSJlOcvK17H+eBTfHggjNkPJu2wsNLzQyZ/XutbFza7iNuGRJEl6kMlvtpJUhkLTQpl2aBpnk88C0M6zHdM7TcfP/t53u42FhaR9/wPJX3+NMSsLAPsB/XF/7z3MvbxMCyT8oHIHOlaZfYl9Lej7CTQdUm5LY4Ljs/gqKJTNsjBpEiEEydHZRF5IIfJCKvFXi++4rTZT4V3fEb+mLvg1ccbZ20bOkpQkSZKkGiT44Fm2bd1KqiYXAO88G1rt2oSjrTnea9dg0779PcfQGXWsOreK5WeXozfqcdA68GEHZdZklc0bMuNg7zyl16RRr5xr+jj0mgrOdcr95eMz8ll9KIyfjkaSla+8vqutlpc7+/NCR38crct/1qYkSZJ0kyxQSlIZ0Bl0rDp/Mym0Mbfh3bbv8kTAE6hVd98tWQhB1rZtJC74DF10NADaRo3wmDwZmw73TkiLSboCO6dB8D/KsYUtdBkLnUaCRflsknIiIo1lQaHsvJRYdK5PE6UwGegjC5O3k5+tI+qSMkMy8mIquZmFxR53cLcqKkj6NHDCXCt7SUqSJElSTWMwGNj8xU9cyApHpzFgLjTUi9XRfP+32Pfpg9cnH6NxdLznOGeTzjLj8AyupF0BoKdvT6Z0mlJ1Z03mpiqth46tAP31lSL1+0CvKeDVotxf/nJ8Jt/sC+PPMzHoDMpN4XpuNrzerS6DW/lgaS7zLkmSpMogC5SSdJ/OJ59n2qFpRUnhQ7UeYkrHKSXqNZl3+jQJ8+aTd+oUAGZubriNHYvD4MdQaUxIjrKTIGgOnFgDwgAqDbR5GXq8D7bupXhXdyeEYO+VJJYFXeVoWCqgTMzsH+jJiB71ZWHyfxiNgsTwTCKuz5JMjMiEm5MkMdNqqNXQCb8mzvg1lTtuS5IkSVJNF33uGpvXbyTBLAtU4Ky3JvDYv/gkX8X9k49xfPLJe858zNHlsOjkIn6+/DMCgaPWkUntJzGwzsCqOWuyIBuOLINDi6BA6fGIb0foPQ38O5frSwshOHw1heX7rrH3SlLR+fZ1nHmjW116NnJHra6C/80kSZIeILJAKUmllFmYyeKTi1kfvB6BwEnrxPvt36d/nf73TAoLo6NJ+vxzMv/ZAoDKygqXV1/F5ZXhqK1NKE4V5sCRr+DAl1CoLAun4QBlp0O3BqV9a3dkMAq2nI9jWdBVLsQqiaW5RsWQVj682b0e9dxsy/w1q6uc9AIiLyoFyahLqRTk6os97uJjg18TF/yaOuNVzxGN+d1n2kqSJEmVZ86cOfz+++9cvnwZKysrOnfuzLx582jYsGHRNS+//DJr164t9rwOHTpw5MiRig5XquL++nId51JDKTDToxYq/DMsaLnjJxzbtMDr2z+xqOVzzzGCooKYeWQmCbkJADxS9xEmtJuAs6VzOUdfCgXZcHylUpjMTVHOeQQqS7kD+pbrztx5hQb+OB3DmoPhBCcoubJaBf0CPXm9W11a+ZV/j0tJkiSpZGSBUpJMJIRgS9gW5h+fT0q+kmQNrDuQie0m3jMp1KemkrJ8BWk//YTQ6UClwmHIENzGjMHcw4SZjrp8OLEa9n8GOdfvAnu3gr4zoXbX0r61O8rXGfjjVAzL910jLDkHACtzDUM7+PFatzp4OchdDfWFBmJD04m6lEbUxVRSYrKLPa61NsO3sTJD0rexC7ZOsuG6JElSdbF3715GjhxJu3bt0Ov1fPjhh/Tt25eLFy9iY2NTdF2/fv1YvXp10bGFKZvbSTVefGgUf675lVizTFCBg8GShmdDqRtxAvdJ7+L0wvOo1He/YZmUm8ScY3PYEbEDgFq2tZjSaQqdvct3BmKp3K4w6VwXHv5Q6TV5j/d6P6LTcvn+SATrjkWRkacDlNz1qba1eLVrHfxdbO4xgiRJklTRZIFSkkwQnhHOzKMzORp3FIDa9rX5qONHdPDqcNfnGbKzSV29htTVqzHmKg3QrTt1xGPiRCwbNy55AAY9nP4R9s6HTKVfJU61oeeUckn0krML+OFIBD8ciSA5W+mT6GBlzsuda/Ny59o42Ty4X7yEUdncJuqSMkMyLjQDg9548wIVuPvb49fUGf+mLrj726HWyFmSkiRJ1dHWrVuLHa9evRp3d3dOnDjBQw89VHReq9Xi6XnvFi/Sg2f71xs4GXuZfDMdKqHCL8uSVjvW49ikAV4bf0db5+6bwuiNetYHr2fpqaVk6bLQqDQMazqMt1q8hZVZFbtRXJANx7+BQ4tvFiad6kD3idDsadCUz1dQIQRHrqWy5lAYOy4mFG3a6OtsxbBOtXmqrS8OVvfeCV2SJEmqHFW6QFmS5TRCCGbMmMGKFStIS0ujQ4cOLF26lKZNm1Zi5FJNk6/PZ9X5Vaw6twqdUYeF2oI3mr/B8MDhWGjuXKQzFhSQ9tPPpCxfjiE9HQDLpk1xGzcOmy6dS94fyGiEC7/DntmQelU5Z+etJHqtXgBN2SZbIQlZrDoQxu+nYii8XnTzdrDkla51eK69HzbaKv3RUW6yUvOLCpLRl9PIz9YVe9zWSUutxs74NnbCt7EzVrYPbgFXkiSpJsvIyADA2bn4yomgoCDc3d1xdHSke/fuzJo1C3f326+QKCgooKCgoOg4MzOz/AKWKk1McDh/f7+RWLMMUIOdQUvD4CjqXzqA6+hRuLzyyj37jp9IOMHso7OL+p0HugQyvfN0Gjo3vOvzKtyNwuTBRZCn9CjHuS48NBGaPVVuhckby7jXHgrncnxW0fmu9V0Z1rk2PRu5o5H9JSVJkqq8Kl1lKMlymvnz5/P555+zZs0aGjRowMyZM+nTpw/BwcHY2dlV8juQqjshBDsjd/LZv58Rkx0DQBefLnzY/kN87X3v/Dy9nvSNG0le+hX6+HgALOrUwW3MGOz+r2/JC5NCQPAW2DMLEs4r56xdoNu70PZVMLe8r/dX/KUEB0KTWbk/rFjz8Ba1HHi1W136B3pi/oDNACzI0xMTnEb0pVSiLqeRnpBb7HFzrQafhk5FBUlHD+uq2ZRekiRJKjNCCMaPH0/Xrl0JDAwsOt+/f3+eeuop/P39CQsLY8qUKfTs2ZMTJ06g1d7a1mPOnDnMmDGjIkOXKtCNHbovZUVQYKZHJcA3x5rmO3/HtXFdZdZk/fp3HSM5L5nP//2czdc2A+CgdWB0q9E8EfAEGnUV2mk6JwWOLYejyyE/XTlXAYXJq0nZ/Hw0kt9ORpOee3MZ9+OtfRjWuTYNPOR3QUmSpOpEJYQQ976sakhKSsLd3Z29e/fy0EMPIYTA29ubsWPHMmnSJEC5G+3h4cG8efN48803SzRuZmYmDg4OZGRkYG9vX55vQapGglODmXd8HsfjjwPgbu3OpHaT6OPf545FKKHXk/nPPyR/tYzC8HAAzDw9cXtnJA6DB6MyK2GCZjTC5b9g33yIP6ec0zpA51HQ8S3Qll3Cla8z8OeZWL49EFZ011mlgv9r4slr3erQxt/pgSm6GQxGEsIylRmSl1JJCM9CGG9+RKrUKjxq212fJemMRx17NA9Y0VaSpKpL5jMVY+TIkfz9998cOHCAWrVq3fG6uLg4/P39WbduHY8//vgtj99uBqWvr6/886sBgg+eZeeWbSSZKX27HQyWBARHEnD1GG7vjsfp2Wfv2mtSb9Tz8+Wf+er0V2TrslGh4okGTzCm1RgcLR0r6F2UQEY0HFoCJ9eC7vpNXOd68NB75VaYLNQb2X4xnh+PRHL4WkrR+VpOyjLup9v64mAtl3FLkiRVlvvJR6v0DMr/9b/LacLCwoiPj6dv375F12i1Wrp3786hQ4fuWKCUS2qku0nLT2PJqSX8FvIbRmFEq9EyPHA4w5sOx9r89jtsC72ejL/+ImXZ1xRGRACgcXLC5c03cHruOdS3mTlxW0YDXPwD9i2AxIvKOXMb6PAGdB4N1mW3M2NUai4/HI3gl+NRpF2/62xtoeHptr4M71L7gWgebjQKkqOyiA5OIyY4ndjQdPQFhmLXOLhb4Xu9IOnT0AmtVbX62JQkSZLK0KhRo/jzzz/Zt2/fXYuTAF5eXvj7+xMSEnLbx7Va7W1nVkrVV2FeAb8v+I5QfRx6MyMaocIvw4IWu9bj0rUTnov+wvwePUoPxhxkwb8LCE0PBZTl3B92/JBA18C7Pq9CJYfAgYVwdj0Yr7e78WwO3cZD40ehHGZ3Rqbk8tOxSH47EVXUF12tgocbujO0gx89Gspl3JIkSdVdtfmmfbvlNPHXl856eHgUu9bDw4OI60Wi25FLaqTb0Rl1/BL8C0tPLyWrUJlJ2Ne/L+PbjsfH1ue2zxF6PRl/biZ5+dfoIiIB0Dg44Dx8OE4vPI/G1rZkL27Qw/kNsH8BJCv9hdDaQ4c3oeOIMitMGo2CfSFJfH84gt3BidyYP+3jaMVLnfx5tr1fjW4eLoyC1Lgcoi+nEXMljdiQdApy9cWusbQxp1YjZcl2rcZO2LtUscbzkiRJUoUTQjBq1Cg2btxIUFAQde6xoQlASkoKUVFReHl5VUCEUmU7vH4nR8+fIF2TBypw0VnT6NRZ/DMj8Px0Dnb/9393XZESmhbKghMLOBhzEABHrSNjW49lSMAQ1Koqsloj+gQcXAiXNgPXk8ja3aDrOKjXU1mCU4Z0BiO7LiXy49EI9ockF513t9PybDtfnmnvh4+jzNMkSZJqimpToHznnXc4e/YsBw4cuOWx//3HXghx1wRg8uTJjB8/vuj4xpIa6cEkhGB7xHYWnVxEZJZSZGzo1JBJ7SfRzrPd7Z+j05Hx558kf70cXVQUABpHR5xfeQWnoUPR2JZw9qG+AM7+Agc+h9RryjlLR6Uo2eFNsHK8z3enyMjV8euJKH44EkF4ys0+it0CXHmpU81tHi6EID0hV+kjGZxOzJVbN7axsNTg3cAJnwaO1GrkhIu3Laoa+N9CkiRJKr2RI0fy008/sWnTJuzs7Ipukjs4OGBlZUV2djbTp0/niSeewMvLi/DwcD744ANcXV0ZMmRIJUcvlaeYy+Fs+eEPos3SQQPmQkPtJEGLPd/h8vSTuI9fisbB4Y7PT85L5qvTX7EhZANGYcRMbcbQRkN5o/kbOGjv/LwKY9ArbYeOfAVRR2+ebzhQKUz63j5Xvh/B8Vn8+m8Uf5yOKZotqVJBtwA3hrb3o1dj9weuL7okSdKDoFoUKO+0nMbz+hKJ+Pj4YnenExMTb5lV+V9ySY10w/H443xx4gvOJSt9Hp0tnRnZcuQdm48b8/JI//13Ur9djS5G2TRH4+yMyyvDlaXcNiUsTOZnwL+r4ejXkBWnnLNyhs7vQLvXwfL+e08JITgTncHPRyPZdCaGfJ2yG7edpRlPtfHlhY5+1HUr4QzPakIIQVZK/vUl22lEB6eRm1FY7BozCzXe9R3xaeiET0Mn3HxtUcskt1R0+fmkxceSGhtNXlYmrf5vUGWHJEmSVC6WLVsGQI8ePYqdX716NS+//DIajYZz587x3XffkZ6ejpeXFw8//DDr16+XmzbWUIX5Bfzx+Q+EFsRSaKa0h/HOs6HxkSBqedri+cs6rJrdeVl2gaGA7y9+z8pzK8nRKb0qe/v1ZlybcfjZ+1XIe7ir/Ew49b2Sq6YrN/BRmyu9JbuMBvfGZfpyGXk6/jwTy2//RnEmOqPovKutlqfa1uK5dn74udy+1ZIkSZJUM1TpAuW9ltPUqVMHT09PduzYQatWrQAoLCxk7969zJs3rzJClqqJkLQQFp5cyL7ofQBYmVnxctOXGdZ0GDbmtxYZDenppP70E2nf/4AhLQ0AjYsLLq+8gtNzz6K2LmHClBEDR5fBv2vg+jJy7LyUGZNtXwHt/RcM03ML2XgqhvXHo4o2vQFo7GXPS538eaylN9YWVfp//RITQpAWn0tsSDpxoenEhqSTnVZQ7BqNmRrPevb4NHCiVkMn3GvbozGTBcmSMhoNZCUnkRYbQ2pcDKmxMaTFRpMaF0N2ys3lVmqNGS1690etqUK7ikqSJJWRe+0paWVlxbZt2yooGqmyHfh5O/9ePFW0nNvBYEm9K7E0ityO+4R3cRgy5I6b4OiNejaFbmLZmWUk5CYA0NSlKe+1e482Hm0q8m3cXlqEshv3ye9u5qpWztDuNeXH7s6TQExlMAoOXU3m13+j2XohnkK9cjPdTK2iV2N3nmrjS/eGbnK2pCRJ0gOiSlcp7rWcRqVSMXbsWGbPnk1AQAABAQHMnj0ba2trhg4dWsnRS1VRTHYMX5/5mj+v/olRGNGoNDzZ4EneavEWrlaut1yvi40lde1a0n79DZGrLI029/HB+ZXhOD7+OGqrEva9SbgIhxbDuV9vNhN3a6RsfNPsKTCzuK/3ZTQKjlxLYd3xqGIJntZMzcBmXjzXwY+2NWA3bqNRkBKdTWyIUoyMu5pOXlbxJdtqtQr32vbUaqQs2/as64CZhSya3Ut+djapsdGkxcWQFhej/D42hrT4WAw63R2fZ2lrh5O3D85etdAV5KO1rvmbK0mSJEkPpqvHL7F701ZizDJAAxZCg1+KiuZ7fsb9mSdxW7Hljsu5jcLI9ojtLD21lPDMcAA8rD0Y03oMA+sOrNw+k0LAtSA4vhKC/wGh5JG4NoROI6D5M2Bedr0eg+Oz+ON0DJtOxRCbkV90vpGnHU+2qcXgVj642srVbpIkSQ+aKl2gvNdyGoCJEyeSl5fHiBEjSEtLo0OHDmzfvl0up5GKic+JZ8XZFWwM2YheKJui9PbrzejWo6njcGuj+/wrV0hdtYqMv/8BvXK9tlEjXF57Dft+/4fKrAT/6xiNcHW3sjQmdMfN8/5dlaUx9fvAHe6ul1RCZj6/nYhm/fEoIlNv9pZs7GXPc+19eayFDw7W1XfTG4POSEJEZtHsyLirGejyi++yrTFX41nXHq/6jngHOOJZxwFzrSxI3o5BryM9IV6ZDRkbTVpcLGlx0aTGxpCXmXHH52nMzHD09MbJyxsn71o4e/kov3r7YGV3/+0IJEmSJKkqS4lJ5O8VvxJBCgYzpXjnk2dD40O78K3tjsevP2PZpMltnyuE4EDMARafWsyl1EsAOGmdeK3ZazzT6Bm0mkosxOWlw5mflcJkSujN83Ufhk7vKBvf3GeuekNcRh5/no7lj9OxXIrLLDpvb2nGYy19eKptLZr5OFT7m+mSJElS6anEvdasPAAyMzNxcHAgIyMDe3v5ZbsmSchJYOW5lWwI2YDu+szFjl4dGdlyJC3dWxa7VhiNZAftJfX778g9fKTovHXHjri89ho2XTqXLGnKz1SSvaPLIfXq9ZMqaPwIdBkDtdre13vKKzSw/WI8v5+MYX9IEsbr/wfbac14tKU3z7bzI9DHvlomePk5OhLCMom/lkFsSDoJYZkYrs8GvcHCygyveg54BzjiVd8Rd387uWT7P4QQ5GakF82A/O+syPSEeITReMfn2jo54+RdCycvb5y9axXNjLR3c5dLtyWpGpD5TPUm//yqnoK8Av5c+COh+bEUqJQb1i46a+pcCaNxVjjuE9/D9uGHb5tzCSH4N+FflpxawsnEkwDYmNswrOkwXmry0m1bClWYuLNKUfLcr6C7foPbwg5aPKss43ZvVCYvk5GnY+v5ODaeiuFoWCo3vnWaa1R0b+DO4Fbe9G7sgaW5zDEkSZJqivvJZ6r0DEpJKq3kvGRWnVvFL8G/UGhUNklp59mOkS1H3tLfx5CdTcaGDaT++BO6yBtNwNXY9emDy2uvYtWsWQlfNBSOrYDTP0JhtnJOaw+tXlCSPZd6pX4/N5Zw/34qhi3n4sgpvDmLsK2/E8+292NAM89q1VtSGAXpibnEXc0g/loG8dcySYvLueU6KzvzomKkd4AjLj62qOUu20Ub1BRbjn29R2RhXu4dn2eutcTJywcnbx+cvHxw9vZRipFe3lhYyebzkiRJkmQwGNixYiMXYkPJ0uSDCuyMWvyiMwm8tAOPd0bg9PQiVOa3rlIRQnA47jDLzywvKkxaqC14rtFzvNrsVZwsnSr67SgKc+DCH3BybfHduN2bKHlq86dBe/8r0HIL9ey5nMTmM7HsDk4sajsE0K62E4+19GFgMy+cbO6vvZEkSZJU81SfaoYklUBUVhRrzq/hj9A/igqTrdxb8U7Ld2jv1b7YtYXh4aT+8CMZv/+O8Xp/SbWDA05PPYnTc89h7uNz7xc0GiBkBxz/BkJ33jzv2gDavwEtnruvjW9CErL4/VQMf5yKIe4/PXp8na0Y0tKHIa1rUce1evT8K8zXkxiRRXxRQTKDglz9Ldc5uFvhWdcB7/qOeNV3wNHDulrOBi0LN5Zkp8fHXi9AxhYVJbNTU+78RJUKBzf3Ysuxb8yKtHV2eWD/e0qSJEnSvQSt/ZtzIRdIMcsFDWiFGb4p0OzgRrxeeAaXL7eiuU0rKSEE+2P2s/zMcs4mnwXAXG3O4wGP81qz1/C08azot6L0low5Cae+g3Mbbm56ozaDxo9C+9fBrxPcZ16QW6hn9+VE/jkXx+7LieTrbhYlA9xtGdzKh0dbeOPrLG+ESpIkSXcmC5RSjXAl7Qqrzq1iW/g2DEKZXdjCrQUjWo6gk1enooKM0OvJ3ruXtPXrydm3v+j5FvXr4fzCizg8+kjJduROj4JTP8Cp7yEz5vpJFTToBx3eUHr3lDLZi07L5e+zcWw+G8v5mOI9egY29+bx1j5VfsMbIQSZyfkkhGUQfzWDuGsZpERn878NJczM1bjXtsezrgOe9RzwrGOPld2DdUdd2SU7uajwqPzEkh4XS0ZiAkLceUm2pa3dzeXYXjdnQjp6emNm8WD9d5QkSZKk+3Hw5x2cunCaZLMcMAONUOGTbUmTQ9vx69UJt7823vbmtVEY2RO1h+Vnlhf1mNRqtDzV4CmGBw7H3dq9ot8K5KbC2fXKTtyJF2+ed6qjrOxp9QLY3V/B9G5FSV9nKwY08+LRFt408aqebYckSZKkiicLlFK1djrxNCvPrWRv9N6ic128u/Bqs1dp69G2KCHSxcaS/ttvpP+2AX1ionKhSoVt9+44v/Qi1p063Tt5MujgyjZlaUzIDuB6tc3KGVoOhXavgnPdUr2P2PQ8/jkXx19n4zgdlV503kytokdDdx5v7UPPRu5VtkdPbmYhieGZJIRnkhiRSWJ4Fvk5t+78bOusVYqRdR3wqueASy1bNJqa3z/yRl/IG7tiFytEJsTddZdsc60ljl7eSgHyevHRycsHJy9vuUGNJEmSJN2nIxuCOHnqXxLNssEM1EKFd64VAScPU791AG4/r8Kidu1bnldgKGDz1c2svbC2aFduKzMrnmn4DMOaDsPVyrVi34hBp2zOeOZnuPw3GJSVRJhZKrMlW7+obNR4H5veZOTpCApOZNuF+DsWJQc18662vdAlSZKkyiULlFK1ozfq2RW5ix8u/sDppNMAqFDRx78PrzZ7lSYuyi6KQq8na9++m7Mlr0/f0zg74/j4EByfegoLf/97v2DqteuzJX+E7Pib5+s8BK2HKZvfmJm+A2NCZn5RUfJERFrReZUKOtZxYUBzLwY288K5ivXoKczXkxSRRUJEZlFRMju14Jbr1GYqXGvZ4XVjdmRde2ydLCsh4oqTn5OtbEYTF0tq3M1CZHp8LIV5eXd8nlpjhqOnl7JL9vXio9P1QqSNk7NM8iVJkiSpjB3ZEMTpUyeIN8sCM1AJFd751tQ/dYyARt64fT0fbUDALc9Ly09jffB6fr78M6n5qQDYmtvybKNnebHJizhbOlfcmxACYk4osyXPb4Dc/7R/8WwOrV+CZk+CVen7Xsak57HjQjw7LiVw9FoqeuPN5TB+ztYMaKbkq7IoKUmSJN0vWaCUqo2Mggx+D/mdny7/RHyOUig0U5vxaL1HGd50OLUdagNKb8n0TZvI+H0j+oSEoudbd+yI09NPYdu7N+p7LX/NS4MLG+HMuuKNxG3clNmSrYeVatObyJRctl+MZ/uFBI5H3NzNUKWCdv7ODGrhRb9AT9ztqkYhz6AzkhKb/Z/ZkVmkxuUUTR4togInTxs8/O1wr22PRx17XLxt0ZjXrNmRQgjys7NIj48jPSGu2K9p8bHkZWbc+ck3+kJ6+RTNgnT28sbRywd7Vze5S7YkSZIklTODwcC+77dwMfQSSdeXciPAu8CGumdO09DPDtfPp2MV2PSW50ZmRvL9xe/5I/QP8g1KX3BPG09ebPwijwc8jq1F6XuOmyz1Gpz9VSlMpl69ed7GDQKfVHbj9m5ZqqGFEFyIzWTHxQR2XEzgYlxmsccD3G3p08SD/oGyKClJkiSVLVmglKq8sIwwfrz0I39e/ZM8vTILzdnSmacbPs3TDZ7GzdoNQ0YGaevWk/HHH+SdPl30XI2TEw6PD8HxySfR1qlz9xfSFyob3Zz5Ga5svbk0RqVWekq2fgkaDgCzks9oFEJwPiaTHRfj2X4xgcvxWcUeb+PvxKDmXvQP9MLToXKLkoX5elKis0mKyiIpKpukyCzSYnMwGv+3Ggm2Tlo8atvjfuPHzw4Lq5rxcSKEICct9WbxMSGOtPg4Mq4XIgtyb91p/L9snJxvzoQsWo7tg4OHJ2a32e1TkiRJkqTypdfp2b78d0ISwknT5BbNmPQqsMb/wkUCA5xx+WIKlo0aFXueURg5EHOAny//zMGYg4jrd2gbOzfm5aYv06d2H8zVFfRve0Y0XNyk3ECPPn7zvLk1NBoEzZ+Buj1AY3o+lluo51BoCkFXEtlzOYmY9JurPtQqaOvvTJ8mHvRp4kHtarI5oyRJklT91IyKglTj6Aw6dkft5tcrv3I07uYMxgZODXih8QsMqDsAC6Oa7AMHiN70J9m7dyMKrxcU1WpsunbBcfDge8+WNBoh5l849xuc/6340hj3ptDyOWj2lEmNxAv1Ro6GpRTdef7v7tsatYr2tZUkr1+gJ96OViUetyzl5+iUQmRkFsnXi5Hpibm3zowEtDZmePj/pxjpb4eNg+lL2qsSo8FAVkoSafHFZ0FmJMSRnhCPvvDWJev/ZevsgqOHF46eXjh6eOHg4Xm9IOmFhZXcoVKSJEmSqoKcjCy2L99IWFYsmZp80IBGqPHOtaT2hdM0aVMflyXTb2n5k56fzsbQjawPXk9MdkzR+a4+XXm56cu092xfMTMH71SUvHHzvPkz0GggaE2bvSmE4GpSNkHBSQQFJ3EsLJVCw81+kpbmah4KcKNPEw96NnLHxbZ6532SJElS9SALlFKVEpUZxW8hv/FH6B9FfX1UqOju252XmrxEG7fW5J8+TdqcBWRu2YIh5WZBUdugAQ6DB2M/aCDm7nfZMfFGUfLCH3Dxj//swg3YeigFyRbPgmezEscdn5HP3iuJ7L2SxP6QZLLy9UWPWVto6N7gZpLnaF1xPSWFEGSl5pMak1OsIJmVmn/b620cLHD1s8PN1w43PztcfW2xc7aslst38rOzyUiMJyMpgYzEGz/xZCTEkZGYgNFguONzVWo19m7uShHyP4VIR08vHNw9MNdWjSX4kiRJkiTdKuJ0KPs2bieGNPJVOtCAudDgnW1BwJkjBPTuiPPqLzD38Ch6jhCC88nn+eXKL2wJ20KBQblZaWdhx5D6Q3im4TP42fuVf/DpUUpR8uIfxYuSqMCvEzQdDE0eM3kX7qx8HUeupRIUnEhQcPFZkqBsctOjgTs9GrrRuZ4rVhay9YwkSZJUsWSBUqp0BYYCgqKC+O3KbxyJO1J03t3KnSEBQxhSfwhOoYlkrd7K1a3vFesrqXFxwWHQIBwGP4a2UaM7F9KEgOh/lWTvwh+QGX3zMQs7aNjfpKUxhXojJyLSCLqSyN7gpFuWbrvaWtC7sbIUpkt91wrZfbsgT09KTDapMdkkx+SQGpNNSmwOhXn6215v72p5vQipFCPdfO2wtq9aG/LcjS4/v1jxMTMp/j+/T7znUmyNufnN2Y+eXjh4eOHk4YWDpxf2ru5ozOTHoyRJkiRVJwfX7eTC+bPEabIQKmVZiLXRAs8MFU1DTlD7yYE4TluLxt6+6DkpeSn8de0v/gj9g9D00KLzjZwb8WzDZxlQdwBWZuW44kUIiDsNwVsg+B+IP/efB1Xg3xmaDFY2ZbT3KvGwBXoDJyPSOXQ1mYOhyZyJzsDwn7Y9FmZqOtRxpkdDpShZ19WmWt6QliRJkmoO+Q1cqhRCCE4lnmLztc1sC99GVqFS4FOhorNPZ54KeJIOaS7kbNtB5rsvkhkbV/Rcta0tdr16Yde/H7ZduqC6U18/fSFEHLyZ8GVE3XzMwlbpJ9l0MNTrBeZ3nxEnhCAyNZcDocnsDU7i0NUUsgtuFv5UKmhey5EeDdzo3tCNFrUc0ajLJ8kzGIykx+eSEptNSkwOKTHZpMRk33YnbQC1WoWjpzWuvrbKzEhfZWak1rpq90M06HVkJicpBccbsx8TE8hIUgqQuRnp9xzD2sERB3cPHNw9cXD3wN7NAydPLxw9vbF1ckalrlmb+EiSJEnSgyYzKY1dqzcTmRVf1F8SwEVvjUd8Ji3zwvAc9jx2Pd9Ddf3mo96o50DMATaGbGRf9D70QsnptBotvf1782zDZ2nh1qL8Cna6fAjfr+SnwVshK/bmYyq1MlOyyWBo8miJZ0oajILzMRkcvJrModAUjoenUqA3FrvG38Wa7g3c6NHQjY51XbC2kF8FJUmSpKpD/qskVaiozCg2X9vM5qubic6+OYvRw9qDx/wG8mhmHbSHzpI1cxZR8fFFj6utrbHt1Qv7/v2w6dr1zn0lc1OVjW6Ctyi/Fvxn50ELW2WmZJPBUL8XmN/9bnhseh6Hr6Zw6GoKR66l3LIUxsXGgu7XC5LdAtxwtinb2YcGvZGMxDzS4nNIi88hNS6X1Fjl90bDbZpFomxe4+Jji4uPDc7etrj42OLkaY3GrGoV4m7shp2ZnERWcpLya8r1X5MTyUpOIjs9jaJtzu9Aa2ODg9v14qO7x/VipAcObh7Yu7nLpdiSJEmSVEMd+30v50+eJk6TiU5lAA2ohQrPfGt8rl2jZT0nXMa/gGWTJoCSe5xLOsc/Yf+wNXwryXnJRWMFugQyJGAI/er0w97C/k4veX9Sw+Dq7us/e0D3n5Ue5jZKbtpwAAT0BRuXew5XqDdyLiaD4+GpHA9L5Xh4Kpn5xVfNuNpq6VLfhS71XOlc34VaTrJPtiRJklR1yQKlVO5is2PZEbGD7eHbOZt8tui8tZk1A126MyjBE7cDEeTu/4Hc3Fxyrz+usrbGrkcPZaZkt26oLW9TbBICkoKVYuSVrRBxCMR/egvauEPDftCgP9R7+K5FyeTsgqKC5OGryYSn5BZ73FyjoqWvIw8FuNGjoTtNve1Rl8EsycJ8PekJuaTF5ZAar/yaFp9LRlIe4jY7aAOYW2pw8VYKkUpB0hZnbxssbarGrEiDXkdWSgpZyYk3i5ApSWQmJRb9Xl9w941oAMwstEVFR3s3Dxzc3HFw9ywqRkSRDH8AAQAASURBVFramNYUXpIkSZKk6is+NJr967cRk59Muiav6JuMjVGLeyY0ig0l4JGHcZj8OhpHRwBC0kLYEraFreFbicq6uZrG2dKZQXUHMbj+YAKcAso+2IJsZZZk6C64ugtSrxV/3M5buXHecADU7nrP1Tw5BXpORqZxPCyVY+GpnI5KJ19XfIakndaMDnVdlKJkfVcC3G3lsm1JkiSp2pAFSqlcRGdFFxUlz6ecLzqvQcUjhmb8X5In3ucSKDj1DxgMZF9/3MzNDduHH8a258PYdOqEWnubXQNzkuFa0M270FlxxR93b3Iz4fNuDbdZxntj98J/w9P4NyKNkxFpXEsu3rNQrYJmtRzpXM+FzvVcaOPvVOqlMEIIctILyUjMJT0xl7S4XGVWZHzOHZdmA5hrNTh5WuPkZYOTpzXOXkpB0s6l8jau0RcWkp2WSnZqMlmpKWT/5+dG8TGnBLMfQVmCbe/qhp2rm/Krizv2rm7Yu7lj5+KKlb2DTKwlSZIk6QGWlZxB0A9/E50UR5JZNkaVKJot6VFgjXtsEm1rGXB95SksmykbHF7LuMaus8pmN//tK2llZkWPWj3oV6cf3Xy6Ya4pwxu7+kKIPakUJa8GQdRRMOpuPq42A98Oyg3z+n3Aq4XSI+g2hBCEp+RyOiqNM1EZnIxM40JsZrEekgBO1ua0re1M+9rOtKvjTKC3PWaaqrVqRpIkSZJKShYopTIhhOBy6mX2Re9jd9RuLqZcLHrMJVvFI6l+dIyywuVcNCLtJAA3ynLaBg2w7fkwdr16Ydm06a19AQtzIfqYshzm6m6IP1v8cTNLpVdPg37KbEmn/2fvzuNsLtsHjn/ONufMvjGrGUbZx04MslYiCvWo9BPxeNqIhkJ2ibQqkpQSkuqxlBalx1JCJJNdaBhjZgxj9vUs9++PMYdjhplhxjFc717nNed8t/s653sy11zf733ftYrFl2e28tfJNHbFpbLreCq74lJJyzEX265BsJe9INk6wg8vU9kTV6UUORkFpCfnkpacQ3py7vmCZC7pZ3KwFNguu6+rpwHfoMIipG+QO37B7vgGu+HuY7xuBbqibtf2YqO98Hj2wrLUc+RlZpR+MEBvcMHzouKjV7WAC4XIatXx9KuG/nJd9YUQQghxy8rLzmXLZz8SGxdLsi6rsAv3+ZTM22qiWpqVhpmnqH1fF7wnPYNyNbHn7B427HqLjSc3ciLjhP1Yeq2eDqEd6BnRk041OuFmqKBuzpYCOLULjm+BE1sg7newOA4HhG+twrHOb+8Gte4EU8ndx89lF/DXyTRizj/+ik8rMU8N9XHljgg/Wtfy444IX26rLndICiGEuHlIgVJctRxzDr8n/s7m+M38eupXknOSAXDPVbQ6paHrmQAiYy2YTiQDxwBQFHbddr/jDtzbt8ejcydcwsIcD5yXXpjknfitsMt2wm7HK9AAgY0Lr0Df1rWwOHlRt5gCi42/T2ey91Q6e+LT2XsqjcNJmZgvGbfRZNDStIYPrWr50qqmH83DffBxu3LBTNkKi5AZZ3MLC48XFSDTk3Mx51svu69Gq8HT34RPddfCYmRw4Z2RfkHumDwqr2t2UeExO/Uc2WlpZKennn+eSnZaqsMdkBZzQZmOqTe44OHn7/Dw9PM/X4AsLES6enpJ0iyEEEKIMsk8m86vK37iVGI8Z/TZFGgs9r9U3GwuVM/WUeNcMs3bh+P9TA8KqnmxI3EHm/56jU0nN3Eu75z9WAatgTbBbbi75t10C++Gt9H72gPMTS0sSJ7cCXHb4OSO4gVJN//C7tq17izMUf1vK3aYM5n57E9IZ39CBgcSM9h3Kp0TlwwrBIWzbEeGeNEszJemYd60ruVHiE8lziYuhBBCOJkUKEWZ2ZSNw+cOsyNpB9sStrEzaScFtgK8sxQNTioeOKWjeYIR/4RsNEoB52ck1GgwNWqEe/v2uLdvh1uzZmiK7pxTCtLjIf4PiNteWJRM2kthKfMiniEQ0bEw2avdGTwDgcJi5LEzWew9dYa98ensOZXOwcQMCizF71YM8DTSqpYvLcJ9aVXLj0YhXhgu6QajlCIv20xmSh4ZZ/PIOJtLRkoemUU/U/KwlnDsIhoNePiZ8AlwxTvADZ8AN7wDXPEJcMPT31Shk9VYCgrsRcbstPPFR/vzVHLSUslKSyUnLQ2b1VL6Ac9z9fRyLD76ni9A+lezLzO5yxV7IYQQQlyb+APH2fndrySnJXNGn41FY7PfKWlUeqrnuhB49hwtI33wGdiTf3wLWJWwla1/jeev5L/ss28DeBo86RjWkS5hXegQ2gF3g/vVB2azwplDEL+zsCAZvxPOHi6+nVu18wXJ84/q9e3dtm02xcmUbPYnZLA/IZ0DCRnsT8ggObPkoX1qV3OnWZgPzcJ9aBbmQ/0gL1xusEkOhRBCiMokBUpxWUopYjNi2ZG4gx1JhY/M3DTCzsDtiYonEhSNT+kIOFt016ANKLzT0SUiArdWLXGPisItKgq9r2/hJrmpEPcrnPqz8Cp0wp+Qdbp44361oWY7qNkearZDeYeTkJHP4aQMDu3K5HBSAoeTMjl2JqvYnZEAXiY9TWr40LiGN01CvYkM9aaGrytKQU56AVmpeZyIOUtWah6Z5wqLkZkphUVIc97l74KEwjshPXyNeFcvKkJe+Onl74rOcHXJpM1mJS8zk5yMdHIz0skpeqQXvrYvSy+8CzI/O7v0g17E5OmFu7cP7j6+hQ9fP9y9fS4qRlbDw9dPul0LIYQQolKY8wrYsXoT/xz8m3Mqm1RtLmiwFyXdbC745+oJyMigeUN/8to3YrfnWd5O2sn2XUNJy09zOF6YZxgdQjvQNbwrLQNbYtBeRY8UqwVSjkDiHkj868KjILP4tn61oUbrwketO6F6PWwKTqXlciQ5k78P/sPfpzM5cjqLo8lZ5JqL55QaDURUc6dRiDcNg71oFOJF0xo+eLvdGBMdCiGEEM4iBUphl2/N50DKAf5K/ou/zvzFX8kxaBLPcHui4vYExehERUQSGB1uxrOARoOxXj3cWrU6/2iJ3t8fMhLg9H7Y90nhz4TdcO5Y8YY1OghsBGF3YAtvR6J3c47kehB7Nptjx7M4vD2BQ0mHycwr+S5AT6OeRqFeNAn1pqGfBxFuRjxsGrJS8wsfv6ew41wCG1LzyE4vuOzM2Bdz83bBy98Vr2omvKq54ulf+NPL34SHrxFtGQYgt5jN5GVlkpeVSW5mhr3QmJORRk5Ghv15bkYGOelp5GZllmlimYvpDAbcffxw97mo8Ojjh7uPL24+vnic/+nm7YPeIInvzU4pBRaFstgKH2bbhefnX+PwWpWyvui5KmW9DWwQOr2dsz8CIYQQNxCL2cJfP/7O0d37Sc3LJFWfS77GAhelUd5WEz45GkJysghupGN/IxM/q1O8nvwLqfsXOhzPw+BBm+A2tAtpR1RwFGFelwwTVJq8dDjzNyTvv1CQPL2/eFdtAIM7hLYoLEaG3UF2QHOO57pyIiWH2LPZ/LMpmyPJv3E0OYucgpIvbrvotNQL8qRRSGEhsmGIF/WDvHA3yp9gQgghxKXkt+MtymqzciLzBIfPHWbPmT0cjN9N1t8HCT1toWayov0ZxaPJ4JlXfF+thwemxpG4RjbGtWUL3BpEoCtIhjMH4fQG+HYunN5XeLdkCZRfbfIDmpHsFckRQ112F4Rx5JyFfw5nc2JrDgXWgw7baxS4KQhBy22ertRyNxHkYsBHo8VkBVuOhZzYAnL+Os0paxKnSnnvGq0Gdx8XPH0Li40evia8qpnwPF+A9PQ3oTfo7NvbrFbysrPIzcwgMyWJMycyyM3MtBcfCwuQ538WLcvMxJxfwodXBiYPT9y8vHH18sbN2/vCc/tPH9x9C4uRRjd36Wp9g1BKgU3Zi3n2Qp5VXZcCYWGb5StwV/hnYFVodPJ9FEKIW1VWaiYxP2wj/sg/pBVkXyhIgv0uSYPS4V9gwjszHz9DGkkNz7ElIJW/sg6Ta8mFuAvHM+qMNKnehFaBrWgX0o7IapHotWX48yUvHc4cLuymnXyoMEc9cxgyLpMlGtxRQY3Jrx7JWY/6HDfczl5zMMfPFRB7LJvjv2eTnLnrss256LTUru5OnUBP6gR4UDfQgzqBntT0c5NZtYUQQogykgLlLSDbnM2xtGMcOneIY3F/ce7IfgpOHKfa2QLCzkDrM4peqQ4Xsy8wGDA1aIBrowa4RgRgCnLBxZiOJvUYpKyD3+fCpnMl7YnS6Mj0iCDJ9Tb+0dZijyWc33LDOZhkQJ2y4WbT4KrATSXiatPgozSEKC0e6PHX6/BCi9EC2vyLxnzMsAGFA4mnldSoBty8XPDwNeF5vvjo7mvA5AYGkwW9wYJWW0BBbg75OWnk52STn53N2RNZnDqYQ352duGynOzzxccs8nPK15XaIRyNFqOHB64enrh5e+PqeXHR0Qc3L6/zhUifwmWeXmh1utIPLIDzRcGLC4BWBWYbynpRUc9eLFSFy8+vd7jTsLR9rSUcy3KheIj1/Drn1gcdaUCj14Jei0avRWPQotFrCp9ftIxLXl9Yryll/YX90YHScZl/RIQQQtyM8rPz2L95Nyf2HCI1I41MXQHp2jxsmvO/DM8XJPVKi6/FhEeODWNBOgl+B/i+RhKxHhdNDJNW+MPTxZMWAS1oEdiCFgEtaOTfCIOuhB4gSkHOOTj3D6TGFv489w+cO/885+xl4y5wCyTN/TZOmW7nsKY2MZZw/sz0I/5EPjlHiu6EzKFogseL+boZqFXNnQh/d2pVc6dOQGEhspa/FCKFEEKIa3XTFCjnz5/P66+/TmJiIo0aNWLOnDnceeedzg7rujFbzSRlJ3E89RinYveSEvc32fEnUKcS8T6dTVCqouY5aFxCD5YiGh9PXGsGYQrywOivweiZi9HlLNrsnZCzDv6h8HERq9KRb/MmWVODk6oWx6zhxBYEccYaQIbywXBOj6sN3JQGV6WhrQ26KA0GrnyXlcpTQD6oAmyqAA1mXNxsGF0VBqMNg4sVnd6KTm9Bo7EABShlxmbJIz8nm5yUbFJPFhYfC3KLz4x4NYxu7pg8PTG5e+Lq6YnJ48LD1dMT16LX59e5enhhdHNDo62aCauynb8j0FpUhDv/3FpUkFMXCn0XL7eqwudFhT2r7cK+Fx3HcdlF+1kvFAQd2rxMkdHZ1Pn/bCjU+dc2FDatDfQalE6D0oHSn/+pw74MHdh0oLTnl2svfiiUFmxaUBqwaQpfK43Cpin8qTTn29KoC3FozsegbNhsZXhYbdjMZdz2Mo8iU6dOddZpEEKIG8bNlpMW5OXzzx+HifvrEGeTzpBtzSNbZyHj4mLkRcNXG5Ueb7MRt1wrKjeR3W6/8r9aBZz1dsz9jDoj9fzq0ci/EQ39G9LIvxG3+dyGFg0UZBcOFXR8S+Fdj+mnICO+cFn6qcJl+RlXjDtNX52TujD+VjXYVxDMX/nBHFWhZOS5Q7Fr6xdyxWoeRkJ9XYnwdyssRlZzp6Z/YVFSxokUQgghKs9NUaD84osvGDVqFPPnz6d9+/Z88MEH9OjRgwMHDhAeHu7s8K6JUopsczYpmadJTTpBevJJMpLiyE5OJO/saWxnUzCczcDzXB7+GQq/LKhWSs3G4qlH663H5GVA567Qu1vQeuixGoyYVR6pSoM505WCDG/MKogC1RqzzZVs5Um6zZccmxcFNldsNh06BUqZAQsoCygz/ljwV5lAKur8MrA4PDcrC2gs6HRWe4ERzNisBdis+VjN+Vx6O1p+BpQwXHmZ6Q0uGN3dcXFzx+TmjoubG0Z3D/tzk7tH4U+389t4eGLy8MDV0wuTu8dl72ws7NoLKGUv6hUW9hTWTHNhkc12vvBm4/zPC9tcWK9QNtslry/droT1xbazOSxX1ovW2S6KxXrJPlaFzWIDa2HRStkuFL0UnC/AXVSM01y8zuZQnLv4v8LiWfF1touOfWG7y6wrdrzz6/SXriss7qFR9sJeYSHvkp+oC0U/uOgIF45n/6ls2JRCqaLnha+LCoCqtHFDFWA5/7jJ2Ww2tFW0IC+EEBWhKuaklnwzpw6e4NT+o5yLP01WVha5lgLytFZytRaytPkXCpHnL64V0SsdXlYX3PM1aHMzOaMO80f1A8TWgjzjhYKkn4sPrd1Dqe1anYam6jQy+BBhBW32OSwnT6IO/gnZZzDnnEGfl4LOWrZhchKUHydsQZxQAZxQjj+zcCu2vYtOS6hnYQGyho8rob6uhF70M8THFZNBerIIIYQQzqBRpf51feNr06YNLVq04P3337cva9CgAX369GHWrFml7p+RkYG3tze/r1+Pq8mEspix2RQ2SwEWqw2bzVLYldNmw2qzgtWGVVlR1sJihbKaC4s5NoXNZsVmKSpemLFazNjMFmxWM8pswWYxg8WCzWzFZjNjM1tRZjOYbWgsCq0VsILOqtBadWis4GLVorVpUFodSqNBabQorRYbGmwaLUqrw6bRYdNpUGixaXXYtFpsOg1oC7sZKzTY0JwvxmhQGiisltlQyoZGYytco2xFW0BhC3B+r6LlhctwGPtQg6ZwWkI0hc/Pv7Yv13BhuX1d0c6F26mi7S9ahga0Oj1anR6dTo9Wp0Or06PR6dFpdWi0WjQaHVqttvC5tvA5Gi1aTeFPjfbCcdX5QpOynS8sFr0+X1xUKHuhsXB9YWHuwnbn7yxE2YtWF8plFxe6uOTn+eeaC1vayrXPxS2UtE/x/dFwUdmwDO2c30dUHK1We9M/NBqN/bnJZJIxUYVwoqJ8Jj09HS8vL2eHc0u6lpz0Qj76E0aDEWWxYLVYsBSYsZ1/brVawWLFarVhtVixWc/no1aFslmxWmxY8y1Y861YzvdCsFoVNsCqwKoFqwYsWoVFY6NAayVXY75QgLwMrdLgYTPiZtHhUmDFmp/Oae0xdlc7RFyQjTyjBp2CAKuGYAuEmS3UKcijQUE2txcU4HfR3fZllalcSVR+JCp/+88E/O2v41V18nFBowFvVwN+bi5U9zQS4GUiwNNY+NzTSICniQCvwufergb5PSWEEEJUomvJR6v8HZQFBQXs2rWLcePGOSy/55572Lp1a4n75Ofnk5+fb3+dkVHYRWT1//6H0WisvGDRAkbQG6/TJ1+UbJY8s6CjoruebpSrxkVFUSi8u7Kg+Ca284/r7Ra/QayoIHVxYaqk56Wtr+j9KqNtnU5XalHuSg8hhBC3jvLmpJfPRzdUTD56yd2OV6TAVRkw2fS4WLUYLAqNJZ88axoJhjj2+h8m39eMp85GNauV6lYrARYr/2exUOOchVCLhUCL9bLN5Ss9GbiRodzJxJVM5UYqnqQoL84obzJ1PmTr/cgz+pFv9MNi9EPvWjjbtZerHl83F7xdDbR1c8HX3YCPmws+rgZ83VzwcjWg00rRUQghhKjqqnyB8uzZs1itVgIDAx2WBwYGkpSUVOI+s2bNYtq0adcjvCu76GL15dMqTSnrL96y5K1KW6opefEVjqe57LZFV6Xt91Har1JfeG1/R5pLt9Ocv9nykmOdv5tSc9GdmIWbX1h+6evCmzSLnmvQXrKsaPnF+2qLlmkv3e/CMvtDq0Gj1aLVFq0ruoNTc36ZFq1Oa9++qKh18aOsy26U/S9eJ4QQQghH5c1Jy5OPFt7gWNQX5UIuVdQ3xf5cadCjRac0aJXG/lOrQGsDnc2GxmpBKTNK5WLRZJGrSyTb/QQF3hY0Oj1apcNg02FSenyVgbq4cJ+qiTbLgFlrxKo1YtaZsOpNWIwmkvSuJBlMYHAFgxsagysYPcHkhcbkjdbVF4PRFaNBi1Gvw0WvJchFx+0mPe5GPe4ueikwCiGEEKLqFyiLXFo0UUpdtpAyfvx4oqOj7a8zMjIICwvjiYcfwcfPH51ej86gR6czYHBxQaOlsKuxRnehqHVpImXvmexYXCspNiGEEEIIcXMqa0562Xz00Ufwq1YdndEFF5MRo9EV3fmhbIQQQgghblZVvkBZrVo1dDpdsSvTycnJxa5gFzEajSV2nQmqHSFjNgkhhBBCiHIrb0562Xy0luSjQgghhLj1VPlLsS4uLrRs2ZL169c7LF+/fj3t2rVzUlRCCCGEEOJWIjmpEEIIIcTVq/J3UAJER0czcOBAWrVqRVRUFAsXLiQuLo6nnnrK2aEJIYQQQohbhOSkQgghhBBX56YoUD788MOkpKQwffp0EhMTiYyM5Pvvv6dmzZrODk0IIYQQQtwiJCcVQgghhLg6GqWUKn2zm1tGRgbe3t6kp6fLmD9CCCGEqJIkn6na5PwJIYQQoqq7lnymyo9BKYQQQgghhBBCCCGEqLqkQCmEEEIIIYQQQgghhHAaKVAKIYQQQgghhBBCCCGcRgqUQgghhBBCCCGEEEIIp5ECpRBCCCGEEEIIIYQQwmn0zg7gRlA0kXlGRoaTIxFCCCGEuDpFeUxRXiOqFslHhRBCCFHVXUs+KgVKICUlBYCwsDAnRyKEEEIIcW1SUlLw9vZ2dhiinCQfFUIIIcTN4mryUSlQAn5+fgDExcVJQl9FZWRkEBYWxsmTJ/Hy8nJ2OKKc5PxVfXIOqz45h1Vfeno64eHh9rxGVC2Sj1Z98u9o1SfnsGqT81f1yTms+q4lH5UCJaDVFg7F6e3tLf8TVHFeXl5yDqswOX9Vn5zDqk/OYdVXlNeIqkXy0ZuH/Dta9ck5rNrk/FV9cg6rvqvJRyWDFUIIIYQQQgghhBBCOI0UKIUQQgghhBBCCCGEEE4jBUrAaDQyZcoUjEajs0MRV0nOYdUm56/qk3NY9ck5rPrkHFZtcv6qPjmHVZ+cw6pNzl/VJ+ew6ruWc6hRVzP3txBCCCGEEEIIIYQQQlQAuYNSCCGEEEIIIYQQQgjhNFKgFEIIIYQQQgghhBBCOI0UKIUQQgghhBBCCCGEEE4jBUohhBBCCCGEEEIIIYTT3PIFyvnz5xMREYHJZKJly5b8+uuvzg5JlMMvv/xC7969CQkJQaPRsGbNGmeHJMph1qxZtG7dGk9PTwICAujTpw+HDx92dliiHN5//32aNGmCl5cXXl5eREVF8cMPPzg7LHGVZs2ahUajYdSoUc4ORZTR1KlT0Wg0Do+goCBnhyWuguSkVZfko1Wb5KNVn+SjNxfJR6umishJb+kC5RdffMGoUaOYMGECu3fv5s4776RHjx7ExcU5OzRRRtnZ2TRt2pR58+Y5OxRxFTZv3syzzz7L9u3bWb9+PRaLhXvuuYfs7GxnhybKqEaNGrz66qv88ccf/PHHH3Tt2pUHHniA/fv3Ozs0UU47d+5k4cKFNGnSxNmhiHJq1KgRiYmJ9sfevXudHZIoJ8lJqzbJR6s2yUerPslHbx6Sj1Zt15qTapRSqpJiu+G1adOGFi1a8P7779uXNWjQgD59+jBr1iwnRiauhkajYfXq1fTp08fZoYirdObMGQICAti8eTMdO3Z0djjiKvn5+fH6668zdOhQZ4ciyigrK4sWLVowf/58ZsyYQbNmzZgzZ46zwxJlMHXqVNasWUNMTIyzQxHXQHLSm4fko1Wf5KM3B8lHqx7JR6u2ishJb9k7KAsKCti1axf33HOPw/J77rmHrVu3OikqIW5t6enpQGFCIaoeq9XKihUryM7OJioqytnhiHJ49tlnue+++7jrrrucHYq4CkeOHCEkJISIiAgeeeQR/vnnH2eHJMpBclIhbiySj1Ztko9WXZKPVn3XmpPqKymuG97Zs2exWq0EBgY6LA8MDCQpKclJUQlx61JKER0dTYcOHYiMjHR2OKIc9u7dS1RUFHl5eXh4eLB69WoaNmzo7LBEGa1YsYI///yTnTt3OjsUcRXatGnDkiVLqFu3LqdPn2bGjBm0a9eO/fv34+/v7+zwRBlITirEjUPy0apL8tGqTfLRqq8ictJbtkBZRKPROLxWShVbJoSofMOHD2fPnj1s2bLF2aGIcqpXrx4xMTGkpaWxcuVKBg0axObNmyUprAJOnjzJyJEj+emnnzCZTM4OR1yFHj162J83btyYqKgobrvtNj799FOio6OdGJkoL8lJhXA+yUerLslHqy7JR28OFZGT3rIFymrVqqHT6YpdmU5OTi52BVsIUblGjBjBN998wy+//EKNGjWcHY4oJxcXF26//XYAWrVqxc6dO3nnnXf44IMPnByZKM2uXbtITk6mZcuW9mVWq5VffvmFefPmkZ+fj06nc2KEorzc3d1p3LgxR44ccXYooowkJxXixiD5aNUm+WjVJfnozelqctJbdgxKFxcXWrZsyfr16x2Wr1+/nnbt2jkpKiFuLUophg8fzqpVq9iwYQMRERHODklUAKUU+fn5zg5DlEG3bt3Yu3cvMTEx9kerVq147LHHiImJkWSwCsrPz+fgwYMEBwc7OxRRRpKTCuFcko/enCQfrTokH705XU1OesveQQkQHR3NwIEDadWqFVFRUSxcuJC4uDieeuopZ4cmyigrK4ujR4/aX8fGxhITE4Ofnx/h4eFOjEyUxbPPPsvy5cv5+uuv8fT0tN894u3tjaurq5OjE2Xx0ksv0aNHD8LCwsjMzGTFihVs2rSJdevWOTs0UQaenp7Fxthyd3fH399fxt6qIsaMGUPv3r0JDw8nOTmZGTNmkJGRwaBBg5wdmigHyUmrNslHqzbJR6s+yUerNslHbw4VkZPe0gXKhx9+mJSUFKZPn05iYiKRkZF8//331KxZ09mhiTL6448/6NKli/110dgGgwYNYvHixU6KSpTV+++/D0Dnzp0dln/yyScMHjz4+gckyu306dMMHDiQxMREvL29adKkCevWrePuu+92dmhC3BLi4+N59NFHOXv2LNWrV6dt27Zs375dcpkqRnLSqk3y0apN8tGqT/JRIZyvInJSjVJKVWKMQgghhBBCCCGEEEIIcVm37BiUQgghhBBCCCGEEEII55MCpRBCCCGEEEIIIYQQwmmkQCmEEEIIIYQQQgghhHAaKVAKIYQQQgghhBBCCCGcRgqUQgghhBBCCCGEEEIIp5ECpRBCCCGEEEIIIYQQwmmkQCmEEEIIIYQQQgghhHAaKVAKIYQQQgghhBBCCCGcRgqUQgghhBBCCCGEEEIIp5ECpRBCCCGEEEIIIYQQwmmkQCmEEEIIIYQQQgghhHAaKVAKIYQQQgghhBBCCCGcRgqUQgghhBBCCCGEEEIIp5ECpRBCCCGEEEIIIYQQwmmkQClEKRYvXoxGo+GPP/5wdigcP34cjUbD4sWLnR1KhahVqxYajcb+cHd3p0WLFsybNw+llMO2mzZtsm+3bdu2YscaPHgwHh4el22rRYsWaDQa3njjjRLXF53nooder6dGjRo88cQTnDp1qtT3cvG+Go0Gb29vOnfuzHfffVfqvjeihIQEpk6dSkxMjLNDKbPBgwej0Who1KgRVqu12HqNRsPw4cMrrL3OnTs7nHOTyUTDhg2ZMWMGBQUFDtsW/b978cPLy4umTZsyZ86cYvGmpKQwfvx4GjZsiLu7O97e3tSvX5+BAweyZ8+eCnsPQgghbk6Sv1a8qVOnFvtdXtKjc+fOQPE899KcoTSX5hmurq72vMFms1Xyu60cM2fOZM2aNc4Oo8yK/j8ymUycOHGi2PrOnTsTGRlZYe1d+h0zGAyEh4czbNgwkpKSim1/6XfMZDJx++23Ex0dzdmzZx22NZvNfPDBB7Ru3Ro/Pz/c3NyoWbMmDzzwAKtXr66w9yDEtdA7OwAhxK2tffv29qJhQkICb731FiNGjCAjI4OXXnqpxH1efPFFfv311zK3ERMTw+7duwFYtGgRY8aMuey2n3zyCfXr1yc3N5dffvmFWbNmsXnzZvbu3Yu7u/sV23nooYcYPXo0NpuNf/75hxkzZtC7d2/Wrl3LfffdV+Z4bwQJCQlMmzaNWrVq0axZM2eHUy4HDhxg8eLFDB06tNLbql27Np999hkAZ86c4aOPPmLSpEnExcWxcOHCYtuPGDGCAQMGAJCWlsY333zD888/z8mTJ3nzzTcByMrKom3btmRlZfHCCy/QtGlTcnNz+fvvv1m1ahUxMTE0adKk0t+bEEIIIS7497//zb333mt/nZiYSL9+/Rx+twN4eXkBsHr1avLz8x2OERcXx8MPP0zfvn3L1ObFeUZycjILFizg+eefJzExkdmzZ1/rW7ruZs6cyUMPPUSfPn2cHUq55OfnM3HiRJYuXXpd2lu3bh3e3t5kZWXx008/8eabb7J161ZiYmIwGAwO2178t1Rubi5//PEHU6dO5ZdffnG4QDFw4EBWrVrFqFGjmDZtGkajkX/++Yd169bx448/lvk7KURlkgKlEMKpfHx8aNu2rf31XXfdRXh4OB988EGJBcp7772XdevWsXbtWnr37l2mNj766CMA7rvvPr777ju2bt1Ku3btStw2MjKSVq1aAdClSxesVisvv/wya9as4bHHHrtiO4GBgfb30q5dO6Kiorj99tuZM2fOZQuUZrPZfsemuOBqP5eiu3CnTJnCgAEDcHV1raQIC7m6ujp8f3v06EHDhg359NNPeffdd4vdIREeHu6w/b333su+ffv4/PPP7QXKr776iqNHj7Jhwwa6dOnisH90dHSVvWtCCCGEqMpq1KhBjRo17K+PHz8OFP/dXqR58+bFlv34449AYbGzLErKM+rXr8+8efOYMWNGsWIVgFKKvLy8Ss+Bqppr+Vzuvfdeli9fzpgxY2jatGklROeoZcuWVKtWDSj82+js2bN88sknbNmypVhueOnfUl26dCEzM5OXX36Zv//+m7p16xIbG8sXX3zB5MmTmTZtmn3bbt26MWzYMMktxQ1DungLUUG2bNlCt27d8PT0xM3NjXbt2hXr3nvmzBmeeeYZGjZsiIeHBwEBAXTt2rXEuwETEhLo378/np6eeHt78/DDD5d4a//l7Nu3jwceeABfX19MJhPNmjXj008/ddimqNv0559/zoQJEwgJCcHLy4u77rqLw4cPl9rG0aNHeeKJJ6hTpw5ubm6EhobSu3dv9u7dW+Y4L+Xl5UXdunU5ffp0iesHDx5Mw4YNGT9+fIndeC+Vl5fH8uXLadmyJW+//TYAH3/8cZnjKfqFX1K3jtLcdtttVK9e3b5v0ee9dOlSRo8eTWhoKEajkaNHj9rjatq0KSaTCT8/P/r27cvBgwcdjlnUlf3QoUN0794dd3d3goODefXVVwHYvn07HTp0wN3dnbp16xY751D6d2PTpk20bt0agCeeeMLebWTq1Kn2bb755huioqJwc3PD09OTu+++u8Su90eOHGHAgAEEBARgNBpp0KAB7733nsM2pX0u5TV79mxOnTrFO++8U+q2cXFx/N///Z9DfG+++eZVJ2p6vZ5mzZpRUFBAWlpamfbx9vZ2+AMjJSUFgODg4BK312rlV7cQQoiKIflrxeSvZaGU4pNPPqF27dp07dr1qo5hMBho2bIlOTk5nDlzBrgwhM2CBQto0KABRqPR/pmV5fwWdWPesGEDw4YNw9/fHy8vLx5//HGys7NJSkqif//++Pj4EBwczJgxYzCbzQ7HOHfuHM888wyhoaG4uLhQu3ZtJkyY4HAHqUajITs7m08//bRYd3go27kHyMjIYMyYMURERODi4kJoaCijRo0iOzvbYbsrfS7l9eKLL+Lv78/YsWNL3TYvL4/x48c7xPfss8+WOS8sSdHNE5f7++hS3t7eAPb8UnJLUVXIN1GICrB582a6du1Keno6ixYt4vPPP8fT05PevXvzxRdf2Lc7d+4cAFOmTOG7776zJymdO3dm06ZN9u1yc3O56667+Omnn5g1axZfffUVQUFBPPzww2WK5/Dhw7Rr1479+/fz7rvvsmrVKho2bMjgwYN57bXXim3/0ksvceLECT766CMWLlzIkSNH6N27d6kFwISEBPz9/Xn11VdZt24d7733Hnq9njZt2pQpQSyJxWLh5MmT1K1bt8T1Op2OWbNmsX///jIlGatWrSI1NZUhQ4ZQp04dOnTowBdffEFWVlaZ4ikqklWvXr3sb+K81NRUUlJSiu07fvx44uLiWLBgAWvXriUgIIBZs2YxdOhQGjVqxKpVq3jnnXfYs2cPUVFRHDlyxGF/s9lMv379uO+++/j666/p0aMH48eP56WXXmLQoEEMGTKE1atXU69ePQYPHsyuXbvs+5blu9GiRQs++eQTACZOnMi2bdvYtm2b/Wr/8uXLeeCBB/Dy8uLzzz9n0aJFpKam0rlzZ7Zs2WJv68CBA7Ru3Zp9+/bx5ptv8u2333Lffffx3HPPOVy9vdLncjWioqLo27cvs2fPtv8/V5IzZ87Qrl07fvrpJ15++WW++eYb7rrrLsaMGXNNY1XGxsbi4+NT4nfGZrNhsViwWCykpKTw8ccfs27dOgYOHOgQP8Djjz/OmjVr7EmlEEIIUZEkf624/LUsfv75Z06cOMGQIUPQaDRXfZxjx46h1+vx9fW1L1uzZg3vv/8+kydP5scff+TOO+8s8/kt8u9//xtvb29WrFjBxIkTWb58OcOGDeO+++6jadOm/Pe//2XQoEG8+eabzJ07175fXl4eXbp0YcmSJURHR/Pdd9/xf//3f7z22mv069fPvt22bdtwdXWlZ8+e9txy/vz5QNnPfU5ODp06deLTTz/lueee44cffmDs2LEsXryY+++/v9gY9iV9LlfD09OTiRMn8uOPP7Jhw4bLbqeUok+fPrzxxhsMHDiQ7777jujoaD799FO6du1arMt/WcXGxgKU+PeRUsqeW2ZlZbFx40bmzJlD+/btiYiIAKBBgwb4+Pgwbdo0Fi5caL/7V4gbjhJCXNEnn3yiALVz587LbtO2bVsVEBCgMjMz7cssFouKjIxUNWrUUDabrcT9LBaLMpvNqlu3bqpv37725e+//74C1Ndff+2w/bBhwxSgPvnkkyvG/Mgjjyij0aji4uIclvfo0UO5ubmptLQ0pZRSGzduVIDq2bOnw3ZffvmlAtS2bduu2E5J76egoEDVqVNHPf/886VuX7NmTdWzZ09lNpuV2WxWJ06cUMOGDVMGg0F9++23DtsWxfrVV18ppZTq0KGDqlGjhsrNzVVKKTVo0CDl7u5erI2uXbsqk8mkUlNTlVIXzueiRYsctitavn37dmU2m1VmZqb69ttvVfXq1ZWnp6dKSkq64nsB1DPPPKPMZrMqKChQBw8eVD169FCAeu+99xzeQ8eOHR32TU1NVa6ursXOQ1xcnDIajWrAgAH2ZYMGDVKAWrlypX2Z2WxW1atXV4D6888/7ctTUlKUTqdT0dHR9mVl/W7s3LmzxO+a1WpVISEhqnHjxspqtdqXZ2ZmqoCAANWuXTv7su7du6saNWqo9PR0h2MMHz5cmUwmde7cuSt+LuV18Xfg0KFDSqfTqdGjR9vXA+rZZ5+1vx43bpwC1O+//+5wnKefflppNBp1+PDhK7bXqVMn1ahRI/v3NzExUU2ePFkBasGCBQ7bxsbGKqDEx+DBg5XFYnHYfvr06crFxcW+TUREhHrqqafUX3/9dVWfjRBCiFuL5K9lV978tUjR7/bXX3+9TNs//PDDSqfTqfj4+DJtf2mekZCQYM9d/vWvf9m3A5S3t7c9rypS1vNb9F0ZMWKEw/59+vRRgHrrrbccljdr1ky1aNHC/nrBggUKUF9++aXDdrNnz1aA+umnn+zL3N3d1aBBg4q917Ke+1mzZimtVlvse/3f//5XAer7778v9XMpj4v/P8rPz1e1a9dWrVq1sn92ReeoyLp16xSgXnvtNYfjfPHFFwpQCxcuvGJ7U6ZMUYBKSkpSZrNZpaamqi+//FK5u7urRx99tNj2NWvWLDG3vOOOO1RiYqLDtt99952qVq2afRt/f3/1r3/9S33zzTdX+/EIUeHkDkohrlF2dja///47Dz30kMMs0jqdjoEDBxIfH+9wNXbBggW0aNECk8mEXq/HYDDwv//9z6Er78aNG/H09OT+++93aOviAbivZMOGDXTr1o2wsDCH5YMHDyYnJ6dYV9xL2ymagKO0bs0Wi4WZM2fSsGFDXFxc0Ov1uLi4cOTIkWJdky/n+++/x2AwYDAYqFmzJh9++CFz584tdVKZ2bNnEx8ff8VuvLGxsWzcuJF+/frh4+MDwL/+9S88PT0v2827bdu2GAwGPD096dWrF0FBQfzwww8EBgaW+l7mz5+PwWDAxcWFBg0asHXrVqZPn84zzzzjsN2DDz7o8Hrbtm3k5uYyePBgh+VhYWF07dqV//3vfw7LNRoNPXv2tL/W6/XcfvvtBAcHO4x35OfnR0BAgMN5LO9341KHDx8mISGBgQMHOnQH8fDw4MEHH2T79u3k5OSQl5fH//73P/r27Yubm5v9yq7FYqFnz57k5eWxffv2K34u16JevXoMHTqUefPmERcXV+I2GzZsoGHDhtxxxx0OywcPHoxS6opXyIvs37/f/v0NDg5m+vTpjB8/nieffLLE7UeOHMnOnTvZuXMnGzduZObMmXz55Zc8+uijDtsVTbTz8ccf8+STT+Lh4cGCBQto2bIln3/+eRk/BSGEEKJkkr9eW/5aXufOnWPNmjXce++9hIaGlnm/i/OMkJAQ3nzzTR577DE+/PBDh+26du3qcEdlec8vQK9evRxeN2jQAKBYTt6gQYNiuaW7uzsPPfSQw3ZFee2leWxJynruv/32WyIjI2nWrJlDbtm9e3c0Go3DHb1Q/HO5Fi4uLsyYMYM//viDL7/88rLvoyjui/3rX//C3d29TJ8FQFBQEAaDAV9fX/r370/Lli0v23OsQ4cO9tzyt99+Y9GiRZw5c4auXbs6zOTds2dP4uLiWL16NWPGjKFRo0asWbOG+++//5p6DglRkaRAKcQ1Sk1NRSlV4pgeISEhwIVxP9566y2efvpp2rRpw8qVK9m+fTs7d+7k3nvvJTc3175fSkpKiQWxoKCgMsWUkpJSpniK+Pv7O7w2Go0ADjGVJDo6mkmTJtGnTx/Wrl3L77//zs6dO+2zDpdF0S/V7du3s3TpUmrVqsXw4cMdugqXpF27dvTp04dXX32V1NTUErf5+OOPUUrx0EMPkZaWRlpaGmazmfvvv5/ffvuNQ4cOFdtnyZIl7Ny5k927d5OQkMCePXto3759md5L//792blzJ3/88QeHDx8mJSWFSZMmFdvu0nNzpXFhQkJCip0vNze3YpOvuLi44OfnV2x/FxcX8vLyHNoqz3fjUqXFarPZ7F3bLRYLc+fOtSfWRY+i4urFSdPljnktpk6dik6nK/EcFL2Xa/ksoHCc0Z07d7Jjxw6++uormjZtyqxZs1ixYkWJ29eoUYNWrVrRqlUrOnfuzPjx45k0aRJfffWVfeD8IoGBgTzxxBMsWLCAPXv2sHnzZlxcXBg5cmSpcQkhhBBXIvnrteWv5bVs2TLy8/PLPDlOkaI8448//mDfvn2kpaWxbNky+xiDRS793Mpzfotcmke6uLhcdvmluWVQUFCxbusBAQHo9foy5VNlPfenT59mz549xXJLT09PlFKVnls+8sgjtGjRggkTJhQbh7MoTr1eX2yYH41GQ1BQUJmH7fn555/ZuXMnP/74Iw8++CC//PILI0aMKHFbb29ve27Zrl07hgwZwvLlyzl48KB9AsYirq6u9OnTh9dff53Nmzdz9OhRGjZsyHvvvcf+/fvL+CkIUXlk2lghrpGvry9arZbExMRi6xISEgDss7AtW7aMzp078/777ztsl5mZ6fDa39+fHTt2FDteWQcZ9/f3L1M812rZsmU8/vjjzJw502H52bNn7XcslqbolypAmzZtaNOmDU2bNuWZZ54hJibmioM2z5o1i8jIyGLtQ+FYf4sXLwZwGP/mYh9//HGxMY0aNGhgj6e8qlevXqZ9L03gihLsy52zijpfRW1dy3ejtFi1Wq39SnXRVfpnn322xGMVjYtT5FrGYypJcHAwo0aN4tVXX2X06NHF1lfE/ycmk8l+zlu3bk2XLl1o1KgRo0aNolevXg53LVxO0R0ff/31F927d7/sdh07duSee+5hzZo1JCcnX/UYnUIIIYTkr9eWv5bXokWLCAwMLHaXYmkuzjOu5NIcqjzn91r5+/vz+++/o5RyiCM5ORmLxVKmdsp67qtVq4arq+tle0Jd2lZF55YajYbZs2dz9913s3DhwmLr/f39sVgsnDlzxqFIqZQiKSnJPgllaZo2bWp/L3fffTfdu3dn4cKFDB06tEzHuDi3vJLw8HD+85//MGrUKPbv30+jRo3KFJ8QlUXuoBTiGrm7u9OmTRtWrVrlcNXVZrOxbNkyatSoYR/QWKPR2K/uFtmzZ0+xLitdunQhMzOTb775xmH58uXLyxRTt27d2LBhg/2XepElS5bg5uZmn5n6WpX0fr777jtOnTp11cesU6cOL774Inv37i1xAO+L1a9fnyFDhjB37txi3Xh//PFH4uPjefbZZ9m4cWOxR6NGjViyZAkWi+WqY60oUVFRuLq6smzZMofl8fHx9i4vFaWs343L3YVQr149QkNDWb58ucNA5NnZ2axcudI+s7ebmxtdunRh9+7dNGnSxH5l9+LHpXc+VIaxY8fi5+fHuHHjiq3r1q0bBw4c4M8//3RYvmTJEjQaDV26dCl3e0WD7p8+fdphAPkriYmJAbAXHE+fPl3iLOJWq5UjR47g5uZWaX9ACSGEuDVI/lqx+euV/PHHH+zZs4dBgwah11+f+4PKc36vVbdu3cjKymLNmjUOy5csWWJfX8RoNJZ4l2pZz32vXr04duwY/v7+JeaWtWrVqpD3dCV33XUXd999N9OnTy826WbRe700p1+5ciXZ2dlXldNrNBree+89dDodEydOLNM+l+aWmZmZl50gtGhYg6K7VYVwJrmDUogy2rBhQ4kznvXs2ZNZs2Zx991306VLF8aMGYOLiwvz589n3759fP755/ard7169eLll19mypQpdOrUicOHDzN9+nQiIiIcCmWPP/44b7/9No8//jivvPIKderU4fvvvy/WBfRypkyZwrfffkuXLl2YPHkyfn5+fPbZZ3z33Xe89tprxbqFXK1evXqxePFi6tevT5MmTdi1axevv/46NWrUuKbjjhkzhgULFjBt2jT69++PTqe77LZTp07ls88+Y+PGjbi7u9uXL1q0CL1ez0svvVTiL9wnn3yS5557ju+++44HHnjgmuK9Vj4+PkyaNImXXnqJxx9/nEcffZSUlBSmTZuGyWRiypQpFdZWWb8bt912G66urnz22Wc0aNAADw8PQkJCCAkJ4bXXXuOxxx6jV69ePPnkk+Tn5/P666+TlpbGq6++am/rnXfeoUOHDtx55508/fTT1KpVi8zMTI4ePcratWvLNMYjYE82r2bGQS8vLyZMmMDzzz9fbN3zzz/PkiVLuO+++5g+fTo1a9bku+++Y/78+Tz99NNXnbg//vjjvPXWW7zxxhs8++yzeHl52dfFxcXZx97Mzs5m27ZtzJo1i5o1a9rv9F26dCkffPABAwYMoHXr1nh7exMfH89HH33E/v37mTx5sr3blRBCCHElkr8WV1n56+UsWrQIgKFDh1bK8S+nrOf3Wj3++OO89957DBo0iOPHj9O4cWO2bNnCzJkz6dmzJ3fddZd928aNG7Np0ybWrl1LcHAwnp6e1KtXr8znftSoUaxcuZKOHTvy/PPP06RJE2w2G3Fxcfz000+MHj2aNm3alBrz4MGD+fTTT4mNjb2qoubs2bNp2bIlycnJDncdFt3tOHbsWDIyMmjfvj179uxhypQpNG/enIEDB5a7LSi8geM///kP8+fPZ8uWLXTo0MG+Li0tzZ5bms1mDh48yMyZMzEajfZeTIcPH6Z79+488sgjdOrUieDgYFJTU/nuu+9YuHAhnTt3pl27dlcVmxAVymnT8whRRRTN3na5R2xsrFJKqV9//VV17dpVubu7K1dXV9W2bVu1du1ah2Pl5+erMWPGqNDQUGUymVSLFi3UmjVr1KBBg1TNmjUdto2Pj1cPPvig8vDwUJ6enurBBx9UW7duLdMsiEoptXfvXtW7d2/l7e2tXFxcVNOmTYvtd+nM2EWKZiUsrZ3U1FQ1dOhQFRAQoNzc3FSHDh3Ur7/+qjp16qQ6depUaow1a9ZU9913X4nr3nvvPQWoTz/99IqxKqXUSy+9pAD7DM5nzpxRLi4uqk+fPleM3dXVVfXu3VspVbbZLq+ES2aILsmV3oNSSn300UeqSZMmysXFRXl7e6sHHnhA7d+/32Gby81WfuksgkVK+ozL8t1QSqnPP/9c1a9fXxkMBgWoKVOm2NetWbNGtWnTRplMJuXu7q66deumfvvtt2LHiI2NVUOGDFGhoaHKYDCo6tWrq3bt2qkZM2aU+XOpVq2aatu2bYnrLna5zyY/P19FRESUeI5OnDihBgwYoPz9/ZXBYFD16tVTr7/+usMM5Zdzuc9cqcKZEgE1bdo0pVTJs3ibTCZVt25dNWrUKIeZFg8cOKBGjx6tWrVqpapXr670er3y9fVVnTp1UkuXLi01LiGEEELy18u71vz10vauNIt3Tk6O8vb2Vh07dizzcYtcKc+42JVy0LKc38vlwEUzSp85c8ZheUn5VkpKinrqqadUcHCw0uv1qmbNmmr8+PEqLy/PYbuYmBjVvn175ebmpgCHz7us+WlWVpaaOHGiqlevnj1nbty4sXr++edVUlJSmT6XBx98ULm6uqrU1NQS15f22Sil1IABAxRQ7Bzl5uaqsWPHqpo1ayqDwaCCg4PV008/XWpbSl3+M1dKqdOnTysPDw/VpUsX+7JLZ/HW6XQqPDxcPfTQQ2r37t327VJTU9WMGTNU165dVWhoqHJxcVHu7u6qWbNmasaMGSonJ6fU2IS4HjRKXdRHTwghhLiBHDhwgEaNGvHtt9+WOrO7EEIIIYQQpQkKCmLgwIG8/vrrzg5FCHERGYNSCCHEDWvjxo1ERUVJcVIIIYQQQlyz/fv3k5OTw9ixY50dihDiEnIHpRBCCCGEEEIIIYQQwmnkDkohhBBCCCGEEEIIIYTTSIFSCCGEEEIIIYQQQgjhNFKgFEIIIYQQQgghhBBCOI0UKIUQQgghhBBCCCGEEE6jd3YANwKbzUZCQgKenp5oNBpnhyOEEEIIUW5KKTIzMwkJCUGrlWvQVY3ko0IIIYSo6q4lH5UCJZCQkEBYWJizwxBCCCGEuGYnT56kRo0azg5DlJPko0IIIYS4WVxNPioFSsDT0xMo/AC9vLycHI0QQgghRPllZGQQFhZmz2tE1SL5qBBCCCGqumvJR6VACfZuNF5eXpIQCiGEEKJKk+7BVZPko0IIIYS4WVxNPioDFAkhhBBCCCGEEEIIIZxGCpRCCCGEEEIIIYQQQginkQKlEEIIIYQQQgghhBDCaWQMSiGEEOIGoJTCYrFgtVqdHYq4Qel0OvR6vYwxKYQQQohKIfmoKE1l5qNSoBRCCCGcrKCggMTERHJycpwdirjBubm5ERwcjIuLi7NDEUIIIcRNRPJRUVaVlY9KgVIIIYRwIpvNRmxsLDqdjpCQEFxcXOQOOVGMUoqCggLOnDlDbGwsderUQauVkXqEEEIIce0kHxVlUdn5qBQohRBCCCcqKCjAZrMRFhaGm5ubs8MRNzBXV1cMBgMnTpygoKAAk8nk7JCEEEIIcROQfFSUVWXmo0699P7LL7/Qu3dvQkJC0Gg0rFmzptg2Bw8e5P7778fb2xtPT0/atm1LXFycfX1+fj4jRoygWrVquLu7c//99xMfH38d34UQQghx7eRuOFEW8j0RQgghRGWRPEOURWV9T5z67cvOzqZp06bMmzevxPXHjh2jQ4cO1K9fn02bNvHXX38xadIkhwrtqFGjWL16NStWrGDLli1kZWXRq1cvGdRVCCGuk5d+fYkl+5dgsVmcHYoQQgghhLgF/fzRGj6Y/Danj8rNSkJUVU7t4t2jRw969Ohx2fUTJkygZ8+evPbaa/ZltWvXtj9PT09n0aJFLF26lLvuuguAZcuWERYWxs8//0z37t0rL3ghhBBsiNvA2n/W8kPsD3QI7UBtn9ql7ySEEEIIIUQFSU08w664/eRqzfz46Roef3m4s0MSQlyFG/b+XZvNxnfffUfdunXp3r07AQEBtGnTxqEb+K5duzCbzdxzzz32ZSEhIURGRrJ169bLHjs/P5+MjAyHhxBCiPLJNmcz8/eZAAxqNEiKk+K6OH78OBqNhpiYGGeHIoQQQogbwOr3PydXa8bDZuTeof9ydjjiFiD5aOW4YQuUycnJZGVl8eqrr3Lvvffy008/0bdvX/r168fmzZsBSEpKwsXFBV9fX4d9AwMDSUpKuuyxZ82ahbe3t/0RFhZWqe9FCCFuRu/FvMfpnNOEeoTyZNMnnR2OcILBgwfTp08fZ4dRKebPn09ERAQmk4mWLVvy66+/OjskIYQQQlzitxXridOeA6BRtdsJqBXo5IjE9Sb56M3jhi1Q2mw2AB544AGef/55mjVrxrhx4+jVqxcLFiy44r5KKTQazWXXjx8/nvT0dPvj5MmTFRq7EELc7A6kHOCzg58BMLHtRFz1rk6OSIiK88UXXzBq1CgmTJjA7t27ufPOO+nRo4fDJH1CCCGEcK6ctCy2HfgDgNACL+4d8ZCTIxKi4tyK+egNW6CsVq0aer2ehg0bOixv0KCB/YQEBQVRUFBAamqqwzbJyckEBl7+yonRaMTLy8vhIYQQomwsNgvTtk3Dpmz0qNWDDqEdnB3STUcpRU6B5bo/lFIV+j42b97MHXfcgdFoJDg4mHHjxmGxXJhMad26dXTo0AEfHx/8/f3p1asXx44dczjGjh07aN68OSaTiVatWrF79+4KjbEkb731FkOHDuXf//43DRo0YM6cOYSFhfH+++9XettCCCGEKJuVc5aQpc3H1Wbgnkf6XvEmJVF+zspHKzonlXy06nDqJDlX4uLiQuvWrTl8+LDD8r///puaNWsC0LJlSwwGA+vXr6d///4AJCYmsm/fPoeJdYQQQlScFYdWcCDlAJ4GT16840Vnh3NTyjVbaTj5x+ve7oHp3XFzqZjU4NSpU/Ts2ZPBgwezZMkSDh06xLBhwzCZTEydOhWA7OxsoqOjady4MdnZ2UyePJm+ffsSExODVqslOzubXr160bVrV5YtW0ZsbCwjR44ste2nnnqKZcuWXXGbAwcOEB4eXmx5QUEBu3btYty4cQ7L77nnniuOby2EEEKI62f397/xj0oGDdRzD6dmkwhnh3TTcVY+ChWXk0o+WrU4tUCZlZXF0aNH7a9jY2OJiYnBz8+P8PBwXnjhBR5++GE6duxIly5dWLduHWvXrmXTpk0AeHt7M3ToUEaPHo2/vz9+fn6MGTOGxo0b22f1FkIIUXGSspOYu3suAKNajqKaazUnRyRuVPPnzycsLIx58+ah0WioX78+CQkJjB07lsmTJ6PVannwwQcd9lm0aBEBAQEcOHCAyMhIPvvsM6xWKx9//DFubm40atSI+Ph4nn766Su2PX36dMaMGXPFbUJCQkpcfvbsWaxWa7GeGKWNby2EEEKI66MgN59N27agdBBc4EnvSY9hzcrCkpyMsbZM2igukHy0anFqgfKPP/6gS5cu9tfR0dEADBo0iMWLF9O3b18WLFjArFmzeO6556hXrx4rV66kQ4cL3Qnffvtt9Ho9/fv3Jzc3l27durF48WJ0Ot11fz9CCHGzm/X7LHIsOTSt3pSH6so4P5XF1aDjwPTuTmm3ohw8eJCoqCiH7lbt27cnKyuL+Ph4wsPDOXbsGJMmTWL79u2cPXvWPv50XFwckZGRHDx4kKZNm+Lm5mY/RlRUVKltBwQEEBAQcE3xX9pNrLTxrYUQQghxfax6awnpulxclJ5OPbqj02lJfO110tesIXDSRHz/JTN5VwRn5aNFbVcEyUerFqcWKDt37lzq2AJDhgxhyJAhl11vMpmYO3cuc+fOrejwhBBCXOR/cf9jw8kN6DV6JkdNRqu5YYcxrvI0Gk2FdbV2lpISqKLf+UXLe/fuTVhYGB9++CEhISHYbDYiIyMpKChw2L68rqVLTbVq1dDpdMWuTpc2vrUQQgghKt+h3/bwd0ECaOB2XRD174wke9s20r78EgCX8JpOjvDmIfmo5KPXW9X+tgkhhLguss3ZzPp9FgCDGg2irm9dJ0ckbnQNGzZk5cqVDonh1q1b8fT0JDQ0lJSUFA4ePMgHH3zAnXfeCcCWLVuKHWPp0qXk5ubi6lo4U/z27dtLbftautS4uLjQsmVL1q9fT9++fe3L169fzwMPPFBq20IIIYSoHFaLlfXrfsSmU1Q3u9Nn4uPYsrNJnDgJAN8Bj+Le5g4nRyluJJKPVi1SoBRCCFGqebvncTrnNKEeoTzZ9ElnhyNuIOnp6cTExDgs8/Pz45lnnmHOnDmMGDGC4cOHc/jwYaZMmUJ0dDRarRZfX1/8/f1ZuHAhwcHBxMXFFRsIfMCAAUyYMIGhQ4cyceJEjh8/zhtvvFFqTNfapSY6OpqBAwfSqlUroqKiWLhwIXFxcTz11FNXfUwhhBBCXJtv3v6MFF02eqWlXbs7cXF1Ienl1zCfOoUhJITq0aOdHaJwEslHbw5SoBRCCHFF+1P2s/zQcgAmtZ2Eq97VyRGJG8mmTZto3ry5w7KisaS///57XnjhBZo2bYqfn589sQPQarWsWLGC5557jsjISOrVq8e7775L586d7cfx8PBg7dq1PPXUUzRv3pyGDRsye/bsYoOZV7SHH36YlJQUpk+fTmJiIpGRkXz//ffUrCndxoQQQghniNt7jANZJ0ADEaoaze9rS87OnaR+9hkAQS9PR+fh7uQohbNIPnpz0Kir7VB/E8nIyMDb25v09HS8vLycHY4QQtwwLDYLA74bwMFzB+kR0YPXOr7m7JBuOnl5ecTGxhIREYHJZHJ2OOIGd6Xvi+QzVZucPyGEKJnNZuODKW9zWpeJn8WNIWOewc2o458+fTCfiMPnXw8R/PLLzg6zSpN8VJRHZeWjMsOBEEKIy1pxaAUHzx3E08WTF1u/6OxwhBBCCCHELeb7uSs4rctEqzS0atQSDx8PzrzzLuYTceiDggh4UXJUIW4GUqAUQghRosSsRObungvA8y2fp5prNSdHJIQQQgghbiWnDh1nz7ljANSy+tPu0W7k7N7NuU8/BSB42lR0np7ODFEIUUGkQCmEEKIYpRTTt08nx5JD84DmPFincsdYEUIIIYQQ4mI2m421n62kQGPFx+pKn+cHYcvNJXHceFAK7wcewKNTJ2eHKYSoIFKgFEIIUcz3sd+z5dQWDFoDU6OmotXIrwshhBBCCHH9/PDelySd79rdsn5zvPw9OTNnDgUnTqAPCCBwwkvODlEIUYHkL04hhBAOUvNSmb1jNgD/afIfavvUdnJEQgghhBDiVpL49wn+OnsUgJoWP+587B5ydu7k3JKlAAS/MgOdTCgmxE1FCpRCCCEczN45m9T8VG73uZ2hkUOdHY4QQgghhLiF2Gw2vl66kgKNpbBr98iB2LKzSXhpAiiFz78ewuPOO50dphCigkmBUgghhN2v8b/y3T/fodVomd5uOgadwdkhCSGEEEKIW8iP7/+XJF0GGqWhRZ1meAf4kPzmW5hPnkQfHEzA2LHODlEIUQmkQCmEEAKAbHM207dPB+CxBo/RuHpjJ0ckhBBCCCFuJUl/nyQm+W8Aaln86Ph4d7K3byd1+XIAQl6Zgc7Dw5khCiEqiRQohRBCAPDun++SlJ1EqEcow5sNd3Y4QgghhBDiFvP10q/I11jwtpq4f8T/Yc3KIvGlCQD4PPoI7u3aOTlCIURlkQKlEEIIYpJj+PzQ5wBMiZqCm8HNyREJUbLjx4+j0WiIiYlxdihCCCGEqEDr3v8viee7djer3QTfIF+SX3sdc0IChtBQAseMcXaIQgCSj1YWKVAKIcQtrsBawJStU1AoHrjtAaJCopwdkqgiBg8eTJ8+fZwdRoX75Zdf6N27NyEhIWg0GtasWVOm/TZv3kzLli0xmUzUrl2bBQsWVG6gQgghxE3i9D/x7E46BEBNiy9dnuhJ1pbfSPvySwCCZ85E6+7uzBDFDUryUUdVOR+VAqUQQtziPtz7If+k/4O/yZ8XWr/g7HCEcLrs7GyaNm3KvHnzyrxPbGwsPXv25M4772T37t289NJLPPfcc6xcubISIxVCCCFuDl9/Uti128tq4oFn/w9rRgaJEycC4DtwIO5t7nByhEJcX7diPioFSiGEuIUdST3CR3s/AmB8m/F4G72dHJEAQCkoyL7+D6Uq9G1s3ryZO+64A6PRSHBwMOPGjcNisdjXr1u3jg4dOuDj44O/vz+9evXi2LFjDsfYsWMHzZs3x2Qy0apVK3bv3l2hMZakR48ezJgxg379+pV5nwULFhAeHs6cOXNo0KAB//73vxkyZAhvvPFGJUYqhBBCVH0/fbCKBF16YdfuWo3xDfHj9KuzsSQlYagZTsDzo5wd4q3JWfloBeekko9WnXxU7+wAhBBCOIfVZmXq1qlYbBY6h3Xmnpr3ODskUcScAzNDrn+7LyWAS8V0nzp16hQ9e/Zk8ODBLFmyhEOHDjFs2DBMJhNTp04FCq8MR0dH07hxY7Kzs5k8eTJ9+/YlJiYGrVZLdnY2vXr1omvXrixbtozY2FhGjhxZattPPfUUy5Ytu+I2Bw4cIDw8vCLeKgDbtm3jnnsc/x/q3r07ixYtwmw2YzAYKqwtIYQQ4mZx+p94diUcAA2EW3zoOvQ+MjdtIn3VKtBoCJk1C62bjI3uFM7KR6HCclLJR6tWPioFSiGEuEV9fuhz9pzdg4fBg4ltJqLRaJwdkriJzJ8/n7CwMObNm4dGo6F+/fokJCQwduxYJk+ejFar5cEHH3TYZ9GiRQQEBHDgwAEiIyP57LPPsFqtfPzxx7i5udGoUSPi4+N5+umnr9j29OnTGVPKQPohIRWbcCclJREYGOiwLDAwEIvFwtmzZwkODq7Q9oQQQoiqzmazsfqTL8nXnZ+1+5kBWNPSSJo0GQC/wYNxa9HCyVGKqkzy0aqVj0qBUgghbkHxmfG8u/tdAJ5v+TyB7oGl7CGuK4Nb4ZVjZ7RbQQ4ePEhUVJRD4bt9+/ZkZWURHx9PeHg4x44dY9KkSWzfvp2zZ89is9kAiIuLIzIykoMHD9K0aVPcLrpzIiqq9EmcAgICCAgIqLD3UlaXFvnV+e5JUvwXQgghivth3hcknZ+1u/ntzfAPrc6p6NFYzpzBJSKC6iOfc3aItzZn5aNFbVcAyUerVj4qBUohhLjF2JSNyVsnk2vJpVVgKx6q+5CzQxKX0mgqrKu1syilSk2QevfuTVhYGB9++CEhISHYbDYiIyMpKChw2L68nNGlJigoiKSkJIdlycnJ6PV6/P39K6wdIYQQ4mYQt+coMSlHQQO1LH50HnQv6d9+R8b334NOR8hrs9GaTM4O89Ym+ajD9uUl+Wj5OXWSnPJMm/7kk0+i0WiYM2eOw/L8/HxGjBhBtWrVcHd35/777yc+Pr5yAxdCiCrsq8NfsTNpJ656V6a3m45WI/OliYrXsGFDtm7d6pDUbd26FU9PT0JDQ0lJSeHgwYNMnDiRbt260aBBA1JTU4sd46+//iI3N9e+bPv27aW2PX36dGJiYq74qOguNVFRUaxfv95h2U8//USrVq1u+PF+hBBCiOvJarWy9ss1mDVWfK2u9IsejDkpiaTp0wGo9vTTuDZu7OQoxc1A8tGqlY869Q7KomnTn3jiiWL9/i+2Zs0afv/99xJP3qhRo1i7di0rVqzA39+f0aNH06tXL3bt2oVOp6vM8IUQoso5lXWKN3e9CcDIFiMJ8wpzckSiqktPTycmJsZhmZ+fH8888wxz5sxhxIgRDB8+nMOHDzNlyhSio6PRarX4+vri7+/PwoULCQ4OJi4ujnHjxjkcZ8CAAUyYMIGhQ4cyceJEjh8/XqZZCK+1S01WVhZHjx61v46NjSUmJgY/Pz/7Ve7x48dz6tQplixZAhReJZ83bx7R0dEMGzaMbdu2sWjRIj7//POrjkMIIYS4GX3z1mec0WehU1paN2mDh687J/89EltGBqbGjan25H+cHaKoYiQfvUnyUXWDANTq1auLLY+Pj1ehoaFq3759qmbNmurtt9+2r0tLS1MGg0GtWLHCvuzUqVNKq9WqdevWlbnt9PR0Baj09PRreQtCCHFDs9lsaui6oSpycaR6/PvHldVmdXZIQimVm5urDhw4oHJzc50dSrkNGjRIAcUegwYNUkoptWnTJtW6dWvl4uKigoKC1NixY5XZbLbvv379etWgQQNlNBpVkyZN1KZNm4rlA9u2bVNNmzZVLi4uqlmzZmrlypUKULt3766097Vx48Yrvq+i996pUyeH/TZt2qSaN2+uXFxcVK1atdT7779f4bFd6fsi+czVmTlzpmrVqpXy8PBQ1atXVw888IA6dOiQwzY2m01NmTJFBQcHK5PJpDp16qT27dvnsE1eXp4aPny48vf3V25ubqp3797q5MmTZY5Dzp8Q4lbw9/Z96uXJ09SUKVPUsonvKaWUSlmyVB2oV18dbNpM5R37x8kR3pokH5V8tDwqKx/VKHWVHeormEajYfXq1fTp08e+zGazcdddd/HAAw8wcuRIatWqxahRoxg1ahQAGzZsoFu3bpw7dw5fX1/7fk2bNqVPnz5MmzatxLby8/PJz8+3v87IyCAsLIz09HS8vLwq5f0JIYSzfXn4S17e/jImnYmV968k3KvixjsRVy8vL4/Y2FgiIiIwyVhLohRX+r5kZGTg7e0t+Uw53XvvvTzyyCO0bt0ai8XChAkT2Lt3LwcOHMDdvXDsrdmzZ/PKK6+wePFi6taty4wZM/jll184fPgwnp6eADz99NOsXbuWxYsX23v1nDt3rsy9euT8CSFudhazhfenv02KLptqFjeGjBuBLjmR2L79UPn5BE6aiN9jjzk7zFuS5KOiPCorH72hJ8mZPXs2er2e554refaupKQkXFxcHIqTUDiN+qUDg15s1qxZly1eCiHEzSghK4E3/7jQtVuKk0IIUWjdunUOrz/55BMCAgLYtWsXHTt2RCnFnDlzmDBhAv369QPg008/JTAwkOXLl/Pkk0+Snp7OokWLWLp0KXfddRcAy5YtIywsjJ9//pnu3btf9/clhBA3mlVvLCZFl41e6WjX9k5cjXqOv/AiKj8f9/bt8R0wwNkhCiGc6IadGWHXrl288847LF68uNzToasSZmq62Pjx40lPT7c/Tp48ea3hCiHEDUspxZStU8ix5NAioAUDGkjyJ4QQl5Oeng4Ujl0FhWM+JSUlcc8999i3MRqNdOrUia1btwKFeavZbHbYJiQkhMjISPs2l8rPzycjI8PhIYQQN6v9G//gUN4pAG7XBNKiVxRn319A3v79aL29CZ75Srn/7hdC3Fxu2ALlr7/+SnJyMuHh4ej1evR6PSdOnGD06NHUqlULKJxCvaCgoNgsS8nJyQQGBl722EajES8vL4eHEELcrFYeWcn2xO0YdUamt5dZu4UQ4nKUUkRHR9OhQwciIyMB7L1yLs0tL+6xczW9embNmoW3t7f9ERYmk5YJIW5OBbl5rN/wP2waRYDZg37jBpP711+c/eADAIImT8Jwhb/fhRC3hhv2r9SBAweyZ8+eYlOwv/DCC/z4448AtGzZEoPB4DCNemJiIvv27aNdu3bOCl0IIW4YiVmJvPFH4SxzzzV/jppeNZ0ckRBC3LiGDx/Onj17Spzt8tI7e0rrsVPaNtKjRwhxq/jvG4tJ0+XiovR0uvtu9DYLCS+OBasVr/vuw/u++5wdohDiBuDUMShLmzbd39/fYXuDwUBQUBD16tUDwNvbm6FDhzJ69Gj8/f3x8/NjzJgxNG7c2D7+jxBC3KqUUkzdNpVsczbNqjfjsQYy6LgQQlzOiBEj+Oabb/jll1+oUaOGfXlQUBBQeJdkcHCwffnFPXYu7tVz8V2UycnJl71objQaMRqNlfFWhBDihvHnt79xxHIaNFDXJYRGnZuSNH06BSdOoA8MJGjyJGeHKIS4QTj1Dso//viD5s2b07x5cwCio6Np3rw5kydPLvMx3n77bfr06UP//v1p3749bm5urF27tkyzJQohxM1s9dHVbE3Yau/ardPKv4tCCHEppRTDhw9n1apVbNiwgYiICIf1ERERBAUFOfTYKSgoYPPmzfbio/TqEUKI4nLSMtm441eURhFU4EmfFx8n69dfSV1eeJd68MxX0Hl7OzlKIcSNwql3UHbu3BmlVJm3P378eLFlJpOJuXPnMnfu3AqMTAghqrak7CRe3/k6ACOajyDCO6KUPYQQ4tb07LPPsnz5cr7++ms8PT3tY0Z6e3vj6uqKRqNh1KhRzJw5kzp16lCnTh1mzpyJm5sbA87POCu9eoQQorgv315MpjYPV5uBu/r2gqxMEl+aAIDvY4/h0b69kyMUQtxInFqgFEIIUfGKunZnmbNoUr0J/9fg/5wdkhBC3LDef/99oPDC+cU++eQTBg8eDMCLL75Ibm4uzzzzDKmpqbRp04affvoJT09P+/Zvv/02er2e/v37k5ubS7du3Vi8eLH06hFC3JI2Ll7LcU0KAA19anNbq7qceu45LGfO4BIRQcCY0U6OUAhxo5ECpRBC3GRWHVnFb6d+w0XrwsvtX5au3UIIcQVl6c2j0WiYOnUqU6dOvew20qtHCCEKnf4nnt//2QNaCCvwodfzj5D23/+Suf5nMBgIef11tK6uzg5TCHGDuWFn8RZCCFF+8ZnxvLbzNQCea/Ectb1rOzkiISrW8ePH0Wg0xMTEODsUIYQQQlzCZrOx+pMvydOa8bKaeODJARTEHuf0zFkABIx8DtfIRk6OUohrI/lo5ZACpRBC3CRsysbE3yaSY8mhRUAL6dotKt3gwYPp06ePs8OocFOnTkWj0Tg8imZyvpLNmzfTsmVLTCYTtWvXZsGCBdchWiGEEOLGsXbOcpJ0GWiVhpZ1muMf7EPCCy+gcnNxa9sWvyFDnB2iuMlIPuqoKuej0sVbCCFuEksPLGXX6V246d2Y0WGGdO0W4ho0atSIn3/+2f66tHEEY2Nj6dmzJ8OGDWPZsmX89ttvPPPMM1SvXp0HH3ywssMVQgghnO7Itn3sTf8HNFDbWo1Oj3cn+c03ydu/H623NyGzX0WjlXukhCirWy0flX8dhBDiJnA09Sjv/vkuAC+0foEwzzAnRySuhVKKHHPOdX+UZSy+8ti8eTN33HEHRqOR4OBgxo0bh8Visa9ft24dHTp0wMfHB39/f3r16sWxY8ccjrFjxw6aN2+OyWSiVatW7N69u0JjvBy9Xk9QUJD9Ub169Stuv2DBAsLDw5kzZw4NGjTg3//+N0OGDOGNN964LvEKIYQQzmTOK+CH77/HorFRzeJOv7FDyN7+OykfLQIg+OXpGAIDnRylKA9n5aMVnZNKPlp18lG5g1IIIao4s83MS1teosBWwJ2hd/JgnRv/6pi4slxLLm2Wt7nu7f4+4HfcDG4VcqxTp07Rs2dPBg8ezJIlSzh06BDDhg3DZDLZJxrJzs4mOjqaxo0bk52dzeTJk+nbty8xMTFotVqys7Pp1asXXbt2ZdmyZcTGxjJy5MhS237qqadYtmzZFbc5cOAA4eHhl11/5MgRQkJCMBqNtGnThpkzZ1K79uXHdN22bRv33HOPw7Lu3buzaNEizGYzBoOh1LiFEEKIquqr1z/hnC4Hg9JxZ8cuGK35/DN2LCiFz78ewuuS35GVwWw1Y9DJ79uK4qx8FCouJ5V8tGrlo1KgFEKIKu7DPR9y8NxBvI3eTGs3DY1G4+yQhGD+/PmEhYUxb948NBoN9evXJyEhgbFjxzJ58mS0Wm2xriaLFi0iICCAAwcOEBkZyWeffYbVauXjjz/Gzc2NRo0aER8fz9NPP33FtqdPn86YMWOuuE1ISMhl17Vp04YlS5ZQt25dTp8+zYwZM2jXrh379+/H39+/xH2SkpIIvOTOkMDAQCwWC2fPniU4OPiK8QghhBBV1Y7VmzhiSQIN1DOE0uTulpwaOQrL6dO41KpF4PjxlR7D10e/ZvH+xbzd+W1qedeq9PZE1SD5aNXKR6VAKYQQVdi+s/tYuGchABPbTKS625Vv+xdVg6veld8H/O6UdivKwYMHiYqKciiYt2/fnqysLOLj4wkPD+fYsWNMmjSJ7du3c/bsWWw2GwBxcXFERkZy8OBBmjZtipvbhSvoUVFRpbYdEBBAQEDAVcfeo0cP+/PGjRsTFRXFbbfdxqeffkp0dPRl97v04kBR9yS5aCCEEOJmlXEmlV92b0NpFcEFnvSdNJj0lSvJ/Okn0OsJef11tG4V0zvjcg6fO8zL218m35rP+hPrGdZkWKW2d6twVj5a1HZFkHy0auWjUqAUQogqKs+Sx0tbXsKqrPSo1YN7I+51dkiigmg0mgrrau0sSqlSE6TevXsTFhbGhx9+SEhICDabjcjISAoKChy2L6+K6FJzMXd3dxo3bsyRI0cuu01QUBBJSUkOy5KTk9Hr9Ze9yi2EEEJUdV/NXUKWNh83mwv3PtIXS9wJkl6ZCUD1kc/h2jiyUtvPLMgkelM0+dZ8OoR2YGjjoZXa3q1E8lHJR683KVAKIUQV9e7ud4lNj6W6a3UmtJ3g7HCEcNCwYUNWrlzpkBhu3boVT09PQkNDSUlJ4eDBg3zwwQfceeedAGzZsqXYMZYuXUpubi6uroVX0rdv315q29fapeZS+fn5HDx40B5nSaKioli7dq3Dsp9++olWrVrd8OP9CCGEEFdj/cLVnNSmgoLG1esQXr8Gxwc8hsrNxa1NG/yHVm6xUCnFxC0TicuMI8Q9hFkdZqHVyDzA4gLJR6tWPioFSiGEqIJ2Ju1k6YGlAExrNw1vo7eTIxK3qvT0dGJiYhyW+fn58cwzzzBnzhxGjBjB8OHDOXz4MFOmTCE6OhqtVouvry/+/v4sXLiQ4OBg4uLiGDdunMNxBgwYwIQJExg6dCgTJ07k+PHjZZqF8Fq71IwZM4bevXsTHh5OcnIyM2bMICMjg0GDBtm3GT9+PKdOnWLJkiVA4VXyefPmER0dzbBhw9i2bRuLFi3i888/v+o4hBBCiBvVyX3H2HlqP2igptmXe4c/xJm33iZv3z603t6EzH4VjbZyi4WL9i1iw8kNGLQG3ur8Fj4mn0ptT9y4JB+9OfJRKVAKIUQVk1WQxcQtEwF4qO5D3Fnj8lfRhKhsmzZtonnz5g7LBg0axOLFi/n+++954YUXaNq0KX5+fvbEDkCr1bJixQqee+45IiMjqVevHu+++y6dO3e2H8fDw4O1a9fy1FNP0bx5cxo2bMjs2bOLDWZe0eLj43n00Uc5e/Ys1atXp23btmzfvp2aNWvat0lMTCQuLs7+OiIigu+//57nn3+e9957j5CQEN59991Kj1UIIYS43ixmC1+vWEWB3oKP1ZU+IwaS8/sOUj76CIDg6dMxBAVVagzbErYxd/dcAF5q8xKNqjWq1PbEjU3y0UJVPR/VqKvtUH8TycjIwNvbm/T0dLy8vJwdjhBCXNGUrVNYdWQVoR6hrLx/Je4Gd2eHJK5BXl4esbGxREREYDKZnB2OuMFd6fsi+UzVJudPCFFVfD5jIYctCeiVlnuadqZFp0bE9umL5cwZvB96kJAZMyq1/YSsBB7+9mHS8tPoV6cf09pNq9T2bgWSj4ryqKx8VAZoEEKIKmRD3AZWHVmFBg2vdHjlhihO5u7fjyU11dlhCCGEEEKISvb7yo38bU4EoI42mNZ9OpAwfjyWM2dwue02gl56qVLbz7fm8/ym50nLT6ORfyNealO57Qkhrh8pUAohRBVxJucMU7ZOAWBw5GBaBrZ0ckRgTkzk5H+e5PiDD1Fw/LizwxFCCCGEEJUkNeEsv/y1DaVRBBd40m/8YM59spjsX35FYzQS+tZbaN0qb9ZnpRSvbH+FAykH8DH68FbntzDqjJXWnhDi+pICpRBCVAE2ZWPSb5NIy0+jgV8DRjQb4eyQsOXmEv/scKwpKWi9vNBfwyDQQgghhBDixmWz2fjq/aVkawtwtxm57/8ewnLoIMlvvw1A4PjxmOrVrdQY/nvkv6w+uhqtRstrHV8jxKPsMyALIW58MkmOEEJUAZ8f+pzfEn7DqDPy6p2vYtAZnBqPUorESZPJO3AAna8vNebNq9Qr5kIIIYQQwnm+fedzEnTpaJSGFjUaERzmR2zff4PFgue99+LzcP9KbX/vmb3M+n0WACOajyAqJKpS2xNCXH9SoBRCiBvckdQjvPXHWwCMaTWG2j61nRwRnPv4EzK+/RZ0OkLnzMGlRqizQxJCCCGEEJXg0K8x7Ek7BhqIsPrTdVhvTkVHY46PxxAaSvD0aWg0mkprPyU3hec3PY/ZZqZbeDeGRg6ttLaEEM4jXbyFEOIGlm/NZ+yvYymwFdCxRkcervews0Mi69ctJL/5JlDYnce9zR1OjkgIIYQQQlSGvKwc1v30IxaNjWoWdx58cQhpX31F5g/rQK8n9K030ZVzpt7ysNgsvPjLi5zOOU0tr1rMaD+jUouhQgjnkQKlEELcwN758x2OpB7Bz+THtHaVe3W6LApOnODU6NFgs+H9YD98Hxvg1HiEEEIIIUTl+eKNT0jT5WJUerp0uxv96VOcfmUmAAHPj8K1adNKbf/dP99lR9IOXPWuzOkyBw8Xj0ptTwjhPE4tUP7yyy/07t2bkJAQNBoNa9assa8zm82MHTuWxo0b4+7uTkhICI8//jgJCQkOx8jPz2fEiBFUq1YNd3d37r//fuLj46/zOxFCiIq3NWErSw8sBeDl9i9TzbWaU+OxZmVz8tlnsWVk4Nq0KUFTpji9YCqEEEIIISrHxsVrieUMAA3ca9GgbT3in38elZ+Pe4cO+D3xRKW2/9Pxn/hk/ydAYS58m89tldqeEMK5nFqgzM7OpmnTpsybN6/YupycHP78808mTZrEn3/+yapVq/j777+5//77HbYbNWoUq1evZsWKFWzZsoWsrCx69eqF1Wq9Xm9DCCEqXFpeGhO3TATg4XoP07FGR6fGo2w2EsaNpeDoMfTVqxP67rtoXVycGpMQQgghhKgcCYeO83vsHgBqFHjzwAuPcXrmLAqOHkNXvRohs19Fo628csKR1CNM/K0wFx7caDDda3WvtLaEEDcGp06S06NHD3r06FHiOm9vb9avX++wbO7cudxxxx3ExcURHh5Oeno6ixYtYunSpdx1110ALFu2jLCwMH7++We6dy/5H7H8/Hzy8/PtrzMyMiroHQkhxLVTSjF121TO5J4hwjuC0a1GOzskzs5/n6yf/4fGYKDGvLkYAgOcHZK4RR0/fpyIiAh2795Ns2bNnB2OEEIIcdOxmC2s/uy/5OnMeFtN9Hn6/8j84QfSvvoKNBpCX3sNvb9/pbWfnp/OcxueI9eSS5ugNoxsMbLS2hLiakg+Wjmq1BiU6enpaDQafHx8ANi1axdms5l77rnHvk1ISAiRkZFs3br1sseZNWsW3t7e9kdYWFhlhy6EEGW2+uhq/hf3P/RaPbPvnI2r3tWp8WT+/DNnz9/pHjR1aqWPNSSqjsGDB9OnTx9nh1HhrjQETRGlFFOnTiUkJARXV1c6d+7M/v37Sz32ypUradiwIUajkYYNG7J69epKeAdCCCHE1ftq9iLO6LLQKy1tGrfBS+WSOHkKAP5P/gf3qKhKa9tis/DC5heIz4on1COU1zu9jl7r1PuqxA1O8tGbJx+tMgXKvLw8xo0bx4ABA/A6P0tYUlISLi4u+Pr6OmwbGBhIUlLSZY81fvx40tPT7Y+TJ09WauxCCFFWJzJO8OqOVwEY0XwEDfwbODWe/CNHSHhxLAC+//d/+DzYz6nxCHE9XGkImiKvvfYab731FvPmzWPnzp0EBQVx9913k5mZedl9tm3bxsMPP8zAgQP566+/GDhwIP379+f333+vjLchhBBClNtvn//I3+ZEAOroQmj7QDviR43ClpWFa4sWVB8+vFLbn7NrDtsSt+Gqd+WdLu/ga/ItfSchbkK3Yj5aJQqUZrOZRx55BJvNxvz580vdXil1xYkbjEYjXl5eDg8hhHA2s83M+F/Hk2vJpXVQawY1HOTUeKxpaZx8dji2nBzc2rQhcOyLTo3nVqKUwpaTc90fSqkKfR+bN2/mjjvuwGg0EhwczLhx47BYLPb169ato0OHDvj4+ODv70+vXr04duyYwzF27NhB8+bNMZlMtGrVit27d1dojCXp0aMHM2bMoF+/kgvySinmzJnDhAkT6NevH5GRkXz66afk5OSwfPnyyx53zpw53H333YwfP5769eszfvx4unXrxpw5cyrpnQghhBBll3T0JFsO/oHSQEiBFw9NGMLpmbPIP3AQna8voW+9iUZfeXczfvvPt3x64FOgcFKcen71Kq0tUTpn5aMVnZNKPuroRs5Hr/pfl4KCApKTk7HZbA7Lw8PDrzmoi5nNZvr3709sbCwbNmxwKCYGBQVRUFBAamqqw12UycnJtGvXrkLjEEKIyvbe7vfYe3Yvni6ezOwwE51W57RYlNlM/MhRmOPiMISEEDrnbTQGg9PiudWo3FwOt2h53dut9+cuNG5uFXKsU6dO0bNnTwYPHsySJUs4dOgQw4YNw2QyMXXqVKDwynB0dDSNGzcmOzubyZMn07dvX2JiYtBqtWRnZ9OrVy+6du3KsmXLiI2NZeTI0seheuqpp1i2bNkVtzlw4MBV5yyxsbEkJSU5DDFjNBrp1KkTW7du5cknnyxxv23btvH88887LOvevfsNkRBWVdcrHxVCiJud1WJl1eIvyNWb8bKauH/oo2R9/x1pX3wBGg0hr7+OISio0trfn7KfqVunAjCs8TCZFOcG4Kx8FCouJ5V8tLgbOR8td4HyyJEjDBkypNgYj0V3LVbk7NlFxckjR46wceNG/C8ZiLdly5YYDAbWr19P//79AUhMTGTfvn289tprFRaHEEJUtq0JW/l438cATGs3jSD3yksAS6OUImnGK+T8/jtaNzdqvP8+el/pXiPKZ/78+YSFhTFv3jw0Gg3169cnISGBsWPHMnnyZLRaLQ8++KDDPosWLSIgIIADBw4QGRnJZ599htVq5eOPP8bNzY1GjRoRHx/P008/fcW2p0+fzpgxY664TUhIyFW/t6JhZAIDAx2WBwYGcuLEiSvuV9I+VxqWRpTseuajQghxK/jy1UUk67PQKS1tGrXGlxxip0wFoNrTT+HRoX2ltX029ywjN4wk35pPxxodGd68cruRi1uH5KMl73ej5qPlLlAOHjwYvV7Pt99+S3Bw8BW7UpcmKyuLo0eP2l/HxsYSExODn58fISEhPPTQQ/z55598++23WK1W+wfm5+eHi4sL3t7eDB06lNGjR+Pv74+fnx9jxoyhcePG9lm9hRDiRnc29ywv/foSCsW/6v6Lu2ve7dR4Upd9duFq+RtvYKpX16nx3Io0rq7U+3OXU9qtKAcPHiQqKsohT2jfvj1ZWVnEx8cTHh7OsWPHmDRpEtu3b+fs2bP2u+Di4uKIjIzk4MGDNG3aFLeLrqBHlWFg/oCAAAICKn+m+UtzoNKGmLnafURxFZmPCiHErW7rF+v525wAGqirDSbq/ihi+/dH5eTg1rYt1Z59ttLaNlvNjN40mtM5p6nlVYtX73wVraZKjER303NWPlrUdkWQfLTi9rkeyl2gjImJYdeuXdSvX/+aG//jjz/o0qWL/XV0dDQAgwYNYurUqXzzzTcAxaZt37hxI507dwbg7bffRq/X079/f3Jzc+nWrRuLFy9Gp3Ne10ghhCgrm7IxcctEUvJSuN3ndl5s7dxxHrO2/MbpWbMACBgzGs+uXUrZQ1QGjUZTYV2tnaWkRKdoPKGi5b179yYsLIwPP/yQkJAQbDYbkZGRFBQUOGxfXpXdpSbofBe3pKQkgoOD7cuTk5OLXZG+dL9Lr06Xto8oWUXmo0IIcSs7fSyeX/fvQGkLx53sO3UwSZMmUnD0GLrq1Qh943U0lfi39as7XuXP5D/xMHjwbtd38XTxrLS2RPlIPir56PVW7gJlw4YNOXv2bIU03rlz5yue7LJ8EUwmE3PnzmXu3LkVEpMQQlxPS/Yv4beE3zDpTLze8XVMepPTYsn/5x9OPf882Gx49+mD35AhTotFVH0NGzZk5cqVDonh1q1b8fT0JDQ0lJSUFA4ePMgHH3zAnXfeCcCWLVuKHWPp0qXk5ubiev5K+vbt20ttu7K71ERERBAUFMT69etp3rw5UDgW4ubNm5k9e/Zl94uKimL9+vUO4/789NNPMm72VajIfFQIIW5VVouVlZ+sIFdvxtNqpPcTD5P99RrSv/4GtFpC33wTfbVqldb+V39/xZd/f4kGDbM7zibCO6LS2hK3JslHi7uR89FyFyhnz57Niy++yMyZM2ncuDGGSyZNkBmxhRCibPae2cs7f74DwIt3vMjtvrc7LRZrWhonn34aW2Ymri1aEDR92g1xm7+48aWnpxMTE+OwzM/Pj2eeeYY5c+YwYsQIhg8fzuHDh5kyZQrR0dFotVp8fX3x9/dn4cKFBAcHExcXx7hx4xyOM2DAACZMmMDQoUOZOHEix48f54033ig1pmvtUnOlIWjCw8PRaDSMGjWKmTNnUqdOHerUqcPMmTNxc3NjwIAB9v0ef/xxQkNDmXX+ruSRI0fSsWNHZs+ezQMPPMDXX3/Nzz//XCwRFqWTfFQIIa7dV69+fGHcyQat8bVkcPzlGQBUHzUK9zvuqLS2dyfvZubvMwEY0XwEHWt0rLS2xM1P8tGbJB9V5aTRaJRGo1FardbhUbSsKkpPT1eASk9Pd3YoQohbRGZ+prr3v/eqyMWRKnpjtLLZbE6LxVZQoI4PGqwO1KuvjnTpqsxnzzotlltRbm6uOnDggMrNzXV2KOU2aNAgBRR7DBo0SCml1KZNm1Tr1q2Vi4uLCgoKUmPHjlVms9m+//r161WDBg2U0WhUTZo0UZs2bVKAWr16tX2bbdu2qaZNmyoXFxfVrFkztXLlSgWo3bt3V9r72rhx4xXfl1JK2Ww2NWXKFBUUFKSMRqPq2LGj2rt3r8NxOnXq5LCPUkp99dVXql69espgMKj69eurlStXliu2K31fbqV8RvJRIYS4Nlu/WK+mTp6qpkyZoj6f+oGyZGSoI/fcow7Uq69O/Oc/yma1VlrbCZkJquOKjjdEHiwKST4q+Wh5VFY+qlGqfB3qN2/efMX1nTp1Ks/hbggZGRl4e3uTnp4uV9yFEJVOKcXYX8byw/EfCHEP4av7v8LLxTn/9iilSJo2jbQVX6B1c6Pm55/LpDjXWV5eHrGxsURERGAyOa+Lv6garvR9uZXyGclHhRDi6p3+5xSLFy8mV2smpMCTwVOfI3nMGDJ/+gl9SDARK1ei9/WtlLZzzDkM/GEgf6f+TT3feizpsQQ3Q9Ue5/BmIPmoKI/KykfL3cW7KiZ8QghxI1lzdA0/HP8BnUbH7I6znVacBEj9bDlpK87P2P2mzNgthKgaJB8VQoirY7VYWfnx5xeNO/kIWSs+J/Onn8BgoMbbb1dacdKmbIz/dTx/p/6Nn8mPuV3nSnFSCGFXpgLlnj17iIyMRKvVsmfPnitu26RJkwoJTAghbkb/pP3DrB2F438Mbz6cZgHNnBZL1pbfOD2zcOyfgDGj8ewiM3YLIW5cko8KIcS1+2LWRxfGnazfCp/sMxx/vXA8vcAXXsC1adNKa3vu7rlsOLkBg9bAO13eIdgjuPSdhBC3jDIVKJs1a0ZSUhIBAQE0a9YMjUZT4gzbGo0Gq9Va4UEKIcTNIN+azwu/vECuJZe2wW0ZEum8WbLzjx27MGN3374yY7cQ4oYn+agQQlybjZ+s5W9LImignj6Ett2bE9vvQTCb8ezeHd+B/1dpba89tpaP9n4EwLR205x6kV4IcWMqU4EyNjaW6tWr258LIYQovzd2vmHv0jKzw0y0Gq1T4rCcPcvJ/zxZOGN3y5YETZsqM3YLIW54ko8KIcTV++fPQ2w7/hdooEaBFw9OeZz4f/8bS3IyLrVrE/zKK5WWD8YkxzBl6xQA/t343/S+rXeltCOEqNrKVKCsWbNmic+FEEKUzfoT61lxeAUAr3R4hepu1Z0Shy0vj5PPPov51CkM4eHUmDcXrYuLU2IRQojykHxUCCGuTl5mDmtXf0OBzoKP1ZV+wwdxds4ccnbuROvmRo15c9F5uFdK2wlZCYzcOBKzzUzXsK6MaD6iUtoRQlR9V3X7ztKlS2nfvj0hISGcOHECgDlz5vD1119XaHBCCHEzOJlxksm/TQbgiUZP0CG0g1PiUDYbCWPHkffXHrTe3oR9sODaB0FXCn4YC9vfr5gghRCijCQfFUKIsvn89Y9I1eXgovR0btcF/V87OPfJJwAEz5qFsXbtSmk3x5zDiA0jOJd3jnq+9Zh15yyn9SASQtz4yv2vw/vvv090dDQ9e/YkLS3NPsaPj48Pc+bMqej4hBCiSsu35jN682iyzFk0D2jOiBbOu2p85u23yfzxRzAYCJs3F2NExLUfdOtc+H0BrBsPSXuv/XhCCFEGko8KIUTZfDtnOSe050BBY+/baHC7LwkTJgLg/++heHW/p1LatSkb434dx9+pf+Nv8pcZu4UQpSp3gXLu3Ll8+OGHTJgwAZ1OZ1/eqlUr9u6VP06FEOJir+98nYPnDuJr9OW1jq9h0BqcEkfql1+S8mHhwOQhr8zArXXraz/ovlWwflLh83tmQFDjaz+mEEKUgeSjQghRur9+2s7u1KMARFj96PHv+4gf8RwqJwe3tm2pPmpUpbX97p/vsvHkRly0LszpMkdm7BZClKrcBcrY2FiaN29ebLnRaCQ7O7tCghJCiJvB9/98zxeHv0CDhpl3ziTIPcgpcWT99htJ06YDUO3ZZ/G+//5rP+iJrbD6ycLnbZ6CqGev/ZhCCFFGko8KIcSVpZ46y/otG7FqbFQ3u/PQmCEkvvQSBcePow8OJvStN9HoyzQlRbmtPbaWRfsWATCtvczYLYQom3IXKCMiIoiJiSm2/IcffqBhw4YVEZMQQlR5semxTNs2DSicrdBZ407mHznCqZGjwGrF6/7eVBteAYXEM3/D54+CtQDq94LuM0FmARfXyfHjx9FoNCXmIuLWIfmoEEJcntVq5Yv3l5ClzcfN5kKPfg+Q+8Vysn7+HxqDgRrvvoPez69S2t51epd9xu5hjYfRq3avSmlHCGeSfLRylLtA+cILL/Dss8/yxRdfoJRix44dvPLKK7z00ku88MILlRGjEEJUKXmWPEZvHk2OJYfWQa15ptkzTonDcuYMJ598CltWFq6tWhI8Ywaaay0kZp6Gzx6EvDSo0Rr6fQhaXam7iZvT4MGD6dOnj7PDqHBTp05Fo9E4PIKCHO+AVkoxdepUQkJCcHV1pXPnzuzfv99JEd96JB8VQojL+++rn5Ckz0CrNLSu1ZSAvDOceecdAAInTcS1ceUMy3Mi44R9xu67wu9iePPhldKOEBeTfPTmyUfLXaB84oknmDJlCi+++CI5OTkMGDCABQsW8M477/DII49URoxCCFGlzNoxiyOpR/A3+TP7ztnotZXTfeZKbLm5nHzmWcwJCbjUrEmNuXPRurhc20Hzs2B5f0iLA7/a8OgKcJHBzsXNqVGjRiQmJtofl45r+Nprr/HWW28xb948du7cSVBQEHfffTeZmZlOivjWUpH56C+//ELv3r0JCQlBo9GwZs0ah/WDBw8u9gdC27ZtHbbJz89nxIgRVKtWDXd3d+6//37i4+Ov9W0KIUS5bVn+I4cKCv/9qaMJokP3ZiSMHgM2G94PPYhv//6V0m5qXirP/PwM6fnpNK7WmJl3zpQZu4W4RrdaPnpV/2IMGzaMEydOkJycTFJSEidPnmTo0KEVHZsQQlQ53xz7hlVHVqFBw+yOs6nuVv26x6CsVhJeHEve3r3ofHwIW/gBel/fazuo1QL/HQKJMeDmD4/9F9yrVUi8ojilFOZ863V/KKUq9H1s3ryZO+64A6PRSHBwMOPGjcNisdjXr1u3jg4dOuDj44O/vz+9evXi2LFjDsfYsWMHzZs3x2Qy0apVK3bv3l2hMV6OXq8nKCjI/qhe/cL/y0op5syZw4QJE+jXrx+RkZF8+umn5OTksHz58usSn6i4fDQ7O5umTZsyb968y25z7733OvyB8P333zusHzVqFKtXr2bFihVs2bKFrKwsevXqZZ9dXAghrofjMUf49fBOlAZCCjzpN+b/iB85CmtaGqbISIImTaqUdvOt+YzcOJK4zDhC3EN4t+u7uOpdK6Utcf04Kx+t6JxU8tGqo9y39XTt2pVVq1bh4+NDtWoX/jjNyMigT58+bNiwoUIDFEKIquJY2jFmbJ8BwNPNnqZNcJvrHoNSitOvziZz/frCMYbem4dLzZrXelD4fgwc+RH0Jnj0C/C/rWICFiWyFNhYOHLzdW/3P+90wmCsmC77p06domfPngwePJglS5Zw6NAhhg0bhslkYurUqUBhYSg6OprGjRuTnZ3N5MmT6du3LzExMWi1WrKzs+nVqxddu3Zl2bJlxMbGMnLkyFLbfuqpp1i2bNkVtzlw4ADh4eGXXX/kyBFCQkIwGo20adOGmTNnUrt2baBwgpakpCTuuece+/ZGo5FOnTqxdetWnnzyyTJ8QuJaVGQ+2qNHD3r06HHFbYxGY7FuVUXS09NZtGgRS5cu5a677gJg2bJlhIWF8fPPP9O9e/cyxyKEEFcrJy2Lr1euIl9nwdtq4v5/DyBlxgzy9u1D5+NDjXfmoDUaK7xdpRSTfpvE7uTdeBo8mX/XfKq5ykXsm4Gz8lGouJxU8tGqpdwFyk2bNlFQUFBseV5eHr/++muFBCWEEFVNjjmH6E3R5FpyiQqO4j+N/+OUOM59spjUpUsBCH51Fm4tW177QTfPhl2fABp48CMIa33txxQ3vfnz5xMWFsa8efPQaDTUr1+fhIQExo4dy+TJk9FqtTz44IMO+yxatIiAgAAOHDhAZGQkn332GVarlY8//hg3NzcaNWpEfHw8Tz/99BXbnj59OmPGjLniNiEhIZdd16ZNG5YsWULdunU5ffo0M2bMoF27duzfvx9/f3+SkpIACAwMdNgvMDCQEydOXLFdUTGudz66adMmAgIC8PHxoVOnTrzyyisEBAQAsGvXLsxms8MfCCEhIURGRrJ169bLFijz8/PJz8+3v87IyKjwuIUQtwabzcbyNz8iVZeLi9LTpV0XXH79idNr1oBOR+jbb2EIDa2Utt+LeY8fYn9Ar9HzVpe3uM1HLmKLG4fko1VLmQuUe/bssT8/cOCA/cOAwlnC1q1bR2gl/aMnhBA3MqUUU7ZO4Z/0fwhwDWDWnbPQOWHimPTvviP5tdcACHjhBbzvu+/aD/rHJ7BpVuHznq9Dg97XfkxRKr2Llv+808kp7VaUgwcPEhUV5TAxU/v27cnKyiI+Pp7w8HCOHTvGpEmT2L59O2fPnsVmswEQFxdHZGQkBw8epGnTpri5XRjrNCoqqtS2AwIC7MWjq3Hx3XSNGzcmKiqK2267jU8//ZTo6Gj7uksnnVJKXftEVOKKnJGP9ujRg3/961/UrFmT2NhYJk2aRNeuXdm1axdGo5GkpCRcXFzwvWQojcDAQIf4LjVr1iymTZtWobEKIW5Nq17/lHhdGhoFzfzrUsfbStzowpwwcOyLuJfhd+fVWHN0DR/s+QCAyVGTaRvctpQ9RFXirHy0qO2KIPlo1VLmAmWzZs3sA4N37dq12HpXV1fmzp1bocEJIURVsOzgMtYdX4deo+fNzm/i7+p/3WPI/n0HiePGA+A7cCB+Q5649oMe/Ba+O//Lr+MLcMewaz+mKBONRlNhXa2dpaTkqGg8oaLlvXv3JiwsjA8//JCQkBBsNhuRkZH2O+OudvyhiuhSczF3d3caN27MkSNHAOxdfZOSkggODrZvl5ycXOwqtqhYzshHH374YfvzyMhIWrVqRc2aNfnuu+/o16/fZfcr7Q+E8ePHO/yBkZGRQVhYWMUELYS4ZWz5/Ef255wADdxuC+CuPm2J/Vf/wklx+vbFd+DASmn398Tfmba18CLLsMbD6Funb6W0I5xH8lHJR6+3MpelY2NjOXbsGEopduzYQWxsrP1x6tQpMjIyGDJkSLkaL23WxLJMmS6zJgohnGnX6V289cdbAIxpPYZmAc2uewx5h/8mfvhwlNmM5z33EDhu7LVfNTuxDVYOBWWDFo9DlwkVE6y4ZTRs2JCtW7c6JHVbt27F09OT0NBQUlJSOHjwIBMnTqRbt240aNCA1NTUYsf466+/yM3NtS/bvn17qW1Pnz6dmJiYKz6u1KXmUvn5+Rw8eNCe/EVERBAUFMT69evt2xQUFLB582batWtX5uOK8quMfLS8goODqVmzpsMfCAUFBcW+v6X9gWA0GvHy8nJ4CCFEeRzf/Te/HiqcFCe4wJN+ox4l/tlnsaWnY2rahKCpUyrlTqp/0v7h+U3PY1EWetTqwfDmwyu8DSEqguSjVUuZ76CseX6ShaLbXStC0ayJTzzxRLF+/3BhyvTFixdTt25dZsyYwd13383hw4fx9PQECmdNXLt2LStWrMDf35/Ro0fTq1cvdu3ahU5Xtav9Qogb25mcM4zZPAaLstAzoicD6g+47jGYk5I4+eST2DIzcW3ZkpDXX0Nzrf/2nT4Anz8Mljyo2wPuexuqaDcBUfnS09OJiYlxWObn58czzzzDnDlzGDFiBMOHD+fw4cNMmTKF6OhotFotvr6++Pv7s3DhQoKDg4mLi2PcuHEOxxkwYAATJkxg6NChTJw4kePHj/PGG2+UGtO1dqkZM2YMvXv3Jjw8nOTkZGbMmEFGRgaDBg0CCq+4jxo1ipkzZ1KnTh3q1KnDzJkzcXNzY8CA6//vwK2kMvLR8kpJSeHkyZP2PxBatmyJwWBg/fr19O/fH4DExET27dvHa+eH3RBCiIqWk5bJmlWryddZ8LG60nfYAFKmTib/yFH01atT4925lTIpTkpuCs/87xkyCzJpVr0ZL3d4Ga2m4oaIEeJqSD56k+Sj6iocPXpUDR8+XHXr1k3dddddasSIEero0aNXcyg7QK1evdr+2mazqaCgIPXqq6/al+Xl5Slvb2+1YMECpZRSaWlpymAwqBUrVti3OXXqlNJqtWrdunVlbjs9PV0BKj09/ZregxDi1lFgLVCPf/+4ilwcqfqs6aOyC7KvewyWjAx1rPf96kC9+upoj57Kkpp67QdNO6nUG/WVmuKl1Id3KZV//d/XrSY3N1cdOHBA5ebmOjuUchs0aJACij0GDRqklFJq06ZNqnXr1srFxUUFBQWpsWPHKrPZbN9//fr1qkGDBspoNKomTZqoTZs2FcsHtm3bppo2bapcXFxUs2bN1MqVKxWgdu/eXWnv6+GHH1bBwcHKYDCokJAQ1a9fP7V//36HbWw2m5oyZYoKCgpSRqNRdezYUe3du7fSYipype/LrZbPVFQ+mpmZqXbv3q12796tAPXWW2+p3bt3qxMnTqjMzEw1evRotXXrVhUb+//s3Xd4VFW3wOHfzKRXSEIaJNRQQwm9SZEiICqoFxVFRFQQ6b036VKlN6kiqAiKhd5Eem+hh4QkhJAQ0tvM7PvHYD6xAEkmlfU+T577nTMze68jucnKOmfvFaT27dunGjRooIoXL67i4uIyxujZs6cqUaKE2r17tzp9+rR68cUXVfXq1ZVer3/mOJ63fz8hRNYZDAa1fPQcNW7cODVl7Ofq7G/HVeT8BepyhYoq0L+qSjp7NkfmTUpPUp1/7qz8V/urtpvbqgfJD3JkHpH7JB+VfDQzciof1SiVuQX1O3bs4NVXX6VGjRo0atQIpRSHDx/m3LlzbNu2jVatWmWpUKrRaNiyZQsdOnQA4NatW5QtW5bTp08TEBCQ8b7XXnuNIkWKsGbNGvbu3UuLFi148ODBYxuTV69enQ4dOvznxuP/1jXRx8eH2NhYWV4jhHgm049PZ33gehwsHdjYfiMlnUrm6vzGtDTufPwJSceOoSvmRqlvNmJVIpuNIZIewFdtIOoquFWAD7eDnYt5Ahb/KSUlhaCgIEqXLo2NjU1ehyPyuSd9v8TFxeHs7Pxc5DPmzEf3799P8+bN/3G+a9euLF68mA4dOnDmzBkePnyIl5cXzZs35/PPP39sv8iUlBSGDBnChg0bSE5OpkWLFhmdQ5/V8/TvJ4TInu+nfcXFlBA0SkNd18o0qlSEsD59AfCaPJkib/z3/rhZpTfqGbB/APvv7MfJyomv231NKedSZp9H5A3JR0Vm5FQ++sxLvP80fPhwBgwYwLRp0/5xftiwYVkuUP7ds7RMl66JQoi88FvQb6wPNG14PLnx5FwvTiqjkbsjRpJ07BhaOzt8ly3LfnEyLQm+edtUnHT0hi4/SHFSCJFvmTMfbdas2RM3wN+xY8dTx7CxsWH+/PnSMFIIkeMObdjBpeSQR01xitH8percfucdAIq+3yVHipNKKaYcm8L+O/ux1lmzoMUCKU4KIcwu05tFBAYG0r1793+c//DDD7l8+bJZgvqrrLRMf9p7RowYQWxsbMbXnTt3zBKrEKLwuxFzg3GHxwHwUdWPeNH3n11kc1rkrFnE/fILWFhQ/MsvsalUKXsDGvTw/Ydw5xjYOJuKk84lzBOsEELkgNzOR4UQIj8IOnOV36+amuJ4pzny+mdvEvrZZ6ikJOzq18dj6NAcmXf5heV8d+07NGiY/sJ0AtwDnv4hIYTIpEwXKIsVK/aPzUcBzp49m60NQP/ury3T/+qvHRGla6IQIjclpCUwYP8AkvXJ1PeqT+8aud+xMHr1ah6s/AoAr88/x6Fxo+wNqBT83B+u/QY6a3hnI7hns+AphBA5LLfyUSGEyC/iH8Ty4w9bSdWYmuJ06P42kSOGkR4aimWJEhSfMxuNRaYXSD7V1htbmX/G9HT48LrDaVGyhdnnEEIIyMIS748//phPPvmEW7du0bBhQzQaDYcOHWL69OkMGjTIbIH9tWX6n3tQ/tkyffr06YB0TRRC5B6lFKP/GM3tuNt42nsyvcl0dNpsdsvOpNgffyRymunnX7EBAyjSsUP2B909Ds6sA40W3vwKSjbM/phCCJHDcisfFUKI/MBgMLBhzkoe6pKxVhY0b/wixg2rSDpyFI2dHSUWLsTib9uemcMfYX8w4bBpa7QP/T+kc6UC2hlYCFEgZLpAOWbMGBwdHZk1axYjRowAwNvbm/Hjx9O3b99MjZWQkMCNGzcyjoOCgjh79iwuLi74+vo+tWW6s7Mz3bt3Z9CgQbi6uuLi4sLgwYOpWrUqLVu2zOylCSHEf1p+YTl7QvZgqbVkdtPZuNjk7v6M8fv3Ez5yFAAuXd/H9ZOPsz/o77Phj3mm//3Kl1CpffbHFEKIXGDOfFQIIfK7jZOXc1cXh1ZpqOXpj0/UNe59/TUA3tOnYVOhvNnnvBR9iQH7B6BXel4u8zL9avYz+xxCCPFXmSpQ6vV6vv76a9555x0GDBhAfHw8AI6Ojlma/OTJk491TRw4cCBg6pq4evVqhg4dSnJyMr169SImJoZ69eqxc+fOx+abM2cOFhYWdOrUKaNr4urVq9HpcvfJJiFE4XXgzgEWnFkAwKh6o6harGquzp90+gxh/QeAwYDTq6/gPmzYU/fifaqTX8GeR83CWk+Cml2yH6gQQuQCc+ejQgiRn/228FuuG03bnlXQetOwajHu9BgNQLGBA3EyU5PavwqND+Wz3Z+RrE+mnlc9Pm/4OVpNpneHE0KITNGoJ7Ut/Bd2dnYEBgZSsmTudq3NSdlpgy6EKNxuxd7i3V/eJSE9gbcqvMXo+qNzdf6Ua9cIfq8Lxrg47Ju8gM/ChWgsLbM36MXN8H13QMELg6DFWLPEKrImJSWFoKAgSpcujY2NTV6HI/K5J32/PE/5jOSjQojnwclth/jt5F4MGiO+aUV5p1s7Qt7pjDEhAefXXsNr2tTs37T+m5iUGN7/7X1ux92mQtEKrG6zGgcrB7POIfIfyUdFZuRUPprp2yD16tXjzJkzmf2YEEIUOPFp8fTb24+E9ARqutdkWJ1huTp/elgYdz76GGNcHLY1alBi7tzsFyev74YfPgEU1O4OL44xS6xCCJGbJB8VQhR2dy7dYs+JAxg0RtzT7Xmjx/8R1uszjAkJ2NaqhefnE81enEzWJ9Nnbx9ux93Gy96LRS0XSXFSCJFrMr0HZa9evRg0aBChoaHUqlULe3v7x16vVq2a2YITQoi8YlRGRvw+gttxt/Gw82B2s9lY6rJZHMwE/YMHhHT/CH1kJNZ+5fBZshitnV32Bg05CpveA6Me/N+EdjPBzImtEELkBslHhRCFWeLDeH7Y+D3JunScDDa07/Q6MaOGkn7nDpYlSlBiwXy0VlZmnVNv1DPs4DDO3T+Ho5Uji1suxt3O3axzCCHEk2S6QPnWW28BPLYBuUajQSmFRqPBYDCYLzohhMgjC88u5EDoAax11sx7cR6utq65NrchIZE7n/Qg7fZtLLy98FmxAl2RItkbNOICfN0J9Mng1xo6LgGt7CUkCp7bt29TunRpzpw5Q40aNfI6HJFHJB8VQhRWBoOBr2etIEaXhJWyoEndJlh8s4LYk6fQOjjgs2Sx2Tt2K6WYeGQi++7sw0prxfwX51O2SFmzziFEYSL5aM7I9F+nQUFB//i6detWxv8VQoiCblfwLpadXwbAuAbjqOJaJdfmNqalEdqnNykXL6IrWhTfFSux9PDI3qDRN2Hd65AaC74N4f/WQC4+DSoKrw8++IAOHTrkdRhmd/DgQV555RW8vb3RaDRs3br1H+9RSjF+/Hi8vb2xtbWlWbNmXLp06bH3pKam0qdPH9zc3LC3t+fVV18lNDQ0l66icJN8VAhRWH07dSXhulg0SkNAsYqUirhE7NatoNVSfM4crMuVM/ucc07PYcuNLWg1WmY0mUEtj1pmn0OInCL5aOHJRzNVoIyPj+fatWtcunQJe3t7SpYs+Y8vIYQoyK7HXGfUoVEAdKnchVfKvpJrcyuDgfChw0g6chStnR0+y5ZhXaZ09gaNDYO1HSAxEjyrQueNYJXNpeJCFHKJiYlUr16dBQsW/Od7ZsyYwezZs1mwYAEnTpzA09OTVq1aZXSUBujfvz9btmxh48aNHDp0iISEBNq3by9P92WT5KNCiMJqx5LNXNWHA1Be40njikW4P3sOAB4jR+LwQmOzz7n64mpWXVwFmG7MtyjZwuxzCCEy73nMR5+5QHn+/HkqVqxImzZtaN++PeXKlWP37t05GZsQQuSq2NRY+u7tS7I+mXpe9RhYa2Cuza2U4u64ccRv347G0pISCxdgW9U/e4Mm3Id1HSE2BFzLwXtbwMbZPAGLHKWUIj0lJde/lFJmvY4DBw5Qt25drK2t8fLyYvjw4ej1+ozXt2/fTuPGjSlSpAiurq60b9+emzdvPjbG8ePHCQgIwMbGhtq1a+dKY5S2bdsyadIkXn/99X99XSnF3LlzGTVqFK+//jr+/v6sWbOGpKQkNmzYAEBsbCwrV65k1qxZtGzZkoCAANavX8+FCxckf8oGyUeFEIXV2e1HOHHX9OSTT1oRXnuzEWFDhoJSFO3cGZf33jX7nFuub2HWqVkADKg1gNf9/v33nng+5VU+au6cVPLRgpOPPvMelMOHD8fX15fvvvsOGxsbJkyYQO/evbly5UpOxieEELlCb9Qz9OBQQhNCKe5QnJlNZmKhzfQ2vVmilCJy2jRiv98MWi3eM2di36BB9gZNegDrOkDUVXAqAV22gEMxs8Qrcp4+NZUvu76Z6/P2XfM9ljY2ZhkrLCyMdu3a8cEHH7B27VquXLnCxx9/jI2NDePHjwdMd4YHDhxI1apVSUxMZOzYsXTs2JGzZ8+i1WpJTEykffv2vPjii6xfv56goCD69ev31Ll79uzJ+vXrn/iey5cv4+vrm6VrCwoKIiIigtatW2ecs7a2pmnTphw+fJgePXpw6tQp0tPTH3uPt7c3/v7+HD58mJdeeilLcz/vJB8VQhRGdy7eYufhfei1RtzS7Xnjg9cI+7gbKjkZ+0aN8Bg5wuxz7g3Zy/gj4wH4oMoHfOj/odnnEAVbXuWjYL6cVPLRgpWPPvNf3ydPnuTXX3+ldu3aAHz11Ve4u7uTkJCAg4NDjgUohBC54YsTX3A4/DC2FrbMaz6PIjZFcm3uqPkLeLBmLQBekyfj9FLrp3ziKVJiYf3rcO8iOHhA15+gSNZ+8QmRVYsWLcLHx4cFCxag0WioWLEi4eHhDBs2jLFjx6LVannjjTce+8zKlStxd3fn8uXL+Pv78/XXX2MwGPjqq6+ws7OjSpUqhIaG8umnnz5x7okTJzJ48OAnvsfb2zvL1xYREQGAx9/2h/Xw8CA4ODjjPVZWVhT9WyMDDw+PjM+LzJN8VAhR2MRHxbB503ck6dJwNFjz8qsv82D4IPT37mFVpgzF58xGY2Hem+YnIk4w5MAQjMrIa2Vfy9VVQ0LkJslHC1Y++sw/6aKioh6r7Lq6umJnZ8f9+/clIRRCFGjfXv2WDVdMj8FPbTyVCi4Vcm3u6JVfEbVoEQAeo0dTpGOH7A2Ylmjq1h1+Buxc4f2fwFW6MBY0FtbW9F3zfZ7May6BgYE0aNAAjUaTca5Ro0YkJCQQGhqKr68vN2/eZMyYMRw9epSoqCiMRiMAISEh+Pv7ExgYSPXq1bGz+9++qQ2e4elid3d33N3dzXYt/+Wv1wZkdJB+kmd5j/hvko8KIQqT9LR0vp67iocWyVgrC5rVaYLuq3kkXg5E5+KCz9Il6JyczDpnYHQgfff2Jc2YRjOfZoxvOF5+L4l/lVf56J9zm4Pko/8uv+ajz1yg1Gg0xMfHY/PoMds/Lyg+Pp64uLiM9zmZ+QeoEELkpGN3jzHl2BQA+gb0zdWNwWM2fUvkF18AUGzAgOzvLZSeDN+8DXeOmvaa7LIF3CuaIVKR2zQajdmWWueVf0t8/txP6M/zr7zyCj4+Pixfvhxvb2+MRiP+/v6kpaU99v7MyuklNZ6enoDprrSXl1fG+cjIyIy72J6enqSlpRETE/PYXevIyEgaNmyYpXmF5KNCiMJlw+SlRFjEoVMaahf3x/vULh4eOIjGxgafJYux8vEx63zBccH03N2ThPQEannU4osmX+Talkai4JF8VPLR3PbMP42UUpQvX/4f5wICAjL+t0ajyZedgIQQ4t8ExwUzcP9ADMrAy2Ve5qOqH+Xa3LHbfibi0b4nrh9/jFuPT7I3oD4VNnWBoINg5QDv/QBe1bMfqBBZVLlyZTZv3vxYYnj48GEcHR0pXrw40dHRBAYGsnTpUl544QUADh069I8x1q1bR3JyMra2tgAcPXr0qXPn9JKa0qVL4+npya5duzLyoLS0NA4cOMD06dMBqFWrFpaWluzatYtOnToBcPfuXS5evMiMGTOyPPfzTvJRIURhsXn6KoI0UQBUsfalpjaKyE2bQKOh+KyZ2FarZtb5IpMi6bGrBw9SHlDRpSLzX5yPjUXBLj4J8TSSjxasfPSZC5T79u3LyTiEECJXxaXF0XtPb+LS4qjmVo0JDSfk2mPu8Xv2ED58eEZXxmIDB2RvQIMevv8QbuwCC1vo/C2UqG2eYIV4itjYWM6ePfvYORcXF3r16sXcuXPp06cPvXv35urVq4wbN46BAwei1WopWrQorq6uLFu2DC8vL0JCQhg+fPhj43Tu3JlRo0bRvXt3Ro8eze3bt5k5c+ZTY8rukpqEhARu3LiRcRwUFMTZs2dxcXHB19cXjUZD//79mTJlCn5+fvj5+TFlyhTs7Ozo3LkzAM7OznTv3p1Bgwbh6uqKi4sLgwcPpmrVqrRs2TLLsT3vJB8VQhQGe1b+yMWkYNBAWb0bLep4ED5oEAAeI0bg2MK8K3piU2PpsasHYQlh+Dr6srjlYhytHM06hxB5SfLRQpKPKqFiY2MVoGJjY/M6FCFELkg3pKtPdn6i/Ff7q5bftVT3k+7n2twJf/yhAv2rqssVKqqwocOU0WDI3oAGvVLfd1dqnJNSE4spdWOveQIVuSY5OVldvnxZJScn53Uomda1a1cF/OOra9euSiml9u/fr+rUqaOsrKyUp6enGjZsmEpPT8/4/K5du1SlSpWUtbW1qlatmtq/f78C1JYtWzLec+TIEVW9enVlZWWlatSooTZv3qwAdebMmRy7rn379j3xupRSymg0qnHjxilPT09lbW2tmjRpoi5cuPDYOMnJyap3797KxcVF2draqvbt26uQkJBsxfak7xfJZwo2+fcT4vlw+pc/1OdjJ6px48apZSNnqYdHjmTkhhFTpph9vvjUePX2treV/2p/1XxTc3Un7o7Z5xAFn+Sjko9mRk7loxqlsrigvhCJi4vD2dmZ2NhY2bNIiOfA1GNT2XBlA7YWtqxtu5aKLrmzT2PS6TOEdO+OSk7GsVWr7HdlNBphWx84sx60FvDW11ChjfkCFrkiJSWFoKAgSpcunbGvnhD/5UnfL5LPFGzy7ydE4Rdy4QbffLeJZG06bnp73urYhpheH2OIjcWxVUuKz52LRqcz23zJ+mR67urJ6cjTFLEuwqqXVlGuaDmzjS8KD8lHRWbkVD6qNWeQQgiR3/29Y3duFSeTL17iTo8eqORk7Bs3xnvWzOwVJ5WC34aaipMaLbyxUoqTQgghhBD5VNz9GH74djPJ2nQcDda83O4lYocPwhAbi031anjPmGHW4mSaIY0B+wZwOvI0DpYOLG21VIqTQoh8TQqUQojnRl517E4JDCSke3eM8fHY1q5FiflforWyyvqAfxYnTywHNNBhCVTpYK5whRBCCCGEGaWnpPH1vK94qEvGWlnQvE4TmDeZ9Dt3sPTxwWfRIrSPmm+Yg96oZ+jBofwR/ge2FrYsarmIyq6VzTa+EELkBClQCiGeC7ce3mLA/gG53rE75eo1Qrp9iDE2FtsaNfBZsjR7CahSsH04HF8GaODV+VD9LbPFK4QQQgghzMdoNLJ2ymLuWcSjU1rqFq9Gsd++JuXceXTOzvgsXYqFq6v55lNGxvwxhj0he7DUWjKv+TwC3APMNr4QQuSULBcob9y4wY4dO0hOTgZAtrIUQuRX0cnR9NrTi/i0eGoUq5FrHbtTb9wgpFs3DA8fYlO1Kj7Ll6FzsM/6gErB9hFwbInp+NX5ULOLeYIVQogCSPJRIUR+t3HSMu5oY9AoqGpfhsp3TpCwbx8aa2tKLFqIdZnSZptLKcWko5P4+dbPWGgsmN1sNg28G5htfCGEyEmZLlBGR0fTsmVLypcvT7t27bh79y4AH330EYMGDTJ7gEIIkR0p+hT67u1LWEIYPo4+fPnil1jrrHN83tRbQQR/0A3DgwfYVK6M74rl6Bwdsz6gUrBjJBxbbDqW4qQQ4jkm+agQoiDYOnMd14wRAJTXeNHIJoaH330PWi3FZ8/CrlYts82llGLWyVl8d+07NGiY8sIUmvk0M9v4QgiR0zJdoBwwYAAWFhaEhIRgZ2eXcf6tt95i+/btZg1OCCGyw6iMjDw0kvNR53GycmJRi0UUtSma4/OmBQcT8sEHGKKisK5YEZ+VK9A5O2d9QKVgxyg4ush0/Mo8qPm+eYIVQogCSPJRIUR+t2flj5yLvwVAqXQXWpd3ImqRKZfzHDcOxxbm3Qt9ybklrLm8BoAJDSfQtnRbs44vhBA5LdMtZHfu3MmOHTsoUaLEY+f9/PwIDg42W2BCCJFdc07NYVfwroz9d0o5l8rxOdNCQwn+oBv6yEis/crh+9VKLIpmoyiqFOwcDUcXmo7bz4VaH5gjVCGEKLAkHxVC5GcnfjzIkZBzKI3CO82Jlxv6ETnE9HS3W+/eFH2rk1nnW3NpDYvOmYqfw+sOp6NfR7OOL4QQuSHTT1AmJiY+dqf6T1FRUVhbm3fZpF6vZ/To0ZQuXRpbW1vKlCnDxIkTMRqNGe9RSjF+/Hi8vb2xtbWlWbNmXLp0yaxxCCEKnm+vfsvqS6sBmNhoIrU9a+f4nOnh4YS83xX93btYlSmD76pVWLi4ZH1ApWDXGDiywHTcfg7U7maeYIUQogDLzXxUCCEy4/qRi+w5/Tt6jZFi6fa81rIu0SOHg1IU6dQJt896mXW+rwO/ZubJmQD0DejLu5XeNev4QgiRWzJdoGzSpAlr167NONZoNBiNRr744guaN29u1uCmT5/OkiVLWLBgAYGBgcyYMYMvvviC+fPnZ7xnxowZzJ49mwULFnDixAk8PT1p1aoV8fHxZo1FCFFw/B76O5OPTQbgsxqf0b5M+xyfMz0iguCuH5AeHo5VyZL4rl6FhZtb1gdUCnaNhcOPft69PBtqf2ieYIUowG7fvo1Go+Hs2bN5HYrIQ7mZjwohxLOKuB7Kj79tI0WTjrPBhpebv8DD4YNQaWk4tGiB59gxZm3UuPHKRqYdnwbAx1U/5uNqH5ttbCHEf5N8NGdkukD5xRdfsHTpUtq2bUtaWhpDhw7F39+fgwcPMn36dLMGd+TIEV577TVefvllSpUqxZtvvknr1q05efIkYHp6cu7cuYwaNYrXX38df39/1qxZQ1JSEhs2bDBrLEKIguHqg6sMPjAYozLyWtnX6FGtR47PmX7vHiFdPyD9zh0sfXzwXbMaS3f3rA+oFOweD4e/NB23mwl1upslViHM6YMPPqBDhw55HYbZjR8/Ho1G89iXp6fnY+95lhUcqamp9OnTBzc3N+zt7Xn11VcJDQ3NzUsptHIzHxVCiGeREB3LprUbSNCmYm+0onX9RqR/PhxjXBy2NWtSfNZMNBaZ3mHtP3137buMG/Ld/LvRJ6CP2cYWoiCRfLTw5KOZLlBWrlyZ8+fPU7duXVq1akViYiKvv/46Z86coWzZsmYNrnHjxuzZs4dr164BcO7cOQ4dOkS7du0ACAoKIiIigtatW2d8xtramqZNm3L48OH/HDc1NZW4uLjHvoQQBd+9xHv02tOLJH0S9TzrMa7BOLPepf436RERBL//PmnBwVh6e1Ny9Sos//aLI1P+7Nb9x1zTcbuZUFfuhguR26pUqcLdu3czvi5cuPDY68+ygqN///5s2bKFjRs3cujQIRISEmjfvj0GgyG3L6fQyc18VAghniY1OZl1c1YQo0vCSlnQuEJtbBZNR3/vHlblyuKzaCFaGxuzzffD9R+YeGQiAF0rd2VAzQE5nvMKIXLf85aPZrpACeDp6cmECRP4+eef+fXXX5k0aRJeXl7mjo1hw4bxzjvvULFiRSwtLQkICKB///688847AERERADg4eHx2Oc8PDwyXvs3U6dOxdnZOePLx8fH7LELIXJXfFo8vfb0IjIpkjLOZZjdfDaWOsscnTM9PJzgLu+THhyCZYkS+K5di2Xx4lkf0GiEXwb9r1v3y7MKRHFSKYXeYHz6G8Vz58CBA9StWxdra2u8vLwYPnw4er0+4/Xt27fTuHFjihQpgqurK+3bt+fmzZuPjXH8+HECAgKwsbGhdu3anDlzJldit7CwwNPTM+OrWLFiGa89ywqO2NhYVq5cyaxZs2jZsiUBAQGsX7+eCxcusHv37ly5hsIut/JRIYR4EoPewNopS7hnEY9OaanjURnPrStIu3kTCw8PfJcvR1ekiNnm23pjK+MPjwfgvUrvMaj2IClOCvEEko8WnHw00wXKVatW8d133/3j/HfffceaNWvMEtSfNm3axPr169mwYQOnT59mzZo1zJw58x/z/P0HslLqiT+kR4wYQWxsbMbXnTt3zBq3ECJ3pRnS6L+vP9diruFq48rCFgtxsnLK2TlDw0zFyUfLukuuXYNViewUJw2wrQ+cXAlo4NUFUOcjs8WbU1L1BgZ+e45RWy6ilMrrcAoNpRTGNEOuf5nz3zAsLIx27dpRp04dzp07x+LFi1m5ciWTJk3KeE9iYiIDBw7kxIkT7NmzB61WS8eOHTOa4SUmJtK+fXsqVKjAqVOnGD9+PIMHD37q3D179sTBweGJXyEhIU8c4/r163h7e1O6dGnefvttbt26lfHas6zgOHXqFOnp6Y+9x9vbG39//yeu8hDPJjfzUSGE+C9Go5H1ny8mTBeLRmmo7liG8se3kXLhAroiRfD9aiWWZrxxsu3mNsb+MRaF4p2K7zC0zlApToock1f5qDlzUslHC1Y+mulNMKZNm8aSJUv+cd7d3Z1PPvmErl27miUwgCFDhjB8+HDefvttAKpWrUpwcDBTp06la9euGevvIyIiHrtjHhkZ+Y+nKv/K2tpaOjwKUUgYlZFRh0ZxPOI4dhZ2LG65mBKOJXJ0zrQ7dwju2hV9+F0sS/pScs2a7C3rNuhh66dw4VvQaKHjUqjWyXwB55CohFR6rjvFyeAYdFoNXRuWorJ3zhaGnxcq3Uj42NxPGrwnNkRjpTPLWIsWLcLHx4cFCxag0WioWLEi4eHhDBs2jLFjx6LVannjjTce+8zKlStxd3fn8uXL+Pv78/XXX2MwGPjqq6+ws7OjSpUqhIaG8umnnz5x7okTJz41cfT29v7P1+rVq8fatWspX7489+7dY9KkSTRs2JBLly7h6ur6xBUcwcHBgCk3sbKyomjRov94z5NWeYhnk5v5qBBC/JdNk1cQpIkCoLJlCQJuHyXh6FG0dnb4LF+OtRm3nPjl1i+M/mM0CsVbFd5iRN0RUpz8u8RosHfN6ygKjbzKR8F8OankowUrH810gTI4OJjSpUv/43zJkiWfWv3NrKSkJLTaxx/y1Ol0GZXs0qVL4+npya5duwgICAAgLS2NAwcOyAbpQjwnZp6cyfbb27HQWDCn+RwquVbK0fnSgoMJ7voB+ogIrEqVwnfNGiw9stEQx5AOm7vD5R9BawFvrIAqHc0XcA4JvBvHR2tOEvYwGUcbCxa/W0uKk+IxgYGBNGjQ4LE/nho1akRCQgKhoaH4+vpy8+ZNxowZw9GjR4mKisr4/R4SEoK/vz+BgYFUr14dOzu7jDEaNGjw1Lnd3d1xz0ajqrZt22b876pVq9KgQQPKli3LmjVrGDhwYMZrmV3B8azvEU+Xm/moEEL8mx9mrOGqIRwAP6MHDRNuErd7DxorK0osWoRtVX+zzbU9aDsjD43EqIy8Wf5NRtYbKb9L/spohN9nwuEF8NFuKFY+ryMS+YTko/8uv+ajmS5Quru7c/78eUqVKvXY+XPnzuHqat67Fa+88gqTJ0/G19eXKlWqcObMGWbPns2HH34ImP4h+vfvz5QpU/Dz88PPz48pU6ZgZ2dH586dzRqLECL/WXNpDesurwPg88af09C7YY7OlxoUREjXD9BHRmJVtiy+q77KXrdufSp89wFc/RW0ltBpDVR82Wzx5pQdlyIYsOksSWkGSrnasaJrHcq5O+R1WIWKxlKL98Sc/X7+r3nN5d8Snz+X6/x5/pVXXsHHx4fly5fj7e2N0WjE39+ftLS0x96fWT179mT9+vVPfM/ly5fx9fV9pvHs7e2pWrUq169fB3imFRyenp6kpaURExPz2F3ryMhIGjbM/X/bwiY381EhhPi73xZ+y4XEINBAqXQXXrSL4eG3W0Cno/ic2djXr2e2uXbc3sHw34djVEZe93udMfXHoNWY7/d1gZcaD1t6wpWfTceBP0Gxpy+/FU+XV/non3Obg+SjBSsfzXSB8u2336Zv3744OjrSpEkTwLTpaL9+/TKWYpvL/PnzGTNmDL169SIyMhJvb2969OjB2LFjM94zdOhQkpOT6dWrFzExMdSrV4+dO3fi6Oho1liEEPnLr7d+ZebJmQAMrDWQ9mXa5+h8qbduEdy1K4b7UVj7lcN31Sos3NyyPmB6Mmx6D27sBp01vP01+LUyX8A5QCnFov03+WLHVQAalXNlYeeaFLGzyuPICh+NRmO2pdZ5pXLlymzevPmxxPDw4cM4OjpSvHhxoqOjCQwMZOnSpbzwwgsAHDp06B9jrFu3juTkZGxtbQE4evToU+fO7pKav0tNTSUwMDAjzmdZwVGrVi0sLS3ZtWsXnTqZtmy4e/cuFy9eZMaMGc88t/h3uZmPCiHEX+1f8wsnIgNRGiiR5kxrTy0PF5lumHtNnoRjixZmm2v77e0MPzgcgzLwWtnXGNdgnBQn/yrqBmzsDFFXQWdlajBZ8/28jqrQkHz0f2NIPppLVCalpqaqTp06KY1GoywtLZWlpaXS6XSqW7duKjU1NbPD5QuxsbEKULGxsXkdihDiGRwJP6JqrK2h/Ff7q2nHpimj0Zij86Vcv66uNmqsLleoqG6+8qpKj47O3oCpCUqtbq/UOCelPvdQ6uY+s8SZk5LT9KrvN6dVyWE/q5LDflZjtl5QaXpDXodVKCQnJ6vLly+r5OTkvA4l07p27aqaNWumzpw589hXcHCwCg0NVXZ2duqzzz5TgYGBauvWrcrNzU2NGzdOKaWUwWBQrq6u6r333lPXr19Xe/bsUXXq1FGA2rJli1JKqfj4eOXm5qbeeecddenSJfXLL7+ocuXKKUCdOXMmx65r0KBBav/+/erWrVvq6NGjqn379srR0VHdvn074z3Tpk1Tzs7O6ocfflAXLlxQ77zzjvLy8lJxcXEZ7+nZs6cqUaKE2r17tzp9+rR68cUXVfXq1ZVer89ybE/6fnme8hnJR4UQeeH4lgPq87ET1bhx49TikTNV6Io16nKFiupyhYoqes1as8617eY2VW1NNeW/2l+N/H2k0huy/rujULq6XakpPqZ8emYFpe6cyOuICjTJRyUfzYycykcz/QSllZUVmzZt4vPPP+fcuXPY2tpStWpVSpYsac66qRBC/KsrD67Qf19/9EY9L5V6iSF1huTo/hkpV64Q8mF3DA8eYF2xIr6rvsLib5sMZ27AWNjwFoQcASsH6PwtlGpkvoBzQGRcCh+vO8W5Ow/RaTWMf7UKXerLz3xhsn///oy7tn/q2rUrq1ev5tdff2XIkCFUr14dFxcXunfvzujRowHQarVs3LiRvn374u/vT4UKFfjyyy9p1qxZxjgODg5s27aNnj17EhAQQOXKlZk+ffo/NjM3t9DQUN555x2ioqIoVqwY9evX5+jRo4/lOs+ygmPOnDlYWFjQqVMnkpOTadGiBatXr0anK9hPI+QHko8KIXLb5X2n2H3mIHqNATe9PS9XLknc52MAcOvdG5f3u5htrq03tmZ0637d73XG1h+LTiu/O4BH+03Ogn2TAQU+9aHTWnD87ya5ovCTfLRw5KMapczUv70Ai4uLw9nZmdjYWJycpMmDEPlVWEIY7/36HlHJUdTxrMOSlkuw0uXc8uLkc+cI+fgTjHFx2FSujO9XK9EVKZL1ARPuw/rXIeI8WDvDe9+DT12zxZsTLoTG8vHak0TEpeBsa8nid2vSsFw2lraLf0hJSSEoKIjSpUtjY2OT1+GIfO5J3y+SzxRs8u8nRP514+hFNv/6I8nadIoYbHmlYnnSpowBg4GiXbrgMdJ8HbW/u/YdE49MBKBT+U6Mqj9KlnX/6e/7TdbuDm2mgYVsN5Rdko+KzMipfDTTT1AaDAZWr17Nnj17iIyMzOhw9Ke9e/dmdkghhHiqqOQoeuzqQVRyFH5F/ZjbfG6OFicTjx0n9NNPMSYlYRsQgM/SJeiy8wdjbCis7QDR18HODbpsAa9qZos3J/xy/i6DvjtLSrqRcu4OrHi/NqXc7PM6LCGEkHxUCJFrgs9fZ8uv20jWpuNksKF1+QqkTRsLBgPOHTrgMWK42YqT31z5hinHpgDwbqV3GVZnWL7stJsnom+a9pu8f8W032S7mVCra15HJYQwo0wXKPv168fq1at5+eWX8ff3lx+YQogcF58Wz6e7PyU4Lhhve28Wt1iMk1XOPV2ScOAAoX37oVJTsWtQH58FC9DaZ6MwF3UD1nWA2DvgVALe3wpufuYK1+yMRsW8PdeZt8fUIa5ZhWJ8+U4ATjaWeRyZEEKYSD4qhMgN4VeD+f77zSRqU3EwWtOyXEW0X4xDpafj1K4tXpMnodGa5+nGtZfW8sXJLwDoWrkrg2oPkp9tf7q2AzZ/DKmx4OAJb60Hnzp5HZUQwswyXaDcuHEj3377Le3atcuJeIQQ4jHJ+mR67+nNlQdXcLVxZXnr5XjY59weM3HbdxA2ZAikp+PQvDnF585Ba22d9QHvnjct6068D67loMtWKOJjtnjNLT4lnUHfnmPn5XsAfNS4NCPaVUKnlQRZCJF/SD4qhMhpUcF32bR+I/G6FOyMVjQvVQmrORNQaWk4tGyB9/TpaMy0h9tXF79izqk5AHxU9SP6BvSV4iSA0QD7p8HBR92Gfeo92m/SM2/jEkLkiCw1ySlXrlxOxCKEEI9JN6Yz+MBgTkeextHSkaWtluLr5Jtj8z3cspW7o0aB0YhTu7amxNMyG08NhhyFrzuZ7vZ6VoX3toBDMfMFbGY37yfwydqT3LyfiJVOy6QO/nSqk3+LqUKI55fko0KInBQT8YCvV6wjVpeMjdGSJr6VcFjwOcaUFOxfeIHis2dnL0f8i6XnlrLg7AIAPq3+KZ9W/1SKkwCJ0bC5O9zaZzqu8xG8NFX2mxSiEMv08+iDBg1i3rx5SG8dIUROMiojow+N5mDoQWx0NixsuZAKLhVybL4HGzZwd8QIMBpxfvMNvL/4InuJ543dpj0nU2NN3QW7/pyvi5N7Au/RYcEf3LyfiIeTNZt61JfipBAi3zJnPnrw4EFeeeUVvL290Wg0bN269bHXlVKMHz8eb29vbG1tadasGZcuXXrsPampqfTp0wc3Nzfs7e159dVXCQ0NzXZsQojcl/Agjq8XrCRGl4SVsqChd0WKLpuGMSkJu3r1KDH/S7RW2S+SKaVYcGZBRnGyT0AfetXoJcVJgNCTsLSJqThpYQuvL4eXZ0lxUohCLtNPUB46dIh9+/bx22+/UaVKFSz/9gf8Dz/8YLbghBDPJ6UUU49N5degX7HQWDC72WwC3ANybL6o5cu5P2s2AEXf74LH8OHZ20/o0lbY/BEY06FcS+i0DqzszBOsmRmNivl7bzBn9zUAapcsyqL3auLuKN37hBD5lznz0cTERKpXr063bt144403/vH6jBkzmD17NqtXr6Z8+fJMmjSJVq1acfXqVRwdHQHo378/27ZtY+PGjbi6ujJo0CDat2/PqVOn0JlpCagQIuelJiSzdvYyoiwSsVQ66ruVx3P1TAxxcdjWrInPooVozdDhWCnFFye/YN3ldQAMrDWQbv7dsj1ugacUnFgB20eY8miXsvDWOvCokteRCSFyQaYLlEWKFKFjx445EYsQQgCw6NwiNl7diAYNU16YwgslXsiReZRS3J83j+glSwFw/bQnxfpmc8+f0+tgW19QRqjcwXTHN5/e7Y1PSWfgt+fY9Wi/yS71SzKmfWWsLMyz2bsQQuQUc+ajbdu2pW3btv/6mlKKuXPnMmrUKF5//XUA1qxZg4eHBxs2bKBHjx7ExsaycuVK1q1bR8uWLQFYv349Pj4+7N69m5deeulfx05NTSU1NTXjOC4uzizXI4TImvSUNFZPW0ykRQI6paV2kXKU2DQfw8OH2Pj747N0SfaaJj5iMBqYeHQiP1w33UgZXnc471Z6N9vjFnhpifDzADi/yXRcsT10WAQ2znkblxAi12S6QLlq1aqciEMIIQBYf3k9S84tAWBUvVG0Lf3vfzRmlzIYuDd5MjEbvgGg2KCBuH38cTYGVPD7LNj7uem45vvQfi5o8+eTM7LfpBCiIMutfDQoKIiIiAhat26dcc7a2pqmTZty+PBhevTowalTp0hPT3/sPd7e3vj7+3P48OH/LFBOnTqVCRMm5Pg1CCGeLj01jVWTFnLXIg6t0lDTvjRlti5FHxWFdcWK+K5Yju7RE9PZmseQzvDfh7MzeCdajZYJDSfQoVyH7F9AQRd1A77tApGXQaODluOhYR+Q5e5CPFey9JiMXq9n9+7dLF26lPj4eADCw8NJSEgwa3BCiOfLlutbmH5iOmDah+etim/lyDzGtDTCBg82FSc1GjzHjc1ecdJogN+G/q842ag/vPJlvi1O7rr8v/0mPZ1s+LZnAylOCiEKnNzIRyMiIgDw8PB47LyHh0fGaxEREVhZWVG0aNH/fM+/GTFiBLGxsRlfd+7cMVvcQohnl56axupJCwm3iEWjNFSz9qX8LyvR37uHVbmy+K5cga5IkWzPk6xPpu++vuwM3omF1oKZTWdKcRIgcBssb24qTtq7Q9efoFFfKU4K8RzK9BOUwcHBtGnThpCQEFJTU2nVqhWOjo7MmDGDlJQUlixZkhNxCiEKuV9v/cq4w+MAeL/y+3xcNRsFwycwJCQS1rcPiYePgKUlxWdMx+k/lvY9E30q/PAJXN4KaKDNVKj/qbnCNSujUfHl3uvM3X0dgDqlirLwXdlvUhQst2/fpnTp0pw5c4YaNWrkdTgij+R2Pvr3rT+UUk/dDuRp77G2tsba2tos8QkhsiY9LZ01kxYRpntUnLT0ocqONRnFyZKrV2Ph6prteRLSEvhsz2ecjjyNjc6Guc3n0qh4IzNcQQFmSIc9E+Hwl6Zj3wbw5ipw8srbuIR4BpKP5oxMP0HZr18/ateuTUxMDLa2thnnO3bsyJ49e8wanBDi+bA7eDcjD41EoehUvhODaw/OkQ6G+gcPCPngAxIPH0FjZ4fv0iXZK06mxML6N0zFSa0lvLky3xYnHyal8dHakxnFyfcblOTrj+pLcVJkywcffECHDh3yOgyze1pXZzBfZ+eYmBi6dOmCs7Mzzs7OdOnShYcPH+bg1RUOuZWPenp6AvzjScjIyMiMpyo9PT1JS0sjJibmP98jhMh/9Ol61ny+kFDdQzRKQ1XLEvjvWvt4cdLNLdvzxKTE0H1nd05HnsbB0oFlrZdJcfLhHVjV7n/FyQa9oes2KU6KLJF8tPDko5kuUB46dIjRo0djZfV404eSJUsSFhZmtsCEEM+Hg6EHGXJwCAZl4NWyrzKq/qgcKU6mhYYR3PldUi5eRFe0KCXXrMa+YcOsDxgfAatehtu/g5UjvLcZ/P/Z/TU/uBAaS/v5h9h7JRIrCy0z3qzGxNf8pRmOEP/hz67OCxYs+M/3/NnZecGCBZw4cQJPT09atWqVsdQYTJ2dt2zZwsaNGzl06BAJCQm0b98eg8GQ8Z7OnTtz9uxZtm/fzvbt2zl79ixdunTJ0esrDHIrHy1dujSenp7s2rUr41xaWhoHDhyg4aPfIbVq1cLS0vKx99y9e5eLFy9mvEcIkb/o0/Wsmfi/4qS/RXGq7lpn9uLkvcR7fLD9Ay5HX8bFxoWvXvqKAPcAM1xBAXb1N1jSGEKPg7UzdFoLL00GnWVeRyZEvvI85qOZ/uvUaDQ+diF/Cg0NxdEMGwcLIZ4fR8KPMGDfAPRGPW1KtWFiw4loNeYvmqVcu0Zw586k3b6NhbcXJb/+GtuqVbM+YNR1WNkK7l0w7ZXT7Rco09R8AZuJUooNx0J4Y/FhQmOS8XWx44dPG9Kptuw3KXLHgQMHqFu3LtbW1nh5eTF8+HD0en3G69u3b6dx48YUKVIEV1dX2rdvz82bNx8b4/jx4wQEBGBjY0Pt2rU5c+ZMjsfdtm1bJk2alNG1+e/+3tnZ39+fNWvWkJSUxIYNGwAyOjvPmjWLli1bEhAQwPr167lw4QK7d+8GIDAwkO3bt7NixQoaNGhAgwYNWL58OT///DNXr17N8essyMyZjyYkJHD27FnOnj0LmBrjnD17lpCQEDQaDf3792fKlCls2bKFixcv8sEHH2BnZ0fnzp0BcHZ2pnv37gwaNIg9e/Zw5swZ3nvvPapWrZrR1VsIkX8Y9AbWTlzIHV0MGgX+Om+q7V5v9uLknfg7dN3elVuxt/Cw82BVm1VUcq1khisooPRpsGMUfPM2pDwE75rQ4wBUfi2vIxOFnOSjBScfzXQloFWrVsydOzfjWKPRkJCQwLhx42jXrp05YxNCFGKn7p2i796+pBnTeNHnRaa8MAVdDjSVSTp9muD3uqCPjMTarxylvvkG6zKlsz5g6ElY2RoehoBLWfhoF3hVN1/AZpKcZmDQd+cYueUCaQYjLSt5sK13Y/yLO+d1aOIZKKVIS0vL9S+llNmuISwsjHbt2lGnTh3OnTvH4sWLWblyJZMmTcp4T2JiIgMHDuTEiRPs2bMHrVZLx44dMRqNGa+3b9+eChUqcOrUKcaPH8/gwYOfOnfPnj1xcHB44ldISEiWr+1pnZ2Bp3Z2Bjhy5AjOzs7Uq1cv4z3169fH2dk54z3i35kzHz158iQBAQEEBJieaho4cCABAQGMHTsWgKFDh9K/f3969epF7dq1CQsLY+fOnY8VQufMmUOHDh3o1KkTjRo1ws7Ojm3btqHT5c9maUI8rwwGA2smLCTkUXGysrY41fZ8/b/i5Jo1ZilOXn1wla6/dSUsIQxfR1/Wtl1LGecyZriCAiomGFa1gSOPngSr3ws+3AEu2cjJRY7Lq3zUnDmp5KMFKx/NdJOc2bNn8+KLL1K5cmVSUlLo3Lkz169fx83NjW+++SYnYhRCFDLn75+n1+5epBhSaFS8EV80/QJLrfmXdcTv309Y/wGolBRsa9TAZ8ni7HVhvL4Lvn0f0pNMd33f/Q7ss5/Emtut+wn0+vo0VyLi0WpgyEsV6dGkDFqtdEMsKNLT05kyZUquzzty5Mh/LJnNqkWLFuHj48OCBQvQaDRUrFiR8PBwhg0bxtixY9FqtbzxxuPbIqxcuRJ3d3cuX76Mv78/X3/9NQaDga+++go7OzuqVKlCaGgon3765L1eJ06c+NTE0dvbO8vX9qTOzsHBwRnveVpn54iICNzd3f8xvru7+xO7Pwvz5qPNmjV74h9CGo2G8ePHM378+P98j42NDfPnz2f+/PmZmlsIkXsMBgNrJywiRPcAFFTWeFNj79+Kk2ZoiHMi4gR99/YlIT0Bv6J+LGu1DDfb/Jcv5prAn+HHXqa9222c4bVFUKl9XkclnkFe5aNgvpxU8tGClY9mukBZvHhxzp49y8aNGzl16hRGo5Hu3bvz7rvvPrZJuRBC/JvA6EB67u5Jkj6Jep71mNtsLlY68xRE/urh5s3cHTsODAbsmzahxNy5aLPzM+rkKvhlECgDlGsJ/7cGrB3MF7CZ/HbhLkO+P09Cqh43B2vmvxNAg7LZT7aFyKzAwEAaNGjw2J6yjRo1IiEhgdDQUHx9fbl58yZjxozh6NGjREVFZdypDgkJwd/fn8DAQKpXr46dnV3GGA0aNHjq3O7u7v+aaJmbOTo7/9v7n2Wc553ko0KIzDDo/3xy0lScrKTxpMae9ejv3zdrcXJX8C6GHxxOmjGNmu41md9iPk5WTma4ggJInwa7xsKxxabj4rXhza+gaMm8jUs8VyQf/Xf5NR/NVIEyPT2dChUq8PPPP9OtWze6deuWU3EJIQqhazHX+GTXJ8SnxRPgHsCXL36JjYV5u0grpYiav4CoRYsAcH7tVbwmTUJjmcUnNI1G2DMB/phrOq7eGV79Mt9t5J1uMDL9tyusOBQEQN1SLizoHIC7k3TpLogsLS0ZOXJknsxrLv+W1Pz5lNqf51955RV8fHxYvnw53t7eGI1G/P39SUtLe+z9mdWzZ0/Wr1//xPdcvnwZX1/fLI3/187OXl7/6zj6X52d/3rXOjIyMqNxiqenJ/fu3fvH+Pfv35fuz08g+agQIjPS09IzunWjoJLyIGDXOvQPHmDt54fv6lVmKU5uurKJyccmo1C08G3BtBemmT3PLTBibsN33SD8tOm4QW9oMQ4szP9Qgsg5eZWP/jm3OUg+WrDy0UwVKC0tLUlNTZW7+kKITLv64Cof7fyIh6kP8Xf1Z1GLRdhZ2j39g5mg0tO5O3YcsVu2AODaswfF+vXL+s+s9BTY2hMumcaj+ShoMgTy2c/Ae3Ep9N5wmhO3YwD4pEkZhrxUAUuddOkuqDQajdmWWueVypUrs3nz5scSw8OHD+Po6Ejx4sWJjo4mMDCQpUuX8sILLwCmzsx/H2PdunUkJydnPBV39OjRp86d00tq/trZ+c99C//s7Dx9+nTg8c7OnTp1Av7X2XnGjBmA6e57bGwsx48fp27dugAcO3aM2NhY6f78BJKPCiGeVXpKGqsnLyRMF2vacxJPqu1YjSE2FpvKlfFZuQKLvy19zCylFAvPLmTp+aUA/F/5/2NUvVE5srd6gXDhe/h5AKTGgU0R6LgEKrTN66hEFkg++r8xJB/NHZle4t2nTx+mT5/OihUrsLDI9MczLSwsjGHDhvHbb7+RnJxM+fLlWblyJbVq1QJMvxAmTJjAsmXLiImJoV69eixcuJAqVarkeGxCiGfz1+JkFdcqLG29FAcr8y6PNiQkENa3H4mHD4NOh+fYsRR9q1PWB0yMho3vwJ1joLWE1xZC9bfMF7CZ7L8aycBvz/EgMQ1Hawu++L/qtPH3zOuwxHMkNjY2o/vxn1xcXOjVqxdz586lT58+9O7dm6tXrzJu3DgGDhyIVqulaNGiuLq6smzZMry8vAgJCWH48OGPjdO5c2dGjRpF9+7dGT16NLdv32bmzJlPjSm7S2oSEhK4ceNGxvGfXZ1dXFzw9fV9rLOzn58ffn5+TJky5T87O7u6uuLi4sLgwYMf6+xcqVIl2rRpw8cff8zSpaY/bD/55JOMjdjFf8vtfFQIUfCkJiezevJi7lrEoVEa/DWe+G9bjjEx0bQ3+bKl6Jyyt/xab9Qz6egkNl/fDECvGr3oWa3n83kDJTUefh0K50zdgylR17Sku4hP3sYlnguSjxaSfFRlUocOHZSjo6Py8vJSrVu3Vh07dnzsy5wePHigSpYsqT744AN17NgxFRQUpHbv3q1u3LiR8Z5p06YpR0dHtXnzZnXhwgX11ltvKS8vLxUXF/fM88TGxipAxcbGmjV+IYRSV6KvqMbfNFb+q/3V29veVrGp5v//s7SICHXz1dfU5QoVVWBATRW/f3/2Boy6odS8GkqNc1Jqqo9Stw6aJ1AzStMb1JRfLquSw35WJYf9rNrOPahu3U/I67BEFiQnJ6vLly+r5OTkvA4l07p27aqAf3x17dpVKaXU/v37VZ06dZSVlZXy9PRUw4YNU+np6Rmf37Vrl6pUqZKytrZW1apVU/v371eA2rJlS8Z7jhw5oqpXr66srKxUjRo11ObNmxWgzpw5k2PXtW/fvidel1JKGY1GNW7cOOXp6amsra1VkyZN1IULFx4bJzk5WfXu3Vu5uLgoW1tb1b59exUSEvLYe6Kjo9W7776rHB0dlaOjo3r33XdVTEzMf8b2pO+X5ymfyc18NLc8T/9+QuS0lPgktXjULDVu3Dg1Yex49cP4pSqwRoC6XKGiut3lfWVIyH7OlJyerHrv6a38V/uramuqqU1XNpkh8gIq9OT/cufxRZTaO1kpffrTPyfyDclHJR/ND/moRqnMLah/2j4/q1atysxwTzR8+HD++OMPfv/99399XSmFt7c3/fv3Z9iwYQCkpqbi4eHB9OnT6dGjxzPNExcXh7OzM7GxsThl8y6aEOJ/rjy4wkc7PyI2NZaqblVZ0mqJ2TcKT7l6jTs9eqCPiEDn5obPkiXY+mfjCergI6YnJ5NjoEhJU6fuYvnrSaY7D5Lou/EMZ0IeAtC1QUlGtKuEjeVzupSogEtJSSEoKIjSpUtjY/Oc7lUlntmTvl+ep3wmN/PR3PI8/fsJkZOSYhNZ88US7lnEo1Maqmm9qLB5ISotDfvGjSkx/8vsNU4EYlNj6bO3D2ciz2CltWJG0xm08G1hpisoQIxGODwP9k4Cox6cSsAby6GkbFNS0Eg+KjIjp/LRTK+Jyc2E76effuKll17i//7v/zhw4ADFixenV69efPzxx4DpEdeIiAhat26d8Rlra2uaNm3K4cOH/7NAmZqaSmpqasZxXFxczl6IEM+hwOhAPt71cUZxcmmrpThaOZp1jsSjRwnt3QdjQgJWZcrgs2wZViWKZ33AC9/D1k/BkAbFa8E7m8ChmPkCNoPtF+8y9PvzxKXocbKxYMab1Wjj7/X0DwohRCFSEAuQQoicl/AgjjWzl3HfIgGd0lJd64nf9wtQ6ek4tGhB8Tmz0WZzT73whHB67e7FzdibOFo5Mv/F+dTyqGWmKyhA4sJhSw8IOmg6rvwavDIPbLO3p6cQ4vmVpQ4Ker2e3bt3s3TpUuLj4wEIDw8nISHBrMHdunWLxYsX4+fnx44dO+jZsyd9+/Zl7dq1gKlbEfCPzkIeHh4Zr/2bqVOn4uzsnPHl4yP7YghhToHRgRlPTlZzq5YjxcnYn34i5ONPMCYkYFu7FqU2fJ314qRScHAmbO5uKk5WbA9df85XxcmUdANjf7xIz/WniUvRU8OnCL/0fUGKk0KI51Zu5aNCiIIhNvIBq2cv5b5FAhZKS02dJ37ffgnp6Ti1a0uJuXOyXZy8FH2Jd399l5uxN3G3dWdNmzXPZ3Hyyi+wuKGpOGlpB68ugP9bI8VJIUS2ZPoJyuDgYNq0aUNISAipqam0atUKR0dHZsyYQUpKCkuWLDFbcEajkdq1azNlyhQAAgICuHTpEosXL+b999/PeN+/tY1/0sbEI0aMYODAgRnHcXFxUqQUwkwuR1/m450fE5cWR7Vi1VjScolZi5NKKaIWLCRq4UIAHNu2wXvaNLTW1lkbMD0FfuoDF741HTfoDa0mQj7qvHjrfgK9N5zh8l3T0949mpZhcGvp0i2EeH7lZj4qhMj/7gXdZeNX64ixSMJC6ailXCm1YQ4Azh074jXpczS67OV2B+4cYMjBISTrk/Er6seiFovwtH/OGhOmJcHO0XBypenYqzq8sRLc/PI2LiFEoZDpv2779etH7dq1iYmJyWixDtCxY0f27Nlj1uC8vLyoXLnyY+cqVapESEgIAJ6epl8If39aMjIy8h9PVf6VtbU1Tk5Oj30JIbLvYtTFx4qTS1ua98lJY0oK4YMGZxQnXbp/SPFZs7JenIy/B6tfNhUnNTp4eRa8NDlfFSe3nAml/fxDXL4bh4u9Fau61WFE20pSnBRCPNdyMx8VQuRvIRdu8PVXa4jRJWGlLKijd6LUt/MBKPruu3hNnpTt4uTGKxvpu68vyfpkGng1YG2btc9fcfLuOVje/H/FyYZ9oPsuKU4KIcwm009QHjp0iD/++AOrvz0eX7JkScLCwswWGECjRo24evXqY+euXbtGyZIlAShdujSenp7s2rWLgIAAANLS0jhw4ADTp083ayxCiCc7de8Un+35jMT0RKoXq86SlktwsHIw2/j6+/e507s3KefOg4UFXuPHUeTNN7M+4N1z8M07EBcGNkWg01oo09Rs8WZXQqqecT9eYvPpUADql3Fh3tsBeDjJptVCCJGb+agQIv+6fuQiW3/bRqIuFVujJXVTrPD8yfQEtVvv3rh91uuJK+uexqiMzD45mzWX1wDwut/rjK4/GkutpVniLxAMevhjLuyfamqE4+ABHZdA2RfzOjIhRCGT6QKl0WjEYDD843xoaCiOjubdY27AgAE0bNiQKVOm0KlTJ44fP86yZctYtmwZYFra3b9/f6ZMmYKfnx9+fn5MmTIFOzs7OnfubNZYhBD/7XD4Yfrt7UeKIYU6nnWY/+J87C3tzTZ+ytWr3On5Kfq7d9E5O1P8yy+xr1c36wNe/hG29IT0JHD1g86bwLWs2eLNrtMhMfTfeJaQB0loNdC3hR99XvRDp816gi3yP6PRmNchiAJAvk9McjMfFULkTxd2HefX33eRrE3H3mhNvbh0im3/CgCPUaNw6fJetsZP0acw8tBIdgXvAqBvQF8+qvpRtgqeBU70TVPOHHrcdFzpFWg/F+zd8jQskXMkzxDPIqe+TzJdoGzVqhVz5859rEiYkJDAuHHjaNeunVmDq1OnDlu2bGHEiBFMnDiR0qVLM3fuXN59992M9wwdOpTk5GR69epFTEwM9erVY+fOnZKcCpFL9obsZfCBwaQb02lcvDFzms3BxsJ8T/nF791H2ODBqKQkrEqVwmfJYqxKlcraYErBwS9g32TTcdkW8OZXYFvEXOFmi95gZOG+m3y59zoGo6J4EVvmvFWDuqVd8jo0kYOsrKzQarWEh4dTrFgxrKysnq8/fsQzUUqRlpbG/fv30Wq1/3hy8HmTm/moECL/OfbDfvacO0SaVo+TwYaG92Nw3v8TWFjgPXUKzq+8kq3xH6Q8oM/ePpy/fx5LrSWfN/qcl8u8bKboCwCl4NQq2DEa0hPB2gnazoDqb4PkKIWS5KPiWeR0PqpRSqnMfCA8PJzmzZuj0+m4fv06tWvX5vr167i5uXHw4EHc3d3NGmBuiIuLw9nZmdjYWNmPUohM+PXWr4w8NBKDMtCqZCumvzAdS515lrwopXiweg2RM2aAUtjVr0+JeXPROTtnbcD0ZPjxM7i42XRc71NoPQl0mb5PkyPuPEii/6aznAqOAeDV6t583sEfZ9vnaAnRcywtLY27d++SlJSU16GIfM7Ozg4vL69/TQifp3xG8lEhnl8H1v3G7zdOotcYKKq3o+GdYByO7UVjbU3xeXNxbNYsW+PfeniLz/Z8RmhCKE5WTsxrPo/anrXNE3xBEB9haiB5fafpuNQL0GERFPHN27hEjpN8VDyrnMpHM12gBEhOTmbjxo2cOnUKo9FIzZo1effddx/bpLwgkYRQiMzbcn0L4w6PQ6FoX6Y9nzf6HAuteYp9Ki2NiM8/5+F33wNQpFMnPMeMRmOZxWJd3F3Y2BnCT4PWwtQMp9YHZok1u5RSbD0bxpitl0hI1eNgbcGkDv50CCie16GJXKaUQq/X/+uyVSEAdDodFhYW//lEw/OWz0g+KsTzZ8fSHzgefgGDRuGmt6fhlQvYXjyB1tERnyWLsatVK1vjHwo7xJADQ0hIT6C4Q3EWtVxEGecyZoq+ALj8I2zrD8kPQGcNLceZbuprC0ZzxtikdJzt5OZ+dkg+Kp4mJ/PRZypQ1qxZkz179lC0aFEmTpzI4MGDsbOzy9RE+ZkkhEJkzteBXzPt+DQA/q/8/zG6/mi0GvMkLvqYGML6DyDp2DHQavEYNpSi77+f9SUGd47Dt+9D/F2wdYG31kGpxmaJNbtik9MZs/UiP50LB6B2yaLMeasGPi6F5+erECL3FPZ8RvJRIZ5vW2eu41z8LZRG4ZHuQMNTh7C6fRWdmxu+K5ZjU7FilsdWSrHhygZmnJiBURmp6V6Tuc3nUtSmqBmvIB9LiYVfh8L5jaZjz6rw+nJwr5S3cT2j+JR0vtxzna+PhfBzn8aUKWa+Rp1CiMzJ8QKlra0t169fp0SJEuh0Ou7evVsgl878F0kIhXh2Ky6sYN7peQB0qdyFIbWHmG1/kpSrVwn9rDfpoaFo7ezwnj0re8t0Tq6CX4eAMR2KVYR3NoJLabPEml3Hgx4wYNNZwh4mo9Nq6NfCj17NymKhKxh3qIUQ+U9hz2ckHxXi+WQ0Gtk4aRnXjBEAeKc50uDANiyi72Hp44PviuVYlSyZ5fHTjelMOTaF76+ZVu50KNeBMfXHYKV7Tvb6vbnPtKQ79g5otNB4ADQdDhb5//qNRsX3p0OZsf0qUQmpAAxsVZ6+LfzyODIhnl/ZyWeeaT1mjRo16NatG40bN0YpxcyZM3Fw+Pe7EmPHjs1UAEKIgkEpxexTs1l9aTUAPav3pFf1XmYrTsZt3074iJGo5GQsfXwosWABNhXKZ20wfaqpMHl6jem40qumvXOs8755VpreyLw911i8/yZGBb4udsx9uwY1fZ+TO/RCCJFFko8K8fxJT01j3aQlhOgeAFAq1Znav65Dl5qMjb8/PksWY+GW9Y7SsamxDNw/kOMRx9GgYWCtgXSt0vX5aA6SEgc7R/8vXy5aGjouBd96eRvXMzoTEsP4bZc5d+chAGXc7BnTvjLNKxaeG1dCPG+e6QnKq1evMm7cOG7evMnp06epXLkyFhb/rG1qNBpOnz6dI4HmJLljLcST6Y16xh0ex083fwJgUK1BfOD/gVnGVgYD97+cT/TSpQDYN2xI8dmz0BUpkrUB48JhUxcIOwlooMVY053gfJBoXg6PY+C3Z7kSEQ/Am7VKMP7VKjhY549GPUKIgq2w5zOSjwrxfEmMiWPdrBVEWMSBAr/UotT4aRlaoxH7pk0oMXs2Wnv7LI8fFBtE7z29CYkPwc7CjhlNZtDUp6kZryAfu7EHfuoLcaGm47o9TDmzdf5fGh3+MJkZ26+w9axpiyQHawv6tijHBw1LY2UhK5GEyGu52iRHq9USEREhS2qEeE4k65MZcmAIB0IPoNPoGN9wPB3KdTDL2Ia4OMKGDCHxwEEAXLp1w33QQDT/8gfnMwk+DN92hcRIsCkCb6wEv5ZmiTU79AYjSw/eYu7ua6QbFEXtLJncsSrtqnrldWhCiELkecpnJB8VonCLDL7LpuXriLZIQqs0VE52pspPppvZzm++gdf48VnPF4HD4YcZvH8w8enxeNt7M7/FfMoXzeLKnYIkJfbRU5NrTcdFS8FrC/PN/uxPkpiqZ+mBmyz7/RYp6UbAdLN/aJsKuDva5HF0Qog/5fgS778yGo2Z/YgQooCKTY2lz94+nIk8g7XOmplNZ9LMp5lZxk69eZPQXp+RFhyMxtoar0mf4/zKK1kbTCk4sQK2DwejHjz84a31+WK/yZv3Exj07TnOPlp+0rKSB1Nfr0oxR+u8DUwIIQowyUeFKLxunbrK1q1biLNIwVLpqBZnRbnfTMVJt969cfss61sMKaVYe3kts0/NxqiM1ChWg7nN5+Jq62rOS8ifru+GbX0hLsx0XK+n6alJq6w/hZobjEbF5tOhfLHjKpHxpn0m65ZyYUz7ylQt4ZzH0QkhzClLt53WrVvHkiVLCAoK4siRI5QsWZI5c+ZQpkwZXnvtNXPHKITIA5FJkfTY1YMbD2/gaOnIghYLqOlR0yxjx+/dS/iQoRgTE7Hw8qLEgvnYVqmStcHSk+HngXBug+nY/w14dX6eJ1tGo2LNkdtM336FlHQjjtYWjH+1Cq/XLP587GskhBA5TPJRIQqfczuPsePQHpJ0adgaLQm4n4rPvg2g0+E1YTxF3nwzy2Mn65MZf3g8vwb9CsBrZV9jbIOxhb8ZTvJD2DkKzqw3HRct/eipyUZ5GtazOHYrms9/uczFsDjAtHf7iLYVaePvKfm0EIVQpjdpWLx4MQMHDqRdu3Y8fPgQg8EAQNGiRZk7d6654xNC5IHbsbfp8msXbjy8QTHbYqxqs8osxUllNHJ/4UJCe32GMTERu9q1Kf39d1kvTsbchq/amIqTGi20nmRa1p3Hxck7D5LovOIoE7ZdJiXdSONybuwY0IQ3apWQZEoIIcxA8lEhCp/9a37h5z92kqRNw8lgQ/0b4fjs+w6NrS0+ixZmqzgZnhDO+7+9z69Bv6LT6BhRdwSfN/q88Bcnr++CRQ0eFSc1UL8XfHo43xcng6MT6bnuFG8tO8rFsDgcrS0Y0bYiuwY2oW1VL8mnhSikMv0E5fz581m+fDkdOnRg2rRpGedr167N4MGDzRqcECL3XYq+RK/dvXiQ8gBfR1+WtlpKCccS2R7XEBtL+LDhJOzfD0DRzp3xGDEcjaVl1ga88gts+RRSY8HWBf5vFZRplu04s0Mpxbcn7/D5z4EkpOqxtdQx8uVKvFfPVxIpIYQwI8lHhShctn6xlnMJQSiNwlVvR+1TJykSdAGdmxs+ixdhW7Vqlsc+fvc4gw8MJiY1BhcbF2Y2nUkdzzpmjD4fSoyGHSPh/EbTsUsZeG0RlGyQt3E9RVxKOgv33mDVH7dJMxjRauCdur4MaFUeNwfZHkmIwi7TBcqgoCACAgL+cd7a2prExESzBCWEyBu/h/7O4AODSdInUcmlEotbLjbLnjwply8T2rcf6aGhaKys8Bw3liJvvJG1wQzpsGcCHJ5vOi5RB/5vNThnv4iaHXdjkxn5wwX2Xb0PQO2SRZn5f9Up5Za/9/URQoiCSPJRIQoHfbqebyYv5Sb3QQNeaY7U2bsV24dRWJcvj8+SxVh6e2dpbKUU6wPXM+vkLAzKQGXXysxtNhcvh0LcpFApOL8Jto+A5AeYnpr8FF4cA1Z2eR3df0o3GPnmeAjzdl8nOjENgBf83Bj9cmUqeDrmcXRCiNyS6QJl6dKlOXv2LCVLlnzs/G+//UblypXNFpgQInd9f+17Jh2dhEEZqOdZj7nN5+Jg5ZDtcR9+/z0REz9HpaVhWaIExefNzfqS7rhw+K4b3DlqOq7fC1pOAIu8W56jlOKb43eY+msg8al6rHRaBr9Unu6Ny6DTylOTQgiREyQfFaLgS4yJY/2sFdy1MO0vWCrZiVo/fYWFMmDftAnFZ81G55C1G70p+hQmHpnItlvbAHilzCuMbTAWG4tC3O055jb8PABu7jUdu1eBV7+EErXzNKwnUUrx28UIZmy/wu3oJADKFLNn9MuVaF7BXVYgCfGcyXSBcsiQIXz22WekpKSglOL48eN88803TJ06lRUrVuREjEKIHKSUYv6Z+Sy/sByAV8u+yvgG47HUZXHp9SPGlBQiPv+c2M0/AODQrBne06ehc85it72be2HzR5AUDdZO8NoCqJy3TRCCoxMZvvkCR25FA1DDpwhfvFkNPw+50yuEEDlJ8lEhCrbwqyF8v34jDyyS0CoNFRLsqfrLcjRA0S5d8Bg2FI1Flvq5EhofysD9Awl8EIhOo2Nw7cG8W+ndwlvsMujh6CLYNwX0yaCzhqZDoVE/yGY+n5OO3Ypm6m9XOHvnIQBuDlb0a+HH23V9sdRlulWGEKIQyPRP/W7duqHX6xk6dChJSUl07tyZ4sWLM2/ePN5+++2ciFEIkUPSDGmM+WNMRjfDT6t/yqfVP812Apd25w6hffuRGhgIWi3F+vbF9ZOP0WizkGwYDXBgBhyYDijwrAr/twZcy2YrxuwwGBWr/ghi5s6rpKQbsbHUMrh1Bbo1Ki1PTQohRC6QfFSIguvi3pP8tn8XibpUrJSOKvcV5feuBK0Wj1EjcXn33SyPfeDOAUYcGkF8WjxFrIsws+lM6nnVM2P0+Uz4WdjWF+6eMx2XegHazwW3cnkZ1RNdvxfP9O1X2B0YCYCdlY6PXyjDx03K4GCdtaK0EKJw0CilVFY/HBUVhdFoxN3dHYCwsDCKFy9utuByS1xcHM7OzsTGxuLk5JTX4QiRK2JTY+m/rz8n753EQmPB2AZj6ejXMdvjxu/bR/iw4Rjj4tC5uFB81kzsG2RxQ+6E+/DDR3Brv+m4ZldoOx0sbbMdZ1bdiIxnyPfnORPyEIAGZVyZ9kZVSrrKXpNCiLz1vOYzko8KUXAcWPcbf9w4SZrGgL3Riho37lHi9E609vYUnzsHhxdeyNK4BqOBhWcXZqwIquZWjVnNZuFp72nO8POPtCTYPwWOLAJlABtnaD0JArpAPn1S9F5cCnN2XePbk3cwKtBpNbxdx4d+Lf1wdyzES++FeM5kJ5/J1i0KNzc3ACIiIpg8eTIrVqwgOTk5O0MKIXJBWEIYvXb34lbsLewt7ZndbDYNvRtma0yVnk7knLk8+OorAGxr1KD43DlYemYxMQw6CD98AvF3wdIO2s+B6nn3VEy6wciyg7eYt/s6aQYjDtYWjGxXibfr+KCVpyaFECLPSD4qRMGwedoqLiYHozTgorej5oljuAZfxtLbmxJLFmNTvnyWxo1KjmL4weEcizgGQOeKnRlce3C2tyvKt27shl8GmfacBKjSEdpMB0ePPA3rv8SnpLP0wC1WHLpFSroRgJeqeDC0TUXKFsv+fvdCiMLjmQuUDx8+5LPPPmPnzp1YWloyfPhwevfuzfjx45k5cyZVqlThq0eFCSFE/nUp+hKf7f6M6JRo3O3cWdRiERVcKmRrzLTQMMIGDSTl3Hng0d5BQwajscpC8xqDHvZPhd9nAQrcykOnteBeKVsxZse5Ow8Z8cMFLt81beL+YkV3Jnf0x8s5757kFEKI55Hko0IUPKmJyXw9bTkhugePOnU7UOu3H7BPjsG2Vi1KzJuLxaMbDZl1JvIMg/cPJjI5ElsLWyY0nEDb0m3NfAX5RGwY7BgBl380HTsVh5dnQ4U2eRvXf0hJN7DhWAgL9t3gwaPO3LVLFmVEu4rUKumSx9EJIfKjZy5Qjhw5koMHD9K1a1e2b9/OgAED2L59OykpKfz22280bdo0J+MUQpjBruBdjPx9JCmGFMoXLc/CFguzvfQlbudO7o4ajTE+Hq2TE95TJuPYsmXWBosJNjXCCT1uOq75PrSZBlZ5s3w6PiWdWTuvsebIbZSCInaWjH+lCq/V8C68G60LIUQ+JvmoEAVL+LU7/LBuI1G6RFBQJsmRmttWokNRpFMnPEePytINbaUU6y6vY86pOeiVnjLOZZjTbA5lipTJgavIY4Z0OLYE9k2F9ETQ6KBeD2g+EqzzX2NGvcHI96dC+XLPdcJjUwBTZ+5hbSrSurKH5NBCiP/0zAXKX375hVWrVtGyZUt69epFuXLlKF++PHPnzs3B8IQQ5qCUYsn5JSw6uwiARt6NmNl0Jg5WWV9WYUxNJXL6DGI2bADAtnp1is+ehWVW9/26tAV+6gepsaYu3a/MBf83shxfdm2/GMH4ny4REWdKrDoGFGfUy5Vwc7DOs5iEEOJ5J/moEAXH6V8Ps+fofhJ1aVgqHRXuK6rsXQEWFniOHkXRLDa0ik2NZfzh8ewO2Q1A21JtGd9wPHaWduYMP38IPmxazh152XRcoi60n21qGpnPGI2KbefDmbv7OkFRiQB4OdvQ50U/OtUugYV05hZCPMUzFyjDw8OpXLkyAGXKlMHGxoaPPvooxwITQphHij6FMX+MYfvt7QC8V+k9BtUehIU261vQpgYFETZwkKlLN+D6UXeK9euHxjILe/2kJcH24XB6jem4RB14YwUULZXl+LIj/GEy4366xK7L9wAo6WrH5A5VaeyXtaVHQgghzEfyUSEKht8WfsepyED0WiMORmuqXrmD7/l96FxcKDFvLnZ16mRp3LORZxl2cBjhieFYaC0YUnsI71R8p/A9lZdwH3aNhXOmBwGwdYFWE6HGu6DNX4U+pRS7AyOZtfMqVyLiAXC1t6JX83K8W88XG0tdHkcohCgonrlCYTQasfxL8UGn02FvL11rhcjPIpMi6bu3L5eiL2GhsWBU/VG8Wf7NbI0Zu20bEePGY0xKQle0KN4zpme54yIRF+H7DyHqKqCBFwZCsxGQB5uaG4yK1YdvM2vnVZLSDFjqNPRoUpbeL5aTxEoIIfIJyUeFyN/0ej2bJq/guooADbjp7Qk4uA+XyCCsK1XCZ8H8LK22MSojqy6uYv6Z+RiUgRIOJZjZdCZV3KrkwFXkIaMBTq2GPRMgJdZ0rmZXaDke7PLfvo1/3Ijiix1XOXvnIQCONhb0aFKGbo1KY2+drX68Qojn0DP/1FBK8cEHH2BtbVremJKSQs+ePf+RFP7www/mjfAvpk6dysiRI+nXr1/GUh6lFBMmTGDZsmXExMRQr149Fi5cSJUqheyXlRCZdCnqEn339iUyOZIi1kWY3Ww2dTyzdrcawBAfT8TnnxP30zYA7OrUwXvmTCw93DM/mNEIRxeZki9DGjh4wuvLoEze7B12ITSWkVsucCHMlAjWLlmUKa9XpbxH/tvXRwghnmf5IR8VQvy7mPD7bFq0lggL01N0PskO1Pr1a6zTU3Bq1xavyZPR2ma+wWBUchSjDo3icPhhANqUasPYBmNxtCpkeVrYafh1MISdMh17VoWX54BP1vP3nHIqOIaZO65y5FY0ALaWOro1KsUnTcpQxC4LTTKFEIJMFCi7du362PF7771n9mCe5MSJEyxbtoxq1ao9dn7GjBnMnj2b1atXU758eSZNmkSrVq24evUqjo6F7JeWEM9o++3tjDk0hhRDCmWdyzK/xXx8HH2yPF7SqVOEDxlKeng4aLW49eqF26c90eiy8GThwzuw9VO4/bvpuHwbeG0h2Of+EurYpHRm7brK+qPBGBU42Vgwol0l3qrtg1ZbyJYKCSFEIZDX+agQ4t+d33WcXb/vId4iFa3SUO6hFdV3rESr1VJs4EBcP/4oS8uwj949yojfRxCVHIWNzobhdYfzut/rhWtJd0Kk6ab9ma8BBVaO8OJoqPMR6PLXU4gXQmOZu/sae65EAmCl09K5ni+9mpfF3dEmj6MTQhR0z/wTb9WqVTkZxxMlJCTw7rvvsnz5ciZNmpRxXinF3LlzGTVqFK+//joAa9aswcPDgw0bNtCjR4+8ClmIPGFURhafW8ySc0sAaFy8MTOazMjyHWaVns79RYuIXroMjEYsS5TAe8YM7GoGZGEwBRe+g18GmxrhWNpDmymmZSu5nGQajYrvT4cy/bcrRCemAfBqdW9Gt68kyZUQQuRjeZmPCiH+3S9fbuRM9DX0WiN2Risq3ozC79Rv6FxcKD5rJvYNGmR6TL1Rz6Kzi1hxYQUKRbki5fiiyReUK1ouB64gj+jT4PhSODADUuNM56p2gtafg6Nn3sb2N+dDHzJv9/WMwqRWA2/WKkHfFn6UKFoImxMJIfJE/rol8x8+++wzXn75ZVq2bPlYgTIoKIiIiAhat26dcc7a2pqmTZty+PDh/yxQpqamkpqamnEcFxeXc8ELkUvi0uIYfnA4v4eZnkx8v/L7DKw1EJ02a/snpgUHEzZkKCnnzwPg/NpreIwZjc4hC52/kx6YOhBeerTkrkQd6LgUXMtmKbbsuBgWy9gfL3I65CEAfu4OTHitCg3LShMcIYQQQohnlZacyoapy7itjTbtN5luR7XfD1As8ha21atTfN5cLD0zX2i7E3eHEYdGcO7+OQDe8HuDYXWHYWuR+eXh+db1XbB9BERfNx171YC2M8C3Xp6G9Xfn7jxk3p7r7P1LYbJDjeL0frEcZYpl4W8CIYR4gnxfoNy4cSOnT5/mxIkT/3gtIiICAA8Pj8fOe3h4EBwc/J9jTp06lQkTJpg3UCHy0LWYa/Tf15878Xew1lkzpv4YXiv3WpbGUkoR+8MWIiZPRiUloXVywmv8OJzatctacDf3wdZeEB8OGh00Gw6NB+b6kpW/L+e2t9LRv2V5PmhUCktd/uqGKIQQQgiRn4VeCmLLN98TbZEIQMkkBwJ+XY+1PpWi772Hx9AhaKwytxehUoqtN7Yy7fg0kvRJOFg6MLbBWNqWbpsTl5A3om+aCpPXd5iO7YtBi3H5rjv3vxYmA4rTu7kUJoUQOSdfFyjv3LlDv3792LlzJzY2/73s8u97kCilnrgvyYgRIxg4cGDGcVxcHD4+Wd+fT4i8tD1oO2MPjyVZn4y3vTdzms+hsmvlLI2lf/CAiPETiN+5E3jUCGfGdCy9vDI/WFoi7J5gWroC4OoHry+F4rWyFFtW/dty7leqezOqXSU8nWU5txBCCCFEZvyxcReHLh8n2SIdC6XD756RavtXorG1xWvaTJzbv5zpMR+mPGTi0YnsCt4FQE33mkx9YSreDt7mDj9vpMTBwS/g6GIwpoPWAur1hKZDwcY5r6PLcPbOQ+btvsa+q/eB/xUm+7zoR2k3+6d8WgghsidfFyhPnTpFZGQktWr9r6BhMBg4ePAgCxYs4OrVq4DpSUqvvxRQIiMj//FU5V9ZW1tndH8UoqDSG/XMPTWXNZfXAFDfqz4zmsygqE3RLI0Xv3s3d8eNxxAdDRYWFOvXF9cPP8xaI5zbf8CPn0FMkOm4zsfQaiJY5e4eNefuPGTCtksZy7nLuTswUZZzCyGEEEJkmsFgYPO0VVxJC8OoVTgZbKh8/iolrx7FqlQpSsz/Ems/v0yPezj8MGMOjSEyORILjQWfBXxGtyrdsrxNUb5iNMC5b0w37RNNTyNSrhW0mQpumf9vlVP+XpjUaTUZS7mlMCmEyC35ukDZokULLly48Ni5bt26UbFiRYYNG0aZMmXw9PRk165dBASYmnakpaVx4MABpk+fnhchC5ErHqQ8YMiBIRyPOA7Ah/4f0jegb5YSOUNcHPcmTyb2x58AsPYrh9e0adhWqZL5wNISYc9EOGZq0oNTCXj1SyjXIvNjZcO9uBSmb7/CD6fDANNy7n4t/ejWqLQs5xZCCCGEyKR7N8P4YdVG7lnEgwY8Ux2ouesnHBPu4/jSS3hNnpTpfcpTDanMPTWX9YHrASjlVIppTaZRxTULOWh+dGMP7BoL9y6ajl3KQptpUL71kz+XS5RSHLkVzaJ9Nzl0IwowFSY7PlrKXUoKk0KIXJavC5SOjo74+/s/ds7e3h5XV9eM8/3792fKlCn4+fnh5+fHlClTsLOzo3PnznkRshA57mLURQbsH0BEYgS2FrZMbjyZViVbZWmshN8PcXf0aPT37oFWi2v37rj16Y02k3sGAf98arLm+9B6Uq4uW0lJN7Di91ss2n+TpDQDAK/XLM7QlyrKcm4hhBBZNn78+H/sX+7h4ZGxH7pSigkTJrBs2TJiYmKoV68eCxcupEpWbvYJkc/8sWkXf1w6QZJFGlqlocxDK2rs+AoLK0s8xo+jyFtvPXF7rX9z9cFVRhwawfUYU5OYTuU7MbjO4MLRCOfeJdg5Bm7uMR3bOEOTIVC3B1hkIcc2M6NRsfdKJAv33+DMo1VGfxYm+7xYjpKuUpgUQuSNfF2gfBZDhw4lOTmZXr16ZSSEO3fuxNHRMa9DE8KslFJsuLKBmSdnojfqKeVUirnN51K2SOY7YRsSEon84gsebtoEgFXJknhNm4rdoyeRMyUfPDWplOLXCxFM+TWQsIfJANT0LcLYV6pQw6dIrsXxvEpN1vPwXhIepZzyOhQhhMgxVapUYffu3RnHur9sgTJjxgxmz57N6tWrKV++PJMmTaJVq1ZcvXpVclJRYOnT9Xw37Suu6cNRWnAwWlPxcghlL+7HqkwZis+ZjU2FCpkaM92YzsoLK1l6bil6pcfFxoWJDSfS1KdpDl1FLoq7C/smwdkNoIygtYS6H5uKk3YueR0deoORXy7cZfH+m1yJiAfA2kLLW3V8+PiFMvi45O5WTEII8XcFrkC5f//+x441Gg3jx49n/PjxeRKPELkhPi2ecYfHZWwc3tK3JRMbTcTRKvN/9CQeP87dkaNIDw0FoGiXLrgPHIDWNgt3rG8fgh975+lTkxfDYpm47TLHbz8AwMvZhuFtK/Jqde9M380Xz06fZuD2hWiun7xH8IVorO0s6DqtEVqt/DcXQhROFhYWeHp6/uO8Uoq5c+cyatQoXn/9dQDWrFmDh4cHGzZsoEePHrkdqhDZFn41hK3rviXSIgE04JXqQPW9P+Mcew/njh3xHDMarV3mClrXY64z+o/RXI6+DEBzn+aMbTAWN9sCvjd4agIc/hIOz4f0JNO5yh2g5ThwKZOnoQGk6g1sPhXGkgM3CXlgis/B2oIuDUryYaPSFHOU3gxCiPyhwBUohXjeXI6+zKD9gwhNCMVCa8Hg2oPpXLFzpotvhvh4ImfOynhq0tLbG68pU7CvXy/zQSXHmPbUOb3WdJwHT03ei0th9s5rfHvqDkqBjaWWnk3L0qNJWWytCsGm6vmQwWAkNDCG6yfucevcfdJTDBmvWdtZkBCTgpNrIViaJYQQ/+L69et4e3tjbW1NvXr1mDJlCmXKlCEoKIiIiAhat/7fvnLW1tY0bdqUw4cP/2eBMjU1ldTU1IzjuLi4HL8GIZ7F7+u3c/jaKZIt0tEpLWWidVTf/RWWdrZ4zpiO86uvZmo8vVHP6kurWXR2EenGdJysnBhRbwQvl365YN9MNujh7HrYO/l/DXB86plu1vvUzdvYgMRUPRuOhbDi0C3uxZl+1rjYW/Fho1J0aVAKZ1vLPI5QCCEeJwVKIfIppRSbrm5ixokZpBvT8bb3ZmbTmVQtVjXTY8Xv3UfEhAmmvSaBIp064T50KDqHTO4xoxRc3gq/Dv1fIlarG7SakGtPTcanpLPs4C2W/36LlHQjAK/V8GZYm4p4F5HimLkpo+LuzYdcOxHJzVORpCSmZ7zm4GKNX20P/Op44FbCoWD/kSGEEE9Qr1491q5dS/ny5bl37x6TJk2iYcOGXLp0KWMfSg8Pj8c+4+HhQXBw8H+OOXXq1H/saylEXkpLTuXb6Su5qSJRWnA0WFP5wk1KXfkD68qVKDF7NlalSmVqzFsPbzH6j9FciDI1Pm1aoiljG4zF3c49B64glygFV36GvZPg/hXTuaKlTflwpVchj/Oh+/GprDtym7VHg3mYZMrbPJ1s+KRJGd6u64OdlZQAhBD5k/x0EiIfik+LZ8KRCey4vQMwLYH5vNHnOFtnrgioj47m3uQpxP36KwCWJX3xmvg59vWycFc3NhR+GQTXtpuO3crDK19CyQaZHysL0g1GNh4PYe7u60QnpgFQq2RRRrarRK2SRXMlhueFUor7IfFcP3GPG6ciSYj53xM+to6WlKvpjl8dDzzLOKORJd1CiOdA27ZtM/531apVadCgAWXLlmXNmjXUr18f4B83aZRST7xxM2LECAYOHJhxHBcXh4+Pj5kjF+LZXD54hl27dhGjSwINeKfYE7D7JxwSoij6fhfcBw/OVBNFg9HAusvrmH9mPmnGNBwtHRlWdxivln21YN/QvLnPtPd6+GnTsW1RaDoManfP8wY4NyITWHnoFptPh5GmN93EL+Vqx6fNytIxoARWFto8jU8IIZ5GCpRC5DNnI88y/PfhhCWEYaGxoH+t/rxf+f1MJXNKKeJ+/pl7k6dgePjQ1KH7w2649e6N1iaT3ayNBjixwpSMpSWYNvx+YRC8MBAscn7PGqUUOy5FMH37VYKiEgEo42bP0DYVeamKR8FOcvMRpRTRYYncOHWPm6fv8/BeUsZrVjY6ygQUw6+OByUqFEWrkwRXCPF8s7e3p2rVqly/fp0OHToAEBERgZeXV8Z7IiMj//FU5V9ZW1tjbS17v4m8ZTAY+GnWei4lBqPXGbFSOspEGKh64Cus3N3xmrcCh0aNMjXm1QdXGX94PBejLwLQqHgjxjcYj6f9P/dwLTDunIC9EyHooOnY0h7qfwoN+4BtkTwLSynF8aAHLP/9FrsDIzPOV/cpwicvlKGNvyc6uZkshCggpEApRD5hMBpYfmE5S84twaAMFHcozvQm06lerHqmxkkPD+fuhAkkHjAlUNYVKuA1aRK2Vf0zH1TERdjWD8JOmo596pmemnSvmPmxsuBU8AMm/xLI6ZCHALg5WNGvZXneruODpRTJss1UlEzgxqnIfxQldZZaSlV1o3wdD3z9XbCwlH09hRDiT6mpqQQGBvLCCy9QunRpPD092bVrFwEBAQCkpaVx4MABpk+fnseRCvHf7l67w49rvyXCIh404JZuR+Wjx/EKu4RjmzZ4jR+HrkiRZx4v1ZDK0nNLWXVxFXqlx8HSgcG1B/O63+sF94byvcumpdxXfzEd66yg9oemm/UOebdMXW8wsuPSPZYdvMm50FjAtLK8ZSUPPmlShtolixbc/+ZCiOeWFCiFyAfCE8IZ8fsITkealou8XOZlRtUblaku3So9nQdr13F/wQJUcjIaS0vcPuuFa/fuaCwzuQl2ShzsnwrHloIygJUjtBoPtT4Ebc4XBq/fi2fmzqvsuGTaM9PWUsfHL5Tmk6ZlcbCWH1vZoZQiKjSBm6ciuXE6ktjI5IzXdBZafKu4ULamO6Wru2FlI/+thRACYPDgwbzyyiv4+voSGRnJpEmTiIuLo2vXrmg0Gvr378+UKVPw8/PDz8+PKVOmYGdnR+fOnfM6dCH+1e4VP3Iq5CLJFulolYZSsdbU2LEWazsbPKdPw+nVzC3FPhFxgolHJnI77jYALX1bMqLeiIK71+SDIFMufP5bQIFGC9U7Q7NhUMQ3z8JKTNXz3ck7rPwjiDsPTDmclYWWN2uVoHvj0pQt5pBnsQkhRHbJX59C5LHtQduZeGQi8enx2FvaM6reKF4p+0qmxkg6fZqIceNJvX4dANtatfD6fCLWZcpkLhil4OJm2DEKEkyb/lP5NWgzDZy8MzdWFoREJzF39zW2nA1DKdBq4K06PvRvWR4Pp0wuTRcZ/ixK3jhlanQTe/+fRclytdwpVdUNK1v5tSCEEH8XGhrKO++8Q1RUFMWKFaN+/focPXqUkiVLAjB06FCSk5Pp1asXMTEx1KtXj507d+Lo+Ow3GoXIDfHRsWyeu5bbumjQgpPBhgqXblPm8gFsa9fCe9p0rEoUf+bx4tLimH1yNpuvbwagmG0xRtYbScuSLXPqEnJWbBj8PgtOrwGj3nSu0qvw4mgoViHPwgp/mMy6o8FsOBZCbLKp8U1RO0u6NCjF+w1K4uYg20UIIQo+jVJK5XUQeS0uLg5nZ2diY2NxcnLK63DEcyIhLYFpx6fx480fAajmVo1pTabh4/jsG+TrY2KInDWL2O9NSaGuSBHchwzBuWMHNJl90vH+VVMTnNu/m45dykC7L6BczieYEbEpzN97nU0n7qA3mn4ktaniyaDW5fHzkD/uskIpRdSdBG6c/peipKWWklVcKVurmKkoKU9KClEoSD5TsMm/n8hph77ZybHAU8RrTc3vfJLsqb7rB+z1iRTr2wfXDz9Eo3u2LV2UUuwJ2cOUY1O4n3wfgDfLv8mAWgNwsiqA37+xYXBojqkwaTA1Y6Tsi/DiGCheM09CUkpxMjiGVX8EsePSPQyPcuRSrnZ0f6EMb9Ysga2VbMEjhMhfspPPyF+lQuSB43ePM+aPMYQnhqNBw8fVPqZn9Z5Yap9tKbZSitgfthD5xRemJjiA85tv4D5oEBZFM9nROjUBDs6AIwtNd4otbEz76jTsC5Y5+9RidEIqi/ffZN3RYFIfdRtsUr4Yg1uXp1qJIjk6d2GkjIqIW7HcOhfFrbP3ift7UdLflXI13SlZ1VWKkkIIIcRzIj46ls3z1nJbY3pq0tZoSdngeKoc24R15Up4T5mCTcVn31/8Tvwdph2fxsFQ037npZxKMa7BOGp71s6pS8g5/1aY9G0IzUdC6RfyJKSUdAPbzoWz+vBtLoXHZZxvUMaVDxqVomUlD2l8I4QolOQvVCFyUYo+hXmn57E+cD0AxR2KM7nxZGp51Hr2Ma5dI2LiRJJPngLA2s8PzwnjsauZybu7SsGlLbBzDMSFms6VbwNtp0PRUpkbK5PiUtJZcfAWKw8FkZhmAKBOqaIMbl2BemVcc3TuwsagNxJ6NYZbZ+8TdC6K5Li0jNcsHhUly9Zyp6S/FCWFEEKI583fn5r0TrGn6v4dFEmMpFi/vrh+9NEz71Weakhl1cVVrLiwglRDKhZaC7pV6UaP6j2w1hWwJcb/WZgcAaVeMHWcyWURsSl8fcy0jDs60RSTtYWW12sWp2vDUlT0LIBPpgohRCbIX6tC5JLz988z6tCojM3D3yz/JoNrD8be0v6ZPm94+JD78xcQs3EjGAxobG0p1vszXN5/P/NNcMLPwvbhEHLEdOzsaypMVmyXuXEyKS4lndV/3GbloaCM/XP8izsxuHUFmpYvJt0Gn1Faip7gi9EEnYsi+EIUaSmGjNesbC0oVdWVMjWK4VPZRYqSQgghxHPoX5+aDImn8tFN2Pr74zVlETblyz/zeIfDDjP52GRC4kMAqOdZj5H1R1LGOZP7nee1fFaYVEpxOuQhqw/f5rcLdzO2OvJ2tqFLg1K8XceHovZWuRqTEELkFfnLVYgclm5IZ/G5xay8uBKjMlLMthgTGk7ghRLPtmxEGQw8/O477s+dl7Gc27FVKzxGDMfSO5ONaxIiYc9EOLMeUGBhC436mb6s7DI3VibEJqfz1aEgvvojiPgU04bj5dwdGNSqPG38PaUw+QyS49MIOm9auh0aGIPh0ZJ4ADsnK0rXKEaZGm4UL18UnUXOd1oXQgghRP70b09N+h/YgUtCJG4DB+L6YTc0Fs/2Z2BEYgQzTsxgV/AuwNQEZ0idIbQp1aZg5W8PbsEf8+DshnxRmExK0/PzubusPxbM+dDYjPN1S7vQrWEpWlX2wEIn+ZwQ4vkiBUohctCl6EuM+2McV2OuAtCudDtG1huJs7XzM30+6eRJIiZPITUwEABrv3J4jByJfYMGmQtEnwpHF8PBmZAWbzrn/ya0mgDOJTI3ViY8TErjq0NBrPrjNvGppsKkn7sDfVr48XJVL9k/5yniopIJerSf5N0bD/lrSzPnYraUqVGMMgHF8CjlhEb+WwohhBDPteiwSH5cspEQ7YN/PjVZvRrek5dgXa7cM42Vbkjn68CvWXRuEcn6ZLQaLZ0rduazGp/hYOWQw1diRvcuwe+z4dIPoB7d3C3ZCJoNz5PC5LV78Ww4FsLm06EZN+2tLLS8Vt2brg1L4V/82f5GEEKIwkgKlELkgBR9CovOLWLtpbUYlIEi1kUYU38MrUu1fqbPp9+9S+QXM4n79VcAtE5OFOvdm6LvvJ255dxKwdVfYccoiAkynfMOgDbTwbdeZi/rmcUkprHi0C3WHA4m4VFhsoKHI31alKOdvxdaKab9K6NRce9WLLcvRBF0PpqYu4mPvV7M15EyNdwoXb0YLt72BevJBSGEEELkCKPRyPaF33Ph/nWSdaYtdLyT7fE/uAOXlAcUGzYMl/e7PFOHbqUUB0MPMvPkzIxtiWoUq8Ho+qOp4FIhJy/DvO6cgN9nwbXf/neuXCt4YSCUbJiroaTqDWy/GMHXR0M4fvtBxvmSrnZ0ruvLm7VK4OpQwPbwFEKIHCAFSiHM7ETECcYfHp+xR0/bUm0ZVncYrrZPb/5iTEwketVqoleuRCUng0ZDkf/7P4r174eFi0vmAgk9CbvGQvAfpmMHT2g5Dqq9DdqcWTJyPz6VlYeCWHfkdkbzm4qejvRr4cdLVTylMPkvUpP13Ln8gNvnowi+GE1KYnrGaxqtBq+yzpSpUYzSNdxwcrXNw0iFEEIIkd/cPHGZnT/+xj2LeNCCg9GaMreiqXRyEw7Nm+M5ehSWxYs/21gPbzLjxAwOhx8GwMXGhf41+/NaudfQagrAcmOl4NY+0xOTt39/dFIDVTpA4wHgVT1XwwmOTmTD8RC+OxnKg0dNb3RaDS0rufNuvZI0LucmubEQQvyFFCiFMJP4tHjmnJrDd9e+A8Ddzp0x9cfQzKfZUz+rDAYe/vAD97/8EsP9KABsa9XCc9RIbCpXzlwg0TdN+0xe3mo6trCBBp9B44FgnTNLcoKjE1l28BbfnQol7dHeiFW8nejbwo9WlTwk+fqb2PtJ3D4fze0LUYRfe4jR+L+129Z2FvhWcaVUNVd8K7tiY5/JBkhCCCGEKPTSUlLZMnMd19PD0VsY0SoNPvE2VNu7BSdnGzzmf4ljy5bPtNoiNjWWhWcX8u3VbzEoA5ZaS96r/B6fVP2kYCznNugh8Ec4PB/Cz5jXi+hZAABJqUlEQVTOaS2h+lvQaAC4PduydnNINxjZeyWS9UeD+f16VMZ5Tycb3qnry1t1fPB0tsm1eIQQoiCRAqUQZrAvZB+Tjk0iMikSgP8r/38MqDUARyvHJ35OKUXi778T+cUXpF6/AYCljw/uAwfg2CaTm48n3IeDM+DkV2DUAxqo8a5p8+8c2mfyUngsSw7c4pfz4fxZYwvwLcJnzcrRopK7LEF+xGgwEnErjtsXorh9PoqYiKTHXi/iYUepqq6UquaGV1lntLIpuhBCCCH+w4mtB/nj1BEe6pJBAy56O/zOB1Lq+jGKvvcexfr1Refw9MJiujGdb69+y6Kzi4hLiwPgRZ8XGVx7MD5OPjl9GdmXGg+n18LRJRBrWrmEhS3U+gAa9s7Rfdb/7kZkAt+dvMPm02FEJZiaE2k00MSvGO/W8+XFiu7S9EYIIZ5CCpRCZENYQhjTjk1jf+h+AHwdfRnfcDx1POs89bMpgYHcmzGDpCNHAdA6O1Os16cUeecdtFZWzx5EWiIcWQR/zIW0BNM5v9bQcjx4VMncBT0DpRRHbz1g8YGbHLx2P+N8swrF+LRpWeqWdpHCJJAUl0bI5WhCLkYTEviA1ER9xmsarQZvP2dKVXWjVFU3injkXAd1IYQQQhQO926E8suazdzRxqB0YKl0lLoP1fatxb5Seby+3YRt1apPHUcpxf47+5l7ei63Ym8B4FfUj2F1hlHPK+f2KDeb2DA4tgROrYHURx2w7dyg7sdQ5yOwd8uVMBJS9fxyPpxvT4ZyKjgm47ybgxVv1vKhc11ffF0lxxNCiGclBUohsiDdkM6ay2tYem4pKYYULDQWdK3SlR7Ve2Br8eR9AtPDwrg/fwGxP/4ISqGxtKRoly649fgEnXMmOvfpU02J2e8zIeGe6Zx3ALSaCKWbZOPq/p3BqNh1+R5LDtzk7J2HAGg10L6aNz2blqWyt5PZ5yxIjAYjEUFxpoLk5QfcD4l/7HVrOwtK+puekvSt7IK1nSzdFkIIIcTTpaem8dOcDVxLDiVVZ7rh6ZlqT+XDB/FIukexEUMp2rkzGoun/2l3NvIsc07N4XTkaQCKWBehT0AfXvd7HQttPv/T8O55OLIALm5+tFoIcPUzbWVU/W2wzPm9upVSnAqOYdOJO/xy4S5Jj/Zc12k1NK9QjE61fWhe0R1LeVpSCCEyLZ//FhIi/zkRcYJJRydl3HGu7VGb0fVHU7ZI2Sd+Tn//PlFLl/Fw0yZUuqkRitPLL1NsQH+sSmRiCYohHc5ugAMzIC7UdK5ISVMDnModzd4AJyFVz3cn77D68G2Co01Lk60stHSqXYJPXij7XN8ZTohJzXhK8s6VGNKS9Y+9XszXEd8qLvhWccWztJMs3RZCCCFEphzetJsTF08R82g5t5PBhtK3Iql4+luKvPkmxQb0f6ZGikGxQcw7PY89IXsAsNZZ816l9/iw6oc4WeXjm8xGA1zbbnpiMujg/86XbAwN+5hWDeVQ88e/ioxP4YfTYXx78g637idmnC/jZs//1fbhjZrFcXeSvSWFECI7pEApxDOKSo5i9snZbLu1DTB1NhxcezDty7R/4pJmw8OHRK/8igfr15s6cwN29evjPnAAttWqPXsARgOc/xYOTIOY26Zzjl7QZDAEvA8WmVgW/gzCHiaz5vBtvjkeQnyKqfDmbGvJu/V86daoNMUcrc06X0Fg0Bu5ezP20VOS0USHJT72urW9Bb6VXU1Fycqu2DmZ999ECCGEEM+H0ItBbN/4I6EWD+HRcm7fB1qq7t9IkSqV8PjuO2z9n76Vz/2k+yw+t5gfrv+AQRnQarR0KNeBXtV74WHvkfMXklVJD+DMOjixAh4+2l9So4MqHU37S3oH5HgIKekGdl6+x9YzYRy4dp//b+++49yq7/zfv466NCNpRtO7xxX3Ci5gWoIxCQQHEkwKJSGbkCVsWG/u/n67+7iPX5IfNyyb+9ub7CawYXdjQltYEkwLzYnBFGPjis242+PpRZrRqExRO+f+cTRFnrE9tsfWyPN5PnIeko7OOfoOiu3PvM+3JJITrtvNRm6eV8Idl1ewpCpXpjYSQogxMq4DykceeYSXXnqJgwcPYrfbWbFiBY8++igzZswYOEbTNH7yk5/wxBNP4Pf7Wbp0Kb/+9a+ZPXvs594TE1M0EeWZA8/wxN4n6I51o6Dw1elf5a8W/RVu66mHZCfC3fiffoqO365HDenDfW3z51H40ENkLV8++gaoKuzfAO/9I/gO6/uyCvRVuZd8a8yHs+yq9/OfH9by1metA4VYdX4W375yErcvLsdhGdd/bYwpTdPoauuh4YCfhgOdNB3yE4skBg9QoGiSi8pZHirn5FFY5ZIVy4UQQghxzsL+EK/+67PUJrzETHrNUdqbxcytmylSAxQ9/BNct9yCcoZeg4FIgN/V/I5nDjxDb1y/QX5txbX8cOEPmZp78Va1Pmutn8Env4G9L0Ky3dhzYdE9+vySORd28R5V1dh6vIMNu5t487NWwpHB0TGLKnNYe3kFX5xXSrZ14tTDQghxsYzrv1k3b97MAw88wOWXX048Hucf/uEfWLVqFfv37ycrKwuAf/qnf+Kf//mfefLJJ5k+fToPP/wwN9xwA4cOHcLpPP0KykKcTv8E4j/f8XMaQg0AzMmbw98v/XvmFpx6AnK1rw//88/T8cS/k+jsBMA6fToFDz1E9nXXjv4uq5qA/a/A+/8vtNfo++y5cOUP4YrvgiXrPH66VLGEyjs1bfznh8fZVd81sH/FlDzuu6qa62YUTpjgrTsQofGgn8aDnTQe9BP2R1LetzvNei/JOR4qZnqwZ0svSSGEEEKcn3gszhv/+gIHu+roMURBgZy4nerDDUw/8BJ599xN3v3fx5h9+vovFA3xzIFneLrmaUIx/Qb5vIJ5rFu8jsVFiy/Gj3L2EnE4+Dp88gTUfTS4v3guXPE9mPuVCz6/5KHWEC/tbuTVPc20BPoG9pfl2PnywjLWLCxjauGZV0YXQghx7hRN07R0N2K0vF4vhYWFbN68mauvvhpN0ygtLeWhhx7if/yP/wFAJBKhqKiIRx99lO9973sjXicSiRCJDIYOwWCQiooKAoEALtc4noNFXDRH/Uf5p+3/xMctHwOQb8/nrxf/NTdPvhmDMvIda7WnB//zL9Dx29+S8PkAMFdVUvBXf4XrppvOeKd7QCIG+16ED/4ZOo7o+6wuWP4DWPZ9sI3d/0fbgn08t62e//qknvaQ/mfCYjTwpQWlfPvK6gmx8E20L07zka6BUPLkYdtGk4GSqW7KL8ulYqaHggonygQJa4UQmSUYDOJ2u6WeyVDy/U1cHz77NjsPforfqM/17VAtVLRFmfP+78ldfQOFf/3XWCorT3uNnlgPzx54lidrniQYDQL6ytwPzH+A6yuvH5/DkANN+jDuXU9BsEnfpxhh5i2w9H6oXAYXsN3twT5e2dPMS7ubONASHNjvspn44rxSvrywjCVVuRPmJr0QQoyF86lnxnUPypMFAgEAPMmJoGtra2ltbWXVqlUDx1itVq655hq2bNlyyoDykUce4Sc/+cmFb7DIOJ19nfzbp//Gfx/6bxJaArPBzD2z7+E7c79DlnnkO9aJcDf+556jc/16En4/AObSUvK+fz85a9agmEe5WnM8ArufgY9+MTjXji0Hlv0lLP2u3ntyDGiaxsfHO3hmax1v17QNDOPOz7by9aWVfHNZJYXOS3eSbzWh0l4XouGA3kOy9XgANZF6nya/IpuKmR4qLvNQPNWN2WJMU2uFEEIIcamq2bybDza+S6spCEYwaUYqAmZmv/cK+bMmU/Rfz2BfsOC01+iN9/L8wedZ/9l6/BG9Dp3snsz3F3yfVVWrTnljPW3UBBz9M+xcry9+o6n6fkc+LL4Xlnwb3GUX7OP93VHermnl9b0tbDnmI1kGYzYqXDejkNsWlXHtjEJsZqn9hBDiYsuYgFLTNNatW8dVV13FnDlzAGhtbQWgqCh1gueioiLq6upOea2/+7u/Y926dQOv+3tQiomrJ9bDMwee4bef/ZbumN6D7nOVn+NvlvwNFc6R/7+RCAbpfPppOp96GjUZnpsrK8n/3vdwf+mW0QeT0W7Y+TvY8i8QatH3ZRXoPSYvvw+sYzNVQbAvxoZdTTy9tY6j7eGB/VdM8vDN5VWsnl2MxTTOitgxoKoaHY1hmg77aTrcRfNhP9G+RMoxzjwbFTM9lF+WS/lluTJse5R6wyF8dbV462rpCQa56s670t0kIYQQYtyr3XmITRveotHYhWbSUDR9nsnLtm2m1Ban4Oc/xfn5z5+212NPrIcXD7/I+s/W09HXAUCVq4r759/PTZNuwmgYZwFbsGWwt2SgYXB/1VV6MDnzFjBfmBvkgd4Y7yRDyY+O+oirgzeml1TlsmZhGTfPKyHHIfWfEEKkU8YElD/4wQ/Yu3cvH3744bD3Tv7HW9O00/6DbrVasVon3grEYri4Gufloy/z2J7H8PZ6AZjpmcm6JetYVrJs5HM6O+l8+mn8Tz+DGtaDPkt1Nfnfvx/XF76AYhrlH6uwF7b/O3zy79Crz1WJq0yfY3LhXWBxnPfPp2ka+5oCPL+9gZd3N9ET1YM5h8XIlxeWcdfyKi4rvrSGkQ0LJI90Ee2NpxxjdZiSYaSHipm5uPLt43Po0zihqgn8Lc1462rx1Z/AW1dLe10t4Q7fwDEGo5Flt9+JabTBvBBCCDHBtBxp4J2nX6Ze8ZMw6T0HC6NZVNccYIrvMPnf/z65a+847U3uQCTAcwef49kDzxKI6DfIy7PLuX/+/Xxx8hcxGcbRr3dqAo5tgp1PwqE3QUveILbnwvyv68FkwfQL8tGhvhh/OtDG65+28P4RL7Eho2Vmlbi4eX4JN88tpTLv/OttIYQQY2Mc/Qt2ag8++CCvvvoq77//PuXl5QP7i4uLAb0nZUlJycD+9vb2Yb0qhRhK0zQ2NWzil7t+SW2gFoCy7DL+auFfsbp69YjDYaJ1dXSsX09gw8toyTlMrdOmkv/97+O88UYU4yjvVPuOwMe/gj3/BYnkXKi5k+Cqv4b5XwPT+YfnXT1RXt7dxAs7GlPm1JlWmM1dy6v48sIynLZLI0gaTSBpsRkpmZZD2bRcymbkkF/hlPmETqEvHMZXf4L2ulp89XrvSF9DPfFoZMTjXQVFFFRVU1BVTSIWk4BSCCGEOIm/yccf//1F6jQfMYMe0uXFHVQcbeKy42+Sf999eL75rxiyTr0AjrfHy9P7n+aFQy/QE9fnqqx0VnLf3Pu4ZcotmA3j6N9f3xHY8yx8+vzg6CCAyuWw+Fsw69YL0luyOxLnzwfbef3TZt477CUaVwfem1Hk5OZ5JXxxXgmTC2SxGyGEGI/GdUCpaRoPPvggGzZs4L333qO6ujrl/erqaoqLi9m4cSMLFy4EIBqNsnnzZh599NF0NFmMc5qmsa11G7/e/Wv2ePcAkGPN4XvzvscdM+7AYhw+tKP300/p+M/fEtq4EZJrStnmziXvL76jD78ZzeI3mgZ1W2DLv8LhNwf3ly2GFQ/CZbeA8fz+OKqqxtbjHTy/vYG3aloHijKLycDq2cV87YpKlk32ZHxPQTWh0tHUfXaBZHk2BuOlN3z9fMQifXQ2NeKtP4GvoY6Ohjp89ScI+ztHPN5ktVJQMYn8qkkDgWRB5SSsjrFbTV4IIYS4lHS1dvDmE3/gRKKdiBLXV+ZO2Civ9zNz32sU3H0XeU/8b4ynWUSgMdTIkzVPsuHIBqJqFIDpudP5i7l/wQ1VN4yfodx9AajZALufhcZPBvfbc2HeWr23ZOHMMf/Yrp4ofzrQzts1rXxwxEtfbDCUnFyQxc3zSrllXgnTisZmyiQhhBAXzrgOKB944AGee+45XnnlFZxO58Cck263G7tdH5L50EMP8bOf/Yxp06Yxbdo0fvazn+FwOPj617+e5taL8WZ763Z+vefX7GzbCYDNaOOuWXfxrTnfwmlJLVo0VSW8eTOd//lbenbsGNiffc01eO77No7LLx9d0BePwoFX4eNfQ/Ou5E4FZnxBDybHYHXC1kAfv9/ZwAs7Gmjo7B3Yf1mxkzsvr2DNwrKMnlMnFknQVhug5ViAlqNdtB4PEoukziFpthkplUByRGpCH57ta9CDSF99Hb6GE3S1tQ4E7idz5hdQUFVNYVU1+ZV6GJlTXIxhvPwSJIQQQoxjnU1e3vqPP1Cn+gaCyWzVSkVLL7O3/TcFX7uDvH9+B1Ne3imvUeOr4an9T/H2ibdJJIdGzy+Yz3fnfZeVZSvHxw1nVYXazbDnOTjwGsSTdahigKk3wMJvwPTVYzI6aKiWQC/v1LTxdk0r22o7BxZ8BKjKc3DzvBJunlfKZcXO8fHfSQghxKiM64Dy8ccfB+Daa69N2b9+/XruvfdeAP72b/+W3t5e/vIv/xK/38/SpUt55513cDrlLpnQ7WrbxWN7HmNb6zYALAYLX53xVe6bcx8FjoKUY9XubgKvvkrnM88SPXZM32k24775ZvK+/S2s06aN7kNDrfp8OzvWQ1gP1jHZ9CHcyx+A/FFe5xS6I3He+qyVDbub+OiYbyBnclpNfGlBKWsvr2BumTsji7LuQITWYwFajgZoOdaFtyGMpqYGadJDcjhN0wj5vMN6RHY2N5KIx0c8x+50kV85ifyKKvIrq8ivqCKvvAqrQ+ZjEkIIIc5WR10bbz35EnVqB9EhwWRpe5Q5n2yg8PY15D38JuZTTEWVUBO81/geT9U8xa72XQP7V5Su4Dtzv8OSoiXpr+00DdpqYN+LsO/3EGwcfC9/hh5KzlsLzuIx/dij7WHermnlnZpWPm0MpLx3WbGTG2cXc+PsYmaWSCgphBCZStG0U3ShmUCCwSBut5tAIIDrNEMsRGbZ3b6bf/v039jSvAUAk8HE7dNu5ztzv0NxVmrRFK2vx//sc3S99BJqKASAITubnLV34Ln77lMWkik0DRq2wSdPwP5XQE2GQtlFsOTbcPl3ICv/nH+eeELlo2MdbNjVyNs1bfTGBnsRXj4pl7WXV/KFucU4LOP6vkMKTdPoausZ6B3ZcjRAwNs77LjsXCslU9yUTM2hZKobT2n2hJ1DUlUTBNvb6Wiqp6Oxgc6mRjqa6ulsaiDaO/y/HYDZatPDx4oqCir1x/yKKrJyci9y64UQF5LUM5lNvr/M1XKkgT8/+xr1WgdRRa/PnKqVsrYIs3a9TdEdt5F3772YCgpGPL8n1sOGoxt49sCzNIT0Fa5NionV1au5a9ZdzMqbddF+llPyn9ADyX2/B++Bwf02N8z5Ciz4BpQtOu+RQf3iCZWddX42HWrnT/vbOObtHnhPUWBxZe5AKCkL3QghxPhxPvVM5iQZQoyCpml82PQh/7HvPwbuPJsUE2umreG7c79LSXZJyrHdH23B/8wzhDdvHhjuaqmqIvcb38B925cxZo9iEu1oD3z2ez2YbN03uL9iGSz9rj6/pOnchlhrmkZNc5ANu5t49dNmvKHBhUom5Tn48sJy1iwspSovM+YBjPbFaT8RpO1EkNbjQVqPB+gLx1IPUiCvNIuSKXoYWTI1B6dn7CdSH+8S8RhdrS10NNbT0dSQDCMb8Dc3EY9FRzzHYDThKSvXe0QO6RXpyi8c3VypQgghhBi1w1v28dEb79Jk7CKuqKCAK2GjrLWH2XteofAbd+L5xzcw5Y58Q7A53Mzzh57n94d+Tyim3yB3WVzcMeMO7pxxJ0VZaV70M+yF/S/rvSUbtg3uN1pg2iqYdwdMu3HMFrzp6omy+bCXPx9oZ/NhL4HewRrRbFRYPiWfG2cXccOsIgqdE682FEKIS50ElOKSkFATvFP3Dv+57z855D8E6D0mb51yK9+Z+x3KnYOrvycCAQKvvob/ueeI1tYO7M+6eiWeu+4i68orRxfmtHwKu56CvS9CJDnUxGSDuV+FK/4CSuaf889zzBvmj3tbeH1vM4fbwgP7cx1mbplfypqFZSysyBnXQ1g0VaOztZu22iBtxwO0nQjS0dwNJ/XZNpoNFE1yDfSQLJ7swuoYRytRXmD9i9V0NjXQ0dRIR6PeG9Lf2oymqiOeYzSb8ZSW4ymrIK+8gryyCjxlFeSWlGE0yV/rQgghxIW0/eUP2L1jOy3GEJpJL2xyEnZKWruZU/MGhd+8E8//eWfExW9UTeWjpo944dALfND0Aaqm/1tf6azkrll38aUpX8JhTmOPwJ5OOPhHPZg89i5o/SN2FKi+Wq9zZ94C9pzz/ihN0zjSHmbTwXY2HWhnR10nQ2f1yXGYuW5GIdddVsg10wtw2ydOfSiEEBOR/CYrMlokEeHVY6+y/rP1A0Ni7CY7d0y/g7tm3TVw51nTNHp37qTrxRcJvvU2WkTviWjIysJ9223kfv1rWE9aJX5EfQH9LvKup/SAsl9OlT6Ee+E3weE5p5+l1tfNH/c28/reFg62hgb2W0wGbphZxJqFZVwzvQCLaXz2hOsJRmk7EaStNqCHkieCxPoSw47L9lgprnZTVO2ieLKbggonRvP4/JnGiqZphDp8+Fua8Dc34W9porOlic6mRoK+9lMuVmOx2/UQMhlA6mFkJa7CQlmwRgghhLiI4vE4763/I4fqjuA1hQd+i8qPOSita2eOdysF3/oW7n/9ewy24b37/H1+NhzdwH8f+m+awk0D+5cWL+Wbs77J1eVXY1DSVA+F2+Hg6/oURbUfDAklgdJFeig557YxmVcy2Bdjy9EOPjjiZfNhL43+1OlpZhQ5uX5mIZ+7rJCFlbkYJ+iUPkIIMRFJQCkykrfHywuHXuDFwy/S2dcJQI41h6/P/Dpfv+zruK1uAOJ+P4GXX6HrxReJHj8+cL51+nRy7rgD95o1GLPPMDxa06B+qx5K1mwYXKHQaNHvIC+6GyZdDecwhPaEr5s/7mvhj3tb2N8SHNhvMiisnJbPF+aWsGp28bi7Yxzti+NrCNNeF6S9LkRbbYCgr2/YcSarkcJKJ8WTXRQlQ8ks99iu5DieRHq68Tfr4aO/pYnOZBjpb2kiHomc8jyb00Xe0N6Q5ZXklVWQ7ckb171khRBCiEtdV7OPjetfob7PS8jYByZQNIXiiIPyQ0eYbQuTd9+3yb7+n4aNwNE0jU+9n/LCoRd4+8TbxFR9yLLT7OTWqbdyx4w7qHaP4gb5hRBs0VfePvAq1H0E2pBRG8VzYdatMOvLkD/1vD4moWrsbezigyM+3j/sZXdDV8qq2xaTgRVT8vjcZXpPyfJcmU9SCCEmKgkoRUap6ajhmf3P8NaJt4gnF6EpySrh7ll3c9u023CYHWiJBN1bttD1+z8Q2rgRLaYXg4rdjuuLXyD3q1/FNm/emYOfjmN6b8m9L0DnYLhJwUw9lJx/51n3ltQ0jYOtId6paeOd/a3UNKeGkldOzeeL80pYNauIHMe5zVs51qJ9cXyNYbx1Idrrg3jrQvjbeoYN1QbILXZQNNlNcbWLomoXnpKsS2517UQ8TqC9dTCAbG7E39JMZ3MjPYGuU55nMBpxFxaTW1pGbkkZnuRjXnklDpf74v0AQgghhDijms272f6nD2gyBIgpCTCCWTNS3GNl0r5dzJhehOf//j6ORQuHnevr9fHasdd45egrHAscG9g/O282a2esZXX1auwm+8X8cfQb7r4jcOgNOPRmck7JIcVc6SKY9SWY+SXIm3JeH9Xc1csHR7y8f8THR0d9dPWkzjc+OT+LldPyWTmtgBVT8zJqgUchhBAXjvxrIMa9mBrj3fp3efbAswML3wAsLFzIN2d+k+srr8dkMNF3+DDtr75K4LXXibe1DRxnmzWLnDvuwHXzF8+86E13B9S8pIeSjdsH95uz9KEti+6B8iVntUJhPKHyyYlONu5vY+P+tpShLEaDwoopedw8r4RVs4rJzUpvKBmLJPA2hPDWhfDWh2ivC54yjMzOtVJQ6aSg0klxtZvCSc5LZu7IeDRKoL2NrrYWulpb9Me2Frpamwm0t51ybkiArJzcwRCypCz5vBx3YZHMDymEEEKMY9G+CO89+TpHG2tpN4YhOZuKU7VS1JngsoNbqbz58+Su+/8wl5SknBtLxNjcuJmXj77Mh00fkkgOk7YZbayuXs3aGWuZkz/n4v5AiTjUfwyH39KDyaE33AHKr9B7Ss68BXKrzvljfOEIHx/r4OPjHWw91sFxX3fK+06biSun5HP19AJWTsunwiO9JIUQQgwnvy2Lcasp3MQfDv+BDUc34Ov1AfqK3KurV/PNmd9kdv5s4l4vgaeeIfDqq0T2Hxg41+B247ppNTlf+Sr2ObNP/0HRHjjyNnz6AhzdCMmemSgGmHyd3lNyxhfAOooVvZPCkTjvH/aycX8bmw62p6xCaDMbuGpqAatmFfH5WUV40hRK9oaj+BrDdDSG9eHa9SG6WrtHnA4xK0cPIwurnMlHFw7X+Ojhea6ifb2D4eNJj6EO3ynnhQQwW23klpSRW1JKbmk5nuRjbkkpVkdmrKguhBBCCN2RbTVsfWMzLVoXPYbowG9IhdEsihrbmd+7n4JvfgPn//m/MFgHp6rRNI3D/sO8fPRl/nj8j/gj/oH35hfMZ83UNdw46UacFufF+2H6AnD0T3ovySPv6K/7Gcz6QjczbtJrW3fZOX1EV0+Urcc7+fiYj4+Pd6Qs6AhgUGB+RQ5XTyvg6un5zC/PwXSJjagRQggx9iSgFONKXI2zuXEzLx5+kS1NW9CSXfc8Ng+3T7udOy+7k7yEnfC771L/+i/p/mgLJJITeZvNZF9zNe4vfYnsa6/FYDlNgBbt1ou2/a/A4XcgNuROb8kCmLcW5twOzqJRtbt/FcLNh/QJvz+p7SSaGOxl58my8LnLCrlhVhErpxVgt1y8BU5UVSPQ3oOvMTwYSDaG6e4aeU7ELLeFgirXQBhZUOnMyHkjNU2jNxQk0N5KV1ur3vuxrRV/q94T8nTDsQHMNjs5xSXkFpWQU1yCu6iE3OIScopLZW5IIYQQIsP1dffy59++Sl1bg95bUgEUsGgmirotVB3ez6zZpeT+6C7s8+alnFsfrOfN2jd5s/bNlCHchfZCbplyC7dOvfXizS2pqtCyB479GY5u0oduD13kxu6B6TfqoeSU68F69mFpZ3eUHSc62VbbycfHOjjQGhx2H/eyYifLp+SxYko+V1R7xt386UIIIcY/CSjFuHAicIJXj73Ky0dfxtvrHdi/rGQZX53+Va7OWUJk8wcE//3HHPnww4F5JQHs8+fjXnMrztWrMeXmnvpDImG9p2TNy3Bk4+BiNwDuSpj3VZh7BxReNqo266sQ+th82MvmQ16aA6mLxEzKc3DDrCJumFXM4qqLswphtC8+EED6mvSekZ3NYeLRkYckuwrs5Jdnk1+eTUGFk4KqzAojIz3dBNrbCLS3EmhvI+htT3keiwxfuGcom9NFblEJ7qJicopLyR0SRNpdbgkhhRBCiEuIpmnseOVDanbuoVUJ0meIpazGXdAeZk7wEKW33YLrx3+ZMjVQa3crb594mzdr36Smo2Zgv9lg5rqK61gzdQ3LS5djMlyEX69CbXBskx5KHtsEPR2p7+dPh+mr9V6SFVeAYfQ3xjVNo76zh+0n/Ow40cn2E50c83YPO25qYTYrpuSxfHIeSyfnpW1EkBBCiEuHBJQibbr6unjrxFu8dvw19nr3Duz32DzcOvVWbi+5iZzthwk++jInPvxRSihpmTIF1+rVuG+5GcukSaf+kLBX7yl56A19uEt8SGCVOym5QuEaKF14xnkl4wmVfU0BthzrYPNhL7vq/MSHrEJoNRlYNjmPa6YXcM2MAibnZ12wgCseTeBv7aGzOUxnSzedzd10tnSPuJI2gMlswFOWTX5FNvlleiCZV5aNxT6+/wqIRfr00NHblgwi2wj2P3rb6OsOn/Ea2bkePYAsKiWnWO8NmVOkb7YzzUkqhBBCiIx3bPtBtr3xHq3xLoLGvoG5JW2amaKwiYpjh5izeDI5f/N1bJcN3qhu72lnU/0m3qx9M2UedKNiZFnJMm6qvonrK6+/8EO4o936XJK1H+ihZOu+1PctTn3o9tTrYcrnwDP63pvxhMqBlhDbT3Syo66T7Sf8eEPDR9lMK8xmySQPy6fksWyyh0Kn7Xx/KiGEECLF+E4nxCUnlojxftP7vHbsNTY3bh5YiduoGFlRuoIvu1ey8FCcnn/7gJ6tT9IzNJScOgXXjatxrb4R67RpI3+ApkFbjT4Z+OG3oHEHKSu8eCbrgeSsW6Fk/mlDSVXVONAa5ONjHWw51sEntZ2EI/GUYybnZ3HNjAKumV7Assl52MxjO3Q7HusPIrtPCiJ7R1y4BvT5IvPLs8lL9ozML8/GXejAcBF6cJ6N/iHYIZ+XYIeXkM9HqMNL0Ocl5Gsn0N52xmHYAHanC3dhEa7CYtwFhbgLi3AX6K9dBYWYzDLESAghhJho2o818eHv/0RzqB2fsVsfwm0Eg6ZQGHWQ3+ZnnqWT4jVfIPv/+SEGmx641QZq2VS/iU31m9jr25tyzUWFi/hC9Rf4fNXnybPnXbjGR3v0odonPoQTH0DTzsE50vuVLICpn9MDyYorwHjmekfTNJq6etnT0MWnDV3saehiX1OAvljqSBuL0cDccjdLJuVyeZWHxVW5aV/IUQghxKVPAkpxwUUTUT5u/ph36t7h3fp3CcVCA+/NzJnBWpaw5CgkNnxC5MBP8Q45d1ShZLQH6rYMhpKBhtT3i+fp8+7MvAWK5pwylFRVjWPeMB8f72DL0Q621nbQ1RNLOcZlM7Fsch4rpxdwzbQCKvPGZhXCSG+crrYeutp68LcOCSK9vadcq8WWZcZTmoWnJGvwsSwLe/b4KCDj0SihTp8eQPq8g48dg6/j0ZHnwRzKYrcPBI7uwqKBzVVQhLugEItdVoIUQgghBLQcqufjDZtoDfvwGrvRFG3gt528uIO8zggzQw1M+sJ1OFd/B1NuLqqmUuOr4c81f2ZTwyZqA7Up15xXMI8bKm9gdfVqirOKL0zDoz3QtEMPJGs/0J8noqnHuCuheiVMvlZfxDG74IyXDfTG2Ns4GEbuaQjgCw+vvVw2E0smefRAcpKHuWXuMb/pLoQQQpyJBJTigogkImxp2sI7de/wXsN7hGODQ3EryePr3XNYVGvAuHU3Ce/v6Ol/02DAvmABzuuvI/u667BOmTL84qoKrZ/CsXfh+LtQvzW1iDPZ9OJt+o36/Duu0hHb2BdL8GlDFzvq/Oys87Or3j8skMyyGLmi2jMw6ffMEtc5zyWZSKiEfH34k0HkQCDZ1kNvMHrK86wO0/AgsjQbu9OctjkSY9EI3Z2dhDs7CPk7CHfq29AQcjS9HwEc7hxc+QU48wv0x7xCnPn5yVCyCFtWtswFKYQQQogRNX5Wy9bX3qWtuwOfsScllHQnbHhCUO1tYOaVs3F/72tYKioIRAL8qeVjPtr/ER81fZQy/7nJYGJp8VKur7yeayuupdBROPaNDjbrPSTrt+mPrXuH95B0luqB5KSV+mPupNNe0t8dpaY5yGfNAWqag9Q0Bzg+wtyRJoPCzBIX8yvcLKjIZUFFDpPzs8bdSBshhBATjwSUYsz4+/x82PQhmxs382HTh3QnV8Y2JjSW+3L4gq+UqUd6MB6qBfXPACQAg8NB1sqVZF93LdnXXDN8oRtNg646OP6eHkrWboZef+oxrjKYdgNMv0mfg8cyvFdde7CPnXV+diS3mqZAyhySoM8juWRSLium5LNsch7zyt2YjYZR/zfQVI2eYJSAtzclgOxq6yHo7UVVT9EdEnC4LOQUOcgpcgyGkaVZOFyWixbQ9Q+77g8cw50dhPqfDwki+8KhM18MMFmtuPKGhI/5BbjyC3Hm5ePML8Dpycd0utXWhRBCCCGGiMfj7HrtI458WoMvHsZv7NGHb6eEkhoVXe3MXV6G+8YbMFZWUNNRw++bXufDfR/yme8zVG1wWHOWOYuVZSu5vvJ6riq7amznlEzEoH0/NHwyGEoG6ocf5yyBqisHQ0nP5BFH/WiaRmuwj5qmIWFkU2DYYo39Kjz2gSByQYWb2aXSO1IIIcT4JAGlOGeapnHYf5j3G9/n/cb3+dT7KRoaiqpR6YUvtmZxVZOLosNelN4OYHCFQcvkyWStWEH2tdfiuOJyDENDKlUF70Go3wJ1H+uTggebUj/c4kwOc7kOplwHeVNTirjO7ij7mgLsa+xib2OAfU0BWkYo3AqdVpZMymVxcn6dWSUuLKbTB5LxWIJQRx8Bby8Bby9BXy9Bby8BXx9BXy+J2MgrZoO+WI27yEFuMogculkv4II1mqrSGw7R3eWnu8tPT/Kxu6uTULInZLizg25/B4l4/MwXBEwWK9keD9mePLJz88j25CVDSD2AdOUXYMt2Su9HIYQQQpyXrpYOtr60iabmJnzGbnoNyREvydIpJ27HE0pQGexg7soqsm/4PI3OKJtad7C97lds3bqVQCSQcs2pOVO5quwqriy7kkWFi7AYx+CGqZoA3xFo3g3Nu/TH1n2pizQCKAYomg0Vy6BiKVQuBXfFsEAy0BvjSFuIQ20hDrcmH9vCdHaPPPJmUp6D2aVuZpW6mF3qYm6Zm7xs6/n/XEIIIcRFIAGlOCveHi9bW7bySesnbG3ZSmt3K6a4xpQWuLVBY0mbg+r6KOaeKBBMbmD0eMhavpysFSvIunIF5uIhc/jEevW7yfUf68O16z+Gvq7UDzaYoGyJHkZOvhbKFg9MBu4LRzh4tEMPJJv0QLLR3zus7YoCM4qcLJmUy5JkIFmeax8WoPX3ggx19hHs6CXo7SOQDCGDvl7CXZFTLlADoBgUnB4r7kI9eBwaRmbnWFHGcAhNrK+P7i4/4a7OIaFjlx5CBvyDgWSgCzWRGPV1He6cZOiYDCCTmzO3/3k+1qwLt0q5EEIIISauvnAPn7z8PnWHj9GlduM39qIqGiTXgTFpBvJidtyBPqaaIky7bgbeeeXsjB7h1dbt7NzyJJ19nSnXdJqdLCtdxlVlV7GidMX5zyeZiEHHUWj9DFr2QNMuaPkUYsOHVWN1Q/niZCB5BZQvAetgL81wJM7xpgCH28IcbgtxqDXE4bbQiDfXAYwGhWmF2cwqdTGn1M3sUhczS124bLIwoBBCiMwlAaU4rUAkwI62HWxr2ca2lm0cDxwnN6QxpUXj2haNWQ0KU1vAFO/vNajPNWnIysK+cCFZy5aSdeWVWGfMQDEYIBEH7wHY+ba+ImHzLmg/MHzeHbMDyi+HqhVQuRzKl9Cn2DjSFuZga5BDe49wsDXEwdbQiJN9g77C9txyN3PL3Mwrz2F2qYssq4lYNEG4s49wax8H9ncR6uwj3NlHKLmF/RHUxGkSSMBsNeIqsOMusOPK73+04S6wk+2xYTyLYeFDJeJxekNBeoMBeoKB5GOQ3mBX8nWQnmDXQBAZ6xsexJ6O3ekiKycXR04uWcktO3dIAOnJIys3F6NJClxxalpCQ4sl0GJqckugxTUsZdnpbpoQQogM1Nfdy+4/fkzdgSN0RkN0mnqJKwkwoG9AlmrB02vCEwwzu8xMaFkRe4p7ecZfw572f8b/Qer0P1ajlQUFC1hcvJilxUuZVzAPk+Ecf/UJt0PbZ9BWk9w+A++h4QvZAJizoGQ+lC7Ut7JFkFtNXINGfy/HfWGOf+LluO8Ex71hjnu7aQ+detHAUreN6cVOZhQ5mZ7cphVlyzBtIYQQlxwJKMUATdM4ETzBnvY9fOr9lD3te2htPcqUFr2H5O0tejDpCaecBeg9JB1LluBYshj74sXYZsxAUSPQfhDaPoG3n4TmPfqd5fgIoVpWYTKQXE64+AqOGidzvCNCra+bYx+HOdi6nRO+bkaawlFRoNLjYE6Zm7mFTqa7HZTbLNCboLsrSndLhI6aJv7YeYywv4/eUGz4RU6+pkEhK8eC02NLDSEL7Ljz7diyR7dATSwaoS8cSgaLeuA4GDoG6Al2DTzvDQbo6w6f8ZonM1mtZOd4kqFjjh48ulNDyKzcXByuHIwm+SN/KdISGlo8GRjG1cHgMD74yMn7BoLFIa+j+jXU/vdiJx0bTV5rpABfgbKfXSW9aoUQQpxR+/EWdr+9hbbmZoJaL13GPuJK8mZ38h6pVTORG7XiCkcpMvURm97L7kkRXo8e4VjXMbR2DdoHr2kz2lhQuIAlRUu4vPhy5uTPObth25qmL17jO6z3jPQd1kPI9v3Q7R35HEs2FM6C0gVQupBI4TwaDOU0dEWp7+yhob6Huj1+an2N1HV0EzvNDfD8bAvTCp3MKNZDyBnF2UwrckqvSCGEEBOGpBUTlKZptPW0cbDzIAc6D1DTtpfWQ3vIbQpS6dWo9sI17RrFXSOcbDBgnTIF29y52BcuwLFgHha3huI7pN9V3vc2/Hk/dNYy4lhoq4t48XwCuXNpdMzkM20y+0JOjvt6OL4pjC/sA3wppxg1yFYVSqwmpjkdVNis5JuMODUDpqhKXzBK9ychon1dHAAOnOHnN1uNOPNsZOfacObZcHqsQ57byHJbMAzpBdkfNPaF/fjq6+gLh+kNh5L7+rcwveEgfeGw/joUIh479ercp6IoBmxOJw6XG7vLhcOVg93lxuFyJR9z9CAy10NWTi4Wm/2sP0NcGJqqQaI/CNRSA8CRgsOU54nBc2IqnHzeac4fMbm/SBSzIbkZ9XYYJaAUQggxyN/sY9+fttNcW08o0k3IGCVo6NMXthnSCdCiGcmJ2XD2xMmOdtFV3MQnM7rZbqwnoibrqSGBZElWCXPz5zKvYB7zC+YzO282ZuMZwjxN0xda9J/Qt/4g0ncYfEdHHp4NgAJ5U0gUzCLgmk6bfSrHjZM41JdLg7+Phroe6nf30B6qA+pO+fFWk4Hq/CwmF2QxOT+byQVZydfZuO0SRAohhJjYJKCcACKJCHXBOo51HeNQ6z7aDn9K+PhRnO1hKnwale0aK3xgOcUUhebKSuxzZmObWo69NAubJ46hux46DkLz67C/HrSRF4aJ2AroyJpCvamaA1oVW6OT+CSQS9fBBDYNHJqCQw3h0MI4NJihKizSzOQajbgNRrI0BVNMhVgygAkBvigQpQ8YaWYes81IlttKVo5Ff3RbcbiNWLM0LLY4JnMcLdFHtLeHvu4uoj3d9AW6CbT0EOnpHtgGgsZwmHj01ENvzkQxGLA7XdidLhzuIWGj043DnTMkeHRjd7mxZWdjMMiwndHSQ0FND+3iKloyIEzZF9cDQ4Y8798/ECgOOZ74SecmtOQ1U89n4PM0tMQpehZebCYFxTQYGiomJfmY3DfwniH1uP59lpNeD3mumRQ0I2AE1QCqQUNVVRKJBKqqopzj1AZCCCEyXzwa4+gnBzix+wCd7T7CiT7CxthgGAkwpENjtmrFFTHh6I2gqm3sy93DW6V+/M6TbnSp4DA5mJM/h7n5c5lbMJd5+fMocBSM3JBICIIt0FU3GET6TyRf10EkeMqfQVOM9Dqr6LJX0WKu4ASl1CTK2dlTRJ0fupqGjsLpTG6psq0mKjwOKnLtVHocVHgcA6FkqduOYQznIhdCCCEuJZdMQPnYY4/x85//nJaWFmbPns0vfvELVq5cme5mXTRxNY63x0tTqJGGhhq8Jw4QbDhOvLEJe2uAYr9GSafGDcGBqXyG0axmbJUl2Erd2ArMWN0xbI4ujNFmCG6H9kTKnWvQb0THNDtdFNDIZI4lqqiNldASL8Kv5qFpNmyaom8qlGgKX9MU7JoZA6cv0DQtBloMjSiaFsVgSGDLUrHYNMxWFbNFxWCOYzTEUQwxIIYa7yMW6SXS001nXTctPd1Ee3rOqSfjyRSDAVu2M7llYx947hzyPBub05V8nY0t24XFPnwhnkwxEP4l9JBOD+gGwzgt+R7xk45Jvkd/uDdwfDIoHHHfkPMSg4Hh0GO0RHKocmJISJjmUFBDQ0UbeNQ3FU3R0MwKmgk0owImBc2ooJpAM/ZvCppR058b9E01gqag7zeAqmhoCqgKaIbk5yj6pqG/ryZboKIHhidv/QHiwBZVUfuGv9f//OTH/udn8uMf//iC//cWQojx7lKvSX0nWjm2fT/ttQ0Eg0F61Cg9xjghQ2RwmHbyZlY/u2rGGbdgjySgL0CDspetJXWcqICIZWiNpFCaVcr03OlM90xnRu4MZnhmUOGswBCPQo9PH25dvx1CzXoQGWqBYNPg89MEkP2CJg9thmJOUMqheDGfRYo4ohZTrxUR6x3p16PBYNJhMVLitlHitlPhsVPhcehBZK7+mOMY3TRAQgghhEh1SQSUL7zwAg899BCPPfYYV155Jb/5zW+46aab2L9/P5WVlelu3nnrifXQEfbS2X6CYGsjIW8T4fYmen1tJLxeTN4uHJ295AU1PCG4LAGXneZ6casBck3YnWaMLgWzI44hy4BqtxLT4oS1HjqjDqLtdmKqh6g2m6jmIKI6CGo5dKtOetUsEqoFg6qgkEDT4qDFgDg2LU61FqOa5pT9aHE0YqDFSWhx4sQwGlUUQwxFiYMWRdNiqIkIajyCmogPa/vJi3ufLYvdjtWRjdXhwJqVhdWhbxZHlr7P0b/PMSSMdGJ3OrHY7PpCPyPQNE0fza5qeqin9gdtGolgdDCQ69/ff1zi5NfqKfYPfVRPej3kUR0S8I2wf9hnJYacMyRM1BIqaiKBppKM3vpDuNRATlOGPE++P/T10ONO+d7Qayunea//taKlPjed1EZD8joGPehDSYZ8hsHra0oyBEy5XrJ9ioaqJUO/5KOqaWiaSkJL7lP1fQl1FCujq8ntzFOfZiSDwYDBYMBoNKKqKoZT/BkRQoiJINNr0qDPT8Pe47QfrSfo66S3p5e+RIw+JUHEmKDbECOqDKnPTvpNwqApZKtWHHEjlkicRMRPs1LDttI66gohataDOwWFEnshi+yFVFk9VJtdTDdmM12xkd0bJN7lRW3cDD1/QOnpQO3rxBAb/TzdIc1Og1ZIg1ZAg1ZAvVZIg1ZIvVZIo1ZAhJHnpnTbzRQ4rckA0kax206p20ZxMpAsdttw2UwSQAohhBAXgKJp2jgYk3h+li5dyqJFi3j88ccH9s2cOZM1a9bwyCOPnPH8YDCI2+1m28aN2G02iMdIqBpqIkY8ntADtZiKpoGaiKOpKqqa0EMcrf95Ak3VUNUEaiIZZiRiJOJx1HgULR5HjcVQ4zG0eJxEPAGJBFo8jhaLocUTKDFQ4hpKAgwqGOIGDAkwqQZMCQUUA6rBgKb0bwqqYkA1GFEVI6pBAcWo7zMaUA16jy0FRT8WJRn0KMmZIdUhmx7PKJqGkoyElORr+u+GayqKMngFvTYbLDRR9M+if0u+VgDNkPpeamE35NyB/QqKon+SQTFgMJkwGE0YTSYMBiMGo/6oGAwYDCYUowFFMQzZZwRFPxfFkPw8RY+hVA1N0wZCxf7nmqYHd/oficHXaMkAbOC4ZM9C9CBLQ///Rn/INRiXcdLj0BBOP0496YzTnzP0+qc4bshRKhooI79/8vkDxyfDO3H++oO7/vBu6OuTtzO9P1bHnOr9ofv7n492n/ySJsT40V/PBAIBXC5XupszIZ1PTdr//W19+x1sVitaNI6qxonH4qjxBGoirvdsj2ugJkioKolYXK9dEgnU5GMioZGIJEhE46hxlURcRVX1miWh6VVfQkluBo24ohJTVCKGOFFlFDfe0BewcSTMWOMGzLE4aiRIh6GBvZ4aThTFCDkUTBrkJxSKElAcTzApFmVKpIcpsQiV8Ti2c/gVJKYZ6cRJq+ahTculVfMkt1xa8Qy87sEGgMVowO0w43FYyHdayM+2Dtks5DutFCRfe7IsWExyk00IIYQ4H+dTj2Z8D8poNMrOnTv5n//zf6bsX7VqFVu2bBnxnEgkQiQyOKdgMKgPBdnw5z9jtVovXGMxAFYwWS/Sf3mNERepGdFJM5WnnTbkUQWG96YEBvPVU7x9wUzw+lVRlIFwqj+wOtfnY3GNC3G98w39hBBCTCxnW5Oeqh59+d1NY1ePKgysij1aRs2ATTNjVY1YEgrGhIYhFiOeCOM3tHHEeRRfvhejXcWjqngSCTwJleJ4nAWJBKVdcUp8cfIS6inLJVVTCOIgiIOQZieEg6DmIIQDv+bEp7kIKC66Tbn0mnPos+QRs+aiWd1k2cw4LEbcdjNuuxmPw8yk5PMcuxm3w0yO3YLbbsZmlhtpQgghRKbI+IDS5/ORSCQoKipK2V9UVERra+uI5zzyyCP85Cc/uRjNO70h2eGpSyflDO8PPXLko860d9j7ypmup6QcN3RffxHY319S7xk5eMTQ9/X/DT1O7zU57FoDvSuTx/fvNwyeM3h+8jil/zgledyQfUryc1JeDznOYEi5Rv97nPTaYDDo5ygGDAYFpf+1YfC1wWhIvU5y6w/Dzmf/WFxjtPtPDgCl2BdCCCFSnW1Nelb1qEZqPTWkPhz6HBQMKBg1BYOmYER/HNzAqGoYEhqKGkdTY6hEUJVeYoYO+iyNxOw+EtkGEooJTTNg1IzYNCPZmpmpmplrtWyI5KJGTcQMNuIGK3GjlYTFRsRoo9Zo45jJhmK2oyUfFbMdzepCsbvB6sZoz8ZiMmE1G7EYDVjNBiqtJhwWI1kWEw6rEatpPN04F0IIIcSFlvEBZb+TAxNN004Zovzd3/0d69atG3gdDAapqKjgW2vvJMeTh9FkSm5mzBYLigFQwKAYU8Ks1AaktmPoZ0uYI4QQQggxMYy2Jj1VPXrv19bi8eRjMJux2KwYTCZsVlvyxqj00BdCCCHEpSnjA8r8/HyMRuOwO9Pt7e3D7mD3s1qtIw6dKZ5cLXM2CSGEEEKIs3a2Nemp6tGSSZOlHhVCCCHEhJPxt2EtFguLFy9m48aNKfs3btzIihUr0tQqIYQQQggxkUhNKoQQQghx7jK+ByXAunXruOuuu1iyZAnLly/niSeeoL6+nvvvvz/dTRNCCCGEEBOE1KRCCCGEEOfmkggo165dS0dHBz/96U9paWlhzpw5vPHGG1RVVaW7aUIIIYQQYoKQmlQIIYQQ4twomqZpZz7s0hYMBnG73QQCAZnzRwghhBAZSeqZzCbfnxBCCCEy3fnUMxk/B6UQQgghhBBCCCGEECJzSUAphBBCCCGEEEIIIYRIGwkohRBCCCGEEEIIIYQQaSMBpRBCCCGEEEIIIYQQIm0koBRCCCGEEEIIIYQQQqSNKd0NGA/6FzIPBoNpbokQQgghxLnpr2P66xqRWaQeFUIIIUSmO596VAJKoKOjA4CKioo0t0QIIYQQ4vx0dHTgdrvT3QxxlqQeFUIIIcSl4lzqUQkoAY/HA0B9fb0U9BkqGAxSUVFBQ0MDLpcr3c0RZ0m+v8wn32Hmk+8w8wUCASorKwfqGpFZpB7NfPL3aOaT7zCzyfeX+eQ7zHznU49KQAkYDPpUnG63W/4QZDiXyyXfYQaT7y/zyXeY+eQ7zHz9dY3ILFKPXjrk79HMJ99hZpPvL/PJd5j5zqUelQpWCCGEEEIIIYQQQgiRNhJQCiGEEEIIIYQQQggh0kYCSsBqtfK//tf/wmq1prsp4hzJd5jZ5PvLfPIdZj75DjOffIeZTb6/zCffYeaT7zCzyfeX+eQ7zHzn8x0q2rms/S2EEEIIIYQQQgghhBBjQHpQCiGEEEIIIYQQQggh0kYCSiGEEEIIIYQQQgghRNpIQCmEEEIIIYQQQgghhEgbCSiFEEIIIYQQQgghhBBpM+EDyscee4zq6mpsNhuLFy/mgw8+SHeTxFl4//33ueWWWygtLUVRFF5++eV0N0mchUceeYTLL78cp9NJYWEha9as4dChQ+luljgLjz/+OPPmzcPlcuFyuVi+fDlvvvlmupslztEjjzyCoig89NBD6W6KGKUf//jHKIqSshUXF6e7WeIcSE2auaQezWxSj2Y+qUcvLVKPZqaxqEkndED5wgsv8NBDD/EP//AP7N69m5UrV3LTTTdRX1+f7qaJUeru7mb+/Pn86le/SndTxDnYvHkzDzzwAFu3bmXjxo3E43FWrVpFd3d3upsmRqm8vJx//Md/ZMeOHezYsYPrr7+eW2+9lZqamnQ3TZyl7du388QTTzBv3rx0N0WcpdmzZ9PS0jKw7du3L91NEmdJatLMJvVoZpN6NPNJPXrpkHo0s51vTapomqZdoLaNe0uXLmXRokU8/vjjA/tmzpzJmjVreOSRR9LYMnEuFEVhw4YNrFmzJt1NEefI6/VSWFjI5s2bufrqq9PdHHGOPB4PP//5z7nvvvvS3RQxSuFwmEWLFvHYY4/x8MMPs2DBAn7xi1+ku1liFH784x/z8ssvs2fPnnQ3RZwHqUkvHVKPZj6pRy8NUo9mHqlHM9tY1KQTtgdlNBpl586drFq1KmX/qlWr2LJlS5paJcTEFggEAL2gEJknkUjw/PPP093dzfLly9PdHHEWHnjgAb74xS/y+c9/Pt1NEefgyJEjlJaWUl1dzZ133snx48fT3SRxFqQmFWJ8kXo0s0k9mrmkHs1851uTmi5Qu8Y9n89HIpGgqKgoZX9RURGtra1papUQE5emaaxbt46rrrqKOXPmpLs54izs27eP5cuX09fXR3Z2Nhs2bGDWrFnpbpYYpeeff55du3axffv2dDdFnIOlS5fy1FNPMX36dNra2nj44YdZsWIFNTU15OXlpbt5YhSkJhVi/JB6NHNJPZrZpB7NfGNRk07YgLKfoigprzVNG7ZPCHHh/eAHP2Dv3r18+OGH6W6KOEszZsxgz549dHV18Yc//IF77rmHzZs3S1GYARoaGvjhD3/IO++8g81mS3dzxDm46aabBp7PnTuX5cuXM2XKFH73u9+xbt26NLZMnC2pSYVIP6lHM5fUo5lL6tFLw1jUpBM2oMzPz8doNA67M93e3j7sDrYQ4sJ68MEHefXVV3n//fcpLy9Pd3PEWbJYLEydOhWAJUuWsH37dn75y1/ym9/8Js0tE2eyc+dO2tvbWbx48cC+RCLB+++/z69+9SsikQhGozGNLRRnKysri7lz53LkyJF0N0WMktSkQowPUo9mNqlHM5fUo5emc6lJJ+wclBaLhcWLF7Nx48aU/Rs3bmTFihVpapUQE4umafzgBz/gpZdeYtOmTVRXV6e7SWIMaJpGJBJJdzPEKHzuc59j37597NmzZ2BbsmQJ3/jGN9izZ48UgxkoEolw4MABSkpK0t0UMUpSkwqRXlKPXpqkHs0cUo9ems6lJp2wPSgB1q1bx1133cWSJUtYvnw5TzzxBPX19dx///3pbpoYpXA4zNGjRwde19bWsmfPHjweD5WVlWlsmRiNBx54gOeee45XXnkFp9M50HvE7XZjt9vT3DoxGn//93/PTTfdREVFBaFQiOeff5733nuPt956K91NE6PgdDqHzbGVlZVFXl6ezL2VIX70ox9xyy23UFlZSXt7Ow8//DDBYJB77rkn3U0TZ0Fq0swm9Whmk3o080k9mtmkHr00jEVNOqEDyrVr19LR0cFPf/pTWlpamDNnDm+88QZVVVXpbpoYpR07dnDdddcNvO6f2+Cee+7hySefTFOrxGg9/vjjAFx77bUp+9evX8+999578RskzlpbWxt33XUXLS0tuN1u5s2bx1tvvcUNN9yQ7qYJMSE0Njbyta99DZ/PR0FBAcuWLWPr1q1Sy2QYqUkzm9SjmU3q0cwn9agQ6TcWNamiaZp2AdsohBBCCCGEEEIIIYQQpzRh56AUQgghhBBCCCGEEEKknwSUQgghhBBCCCGEEEKItJGAUgghhBBCCCGEEEIIkTYSUAohhBBCCCGEEEIIIdJGAkohhBBCCCGEEEIIIUTaSEAphBBCCCGEEEIIIYRIGwkohRBCCCGEEEIIIYQQaSMBpRBCCCGEEEIIIYQQIm0koBRCiPPw3nvvoSgKXV1d6W6KEEIIIYSYoKQmFUJkOkXTNC3djRBCiExx7bXXsmDBAn7xi18AEI1G6ezspKioCEVR0ts4IYQQQggxIUhNKoS41JjS3QAhhMhkFouF4uLidDdDCCGEEEJMYFKTCiEynQzxFkKIUbr33nvZvHkzv/zlL1EUBUVRePLJJ1OG0zz55JPk5OTw+uuvM2PGDBwOB1/5ylfo7u7md7/7HZMmTSI3N5cHH3yQRCIxcO1oNMrf/u3fUlZWRlZWFkuXLuW9995Lzw8qhBBCCCHGLalJhRCXIulBKYQQo/TLX/6Sw4cPM2fOHH76058CUFNTM+y4np4e/uVf/oXnn3+eUCjEbbfdxm233UZOTg5vvPEGx48f5/bbb+eqq65i7dq1AHzrW9/ixIkTPP/885SWlrJhwwZWr17Nvn37mDZt2kX9OYUQQgghxPglNakQ4lIkAaUQQoyS2+3GYrHgcDgGhtAcPHhw2HGxWIzHH3+cKVOmAPCVr3yFp59+mra2NrKzs5k1axbXXXcd7777LmvXruXYsWP813/9F42NjZSWlgLwox/9iLfeeov169fzs5/97OL9kEIIIYQQYlyTmlQIcSmSgFIIIcaYw+EYKAQBioqKmDRpEtnZ2Sn72tvbAdi1axeapjF9+vSU60QiEfLy8i5Oo4UQQgghxCVFalIhRCaRgFIIIcaY2WxOea0oyoj7VFUFQFVVjEYjO3fuxGg0phw3tIAUQgghhBBitKQmFUJkEgkohRDiLFgslpSJxMfCwoULSSQStLe3s3LlyjG9thBCCCGEuPRITSqEuNTIKt5CCHEWJk2axLZt2zhx4gQ+n2/gjvP5mD59Ot/4xje4++67eemll6itrWX79u08+uijvPHGG2PQaiGEEEIIcSmRmlQIcamRgFIIIc7Cj370I4xGI7NmzaKgoID6+voxue769eu5++67+Zu/+RtmzJjBl770JbZt20ZFRcWYXF8IIYQQQlw6pCYVQlxqFE3TtHQ3QgghhBBCCCGEEEIIMTFJD0ohhBBCCCGEEEIIIUTaSEAphBBCCCGEEEIIIYRIGwkohRBCCCGEEEIIIYQQaSMBpRBCCCGEEEIIIYQQIm0koBRCCCGEEEIIIYQQQqSNBJRCCCGEEEIIIYQQQoi0kYBSCCGEEEIIIYQQQgiRNhJQCiGEEEIIIYQQQggh0kYCSiGEEEIIIYQQQgghRNpIQCmEEEIIIYQQQgghhEgbCSiFEEIIIYQQQgghhBBp8/8DHUMuVtHcBLoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from biocrnpyler.components import Protein, Promoter\n",
    "from biocrnpyler.mechanisms import Transcription_MM\n",
    "#Because we are lazy, all parameters will use the default \"param_name\"-->value key mapping.\n",
    "#Parameter warnings will be later suppressed in the Mixture constructor\n",
    "#For details on how parameter loading and defaulting works, see the Parameter notebook.\n",
    "kb, ku, ktx, ktl, kdeg = 100, 10, 3.0, 5.0, 2\n",
    "parameters = {\"kb\":kb, \"ku\":ku, \"ktx\":ktx, \"ktl\":ktl, \"kdeg\":kdeg}\n",
    "\n",
    "#A constituitively expressed reporter\n",
    "#By default the promoter 'P' will use the polymerase 'rnap'\n",
    "reference_assembly = DNAassembly(name = \"ref\", promoter = \"P\", rbs = \"BCD\")\n",
    "#A constiuitively expressed load (RNA and Protein)\n",
    "full_load_assembly = DNAassembly(name = \"Load\", promoter = \"P\", rbs = \"BCD\")\n",
    "#A constiutively transcribed (but not translated) load\n",
    "#By putting rbs = None, DNAassembly automatically knows not to include translation\n",
    "RNA_load_assembly = DNAassembly(name = \"TxLoad\", promoter = \"P\", rbs = None)\n",
    "\n",
    "#Load genes on orthogonal polymerases\n",
    "T7 = Protein(\"T7\") #Create a new protein (polymerase) called 'T7'\n",
    "\n",
    "#Create a custom promoter with a custom mechanism that uses T7 instead of RNAP\n",
    "#instantiate a new mechanism Transcription_MM with its own name and overwrote the default parameter rnap\n",
    "mechanism_txt7 = Transcription_MM(name = \"T7_transcription_mm\", rnap=T7.get_species())\n",
    "#Create an instance of a promoter with this mechanism for transcription\n",
    "T7P = Promoter(\"T7P\", mechanisms={\"transcription\":mechanism_txt7})\n",
    "#Create A load assembly with the custom T7 promoter\n",
    "T7_load_assembly = DNAassembly(name = \"T7Load\", promoter = T7P, rbs = \"BCD\")\n",
    "\n",
    "#Each new assembly requires its own promoter instance - so here I create another here\n",
    "T7P = Promoter(\"T7P\", mechanisms={\n",
    "    \"transcription\":Transcription_MM(name = \"T7_transcription_mm\", rnap=T7.get_species())})\n",
    "#A load assembly with the custom T7 promoter and no RBS\n",
    "T7RNA_load_assembly = DNAassembly(name = \"T7TxLoad\", promoter = T7P, rbs = None)\n",
    "\n",
    "#Add all the assemblies to a mixture\n",
    "components = [reference_assembly, full_load_assembly, T7_load_assembly, T7, RNA_load_assembly, T7RNA_load_assembly]\n",
    "myMixture = TxTlExtract(name = \"txtl\", parameters = parameters, components = components)\n",
    "\n",
    "#Print the CRN\n",
    "myCRN = myMixture.compile_crn()\n",
    "\n",
    "#The Species, Reaction, and CRN pretty_print functions return text which has been formated with a number of formatting options\n",
    "print(\"\\npretty_print gives a nicely formatted repesentation of the CRNS, reactions, and species. The names of species are formatted for clarity, but are not the actual species representations. Additionally a number of printing options are available.\",\n",
    "      \"\\n\", myCRN.pretty_print(show_material = True, show_rates = True, show_attributes = True, show_keys = False))\n",
    "\n",
    "#Simulate with BioSCRAPE if installed\n",
    "\n",
    "print(\"Simulating\")\n",
    "try:\n",
    "    %matplotlib inline\n",
    "    import numpy as np\n",
    "    import pylab as plt\n",
    "    timepoints = np.arange(0, 3, .01)\n",
    "    stochastic = False #Whether to use ODE models or Stochastic SSA models\n",
    "    plt.figure(figsize = (16, 8))\n",
    "    plt.subplot(221)\n",
    "    plt.title(\"Load on a RNAP Promoter\")\n",
    "    loads = [0, 1.0, 5., 10., 50, 100, 500, 1000]\n",
    "    for dna_Load in loads:\n",
    "        #print(\"Simulating for dna_Load=\", dna_Load)\n",
    "        x0_dict = {\"protein_T7\": 10., \"protein_RNAP\":10., \"protein_RNAase\":5.0, \"protein_Ribo\":50.,\n",
    "                   'dna_ref':5., 'dna_Load':dna_Load}\n",
    "\n",
    "        results = myCRN.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0_dict, stochastic = stochastic)\n",
    "        if results is not None:\n",
    "            plt.plot(timepoints, results[\"protein_ref\"], label = \"Load = \"+str(dna_Load))\n",
    "\n",
    "    plt.xlim(0, 5)\n",
    "    #plt.xlabel(\"time\")\n",
    "    plt.ylabel(\"Reference Protein\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(222)\n",
    "    plt.title(\"Load on a T7 Promotoer\")\n",
    "    for dna_Load in loads:\n",
    "        #print(\"Simulating for dna_T7Load=\", dna_Load)\n",
    "        x0_dict = {\"protein_T7\": 10., \"protein_RNAP\":10., \"protein_RNAase\":5.0, \"protein_Ribo\":50.,\n",
    "                   'dna_ref':5., 'dna_T7Load':dna_Load}\n",
    "        results = myCRN.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0_dict, stochastic = stochastic)\n",
    "        if results is not None:\n",
    "            plt.plot(timepoints, results[\"protein_ref\"], label=\"Load = \" + str(dna_Load))\n",
    "    plt.xlim(0, 5)\n",
    "    #plt.xlabel(\"time\")\n",
    "    plt.ylabel(\"Reference Protein\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(223)\n",
    "    plt.title(\"Load on a RNAP Promotoer, No RBS\")\n",
    "    for dna_Load in loads:\n",
    "        #print(\"Simulating for dna_TxLoad=\", dna_Load)\n",
    "        x0_dict = {\"protein_T7\": 10., \"protein_RNAP\":10., \"protein_RNAase\":5.0, \"protein_Ribo\":50.,\n",
    "                   'dna_ref':5., 'dna_TxLoad':dna_Load}\n",
    "        results = myCRN.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0_dict, stochastic = stochastic)\n",
    "        if results is not None:\n",
    "            plt.plot(timepoints, results[\"protein_ref\"], label=\"Load = \" + str(dna_Load))\n",
    "    plt.xlim(0, 5)\n",
    "    plt.xlabel(\"time\")\n",
    "    plt.ylabel(\"Reference Protein\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(224)\n",
    "    plt.title(\"Load on a T7 Promotoer, No RBS\")\n",
    "    for dna_Load in loads:\n",
    "        #print(\"Simulating for dna_T7TxLoad=\", dna_Load)\n",
    "        x0_dict = {\"protein_T7\": 10., \"protein_RNAP\":10., \"protein_RNAase\":5.0, \"protein_Ribo\":50.,\n",
    "                   'dna_ref':5., 'dna_T7TxLoad':dna_Load}\n",
    "        results = myCRN.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0_dict, stochastic = stochastic)\n",
    "        if results is not None:\n",
    "            plt.plot(timepoints, results[\"protein_ref\"], label=\"Load = \" + str(dna_Load))\n",
    "    plt.xlim(0, 5)\n",
    "    plt.xlabel(\"time\")\n",
    "    plt.ylabel(\"Reference Protein\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "except ModuleNotFoundError:\n",
    "    print('please install the plotting libraries: pip install biocrnpyler[all]')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# End"
   ]
  }
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