= single element of list)\n",
" cart_in_file = [\n",
" s_in_file for s_in_file in in_files for s_fwhm in fwhms\n",
" ]\n",
" cart_fwhm = [\n",
" s_fwhm for s_in_file in in_files for s_fwhm in fwhms\n",
" ]\n",
" cart_usans = [\n",
" s_usans for s_usans in usans for s_fwhm in fwhms\n",
" ]\n",
" cart_btthresh = [\n",
" s_btthresh for s_btthresh in btthresh for s_fwhm in fwhms\n",
" ]\n",
" return cart_in_file, cart_fwhm, cart_usans, cart_btthresh\n",
"\n",
"def getusans(x):\n",
" return [[tuple([val[0], 0.5 * val[1]])] for val in x]\n",
"\n",
"def create_fsl_FEAT_workflow_func(whichrun = 0,\n",
" whichvol = 'middle',\n",
" workflow_name = 'nipype_mimic_FEAT',\n",
" first_run = True,\n",
" func_data_file = 'temp',\n",
" fwhm = 3):\n",
" from nipype.workflows.fmri.fsl import preprocess\n",
" from nipype.interfaces import fsl\n",
" from nipype.interfaces import utility as util\n",
" from nipype.pipeline import engine as pe\n",
" \"\"\"\n",
" Setup some functions and hyperparameters\n",
" \"\"\"\n",
" fsl.FSLCommand.set_default_output_type('NIFTI_GZ')\n",
" pickrun = preprocess.pickrun\n",
" pickvol = preprocess.pickvol\n",
" getthreshop = preprocess.getthreshop\n",
" getmeanscale = preprocess.getmeanscale\n",
"# chooseindex = preprocess.chooseindex\n",
" \n",
" \"\"\"\n",
" Start constructing the workflow graph\n",
" \"\"\"\n",
" preproc = pe.Workflow(name = workflow_name)\n",
" \"\"\"\n",
" Initialize the input and output spaces\n",
" \"\"\"\n",
" inputnode = pe.Node(\n",
" interface = util.IdentityInterface(fields = ['func',\n",
" 'fwhm',\n",
" 'anat']),\n",
" name = 'inputspec')\n",
" outputnode = pe.Node(\n",
" interface = util.IdentityInterface(fields = ['reference',\n",
" 'motion_parameters',\n",
" 'realigned_files',\n",
" 'motion_plots',\n",
" 'mask',\n",
" 'smoothed_files',\n",
" 'mean']),\n",
" name = 'outputspec')\n",
" \"\"\"\n",
" first step: convert Images to float values\n",
" \"\"\"\n",
" img2float = pe.MapNode(\n",
" interface = fsl.ImageMaths(\n",
" out_data_type = 'float',\n",
" op_string = '',\n",
" suffix = '_dtype'),\n",
" iterfield = ['in_file'],\n",
" name = 'img2float')\n",
" preproc.connect(inputnode,'func',\n",
" img2float,'in_file')\n",
" \"\"\"\n",
" delete first 10 volumes\n",
" \"\"\"\n",
" develVolume = pe.MapNode(\n",
" interface = fsl.ExtractROI(t_min = 10,\n",
" t_size = 508),\n",
" iterfield = ['in_file'],\n",
" name = 'remove_volumes')\n",
" preproc.connect(img2float, 'out_file',\n",
" develVolume, 'in_file')\n",
" if first_run == True:\n",
" \"\"\" \n",
" extract example fMRI volume: middle one\n",
" \"\"\"\n",
" extract_ref = pe.MapNode(\n",
" interface = fsl.ExtractROI(t_size = 1,),\n",
" iterfield = ['in_file'],\n",
" name = 'extractref')\n",
" # connect to the deleteVolume node to get the data\n",
" preproc.connect(develVolume,'roi_file',\n",
" extract_ref,'in_file')\n",
" # connect to the deleteVolume node again to perform the extraction\n",
" preproc.connect(develVolume,('roi_file',pickvol,0,whichvol),\n",
" extract_ref,'t_min')\n",
" # connect to the output node to save the reference volume\n",
" preproc.connect(extract_ref,'roi_file',\n",
" outputnode, 'reference')\n",
" if first_run == True:\n",
" \"\"\"\n",
" Realign the functional runs to the reference (`whichvol` volume of first run)\n",
" \"\"\"\n",
" motion_correct = pe.MapNode(\n",
" interface = fsl.MCFLIRT(save_mats = True,\n",
" save_plots = True,\n",
" save_rms = True,\n",
" stats_imgs = True,\n",
" interpolation = 'spline'),\n",
" iterfield = ['in_file','ref_file'],\n",
" name = 'MCFlirt',\n",
" )\n",
" # connect to the develVolume node to get the input data\n",
" preproc.connect(develVolume, 'roi_file',\n",
" motion_correct, 'in_file',)\n",
" ######################################################################################\n",
" ################# the part where we replace the actual reference image if exists ####\n",
" ######################################################################################\n",
" # connect to the develVolume node to get the reference\n",
" preproc.connect(extract_ref, 'roi_file', \n",
" motion_correct, 'ref_file')\n",
" ######################################################################################\n",
" # connect to the output node to save the motion correction parameters\n",
" preproc.connect(motion_correct, 'par_file',\n",
" outputnode, 'motion_parameters')\n",
" # connect to the output node to save the other files\n",
" preproc.connect(motion_correct, 'out_file',\n",
" outputnode, 'realigned_files')\n",
" else:\n",
" \"\"\"\n",
" Realign the functional runs to the reference (`whichvol` volume of first run)\n",
" \"\"\"\n",
" motion_correct = pe.MapNode(\n",
" interface = fsl.MCFLIRT(ref_file = first_run,\n",
" save_mats = True,\n",
" save_plots = True,\n",
" save_rms = True,\n",
" stats_imgs = True,\n",
" interpolation = 'spline'),\n",
" iterfield = ['in_file','ref_file'],\n",
" name = 'MCFlirt',\n",
" )\n",
" # connect to the develVolume node to get the input data\n",
" preproc.connect(develVolume, 'roi_file',\n",
" motion_correct, 'in_file',)\n",
" # connect to the output node to save the motion correction parameters\n",
" preproc.connect(motion_correct, 'par_file',\n",
" outputnode, 'motion_parameters')\n",
" # connect to the output node to save the other files\n",
" preproc.connect(motion_correct, 'out_file',\n",
" outputnode, 'realigned_files')\n",
" \"\"\"\n",
" plot the estimated motion parameters\n",
" \"\"\"\n",
" plot_motion = pe.MapNode(\n",
" interface = fsl.PlotMotionParams(in_source = 'fsl'),\n",
" iterfield = ['in_file'],\n",
" name = 'plot_motion',\n",
" )\n",
" plot_motion.iterables = ('plot_type',['rotations',\n",
" 'translations',\n",
" 'displacement'])\n",
" preproc.connect(motion_correct, 'par_file',\n",
" plot_motion, 'in_file')\n",
" preproc.connect(plot_motion, 'out_file',\n",
" outputnode, 'motion_plots')\n",
" \"\"\"\n",
" extract the mean volume of the first functional run\n",
" \"\"\"\n",
" meanfunc = pe.Node(\n",
" interface = fsl.ImageMaths(op_string = '-Tmean',\n",
" suffix = '_mean',),\n",
" name = 'meanfunc')\n",
" preproc.connect(motion_correct, ('out_file',pickrun,whichrun),\n",
" meanfunc, 'in_file')\n",
" \"\"\"\n",
" strip the skull from the mean functional to generate a mask\n",
" \"\"\"\n",
" meanfuncmask = pe.Node(\n",
" interface = fsl.BET(mask = True,\n",
" no_output = True,\n",
" frac = 0.3,\n",
" surfaces = True,),\n",
" name = 'bet2_mean_func')\n",
" preproc.connect(meanfunc, 'out_file',\n",
" meanfuncmask, 'in_file')\n",
" \"\"\"\n",
" Mask the motion corrected functional data with the mask to create the masked (bet) motion corrected functional data\n",
" \"\"\"\n",
" maskfunc = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix = '_bet',\n",
" op_string = '-mas'),\n",
" iterfield = ['in_file'],\n",
" name = 'maskfunc')\n",
" preproc.connect(motion_correct, 'out_file',\n",
" maskfunc, 'in_file')\n",
" preproc.connect(meanfuncmask, 'mask_file',\n",
" maskfunc, 'in_file2')\n",
" \"\"\"\n",
" determine the 2nd and 98th percentiles of each functional run\n",
" \"\"\"\n",
" getthreshold = pe.MapNode(\n",
" interface = fsl.ImageStats(op_string = '-p 2 -p 98'),\n",
" iterfield = ['in_file'],\n",
" name = 'getthreshold')\n",
" preproc.connect(maskfunc, 'out_file',\n",
" getthreshold, 'in_file')\n",
" \"\"\"\n",
" threshold the functional data at 10% of the 98th percentile\n",
" \"\"\"\n",
" threshold = pe.MapNode(\n",
" interface = fsl.ImageMaths(out_data_type = 'char',\n",
" suffix = '_thresh',\n",
" op_string = '-Tmin -bin'),\n",
" iterfield = ['in_file','op_string'],\n",
" name = 'tresholding')\n",
" preproc.connect(maskfunc, 'out_file',\n",
" threshold,'in_file')\n",
" \"\"\"\n",
" define a function to get 10% of the intensity\n",
" \"\"\"\n",
" preproc.connect(getthreshold,('out_stat',getthreshop),\n",
" threshold, 'op_string')\n",
" \"\"\"\n",
" Determine the median value of the functional runs using the mask\n",
" \"\"\"\n",
" medianval = pe.MapNode(\n",
" interface = fsl.ImageStats(op_string = '-k %s -p 50'),\n",
" iterfield = ['in_file','mask_file'],\n",
" name = 'cal_intensity_scale_factor')\n",
" preproc.connect(motion_correct, 'out_file',\n",
" medianval, 'in_file')\n",
" preproc.connect(threshold, 'out_file',\n",
" medianval, 'mask_file')\n",
" \"\"\"\n",
" dilate the mask\n",
" \"\"\"\n",
" dilatemask = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix = '_dil',\n",
" op_string = '-dilF'),\n",
" iterfield = ['in_file'],\n",
" name = 'dilatemask')\n",
" preproc.connect(threshold, 'out_file',\n",
" dilatemask, 'in_file')\n",
" preproc.connect(dilatemask, 'out_file',\n",
" outputnode, 'mask')\n",
" \"\"\"\n",
" mask the motion corrected functional runs with the dilated mask\n",
" \"\"\"\n",
" dilateMask_MCed = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix = '_mask',\n",
" op_string = '-mas'),\n",
" iterfield = ['in_file','in_file2'],\n",
" name = 'dilateMask_MCed')\n",
" preproc.connect(motion_correct, 'out_file',\n",
" dilateMask_MCed, 'in_file',)\n",
" preproc.connect(dilatemask, 'out_file',\n",
" dilateMask_MCed, 'in_file2')\n",
" \"\"\"\n",
" We now take this functional data that is motion corrected, high pass filtered, and\n",
" create a \"mean_func\" image that is the mean across time (Tmean)\n",
" \"\"\"\n",
" meanfunc2 = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix = '_mean',\n",
" op_string = '-Tmean',),\n",
" iterfield = ['in_file'],\n",
" name = 'meanfunc2')\n",
" preproc.connect(dilateMask_MCed, 'out_file',\n",
" meanfunc2, 'in_file')\n",
" \"\"\"\n",
" smooth each run using SUSAN with the brightness threshold set to \n",
" 75% of the median value for each run and a mask constituing the \n",
" mean functional\n",
" \"\"\"\n",
" merge = pe.Node(\n",
" interface = util.Merge(2, axis = 'hstack'), \n",
" name = 'merge')\n",
" preproc.connect(meanfunc2, 'out_file', \n",
" merge, 'in1')\n",
" preproc.connect(medianval,('out_stat',get_brightness_threshold_double), \n",
" merge, 'in2')\n",
" smooth = pe.MapNode(\n",
" interface = fsl.SUSAN(dimension = 3,\n",
" use_median = True),\n",
" iterfield = ['in_file',\n",
" 'brightness_threshold',\n",
" 'fwhm',\n",
" 'usans'],\n",
" name = 'susan_smooth')\n",
" preproc.connect(dilateMask_MCed, 'out_file', \n",
" smooth, 'in_file')\n",
" preproc.connect(medianval, ('out_stat',get_brightness_threshold),\n",
" smooth, 'brightness_threshold')\n",
" preproc.connect(inputnode, 'fwhm', \n",
" smooth, 'fwhm')\n",
" preproc.connect(merge, ('out',getusans),\n",
" smooth, 'usans')\n",
" \"\"\"\n",
" mask the smoothed data with the dilated mask\n",
" \"\"\"\n",
" maskfunc3 = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix = '_mask',\n",
" op_string = '-mas'),\n",
" iterfield = ['in_file','in_file2'],\n",
" name = 'dilateMask_smoothed')\n",
" # connect the output of the susam smooth component to the maskfunc3 node\n",
" preproc.connect(smooth, 'smoothed_file',\n",
" maskfunc3, 'in_file')\n",
" # connect the output of the dilated mask to the maskfunc3 node\n",
" preproc.connect(dilatemask, 'out_file',\n",
" maskfunc3, 'in_file2')\n",
" \"\"\"\n",
" scale the median value of the run is set to 10000\n",
" \"\"\"\n",
" meanscale = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix = '_intnorm'),\n",
" iterfield = ['in_file','op_string'],\n",
" name = 'meanscale')\n",
" preproc.connect(maskfunc3, 'out_file',\n",
" meanscale, 'in_file')\n",
" preproc.connect(meanscale, 'out_file',\n",
" outputnode,'smoothed_files')\n",
" \"\"\"\n",
" define a function to get the scaling factor for intensity normalization\n",
" \"\"\"\n",
" preproc.connect(medianval,('out_stat',getmeanscale),\n",
" meanscale,'op_string')\n",
" \"\"\"\n",
" generate a mean functional image from the first run\n",
" should this be the 'mean.nii.gz' we will use in the future?\n",
" \"\"\"\n",
" meanfunc3 = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix = '_mean',\n",
" op_string = '-Tmean',),\n",
" iterfield = ['in_file'],\n",
" name = 'gen_mean_func_img')\n",
" preproc.connect(meanscale, 'out_file',\n",
" meanfunc3, 'in_file')\n",
" preproc.connect(meanfunc3, 'out_file',\n",
" outputnode,'mean')\n",
" \n",
" \n",
" # initialize some of the input files\n",
" preproc.inputs.inputspec.func = os.path.abspath(func_data_file)\n",
" preproc.inputs.inputspec.fwhm = 3\n",
" preproc.base_dir = os.path.abspath('/'.join(\n",
" func_data_file.split('/')[:-1]))\n",
" \n",
" output_dir = os.path.abspath(os.path.join(\n",
" preproc.base_dir,\n",
" 'outputs',\n",
" 'func'))\n",
" MC_dir = os.path.join(output_dir,'MC')\n",
" for directories in [output_dir,MC_dir]:\n",
" if not os.path.exists(directories):\n",
" os.makedirs(directories)\n",
" \n",
" # initialize all the output files\n",
" if first_run == True:\n",
" preproc.inputs.extractref.roi_file = os.path.abspath(os.path.join(\n",
" output_dir,'example_func.nii.gz'))\n",
" \n",
" preproc.inputs.dilatemask.out_file = os.path.abspath(os.path.join(\n",
" output_dir,'mask.nii.gz'))\n",
" preproc.inputs.meanscale.out_file = os.path.abspath(os.path.join(\n",
" output_dir,'prefiltered_func.nii.gz'))\n",
" preproc.inputs.gen_mean_func_img.out_file = os.path.abspath(os.path.join(\n",
" output_dir,'mean_func.nii.gz'))\n",
" \n",
" return preproc,MC_dir,output_dir"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "P3HYGeauF850",
"colab_type": "code",
"outputId": "90d34252-3576-46bb-8bb8-3dfbd414f5e7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 90
}
},
"source": [
"preproc,MC_dir,output_dir = create_fsl_FEAT_workflow_func(\n",
" workflow_name = 'preprocess',\n",
" first_run = True,# or False\n",
" func_data_file = '.',\n",
" fwhm = 3,\n",
" )\n",
"preproc.write_graph('preproc')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"190715-19:27:21,451 nipype.workflow INFO:\n",
"\t Generated workflow graph: /content/preprocess/preproc.png (graph2use=hierarchical, simple_form=True).\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'/content/preprocess/preproc.png'"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y9xAYKHtEDB7",
"colab_type": "text"
},
"source": [
"## 7.2. FSL fsf file"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GtqxK1ZsqPV-",
"colab_type": "text"
},
"source": [
"```\n",
"\n",
" # FEAT version number\n",
" set fmri(version) 6.00\n",
"\n",
" # Are we in MELODIC?\n",
" set fmri(inmelodic) 0\n",
"\n",
" # Analysis level\n",
" # 1 : First-level analysis\n",
" # 2 : Higher-level analysis\n",
" set fmri(level) 1\n",
"\n",
" # Which stages to run\n",
" # 0 : No first-level analysis (registration and/or group stats only)\n",
" # 7 : Full first-level analysis\n",
" # 1 : Pre-processing\n",
" # 2 : Statistics\n",
" set fmri(analysis) 1\n",
"\n",
" # Use relative filenames\n",
" set fmri(relative_yn) 0\n",
"\n",
" # Balloon help\n",
" set fmri(help_yn) 1\n",
"\n",
" # Run Featwatcher\n",
" set fmri(featwatcher_yn) 1\n",
"\n",
" # Cleanup first-level standard-space images\n",
" set fmri(sscleanup_yn) 0\n",
"\n",
" # Output directory\n",
" set fmri(outputdir) \"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01\"\n",
"\n",
" # TR(s)\n",
" set fmri(tr) 0.850000 \n",
"\n",
" # Total volumes\n",
" set fmri(npts) 518\n",
"\n",
" # Delete volumes\n",
" set fmri(ndelete) 10\n",
"\n",
" # Perfusion tag/control order\n",
" set fmri(tagfirst) 1\n",
"\n",
" # Number of first-level analyses\n",
" set fmri(multiple) 1\n",
"\n",
" # Higher-level input type\n",
" # 1 : Inputs are lower-level FEAT directories\n",
" # 2 : Inputs are cope images from FEAT directories\n",
" set fmri(inputtype) 2\n",
"\n",
" # Carry out pre-stats processing?\n",
" set fmri(filtering_yn) 1\n",
"\n",
" # Brain/background threshold, %\n",
" set fmri(brain_thresh) 10\n",
"\n",
" # Critical z for design efficiency calculation\n",
" set fmri(critical_z) 5.3\n",
"\n",
" # Noise level\n",
" set fmri(noise) 0.66\n",
"\n",
" # Noise AR(1)\n",
" set fmri(noisear) 0.34\n",
"\n",
" # Motion correction\n",
" # 0 : None\n",
" # 1 : MCFLIRT\n",
" set fmri(mc) 1\n",
"\n",
" # Spin-history (currently obsolete)\n",
" set fmri(sh_yn) 0\n",
"\n",
" # B0 fieldmap unwarping?\n",
" set fmri(regunwarp_yn) 0\n",
"\n",
" # EPI dwell time (ms)\n",
" set fmri(dwell) 0.7\n",
"\n",
" # EPI TE (ms)\n",
" set fmri(te) 35\n",
"\n",
" # % Signal loss threshold\n",
" set fmri(signallossthresh) 10\n",
"\n",
" # Unwarp direction\n",
" set fmri(unwarp_dir) y-\n",
"\n",
" # Slice timing correction\n",
" # 0 : None\n",
" # 1 : Regular up (0, 1, 2, 3, ...)\n",
" # 2 : Regular down\n",
" # 3 : Use slice order file\n",
" # 4 : Use slice timings file\n",
" # 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )\n",
" set fmri(st) 0\n",
"\n",
" # Slice timings file\n",
" set fmri(st_file) \"\"\n",
"\n",
" # BET brain extraction\n",
" set fmri(bet_yn) 1\n",
"\n",
" # Spatial smoothing FWHM (mm)\n",
" set fmri(smooth) 3.0\n",
"\n",
" # Intensity normalization\n",
" set fmri(norm_yn) 0\n",
"\n",
" # Perfusion subtraction\n",
" set fmri(perfsub_yn) 0\n",
"\n",
" # Highpass temporal filtering\n",
" set fmri(temphp_yn) 0\n",
"\n",
" # Lowpass temporal filtering\n",
" set fmri(templp_yn) 0\n",
"\n",
" # MELODIC ICA data exploration\n",
" set fmri(melodic_yn) 0\n",
"\n",
" # Carry out main stats?\n",
" set fmri(stats_yn) 0\n",
"\n",
" # Carry out prewhitening?\n",
" set fmri(prewhiten_yn) 1\n",
"\n",
" # Add motion parameters to model\n",
" # 0 : No\n",
" # 1 : Yes\n",
" set fmri(motionevs) 0\n",
" set fmri(motionevsbeta) \"\"\n",
" set fmri(scriptevsbeta) \"\"\n",
"\n",
" # Robust outlier detection in FLAME?\n",
" set fmri(robust_yn) 0\n",
"\n",
" # Higher-level modelling\n",
" # 3 : Fixed effects\n",
" # 0 : Mixed Effects: Simple OLS\n",
" # 2 : Mixed Effects: FLAME 1\n",
" # 1 : Mixed Effects: FLAME 1+2\n",
" set fmri(mixed_yn) 2\n",
"\n",
" # Number of EVs\n",
" set fmri(evs_orig) 1\n",
" set fmri(evs_real) 2\n",
" set fmri(evs_vox) 0\n",
"\n",
" # Number of contrasts\n",
" set fmri(ncon_orig) 1\n",
" set fmri(ncon_real) 1\n",
"\n",
" # Number of F-tests\n",
" set fmri(nftests_orig) 0\n",
" set fmri(nftests_real) 0\n",
"\n",
" # Add constant column to design matrix? (obsolete)\n",
" set fmri(constcol) 0\n",
"\n",
" # Carry out post-stats steps?\n",
" set fmri(poststats_yn) 0\n",
"\n",
" # Pre-threshold masking?\n",
" set fmri(threshmask) \"\"\n",
"\n",
" # Thresholding\n",
" # 0 : None\n",
" # 1 : Uncorrected\n",
" # 2 : Voxel\n",
" # 3 : Cluster\n",
" set fmri(thresh) 3\n",
"\n",
" # P threshold\n",
" set fmri(prob_thresh) 0.05\n",
"\n",
" # Z threshold\n",
" set fmri(z_thresh) 2.3\n",
"\n",
" # Z min/max for colour rendering\n",
" # 0 : Use actual Z min/max\n",
" # 1 : Use preset Z min/max\n",
" set fmri(zdisplay) 0\n",
"\n",
" # Z min in colour rendering\n",
" set fmri(zmin) 2\n",
"\n",
" # Z max in colour rendering\n",
" set fmri(zmax) 8\n",
"\n",
" # Colour rendering type\n",
" # 0 : Solid blobs\n",
" # 1 : Transparent blobs\n",
" set fmri(rendertype) 1\n",
"\n",
" # Background image for higher-level stats overlays\n",
" # 1 : Mean highres\n",
" # 2 : First highres\n",
" # 3 : Mean functional\n",
" # 4 : First functional\n",
" # 5 : Standard space template\n",
" set fmri(bgimage) 1\n",
"\n",
" # Create time series plots\n",
" set fmri(tsplot_yn) 1\n",
"\n",
" # Registration to initial structural\n",
" set fmri(reginitial_highres_yn) 0\n",
"\n",
" # Search space for registration to initial structural\n",
" # 0 : No search\n",
" # 90 : Normal search\n",
" # 180 : Full search\n",
" set fmri(reginitial_highres_search) 90\n",
"\n",
" # Degrees of Freedom for registration to initial structural\n",
" set fmri(reginitial_highres_dof) 3\n",
"\n",
" # Registration to main structural\n",
" set fmri(reghighres_yn) 1\n",
"\n",
" # Search space for registration to main structural\n",
" # 0 : No search\n",
" # 90 : Normal search\n",
" # 180 : Full search\n",
" set fmri(reghighres_search) 180\n",
"\n",
" # Degrees of Freedom for registration to main structural\n",
" set fmri(reghighres_dof) 7\n",
"\n",
" # Registration to standard image?\n",
" set fmri(regstandard_yn) 1\n",
"\n",
" # Use alternate reference images?\n",
" set fmri(alternateReference_yn) 0\n",
"\n",
" # Standard image\n",
" set fmri(regstandard) \"/opt/fsl/fsl-5.0.9/fsl/data/standard/MNI152_T1_2mm_brain\"\n",
"\n",
" # Search space for registration to standard space\n",
" # 0 : No search\n",
" # 90 : Normal search\n",
" # 180 : Full search\n",
" set fmri(regstandard_search) 180\n",
"\n",
" # Degrees of Freedom for registration to standard space\n",
" set fmri(regstandard_dof) 12\n",
"\n",
" # Do nonlinear registration from structural to standard space?\n",
" set fmri(regstandard_nonlinear_yn) 1\n",
"\n",
" # Control nonlinear warp field resolution\n",
" set fmri(regstandard_nonlinear_warpres) 10 \n",
"\n",
" # High pass filter cutoff\n",
" set fmri(paradigm_hp) 99999\n",
"\n",
" # Total voxels\n",
" set fmri(totalVoxels) 264751872\n",
"\n",
"\n",
" # Number of lower-level copes feeding into higher-level analysis\n",
" set fmri(ncopeinputs) 0\n",
"\n",
" # 4D AVW data or FEAT directory (1)\n",
" set feat_files(1) \"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/sub-01_unfeat_run-01_bold\"\n",
"\n",
" # Add confound EVs text file\n",
" set fmri(confoundevs) 0\n",
"\n",
" # Subject's structural image for analysis 1\n",
" set highres_files(1) \"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/sub-01-T1W_mprage_sag_p2_1iso_MGH_day_6_nipy_brain\"\n",
"\n",
" # EV 1 title\n",
" set fmri(evtitle1) \"\"\n",
"\n",
" # Basic waveform shape (EV 1)\n",
" # 0 : Square\n",
" # 1 : Sinusoid\n",
" # 2 : Custom (1 entry per volume)\n",
" # 3 : Custom (3 column format)\n",
" # 4 : Interaction\n",
" # 10 : Empty (all zeros)\n",
" set fmri(shape1) 0\n",
"\n",
" # Convolution (EV 1)\n",
" # 0 : None\n",
" # 1 : Gaussian\n",
" # 2 : Gamma\n",
" # 3 : Double-Gamma HRF\n",
" # 4 : Gamma basis functions\n",
" # 5 : Sine basis functions\n",
" # 6 : FIR basis functions\n",
" set fmri(convolve1) 2\n",
"\n",
" # Convolve phase (EV 1)\n",
" set fmri(convolve_phase1) 0\n",
"\n",
" # Apply temporal filtering (EV 1)\n",
" set fmri(tempfilt_yn1) 1\n",
"\n",
" # Add temporal derivative (EV 1)\n",
" set fmri(deriv_yn1) 1\n",
"\n",
" # Skip (EV 1)\n",
" set fmri(skip1) 0\n",
"\n",
" # Off (EV 1)\n",
" set fmri(off1) 30\n",
"\n",
" # On (EV 1)\n",
" set fmri(on1) 30\n",
"\n",
" # Phase (EV 1)\n",
" set fmri(phase1) 0\n",
"\n",
" # Stop (EV 1)\n",
" set fmri(stop1) -1\n",
"\n",
" # Gamma sigma (EV 1)\n",
" set fmri(gammasigma1) 3\n",
"\n",
" # Gamma delay (EV 1)\n",
" set fmri(gammadelay1) 6\n",
"\n",
" # Orthogonalise EV 1 wrt EV 0\n",
" set fmri(ortho1.0) 0\n",
"\n",
" # Orthogonalise EV 1 wrt EV 1\n",
" set fmri(ortho1.1) 0\n",
"\n",
" # Contrast & F-tests mode\n",
" # real : control real EVs\n",
" # orig : control original EVs\n",
" set fmri(con_mode_old) orig\n",
" set fmri(con_mode) orig\n",
"\n",
" # Display images for contrast_real 1\n",
" set fmri(conpic_real.1) 1\n",
"\n",
" # Title for contrast_real 1\n",
" set fmri(conname_real.1) \"\"\n",
"\n",
" # Real contrast_real vector 1 element 1\n",
" set fmri(con_real1.1) 1\n",
"\n",
" # Real contrast_real vector 1 element 2\n",
" set fmri(con_real1.2) 0\n",
"\n",
" # Display images for contrast_orig 1\n",
" set fmri(conpic_orig.1) 1\n",
"\n",
" # Title for contrast_orig 1\n",
" set fmri(conname_orig.1) \"\"\n",
"\n",
" # Real contrast_orig vector 1 element 1\n",
" set fmri(con_orig1.1) 1\n",
"\n",
" # Contrast masking - use >0 instead of thresholding?\n",
" set fmri(conmask_zerothresh_yn) 0\n",
"\n",
" # Do contrast masking at all?\n",
" set fmri(conmask1_1) 0\n",
"\n",
" ##########################################################\n",
" # Now options that don't appear in the GUI\n",
"\n",
" # Alternative (to BETting) mask image\n",
" set fmri(alternative_mask) \"\"\n",
"\n",
" # Initial structural space registration initialisation transform\n",
" set fmri(init_initial_highres) \"\"\n",
"\n",
" # Structural space registration initialisation transform\n",
" set fmri(init_highres) \"\"\n",
"\n",
" # Standard space registration initialisation transform\n",
" set fmri(init_standard) \"\"\n",
"\n",
" # For full FEAT analysis: overwrite existing .feat output dir?\n",
" set fmri(overwrite_yn) 0\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QxWpJHX_EFU2",
"colab_type": "text"
},
"source": [
"## 7.3. log report of the FSL GUI pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vQ1W-m3sqWt4",
"colab_type": "text"
},
"source": [
"\n",
"\n",
"\n",
"\n",
"\n",
"FSL\n",
"Progress Report / Log
\n",
"Started at Tue Apr 30 13:04:17 CEST 2019\n",
"Feat main script
\n",
"\n",
"/bin/cp /bcbl/home/public/Consciousness/uncon_feat/scripts/MRI/standard/session02.run01/run01.fsf design.fsf\n",
"\n",
"mkdir .files;cp /opt/fsl/fsl-5.0.10/fsl/doc/fsl.css .files;cp -r /opt/fsl/fsl-5.0.10/fsl/doc/images .files/images\n",
"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fsl_sub -T 10 -l logs -N feat0_init /opt/fsl/fsl-5.0.10/fsl/bin/feat /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/design.fsf -D /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat -I 1 -init\n",
"1323378\n",
"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fsl_sub -T 324 -l logs -N feat2_pre -j 1323378 /opt/fsl/fsl-5.0.10/fsl/bin/feat /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/design.fsf -D /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat -I 1 -prestats\n",
"1323379\n",
"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fsl_sub -T 75 -l logs -N feat3_film -j 1323379 /opt/fsl/fsl-5.0.10/fsl/bin/feat /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/design.fsf -D /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat -I 1 -stats\n",
"1323380\n",
"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fsl_sub -T 119 -l logs -N feat4_post -j 1323380 /opt/fsl/fsl-5.0.10/fsl/bin/feat /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/design.fsf -D /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat -poststats 0 \n",
"1323381\n",
"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fsl_sub -T 1 -l logs -N feat5_stop -j 1323379,1323380,1323381 /opt/fsl/fsl-5.0.10/fsl/bin/feat /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/design.fsf -D /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat -stop\n",
"1323382\n",
"
Initialisation
\n",
"\n",
"### convert image to float\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/sub-01_unfeat_run-01_bold prefiltered_func_data -odt float\n",
"Total original volumes = 518\n",
"Deleting 10 volume(s) - BE WARNED for future analysis!\n",
"\n",
"### remove the first 10 volumes\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslroi prefiltered_func_data prefiltered_func_data 10 508\n",
"\n",
"### extract example volume from the middle\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslroi prefiltered_func_data example_func 254 1\n",
"
Preprocessing:Stage 1
\n",
"\n",
"### setup some hyperparameters and create folder\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/mainfeatreg -F 6.00 -d /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat -l /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/logs/feat2_pre -R /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/report_unwarp.html -r /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/report_reg.html -i /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/example_func.nii.gz -n 10 -h /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/sub-01-T1W_mprage_sag_p2_1iso_MGH_day_6_nipy_brain -w 7 -x 180 -s /opt/fsl/fsl-5.0.9/fsl/data/standard/MNI152_T1_2mm_brain -y 12 -z 180 \n",
"Option -F ( FEAT version parameter ) selected with argument \"6.00\"\n",
"Option -d ( output directory ) selected with argument \"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat\"\n",
"Option -l ( logfile )input with argument \"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/logs/feat2_pre\"\n",
"Option -R ( html unwarping report ) selected with argument \"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/report_unwarp.html\"\n",
"Option -r ( html registration report ) selected with argument \"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/report_reg.html\"\n",
"Option -i ( main input ) input with argument \"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/FEAT.session02.run01.feat/example_func.nii.gz\"\n",
"Option -n ( use nonlinear reg ) input with argument \"10\"\n",
"Option -h ( high-res structural image ) selected with argument \"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/sub-01-T1W_mprage_sag_p2_1iso_MGH_day_6_nipy_brain\"\n",
"Option -w ( highres dof ) selected with argument \"7\"\n",
"Option -x ( highres search ) selected with argument \"180\"\n",
"Option -s ( standard image ) selected with argument \"/opt/fsl/fsl-5.0.9/fsl/data/standard/MNI152_T1_2mm_brain\"\n",
"Option -y ( standard dof ) selected with argument \"12\"\n",
"Option -z ( standard search ) selected with argument \"180\"\n",
"\n",
"\n",
"
Preprocessing:Stage 2
\n",
"\n",
"### MCFLIRT, motion correction\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/mcflirt -in prefiltered_func_data -out prefiltered_func_data_mcf -mats -plots -reffile example_func -rmsrel -rmsabs -spline_final\n",
"### create MCFLIRT folder\n",
"/bin/mkdir -p mc ; /bin/mv -f prefiltered_func_data_mcf.mat prefiltered_func_data_mcf.par prefiltered_func_data_mcf_abs.rms prefiltered_func_data_mcf_abs_mean.rms prefiltered_func_data_mcf_rel.rms prefiltered_func_data_mcf_rel_mean.rms mc\n",
"\n",
"### plot motion correction results\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fsl_tsplot -i prefiltered_func_data_mcf.par -t 'MCFLIRT estimated rotations (radians)' -u 1 --start=1 --finish=3 -a x,y,z -w 640 -h 144 -o rot.png \n",
"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fsl_tsplot -i prefiltered_func_data_mcf.par -t 'MCFLIRT estimated translations (mm)' -u 1 --start=4 --finish=6 -a x,y,z -w 640 -h 144 -o trans.png \n",
"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fsl_tsplot -i prefiltered_func_data_mcf_abs.rms,prefiltered_func_data_mcf_rel.rms -t 'MCFLIRT estimated mean displacement (mm)' -u 1 -w 640 -h 144 -a absolute,relative -o disp.png \n",
"\n",
"### calculate the mean functional image across time\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_mcf -Tmean mean_func\n",
"\n",
"### bet2 skull stripping on the mean functional image and rename the mask\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/bet2 mean_func mask -f 0.3 -n -m; /opt/fsl/fsl-5.0.10/fsl/bin/immv mask_mask mask\n",
"\n",
"### mask the motion corrected images\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_mcf -mas mask prefiltered_func_data_bet\n",
"\n",
"### calculate the 2 and 98 percentile threshold on the bet2 images\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslstats prefiltered_func_data_bet -p 2 -p 98\n",
"0.000000 13825.000000 \n",
"\n",
"### apply the 10% of the 98 percentile threshold to create a binary mask - threshold mask\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_bet -thr 1382.5 -Tmin -bin mask -odt char\n",
"\n",
"### calculate the scaling intensity for later use\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslstats prefiltered_func_data_mcf -k mask -p 50\n",
"8256.169922 \n",
"\n",
"### dilatemask\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths mask -dilF mask\n",
"\n",
"### apply the dilate mask to the motion corrected images\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_mcf -mas mask prefiltered_func_data_thresh\n",
"\n",
"### compute the mean functional image across time according to the dilatemasked images\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_thresh -Tmean mean_func\n",
"\n",
"### susan smoothing - 6192.1274415 is 75% of the median intensity differences. \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/susan prefiltered_func_data_thresh 6192.1274415 1.27388535032 3 1 1 mean_func 6192.1274415 prefiltered_func_data_smooth\n",
"\n",
"### apply the dilate mask to the smoothed data\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_smooth -mas mask prefiltered_func_data_smooth\n",
"\n",
"### mean scale the masked smoothed data using the scaling intensity value: 10000 / [that_value]\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_smooth -mul 1.21121538128 prefiltered_func_data_intnorm\n",
"\n",
"### rename the data\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_intnorm filtered_func_data\n",
"\n",
"### calculate the mean functional image of the smoothed and masked images, and it is the \"mean.nii.gz\" in the folder\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths filtered_func_data -Tmean mean_func\n",
"\n",
"/bin/rm -rf prefiltered_func_data*\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ibML9EsL4oal",
"colab_type": "text"
},
"source": [
"## 7.4. call the function to create the workflow\n",
"## 7.4. setup the functional diretory, the output directory, and the mcflirt output directory"
]
},
{
"cell_type": "code",
"metadata": {
"id": "hihb2u7hnxBk",
"colab_type": "code",
"colab": {}
},
"source": [
"import os\n",
"functional_data = 'func1.nii'\n",
"output_dir = 'outputs'\n",
"if not os.path.exists(output_dir):\n",
" os.mkdir(output_dir)\n",
"MC_dir = 'MC'\n",
"if not os.path.exists(os.path.join(output_dir,MC_dir)):\n",
" os.mkdir(os.path.join(output_dir,MC_dir))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "UNV6u-HN4z5f",
"colab_type": "text"
},
"source": [
"## 7.5. call the the workflow function and setup the input values like functional data directory and the spatial smoothing size, as well as the output directory.\n",
"## since this is a demonstration of preprocessing the first run, we will extract a volume as the reference volume (example_func.nii.gz)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QRoEsJBEobjr",
"colab_type": "code",
"outputId": "a14419c7-6a65-4dbf-b8b4-55f703b088a8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 90
}
},
"source": [
"workflow_name = 'nipype_mimic_FEAT'\n",
"first_run = True\n",
"preproc,_,_ = create_fsl_FEAT_workflow_func(workflow_name = workflow_name,)\n",
"# if have run the first run, you will get a example_func.nii.gz as the functional reference, and you can pass this to the function to create a \n",
"# slightly different work flow\n",
"# initialize some of the input files\n",
"preproc.inputs.inputspec.func = functional_data\n",
"preproc.inputs.inputspec.fwhm = 3\n",
"preproc.base_dir = output_dir\n",
"\n",
"# initialize all the output files\n",
"if first_run == True:\n",
" preproc.inputs.extractref.roi_file = os.path.abspath(os.path.join(output_dir,\n",
" 'example_func.nii.gz'))\n",
"else:\n",
" copyfile(first_run,output_dir,'example_func.nii.gz')\n",
"\n",
"preproc.inputs.dilatemask.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'mask.nii.gz'))\n",
"preproc.inputs.meanscale.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'prefiltered_func.nii.gz'))\n",
"preproc.inputs.gen_mean_func_img.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'mean_func.nii.gz'))\n",
"preproc.write_graph()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"190715-19:32:00,267 nipype.workflow INFO:\n",
"\t Generated workflow graph: outputs/nipype_mimic_FEAT/graph.png (graph2use=hierarchical, simple_form=True).\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'outputs/nipype_mimic_FEAT/graph.png'"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_h7TAzuepepM",
"colab_type": "code",
"outputId": "8178fc78-f8b1-439e-b3bf-d15268470d78",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 917
}
},
"source": [
"Image(f'{output_dir}/{workflow_name}/graph.png',height = 900)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/png": 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/lZUVg8FgMBjDhw8/dOhQ+zfr6+tLp9MbGho+oODvvvuu9Tfql19+IaeWWSzWzJkz23wb\n2xxbXFzccsbhw4dv2rTprT+LadOm6erq9kz/u5UrVyoqKmZmZvbAuUAC4ZbmAADQv4jF4rlz54aH\nh4eHh7fM5lJu+fLlly5dqqio6I3jZ2ZmmpmZnThxYsGCBd0xfmdMmzbN39/fyMiozfaKigo9Pb3t\n27evXbu2u2v44YcfduzYceHCBU9Pz+4+F0gmLAUBAID+hUajnT17dty4cc7OzhcvXqS6nP+vu6//\n677xTU1Nt23btm3btvr6+m46xVu1LAVJTk4mZ8fb7+Pn5zds2DBfX99uraSpqWnRokU7d+4MDAxE\nqu7PEKwBAKDfkZWVvXbt2tdffz137tw5c+Z8QFc7aGPTpk2enp5eXl5trmLsVhs2bHj16tXLly8X\nLVr01nut//rrr0lJSdevX6fT6d1XxqNHj6ytra9cuXL16lUfH5/uOxFIPgRrAADoj6SlpXfv3h0W\nFhYbGzt48OCAgAAKi9m8efOJEydqamqMjIwuX77c68Yn7dixw9fXd9euXd00fntMJvOTTz5xcXHx\n8/MzNzdv82pwcHBTU9O9e/c4HE43FdDQ0LBx48YxY8bo6+unpKS4u7t304mgt8AaawAA6Ndqa2s3\nbdp05MgRBweHLVu2tL7RIMC7cLncgICAvXv38vn8ffv2Ubi4HCQKZqwBAKBfY7PZhw4diomJYbFY\nkydPtrOzCwkJwawTvEttbe2uXbuMjIy+//77zz777Pnz50jV0AIz1gAAAP8nPj5+x44dISEhZmZm\nS5cuXbBggZqaGtVFgaRISko6duzY2bNnRSLRqlWrvvnmG/znAW0gWAMAAPyP5ORkf3//v/76i8fj\nTZ8+3cfHZ+LEiVJS+BtvP1VdXX3u3Lnjx48/ffp00KBBPj4+y5YtU1ZWproukEQI1gAAAG/B5XIv\nX7587Nix6OhoHR0dDw8PDw+PcePGdWt/CZAc5eXlYWFhQUFB4eHh0tLSs2fPXrx4saOjI41Go7o0\nkFwI1gAAAB15+fLl+fPng4ODk5KSlJWVp02b5uHhMXnyZAUFBapLg66XnZ0dFBQUHBz84MEDOp3u\n7Ow8c+bM2bNnk3fKBOgYgjUAAECn5OTkhIeHh4aGhoeHi8XioUOHuri4uLi4ODo6MhgMqquDD1dW\nVvb48eOHDx9GREQ8ffqUyWROmDDB09PTw8MDeRreC4I1AADA+ykvLw8PD797925kZOSbN2/k5eUd\nHBycnJwcHR1HjBjBYrGoLhD+XVFRUWxs7L179+7evZuSkiItLT1y5EgnJycnJ6exY8diwQ98GARr\nAACAD5ednR0ZGRkZGXn37t3CwkIZGRkLCwtbW1s7OztbW1tzc3NpaWmqawSCIIi6urqEhIS4uLjY\n2Ni7d+9WVVVJSUkNGTJkwoQJTk5O48aNU1RUpLpG6PUQrAEAALpGTk5ObGxsXFxcXFzc06dPuVyu\ngoLCsGHDLC0thwwZYmlpaWVlhW4SPSY7Ozs1NTU1NTU5OTk5OTkjI0MoFGpra1tbWz948KCmpmbQ\noEGenp6zZs0aPnw41cVCH4FgDQAA0PUEAkFaWlpsbOzTp0/JeFdTU0MQhL6+PpmwzczMBg4caGpq\nqqmpSXWxvZ5AIMjOzs7MzHz16lXqP2prawmCMDQ0tLCwsLKysrGxsbW11dfXJwhCJBIlJiaGhoae\nO3cuMzPT0NBw+vTpnp6eDg4OaPoBHwPBGgAAoCfk5OSkpqampKSkpKSkpaW9ePGCx+MRBMFms03/\nl4GBgY6ODpb5vlV9fX1ubm5OTg4ZozMzMzMzM7Ozs5ubmwmCUFVVtbCwID+6WFlZWVpaKikpdTxg\nWlrapUuX/vzzz5cvX+rr68+YMcPT09Pe3h6dy+EDIFgDAABQQCQS5efnZ7bT2NhIEISUlJSWlpaB\ngYGenp6enp6hoaGenp6urq6mpqampmafvz6yrKysrKyspKQkLy8vNzc3Pz8/Pz8/JycnPz+/urqa\n3EddXZ38HEJO/JM4HM4Hn5RM2BcvXnz+/Lm6uvrkyZM9PT2nTJkiIyPTRW8L+j4EawAAAAlSWFjY\nJkrm5+fn5uYWFxeLRCJyHyaTqaampq2tra6urqamRqZtDoej9A8Oh6OsrKykpCRRl042NjbW1NRU\nV1fX/KOqqorM0OXl5SUlJSUlJeXl5WVlZQKBgDyEwWCQHy0MDAz09fX19PT09fUNDQ319fX/dSr6\ngyUnJ1+5cuXy5cvp6emampqzZs2aN2+evb09VonAv0KwBgAA6AWam5uLi4uLiorK/lFcXEzG0JKS\nktLS0qqqKi6X2+YoBQUFJSUlZWVlBoOhqKgoIyOjpKQkJSXF4XCkpKSUlJRkZGRaumGw2ezWQbz1\nSwRBiEQicpl4i8bGRnI1C/mSQCCoq6vj8/lcLpfH4zU2NpI71NbWknmaz+e3KY/D4aipqamrq6ur\nq2toaGhqapIfFbS0tFq2dOH38H09f/788uXLf/31V1pampGR0bx58+bPn29mZkZhSSDhEKwBAAD6\nCIFA0DIT3GZuuLGxsb6+vrm5ubKyMikpSUdHR1paura2lszBBEGIxeKWVRYkMhy33kKG8pandDq9\n5faTHA5HWlqazWaTGxkMhry8PJPJlJOTY7PZ5PQ5qfXj7v+WdA1ylcjp06ffvHljbm7u6enp7e09\nYMAAqusCiYNgDQAA0I9s2LDhyJEjaWlpenp6VNfSy4hEopiYmEuXLp0/f76ysnL06NGenp4LFixQ\nVVWlujSQFAjWAAAA/cWzZ89sbGwOHDiwfPlyqmvpxZqamm7dunXp0qUrV66IRCIXF5eFCxdOnz5d\nVlaW6tKAYgjWAAAA/YJQKLSzs5OVlY2OjkYvuS5RXV0dEhJy6dKlGzduKCoqurm5LVy40NnZGZc5\n9lsI1gAAAP3Czz///P333z99+tTc3JzqWvqagoKCy5cvnzlzJiEhQU9Pb/78+StWrDA0NKS6Luhp\nCNYAAAB9X3Z2tqWl5YYNG77//nuqa+nLkpOTT5w4cebMmerq6ilTpixZsmTatGnohN1/IFgDAAD0\nfZMmTcrNzU1KSpKTk6O6lr6Pz+cHBwefPn36xo0b6urqc+bMWbp0qaWlJdV1QbdDsAYAAOjjTp06\n5ePjExUV5eDgQHUt/Ut+fv65c+eOHDmSnZ1tbW395Zdfzp8/v8/fOLM/Q7AGAADoy8rLy83Nzb28\nvA4cOEB1Lf2USCSKjIwMCAgICgpiMplz585dtmzZiBEjqK4Luh6CNQAAQF82b968hw8fpqamtr6N\nIlCiuLj4r7/+Onr0aGpqqrm5+cKFC5cuXaqiokJ1XdBlEKwBAAD6rBs3bkydOjU4ONjd3Z3qWuD/\niMXiqKioY8eOXb58mUajzZkz56uvvrKxsaG6LugCCNYAAAB9U0NDg6WlpZ2d3Z9//kl1LfAW1dXV\n586dCwgISE5OtrW1Xbly5Zw5cxgMBtV1wYdDsAYAAOib1qxZc+bMmfT0dE1NTaprgY4kJCT89ttv\nFy5cYLFYCxcuXLNmjZGREdVFwYdAsAYAAOiD4uLi7O3tAwMDFy1aRHUt0ClFRUWBgYF//PFHSUnJ\ntGnTVq5c+emnn+Imjr0LgjUAAEBfIxAIbGxslJWVIyMjkcx6F6FQeP369QMHDty5c8fU1HTlypVL\nly5lMplU1wWdIkV1AQAAANDFdu/e/eLFi8DAQKTqXkdaWtrNze327dvPnj0bM2bMxo0bDQ0Nt2zZ\nUlhYSHVp8O8wYw0AANCnvHz5cujQoT/++OP69euprgU+Vmlp6YkTJw4ePFhWVjZ9+vT//Oc/o0aN\norooeCcEawAAgL5DLBa7uLiUl5c/efKETqdTXQ50jaampvPnz+/fvz85OXns2LFr1651c3PDnyMk\nEJaCAAAA9B2BgYH3798/fvw4UnVfIicnt2jRomfPnkVERCgqKnp4eFhaWp48eZLP51NdGvwPzFgD\nAAD0EcXFxebm5osXL/7555+prgW60atXr/z9/QMCApSUlJYvX75mzRplZWWqiwKCQLAGAADoM2bN\nmvX06dOUlBQFBQWqa4FuV1xcfOTIkd9++00oFC5atGj9+vW6urpUF9XfIVgDAAD0BWFhYW5ubuHh\n4RMnTqS6Fug5VVVVhw8fPnjwYE1Njbe394YNGwYMGEB1Uf0XgjUAAECvV1tba2Fh4ezsfPLkSapr\nAQrweLyTJ0/+9NNP+fn5CxYs2Lx5s6mpKdVF9Ue4eBEAAKDX27BhA4/Hw9LqfovBYCxfvvzFixdH\njx6NiYkZPHiwm5tbYmIi1XX1OwjWAAAAvdvjx48DAgJ+++03dXV1qmsBKtHp9IULF6anpwcFBRUU\nFIwcOdLNze3JkydU19WPYCkIAABAL9bU1DRixAgDA4MbN25QXQtIEJFIdPXq1e3btycnJ7u5uW3b\ntm3o0KFUF9X3YcYaAACgF9uxY0d2dvahQ4eoLgQki5SU1OzZsxMTE4OCgvLz84cPHz537tznz59T\nXVcfh2ANAADQW2VkZPz00087d+40NjamuhaQRDQazd3dPSEh4datWy9fvrS0tJwzZ86rV6+orqvP\nwlIQAACAXkkkEo0dO5bP5z969EhaWprqckDSiUSiK1eufPfdd2/evPHy8vLz88PnsS6HGWsAAIBe\n6dChQ7GxsX/88QdSNXSGlJSUp6dnWlrakSNHoqKizM3NV69eXVZWRnVdfQpmrAEAAHqf3NxcS0tL\nX1/f7du3U10L9D58Pv/o0aPbt2/ncrnffvvtf/7zHyaTSXVRfQGCNQAAQO8zffr0jIyMZ8+eMRgM\nqmuB3qqhoeHgwYO7du2SlZXdsmXLypUrZWRkqC6qd8NSEAAAgF7mwoULoaGhv//+O1I1fAwmk7lh\nw4bXr1/7+Phs2LDBysrq0qVLVBfVuyFYAwAASLScnJzWTysrK9esWbN06VInJyeqSoK+RFVVdffu\n3cnJyRYWFnPnzh03blxcXBzVRfVWCNYAAACSKysry8zMbO/evQKBgNyybt06Go22e/duaguDPmbQ\noEGXL19+/PgxjUYbNWrUnDlz2nyig85AsAYAAJBcDx48aGpq2rBhg7W1dWJi4r17906ePOnv78/h\ncKguDfogW1vbe/fuXbp06cmTJxYWFjt27ODxeFQX1Zvg4kUAAADJtXTp0lOnTjU3N8vIyIhEokGD\nBpmamoaGhlJdF/RxfD7/999//+GHH9hs9o4dOz7//HMajUZ1Ub0AZqwBAAAkV2RkZHNzM0EQAoFA\nJBJlZmYmJibevn2b6rqgj5OVlV29evXz58+nTp26aNEiJyenZ8+eUV1UL4BgDQAAIKHKy8vfvHnT\neotAICguLp44ceLs2bPLy8upKgz6CR0dnT/++OPhw4cNDQ3W1tZfffVVZWUl1UVJNARrAAAACRUd\nHd1+o1AoJAji2rVr5ubmSUlJPV4U9DujRo16/PjxsWPHrl27ZmZmdvr0aSwkfhcEawAAAAkVHR1N\np9PbbydXu65du3bo0KE9XhT0RzQa7YsvvsjIyPDy8vLx8Rk3blxaWhrVRUkiBGsAAAAJFRkZyefz\n22yk0+lsNvvGjRsbNmzA9WTQk5SUlH777bf4+Hg+nz98+PDVq1fX19dTXZRkQVcQAAAASdTQ0KCk\npNTSvpokIyNjZmYWEhIyYMAAiuoCIEQi0dGjR9evX6+iouLv7z916lSqK5IUmLEGAACQRI8ePWqT\nqmk02ty5c2NjY5GqgVpSUlJffvnl8+fPbW1tp02b5unpWVxcTHVREgHBGgAAQBJFR0fLysqSj6Wl\npel0+pEjR86ePSsvL09tYQAkbW3tCxcuhIeHJyQkWFhYnD59muqKqIdgDQAAIInu3btHdrCm0+lq\namoPHjz48ssvqS4KoK2JEyempaUtXbrUx8dnypQpubm5VFdEJQRrAAAAiSMQCGJjY8VisbS0tIOD\nQ2pqqp2dHdVFAbydvLz87t27Hzx4kJ2dbWVl9dtvv4lEIqqLogYuXgQAAPhYVVVVPB6vsbGxurpa\nLBZzudzW3Tzq6+vJuWeSrKwsi8Vq85RGoykrK8vLyzMYDA6H8+TJExsbG38Xhd4AACAASURBVIIg\n1q9fv3PnTmlp6Z58OwAfhsfj7d69e+fOnaNGjTp69OigQYOorqinIVgDAAC8RUNDQ15eXklJSVlZ\nWWVlZXl5eeU/KioqKisruVxuTU1NY2Mjj8fr8rPLyMgIhUJ1dXV1dXVVVVWVf6j+Q01NTUtLS09P\nj8lkdvnZAT5GfHz84sWLs7Kydu/evXLlyn7VFBLBGgAA+q/m5uacnJysrKzXr18XFBTk5+cXFhYW\nFBQUFBTU1NS07KakpKSmpkbm2paAy2KxlJSUWuaY5eTkmEymkpKSlJQU+bjlcAaD0fqKwzZZvKGh\noampSSgU1tbWko+rqqrOnDkzYsQIFovF5XLJHE8GelLr2pSVlXV0dHR1dXV0dPT09PT09IyNjY2N\njQ0NDd96cxmAHsDn87dv375r1y4XF5fjx49ra2tTXVEPQbAGAIB+QSwWv3nzJjU1NT09PSsriwzT\neXl55B3CORyOvr6+vr6+trY2GVLJtKqtra2urt7zKzGEQmEHJxUKhWVlZUVFRQUFBYWFhYWFhfn5\n+cXFxXl5eXl5eVVVVQRBSEtLGxgYkCHbxMTEzMzM0tLSyMioX00fArXi4uI+//zz0tLSw4cPf/bZ\nZ1SX0xMQrAEAoG+qqKhISEhITU1NS0tLSUlJT0/ncrkEQRgaGpqYmJBxs+Urh8Ohut4uU1VV9fr1\na/KTA/n19evXZK8GBQUFMzMzKysrCwsLKyurESNGqKqqUl0v9GWNjY0bN248cODA559/fujQIUVF\nRaor6l4I1gAA0Ec0NzcnJydHR0cnJCQkJCQ8f/5cLBZzOBxzc3MLCwvy67Bhw9TU1KiulAK1tbWv\nXr1KS0tLT08nv2ZlZREEoa2tbW1tbW1t7ejoaG9vjxXb0B1u3rzp4+MjJyd3+vTpMWPGUF1ON0Kw\nBgCAXqy+vj4qKioyMvLevXvPnj0TCATq6uo2NjY2Nja2trYjR47U0NCgukYJVVpaGv+PuLi48vJy\nGRmZYcOGjR8/3snJacyYMQoKClTXCH1HcXGxj4/P7du3N2/e/MMPP/TVRjcI1gAA0MsIBILo6OiI\niIi7d+/GxcUJhUILCwsnJyd7e3tbW1sjIyOqC+yVsrKy4uLiYmJiIiMj09LSZGRkbG1tnZycnJ2d\nHR0dZWRkqC4Qej2xWPz777+vW7fO1tb2/PnzOjo6VFfU9RCsAQCgd2hoaLhz505YWFhwcHBJSYmx\nsbGDg4Ojo+PUqVP19PSorq5PKS0tvX//fnR09MOHDxMSEjgcjouLi6urq4eHB5vNpro66N3S09Pn\nzJlTWlp65syZSZMmUV1OF0OwBgAAicblcq9cufLnn3/evXtXIBA4Ojq6u7tPnz7dxMSE6tL6hdev\nXwcHBwcHBz98+JBOpzs5OXl5ec2aNQurseGD1dXVLVu27MKFC+vXr9+xY0dfWhaCYA0AAJJILBY/\nfPjw5MmTFy9ebGpqmjJlyowZM6ZNm9Y/Lz2UBOXl5WFhYdeuXbtx44a8vPycOXO8vb0dHByorgt6\nq9OnT69YsYJcFtJnGl0jWAMAgGThcrnHjx/39/d/+fLlsGHDvL2958+fjzwtOUpLS8+fP3/ixInk\n5ORBgwZ9/fXXPj4+mMCGD5CYmDhnzpyampqzZ89OnDiR6nK6AII1AABIivLycn9//0OHDjU0NHh7\ney9dunTYsGFUFwXv9PTp08DAwFOnTrFYrFWrVq1atQpdseF91dbWLlmy5OrVq7t27fr222+pLudj\nIVgDAAD16urq/vvf/x46dEheXp6MaJii7i3KysoOHjx46NChpqamr7/+esuWLejTB+9r375969ev\nnzt3bmBgoLy8PNXlfDgEawAAoJJYLD5z5szGjRubmpq+//77pUuXslgsqouC91ZfXx8QELB9+3Z5\nefk9e/bMnz8f906H9xIVFTV79mxdXd2goCBDQ0Oqy/lAUlQXAAAA/VdmZqaDg4OPj4+Hh8fLly/X\nrFmDVN1LKSgo/Oc//3n58qWbm5u3t/eYMWPIOzsCdNLYsWMfPXokEAhGjhx57949qsv5QAjWAABA\njaCgoJEjRzY1NT158uTw4cNYntsHqKmpHTlyJD4+vrGx0draOiQkhOqKoDcxMTF59OjR2LFjP/30\n04MHD1JdzodAsAYAgJ4mEonWr18/c+ZMLy+vhw8fdtUVitevX1dSUgoNDe2S0Vrbtm2bubk5m82W\nk5MzNTVdv359fX196x2amppWr16tpaXFZDJdXFw0NDRoNNqRI0e6vJL2IiIiNm3a1L6Mmzdvtt95\n7969rWsLCQnZs2ePUCjs2pKGDx/+8OFDT09PDw+PjRs3YtEpdJ6CgsLly5e3bNmyevXqlStXCgQC\nqit6P7hDKQAA9CiRSLR06dJz586dOnXq888/78KRuy/ARUZGrlq1ysvLi06n37hxY8GCBSkpKTdu\n3GjZ4Zdffrl582ZGRsbFixdVVFSGDRs2cODAbiqmta1btyYmJp47d659GW2iP2ndunUeHh4ttbm7\nu79588bZ2TkoKEhZWbkLC2MwGAEBAQ4ODl9++WVlZeUff/yBJdfQSTQazc/Pb8iQIQsXLszKyrp0\n6VJvuhxWDAAA0IO+++47WVnZ69evU13Ie5g2bZpAIGh5OmfOHIIgcnNzW7bY2NjMmzev5emrV68I\ngvj9998/5qQNDQ2jR4/uYIddu3YNGjSosbHxXWW8VfvafH19R48e3dzc/DHVvktoaCidTt+6dWt3\nDA5927Nnz3R1da2srPLy8qiupbOwFAQAAHpOZGTkjh07Dh06NGXKFKpr6YhYLL506VJAQAD5NCws\nrPVdl8lWgA0NDS1b8vPz6XR619Zw7Nix0tLSd72amZn5/fff//jjjwwG4yPL8PPzS0pK2r9//wcW\n2iFXV9eDBw9u27at916OBlQZMmRIdHR0c3PzqFGjkpOTqS6nUxCsAQCgh4hEopUrV06fPn3JkiVd\nPnh0dLSBgQGNRvP39ycIYv/+/SwWS0pKytraWlNTk06ns1isESNGjBkzRl9fn8FgKCsrr1+/vuVw\noVC4c+fOwYMHy8vLq6mpGRkZ7dy5k5yZbq+goEBeXt7IyIggiNu3b5uamhYVFZ06dYpGo731b9Zi\nsfjXX381MzOTk5PjcDgeHh4ZGRktrz548MDc3FxJSYnBYFhZWYWHhxMEsWbNmrVr175+/ZpGo5ma\nmrYf88CBA2Kx2N3dnXz61jLu379va2vLZDLZbLaVlVVtbe1b3w6Hwxk3btz+/fvF3bOWZtmyZa6u\nritXrhSJRN0xPvRhAwYMiImJMTExGT9+/P3796kupxOonTAHAID+IyQkREpKKiMjo5vGz8vLIwji\n4MGD5NOtW7cSBBEbG8vlcsvLyydPnkwQxN9//11WVsblcn19fQmCSEpKInfesWOHtLR0cHBwQ0ND\nQkKCpqbm+PHj33oWLperqKjo6+vbeqOmpuYXX3zR8rTNcosffvhBVlb2zJkz1dXVycnJI0aMUFNT\nKy4uJl+9dOmSn59fZWVlRUXFqFGjVFVVye2zZs0yMTF515s1NjY2Nzdvs7F1GfX19Ww2e8+ePY2N\njcXFxTNnziwrK2tfG4m8/DExMfFdp/tI6enpNBotLCysm8aHvo3H43l5ecnJyZ07d47qWv4FZqwB\nAKCHhISEjB49evDgwT15UnNzcyaTqaqq+tlnnxEEYWBgoKamxmQyFyxYQBBEy8xxUFCQtbW1u7u7\nvLz8iBEjpk+fHhUVxefz2w+4c+dObW3t7du3d7KAxsbGX3/9debMmQsWLFBSUrKysjpy5Eh5eXnL\nOpPZs2dv3bqVw+GoqKi4u7tXVFSUlZV1PCaXy33z5o2JiUkH+2RnZ9fW1lpYWDAYDE1NzStXrnRw\nM0vycsaUlJROvqn3ZWZmZmdnh+578GHISL1ixYrPP//8119/pbqcjiBYAwBAD0lNTbW1taXq7LKy\nsgRBtHTvItciNzc3k095PJ641UIIoVBIp9Nbr6smXb169eLFi+Hh4YqKip08b1paWn19/ciRI1u2\n2NjYyMrKxsbGtt+ZrOpf+9+VlpaKxWImk9nBPsbGxhoaGgsWLPDz88vOzu54QHKokpKSjnf7GHZ2\ndt0X3KHPk5KS2rdv3969e9etW+fn50d1Oe+EYA0AAD2krq6u83m0h02dOjUhISE4OLixsfHJkydB\nQUGurq5tgvWFCxd279597969AQMGdH7k6upqgiDarL1WVlauq6sjH//999/jx49XV1eXk5Nrvey7\nAzwejyAIOTm5DvaRl5ePjIx0dHTcsWOHsbGxl5dXY2NjBzu3DNtNlJSUWt4ywIf55ptvTp8+vWPH\njq+//loskf3REawBAKCHaGpqFhQUUF3F2/n5+Tk5OXl7e7PZ7JkzZ86ZMycwMLD1DgcPHjx79mxk\nZKSOjs57jUz2h26TKaurq/X09AiCyM3NnTFjhpaWVmxsbE1NzZ49ezozJpmD/3Vi28LCIjQ0tLCw\ncMOGDX/99dfevXvftSe56IUctpvk5eVpaWl13/jQTyxYsODMmTN//PHHihUrJPByWNwgBgAAeoi9\nvf3Zs2dFIpGUlMRN66Slpb1+/bqsrExGpu2/jGKxeOPGjVVVVUFBQe1f/VeWlpYKCgpPnjxp2RIb\nG8vn862trQmCSElJaW5u/uqrr4yNjQmC6ORdVMi7J9bU1HSwT2FhYXV1tbm5ubq6+q5du27dupWe\nnv6uncmhNDU1O/mm3pdIJLpz5463t3c3jQ/9ipeXl4KCgqenZ11d3alTpz7g/8ruI3G/2gAAoK+a\nN29eTk6OZF7BtmrVKgMDg7ferTA9Pf2nn34KDAyk0+m0VjqYAG6NwWCsXbv26tWrZ8+era2tTUlJ\nWbFihba29rJlywiCMDAwIAgiIiKCx+O9evWq9cJrFRWVwsLC7Ozsurq6lrXgJCaTaWxsnJ+f38F5\nCwsLly9fnpGRwefzExMTc3JyRo0a9a6dyaGsrKw6844+QFBQUH5+/rx587ppfOhvXF1dr127du3a\ntfnz57f5v4Ni1DYlAQCAfmXu3LnGxsb19fVdPvLBgwfJlQZMJtPd3X3//v3kBXkDBgx48ODB7t27\nlZSUCILQ1NQ8d+7chQsXyNlZDofz559/isXiyMhIVVXVln8c6XS6mZnZlStXxGLxuy65+/nnn8Vi\ncXZ29vDhwwmCkJGRGTFixOXLl3/55RdycBaLNXPmTLFYLBKJfv7554EDB9LpdA6HM2PGjBcvXrRU\nvmHDBhUVFWVlZU9PT7IJt4mJSW5u7tOnTw0NDeXl5R0dHVt687Xw9fWl0+kNDQ3k0/ZlZGdn29vb\nczgcaWlpHR2dLVu2CASC9rWRpk2bpqurKxKJuvznIhaL6+rqBgwY8Nlnn3XH4NCfRUZGKigouLu7\nNzU1UV3L/6GJJXLpNwAA9ElFRUVDhgwZP378xYsXO7nsoWccPnz41atX+/btI5/y+fyNGzcePny4\nqqqqW1cef7DMzEwzM7MTJ06QfQM/RkVFhZ6e3vbt29euXdsltbUmEolmz54dHR2dnJyMNdbQ5R49\nejR58mRnZ+eLFy9KwpoQLAUBAICeo62tffny5ZCQkGXLlknOhUfFxcW+vr6LFy9u2SIrK2tgYNDc\n3CxZf2VuxdTUdNu2bdu2bXvr8pX34ufnN2zYMPKOOV1LJBItXbr0+vXrV65cQaqG7jB69OibN2/e\nvn170aJFkvArBcEaAAB61Lhx465cuXL69Gl3d/fKykqqyyEIgpCXl6fT6ceOHSspKWlubi4sLDx6\n9OgPP/zg5eXFZrOpru6dNm3a5Onp6eXl1fFVjB379ddfk5KSrl+/TrbQ7kKVlZWurq7nz5+/du3a\nmDFjunZwgBajR4++du3a5cuXlyxZQvlCDCwFAQAACiQkJMyePVsoFF66dMnOzo7qcogHDx5s27Yt\nLi6Oy+UqKChYWFjMnz//yy+/lIQ/Lnfs1q1bkZGRu3fv/oBjg4OD09PT169f3/5WOB/p6dOnnp6e\nfD7/0qVLHVw0CdBVQkJCZs+evWLFit9++43CMhCsAQCAGuXl5fPmzYuKilq3bt2mTZtYLBbVFUEX\n4HK5O3fu/OWXX8aPH3/u3LnWl4QCdKsrV67MnTv3u+++o/DWjNKSfFtIAADow5hM5rx589hs9s8/\n/xwQEKCjo2NhYSFRVzTCexGLxefPn58+fXpMTMzOnTt/++03fFiCnmRubm5oaPif//xHXl7ewcGB\nkhqwxhoAACgjLS29Zs2aFy9eTJ48ef78+fb29sHBwZJwBRK8F5FIdPXqVTs7u4ULF06dOvXly5e+\nvr4SeBsg6PO++OKLffv2bdy48fTp05QUgP/oAQCAYhoaGoGBgXFxcZqamjNnzrS0tDx+/Dh5k22Q\ncE1NTUePHjU3N/f09NTV1X3y5ElAQIC6ujrVdUH/tXr16vXr1y9ZsuTOnTs9f3assQYAAAny/Pnz\nn3/+mVybu3DhQm9v708++YTqouAt0tPTT548eebMmcrKygULFnz77bf4SYGEEIvFn3/+eVhYWHR0\ntKWlZU+eGsEaAAAkTkFBwR9//HHq1Knc3NzRo0d7e3vPnTuXvHUiUKu6uvrChQsnT56MjY01NDRc\nuHDhsmXLdHV1qa4L4H/weDxnZ+eioqJHjx6RdxvtGQjWAAAgoUQiUUxMzJkzZ86fP8/j8ezs7Dw9\nPWfNmqWnp0d1af1OWVnZjRs3Ll26dPv2bRqN5ubm9vnnn0+dOrXL+/QBdJWKiorRo0crKSndv3+f\nyWT2zEkRrAEAQNLV1tYGBwcHBweHh4dzuVwbGxsPD49JkyYNGzYMV8h1H5FIlJiYGB4eHhwcHB8f\nz2KxJk+ePH36dHd3d0m+bw5Ai9evX48ePXrs2LEXL17smd8VCNYAANBr8Hi8c+fOnTp1Ki4urqmp\nicPhjBs3bsKECU5OTmjV1yXEYnFqaurdu3cjIyOjoqKqqqq0tLTc3NymT5/u7OzMYDCoLhDg/URF\nRU2cOHH16tV79uzpgdMhWAMAgKRramqKioq6fv3633///erVK2Vl5YkTJ44cOVJOTq4l/2loaNjb\n29vY2Nja2trY2GBBdudVV1fHx8fHxcXFx8fHxMSUlZXhEwv0JadPn/b29r548eLs2bO7+1wI1gAA\nIKFKS0tv3rwZFhZ269atmpoaY2NjV1dXNze3sWPHysrKtuwmFAqTkpLu3bsXExMTHx+fl5dHo9EG\nDx5sY2NjY2NjaWlpZWWlpqZG4RuRNGVlZSkpKampqU+ePImLi3v58qVYLDYwMLCxsbG3tx8/fjzW\n2EAfs3LlytOnT8fGxpqbm3friRCsAQBAgpDrekNDQ8PCwp4+fcpgMBwcHFxdXT08PAwNDTszQlFR\nETn/GhcXl5CQUFlZSRCEhoaGpaWlhYWFpaWlubn5wIEDe7JRALWKi4szMzPT09NTU1PT0tJSU1NL\nS0sJglBRURk5ciQ5x29ra6ulpUV1pQDdpbm5ecKECeXl5fHx8YqKit13IgRrAACgXmVl5Z07dyIi\nIkJCQoqLiwcMGDBx4kQXF5cpU6YoKCh8zMiFhYVkmkxLS0tJSXn+/HldXR1BECwWy+R/6enp6evr\nf+TpKFRfX5+Xl5efn//6f3G5XIIgFBUVzc3NrayszM3Nyc8YOjo6VJcM0HPy8vKsra2dnZ3//PPP\n7jsLgjUAAFAmLS0tLCwsIiLi/v37IpFo1KhRbm5uLi4u1tbW3XRGsVicl5dHJs6srKyWB1VVVeQO\nCgoKenp6Wlpaenp62traurq6ampqKioqqqqq5FcOh9NNtXWsqqqqoqKioqKisrKSfFBQUFBUVJSf\nn19cXJyXl0cGaIIgOByOiYmJsbEx+YGBfKCvr4+l0tDP3blzZ9KkSfv371+1alU3nQLBGgAAelRD\nQ0NMTExoaOi1a9fy8vLU1dXHjx/v6urq7u6urKxMVVWVlZUFBQV5eXnFxcX5+flFRUUFBQWFhYWF\nhYUVFRWt768uJSVFhmwFBQUlJSU5OTkFBQVFRUU5OTk2m81kMuXk5AiCUFRUlJGRaTmk9cWUNTU1\nIpGIfCwQCMgZ9KampoaGhtraWh6PV19fX19f39TUVFNTU19fTybplkMIgpCVlVVVVdXR0dHR0dHV\n1dXW1iY/DOjr6+vq6qqoqPTAdwygN9q2bdv27dvv3r3r4ODQHeMjWAMAQE/IysqKiIgIDQ29fft2\nc3Pz8OHDXVxcXF1d7e3tJf86ubq6upZ5YnLOuLKysr6+viUH19XV8Xi8urq6+vr65uZmgiBapsAJ\nguDz+S3TyQRBsFis1hdfklPgdDqdDOgMBkNRUVFBQYHBYLDZbAUFBRUVFXKynJw7V1FR6dZFogB9\nmEgkcnV1TU1NffbsWXf89QnBGgAAuotAIHj8+HFYWFhoaGh6ejqLxZowYYKbm5urqysW+AIAJSoq\nKqysrCZMmHDu3LkuHxzBGgAAulhLm7zw8PDa2tp3tckDAKDE7du3J02adOHChTlz5nTtyAjWAADQ\nBchm0h/TJg8AoMcsW7bsypUrKSkp2traXTgsgjUAAHy4ioqKyMjIljZ5RkZGn376aZe0yQMA6D5c\nLnf48OFGRkY3b97swoY5CNYAAPDeer5NHgBA14qJiRk7duyRI0eWLFnSVWMiWAMAQKe0tMm7evVq\nfn5+S5u86dOnt+4lBwDQW2zYsOHQoUNJSUmmpqZdMiCCNQAAdORdbfIcHBxwwxEA6NWamppsbGxU\nVVUjIyO75BcagjUAALTV0iYvJCTk+fPnCgoK48ePd3Nzc3Nz69oLfQAAqBUfHz9q1KhTp04tWLDg\n40dDsAYAgP9TUlISHh7evk3euHHj6HQ61dUBAHSL5cuXBwUFvXjx4uNXtSFYAwD0a+9qkzdjxgwD\nAwOqqwMA6HaVlZWffPLJggULfv31148cCsEaAKA/ItvkkXm6qqqKbJPn6ur66aefMhgMqqsDAOhR\ngYGBX3311ZMnT4YOHfox4yBYAwD0Iy1t8u7duycWi9EmDwCAIAiRSOTg4CAjIxMVFfUxVzEiWAMA\n9HENDQ137twJCwu7fv062SZv8uTJbm5uEydORJs8AABSQkKCnZ3dyZMnP+YqRgRrAIC+CW3yAADe\ny4oVK8irGNls9oeNgGANANB38Hi86Oho8gbjz58/V1FRcXZ2dnFxQZs8AIB/VVlZaWJi8s033/zw\nww8fNgKCNQBAr4c2eQAAXeK///3vL7/8kpWVpaKi8gGHI1gDAPRKaJMHANDl6uvrTUxMFi9evHPn\nzg84HMEaAKA3adMmz9jYmFw5jTZ5AABdYu/evX5+fq9fv9bU1HzfYxGsAQB6gdZt8giCsLOzQ5s8\nAIDuwOPxTE1NPT099+3b977HIlgDAEgoLpcbGRkZFhb2999/FxQUaGhoTJo0CW3yAAC6m7+//7ff\nfvvy5Ut9ff33OhDBGgBAsrS0ybt165ZAIECbPACAHsbn8z/55JNJkyb9/vvv73UggjUAAPVa2uQF\nBwdnZGS0tMlzd3fX0tKiujoAgH7n2LFjK1asePPmja6ubuePQrAGAKBMS5u8mzdv1tXVoU0eAICE\n4PP5hoaGixcv3r59e+ePQrAGAOhRbdrkycvL29vbu7q6zpw5830X8wEAQPf58ccf/f39c3Nz5eXl\nO3kIgjUAQE8oLy+/e/du+zZ5EydOlJOTo7o6AABoq7S01NDQ0N/ff/HixZ08BMEaAKAbkW3yQkND\nHz16JCUlRbbJc3NzMzc3p7o0AAD4F1988cXTp0+Tk5M7ee04gjUAQBd7V5u8SZMmsdlsqqsDAIDO\nSkxMHDFiRGRk5IQJEzqzP4I1AEDXyMrKIld6REVFCYXCYcOGkVcijhgxAm3yAAB6qTFjxqiqqgYF\nBXVmZwRrAIAP16ZNnqqqqpOTE9rkAQD0GZcvX/by8nr16pWRkdG/7oxgDQDw3nJycsLDwyMiIsg2\neebm5uQNxtEmDwCgjxEIBAMGDPD29u5M3z0EawCATkGbPACA/mnjxo3nzp3Lzs6WlpbueE8EawCA\njrS0yQsNDa2urkabPACA/ubly5eDBw+OiIhwdnbueE8EawCAt0CbPAAAaGFnZzd48ODTp093vBuC\nNQDA/0GbPAAAeCt/f//NmzeXlJR0fBdGBGsA6O/QJg8AADpWXFysp6d36dKlGTNmdLAbgjUA9EeN\njY0PHz6MiIgICgp68eIF2uQBAEDHxo0bp6ure/78+Q72kemxagAAKJednX3r1q2IiIgbN27U19eb\nm5t7eHi4uLiMHz9eRga/DwEA4J08PT03b97c2NjYwWoQzFgDQB/Xuk1eQkICk8kk2+TNmjVLT0+P\n6uoAAKB3KCws1NPTCw0NnTZt2rv2QbAGgL4JbfIAAKBrjRgxwt7e3t/f/107IFgDQN8hEokSExMj\nIiLINnmysrKOjo7kymkzMzOqqwMAgN7tu+++O3PmTE5Ozrt2QLAGgF6vpU1eWFhYYWGhpqbmxIkT\n0SYPAAC6VkxMjIODQ1pa2rvuaYCLdQCgt2rfJm/p0qVokwcAAN3Ezs6Ow+Hcvn37XcEaM9YAQCUe\nj7dx48ZvvvnG0NCwM/uTbfJCQ0ODg4NzcnLINnlk22kOh9Pd1QIAQD/n4eFBo9GuXbv21lcxYw0A\nlElLS/P09Hz+/PmgQYO++uqrDvZs3ybPy8sLbfIAAKCHTZgwwc/PTygUSktLt38VM9YAQAGxWHzo\n0KG1a9eKRCKRSDRx4sQbN2602UcoFD569CgsLCwiIoJsk+fk5OTm5jZ16lS0yQMAAEokJycPHTr0\n6dOnw4cPb/8qgjUA9LSysjJvb+8bN260/P6RlZWtqqpiMpnkq/fu3UObPAAAkEBisVhNTW3r1q2+\nvr7tX8WfUAG6i0gkqqmpIQiipqZGJBJxuVw+n08QBJ/P53K5bXYWHJ/ClgAAIABJREFUCoW1tbXt\nB2EwGO3v8CQrK8tiscjH5MJiRUVFGRkZeXl5BoPR5W+ka0VERMyfP7+qqqr1p3o+n3/s2LGGhobW\nbfI2btyINnkAACBRaDSara1tXFzc21/FjDXAu4jF4srKyoqKisrKytra2pqamtraWi6XW19fX1tb\nW1tbW19fz+VyyZe4XC6Px2toaGhqanprdO4xdDpdQUGBRqMpKytLSUkpKSmx2WwWi6WgoMBms9ls\ntoKCAovFYrPZSkpKLBZLSUlJ5R/dOiXM4/H8/Px++uknGo0mEona1Eyj0TgcDtkmb/LkyYqKit1X\nCQAAwAfbunXr+fPnX7161f4lBGvop7hcblFRUUlJSUlJSWFhIZmeya8k8nGbo8iESobR1o8VFRUV\nFBTICWN5eXkZGRkyF751OpmMvO1LUlJSkpKSarOxrq5OIBC0L56c/H7rpHhTU1NDQwM5BS4QCOrq\n6sjcX19f3/5xm4CroKDQErLV1NTIB6qqqhoaGtra2lpaWhoaGurq6h/wDU9PT587d25GRkb7t0PS\n0tIqKir6gJEBAAB6UlhYmLu7e1lZmaqqapuXEKyhz2pqasrLy8vNzc3Nzc3LyystLW2dpFvPKGto\naLSESDJHtjwgH6uqqpJJmsK3000aGxtrampaf5ZoeVBRUdHyoLS0lIzyBEHIysqqq6uTOVtTU1NH\nR0dTU9PQ0HDAgAEGBgbtv0tisTgwMNDX11coFL4rVZOeP3/+ySefdNdbBQAA6AqlpaWamprh4eET\nJ05s8xLWWEOvx+PxMjMz37x5k5OTk/uP7Ozs4uJi8nMjg8HQ19fX1NTU0tIaPny4hoaGrq6uhoaG\nlpYWOQVLp9OpfhOUkZeXl5eX19LS+tc9y8vLS0pKiouLi4qKSktLCwsLS0tLc3NzY2NjCwsLW2b3\nlZWVDQwMDA0NDQ0NDQwMlJWVT5w48ejRo38dn06n//333wjWAAAg4TQ0NDQ1NVNTU9sHa8xYQ28i\nEAhyc3Oz/pGWlpaenp6TkyMUCgmC4HA42traOjo6xv8gnw4YMKD9EgvoWjwer7CwMCsrq7CwsKio\nqOUH9NbVHVJSUtLS0uQPRSQSCQQC8hfR2LFj79+/39OlAwAAvCcXFxcDA4Pjx4+32Y4Za5BcIpHo\n9evXycnJKSkpKSkpycnJ2dnZ5FoCbW3tQYMGmZqaOjo6Dhw4cODAgaampu27Z0CPYTAY5IeZNtub\nm5vT09NTUlIyMjLIPyyQy3Kam5sJgmCxWMrKygoKCrKyslJSUg0NDY2Njfg5AgCAhLOysoqOjm6/\nHTPWIEG4XG5CQkJSUlJKSsqzZ8/S0tIaGhqkpaVNTEyGDBkyZMiQQYMGkTEaLSN6tebm5uzs7Fev\nXr148SI9Pf2tP+shQ4bY2Njo6OhQXSwAAEBbx44d8/X1raura/MncQRroJJQKMzIyEj4R3x8PJ/P\nV1ZWtrCwsLCwMDc3t7a2Hj58eEvPZujDCgsLExIS0tPT09LSEhISMjIyRCKRtra29T/GjBnz1m4q\nAAAAPSwqKmrcuHH5+fm6urqttyNYQ0/j8XgxMTGRkZEPHz5MSEioq6tjsVjW1ta2trajRo2ytbXV\n19enukagXl1d3ZMnTx4/fhwXFxcbG1tUVCQtLW1hYTF69Ojx48c7OTlpaGhQXSMAAPRT+fn5+vr6\nUVFRY8aMab0dwRp6glAoTEhIuHPnDpmnGxsbBw4cOGbMGDs7Ozs7OwsLCxkZLPeHjuTl5cXGxsbG\nxsbExMTFxQmFQisrK2dnZ2dn57Fjx2JpEAAA9CSxWMxkMo8cOfLFF1+03o5gDd2opqYmLCzs2rVr\nd+7cqa6u1tbWdnJyIsOQgYEB1dVBb1VXV3f//v3IyMg7d+6kpKRIS0uPGjXK3d195syZJiYmVFcH\nAAD9grm5+ezZs7dt29Z6I4I1dL2ysrLg4OCrV6/euXNHLBZPmDBh2rRpzs7OFhYWVJcGfU1paWlk\nZOStW7dCQkIqKiqGDh06Y8aMmTNnWllZUV0aAAD0ZZMnT9bR0WnTcQ/BGroMn8+/evVqYGDg/fv3\nZWVlJ02aNGPGDDc3N/K23gDdSiAQ3L9//+rVq0FBQYWFhYMGDfL29vbx8dHU1KS6NAAA6IMWLVpU\nXFx848aN1hsRrKELZGVlBQQEnDhxoqKiwtXVdf78+VOnTkUrD6CESCR6/PjxxYsXz5w5U19f7+Hh\nsXz58vHjx9NoNKpLAwCAvmPTpk03b95MTExsvRG3o4OP8vjx46lTpw4cOPDs2bMrVqzIzs4OCgry\n9PREqgaqSElJ2dvb79+/v6CgIDAwMC8vz8nJyczM7NixY+TdhQAAAD6etrZ2+7sLI1jDB0pPT58x\nY8bo0aPr6+uvXLmSnZ3t5+enp6dHdV0A/4fBYCxcuDAmJubZs2djx45dsWKFlZXV1atX8Wc6AAD4\neJqamuXl5W3+TUGwhvdWWlrq4+MzZMiQN2/e/P3331FRUR4eHuiX10327t2roaFBo9GOHDlCdS1v\nFxERsWnTJvJxU1PT6tWrtbS0mEzmzZs32+/c5u2EhITs2bNHKBR2a4VDhgwJCAh4/vz58OHDZ8+e\nPWrUqJiYmG49IwAA9HlKSkpCobC+vr71RgRreD/BwcGWlpaRkZGnTp16+vTp1KlTqa6oj1u3bp0k\np8CtW7ceOHBg8+bN5NNffvnl5s2bGRkZ+/fvb/O7htTm7bi7uzMYDGdn5+rq6u4u1cTE5Pz58wkJ\nCWw2e+zYsf+PvfuMi+La+wA+S11671KkKF0pAiJGBTVWjNhjS2KvXMUIduySqFgSC5ooGhMBO7FF\nEFRAUBCkg4CAdAEFlr7leTFP9nJREWGXgd3f98X97M7OnPMbNNf/Hs6c4+Pj09rayu9OAQBAUCko\nKBAEUVNT0/YgCmvoLA6Hs3v37qlTp7q7u6ekpMydO1dEBH9/hNqBAwcuX74cHBzM3Z/lxo0b9vb2\nioqKS5cunT59emca8fT0HDRo0IQJE3pmArSNjc0///xz8uTJX3/9ddy4ce/eveuBTgEAQPDIy8sT\nKKyhy3x8fHbu3Hn8+PGzZ89iozvIycnZtm3bzp076XQ692BRUZG4uPiXNuXr65uUlHTkyBGeBvwk\nGo22ZMmSmJiY3NzckSNH9sBgOQAACB5yxLrdPyIorKFTzp07d/DgwcDAwFWrVvVYpz/99JO0tLSc\nnFxFRYWXl5eOjk5WVhaLxdq+fbuenp6UlJS1tXVQUBBBEEeOHJGRkREREbGzs9PQ0BAXF5eRkbG1\ntR0+fLiuri6dTldUVNy4cSO3ZQ6Hc/jwYTMzM0lJSSUlpW+++SYzM5P8yMzMjEajkU01NDQQBLFx\n40YFBQU6nX7+/HmCID4aoGOfbbODPO2sXbtWQkJCU1OTfLtq1SoZGRkajVZZWdmFn8On7uXRo0cO\nDg7S0tLy8vJWVla1tbUfJjl27BiHw3F3dyffPnjwwNjYuLS0NDAwkEajycrKdrIdgiCUlJRGjBhx\n5MiRnnys0MrKKiIiorKyst1utAAAAJ1Bjis1Nzf/z1EOwOdUVlYqKip6eXn1fNdbtmwhCMLT0/P4\n8eMeHh4ZGRkbNmyQlJS8cuXKu3fvNm/eLCIi8vz5cw6Hs2PHDoIg4uLi6uvrKysrx40bRxDE7du3\n3759W19fv3btWoIgkpKSyGa3b98uISFx8eLF9+/fJycn29raqqqqlpWVcTgcJpNpYGCgp6fHZDK5\nMdatW+fv70++/lSADny2zQ7ycDicV69eEQRx8uRJ8u3cuXM1NDS47fz8888EQbx9+5Z8+0U/h4/e\nC4PBkJeX9/Pza2xsLCsr8/Dw4DbelqGhobm5ebuDGhoaCxcuJF9/qp12t0MiH39MTEzs+CfJc1FR\nUSIiIsHBwT3cLwAA9HXkWPW9e/faHkRhDZ/n7++voKDAYDB6vmuysG5sbCTfNjY2SktLz549m3zb\n0NAgKSm5cuVKzr8FZV1dHflRYGAgQRApKSnk22fPnhEEcfnyZfIqWVlZbiPcT3ft2kW+9ff3JwiC\nW2zV19fr6enV1NR0HKBjHbT52TxdKKw783P41L2kpqYSBPH33393cDsMBoNGo02ePLnd8baF9afa\n+WhhTW4Je+HChQ465ZOZM2cOHz685/sFAIA+jfwVdGhoaNuDmAoCnxcVFTV27NjesOdLVlZWQ0OD\npaUl+VZKSkpTU/OjsyYkJCQIguA+D0dO/CVXgUhLS2MwGPb29tyThwwZIiEhERcXR75dvHixgoIC\nd8rvH3/88c0335DPKHQ+QDsdtPnZPN3Rwc/hU/diaGiorq4+b948X1/f/Pz8jzZbUVHB4XCkpaU7\n6Loz7XCRTZWXl3/JzfGGh4fH06dPsUIIAAB8kbb/pHKhsIbPq66uVlVVpToFQRBEfX09QRBbt26l\n/augoID8yth55O9uyEnAXIqKinV1deRrWVnZpUuXxsTEkOO7J0+eJGdQdCdAB21+Ng+ffOpepKSk\nHj586OLisnfvXkNDw9mzZzc2Nra7tqmpiSAISUnJDtrvTDttT+Y228PU1NSYTOan5n8DAAB8FLmD\nR7slrVBYw+fp6+tnZGRQnYIgCEJNTY0gCO7UZNLTp0+/qBFFRUWCINqVre/fv2+7beTatWvFxcX9\n/f0fP36sq6trZGTU/QCfarMzefihg3uxsLAIDQ0tKSnx9vYOCgo6ePBgu2vJOvizG7t8th2ulpYW\nbrM9LD09XV5eXllZuee7BgCAvo7zv4/dY7c8+DwPD49vvvkmIyPDzMyM2iTk0hZJSUndacTS0lJW\nVjY+Pp57JC4urqWlxc7OjnukX79+M2fODAoKKikpIWctdz/Ap9rsTJ62xMTEeDJv4VP3UlJS8v79\ne3NzczU1tf379//zzz/p6entziF3T2y3eGcX2uEim9LQ0Ojq3XQRi8U6e/bs1KlTaTRaD3cNAACC\nByPW8HkTJkywtbVdvHgxOaxIITqd/v333//1118nTpyora1lsVhFRUWlpaVf2oiXl9e1a9f++OOP\n2tralJSUFStWaGlpLVu2rO1pXl5eTCbz3bt3rq6uvArwqTY7k4fL2Ni4urr6xo0bra2tb9++LSgo\n+KLb/+y9lJSULF++PDMzs6WlJTExsaCgwMnJqd210tLShoaGRUVFHbTfmXa4yKasrKy6di9dduDA\ngczMTG9v7x7uFwAABBO/HpUEwZKamiovLz9jxoyWlpYe69TPz4+cG6Crq3vx4kXyYHNzs7e3t56e\nnpiYmJqa2rRp09LS0o4cOUI+/WZgYPDkyZMDBw6Qy7ZraGhcunTp8uXL5FCokpLSX3/9xeFw2Gz2\nzz//bGJiIi4urqSkNHXq1KysrA8DjBo16uzZs+0OfjRA52/qo212kOfQoUNkeBkZGQ8PDw6HU1VV\nNWrUKDqd3r9//zVr1vz4448EQRgbGxcWFn7pz+Gj95Kfn+/s7KykpCQqKqqtrb1ly5a2qwRykTNb\nGhoayLf5+fk2NjYEQYiJidna2l65cuWj7Xx4O6SJEyfq6Oiw2ezO/yS7LzAwUERE5OjRoz3ZKQAA\nCAyCIIKCgtoeoXF6cEcG6NMiIyMnT57s6OgYHByMCamQk5NjZmZ27ty5efPmdbOpqqqqfv367dmz\nx8vLiyfZPovD4ezZs2fHjh0+Pj779u3rmU4BAEDA0Gi0oKCgmTNnco9gKgh01siRI588eZKdnW1l\nZXXv3j2q4wDFjI2Nd+3atWvXLgaD0c2mfH19Bw8ezF0mhd8KCgpGjx69e/fuX3/9FVU1AADwEApr\n+AKDBw9OSUmZNGnS+PHjx4wZk5iYSHWiXiEzM5P2abNnz6Y6IL9s2rRpxowZs2fP7vgpxo4dPnw4\nKSnpzp075IKgfFVXV+fr62tmZlZaWhoTE7NixQp+9wgAAEIFhTV8GQUFhdOnTz948ODdu3f29vYz\nZ87Mzc2lOhTFTE1NO5iAdfnyZaoD8tHevXvXrl27f//+rl1+8+bN5ubmyMhIJSUl3gZrp6WlJSAg\nwMjI6JdfftmxY8eLFy/a7sgDAADAE5hjDV3E4XCCg4O3bt1aWFjo4eGxbNmyESNGYM0y6G3y8/PP\nnDnz+++/MxiMdevWbdiwgdzwEgAAoJswxxp4hkajzZo1Kz09PSAg4PXr16NGjTI3Nz9y5Eh1dTXV\n0QAIFot18+bNCRMmGBkZnT9/fsmSJTk5Obt27UJVDQAA/IPCGrpFXFx84cKFsbGxiYmJI0aM2L59\nu46OzrRp0y5dutSdebcAXcNisSIjI9esWaOnp+fh4cFisa5cuVJQULBr166e330GAACEDaaCAC/V\n1dUFBQWFhIRERETQaDQ3NzcPD48pU6aQu2cD8ElLS0t4ePi1a9du3rz59u1bS0tLDw+PBQsWcPeN\nBwAA4LkPp4KgsAa+ePfuXWho6LVr1/7555+WlhZHR0dXV1c3N7ehQ4dKSkpSnQ4ERHp6enh4eHh4\neGRkZG1trb29vYeHh4eHx4ABA6iOBgAAgg+FNfQ0BoNx9+7dBw8ehIeH5+XlSUtLu7i4kEW2jY2N\nqKgo1QGhjykoKHj48GF4ePjDhw9LS0sVFRVHjBgxevRod3d3PT09qtMBAIAQQWENVCotLY2KigoL\nC7t79+6bN29kZGQGDx5sZ2dnZ2c3fPjw/v37Ux0QeqP6+vrExMSEhISEhITo6Oi8vDwpKSlbW1sX\nF5fRo0ePGDGiBxbABgAA+BAKa+gVOBxOWlpaTExMXFxcXFxcRkYGm83W1dV1dHR0cnKysbGxtrZW\nVVWlOiZQo7GxMT09/eXLl8+ePYuNjU1LS2MymVpaWo6Ojo6OjkOHDnV0dKTT6VTHBAAAYYfCGnqj\n2tra+Pj42NjYZ8+excXFlZWVEQShpaVlaWk5aNAgS0tLKysrCwsLTM4WSBwO5/Xr18nJySkpKSkp\nKcnJyTk5OSwWS1pa2s7OzsHBwcnJydHRUVdXl+qkAAAA/wOFNfQB5eXl3DIrJSUlLS2tqalJTEzM\n2NjY1NTU5F8DBgzQ1tamOix8mdra2lf/ysrKevXqVXp6OoPBEBER6d+/P/d7lLW1tZGREabgAwBA\nb4bCGvoeFov16tUrssjOzs4ma7K6ujqCIGRlZbl1tpGRkZ6enq6urp6eHuYJUI7NZpeWlhYUFBQW\nFubn55N/atnZ2eXl5QRBiIuL9+/fn/x2ZG5ubm1tbWFhISMjQ3VqAACAL4DCGgREWVkZt8gm5ebm\nNjQ0kJ9qampyi2x9fX09Pb1+/fppaWmpq6tLSEhQm1zAVFRUVFRUFBUVFRYWvnnzpqCgoKCg4M2b\nN0VFRa2trQRBiImJ9evXj/tLhgEDBpiYmBgYGIiJiVGdHQAAoFtQWIMgq6ysLPwXOVZKIidtk9TU\n1NTV1bW0tMg6W0dHR11dXVtbW1VVVVlZWVlZWUpKisJb6FVYLFZVVVV1dXVVVVVFRUVxcXFFRUVJ\nSUl5eXlZWVlpaWlFRQVZPRMEIScnp6enZ2BgoKenR36r0dfX19fX19bWxowOAAAQSB8W1hg0AsGh\nqqqqqqpqa2vb7nhTU1NJSQlZCLatDjMyMtpVhwRBSElJKSsrq6ioKLehoqKiqKiooKAgIyMjKysr\nJyfX9nXP3mXXNTU11dfX19TU1NbW1tfXc1/X1dVVV1eTBXTbF+02pVdTU9PQ0NDU1NTS0ho4cCD3\nO4mGhoa2traioiJV9wUAANBLoLAGwUen0w0NDQ0NDT91QkVFRduysq3CwsLExMSqqqqampqamho2\nm/3h5QoKCrKysjIyMnJyckwmk9y/XUFBQUREREZGRkJCQlJSUlpaWlRUVF5enrxEREREQUGhXTvk\naW2PMJlMcjZ5Ww0NDc3NzeTrpqamxsZG7mnv3r0jCKKuro7JZDY2NjY1NXGLafLgh+Hl5eXl5OS4\nXyF0dXUHDRqkoqLS9quFioqKuro6losGAADoGAprAEJdXV1dXb0zZzY2NjIYjLq6upqaGsa/3r9/\nT77Iy8u7ePHihAkTtLS0yBq3rKyMW+O2trYyGAyynebm5oaGBiaTyWKxuMsIMhiMtmPnJCUlpXZH\nxMXFZWVlydfklHEmk2lgYED8W81rampyq3kJCQkZGRklJSVZWVmy+ldUVJSTkyNff1jcAwAAQJeh\nsAb4AlJSUlJSUuSYdDtNTU1Dhgxxdna+fv16Z2YVs9nsIUOGaGtrh4aGdifSjz/+GBgYeO3atT40\nKQUAAEAgiVAdAEBAbNiwoaio6I8//ujks3oXL158+fLlgQMHutmvj49Pc3Pz0aNHu9kOAAAAdBMK\nawAeuH///okTJ06ePKmnp9eZ85uamrZv375o0SILC4tudq2iouLl5XXw4MGqqqpuNgUAAADdgcIa\noLsqKyu/++67efPmzZ49u5OX+Pv7V1dX+/r68iTA+vXr6XS6n58fT1oDAACArkFhDdBdK1asEBMT\n6/xkjMrKSj8/vw0bNmhpafEkgKysrI+Pz/Hjx4uKinjSIAAAAHQBCmuAbgkICLh27drFixc/XL7j\nU3bu3CkpKbl+/Xoexli5cqWWltbevXt52CYAAAB8ERTWAF2Xm5u7YcMGb2/vkSNHdvKSvLy8gICA\nPXv28HYRDwkJiS1btvz22285OTk8bBYAAAA6D1uaA3QRi8UaNmxYa2vr06dPyfWkO2PatGnp6ekp\nKSliYjxe7JLFYllZWdnY2Fy6dIm3LQMAAMCHPtzSHCPWAF106NChpKSkixcvdr6qfvLkyfXr13/+\n+WeeV9UEQYiKiu7cufPy5ctJSUk8bxwAAAA+CyPWAF2RmZlpY2OzY8cOHx+fTl7CYrHs7e1VVVUf\nPHjAp1QcDsfJyUldXb2bm84AAADAZ304Yo2dFwG+GJvNXrx4sbm5uZeXV+evOn36dHp6+suXL/kX\njEaj7dy5c/z48c+ePXNwcOBfRwAAAPAhTAUB+GIHDx6Mj48PDAwUFxfv5CXV1dU7duzw9PQ0NTXl\na7Zx48YNHTp03759fO0FAAAAPoTCGuDLZGVl+fr67tixw9LSsvNXbdmyRVRUdMuWLfwLxuXt7X3r\n1q3k5OQe6AsAAAC4MMca4Auw2ewRI0bU19fHxcV1frg6MTFxyJAh586dmz9/Pl/jkTgczuDBg83N\nzf/6668e6A4AAEA4YVUQgG45dOjQs2fPvmgSCIfD+c9//uPg4DBv3jy+ZuOi0WibNm0KCQnJzs7u\nmR4BAACAQGEN0HlZWVk7duzYvn27lZVV56+6dOlSVFTUkSNHaDQa/7K1M2PGDCMjowMHDvRYjwAA\nAIDCGqBT2Gz2okWLzMzMvL29O38Vg8Hw8fFZvHhxD6/RISoqunHjxkuXLpWUlPRkvwAAAMIMhTVA\np5w+ffrZs2e///77F+3tsnv37oaGhj179vAv2KfMmzdPRUXll19+6fmuAQAAhBMKa4DPKy0t3bx5\ns5eX16BBgzp/VWpqqr+//+7du9XU1PiX7VMkJSVXrFhx6tQpBoPR870DAAAIIRTWAJ+3evVqRUXF\nrVu3dv4SNpu9fPnywYMHL1++nH/BOrZq1aqWlpbAwECqAgAAAAgVFNYAn3Hnzp1r164FBATIyMh0\n/qozZ87ExcWdPn1aVFSUf9k6pqysPG/evKNHj7LZbKoyAAAACA8U1gAdqaurW758+YIFC8aMGdP5\nq8rLyzdt2uTp6WljY8O/bJ2xfv363Nzc0NBQamMAAAAIAxTWAB3ZsmVLQ0PDzz///EVXrV+/Xk5O\nztfXlz+hvsCAAQPGjRt3/PhxqoMAAAAIPhTWAJ/0/PnzEydOHDp0SF1dvfNXPXjw4M8//zx+/Lis\nrCz/snXeihUrHj58iM1iAAAA+A1bmgN8HJPJdHBwUFBQePjwYef3dmlubh40aJCFhcXVq1f5Gq/z\n2Gy2kZHR9OnTv3TcHQAAADqALc0BOuvw4cOZmZlnzpz5oh0Td+/eXVJScvToUf4F+1IiIiKLFi06\nd+5cU1MT1VkAAAAEGQprgI8oLCzctWvX1q1bjY2NO39VVlbWwYMH9+zZ069fP/5l64LFixfX1tZe\nuXKF6iAAAACCDFNBAD5i+vTpL1++TE1NlZSU7OQlbDZ7xIgRTU1NsbGxFC6x9ykzZswoKyt78uQJ\n1UEAAAAExIdTQb5gc2YAIREWFnb16tU7d+50vqomCOKXX36JjY3tnVU1QRDLli0bM2ZMRkaGmZkZ\n1VkAAAAEE6aCAPyPlpaWNWvWTJ06dfz48Z2/Kj8/f8uWLZs3b7azs+Nftu5wdXXV1dW9ePEi1UEA\nAAAEFgprgP/h7+9fUFBw6NChzl/C4XCWLVumq6u7adMm/gXrJhERkblz5164cAG7MAIAAPAJCmuA\n/yoqKtqzZ8/mzZv79+/f+avOnDkTFhZ29uxZOp3Ov2zdt2DBguLi4kePHlEdBAAAQDChsAbh9eTJ\nk+vXr7c94uXlpaGhsWHDhs43UlJS4u3tvWHDBmdnZ14H5DEzMzNbW1vMBgEAAOATFNYgvB4+fOjh\n4eHs7Pz8+XOCIMLDw4ODg48cOdLBwHNERERzc3PbIytWrFBXV+8Nu5d3xvz5869evdrQ0EB1EAAA\nAAGEwhqEV0pKCo1Ge/78uaOj46xZs1avXj158uRJkyZ1cMmSJUtsbGxSU1PJt4GBgX///ffZs2el\npKR6JHJ3zZkzp6GhITQ0lOogAAAAAgiFNQivFy9ecDgcJpPJ4XCuX7+elZWlrKz8/v37T52fn5+f\nm5ublZVla2t7+PDh0tLS9evXr169evjw4T0Zuzs0NDRGjBhx7do1qoMAAAAIIBTWIKSam5sLCwu5\nb1tbWzkczp9//qmvr+/n59duvgfp7t27oqKibDa7tbX1xx9/HDx4sIyMzL59+3owNQ988803d+7c\nwfbmAAAAPIfCGoRUZmYmi8Vqd7C1tbW2tnbz5s0WFhbJyclv6dwCAAAgAElEQVTtPr179y73NZvN\nrq6urqys7HOjv1OnTq2vr3/48CHVQQAAAAQNCmsQUqmpqSIiH//7z+FwXF1dLSws2h5sbW0NDw9v\nW4szmczGxsYFCxZMnz69urqav3F5R0dHx87O7saNG1QHAQAAEDQorEFIpaamiouLf/Sj7du3BwQE\ntNuZPDo6+lOLady8edPKyurp06e8T8kf33zzzc2bNz8csAcAAIDuQGENQiopKamlpaXtERERERER\nkdOnT3907bx79+5JSEh8tCkWizVkyJCBAwfyIyc/TJ06taKiIjY2luogAAAAAgWFNQiply9fcjgc\n7ltRUVEJCYlbt24tXbr0o+ffunWrXSFOEIS4uLiEhIS/v/+NGzeUlZX5GJenzM3NDQ0N79+/T3UQ\nAAAAgSJGdQAACtTV1ZWVlXHfiouLS0tL3717d+jQoR89v6ysLDMzs91BUVFRIyOj4OBgKysrPmbl\nD1dX14iICKpTAAAACBSMWIMwSk1N5Q5Xi4uLa2pqPn/+/FNVNUEQd+/ebfukI/l65cqVSUlJfbGq\nJghi1KhRsbGxdXV1VAcBAAAQHCisQRilpqaSzyaKiYlZWFjEx8ebmJh0cP7du3dpNBr5WlxcXFFR\n8fbt28eOHZOUlOyJuHzg5ubGYrGio6OpDgIAACA4UFiDMEpLS+NwOKKiomPGjImKilJXV+/gZBaL\ndf/+fSaTSRCEiIjI8OHDU1NTJ0yY0FNh+UJDQ8Pc3ByrWQMAAPAQ5lhDH/b+/fvGxsbGxsZ3794R\nBFFXV0eWv9xP2z6eKCsry11fLyIigs1mf/PNN3v37q2qqmppaWn7aTvPnj2rra0VERERFRU9ePDg\nmjVruKPXfZqrqysKawAAAB5CYQ29C5vNLi8vLyoqKi8vr6qqqq6urqqqqqqqqqysJF8wGAxuPd3N\nvm7cuNF2nxQxMTE5OTlZWVlZWVllZWWVfyUmJhIEoaGhsW/fvq+++qqlpaXvzgBpa9SoUb/++mtN\nTY2CggLVWQAAAAQBCmugRn19fW5ubk5OzuvXr9+8eVNcXFxSUlJYWFheXt7a2kqeIyUlRZa2ysrK\nqqqqpqamKioqsrKySkpKdDpdSkpKUVGRTqdLS0srKCiIiIhISUnR6XRuF+0GobkD2O/fv79169bk\nyZMJgqirq2tqaqqrq+O+YDAYDAaDLOLLysrS0tJevnwpISFRWlr6/fffk02pq6traWnp6urq6Oho\na2v379/f2NjYyMio4yklvc2wYcPYbPbz589Hjx5NdRYAAABBQGv7u3IAfmCz2bm5ucnJyRkZGTk5\nOWQ9Ta52R6PRdHR09PT0tLW1dXR0dHV1yYJVW1tbS0tLWlqa6uxEc3PzgwcPJk2axGQyKyoquF8A\nSktLi4qKioqKSkpK8vPzm5ubCYKQk5MjK2wjI6MBAwZYW1tbWFhISUlRfROfZGBgsGTJki1btlAd\nBAAAoO+h0WhBQUEzZ8787xEU1sBztbW18fHxKSkpKSkpycnJaWlpDQ0NIiIi5Mgut/Q0MTExNDQU\ngGkVbDa7qKiI+52B/N/s7OzGxkZyrWtra2srKysrKys7Ozs9PT2q8/7XrFmzGhsbb926RXUQAACA\nvgeFNfAFi8XKzMxMSEhISEiIjo5OTExks9lKSkrm5uYWFhbm5uZ2dnY2NjYyMjJUJ+1RJSUlCQkJ\n6enpaWlpCQkJWVlZLBZLU1PT3t7ezs7Ozs7OxcVFSUmJwoSHDx8+cOBARUUFhRkAAAD6KBTWwDNs\nNjsxMTEsLCw8PPzp06cMBkNWVtbe3t7JycnJycnBwUFLS4vqjL1LfX19QkJCXFxcbGxsXFxccXGx\nqKiolZWVm5ubm5vbV1991fNfPGJiYoYNG5abm2toaNjDXQMAAPR1KKyhu968eXPnzp2wsLCIiIiq\nqioNDQ03N7cRI0YMHTrU3Nyc3HUFOqOoqCguLu7JkydhYWFpaWkSEhJOTk5ubm7jxo0bMmRIz6zo\n19TUpKCgcP78+Tlz5vRAdwAAAIIEhTV0UV5eXmhoaEhISExMjJSUlLOz8+jRo0ePHm1raysYizpT\nq6Ki4tGjR2FhYffv3y8oKFBTUxs3btyMGTPGjRv3qdW1ecXe3n748OH+/v587QUAAEDwfFhYY7k9\n6Eh+fv65c+dCQkIyMjLU1dWnTJmyZcsWV1dXAXjisFdRV1efMWPGjBkzCIJ4+fLl9evXr1+/fvHi\nRRUVFXd39++++2748OF8+gJjY2OTnJzMj5YBAACEDQpr+Ijm5uYbN2789ttv4eHhGhoas2bNOnXq\n1LBhwzDTowcMGjRo0KBBvr6+OTk5165dCw4OHjFixIABA3744YeFCxdqamrytjsrK6vr16/ztk0A\nAADhJEJ1AOhdysvLfXx8dHR05s6dS6fTr127VlhY6O/v/9VXX6Gq7mHGxsYbN26Mj49PTEwcO3as\nn5+fnp7e9OnTnz9/zsNerK2tq6qqiouLedgmAACAcEJhDf+voKBgzZo1/fv3P3/+/H/+85/CwsJb\nt25NmTJFTAy/1qDY4MGDjx8/XlJScu7cucLCQgcHh7Fjx0ZGRvKkcWtra4IgMBsEAACg+1BYA1FV\nVbV8+XITE5Nbt2799NNPr1+/3rp1q7a2NtW54H/Q6fS5c+c+e/bsn3/+aW1tHTVqlIuLS0JCQjeb\nVVZW7tevHwprAACA7kNhLdTYbPbZs2cHDhx469atU6dO5eTkrF69ujdvwQ0EQYwZMyYiIiI6OlpE\nRMTR0XHVqlXv3r3rToPW1tapqam8igcAACC0UFgLr9zc3GHDhq1YsWL+/PmZmZk//PADv1d2Ax5y\ndnZ+9OjR77//fvXqVVNT02vXrnW5KSMjo7y8PB5mAwAAEE4orIXU3bt3hwwZ0tzcnJCQ4O/vLy8v\nT3Ui+GI0Gm3BggVZWVnffPPN9OnTN23axGKxutCOvr5+QUEBz+MBAAAIGxTWwmj//v2TJk2aPHly\ndHQ0+ewaEATR3Nzs6empqakpLS197969T50WFha2adOmTl5y8OBBdXV1Go126tQpgiBu3brl5+fX\ntfL3UxQUFE6fPn3u3LkjR45MmDChpqbmS1vQ19cvLS1taWnhYSoAAAAhhMJa6Gzbtm3btm1Hjx4N\nDAzEdOq2Dh06dO/evczMzCNHjjAYjI+es2PHjmPHjm3evLmTl2zYsCEmJob71t3dnU6nu7m5vX//\nnrfhFy5cGBUVlZaWNm7cuLq6ui+6Vl9fn81mFxUV8TYSAACAsEFhLVzOnz+/d+/es2fPrl69muos\nBEEQjY2Nzs7OvaTBGzdu2NvbKyoqLl26dPr06R+ecODAgcuXLwcHB8vJyXXykg95enoOGjRowoQJ\nTCazazk/xc7O7uHDh/n5+fPmzeNwOJ2/UF9fnyAIzAYBAADoJhTWQiQ3N3flypXe3t7fffcd1Vn+\n32+//VZRUdFLGiwqKurg8c2cnJxt27bt3LmTTqd38pJP8fX1TUpKOnLkSNdydmDAgAFXrly5e/fu\nr7/+2vmr1NTUxMTEysrKeJ4HAABAqKCwFiI//vijkZHRrl27+NE4i8Xavn27np6elJSUtbV1UFAQ\nQRDnz5+XlZWl0WhKSko3btyIj4/X19cXFRX99ttvCYL4z3/+4+XllZubS6PRjI2Nf/rpJ2lpaTk5\nuYqKCi8vLx0dnaysrCdPnpibmysoKNDpdCsrq/v373N7vHjxor29PZ1Ol5GRMTAw2L17d2ca/GjO\nBw8eGBsbl5aWBgYG0mg0WVnZD2/w2LFjHA7H3d2dfPvRSx49euTg4CAtLS0vL29lZVVbW/vRn5WS\nktKIESOOHDnyRePKnTRs2LANGzZs3bq187NNaDSatLR0Q0MDz8MAAAAIFw4Ih9evX4uIiFy9epVP\n7W/YsEFSUvLKlSvv3r3bvHmziIjI8+fPORxOenq6tLT0woULydM2bdp09uxZ7lXTpk0zMjLivt2y\nZQtBEJ6ensePH/fw8MjIyAgJCfH19a2urq6qqnJyclJRUSHP9Pf3Jwhi//79VVVV1dXVp0+fnjt3\nbmca/FRODoejoaHBzfkhQ0NDc3PzdgfbXsJgMOTl5f38/BobG8vKyjw8PN6+fcvhcF69ekUQxMmT\nJ9teSD7+mJiY2PmfcOfV1NTIy8v7+/t3/hItLS2y0AcAAIBOIggiKCio7RGMWAuLO3fuyMnJcQdc\neaupqenEiRNTp06dNm2aoqLi1q1bxcXFz507RxCEmZmZv79/YGDgpUuX/vrrr+bm5kWLFnXc2oED\nB1avXk0uzzx9+vQdO3YoKSkpKyu7u7tXVVW9ffu2tbV1586do0aN8vHxUVZWVlJSWrRo0ZAhQz7b\noIGBwadydqy+vv7169dGRkYdnJOfn19bW2thYUGn0zU0NK5evaqqqvqpk01MTAiCSElJ+WzXXSAv\nLz9lypTQ0NDOXyIjI1NfX8+PMAAAAMIDhbWwSEtLGzx4sJiYGD8az8rKamhosLS0JN9KSUlpampm\nZmaSb8kH+5YvXx4cHPzTTz91uRdyNjOLxUpOTn7//v3XX3/N/UhUVNTT07ObOTtQUVHB4XCkpaU7\nOMfQ0FBdXX3evHm+vr75+fkdN0g2VV5e/tmuu2bIkCFpaWmdPx+FNQAAQPehsBYWDAaDu5YFz5E1\n2datW2n/KigoaDtnd+/evQwGowuPFd6+fXvkyJFqamqSkpIbN24kD5JzlxUVFXme81OampoIgpCU\nlOzgHCkpqYcPH7q4uOzdu9fQ0HD27NmNjY0dnMxtlh/k5eW/aNE9SUlJrGMNAADQTSishYW6unpJ\nSQmfGldTUyMIot2k3qdPn5Kftra2enp6Hj58+OnTp3v27Ol8s4WFhVOnTtXU1IyLi6upqfHz8yOP\na2trEwRRWVnJ25wdIOvgz27sYmFhERoaWlJS4u3tHRQUdPDgwU+dSVax/FtHvLi4WENDo/Pn19fX\ndzweDwAAAJ+FwlpYDB069OXLl10oRjtDV1eXTqcnJSV99NM1a9YsWbJk3bp169ev3717d2cKWVJK\nSkpra+vKlSsNDQ3pdDqNRiOPGxgYKCsr//PPP7zN2QFy98SONzUsKSlJT08nCEJNTW3//v22trbk\n248im/qi2veLhIeHDx06tPPn19fXy8jI8CkMAACAkEBhLSy+/vpreXn506dP86NxOp3+/fff//XX\nXydOnKitrWWxWEVFRaWlpQRB/Prrrzo6Oh4eHgRB7Nu3z9zcfO7cudx16JSVlUtKSvLz8+vq6lpb\nW9s1q6enRxBEWFhYU1PTq1ev4uLiyOOSkpKbN29+/Pjx2rVri4uL2Wx2XV0dWcV23GAHOTsmLS1t\naGjY8d6EJSUly5cvz8zMbGlpSUxMLCgocHJy+tTJZFNWVlaf7boL0tLSIiIiyDUNOwmFNQAAAA/0\nwFok0Evs3LlTTk6usLCQH403Nzd7e3vr6emJiYmpqalNmzYtLS1t8uTJNBpNWVk5JiaGw+GsW7dO\nRESEIAgFBYX4+HgOh/PixQt9fX0pKSkXF5f169eTUyN0dXUvXrxINuvt7a2srKyoqDhjxoxffvmF\nIAgjIyPyFn755RcrKys6nU6n021sbH799dfONPjRnPn5+TY2NgRBiImJ2draXrly5cMbXLt2rbi4\neENDA/n2w0vy8/OdnZ2VlJRERUW1tbW3bNnCZDIPHTpEDkvLyMh4eHhwW5s4caKOjg6bzeb5HwSb\nzXZzc7OxsWGxWJ2/Slpa+ty5czwPAwAAIMCID5bbo3H4sEUF9E5NTU22trYKCgqRkZEdP4cHH8rJ\nyTEzMzt37ty8efO62VRVVVW/fv327Nnj5eXFk2xt7dmzZ9euXVFRUQ4ODp28pLW1VVJSMiQkZNq0\naTzPAwAAIKhoNFpQUNDMmTO5RzAVRIjQ6fRr165lZGTMnDnzw2kS0DFjY+Ndu3bt2rWLwWB0sylf\nX9/BgwevXbuWJ8HaCggI2L59u7+/f+eraoIgiouLORyOrq4uz/MAAAAIFRTWwsXU1PTu3bsRERFj\nxozpwuJ3Qm7Tpk0zZsyYPXt2x08xduzw4cNJSUl37twhl+XmFTabvXXr1uXLl/v6+q5ateqLriVX\n3dbX1+dhHgAAACGEwlroDB069NmzZ+Xl5ba2trGxsVTH6WP27t27du3a/fv3d+3ymzdvNjc3R0ZG\nKikp8TBVbW2th4fHwYMHT58+vX379i+9vKCggE6nq6ur8zASAACAEEJhLYxMTU1jYmKsra1Hjhy5\nY8eODvYxgQ+NHTv2wIEDXbt2ypQpmzZtEhUV5WGemzdvWltbJyQkPH78eMmSJV1ooaCgQE9Pj7ua\nIQAAAHQNCmshpaSk9Pfff+/bt8/f35/c1oTqRPDFcnJyJk6cOHXq1OHDhyckJHzRvOq2CgoKMA8E\nAACg+1BYCy8REZH169dnZmY6OTm5u7uPHTv20aNHVIeCTiksLFy7dq2VlVVhYWFkZOTFixe7M5Ej\nMzPTxMSEh/EAAACEEwprYaetrf3nn39GRES0tLSMHDnSxcXlzp07WISx18rOzl60aJGJicnNmzcP\nHjyYmJj41VdfdadBDoeTmpo6aNAgXiUEAAAQWiisgSAIYuTIkZGRkVFRUQoKCpMmTbK2tj569GhV\nVRXVueD/MZnMmzdvuru7m5mZRUVFnTp1KicnZ9WqVWJiYt1sOS8vr7a21tramic5AQAAhBkKa/iv\nYcOG3b59OyEhwcnJadu2bTo6OrNnz37w4AGbzaY6mvDKzs728fHR1dX18PBoamoKCgrKyMj4/vvv\nebVaX3JysoiIiKWlJU9aAwAAEGYorKE9GxubM2fOVFRUXLx4saqq6uuvv9bQ0FiwYEFoaGhLSwvV\n6YRFXl7e0aNHXVxcTE1NL168uHDhwlevXv3zzz/Tp08nt4XnleTkZENDQ1lZWR62CQAAIJywpTl8\nRkZGRkhIyPXr15OSkpSVlSdNmjRlyhRXV1dFRUWqowkaJpP57Nmzv//++9q1a1lZWZqamlOmTJk2\nbZqrqytvV+hra/r06QRBXLlyhU/tAwAACKoPtzRHYQ2dlZeXd/Xq1fPnz2dkZNBoNHt7+9GjR7u5\nuTk7O9PpdKrT9VXks4Ph4eHh4eGPHj2qq6szMDCYOnWqh4eHs7MzbwenP8rQ0PD777/ftm0bvzsC\nAAAQMB8W1t198gmERHp6emBg4B9//FFSUuLo6Dhq1Kj379+HhITs27dPSkrK0dFx6NChjo6OTk5O\nGhoaVIft7RoaGhISEuLi4p4+fRodHV1eXq6srDxy5MgDBw64ubkNHDiwx5JUVFS8fv3aycmpx3oE\nAAAQYCisoSPv378PDg6+cOFCdHS0jo7O/PnzybXeuCcUFBSEh4c/efLk5s2bfn5+bDbbwMDAycnJ\nwcHB2tra2tpaTU2Nwvy9RENDQ3p6ekpKSnx8fGxsbHJyMpPJ1NTUdHR0XL9+vaurq42NDf8me3Qg\nNjaW/OVDz3cNAAAgeDAVBD6CxWJFRERcuHDhypUrHA5n8uTJ8+fPnzBhQsfFX01NzbNnz2JjY+Pi\n4p4/f15RUUEQhIaGhpWVlZWVlaWlpaWlpbGxsbKyck/dBzWamppyc3MzMjJSU1NTU1OTk5Pz8vJY\nLJa0tPTgwYMdHR3Jof3esNnhli1brl+/np6eTnUQAACAvgdzrOEz0tPTL1y4cP78+fLycjs7u6VL\nl86ZM0dOTq4LTZWXl6ekpCQnJ6empqakpKSlpTU2NhIEoaysbGxsbGxsbGRkZGJiYmhoqKenp6mp\nyav143pSRUVFSUlJXl5eTk5OTk5Obm5uTk7OmzdvOByOiIiIkZGRtbW1paWllZWVtbW1oaEhJcPS\nHXBzc9PX1//999+pDgIAAND3YI41fFxtbe2lS5fOnDmTmJhoZGS0cuXKBQsWGBgYdKdNDQ0NDQ2N\n0aNHk29ZLFZ+fj63+szJybly5UpeXl5zczNBEDQaTVNTU1tbW0dHp1+/flpaWtra2iptKCsrd38z\nlC9VU1Pz9u3bqn9VVla+efOmpKSkuLi4uLi4pKSEDC8iItKvXz/yq8LXX3/N/c4gLS3dw4G/CJvN\njo+Pb/t/BwAAANAdKKyFXXx8/OnTpy9fvsxms2fOnEmunUyj0XjekaioqJGRkZGRUduDbDa7pKSk\nsLCwtLS0qKiIrFZTU1MfPHhQWlrKYDDanqyoqKiqqqqgoCArKyslJSUnJycrK0un0+Xl5WVkZCQk\nJAiCUFBQ4K6kISkpyS1tmUxmXV0dt6m6ujomk0kQxLt375qamhobG8kXDAajpqamqamJrKTJc7it\nqaqq9uvXT1tb28bGZtKkSdra2uRbAwMDSUlJnv/E+C09Pb22ttbR0ZHqIAAAAAIChbWQampqCg4O\nPnr06IsXLwYOHLh169bFixerqKj0cAxyrLdfv34f/bS5ubm6urqqjcrKypqaGgaD0djYWFdXV1ZW\nRr4gC2U2m11TU8O9vKGhgRxRJgiCRqO1XXhbSkqKXCJQUVFRSkpKSkpKUVFRUVExJiamf//+M2fO\n5I6Uq6qqki8EbwuVp0+fysjIYM9FAAAAXkFhLXTIWdQBAQENDQ3u7u5+fn5ubm78GKLuPklJSS0t\nLS0trR7r8ezZs8uXLz916pSdnV2PdUqViIgIFxeXnp9gAwAAIKiwpbmwaGpqCgkJGTNmjIWFxfXr\n1729vYuKioKDg0ePHt07q2pKLFq06Kuvvlq2bBmLxaI6C989evRo5MiRVKcAAAAQHCisBV9mZqaP\njw+5CrWSktKDBw8yMzO9vb1VVVWpjtbr0Gi0kydPpqam/vLLL1Rn4a/MzMySkhIU1gAAADyEwlpg\nMZnMkJAQFxcXMzOzGzdubN68ubi4GEPUnzVw4MAff/xx27ZtRUVFVGfho8jISFlZWWGY8QIAANBj\nUFgLoHfv3v38889GRkZz5sxRV1d/+PBhRkaGl5dXzz+b2Edt3bpVW1t77dq1VAfho/v3748cObIv\nrh0OAADQa6GwFig5OTmenp66urq7d+8eN25cWlratWvXRo0ahSHqLyIpKXnq1KkbN27cvHmT6ix8\n0dzcHBYWNnHiRKqDAAAACBQU1gIiKipq8uTJAwYMuH379rZt2woLC0+fPj1w4ECqc/VVI0eOnDt3\n7po1a9qufi0wIiMjGQzG+PHjqQ4CAAAgUFBY923Nzc0XLlywsrIaPnz4u3fvgoKCsrKyvL29267Z\nDF3j7+/f2Ni4Y8cOqoPw3u3bt62trfX19akOAgAAIFBQWPdVpaWlvr6+Ojo6S5cutbGxSU5OjoqK\nmjFjhqioKNXRBISqqur+/fuPHTuWkJBAdRYeu3379oQJE6hOAQAAIGhQWPc9cXFxs2bN0tPTO3Pm\nzPr164uKishBa6pzCSCBXNY6ISEhLy9v2rRpVAcBAAAQNCis+5KwsDBXV1cnJ6fXr18HBgbm5+dv\n3rwZy1Hzj0Auax0SEmJgYICF9gAAAHgOhXUfwOFwQkNDhw4dOmbMmJaWllu3bj179uzbb7/FWmk9\nQPCWtb569eqMGTOwUAwAAADPobDu1dhsdkhIiKWl5ZQpU1RVVZ8+fUqu/kF1LuEiSMtav3jxIicn\nZ8aMGVQHAQAAEEAorHuplpaWCxcumJqazp4928LCIjU1NTQ01MnJiepcwkiQlrUOCgrq37+/vb09\n1UEAAAAEEArrXofBYBw9etTQ0HDJkiVOTk6ZmZnBwcHm5uZU5xJq3GWtGQwG1Vm6js1m//nnn/Pm\nzcM8EAAAAH5AYd2LVFZW+vr66uvrb926ddq0aXl5eRcuXDAxMaE6FxCEQCxrHRYWVlRUNHfuXKqD\nAAAACCYxqgMAQRBEeXn5yZMn/f39JSQk1qxZs3btWmVlZapDwf8gl7Vevnz53LlzbW1tqY7TFRcv\nXhw6dCj24wQAAOATjFhTrKKiwsfHx8DA4NSpU+vWrcvNzfX19UVV3Tv16WWt6+rqrl+/Pn/+fKqD\nAAAACCwU1pQpKytbt26dvr7+n3/+6e/vX1BQ4OvrKy8vT3Uu+CRyWeuUlJRff/2V6ixf7I8//uBw\nOLNnz6Y6CAAAgMBCYU2BqqoqX1/fAQMG/Pnnn76+vtnZ2cuXL5eUlKQ6F3weuaz11q1b+9yy1idP\nnpwzZ46SkhLVQQAAAAQWCuseVV1dvXnzZnLix86dO/Pz8729vel0OtW54AuQy1p7enpSHeQLPH78\nOCUlZcWKFVQHAQAAEGQorHtIQ0ODn5+fkZFRQECAl5dXdnb2unXrpKSkqM4FX4xc1vr69et9aFnr\nEydOODk5YRtzAAAAvsKqIHzX2tp69uzZ3bt3MxiMDRs2rF+/XlZWlupQ0C3cZa3d3Nx6/59mWVnZ\n9evXz5w5Q3UQAAAAAYcRaz7icDghISEWFhaenp6TJ09+9erV9u3be38dBp3Rh5a1PnPmjJyc3MyZ\nM6kOAgAAIOBQWPNLWFjYkCFDZs+ePXjw4IyMjNOnT2toaFAdCniGXNb66NGjL168oDpLR1gs1m+/\n/bZo0SJM5QcAAOA3FNa89/LlyzFjxowZM0ZLSyspKSk4ONjIyIjqUMB7fWJZ61u3bhUWFi5evJjq\nIAAAAIIPhTUvlZaWLl682NbWtqam5vHjx6GhoVZWVlSHAn7pE8tanzx5cty4cSYmJlQHAQAAEHwo\nrHmjsbHRz8/P1NT0/v37J0+ejI2NHT58ONWhgO96+bLWL1++DAsLW7t2LdVBAAAAhAKNw+FQnaFv\n43A4V65c2bhx49u3bzds2ODt7Y1F9IRKc3PzoEGDLCwsrl69SnWW9ubMmZORkZGYmEij0ajOAgAA\nIGhoNFpQUFDb5QEwYt0tMTExQ4YMmTNnztixY3Nzc319fVFVC5teu6x1Xl7elStXfHx8UFUDAAD0\nDBTWXVRWVrZw4UIXFxclJaXExEQs+iHMuMtaMxgMqm9fqx4AACAASURBVLP8188//6ynpzd9+nSq\ngwAAAAgLFNZfjMlkHj161NTUNDw8/Pz58w8ePMATitDblrUuLy8PDAz88ccfxcSwCRQAAEAPQWH9\nZSIjI21sbDZu3Lhw4cLMzMwFCxZQnQh6hU8ta93Q0FBfX8/v3ltbWysqKtoeOXLkiLy8/MKFC/nd\nNQAAAHChsO6sN2/ezJw5c9SoUf37909PTz969Cj2UIS2PlzW+t69e+bm5mFhYfzuOjc319DQcNu2\nbTU1NQRB1NbWnjp16j//+Q9m/AMAAPQkFNafR879sLCwePHiRWho6K1bt7DhC3yo7bLWZWVls2bN\nGj9+fGFh4dOnT/nddXZ2dn19/YEDB3R1dX/66aejR4+y2ezly5fzu18AAABoC/MvP+P58+fLli1L\nT0/ftGmTj4+PpKQk1Ymg9yKXtfb29t66dWtjYyNBEBwO59GjR/zuNysrS0JCoqWlpa6ubsuWLWJi\nYs7OzhiuBgAA6GEYsf6k+vp6Hx+foUOHysnJJSYm7tixA1U1dCwlJeX+/fvNzc11dXVMJpM8+OLF\ni5aWFr72m52dzV2QnslkNjU1PXr0yNDQMCAgoDdvtw4AACBgUFh/XGhoqJmZWUBAwIkTJyIjI83M\nzKhOBL1aY2Ojr6+vra1tYmJiu02XWlpakpKS+Np7Wlpaa2tr2yMsFqu0tHT58uWWlpZ3797la+8A\nAABAQmHdXlFRkbu7+5QpU9zc3F69erV06VLsrwEdKykpsbS03LlzJ5PJ5A5Uc4mLi8fExPA1QFZW\n1ocHORwOjUZ7/fo1/gIDAAD0DBTW/8XhcM6ePWtpaZmdnR0REXHu3DkVFRWqQ0EfoK2tvWfPHklJ\nyY8uGs1ms6Ojo/nXe21tbXV19YfHRUVFpaWlw8PDx40bx7/eAQAAgAuF9f8rKSmZMmXKsmXLZs2a\nlZCQMGLECKoTQV8yZ86cZ8+eaWpqiouLt/uIxWLx9fnFjw5Xi4mJKSgoPHnyZNiwYfzrGgAAANpC\nYU1wOJyAgABTU9OMjIzIyMjTp0/LyMhQHQr6Hmtr66SkJBcXF1FR0XYfvX379s2bN3zqNzs7W0Tk\nf/5DFhMTU1dXj42NHTx4MJ86BQAAgA8Je2Gdn58/duzYVatWrVy5MjU1dfjw4VQngj5MRUXlwYMH\nGzZsIAii7cxmERER/k2zfvXqVdthcnFxcQMDg7i4OBMTEz71CAAAAB8lvIU1h8M5efKklZVVaWlp\nTEzMgQMHsJoedJ+oqOiBAwf++usvCQkJ7pRrMTEx/m0Tk5mZyV0SRFxc3MLCIiYmpl+/fnzqDgAA\nAD5FSAvr8vLySZMmrVmzZu3atQkJCUOGDKE6EQiU2bNnx8XFcadct7S0REZG8qmvtLQ0NptNEIS4\nuLijo+OjR4/U1NT41BcAAAB0QBgL6/v379vY2KSnpz98+HDv3r0YqAZ+GDRoUGJiorOzMznlOjU1\nldyLkbc4HE5ubi5BEKKiouPGjQsLC5OXl+d5LwAAANAZwrWleWNjo4+Pz/Hjx6dPnx4QEKCoqEh1\nIhBkqqqq4eHhW7Zs8fPzY7FYz58//+qrr9hsdk1NDYPBaGhoYDAYTCazrq6Oe0nbLRsJglBUVOTO\n1ZaUlJSWlpaQkJCRkVFSUpKWlpaUlCwtLSXr9VmzZgUGBn50vT8AAADoGUL0z/Dz58/nzZtXUVFx\n6dKlOXPmUB0HBA2TySwpKSksLKyoqKioqKj8V1VVlYGBQUFBwfjx4zkcDg/HrcmVqgmCUFFRKS4u\nnjNnjrq6uqqqqoqKiqqqqoaGhra2tr6+PnkOAAAA8JtQFNYsFmvfvn27du1ydXWNiIjQ1tamOhH0\nYWw2u7CwMDs7Ozs7u6Cg4M2bN2/evCkoKCgrK2OxWOQ5SkpKampqqqqqqqqq2tragwYNamxsTE1N\nXbhwobS0tKKiooyMjLS0tJycHI1Ga/ubE3IcmttRTU0N96P6+vqWlpampqaGhob379/X19c3NDTc\nvn27pKRk+PDhlZWVb9++TUtLq6qqIgt67si3iopKv3799PT09PT0dHV1jY2NBwwYYGJiQqfTe+pn\nBgAAIBRoHA6H6gz8VVZWNnfuXHLdj7Vr12J7Z/giTCYzMzMzMTExPT2dLKZfvXrV3NxMEISqqqqB\ngYGuri5Zs/br109XV1dfX19NTe3DbWIIgmhqauJ5LVtaWqqlpfXRjyorK4uLiwsLC8nSv6ioqKCg\ngHzLZrNFRET09PRMTEwGDBhgZmY2aNCgQYMGycnJ8TYeAACAAKPRaEFBQTNnzvzvEcEurCMjI7/9\n9lsJCYmgoCBHR0eq40Af0NLS8uLFi8R/paamNjU1SUpKmpqaDvjXwIEDTUxMlJWVqQ7bFc3Nza9e\nveJ+ScjKysrIyKiurqbRaMbGxoMHD7axsbGxsXFwcOijNwgAANAzhKiw5nA4P/3005YtWyZPnnzu\n3Dk8pwgdqKuri4uLi4qKio6Ojo6ObmxslJeXt7KysrOzs7CwMDc3t7e3F+yJEyUlJQkJCenp6Wlp\naQkJCRkZGRwOx9DQcNiwYS4uLsOGDTM3N8dvewAAANoSlsL67du38+fPj4yM9PPz8/T0pDoO9Eat\nra1RUVH37t27d+9eamoqm802NTUl60hnZ+cBAwZQHZBKlZWVT58+Jb9pxMfHNzc3a2trjxkzZty4\ncWPHjsVINgAAACEkhXVERMS3334rKysbFBRka2tLdRzoXcrKym7dunXv3r2wsLC6urqBAweOGzfO\n1dXV2dlZVVWV6nS9UVNTU3x8/JMnT+7fvx8dHc3hcBwcHMaPHz9p0iQbGxuq0wEAAFBG8AvrgICA\nVatWTZo06ffff1dSUqI6DvQW7969Cw0NDQkJuXfvnri4+LBhw0aPHu3u7m5mZkZ1tL6kvr7+4cOH\nf//997179woLCw0MDNzd3RcuXIhvsAAAIIQEubBuampatmzZpUuX9u7d6+3tTXUc6BWYTOaNGzd+\n//33sLAwMTGxiRMnzpo1a+LEiVJSUlRH69s4HM6zZ88uX74cEhJSXFxsZWU1f/78H374QUVFhepo\nAAAAPURgC+s3b95MnTo1Pz//8uXLo0ePpjoOUK+srOzMmTOnT58uLS0dP378nDlz3N3dsZwcz7HZ\n7KioqMuXL//555/Nzc2zZ89euXLlkCFDqM4FAADAdx8W1iIUpuGVR48e2dvbM5nM58+fo6qG7Ozs\n+fPn6+vrHzt2bO7cuTk5OX///ffcuXNRVfODiIjIV199deLEieLi4qNHj7548cLBwcHJyenvv/+m\nOhoAAEBP6/OFdUBAwOjRo11dXWNiYvr37091HKBSfn7+Dz/8YGFhER8ff/r06Tdv3vj5+eFvRc+Q\nkZFZunTpy5cvnzx5oqWl5e7uPnTo0AcPHlCdCwAAoOf04cK6qanp22+/XbVqlZ+f319//SUtLU11\nIqBMfX39unXrBg4c+Pjx499//z01NfW7774T7JWney0XF5fr168/e/ZMSUlp7Nixo0aNyszMpDoU\nAABAT+irhfXbt2/d3NzIRYjXr19PdRyg0oMHD6ysrC5cuHD8+PGMjIz58+eLiopSHUrY2dvb37lz\nJzo6uq6uzsbGZt++fa2trVSHAgAA4K8+WVjn5OS4uLiUlpZGR0e7ublRHQco09zcvGzZsq+//trO\nzi49PX3p0qXi4uJUh4L/cnZ2jo2N3bVr1549exwcHHJycqhOBAAAwEd9r7COjo4eOnSokpJSbGws\nFiEWZpWVlWPGjAkKCrpy5UpISIiGhgbVieAjxMTEfvzxx5cvX4qJiTk5OT1+/JjqRAAAAPzSxwrr\noKCg0aNHjxgxIiIiQl1dneo4QJlXr145OTkVFRXFxMR4eHh0s7WrV68aGhrSaDQajbZt27aPnnP4\n8GEajSYiImJqatq2OszKylqzZo2FhYWcnJyYmJiCgsKAAQMmTpz49OnTdi23Y2BgQBDEwYMH1dXV\naTTaqVOnyAbv3LmjoKAQGhrazZsKCwvbtGkT+bq5udnT01NTU1NaWvrevXsfntwuxq1bt/z8/Fgs\nVjczcJmYmDx69GjkyJFjx469dOkSr5oFAADoVfpMYc3hcHx9fefMmbN06dLg4GBs8CHMioqKRo8e\nraKiEhsba25u3v0Gp02blpeXZ2RkRBDE2bNnP5wNzGKxjh07RhCEq6trZmbmV199RR7/7bffrKys\nkpOTDx8+/ObNm/r6+sTExN27d79//z4lJaVtywoKChwOh8PhMJnMhoaG8vJy8nHbDRs2xMTEtO2L\nJ0vL79ix49ixY5s3bybfHjp06N69e5mZmUeOHGEwGB+e3y6Gu7s7nU53c3N7//5998OQpKWlg4OD\nV69evXDhwuvXr/OqWQAAgN5DjOoAncJkMpcsWXLp0qWAgIDFixdTHQeoxGQyZ86cKSMjc/fuXWVl\nZd42bmdnl5CQcOPGjRkzZrQ9fvXqVR0dnYKCgrYHY2Njly1bNmLEiPv374uJ/f9/SoaGhoaGhoqK\niq9evfpoF6KiolJSUlJSUgMGDPjoCRMnTqypqekgZGNjo5ubW7tyvK0DBw5cvnz55cuX3HVRbty4\nYW9vr6iouHTp0g5absvT0zMvL2/ChAmPHz/m3l03iYiIHDx4sKGhYf78+fHx8aampjxpFgAAoJfo\nAyPWTU1NM2bMCA4OvnnzJqpqOHTo0MuXL69cucLzqpogiJUrVxIEcfLkyXbHDx8+7OXl1e7gnj17\nWCzW/v37P6w7v/7669WrV3fc140bN7oW8rfffquoqPjUpzk5Odu2bdu5c2fb1QaLioq68Finr69v\nUlLSkSNHupbzU44ePWppafndd98JxravAAAAXL29sGYwGO7u7pGRkffv3x8/fjzVcYBi79+/37t3\nr4+PD09mgHzI1dXVzMwsIiIiKyuLezA6OrqhoWHs2LFtz2xpaQkPD1dRUXFwcOBhgKioKD09PRqN\n9ssvvxAE8dNPP0lLS8vJyVVUVHh5eeno6IwfP97Lyys3N5dGoxkbG3/YwrFjxzgcjru7O/n2wYMH\nxsbGpaWlgYGBNBpNVlaWIIhHjx45ODhIS0vLy8tbWVnV1tZ+NIySktKIESOOHDnC2wpYXFw8ICAg\nPj7+6tWrPGwWAACAcr26sK6urh4zZkxycnJERISLiwvVcYB6ly5d4nA4np6e/Oti+fLlBEFwHyUk\nCOLQoUMfrpVeUFDQ1NRkYmLShS4ePnx48ODBj37k4uLSdo7Hxo0b169fz2Aw9u3b179/fycnJ39/\n/8mTJxsZGXE4nI+uXnf79u2BAwdy90saM2ZMTk6OhobGwoULORwOg8Gor693d3efPn16dXX1q1ev\nBgwY0NLS8qmoNjY2xcXFL1++7MJtdsDa2nry5Mkf/mYAAACgT+u9hXVpaenIkSPLysqePHkyePBg\nquNAr3D//v1x48bJy8vzr4uFCxfKyMgEBgY2NjYSBJGXl/f8+fNvv/223WnkKC85ANwZNTU13PVA\nurD4+oEDB1avXn316tWO5yXX19e/fv2afArzU/Lz82tray0sLOh0uoaGxtWrV1VVVT91MvnNgXwQ\nk7dmzpz55MmT+vp6nrcMAABAlV5aWOfl5bm4uDCZzCdPnnRtUBAEUmZmpo2NDV+7UFBQ+Pbbb9+9\ne3f58mWCIPz9/VeuXCkhIdHuNLKkbmho6HyznH9FRETwNjNXRUUFh8PhDld/lKGhobq6+rx583x9\nffPz8ztukGyqvLychyFJtra2ra2tubm5PG8ZAACAKr2xsE5PT3d2dlZTU4uKiurXrx/VcaAXqa+v\nl5GR4Xcv5COMp06dev/+fUhICDk5pB0DAwM6nZ6dnd2F9keOHLlhw4bupvyYpqYmgiAkJSU7OEdK\nSurhw4cuLi579+41NDT8P/buM66ps+8D+AEChLCXIEMQEJlhBQSMCgoiKjgQ0KrgqFtBWxWtWtHW\nqq0LbLVOLC6GA3ELCBhAZIYpskH2kj0DeV6cuzzWARGSnCT8vy/uj8DJdX5ob/3lcI2lS5eiz+a/\ndvHgsMyFvjNpa2tj+sgAAAAAVjiuWOfn59vb22tqakZERLBi2wfA1eTk5Gpqalh9F2NjY0tLy6Sk\npA0bNri6ukpLS39+jbCwsIODQ0NDQ3x8/OdfbWpqwmQHG7QHD3uwi76+/sOHD6uqqnx8fIKDg782\n4RtBEHT6NSu2ja+urkYQBM7LBAAAwEs4q1jn5+fb2tqqqak9ffpUXFwc6ziA45ibm1MoFDbcCH1o\nfefOnR07dnztGl9fX2Fh4R9++OHzJ77Z2dnM2vv5m6CnJw69DXZVVVVubi6CIPLy8kePHjU1NUU/\n/CJ0KFbU31evXsnIyGhoaDB9ZAAAAAArHFSsCwoKbG1tJ0yY8OzZM5auTgPcy8XFJSEh4e3bt6y+\nkZubm5yc3KJFi4ZofsbGxjdv3szOzp42bdqTJ09aWlr6+vpKSkouXbq0du3aEewbzSAZGZmqqqrS\n0tK2trZPDokkEAgaGhoVFRVDvLyqqmrjxo15eXm9vb3p6ellZWWWlpZfuxgdytDQkFnhUQMDA1ev\nXl28eDE/Pwf9FQQAAACMFp0zFBQUKCsrm5qaNjU1YZ0FcK7+/n4DAwNnZ2fmDnvv3j10Jw05Obmt\nW7ein9y9e3dCQgL66/379ysqKiIIws/Pr6enR6FQBl9bXl6+c+dOQ0NDMTExAQEBKSkpExOTtWvX\nxsfH0+n0+Pj4wRMWFRUVZ82a9cmtT548iT4PFhUVXbx48dmzZ9EbEQgEZ2fn48ePo9MwVFVVr1+/\njr4kLS1NTU1NRESETCbX1NR8MqCXl5egoGBnZyf6YWlpKbrcE4fDmZqa3rlzp7S01NraWlpaWkBA\nQElJad++fTQa7ZMYg6PNmzdPWVl5YGCAab/XdDqdTr927ZqAgEB2djZzhwUAAADYCUGQ4ODgjz/D\nR+eAw8/KyspsbGykpaUjIyNhXjUYWnR0tJ2d3eXLl1evXo11Fk5UWFioq6sbEBCwYsWKUQ7V2Nio\noqLy66+/fn7q5GiUlpaampquWLHC39+ficMCAAAAbMbHxxccHOzm5jb4Gex/DltRUTF9+nQ5ObmX\nL19CqwbDsrW13bVr16ZNm1i3aR1X09LSOnz48OHDh9vb20c5lK+vr7GxsZeXF1OCoZqamubNmzdh\nwoTjx48zcVgAAACAE2BcrJuamhwcHMTFxZ8/fy4lJYVtGMAtfvvtt4ULFzo5OYWFhWGdhRPt3bvX\n1dV16dKlQ69iHNqpU6eoVOqTJ0+YOFkc/dlUR0fH48ePWbHTCAAAAIAtLIt1V1eXs7Nza2vrkydP\n4Fk1YBw/P//NmzdXr169ePFiX19frONwoiNHjnh5eR09enRkL3/w4EFPT09MTMwXtxocmaSkJHSV\n5KtXr5SVlZk1LAAAAMA5MJtj3dfXt3Dhwjdv3lAoFF1dXUwyAG53+vTpXbt2ubi4nD17dty4cVjH\nAV/W399/5syZAwcO2NjYBAcHw06aAAAAeAOnzLGm0+kbNmyIiYkJDw+HVg1GbMeOHU+fPk1KStLT\n07t+/TrWccAXZGZmWlpa+vj4ODk5DW5yAgAAAPAkbIr1rl27bty4cefOHWtra0wCAJ5hb2+flZX1\n3XffrVq1aubMmQkJCVgnAv9TXV3t5eVFIpHodLq6uvq9e/eIRKKoqKipqamHh8fvv//+/PnzyspK\nrGMCAAAATINBsT516tTp06evXbvm6OjI/rsD3iMmJubv7x8fH0+j0aZOnTpv3rzU1FSsQ41pDQ0N\nu3bt0tLSunfv3p9//pmUlFRYWNjZ2ZmdnX3jxg1nZ+cPHz5cuHDB0dFRRUVFSkqKTCZv2LDBz88v\nMjKyoaEB6/gAAADACLF7jvXjx48XLFhw/Phx5u6MCwDqxYsXP//8c1JSkoODw+bNm+fNmwdn+7FT\nbm7uuXPn/vnnH1FR0T179mzcuBGPx3/t4paWlqysrNzc3JycnNzc3PT09MbGRgRBxo8fr6+vr6en\nZ2Zmpq+vb2BgICwszMZvAgAAAGDI53Os2Vqs8/LyLC0tFy5ceO3aNbbdFIxBjx8/Rh9/qqurb9iw\nYe3atXJycliH4mV9fX0PHjw4d+5cdHT0pEmTNm/evG7dOlFR0W8dp6qqCu3Zqampubm52dnZPT09\ngoKCkyZN+rhqT5w4kY+PjxXfCAAAAMA4LIt1U1PTlClTxo0b9/LlS3j+BNigsLDw8uXLly5damlp\nsbW1Xbly5cKFCyUkJLDOxTsGBgYSEhJCQ0ODg4Pr6+tnzpy5fv36xYsXCwgIMGX8np4etF5nZWVl\nZmZmZ2ejc7JlZGQMDQ0NDQ2JRKKJiYmhoSH8lQIAAID9MCvWNBptzpw5hYWFb968UVBQYMMdAUB1\ndHTcu3cvKCgoIiICh8PNmzfPzc3N3t4eDiQaMRqNlpiYeO/evZCQkMrKSiKR6O7uvnz5cjU1NVbf\nuqmpCW3Yg1W7vb1dUFBQV1fX5F/Gxsbw9gkAAAAbYFasN2/efO3aNQqFYmZmxobbAfC5xsZGtGHH\nxsby8fFZWVnNmTPH0dHR2NgY5hUworKy8tmzZ8+ePYuMjGxubtbW1nZ3d1+6dKmenh6GqaqqqlL/\nlZycXFtbiyDI+PHjzf5FIpHGjx+PYUIAAAC8CptiffXq1e+//z4oKOjjGwOAlaampoiICLQj1tTU\nKCoq2tjYTJ06lUwmGxoaMmsaA294//59XFxcfHz8q1evsrKyREREZsyYgb4h0dbWxjrdF6A9e3Ci\n9tu3b+l0urS0NDo/G6WnpwdvpQAAAIweBsU6MTFx+vTpBw4cOHDgAEtvBMC3otPpVCr1+fPnFAol\nISGhublZQkLCyspq6tSpJBLJ2Nh4DD7pbG9vz8zMTE9PT0hIiIuLKy8vFxQUNDU1nTp1qr29/YwZ\nM7jrhBd045HBR9rv3r3r7++XkJAwNDRES7a+vr6hoaGQkBDWSQEAAHAfdhfr5uZmU1NTHR2dx48f\nwyMiwMkGBgZycnLQhh0fH19aWoogiIKCgrGxMTpt18DAQEtLi8cWydHp9PLy8ry8vPT09PT0dCqV\nWlhYODAwICUlZWlpaW1tPW3aNAsLCwKBgHVS5ujt7S0oKBjs2WlpaV1dXeiuI4PPs01NTXnm+wUA\nAMBSbC3WdDrdxcUlKSmJSqXCZmeAu3z48AEtmqi3b9/SaDQBAQE1NbWJEyfq6Ojo6+tra2urq6ur\nqKhwRdum0+k1NTVlZWUFBQXv3r3L/1dXVxeCIKqqquhbiPLy8uvXr0+ePPnnn392dXXl7V3A+/r6\ncnJy0v+VkZHR1taGw+EGl0KSSCQTE5MR7BsIAABgLGBrsfbz8/vxxx9fvHgxc+ZMFt0CAPbo6elB\ny+iLFy9u3bolISHR3d394cMH9KuKiooqKiqqqqqqqqoTJkxQUFCQlZUdN26cvLy8nJwc2x5/0mi0\nhn/V1dXV1dVVVla+f/++vLz8/fv3VVVVvb29CIIICwtraWlNnjxZW1tbW1t78uTJOjo6MjIyg+OU\nlJQcO3bsypUrkydP9vHxWb58+RiZdz4wMFBYWEilUtPS0tCqXV9fLyAgoKenRyKRzM3NSSSSkZER\nzBsBAACAYl+xTklJIZPJBw4c2LdvHyvGB4DN6HS6v7//rl27HB0dr169KisrW19fX1ZWVlFRUV5e\nXl5eXlFR8f79+7Kysvr6erTCoggEgpycnJycnJiYGIFAEBcXl5CQIBAIBAJBWloaQRA8Hj84cRmH\nw4mLi6O/7unp6ezsRH9No9Ha2toQBOno6Ojs7Gxra2ttbe3s7Ozs7Pzw4UN7e3t9fX1TU9PgTfn4\n+OTl5ZWUlNCuj/b+CRMmoO2fkaL89u3bo0eP3rp1S0dHZ/fu3WOnXn/s4y1HXr9+3djYiMPhtLW1\nB+eNWFhYQM8GAIAxi03Fuq2tjUQiKSsrR0REjMF/jAHvqa2t9fDwiI2NPX78uJeX17ALBlpbW+vq\n6hr+q6Ojo6OjAy3Bb968UVdX7+npQRCko6NjsIh//OuPSzaCIGgLJxAIIiIi7e3tGhoa0tLSaDsX\nExOTk5NDn5TL/Yspszg+qdcrVqzg7ckhQ6DT6fn5+cnJySkpKcnJyVQqtbOzU1RUFJ0xgj7PnjRp\nEiwmAQCAsYNNxdrNzY1CoVCpVDgLBvCAiIgIDw8PCQmJoKAgExOT0Q94+fJlLy+v+vr6zyfv6urq\nLlmy5Ndff/3k/6gf6+rqUlFR2bVr1549e0YfhhG5ubnHjh27deuWrq7uzz//vGTJEqiP/f39eXl5\nqR/p7u5G9xshk8lTp061srKCtSUAAMDbPi/WzH/4dPPmzTt37ty4cQNaNeB2NBrN19d3zpw59vb2\nqampTGnVCII8fPjQzs7u81bd09NTWFhoYGAw9MtFRERWrFhx6dKlgYEBpuQZlp6eXmBgYEZGhomJ\nybJly4yMjEJDQ9lzthTHEhAQ0NfX9/Dw8PPzi4uLa25uTkxM/OWXX9TU1EJDQ52dnceNG6ejo+Pp\n6Xnu3Lm0tDQajYZ1ZAAAACzH5GJdX1+/Y8eOLVu2zJo1i7kjA8BmpaWl06dPP3ny5LVr1wIDA8XE\nxJgybFdXV2RkpJOT0+dfQvceGbZYIwiycePGkpKSly9fMiUSg/T19dF6bWxsvHTpUmNjY6jXg4SF\nhadMmeLl5XXz5s2ioqLm5uYXL14sXbq0qanpwIEDZmZmBAKBRCJ5e3sHBgbm5ORgnRcAAABLMLlY\nb9q0SVRU9LfffmPusACw2d27d01MTHp6elJTU1euXMnEkSMiIrq6uubOnfv5l7Kzs9E9lYcdRFdX\n19ra+uLFi0wMxqDBej158mR3d3eo118kKSlpZ2fn6+v78OHD+vr67Ozsv//+28TEJDo6es2aNQYG\nBioqKkuWLDl16lRiYmJfXx/WeQEAADAHM4t1knqH+wAAIABJREFUUFDQvXv3Lly48PGKKwC4S1dX\nl7e395IlS5ycnOLi4ph+cPfDhw8tLCyUlZU//1J2draOjg6Du0ysW7cuLCysurqaufEYZGBgEBIS\nMlivTUxMoF5/DT8/v76+/po1ay5dupSZmfnhw4eXL19u3ry5r6/v2LFjVlZWkpKS06dP/+mnnx49\nevTx1i4AAAC4DtOKdUNDg7e394YNG2bPns2sMQFgs9zc3ClTpvzzzz8hISGBgYFMP757YGDg0aNH\nX5wHgiBIdna2oaEhg0O5ubmJi4sHBgYyL903MzQ0DAkJoVKp2tra7u7u1tbWDx8+xDAPVxAXF7e1\ntf3pp58ePHhQV1dXVFSEPsx+8eLFggULZGVlNTU1PTw8Ll68mJOTA+9VAACAuzCtWG/atElEROT3\n339n1oAAsFlgYKC5uTmBQEhPT3d1dWXFLZKSkmpqapydnb/41ezsbH19fQaHQpcwXrx4kW1LGL+G\nSCSi9VpVVXXBggVWVlZQrxmnoaGBroBMSUn58OFDRETEypUrq6urt2/fbmBgIC0tbW9v7+vrGxkZ\n2d3djXVYAAAAw2BOsb579+7du3cvXrwIk0AAN2ppaVmyZMmaNWt27twZHx8/ceJEFt3o4cOHampq\nX3ws3dbWVl5ezvgTawRBNm7cWFxcHB0dzbyAI4fW6/T0dLRew9PrEZCQkEBnZkdERDQ3NyckJBw4\ncEBMTOzvv/+2t7eXlpa2sbE5ePBgVFTU4MlBAAAAOAoTinVHR8f27dtXrVoFk0AAN6JSqWZmZgkJ\nCZGRkYcOHWLpkUbh4eELFy784peysrLodDojW4IMwnAJ49cYGRmFhIQkJCTIyso6OztPnToV6vXI\nCAkJWVlZ/fjjj/fv36+pqcnPz//77781NDRu3rxpZ2cnLS1NJpP37dv3/Pnz9vZ2rMMCAAD4HyYU\n66NHj7a2tsJOIIAbBQYGTp06VVlZOTU11cbGhqX3Ki0tzc7Onj9//he/mp2dLSYmpq6u/k1jrl+/\nPiwsrK6ujgn5mMfS0vLhw4evX7+WkZFxdnYmk8mRkZFYh+JukyZN8vT0vHr1amFhYXV19Y0bN8zM\nzJ4/f+7o6CglJYVu5BcaGgprHwEAAFujLdbFxcUnT548dOiQoqIiUwIBwB7d3d3r169ftWrV999/\nHxkZOX78eFbf8cmTJ2JiYtOmTfviV3NycvT19b/1REM3NzdRUdFr164xIR+zofU6ISEBnShMJpOj\noqKwDsULFBUVXV1d0WnZVVVVt27dmjJlSlRUlLu7+7hx48zMzHbs2PHgwQMo2QAAwH6jLdY//vjj\nxIkTt2zZwpQ0ALBHQUHBlClTgoOD79y54+fnJygoyIabPn361N7eXlhY+Itfzc7O/qZ5ICjOWcL4\nNehaxvj4eGlpaTs7OzKZzOZzbXiboqKim5vbX3/9lZ2d3dzc/OzZM3t7ewqFsnjxYnSDkQ0bNoSG\nhra2tmKdFAAAxoRRFeuoqKiwsLBTp06xp5cAwBQPHjywsLDA4XDp6emLFy9mz017enpiYmIcHR2/\ndsHIijWCIJs2bSoqKuKQJYxfg65ljIuLExERmTVrFplM5vDA3Ahd+3js2LGUlJSGhoawsLB58+bF\nxcW5ubmNGzfO1tb2119/ff36NRyuDgAArDPyYk2j0Xbs2LFgwYI5c+YwMRAArIP+R7to0aJly5Yl\nJCRoaGiw7dYxMTHt7e0ODg5f/GptbW1dXd3IijW6hPHSpUujC8gOU6dOjYiIoFAoeDx+5syZZDI5\nJiYG61C8SVpaesGCBf7+/jk5OTU1NdevX9fW1r58+bK1tbWUlJS9vf3x48dTU1Nhn2wAAGCukRfr\nc+fO5efnnzx5kolpAGCduro6Ozu7ixcvXr9+/dy5c1+bksEiT58+JRKJEyZM+OJXs7OzEQQZWbFG\nEGT9+vX3799vaGgYeT42QtcyUigUYWFhW1tbMpkcGxuLdShepqCg4OrqeuHChdLS0rdv3x4/flxc\nXPz48eMkEklJSWnFihUBAQHl5eVYxwQAAF4wwmLd3t7+yy+/bN++XVNTk7mBAGCFtLQ0CwuLkpKS\n2NjY5cuXsz/AkydPhp4HIicnN+IVwK6ursLCwrdv3x5pOgygaxkpFIqQkJCNjY29vX1SUhLWoXif\njo7Oli1b7t27V19fn5KSsn379tra2s2bN6upqaETsh8+fNjT04N1TAAA4FYjLNZnz57t7u7euXMn\nc9MAwArXr18nk8m6urrp6ekkEon9AYqLiwsKCoYu1t90NMwnCASCi4sLtsebjwy6lpFCodBotClT\nptjb2ycnJ2MdakwQEBAwMzPz8fGJiIhAT3xcuHAhhUJxdnaWlZVdsGDB+fPnS0tLsY4JAABcZiTF\nur29/fTp09u3b5eTk2N6IACYqKenx9vb29PT08vL69GjRzIyMpjEePz4sYSEhLW19dcuGPHKxUEe\nHh4pKSlZWVmjGQQr6FpGCoXS19dnYWEB9ZrN8Hi8nZ3dyZMnc3Nzq6ur0YlSe/funThx4uBjbDhQ\nHQAAGDGSYu3n59fT07Njxw6mpwGAiaqqqmxtbQMCAkJDQ48dO8bSIxWH9vTp09mzZ39t8xw6nZ6b\nmzvKYm1jYzNx4sSbN2+OZhBsoWsZIyIiWltb0XqdkpKCdagxR1FR0cPDIyQkpKGhgUKhuLq6pqam\nLliwQEZGBl3ymJeXh3VGAADgXN9crNvb2/38/Ly9vbF6+AcAI+Li4szMzD58+JCUlOTi4oJhkq6u\nrtjY2CHmgZSVlbW2to6yWPPx8S1fvvz69ev9/f2jGQdzdnZ2b968iYiIaGlpMTc3t7e3T01NxTrU\nWITD4chkMrp5X0lJyalTp0RFRX/99VddXV19ff2ffvopOTkZNhUBAIBPfHOxPnPmTE9Pz/bt21mR\nBgCmuHTp0qxZsywtLd+8eaOjo4NtmOjo6K6uriF2pczOzubj49PT0xvljTw9Paurq3njdEM7O7uk\npKTBeu3k5JSWloZ1qLFLTU1t48aNYWFhjY2NkZGR9vb2t2/ftrCwUFVV3bx5c0RERF9fH9YZAQCA\nI3xbsW5tbUVnV8PjasCZ+vv79+zZs2HDhh07dty9e1dCQgLrRMizZ8+MjY2VlJS+dkF2draqqqqU\nlNQob6SlpWVpacmNSxi/Bq3XL168qKmpIZFIUK8xJyQkNGvWrDNnzpSUlGRnZ2/bti0zM9PBwUFa\nWtrJySkwMLClpQXrjAAAgKVvK9bnzp3r7++Hx9WAMzU1Nc2ZM8fPzy8wMPDYsWP8/KM6WJRZnj9/\nPvQhSpmZmaPZEuRjHh4e9+7d47FyY2dnl5yc/OLFi+rqarRep6enYx0KIPr6+j4+PnFxcehEEQRB\nvv/+ewUFBXt7ez8/v6qqKqwDAgAABr6hefT39//999/r1q2TlpZmXSAARiY/P9/a2vrt27cUCmXF\nihVYx/mfsrKy/Px8e3v7Ia6hUqnGxsZMud2yZcsQBLl79y5TRuMoaL1+8OBBVVUVWq+pVCrWoQCC\nIIiamtr69esfPnxYXV194cIFMTGxn376SVVVderUqadPn66srMQ6IAAAsM83FOuwsLD3799v2rSJ\ndWkAGJmnT59aWFjIysqmpKRgslP11zx//lxUVHSIjfa6uroKCgqMjIyYcjtJScn58+fz0myQj/Hx\n8Tk5OaWkpISFhVVWVpqZmbm5ucEmFZxDVlbW09MTPQT03r172trahw8fnjBhgo2NzYULFxobG7EO\nCAAALPcNxfrPP/+cN2+ehoYG69IAMAJ+fn7z58+fP39+VFTUiA8vZJEXL17Y2NgMcXx6dnY2jUZj\nVrFGEMTDw+PVq1fFxcXMGpDToPU6NTU1LCyssLBQX18f6jWnERERWbBgQUBAQE1NTVhY2IQJE3bu\n3KmgoEAmky9evNja2op1QAAAYBVGi3Vubm5sbOzWrVtZmgaAb9LT07NixYqdO3eePHnyxo0beDwe\n60T/0d/fHx0dPXv27CGuycjIEBUV1dLSYtZN58yZo6CgcOPGDWYNyJk+fnpdUFCA1ut3795hnQv8\nh7CwMLqosa6u7v79+0pKStu2bVNQUEA/2dHRgXVAAABgMkaLtb+/v5aWlp2dHUvTAMC4xsZGe3v7\nR48ePX78mDMX1CYlJTU1NQ1brIlEIhPXWeJwuGXLll27dm0s7DHMz8+PPr0OCgrKysrS09Nzc3PL\nz8/HOhf4lIiIiJOTU0hISHV19dmzZzs7O9esWaOkpOTp6fns2TNu33wdAAAGMfTPeXNz840bN7Zt\n28Yh2ywAUFRUNHXq1OLi4piYmKGbK4aeP3+uoqIy9EbaVCqVifNAUB4eHiUlJQkJCcwdlmPx8/O7\nurrm5OQEBQVlZmbq6uq6ubkVFBRgnQt8gYyMzPfffx8VFVVbW/vHH38UFRXNnTtXVVV1z549RUVF\nWKcDAIDRYqgoX7t2jZ+f39PTk9VpAGDE69evraysREREEhMTmbWfBitEREQMvdEenU7PyspierE2\nNjY2MjL6559/mDssh0PrdW5uLlqvdXR0oF5zMllZ2fXr18fFxRUUFKxevfr69eva2tr29vZBQUE9\nPT1YpwMAgBFiqFgHBAR89913nHDWBgChoaGzZs0ik8nx8fEqKipYx/mq5ubmpKSkoZ+ml5SUtLS0\nML1YIwiycuXK4ODgrq4upo/M4T6u1xkZGfr6+h4eHoWFhVjnAl+lqal55MiR9+/fP3/+XFpa2sPD\nQ1FRccOGDbCdIgCAGw1frPPy8jIzM7/77js2pAFgaH5+fkuXLl23bt2dO3cIBALWcYby8uVLOp0+\na9asIa7JyMjg5+dn1ukwH1uxYkVnZ2d4eDjTR+YKaL1++/btzZs3ExMT9fT0PDw8YKYBJ+Pn57ez\nswsJCSkvL9+zZ8/Lly9NTExIJNLFixfb2tqwTgcAAIwavljfunVLSUmJTCazIQ0AX9Pb2+vp6fnj\njz/6+/v7+flx/nT/Fy9ekEgkGRmZIa7JyMjQ1NQUExNj+t0VFBRmz57N83uDDG1w7vXly5cTExN1\ndXWhXnM+RUVFHx+f/Pz8qKgobW1tb29vZWXlDRs2ZGVlYR0NAACGN3w7CQ4OXrZsGef3GMDDWltb\n586de//+/UePHm3ZsgXrOAyJiIgYdlUlE89c/NzSpUufP38Op3IICgp6eHig9fr169dovebhfb55\nAx8f38yZM2/dulVZWfnrr79SKBQikWhnZxceHj4wMIB1OgAA+Kph6nJqamp+fr67uzt70gDwuZqa\nGhsbm9zc3FevXg29FpBzFBQUFBcXD32SOYIgGRkZrJhgjVq4cCEOh7t//z6LxucuaL3Ozc29fPly\nQkKCrq7uhg0bKioqsM4FhiEjI+Pl5ZWTk/Ps2TMhIaGFCxdqa2v7+fm1t7djHQ0AAL5gmGIdHBys\noaHBUWdEgzGlpKRk+vTpLS0tr1694uQNQD4REREhLi5uaWk5xDXNzc1lZWWsK9bi4uJz584NCgpi\n0fjcCK3Xb9++vXTpUmRkpKam5oYNGyorK7HOBYbBx8fn4ODw5MmTt2/fOjg47Nu3T01N7eeff25o\naMA6GgAA/MdQxZpOp4eGhi5btoyPj49tgQAYlJqaamlpKSkp+fr1ayaeTcgGUVFRM2bMEBQUHOKa\nzMxMOp3OumKNIMjSpUujo6OrqqpYdwtu9Em91tDQgHrNLSZPnvzXX39VVVX9/PPPly5dUlFR2bBh\nQ3l5Oda5AADgf4Yq1q9fvy4tLXVzc2NbGgAGvXz5cubMmYaGhlFRUePGjcM6zjcYGBiIiYkZej8Q\nBEGoVKqMjIyqqirrksyfP19MTOzu3busuwX3EhISQuv12bNnnzx5gtZreBPCFSQkJLy9vQsLC48f\nP/706VMtLa2NGze+f/8e61wAADBksX7y5ImmpiaRSGRbGgBQd+/enTdvnrOz89OnT7luA/W0tLSm\npqZhi3VGRgarJ7fg8XhnZ+fg4GCW3oWrCQkJrV+/vqio6OzZs48fP0brdXV19dCvghNMOIGoqKi3\nt3dRUdH58+efP38+adIkLy+vYf/sAACApYYq1hEREcOuvgKA6U6fPu3q6rply5bAwMChZ1NwJvQR\nu4GBwdCXsXTl4iB3d/eEhITS0lJW34irofW6uLjY39//0aNHWlpa3t7eX6toubm5ZDK5ubmZzSHB\nFwkKCq5du/bdu3enT5++d++elpaWj48P/OkAALDy1WLd3NycmppqZ2fHzjQAHDx48Mcffzxx4sSJ\nEye4dHJ/VFTUrFmzhg5Po9FycnLYUKwdHBxkZGRCQkJYfSMegNbrkpKS06dP37lzZ9KkSd7e3jU1\nNZ9c5uvrm5KSYm9vDweXcA4hIaFNmzYVFhYeOXLk6tWr2tra58+fp9FoWOcCAIw5Xy3WL1++RBDE\n1taWjWHAmEan03fu3HnkyJFLly798MMPWMcZod7e3oSEhGHngbx79667u5sNxVpQUHDRokUwG4Rx\ng0+vT506FRoaij69HqzXOTk5d+7cQRCESqXOmTOns7MT07DgP/B4/Pbt2wsKCjw8PLZv325sbPzi\nxQusQwEAxpavFuvIyEgzM7Ohz40DgFnodPr27dvPnDlz9erVtWvXYh1n5BISEjo6OmbOnDn0ZVQq\nVVBQUE9Pjw2R3N3d09LS4MTBbyIsLLx+/Xr0Cehgva6trT148CAOh0MQhEajJScnz58/v7u7G+uw\n4D+kpKROnDiRk5Ojp6fn4ODg5OQEG5YDANhmqGIN80AAe/T393///fd///13SEiIh4cH1nFGJSoq\nSlNTc+LEiUNflpGRoaurKyQkxIZINjY28vLysDfICBAIBG9v74KCgp9//vnWrVuampr37t3r6+tD\nv9rX10ehUJycnGAtIwfS0tIKCQmJjo7Oz883NDT08/ODIxsBAGzw5WJdVlZWUFAAxRqwQX9//+rV\nq2/evBkaGrp48WKs44wWOsF62MvYsCXIIBwO5+TkBMV6xERFRXfv3l1SUqKtrY0+rh5Eo9Gio6Nd\nXV1hOi9nsrGxSUtLW7t27c6dO2fOnAk7XgMAWO3LxToyMpJAIFhbW7M5DRhrent7XV1d79279+jR\nI2dnZ6zjjFZbW1tKSgqDxZoNE6wHubi4JCcnl5WVse2OvKesrIxKpQ4+rh7U39//5MkTd3d36Nac\nSVRU9MSJE4mJifX19cbGxqGhoVgnAgDwMtwXPxsfH29paSksLMzmNGBM6ezsXLx48Zs3byIiIqys\nrLCOwwQxMTE0Gm3YJb81NTW1tbXsLNb29vZSUlL37t3bsWMH227KY/bt24fD4T4v1giC9Pf3P3jw\nYO3atQEBAfz8Q+1hOhptbW09PT2tra0dHR29vb29vb0dHR2DX/3kQwRBpKWlP/9QTEwMj8dLSEgQ\nCIQx9Te8mZlZSkrKDz/84ObmtnbtWn9/fwKBgHUoAAAP+nKxTkpKcnJyYnMUMKZ0d3cvXLgwPT09\nKirK1NQU6zjMERUVZWRkJC8vP/RlVCoVQRB2FmtBQcH58+ffvXsXivXIZGRkhIeH0+n0r13Q399/\n48YNERGR8+fPM75NZE9PT01NTUVFRUNDQ1NTU1NTU2Nj48f/29bW1tra2t3d3d7ezqRv5f/x8fFJ\nSUmJiIgQCAQZGRlZWVmZj6AfKioqjh8/XkFBgUv3vvwY+qfj4OCwdu3a1NTU+/fvq6urYx0KAMBr\nvlCs29ra8vLyDh8+zP40YIxAZ4AkJydHRETwTKtGECQqKsrBwWHYyzIyMpSVleXk5NgQaZCLi8vi\nxYurqqqUlJTYeV/ecOXKFQEBAXSyh4CAAPro+pPFcAMDA5cuXRIRETl9+vQnn6+oqCgqKiouLn7/\n/n1lZWV1dfX79+9ramrq6uoGLxMTExsstXJyctra2jIyMhISEpKSksLCwmJiYmJiYsLCwpKSkujD\nZn5+fklJycGXCwgIfHxGaX9/f2tr6+CHNBoN3XX748fePT09zc3N3d3dHR0daJVvamoqKytr+ldv\nby/6ckFBQUVFRVVVVUVFRRUVFSUlJXV1dQ0NDQ0NDVlZWWb+RrPewoULzczMFi1aRCKRgoKCYCkR\nAIC5vlCs09LS+vv7SSQS+9OAsaCvr8/V1ZVCobx48YKX/jOrra3Nycn5/fffh72SzROsUQ4ODmJi\nYvfu3du6dSubb80D/P39T506VVFRUVJSUlxcXFJSUlJSUlBQUFRU1NTUhF4jJCREo9HOnDlTU1ND\nIpGK/1VaWoo2VDExMTU1NSUlJSUlJSMjo/Hjx6uoqKCFVU5OjrkTMwQEBD6ZCjLsD1I+19bWVlVV\nhT5Tr66urqysrKqqSktLCw8Pr6ioQN9mSElJafxLS0vL0NBQT0/v44rPgVRVVePi4tavXz9nzpwj\nR474+PhgnQgAwDu+UKypVKqcnNyECRPYnwbwvP7+fg8Pj6ioqKdPn1pYWGAdh5levnyJw+GmTZs2\n7JWZmZnsX6mJx+MdHR3v3r0LxXpkcDicurq6urr6x3PoW1paXr9+/erVq7S0tIKCgsrKyp6enqCg\noMePHxsbG2toaFhZWWlqaqK9U0FBAcP8IyAuLj558uTJkyd//iUajVZWVoa+c0AfxkdERPz111/o\nPG81NTV9fX0DAwN9fX0ikWhgYPDJbiqYw+Px//zzj76+/r59+7KystAfNWAdCgDAC77wl112draB\ngQH7owCeh7bq8PDwx48fM1JAuUtMTIy5ubmYmNjQl3V1db179479T6wRBHFxcVm2bFldXd24cePY\nf3feQKPR3r17l5qaGh8fHxcXl5eXNzAwICUlpampOXXqVDMzM7RQKioqYp2UtXA4nKampqam5ief\nr6qqys3NzcnJyc3NjY+P//PPPzs7OwUFBYlEIvr7Y2Zmpqenxwkztvn4+Hx8fIyMjJYtW0Ymk+/f\nvw+PkwAAo/eFYp2Tk2NmZsb+KIC3DQwMrF69+v79+48fP7axscE6DvPFxsa6uLgMe1lWVhaNRjMx\nMWFDpE/MnTtXSEjo4cOHXH22Jft1dnbGx8dHR0dHR0enpaX19vZKSUmZm5svWrTI3NycRCIpKytj\nnZFToBNdBicu02i0t2/fJicnJycnx8XFnT9/vq+vT0ZGZurUqTNnzrS1tSUSidiW7Dlz5iQnJy9Y\nsIBEIoWGhs6YMQPDMAAAHvBpsabT6W/fvuX20+8Ap6HT6Zs3bw4ODr5///6wu9Fxo+rq6nfv3jHy\nrzKVShUTE9PS0mJDqk+IiorOmjUrPDwcivWw+vv7X79+HRkZ+fLlyzdv3vT29uro6Nja2m7dutXc\n3HzSpEmc8MyV8+FwOENDQ0NDwzVr1iAI0t3dTaVSk5KSYmNjf/311x07dsjJydnY2Nja2jo4OHz+\n/Js9tLS0EhMTPT09Z8+e/ddff33//feYxAAA8IZPi3VNTU1zc7OOjg4maQBPotPpW7duDQgIuHv3\n7ty5c7GOwxIxMTE4HI6R3bipVKqRkRHrdjsemrOzs7e3d0dHh6ioKCYBOFxXV1dkZOSjR4/Cw8Nr\namrGjx9PJpPPnj07Z84cmCcweng83tLS0tLS0svLC0GQ4uLiyMjIyMjI/fv3b9myRUNDY/78+a6u\nrtbW1mz+P4i4uPidO3cOHjy4fv16KpXq5+cnICDAzgAAAJ7x6V9eRUVFCIJg9eQA8B46ne7l5XXp\n0qXQ0ND58+djHYdVYmNjzc3NxcXFh70yPT2dbYeZf87Z2bmnpycyMhKrAJyps7Pzxo0b8+fPl5GR\nWbRoUU5Ozg8//PDu3buqqqqQkJD169dDq2YFDQ2N9evXh4SE1NXVRUVFOTk5hYeHT5s2TUVFZdOm\nTfHx8ewMw8/P/8svv4SGhgYEBCxatKizs5OddwcA8IxPi3VxcbGwsDBMGQTMsnfv3vPnzwcGBvLA\nieVDiImJYWTi+MDAQHZ2NobFWkFBwdzcPDw8HKsAHIVOp8fHx69bt278+PFr1qzh5+f/66+/qqur\n4+Lidu3apa2tjXXAsQKHw82cOfPMmTMlJSVUKhVt1WQyefLkyb/99ltFRQXbkri4uERHR79588bG\nxubjXcYBAIBBXyjW6urqWP2cGvCYQ4cOnThx4ubNm0uXLsU6CwvV1NTk5+czMsE6Pz+/vb0dw2KN\nIIizs/OjR4/6+/sxzIC5jo4Of3//yZMnk8nkpKQkX1/fioqK8PDwNWvWjGC/Z8BERkZGBw4cyMzM\nTE1NdXBwOHXqlJqamqOjI9t+zGJhYfH69euWlhYrK6v8/Hz23BQAwDM+LdBlZWVwyitgivPnzx86\ndOj8+fPu7u5YZ2GtmJgYAQEBa2vrYa+kUqk4HA7b7SydnZ3r6urevHmDYQYM1dfXHzx4UE1Nbe/e\nvXZ2dqmpqRkZGTt27IAtCDmNqampv78/Ohunt7fX3t7ezMwsKCgIPZiGpTQ0NBISEhQVFa2trdk8\nIwUAwO0+LdbV1dUwDwSM3v3797dt2/bbb7+tW7cO6ywsFxsbSyKRGJlgTaVSdXV18Xg8G1J9jYGB\nwaRJk8bgbJDW1tZdu3apq6ufO3du69atpaWl586dMzU1xToXGIqQkJCLi0tUVFRKSoqWltaKFSu0\ntbWDgoLodDpL7ysrK/vixQsrK6vZs2c/fPiQpfcCAPCST4t1TU0Nzx9tAFjt5cuXy5Yt27hx4549\ne7DOwg4MTrBGEIRKpWI7DwQ1f/78MVWsBwYGAgICtLW1AwICjh49WlZW5uvrC1M+uIuZmVlwcPC7\nd+9sbW2XL18+Y8aM9PR0lt5RVFQ0LCzMw8Nj0aJF58+fZ+m9AAA84wvFmuvO3QUcBT1tYcmSJf7+\n/lhnYYe6ujoGd7BGOKZYOzs7v337dozMH3337p2VldX69etdXV3z8/O9vLwIBALWocAIaWpqXrly\nJSkpqb+/39zcfOvWrd3d3ay7nYCAwPngZ9AvAAAgAElEQVTz548cObJlyxZvb29WPyYHAPCA/xTr\ngYGBhoYGKNZgxAoKCubPnz9jxoyAgIAxsgQ2OjpaQEBg6tSpw15ZVVVVW1vLCcWaTCbLyMiMhR9w\nBwUFmZub8/Hxpaennz17VkZGButEgAnMzMzi4uKuXbt269YtKysrdJdY1vHx8bly5cr58+fXrVs3\nxlf9AgCG9Z/q09bW1t/fLy0tjVUawNUqKyvt7e0nTpwYHBwsKCiIdRw2iY2NNTMzY3CCNYIgRCKR\n9aGGgcPhZs+e/fTpU6yDsFB/f/+2bdu+++67NWvWvHr1CtsFo8zS09Pj7e2tqKhIIBCePXvGzltH\nRkbu3buXwRgnTpwYN24cHx/f33//jSBIeHj48ePHmVtJ+fj4VqxYkZqaKiAgYGZmxup3iatXrw4L\nC7t169bKlSvZsHoSAMC9/lOsW1paEASRlJTEKAzgYo2Njfb29mJiYk+ePBlTp/p90wTrCRMmyMnJ\nsTgRQxwdHSkUSltbG9ZBWIJGoy1fvvzq1ashISFnzpwREhLCOhFznDx58tmzZ3l5eWfOnGlvb2fb\nfQ8ePOjv7//TTz8xGGPnzp0JCQmDHzo7O+Px+FmzZjU3NzM32MSJE+Pj411dXRcvXhwcHMzcwT8x\nd+7cZ8+ePXr0aNGiRSydfwIA4Gr/OdIcLdYSEhIYhQHcqrOz09nZub29PT4+fkz9tL2uri4vL+/k\nyZOMXEylUk1MTFgdiUGOjo40Gi06OponD+7ZuXPnw4cPHz9+zOB7Hm4RFhZGIpGkpKTWr1/Ptpse\nO3YsKCgoIyNjcDebEcTw9vYuLi6eO3fuq1evcDjc8C9gmLCw8KVLl8TFxT08PMaPHz99+nQmDv6J\n6dOnR0VFzZkzZ+7cueHh4WJiYqy7FwCAS/3niXVraysCxRp8o76+PhcXl/z8/IiICFVVVazjsBW6\ngzUjE6wRjlm5iJKXlzcxMeHJ2SBhYWF+fn5Xr17lsVaNIEhFRQWbJ1kVFhYeOHDg0KFDH+8RObIY\nvr6+VCr1zJkzTA34PydPnnRycnJ3d//w4QMrxh9kbm7+4sWLzMzMhQsXdnV1sfReAABu9J9i3dnZ\niSAILJkHjBsYGPD09ExISHj27NnkyZOxjsNusbGxpqamjLwXbW9vLyoq4pxijSCIo6Pj48ePsU7B\nZL29vdu2bfP09GTFsURnzpwRFRXl5+c3MzNTUFAQFBQUFRU1NTWdNm2aqqoqHo+XkpLavXv34PX9\n/f0///zzhAkTREREiETi4FwFCoWip6cnKSmJx+MNDQ2fP3+OIMi5c+dERUUJBMKDBw8cHR0lJCRU\nVFRu376NviQiIkJLS6u6uvqff/7h4+MTExPz8vISEhIa3B11y5YtoqKifHx8DQ0Nw46Gun79OolE\nwuPxoqKi6urqv/zyy+ffsr+/P51OH/yxxucxEASJjY21sLAgEAgSEhKGhoboA5rPSUtLz5gx48yZ\nM6zYW4OPj+/SpUsIgvj6+jJ98E+YmZlFRUVRqdSFCxfCnBAAwKfoH3ny5AmCIG1tbXQAGLN7924h\nIaGoqCisg2BDT09v9+7djFxJoVAQBCkpKWHkYgRBgoODR5WMAeiRcrm5uay+ETsFBgYKCQlVVFSw\naPyDBw8iCPLmzZuOjo6GhoY5c+YgCPL48eP6+vqOjg4vLy8EQahUKnrxzp07hYWF79y58+HDh59+\n+omfnz85OZlOp4eGhvr6+jY1NTU2NlpaWsrKyqLX79u3D0GQqKiolpaWurq6adOmiYqK9vb2Dt5d\nQUHB09Nz8MPly5crKCgMfvjHH38gCFJfX8/IaKdPn0YQ5OjRo42NjU1NTRcuXFi+fPnn36+Ghoae\nnt4nn/w4Rnt7u4SExPHjx7u6umpqahYvXowGKCgoQBDk/PnzH78QXf6Ynp7+bb/pDPP39ycQCB8+\nfGDR+B9LT0+XlZWdPXt2V1cXG24HAOBMn/97/Z8n1n19fQiCMHcCHOBhly9f/uOPPy5dujRz5kys\ns2Cgrq7u7du3jO9gLSUlpaamxupUjJsyZYqsrCyPzQYJDw+3s7Nj9fGxenp6BAJBVlZ22bJlCIKg\na1IJBMKKFSsQBMnLy0MQpLu7+9y5c4sWLXJxcZGSktq/f7+goGBAQACCIEuWLDl48KC0tLSMjIyz\ns3NjY2N9ff3g4NbW1hISEvLy8kuXLu3o6CgvLx9N1C+O1tfXd+jQIVtb2z179sjIyEhLS69du9bc\n3PyT13Z0dJSUlGhqag4xfmlpaWtrq76+Ph6PV1BQuHv37hDLcydNmoQgSFZW1mi+oyGsXLmyt7c3\nIiKCReN/zNjY+NmzZ0lJSW5ubr29vWy4IwCAK/ynWKO7CI2djdLAaDx9+nTTpk2HDh3y8PDAOgs2\nYmNj+fn5v2mCNR8fH6tTMU5AQMDOzo7HinV2dvaUKVPYdjt0v5HB/dfQvzzRJxTv3r3r7Owc3OZP\nREREUVER7dwfQ1/yxa3o0MHR0ZgVFR0tMzOzubnZwcFh8KsCAgLe3t6fvKSuro5Opw89OVBDQ2Pc\nuHErVqzw9fUtLS0dOgM6VG1t7Yi+g+FJSUlNnjw5OzubReN/gkQiPXv2LDY2duXKlbC/NQAA9Z9i\njf7VICAggFEYwDXS09Pd3NxWrlx54MABrLNgBp1gzeD2lBy1JcggR0fHV69esXPjNlZra2tjZE9x\nNujo6EAQZP/+/Xz/KisrQ9exoNuVyMvLCwsLfzwnm23QadBSUlJDX4ZOIBYWFh7iGhERkZcvX5LJ\n5CNHjmhoaCxdunSIJX0iIiKDw7KIhITE1yZ5s8KUKVOePn366NGjLVu2sO2mAABO9p9iTYfzWgED\nKisrnZ2dSSQSevrDmMX4DtY0Gi0nJ4ejVi6iBjfdwzoI0ygoKFRWVmKdAkEQRF5eHkGQ06dPfzz3\n7vXr1+Xl5YsWLVJUVHzz5k1LS8vx48fZn01JSQlBEHSZ4xDQHjzss1h9ff2HDx9WVVX5+PgEBwef\nOHHia1eiUybQYVmkoqJicEEne1hbWwcFBV25cmX//v3svC8AgDONiUOnARO1trbOnTtXUlLy/v37\nPHPuxgjU19fn5uYyOME6Nze3u7ubA4v1uHHjeGzTPWtra/ZMsR0Wuk8Ietzmx7Kysvr6+jZv3qyh\noYHH40czOwiHw41sooi6urqMjMyLFy+Gvgw9PRE93+BrqqqqcnNzEQSRl5c/evSoqakp+uEXoUMp\nKCiMIDMj8vLy3r9/z+DsLCZycnK6fPnyb7/9hi4JBQCMZVCswTfo6+tbsmRJfX39kydPhv05Mm/7\n1gnWQkJCOjo6rE41Avb29lFRUVinYJrly5dnZmbGxMRgHQTB4/GrV6++ffv2uXPnWltb+/v7Kyoq\nqqurJ0yYgCBIZGRkd3d3QUHBmzdvRnwLLS2tpqamsLCwvr6++vr6srIyBl8oLCz8008/vXr1ysvL\nq7KycmBgoK2t7fNCTCAQNDQ0Kioqhhiqqqpq48aNeXl5vb296enpZWVllpaWX7sYHcrQ0JDBnN/q\nzJkzmpqaVlZWLBp/CJ6enn/88cfOnTvv3LnD/rsDADjIxz+mRLdZZf3mJIArDQwMrFq1SlxcPC0t\nDess2Nu6dSuJRGLw4h07dpiamjI+OMKW7fZQ6PPd0tJS9tyODRwcHAwMDLq7u5k+8pkzZ9Dld+rq\n6hQK5dixY+gMewUFhZs3bwYFBaHPYqWlpW/fvk2n03t6enx8fCZMmIDD4eTl5V1cXHJycuh0uo+P\nj4yMjJSUlKur659//okgiKam5p49e9DBJ02aVFRUdPHiRXR/dDU1tfz8/NLSUnSOPg6HMzU1vXPn\nDp1Ob2xstLW1xePxEydO3LZt265duxAE0dLSKi8v/+uvv4YYDf12/vzzT0NDQzwej8fjTUxM/vrr\nr8+/ZS8vL0FBwc7OTvTDz2OUlpZaW1tLS0sLCAgoKSnt27ePRqOdPHkS/a0QFRVdvHjx4Gjz5s1T\nVlYeGBhg+h8NnU5/8+aNgIBAQEAAKwZn0LZt2/B4fHx8PIYZAADs9Pm/11CsAaN8fX0FBAQePHiA\ndRCOYGBgsHPnTgYvtrW1Xbt2LeODs7NYd3V14fH4a9eused2bFBUVCQpKbl69Wqsg/CCgoICHA53\n/fr10Q/V0NCAx+NPnDgx+qE+V1NTo6amZm9vz6LWzqD+/v4FCxbIyckVFBRgGAMAwDaf/3sNU0EA\nQ4KCgg4dOuTv7z94BttYVl9fn5OTw+AEawRBMjIyjIyMWBppxPB4vKWlJS/NBtHQ0Lh169b169d3\n7NhBhwXZo6OlpXX48OHDhw+PfusYX19fY2Nj9Awd5qqrq7O3txcSErp9+za2O1ry8/PfvHlzwoQJ\nTk5Ozc3NGCYBAGAFijUYXkxMjKenp4+Pz+bNm7HOwhHQCdZkMpmRi8vKypqamjhwr71Bs2bN4qVi\njSDI3Llzb968+eeffy5ZsoSdm6/xpL1797q6ui5dunToVYxDO3XqFJVKffLkCdPPSUhJSZkyZUpX\nV1dUVJSsrCxzBx8BUVHRhw8ftre3f/fddwMDA1jHAQCwGxRrMIzCwsIlS5YsXLjwt99+wzoLp4iN\njTUxMWFw+WZ6ejofHx+RSGR1qhGbNWtWVVXV52eXcDU3N7eXL18mJiaSSKTMzEys43C3I0eOeHl5\nHT16dGQvf/DgQU9PT0xMjLS0NHODBQYGTp8+feLEiXFxcaqqqswdfMSUlJTCw8NjYmLG8jb/AIxZ\nUKzBUNra2hYtWqSmphYQEMBRpwZiKzY2lvF5IFQqVVNTE106xpnMzc0lJCR47KE1giDTpk1LTU1V\nVla2tLQ8dOgQejgLGJnZs2cfO3ZsZK9dsGDB3r17mXv0WEFBgZOT0+rVq3fv3h0ZGcm6LfxGxsTE\n5MKFC0ePHg0JCcE6CwCAraBYg68aGBj47rvvGhsbHzx4MPSxxmNKY2PjN02wRg8zZ2mkUcLhcNOn\nT+e9Yo0giKKiYkRExOHDh0+dOqWrqwsthwe0trbu3r3bwMCgtLQ0KirK19eXn58T/yFbuXLl5s2b\n165dO8TG3gAA3sOJfx8BDvHjjz9GRkbev39fRUUF6ywcJCYmho+Pb9q0aQxez/nFGkGQWbNmRUdH\nD3vGHjfC4XA7d+589+7drFmzli1bZmVl9eDBA5j8yo2am5uPHTumra195cqVU6dOpaenM3j0KVZO\nnz5NJBLd3d3hpyUAjB1QrMGXXbt2zc/P78qVK1OmTME6C2eJjY01NjZmcIJ1c3NzeXk5J69cRM2a\nNau5uTk9PR3rIKyiqKh49erVxMREeXn5RYsW6evrX7lypaenB+tcgCGVlZW7du1SU1M7duzYqlWr\n8vPzt2zZgsPhsM41DEFBwaCgoOrq6u3bt2OdBQDAJlCswRfExcVt3LjxwIED3333HdZZOE5MTAzj\n80DS09PpdDrnP7E2MDBQUFDgydkgHzM3Nw8PD8/Ozra0tNy8ebO6uvru3bvhJ/Uci0ajPX782NXV\nFd1Ccf/+/eXl5ceOHeOE3T8YpKqqeu3atcuXL9+4cQPrLAAAdoBiDT5VWlq6ePHiefPmHTx4EOss\nHKepqembJlinp6fLy8srKSmxNNXo8fHxzZgx49WrV1gHYQc9Pb2AgICioqJ169YFBwfr6+tPmTLl\n3LlzHz58wDoa+J+cnJxdu3apqqo6OTnV1dVdvHixpKRk165dnLwI+Gvmz5+/bdu2zZs3FxcXY50F\nAMByUKzBf7S1tTk7OysrKwcGBnLmkiBsxcXF0en0qVOnMnh9RkYG588DQZHJ5Pj4eJ6cZv1FKioq\nhw8fLikpoVAoxsbGPj4+8vLyZDLZz8/v/fv3WKcbo3Jycnx9fUkkkoGBQVBQkKenZ35+fmxsrKen\np5CQENbpRu7333+fOHHi6tWrYXI/ADwPmhP4fwMDA8uXL6+vrw8PDxcVFcU6DieKi4vT19dn/CfR\n6enpXFSsW1pacnJysA7CVuhBPxcuXKisrPznn3+UlJQOHDigpqZGIpF++eWXpKSksfNOAyutra0P\nHz7csGGDkpKSgYFBYGDgtGnTYmJiysrKjh07pqWlhXVAJhAWFg4MDExMTPTz88M6CwCAtaBYg/+3\ne/fuiIiI+/fvc85RC5yGQqEweOAigiA9PT15eXkce5j5J4hEoqSkJIVCwToINiQkJJYvXx4SElJf\nX//kyRNzc/MLFy5MmTJFRkbGyckJ3YMCHjcyS0dHx7Nnz/bs2YP+Di9YsCAtLW3z5s0ZGRnFxcWn\nT5+eMWMGj/3EzMjIaP/+/Xv37h1r710BGGv46HT64AchISHu7u4ffwaMHdeuXVuzZs3169eXL1+O\ndRYO1dXVJSUlFRAQwOCaztTUVBKJ9PbtWx0dnW+6ER8fX3BwsJub24hijpyjo6OUlNTt27fZfF+O\nlZubGx0dHRMTExMT09DQICMjY21tbW5ubm5ubmFhwUVL6DBHp9MLCgqSk5OTkpKSk5NTUlL6+vr0\n9PRsbGxsbW1nzJghLy+PdUaWo9FoVlZWOBwuPj6ex942ADBmff7vNadvVwTYIykpadOmTT4+PtCq\nh5CYmNjb28v4E+v09HQCgTBp0iSWpmIiMpl8/vx5rFNwED09PT09vS1bttDp9KysrOjo6MTExH/+\n+Qdd16upqWlubk4ikYhEor6+PucvUWUnGo1WUFCQnZ2dkZGBlunm5mYhISEjIyMLC4tt27bZ2toq\nKipiHZOtcDjc1atXzczMLl68uHHjRqzjAABYAoo1QGpra11cXKZNm/brr79inYWjxcXFqaioTJgw\ngcHrMzIyiEQic09yZikymbx///7S0lJ1dXWss3AWPj4+IpFIJBK9vb0RBGloaEDLYnJy8h9//FFb\nW4sgiLS0tIGBgZ6enqGhoZ6enpaWloqKCh8fH9bZ2aGnp6e4uPjdu3e5ublZWVm5ubl5eXm9vb0C\nAgKTJk0yNzf/5ZdfzM3NjY2NhYWFsQ6LJUNDw+3bt+/Zs8fZ2RneiQHAk6BYj3V9fX3u7u44HO72\n7dtcVAExQaFQGN9oD0GQ9PR0zt/B+mNTpkwRFhamUChQrIcmJyc3d+7cuXPnoh82NDSgbTI7Ozsn\nJyckJATduU9YWFhDQ0NDQ0PzX2pqauPHj+feOSQ0Gq22tvb9+/elpaVFRUXFxcVFRUVFRUWVlZV0\nOp2Pj09dXV1fX9/R0XHXrl3o8348Ho91as7i6+t7586d3bt3w87WAPAkKNZj3fbt21NTU1+/fs29\n/9izR39/f2Ji4h9//MHg9ejkgZUrV7I0FXPh8XgzM7O4uDjuio05OTk5W1tbW1vbwc/U1NQUFhai\nvbO4uDg5OTk4OBh9sI0gCB6PV/rX+PHjVVRU5OXlZWRkZGVlZWRk0F9g8i63q6urqampsbER/d+G\nhoaamprq6uqqqqrKysrq6ura2lp0BScOh5swYYKmpqaOjs68efPQNw9aWlpiYmLsj81dCASCn5+f\ns7Pz2rVrP/5vBgDAG6BYj2nXr18/f/58UFCQgYEB1lk4HZVKbWtrY3yCdWFhYWtrK3c9sUYQhEwm\nP3r0COsUXE9RUVFRUfGT/1ra29vLy8urPlJZWZmcnHzv3r2Ghoaurq6PL5aUlJSTk5OSkiIQCHg8\nXkpKCo/Hi4iISEpK4vF4dDdM9DODL5GQkBis411dXd3d3YNfamlpQQvxhw8furu7u7q6Wlpaenp6\n2tvb29vbOzs70Sb9SQYpKSn0G1FRUdHR0VFWVh4/frySkpKysrKKioqgoCCzf9vGCicnJ0dHxx9/\n/DElJQVWMQLAY6BYj11paWkbNmzYu3cv+3ef4EYUCkVGRkZXV5fB66lUqoCAgKGhIUtTMR2ZTP7j\njz/q6+vHwi4NbCYmJobOjvjiVz9+WoxqaGhobm7u7Ozs7u5ubm7+8OFDdXV1c3Nzd3d3Z2cngiDt\n7e19fX2DI3x8cqSQkJCoqGhvby8/Pz8OhxMVFUUPWEELOoFAkJCQIBAI48aNExcXFxERGXxS/vEv\nYG4Y65w8eZJIJAYGBq5atQrrLAAAZoJiPUbV1tYuWLCATCYfPnwY6yzcIS4ujkwmM/54iUqlTp48\nmUAgsDQV06GHSr5+/drZ2RnrLGOLiIiIsrKysrIyE8ckkUgzZsw4efIkE8cETKGrq/v999/v27fP\n1dUVTuMCgJfAD6HGInTBoqCgICxYZFxCQgLj80AQBKFSqVw3DwRBEBkZmUmTJiUnJ2MdBDABkUjM\nzMzEOgX4skOHDrW3t585cwbrIAAAZoJiPRahCxbDw8NhwSKD8vPzq6urp02bxvhLuG5LkEHm5uZJ\nSUlYpwBMQCQSMzIysE4BvmzcuHE7duw4ceJEc3Mz1lkAAEwDxXrMQRcsXrlyBRYsMo5CoYiIiJia\nmjJ4fV1dXXV1NfcW65SUFDiBlQcQicT6+vqamhqsg4Av27FjBx8f359//ol1EAAA00CxHluSk5PX\nr18PCxa/VVxcnKWlJbr8ixHp6ekIghgZGbEyFKtYWFg0NTUVFRVhHQSMFvpfIDy05liSkpJeXl4n\nT56Eh9YA8Awo1mNIY2PjkiVLbGxsfvnlF6yzcBkKhfKtE6xVVFTGjRvHukisY2JiIigoCNOseYCs\nrKySkhJMs+Zk27dvp9Pp58+fxzoIAIA5oFiPFQMDAytWrEAQ5Pr167Bz6jepqakpKir6pgnWXLpy\nEYXH4w0MDKBY8wYikZiVlYV1CvBVUlJSmzZtOnv2bG9vL9ZZAABMAAVrrDh06FB0dPSdO3fk5OSw\nzsJlKBQKDoeztLRk/CVcXawRBLGwsID1i7wB1i9yvm3btjU2Nt6+fRvrIAAAJoBiPSZERkYeOXLE\nz8/P3Nwc6yzcJy4uzsjISFxcnMHrOzo6CgoKuLpYm5ubp6en02g0rIOA0SISiW/fvoWnoZxMSUnJ\n1dX15MmTsGIYAB4AxZr3lZeXL1u2zN3dfcOGDVhn4Uro0TCMX5+Zmdnf38/txbqzszMnJwfrIGC0\niERiX19fXl4e1kHAUHbs2JGVlRUTE4N1EADAaEGx5nE9PT1LliwZP378pUuXsM7ClTo6OjIzM9Hz\nCBlEpVIlJCQ0NDRYl4rV9PX1RUVFYZo1D9DV1RUWFob1ixzOzMzM0tLy4sWLWAcBAIwWFGse5+3t\nnZeXFxISwnVna3OIxMREGo1mZWXF+EvQCdZ8fHysS8VqAgICJiYmUKx5AA6H09HRgfWLnG/t2rX3\n799vaGjAOggAYFSgWPOyW7duXbx4MSAgQEdHB+ss3Co+Pl5dXV1FRYXxl3D7ykUUiURKS0vDOgVg\nAiMjI1i/yPmWLl0qJCQESxgB4HZQrHlWVlbWunXrdu7c6eLignUWLpaQkPBN80D6+/uzs7N5oFgb\nGhrm5OT09/djHQSMlqGhIUwF4XxiYmKurq6XL1/GOggAYFSgWPOmtrY2Nzc3Y2PjI0eOYJ2Fiw0M\nDCQmJlpbWzP+kry8vM7OTh4o1kZGRl1dXQUFBVgHAaNFJBKrq6vr6uqwDgKGsWrVqszMTFg0DABX\ng2LNg+h0+qpVq1paWu7cuSMoKIh1HC6WnZ3d0tLyrSsXhYSE9PT0WJeKPfT19XE4HDzp5AFEIhFB\nEJhmzfmmTp2qrKwcGhqKdRAAwMhBseZBZ8+effDgwe3bt8ePH491Fu4WHx8vISFhYGDA+EuoVKqe\nnp6wsDDrUrEHHo+fNGkStDEeoKioqKCgAO+ROB8/P7+Li0twcDDWQQAAIwfFmtekpKTs3r3b19d3\nxowZWGfhegkJCZaWlgICAoy/hDdWLqKIRCK0Md4Af5TcwtXVNS8vLzc3F+sgAIARgmLNU5qbm93d\n3a2trffu3Yt1Fl4QHx//TROsEd4q1rDojWdAseYW1tbW48ePDwsLwzoIAGCEoFjzlLVr13Z0dNy6\ndeubHrKCL6qtrS0pKfmmCdYVFRUNDQ08U6yJRGJZWVlzczPWQcBooXu8wBn1nI+fn9/BweHZs2dY\nBwEAjBAUa97h5+cXFhZ28+ZNRUVFrLPwgri4OAEBAQsLC8Zfkp6ezsfHh64V4wFEIpFOp8MeBTyA\nSCT29PTk5+djHQQMb86cOa9fv4Y3tABwKSjWPCIlJeX/2LvvuKbO/n/8V0hCwgohsvcGmQKKKKiA\naLUoinXXW7Sts617t70dt1a8qy1119aqt3XWWesEJyobEpZsASFshISd9fvj/JoPX1CEkJNDwvv5\nhw8yzjmvoMI7V97XdW3evHnXrl3jx48nOouKePHihbu7O4PB6P0hbDbb2tpaT08Pv1SKZGlpyWQy\noYVABbi4uFCpVPirVAoTJ05ECMXExBAdBAAgCyisVQHWWu3v779lyxais6iOvm4NgxBis9leXl44\n5VE8Eonk7u4OC4OoABqN5uTkBIW1UtDT0/P19X3w4AHRQQAAsoDCWhVgrdXnzp2D1mp5aW1tTUtL\nk6Gw9vT0xCkSIWDSm8qAv0olMm7cuNjYWKJTAABkAYW10oPWajwkJSV1dHT0aUkQHo/3+vVrVRqx\nRgi5uLi8evWK6BRADmCNFyXi7++fm5tbU1NDdBAAQJ9BYa3csFWrobVa7l68eGFqamplZdX7Q9LS\n0iQSicosCYJxcHCor6+vr68nOgjoL09Pzzdv3tTV1REdBHyYv78/iUSKi4sjOggAoM+gsFZiPB5v\n3rx5gYGBsGq13L18+TIgIKBPh7DZ7CFDhlhYWOAUiRAODg4Iofz8fKKDgP7CFqvJzMwkOgj4MCaT\n6eLi8uLFC6KDAAD6DAprJfbll1/yeLzTp0+rqcHfozxJJJL4+PjBvDWMlKWlJZ1Oh2XaVICZmZm+\nvj6HwyE6COiVkSNHJicnE50CANBnUJApq9OnT587d+7kyZMmJiZEZ1E1OTk5tbW1g3xJEIyampqt\nrS2MWKsGWONFiXh6erLZbKJTAB+ibsoAACAASURBVAD6DAprpVRQULBq1aoNGzZMmTKF6CwqKC4u\nTlNTs0/re3R0dGRnZ6veiDVCyMHBAQpr1QALgyiRYcOG1dfXl5WVER0EANA3UFgrn/b29jlz5jg5\nOe3evZvoLKrpxYsXvr6+VCq194e8evWqo6NDxdbaw0BhrTI8PDwyMjJEIhHRQcCHeXp6kkgkGLQG\nQOlAYa18Nm/enJeXd+7cOXV1daKzqKa4uLhRo0b16RA2m02j0ZydnXGKRCAHBwfosVYNHh4era2t\nhYWFRAcBH8ZgMKytraF1BwClQyE6AOjJ8+fPxWLx2LFjpffcvXv34MGD//vf/xwdHQkMpsIaGhpy\nc3P7WlhzOBw3NzcKRQX/Qzk6OvL5/KqqKiMjI6KzgH5xdXUlk8nR0dHZ2dkZGRlsNpvD4Vy7dg1b\nMAQMNA4ODgUFBUSnAAD0jQrWAark3LlzJ06c2LJly44dO6hUalVV1eLFiyMiIhYsWEB0NJUVHx8v\nFot9fX37dJRKLgmCka64B4W1MuLz+ZmZmenp6RwOJzU1lUwmf/XVVwghOp3e0dEhkUiwv18wANnb\n28PyiAAoHWgFGdBu3rwpFov37ds3cuTI3NzcTz/9VEdH5+DBg0TnUmVxcXH29vZ9LSIzMjJUssEa\nIWRqaqqlpQXdIEoqKipq9OjRX3311W+//ZaQkNDR0YHd39bWJhaLTU1NNTQ0iE0I3sfOzg76dgBQ\nOlBYD1yZmZkVFRUIIZFIlJGR4e7unpKScvnyZR0dHaKjqTIZGqzfvHlTW1urqoU1iUSyt7eH+YtK\nasOGDcbGxmKxWCAQdHmIRCK5ubkRkgr0hp2dHZfLbW5uJjoIAKAPoLAeuO7cuSNdmEIoFAoEgsbG\nxh07dsCmxPgRi8WJiYkyzFwkkUgq3KhqY2NTUlJCdAogCw0NjQMHDkgkku4PUalUKKwHMhsbG4lE\n8ubNG6KDAAD6AArrgevWrVtCobDzPRKJ5O7du05OTnfv3iUqlWrLzs5ubGyUYeaitbU1k8nEKRXh\nzM3NYT1d5TVv3rwRI0Z0n1krEomGDh1KSCTQG6ampggh7HNLAICygMJ6gGpsbIyLi+s+ziQQCN6+\nfRsaGrp3715Cgqm2uLg4LS2tvg7jcTgcVe0DwZiZmUFhrbxIJNLPP//cfflqKKwHuCFDhqirq0Nh\nDYBygcJ6gIqOjhaLxe97dOzYsQsXLlRknkEiLi7O19e3r6vmsdls1S6szc3Ny8vLe/gHCQY4Pz+/\nWbNmdd/zyMnJiZA8oDdIJJKhoSEU1gAoFyisB6g7d+50L+8oFAqZTP7uu+8ePXpkZmZGSDDVJsPM\nxaampqKiIlVdaw9jYWHR0dFRU1NDdBAguwMHDqip/T8/8Fks1pAhQ4jKA3rDxMSksrKS6BQAgD6A\nwnogkkgkt27d6jKLn0KhWFtbJyUl7dixo8svSCAXDQ0NeXl5fS2s09PTxWKxyo9YI4RgEpVSMzc3\nX79+fee369AHMvCxWKy3b98SnQIA0AdQnw1EaWlptbW10ptYGb148WIOh+Pl5UVcLhWHNbWPHDmy\nT0ex2Wxs82F8Qg0IZmZmJBIJ2qyV3datW5lMJolEQghRqVR3d3eiE4EP0NXVbWxsJDoFAKAPoLAe\niDovtEelUplM5q1bt06cOKGpqUlsMNWGbQ1jYGDQp6M4HM6wYcOwYkVV0el0fX19KKyVnba2dmRk\npPTfKoxYD3wMBoPH4xGdAgDQB1BYD0Q3b97EFtojkUjjxo3LzMycMmUK0aFUnwwN1mgQLAmCweYv\nEp0C9NeiRYtcXFzIZLJAIHB2diY6DvgABoMBI9YAKBcorAecurq61NRUhBCNRjt27Fh0dLSJiQnR\noVSfWCxOSkrqa2EtFoszMzMHSWENPdYqgEwmHzp0CFt6z8XFheg44ANgxBoApdO3ZcXwlpeXx+Fw\niE5BsOfPn4vFYisrq7Vr17JYrD///JPoRB8mEon8/f0tLCyIDiK7rKwsGbaGyc/Pb25uHiSFdXZ2\nNtEpwAfw+XyhUNja2trW1tbR0YHthi0Wi7uMevr5+aWlpb18+bJ7C5OGhgadTu9yp6amJo1GQwhR\nqVRtbW01NTVdXV2EkLRjG+BEXV29vb2d6BQAgD4YQIU1m80eP358fX090UEGhJKSkjVr1hCdog/O\nnz8/b948olPIDtsaxtXVtU9HsdlsMpnc16OUkbm5+YMHD4hOofqEQmFdXV19fX1dXV1jY2NTU1Nj\nYyOPx2tqampqauLz+Q0NDdjXzc3Nb9++lRbNDQ0N79y3vAdz5syRS2ZtbW0qlYpV5FpaWtra2tra\n2kwmU0dHB/uawWDo6upiX+vq6mLL/LFYLJg08kFUKrXL/rsAgAFuoBTWHA4nJCTEzs7u4cOHg/yn\nbX5+voODA9Epeuv06dPYHpBkMpnoLP0SFxc3cuTIvm4Nw+FwnJ2dNTQ0cEo1cBgYGHReqQbIoLW1\ntaKioqKioqqqisvl1tbWYgU09mdtbW1dXV2Xz/2xseEuFaq+vr61tbWOjg42YKynp4cQYjAYZDJZ\nS0tLXV2dRqNpampSKBQdHR3sPNijnc+cmpoaGBjY/b8tj8frvkdjY2Mjtj1Qe3t7S0uLUCjk8/kS\niaShoUH6aHNzc0dHB5/Pb25uxt4PlJeXY28AeDwe9iahy+CrhoYGVmRjdba+vj72taGhoampqbGx\nsbGxMYvFksO3XmlRqdQu664CAAa4AVFYczic8ePH29rawsIXCCGlq6rXr19/4MABorP0V1xc3MyZ\nM/t61CCZuYgQYrFYPB5PKBT29b3HoCIWiysqKoqLi0tKSt68eSOtoaurq8vLy/l8vvSZhoaGWB3J\nYrFMTU3d3NykZaW01tTV1cXv5+H48ePfeT+Dweh+J1a7959AIGhsbMTeSEjfUUhv5ufnx8fH19XV\nVVdXd3R0YIfQaDQjIyNTU1Ppn+bm5lZWVpaWllZWVliDigqjUCgwYg2AciH+dyRWVdvY2EBVrVyk\nVfXSpUuVvbCuq6uTYWsYhBCbzV69ejUekQYaPT09bIRSX1+f6CwDQllZWUFBQUlJCVZGY968eYNV\nhFQq1dTU1NTU1NDQ0M3NzcjIyMTEBBuCxe7svrv4YEClUvX19XvzT6impqaqqqqioqKysrKyshJ7\nc5Kdnf3kyZPS0lKsdxwhZGJiYvX/sre3t7GxUVdXx/mlKAiMWAOgdAgurKVV9a+//gpVtRLpXFUT\nnUUOEhISEEK+vr59Oqquro7L5ar2ZuZS2Cfy9fX1g7CwbmhoKCwsLCoqKioqysrKys7OzsvLw4af\naTSamZmZra2tmZnZiBEjbP9haWkJQ/v9YWBgYGBg4Obm9s5H3759y+VyKyoqiv7x8OFDLpdbWVmJ\nNZqbmJi4urpifxcuLi6urq7W1tbKuGGtSCRS9i47AAYbgn/0jxkzhs/n19XVwYaCysXQ0HDLli2L\nFy8mOoh8JCQk2NnZ9XVrmLS0NITQ4GkFQQgNht2V29rasrKy2Gx2enp6enp6VlZWTU0NQohGo9nb\n2zs6OoaEhKxYscLR0dHR0dHIyIjovIORnp6enp5e90nDfD6/oKAgPz8/Ly8vNzeXzWb/+eef2D9a\nTU1NZ2dnj394enoqxVtEgUAwOD/cAEB5EVxY8/n8iIgIqKqVS1pa2pkzZ1SmqkYIxcfH+/n59fUo\nNpttbGw8SEor6Yg10UHkr66uLjExkcPhYMV0fn6+UCjElojx9PQMDw93cnJydHS0tLSEscMBTkdH\nx8vLq8svlJqaGqzOzsnJYbPZd+/eraqqQgiZmppiFbanp+fw4cMH5uQWKKwBUDrEf1jp5eU1efJk\nolOAvjlz5gzREeRGIpEkJSXt2rWrrwdim5njEWkA0tbWVldXV43CWiQS5eTkpKSkvHjx4vnz569e\nvZJIJFjzwIQJE7Zs2eLj4+Ps7AxltGrAukr8/f2l97x9+zYrKyslJSU7O/v58+c///xzW1sbg8Hw\n9fX19/f38fEZPXr0kCFDCMwsBYU1AEqH+MIaAGLl5ua+fft25MiRfT2Qw+F8/PHHeEQamPT09JS3\nsG5ra3v+/PnDhw9fvHiRkpLS0tKiq6s7cuTImTNnjhw5cuTIkQOkkAIKoKenFxAQEBAQgN3s6OhI\nTU1NSEiIj48/ffr0zp071dTUhg4dOmrUqODg4PHjxxsaGhIVFQprAJQOFNZgsEtISKDRaB4eHn06\nqr29PScnZ+vWrTilGoBYLJZy9ViLxWI2mx0TExMTE/P8+fPW1lYnJ6exY8cuWrTIz8/P2dlZGWez\nAblTV1f38/Pz8/PDVviprKyMj4+Pj49/8eLFmTNnhEKhh4dHSEhISEjI2LFjFTzJvrm5WUtLS5FX\nBAD0ExTWYLBLSEjw8vLq64K4WVlZAoFg8LSCIOUprFtbW+/fv3/lypX79+/X1tYaGhqOHz/+8OHD\nISEhlpaWRKcDA52xsfH06dOnT5+OEGpqanry5ElMTMy9e/cOHDhAo9H8/f3Dw8NnzJhhamqqgDA8\nHu+dK4sDAAYsKKzBYJeQkDBmzJi+HsXhcDQ0NAbmhCecsFisgdwK0tLScufOnStXrty+fbulpcXf\n33/z5s0hISGenp4kEonodEApaWtrT5kyZcqUKQghLpcbExNz9+7dbdu2rV69evTo0TNnzpwxY4aF\nhQV+Afh8PhTWACgX+CQUDGqtra0ZGRmyNVi7ubkNqrWKmUzmwByxjo2NnT9/voGBwdy5c6urq/ft\n21dWVvbs2bMNGzYMGzYMqmogF6ampgsXLrxw4UJ1dfW1a9esra23b99uZWXl7+9/+vTptrY2PC7K\n5/Ol+9IDAJQCFNZgUEtNTRUIBDKstTd4NjOX0tDQwKl6kA2fzz9+/LiHh8fYsWOLiop++umnioqK\nR48erVy50sTEhOh0QGXR6fRp06adPXu2qqrq1q1bVlZWy5YtMzc337hxY2FhoXyvBYU1AEoHCmsw\nqCUkJBgYGNjY2PT1wEFYWKurq2P7dROupqZm3bp15ubm69atGzFiRHJycnx8/NKlS/u6xQ8A/UGj\n0UJDQ8+fP19aWrpu3brLly87OjqGhoYmJSXJ6xJ1dXWwXg0AygUKazCoJSQkyNAHUlJS8vbt20E1\ncxENjMK6paVlz5499vb2Fy9e3L59e3l5+cmTJ318fIhNBQY5IyOjbdu2FRUVXbt2rbGxceTIkXPn\nzpXL6HV1dTW8XQRAuSh3YX3y5MlRo0Y5OTlduHCB6Cx98PTpUx8fn0ePHuF3iZcvXx44cAD7uqOj\nY8+ePf7+/p6enrGxsd2f3OXb+OjRo19//VUkEuEXb+CQrbDmcDgkEsnd3R2PSAOWurp6e3s7gQFO\nnTrl4OCwb9++jRs35ufnr1u3Tk9PT+5XGTFiBJlM7v+7pl27drm4uDAYDGwv9E2bNjU1NcklIbHa\n29tXr15tbGysqal57949RV46JiZGusDlB2Ps37/f0NCQRCIdP34cIfTXX3/t27cP1x9rZDJ52rRp\nz58/v379enp6uouLy6pVq3g8nswnlEgk2LI2cgwJAMCbchfWn3/++cWLF4lO0WcSiQTX8x88ePDs\n2bPLly/Hbv7+++/Pnj3DJrO3tLR0f36Xb2NwcDCNRlu0aFF/fiUoherq6pKSEhkKazabbWNjo6ur\ni0eqAYtGoxE1Yl1TUxMWFrZkyZLw8PDCwsJvv/0Wv8V9k5KSgoKC+n+eR48effXVV8XFxbW1td9/\n/31UVNSsWbP6f1rCHThw4N69ezk5OVFRUYp8q7B9+/aDBw9u27atlzE2bNjw8uVL6c2wsDA6nT5+\n/PiGhga8o06bNi09Pf3QoUOXLl3y9PR88eKFbOepr68XCAQwYg2AclHuwrqv2tra5s6dS/h1AwMD\nU1JSgoOD8bjWiRMnbt++HRUVJa08YmJi3N3dGQzGnDlzPvroo96cZOHChc7OzkuXLlXtceu4uDgS\niTR8+PC+HjioNjOXolKphBTWeXl5o0aNysjIePLkyeHDhxVTZ8iwlkhra+vo0aOlN7W1tZctW8Zi\nsXR0dGbPnh0eHn7v3r03b97INSYBbty4MXz4cCaTuXTp0pkzZyrmopGRkRcvXrx8+bJ0Jp8MMVav\nXu3p6fnxxx8LhUI8wyKEEIVCWbp0aUZGhqura3Bw8NmzZ2U4SU1NDUIIRqwBUC6Dq7C+cuVKXV2d\nCl+3pKQkKipq1apVnbc7qayslGFVuK+//vrVq1enT5+WZ74BJiEhwcnJSYZ2gkE4cxER1ApSVFQ0\nbtw4fX39hIQE6R7UCiDDPtInT56srq6W3vz777/JZLL0pr6+PkLonR8ZKZeysjIFb7JdUFDw3Xff\n7dy5k06n9zPGjh072Gx2VFSUXAO+l6Gh4a1bt7Zu3RoREfH777/39fCqqioEhTUAymagF9b/+9//\n3N3dR40atX37dn9/f3d397lz53I4nPc9XyKRnDp1avLkyW5ubiNGjFi5cmVRURH20J49eyIjI0tL\nS52cnCZMmNDzdc+cOTNs2DBnZ+cZM2aMHj3axcVl2LBh4eHh8+fPHzdunLu7+/Dhw3/44QcZrpuS\nkhIYGOjk5PTHH3988Njz588PGzbM09Pz4cOHX3zxhbe399ixY//+++/3xcbGRaRj4S9evJgwYUJN\nTc3169ednJywQdbExMSZM2d6enp6e3tPmTLlfR/mMhiMESNGnDlzBu/GFQIlJCTIsNAen88vKioa\nhIW14ltB2trapkyZYm5uHhMTo+DyoqCgwNnZWUtLS0NDY8yYMc+fP5c+JBKJ/v3vf1taWmpoaHh4\neFy6dAkhtGbNmvXr1xcWFpJIJHt7++4nLC8v19DQ6M36M9jHTWpqaj4+PkZGRlQqVUtLy9vbe8yY\nMRYWFnQ6nclkbtq0qec8CKHY2FgXFxddXV06ne7u7n7//n2E0NGjR7W0tDQ1NW/evDl58mQGg2Fu\nbt7LOSrR0dH29vYVFRVnzpwhkUja2tqrVq1SV1c3NjbGnvDll19qaWmRSKTa2tpeXuvs2bPDhw+n\n0+laWlrW1tb/+c9/ul/34MGDEokkLCzsfTEQQk+fPvX19dXU1GQwGO7u7u9rY9PT0xs3blxUVJTC\nfqyRSKQdO3Zs3bp1+fLlCQkJfTr2zZs36urqUFgDoFwGemG9cOHCGTNmtLa2Lly48PHjx9evXxcK\nhYsXL66oqHjn8w8dOnTgwIEVK1bExcWdO3eusrJy/vz52E/5b775Jjg42NLSMjc3Nzo6uufrRkRE\nfP755xKJZMeOHQ8fPnz58uXw4cOzs7OXLVt248aNxMTE8PDw3377LScnp6/X9fHx6dIX3sOx8+fP\nX7RoUVtbm5aWVlRUVExMjIWFxbfffvu+jzKfPHliY2OjoaGB3fT394+OjtbX1w8PD8/NzWWz2a2t\nrStWrJg0aVJiYuKDBw9sbGwEAsH7vgkuLi5VVVXS16hixGJxcnKybDMXJRLJIGwFUfyI9f79+8vK\nyq5fv44VT4qkp6d37969xsbG5ORkgUAwYcKE/Px87KEtW7b897//xZbNnjp16vz585OTk6OioqZO\nnWpnZyeRSAoKCrqcraWl5dGjR0uWLFFXV//gpdesWbNx40aJRHLs2LHXr19XVlaOHTs2LS1t69at\naWlp9fX1ERERP/zwg3R84Z15EEJVVVVz5swpLi7mcrna2tqffvopQmjlypVr165tbW3V0dG5dOlS\nYWGhra3tkiVLevg5IDVhwoSCggIjI6OIiAiJRNLU1HTw4MHZs2dLn3DkyJGdO3dKb37wWlFRUQsX\nLpw5cyaXyy0rK9u2bVtubm73696+fdvJyUlTU/N9MZqbm8PCwmbOnFlfX5+fn+/o6NjDO0AvL6/y\n8vIeRmfwsHv37nHjxi1btqxPBf2bN2/MzMzU1Ab6r2kAQGfK8T+WQqHY2dmpq6vb29vv2LGjubn5\n2rVr3Z/W1tZ26tSpiRMnhoWF6ejoODo67ty58+3bt5cvX5b50vb29hoaGkwmE9vV1tTUVE9PT0ND\nAxs+wYaW+3PdXh7r5eWlra3NYrFCQ0NbW1u5XG73U7W2tpaVlVlaWvZwubKysqamJnt7exqNpq+v\nf+jQoR4aIaysrBBCeXl5H3wVyujVq1c8Hk+2wprJZPb8fVZJCl5uTyQSHTt2bPXq1ebm5gq7qJSO\njo61tTWFQnF1df3111/b2tpOnDiBEGprazt69Gh4ePgnn3zCZDK//fZbKpV66tSpns/2/fffm5iY\n7N69u08ZXFxcNDU1hwwZMm/ePISQpaWlvr6+pqbmggULEELYO94e8sycOXP79u16enosFissLKyu\nrg7r2cWMHj2awWBg21U2NzeXlpb28TvUB++8lkAg2LlzZ1BQ0JYtW1gslp6e3ueffz5ixIguxzY3\nN79+/drOzq6H8xcXF/N4PFdXVzqdbmRkdPXqVazx5p0cHBwQQhkZGfJ4Zb1FIpH279/P4XD6NJGx\nrKwM1/3SAQB4UI7CujM3NzcNDQ1ps0RnBQUFLS0tbm5u0nvc3d2pVKpcBiewfj7pUDHWtYzd7M91\n+3pslxid1dXVSSSSzm2I3VlYWAwZMmTjxo2HDh0qLy/vORs28o2NnauehIQEDQ2Nzt/5XsIarAfh\nRtkKLqyLioq4XO706dMVdsX3cXd319XVTU9PRwjl5uZ2/g+roaFhbGzc86c6165du3z58v3792Xe\nQg8b55b+r8d+CGDjvr3Mgx3yzrnI2Ml7M2Ldf52vlZ6e3tDQ0Hk6NZlMXr16dZdDqqurJRKJdLj6\nnWxtbQ0NDRcsWLBjx47i4uKeM2CnwtqXFcnT09PW1vadC56+DxTWACgj5SusEUJUKrW+vr77/Vhf\nXZcfwQwGo7m5Gdc8/bmuHDNjH9P3/FkznU4/c+aMj4/PsWPHxo8fv3bt2h42qcZqdGKXLsZPQkKC\nj4+PDPOf2Gz2IOwDQQovrN++fYsQGiDbzlGpVKwcxP5jfvvtt6R/lJSU9DAl8eLFi5GRkU+ePLG2\ntsYjWA95bt++HRgYaGBgQKPROvdkDxDYjz4mk9nz07AfUJ1nY3enoaHx6NGjgICAPXv22Nrazp07\nt7W1tYcnS0+rYPr6+u/8zfU+ZWVlZmZm+OUBAOBB+QproVDI5/Ol02U6YzAY6J/fNFI8Hu+dT5aj\n/lxXjpmxOviDC+Q5ODgcP378+fPnS5YsuXPnTg9z1bFKouchcOUl29YwIpEoKytrEM5cVDys2WYg\ntPgLhcL6+nosD7bY308//STpJC4u7p0HHjp06I8//nj06JGpqSlO2d6Xp7S0NDw83NjYOCEhobGx\ncd++fTgFkBn2PfngB2JYHfzBH2uurq63bt3icrmbN2++dOnS/v373/dM7M2hdCKKwggEgvz8fKy/\nrpcKCgreOREWADCQKV9hnZCQIJFI3lnZODg4aGpqZmZmSu/hcDgCgcDV1RXXSP25rhwzs1gsEonU\n85YN1dXV2MwqFou1fv16V1fX7hOtpPh8PvpnmTAV09LSkpWVJUNhnZeX19LSAoW1AhgbG48aNerX\nX38lOgh6/PixWCz29vZGCGHrcrDZ7J4PkUgkmzdvzsjIuHHjBq4zL9+XJyMjQyAQrFy50tbWlk6n\n49e5RKFQZGsjsba2ZrFYDx486Plp2O6JjY2NPTyHy+VmZ2cjhAwMDPbu3evt7Y3dfCfsVEZGRjJk\n7o8rV640NjZOmzatl8/ncrk8Hs/JyQnXVAAAuVOOwlosFvN4PJFIlJub+/3335uams6YMaP702g0\n2mefffbgwYO//vqrqakpLy9vx44d2IwZ7Am6urrV1dXl5eXNzc1y3COgP9f94LG9p6GhYWFhUVlZ\n2cNzqqurt2/fXlRUJBAIsrOzy8vLe+hqwE7l6OjY1yQDX3JyslAolG3mIoVCcXFxwSMV6OLbb7+9\ndu3azZs3FX/pjo6OxsZGoVCYmpq6atUqKyurRYsWIYTodPrixYsvXLhw9OhR7IdSWVkZtkgRi8Xi\ncrnFxcV8Pp/D4fz3v//99ddfqVQqqZMeRlJl87482Ph6TExMW1tbfn5+Xxd66z17e/v6+vobN24I\nBIKampqSkpJeHkij0bZt2/bs2bNVq1aVl5eLxWI+n9+9INbU1LS1tS0rK+vhVFwud/ny5Tk5OR0d\nHWlpaSUlJT0so4mdyt3dvZc55aKmpmbDhg0RERG975nGPquBwhoA5dP5A0RsAVSJAiGEoqKicns0\nd+5cCoViZGREJpO1tbVDQkJiYmKwh7Zs2YKNp2poaEycODE3NzcnJ2fTpk1WVlYUCoXBYEyYMOH+\n/fvSU12/ft3U1JROp/v4+Lx48aKHi37zzTfYZ4VmZmbnz5/fsGEDNvFIX19///79P/30E3ZdBoPx\n448/9um6K1eulGYODg7u+djt27djMaysrGJiYv7zn/9go1+mpqYPHjzoHnvhwoUUCoXD4WA3Hz9+\njJWAZDLZ1dX14MGDjx8/9vLyYjAYZDLZ0NBwxYoV2dnZ3b+NmMDAQCMjo5ycnC5XwbZX6HwPQujS\npUuK/JfTTz/88IOxsbEMB27ZssXNzU3ueboYmN9Pxf98kEgkX3zxhba2dnx8vCIveurUqaCgIEND\nQwqFgi3KUVJSIn20vb198+bNlpaWFArFwMDgk08+ycrKkkgkqampVlZWGhoaAQEBMTEx7/x5+8MP\nP3zw6lFRUdikC2tr69jY2MjISF1dXYSQkZHRuXPnLl68iI226unpXbhwoYc8mzdvZrFYTCZz1qxZ\nhw8fRgjZ2dlt2bIFO7mDg0NhYeGJEyewbjQrK6u8vLyegxUXF3t5eSGEKBSKt7f3lStXJBJJXV1d\nUFAQnU63sbH5+uuvN27ciBCyt7cvLS09cuTIB691+PBhd3d3Op1Op9O9vLyOHDnS/bqrVq2iUqkt\nLS3vi1FcXDx69Gg9PT0yjcIr1wAAIABJREFUmWxqavrNN98IhcIDBw5g3ygtLa0ZM2ZIzxYaGmpm\nZiYWiz/4FyEvjY2Nvr6+dnZ22PzyXjp69CiTycQvFQBALrr/viZJOi2refny5Tlz5kgUuCEIiUSK\nioqaPHlyD8/Zvn373bt3ExMTFZZKeZWUlEyePDkyMlK6mYLMGhoaxo4du2bNms8++6zLQ3fv3l2z\nZk1upxVnnZycLl261HlF2wFu9uzZ7e3tMgyFhoaG6unpSTf3wQmJRBqA30/F/3xACAkEgk8++eTR\no0fnzp3r/cfoQJUUFBQMHTr01KlT2DqD/VFXV2dubr579+7169fLJdsHFRcXY2sdPn36tE8N02vW\nrImPj4+Pj8cvGwCg/7r/vlaaVhCiIygHKyur1atXHz58uP87Jx86dGjo0KELFy6US7CBJikpqfuK\nub2Rnp6u4A+RBzkqlXrt2rVPP/00PDz866+/VoEtwUFf2dvb79q1a9euXT1PIOmNHTt2DBs2bNWq\nVXIJ9kHnz5/38vIikUjx8fF9nYaYm5sLfSAAKCPlKKzlrqioyOn91q5dS3RA2S1btmzy5Mlr167F\nph7K5tSpU69evfr111+x5bpVTE1NTXFxsQyFdX19fVlZmYeHBx6pwPtQKJRffvnl0qVL58+fd3R0\nPHHixAfXiBjIcnJySO8nw+QKlQ+GENq6deusWbPmzp3b8yzGnv34449sNvvOnTsyLLLZV8nJycHB\nwQsWLJg9e/bLly9lWI4aCmsAlNRAL5t+/PHHq1evCgSC4ODgzZs3d95NoD9sbW1z37V3rmpYu3bt\n8+fPf/nllw0bNshw+MOHDzs6Os6ePUsmk+WebSBITEwkkUjDhw/v64HYFiFQWBNi1qxZQUFB+/fv\n//rrr6Oionbu3Dlr1iyiQ8nC2dlZwe00vTRgg2H27Nnz4MGDvXv3RkZGynD4zZs329vbnzx5gveP\ntdLS0j179vz2228jRox4+vTpmDFjZDhJW1tbaWkpFNYAKKOBXlivW7du3bp1RKdQPgEBAQEBAbId\nO378+PHjx8s3z4CSlJRkZ2cnw84j6enpLBYLtmwgir6+fmRk5OLFi7du3Tp79mxfX9+VK1fOmTNH\nVZdaB11MnDhx4sSJsh07bdo0vHv0Y2Njjx07dvXqVRsbmytXroSHh8t8qvz8fJFI5OzsLMd4AADF\nGKStIGAwk7nBOiMjA1awJpyTk9O1a9fi4uJsbW2XLl1qbm6+cePGwsJConOBQYrP5x87dszDw2Ps\n2LEFBQUnTpzIzMzsT1WNEMrNzVVTU7Ozs5NXSACAwkBhDQad5ORkmWcuQh/IAOHn53fhwoXS0tK1\na9devnzZ0dExKCjoyJEj2JLSAOCttbX1xo0b//rXv8zMzDZs2DBixIikpKTExMSIiIj+T03Jycmx\ntraGj2IAUEZQWIPBpbi4uLq62tfXt68HisXirKwsWBJkQDEyMvrmm2+KioquX79ubGy8detWc3Pz\nMWPG/Pzzzz1vKQKAbFpaWq5evTpv3jxDQ8NPPvmkpKRkz549ZWVlJ0+elGHaxvtkZWXBLlQAKKmB\n3mMNgHwlJiaSyeQe9pt8n4KCgubmZhixHoDIZHJYWFhYWFhbW9u9e/euXLny73//e+3atd7e3iEh\nISEhIQEBATD4B2QmkUgyMzNjYmKio6OfPn3a3t4+ZsyYyMjIGTNmmJiY4HHF5OTkTz/9FI8zAwDw\nBoU1GFySkpLc3Ny0tLT6emB6ejq2gSUeqYBc0On06dOnT58+vb29PTo6+u7du9evX9+3bx+2GyJW\nZA8bNkxNDT6pAx9WVlYW84+qqqohQ4YEBQVFRUVNmzbN0NAQv+s2NjYWFhb6+PjgdwkAAH6gsAaD\nS39mLtrb22NbNIMBjkajTZkyZcqUKQihysrK2NjYmJiYgwcPbt68WVtb29PT08fHJyAgIDAw0MDA\ngOiwYKAQCoW5ubkvXrx4/vx5SkpKdnY2hULx9PRctGhRSEhIYGCgYtb1T01NlUgkUFgDoKSgsAaD\niFgsTk1NnT9/vgzHwsxFJWVsbDxr1qxZs2ZJJJKMjIznz5/Hx8ffu3fv4MGDCCEnJ6eRI0f6+fl5\ne3vL9lEGUF4ikSg/P5/NZickJCQkJKSmpra3t+vr648cOXLevHmjR48eNWqUhoaGglOlpKQYGRmZ\nmpoq+LoAALmAwhoMItnZ2Xw+X4aZiwih9PT0RYsWyTsRUBwSieTh4eHh4bFy5UqEUF1dXXx8fEJC\nQlxc3JYtW3g8npqamq2traenp8c/bGxsSCQS0cGB3NTX13M4nPR/ZGVltba2UigUDw8PPz+/FStW\n+Pn5OTg4EBsyJSVFjvMgAQAKBoU16K/W1laiI/RWUlISnU6XoU+az+e/fv0aRqxVyZAhQ0JDQ0ND\nQxFCEonk9evX0nrr7NmzRUVFYrGYwWC4uLg4Ozs7Ojo6ODhgfyp+CBPIQCQSlZaW5uXl5eXl5ebm\n5uXlvXr1ClsrxsDAwNPTc+zYsV999ZW7u7urqyuNRiM67/9JSUmZN28e0SkAADKCwhr0S2tr64oV\nK1gslgzrbCheUlKSt7c3lUrt64EZGRkSiQQKa1VFIpFsbW1tbW2nT5+O3dPU1JSZmYkNaubm5j57\n9qykpEQkEpFIJEtLS2mdbW1tbWVlZWlpyWKxiH0Jg1lbW1tJSUlJSUlpaWlBQQFWTBcUFLS3tyOE\nDAwMHB0dnZycQkJCsI8jcFrKQy54PB7MXARAqUFhDWTX2tq6cuXKV69eRUdHOzo6Eh3nwxITE8eM\nGSPDgenp6To6OtbW1vJOBAYobW1tPz8/Pz8/6T3t7e3Soi0vLy89Pf3atWuVlZXYozo6OpaWltI6\nG/vTxMTExMQEVvqTC5FIVF1dXVlZWVZW9vr169LSUqySLi0t7fy3YGdn5+joOH36dCcnJycnJwcH\nBz09PWKT90lqaqpYLIbCGgDlBYU1kBFWVWdnZ0dHRytFR2B7e3tmZua6detkODYjI8PDwwPabQcz\nGo3m6urapY+o81gp9kVGRsbt27fLy8uFQiH2HCaTaWpqik1HMzIyMjMzMzQ0NDMz09fXHzJkCIvF\ngsobISQWi+vq6urr6+vq6iorK7lcblVVVXl5eXV1dXl5eVVVVVVVlVgsxp5sYGBgZWVlZWXl7+8/\nf/58a2tr7M2MCnxugM1cNDMzIzoIAEBGUFgDGSlXVY0QYrPZ7e3tsJk5kCM6nY6NjHa5XygUVlZW\nYhUhl8utrKzEisX8/Hwul1tdXd3R0SF9spaWFovFwopsabXNYrEYDIauri6DwdDW1tbW1tbR0WEy\nmdra2urq6op9lbKQSCQNDQ18Pr+pqampqYnH4/F4POwLrIDGamjsz7q6urdv33Y+3NDQEHv7YWRk\n5ObmZmJiYmxsbGpqamhoaGlpqcJt7ikpKTBcDYBSI76wZrPZMBCoXNhsNkIoJyfnyZMnnp6eRMfp\nraSkJCaTaW9v39cDsX3XZFukDwxOFArF3Nzc3Nz8fU+orq6WlpVdSsycnBzsaz6f39jY2P1YdXV1\nLS0tPT09bW1tKpWqpaWlrq5Op9M1NDSoVKq2traampquri5CSFdXV7obTuevO5+n8z0CgaCpqanL\n5fh8vnT0Hfu6paWlvb29vb29paVFKBTy+XyEEFYZY+8ZmpqampubuyfX0NBgMBjSdxHGxsYuLi7Y\nTX19fen9RkZGMkyEUA0pKSlz5swhOgUAQHbEF9anT58+ffo00SlA3+jo6Dx69EiJqmqEUFJSkq+v\nrwzv4kpKShoaGmDEGsgRNiLbm2diA71NTU18Pv/t27dN/2hsbOTxeCKRqHOx29TUVF1d3aXYRQiJ\nRCIej9flzNghne8hkUhMJrPL07CSHfu6exFPpVJtbW0RQrq6uvX19adOnZo6deq0adO0tbWx0h+D\nDb2TyeS+f6sGkbdv3xYUFMCINQBKjeDCWiKREBtAASZNmmRmZnby5Emigwx2iYmJn3zyiQwHpqen\nk0gk2MwcEILBYDAYDKJT9Nbw4cO//PJLZ2fnyMhIorMon9jYWIlE4u/vT3QQAIDsiB+xVnlkMlk6\n5wYQpbGxMS8vT+YGa2tr6+4jeQCALpYvX04mk5cvX44Qgtq6r549e+bm5qavr090EACA7KCwxp2a\nmhoU1oRLSUkRi8WyFdbYkiByjwSASlqyZImWllZERIRIJPrhhx+IjqNMnj17NnbsWKJTAAD6BQpr\n3KmpqYlEIqJTDHbJyclmZmampqYyHJuenj5r1iy5RwJAVc2fP59MJi9YsKCpqeno0aMwPb03+Hx+\nWlrahg0biA4CAOgXKKxxB60gA0FycrJsywK2trbm5+e7u7vLPRIAKmzOnDlkMnn+/Pkikej48eNd\nFiQB3T1+/FgsFgcGBhIdBADQL1BY4w5aQQaC5OTkxYsXy3BgVlaWSCSCVhAA+mrmzJkaGhozZ85s\nbm4+c+YMhQK/bnpy7949Hx+fXq4VAwAYsGAUAXfQCkK4+vr64uJi2UasMzIyNDQ0ZFj9GgAQGhp6\n/fr169ev/+tf/5Iuhg3e6f79+5MmTSI6BQCgv6Cwxh20ghAuOTlZIpHItjpsRkaGm5sbrL8LgGwm\nTZp09+7dv//+e/78+QKBgOg4A1ROTk5RUREU1gCoACiscQetIIRLTk62tLSU7TPW9PR0aLAGoD/G\njRt3586de/fuhYeHt7W1ER1nILp79y6LxRo5ciTRQQAA/QWFNe6gsCZcSkqKbH0gCKGMjAworAHo\npzFjxjx8+PDly5czZsxobW0lOs6Ac+3atSlTpsAnYwCoACiscQc91oRLSUmRrQ+koqKiuroaZi4C\n0H8jRoyIjo5OTEz8+OOPm5qaiI4zgFRVVcXFxc2YMYPoIAAAOYDCGnfQY02s2trakpIS2Qrr9PR0\nhBCMWAMgFz4+PjExMZmZmR9//DGfzyc6zkBx7do1Go02YcIEooMAAOQACmvcQSsIsZKTkxFC3t7e\nMhybnp5uampqYGAg71AADFLDhg179uxZQUHB5MmTeTwe0XEGhGvXroWGhmpqahIdBAAgB1BY4w5a\nQYiVnJxsbW0tW3EMm5kDIHdDhw59/PhxcXFxcHBwXV0d0XEIVlNT8+TJE+gDAUBlQGGNO2gFIVZ/\nZi6mp6dDYQ2A3Dk5OcXGxtbX10+YMKG2tpboOES6fPkyjUabMmUK0UEAAPIBhTXuoBWEWDLPXBQI\nBK9evYIGawDwYGNj8/jxYx6PN3bs2IqKCqLjEOb8+fPTp0/X1tYmOggAQD6gsMYdtIIQqKam5s2b\nN7IV1jk5OR0dHTBiDQBOrKysHj9+LBAIgoODuVwu0XEIUFJSEhcXN3/+fKKDAADkBgpr3MGINYGS\nkpJIJJLMMxepVKqzs7PcUwEAMBYWFrGxsWQyOSgoqKysjOg4inb+/Hl9fX1YDwQAVQKFNe6gx5pA\nycnJNjY2Q4YMkeHYjIyMoUOHqquryz0VAEDK2Nj40aNHNBotICCgqKiI6DiKI5FITp48OW/ePCqV\nSnQWAIDcQGGNOxixJhDMXARg4DM0NHz69KmhoWFQUFBBQQHRcRTk4cOHhYWFS5YsIToIAECeoLDG\nHfRYEyg5OVm2BmuEUHp6OsxcBEAx9PT0Hjx4YGJiMmbMmKysLKLjKMKJEyf8/f3d3NyIDgIAkCco\nrHEHrSBEqays5HK5so1Y19fXl5eXw4g1AArDZDLv379vY2Mzfvz4jIwMouPgq7a29q+//lq6dCnR\nQQAAcgaFNe6gFYQoycnJMs9c5HA4CCEorAFQJF1d3ejoaFdX1/Hjx2P/B1XVyZMnNTQ0Zs6cSXQQ\nAICcQWGNO2gFIUpycrK9vT2TyZTh2PT09CFDhpiamso9FQCgB1paWrdu3fL09AwMDExMTCQ6Di6w\naYsLFy6EbcwBUD1QWOMOWkGIIvPWMAihjIwMT09P+eYBAPSGpqbm33//PWbMmIkTJ8bFxREdR/4e\nPXqUn5//2WefER0EACB/UFjjjkQiQWFNiP4U1rAkCAAEotFoV65cCQoKmjBhwuPHj4mOI2cnTpwY\nPXo0vHUHQCVBYY076LEmRHl5eUVFhWwzF0UiUVZWFiwJAgCB1NXVL1++PGnSpClTpsTExBAdR26q\nq6tv3LgBq+wBoKqgsMYdmUyGHmvFw2Yuenl5yXBsQUFBS0sLjFgDQCwqlXrp0qVPPvkkLCzswYMH\nRMeRj6NHjzIYjNmzZxMdBACACyiscQcj1oRITU11cHDQ1dWV4dj09HQymezi4iL3VACAPiGTyadO\nnZozZ87UqVNv3LhBdJz+amtrO378+MqVK2HaIgCqikJ0ANUHhTUh+rPnYmZmpr29PfzmA2AgIJPJ\nv//+u5aW1pw5cy5cuDBjxgyiE8nu9OnTDQ0NK1asIDoIAAAvMGKNO2gFIURqaqpsfSAIoczMTGiw\nBmDgIJFIhw4dWr58+ezZs//44w+i48hIIpEcPHhw4cKFxsbGRGcBAOAFRqxxByPWildVVVVRUSHb\n1jAIoczMzHnz5sk3EgCgP0gkUlRUFJlMXrRokUgkioiIIDpRn/311185OTlXrlwhOggAAEdQWOMO\nCmvFw2YuDhs2TIZj29raCgsL3dzc5J4KANAfJBLpxx9/1NbWXrx4cXNz88qVK4lO1JPa2tq2tjZz\nc3PpPQcOHAgNDYXJGwCoNmgFwR20giheamqqjY0Ni8WS4djs7GyRSASFNQAD065du/bu3fvVV18d\nOnSI6Cw9iY+Pt7Oz+/rrr7lcLkIoOTk5NjZ2/fr1ROcCAOALRqxxByPWipeWltafBmsajWZvby/f\nSAAAedm8eTOJRFq9erVIJFqzZk3nh8Ri8cuXLwMCAojKJlVUVCQSiX755ZcTJ06sWLGiuLjYx8cn\nMDCQ6FwAAHxBYY07KKwVLzU1Veb9FzIzM4cOHUqhwH8NAAauTZs2kcnkdevWNTU1ffvtt9idEolk\n+fLlV65cKSkp0dHRITZhUVERmUzu6OhACB09elQkEgUHB1dUVJiYmBAbDACAK2gFwR0U1gpWX19f\nWlran5mL0AcCwMC3fv36Y8eObd++fcuWLQghiUSyatWqkydP8ni8w4cPE50OFRQUCAQC7GuBQCAW\ni58+fWpjY7N69erKykpiswEA8APDcriDHmsFS0lJkUgk/WkF+fLLL+UbCQCAh2XLlpHJ5GXLlmE3\njxw5IpFIEEKRkZFfffUVsYPWeXl5WBgprM4+duzYr7/+GhUVtXTpUoKiAQBwBIU17mDEWsFSU1PN\nzMxkWym2sbGxrKwMRqwBUBZffPEFhULZtGlTbW2ttJBtaWk5cuQINpJNCIlE8ubNm/c9am5uPnXq\nVEXmAQAoDLSC4A4KawVLS0vrTx+IRCKBwhoAJVJSUlJTU9N5eFgoFEZGRvL5fKIiVVVVtbW1db+f\nSqU6Ojq+ePECOq0BUFVQWOMOWkEULDU1tT+FtY6OjqWlpXwjAQBw8uOPP+7YsaP7/c3NzUeOHFF4\nnP9fUVFR9zupVKqrq+vTp08NDAwUHwkAoBhQWOMORqwVicfjFRYWytxgnZWV5ebmRiKR5JsKAICH\nn3766X0rQ2OD1k1NTQqOhCkqKlJT+39+vVIoFB8fn6dPnw4ZMoSQSAAAxYDCGndQWCtSWlqaWCyG\nJUEAUHnl5eWHDx8mkUhdSlgpAgeti4qKqFSq9CaVSvXz84uOjmYwGITkAQAoDBTWuMMK6y7TwwFO\nUlNT9fX1LSwsZDs8MzPT1dVVvpEAAHgwMzPLz8+/efPmsGHDEELd154XCoV79+4lpNP69evX0g5A\nCoUSHBwcHR2tra2t+CQAAAWDwhp3ZDIZIQSD1oqRlpbm4+Mj27FVVVU1NTUwYg2AslBTU5s6dWpK\nSkpsbGxISAiJROo8TowQam5uPnbsmOKD5ebmCoVChBCZTA4LC7t16xadTld8DACA4kFhjTvsY0oo\nrBWjnzMXEUJQWAOgdAICAu7evZuWljZ79mwymSwtr4VC4ffff6/4QevCwkKEkJqa2pw5cy5fvtyl\n3AcAqDBYxxp3UFgrTEtLS25urswzFzMyMvT19Y2MjOSbCgCgGJ6enn/88cfOnTv379//+++/SyQS\ngUDA4/GOHTu2adOmHg5sampqb29vbGxsaWlpb28XCASdZz12dHQ0Nzd3fr6enl73mzo6OjQajcFg\nUCiUmpoahNDixYtPnDjxvhZwAIBKgsIad9AKojAcDkcoFMo8Yp2VleXh4SHfSAAABbOzszt27Ni3\n3367b9++kydPtrS07Ny5UywWNzU11dfX19fX19bW1tfXNzY28vn8trY2nMazaTTavXv3hg4dyvrH\nkCFDOn9hampqYmKir6+Px9UBAESBwhp32HAFLGWtAKmpqbq6ura2trIdnpmZ6evrK99IAABcVVRU\nFBUVFRUVFRcXV1RUlJWVcblcLpdbVVUlHc5obW2NjIy0srLCKlo7OztfX19dXV0dHR06na6jo6Ol\npUWn03V1dTU0NOh0upqamq6urvQSXW6KRCIejye9KRQKsdKcx+O1t7fz+fynT5+mp6eHhoa2trZK\nq/m6urrXr19jX9TX12Md2AghOp1uYmJiZmZmZmZmYmJiYWFhbW1tZ2dna2tL7JbsAADZQGGNO2gF\nURhsz0XZVqGWSCTZ2dmLFy+WeyoAgFzU1dVlZGRkZ2fn5+cX/aOlpQUhRKPRrK2tTUxMzM3NnZ2d\npXWqubm5oaEhmUyOjo4ODQ2VSwwymdylFaTLhi/e3t4sFqvnkzQ0NFRUVHC53PLycuydQFlZWUJC\nwp9//snlcrFVpAwMDGxtbbEi28nJydXV1cXFhUajyeVVAABwAoU17qAVRGFSU1ODg4NlO7akpITH\n48HMRQAGiPb2dg6Hk56enpWVlZmZmZmZWVlZiRDS09NzcHCws7ObNm0aVnTa2tqam5v3/I5aXlV1\nb3ywqkYIMZlMJpM5dOjQ7g+1t7e/fv26sLCwqKgI+/PatWsFBQUdHR1kMtne3t7tH56eng4ODji8\nAgCA7KCwxh20gihGR0dHVlbW+7Zh+6DMzEwSieTi4iLfVACA3isqKnr+/HnKP9ra2tTV1e3t7V1d\nXZctW+bj4+Pq6mpjY6Pae6PSaDRnZ2dnZ+fOdwqFwtLS0qysrOzs7KysrD///HP37t0ikYjBYLi7\nu/v4+AQEBIwZM8bY2Jio2AAADBTWuINWEMXIyMjo6Ojoz1p7FhYWTCZTvqkAAD0QiURpaWmPHj16\n/Pjxy5cveTwenU4fNmyYr6/v8uXLfX197e3tYVUNhBCFQsHG5qdOnYrd09raymazk5KSEhMT7927\nd+jQIYlEYmVlNW7cuODg4ODgYJn3yQIA9AcU1riDwloxUlNTNTU1HR0dZTs8KysL+kAAUIzc3Nz7\n9+8/evTo6dOnDQ0NRkZGQUFBkZGRvr6+Hh4esOpzb2hoaIwaNWrUqFHYzYaGhsTExPj4+CdPnixf\nvrytrc3e3h6rsCdOnNilKRwAgB8orHEHPdaKkZaW5uXlhX23ZZCRkfHRRx/JNxIAQEosFqelpd26\ndevPP//Mzs7W1tb28/PbsmVLSEiIzHOOgRSTyZw4ceLEiRP//e9/C4VCDocTExMTExNz5swZoVDo\n5+c3derU8PBwmYceAAC9BB+x4Q56rBWDzWYPGzZMtmOFQmFOTg6MWAMgdxKJ5NmzZ0uWLDExMRk+\nfPi5c+cmT5787NmzhoaG6OjozZs3+/j4QFUtXxQKxcfHZ/PmzdHR0VVVVX/88YeFhcXevXudnJy8\nvLx2795dWlpKdEYAVBYU1riDVhAFEIvFGRkZMhfW+fn57e3tUFgDIEdv3rzZvXu3g4PDuHHjUlJS\n1qxZk5mZmZ+fv3///jFjxsj84RLoE11d3blz5164cKG6uvr+/fujRo06ePCgjY3NhAkTzp0719ra\nSnRAAFQNFNa4g1YQBcjNzW1qapJ5M/PMzEwymdxlGj4AQDYPHjyYNGmStbX1wYMHp06dyuFwUlNT\nt27d6urqSnS0wUtdXX3ixIlHjx4tKyu7cuWKpqbm4sWLTUxMvv7669evXxOdDgDVAYU17qAVRAHY\nbDaFQpH513ZWVpa9vb2GhoZ8UwEwqAiFwgsXLnh7e3/00Udisfjq1avl5eU//fSTh4cH0dHA/1FX\nVw8PD7958+abN2++++67W7duOTo6zp8/n81mEx0NAFUAhTXuoBVEATgcztChQ+l0umyHZ2ZmQh8I\nADKTSCQXL150dHT817/+5eTklJKS8uDBg+nTp8P6HgOZkZHR+vXrCwoKTp8+nZWV5eXlFRoa+urV\nK6JzAaDcoLDGHbSCKEB/Zi4iKKwB6AcOhxMYGPjpp58GBQXl5eVhg9ZEhwK9RaFQPv30Uzabfffu\n3YqKCk9Pz3Xr1jU2NhKdCwBlBYU17qAVRAH6U1i3tbUVFBRA9ycAfdXe3r5mzRofH5+Ojo74+PiT\nJ0/a2toSHQrIgkQiTZo0KTk5+fDhw2fPnnVycrp69SrRoQBQSlBY4w5aQfBWUVFRVVUlc2H96tUr\nkUgEI9YA9ElxcXFAQMCpU6d+++23ly9fjhgxguhEoL/U1NSWLl2al5c3ffr0WbNmrV27ViAQEB0K\nACUDhTXuoLDGW1paGkJI5glSmZmZNBrNwcFBrqEAUGX379/38fERCATJycmLFi0a4AtR7927V1dX\nl0Qi9TA/r729ffXq1cbGxpqamvfu3VNkvJiYmK1bt/Yyxv79+w0NDUkk0vHjxxFCf/311759++T7\niaient7x48fPnTv322+/BQYGVlZWyvHkAKg8KKxxBz3WeGOz2ZaWlvr6+rIdnpmZOXToUAoFdiEF\noFdu3rwZFhYWGhoaFxenFO9It27d+ssvv/T8nAMHDty7dy8nJycqKqqpqUkxwRBC27dvP3jw4LZt\n23oZY8OGDS9fvpSJR6BHAAAgAElEQVTeDAsLo9Pp48ePb2hokG+wefPmJSYm1tbWBgUFVVRUyPfk\nAKgwKCZwBz3WeONwODBzEQDFSExMnDt37meffXb06NEBPlDdJzdu3Bg+fDiTyVy6dKnCLhoZGXnx\n4kUOhyNd0UiGGKtXry4qKvr444+fPXsm3wGCoUOHPnnyJCgoKCws7NmzZ7AgKQC9ASPWuINWELz1\nf0kQmLkIQG80NzfPmTMnMDDw8OHDqlRVI4TKysoUvDhgQUHBd999t3Pnzs7rhMoWY8eOHWw2Oyoq\nSq4BEULIxMTk77//Lioq2rhxo9xPDoBKgsIad9AKgqumpqaCggKZC2sej/fmzRsYsQagN/bu3cvj\n8U6dOoXHhuRRUVFaWlpqamo+Pj5GRkZUKlVLS8vb23vMmDEWFhZ0Op3JZG7atEn6/NjYWBcXF11d\nXTqd7u7ufv/+fez+p0+f+vr6ampqMhgMd3d3Ho/X5UJVVVXW1tYUCmXSpEkIoejoaHt7+4qKijNn\nzpBIJG1t7VWrVqmrqxsbG2PP//LLL7W0tEgkUm1tLULo6NGjWlpampqaN2/enDx5MoPBMDc3v3Dh\nQudLnD17dvjw4XQ6XUtLy9ra+j//+U/313vw4EGJRBIWFobd7B6jN68Fo6enN27cuKioKIlE0vdv\n/AfY29tHRUUdO3YsPT1d7icHQPVAYY07aAXBFYfDEYvFMhfWmZmZEokECmsAPqi1tfXo0aPr1q2T\nVpzytWbNmo0bN0okkmPHjr1+/bqysnLs2LFpaWlbt25NS0urr6+PiIj44YcfOBwO9vyqqqo5c+YU\nFxdzuVxtbe1PP/0UIdTc3BwWFjZz5sz6+vr8/HxHR8eOjo4uF2KxWMOHD7927Ro2O3DChAkFBQVG\nRkYRERESiaSpqengwYOzZ8+WPv/IkSM7d+6U3ly5cuXatWtbW1t1dHQuXbpUWFhoa2u7ZMkS6QIa\nUVFRCxcunDlzJpfLLSsr27ZtW25ubvfXe/v2bScnJ01NTexm9xi9eS1SXl5e5eXl0m+OfC1YsMDN\nze3HH3/E4+QAqBgorHEHrSC4YrPZTCbT2tpatsMzMzO1tbWtrKzkGgoAFfT8+fO3b99GRETgfSEX\nFxdNTc0hQ4bMmzcPIYRNTdbU1FywYAFCKCcnB3vazJkzt2/frqenx2KxwsLC6urqampqiouLeTye\nq6srnU43MjK6evVql2nNQqEwIiLiiy++kA4Vy2z06NEMBsPAwGDu3LnNzc2lpaUIIYFAsHPnzqCg\noC1btrBYLD09vc8//7z7WoTNzc2vX7+2s7Pr4fwffC2dYbNIMzIy+vmi3olEIkVERPz999/wiwyA\nD4LCGndQWOOKw+F4enrK3O6ZlZXl5uamYt2iAOAhIyPDzMzM3NxcYVdUV1dHCAmFQuwm1nz8zpWV\nsYdEIpGtra2hoeGCBQt27NhRXFzc5WkikWj+/PmGhoZYE4h8c2LB0tPTGxoaPvroI+mjZDJ59erV\nXQ6prq6WSCTS4ep36vm1dIGdqqqqSqZX8GEjR46sq6vjcrk4nR8AlQGFNe6gxxpXsJk5AIrR1NTE\nYDCITvF/bt++HRgYaGBgQKPRpL3XGhoajx49CggI2LNnj62t7dy5c1tbW6WHfPXVV/n5+cePH8/O\nzsYpFdYGzWQye35aW1sbQohGo/XwnJ5fS/cnS0+LB+yvns/n43R+AFQGFNa4gx5r/IhEoqysLCis\nAVAAQ0NDLpc7QMYISktLw8PDjY2NExISGhsb9+3bJ33I1dX11q1bXC538+bNly5d2r9/v/Sh2bNn\nR0dHM5nMhQsXSgfC5cvU1BQhhE1z7AFWB3/w90IPr6ULrP0avxXxysrKEEI4tdcDoEqgsMYdtILg\n59WrVy0tLTIX1jU1NdXV1VBYA9Abo0ePbmxsTExMJDoIQghlZGQIBIKVK1fa2trS6XRpNxeXy8VG\now0MDPbu3evt7d15cDooKEhfX//EiRMpKSm7d+/u4fwUCkW23bytra1ZLNaDBw96fhq2e2JjY2MP\nz+n5tXSBncrIyEiGzL0RHR09dOhQPT09nM4PgMqAwhp3WCsIjFjjgc1mq6uru7i4yHY4tnoUFNYA\n9Ia7u7uHh8ehQ4eIDoIQQpaWlgihmJiYtra2/Pz8hIQE7H4ul7t8+fKcnJyOjo60tLSSkhI/P78u\nx4aFhS1atGjPnj0pKSnvO7+9vX19ff2NGzcEAkFNTU1JSUkvg9FotG3btj179mzVqlXl5eVisZjP\n53cviDU1NW1tbbFh4PfpzWuRwk7l7u7ey5x90tjYePr0aWzyKADgAySdXLp0qcs9oP+wlrs7d+4Q\nHUQFbdiwYdiwYTIfHhUVpa+vL8c88oIQunTpEtEpuoKfD+DKlSskEunx48d4nDwqKgqbgWdtbR0b\nGxsZGamrq4sQMjIyOnfu3MWLF7HhWD09vQsXLkgkks2bN7NYLCaTOWvWrMOHDyOE7OzsYmNjR48e\nraenRyaTTU1Nv/nmG6FQePXqVWyo1draurq6msfjWVhYIIS0tbX/97//FRcXe3l5IYQoFIq3t/eV\nK1ckEkldXV1QUBCdTrexsfn666+x7VHs7e1LS0uPHDmC5XRwcCgsLDxx4gTWf2xlZZWXl4e9lsOH\nD7u7u9PpdDqd7uXldeTIke6vd9WqVVQqtaWlBbvZPUZxcXH313LgwAHs+6ClpTVjxgzp2UJDQ83M\nzMRiMR5/NStWrDAwMKivr8fj5AAote6/r6Gwxl1TUxNC6Pbt20QHUUEhISGLFi2S+fAlS5YEBQXJ\nMY+8QGENBqzw8HATE5M3b94QHUTp5efnUyiUs2fP9v9UtbW1dDp9//79/T9Vd2fPniWRSBcvXsTj\n5AAou+6/r6EVBHdY8x/0WOMBlgQBQMFOnz7NYrFCQkJg5bV+sre337Vr165du7DBl/7YsWPHsGHD\nVq1aJZdgnd24cePzzz/fsGHDnDlz5H5yAFQSFNa4wyYvSnDYaXaQe/PmTW1trcyFtUQiyc7OdnV1\nlW8qAFQbg8GIiYlRU1MbOXJkXFwc0XGU29atW2fNmjV37tyeZzH27Mcff2Sz2Xfu3MEW85YXiUTy\n3//+d9asWV988UXnRVcAAD2Dwhp3MGKNEzabTSKRPDw8ZDu8tLS0sbERp7k+AKgwY2Pj+Ph4X1/f\nsWPHQsnVT3v27Fm1atXevXtlO/zmzZvt7e1PnjyR73odPB5v9uzZ33zzze7duw8fPgxbaAHQexSi\nA6g+GLHGCZvNtra2lvnXSWZmJolEknlFEQAGMwaDceXKlb17937zzTdPnjz5+eefHR0diQ6lrCZO\nnDhx4kTZjp02bdq0adPkm+fKlSsbNmwQCARPnjzx9/eX78kBUHkwYo07GLHGCYfD6WeDtYWFxQc3\nSAMAvBOJRNq2bduTJ08qKirc3d03bdoE2/Ipu4yMjODg4NmzZwcGBqalpUFVDYAMoLDGHYxY44TD\n4Xh6esp8eFZWFjRYA9BPAQEBKSkpP/300++//+7g4LBv377+tAsDomRmZkZERHh7ezc3N8fFxZ0+\nfdrQ0JDoUAAoJSiscQcj1nhoamoqKirqZ2ENS4IA0H9kMnnlypV5eXkRERHff/+9paXl5s2bYc0Q\nZREbGzt16lQPD4/k5OTff/89Li5u5MiRRIcCQIlBYY07rLCGEWv5Sk9PF4vFMhfWYrE4Nzd36NCh\n8k31/7F333FN3fv/wE9IQhJmwg4jQJiyFBALiANFrVpxVUXrbBVHVbRqnVXkVit1a/H61VrruFWr\ntmq15SrgQtE6WQkhCXujEEYIIST5/XF+zZevIAInyQnwfv5xH2R9zjv2al558znvA0C/ZWZmFh8f\nX1RUtHXr1nPnzrHZ7KioqMTERLjorG6qra09duxYUFDQ8OHDa2trr127lpWVNW/ePPRXrACAHoO/\nQtqgp6cHHWv1ysjIMDY2dnJy6tnLCwsLxWIxBGsA1MvU1PTrr7/Oz88/fvx4aWnphAkTHB0dt2zZ\nwuPx8C4NIAiCyOXyxMTEqKgoW1vbDRs2DBgwIDU1NTU1NTIyEkZ/AKAWEKy1gUAgQMdavTIyMvz8\n/Hr8ScDhcBAEgWANgCZQKJSFCxc+fPiwoKBg1apVFy9e9PT09Pb23rRpU2pqKvxjqH3Nzc1JSUkx\nMTEODg7jx48XCASHDx+uqKg4e/YsnKEIgHpBsNYG6FirXUZGBpYN1hwOx87OztTUVI0lAQDege63\nFggEKSkpo0ePvnjx4rBhwxwdHVeuXJmYmIj9ioOgc8XFxadPn546daq5ufm4ceOeP3++Zs0agUDw\n/Pnz6OhoY2NjvAsEoA+COdbaAB1r9VIqlZmZmXPnzu3xClwuFyZYA6Adenp64eHh4eHhR44cefXq\n1fXr169fv56QkEAmk4cMGYI+FBISQqPR8K60L6ioqLj7D4FAQKVSR48efejQoUmTJtnY2OBdHQB9\nHwRrbYCOtXrl5+fX19dj6VhzuVw48x0A7fP39/f394+NjVXlv4sXL3777bdUKjUoKGjIP3p8+kQ/\nJJPJMjIy/v7772fPnj158oTL5aLfWKKiouAbCwDaB8FaG6BjrV4ZGRkEAgHLFGoul7tw4UL1VQQA\n6B4bG5vZs2fPnj0bQZDi4uKUlJTU1NSkpKTDhw+3trZaWVkNGTIkKCjIz8/P19fX2dkZplWoSCQS\nDoeTnZ394sWLZ8+evXr1qrm52cTEJDAwcMqUKQcOHAgLCzMyMsK7TAD6KQjW2gAda/XKyMhgs9km\nJiY9e3lJSUldXR1sBQFARzg4OCxYsGDBggUIgojF4pcvXz579uzvv/8+c+ZMfn6+Uqk0MDAYMGCA\nj4+Pt7e3r6+vm5sbi8Uik8l4F64NjY2NQqEwJycnMzOTw+FkZmbm5eUpFAoKheLr6ztkyJDo6Ogh\nQ4Z4enrCdw8AdAEEa22AjrV6YTxzkcvlIjASBACdZGhoOGzYsGHDhqE3GxoauFxuZmZmdnZ2VlbW\nnTt30EvPkEgkR0dHNpvt8g9nZ2cmk2ltbY1r+T3X0tJSUVFRXFycl5cn/AeXyxWJRAiCkEgkNzc3\nb2/vuXPnent7+/j4uLq6kkjwCQ6AzoG/ltoAHWv1Sk9Px3LmIofDsbKysrCwUGNJAABNMDY2Rndd\nq+6pra0VCARo7szLy+NyuTdv3iwtLUWbF/r6+tbW1g4ODjY2NnZ2dra2ttbW1ubm5mZmZmZmZugP\nuLS6xWJxTU1NTU3N27dv3759W11dXVlZWVpaiobpysrKyspK9JkUCsXZ2dnFxcXV1fXvv/8ODAw8\nfPhwUFCQvr6+9ssGAHQXBGttgGCtRmKxOC8vz8/Pr8crcLlcaFcD0EsxGIygoKCgoKC2d0ql0sLC\nwvLy8pKSkvLy8tLS0rKyslevXt28ebOqqkosFrd9somJCRqyaTQalUplMBgUCsXAwMDExIRKpaK7\nk6lUattz/kxMTIhEIvqzRCJpbm5WPVRXV6dQKJRKpUgkQh8SiURSqVQsFtfX10skkrdv39bU1LR9\nCYIgFhYWVlZWdnZ2TCZz4MCBNjY26JcBBwcHJpOp2tSxatWq2bNnT5s27cyZMx9//LFa/yABABoB\nwVobYCuIGmVmZmK5mDmCIBwOx8fHR40lAQDwRaFQ3N3d3d3dO3xUKpWi6RZtGKt+QHNwbW1tXV1d\nRUVFXV1dc3MzmsKrq6sJBIIqTNfW1qpW09fXNzQ0VN00MjJCW+AMBgON46ampkZGRtbW1iYmJjQa\nTdUpb9s47+LFrYKCgl6+fLl8+fIJEyasWrVq79690LcGQMdBsNYG6FirUXp6urGxsbOzc49X4HK5\nM2bMUGNJAABdRqFQbG1tbW1tu/j85uZmMzOzhISERYsWabSwrjAxMfnPf/4zbty4FStWpKWlXbhw\nwcXFBe+iAADvBScRawN0rNUoMzPT19e3xxczr6qqevPmDYwEAQC8z6NHjyQSSXh4ON6F/K/58+f/\n/fffzc3NgwcPvn79Ot7lAADeC4K1NkDHWo3S09OxbLDmcDgIgkCwBgC8T3Jyspubm65dpMbLy+vv\nv/+eMWPG1KlTt2/fDp8pAOgmCNbaAB1rdVEqlVlZWRiDNZ1OZzKZaqwKANCXJCUlRURE4F1FB6hU\n6okTJ37++ed9+/Z98sknbXd+AwB0BARrbYCOtboUFhaKRCKMQ6yhXQ0AeB+RSPTy5cvRo0fjXch7\nzZ8/PzU1lcPhDBkyJCsrC+9yAAD/BwRrbYCOtbqkp6cTCAQsMz1g1h4AoBN3795VKpUjRozAu5DO\nBAQEPHv2jMVihYSEXL58Ge9yAAD/C4K1NkDHWl0yMjKcnZ17fDFzBEE4HA4EawDA+yQnJwcEBOj+\nBaQsLS0TExMXLlw4a9aszZs3w0cMADoCxu1pA3Ss1QXjxcxFIlF5eTlsBQEAvE9SUtKUKVPwrqJL\nyGTy0aNHAwMDly1bJhQKz549S6VS8S4KgP4OOtbaAB1rdcnIyICRIAAADSktLeXxeLq8wbq9hQsX\npqSk3L17Nzw8vLq6Gu9yAOjvIFhrA3Ss1UIsFgsEAozB2tDQkMViqbEqAECfcefOHQqFEhYWhnch\n3RMaGpqWlvbmzZuQkJDc3Fy8ywGgX4NgrQ3QsVaLrKwshUKBJVijZy72+OIyAIC+LTk5eejQoTQa\nDe9Cus3V1fXhw4cMBiM0NDQ1NRXvcgDovyBYawN0rNUiIyPD0NCQzWb3eAUOhwP7QAAA73P37t3e\ntQ+kLRsbm3v37oWGhkZERFy6dAnvcgDopyBYawN0rNUC3WCtp9fz/9PCSBAAwPtwOJzS0lLdvDRM\nFxkaGv7++++LFi2aM2fO8ePH8S4HgP4IpoJoA3Ss1QLjxcwbGxuLi4shWAMAOpSUlESn0wMDA/Eu\nBBMikfjvf//b1tZ2xYoVUqk0JiYG74oA6F8gWGsDdKzVIisra9asWT1+OZfLVSqVsBUEANCh5OTk\nkSNHEolEvAtRg2+++cbAwGDNmjUNDQ3btm3DuxwA+hEI1toAHWvsSkpKamtrfX19e7wCh8OhUqlY\ntmgDAPqq1tbW+/fvf/vtt3gXojbr1q0zNDRcsWJFY2Pjnj178C4HgP4CgrU26OnpQbDGKDMzE0EQ\nb2/vHq/A5XLd3d37RjsKAKBez549q6ur69UbrNtbtmwZkUhctmwZgiCQrQHQDgjW2kAgEGArCEZZ\nWVl2dnbm5uY9XoHL5cI+EABAh5KTk21tbT09PfEuRM2WLFliaGi4YMECuVy+d+9evMsBoO+DYK0N\n0LHGLisry8fHB8sKHA5n3rx56qoHANCXJCcnjxkzBu8qNGLOnDlKpXLhwoVyufzAgQN4lwNAHwfB\nWhugY41dZmYmlvmyzc3N+fn50LEGALTX1NSUlpb2+eef412Ipnz22WckEumzzz4zNjbeuXMn3uUA\n0JdBsNYG6FhjJJfLc3JysMyN4vF4crkcgjUAoL2HDx9KpdLw8HC8C9GgWbNmNTc3L1q0yMjIaMOG\nDXiXA0CfBcFaG6BjjZFAIJBIJBhHgpBIJBcXFzVWBQDoG5KTkwcMGGBvb493IZq1YMGCmpqadevW\nmZub9+H2PAD4gmCtDdCxxigzM5NIJGK5tguXy3Vzc6NQKGqsCgDQNyQnJ/exeSDvs3bt2tra2qVL\nl1pbW0+cOBHvcgDog+CS5toAHWuMsrKyXFxcaDRaj1eAi5kDADr09u3b169fYzmFo3eJi4tbsmTJ\nzJkznzx5gnctAPRBEKy1Aa68iFFmZiaWfSAIgnA4HNhgDQBoLyUlhUAgDB8+HO9CtOfw4cPDhw+P\njIwUCAR41wJAXwPBWhsIBALeJfRuGGftyWQyoVAIHWsAQHvJycmDBw9mMBh4F6I9ZDL5119/tbe3\nnzJlSkNDA97lANCnQLDWEthj3WMSiUQoFGLpWPP5/JaWFuhYAwDaS0pK6icbrNsyNja+deuWSCSa\nOXOmXC7HuxwA+g4I1tpAIBAgWPcYh8ORy+VYOtYcDkdPT8/d3V2NVQEA+oDCwkKhUNh/Nli3xWQy\nL1++fPfu3R07duBdCwB9BwRrbYCtIFhkZWVRqVRXV9cer8Dlcp2dnQ0MDNRYFQCgD0hKSqJSqSEh\nIXgXgo+QkJBjx47t3r378uXLeNcCQB8B4/a0BDrWPZaZmenl5UUkEnu8ApfLhX0gAID2kpOThw8f\nTqVS8S4EN59//vmLFy8WL148aNAgNzc3vMsBoNeDjrU2QMcaC4xnLiIwaw8A0BGlUnn37t3+uQ+k\nrUOHDnl7e0+fPr2pqQnvWgDo9SBYawl0rHsM46w9uVyem5sLwRoA0NjY2PZEvczMzIqKCgjWZDL5\n4sWLZWVla9aswbsWAHo9CNbaACcv9lhtbW1ZWRmWjnV+fr5EIoGtIACAO3fuMBiMSZMmHT16lMPh\nJCUlmZmZ+fv7410X/lgs1qlTp3788UfYbA0ARrDHWhtgK0iPZWZmIgiCpWPN4XAIBIKnp6f6igIA\n9EoWFhYNDQ23bt3666+/5HI5jUZjMpnnzp2LiIiws7PDuzqcTZ48OTo6eunSpSEhIfb29niXA0Bv\nBR1rLYGOdc9kZmYyGAwsn3kcDsfe3t7ExESNVQEAeiMLCwsEQZRKJbohRCKRFBYWfv755/b29s7O\nzitXruTz+XjXiKf9+/dbWlp+8cUX8IEFQI9BsNYG6Fj3GPYzF2EkCAAAZWlp+c49crlcoVAgCFJY\nWHjjxg0mk4lHXbrC0NDw559/Tk5OPnnyJN61ANBbQbDWEmgA9AzGMxcRBOFwOBCsAQAIgpiZmenp\nvfdT79y5c0ZGRtqsRweFhISsX7/+66+/Lisrw7sWAHolCNbaACcv9lh2djaWjrVSqeTxeDASBACA\nIIienp6xsXH7+0kkUkxMzIgRI7Rfkg7asWOHhYXF6tWr8S4EgF4JgrU2wFaQnikuLhaJRFg61kVF\nRQ0NDRCsAQAoMzOzd+4hEokODg67d+/GpR4dRKPRTp48+dtvv12/fh3vWgDofSBYawl0rHsAHQmC\nZSMHh8NBEASCNQAA1X6btUKhOHv2LI1Gw6Ue3RQeHv7ZZ5+tWrUKLhkDQHdBsNYG2ArSM1lZWfb2\n9u07TF3H5XJtbGzMzc3VWBUAoPeysbFpe5NIJG7cuDEsLAyvenTW/v376+rq9u3bh3chAPQyEKy1\nAbaC9ExWVhbGMxe5XC60qwEAKtbW1iTS/7+AA4lEcnZ23rFjB74l6SYrK6tNmzbFx8cXFxfjXQsA\nvQkEay2BjnUPZGZmYpy1ByNBAABtWVhYEIlE9GelUvnLL79QqVR8S9JZa9eutbKy2r59O96FANCb\nQLDWBuhY94BcLs/JycE+xBo61gAAFXNzc7TNQSKRvvnmm6CgILwr0l1UKvW77747e/ZsVlYW3rUA\n0GtAsNYS6Fh3l1AobG5u9vb27vEKlZWVtbW1cDFzAICKpaVla2sriUTy9PTcsmUL3uXoulmzZvn4\n+Hz77bd4FwJArwHBWhvg5MUeyM7O1tPTwxKLc3JyEASBYA0AULGwsFAoFAQC4ZdffiGTyXiXo+sI\nBMK2bdsuX76MzmgCAHwQCe8C+gXYCtID2dnZTk5OhoaGPV4hJyfH2NjY1tZWjVUBAHAnEolaW1vr\n6+ulUik6D66hoaG1tVX1BKVSKRKJ3nkVnU4nEAh5eXkIgsybN+/t27cvXrxAEMTAwIBCoZiYmJBI\nJDqdrsX30Tt8+umnPj4+u3btunjxIt61ANALQLDWEuhYdxeHw8GyDwRBEB6P5+npCd9qANBljY2N\n5eXlVVVVNTU1ovdrbm6WSCSNjY0ymQz7QX/66aeffvqpw4fIZLKRkRGNRqNSqfT3MzMzs7a2ZjKZ\nWL789wpo0zoqKmrXrl0uLi54lwOAroNgrQ2wFaQHsrOzJ0yYgGWFnJwcDw8PddUDAOiZ+vr6oqKi\nwsLCoqKiin9UV1dXVFRUVla2vQRJ+yzr7u6O/oAmXSMjIzKZTKfTiUSiqampvr4+mmvRR9se1MTE\nRDX9A0EQuVxeX1+PIIhYLC4rK3Nzc0OTOoIgaFivq6uTy+UikUgmkzU2NqKPqmJ9WVkZh8Npm/JV\nKxsYGFhbW9vY2FhZWdnY2KBp28HBwcnJycHBwcTERNN/vFowbdo0R0fHhISEAwcO4F0LALoOgrU2\nQNO0u1pbW3k83vr167EskpOT8/nnn6urJABA55qbm3Nzc3Nzc4VCYVFRkSpM19XVoU+g0+l2dnZW\nVlZMJpPNZr+TR83NzTU3+Y5IJDIYDARBGAyGvb09xtWam5vfvn1bWVlZUVFRVVWFfkOoqqrKzc1N\nTU0tLS1VbUSh0+kODg6Ojo4sFovFYrm6urq7u7u5ufWuGX9EInHZsmXffvttbGxs3/iqAIDmQLDW\nEuhYd4tQKJRKpVi2gjQ3NxcVFcGZiwBoSGlpaVZWVm5uLo/HQ/N0cXGxQqEgEokODg4sFsvR0XHg\nwIEODg6qZGlsbIx31epBpVLt7Ozs7Oze94SGhgb0e0VxcXFRUVFxcXFmZuatW7fQPyI9PT0Wi4Um\nbE9PT3d3d29v705W0wVLliyJi4s7e/bsypUr8a4FAJ0GwVoboGPdXehIECwbOXJzc+VyOQRrANRC\nJpPl5ua+ePGCw+FkZ2c/e/assrISQRAGg8Fms9ls9sKFC729vdlstqenZ5/fdvxBxsbG3t7e7VsD\nLS0tJSUleXl52dnZHA6Hx+P99ddf6PmUdDrd29s7MDDQ29vby8srMDCQRqPhUXvHGAzGnDlzfvjh\nhy+//BI+0QDoBARrLYGOdbdkZ2c7OztjHAlCJBJdXV3VWBUA/QqPx3vy5ElaWlpaWhqXy5XJZDQa\nzdvbe+DAgVu3bvXz8/Pz80P3V4Au0tfXR7+HREREqO6sqanJ+MejR49OnDjR3NxMJpO9vLxCQkKC\ng4NDQkLc3exM3cgAACAASURBVN1xLBv15Zdfnjx58uHDh8OHD8e7FgB0FwRrbYCTF7sL+0iQnJwc\nJyen3rWREQB8tbS0PHny5OHDh2iefvv2LZVKDQwMHDt27JYtW/z8/Nzd3dueEQjUwszMbOTIkSNH\njkRvtra25ubmZmRkPH/+/MmTJz///HNzc7OFhUVwcHBwcPCwYcOCg4P19fW1X+fAgQMHDhx4/vx5\nCNYAdAKCtTbAL866Kzs7+5NPPsGyAjprT131ANCH5eXlJSUlJSUl3b59u66ujslkBgYGbtiwYejQ\noYMHD4Zvp1pGIpG8vLy8vLyioqKQf87kfvToUWpq6k8//bRt2zYDA4PQ0NCIiIiIiIiAgABtfr7M\nmzcvLi7u8OHDOrVNBQCdAlde1BLoWHcd2rDx8vLCskhOTg4EawDeRyKR/P777wsWLLC1tXVxcdm8\nebNSqYyPjxcKhWVlZX/88cfGjRvDwsIgVeOORCJ5e3tHR0efPXtWKBQKhcIDBw6YmprGx8cPHjzY\nzs5u4cKF165dQ0cHatqcOXPEYvHNmze1cCwAeikI1toAW0G6RSAQYBwJolQqc3NzYYg1AO9oamq6\ncuVKVFSUlZXVp59+mp+fv3Llyr///ruqqury5ctLly5ls9l41wg6w2azly5deuXKlerq6qdPn375\n5ZdCoXD69OlWVlazZ8++evVq27ngasdkMiMiIs6dO6e5QwDQ28FWEG2ArSDdgo4EwdJvLikpaWxs\nhI41ACilUnn37t2TJ0/euHFDKpWOGDHi+++/nzp1qo2NDd6lgR4iEolDhgwZMmTI1q1by8vLf/vt\nt6tXr86aNYtCoUyePHnJkiUjR47UxEfPnDlzlixZUl9fDwOtAegQdKy1BDrWXZednc1ms7Hs4cvJ\nyUEQBII1AG/evNm3b5+Hh8fo0aOLi4sPHjxYXl6enJy8fPlySNV9BpPJ/PLLL1NSUsrKyg4cOFBQ\nUDBq1ChPT8/9+/e/fftWvcf65JNPFApFYmKiepcFoM+AYK0N0LHuFrWMBDEzM7O0tFRXSQD0OkKh\ncOHChfb29t9+++24ceMyMzNTU1Ojo6Ph70UfZmVltXTp0sePH6enp0dERMTFxdnZ2X3++efoqGy1\nMDMzCwsLu3HjhroWBKCPgWCtJdCx7rrs7GyMwRpGgoD+rLCwcPHixQMGDEhLS0tISCgtLT169KiP\njw/edQHt8fPzS0hIKCsrO3r06MOHDz09PaOjo4uKitSyeGRk5K1bt2QymVpWA6CPgWCtDXDyYte1\ntrby+XyMI0EgWIP+qbGxMSYmxt3dPSUl5cSJE9nZ2V988QVcB7HfMjQ0XLJkCZfL/Z//+Z+kpCQ3\nN7e1a9c2NjZiXHby5MkikSg1NVUtRQLQx0Cw1gbYCtJ1fD4f40gQBEFycnJgJAjob1JSUnx9ff/z\nn/8cPXqUx+MtXLiQRILT0wFCIpEWLVrE4/GOHDly7ty5gQMH3rt3D8uCbDbby8vrr7/+UlOBAPQp\nEKy1BDrWXZSdnU0kErHE4sbGxtLSUuhYg/5DKpV++eWXERER/v7+2dnZ0dHRZDIZ76Lea9GiRVQq\nlUAgNDc369Ti+/bts7KyIhAIx48fb//o4sWLjY2NCQTC69evO18nKSlp8+bN6M9SqTQmJsbGxsbA\nwKDDE/7eOeiNGzfi4+Plcnl3i/8gMpm8dOnSrKwsPz+/UaNGrV69WiqV9ni18PDwu3fvqrE8APoM\nCNbaAFtBuk4tI0GUSiUEa9BPvH37NiIi4pdffvnPf/7z22+/WVtb413RB5w+fXr9+vU6uPj69esf\nP378vkd//PHHkydPfnCRHTt2HDlyZMuWLejN/fv3JyYm5uTkHDp0qMM9GO8cNDIykkqljh49WiQS\ndf8dfJiNjc3vv/9+7ty5s2fPjh07tra2tmfrhIeHv3r1SkNFAtCrQbDWBtgK0nXYR4LweDwymezs\n7KyukgDQWfX19R9//HFxcfGjR49mz56Ndzn/SyKRhIaGvu9mX7Vnz56LFy/++uuvxsbG6D3Xrl0b\nPHgwnU6Pjo7+9NNPu7JITEzMwIEDJ0yY0NraqqE6P/vss9TU1Pz8/I8//rihoaEHK4wcOVKpVD58\n+FDttQHQ20Gw1hLoWHeRWkaCuLq66vKvwgFQC6VSOX/+/NLS0uTkZIzn+3broJcvXz5x4kTnTzt1\n6lRVVdX7bqI02nHQxOKdrykQCL755pudO3e2vQ58SUlJD/4tio2Nff369aFDh3pSZdf4+PikpKQU\nFRUtWLCgBy83Nzf39fWF3SAAtAfBWhugY91FMpkM+0iQnJwc2AcC+oMzZ87cunXr119/dXFx0dxR\n5HL57t27PTw8aDSahYWFs7Pz7t27Z86cqXp0+/btLBaLRqP5+fldunQJQZA1a9asW7dOKBQSCARX\nV9d3bqIv1NPTu3Xr1vjx401NTZlM5k8//YTe//333xsYGBgbG1dVVa1bt87Ozo7H43V4FARB7t+/\nP2TIEAMDAxMTE19f3/r6+s4XRxBEqVQeOHBgwIABFAqFwWBMmTIFvZ5Uh5RK5d69ez08PCgUiqmp\n6YYNGzr5gzpy5IhSqYyMjERv3rlzx9XVtby8/MyZMwQCwcjIqJOC38FgMEaMGHHo0CGNdmRcXV0v\nXrx448aNnl2ifOTIkffv31d7VQD0dhCstQQ61l3B5/NbWlqwjwSBYA36PLlcHhsbu3jx4rCwMI0e\nKD4+fvv27Xv37q2pqbl9+3ZzczOdTqfT6eijmzZt+v7779ELOk6aNGnOnDnPnz8/dOjQpEmTXFxc\nlEqlQCB45yb6QoVCQafTL168WFBQEBAQsGLFiqamJgRBvv7666+++qqxsXH37t3Ozs7BwcFKpbLD\no4jF4sjIyE8//bSmpobP57u7u7e0tHS+OIIgsbGxmzdv3rp1a1VV1YMHD4qLi4cNG1ZZWdnhe//m\nm282bty4dOnSysrKioqKTZs2dfIHdevWLQ8PDwMDA/TmmDFjBAKBtbX1ggULlEplY2NjJwW35+/v\nX1pamp6e3o3/VN03YsSIzz//fMeOHQqForuv/eijjzIzMyUSiSYKA6D3gmCtDXDyYhehI0Hc3d17\nvIJCoeDz+TBrD/R5aWlphYWFa9as0fSBrl27FhgYGBkZSaPRAgICJk+e/ODBAzQRNjc3Hzt2bOrU\nqdOnT6fT6du2bSOTyadPn+7iyqGhoaampgwGIyoqSiqV5ufnt310z549K1euvHr1qpOTU4dHKSgo\nqK+v9/b2plKp1tbWV69etbCw6HxxiURy4MCBadOmzZ0719TU1NfX9/jx42/evOlwZ4tEIjl48GBE\nRMRXX31Fp9NpNJqZmdn73otYLM7Pz+/8VwedF/wONzc3BEEyMzM7WVAt1q5dm5+fn5aW1t0XBgUF\nyWSyjIwMTVQFQO8FwVobYCtIF2VnZ7u4uGAZCVJQUNDc3Awda9DnvXz50sbGRgvfIZubm9v2BeRy\nOZlMJhKJCILweLympibVNR1pNJqNjU0nOyveB92F/L4r+b3vKGw228rKau7cubGxsQUFBV1ZPDs7\nu7GxcfDgwapHg4KC9PX1nz592v6FAoGgqalp9OjRXXkLVVVVSqVS1a7uUBcLRqFLva+VrkYDBgyw\ntrZ++fJld1/o4uJiZmb27NkzTVQFQO8FwVoboGPdRdhHgqAf6lh63gD0CvX19aamplo40IQJE168\neHH9+nWJRPL8+fNr16598sknaLAWi8UIgmzbto3wj8LCQtWmC3V531FoNFpKSkpYWNiuXbvYbHZU\nVNQHtyWg4+HQ7c4qdDq9w8kYJSUlCIJYWlp2pUh0bDaFQunkOd0qGO0vaGLUd3umpqZ1dXXdfRWB\nQAgICHj+/LkmSgKg94JgDXQIh8PBfjFzGxsbBoOhrpIA0E1MJrOsrExzE9lUYmNjR40atXDhQhMT\nk2nTps2cOVM1zhkNnQcPHlS20YNNBZ3r5Cje3t5//PFHWVnZxo0bL126tG/fvs6XQreGvxOjRSKR\nvb19+yejwz26eBUVNAd/8MIuXS8Y3WyD5dd3XSSTyUpLS21tbXvw2sGDB0OwBuAdEKyBrmhtbRUI\nBNiDNWywBv3BqFGjGhoakpKSNH2g7OxsoVBYXV0tk8mKioqOHTum+uLq4OBApVI/eCVCjN53lLKy\nMg6HgyCIpaXld999FxAQgN7shI+Pj5GRUdss+PTp05aWlsDAwA6frKen18XBF+jVEzvv+3arYHQp\nLVzu586dO2KxuIs7Xt7h5+eXm5vbySmYAPRDEKyBrhAKhVKpdMCAAVgWgWAN+glnZ+dx48bt3Lmz\nB/McumXlypUsFqvDqwZSqdRFixZduHDh2LFj9fX1crm8pKSkvLwcQRAzM7OysrKCgoKGhgaZTPbO\nzW4V8L6jlJWVLVu2LCcnp6Wl5dWrV4WFhcHBwR9cat26db/99tv58+fr6+szMzOXL1/OZDKXLl3a\n/smWlpbTp0+/cuXKqVOn6uvrMzIyOpnebWBgwGaz0d0j79OtgtGlfH19O39HGKGzZcaPH+/o6NiD\nl3t5eaEzUtVeGAC9WNtfrqHDQZVA3WbMmDFjxgy8q9B1v//+u56eXmNjI5ZFbG1t9+7dq66S8IIg\nyKVLl/Cu4l3w74Ouef36NYVC+de//qXRo6SkpJibm6s+Mshk8oABA65evYo+KpVKN27cyGKxSCQS\nmkSzs7OVSuXLly8dHR1pNFpYWFhFRUXbm1999RW6w8HNzU0oFJ4/fx5tgdvb22dlZcXHx6OPOjg4\nnDt3rpOjFBQUhIaGMhgMIpFoa2u7devW1tZW1cs7XFypVCoUir1797q5uZHJZAaDMXXqVB6Phx5l\n//79aIfY0NBw2rRpSqWyoaFh8eLF5ubmRkZGYWFh27dvR5dKT09v/we1evVqMpnc1NSE3iwoKPD3\n90cQhEQiBQQEXLlypcOC2x8UNXHiRDs7O4VCoan/rkqlUqncuXMnhULJzMzs2cslEgmRSPz111/V\nWxUAvUj7z2sI1toAwbordu/e7eTkhGWFxsZGAoFw/fp1dZWEFwjWoIuOHj1KIBB+/vlnzR0iISFh\nzZo1qptSqXTt2rUUCkWVIAGKz+eTSCTVlwEs3rx5Q6VS9+3bh32pTpw6dYpAICQkJGBZxM3NLTY2\nVl0lAdDrtP+8JmmvNw5Ap7hcLsYxeWjnCbaCgP5j5cqVFRUVn3/+eXV19fr169W+fkVFxerVq9vu\nb9bX12exWDKZTCaTaeHUul7E1dU1Li4uLi5uypQp7wwe6a7Y2NhBgwatXr1aXbW1Fx8fv3nz5m3b\ntq1YsQLLOl5eXlwuV11VAdAHwB5roCtycnIwbrDOzc0lkUjOzs7qKgkA3fftt98eOHBg06ZNEyZM\n6HyPbw/QaDQymXzq1KnKykqZTFZWVvbjjz9u3749KirKxMREvcfqAzZv3jxjxoyoqKgeTK9TOXDg\nwOvXr//88090ArfaVVRUTJkyZevWrYcOHYqLi8O4moeHR25urloKA6BvgGANdIJSqeTxeNjPXHR2\ndtbX11dXVQD0CjExMQ8ePBAKhb6+vp2cYNcDpqamt2/fzsrKcnd3p9FoXl5ep0+f3rNnz5kzZ9R4\nlL5k165dq1ev/u6773r28uvXr0ul0nv37mloZujly5d9fHwyMjKSkpLU0hF3dHT84MVuAOhXYCsI\n0AmlpaX19fUYgzWfz4dLw4D+KTQ09OXLl5s2bVq2bNmlS5fi4uKGDh2qlpWHDRt2584dtSzVT4wd\nO3bs2LE9e+3kyZMnT56s3npQqamp27dvv3///sqVK7/77rvOLxLZdc7OzrW1tXV1ddq5XBEAug86\n1kAnoLv0sO+xhg3WoN8yNDQ8evTogwcPZDJZWFjYxx9/3OGVukF/8+TJk3Hjxg0bNkyhUDx8+PDw\n4cPqStUIgjg5OSEIAk1rAFQgWAOdwOVyLS0tLSwssCzC5/Pd3NzUVRIAvVFYWNiDBw/u3LnT0NAQ\nHBw8ZsyYK1eudHd0NOgDZDLZ5cuXR48eHRISIhaLk5OT7927Fxoaqt6jODk5EQgECNYAqECwBjqB\ny+Vi3AdSUVFRV1cHW0EAQBAkIiLi0aNHiYmJFAolKiqKxWJt27YN0k8/kZ+fv2XLFgcHh9mzZxsa\nGt6+fTs1NXXUqFGaOBaNRrO0tCwsLNTE4gD0RhCsgU7APmsPPTMdtoIAoDJu3LibN2/m5eUtXrz4\n9OnTLi4uo0ePPnbsWEVFBd6lAfUrLy9PSEgYNWqUq6vr2bNnly5dmp+ff+PGjTFjxmj0uDY2NpWV\nlRo9BAC9CARroBOwd6xzc3MNDQ1tbW3VVRIAfQOLxfrXv/5VWFh49epVS0vLjRs32tnZDR8+/MiR\nI2ofzwe0r7i4+NChQ8OGDbO3t9+8ebO1tfVvv/1WUFCwc+dOBwcHLRRgbW0NwRoAFZgKAvBXW1tb\nVVWFPVi7u7sTCAR1VQVAX0IikaZMmTJlypTm5uY7d+5cvnx5+/btMTExbDY7IiIiIiJi3LhxMJq6\nt2hqanr8+HFSUlJSUtLLly9pNNqoUaNOnz49ffp0Q0NDLRcDHWsA2oJgDfDH4XAQBFFLsFZTRQD0\nWVQqddKkSZMmTZJKpSkpKbdv305KSjpx4gSFQgkNDR0zZszw4cMDAwOpVCrelYL/o7m5+cWLF+iZ\nqY8ePWppafHx8RkzZsy3334bHh5OoVDwKsza2jonJwevowOgayBYA/xxuVxDQ0OMv7Xk8XgzZ85U\nV0kA9HkUCmX8+PHjx49HEKS8vPzOnTt37tw5cuTIli1b9PX1AwICgoODQ0JCQkJCtLOjALRXXFz8\n+PHjJ0+epKWlvXr1qqWlxcbGJiIi4uTJk2PGjGEymXgXiCCwFQSA/wuCNcBfTk6Op6cnll0ccrk8\nLy8PZu0B0DNMJnP+/Pnz589HEEQgEDx58uTJkyf379//4YcfWltb7e3tAwMD/fz8Bg4cOGjQIDab\nDXuuNEGhUOTl5b1+/TojIyM9Pf3FixelpaUkEsnPzy8kJGTlypUhISEuLi54l/kuBoNRW1uLdxUA\n6AoI1gB/2M9czM/Pb2lpga0gAGDn6urq6uo6d+5cBEHEYvGzZ8+ePHny6tWrX3/9dffu3XK53NjY\n2NfXF83Zbm5u7u7u9vb2ELW7S6lUFhcX8/n83Nzc9PT0jIyMzMzMxsZGIpHo5ubm5+e3atWq4ODg\nwYMHa3/bdLeYmJg0NjYqFAo9PRiHAAAEa6ADuFzuF198gWUFdNYeBGsA1MvQ0HDkyJEjR45EbzY1\nNWVlZaWnp6NB8NKlS2ir0sDAAE3Yqv91cnKysbGBpIVSKBQVFRUFBQVojM7NzUV/kEgkCIIwGAwf\nH5/AwMBFixYNGjTIx8eHRqPhXXI3mJqaKpXKhoYGuKo5AAgEa4A7iURSWFiI/cxFa2trOp2urqoA\nAO0ZGBgMGTJkyJAhqnuqq6tzc3N5PB6aFP/444/c3FypVIogiL6+vr29vYODg6Ojo6OjI4vFcnBw\nsLOzs7KysrS07HsdbqVSWV1dXVVVVVpaWlRUVFxcXFhYWFhYWFRUVFJSgl78kkqlot89xo8fHxMT\n4+Hh4e7ujvGKs7hDh8nU1dVBsAYAgWANcMfj8RQKBYwEAaA3srS0tLS0HDp0qOoehUJRUlJSVFSE\nZkrUixcvCgsLGxsb0eeQSCT0hba2tpaWllZWVra2tgwGg06n0+l01Q+681VZJBKJRKLa2lrRP2pq\nasrLy6uqqqqqqsrLy9FILZfL0ecbGxuzWCxHR8cBAwaMGzcO/dnR0dHOzq7vdfHRPF1XV4d3IQDo\nBAjWAGdcLpdMJmM8IweCNQA6Qk9Pj8VisVissLCwdx5ShVFVEkV/yMnJqaioqKmpEYvF77xEFbL1\n9fWNjY2pVCqNRjM0NNTX1zcxMSGRSKrwbWxsTCL97yca+kzVTYlE0tzcrLrZ2tra0NCA/iwSiVpb\nW+vr61taWsRicVNTk1QqbWhoaGlpUeXpd6oyMjJiMBhMJhP9ShAYGIi24dHvCeiXBGx/ir2JkZER\ngiCqb00A9HMQrAHOuFyui4uLvr4+lkVyc3PHjh2rrpIAAJpgZmZmZmbm7e39vifIZDLR/6VqEstk\nsvr6eqlU2tTUVFVV1dLSUldX19raqmqUvhN/xWJxS0uL6qa+vv47pwCqsq+pqSmJRDI1NUWfY2Fh\nQaFQTExMyGTyOx10BoPB5/PnzZvn4uJy48YNa2trtf3R9GboCG10/w8AAII1wBn2kSBisbikpAQ6\n1gD0dmQyGd0ignch7+Xq6vr06dOJEycGBwf/+eefGP/t6hsgWAPQVl/b7AV6HezBms/nK5VKDw8P\ndZUEAADv4+Li8vjxYwcHh6FDh969exfvcvCHXqQTgjUAKAjWAE9yuVwgEHh6emJZJDc3l0gkstls\ndVUFAACdMDMzu3Pnzvjx4z/++ONz587hXQ7O9PX1CQQCBGsAULAVBOApLy9PKpViHwni5OSE/joS\nAAC0gEKhnD9/3s3NbcGCBUKhcMeOHX1vgGAXEQgEMpncdkc7AP0ZBGuAJy6XSyAQMO7igJEgAADt\nIxAIsbGxDg4Oy5cvLygoOHHiBMaTsHsvuVze98YIAtAz8DcB4InL5drb2xsbG2NZBII1AAAvX3zx\nxa1bt37//ffx48eLRCK8y8GHQqEgEol4VwGAToBgDfCUk5ODcYM1giB8Ph+CNQAAL2PGjElNTeXz\n+WFhYYWFhXiXo21yuVypVLYdIg5AfwbBGuAJe7B+8+ZNTU2Nm5ubukoCAIDu8vX1ffLkib6+fnBw\n8PPnz/EuR6vQ601CxxoAFARrgKfc3FyMG6z5fD6CINCxBgDgy9bW9sGDBwEBASNHjrxx4wbe5WgP\nBGsA2oJgDXBTXV1dU1ODPVhTKBR7e3t1VQUAAD1jZGR0/fr1efPmTZs27ejRo3iXoyWtra0IBGsA\n/gGbogBueDwegiAYg7VAIGCz2fBvOgBAF5BIpH//+9+enp5r1qwRCAQHDx7s8+My0I417LEGAAV/\nEwBueDyegYGBnZ0dlkX4fD5ssAYA6JSYmBg7O7v58+cXFxefP3/ewMAA74o0CLaCANBWH/8mDXQZ\nj8dzd3fH2M6BYA0A0EGffvppcnJyampqeHh4ZWUl3uVoEGwFAaAtCNYANzweD+M+EARBBAKBq6ur\nWuoBAAA1CgkJSUtLE4lEISEhOTk5eJejKdCxBqAtCNYAN9iDdVVVVV1dHXSsAQC6ycXF5fHjx3Z2\ndqGhoffv38e7HI0Qi8UIghgaGuJdCAA6AYI1wEdra2t+fj72MxcRBIFgDQDQWebm5klJSePGjRs7\nduz58+fxLkf96urqEAQxNTXFuxAAdAIEa4CPvLy8lpYWmLUHAOjzKBTKL7/8snnz5vnz58fGxuJd\njppBsAagLZgKAvCRm5uLYG42oxus+/w0KwBAb0cgEGJjY+3t7ZcvX15YWHjixAkymYx3UeohEokQ\nCNYA/AOCNcAHj8eztbU1MTHBsgifz4czFwEAvcXixYsdHBxmzpxZXFx89erVvhFG6+rqjIyMYI41\nACho9QF8qGUkCMzaAwD0LuPGjXv48CGPxwsLCysqKsK7HDWoq6vrG98QAFALCNYAH2oJ1kKhEDrW\nAIDexc/P78mTJyQSKTg4+MWLF3iXg1VdXR2dTse7CgB0BQRrgA/06jBYVqisrIRZewCA3sjOzu7B\ngweDBg0aMWLEzZs38S4HE5FIBB1rAFQgWAMc1NXVVVZWwqw9AEC/ZWxsfOPGjblz506ZMiUhIQHv\ncnoOtoIA0BacbQBwwOPxEATBPmuPRqPZ2dmpqSgAANAqEol0/PjxAQMGrFq1Kjc39+DBg71xxlF9\nfT0EawBUIFgDHPB4PH19fUdHRyyLCAQCFxeX3vg5BAAAKjExMba2tvPnzy8pKTl//jyNRsO7ou6p\nrq4eOHAg3lUAoCsglAAc8Hg8V1dXjOOZYNYeAKBvmDFjRnJy8oMHD8LDw6uqqvAup3tKS0ttbW3x\nrgIAXQHBGuAAZu0BAEBboaGhaWlpNTU1ISEhOTk5eJfTDRUVFRCsAVCBYA1woJZgLRAIIFgDAPoM\nV1fXtLQ0JpM5dOjQBw8e4F1Ol4hEoqamJiaTiXchAOgKCNZA2xQKhUAgwBisKyoqGhoaYCsIAKAv\nMTc3T0pKGjNmzNixY3/55Re8y/mwsrIyBEGgYw2ACgRroG1FRUUSiQRm7QEAQHtUKvXChQubNm2a\nO3dubGws3uV8AARrAN4BU0GAtqGz9jBeHQadtQf/mgMA+h4CgRAbG2tra/vll18WFxcfP36cTCbj\nXVTHysvLKRQKg8HAuxAAdAUEa6BtPB7PwsLC3NwcyyICgcDV1RVm7QEA+qro6GgWizVz5szi4uLL\nly/r5qzosrIyW1tbAoGAdyEA6ArIJUDbcnNz1TISBDZYAwD6to8//vjhw4ccDmfYsGFFRUV4l4Mg\nCHLv3r09e/ZcvXo1IyNDIpGUl5fDbw4BaAs61kDb1DVrb+zYsWqpBwAAdNbAgQOfPn06ceLE4ODg\nmzdvBgQE4FuPVCrdvHkz+jOBQDAxMbG0tFy2bJmbm5u7u7u7u7uzs7O+vj6+RQKAIwjWQNt4PN6Y\nMWMwLiIUCuHMRQBAf2BnZ/fw4cOZM2eOGDHi4sWLEydObP8chUKhna1x/v7+qp+VSmVdXV1dXV1h\nYSGBQGhpaUEQ5NKlSzNnztRCJQDoJtgKArRKLBaXlJRg7FiXl5fDrD0AQP9hbGx8/fr1adOmTZ48\n+dixY+88euvWrQ0bNminEisrKysrq3fulMlkLS0tenp63t7eM2bM0E4lAOgmCNZAq/h8vlKphFl7\nAADQLfr6+j///PO2bdtWrlwZExOjUCjQ+589ezZ9+vTDhw9r7XqNQUFBHXbHFQpFfHw8nMgI+jkI\n1kCr3LXQSQAAIABJREFUcnNziUQim83GsohAIDAwMIAzZgAA/Qo6hu/nn38+fvz4zJkzJRJJQUHB\n+PHjW1tb9fT01qxZo50yBg8eTCK9u4+USCQOGTKkw20qAPQrsMcaaBWfz3d0dMR4aotAIHBxcYG+\nCACgH5o/f769vf306dNHjx799u3b+vp6uVwul8v/+9///vXXX+PHj9d0Af7+/uh26rbkcvmePXs0\nfWgAdB90rIFW8fl87Fs4hEIhxp43AAD0XqNGjbp3755QKMzLy5PJZOidRCJx1apVqpua034yCZlM\nHjVqVHh4uKYPDYDug2ANtEpdwdrFxUUt9QAAQK+jVCr3799fU1PT2tqqulMul+fn5x8/flzTR3dw\ncKDT6W3vkclku3fv1vRxAegVIFgDrVLLhV0gWAMA+rOdO3eeP3++bapGKRSKrVu3vn37VtMFBAYG\nqjbjkcnkyZMnf/TRR5o+KAC9AgRroD319fXV1dUYO9Yikai2thaCNQCgfzpz5kxcXJxSqezwUYlE\nEhsbq+kagoKCyGQy+nNra2tcXJymjwhAbwHBGmgPn89HMI/JQ2ftQbAGAPRDcrk8LS2NQqEQicQO\nT+BubW3997//zeVyNVqGv78/upmbTCbPmTPHz89Po4cDoBeBYA20h8/nk0gkJycnLIsIhUIikchi\nsdRUFAAA9BpEIvH48eOVlZXHjh3z8fFBEETVOVbR09NbsWKFRsvw9/dHW+YKhWLnzp0aPRYAvQsE\na6A9fD7fycmp/cdAtwiFQhaLhXFgHwAA9F4mJibR0dEZGRnPnz9fuHChgYGBnp6e6qItMpns3r17\nf/31l+YKcHV1NTAwQBBk8eLF8PtDANqCOdZAe2AkCAAAqFFgYOCJEyf2799/8eLFhISE9PR0Mpks\nk8n09PRWrVrF5XI/2MiQSCTNzc319fUtLS319fUIgojF4rZjqltbWxsaGtq+hEKhGBgYODk58fn8\n8PDwpKQkBoOBIAiNRqNSqSYmJvr6+iYmJhp4uwD0AhCsgfYIBIKgoCCMiwiFQk9PT7XUAwAAfYCx\nsfGSJUuWLFny6tWr48ePnz9/vqmpSSgUfvHFF0FBQbW1teg53ypisbiurk4mk6FJGouoqKj3PUSl\nUmk0momJCY1Go9PpjI6Ym5tbWVnZ2dkZGRlhrAQAHQHBGmgPn8+fPXs2xkXy8vLgqrkAgP6surq6\nsLCwoKCguLi4pKSkqqqqvLy8oqKioqKi7ay9c+fOJSYmmpubq4Ksg4MDg8EwNDQ0NTUlk8kmJiZo\n/DU2NiaTyeh0arQh3fZwaENaBW1p37x586OPPrK0tFS1tJuamqRSqSqyNzc3SySShoYGiUSiyvSl\npaWqnxsbG1VrGhgYMJlMGxsbGxsbW1tbKysrFovl5OTk6OhoZ2fX/grqAOgs+D8r0BKRSPTmzRuM\nW0GkUmlpaSlsBQEA9Aetra15eXlcLjc3N7egoKCwsDA/P7+goKCpqQlBED09PRsbGzs7OxsbGzab\nPXToUGtrazs7OysrK1tbW0tLS6FQ2NTUpPYJ04aGhoaGhvPmzVPdY2lp2YN1ZDLZmzdvKisry8rK\nqqqqSktL0W8Ir169qqioKC4ulkqlCIKQSCR7e3s0ZDs5Obm5uXl6enp4eECTG+gmCNZAS9Qyay8/\nP1+hUECwBgD0PS0tLZmZmVwul8vl8ng8LpcrEAhaWloIBAKaLJ2cnAICAtB86ejo+MHTuH19fbVW\nfA+QyWQmk8lkMgcNGtT+UaVSWV5ejn6dQNvzhYWFT58+zcvLQ7eAs1gsDw8PT0/PAQMGeHp6Dho0\n6J3OOgC4gGANtITP55PJZIxj8oRCIYIgzs7OaioKAABw09jYyOPxsrOzX/yjubmZTCY7ODh4eXlN\nmjSJzWZ7eXkNHDjQ2NgY72K1jUAg2Nra2trahoaGtr2/tbW1qKgoLy8vOzubw+Fwudxff/21srIS\nQRAmkxn4D29vbzabjVPtoF+DYA20hM/nOzs7Y5+1Z2VlBeebAwB6KYFA8OjRo4cPHz569Cg3N1eh\nUDAYjICAgJCQkBUrVgQEBLi6usKW4k6QSCQ2m81msyMiIlR3lpWVvX79+tWrV69evTp79iw6Wtva\n2jo4OHjYsGFhYWEBAQEYP30A6CL42wu0RCAQuLq6YlwEZu0BAHqdzMzMu3fvpqampqamlpeXU6nU\noKCgadOmDR482N/fH+M1swCCIGhve8KECehNkUj08uXLV69epaamxsfHr1+/3sDA4KOPPho2bNjw\n4cPDwsIoFAq+BYM+DII10BI+nx8cHIxxEQjWAIBeQSwWp6Sk3Lx5MzExsaioyNjY+KOPPoqOjg4L\nCwsLC6NSqXgX2JfR6fRRo0aNGjVq3bp1CILk5eWlpqY+evToypUr//rXv6hU6tChQyMiIiIjIwcM\nGIB3saCvgWANtITP57c9i7xnhELhrFmz1FIPAACoXUlJyaVLl27cuPH48WOlUjlkyJDFixd//PHH\ngYGBqisjAi1Dt47Mnz8fQZCioqLExMTExMRdu3Zt2rTJ3d194sSJs2bNUvvsFNBvQbAG2lBbW1tT\nU4NxJIhCoSgoKICONQBA17x58+by5csXL15MTU01NTWdNGnS8uXLx44da2Zmhndp4P9gsVjR0dHR\n0dEymSw1NTUxMfH69esHDx5ks9lRUVGzZ8/28fHBu0bQu8EXaKANubm5COZZe6Wlpc3NzRCsAQA6\nQqFQ/Pnnn5988gmTydywYYOdnd3vv/9eUVFx5syZqKgoSNW6jEwmh4eHx8fH5+TkvHz5cvr06efP\nn/f19fX19T169Cj2a1KCfguCNdAGPp+vr6+vlll7EKwBALgTiUQHDx708PD45JNPWlpazp07V1VV\n9csvv0RGRnY+WxroIH9//++//76goODhw4dDhw7dsmWLvb39ypUrc3Jy8C4N9D4QrIE2CAQCZ2dn\nIpGIZRGhUGhkZGRlZaWuqgAAoLvevHnz1Vdf2dvb79ixY9y4cRwO5/bt21FRUe9cBhz0OgQCISws\n7Pjx48XFxTt37vzvf//r5eU1fvz4Fy9e4F0a6E0gWANt4PP5GPeBIAgiFArZbDaBQFBLSQAA0C1i\nsXjXrl2urq4XLlzYtWtXSUnJDz/84OnpiXddQM3odPratWt5PN7Nmzfr6uqCgoKioqIEAgHedYHe\nAYI10AY1Bmu11AMAAN1y4cIFNze377//ft26dQKBICYmBq5U1bfp6elNmDDh8ePHV69ezcjI8PLy\nWrNmjUQiwbsuoOsgWANtEAgEagnWsMEaAKBlIpHos88+++yzz6ZMmSIQCL755htDQ0O8iwLaM3Xq\n1IyMjISEhLNnzwYGBr58+RLvioBOg2ANNO7Nmze1tbXYL7uYl5cHwRoAoE2PHj0aOHDg3bt3ExMT\njx07ZmlpiXdFHVi0aBGVSiUQCM3Nzd197b59+6ysrAgEwvHjx9s/unjxYmNjYwKB8Pr1a/SeP//8\n09TU9I8//sBa9PslJSVt3rwZ/VkqlcbExNjY2BgYGCQmJn6w/hs3bsTHx8vlcvWWRCKRlixZkp6e\nzmQyQ0JC9u3bp971QV8CwRpoHLo1DWPHuqampra2FoI1AEBrrl27FhERMWjQoMzMzLFjx+Jdznud\nPn16/fr1PXvt+vXrHz9+/L5Hf/zxx5MnT7a9R6lU9uxAXbRjx44jR45s2bIFvbl///7ExMScnJxD\nhw41Nja2f/479UdGRlKp1NGjR4tEIrXX5uDgcOfOnV27dm3evHn58uUKhULthwB9AFwgBmgcn8+n\nUCgODg5YFoFZewAAbUpKSpo1a9YXX3zxww8/aOKiiRKJZPTo0Z2EWt00ceLEuro6DS2+Z8+eixcv\npqenqy75fu3atcGDB9Pp9Ojo6C4uEhMTk5eXN2HChAcPHpBIag45enp669evd3Nzi4qK0tfXP3z4\nsHrXB30AdKyBxvH5fDabjX3WHolEwjgJGwAAuqK4uHjGjBkzZsxISEjQ0KXIT506VVVVpd41NTE0\nSY1rKpXKy5cvnzhxosNH0f3rO3fuVKVqBEFKSkrIZHJ3DxQbG/v69etDhw71vNZOTZ48+ezZsz/8\n8MOZM2c0dAjQe0GwBhqnlpEgeXl5LBarB//CAgBAd61YsYLJZJ48eVJD8z3XrFmzbt06oVBIIBBc\nXV2///57AwMDY2PjqqqqdevW2dnZ8Xg8uVy+fft2FotFo9H8/PwuXbqEvvb+/ftDhgwxMDAwMTHx\n9fVVXSNQT0/v1q1b48ePNzU1ZTKZP/30k+pwSqXywIEDAwYMoFAoDAZjypQpnVz6RKlU7t2718PD\ng0KhmJqabtiwQfVQamoqi8UiEAg//PADgiDHjh0zNDQ0MDC4fv36+PHjTUxM7O3tL1y4oHq+XC7f\nvXu3h4cHjUazsLBwdnbevXv3zJkzOzzukSNHlEplZGQkevPOnTuurq7l5eVnzpwhEAhGRkadvPd3\nMBiMESNGHDp0SHMbV2bMmLFmzZqYmJg3b95o6BCgl4JgDTROKBRiP3MRRoIAALTj9evXN2/ePHjw\nII1G09AhDh06NGnSJBcXF6VSKRAIvv7666+++qqxsXH37t3Ozs7BwcFKpXLTpk3ff//9wYMHy8vL\nJ02aNGfOnOfPn4vF4sjIyE8//bSmpobP57u7u7e0tKBrKhQKOp1+8eLFgoKCgICAFStWNDU1oQ/F\nxsZu3rx569atVVVVDx48KC4uHjZsWGVlZYe1ffPNNxs3bly6dGllZWVFRcWmTZtUD4WFhbXdu7Ji\nxYq1a9dKJBJjY+NLly6hE1GXLFkik8nQJ8THx2/fvn3v3r01NTW3b99ubm6m0+l0Or3D4966dcvD\nw0N1nZ0xY8YIBAJra+sFCxYolcrGxsZO3nt7/v7+paWl6enpXf1P0n1xcXH6+vpHjhzR3CFAbwTB\nGmicQCDAnonz8vJgiDUAQAuuXLnCZrPHjRun/UPv2bNn5cqVV69edXJyOnbs2NSpU6dPn06n07dt\n20Ymk0+fPl1QUFBfX+/t7U2lUq2tra9evWphYaF6eWhoqKmpKYPBiIqKkkql+fn5CIJIJJIDBw5M\nmzZt7ty5pqamvr6+x48ff/PmTYdbMiQSycGDByMiIr766is6nU6j0czMzD5YdmhoqImJiaWlZVRU\nlFgsLioqQu+/du1aYGBgZGQkjUYLCAiYPHnygwcPOkzDYrE4Pz+/80+Kzt/7O9Bfk2ZmZn6w+B4z\nNDRcsGDB5cuXNXcI0BtBsAaaJRKJ1DLNIz8/39nZWS0lAQBAJ16/fj106FB8a+DxeE1NTT4+PuhN\nGo1mY2OTk5PDZrOtrKzmzp0bGxtbUFDwvpeju+bQznF2dnZjY+PgwYNVjwYFBenr6z99+rT9CwUC\nQVNT0+jRo3tWtr6+vuq4CII0Nze33Ywhl8vJZHKH59tUVVUplcrOLwvfxfeOQpd6X1deXYYNG5aT\nkwNXjQFtQbAGmqWWaR4ymaykpASCNb7QzaaaHrYFAO7q6+tNTU3xrUEsFiMIsm3bNsI/CgsLm5qa\naDRaSkpKWFjYrl272Gx2VFTUB1MdOngO3aOsQqfTGxoa2j+5pKQEQRB1jeueMGHCixcvrl+/LpFI\nnj9/fu3atU8++aTDYI1O4KZQKJ2s1q33jm7j6cFg725B/3/yvq3eoH+CYA00SygUEolEjNM8ioqK\n5HI5bAXBFzq4qrW1Fe9CANAsJpOp2syAFzTaHjx4UNlGWloagiDe3t5//PFHWVnZxo0bL1269MGL\nlaB7mt+J0SKRyN7evv2T0YkcUqlULe8iNjZ21KhRCxcuNDExmTZt2syZM9+Ziq2C5uAPXtil6+8d\n3XCiuV3yqMLCQn19/U52pIB+CII10CyhUOjg4ID+frDH0J2C0LHGF/rLZQjWoM8bPnz4vXv30J4x\nXhwcHKhUqup6hyplZWUcDgdBEEtLy++++y4gIAC92QkfHx8jI6Pnz5+r7nn69GlLS0tgYGCHT9bT\n07t//z7md4AgCJKdnS0UCqurq2UyWVFR0bFjxxgMRofPRK+e2PmE7G69d3Qpa2trbO/gA27evBkW\nFoZxmCzoYyBYA81Sy3XI8/PzjY2Nzc3N1VIS6Bm0Y63aPQlAXzVr1iyFQpGQkKDRo5iZmZWVlRUU\nFDQ0NLT/a0WlUhctWnThwoVjx47V19fL5fKSkpLy8vKysrJly5bl5OS0tLS8evWqsLAwODi48wNR\nqdR169b99ttv58+fr6+vz8zMXL58OZPJXLp0afsnW1paTp8+/cqVK6dOnaqvr8/IyHjf2OmuWLly\nJYvF6vCKie8wMDBgs9noRpT36dZ7R5fy9fXtQdldlJOT89tvv33xxReaOwToldr+mgkdk6kE6oZe\naADvKvARHh4eHR2NcZHNmzf7+fmppZ5eAUGQS5cu4V3Fu5KSkhAEefPmDd6FAKBxO3fuNDAw4HA4\nmjvEy5cvHR0daTRaWFjYV199hW5acHBwOHfuHPoEqVS6ceNGFotFIpHQvJudnV1QUBAaGspgMIhE\noq2t7datW1tbW+Pj49GXu7m5CYXC8+fPo41he3v7rKwspVKpUCj27t3r5uZGJpMZDMbUqVN5PB56\nlP3796NtXUNDw2nTpimVyoaGhsWLF5ubmxsZGYWFhW3fvh1dKj09/ejRozY2NgiCGBgYREZGJiQk\noOcIosc9ceKEiYkJgiCOjo65ublKpTIlJaVtQ4RMJg8YMODq1asd/oGsXr2aTCY3NTWhNwsKCvz9\n/REEIZFIAQEBV65c6fC9t68fNXHiRDs7O4VCoaH/fC0tLcHBwYGBga2trRo6BOgV2n9eQ7DWhv4c\nrFksVnx8PMZFoqKipkyZopZ6egXdDNaPHj1CEKS4uBjvQgDQuJaWlpCQEDabXVFRgXctvVhCQsKa\nNWtUN6VS6dq1aykUiio9t8Xn80kkkup7BRZv3ryhUqn79u3DvlSHFArF/PnzjYyMsrOzNXQI0Fu0\n/7yGrSBAg6RSaWlpKfaTDmHWni5Ae1Fw/jvoD8hk8vXr10kk0vDhwz842Q10qKKiYvXq1W13Sujr\n67NYLJlM1uGOMldX17i4uLi4uK5sHelcbGzsoEGDVq9ejXGdDslksvnz51+8ePHq1ateXl6aOATo\n1SBYAw0qKCiQy+VquToMBGvcQbAG/YqlpeWDBw+MjIz8/f3bXqYbdBGNRiOTyadOnaqsrJTJZGVl\nZT/++OP27dujoqLQf0za27x584wZM6Kiojo/i7FzBw4ceP369Z9//omeb61eBQUF4eHhv//++40b\nN8aOHav29UEfAMEaaFBeXh6CeZqHWCyurq6GYI07CNagv7G2tk5NTZ0/f/5nn302f/78Dgc/g/cx\nNTW9fft2VlaWu7s7jUbz8vI6ffr0nj17zpw508mrdu3atXr16u+++65nB71+/bpUKr137977xo9g\ncfbsWV9f3/r6+rS0NFwuzAl6BRLeBYC+TCgUWlhYoFNUewxm7ekIExMTAoEAwRr0KzQa7fDhw6NH\nj168eLGPj09cXNy8efP09KAn1SXDhg27c+dOd181duzYHjeDJ0+ePHny5J69thMvXrzYtGlTSkrK\nhg0b4uLiMA6QBX0b/OsANEgoFKpl1h6CII6OjuqoCPScnp6emZlZVVUV3oUAoG2RkZGZmZnjxo1b\nvHjxwIEDb968iXdFQEv4fP6sWbOCgoLEYvGDBw/27NkDqRp0DoI10KC8vDzsZy7m5eVZW1u/cz1e\ngAsbG5vKykq8qwAAB9bW1idOnMjMzPTw8IiMjAwODr5w4QJ6eT/QJ/39/9i783go1/9/4PeMwRg7\nMbasLZaSIiWjsitLqbTaotVJqpNSpzrak845fMpRaTmpDumkIylFCKflCGkRCmMbjLKN3TC/P+b3\n8fHVhrnHPcb7+UcPo5nrflHp5XLd1/Xvv25ubnp6em/evImNjX3y5ImpqSnWocAoAMUacBFaM9aw\nDoRHKCgo1NTUYJ0CAMxoa2v/9ddfz549Gz9+vLu7u7q6+sGDB+EfBT/p7Oy8du3arFmzZs2a9fbt\n2wsXLrx69Wrx4sVY5wKjBhRrwC0sFqu0tBSKNT8hk8nQIQAwNja+efNmaWnp2rVrw8LC1NTUFi9e\nHB0d3dbWhnU0MEwsFuuff/7x9fVVVVX18vLS0NDIzMzMyclxd3eHE8vBkECxBtxSXV3d1tYGm1jz\nE0VFRRqNhnUKAHiCiorK0aNHy8vLL1y40NnZ6ebmJi8vv3r16jt37nR2dmKdDgxWTk7Orl271NXV\nKRRKamrq1q1bqVRqdHQ0LPwAwwO7ggBuKS4uRhCE8xlrKpUKxZpHqKur//HHH1inAICHEIlENzc3\nNze3+vr6u3fv3rx5c8mSJcLCwnPmzHFwcFi8eDHceM2D2tranjx5Eh8fHxcXV1ZWpqqqunjxYhcX\nFwqFgnU0MOpBsQbcUlJSQiQSFRUVORnk48ePDAaD82lvgAoNDY1Pnz41NTVJSkpinQUA3iIjI+Pu\n7u7u7l5ZWRkfH3///v2ffvpp27Zturq6CxYssLKymjNnztcORgEjgMlkvnz5MjU1NTExMTMzk8lk\nGhkZeXp62tvbz5w5E+t0gH9AsQbcUlxcrKmpyeGGr6gcMQPQwv4Op7S01MDAAOssAPAoFRWVzZs3\nb968ubOzMyMjIzEx8f79+7/88ouAgMDUqVPNzMxMTU3NzMyUlJSwTsr/Wlpanj9/npmZmZmZ+ezZ\ns5aWFjk5ORsbm0uXLtnY2MjJyWEdEPAhKNaAW0pKSlC5c5FAIKioqKASCXBIQ0MDj8eXlJRAsQbg\nu4SFha2srKysrE6dOlVbW5v5X+Hh4UwmU0NDw8jIaMaMGdOnT58xYwaUPFS0t7e/evUqNzc3Jycn\nOzv71atX7E81hUL55ZdfKBSKjo4ODofDOibgZ1CsAbcUFxfPmjWLw0FKS0tVVFQEBQVRiQQ4RCQS\nx48fX1hYiHUQAEYZMpm8dOnSpUuXIgjS0tLy7Nmzf/75Jycn5/fff6+oqEAQREVFhd2w9fT0tLW1\nJ02aJCwsjHVqXsdiscrKygoLC9++ffvy5cvc3NyCggImkykhIWFgYDB37txdu3ZRKBRlZWWsk4Ix\nBIo14Jbi4uLVq1dzOEhpaSkssOYpenp6b9++xToFAKOYmJgYeyab/bCuri43N5c9yfrnn3+WlJT0\n9PQICAioq6tra2vr6OhMnjx58uTJmpqaioqKY/k09YaGBiqVWlRUVFBQUFBQUFhYWFhYyN7ikEwm\nGxgYODg4HDhwYPr06VpaWjAtDbACxRpwBYPBqKurg02s+c+UKVMePHiAdQoA+Ad71a+NjQ37YWdn\nJ7s7FhYWvnv3LjU1NTw8vLW1FUEQISEhVVVVdXV1dXV1NTU19huKioqKiookEgnTDwI1TCaTTqdX\nV1eXl5eXlZWVlpZS/6u5uRlBEAKBoKGhoaOjY21tvWXLFh0dHW1tbWlpaayDA/D/QbEGXMHeaw+V\nTaznzZuHRiKADj09vdDQUCaTSSDAVw8A0CcsLDx16tSpU6f2f2dlZSWVSi0tLS0rK2O/kZqaWlFR\n0XemupiYmLKysry8vJKSEplMVlBQkJeXl/6/MN+TpKOjo6GhoaGhob6+vu+N6urqmpqa2tpaGo1G\np9PpdDqLxWI/X1FRkf3Ng729fd/3EhoaGkJCQth+IAB8A/zXCLiipKQEj8erq6tzMkhvb295eTnM\nWPOUqVOndnZ2FhYW6unpYZ0FgLFCRUVFRUVlwC7Lvb291dXVNBqtr5jW1dVVVVVlZ2dXV1d//PiR\nPcXbR0BAoK9hi4qKCgkJSUlJCQoKiouLi4iIEIlECQkJ9imDYmJi/e9sYT+572FDQ0P/YRsbG9lV\nuKmpqbu7u7m5uaOjo729ncFgdHd3NzY29vXp9vb2/i8kEokyMjIKCgqKiopKSkozZ86Ul5dXVFRU\nUFAgk8njx48nEonofQoBGCFQrAFXFBcXKysrc/hlsbKysqurC4o1T5kyZYqIiEhWVhYUawCwhcfj\nlZWVv3Fn3pUrV7y9vfft27dw4cKGfpqbm9va2jo7O9mtt66ujv2wqampt7cX6deV2frez9bXv9nE\nxMQQBGltbR0/fryQkJCkpKSwsDCJRFJSUhIUFJSSkiISidJfIiIiwpXPCwCYgmINuKK4uBiVBdYI\nbGLNYwQFBQ0MDLKysjw9PbHOAgD4qoiIiE2bNu3cuTMwMJDb10pOTra2tk5PT4fvtwEYu/cXA65C\naxNrERERMpmMSiSAFmNj43///RfrFACArwoPD9+0aZO/v39QUNAIXM7S0lJLS+vSpUsjcC0AeBwU\na8AV7GMXORyEvSUI7JrEa4yNjV+9etXR0YF1EADAFwQHB/v4+Bw8ePDEiRMjc0UcDufh4XHlypXO\nzs6RuSIAPAuKNUAfk8ksLy9Hq1ijEgmgiEKhdHV1PX/+HOsgAICBgoKCdu/eHRISsm/fvpG8rpeX\nV2Nj4507d0byogDwICjWAH0VFRVMJhOKNb9i76SblpaGdRAAwP9x4MCBvXv3RkRE+Pn5jfCllZWV\nbW1tL168OMLXBYDXQLEG6EPrpsOysjI1NTU0EgGUzZ8/H4o1ALyDxWJt37792LFjly5d8vb2xiSD\nt7d3UlJSWVkZJlcHgEdAsQboKy0tFRUVlZOT42SQ7u5uGo3G4U7YgEvmz5//7NkzWGYNAC9gsVi+\nvr5hYWHR0dEeHh5YxXBycpKXl798+TJWAQDgBVCsAfrKyso4L8SVlZU9PT0wY82bzM3NOzo60tPT\nsQ4CwFjX09Pj5eUVERERExOzbNkyDJMQCAQ3N7eLFy/29PRgGAMAbEGxBuhDZW00++eJUKx5k6qq\n6pQpU+7du4d1EADGNCaT6enpGRMTEx8fv3jxYqzjIOvWrauqqkpOTsY6CACYgWIN0EelUjmfsaYx\nHvYdAAAgAElEQVRSqSIiIhyuJwHcY29vHx8fj3UKAMaurq6u5cuX3759Oz4+3sbGBus4CIIgkyZN\nMjU1hVsYwVgGxRqgr7S0lPNizb5zETax5lkLFy4sKSkpKirCOggAY1FbW5ujo2NKSkpSUpKFhQXW\ncf7H29s7Li6urq4O6yAAYAOKNUBZV1dXdXU1bAnC9+bMmSMjIwPb1gIw8lpbW52cnLKysh4+fGhi\nYoJ1nP9jxYoVJBLp2rVrWAcBABtQrAHKysrKent70ZqxRiMR4AoCgbBo0aKbN29iHQSAsaWpqcnG\nxub169dpaWnGxsZYxxlIRERkxYoV58+fxzoIANiAYg1QBptYjx0uLi5ZWVlUKhXrIACMFQ0NDba2\ntiUlJSkpKfr6+ljH+TJvb++CgoKnT59iHQQADECxBiijUqkSEhLS0tKcDNLb21tZWQnFmsdZWVlJ\nS0vfunUL6yAAjAl0On3+/PnV1dUZGRl6enpYx/mqmTNnGhgYwC2MYGyCYg1QRqVSOZ+urq6u7uzs\nhNNheJygoKCTk9ONGzewDgIA/6upqbG0tGQwGGlpaRMmTMA6znesXbv2xo0bDAYD6yAAjDQo1gBl\nsIn1mOLq6pqVlfXmzRusgwDAz8rLy83MzJhMZkZGBudfYEeAq6srk8mMiYnBOggAIw2KNUAZKptY\nl5WVCQoKKioqopEIcJG5ubmqqirsAAAA91CpVHNzcyEhoZSUFGVlZazjDIqMjIyzszOsBgFjEBRr\ngDK0NrFWUVEREBBAIxHgIjwe7+bmdvXqVTjEGABuKCwspFAo0tLS6enpo2uuwdvb++nTp2/fvsU6\nCAAjCoo1QFN7ezudTkdlKQgssB4tPDw8qqurHzx4gHUQAPhNfn6+ubm5urp6SkqKrKws1nGGxsLC\nQktL69KlS1gHAWBEQbEGaKJSqSwWC5XzzGGB9WgxceJEc3Pz8PBwrIMAwFdycnLmzZs3ceLE+/fv\nS0hIYB1nyHA4nKen55UrVzo7O7HOAsDIgWIN0MTexJrzTgybWI8uPj4+9+7dY//pAwA4l5WVZW1t\nbWRklJiYKC4ujnWcYVq7dm1jYyOczwrGFCjWAE1UKlVWVlZSUpLDccrLy6FYjyKLFi1SUlI6d+4c\n1kEA4Afp6emWlpZz5sy5ffu2iIgI1nGGT1lZ2dbWFm5hBGMKFGuAJlS2BKmrq2ttbYViPYoQCIT1\n69dfvHixra0N6ywAjG6JiYl2dnYLFiyIjY0lEolYx+GUt7d3UlISewdVAMYCKNYATShuYg03L44u\nmzZtam1t/eOPP7AOAsAolpCQ4Ozs7OzsfP36dUFBQazjoMDJyYlMJl++fBnrIACMECjWAE1obWKN\nx+NVVFTQSARGiLy8vIeHx6lTp5hMJtZZABiVYmJinJ2d2ftXEggErOOgg0AguLq6Xrx4EXbkBGME\nFGuAJrSKtaKiopCQEBqJwMjx9/cvLy+PjY3FOggAo09UVNSaNWvWr19/7tw5PJ6v/mtet25dVVVV\ncnIy1kEAGAl89a8XYIvBYHz8+BE2sR6zNDU1nZ2dg4KCWCwW1lkAGE0iIiJcXV137NgRFhaGw+Gw\njoOySZMmmZqawi2MYIyAYg1QQ6VSETTWRsNee6PXTz/9lJubGx8fj3UQAEaN8PDwTZs2+fv7BwUF\nYZ2FW7y9vePi4urq6rAOAgDXQbEGqGEXa847MZwOM3oZGBgsWrQoMDAQJq0BGIzg4GAfH5+DBw+e\nOHEC6yxctGLFChKJdPXqVayDAMB1UKwBakpLS8lksqioKIfjwIz1qHb48OG8vLy4uDisgwDA64KC\ngnbv3h0SErJv3z6ss3CXiIjIihUrIiIisA4CANdBsQaoQeXOxebm5sbGRijWo9eUKVOcnZ0PHDgA\nmwAA8A0HDhzYu3dvRESEn58f1llGgre3d0FBwdOnT7EOAgB3QbEGqEGlWKO1ngRg6OjRowUFBVeu\nXME6CAC8iMVibd++/dixY5cuXfL29sY6zgiZOXOmgYFB/1sYKysr4bhWwH+gWAPUoHg6jKqqKhqJ\nADYmT57s7e29f/9+OIgRgAFYLJavr29YWFh0dLSHhwfWcUbU2rVrb9y4UV9fHxsba2dnp6amtn//\nfqxDAYAyKNYANWhtYi0nJ8f5Qm2ArcDAQAaDERISgnUQAHhIT0+Pl5dXRERETEzMsmXLsI4z0mbN\nmiUqKqqpqbls2bJHjx719va2t7djHQoAlEGxBuhobGxsbGyEvfYAG5lM3rVr1/HjxysqKrDOAgBP\nYDKZnp6eMTEx8fHxixcvxjrOyOno6Lh586a5ubmJiUl9fX1TUxOLxWIf0drV1YV1OgBQBsUaoIO9\nhIPzTlxeXg7Fmj/4+/srKytv27YN6yAAYK+rq2v58uW3b9+Oj4+3sbHBOs7IefTokZyc3MqVK9PT\n01ksVnd3d//f7e7u7u3txSobANwAxRqgo7y8HEGQ8ePHcz4OLLDmD8LCwqdPn46NjYXzYsAY19bW\n5ujomJKSkpSUZGFhgXWcEWVubm5raysgIPDFAs1isTo6OkY+FQDcA8UaoAOttdEVFRWct3PAI6yt\nrVetWrVly5bW1lasswCAjdbWVicnp6ysrIcPH5qYmGAdZ6Th8firV68aGBgICgp+8QlQrAGfgWIN\n0FFRUcH5THN3d3dNTQ0Ua37y22+/NTc3Hz16FOsgAGCgqanJxsbm9evXaWlpxsbGWMfBhoiIyL17\n9xQVFQkEwue/C/cvAj4DxRqgA5UlHDQaraenB4o1PyGTyYcPHw4ODn716hXWWQAYUQ0NDba2tiUl\nJSkpKfr6+ljHwdK4ceMSExOJRCIeP7B1QLEGfAaKNUAHKsWavYMEFGs+4+PjY2RktGXLFhaLhXUW\nAEYInU6fP39+dXV1RkaGnp4e1nGwp6OjEx8f/3mxhqUggM9AsQboKCsr47wQV1RUCAoKkslkVCIB\nHoHH48+cOfPkyZOIiAisswAwEmpqaiwtLRkMRlpa2oQJE7COwyvmz59/9uzZAe+EGWvAZ6BYAxSw\n10ajMmOtpKQkICCASirAOwwNDf39/Xfs2FFYWIh1FgC4q7y83MzMjMlkZmRkcH4YLZ/x9vbeuXNn\n/3lrKNaAz0CxBiiorKzs6enhfP9p2BKEjx0+fHjKlClr1qyBIyEAH6NSqebm5kJCQikpKcrKyljH\n4UVBQUEODg59NzJCsQZ8Boo1QAF7E2tUZqyhWPMrAoFw7dq1wsLCQ4cOYZ0FAK4oLCykUCjS0tLp\n6emKiopYx+FReDw+KipqypQp7A34oFgDPvOFvW8AGKry8nJhYWHO10ZXVFRYWVmhEgnwoAkTJpw6\ndcrHx8fS0tLc3BzrOACgKT8/38rKSlNT8969exISEljH4V0MBqOzs/Ps2bOOjo51dXVv374VExPr\n/4Tm5uaenp6+hwICAgM+n5KSkng8nkgkioiISEhICAsLi4uLj1B6AL4HijVAQXl5+fjx43E4HCrj\noBIJ8KaNGzcmJye7u7u/evVKWloa6zgAoCMnJ8fW1lZXV/fu3btjreQxmUw6nU6j0eh0ekNDQ0ND\nQ319/YA32traGhsbOzs729raBrx83759qMQgkUjCwsJSUlIkEklaWlpaWlpGRmbAG/Ly8kpKSvLy\n8l/cURsAVMDfLYACVPba6+jo+PTpE5xnzvfCw8P19fU3bNhw8+ZNrLMAgIKsrCw7OztjY+PY2FgR\nERGs43BFR0cHlUotLS2lUqk0Gq2yspJOp1dVVdXW1tbW1vbtpCkkJNS/y8rIyGhoaEhLS5NIJCkp\nKWFhYRKJJC4uLiQkJCkpSSQSs7KyaDTaxo0b+9/OKCoqKiQk1PdwQB3v6elpbm5GEKStra2zs7Op\nqamrq4vBYLAfNjY2trW1sdv8x48f379/zy739fX13d3d7BHweLy8vDyZTFZWVmb/qqSkpK6urqGh\noa6uTiQSR+DzCfgYFGuAArQ2sWaxWDBjzffGjRt3+fLlBQsW/PHHH56enljHAYAj6enpDg4O8+bN\nu3nzJn90sqampoKCgvz8/OLiYnaTLi0tra6uZv+utLS0srIyu5Lq6+sr9kMmkwcs6vguPT29lpaW\nb79KWFhYWFi4/3vGjRs31A8KQZCWlpba2trqfmg0WlVVVVZWVmVlZWNjI/tpioqK7IatoaGhpaWl\np6enra0Na3vA4EGxBigoLy/n/LReOB1m7LC1td21a9fmzZv19PRmzpyJdRwAhikxMXHJkiWOjo7X\nrl1j34o36jQ3N+fl5eXn5+fn57979+7du3eVlZUIgoiIiGhpaWloaBgZGS1btozdNdXV1aWkpNAN\nMNQuzsmFxMTEtLS0vvi7jY2NfVPy7Dfi4uKKi4vZ91aqqKjo6Ojo6Ojo6urq6upOmzYNqjb4GijW\nAAUVFRWozFgTiURZWVlUIgEed+zYsdevXy9atOjFixdKSkpYxwFgyBISEpYtW7ZkyZIrV66MojW7\nDAYjLy8v+78KCgp6e3slJCQmTpyoqanp7e2tp6enq6urra09po4UkJKSMjAwMDAwGPB+Go2Wn5//\n9u1b9vceUVFRdXV1CIIoKioa/peJicnwJtEBXxo1XwsAz6qvr2cwGGjttcf5HZBgVMDj8deuXTM2\nNnZxcUlNTe2/pBIA3hcTE+Pq6urp6Xn27NnPj+nmNSUlJRkZGY8fP/7nn3/ev3/PYrEUFBQMDQ2X\nLl1qaGg4ffp0uLnla5SUlJSUlPpvV1VeXp6Tk5OTk5OdnX327Nna2locDjdx4kRTU9N58+bNnTsX\nTgUa46BYA06VlZUhsIk1GDppaen4+PhZs2b5+fmFh4djHQeAwYqKinJ3d9+wYcOZM2d4di6guLg4\nOTmZ3acrKyuJRKKxsfGKFStmzpw5Y8YMOLxm2FRVVVVVVRcvXsx+WFVVlZ2dnZWV9fjx46ioqI6O\nDhUVlXnz5pmZmVlbW2tqamKbFow8KNaAU+zTYTjvxLDX3hikra0dGRnp7Ow8ffr0DRs2YB0HgO+L\niIjYtGnTzp07g4KCsM4yUE9Pz9OnT+/evZucnJydnS0qKmpgYLBmzRorKysKhcIf91byGvatnE5O\nTgiCMJnMvLy85OTkzMzMgICATZs2aWpqWllZOTg42NjYDLgFE/ArKNaAU+Xl5XJyciQSicNxKioq\njIyMUIkERpFFixbt27dvy5Yt2trac+fOxToOAN8SHh6+ZcsWf3//EydOYJ3lf9ra2uLi4v7666+k\npCQGg6Grq7tw4cLg4GAKhTJKb6kcpQgEAnvV9e7du7u7uzMzM+/du3fv3r3z589LSEhYW1u7uLg4\nOTnx656MgI3XV4YB3ofKnYsILAUZwwIDA+3s7FasWFFSUoJ1FgC+Kjg42MfH5+DBgzzSqnt6eh48\neODu7q6goODu7t7a2nr8+PGSkpK3b98GBwebm5tDq8aQoKCgubl5cHDw27dvS0pKjh071tLS4urq\nqqCg4OnpmZSU1P90ScBPoFgDTpWVlXFerBkMRnNzMxTrsQmPx1+/fl1JScnOzo5Op2MdB4AvCAoK\n2r17d0hICFonBXKisrJyz549KioqdnZ2RUVFR48eraqqSkxM/OGHH+DOOR6koaHxww8/JCYmVlVV\nHTlypKCgwMbGZvz48Xv37q2qqsI6HUAZFGvAqfLycjU1Nc4HQWAT6zFMXFw8MTERh8MtXLiQwWBg\nHQeA/+PAgQN79+6NiIjw8/PDNsnz589XrVqlqal55cqVjRs3vn///tmzZ76+vvLy8tgGA4MhLy/v\n6+v77NmzoqKi9evXX758WUNDY/Xq1f/++y/W0QBqoFgDTqFy0yGcDgPk5OTu379fVVW1ePHizs5O\nrOMAgCAIwmKxtm/ffuzYsUuXLnl7e2OY5PHjx6amprNnz/7w4cOlS5eoVGpgYOCECRMwjASGbeLE\niQcPHiwrK7tw4UJhYeGsWbMoFEpGRgbWuQAKoFgDjnR1ddXU1HC+FKS8vFxcXFxSUhKVVGCU0tTU\nfPDgQU5OjqenZ29vL9ZxwFjHYrF8fX3DwsKio6M9PDywilFYWLh48eL58+eLi4tnZGRkZWW5urrC\n1u98QEhIyN3dPTs7Oz09nUQizZ0719nZubCwEOtcgCNQrAFHKisre3t7UdnEGk4oAAiC6Ovrx8bG\n3r59e+vWrVhnAWNaT0+Pl5dXRERETEzMsmXLMMnQ3t6+bdu2KVOmlJaWPnjwIDExkUKhYJIEcJWZ\nmdnDhw/v379fXFw8ZcqUbdu2sY9SB6MRFGvAEfbaaDgdBqDI3Nz8jz/+CA8PP3nyJNZZwBjFZDI9\nPT1jYmLi4+P7jgIZYW/evDE2No6MjDx37lxubq6NjQ0mMcCIsbOzy83NDQ8Pv3LlirGx8du3b7FO\nBIYDijXgSFlZmbCwMJlM5nAcKNagv5UrV4aEhAQEBISEhGCdBYw5XV1dy5cvv337dnx8PFZ19ty5\nc8bGxlJSUi9fvvTy8uLGqenr1q0TFxfH4XAvX75kv+fevXuSkpLx8fEIgpw6dUpeXh6Hw509exb1\nS6Po+PHjkpKS/T+KQUpOTt6zZw/77c7OTj8/PwUFBRKJlJiY+PmTB3w27ty5ExQUxI398gQEBNat\nW/fy5UsJCYmZM2deuHAB9UsAboNiDTjCvnOR80N9oViDAXx9fX/99dft27cfP34c6yxgDGlra3N0\ndExJSUlKSrKwsMAkw4EDBzZv3rxz5860tDTurZG7cOFCRERE//ewWKy+t3fu3PnkyRMuXRpFe/bs\nOXfu3FBf9fPPP//nP//Zu3cv++Evv/ySmJhYUFAQEhLS0tLy+fMHfDacnJyIRKKlpWVjY+Owk3+D\nmpra48ePd+zYsWHDhkOHDnHjEoB74ORFwJGKigrO99pDEKSqqgqKNRhg27ZtRCLRx8eHxWL1/RcI\nAPe0trYuWrQoJyfn4cOHxsbGmGT49ddfjx49euHCBS8vrxG+tL29fVNT01Bf1d7ebmlpOSpaONuJ\nEyeio6Pz8vL6znj/+++/jYyMpKSkNmzYMMhB/Pz8SkpKFi5cmJ6eTiCgX6UIBMKRI0fU1NQ2bdok\nKSmJ+T6PYPCgWAOOlJeXcz6h8vHjx7a2NijW4HObNm3C4XCbN29msVg//fQT1nEAP2tqalq4cOGH\nDx/S0tL09fUxyZCRkeHv73/y5MmRadWc/7ARQZCLFy+OonOdPnz4sH///qtXr/a1agRBKisrdXV1\nhzpUYGCgsrJySEjIzp07Uc34P+vXr29sbPzxxx+NjIxMTU25dBWALlgKAjiCyibWcDoM+IaNGzee\nPXt2//79R44cwToL4FsNDQ22trYlJSUpKSlYteqenp7169fb29v/+OOPXLoEi8UKDg6ePHmysLCw\npKSkv79/329lZmaqqqricLgzZ8588bUZGRm6urqSkpJEInHq1KkPHjxAEGTbtm0//vhjcXExDodj\nb6rd09Nz4MABVVVVERERfX39GzduIAgSEhIiKiqKx+MNDQ3JZLKgoKCoqOiMGTPMzMzGjx9PJBKl\npKR27dr17WshCPL48WNjY2MSiSQhITF16tTm5uYBIWtra9XV1QkEgp2d3Rc/iv/85z8sFsvJyYn9\nMCkpacKECdXV1VeuXMHhcGJiYoO5Cpu0tPS8efNCQkL6L6FBnb+/v62t7fr16+EI9NECijXgCCpr\no9lnuiorK6ORCPChDRs2nDt37ueffz58+DDWWQAfotPp8+fPr66uzsjI0NPTwyrGrVu3Pnz4wNUb\ndvfv37979+6NGzfW1tbW1NQEBAT0/RaFQvn2co7a2toVK1ZQqVQajSYmJrZmzRoEQUJCQhwdHbW0\ntFgs1ocPHxAECQgIOHny5G+//VZdXe3o6Lh69eoXL15s27bN39+fxWKFh4eXlpbW1NTMnTs3Nzd3\nz549ubm59fX1Hh4ewcHBeXl537hWa2urk5PTsmXL6uvr379/P2nSpK6urgEhZWRkjIyMYmNjv3gP\nIoIgCQkJkydPJpFI7IfW1tYfPnwgk8keHh4sFqulpWUwV+kzffr0qqqqvthcEhISUlRUdPv2ba5e\nBaAFijUYvsbGxpaWFs6LdWVlpbS0tKioKCqpAF9av379mTNnfv755z179nB1fgiMNTU1NZaWlgwG\nIy0tDduDDGNjYy0tLTU1Nbk0fnt7+2+//WZlZbVjxw4pKSkREREZGZnBv3zZsmU///yztLS0jIyM\nk5PTp0+f6urqBjyno6Pj999/d3Z2Xrp0qZSU1L59+wQFBS9fvtz3BF1dXRKJJCsru2rVKgRBVFVV\nx40bRyKRXF1dEQQpKCj4xrWoVGpzc7Oenh6RSCSTybdu3Ro3blz/qzOZTA8Pj3Xr1vVNSA/Q2tpa\nWlqqpaX1jQ/zu1fpb+LEiQiCvH79+hsDcm7ixInm5uaxsbFcvQpACxRrMHyVlZUIGjPNVVVVMF0N\nvmvz5s2RkZG//PKLh4dHd3c31nEAPygvLzczM2MymRkZGRoaGtiGycvLmzNnDvfG//DhQ1tbm6Wl\nJedDCQoKIgjy+eKEwsLCtra2KVOmsB+KiIgoKCj01eX+2CdHMpnM/gN+8d9137U0NTXl5eVdXV0D\nAwOpVOqAp/X09KxevVpeXv5ri0AQBKHT6SwWq2+6+ou+fZUB2EPV1tZ++2mcMzU15fa8OEALFGsw\nfOxiraKiwuE4UKwHwOFwMCn7Ra6urvfu3fv7778XLlzIYDCwjgNGNyqVam5uLiQklJKSwgtfghgM\nhoSEBPfGZ3/FlpOTG97LExIS5s+fLycnJyws3H89dH+tra0Iguzbtw/3X2VlZW1tbahcS0REJCUl\nhUKhHD16VFNTc+XKlf2PJ9yyZcv79+/Pnj2bn5//tWE7OjoQBBEWFv7Gpb99lc+f3DcsV0lISHxt\nqTfgNVCswfBVVlaKiopKSUlxOA6NRuOF/9V4B4FA6JvIAQNYWVmlpKS8evXK0tJyFO1FAHhNYWEh\nhUKRlpZOT09XVFTEOg6CIIiCgkJFRQX3xmfvg9HZ2TmM15aXlzs7OysoKDx//rypqSkoKOiLT2O3\n9t9++43Vz9OnT9G6lp6eXnx8PI1G2717940bN06dOtX3W8uXL09KSpKSknJ3d//a1092D/7uXYDf\nuMoA7OXX7GG5qry8nEf+loLvgmINhq+qqorz6WoEZqw/IygoCEsdvsHIyOjp06eNjY0mJibs+6UA\nGJL8/Hxzc3N1dfWUlBRZWVms4/x/ZmZmiYmJ3Ptp1ZQpU/B4/OPHj4fx2tevX3d3d/v4+GhqahKJ\nxK/t08fe4mOohyAO8lo0Go09Gy0nJ3f8+PEZM2b0n5w2NzcfN27c+fPns7Ozv7aDEPv0xG/v1f3t\nqwzAHorzs4e/jcViJSYmmpmZcfUqAC1QrMHwoVislZSUOB+Hb8CM9Xdpamqmp6dLSUmZmZnl5ORg\nHQeMJjk5OfPmzZs4ceL9+/e5uvRiqDw9PQsKChISErg0vpyc3NKlS//666+LFy82Nze/evXq/Pnz\ng3wt+7yC5OTkjo6O9+/fP3/+vO+3ZGRkaDQalUplMBgCAgJr166Nior6/fffm5ube3p6Kisrq6ur\nh5Tza9ei0WibNm0qKCjo6urKzc0tKyubPXv2gNc6OTl5enoePXo0Ozv785FJJJKmpiZ7SczXDOYq\nfdhDTZ06dUgf4FDFxcUVFRV5enpy9SoANf1/XsPeb5IF0Obi4uLi4oJ1CvQtWLCAvUURJ9hr8u7c\nuYNGIj4hKyv7+++/Y51iFGhqarK0tBQXF4e/P2CQ/v33XxkZGTs7u7a2NqyzfMGKFSvU1NQaGxu5\nND6DwVi3bp2srKyYmBiFQjlw4ACCICoqKnl5eadPn1ZQUEAQhEQiOTk5/fLLL+yJWFFR0SVLlrBY\nrN27d8vIyEhJSbm4uLD3utbS0iovL8/JyVFTUxMREaFQKDU1NZ2dnbt371ZVVSUQCOwq//bt25CQ\nEPZ9furq6hkZGSdOnJCUlEQQhEwmX79+PTo6mn0taWnpqKior10rIyNjzpw50tLSAgICSkpKP/30\nE5PJvHXrlrS0NHtkOp3e3NzM3qhKTEwsMjLy88/A1q1bBQUF+/70qVTq9OnTEQQhEAgzZsz466+/\nqFTq51f5/LPBZm9vr6ys3Nvby6U/LxaL1dDQMH78+NWrV3PvEoATCILcuHHj/7yn/wMo1lzCr8V6\n6tSpe/fu5XCQoqIiBEGys7NRicQfFBUV2YcOgO/q7Oxct24dHo8/evQoV/97A3zg8ePH4uLiDg4O\n7e3tWGf5MjqdrqSkZG1t3dXVhXUW/vT+/XsCgXD16lXOh/r48SORSDx16hTnQ31NZ2enhYWFsrJy\nXV0d964COPF5sYalIGD4UFkbDafDfE5ERGQY99GPTUJCQhEREeHh4YGBgStXroTPG/iaxMREOzu7\nBQsWxMbG9j/OmqfIycndvXv32bNnixcv/sZmFGDYJkyYcOjQoUOHDrW0tHA4VGBgoIGBwdatW1EJ\n9rm2tjYnJ6cXL14kJCR8Yy9twGugWINhamtrq6+vR2WvPUFBwWHvAMWXJCUlYS+5IdmwYcOjR4/S\n0tLmzJlTVlaGdRzAcxISEpydnZ2dna9fv87eF5lnTZ8+PTU1NSsry8jI6NWrV1jH4UN79uxxcXFZ\nuXLlt+9i/LZff/315cuX9+7d49Jfp3fv3s2ZMycrK+vBgwfTpk3jxiUAl0CxBsOE1ibWNBpNUVER\nj4e/iv8jISHByVf8scnMzOzp06c9PT1GRkZpaWlYxwE8JCYmxtnZ2c3N7erVqwQCAes432doaPji\nxQsZGZlZs2aFhoZiHYcPHT16dOvWrcePHx/ey+Pi4jo7O9PS0tjLu1EXGRk5c+ZMYWHhrKysb9w6\nCXgTtBkwTGgt4YC99j4nKSkJZwEMg6am5pMnT0xNTW1tbS9cuIB1HMAToqKi1qxZs379+hG8PL0A\nACAASURBVHPnzo2ib+BVVVVTU1N37NixY8cOGxsbOHUPdTY2NidOnBjeaxctWrRnzx4BAQF0IyEI\nkpeXZ2Nj4+XltX379n/++Yd759sD7hk1X2UAr6msrBQSEuJ8CQfstfc5OGRr2MTFxWNjY3fv3r1h\nwwZvb29Ycj3GRUREuLq67tixIyws7GtbL/MsAoFw9OjRx48fNzY2zpgxw8vLiz2dAfhSZWXl2rVr\nZ8yY0dTU9Pjx48OHD4+Kn66Az0GxBsNUWVmprKzM+QwQzFh/TkpKqrGxEesUoxUejz906NCdO3fi\n4uIMDQ1fv36NdSKAjfDw8I0bN/r7+3/tmMBRgUKhPH/+/Nq1a6mpqZMmTdqyZQt7JyXANwoLC318\nfCZNmpSenv7nn38+e/bM1NQU61Bg+KBYg2FCqxBDsf6cvLx8bW0t1ilGNwcHh9zcXFlZWVilOjYF\nBwf7+PgcOnRo2D/u5x04HG7VqlUFBQUnTpxITEzU0dFxcHBISkpice2MRjACWCzWw4cPFy5cqKOj\nk5SUFBwc/O7duxUrVoy6H62AAaBYg2FC5djF3t7empoaKNYDkMlkKNacGz9+fFpa2q5du3bs2OHu\n7s757lpgtAgKCtq9e3dISMi+ffuwzoIaYWFhX1/foqKi2NjY1tZWGxsbXV3dI0eOlJaWYh0NDE1J\nScmRI0d0dHRsbW07Ozvj4uIKCwt/+OEHISEhrKMBFECxBsNUWVnJebGuq6vr6uqCYj0AmUxubGzs\n6OjAOsioRyAQAgMDHzx48PDhQ9i8bIw4cODA3r17IyIi/Pz8sM6CPjwev2jRotTU1NzcXCsrq9On\nT2tpaVEolN9///3jx49YpwPfUldXFxYWZmpqOmHChDNnztja2ubl5T169MjR0XEU3VYLvgv+LMEw\nsddYczgIjUZDEARuXhyAfbAwTFqjxcrKKisra9y4cSYmJufPn8c6DuAWFou1ffv2Y8eOXbp0ydvb\nG+s43GVgYHD69GkajZaenq6npxcQEKCgoGBkZBQYGMg+yBbrgOD/KykpCQ0Ntba2VlZW9vf3V1ZW\njouLq6ioCA0N1dfXxzodQB8UazAc3d3ddDodrWMXoVgPQCaTESjWqGIvC9m6dauPj4+Dg0NNTQ3W\niQDKWCyWr69vWFhYdHS0h4cH1nFGiICAAIVCOXfuXHV1dXR0tL6+/tmzZ42MjNTU1DZt2hQXF/fp\n0yesM45Fnz59iouL27Rpk6qqqpaW1vHjx8ePHx8VFfXx48eYmBhHR0ceP6UIcAI2cwHDQaPRent7\nUTl2UVJSUkxMDJVUfENBQQGHw1VWVhobG2OdhX8QCITjx48vWrTI3d1dR0fnzJkza9aswToUQEdP\nT8+6dev+/PPPmJiYxYsXYx0HA6KiosuWLVu2bFlvb292dnZCQkJCQkJERASLxdLT05s7d66Zmdnc\nuXNhFoN72D89yMjISE9Pf/v2LQ6HY2+SaG9vb2hoCIs9xg4o1mA40Dp2EbYE+SIikaigoEClUrEO\nwodmz5798uXLPXv2uLm5xcXFnT17VkZGButQgCNMJnPt2rWxsbHx8fE2NjZYx8EYHo+fOXPmzJkz\nAwMD6+vrMzMz2W3v/PnzTCZTS0vL2NjY0NBwxowZM2bMkJSUxDrvKNbU1JSTk5OdnZ2dnZ2VlVVc\nXEwgEAwNDe3s7I4dO2ZqagpfW8YmKNZgOKqqqvB4vKKiIufjQLH+InV19bKyMqxT8CcSiRQaGrpw\n4UJvb289Pb0LFy7Y29tjHQoMU1dX18qVKx8+fBgfH29hYYF1HN4iIyPj5OTk5OSEIEhLS8uTJ0/+\n+eefFy9eBAcH19bW4nC4CRMmsEv2lClTtLW11dXVYa+3r+nt7S0rK3v37t3r169zc3NzcnI+fPjA\nYrEUFBQMDQ3XrFlDoVBMTEzgB7AAijUYjsrKSgUFBc7PhaLRaFCsv0hDQwNmrLmKfUv+5s2bHR0d\nf/jhh6CgIBKJhHUoMDRtbW3Ozs7Pnz9PSkoyMTHBOg5PExMTs7Gx6ZvRr6qqyv6v0NBQ9u0uJBJJ\nW1tbW1tbV1dXW1t78uTJ6urqY7MptrS0lJaWFhUVFRQUvH37tqCgoLCwkH2SK4lEIpFIS5YsOXXq\nlKGhIfwXBgaAYg2GA5VNrNnjGBoacj4O/1FXV4+Pj8c6BZ+TlZWNiYn5888/t2zZkpCQEB4ebmtr\ni3UoMFitra2LFi3Kycl5+PAh3I0wVMrKysrKyuzJbARBGhsb2Q2ysLAwPz//0qVLVCq1t7cXQRA5\nOTkNDQ11dXWN/1JRUVFUVJSWlsbyA0BJQ0NDdXV1ZWVl6X+9fv3648ePdXV1CILg8Xh1dXUdHR0r\nK6stW7awv9/IzMw8cOBARETEx48f1dXVoViDAaBYg+FAZRNrBEGqqqrgZpovghnrEbN69WpLS0t/\nf387OzsXF5czZ87Iy8tjHQp8R1NT08KFCz98+JCWlgZ7lnFOSkpq9uzZs2fP7ntPe3t7SUlJaT+J\niYlUKrWpqYn9BPatIEpKSmQyWVlZWV5ent22ZWRk+n4VFxfH6ANCEARhMBgNDQ319fXsX+vr62tq\nauh0elVVVW1tLY1Gq6mp6TsuQFJSUkNDg0wmFxUVTZgwISgoyNjYWEtLi0gkDhjWwcHBwcEhOTl5\nz549BgYG9vb2Bw8enDFjxoh/fIBHQbEGw1FVVcX515GOjo6Ghgb4dv+LJk6cyGAwaDQafOMxAshk\ncmRk5IoVK3x8fLS1tU+cOLF+/XpYbMqzGhoaFixYUFZWlpKSoqenh3Uc/iQiIqKnp/f5p7e+vp5G\no/XvpjU1NXl5ebW1tbW1tX21m01QUJBdskVFRSUkJISEhCQkJERERIhEYt9DBEEEBATYb/S9qv/6\nEwaDwWQy+x42Nzf39PSw3+jq6mpubm5vb+/o6Oh72Nrayi7T3d3d/cNISkqSyWQymaykpDRz5kxF\nRUVFRUX2dwVKSkp9Nxrm5eV5eHhs27YtODhYV1f3a58fKysrKyur5OTkgIAAIyMjqNegDxRrMByV\nlZWOjo4cDlJVVcVisaBYf5GOjg6CIO/evYNiPWLs7e3fvXt36NAhHx+f69evnzt3TltbG+tQYCA6\nnW5tbd3Y2JiRkTFhwgSs44w5MjIyMjIyU6ZM+eLv9vT09E0S958tbm1tZTAYnZ2dzc3Nnz59evv2\nraSkJIvFamlpQRCks7OTvXyZraOjo729ve8hu4j3PSSRSMLCwgiCiIuLCwkJSUpKEolEERERFRUV\nYWFhcXFxUVHR/rPmfb8OcsO7adOmPXv2LDAw0MfHJzY29sKFC9/48Sz78Km///774MGDM2fOXLZs\nWWBgIPurNxi7WP3cuHFjwHsAKlxcXFxcXLBOgZqenh4hIaFr165xOM7jx48RBKHRaKik4j+ysrJn\nzpzBOsVY9OzZM319fRERkePHj3d1dWEdB/xPdXX1lClTNDQ0SkpKsM4ChikmJgaPx7969QrrIN/x\n5MmTyZMnS0pKnjt37rtP7u3tvXXrlr6+voCAwLp16yorK0cgIeAFCILcuHGj/3tgx3IwZHQ6vaur\nC5VNrAkEAqxn/ZrJkye/e/cO6xRj0axZs7Kzs48fP37kyJEpU6bcu3cP60QAQRCkvLzczMyMyWRm\nZGRoaGhgHQcMB4vFOnLkiIuLy9SpU7HO8h0mJiY5OTlubm6bNm1asmTJt8+wxOFwS5YsefnyZVRU\nVEpKyqRJkwICAhobG0csLeAdUKzBkLFPh+F8CQeNRlNQUBAQEEAjFB/S0dEpKCjAOsUYRSAQ/Pz8\n3r17N2vWLHt7e0dHx5KSEqxDjWlUKtXc3FxISCglJQXWj41ef/3115s3b3766SesgwwKiUQ6ffr0\no0ePXrx4MW3atNTU1G8/H4fDubi4vHv37rfffrt8+bKWllZQUFDf/ZFgjIBiDYYMrWINp8N8m56e\n3ps3b7BOMaaNHz8+MjLy0aNHpaWlOjo6fn5+7CWhYIQVFhZSKBRpaen09HTOz6UCWBlF09X9mZub\nv3792szMzNLS0s/Pr6ur69vPFxIS2rBhQ3Fxsa+v78GDBydNmnT+/Hn2DZdgLIBiDYaMRqPJyMiI\niIhwPg7cmfcNBgYGtbW11dXVWAcZ6ywsLHJzc0+ePHnlyhVtbe3IyEj2ujowMvLz883NzdXV1VNS\nUmRlZbGOA4ZvdE1X9ycpKRkVFfXHH39cvHiRQqF8+PDhuy8RExMLDAwsKiqysbHx8fExNDR88ODB\nCEQFmINiDYYMreMSoVh/2/Tp03E4XG5uLtZBACIoKOjn51dQUGBlZeXp6WlhYfHq1SusQ40JOTk5\n8+bNmzhx4v379/vvyAZGnVE6Xd2fu7v7ixcvuru7DQ0Nr127NpiXqKioXLhw4c2bN5MmTbKzs7O2\nts7Pz+d2ToAtKNZgyKqrq1H5aWxNTY2CggLn4/ArKSkpNTU1KNa8Q0FB4Y8//njy5Elra+v06dPX\nrl1bUVGBdSh+lpWVZW1tbWRklJiYiO1RI4Bzo3e6uj9tbe2nT5+6u7u7u7uvXbu2tbV1kK+KiYlJ\nSUmpra2dPn16QEAALCrjY1CswZChVazRGoePTZ8+HYo1r5k9e/bz58+jo6PZ+yj7+fnBvf/ckJ6e\nbmlpOWfOnNu3b3O+8Axgiw+mq/sQicTTp0/HxcXdvXt39uzZRUVFg3yhubn5y5cvIyIiLl68OGnS\nJFhUxq+gWIMhQ6UQt7a2trS0wIz1t0Gx5k199/6fPn06Ojoa7v1HXWJiop2d3YIFC2JjYz8/UxqM\nOvwxXd2fo6Njbm6umJiYkZHRX3/9NchX4fF4d3f3wsJCFxcXLy+vWbNm/fvvv1zNCUYeFGswZDQa\njfNizb4nD2asv23mzJklJSW1tbVYBwFfICgoCPf+c0NCQoKzs7Ozs/P169cFBQWxjgM4xU/T1f2p\nqKikpaWtXbt2+fLlfn5+A05Q/wYZGZnQ0NCsrCwhISETExN3d/e6ujquRgUjCYo1GBomk/nx40e0\nijXMWH/b7Nmz8Xg8TGnwsr57/62srHx8fIyNjeFAGU7ExMQ4Ozu7ubldvXqVQCBgHQeggP+mq/sI\nCwuHhoZGRkZeuHDB2tq6pqZm8K+dPn16RkbG5cuXk5OTdXR0zp4929vby72oYMRAsQZDU1tb29vb\ny/luHjU1NXg8Ho5d/DYpKanJkyc/ffoU6yDgO1RUVC5duvTy5UtlZWUHB4c5c+YkJSVhHWr0iYqK\nWrNmzfr168+dO4fHw39P/IBfp6v7c3V1/eeffyoqKoyMjJ49ezb4F+JwOPbKkLVr127dunXu3Llw\n2i4fgK9cYGjQWsJRXV0tJycHM1LfNXv2bCjWo8WUKVPu3Lnz8uVLFRUVW1vbOXPmxMfHYx1q1IiI\niHB1dd2xY0dYWBgOh8M6DkAHH09X92dgYJCVlaWvrz9//vwLFy4M6bXi4uLBwcHZ2dnd3d3Tpk0L\nCAjo7OzkUk4wAqBYg6Gh0WgIGks4YK+9QTIxMcnKymIymVgHAYOlr68fExPz5MkTWVlZJycnCoWS\nkpKCdSheFx4evnHjRn9//6CgIKyzANSMhenqPjIyMnfv3t21a9eGDRv8/f2Huq5j6tSpT58+PXPm\nTFhYmJGR0fPnz7mUE3AbFGswNNXV1ZKSkiQSifNx4M7FwTAxMWltbc3Ly8M6CBia2bNnx8fHp6en\nCwkJWVpa2tjYPHnyBOtQPOrkyZM+Pj6HDh06ceIE1lkAmsbIdHUfPB5/6NChGzduhIWFOTg4MBiM\nob58w4YNr169UlRUnDNnzsaNG2G769EIijUYmurqalSOS6ypqYFiPRh6enry8vKPHz/GOggYDjMz\ns5SUlIyMjJ6eHlNTUwqFEh8fD5vX9hcUFBQQEBASErJv3z6sswA0janp6v5cXFxSUlKys7MpFEp5\neflQX66hofHw4cPo6Ohbt25NnTr14cOH3AgJuAeKNRgaFE+HgaUgg4HD4czMzNLS0rAOAoaPQqE8\nevQoIyNDWlp60aJF06dPj4yMhI35EAQ5cODA3r17IyIi/Pz8sM4CUDbWpqv7mz179rNnz5hMpomJ\nSXZ29jBGcHFxefPmjZGRka2traenZ319PeohAZdAsQZDA+eZj7z58+enp6dDDxvt2NPVubm5+vr6\nXl5ekyZNCg0NHbPHyrBYrO3btx87duzSpUve3t5YxwEoG7PT1X00NDSePXtmYGAwb968uLi4YYyg\noKBw8+bN+Pj4lJQUXV1d2MdztIBiDYYGlWKN1mbYY8T8+fObmprgCEb+MG3atMjIyKKiIgcHh4CA\nAA0NjaCgoNbWVqxzjSgWi+Xr6xsWFhYdHe3h4YF1HIC+sTxd3UdcXPzvv/9evXr1kiVLQkJChjeI\ng4PD69evraysHBwc/Pz8YMMQ3gfFGgwNKscu0un0np4emLEeJPYy69TUVKyDANRoamqGhoaWlpZ6\neHgcPnxYWVnZz8+voqIC61wjoaenx8vLKyIiIiYmZtmyZVjHAeiD6eo+goKC58+fDwoK2rFjx7C/\nzZCUlLx27dqNGzciIyMNDQ1fvXqFbkiALijWYAh6e3vpdDrnxZp9PBXMWA8SDoezsrJKTEzEOghA\nmYKCwokTJ6hU6o8//hgTEzNhwgQ3N7ecnBysc3ERk8n09PSMiYmJj49fvHgx1nEAV8B09QA7d+6M\njIw8efLk5s2bh328oouLy8uXL6WlpWfNmhUaGgr3QPMsKNZgCOrq6phMJue7gsB55kO1cOHCjIyM\npqYmrIMA9I0bN27//v1lZWXXrl0rKioyNDSkUCg3b97kv1X1XV1dy5cvv337dnx8vI2NDdZxAFfA\ndPUXubq6xsbG/vHHH66urt3d3cMbRE1NLTU1NTAw0N/f387Ojv0/KeA1UKzBEKB17GJNTY2YmJiY\nmBgaocaEBQsWsFis5ORkrIMAbhESEnJxcXn+/HlGRoaSktKqVasmTZoUFBTU2NiIdTR0tLW1OTo6\npqSkJCUlWVhYYB0HcAtMV3+No6Pj/fv379696+zs3N7ePrxBCATC7t27MzIySkpKpk2bBme78iAo\n1mAIUDzPHNaBDImMjMysWbMSEhKwDgK4jkKhxMTEFBQUODg4HD58WE1NbePGja9fv8Y6F0daW1ud\nnJyysrIePnxoYmKCdRzALTBd/W3z589/9OjR8+fP7ezsmpubhz3OrFmzsrOzbW1tFy1a5OvrC3c0\n8hQo1mAIaDSaqKiouLg4h+PA6TDDYG9vn5CQMOz1eWB0mTBhQmhoaFlZ2b59+5KTk6dNm2ZpaXnr\n1q3ReLh9U1OTjY3N69ev09LSjI2NsY4DuAimq79r5syZjx8/Li4utrCwqKurG/Y4EhISV69evX79\n+tWrVykUCpVKRS8j4AgUazAEaB27CKfDDIO9vT2dTufvO9vAALKysv7+/u/fv3/48KGsrOzKlSvH\njx8fEBAwjOPcsNLQ0GBra1tSUpKSkqKvr491HMBFMF09SLq6uo8fP66vrzc3N//48SMnQ61atSor\nK6urq8vAwOD27dtoJQScgGINhgDF02Fgxnqo9PX11dTUYDXIGITH462srGJiYt6/f+/p6Xnp0qUJ\nEyawj03m8Z0B6HT6/Pnzq6urMzIy9PT0sI4DuAumqwdPS0srPT29ra3NxsaGw/soJk6c+Pz5cxcX\nl6VLlwYEBPDfTc+jDhRrMARwnjm2FixYAMV6LFNXVz9+/HhlZeX169cbGxstLS3V1NQCAgLKysqw\njvYFNTU1lpaWDAYjLS1twoQJWMcB3AXT1UOloqKSlpZWX19vZ2fHYDA4GYpIJEZERJw9ezY0NHTh\nwoUNDQ1ohQTDAMUaDAFaxbq2thaK9TDY29tnZ2ezdwEHYxZ7/5CkpKT8/PzVq1dfunRJU1PT2tr6\n5s2bw97GC3Xl5eVmZmZMJjMjI0NDQwPrOIDrYLp6GFRVVZOSksrLyxcsWMD58asbNmzIyMjIz8+f\nNWvWu3fvUEkIhgGKNRgCVIp1U1NTW1sbLAUZBktLSyKReP/+fayDAJ6go6Nz4sSJioqK6OhoIpG4\natUqVVVVPz+/N2/eYBuMSqWam5sLCQmlpKQoKytjGwaMAJiuHraJEyc+ePCgoKDA2dm5o6ODw9GM\njIxyc3OVlJRmzZoVFxeHSkIwVFCswWCxWCxU1kbD6TDDJiIiMm/ePFgNAvoTFhZ2cXGJj49///79\n+vXrY2Nj9fX158+ff/nyZU728xq2wsJCCoUiLS2dnp4O3z+PETBdzYmpU6cmJye/ePFi5cqVnP/Q\nady4cQ8ePHBxcVmyZMmJEydQSQiGBIo1GKz6+vrOzk7OdwWB88w54ezsfP/+/ZaWFqyDAJ6joaFx\n6NAhKpV69+7dcePGbd68WUFBYc2aNffv3x+x+5ny8/PNzc3V1dVTUlJkZWVH5qJghA242Q6mqzln\nYGCQkJDw6NGj1atXc/6vVVhY+OLFi7/99tv+/fu9vLx4Z4XYGAHFGgwWiqfDEAiEcePGoRFqzFm2\nbBmTyYSf8YGvERAQWLhw4V9//VVTU3P27Fk6nW5vby8vL79x48bMzEyuXjonJ2fevHkTJ068f/++\nhIQEV68FMLR//34KhZKamsp+CNPVqDAxMYmLi7t79+7WrVtRGXDr1q337t27devWggULmpqaUBkT\nDAYUazBYKJ5nLi8vj8fD373hkJaWtrGxiYqKwjoI4HVSUlLu7u5JSUnv37/39fV99OiRmZnZ1KlT\ng4ODKysrUb9cVlaWtbW1kZFRYmIi52dIAV5WXFz85MkTCwsLU1PTlJQUmK5Gi4WFRUxMzLlz54KD\ng1EZ0NraOjMzs6ioyNTUdBRtfj/aQbkBg0Wj0YSFhaWkpDgcB84z59DKlSsfPnz46dMnrIOA0UFL\nSyswMPDDhw8vXrywsLAIDg5WU1OjUCihoaFo7TCTnp5uaWk5Z86c27dvi4iIoDIm4Fnv379nb6D+\n77//Wlpa0mg0CoWCdSg+4ejoeObMmd27d1+7dg2VAadOnZqZmYnD4UxMTPLy8lAZE3wbFGswWOxj\nF3E4HIfjwOkwHFq8eLGQkNCtW7ewDgJGGUNDw9DQ0IqKir///ltTU3P//v3Kysrshs3J0cqJiYl2\ndnYLFiyIjY0lEokoBga8qaqqiv0Gk8lEEKSxsdHX13f27Nnx8fGY5uITmzZt8vPzW79+PVprt1RV\nVTMzM7W1tefNm8ft9WAAgWINBq+mpgaVrTzgdBgOiYqK2tvbw2oQMDzCwsKOjo6RkZE1NTVRUVFk\nMjkgIEBFRcXR0fH69etDPagiISHB2dnZ2dn5+vXrgoKCXMoMeAedTm9vb+//Hna9zs7OdnJyMjU1\nff36NUbR+Mcvv/yyYMGCpUuXorV+Q1JS8v79+zY2NlZWVn///TcqY4KvgWINBgutU13odDqZTOZ8\nnLFs1apV6enpffNGAAwDiURavnz5rVu3Pn36xP65s5eXl7y8vKOj4/nz5+l0+ndHiImJcXZ2dnNz\nu3r1KoFA4H5kgD0qlfrF9zOZTAEBARwOB+cBcQ6Px0dGRiooKDg5OXF+cAybkJBQVFSUm5ubi4vL\nlStXUBkTfBEUazBYNTU1qBTi2tpaeXl5zscZyxYsWCAhIRETE4N1EMAPSCQSeyfs6urqM2fO9Pb2\nbt26VUVFxdbW9hsNOyoqas2aNevXrz937hzcizx2lJaWfnFBIIFAMDExSUxMFBMTG/lU/EdMTOzO\nnTs0Gs3d3Z29op1zAgIC58+f9/Pz8/LyOnv2LCpjgs/BV0MwWKjMNPf29n769AmKNYeEhYWdnZ1h\nNQhAl4yMjLe3d0JCQn19/a1bt8hk8s6dOxUVFSkUSlBQUHFxcd8zIyIiXF1dd+zYERYWxvl9F2AU\nKS0tFRISGvBOAQEBCwuLpKQkaNUoUlNTu3Hjxp07d4KCgtAaE4fDnTp16tChQz4+PmFhYWgNC/qD\nH96BwaLT6ZwX4k+fPjGZTCjWnFu5cqWtre379+8nTpyIdRbAb0gkkqOjo6OjY3t7e3Jy8s2bN48f\nPx4QEKCrq+vo6MhkMn/99dddu3bBuW5jEJVK7e3t7f8eAoFgbW19+/ZtYWFhrFLxK3Nz85MnT+7a\ntWvOnDlz585Fa9iffvqJSCT6+vr29vb6+vqiNSxgg2INBoXJZNbX13NeiNk/VoZizTkLCwt5efmo\nqKgDBw5gnQXwLREREXbD7uzsfPToUVxcXHh4eHNzs5SUVGtra1JS0rx58z6fvwR87P379/1P8iMQ\nCIsWLYqKioJbV7lk27ZtmZmZy5cvz83NRXFDrR9//BFBED8/PwRBoFujC5aCgEGpq6vr7e3lfCkI\nFGu0EAgENze3CxcujNhp1WAsExYWXrhwoaamJoPB2LFjx5YtWzIyMmxsbOTk5FasWHHt2jXYWH2M\n6L8iSEBAYOnSpdHR0dCquQeHw128eFFUVHTNmjXofrX/8ccfjx075ufnB2tC0AXFGgwKWoWYTqcL\nCAjIysqiEWqsW79+fWVlZVJSEtZBwJhw4MCBvXv3RkRE/PLLL4cPH3758iWVSg0ODm5ra/P29paX\nlzcyMgoICEhOTu4/own4CYvFYh/BiyAIHo/38PD4888/YUMYbpOSkoqJiXny5MnJkyfRHTkgIODI\nkSO+vr5Xr15Fd+SxDIo1GJTa2loEpWItKysrICCARqixbvLkyWZmZhEREVgHAXyOxWJt37792LFj\nly5d8vb27nu/mprahg0b4uPj6XT6zZs3DQ0No6Ojra2tyWTy8uXLL168yI2z0wGGaDRaV1cXgiA4\nHG7jxo0XLlyADWFGhqGh4dGjRw8cOJCVlYXuyHv37t21a9fatWvh0DG0wD8JMCh0Ol1ISIjz88zr\n6upgHQiK1q9fz96SCesggG+xWCxfX9+wsLDo6GgPD48vPkdSUnLJkiXnzp2jUqnFGe1vGQAAIABJ\nREFUxcXsmxq3b98+fvx4LS2tjRs33rx5s6WlZWSDA/SVlpay39i+fTtsCDPCtm/fPnfuXA8PjwEH\n9HDu+PHj3t7erq6ujx8/RnfksQl+ggMGhb35NOdfRqFYo2vZsmV+fn6RkZEBAQFYZwF8qKenZ926\ndX/++WdMTMzixYsH8xJNTc0NGzZs2LCho6MjMzMzOTk5OTk5IiKCSCSamppaWVlZWVnNmDEDOhmX\nMBgMJpPZ2dnZ1tbGYrEaGxvZ729vb+/o6Bjw5P5PGEBKSurzP6P09HQEQdauXbt69Wp2yRYVFRUS\nEhIUFISN9rgNj8dfvnxZX19/z549ISEhKI6Mw+F+//33+vp6Jyen1NTUGTNmoDj4GATFGgwKWoUY\nlT37QB8ikejm5hYREbFr1y74mSxAF5PJXLt2bWxsbHx8vI2NzVBfTiQS2TUaQZDa2tr09PT4+PiT\nJ08GBAQoKChYW1s7OjpaW1sP7+dgFRUVysrK/Pd3vqWlpb6+vqGhob6+nsFgtLW1NTc3Nzc3t7W1\ntba2NjY2tra2trW1MRiMpqamtrY2dl1ub2/v7e1tamoamZCXL1++fPnyF3+LXcdJJJKwsLCIiIio\nqKiEhIS4uDiJRBIVFZWWlma/IS4uLiEhQSKRJCQkpKWlZWRkpKWloZp/l6qq6unTpz09PZcsWYLi\n7nsIgggICFy/ft3BwcHe3v7p06fq6uooDj7WQLEGg1JbW4vKsYt0Oh2+G0bXxo0bQ0NDU1NTLS0t\nsc4C+EdXV9fKlSsfPnwYHx9vYWHB4WhkMtnFxcXFxaWnp+fly5fsaew1a9b09vYaGBiYmppSKBQr\nKytpaelBDrh///78/PyLFy9OnTqVw2wjo76+vra2tq6urqampra2tr6+vq9A9/91wH2fAgICEhIS\n7A5KIpHYxZREIsnJyfW11b7Z4v6llkAgiIuLI/0mnvveM4C4uPjndx8ymUwGg/H5k/Py8lRVVREE\n6evxX5wgb2lp6e7u7vseoLm5mcFg1NbWNjQ09H/ngD0uhISEpKWl+3p2368yMjJkMllBQUFOTo5M\nJsvIyHDw5zDqubm53bp1a926dXl5eSL/j737jGsi+/sGPAkJIaG3QOhNepGqNEEQFBUEFWysrg0r\nKIp1LdixYwXFtmuhqOguq7JSlCKKILpKlSIgNfReQ54Xc28e/vQyySRwrhd8QkjOfAkh+c3JKUQi\ngi1zc3M/ffp0xowZc+fOfffu3cj/GYE+QGENjAhSPc1UKlVcXHz87QAMGhoaZmZmQUFBoLAGkNLa\n2uri4pKcnBwVFWVqaopgy1xcXIaGhoaGhnv27KmpqYmJiYmLi4uOjr58+TIOhzMwMLCysrKysrK0\ntBQQEBiinbdv3xYXF+vr6/v4+Bw6dIhEIiEYcgx6enoqKiqKiopKSkpKSkrg6plRRldVVcFz/iAI\nwmKx4uLioqKicNUoKio6ZcqU/tWksLCwgIAAWluu4HC4Aesqa2trBI/S0dHR2NjY/+yC8bWgoKCu\nrq6mpgZe7xW+Fzc3N1xhM0ptCoUiLS0tIyMjLy8vKSk58T7H6OP69etaWlpHjhxBfIcmfn7+Fy9e\nmJqaOjs7v379Guz4MzagsAZGpLKyUltbe/ztgKEgzLB+/foNGzaAxxZAREtLy4IFC9LS0l6/fm1i\nYsK8A4mKirq5ubm5uUEQRKVSk5OT3717Fx0dfe7cOSwWq6amBndj9+/Jrq6uLi4uptPpNBrtwoUL\nDx8+vHnzpoODA/OiMjQ1NeXn5xcXFzNq6OLi4uLi4vLycrizGYvFSv5HXFxcW1ubUf/BF8hk8oSv\n/EaIQCCIi4uPpKulp6eHSqX2PlFhXPj69WtlZWVFRQVceePxeCkpKVlZWTk5ORkZGbjalpOTU1FR\nmTBDTaSkpE6dOuXp6bl48WIjIyPEG3/16pW5uTm8liJ4ro4BKKyBEUGkaOvs7GxoaADFH+KWLFmy\nY8eOP/74w8fHB+0sAGdraGiYO3duXl7e27dvdXV1WXZcMpkMb/EIQVBVVdWHDx/gIvvWrVsYDIZR\nZNva2oqIiCQlJdHpdPiOXV1dZWVlc+fOnTt37o0bN2RkZJCKBPeY9vHjxw/40MLCwhQKRUpKSlNT\n09bWVkpKSklJSUlJSVZWFuyWgjjG6cpgI3+6urqqqqrKy8sLCgrKysrgC+/evSsvL2fswS4sLKw0\nEJb+JgjZsGHD06dP165dm5qaivjzTVNT89mzZ3PmzDlw4MDJkyeRbXwyAIU1MCKITF6kUql0Oh0U\n1ogjEonu7u4BAQHe3t5gjXBgzOrq6hwcHIqKimJjY7W0tNCKIS4u3rvIjo+Pf/v27du3b4OCgrBY\n7NSpU+ExEh0dHfDt4bIpKipKXV39xIkTW7duHcN/QUlJSWZm5rdv3+Cv379/h8cQc3NzKygoKCsr\na2hozJ8/X0VFRUVFRV5enoeHB9FfGhgXuKNaSkrK0NCwz4/a29vhVSDz8vLy8vLy8/PDw8MLCwvh\nTxiEhIRUVVW1tbU1NTV1dHQ0NTURPDdjHgwGc/PmTR0dnTNnzvz222+It29tbX337t0VK1ZIS0tv\n2bIF8fYnNlBYA8Orr69vb29HpLCGwH7mzOHl5XXt2rU///xz4cKFaGcBOBKVSrWzs6uvr09ISFBR\nUUE7zv8RFxdftGjRokWLIAiqrq6Gi+znz58zqmqGrq6urq6uHTt23Llz5/bt20N/RN7Y2JiWlvbt\n27f09PSMjIyMjAx41p2kpKS2traZmdmaNWuUlZVVVFTk5OTAySpH4+HhUVdXV1dX731ld3d3cXFx\nfn5+fn5+VlZWRkbGixcv4H3QhISEtLS0tLS0tLW1dXR0DAwMhh7ujxZFRcXDhw8fPHjQxcVFU1MT\n8faXLVv2/fv3bdu2ycjILFiwAPH2JzAM4wM1CILCwsKWLFnS+xoAEfAgwrCwMLSDjNH379/V1NQ+\nf/48derU8bQTGRnp4OBQX18vKCiIVDaAwdnZuaamJiEhAe0gAOepqKiws7NraWmJiYlRVFREO85Q\nurq6+Pn5+xfWDDgcrqenZ+vWrSdOnGAMq+3q6vr69WtiYuKnT58+ffqUnZ3d09MjJCSkrKysqalp\naGgIF1KSkpKs+j0AtlNfX5+fn5+RkfHp06fMzMz09PSKigoIgigUiqGhoYWFhbm5uYGBAeozZRlo\nNJqpqSkXF1diYiIzTv/odPratWtDQ0NjY2OnTZuGePsTAwaDCQ0Nhcs8GOixBoaHVE8zlUolEAig\nqmYSb29va2vr5ORk8AoIjEpxcbGtrS0Oh0tISJCWlkY7zjC+fv06RFUNQVB3dzcEQZcvXw4LC1u+\nfHldXV1qampmZiaNRhMVFTUyMnJxcTEyMjIyMuKID/0BlhESEoKXrFm5ciV8TUlJSep/zp49u3fv\nXhwOp6mpaWxsPGPGDCsrK3l5eRQDc3Fx3bp1y8jIKDAwkBkDNjAYzI0bN0pLS11cXN6/f4/uL8tB\nQGENDK+yshKDwYx/mTywbAVTWVlZGRsb+/v7BwcHo50F4BiFhYW2trY8PDzR0dEUCgXtOMNLSkrC\n4/F91nuGIAiHw3FxcXV1dTHWZauoqPD391dVVXV0dDxw4ICxsTGbd8YD7AZeV4Sx5+iPHz9SUlJS\nU1OTk5MfPnzY3t6uqKhoZWVlbW1tZWWFyqYqurq6O3fu3L9/v4uLi5SUFOLt4/H4J0+ezJgxw8HB\nASxuPUKgsAaGR6VSRURExj/1GOxnzmxeXl6rV68+ffo0vIkDAAwtJyfH1tZWUlLyn3/+ERUVRTvO\niLx//753Vc3DwyMlJSUgINDe3l5RUdHR0cHNza2vr29nZzdr1qxp06aBWYYAUhQVFRUVFeEP/dvb\n2z98+ABPqw0JCWlvb1dQULC3t3dycoLPVFmW6tChQ6Ghobt3737w4AEz2ocXt54+fTpY3HqEQGEN\nDK+yshLsZ84RlixZsm/fvqtXr545cwbtLAC7y8zMnDVrlpKS0suXL9lzetaApk+fbm5uLicnx8/P\nn5GRERMT8/r16x8/fhgbGy9dunTmzJnTpk0D7/0As/Hw8FhbW8M75jCK7BcvXgQFBZFIpNmzZzs6\nOs6fP19MTIzZSYhE4rVr1+bOnbt69WombRMGL25tYWHx66+/Pnr0CN7LExgMWPobGB6VSkVqP3NQ\nWDMVHo/fvHnzjRs3Ghsb0c4CsLW0tDQrK6spU6a8evWKg6pqCILWrFlDJBLPnTs3a9YsHx+fzs7O\nCxculJaWJicnHz58eMaMGaCqBlgMLrJ9fX1TUlJ+/vx5/vz5tra2TZs2SUpKWltb37t3r6WlhakB\nHBwcnJycNm3aNPT0g/HQ0tIKDw8PDw8/ePAgkw4xYYDCGhgegvuZg8Ka2TZu3NjT03Pv3j20gwDs\nKyUlxc7OzsjIKDIykp+fH+04I/X+/ft169ZJSUlt2bJFSkoqLCysurr677//9vDw4IjR4cBkIC0t\nvWHDhpcvX1ZVVYWGhkpKSm7cuJFCoXh4eHz48IF5x71y5UpZWdm5c+eYd4iZM2cGBgaeOHHi+vXr\nzDvKBAAKa2B4SBXEVVVV458BCQxNWFh45cqV/v7+NBoN7SwAO4qPj7e1tTUzM3v27BmRSEQ7zvDa\n29sDAgK0tLTMzMxSU1OPHz9eWloaHBy8cOFCXl5etNMBwMD4+PgWLVoUEhJSVlZ2/Pjx5ORkU1NT\nbW3twMDA9vZ2xA8nJyd34MCBEydOFBQUIN44w+rVqw8dOuTl5fXXX38x7yicDhTWwPDAGGvOsm3b\ntqKiomfPnqEdBGA7kZGRc+bMcXBwCA8PZ/9Zfe3t7efOnVNQUNixY4eFhUVKSsqXL1+8vLxERETQ\njgYAIyUiIuLl5fXvv/9+/PjRzMzM29tbUVHxwoULiJfXPj4+KioqzN4o0dfX193d3d3d/du3b0w9\nEOcChTUwPETGWDc1NbW1tYHCmgVUVVUXLlx4/PhxsNkT0NuLFy9cXFxcXFwePnw4/kV+mC04OFhN\nTc3X13f16tWFhYU3btwYejNFpKxbt46fnx+DwXz58gW+5uXLl4KCghERERAEnTt3jkwmYzCYwMDA\n8R/r6dOnSkpKGAwGg8EMNnT1woULGAwGi8Wqq6vHx8ePqv1Tp04JCgr2/l1Yky0nJ8fT01NLS4uf\nnx+HwwkKCqqqqs6bN+/9+/cjD9D/DzGY6Ojoffv2wZc7Ojq2bdsmKSlJIpEiIyP737jPX/Cvv/46\nffo0az7fMzY2vnnz5o8fP1auXHnw4EF1dfXQ0FAE28fhcFeuXPnnn3/g5yqTYDCYoKAgIyMjR0dH\neI8LoC96L/DfmA4gzdXV1dXVFe0UY9Te3o7BYJ49ezbOdvLy8iAISk1NRSQVMLQvX75gMJi//voL\n7SAAuwgNDcXj8R4eHjQaDe0swygtLZ03bx4Wi123bl1ZWRnrA8ArwX/+/Bn+9u+//xYQEGD8N+Xm\n5kIQFBAQgNThlJWVIQiSlJTs7Ozs86Pu7m54Vw5bW9uxNd7nd2FBtlu3buHx+BkzZkRGRtbV1bW3\nt+fn54eEhJiZmd24cQPx8IcOHXJ0dGxsbIS/PXHihKqqal1d3Y0bNx4/fjzgXfr8Bf39/a2srOrq\n6kaVbZxKSkrWrFmDwWAcHR2RfZK7urqqqKh0dHQg2GZ/1dXVKioqZmZm7e3tTD0Q+4MgKDQ0tPc1\noMcaGAaVSqXT6ePvsUZq+0ZgJPT09BwdHY8cOUIHndYABAUHB69YsWL9+vWBgYFYLFu/7L9//97I\nyCg3NzcuLi4oKIgdZiXOmzevoaHB0dFxVPdqa2szMzMb4Y0NDQ0rKiqeP3/e5/qnT5+ivhfmqLJ9\n+PBhw4YNlpaWMTExs2fPFhISIhAISkpKS5YsOXToUGdnJ7LZ/Pz8QkJCwsLCGHNwnz9/bmRkJCQk\n5OHhsXjx4pE0sm3bNj09vblz58J7drKGtLT07du33759m5WVZWhoiOC8xtOnT5eUlAQEBCDV4IBE\nRUUjIiIyMzM9PDyYeiBOxNavsAA7QHA/cwiCwORFljl8+HBaWlpUVBTaQQCUBQUFubu779ix49q1\na2y+AG1MTIytra2+vv7Hjx8tLCzQioHIo3T79u2Rf1C+efNmCIL6F0MXLlzYuXPn+MOMx6iyHT9+\nnEajnTp1Cofru0vG7Nmzt27dOqpDD/2HyMvLO3jw4JEjR3rPFigpKRnDMCdfX98vX774+/uP9o7j\nNGPGjLS0tGnTptnY2Lx58waRNhUVFbdv337kyJGamhpEGhyMurp6SEjIo0ePzp49y9QDcRxQWAPD\nqKyshCAIkR5rQUFB9p8vNWEYGBjY29v7+vqiHQRAU0BAwIYNG3bt2nX69Gm0swwjOzt7wYIFCxcu\njIiIEBQUZOWh6XT62bNn1dTUCASCoKDgrl27GD9KTEyUk5PDYDBXr14d8L4JCQmamprwi5uOjs4/\n//wDQdD27dt37tyZn5+PwWBUVFQgCKLRaIcOHZKTkyMSibq6un0G19rY2GhoaLx58yYnJ4dx5bt3\n71pbW+3t7UdyRAiC4uLiTExMSCSSgICAjo5O/8XsKysrFRQUcDjcnDlzRv7gjDxbZ2dnTEyMqKio\niYnJ0G0O9mgM8Yfo7/Lly3Q63cnJCf42KipKRUWlvLz8999/x2AwfHx80AgeE5iwsLCVlZW/vz/r\nP+Lj5+d/8uTJggULnJycvn//jkib+/fv5+HhOXr0KCKtDWH27NlnzpzZu3fvn3/+yexjcRBQWAPD\noFKpRCIRfpEaZztgHAiLHT58+P3793FxcWgHAdBx5syZzZs3Hz161M/PD+0sw1u7dq2Wlta9e/dY\nP1jl4MGDe/bs2bBhQ2VlZUVFxd69exk/srCwSEpKGuK+lZWVS5YsKSwsLCsr4+PjW7FiBQRB/v7+\njo6OysrK9P+ml+zdu/fMmTMXL14sLy93dHRcvnx5ampq73Y2btwIQVDvOZHnz5/fsWPHCI/Y0tLi\n5OS0ePHi2tra3NxcVVXV/uMuREREjIyMwsPDB5zVN4QRZisqKmpvb58yZcqwDQ72aAzxh+jvxYsX\nampqJBIJ/tbOzi4vL09CQmLVqlV0Or25uXkkjwmDvr5+aWnpv//+O2x4xHFxcf3xxx9qampr165F\npEF+fv4jR45cv349IyMDkQaH4O3tvX79end3969fvzL7WByj94BrMHmRSTh68uKZM2fk5eXH3872\n7dtNTU3H3w4wKjNnzhzztCeAo/n5+WEwGLgTjv3B60V8+PCB9YdubW0lkUh2dnaMa/rMmfv58ycE\nQVeuXIG/HWLy4smTJ6H/5qUsWrQILqzpdHpbWxuJRFq6dCnjiAQCYfPmzfC3ysrKP378qK+v5+Xl\nFRYWbm1tpdPp+fn5MjIyHR0dTU1N0OCTFxlHTE9PhyDo77//7nMDxu/S1dW1bNmyV69ejerBGVU2\nuDieNWvW0G0O9mgM+4forbm5GZ751+d6RmFNp9MHe0wG/AveuXMHgqA//vhj6PDMA5+/IfUvQKPR\nDA0N582bh0hrQ+vs7LSyslJQUKiurmbB4dgNBCYvAqOF1K4uYHcYVBw4cCAmJubdu3doBwFY6tCh\nQ/v37w8KCtq2bRvaWUYkLi5OXl5+2rRprD90Xl5ea2urra3t+JuCR/f2X7stJyentbVVW1sb/pZI\nJEpKSmZnZ/e+jaCg4PLly+vq6kJCQiAIunjx4ubNm7m5uUd4RCUlJTKZ7O7u7uvrW1hY2OdmNBpt\n+fLlZDJ5VINARpsN/mCztbV16NYGezRG9YeAz14Y3dUDGvox6QNuCh76iApTU1M5OTmkPmDEYrFn\nzpx58eJFTEwMIg0OAY/Hh4eHYzCYJUuWgI3JIDAUBBhWTU2NmJgY+7QDjIqNjY2FhQXcrQVMBnQ6\n3dvb++TJk3fu3EHqk2UWqKmpQevEu6SkBBrHvOoXL15YW1uLi4sTCITdu3cPeJuWlhYIgg4cOID5\nT1FRUf8CFJ4mGBgYWF9f//jxY3gAxgiPSCQSY2NjLSwsTpw4oaSktHTp0ra2NsZdtm7dmpubGxgY\nmJmZObZfcyTZFBQUeHh4hh0oPNijMao/BLy7CoFAGOI2Qz8m/W/MaBYtZDK5uroaqdZsbGwcHBwO\nHDiAVINDEBERCQ8PT0pKGmzJ80kFFNbAMKqrq0VFRdmnHWC0fvvtt5cvX378+BHtIADT0el0T0/P\na9euhYSErFq1Cu04o6CoqJiXl9fR0cH6Q8Mzqsd26OLiYhcXF0lJyeTk5IaGhsFmiMLF4sWLF3t/\nXtx/t5SpU6dOnz7948ePGzZscHV1FRYWHtURtbS0IiIiysrK9uzZExoaeu7cOcaP3NzcoqKihISE\nVq5cObZF5UaSjUAgzJ49u7q6esCPyGpra9etWzfEozGqPwRcBw/bPzrEY9IHPPwabhYVHR0dubm5\nSkpKCLZ57Nix5OTkv//+G8E2BzN16tQbN274+fk9fvyYBYdjZ6CwBoaBYI81KKxRMWfOHCsrq8H6\n0oAJg0ajrVmzJigoKCwsbIQr+LIPZ2fnlpaW+/fvs/7Q2traWCx2bB/Bf/v2raura/PmzUpKSjw8\nPIMtDycrK8vDwzOS7Q/hjuEnT554e3uP6ohlZWVwb7S4uPipU6cMDAx6d07PnDlTTEzs5s2bnz59\nOn78+Gh/zRFmgyDI19eXQCDs2LGjf99weno6vAbfYI/GqP4Q8O6JDQ0NQ9xm6MekD7ip8a9/NWa/\n//57W1ubs7Mzgm0aGhouWLBg//79PT09CDY7mF9++WXDhg1r1qxhwaRJdgYKa2AYSPU0g8IaRceP\nH4+Li2PBYDsALd3d3b/++mtYWFhERASy782sQaFQNm/evHv37oKCAhYfWlxcfNGiRU+ePLl9+3Zj\nY+PXr19v3rw5wvvKyclBEBQdHd3e3p6bm5ucnMz4kYiISFlZWWFhYVNTExcX1+rVq4ODg69fv97Y\n2Eij0UpKSsrLy/s36ObmJiYm5uLiMljP5WBHLCsr27hxY3Z2dmdn5+fPn4uKiqZPn97nvk5OTr/+\n+uuJEyc+ffo0wl9wVNkgCJo6derDhw/T09MtLS1fvnzZ0NDQ1dX148ePoKCgtWvXwiPCeXh4Bnw0\nRvWHIJFISkpK8OiRwYzkMWGAm9LR0RnRY4G0vLy83bt3b926VVJSEtmWjx07lpGRER4ejmyzg7l8\n+bK+vv7ChQuHPueZ4Hp/FgNWBWESjl4VRFRU9Pr16+NspLOzE4PBhIeHIxIJGAMHBwcjI6Oenh60\ngwDI6+jocHFx4eXljYmJQTvL2LW2thoZGSkoKBQUFLD40E1NTevWrRMVFeXj47OwsDh06BAEQTIy\nMv/++++VK1fgWodEIjk5OZ0/fx7u1OTl5V24cCGdTt+zZ4+IiIiQkJCrqyu81rWysnJxcXFaWpq8\nvDyRSLSwsKioqOjo6NizZ4+cnBwOh4MrSLjcgfcMFxMT27p1Kxxm9+7dSUlJ8OUDBw7AR8disZqa\nmgkJCYMdMSEhwczMTFhYmIuLS0pK6rfffuvu7n769Ck8ZkNBQYFKpTY2NsrKykIQxMfHN+zyF2PL\nBisuLvbx8dHR0eHj4+Pi4hISEtLX11+7du27d+/gGwz4aAz9h+if0MvLC4/HwwuV0On0wsJCfX19\nCIJwOJyBgcGTJ08KCwv7Pyb9/4KwefPmSUtLo/IKmZ+fLy8vb2Ji0tbWxoz2ly5dqqmpSaPRmNF4\nfyUlJZKSks7OzpPk7QbqtyoIKKxZgXMLaxqNxsXF1edJMwZw30xcXBwiqYAx+Pfff7FY7NOnT9EO\nAiCspaXF3t5eUFCQUfFwrurqan19fTExsejoaLSzAOwuNzcXh8Pdv39//E1VV1fz8PCcO3du/E2N\n1uvXr0VFRQ0NDWtqaph0iO/fv+NwuAcPHjCp/f7i4uJwONz58+dZdkQU9S+swVAQYCh1dXU0Gm38\nY6zhvVXBqiAo0tXVdXNz279//9imLgHsCd4CIyUl5fXr16ampmjHGS9RUdHExMRZs2bZ29tv3boV\nXiYZAAakoqJy9OjRo0ePNjc3j7MpX1/fqVOnenl5IRJshBobGzdv3jx79uzZs2fHx8eLiIgw6UBT\npkxxd3c/cuQIy9bCmzFjhq+v7969ez98+MCaI7IVUFgDQ4GX/hn/2Gi4sAZjrNF19OjRgoICVOaH\nAczQ0NBgb2//7du3t2/fDruJNKcgkUjBwcEhISFhYWFKSkqXLl0Cp4LMkJ2djRnc0qVL0Q44Ivv2\n7XN1dV26dOl4RvReuHDhy5cvL1++hIeAs0B3d/fNmzfV1NRCQkICAwMfPnw49ILc43fw4MGCgoLn\nz58z9Si97du3z9ra2t3dfbBt5CcwUFgDQ4ELa6R6rAdcoQlgmSlTpqxevdrX1xeVRc0AZNXV1c2e\nPbugoCA2NlZXVxftOAhzdXXNyspyd3ffvXu3mprajRs3wJMWWerq6kN8ug1vBMMRTpw44eXlderU\nqbHd/c8//+zo6Hj79i1r3p7a29sDAgJUVVU9PT2XLVuWl5fn4eHBguMqKSktWLBgsOUgmQGLxd6/\nf7+lpWX9+vUsOyibAIU1MBSkeppramoEBASG3UUMYLbDhw9XV1cHBASgHQQYFyqVam1tXV5enpCQ\noKWlhXYcphAVFb148WJ2dra9vf22bdvk5OR27dqVlZWFdi6A7djb2/v5+Y3tvgsWLNi3bx8XFxey\nkfrLysry8fGRl5ffsWPHnDlzcnJyLly4wLzhH/3t3bs3JSUlPj6eZUeUkJC4d+/ekydP4O3iJw9Q\nWANDqa6u5uPjg9ftH2c7YIA1O5CSktqyZcuJEycm4cdzE0ZFRYWtrW1TU9Pbt29VVFTQjsNcioqK\nAQEBP3782LJly5MnTzQ1Nc3Nze/cuTP+YbUAwAJNTU23b982MzPT1NQMDw+CfN8yAAAgAElEQVTf\nunXrjx8/rl+/rqCgwOIkxsbGFhYWZ8+eZeVBZ8+e7ePj4+XlNalOiUFhDQwFqcWnwSLW7GPPnj1d\nXV0sfnkFkFJcXGxpadnd3Z2QkKCoqIh2HBahUCiHDh3Kz8+PioqSl5ffsmULhUJxc3N7+PBhbW0t\n2ukAoK/a2tr79++7urpSKJStW7cqKipGR0fn5eUdPHgQ8ZWqR27Xrl0vXrxg8e4tx48f19bWXrZs\nGby35WQACmtgKGDbxYlHVFT0wIED58+fLyoqQjsLMDqFhYUzZ87k5uaOjY2VlpZGOw6rYbHYWbNm\nPXr0qKys7MyZMw0NDWvWrJGQkLCxsfH392f9zjIA0Ed+fv6FCxdmzpxJJpPXr1/f1NR0/vz5srKy\nhw8f2traYrEoV1yOjo4aGhrnz59n5UHxePzDhw/z8vLGvOUnxwGFNTAUpIZwgMKarXh5ecnIyOzf\nvx/tIMAo5OTkWFhYCAsLx8fHUygUtOOgSVhYeNOmTf/8809VVdXDhw+lpKSOHj2qrKysqam5efPm\n0NDQiooKtDMCk0V5eXlwcPDGjRvV1dVVVFSOHz8uLS396NEjKpUaGRm5YcMG9pm1j8FgvL29Hz16\nBC9LwDLKysqnTp06depUamoqK4+LFlBYA0MB+5lPSNzc3KdPnw4ODn737h3aWYARyczMnDlzpoKC\nQmxsLPhXYhAQEHBzc3vw4AGVSo2JiVmwYMGXL1/c3d0pFIqmpuamTZtCQkIG3DkcAMajrKyMUUxL\nSUmtXLny69evLi4uMTExVCr1wYMHbm5uAgICaMccwIoVK4hE4h9//MHi427ZssXCwmLVqlXt7e0s\nPjTr4dAOALC1mpoaeXl5RNoB1QBbcXFxsbW19fHxSUpKwmAwaMcBhpKWljZ79mxNTc2///6bn58f\n7TjsCIfD2djY2NjYQBDU2tqalJSUmJj47t27u3fvdnR0UCgUw/9Mnz5dXFwc7bwAh2lsbPz69eun\n/2RmZnJxcU2dOnX27NnHjh2zs7MTEhJCO+OIEInEZcuW3bx509vbm5Wv/Fgs9u7du7q6uidOnDh2\n7BjLjosKUFgDQ0GqxxqsCsKGzp49a2RkFBISsmzZMrSzAINKSUmZM2eOiYlJeHg4kUhEOw4HIJFI\ns2bNmjVrFgRBLS0tHz58+PjxY2pq6p07d44cOQJBkIqKipGRkZGRkZ6enra2NoqTyQC2VV5enpGR\n8e+//6ampqampubl5UEQJCcnZ2Rk5O7ubmJiMn36dF5eXrRjjsXGjRsDAgISExMtLS1ZeVwFBYVT\np05t377dycnJ2NiYlYdmMVBYA0NBpCCm0+n19fWgx5rdTJ06ddWqVbt3716wYAGz9/0CxiY+Pn7+\n/PlWVlaPHz8e/6qXkxAvL6+tra2trS38bWVlZWpqakpKSmpq6tmzZysrKyEIEhER0dbW1tTUhL/q\n6OiAXoDJpqqqKj09PTMzk/EVXm1GQkICrqThMzEJCQm0kyJAV1fXyMgoKCiIxYU1BEGbN29++vTp\n2rVrU1NTJ/C+FqCwBgbV09NTV1c3/oK4vr6+u7sbFNZs6OTJk0+ePLl48eJvv/2Gdhagr8jIyIUL\nFzo6Oj548IBlmy1PbBISEvPmzZs3bx78LZVKTU9Pz8jIyMjISE9PDw0NraurgyCITCZraGgoKyur\nqKgwvgoKCqKaHUBGQ0NDXl5efn4+42tWVlZVVRUEQcLCwlpaWlpaWq6urvCJFplMRjsvU6xfv37b\ntm3+/v6s3KEGgiAMBnPr1i1tbe1Lly7t2rWLlYdmJVBYA4Oqr6+n0WhI7WcOCms2JCEhsXv3bj8/\nvzVr1kzyhSbYzYsXLxYvXrxw4cLff/8dhwMv1ExBJpMZI7NhZWVlcJH9/fv3vLy8N2/eFBcX02g0\nCILExcXhIltFRUVRUVFGRkZGRkZWVhaMz2FPbW1txcXFJSUlJSUlP378YJTR8IIYXFxccnJyKioq\nmpqaLi4ucD0tJSWFdmoWWbZs2c6dOx8+fOjp6cniQyspKe3Zs+fYsWPLly+fqGuGYuh0OuObsLCw\nJUuW9L4GQISbmxsEQWFhYWgHGZ3v37+rqal9/vx56tSp42nnw4cPpqamRUVFcnJySGUDkNLW1qah\noWFjYzPZdp1lZ2FhYe7u7qtXrw4ICEB97dtJrrOzs7CwsHcHZ35+flFRUVtbG3wDMTExuMKWk5OT\nlpaGL5DJZElJSU6Z0Ma56uvrKyoqKisrGTX0z58/i4uLS0tLGSvKEYlEeXl5xkkRfEFBQWGSfwr0\n66+/fv/+PSkpifWHbmtr09LSMjMze/DgAeuPjjgMBhMaGgqXeTDQEQIMCn5hQqrHGgxbZE9EIvHs\n2bNLlixZt26dmZkZ2nEAKDg4eOXKlR4eHlevXgULtqCOm5tbVVVVVVW1z/VVVVVwJcco6b5+/fry\n5cvS0lLGDnMEAgGusCUkJMTFxaWkpMhkMplMplAoIiIiIiIiwsLCYHrDYFpbW2tra+vq6mpra8vK\nyqhUalVVVVlZWVVVVWVlZXl5eVVVVUdHB3xjbm5uaWlpGRkZeXl5BwcHGRkZOTk5WVlZGRkZ8NYz\nIFdXV0dHx6KiIkQW/hoVIpF44cIFFxeXdevWWVtbs/joLAAKa2BQSA3hqKmp4eHhAe8fbMvV1fXu\n3bsbN25MS0sDow7QFRQUtHHjxl27dvn5+aGdBRiKuLi4uLi4vr5+n+vpdHpFRUVVVVV5eTmVSqVS\nqXAtWFZW9unTJ7g6hMeWwAgEAlxhCwsL97nAx8fHx8cnJCREIpFIJJKgoCAfHx8vLy8nLkbR0tLS\n0tLS3Nzc0NDQ2tra0tLS0NDQ3Nzc3NwMV89wAd37AqNohiCIi4uLTCaLi4tTKBQymaympiYlJSUu\nLg6fpZDJZAkJCXAWOir29vbCwsLh4eHe3t6sP7qzs/PcuXO3bt365cuXifemM9F+HwBB1dXVvLy8\n4x9BCBaxZn+XLl3S0dEJCAhg/ZA7gCEgIGDLli3wqHe0swBjhMFgKBQKhULR1dUd8AZ0Op1KpQ5W\nTdbW1ubn58OX4bpzwEbgapuXlxfehURISAiDwZBIJAKBgMPh4MXOBQUFsVgskUhkrCeDx+P5+Pj6\nNMW4fW9NTU3d3d1DXNne3t7W1tbT09PQ0MD4UUdHR2trK7wMFARBcA3d2toKf9sffObAOJ0QFhZW\nVlaGL/Dx8Z07dy43N3fdunX79++XkZEBdTOy8Hi8k5PT48ePUSmsIQi6dOmStrb2hHzTAYU1MCik\nCmJQWLO/KVOm7Ny588CBA4sWLZo8M3jYypkzZ+A5PQcOHEA7C8BEGAxGQkJi5Au31dfXw128jY2N\nTU1NLS0tra2tdXV18IWmpiZGIdvc3NzV1dXa2gqvcQGvcNLS0sIYmtLW1tZ/37sBr+Th4enfpdL7\nSm5ubrjjHN6vm5eXl5ubm4+PD14UHC70+fn54eofHvHCy8vLz88vKCgIXx52lZXVq1cHBQX5+PhE\nR0ffvHlz5syZI3zEgBFydXWdP38+KqNBIAhSUVHZvn27r6/vypUrJ9iSO6CwBgZVU1ODyOg0pNoB\nmOrAgQPBwcF79+5l/W63wOnTp/ft2+fv779t2za0swDsRUhIiNmTIM3NzY2Njf39/Zl6lNHCYDAe\nHh7z58/fsmWLra3t+vXrz549y577hHMoOzs7FEeDQBC0b9++oKAgf3//w4cPoxKAScB8c2BQSG2X\niNT2jQBTwRNKHjx48ObNG7SzTC6HDh3av39/UFAQqKoBVFRVVbHtNu9SUlLPnj27f//+s2fPtLS0\nIiIi0E40ceDx+Pnz5z9//hytAIKCgt7e3hcvXoS345kwQGENDAqpghgMBeEU8IQST0/Prq4utLNM\nCnQ63dvb++TJk3fu3Fm7di3acYBJip0La9iKFStycnLmzp3r5OS0cuXKwYaeA6M1Z86c9+/fNzY2\nohVg+/btBALh/PnzaAVgBlBYA4NCcCgIKKw5xaVLl/Lz8y9fvox2kImPTqd7enpeu3YtJCRk1apV\naMcBJqmurq6GhgY2L6whCBIWFr5x48azZ89evXqlo6OTmJiIdqKJwM7OjkajxcXFoRWAj4/Px8fn\n0qVLVCoVrQyIA4U1MCjQYz0JKSsr79mz58iRIyUlJWhnmchoNBo8NyssLGzx4sVoxwEmr6qqKjqd\nzil7dzs7O6enp2tpac2cOXPv3r3gs7VxEhMT09PTi4qKQjHD1q1bhYSEzpw5g2IGZIHCGhgUUgVx\nbW0tKKw5yN69eyUlJbds2YJ2kAmru7v7119/ffz4cUREhLOzM9pxgEkN7ilk/x5rBgkJiYiIiGvX\nrl25csXS0jIvLw/tRJzNzs4O3cKaSCTu3r37+vXrFRUVKMZAECisgUHV1dWJiIiMs5G2tra2trbx\ntwOwDA8PT1BQUERExOPHj9HOMgF1dna6ubk9e/YsIiLC3t4e7TjAZAevzcdBhTX034IhKSkpnZ2d\nBgYGN2/eRDsRB7Ozs8vOzi4uLkYxg4eHBx8fX2BgIIoZEAQKa2BgTU1NXV1d4y+I4dm+oLDmLFZW\nVmvWrPHy8oKXwgWQ0tra6ujoGBsbGxUVZWNjg3YcAICqqqrweDyzV/RjBk1NzeTk5B07dmzatMnd\n3b21tRXtRBzJwsKCRCKh22nNw8Pj4eEREBDQe7tNzgUKa2BgcEU1/ldbeOcCTnzVnuTOnz+PxWJ3\n796NdpCJo6WlxcnJKSUl5fXr16ampmjHAQAIgiAqlSomJsah+xri8XhfX9+IiIiXL1+am5v/+PED\n7USch4eHx8TE5P379+jG2Lx5c11d3cT4mBQU1sDA4IIY3lVr/O2AwprjCAoKXrx48fbt2zExMWhn\nmQgaGhrs7e2/ffv29u1bExMTtOMAwP+pqqrilJmLg5k7d+6XL1/wePzUqVNRXJWZc5mYmHz8+BHd\nDFJSUosWLbp48SK6MRABCmtgYEgVxEj1fAOs5+bm5uTktGnTpra2NrSzcLa6urrZs2cXFBTExsbq\n6uqiHQcA/j/2X8R6JOTk5OLj411dXRcuXLh3796enh60E3ESY2PjjIyMpqYmdGN4enqmpaWh3nc+\nfqCwBgaG4FAQbm5uEomERCiA1a5fv15VVXXs2DG0g3AwKpVqbW1dXl6ekJCgpaWFdhwA+B8To7CG\nIIiHh+fWrVuBgYEXL150dHQE80NGzsTEpKenJy0tDd0YZmZmxsbGV65cQTfG+IHCGhhYfX09iUQi\nEAjjb2f840kAtEhJSR0/fvzs2bOfP39GOwtHqqiosLW1bWpqevv2rYqKCtpxAKAvKpXK6UNBevPw\n8Hj79u2///5rYmKSmZmJdhzOICcnJyUllZycjHYQaMuWLU+ePKmurkY7yLiAwhoYWF1dHSLjN+rr\n68E4EI62adMmExOTdevWgb0YRqu4uNjS0rK7uzshIUFRURHtOAAwgAnTY81gamr66dMnSUlJCwsL\nFPcU5CzGxsYpKSlop4AWL15MIBA4fQojKKyBgSHV0wwKa06HxWLv3LmTlZV16tQptLNwksLCwpkz\nZ3Jzc8fGxkpLS6MdBwAGNvEKawiCJCQkoqOj58yZY29v/+jRI7TjcAAjI6NPnz6hnQLi5eV1dHQM\nDg5GO8i4gMIaGBhSBTEorCcANTW1Y8eOHTt2jB1eeTlCTk6OhYWFsLBwfHw8hUJBOw4ADKyrq6uh\noWHiFdYQBBEIhIcPH+7bt8/d3d3X1xftOOxOU1OzqKiIHdYCX7ZsWWJiYmFhIdpBxg4U1sDA6urq\nkOqxBmOsJwBvb28zM7O1a9d2dnainYXdZWZmzpw5U0FBITY2VlRUFO04ADCoqqoqOp0+kcZY94bB\nYHx9ff39/Y8dO7Z+/fru7m60E7EvDQ2Nnp6e79+/ox0EmjNnjoiISFhYGNpBxg4U1sDAkOppRmqs\nNoAuLBZ79+7d/Pz8EydOoJ2FraWlpVlZWU2ZMuXVq1cCAgJoxwGAoVCpVIjT9jMfLS8vr6dPnz58\n+HDx4sXs0CPLnlRUVPB4fFZWFtpBIDwev2jRIo4eDQIKa2BgCPZYg8J6YlBSUjp+/PjJkydTU1PR\nzsKmUlJS7OzsjIyMIiMj+fn50Y4DAMOoqqqCJnphDUGQs7NzVFRUYmLirFmzwDJ8A8Lj8crKytnZ\n2WgHgSAIWrZs2ZcvXzIyMtAOMkagsAYGBsZYA/15enqam5uvWrWqvb0d7SxsJz4+3tbW1szM7Nmz\nZ0QiEe04ADC8qqoqPB4/GV6izc3N3717V1paamNjA59OAH2oq6uzQ481BEEzZsygUCicu4kmKKyB\ngYHl9oD+4AEhP3/+PH78ONpZ2EtkZOScOXMcHBzCw8N5eHjQjgMAI0KlUsXExDAYDNpBWEFNTS0h\nIaG5uXnGjBllZWVox2E7GhoabNJjjcVi7e3to6Ki0A4yRqCwBgaGyKRDOp3e0NAACuuJRFFR8dSp\nU6dPn2aH3QTYxIsXL1xcXFxcXB4+fIjH49GOAwAjVVVVNVFnLg5ITk7uzZs3PT09NjY2oLbuQ1VV\nNS8vj06nox0EgiDI3t4+KSmpsbER7SBjAQprYABdXV0tLS3jL4gbGxtpNBoorCeYzZs329jY/PLL\nL83NzWhnQV9YWJiLi8vKlSvv37+Pw+HQjgMAozAhF7EemoyMTFxcHBcXl62tLTx3E4DJycm1tbXV\n1NSgHQSCIMje3p5Go719+xbtIGMBCmtgAPX19XQ6ffw91vX19RAEgeX2JhgMBnP37t3a2todO3ag\nnQVlwcHBK1asWL9+fWBgIBYLXk4BDjMJC2sIgiQlJd+8eQNB0KxZs9ikjmQHsrKyEAT9/PkT7SAQ\nBEFiYmJTp07l0NEg4J0AGABcEI+/pxmpdgB2IyUlFRQUFBQU9OTJE7SzoCYoKMjd3X3nzp3Xrl2b\nJKNUgQmGSqVOqqEgDGQy+fXr101NTXZ2dvD7FCArK4vBYNiksIYgyN7e/p9//kE7xViAwhoYALwg\nEVI91qCwnpBcXFx+/fXXTZs2lZeXo50FBQEBARs2bNi1a5efnx/aWQBgjCZnjzVMVlY2KiqqoqJi\n4cKFHR0daMdBHw8Pj4iICFsV1rm5uQUFBWgHGTVQWAMDQKoghgt0QUFBBDIB7OfKlSvCwsK//vor\nm8x3YZkzZ85s3rz56NGjoKoGONpkLqwhCFJRUXn9+vXnz59XrVrV09ODdhz0ycrKsk9hbW5uzsvL\nGxsbi3aQUQOFNTCA+vp6Li6u8e9wUV9fTyKRCAQCIqkAdsPHx3fv3r2YmJjr16+jnYV1Tp8+vXfv\nXn9//wMHDqCdBQDGrqurq6GhYTIX1hAEaWtrP3v27Pnz556enmhnQZ+srGxJSQnaKf4PNze3gYFB\nSkoK2kFGDRTWwADq6uoEBQXHPxkLkTX7AHZmZma2f//+Xbt2scnOAsx26NCh/fv3BwUFbdu2De0s\nADAuVVVVdDp9co6x7s3a2vru3bsBAQGXL19GOwvKpKWl2WoVQmNj448fP6KdYtRAYQ0MAGy7CIzc\nwYMHdXR0li9fPrG3Y6TT6d7e3idPnrxz587atWvRjgMA4wUvNjfJe6xhy5YtO3HixM6dO6Ojo9HO\ngiYxMbHq6mq0U/x/xsbG6enpbW1taAcZHVBYAwNAqqcZFNaTAR6PDw4OLigo2L17N9pZmIVOp3t6\nel67di0kJGTVqlVoxwEABMA7e4PCGrZ37143Nzc3N7fc3Fy0s6BGVFSUrdYfNDY27u7u/vLlC9pB\nRgcU1sAAwH7mwKgoKSkFBQVdvXr1+fPnaGdBHo1GW716dVBQUFhY2OLFi9GOAwDIqKqqwuPx4CUa\nhsFgbt26pays7OzszKEb/o0fu/VYKykpiYqKctwwa7BPGDAABHuswRjrScLNzS0yMnLNmjX6+vry\n8vKM65ubm5uamigUCorZxqO7u3v16tXh4eERERH29vZoxwGAsUtPT799+7aYmJikpKSYmFhycrKQ\nkFBjYyNYuAlGJBKfPXtmbGy8fv360NBQtOOgQFRUtLOzs7m5mY+PD+0sEARBGAzGyMiI4wpr0GMN\nDACpHmuk2gE4wpUrVyQlJX/55RcajQZfk5WVZWhoeOPGDXSDjURbW1v/kXydnZ2urq7Pnj0DVTUw\nAZDJ5EuXLvn6+m7cuNHZ2fny5ctVVVVCQkJ4PJ5MJmtqap48eRLtjCiTkZEJDg5++vTppFrpiEFM\nTAyCILbqtDY2NgaFNTARgMmLwBjw8vKGhYWlpqYeO3YMgqCQkBBDQ8Pc3NxHjx6hHW14gYGBCxYs\n6L1PRGtrq6Oj45s3b6KiomxsbFDMBgCIIJPJqqqq3d3d3d3dva/v7u6uqqrKysqaPn06WtnYh7W1\n9YEDB3bs2JGWloZ2FlYTFRWFIIithlnr6+vn5uZy1vxFUFgDAwBjrIGx0dbWPnPmzLFjx5YtW7Zs\n2bL29nY6nZ6bm5uZmYl2tKG0trYeP348Kipq0aJFXV1dEAS1tLQ4OTmlpKS8fv3a1NQU7YAAgIw5\nc+Zwc3P3vx6LxWpoaMycOZP1kdjQwYMHzc3NlyxZ0tzcjHYWlmLDwnrKlCk9PT2ctf8iKKyBAYBV\nQYAxc3Z2FhMTe/z4MQRB8I6M3NzcT548QTvXUK5fv97Q0ABBUGRkpKura01Njb29/bdv396+fWti\nYoJ2OgBAjI2NDXzq2N++ffswGAyL87AnLi6uhw8f1tfX+/j4oJ2Fpfj5+TEYDFvN3VRRUcFisd+/\nf0c7yCiAwhoYQENDw/gLYhqN1tTUBArrSeXNmzd6enp1dXWMYdYQBHV2drLzaJCWlpZTp07BgWk0\n2t9//21ra1tUVBQfH6+rq4t2OgBAkpWV1YDVs6io6JIlS1ifh21JSkoGBQXdvHnz1atXaGdhHSwW\nSyQS2aqfnkgkysjIcNYaiKCwBvpqaWnp7Owcf0Hc0NBAp9NBYT1J9PT0HDlyxNbWtr6+vn+XWE5O\nTk5ODirBhnXt2jW4uxpGo9HS09PNzc1VVVVRTAUAzCAoKKinp9fnShwOt2PHjgGHiExmzs7Obm5u\n69atq6urQzsL6/Dx8bW0tKCd4n9MmTIFFNYAZ4M/Bhr/AkxItQNwhPDw8GPHjmEwmJ6env4/xePx\n7DkapKWlxc/Pr3f/OgRBNBrtyZMnXl5eaKUCAOaZPXt2nxqai4tr/fr1aOVhZ9euXevp6dm1axfa\nQViHl5eXrXqsIQhSVVUFQ0EAzgYXxPz8/ONsp6mpCZF2AI6wePHiT58+aWlpcXFx9f9pV1cXe44G\nuXLlCvxE7aOnp+f69esTeC9JYNKysbHp7OxkfIvH49etWwfPWgP6EBUV9ff3v3PnTlJSEtpZWAT0\nWI8fKKyBvuA6Q0BAYJztIFWgA5xCT08vLS3txIkTeDweh+u7+VRmZmZeXh4qwQbT1NTk5+fXZ+kx\nBjqdfvbs2YCAABanAgCmMjc3x+PxjG+7u7u3b9+OYh42t2TJkjlz5mzcuHGwSZ8TDB8fH7v1WE+Z\nMqW8vHzAHhD2BAproC+kepqRKtABDoLD4fbs2ZOWlqajo4PF/s/LCxuOBrly5cqAfTPwWYGWltbv\nv/8OPiIHJhgSiWRiYgJPYcThcI6OjioqKmiHYmuXL1/Ozc29du0a2kFYgQ2HgsDPTw5acQ8U1kBf\nCBbWWCyWRCIhEQrgJNra2qmpqQEBATw8PIy+MXYbDdLc3Hz27Nk+3dVwWiMjo7/++uvbt28rV67s\n3/UOAJzO3t4efqp3d3dPthXlxkBFRcXHx8fX15ettiRkEjbssZaSkoIgqLy8HO0gIwUKa6CvxsZG\nAoEw/hniTU1NvLy8fbotgUkCi8V6eHhkZGRMnz6d8Rz49u1bfn4+usEY/P39e79/4PF4Li6uJUuW\nfPv27f37946OjihmAwCmsrW17ezsxGAwenp6lpaWaMfhAHv27CESiZNhy3cCgdB7A1p2ICAgwMvL\nW1FRgXaQkQJFD9BXU1MTIgOjkWoH4FxKSkpv3769fPkykUiEu37Dw8PRDgVBENTQ0MDorsbhcEQi\ncdOmTT9+/Lh//762tjba6QCAuaZNm0YkEul0+t69e9HOwhn4+PgOHjx47do1DhqQMDZsWFhDECQh\nIcFBhTX4lBPoCxTWAIIwGMzy5cstLS29vLzi4uLu3bu3cOHC+vp6xg3gddMHvG9PT0/vFab7ExQU\nHOwjEW5ubl5eXsa3QkJC8KBS+MKZM2fgybViYmK7du3asGEDWBcS4Gg0Gq2xsbG9vb2tra2xsZFG\no3V2dvaZQlBfXw9vhgpB0JQpU4qLi4WEhKKjoxk34OPjgz+6ERAQIBKJPDw8AgICA67zMwl5eHhc\nuXLl8OHD9+/fRzsLE3Fzcw/2gowiSUlJUFgDHKypqQmRGYegsOZEDQ0NTf9pbGyEq96Ghobu7u6G\nhoaOjo7W1tY+V8Lv362trXA/B2MzhQF3VcjMzGSrmVLV1dV+fn5+fn4QBAkLC0MQRCAQSCQSLy8v\nNze3kJAQDocTEBDofaWgoCAOhxMUFIRrdwEBAf7/gOocQAqdTqdSqVQqtaKioq6f+vp6+EJbW1t7\neztcSY/hKA4ODsPeBq6zeXh4iESicC9CQkKMyyIiIhISEmQymUwmT9R90XE4nK+v7/Lly48cOaKk\npIR2HGYhEAhsWFhTKBRQWAMcDPRYTxgtLS21tbW1tbV1dXW1/2lsbGSUzvX19b2/Hax7uE99SSKR\nCASCgIAADodTUlLC4XD8/Pzw+y4EQYwuLkZ3cu8LtbW1NTU106ZNY7z74vF4Pj6+wX4Ffn7+waYP\ndnd3D7EAU1NTE2NiIp1Oh/vI4S7wlJSU7u5uNTU1eDsb+LeGO/wgCIIrFfjudXV1nZ2dBQUFvc8o\nGhsbu7u7e3e69yYoKMioswUEBISEhHp/K9ILXI707lYHJpvW1tbCwozhE4EAACAASURBVMLCwsKi\noqLy8vLS0lIqlVpeXl5eXk6lUhlPYAwGI/y/lJSU4AvwP6OgoCAej2eUv/B/DQaD6bPxLdwhDV/O\nysoSFxfv0xsNd2l3dXU1NzfDp8oNDQ1dXV2MvnBGZV9QUNC7ymd0hONwODKZTKFQKBSKhISElJQU\nhUJRUFCQl5dXVFSEXyI41+LFi/ft23ft2rXz58+jnYVZuLm52XAoiKSkZHp6OtopRgoU1kBfjY2N\nSBXWYK09JmlpaamoqKisrKyqqqqoqKiurq7thVFG93l9FBQUFBERgXtY4a9KSkpDFIL8/Py934nZ\nDQ6Hg/uYBzTEj2bNmoVIALj+aOqFcaLC+FpfX19RUZGbmwt/W1tb2+fshUAg9Cm1YWJiYpKSkuLi\n4pKSkhISEqD+5nTl5eVZWVk5OTkFBQVFRUVwMU2lUuGfCgsLS0tLUygUSUlJDQ0NKSkpMpksLS1N\nJpMlJCREREQQz6OhodH/yiH+a4ZWW1tbWVlZWVkJnxuUlZVVVlYWFxcnJyeXlJQwzkLJZDJcZMvL\nyyspKamrq2toaEhKSo7912AtLi6uDRs2+Pn5nTx5kkAgoB2HKdizx1pCQqL3mCU2BwproC+kepob\nGxtBYT02HR0dZWVlJSUllZWVFRUVVVVVjAtwPd176KSwsLC4uDijMpOXl+/fLQoDYyWRhcfj4V7D\nUd2LRqMNeBYEq6yszMrKqq2traqq6t0pzsvLKyEh0bvUFhcXp1AoZDJZRkZGSkpqor7Nc6iCgoKv\nX7/m5ORkZ2dnZWVlZ2fDJ1RwZ7O8vLylpaW7u7uioiJcZXL6ICL4FWbAYh2CoIaGBvhcgtE9/+bN\nm1u3bsHPcCEhITU1NU1NTTU1NXV1dR0dHXYeaOHu7r5v376YmJi5c+einYUp2LbHmoOW2wOFNdBX\nU1OTrKwsIu1IS0uPv52JqqOjo6ampry8vKCgoKysrPeFoqIixohJHh4e+ONUYWFhbW1tOzs7YWFh\nxjVycnJgvA1n4eLiEhcXFxcXH/aW8DOkrq6uvLy8rKyMcaG0tDQ1NbWsrIxKpTKeJ8LCwhQKRUpK\nSklJqfcFWVlZcH7LbDQaraioKCMj49OnT58+ffrw4QO84LGwsLCmpqaent6yZcu0tLSUlJQUFRUn\n6hDkIQgKCurq6urq6va5Hh5SkpGRkZmZmZGRERcXV1hY2NPTIyAgoKOjY2hoaGhoqKWlpaOjM/7l\nX5EiLS1tbGz87NmzCVxYs2GPNZlMbmxs7Ojo4IgeBFBYA30hOBQE1HwQBHV1dRUXFxf8Jz8/v6Cg\n4OfPn4y9BggEgrS0tLS0tJycnJmZmYyMjKysrLS0tIyMDJlMBhuUTFoEAkFKSkpKSkpLS2vAG3R3\nd1Op1J8/f5aVlf38+bOkpKS0tDQrKys6Orq0tJTR7SQmJiYnJ6f0v+Tk5Nh2kA9HqKure/fuXWJi\nYmJiYlpaWltbGzc3t46Ojr6+/pEjR/T19XV1dcEAnqEJCwvD1TPjmubm5m/fvqWlpX3+/DkxMTEw\nMLCzs5NEIhkYGFhYWFhYWJibm/cZOM56CxYsuHr1KroZmIc9C2u4lmhubgaFNcCRwOTFMevo6MjO\nzoaHUTL8/PkTnoQkJCQE1zT29vZycnKysrLwh/gSEhJoBwc4Eg6HgyvvAX8KD3gtLS0tLi6GT+2i\no6Pz8/PhMQk4HE5WVpZRZysrK6uqqqqrq3PE+xZaqFRqdHR0YmJiQkJCZmYmnU7X0NCwsLBYu3at\nvr6+lpYWOFcZJz4+PlNTU1NTU/jbrq6u9PT0z58/f/jw4c8//zx9+jQGg9HS0rK0tLSwsLC1tSWT\nyawPOXPmzN9++y03N3fKlCmsPzqzYTAYxlRU9gHXEk1NTaKiomhnGR4orIG+QGE9Qo2Njbm5ub0/\nyszJyYE/modHUiopKRkZGfXuJkQ7MjCJSEhISEhIGBgY9Lke/vy9t5iYmOLiYvj0j0KhaGlpaWpq\nwkMX9PT0RjJqZQKj0WhfvnyJjo6OiIh4//49FovV09OzsbE5dOiQtbX1JH9wmA2Px+vr6+vr669Z\nswaCoMbGxo8fPyYmJr579+727dsdHR2ampqOjo6zZs2ysrJi2VmNkZERiUSKj4+fqIV1T08P2in6\nYhTWaAcZEVBYA32BwnpA3d3d2dnZaWlpaWlpGRkZWVlZpaWlEATx8PCoq6urq6svXbpUQ0NDQ0Nj\nypQp7DMiEAD66P/5OwRBnZ2d379/h6fZZWZmxsfH37x5s729HYIgGRkZdXV1bW1tfX19AwMDDQ2N\nyTALtq2tLSIi4unTp1FRUXV1dYqKinPmzNmzZ4+NjQ0Y4IEWAQGBWbNmwav6NDc3x8bGvnr1KjQ0\n9PTp0yIiInZ2dosXL543bx6zF/XD4/HTpk179+7d2rVrmXogVGCxWHbusUY7yIiAwhroC5Fl8uh0\nektLC0cX1vCnkGn/+ffff9va2nh4eHR0dHR0dOzt7TU1NTU0NBQUFAbb/A8AOAU3N7e2tnbv7dx7\nenp+/PiRlZUFV9uJiYkBAQEdHR0kEklXV9fQ0NDAwMDAwGCCjX/o6up6/fp1cHDwn3/+2dbWNnPm\nzEOHDjk4OKipqaEdDfgffHx8Tk5OTk5OEARlZ2e/evXqxYsXS5cuJZFIzs7OS5cutbOzY94z08jI\nKCoqikmNowuLxYIe63EChTXwP9rb27u6usZfEDc3N8OTuxFJxTKVlZWJiYnx8fFJSUlfv36F583o\n6ekZGhp6eHgYGBhoampOpDICAAaDxWKVlZWVlZXnz58PX9PV1ZWRkcE41bx7925rayuBQNDV1TU1\nNZ0xY4alpSUqY14RkZOTc/Xq1UePHtXV1ZmZmfn5+bm6unLurzOpwJ8Zent7V1ZWhoWFhYSEzJ8/\nX0REZMWKFVu2bFFVVUX8iHp6epcuXers7Jx4H06yZ2HNx8eHwWBAYQ1wJPiJO/7CGql2WKCoqCg+\nPj4hISEhISE7O5uLi0tPT8/CwsLLy8vAwEBdXX0yfPANAMPC4/FTp06dOnUqPOCVRqNlZ2fDC8wl\nJCRcu3aNRqNpaGhYWlpaWlrOmDFDTk4O7cjD6+npefHixdWrV6OiopSVlffs2bN06VKOSA70JyEh\n4enp6enpWVRUFBwcHBQUdOXKFXt7e09PTwcHBwQ/Wpw6dWpnZ2dWVpaenh5SbbIJ9py8iMViSSQS\nKKwBjgTv6jz+nmY2L6zb2tqio6P/+uuv169fFxcXc3NzGxsbu7i4WFpampubc1xHOwCwHhcXl5aW\nlpaW1sqVKyEIamxshJfLSEhIuHfvXmdnp7y8vL29vZOTk62tLRvuZU2j0f7444/jx4//+PHD3t4+\nIiIC2doLQJG8vPzevXt3794NnzU5OjoqKSkdPHjwl19+QeRPrKamhsPhcnJyJl5hzZ491hAE8fPz\ng8Ia4EgTu8eaSqVGRERERERERUW1t7cbGRmtXbvWysrKxMSEDd/4AYCDCAgIzJ07F941o62tLTk5\nOS4u7sWLF7du3SISifb29o6OjvPnz2eTwRURERH79u37/v37mjVrduzYwYzRAgDqsFiso6Ojo6Nj\nTk7O+fPn161bd+7cuVOnTjFGN40ZvNJlUVERIjnZCtsW1gICAnDHH/sDZ+fA/5iQhXVVVdXly5fN\nzMwoFIqnpyeNRvP39y8pKUlOTj506JCVlRWoqgEAQUQi0dra+vDhwx8/fiwtLb148WJ3d/fWrVsp\nFIq5ufnly5cZuyOxXnZ29owZMxYsWKCpqZmenh4YGAiq6glPTU3t5s2b6enpampqTk5OM2bMyMnJ\nGWebCgoKE7KwZs+hIBAEEQgENty5ZkCgsAb+B3xGODEKazqdHhkZ6ezsLC0tffDgQTU1tadPn1ZX\nV0dERKxfv55CoaCYDQAmCQqF4uHhERERUV1d/fTp0ylTphw4cEBaWtrFxeX169esfAun0+kBAQGG\nhoYdHR3JyclhYWGgpJ5U1NTUnjx58v79+7a2NgMDg8DAwPG0pqCgUFhYiFA0NsK2PdY4HA5ea5/9\ngcIa+B9NTU1cXFwkEmn87RAIBLRmTHd2dgYGBmpoaMydO7e5ufnu3bvl5eV37951dnYe/6/GDOfO\nnSOTyRgMZoSv9S9fvhQUFIyIiGB2MI6IMaDRPqRD6Ojo2LZtm6SkJIlEioyMRCTe+EVHR+/btw++\nPGzCPo/GX3/9dfr0aXgzI5aB10G7d+9eRUXF7du3GxoaZs+erampefPmTRZ0RNXU1Dg6Onp5efn4\n+Lx7987Y2JjZR2QNFjw5jx49qqmpKSAgQCAQVFRUdu/e3dzcPOy92Pb5OW3atKSkJG9v761btzo5\nOdXV1Y2tHXl5eVBYsxIorAFOhdSuLo2Njah0V/f09Ny5c0dVVdXb29vKyurbt2/R0dErVqxgz3qa\nwcfHJykpaeS3Z5OP6tgkxoBG+5AO4fz585GRkdnZ2f7+/iMpKVjg8OHDly9f3r9/P/ztsAn7PBpO\nTk48PDy2trb19fUsStwLiURyd3ePjY399u2bpaWll5eXmpravXv3mPd2/vPnT0tLy/T09Pj4+CNH\njuBwE2dyEQuenLGxsVu3bi0sLKyurj558qS/v7+rq+vQd2Hz5ycejz9+/HhcXNyXL18sLS3hrb5G\na6IW1mw7FAQU1gCnQmR3GAilbRczMzMtLS03btzo4OCQm5t748YNLS0tFmdgjXnz5jU0NDg6Oo7k\nxm1tbWZmZiyIwbwDoev58+dGRkZCQkIeHh6LFy8e7d0Rf1j8/PxCQkLCwsIY/2JjSLht2zY9Pb25\nc+ei+F6lra198+bN3Nxce3v79evXz5gxIzMzE/Gj1NbW2tvbc3FxJSUlmZqaIt4+usb55BwJPj6+\nDRs2iIiI8PPzu7m5ubi4REZG/vz5c7Dbc8rz09zcPCkpiU6n29vbj6HfWkFBoaWlBcXZAkwCeqzH\nDxTWwP9AqqeZ9YV1aGiosbFxa2vr+/fvAwICZGRkWHl0dnb79m0qlTqRDsRiJSUl49kVCNmHJS8v\n7+DBg0eOHOHh4WFcObaEvr6+X7588ff3Ryrb2MjKyt64cePTp09dXV2Ghob3799HsHE6nb5ixYq2\ntrZ//vlHSkoKwZbZxDifnCPx999/917LX0xMDIKg1tbWAW/MWc9PGRmZ169fNzU1/fLLL6PtppWX\nl4cgaOJ1WoPCevxAYQ38D6QKYhYX1levXl22bNnGjRtTU1MNDQ1Zdtyh3b9/38jIiIeHh5eXV0FB\n4dixYxAEJSQkaGpqCgoKwruj//PPP6NtNjExUU5ODoPBXL16FYKg69ev8/LykkikP//808HBQUBA\nQEZGJjg4GL7x9u3bd+7cmZ+fj8FgVFRUIAii0WiHDh2Sk5MjEom6urqhoaHDNgJBUFxcnImJCYlE\nEhAQ0NHRgRct7h2jz4HWrVuHwWAwGIyysvLnz58hCFq9ejWJRBIUFPzrr7+G/R37H278D+mAv/jQ\noqKiVFRUysvLf//9dwwGw8fHN/Th+sfr//jT6fQLFy5oaGgQCARhYWFnZ+fs7Gz47mfOnCGRSPz8\n/FQqdefOndLS0v3XLrh8+TKdTod3ch4s4WCPXh/CwsJWVlb+/v7s8Mmvrq7uu3fvNm7cuGrVqvEP\ni2e4f/9+dHR0SEgIk6pqf39/Xl5eLBZraGgoISGBx+N5eXkNDAwsLS1lZWV5eHiEhIR2797NuP1g\nT8IBn1RD/1f2/9N7eXlxc3NLSkrCN9iyZQsvLy8Gg4F7VYf9H4cG+f/qo7S0lEgkKioqDviAcNzz\nU1paOjg4ODIy8tGjR6O9IwRB5eXlzMmFGjAUBAH0XuB/cjqANFdXV1dXV7RTjMj69evt7OzG386a\nNWvmzJkz/nZGIjY2louL69SpU6w53AhdvHgRgqBTp07V1NTU1tbeuHFjxYoVdDr98ePHvr6+tbW1\nNTU106dPFxUVhW+fm5sLQVBAQMBIGoc/h71y5Qr87W+//QZBUExMTENDA5VKtbS05OXl7ezshH+6\naNEiZWVlxn19fHwIBMKTJ0/q6ur279+PxWJTUlKGbqT5/7F3n3FNXX0cwG8ggYQ9Q9iQoCBDmaIM\ntYqtioALRK0UK4LWFlwVHBXEBa3WIjigTlygVsH9WFQqKlVAEQIKAjIDhL1nkufFoSllGSDJTeB8\nX/ghN7nn/hMu8ZeTc89pbpaRkQkNDW1ra6uoqFiyZElVVVX/MvocaOnSpaKiomVlZewtK1euvH37\n9mef3WCHG+VLOtgT/ywVFZVvvvmGfXOwww1WXp+XZc+ePWJiYhcvXqyvr8/IyDA3N1dSUqqoqAD3\ngt+Cn59feHj4kiVL3r9/36cYMplsaGg4RIWDvXoDnmDg8rK3b99y8jrwx/79+0VFRRMTE0ffFJPJ\nnDhx4tq1a0ff1BACAwMRBHn16hUYGDBv3jwEQe7du1dVVdXS0uLr64sgSHp6OnjwYCfhYCfV0H/a\nrH4n56pVq1RUVNg3f/nlFwRBwAnw2dYGO4F7a2lpkZaW9vX1HezVENLz09PT08DAYLh74fH4Cxcu\n8KIeFF26dAmHw6FdxQDmz5+/Zs0atKsYAIIgsbGx/9nS+wYM1jwiRMHa3d196dKlo2+Hn0/ZysrK\nycmJP8fiUGdnp5yc3BdffMHe0t3dDbpeejt48CCCIHQ6ncWNYN3W1gZuHj9+HEGQvLw8cLN3sGtr\na5OQkHB3dwc3W1tbxcXFv/vuu6EboVKpCILcvXt36DL6JMiEhAQEQfbv3w9uNjQ0TJgwobu7+7PP\nbsDDjfIlHeKJf1af7DLg4YYor/fL0traKiUlxS6DxWK9fv0aQZDg4GBws89voY/m5mYMBtP/bO9d\n4WC/rAFPsLNnzyIIEh0dPcTT578FCxZMnTp19O2Ar0pSU1NH39QQQLBuamoCNy9cuIAgSGZmJrgJ\nfr8xMTEsjk/C3ufw0H/arBEF6wFb4/Dva9euXRMnTmxsbBzwpRDe8xP8mjIyMoa1l6qqav+XSNhd\nuXJFVFQU7SoG4OTkBEbsCJr+wRoOBYH+g4tDQfizMHhFRUVKSsrmzZv5cCzOZWRk1NfXf/XVV+wt\noqKifn5+fR4Gxh1yfVYpMMthV1dX/7tycnJaW1uNjY3BTQKBQCKR2EMRBmuETCYTicSvv/46KCiI\n8zGFs2fPnjhx4tmzZ8FbT0xMjLu7e+/BmoMZ8HCjfEk5f+LDwj4ch+VlZWU1NzdbWlqyt1hZWYmJ\nib169YqTw4GwNfQUN8P6ZYGmKisrOTk632zZsuX169cVFRWjbCc9PV1CQsLc3JwrVXEI/OGwv7MG\nZwj4O+LwJBzibWGIP+0Rlwpa4+QEvnnz5rVr1/73v/8N9n+E8J6flpaWBAIBfBLjnLy8/Ihn6xNY\ncCjI6MFgDf1Hc3MzGAYnIO18FhjiJmiXKoJRg3Jycv3vunfv3qxZs5SVlcXFxXsPvuSPlpYWBEF2\n796N+UdRUdFg1yGxEQiEJ0+e2NnZHThwgEwmu7u7t7W1ffZYGAxm/fr1BQUFjx8/RhAkOjp67dq1\nnBQ54OFG+ZKO7IkPaMDDDVFeb2D6sD5/GnJycmBBpc9qb29HEERcXHyIxwzrlwXWHAXNCg5NTU0E\nQUYfrEE3AQaD4UZRXDDESYju2wLCwQkcExMTEhKSmJioo6Mz2GOE9/zEYDAjWDF7TAZrgb14UVRU\nVACDNfgQ0udNBgZr6D9aWlokJSVH305raytX2vmsiRMnYrFYbs1YzC3gSqn+MzEVFxcvXryYRCK9\nevWqoaEhNDSUz4UpKysjCHL06NHe31slJyd/dkcjI6M7d+7QaDR/f//Y2NjDhw9zcjhPT088Hn/6\n9OmcnBwZGRlwHT0n+h9ulC/piJ84h4cbrLw+QHDpE6Pr6+s5/GQIcsZnv+Lg/JcFVmYBzQqOly9f\n4nC40S+LSCKRampqRvbxiRcGOwlRf1tAPncCh4eHX7p06cmTJ0NfAyq85ycYHz/c5Xjl5OTGXrAW\nWILzCbk38IVPn0lvYLCG/qOtrY0r72Ktra38+d9aUlJy9erVwcHBAvUGp6Ojo6Cg8OjRoz7bMzMz\nu7q6vvvuOzKZjMfj+f9OAWYqSE9PH9ZeNBoNTDCsrKx86NAhc3NzDucblpeXX758eVxc3OHDh9et\nWzeaw43yJR3ZE+9vsMMNVl4fxsbGUlJSqamp7C2vXr3q7OzkcCobsDpdQ0PDEI8Z1i8LNKWiosLJ\n0fmjtrY2ODjYw8Nj9Is62dvbM5lMwVksc7CTkItvC1gsdmQDRQY7gVkslr+/f2ZmZlxc3Ge/hBTe\n8/PBgwcsFsve3n5Ye43JHmtoWEAnOgzW0FC4FYj5FqwRBDlw4EBXV9fixYuHfkPnJ3Fx8Z07dz57\n9szX17esrIzJZDY1NWVnZ2tpaSEIkpCQ0N7e/vHjRw5H1o6SgoICjUYrLCwE69WvWbPm6tWrJ06c\naGxsZDAYpaWln50xikajrV+//sOHD52dnW/fvi0qKpo2bdrQB2L/775hw4aOjo67d+9yuJzNYIcb\n5UuKx+NH8MT7G+xwg5WH9Hv9t27devPmzUuXLjU2NmZmZm7YsEFVVdXHx4eTo0tISJDJ5NLS0iEe\nw+EvCwBNmZiYcP4K8FR9ff2iRYuYTOb+/ftH3xqJRHJ0dAwNDRWQr7YHOwm5+Lagp6dXW1sbFxfX\n1dVVVVVVVFTE4Y6DncDZ2dk///zz77//jsPhML0M2M0spOcnk8kMCQlxdnYmEonD2hEGa2jAHms4\nKwg/CNGsICoqKuxJHkZDSUnpxIkTo2+HQ5mZmWpqapMmTaJSqXw76GdFRESYmJjg8Xg8Hm9mZnb8\n+HEWi+Xv76+goCAnJ+fq6gpmgKZQKJs2bQK9MpKSkkuWLBm62fDwcDBVrYSEhLOz8/Hjx0Hf3oQJ\nE/Lz86OiosBlo9ra2rm5uSwW682bN9ra2gQCwc7OrqKioqOjw9/fX0tLC4vFKisrL126NCsra+hG\nCgsLbWxs5OXlRUVF1dTUdu3a1d3d3aeM/gdiF2xmZrZjxw7OX7cBDzf6l3TAJ/7ZSszMzBAEwWKx\n5ubmN27cGOxwxcXFg5XX52VhMpm//PLLhAkTcDicvLz84sWLc3JywOFCQ0PBx1FNTc2LFy8OWJKv\nry8Oh2ttbR2swgFfvSNHjgx4gjk6OqqrqzOZTM5/O7yTmZmpr6+voaHx2d8L59LT08XExH755Rdu\nNdjHb7/9Bv5wdHR0kpKSQkJCZGVlEQRRUVG5fPlyTEwMeNnl5eWvXr3KGvwkHPCkCggIGPqvsv/J\nWVNT88UXX+DxeF1d3R9++OHHH39EEERPT6+4uPizbxSsgU7gzMzMAfPEYC+pMJ6foaGhYmJi7Ilc\nOLdnz57+cwsKO4HNgYKZo8DiX0+ePOm9EQZrfhDME2JA0tLSZ86cGX07kpKS58+fH307nCsrK7Ox\nsRETE9uzZ09LSws/Dw0NbcGCBQUFBWhXMUZ8/PgRi8UOFruHpbq6Go/HHz58ePRNjVJzc/OuXbtw\nOJytrS2NRuNu46GhoVgslpMJ1KHRE7rz89atW6KioiM7ypEjRzQ0NLheEroENgcKZo7Kz89HEKTP\nkghwKAj0H2Cm1VE2wmKxuDVWm3NqamrPnj375Zdfjh49SqFQjh07JmhzHYwr7KEgGRkZoP8M3XrG\nDD09veDg4ODg4Obm5lE2FRQUZGpqClYwQUtbW1tYWBiFQgkPD//111+fPXs23AvIPmv79u3ffvut\nq6vrtWvXuNsy1J9wnZ9Xr15dvny5j4/P1q1bR7C7mJgYuLwSGrfAleh9JqCEwRr6V2dnZ3d39+gD\ncXt7O5PJHH1AHy5RUVFfX9/8/PyVK1cGBARoaWnt3r2bRqPxuYxR+vDhA2Zw7u7uaBfIEX9//48f\nP+bm5q5Zs6bPwsiC8wQFp5Jh2bFjh6urq7u7+2guKvj111/T09Pv37/fd3Qgv5SWlu7cuVNLS2vn\nzp2rV6/Oz8///vvvRUR48l/SqVOnfH193d3dfXx8BGeSkLFKKM7P9vb2gICAr7/+ev369eHh4SNr\nBAZrCEzR2GfVDhisoX+B/3JGH4jBvKT8D9aAsrLykSNHPn36tHHjxjNnzmhrazs5Od24caOjowOV\neoZr6JV1Y2Ji0C6QIxISEgYGBg4ODkFBQYaGhr3vEpwnKDiVDNeBAwd8fX0PHTo0st3j4+M7OjoS\nExPl5eW5W9hndXR0XL9+3dHRUUdH5/z58z/88MOnT59++eUXJSUl3h0Ug8H8/PPPsbGx169ft7Ky\nGv3kMNDQBPz8zMrKmjZt2smTJ8+fPx8WFjbij3MwWEOgxxoGa2hQ3ArWoB10J8dVUVEJDAwsKiq6\ndOkSg8Fwd3cnkUgrVqy4evUqWKQD4qn9+/czGIzi4mLOJwOBhuXLL78MCQkZ2b4uLi47duzgZBVM\nbqmrq7t69eqKFSvAnyGCIFeuXCksLNyzZ89wp2IYMVdX1zdv3igoKEybNm3btm01NTX8Oe74JJjn\nZ01NzZYtWywsLGRkZDIyMlavXj2a1mCwhmg0mrS0dJ9VO2Cwhv7F3WCNVo91b2JiYsuXL79//35x\ncXFQUFBVVdU333yjrKw8Z86c3377raCgAO0CIWgsy8/P/+233+bMmUMkEr/55puqqqq9e/cWFxff\nu3fPzc0NLKnNTzo6OomJiYcPH7548SKFQjl06BAcGTJOtLS0HDhwgEKhXLly5ciRI0+fPuV8varB\niImJdXd3C8hkjhAqysrK+i/vBYM19K+xF6zZ1NTU/Pz8EhIS6HT6xYsXiUTi3r17KRTKpEmTfHx8\nLl++XFJSgnaNEDQWlJSUXLp0ycfHx8DAAFzKpqKicvHiRTqdnpCQ4OvrO/TqfbwmKir6/fff5+fn\nb968+dChQxQKZd++fZWVlSiWBPFURUUFeLf/+eeft27dmpeXqpDM5wAAIABJREFUt3HjRq50h4NP\nhrDTejwrKytTV1fvsxGLSimQYAJjo0c/hINb7fCCnJycu7u7u7t7V1fXs2fP/vzzz6SkpHPnznV1\ndeno6MyYMWPGjBn29vajX04ZgsaPnJycpKSkZ8+eJSUlFRYWiomJWVpaLl68eO7cuTNmzMBiBe4/\nGikpqcDAwA0bNhw9evTYsWMHDhxwdXX94Ycfpk6dinZpENf8/fff4eHhN27ckJWVXbdu3ebNm7k7\nlJ8drPF4PBebhYRIaWlp/x5rgXu/g1A0hnus+8PhcHPmzJkzZw6CIK2trX///TeIBb6+vq2trSoq\nKhYWFub/GP2XhhA0lhQVFb35R1paWmVlpYSExLRp0zw9PWfMmGFtbS3gf/4AkUg8dOhQYGDg1atX\nw8PDra2tzczMVqxYsXz5crAaIiSMioqKYmNjr1y58u7dOwsLi6ioqOXLl/Mi+4qLiyMIIiyXxUO8\nUFZWZm5u3mcjDNbQv8ZVsO5NQkJi9uzZs2fPRhCkq6srJSUlOTk5LS3t+vXrBw8eZDKZioqKIGGD\ntE0mkzEYDNpVQxCfgHUQ3vRSU1MjIiIyceJEc3PzH3/80cbGxtLSEq2Z+0YJrDS+Zs2aFy9eXLhw\nISQkxN/f38bGZsWKFcuWLQMrAkKCr6Ki4vr16zExMcnJyQoKCkuXLj158uT06dN5d0Q4FAQqLS2F\nQ0GgobS2tmIwmNF/sm9raxMREQGf5oUODoezsbGxsbEBN5ubm9PT07Ozs7Oysp4/f3706NHOzk4x\nMTE9PT0jIyNDQ0MjIyMymWxkZAS/DYTGhu7u7uLi4qysrOzs7IKCgqysrHfv3jU3N4uKiurr61tY\nWPz444+Ghoa2trYKCgpoF8tNtra2tra2J0+efPr0aXR09M6dO319fc3MzBwcHBwcHGbNmiWAY1rG\nOSaT+fbt24SEhISEhMTERBwOt3Dhwvj4+K+++ooPl8aCT5LsxbCg8aatra22thYGa2gora2teDx+\n9Gs0tLa2EgiEsdGnKyUlZWdnZ2dnB262tbVlZGRQqdQPHz5kZWVFR0cXFRUxmUwQtQ0NDQ0MDCZN\nmkShUMhksrKyMrrFQ9Bn0en0goKCgoKC9+/ff/jwITs7++PHj11dXSIiItra2pMmTZo+ffqaNWtM\nTExMTEwE88IJ7hIVFQVJuq2t7eHDhw8ePLhy5UpoaKiSktKXX345b968GTNmwLFh6CoqKnr27NnD\nhw8fPXpUXV2tpaU1f/78jRs3fvXVV/w8RcF8IDxa1QgSfGVlZQiCwGANDYUr65kj/wTr0bcjgAgE\ngrW1tbW1NXtLa2vrhw8fQM7+8OHDjRs38vPzQR+GtLQ0uR8dHR3+zzIGQR0dHYWFhQX9gKWncTgc\nhUIxNDRctGiRkZGRgYGBgYGBEI3m4gUCgbB48eLFixcjCEKlUkHI9vLy6uzs1NDQsLe3t7W1tbe3\nNzY2htGK1xgMBpVKTUpKevHixfPnz0tLS8XFxe3s7Pz9/efPn29kZIRKVTBYj3PZ2dkYDGbChAl9\ntsNgDf2rtbWVW8F6/PyXLCEhAYZfs7d0d3eXlJT0zi5//fXX+fPnwYIUIiIiGhoaWlpaWlpaampq\nmpqaGhoa6urqmpqaJBIJvkdDo8FkMisqKkpKSsrKykpLS8EPJSUlRUVFZWVlIAcoKiqCz3iOjo5k\nMllXV5dMJmtqasJxDkMwNjY2Njbetm1bW1tbSkoKSHg7d+5sbGyUlZWdOnWqubm5mZmZmZmZnp4e\n/CsePSaT+fHjx7dv3759+/bNmzevX78GL7WNjc369evt7e2trKxQ776BwXqcy8jI0NXV7bPsIgKD\nNdQbt3qaudXzLaSwWKyurq6uri6YcoStoaGBHbVB1klMTCwtLa2srGSxWGBHVVVVELXV1NS0tbWV\nlZVJJJKKioqysrKysjJ8B4eYTGZVVVVVVVVFRUVlZSWdTi8uLi4rKysrKysuLq6oqOju7kYQBIPB\nkEgkdXV1dXV1MzMzFxcXEKDJZLKsrCzaT0KIEQgEMCkn0qsbNSUl5f79+0eOHOnu7paWljY1NTUz\nM5s8ebKhoaG+vv4YG4nOI7W1tR8+fHj//n1GRsabN2/evXvX1NSExWINDQ3NzMwOHjwogF8OwGA9\nzmVkZEyePLn/dhisoX9xKxC3tbWh3pcggGRlZUGfVp/tnZ2dNBqtd/9iaWlpcnLytWvXqqqq2FfG\niIqKgnjNjtq9M7eioqKioiLMTMKuvr6+pqamtrYWpOfKysqKigp2jK6qqqLT6eyV3nA4nLKysqam\nprq6upWV1eLFi8FXH+CDGRxxxGuioqJTpkyZMmUKuNne3p6ZmQlmTUlOTj59+jSYH0lZWRkkbH19\nfUNDQ11dXR0dHSG9tpsr2tvbi4qKwLD+nJwckKerqqoQBJGUlDQyMjIzM1u9erW5ubmxsbEgXxQO\ng/U49+7dO3d39/7bYbCG/gWHgqBCTExMR0dHR0dnwHurq6vpdHpVVVV5eXnvH/Ly8sAPYDkeQFRU\nVOFzZGRkpKWlpaWl+3+BBfFCY2NjU1NTU1NTY2NjZWVlQ0NDfX19bT8gT/deHplAIKioqJBIJGVl\nZW1t7WnTpikrKxOJRFVVVfADd1e7gEYJj8dbWVlZWVmBmywWq6ioCARHID4+HizxCL5P0NXV1dbW\n1vmHuro6iURSVFRE9UlwU01NTUVFRWlpaVFRUWEv5eXl4AEkEmnSpEmGhoZLliyZNGmSvr6+lpaW\nEF31DoP1eNbW1pafnw97rKHPgMFaACkpKQ2dn5qbm6uqqkAs66+goID9c/9poeTk5KSlpaWkpEDO\nBjfZ5OXlxcTEJCUlJSQkxMXFZWRksFisnJwcDoeTkpIiEAiC3JnELW1tbe3t7U1NTd3d3fX19d3d\n3Y2NjR0dHa2trS0tLR0dHfX19U291NfXs5N0c3NzfX19/zbxeDyRSNTU1FRQUFBSUpo4cWKfDz+K\niorKyspSUlL8f74Qt2AwGJCYv/rqK/bG+vr6T58+sSPmp0+f7t69W1RU1NjYCB4gLi5OJBLV1NTA\nZypVVVUikaigoCDfi4KCArphjsFg1P1XTU0N+MwPPu2XlZXR6XT2BM8yMjLgpbCysnJ1dQU/j4FR\nSSBYc2V1dEjoZGZmMhgMGKyhz+DWEI4xPCuIAJKSkpKSktLV1f3sI5uammpra3t3oDY0NLATYWNj\nY319fUVFRV5eHvtmZ2cnmDViiKPjcDg5OTksFgu6wEEWRxBEXFwcfL5i/4DH48GJwf6BTV5efsD2\nRUREBvvft6GhoXf/bm91dXW9b4JwjCBIa2srWCat/w8tLS0gBzQ2NnZ3d4OeRfBV/hDPXUxMTE5O\njv0lAJgHpvdNWVlZWVlZ9keXxsbGuLi4mJiY7OxsBEGmT5/u7u5uYWExxFGgsUROTm7A8WC1tbU0\nGq28vByM+aHRaHQ6/ePHj8+fP6fT6X2+ykAQRFZWFoRscXFxKSkp8NFXVlYWh8PJyMiw/77Yf3oA\n+28TYJ/zfW6Cv5fGxsaurq6GhgbwMbK5ubmjo6O2trauro79MQAAX5QRiUQVFRVVVVU9PT11dXXw\n1QqJRFJTUxuro8wZDAYCe6zHq4yMDElJSQqF0v8uGKyhf3Grp3mcX7wosEDUG8GO7P9oQa9tV1dX\nc3PzgBuRXimW/UNdXV1FRQUyUJwFwP/cAx66vb2991iX3oboMgc5g31ziIivoKDQJ+uDfuLIyMiq\nqqo1a9YsWrRIQkKC/fkBh8NJS0v3/2DAORMTk59++ikrK+v69etXr149fPiwtra2i4uLq6sre7p0\naLwB31QYGxsP9oCGhoa6gXR2djY1NYE/qOLiYnCz/ydJgL0d6HMa9/nrkJaWFhMTk5WVlZGRIZFI\n0tLS4uLi8gMZt4PK4FCQ8ezdu3eDXU0LgzX0r9bWVhKJxJV25OTkRt8OJCAIBAKBQBisU5n/EhIS\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+XY8ADNZjTHd397fffmtra/vtt9+OvjUYrKEeXV1dAw7CQ6sdCOKQhoaGuLj4eO6kNDU1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+JCYmhqKox48ft7W1SSSSTZs2mZJra2tLCMnIyDDcVFdbW7sMS1iEDRs2EEIGBwdXqoDCwsKo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\nawAAAAAAFvwPnewrKG4BXogAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"image/png": {
"height": 900
}
},
"execution_count": 26
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QF77EkMJrrrI",
"colab_type": "text"
},
"source": [
"## 7.5. How do we map the nipype workflow node to the lines excuted by the FSL pipeline?\n",
"\n",
"### Many thanks to this github repo: https://github.com/vsoch/ica-/blob/master/melodic_ss.sh, but the SUSAN smoothing is wrongly interpreted. \n",
"\n",
"#### nipype workflow node:\n",
"1. Mapnode()\n",
"2. interface: a fsl wrapper function\n",
"3. iterfield: input placeholders\n",
"4. name: name of node"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "23T-45w2ryW-",
"colab_type": "text"
},
"source": [
"### 7.5.1. Inputspec: takes 2 parameters: \"func\" for the next processing, \"fwhm\" for spatial smoothing (in mm)\n",
"```\n",
"inputnode = pe.Node(\n",
" interface = util.IdentityInterface(\n",
" fields=['func','fwhm','anat']),\n",
" name = 'inputspec')\n",
"``` \n",
"#### \"anat\" is for later implementation\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z3OCDny7uL4S",
"colab_type": "text"
},
"source": [
"### 7.5.2. img2float: convert nii to float values\n",
"```\n",
"img2float = pe.MapNode(\n",
" interface=fsl.ImageMaths(\n",
" out_data_type='float',op_string='',suffix='_dtype'),\n",
" iterfield=['in_file'],\n",
" name = 'img2float')\n",
" preproc.connect(inputnode,'func',img2float,'in_file')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"fslmath -odt float\n",
"```\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/sub-01_unfeat_run-01_bold prefiltered_func_data -odt float\n",
"\n",
"# fslmaths -odt float\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PFfPYzK6vKpE",
"colab_type": "text"
},
"source": [
"### 7.5.3. remove_volumes\n",
"```\n",
"develVolume = pe.MapNode(\n",
" interface = fsl.ExtractROI(t_min = 10,t_size = 508),\n",
" iterfield = ['in_file'],\n",
" name = 'remove_volumes')\n",
"preproc.connect(img2float,'out_file',develVolume,'in_file')\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```fslroi \n",
"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/img2float/mapflow/_img2float0/sub-01_unfeat_run-01_bold_dtype.nii.gz \n",
"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/remove_volumes/mapflow/_remove_volumes0/sub-01_unfeat_run-01_bold_dtype_roi.nii.gz 10 508\n",
"\n",
"# fslroi \n",
"```\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslroi prefiltered_func_data prefiltered_func_data 10 508\n",
"\n",
"# fslroi \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qF81VEX6vyZP",
"colab_type": "text"
},
"source": [
"### 7.5.4. optional: difference between the first run and the rest is to extract the reference for motion correct (realignment)\n",
"```\n",
"if first_run == True:\n",
" \"\"\"\n",
" Realign the functional runs to the reference (`whichvol` volume of first run)\n",
" \"\"\"\n",
" motion_correct = pe.MapNode(\n",
" interface = fsl.MCFLIRT(\n",
" save_mats = True, # save transform matrices\n",
" save_plots = True, # save transform parameters for plotting\n",
" save_rms = True, # save rms displacement parameters\n",
" stats_imgs = True, # produce variance and std. dev. images\n",
" interpolation = 'spline' # interpolation method for transformation\n",
" ),\n",
" iterfield = ['in_file','ref_file'],\n",
" name = 'MCFlirt',\n",
" )\n",
" # connect to the develVolume node to get the input data\n",
" preproc.connect(develVolume,'roi_file',\n",
" motion_correct,'in_file',)\n",
" ######################################################################################\n",
" ################# the part where we replace the actual reference image if exists ####\n",
" ######################################################################################\n",
" # connect to the develVolume node to get the reference\n",
" preproc.connect(extract_ref, 'roi_file', \n",
" motion_correct,'ref_file')\n",
" ######################################################################################\n",
" # connect to the output node to save the motion correction parameters\n",
" preproc.connect(motion_correct,'par_file',\n",
" outputnode,'motion_parameters')\n",
" # connect to the output node to save the other files\n",
" preproc.connect(motion_correct,'out_file',\n",
" outputnode,'realigned_files')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"fslroi \n",
"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/remove_volumes/mapflow/_remove_volumes0/sub-01_unfeat_run-01_bold_dtype_roi.nii.gz \n",
"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/outputs/func/example_func.nii.gz 254 1\n",
"# fslroi \n",
"\n",
"\n",
"mcflirt \n",
"-in /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/remove_volumes/mapflow/_remove_volumes0/sub-01_unfeat_run-01_bold_dtype_roi.nii.gz \n",
"-spline_final \n",
"-out /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/MCFlirt/mapflow/_MCFlirt0/sub-01_unfeat_run-01_bold_dtype_roi_mcf.nii.gz \n",
"-reffile /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/outputs/func/example_func.nii.gz \n",
"-mats -plots -rmsabs -rmsrel -stats\n",
"# mcflirt -in -out -reffile \n",
"-mats -plots -rmsabs -rmsrel # see below\n",
"-stats # produce variance and std. dev. images\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslroi prefiltered_func_data example_func 254 1\n",
"# fslroi \n",
"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/mcflirt -in prefiltered_func_data -out prefiltered_func_data_mcf -mats -plots -reffile example_func -rmsrel -rmsabs -spline_final\n",
"\n",
"# mcflirt -in -out \n",
" -mats # save transformation matrices\n",
" -plots # save transformation parameters\n",
" -reffile \n",
" -rmsrel -rmsabs # save rms displacement parameters\n",
" -spline_final # interpolation method for transformation\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zCjzMpnQzlYW",
"colab_type": "text"
},
"source": [
"#### 7.5.4.1. if there exist a reference \"example_func.nii.gz\", we can directly input it to the mcflirt algorithm\n",
"\n",
" else:\n",
" \"\"\"\n",
" Realign the functional runs to the reference ref_file\n",
" \"\"\"\n",
" motion_correct = pe.MapNode(\n",
" interface = fsl.MCFLIRT(\n",
" ref_file = first_run, # the only difference from above\n",
" save_mats = True,\n",
" save_plots = True,\n",
" save_rms = True,\n",
" stats_imgs = True,\n",
" interpolation = 'spline'),\n",
" iterfield = ['in_file','ref_file'],\n",
" name = 'MCFlirt',\n",
" )\n",
" # connect to the develVolume node to get the input data\n",
" preproc.connect(develVolume,'roi_file',\n",
" motion_correct,'in_file',)\n",
" # connect to the output node to save the motion correction parameters\n",
" preproc.connect(motion_correct,'par_file',\n",
" outputnode,'motion_parameters')\n",
" # connect to the output node to save the other files\n",
" preproc.connect(motion_correct,'out_file',\n",
" outputnode,'realigned_files')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BIwuxKTK0Dij",
"colab_type": "text"
},
"source": [
"### 7.5.5. plot_motion: plot the estimated motion parameters\n",
"```\n",
"plot_motion = pe.MapNode(\n",
" interface=fsl.PlotMotionParams(in_source='fsl'\n",
" ),\n",
" iterfield = ['in_file'],\n",
" name = 'plot_motion',\n",
" )\n",
" plot_motion.iterables = ('plot_type',['rotations', # plot rotations (radians)\n",
" 'translations', # plot translations (mm)\n",
" 'displacement' # plot mean displacement (mm)\n",
" ])\n",
" preproc.connect(motion_correct,'par_file',\n",
" plot_motion,'in_file')\n",
" preproc.connect(plot_motion,'out_file',\n",
" outputnode,'motion_plots')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"fsl_tsplot -i /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/MCFlirt/mapflow/_MCFlirt0/sub-01_unfeat_run-01_bold_dtype_roi_mcf.nii.gz.par -o /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/MCFlirt/mapflow/_MCFlirt0/sub-01_unfeat_run-01_bold_dtype_roi_mcf.nii.gz_disp.png -t 'MCFLIRT estimated mean displacement (mm)' -a abs,rel\n",
"\n",
"fsl_tsplot -i /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/MCFlirt/mapflow/_MCFlirt0/sub-01_unfeat_run-01_bold_dtype_roi_mcf.nii.gz.par -o /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/MCFlirt/mapflow/_MCFlirt0/sub-01_unfeat_run-01_bold_dtype_roi_mcf.nii.gz_rot.png -t 'MCFLIRT estimated rotations (radians)' --start=1 --finish=3 -a x,y,z\n",
"\n",
"fsl_tsplot -i /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/MCFlirt/mapflow/_MCFlirt0/sub-01_unfeat_run-01_bold_dtype_roi_mcf.nii.gz.par -o /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/MCFlirt/mapflow/_MCFlirt0/sub-01_unfeat_run-01_bold_dtype_roi_mcf.nii.gz_trans.png -t 'MCFLIRT estimated translations (mm)' --start=4 --finish=6 -a x,y,z\n",
"```\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fsl_tsplot -i prefiltered_func_data_mcf.par -t 'MCFLIRT estimated rotations (radians)' -u 1 --start=1 --finish=3 -a x,y,z -w 640 -h 144 -o rot.png \n",
"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fsl_tsplot -i prefiltered_func_data_mcf.par -t 'MCFLIRT estimated translations (mm)' -u 1 --start=4 --finish=6 -a x,y,z -w 640 -h 144 -o trans.png \n",
"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fsl_tsplot -i prefiltered_func_data_mcf_abs.rms,prefiltered_func_data_mcf_rel.rms -t 'MCFLIRT estimated mean displacement (mm)' -u 1 -w 640 -h 144 -a absolute,relative -o disp.png \n",
"\n",
"# fsl_tsplot -i -t -u \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x9OqyQEA1LG1",
"colab_type": "text"
},
"source": [
"### 7.5.6. meanfunc: extract the mean volume of the functional run\n",
"\n",
" meanfunc = pe.Node(\n",
" interface=fsl.ImageMaths(op_string = '-Tmean', # string defining the operation\n",
" suffix='_mean', # out_file suffix\n",
" ),\n",
" name = 'meanfunc')\n",
" preproc.connect(motion_correct,('out_file',pickrun,whichrun),\n",
" meanfunc,'in_file')\n",
"\n",
"*[nipype-generated]*\n",
"#### fslmaths \n",
"#### /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/MCFlirt/mapflow/_MCFlirt0/sub-01_unfeat_run-01_bold_dtype_roi_mcf.nii.gz \n",
"#### -Tmean \n",
"#### /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/meanfunc/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mean.nii.gz\n",
"##### fslmaths -Tmean \n",
"\n",
"*[FSL GUI]*\n",
"### /opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_mcf -Tmean mean_func\n",
"#### fslmaths -Tmean "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7kc__h3K2Usm",
"colab_type": "text"
},
"source": [
"### 7.5.7. bet2_mean_func: strip the skull from the mean functional to generate a mask --- here is why the smoothing is different from the FSL GUI pipeline. The mask is estimated essentially different commands (bet vs bet2)\n",
"```\n",
"meanfuncmask = pe.Node(\n",
" interface=fsl.BET(mask=True, # create binary mask image\n",
" no_output=True, # Don't generate segmented output\n",
" frac=0.3,\n",
" surfaces=True\n",
" ),\n",
" name='bet2_mean_func')\n",
"preproc.connect(meanfunc,'out_file',\n",
" meanfuncmask,'in_file')\n",
"```\n",
"#### bet \n",
"#### /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/meanfunc/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mean.nii.gz \n",
"#### /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/bet2_mean_func/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mean_brain.nii.gz \n",
"#### -f 0.30 \n",
"#### -m \n",
"#### -n\n",
"#### -A\n",
"\n",
"##### bet \n",
"##### \n",
"##### -f \n",
"##### -n # Don't generate segmented output\n",
"##### -m # create binary mask image\n",
"##### -A # run bet2 and then betsurf to get additional skull and scalp surfaces\n",
"\n",
"\n",
"### /opt/fsl/fsl-5.0.10/fsl/bin/bet2 mean_func mask -f 0.3 -n -m\n",
"#### bet2 \n",
"#### -f \n",
"#### -n # Don't generate segmented output\n",
"#### -m # create binary mask image\n",
"\n",
"`bet2 has virtually the same functionality as the original bet command-line program. You can use the same command-line syntax as before, with the main addition being the -e option for outputting a \"mesh\" version of the estimated brain mask. `\n",
"\n",
"`bet is a script which calls the bet2 binary - hence by default it will give the same results. bet has more script-level options that wrap around bet2.`"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HyNlJFZO32mU",
"colab_type": "text"
},
"source": [
"### 7.5.8. maskfunc: Mask the motion corrected functional data with the mask to create the masked (bet) motion corrected functional data\n",
"```\n",
"maskfunc = pe.MapNode(\n",
" interface=fsl.ImageMaths(suffix='_bet', # out_file suffix\n",
" op_string='-mas' # string defining the operation\n",
" ),\n",
" iterfield=['in_file'],\n",
" name='maskfunc')\n",
"preproc.connect(motion_correct,'out_file',\n",
" maskfunc,'in_file')\n",
"preproc.connect(meanfuncmask,'mask_file',\n",
" maskfunc,'in_file2')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"fslmaths \n",
"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/MCFlirt/mapflow/_MCFlirt0/sub-01_unfeat_run-01_bold_dtype_roi_mcf.nii.gz \n",
"-mas /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/bet2_mean_func/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mean_brain_mask.nii.gz \n",
"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/maskfunc/mapflow/_maskfunc0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_bet.nii.gz\n",
"\n",
"# fslmaths -mas \n",
"```\n",
"#### *FSL GUI*\n",
"```\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_mcf -mas mask prefiltered_func_data_bet\n",
"# fslmaths -mas \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i0lBGaeV4gGK",
"colab_type": "text"
},
"source": [
"### 7.5.9. getthreshold: determine the 2nd and 98th percentile of each functional run\n",
"```\n",
"getthreshold = pe.MapNode(\n",
" interface=fsl.ImageStats(op_string='-p 2 -p 98'), # string defining the operation\n",
" iterfield = ['in_file'],\n",
" name='getthreshold')\n",
"preproc.connect(maskfunc,'out_file',getthreshold,'in_file')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"fslstats \n",
"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/maskfunc/mapflow/_maskfunc0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_bet.nii.gz \n",
"-p 2 \n",
"-p 98\n",
"\n",
"# fslstats -p -p \n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/fslstats prefiltered_func_data_bet -p 2 -p 98\n",
" \n",
"# fslstats -p -p \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BSuqUJHMj1vw",
"colab_type": "text"
},
"source": [
"### 7.5.10. Thresholding"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oexq_NkX438i",
"colab_type": "text"
},
"source": [
"#### 7.5.10.2 thresholding: threshold the functional data at 10% of the 98th percentile\n",
"```\n",
"threshold = pe.MapNode(\n",
" interface=fsl.ImageMaths(out_data_type='char', # output datatype, one of (char, short, int, float, double, input)\n",
" suffix='_thresh', # out_file suffix\n",
" op_string = '-Tmin -bin'), # string defining the operation\n",
" iterfield=['in_file','op_string'],\n",
" name='tresholding')\n",
"preproc.connect(maskfunc,'out_file',threshold,'in_file')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"fslmaths \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/maskfunc/mapflow/_maskfunc0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_bet.nii.gz \n",
" -thr 1382.5000000000 \n",
" -Tmin \n",
" -bin \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/tresholding/mapflow/_tresholding0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_bet_thresh.nii.gz \n",
" -odt char\n",
"\n",
"fslmaths -thr <10 * upper percentile> \n",
" -Tmin # we want the minimum across time\n",
" -bin # binary mask\n",
" \n",
" -odt char\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_bet -thr 1382.5 -Tmin -bin mask -odt char\n",
"# fslmaths -thr <10_%_upper_percentile> \n",
" -Tmin # Tmin to specify we want the minimum across time\n",
" -bin # use \"mask\" as a binary mask\n",
" \n",
" -odt \n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BDiNI0e-jb7H",
"colab_type": "text"
},
"source": [
"#### 7.5.10.2 define a function to get the 10% of the intensity\n",
"```\n",
"preprocec.connect(getthreshold,('out_stat',getthreshop),threshold,'op_string')\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Pk-hSjeI57PM",
"colab_type": "text"
},
"source": [
"### 7.5.11. dilatemask: difF is a spatial filtering option that specifies maximum filtering of all voxels\n",
"```\n",
"dilatemask = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix='_dil',# out_file suffix\n",
" op_string='-dilF'), # string defining the operation\n",
" iterfield=['in_file'],\n",
" name = 'dilatemask')\n",
"preproc.connect(threshold,'out_file',dilatemask,'in_file')\n",
"preproc.connect(dilatemask,'out_file',outputnode,'mask')\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"fslmaths \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/tresholding/mapflow/_tresholding0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_bet_thresh.nii.gz \n",
" -dilF \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/outputs/func/mask.nii.gz\n",
"# fslmaths -dilF \n",
"```\n",
"\n",
"#### FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths mask -dilF mask\n",
"\n",
"# fslmaths -dilF \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VD9s9HFm6zDf",
"colab_type": "text"
},
"source": [
"### 7.5.12. dilateMask_MCed: mask the motion corrected functional runs with the dilated mask\n",
"```\n",
"dilateMask_MCed = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix='_mask',op_string='-mas'),\n",
" iterfield=['in_file','in_file2'],\n",
" name='dilateMask_MCed')\n",
"preproc.connect(motion_correct,'out_file',dilateMask_MCed,'in_file',)\n",
"preproc.connect(dilatemask,'out_file',dilateMask_MCed,'in_file2')\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"fslmaths \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/MCFlirt/mapflow/_MCFlirt0/sub-01_unfeat_run-01_bold_dtype_roi_mcf.nii.gz \n",
" -mas \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/outputs/func/mask.nii.gz \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/dilateMask_MCed/mapflow/_dilateMask_MCed0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mask.nii.gz\n",
"\n",
"# fslmaths -mas \n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_mcf -mas mask prefiltered_func_data_thresh\n",
"\n",
"\n",
"# fslmaths -mas \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hGlBiE3L6mvR",
"colab_type": "text"
},
"source": [
"### 7.5.13. meanfunc2: We now take this functional data that is motion corrected, high pass filtered, and create a \"mean_func\" image that is the mean across time (Tmean)\n",
"```\n",
"meanfunc2 = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix='_mean', # suffix of the output file\n",
" op_string='-Tmean',), # string defining the operation\n",
" iterfield = ['in_file'],\n",
" name = 'meanfunc2')\n",
"preproc.connect(dilateMask_MCed,'out_file',meanfunc2,'in_file')\n",
"\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"fslmaths \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/dilateMask_MCed/mapflow/_dilateMask_MCed0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mask.nii.gz \n",
" -Tmean \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/meanfunc2/mapflow/_meanfunc20/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mask_mean.nii.gz\n",
"\n",
"# fslmaths -Tmean \n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_thresh -Tmean mean_func\n",
"\n",
"# fslmaths -Tmean \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NFPbFqsxdUUF",
"colab_type": "text"
},
"source": [
"### 7.5.14. SUSAN smoothing: smooth each run using SUSAN with the brightness threshold set to 75% of the median value for each run and a mask constituing the mean functional\n",
"\n",
"https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=FSL;d23aeab5.1004\n",
"\n",
"https://nipype.readthedocs.io/en/latest/users/examples/fmri_fsl.html\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xMwLmlTQec_-",
"colab_type": "text"
},
"source": [
"#### 7.5.14.1. merge: merge multiple input spaces for SUSAN smoothing, because USAN input placeholder takes a tuple of mask and a thresholding value\n",
"```\n",
" def get_brightness_threshold_double(thresh):\n",
" return [2 * 0.75 * val for val in thresh]\n",
"\n",
" merge = pe.Node(\n",
" interface = util.Merge(2, axis = 'hstack'), \n",
" name = 'merge')\n",
"preproc.connect(meanfunc2, 'out_file', \n",
" merge, 'in1')\n",
"preproc.connect(medianval,('out_stat',get_brightness_threshold_double), \n",
" merge, 'in2')\n",
" \n",
"```\n",
"#### no corresponding fsl command"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uSgMvZwYe5EQ",
"colab_type": "text"
},
"source": [
"#### 7.5.14.2. susan_smooth: calling susan smooth algorithm. The algorithm takes 4 inputs: input image.nii, brightness threshold value, spatial smoothing size, and whether the smoothing area is to be found from secondary images\n",
"```\n",
"def get_brightness_threshold(thresh):\n",
"return [0.75 * val for val in thresh]\n",
"def getusans(x):\n",
"return [[tuple([val[0], 0.5 * val[1]])] for val in x]\n",
"\n",
"smooth = pe.MapNode(\n",
" interface = fsl.SUSAN(dimension = 3,\n",
" use_median = True),\n",
" iterfield = ['in_file',\n",
" 'brightness_threshold',\n",
" 'fwhm',\n",
" 'usans'],\n",
" name = 'susan_smooth')\n",
"preproc.connect(dilateMask_MCed, 'out_file', \n",
" smooth, 'in_file')\n",
"preproc.connect(medianval, ('out_stat',get_brightness_threshold),\n",
" smooth, 'brightness_threshold')\n",
"preproc.connect(inputnode, 'fwhm', \n",
" smooth, 'fwhm')\n",
"preproc.connect(merge, ('out',getusans),\n",
" smooth, 'usans')\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"susan \n",
"/bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/dilateMask_MCed/mapflow/_dilateMask_MCed0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mask.nii.gz \n",
" 6221.2500000000 \n",
" 1.2739827004 \n",
" 3 \n",
" 1 \n",
" 1 \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/meanfunc2/mapflow/_meanfunc20/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mask_mean.nii.gz \n",
" 6221.2500000000 \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/susan_smooth/mapflow/_susan_smooth0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mask_smooth.nii.gz\n",
"\n",
"# susan\n",
" \n",
" brightness_threshold = 0.75 * median value\n",
" dimension: fully 3D\n",
" to use a local median filter\n",
" to use USAN \n",
" what is the mask\n",
" should the same as the brightness threshold\n",
" \n",
"```\n",
"#### *FSL GUI*\n",
"````\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/susan prefiltered_func_data_thresh 6192.1274415 \n",
"1.27388535032 \n",
"3 \n",
"1 \n",
"1 \n",
"mean_func \n",
"6192.1274415 \n",
"prefiltered_func_data_smooth\n",
"\n",
"# susan \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qjj5xqbBibD8",
"colab_type": "text"
},
"source": [
"### 7.5.15. dilateMask_smoothed: dilate mask the smoothed data\n",
"```\n",
"maskfunc3 = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix='_mask',# suffix of the output file\n",
" op_string='-mas'), # string defining operation\n",
" iterfield = ['in_file','in_file2'],\n",
" name='dilateMask_smoothed')\n",
"# connect the output of the susam smooth component to the maskfunc3 node\n",
"preproc.connect(smooth,'smoothed_file',\n",
" maskfunc3,'in_file')\n",
"# connect the output of the dilated mask to the maskfunc3 node\n",
"preproc.connect(dilatemask,'out_file',\n",
" maskfunc3,'in_file2')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"\n",
"fslmaths \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/susan_smooth/mapflow/_susan_smooth0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mask_smooth.nii.gz \n",
" -mas \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/outputs/func/mask.nii.gz \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/dilateMask_smoothed/mapflow/_dilateMask_smoothed0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mask_smooth_mask.nii.gz\n",
"\n",
"# fslmaths -mas \n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_smooth -mas mask prefiltered_func_data_smooth\n",
"\n",
"# fslmaths -mas \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qo_-PMMNnGae",
"colab_type": "text"
},
"source": [
"### 7.5.16. meanscaling"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gdoJNzWQeW6u",
"colab_type": "text"
},
"source": [
"#### 7.5.16.1. medianval: Take the motion corrected functional data, and using \"mask\" (from maskfunc)as a mask (the -k option), output the 50th percentile (the mean?), We will need this later to calculate the intensity scaling factor\n",
"```\n",
"\n",
"medianval = pe.MapNode(\n",
" interface = fsl.ImageStats(op_string = '-k %s -p 50'), # string defining the operation\n",
" iterfield = ['in_file','mask_file'],\n",
" name='cal_intensity_scale_factor')\n",
"preproc.connect(motion_correct,'out_file',\n",
" medianval,'in_file')\n",
"preproc.connect(threshold,'out_file', # here was where I made a mistake in the first implementation\n",
" medianval,'mask_file')\n",
"```\n",
"##### *nipype-generated*\n",
"```\n",
"fslstats \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/MCFlirt/mapflow/_MCFlirt0/sub-01_unfeat_run-01_bold_dtype_roi_mcf.nii.gz \n",
" -k \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/maskfunc/mapflow/_maskfunc0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_bet.nii.gz \n",
" -p 50 \n",
"\n",
"\n",
"# fslstats -k -p 50\n",
"```\n",
"\n",
"##### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslstats prefiltered_func_data_mcf -k mask -p 50\n",
"# fslstats -k -p 50\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_QHeeskoi-W1",
"colab_type": "text"
},
"source": [
"#### 7.5.16.2. meanscale: scale the median value of the functional data, calculate the intensity scaling factor applied to the whole 4D dataset so that it's mean is 10000\n",
"```\n",
"meanscale = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix='_intnorm'),\n",
" iterfield = ['in_file','op_string'],\n",
" name = 'meanscale')\n",
"preproc.connect(maskfunc3,'out_file',\n",
" meanscale,'in_file')\n",
"preproc.connect(meanscale,'out_file',\n",
" outputnode,'smoothed_files')\n",
"```\n",
"##### *nipype-generated*\n",
"```\n",
"fslmaths \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/nipype_mimic_FEAT/dilateMask_smoothed/mapflow/_dilateMask_smoothed0/sub-01_unfeat_run-01_bold_dtype_roi_mcf_mask_smooth_mask.nii.gz \n",
" -mul \n",
" 1.2709972880 \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/outputs/func/prefiltered_func.nii.gz\n",
" fslmaths\n",
" \n",
" -mul\n",
" 10000 / medianval\n",
" \n",
"```\n",
"##### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_smooth -mul 1.21121538128 prefiltered_func_data_intnorm\n",
"\n",
"# fslmaths -mul <10000/medianinval>\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7-n0e_K6oTmZ",
"colab_type": "text"
},
"source": [
"### /opt/fsl/fsl-5.0.10/fsl/bin/fslmaths prefiltered_func_data_intnorm filtered_func_data\n",
"#### this is not used in the nipype-pipeline, because this is to re-name the functional data, and because no high pass was performed"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ymzl3ESEnk1s",
"colab_type": "text"
},
"source": [
"### 7.5.17. gen_mean_func_img: generate a mean functional image from all the functional images across time. Should this be the \"mean.nii.gz\" we will use in the future?\n",
"```\n",
"meanfunc3 = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix='_mean', # suffix of the output file\n",
" op_string='-Tmean',), # string defining operation\n",
" iterfield = ['in_file'],\n",
" name='gen_mean_func_img')\n",
"preproc.connect(meanscale,'out_file',meanfunc3,'in_file')\n",
"preproc.connect(meanfunc3,'out_file',outputnode,'mean')\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"fslmaths \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/outputs/func/prefiltered_func.nii.gz \n",
" -Tmean \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/func/session-02/sub-01_unfeat_run-01/outputs/func/mean_func.nii.gz\n",
"\n",
"# fslmaths -Tmean \n",
"```\n",
"\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fslmaths filtered_func_data -Tmean mean_func\n",
"\n",
"# fslmaths -Tmean \n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B3onupJwQsdm",
"colab_type": "text"
},
"source": [
"## 7.6. ICA-based automatic removal of motion artifacts (IROMA) -- in fact, we cannot perform ICA-AROMA without coregistration to the structural scan (section 8)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wpIPL1rMRBdK",
"colab_type": "text"
},
"source": [
"### 7.6.1. download all the ICA_AROMA code from this [github repo](https://github.com/maartenmennes/ICA-AROMA)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gxpmmHB9RRYe",
"colab_type": "text"
},
"source": [
"### 7.6.2. upzip the downloaded file and put the files in the unzipped folder to the folder that contain the other preprocessing scripts"
]
},
{
"cell_type": "code",
"metadata": {
"id": "GMflgCYhSqFG",
"colab_type": "code",
"outputId": "d60b159d-f176-44c6-f16c-0964197f636e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 637
}
},
"source": [
"Image('ICA.jpg',height=600)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/jpeg": 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Jv/iqP+ElvP8Anpc/+Bkv/wAVQB9RUV8u/wDCS3n/AD0uf/AyX/4qj/hJbz/n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\nVuigCp/Ztr/dk/7+v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AHI5PHI9a0qACjrVDUNa07Srqytr25WGa+l8m3UqTvf\n04HHUDJwMkDvVbVPFGk6NerZ3ktwLhovO2Q2ks2EzjcSikAZ9aAKWoeCbC/vpbtbu8tXlIaRYTGV\nZsAZw6Ng4AHHpVX/AIV9af8AQX1L/vm3/wDjVbdx4j0i20q31OS+jNpc7fIdAXMpIyAiqCWPsBmk\ntPEek31jd3kF1mKzBNwrxukkWBu+ZGAYcc9Oa1VaaVkyHSg3doxf+FfWn/QX1L/vm3/+NUf8K+tP\n+gvqX/fNv/8AGq1ZfFuhw+GV8RSX6jSWAIn8tznLbfu43ZzxjHFWdV13TNEsEvtRulgtndUVypOS\n3TgDPv7AEmj29TuL2UOxg/8ACvrT/oL6l/3zb/8AxqtXRfDNlockssUs888oAaWcrnAzgAKAAOT2\n71tUUpVZyVmxqnFO6QUUUVmWFFFYtj4nstQ/sryorhf7Tjlkh3qBtCYzu54PPGM0AbVFFUtI1OHW\ndKt9Rt1kWGddyrIAGAzjnBPpQBdooooAKKKKACiiigArL1rQbTXIES4aWN423xyxEBkOCOMgjoSO\nQetalFNNp3Qmk1ZnIf8ACvrT/oL6l/3zb/8Axqj/AIV9af8AQX1L/vm3/wDjVdfRWnt6ncj2UOxy\nH/CvrT/oL6l/3zb/APxqj/hX1p/0F9S/75t//jVdfVfUL2PTtNur6ZXaK2heZwgyxCgk4z34o9vU\n7h7KHY5238BafDcxSy319crG4cRS+UFLA5BOxFPBAPXtXUqoVQo6Co7edbm1iuEBCSoHUN1wRnmp\naiU5S+JlRio7BRRRUlBRRRQAUUUUAFFFFABRRRQAUUUUAMljSaMxuMqeCK5Q/D3T1+WHUdRhiHCR\nr5JCDsAWjJwPcmuuqtZ39tfrM1tJ5ghmeCT5SMOpww59D36VcZyh8LJlCMt0cz/wr60/6C+pf982\n/wD8ao/4V9af9BfUv++bf/41XX0VXt6ncn2UOxyH/CvrT/oL6l/3zb//ABqj/hX1p/0F9S/K3/8A\njVdEmpwvrc2khZPPitkuWbA27XZ1ABznOUPb0q7R7ep3D2UOxR0nSbXRrJbS1D7ASSznLMSckn3J\nJNXqKpaRqcOtaPaanbLIkN1EsqLIAGAIzzgkZ/Gs27u7NErF2iiikAUUUUAFFFFABRRRQAUVSv8A\nU4dOmsIplkZr25+zR7ACA2xnycnphD69qL/U4dOmsIplkZr25+zR7ACA2xnycnphD69qALtFVpb+\n2gvraykk23FyrtEm0ncExu56DGR1qzQBm607JaRkIzKs8TttGSAsisePoK5J3Ih1qySSM218JXjl\naKcOrugG1l8rGM5Oc9MDFd+QCMEZrK1fVoNKktIRYz3lzduyQwWypubapZjl2VQAB3NAHFX2mWD3\n2myWbCK3tgN0aW7x7HDqxkU+Q2WO3BxtJwOear/2ZHMmti6a0cX0aGOMWcgQyK7tuYCEYyGUZO9u\nvJrvE1i2OoWljJZXEM1006xiSNQMREAseejZBX1B5xWp5af3F/KgDn7bWLCO1hjw0O1FXyoraUom\nB91fkHA6DgfQVoaK7NZuWRlDTyyLuGDtaRmHH0NaHlp/cX8qgW+tf7SbThJ/pSwicx7TwhJUHOMd\nQeOtAFmiiszV9aj0qS0hFpc3lzduyQwWwTc21SzHLsqgADuaANOis6x17TNSudQt7S7V5dPk8q6U\nqy+W3PqBkcHkZHBqkvjPQZNDg1mO8kksbibyInjtpWZ5MkbQgXd1U9u1AG9RWNF4r0WbTLnUVvCL\ne1cRz74XV4mJAAZCoYZ3Dt3rZoA8/wDjb/ySHXf+3f8A9KI65/8AZx/5J5qH/YVk/wDRUVdB8bf+\nSQ67/wBu/wD6UR1z/wCzj/yTzUP+wrJ/6KioA9gooqkNThOtvpO2Tz1tluS2Bt2liuM5znIPagC7\nRRRQAUjEqpIGSBUNzcm3aAC3mm82URkxKDsyCdzc8Lx19xWYniNJr27gttM1C4jtZlt5LiNUKGQs\ngIALhjtD7iduMK3JPBAOT8T2X9q6W1o6RGYG6HlXUUpjBkL7JAURuV3DH1PIpLOytYvEF7e3U3nw\n3CuuWgkJZWCjYy+RkqNvGXI9ua9FKIeqr+VHlp/cX8qAOU0K607R9CsdPDbDbwIjiG0lClgBuI+Q\ndTk1Bolzaaa975qRh5pnk+0xW8xknBdmHmZjGCoYAct+HSuih1S0u7KzvLKGS6t7twqSRRfdBz8z\nBsELxj8RxWh5af3F/KgDEtb1LvV7eS2EjxpFKjs0TIAWKEfeAz901u0gVV6KB9BS0AFFFUtI1OHW\ndKt9Rt1kWGddyrIAGAzjnBPpQBdoqtLf20F9bWUkm24uVdok2k7gmN3PQYyOtWaACiiigAooooAK\nKpatqcOj6dJfXCyNEjIpEYBbLMFHUjuwq7QAUUUUAFYup3It9UhkmVxCsEib1jZ+SyEcKCf4TRde\nI0tbewb+zNQkur5mWGzVUEvygli25wowB/e7itK2nF5EztbSw7ZHj2zKATtYruHJ+U4yPYigDHGs\n2A/5ay/+A0v/AMTThrdgP+Wsv/gLL/8AE1ueWn9xfyqsLuzOpNpwZftSwicx7D9wkqDnGOoPHWgD\nN/t3T/8AnrN/4Cy//E1XvtUtbrT7q3gaZ5ZYXjRfs8gySpA5KgDrW5Zyrd2kdwbSS3LjPlToA6+x\nAJ/nU/lp/cX8qAEicyRKzDBI6U+iigAoqkNThOtvpO2Tz1tluS2Bt2liuM5znIParF1cw2VnNd3D\n7III2kkbBO1VGScDnoKAJaKZDKk8Ec0Tbo5FDKcYyCMin0AeP/tHf8k80/8A7Csf/oqWug+CX/JI\ndC/7eP8A0okrn/2jv+Seaf8A9hWP/wBFS10HwS/5JDoX/bx/6USUAegUUUUAFFFFABRRRQBQ1oSN\no13HEhZ5IXQAe4Irmxerb+Irm7iYNa3YTzfMimWSJlDDgCMhgcjuMc9a6qG+tbq7urSKTdNasqzJ\ntI2llDDkjB4I6VP5af3F/KgDy670SyfQtPs4GjEkOftKi3kRZmKFd24wPyMnBK554Iqz9jim166u\nrmaF7We1lt3/ANEkLSbhHtLjyQWI2Hkse2AOldrd6tBa6za6XHYz3NzOnmN5KpthjDBS7lmHGT0G\nTweK0/LT+4v5UAcpouoWWn6LaWbqsDwxhDHb20xTPqP3a9evTqT161saPMZp76RUcRSyh0ZlKkgR\novQ89VNTaRf2us6Vb6jbxMsM67lWRQGAzjnBPpS6XqUGqQTy26Oiw3MtswcAZaNyjEYJ4yDigC9R\nRWfq+rQ6PZpcSwzTtJKkMUMABeR2OAoyQPxJAoA0KKjgkaW3jkeJ4WdQxikxuQkdDgkZHTgke9SU\nAFFFFABRRRQAUUUUAFFFFABRRRQBxutMztN/yzuY76O6hWSOQpIFVFwWRWxna3Y44ODVaQWd7qT3\n92US4NksMTJbTSGCTLkspMY6bhg8HjtXclFJyVB/CsUeKNBOqDTTc7bgzGBd9u6xtIDgoJCuwtkE\nYBzQB5/Jorf8I3JpsUtusjsWO+B2VW8rYHU/ZhtOeThd3ffmumsZLCz8Q3moCOL/AEmFA062sol3\njqD+75B4Oc9R0711dnd2d+szWzLIIZngk+QjDqcMOR2PfpVny0/uL+VAGD/aEV3eWX2USyGOcu+Y\nXQBfLderAd2FdBSBFByFA+gpaACiiigAooooAKKKKACiiigAooooAKKKKACsnWJjDNZOyOYo5WZ2\nVSxGY3XoOerCqdv458O3TxBL2RElfy45Z7WaKJ2zjAkdAp54610JAPUA/WgDAGs2A/5ay/8AgNL/\nAPE04a3YD/lrL/4Cy/8AxNbnlp/cX8qpabf2uqJcvBEyi3uZLZ96gZZG2kjBPGelAFH+3dP/AOes\n3/gLL/8AE0LrlhuBEkzEHOBay8/+O1t+Wn9xfyqnql9aaPpdxqF0h8mBC7BFyx9AB6k8UAJoqyJo\ntnFKhV44UQj3CgVfrDm8SLZ2Ml1e6TqNqkaxMyyLGTl5DGFBVyCRgMeeAw78DcoAKKKKACiiigAo\noooAKKKKACiiigAooqtLf20F9bWUkm24uVdok2k7gmN3PQYyOtAE0zmOJnVdxA6Vw6zNaXunXMW0\nvHZraXMU0cylcbfmUrGwPRuOM8cjFddpupwapHcvAkii3uZLZ94AyyNtJGCeKt+Wn9xfyoA83vNN\nsptNukieMXc961w7G0k/fR+YWCMzRNxjHBUjI/GmQ2Sx6tpE5miNvZRrG+63kaQp5bqULeQCwywx\nyowPu5r0vy0/uL+VHlp/cX8qAOO8PT2GjaY1nsjhCzOy/Z7WXDgnIZh5Yw2MDHPQc1tabci51Wea\nFXMLQRpvaNkyVZyeGAP8Qq1q1/a6Ppz3txEzRI6IRGoJy7hB1I7sKvBQvQAfQUALRWPceI7a31W5\nsjbXLpaQ+fd3SqvlW6lWYbstuJIU8KrdRnFNl8W6HD4ZXxFJfqNJYAify3Octt+7jdnPGMcUAbVF\nY2o+KtI0q6jtrqa486SHzwsNpNNiPONx2KcDPrirMOuabcNp4hu0k/tFGktSgJEqqAScgYHB74oA\n0KKKKACiiigAooooAKKKKACiiigDzXxEscniPUCyxnbcIG3u4+XyY+AFYDPI5NZtrq8zwJHB5VsA\nqSBAzMuxs8diG4PXPPrnNdZrXgmbVNWnvotSiiWZ1k8uS2Z9rhFTIKyLxhRwQe9UH+HVy4A/tPT1\nwQ37vTWTkdCdsoz+NehCtS5UmzilSqczaRoW77/hpqTZz+5vhn/gctYlxo2m6B4R0PWdHtY7HU99\nkqm2Gz7SZHRXRwOHyrMefTPau00vQorHw7/Y9xJ9qjdZRM2Cm/zGZm4ByB8xHX8agsPB2h6bcwXE\nFpK8tuMQG5upZ/K4x8gkZgvHHGK4ZtOTaOuCtFJnNW2ueKtTvJr2wtL6SCLUHtxAFtBbGKOUxtuZ\nnEwfAJzgDOBgjk6On6lqkPicW+t3l/bGe5mjtYPs0RtJkG4xhZAC4fYAxDMOQRitmTwto8mpNf8A\n2aRJ3kEriO4kSORxjDNGrBWPA5INLB4Y0m31JdQSCVrhHZ4/MuZJEjZsglEZiqkgkZAHU1JRyHw8\n1C91bTLaxS6bT4NPtgpiCKZrguDiX5gQIxzjHVgc4xgx+HpNTsPCPhqzttXuA+rTiASzRREWqqks\njeWAgyzbMfPurtrfw7pVqbAwWvltYKyWzLI2UU9VJzllPo2RkA9hUA8I6ILSa0+yyfZ5ZBL5f2mX\nEbgkho/m/dHJP3NtAHNXOt63DOdIGqjz4dbhsjfGBN0kMkPmYK42hxnGQAOBx1B6LQby8OraxpV3\ndNefYXiMdw6KrlZE3bWCgLkEHkAcEVV1LwZZT2On2FnBGlrDqAvLgSSOXk+VgW3nLF8leSc8deBW\n1pekWOjQSQ2MJQSOZJGeRpHkYgDLOxLMcADk9qAL1FFFABRRRQAUUUUAFFFFAFa7/g/GpLf/AFC/\nj/Oo7v8Ag/GpLf8A1C/j/OgCWiiigAooooAKKKKAOCk/sH/hMNV/4SnyPtn2iL+zPtfTyfLTHk5/\ni8zzM7ec49qwr69gbxJbX8C6XZ6gNejtnhjhc3pjM/llpH3DCOpyAVK4ZQDnFetUUAcFoH9g/wBt\n3H9teR/wlH9ozbPtH+u2b28rys8+X5e3px1zzmsnQtMttP0PwZqllEE1G4ujDJOSd0iNFMdjHuoK\nrgdsDFep0UAeS+FLZZLzSpZNa02DxChJu7YWMi3ssm1g6TMZTuXPOSmOAVxxRpaaOs3gsxMP7fN6\nP7UGT5vm+RL5nne+/O3d2zjjNetUUAeYaNp9pqcPga3vYUng+yXjNFIMo/3OGHQjvg+gr02NEijW\nONQqKAqqowAB0Ap1FABRRRQAUUUUAFFFFAHM+MfveH/+wzb/AMmrM0HXNM8M/wBqadrd0llfHUbi\nf98CDcpI5ZHT+/8AKVXAyRtxXc0UAebatDqnizU9Xl0mwtp7aCBbC1muLpoGgmG2ZnVfLbJDeUOc\ncxEV3Gg6out6DZaiF2GeIM6f3H6Mv4MCPwrRrJvNIvrm6eaLxHqdojYxDDHbFF47F4Wb35J60Acf\nrlrq3ifWtaOmWNrcQ2sQ0+3nmuzEYZwVld1Ajbdh/KHUcx4pkUt54r8SWl5p2qz6TPPoIZ2hjjcq\n/mkMhDqfutkcYPHWvRokaOFEaV5WVQDI4G5j6nAAyfYAU+gDzDS7q103/hFtQurf7NYaXDdabd9X\nWzusoNzHkgNsf5j2cc81du7mDWdR8S6vpjebpq6E1q9yg/dzyjew2no21T1H97FehUUAeQaloWoJ\n4NvUaEf2HFpM2pRPuHNw9ttKbeuAxlkz0yw9K6HWVvNd12306z0621C106xzcxXF0YF8ydCi8hHy\nRGH4x/y0Fd9RQBgeDb24u/DkMF8R9vsWayusNn95Gduc99w2t/wKue8Qv4fj+IbHxEIPsn9jrtN0\nMxbvNfrnjdjOO/XFddqGmXd5cCSDXdQsUChfKt0typPr+8iY5/HHHSkstFW11H+0Jb26urs2wtmk\nm2DcodmBIRVGfmxwAMAcd6APMriRbrSLGz1iHT4biLSxLBPqsLyzygs4RIlDKfMVVQkglssOK2tB\ntYNd1nSLzVYFvZf+EatJysw3hpCzHdg8Fs9D2ya9GooA8m8PXcD+LPDl3ZjSrWW/aZbu0sYnEsY8\nl32XDlvmcMo4Kg5BxwDWl4c3+X4J8vG/7Fe7c9M/JivR6KAPJ/B9tFJqGkS3GtadBrqOTe2y2Mi3\nsr7WEiTMZTuXOTkpjgEY4rtvAf8AyI+k/wDXH/2Y10VFABRRRQAUUUUAFFFZN5pF9c3TzReI9TtE\nbGIYY7YovHYvCze/JPWgDWryNpNKCa0kAX/hJzrsn2Hg+dnzh9w9fLxu3Y4+9mvQBoWohgf+Es1k\njPTyrPH/AKIq/pumQ6WlykDSMLi5kuX3kHDO24gYA4z0oA4kW8v/AAlI8HeW32Fb06xnHy/Z87wn\n/gR2/uipdCbQhrNydZEB8UjUJygn/wBcU3t5XlZ52eXt6cdc966yy0aKz1S91Jri4ubq6wpaYr+6\njUkrGgUDCgseuSe5NaVAHkmhXcT+KPD13p40m1uL8zrc2lnE4lj/AHDuEuHLfMwZRwVByDjgGrOn\n/wDCP/8ACF32/wAn/hKv7KuPt/m/8fXm+WfM35527s4zxjGK9SooA8wvtMk0aKxPhyJodRvdBu9z\nRkl55VSIox/vOCxwTzzVnwVbacNcgn07WtKdxasLizsbGSGR87fmn3Sv86nuwDcnmvRqr3tvLdWr\nww3k9m7YxNAELrz2Dqy89ORQBYorD/sHUsf8jbrP/fqz/wDjFXtOsbiyWQXGrXmoF8YNykK7Pp5c\nadffPSgC9RRRQAUUUUAFFFFABRRRQAUUUUAeb6o+m2PjprknS9WvZr2BVtZQRe2v3FzEecoPvkYU\nctk1Vh/sHR9O8SRJpulnUxqEiSwvGqMls86APJtG7yVDKx7YFepUUAeO4hOl+K9OtriyksAdNdP7\nMjaGAM8xDGMb2H8K5KnGR6g1r+IbGLS9T1ax02A2unS2ljPew2oK/u/tLrM4C9zGCGI5IFel0UAe\nUTz6HY6tr7aJbWdzpp0+yjZFJNqm6eRWf5eNighm28cNnnNVMQnSvFenW1zZPYA6a6f2ZG0MAZ5i\nGMY3sP4VyVOMj1Br2KigDz7UNM8O6f4png1a3t7XT1sUbTUA2L5xeTzjHjrN/quR83THet34f/8A\nJPtAz/z4xdf92ukooAKKKKACiiigAooooARjtUkAnAzgd68l0K7jfxR4eutPGlWtzfmdbm0sonEs\nX7h3CXDlvmYMo4Kg5BxwDXrdFAHkenppSnwcbZl/4SM3n/EyyT5nneRLv8733/d3ds44zTtLTR1m\n8FmJh/b5vR/agyfN83yJfM8733527u2ccZr1qigDifGFtoY8U+H7zXbewNmEuInmvY0MYYhSiksM\ndmxn3rL0z+y7bxqLeMaVrE95d3AknRSL22Vg7ES5zmMfcH3eq4Br0qqGp6zY6OkJvJJA0z7Io4oX\nldzgk4RAWOACTxxQBxXhoS3mvWXh+cOyeFvN8wt/Exylsf8AvyWP1xWr4n0jTNS8YeGxf6daXQb7\nQrCeBXyBHkA5HQHn6119VrO/tr9ZmtpPMEMzwSfKRh1OGHPoe/SgDz7w9CttqujwWkUcSR3mtJFG\nq7UUCfAAA6AccVn+FLeFrzSprnXNPttcjLNfQJYyLfSNtbzElYyncM5IJTHAKgcCvUbe/trq6u7a\nGTdNaOsc67SNjFQ4GT1+VgePWrNAHnPgVtOs9bXT7EaVqL/ZGMmq2IIlO1l4uAc/O2c53ZyrcCr2\np22jQ/EyO51C309bmewjFlLdRpl7hZWxsYj74ynTnGK7iigDyzw79g+0+GP7P3f8JL5p/tvr5uPK\nfzfP9vM27c+23iul8SaVp1/4z8MteWFrcEm4BM0KvwI9wHI7Hke9ddRQB5bFo2pXl3rEmlplb/Ur\nrTr9g4UxwF1PmDPUqPNAA5zJR9mZvDWnW1tM1of+EsnSOSJVJj/0ibGAwI49wa9SooA8r1aGTS9L\n8WaZf3D3WoyvbXgvJAFa5hLoi5UYUFCpU4AHQ45r1SiigDz/AONv/JIdd/7d/wD0ojrn/wBnH/kn\nmof9hWT/ANFRV0Hxt/5JDrv/AG7/APpRHXP/ALOP/JPNQ/7Csn/oqKgDr/F/2D+3tI/t/Z/YHlT+\nZ5/+o+0ZTy/Nzxjb5mM8Z98VRlubGz1TULnSbVbuyi8Ob4LeAkCVRJIdq47Hpx+Fd/RQB5Jpos/7\nW1CytNU0y1srzQp3nm0aBkihcPGoYneQzqHbJG04Iz1FTWjaTcaFrGnxSaJY2qfZnk1PT0b7JOd5\nPlyjI/u4Ybjw/Jr1WigDzPT7m1ns9Jjs7GwtoIfEMaLLp2fs1x+5Y74+BxztOMjKnk1Wl0LR4NN1\nW8i0qxjuofE0CRTpboHRftMAwrYyByeB6mvVaKAOB0JtCGs3J1kQHxSNQnKCf/XFN7eV5Wednl7e\nnHXPesLQp7S58T+HpoI9IhOpGeO9srSBvMRTA7eXcsWIZgyjhlByDjjNet0UAeU6BFo9voXhqCwi\nsotQi1WFNQSBFWVXCzACUDnPXGfeiT7H50uc/wDCa/218nXzvK+0cY/6Y+R/wHr3r1aigDzCTTrS\nHRtW1hIVGow+I/3Vz/HGDdopVT2UgkEdDk5r0+g8gjOPese30a/huI5ZPE+rTojAmKSO1CuPQ7YQ\ncfQg0Aed+FZNK/s/wsmlBf7f+1ZutoPneR8+/wAw9fL24254ztxViy/s/wDsbwl/wkPl/wBg/Y7j\nf5/+o+0bl8vzM8fd8zGe/vivR9I0yHRtJttOt2kaG3TYjSEFiPfAA/SrtAHk1vbaQJfD15r1vanS\nRNfxWs2pRggQF826sZB02g7c9q0dIfTbLxykdudL1a6uby4LXMYIvrQEOxEvXKD7g+71XANekUUA\nVrC/tdTs0u7OUSwOWCuARkglT19wRVmiigArK8Tfb/8AhF9V/svd9v8Askv2fZ97ftOMe+envWrR\nQB5pef8ACJf8Ihd/8I79l+0f6L9o8v8A1v8Ar4/9b33Z/vc9aoePr2D7Tr1yq6XZ6jpyobZ5YXe9\nmwiuJIiGG1ASRkBh8rZr1qigDzfVH02x8dNck6Xq17NewKtrK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k4AGByetUj4b0p40T7Oy+WgRGSZ1ZQG3DDA5yDnB6jJHQmnDU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\nx93FAHr1Fee+G4ri58QW2g3CuYfC2/536SF8rbH3xCWz7kV6FQBHPClzbyQ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zR8yk9AhBK56mtjRbbVdIa50h9LkltpryeZNRinjC7JZGkyykh9w3Y4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\nZX9vILtfFDmPTZrWS3k0+KERxuAGRWSFA54GACw4qZFudQ0vUbS/i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bmaM0AYf9qaZ/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\n/wDgNJ/8TUNxdLfolvBHMWMqMS8TIFCsG/iA9KKKzLP/2Q==\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"image/jpeg": {
"height": 600
}
},
"execution_count": 28
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RID31Va_Rcwh",
"colab_type": "text"
},
"source": [
"### 7.6.3. setup the ICA_AROMA script that calls \"ICA_AROMA.py\""
]
},
{
"cell_type": "code",
"metadata": {
"id": "tmn2XuQ5S5kw",
"colab_type": "code",
"colab": {}
},
"source": [
"from nipype.interfaces.fsl import ICA_AROMA\n",
"sub = 'sub-01'\n",
"fMRI_dir = '{}/func'.format(sub)\n",
"if not os.path.exists(fMRI_dir):\n",
" os.makedirs(fMRI_dir)\n",
"parent_dir = fMRI_dir\n",
"sample = os.path.join(parent_dir,'prefiltered_func.nii.gz') # this is what we called the preprocessed funtional data: [cell](https://colab.research.google.com/drive/19vcwpQ2su92HJ1IMf_spVlPAQ0GKVuyq#scrollTo=QRoEsJBEobjr&line=7&uniqifier=1)\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "SNHbZtTPUFes",
"colab_type": "text"
},
"source": [
"#### codes that cannot be run on colab because we have not run the preprocessing and not saved the preprocessed data\n",
"```\n",
"first_session = 2\n",
"first_run_dir = os.path.join(MRI_dir,\n",
" 'session-0{}'.format(first_session),\n",
" 'sub-01_unfeat_run-01',)\n",
"first_run_dir = os.path.abspath(first_run_dir)\n",
"# setup the ICA_AROMA from nipype\n",
"AROMA_obj = ICA_AROMA()\n",
"# these two are only found in the very first run\n",
"## input the functional to structural transformation matrix\n",
"func_to_struct = os.path.join(first_run_dir,'outputs',\n",
" 'reg',\n",
" 'example_func2highres.mat')\n",
"## input the warp-file describing the non-linear registration (e.g., FSL FNIRT) of the structural data to MNI152 space\n",
"warpfield = os.path.join(first_run_dir,'outputs',\n",
" 'reg',\n",
" 'highres2standard_warp.nii.gz')\n",
"# these tow are customized for each run\n",
"## input the MCflirt motion correction parameters\n",
"fsl_mcflirt_movpar = os.path.join(parent_dir,\n",
" 'func',\n",
" 'MC',\n",
" 'MCflirt.par')\n",
"## input the dilate mask\n",
"mask = os.path.join(parent_dir,\n",
" 'func',\n",
" 'mask.nii.gz')\n",
"# output directory. If not exists, it will be created automatically. If it exists, you have to specify \"over-writing\" to ignore the error\n",
"output_dir = os.path.join(parent_dir,\n",
" 'func',\n",
" 'ICA_AROMA')\n",
"AROMA_obj.inputs.in_file = os.path.abspath(sample)\n",
"AROMA_obj.inputs.mat_file = os.path.abspath(func_to_struct)\n",
"AROMA_obj.inputs.fnirt_warp_file = os.path.abspath(warpfield)\n",
"AROMA_obj.inputs.motion_parameters = os.path.abspath(fsl_mcflirt_movpar)\n",
"AROMA_obj.inputs.mask = os.path.abspath(mask)\n",
"\n",
"# Type of denoising strategy: \n",
"# \\'no\\': only classification, no denoising; \n",
"# \\'nonaggr\\': non-aggresssive denoising (default); \n",
"# \\'aggr\\': aggressive denoising; \n",
"# \\'both\\': both aggressive and non-aggressive denoising (seperately)\n",
"AROMA_obj.inputs.denoise_type = 'nonaggr' \n",
"\n",
"AROMA_obj.inputs.out_dir = os.path.abspath(output_dir)\n",
"\n",
"cmdline = 'python ' + AROMA_obj.cmdline + ' -ow' # overwrite if exists previous ran ICA_AROMA results\n",
"\n",
"print(cmdline)\n",
"# use the system line to call the cmdline\n",
"os.system(cmdline)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "P1yugwLmVasL",
"colab_type": "text"
},
"source": [
"### 7.6.4. output file: denoised_func_data_nonaggr.nii.gz"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Sd_c65yYVtwS",
"colab_type": "text"
},
"source": [
"## 7.7. highpass filter"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eCU8ko-19dy-",
"colab_type": "text"
},
"source": [
"### 7.7.0: the graph for the workflow"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bT5Rvx3t9hJk",
"colab_type": "code",
"outputId": "40403ade-ced3-4233-e935-20b0ca0ca1ea",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1002
}
},
"source": [
"Image('highpass.jpg')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/jpeg": 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U1Jr+tttY3/ve2Gn1La/G2rVrUVVVhfT0dFhbW9e7LRkZGQDQKgpQYGAgevTo0SqyEgXF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\nRGbs/wN60RYar86wNAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j3BEwrGl1oeY",
"colab_type": "text"
},
"source": [
"### 7.7.1. calculate the spatial sigma for a given highpass cutoff (in sec) value and a given TR (in sec):\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sWsH6YDjI8o7",
"colab_type": "text"
},
"source": [
"$$highpass = \\frac{\\frac{hpf_freq}{2}}{TR}$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5sMUhE-N3sYF",
"colab_type": "text"
},
"source": [
"### 7.7.2. initialize a workflow\n",
"\n",
"```\n",
"from nipype.workflows.fmri.fsl import preprocess\n",
"from nipype.interfaces import fsl\n",
"from nipype.pipeline import engine as pe\n",
"from nipype.interfaces import utility as util\n",
"fsl.FSLCommand.set_default_output_type('NIFTI_GZ')\n",
"getthreshop = preprocess.getthreshop\n",
"getmeanscale = preprocess.getmeanscale\n",
"highpass_workflow = pe.Workflow(name = workflow_name)\n",
"```\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F9yw0-XN32n0",
"colab_type": "text"
},
"source": [
"### 7.7.3. input and output nodes\n",
"\n",
"\n",
"\n",
"```\n",
"inputnode = pe.Node(interface = util.IdentityInterface(\n",
" fields = ['ICAed_file',]),\n",
" name = 'inputspec')\n",
"outputnode = pe.Node(interface = util.IdentityInterface(\n",
" fields = ['filtered_file']),\n",
" name = 'outputspec')\n",
"```\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z7Zat8cO39aE",
"colab_type": "text"
},
"source": [
"### 7.7.4. image2float: float chart the image data\n",
"\n",
"\n",
"```\n",
"img2float = pe.MapNode(interface = fsl.ImageMaths(out_data_type = 'float',\n",
" op_string = '',\n",
" suffix = '_dtype'),\n",
" iterfield = ['in_file'],\n",
" name = 'img2float')\n",
"highpass_workflow.connect(inputnode,'ICAed_file',\n",
" img2float,'in_file')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"fslmaths -odt float\n",
"```\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.9/fsl/bin/fslmaths ICA_AROMA/denoised_func_data_nonaggr prefiltered_func_data -odt float\n",
"Total original volumes = 508\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YAjIMExe4G8J",
"colab_type": "text"
},
"source": [
"### 7.7.5. thresholding the image\n",
"\n",
"\n",
"```\n",
"getthreshold = pe.MapNode(interface = fsl.ImageStats(op_string = '-p 2 -p 98'),\n",
" iterfield = ['in_file'],\n",
" name = 'getthreshold')\n",
"highpass_workflow.connect(img2float, 'out_file',\n",
" getthreshold, 'in_file')\n",
"thresholding = pe.MapNode(interface = fsl.ImageMaths(out_data_type = 'char',\n",
" suffix = '_thresh',\n",
" op_string = '-Tmin -bin'),\n",
" iterfield = ['in_file','op_string'],\n",
" name = 'thresholding')\n",
"highpass_workflow.connect(img2float, 'out_file',\n",
" thresholding, 'in_file')\n",
"highpass_workflow.connect(getthreshold,('out_stat',getthreshop),\n",
" thresholding,'op_string')\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"fslstats -p 2 -p 98\n",
"fslmaths -thr 1729.0583984000 -Tmin -bin -odt char\n",
"```\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.9/fsl/bin/fslstats prefiltered_func_data -p 2 -p 98\n",
"/opt/fsl/fsl-5.0.9/fsl/bin/fslmaths prefiltered_func_data -thr 1617.8395508 -Tmin -bin mask -odt char\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L4c5Ak_Q5pXa",
"colab_type": "text"
},
"source": [
"### 7.7.6. dilatemasking\n",
"\n",
"```\n",
"dilatemask = pe.MapNode(interface = fsl.ImageMaths(suffix = '_dil',\n",
" op_string = '-dilF'),\n",
" iterfield = ['in_file'],\n",
" name = 'dilatemask')\n",
"highpass_workflow.connect(thresholding,'out_file',\n",
" dilatemask,'in_file')\n",
"```\n",
"#### *nipype-generated*\n",
"\n",
"```\n",
"fslmaths -dilF \n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.9/fsl/bin/fslmaths mask -dilF mask\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qNxgPN7i6BGG",
"colab_type": "text"
},
"source": [
"### 7.7.7. masking\n",
"\n",
"```\n",
"maskfunc = pe.MapNode(interface = fsl.ImageMaths(suffix = '_mask',\n",
" op_string = '-mas'),\n",
" iterfield = ['in_file','in_file2'],\n",
" name = 'apply_dilatemask')\n",
"highpass_workflow.connect(img2float, 'out_file',\n",
" maskfunc, 'in_file')\n",
"highpass_workflow.connect(dilatemask, 'out_file',\n",
" maskfunc, 'in_file2')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"fslmaths -mas \n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.9/fsl/bin/fslmaths prefiltered_func_data -mas mask prefiltered_func_data_thresh\n",
"```\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0yd8Glod6cd5",
"colab_type": "text"
},
"source": [
"### 7.7.8. calculate median value of the functional images across time: input image would all the volumes, and there is also a mask image we get from the thresholding (10% of the upper bound)\n",
"\n",
"```\n",
"medianval = pe.MapNode(interface = fsl.ImageStats(op_string = '-k %s -p 50'),\n",
" iterfield = ['in_file','mask_file'],\n",
" name = 'cal_intensity_scale_factor')\n",
"highpass_workflow.connect(img2float, 'out_file',\n",
" medianval, 'in_file')\n",
"highpass_workflow.connect(thresholding, 'out_file',\n",
" medianval, 'mask_file')\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"fslstats -k -p 50\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"\n",
"```\n",
"/opt/fsl/fsl-5.0.9/fsl/bin/fslstats prefiltered_func_data -k mask -p 50\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GnqOb1Vl7Hqt",
"colab_type": "text"
},
"source": [
"### 7.7.9. meanscale\n",
"\n",
"```\n",
"meanscale = pe.MapNode(interface = fsl.ImageMaths(suffix = '_intnorm'),\n",
" iterfield = ['in_file','op_string'],\n",
" name = 'meanscale')\n",
"highpass_workflow.connect(maskfunc, 'out_file',\n",
" meanscale, 'in_file')\n",
"highpass_workflow.connect(medianval, ('out_stat',getmeanscale),\n",
" meanscale, 'op_string')\n",
"```\n",
"\n",
"\n",
"#### *nipype-generated*\n",
"\n",
"```\n",
"fslmaths -mul 0.9933852571 \n",
"```\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.9/fsl/bin/fslmaths prefiltered_func_data_thresh -mul 1.06308197635 prefiltered_func_data_intnorm\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hctuIOYf7tWW",
"colab_type": "text"
},
"source": [
"### 7.7.10. calculate the mean image.nii.gz across time\n",
"\n",
"```\n",
"meanfunc = pe.MapNode(interface = fsl.ImageMaths(suffix = '_mean',\n",
" op_string = '-Tmean'),\n",
" iterfield = ['in_file'],\n",
" name = 'meanfunc')\n",
"highpass_workflow.connect(meanscale, 'out_file',\n",
" meanfunc, 'in_file')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"fslmaths -Tmean \n",
"```\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.9/fsl/bin/fslmaths prefiltered_func_data_intnorm -Tmean tempMean\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1TMFSW578Fwn",
"colab_type": "text"
},
"source": [
"### 7.7.11. apply the temporal highpass filer and then add the mean back to the filtered images\n",
"\n",
"```\n",
"hpf = pe.MapNode(interface = fsl.ImageMaths(suffix = '_tempfilt',\n",
" op_string = '-bptf %.10f -1' % (HP_freq/2/TR)),\n",
" iterfield = ['in_file'],\n",
" name = 'highpass_filering')\n",
"highpass_workflow.connect(meanscale,'out_file',\n",
" hpf, 'in_file',)\n",
"\n",
"addMean = pe.MapNode(interface = fsl.BinaryMaths(operation = 'add'),\n",
" iterfield = ['in_file','operand_file'],\n",
" name = 'addmean')\n",
"highpass_workflow.connect(hpf, 'out_file',\n",
" addMean, 'in_file')\n",
"highpass_workflow.connect(meanfunc, 'out_file',\n",
" addMean, 'operand_file')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"fslmaths \n",
"-bptf 35.2941176471 -1 # 35.295176471 = (60 / 2) / 0.85 = (hpf in sec / 2) / TR\n",
"\n",
"\n",
"fslmaths -add \n",
"```\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.9/fsl/bin/fslmaths prefiltered_func_data_intnorm -bptf 35.2941176471 -1 -add tempMean prefiltered_func_data_tempfilt\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "byHZVofb9B17",
"colab_type": "text"
},
"source": [
"### 7.7.12. connect the last output to the output node and run the workflow\n",
"\n",
"```\n",
"highpass_workflow.connect(addMean, 'out_file',\n",
" outputnode, 'filtered_file')\n",
"# define the necessary configurations for a nipype workflow: base_dir, input, output\n",
"highpass_workflow.base_dir = 'hpf'\n",
"highpass_workflow.write_graph(dotfilename='session {} run {}.dot'.format(n_session,n_run))\n",
"\n",
"highpass_workflow.inputs.inputspec.ICAed_file = os.path.abspath(file_name)\n",
"highpass_workflow.inputs.addmean.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'filtered.nii.gz'))\n",
"\n",
"highpass_workflow.run()\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2MqApPxs6n95",
"colab_type": "text"
},
"source": [
"# 8. registration structural to example_func, structural to standard, standard to example_func"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nv-e6jv7Dz7Z",
"colab_type": "text"
},
"source": [
"## 8.1. function to create a registration workflow"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zx8oSugwp6Jq",
"colab_type": "code",
"colab": {}
},
"source": [
"def create_registration_workflow(\n",
" anat_brain,\n",
" anat_head,\n",
" example_func,\n",
" standard_brain,\n",
" standard_head,\n",
" standard_mask,\n",
" workflow_name = 'registration',\n",
" output_dir = 'temp'):\n",
" from nipype.interfaces import fsl\n",
" from nipype.interfaces import utility as util\n",
" from nipype.pipeline import engine as pe\n",
" fsl.FSLCommand.set_default_output_type('NIFTI_GZ')\n",
" registration = pe.Workflow(name = 'registration')\n",
" inputnode = pe.Node(\n",
" interface = util.IdentityInterface(\n",
" fields = [\n",
" 'highres', # anat_brain\n",
" 'highres_head', # anat_head\n",
" 'example_func',\n",
" 'standard', # standard_brain\n",
" 'standard_head',\n",
" 'standard_mask'\n",
" ]),\n",
" name = 'inputspec')\n",
" outputnode = pe.Node(\n",
" interface = util.IdentityInterface(\n",
" fields = ['example_func2highres_nii_gz',\n",
" 'example_func2highres_mat',\n",
" 'linear_example_func2highres_log',\n",
" 'highres2example_func_mat',\n",
" 'highres2standard_linear_nii_gz',\n",
" 'highres2standard_mat',\n",
" 'linear_highres2standard_log',\n",
" 'highres2standard_nii_gz',\n",
" 'highres2standard_warp_nii_gz',\n",
" 'highres2standard_head_nii_gz',\n",
" # 'highres2standard_apply_warp_nii_gz',\n",
" 'highres2highres_jac_nii_gz',\n",
" 'nonlinear_highres2standard_log',\n",
" 'highres2standard_nii_gz',\n",
" 'standard2highres_mat',\n",
" 'example_func2standard_mat',\n",
" 'example_func2standard_warp_nii_gz',\n",
" 'example_func2standard_nii_gz',\n",
" 'standard2example_func_mat',\n",
" ]),\n",
" name = 'outputspec')\n",
" \"\"\"\n",
" fslmaths /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/sub-01-T1W_mprage_sag_p2_1iso_MGH_day_6_nipy_brain highres\n",
" fslmaths /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/sub-01-T1W_mprage_sag_p2_1iso_MGH_day_6_nipy_brain highres_head\n",
" fslmaths /opt/fsl/fsl-5.0.9/fsl/data/standard/MNI152_T1_2mm_brain standard\n",
" fslmaths /opt/fsl/fsl-5.0.9/fsl/data/standard/MNI152_T1_2mm standard_head\n",
" fslmaths /opt/fsl/fsl-5.0.9/fsl/data/standard/MNI152_T1_2mm_brain_mask_dil standard_mask\n",
" \"\"\"\n",
" # skip\n",
" \n",
" \"\"\"\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/flirt \n",
" -in example_func \n",
" -ref highres \n",
" -out example_func2highres \n",
" -omat example_func2highres.mat \n",
" -cost corratio \n",
" -dof 7 \n",
" -searchrx -180 180 \n",
" -searchry -180 180 \n",
" -searchrz -180 180 \n",
" -interp trilinear \n",
" \"\"\"\n",
" linear_example_func2highres = pe.MapNode(\n",
" interface = fsl.FLIRT(cost = 'corratio',\n",
" interp = 'trilinear',\n",
" dof = 7,\n",
" save_log = True,\n",
" searchr_x = [-180, 180],\n",
" searchr_y = [-180, 180],\n",
" searchr_z = [-180, 180],),\n",
" iterfield = ['in_file','reference'],\n",
" name = 'linear_example_func2highres')\n",
" registration.connect(inputnode, 'example_func',\n",
" linear_example_func2highres, 'in_file')\n",
" registration.connect(inputnode, 'highres',\n",
" linear_example_func2highres, 'reference')\n",
" registration.connect(linear_example_func2highres, 'out_file',\n",
" outputnode, 'example_func2highres_nii_gz')\n",
" registration.connect(linear_example_func2highres, 'out_matrix_file',\n",
" outputnode, 'example_func2highres_mat')\n",
" registration.connect(linear_example_func2highres, 'out_log',\n",
" outputnode, 'linear_example_func2highres_log')\n",
" \n",
" \"\"\"\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/convert_xfm \n",
" -inverse -omat highres2example_func.mat example_func2highres.mat\n",
" \"\"\"\n",
" get_highres2example_func = pe.MapNode(\n",
" interface = fsl.ConvertXFM(invert_xfm = True),\n",
" iterfield = ['in_file'],\n",
" name = 'get_highres2example_func')\n",
" registration.connect(linear_example_func2highres,'out_matrix_file',\n",
" get_highres2example_func,'in_file')\n",
" registration.connect(get_highres2example_func,'out_file',\n",
" outputnode,'highres2example_func_mat')\n",
" \n",
" \"\"\"\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/flirt \n",
" -in highres \n",
" -ref standard \n",
" -out highres2standard \n",
" -omat highres2standard.mat \n",
" -cost corratio \n",
" -dof 12 \n",
" -searchrx -180 180 \n",
" -searchry -180 180 \n",
" -searchrz -180 180 \n",
" -interp trilinear \n",
" \"\"\"\n",
" linear_highres2standard = pe.MapNode(\n",
" interface = fsl.FLIRT(cost = 'corratio',\n",
" interp = 'trilinear',\n",
" dof = 12,\n",
" save_log = True,\n",
" searchr_x = [-180, 180],\n",
" searchr_y = [-180, 180],\n",
" searchr_z = [-180, 180],),\n",
" iterfield = ['in_file','reference'],\n",
" name = 'linear_highres2standard')\n",
" registration.connect(inputnode,'highres',\n",
" linear_highres2standard,'in_file')\n",
" registration.connect(inputnode,'standard',\n",
" linear_highres2standard,'reference',)\n",
" registration.connect(linear_highres2standard,'out_file',\n",
" outputnode,'highres2standard_linear_nii_gz')\n",
" registration.connect(linear_highres2standard,'out_matrix_file',\n",
" outputnode,'highres2standard_mat')\n",
" registration.connect(linear_highres2standard,'out_log',\n",
" outputnode,'linear_highres2standard_log')\n",
" \"\"\"\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/fnirt \n",
" --iout=highres2standard_head \n",
" --in=highres_head \n",
" --aff=highres2standard.mat \n",
" --cout=highres2standard_warp \n",
" --iout=highres2standard \n",
" --jout=highres2highres_jac \n",
" --config=T1_2_MNI152_2mm \n",
" --ref=standard_head \n",
" --refmask=standard_mask \n",
" --warpres=10,10,10\n",
" \"\"\"\n",
" nonlinear_highres2standard = pe.MapNode(\n",
" interface = fsl.FNIRT(warp_resolution = (10,10,10),\n",
" config_file = \"T1_2_MNI152_2mm\"),\n",
" iterfield = ['in_file','ref_file','affine_file','refmask_file'],\n",
" name = 'nonlinear_highres2standard')\n",
" # -- iout\n",
" registration.connect(nonlinear_highres2standard,'warped_file',\n",
" outputnode,'highres2standard_head_nii_gz')\n",
" # --in\n",
" registration.connect(inputnode,'highres',\n",
" nonlinear_highres2standard,'in_file')\n",
" # --aff\n",
" registration.connect(linear_highres2standard,'out_matrix_file',\n",
" nonlinear_highres2standard,'affine_file')\n",
" # --cout\n",
" registration.connect(nonlinear_highres2standard,'fieldcoeff_file',\n",
" outputnode,'highres2standard_warp_nii_gz')\n",
" # --jout\n",
" registration.connect(nonlinear_highres2standard,'jacobian_file',\n",
" outputnode,'highres2highres_jac_nii_gz')\n",
" # --ref\n",
" registration.connect(inputnode,'standard_head',\n",
" nonlinear_highres2standard,'ref_file',)\n",
" # --refmask\n",
" registration.connect(inputnode,'standard_mask',\n",
" nonlinear_highres2standard,'refmask_file')\n",
" # log\n",
" registration.connect(nonlinear_highres2standard,'log_file',\n",
" outputnode,'nonlinear_highres2standard_log')\n",
" \"\"\"\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/applywarp \n",
" -i highres \n",
" -r standard \n",
" -o highres2standard \n",
" -w highres2standard_warp\n",
" \"\"\"\n",
" warp_highres2standard = pe.MapNode(\n",
" interface = fsl.ApplyWarp(),\n",
" iterfield = ['in_file','ref_file','field_file'],\n",
" name = 'warp_highres2standard')\n",
" registration.connect(inputnode,'highres',\n",
" warp_highres2standard,'in_file')\n",
" registration.connect(inputnode,'standard',\n",
" warp_highres2standard,'ref_file')\n",
" registration.connect(warp_highres2standard,'out_file',\n",
" outputnode,'highres2standard_nii_gz')\n",
" registration.connect(nonlinear_highres2standard,'fieldcoeff_file',\n",
" warp_highres2standard,'field_file')\n",
" \"\"\"\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/convert_xfm \n",
" -inverse -omat standard2highres.mat highres2standard.mat\n",
" \"\"\"\n",
" get_standard2highres = pe.MapNode(\n",
" interface = fsl.ConvertXFM(invert_xfm = True),\n",
" iterfield = ['in_file'],\n",
" name = 'get_standard2highres')\n",
" registration.connect(linear_highres2standard,'out_matrix_file',\n",
" get_standard2highres,'in_file')\n",
" registration.connect(get_standard2highres,'out_file',\n",
" outputnode,'standard2highres_mat')\n",
" \"\"\"\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/convert_xfm \n",
" -omat example_func2standard.mat -concat highres2standard.mat example_func2highres.mat\n",
" \"\"\"\n",
" get_exmaple_func2standard = pe.MapNode(\n",
" interface = fsl.ConvertXFM(concat_xfm = True),\n",
" iterfield = ['in_file','in_file2'],\n",
" name = 'get_exmaple_func2standard')\n",
" registration.connect(linear_example_func2highres, 'out_matrix_file',\n",
" get_exmaple_func2standard,'in_file')\n",
" registration.connect(linear_highres2standard,'out_matrix_file',\n",
" get_exmaple_func2standard,'in_file2')\n",
" registration.connect(get_exmaple_func2standard,'out_file',\n",
" outputnode,'example_func2standard_mat')\n",
" \"\"\"\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/convertwarp \n",
" --ref=standard \n",
" --premat=example_func2highres.mat \n",
" --warp1=highres2standard_warp \n",
" --out=example_func2standard_warp\n",
" \"\"\"\n",
" convertwarp_example2standard = pe.MapNode(\n",
" interface = fsl.ConvertWarp(),\n",
" iterfield = ['reference','premat','warp1'],\n",
" name = 'convertwarp_example2standard')\n",
" registration.connect(inputnode,'standard',\n",
" convertwarp_example2standard,'reference')\n",
" registration.connect(linear_example_func2highres,'out_matrix_file',\n",
" convertwarp_example2standard,'premat')\n",
" registration.connect(nonlinear_highres2standard,'fieldcoeff_file',\n",
" convertwarp_example2standard,'warp1')\n",
" registration.connect(convertwarp_example2standard,'out_file',\n",
" outputnode,'example_func2standard_warp_nii_gz')\n",
" \"\"\"\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/applywarp \n",
" --ref=standard \n",
" --in=example_func \n",
" --out=example_func2standard \n",
" --warp=example_func2standard_warp\n",
" \"\"\"\n",
" warp_example2stand = pe.MapNode(\n",
" interface = fsl.ApplyWarp(),\n",
" iterfield = ['ref_file','in_file','field_file'],\n",
" name = 'warp_example2stand')\n",
" registration.connect(inputnode,'standard',\n",
" warp_example2stand,'ref_file')\n",
" registration.connect(inputnode,'example_func',\n",
" warp_example2stand,'in_file')\n",
" registration.connect(warp_example2stand,'out_file',\n",
" outputnode,'example_func2standard_nii_gz')\n",
" registration.connect(convertwarp_example2standard,'out_file',\n",
" warp_example2stand,'field_file')\n",
" \"\"\"\n",
" /opt/fsl/fsl-5.0.10/fsl/bin/convert_xfm \n",
" -inverse -omat standard2example_func.mat example_func2standard.mat\n",
" \"\"\"\n",
" get_standard2example_func = pe.MapNode(\n",
" interface = fsl.ConvertXFM(invert_xfm = True),\n",
" iterfield = ['in_file'],\n",
" name = 'get_standard2example_func')\n",
" registration.connect(get_exmaple_func2standard,'out_file',\n",
" get_standard2example_func,'in_file')\n",
" registration.connect(get_standard2example_func,'out_file',\n",
" outputnode,'standard2example_func_mat')\n",
" \n",
" registration.base_dir = output_dir\n",
" \n",
" registration.inputs.inputspec.highres = anat_brain\n",
" registration.inputs.inputspec.highres_head= anat_head\n",
" registration.inputs.inputspec.example_func = example_func\n",
" registration.inputs.inputspec.standard = standard_brain\n",
" registration.inputs.inputspec.standard_head = standard_head\n",
" registration.inputs.inputspec.standard_mask = standard_mask\n",
" \n",
" # define all the oupput file names with the directory\n",
" registration.inputs.linear_example_func2highres.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'example_func2highres.nii.gz'))\n",
" registration.inputs.linear_example_func2highres.out_matrix_file = os.path.abspath(os.path.join(output_dir,\n",
" 'example_func2highres.mat'))\n",
" registration.inputs.linear_example_func2highres.out_log = os.path.abspath(os.path.join(output_dir,\n",
" 'linear_example_func2highres.log'))\n",
" registration.inputs.get_highres2example_func.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'highres2example_func.mat'))\n",
" registration.inputs.linear_highres2standard.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'highres2standard_linear.nii.gz'))\n",
" registration.inputs.linear_highres2standard.out_matrix_file = os.path.abspath(os.path.join(output_dir,\n",
" 'highres2standard.mat'))\n",
" registration.inputs.linear_highres2standard.out_log = os.path.abspath(os.path.join(output_dir,\n",
" 'linear_highres2standard.log'))\n",
" # --iout\n",
" registration.inputs.nonlinear_highres2standard.warped_file = os.path.abspath(os.path.join(output_dir,\n",
" 'highres2standard.nii.gz'))\n",
" # --cout\n",
" registration.inputs.nonlinear_highres2standard.fieldcoeff_file = os.path.abspath(os.path.join(output_dir,\n",
" 'highres2standard_warp.nii.gz'))\n",
" # --jout\n",
" registration.inputs.nonlinear_highres2standard.jacobian_file = os.path.abspath(os.path.join(output_dir,\n",
" 'highres2highres_jac.nii.gz'))\n",
" registration.inputs.nonlinear_highres2standard.log_file = os.path.abspath(os.path.join(output_dir,\n",
" 'nonlinear_highres2standard.log'))\n",
" registration.inputs.warp_highres2standard.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'highres2standard.nii.gz'))\n",
" registration.inputs.get_standard2highres.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'standard2highres.mat'))\n",
" registration.inputs.get_exmaple_func2standard.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'example_func2standard.mat'))\n",
" registration.inputs.convertwarp_example2standard.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'example_func2standard_warp.nii.gz'))\n",
" registration.inputs.warp_example2stand.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'example_func2standard.nii.gz'))\n",
" registration.inputs.get_standard2example_func.out_file = os.path.abspath(os.path.join(output_dir,\n",
" 'standard2example_func.mat'))\n",
" return registration"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "d-5tB_jDEbhe",
"colab_type": "text"
},
"source": [
"## 8.2. call the function to create the workflow"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1_5Wqk9X6hIr",
"colab_type": "code",
"outputId": "9b6a2552-55da-4c71-aa5d-3b75ae4a01a8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 70
}
},
"source": [
"import os\n",
"func_dir = ''\n",
"anat_dir = '../../data/MRI/{}/anat/'\n",
"ref_dir = 'example_func.nii.gz'\n",
"standard_brain = 'MNI152_T1_2mm_brain.nii.gz'\n",
"standard_head = 'MNI152_T1_2mm.nii.gz'\n",
"standard_mask = 'MNI152_T1_2mm_brain_mask_dil.nii.gz'\n",
"\n",
"sub = 'sub-01'\n",
"anat_brain = 'T1_brain.nii'\n",
"anat_head = 'T1.nii'\n",
"func_ref = ref_dir.format(sub,'02',sub,'01')\n",
"\n",
"session = '02'\n",
"run = '01'\n",
"\n",
"registration = create_registration_workflow(\n",
" anat_brain = anat_brain,\n",
" anat_head = anat_head,\n",
" example_func = func_ref,\n",
" standard_brain = standard_brain,\n",
" standard_head = standard_head,\n",
" standard_mask = standard_mask,\n",
" workflow_name = 'registration')\n",
"\n",
"output_dir = 'tmp'\n",
"if not os.path.exists(output_dir):\n",
" os.mkdir(output_dir)\n",
"\n",
"\n",
"\n",
"registration.write_graph()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"190715-19:20:58,86 nipype.workflow INFO:\n",
"\t Generated workflow graph: temp/registration/graph.png (graph2use=hierarchical, simple_form=True).\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'temp/registration/graph.png'"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oBkLRqIdMbaz",
"colab_type": "text"
},
"source": [
"## 8.3. plot the workflow"
]
},
{
"cell_type": "code",
"metadata": {
"id": "s7zP-EgR6ksM",
"colab_type": "code",
"outputId": "7b19258b-74d7-4fa7-895b-6b224d34240d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 257
}
},
"source": [
"Image(os.path.join('temp/registration/graph.png'),width=800)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
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pKyu7d+/e58+fBwYGlpeXd3R0sFisrqm0goKCubl5WVmZ6Bd7vuDexs1vnbt+/fr29vb7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kWLoqKiQIJAY8rJkydjYmJiY2PV1NSwzgUDXFxcLi4uZWVlmzdvJhKJ06ZNS05OxjopCGls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\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NZrPZbB6Px2KxOBwOmsuOyWRiswfb29s7ODgQiUQikejg4ODs7Ozg4ODk5EQgEBwdHW1s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\nKd5lrge+VKJLLR2Y6zW/1OrX4/FQJwdq70CNkh0Oh81m83q91dXVXM5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"text/plain": [
""
]
},
"metadata": {
"tags": [],
"image/png": {
"width": 800
}
},
"execution_count": 13
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1PTwXPKN8p2L",
"colab_type": "text"
},
"source": [
"## 8.4. How do we map the nipype function to the FSL GUI commands?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cVHp1UoI8xBX",
"colab_type": "text"
},
"source": [
"### 8.4.1. initialize the input and output spaces\n",
"```\n",
"inputnode = pe.Node(\n",
" interface = util.IdentityInterface(\n",
" fields = [\n",
" 'highres', # anat_brain BET T1\n",
" 'highres_head', # anat_head \n",
" 'example_func',\n",
" 'standard', # standard_brain\n",
" 'standard_head',\n",
" 'standard_mask'\n",
" ]),\n",
" name = 'inputspec')\n",
" outputnode = pe.Node(\n",
" interface = util.IdentityInterface(\n",
" fields = ['example_func2highres_nii_gz',\n",
" 'example_func2highres_mat',\n",
" 'linear_example_func2highres_log',\n",
" 'highres2example_func_mat',\n",
" 'highres2standard_linear_nii_gz',\n",
" 'highres2standard_mat',\n",
" 'linear_highres2standard_log',\n",
" 'highres2standard_nii_gz',\n",
" 'highres2standard_warp_nii_gz',\n",
" 'highres2standard_head_nii_gz',\n",
" # 'highres2standard_apply_warp_nii_gz',\n",
" 'highres2highres_jac_nii_gz',\n",
" 'nonlinear_highres2standard_log',\n",
" 'highres2standard_nii_gz',\n",
" 'standard2highres_mat',\n",
" 'example_func2standard_mat',\n",
" 'example_func2standard_warp_nii_gz',\n",
" 'example_func2standard_nii_gz',\n",
" 'standard2example_func_mat',\n",
" ]),\n",
" name = 'outputspec')\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VqoOVQx6iuSg",
"colab_type": "text"
},
"source": [
"#### 8.4.1.1. moving things around in FSL, but I would just skip these because I could simply specify those images with `os`"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "N_cMawK1i6YZ",
"colab_type": "text"
},
"source": [
"**FSL commands**\n",
"```\n",
"fslmaths /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/sub-01-T1W_mprage_sag_p2_1iso_MGH_day_6_nipy_brain highres\n",
"fslmaths /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/sub-01-T1W_mprage_sag_p2_1iso_MGH_day_6_nipy_brain highres_head\n",
"fslmaths /opt/fsl/fsl-5.0.9/fsl/data/standard/MNI152_T1_2mm_brain standard\n",
"fslmaths /opt/fsl/fsl-5.0.9/fsl/data/standard/MNI152_T1_2mm standard_head\n",
"fslmaths /opt/fsl/fsl-5.0.9/fsl/data/standard/MNI152_T1_2mm_brain_mask_dil standard_mask\n",
"\n",
"```\n",
"\n",
"**We can do the same thing with nipype:**\n",
"```\n",
"fslmaths = fsl.ImageMaths()\n",
"fslmaths.inputs.in_file = anat_brain\n",
"fslmaths.inputs.out_file = os.path.abspath(os.path.join(output_dir,'highres.nii.gz'))\n",
"fslmaths.cmdline\n",
"fslmaths.run()\n",
"\n",
"fslmaths = fsl.ImageMaths()\n",
"fslmaths.inputs.in_file = anat_head\n",
"fslmaths.inputs.out_file = os.path.abspath(os.path.join(output_dir,'highres_head.nii.gz'))\n",
"fslmaths.cmdline\n",
"fslmaths.run()\n",
"\n",
"fslmaths = fsl.ImageMaths()\n",
"fslmaths.inputs.in_file = standard_brain\n",
"fslmaths.inputs.out_file = os.path.abspath(os.path.join(output_dir,'standard.nii.gz'))\n",
"fslmaths.cmdline\n",
"fslmaths.run()\n",
"\n",
"fslmaths = fsl.ImageMaths()\n",
"fslmaths.inputs.in_file = standard_head\n",
"fslmaths.inputs.out_file = os.path.abspath(os.path.join(output_dir,'standard_head.nii.gz'))\n",
"fslmaths.cmdline\n",
"fslmaths.run()\n",
"\n",
"fslmaths = fsl.ImageMaths()\n",
"fslmaths.inputs.in_file = standard_mask\n",
"fslmaths.inputs.out_file = os.path.abspath(os.path.join(output_dir,'standard_mask.nii.gz'))\n",
"fslmaths.cmdline\n",
"fslmaths.run()\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OzjoMuS8-I4R",
"colab_type": "text"
},
"source": [
"### 8.4.2. example2highres_flirt: FLIRT mapping from example_func to T1 (was wrong but now correct for x, y, z range of search)\n",
"```\n",
"linear_example_func2highres = pe.MapNode(\n",
" interface = fsl.FLIRT(cost = 'corratio',\n",
" interp = 'trilinear',\n",
" dof = 7,\n",
" save_log = True,\n",
" searchr_x = [-180, 180],\n",
" searchr_y = [-180, 180],\n",
" searchr_z = [-180, 180],),\n",
" iterfield = ['in_file','reference'],\n",
" name = 'linear_example_func2highres')\n",
"registration.connect(inputnode, 'example_func',\n",
" linear_example_func2highres, 'in_file')\n",
"registration.connect(inputnode, 'highres',\n",
" linear_example_func2highres, 'reference')\n",
"registration.connect(linear_example_func2highres, 'out_file',\n",
" outputnode, 'example_func2highres_nii_gz')\n",
"registration.connect(linear_example_func2highres, 'out_matrix_file',\n",
" outputnode, 'example_func2highres_mat')\n",
"registration.connect(linear_example_func2highres, 'out_log',\n",
" outputnode, 'linear_example_func2highres_log')\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"flirt \n",
" -in example_func.nii.gz \n",
" -ref T1.nii.gz \n",
" -out example_func2highres.nii.gz \n",
" -omat example_func2highres.mat \n",
" -cost corratio \n",
" -dof 7 \n",
" -interp trilinear \n",
" -searchrx -180 180 \n",
" -searchry -180 180 \n",
" -searchrz -180 180 \n",
" -verbose 1\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/flirt \n",
" -in example_func # \n",
" -ref highres # \n",
" -out example_func2highres # ? an image.nii.gz?\n",
" -omat example_func2highres.mat # transformation matrix\n",
" -cost corratio \n",
" -dof 7 \n",
" -searchrx -180 180 \n",
" -searchry -180 180 \n",
" -searchrz -180 180 \n",
" -interp trilinear \n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rlRssm7BBXm1",
"colab_type": "text"
},
"source": [
"### 8.4.3. inverse_example2highres: get inverse transformation matrix\n",
"```\n",
"get_highres2example_func = pe.MapNode(\n",
" interface = fsl.ConvertXFM(invert_xfm = True),\n",
" iterfield = ['in_file'],\n",
" name = 'get_highres2example_func')\n",
" registration.connect(linear_example_func2highres,'out_matrix_file',\n",
" get_highres2example_func,'in_file')\n",
" registration.connect(get_highres2example_func,'out_file',\n",
" outputnode,'highres2example_func_mat')\n",
"```\n",
"#### *nipype-generated*\n",
"```\n",
"convert_xfm \n",
"-omat /reg/highres2example_func.mat \n",
"-inverse /reg/example_func2highres.mat\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/convert_xfm \n",
" -inverse -omat highres2example_func.mat example_func2highres.mat\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NHSYmW7y_HUA",
"colab_type": "text"
},
"source": [
"### 8.4.4. highres2standard_flirt: map structural image to standard, thus, it takes 12 degree of freedom\n",
"```\n",
"linear_highres2standard = pe.MapNode(\n",
" interface = fsl.FLIRT(cost = 'corratio',\n",
" interp = 'trilinear',\n",
" dof = 12,\n",
" save_log = True,\n",
" searchr_x = [-180, 180],\n",
" searchr_y = [-180, 180],\n",
" searchr_z = [-180, 180],),\n",
" iterfield = ['in_file','reference'],\n",
" name = 'linear_highres2standard')\n",
"registration.connect(inputnode,'highres',\n",
" linear_highres2standard,'in_file')\n",
"registration.connect(inputnode,'standard',\n",
" linear_highres2standard,'reference',)\n",
"registration.connect(linear_highres2standard,'out_file',\n",
" outputnode,'highres2standard_linear_nii_gz')\n",
"registration.connect(linear_highres2standard,'out_matrix_file',\n",
" outputnode,'highres2standard_mat')\n",
"registration.connect(linear_highres2standard,'out_log',\n",
" outputnode,'linear_highres2standard_log')\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"flirt \n",
" -in /standard/MNI152_T1_2mm_brain.nii.gz \n",
" -out /reg/highres2standard.nii.gz \n",
" -omat /reg/highres2standard.mat \n",
" -cost corratio \n",
" -dof 12 \n",
" -interp trilinear \n",
" -searchrx -180 180 \n",
" -searchry -180 180 \n",
" -searchrz -180 180 \n",
" -verbose 1\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/flirt \n",
" -in highres \n",
" -ref standard \n",
" -out highres2standard \n",
" -omat highres2standard.mat \n",
" -cost corratio \n",
" -dof 12 \n",
" -searchrx -180 180 \n",
" -searchry -180 180 \n",
" -searchrz -180 180 \n",
" -interp trilinear\n",
"```\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7SBuuYlVATBd",
"colab_type": "text"
},
"source": [
"### 8.4.5. highres2standard_fnirt: nonlinear mapping from structural to standard\n",
"```\n",
"nonlinear_highres2standard = pe.MapNode(\n",
" interface = fsl.FNIRT(warp_resolution = (10,10,10),\n",
" config_file = \"T1_2_MNI152_2mm\"),\n",
" iterfield = ['in_file','ref_file','affine_file','refmask_file'],\n",
" name = 'nonlinear_highres2standard')\n",
"# -- iout\n",
"registration.connect(nonlinear_highres2standard,'warped_file',\n",
" outputnode,'highres2standard_head_nii_gz')\n",
"# --in\n",
"registration.connect(inputnode,'highres',\n",
" nonlinear_highres2standard,'in_file')\n",
"# --aff\n",
"registration.connect(linear_highres2standard,'out_matrix_file',\n",
" nonlinear_highres2standard,'affine_file')\n",
"# --cout\n",
"registration.connect(nonlinear_highres2standard,'fieldcoeff_file',\n",
" outputnode,'highres2standard_warp_nii_gz')\n",
"# --jout\n",
"registration.connect(nonlinear_highres2standard,'jacobian_file',\n",
" outputnode,'highres2highres_jac_nii_gz')\n",
"# --ref\n",
"registration.connect(inputnode,'standard_head',\n",
" nonlinear_highres2standard,'ref_file',)\n",
"# --refmask\n",
"registration.connect(inputnode,'standard_mask',\n",
" nonlinear_highres2standard,'refmask_file')\n",
"# log\n",
"registration.connect(nonlinear_highres2standard,'log_file',\n",
" outputnode,'nonlinear_highres2standard_log')\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"fnirt \n",
" --aff=/reg/highres2standard.mat \n",
" --config=T1_2_MNI152_2mm \n",
" --cout=/reg/highres2standard_warp.nii.gz \n",
" --in=T1.nii \n",
" --jout=/reg/highres2highres_jac.nii.gz \n",
" --logout=log.txt \n",
" --ref=/standard/MNI152_T1_2mm.nii.gz \n",
" --refmask=/standard/MNI152_T1_2mm_brain_mask_dil.nii.gz \n",
" --warpres=10,10,10 \n",
" --iout=highres2standard_head.nii.gz\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/fnirt \n",
" --iout=highres2standard_head \n",
" --in=highres_head \n",
" --aff=highres2standard.mat \n",
" --cout=highres2standard_warp \n",
" --iout=highres2standard \n",
" --jout=highres2highres_jac \n",
" --config=T1_2_MNI152_2mm \n",
" --ref=standard_head \n",
" --refmask=standard_mask \n",
" --warpres=10,10,10\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "enQ24N4GA3dE",
"colab_type": "text"
},
"source": [
"### 8.4.6. warp_highres2standard:\n",
"```\n",
"warp_highres2standard = pe.MapNode(\n",
" interface = fsl.ApplyWarp(),\n",
" iterfield = ['in_file','ref_file','field_file'],\n",
" name = 'warp_highres2standard')\n",
"registration.connect(inputnode,'highres',\n",
" warp_highres2standard,'in_file')\n",
"registration.connect(inputnode,'standard',\n",
" warp_highres2standard,'ref_file')\n",
"registration.connect(warp_highres2standard,'out_file',\n",
" outputnode,'highres2standard_nii_gz')\n",
"registration.connect(nonlinear_highres2standard,'fieldcoeff_file',\n",
" warp_highres2standard,'field_file')\n",
"```\n",
"#### *nipype-generate*\n",
"```\n",
"applywarp \n",
" --in=T1.nii.gz \n",
" --ref=/standard/MNI152_T1_2mm_brain.nii.gz \n",
" --out=/reg/highres2standard.nii.gz \n",
" --warp=/reg/highres2standard_warp.nii.gz\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/applywarp \n",
" -i highres \n",
" -r standard \n",
" -o highres2standard \n",
" -w highres2standard_warp\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "v0n9BNDvCJlU",
"colab_type": "text"
},
"source": [
"### 8.4.7. inverse_highres2standard:\n",
"```\n",
"get_standard2highres = pe.MapNode(\n",
" interface = fsl.ConvertXFM(invert_xfm = True),\n",
" iterfield = ['in_file'],\n",
" name = 'get_standard2highres')\n",
"registration.connect(linear_highres2standard,'out_matrix_file',\n",
" get_standard2highres,'in_file')\n",
"registration.connect(get_standard2highres,'out_file',\n",
" outputnode,'standard2highres_mat')\n",
"```\n",
"#### *nipype-generate*\n",
"```\n",
"convert_xfm \n",
"-omat /reg/standard2highres.mat \n",
"-inverse /reg/highres2standard.mat\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/convert_xfm \n",
" -inverse -omat standard2highres.mat highres2standard.mat\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wAxh3x6GCZiu",
"colab_type": "text"
},
"source": [
"### 8.4.8. example2standard - surprisingly, the order of file1 and file2 is important:\n",
"```\n",
"get_exmaple_func2standard = pe.MapNode(\n",
" interface = fsl.ConvertXFM(concat_xfm = True),\n",
" iterfield = ['in_file','in_file2'],\n",
" name = 'get_exmaple_func2standard')\n",
"registration.connect(linear_example_func2highres, 'out_matrix_file',\n",
" get_exmaple_func2standard,'in_file')\n",
"registration.connect(linear_highres2standard,'out_matrix_file',\n",
" get_exmaple_func2standard,'in_file2')\n",
"registration.connect(get_exmaple_func2standard,'out_file',\n",
" outputnode,'example_func2standard_mat')\n",
"```\n",
"\n",
"#### *nipype-generate*\n",
"```\n",
"convert_xfm \n",
" -omat /reg/example_func2standard.mat \n",
" -concat /reg/highres2standard.mat /reg/example_func2highres.mat \n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/convert_xfm \n",
" -omat example_func2standard.mat \n",
" -concat highres2standard.mat \n",
" example_func2highres.mat\n",
"``` "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HAZ0sTOfCvzs",
"colab_type": "text"
},
"source": [
"### 8.4.9. convertwarp_standard_brain:\n",
"```\n",
"convertwarp_example2standard = pe.MapNode(\n",
" interface = fsl.ConvertWarp(),\n",
" iterfield = ['reference','premat','warp1'],\n",
" name = 'convertwarp_example2standard')\n",
"registration.connect(inputnode,'standard',\n",
" convertwarp_example2standard,'reference')\n",
"registration.connect(linear_example_func2highres,'out_matrix_file',\n",
" convertwarp_example2standard,'premat')\n",
"registration.connect(nonlinear_highres2standard,'fieldcoeff_file',\n",
" convertwarp_example2standard,'warp1')\n",
"registration.connect(convertwarp_example2standard,'out_file',\n",
" outputnode,'example_func2standard_warp_nii_gz')\n",
"```\n",
"#### *nipype-generate*\n",
"```\n",
"convertwarp \n",
" --ref=/standard/MNI152_T1_2mm_brain.nii.gz \n",
" --premat=/reg/example_func2highres.mat \n",
" --warp1=/reg/highres2standard_warp.nii.gz \n",
" --out=/reg/example_func2standard_warp.nii.gz\n",
"```\n",
"\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/convertwarp \n",
" --ref=standard \n",
" --premat=example_func2highres.mat \n",
" --warp1=highres2standard_warp \n",
" --out=example_func2standard_warp\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Jr2gvq5BDFl1",
"colab_type": "text"
},
"source": [
"### 8.4.10. warp_example2stand:\n",
"```\n",
"warp_example2stand = pe.MapNode(\n",
" interface = fsl.ApplyWarp(),\n",
" iterfield = ['ref_file','in_file','field_file'],\n",
" name = 'warp_example2stand')\n",
"registration.connect(inputnode,'standard',\n",
" warp_example2stand,'ref_file')\n",
"registration.connect(inputnode,'example_func',\n",
" warp_example2stand,'in_file')\n",
"registration.connect(warp_example2stand,'out_file',\n",
" outputnode,'example_func2standard_nii_gz')\n",
"registration.connect(convertwarp_example2standard,'out_file',\n",
" warp_example2stand,'field_file')\n",
"```\n",
"\n",
"#### *nipype-generate*\n",
"```\n",
"applywarp \n",
" --in=/func/example_func.nii.gz \n",
" --ref=/standard/MNI152_T1_2mm_brain.nii.gz \n",
" --out=/reg/registration/warp_standard_brain/mapflow/_warp_standard_brain0/example_func_warp.nii.gz \n",
" --warp=/reg/example_func2standard_warp.nii.gz\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/applywarp \n",
" --ref=standard \n",
" --in=example_func \n",
" --out=example_func2standard \n",
" --warp=example_func2standard_warp\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mqhNSWhwDW0r",
"colab_type": "text"
},
"source": [
"### 8.4.11. inverse_example2standard:\n",
"```\n",
"get_standard2example_func = pe.MapNode(\n",
" interface = fsl.ConvertXFM(invert_xfm = True),\n",
" iterfield = ['in_file'],\n",
" name = 'get_standard2example_func')\n",
"registration.connect(get_exmaple_func2standard,'out_file',\n",
" get_standard2example_func,'in_file')\n",
"registration.connect(get_standard2example_func,'out_file',\n",
" outputnode,'standard2example_func_mat')\n",
"```\n",
"#### *nipype-generate*\n",
"```\n",
"convert_xfm \n",
" -omat /reg/standard2example_func.mat \n",
" -inverse \n",
" /reg/example_func2standard.mat\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/convert_xfm \n",
" -inverse \n",
" -omat standard2example_func.mat \n",
" example_func2standard.mat\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4pPUvRWwm1h8",
"colab_type": "text"
},
"source": [
"### 8.4.12. plot the co-registrations - must be explicit not called from a function"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UmuDoys9m6s-",
"colab_type": "text"
},
"source": [
"```\n",
"######################\n",
"###### plotting ######\n",
"example_func2highres = os.path.abspath(os.path.join(output_dir,\n",
" 'example_func2highres'))\n",
"example_func2standard = os.path.abspath(os.path.join(output_dir,\n",
" \"example_func2standard\"))\n",
"highres2standard = os.path.abspath(os.path.join(output_dir,\n",
" 'highres2standard'))\n",
"highres = os.path.abspath(anat_brain)\n",
"standard = os.path.abspath(standard_brain)\n",
"\n",
"plot_example_func2highres = f\"\"\"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/slicer {example_func2highres} {highres} -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png {example_func2highres}1.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/slicer {highres} {example_func2highres} -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png {example_func2highres}2.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/pngappend {example_func2highres}1.png - {example_func2highres}2.png {example_func2highres}.png; \n",
"/bin/rm -f sl?.png {example_func2highres}2.png\n",
"/bin/rm {example_func2highres}1.png\n",
"\"\"\".replace(\"\\n\",\" \")\n",
"\n",
"plot_highres2standard = f\"\"\"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/slicer {highres2standard} {standard} -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png {highres2standard}1.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/slicer {standard} {highres2standard} -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png {highres2standard}2.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/pngappend {highres2standard}1.png - {highres2standard}2.png {highres2standard}.png; \n",
"/bin/rm -f sl?.png {highres2standard}2.png\n",
"/bin/rm {highres2standard}1.png\n",
"\"\"\".replace(\"\\n\",\" \")\n",
"\n",
"plot_example_func2standard = f\"\"\"\n",
"/opt/fsl/fsl-5.0.10/fsl/bin/slicer {example_func2standard} {standard} -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png {example_func2standard}1.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/slicer {standard} {example_func2standard} -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png {example_func2standard}2.png ; \n",
"/opt/fsl/fsl-5.0.10/fsl/bin/pngappend {example_func2standard}1.png - {example_func2standard}2.png {example_func2standard}.png; \n",
"/bin/rm -f sl?.png {example_func2standard}2.png\n",
"\"\"\".replace(\"\\n\",\" \")\n",
"for cmdline in [plot_example_func2highres,plot_example_func2standard,plot_highres2standard]:\n",
" os.system(cmdline)\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0EommK3WM-hl",
"colab_type": "text"
},
"source": [
"# 9. Recon-all (freesurfer): [detail descption](http://www.alivelearn.net/?p=175)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "A5F-kg2U8nu2",
"colab_type": "code",
"outputId": "5a71501d-5c1a-4ecd-d3a0-50c2dd6dea46",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"from nipype.interfaces.freesurfer import ReconAll\n",
"import os\n",
"subject_dir = '/content/reconall'\n",
"if not os.path.exists(subject_dir):\n",
" os.mkdir(subject_dir)\n",
"sub = 'sub-01'\n",
"reconall = ReconAll()\n",
"reconall.inputs.subject_id = sub # can be anything\n",
"reconall.inputs.directive = 'all' # process directive\n",
"reconall.inputs.subjects_dir = subject_dir\n",
"reconall.inputs.T1_files = os.path.abspath('T1.nii')\n",
"print(reconall.cmdline)\n",
"# reconall.run()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"recon-all -all -i /content/T1.nii -subjid sub-01 -sd /content/reconall\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JBPX57b8OLrp",
"colab_type": "text"
},
"source": [
"\n",
"## 9.1. recon-all command line: takes a while to finish running\n",
"\n",
"### *nipype-generated*\n",
"```\n",
"recon-all \n",
" -all \n",
" -i /content/T1.nii \n",
" -subjid sub-01 \n",
" -sd /content/reconall\n",
"```\n",
"### *freesurfer command*\n",
"```\n",
"recon-all \\\n",
" -i \\\n",
" -s \\\n",
" -sd \\\n",
" -all <'all' or 'autorecon1' or 'autorecon2' or\n",
" 'autorecon2-volonly' or 'autorecon2-perhemi' or\n",
" 'autorecon2-inflate1' or 'autorecon2-cp' or 'autorecon2-wm' or\n",
" 'autorecon3' or 'autorecon3-T2pial' or 'autorecon-pial' or\n",
" 'autorecon-hemi' or 'localGI' or 'qcache'>\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ibffvytWPh4G",
"colab_type": "text"
},
"source": [
"## 9.2. Extract ROIs from finished Recon-all jobs"
]
},
{
"cell_type": "code",
"metadata": {
"id": "YGlhtw1UNZ5D",
"colab_type": "code",
"outputId": "1aba2c1e-74dd-4359-8f68-f108cf1db831",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
}
},
"source": [
"import os\n",
"import pandas as pd\n",
"from nipype.interfaces import freesurfer,fsl\n",
"fsl.FSLCommand.set_default_output_type('NIFTI_GZ')\n",
"\n",
"freesurfer_list = pd.read_csv('FreesurferLTU.csv')\n",
"print(freesurfer_list.iloc[1000:1010])"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
" #No. Label Name R G B A\n",
"1000 11112 ctx_lh_G_front_inf-Opercular 220 20 100 0\n",
"1001 11113 ctx_lh_G_front_inf-Orbital 140 60 60 0\n",
"1002 11114 ctx_lh_G_front_inf-Triangul 180 220 140 0\n",
"1003 11115 ctx_lh_G_front_middle 140 100 180 0\n",
"1004 11116 ctx_lh_G_front_sup 180 20 140 0\n",
"1005 11117 ctx_lh_G_Ins_lg_and_S_cent_ins 23 10 10 0\n",
"1006 11118 ctx_lh_G_insular_short 225 140 140 0\n",
"1007 11119 ctx_lh_G_occipital_middle 180 60 180 0\n",
"1008 11120 ctx_lh_G_occipital_sup 20 220 60 0\n",
"1009 11121 ctx_lh_G_oc-temp_lat-fusifor 60 20 140 0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dIr6Bzs2P5kL",
"colab_type": "text"
},
"source": [
"### 9.2.1. codes that cannot be run on colab, because you have to setup fsl, freesurfer and their corresponding environments\n",
"```\n",
"# setup the environment for Freesurfer called by Nipype\n",
"os.environ['SUBJECTS_DIR'] = os.path.abspath('{}/'.format(sub))\n",
"\n",
"# All information of the extracted ROIs\n",
"in_file = os.path.abspath('{}/mri/aparc+aseg.mgz'.format(sub))\n",
"\n",
"# orignal T1 scan with the skull but in .mgz format that Freesurfer reads\n",
"original = os.path.abspath('{}/anat/{}/mri/orig/001.mgz'.format(sub))\n",
"\n",
"# define output directory\n",
"ROI_anat_dir = '../../data/MRI/{}/anat/ROIs'.format(sub)\n",
"if not os.path.exists(ROI_anat_dir):\n",
" os.mkdir(ROI_anat_dir)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vFoOYLEuWRwU",
"colab_type": "text"
},
"source": [
"### 9.2.3. define roi names and pick them from the freesurferLUT.csv file. Since we only want to have the cortical regions, we need to specify \"ctx\" in the ROI names. Additionally, we only want to have those ROIs created by mri_aparc2aseg, thus, we need to specify the second digit of the label number must be \"0\""
]
},
{
"cell_type": "code",
"metadata": {
"id": "a29diEPMP06C",
"colab_type": "code",
"outputId": "0312953c-ff01-4af5-ef87-bae7170deb23",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 503
}
},
"source": [
"roi_names = \"\"\"fusiform\n",
"inferiorparietal\n",
"superiorparietal\n",
"inferiortemporal\n",
"lateraloccipital\n",
"lingual\n",
"parahippocampal\n",
"pericalcarine\n",
"precuneus\n",
"superiorfrontal\n",
"parsopercularis\n",
"parsorbitalis\n",
"parstriangularis\n",
"rostralmiddlefrontal\"\"\"\n",
"# preallocation\n",
"idx_label,label_names = [],[]\n",
"\n",
"for name in roi_names.split('\\n'):# for each of the roi name\n",
" for label_name in freesurfer_list['Label Name']: # for each of the roi defined by Freesurfer\n",
" if (name in label_name) and ('ctx' in label_name): # if the roi name matches to the Freesurfer defining one and it is the cortical region\n",
" if str(idx)[1] == '0': # if the 2nd digit of the label number is \"0\", meaning it is segmented by mri_aparc2aseg\n",
" idx = freesurfer_list[freesurfer_list['Label Name'] == label_name]['#No.'].values[0]\n",
" print(f'label No. is {idx},ROI name is {label_name}')\n",
" idx_label.append(idx)\n",
" label_names.append(label_name)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"label No. is 1007,ROI name is ctx-lh-fusiform\n",
"label No. is 2007,ROI name is ctx-rh-fusiform\n",
"label No. is 1008,ROI name is ctx-lh-inferiorparietal\n",
"label No. is 2008,ROI name is ctx-rh-inferiorparietal\n",
"label No. is 1029,ROI name is ctx-lh-superiorparietal\n",
"label No. is 2029,ROI name is ctx-rh-superiorparietal\n",
"label No. is 1009,ROI name is ctx-lh-inferiortemporal\n",
"label No. is 2009,ROI name is ctx-rh-inferiortemporal\n",
"label No. is 1011,ROI name is ctx-lh-lateraloccipital\n",
"label No. is 2011,ROI name is ctx-rh-lateraloccipital\n",
"label No. is 1013,ROI name is ctx-lh-lingual\n",
"label No. is 2013,ROI name is ctx-rh-lingual\n",
"label No. is 1016,ROI name is ctx-lh-parahippocampal\n",
"label No. is 2016,ROI name is ctx-rh-parahippocampal\n",
"label No. is 1021,ROI name is ctx-lh-pericalcarine\n",
"label No. is 2021,ROI name is ctx-rh-pericalcarine\n",
"label No. is 1025,ROI name is ctx-lh-precuneus\n",
"label No. is 2025,ROI name is ctx-rh-precuneus\n",
"label No. is 1028,ROI name is ctx-lh-superiorfrontal\n",
"label No. is 2028,ROI name is ctx-rh-superiorfrontal\n",
"label No. is 1018,ROI name is ctx-lh-parsopercularis\n",
"label No. is 2018,ROI name is ctx-rh-parsopercularis\n",
"label No. is 1019,ROI name is ctx-lh-parsorbitalis\n",
"label No. is 2019,ROI name is ctx-rh-parsorbitalis\n",
"label No. is 1020,ROI name is ctx-lh-parstriangularis\n",
"label No. is 2020,ROI name is ctx-rh-parstriangularis\n",
"label No. is 1027,ROI name is ctx-lh-rostralmiddlefrontal\n",
"label No. is 2027,ROI name is ctx-rh-rostralmiddlefrontal\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7PFZ2oJbXaW4",
"colab_type": "text"
},
"source": [
"### 9.2.4. start the for-loop\n",
"```\n",
"for idx,label_name in zip(idx_label,label_names):\n",
"\n",
" binary_file = os.path.abspath(os.path.join(ROI_anat_dir,'{}.nii.gz'.format(label_name)))\n",
"\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xpXRDOYOJlPt",
"colab_type": "text"
},
"source": [
"### 9.2.5. generate a binarized mask image in nii.gz format from the freesurfer label file if the name of the ROI matches to what we want\n",
"```\n",
"binarizer = freesurfer.Binarize(in_file = in_file,\n",
" match = [idx],\n",
" binary_file = binary_file)\n",
"print(binarizer.cmdline)\n",
"binarizer.run()\n",
"```\n",
" \n",
"#### *nipype-generated*\n",
"```\n",
"mri_binarize \n",
" --o # can be loaded in fslview but no align with the T1.nii scan\n",
" --i mri/aparc+aseg.mgz # freesurfer segmented region labels\n",
" --match <2027> # an integer that matches to the ROI we want to have\n",
"```\n",
"\n",
"#### *Freesurfer command, FSL GUI*\n",
"```\n",
"mri_binarize \n",
" --i \n",
" --match # interger\n",
" --o # output image that is not aligned with FSL dimensionality (flipped y axis)\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-ftpu8t3J2u4",
"colab_type": "text"
},
"source": [
"### 9.2.6. swap the dimensionality that was defined in freesurfer to the dimensionality that is defined in fsl\n",
"```\n",
"fsl_swapdim = fsl.SwapDimensions(new_dims = ('x', 'z', '-y'),)\n",
"fsl_swapdim.inputs.in_file = binarizer.inputs.binary_file\n",
"fsl_swapdim.inputs.out_file = binarizer.inputs.binary_file.replace('.nii.gz', '_fsl.nii.gz')\n",
"print(fsl_swapdim.cmdline)\n",
"fsl_swapdim.run()\n",
"```\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"fslswapdim \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/ROIs/ctx-rh-rostralmiddlefrontal.nii.gz # image with wrong x,y,z oriantation\n",
" x z -y \n",
" /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/ROIs/ctx-rh-rostralmiddlefrontal_fsl.nii.gz\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"fslswapdim \n",
" \n",
" x z -y \n",
" # rename it with a suffix so that we keep both\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4B1ZlGbEKVBO",
"colab_type": "text"
},
"source": [
"### 9.2.7.convert .nii.gz reslice output to match file\n",
"```\n",
"mc = freesurfer.MRIConvert()\n",
"mc.inputs.in_file = fsl_swapdim.inputs.out_file\n",
"mc.inputs.reslice_like = original\n",
"mc.inputs.out_file = fsl_swapdim.inputs.out_file\n",
"print(mc.cmdline)\n",
"mc.run()\n",
"```\n",
"\n",
"\n",
"#### *nipype-generated*\n",
"```\n",
"mri_convert \n",
" --reslice_like /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/sub-01/mri/orig/001.mgz # resclicing base image, the original T1 scan in freesurfer format\n",
" --input_volume /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/ROIs/ctx-rh-rostralmiddlefrontal_fsl.nii.gz \n",
"# roi_mask.nii.gz that can be loaded in fslview align with the T1.nii scan but off by a bit\n",
" --output_volume /bcbl/home/public/Consciousness/uncon_feat/data/MRI/sub-01/anat/ROIs/ctx-rh-rostralmiddlefrontal_fsl.nii.gz # output_roi_mask.nii.gz\n",
"```\n",
"\n",
"#### *FSL GUI*\n",
"```\n",
"mri_convert \n",
" -i \n",
" -rl # reslice output to match file 001.mgz\n",
" -o # just overwrite it\n",
"```\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Bl8cepTrab4B",
"colab_type": "text"
},
"source": [
"# 10. transform ROIs from structural space to BOLD space"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wArfqQ_wDz4j",
"colab_type": "text"
},
"source": [
"## 10.1. the simple workflow steps"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QFuYIPGWD39H",
"colab_type": "code",
"outputId": "c726a776-8d10-4d5c-ff90-7983d089fbd0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 517
}
},
"source": [
"Image('struc2bold.jpg',height = 500)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/jpeg": 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YDChhSmVlpdj0ENsLM+0N/Z/2Q6gODg4YPXo0fvnlF6YjUb1ACyIlFvh8Pnbt\n2oXx48fD3d0dDx48gIeHB9OxGNfU1AQNDQ2mYwAAFBQUAIBOgP0CLS0thIWFYc2aNVi4cCE2bdoE\nQu+7LpHkmQ5AUVwuF/Pnz8ft27dx4MABvPfee0xHEhv19fVMRxBQV1dnOoLYYrPZ2Lt3L4YOHYpl\ny5YhKysLp06dgqKiItPRqB6gBZFi1OPHjzFt2jQ0NTXh7t27eOONN5iORHWh/dB1bW0tw0nE17x5\n82Bubo533nkH48ePx++//y42h7ypV6OHTCnGXLx4ER4eHjAyMkJ8fDwthp0ghIjNIdN29HDgy40c\nORIxMTGoqKiAh4cHvfmwBKEFkWLErl27MHPmTCxZsgTh4eEyO4r0VcSpN0YP/3WftbU1YmJiYGho\niDfffJPOgyohaEGk+lVLSwuWLl2KXbt24YcffsCBAwfA4XCYjkV1w4ABAwD8O9CHejVtbW3cuHED\nw4YNg5eXF6KiopiORL0CLYhUv6mqqoKfnx8uX76Ma9euYfny5UxHkgiqqqpMRxDS3NzMdASJoaSk\nhIsXL2LixInw8/Oj072JOVoQqX6Rk5ODUaNGIS8vD1FRUfD29mY6kkSoq6sDm81mOgaAf+fzpHqO\nw+Hgp59+wqJFizB16lRcu3aN6UhUF+i/cErkkpOTMXLkSGhqaiImJgYODg5MR5IYfD6f6QgCampq\nTEeQWHJycvj+++/x/vvvY/r06bh+/TrTkahO0IJIidTff/+NsWPHwsXFBTdv3oSenh7TkSSKkpKS\n2Nx/T5wG+Eiqr7/+GqtXr8aMGTPo4VMxRAsiJTK///47JkyYgClTpuDy5ctSeUd7UVNSUhKbi/Pb\nB9PQC/RfH4vFwr59+7BixQpMnz4dt27dYjoS9RxaECmR+OWXXzBjxgysWrUKp06doiNJX5Oqqioa\nGhqYjgHg/woivfyid1gsFr799lssWrQI06ZNQ2xsLNORqP+PFkSqz506dQr+/v4ICgrCvn37ZHpy\n7t5SUVERm7lD2+9yQQti77FYLHz33Xfw9vbG1KlTkZmZyXQkCrQgUn0sJCQEy5Ytw/bt27Fz506m\n40g8JSUlPHv2jOkYAP6vhyhuM+dIKjabjXPnzsHJyQmTJk1CUVER05FkHi2IVJ85duwYVqxYgc8+\n+wybN29mOo5UUFZWFpvBLPSQad9TUFBAaGgolJSUMHHiRLE5XyyraEGk+iA924gAACAASURBVMSp\nU6ewevVqfPHFFwgMDGQ6jtRQV1cXm5lh2gtz+4w1VN/Q0NDA1atXUV1djYULF9K5YhlE73ZB9dqF\nCxewbNkybN26FZ988gnTcaSKoqIiGhsbUV1djZycHMHD2dm5y8kNwsLCOj3MGhsb22Fwk7e3N7S1\ntTs8t7y8HMeOHYO5uTksLCxgYWEh2CY9ZNr3TExMcOHCBXh5eWHz5s3YvXs305FkEovQryNUL1y/\nfh3vvPMOPvjgA3z99ddMx5FoOTk5SEhIQG5uLnJycpCVlYXExESUl5ejpaVF6LmhoaGYPn16p9tZ\nsGBBt+/c3tjY2Okh0La2tg7Fk81mQ01NDUOHDoW1tbWgUDo4OGDEiBHd3EvqZX755RcsWrQIp0+f\nxvz585mOI3sIRb2mO3fuEGVlZRIQEED4fD7TcSTe33//TQAQAERBQUHwc2ePoqKiLrfzxx9/vHRd\nAITD4ZBFixa9NM+IESO6XF9eXl6Qce3atX39Usi0oKAgoqysTBISEpiOInNoD5F6LZmZmfDw8ICf\nnx9Onz4tNvNtSrqxY8ciKioKbW1tXT5HT08PpaWlXf69tbUVOjo6rxyMc+3aNUycOLHLv69fvx6H\nDh3q0Dt9HofDQU5ODoyMjF7aFtV9fD4fEyZMQH5+PuLi4uiUef2IDqqheqy8vByTJk2CjY0NQkJC\naDHsQ7t27XppMZSTk8Po0aNfug0Oh4O5c+e+dDIELS0t+Pj4vHQ7np6eaG1tfWk7K1eupMWwj8nJ\nyeGnn35CTU0NVq9ezXQcmUILItUjz549w9SpUwEAV65coUPw+9ibb76J0aNHQ16+8/FubDYbo0aN\neuV2FixY0GUxU1BQwIIFC7pso93w4cNfOeJx06ZNr8xC9Zy+vj5+/vlnnDt3Dj/88APTcWQGLYhU\ntxFCMH/+fGRnZ+PatWv0Lvci8rJeYmtrK4YPH/7KbYwZMwYGBgad/q2lpQVz58595TYsLCwwcODA\nTv/G4XCwfPlyGBsbv3I71OsZP348Nm/ejHXr1iE1NZXpODKBFkSq23bu3Ilr167h8uXLsLGxYTqO\n1Bo3bhxGjhzZaQ+OzWZ3qyDKyclhwYIFUFBQ6PA3IyOjbvUyAWDUqFGd3geREIKgoKBubYN6fVu2\nbMGQIUOwbNkysbnriTSjBZHqlitXrmDHjh04dOgQPD09mY4j9brqJTo6Onb7riHz5s3rMCCGw+HA\n39+/2/PLenp6dijM7b1DU1PTbm2Den1sNhshISFISEjAvn37mI4j9WhBpF4pLS0NixYtwvLlyxEQ\nEMB0HJkwfvx4eHh4CBUjDofzygE1zxs2bBgsLS2FlrW2tvbo+jY3N7cORZUQQs8d9qPBgwdjx44d\n2LJlCz10KmK0IFIvVV9fj2nTpsHR0RHfffcd03Fkyou9RD6fDzc3tx5tY8mSJUKjTW1sbODk5NTt\n9d3c3IQOmXI4HCxduhTm5uY9ykH1zscffwxnZ2csX76cTu0mQrQgUi+1du1aVFRU4Pz5852ej6JE\nx8fHB+7u7oLLWng8Xo8PV8+bN08w2pTD4WDJkiU9Wl9VVRV2dnaC3/l8Pj13yAA2m42jR48iNjYW\np06dYjqO1KIFkerS2bNnceLECYSEhNDRhAzZuXOnYDCFmpqaUHHqDltbWzg7OwP4dzq2efPm9TjD\nqFGjwOFwBOcfLSwserwNqvdcXFywcuVKBAYGis0dUKQNLYhUp3JycrB69Wq8//77gusOqf7n5+eH\nYcOGAQBGjBjxWjdbXrx4MYB/z0VZWVn1eP32C/RbW1vpbb0Y9vnnn4PH49F7jYoIvdsF1QGPx8P8\n+fNhbm6Or776iuk4Uo/P54PL5YLL5aK6uhpcLhf19fVoaWlBbW0tPD09ER8fj+rqagQGBqKpqQmN\njY1oaGgQGvDy4u/Av+9lZWUlgH9nGBo9ejSUlJQ6ZNDS0urwu7y8PNTU1AS9EXNzc0REREBdXR3y\n8vLQ1NSEmpoaNDU1oampCQ0NDTpRg4hpamri888/x3vvvYcVK1bAwcGB6UhShc5lSnXwzTffICgo\nCPHx8Rg8eDDTcSQKIQTl5eUoKytDWVkZiouLUV5ejpKSEpSUlAgK3vOPlx3+UlNTg7y8PJ49e4aB\nAwfC0NAQAwYMgLKyMpSVlYXuTaioqNhpsdPU1MSvv/6KKVOmgBDSYQabtrY21NXVCe0Dl8tFa2sr\n6uvr0dTUhCdPnkBLSwt8Ph81NTXg8/md5lVUVBQUyPaHtrY2dHV1oa+vD0NDQ8HPBgYG0NPTo+em\ne4jP52PYsGGwsLDAxYsXmY4jVWhBpIRkZWXByckJQUFB9PDYC3g8HoqLi5Gbm4u8vDzk5+cjLy9P\n8HNpaSnKy8uFRoZyOBzo6ekJCoC2tjY0NDQ6FA1NTU1oaWkJflZVVRUqdmFhYXBzc3vt2YEeP34M\nR0fH1973U6dOwd/fX/B7e2Gsq6vrUOBffFRXV6OsrAylpaUoKSlBQ0OD0La1tbWhr68PIyMjmJqa\nwtzcHObm5jA1NRX8Tnuewq5fv45JkyYhKiqKXhfch2hBpAQIIRg/fjyqq6sRFxf30smhpVVzczMy\nMjKQnp6O9PR0ZGRkIDMzE7m5uSgqKhIasWliYiL4wDYxMYG+vj709PRgZGQk6AV1NfWZLGtoaBAU\nx/LychQXF6O0tBTFxcXIy8sTfOGor68XrKOnpwczMzNYWlrCzs4OdnZ2sLe3h52dXYfDvbLCy8sL\nfD4fd+7cYTqK1KAFkRI4evQoPvjgA8TExAgGckir2tpaJCcnIyUlBY8fPxYUwdzcXPD5fLDZbJib\nm8POzg42NjYwMzODqakpzMzMYGFhAQMDg06nNKP6TlVVlaAX3l4knz59Kvii0tjYCADQ0dERFEd7\ne3sMGTIELi4uUj8yOjY2Fp6enrhy5QomT57MdBypQAsiBQAoKSmBg4MDVq1ahS+++ILpOH2GEIKn\nT58iKSkJSUlJSE5ORlJSEp4+fQpCCHR0dDBo0CBBr8PW1hb29vawtrYWOmRJiRdCCPLy8oR682lp\naUhPTxe8t9ra2nB1dYWTkxNcXFzg5OSEIUOGSNU5yxkzZiA7OxsJCQn0C1ofoAWRAgAsX74cERER\nSEtLk+jzNXV1dYiNjUVUVBSio6MRHR2NmpoayMvLw9bWFi4uLnB2dhb8l97LT/rU19cjOTkZycnJ\nSExMRFJSEh4+fIj6+npwOBy88cYb8PT0hJubG0aNGgUzMzOmI7+21NRUODk5ISQkBIsWLWI6jsSj\nBZFCfHw83NzccObMGcyZM4fpOD2Sn5+PW7duISoqClFRUXj8+DEIIXBwcICnpyc8PT3h4uKCIUOG\nSHShp3qHz+cjOzsbiYmJiImJQXR0NOLj49Hc3AxjY2OMHDkSHh4eGDt2LFxdXSWqt7VixQrcunUL\nqampUtX7ZQItiDKOEIKxY8cCAO7cufNaF373p9raWkRGRiI8PBwRERFITU2Fmpoa3N3d4eHhAQ8P\nD3h6ekJbW5vpqJSYa2lpwYMHDxAdHS34QlVYWAgdHR2MHz8evr6+8PHxEfuZeQoKCmBnZ4c9e/bg\nv//9L9NxJBotiDLu/PnzePfddxEbG9ut++wxITk5GZcuXcKNGzcQGxsLFosFDw8PwQeWm5ubYL5P\niuqNrKwsREREIDw8HLdv30ZVVRVsbW3h6+uLKVOmwNvbWyx7YRs2bMDJkyeRlZUFVVVVpuNILFoQ\nZVhLSwvs7e3h5eWFkJAQpuMISUhIwG+//YbffvsNGRkZsLKywpQpU+Dr64tx48bR/+kpkePxeIiP\nj0d4eDiuX7+OqKgoqKur45133sHMmTPh5+cnNgOvqqqqYGlpiU2bNiEwMJDpOBKLFkQZdvToUaxb\ntw6ZmZliMUQ9LS0NISEh+O2335CdnQ17e3vMnDkTs2bNgqurK9PxKBlXUlKC0NBQXLhwAXfu3IGK\nigqmTJkCf39/+Pr6Mn7eccuWLfj+++/x9OlTqKmpMZpFUtGCKKOam5thY2ODmTNnYv/+/YzlaGlp\nwcWLF3H06FFERkbC0tISCxcuxMyZMwV3aaAocVNWVoZLly7h7Nmzgn+3AQEBWLZsGfT09BjJVF1d\nDUtLS2zcuJHewPl1EUomHTx4kCgrK5Pi4mJG2i8qKiKBgYFET0+PcDgcMmPGDPLnn38SHo/HSB6K\nel1paWlk/fr1ZODAgYTD4ZA5c+aQqKgoRrJs2bKFDBw4kNTW1jLSvqSjBVEGPXv2jBgZGZGPP/64\n39suLS0lH330EVFSUiLGxsZk165dpLCwsN9zUFRfa2pqIqdPnyaenp4EAJk0aRK5d+9ev2aoqqoi\nGhoaZPfu3f3arrSgBVEGHThwgKiqqpLS0tJ+a7Oqqops3LiRqKqqEgMDA/Ltt9+SxsbGfmufovpT\neHi4oDBOnTqVPHjwoN/a3rp1K9HW1iY1NTX91qa0kJyrT6k+0dbWhv/9739YvXp1v53ruHz5MgYN\nGoSQkBBs374d2dnZWLt2Lb1QnpJaPj4+iIqKQlhYGIqLizFs2DCsXbu2X+50/+GHH4LP5+PAgQMi\nb0vqMF2Rqf517tw5Ii8vT3Jzc0XeVmVlJVm4cCEBQJYuXUq4XK7I2+yNyspKcunSJfL5558zHYXq\nBkl5v/h8Pjlx4gTR0dEh5ubm5J9//hF5m9u3b6e9xNdAC6KM8fDwIAsWLBB5O/fv3yfGxsbE2NiY\nhIWFiby9nuLxeOTzzz8nmzZtIiNHjiQsFou89dZbBACxt7cXeq69vT1Zvnw5Q0mlV0pKCvnmm28E\nv7/4ngwaNIgkJyd3uu6TJ0/Ixo0bhd6v1tZWsmnTpn75svc6ysvLydtvv03k5eXJwYMHRdpWdXU1\n0dTUpOcSe4gWRBly+/ZtAoAkJCSItJ1bt24RVVVVMmXKFFJVVSXStl7Xl19+SfT19QmfzyfV1dVk\n0qRJgtfnxYI4ZsyYbg9Aevr0qQjSSoae7PvVq1fJ4sWLSVtbm2BZZ+/JX3/91eU22traOrxf9fX1\nZNasWSQjI+O19kHU+Hw++frrrwmbzSaBgYEibevTTz8l+vr65NmzZyJtR5rQgihD3nnnHTJu3DiR\nthEXF0fU1NTIihUrhD7sxI2lpWWHwkcI6bQgdld+fj4ZM2ZMb6NJpJ7se1JSErGxselwaUBX78nL\ndPZ+ZWVlkcGDB4v1IfrQ0FCioKBA9u7dK7I2SktLiZKSEjl8+LDI2pA2dFCNjMjPz8cff/yBdevW\niayN2tpavPvuu3j77bdx7NgxsZ5fNDc3t0+3V15ejsmTJ6OsrKxPtysJerLvPB4P/v7+WLZsWYfZ\nVPrqPbGysoKjoyM++uijPtmeKEyfPh2nT5/G5s2bERkZKZI29PT0sHTpUnz99ddoa2sTSRtSh+mK\nTPWPbdu2ETMzM5Fe+P7JJ58QJycnsT5Ec+XKFbJq1SoCgGhoaJBVq1aRVatWkbq6OkKIcI+jra2N\nnDt3jvj7+5MxY8aQtrY28vfff5MNGzYQKysrkpWVRZydnYm2tjZZuXKl0DZ7or6+nmzbto0sWrSI\nrFu3jri5uZHt27cLetilpaXkgw8+IOvWrSMff/wx8fT0JCtXriRFRUWEz+eTP/74g3zwwQfEzMyM\n5ObmEh8fH8Jms8mQIUPI/fv3CSGEXLt2jejo6BAAZNeuXYK2f/zxR8LhcMipU6cIIYTU1taSHTt2\nkOXLl5MxY8YQT09PEhsb22f7fv78eQKApKSkdOs9SUxMJOPGjSOfffYZ2bRpE5GTkxPqWaKLHv2J\nEycIi8UiaWlpPXov+tuGDRuInZ0daW1tFcn2s7KyiLy8PDlz5oxIti9taEGUAa2trcTExITs2LFD\nZG0UFxcTJSUlcv36dZG10Ze6+iB9cXltba1gWXNzM4mPjyfq6uoEANm/fz+JjIwks2fPJpWVla91\nuLWuro64urqS5cuXEz6fTwgh5NixYwQAOXv2LCkrKyMWFhZCIym5XC4ZNGgQMTY2JgUFBaSiooJo\na2sTAGT37t2kuLiYREZGEhaLRVxdXQXrBQcHEwDk2rVrgmW5ublk8eLFhJB/B7W89dZbQrMXzZ07\nl2hpaZHS0tI+2fcZM2YQeXn5TgtAZ9uwsrIiZmZmgt8DAgKErp/tqt3k5GQCgGzduvWVmZjU0NBA\ndHV1SUhIiMjamDdvHnF1dRX8+6K6RguiDLh8+TKRl5cnBQUFImvj0KFDRF9fX2KmXutuQeTz+R2W\n2dnZEQAdesKvUxC3bdtGAJDs7GzBssbGRnLo0CFSVlZGPvroIwKAVFRUCK139uxZAoC89957Qpme\nZ2VlRVgsluD3lpYWYmZmRiZPnixY9umnnwoGWV29epUA6PRx4cKFPtl3Y2NjYmJi0unfOtuGpqYm\nAUAOHz5MeDweefz4sdClBF21W1VVRQAQX1/fV2Zi2urVq4mXl5fItv/gwQPCYrEk5ssqk+g5RBlw\n9OhRTJ48WaR3tLh//z6GDRvG+Iz/fa2zGya3L1NSUur19q9duwYAMDExESxTVFTE+++/D11dXdy5\ncwcAoKGhIbTeuHHjAAD//PNPlzk5HA7Ic3P3czgcrF27FlevXkVmZiZaWlqQlpYmuJNIdHQ0XFxc\nQP79oiz0mDFjRp/se0lJSY/W3b9/P9hsNt577z24ubmhuroa6urqr1yv/fxkcXHxa+XsT25uboiL\nixPZ9l1cXDBhwgTs3btXZG1IC+n69KI6KCoqwvXr17Fy5UqRtsPlcrv1QUUJa25uBvDvjWk7w+fz\nAQA5OTlCy7W1tQEAysrKPWpvxYoVUFFRwaFDh3Dx4kXMnj1b8Dcej4eMjAxBpufxeLwetdMVFosl\nVKRfZfHixYiLi4O3tzfi4+MxatQo7Nu3r1vtSAoNDQ3U19eLdODLxo0bERkZidjYWJG1IQ1oQZRy\nZ8+ehZ6eHiZMmCDSdkxMTFBYWCjSNiRBTz7sAWDEiBEAgM8//1xo3YqKCly4cAHe3t4AgD///FNo\nvYKCAgDAlClTetSehoYGVqxYgePHj+PXX3/F9OnTBX8bPHgwGhoacPjwYaF1iouLOyzrTHf23djY\nGHV1dd3O+8UXX8DV1RUREREIDQ0Fi8XCli1bXrlefX29oD1xV1BQAENDQ8jLy4usjXHjxsHNzQ3f\nfvutyNqQCkwdq6X6x7Bhw8h///tfkbdz8eJFMmDAAIm47UxLSwsBQGxsbISWt1/obWtr+9Jltra2\nBECH6yx1dHSIhoZGj+7ekZGRQTQ0NAR3RwgODib79+8nEydOJLW1taSiooJYW1sTc3NzUl1dLVhv\nw4YNxNXVldTX1wtlen7ghLW1dYdlhBCSnZ1N5OTkhEabEvLvaFczMzMiJydH1q9fT37//Xdy8OBB\n4uvrK7imr7f7Pn/+fMJisTqcg+zqPdHT0xOa3MHU1FQwUKiz96bdo0ePCACybdu2l+YRB1OnTiWL\nFi0SeTsnT54kCgoKpKioSORtSSpaEKVYamoqAdAvt6BpaWkhVlZW5KuvvhJ5W72RlpZGduzYQQAQ\nNptNvv/+e/LkyROSm5tLPvvsMwKAKCgokJMnT5KCggKyb98+wbIjR46QnTt3Eg6HIxjB+OjRI8G2\njxw5QlRVVcmaNWt6lOnhw4dkypQpRFVVlSgpKZE5c+YIfWiVlZWR//znP8TT05N88sknZM2aNWTD\nhg2CLx8///wzUVBQIADId999R2pqasipU6cIm80mAMjevXs73Flk6dKlpKysrNPXx8/PjygqKhIN\nDQ2yaNEiUlJSQurr68m+fft6ve9//vknASB0v8Cu3hNC/m/QzN69e0lgYCCZNGkSycrK6vT9ev4L\nw08//URYLBZJTU3twTvR/9LS0giHwyGxsbEib6u5uZkYGhqSzZs3i7wtScUipIfHeCiJsWXLFpw/\nfx6pqan90t6ZM2cQEBCA+/fvw8HBoV/apCQLIQR+fn4YNmyYSAd5zJo1C6qqqjhx4oTI2uit1tZW\njB07Fqampjh37ly/tLljxw589913KCgogIKCQr+0KUnoOUQp9ssvv2D+/Pn91t68efMwZcoUTJky\nRXCOS5axWKxXPvrry4q4YLFYOH78OMLCwsDlckXSxsOHD5GSktKtwTdM4fF4WLp0KXJycnDw4MF+\na3flypWoqanB2bNn+61NicJwD5USkfj4eAKAPH78uF/bra2tJe7u7sTW1pakp6f3a9uU5IiPjyeL\nFy8mLS0tfbrdiooKMnXqVJKZmdmn2+1LTU1NZO7cuURDQ4PExMT0e/v+/v5CEzZQ/4f2EKXUpUuX\nYG9vj0GDBvVru2pqaggLC4Oenh5GjhyJy5cv92v7lGR44403EBQU1Ke9o9bWVvzwww84efIkrK2t\n+2y7fSkrKwteXl4IDw/H1atX4e7u3u8ZPvjgAzx48AB3797t97bFHT2HKKWcnJwwefJkxi7GbWpq\nwvvvv4+QkBAsXLgQBw4cgJaWFiNZKIpphBAcOnQImzZtgo2NDc6fPw9bW1vG8nh6esLMzKzfzl1K\nCtpDlEKZmZl4+PCh0DVm/U1RURE//vgj/vjjD9y+fRuDBw/GhQsXenydHkVJutTUVIwfPx4fffQR\n1q9fj7i4OEaLIQCsWbMGly5dksm7s7wMLYhS6NKlSzAyMoKbmxvTUTB58mSkpKTAz88Ps2fPhpub\nG8LCwpiORVEil5GRAX9/fwwZMgRVVVW4d+8eduzYAQ6Hw3Q0zJgxA6qqqjh58iTTUcQKLYhS6PLl\ny3jnnXfEZvoqLS0tnDhxAgkJCTAyMsLUqVPh4eGBGzduMB2NovpcdnY2li5dCkdHR9y7dw+nT5/G\ngwcPBHPGigNFRUUsXLgQP/74Iz1q8xxaEKVMTU0NYmJiRD5V2+twcXHB5cuXce/ePQwcOBATJkyA\no6Mjvv32W1RXVzMdj6JeG4/Hw5UrVzBlyhTY2trin3/+QUhICB49eoR58+aJ5aT3K1asQFpammCC\neIoWRKlz8+ZNsFgseHl5MR2lS8OHD0dYWBgSExMxbtw4bN26FcbGxliyZAliYmKYjkdR3VZYWIid\nO3fC0tIS06ZNA4/Hw4ULF5CamopFixaBzWYzHbFLTk5OcHd3xw8//MB0FLFBR5lKmVWrViE1NVVw\n2yBJUF9fjzNnzuDIkSNISEiAjY0NZs+ejZkzZ2LYsGFMx6MoIUVFRbh48SLOnz+Pf/75Bzo6Oli2\nbBkCAgJgaWnJdLweCQ4Oxtq1a1FUVARNTU2m4zCOFkQpY2FhgVWrVmHTpk1MR3ktiYmJOH/+PC5c\nuIC0tDRYWlpi1qxZmDlzJtzc3MTmvCglW/Ly8hAaGooLFy4gKioKGhoaePvttzF79mz4+fmJxUCZ\n11FfXw9DQ0N88cUXeO+995iOwzhaEKVIamoqBg0ahPj4eLzxxhtMx+m1hw8f4vz58wgNDcXDhw+h\np6cHHx8f+Pr6wtfXVyJu7UNJpoaGBty5cwcRERG4ceMGHj16BB0dHUybNg2zZs3C+PHjJbYIvigg\nIADx8fFISEhgOgrjaEGUIocPH8bWrVtRVlYmlifxeyMzMxN//vknwsPDERkZiZqaGjg6OsLX1xfj\nx4+Hp6cndHV1mY5JSajm5mbEx8fjzp07CA8Px927d8Hj8eDm5ib4Aubp6SnW5wRfV3R0NEaOHIlH\njx7B0dGR6TiMogVRisydOxfNzc24dOkS01FEqq2tDbGxsQgPD0d4eDju3buHtrY22NnZwcPDA+7u\n7hg1ahSGDBkilR9gVO/l5eUhJiYG0dHRiI6ORkJCAlpbW2FjYwMfHx/4+flh/Pjx0NDQYDqqyBFC\nYGNjg3nz5uGzzz5jOg6jaEGUIgYGBggMDMS6deuYjtKv6uvrcf/+fdy9exfR0dGIjY1FRUUF1NTU\nMHz4cLzxxhtwdnbG0KFD4ejoKDWHuqjuefr0KVJSUpCYmIjk5GTExMSgsLAQAwYMwLBhw+Dp6Ql3\nd3eMHDlSZg/Db968GWfOnEFmZqZMn6enBVFKPHnyBI6OjkhISBCrC4CZkp6eLvj2n5iYiJSUFDx7\n9gwcDgeOjo4YOnSooEja29vD1NSU6chUL3G5XKSnpyMpKUlQAFNSUsDlcsFms2Fra4uhQ4fCw8MD\nHh4ecHV1xYABA5iOLRYeP36MwYMHIyoqCp6enkzHYQwtiFLi6NGjCAwMRGVlpdSdP+wLPB4PWVlZ\ngl5C+4dlXl4eAEBZWRl2dnaws7ODra0t7O3tBT9ra2sznJ5q19zcjIyMDKSnpwv+m56ejrS0NJSX\nlwMANDQ04OzsLPjC4+LigsGDB0NZWZnh9OLN1dUVo0eP7tf7M4obWhClxPz581FXV4crV64wHUWi\nVFVVIS0tDWlpaUIfsBkZGWhsbAQAaGtrw8zMDGZmZrCwsBD83P4wNDRkeC+kR21tLfLy8pCTk4O8\nvDyhR05ODoqLi8Hn88Fms2Fubi705aX9C4yFhQXTuyGRvvrqK/zvf/9DYWEh5OXlmY7DCFoQpYS1\ntTWWL1+OoKAgpqNIBUII8vLykJGRgczMTMGHcm5uLnJzc1FUVAQejwcAGDBgAIyNjWFgYABdXV0Y\nGhpCT09P8LO+vr7gZ3V1dYb3rP81NTWhvLwcRUVFKC8vR1lZGYqLi1FWVia0vKioCFwuV7Cejo6O\n4EuHubk5zM3NYWFhAXt7e1hbW9PDnX0sPz8fFhYWCAsLw8SJE5mOwwhaEKVAeXk59PT0EBERAW9v\nb6bjyIS2tjYUFhYiPz8fOTk5KCgoQGlpKcrKylBSUoKnT58iPz8fPB5PaPJkNpsNTU1NwUNLSwta\nWlpCyzQ1NaGiogJlZWUoKytjwIABUFdXB5vNhpaWFuTl5aGmpoYBAwaI5DBgW1sb6urq0NTUhMbG\nRjQ0NKClpQU1NTXg8XjgcrlobW1FfX09ampqwOVyUV1dDS6XK/RoJrliwwAAIABJREFUX9bU1CS0\nfVVVVaEvDUZGRtDV1YWxsbFQAaSHOPvfuHHjYGlpiePHjzMdhRGy2S+WMjExMZCTk8Pw4cOZjiIz\n5OXlBb2W0aNHC5a3tLRg69atiIyMxOTJk3HkyBGwWCxBD6irwvH06VOhZQ0NDYJDtt0lJyfX6WUC\nmpqaaGpqgoKCAhobG9Ha2ir098bGxg5Fqzv7r6amBnV1daGCrq+vD3t7e6FlWlpaQj1nJSWlHrVF\n9Z9Zs2Zh27Zt4PF4MnnJEu0hSoHNmzfj0qVLePjwIdNRZNqjR4+wcOFCZGZm4ptvvkFAQECvt/my\n3ll7D65dS0sLGhoahNbn8Xiora1FYGAg3n33XXh4eHQoSAoKClBRURH8zmazoa6uLuiBdtZLpaRT\nfn4+zM3NcfPmTbG+QYCo0IIoBfz8/GBmZobg4GCmo8gkQgj279+PoKAguLq64vTp07C2tmY6lhAW\ni4Wff/4Z8+fPZzoKJeaGDx+O0aNHY//+/UxH6Xd0fL4UkJa5SyVRfn4+fH19sWHDBgQFBeGvv/4S\nu2JIUT3xzjvv4PLly0zHYAQtiBKusLAQVVVVcHZ2ZjqKzDl37hyGDh2KgoICREVFYcuWLTI7XJ2S\nHtOmTUNOTg4SExOZjtLvaEGUcCkpKQD+vdkn1T+4XC4WLFiAefPmYd68eUhISMCIESOYjkVRfcLJ\nyQnW1tYy2UukBVHCpaSkwMzMTCYmIRYHt27dgrOzM27fvo2wsDB899139PIASurI6mFTWhAlXEpK\nCu0d9oOmpiasX78evr6+cHNzQ3JyMiZNmsR0LIoSialTpyIxMRHFxcVMR+lXtCBKOFoQRS8pKQkj\nRoxAcHAwgoOD8dtvv0FHR4fpWBQlMqNGjYKKigoiIiKYjtKvaEGUYG1tbXjy5AktiCLC5/Px1Vdf\nwd3dHZqamkhMTMTSpUuZjkVRIsfhcDBu3DiEh4czHaVf0YIowdLT09Hc3EwLogjk5ubC29sbmzdv\nxvbt2xEZGQlLS0umY1FUv/Hz80NERARk6VJ1WhAlWEpKCjgcDhwcHJiOIlVOnz6NoUOHoqysDDEx\nMQgMDJTJaawo2ebt7Y3i4mKZmgGLFkQJlpKSAgcHB3oH+D5SVVWFOXPmYPHixViyZAni4+PpzZYp\nmeXo6AgTExOZOmxKC6IEowNq+s6NGzfg5OSEqKgo3LhxA/v374eioiLTsSiKUb6+vjI1sIYWRAmW\nlpaGQYMGMR1DojU2NmLt2rWYOHEiRo8ejeTkZPj4+DAdi6LEgo+PD+7cuYOWlhamo/QLWhAlFJ/P\nx9OnT+ndwXshISEBw4cPx6lTp3D69GmcO3cO2traTMeiKLExatQoPHv2TGamcaMFUUIVFRWhpaUF\nVlZWTEeRODweD3v27IGHhwf09fWRnJyMBQsWMB2LosSOubk5TExM8M8//zAdpV/QmYglVHZ2NgDQ\nHmIPZWdnw9/fH/fv38eePXvw4YcfQk6Ofi+kqK54eHggOjqa6Rj9gn4SSKicnBwoKSnByMiI6SgS\nIyQkBC4uLqitrUVcXBzWr19PiyFFvcLo0aMRFRXFdIx+QT8NJFRWVha9ULybKioqMH36dAQEBGDl\nypWIi4ujo3Mpqps8PT1RVFSEnJwcpqOIHD1kKqGePn1KC2I3XL16FcuXL4eCggIiIiLg5eXFdCTq\n/7F353FRVf0fwD8DM4DsqGwiA7K7DVsiiwuh4JpaYvVoLj8NNUsfLZe0skwNLEtL09R61Mr0ZS5p\nkhGomAiKgjAosroAwgAKAwzbDDPn94cP95FY1Zm5w3Der9e8cu7Mvfdzx5zvnHvOPZfqVry8vGBo\naIikpCSt76KhLcRuihbEjtXV1eGtt97C5MmTMWbMGAiFQloMKeoZ6Onp4YUXXsDly5fZjqJytCB2\nU3fu3KEFsR3Jycnw9vbG0aNHceTIEfz888/0fpEU9Rx8fHyQmprKdgyVowWxG2poaEBJSQmcnZ3Z\njqJRmpqasGHDBgQFBYHP50MoFOLVV19lOxZFdXuenp7IyMiAQqFgO4pK0YLYDd27dw+EEK0/n/80\n8vLyMGLECERFRWHr1q3466+/YGdnx3YsitIKnp6ekEgkzOVe2ooWxG6ouLgYAOglF/+1d+9eeHl5\nQSqVIiUlBf/+97/B4XDYjkVRWmPQoEHgcrlaP2MNLYjdUGlpKXg8Xo+/a3tpaSleeuklLFmyBEuX\nLsWVK1cwaNAgtmNRlNbR19fHwIEDIRQK2Y6iUvSyi26otLQUffv27dGtoFOnTmHhwoUwMjJCfHw8\nRowYwXYkitJqXl5eSE9PZzuGStEWYjdUWloKW1tbtmOwoqamBhEREZg2bRomTZqEtLQ0WgwpSg0E\nAoHWnzKlLcRuqLS0FFZWVmzHULvExETMmTMHVVVVOHbsGKZPn852JIrqMTw8PFBQUIDa2loYGRmx\nHUclaAuxGxKJRLCxsWE7htrIZDJ89NFHGD16NNzc3CAUCmkxpCg1c3FxAfB42khtRQtiN1RWVgZr\na2u2Y6hFVlYWAgMD8dVXX+Hrr79GdHR0jz1dTFFscnJygq6uLvLy8tiOojK0IHZDIpFI6wsiIQTf\nfvstfH19weFwkJqaiiVLlvTogUQUxSY9PT3Y29sjNzeX7SgqQwtiN6TtLcSSkhJMmDABy5cvx7vv\nvovExES4u7uzHYuiejxXV1etvjifDqrRcBMmTMCdO3fg6ekJExMTcLlcGBkZISEhAWKxGMbGxjAy\nMoKZmRmCgoLQq1cvtiM/l+PHj2PRokUwNzfH33//jYCAALYjURT1X87OzsjKymI7hsrQgqjh5HI5\ncnJykJOTAy6XCx0dHXA4HPznP/+BQqGAQqGAXC4Hl8tFY2Mj23GfWXV1Nf7973/jwIEDiIiIwFdf\nfQVjY2O2Y1EU9QQXFxdER0ezHUNlaEHUcPPmzcO5c+egUCjQ1NTU5nu4XC42bNjQbe/+funSJcyZ\nMwd1dXU4ffo0XnrpJbYjURTVBkdHRzx48ABNTU3gcrWvfHTPb9AeZPz48V16X0REhIqTPD2xWIzt\n27e3+7pUKsX777+P4OBgDB06FBkZGbQYUpQGs7W1hUKhQFlZGdtRVIIWRA3Xu3dv+Pn5tTu6ksfj\nYfr06bC0tFRzss69+uqrWLFiBS5cuNDqtVu3bmH48OHYuXMnvvvuO5w+fbpHTjZAUd1J8/XPzTcY\n0Da0IHYDr7zySrunJ2QyGZYsWaLmRJ07ePAgYmNjAQCzZs2CWCwG8Phyiu3bt+OFF16AgYEB0tLS\nNLJ1S1FUa8132KEFkWLNSy+9BJlM1mo5h8OBi4sLRo0axUKq9uXm5uKtt95inj98+BBLlixBUVER\nwsLCsGrVKqxbtw6XLl1iZr+gKErzGRgYwMLCAiUlJWxHUQnt6xXVQh4eHrC3t0dhYWGL5To6Onjn\nnXdYStU2qVSKGTNmtBgAJJPJcPjwYcTHx8PU1BSJiYkYNmwYiykpinpWtra2WlsQaQuxm3j55ZfB\n4/FaLNPV1cXcuXNZStS2jz76CDdv3mzVouVwOKiursaZM2doMaSobqxfv370lCnFrkmTJrUoMjwe\nD//6179gbm7OYqqW4uLi8MUXX0Aul7d6jRACqVSKhQsXghDCQjqKopShT58+ePToEdsxVIIWxG5i\n9OjRMDAwYJ5r2mCa8vJy/Otf/+pwrlGZTIaLFy/im2++UWMyiqKUydTUFNXV1WzHUAlaELsJfX19\nhIaGQldXFxwOB0OHDoWfnx/bsQA8bv3NmzcPVVVVUCgUHb5XoVBg+fLlyMzMVFM6iqKUyczMjBZE\nin1TpkyBXC4HIUSjBtPs2rULZ8+ebXMkbDM9PT3mv15eXrh//7664lEUpUSmpqaoqqpiO4ZK0FGm\nGqipqQk1NTWtlnt5eTF/DgwMhEgkgr6+fov36OjowMzMTOUZm2VkZGDFihWt+gW5XC4IIVAoFBg0\naBAmTpyI8ePHY8SIEUxxpCiq+9HmFiItiM+huroaYrG4xaOqqqrF85qaGojFYqbINTQ0oL6+HrW1\ntZBKpaiqqoJcLmcuXO+qoUOHdul9PB4PxsbGMDAwQK9evWBsbAwejwczMzNwuVyYmZmBx+PBxMQE\n5ubmsLCwgLm5OczMzGBubt7q8WQ/Zn19PcLCwiCTycDhcMDlciGTyWBqaorx48djwoQJGD9+PDO7\nBUVR3R9tIfYgDx8+RElJCUpLSyESiVBeXs48Ly8vR3FxMcrLy1FWVtbmZNsGBgYtCoixsTHMzc2h\nr68PS0tL6OnpwcjICIaGhtDX12du6WRhYdFiO/98DgC9evXCgQMHMHbsWLi4uKC6urrViE6pVIra\n2tpWz+vr69HQ0ICamho0NTUxRbq6uhoSiQSlpaUQi8WorKxkinlb/YGGhoawsbGBtbU1ioqKIBKJ\nAAD29vYIDAxESEgIRo4cCQcHh25/KyqKolozMzNDXV2dVk7wrV1H0wmZTIaCggIUFhaioKAA9+/f\nZ/5cWFiIe/fuoa6ujnm/np4erKysmAJgY2MDb29vWFlZwdLSEr179+6wBaUK7u7u6Nu3r0r30ayt\nFnBFRQVKS0uRnp6O8vJyDB48GHK5HBUVFThy5AiOHDnCrG9lZQV7e3vw+Xzw+Xw4ODgwzx0cHLT6\nJscUpa2auzxqa2vV2j2jDlpZEIuKipCTk4Pc3Fzk5OQgKysLubm5uHv3LtOq69WrF/NFbW9vj4CA\nADg4OIDP58PW1hY2NjZtttLYpq5iCDw+NWJqago+n9+l9zc1NaG8vBylpaXMD43mx/Xr13Hs2DGI\nRCKmVWtiYgJXV1e4ubnB3d0dbm5uzMPU1FSVh0ZRFNVKty6I5eXlEAqFSEtLQ0ZGBoRCIXJycphT\nhr1792a+YIOCguDm5gZHR0fw+Xx6ZwUV4HK5sLW1ha2tbYsBQE+SyWR48OABCgoKkJuby/xoOXbs\nGPLy8pibHFtbW2PQoEEQCAQQCATw9PTE4MGDVd4CpyiqY9r8Y7XbFMQ7d+7g2rVrSEtLYwrggwcP\nADzuvxIIBBg3bhyWLl0KDw8PuLq6qrU1RXUNj8eDo6MjHB0dW01KLpfLUVBQgJycHGRnZyMrKwvX\nrl3D/v37UV1dDS6XC3d3d6ZAenl5wd/fX+tO21CUJmuefEMikWjdvz2NLIj19fVITU3FlStXcPny\nZSQlJUEkEkFPTw9DhgyBp6cnxo0bB4FAAC8vL/Tu3ZvtyJQS6OrqYsCAARgwYADGjRvHLCeE4M6d\nO0hPT0d6ejoyMjKwZ88e3L17Fzo6Ohg0aBACAwMREBCA4cOHw8PDo8MZcyiKen5tTdHY3WlEQWxo\naMClS5cQFxeHixcvIjU1FTKZDHw+H0FBQVi3bh2GDx8Ob2/vVhNcU9qPw+HA2dkZzs7OeOWVV5jl\nFRUVSE5ORlJSEhITE3H48GHU1NSgd+/eCAgIQEhICEJDQzFkyBBaIClKSXR1ddmOoDIcwsJMy4QQ\npKWlIS4uDrGxsbh06RKkUil8fHwwevRo+Pv7IyAgAHZ2duqORnVjcrkct27dwpUrV5CYmIhz586h\nqKgINjY2GDt2LEJDQxEaGgpbW1u2o6odh8PBoUOHMHPmTLajUN1cRkYGBAIB7t+/3+UBd92F2lqI\njY2NiIuLw7FjxxAdHY3y8nI4Ojpi7NixePPNNzFmzBj06dNHXXEoLaSrq8sMwlm4cCEAICsrC7Gx\nsYiNjcXbb78NiUSCoUOHYtq0aQgPD4dAIGA5NUV1L09emqZtVFoQGxoaEBMTg2PHjuHMmTOoqanB\niBEj8NFHH2H8+PFwdXVV5e4pCh4eHvDw8MDSpUvR1NSEpKQkREdH49ChQ9i4cSNcXV0RHh6O8PBw\n+Pj4sB2XojReR3MWd3cqmdw7OTkZCxYsgJWVFaZPnw6RSITIyEg8ePAA8fHxWLp0KS2GlNpxuVyM\nHDkSUVFRyM/PR2pqKmbMmIETJ07A19cXrq6uiIyMZGbfoSiqteYZrExMTFhOonxKK4gSiQR79uyB\nj48Phg8fjtTUVGzZsgUikQixsbFYvHgxnZmE0ije3t7YvHkzsrKykJ6ejmnTpuGrr74Cn8/HjBkz\ncO7cOXozY4r6h+YbD+joaN/Nkp77iEQiEVasWIF+/frh3Xffhbe3N65evYobN27grbfeotcCUt2C\nQCDAF198gaKiIhw4cADl5eUYO3Ys3N3d8cMPP7Q5by1F9WTaOOL/mQtieXk5Vq1aBWdnZxw9ehSf\nfvopiouL8cMPP2jMjWsp6mnp6+tj5syZiI+PR2ZmJoKDg7FkyRJ4eHjgxx9/pIWR6vHq6+sBPJ7o\nX9s8dUFsaGjAxx9/DCcnJ/z000/YvHkz8vLysHz5cq2btYDq2QYOHIi9e/ciOzsbo0ePxoIFCzBk\nyBCcPHmS7WgUxRqpVKq11yI+VUFMTk6Gr68vtm/fjo8++gj5+flYvnw5vc0PpdUcHR3xww8/4Pbt\n2/Dx8cH06dMxZcoUZupAiupJqqurtbbx06WCKJVKsW7dOgQFBcHOzg5CoRCrV6+GkZGRqvNpHLbu\nFK2M/VZUVODUqVOIjIxUy/666mlyscnFxQW//PILzp8/j5ycHHh6euL06dNsx6Iotaqqquq5BbGy\nshLjxo3Dzp07sXPnTsTExMDBwUEd2TTK9u3bMWbMGLUPElLWfrOysvD5559j2rRpOHjwYJvvaWho\nwNatW/Hiiy8+1SQJN2/exLZt25jnCoUCkZGRzI+oQYMGISMjo8u5mpqasG7dOhQUFDzFEapPcHAw\nUlNT8fLLL2PatGmIiopiOxJFqU1VVRXMzc3ZjqEapANisZh4eXmRAQMGkMzMzI7e2u3dvXu3w2Uy\nmYzY2tqSTj4ypVPmfpuamggA4u7u3uH+bGxsury/P/74g8ydO5c0NTUxyz7//HNibW1NFAoFqays\nJBMmTCB///33U+WSSCQkPDyc5ObmdikHW/bu3Uu4XC5Zu3Yt21E6BIAcOnSI7RiUFliyZAkJDg5m\nO4ZKtNtClMvlCA8Ph0QiQVJSEgYOHKieCs2CoqIizJkzp8NlXC6XlfuAKXO/XekI53K5XT4dIhQK\nsWzZMuzYsaPFtnfv3g1zc3NwOByYm5vjjz/+wMiRI58ql5GREbZs2YJp06ahqqqqS3nYEBERgV9/\n/RVbt27Frl272I5DUSonFot73inTzz//HEKhEBcuXNDqC+rLy8sxadIklJWVdbiMakkul2POnDmY\nP39+qxkr7t+/r5R9ODk5YdCgQXj33XeVsj1VmTZtGvbt24f33nsPt27dYjsORalUj+tDFIlE2LRp\nE/bu3Yv+/furO9NTae6vmj9/PpYtWwYjIyNwOBzmATyeWeHTTz/Fm2++iVGjRiEwMBDJyckAgF27\ndkEoFEIkEmHx4sXtLntSZmYm/Pz8wOVyMWTIEFy/fh1yuRwJCQlYs2YNnJ2dcefOHXh6eqJPnz4o\nLi7uMAMApKen48UXX8TmzZuxbt066OrqMjNCdLTfZmVlZVi6dClWrFiBVatWITAwEIsWLUJJSUmH\nn59EIsGKFSswb948rFmzBsuXL4dEIun0cz958iTS09Px0ksvMcvOnDmDxYsXQ6FQMJ/d4sWLIZFI\nunR8bZk0aRL279+PnJycTt/Lprlz5+Lll1/GqlWr2I5CUSpVUVEBCwsLtmOoRlvnUd9//33i4+Oj\n7tO3zyQyMpLo6emRhoYGQggh+/btIwDIG2+8QQghRC6Xk4kTJ5KSkhJmnddee41YWFiQyspKQghp\ns1+trWXu7u4EAPnwww9JaWkpiY+PJwCIr68vaWxsJCkpKcTU1JQAINu3byfx8fFkxowZpLy8vNMM\nTk5OhM/nM69HRESQ0tLSTvdLCCFlZWXE0dGRfPbZZ8z6YrGYDBw4kNjZ2ZEHDx60eVyNjY0kICCA\nLF68mHn97t27RE9Pr9M+xFdeeYVwuVwik8lavdbWZ9fR8bW3DiGECIVCAoCsX7++wzyaIDs7mwAg\nqampbEdpBbQPkVISFxcXEhkZyXYMlWjzW8/d3Z188skn6s7yTIKDg1t8MVdUVBAAZMiQIYSQx4M+\nALT5OH78OCHk6QuiXC5nljk4OBBdXV3muZubGwFA6urqmGVdyWBubk4AkF27dhG5XE4yMzNJVVVV\nl/b77rvvEgDk4cOHLfIeOXKEACBLlixp87h27NhBALQaMOXq6tppQbSzsyP9+/dv87W2PruOjq+9\ndQj5399naGhoh3k0xaBBg8jHH3/MdoxWaEGklKVXr15k//79bMdQiVanTBUKBfLz8+Hh4aGsRqhK\nBQUFoampCTExMQDATK01duxYAEBSUhK8vLxAHhf/Fo8n777+NJ6c1NbAwAByuZx53nya9snJCrqS\nYfv27dDV1cWSJUvg5+eHysrKVoNp2tvvxYsXAaDVef3g4GAAQEJCQpvHceLECQCPr69rbz/tEYlE\nTzUhQ1eOry3N/ZOdnfrVFB4eHsjKymI7BkWpRFVVFerr69GvXz+2o6hEt5+u/JNPPsHGjRsxb948\nfPDBB1i2bBnWr1+PLVu2AHg8+CM3NxeNjY2t1n2ykKlSVzLMnTsX165dw5gxY5CSkoKgoKAW1/Z1\npPl2LPfu3WuxvHfv3gDan3OwedBQV/oM/4nD4TzVnSCe9fiaf2BQFMW+5lujaetAy1YFUUdHB66u\nrt3mV65CoUBVVRWuX7+OzZs34/Dhw9iwYQP09PQAAIMHD0ZtbW2rIfElJSUtlrX15f40X/gd6UqG\nLVu2wNvbG3FxcThx4gQ4HA4++uijLm1/zJgxAMC0kpsVFRUBACZPntzmek5OTm2u1xV2dnZdGhTT\n7FmPr7lY29nZPXVGNmRlZXWbsysU9bSapyu0tbVlOYmKtHUede3atcTb21sNZ2yf39q1a8nAgQPJ\njz/+SM6ePUsuX75Mbt68yQyykUgkhM/nEx0dHfLee++R06dPkx07dpDQ0FAiFosJIYT07duXmJmZ\ntRh80tYyFxcXAqDFRejOzs4t+vea+9+efE9XMlhZWZGKigpmHXt7e+bvoLP9Pnz4kDg7OxMHBwdm\nkA4hhKxevZp4e3sTiURCCPnfBfCurq6EEEIuXLhAdHR0iI2NDUlISCAKhYKkp6cz/X3l5eXtfu4z\nZ84kHA6nRV8pIYRIpVICgLi4uLRY3tHx/TPXk27dukUAaGS/3D9lZWURACQtLY3tKK2A9iFSSnD4\n8GHC4/GIQqFgO4pKtFkQRSIRMTIyYgZ8aLLffvuN9OnTp9VgFRMTE/LDDz8QQh6P/gsLCyMGBgbE\nzMyMzJ49m4hEImYb3333HTE2NiZLly5tc5lcLif79u0jXC6XACCbNm0iNTU1ZN++fURXV5cAIB98\n8AGJiooiPB6PGRV569YtZnudZcB/B5VERUWR999/n0yYMIHk5uZ2ut/PPvuMNDQ0kLKyMvLWW2+R\ngIAAsmrVKrJ06VKyevVqUl1dTQgh5P79+2TTpk0EANHT0yMHDx4klZWV5OzZs2TYsGGEx+ORPn36\nkJUrV5IRI0aQRYsWkXPnzrUowk+KiYkhAEhiYmKLY9ywYQMBQHR1dcnu3bvJ7du32z2+/Pz8dnM1\n+/nnnwmHwyFZWVnP87+JWsyYMYNMnjyZ7RhtogWRUoYvvviixWhxbcMhpO3zglu2bMHWrVtx7do1\nODo6qqBt+vwIIdi5cycIIVi2bBmzrL6+HjExMZg9e/Yz9Y9RnSOEICwsDL6+viqdyzM8PBzGxsY4\ncOCAyvahDPv378fbb7+N1NRUjTxlyuFwcOjQIcycOZPtKFQ39s477yAjI4MZyKdt2h1Us3LlSnh7\neyM0NJTpSNU069evx7Jly/Dmm28yyzgcDgwNDeHn59cjJyFXFw6Hg/379yM6OhpisVgl+7h58yYy\nMjK6PLiILSdPnkRERAS+/vprjSyGFKUs9+7d09gGkjK0WxB1dXXx66+/wtzcHMOHD9fIKani4+MB\nANu2bYNMJgPwuOWSnp6OFStW4NChQyym0379+/fHwYMHsXz5cubzV5ZHjx5h3bp1+OOPPzR6Vozd\nu3djxowZWLduHSIiItiOQ1EqdffuXWYwnlbq7JyqWCwmY8aMIUZGRmTnzp0a1ZlaWFhIIiIiCJ/P\nJ2ZmZsTT05NMnDiRREVFMYNVKNXLzs4mX375pdK2J5VKSWRkZItBOJrm0aNH5PXXXyc6Ojrkiy++\nYDtOp0D7ECkl6NWrFzl48CDbMVSm3T7EJ8lkMnz66aeIiorCyJEj8cMPP2DAgAFqKNcUpXlOnz6N\nhQsXgsfj4fvvv8e4cePYjtQp2odIPS+RSARbW1tcvHgRo0aNYjuOSnTpwnwej4eNGzfiypUrKC8v\nh6enJyIjI+mAFapHyc7OxmuvvYapU6diwoQJuHnzZrcohhSlDM0Tf/TIPsS2+Pr64vr161i5ciUi\nIyPh5OSErVu3oq6uTlX5KIp1d+7cwdy5czFkyBBkZGQgOjoa+/fv19pb4FBUW/Lz86Gvr6/xd0B6\nHk89dZu+vj7Wr1+Pu3fvYv78+diwYQOcnZ3x5ZdfoqKiQhUZKYoVN2/exIIFC+Du7o4rV67gwIED\nuHnzJiZOnMh2NIpSu8zMTLi5uXVpruPu6pmPrE+fPoiKisKdO3cwa9YsbNy4Ef3798fcuXORmJio\nzIwUpTYNDQ346aefMGLECAwdOhSJiYn44YcfcOvWLcyaNUurvwwoqiPZ2dkYOHAg2zFU6rn/dVta\nWmLr1q148OABduzYgdu3byMoKAienp7YuXMnves81S3cuHED7733Huzs7BAREQF7e3tcuHABmZmZ\nmDNnDrhcLtsRKYpVPWGeXqX93DUyMsKCBQuQnJyMlJQUDB8+HB988AH69euH4OBgfPvttyguLlbW\n7ijquV27dg3vv/8+XFxc4OPjg99//x1r1qxBQUEBDh8+jOCcTcn0AAAgAElEQVTgYHq3DYrC49vq\n5eTkaH1B7NJlF8+qsbERcXFxOHbsGE6dOoWqqioEBgbi5ZdfxsSJE7X+w6U0i1QqxeXLl3HmzBmc\nOHEC9+7dw8CBAzF9+nTMmDEDAoGA7YgqQy+7oJ5HdnY2PDw8kJaWBk9PT7bjqIxKC+KTZDIZzp8/\nj2PHjuHMmTMQiUTo378/QkNDERYWhjFjxsDS0lIdUage5NatW/jrr78QFxeHixcvoq6uDl5eXpg2\nbRqmT5+OwYMHsx1RLWhBpJ7HqVOn8Morr0AikTzVjcG7G7V1jPB4PIwbN465bisjIwOxsbGIjY3F\nggUL0NDQAE9PT4wePRqBgYHw9/eHvb29uuJRWkAul+PmzZu4fPkykpKScP78eRQXF8POzg6hoaHY\ns2cPQkNDYWVlxXZUiupWbt68CQcHB60uhoAaW4gdaWxsxOXLlxEbG4u///4bKSkpaGxshJ2dHQID\nAxEYGAg/Pz/4+vpCX1+f7biUhigvL8fVq1dx5coVXL58GdeuXUNtbS0sLS3h7++PkJAQhIaG9phW\nYEdoC5F6Hq+++iqamppw4sQJtqOolEYMndPX10dISAhCQkIAPC6QKSkpSE5ORmJiIjOKlcfjYeDA\ngRAIBPD09GQe9Be/dlMoFMjLy0NaWhqEQiHS0tKQkZGBgoIC6OrqYsiQIQgKCsK8efMQEBAANzc3\ntiNTlFYRCoX417/+xXYMldOIFmJXFBYWIjk5GTdu3IBQKER6ejoKCgoAADY2NvD09IRAIICrqyvc\n3Nzg7u4OGxsbllNTT0Mmk+HevXvIzs5GTk4OsrKyIBQKkZGRgbq6OvB4PAwaNIj5QeTj44MXXngB\nJiYmbEfXeLSFSD2r2tpamJqa4tixY3j55ZfZjqNS3aYgtkUsFjOthvT0dGRkZCAnJwdVVVUAAFNT\nU7i5ucHV1RXu7u5wc3ODo6MjHBwcYGtrS4fUs6CxsREFBQUoKChAXl4ecnNzmQJ49+5d5jZS9vb2\ncHNzg5eXF1MABw0aBB6Px/IRdE+0IFLP6sqVKwgICMCdO3e0/qYOGnHK9FmZm5sjODgYwcHBLZaX\nlpYiOzsbubm5yMnJQU5ODo4ePYq8vDxIpVIAgJ6eHuzt7cHn88Hn8+Ho6Mg8t7Ozg6WlJR31+pSk\nUinKy8shEonw4MED3Lt3jyl+zY+SkhLm/RYWFnBzc4ObmxvmzJnDtO5dXV1hZGTE4pFQFNVMKBTC\nzMxMqyf1btatC2J7rK2tYW1t3eoWJXK5HMXFxbh//z7u37+PgoICFBYWoqCgANevX0dBQQFqamqY\n9/N4PFhaWsLKygq2trat/mxhYQFzc3PmYWFhoVVf5JWVlRCLxcx/xWIxKioqIBKJUFZWhrKyMuTn\n56O2thZlZWV49OhRi/VtbW3B5/Nhb2+PoKAgzJw5Ew4ODsyPkD59+rB0ZBRFdVVaWhoEAkGPOKOm\nlQWxPbq6urC3t4e9vT1GjBjR5nsqKytRXFyMsrIylJSUoLy8HKWlpUwRuH37NrO8sbGx1fo8Hq9F\nkTQzM4OJiQm4XC7Mzc3B5XJhamoKPT09GBkZwdDQEPr6+sx7mjUvf5K+vj4MDQ2Z52KxGP88411T\nU4OmpibmeV1dHRobG5nllZWVkMvlqK6uRmNjI+rq6pjHk4VPLBa3+fmYmJigX79+sLS0hI6ODq5f\nvw5/f3+89dZbsLe3h5WVFaytrdG/f386IpiitEBqair8/PzYjqEWPaogdoWFhQUsLCy6NFS/rq6u\nRQH5Z2tKLBajuroa1dXVaGpqwt27d9HU1ISamho0NDSgvr4etbW1kEqlqKqqgkKhUPrxGBgYoFev\nXjA2NgaPx4OZmRm4XC7MzMzA4/FgbGyMPn36gM/nt2rt/rMF3FzQn/TTTz9h6dKl+O677/Dzzz/D\n29tb6cdAURQ7pFIp0tPTsWTJErajqAUtiM/B0NAQhoaG6Nevn9K3/c+WHvC/1l6z5iL3z0zqbJnN\nnj0bo0aNwty5c+Hv748NGzZg1apV0NXVVVsGiqJUIyMjAw0NDRg+fDjbUdSCFkQN1dalBBYWFiwk\n6ZyDgwPOnz+PrVu3Yv369YiOjsaPP/6o9SPSKErbJScnw8zMDK6urmxHUQt6czdKKXR0dLB69Wpc\nvXoVYrEYXl5eOHDgANuxKIp6DsnJyXjhhRd6zH1Ae8ZRUmrj6emJa9eu4c0338SCBQsQHh6Ohw8f\nsh2LoqhncO3atR4zoAagBZFSAQMDA3z55ZeIjY1FcnIyBAIBzp49y3YsiqKegkQiwe3btzFs2DC2\no6gNLYiUyoSEhEAoFOLFF1/EpEmT8Pbbb6Ouro7tWBRFdUFycjIUCgVtIVKUspibm+PQoUP45Zdf\ncPjwYfj4+ODatWtsx6IoqhOJiYlwcHCAnZ0d21HUhhZESi1ef/11pKeno3///ggMDMSmTZtaXVZC\nUZTmSExMREBAANsx1IoWREpt7O3tERsbi88//xybN2/GqFGjkJ+fz3YsiqL+QaFQICkpqd0ZvbQV\nLYiUWnE4HKxYsQLXr19HfX09vLy8sG/fPrZjURT1hMzMTIjFYgQGBrIdRa1oQaRYMXjwYFy9ehVv\nv/02Fi9ejClTpqCsrIztWBRFAbh8+TKMjY0hEAjYjqJWtCBSrNHT00NUVBTi4+ORkZGBoUOH4vff\nf2c7FkX1eJcvX0ZAQECPm4KRFkSKdSNHjkR6ejrGjx+PKVOmYOHChZBIJGzHoqgeKzExscedLgVo\nQaQ0hKmpKQ4ePIhff/0VJ06cgLe3N65cucJ2LIrqcYqKipCfn9/qfrI9AS2IlEYJDw9HRkYGnJ2d\nMXLkSKxfv55enkFRanT+/Hno6+v3uEsuAFoQKQ1ka2uLs2fPYvv27fjyyy8RGBiI7OxstmNRVI9w\n4cIFDB8+HL169WI7itrRgkhpJA6Hg7fffhspKSkghMDHxwe7du0CIYTtaBSl1eLj4zFmzBi2Y7CC\nFkRKo3l4eCAxMRHvvvsuli1bhkmTJqGkpITtWBSlle7cuYN79+4hODiY7SisoAWR0ng8Hg8bN27E\n33//jZycHAgEApw4cYLtWBSldS5cuABDQ0MMHz6c7SisoAWR6jYCAwNx48YNTJs2DdOnT8f8+fNR\nXV3NdiyK0hoXLlxAUFAQ9PX12Y7CCloQqW7FxMQE+/btw2+//Ybo6Gh4eXkhISGB7VgUpRUuXLjQ\nY0+XArQgUt3U1KlTIRQKMXjwYAQHB2Pt2rWQSqVsx6Kobis7OxvFxcV48cUX2Y7CGloQqW7L2toa\nv//+O3bt2oUdO3bA398fmZmZbMeiqG7p/PnzMDExwbBhw9iOwhpaEKlub+HChUhLS4Oenh58fX3x\n9ddf08szKOopxcfHY8SIEeByuWxHYQ0tiJRWcHFxQUJCAt5//32sXLkSYWFhePDgAduxKKpbIIQg\nPj4eISEhbEdhFS2IlNbgcrn4+OOPcfnyZRQUFEAgEODo0aNsx6IojXfz5k2UlZX16P5DgBZESgv5\n+fnhxo0bePXVV/H666/jjTfeQFVVFduxKEpjxcfHw8LCAl5eXmxHYRUtiJRWMjQ0xO7du3HmzBmc\nO3cOAoEAFy5cYDsWRWmk8+fPY9SoUT3u/of/RAsipdUmTpyIjIwM+Pj4YOzYsVi5ciUaGxvZjkVR\nGkMul+PChQs9dv7SJ9GCSGm9vn374uTJk9i3bx/27t2LYcOGQSgUsh2LojRCUlISqqqqEBYWxnYU\n1tGCSPUY8+fPR1paGkxNTeHn54cvv/wSCoWC7VgUxaqYmBgMGDAA7u7ubEdhHS2IVI/i5OSEixcv\n4uOPP8batWsxduxYFBQUsB2LolgTExOD8ePHsx1DI3AIvYKZ6qFSU1Mxe/ZsPHjwAN9++y1mzZrF\ndiSluHTpUqt+0tDQUKxdu7bVdWbDhg2DmZmZOuNRGuThw4ewtrbG8ePHMW3aNLbjsI4WRKpHq6+v\nx/vvv48dO3ZgxowZ2L17N3r37s12rOcyY8YMHDt2rEvvlUgkMDIyUnEiSlP98ssvmDdvHh49egQT\nExO247COnjKlerRevXrh66+/xp9//omEhAQIBALExsayHeu5vP76652+R1dXF1OnTqXFsIeLiYlB\nYGAgLYb/RQsiRQEICwtDRkYGAgMDMW7cOCxfvhwNDQ0driOTydSU7ulMmjQJhoaGHb5HoVBg/vz5\nakpEaSJCCP766y/af/gEWhAp6r969+6No0eP4uDBgzhw4AB8fX1x48aNNt8rl8sRFhaGlJQUNafs\nnIGBAWbMmAEej9fue0xMTOgXYQ+XlpYGkUhEL7d4Ai2IFPUPs2fPRnp6OqysrODv74+oqCjI5fIW\n74mKikJ8fDzCwsIgkUhYStq+WbNmtduC5fF4CA8Ph56enppTUZokJiYG1tbW8Pb2ZjuKxqAFkaLa\n4ODggHPnzmHTpk345JNPEBwcjLt37wIAUlJS8MknnwAAqqur8c4777CYtG0hISHtDg6SyWRaM6KW\nenYxMTEICwsDh8NhO4rGoAWRotqho6ODVatWITk5GVVVVfDy8sKePXtaDFppamrCwYMHNe6uGrq6\nupg5c2abrUBLS0sEBwerPxSlMSQSCS5fvkxPm/8DLYgU1QmBQIDk5GS8+eab+PTTT3Hv3j00NTUx\nr3M4HCxYsAD3799nMWVrM2fOhFQqbbFMT08Ps2fPho4O/affk507dw5yuRyhoaFsR9Eo9F8FRXWB\ngYEBxo0bh5KSkhbFEHg8Wq+xsRGvvfZaq75GNvn7+6N///4tlkmlUrz22mssJaI0RUxMDHx8fGBp\nacl2FI1CCyJFdUFFRQXeeOONdvtbZDIZrl+/jo0bN6o5Wfs4HA7mzJnT4rQpn8+Hn58fi6koTRAT\nE4Nx48axHUPj0IJIUV0QEREBsVjc4WTgcrkcGzduREJCghqTdez1119nTpvyeDzMmzeP3UAU63Jy\ncnDnzh3af9gGWhApqhM//fQTTpw40aUL8TkcDl577TWIxWI1JOvc0KFD4eHhAeBxK3bmzJksJ6LY\n9ueff8LMzAz+/v5sR9E4tCBSVCfCwsLw888/Y+bMmTA3NwfweHBKW6dP5XI5ysvLERERoe6Y7Zo7\ndy4AwM3Njd7ih0J0dDTCwsLA5XLZjqJx6OTeFPUUFAoFUlJSEBsbizNnziA5ORkKhQJcLrdFC5LD\n4eD7779/7unRqqqqIJfLIRaLIZPJIJFI0NDQgPr6ekgkkhb7rKmpaTXgRyaTobCwEFu2bEFAQABe\nffXVNqd1MzMzazHy1MLCArq6ujA1NYW+vj4MDQ1haGgIfX19mJqaQldX97mOi2KHRCJB3759sWfP\nHuaHEvU/tCBS1HOoqqpCXFwcYmJiEB0djeLiYvB4PKZQXb9+HdbW1hCJRCgtLUVFRQXEYjHEYjGq\nqqqYPzc/KisrIRaLIZFIWl0y0ZlevXrBwMCgxTIOhwNzc3MUFxfDysoKUqm01a2hFAoFqqqqnvrY\nLSwsYGxsDHNz8w4fvXv3hqWlJaytrWFjYwNjY+On3helHL/99humT5+OkpISWFlZsR1H49CCSFHP\nQCqVoqioCIWFhbh//z4KCwtRVlaG3Nxc5OTkoKSkBHV1da3W09fX77B4WFhYwNzcHEZGRi1aYxYW\nFuByuTAxMWFabEZGRl2efi0lJQW+vr5dei8hBGKxGE1NTaipqWFapLW1tZBKpS1arTU1Na2K+j8f\n/5zaztDQkCmOVlZWzJ9tbW3B5/Ph4OAAPp9P78CgAhEREcjIyMCVK1fYjqKRaEGkqDbI5XLcu3cP\nOTk5yMvLQ0FBAQoKCpgCKBKJmBGnBgYGsLe3h7W1NSwtLdGvXz9YWlqid+/eqKiowIABAzBixAhY\nW1v3yNstyWQylJWVoaysDCUlJSgvL4dIJIJIJGL+XFpaiuLiYlRUVDDrmZubtyiQfD4fAwYMgKur\nK9zc3Dq9owfVmr29PRYuXIiPPvqI7SgaiRZEqkd7+PAhMjMzkZOTw7TusrOzkZ+fz5yytLa2hoOD\nA+zt7WFvbw9HR0fY29szX9L01JPySCQSFBQUMK3u5h8izc8LCwvR1NQEDocDe3t7pjg2Dxhyc3PD\ngAED6Ew8bcjIyIBAIMD169e7fLagp6EFkeoRmpqakJ2dDaFQiLS0NOa/IpEIAGBqasp8sTZ/uTZ/\n2dJTd5pDJpPh7t27yM7ORk5ODvNDJjs7G8XFxQAAY2NjDB06FAKBAF5eXhAIBBAIBD2+7zIqKgrb\ntm1DSUkJ/cHQDloQKa1DCEFWVhaSkpKQmJiIGzduIDMzEw0NDTAwMMDgwYPh6enJfFEOHDgQNjY2\nbMemnlNNTQ1ycnKQkZGB9PR0ZGRk4MaNG6ioqACHw4GTkxO8vb3h7++P4cOHw9fXF7169WI7ttqM\nHDkSzs7OOHDgANtRNBYtiFS3V1NTg6tXryIxMRFJSUlITk5GRUUFc/Gxp6cn01Jwd3en11/1MEVF\nRS0K5JUrV1BQUAAejwcfHx8EBAQgICAA/v7+4PP5bMdViYqKClhZWeHw4cOYMWMG23E0Fi2IVLcj\nk8mQlJSEuLg4xMbG4tq1a1AoFPDw8ICfnx8CAwMRGBiIQYMG0VNDVJsePHjQ4kdUSkoKGhsb4eTk\nhNDQUIwdO7bDe0p2N7/88gvmzZuH8vJymJmZsR1HY9GCSHULWVlZ+OuvvxAbG4v4+HhIJBIMHToU\nY8eOxZgxYxAQEKA1X16U+jU2NiI1NRV///03YmNjcfnyZchkMvj4+CA0NBShoaEICgoCj8djO+oz\nmTVrFkpKSnD+/Hm2o2g0WhApjXXjxg0cP34cx44dQ3Z2NmxtbTF27FjmC4r2+1GqUldXh4SEBMTG\nxiI2NhZCoRDm5uaYOnUqwsPDMXbsWOjr67Mds0sUCgWsrKywdu1avPfee2zH0Wi0IFIaJTU1FUeP\nHsXx48eRl5cHFxcXhIeHIzw8nA4Vp1hTXFyMEydO4NixY0hISICJiQkmT56M8PBwjB8/XqOL49Wr\nV+Hv74/bt28zE71TbaMFkWJdTU0NDh06hD179iAtLQ0eHh5MEfT09GQ7HkW1UFpaipMnT+LYsWOI\nj49Hnz59MG/ePERERMDFxYXteK2sX78eBw8exP3799mOovFoQaRYk5qaiu+++w6HDx8GIQSvv/46\nFi1ahGHDhrEdjaK6pKSkBP/5z3+wb98+FBQUYOzYsVi0aBGmTJmiMf2Nvr6+GD58OHbt2sV2FI1H\nh+BRanf16lWMGzcOvr6+uHr1KqKiovDgwQN8//33tBhS3YqtrS0++OAD3LlzB2fOnEGvXr3w2muv\nwd3dHfv372919xF1Ky4uxo0bNzBp0iRWc3QXtCBSapOSkoKXXnoJ/v7+qK2tRVxcHNLT0/H222/T\noeBUt6ajo4OJEyfi1KlTyM/PZ1qKgwYNwk8//cTMe6tuf/zxBwwMDBASEsLK/rsbWhAplauoqMDs\n2bMxbNgwlJWV4c8//0RCQgLGjBnDdjSKUjoHBwfs3bsXWVlZCAwMxPz58yEQCHDp0iW1Z4mOjkZI\nSEiPmpHnedCCSKnU6dOnMWTIEFy4cAEnT55kTpdSlLZzcnLCgQMHcOvWLTg6OmL06NFYtGhRm7cF\nUwWpVIq4uDh6uvQp0IJIqUR9fT3mzp2LqVOnIiwsDDdv3sTUqVPZjtWh6urqbrvfiooKnDp1CpGR\nkWrZnyZ4mmNmk5ubG86cOYMjR47g+PHjeOGFF5CVlaXy/V6+fBkSiYQWxKdBKErJysvLib+/P7Gy\nsiJnzpxhO06ntm3bRkJCQgiPx+uW+719+zZZs2YNAUDc3d3bfE99fT354osvSHBwMOFyuS1ec3d3\nJwsWLHiuDM8iIyODfPXVV8xzuVxOPvvsM7J27VoSGBhIBg4cSIRCYZvrtnXMMpmMrF27lty/f18t\n+Z9FUVERGTlyJLGwsCCXLl1S6b7ee+89MmjQIJXuQ9vQgkgplVgsJn5+fsTd3Z3k5eWxHaddd+/e\nZf4sk8mIra0tUffvQ2Xut6mpqcOC2Lw/GxubVvsbOXIkWbly5XNneBp//PEHmTt3LmlqamKWff75\n58Ta2pooFApSWVlJJkyYQP7+++92t9HWMUskEhIeHk5yc3NVmv95NDY2klmzZhFjY2Ny5coVle3H\nw8ODvPfeeyrbvjaiBZFSqvDwcOLi4kIePHjAdpR2FRYWkpEjR7ZY5u7urvaCqOz9dlYQlb2/Z5We\nnk5cXFxIdXV1i+UDBgzoNP8/tXXM+fn5ZPDgwUQsFj93VlVRKBRk9uzZxMbGhohEIqVvPy8vjwAg\n58+fV/q2tRntQ6SU5pdffsGff/6JM2fOoF+/fmzHaVN5eTkmTZqEsrIytqP0SHK5HHPmzMH8+fNb\n3XhZWTOpODk5YdCgQXj33XeVsj1V4HA42L9/P5ydnbFkyRKlbz86OhpmZmYYMWKE0retzWhBpJRC\nKpXiww8/xKeffgp3d3e247Rr165dEAqFEIlEWLx4cavXMzMz4efnBy6XiyFDhuD69euQy+VISEjA\nmjVr4OzsjDt37sDT0xN9+vRBcXExampq8Omnn+LNN9/EqFGjEBgYiOTkZGab6enpePHFF7F582as\nW7cOurq6qKmp6XS/zcrKyrB06VKsWLECq1atQmBgIBYtWoSSkpIOj1UikWDFihWYN28e1qxZg+XL\nl0MikTCvy+VyHD16FHPnzsWoUaNACEF0dDSWLl0KBwcHFBQUIDQ0FFwuF0OHDkVKSgqzrkKhQGRk\nJObPn49ly5bByMgIHA6HebTn5MmTSE9Px0svvcQsO3PmDBYvXgyFQsH8vSxevBgSiaRLn11bJk2a\nhP379yMnJ6fT97JFV1cXBw4cwOnTp3H16lWlbjs6OhphYWEaM1tOt8F2E5XSDidPniT6+vqkpqaG\n7SidQhun2ZpPJX744YektLSUxMfHEwDE19eXNDY2kpSUFGJqakoAkO3bt5P4+HgyY8YMUl5eTiZO\nnEhKSkqYbb322mvEwsKCVFZWEkIIcXJyInw+n3k9IiKClJaWdrpfQggpKysjjo6O5LPPPmPWF4vF\nZODAgcTOzq7Fqeknj6uxsZEEBASQxYsXM6/fvXuX6OnptThlWl1dzaynUCjIw4cPSe/evQkAsnnz\nZlJSUkLi4+MJh8Mh3t7ezHqRkZFET0+PNDQ0EEII2bdvHwFA3njjjQ4/+1deeYVwuVwik8m69PfS\n0WfX3jqEECIUCgkAsn79+g7zaIIpU6Z0+rk9jZqaGqKvr08OHDigtG32FLQgUkqxZMmSVv1ymqqj\ngiiXy5llDg4ORFdXl3nu5uZGAJC6ujpm2R9//EEAtPk4fvw4IYQQc3NzAoDs2rWLyOVykpmZSaqq\nqrq033fffZcAIA8fPmyR98iRIwQAWbJkSZvHtWPHDgKAZGZmtljP1dW1RUFUKBStPo/m43ySk5MT\n4XA4zPPm0arNha2iooIAIEOGDCEdsbOzI/3792/ztbb+Xjr67Npb58k8oaGhHebRBN988w2xtbVV\n2vbOnDlDdHR0WvxwoLqGnjKllKK4uBh2dnZsx3huOjr/+ydhYGAAuVzOPG8+FfjkrB9JSUnw8vIC\nefzjssXjlVdeAQBs374durq6WLJkCfz8/FBZWQlTU9Mu7ffixYsA0Gpqu+DgYABAQkJCm8dx4sQJ\nAGh194Un9/PkMXW2jMfjgTxxH4CgoCA0NTUhJiYGAJg5O8eOHdtmnmYikeipZk3pymfXlub+yc5O\nK2sCOzs7lJSUKG3e0+joaPj6+sLKykop2+tJaEGklMLc3FxrLvh+GnK5HLm5uWhsbGzzNQCYO3cu\nrl27hjFjxiAlJQVBQUHYtm1bl7bfPAfmvXv3Wizv3bs3AMDQ0LDN9ZoHDT3ZZ6hMn3zyCTZu3Ih5\n8+bhgw8+wLJly7B+/Xps2bKlw/U4HE6LwtqZZ/3sOurH1DRisRjGxsbgcrlK2V50dDTGjx+vlG31\nNLQgUkoxbNgwXL9+nbVJjJ/W03wpd2Tw4MGora1tdWudkpISZtmWLVvg7e2NuLg4nDhxAhwOBx99\n9FGXtt8832tzS6xZUVERAGDy5Mltrufk5NTmesqiUChQVVWF69evY/PmzTh8+DA2bNgAPT29Dtez\ns7Pr0qCYZs/62TX/EOgOZy2Sk5Ph5+enlG1lZGSgoKCAzk7zrNg7W0tpE5FIRAwNDcnZs2fZjtKp\nvn37EjMzsxYDUlxcXAiAFheKOzs7t+jfa+5/e/I9EomE8Pl8oqOjQ9577z1y+vRpsmPHDhIaGspc\nB2dlZUUqKiqYdezt7ZkBKp3t9+HDh8TZ2Zk4ODgwg3QIIWT16tXE29ubSCQSQsj/LlJ3dXUlhBBy\n4cIFoqOjQ2xsbEhCQgJRKBQkPT2d6ZMrLy9vc70nj1OhULTK1Lxs7dq1ZODAgeTHH38kZ8+eJZcv\nXyY3b95kBtm0Z+bMmYTD4bTohyWEEKlUSgAQFxeXFss7+uzayt7s1q1bBAD5+OOPO8zDNolEQiwt\nLcn+/fuVsr3IyEhiZWXVok+a6jpaECmlWb16NRk8eDCpra1lO0qHvvvuO2JsbEyWLl1K5HI52bdv\nH+FyuQQA2bRpE6mpqSH79u0jurq6BAD54IMPSFRUFOHxeMzIxVu3bjHby87OJmFhYcTAwICYmZmR\n2bNnt7jYGv8d+BEVFUXef/99MmHCBJKbm9vpfj/77DPS0NBAysrKyFtvvUUCAgLIqlWryNKlS8nq\n1auZC9vv379PNm3aRAAQPT09cvDgQVJZWUnOnj1Lhg0bRng8HunTpw9ZuXIlGTFiBFm0aBE5d+4c\nqaqqItu2bWPW++mnn8h3333HjET99ttvSVVVFfnxxx+ZTFFRUaS+vp789ttvpE+fPq0GEpmYmJAf\nfvih3c8+JiaGACCJiYktPr8NGzYQAERXV5fs3r2b3AAJZREAACAASURBVL59u93PLj8/v91jbvbz\nzz8TDodDsrKylPb/jSqsXLmSDBw4sM1Rt89ixIgRZO7cuUrZVk/EIURJ546oHq+mpga+vr4YNmwY\nfvzxR+jq6rIdiVIBQgh27twJQgiWLVvGLKuvr0dMTAxmz57dbt8lIQRhYWHw9fVFVFSUyjKGh4fD\n2NgYBw4cUNk+ntfhw4cxZ84cnD9/HiNHjnzu7VVUVMDKygqHDx/GjBkzlJCwB2KzGlPaJyUlhZiZ\nmZH/+7//U9qvXkqzfPjhhwRAm2cCioqKOp1QurCwkAwZMqRFi06ZMjIyiJubW4tTrZrmyJEjRE9P\nj0RFRSltm4cPHyY8Hk+jp6zTdHRQDaVUPj4++O2333Ds2DFMmTIFFRUVbEeilCw+Ph4AsG3bNshk\nMgCPW37p6elYsWIFDh061OH6/fv3x8GDB7F8+XJmfWV59OgR1q1bhz/++AMWFhZK3bYyKBQKREVF\nYebMmVizZg3WrFmjtG3/+eefCAoKanWJDvUU2K7IlHa6ceMGsbe3J7a2tuTUqVNsx6GUqLCwkERE\nRBA+n0/MzMyIp6cnmThxIomKinqq1kl2djb58ssvlZZLKpWSyMhIjW0Z5ufnk9GjRxMej0d2796t\n1G3L5XJiZWVFIiMjlbrdnob2IVIqU1lZiRUrVuDgwYOYPXs2vv76a4381U5RqkQIwe7du7F69Wq4\nuLhg//798Pb2Vuo+rl69Cn9/fwiFQgwdOlSp2+5J6ClTSmUsLCxw4MAB/P7774iLi4Obmxu2bNmC\n2tpatqNRlFr8/vvveOGFF/Dvf/8bK1asQHJystKLIfD4Ynw+n0+L4XOiBZFSucmTJyMzMxOLFi3C\npk2b4OTkhK+++gr19fVsR6Molfjzzz/h5+eHqVOngs/nIzU1FRs3bux04oJnFR0dTS/GVwJaECm1\nMDc3x6ZNm3D37l3MmzcP69evh5OTEz755BNm1hWK6s7q6+tx8OBB+Pn5YcKECbC2tsb169dx8uRJ\nlbbciouLcePGDVoQlYAWREqt+vbtiy1btiA/Px8LFizA3r174ejoiKlTp+Ls2bPdZuo3imp2+/Zt\nLF++HHZ2dli0aBFcXV2RnJyM33//HT4+Pirf/9mzZ2FgYICQkBCV70vb0UE1FKtkMhlOnz6NPXv2\nIC4uDg4ODpg1axamT5+ukr4WilIGkUiEkydP4siRI/j777/h6uqKhQsXYt68eejbt69as7z66quo\nq6vDmTNn1LpfbUQLIqUx8vLysH//fhw9ehR5eXlwdnZGeHg4pk+fjmHDhrEdj+rhioqKcPLkSRw7\ndgwJCQkwNTXFlClTMGfOHISEhLByhw2ZTAZLS0ts2rQJ77zzjtr3r21oQaQ0klAoxK+//orjx4/j\n9u3bcHR0xKRJkxAaGorg4GB68TGlcnK5HMnJyYiNjcXZs2dx9epV9O7dG1OmTMGMGTMwZswYlQ2S\n6aoLFy4gJCSE+QFJPR9aECmNd+vWLZw6dQoxMTG4cuUKFAoFhg8fjtDQUISGhsLPz09p95Kjera8\nvDzExcXhr7/+wvnz51FVVQUPDw+EhoZi8uTJCAkJ0aj/11auXIno6Gjcvn2b7ShagRZEqluRSCS4\nePEiYmNj8ddff+H27dswNjbGCy+8gKCgIAQEBMDPzw+WlpZsR6U0XGNjI1JSUpCUlIQrV64gKSkJ\nDx48gKWlJcaMGYPQ0FCEhYWhf//+bEdt18CBAzFp0iRs3bqV7ShagRZEqlsrKirC+fPnceXKFSQm\nJuLmzZuQy+Vwc3NjiqOvry+GDBkCIyMjtuNSLJHL5cjJyUF6ejquXr2KpKQk3LhxA1KpFPb29vD3\n90dgYCBGjRoFLy8v6Oho/gD8/Px8uLi44Pz583jxxRfZjqMVaEGktEpNTQ2uXbuGpKQk5pf/o0eP\noKOjAxcXFwgEAnh5eUEgEMDT0xN8Pp/tyJSSicViCIVCpKenIyMjAzdu3MCtW7dQX18PfX19+Pr6\nwt/fnymCdnZ2bEd+Jt988w3Wr1+P8vJy8Hg8tuNoBVoQKa1XUFCA9PR0CIVCpKWlQSgUIi8vDwqF\nAhYWFnB3d4ebmxvc3d3h6uoKNzc3uLq6wtDQkO3oVDvkcjnu3buH3NxcZGdnIycnBzk5OcjNzcX9\n+/cBANbW1hAIBPD29sbQoUPh6ekJDw8PrSkekydPhqGhIY4ePcp2FK1BCyLVI9XW1uLmzZtIS0tD\nTk4OsrKykJubi7t376KpqQkcDgf29vZwdXWFi4sL+Hw++Hw+HB0dwefz0a9fP40aXKGNysrKUFhY\niIKCAhQUFODevXu4d+8esrOzkZ+fD6lUCgCwtbWFm5sb8xAIBBAIBLCxsWH5CFSnvr4effr0wbff\nfov/+7//YzuO1qAFkaKeIJPJcOfOHabFkZOTg7y8PBQUFKCwsBCNjY0AAF1dXfTr1w8ODg5wcHCA\nnZ0dbG1tYWlpCRsbG1hbW8PS0hJWVlasXJ+myWpqalBcXIzy8nKUlZWhpKQE5eXlePDgAVP87t+/\nz8x1q6OjAxsbGzg4OGDAgAGtWvMmJiYsH5H6RUdH46WXXkJRURH69evHdhytQQsiRXURIQQikYj5\n0n6y5VJUVITS0lKUlZW1uOmtrq4uUxhtbGzQu3dvmJubMw8LC4s2/2xoaKjRg4CkUilqa2tRVVWF\nyspKiMXiFo9/LmsufGVlZWhoaGixrebPp1+/fq1a4nw+H/3792f9ej9Ns2TJEly9ehUpKSlsR9Eq\ntCBSlJI9evQIZWVlKCsrg0gkQmlpKcrLy1FSUsIUiicLRmVlZbvb6tWrFwwMDGBiYgIulwtzc3Nw\nuVyYmppCX1+/RT8nh8OBubl5q22YmZkxoyarq6shl8tbvF5bW8ucfgQet5IlEgkaGhpQX18PiUQC\nmUwGsVgMuVyOqqqqdvMaGxu3KPjNhd7S0hK2trawsrKCpaUl+vXrxxRCeur56Tk4OGDu3Ln49NNP\n2Y6iVWhBpCgN8M+WVV1dHerq6phiVV1djaamJojFYjQ1NaGmpoYpWM2alz+JEAKxWMw8NzQ0hL6+\nfov3/LOwcrlcmJiYMMuNjIygp6cHU1NTXLp0CWfPnsX27dthZ2cHMzOzFsVPWwasaLK0tDR4e3sj\nOTmZTmmoZLQgUhTVZWKxGN7e3hgyZAhOnz5N+0dZ8Mknn+D7779HYWEh/fyVTPOvPqUoSmOYm/8/\ne/cdFsW5/g38uyxLXZrSpDdBigWxRCygBmNNLDHx57HX9KNJ1GhiNMWoOUkwiSFGk6NoYo9JrLH3\nDgqKCqio9F6Xvrv3+0cO87qyKEgZkPtzXXvBPjvzzD0rznf6mOPXX3/FgQMHEBYWJnY5rdJff/2F\nkSNHchg2Ag5Exlid9O7dGx999BHmzZuHmJgYsctpVe7du4eoqCi89NJLYpfyTOJdpoyxOlOpVOjb\nty8UCgUuXboEAwMDsUtqFVatWoVPPvkEmZmZfLy2EfAWImOszqRSKTZv3owHDx5gwYIFYpfTamza\ntAljxozhMGwkHIiMsafi4uKCH3/8Ed9//z32798vdjnPvJs3b+LKlSuYOHGi2KU8s3iXKWOsXiZO\nnIjDhw8jOjoaNjY2YpfzzPrggw+wdetWJCQktIincbRE/K0yxurlhx9+gLGxMaZOnQpev24carUa\nmzdvxoQJEzgMGxF/s4yxejE1NcVvv/2Gw4cP47vvvhO7nGfSkSNHkJyczLtLGxnvMmWMNYjPPvsM\ny5Ytw6VLl9CpUyexy3mmjBo1Cvn5+Th+/LjYpTzTOBAZYw1CpVKhf//+yM3NxeXLl2FoaCh2Sc+E\nxMREuLu7Y/PmzRg7dqzY5TzTeJcpY6xBSKVSbNq0CSkpKXj//ffFLueZsW7dOlhZWWHkyJFil/LM\n40BkjDUYZ2dnrFmzBj/++CP27NkjdjktXkVFBX755RfMnDmTrz1sArzLlDHW4KZOnYp9+/YhOjoa\n7dq1E7ucFmvjxo2YOXMm7t69CwcHB7HLeeZxIDLGGpxCoYC/vz9cXV1x8OBBvhH1UyAidOnSBV26\ndEF4eLjY5bQKvMuUMdbg5HI5Nm/ejBMnTiA0NFTsclqkQ4cO4fr163jvvffELqXV4C1ExlijWb58\nOZYuXYqLFy+iS5cuYpfTogwcOBAymQx///232KW0GhyIjLFGo1Kp8PzzzyM9PR2RkZEwMjISu6QW\nITIyEt26dcPRo0cxYMAAsctpNTgQGWONKikpCV26dMHYsWOxZs0asctpEV588UVkZGTg4sWLYpfS\nqvAxRMZYo3J0dMTatWvx008/4c8//xS7nGYvIiICe/fuxZIlS8QupdXhLUTGWJOYOXMm/vjjD0RH\nR8Pe3l7scpqtESNGIDMzk7cORcCByBhrEsXFxejatSscHR1x6NAhfmqDFhEREejRowf27duHIUOG\niF1Oq8OByBhrMpGRkQgMDMRnn32G+fPni11OszN48GAUFBTg/PnzYpfSKvEqGmOsyQQEBOCzzz7D\n4sWLERkZKXY5zcqxY8dw8OBBLFu2TOxSWi3eQmSMNSm1Wo1BgwYhKSkJV65cgbGxsdgliY6I0KNH\nD1haWuLAgQNil9Nq8RYiY6xJ6ejoYOPGjcjJycG///1vsctpFnbs2IErV65gxYoVYpfSqvEWImNM\nFH/99RdGjhyJHTt24OWXXxa7HNGUl5fDz88Pzz33HDZt2iR2Oa0abyEyxkTx0ksvYfbs2Zg9ezaS\nkpLELkc0oaGhSE1N5WOHzQBvITLGRFNSUoKAgADY2triyJEjkEqlYpfUpFJSUtChQwfMnz8fixcv\nFrucVo+3EBljojEyMsKWLVtw7tw5fPnll2KX0+Tmz58PKysrzJs3T+xSGDgQGWMi69KlC5YvX44l\nS5bg0qVLYpfTZE6dOoUtW7bgm2++gYGBgdjlMPAuU8ZYM0BEGDx4MBISEnDlyhWYmJiIXVKjUiqV\n8Pf3h52dHQ4ePCh2Oex/eAuRMSY6iUSC8PBwFBQU4J133qn2eXl5eYu8MXhpaSlmzpyJgoICjfaw\nsDDcvn0bq1evFqkypg0HImOsWbC1tcUvv/yC8PBwbNu2TWi/efMmTExMMGrUKKhUKhErrLuzZ8/i\n559/hqOjo/Cg36ysLCxZsgRz585F+/btRa6QPYx3mTLGmpU333wTmzdvxtWrV3HgwAHMmTMHlZWV\nICKcPn0affr0EbvEWlu0aBG+/PJLEBHUajWmTp0KlUqFo0ePIjY2FnK5XOwS2UM4EBljzUppaSn8\n/f2Rl5eHrKwsVC2i9PT08O6772L58uUiV1h73bp107hnq66uLoyNjfHOO+/g008/FbEypg3vMmWM\nNStnzpxBZmYmcnNz8fD6ekVFBf766y8RK6ubwsJCXL16VaNNqVSiqKgIn3/+OWbMmIHCwkKRqmPa\ncCAyxpqF8vJyzJs3Dy+88AIKCgqgVCqrDRMbG4vU1FQRqqu7kydPQq1WV2tXq9UgImzcuBEeHh44\nfPiwCNUxbTgQGWOiu3PnDmxtbfHVV18Jx9u00dHRwf79+5u4uqdz7Ngx6Onp1fh5ZWUlsrKyhCd/\nMPFxIDLGRNeuXTsMGjQIwD+h9zi7d+9uipLq7eDBg6ioqKjxc6lUil69eiE5ORmOjo5NWBmrCZ9U\nwxhrNv766y9MnToVCoUClZWVWocxMDBAfn4+9PX1m7i62svOzoa1tTW0LV51dHRARJg/fz4+//xz\n6OrqilAh04a3EBljzcZLL72EW7duYcCAAZBIJFqHKSsrw8mTJ5u4sro5duyY1naZTAYTExPs2bMH\nK1as4DBsZjgQGWPNio2NDQ4cOIDVq1dDX18fMplM43OZTIZ9+/aJVF3tHDt2rFrYSaVSBAQE4MaN\nGxg2bJhIlbHH4V2mjLFm69atWxg3bhxu3LihcZcaR0dHJCYmiljZ47m4uODBgwcAeBdpS8JbiIyx\nZsvb2xsRERGYN28edHR0hOclJiUlITY2VuTqtEtOThbCkHeRtiwciIyxZk0mk2H58uU4ceIEbGxs\nhFBprrtNHz5+2LlzZ1y7do13kbYQvMuUMdYsFBcXQ6FQQKFQID8/H0VFRSgvL0dhYSGICPn5+Sgr\nK8OGDRuEO8AsWLAApaWlKCsrQ0VFBYqLizX6rPpMm4KCAo3rHfX19WFkZKR1WFNTU2HrtIqFhYXG\nZ8bGxtDT08OqVauQnp6OIUOGYNKkSZDJZDAzM4OhoSFMTEwgl8thYWEBuVxe7fgoExcHImOsQSiV\nSuTk5Gi8cnNzkZOTg6ysLOTk5KCwsFAIu6rwq2p70qLI3NwcEokEJiYmKCkpQV5eHjp27AhjY2MY\nGhpCJpNVu1m2trYqVQFWpaZLParCuKa2qmCtGj8xMREGBgZQq9UoKSl57Dzp6+vDxMQEpqamMDMz\ng1wuh1wuh6mpKdq2bSu82rRpo/He0tIS5ubmj+2b1R0HImOsRpWVlcjMzERKSgoyMjKQmpqK9PR0\npKWlIS0tDRkZGcjKykJ2drbW+3KamJhoLMRNTU2FrSO5XK41DKq2oExMTKCnpwczMzOttSUlJaGy\nshJubm6N/TXUWmlpKbKysuDk5CS0Vd2/tKSkRFgRqGml4OH3D69Q5OTkoLy8XGNaUqlU47u1s7OD\nra2t8LNdu3Zo164dbG1tYWlp2dRfRYvEgchYK0VESEtLw/379/HgwQPhZ2JiIpKTk5GZmYmMjAyN\ncSwsLISFrJ2dHaytrWFpaQkrKyutWzOPu3UZqxuFQiGEZNUW98PvH11ZeXhXsZ6eHmxsbGBvbw97\ne3s4OTnBxcUFrq6ucHZ2hrOzc40rHq0JByJjz7CSkhLEx8cjLi4Od+7cwf3793H//n0kJibiwYMH\nwlaHnp4eHB0d4ezsDCcnJzg4OMDGxqbaVoeBgYHIc8RqKz8/H6mpqcjIyBC28FNSUpCamir8DaSl\npQnDW1hYCEFZFZaenp7w8vKCs7NztWOozyIORMZaOLVajcTERMTHxyM2NhZxcXGIj49HfHw8kpKS\nQESQyWRwdXWFi4uLxkLP2dkZLi4usLOze+I9RNmzp6ysTNgzULWXIDExEffv30dCQoIQmPr6+vDw\n8ICXlxc8PT3h6emJDh06wNPTE23bthV5LhoOByJjLUhhYSFiYmIQFRWFqKgoXLt2DdevXxdO3rCx\nsREWVF5eXsLL1dWVr4FjdVZUVCSsXMXFxSE2NlZ4X3VGr62tLTp16oQuXbqgS5cu6NSpE7y8vFrk\n3xsHImPNVFZWFi5duoQrV67g2rVruHr1KhISEkBEsLKyEhZCnTt3FkKQjwOxppKUlIT4+HjcvHlT\nWDmLiYlBWVkZDAwM4Ofnh86dO6Nz584ICAhAQEBAs74hO8CByFizoFKpEBMTg7Nnz+LSpUs4f/48\n4uPjIZVK4e7uji5dusDf3x+dOnVC586dYW9vL3bJjFWjVCoRGxuL69evIyoqClevXsX169eRnp4O\nfX19dO3aFT179kRgYCACAwOb3d8xByJjIqioqMCFCxdw+PBhnDlzBpGRkSgqKkLbtm3x3HPPoVev\nXujVqxe6d+8OExMTsctlrF6Sk5Nx/vx5nDt3DhcuXMDVq1dRXl4Oe3t7BAYGol+/fggJCYGXl5eo\ndXIgMtZEYmNjcfjwYRw6dAgnTpxAcXExfH190adPHyEEPT09xS6TsUZXXl6OyMhInD9/HhcvXsSp\nU6eQkZEBZ2dnhISEICQkBM8//zzatGnTpHVxIDLWSMrKynDw4EHs3r0bhw8fRlJSEmxtbRESEoJB\ngwbh+eefh62trdhlMiY6IsK1a9dw6NAhHDlyBKdOnUJFRQUCAgIQEhKCMWPGoGvXro1eBwciYw2o\nKgR37NiBPXv2oLi4GP3798fgwYMxaNAg+Pn51fjgW8bYP8rKynDq1CkcOXIEu3fvRlxcHNzd3TF2\n7FiMHTu20cKRA5GxelKr1Th06BB+/fVXjRAcO3YsRo8ezbfNYqyerl27hu3bt2PHjh2Ij4+Hh4cH\nXnnlFUyZMgXt27dvsOlwIDL2lPLz87F+/Xr8+OOPuHPnDoKDg/Hqq69i9OjRsLKyErs8xp5J0dHR\n2LFjB7Zs2YJ79+5h0KBBePvttzFkyJB631yCA5GxOoqJicHq1avx66+/QiqVYvLkyXjzzTdFP0OO\nsdZErVbjwIEDWL16NQ4dOgRnZ2e88cYbmDFjxlM/CYQDkbFaunHjBj755BPs3LkT3t7eeOuttzBh\nwgS+LIIxkd2+fRthYWHYsGEDAGDu3LmYO3dunf9v8s0LGXuC/Px8vPXWW+jcuTNiY2Oxa9cuxMTE\n4PXXX2+WYajtMUzNzdPU2BLmqz6USiXOnTsnag25ubn466+/sHz5clHrqKv27dsjNDQUiYmJmDt3\nLkJDQ9G+fXuEh4c/8TmbGogxVqM9e/aQtbU1WVtb04YNG0ilUoldklaVlZUUGhpKAwYMIJlMJnY5\nWpWWltJ//vMfCg4OJl1d3VqN0xLmq75ycnLo888/J1NTU6rvIvn69ev0zTffCO9VKhV98cUXtHDh\nQgoMDCRvb2+6du2a1nFv3bpFCxYsIADk5eVFRP98/wsXLqQHDx7Uq66mlpubS2+++SZJpVIKDg6m\nxMTEWo3HgciYFkqlkt59912SSCQ0depUysvLE7ukJ1IqlWRnZ1fvhWpjqqysJFtb2zrV2BLmqyE4\nOjrWax73799PkydPJqVSKbR9+eWXZGNjQ2q1mvLy8mjIkCF06tSpGvtQKpUagUhEpFAo6OWXX6bb\nt28/dW1iiYyMJB8fH7KwsKADBw48cfhn+y+MsadQXl5Oo0ePJmNjY9qyZYvY5dSJl5dXsw+Op6mx\nJcxXfdVnHqOjo8nDw4MKCws12l1dXTXCrTYeDUQiort375Kvry/l5+c/VX1iKikpoRkzZpCuri5t\n2LDhscPyMUTGHjFt2jScOnUKx44dw7hx48Quh7HHUqlUmDRpEqZNm1btmPaDBw8aZBpubm7w8fHB\nu+++2yD9NSVDQ0OsW7cOn3zyCaZPn449e/bUOCwHImMP2bp1K/744w/s3bsXPXr0ELucp3b+/Hn4\n+/tDJpOha9euOHr0qPBZZmYm3n77bcydOxfz5s1DYGAgZs+eLTwMduvWrTAwMBDuqFNUVISff/5Z\naCMi7Nu3D2+//TacnZ2RmJiIkJAQ6OrqomPHjoiMjBSmpVAoMHfuXEyZMgULFizAnDlzoFAoGny+\ntmzZAmNjY+jo6GDVqlVQKpUAgO3bt8PIyAibN2+uVf/FxcXYuXMnpk6dij59+uDXX3+FhYUF3Nzc\ncPHiRZw8eRLPPfccZDIZfH19ERUVpTH+jRs38OKLL+Ljjz/GjBkz0K1bN5w9e1b4PDo6Gv3798ey\nZcuwaNEiSKVSFBUVaa3lxx9/hKGhIT788MPHnmzzxx9/IDo6GiNGjBDa9u7di9deew1qtRrp6el4\n7bXX8Nprr0GhUNSphocNGzYM69evR3x8/BOHbY4WLVqEefPmYerUqcjOztY+UNNstDLW/BUVFZGt\nrS2tXr1a7FKeWtVut/fee48uXLhA4eHhZGpqSjKZjK5cuUKZmZnk4uJCX3zxhTBOfn4+eXt7k729\nPaWkpBARkaenZ7Xdd1VtarWasrOzqU2bNgSAli1bRmlpaXTixAmSSCTk7+9PRP/seu7Vqxe99tpr\nQh/37t0jPT29p95lWtN8EZFwQsitW7eE8RISEmjUqFG1no5KpaK0tDQCQBYWFnT8+HFKS0sjmUxG\n9vb2tGrVKiorK6P4+HjS1dWlfv36aYzv6OhI7du3JyIitVpNdnZ25O7uLnzu5uZGTk5OwvuZM2dS\nRkaGxjwS/XOizbRp0ygmJuaJNY8ePZp0dXWpsrKy2mfQsvvzcTXUNA4R0bVr1wgAffzxx0+sqblS\nq9XUr18/mjVrltbPORAZ+5/w8HCysLCgsrIysUt5alUL1YqKCqHtxx9/JAA0fvx4evfddwkAZWdn\na4y3detWAkBvvPGGRj/a+q6iLTTd3NxIIpEQEdH3339PAOjmzZsaw7Rv3/6pA7Gm+SIiSk9PJwMD\nA5o+fbowzKeffkp79uyp07TUanW1UHB3d9c6r4aGhhptK1eupG+//ZaI/gnXh78PIiJzc3MCQGFh\nYaRSqejmzZtUUFCgMY93796ladOmVfs3qom9vT05ODho/UxbuD2uhprGIfrnzE0AFBISUqu6mqvd\nu3eTgYEB5ebmVvuMd5ky9j/Hjx9Hr169mv1TvWtDJpMJv7/00ksA/rkf5MmTJwEAZmZmGsMHBwcD\nAM6cOVPraWi7SblMJhOu+9q1axcAwMPDQ2OY+txeq6b5AgAbGxvMmDEDGzduREpKCogIx48fx+DB\ng+s0DW3zpaurq7WW0tJSjbb58+dj0qRJWLVqFVavXo3y8nKN6+BWrVoFqVSKN954Az169EBeXh5M\nTU01+hg2bBiKi4tr/eij9PR0GBoa1mrY2tagTdXxyapd6y1VcHAwysrKcOHChWqfcSAy9j95eXlP\nfcun5qzq5uLm5uZQq9UAgPv372sMU7XwNTIyarDpZmZmAkC9jhk+zsPzVWXevHkgIoSGhuLy5ct4\n7rnntIZZYzl27Bg8PT3RpUsXvPPOO5DL5RqfT548GZcvX8bAgQMRGRmJ3r17IzQ0VGOYr776Ctu2\nbcPKlStrNc2q47q1VZsaaprOs8DExAR6enrIy8ur9hkHImP/4+zs3GBn5TUnqampAICRI0di4MCB\nAICDBw9qDJOcnAwAGD58uEa7SqUSfq86UaW23NzctE6roTw8X1WcnJwwYcIE/PTTT1i9ejWmTZvW\nKNOuyZQpUyCXy4Ut7keDauXKlfD398eRI0ewa9cuSCQSLF68WGOYYcOGYdGiRVi0aBH279//xGna\n29vX6qSYutSgTdWKjb29fa2n1RwlJSWhoqICjXkVagAAIABJREFULi4u1T9s0p23jDVjJ0+eJKlU\nSsnJyWKX8tSqjkOVlpYKbe+99x4FBgZSWVkZZWdnk7u7Ozk7O2vcbGD+/Pnk7+9PCoWCiIiGDh1K\nAOi7776jxMREWrt2rXASTWRkJCmVSuFYoFqtFvqpOtamVqvp+PHjpKOjQ7a2tnTmzBlSq9UUHR0t\nHMPKyspqsPl62L1790gmk1FQUFBdvz4i+ufYHwDy9PQU2qrm9eGL3rXNv4mJCenr61NMTAxt2bKF\n2rZtSwAoPj6e0tLSyNraWuPYlaOjo3AS0sMnLVVWVlL//v3JzMys2jHYR40fP54kEgmVlJRotFdU\nVBAA8vDw0Gh/XA1VF+ZXnRj0sBs3bhAAWrJkyWPrae5CQ0PJ3t5e612npEuXLl3a6JHMWAvg7OyM\nEydO4MKFCxgzZozY5TwVNzc3ZGZmYu3atbh48SL++OMPtG3bFmvXroWhoSGMjIzwf//3f0hJScHX\nX3+NuLg47NmzBzKZDOvXrxd28fXs2RORkZH45ZdfcOjQIcyePRvR0dEYNGgQLC0tcenSJfz2229Q\nqVSwtraGl5cXtm/fLtw70tDQEC+//DJ69+6NK1euYOXKlfjuu++go6ODiooKDBkyBJaWlnBycqrV\nMcUnzdfDzM3NcfXqVYwfPx6dO3eu0/eXlZWFsLAwHDlyBBUVFejbty8ePHiAsLAwKJVKGBgYwNfX\nFzt27MCmTZugUqlgZ2cHZ2dnGBkZwdzcHMeOHcPu3bsxZswY2Nvb48yZM4iIiMCrr76KTz75BH/+\n+SeKi4uxb98+Yffu77//jh07dgjfp4ODA6ytrfHbb79h+/btMDY2hqenJwwMDKrVbGJigk2bNmHY\nsGFwdHQEAMTHx2PNmjU4ceIECgoKYG1tDblcDktLS8yfP79aDRs2bEBRURHCwsJw7NgxKBQKODs7\nw9nZWZjmoUOH8Mcff2DNmjUt9hmfWVlZ+Ne//oWPP/4Y3bt3r/Y5P+2CsYfExcWha9eu+Oijj7Bw\n4UKxy2FPQa1Wo3fv3jh69GiDHhNtrogIgwYNQkBAAFasWNFo03n55Zchl8uFJ0q0NCUlJRgwYAB0\ndHRw5swZrStifAyRsYd4eXlhy5YtWLx4MT766KO63Smf1ZlEInniKzY2tk59/vLLL+jTp0+1MGyM\naTUHEokE69evx759+5Cfn98o04iJicH169drdfJNc5SVlYUBAwYgJSUF27dvr3mvRFPuu2Wspdiy\nZQvp6+vTyJEja309GBPP/v37ydvbm9q3b0+WlpaUmZkpdklNLjIykiZPnqxxrWZDyM7OphEjRtCd\nO3catN+mcubMGXJ2diYvLy9KSEh47LC8hciYFuPGjcPRo0dx5coVdOrUCTt37hS7JPYYdnZ2yM7O\nRnl5OXbu3AkrKyuxS2pyXbt2xaJFi/D99983WJ+VlZVYt24dwsPD4e7u3mD9NgWFQoEFCxYgKCgI\nnTt3xrlz5+Dq6vrYcfgYImOPkZ+fj7lz5yI8PBzPP/88/vOf/9T5RA3GWNNRq9XYvHkzFi5cCIVC\ngZUrV2LWrFm1Gpe3EBl7DHNzc6xfvx6nT59GXl4e/P398corr1S7qTNjTFxKpRLbtm1Dp06dMHXq\nVAwaNAjx8fG1DkOAA5GxWunduzcuXbqEXbt2IS4uDv7+/ujXrx+2bt2KyspKsctjrNXKyMjA559/\nDhcXF/zrX/9Cx44dERMTg19++aXOu855lyljT+HEiRP44Ycf8Oeff8LKygqzZs3CrFmzYGdnJ3Zp\njLUK586dww8//ICdO3fCzMwM06dPx+uvvw4nJ6en7pMDkbF6SE5Oxk8//YR169YhKysLffv2xdix\nYzFmzBjY2tqKXR5jz5TIyEjs2LED27dvx71799CjRw+88cYbGDduXIPclJ8DkbEGUFFRgSNHjmDH\njh34888/UVRUJITj6NGjORwZe0pXrlwRQjAhIQEdOnTA2LFjMXbsWHTs2LFBp8WByFgDq6iowOHD\nh7Fjxw789ddfKCwsRNeuXRESEoLnn38effr0gZ6enthlMtYsZWVl4ciRIzh06BCOHDmC5ORkeHl5\nCSHYqVOnRps2ByJjjai8vBynT58W/nNHRUXB0NAQQUFBQkD6+vrW6xmBjLVkxcXFuHDhAg4dOoTD\nhw8jOjoaBgYG6NevHwYNGoSQkBD4+fk1SS0ciIw1oaysLBw+fBiHDx8W1n7NzMzQq1cv4dWjR49q\nD/Bl7FmRmJiIs2fP4vz58zh37hyio6OhVqvh7++PkJAQhISEoHfv3qI8qJsDkTER3bx5ExcuXBAW\nELGxsZBIJPDx8UFgYCB69eoFf39/eHt7825W1uLk5+cjKioKkZGRwt94eno6DA0N0a1bN/Tq1QvP\nPfcc+vTp0yzuLsSByFgzkpubiwsXLuDixYs4d+4cLl68iKKiIshkMvj4+KBz587o1KkTunTpgk6d\nOjWLhQhjarUaCQkJiIqKQnR0NK5du4aoqCgkJiYC+OfBzYGBgejZsyd69eqFrl27QiaTiVx1dRyI\njDVjarUad+7cQXR0NKKjoxEVFYXr168LCxp7e3t07NgRPj4+8PT0hKenJ7y8vPh6SNYoKisrkZCQ\ngNjYWMTHxyM+Ph43b97EtWvXoFAoIJPJ0KFDB40Vty5durSYFTcORMZaoNzcXERFReHatWu4du0a\nbt26hfj4eOTm5gL456GxVQHp7e0NT09PeHh4wMXFBW3bthW5etacqVQqpKSk4N69e0LoxcXFITY2\nFvfu3YNSqYREIoGzszM8PT019lz4+fm16F37HIiMPUOys7MRGxuLuLg4YWEWGxuLhIQEVFRUAACM\njY3h4uICV1dXODk5wcXFRXg6uouLC2xsbESeC9aYKisrkZSUhPv37yMxMRH379/HvXv3hN+Tk5Oh\nVCoB/HMv36q9Dl5eXsJKlqenJwwNDUWek4bHgchYK6BUKoUFXtWC8N69e8LvDy8EDQwMYGdnh3bt\n2sHW1hZ2dnawtbWFvb09bGxshJ/W1tYizxV7WEVFBTIzM5GcnIyMjAykpKRo/ExNTUVaWhoyMjKg\nVqsB/P+Vo6qVoYdXjFrjyhEHImMMSqUSKSkpePDgAe7fv4+0tDThlZqaivT0dKSmpqKkpEQYR09P\nD9bW1rC0tESbNm1gZWWFNm3aoG3btmjbtm2Nv7Mnq6ioQE5ODnJzc5GTkyP8np2dLbx/+PPs7Gxk\nZmZq9GFpaamxQlP108HBAS4uLnBycmoxx/aaCgciY6zWCgsLq215VC2QtS2oy8rKqvUhl8thYmIC\nuVwOU1NTmJuba7SZmZnBzMwMcrkc+vr6MDMzg46ODkxMTKCrqwsjIyPo6+tDX18fRkZG0NXVhYmJ\nCSQSCczNzZvke6isrIRCoYBarUZBQQGAfy4xICIUFRVBqVSipKQE5eXlKC8vR0lJCYqKiqBQKFBU\nVISioiLk5+cL7xUKBQoKClBQUACFQoHy8vJq0zQ1Na1xxcPS0hIODg4aW/BiXMfX0nEgMsYaTXFx\ncbWtnKpAqAqDvLw8jWAoLCwUwqKiogJ5eXn1qqEqUB9lbGwMqVSKiooKGBoaQqFQaH2UV1lZGUpL\nS596+np6ejA2NoZcLheC39TUFGZmZsJKgImJCczMzGBqaiq0Pbp13RwvU3jWcCAyxlqEwsJCqFQq\nFBcXo6KiQgiqqq01lUqFwsJCjXEe3oJ7VEFBAa5cuYKtW7dixYoVMDAw0HqiiFQqhampaY1tFhYW\nAP5/8MrlcshkMhgaGsLAwKAhZp01EQ5ExlirtXnzZvzrX/8CLwYZAPAdhRljjDFwIDLGGGMAOBAZ\nY4wxAByIjDHGGAAORMYYYwwAByJjjDEGgAORMcYYA8CByBhjjAHgQGSMMcYAcCAyxhhjADgQGWOM\nMQAciIwxxhgADkTGGGMMAAciY4wxBoADkTHGGAPAgcgYY4wB4EBkjDHGAHAgMsYYYwA4EBljjDEA\nHIiMMcYYAA5ExhhjDAAHImOMMQaAA5ExxhgDwIHIGGOMAeBAZIwxxgBwIDLGGGMAOBAZY4wxAByI\njDHGGAAORMYYYwwAByJjjDEGgAORMcYYAwDoil0AY4w1hdTUVLz55psoLS2Fqamp0GZsbIxXXnlF\nGC4nJwf+/v746quvxCqViURCRCR2EYwx1hQkEkmthvvggw+wfPnyRq6GNTe8y5Qx1mp89NFHkMlk\nTxxu/PjxTVANa254C5Ex1mrcvHkTvr6+jx2mffv2iI+Pb6KKWHPCW4iMsVbDx8cHvr6+Ne46lclk\nmDx5chNXxZoLDkTGWKsyadIk6OpqP59QqVTi//7v/5q4ItZc8C5Txlir8uDBA7i6uuLRRZ9EIkFA\nQAAuX74sUmVMbLyFyBhrVZydndGjRw/o6Ggu/qRSKSZMmCBSVaw54EBkjLU6kydPrnYcUa1W49VX\nXxWpItYccCAyxlqdsWPHaryXSqUICgqCra2tSBWx5oADkTHW6lhaWqJ///6QSqUAACLCpEmTRK6K\niY0DkTHWKk2aNEk4sUYqlWLUqFEiV8TExoHIGGuVRo4cKdy1ZsiQITAzMxO5IiY2vrk3Y+yZVlBQ\nALVarbXdz88PkZGRGDRoEFJSUmBkZFRtOAMDAxgaGjZFqUxkfB0iY0w0paWlKCwsREFBAQoLC5Gf\nn4+CggLhfdVnCoVCCLb8/HwQEfLy8gAAeXl5ICLk5+dDrVajoKCgUWvW1dWFiYkJZDIZ5HI59PT0\nYGxsLPzU19eHkZERDAwMYGRkBHNzc5iZmcHMzAympqYwNTUVfjczMxM+rzqeycTDgcgYazAlJSVI\nTk5GVlYWsrOzkZ2djfT0dOH3rKwsZGRkCJ+Xl5dr7Ucul2sEh4mJCUxNTSGVSoWfZmZm0NHRgbm5\nOSQSCSwsLABA+FmlKqy0TYOI8N133+H9999HcXExKioqqg2nUChQWVkpvK+srIRCoUBFRQWKi4tR\nXl6OkpISlJWVobS0VPhZWlqKkpISFBQUCEFfWFiodRoAYGJiAhsbG1hZWcHS0hJWVlawtraGlZWV\n0GZtbQ0bGxvY2trWeLcd9vQ4EBljtZKbm4uUlBQkJiYiNTUVKSkpSEpKQmpqKpKSkpCSkoL8/HyN\ncUxNTTUW8paWlhrvq7aOqracLCwshKBrKsXFxTA2Nm6y6ZWVlWlsAefl5QmhmZGRIaw4ZGdna6w8\nlJWVCX3o6OjA1tYWDg4OsLOzg6OjI+zt7WFvbw9HR0ehzcDAoMnm61nAgcgYA/DPhenJycm4e/du\ntVdCQoJG2JmZmcHBwUHrAtne3l4IPH19fRHn6NmiUCiQkZGBjIyMGldIUlNTha1uiUQCe3t7uLu7\nw83NDR4eHnBzc4O7uzvc3d3Rpk0bkeeo+eFAZKyVKS8vx61bt3Dr1i1cv35d+P3+/fvCwtTU1FRY\ncFa93NzcYGdnBycnpybdomJ1UxWYSUlJuHPnDhISEoQVm/v37wu7fy0sLODu7g5fX1/hKSC+vr5w\ndnau9YOUnzUciIw9o4gId+7cwZUrVxATE4ObN28iJiYGd+/ehUqlgr6+Pry9vYXXw1sSlpaWYpfP\nGoFKpUJiYqLG1v/Nmzdx48YNPHjwAEQEuVwOb29v+Pn5wcfHB506dUJAQADatm0rdvmNjgORsWdE\nUlISLl++jIiICOFnfn4+9PT04OXlBR8fH3Ts2FFY2Lm7u/OZjUygUChw69YtxMTECHsPbt68icTE\nRACAm5sbunfvjm7duiEgIAABAQEwNTUVueqGxYHIWAukVCpx+fJlnDp1CqdPn8bly5eRmZkJmUwG\nPz8/dO/eHQEBAejevTv8/PyEC9AZq6v8/HxEREQgMjJSWNF68OABdHR04OnpiZ49eyIoKAhBQUFw\nc3MTu9x64UBkrAWoqKjA5cuXceLECZw6dQpnz55FcXExXF1d0bdvX2HNvUuXLnxmIWt0mZmZiIiI\nQEREBC5duoTTp0+jsLAQDg4OCA4OFgKyffv2YpdaJxyIjDVTDx48wL59+7B7926cPn0aJSUlaN++\nPYKCgtCvXz8EBwfD0dFR7DIZg0qlQlRUFE6ePIkTJ07gzJkzyMvLg52dHYYMGYLhw4cjJCSk2Z+M\nxYHIWDOhVqsRERGB3bt3Y+/evYiOjkbbtm0xdOhQDB48GMHBwbCzsxO7TMaeSK1WIzo6GidOnMDe\nvXtx6tQp6Orqon///njxxRcxfPhwODg4iF1mNRyIjIksMjISmzZtwrZt25Ceng5vb2+MGDECw4cP\nR2BgIJ/4wlq8/Px8HDx4ELt378bff/+N3NxcdO/eHePHj8f48eNhbW0tdokAOBAZE0Vqaio2b96M\njRs34vr16/D29sakSZPw8ssvw8PDQ+zyGGs0SqUSZ86cwZYtW7B9+3YUFxdj6NChmDBhAkaMGCHq\nzRw4EBlrQidPnsTXX3+N/fv3w8zMDOPHj8eUKVMQEBAgdmmMNbny8nLs3r0bGzZswMGDB2FmZoZp\n06Zhzpw5sLe3b/J6OBAZa2RqtRp79uzBihUrcOHCBQQFBWHOnDkYNmwYXw7B2P9kZGRg48aN+Pbb\nb5GVlYUJEyZg3rx56NChQ5PVwA8IZqyREBG2bNmCjh07YvTo0bC1tcX58+dx4sQJjYfTMsYAGxsb\nzJs3DwkJCfjxxx9x7tw5+Pr6YvTo0bh582aT1MCByFgjiIiIQGBgICZMmIBu3brh+vXr+OOPP/Dc\nc8+JXRpjzZqenh6mTZuGGzduYOfOnbh//z46d+6MN998s9rTVBoaByJjDai8vBwLFy5Er169oK+v\nj4iICISHh8PHx0fs0lgLlZubi7/++gvLly8Xu5QmpaOjg1GjRiEiIgI//fQTdu3aBT8/P+zbt6/x\nptloPTPWyqSkpCA4OBg//PADVq9ejePHj8Pf31/sslgTiYmJQWhoaL376dChA2bMmAEAiI2NxZdf\nfomRI0ciPDy8xuGellKpxKJFi4T7lTZHOjo6whZj//79MWLECCxcuBAqlarhp9XgPTLWCsXHx6Nn\nz54oKipCZGQkZs+e3SoeoXP//v0W2XdDO3DgAL766iu88847dRpP2zxaW1vDwsICwD+ht2zZMq3j\nPjzc4/p7HF1dXXz44Yd47733cOfOnTqN29TatGmDTZs2ITw8HN9++y3GjRuHioqKhp0IMcbqJSkp\niezt7WnAgAFUVFQkdjlNJikpifr27dvi+m5o0dHR5OHhQYWFhXUary7zCIC8vLwarL9H3b17l3x9\nfSk/P/+pxm9qly5dojZt2tC4ceNIrVY3WL+8hchYPahUKowdOxbu7u7Ys2cP5HK52CU1iaysLAwb\nNgyZmZktqu+GplKpMGnSJEybNg0mJia1Hq+h57G+/bm5ucHHxwfvvvtug9TT2Lp3746jR49i7969\n+Pbbbxuu4waLVsZaobVr15KtrS1lZmaKXUqtZWRk0FtvvUVz5syh999/n3r16kWzZs2i1NRUIiLa\nsmUL6evrU9XiobCwkNatW6fRtnTpUgJAZmZmNHv2bFKr1XTp0iVauHAhubm50Y0bNygwMJB0dXXJ\n29ub9u7d+9R9V4mKiqLg4GD6/PPPaeHChaSjo0MFBQW1mm7VtD755BOaPn069e3bl3r16kUXL14U\nPlcoFLRkyRKaOHEizZkzh3r06EFLly4lpVJZ43e5Y8cOAkDXr18X2p52HpVKJW3bto0mTZpUbUsP\nD20hahtOW3+bN28mIyMjkkgkFBoaSpWVlUREtG3bNjI0NKTffvtNYxobNmwgiURCcXFxNc5vc7Nt\n2zaSy+WUlpbWIP1xIDL2lJRKJdnZ2dH3338vdim1lpmZSS4uLvTFF18Ibfn5+eTt7U329vaUkpJC\nRESenp706Pryo22PLqQPHz5MpqamBIDef/99ioqKol27dpG5uTlJpVKKiIh4qr6ruLm5kZOTk/B+\n5syZlJqaWqvpqlQqGjp0qMaC89VXXyULCwvKy8ujoqIi8vf3p+nTpwu74NauXUsAaOvWrTV+n6NH\njyZdXV0hbGqan9rOY2Fhodb2R9u0DadtvAULFhAAunXrltCWkJBAo0aNqjYv165dIwD08ccf1zi/\nzVHv3r3p7bffbpC+OBAZe0pHjx4lAC1q6/Ddd98lAJSdna3RvnXrVgJAb7zxBhEReXl5VVugP9qm\nbQFctdB/OCDCwsIIAE2cOLFefZubmxMACgsLI5VKRTdv3qSCgoJaTXf//v0EQOvr999/pyVLlhAA\nSkhIEMYvLS2l1atXP/bf197enhwcHKq1P+08qtXqWgWituG0jZeenk4GBgY0ffp0oe3TTz+lPXv2\nVKs5NzeXAFBISEiN89scfffdd2RjY9MgffExRMaeUmxsLKytrWFlZSV2KbV28uRJAICZmZlGe3Bw\nMADgzJkz9eq/6sxaXV1doW3EiBEAgOjo6Hr1vWrVKkilUrzxxhvo0aMH8vLyYGpqWqvpnj9/Hl26\ndAH9sxGg8Ro9ejQOHDgAABqPJDIwMMCbb7752H/f9PR0GBoa1mu+HlbbM5NrO5yNjQ1mzJiBjRs3\nIiUlBUSE48ePY/DgwdWGrToGmpaWVvuCmwFfX19kZGQgOzu73n1xIDLWiqjVagDVT89v06YNAMDI\nyKjBp2lrawsA9X6KweTJk3H58mUMHDgQkZGR6N2792Ov+3t4uiqVCrdv30Z5eXm14VQqldB+9+7d\nOtUkkUhAzfx20PPmzQMRITQ0FJcvX8Zzzz2nseJQpTVcJvQkHIiMPSVvb29kZma2iLMhqwwcOBAA\ncPDgQY325ORkAMDw4cM12h+++FmpVFbrrzZhkJeXBwB4/vnn69X3ypUr4e/vjyNHjmDXrl2QSCRY\nvHhxrabr6+uL4uJihIWFaQyTlpaGsLAwdO/eHQDwxRdfaEw3Ozsbv//+e43TsLe3R1FRUY2fN8T3\nVxfa+nNycsKECRPw008/YfXq1Zg2bZrWcRUKBQCI8pSJ+rhx4wZsbW1haWlZ/84aZMcrY61QSzyp\nJjs7m9zd3cnZ2Zny8vKE9vnz55O/vz8pFAoiIho6dCgBoO+++44SExNp7dq11KZNGwJAkZGRpFQq\nydLSkszMzIQTcYj+/3EylUoltG3ZsoVcXV0pJyenXn1bW1tTbm6u8N7R0ZH8/f1rNV2FQkFOTk6k\no6ND7733Hu3evZu+//57CgkJofz8fLp9+zaZmZkRABoyZAj9/PPPtGrVKho8ePBjry8cP348SSQS\nKikp0Wh/2nlUKpUEgNq3b1/nNm39Vbl37x7JZDIKCgqqcV5u3LhBAGjJkiU1DtMcBQYG0r///e8G\n6YsDkbF6+Pnnn8nGxobS09PFLqXWMjMz6fXXX6devXrRvHnz6O2336b58+drLPhv375NgYGBJJVK\nyc/Pjy5dukRBQUE0c+ZM+v3336msrIzWrFlDcrlc4wy/qmD6/vvvqaCggJKSkmjp0qUaZ3c+bd/4\n30kjK1asoA8++ICGDBlCd+/erfV04+LiaNCgQWRgYEBmZmY0ceJEjX+3mJgYGj58OMnlcjI0NKRX\nXnlFuBSlJgcPHiQAdO7cOY32p5lHhUJBoaGhBID09PRo06ZNFBMTQ59//rnQFh4eTsnJydWGKyws\n1PqdPWzUqFG0cePGGufl119/JYlEQrGxsY+d5+Zk8+bNZGpq2mCXXfDzEBmrB7Vajb59+0IikeDQ\noUONcgyuJenQoQPi4uKa/LiaWNMlIgwaNAgBAQFYsWJFk067LtRqNXr37o2jR4/W+Df68ssvQy6X\nY8OGDU1b3FO6evUq+vbtiy+++KLOt8yrCR9DZKwedHR0sH37djx48ABDhw597PEk9uyRSCRYv349\n9u3b1+iPJqqPX375BX369KkxDGNiYnD9+vUGuTl5U7hw4QIGDhyIl156CW+//XaD9cuByFg92dvb\n4/jx47h37x569uyJuLg4sUsSTdWJI9pOIHkWpwv8c6lGeHg45syZg8rKyiaffk0OHDgAHx8feHp6\nYtGiRZg/f77W4XJycrBo0SLs37+/2s3Cm6MNGzagf//+CAkJwYYNGxr07FgORMYagIeHBy5cuIA2\nbdogICAAP/zwQ7M/Hb8hFRcXY8WKFbh37x4AYMGCBYiMjHxmp/uorl27YtGiRfj++++bfNo1sbOz\nQ3Z2NsrLy7Fz506t11NWVlZi3bp1CA8Ph7u7uwhV1l52djbGjRuHadOmYd68ediyZQtkMlmDToOP\nITLWgCorK/HZZ59h+fLl6NatG8LCwviZiIzVg1qtxn//+18sXLgQhoaGWLduHV544YVGmRZvITLW\ngGQyGT799FNERERAJpOhW7dumDhxImJiYsQujbEWRa1WY+fOnQgICMDrr7+OCRMmICYmptHCEOBA\nZKxRdO7cGSdPnsRvv/2GqKgodOrUCSNGjKj3rdEYe9aVl5dj7dq16NChA1599VV4eHggOjoaoaGh\nwq36GgsHImONRCKRYNy4cbh27Rp2796N/Px89O3bF3369MGOHTu03kaMsdYqNTUVy5cvh4uLC955\n5x30798fsbGx2LFjB3x8fJqkBj6GyFgTOnv2LL7++mvs3bsXJiYmeOWVVzBp0iT06tVL7NIYa3Kl\npaXYtWsXfv31Vxw+fBht27bF1KlTMWfOHOFetE2JA5ExEWRmZuK3335DeHg4oqOj0aFDB0ycOBFj\nxoyBl5eX2OUx1miUSiVOnjyJbdu2Yfv27SgrK8PgwYMxdepUDB06tMHPHK0LDkTGRHb9+nWsX78e\n27ZtQ2pqKtq3b48XX3wRw4YNQ9++fbU+mYCxliQ3NxcHDhzAnj178Pfff6OwsBDdu3fHxIkTMX78\neOFpK2LjQGSsmSAiREZGYu/evdizZw+uXr0Kc3NzDB48GEOGDEFQUBCcnJzELpOxJ1KpVLhy5QpO\nnDiBvXv34uzZs9DX18fAgQMxYsQIDBtQHytaAAAMXUlEQVQ2DHZ2dmKXWQ0HImPNVHJyMvbt24c9\ne/bg5MmTUCgUcHV1RVBQEIKDgxEUFAQXFxexy2QMSqUSV65cwcmTJ3Hy5EmcPn0ahYWFcHJywuDB\ngzFixAgMHDiwQR+m3Bg4EBlrAZRKJSIiIoQFzpkzZ1BUVARnZ2f07dsX3bt3R0BAAPz9/Vv9DcZZ\n40tLS0NkZCQiIiJw6dIl4e/RxcUF/fr1Q//+/REUFARXV1exS60TDkTGWqCH18hPnz6NiIgIpKWl\nQVdXFz4+PujWrZvw6ty5M/T09MQumbVQOTk5iIiI0HglJydDKpWiQ4cO6N69O/r3749+/fq1+D0W\nHIiMPSOSk5OFtfbLly8jIiICOTk50NXVRfv27eHr6wsfHx/4+fkJN30W84w+1rwUFBTg5s2biImJ\n0fiZmpoKiUQCDw8PdO/eHd26dUNAQAC6du0KuVwudtkNigORsWfY/fv3ERERobGAi4+PR2VlJWQy\nGby8vODj44MOHTrAzc0N7u7ucHd3R7t27cQunTWCiooKPHjwAHfv3sXdu3eRkJCAmJgY3Lp1C0lJ\nSQAAMzMz+Pr6CitQnTp1QteuXWFubi5y9Y2PA5GxVqayshLx8fG4ceOG8IqLi8Pdu3dRWloKADAy\nMhLCsSooXV1d4eTkBHt7+1axcGyJ1Go10tPTkZycjMTERCQkJGiEX2JiIlQqFQDAysoK7u7u8PX1\nhbe3Nzp27Ahvb284OjqKPBfi4UBkjAlSU1OFheedO3c0FqjZ2dnCcEZGRnB2doadnR3s7e3h5OQE\nOzs7ODg4wM7ODtbW1rC0tGz2ZxW2JAUFBcjIyEBWVhZSUlKQkpKCxMRE4fekpCSkpaUJz4SUSqVw\ncnLS2PJ3c3ODh4cH3NzcGv2+oC0RByJjrFYUCgUSExORnJyM1NTUagvjlJQU5OTkaIwjl8thZWUl\nBKSlpSWsrKxgY2MDS0tLmJmZCS9zc3OYmZnB1NT0mT4JSKFQoLCwEAUFBcLPgoICZGdna7yqwq/q\nfUVFhdCHrq4ubGxsalwpcXR0hJ2dHR8jriMORMZYgyktLUVqaioyMzOFBXlWVhYyMjK0LuxLSkq0\n9mNoaAhTU1ONwDQyMoK+vj6MjY2hp6cn/JTL5ZDJZDAxMYGuri5MTU0hlUqFvqRSqdatoar+qhQV\nFQlbV1XUajUKCgqqzWNZWRlKSkpQXl6O4uJiVFRUQKFQoLKyUvhZVFSEsrIyIfDy8/NRUFAg7LJ8\nVJs2baqtOFhbW8PKykpos7GxgZWVFWxtbTXmkTUMDkTGmGgqKyuFraS8vDyNLaZHt6IeDZ6qAKv6\nWVhYCJVKhYKCAqjV6kar2cDAAIaGhjA0NBR+NzAwqBbYcrlcCHZzc3OYm5sLIf9w2Fd9zsTHgcgY\ne6aVl5dr3RJ9dIuwKtgeZW5uDolE0qg1suaBA5ExxhgDPyCYMcYYA8CByBhjjAHgQGSMMcYAcCAy\nxhhjADgQGWOMMQAciIwxxhgADkTGGGMMAAciY42msrISS5cuhYODA3R1deHn54dffvkFVZf+Lly4\nEJ6enpBIJJDJZHjxxRcxdOhQ+Pn5ISgoCOvXr0dNlwnHxcVh8uTJ6N+/P0aPHo1+/fphypQpuH37\ntsZw58+fx2uvvQaJRAKJRILFixcjIiJCa5+nTp3CxIkTIZFIoKOjgxdeeAGDBw9GQEAABgwYgG+/\n/RYKhaJhvyTGmhNirBW5d+9ek01rxowZNGXKFPrpp5/o/fffJ2NjYwJA33zzjTBMfn4+ASBPT0+h\nTalU0kcffUQAaPHixdX63bdvHxkZGdFPP/1EarWaiIjUajWtWbOGjI2Nae/evRrDl5WVEQBycXF5\nYs2lpaUEgNq3by+0qdVqOnXqFHl6epKDgwNFRETU+btgrCXgQGStRlJSEvXt27dJphUbG0vz5s3T\naDt+/DgBoHbt2mm0AyAvLy+NNqVSSRYWFmRhYaHRnpiYSHK5nGbOnKl1urNmzSIjIyO6f//+E6dR\nk5qGzcjIIAcHB7KysqL09PRa9cVYS8K7TFmrkJWVhWHDhiEzM7NJppeRkYGPPvpIoy04OBj29vbV\nHpGkjVQqhbGxMcrLyzXaQ0NDoVAoMHPmTK3jvf766ygpKcE333zz9MXXwNraGsuWLUNWVha+/vrr\nBu+fMbFxILJnSnR0NPr3749ly5Zh0aJFkEqlKCoqQlhYGK5du4b09HS89tprUKlUOHPmDBYsWAB3\nd3ckJCSgc+fOaNu2Lb755hsYGBgIN3QuKirCzz//rNEGAMXFxVi6dCkmTZqEuXPnomfPnvjkk0+g\nUqnQr1+/ao8cIiKUlpaid+/eT5yPPXv2IDk5GVOmTNFo37dvHwwMDNC1a1et43Xq1AmGhoY4ePBg\nHb+52hk9ejR0dHSwe/fuRumfMVGJvYnKWENyc3MjJycn4f3MmTMpIyODiDR3BZaXl1NkZCSZmpoS\nAFq1ahWdOHGCxo4dSzk5OeTp6UmP/vd4uK2oqIj8/f1p+vTpwnG8tWvXEgDaunWr1trOnz9PAOj4\n8eMa7QDI1NSUJk+eTJMmTaLg4GAyMzOj0NBQUiqVGsPKZDJydXV97Hfg4uJCMpms2jTqu8u0ipOT\nE+nq6taqL8ZaEl3RkpixRpCbm4v8/Hz8+OOPmD17NubOnav1kT56enro2rUrbG1tUVhYiFmzZsHQ\n0BBBQUEAoPVxPw+3ffXVV7h69Sp+//13oX3ixImoqKjAgAEDqo1LRPjkk0+wdOlSBAcHV/vcysoK\ny5YtQ2lpKVJSUrB//34sXrwYt2/fRmhoqMYT5J/0KKLGflSRrq4uP5yWPZN4lyl7pqxatQpSqRRv\nvPEGevTogby8PK1PS69SFR6GhoZ1ms6BAwcAAA4ODkKbgYEB3nzzTVhZWVUbfs2aNfDz88PHH3+s\ntT9dXV3Y29vDw8MDQUFBWLlyJdatW4ewsDB8+umnwnCurq5IS0ur8QG4SqUSqampcHV1rdP81FZV\n/15eXo3SP2Ni4kBkz5TJkyfj8uXLGDhwICIjI9G7d2+EhoY2+HSqTna5e/fuE4fdvXs3cnNz8eWX\nX9Zp623kyJEA/jmeWGXIkCEoLS1FdHS01nGioqJQXl6OIUOG1Ho6wD8nHVVWVj5xuFOnTqGsrAyj\nRo2qU/+MtQQciOyZsnLlSvj7++PIkSPYtWuXcDF6Farj87BVKpXw+8NPV+/evTsA4IsvvtDoMzs7\nG7///rvw/u+//0ZiYiI+/PBDjTC8dOnSE6ednp4OALCxsRHa5syZAyMjI6xdu1brOP/9739hbGyM\nd999V2h70jwTET744IMn7gatqKjAhx9+iHbt2uHf//73E+tnrKXhY4jsmfLNN99g1qxZsLCwwKhR\no+Dg4ABLS0sAgKWlJTIyMpCamgo7OzsAEHY9qlQqjUBwd3dHXFwcwsLCMHLkSPz999/Iy8sDAFy5\ncgXvv/8+duzYgU2bNiE7OxtjxoyBQqHA33//je3btwMADh8+jJUrV2LMmDFYvXo1gH/CJzk5Gfr6\n+ujRowfKysqE6T8sKysLs2fPhlQqxQcffCC0u7i4YMuWLRg/fjx69OiBKVOmQCKRgIjw888/Y+PG\njdi2bRucnJyEcUpLSwFoBvrDn3322WcoLy+Hjo6OUM+ju2Tj4+Px1ltvISkpCfv27YOFhUWd/l0Y\naxFEPKGHsQaH/50huWLFCvrggw9oyJAhdPfuXSIiWrNmDcnlcnr77bdJoVBQaGgoyWQyAkAff/wx\n3bhxQ+jn9u3bFBgYSFKplPz8/OjSpUsUFBREM2fOpN9//53KysooJiaGhg8fTnK5nAwNDemVV16h\n1NRUIiI6e/YsGRoaEgCtr7t379KBAwdo+vTpQtvAgQNp+PDh1LNnT7Kzs6MRI0bQ6dOntc5nbGws\nTZw4kfr160ejR4+mgQMH0tSpUykuLk5juHPnzmlMIyAggAYOHEgDBw6knj17CnfP2bBhA509e5Zm\nz54tDPvCCy/QkCFDKDAwkIKCguibb76hwsLCRvqXY0x8EqI67kNijDHGnkF8DJExxhgDByJjjDEG\ngAORMcYYA8CByBhjjAHgQGSMMcYAcCAyxhhjADgQGWOMMQAciIwxxhgADkTGGGMMAAciY4wxBoAD\nkTHGGAPAgcgYY4wB4EBkjDHGAHAgMsYYY/h/7dWBAAAAAICg/akXKYlKiABQCREAKiECQCVEAKiE\nCABVDTcI9NQDcdQOAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"image/jpeg": {
"height": 500
}
},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x5Ol0bVD_t1a",
"colab_type": "text"
},
"source": [
"## 10.2. Import the nipype interfaces"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-HnorSrXWFEv",
"colab_type": "code",
"colab": {}
},
"source": [
"from nipype.interfaces import fsl\n",
"from nipype.pipeline import engine as pe\n",
"from nipype.interfaces import utility as util\n",
"fsl.FSLCommand.set_default_output_type('NIFTI_GZ')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "qXAWZMPL_qN8",
"colab_type": "text"
},
"source": [
"### 10.2.1. codes that cannot be run on colab because we don't have the ROI masks of the FSL-Freesurfer extracted ones. \n",
"```\n",
"# define the directory of the anatomical scans (T1.nii)\n",
"anat_dir = '/'\n",
"\n",
"# search for all the FSL-Freesurfer processed masks of the ROIs\n",
"ROI_in_structural = glob(os.path.join(anat_dir,'ROIs','*fsl.nii.gz'))\n",
"\n",
"# define the directory of the first session first run, from which we extract the example_func.nii.gz (the reference image)\n",
"preprocessed_functional_dir = '/func/session-1/run-01/outputs'\n",
"\n",
"# define the output directory, to which we want to store the mask in BOLD space\n",
"output_dir = os.path.join(anat_dir,'ROI_BOLD')\n",
"if not os.path.exists(output_dir):\n",
" os.mkdir(output_dir)\n",
" \n",
"``` \n",
"```\n",
"for roi in ROI_in_structural:\n",
" roi = os.path.abspath(roi)\n",
" roi_name = roi.split('/')[-1]\n",
" simple_workflow = pe.Workflow(name = 'struc2BOLD')\n",
"\n",
" inputnode = pe.Node(interface = util.IdentityInterface(\n",
" fields=['flt_in_file',\n",
" 'flt_in_matrix',\n",
" 'flt_reference',\n",
" 'mask']),\n",
" name = 'inputspec')\n",
" outputnode = pe.Node(interface = util.IdentityInterface(\n",
" fields=['BODL_mask']),\n",
" name = 'outputspec')\n",
" \"\"\"\n",
" flirt \n",
" -in /export/home/dsoto/dsoto/fmri/$s/sess2/label/$i \n",
" -ref /export/home/dsoto/dsoto/fmri/$s/sess2/run1_prepro1.feat/example_func.nii.gz \n",
" -applyxfm \n",
" -init /export/home/dsoto/dsoto/fmri/$s/sess2/run1_prepro1.feat/reg/highres2example_func.mat \n",
" -out /export/home/dsoto/dsoto/fmri/$s/label/BOLD${i}\n",
" \"\"\"\n",
" flirt_convert = pe.MapNode(\n",
" interface = fsl.FLIRT(apply_xfm = True),\n",
" iterfield = ['in_file','reference','in_matrix_file'],\n",
" name = 'flirt_convert')\n",
" simple_workflow.connect(inputnode,'flt_in_file',flirt_convert,'in_file')\n",
" simple_workflow.connect(inputnode,'flt_reference',flirt_convert,'reference')\n",
" simple_workflow.connect(inputnode,'flt_in_matrix',flirt_convert,'in_matrix_file')\n",
"\n",
" \"\"\"\n",
" fslmaths /export/home/dsoto/dsoto/fmri/$s/label/BOLD${i} -mul 2 \n",
" -thr `fslstats /export/home/dsoto/dsoto/fmri/$s/label/BOLD${i} -p 99.6` \n",
" -bin /export/home/dsoto/dsoto/fmri/$s/label/BOLD${i}\n",
" \"\"\"\n",
" def getthreshop(thresh):\n",
" return ['-thr %.10f -Tmin -bin' % (val) for val in thresh]\n",
" getthreshold = pe.MapNode(\n",
" interface=fsl.ImageStats(op_string='-p 99.6'),\n",
" iterfield = ['in_file','mask_file'],\n",
" name='getthreshold')\n",
" simple_workflow.connect(flirt_convert,'out_file',getthreshold,'in_file')\n",
" simple_workflow.connect(inputnode,'mask',getthreshold,'mask_file')\n",
"\n",
" threshold = pe.MapNode(\n",
" interface=fsl.ImageMaths(out_data_type='char',suffix='_thresh',\n",
" op_string = '-Tmin -bin'),\n",
" iterfield=['in_file','op_string'],\n",
" name='thresholding')\n",
" simple_workflow.connect(flirt_convert,'out_file',threshold,'in_file')\n",
" simple_workflow.connect(getthreshold,('out_stat',getthreshop),threshold,'op_string')\n",
"# simple_workflow.connect(threshold,'out_file',outputnode,'BOLD_mask')\n",
"\n",
" bound_by_mask = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix='_mask',op_string='-mas'),\n",
" iterfield=['in_file','in_file2'],\n",
" name = 'bound_by_mask')\n",
" simple_workflow.connect(threshold,'out_file',bound_by_mask,'in_file')\n",
" simple_workflow.connect(inputnode,'mask',bound_by_mask,'in_file2')\n",
" simple_workflow.connect(bound_by_mask,'out_file',outputnode,'BOLD_mask')\n",
"\n",
" # setup inputspecs \n",
" simple_workflow.inputs.inputspec.flt_in_file = roi\n",
" simple_workflow.inputs.inputspec.flt_in_matrix = os.path.abspath(os.path.join(preprocessed_functional_dir,\n",
" 'reg',\n",
" 'highres2example_func.mat'))\n",
" simple_workflow.inputs.inputspec.flt_reference = os.path.abspath(os.path.join(preprocessed_functional_dir,\n",
" 'func',\n",
" 'example_func.nii.gz'))\n",
" simple_workflow.inputs.inputspec.mask = os.path.abspath(os.path.join(preprocessed_functional_dir,\n",
" 'func',\n",
" 'mask.nii.gz'))\n",
" simple_workflow.inputs.bound_by_mask.out_file = os.path.abspath(os.path.join(output_dir,\n",
" roi_name.replace('_fsl.nii.gz',\n",
" '_BOLD.nii.gz')))\n",
" simple_workflow.base_dir = os.path.abspath(output_dir)\n",
" simple_workflow.write_graph(dotfilename='{}.dot'.format(roi_name.split('.')[0]))\n",
" simple_workflow.run()\n",
"```\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oryyNPc7A-nz",
"colab_type": "text"
},
"source": [
"## 10.3. Let's split the code from the cell above to explain it"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X291Yg3QBDGv",
"colab_type": "text"
},
"source": [
"### 10.3.1. start the for-loop:\n",
"```\n",
"for roi in ROI_in_structural:\n",
" # make sure it is the absulote path because reasons\n",
" roi = os.path.abspath(roi)\n",
" # the name of each ROI is the last item of the path splitted by \"/\"\n",
" roi_name = roi.split('/')[-1]\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G4mKw8pcBSwU",
"colab_type": "text"
},
"source": [
"### 10.3.2.initialize the workflow as usual:\n",
"```\n",
"simple_workflow = pe.Workflow(name = 'struc2BOLD')\n",
"\n",
"inputnode = pe.Node(interface = util.IdentityInterface(\n",
" fields=['flt_in_file', # input file for flirt command\n",
" 'flt_in_matrix', # input mat file for flirt command\n",
" 'flt_reference', # reference file for flirt command\n",
" 'mask']), # mask file for the last step of maksing the boundaries\n",
" name = 'inputspec')\n",
"outputnode = pe.Node(interface = util.IdentityInterface(\n",
" fields=['BODL_mask']), # output the mask in BOLD space\n",
" name = 'outputspec')\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q9WzV4-KBoGm",
"colab_type": "text"
},
"source": [
"### 10.3.3. flirt: inverse transformation of the masks in structural space to BOLD space\n",
"```\n",
"flirt_convert = pe.MapNode(\n",
" interface = fsl.FLIRT(apply_xfm = True), # perform inverse transformation instead of estimate the transformation matrix\n",
" iterfield = ['in_file','reference','in_matrix_file'],\n",
" name = 'flirt_convert')\n",
" # connect the input file (mask_in_structural_space.nii.gz)\n",
"simple_workflow.connect(inputnode,'flt_in_file',flirt_convert,'in_file')\n",
"# connect the input reference file (example_func.nii.gz)\n",
"simple_workflow.connect(inputnode,'flt_reference',flirt_convert,'reference')\n",
"# connect the input transformation matrix file (highres2example.mat)\n",
"simple_workflow.connect(inputnode,'flt_in_matrix',flirt_convert,'in_matrix_file')\n",
"```\n",
"\n",
"### corresponding FSL command:\n",
"```\n",
"flirt \n",
" -in /export/home/dsoto/dsoto/fmri/$s/sess2/label/$i # \n",
" -ref /export/home/dsoto/dsoto/fmri/$s/sess2/run1_prepro1.feat/example_func.nii.gz # \n",
" -applyxfm # apply transformation matrix\n",
" -init /export/home/dsoto/dsoto/fmri/$s/sess2/run1_prepro1.feat/reg/highres2example_func.mat # \n",
" -out /export/home/dsoto/dsoto/fmri/$s/label/BOLD${i} # output file \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "N3efMnQFCmh5",
"colab_type": "text"
},
"source": [
"### 10.3.4. thresholding\n",
"```\n",
"# define a \"get threshold value function\", which takes the threshold value at a given percentile and transform it to a string format that can be passed to the FSL command line\n",
"def getthreshop(thresh):\n",
" return '-mul 2 -thr %.10f -bin' % (val) for val in thresh]\n",
"\n",
"# the getthreshold node that take the flirt transformed mask and binarizes the mask at 99.6 percentile, but this node does only the estimate of the cut point value but not applying the value\n",
"getthreshold = pe.MapNode(\n",
" interface=fsl.ImageStats(op_string='-p 99.6'),\n",
" iterfield = ['in_file','mask_file'],\n",
" name='getthreshold')\n",
"simple_workflow.connect(flirt_convert,'out_file',getthreshold,'in_file')\n",
"simple_workflow.connect(inputnode,'mask',getthreshold,'mask_file')\n",
"\n",
"\n",
"# the thresholding node that take the threshold value and binarize the mask in BOLD space\n",
"threshold = pe.MapNode(\n",
" interface=fsl.ImageMaths(out_data_type='char',suffix='_thresh',\n",
" op_string = '-Tmin -bin'),\n",
" iterfield=['in_file','op_string'],\n",
" name='thresholding')\n",
"simple_workflow.connect(flirt_convert,'out_file',threshold,'in_file')\n",
"simple_workflow.connect(getthreshold,('out_stat',getthreshop),threshold,'op_string')\n",
"```\n",
"### corresponding FSL command:\n",
"```\n",
"fslmaths \n",
" /export/home/dsoto/dsoto/fmri/$s/label/BOLD${i} # \n",
" -mul 2 # multiple the image values by 2\n",
" -thr `fslstats /export/home/dsoto/dsoto/fmri/$s/label/BOLD${i} -p 99.6` # estimate the threshold value at 99.6 percentile and binarize the mask based on this value\n",
" -bin /export/home/dsoto/dsoto/fmri/$s/label/BOLD${i} # \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pr2jLQ2EHyzN",
"colab_type": "text"
},
"source": [
"### 10.3.5. bound the binarized mask by the whole brain mask (dilate mask generated in preprocessing 7.5.11)\n",
"```\n",
"bound_by_mask = pe.MapNode(\n",
" interface = fsl.ImageMaths(suffix='_mask',op_string='-mas'),\n",
" iterfield=['in_file','in_file2'],\n",
" name = 'bound_by_mask')\n",
"# get the binarized ROI mask\n",
"simple_workflow.connect(threshold,'out_file',bound_by_mask,'in_file')\n",
"# get the whole brain mask\n",
"simple_workflow.connect(inputnode,'mask',bound_by_mask,'in_file2')\n",
"# multiple them. Because both masks are 1-0 matrices, so that they will zero-out the out-of-bound voxels that is wrongly transform by the highres2example transformation matrix\n",
"simple_workflow.connect(bound_by_mask,'out_file',outputnode,'BOLD_mask')\n",
"```\n",
"\n",
"### corresponding FSL command:\n",
"```\n",
"fslmaths \n",
" \n",
" -mas \n",
" \n",
" \n",
"```"
]
},
{
"cell_type": "code",
"metadata": {
"id": "s7VhZY21_Xjv",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}