XCMS3_Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess your data using XCMS3 and export data files for feature-based molecular networking through GNPS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To follow this example tutorial, download the folder named <tt>'peak/AMG_Plant_subset'</tt> from the link below and place it under the same directory as your jupyter notebook: <br>\n",
    "https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=de2d18fd91804785bce8c225cc94a444 <br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the settings for `xcms` used in this tutorial were not optimized,\n",
    "specifically the alignment based on the default *obiwarp* parameters might\n",
    "perform a little too strong retention time adjustment.\n",
    "For more information on optimization of the parameters see the [xcms vignette](https://bioconductor.org/packages/release/bioc/vignettes/xcms/inst/doc/xcms.html)\n",
    "or the [xcms preprocessing](https://jorainer.github.io/metabolomics2018/xcms-preprocessing.html) workshop."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load required libraries and utility functions for GNPS export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: Biobase\n",
      "Loading required package: BiocGenerics\n",
      "Loading required package: parallel\n",
      "\n",
      "Attaching package: ‘BiocGenerics’\n",
      "\n",
      "The following objects are masked from ‘package:parallel’:\n",
      "\n",
      "    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n",
      "    clusterExport, clusterMap, parApply, parCapply, parLapply,\n",
      "    parLapplyLB, parRapply, parSapply, parSapplyLB\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    IQR, mad, sd, var, xtabs\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n",
      "    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n",
      "    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n",
      "    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n",
      "    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n",
      "    union, unique, unsplit, which, which.max, which.min\n",
      "\n",
      "Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
      "Loading required package: BiocParallel\n",
      "Loading required package: MSnbase\n",
      "Loading required package: mzR\n",
      "Loading required package: Rcpp\n",
      "Warning message in fun(libname, pkgname):\n",
      "“mzR has been built against a different Rcpp version (1.0.1)\n",
      "than is installed on your system (1.0.2). This might lead to errors\n",
      "when loading mzR. If you encounter such issues, please send a report,\n",
      "including the output of sessionInfo() to the Bioc support forum at \n",
      "https://support.bioconductor.org/. For details see also\n",
      "https://github.com/sneumann/mzR/wiki/mzR-Rcpp-compiler-linker-issue.”Loading required package: S4Vectors\n",
      "Loading required package: stats4\n",
      "\n",
      "Attaching package: ‘S4Vectors’\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    expand.grid\n",
      "\n",
      "Loading required package: ProtGenerics\n",
      "\n",
      "This is MSnbase version 2.10.1 \n",
      "  Visit https://lgatto.github.io/MSnbase/ to get started.\n",
      "\n",
      "\n",
      "Attaching package: ‘MSnbase’\n",
      "\n",
      "The following object is masked from ‘package:stats’:\n",
      "\n",
      "    smooth\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    trimws\n",
      "\n",
      "\n",
      "This is xcms version 3.6.1 \n",
      "\n",
      "\n",
      "Attaching package: ‘xcms’\n",
      "\n",
      "The following object is masked from ‘package:stats’:\n",
      "\n",
      "    sigma\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(xcms)\n",
    "source(\"https://raw.githubusercontent.com/jorainer/xcms-gnps-tools/master/customFunctions.R\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use socket based parallel processing on Windows systems. The number (`4`)\n",
    "defines the number of parallel tasks. Adapt this setting to the number of CPUs\n",
    "available on your system. Also note that it is usually better to not use all\n",
    "CPUs of a system as a) during the analysis the MS data has to be imported from\n",
    "the original mzML/mzXML/CDF files and it will thus be limited by the I/O of the\n",
    "hard disks and b) the computer needs to have enough memory to load the complete\n",
    "MS data of as many raw data files as there are parallel jobs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "if (.Platform$OS.type == \"unix\") {\n",
    "  register(bpstart(MulticoreParam(4)))\n",
    "} else {\n",
    "  register(bpstart(SnowParam(4)))\n",
    "} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load all .mzXML files and define sample grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mzMLfiles <- paste0('AMG_Plant_subset/',list.files(path = 'AMG_Plant_subset/', pattern = \".mzXML$\", recursive = TRUE))\n",
    "s_groups <- rep(\"sample\", length(mzMLfiles))\n",
    "pheno <- data.frame(sample_name = sub(basename(mzMLfiles), pattern = \".mzML\",replacement = \"\", fixed = TRUE),\n",
    "                    sample_group = s_groups, stringsAsFactors = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sample_name</th><th scope=col>sample_group</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>10712_P4_RH3_01_521.mzXML </td><td>sample</td></tr>\n",
       "\t<tr><td>10715_P4_RA4_01_415.mzXML </td><td>sample</td></tr>\n",
       "\t<tr><td>10765_P4_RE9_01_482.mzXML </td><td>sample</td></tr>\n",
       "\t<tr><td>11035_P4_RB4_01_431.mzXML </td><td>sample</td></tr>\n",
       "\t<tr><td>13917_P4_RA10_01_423.mzXML</td><td>sample</td></tr>\n",
       "\t<tr><td>14153_P4_RH8_01_526.mzXML </td><td>sample</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " sample\\_name & sample\\_group\\\\\n",
       " <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 10712\\_P4\\_RH3\\_01\\_521.mzXML  & sample\\\\\n",
       "\t 10715\\_P4\\_RA4\\_01\\_415.mzXML  & sample\\\\\n",
       "\t 10765\\_P4\\_RE9\\_01\\_482.mzXML  & sample\\\\\n",
       "\t 11035\\_P4\\_RB4\\_01\\_431.mzXML  & sample\\\\\n",
       "\t 13917\\_P4\\_RA10\\_01\\_423.mzXML & sample\\\\\n",
       "\t 14153\\_P4\\_RH8\\_01\\_526.mzXML  & sample\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| sample_name &lt;chr&gt; | sample_group &lt;chr&gt; |\n",
       "|---|---|\n",
       "| 10712_P4_RH3_01_521.mzXML  | sample |\n",
       "| 10715_P4_RA4_01_415.mzXML  | sample |\n",
       "| 10765_P4_RE9_01_482.mzXML  | sample |\n",
       "| 11035_P4_RB4_01_431.mzXML  | sample |\n",
       "| 13917_P4_RA10_01_423.mzXML | sample |\n",
       "| 14153_P4_RH8_01_526.mzXML  | sample |\n",
       "\n"
      ],
      "text/plain": [
       "  sample_name                sample_group\n",
       "1 10712_P4_RH3_01_521.mzXML  sample      \n",
       "2 10715_P4_RA4_01_415.mzXML  sample      \n",
       "3 10765_P4_RE9_01_482.mzXML  sample      \n",
       "4 11035_P4_RB4_01_431.mzXML  sample      \n",
       "5 13917_P4_RA10_01_423.mzXML sample      \n",
       "6 14153_P4_RH8_01_526.mzXML  sample      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(pheno)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read all raw data, including MS2 level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawData <- readMSData(mzMLfiles, centroided. = TRUE, pdata = new(\"NAnnotatedDataFrame\", pheno), mode = \"onDisk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a base peak chromatogram (BPC) of your data for visual inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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x89TWqGFymjadt2PHS8zfNwDAEIQAAC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XHCffcWWa+U1shrXcx82QNtJms5zj/gQq\n99RatoNUnYucY9PjJhfvGtfrLddo7gVN+00fH/2DAAS2iMCQG4xvSm+TzpGG1NsiHHR1yQTq\ndVLTSz7N5Juri4iaNpjmcRss22Tx0VY+L891+5xnXjsuT19qm87bxgVfn/EOtWlyDpvwqdza\n2na5F+OuVxflqddsv62NPjZt9cjbIdDGb9n3xzgR28S8cnE6Y3Da8jXqkLja75TwEwIQgMCS\nCAy9Kb9S5/U/g/0F6TTpROnJLfLb7QgQyAKs68u2EFoOgbpQaP5O17I+Z8tCuY9tXbT0se9j\n4/43F0CrOE+fvmyyTRg1+1jnO+nKz2kzdpzytJW42eZer6lme1M/DvfKIffKmrdo2vNl56Lt\nPIu2uY569Tr7/9l7t1j7vqu+Lw8N5mpMgw3G9v+PjYnNxRauSxIgdtOUOqUtERWiraOKl7a0\nVVXUlEp5QKKyBFKlClGkvrjihQc7ahUkg9SWqFUqiDBSII5K1ISU+G/jKzebmw3h8tDx2ft8\nzn+c+ZtrrbnWXvucs88eQxp7zjnmGGOO8Z1zrTXn3vvsw1qcy4G+rH8f8dUYhUAhcEUIrL3B\n/FRgw897/3vBPxP8q8G/1eG/FbKiQoD1xYPuT4PXrrVCbxyBvBHK9VEP2Lg5ptzqY3S8OT3G\nZvNjPOgq2xIX9k+ZWkwybjlv5Pa5GW+vydZXtqfPH8qY08s2Ve8j0MPPOelbrJfOHS7We7sf\nC3FxnfoVO+SwcqKhrj7tokKgECgEdkVgzY80MPA/Cv7EQAT/dECnVJ4+AmzA3Oy2m7Gnn/39\nZTi3UZjrM8K8+diy8cgbF31uLY0lx4GMdVT04mFRLFq8aLeUZeDqZpxr0rmjzJi3Pui71jng\nOcmbPHtRng99Oie2TyldE71xTvF7bts2XtdmKycO1mI9U849I+W/ELhiBNYekP6LK8aqUl+P\ngAek+ordeuzWWPQ2ENrP9amTy7lNctY7V901k/0r23MTmf1fWj3PqXU3k5bkZJ+lMvBEluX2\nUbakLpvS1qbVfYrtb4ukfjn4V3ZKDvxb2nNtM0eXOFeuLdewObj+lIMddfVpFxUChUAhsCsC\nSwekL4nR/mzwp4N5B42/Nxq5kfNLd/VrdwHClZMb23q377wLYW6jMNdnVOjkzceIjbaUe25W\njCX7VJbHvNa6cwM+GSPr9oOP9Ywfet7D3ajrC5s54jqGtDu2nv4rz0B4L3Jesj/nJMu21PXt\n4WKLj4eyyevKNWk+eQ0TXz1THmqWatxC4EoQyDekXsr/dwh/Pfgbbjp/4aaNbI7/2xv9Kq4b\nAR76fHrEw25prV03Uqdl7yai52Wur6f/0BurdiNEjMgeOq4eVg8l43qCvKbABp4jbcASO8re\nphx5S8qWxmjtnkpbzPbKRzyzP+cyy06pO9+n+LhvWzAwbkqv+R5e9Pfk9x1zjVcIFAJPFIGl\nT5D+r8ibrxX89k3+/0eU/EjDEv2TJYXqvwoEeOC5eauH2fmmvMV27eYBezcma6Lcajc3RvZJ\nHYbW5nS0etqv4tPOXcYQBNq2932xRaf1gUxS71oPSNzH9jzAiKf4UvYOq7l/tK5vDxejdo9B\nj9hz3K435HBeo/TtOSfhrqgQKAQKgRcR8EH5ouRurf0k6L+8212tQmAWgXxAqofZLFQndbop\n0gmf2nltt33qTJUPfRBhnRCDmyHjd7M0Ffe1yMFDLMSI3J038UKW6+pmvRbr1oa2lMe9tmuZ\n3PfMueerDkjPHoLy2rNu6Tq2XWUhUAgUArsi0LtR7zpAObtqBFhfbObq3b7zLoO8EWak/Itb\nbV8vEnTaDXRPb0q252aFWFgv+jR+ZNan4rgmufMlJrYtMxbqIMv10fu/NswBZPvYevqv5DuK\n1QgaPfzqgPQicuDDOs7PDWVq5T5lVRYChUAhsBsCe970dwuqHD0ZBOqAdD9T2W64thyQjJSN\nSevPvvso242Q9yg+FSt6FgHx8mCU58561lEPT2JLfWTe/XtC/WJ3DQROGatTc8744ZcfgNjL\nv75H5vPUPPa2d53il/jzgdy8HPMS8zP2KguBQuACENjrpnwBqVaID4AA74rykKt3+84Lfrt5\neFkM569utX1TkbDhgNZsPPS9xuY4yvQrPvGnT+9RrCHHm7Z++j0Zn5yt+IgdfS1etttSP2Ju\nO5eOi8w5yf1PuU7ue+Ys/mD2quA3BNcnSMf1mtega1q86JPo2wszfVZZCBQChcAtAnve9G+d\nVqUQuEGA9cW7zjzMaq3dgHKGwg2Erl8fFX6iH2r7jtK7r+i4+cgblLta99PKsTCi8Rvf/UTx\nsKM8F8P/j4MhiM+cupi2GPp3asjn5h17mGt5ZLxQe1LEvWuv+9cXh69/JaGDX7Dfa7Pv/Hi4\nSEM9+iqxuw7zmkRun0lcYn7GXmUhUAhcAAJ73fQvINUK8QEQYH3xIINrrd3fBID3VnKDMmqP\n/p7kRqiNo23vOeZj8/VFEdDnB/c2zeAjZUys5371erIt1yRjwNd2LYNfD0PxXVPy6e6XNgYe\nPPcaA/fM0yUTWJxyH7vk3Cv2QqAQeAQIXNuD7hFAflUh1AHpfqa7vY7dLDP6yKYLnVM2VHm8\nUzNuYzH+a9oskTP5mnuLaTtXtLPMuvaUyNp2u24YRx3qkjI28pDtY+uyXrfEjk3vsLol83b8\nPAdt3xb/+ptbP1v83ocN+btOKc2BnOwzDvrNVVmVhUAhUAjshkDdYHaDshx1EGBTwUMOrrXW\nAehMIjYP0simK+s89MbDjRBxtHHltvk9xVIM5q4Z8MlzRT1Ti5X66FDnmvQrdsooe6QvbKC5\nuI4aj/f1P4zQXr4yPPLdK2exzCGIa68v662p43NPf2vG3qpLvK5j1yi+etirt3WssisECoFC\nYBaB3o1n1qA6C4EVCLC+6m+QVgC2UdWNkJsGv7KDO/uWXGtruaR/rn7WjBtGxjD+h47rXPn2\n/IqBuWedLMt18MltbGxbZj9ijF3GtqernXqX/Nx4SSTzShMaKMVjr5x7fpyLgXAWVYzXubK9\naPgIFIiVuMGIMh/y7Avxgei/pNxuwq6iECgELgWB3s36UmKvOB8/Aj7UeNDVWjvffLlRcKPl\n5mh0ROcJ/TUbD+3W2CzFNOVzzzGWYnjofjCYu2by/KILZdlRcncDmfvFOH9tLPdrb+kYri/l\nl1aaxytWBO59S9sVpl3V1g+Yin3b13WwINTHU5grcCEfc8qp25dlVS8ECoFCYDcEvPnv5rAc\nFQIJAdYXD2q41loCZueqGwg3RXnzYN/okNl21GZPPeIlBuMwftt7jvVYfXndmPtUnGLCvFOH\nLI+t42vGVHm+Jns26uUSPXgprmzzGOtrvmJnrnvdv/QnLnnu2j511pT68F5ge42Ph9IlVtcX\nZc7BPmNTz3aVhUAhUAjsisBeN/1dgypnTwYBN3p5M/ZkkntEibgJckNhSYj2zYWbNx9bNh5b\nbHrxGCv+pJ7Mvqdact1MYepc0a+OMkrx6mFjH2V7TWZ/PVtkrqtLfW6YP1+zGyVt8qdto7Y9\nPf3ZB6Zgvxfp37myvZf/c/ohVrGgzGu0zYP+VnbO2Mp3IVAIXBkC+Y90ryz1SvceEOABxkMu\nP+juYdirG8KNgpsL8FZmOQdK1nnIjYdxmEeO+SHjynHcRx0c5q6ZjBN1sOlhpl5PR/89uzZH\n/WAD2T62Luc1xy0mS9F7GMy2SzZz/a2fXwvlL7wxaPvm/Cz1XeJctXPi2hQX2+ROXfkSFtVf\nCBQChcBqBLz5rzYsg0JgAAHWFw9qN2MDJqWyAgHe4ODrQm4U3BTljYR9S2632Cz5XNtvrMTi\nBijL1vq7VH2vG3PPeSBr8VGW9dr5FE91aPfu/70xsw31OR11n0pprj2stuSoH+fnV8PJp24c\nOdYWv9q0/vfwqe9zl8TqOqXkfoZMFrMQHeiScjPmKguBQuBCEPBmeiHhVpgXhgDrqw5I55u0\nV4frfz3YjYIHJDcWoyNj7+bDDcqoLXpbbHr+zcNYso65ZdlTrXvdiMdSnuDVm4PWXlyRg6df\nG9OecVobZdr0xkHnEijnlutzsfuMtJzTHelz3M+G8geD/3TEaIOO14vjbXBx7ybGSpnXZC+Q\ntfe4no+SFQKFQCEwicBeN/3JAarjqhHwQcfDrNba/kuBDS64urFwU2TJiPZRHyE30SO6zu+I\n7oiOsbo5oq0M+1wf8bdVh4Pn12013sGOOR29ZsBEvBi6hxH+2nnFf08XHz1yDGyewrU8mrt6\ne+Wsvz8OHP9+MLgqswzRZtKH8217s8N7NhQP4s455PY9h1TDFQKFwDUisNdN/xqxq5yXEWB9\nsRHjQVdrbRmvtRpgKoMzDPF/kKSRDVLefLhB0f4+S2N1zdBWdp9x8TPQr7nPxJuxmNOpfPNc\nZTP1KSWxo93KwThfk/RnHWxayvPS9l1CO+OR63Oxq5exmtNf6tOfWNNWZrnkY65fH94L5nQf\nWx+xi4vr0XwsjZn+VmZflYVAIVAInIzAXjf9kwMpB08SATd6PKzrYbb/FINvxpX288Hg/feC\n+fpO7o9ml7LO1o1H9tEdZECoD2Jo6T43fODo18/aOO6jDQ5L14zz5DXWw2wuVvz3cmRs50F7\n2/c5B459rtKclvyDL2R5bG1/dVzmi4P452x31bXUv3Nlu6v8yITE6rqm9Bowh7zG1XtkKVQ4\nhUAh8FQQ2Oum/1TwqDz2RYAHGw85PtHobcb2He36vHH9unkg+88L5pMPZPyNw+cGIxshNx9b\nNh7ajowzp+P9CH/GYX625+z36mNMY9nL5xo/jM91sxQDmLgRpt6S2Okv96OvTa5nnbau3lJc\nrd1jaYsH8eT6XHzq7ZWzfpi3bw1+ZRrcsZJodVUfrgvbqx09gIGxWhoCbdj1ipx6q4e8qBAo\nBAqBXRDwZr2Ls3JSCDQIsL54UPMwq7XWgLND042Dmwc3EMpfG2O8cWAc7VFds/HIdgPDLKrg\nDzKPY+v42pPl/j3rrNWHPNB73YhHzq3F3OvKedMm46Us+6GubStv29jDbrpH7Vo/l9g2V8tT\nc3AumB982sZvrtPeQvpwrmxv8fUQNq5jSnPoxaFer69khUAhUAicjMBeN/2TAykHTxIBHs4+\n6C7tQX0JE8L16yYLnNtNA5ivxb31MYIDNnuQseovx78lrq0xMe5DHpAYm83h3P0540FdzCxD\n1J17ckMH/9ajeksZc4Xq6Zv2JVKOO9fnclFvbi7m7Ns+/YGldXXatvI1pT4uca6IPcctRshz\nH3jYR72oECgECoHdEdjrpr97YOXwSSDA+mIjtrTZexLJPkASbKTdEDG8mwvqbipyP/IetZuP\nEZspPz35qMxxySPngv19boiI4yHvjcwrX0sVj6jeErIWG2QeeOZssh11c6Se+6LZJcZArzdG\n1+ARC0dzUE+sTk1JfxlHZaf6zvbMFXQO30fP+78Sq+uQ0jojtXlk/OgvKgQKgUJgVwT2uunv\nGlQ5ezIIsL54UNffIJ1nSt00eB27KWI0+mD7kE0Rem5G8KHfKf1zyR03b46y7Fzjtn7B7KE/\nQeKaWZo7sIGdu6jeUt5AqtNiaVsj/bRy2vBUv/aPvcx55fpc3M6B5ZzuSJ/jtvODrX0jfqZ0\n9OG9wPaU/mOSG6ulGOW28dpnu8pCoBAoBHZFYK+b/q5BlbMngwAPNh5k9TA7z5R6/YpzPtwg\nk9eMzlzpd9QOmz2IeKHsryc7ap3vlTEfwwHJ3HuZipE6ttVVbtt+5XmtoGO/+r3yEjfdOQ9z\nR5brWaetq7f2mmj92NZfi7/9p5b6H5nPU8c6hz1xkwOlOdBWFtUDqWe7ykKgECgEdkVgr5v+\nrkGVsyeDAOuLjQAPs1pr+0+rmFq6oXAkN0u2p8q8+Thl4zY63lwc9LlmqOvzPjdE4CmmxHDf\ntPYrdmDTzlvGK2OYc1GuLNsoyyX9UGt3lD7NV9eB5alZzmE31zc6Lj6Yp0ucK2MXh6X1OIpJ\n6RUChUAhsBqBvW76qwcug6tAwAdeu3m7iuTvIUmvX0o3RWCeWZ3RcNZsSpzfUd9LeviD3NxR\nV8Yaui8Cs8fwCdLU3InJEh5Zz3nVZ74mXTv6y3bIbDsH+lD/UkrzyDktxa6N5ZL+Ur9+8hrX\nxj7bW0p89Hxv8XXfNr3YXbdtn/L7jrHGKwQKgStB4FIfdFcyPRefJuuLTVU9zM4zlV6/lnlj\nxIZCXho9bz62zJXj4ucUynnoM8tO9T8aoW76EgAAQABJREFUm+M49qjdXnqMO/UjDY7hPBmr\neNmfS3RyP/Xczrpzde0cc073sfeN5pDXQK5vza/nw1gst/rGDh/Ok23KSyDzb3MgdvvMgxxb\nmX1VFgKFQCFwMgK9m/XJTstBIXCDAOuLAxJca+0GlB0LMW03FLRldeaG1R6dh9x4EAdEDC3d\nZ1zG8VCfIi19xU5sxITST3fEzlLdXjmyNrATD8ew3fP5mGU57lyfiznrjeK15O/3Q+Gjc0on\n9BEvcz8y/ycMcxZTYmeNZcyp2845UVce1aJCoBAoBPZFYI8b/r4RlbenhAAPMB/W9TDbf2a9\nfi3nNhejo2/ZeOSNy+g4PT3XiGsGnSzr2ZxDJp6W5xhjzifjMpdz4zvXYu+8iRf+rVOKqbKe\nvTbq0IZsYwPZPrYu83U0hzwHub41a8b9zeAPJwfGYpm6VlddOxru4VNf91USs+s1r2vqknLb\nVRYChUAhsCsCe9zwdw2onD0pBHxY18PsPNPq9UsJxhlnNhny0uhuSNDLPpbs2n78nELaE4OU\nZdbtO1fpOA/1CRLje4BpczQ25ODkGhCz3K8tMvuRUYfVzX30T5F2jjml91jl5kt8uT4Xb9bb\nI2/8jcztXExzffh3nub0HmOfsRvb3LqkL8+NNlUWAoVAIbALAnvc8HcJpJw8SQRYX2wG6mF2\nnun1+nWjkDdGyOQ1oz/kXE3lQfzEdV8krg99QDKOXt7tPNnOOIlntlemfu7TVh37aMPXeC3n\nOch1sVlbgmOLPbLXBP/Ztc46+vq3i/alkLEbszjRts9c7LNdZSFQCBQCuyKwxw1/14DK2ZNC\nwIea75j64HtSST5gMl6/lnnT4KZiBHPniVScq5G0st2I/pKOsZKHlGXW7TtX6Tjieq5xpvwy\n/tw80C9G1nO79asOJYQubFsZZZbRzuQYWXZJ9Zxbrs/lkPX2WA/46GH/XMhfOhfIyj7nKse/\n0sW9qxOrcbdlm0eL4b0HWwMWAoXA00Zgjxv+00aosjsFAdYXGz0fdu1D7hTfZfvi16vcdLlp\nAGev7bWYb5krbU6dE2LVl6V52D51jBF7MXuMnyCJEXgYZ84p42R/z0a93Kcs+2vr6Dgnbd8l\ntcVmKWb0uIdBozZH7f6rePd89WR9L9PSPXxMe7+fHnNwPbZto1Buu8pCoBAoBHZD4Ck86HYD\noxztjgAPMB5ye24wdg/ygh16/bY4k9Ln3OT1L0f5nTf1qUJ7+t2U6HvKpic/dcOS48j+iQk+\n1X/2OVc394c6IBHbXL7ikHW4xpB/bvBXBveI/tYWHy2po9w2urD42H8ppXkQb67PxU+u3r/m\n9Eb7GDfPG3bOwV64Ok/4Hs0T3YemFhtxQm6fMbrebVdZCBQChcCuCOx1Q941qHL2JBDwocaD\nzA0AsqL9EPD6tdQz7XcEc0h6SfAXBY/SQ84VcbsZNQ5k1kdzOFXPddrieqrfUXvGn9oAGhuY\nWMevGPE1rVchCLKf8kuC/6Vg9OSo3upQXyLt9Luk/9j6c9y5PhcnevxPKmiP9YA/5rbnqydj\n3DWEf9fCGrvHoGvslsR0qbk8BjwrhkKgEDgBAR6YRYXAORDwYZ8PSMrOMd41+hRPSzfVtLm2\n2WjYlzcdLVa5zw0JsiXSbo3NnE/99XQYYySmnu1amZg91CdI5Dm1iRZrS3Ozbez53k4e/0Yw\nfWKofmtvv3JKZdrYzjpPtQ5mzMVehD9w7GHo3O0xlnO1h6/78gEmYkNpDsjtMxb69sRLv1UW\nAoVAIXBAoG4wtRDOhYAbgPZBd67xrtGv1y+lmwlwAPvch8w29Zby5kM/zl+re872VBzGdM6x\ns29zn8Ms6+9Zd2wPu61v+8HEOjq2lXEoss5hia/e0YZfGcyvpplfi692oXIg7Wi0ukeNy3tt\nc5zKAD0PSKM2U76Q48O5Uk9M3xKCv6RwY6l/zfeIWV/3VYqHsVO2ealzXzHVOIVAIXBlCPiA\nvLK0K917QMC1xebCDYayexj+KoYQTz/pcFPNZsK+vMkYAcWNh3ZrbEZ053TaTRC6xkFc1ud8\n7NHnOOK6h8+1PpzLOTvnCh3rvdhZC/nAxGEJXkuO4dpaa//Q+mLzhRHI1w0Gg433L+0HTbtq\n+Jhay8zJ53WtxoXG6FzZHvfwcJo5VjA3h15EUxj2dEtWCBQChcBqBC71Qbc60TK4dwR82OUH\nnbJ7D+aJDsj1y/9O+bab0g0FOHttW85hT5+2lnP6od6lLTbZUS8O4icm48r656qL2UMckMSQ\nfI3DPO0TJ0o2+y8PFh9t8oEIGbqZXxHtLwuWMsboZbKtju2sc0l1/ibvtYMBg51/g7RH3vgA\nx54v5sz5i+pmcp42O3ggQzCRyUHqyaYw1KbKQqAQKAROQmCPm/FJAZTxk0XAtZUf1r1NwZMF\n4B4SA2M3VXyNyk0FOIu/YYxiv+e75Y49WhKjOUzZjOYxZT8id4wWwxHbU3Ucm0259Z5PceKA\nzA8w2NYmH+6o0w/nfv9OLcTP2CNrqfXR9j/2trkzr6Nzi80p1wS/Ipk/rdOfsWTMmI88b7lv\ntN76bdujfh5Cj1hdx5ZTceS1PKVT8kKgECgENiMw+pDYPEAZXi0CPpjZXJyywbhaAAcSB2MZ\ndTcNWaYb58N2LulzQ2I5p59tqWvTyte2e3Fwj8L/XmOMxOR98dTN6shYrY64k6/1Voe2eKBD\nvHySBOXYtecQBdm2bltfB6WZF/UcY0b1UXaR71cH80t/5r4UKLmecv/ib4oYU2LcqbllrD2w\nxb9z5biXUoqPGNGWc072X0peFWchUAhcGAK8Y1VUCJwDAR/0PMh8sCk7x3jX6FM8Kb8+4QwW\n9rG5gGwfW3df1UG6Zq6wUx/b7If2WiLG7K9n347Z0zlVZh4PeUDiE6R2zoyL/MAptz0gKcux\n+z+x6JPx0fpHBunj2HqxzZjtuOpcSvmylTmAxSlfsWMe8nMWf2DYYq+slYfqSdTO5UnOzmyc\nY3WtTa23KfmZQyz3hUAhcC0I7H0zvhbcKs9lBFxbvPvKwwzKD8CjpF5PQcBNMLjyKYGfFIC9\n+Ot/CXvnyHJJX797lozpu/XGQR7Ubd9HXGJ3H2O1+DkmOFif0lEONupasjass0HPOtiRo+tn\nDb6tH3xdCoEHTO7O8VLs6LkmxXPJJvdjI87I8TeH4Whc+OoR4+H/Ekl8LXMOvbx6etmm6oVA\nIVAIbEYgv7O12UkZFgIdBHx41QGpA85OIjAWZ+u2242Y8t7Q9LmpspzTb31ssWl90M5x5H79\nZ9k56+Zuec6xpnxz3bSbZeMBDzFBphxfbsbzvV2Zepatf3zahy9JmePatv+SSnMezQE9D0hb\n8sRe/LGn3cNZWdZFfy3lvPS51sdD6YsN44O5uVjmuC4ttxx71QuBQuACEPBhcQGhVogXhgBr\ni1+LgnmYQbXejjjs9SqelGwibLd1xuttMpC35FyN6rf2p7QZ0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xz8/uCi7QjwwGQD4MPeB952j2WZEXAz4ebYNnhbV5/2mk3G1k1JO67j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\nDwrk0T71THk25MR+Py5Ik4QDjgw8SQY8TAccGXjaGbCvOhx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yhe4bWzRIJn8/PbxPbCOXP9jb79nVR/615uVPaT40SEaM+MkMeYpvjb/c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KT2JTBc5ZAR9vD2kHPHIZrT7gh4mL+M/40mS2vv3lbF/gEXEy8GhwQeC5LD2GXMTkezG7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\nUbA+zgpidt/R+Kx3Y3atrvYc9w5j/NaH9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njUh3Ylc+PlrQ9zqbw++DK/+hkuxxzckrxzw/ZB3MqaRzGYE+W+GqynB8NDQVu7BmVcG6Pn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\nAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “  74.8699 - 2005.8185”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bpis <- chromatogram(rawData, aggregationFun = \"max\")\n",
    "plot(bpis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Peak picking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define settings for the centWave peak detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cwp <- CentWaveParam(snthresh = 5, noise = 1000, peakwidth = c(3, 30), ppm = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform the chromatographic peak detection using *centWave*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "110681"
      ],
      "text/latex": [
       "110681"
      ],
      "text/markdown": [
       "110681"
      ],
      "text/plain": [
       "[1] 110681"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(rawData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n"
     ]
    }
   ],
   "source": [
    "processedData <- findChromPeaks(rawData, param = cwp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get an overview of the detected peaks, using a heatmap which represents the\n",
    "number of peaks detected for each file along the retention time range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FgKKKCAAgoooIACCiigwE8E6hqkNBtT\nySSSKWpbk6Ukz3uD7EumkXHkW7ImyRS4NEvPk+w/lVTVWDYWDVL2y/P3I7n5Qhqic8ha5HiS\nhqxdpTHLVaajyPVNO+Xc07zlCpQNUhOOqwoooIACCiiggAIKKLBcoNMpdhez+9Pk1+Q35F/I\nNmQ+yd3j9iLrk1z5WZdcRFLPkg2bsh7rF5LU3eSJZUsrfuRq0AnkK5KpdrliNZE8TqoqV59y\nlSsNUrnyHo8m08k35Q1dXs4Vq3/oMFt0+Vw8vAIKKKCAAgoooIACCnQgUHcFqTjEQhZOI0WD\nMZnl3P3te5KrM5+R1HXkL2S7rFDfkc+XLa34cQSLV5HZ5JQVwystvcbaLSR3m/uU5Lh1tZQd\nHiBnkjQcfyOpfchIkmZsoGo0L/TGv+HFOm1Y/w2H9ikKKKCAAgoooIACCihQJ9DpP8hf4UBF\nc5RjvtQ48Os8Fs1RhnKDhffIBllpUXswlil5b5FMoSsfk9UfKw3G6Y21jXic9OOW6oVMsxtG\nynezSwOXKXyPkYGq/8ELbUrSmHWSXHlLrb78wZ8KKKCAAgoooIACCijwSwh02iC93XRyuTKU\n+mT5w0o/i20rDbKSpmcGWUxy44ZMh2tV+b5RGp00WePJ38m55ABSV4+zQxq0TKlLrUHSLOV3\nJ7U7LzZ1pfL+/r8Ok6tklgIKKKCAAgoooIACCvzCAp02SPleUF9qE548k+RqUKbYZQpdu7qG\nDWPIrWQaOYPkqtBUkqsyVZUpf/eT3LUu0+z2I7mT3kBOr+PlLAUUUEABBRRQQAEFFOhFgU4b\npL68t7V5cq4cbUPOIrNIu8ptvc8maaBy1SiV504hm5HbSV3lalEqjViuJC0gucGEpYACCiig\ngAIKKKCAAgpUCnS7QcqVnzvJnuQKchtpV1uyIdtzteo48hUp6nwW5pODyYRisM3jk4wvJPnF\nsPme0z3EUkABBRRQQAEFFFBAAQVqBfIdnaJ2YuFFMpfsXAz28fFCnn8UyS95zfdsJpLmGxE8\nxdijJI3MxiTN0AukXF+yciKZQzIF7zHyMmlVuVFEptkVV6A6nV6X1825tqqbGXy41QbHFFBA\nAQUUUEABBRRQYNURKDdI3XhXoxsHHcnjlW1eYDLj48juJE3IDaRVPcPgRHI5uYvsSr4lrSo3\neUiDNI+k4eukxrBT0qpsjlqpOKaAAgoooIACCiigwComUG6Q0khkSly5Wo1le37nUPO+xfNG\nFQs85gYLSSeVq011NYkdknK1Oo9856jVeG5P3jx+EWOJpYACCiiggAIKKKCAAkNcoNvfQRri\nvL59BRRQQAEFFFBAAQUU6CUBG6Re+rQ8VwUUUEABBRRQQAEFFOiqgA1SV3k9uAIKKKCAAgoo\noIACCvSSgA1SL31anqsCCiiggAIKKKCAAgp0VcAGqau8HlwBBRRQQAEFFFBAAQV6ScAGqZc+\nLc9VAQUUUEABBRRQQAEFuipQvs13V1/IgyugQP8IDB8xqvZASz5YULvPL7rD+ptUv/wXH1Zv\nr9vaD8fvxLnyNOrOofLJq622xmbb1ezRt811x+/z++/b6S17dl/Poa/P7/NbqPsz0Nc/5znB\numPUnUNf32RfX7/u+XXn1+33V/f6blegvwTq/iz39b+V/jrPIXIcryANkQ/at6mAAgoooIAC\nCiiggAL1AjZI9UbuoYACCiiggAIKKKCAAkNEwAZpiHzQvk0FFFBAAQUUUEABBRSoF7BBqjdy\nDwUUUEABBRRQQAEFFBgiAjZIQ+SD9m0qoIACCiiggAIKKKBAvcBguYvdepzq/uQH8jBZTFrV\n5gzuQt4lT5Fh5DCS+prkuVV1CBvznt8hzzZ23IHHbRvrGbcUUEABBRRQQAEFFFBgiAoMlitI\nX+J/AnmQXNPms1iH8YfIfaQ47zxOb2Qmj2mg2tXObJhBsv95pZ3GN8bGlsZcVEABBRRQQAEF\nFFBAgSEoUDQag+Gtn8lJLCQTyIEtTuhGxnYkl5DZTdtz5SlXk45sGi+vHltecVkBBRRQQAEF\nFFBAAQUUaBYYTA3SR5zcSSTNzh1kU1LUySycSjKF7upisPT4Kssfk6NLY82LxzAwr3nQdQUU\nUEABBRRQQAEFFFCgEKj7DtLG7HgomUM+IAeQMWQueYS8T9Ym+f7Q3iRNyr3kTZIaQQ5attT+\nR46T7xSlZpFryQVkCjmcbE9uIotI0UCxuFItYS3T804hmWbX/F2i3RjbilxKLiOWAgoooIAC\nCiiggAIKKPATgboGKc3GVDKJZIra1mQpyfPeIPuSaWQc+ZasSTIFLs3S8yT7TyVVNZaNRYOU\n/fL8/UhuvpCG6ByyFjmepCFrV2nMcpXpKHJ900459zRvuQJlg9SE46oCCiiggAIKKKCAAgos\nF+h0it3F7P40+TX5DfkXsg2ZT3L3uL3I+iRXftYlF5HUs2TDpqzH+oUkdTd5YtnSih+5GnQC\n+Ypkql2uWE0kj5OqytWnXOVKg1SuvMejyXTyTXlDl5eHcfxO0+VT8fAKKKCAAgoooIACCijQ\niUCnDdJCDnYayTS398hkkvqe5OpMpuClsbmO5ErSdiT1Hfm8KQeyfhWZTU4hreo1Bm8hOb9P\nSY5bV0vZ4QGS6XRblHbeh+WRJM3YQNXWvFA84tNJpgzUifk6CiiggAIKKKCAAgoo0F6gbopd\n8cxXWChffXmpseF1Hj8rduIxN1hIA7VBaay8uAcrmZL3FskUuvIxWf2xRrN0emNtIx4nkfMb\n61UP97LxjyR3syum2aWByxS+x8hvyUDUm7xIGrPhHb7YweyXq2+WAgoooIACCiiggAIK/IIC\nnTZIbzedY64MpT5Z/rDSz2LbSoOspOmZQRaT3Lgh0+FaVb5vlEYnTdZ4kqtH55JHGuGhbT3O\nljRomVKXBinvL83SXaTdebGp3yuNYqYkdlr/0OmO7qeAAgoooIACCiiggALdE+h0il2mi/Wl\nNuHJM0muBh1BMoWuXV3DhjHkVjKNnEHyXZ6pZFNSVZnOdj8pptntx3LupDeQ0+t4OUsBBRRQ\nQAEFFFBAAQV6UaDTBqkv721tnpwrR9uQs8gs0q5yW++zSRqoXDVK5bn5js5m5HZSV/c1dkgj\nlitJC8jPuZrD7pYCCiiggAIKKKCAAgoMRYFuN0i58nMn2ZNcQW4j7WpLNmR7rlYdR74iRZ3P\nwnyS7+pMKAbbPD7J+EJyDMn3nO4hlgIKKKCAAgoooIACCihQK5Dv6BS1Ewsvkrlk52Kwj48X\n8vzcdnsRyd3oJpLVSbmeYuVRkkZmY5Jm6AVSri9ZOZHMIZmC9xh5mbSqfP8n0+yKK1CdTq/L\n6+ZcW9XNDD7caoNjCiiggAIKKKCAAgoosOoIlBukbryr0Y2DjuTxyjYvMJnxcWR3kibkBtKq\nnmFwIrmc5KYLu5LcUrxV5SYPaZDmkTR8ndQYdkpalc1RKxXHFFBAAQUUUEABBRRYxQTKDVIa\niUyJK1ersWzP7xxq3rd43qhigcfcYCHppHK1qa4msUNSrlbnke8ctRrP7cmbxy9iLLEUUEAB\nBRRQQAEFFFBgiAt0+ztIQ5zXt6+AAgoooIACCiiggAK9JGCD1EuflueqgAIKKKCAAgoooIAC\nXRWwQeoqrwdXQAEFFFBAAQUUUECBXhKwQeqlT8tzVUABBRRQQAEFFFBAga4K2CB1ldeDK6CA\nAgoooIACCiigQC8J2CD10qfluSqggAIKKKCAAgoooEBXBcq3+e7qC3nwoSHw1cvVvzJq3d/9\noRZiyQcLKvcZPur3ldt7feOSBc9VvoWv/+s/VW7PxqVvVe+yzrHHVe7QyedUeYC6jV98WLdH\n37Z3+/icXd2f09Vq/hyvuVv1Z/D5DdWf81pj/6HS6Fcj2/1at8qndbxx6buv1e67xmbbVe5T\nd4y67XXHHz5iVOXr98ffV5UvMAB/Dldbf5PKU+j6xl/69bv+Bn/5F/h+0V9rTuKEmu1u7gmB\ngfj7oicgBsdJegVpcHwOnoUCCiiggAIKKKCAAgoMAgEbpEHwIXgKCiiggAIKKKCAAgooMDgE\nbJAGx+fgWSiggAIKKKCAAgoooMAgELBBGgQfgqeggAIKKKCAAgoooIACg0PABmlwfA6ehQIK\nKKCAAgoooIACCgwCgcFyF7v1sNif/EByG7TFpFVtzuAu5F3yFBlGDiOpr0n1LdRWW+0Q9sl7\nfoc8S1I7kG1J1jNuKaCAAgoooIACCiigwBAVGCxXkL7EP/epfJBc0+azWIfxh8h9pDjvPE5v\nZCaPaaDa1c5smEGy/3mlncY3xsaWxlxUQAEFFFBAAQUUUECBIShQNBqD4a2fyUksJBPIgS1O\n6EbGdiSXkNlN23PlKVeTjmwaL68eW15xWQEFFFBAAQUUUEABBRRoFhhMDdJHnNxJJM3OHWRT\nUtTJLJxKMoXu6mKw9Pgqyx+To0tjzYvHMDCvedB1BRRQQAEFFFBAAQUUUKAQqPsO0sbseCiZ\nQz4gB5D8eva55BHyPlmb5PtDe5M0KfeSN0lqBDlo2VL7HzlOvlOUmkWuJReQKeRwsj25iSwi\nRQPF4kq1hLVMzzuFZJpd83eJdmNsK3IpuYxYCiiggAIKKKCAAgoooMBPBOoapDQbU8kkkilq\nW5OlJM97g+xLppFx5FuyJskUuDRLz5PsP5VU1Vg2Fg1S9svz9yO5+UIaonPIWuR4koasXaUx\ny1Wmo8j1TTvl3NO85QqUDVITjqsKKKCAAgoooIACCiiwXKDTKXYXs/vT5NfkN+RfyDZkPsnd\n4/Yi65Nc+VmXXERSz5INm7Ie6xeS1N3kiWVLK37katAJ5CuSqXa5YjWRPE6qKlefcpUrDVK5\n8h6PJtPJN+UNLiuggAIKKKCAAgoooIACZYG6K0jFvgtZOI0UDcZklnP3t+9Jrs58RlLXkb+Q\n7bJCfUc+X7a04scRLF5FZpNMiWtVrzF4C8nd5j4lOW5dLWWHB8iZZAvyN5Lah4wkacYGqvL6\nuVo1vMMX3KDD/dxNAQUUUEABBRRQQAEFuijQaYP0CudQNEc5nZca5/Q6j0VzlKHcYOE90u4f\n/HuwLVPy3iKZQlc+Jqs/1miWTm+sbcTjJHJ+Y73qIdPs/khyN7timl0auEzhe4z8lgxE5fWu\nIJ02SGniciMKSwEFFFBAAQUUUEABBX5BgU4bpLebzjFXhlKfLH9Y6WexbaVBVtL0zCCLSW7c\nkOlwrWotBtPopMkaT64j55JHGuGhbWUaXhq0TKlLg5T3l2bpLtLuvNjU75VpgpmG2GllGqAN\nUqda7qeAAgoooIACCiigQJcEOv0OUv7B35fahCfPJLkalCl2mULXrq5hwxhyK5lGziDDyFSy\nKamqTPm7n+SudZnmth/JnfQGcnodL2cpoIACCiiggAIKKKBALwp02iD15b2tzZNz5WgbchaZ\nRdpVbut9NkkDlatGqTx3CtmM3E7q6r7GDmnEciVpAXmaWAoooIACCiiggAIKKKBApUC3G6Rc\n+bmT7EnynZzbSLvakg3ZnqtVx5GvSFHnszCfHEwmFINtHp9kfCHJL4bN95zuIZYCCiiggAIK\nKKCAAgooUCuQ7+gUtRMLL5K5ZOdisI+PF/L83HZ7Ecnd6CaS1Um5nmLlUZJGZmOSZugFUq4v\nWTmRzCGZgvcYeZm0qh8YzDS74gpUp9Pr8ro511Z1M4MPt9rgmAIKKKCAAgoooIACCqw6AuUG\nqRvvanTjoCN5vLLNC0xmfBzZnaQJuYG0qmcYnEguJ7npwq7kW9KqcpOHNEjzSBq+TmoMOyWt\nyuaolYpjCiiggAIKKKCAAgqsYgLlBimNRKbElavVWLbndw4171s8b1SxwGNusJB0UrnaVFeT\n2CEpV6vzyHeOWo3n9uTN4xcxllgKKKCAAgoooIACCigwxAW6/R2kIc7r21dAAQUUUEABBRRQ\nQIFeErBB6qVPy3NVQAEFFFBAAQUUUECBrgrYIHWV14MroIACCiiggAIKKKBALwnYIPXSp+W5\nKqCAAgoooIACCiigQFcFbJC6yuvBFVBAAQUUUEABBRRQoJcEbJB66dPyXBVQQAEFFFBAAQUU\nUKCrAuXbfHf1hTz40BBY93d/qH6j629SvZ2twzvYp/YgPbzD8FG/rz77//TP1dvZuvTd1yr3\nWWOz7Sq3u3G11ZZ8sKBPDMNHjKp8/uc3/FPl9nWOPa5ye93G7xf9tXKXX41s92vfKp/248ZO\n/gzV/Tn88WBdWqj7DDt5D106tc4P+8WH1fv29e/LuuNXv3r3t9a9v/44/7rX6Ou7rDvHmtfv\n63+rfT19n6/AUBTwCtJQ/NR9zwoooIACCiiggAIKKNBSwAapJYuDCiiggAIKKKCAAgooMBQF\nbJCG4qfue1ZAAQUUUEABBRRQQIGWAjZILVkcVEABBRRQQAEFFFBAgaEoYIM0FD9137MCCiig\ngAIKKKCAAgq0FLBBasnioAIKKKCAAgoooIACCgxFgcFym+/1wN+f/EAeJotJq9qcwV3Iu+Qp\nMowcRlJfkzy3qg5hY97zO+TZxo478LhtYz3jlgIKKKCAAgoooIACCgxRgcFyBelL/E8gD5Jr\n2nwW6zD+ELmPFOedx+mNzOQxDVS72pkNM0j2P6+00/jG2NjSmIsKKKCAAgoooIACCigwBAWK\nRmMwvPUzOYmFZAI5sMUJ3cjYjuQSMrtpe6485WrSkU3j5dVjyysuK6CAAgoooIACCiiggALN\nAoOpQfqIkzuJpNm5g2xKijqZhVNJptBdXQyWHl9l+WNydGmsefEYBuY1D7qugAIKKKCAAgoo\noIACChQCdd9B2pgdDyVzyAfkADKGzCWPkPfJ2iTfH9qbpEm5l7xJUiPIQcuW2v/IcfKdotQs\nci25gEwhh5PtyU1kESkaKBZXqiWsZXreKSTT7Jq/S7QbY1uRS8llxFJAAQUUUEABBRRQQAEF\nfiJQ1yCl2ZhKJpFMUduaLCV53htkXzKNjCPfkjVJpsClWXqeZP+ppKrGsrFokLJfnr8fyc0X\n0hCdQ9Yix5M0ZO0qjVmuMh1Frm/aKeee5i1XoGyQmnBcVUABBRRQQAEFFFBAgeUCdQ1S4XQx\nC3eRNDPfkdxIYTyZTx4ne5HcFe7PJNsuIpnSlrENSbny/D+RTJW7mzxBypWrQblhw3MkU+0y\nDfBSktepqllszFWu5gYpz8/Uu+nkGzIQ9e95kSlkeIcv9psO93M3BRRQQAEFFFBAAQUU6KJA\npw3SQs7hNFI0GJNZToP0PcnVmc9I6jryF7JdVqg0Q58vW1rx4wgWryKzySkrhldaeo21W0ju\nNvcpyXHraik7PEDOJFuQv5HUPmQkSTM2UPUVL5RpiLmi1mn9rtMd3U8BBRRQQAEFFFBAAQW6\nI9Bpg/QKL180RzmTlxqn8zqPRXOUodxg4T2yQVZa1B6MZUreWyRT6MrHZPXHGs3S6Y21jXic\nRM5vrFc9ZJrdH0nuZldMs0sDlyl8j5HfkoGoNIW56tVpncGOf+h0Z/dTQAEFFFBAAQUUUECB\n7ghk+lkn9XbTTrkylPpk+cNKP4ttKw2ykqZnBllMDiKZDteq8n2jNDppssaTv5NzyQGkrjIN\nLw1aptSl1iBplu4j7c6LTZYCCiiggAIKKKCAAgoosOIXrtZZ5HtBfalNePJMkqtBmWKXKXTt\nKt9hGkNuJdNIrq4MI1PJpqSqMuXvfpK71mWaXW72kDvpDeT0Ol7OUkABBRRQQAEFFFBAgV4U\n6PQKUl/e29o8OVeOtiFnkVmkXeW23meTNFC5apTKc3PDg83I7aSucrUolUYsV5IWkKeJpYAC\nCiiggAIKKKCAAgpUCnS7QcqVnzvJnuQKchtpV1uyIdtzteo4khsdFHU+C/PJwWRCMdjm8UnG\nF5LcRS/fc7qHWAoooIACCiiggAIKKKBArUC+o1PUTiy8SOaSnYvBPj5eyPNz2+1FJHejm0hW\nJ+V6ipVHSRqZjUmaoRdIub5k5UQyh2QK3mPkZdKqcqOITLMrrkB1Or0ur5tzbVU3M/hwqw2O\nKaCAAgoooIACCiigwKojUG6QuvGuRjcOOpLHK9u8wGTGx5HdSZqQG0ireobBieRychfZleSX\n07aq3OQhDdI8koavkxrDTkmrsjlqpeKYAgoooIACCiiggAKrmEC5QUojkSlx5Wo1lu35nUPN\n+xbPG1Us8JgbLCSdVK421dUkdkjK1eo88p2jVuMvtRi/iLHEUkABBRRQQAEFFFBAgSEu0O3v\nIA1xXt++AgoooIACCiiggAIK9JKADVIvfVqeqwIKKKCAAgoooIACCnRVwAapq7weXAEFFFBA\nAQUUUEABBXpJwAaplz4tz1UBBRRQQAEFFFBAAQW6KmCD1FVeD66AAgoooIACCiiggAK9JFC+\ni10vnbfnOkgFlnywoPLMhldubWxcf5NO9lp19/niw8r3tvTd1yq3Z+PX91T/+q81tqo5xH/6\n58odho8YVbm99s9BzfMrD87GLx79S+Uuw8ccX729j6+fg3/9X/+p8jWWbP4Plds3OLfauPLJ\nbPxv/8v/WbnL3v/ct7/elyz6a+Xx+2Nj3edU9xqf31D9GaxzbH7nePv69r9X/3fy4X/Lr9Vr\nX+v+x1Y3S12x/5q7Vb/+9x0Y/2pku98+sfx11thsuxUv2GKp7u+Luue3OOTPGurr3xWr1f0/\npR/+W/5Zb+jfsnPd/9Nq/s5f93d/+Le8qs/pNYG6Pye99n56/Hy9gtTjH6Cnr4ACCiiggAIK\nKKCAAv0nYIPUf5YeSQEFFFBAAQUUUEABBXpcwAapxz9AT18BBRRQQAEFFFBAAQX6T8AGqf8s\nPZICCiiggAIKKKCAAgr0uIANUo9/gJ6+AgoooIACCiiggAIK9J+ADVL/WXokBRRQQAEFFFBA\nAQUU6HGBvt0Htv/e/Hocan+Se6o+TBaTVrU5g7uQd8lTJPdYPYykviZ5blUdwsa853fIs40d\nd+Bx28Z6xi0FFFBAAQUUUEABBRQYogKD5QrSl/ifQB4k17T5LNZh/CFyHynOO4/TG5nJYxqo\ndrUzG2aQ7H9eaafxjbGxpTEXFVBAAQUUUEABBRRQYAgKFI3GYHjrZ3ISC8kEcmCLE7qRsR3J\nJWR20/ZcecrVpCObxsurx5ZXXFZAAQUUUEABBRRQQAEFmgUGU4P0ESd3EkmzcwfZlBR1Mgun\nkkyhu7oYLD2+yvLH5OjSWPPiMQzMax50XQEFFFBAAQUUUEABBRQoBOq+g7QxOx5K5pAPyAFk\nDJlLHiHvk7VJvj+0N0mTci95k6RGkIOWLbX/kePkO0WpWeRacgGZQg4n25ObyCJSNFAsrlRL\nWMv0vFNIptk1f5doN8a2IpeSy4ilgAIKKKCAAgoooIACCvxEoK5BSrMxlUwimaK2NVlK8rw3\nyL5kGhlHviVrkkyBS7P0PMn+U0lVjWVj0SBlvzx/P5KbL6QhOoesRY4nacjaVRqzXGU6ilzf\ntFPOPc1brkDZIDXhuKqAAgoooIACCiiggALLBeoapMLpYhbuImlmviO5kcJ4Mp88TvYiuSvc\nn0m2XUQypS1jG5Jy5fl/Ipkqdzd5gpQrV4Nyw4bnSKbaZRrgpSSvU1Wz2JirXM0NUp6fqXfT\nyTdkIGojXiTnPLzDF9uhw/3cTQEFFFBAAQUUUEABBbookOahk1rITqeRTHN7j0wmqe9Jrs7M\nIWlsriO5krQdSaUZ+rwpB7J+FZlNTiGt6jUGbyE5v09JjltXubL1AMl0ui1KO+/D8kiSZmyg\nKo3Rb8ivO8z/NFAn5usooIACCiiggAIKKKBAe4FOryC9wiHKV19eahzydR4/Kx0+N1hIA7VB\naay8uAcrmZL3FskUuvIxWf2xRrN0emMtV2MmkfMb61UP97LxjyR3syum2aWByxS+x8hvyUDU\n+7xIXrfTOoMdp3S6s/spoIACCiiggAIKKKBAdwQ6vYL0dtPL58pQ6pPlDyv9LLatNMhKmp4Z\nZDE5iGQ6XKvK943S6KTJGk/+Ts4lB5C6yjS8NGjF3ezWYDnN0n2k3XmxyVJAAQUUUEABBRRQ\nQAEFVvzC1TqLTJ/rS23Ck2eSXA06gmQKXbu6hg1jyK1kGsnVlWFkKtmUVFWm/N1Piml2udlD\n7qQ3kNPreDlLAQUUUEABBRRQQAEFelGg0ytIfXlva/PkXDnahpxFcjOFdpXbep9N0kDlqlEq\nz830s83I7aSucrUolUYsV5IWkKeJpYACCiiggAIKKKCAAgpUCnS7QcqVnzvJnuQKchtpV1uy\nIdtzteo48hUp6nwW5pODyYRisM3jk4wvJLmLXr7ndA+xFFBAAQUUUEABBRRQQIFagXxHp6id\nWHiRzCU7F4N9fLyQ5+e227n7Xe5GN5GsTsr1FCuPkjQyG5M0Qy+Qcn3Jyokkd8vLFLzHyMuk\nVeVGEZlmV1yB6nR6XV4359qqbmbw4VYbHFNAAQUUUEABBRRQQIFVR6DcIHXjXY1uHHQkj1e2\neYHJjI8ju5M0ITeQVvUMgxPJ5eQusivJLcVbVW7ykAZpHknD10mNYaekVdkctVJxTAEFFFBA\nAQUUUECBVUyg3CClkciUuHK1Gsv2/M6h5n2L540qFnjMDRaSTipXm+pqEjsk5Wp1HvnOUavx\n3J68efwixhJLAQUUUEABBRRQQAEFhrhAt7+DNMR5ffsKKKCAAgoooIACCijQSwI2SL30aXmu\nCiiggAIKKKCAAgoo0FUBG6Su8npwBRRQQAEFFFBAAQUU6CUBG6Re+rQ8VwUUUEABBRRQQAEF\nFOiqgA1SV3k9uAIKKKCAAgoooIACCvSSQPMd3Xrp3Felc82d/qaQ9Ul+51PX6ocf/jW/J6p7\n9cWH1cdef5Pq7W7tH4G+fg51z++fsxzUR1nywYLK81v67muV29f93R8qt9cdv/LJbBw+YlTd\nLr/49rr3WPse6v6+6Ouf07rj/+KCnoACq6321dP/pZKh7u+a1fxzXuk3WDYuWfBc5akMH/X7\nyu11G4cN+8du/5t/OOfwDdmT5Hec9nR5BamnPz5PXgEFFFBAAQUUUEABBfpTwAapPzU9lgIK\nKKCAAgoooIACCvS0gA1ST398nrwCCiiggAIKKKCAAgr0p4ANUn9qeiwFFFBAAQUUUEABBRTo\naQEbpJ7++Dx5BRRQQAEFFFBAAQUU6E8BG6T+1PRYCiiggAIKKKCAAgoo0NMCawySs1+P89if\n5BbUD5PFpFVtzuAu5F2SWwjmloWHkdTXJM+tqkPYmPf8Dnm2seMOPG7bWM+4pYACCiiggAIK\nKKCAAkNUYLBcQcrv/jmBPEiuafNZrMP4Q+Q+Upx3Hqc3MpPHNFDtamc2zCDZ/7zSTuMbY2NL\nYy4qoIACCiiggAIKKKDAEBQoGo3B8NbP5CQWkgnkwBYndCNjO5JLyOym7bnylKtJRzaNl1eP\nLa+4rIACCiiggAIKKKCAAgo0CwymBukjTu4kkmbnDrIpKepkFk4lmUJ3dTFYenyV5Y/J0aWx\n5sVjGJjXPOi6AgoooIACCiiggAIKKFAI1H0HaWN2PJTMIR+QA8gYMpc8Qt4na5N8f2hvkibl\nXvImSY0gBy1bav8jx8l3ilKzyLXkAjKFHE62JzeRRaRooFhcqZawlul5p5BMs2v+LtFujG1F\nLiWXEUsBBRRQQAEFFFBAAQUU+IlAXYOUZmMqmUQyRW1rspTkeW+Qfck0Mo58S9YkmQKXZul5\nkv2nkqoay8aiQcp+ef5+JDdfSEN0DlmLHE/SkLWrNGa5ynQUub5pp5x7mrdcgbJBasJxVQEF\nFFBAAQUUUEABBZYL1DVIhdPFLNxF0sx8R3IjhfFkPnmc7EVyV7g/k2y7iGRKW8Y2JOXK8/9E\nMlXubvIEKVeuBuWGDc+RTLXLNMBLSV6nqmaxMVe5mhukPD9T76aTb8hAVBq6/42kYeyk9uxk\nJ/dRQAEFFFBAAQUUUECB7gp02iAt5DROI0WDMZnlNEjfk1yd+YykriN/IdtlhUoz9PmypRU/\njmDxKjKbnLJieKWl11i7heRuc5+SHLeucmXrAXIm2YL8jaT2ISNJmrGBqnx/KucxvMMXzFRG\nSwEFFFBAAQUUUEABBX5hgU4bpFc4z6I5yim/1Djv13ksmqMM5QYL75ENstKi9mAsU/LeIplC\nVz4mqz/WaJZOb6xtxOMkcn5jveoh0+z+SHI3u2KaXRq4TOF7jPyWDET9v7zI73/GC53BvlN+\nxv7uqoACCiiggAIKKKCAAl0QyPSzTurtpp1yZSj1yfKHlX4W21YaZCVNzwyymBxEMh2uVWV6\nWhqdNFnjyd/JueQAUleZhpcGrbib3Rosp1m6j7Q7LzZZCiiggAIKKKCAAgoooMCKX7haZ5Hv\nBfWlNuHJM0muBmWKXabQtat8h2kMuZVMI7m6MoxMJZm6VlWZ8nc/yV3rMs1uP5I76Q3k9Dpe\nzlJAAQUUUEABBRRQQIFeFOj0ClJf3tvaPDlXjrYhZ5FZpF3ltt5nkzRQuWqUynMz/Wwzcjup\nq1wtSqURy5WkBeRpYimggAIKKKCAAgoooIAClQLdbpBy5edOsie5gtxG2tWWbMj2XK06jnxF\nijqfhfnkYDKhGGzz+CTjC0nuopfvOd1DLAUUUEABBRRQQAEFFFCgViDf0SlqJxZeJHPJzsVg\nHx8v5Pm57fYikrvRTSSrk3I9xcqjJI1M7uaWZugFUq4vWTmRzCGZgvcYeZm0qtwoItPsiitQ\nnU6vy+vmXFvVzQw+3GqDYwoooIACCiiggAIKKLDqCJQbpG68q9GNg47k8co2LzCZ8XFkd5Im\n5AbSqp5hcCK5nNxFdiX55bStKjd5SIM0j6Th66TGsFPSqmyOWqk4poACCiiggAIKKKDAKiZQ\nbpDSSGRKXLlajWV7fudQ877F80YVCzzmBgtJJ5WrTXU1iR2ScrU6j3znqNX4Sy3GL2IssRRQ\nQAEFFFBAAQUUUGCIC3T7O0hDnNe3r4ACCiiggAIKKKCAAr0kYIPUS5+W56qAAgoooIACCiig\ngAJdFbBB6iqvB1dAAQUUUEABBRRQQIFeErBB6qVPy3NVQAEFFFBAAQUUUECBrgrYIHWV14Mr\noIACCiiggAIKKKBALwmU72LXS+ftuSowZAWWLHiu9r0vffe1yn2+X/TXyu3DxxxfvX3EqMrt\nv/TGJR8sqDyF4R2cf90+ddv74xyq3sRrv8+vhmtfv7nit+03DpItS0e2+80Ky09wjc22qzzT\nJX/Nb3xoX7+qOf63/73TX5PX+jXW3C2/07x91Z1/3X+nOXLdMdq/emdbho/6fWc7ttvriw/b\nbemf8fU3qT5OJ69fd4zqV+j+1pr3sO7v/lB9DoP9/VWfvVsbAn3+b1HJfhXwClK/cnowBRRQ\nQAEFFFBAAQUU6GUBG6Re/vQ8dwUUUEABBRRQQAEFFOhXARukfuX0YAoooIACCiiggAIKKNDL\nAjZIvfzpee4KKKCAAgoooIACCijQrwI2SP3K6cEUUEABBRRQQAEFFFCglwVskHr50/PcFVBA\nAQUUUEABBRRQoF8FBsttvtfjXe1PfiAPk8WkVW3O4C7kXfIUGUYOI6mvSZ5bVYewMe/5HfJs\nY8cdeNy2sZ5xSwEFFFBAAQUUUEABBYaowGC5gvQl/ieQB8k1bT6LdRh/iNxHivPO4/RGZvKY\nBqpd7cyGGST7n1faaXxjbGxpzEUFFFBAAQUUUEABBRQYggJFozEY3vqZnMRCMoEc2OKEbmRs\nR3IJmd20PVeecjXpyKbx8uqx5RWXFVBAAQUUUEABBRRQQIFmgcHUIH3EyZ1E0uzcQTYlRZ3M\nwqkkU+iuLgZLj6+y/DE5ujTWvHgMA/OaB11XQAEFFFBAAQUUUEABBQqBuu8gbcyOh5I55ANy\nABlD5pJHyPtkbZLvD+1N0qTcS94kqRHkoGVL7X/kOPlOUWoWuZZcQKaQw8n25CayiBQNFIsr\n1RLWMj3vFJJpds3fJdqNsa3IpeQyYimggAIKKKCAAgoooIACPxGoa5DSbEwlk0imqG1NlpI8\n7w2yL5lGxpFvyZokU+DSLD1Psv9UUlVj2Vg0SNkvz9+P5OYLaYjOIWuR40kasnaVxixXmY4i\n1zftlHNP85YrUDZITTiuKqCAAgoooIACCiigwHKBugapcLqYhbtImpnvSG6kMJ7MJ4+TvUju\nCvdnkm0XkUxpy9iGpFx5/p9IpsrdTZ4g5crVoNyw4TmSqXaZBngpyetU1Sw25ipXc4OU52fq\n3XTyDRmIymuOI2kYO6nfdrKT+yiggAIKKKCAAgoooEB3BTptkBZyGqeRosGYzHIapO9Jrs58\nRlLXkb+Q7bJCpRn6fNnSih9HsHgVmU1OWTG80tJrrN1Ccre5T0mOW1e5svUAOZNsQf5GUvuQ\nkSTN2EDVKF7o/yHDO3zBNFSWAgoooIACCiiggAIK/MICnf7D/BXOs2iOcsovNc77dR6L5ihD\nucHCe2SDrLSoPRjLlLy3SKbQlY/J6o81mqXTG2sb8Tjpxy3VC5lmN4yU72aXBi5T+B4jA1Vv\n8kK5crZ2h8kVNUsBBRRQQAEFFFBAAQV+YYFOG6S3m84zV4ZSnyx/WOlnsW2lQVbS9Mwgi8lB\nJNPhWlW+b5RGJ03WePJ3ci45gNRVpuGlQSvuZrcGy2mW7iPtzotNlgIKKKCAAgoooIACCiiw\n4heu1lnke0F9qU148kySq0GZYpcpdO0q32EaQ24l08gZJFeFppJNSVVlyt/9JHetyzS73Owh\nd9IbyOl1vJylgAIKKKCAAgoooIACvSjQ6RWkvry3TDPLlaNtyFlkFmlXua332SQNVK4apfLc\nKWQzcjupq1wtSqURy5WkBeRpYimggAIKKKCAAgoooIAClQLdbpBy5edOsie5gtxG2tWWbMj2\nXK06jnxFijqfhfnkYDKhGGzz+CTjC0nuopfvOd1DLAUUUEABBRRQQAEFFFCgViDf0SlqJxZe\nJHPJzsVgHx8v5Pm57fYikrvRTSSrk3I9xcqjJI3MxiTN0AukXF+yciKZQzIF7zHyMmlVuVFE\nptkVV6A6nV6X1825tqqbGXy41QbHFFBAAQUUUEABBRRQYNURKDdI3XhXoxsHHcnjlW1eYDLj\n48juJE3IDaRVPcPgRHI5uYvsSvLLaVtVbvKQBmkeScPXSY1hp6RV2Ry1UnFMAQUUUEABBRRQ\nQIFVTKDcIKWRyJS4crUay/b8zqHmfYvnjSoWeMwNFpJOKleb6moSOyTlanUe+c5Rq/Hcnrx5\n/CLGEksBBRRQQAEFFFBAAQWGuEC3v4M0xHl9+woooIACCiiggAIKKNBLAjZIvfRpea4KKKCA\nAgoooIACCijQVQEbpK7yenAFFFBAAQUUUEABBRToJQEbpF76tDxXBRRQQAEFFFBAAQUU6KqA\nDVJXeT24AgoooIACCiiggAIK9JJA8x3deuncV6VzzZ3+ppD1SX7nU9fqhx/+Nb8nqnv1xYfV\nx15/k+rtbq0XqDOuPwJ/0mo+h/54jarz6Ovr9/X5VefW2PbVy9V3919js+06OEr3dln67muV\nB+/2+Q0fMary9XtiY92fo554E57kqi7wxfT/XPkW19//4srttX/fVz/brQMksGTBc5WvNHzU\n7yu3120cNuwfu/1v/uGcwzdkT5LfcdrT5RWknv74PHkFFFBAAQUUUEABBRToTwEbpP7U9FgK\nKKCAAgoooIACCijQ0wI2SD398XnyCiiggAIKKKCAAgoo0J8CNkj9qemxFFBAAQUUUEABBRRQ\noKcFbJB6+uPz5BVQQAEFFFBAAQUUUKA/BWyQ+lPTYymggAIKKKCAAgoooEBPC6wxSM5+Pc5j\nf5JbUOfeuotJq9qcwV3IuyS3EMwtCw8jqa9J9X15V1vtEPbJe36HPEtSO5BtSdYzbimggAIK\nKKCAAgoooMAQFRgsV5Dyu39OIA+Sa9p8Fusw/hC5jxTnncfpjczkMQ1Uu9qZDTNI9j+vtNP4\nxtjY0piLCiiggAIKKKCAAgooMAQFikZjMLz1MzmJhWQCObDFCd3I2I7kEjK7aXuuPOVq0pFN\n4+XVY8srLiuggAIKKKCAAgoooIACzQKDqUH6iJM7iaTZuYNsSoo6mYVTSabQXV0Mlh5fZflj\ncnRprHnxGAbmNQ+6roACCiiggAIKKKCAAgoUAnXfQdqYHQ8lc8gH5AAyhswlj5D3ydok3x/a\nm6RJuZe8SVIjyEHLltr/yHHynaLULHItuYBMIYeT7clNZBEpGigWV6olrGV63ikk0+yav0u0\nG2NbkUvJZcRSQAEFFFBAAQUUUEABBX4iUNcgpdmYSiaRTFHbmiwled4bZF8yjYwj35I1SabA\npVl6nmT/qaSqxrKxaJCyX56/H8nNF9IQnUPWIseTNGTtKo1ZrjIdRa5v2innnuYtV6BskJpw\nXFVAAQUUUEABBRRQQIHlAp1OsbuY3Z8mvya/If9CtiHzSe4etxdZn+TKz7rkIpJ6lmzYlPVY\nv5Ck7iZPLFta8SNXg04gX5FMtcsVq4nkcVJVufqUq1xpkMqV93g0mU6+KW/o8nKc/kOHyZU2\nSwEFFFBAAQUUUEABBX5hgborSMXpLWThNFI0GJNZzt3fvie5OvMZSV1H/kK2ywr1Hfl82dKK\nH0eweBWZTTIlrlW9xuAtJHeb+5TkuHW1lB0eIGeSLcjfSGofMpKkGRuoGs0LvTFQL+brKKCA\nAgoooIACCiigQP8IdHoF6RVermiO8sovNV7+dR6L5ihDucHCe2SDrLSoPRjLlLy3SKbQlY/J\n6o+VBuP0xtpGPE76cUv1QqbZDSPlu9mlgcsUvsfIQNX/4IX+Z5JGrZP87wN1Yr6OAgoooIAC\nCiiggAIKtBdYo/2mlba8vdLa8itDGfqkaTyruWrUqtL0zCCLSW7ckOlwrSrfN0qjkyYrV6ly\n9ehc8kgjPLStTMNLg5YpdfkeUt5fmqW7SLvzYlNXqvy9qroXyPejLAUUUEABBRRQQAEFFPiF\nBTq9gpTvBfWlNuHJM0muBmWKXabQtatr2DCG3EqmkTNIrgpNJZuSqsqUv/tJ7lqXKzf7kXy/\nZyCn1/FylgIKKKCAAgoooIACCvSiQKcNUl/e29o8OVeOtiFnkVmkXeW23meTNFC5apTKc6eQ\nzcjtpK7ua+yQRixXkhaQp4mlgAIKKKCAAgoooIACClQKdLtBypWfO8me5ApyG2lXW7Ih23O1\n6jjyFSnqfBbmk4PJhGKwzeOTjC8kx5B8z+keYimggAIKKKCAAgoooIACtQL5jk5RO7HwIplL\ndi4G+/h4Ic/PbbcXkdyNbiJZnZTrKVYeJWlkNiZphl4g5fqSlRPJHJIpeI+Rl0mryo0iMs2u\nuALV6fS6vG7OtVXdzODDrTY4poACCiiggAIKKKCAAquOQLlB6sa7Gt046Eger2zzApMZH0d2\nJ2lCbiCt6hkGJ5LLSW66sCv5lrSq3OQhDdI8koavkxrDTkmrsjlqpeKYAgoooIACCiiggAKr\nmEC5QUojkSlx5Wo1lu35nUPN+xbPG1Us8JgbLCSdVK421dUkdkjK1eo88p2jVuO5PXnz+EWM\nJZYCCiiggAIKKKCAAgoMcYFufwdpiPP69hVQQAEFFFBAAQUUUKCXBGyQeunT8lwVUEABBRRQ\nQAEFFFCgqwI2SF3l9eAKKKCAAgoooIACCijQSwI2SL30aXmuCiiggAIKKKCAAgoo0FUBG6Su\n8npwBRRQQAEFFFBAAQUU6CUBG6Re+rQ8VwUUUEABBRRQQAEFFOiqQPk23119IQ+ugAKDSOCL\nD7t7MutvUn38vr5+X59ffXbLtq6x2XYd7NV+l6XvvtZ+I1vqjl/3/MqDd7Bx+IhRlXst+WBB\n5fZONvb1GHXn2Mk5VO7T1z9HdX/OK1/cjR0J1H1GfgYdMbrTEBCo+29lCBD051v0ClJ/anos\nBRRQQAEFFFBAAQUU6GkBG6Se/vg8eQUUUEABBRRQQAEFFOhPARuk/tT0WAoooIACCiiggAIK\nKNDTAjZIPf3xefIKKKCAAgoooIACCijQnwI2SP2p6bEUUEABBRRQQAEFFFCgpwUGy13s1kNx\nf/IDeZgsJq1qcwZ3Ie+Sp8gwchhJfU3y3Ko6hI15z++QZxs77sDjto31jFsKKKCAAgoooIAC\nCigwRAUGyxWkL/E/gTxIrmnzWazD+EPkPlKcdx6nNzKTxzRQ7WpnNswg2f+80k7jG2NjS2Mu\nKqCAAgoooIACCiigwBAUKBqNwfDWz+QkFpIJ5MAWJ3QjYzuSS8jspu258pSrSUc2jZdXjy2v\nuKyAAgoooIACCiiggAIKNAsMpgbpI07uJJJm5w6yKSnqZBZOJZlCd3UxWHp8leWPydGlsebF\nYxiY1zzougIKKKCAAgoooIACCihQCNR9B2ljdjyUzCEfkAPIGDKXPELeJ2uTfH9ob5Im5V7y\nJkmNIActW2r/I8fJd4pSs8i15AIyhRxOtic3kUWkaKBYXKmWsJbpeaeQTLNRPQdTAABAAElE\nQVRr/i7RboxtRS4llxFLAQUUUEABBRRQQAEFFPiJQF2DlGZjKplEMkVta7KU5HlvkH3JNDKO\nfEvWJJkCl2bpeZL9p5KqGsvGokHKfnn+fiQ3X0hDdA5ZixxP0pC1qzRmucp0FLm+aaece5q3\nXIGyQWrCcVUBBRRQQAEFFFBAAQWWC3Q6xe5idn+a/Jr8hvwL2YbMJ7l73F5kfZIrP+uSi0jq\nWbJhU9Zj/UKSups8sWxpxY9cDTqBfEUy1S5XrCaSx0lV5epTrnKlQSpX3uPRZDr5przBZQUU\nUEABBRRQQAEFFFCgLNBpg7SQJ51GMs3tPTKZpL4nuTqTKXhpbK4juZK0HUl9Rz5vyoGsX0Vm\nk0yJa1WvMXgLyfl9SnLculrKDg+QTKfborTzPiyPJGnGBqr+Ay+UxjHvv5PcPFAn5usooIAC\nCiiggAIKKKBAe4G6KXbFM19hoXz15aXGhtd5/KzYicfcYCEN1AalsfLiHqxkSt5bJFPoysdk\n9ccazdLpjbWNeJxEzm+sVz3cy8Y/ktzNrphmlwYuU/geI78lA1F5f/uTNTt8sf+V/f5zh/u6\nmwIKKKCAAgoooIACCnRJoNMG6e2m189VkdQnyx9W+llsW2mQlTQ9M8hikhs3ZDpcq8r3jdLo\npMkaT3L16FzySCM8tK1Mw0uDlil1aZDy/tIs3UXanReb+r3SKDZPHax6ka2rNrpNAQUUUEAB\nBRRQQAEFBkag0yl2mT7Xl9qEJ88kuRp0BMkUunZ1DRvGkFvJNHIGGUamkk1JVWXK3/2kmGa3\nH8u5k95ATq/j5SwFFFBAAQUUUEABBRToRYFOG6S+vLe1eXKuHG1DziK5mUK7ym29zyZpoHLV\nKJXnTiGbkdtJXd3X2CGNWK4kLSC5wYSlgAIKKKCAAgoooIACClQKdLtBypWfO8me5ApyG2lX\nW7Ih23O16jjyFSnqfBbmk4PJhGKwzeOTjC8kx5B8z+keYimggAIKKKCAAgoooIACtQL5jk5R\nO7HwIplLdi4G+/h4Ic/PbbcXkdyNbiJZnZTrKVYeJWlkNiZphl4g5fqSlRPJHJIpeI+Rl0mr\nyvd/Ms2uuALV6fS6vG7OtVXdzODDrTY4poACCiiggAIKKKCAAquOQLlB6sa7Gt046Eger2zz\nApMZH0d2J2lCbiCt6hkGJ5LLSW66sCvJLcVbVW7ykAZpHknD10mNYaekVdkctVJxTAEFFFBA\nAQUUUECBVUyg3CClkciUuHK1Gsv2/M6h5n2L540qFnjMDRaSTipXm+pqEjsk5Wp1HvnOUavx\nl1qMX8RYYimggAIKKKCAAgoooMAQF+j2d5CGOK9vXwEFFFBAAQUUUEABBXpJwAaplz4tz1UB\nBRRQQAEFFFBAAQW6KmCD1FVeD66AAgoooIACCiiggAK9JGCD1EuflueqgAIKKKCAAgoooIAC\nXRWwQeoqrwdXQAEFFFBAAQUUUECBXhKwQeqlT8tzVUABBRRQQAEFFFBAga4KlG/z3dUX8uBD\nQ2DJBwsq3+jw9Tep3O7GDgQ6Mfziww4O5C7dFFhjs+0qDz98xKjK7XUb+/r82uOP+n31Lh38\nGRve12N08me9+izd2usC/hmo/wQ1qjdaFfao+zvXPwf9+il7BalfOT2YAgoooIACCiiggAIK\n9LKADVIvf3qeuwIKKKCAAgoooIACCvSrgA1Sv3J6MAUUUEABBRRQQAEFFOhlARukXv70PHcF\nFFBAAQUUUEABBRToVwEbpH7l9GAKKKCAAgoooIACCijQywKD5S5264G4P/mBPEwWk1a1OYO7\nkHfJU2QYOYykviZ5blUdwsa853fIs40dd+Bx28Z6xi0FFFBAAQUUUEABBRQYogKD5QrSl/if\nQB4k17T5LNZh/CFyHynOO4/TG5nJYxqodrUzG2aQ7H9eaafxjbGxpTEXFVBAAQUUUEABBRRQ\nYAgKFI3GYHjrZ3ISC8kEcmCLE7qRsR3JJWR20/ZcecrVpCObxsurx5ZXXFZAAQUUUEABBRRQ\nQAEFmgUGU4P0ESd3EkmzcwfZlBR1Mgunkkyhu7oYLD2+yvLH5OjSWPPiMQzMax50XQEFFFBA\nAQUUUEABBRQoBOq+g7QxOx5K5pAPyAFkDJlLHiHvk7VJvj+0N0mTci95k6RGkIOWLbX/kePk\nO0WpWeRacgGZQg4n25ObyCJSNFAsrlRLWMv0vFNIptk1f5doN8a2IpeSy4ilgAIKKKCAAgoo\noIACCvxEoK5BSrMxlUwimaK2NVlK8rw3yL5kGhlHviVrkkyBS7P0PMn+U0lVjWVj0SBlvzx/\nP5KbL6QhOoesRY4nacjaVRqzXGU6ilzftFPOPc1brkDZIDXhuKqAAgoooIACCiiggALLBTqd\nYncxuz9Nfk1+Q/6FbEPmk9w9bi+yPsmVn3XJRST1LNmwKeuxfiFJ3U2eWLa04keuBp1AviKZ\napcrVhPJ46SqcvUpV7nSIJUr7/FoMp18U97gsgIKKKCAAgoooIACCihQFqi7glTsu5CF00jR\nYExmOXd/+57k6sxnJHUd+QvZLivUd+TzZUsrfhzB4lVkNsmUuFb1GoO3kNxt7lOS49bVUnZ4\ngJxJtiB/I6l9yEiSZmygKlfeHiJrdviCG3W4n7spoIACCiiggAIKKKBAFwU6bZBe4RyK5iin\n81LjnF7nsWiOMpQbLLxHNshKi9qDsUzJe4tkCl35mKz+WKNZOr2xluZhEjm/sV71kGl2fyS5\nm10xzS4NXKbwPUZ+SwaiYnADGd7hi+UK3Ikd7utuCiiggAIKKKCAAgoo0CWBThukt5teP1eG\nUp8sf1jpZ7FtpUFW0vTMIItJbtyQ6XCtKt83SqOTJms8ydWjc8kjjfDQtjINL81JptSlQcr7\nS7N0F2l3Xmzq98o0wdt/xlFzbjZIPwPMXRVQQAEFFFBAAQUU6IZAp99Byj/4+1Kb8OSZJFeD\nMsUuU+ja1TVsGENuJdPIGWQYmUo2JVWVKX/3k9y1LtPs9iO5k95ATq/j5SwFFFBAAQUUUEAB\nBRToRYFOG6S+vLe1eXKuHG1DziKzSLvKbb3PJmmgctUoledOIZuRTq7K3Md+qTRiuZK0gOQG\nE5YCCiiggAIKKKCAAgooUCnQ7QYpV37uJHuSK8htpF1tyYZsz9Wq48hXpKjzWZhPDiYTisE2\nj08yvpDkF8MeRu4hlgIKKKCAAgoooIACCihQK5Dv6BS1Ewsvkrlk52Kwj48X8vzcdnsRyd3o\nJpLVSbmeYuVRkkZmY5Jm6AVSri9ZyXd05pBMwXuMvExaVW4UkWl2xRWoTqfX5XVzrq3qZgYf\nbrXBMQUUUEABBRRQQAEFFFh1BMoNUjfe1ejGQUfyeGWbF5jM+DiyO0kTcgNpVc8wOJFcTnLT\nhV3Jt6RV5SYPaZDmkTR8ndQYdkpalc1RKxXHFFBAAQUUUEABBRRYxQTKDVIaiUyJK1ersWzP\n7xxq3rd43qhigcfcYCHppHK1qa4msUNSrlbnke8ctRrP7cmbxy9iLLEUUEABBRRQQAEFFFBg\niAt0+ztIQ5zXt6+AAgoooIACCiiggAK9JGCD1EuflueqgAIKKKCAAgoooIACXRWwQeoqrwdX\nQAEFFFBAAQUUUECBXhKwQeqlT8tzVUABBRRQQAEFFFBAga4K2CB1ldeDK6CAAgoooIACCiig\nQC8J2CD10qfluSqggAIKKKCAAgoooEBXBcq3+e7qC3nwoSEwfNTvh8YbHezvcv1NBvcZDvbz\nQ2/4L3yOv/Tr1/4B6g+f/jhG7Ym6gwK9LfCrke1+RWPjfX3xYfUb9L+zap9e2ernOKCflFeQ\nBpTbF1NAAQUUUEABBRRQQIHBLGCDNJg/Hc9NAQUUUEABBRRQQAEFBlTABmlAuX0xBRRQQAEF\nFFBAAQUUGMwCNkiD+dPx3BRQQAEFFFBAAQUUUGBABWyQBpTbF1NAAQUUUEABBRRQQIHBLGCD\nNJg/Hc9NAQUUUEABBRRQQAEFBlRgsNzmez3e9f7kB/IwWUxa1eYM7kLeJU+RYeQwkvqa5LlV\ndQgb857fIc82dtyBx20b6xm3FFBAAQUUUEABBRRQYIgKDJYrSF/ifwJ5kFzT5rNYh/GHyH2k\nOO88Tm9kJo9poNrVzmyYQbL/eaWdxjfGxpbGXFRAAQUUUEABBRRQQIEhKFA0GoPhrZ/JSSwk\nE8iBLU7oRsZ2JJeQ2U3bc+UpV5OObBovrx5bXnFZAQUUUEABBRRQQAEFFGgWGEwN0kec3Ekk\nzc4dZFNS1MksnEoyhe7qYrD0+CrLH5OjS2PNi8cwMK950HUFFFBAAQUUUEABBRRQoBCo+w7S\nxux4KJlDPiAHkDFkLnmEvE/WJvn+0N4kTcq95E2SGkEOWrbU/keOk+8UpWaRa8kFZAo5nGxP\nbiKLSNFAsbhSLWEt0/NOIZlm1/xdot0Y24pcSi4jlgIKKKCAAgoooIACCijwE4G6BinNxlQy\niWSK2tZkKcnz3iD7kmlkHPmWrEkyBS7N0vMk+08lVTWWjUWDlP3y/P1Ibr6QhugcshY5nqQh\na1dpzHKV6ShyfdNOOfc0b7kCZYPUhOOqAgoooIACCiiggAIKLBeoa5AKp4tZuIukmfmO5EYK\n48l88jjZi+SucH8m2XYRyZS2jG1IypXn/4lkqtzd5AlSrlwNyg0bniOZapdpgJeSvE5VzWJj\nrnI1N0h5fqbeTSffkIGof8eL/N8kDWMntUUnO7mPAgoooIACCiiggAIKdFeg0wZpIadxGika\njMksp0H6nuTqzGckdR35C9kuK1Saoc+XLa34cQSLV5HZ5JQVwystvcbaLSR3m/uU5Lh1lStb\nD5AzSRqOv5HUPmQkSTM2ULWYF0rzOLzDF8w0RUsBBRRQQAEFFFBAAQV+YYFOG6RXOM+iOcop\nv9Q479d5LJqjDOUGC++RDbLSovZgLFPy3iKZQlc+Jqs/1miWTm+sbcTjJHJ+Y73qIdPs/khy\nN7timl0auEzhe4z8lgxExeT/+BkvdAb7/sefsb+7KqCAAgoooIACCiigQBcEMv2sk3q7aadc\nGUp9svxhpZ/FtpUGWUnTM4Pk6spBJNPhWlW+b5RGJ03WePJ3ci45gNRVpuGlQcuUutQaJM3S\nfaTdebHJUkABBRRQQAEFFFBAAQVW/MLVOot8L6gvtQlPnklyNShT7DKFrl3lO0xjyK1kGsnV\nlWFkKtmUVFWm/N1Pcte6TLPLzR5yJ72BnF7Hy1kKKKCAAgoooIACCijQiwKdXkHqy3vL92ty\n5WgbchaZRdpVbut9NkkDlatGqTx3CtmM3E7qKleLUmnEciVpAXmaWAoooIACCiiggAIKKKBA\npUC3G6Rc+bmT7EmuILeRdrUlG7I9V6uOI1+Ros5nYT45mEwoBts8Psn4QpK76OV7TvcQSwEF\nFFBAAQUUUEABBRSoFch3dIraiYUXyVyyczHYx8cLeX5uu72I5G50E8nqpFxPsfIoSSOzMUkz\n9AIp15esnEjmkEzBe4y8TFpVbhSRaXbFFahOp9fldXOurepmBh9utcExBRRQQAEFFFBAAQUU\nWHUEyg1SN97V6MZBR/J4ZZsXmMz4OLI7SRNyA2lVzzA4kVxO7iK7kvxy2laVmzykQZpH0vB1\nUmPYKWlVNketVBxTQAEFFFBAAQUUUGAVE8gUOOuXF8iNKKaQ9UmulnWtfvjhX3OFzVJAAQUU\nUECBHhD46un/UnmW6/7uD5XbV1s/98myBrvAkgXPVZ7i8FG/r9xet3HYsH/s9r/5h3MO+fU9\ne5LMDuvp6vZ3kHoax5NXQAEFFFBAAQUUUECBoSVggzS0Pm/frQIKKKCAAgoooIACClQI2CBV\n4LhJAQUUUEABBRRQQAEFhpaADdLQ+rx9twoooIACCiiggAIKKFAhYINUgeMmBRRQQAEFFFBA\nAQUUGFoC3b7N99DS9N2u1u27sAwJ4i8+rHybSz5YULk9G7/+r/9Uuc+au+V3MbevNTbbrv3G\nAdiy9N3X+vQq/XH+dYbr/Kd/rjzHJX/NbyNoX78a2e63Cix/ztf3VP8Kt0v+rxHtD86W6576\nj5Xbv1/018rtdRvrzj/Pr3uN4WOOr3yZOsO651cenI1vHnBJ5S6/ueK3ldv7+vp17y8v3olz\n1UnW/bcwfMSoqqev1snfN5UH6PLGuvNf9vKD/S5uNX/ne5e6Lv8hGiSH7+td6gbJ21hlTsMr\nSKvMR+kbUUABBRRQQAEFFFBAgb4K2CD1VdDnK6CAAgoooIACCiigwCojYIO0ynyUvhEFFFBA\nAQUUUEABBRToq4ANUl8Ffb4CCiiggAIKKKCAAgqsMgI2SKvMR+kbUUABBRRQQAEFFFBAgb4K\n2CD1VdDnK6CAAgoooIACCiigwCojMFhu870eovuTH8jDZDFpVZszuAt5lzxFhpHDSOprkudW\n1SFszHt+hzzb2HEHHrdtrGfcUkABBRRQQAEFFFBAgSEqMFiuIH2J/wnkQXJNm89iHcYfIveR\n4rzzOL2RmTymgWpXO7NhBsn+55V2Gt8YG1sac1EBBRRQQAEFFFBAAQWGoEDRaAyGt34mJ7GQ\nTCAHtjihGxnbkeQ3+81u2p4rT7madGTTeHn12PKKywoooIACCiiggAIKKKBAs8BgapA+4uRO\nIml27iCbkqJOZuFUkil0VxeDpcdXWf6YHF0aa148hoF5zYOuK6CAAgoooIACCiiggAKFQN13\nkDZmx0PJHPIBOYCMIXPJI+R9sjbJ94f2JmlS7iVvktQIctCypfY/cpx8pyg1i1xLLiBTyOFk\ne3ITWUSKBorFlWoJa5medwrJNLvm7xLtxthW5FJyGbEUUEABBRRQQAEFFFBAgZ8I1DVIaTam\nkkkkU9S2JktJnvcG2ZdMI+PIt2RNkilwaZaeJ9l/KqmqsWwsGqTsl+fvR3LzhTRE55C1yPEk\nDVm7SmOWq0xHkeubdsq5p3nLFSgbpCYcVxVQQAEFFFBAAQUUUGC5QF2DVDhdzMJdJM3MdyQ3\nUhhP5pPHyV4kd4X7M8m2i0imtGVsQ1KuPP9PJFPl7iZPkHLlalBu2PAcyVS7TAO8lOR1qmoW\nG3OVq7lByvMz9W46+YYMROU9/x9keIcvlu9WWQoooIACCiiggAIKKPALC3TaIC3kPE8jRYMx\nmeU0SN+TXJ35jKSuI38h22WFSjP0+bKlFT+OYPEqMpucsmJ4paXXWLuF5G5zn5Ict65yZesB\ncibZgvyNpPYhI0masYGqTDvcluSKWif17zrZyX0UUEABBRRQQAEFFFCguwKdNkivcBpFc5Qz\neqlxWq/zWDRHGcoNFt4jG2SlRe3BWKbkvUUyha58TFZ/rNEsnd5Y24jHSeT8xnrVQ6bZ/ZHk\nbnbXN3ZMA5cpfI+R35KBqL/zImkEO60z2HFKpzu7nwIKKKCAAgoooIACCnRHINPPOqm3m3bK\nlaHUJ8sfVvpZbFtpkJU0PTPIYnIQyXS4VpXvG6XRSZM1nqTZOJccQOoq0/DSoGVKXWoNkmbp\nPtLuvNhkKaCAAgoooIACCiiggAIrfuFqnUW+F9SX2oQnzyS5GpQrK5lC167yHaYx5FYyjeTq\nyjAylWxKqipT/u4nuWtdptnlZg+5k95ATq/j5SwFFFBAAQUUUEABBRToRYFOryD15b3l+zi5\ncrQNOYvMIu0qt/U+m6SBylWjVJ6b6WebkdtJXeVqUSqNWK4kLSBPE0sBBRRQQAEFFFBAAQUU\nqBTodoOUKz93kj3JFeQ20q62ZEO252rVceQrUtT5LMwnB5MJxWCbxycZX0hyF718z+keYimg\ngAIKKKCAAgoooIACtQL5jk5RO7HwIplLdi4G+/h4Ic/PbbcXkdyNbiJZnZTrKVYeJWlkNiZp\nhl4g5fqSlRPJHJIpeI+Rl0mryo0iMs2uuALV6fS6vG7OtVXdzODDrTY4poACCiiggAIKKKCA\nAquOQLlB6sa7Gt046Eger2zzApMZH0d2J2lCbiCt6hkGJ5LLyV1kV5JfTtuqcpOHNEjzSBq+\nTmoMOyWtyuaolYpjCiiggAIKKKCAAgqsYgLlBimNRKbElavVWLbndw4171s8b1SxwGNusJB0\nUrnaVFeT2CEpV6vzyHeOWo2/1GL8IsYSSwEFFFBAAQUUUEABBYa4QLe/gzTEeX37CiiggAIK\nKKCAAgoo0EsCNki99Gl5rgoooIACCiiggAIKKNBVARukrvJ6cAUUUEABBRRQQAEFFOglARuk\nXvq0PFcFFFBAAQUUUEABBRToqoANUld5PbgCCiiggAIKKKCAAgr0kkCrO7310vmvKueaO/1N\nIeuT/M6nrtUPP/xrfk9U12rJgucqjz18xKjK7cs2rr9J9T5ffFi9vdvPr371vm+te399f4XV\nlnywoPIoHX1OlUfo7sa+nn/d8/vj7OsM+3oOdcevfQ99/e+k9gVWgR3qjOreYt1/y309ft3r\nZ3u3z6Hbx+/kPa7i+3z19H+pfIfr7n5C5XY39oZA7b+fRv2++o3U/Lc4bINjuv1v/uGc4Ddk\nT5LfcdrT5RWknv74PHkFFFBAAQUUUEABBRToTwEbpP7U9FgKKKCAAgoooIACCijQ0wI2SD39\n8XnyCiiggAIKKKCAAgoo0J8CNkj9qemxFFBAAQUUUEABBRRQoKcFbJB6+uPz5BVQQAEFFFBA\nAQUUUKA/BWyQ+lPTYymggAIKKKCAAgoooEBPC6wxSM5+Pc5jf5JbUD9MFpNWtTmDu5B3SW4h\nmFsWHkZSX5M8t6oOYWPe8zvk2caOO/C4bWM945YCCiiggAIKKKCAAgoMUYHBcgUpv/snN/J/\nkFzT5rNYh/GHyH2kOO88Tm9kJo9poNrVzmyYQbL/eaWdxjfGxpbGXFRAAQUUUEABBRRQQIEh\nKFA0GoPhrZ/JSSwkE8iBLU7oRsZ2JJeQ2U3bc+UpV5OObBovrx5bXnFZAQUUUEABBRRQQAEF\nFGgWGEwN0kec3Ekkzc4dZFNS1MksnEoyhe7qYrD0+CrLH5OjS2PNi8cwMK950HUFFFBAAQUU\nUEABBRRQoBCo+w7Sxux4KJlDPiAHkDFkLnmEvE/WJvn+0N4kTcq95E2SGkEOWrbU/keOk+8U\npWaRa8kFZAo5nGxPbiKLSNFAsbhSLWEt0/NOIZlm1/xdot0Y24pcSi4jlgIKKKCAAgoooIAC\nCijwE4G6BinNxlQyiWSK2tZkKcnz3iD7kmlkHPmWrEkyBS7N0vMk+08lVTWWjUWDlP3y/P1I\nbr6QhugcshY5nqQha1dpzHKV6ShyfdNOOfc0b7kCZYPUhOOqAgoooIACCiiggAIKLBeoa5AK\np4tZuIukmfmO5EYK48l88jjZi+SucH8m2XYRyZS2jG1IypXn/4lkqtzd5AlSrlwNyg0bniOZ\napdpgJeSvE5VzWJjrnI1N0h5fqbeTSffkIGo4bzIiSSPnVT8LAUUUEABBRRQQAEFFPiFBTpt\nkBZynqeRosGYzHIapO9Jrs58RlLXkb+Q7bJCpRn6fNnSih9HsHgVmU1OWTG80tJrrN1Ccre5\nT0mOW1dL2eEBcibZgvyNpPYhI0masYGqf88LnUtyRa2T2qiTndxHAQUUUEABBRRQQAEFuiuQ\nqyud1CvsVDRH2f+lxpNe57FojjKUGyy8RzbISovag7FMyXuLZApd+Zis/lijWTq9sZbmYdKP\nW6oXMs1uGCnfzS4NXKbwPUYGqt7hhXYi23eYfxqoE/N1FFBAAQUUUEABBRRQoL1Apw3S202H\nyJWh1CfLH1b6WWxbaZCVND0zyGJyEMl0uFaV7xul0UmTNZ78neRqzAGkrjINLw1acTe7NVhO\ns5TfndTuvNhkKaCAAgoooIACCiiggAIrfuFqnUW+F9SX2oQnzyS5GpQpdplC167yHaYx5FYy\njZxBclVoKtmUVFWm/N1Pcte6TLPbj+ROegM5vY6XsxRQQAEFFFBAAQUUUKAXBTq9gtSX97Y2\nT86Vo23IWWQWaVe5rffZJA1Urhql8twpZDNyO6mrXC1KpRHLlaQF5GliKaCAAgoooIACCiig\ngAKVAt1ukHLl506yJ7mC3Eba1ZZsyPZcrTqOfEWKOp+F+eRgMqEYbPP4JOMLSe6il+853UMs\nBRRQQAEFFFBAAQUUUKBWIN/RKSo3FXiRzCU7F4N9fLyQ5+e224tI7kY3kaxOyvUUK4+SNDIb\nkzRDL5ByfclKbps9h2QK3mPkZdKqcqOITLMrrkB1Or0ur5tzbVU3M/hwqw2OKaCAAgoooIAC\nCiigwKojUG6QuvGuRjcOOpLHK9u8wGTGx5HdSZqQG0ireobBieRychfZlXxLWlVu8pAGaR5J\nw9dJjWGnpFXZHLVScUwBBRRQQAEFFFBAgVVMoNwgpZHIlLhytRrL9vzOoeZ9i+eNKhZ4zA0W\nkk4qV5vq6v9v717g5qrre9+LQAj3UkibRoEooCgKu5HNTaFYWpCCCFsuIgbK1fISb7RQypGe\n7EoURTluir4Aj5pDKQhswindcIB2p7CVS8tFud9aCGLYoFzlHgKczzeZRVYWa2bN8DzzZCbz\n+b1e32et9V/Xec8Q8s/6z3pms0FSrrrryHeO6trzePJq+wm0JZYCCiiggAIKKKCAAgqMuEC/\nv4M04ry+fAUUUEABBRRQQAEFFBgmATtIw/Ruea0KKKCAAgoooIACCijQVwE7SH3l9eAKKKCA\nAgoooIACCigwTAJ2kIbp3fJaFVBAAQUUUEABBRRQoK8CdpD6yuvBFVBAAQUUUEABBRRQYJgE\nyk+xG6br9loHVGDRo/d0vLJJG0zvuH7xyuee6LzNWut3Xt+0dqz7Nx2/z+tfuKP5qfOv/Gvn\nX/+16rb5Xcztq+l9XGO7g9rv3M2ahve46TWuMnXzbs7SdpuFt+Q3BXSut09r99T/zvt1u/aJ\nY/6646ZrH9b5NeY3aneqVx++t9PqxnVNn5HGA7DBa4/c0nGzJuOmz/HKG753TMdv/Bw9Pr/j\n8ce8st/H7+ICJzVss3CM19jv4zdc/uLVk6Z/qJvN3EaB5SrQ+Pejhv9vvm3I/26zXPFrTu4d\npBoUmxRQQAEFFFBAAQUUUGA0Bewgjeb77qtWQAEFFFBAAQUUUECBGgE7SDUoNimggAIKKKCA\nAgoooMBoCthBGs333VetgAIKKKCAAgoooIACNQJ2kGpQbFJAAQUUUEABBRRQQIHRFLCDNJrv\nu69aAQUUUEABBRRQQAEFagQG5THfa3Jtu5LXSZ5h/BKpqw1p3Jo8Sq4nK5G9SepF0vT844+z\nTV7zw+QmktqCvIdkOe2WAgoooIACCiiggAIKjKjAoNxBeh7//GKVS8ipbd6L1Wm/jFxIiuvO\ndG4rlzNNB6pdbcWKS0m2/3Jpo5mttp1Kbc4qoIACCiiggAIKKKDACAoUHY1BeOlHcRELyDFk\n95oLOoO2D5KvkGsr63PnKXeT9q20lxc/VV5wXgEFFFBAAQUUUEABBRSoCgxSB+lJLu5gks7O\nj8gUUtQhzBxGMoTum0VjaXo380+R/Utt1dkDaLir2uiyAgoooIACCiiggAIKKFAINH0HaT02\n3ItcRx4nu5EZ5DZyJfk1mUzy/aGPkHRSLiAPkNQGZI/Fc+1/5Dj5TlFqHvkWOY6cTfYh7yPf\nI4+QogPF7DK1kKUMzzuUZJhd9btE29L2LnIS+SqxFFBAAQUUUEABBRRQQIE3CTR1kNLZmENm\nkwxR24QsItnvfvJRci7ZmbxCViUZApfO0s9Itp9DOtVOrCw6SNku++9C8vCFdIi+SFYjnybp\nkLWrdMxyl2k/clplo1x7Om+5A2UHqYLjogIKKKCAAgoooIACCiwRaOogFU4nMnMeSWfmVZIH\nKcwk95FryIdJngL3BZJ1J5AMaUvbOqRc2f9zJEPlzic/IeXK3aA8sOFmkqF2GQZ4Esl5OtU8\nVuYuV7WDlP0z9G4ueZlMRK3ESdJJTIexm3pvNxu5jQIKKKCAAgoooIACCvRXoNsO0gIu43BS\ndDBOZz4dpNdI7s78hqS+Tb5GNs8Clc7Qs4vnlv74JLPfINeSQ5c2LzN3D0tnkTxt7hmS4zbV\nIja4mBxFNiK/IKkdyTSSzthE1bs50VVk0kSd0PMooIACCiiggAIKKKDA2AVyd6WbupONis5R\ntr+9tdO9TIvOUZrygIXHyNpZqKntacuQvAdJhtCVj8niG7Upc0e0ltZlOvuNNZ1nMswud2/K\nT7NLBy5D+K4mE1X/wYlWJyt3mT+bqAvzPAoooIACCiiggAIKKNBeoNsO0kOVQ+TOUOrpJZNl\nfhbrlmlkIZ2eS8lLZA+S4XB1le8bpaOTTtZM8ivyJbIbaapr2CAdtOJpdqswn85SfndSu+ti\nlaWAAgoooIACCiiggAIKLP2Fq00W+V7QWGp9dr6c5G5QhthlCF27yneYZpDvk3PJkSR3heaQ\nKaRTZcjfRSRPrcswu11InqQ3kcPrOJ2lgAIKKKCAAgoooIACwyjQ7R2ksby2yeycO0ebkaPJ\nPNKu8ljvz5N0oHLXKJV9zyZTyQ9JU+VuUSodsdxJmk9uIJYCCiiggAIKKKCAAgoo0FGg3x2k\n3Pk5h+xAvk5+QNrVxqzI+tytOpC8QIo6lpn7yJ7kmKKxzfSntC8geYpevuf0Y2IpoIACCiig\ngAIKKKCAAo0C+Y5OUVsycyu5jWxVNI5xejz757Hbj5A8jW4WyYMLynU9C1eRdGTWI+kM/ZyU\n63kWPkOuIxmCdzW5g9RVHhSRYXbFHahuh9flvLnWujqTxivqVtimgAIKKKCAAgoooIACK45A\nuYPUj1e1aeug05ie0uYEp9O+M9mOpBPyHVJXN9I4i5xMziPbkPxy2rrKQx7SQbqLpMPXTc1g\no6Su7BzVqdimgAIKKKCAAgoooMAKJlDuIKUjkSFx5apry/r8zqHqtsV+04sZpnnAQtJN5W5T\nU81mg6RcddeR7xzVtefx5NX2E2hLLAUUUEABBRRQQAEFFBhxgX5/B2nEeX35CiiggAIKKKCA\nAgooMEwCdpCG6d3yWhVQQAEFFFBAAQUUUKCvAnaQ+srrwRVQQAEFFFBAAQUUUGCYBOwgDdO7\n5bUqoIACCiiggAIKKKBAXwXsIPWV14MroIACCiiggAIKKKDAMAlUn+g2TNe+Il1rnvR3NlmL\n5Hc+9a1ef/2f83uiLAVWbIHnnljur2/h4/M7XsOkDaZ3XL+89+94caxsur7s3/Qam87RuH6t\n9Ttv0vQ5aNq/89Fdq8CECDw39887nmetXU/suP5tfs47+wzI2oXzb+54JZOmf6jj+qaVK630\nR/3+O/8kruFlsgPJ7zgd6vIO0lC/fV68AgoooIACCiiggAIKjKeAHaTx1PRYCiiggAIKKKCA\nAgooMNQCdpCG+u3z4hVQQAEFFFBAAQUUUGA8Bewgjaemx1JAAQUUUEABBRRQQIGhFrCDNNRv\nnxevgAIKKKCAAgoooIAC4ylgB2k8NT2WAgoooIACCiiggAIKDLXAKgNy9WtyHbuSPIL6CvIS\nqasNadyaPEryCME8snBvknqRZN9O9XFW5jU/TG5qbbgF0/e0ltNuKaCAAgoooIACCiigwIgK\nDModpPzun4PIJeTUNu/F6rRfRi4kxXVnOreVy5mmA9WutmLFpSTbf7m00cxW206lNmcVUEAB\nBRRQQAEFFFBgBAWKjsYgvPSjuIgF5Biye80FnUHbB8lXyLWV9bnzlLtJ+1bay4ufKi84r4AC\nCiiggAIKKKCAAgpUBQapg/QkF3cwSWfnR2QKKeoQZg4jGUL3zaKxNL2b+afI/qW26uwBNNxV\nbXRZAQUUUEABBRRQQAEFFCgEmr6DtB4b7kWuI4+T3cgMchu5kvyaTCb5/tBHSDopF5AHSGoD\nssfiufY/cpx8pyg1j3yLHEfOJvuQ95HvkUdI0YFidplayFKG5x1KMsyu+l2ibWl7FzmJfJVY\nCiiggAIKKKCAAgoooMCbBJo6SOlszCGzSYaobUIWkex3P/koOZfsTF4hq5IMgUtn6Wck288h\nnWonVhYdpGyX/XchefhCOkRfJKuRT5N0yNpVOma5y7QfOa2yUa49nbfcgbKDVMFxUQEFFFBA\nAQUUUEABBZYIdDvE7kQ2v4G8g7yT/B3ZjNxH8vS4D5O1SO78rEFOIKmbyDqVrMny8SR1PvnJ\n4rmlP3I36CDyAslQu9yxmkWuIZ0qd59ylysdpHLlNe5P5pKXyyv6PD+V46eD2U1yp85SQAEF\nFFBAAQUUUECB5SzQdAepuLwFzBxOig7G6czPJK+R3J35DUl9m3yNbJ4F6lXy7OK5pT8+yew3\nyLUkQ+Lq6h4azyJ52twzJMdtqkVscDE5imxEfkFSO5JpJJ2xiapNOdH9E3Uyz6OAAgoooIAC\nCiiggALjI9DtHaQ7OV3ROcqZb2+d/l6mRecoTXnAwmNk7SzU1Pa0ZUjegyRD6MrHZPGNSgfj\niNbSukxnv7Gm80yG2a1Eyk+zSwcuQ/iuJhNV/86Jcudoky6TO3SWAgoooIACCiiggAIKLGeB\nVbo8/0OV7XJnKPX0kskyP4t1yzSykE7PpeQlkgc3ZDhcXeX7RunopJM1k+Tu0ZfIla0waVsZ\nhpcOWobU5XtIeX3pLJ1H2l0Xq/pSv+zhqO0sejiEmyqggAIKKKCAAgoooMBYBbq9g5TvBY2l\n1mfny0nuBmWIXYbQtatTWTGDfJ+cS44kuSs0h0whnSpD/i4ieWpdhtntQvIkvYkcXsfpLAUU\nUEABBRRQQAEFFBhGgW47SGN5bZPZOXeONiNHk3mkXeWx3p8n6UDlrlEq+55NppIfkqa6sLVB\nOmK5kzSf5AETlgIKKKCAAgoooIACCijQUaDfHaTc+TmH7EC+Tn5A2tXGrMj63K06kLxAijqW\nmfvInuSYorHN9Ke0LyD5xbD5ntOPiaWAAgoooIACCiiggAIKNArkOzpFbcnMreQ2slXROMbp\n8eyfx24/QvI0ullkZVKu61m4iqQjsx5JZ+jnpFzPs/AZch3JELyryR2krvKgiAyzK+5AdTu8\nLufNtdbVmTReUbfCNgUUUEABBRRQQAEFFFhxBModpH68qk1bB53G9JQ2Jzid9p3JdiSdkO+Q\nurqRxlnkZJKHLmxDXiF1lYc8pIN0F0mHr5uawUZJXdk5qlOxTQEFFFBAAQUUUECBFUyg3EFK\nRyJD4spV15b1+Z1D1W2L/aYXM0zzgIWkm8rdpqaazQZJuequI985qmvP48mr7SfQllgKKKCA\nAgoooIACCigw4gL9/g7SiPP68hVQQAEFFFBAAQUUUGCYBOwgDdO75bUqoIACCiiggAIKKKBA\nXwXsIPWV14MroIACCiiggAIKKKDAMAnYQRqmd8trVUABBRRQQAEFFFBAgb4K2EHqK68HV0AB\nBRRQQAEFFFBAgWESsIM0TO+W16qAAgoooIACCiiggAJ9FSg/5ruvJ/LgoyHwwg1/3/GFrvGB\nj3Vc39XKtdbvarOh3ei5Jzpe+sLH53dc383KSRtM72az/m3T9B42GIz1wroxbDJ67qqvdbyM\nt09r92vVOu72xspFj97zxnzdzMJb8uvg2tdYz9/+yN2v6ca509Ga3oO3NXxOms4/qdPJs67p\nc9q0f8P1jfn4Ted3/XAINH1Oml5F0+e06fhN+zed3/XjIjBp+oc6Hmfh/Js7rm/av+POrnyT\ngHeQ3kRigwIKKKCAAgoooIACCoyqgB2kUX3nfd0KKKCAAgoooIACCijwJgE7SG8isUEBBRRQ\nQAEFFFBAAQVGVcAO0qi+875uBRRQQAEFFFBAAQUUeJOAHaQ3kdiggAIKKKCAAgoooIACoyow\nKE+xW5M3YFfyOrmCvETqakMatyaPkuvJSmRvknqRZN9O9XFW5jU/TG5qbbgF0/e0ltNuKaCA\nAgoooIACCiigwIgKDModpOfxP4hcQk5t816sTvtl5EJSXHemc1u5nGk6UO1qK1ZcSrL9l0sb\nzWy17VRqc1YBBRRQQAEFFFBAAQVGUKDoaAzCSz+Ki1hAjiG711zQGbR9kHyFXFtZnztPuZu0\nb6W9vPip8oLzCiiggAIKKKCAAgoooEBVYJA6SE9ycQeTdHZ+RKaQog5h5jCSIXTfLBpL07uZ\nf4rsX2qrzh5Aw13VRpcVUEABBRRQQAEFFFBAgUKg6TtI67HhXuQ68jjZjeTXw99GriS/JpNJ\nvj/0EZJOygXkAZLagOyxeK79jxwn3ylKzSPfIseRs8k+5H3ke+QRUnSgmF2mFrKU4XmHkgyz\nq36XaFva3kVOIl8llgIKKKCAAgoooIACCijwJoGmDlI6G3PIbJIhapuQRST73U8+Ss4lO5NX\nyKokQ+DSWfoZyfZzSKfaiZVFBynbZf9dSB6+kA7RF8lq5NMkHbJ2lY5Z7jLtR06rbJRrT+ct\nd6DsIFVwXFRAAQUUUEABBRRQQIElAt0OsTuRzW8g7yDvJH9HNiP3kTw97sNkLZI7P2uQE0jq\nJrJOJWuyfDxJnU9+snhu6Y/cDTqIvEAy1C53rGaRa0inyt2n3OVKB6lceY37k7nk5fIK5xVQ\nQAEFFFBAAQUUUECBskC3HaQF7HQ4yTC3x8jpJPUayd2ZDMFLx+bbJHeSNiepV8mzlezO8jfI\ntSRD4urqHhrPIrm+Z0iO21SL2OBikuF0G5U23pH5aSSdsYmqd3Oi35CXusx3J+rCPI8CCiig\ngAIKKKCAAgq0F2gaYlfseScz5bsvt7dW3Ms0HYGi8oCFdKDWLhoq0+1ZzpC8B0mG0JWPyeIb\ntSlzR7SW1mU6mxzbWu40uYCVnyV5ml0xzC4duAzhu5q8n0xEzecknyCTujzZx9juS11u62YK\nKKCAAgoooIACCijQJ4FuO0gPVc6fO0Opp5dMlvlZrFumkYV0ei4luauSBzdkOFxd5ftG6eik\nkzWT5O5ROg9XtsKkbWUYXjpoGVKXDlJeXzpL55F218Wqca/cWfuXHo5avuPVw25uqoACCiig\ngAIKKKCAAuMp0O0QuwyfG0utz86Xk9wN+iTJELp2dSorZpDvk3PJkWQlModMIZ0qHZOLSDHM\nbhfm8yS9iRxex+ksBRRQQAEFFFBAAQUUGEaBbjtIY3ltk9k5d442I0eTeaRd5bHenyfpQBVD\nzrLv2WQq+SFpqgtbG6QjljtJ88kNxFJAAQUUUEABBRRQQAEFOgr0u4OUOz/nkB3I18kPSLva\nmBVZn7tVB5IXSFHHMnMf2ZMcUzS2mf6U9gXkALI3+TGxFFBAAQUUUEABBRRQQIFGgXxHp6gt\nmbmV3Ea2KhrHOD2e/fPY7UdInkY3i6xMynU9C1eRdGTWI+kM/ZyU63kWPkOuIxmCdzW5g9RV\nHhSRYXbFHahuh9flvLnWujqTxivqVtimgAIKKKCAAgoooIACK45AuYPUj1e1aeug05ie0uYE\np9O+M9mOpBPyHVJXN9I4i5xM8tCFbUgeKV5XechDOkh3kXT4uqkZbJTUlZ2jOhXbFFBAAQUU\nUEABBRRYwQTKHaR0JDIkrlx1bVmf3zlU3bbYb3oxwzQPWEi6qdxtaqrZbJCUq+468p2juvY8\nnrzafgJtiaWAAgoooIACCiiggAIjLtDv7yCNOK8vXwEFFFBAAQUUUEABBYZJwA7SML1bXqsC\nCiiggAIKKKCAAgr0VcAOUl95PbgCCiiggAIKKKCAAgoMk4AdpGF6t7xWBRRQQAEFFFBAAQUU\n6KuAHaS+8npwBRRQQAEFFFBAAQUUGCYBO0jD9G55rQoooIACCiiggAIKKNBXgfJjvvt6Ig8+\nGgJrfOBjnV/oWut3Xu/at72twWhSw/oVgrDPr7Erw+ee6Ei51q4ndly/8PH5Hdc3rWz6b2nh\n1M07HmLSBtM7rl8hVjZ8TiY1vciG/Zt2b1zf7+M3XoAbDIVAw+fk7dPa/YrG1qtr+LOq6f8p\nQ2E0Ahe5cP7NHV/lpOkf6rjeleMr4B2k8fX0aAoooIACCiiggAIKKDDEAnaQhvjN89IVUEAB\nBRRQQAEFFFBgfAXsII2vp0dTQAEFFFBAAQUUUECBIRawgzTEb56XroACCiiggAIKKKCAAuMr\nYAdpfD09mgIKKKCAAgoooIACCgyxwKA8xW5NDHclr5MryEukrjakcWvyKLmerET2JqkXSfbt\nVB9nZV7zw+Sm1oZbMH1PazntlgIKKKCAAgoooIACCoyowKDcQXoe/4PIJeTUNu/F6rRfRi4k\nxXVnOreVy5mmA9WutmLFpSTbf7m00cxW206lNmcVUEABBRRQQAEFFFBgBAWKjsYgvPSjuIgF\n5Biye80FnUHbB8lXyLWV9bnzlLtJ+1bay4ufKi84r4ACCiiggAIKKKCAAgpUBQapg/QkF3cw\nSWfnR2QKKeoQZg4jGUL3zaKxNL2b+afI/qW26uwBNNxVbXRZAQUUUEABBRRQQAEFFCgEmr6D\ntB4b7kWuI4+T3Uh+pfNt5EryazKZ5PtDHyHppFxAHiCpDcgei+fa/8hx8p2i1DzyLXIcOZvs\nQ95HvkceIUUHitllaiFLGZ53KMkwu+p3ibal7V3kJPJVYimggAIKKKCAAgoooIACbxJo6iCl\nszGHzCYZorYJWUSy3/3ko+RcsjN5haxKMgQunaWfkWw/h3SqnVhZdJCyXfbfheThC+kQfZGs\nRj5N0iFrV+mY5S7TfuS0yka59nTecgfKDlIFx0UFFFBAAQUUUEABBRRYItDtELsT2fwG8g7y\nTvJ3ZDNyH8nT4z5M1iK587MGOYGkbiLrVLImy8eT1PnkJ4vnlv7I3aCDyAskQ+1yx2oWuYZ0\nqtx9yl2udJDKlde4P5lLXi6vcF4BBRRQQAEFFFBAAQUUKAs03UEqtl3AzOGk6GCcznye/vYa\nyd2Z35DUt8nXyOZZoF4lzy6eW/rjk8x+g1xLMiSuru6h8SySp809Q3LcplrEBheTo8hG5Bck\ntSOZRtIZm6hKJ/IfyKQuT7hel9u5mQIKKKCAAgoooIACCvRRoNsO0p1cQ9E5yuXc3rqme5kW\nnaM05QELj5G1s1BT29OWIXkPkgyhKx+TxTdqU+aOaC2ty3Q2Oba13GmSYXafJXmaXTHMLh24\nDOG7mryfTERlKODZZNUuT7YD2x3Y5bZupoACCiiggAIKKKCAAn0S6LaD9FDl/LkzlHp6yWSZ\nn8W6ZRpZSKfnUvISyYMbMhyurvJ9o3R00smaSXL36EvkylaYtK0Mw0sHLUPq0kHK60tn6TzS\n7rpYNe6Vjt9ZPRw12x/Yw/ZuqoACCiiggAIKKKCAAn0Q6PY7SPle0FhqfXa+nORuUIbYZQhd\nuzqVFTPI98m55EiyEplDppBOlSF/F5E8tS7D7HYheZLeRA6v43SWAgoooIACCiiggAIKDKNA\ntx2ksby2yeycO0ebkaPJPNKu9mHF50k6ULlrlMq+Ga42lfyQNNWFrQ3SEcudpPkkD5iwFFBA\nAQUUUEABBRRQQIGOAv3uIOXOzzkk37H5OvkBaVcbsyLrc7cqw81eIEUdy8x9ZE9yTNHYZvpT\n2heQ/GLYfM/px8RSQAEFFFBAAQUUUEABBRoF8h2dorZk5lZyG9mqaBzj9Hj2z2O3HyHPkFlk\nZVKu61m4iqQjk6e5pTP0c1Ku51n4DLmOZAje1eQOUld5UESG2RV3oLodXpfz5lrr6kwar6hb\nYZsCCiiggAIKKKCAAgqsOALlDlI/XtWmrYNOY3pKmxOcTvvOZDuSTsh3SF3dSOMscjLJQxe2\nIa+QuspDHtJBuoukw9dNzWCjpK7sHNWp2KaAAgoooIACCiigwAomUO4gpSORIXHlqmvL+vzO\noeq2xX7TixmmecBC0k3lblNTzWaDpFx115HvHNW15/Hk1fYTaEssBRRQQAEFFFBAAQUUGHGB\nfn8HacR5ffkKKKCAAgoooIACCigwTAJ2kIbp3fJaFVBAAQUUUEABBRRQoK8CdpD6yuvBFVBA\nAQUUUEABBRRQYJgE7CAN07vltSqggAIKKKCAAgoooEBfBewg9ZXXgyuggAIKKKCAAgoooMAw\nCdhBGqZ3y2tVQAEFFFBAAQUUUECBvgqUH/Pd1xN58BERWGv9EXmhK/jLfO6Jzi9wrO9z0/E7\nn/1tb2s6/1iPz/lfuKPzrz9bZermHa9y0aP3dFy/xgc+1nH9wsfnd1zfdPyOO7Ny0gbTO27S\ndP6m/XPwpmN0vABWNp5jrO9z0/5Nn7OmF+B6BcZB4LVHbul8lIY/Szrv7FoFFKgT8A5SnYpt\nCiiggAIKKKCAAgooMJICdpBG8m33RSuggAIKKKCAAgoooECdgB2kOhXbFFBAAQUUUEABBRRQ\nYCQF7CCN5Nvui1ZAAQUUUEABBRRQQIE6ATtIdSq2KaCAAgoooIACCiigwEgK2EEaybfdF62A\nAgoooIACCiiggAJ1AoPymO81ubhdyeskz9Z9idTVhjRuTR4l15OVyN4k9SLp/Fzet73t42yT\n1/wwuYmktiDvIVlOu6WAAgoooIACCiiggAIjKjAod5Cex/8gcgk5tc17sTrtl5ELSXHdmc5t\n5XKm6UC1q61YcSnJ9l8ubTSz1bZTqc1ZBRRQQAEFFFBAAQUUGEGBoqMxCC/9KC5iATmG7F5z\nQWfQ9kHyFXJtZX3uPOVu0r6V9vLip8oLziuggAIKKKCAAgoooIACVYFB6iA9ycUdTNLZ+RGZ\nQoo6hJnDSIbQfbNoLE3vZv4psn+prTp7AA13VRtdVkABBRRQQAEFFFBAAQUKgabvIK3HhnuR\n68jjZDcyg9xGriS/JpNJvj/0EZJOygXkAZLagOyxeK79jxwn3ylKzSPfIseRs8k+5H3ke+QR\nUnSgmF2mFrKU4XmHkgyzq36XaFva3kVOIl8llgIKKKCAAgoooIACCijwJoGmDlI6G3PIbJIh\napuQRST73U8+Ss4lO5NXyKokQ+DSWfoZyfZzSKfaiZVFBynbZf9dSB6+kA7RF8lq5NMkHbJ2\nlY5Z7jLtR06rbJRrT+ctd6DsIFVwXFRAAQUUUEABBRRQQIElAk0dpMLpRGbOI+nMvEryIIWZ\n5D5yDfkwyVPgvkCy7gSSIW1pW4eUK/t/jmSo3PnkJ6RcuRuUBzbcTDLULsMATyI5T6eax8rc\n5ap2kLJ/ht7NJS+TiagMD/xbkg5jNzW9m43cRgEFFFBAAQUUUEABBforkM5DN7WAjQ4nGeb2\nGDmdpF4juTtzHUnH5tskd5I2J6l0hp6tZHeWv0GuJYeSurqHxrNIru8ZkuM2Ve5sXUwynG6j\n0sY7Mj+NpDM2URWLX5K4dZOnJ+rCPI8CCiiggAIKKKCAAgq0F+j2DtKdHKJ89+X21iHvZfqb\n0uHzgIV0oNYutZVnt2chQ/IeJBlCVz4mi2/Upswd0Vpal+lscmxrudPkAlZ+luRpdsUwu3Tg\nMoTvavJ+MhGVTt1f9HCiI9n2D3vY3k0VUEABBRRQQAEFFFCgDwLd3kF6qHLu3BlK1d35KNYt\n2WLpz3R6LiUvkT1IhsPVVb5vlI5OOlkzya/Il8hupKkyDC8dtOJpdqswn87ShaTddbHKUkAB\nBRRQQAEFFFBAAQWW/sLVJosMGRtLrc/Ol5PcDfokyRC6dpXvMM0g3yfnktxdWYnMIfluT6fK\nkL+LSDHMLg97yJP0JnJ4HaezFFBAAQUUUEABBRRQYBgFur2DNJbXNpmdc+doM3I0ycMU2lUe\n6/15kg5U7hqlsu/ZZCr5IWmq3C1KpSOWO0nzyQ3EUkABBRRQQAEFFFBAAQU6CvS7g5Q7P+eQ\nHcjXyQ9Iu9qYFVmfu1UHkhdIUccycx/ZkxxTNLaZ/pT2BSRP0cv3nH5MLAUUUEABBRRQQAEF\nFFCgUSDf0SlqS2ZuJbeRrYrGMU6PZ/88djtPv8uDC2aRlUm5rmfhKpKOzHoknaGfk3I9z8Jn\nSJ6WlyF4V5M7SF3lQREZZlfcgep2eF3O5BB3FgAAMjRJREFUm2utqzNpvKJuhW0KKKCAAgoo\noIACCiiw4giUO0j9eFWbtg46jekpbU5wOu07k+1IOiHfIXV1I42zyMnkPLINySPF6yoPeUgH\n6S6SDl83NYONkrqyc1SnYpsCCiiggAIKKKCAAiuYQLmDlI5EhsSVq64t6/M7h6rbFvtNL2aY\n5gELSTeVu01NNZsNknLVXUe+c1TXnseTV9tPoC2xFFBAAQUUUEABBRRQYMQF+v0dpBHn9eUr\noIACCiiggAIKKKDAMAnYQRqmd8trVUABBRRQQAEFFFBAgb4K2EHqK68HV0ABBRRQQAEFFFBA\ngWESsIM0TO+W16qAAgoooIACCiiggAJ9FbCD1FdeD66AAgoooIACCiiggALDJFB+it0wXbfX\nOqgCzz0x9itba/2xH8MjdBYY6/s01v07X13z2gk4/ypTN2++jg5bNO2/8PH5HfbmUaGP3tNx\nfdPxO+7MyqbzN+0/HusnbTB9PA7z1o/hnzVv3c49J0zg7dPa/QaSJZfQ9N/yJD/nE/ZeeaIV\nR8A7SCvOe+krUUABBRRQQAEFFFBAgTEK2EEaI6C7K6CAAgoooIACCiigwIojYAdpxXkvfSUK\nKKCAAgoooIACCigwRgE7SGMEdHcFFFBAAQUUUEABBRRYcQTsIK0476WvRAEFFFBAAQUUUEAB\nBcYoYAdpjIDuroACCiiggAIKKKCAAiuOwKA85ntNSHclr5MryEukrjakcWvyKLmerET2JqkX\nSfbtVB9nZV7zw+Sm1oZbMH1PazntlgIKKKCAAgoooIACCoyowKDcQXoe/4PIJeTUNu/F6rRf\nRi4kxXVnOreVy5mmA9WutmLFpSTbf7m00cxW206lNmcVUEABBRRQQAEFFFBgBAWKjsYgvPSj\nuIgF5Biye80FnUHbB8lXyLWV9bnzlLtJ+1bay4ufKi84r4ACCiiggAIKKKCAAgpUBQapg/Qk\nF3cwSWfnR2QKKeoQZg4jGUL3zaKxNL2b+afI/qW26uwBNNxVbXRZAQUUUEABBRRQQAEFFCgE\nmr6DtB4b7kWuI4+T3cgMchu5kvyaTCb5/tBHSDopF5AHSGoDssfiufY/cpx8pyg1j3yLHEfO\nJvuQ95HvkUdI0YFidplayFKG5x1KMsyu+l2ibWl7FzmJfJVYCiiggAIKKKCAAgoooMCbBJo6\nSOlszCGzSYaobUIWkex3P/koOZfsTF4hq5IMgUtn6Wck288hnWonVhYdpGyX/XchefhCOkRf\nJKuRT5N0yNpVOma5y7QfOa2yUa49nbfcgbKDVMFxUQEFFFBAAQUUUEABBZYINHWQCqcTmTmP\npDPzKsmDFGaS+8g15MMkT4X7Asm6E0iGtKVtHVKu7P85kqFy55OfkHLlblAe2HAzyVC7DAM8\nieQ8nWoeK3OXq9pByv4ZejeXvEwmotbmJMeTdBi7qa262chtFFBAAQUUUEABBRRQoL8C3XaQ\nFnAZh5Oig3E68+kgvUZyd+Y3JPVt8jWyeRaodIaeXTy39Mcnmf0GuZYcurR5mbl7WDqL5Glz\nz5Act6lyZ+tichTZiPyCpHYk00g6YxNVa3CiLcmkLk/4zi63czMFFFBAAQUUUEABBRToo0C3\nHaQ7uYaic5TLub11TfcyLTpHacoDFh4juYNSV9vTmCF5D5IMoSsfk8U3alPmjmgtrct0Njm2\ntdxpkmF2nyV5mt1prQ3TgcsQvqvJ+8lEVAw+0cOJjmTbs3vY3k0VUEABBRRQQAEFFFCgDwIZ\nftZNPVTZKHeGUk8vmSzzs1i3TCML6fRcSl4ie5AMh6urfN8oHZ10smaSX5Evkd1IU2UYXjon\nGVKXWoWks3QhaXddrLIUUEABBRRQQAEFFFBAgaW/cLXJIt8LGkutz86Xk9wNyhC7DKFrV/kO\n0wzyfXIuyd2VlcgcMoV0qgz5u4jkqXUZZpeHPeRJehM5vI7TWQoooIACCiiggAIKKDCMAt3e\nQRrLa5vMzrlztBk5mswj7SqP9f48SQcqd41S2TfDz6aSH5Kmyt2iVDpiuZM0n9xALAUUUEAB\nBRRQQAEFFFCgo0C/O0i583MO2YF8nfyAtKuNWZH1uVt1IHmBFHUsM/eRPckxRWOb6U9pX0Dy\nFL18z+nHxFJAAQUUUEABBRRQQAEFGgXyHZ2i8tS1W8ltZLweO308x8pjtx8heRrdLLIyKdf1\nLFxF0pFZj6Qz9HNSrudZ+Ay5jmQI3tXkDlJXeVBEhtkVd6C6HV6X8+Za6+pMGq+oW2GbAgoo\noIACCiiggAIKrDgC5Q5SP17Vpq2DTmN6SpsTnE77zmQ7kk7Id0hd3UjjLHIyOY9sQ/LLaesq\nD3lIB+kukg5fNzWDjZK6snNUp2KbAgoooIACCiiggAIrmEC5g5SORIbElauuLevzO4eq2xb7\nTS9mmOYBC0k3lbtNTTWbDZJy1V1HvnNU157Hk1fbT6AtsRRQQAEFFFBAAQUUUGDEBfr9HaQR\n5/XlK6CAAgoooIACCiigwDAJ2EEapnfLa1VAAQUUUEABBRRQQIG+CthB6iuvB1dAAQUUUEAB\nBRRQQIFhErCDNEzvlteqgAIKKKCAAgoooIACfRWwg9RXXg+ugAIKKKCAAgoooIACwyRQford\nMF231zqoAmutP6hX5nX1ItD0Pj73ROejNe3fee+3vW2sx2/av+n8A7B+lambj+kqJm0wfUz7\nj8fOg3AN4/E6PIYCgyzgf2eD/O54bcMq4B2kYX3nvG4FFFBAAQUUUEABBRQYdwE7SONO6gEV\nUEABBRRQQAEFFFBgWAXsIA3rO+d1K6CAAgoooIACCiigwLgL2EEad1IPqIACCiiggAIKKKCA\nAsMqYAdpWN85r1sBBRRQQAEFFFBAAQXGXcAO0riTekAFFFBAAQUUUEABBRQYVoFBecz3mgDu\nSl4nV5CXSF1tSOPW5FFyPVmJ7E1SL5Ls26k+zsq85ofJTa0Nt2D6ntZy2i0FFFBAAQUUUEAB\nBRQYUYFBuYP0PP4HkUvIqW3ei9Vpv4xcSIrrznRuK5czTQeqXW3FiktJtv9yaaOZrbadSm3O\nKqCAAgoooIACCiigwAgKFB2NQXjpR3ERC8gxZPeaCzqDtg+Sr5BrK+tz5yl3k/attJcXP1Ve\ncF4BBRRQQAEFFFBAAQUUqAoMUgfpSS7uYJLOzo/IFFLUIcwcRjKE7ptFY2l6N/NPkf1LbdXZ\nA2i4q9rosgIKKKCAAgoooIACCihQCDR9B2k9NtyLXEceJ7uRGeQ2ciX5NZlM8v2hj5B0Ui4g\nD5DUBmSPxXPtf+Q4+U5Rah75FjmOnE32Ie8j3yOPkKIDxewytZClDM87lGSYXfW7RNvS9i5y\nEvkqsRRQQAEFFFBAAQUUUECBNwk0dZDS2ZhDZpMMUduELCLZ737yUXIu2Zm8QlYlGQKXztLP\nSLafQzrVTqwsOkjZLvvvQvLwhXSIvkhWI58m6ZC1q3TMcpdpP3JaZaNcezpvuQNlB6mC46IC\nCiiggAIKKKCAAgosEWjqIBVOJzJzHkln5lWSBynMJPeRa8iHSZ4K9wWSdSeQDGlL2zqkXNn/\ncyRD5c4nPyHlyt2gPLDhZpKhdhkGeBLJeTrVPFbmLle1g5T9M/RuLnmZTERN4iR5/Zl2U/Gz\nFFBAAQUUUEABBRRQYDkLdNtBWsB1Hk6KDsbpzKeD9BrJ3ZnfkNS3ydfI5lmg0hl6dvHc0h+f\nZPYb5Fpy6NLmZebuYekskqfNPUNy3KbKna2LyVFkI/ILktqRTCPpjE1UTeVEf0W67SCtPVEX\n5nkUUEABBRRQQAEFFFCgvUDurnRTd7JR0TnK9re3drqXadE5SlMesPAYafcX/u1ZlyF5D5IM\noSsfk8U3alPmjmgtrct09htrOs9kmN1KpPw0u3TgMoTvajJRlc7Z+0leRzfJsEJLAQUUUEAB\nBRRQQAEFlrNAtx2khyrXmTtDqaeXTJb5WaxbppGFdBQuJS+RPUiGw9VVvm+Ujk46WTPJr8iX\nyG6kqTIMLx204ml2qzCfztKFpN11scpSQAEFFFBAAQUUUEABBZb+wtUmi3wvaCy1PjtfTnI3\nKEPsMoSuXeU7TDPI98m55EiSu0JzyBTSqTLk7yKSp9ZlmF0e9pAn6U3k8DpOZymggAIKKKCA\nAgoooMAwCnR7B2ksr20yO+fO0WbkaDKPtKs81vvzJB2o3DVKZd+zyVTyQ9JUuVuUSkcsd5Lm\nkxuIpYACCiiggAIKKKCAAgp0FOh3Byl3fs4hO5Cvkx+QdrUxK7I+d6sOJC+Qoo5l5j6yJzmm\naGwz/SntC0ieIpfvOf2YWAoooIACCiiggAIKKKBAo0C+o1PUlszcSm4jWxWNY5wez/557PYj\nJE+jm0VWJuW6noWrSDoy65F0hn5OyvU8C58h15EMwbua3EHqKg+KyDC74g5Ut8Prct5ca12d\nSeMVdStsU0ABBRRQQAEFFFBAgRVHoNxB6ser2rR10GlMT2lzgtNp35lsR9IJ+Q6pqxtpnEVO\nJueRbcgrpK7ykId0kO4i6fB1UzPYKKkrO0d1KrYpoIACCiiggAIKKLCCCZQ7SOlIZEhcuera\nsj6/c6i6bbHf9GKGaR6wkHRTudvUVLPZIClX3XXkO0d17Xk8ebX9BNoSSwEFFFBAAQUUUEAB\nBUZcoN/fQRpxXl++AgoooIACCiiggAIKDJOAHaRhere8VgUUUEABBRRQQAEFFOirgB2kvvJ6\ncAUUUEABBRRQQAEFFBgmATtIw/Ruea0KKKCAAgoooIACCijQVwE7SH3l9eAKKKCAAgoooIAC\nCigwTALlp9gN03V7rQqMrsBzTzS/9rXW77xN0/qmc4x1/85Xt0KsXfToPR1fxypTN++4vmnl\nwlvy2w7a16RdT2y/chzWLHx8fuNRJm0wveM2Tcdo2r/jwbtZ2fQ5bzpG038HTfu7XoEuBF57\n5JbOW33gY53Xu3bFEGj688o/j8b1ffYO0rhyejAFFFBAAQUUUEABBRQYZgE7SMP87nntCiig\ngAIKKKCAAgooMK4CdpDGldODKaCAAgoooIACCiigwDAL2EEa5nfPa1dAAQUUUEABBRRQQIFx\nFbCDNK6cHkwBBRRQQAEFFFBAAQWGWcAO0jC/e167AgoooIACCiiggAIKjKvAoDzme01e1a7k\ndXIFeYnU1YY0bk0eJdeTlcjeJPUiyb6d6uOszGt+mNzU2nALpu9pLafdUkABBRRQQAEFFFBA\ngREVGJQ7SM/jfxC5hJza5r1YnfbLyIWkuO5M57ZyOdN0oNrVVqy4lGT7L5c2mtlq26nU5qwC\nCiiggAIKKKCAAgqMoEDR0RiEl34UF7GAHEN2r7mgM2j7IPkKubayPneecjdp30p7efFT5QXn\nFVBAAQUUUEABBRRQQIGqwCB1kJ7k4g4m6ez8iEwhRR3CzGEkQ+i+WTSWpncz/xTZv9RWnT2A\nhruqjS4roIACCiiggAIKKKCAAoVA03eQ1mPDvch15HGyG5lBbiNXkl+TySTfH/oISSflAvIA\nSW1A9lg81/5HjpPvFKXmkW+R48jZZB/yPvI98ggpOlDMLlMLWcrwvENJhtlVv0u0LW3vIieR\nrxJLAQUUUEABBRRQQAEFFHiTQFMHKZ2NOWQ2yRC1Tcgikv3uJx8l55KdyStkVZIhcOks/Yxk\n+zmkU+3EyqKDlO2y/y4kD19Ih+iLZDXyaZIOWbtKxyx3mfYjp1U2yrWn85Y7UHaQKjguKqCA\nAgoooIACCiigwBKBpg5S4XQiM+eRdGZeJXmQwkxyH7mGfJjkqXBfIFl3AsmQtrStQ8qV/T9H\nMlTufPITUq7cDcoDG24mGWqXYYAnkZynU81jZe5yVTtI2T9D7+aSl8lE1EqcJHetJnV5ss26\n3M7NFFBAAQUUUEABBRRQoI8C3XaQFnANh5Oig3E68+kgvUZyd+Y3JPVt8jWyeRaodIaeXTy3\n9Mcnmf0GuZYcurR5mbl7WDqL5Glzz5Act6lyZ+tichTZiPyCpHYk00g6YxNV7+ZE6fh16ztR\n1+V5FFBAAQUUUEABBRRQoINA7q50U3eyUdE5yva3t3a6l2nROUpTHrDwGFk7CzW1PW0Zkvcg\nyRC68jFZfKM2Ze6I1tK6TGe/sabzTIbZ5e5N+Wl26cBlCN/VZKLqPzhR7h7Ft5ukU2cpoIAC\nCiiggAIKKKDAchbotoP0UOU6c2co9fSSyTI/i3XLNLKQTs+l5CWyB8lwuLrK943S0Uknayb5\nFfkS2Y001TVskA5a8TS7VZhPZym/O6nddbGqL5XOYrfpywV4UAUUUEABBRRQQAEFFOhNoNsO\nUr4XNJZan50vJ7kblCF2GULXrvIdphnk++RcciTJXaE5ZArpVBnydxHJ938yzG4XkifpTeTw\nOk5nKaCAAgoooIACCiigwDAKdNtBGstrm8zOuXO0GTmazCPtKo/1/jxJByp3jVLZ92wylfyQ\nNFXuFqXSEcudpPnkBmIpoIACCiiggAIKKKCAAh0F+t1Byp2fc8gO5OvkB6RdbcyKrM/dqgPJ\nC6SoY5m5j+xJjika20x/SvsCkqfo5XtOPyaWAgoooIACCiiggAIKKNAokO/oFLUlM7eS28hW\nReMYp8ezfx67/QjJ0+hmkZVJua5n4SqSjsx6JJ2hn5NyPc/CZ8h1JEPwriZ3kLrK934yzK64\nA9Xt8LqcN9daV2fSeEXdCtsUUEABBRRQQAEFFFBgxREod5D68ao2bR10GtNT2pzgdNp3JtuR\ndEK+Q+rqRhpnkZPJeWQbkl9OW1d5yEM6SHeRdPi6qRlslNSVnaM6FdsUUEABBRRQQAEFFFjB\nBModpHQkMiSuXHVtWZ/fOVTdtthvejHDNA9YSLqp3G1qqtlskJSr7jrynaO69jyevNp+Am2J\npYACCiiggAIKKKCAAiMu0O/vII04ry9fAQUUUEABBRRQQAEFhknADtIwvVteqwIKKKCAAgoo\noIACCvRVwA5SX3k9uAIKKKCAAgoooIACCgyTgB2kYXq3vFYFFFBAAQUUUEABBRToq4AdpL7y\nenAFFFBAAQUUUEABBRQYJoHyU+yG6bq91kEVeO6Jzle21vqd1w/C2kF/Dd0YNr2GsTr3+/hN\n17e8z990faxfZermXWz11jeZNOPTb31n9lz4+PyO+0/aYHrH9d2snIhzdHMdb3mbbv5be8sH\nd8fFAmP9b9n3yA+SAgr0QcA7SH1A9ZAKKKCAAgoooIACCigwnAJ2kIbzffOqFVBAAQUUUEAB\nBRRQoA8CdpD6gOohFVBAAQUUUEABBRRQYDgF7CAN5/vmVSuggAIKKKCAAgoooEAfBOwg9QHV\nQyqggAIKKKCAAgoooMBwCthBGs73zatWQAEFFFBAAQUUUECBPggMymO+1+S17UpeJ1eQl0hd\nbUjj1uRRcj1ZiexNUi+S7NupPs7KvOaHyU2tDbdg+p7WctotBRRQQAEFFFBAAQUUGFGBQbmD\n9Dz+B5FLyKlt3ovVab+MXEiK6850biuXM00Hql1txYpLSbb/cmmjma22nUptziqggAIKKKCA\nAgoooMAIChQdjUF46UdxEQvIMWT3mgs6g7YPkq+Qayvrc+cpd5P2rbSXFz9VXnBeAQUUUEAB\nBRRQQAEFFKgKDFIH6Uku7mCSzs6PyBRS1CHMHEYyhO6bRWNpejfzT5H9S23V2QNouKva6LIC\nCiiggAIKKKCAAgooUAg0fQdpPTbci1xHHie7kRnkNnIl+TWZTPL9oY+QdFIuIA+Q1AZkj8Vz\n7X/kOPlOUWoe+RY5jpxN9iHvI98jj5CiA8XsMrWQpQzPO5RkmF31u0Tb0vYuchL5KrEUUEAB\nBRRQQAEFFFBAgTcJNHWQ0tmYQ2aTDFHbhCwi2e9+8lFyLtmZvEJWJRkCl87Sz0i2n0M61U6s\nLDpI2S7770Ly8IV0iL5IViOfJumQtat0zHKXaT9yWmWjXHs6b7kDZQepguOiAgoooIACCiig\ngAIKLBHodojdiWx+A3kHeSf5O7IZuY/k6XEfJmuR3PlZg5xAUjeRdSpZk+XjSep88pPFc0t/\n5G7QQeQFkqF2uWM1i1xDOlXuPuUuVzpI5cpr3J/MJS+XV/R5PnfPfq/LrNvna/HwCiiggAIK\nKKCAAgoo0IVA0x2k4hALmDmcFB2M05nP099eI7k78xuS+jb5Gtk8C9Sr5NnFc0t/fJLZb5Br\nSYbE1dU9NJ5F8rS5Z0iO21SL2OBichTZiPyCpHYk00g6YxNVm3Ki+9/CyeJlKaCAAgoooIAC\nCiigwHIS6PYO0p1cX9E5yqXe3rree5kWnaM05QELj5G1s1BT29OWIXkPkgyhKx+TxTcqHYwj\nWku5uzL7jTWdZzLMbiVSfppdOnAZwnc1maj6d070bpKOYjfJNabS4bQUUEABBRRQQAEFFFBg\nOQms0uV5H6psV9zpeLrSnsViXXVVOj2XkpdIHtyQ4XB1le8bpaOTTtZMkrtHXyJXtsKkbWUY\nXjpoGVKX7yHl9aWzdB5pd12s6kulE9ht/Xa3G7qdAgoooIACCiiggAIK9E+g2ztI+V7QWGp9\ndr6c5G5QhthlCF27OpUVM8j3ybnkSJK7QnPIFNKpcgfmIpKn1mWY3S4k3wWayOF1nM5SQAEF\nFFBAAQUUUECBYRTotoM0ltc2mZ1z52gzcjSZR9pVHuv9eZIOVO4apbLv2WQq+SFpqgtbG6Qj\nljtJ88kNxFJAAQUUUEABBRRQQAEFOgr0u4OUOz/nkB3I18kPSLvamBVZn7tVB5IXSFHHMnMf\n2ZMcUzS2mf6U9gUkvxg233P6MbEUUEABBRRQQAEFFFBAgUaBfEenqC2ZuZXcRrYqGsc4PZ79\n89jtR0ieRjeLrEzKdT0LV5F0ZNYj6Qz9nJTreRY+Q64jGYJ3NbmD1FUeFJFhdsUdqG6H1+W8\nuda6OpPGK+pW2KaAAgoooIACCiiggAIrjkC5g9SPV7Vp66DTmJ7S5gSn074z2Y6kE/IdUlc3\n0jiLnEzy0IVtyCukrvKQh3SQ7iLp8HVTM9goqSs7R3UqtimggAIKKKCAAgoosIIJlDtI6Uhk\nSFy56tqyPr9zqLptsd/0YoZpHrCQdFO529RUs9kgKVfddeQ7R3XteTx5tf0E2hJLAQUUUEAB\nBRRQQAEFRlyg399BGnFeX74CCiiggAIKKKCAAgoMk4AdpGF6t7xWBRRQQAEFFFBAAQUU6KuA\nHaS+8npwBRRQQAEFFFBAAQUUGCYBO0jD9G55rQoooIACCiiggAIKKNBXgfJDGvp6Ig/elcCk\nrrZyIwUUUEABBRRQQAEFuhdYs/tNF2/Z7knR7Q6zQv0d1g5Su7d5YtuLD+Gz/T7t2Wf/j/6e\n4qX8yqoONbnX/z47HKtfq0bhNfTLbgU67qLnnhjqV7PKWv/e8fqbXt9Y9+948tbKpnN0c4y+\nbjMMf171FWACDt70523TJYzAe/TSLZ3/LJr8y3/urDQCRp0BhmPtosd/0fFCG/+8bH6fn+t4\ngvFbuXD8DrX8jlR95PXyuxLPvDUEq04wQx6tviE5a4LP6+kUKAvsy8Lvku+WG51XYIIF9uF8\nG5H/NsHn9XQKlAX2ZOG95NvlRucVmGCBj3G+95HDezxvOkc397jPQG7uHaTBeVtuWg6X8kvO\n+Sr5/nI4t6dUoBDIXwbyh6qfw0LE6fIQeBcnXZn4OVwe+p6zEJjGzLrEz2Eh4nR5CGzASX+P\nXL88Tj4I5/QhDYPwLngNCiiggAIKKKCAAgooMBACdpAG4m3wIhRQQAEFFFBAAQUUUGAQBOwg\nDcK74DUooIACCiiggAIKKKDAQAjYQRqIt8GLUEABBRRQQAEFFFBAgUEQsIM0CO+C16CAAgoo\noIACCiiggAIDIWAHaSDeBi9CAQUUUEABBRRQQAEFBkHADtIgvAtegwIKKKCAAgoooIACCgyE\ngB2kgXgbvAgFFFBAAQUUUEABBRQYBAE7SIPwLngNCiiggAIKKKCAAgooMBACdpAG4m1Ybhex\nkDO/stzO7okVWCKQz2FiKbA8BfzzcHnqe+5CIP9P9s/DQsPp8hLwz8PlJe95B0JgXa5iykBc\niRcxygJr8+J/Z5QBfO0DIbAWV/G7A3ElXsQoC6zJi/+9UQbwtQ+EwBpcxbSBuBIvQgEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFVnSBlVf0F+jrqxXI+7492YYsIk8SS4HxEFiDg8wgHya/\nRX5DXibt6p2s+AOS6a/IK6Rd9bJtu2PYPnoC03jJf0Ty+Xqxzcvv5bPVy7ZtTmfzCAlM5bXm\nz7h3k2fJ86Rd9fLZ6mXbduezfTQEJvMyP0R2IGuRx8mrpF318tnqZdt257NdgYEQ2IyruJu8\nXsqdzG9ILAXGInAwOz9Gyp+tdJC+0Oag/5X2dIiK7Rcxf/w4bNvmEDaPoED+Meg6ks9Y/lGo\nrvwc1qnYNlaBdTjAXFL8+ZZpOuh/RerKz2Gdim1jFfhDDjCflD+HD7Kc9rryc1inYtsKL7AS\nr/B/kfyl9TNkU3IkeYE8RNYklgJvReCP2ek1kj948xeAD5B0jO4h+YN5JilXtk97/gLx+yR3\nM68gafs8KVcv25b3c16Bv4Ygn6mkroPUy2erl22VV+DfWp+7rzH9IPlTkn+MzGfxU6RcvXy2\netm2fA7nR09gI17y0+Qpkn983IIcR54gz5DppFy9fLZ62bZ8DucVGEiBo7mq/OH82crVHdmm\nvbKZiwq0FfgX1uSztWtli//cas9fDIrKMLwHyS9JeZjvpFb7w6X2XrZlN0uBNwTS6c4dygyt\ny2ez2kHq5bPVy7acyhpxgT14/fnMnVlxeH+r/epSey+frV62LZ3C2REV+Atedz6Hf1N5/bNa\n7V8ptffy2epl29IpnFVgcAX+lUt7ieS7IeXKUIDc+r+x3Oi8Al0KvJ3t/o2kE1Tu8BS738NM\nhs8V63ZnPn9on0KqNZuGrMtfMFK9bLtkD38qsORu+P1A/IScSvKZ2o6Uq5fPVi/bls/h/GgK\n/AsvO/9qn+9+VCtDm/IPR0X18tnqZdvi+E5HVyD/j82ffXtVCPIZTPt3S+29fLZ62bZ0iuGa\nzV9srNEQWJWX+Z/IfSS3XMuVIXf5S+xWJNtZCvQikKF1+df6LUj1i5/5C8Lvkfmlddk2lU5V\ntYq2rVsretm2eiyXR1fg/+Kl/y45mFQ/k4VKL5+tXrYtju90dAXyhfh0zvMPkhnanj8btySr\nkHnkRlJUL5+tXrYtju90dAX+qfXS/7RCcEhruVifxV4+W71sWzn18CzaQRqe92qsV7oeB8gQ\npow9rasnaUznaErdStsUeIsCf8l+uUN5cWn//MU1VfdZzOcw9Y4lk8V/yc1sN9u2dnEy4gKf\n4PUfSb5EHuxg4eewA46r3rJA/rxbm/yC7EN+Re4gt5LHyCdJufwcljWcH0+BqznY35C9SD6D\nuaN0C8l3gk8jl5Gi/BwWEq2pHaQKyAq8mD+0U3m8Y1092Wpcs26lbQq8BYH92eevSYY6zSJF\ndfosVj+HvWxbHN/p6ApM5aX/3+QfyA8bGHr5bPWybcNpXb2CCxT/uLMjr/N8cg75L+Q4kvrv\nZLfFc0t+9PLZ6mXb0imcHVGB3D3P5+8ukruYf0l+nzxAziSvkKJ6+Wz1sm1x/KGb2kEaurfs\nLV9wbvWn2r3nxfdD2g1HWbK3PxXoTuBP2exc8muSf9F/kRTV6bNY/Rz2sm1xfKejK5BO0Wsk\nd5CaqpfPVi/bNp3X9Su2QPGXxy15mUeRPyeXkG+R/KNR6jtLJot/9vLZ6mXb0imcHVGBfN5u\nJ8+TbcharemjTH9Ois8js4uHg2Za93fEkfz/ch1EgKwVTyD/QeRLeb/d5qUV7c+0WW+zAt0K\n/DUb/ojkKXU7kbtJuR5pLRSfufK6oq34HPaybfk4zo+ewOd4yfny8BdI/kKwRivF9yrzfbi0\n5TshqV4+W71su+To/hxVgf/deuH5x6FzKgj/wnL+X7w5+a3Wul4+W71s2zq8kxEW+DKv/QWy\nB7mR5M/FTLOc/8f+H6SoXj5bvWxbHH/opnaQhu4te8sXnKeIZSx08RfQ6oHSnv+Qnq6ucFmB\nLgXyF8//Rv4ryR/CeaxyHgpSrW7+cF3Q2qmXbavncXm0BIrvdvyYl52/CBQ5tsWQv5ym7T2t\n5V4+W71s2zq8kxEVyGcldzHz/9tqpT2fw9SUJZOuOur+edjCctK1QD5fuWuUh4U8WdkrnaN/\nIrnLuVFrXS9/xvWybevwwzexgzR879lYrjj/kv9+skHlIPkP6X3kZuIQuwqOi10J5M+SH5L8\n6/3/S3Ym+UJyXRV3lP6gZmXR9m+tdb1sW3M4m0ZIIMOY/rYmt7QMLmqte6q13Mtnq5dtW4d3\nMqIC+cfIfyfvJWvUGOSpnvkMZptUL5+tXrZdcnR/jqpA/i6X/y//ThuASa32YvhcL5+tXrZt\nc3qbFRgsgf/C5bxOjq9c1gmt9n0r7S4q0K3A0WyYz9ZcUvyB22nf21iZoSjrlDZal/kMP/kZ\nyeNwi+pl22IfpwoUAqcwk8/mdkVDadrLZ6uXbUuncHYEBf6M15zP3KzKa9+S5XSg/rHS3stn\nq5dtK6dxccQE7uT1LiRbV153HiSS0UIZBl+uXj5bvWxbPofzCgykQP414S6Sf1n4KvkjcnJr\nOX+xtRR4KwLrs1P+RTR/IfifJHeQ6pIviBZ1IDPZPnct0zHfj9xC8peHGaRcvWxb3s95BSLQ\nqYPUy2erl22VH22B1Xj5+X9t/oz7LvkYyYNDclf9UfJuUq5ePlu9bFs+h/OjJ7AjLzl/33uS\n/CX5KDmCPETy2fwTUq5ePlu9bFs+h/MKDKxAhtf9fyRjofMfSHIlmUosBd6KwCfYqfgsdZqu\nVzn4QSznD+5in8wfXtmmWOxl22IfpwpEoFMHKet7+Wz1sm2ObY2uQH4X0t+Tl0n+jHuFXEuq\n/wBE0+Lq5bPVy7bF8Z2OpkA6SbeT4v+zmd5L/pjUVS+frV62rTuXbQoMpED+8P4QsWM0kG/P\nyFxUHuywKdmC5F9dO1Uv23Y6jusUqAr08tnqZdvqeVwePYF812NLkv/nNlUvn61etm06r+tX\nfIGM9Mjf+fKd86bq5bPVy7ZN53W9AgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\nwAQKrDyB5/JUCiiggAIK9EtgbQ68B1mNPNavk7yF427DPskD5NW3sL+7KKCAAgoooIACCiig\nwJAJrMr1HkcOmKDrrjvfFpz7dfLdCbqGutN8gMa/r6y4iOVc15RKu4sKKKCAAgMq8PYBvS4v\nSwEFFFBgeAT251K/SdaaoEuuO99znPsycscEXUPdaS6hcafKiltYznUtrLS7qIACCigwoAKr\nDOh1eVkKKKCAAgr0IvAQG+/Zyw4TtO3XJ+g8nkYBBRRQYJwE/A7SOEF6GAUUUGAIBf6Qa55B\n/oMcSXYjT5FfkaI2Y2Y/chB5L3mRlL/jsyPL6ZhsTR4n65EHycsklX+I+zj5FMnx1yf5Ps4i\nUlT2yfC8HPsVshf5U7IleZ50c77V2S7HmEwWkHJl+F2uP+vzGnJtj5Jy7czCfyZ3kZgcSPYm\nU8j9pNP3h9ZhfV5fHHL+HHtdkk7bH5Cdyb0kr7l4rS8wn6F3OcfB5HfJ/yZpzzH+hBxO4voI\nyftSrab3prq9ywoooIACCiiggAIKKNBB4B9Zl7/En0Hyl/Xkn0hRf85MOhOvkYdJ/oKfjsJs\nshJJ/T0p9s0026YTkno3+VeS9mdIOlCZTycknZ+iMp/2vyF3tuYzJC1t6TAdRYpqd75230E6\njR1z3bmudDQyzWtIe3kUxVyWs/5Ykm1y3pw/uYX8NmlX6ahkn2L7zJ/f2rj6HaQtW9udzPTf\nW/PFue5j+R3kX1rthUE6ib9PytXNe1Pe3nkFFFBAAQUUUEABBRRoEEgHKZ2H3J04guSuyU4k\nlbs++Qv/NWQaSeVJceeRtB9CijqImbTljkdR6UDdSNIZ+QwpOlR/zPwTJHdUJpFU0WlIx+JC\nsj1J5+UTJB20J8kapKi689V1kA5jh1zXFSR3aFIbkEtJ2tPJKGouMzl/7p79Gcmdnqnkf5Bs\n+1ekqe5ng3Qky9Wug5RznUtim2s7h+Q86QxdTnYg8fkLkvYLSFG9vDfFPk4VUEABBRRQQAEF\nFFCgQSAdpPzl+5ia7e5prftQZd2aLGcYWO62FJ2eug5Lhpzl2DlHtXKnKOs+21pRdJDms1x0\nmlqrFneYsm22KarufNUOUq4tHb90xtYtdmxN8xoynO1ZkvlUOkjla1rcyI/3t9qzvql66SCl\nI7Va6YBbM5/z55rWKbXndeRO0q2ltl7em9JuziqggAIKdCPw9m42chsFFFBAgRVaIMPgyvVb\nLLyX5C/8r5B0TopswvyN5PdIcWeJ2TfVdq2WeUyLfYtphtil0iko180spDNQrvmthXXKjV3M\nb8w2eR2XkQzvK1fu0lxC1iKbl1cwf11leX5rudfzVw7zpsU7acndsaJub83cy/Q3RSPTdJoe\nI2u32sbjvWkdyokCCiigQJ1AhjBYCiiggAKjLfBg5eXnOzWpTMt3LhY3ln5syvyC0nJ5tjjG\naeXGynz2L1ceblCtl1oNvf6DXjp4qYeWTN70s2jPNaRjVlT1Gt7q+YvjtZsW5y/WFw+BeLpo\nKE2LdWkqXMfy3pQO7awCCiigQFXADlJVxGUFFFBg9ASqd22KTsGVUJzageOODuuKY2Q4XO6A\n1FX5TknW53s541W5S5QqhtAtWVr6s7gjU1xnsWY8r6E4Zt20al63TV1bcb1jeW/qjmubAgoo\noEBLwA6SHwUFFFBAgapA8XS1DVjxP6srWd6W5K5Gvi/Tru5rrUgnqHqMDBPL8Lrq3ZrWLuMy\nyfDAVL5DVFdFe/VOTt22g9Q2Hu/NIL0er0UBBRQYOIFehywM3AvwghRQQAEFxl3gRY54FfkQ\n+ZPK0bdg+X+RH5B8PyaV7ymlyndriifFnUj7yovXLv1xBrP/RLZf2tTTXN35qgfIXasbyK4k\nnbFyfYCFvUiGFnYaQljep5v5XFfZoJt9et2m1/em1+O7vQIKKDDyAnaQRv4jIIACCihQK/BF\nWjOc62Lyf5I/JseTfyDp8BxOio7Kr5hP5Wl43yAbkuvJHJJO0DXkALIP+X/IQSQdqAvJW6m6\n89UdJ9eTx5j/M/lzsgvJ68r1PENyPUUnj9kxV65rPfIjctiYj9b+AL28N+2P4hoFFFBAAQUU\nUEABBRRYRuAfWUoHod0T2jZnXe4WZThdtkt+SQ4h5cpw7R+TdJiyzb4klX+EO47kwQPF/vmO\nz38nU0lRWzKT9X9bNJSmf9Nat1Opre58ubOVY3y3tF1mf5/cRIrz5xHl6TDNIOWay0K2Wb/c\nyHxeQ9rnVdrrFv+Axty5yvZ3tDa4qLU8pbXc7rXmNWW/XFu15tPwQKWx2/emspuLCiiggAIK\nKKCAAgooMFaBNTjAfyIbk+pwufKxV2fhd8oNpfmNmN+KtOuMlTbterbT+aoHyXnTOan+nqXq\nduOx/LscZPJ4HKiLY3T73nRxKDdRQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUECBFVPg/weJdPIH4g8aYgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotChromPeakImage(processedData, binSize = 10) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retention time alignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do an *obiwarp*-based alignment using the default settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sample number 11 used as center sample.\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "Apply retention time correction performed on MS1 to spectra from all MS levels\n",
      "Applying retention time adjustment to the identified chromatographic peaks ... OK\n"
     ]
    }
   ],
   "source": [
    "processedData <- adjustRtime(processedData, param = ObiwarpParam())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the difference between adjusted and raw retention times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OIGMmr0dzCrzSqrMu8L8ClYos1yzhod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AtjjSIlKZwP20ERIwnSrUWsmHXqn4FOE6QPUJVrYV9Y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KpH4vWN+hYL72MUwEt8drIz+Mu6b11diO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\nPs5Vk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cL4BjHAXNYApMg1NBP38CjOFp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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotAdjustedRtime(processedData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Peak grouping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the parameters for the *peak density*-based peak grouping (correspondence\n",
    "analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 10757 mz slices ... OK\n"
     ]
    }
   ],
   "source": [
    "pdp <- PeakDensityParam(sampleGroups = processedData$sample_group, minFraction = 0.10)\n",
    "processedData <- groupChromPeaks(processedData, param = pdp) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gap filling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill-in missing peaks. Peak detection might have failed for some features in\n",
    "some samples. The `fillChromPeaks` function allows to integrate for such cases\n",
    "all signal in the respective m/z - retention time range. Below we first define\n",
    "the median width of identified chromatographic peaks in retention time dimension\n",
    "and use this as parameter `fixedRt` for the `fillChromPeaks`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Defining peak areas for filling-in .... OK\n",
      "Start integrating peak areas from original files\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n",
      "R_zmq_msg_send errno: 4 strerror: Interrupted system call\n"
     ]
    }
   ],
   "source": [
    "medWidth <- median(chromPeaks(processedData)[, \"rtmax\"] -\n",
    "                   chromPeaks(processedData)[, \"rtmin\"])\n",
    "## fill missing peaks\n",
    "processed_Data <- fillChromPeaks(processedData,\n",
    "                                 param = FillChromPeaksParam(fixedRt = medWidth))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Export MS1 and MS2 features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we use the `featureSpectra` function to extract all MS2 spectra with their\n",
    "precursor <i>m/z</i> being within the <i>m/z</i> range of a feature/peak and their retention\n",
    "time within the rt range of the same feature/peak. Zero-intensity\n",
    "values are removed from each spectrum with the `clean` function, and\n",
    "subsequently processed into the expected format using the `formatSpectraForGNPS`\n",
    "function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "## export the individual spectra into a .mgf file\n",
    "filteredMs2Spectra <- featureSpectra(processedData, return.type = \"Spectra\")\n",
    "filteredMs2Spectra <- clean(filteredMs2Spectra, all = TRUE)\n",
    "filteredMs2Spectra <- formatSpectraForGNPS(filteredMs2Spectra)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The extracted MS2 spectra are saved as *ms2spectra_all.mgf* file. This file can\n",
    "for example be used to do *in silico* structure prediction through\n",
    "[SIRIUS+CSI:FingerID](https://bio.informatik.uni-jena.de/software/sirius/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "writeMgfData(filteredMs2Spectra, \"ms2spectra_all.mgf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export peak area quantification table. To this end we first extract the *feature\n",
    "definitions* (i.e. the <i>m/z</i> and retention time ranges and other metadata for all\n",
    "defined features in the data set) and then the integrated peak areas (with the\n",
    "`featureValues` function). This peak area quantification table contains features\n",
    "and respective per sample peak areas in columns. The combined data is then saved\n",
    "to the file *xcms_all.txt*. Note that it is now also possible to use the entire\n",
    "feature table in the FBMN workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## get data\n",
    "featuresDef <- featureDefinitions(processedData)\n",
    "featuresIntensities <- featureValues(processedData, value = \"into\")\n",
    "\n",
    "## generate data table\n",
    "dataTable <- merge(featuresDef, featuresIntensities, by = 0, all = TRUE)\n",
    "dataTable <- dataTable[, !(colnames(dataTable) %in% c(\"peakidx\"))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 31</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Row.names</th><th scope=col>mzmed</th><th scope=col>mzmin</th><th scope=col>mzmax</th><th scope=col>rtmed</th><th scope=col>rtmin</th><th scope=col>rtmax</th><th scope=col>npeaks</th><th scope=col>sample</th><th scope=col>10712_P4_RH3_01_521.mzXML</th><th scope=col>⋯</th><th scope=col>10546_P4_RG11_01_515.mzXML</th><th scope=col>11111_P4_RB1_01_428.mzXML</th><th scope=col>11163_P4_RB11_01_439.mzXML</th><th scope=col>12262_P1_RE5_01_94.mzXML</th><th scope=col>13541_P4_RG3_01_506.mzXML</th><th scope=col>14563_P1_RE10_01_99.mzXML</th><th scope=col>15581_P2_RD4_01_203.mzXML</th><th scope=col>15623_P2_RC3_01_187.mzXML</th><th scope=col>16500_P2_RD8_01_209.mzXML</th><th scope=col>16501_P2_RD9_01_210.mzXML</th></tr>\n",
       "\t<tr><th scope=col>&lt;I&lt;chr&gt;&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>FT0001</td><td>74.93800</td><td>74.93730</td><td>74.93844</td><td>635.1414</td><td>619.8896</td><td>668.8052</td><td>13</td><td>13</td><td>  5992.335</td><td>⋯</td><td>  5994.643</td><td>  6612.649</td><td>  7339.393</td><td>NA</td><td>  6213.569</td><td>NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td></tr>\n",
       "\t<tr><td>FT0002</td><td>79.01845</td><td>78.99923</td><td>79.01991</td><td>632.4878</td><td>612.9404</td><td>672.1768</td><td>24</td><td>19</td><td> 35500.207</td><td>⋯</td><td> 38460.364</td><td> 44295.866</td><td>  5187.798</td><td>NA</td><td>  5745.190</td><td>NA</td><td> 43961.04</td><td> 44742.50</td><td> 37134.69</td><td> 37756.04</td></tr>\n",
       "\t<tr><td>FT0003</td><td>79.01919</td><td>79.01781</td><td>79.02002</td><td>768.7201</td><td>748.2793</td><td>814.7120</td><td>17</td><td>15</td><td>  9027.811</td><td>⋯</td><td>        NA</td><td> 17532.640</td><td> 19516.889</td><td>NA</td><td> 15355.086</td><td>NA</td><td> 13430.69</td><td> 11438.27</td><td> 12591.96</td><td> 12287.22</td></tr>\n",
       "\t<tr><td>FT0004</td><td>82.53777</td><td>82.53682</td><td>82.53828</td><td>636.0318</td><td>622.7896</td><td>636.3755</td><td> 5</td><td> 5</td><td>        NA</td><td>⋯</td><td>        NA</td><td>        NA</td><td> 17877.379</td><td>NA</td><td>        NA</td><td>NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td></tr>\n",
       "\t<tr><td>FT0005</td><td>84.96005</td><td>84.95913</td><td>84.96120</td><td>632.6655</td><td>614.6725</td><td>672.1768</td><td>19</td><td>19</td><td>126988.570</td><td>⋯</td><td>127101.080</td><td>132847.951</td><td>121974.027</td><td>NA</td><td>125743.087</td><td>NA</td><td>136783.16</td><td>139987.26</td><td>134410.81</td><td>139653.48</td></tr>\n",
       "\t<tr><td>FT0006</td><td>84.96005</td><td>84.95916</td><td>84.96123</td><td>768.4869</td><td>748.9986</td><td>815.6020</td><td>19</td><td>19</td><td> 58810.147</td><td>⋯</td><td> 57752.126</td><td> 69500.498</td><td> 48341.732</td><td>NA</td><td> 61066.160</td><td>NA</td><td> 48422.23</td><td> 56365.87</td><td> 49438.42</td><td> 50955.62</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 31\n",
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllll}\n",
       " Row.names & mzmed & mzmin & mzmax & rtmed & rtmin & rtmax & npeaks & sample & 10712\\_P4\\_RH3\\_01\\_521.mzXML & 10715\\_P4\\_RA4\\_01\\_415.mzXML & 10765\\_P4\\_RE9\\_01\\_482.mzXML & 11035\\_P4\\_RB4\\_01\\_431.mzXML & 13917\\_P4\\_RA10\\_01\\_423.mzXML & 14153\\_P4\\_RH8\\_01\\_526.mzXML & 14880\\_P3\\_RA3\\_01\\_290.mzXML & 18404\\_P3\\_RF6\\_01\\_370.mzXML & 27373\\_P2\\_RD5\\_01\\_204.mzXML & 29342\\_P5\\_RF3\\_01\\_620.mzXML & 31878\\_P1\\_RH3\\_01\\_131.mzXML & 10232\\_P4\\_RE4\\_01\\_476.mzXML & 10546\\_P4\\_RG11\\_01\\_515.mzXML & 11111\\_P4\\_RB1\\_01\\_428.mzXML & 11163\\_P4\\_RB11\\_01\\_439.mzXML & 12262\\_P1\\_RE5\\_01\\_94.mzXML & 13541\\_P4\\_RG3\\_01\\_506.mzXML & 14563\\_P1\\_RE10\\_01\\_99.mzXML & 15581\\_P2\\_RD4\\_01\\_203.mzXML & 15623\\_P2\\_RC3\\_01\\_187.mzXML & 16500\\_P2\\_RD8\\_01\\_209.mzXML & 16501\\_P2\\_RD9\\_01\\_210.mzXML\\\\\n",
       " <I<chr>> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t FT0001 & 74.93800 & 74.93730 & 74.93844 & 635.1414 & 619.8896 & 668.8052 & 13 & 13 &   5992.335 &   6122.173 &   5164.236 &   6883.096 &   7538.983 &   4957.674 &        NA &        NA &   5531.71 &   5954.468 & NA &   5659.586 &   5994.643 &   6612.649 &   7339.393 & NA &   6213.569 & NA &        NA &        NA &        NA &        NA\\\\\n",
       "\t FT0002 & 79.01845 & 78.99923 & 79.01991 & 632.4878 & 612.9404 & 672.1768 & 24 & 19 &  35500.207 &  43767.917 &  38103.877 &   5065.469 &  47939.932 &   4957.661 &  42037.97 &  38253.62 &  39491.59 &  34126.862 & NA &  44602.591 &  38460.364 &  44295.866 &   5187.798 & NA &   5745.190 & NA &  43961.04 &  44742.50 &  37134.69 &  37756.04\\\\\n",
       "\t FT0003 & 79.01919 & 79.01781 & 79.02002 & 768.7201 & 748.2793 & 814.7120 & 17 & 15 &   9027.811 &  14229.897 &         NA &  14607.718 &         NA &  11561.115 &  18110.88 &  11001.74 &  12263.00 &         NA & NA &  12769.920 &         NA &  17532.640 &  19516.889 & NA &  15355.086 & NA &  13430.69 &  11438.27 &  12591.96 &  12287.22\\\\\n",
       "\t FT0004 & 82.53777 & 82.53682 & 82.53828 & 636.0318 & 622.7896 & 636.3755 &  5 &  5 &         NA &         NA &         NA &  10529.828 &   6822.693 &         NA &  12581.50 &        NA &   9303.16 &         NA & NA &         NA &         NA &         NA &  17877.379 & NA &         NA & NA &        NA &        NA &        NA &        NA\\\\\n",
       "\t FT0005 & 84.96005 & 84.95913 & 84.96120 & 632.6655 & 614.6725 & 672.1768 & 19 & 19 & 126988.570 & 134311.111 & 123673.313 & 134965.833 & 131305.492 & 125368.799 & 147064.17 & 124015.91 & 131591.15 & 123638.900 & NA & 126758.017 & 127101.080 & 132847.951 & 121974.027 & NA & 125743.087 & NA & 136783.16 & 139987.26 & 134410.81 & 139653.48\\\\\n",
       "\t FT0006 & 84.96005 & 84.95916 & 84.96123 & 768.4869 & 748.9986 & 815.6020 & 19 & 19 &  58810.147 &  51286.966 &  57856.394 &  61844.896 &  45413.869 &  62556.181 &  56385.04 &  64976.17 &  45994.37 &  44476.584 & NA &  52225.752 &  57752.126 &  69500.498 &  48341.732 & NA &  61066.160 & NA &  48422.23 &  56365.87 &  49438.42 &  50955.62\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 31\n",
       "\n",
       "| Row.names &lt;I&lt;chr&gt;&gt; | mzmed &lt;dbl&gt; | mzmin &lt;dbl&gt; | mzmax &lt;dbl&gt; | rtmed &lt;dbl&gt; | rtmin &lt;dbl&gt; | rtmax &lt;dbl&gt; | npeaks &lt;dbl&gt; | sample &lt;dbl&gt; | 10712_P4_RH3_01_521.mzXML &lt;dbl&gt; | ⋯ ⋯ | 10546_P4_RG11_01_515.mzXML &lt;dbl&gt; | 11111_P4_RB1_01_428.mzXML &lt;dbl&gt; | 11163_P4_RB11_01_439.mzXML &lt;dbl&gt; | 12262_P1_RE5_01_94.mzXML &lt;dbl&gt; | 13541_P4_RG3_01_506.mzXML &lt;dbl&gt; | 14563_P1_RE10_01_99.mzXML &lt;dbl&gt; | 15581_P2_RD4_01_203.mzXML &lt;dbl&gt; | 15623_P2_RC3_01_187.mzXML &lt;dbl&gt; | 16500_P2_RD8_01_209.mzXML &lt;dbl&gt; | 16501_P2_RD9_01_210.mzXML &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| FT0001 | 74.93800 | 74.93730 | 74.93844 | 635.1414 | 619.8896 | 668.8052 | 13 | 13 |   5992.335 | ⋯ |   5994.643 |   6612.649 |   7339.393 | NA |   6213.569 | NA |        NA |        NA |        NA |        NA |\n",
       "| FT0002 | 79.01845 | 78.99923 | 79.01991 | 632.4878 | 612.9404 | 672.1768 | 24 | 19 |  35500.207 | ⋯ |  38460.364 |  44295.866 |   5187.798 | NA |   5745.190 | NA |  43961.04 |  44742.50 |  37134.69 |  37756.04 |\n",
       "| FT0003 | 79.01919 | 79.01781 | 79.02002 | 768.7201 | 748.2793 | 814.7120 | 17 | 15 |   9027.811 | ⋯ |         NA |  17532.640 |  19516.889 | NA |  15355.086 | NA |  13430.69 |  11438.27 |  12591.96 |  12287.22 |\n",
       "| FT0004 | 82.53777 | 82.53682 | 82.53828 | 636.0318 | 622.7896 | 636.3755 |  5 |  5 |         NA | ⋯ |         NA |         NA |  17877.379 | NA |         NA | NA |        NA |        NA |        NA |        NA |\n",
       "| FT0005 | 84.96005 | 84.95913 | 84.96120 | 632.6655 | 614.6725 | 672.1768 | 19 | 19 | 126988.570 | ⋯ | 127101.080 | 132847.951 | 121974.027 | NA | 125743.087 | NA | 136783.16 | 139987.26 | 134410.81 | 139653.48 |\n",
       "| FT0006 | 84.96005 | 84.95916 | 84.96123 | 768.4869 | 748.9986 | 815.6020 | 19 | 19 |  58810.147 | ⋯ |  57752.126 |  69500.498 |  48341.732 | NA |  61066.160 | NA |  48422.23 |  56365.87 |  49438.42 |  50955.62 |\n",
       "\n"
      ],
      "text/plain": [
       "  Row.names mzmed    mzmin    mzmax    rtmed    rtmin    rtmax    npeaks sample\n",
       "1 FT0001    74.93800 74.93730 74.93844 635.1414 619.8896 668.8052 13     13    \n",
       "2 FT0002    79.01845 78.99923 79.01991 632.4878 612.9404 672.1768 24     19    \n",
       "3 FT0003    79.01919 79.01781 79.02002 768.7201 748.2793 814.7120 17     15    \n",
       "4 FT0004    82.53777 82.53682 82.53828 636.0318 622.7896 636.3755  5      5    \n",
       "5 FT0005    84.96005 84.95913 84.96120 632.6655 614.6725 672.1768 19     19    \n",
       "6 FT0006    84.96005 84.95916 84.96123 768.4869 748.9986 815.6020 19     19    \n",
       "  10712_P4_RH3_01_521.mzXML ⋯ 10546_P4_RG11_01_515.mzXML\n",
       "1   5992.335                ⋯   5994.643                \n",
       "2  35500.207                ⋯  38460.364                \n",
       "3   9027.811                ⋯         NA                \n",
       "4         NA                ⋯         NA                \n",
       "5 126988.570                ⋯ 127101.080                \n",
       "6  58810.147                ⋯  57752.126                \n",
       "  11111_P4_RB1_01_428.mzXML 11163_P4_RB11_01_439.mzXML 12262_P1_RE5_01_94.mzXML\n",
       "1   6612.649                  7339.393                 NA                      \n",
       "2  44295.866                  5187.798                 NA                      \n",
       "3  17532.640                 19516.889                 NA                      \n",
       "4         NA                 17877.379                 NA                      \n",
       "5 132847.951                121974.027                 NA                      \n",
       "6  69500.498                 48341.732                 NA                      \n",
       "  13541_P4_RG3_01_506.mzXML 14563_P1_RE10_01_99.mzXML 15581_P2_RD4_01_203.mzXML\n",
       "1   6213.569                NA                               NA                \n",
       "2   5745.190                NA                         43961.04                \n",
       "3  15355.086                NA                         13430.69                \n",
       "4         NA                NA                               NA                \n",
       "5 125743.087                NA                        136783.16                \n",
       "6  61066.160                NA                         48422.23                \n",
       "  15623_P2_RC3_01_187.mzXML 16500_P2_RD8_01_209.mzXML 16501_P2_RD9_01_210.mzXML\n",
       "1        NA                        NA                        NA                \n",
       "2  44742.50                  37134.69                  37756.04                \n",
       "3  11438.27                  12591.96                  12287.22                \n",
       "4        NA                        NA                        NA                \n",
       "5 139987.26                 134410.81                 139653.48                \n",
       "6  56365.87                  49438.42                  50955.62                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(dataTable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.table(dataTable, \"xcms_all.txt\", sep = \"\\t\", quote = FALSE, row.names = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Export MS2 features only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `filteredMs2Spectra` contains all MS2 spectra with their precursor <i>m/z</i>\n",
    "within the feature's <i>m/z</i> range and a retention time that is within the retention\n",
    "time of the chromatographic peak/feature. We thus have multiple MS2 spectra for\n",
    "each feature (also from each sample). Metadata column `\"feature_id\"` indicates\n",
    "to which feature a MS2 spectrum belongs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spectra with 49561 spectra and 2 metadata column(s):\n",
       "                             msLevel     rtime peaksCount |  feature_id\n",
       "                           <integer> <numeric>  <integer> | <character>\n",
       "  FT0006.CP02887.F01.S4887         2   767.064          4 |      FT0006\n",
       "  FT0006.CP02887.F01.S4923         2   770.735          1 |      FT0006\n",
       "  FT0006.CP05806.F02.S4944         2   767.423          1 |      FT0006\n",
       "  FT0006.CP08069.F03.S4901         2   794.810          0 |      FT0006\n",
       "  FT0006.CP16057.F06.S4949         2   774.844          3 |      FT0006\n",
       "                       ...       ...       ...        ... .         ...\n",
       "  FT3194.CP48469.F16.S1795         2   317.441        476 |      FT3194\n",
       "  FT3194.CP56479.F18.S1834         2   288.986        660 |      FT3194\n",
       "  FT3194.CP56479.F18.S1841         2   289.973        508 |      FT3194\n",
       "  FT3194.CP70770.F21.S1823         2   286.579        363 |      FT3194\n",
       "  FT3194.CP70770.F21.S1829         2   287.456        370 |      FT3194\n",
       "                               peak_id\n",
       "                           <character>\n",
       "  FT0006.CP02887.F01.S4887     CP02887\n",
       "  FT0006.CP02887.F01.S4923     CP02887\n",
       "  FT0006.CP05806.F02.S4944     CP05806\n",
       "  FT0006.CP08069.F03.S4901     CP08069\n",
       "  FT0006.CP16057.F06.S4949     CP16057\n",
       "                       ...         ...\n",
       "  FT3194.CP48469.F16.S1795     CP48469\n",
       "  FT3194.CP56479.F18.S1834     CP56479\n",
       "  FT3194.CP56479.F18.S1841     CP56479\n",
       "  FT3194.CP70770.F21.S1823     CP70770\n",
       "  FT3194.CP70770.F21.S1829     CP70770"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filteredMs2Spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We next select a single MS2 spectrum for each feature and export this reduced\n",
    "set also as an .mgf file. This file can be submitted to GNPS [feature-based\n",
    "molecular\n",
    "networking](https://ccms-ucsd.github.io/GNPSDocumentation/featurebasedmolecularnetworking/). As\n",
    "*representative* MS2 spectrum for each feature we select the spectrum with the\n",
    "largest total signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Select for each feature the Spectrum2 with the largest TIC.\n",
    "filteredMs2Spectra_maxTic <- combineSpectra(filteredMs2Spectra,\n",
    "                                            fcol = \"feature_id\",\n",
    "                                            method = maxTic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "writeMgfData(filteredMs2Spectra_maxTic, \"ms2spectra_maxTic.mgf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At last we subset the peak area quantification table to features for which we\n",
    "have also an MS2 spectrum and export this to the *xcms_onlyMS2.txt* file. This\n",
    "file can be submitted to GNPS [feature-based molecular\n",
    "networking](https://ccms-ucsd.github.io/GNPSDocumentation/featurebasedmolecularnetworking/):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## filter data table to contain only peaks with MSMS DF[ , !(names(DF) %in% drops)]\n",
    "filteredDataTable <- dataTable[which(dataTable$Row.names %in% filteredMs2Spectra@elementMetadata$feature_id),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 31</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Row.names</th><th scope=col>mzmed</th><th scope=col>mzmin</th><th scope=col>mzmax</th><th scope=col>rtmed</th><th scope=col>rtmin</th><th scope=col>rtmax</th><th scope=col>npeaks</th><th scope=col>sample</th><th scope=col>10712_P4_RH3_01_521.mzXML</th><th scope=col>⋯</th><th scope=col>10546_P4_RG11_01_515.mzXML</th><th scope=col>11111_P4_RB1_01_428.mzXML</th><th scope=col>11163_P4_RB11_01_439.mzXML</th><th scope=col>12262_P1_RE5_01_94.mzXML</th><th scope=col>13541_P4_RG3_01_506.mzXML</th><th scope=col>14563_P1_RE10_01_99.mzXML</th><th scope=col>15581_P2_RD4_01_203.mzXML</th><th scope=col>15623_P2_RC3_01_187.mzXML</th><th scope=col>16500_P2_RD8_01_209.mzXML</th><th scope=col>16501_P2_RD9_01_210.mzXML</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;I&lt;chr&gt;&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>6</th><td>FT0006</td><td>84.96005</td><td>84.95916</td><td>84.96123</td><td>768.4869</td><td>748.9986</td><td>815.6020</td><td>19</td><td>19</td><td> 58810.15</td><td>⋯</td><td> 57752.13</td><td> 69500.50</td><td>48341.73</td><td>NA</td><td>61066.160</td><td>NA</td><td>48422.23</td><td> 56365.87</td><td> 49438.42</td><td> 50955.62</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>FT0009</td><td>87.00453</td><td>87.00386</td><td>87.00597</td><td>647.4418</td><td>612.9404</td><td>715.0927</td><td>40</td><td>19</td><td> 44242.49</td><td>⋯</td><td> 41532.80</td><td> 26743.93</td><td>49569.10</td><td>NA</td><td>46971.061</td><td>NA</td><td>42156.38</td><td> 29162.54</td><td> 45870.67</td><td> 46071.95</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>FT0010</td><td>87.00450</td><td>87.00403</td><td>87.00550</td><td>763.8388</td><td>749.9156</td><td>810.2850</td><td>17</td><td>15</td><td> 18681.10</td><td>⋯</td><td> 16352.62</td><td> 25489.00</td><td>27538.64</td><td>NA</td><td>30657.713</td><td>NA</td><td>19938.46</td><td>       NA</td><td>       NA</td><td>       NA</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>FT0011</td><td>87.04386</td><td>87.04377</td><td>87.04416</td><td>163.3574</td><td>159.4496</td><td>164.1130</td><td> 3</td><td> 3</td><td>       NA</td><td>⋯</td><td>       NA</td><td>       NA</td><td>      NA</td><td>NA</td><td> 7893.677</td><td>NA</td><td>      NA</td><td>       NA</td><td>       NA</td><td>       NA</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>FT0012</td><td>88.01455</td><td>88.00423</td><td>88.02490</td><td>631.3002</td><td>611.2421</td><td>672.1768</td><td>40</td><td>19</td><td>107234.14</td><td>⋯</td><td>137523.76</td><td>137125.07</td><td>45488.69</td><td>NA</td><td>39777.838</td><td>NA</td><td>33678.81</td><td>154896.49</td><td>111179.41</td><td>132350.13</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>FT0013</td><td>88.02322</td><td>88.00406</td><td>88.02502</td><td>766.8456</td><td>743.2274</td><td>815.6020</td><td>34</td><td>19</td><td> 48550.50</td><td>⋯</td><td> 54132.12</td><td> 56679.59</td><td>12947.44</td><td>NA</td><td>12640.055</td><td>NA</td><td>45822.21</td><td> 50789.33</td><td> 47394.42</td><td> 42487.33</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 31\n",
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllll}\n",
       "  & Row.names & mzmed & mzmin & mzmax & rtmed & rtmin & rtmax & npeaks & sample & 10712\\_P4\\_RH3\\_01\\_521.mzXML & 10715\\_P4\\_RA4\\_01\\_415.mzXML & 10765\\_P4\\_RE9\\_01\\_482.mzXML & 11035\\_P4\\_RB4\\_01\\_431.mzXML & 13917\\_P4\\_RA10\\_01\\_423.mzXML & 14153\\_P4\\_RH8\\_01\\_526.mzXML & 14880\\_P3\\_RA3\\_01\\_290.mzXML & 18404\\_P3\\_RF6\\_01\\_370.mzXML & 27373\\_P2\\_RD5\\_01\\_204.mzXML & 29342\\_P5\\_RF3\\_01\\_620.mzXML & 31878\\_P1\\_RH3\\_01\\_131.mzXML & 10232\\_P4\\_RE4\\_01\\_476.mzXML & 10546\\_P4\\_RG11\\_01\\_515.mzXML & 11111\\_P4\\_RB1\\_01\\_428.mzXML & 11163\\_P4\\_RB11\\_01\\_439.mzXML & 12262\\_P1\\_RE5\\_01\\_94.mzXML & 13541\\_P4\\_RG3\\_01\\_506.mzXML & 14563\\_P1\\_RE10\\_01\\_99.mzXML & 15581\\_P2\\_RD4\\_01\\_203.mzXML & 15623\\_P2\\_RC3\\_01\\_187.mzXML & 16500\\_P2\\_RD8\\_01\\_209.mzXML & 16501\\_P2\\_RD9\\_01\\_210.mzXML\\\\\n",
       "  & <I<chr>> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t6 & FT0006 & 84.96005 & 84.95916 & 84.96123 & 768.4869 & 748.9986 & 815.6020 & 19 & 19 &  58810.15 &  51286.966 & 57856.39 & 61844.90 &  45413.87 & 62556.18 & 56385.04 &  64976.17 & 45994.366 &  44476.58 & NA & 52225.75 &  57752.13 &  69500.50 & 48341.73 & NA & 61066.160 & NA & 48422.23 &  56365.87 &  49438.42 &  50955.62\\\\\n",
       "\t9 & FT0009 & 87.00453 & 87.00386 & 87.00597 & 647.4418 & 612.9404 & 715.0927 & 40 & 19 &  44242.49 &  27554.836 & 41298.02 & 49304.50 & 103095.82 & 40104.30 & 57138.25 &  28730.82 & 47305.543 &  71520.57 & NA & 39348.12 &  41532.80 &  26743.93 & 49569.10 & NA & 46971.061 & NA & 42156.38 &  29162.54 &  45870.67 &  46071.95\\\\\n",
       "\t10 & FT0010 & 87.00450 & 87.00403 & 87.00550 & 763.8388 & 749.9156 & 810.2850 & 17 & 15 &  18681.10 &  30885.277 & 32277.13 & 28647.09 &  44674.04 & 15693.61 & 21368.79 &  29214.61 &        NA &  69871.83 & NA & 20408.02 &  16352.62 &  25489.00 & 27538.64 & NA & 30657.713 & NA & 19938.46 &        NA &        NA &        NA\\\\\n",
       "\t11 & FT0011 & 87.04386 & 87.04377 & 87.04416 & 163.3574 & 159.4496 & 164.1130 &  3 &  3 &        NA &   6889.364 & 18723.88 &       NA &        NA &       NA &       NA &        NA &        NA &        NA & NA &       NA &        NA &        NA &       NA & NA &  7893.677 & NA &       NA &        NA &        NA &        NA\\\\\n",
       "\t12 & FT0012 & 88.01455 & 88.00423 & 88.02490 & 631.3002 & 611.2421 & 672.1768 & 40 & 19 & 107234.14 & 149518.154 & 39238.34 & 45934.66 & 148660.70 & 38567.50 & 34646.35 & 119404.39 & 31743.365 & 108636.60 & NA & 36593.81 & 137523.76 & 137125.07 & 45488.69 & NA & 39777.838 & NA & 33678.81 & 154896.49 & 111179.41 & 132350.13\\\\\n",
       "\t13 & FT0013 & 88.02322 & 88.00406 & 88.02502 & 766.8456 & 743.2274 & 815.6020 & 34 & 19 &  48550.50 &  74477.307 & 11799.31 & 73824.54 &  70792.67 & 13213.24 & 57125.25 &  45981.83 &  7481.007 &  43686.76 & NA & 12810.05 &  54132.12 &  56679.59 & 12947.44 & NA & 12640.055 & NA & 45822.21 &  50789.33 &  47394.42 &  42487.33\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 31\n",
       "\n",
       "| <!--/--> | Row.names &lt;I&lt;chr&gt;&gt; | mzmed &lt;dbl&gt; | mzmin &lt;dbl&gt; | mzmax &lt;dbl&gt; | rtmed &lt;dbl&gt; | rtmin &lt;dbl&gt; | rtmax &lt;dbl&gt; | npeaks &lt;dbl&gt; | sample &lt;dbl&gt; | 10712_P4_RH3_01_521.mzXML &lt;dbl&gt; | ⋯ ⋯ | 10546_P4_RG11_01_515.mzXML &lt;dbl&gt; | 11111_P4_RB1_01_428.mzXML &lt;dbl&gt; | 11163_P4_RB11_01_439.mzXML &lt;dbl&gt; | 12262_P1_RE5_01_94.mzXML &lt;dbl&gt; | 13541_P4_RG3_01_506.mzXML &lt;dbl&gt; | 14563_P1_RE10_01_99.mzXML &lt;dbl&gt; | 15581_P2_RD4_01_203.mzXML &lt;dbl&gt; | 15623_P2_RC3_01_187.mzXML &lt;dbl&gt; | 16500_P2_RD8_01_209.mzXML &lt;dbl&gt; | 16501_P2_RD9_01_210.mzXML &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 6 | FT0006 | 84.96005 | 84.95916 | 84.96123 | 768.4869 | 748.9986 | 815.6020 | 19 | 19 |  58810.15 | ⋯ |  57752.13 |  69500.50 | 48341.73 | NA | 61066.160 | NA | 48422.23 |  56365.87 |  49438.42 |  50955.62 |\n",
       "| 9 | FT0009 | 87.00453 | 87.00386 | 87.00597 | 647.4418 | 612.9404 | 715.0927 | 40 | 19 |  44242.49 | ⋯ |  41532.80 |  26743.93 | 49569.10 | NA | 46971.061 | NA | 42156.38 |  29162.54 |  45870.67 |  46071.95 |\n",
       "| 10 | FT0010 | 87.00450 | 87.00403 | 87.00550 | 763.8388 | 749.9156 | 810.2850 | 17 | 15 |  18681.10 | ⋯ |  16352.62 |  25489.00 | 27538.64 | NA | 30657.713 | NA | 19938.46 |        NA |        NA |        NA |\n",
       "| 11 | FT0011 | 87.04386 | 87.04377 | 87.04416 | 163.3574 | 159.4496 | 164.1130 |  3 |  3 |        NA | ⋯ |        NA |        NA |       NA | NA |  7893.677 | NA |       NA |        NA |        NA |        NA |\n",
       "| 12 | FT0012 | 88.01455 | 88.00423 | 88.02490 | 631.3002 | 611.2421 | 672.1768 | 40 | 19 | 107234.14 | ⋯ | 137523.76 | 137125.07 | 45488.69 | NA | 39777.838 | NA | 33678.81 | 154896.49 | 111179.41 | 132350.13 |\n",
       "| 13 | FT0013 | 88.02322 | 88.00406 | 88.02502 | 766.8456 | 743.2274 | 815.6020 | 34 | 19 |  48550.50 | ⋯ |  54132.12 |  56679.59 | 12947.44 | NA | 12640.055 | NA | 45822.21 |  50789.33 |  47394.42 |  42487.33 |\n",
       "\n"
      ],
      "text/plain": [
       "   Row.names mzmed    mzmin    mzmax    rtmed    rtmin    rtmax    npeaks\n",
       "6  FT0006    84.96005 84.95916 84.96123 768.4869 748.9986 815.6020 19    \n",
       "9  FT0009    87.00453 87.00386 87.00597 647.4418 612.9404 715.0927 40    \n",
       "10 FT0010    87.00450 87.00403 87.00550 763.8388 749.9156 810.2850 17    \n",
       "11 FT0011    87.04386 87.04377 87.04416 163.3574 159.4496 164.1130  3    \n",
       "12 FT0012    88.01455 88.00423 88.02490 631.3002 611.2421 672.1768 40    \n",
       "13 FT0013    88.02322 88.00406 88.02502 766.8456 743.2274 815.6020 34    \n",
       "   sample 10712_P4_RH3_01_521.mzXML ⋯ 10546_P4_RG11_01_515.mzXML\n",
       "6  19      58810.15                 ⋯  57752.13                 \n",
       "9  19      44242.49                 ⋯  41532.80                 \n",
       "10 15      18681.10                 ⋯  16352.62                 \n",
       "11  3            NA                 ⋯        NA                 \n",
       "12 19     107234.14                 ⋯ 137523.76                 \n",
       "13 19      48550.50                 ⋯  54132.12                 \n",
       "   11111_P4_RB1_01_428.mzXML 11163_P4_RB11_01_439.mzXML\n",
       "6   69500.50                 48341.73                  \n",
       "9   26743.93                 49569.10                  \n",
       "10  25489.00                 27538.64                  \n",
       "11        NA                       NA                  \n",
       "12 137125.07                 45488.69                  \n",
       "13  56679.59                 12947.44                  \n",
       "   12262_P1_RE5_01_94.mzXML 13541_P4_RG3_01_506.mzXML 14563_P1_RE10_01_99.mzXML\n",
       "6  NA                       61066.160                 NA                       \n",
       "9  NA                       46971.061                 NA                       \n",
       "10 NA                       30657.713                 NA                       \n",
       "11 NA                        7893.677                 NA                       \n",
       "12 NA                       39777.838                 NA                       \n",
       "13 NA                       12640.055                 NA                       \n",
       "   15581_P2_RD4_01_203.mzXML 15623_P2_RC3_01_187.mzXML\n",
       "6  48422.23                   56365.87                \n",
       "9  42156.38                   29162.54                \n",
       "10 19938.46                         NA                \n",
       "11       NA                         NA                \n",
       "12 33678.81                  154896.49                \n",
       "13 45822.21                   50789.33                \n",
       "   16500_P2_RD8_01_209.mzXML 16501_P2_RD9_01_210.mzXML\n",
       "6   49438.42                  50955.62                \n",
       "9   45870.67                  46071.95                \n",
       "10        NA                        NA                \n",
       "11        NA                        NA                \n",
       "12 111179.41                 132350.13                \n",
       "13  47394.42                  42487.33                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(filteredDataTable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.table(filteredDataTable, \"xcms_onlyMS2.txt\", sep = \"\\t\", quote = FALSE, row.names = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Export MS2 consensus spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, instead of selecting the spectrum with the largest total signal as representative MS2 spectrum for each feature, we can create a consensus MS2 spectrum. A consensus MS2 spectrum can for example be created by combining all MS2 spectra for a feature into a single spectrum that contains peaks present in the majority of spectra. Note however that this feature is experimental at present."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To this end we can use the `combineSpectra` function. The parameter `minProp` defines the mimimal proportion of spectra in which a peak has to be present in order for it to be added to the consensus spectrum (0.8 -> 80% of spectra). The parameters `mzd` and `ppm` allow to define how to group peaks between spectra with `mzd` being a fixed, constant value and all peaks between spectra with a difference in their <i>m/z</i> < mzd are combined into the final mass peak in the consensus spectrum. Finally, the parameter `ppm` allows to perform an <i>m/z</i> dependent grouping of mass peaks, i.e. mass peaks with a difference in their <i>m/z</i> smaller than ppm are combined. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more details see the documentation of the [`consensusSpectrum`](https://rdrr.io/bioc/MSnbase/man/consensusSpectrum.html) function in the MSnbase R package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "“No peak present in more than 80% of spectra.”Warning message in FUN(X[[i]], ...):\n",
      "“No peak present in more than 80% of spectra.”"
     ]
    }
   ],
   "source": [
    "filteredMs2Spectra_consensus <- combineSpectra(filteredMs2Spectra, fcol = \"feature_id\", method = consensusSpectrum,mzd = 0, minProp = 0.8, ppm = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "writeMgfData(filteredMs2Spectra_consensus, \"ms2spectra_consensus.mgf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analogously we subset the peak area quantification table to features for which we\n",
    "have an MS2 consensus spectrum and export this to the *xcms_consensusMS2.txt* file. This\n",
    "file can be submitted to GNPS [feature-based molecular\n",
    "networking](https://ccms-ucsd.github.io/GNPSDocumentation/featurebasedmolecularnetworking/):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## filter data table to contain only peaks with MSMS DF[ , !(names(DF) %in% drops)]\n",
    "consensusDataTable <- dataTable[which(dataTable$Row.names %in% filteredMs2Spectra_consensus@elementMetadata$feature_id),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 31</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Row.names</th><th scope=col>mzmed</th><th scope=col>mzmin</th><th scope=col>mzmax</th><th scope=col>rtmed</th><th scope=col>rtmin</th><th scope=col>rtmax</th><th scope=col>npeaks</th><th scope=col>sample</th><th scope=col>10712_P4_RH3_01_521.mzXML</th><th scope=col>⋯</th><th scope=col>10546_P4_RG11_01_515.mzXML</th><th scope=col>11111_P4_RB1_01_428.mzXML</th><th scope=col>11163_P4_RB11_01_439.mzXML</th><th scope=col>12262_P1_RE5_01_94.mzXML</th><th scope=col>13541_P4_RG3_01_506.mzXML</th><th scope=col>14563_P1_RE10_01_99.mzXML</th><th scope=col>15581_P2_RD4_01_203.mzXML</th><th scope=col>15623_P2_RC3_01_187.mzXML</th><th scope=col>16500_P2_RD8_01_209.mzXML</th><th scope=col>16501_P2_RD9_01_210.mzXML</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;I&lt;chr&gt;&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>3</th><td>FT0003</td><td>84.96008</td><td>84.95917</td><td>84.96123</td><td>768.7932</td><td>748.9986</td><td>816.4870</td><td>18</td><td>18</td><td> 47386.26</td><td>⋯</td><td> 44219.90</td><td>  63143.97</td><td> 63463.52</td><td>NA</td><td> 56322.53</td><td>NA</td><td> 33831.92</td><td> 54578.64</td><td> 41726.33</td><td> 36886.44</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>FT0006</td><td>88.02374</td><td>88.02312</td><td>88.02502</td><td>768.6035</td><td>748.2793</td><td>815.6020</td><td>14</td><td>14</td><td>       NA</td><td>⋯</td><td> 52712.53</td><td>  56453.99</td><td> 40019.00</td><td>NA</td><td>       NA</td><td>NA</td><td> 38260.09</td><td> 49078.86</td><td>       NA</td><td> 33403.81</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>FT0007</td><td>88.02381</td><td>88.02301</td><td>88.02486</td><td>632.3018</td><td>613.8012</td><td>672.1768</td><td>19</td><td>19</td><td>106601.48</td><td>⋯</td><td>117296.73</td><td> 143670.22</td><td>124281.27</td><td>NA</td><td>116959.66</td><td>NA</td><td>116065.87</td><td>134373.45</td><td>113570.83</td><td>111622.61</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>FT0010</td><td>97.01013</td><td>97.00943</td><td>97.01124</td><td>629.6270</td><td>611.2421</td><td>668.8052</td><td>19</td><td>19</td><td>104805.37</td><td>⋯</td><td>103491.04</td><td> 116757.48</td><td>126878.14</td><td>NA</td><td>112133.41</td><td>NA</td><td> 97458.39</td><td>104212.58</td><td> 88544.07</td><td> 92049.22</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>FT0011</td><td>97.01005</td><td>97.00962</td><td>97.01075</td><td>764.7314</td><td>751.1205</td><td>811.1670</td><td>13</td><td>13</td><td> 37995.65</td><td>⋯</td><td> 42610.06</td><td>  44654.40</td><td> 48893.62</td><td>NA</td><td> 39757.19</td><td>NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td></tr>\n",
       "\t<tr><th scope=row>16</th><td>FT0016</td><td>98.51246</td><td>98.51178</td><td>98.51361</td><td>664.4330</td><td>612.9404</td><td>714.4675</td><td>76</td><td>19</td><td>406179.85</td><td>⋯</td><td>820176.67</td><td>1768152.28</td><td>962126.03</td><td>NA</td><td>524106.60</td><td>NA</td><td>270742.59</td><td>458656.61</td><td>556087.14</td><td>214308.59</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 31\n",
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllll}\n",
       "  & Row.names & mzmed & mzmin & mzmax & rtmed & rtmin & rtmax & npeaks & sample & 10712\\_P4\\_RH3\\_01\\_521.mzXML & 10715\\_P4\\_RA4\\_01\\_415.mzXML & 10765\\_P4\\_RE9\\_01\\_482.mzXML & 11035\\_P4\\_RB4\\_01\\_431.mzXML & 13917\\_P4\\_RA10\\_01\\_423.mzXML & 14153\\_P4\\_RH8\\_01\\_526.mzXML & 14880\\_P3\\_RA3\\_01\\_290.mzXML & 18404\\_P3\\_RF6\\_01\\_370.mzXML & 27373\\_P2\\_RD5\\_01\\_204.mzXML & 29342\\_P5\\_RF3\\_01\\_620.mzXML & 31878\\_P1\\_RH3\\_01\\_131.mzXML & 10232\\_P4\\_RE4\\_01\\_476.mzXML & 10546\\_P4\\_RG11\\_01\\_515.mzXML & 11111\\_P4\\_RB1\\_01\\_428.mzXML & 11163\\_P4\\_RB11\\_01\\_439.mzXML & 12262\\_P1\\_RE5\\_01\\_94.mzXML & 13541\\_P4\\_RG3\\_01\\_506.mzXML & 14563\\_P1\\_RE10\\_01\\_99.mzXML & 15581\\_P2\\_RD4\\_01\\_203.mzXML & 15623\\_P2\\_RC3\\_01\\_187.mzXML & 16500\\_P2\\_RD8\\_01\\_209.mzXML & 16501\\_P2\\_RD9\\_01\\_210.mzXML\\\\\n",
       "  & <I<chr>> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t3 & FT0003 & 84.96008 & 84.95917 & 84.96123 & 768.7932 & 748.9986 & 816.4870 & 18 & 18 &  47386.26 &  38136.82 &  51460.58 &   47388.56 &   44419.83 &   45890.89 &  47230.00 &  44974.42 &  40492.40 &        NA & NA &  50286.34 &  44219.90 &   63143.97 &  63463.52 & NA &  56322.53 & NA &  33831.92 &  54578.64 &  41726.33 &  36886.44\\\\\n",
       "\t6 & FT0006 & 88.02374 & 88.02312 & 88.02502 & 768.6035 & 748.2793 & 815.6020 & 14 & 14 &        NA &  55110.19 &  43578.91 &   65758.46 &   62490.15 &         NA &  40370.59 &  47018.33 &  39557.42 &        NA & NA &  52415.23 &  52712.53 &   56453.99 &  40019.00 & NA &        NA & NA &  38260.09 &  49078.86 &        NA &  33403.81\\\\\n",
       "\t7 & FT0007 & 88.02381 & 88.02301 & 88.02486 & 632.3018 & 613.8012 & 672.1768 & 19 & 19 & 106601.48 & 146353.37 & 121914.65 &  138391.79 &  146558.84 &  107162.40 & 138499.15 & 121319.22 & 123077.82 & 109997.47 & NA & 126375.18 & 117296.73 &  143670.22 & 124281.27 & NA & 116959.66 & NA & 116065.87 & 134373.45 & 113570.83 & 111622.61\\\\\n",
       "\t10 & FT0010 & 97.01013 & 97.00943 & 97.01124 & 629.6270 & 611.2421 & 668.8052 & 19 & 19 & 104805.37 & 129353.45 & 112135.02 &  122777.92 &  139870.32 &  110140.66 & 102883.02 & 113450.47 &  86826.79 &  97819.12 & NA & 102370.22 & 103491.04 &  116757.48 & 126878.14 & NA & 112133.41 & NA &  97458.39 & 104212.58 &  88544.07 &  92049.22\\\\\n",
       "\t11 & FT0011 & 97.01005 & 97.00962 & 97.01075 & 764.7314 & 751.1205 & 811.1670 & 13 & 13 &  37995.65 &  47879.08 &  35321.23 &   49483.81 &   51316.68 &   34469.26 &        NA &  48657.76 &        NA &  34359.08 & NA &  39917.78 &  42610.06 &   44654.40 &  48893.62 & NA &  39757.19 & NA &        NA &        NA &        NA &        NA\\\\\n",
       "\t16 & FT0016 & 98.51246 & 98.51178 & 98.51361 & 664.4330 & 612.9404 & 714.4675 & 76 & 19 & 406179.85 & 848457.43 & 331905.92 & 1102072.50 & 2981035.85 & 1153126.52 & 309184.92 & 549799.64 & 821079.07 & 664890.94 & NA & 465365.44 & 820176.67 & 1768152.28 & 962126.03 & NA & 524106.60 & NA & 270742.59 & 458656.61 & 556087.14 & 214308.59\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 31\n",
       "\n",
       "| <!--/--> | Row.names &lt;I&lt;chr&gt;&gt; | mzmed &lt;dbl&gt; | mzmin &lt;dbl&gt; | mzmax &lt;dbl&gt; | rtmed &lt;dbl&gt; | rtmin &lt;dbl&gt; | rtmax &lt;dbl&gt; | npeaks &lt;dbl&gt; | sample &lt;dbl&gt; | 10712_P4_RH3_01_521.mzXML &lt;dbl&gt; | ⋯ ⋯ | 10546_P4_RG11_01_515.mzXML &lt;dbl&gt; | 11111_P4_RB1_01_428.mzXML &lt;dbl&gt; | 11163_P4_RB11_01_439.mzXML &lt;dbl&gt; | 12262_P1_RE5_01_94.mzXML &lt;dbl&gt; | 13541_P4_RG3_01_506.mzXML &lt;dbl&gt; | 14563_P1_RE10_01_99.mzXML &lt;dbl&gt; | 15581_P2_RD4_01_203.mzXML &lt;dbl&gt; | 15623_P2_RC3_01_187.mzXML &lt;dbl&gt; | 16500_P2_RD8_01_209.mzXML &lt;dbl&gt; | 16501_P2_RD9_01_210.mzXML &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 3 | FT0003 | 84.96008 | 84.95917 | 84.96123 | 768.7932 | 748.9986 | 816.4870 | 18 | 18 |  47386.26 | ⋯ |  44219.90 |   63143.97 |  63463.52 | NA |  56322.53 | NA |  33831.92 |  54578.64 |  41726.33 |  36886.44 |\n",
       "| 6 | FT0006 | 88.02374 | 88.02312 | 88.02502 | 768.6035 | 748.2793 | 815.6020 | 14 | 14 |        NA | ⋯ |  52712.53 |   56453.99 |  40019.00 | NA |        NA | NA |  38260.09 |  49078.86 |        NA |  33403.81 |\n",
       "| 7 | FT0007 | 88.02381 | 88.02301 | 88.02486 | 632.3018 | 613.8012 | 672.1768 | 19 | 19 | 106601.48 | ⋯ | 117296.73 |  143670.22 | 124281.27 | NA | 116959.66 | NA | 116065.87 | 134373.45 | 113570.83 | 111622.61 |\n",
       "| 10 | FT0010 | 97.01013 | 97.00943 | 97.01124 | 629.6270 | 611.2421 | 668.8052 | 19 | 19 | 104805.37 | ⋯ | 103491.04 |  116757.48 | 126878.14 | NA | 112133.41 | NA |  97458.39 | 104212.58 |  88544.07 |  92049.22 |\n",
       "| 11 | FT0011 | 97.01005 | 97.00962 | 97.01075 | 764.7314 | 751.1205 | 811.1670 | 13 | 13 |  37995.65 | ⋯ |  42610.06 |   44654.40 |  48893.62 | NA |  39757.19 | NA |        NA |        NA |        NA |        NA |\n",
       "| 16 | FT0016 | 98.51246 | 98.51178 | 98.51361 | 664.4330 | 612.9404 | 714.4675 | 76 | 19 | 406179.85 | ⋯ | 820176.67 | 1768152.28 | 962126.03 | NA | 524106.60 | NA | 270742.59 | 458656.61 | 556087.14 | 214308.59 |\n",
       "\n"
      ],
      "text/plain": [
       "   Row.names mzmed    mzmin    mzmax    rtmed    rtmin    rtmax    npeaks\n",
       "3  FT0003    84.96008 84.95917 84.96123 768.7932 748.9986 816.4870 18    \n",
       "6  FT0006    88.02374 88.02312 88.02502 768.6035 748.2793 815.6020 14    \n",
       "7  FT0007    88.02381 88.02301 88.02486 632.3018 613.8012 672.1768 19    \n",
       "10 FT0010    97.01013 97.00943 97.01124 629.6270 611.2421 668.8052 19    \n",
       "11 FT0011    97.01005 97.00962 97.01075 764.7314 751.1205 811.1670 13    \n",
       "16 FT0016    98.51246 98.51178 98.51361 664.4330 612.9404 714.4675 76    \n",
       "   sample 10712_P4_RH3_01_521.mzXML ⋯ 10546_P4_RG11_01_515.mzXML\n",
       "3  18      47386.26                 ⋯  44219.90                 \n",
       "6  14            NA                 ⋯  52712.53                 \n",
       "7  19     106601.48                 ⋯ 117296.73                 \n",
       "10 19     104805.37                 ⋯ 103491.04                 \n",
       "11 13      37995.65                 ⋯  42610.06                 \n",
       "16 19     406179.85                 ⋯ 820176.67                 \n",
       "   11111_P4_RB1_01_428.mzXML 11163_P4_RB11_01_439.mzXML\n",
       "3    63143.97                 63463.52                 \n",
       "6    56453.99                 40019.00                 \n",
       "7   143670.22                124281.27                 \n",
       "10  116757.48                126878.14                 \n",
       "11   44654.40                 48893.62                 \n",
       "16 1768152.28                962126.03                 \n",
       "   12262_P1_RE5_01_94.mzXML 13541_P4_RG3_01_506.mzXML 14563_P1_RE10_01_99.mzXML\n",
       "3  NA                        56322.53                 NA                       \n",
       "6  NA                              NA                 NA                       \n",
       "7  NA                       116959.66                 NA                       \n",
       "10 NA                       112133.41                 NA                       \n",
       "11 NA                        39757.19                 NA                       \n",
       "16 NA                       524106.60                 NA                       \n",
       "   15581_P2_RD4_01_203.mzXML 15623_P2_RC3_01_187.mzXML\n",
       "3   33831.92                  54578.64                \n",
       "6   38260.09                  49078.86                \n",
       "7  116065.87                 134373.45                \n",
       "10  97458.39                 104212.58                \n",
       "11        NA                        NA                \n",
       "16 270742.59                 458656.61                \n",
       "   16500_P2_RD8_01_209.mzXML 16501_P2_RD9_01_210.mzXML\n",
       "3   41726.33                  36886.44                \n",
       "6         NA                  33403.81                \n",
       "7  113570.83                 111622.61                \n",
       "10  88544.07                  92049.22                \n",
       "11        NA                        NA                \n",
       "16 556087.14                 214308.59                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(consensusDataTable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.table(consensusDataTable, \"xcms_consensusMS2.txt\", sep = \"\\t\", quote = FALSE, row.names = FALSE)"
   ]
  }
 ],
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