Mercurial > repos > iuc > falco
changeset 4:959a14c1f2dd draft default tip
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/falco commit a71120623d1dc818a107ca32f2c232fd47d819ea
| author | iuc |
|---|---|
| date | Fri, 27 Sep 2024 17:41:40 +0000 |
| parents | babbcf02d35c |
| children | |
| files | falco.xml test-data/1000trimmed.fastq.bz2 test-data/contaminant_list.txt test-data/fastqc_customlimits.txt test-data/fastqc_data.txt test-data/fastqc_data_adapters.txt test-data/fastqc_data_adapters_summary.txt test-data/fastqc_data_contaminant_summary.txt test-data/fastqc_data_contaminants.txt test-data/fastqc_data_customlimits.txt test-data/fastqc_data_customlimits_summary.txt test-data/fastqc_data_hisat.txt test-data/fastqc_data_nogroup.txt test-data/fastqc_data_nogroup_summary.txt test-data/fastqc_data_summary.txt test-data/fastqc_report.html test-data/fastqc_report_adapters.html test-data/fastqc_report_bisulfite.html test-data/fastqc_report_bisulfite.txt test-data/fastqc_report_bisulfite_summary.txt test-data/fastqc_report_contaminants.html test-data/fastqc_report_customlimits.html test-data/fastqc_report_hisat.html test-data/fastqc_report_kmer.html test-data/fastqc_report_min_length.html test-data/fastqc_report_nogroup.html test-data/fastqc_report_reverse_complement.html test-data/fastqc_report_reverse_complement.txt test-data/fastqc_report_reverse_complement_summary.txt test-data/fastqc_report_subsample.html test-data/fastqc_report_subsample.txt test-data/limits.txt test-data/summary.txt |
| diffstat | 33 files changed, 1623 insertions(+), 3463 deletions(-) [+] |
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--- a/falco.xml Tue Sep 10 19:02:42 2024 +0000 +++ b/falco.xml Fri Sep 27 17:41:40 2024 +0000 @@ -1,7 +1,7 @@ <tool id="falco" name="Falco" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="21.05"> <description>An alternative, more performant implementation of FastQC for high throughput sequence quality control</description> <macros> - <token name="@TOOL_VERSION@">1.2.3</token> + <token name="@TOOL_VERSION@">1.2.4</token> <token name="@VERSION_SUFFIX@">0</token> </macros> <xrefs> @@ -74,28 +74,81 @@ </data> </outputs> <tests> + <!-- Test with fastq input --> <test expect_num_outputs="2"> <param name="input_file" value="1000trimmed.fastq"/> - <output name="html_file" file="fastqc_report.html" ftype="html" lines_diff="2"/> - <output name="text_file" file="fastqc_data.txt" ftype="txt"/> + <output name="html_file" ftype="html"> + <assert_contents> + <has_line_matching expression="<html><head>.+<title> 1000trimmed_fastq - report.+"/> + </assert_contents> + </output> + <!-- two lines diff to allow for reported version to change --> + <output name="text_file" file="fastqc_data.txt" ftype="txt" lines_diff="2"/> + </test> + <!-- Test with fastq.gz input --> + <test expect_num_outputs="2"> + <param name="input_file" value="1000trimmed.fastq.gz"/> + <output name="html_file" ftype="html"> + <assert_contents> + <has_line_matching expression="<html><head>.+<title> 1000trimmed_fastq_gz - report.+"/> + </assert_contents> + </output> + <!-- four lines diff to allow for reported version to change; two more to accomodate changed input file name --> + <output name="text_file" file="fastqc_data.txt" ftype="txt" lines_diff="4"/> + </test> + <!-- Test with BAM input --> + <test expect_num_outputs="2"> + <param name="input_file" value="hisat_output_1.bam"/> + <output name="html_file" ftype="html"> + <assert_contents> + <has_line_matching expression="<html><head>.+<title> hisat_output_1_bam - report.+"/> + </assert_contents> + </output> + <!-- four lines diff to allow for reported version to change; two more to accomodate changed input file name --> + <output name="text_file" file="fastqc_data_hisat.txt" ftype="txt" lines_diff="4"/> + </test> + <!-- Test summary file option --> + <test expect_num_outputs="3"> + <param name="input_file" value="1000trimmed.fastq"/> + <param name="generate_summary" value="true"/> + <output name="html_file" ftype="html"> + <assert_contents> + <has_line_matching expression="<html><head>.+<title> 1000trimmed_fastq - report.+"/> + </assert_contents> + </output> + <output name="text_file" file="fastqc_data.txt" ftype="txt" lines_diff="2"/> + <output name="summary_file" file="fastqc_data_summary.txt" ftype="txt"/> </test> <test expect_num_outputs="2"> <param name="input_file" value="1000trimmed.fastq"/> <param name="contaminants" value="contaminant_list.txt" ftype="tabular"/> - <output name="html_file" file="fastqc_report_contaminants.html" ftype="html" lines_diff="2"/> - <output name="text_file" file="fastqc_data_contaminants.txt" ftype="txt"/> + <output name="html_file" ftype="html"> + <assert_contents> + <has_line_matching expression="<html><head>.+<title> 1000trimmed_fastq - report.+"/> + </assert_contents> + </output> + <output name="text_file" file="fastqc_data_contaminants.txt" ftype="txt" lines_diff="2"/> </test> <test expect_num_outputs="2"> <param name="input_file" value="1000trimmed.fastq"/> <param name="adapters" value="adapter_list.txt" ftype="tabular"/> - <output name="html_file" file="fastqc_report_adapters.html" ftype="html" lines_diff="2"/> - <output name="text_file" file="fastqc_data_adapters.txt" ftype="txt"/> + <output name="html_file" ftype="html"> + <assert_contents> + <has_line_matching expression="<html><head>.+<title> 1000trimmed_fastq - report.+"/> + </assert_contents> + </output> + <output name="text_file" file="fastqc_data_adapters.txt" ftype="txt" lines_diff="2"/> </test> - <test expect_num_outputs="2"> + <test expect_num_outputs="3"> <param name="input_file" value="1000trimmed.fastq"/> <param name="limits" value="limits.txt" ftype="txt"/> - <output name="html_file" file="fastqc_report_customlimits.html" ftype="html" lines_diff="2"/> - <output name="text_file" file="fastqc_data_customlimits.txt" ftype="txt"/> + <param name="generate_summary" value="true"/> + <output name="html_file" ftype="html"> + <assert_contents> + <has_line_matching expression="<html><head>.+<title> 1000trimmed_fastq - report.+"/> + </assert_contents> + </output> + <output name="summary_file" file="fastqc_data_customlimits_summary.txt" ftype="txt"/> </test> <!-- ## The kmers param is ignored in Falco and always set to 7. If this ever gets reconsidered, this test could be uncommented. <test expect_num_outputs="2"> @@ -116,40 +169,49 @@ <output name="html_file" file="fastqc_report_min_length.html" ftype="html" lines_diff="2"/> <output name="text_file" file="fastqc_data_min_length.txt" ftype="txt"/> </test> --> - <test expect_num_outputs="3"> + <test expect_num_outputs="2"> <param name="input_file" value="1000trimmed.fastq" ftype="fastq"/> <param name="nogroup" value="--nogroup"/> - <param name="generate_summary" value="true"/> - <output name="html_file" file="fastqc_report_nogroup.html" ftype="html" lines_diff="2"/> - <output name="text_file" file="fastqc_data_nogroup.txt" ftype="txt"/> - <output name="summary_file" file="fastqc_data_nogroup_summary.txt" ftype="txt"/> - <assert_command> - <has_text text="--nogroup"/> - </assert_command> + <output name="html_file" ftype="html"> + <assert_contents> + <has_line_matching expression="<html><head>.+<title> 1000trimmed_fastq - report.+"/> + </assert_contents> + </output> + <output name="text_file" file="fastqc_data_nogroup.txt" ftype="txt" lines_diff="2"/> </test> <test expect_num_outputs="3"> <param name="input_file" value="1000trimmed.fastq"/> <param name="subsample" value="10"/> <param name="generate_summary" value="true"/> - <output name="html_file" file="fastqc_report_subsample.html" ftype="html" lines_diff="2"/> - <output name="text_file" file="fastqc_report_subsample.txt" ftype="txt"/> + <output name="html_file" ftype="html"> + <assert_contents> + <has_line_matching expression="<html><head>.+<title> 1000trimmed_fastq - report.+"/> + </assert_contents> + </output> + <output name="text_file" file="fastqc_report_subsample.txt" ftype="txt" lines_diff="2"/> <output name="summary_file" file="fastqc_report_subsample_summary.txt" ftype="txt"/> </test> <test expect_num_outputs="3"> <param name="input_file" value="1000trimmed.fastq"/> <param name="bisulfite" value="-bisulfite"/> <param name="generate_summary" value="true"/> - <output name="html_file" file="fastqc_report_bisulfite.html" ftype="html" lines_diff="2"/> - <output name="text_file" file="fastqc_report_bisulfite.txt" ftype="txt"/> + <output name="html_file" ftype="html"> + <assert_contents> + <has_line_matching expression="<html><head>.+<title> 1000trimmed_fastq - report.+"/> + </assert_contents> + </output> + <output name="text_file" file="fastqc_report_bisulfite.txt" ftype="txt" lines_diff="2"/> <output name="summary_file" file="fastqc_report_bisulfite_summary.txt" ftype="txt"/> </test> - <test expect_num_outputs="3"> + <test expect_num_outputs="2"> <param name="input_file" value="1000trimmed.fastq"/> <param name="reverse_complement" value="-reverse-complement"/> - <param name="generate_summary" value="true"/> - <output name="html_file" file="fastqc_report_reverse_complement.html" ftype="html" lines_diff="2"/> - <output name="text_file" file="fastqc_report_reverse_complement.txt" ftype="txt"/> - <output name="summary_file" file="fastqc_report_reverse_complement_summary.txt" ftype="txt"/> + <output name="html_file" ftype="html"> + <assert_contents> + <has_line_matching expression="<html><head>.+<title> 1000trimmed_fastq - report.+"/> + </assert_contents> + </output> + <output name="text_file" file="fastqc_report_reverse_complement.txt" ftype="txt" lines_diff="2"/> </test> </tests> <help><![CDATA[ @@ -159,15 +221,15 @@ 💚️ With its superior performance Falco saves computational resources and gives you back results faster than FastQC. -We recommend it for most use cases (but see below for exceptions). 💚️ +We recommend it for most use cases (but see below for rare exceptions). 💚️ The main functions of Falco are very similar to those of FastQC: - Import of data from BAM, SAM or FastQ/FastQ.gz files (any variant), - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data -- Export of results to an HTML based permanent report -- Offline operation to allow automated generation of reports without running the interactive application +- Export of results to an HTML-based report + .. class:: infomark @@ -181,13 +243,10 @@ Falco doesn't currently support fastq.bz2 as input format meaning Galaxy has to perform a relatively slow format conversion before running the tool, which together makes the analysis slower than with FastQC. -- you are interested in PolyA and PolyG statistics in the Adapter Content section of the quality report - - Falco doesn't currently calculate statistics for these "Adapters" by default. +- you need the HTML report to be viewable offline -- your input consists of *mapped* reads in SAM/BAM format - - Due to a bug in the current version of Falco, reads mapped to the reverse strand of the reference genome are not handled correctly and reported metrics are wrong! + The current version of Falco relies on plotly to generate the graphs in the HTML report dynamically each time it's viewed. + MultiQC plots generated from Falco's raw data output are, of course, viewable offline just like the ones generated from FastQC output. -----
--- a/test-data/contaminant_list.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/contaminant_list.txt Fri Sep 27 17:41:40 2024 +0000 @@ -1,192 +1,194 @@ -# This file contains a list of potential contaminants which are -# frequently found in high throughput sequencing reactions. These -# are mostly sequences of adapters / primers used in the various -# sequencing chemistries. -# -# Please DO NOT rely on these sequences to design your own oligos, some -# of them are truncated at ambiguous positions, and none of them are -# definitive sequences from the manufacturers so don't blame us if you -# try to use them and they don't work. -# -# You can add more sequences to the file by putting one line per entry -# and specifying a name[tab]sequence. If the contaminant you add is -# likely to be of use to others please consider sending it to the FastQ -# authors, either via a bug report at www.bioinformatics.babraham.ac.uk/bugzilla/ -# or by directly emailing simon.andrews@babraham.ac.uk so other users of -# the program can benefit. - -Illumina Single End Adapter 1 GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG -Illumina Single End Adapter 2 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT -Illumina Single End PCR Primer 1 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT -Illumina Single End PCR Primer 2 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT -Illumina Single End Sequencing Primer ACACTCTTTCCCTACACGACGCTCTTCCGATCT - -Illumina Paired End Adapter 1 ACACTCTTTCCCTACACGACGCTCTTCCGATCT -Illumina Paired End Adapter 2 GATCGGAAGAGCGGTTCAGCAGGAATGCCGAG -Illumina Paried End PCR Primer 1 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT -Illumina Paired End PCR Primer 2 CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT -Illumina Paried End Sequencing Primer 1 ACACTCTTTCCCTACACGACGCTCTTCCGATCT -Illumina Paired End Sequencing Primer 2 CGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT - -Illumina DpnII expression Adapter 1 ACAGGTTCAGAGTTCTACAGTCCGAC -Illumina DpnII expression Adapter 2 CAAGCAGAAGACGGCATACGA -Illumina DpnII expression PCR Primer 1 CAAGCAGAAGACGGCATACGA -Illumina DpnII expression PCR Primer 2 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA -Illumina DpnII expression Sequencing Primer CGACAGGTTCAGAGTTCTACAGTCCGACGATC - -Illumina NlaIII expression Adapter 1 ACAGGTTCAGAGTTCTACAGTCCGACATG -Illumina NlaIII expression Adapter 2 CAAGCAGAAGACGGCATACGA -Illumina NlaIII expression PCR Primer 1 CAAGCAGAAGACGGCATACGA -Illumina NlaIII expression PCR Primer 2 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA -Illumina NlaIII expression Sequencing Primer CCGACAGGTTCAGAGTTCTACAGTCCGACATG - -Illumina Small RNA Adapter 1 GTTCAGAGTTCTACAGTCCGACGATC -Illumina Small RNA Adapter 2 TGGAATTCTCGGGTGCCAAGG -Illumina Small RNA RT Primer CAAGCAGAAGACGGCATACGA -Illumina Small RNA PCR Primer 2 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA -Illumina Small RNA Sequencing Primer CGACAGGTTCAGAGTTCTACAGTCCGACGATC - -Illumina Multiplexing Adapter 1 GATCGGAAGAGCACACGTCT -Illumina Multiplexing Adapter 2 ACACTCTTTCCCTACACGACGCTCTTCCGATCT -Illumina Multiplexing PCR Primer 1.01 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT -Illumina Multiplexing PCR Primer 2.01 GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT -Illumina Multiplexing Read1 Sequencing Primer ACACTCTTTCCCTACACGACGCTCTTCCGATCT -Illumina Multiplexing Index Sequencing Primer GATCGGAAGAGCACACGTCTGAACTCCAGTCAC -Illumina Multiplexing Read2 Sequencing Primer GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT - -Illumina PCR Primer Index 1 CAAGCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGTTC -Illumina PCR Primer Index 2 CAAGCAGAAGACGGCATACGAGATACATCGGTGACTGGAGTTC -Illumina PCR Primer Index 3 CAAGCAGAAGACGGCATACGAGATGCCTAAGTGACTGGAGTTC -Illumina PCR Primer Index 4 CAAGCAGAAGACGGCATACGAGATTGGTCAGTGACTGGAGTTC -Illumina PCR Primer Index 5 CAAGCAGAAGACGGCATACGAGATCACTGTGTGACTGGAGTTC -Illumina PCR Primer Index 6 CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGGAGTTC -Illumina PCR Primer Index 7 CAAGCAGAAGACGGCATACGAGATGATCTGGTGACTGGAGTTC -Illumina PCR Primer Index 8 CAAGCAGAAGACGGCATACGAGATTCAAGTGTGACTGGAGTTC -Illumina PCR Primer Index 9 CAAGCAGAAGACGGCATACGAGATCTGATCGTGACTGGAGTTC -Illumina PCR Primer Index 10 CAAGCAGAAGACGGCATACGAGATAAGCTAGTGACTGGAGTTC -Illumina PCR Primer Index 11 CAAGCAGAAGACGGCATACGAGATGTAGCCGTGACTGGAGTTC -Illumina PCR Primer Index 12 CAAGCAGAAGACGGCATACGAGATTACAAGGTGACTGGAGTTC - -Illumina DpnII Gex Adapter 1 GATCGTCGGACTGTAGAACTCTGAAC -Illumina DpnII Gex Adapter 1.01 ACAGGTTCAGAGTTCTACAGTCCGAC -Illumina DpnII Gex Adapter 2 CAAGCAGAAGACGGCATACGA -Illumina DpnII Gex Adapter 2.01 TCGTATGCCGTCTTCTGCTTG -Illumina DpnII Gex PCR Primer 1 CAAGCAGAAGACGGCATACGA -Illumina DpnII Gex PCR Primer 2 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA -Illumina DpnII Gex Sequencing Primer CGACAGGTTCAGAGTTCTACAGTCCGACGATC - -Illumina NlaIII Gex Adapter 1.01 TCGGACTGTAGAACTCTGAAC -Illumina NlaIII Gex Adapter 1.02 ACAGGTTCAGAGTTCTACAGTCCGACATG -Illumina NlaIII Gex Adapter 2.01 CAAGCAGAAGACGGCATACGA -Illumina NlaIII Gex Adapter 2.02 TCGTATGCCGTCTTCTGCTTG -Illumina NlaIII Gex PCR Primer 1 CAAGCAGAAGACGGCATACGA -Illumina NlaIII Gex PCR Primer 2 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA -Illumina NlaIII Gex Sequencing Primer CCGACAGGTTCAGAGTTCTACAGTCCGACATG - -Illumina 5p RNA Adapter GTTCAGAGTTCTACAGTCCGACGATC -Illumina RNA Adapter1 TGGAATTCTCGGGTGCCAAGG - -Illumina Small RNA 3p Adapter 1 ATCTCGTATGCCGTCTTCTGCTTG -Illumina Small RNA PCR Primer 1 CAAGCAGAAGACGGCATACGA - -TruSeq Universal Adapter AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT -TruSeq Adapter, Index 1 GATCGGAAGAGCACACGTCTGAACTCCAGTCACATCACGATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 2 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCGATGTATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 3 GATCGGAAGAGCACACGTCTGAACTCCAGTCACTTAGGCATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 4 GATCGGAAGAGCACACGTCTGAACTCCAGTCACTGACCAATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 5 GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 6 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGCCAATATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 7 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCAGATCATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 8 GATCGGAAGAGCACACGTCTGAACTCCAGTCACACTTGAATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 9 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGATCAGATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 10 GATCGGAAGAGCACACGTCTGAACTCCAGTCACTAGCTTATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 11 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGGCTACATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 12 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCTTGTAATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 13 GATCGGAAGAGCACACGTCTGAACTCCAGTCACAGTCAACTCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 14 GATCGGAAGAGCACACGTCTGAACTCCAGTCACAGTTCCGTCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 15 GATCGGAAGAGCACACGTCTGAACTCCAGTCACATGTCAGTCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 16 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCCGTCCCTCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 18 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTCCGCATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 19 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTGAAACTCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 20 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTGGCCTTCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 21 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTTTCGGTCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 22 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCGTACGTTCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 23 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCCACTCTTCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 25 GATCGGAAGAGCACACGTCTGAACTCCAGTCACACTGATATCTCGTATGCCGTCTTCTGCTTG -TruSeq Adapter, Index 27 GATCGGAAGAGCACACGTCTGAACTCCAGTCACATTCCTTTCTCGTATGCCGTCTTCTGCTTG - -Illumina RNA RT Primer GCCTTGGCACCCGAGAATTCCA -Illumina RNA PCR Primer AATGATACGGCGACCACCGAGATCTACACGTTCAGAGTTCTACAGTCCGA - -RNA PCR Primer, Index 1 CAAGCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 2 CAAGCAGAAGACGGCATACGAGATACATCGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 3 CAAGCAGAAGACGGCATACGAGATGCCTAAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 4 CAAGCAGAAGACGGCATACGAGATTGGTCAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 5 CAAGCAGAAGACGGCATACGAGATCACTGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 6 CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 7 CAAGCAGAAGACGGCATACGAGATGATCTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 8 CAAGCAGAAGACGGCATACGAGATTCAAGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 9 CAAGCAGAAGACGGCATACGAGATCTGATCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 10 CAAGCAGAAGACGGCATACGAGATAAGCTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 11 CAAGCAGAAGACGGCATACGAGATGTAGCCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 12 CAAGCAGAAGACGGCATACGAGATTACAAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 13 CAAGCAGAAGACGGCATACGAGATTTGACTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 14 CAAGCAGAAGACGGCATACGAGATGGAACTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 15 CAAGCAGAAGACGGCATACGAGATTGACATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 16 CAAGCAGAAGACGGCATACGAGATGGACGGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 17 CAAGCAGAAGACGGCATACGAGATCTCTACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 18 CAAGCAGAAGACGGCATACGAGATGCGGACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 19 CAAGCAGAAGACGGCATACGAGATTTTCACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 20 CAAGCAGAAGACGGCATACGAGATGGCCACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 21 CAAGCAGAAGACGGCATACGAGATCGAAACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 22 CAAGCAGAAGACGGCATACGAGATCGTACGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 23 CAAGCAGAAGACGGCATACGAGATCCACTCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 24 CAAGCAGAAGACGGCATACGAGATGCTACCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 25 CAAGCAGAAGACGGCATACGAGATATCAGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 26 CAAGCAGAAGACGGCATACGAGATGCTCATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 27 CAAGCAGAAGACGGCATACGAGATAGGAATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 28 CAAGCAGAAGACGGCATACGAGATCTTTTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 29 CAAGCAGAAGACGGCATACGAGATTAGTTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 30 CAAGCAGAAGACGGCATACGAGATCCGGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 31 CAAGCAGAAGACGGCATACGAGATATCGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 32 CAAGCAGAAGACGGCATACGAGATTGAGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 33 CAAGCAGAAGACGGCATACGAGATCGCCTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 34 CAAGCAGAAGACGGCATACGAGATGCCATGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 35 CAAGCAGAAGACGGCATACGAGATAAAATGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 36 CAAGCAGAAGACGGCATACGAGATTGTTGGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 37 CAAGCAGAAGACGGCATACGAGATATTCCGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 38 CAAGCAGAAGACGGCATACGAGATAGCTAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 39 CAAGCAGAAGACGGCATACGAGATGTATAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 40 CAAGCAGAAGACGGCATACGAGATTCTGAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 41 CAAGCAGAAGACGGCATACGAGATGTCGTCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 42 CAAGCAGAAGACGGCATACGAGATCGATTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 43 CAAGCAGAAGACGGCATACGAGATGCTGTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 44 CAAGCAGAAGACGGCATACGAGATATTATAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 45 CAAGCAGAAGACGGCATACGAGATGAATGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 46 CAAGCAGAAGACGGCATACGAGATTCGGGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 47 CAAGCAGAAGACGGCATACGAGATCTTCGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA -RNA PCR Primer, Index 48 CAAGCAGAAGACGGCATACGAGATTGCCGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA - -ABI Dynabead EcoP Oligo CTGATCTAGAGGTACCGGATCCCAGCAGT -ABI Solid3 Adapter A CTGCCCCGGGTTCCTCATTCTCTCAGCAGCATG -ABI Solid3 Adapter B CCACTACGCCTCCGCTTTCCTCTCTATGGGCAGTCGGTGAT -ABI Solid3 5' AMP Primer CCACTACGCCTCCGCTTTCCTCTCTATG -ABI Solid3 3' AMP Primer CTGCCCCGGGTTCCTCATTCT -ABI Solid3 EF1 alpha Sense Primer CATGTGTGTTGAGAGCTTC -ABI Solid3 EF1 alpha Antisense Primer GAAAACCAAAGTGGTCCAC -ABI Solid3 GAPDH Forward Primer TTAGCACCCCTGGCCAAGG -ABI Solid3 GAPDH Reverse Primer CTTACTCCTTGGAGGCCATG - - - -Clontech Universal Primer Mix Short CTAATACGACTCACTATAGGGC -Clontech Universal Primer Mix Long CTAATACGACTCACTATAGGGCAAGCAGTGGTATCAACGCAGAGT -Clontech SMARTer II A Oligonucleotide AAGCAGTGGTATCAACGCAGAGTAC -Clontech SMART CDS Primer II A AAGCAGTGGTATCAACGCAGAGTACT -Clontech_Universal_Primer_Mix_Short CTAATACGACTCACTATAGGGC -Clontech_Universal_Primer_Mix_Long CTAATACGACTCACTATAGGGCAAGCAGTGGTATCAACGCAGAGT -Clontech_SMARTer_II_A_Oligonucleotide AAGCAGTGGTATCAACGCAGAGTAC -Clontech_SMART_CDS_Primer_II_A AAGCAGTGGTATCAACGCAGAGTACT -Clontech_SMART_CDS_Primer_II_A ACGTACTCTGCGTTGATACCACTGCTTCCGCGGACAGGCGTGTAGATCTCGGTGGTCGC -Clontech_SMART_CDS_Primer_II_A GAGTACGTACTCTGCGTTGATACCACTGCTTCCGCGGACAGGCGTGTAGATCTCGGTGGT - +# This file contains a list of potential contaminants which are +# frequently found in high throughput sequencing reactions. These +# are mostly sequences of adapters / primers used in the various +# sequencing chemistries. +# +# Please DO NOT rely on these sequences to design your own oligos, some +# of them are truncated at ambiguous positions, and none of them are +# definitive sequences from the manufacturers so don't blame us if you +# try to use them and they don't work. +# +# You can add more sequences to the file by putting one line per entry +# and specifying a name[tab]sequence. If the contaminant you add is +# likely to be of use to others please consider sending it to the FastQ +# authors, either via a bug report at www.bioinformatics.babraham.ac.uk/bugzilla/ +# or by directly emailing simon.andrews@babraham.ac.uk so other users of +# the program can benefit. + +Test sequence ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT + +Illumina Single End Adapter 1 GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG +Illumina Single End Adapter 2 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT +Illumina Single End PCR Primer 1 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT +Illumina Single End PCR Primer 2 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT +Illumina Single End Sequencing Primer ACACTCTTTCCCTACACGACGCTCTTCCGATCT + +Illumina Paired End Adapter 1 ACACTCTTTCCCTACACGACGCTCTTCCGATCT +Illumina Paired End Adapter 2 GATCGGAAGAGCGGTTCAGCAGGAATGCCGAG +Illumina Paried End PCR Primer 1 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT +Illumina Paired End PCR Primer 2 CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT +Illumina Paried End Sequencing Primer 1 ACACTCTTTCCCTACACGACGCTCTTCCGATCT +Illumina Paired End Sequencing Primer 2 CGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT + +Illumina DpnII expression Adapter 1 ACAGGTTCAGAGTTCTACAGTCCGAC +Illumina DpnII expression Adapter 2 CAAGCAGAAGACGGCATACGA +Illumina DpnII expression PCR Primer 1 CAAGCAGAAGACGGCATACGA +Illumina DpnII expression PCR Primer 2 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA +Illumina DpnII expression Sequencing Primer CGACAGGTTCAGAGTTCTACAGTCCGACGATC + +Illumina NlaIII expression Adapter 1 ACAGGTTCAGAGTTCTACAGTCCGACATG +Illumina NlaIII expression Adapter 2 CAAGCAGAAGACGGCATACGA +Illumina NlaIII expression PCR Primer 1 CAAGCAGAAGACGGCATACGA +Illumina NlaIII expression PCR Primer 2 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA +Illumina NlaIII expression Sequencing Primer CCGACAGGTTCAGAGTTCTACAGTCCGACATG + +Illumina Small RNA Adapter 1 GTTCAGAGTTCTACAGTCCGACGATC +Illumina Small RNA Adapter 2 TGGAATTCTCGGGTGCCAAGG +Illumina Small RNA RT Primer CAAGCAGAAGACGGCATACGA +Illumina Small RNA PCR Primer 2 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA +Illumina Small RNA Sequencing Primer CGACAGGTTCAGAGTTCTACAGTCCGACGATC + +Illumina Multiplexing Adapter 1 GATCGGAAGAGCACACGTCT +Illumina Multiplexing Adapter 2 ACACTCTTTCCCTACACGACGCTCTTCCGATCT +Illumina Multiplexing PCR Primer 1.01 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT +Illumina Multiplexing PCR Primer 2.01 GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT +Illumina Multiplexing Read1 Sequencing Primer ACACTCTTTCCCTACACGACGCTCTTCCGATCT +Illumina Multiplexing Index Sequencing Primer GATCGGAAGAGCACACGTCTGAACTCCAGTCAC +Illumina Multiplexing Read2 Sequencing Primer GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT + +Illumina PCR Primer Index 1 CAAGCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGTTC +Illumina PCR Primer Index 2 CAAGCAGAAGACGGCATACGAGATACATCGGTGACTGGAGTTC +Illumina PCR Primer Index 3 CAAGCAGAAGACGGCATACGAGATGCCTAAGTGACTGGAGTTC +Illumina PCR Primer Index 4 CAAGCAGAAGACGGCATACGAGATTGGTCAGTGACTGGAGTTC +Illumina PCR Primer Index 5 CAAGCAGAAGACGGCATACGAGATCACTGTGTGACTGGAGTTC +Illumina PCR Primer Index 6 CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGGAGTTC +Illumina PCR Primer Index 7 CAAGCAGAAGACGGCATACGAGATGATCTGGTGACTGGAGTTC +Illumina PCR Primer Index 8 CAAGCAGAAGACGGCATACGAGATTCAAGTGTGACTGGAGTTC +Illumina PCR Primer Index 9 CAAGCAGAAGACGGCATACGAGATCTGATCGTGACTGGAGTTC +Illumina PCR Primer Index 10 CAAGCAGAAGACGGCATACGAGATAAGCTAGTGACTGGAGTTC +Illumina PCR Primer Index 11 CAAGCAGAAGACGGCATACGAGATGTAGCCGTGACTGGAGTTC +Illumina PCR Primer Index 12 CAAGCAGAAGACGGCATACGAGATTACAAGGTGACTGGAGTTC + +Illumina DpnII Gex Adapter 1 GATCGTCGGACTGTAGAACTCTGAAC +Illumina DpnII Gex Adapter 1.01 ACAGGTTCAGAGTTCTACAGTCCGAC +Illumina DpnII Gex Adapter 2 CAAGCAGAAGACGGCATACGA +Illumina DpnII Gex Adapter 2.01 TCGTATGCCGTCTTCTGCTTG +Illumina DpnII Gex PCR Primer 1 CAAGCAGAAGACGGCATACGA +Illumina DpnII Gex PCR Primer 2 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA +Illumina DpnII Gex Sequencing Primer CGACAGGTTCAGAGTTCTACAGTCCGACGATC + +Illumina NlaIII Gex Adapter 1.01 TCGGACTGTAGAACTCTGAAC +Illumina NlaIII Gex Adapter 1.02 ACAGGTTCAGAGTTCTACAGTCCGACATG +Illumina NlaIII Gex Adapter 2.01 CAAGCAGAAGACGGCATACGA +Illumina NlaIII Gex Adapter 2.02 TCGTATGCCGTCTTCTGCTTG +Illumina NlaIII Gex PCR Primer 1 CAAGCAGAAGACGGCATACGA +Illumina NlaIII Gex PCR Primer 2 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA +Illumina NlaIII Gex Sequencing Primer CCGACAGGTTCAGAGTTCTACAGTCCGACATG + +Illumina 5p RNA Adapter GTTCAGAGTTCTACAGTCCGACGATC +Illumina RNA Adapter1 TGGAATTCTCGGGTGCCAAGG + +Illumina Small RNA 3p Adapter 1 ATCTCGTATGCCGTCTTCTGCTTG +Illumina Small RNA PCR Primer 1 CAAGCAGAAGACGGCATACGA + +TruSeq Universal Adapter AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT +TruSeq Adapter, Index 1 GATCGGAAGAGCACACGTCTGAACTCCAGTCACATCACGATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 2 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCGATGTATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 3 GATCGGAAGAGCACACGTCTGAACTCCAGTCACTTAGGCATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 4 GATCGGAAGAGCACACGTCTGAACTCCAGTCACTGACCAATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 5 GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 6 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGCCAATATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 7 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCAGATCATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 8 GATCGGAAGAGCACACGTCTGAACTCCAGTCACACTTGAATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 9 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGATCAGATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 10 GATCGGAAGAGCACACGTCTGAACTCCAGTCACTAGCTTATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 11 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGGCTACATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 12 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCTTGTAATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 13 GATCGGAAGAGCACACGTCTGAACTCCAGTCACAGTCAACTCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 14 GATCGGAAGAGCACACGTCTGAACTCCAGTCACAGTTCCGTCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 15 GATCGGAAGAGCACACGTCTGAACTCCAGTCACATGTCAGTCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 16 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCCGTCCCTCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 18 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTCCGCATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 19 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTGAAACTCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 20 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTGGCCTTCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 21 GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTTTCGGTCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 22 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCGTACGTTCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 23 GATCGGAAGAGCACACGTCTGAACTCCAGTCACCCACTCTTCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 25 GATCGGAAGAGCACACGTCTGAACTCCAGTCACACTGATATCTCGTATGCCGTCTTCTGCTTG +TruSeq Adapter, Index 27 GATCGGAAGAGCACACGTCTGAACTCCAGTCACATTCCTTTCTCGTATGCCGTCTTCTGCTTG + +Illumina RNA RT Primer GCCTTGGCACCCGAGAATTCCA +Illumina RNA PCR Primer AATGATACGGCGACCACCGAGATCTACACGTTCAGAGTTCTACAGTCCGA + +RNA PCR Primer, Index 1 CAAGCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 2 CAAGCAGAAGACGGCATACGAGATACATCGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 3 CAAGCAGAAGACGGCATACGAGATGCCTAAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 4 CAAGCAGAAGACGGCATACGAGATTGGTCAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 5 CAAGCAGAAGACGGCATACGAGATCACTGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 6 CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 7 CAAGCAGAAGACGGCATACGAGATGATCTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 8 CAAGCAGAAGACGGCATACGAGATTCAAGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 9 CAAGCAGAAGACGGCATACGAGATCTGATCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 10 CAAGCAGAAGACGGCATACGAGATAAGCTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 11 CAAGCAGAAGACGGCATACGAGATGTAGCCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 12 CAAGCAGAAGACGGCATACGAGATTACAAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 13 CAAGCAGAAGACGGCATACGAGATTTGACTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 14 CAAGCAGAAGACGGCATACGAGATGGAACTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 15 CAAGCAGAAGACGGCATACGAGATTGACATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 16 CAAGCAGAAGACGGCATACGAGATGGACGGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 17 CAAGCAGAAGACGGCATACGAGATCTCTACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 18 CAAGCAGAAGACGGCATACGAGATGCGGACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 19 CAAGCAGAAGACGGCATACGAGATTTTCACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 20 CAAGCAGAAGACGGCATACGAGATGGCCACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 21 CAAGCAGAAGACGGCATACGAGATCGAAACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 22 CAAGCAGAAGACGGCATACGAGATCGTACGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 23 CAAGCAGAAGACGGCATACGAGATCCACTCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 24 CAAGCAGAAGACGGCATACGAGATGCTACCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 25 CAAGCAGAAGACGGCATACGAGATATCAGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 26 CAAGCAGAAGACGGCATACGAGATGCTCATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 27 CAAGCAGAAGACGGCATACGAGATAGGAATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 28 CAAGCAGAAGACGGCATACGAGATCTTTTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 29 CAAGCAGAAGACGGCATACGAGATTAGTTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 30 CAAGCAGAAGACGGCATACGAGATCCGGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 31 CAAGCAGAAGACGGCATACGAGATATCGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 32 CAAGCAGAAGACGGCATACGAGATTGAGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 33 CAAGCAGAAGACGGCATACGAGATCGCCTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 34 CAAGCAGAAGACGGCATACGAGATGCCATGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 35 CAAGCAGAAGACGGCATACGAGATAAAATGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 36 CAAGCAGAAGACGGCATACGAGATTGTTGGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 37 CAAGCAGAAGACGGCATACGAGATATTCCGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 38 CAAGCAGAAGACGGCATACGAGATAGCTAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 39 CAAGCAGAAGACGGCATACGAGATGTATAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 40 CAAGCAGAAGACGGCATACGAGATTCTGAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 41 CAAGCAGAAGACGGCATACGAGATGTCGTCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 42 CAAGCAGAAGACGGCATACGAGATCGATTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 43 CAAGCAGAAGACGGCATACGAGATGCTGTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 44 CAAGCAGAAGACGGCATACGAGATATTATAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 45 CAAGCAGAAGACGGCATACGAGATGAATGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 46 CAAGCAGAAGACGGCATACGAGATTCGGGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 47 CAAGCAGAAGACGGCATACGAGATCTTCGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA +RNA PCR Primer, Index 48 CAAGCAGAAGACGGCATACGAGATTGCCGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA + +ABI Dynabead EcoP Oligo CTGATCTAGAGGTACCGGATCCCAGCAGT +ABI Solid3 Adapter A CTGCCCCGGGTTCCTCATTCTCTCAGCAGCATG +ABI Solid3 Adapter B CCACTACGCCTCCGCTTTCCTCTCTATGGGCAGTCGGTGAT +ABI Solid3 5' AMP Primer CCACTACGCCTCCGCTTTCCTCTCTATG +ABI Solid3 3' AMP Primer CTGCCCCGGGTTCCTCATTCT +ABI Solid3 EF1 alpha Sense Primer CATGTGTGTTGAGAGCTTC +ABI Solid3 EF1 alpha Antisense Primer GAAAACCAAAGTGGTCCAC +ABI Solid3 GAPDH Forward Primer TTAGCACCCCTGGCCAAGG +ABI Solid3 GAPDH Reverse Primer CTTACTCCTTGGAGGCCATG + + + +Clontech Universal Primer Mix Short CTAATACGACTCACTATAGGGC +Clontech Universal Primer Mix Long CTAATACGACTCACTATAGGGCAAGCAGTGGTATCAACGCAGAGT +Clontech SMARTer II A Oligonucleotide AAGCAGTGGTATCAACGCAGAGTAC +Clontech SMART CDS Primer II A AAGCAGTGGTATCAACGCAGAGTACT +Clontech_Universal_Primer_Mix_Short CTAATACGACTCACTATAGGGC +Clontech_Universal_Primer_Mix_Long CTAATACGACTCACTATAGGGCAAGCAGTGGTATCAACGCAGAGT +Clontech_SMARTer_II_A_Oligonucleotide AAGCAGTGGTATCAACGCAGAGTAC +Clontech_SMART_CDS_Primer_II_A AAGCAGTGGTATCAACGCAGAGTACT +Clontech_SMART_CDS_Primer_II_A ACGTACTCTGCGTTGATACCACTGCTTCCGCGGACAGGCGTGTAGATCTCGGTGGTCGC +Clontech_SMART_CDS_Primer_II_A GAGTACGTACTCTGCGTTGATACCACTGCTTCCGCGGACAGGCGTGTAGATCTCGGTGGT +
--- a/test-data/fastqc_customlimits.txt Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,84 +0,0 @@ -# For each of the modules you can choose to not run that -# module at all by setting the value below to 1 for the -# modules you want to remove. -duplication ignore 0 -kmer ignore 0 -n_content ignore 0 -overrepresented ignore 0 -quality_base ignore 0 -sequence ignore 0 -gc_sequence ignore 0 -quality_sequence ignore 0 -tile ignore 0 -sequence_length ignore 0 -adapter ignore 0 - -# For the duplication module the value is the percentage -# remaining after deduplication. Measured levels below -# these limits trigger the warning / error. -duplication warn 70 -duplication error 50 - -# For the kmer module the filter is on the -log10 binomial -# pvalue for the most significant Kmer, so 5 would be -# 10^-5 = p<0.00001 -kmer warn 2 -kmer error 5 - -# For the N module the filter is on the percentage of Ns -# at any position in the library -n_content warn 5 -n_content error 20 - -# For the overrepresented seqs the warn value sets the -# threshold for the overrepresented sequences to be reported -# at all as the proportion of the library which must be seen -# as a single sequence -overrepresented warn 0.1 -overrepresented error 1 - -# The per base quality filter uses two values, one for the value -# of the lower quartile, and the other for the value of the -# median quality. Failing either of these will trigger the alert -quality_base_lower warn 10 -quality_base_lower error 5 -quality_base_median warn 25 -quality_base_median error 20 - -# The per base sequence content module tests the maximum deviation -# between A and T or C and G -sequence warn 10 -sequence error 20 - -# The per sequence GC content tests the maximum deviation between -# the theoretical distribution and the real distribution -gc_sequence warn 15 -gc_sequence error 30 - -# The per sequence quality module tests the phred score which is -# most frequently observed -quality_sequence warn 27 -quality_sequence error 20 - -# The per tile module tests the maximum phred score loss between -# and individual tile and the average for that base across all tiles -tile warn 5 -tile error 10 - -# The sequence length module tests are binary, so the values here -# simply turn them on or off. The actual tests warn if you have -# sequences of different length, and error if you have sequences -# of zero length. - -sequence_length warn 1 -sequence_length error 1 - -# The adapter module's warnings and errors are based on the -# percentage of reads in the library which have been observed -# to contain an adapter associated Kmer at any point - -adapter warn 5 -adapter error 10 - - -
--- a/test-data/fastqc_data.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/fastqc_data.txt Fri Sep 27 17:41:40 2024 +0000 @@ -1,4 +1,4 @@ -##Falco 1.2.3 +##Falco 1.2.4 >>Basic Statistics pass #Measure Value Filename 1000trimmed_fastq @@ -1597,114 +1597,114 @@ #Sequence Count Percentage Possible Source ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT 33 0.672783 No Hit >>END_MODULE ->>Adapter Content warn +>>Adapter Content pass #Position Illumina Universal Adapter Illumina Small RNA 3' Adapter Illumina Small RNA 5' Adapter Nextera Transposase Sequence PolyA PolyG 1 0 0 0 0 0.0203874 0 -2 0 0 0 0 0.0815494 0 -3 0 0 0 0 0.142712 0 -4 0 0 0 0 0.183486 0 -5 0 0 0 0 0.285423 0 -6 0 0 0 0 0.38736 0 -7 0 0 0 0 0.489297 0 -8 0 0 0 0 0.591233 0 -9 0 0 0 0 0.672783 0 -10 0 0 0 0 0.754332 0 -11 0 0 0 0 0.835882 0 -12 0 0 0 0 0.917431 0 -13 0 0 0 0 1.01937 0 -14 0 0 0 0 1.1213 0 -15 0 0 0 0 1.24363 0 -16 0 0 0 0 1.34557 0 -17 0 0 0 0 1.46789 0 -18 0 0 0 0 1.59021 0 -19 0 0 0 0 1.67176 0 -20 0.122324 0 0 0 1.75331 0 -21 0.122324 0 0 0 1.83486 0 -22 0.122324 0 0 0 1.89602 0 -23 0.122324 0 0 0 1.95719 0 -24 0.122324 0 0 0 2.01835 0 -25 0.122324 0 0 0 2.07951 0 -26 0.122324 0 0 0 2.14067 0 -27 0.142712 0 0 0 2.20183 0 -28 0.183486 0 0 0 2.263 0 -29 0.224261 0 0 0 2.32416 0 -30 0.224261 0 0 0 2.38532 0 -31 0.224261 0 0 0 2.44648 0 -32 0.224261 0 0 0 2.50765 0 -33 0.224261 0 0 0 2.60958 0 -34 0.224261 0 0 0 2.71152 0 -35 0.265036 0 0 0 2.81346 0 -36 0.285423 0 0 0 2.89501 0 -37 0.326198 0 0 0 2.99694 0 -38 0.407747 0 0 0 3.09888 0 -39 0.468909 0 0 0 3.20082 0 -40 0.468909 0 0 0 3.30275 0 -41 0.468909 0 0 0 3.40469 0 -42 0.468909 0 0 0 3.48624 0 -43 0.468909 0 0 0 3.56779 0 -44 0.468909 0 0 0 3.60856 0 -45 0.468909 0 0 0 3.62895 0 -46 0.468909 0 0 0 3.64934 0 -47 0.468909 0 0 0 3.66972 0 -48 0.468909 0 0 0 3.69011 0 -49 0.468909 0 0 0 3.7105 0 -50 0.468909 0 0 0 3.7105 0 -51 0.468909 0 0 0 3.7105 0 -52 0.468909 0 0 0 3.7105 0 -53 0.468909 0 0 0 3.7105 0 -54 0.468909 0 0 0 3.7105 0 -55 0.468909 0 0 0 3.7105 0 -56 0.468909 0 0 0 3.7105 0 -57 0.468909 0 0 0 3.7105 0 -58 0.468909 0 0 0 3.7105 0 -59 0.468909 0 0 0 3.73089 0 -60 0.468909 0 0 0 3.75127 0 -61 0.468909 0 0 0 3.77166 0 -62 0.468909 0 0 0 3.81244 0 -63 0.468909 0 0 0 3.85321 0 -64 0.468909 0 0 0 3.89399 0 -65 0.468909 0 0 0 3.93476 0 -66 0.468909 0 0 0 3.97554 0 -67 0.468909 0 0 0 4.01631 0 -68 0.468909 0 0 0 4.05708 0 -69 0.468909 0 0 0 4.09786 0 -70 0.468909 0 0 0 4.13863 0 -71 0.468909 0 0 0 4.17941 0 -72 0.468909 0 0 0 4.22018 0 -73 0.468909 0 0 0 4.26096 0 -74 0.489297 0 0 0 4.32212 0 -75 0.489297 0 0 0 4.38328 0 -76 0.489297 0 0 0 4.42406 0 -77 0.489297 0 0 0 4.46483 0 -78 0.489297 0 0 0 4.50561 0 -79 0.489297 0 0 0 4.54638 0 -80 0.489297 0 0 0 4.58716 0 -81 0.489297 0 0 0 4.62793 0 -82 0.489297 0 0 0 4.66871 0 -83 0.509684 0 0 0 4.70948 0 -84 0.509684 0 0 0 4.75025 0 -85 0.509684 0 0 0 4.79103 0 -86 0.509684 0 0 0 4.8318 0 -87 0.509684 0 0 0 4.91335 0 -88 0.509684 0 0 0 4.9949 0 -89 0.509684 0 0 0 5.05607 0 -90 0.509684 0 0 0 5.09684 0 -91 0.509684 0 0 0 5.158 0 -92 0.570846 0 0 0 5.21916 0 -93 0.632008 0 0 0 5.28033 0 -94 0.632008 0 0 0 5.34149 0 -95 0.632008 0 0 0 5.40265 0 -96 0.632008 0 0 0 5.46381 0 -97 0.632008 0 0 0 5.52497 0 -98 0.632008 0 0 0 5.52497 0 -99 0.632008 0 0 0 5.52497 0 -100 0.632008 0 0 0 5.52497 0 -101 0.632008 0 0 0 5.52497 0 -102 0.632008 0 0 0 5.52497 0 -103 0.632008 0 0 0 5.52497 0 -104 0.632008 0 0 0 5.52497 0 -105 0.632008 0 0 0 5.52497 0 -106 0.632008 0 0 0 5.52497 0 -107 0.632008 0 0 0 5.52497 0 -108 0.632008 0 0 0 5.52497 0 +2 0 0 0 0 0.0611621 0 +3 0 0 0 0 0.0611621 0 +4 0 0 0 0 0.0611621 0 +5 0 0 0 0 0.122324 0 +6 0 0 0 0 0.122324 0 +7 0 0 0 0 0.122324 0 +8 0 0 0 0 0.142712 0 +9 0 0 0 0 0.142712 0 +10 0 0 0 0 0.142712 0 +11 0 0 0 0 0.142712 0 +12 0 0 0 0 0.142712 0 +13 0 0 0 0 0.163099 0 +14 0 0 0 0 0.163099 0 +15 0 0 0 0 0.183486 0 +16 0 0 0 0 0.203874 0 +17 0 0 0 0 0.224261 0 +18 0 0 0 0 0.224261 0 +19 0 0 0 0 0.224261 0 +20 0.122324 0 0 0 0.244648 0 +21 0.122324 0 0 0 0.244648 0 +22 0.122324 0 0 0 0.244648 0 +23 0.122324 0 0 0 0.244648 0 +24 0.122324 0 0 0 0.244648 0 +25 0.122324 0 0 0 0.244648 0 +26 0.122324 0 0 0 0.244648 0 +27 0.142712 0 0 0 0.244648 0 +28 0.183486 0 0 0 0.244648 0 +29 0.224261 0 0 0 0.244648 0 +30 0.224261 0 0 0 0.244648 0 +31 0.224261 0 0 0 0.244648 0 +32 0.224261 0 0 0 0.244648 0 +33 0.224261 0 0 0 0.285423 0 +34 0.224261 0 0 0 0.285423 0 +35 0.265036 0 0 0 0.30581 0 +36 0.285423 0 0 0 0.30581 0 +37 0.326198 0 0 0 0.326198 0 +38 0.407747 0 0 0 0.326198 0 +39 0.468909 0 0 0 0.326198 0 +40 0.468909 0 0 0 0.326198 0 +41 0.468909 0 0 0 0.326198 0 +42 0.468909 0 0 0 0.326198 0 +43 0.468909 0 0 0 0.326198 0 +44 0.468909 0 0 0 0.326198 0 +45 0.468909 0 0 0 0.326198 0 +46 0.468909 0 0 0 0.326198 0 +47 0.468909 0 0 0 0.326198 0 +48 0.468909 0 0 0 0.326198 0 +49 0.468909 0 0 0 0.326198 0 +50 0.468909 0 0 0 0.326198 0 +51 0.468909 0 0 0 0.326198 0 +52 0.468909 0 0 0 0.326198 0 +53 0.468909 0 0 0 0.326198 0 +54 0.468909 0 0 0 0.326198 0 +55 0.468909 0 0 0 0.326198 0 +56 0.468909 0 0 0 0.326198 0 +57 0.468909 0 0 0 0.326198 0 +58 0.468909 0 0 0 0.326198 0 +59 0.468909 0 0 0 0.326198 0 +60 0.468909 0 0 0 0.326198 0 +61 0.468909 0 0 0 0.326198 0 +62 0.468909 0 0 0 0.326198 0 +63 0.468909 0 0 0 0.326198 0 +64 0.468909 0 0 0 0.326198 0 +65 0.468909 0 0 0 0.326198 0 +66 0.468909 0 0 0 0.326198 0 +67 0.468909 0 0 0 0.326198 0 +68 0.468909 0 0 0 0.326198 0 +69 0.468909 0 0 0 0.326198 0 +70 0.468909 0 0 0 0.326198 0 +71 0.468909 0 0 0 0.326198 0 +72 0.468909 0 0 0 0.326198 0 +73 0.468909 0 0 0 0.326198 0 +74 0.468909 0 0 0 0.326198 0 +75 0.468909 0 0 0 0.326198 0 +76 0.468909 0 0 0 0.326198 0 +77 0.468909 0 0 0 0.326198 0 +78 0.468909 0 0 0 0.326198 0 +79 0.468909 0 0 0 0.326198 0 +80 0.468909 0 0 0 0.326198 0 +81 0.468909 0 0 0 0.326198 0 +82 0.468909 0 0 0 0.326198 0 +83 0.468909 0 0 0 0.326198 0 +84 0.468909 0 0 0 0.326198 0 +85 0.468909 0 0 0 0.326198 0 +86 0.468909 0 0 0 0.326198 0 +87 0.468909 0 0 0 0.326198 0 +88 0.468909 0 0 0 0.326198 0 +89 0.468909 0 0 0 0.326198 0 +90 0.468909 0 0 0 0.326198 0 +91 0.468909 0 0 0 0.326198 0 +92 0.468909 0 0 0 0.326198 0 +93 0.468909 0 0 0 0.326198 0 +94 0.468909 0 0 0 0.326198 0 +95 0.468909 0 0 0 0.326198 0 +96 0.468909 0 0 0 0.326198 0 +97 0.468909 0 0 0 0.326198 0 +98 0.468909 0 0 0 0.326198 0 +99 0.468909 0 0 0 0.326198 0 +100 0.468909 0 0 0 0.326198 0 +101 0.468909 0 0 0 0.326198 0 +102 0.468909 0 0 0 0.326198 0 +103 0.468909 0 0 0 0.326198 0 +104 0.468909 0 0 0 0.326198 0 +105 0.468909 0 0 0 0.326198 0 +106 0.468909 0 0 0 0.326198 0 +107 0.468909 0 0 0 0.326198 0 +108 0.468909 0 0 0 0.326198 0 >>END_MODULE
--- a/test-data/fastqc_data_adapters.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/fastqc_data_adapters.txt Fri Sep 27 17:41:40 2024 +0000 @@ -1,4 +1,4 @@ -##Falco 1.2.3 +##Falco 1.2.4 >>Basic Statistics pass #Measure Value Filename 1000trimmed_fastq @@ -1672,39 +1672,39 @@ 71 0.468909 0 0 0 0 72 0.468909 0 0 0 0 73 0.468909 0 0 0 0 -74 0.489297 0 0 0 0 -75 0.489297 0 0 0 0 -76 0.489297 0 0 0 0 -77 0.489297 0 0 0 0 -78 0.489297 0 0 0 0 -79 0.489297 0 0 0 0 -80 0.489297 0 0 0 0 -81 0.489297 0 0 0 0 -82 0.489297 0 0 0 0 -83 0.509684 0 0 0 0 -84 0.509684 0 0 0 0 -85 0.509684 0 0 0 0 -86 0.509684 0 0 0 0 -87 0.509684 0 0 0 0 -88 0.509684 0 0 0 0 -89 0.509684 0 0 0 0 -90 0.509684 0 0 0 0 -91 0.509684 0 0 0 0 -92 0.570846 0 0 0 0 -93 0.632008 0 0 0 0 -94 0.632008 0 0 0 0 -95 0.632008 0 0 0 0 -96 0.632008 0 0 0 0 -97 0.632008 0 0 0 0 -98 0.632008 0 0 0 0 -99 0.632008 0 0 0 0 -100 0.632008 0 0 0 0 -101 0.632008 0 0 0 0 -102 0.632008 0 0 0 0 -103 0.632008 0 0 0 0 -104 0.632008 0 0 0 0 -105 0.632008 0 0 0 0 -106 0.632008 0 0 0 0 -107 0.632008 0 0 0 0 -108 0.632008 0 0 0 0 +74 0.468909 0 0 0 0 +75 0.468909 0 0 0 0 +76 0.468909 0 0 0 0 +77 0.468909 0 0 0 0 +78 0.468909 0 0 0 0 +79 0.468909 0 0 0 0 +80 0.468909 0 0 0 0 +81 0.468909 0 0 0 0 +82 0.468909 0 0 0 0 +83 0.468909 0 0 0 0 +84 0.468909 0 0 0 0 +85 0.468909 0 0 0 0 +86 0.468909 0 0 0 0 +87 0.468909 0 0 0 0 +88 0.468909 0 0 0 0 +89 0.468909 0 0 0 0 +90 0.468909 0 0 0 0 +91 0.468909 0 0 0 0 +92 0.468909 0 0 0 0 +93 0.468909 0 0 0 0 +94 0.468909 0 0 0 0 +95 0.468909 0 0 0 0 +96 0.468909 0 0 0 0 +97 0.468909 0 0 0 0 +98 0.468909 0 0 0 0 +99 0.468909 0 0 0 0 +100 0.468909 0 0 0 0 +101 0.468909 0 0 0 0 +102 0.468909 0 0 0 0 +103 0.468909 0 0 0 0 +104 0.468909 0 0 0 0 +105 0.468909 0 0 0 0 +106 0.468909 0 0 0 0 +107 0.468909 0 0 0 0 +108 0.468909 0 0 0 0 >>END_MODULE
--- a/test-data/fastqc_data_adapters_summary.txt Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,11 +0,0 @@ -PASS Basic Statistics 1000trimmed_fastq -PASS Per base sequence quality 1000trimmed_fastq -FAIL Per tile sequence quality 1000trimmed_fastq -PASS Per sequence quality scores 1000trimmed_fastq -FAIL Per base sequence content 1000trimmed_fastq -WARN Per sequence GC content 1000trimmed_fastq -PASS Per base N content 1000trimmed_fastq -WARN Sequence Length Distribution 1000trimmed_fastq -PASS Sequence Duplication Levels 1000trimmed_fastq -WARN Overrepresented sequences 1000trimmed_fastq -PASS Adapter Content 1000trimmed_fastq
--- a/test-data/fastqc_data_contaminant_summary.txt Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,11 +0,0 @@ -PASS Basic Statistics 1000trimmed_fastq -PASS Per base sequence quality 1000trimmed_fastq -FAIL Per tile sequence quality 1000trimmed_fastq -PASS Per sequence quality scores 1000trimmed_fastq -FAIL Per base sequence content 1000trimmed_fastq -WARN Per sequence GC content 1000trimmed_fastq -PASS Per base N content 1000trimmed_fastq -WARN Sequence Length Distribution 1000trimmed_fastq -PASS Sequence Duplication Levels 1000trimmed_fastq -WARN Overrepresented sequences 1000trimmed_fastq -PASS Adapter Content 1000trimmed_fastq
--- a/test-data/fastqc_data_contaminants.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/fastqc_data_contaminants.txt Fri Sep 27 17:41:40 2024 +0000 @@ -1,4 +1,4 @@ -##Falco 1.2.3 +##Falco 1.2.4 >>Basic Statistics pass #Measure Value Filename 1000trimmed_fastq @@ -1595,116 +1595,116 @@ >>END_MODULE >>Overrepresented sequences warn #Sequence Count Percentage Possible Source -ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT 33 0.672783 No Hit +ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT 33 0.672783 Test sequence >>END_MODULE ->>Adapter Content warn +>>Adapter Content pass #Position Illumina Universal Adapter Illumina Small RNA 3' Adapter Illumina Small RNA 5' Adapter Nextera Transposase Sequence PolyA PolyG 1 0 0 0 0 0.0203874 0 -2 0 0 0 0 0.0815494 0 -3 0 0 0 0 0.142712 0 -4 0 0 0 0 0.183486 0 -5 0 0 0 0 0.285423 0 -6 0 0 0 0 0.38736 0 -7 0 0 0 0 0.489297 0 -8 0 0 0 0 0.591233 0 -9 0 0 0 0 0.672783 0 -10 0 0 0 0 0.754332 0 -11 0 0 0 0 0.835882 0 -12 0 0 0 0 0.917431 0 -13 0 0 0 0 1.01937 0 -14 0 0 0 0 1.1213 0 -15 0 0 0 0 1.24363 0 -16 0 0 0 0 1.34557 0 -17 0 0 0 0 1.46789 0 -18 0 0 0 0 1.59021 0 -19 0 0 0 0 1.67176 0 -20 0.122324 0 0 0 1.75331 0 -21 0.122324 0 0 0 1.83486 0 -22 0.122324 0 0 0 1.89602 0 -23 0.122324 0 0 0 1.95719 0 -24 0.122324 0 0 0 2.01835 0 -25 0.122324 0 0 0 2.07951 0 -26 0.122324 0 0 0 2.14067 0 -27 0.142712 0 0 0 2.20183 0 -28 0.183486 0 0 0 2.263 0 -29 0.224261 0 0 0 2.32416 0 -30 0.224261 0 0 0 2.38532 0 -31 0.224261 0 0 0 2.44648 0 -32 0.224261 0 0 0 2.50765 0 -33 0.224261 0 0 0 2.60958 0 -34 0.224261 0 0 0 2.71152 0 -35 0.265036 0 0 0 2.81346 0 -36 0.285423 0 0 0 2.89501 0 -37 0.326198 0 0 0 2.99694 0 -38 0.407747 0 0 0 3.09888 0 -39 0.468909 0 0 0 3.20082 0 -40 0.468909 0 0 0 3.30275 0 -41 0.468909 0 0 0 3.40469 0 -42 0.468909 0 0 0 3.48624 0 -43 0.468909 0 0 0 3.56779 0 -44 0.468909 0 0 0 3.60856 0 -45 0.468909 0 0 0 3.62895 0 -46 0.468909 0 0 0 3.64934 0 -47 0.468909 0 0 0 3.66972 0 -48 0.468909 0 0 0 3.69011 0 -49 0.468909 0 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--- a/test-data/fastqc_data_customlimits.txt Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1710 +0,0 @@ -##Falco 1.2.3 ->>Basic Statistics pass -#Measure Value -Filename 1000trimmed_fastq -File type Conventional base calls -Encoding Sanger / Illumina 1.9 -Total Sequences 4905 -Sequences flagged as poor quality 0 -Sequence length 1-108 -%GC 41 ->>END_MODULE ->>Per base sequence quality pass -#Base Mean Median Lower Quartile Upper Quartile 10th Percentile 90th Percentile -1 29.2828 31 27 33 23 34 -2 28.8329 30 27 32 22 33 -3 28.897 30 27 32 23 34 -4 29.0068 30 27 32 23 34 -5 28.9378 30 27 32 22 34 -6 29.0029 30 27 32 23 34 -7 28.8998 30 27 32 22 34 -8 28.9007 30 27 32 22 33 -9 28.7546 30 27 32 22 34 -10-11 28.8221 30 27 32 22.5 33 -12-13 28.749 30 26.5 32 22 33 -14-15 28.5337 30 26 32 22 33.5 -16-17 28.5266 30 26 32 22 33 -18-19 28.6834 30 26 32 22.5 33 -20-21 28.3701 29.5 26 32 22 33 -22-23 28.1859 29 26 32 22 33 -24-25 28.1301 29 26 32 21.5 33 -26-27 28.0758 29 26 32 21.5 33 -28-29 27.828 29 25 32 21 33 -30-31 27.7928 29 25 31.5 21 33 -32-33 27.491 28 25 31 21 33 -34-35 27.3572 28 24.5 31 21 33 -36-37 27.0622 28 24 31 20.5 33 -38-39 27.238 28 24 31 21 33 -40-41 27.029 28 24 31 20.5 33 -42-43 26.9086 27 24 31 20.5 33 -44-45 26.632 27 24 30 20 32 -46-47 26.8304 27.5 24 31 20.5 32 -48-49 26.4317 27 23.5 30 20 32 -50-51 26.219 27 23 30 20 32 -52-53 25.9257 26.5 22.5 29.5 19.5 31.5 -54-55 27.9952 29.5 25.5 31.5 20.5 33 -56-57 30.4109 31.5 28.5 33 25.5 34 -58-59 30.4084 31.5 28.5 33 26 34 -60-61 30.4448 31.5 29 33 26 34 -62-63 30.4404 31.5 29 33 25.5 34 -64-65 30.4417 31.5 29 33 25.5 34 -66-67 30.5114 32 29 33 25.5 34 -68-69 30.0512 31 28 33 24.5 34 -70-71 30.2761 31 29 33 25 34 -72-73 30.3855 31.5 29 33 25.5 34 -74-75 30.2135 31 28.5 33 25.5 34 -76-77 29.6789 31 28 33 24.5 34 -78-79 30.0191 31 28 33 24.5 34 -80-81 29.6642 31 27.5 33 24 34 -82-83 29.6535 31 28 32.5 24.5 34 -84-85 29.4256 30.5 27 32 24 34 -86-87 29.3406 30 27 32.5 24 34 -88-89 28.7857 30 27 32 22.5 33.5 -90-91 28.6675 29.5 26 32 23 33 -92-93 28.5659 29 26 32 23 33 -94-95 28.2717 29 26 32 22.5 33 -96-97 27.9496 29 25.5 31 22 33 -98-99 27.3152 28 25 31 21.5 33 -100-101 27.6147 28 25 31 21.5 33 -102-103 27.1962 28 24.5 31 21 33 -104-105 26.8709 27.5 24 31 20 32.5 -106-107 26.3676 27 23.5 30 20.5 32 -108 27.5651 28 24 31 22 33 ->>END_MODULE ->>Per tile sequence quality fail -#Tile Base Mean -0 1 -29.4857 -0 2 -28.7721 -0 3 -28.5832 -0 4 -28.7282 -0 5 -28.9662 -0 6 -28.9765 -0 7 -29.0324 -0 8 -28.9563 -0 9 -28.4479 -0 10 -28.2935 -0 11 -28.6264 -0 12 -28.711 -0 13 -28.5163 -0 14 -28.3915 -0 15 -28.2392 -0 16 -28.5304 -0 17 -28.3975 -0 18 -28.5859 -0 19 -28.6272 -0 20 -28.1273 -0 21 -28.2058 -0 22 -28.1046 -0 23 -28.3908 -0 24 -28.0801 -0 25 -28.0111 -0 26 -27.9571 -0 27 -28.1062 -0 28 -27.7915 -0 29 -27.6043 -0 30 -27.7709 -0 31 -27.5781 -0 32 -27.8211 -0 33 -27.6197 -0 34 -27.447 -0 35 -27.3368 -0 36 -27.2226 -0 37 -27.2582 -0 38 -27.2825 -0 39 -26.7901 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1.66503 -10 79 -1.23938 -10 80 -0.484477 -10 81 0.558824 -10 82 -0.604575 -10 83 0.291667 -10 84 1.60458 -10 85 1.62337 -10 86 1.46078 -10 87 -1.89624 -10 88 -0.816176 -10 89 -1.13807 -10 90 0.363562 -10 91 1.05801 -10 92 0.189542 -10 93 -0.71732 -10 94 2.33252 -10 95 -3.34069 -10 96 -1.5915 -10 97 0.352941 -10 98 1.76716 -10 99 -1.22631 -10 100 -0.515523 -10 101 -1.59314 -10 102 1.67565 -10 103 3.30065 -10 104 1.91748 -10 105 -1.28676 -10 106 -2.16993 -10 107 1.53234 -10 108 1.7686 ->>END_MODULE ->>Per sequence quality scores pass -#Quality Count -20 7 -21 24 -22 47 -23 78 -24 226 -25 513 -26 830 -27 1017 -28 947 -29 645 -30 352 -31 157 -32 55 -33 6 -34 1 ->>END_MODULE ->>Per base sequence content fail -#Base G A T C -1 21.2232 33.0071 25.9735 19.7961 -2 23.7658 31.4157 27.3154 17.5031 -3 21.8769 28.7058 29.4623 19.955 -4 21.5505 28.2953 28.8505 21.3037 -5 21.2191 30.2059 28.2713 20.3037 -6 22.1243 28.9463 26.3644 22.5651 -7 21.944 29.966 28.3956 19.6944 -8 21.3781 28.8252 27.8622 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18.8374 -60-61 17.4025 33.1862 29.507 19.9043 -62-63 18.543 29.7277 32.2664 19.4628 -64-65 16.8936 32.9407 30.1803 19.9853 -66-67 18.5199 28.9028 32.8056 19.7717 -68-69 17.268 30.3756 31.6642 20.6922 -70-71 17.673 27.7982 35.3461 19.1826 -72-73 16.2003 29.5287 33.8733 20.3976 -74-75 18.3726 28.0191 34.5361 19.0722 -76-77 16.6789 31.701 31.7378 19.8822 -78-79 18.7776 28.3873 32.6215 20.2135 -80-81 16.3844 30.9278 32.9161 19.7717 -82-83 17.0839 30.9278 32.5479 19.4404 -84-85 16.9367 31.5906 31.4433 20.0295 -86-87 17.489 27.9087 34.2047 20.3976 -88-89 16.7894 32.3638 31.0751 19.7717 -90-91 19.5876 29.7496 33.542 17.1208 -92-93 17.7467 30.0442 31.3328 20.8763 -94-95 18.9249 28.461 33.3947 19.2194 -96-97 16.4212 29.4183 32.4742 21.6863 -98-99 18.1581 29.3438 33.1469 19.3512 -100-101 17.7736 29.8491 33.283 19.0943 -102-103 18.3396 29.5094 30.9057 21.2453 -104-105 18.0377 28.6792 31.7736 21.5094 -106-107 17.2783 29.3578 33.5245 19.8394 -108 20.0535 26.3815 31.7291 21.836 ->>END_MODULE ->>Per sequence GC content warn -#GC Content Count -0 15 -1 15.5 -2 16.5 -3 17 -4 18 -5 21.5 -6 26.5 -7 30 -8 33.5 -9 36 -10 41 -11 47 -12 47.5 -13 56 -14 65.5 -15 69 -16 72.5 -17 77.5 -18 85.5 -19 94.5 -20 105.5 -21 113 -22 120 -23 131.5 -24 150 -25 172.5 -26 198 -27 217.5 -28 244.5 -29 281.5 -30 314.5 -31 337 -32 365 -33 402.5 -34 436 -35 463 -36 481.5 -37 505 -38 525 -39 510.5 -40 490.5 -41 493 -42 487 -43 483.5 -44 488 -45 475.5 -46 468 -47 468.5 -48 477 -49 473 -50 437.5 -51 416 -52 405.5 -53 397 -54 386 -55 365 -56 346 -57 343 -58 334 -59 320 -60 319 -61 301.5 -62 276.5 -63 245.5 -64 207.5 -65 191 -66 182 -67 173 -68 167 -69 151.5 -70 131.5 -71 121 -72 117.5 -73 110.5 -74 104 -75 90.5 -76 75 -77 67.5 -78 62.5 -79 61.5 -80 59 -81 57 -82 55 -83 47 -84 39 -85 38 -86 36.5 -87 35.5 -88 28.5 -89 21 -90 19 -91 17 -92 15.5 -93 14.5 -94 14 -95 13.5 -96 14.5 -97 15.5 -98 15.5 -99 16 -100 15 ->>END_MODULE ->>Per base N content pass -#Base N-Count -1 0 -2 0 -3 0 -4 0 -5 0 -6 0 -7 0 -8 0 -9 0 -10-11 0 -12-13 0 -14-15 0 -16-17 0 -18-19 0 -20-21 0 -22-23 0 -24-25 0 -26-27 0 -28-29 0 -30-31 0 -32-33 0 -34-35 0 -36-37 0 -38-39 0 -40-41 0 -42-43 0 -44-45 0 -46-47 0 -48-49 0 -50-51 0 -52-53 0 -54-55 0 -56-57 0 -58-59 0 -60-61 0 -62-63 0 -64-65 0 -66-67 0 -68-69 0 -70-71 0 -72-73 0 -74-75 0 -76-77 0 -78-79 0 -80-81 0 -82-83 0 -84-85 0 -86-87 0 -88-89 0 -90-91 0 -92-93 0 -94-95 0 -96-97 0 -98-99 0 -100-101 0 -102-103 0 -104-105 0 -106-107 0 -108 0 ->>END_MODULE ->>Sequence Length Distribution warn -#Length Count -1 3.0 -2 11.0 -3 28.0 -4 56.0 -5 43.0 -6 52.0 -7 39.0 -8 56.0 -9 60.0 -10 57.0 -11 43.0 -12 46.0 -13 45.0 -14 66.0 -15 59.0 -16 49.0 -17 73.0 -18 54.0 -19 44.0 -20 52.0 -21 73.0 -22 72.0 -23 68.0 -24 56.0 -25 86.0 -26 92.0 -27 75.0 -28 69.0 -29 74.0 -30 96.0 -31 72.0 -32 81.0 -33 65.0 -34 87.0 -35 86.0 -36 87.0 -37 100.0 -38 82.0 -39 78.0 -40 76.0 -41 79.0 -42 88.0 -43 83.0 -44 75.0 -45 74.0 -46 72.0 -47 84.0 -48 74.0 -49 81.0 -50 91.0 -51 80.0 -52 98.0 -53 43.0 -54 8.0 -55 4.0 -56 1.0 -64 1.0 -97 1.0 -98 32.0 -106 34.0 -107 169.0 -108 1122.0 ->>END_MODULE ->>Sequence Duplication Levels pass -#Total Deduplicated Percentage 98.736 -#Duplication Level Percentage of deduplicated Percentage of total -1 99.4425 98.1855 -2 0.474912 0.937819 -3 0.0412967 0.122324 -4 0.0206484 0.0815494 -5 0 0 -6 0 0 -7 0 0 -8 0 0 -9 0 0 ->10 0.0206484 0.672783 ->50 0 0 ->100 0 0 ->500 0 0 ->1k 0 0 ->5k 0 0 ->10k+ 0 0 ->>END_MODULE ->>Overrepresented sequences warn -#Sequence Count Percentage Possible Source -ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT 33 0.672783 No Hit ->>END_MODULE ->>Adapter Content warn -#Position Illumina Universal Adapter Illumina Small RNA 3' Adapter Illumina Small RNA 5' Adapter Nextera Transposase Sequence PolyA PolyG -1 0 0 0 0 0.0203874 0 -2 0 0 0 0 0.0815494 0 -3 0 0 0 0 0.142712 0 -4 0 0 0 0 0.183486 0 -5 0 0 0 0 0.285423 0 -6 0 0 0 0 0.38736 0 -7 0 0 0 0 0.489297 0 -8 0 0 0 0 0.591233 0 -9 0 0 0 0 0.672783 0 -10 0 0 0 0 0.754332 0 -11 0 0 0 0 0.835882 0 -12 0 0 0 0 0.917431 0 -13 0 0 0 0 1.01937 0 -14 0 0 0 0 1.1213 0 -15 0 0 0 0 1.24363 0 -16 0 0 0 0 1.34557 0 -17 0 0 0 0 1.46789 0 -18 0 0 0 0 1.59021 0 -19 0 0 0 0 1.67176 0 -20 0.122324 0 0 0 1.75331 0 -21 0.122324 0 0 0 1.83486 0 -22 0.122324 0 0 0 1.89602 0 -23 0.122324 0 0 0 1.95719 0 -24 0.122324 0 0 0 2.01835 0 -25 0.122324 0 0 0 2.07951 0 -26 0.122324 0 0 0 2.14067 0 -27 0.142712 0 0 0 2.20183 0 -28 0.183486 0 0 0 2.263 0 -29 0.224261 0 0 0 2.32416 0 -30 0.224261 0 0 0 2.38532 0 -31 0.224261 0 0 0 2.44648 0 -32 0.224261 0 0 0 2.50765 0 -33 0.224261 0 0 0 2.60958 0 -34 0.224261 0 0 0 2.71152 0 -35 0.265036 0 0 0 2.81346 0 -36 0.285423 0 0 0 2.89501 0 -37 0.326198 0 0 0 2.99694 0 -38 0.407747 0 0 0 3.09888 0 -39 0.468909 0 0 0 3.20082 0 -40 0.468909 0 0 0 3.30275 0 -41 0.468909 0 0 0 3.40469 0 -42 0.468909 0 0 0 3.48624 0 -43 0.468909 0 0 0 3.56779 0 -44 0.468909 0 0 0 3.60856 0 -45 0.468909 0 0 0 3.62895 0 -46 0.468909 0 0 0 3.64934 0 -47 0.468909 0 0 0 3.66972 0 -48 0.468909 0 0 0 3.69011 0 -49 0.468909 0 0 0 3.7105 0 -50 0.468909 0 0 0 3.7105 0 -51 0.468909 0 0 0 3.7105 0 -52 0.468909 0 0 0 3.7105 0 -53 0.468909 0 0 0 3.7105 0 -54 0.468909 0 0 0 3.7105 0 -55 0.468909 0 0 0 3.7105 0 -56 0.468909 0 0 0 3.7105 0 -57 0.468909 0 0 0 3.7105 0 -58 0.468909 0 0 0 3.7105 0 -59 0.468909 0 0 0 3.73089 0 -60 0.468909 0 0 0 3.75127 0 -61 0.468909 0 0 0 3.77166 0 -62 0.468909 0 0 0 3.81244 0 -63 0.468909 0 0 0 3.85321 0 -64 0.468909 0 0 0 3.89399 0 -65 0.468909 0 0 0 3.93476 0 -66 0.468909 0 0 0 3.97554 0 -67 0.468909 0 0 0 4.01631 0 -68 0.468909 0 0 0 4.05708 0 -69 0.468909 0 0 0 4.09786 0 -70 0.468909 0 0 0 4.13863 0 -71 0.468909 0 0 0 4.17941 0 -72 0.468909 0 0 0 4.22018 0 -73 0.468909 0 0 0 4.26096 0 -74 0.489297 0 0 0 4.32212 0 -75 0.489297 0 0 0 4.38328 0 -76 0.489297 0 0 0 4.42406 0 -77 0.489297 0 0 0 4.46483 0 -78 0.489297 0 0 0 4.50561 0 -79 0.489297 0 0 0 4.54638 0 -80 0.489297 0 0 0 4.58716 0 -81 0.489297 0 0 0 4.62793 0 -82 0.489297 0 0 0 4.66871 0 -83 0.509684 0 0 0 4.70948 0 -84 0.509684 0 0 0 4.75025 0 -85 0.509684 0 0 0 4.79103 0 -86 0.509684 0 0 0 4.8318 0 -87 0.509684 0 0 0 4.91335 0 -88 0.509684 0 0 0 4.9949 0 -89 0.509684 0 0 0 5.05607 0 -90 0.509684 0 0 0 5.09684 0 -91 0.509684 0 0 0 5.158 0 -92 0.570846 0 0 0 5.21916 0 -93 0.632008 0 0 0 5.28033 0 -94 0.632008 0 0 0 5.34149 0 -95 0.632008 0 0 0 5.40265 0 -96 0.632008 0 0 0 5.46381 0 -97 0.632008 0 0 0 5.52497 0 -98 0.632008 0 0 0 5.52497 0 -99 0.632008 0 0 0 5.52497 0 -100 0.632008 0 0 0 5.52497 0 -101 0.632008 0 0 0 5.52497 0 -102 0.632008 0 0 0 5.52497 0 -103 0.632008 0 0 0 5.52497 0 -104 0.632008 0 0 0 5.52497 0 -105 0.632008 0 0 0 5.52497 0 -106 0.632008 0 0 0 5.52497 0 -107 0.632008 0 0 0 5.52497 0 -108 0.632008 0 0 0 5.52497 0 ->>END_MODULE
--- a/test-data/fastqc_data_customlimits_summary.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/fastqc_data_customlimits_summary.txt Fri Sep 27 17:41:40 2024 +0000 @@ -1,11 +1,2 @@ PASS Basic Statistics 1000trimmed_fastq -PASS Per base sequence quality 1000trimmed_fastq -FAIL Per tile sequence quality 1000trimmed_fastq -PASS Per sequence quality scores 1000trimmed_fastq -FAIL Per base sequence content 1000trimmed_fastq -WARN Per sequence GC content 1000trimmed_fastq -PASS Per base N content 1000trimmed_fastq WARN Sequence Length Distribution 1000trimmed_fastq -PASS Sequence Duplication Levels 1000trimmed_fastq -WARN Overrepresented sequences 1000trimmed_fastq -PASS Adapter Content 1000trimmed_fastq
--- a/test-data/fastqc_data_hisat.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/fastqc_data_hisat.txt Fri Sep 27 17:41:40 2024 +0000 @@ -1,448 +1,601 @@ -##FastQC 0.12.1 +##Falco 1.2.4 >>Basic Statistics pass #Measure Value Filename hisat_output_1_bam File type Conventional base calls Encoding Sanger / Illumina 1.9 Total Sequences 20 -Total Bases 1.4 kbp Sequences flagged as poor quality 0 Sequence length 70 -%GC 43 +%GC 44 >>END_MODULE ->>Per base sequence quality pass +>>Per base sequence quality fail #Base Mean Median Lower Quartile Upper Quartile 10th Percentile 90th Percentile -1 17.0 NaN NaN NaN NaN NaN -2 17.0 NaN NaN NaN NaN NaN -3 17.0 NaN NaN NaN NaN NaN -4 17.0 NaN NaN NaN NaN NaN -5 17.0 NaN NaN NaN NaN NaN -6 17.0 NaN NaN NaN NaN NaN -7 17.0 NaN NaN NaN NaN NaN -8 17.0 NaN NaN NaN NaN NaN -9 17.0 NaN NaN NaN NaN NaN -10 17.0 NaN NaN NaN NaN NaN -11 17.0 NaN NaN NaN NaN NaN -12 17.0 NaN NaN NaN NaN NaN -13 17.0 NaN NaN NaN NaN NaN -14 17.0 NaN NaN NaN NaN NaN -15 17.0 NaN NaN NaN NaN NaN -16 17.0 NaN NaN NaN NaN NaN -17 17.0 NaN NaN NaN NaN NaN -18 17.0 NaN NaN NaN NaN NaN -19 17.0 NaN NaN NaN NaN NaN -20 17.0 NaN NaN NaN NaN NaN -21 17.0 NaN NaN NaN NaN NaN -22 17.0 NaN NaN NaN NaN NaN -23 17.0 NaN NaN NaN NaN NaN -24 17.0 NaN NaN NaN NaN NaN -25 17.0 NaN NaN NaN NaN NaN -26 17.0 NaN NaN NaN NaN NaN -27 17.0 NaN NaN NaN NaN NaN -28 17.0 NaN NaN NaN NaN NaN -29 17.0 NaN NaN NaN NaN NaN -30 17.0 NaN NaN NaN NaN NaN -31 17.0 NaN NaN NaN NaN NaN -32 17.0 NaN NaN NaN NaN NaN -33 17.0 NaN NaN NaN NaN NaN -34 17.0 NaN NaN NaN NaN NaN -35 17.0 NaN NaN NaN NaN NaN -36 17.0 NaN NaN NaN NaN NaN -37 17.0 NaN NaN NaN NaN NaN -38 17.0 NaN NaN NaN NaN NaN -39 17.0 NaN NaN NaN NaN NaN -40 17.0 NaN NaN NaN NaN NaN -41 17.0 NaN NaN NaN NaN NaN -42 17.0 NaN NaN NaN NaN NaN -43 17.0 NaN NaN NaN NaN NaN -44 17.0 NaN NaN NaN NaN NaN -45 17.0 NaN NaN NaN NaN NaN -46 17.0 NaN NaN NaN NaN NaN -47 17.0 NaN NaN NaN NaN NaN -48 17.0 NaN NaN NaN NaN NaN -49 17.0 NaN NaN NaN NaN NaN -50 17.0 NaN NaN NaN NaN NaN -51 17.0 NaN NaN NaN NaN NaN -52 17.0 NaN NaN NaN NaN NaN -53 17.0 NaN NaN NaN NaN NaN -54 17.0 NaN NaN NaN NaN NaN -55 17.0 NaN NaN NaN NaN NaN -56 17.0 NaN NaN NaN NaN NaN -57 17.0 NaN NaN NaN NaN NaN -58 17.0 NaN NaN NaN NaN NaN -59 17.0 NaN NaN NaN NaN NaN -60 17.0 NaN NaN NaN NaN NaN -61 17.0 NaN NaN NaN NaN NaN -62 17.0 NaN NaN NaN NaN NaN -63 17.0 NaN NaN NaN NaN NaN -64 17.0 NaN NaN NaN NaN NaN -65 17.0 NaN NaN NaN NaN NaN -66 17.0 NaN NaN NaN NaN NaN -67 17.0 NaN NaN NaN NaN NaN -68 17.0 NaN NaN NaN NaN NaN -69 17.0 NaN NaN NaN NaN NaN -70 17.0 NaN NaN NaN NaN NaN +1 17 17 17 17 17 17 +2 17 17 17 17 17 17 +3 17 17 17 17 17 17 +4 17 17 17 17 17 17 +5 17 17 17 17 17 17 +6 17 17 17 17 17 17 +7 17 17 17 17 17 17 +8 17 17 17 17 17 17 +9 17 17 17 17 17 17 +10 17 17 17 17 17 17 +11 17 17 17 17 17 17 +12 17 17 17 17 17 17 +13 17 17 17 17 17 17 +14 17 17 17 17 17 17 +15 17 17 17 17 17 17 +16 17 17 17 17 17 17 +17 17 17 17 17 17 17 +18 17 17 17 17 17 17 +19 17 17 17 17 17 17 +20 17 17 17 17 17 17 +21 17 17 17 17 17 17 +22 17 17 17 17 17 17 +23 17 17 17 17 17 17 +24 17 17 17 17 17 17 +25 17 17 17 17 17 17 +26 17 17 17 17 17 17 +27 17 17 17 17 17 17 +28 17 17 17 17 17 17 +29 17 17 17 17 17 17 +30 17 17 17 17 17 17 +31 17 17 17 17 17 17 +32 17 17 17 17 17 17 +33 17 17 17 17 17 17 +34 17 17 17 17 17 17 +35 17 17 17 17 17 17 +36 17 17 17 17 17 17 +37 17 17 17 17 17 17 +38 17 17 17 17 17 17 +39 17 17 17 17 17 17 +40 17 17 17 17 17 17 +41 17 17 17 17 17 17 +42 17 17 17 17 17 17 +43 17 17 17 17 17 17 +44 17 17 17 17 17 17 +45 17 17 17 17 17 17 +46 17 17 17 17 17 17 +47 17 17 17 17 17 17 +48 17 17 17 17 17 17 +49 17 17 17 17 17 17 +50 17 17 17 17 17 17 +51 17 17 17 17 17 17 +52 17 17 17 17 17 17 +53 17 17 17 17 17 17 +54 17 17 17 17 17 17 +55 17 17 17 17 17 17 +56 17 17 17 17 17 17 +57 17 17 17 17 17 17 +58 17 17 17 17 17 17 +59 17 17 17 17 17 17 +60 17 17 17 17 17 17 +61 17 17 17 17 17 17 +62 17 17 17 17 17 17 +63 17 17 17 17 17 17 +64 17 17 17 17 17 17 +65 17 17 17 17 17 17 +66 17 17 17 17 17 17 +67 17 17 17 17 17 17 +68 17 17 17 17 17 17 +69 17 17 17 17 17 17 +70 17 17 17 17 17 17 +>>END_MODULE +>>Per tile sequence quality pass +#Tile Base Mean +470 1 0 +470 2 0 +470 3 0 +470 4 0 +470 5 0 +470 6 0 +470 7 0 +470 8 0 +470 9 0 +470 10 0 +470 11 0 +470 12 0 +470 13 0 +470 14 0 +470 15 0 +470 16 0 +470 17 0 +470 18 0 +470 19 0 +470 20 0 +470 21 0 +470 22 0 +470 23 0 +470 24 0 +470 25 0 +470 26 0 +470 27 0 +470 28 0 +470 29 0 +470 30 0 +470 31 0 +470 32 0 +470 33 0 +470 34 0 +470 35 0 +470 36 0 +470 37 0 +470 38 0 +470 39 0 +470 40 0 +470 41 0 +470 42 0 +470 43 0 +470 44 0 +470 45 0 +470 46 0 +470 47 0 +470 48 0 +470 49 0 +470 50 0 +470 51 0 +470 52 0 +470 53 0 +470 54 0 +470 55 0 +470 56 0 +470 57 0 +470 58 0 +470 59 0 +470 60 0 +470 61 0 +470 62 0 +470 63 0 +470 64 0 +470 65 0 +470 66 0 +470 67 0 +470 68 0 +470 69 0 +470 70 0 +473 1 0 +473 2 0 +473 3 0 +473 4 0 +473 5 0 +473 6 0 +473 7 0 +473 8 0 +473 9 0 +473 10 0 +473 11 0 +473 12 0 +473 13 0 +473 14 0 +473 15 0 +473 16 0 +473 17 0 +473 18 0 +473 19 0 +473 20 0 +473 21 0 +473 22 0 +473 23 0 +473 24 0 +473 25 0 +473 26 0 +473 27 0 +473 28 0 +473 29 0 +473 30 0 +473 31 0 +473 32 0 +473 33 0 +473 34 0 +473 35 0 +473 36 0 +473 37 0 +473 38 0 +473 39 0 +473 40 0 +473 41 0 +473 42 0 +473 43 0 +473 44 0 +473 45 0 +473 46 0 +473 47 0 +473 48 0 +473 49 0 +473 50 0 +473 51 0 +473 52 0 +473 53 0 +473 54 0 +473 55 0 +473 56 0 +473 57 0 +473 58 0 +473 59 0 +473 60 0 +473 61 0 +473 62 0 +473 63 0 +473 64 0 +473 65 0 +473 66 0 +473 67 0 +473 68 0 +473 69 0 +473 70 0 >>END_MODULE >>Per sequence quality scores fail #Quality Count -17 20.0 +17 20 >>END_MODULE >>Per base sequence content fail #Base G A T C -1 20.0 5.0 35.0 40.0 -2 10.0 10.0 45.0 35.0 -3 35.0 20.0 20.0 25.0 -4 35.0 30.0 25.0 10.0 -5 20.0 20.0 30.0 30.0 -6 20.0 35.0 20.0 25.0 -7 15.0 40.0 35.0 10.0 -8 20.0 15.0 45.0 20.0 -9 20.0 25.0 35.0 20.0 -10 20.0 20.0 30.0 30.0 -11 15.0 20.0 45.0 20.0 -12 10.0 40.0 35.0 15.0 -13 25.0 35.0 20.0 20.0 -14 35.0 20.0 20.0 25.0 -15 30.0 35.0 15.0 20.0 -16 10.0 45.0 25.0 20.0 -17 25.0 25.0 40.0 10.0 -18 25.0 35.0 10.0 30.0 -19 5.0 30.0 25.0 40.0 -20 20.0 15.0 40.0 25.0 -21 25.0 25.0 25.0 25.0 -22 15.0 30.0 20.0 35.0 -23 20.0 5.0 45.0 30.0 -24 10.0 30.0 35.0 25.0 -25 30.0 40.0 15.0 15.0 -26 15.0 35.0 20.0 30.0 -27 15.0 35.0 30.0 20.0 -28 25.0 25.0 30.0 20.0 -29 15.0 30.0 20.0 35.0 -30 20.0 35.0 30.0 15.0 -31 20.0 35.0 25.0 20.0 -32 35.0 15.0 35.0 15.0 -33 30.0 35.0 15.0 20.0 -34 25.0 25.0 25.0 25.0 -35 25.0 20.0 35.0 20.0 -36 30.0 25.0 20.0 25.0 -37 15.0 45.0 25.0 15.0 -38 30.0 25.0 35.0 10.0 -39 20.0 45.0 15.0 20.0 -40 15.0 35.0 20.0 30.0 -41 35.0 25.0 20.0 20.0 -42 30.0 30.0 35.0 5.0 -43 25.0 15.0 40.0 20.0 -44 40.0 20.0 30.0 10.0 -45 15.0 35.0 25.0 25.0 -46 15.0 30.0 40.0 15.0 -47 35.0 15.0 30.0 20.0 -48 30.0 35.0 20.0 15.0 -49 10.0 55.00000000000001 30.0 5.0 -50 40.0 25.0 20.0 15.0 -51 25.0 35.0 10.0 30.0 -52 30.0 25.0 20.0 25.0 -53 30.0 10.0 30.0 30.0 -54 20.0 40.0 20.0 20.0 -55 10.0 35.0 10.0 45.0 -56 50.0 10.0 30.0 10.0 -57 15.0 45.0 30.0 10.0 -58 20.0 35.0 20.0 25.0 -59 30.0 35.0 30.0 5.0 -60 20.0 35.0 25.0 20.0 -61 25.0 15.0 35.0 25.0 -62 10.0 20.0 55.00000000000001 15.0 -63 25.0 20.0 35.0 20.0 -64 20.0 35.0 25.0 20.0 -65 30.0 35.0 25.0 10.0 -66 15.0 40.0 35.0 10.0 -67 20.0 35.0 20.0 25.0 -68 20.0 25.0 30.0 25.0 -69 15.0 35.0 25.0 25.0 -70 5.0 40.0 40.0 15.0 +1 20 5 35 40 +2 10 10 45 35 +3 35 20 20 25 +4 35 30 25 10 +5 20 20 30 30 +6 20 35 20 25 +7 15 40 35 10 +8 20 15 45 20 +9 20 25 35 20 +10 20 20 30 30 +11 15 20 45 20 +12 10 40 35 15 +13 25 35 20 20 +14 35 20 20 25 +15 30 35 15 20 +16 10 45 25 20 +17 25 25 40 10 +18 25 35 10 30 +19 5 30 25 40 +20 20 15 40 25 +21 25 25 25 25 +22 15 30 20 35 +23 20 5 45 30 +24 10 30 35 25 +25 30 40 15 15 +26 15 35 20 30 +27 15 35 30 20 +28 25 25 30 20 +29 15 30 20 35 +30 20 35 30 15 +31 20 35 25 20 +32 35 15 35 15 +33 30 35 15 20 +34 25 25 25 25 +35 25 20 35 20 +36 30 25 20 25 +37 15 45 25 15 +38 30 25 35 10 +39 20 45 15 20 +40 15 35 20 30 +41 35 25 20 20 +42 30 30 35 5 +43 25 15 40 20 +44 40 20 30 10 +45 15 35 25 25 +46 15 30 40 15 +47 35 15 30 20 +48 30 35 20 15 +49 10 55 30 5 +50 40 25 20 15 +51 25 35 10 30 +52 30 25 20 25 +53 30 10 30 30 +54 20 40 20 20 +55 10 35 10 45 +56 50 10 30 10 +57 15 45 30 10 +58 20 35 20 25 +59 30 35 30 5 +60 20 35 25 20 +61 25 15 35 25 +62 10 20 55 15 +63 25 20 35 20 +64 20 35 25 20 +65 30 35 25 10 +66 15 40 35 10 +67 20 35 20 25 +68 20 25 30 25 +69 15 35 25 25 +70 5 40 40 15 >>END_MODULE >>Per sequence GC content fail #GC Content Count -0 0.0 -1 0.0 -2 0.0 -3 0.0 -4 0.0 -5 0.0 -6 0.0 -7 0.0 -8 0.0 -9 0.0 -10 0.0 -11 0.0 -12 0.0 -13 0.0 -14 0.0 -15 0.0 -16 0.0 -17 0.0 -18 0.0 -19 0.0 -20 0.0 -21 0.0 -22 0.0 -23 0.0 -24 0.0 -25 0.0 -26 0.0 -27 0.0 -28 0.0 +0 0 +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +21 0 +22 0 +23 0 +24 0 +25 0 +26 0 +27 0 +28 0 29 0.5 -30 1.0 +30 1 31 0.5 32 0.5 -33 1.0 +33 1 34 0.5 -35 0.0 -36 0.0 -37 0.0 -38 1.0 +35 0 +36 0 +37 0 +38 1 39 2.5 -40 3.0 -41 2.0 +40 3 +41 2 42 1.5 -43 2.0 +43 2 44 2.5 45 2.5 -46 1.0 -47 0.0 +46 1 +47 0 48 0.5 49 0.5 -50 0.0 +50 0 51 1.5 52 1.5 -53 0.0 +53 0 54 0.5 55 0.5 -56 0.0 -57 0.0 -58 0.0 -59 0.0 -60 0.0 -61 0.0 -62 0.0 -63 0.0 -64 0.0 -65 0.0 -66 0.0 -67 0.0 -68 0.0 -69 0.0 -70 0.0 -71 0.0 -72 0.0 -73 0.0 -74 0.0 -75 0.0 -76 0.0 -77 0.0 -78 0.0 -79 0.0 -80 0.0 -81 0.0 -82 0.0 -83 0.0 -84 0.0 -85 0.0 -86 0.0 -87 0.0 -88 0.0 -89 0.0 -90 0.0 -91 0.0 -92 0.0 -93 0.0 -94 0.0 -95 0.0 -96 0.0 -97 0.0 -98 0.0 -99 0.0 -100 0.0 +56 0 +57 0 +58 0 +59 0 +60 0 +61 0 +62 0 +63 0 +64 0 +65 0 +66 0 +67 0 +68 0 +69 0 +70 0 +71 0 +72 0 +73 0 +74 0 +75 0 +76 0 +77 0 +78 0 +79 0 +80 0 +81 0 +82 0 +83 0 +84 0 +85 0 +86 0 +87 0 +88 0 +89 0 +90 0 +91 0 +92 0 +93 0 +94 0 +95 0 +96 0 +97 0 +98 0 +99 0 +100 0 >>END_MODULE >>Per base N content pass #Base N-Count -1 0.0 -2 0.0 -3 0.0 -4 0.0 -5 0.0 -6 0.0 -7 0.0 -8 0.0 -9 0.0 -10 0.0 -11 0.0 -12 0.0 -13 0.0 -14 0.0 -15 0.0 -16 0.0 -17 0.0 -18 0.0 -19 0.0 -20 0.0 -21 0.0 -22 0.0 -23 0.0 -24 0.0 -25 0.0 -26 0.0 -27 0.0 -28 0.0 -29 0.0 -30 0.0 -31 0.0 -32 0.0 -33 0.0 -34 0.0 -35 0.0 -36 0.0 -37 0.0 -38 0.0 -39 0.0 -40 0.0 -41 0.0 -42 0.0 -43 0.0 -44 0.0 -45 0.0 -46 0.0 -47 0.0 -48 0.0 -49 0.0 -50 0.0 -51 0.0 -52 0.0 -53 0.0 -54 0.0 -55 0.0 -56 0.0 -57 0.0 -58 0.0 -59 0.0 -60 0.0 -61 0.0 -62 0.0 -63 0.0 -64 0.0 -65 0.0 -66 0.0 -67 0.0 -68 0.0 -69 0.0 -70 0.0 +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +21 0 +22 0 +23 0 +24 0 +25 0 +26 0 +27 0 +28 0 +29 0 +30 0 +31 0 +32 0 +33 0 +34 0 +35 0 +36 0 +37 0 +38 0 +39 0 +40 0 +41 0 +42 0 +43 0 +44 0 +45 0 +46 0 +47 0 +48 0 +49 0 +50 0 +51 0 +52 0 +53 0 +54 0 +55 0 +56 0 +57 0 +58 0 +59 0 +60 0 +61 0 +62 0 +63 0 +64 0 +65 0 +66 0 +67 0 +68 0 +69 0 +70 0 >>END_MODULE >>Sequence Length Distribution pass #Length Count 70 20.0 >>END_MODULE >>Sequence Duplication Levels pass -#Total Deduplicated Percentage 100.0 -#Duplication Level Percentage of total -1 100.0 -2 0.0 -3 0.0 -4 0.0 -5 0.0 -6 0.0 -7 0.0 -8 0.0 -9 0.0 ->10 0.0 ->50 0.0 ->100 0.0 ->500 0.0 ->1k 0.0 ->5k 0.0 ->10k+ 0.0 +#Total Deduplicated Percentage 100 +#Duplication Level Percentage of deduplicated Percentage of total +1 100 100 +2 0 0 +3 0 0 +4 0 0 +5 0 0 +6 0 0 +7 0 0 +8 0 0 +9 0 0 +>10 0 0 +>50 0 0 +>100 0 0 +>500 0 0 +>1k 0 0 +>5k 0 0 +>10k+ 0 0 >>END_MODULE >>Overrepresented sequences fail #Sequence Count Percentage Possible Source -CCTTTCGCCATCAACTAACGATTCTGTCAAAAACTGACGCGTTGGATGAG 1 5.0 No Hit -TGGCGCTCTCCGTCTTTCTCCATTTCGTCGTGGCCTTGCTATTGACTCTA 1 5.0 No Hit -ACCATAAACGCAAGCCTCAACGCAGCGACGAGCACGAGAGCGGTCAGTAG 1 5.0 No Hit -TGTTTTCCGTAAATTCAGCGCCTTCCATGATGCGACAGGCCGTTTGAATG 1 5.0 No Hit -CTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGGT 1 5.0 No Hit -TCTGCGTTTGCTGATGAACTAAGTCAACCTCAGCACTAACCTTGCGAGTC 1 5.0 No Hit -CCATACAAAACAGGGTCGCCAGCAATATCGGTATAAGTCAAAGCACCTTT 1 5.0 No Hit -TAAGCATTTGTTTCAGGGTTATTTGAATATCTATAACAACTATTTTCAAG 1 5.0 No Hit -CAAATTAGCATAAGCAGCTTGCAGACCCATAATGTCAATAGATGTGGTAG 1 5.0 No Hit -GCGTTAAGGTACTGAATCTCTTTAGTCGCAGTAGGCGGAAAACGAACAAG 1 5.0 No Hit -CTGAATGGAATTAAGAAAACCACCAATACCAGCATTAACCTTCAAACTAT 1 5.0 No Hit -GCGACCATTCAAAGGATAAACATCATAGGCAGTCGGGAGGGTAGTCGGAA 1 5.0 No Hit -GTGAAATTTCTAGGAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAG 1 5.0 No Hit -CTCAAATCCGGCGTCAACCATACCAGCATAGGAAGCATCAGCACCAGCAC 1 5.0 No Hit -TTCTGGTGATTTGCAAGAACGCGTACTTATTCGCCACCATGATTATGACC 1 5.0 No Hit -CTCGCGATTCAATCATGACTTCGTGATAAAAGATTGAGTGTGAGGTTATA 1 5.0 No Hit -TTAGGTGTGTGTAAAACAGGTGCCGAAGAAGCTGGATTAACAGAATTGAG 1 5.0 No Hit -GCGGTATTGCTTCTGCTCTTGCTGGTGGCGCCATGTCTAAATTGTTTGGA 1 5.0 No Hit -TTTCGGATATTTCTGATGAGTCGAAAAATTATCTTGATAAAGCAGTAATT 1 5.0 No Hit -CTCGCCAAATGACGACTTCTACCACATCTATTGACATTATGGGTCTGCAA 1 5.0 No Hit +ACCATAAACGCAAGCCTCAACGCAGCGACGAGCACGAGAGCGGTCAGTAGCAATCCAAACTTTGTTACTC 1 5 No Hit +GTGAAATTTCTAGGAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGTTTAAGATTGCTGAGGGTCA 1 5 No Hit +CCATACAAAACAGGGTCGCCAGCAATATCGGTATAAGTCAAAGCACCTTTAGCGTTAAGGTACTGAATCT 1 5 No Hit +CTCGCCAAATGACGACTTCTACCACATCTATTGACATTATGGGTCTGCAAGCTGCTTATGCTAATTTGCA 1 5 No Hit +CAAATTAGCATAAGCAGCTTGCAGACCCATAATGTCAATAGATGTGGTAGAAGTCGTCATTTGGCTAGAA 1 5 No Hit +CTCGCGATTCAATCATGACTTCGTGATAAAAGATTGAGTGTGAGGTTATAACGCCGAAGCGGTAAAAAAT 1 5 No Hit +TGGCGCTCTCCGTCTTTCTCCATTTCGTCGTGGCCTTGCTATTGACTCTACTGTAGACATTTTTACTTTT 1 5 No Hit +TAAGCATTTGTTTCAGGGTTATTTGAATATCTATAACAACTATTTTCAAGCGCCGAGGATGCGTGACCGT 1 5 No Hit +TGTTTTCCGTAAATTCAGCGCCTTCCATGATGCGACAGGCCGTTTGAATGTTGACGGGATGAACATAATA 1 5 No Hit +TTCTGGTGATTTGCAAGAACGCGTACTTATTCGCCACCATGATTATGACCAGTGTTTCCAGTCCGTTCAG 1 5 No Hit +TCTGCGTTTGCTGATGAACTAAGTCAACCTCAGCACTAACCTTGCGAGTCATTTCATTGATTTGGTCATT 1 5 No Hit +GCGTTAAGGTACTGAATCTCTTTAGTCGCAGTAGGCGGAAAACGAACAAGCGCAAGAGTAAACATAGTGC 1 5 No Hit +CCTTTCGCCATCAACTAACGATTCTGTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGCTTAATATGCT 1 5 No Hit +TTTCGGATATTTCTGATGAGTCGAAAAATTATCTTGATAAAGCAGTAATTACTACTGCTTGTTTACGAAT 1 5 No Hit +TTAGGTGTGTGTAAAACAGGTGCCGAAGAAGCTGGATTAACAGAATTGAGAACCAGCTTATCAGAAAAAA 1 5 No Hit +CTGAATGGAATTAAGAAAACCACCAATACCAGCATTAACCTTCAAACTATCAAAATATAACGTTGACGAT 1 5 No Hit +GCGACCATTCAAAGGATAAACATCATAGGCAGTCGGGAGGGTAGTCGGAACCGACGAAGACTCAAAGCGA 1 5 No Hit +GCGGTATTGCTTCTGCTCTTGCTGGTGGCGCCATGTCTAAATTGTTTGGAGGCGGTCAAAAAGCCGCCTC 1 5 No Hit +CTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGGTGGCAAGTCTGCCGCTGATAA 1 5 No Hit +CTCAAATCCGGCGTCAACCATACCAGCATAGGAAGCATCAGCACCAGCACGCTCCCAAGCATTAATCTCA 1 5 No Hit >>END_MODULE >>Adapter Content pass #Position Illumina Universal Adapter Illumina Small RNA 3' Adapter Illumina Small RNA 5' Adapter Nextera Transposase Sequence PolyA PolyG -1 0.0 0.0 0.0 0.0 0.0 0.0 -2 0.0 0.0 0.0 0.0 0.0 0.0 -3 0.0 0.0 0.0 0.0 0.0 0.0 -4 0.0 0.0 0.0 0.0 0.0 0.0 -5 0.0 0.0 0.0 0.0 0.0 0.0 -6 0.0 0.0 0.0 0.0 0.0 0.0 -7 0.0 0.0 0.0 0.0 0.0 0.0 -8 0.0 0.0 0.0 0.0 0.0 0.0 -9 0.0 0.0 0.0 0.0 0.0 0.0 -10 0.0 0.0 0.0 0.0 0.0 0.0 -11 0.0 0.0 0.0 0.0 0.0 0.0 -12 0.0 0.0 0.0 0.0 0.0 0.0 -13 0.0 0.0 0.0 0.0 0.0 0.0 -14 0.0 0.0 0.0 0.0 0.0 0.0 -15 0.0 0.0 0.0 0.0 0.0 0.0 -16 0.0 0.0 0.0 0.0 0.0 0.0 -17 0.0 0.0 0.0 0.0 0.0 0.0 -18 0.0 0.0 0.0 0.0 0.0 0.0 -19 0.0 0.0 0.0 0.0 0.0 0.0 -20 0.0 0.0 0.0 0.0 0.0 0.0 -21 0.0 0.0 0.0 0.0 0.0 0.0 -22 0.0 0.0 0.0 0.0 0.0 0.0 -23 0.0 0.0 0.0 0.0 0.0 0.0 -24 0.0 0.0 0.0 0.0 0.0 0.0 -25 0.0 0.0 0.0 0.0 0.0 0.0 -26 0.0 0.0 0.0 0.0 0.0 0.0 -27 0.0 0.0 0.0 0.0 0.0 0.0 -28 0.0 0.0 0.0 0.0 0.0 0.0 -29 0.0 0.0 0.0 0.0 0.0 0.0 -30 0.0 0.0 0.0 0.0 0.0 0.0 -31 0.0 0.0 0.0 0.0 0.0 0.0 -32 0.0 0.0 0.0 0.0 0.0 0.0 -33 0.0 0.0 0.0 0.0 0.0 0.0 -34 0.0 0.0 0.0 0.0 0.0 0.0 -35 0.0 0.0 0.0 0.0 0.0 0.0 -36 0.0 0.0 0.0 0.0 0.0 0.0 -37 0.0 0.0 0.0 0.0 0.0 0.0 -38 0.0 0.0 0.0 0.0 0.0 0.0 -39 0.0 0.0 0.0 0.0 0.0 0.0 -40 0.0 0.0 0.0 0.0 0.0 0.0 -41 0.0 0.0 0.0 0.0 0.0 0.0 -42 0.0 0.0 0.0 0.0 0.0 0.0 -43 0.0 0.0 0.0 0.0 0.0 0.0 -44 0.0 0.0 0.0 0.0 0.0 0.0 -45 0.0 0.0 0.0 0.0 0.0 0.0 -46 0.0 0.0 0.0 0.0 0.0 0.0 -47 0.0 0.0 0.0 0.0 0.0 0.0 -48 0.0 0.0 0.0 0.0 0.0 0.0 -49 0.0 0.0 0.0 0.0 0.0 0.0 -50 0.0 0.0 0.0 0.0 0.0 0.0 -51 0.0 0.0 0.0 0.0 0.0 0.0 -52 0.0 0.0 0.0 0.0 0.0 0.0 -53 0.0 0.0 0.0 0.0 0.0 0.0 -54 0.0 0.0 0.0 0.0 0.0 0.0 -55 0.0 0.0 0.0 0.0 0.0 0.0 -56 0.0 0.0 0.0 0.0 0.0 0.0 -57 0.0 0.0 0.0 0.0 0.0 0.0 -58 0.0 0.0 0.0 0.0 0.0 0.0 -59 0.0 0.0 0.0 0.0 0.0 0.0 +1 0 0 0 0 0 0 +2 0 0 0 0 0 0 +3 0 0 0 0 0 0 +4 0 0 0 0 0 0 +5 0 0 0 0 0 0 +6 0 0 0 0 0 0 +7 0 0 0 0 0 0 +8 0 0 0 0 0 0 +9 0 0 0 0 0 0 +10 0 0 0 0 0 0 +11 0 0 0 0 0 0 +12 0 0 0 0 0 0 +13 0 0 0 0 0 0 +14 0 0 0 0 0 0 +15 0 0 0 0 0 0 +16 0 0 0 0 0 0 +17 0 0 0 0 0 0 +18 0 0 0 0 0 0 +19 0 0 0 0 0 0 +20 0 0 0 0 0 0 +21 0 0 0 0 0 0 +22 0 0 0 0 0 0 +23 0 0 0 0 0 0 +24 0 0 0 0 0 0 +25 0 0 0 0 0 0 +26 0 0 0 0 0 0 +27 0 0 0 0 0 0 +28 0 0 0 0 0 0 +29 0 0 0 0 0 0 +30 0 0 0 0 0 0 +31 0 0 0 0 0 0 +32 0 0 0 0 0 0 +33 0 0 0 0 0 0 +34 0 0 0 0 0 0 +35 0 0 0 0 0 0 +36 0 0 0 0 0 0 +37 0 0 0 0 0 0 +38 0 0 0 0 0 0 +39 0 0 0 0 0 0 +40 0 0 0 0 0 0 +41 0 0 0 0 0 0 +42 0 0 0 0 0 0 +43 0 0 0 0 0 0 +44 0 0 0 0 0 0 +45 0 0 0 0 0 0 +46 0 0 0 0 0 0 +47 0 0 0 0 0 0 +48 0 0 0 0 0 0 +49 0 0 0 0 0 0 +50 0 0 0 0 0 0 +51 0 0 0 0 0 0 +52 0 0 0 0 0 0 +53 0 0 0 0 0 0 +54 0 0 0 0 0 0 +55 0 0 0 0 0 0 +56 0 0 0 0 0 0 +57 0 0 0 0 0 0 +58 0 0 0 0 0 0 +59 0 0 0 0 0 0 +60 0 0 0 0 0 0 +61 0 0 0 0 0 0 +62 0 0 0 0 0 0 +63 0 0 0 0 0 0 +64 0 0 0 0 0 0 +65 0 0 0 0 0 0 +66 0 0 0 0 0 0 +67 0 0 0 0 0 0 +68 0 0 0 0 0 0 +69 0 0 0 0 0 0 +70 0 0 0 0 0 0 >>END_MODULE
--- a/test-data/fastqc_data_nogroup.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/fastqc_data_nogroup.txt Fri Sep 27 17:41:40 2024 +0000 @@ -1,4 +1,4 @@ -##Falco 1.2.3 +##Falco 1.2.4 >>Basic Statistics pass #Measure Value Filename 1000trimmed_fastq @@ -1744,114 +1744,114 @@ #Sequence Count Percentage Possible Source ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT 33 0.672783 No Hit >>END_MODULE ->>Adapter Content warn +>>Adapter Content pass #Position Illumina Universal Adapter Illumina Small RNA 3' Adapter Illumina Small RNA 5' Adapter Nextera Transposase Sequence PolyA PolyG 1 0 0 0 0 0.0203874 0 -2 0 0 0 0 0.0815494 0 -3 0 0 0 0 0.142712 0 -4 0 0 0 0 0.183486 0 -5 0 0 0 0 0.285423 0 -6 0 0 0 0 0.38736 0 -7 0 0 0 0 0.489297 0 -8 0 0 0 0 0.591233 0 -9 0 0 0 0 0.672783 0 -10 0 0 0 0 0.754332 0 -11 0 0 0 0 0.835882 0 -12 0 0 0 0 0.917431 0 -13 0 0 0 0 1.01937 0 -14 0 0 0 0 1.1213 0 -15 0 0 0 0 1.24363 0 -16 0 0 0 0 1.34557 0 -17 0 0 0 0 1.46789 0 -18 0 0 0 0 1.59021 0 -19 0 0 0 0 1.67176 0 -20 0.122324 0 0 0 1.75331 0 -21 0.122324 0 0 0 1.83486 0 -22 0.122324 0 0 0 1.89602 0 -23 0.122324 0 0 0 1.95719 0 -24 0.122324 0 0 0 2.01835 0 -25 0.122324 0 0 0 2.07951 0 -26 0.122324 0 0 0 2.14067 0 -27 0.142712 0 0 0 2.20183 0 -28 0.183486 0 0 0 2.263 0 -29 0.224261 0 0 0 2.32416 0 -30 0.224261 0 0 0 2.38532 0 -31 0.224261 0 0 0 2.44648 0 -32 0.224261 0 0 0 2.50765 0 -33 0.224261 0 0 0 2.60958 0 -34 0.224261 0 0 0 2.71152 0 -35 0.265036 0 0 0 2.81346 0 -36 0.285423 0 0 0 2.89501 0 -37 0.326198 0 0 0 2.99694 0 -38 0.407747 0 0 0 3.09888 0 -39 0.468909 0 0 0 3.20082 0 -40 0.468909 0 0 0 3.30275 0 -41 0.468909 0 0 0 3.40469 0 -42 0.468909 0 0 0 3.48624 0 -43 0.468909 0 0 0 3.56779 0 -44 0.468909 0 0 0 3.60856 0 -45 0.468909 0 0 0 3.62895 0 -46 0.468909 0 0 0 3.64934 0 -47 0.468909 0 0 0 3.66972 0 -48 0.468909 0 0 0 3.69011 0 -49 0.468909 0 0 0 3.7105 0 -50 0.468909 0 0 0 3.7105 0 -51 0.468909 0 0 0 3.7105 0 -52 0.468909 0 0 0 3.7105 0 -53 0.468909 0 0 0 3.7105 0 -54 0.468909 0 0 0 3.7105 0 -55 0.468909 0 0 0 3.7105 0 -56 0.468909 0 0 0 3.7105 0 -57 0.468909 0 0 0 3.7105 0 -58 0.468909 0 0 0 3.7105 0 -59 0.468909 0 0 0 3.73089 0 -60 0.468909 0 0 0 3.75127 0 -61 0.468909 0 0 0 3.77166 0 -62 0.468909 0 0 0 3.81244 0 -63 0.468909 0 0 0 3.85321 0 -64 0.468909 0 0 0 3.89399 0 -65 0.468909 0 0 0 3.93476 0 -66 0.468909 0 0 0 3.97554 0 -67 0.468909 0 0 0 4.01631 0 -68 0.468909 0 0 0 4.05708 0 -69 0.468909 0 0 0 4.09786 0 -70 0.468909 0 0 0 4.13863 0 -71 0.468909 0 0 0 4.17941 0 -72 0.468909 0 0 0 4.22018 0 -73 0.468909 0 0 0 4.26096 0 -74 0.489297 0 0 0 4.32212 0 -75 0.489297 0 0 0 4.38328 0 -76 0.489297 0 0 0 4.42406 0 -77 0.489297 0 0 0 4.46483 0 -78 0.489297 0 0 0 4.50561 0 -79 0.489297 0 0 0 4.54638 0 -80 0.489297 0 0 0 4.58716 0 -81 0.489297 0 0 0 4.62793 0 -82 0.489297 0 0 0 4.66871 0 -83 0.509684 0 0 0 4.70948 0 -84 0.509684 0 0 0 4.75025 0 -85 0.509684 0 0 0 4.79103 0 -86 0.509684 0 0 0 4.8318 0 -87 0.509684 0 0 0 4.91335 0 -88 0.509684 0 0 0 4.9949 0 -89 0.509684 0 0 0 5.05607 0 -90 0.509684 0 0 0 5.09684 0 -91 0.509684 0 0 0 5.158 0 -92 0.570846 0 0 0 5.21916 0 -93 0.632008 0 0 0 5.28033 0 -94 0.632008 0 0 0 5.34149 0 -95 0.632008 0 0 0 5.40265 0 -96 0.632008 0 0 0 5.46381 0 -97 0.632008 0 0 0 5.52497 0 -98 0.632008 0 0 0 5.52497 0 -99 0.632008 0 0 0 5.52497 0 -100 0.632008 0 0 0 5.52497 0 -101 0.632008 0 0 0 5.52497 0 -102 0.632008 0 0 0 5.52497 0 -103 0.632008 0 0 0 5.52497 0 -104 0.632008 0 0 0 5.52497 0 -105 0.632008 0 0 0 5.52497 0 -106 0.632008 0 0 0 5.52497 0 -107 0.632008 0 0 0 5.52497 0 -108 0.632008 0 0 0 5.52497 0 +2 0 0 0 0 0.0611621 0 +3 0 0 0 0 0.0611621 0 +4 0 0 0 0 0.0611621 0 +5 0 0 0 0 0.122324 0 +6 0 0 0 0 0.122324 0 +7 0 0 0 0 0.122324 0 +8 0 0 0 0 0.142712 0 +9 0 0 0 0 0.142712 0 +10 0 0 0 0 0.142712 0 +11 0 0 0 0 0.142712 0 +12 0 0 0 0 0.142712 0 +13 0 0 0 0 0.163099 0 +14 0 0 0 0 0.163099 0 +15 0 0 0 0 0.183486 0 +16 0 0 0 0 0.203874 0 +17 0 0 0 0 0.224261 0 +18 0 0 0 0 0.224261 0 +19 0 0 0 0 0.224261 0 +20 0.122324 0 0 0 0.244648 0 +21 0.122324 0 0 0 0.244648 0 +22 0.122324 0 0 0 0.244648 0 +23 0.122324 0 0 0 0.244648 0 +24 0.122324 0 0 0 0.244648 0 +25 0.122324 0 0 0 0.244648 0 +26 0.122324 0 0 0 0.244648 0 +27 0.142712 0 0 0 0.244648 0 +28 0.183486 0 0 0 0.244648 0 +29 0.224261 0 0 0 0.244648 0 +30 0.224261 0 0 0 0.244648 0 +31 0.224261 0 0 0 0.244648 0 +32 0.224261 0 0 0 0.244648 0 +33 0.224261 0 0 0 0.285423 0 +34 0.224261 0 0 0 0.285423 0 +35 0.265036 0 0 0 0.30581 0 +36 0.285423 0 0 0 0.30581 0 +37 0.326198 0 0 0 0.326198 0 +38 0.407747 0 0 0 0.326198 0 +39 0.468909 0 0 0 0.326198 0 +40 0.468909 0 0 0 0.326198 0 +41 0.468909 0 0 0 0.326198 0 +42 0.468909 0 0 0 0.326198 0 +43 0.468909 0 0 0 0.326198 0 +44 0.468909 0 0 0 0.326198 0 +45 0.468909 0 0 0 0.326198 0 +46 0.468909 0 0 0 0.326198 0 +47 0.468909 0 0 0 0.326198 0 +48 0.468909 0 0 0 0.326198 0 +49 0.468909 0 0 0 0.326198 0 +50 0.468909 0 0 0 0.326198 0 +51 0.468909 0 0 0 0.326198 0 +52 0.468909 0 0 0 0.326198 0 +53 0.468909 0 0 0 0.326198 0 +54 0.468909 0 0 0 0.326198 0 +55 0.468909 0 0 0 0.326198 0 +56 0.468909 0 0 0 0.326198 0 +57 0.468909 0 0 0 0.326198 0 +58 0.468909 0 0 0 0.326198 0 +59 0.468909 0 0 0 0.326198 0 +60 0.468909 0 0 0 0.326198 0 +61 0.468909 0 0 0 0.326198 0 +62 0.468909 0 0 0 0.326198 0 +63 0.468909 0 0 0 0.326198 0 +64 0.468909 0 0 0 0.326198 0 +65 0.468909 0 0 0 0.326198 0 +66 0.468909 0 0 0 0.326198 0 +67 0.468909 0 0 0 0.326198 0 +68 0.468909 0 0 0 0.326198 0 +69 0.468909 0 0 0 0.326198 0 +70 0.468909 0 0 0 0.326198 0 +71 0.468909 0 0 0 0.326198 0 +72 0.468909 0 0 0 0.326198 0 +73 0.468909 0 0 0 0.326198 0 +74 0.468909 0 0 0 0.326198 0 +75 0.468909 0 0 0 0.326198 0 +76 0.468909 0 0 0 0.326198 0 +77 0.468909 0 0 0 0.326198 0 +78 0.468909 0 0 0 0.326198 0 +79 0.468909 0 0 0 0.326198 0 +80 0.468909 0 0 0 0.326198 0 +81 0.468909 0 0 0 0.326198 0 +82 0.468909 0 0 0 0.326198 0 +83 0.468909 0 0 0 0.326198 0 +84 0.468909 0 0 0 0.326198 0 +85 0.468909 0 0 0 0.326198 0 +86 0.468909 0 0 0 0.326198 0 +87 0.468909 0 0 0 0.326198 0 +88 0.468909 0 0 0 0.326198 0 +89 0.468909 0 0 0 0.326198 0 +90 0.468909 0 0 0 0.326198 0 +91 0.468909 0 0 0 0.326198 0 +92 0.468909 0 0 0 0.326198 0 +93 0.468909 0 0 0 0.326198 0 +94 0.468909 0 0 0 0.326198 0 +95 0.468909 0 0 0 0.326198 0 +96 0.468909 0 0 0 0.326198 0 +97 0.468909 0 0 0 0.326198 0 +98 0.468909 0 0 0 0.326198 0 +99 0.468909 0 0 0 0.326198 0 +100 0.468909 0 0 0 0.326198 0 +101 0.468909 0 0 0 0.326198 0 +102 0.468909 0 0 0 0.326198 0 +103 0.468909 0 0 0 0.326198 0 +104 0.468909 0 0 0 0.326198 0 +105 0.468909 0 0 0 0.326198 0 +106 0.468909 0 0 0 0.326198 0 +107 0.468909 0 0 0 0.326198 0 +108 0.468909 0 0 0 0.326198 0 >>END_MODULE
--- a/test-data/fastqc_data_nogroup_summary.txt Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,11 +0,0 @@ -PASS Basic Statistics 1000trimmed_fastq -PASS Per base sequence quality 1000trimmed_fastq -FAIL Per tile sequence quality 1000trimmed_fastq -PASS Per sequence quality scores 1000trimmed_fastq -FAIL Per base sequence content 1000trimmed_fastq -WARN Per sequence GC content 1000trimmed_fastq -PASS Per base N content 1000trimmed_fastq -WARN Sequence Length Distribution 1000trimmed_fastq -PASS Sequence Duplication Levels 1000trimmed_fastq -WARN Overrepresented sequences 1000trimmed_fastq -WARN Adapter Content 1000trimmed_fastq
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/fastqc_data_summary.txt Fri Sep 27 17:41:40 2024 +0000 @@ -0,0 +1,11 @@ +PASS Basic Statistics 1000trimmed_fastq +PASS Per base sequence quality 1000trimmed_fastq +FAIL Per tile sequence quality 1000trimmed_fastq +PASS Per sequence quality scores 1000trimmed_fastq +FAIL Per base sequence content 1000trimmed_fastq +WARN Per sequence GC content 1000trimmed_fastq +PASS Per base N content 1000trimmed_fastq +WARN Sequence Length Distribution 1000trimmed_fastq +PASS Sequence Duplication Levels 1000trimmed_fastq +WARN Overrepresented sequences 1000trimmed_fastq +PASS Adapter Content 1000trimmed_fastq
--- a/test-data/fastqc_report.html Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -<html><head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <title> 1000trimmed_fastq - report </title><link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous"><link href="https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-wvfXpqpZZVQGK6TAh5PVlGOfQNHSoD2xbE+QkPxCAFlNEevoEH3Sl0sibVcOQVnN" crossorigin="anonymous"><style type="text/css"> @media screen { div.summary { width: 18em; position:fixed; top: 4em; margin:1em 0 0 1em; } div.main { display:block; position:absolute; overflow:auto; height:auto; width:auto; top:4.5em; bottom:2.3em; left:18em; right:0; border-left: 1px solid #CCC; 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list-style-type:none; } div.summary ul li img { margin-bottom:-0.5em; margin-top:0.5em; } div.main { background-color: white; } div.module { padding-bottom:3em; padding-top:3em; border-bottom: 1px solid #990000 } div.footer { background-color: #EEE; border:0; margin:0; padding: 0.5em; font-size: 100%; width:100%; } h2 { color: #2a5e8c; padding-bottom: 0; margin-bottom: 0; clear:left; }table { margin-left: 3em; text-align: center; } th { text-align: center; background-color: #000080; color: #FFF; padding: 0.4em;} td { font-family: monospace; text-align: left; background-color: #EEE; color: #000; padding: 0.4em;}img { padding-top: 0; margin-top: 0; border-top: 0;} p { padding-top: 0; margin-top: 0;}.pass { color : #009900;}.warn { color : #999900;}.fail { color : #990000;}</style><script src="https://cdn.plot.ly/plotly-latest.min.js"></script></head><body><div class="header"> <div id="header_title">Report</div> <div id="header_filename">Sun Sep 1 15:39:03 2024 -<br/> 1000trimmed_fastq </div></div><div class="summary"><h2>Summary</h2><ul> <li><a class="pass" href="#basicstatistics"> Basic Statistics </a></li> <li><a class="pass" href="#perbasesequencequality"> Per base sequence quality</a></li> <li><a class="fail" href="#pertilesequencequality">Per tile sequence quality</a></li> <li><a class="pass" href="#persequencequalityscores">Per sequence quality scores</a></li> <li><a class="fail" href="#perbasesequencecontent">Per base sequence content</a></li> <li><a class="warn" href="#persequencegccontent">Per sequence GC content</a></li> <li><a class="pass" href="#perbasencontent">Per base N content</a></li> <li><a class="warn" href="#sequencelengthdistribution">Sequence Length Distribution</a></li> <li><a class="pass" href="#sequenceduplicationlevels">Sequence Duplication Levels</a></li> <li><a class="warn" href="#overrepresentedsequences">Overrepresented sequences</a></li> <li><a class="warn" href="#adaptercontent">Adapter Content</a></li> <!-- <li><a class="{{passkmercontent}}" href="#kmercontent">{{kmercontentname}}</a></li> --></ul></div><div class="main"><div class="module"> <h2 class="pass" id="basicstatistics"> Basic Statistics: pass </h2> <table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>1000trimmed_fastq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4905</td></tr><tr><td>Sequences Flagged As Poor Quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1 - 108</td></tr><tr><td>%GC:</td><td>41</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="perbasesequencequality"> Per base sequence quality: pass</h2> <div id="seqbasequalityboxplot"></div></div><div class="module"> <h2 class="fail" id="pertilesequencequality"> Per tile sequence quality : fail </h2> <div id="tilequalityheatmap"></div></div><div class="module"> <h2 class="pass" id="persequencequalityscores"> Per sequence quality scores : pass </h2> <div id="seqqualitylineplot"></div></div><div class="module"> <h2 class="fail" id="perbasesequencecontent"> Per base sequence content : fail </h2> <div id="basesequencecontentlineplot"></div></div><div class="module"> <h2 class="warn" id="persequencegccontent"> Per sequence GC content: warn </h2> <div id="sequencegccontentlineplot"></div></div><div class="module"> <h2 class="pass" id="perbasencontent"> Per base N content : pass </h2> <div id="basencontentlineplot"></div></div><div class="module"> <h2 class="warn" id="sequencelengthdistribution"> Sequence Length Distribution : warn </h2> <div id="sequencelengthdistributionlineplot"></div></div><div class="module"> <h2 class="pass" id="sequenceduplicationlevels"> Sequence Duplication Levels : pass </h2> <div id="seqduplevelslineplot"></div></div><div class="module"> <h2 class="warn" id="overrepresentedsequences"> Overrepresented sequences : warn</h2> <table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT</td><td>33</td><td>0.672783</td><td>No Hit</td></tr></tbody></table></div><div class="module"> <h2 class="warn" id="adaptercontent"> Adapter Content : warn </h2> <div id="adapterlineplot"></div></div><!--<div class="module"> <h2 class="{{passkmercontent}}" id="kmercontent"> {{kmercontentname}} : {{passkmercontent}} </h2> <div id="kmerlineplot"></div></div>--></div><div class="footer">Falco 1.2.3</div></body><script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1"crossorigin="anonymous"></script><script 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padding-left:0; list-style-type:none; } div.summary ul li img { margin-bottom:-0.5em; margin-top:0.5em; } div.main { background-color: white; } div.module { padding-bottom:3em; padding-top:3em; border-bottom: 1px solid #990000 } div.footer { background-color: #EEE; border:0; margin:0; padding: 0.5em; font-size: 100%; width:100%; } h2 { color: #2a5e8c; padding-bottom: 0; margin-bottom: 0; clear:left; }table { margin-left: 3em; text-align: center; } th { text-align: center; background-color: #000080; color: #FFF; padding: 0.4em;} td { font-family: monospace; text-align: left; background-color: #EEE; color: #000; padding: 0.4em;}img { padding-top: 0; margin-top: 0; border-top: 0;} p { padding-top: 0; margin-top: 0;}.pass { color : #009900;}.warn { color : #999900;}.fail { color : #990000;}</style><script src="https://cdn.plot.ly/plotly-latest.min.js"></script></head><body><div class="header"> <div id="header_title">Report</div> <div id="header_filename">Sun Sep 1 15:39:42 2024 -<br/> 1000trimmed_fastq </div></div><div class="summary"><h2>Summary</h2><ul> <li><a class="pass" href="#basicstatistics"> Basic Statistics </a></li> <li><a class="pass" href="#perbasesequencequality"> Per base sequence quality</a></li> <li><a class="fail" href="#pertilesequencequality">Per tile sequence quality</a></li> <li><a class="pass" href="#persequencequalityscores">Per sequence quality scores</a></li> <li><a class="fail" href="#perbasesequencecontent">Per base sequence content</a></li> <li><a class="warn" href="#persequencegccontent">Per sequence GC content</a></li> <li><a class="pass" href="#perbasencontent">Per base N content</a></li> <li><a class="warn" href="#sequencelengthdistribution">Sequence Length Distribution</a></li> <li><a class="pass" href="#sequenceduplicationlevels">Sequence Duplication Levels</a></li> <li><a class="warn" href="#overrepresentedsequences">Overrepresented sequences</a></li> <li><a class="pass" href="#adaptercontent">Adapter Content</a></li> <!-- <li><a class="{{passkmercontent}}" href="#kmercontent">{{kmercontentname}}</a></li> --></ul></div><div class="main"><div class="module"> <h2 class="pass" id="basicstatistics"> Basic Statistics: pass </h2> <table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>1000trimmed_fastq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4905</td></tr><tr><td>Sequences Flagged As Poor Quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1 - 108</td></tr><tr><td>%GC:</td><td>41</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="perbasesequencequality"> Per base sequence quality: pass</h2> <div id="seqbasequalityboxplot"></div></div><div class="module"> <h2 class="fail" id="pertilesequencequality"> Per tile sequence quality : fail </h2> <div id="tilequalityheatmap"></div></div><div class="module"> <h2 class="pass" id="persequencequalityscores"> Per sequence quality scores : pass </h2> <div id="seqqualitylineplot"></div></div><div class="module"> <h2 class="fail" id="perbasesequencecontent"> Per base sequence content : fail </h2> <div id="basesequencecontentlineplot"></div></div><div class="module"> <h2 class="warn" id="persequencegccontent"> Per sequence GC content: warn </h2> <div id="sequencegccontentlineplot"></div></div><div class="module"> <h2 class="pass" id="perbasencontent"> Per base N content : pass </h2> <div id="basencontentlineplot"></div></div><div class="module"> <h2 class="warn" id="sequencelengthdistribution"> Sequence Length Distribution : warn </h2> <div id="sequencelengthdistributionlineplot"></div></div><div class="module"> <h2 class="pass" id="sequenceduplicationlevels"> Sequence Duplication Levels : pass </h2> <div id="seqduplevelslineplot"></div></div><div class="module"> <h2 class="warn" id="overrepresentedsequences"> Overrepresented sequences : warn</h2> <table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT</td><td>33</td><td>0.672783</td><td>No Hit</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="adaptercontent"> Adapter Content : pass </h2> <div id="adapterlineplot"></div></div><!--<div class="module"> <h2 class="{{passkmercontent}}" id="kmercontent"> {{kmercontentname}} : {{passkmercontent}} </h2> <div id="kmerlineplot"></div></div>--></div><div class="footer">Falco 1.2.3</div></body><script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1"crossorigin="anonymous"></script><script 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--- a/test-data/fastqc_report_bisulfite.html Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -<html><head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <title> 1000trimmed_fastq - report </title><link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous"><link href="https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-wvfXpqpZZVQGK6TAh5PVlGOfQNHSoD2xbE+QkPxCAFlNEevoEH3Sl0sibVcOQVnN" crossorigin="anonymous"><style type="text/css"> @media screen { div.summary { width: 18em; position:fixed; top: 4em; margin:1em 0 0 1em; } div.main { display:block; position:absolute; overflow:auto; height:auto; width:auto; top:4.5em; bottom:2.3em; left:18em; right:0; border-left: 1px solid #CCC; padding:0 0 0 1em; background-color: white; z-index:1; } div.header { background-color: #EEE; border:0; margin:0; padding: 0.2em; font-size: 200%; position:fixed; width:100%; top:0; left:0; z-index:2; } div.footer { background-color: #EEE; border:0; margin:0; padding:0.5em; height: 2.5em; overflow:hidden; font-size: 100%; position:fixed; bottom:0; width:100%; z-index:2; } img.indented { margin-left: 3em; } } @media print { img { max-width:100% !important; page-break-inside: avoid; } h2, h3 { page-break-after: avoid; } div.header { background-color: #FFF; } } body { color: #000; background-color: #FFF; border: 0; margin: 0; padding: 0; } div.header { border:0; margin:0; padding: 0.5em; font-size: 200%; width:100%; } #header_title { display:inline-block; float:left; clear:left; } #header_filename { display:inline-block; float:right; clear:right; font-size: 50%; margin-right:2em; text-align: right; } div.header h3 { font-size: 50%; margin-bottom: 0; } div.summary ul { padding-left:0; list-style-type:none; } div.summary ul li img { margin-bottom:-0.5em; margin-top:0.5em; } div.main { background-color: white; } div.module { padding-bottom:3em; padding-top:3em; border-bottom: 1px solid #990000 } div.footer { background-color: #EEE; border:0; margin:0; padding: 0.5em; font-size: 100%; width:100%; } h2 { color: #2a5e8c; padding-bottom: 0; margin-bottom: 0; clear:left; }table { margin-left: 3em; text-align: center; } th { text-align: center; background-color: #000080; color: #FFF; padding: 0.4em;} td { font-family: monospace; text-align: left; background-color: #EEE; color: #000; padding: 0.4em;}img { padding-top: 0; margin-top: 0; border-top: 0;} p { padding-top: 0; margin-top: 0;}.pass { color : #009900;}.warn { color : #999900;}.fail { color : #990000;}</style><script src="https://cdn.plot.ly/plotly-latest.min.js"></script></head><body><div class="header"> <div id="header_title">Report</div> <div id="header_filename">Sun Sep 1 15:40:53 2024 -<br/> 1000trimmed_fastq </div></div><div class="summary"><h2>Summary</h2><ul> <li><a class="pass" href="#basicstatistics"> Basic Statistics </a></li> <li><a class="pass" href="#perbasesequencequality"> Per base sequence quality</a></li> <li><a class="fail" href="#pertilesequencequality">Per tile sequence quality</a></li> <li><a class="pass" href="#persequencequalityscores">Per sequence quality scores</a></li> <li><a class="warn" href="#perbasesequencecontent">Per base sequence content</a></li> <li><a class="warn" href="#persequencegccontent">Per sequence GC content</a></li> <li><a class="pass" href="#perbasencontent">Per base N content</a></li> <li><a class="warn" href="#sequencelengthdistribution">Sequence Length Distribution</a></li> <li><a class="pass" href="#sequenceduplicationlevels">Sequence Duplication Levels</a></li> <li><a class="warn" href="#overrepresentedsequences">Overrepresented sequences</a></li> <li><a class="warn" href="#adaptercontent">Adapter Content</a></li> <!-- <li><a class="{{passkmercontent}}" href="#kmercontent">{{kmercontentname}}</a></li> --></ul></div><div class="main"><div class="module"> <h2 class="pass" id="basicstatistics"> Basic Statistics: pass </h2> <table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>1000trimmed_fastq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4905</td></tr><tr><td>Sequences Flagged As Poor Quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1 - 108</td></tr><tr><td>%GC:</td><td>41</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="perbasesequencequality"> Per base sequence quality: pass</h2> <div id="seqbasequalityboxplot"></div></div><div class="module"> <h2 class="fail" id="pertilesequencequality"> Per tile sequence quality : fail </h2> <div id="tilequalityheatmap"></div></div><div class="module"> <h2 class="pass" id="persequencequalityscores"> Per sequence quality scores : pass </h2> <div id="seqqualitylineplot"></div></div><div class="module"> <h2 class="warn" id="perbasesequencecontent"> Per base sequence content : warn </h2> <div id="basesequencecontentlineplot"></div></div><div class="module"> <h2 class="warn" id="persequencegccontent"> Per sequence GC content: warn </h2> <div id="sequencegccontentlineplot"></div></div><div class="module"> <h2 class="pass" id="perbasencontent"> Per base N content : pass </h2> <div id="basencontentlineplot"></div></div><div class="module"> <h2 class="warn" id="sequencelengthdistribution"> Sequence Length Distribution : warn </h2> <div id="sequencelengthdistributionlineplot"></div></div><div class="module"> <h2 class="pass" id="sequenceduplicationlevels"> Sequence Duplication Levels : pass </h2> <div id="seqduplevelslineplot"></div></div><div class="module"> <h2 class="warn" id="overrepresentedsequences"> Overrepresented sequences : warn</h2> <table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT</td><td>33</td><td>0.672783</td><td>No Hit</td></tr></tbody></table></div><div class="module"> <h2 class="warn" id="adaptercontent"> Adapter Content : warn </h2> <div id="adapterlineplot"></div></div><!--<div class="module"> <h2 class="{{passkmercontent}}" id="kmercontent"> {{kmercontentname}} : {{passkmercontent}} </h2> <div id="kmerlineplot"></div></div>--></div><div class="footer">Falco 1.2.3</div></body><script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1"crossorigin="anonymous"></script><script 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--- a/test-data/fastqc_report_bisulfite.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/fastqc_report_bisulfite.txt Fri Sep 27 17:41:40 2024 +0000 @@ -1,4 +1,4 @@ -##Falco 1.2.3 +##Falco 1.2.4 >>Basic Statistics pass #Measure Value Filename 1000trimmed_fastq @@ -1597,114 +1597,114 @@ #Sequence Count Percentage Possible Source ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT 33 0.672783 No Hit >>END_MODULE ->>Adapter Content warn +>>Adapter Content pass #Position Illumina Universal Adapter Illumina Small RNA 3' Adapter Illumina Small RNA 5' Adapter Nextera Transposase Sequence PolyA PolyG 1 0 0 0 0 0.0203874 0 -2 0 0 0 0 0.0815494 0 -3 0 0 0 0 0.142712 0 -4 0 0 0 0 0.183486 0 -5 0 0 0 0 0.285423 0 -6 0 0 0 0 0.38736 0 -7 0 0 0 0 0.489297 0 -8 0 0 0 0 0.591233 0 -9 0 0 0 0 0.672783 0 -10 0 0 0 0 0.754332 0 -11 0 0 0 0 0.835882 0 -12 0 0 0 0 0.917431 0 -13 0 0 0 0 1.01937 0 -14 0 0 0 0 1.1213 0 -15 0 0 0 0 1.24363 0 -16 0 0 0 0 1.34557 0 -17 0 0 0 0 1.46789 0 -18 0 0 0 0 1.59021 0 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-54 0.468909 0 0 0 3.7105 0 -55 0.468909 0 0 0 3.7105 0 -56 0.468909 0 0 0 3.7105 0 -57 0.468909 0 0 0 3.7105 0 -58 0.468909 0 0 0 3.7105 0 -59 0.468909 0 0 0 3.73089 0 -60 0.468909 0 0 0 3.75127 0 -61 0.468909 0 0 0 3.77166 0 -62 0.468909 0 0 0 3.81244 0 -63 0.468909 0 0 0 3.85321 0 -64 0.468909 0 0 0 3.89399 0 -65 0.468909 0 0 0 3.93476 0 -66 0.468909 0 0 0 3.97554 0 -67 0.468909 0 0 0 4.01631 0 -68 0.468909 0 0 0 4.05708 0 -69 0.468909 0 0 0 4.09786 0 -70 0.468909 0 0 0 4.13863 0 -71 0.468909 0 0 0 4.17941 0 -72 0.468909 0 0 0 4.22018 0 -73 0.468909 0 0 0 4.26096 0 -74 0.489297 0 0 0 4.32212 0 -75 0.489297 0 0 0 4.38328 0 -76 0.489297 0 0 0 4.42406 0 -77 0.489297 0 0 0 4.46483 0 -78 0.489297 0 0 0 4.50561 0 -79 0.489297 0 0 0 4.54638 0 -80 0.489297 0 0 0 4.58716 0 -81 0.489297 0 0 0 4.62793 0 -82 0.489297 0 0 0 4.66871 0 -83 0.509684 0 0 0 4.70948 0 -84 0.509684 0 0 0 4.75025 0 -85 0.509684 0 0 0 4.79103 0 -86 0.509684 0 0 0 4.8318 0 -87 0.509684 0 0 0 4.91335 0 -88 0.509684 0 0 0 4.9949 0 -89 0.509684 0 0 0 5.05607 0 -90 0.509684 0 0 0 5.09684 0 -91 0.509684 0 0 0 5.158 0 -92 0.570846 0 0 0 5.21916 0 -93 0.632008 0 0 0 5.28033 0 -94 0.632008 0 0 0 5.34149 0 -95 0.632008 0 0 0 5.40265 0 -96 0.632008 0 0 0 5.46381 0 -97 0.632008 0 0 0 5.52497 0 -98 0.632008 0 0 0 5.52497 0 -99 0.632008 0 0 0 5.52497 0 -100 0.632008 0 0 0 5.52497 0 -101 0.632008 0 0 0 5.52497 0 -102 0.632008 0 0 0 5.52497 0 -103 0.632008 0 0 0 5.52497 0 -104 0.632008 0 0 0 5.52497 0 -105 0.632008 0 0 0 5.52497 0 -106 0.632008 0 0 0 5.52497 0 -107 0.632008 0 0 0 5.52497 0 -108 0.632008 0 0 0 5.52497 0 +2 0 0 0 0 0.0611621 0 +3 0 0 0 0 0.0611621 0 +4 0 0 0 0 0.0611621 0 +5 0 0 0 0 0.122324 0 +6 0 0 0 0 0.122324 0 +7 0 0 0 0 0.122324 0 +8 0 0 0 0 0.142712 0 +9 0 0 0 0 0.142712 0 +10 0 0 0 0 0.142712 0 +11 0 0 0 0 0.142712 0 +12 0 0 0 0 0.142712 0 +13 0 0 0 0 0.163099 0 +14 0 0 0 0 0.163099 0 +15 0 0 0 0 0.183486 0 +16 0 0 0 0 0.203874 0 +17 0 0 0 0 0.224261 0 +18 0 0 0 0 0.224261 0 +19 0 0 0 0 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0.468909 0 0 0 0.326198 0 +87 0.468909 0 0 0 0.326198 0 +88 0.468909 0 0 0 0.326198 0 +89 0.468909 0 0 0 0.326198 0 +90 0.468909 0 0 0 0.326198 0 +91 0.468909 0 0 0 0.326198 0 +92 0.468909 0 0 0 0.326198 0 +93 0.468909 0 0 0 0.326198 0 +94 0.468909 0 0 0 0.326198 0 +95 0.468909 0 0 0 0.326198 0 +96 0.468909 0 0 0 0.326198 0 +97 0.468909 0 0 0 0.326198 0 +98 0.468909 0 0 0 0.326198 0 +99 0.468909 0 0 0 0.326198 0 +100 0.468909 0 0 0 0.326198 0 +101 0.468909 0 0 0 0.326198 0 +102 0.468909 0 0 0 0.326198 0 +103 0.468909 0 0 0 0.326198 0 +104 0.468909 0 0 0 0.326198 0 +105 0.468909 0 0 0 0.326198 0 +106 0.468909 0 0 0 0.326198 0 +107 0.468909 0 0 0 0.326198 0 +108 0.468909 0 0 0 0.326198 0 >>END_MODULE
--- a/test-data/fastqc_report_bisulfite_summary.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/fastqc_report_bisulfite_summary.txt Fri Sep 27 17:41:40 2024 +0000 @@ -8,4 +8,4 @@ WARN Sequence Length Distribution 1000trimmed_fastq PASS Sequence Duplication Levels 1000trimmed_fastq WARN Overrepresented sequences 1000trimmed_fastq -WARN Adapter Content 1000trimmed_fastq +PASS Adapter Content 1000trimmed_fastq
--- a/test-data/fastqc_report_contaminants.html Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -<html><head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <title> 1000trimmed_fastq - report </title><link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous"><link href="https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-wvfXpqpZZVQGK6TAh5PVlGOfQNHSoD2xbE+QkPxCAFlNEevoEH3Sl0sibVcOQVnN" crossorigin="anonymous"><style type="text/css"> @media screen { div.summary { width: 18em; position:fixed; top: 4em; margin:1em 0 0 1em; } div.main { display:block; position:absolute; overflow:auto; height:auto; width:auto; top:4.5em; bottom:2.3em; left:18em; right:0; border-left: 1px solid #CCC; padding:0 0 0 1em; background-color: white; z-index:1; } div.header { background-color: #EEE; border:0; margin:0; padding: 0.2em; font-size: 200%; position:fixed; width:100%; top:0; left:0; z-index:2; } div.footer { background-color: #EEE; border:0; margin:0; padding:0.5em; height: 2.5em; overflow:hidden; font-size: 100%; position:fixed; bottom:0; width:100%; z-index:2; } img.indented { margin-left: 3em; } } @media print { img { max-width:100% !important; page-break-inside: avoid; } h2, h3 { page-break-after: avoid; } div.header { background-color: #FFF; } } body { color: #000; background-color: #FFF; border: 0; margin: 0; padding: 0; } div.header { border:0; margin:0; padding: 0.5em; font-size: 200%; width:100%; } #header_title { display:inline-block; float:left; clear:left; } #header_filename { display:inline-block; float:right; clear:right; font-size: 50%; margin-right:2em; text-align: right; } div.header h3 { font-size: 50%; margin-bottom: 0; } div.summary ul { padding-left:0; list-style-type:none; } div.summary ul li img { margin-bottom:-0.5em; margin-top:0.5em; } div.main { background-color: white; } div.module { padding-bottom:3em; padding-top:3em; border-bottom: 1px solid #990000 } div.footer { background-color: #EEE; border:0; margin:0; padding: 0.5em; font-size: 100%; width:100%; } h2 { color: #2a5e8c; padding-bottom: 0; margin-bottom: 0; clear:left; }table { margin-left: 3em; text-align: center; } th { text-align: center; background-color: #000080; color: #FFF; padding: 0.4em;} td { font-family: monospace; text-align: left; background-color: #EEE; color: #000; padding: 0.4em;}img { padding-top: 0; margin-top: 0; border-top: 0;} p { padding-top: 0; margin-top: 0;}.pass { color : #009900;}.warn { color : #999900;}.fail { color : #990000;}</style><script src="https://cdn.plot.ly/plotly-latest.min.js"></script></head><body><div class="header"> <div id="header_title">Report</div> <div id="header_filename">Sun Sep 1 15:39:23 2024 -<br/> 1000trimmed_fastq </div></div><div class="summary"><h2>Summary</h2><ul> <li><a class="pass" href="#basicstatistics"> Basic Statistics </a></li> <li><a class="pass" href="#perbasesequencequality"> Per base sequence quality</a></li> <li><a class="fail" href="#pertilesequencequality">Per tile sequence quality</a></li> <li><a class="pass" href="#persequencequalityscores">Per sequence quality scores</a></li> <li><a class="fail" href="#perbasesequencecontent">Per base sequence content</a></li> <li><a class="warn" href="#persequencegccontent">Per sequence GC content</a></li> <li><a class="pass" href="#perbasencontent">Per base N content</a></li> <li><a class="warn" href="#sequencelengthdistribution">Sequence Length Distribution</a></li> <li><a class="pass" href="#sequenceduplicationlevels">Sequence Duplication Levels</a></li> <li><a class="warn" href="#overrepresentedsequences">Overrepresented sequences</a></li> <li><a class="warn" href="#adaptercontent">Adapter Content</a></li> <!-- <li><a class="{{passkmercontent}}" href="#kmercontent">{{kmercontentname}}</a></li> --></ul></div><div class="main"><div class="module"> <h2 class="pass" id="basicstatistics"> Basic Statistics: pass </h2> <table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>1000trimmed_fastq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4905</td></tr><tr><td>Sequences Flagged As Poor Quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1 - 108</td></tr><tr><td>%GC:</td><td>41</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="perbasesequencequality"> Per base sequence quality: pass</h2> <div id="seqbasequalityboxplot"></div></div><div class="module"> <h2 class="fail" id="pertilesequencequality"> Per tile sequence quality : fail </h2> <div id="tilequalityheatmap"></div></div><div class="module"> <h2 class="pass" id="persequencequalityscores"> Per sequence quality scores : pass </h2> <div id="seqqualitylineplot"></div></div><div class="module"> <h2 class="fail" id="perbasesequencecontent"> Per base sequence content : fail </h2> <div id="basesequencecontentlineplot"></div></div><div class="module"> <h2 class="warn" id="persequencegccontent"> Per sequence GC content: warn </h2> <div id="sequencegccontentlineplot"></div></div><div class="module"> <h2 class="pass" id="perbasencontent"> Per base N content : pass </h2> <div id="basencontentlineplot"></div></div><div class="module"> <h2 class="warn" id="sequencelengthdistribution"> Sequence Length Distribution : warn </h2> <div id="sequencelengthdistributionlineplot"></div></div><div class="module"> <h2 class="pass" id="sequenceduplicationlevels"> Sequence Duplication Levels : pass </h2> <div id="seqduplevelslineplot"></div></div><div class="module"> <h2 class="warn" id="overrepresentedsequences"> Overrepresented sequences : warn</h2> <table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT</td><td>33</td><td>0.672783</td><td>No Hit</td></tr></tbody></table></div><div class="module"> <h2 class="warn" id="adaptercontent"> Adapter Content : warn </h2> <div id="adapterlineplot"></div></div><!--<div class="module"> <h2 class="{{passkmercontent}}" id="kmercontent"> {{kmercontentname}} : {{passkmercontent}} </h2> <div id="kmerlineplot"></div></div>--></div><div class="footer">Falco 1.2.3</div></body><script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1"crossorigin="anonymous"></script><script 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--- a/test-data/fastqc_report_customlimits.html Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -<html><head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <title> 1000trimmed_fastq - report </title><link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous"><link href="https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-wvfXpqpZZVQGK6TAh5PVlGOfQNHSoD2xbE+QkPxCAFlNEevoEH3Sl0sibVcOQVnN" crossorigin="anonymous"><style type="text/css"> @media screen { div.summary { width: 18em; position:fixed; top: 4em; margin:1em 0 0 1em; } div.main { display:block; position:absolute; overflow:auto; height:auto; width:auto; top:4.5em; bottom:2.3em; left:18em; right:0; border-left: 1px solid #CCC; padding:0 0 0 1em; background-color: white; z-index:1; } div.header { background-color: #EEE; border:0; margin:0; padding: 0.2em; font-size: 200%; position:fixed; width:100%; top:0; left:0; z-index:2; } div.footer { background-color: #EEE; border:0; margin:0; padding:0.5em; height: 2.5em; overflow:hidden; font-size: 100%; position:fixed; bottom:0; width:100%; z-index:2; } img.indented { margin-left: 3em; } } @media print { img { max-width:100% !important; page-break-inside: avoid; } h2, h3 { page-break-after: avoid; } div.header { background-color: #FFF; } } body { color: #000; background-color: #FFF; border: 0; margin: 0; padding: 0; } div.header { border:0; margin:0; padding: 0.5em; font-size: 200%; width:100%; } #header_title { display:inline-block; float:left; clear:left; } #header_filename { display:inline-block; float:right; clear:right; font-size: 50%; margin-right:2em; text-align: right; } div.header h3 { font-size: 50%; margin-bottom: 0; } div.summary ul { padding-left:0; list-style-type:none; } div.summary ul li img { margin-bottom:-0.5em; margin-top:0.5em; } div.main { background-color: white; } div.module { padding-bottom:3em; padding-top:3em; border-bottom: 1px solid #990000 } div.footer { background-color: #EEE; border:0; margin:0; padding: 0.5em; font-size: 100%; width:100%; } h2 { color: #2a5e8c; padding-bottom: 0; margin-bottom: 0; clear:left; }table { margin-left: 3em; text-align: center; } th { text-align: center; background-color: #000080; color: #FFF; padding: 0.4em;} td { font-family: monospace; text-align: left; background-color: #EEE; color: #000; padding: 0.4em;}img { padding-top: 0; margin-top: 0; border-top: 0;} p { padding-top: 0; margin-top: 0;}.pass { color : #009900;}.warn { color : #999900;}.fail { color : #990000;}</style><script src="https://cdn.plot.ly/plotly-latest.min.js"></script></head><body><div class="header"> <div id="header_title">Report</div> <div id="header_filename">Sun Sep 1 15:40:01 2024 -<br/> 1000trimmed_fastq </div></div><div class="summary"><h2>Summary</h2><ul> <li><a class="pass" href="#basicstatistics"> Basic Statistics </a></li> <li><a class="pass" href="#perbasesequencequality"> Per base sequence quality</a></li> <li><a class="fail" href="#pertilesequencequality">Per tile sequence quality</a></li> <li><a class="pass" href="#persequencequalityscores">Per sequence quality scores</a></li> <li><a class="fail" href="#perbasesequencecontent">Per base sequence content</a></li> <li><a class="warn" href="#persequencegccontent">Per sequence GC content</a></li> <li><a class="pass" href="#perbasencontent">Per base N content</a></li> <li><a class="warn" href="#sequencelengthdistribution">Sequence Length Distribution</a></li> <li><a class="pass" href="#sequenceduplicationlevels">Sequence Duplication Levels</a></li> <li><a class="warn" href="#overrepresentedsequences">Overrepresented sequences</a></li> <li><a class="warn" href="#adaptercontent">Adapter Content</a></li> <!-- <li><a class="{{passkmercontent}}" href="#kmercontent">{{kmercontentname}}</a></li> --></ul></div><div class="main"><div class="module"> <h2 class="pass" id="basicstatistics"> Basic Statistics: pass </h2> <table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>1000trimmed_fastq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4905</td></tr><tr><td>Sequences Flagged As Poor Quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1 - 108</td></tr><tr><td>%GC:</td><td>41</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="perbasesequencequality"> Per base sequence quality: pass</h2> <div id="seqbasequalityboxplot"></div></div><div class="module"> <h2 class="fail" id="pertilesequencequality"> Per tile sequence quality : fail </h2> <div id="tilequalityheatmap"></div></div><div class="module"> <h2 class="pass" id="persequencequalityscores"> Per sequence quality scores : pass </h2> <div id="seqqualitylineplot"></div></div><div class="module"> <h2 class="fail" id="perbasesequencecontent"> Per base sequence content : fail </h2> <div id="basesequencecontentlineplot"></div></div><div class="module"> <h2 class="warn" id="persequencegccontent"> Per sequence GC content: warn </h2> <div id="sequencegccontentlineplot"></div></div><div class="module"> <h2 class="pass" id="perbasencontent"> Per base N content : pass </h2> <div id="basencontentlineplot"></div></div><div class="module"> <h2 class="warn" id="sequencelengthdistribution"> Sequence Length Distribution : warn </h2> <div id="sequencelengthdistributionlineplot"></div></div><div class="module"> <h2 class="pass" id="sequenceduplicationlevels"> Sequence Duplication Levels : pass </h2> <div id="seqduplevelslineplot"></div></div><div class="module"> <h2 class="warn" id="overrepresentedsequences"> Overrepresented sequences : warn</h2> <table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT</td><td>33</td><td>0.672783</td><td>No Hit</td></tr></tbody></table></div><div class="module"> <h2 class="warn" id="adaptercontent"> Adapter Content : warn </h2> <div id="adapterlineplot"></div></div><!--<div class="module"> <h2 class="{{passkmercontent}}" id="kmercontent"> {{kmercontentname}} : {{passkmercontent}} </h2> <div id="kmerlineplot"></div></div>--></div><div class="footer">Falco 1.2.3</div></body><script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1"crossorigin="anonymous"></script><script 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[0.0203874,0.0815494,0.142712,0.183486,0.285423,0.38736,0.489297,0.591233,0.672783,0.754332,0.835882,0.917431,1.01937,1.1213,1.24363,1.34557,1.46789,1.59021,1.67176,1.75331,1.83486,1.89602,1.95719,2.01835,2.07951,2.14067,2.20183,2.263,2.32416,2.38532,2.44648,2.50765,2.60958,2.71152,2.81346,2.89501,2.99694,3.09888,3.20082,3.30275,3.40469,3.48624,3.56779,3.60856,3.62895,3.64934,3.66972,3.69011,3.7105,3.7105,3.7105,3.7105,3.7105,3.7105,3.7105,3.7105,3.7105,3.7105,3.73089,3.75127,3.77166,3.81244,3.85321,3.89399,3.93476,3.97554,4.01631,4.05708,4.09786,4.13863,4.17941,4.22018,4.26096,4.32212,4.38328,4.42406,4.46483,4.50561,4.54638,4.58716,4.62793,4.66871,4.70948,4.75025,4.79103,4.8318,4.91335,4.9949,5.05607,5.09684,5.158,5.21916,5.28033,5.34149,5.40265,5.46381,5.52497,5.52497,5.52497,5.52497,5.52497,5.52497,5.52497,5.52497,5.52497,5.52497,5.52497,5.52497], type : 'line', name : "PolyA"},{x : [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108], y : [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], type : 'line', name : "PolyG"} ], { margin: { t: 0 }, showlegend: true, xaxis : {title : 'Base position'}, yaxis : {title : '% sequences with adapter before position'} } );}if (document.getElementById('kmerlineplot') !== null) { Plotly.newPlot('kmerlineplot', [ ], { margin: { t: 0 }, showlegend: true, xaxis : {title : 'Base position'}, yaxis : {title : 'log2(obs/ exp max)'} } );}</script></html> \ No newline at end of file
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src="data:image/png;base64,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" alt="FastQC"/>FastQC Report</div><div id="header_filename">Thu 8 Jun 2023<br/>hisat_output_1_bam</div></div><div class="summary"><h2>Summary</h2><ul><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M0">Basic Statistics</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M1">Per base sequence quality</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M3">Per sequence quality scores</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M4">Per base sequence content</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M5">Per sequence GC content</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M6">Per base N content</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M7">Sequence Length Distribution</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M8">Sequence Duplication Levels</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M9">Overrepresented sequences</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M10">Adapter Content</a></li></ul></div><div class="main"><div class="module"><h2 id="M0"><img src="data:image/png;base64,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" alt="[OK]"/>Basic Statistics</h2><table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>hisat_output_1_bam</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>20</td></tr><tr><td>Total Bases</td><td>1.4 kbp</td></tr><tr><td>Sequences flagged as poor quality</td><td>0</td></tr><tr><td>Sequence length</td><td>70</td></tr><tr><td>%GC</td><td>43</td></tr></tbody></table></div><div class="module"><h2 id="M1"><img src="data:image/png;base64,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" alt="[OK]"/>Per base sequence quality</h2><p><img class="indented" 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" alt="Per base quality graph"/></p></div><div class="module"><h2 id="M3"><img src="data:image/png;base64,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" alt="[FAIL]"/>Per sequence quality scores</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per Sequence quality graph"/></p></div><div class="module"><h2 id="M4"><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAEzElEQVR4XsWXV2gVWxSGd2L0RFEMiKA+CDYMdsQnS8CAFREVsV4VUR9sWMECVuzYsETsGnvvir1F0MRUuHp91QfzqpLkppz57vxnMslkZo6JwsUNP5zZZ9b6/7PW2mvtYwDzJxHYqBfGJL43JjXXmPRsY/4S9Fl7+i7wfj0IbMRDjjEjbKJMm+ibjaoCY0r+NuaHoM/a03d6R+/67eMhsOGH/QvTbKeFecaUfUlOriodNgw2boSjR+HaNThzBtatw5o6ldL+/fkSiVTpXdnI1u/Pj8CGi+fGJNmOMmxH5V+TkqLWsmXw4QN8+gRFRZCTA2/ewLNn8OAB3LoFly9DRgbW8OEURyJR2cqHfPn9/1RAvjEptmFWkTGlFR07wo0b8PEjFBZCdjZkZcHTp3D/Pty8CZcuOZE4diwmgD17YM0aKnv3Rj7kSz79PKECqn951j/G/KuwkpcHBQXw7h28fg1PnsC9e46oixchM9NJx8GDsHs3bN0KGzbA6tVgR83q1Qv5ks+wSAQEKGRSbU2eDPn58PYtvHoFjx/D3btw/TpcuACnT8ORI7B/P+zaBVu2wPr1sGoVLF0KCxbAnDkwYwZWaqobiQw/X52H6oIrr2jb1sntixfw6BHcueMU3PnzcOoUHD4M+/bBzp2weXOsCFm50iGePx9mz4bp02HiRBg7FkaOpLJVK+TbX5h1BKhyvxoTZccOePgQbt+Gq1fh3Dk4edJJQ26uQ7xpE6xdCytWwOLFsHChUxtKy4QJMGYMjLBPY3o6DBwIPXtSnJCgwiwMFaCzq+NjDRniVPSVK3D2LJw4AYcOORVfWQlVVc5nES9aBHPnOqFWfUSjUF4Ox4/D4MEwYAD06xcjp2tXrJYtEYe3T3h/febnhISqWEH5K/rlS6iooGZJiN7r0AG6d3dSJGHukoi9e6FbN+jcGdq0AZucSITPdsMSF3UEOO31W0mXLnUrets2WL7cKTqRepcESZzEesm19KyUJSY6FB58tyEuceIKUB9XK2X0aKeidZRU0bNmQfPmjiNVu1+EmxLv0rOOZlJSgFywHAFq26m4AjRM1M+ZMqW2osePhyZNao3jifAul7xx4wCxF+ISJ64ATTQNFaZNgyVLQD2gUaOAYUzEgQPhIkSudNRDLohLnAQEiHjmTGjdOmAUg8Kq3PrDriVRqomEhKBdfQJqUqAj2KdPwKBecnepMFW89YgIpKCmCO2WGRr6eOR69qdDIlTIcUSEFiHVx/B7mJHyrmYURi5RYTUhEeqWIf5Cj6EQa0SKgl+AoK5XVlZL4JIrMmGno7TUGUp+P8nJcRoRta046jfyi/CSu995RYhcx9hrq3cHDcLq1Cl+KxZqhpGf3CtC6QhrMhKhCeknb9HC6Sn2Ea++JYUPI8Edx+V+534i/54Lf87bt4d582JjuqIh41hwLyRxU9EQSGRamtNV7YtK1A59gy4kwnPPley3RKSkOONZR9Ee45Y9kn/pSiZ4L6U/TYcXdoXr5hNrRJqodj1U9u3765dSF1KrkClvKszQaCjUuhOMGgXbt9f0BWvSJIqbNv39a7kX3j8mOsM/mjXDatcOevQgNj01wMaNwxo6lBJ7T5cavSsbf8GFIbARD3/sr1kAf+rP6f+F/wAgd4HvpfuYlgAAAABJRU5ErkJg" alt="[FAIL]"/>Per base sequence content</h2><p><img class="indented" 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" alt="Per base sequence content"/></p></div><div class="module"><h2 id="M5"><img src="data:image/png;base64,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" alt="[FAIL]"/>Per sequence GC content</h2><p><img class="indented" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAyAAAAJYCAIAAAAVFBUnAACAAElEQVR4XuzdibNmd1ngcf4WhZnRcSzF0lLH0SlLGa3RGXUUWWRzYxEGkYFBQEFEtiAEQgwGSAgQdgIEwm4gbGHfZN8N4YqICg4gIiRz+vum33v6c859u0kusdv7+1RX6rzP73tOd/PS732KNN03uXYYhmEYhmE4VDdxMAzDMAzDMNwwY8EahmEYhmE4ZGPBGoZhGIZhOGRjwRqGYRiGYThkY8EahmEYhmE4ZGPBGoZhGIZhOGRjwRqGYRiGYThkY8EahmEYhmE4ZGPBGoZhGIZhOGRjwRqGYRiGYThkY8EahmEYhmE4ZGPBGoZhGIZhOGRjwRqGYRiGYThkY8EahmEYhmE4ZGPBGoZhGIZhOGRjwRqGYRiGYThkY8EahmEYhmE4ZGPBGoZhGIZhOGRjwRqGYRiGYThkY8Eahn/LbnKT636Nby++JYd1++nsBv4ch2EYVo0PlGE4fd3kRAcdzefYfbpxWM3GqZenie0P+Iz7kQ/DcDobHyjDcJra8fV+frQju/ZkpxuH1WycenmaGAvWMAzfDuMDZRhORzu+2O842rrJzHayerR8uR1y1zyeD1eD7ct5sJ2v3rKdb3FEM5/zhM01w+1kNZ5fbB3UM5kPt/N5wPU8256uzk99Mh8Ow3A6GL8mh+F0tOPr5Y6jDb4AH3SxRX/Q7csnHFSe+nA1WE6W2fbeg7LlA7H6zP3jU5is3nj9hsvgenzXwzCcVsYvzmE4HfHVdPVr8EF2fyVe3r7a83J1vnrjqQx33L46Wd6yvDhoeJBlvLxr92T1xm/HcDnZkQ3DcJoYvziH4XS0/MJ56l9TT/qV+CaZv1y9nr9cna/eeNLh9uVqedCEW+YXc5xid7x617zcvtzaDufB7uHc7nI73E6W927ns3AYhtPC+GU5DKej5ZfM7WR5BL76LodMVnters5XbzyV4Y7bVyfbW7bX85fbbGt1eO3imQddLO1udj/2oOHWKZbLydzu02EYbnzj1+QwnKb4krn6ZXjVarm8ZfXooO/0JlkONxeHOFydcH3Q0e7htd/KTwe7m9Ufz0mHW6dYLidzu0+HYbjxjV+Tw3D6OrYFzBx0NJ8fdDq/4Gg7nGfb+fzioBtXh9u7KOeT1ev5kFu2w+VkPifYmmfLeHnXtp8fHfqQ0/n1PJtPtsPlZBiG08T4NTkMw3BoxqIzDMPG+CwYhmE4NGPBGoZhY3wWDMMwHJqxYA3DsDE+C4ZhGIZhGA7ZWLCGYRiGYRgO2ViwhmEYhmEYDtlYsIZhGIZhGA7Zrj9hZRiGYRiGYbgeTliwVq+HYRiGYRiGb8n6IjUWrGH4Nnn7hZddfKsHvePCyzwYhmEY/g1ZWaSW/5bwpcMwHJJzbnGPR3zHr5zz3+7hwTAMR8/rhjPcfFnCCYvUQb8HazmZ7K0xilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYZXP0tF9+wLRgPfvXHnLSEkYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjt7e3+Cj2c5na/fSub07WLjWosWDCKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYyyObrw5+87LVjPueNDT1rCKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUo7FgneF2v30rm9O1i41qLFgwilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMsjl60k/fswXrT05awihGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKOxYJ3hdr99K5vTtYuNaixYMIpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjLI5Ovc/32X8L1gbRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGI0Fqwz3O63b39z2v4GrOU6tZxcu/ZflL1T/q/U3igXjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMcrm6Ozvu8O0YD3rdn980hJGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYjQXrBps+TqdvTm8su9++lc1paSxYMIpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjHLs4JprHnnTX50+Dp55mwefpFwwilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFaG3B2mwM228HHc3n31Y35vd1PdzI/2lg+fbNrWxOS2PBglGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYpRp/k9f/PLm4+Bpv3T/3eWSUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRosF6xS3qxttpbgxv6/r51/3RzgWrBVGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUo0zzv//U3ubj4Ck/e+/d5ZJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGJy5YO3aFHUdby/XroMnq9bzfDOeT+ZD5QUfzyXa+363NV++dfzvp/EY2FqwVRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKNM86vf+eHNx8ET/+vdd5dLRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGB3egrXcNlZXkN3X9MvJ1mq/nC+H+49YzFfvXWbLch7fyMaCtcIoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxilGn+0Ve/bfNx8IQf/u3d5ZJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGByxYy9XhpGvE8nQ+4ckMT/F67qBmcz3/RjDHfPVejrYvOdq+vJGNBWuFUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxSjT/L3P+YvNx8Fjv/f2u8sloxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjMaCtfhBrt47n2yG3LV9eSMbC9YKoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGm+ZXnvXDzcfCom91qd7lkFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGK0QEL1vLlSdeI5enqow565qlczx3UnDTeMV82BwUMlzfeaMaCtcIoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxilGl++cOeNn0WPPKmt5z++c1/+caOcskoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjtQWLbwcdbe9aDVYn2+H8lPnq9XyynB/UM9y/+TjmO+7dPZw/5EY2FqwVRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKNM85fe+wnTZ8Gjv/u20z+/9qUv7yiXjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMVr8MQ3XLraHk85Xg4MmDCmX1/N+O5kPdx/NJ/PmoPlB925ezuP5cPXhN47l2ze3sjktjQULRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUab5c+/0sOmz4Oyb32n65//73N/vKJeMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxWluwzgj/umvN6WP327eyOS2NBQtGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRpvlFv3C/6VPp3B899tcR/v2n/3pHuWQUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYrRWLDOcLvfvpXNaWksWDCKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYwyzc/78btNn0rn//TvTv/8mw9+eke5ZBSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFYsM5wu9++lc1paSxYMIpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjDLNH/M9t5s+lS76xWP/O9bV7/rIjnLJKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUozN2wRo2dr99K5vT0liwYBSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGOVfvvb1aa866z/c5lm/9pDp4tNvet9BpY+LUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRmPBOsPtfvtWNqelsWDBKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxypf2vnDsN2D9yJ2f/1uPmC4+9pp3HFT6uBjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGI0Fqwz3O63b2VzWhoLFoxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKN87gOfnPaqC37uPi++x2Oniw++5I0HlT4uRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGI0F6wy3++1b2ZyWxoIFoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKJ98/XumverZt3vIy/7vn00X733u5QeVPi5GMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYjQXrDLf77VvZnJbGggWjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoH3jxG6a96tJ7nv2qBz9lunjnRS8/qPRxMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxWgsWGe43W/fyua0NBYsGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRnn7hZdNe9WrH3zBax/xjOniyie+6KDSx8UoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjsWCd4Xa/fSub09JYsGAUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjlij991rRXvemc57/x8c+bLt7w2OccVPq4GMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYnQKC9YPfMcjpm9Ov82ux3f6rd7yrfZz83tvyHOuvcG37377VjanpbFgwShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMcorHnD+tFe96xmvfOv5l04Xlz/8aQeVPi5GMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYzRaszVd6vm3nJzzl22P+vVyP73H1x7n86cyP5i+XdgT8UHeUq27gz3RuLFgrjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEa55K6PnvaqD7/sync9/RXTxSv/4EkHlT4uRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGJ02C9YN/15Wn7D6MzoVpx6fernxrfa7jQVrhVGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoF9/qD6e96qq3fOB9z3/tdPHS//OEg0ofF6MYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYzW/hUhX/53LCi7h9v55oIJN3LXqZzyEG5ZHR7UH/S07WT7Ty4ot8PVZtlztDzdTObzbbmxfPvmVjanpbFgwShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMcqTbnHsryD8249e9aHL3jxdvOjujzmo9HExilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjF6FtfsOZf+K/HcLU5aLJ6+0FDgtXhQT1Pm79cTnbcu5xzvSyX2epzlvON5ds3t7I5LY0FC0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilEe9wO/Pu1VX/nCFz9++Tuni+f9xsMPKn1cjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMTrlBeuga77wz++dD5fz5Y3LZnu9nc/Nb9++PDFxeFC/vHfHjby8IdfLo+V8x73XjgXLKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjvb3PXv3ZR970lo/4zlte841v/tWV758WrGfe+sGr5d4pP3NvlAtGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEZn5oK1vH07PDF0uKPf/bQdL2/I9fJoOd9x77VjwTKKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxWhv71Mf+ti0VJ198zsdO33PseuLfvH3V8u9U37m3igXjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMbtiCtR8dd9IVgWY+XL13c72dc7R5uTpfHfLYHf2OG3l5Q66XR8v5jnuvHQuWUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGe3sfuvLd01J1/k/dczr9/Eeumq6f8jO/t1runfIz90a5YBSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxidMMWrPm3kw4317ub7eSgo/np6hM211uU82B5747JKd67nM+DZb/7UTyH643l2ze3sjktjQULRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGK0d7ee15+7O/JecavPHA6/YerPjddn/cTv7Na7p3yM/dGuWAUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYnQDFqzty5MOl2vBspkPt9fLo/np9nrebG+ZD+c9R8uMYDNZPdpeE5Mtj0765PnwoOuN5ds3t7I5LY0FC0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitHe3lue9fJpqXrBnR81nX75b784XZ/zQ7+5Wu6d8jP3RrlgFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGK0tmANZ5Ddb9/K5rQ0FiwYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShG08foE58/LVUv//0nTqf//OWvTteP+U+3Xy33TvmZe6NcMIpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxGgvWGW7327eyOS2NBQtGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYrR3t4rH/7Uaal63VnPnE6v+cY3p+tH3uxXV8u9U37m3igXjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMxoJ1htv99q1sTktjwYJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGK0t/fi+507LVVve8pLN8FZ//7W08tv/PPX7XLiw65jFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGK0ViwznC7376VzWlpLFgwilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGM9vaec5dHThvV+y+5YhOc/f13nF7+0xe/bJcTH3YdoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjMaCdYbb/fatbE5LY8GCUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxitLf3tFv9wbRRfeKKd2+Cc3/kztPLf/zc39nlxIddxyhGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKOxYJ3hdr99K5vT0liwYBSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGO3tnf8z95o2qr9+38c3wZ//5P+eXv7dJ802TnzYdYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjEaC9YZbvfbt7I5LY0FC0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitHe3jk//FvTRvWlqz+/CS74uftML//mA5+yy4kPu45RjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGY8E6w+1++1Y2p6WxYMEoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjHa2zvru24zbVRf/+rXNsHTf/mB08ur3/lhu5z4sOsYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxi1II1nNF8j2dWNqelsWDBKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEahueoTn57WqUd/9223wbNv95Bp8qk3vJdyY/akfUYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjE6qlY2p6WxYMEoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRqH56DvfP61Tf/Zjd90Gz//tY/+nwo+++m2UG7Mn7TOKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYrRUbWyOS2NBQtGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMQrNX17+lmmduvDn77sNLr3n2dPkA5e+kXJj9qR9RjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMTqqVjanpbFgwShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGoXn7Ja+Z1qnn3PGh2+Bl9ztvmrzn2a+h3Jg9aZ9RjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMjqqVzWlpLFgwilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilFo3njBi6d16iW/9/ht8OoHXzBN3vHUl1FuzJ60zyhGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEZH1crmtDQWLBjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKDSveczF0zr1mj9+6jZ43aOOTa4874WUG7Mn7TOKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYrRUbWyOS2NBQtGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMQrNSx90/rROvfncS7bBm855/jR5/WOeTbkxe9I+oxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGB1VK5vT0liwYBSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSj0Lzgno+Z1ql3P+vV2+CtT7p0mvzFn1xEuTF70j6jGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYHVUrm9PSWLBgFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKPQXHz7Y3/q1Ude+dZt8K6LXzVNXvnA8yk3Zk/aZxSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKOjamVzWhoLFoxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFJoL/ud9p3XqM2//0Db4y0teN01eeu8nUG7MnrTPKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRkfVyua0NBYsGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoNH/2E3eb1qkvfPzqbfDhl105TV54t0dTbsyetM8oRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGR9XK5rQ0FiwYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxSg0j/m+O0zr1Ff/4R+3wcdf+65p8tw7PYxyY/akfUYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjE6qlY2p6WxYMEoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRpkHV3/mM4/4zls+8ma/es03r9kGV73lA9OCdfGtHzQvt2ZP2mcUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjo2plc1oaCxaMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYpR58In3f2Tapc6++Z1OCN778Wl40S/cb15uzcstoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGB1VK5vT0liwYBSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjzIMPvumd0y71xJ+8xzz4249eNQ2ffIt7zcutebllFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUo6NqZXNaGgsWjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGKUefCuy66YdqkLf+l+8+CLn/n8NDzvx+82L7fm5ZZRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMjqqVzWlpLFgwilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilHmwZUXv2zapZ716/t/0/PkK1/44jR8/A/+5rzcmpdbRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMTqqVjanpbFgwShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGmQevPfe50y51yb3Ongf//JV/moZ/+j2/Ni+35uWWUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjI6qlc1paSxYMIpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpR5sHL/+SCaZd62R89eR5c881rpuEjb3rLebk1L7eMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxidFStbE5LY8GCUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYwyD150n3OmXeryxz+b5tHfdZtp/plPXzWPNyg3jGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYnRUrWxOS2PBglGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMMg+e/VsPnxapN130Epqzb36naf6pD39sHm9QbhjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMXoqFrZnJbGggWjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGGUeXHTLB0yL1Dtf/Fqac3/0LtP8Y+/54DzeoNwwilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGK0VG1sjktjQULRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjHKPPjzW9xzWqTef8XbaM7/qWPzD7/lPfN4g3LDKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRkfVyua0NBYsGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUo8+BxP/Qb0yL18fd+kObCn7vvZvGaxxuUG0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjE6qlY2p6WxYMEoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRpkHj/p3t9r8ZnaaZ/zKA6f5e17xxnm8QblhFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUo6NqZXNaGgsWjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGKU7emnP/bJY3/e1X889udd0TznDg+djt7xosv3n3Uc5YZRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMjqqVzWlpLFgwilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilG2px95x/umLeoJP3aXZfmCOz9qOnrrc165/6zjKDeMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxidFStbE5LY8GCUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYyyPX3vq988bVFP/u/3XpaX/u7jjv3xDU+9dP9Zx1FuGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxeioWtmclsaCBaMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYZXv6tue9atqinnHbBy3Ll9//idPRFU98wf6zjqPcMIpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFRtbI5LY0FC0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxyvb09U9+4bRFPe93zlqWr3nIhdPRXzzuWfvPOo5ywyhGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEZH1crmtDQWLBjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKNvTVz366dMW9ZIHnLcsX3fWM6ejVz7iov1nHUe5YRSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKOjamVzWhoLFoxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxilO3pSx547N8Dvuqspy/LNz3h+dPRZX/0pP1nHUe5YRSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKOjamVzWhoLFoxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxilO3p8+7+6GmLev35lyzLtz3lpdPRpb9/7v6zjqPcMIpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFRtbI5LY0FC0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxyvb0Gbf7o2mLettzX7Us3/3MY7///ZJ7nb3/rOMoN4xiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGJ0VK1sTktjwYJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjLI9fcr/uM+0Rb331W9elu+/5Irp6Ll3feQ23qLcMIpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFRtbI5LY0FC0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxyvb03P9y12mL+sjb3rssP/KKt0xHz7zTQ/efdRzlhlGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYyOqpXNaWksWDCKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUbanj/ne209b1F999BPL8hNXvHs6evqt/3D/WcdRbhjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMXoqFrZnJbGggWjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGGVzdPVVn5lWqEfd7FZ7n/3ssrzqrR+cTi/4xfud8LhQbhjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMXoqFrZnJbGggWjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGGVz9In3fXhaoR73g7+xWv71X35iOn3Sz/7e/rOOo9wwilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGK0VG1sjktjQULRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjHK5uiDb3jHtEL9+U/fc7X8wseunk7P+8m77z/rOMoNoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGB1VK5vT0liwYBSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjbI7e9ZLXTSvUU3/5/qvll67+/HR6zo/89vZRW5QbRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMTqqVjanpbFgwShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShG2Ry9+RmXTSvUs3/rYavlV/7uS9PpY7//jseftI9ywyhGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEZH1crmtDQWLBjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKJujF9/3nGmFuvgOf7xafv2rX5tOH/3dt91/1nGUG0bZHL39wssuvtWDLrvPuW84+7nTt5c/9ILp2/PuftaF/+v+l5993V8mfeLDrnPiw65jFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKP+fvTv9lvUsDzvd/4uNO7bTdpYds5w0idO2cYgdEy9jsIBY2GCFKSBAEYNkzGjQhEASEhrQhCQ0zxJCA5olNCMhCTQdoXHHnfaKk3TaGRxz+tm/OtQuXVW16+hz1W/p0/Nedb/v0qd71aldJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVa1xZsTvNtFiwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlagml878/U+MFerMN31isfzxj4/4qbeM/yZ/YzibsjCTJpfGdjVutPC/s5bdvV45bF+iEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVa1xZsTvNtFiwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlagmly569xFjxbnsQ/t+DEe0d+8xP/f2AV549rmdcUvk3l3vfum7jxpzLn7nF+487qLx33WfP338d/abDx+HF75n3zfFv3LYvl45bF+iEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVa1xZsTvNtFiwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlagml676xIljxbnp2POWyeN++Z0D/OiHT++MWyL37nr3K/7tsWPOD665a1aO+47Dqw47aVbSzqCZRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKtK4t2Jzm2yxYJCpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRTS5d/u+OHyvOzSdcuEye9LrtH9J5+nuP74xbIvfuevdvvu3TY85zdz86K8d9x+EVhx4/K2ln0EyiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVa1xZsTvNtFiwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlagmly49ePuNpdtOuXSZPO31Hxrgh3d/b2fcErl317uf/oaPjDn/4YfPzcpx33E4nmFW0s6gmUQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrSuLdic5tssWCQqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUU0uXfS+7Y9G3XnGlcvkWW/82ACP3nLvzrglcu+udz/htX865vy///d/nJXjvuNwPMOspJ1BM4lKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWhdW7A5zbdZsEhUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUoppcuuCgL4wV5+5zrl0mz/3DPx/g4evu2Bm3RO7d5e4//vFRrzlgzPm7v/1fs3Lc94jl38I16SdjXpGoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYnWtQWb03ybBYtEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJarJpfMO/OxYce696Ppl8sI//vwA91/+nZ1xS+Te5Xf/b//pv44hX/7FdyDvvfD6cT6eYSrn2xk0k6hEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRida1BZvTfJsFi0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlqsmlc966/QbVdH8S7d172XuOHuCe86/bGbdE7l1+9//4o60x5JT/6wPI+y+7aZyf87ZPTeV8O4NmEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0bq2YHOab7NgkahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahENbl01h8cNlach665bZm8+iPbf2Z455lX7YxbIvcuv/tLDz4xhnzjTYchH+yXEM9+8+FTOd/OoJlEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0ri3YnObbLFgkKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlFNLp3+xo+OFeeRG+5aJr992CkD3HryJTvjlsi9y+/+1I33jSGXHHQE8uHr7xrnZ/yrj07lfDuDZhKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtG6tmBzmm+zYJGoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRDW5dNq/2P72hEdvvmeZvOlzZw1w05e/OZ22TO5dfvdHLvzOGPKtj52EHPcd56f99iFTOd/OoJlEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0ri3YnObbLFgkKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlFNLp3ymx8cK87jdzywTN72pfMHuP6LZ+2MWyL3Lr/7d792+RhyyxHnIsd9x/kprz94KufbGTSTqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJ1rUFm9N8mwWLRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCWqyaWTfu19Y8V54t5Hlsm7T7xsgGs/ferOuCVy7/K73/yFb4wh9556FXLcd5yPZ5jK+XYGzSQqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKJ1bcHmNN9mwSJRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiWpy6YR//O6x4jz90GPL5P1nXDPAlR/76s64JXLv8rtfc8hXx5DvX3ILctx3nI9nmMr5dgbNJCpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUonVtweY032bBIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJanLpuF9511hx9jz6xDL58Pnb36Rw6Yf2/ZrNNF1hJo3zi/5k++tMn7n5QeS47zgfzzCV8+0MmklUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqETr2oLNab7NgkWiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEtXk0rG/eOBYcZ57cs8y+fgVtw9w4XuO3Bm3RO5dfvezf+/jY8jWw08jn3vimXE+nmEq59sZNJOoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYnWtQWb03ybBYtEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJarJpWN+7u1jxXnh2eeWySevv3eA896x78vWp+kKM2mcf+2fbn/S669f+EvkC3ueG+fjGaZyvp1BM4lKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWhdW7A5zbdZsEhUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUoppcOvI1fzhWnJdffGmZfPa2hwf4xgGf3Bm3RO5dfvdj//4fjSH/47/+DfKlF18c50e95oCpnG9n0EyiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVa1xZsTvNtFiwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlajG+dirxn4zdqxd5Iv3/3CYM37vYzvjlsi9S+7+t//9f44JR/+9ty2UR/70W7Y3vJde3no1M0UlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrSuLdic5tssWCQqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUY3zF559xb/QLZR/+dizw5z6hg/vjFsi9y65+3/e+qsx4cR/9O6F8uiffdu4+uKPnt96NTNFJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0ri3YnObbLFgkKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGN8+ee3DOWmy/9wr7PmC+Uf/XM9rtcJ/2z9++MWyL3Lrn7Xz6+vaKd8S8PXSjH3cfV55/a/pT9LJi2M2gmUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISrWsLNqf5NgsWiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpU43zPY0+O5ea4f/jOXeTk/acTfvWgnXFL5N4ld3/29u1PcV3wR59dKMfdx9XxJFuvZqaoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYnWtQWb03ybBYtEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJapx/vT3Ht9env7Rv9lF/s1f/5dhvvwP/nhqlsm9S+7++JV3jAlXHfyVhXLcfVwdT7L1amaKSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpVoXVuwOc23WbBIVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKIa50/et/1LNSf+032/VLNQ/u1/+x9HbH9E/a0745bIvUvufv+Z144JN376jIXyxL7BYTzJ1quZKSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUonVtweY032bBIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJapw/fueDY7k55Tc/uLs84qd2/tBvdzkLpk1+LvquEy5ZKE/+jX5t+s4Ht17NTFGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEq1rCzan+TYLFolKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVOP80Vu2v0T0tH/xkd3lMT/fl5Hu2fdlpLvIWTDt24efOl7+0LnXL5SnvuHD4+p4kq1XM1NUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqETr2oLNab7NgkWiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEtU4f+SGu8Zyc/obP7q7/Mov/clgzz6+/Tn03eUsmHbZe48ZL3/iW99dKE//3UPH1UduvHvr1cwUlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRurZgc5pvs2CRqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQ1zh+65rax3Jz1B4ftLk/4x+8e7KkHH9uZuETOgmnnHfDn22+A3fP4QnnWmw7bfn/r2tu2Xs1MUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISrWsLNqf5NgsWiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpU4/z+y78zlptz3vrnu8uTf/0Dg/3g7od2Ji6Rs2Daab/1ofHy/+epFxbKb7z1k+PqA5ffvPVqZopKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWhdW7A5zbdZsEhUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohrn9150/Vhuzj3wM7vL0377kO2PSd18z87EJXIWTDvul7e/6er/+6v/tFCOu4+r9110w9armSkqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKJ1bcHmNN9mwSJRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiWqc333O9hcoXPCnX9hdnvn7Hx/se9fePmXL5CyY9PJLL2//2uBPveXH/+vvFspx9zH87nOv3drvmVsbOZeoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKtaws2p/k2CxaJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlTj/M4zrxrLzUXvPXJ3ec7bPjXY/ZfdtDNxiZwFk370w6fHa7/yS3+yTI67DzCeZGu/Z25t5FyiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0ri3YnObbLFgkKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGN89tPvWwsN5d88Eu7y/Pf+fnBvvvN63YmLpGzYNIPv/u98dpTf/PgZXLcfYDxJFv7PXNrI+cSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSidW3B5jTfZsEiUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlqnN/y1QvHcnP5vzt+d3nR+44a7I4zrtiZuETOgkkPX7f9OznnvvnPlsnLDzlugPEkW/s9c2sj5xKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKJ1bcHmNN9mwSJRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiWqc3/Tlb47l5qpPnLi7vPwjX9negU66eGfiEjkLJt1z/nXjtZe++6hl8sqPnzjAeJKt/Z65tZFziUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0bq2YHOab7NgkahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahENc5vOOobY7m55s9P2V1eddhJ2zvQseftTFwiZ8GkW0++ZLz2uk+cvExe88mTB7jh6HO29nvm1kbOJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoROvags1pvs2CRaISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKIS1Ti/7vOnb28/nz9jd/mtz3x9sG9/4cydiUvkLJh0/RfPGq+99ehvLpPXfa5n+IvtZ8BM2pk1k6hEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRida1BZvTfJsFi0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlqnF+zae2fyXwhiPP3l0OwBtdy+QsmDT5F8D7Tr9mmbz+iO3h13761K39nrm1kXOJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRurZgc5pvs2CRqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQ1zif/9nfjl87dXd58/AWDXfHRE3YmLpGzYNKF79n+FobHLrttmRx3H+Dqw7+2td8ztzZyLlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJVrXFmxO820WLBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqMb5FYceP5abW07Y/gu+XeRtp1w62KUHH7szcYmcBZO+ccD2L+Hsue17y+S4+wCXH7r9l4yYSTuzZhKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtG6tmBzmm+zYJGoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRDXOL/3QsWO5ue3kS3eXd5199WAX/psv7kxcImfBpFPf8OHx2n///WeWyXH37e3tQ9vbG2bSzqyZRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKtK4t2Jzm2yxYJCpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRjfOL33f0Ea/8gitRTX6y8LwDP7szcYmcBZNO+NWDxmv/80v/YZkcdx9gPMnWfs/c2si5RCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaF1bsDnNt1mwSFSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSi2nrl7wDuIh+88pbBzv7DP9uZuETOgklH/+zbxmv/59/892Vy3P2In/weImbSVM4mKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSidW3B5jTfZsEiUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlqnJ/3js+O5ebeC6/fXT58/V2Dnf7Gj+5MXCJnweiFPc+NFx7zc2/Xzch7Lvj29ttj79h+e0xUO+NmEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0bq2YHOab7NgkahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahENc7nf8VZVI/ddt9gp/zzD+1MXCJnweipBx8bLzzpde/Vzcj7Lr1pmHPf/qmt/Zs5SVSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoROvags1pvs2CRaISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKIS1Tg/+82Hj+Xmwatv3V0+ce8j23vSr71vZ+ISOQtGj958z3jhmb/7Ud2MfOjqW4c5682Hb+3fzEmiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVa1xZsTvNtFiwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlajG+Rn/6qNjuXn4+rt2l8888oPBjn/tn+5MXCJnwej+y7bfnbrwjz+vm5EPf/vOYc74vY9t7d/MSaISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJVrXFmxO820WLBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqMb5ab99yFhuHr35nt3lc088M9ixv3jgzsQlchaM7jzjyvHCqz9yvG5Gfv872+9yff13Dtnav5mTRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKtK4t2Jzm2yxYJCpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRjfNTXn/wWG5+cMcDu8uXnn9hsKP+97fuTFwiZ8HopmPPGy+86XNn6WbkuPswJ7/+4K39mzlJVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahE69qCzWm+zYJFohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohLVOD/pn71/LDdP3PPwSnnkT79lyJdffGmlnG3yW4d3n3iZbkaOuw8znmRr/2ZOEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0bq2YHOab7NgkahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahENc6/+n++eyw3Tz342Er5pZ//10M+/8yzK+Vslx68/U3x3zv/Rt2MnPyl4XiSrf2bOUlUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqETr2oLNab7NgkWiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEtU4P/61fzqWm2e+/8OV8rh/+M4h9zz25Eo523kHbn/P1pPX36ubkePuR/zkE/Si2hk3k6hEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRida1BZvTfJsFi0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlqnF+7D94x1hunnvimZXyq697z/Z7XQ88ulLONvkrxRfv/6FuRv7oiaeHGU+ytX8zJ4lKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWhdW7A5zbdZsEhUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohrnx/z828dy88Ke51bKk3/jg0M+fueDK+VsJ/3a+8ar/uqZl3Qz8vk9PxpmPMnW/s2cJCpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUonVtweY032bBIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJapwf9ZoDxnLz0osvrpRf/53tL3T4/k3fXSlnm7xD9jd//V90M/KlF14c5qifOWBr/2ZOEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0bq2YHOab7NgkahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahE9fJLL4/N5siffstKOc7PetNhAz907W0r5bSxtx3xU2858jV/+OO/+7HulXKwMXw8j6hm5TRRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKtaws2p/k2CxaJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlQv/uj5sdYc/bNvWynH+blv3/7VwvsuXfGrhTuDtrb2PPbkeMlXfulPRDUrj/57b93+qNZzL4hqVk4TlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRurZgc5pvs2CRqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqET1/FN7tj9d/gv79f3s57/rLwa++9xrV8ppP7jrwfGSk3/9A6KaleMZhnzuqT2impXTRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKtK4t2Jzm2yxYJCpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpR7XuH6ZffuVKO84vff/TAd5x+xUo57aFrbxsvOfP3Py6qWTmeYchnH39SVLNymqhEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRida1BZvTfJsFi0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlqqcf3v4J5xN+9aCVcpxffshxA99y4kUr5bTvnvet8ZLz37ngl573vlKOZxhyPI+oZuU0UYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISrWsLNqf5NgsWiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUT97//bHWnPhP3rtSjvOrD//awDcec+5KOW1sY+Mll334K6KaleMZhhzPI6pZOU1UohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqETr2oLNab7NgkWiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEtW+z0j9xgdXynF+3edOH/i6vzhjpZw28HjJtz7zdVHNypN//QNDjucR1aycJipRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUonVtweY032bBIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJ6rFb7xtrzalv+PBKOc5vOPqcga/55Mkr5bQrDj1hvOTm4y8Q1aw89Z9/eMjxPKKaldNEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0ri3YnObbLFgkKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlE9cuPdY605/XcPXSnH+c0nXDDw5Ycev1JOu+CgL46X3HX21aKalae/8dAhH7nhblHNymmiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVa1xZsTvNtFiwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlagmf+V31psOWynH+e2nXjbwJR84ZqWcdvZbDh8vefDKW0Q1K8980yeG/N61t4tqVk4TlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRurZgc5pvs2CRqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqET1wOU3j7XmG2/95Eo5zu8+55qBLzjoiyvltFN+6+Dtf/i7bfU//H3jgE8O+cAVN4tqVk4TlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRurZgc5pvs2CRqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqER130U3jLXm3D/69Eo5zvfhAz+zUk477rXvGi955pHVX74wnmHI+y6+UVSzcpqoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYnWtQWb03ybBYtEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJaq7z712rDXnv+svVspx/uBVt27/e+KbD18ppx31M9u/JL0/P4Az+Zr47573LVHNymmiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVa1xZsTvNtFiwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlajuOuuqsdZc+J4jV8pxPv+JeFFNrz739LPDf+nn//VKORrPMPB4HlHNymmiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVa1xZsTvNtFiwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahuP+3yI175ufVlcpw/fvsDA5/yWwevlJOefGD7W0y/+rr3rJSj8QwDj+cR1aycJipRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUonVtweY032bBIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJavJN65cfctxKOc6fvO+RI175te+iml6dvOP19d85ZKXcmvkdHlHNymmiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVa1xZsTvNtFiwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlai+85VvjrXmyo+fuFKO8z3ff2Lg4177rpVy0n0X96H4t39qpRxd+bGvDjyeR1SzcpqoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYnWtQWb03ybBYtEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJar5L2dfJsf580/t2f5M1S8cuFJOuv3r2//+ePH7j14pR1f/2clHbP/Q4TmimpXTRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKtK4t2Jzm2yxYJCpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRTX4r8LrPnb5SjvOXXnhx4KN+5oCVctINx2xvb1cf/rWVcvStz3594G9/4UxRzcppohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlWtcWbE7zbRYsEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWorv30qWOtuf6Is1fKyaWjXrP9tQsvvfjiSrn1kzelbjj6nJVydP0Xzxr42s+cJqpZOU1UohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqETr2oLNab7NgkWiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEtXVh3+tf5g7d6WcXDr2//ij4Z97+tmVcnTxv+0PA0+9bKUcjWfYfrvrz74mqlk5TVSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoROvags1pvs2CRaISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKIS1eWHHj/WmptPuGClnFw67le2v5l9z6NPrJRb21/O/pmB77vohpVy6ye/JH3FoSeIalZOE5WoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0bq2YHOab7NgkahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEddmHvjzWmltPvmSlnFw68XXvHf7J+7+/Uo5Of+OhAz9y490r5Wg8w8DjeUQ1K6eJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpVoXVuwOc23WbBIVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKK6+P1Hj7XmjtOvWCknl055/faPNz9+xwMr5ejEf9I2dt8jK+VoPMMR/cmhqGblNFGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEq1rCzan+TYLFolKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVBcc9MWx1tx9zjUr5eTS6b+786bU7nJ07C8cOPDzT+1ZKUfjGQYezyOqWTlNVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahE69qCzWm+zYJFohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohLVN//4c2OtueeCb6+Uk0tn/cFhwz949a0r5b7vdHjNAVsvv7y7nHTP+dcNP55HVLNymqhEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRida1BZvTfJsFi0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlqnPf/qmx1tx36U0r5eTSvs+tX7z9ufXd5b6vff+VfV/7LqrJpUn3XXLjEX3tu6hm5TRRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKtaws2p/k2CxaJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlRnvfnwsdY89JN3pHaRk0sXHPSF/knx2pXyB3f0y9Cv3/fL0KKaXJr04FW3Dn/2Ww4X1aycJipRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUonVtZ3P632aaAfsucbL31fxvFZWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJaozfu9jY615+Nt3rpSTS5f01VZ3fP3ylXIsbUP+/+zd+bd1dX3g+f+FdGnaqdMxZeIUrXSpq+KQFdPlkCgaNSYaE7UjaKltgibyKIMiIKgIDqAoDuAAyqg4gKCAWCgCMnOinWgSjEaNcvp73xueXF77+Jxr+eM578W667D3a3/uXs9Pn3XvPfu86/9+5Vo5ddU5nxv+5N9/hah2y/2JSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpVoU7vXgrXy9fx/p/wXLVGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKIS1UmP/6ux1lx9waVr5XTqoy+bnpv1gbXyi6d+ct/OH62/fq2cuvr8S4Y/6QkvE9VuuT9RiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohJtais2p+Vso9ouWCQqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUZ04PXbh4rsfu3AAOZ36YO86/MhL37xWnnXocUOe9qzXrZVT4x52fqX42BeLarfcn6hEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiTa1FZvTcrZRfWLbtm2/SG966PPGWvPhd57miZ/TsY/9i+HHV0/MOvYxL9qjnBr3MPy4H09s27Zt27Zfut3LEq1YsOY/r5ofWf4ie6uoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRHfeIPxtrzXVf/tpaOZ3a+8+l3vesnQdAnPXy49fKqXEPw4/7EdVuuT9RiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohJtam5OK3eplQf9Fy1RiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEtUxD+mzBa+++7MFDyCnU3f/ZdXzXr9WDrNvb+83nLr7sQ4Pea6odsv9iUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaFNb/7dWy59z3H/REpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRvenXnj3WmpuuvX6tnE59ZXpv4B+sf2/gMPv29kjSqZu+cf3Orwh/7dmi2i33JypRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUok3tPzanlVvU1MpT/ouWqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYnqqPs/Y6w1t3zrprVyOnXN9HSr//qXa+WJ/9df7tvzpxaObrnhxuHH/Yhqt9yfqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJNrV7LVi722W2C5aJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlRvvM/Txlpz+623rZXTqW99rV/k/cZz1so3P/g5O798/Nrdv3wU1XRq6u6P1rnP00S1W+5PVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEm9qKzWnedsEiUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlGd901dpp9v/KU9fKemdMa9Ib/9NQ1nzB4xx3DjMm333b36iaqu7/fPQ0/ho+70s3klKhEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiTa1FZvTvO2CRaISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISLZf//qOfjIXmiF99+lq53DXzqAcdPK66+dobDiDH2WGO/j8O3uPMqTfe9+njqp/++Ce6mZwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRprZic5q3XbBIVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKLl8kd3/mBnDXrgM9fK5a6Zx//2C8dV11561QHkODvMWx/1wj3OnBp3Mq760fd/qJvJKVGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEm1qKzanedsFi0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlWi5/8I//PBaaN//6H6+Vy10z3/mkQ/fd8/GFohrHx9lhhtzjzKlxJ+OqH3z3X3QzOSUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKJNbcXmNG+7YJGoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRMvlnX//j2Ohectv/slaudw187SDXzuu+tIHPn0AeenpnxpmyD3OnBp3Mq4ad6WbySlRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohJtais2p3nbBYtEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJVou/+mWb4+F5vhHvmCtXO6a+eEXHz2u+uyJHz6A/MyJHxpmyD3OnBp3Mq4ad6WbySlRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohJtais2p3nbBYtEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJVou/+H6nbcEnvg7f7FWLnfN/MRr3jau+vS+dx9Afvrwdw3zib9++x5nTp3wOzsfXzjuSjeTU6ISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJdrUVmxO87YLFolKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKtFx++5qdZ3u+/XEvXSuXu2aef/TOpzKf9YqdDxkU1Th+1st3PrLw/De9b/9Votp/duptj3vJuGrclW4mp0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrSprdic5m0XLBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaLm844rr9u38Kfoha+Vy18zPn3zWuOoDf/aGA8hxdpjPn/KxPc6ceucTDxlX3XHldbqZnBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtGmtmJzmrddsEhUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUouXy1kuvGQvNKX/wP9bK5a6Zl3/0gnHVe572/x5AjrPDDLnHmVOnPPkV46pbv/R13UxOiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaFNbsTnN2y5YJCpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRcnnjxTtPq5pWpQPL5a6ZX7voS/t/sSiqcXycHWbIPc6cevdTXz2uuulzX9XN5JSoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYk2tRWb07ztgkWiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEi2X15+/88nNpz7zsLVyuWvm9Vfu/Nzr2N/aebiDqBb3PHBhyD3OnBp3Mq664YIv62ZySlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRJvais1p3nbBIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJlstrz75kLDTvf87frpXLXTNvvfHmffd8wI6oFvd86M1tN92yx5lT407GVdeec6luJqdEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0qa3YnOZtFywSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWi5vOasz42F5gN/um+tXN575pH3+6Nx4S3fuklUt9yw8+bEI+//R7svEdVuMBp3Mi4cd6WbySlRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohJtais2p3nbBYtEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJVourz7jwrHQnPGiI9fK5b1nHvuwPx0XXvflr4lqHB9nj3v4n+6+RFS7wWjcybhw3JVuJqdEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0qa3YnOZtFywSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWi5vOK0c8dC89H/581r5fLeM9/xu3+1swmdf4moxvFx9qTH/9XuS0S1G4zGnYwLx13pZnJKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEm9qKzWnedsEiUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYmWy8tP+eRYaM56+XFr5fLeM9/7h389LrzsQ+eJahwfZ4fZfYmodoPRmYceOy4cd6WbySlRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohJtais2p3nbBYtEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJVouL33bmWOh+firT1wrl/eeecYLjxgXfg811bAAAIAASURBVO6dZ4rq4pM+uvObxxcesfsSUe0Go0+8+sRx4aVvP0s3k1OiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCXa1FZsTvO2CxaJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrRcfuHYnY9kPvu1J62Vy3vP/PirThgXnnfkqaI698j37uxtrzph9yWi2g1GZx920rjwC8d9SDeTU6ISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJdrUVmxO87YLFolKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKtFx+9qj3j4Xm04e/a61c3nvmuW98z/SjL1FNP4g694j37r5EVLvB6FOHnzIuvPio03UzOSUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKJNbcXmNG+7YJGoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRMvlhYfv7EnnHXnqWrm898yL3/6RnV8CvuhIUU1vBrz4HR/dfYmodoPR9KOviw5/r24mp0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrSprdic5m0XLBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaLk8729OHgvNhW85fa1c3nvmZR/cefvhqc88TFTTA9kvO+O83ZeIajcYjTvZWfgOO1k3k1OiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCXa1FZsTvO2CxaJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrRcfupVbx8LzWdOOGOtXN575lfP++K48J1PPERU4/g4O8zuS0S1G4zGnYwLx13pZnJKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEm9qKzWnedsEiUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYmWy08ecvzO7/JOutfv8lbK5b1nfvPyq8eFxz/yBaI6/hEvGGeH2X2JqHaD0fT2w3FXupmcEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0aa2YnOat12wSFSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSi5fJjL955sOcX3vOJtXJ575k3X/etceHRD3ymqI56wDPH2WF2XyKq3WA07mRcOO5KN5NTohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQl2tRWbE7ztgsWiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0XH7kBTt/jX7p+89ZK5ezmW+8z9PGtT/9yb/Dfvrjn4zj4yweNoW55H3njGvHXelmckpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqESb2orNad52wSJRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiZbLM557+L4eyL5WLmcz3/KQ541r7/z2d2HjyDh+zEOei4dNYS47Y+dv58dd6WZySlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRJvais1p3nbBIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJlsvTn/nasdB85WOfWSuXs5knPubF49rvXHMTbBwZx9/22BfjYVOYr5x10bj29INfp5vJKVGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEm1qKzanedsFi0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlWi5PfeprxkJz1TmfWyuXs5nv+u+vGtfe9LmvwsaRcXycxcOmMFee/blx7WlPe41uJqdEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0qa3YnOZtFywSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWi5fPfv/4+x0Fx9/iVr5XI28/Q/2fn14jUf+zzsmrN2lqRxFg+bwkxPf3jPk1+pm8kpUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISbWorNqd52wWLRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVaLk9+/Mt2lqTPXr5WLmczzzzk2HHtl991NuzyUz45jo+zeNgUZtzJuPbkJ6x4vBZySlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRJvais1p3nbBIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJlst3POYlY6H5xhevXCuXs5nn/N3OU+Dnnxs4fb7hp15/Ch42hRl3Mq59x2NfopvJKVGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEm1qKzanedsFi0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlWi5PfNSLxkLzzS/f64mgK+VyNvOi4z+4s0jNnro+PR3+orf+wk+HH407GdeOu9LN5JSoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYk2tRWb07ztgkWiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEi2Xxz/0T8dCc8PV31grl7OZl5x29r5Vz6yanq11yfv+V56tNe5kXDvuSjeTU6ISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJdrUVmxO87YLFolKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKtFwe8+DnjIXmpm9cv1YuZzOv/OTF49pTn+o7/qZ3Jo6zeNgU5savXzeuHXelm8kpUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISbWorNqd52wWLRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCVaLo9+4MFjobnlhhvXyuVs5te/eMXKv5ea/q5rnMXDpjA3X3/jvp1P4DlYN5NTohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQl2tRWbE7ztgsWiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0XB5x36ePheb2W29bK5ezmT/vp03H/PrOT8XGWTxsCnP7LbeOa4/41T/UzeSUqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJNrUVm9O87YJFohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohLdddfYZsZ/urkszB233f6G/+0p47+7fnbXfnPXT382Hbzj9jvwuyb9R5jFHXdMt6SbyxKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtGmtmJzmrddsEhUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUmLs/lfm+T9fN5JRosXjTrz17TPjBd/9lvxmvx5E3/Z/Plu555s/7DGldYaZEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiTa1FZvTvO2CRaISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKLC/Oj7PxyrzFEPeKZuJqdEi8UJ/2XnKQ//cP1t+814PY6c8Dsvku555rifMeHH//rDtXKx55mLrZwlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISbWorNqd52wWLRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRIX55X/adPLvv2JMuPXSa/ab8XocGcele5457mdM+OH37lwrF3ueudjKWaISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtGmtmJzmrddsEhUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUmDu//d2xyrzlIc/TzeSUaLF43x//7Zhw7TmX7jfXnn3JODKOS/c8c9zPmDDuba1c7HnmYitniUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRJvais1p3nbBIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlFh/unWb49V5vhHvEA3k1OixeIjL33zmHDFaefuN1ec+ulxZByX7nnmuJ8xYdzbWrnY88zFVs4SlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJNrUVm9O87YJFohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUosL84/W3/5J/L/XJw94xJnzh2A/tN59/yxnjyNmHnSTd88xxP2PCuLe1crHnmYutnCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohJtais2p3nbBYtEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEhfnONTeNVeZtj3uJbianRIvFBcfsfK7zeX9z8n4zXo8jF77ldOmeZ77tsS8eE8a9rZWLPc9cbOUsUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaFNbsTnN2y5YJCpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrMHVfuPCn0nU88RDeTU6LF4gvv/viYcNZfvnm/Ga93fqb1nk9I9zzzpCe8bExYXHX9WrnY88zFVs4SlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJNrUVm9O87YJFohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUosLc+qWvj1XmlCf/r7/j7ytnXTQmvP8Zr91vxutxZByX7nnm9M7E2y77+lq52PPMxVbOEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiTa1FZvTvO2CRaISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKLC3PS5r45V5t1PfbVuJqdEi8U1n718TDj58S/bb975uzs/fxrHpXue+e6nvGpMuOnzV6+Viz3PXGzlLFGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWhTW7E5zdsuWCQqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqzA0XfHmsMqc+4290MzklWixuuPobY8JxD33+fnPcbz1/HBnHpXueOe5nZ8KFX1krF3ueudjKWaISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtGmtmJzmrddsEhUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUmGvPuXTfL/fMqumzmd94n6ftN9MH3cw/PXqx55nTs7W++an/eLbWz5OLPc9cbOUsUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaFNbsTnN2y5YJCpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrMNR/7/FhlPvD8w3UzOSWqox948Bjyozt/MMCP/uVfx+s3PehgUTmuRIvFuJ+dXzJ+7PNr5WLPMxdbOUtUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCXa1FZsTvO2CxaJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJCnP1h3b+RP1DLzpSN5NTojrxUTuPrfrezX8/wPg6Xp/46BUP1lrseea4nzFk3NtaudjzzMVWzhKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYk2tRWb07ztgkWiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiwlz5vnP3/XJPXR+958mv3Pmd4BXfHGB8Ha/HEVE5rkT3PB1+3NtaudjzzMVWzhKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYk2tRWb07ztgkWiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiwnz5XWePVebMQ4/VzeSUqM543r4x5PrzLh9gfB2vxxFROa5Ei8WZhxw7hox7WysXe5652MpZohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0aa2YnOat12wSFSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSYS99+1lhlPvHqE3UzOSWqTx761jHkqx+8cICvfuCC8XocEZXjSrRYfPzVJ4wh497WysWeZy62cpaoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0qa3YnOZtFywSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISFeYLx31o3y/3uYGjiw5/7xhyyYlnDnDJCR8dry/ad6qoHFeiez7f8IvHfXitXOx55mIrZ4lKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqESb2orNad52wSJRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRYS4+6vSxynzq8FN0MzklqunHYBe+/j0DXPB37x6vv/T2j4nKcSVaLD71+lPGkIuPPn2tXOx55mIrZ4lKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqESb2orNad52wSJRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRYaYfPp175Ht1MzklqumtiJ/4q+MGGF/H6699+DOiclyJFovzjty5q4v2nbpWLvY8c7GVs0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKJNbcXmNG+7YJGoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoMOcddvJYZS445v26mZwS1Q0XfmUM+eAfv36A8XW8vuGiK0TluBItFuN+xpDzX3vKWrnY88zFVs4SlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJNrUVm9O87YJFohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUosJ8+tVv3/lZ0VvP0N0jP3vB9U953EkXX3DD9L+iqauuH0Pe9XuvGGB8Ha/HEU3t/767Ey0W437GkHFva+VizzMXWzlLVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQl2tRWbE7ztgsWiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiQozvQHwcyd9FHb77Xd89vzrX/DM0x980L7pv/F6HBnHkaN/vvU7Y8gJv/3CMfCtj3zheD2OiIrvPiVaLMb97OutiGvlYs8zF1s5S1SiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJdrUVmxO87YLFolKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYkK87GXHDNWmS+8++P7wfXX3fLWo89//COOn/aqh93vyKc87qSH3+/I6X/H8XH2+utu3jVy8eMf/NsYctQDnjEGHnX/Z4zX48husD+++5RosRj3M4aMe1srF3ueudjKWaISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtGmtmJzmrddsEhUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUmI++cOdDaS553znj1PnnXPmERx7/sHt2qSc96sTT3nnZ9+/80WDj63g9jkynHn6/I574yLeef85V+2ce+b//4Zjzw+/dOb6O13v87lOixeKS03Yefzruba1c7HnmYitniUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRJvais1p3nbBIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlFhpoewX3bGubfddvtjHnLMtD8996nvPvOMy372s7vA48g4Ps5ObPjbb9v5jeE4dfzD/mzMuaPPyXnrw/9subfvPiVaLC4747x9PQ5+rVzseeZiK2eJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEm9qKzWnedsEiUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUWFOP/h1Y5X5ylkX/d2rPz79QvD8c65cKaemU+efc9XD77/zg65x1SRPecKhY87/PPPi8fWUJx66X5LjSrRYfPnMC8ecDzzrb9fKxZ5nLrZylqhEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrSprdic5m0XLBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohIV5rSnvWasMu98/ZljW/rN+7zxwnO/+vPk1P6zF3z6quHHVR963yXj+FiGxpwvHP/h/YvRfrk7x5VosbjykzuL2mlP/+u1crHnmYutnCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohJtais2p3nbBYtEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEhXnPk1/5qoOe+fD7HTFWpbe95cIDyKnd4MRjLhhXPeIBR938re9+7KVvGSvRp1+189CH8Rq5P8eVaLH46rlfHHPe8wevXCsXe5652MpZohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0aa2YnOat12wSFSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSYt/3uof/loL8Ze9JL/uT0A8spzLhqXPvfH3vS2a/Z+XCbDz//jePr+a9711xOOa5Ei8X//OxlO79qfMLOrxoPLBd7nrnYylmiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRprZic5q3XbBIVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVJinPGhnu3rSI4+78Vu3HlhOYcZVv/87O28t/JP/dnw/c3rV+PrF4z8yl1OOK9Fi8fUvXjHmnPTYl66Viz3PXGzlLFGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWhTW7E5zdsuWCQqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCWq3eB9p1y+86dXB/3dZ866RLfnmZdfeu0jH3j0znsPD/rzd/zXl4zF6Kr3n79SLvY885uXXz3mnPjoF62Viz3PXGzlLFGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWhTW7E5zdsuWCQqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCWq/Wev+vJt01+pv+ig59/w1a/rfpGZF5xz7Zjznw86/DW//hdjMbru3Mt+nnRciRaLcT9jzvEP23ncw4HlYs8zF1s5S1SiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJdrUVmxO87YLFolKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlqOnX1FXc8/P5Hja3oWfd7+Vhlbvz6dbpfcObrXvyhnR+G/crrDzno2bdd/o0DyHmixeLGa3aep/WW33juWrnY88zFVs4SlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJNrUVm9O87YJFohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUoppO/d6jd/5w6nG/ddzRD3zWWGVuvv5G3S848/+7/rZHHPS6MfNRBx323W/dcQA5T7RY3Hzdt8ZdvelBB6+Viz3PXGzlLFGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWhTW7E5zdsuWCQqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCWqcfwfvvP9h973jb9x0BuuvuKOI3515yNubr/Fv3Cf5DxRjeP/9k/fP+SgZ//GQfsectDrb7/+2weQ80SLxW0337rvno/cObBc7HnmYitniUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRJvais1p3nbBIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGN48fsu+jBB+079M8/Ol7v+5WnjFVmccfOJ97M5TxR7Zy46643/Ken/t5BLx+Tx/wDyVmi0R13jLsa97Ze7n3mVs4SlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJNrUVm9O87YJFohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUovr+nT/67Qe9aaxBX//a3//0J/8+9pgj7vt0UTmuRDWdOubBzz30oGePyWP++C6iuvewuxPVG+/ztHFvP/v3n66Vu8H+RCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYk2tRWb07ztgkWiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiOuWES8YO9IJnvH+AH//rD8cSc/QDnikqx5WoplMnPnrnLYRPut9hY/74LqK697C7E9W4qzHtxz/4t7VyN9ifqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtGmtmJzmrddsEhUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0WNxy822PecixYwH60udvGuCH37tzLDHH/PpzdOW4EtV06uTHHzKmHfboV475j/3NY2+55XbdLzJz3NWY9sN/unOt3A32JypRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0qa3YnOZtFywSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISLRYnn/CZsf0840nvmsD3v/29scQc99Dn6+rew+5OVNOp6THu7/2DV/3RE08Z32V8L90vMnPc1Zg27nCt3A32JypRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUq0qa3YnOZtFywSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKLC3H7bHU945PFj9TnvkzvPqRr9863fGUvMCY/6c+TUvYfdnej/Z+/Ov72t60L//y+kdSrPyROHtNLKRq0sO+aAmlNqqeHJeR5T40ZABETmWZBJEBlkEAEVERBlMAZBQeC2sFJTwgG8v+/9/OD1uXh89n3vvmv1A2vt/VisVut6P31f72v/9Fr3/uzrk8XSR5/1jrHbic96x4Xn3DTuMu51991+dv7hmz2EZmGcauw2TrhhOQ8mRjHKYuniD510zNPfctor9z3/vUeP/z57wKnjv3Nef/BH93zXNcecOy/x8M0eYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYx2qzWmZxWbQ1YMIpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGoTnlhCvG3PO0Jx324IM/XQT/etvdY4g54g/+nnLh4Zs9xCiLpY8++51jtzGRjP3HXca9xh3XLUGzME41dhsn3LCcBxOjGGWxNKarcbt1/zvpOe+cl3jYXj9jFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjHarNaZnFZtDVgwilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEaheeaTDx9Dzxknf3kK/uWf7hgDxNF/8jrKhdlOS0ZZLF1zzLlr/95z7Pnj/z/jpC+Pe407rluCZuGoP37dONs44YblPJgYxSiLpVP/Zu9xu+Oe+dbz33vU+O9zHzp1/Pex5717XDz71QfOSzx8s4cYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYw2q3Ump1VbAxaMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilHmwXmf+NKYeH5/jw/96EfLFx9s/8rX1qaKv3jzvJzMdloyitGOHeMu417jjuO+uy537GTP4572pnG27dfdtmE5DyZGMcpi6ew3f3jc7uL9Pzovrzryk+Pip9933LzEcqMZoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYrRZrXO5LRqa8CCUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjHKPHjxM44b485BH7hwHtx11dp3Kp/4rHfMy8m8nBjFKAfuc+G444ufceyG5TyYnPjMt4+zLb7WcNflPJgYxSiLpTNfd+C43SUHnTIvv3T8p8bFC9955LzEcqMZoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYrRZrXO5LRqa8CCUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjHKtHrZJTeMWeeJ/3P/r936sN+4feNz148B4mN/9Z7ZTkvzcmIUo4x7jTuO+46777qc7bR08vPeM852x+ev37CcBxOjGGWxdMb/23/c7rLDzpiXX/nYxePi+W8+dF5iudGMUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMVos1pnclq1NWDBKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxhlWn3VX588Bp33ve2TlLdd8qUxQJz21/+43GhmXk6MYpRxfdxx3HfcfcNy1Wkvfv84222fuXbDch5MjGKUxdKpr9hn3O5zR39iXt748cvGxU++9qB5ieVGM0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUo81qnclp1daABaMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxilMXSmadd9X9+bu/H/fw+N15/G+Utn7pyDBAf/9sPPGyvn5mXE6MYZVwfdxz3HXcfZ9h1uWqcapztlgu+uGE5DyZGMcpi6WMvXZvnrjjh3Hl50zlXjItn7bX/vMRyoxmjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFmtc7ktGprwIJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMcpi6f/+/qG777b3+L+r5T998nNjgPjEqw5YbjQzLydGMcpiaRd3nyw3mhmnGmcbJ9ywnAcToxhlsfTR56/9RvKLp14wL2+98Kpx8YyX7TMvsdxoxihGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxitFmtMzmt2hqwYBSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMMq7fddfdv/3Yta92/vgpX1wtbzjj0jFAnPO6gx+218/My4lRjLJYGvcdd/+dx35wnGTXJcapxtnGCTcs58HEKEZZLJ2w59pbUq8+89Pz8vZLrx0XT33R++cllhvNGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMNqt1JqdVWwMWjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRxvWzTruqf0A6bN3yyydfNAaIT71luTo3LydGMcq0+he/t/aPWGf1W0KjLDeaGacaZxsn3LCcBxOjGGWxdOxfrr1o9NpzLpuXd1xxw7h48nPfPS+x3GjGKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGK0Wa0zOa3aGrBgFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYwyru/14rWPtx+074Xrltccd94YIC5619HLjWbm5cQoRplWD973oumj7kZZbjRz4buOGmcbJ9ywnAcToxhlsXTUn71h3O66Cz8/L++65uZx8cRnvG1eYrnRjFGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFaLNaZ3JatTVgwShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMY5dZb7nj8L+yzx6O33Xj97euWXzz87DFAXPKPx892WpqXE6MYZVodd9/jUdvGScZ5jDLbaemS9x+/9qGow8/esJwHE6MYZbF0xFNes/YbyUuunJffuv62cfHYP3/TvMRyoxmjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFmtc7ktGprwIJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMcpRh3xm9932fumeD5uf5sEVB58xBojL9lu+RmFuXk6MYpR58JJnHz9OMs5jlHk5uWzfk8bZrvjwxzcs58HEKEZZLB32+68at/vqZ6+Zl/fefOe4eNRTXjsvsdxoxihGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxitFmtMzmt2hqwYBSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGM8tw/O2qMNScf99CvvVbLy/f/2BggPn/Q6fNgMi8nRjHKPDjp2M+Pkzz3qUcZZV5OPnfgaeNsn/3gKRuW82BiFKMslg757VeM2938xevm5b99ffu4ePjvvWpeYrnRjFGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFaLNaZ3JatTVgwShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMY7dhx5zf+fcw0T3jMfnd8466dlZ/Z+8QxQFx52FnzYDIvJ0Yxyjz4xte/OU4yzjNOZbeTPb9w6FnjbJduO3HDch5MjGKUxdJBv/E343Zfu/bGefm9u+8dFw954ivmJZYbzRjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjDardSanVVsDFoxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGK0Y4dh+x3+Rho3riX/zo1by5+zzFjgLj66HNoFublxChGoXnj3502zjNOZbdSLoxTjbNd/A/HbljOg4lRjLJY+tAefz1ud/sNN8/LH9z7nXHxoMe9dF5iudGMUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMVos1pnclq1NWDBKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUo9D89Kc7/vSJh4yB5sJzv7yL8oK3HT4GiGtPfOjtmpiXE6MYheaCc68d5xmnGmfbdbkwTjXOdsHbj9iwnAcToxhlsbT/rzx/3O6OWx76a4DF0v3f+f64eMD/fuG8xHKjGaMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGK0Wa1zuS0amvAglGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEahuebKO8c088e/efA99+yqPO+Nh4wB4rpTLzHKvJwYxSg04yRP+Y2Dxqm+dOU3d10ujFONs40TbljOg4lRjLJY2veXnjtu982v3zEvf3zfD8fF/X75efMSy41mjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGm9U6k9OqrQELRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGIXm3W84d4wyH3jPeXYPL89+9YFjgPjqWZcbZV5OjGIUo+3b93n3eeNU42wblsM41TjbOOGG5TyYGMUoi6Vtj37WuN09vW5+Kh984IFxcZ9HP3teYrnRjFGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFaLNaZ3JatTVgwShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKPMg/v/88dP/J/7j1HmmqtusXt4eeYr9xsDxM3nfcEo83JiFKMYbd8+zjNONc42Trjrcrjp3LUvWh4n3LCcBxOjGGVcH3PVuNeYsVbLbY965lj66YNrv9dcbjSz3GjGKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGK0Wa0zOa3aGrBgFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUebBeWd9dcwxL3r6CUaZl6e/dO8xPXzt4ode/oR5OTGKUYwyTjXOdv4nvrph+bWLrh5nGyfcsJwHE6MYZVz/5tfvGPfa95eeu1oufnX4k/t/tChXLTeaMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYbVbrTE6rtgYsGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYpR58MoXnDKGmNM/eq1R5uUpL3jvmB6+fvlXjDIvJ0YxilHGqcbZxgk3LL9+2VfG2U554Xs3LOfBxChGGdfvuOX2tc9a/a/nr5YffOwLx9IPv/eDRblqudGMUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMVos1pnclq1NWDBKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUo0yr9/7z9/d41LZf/x8f+N537zfKbKcdH93znWN6+OaVXzXKvJwYxShGGacaZxsnHOfcdXnnlTeOs52057tmW65fzoOJUYwyrt9+4y3jXh/6Py9eLQ/6tZeMpR98+7uLctVyoxmjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFmtc7ktGprwIJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGmVaPO+zK3Xfb+w2vPHPDcjj+6W8Z08M9195qlHk5MYpRjDKuj7ONEx53+JW7Lu++dm3oOeHpb13uuJNyHkyMYpRx/Wtf/uq410G//rLV8pAnvHwsfe+eby/KVcuNZoxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRpvVOpPTqq0BC0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxhlWn3GHx05xpfLLv7ahuVwzJ++YUwP/3zj140yLydGMYpRxvVxtnHCZz55+bU5RhmnGmc75qlvWO64k3IeTIxilHH9lquuG/f68G+9YrU87HfXvqPw37+xdmW50cxyoxmjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFmtc7ktGprwIJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGWSyd28fbn/TYAx544MFdlwtH/uGrx/Tw7Vu/aZR5OTGKUYwyro+z/c5jDxjnHKfdRTlONc525B+95r+y5yqjGGVc/+rnvjTuddjvv2q1HAcYS/fesvburuVGM8uNZoxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRpvVOpPTqq0BC0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxhlsfTcpx4zBpfn/dmxG5YLhz1pr7V/nrnjW0aZlxOjGMUoi6Vxwg3POU61NvQ8aa/ljjsp58HEKEYZ12/4zBfHvY54ymtWy2OeuvaPfN+64fZFuWq50YxRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxWizWmdyWrU1YMEoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjLJb+5IkfGYPL2affsGG5cMhv/u3aB4y2/6tR5uXEKEYxymLpE6ddP845TruLcpxqnG2ccLnjTsp5MDGKUcb16y78/LjXUX/2htXyhKe/dSzdfe0ti3LVcqMZoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYrRZrXO5LRqa8CCUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRhnX77j938bU8ru/+qEHH3jo2/6MMt/qwD3W/kTuvn/9rlHm5cQoRjHKYmmccJxznHaceWflONU42zjhf3FPGMUo4/q151w27nXsX75ltTxpz3eNpTuvvHFRrlpuNGMUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMdqs1pmcVm0NWDCKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUo4/pxh6/9/eA7XnvOhuXkg7/ygjE9/PA/7jPKvJwYxShGmVbf/tpPTn9LaJQffu8H42zjhNP/ZGflPJgYxSjj+tVnfnrc64Q937FanvLCXhV22VcW5arlRjNGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKPNap3JadXWgAWjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMMq7/9TNPHCPLp8+/ecNysu8vPmdMDz/54Y+NMi8nRjGKUabVi8+7eZx2nHln5U/u/9HevV19ueNOynkwMYpRxvUvnnrBuNeJz3/3ann6y7aNpa9ddPWiXLXcaMYoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYrRZrTM5rdoasGAUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRvvPv/7l4v+h99619qcsuytlOO/b+ubUv2tvx058aZV5OjGIUo0yr9/3gR4//hQ/s8eht4+RG+emDPx1n2/aoZ8623GDPOaMYZVy/4oRzx70+9pL3r5Zn/d3+Y+mmc69YlKuWG80YxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYw2q3Ump1VbAxaMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxyifPuGH33fbe64WnblhOqw/+5IExOuzz88/esJwzilGMMg/+7oVrX+lzzsdvMMoI9nn0s8cJH3zggel/YpTljjNGMcq4/rljzh43OvXl21bLT77mwLF045mXL8pVy41mjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGm9U6k9OqrQELRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGOX1L//4GFZOO/HaDctp9Uf33T9Gh/0e87wNyzmjGMUo8+DUE9a+l/D1rzjTKCMYZxsn/PF9P5z+J0ZZ7jhjFKOM65cf/vFxo9Nftd9qed6bPjKWrjvl04ty1XKjGaMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGK0Wa1zuS0amvAglGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEbbt995591PfMz+Y1j5l2/9x67L7bM9//M7/zFGhwN+9UUblnNGMYpR5sE/b//eOPM4+Ti/XeU42zjh/d/Z4FsLlzvOGMUo4/pnDj5l3OjM1x24Wl7w9iPG0rUnfGpRrlpuNGMUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMdqs1pmcVm0NWDCKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMVo+/ZPfvzqMak850+P2bDcPtvz+//y72N0OPjxL9uwnDOKUYxCM848Tv7JM6+2qxxnGycc55x6oyy3mzGKUcb1iz940rjR2W/+8Gp58T8cO5auOmrtbzOXG80sN5oxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxhtVutMTqu2BiwYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxitH37216z9vXJhx3w2Q3L7bM9v3vXvWN0+MhvvWLDcs4oRjEKzTjzOPnbX7vObwnH6jjbOOE459QbZbndjFGMMq5fuM/x40bnvOOw1fLSbSeOpS8cetaiXLXcaMYoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYrRZrTM5rdoasGAUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYrR9u1/+GsHjjHlphv/ecNy+2zPf7v9njE6HP77/2/Dcs4oRjEKzTjzOPkfPm75u7l5Oc42TjjOOfVGWW43YxSjjOvnv/eocaPz33v0avnZD6799vBzB562KFctN5oxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxhtVutMTqu2BiwYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxC89lLbxwzylN+48N2K+XCtHrvTXeM0eGoP37dhuWcUYxiFKMdO8bJx/nHU6yWRz3lteOE45xTTLOw3GvGKEYZ18955+HjRhduO261vOLDa59/v2zfkxblquVGM0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUo81qnclp1daABaMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUWj2e+/5Y0D5x7dfYLdSLixXr7ttjA7HPe1NG5ZzRjGKUYx27BgnH+cfT7FaHvvnbxonHOecYpqF5V4zRjHKuH72Ww4ZN7povxNXyy8esfYGh0vef/yiXLXcaMYoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYrRZrTM5rdoasGAUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMQrNM/7o8DGgfP6y2+1WyoVp9a6rbxqjw4nPfPuG5ZxRjGIUox07xsnH+cdTrJYnPuNt44R3XfP/4930E6MYZVw/6/VrL7v6zEGnrJZfOu78tX/cetdRi3LVcqMZoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYrRZrXO5LRqa8CCUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUo8+D6r3xtTCdP+OX9fvSj5ds41y0n0+odn79+jA4nP+89G5ZzRjGKUYx27BgnH+cfT3HDdbdRnvzcd48T3nHFDVM8DybLvWaMYpRx/Yy/X3td+2WHnrFafuXki8fS+W85dFGuWm40YxSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYx2qzWmZxWbQ1YMIpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYZR4ccfBnxmiy14tPNsq8nEyrt33m2jE6nPbi929YzhnFKEYxyl4vOmk8xXgWylNf9P5xwtsvXb49dR5MlhvNGMUo4/ppr/jAuNFnjz5rtbzh45eOpXNee/CiXLXcaMYoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYrRZrTM5rdoasGAUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMco8eOmex4/R5MSjfUHDwrycTKu3XPDFMTqc8Tf7bFjOGcUoRjHKCUetvazhZXueQDnONk5464VXTeU8mCw3mjGKUcb1j71kbZK74sRzV8t/OufzY+kTe31wUa5abjRjFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjHarNaZnFZtDVgwilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxhlWr39tjsf9/P77PHobbfc/A2jzHZamlYfOaPDzTd9Y49HbXv8L+xz++13zstxtnHCcc6pnO20tNxoxihGGdc/+oL3jBt98ZQLVstHzhhqFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjzWqdyWnV1oAFoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRptXTPvqF3Xfb+/l/sfb2JqMsN5qZVh9Rv/z6q6cdPZ7l9JO+MC/H2cYJxznn5arlRjNGMcq4fsJz3jludPXHL14tHzm/SDWKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFmtc7ktGprwIJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxSjT6uteceoYSg7e96INy7lp9RH18e2D9r1w97Uvfj5tXo6zjROOc87LVcuNZoxilHH9uGe8ddzoS5+8dLV8hPwpwI6tcoVRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxhtVutMTqu2BiwYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYyyWLr7rnt+57EfHEPJ1VfevOsS0+oj6gUE4ynGs4wnGs81lRe+88i1uee48+flquVGM0Yxyrh+9J+/YW2Su+Dzq+Uj5GUWO7bKFUYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYrRZrTM5rdoasGAUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMcpi6ahDLh0TyZN//aANS0yrj7RXaI5nGU909Ecum8pPv++4tY9GHXE2JZYbzRjFKOP6EX+89sr4Gy65crV8hLyOdcdWucIoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYw2q3Ump1VbAxaMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEZZLO35J0eOcWTPPzliwxLT6iPtS2B+9kRHTuWlHzhpnPALh3ycEsuNZoxilHH9sD9Y+9LDf/rsNavlI+QLhXZslSuMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMVos1pnclq1NWDBKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGKUxdKf/tbaV/gdechDHxsyynKjmWn1kfY1xkcesvZOr6f+1oen8nMfOnWc8LMHnEqJ5UYzRjHKuH7I77xy3OjmK7+yWj4SvhJ7wShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRpvVOpPTqq0BC0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUo4zrN16/9vUyT/jl/b75zbt3Xa6aVi/dduLavw8detaG5ZxRjGIUoyyWxrP85i/vO57rxuvXXuk+rn/hI2eOE166z0cpsdxoxihGGdcP/s2/GTe69UvLL5yeVr97171j6SO/9YpFuWq50YxRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxWizWmdyWrU1YMEoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYpRx/fgj197M+fK/WufLieeWG81Mqxf/w7FjdLjqqHM2LOeMYhSjGGVaHU80nuv4Iy9flONs44Sffu+xq+XccqMZoxhlXD/w114ybnTb9Tetlt//l38fSwc//mWLctVyoxmjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFmtc7ktGprwIJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxSjj+t+/5GO7P/y7ZYyy3GhmWr3g7UeM0eHaEz61YTlnFKMYxSjT6uEHXTKeazzdohxnGye84B1HrpZzy41mjGKUcX3/x75g3OiOm5dfhjit/ud3/mMsHfCrL1qUq5YbzRjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjDardSanVVsDFoxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRrn77nt++7H7j0HkS1ffsutyttPStHremz4yRofrTvn0huWcUYxiFKNMq+OJxnONpxvPOK6Ps40TjnOulnPLjWaMYpRxfd9ffu640Tdvv2O1/NF994+l/R7zvEW5arnRjFGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFaLNaZ3JatTVgwShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxilEsuvG5MIX/6xIc+DL6Lch5MptVPvubAMTrceOblG5ZzRjGKUYwyD8Zzjaf7zEXXjevjbOOE45zrlpPlRjNGMcq4vs+jnz1udPddd62WD/7kgbG0z88/e1GuWm40YxSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYx2qzWmZxWbQ1YMIpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYZd/3nj9GkHe8/swNy3kwmVbP+rv9x+hw07lXbFjOGcUoRjHKPHjn688aTzeecVy/6ZwrxgnHOdctJ8uNZoxilHvuXvs7wW2PeubOyr1/7pkj+OmDP50Hk3k5MYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYbVbrTE6rtgYsGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGM8tw/O2qMIGeddtWG5TyYTKunv2zbmBu+dtHVG5ZzRjGKUYwyD8Zzjacbzziu33rRVeOE45zrlpPlRjNGMcpd37hz3OUDv/icnZX7/uJzRvCTH/54Hkzm5cQoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYrRZrTM5rdoasGAUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMdqx43vfuX+PR2173M/v8/Xbv7nrcsdGe57ywveOueHrl31lw3LOKEYxilHmwXiu8XR7PHrb9757/+2XfXmc8NQXvm/dcjLbackoRrnz1tvHXfb/X8/fWfnBX1n7CPwP/+O+eTCZlxOjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFmtc7ktGprwIJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxWjHjgvPuWn33fZ+8TOO3bDcsdGeJ+35rjE33HnljRuWc0YxilGMQvPiZxw3nnE86Z1fuHGc8KTnvGtn5cJspyWjGOX2G28Zdzlg9xfvrDxwj7WXONz3r9+dB5N5OTGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGG1W60xOq7YGLBjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjHbsePcbzh3Dx0H7XrhhuWOjPU94+lvH3HD3tbdsWM4ZxShGMQrNeLrxjONJ7/7SzeOEJ/zl23ZWLsx2WjKKUb72lX8adzno8S/dWXnIb/7tCL63/V/nwWReToxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRpvVOpPTqq0BC0YxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUox07nvzrB4/h44rPfnXDcsdGex7z1DeMueFbN9y+YTlnFKMYxSg04+l2X/se64PH2cYJj/2zN+6sXJjttGQUo9xy9fXjLh9+4st3Vh72pL1G8O93fGseTOblxChGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxitFmtMzmt2hqwYBSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEahufWme8fk8Qe/9iG7lXLBKNPqkX/0mjE33HvLNzcs54xiFKMYxWj79vGM40mvumBt9Dnqya/ZRbn9v7zn9p2U//T5L427HPp7r9pZeeQfvnoE3771YR96m8zLiVGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFaLNaZ3JatTVgwShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxCc9zhV46x4417nW63Ui4YZVo97HdftfYPM99Yu2KU5UYzRjGKUYxitH37G//utPGkh267cJxwnHMX5fb/8p7bd1LeeOna3yoe/uRX76w85k/X/qnvn2/8+jyYzMuJUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMVos1pnclq1NWDBKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjELzt889eYwdp5xwhd1KuWCUafWQJ7x87aNF93x7w3LOKEYxilGMtm8fzzie9KXPOH6ccJxzF+X2//Ke23dSXnfR2tu2jnrq63dWHv/0t4zgnmtvnQeTeTkxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxhtVutMTqu2BiwYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUebBf97348f/wgf2ePS2W29ZfqPLuuXEKNPqQX3D8Q++/d0NyzmjGMUoRjHavv3Wm+/Y41HbxvO+d7dnjXPuotz+X95z+07Ka8+9bO2TXk9/y87Kj+75zhF880o/97YwLydGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKPNap3JadXWgAWjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxyjy4/NO37b7b3i96+glGmZcTo0yrH3zsC8fc8MPv/WDDcs4oRjGKUYzywqcfP573Vbu9dJxz1+VspyWjGOWaMy8ZP4fj93z7zspTXtALwy7/yjyYzMuJUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMVos1pnclq1NWDBKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjDIPtr1z7eUFh33oc0aZlxOjTKv7/tLaNxz/5P4fbVjOGcUoRjGKUcaTjud91m6v2e+X175oeRflbKcloxjlqlPXPul14l+9a2fl6S/dewRfu/iaeTCZlxOjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFmtc7ktGprwIJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYZR78+e8cOgaOG758j1Hm5cQo0+q2Rz30FXsblnNGMYpRjGKU66+9Zzzvb+/27m2Pftauy9lOS0YxyhUnnjt+Dif/9ft2Vp75yv1GcPN5X5gHk3k5MYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYbVbrTE6rtgYsGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGm1bvu/M6YNp70qwc8+N/0lcMPPvDAGBr2efSzNyxhFKMYxShGGU86nnc89dt2e95PH3hwF+XDN3uIUYzy+WPPHj+KU/52287Ks1994Ai+etbl82AyLydGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKPNap3JadXWgAWjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxyrR6yvFfGqPGm/f6xIblnFEWSz++74djaNjvMQ/99s0oy41mjGIUoxjFKOP6eN7x1C/Z7ZU//s8f7rpcZRSjXH7EmeNHcfpe++6sPO+Nh4zgulMvmQeTeTkxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxhtVutMTqu2BiwYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUabVv3/J2nuhzj79+g3LOaMslu7/zvfH0HDA/37o8+NGWW40YxSjGMUoRhnXP3Ha9eOpn7rbm+//7ton8XdRrjKKUT7z4VPHj+LM1z7s9a3z4IK3HzGCa0+8YB5M5uXEKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGK0Wa0zOa3aGrBgFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRlks/fjHDzzhMfuNUePb//L9XZcwymLpB/d+ZwwNBz3upRuWMIpRjGIUo4zr9/7z98dTP26393/nnn/ddbnKKEb59AEnjR/FJ9704Z2VF7/nmBFcffQ582AyLydGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKPNap3JadXWgAWjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxymLp1BPWfj/4tN89dMMSRlksfe/ue8fQcMgTX7FhCaMYxShGMcpi6Uk//w/j2U84+PINSxjFKBd+4ITxozjn7YfurPzM3ieO4MrDzpoHk3k5MYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYbVbrTE6rtgYsGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilEWSy942toboZ7/F8dtWMIoi6V/+/r2MTQc/nsPfQuNUZYbzRjFKEYxilEWS0/5H2sD1nP++MgNSxjFKOe/7+jxozj/H47aWXn5/h8bwecPWucbirbvZE+jGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFmtc7ktGprwIJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYZbH0tCcdNoaM0z967YYljLJYuvfmO8fQcNRTXrthCaMYxShGMcpi6dWPf+vax7CecPCGJYxilHPfdfj4UVyw93E7K684+IwRXLbfyfNgMi8nRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjzWqdyWnV1oAFoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMcqOn30O6YmP2f/HP35g1+UqoyyWvnX9bWNoOPbP37RhCaMYxShGMcpi6ainvvFxu73/v/fzZ/jkWz8yfhQX7XvizsovHr72HodL/vH4eTCZlxOjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxitFmtc7ktGprwIJRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYZcfP/pLu1S89fcNylVEWS3ddc/MYGk58xts2LGEUoxjFKEZZLJ3wl2976m5v/u/9C0qc+fq111xdctDHdlZec9x5axPYu46eB5N5OTGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGG1W60xOq7YGLBjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRdvzsXVCnnvDQ7wd3Ua4yymLpjituGEPDyc9994YljGIUoxjFKIulk57zrpfs9ord/1vfAYaP//0Hx4/i0kMf9hGrefDlky8awafectg8mMzLiVGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShGMYpRjGIUoxjFaLNaZ3JatTVgwShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYwyvc38rju/s+tyttOSURZLt1967RgaTn3R+zcsYRSjGMUoRlksnfrC971tt+ft/t/6Fnuc9soPjB/FZ4962B8JzoMbzrh0BOe87uB5MJmXE6MYxShGMYpRjGIUoxjFKEYxilGMYhSjGMUoRjGKUYxiFKMYxShG/x979/0l21UY+P79LQIcxoyfbQweHADjAc+MHMAMOctkMMEggsEkY7gKCEUUkBASEhIooIgCCIEQSCgBsgTKAoU2ySYLjIF+u75Ht7r0qb7dV89+vPdW93exehXnfGr3uVs/1F5V1fuUqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEW7V1Vk7LbS+wSFSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQ13Y/vcX985KZyEcwT1XTqpguvGIuG016w76aSRCUqUYlKVNOpcYXjOvf8vYMf8p96H8bFTn7+u8av+NwH77PN1SL48tmXDnDmyw9cBPMW5TxRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWirts7KabntBRaJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqI486NKxsNjnrRduKhfBPFFNp758zmfHouGMvzlgU0miEpWoRCWq6dS4wnGdr3/ObKOKMRuiuu9g9yYqUX3oOf8wfsXlJ5+/K3nj+ZcPcPqL9lsE8xblPFGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaKu2zsppue0FFolKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWonvOE2cLikotu2VQugnmimk5dd/qnx6Lh7FcfsqkkUYlKVKIS1XRqXOG4zhPeccaYhzEborrvYPcmKlGd8PS3jF9xxWmf2JW85ZNXD3DKXu9cBPMW5TxRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWirts7KabntBRaJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaGXlphu++tAH7vPffnW/e370043lym6PubJTfvHkT4xFw3lv+M/cHZ5EJSpRTafGFY7r/PzxF455eOiD9rnpxq/q7v+Y9IEnvnH8imvO/tSu5O2XXjvAyc98+yKYtyjniUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEWzVXTuuupdY96IyWqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEq2sfPiDn3vIHjte/IyTNpUruz3myk559fHnj0XDhW/5f3CHdFGJSlTTqQvefPS4znG1L3r6SWM2xpzo7v+Y9P7HvW78ii+ef5/PHxfBnVd8efYu2pPfvAjmLcp5ohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRVu0+K6f/oxaPzI976P5Mq6hEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUopWV173slLGkOO6oyzeVK7s95spOecXRZ49Fw0X/+J95+x0SlahENZ266B0fmH1+d/TZYx7GbIw50d3/MenoPV89fsW1F12+K7nyxZsHOO7xb1gE8xblPFGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaKu2tnKaVlHrrqXWPeiMlqhEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRTaeuOvbcE5/ylvFzU0nTqU8eeNKxf/WG8XM8fszDDhpLiptv+Oa6kjBToppOnfeG2fblZ73q4E0liUpUohKVqKZTZ73yoB3tQTXmYczGmBPd/R+TjnjMK8avuP6Sq3Ylv/Hlrw7w/j1fswjmLcp5ohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRVs2V07prqXO22+7/pQ55zMvGi/Ehj/0bT+xe86cfc9RpYz3xqN/eX/Gf1PgVs1/0py/zxP/HWrzOMRtjTo456lTRf6z3PPx541ecdsyJntjZODXAYJ7Ybrvttvv/Wy6YFnI5te4Ca92DLllLVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlaimUyc++c3T93U2lTSdOvbxr5+9WfL41x+y/4VjMfG2131Md//HpOnUWS8/cPyiC9501KaSRCUqUYlKVNOp89945OydtpcfOB6/7bXnjjkZM7OuJMyUqA79wxeNX3HTVf+0K/ndO74xwOGPfOkimLco54lKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRFs1V07rrqXWPeiMlqhEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRTaeOf8LsT9KO/99/t6mk6dQxf7H3ePr4udcTjxuLiQvP+Yru/o9J06n5l8c3lSQqUYlKVKKaTi1+GX/MxpiTvZ74gXUlYaZEddDD/nr8iluvvWFX8gdf/9cBDn34CxbBvEU5T1SiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQl2qq5clp3LbXuQWe0RCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlqOvX+PV8zewtqz703lTSdOupPXzl7LX/Mq37vV/Z96IP2+d53f6y7/2PSdOrcvQ8bv+jaUy/eVJKoRCUqUYlqOnXtKZ8c1zmudjz+3nd+/NAH7jNm5rZb71iWtAjmieo9v/Wc8Stuv+HmXcl7/vX7Axz0O3stgnmLcp6oRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrRVc+W07lpq3YPOaIlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKIS1XTqqD+efYnqyEe/fFNJ06n3PuLF4+mvf9irH7LHjqf/+TGiuu9g9yYqUU2nph3Sv3LO5zaVJCpRiUpUoppOseP80//imDEzZ5xyxbKkRTBPVO/+jWeMX/G1W2/flfy3H94zwAEPfuYimLco54lKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRFs1V07rrqXWPeiMlqhEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRTacOfsjs86aDH/rXm0qaTh34O88dT3/ar79+LCP2f8d5orrvYPcmKlFNp07569kN+G656KpNJYlKVKISlaimU+MKx3We8rwd0/8dczJm5s17f3RZ0iKYJ6p9f+Up41fcdcedu5I/++m/D7Dfrz51EcxblPNEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKKt2jorp+W2F1gkKlGJSlSiEpWoRCUqUYlKVKISlahEJarZiV/8Yt8HzV6tx8/xeCO51OzE3XdPT3/kHm8by4iLP/4lUTlciUpU06kPPe2ts7dtLrtuU0miEpWoRCWq6dRXP/dP4zrH1U7/95MXfmnMzJ/90WHLkhbBPNHq7D/ZGH/HA560sRxgsPEfCLYsp0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUoq3aOiun5bYXWCQqUYlKVKISlahEJSpRiUpUohKVqEQlqnH8x9/94fRqPX7+5Hs/3EAuN45/9cZbxxPf9IBnjjXEI//PA+66625ROVyJSlTTqeP/6u/G77r7CzdtKklUohKVqEQ1nRpXOK5zXO30f8ecPPK3Dhjzc/WVNyJpYaS1RKur//6Tn87enfo1352C7fdrT5u9y/W1+3z3a105JSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISbdXWWTktt73AIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRjeP/evvK7O2rPnIajzeQy43jN17xpfHE5z/gpWMB8bKnH7eBXE5UoppOHfM//nb8rm/e8LVNJYlKVKISlaimU+MKx3Ue8z9fPZevfN7JY37ed+jFSFobaCHR6upPvv+jMf4B/9XvV8He85vPGuyrN98GW5ZTohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRVm2dldNy2wssEpWoRCUqUYlKVKISlahEJSpRiUpUohLVOH7X1TdMr9azdzuuuXEDudw4/qULZ1/o/osH/d1YQBz29tM3kMuJSlTTqSMeOfsy/nfu+PqmkkQlKlGJSlTTqe987evjOsfVzuXxR39mzM+Ln3kCktYGWki0uvqjb393jH/g7zx3Y3nw786+Wnfbl2+CLcspUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpVoq7bOymm57QUWiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlqHL/p41eMl+GDH/b88fPmj1+5gVxuHP/8Ry545x5P+r0HvHMsID5+9LkbyOVEJarp1CEPm21f/sNvfmdTSaISlahEJarp1LjCcZ2H/N7z5/K6a28d8/OHv/HuO+64a1HSfcbamWh19fv//O3Zf7Lfe/7G8rDfn21GesuXvgxbllOiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtFWbZ2V03LbCywSlahEJSpRiUpUohKVqEQlKlGJSlSiEtU4/qUPX7SjPRrGz/F4A7ncOH7JUac/b4+XzL6Atcdbx+MN5HKiEtV06oD/OvvM6yc/uGdTSaISlahEJarp1LjCcZ3v+c1nLco/+6PDxiwdfdjFi5LWBlpItPMdssP+6MUbyyMeNXvD78Yrr4UtyylRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWirts7KabntBRaJSlSiEpWoRCUqUYlKVKISlahEJSpRiWocv+zwj46X4eMeP/v++GVHnLGBXG4cv3Df4x+zx5vH0uExe/z9hft9cAO5nKhENZ3a50FPHhf5i5/9fFNJohKVqEQlqunUz3/2s3Gd42oX5VP3nO2G9dQ9j16UtDbQQqLV1W/dfOcY/4hH/83G8n2PnW0A++XPXQNbllOiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtFWbZ2V03LbCywSlahEJSpRiUpUohKVqEQlKlGJSlSiEtU4/sl3Hj9ehqctpsbjDeRy4/jZbzz8UXu8dSwdnr/HS8bjDeRyohLVOP6zf+tP5371qZvK5UQlKlGJSlTzs+M6x9X+7Kf/PpfvO/TiMUt//oh7N2tYG2ihtYEWEq2ufv3628bgR/3pqzaW7/+z2S2Mrvv0fTY4XVdOiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEW7V1Vk7LbS+wSFSiEpWoRCUqUYlKVKISlahEJSpRiUpU4/g5rz50vAx/7PWHj5/nvObQDeRy4/ixe+031g0Pf9A+79zjSae8dL8N5HKiEtXqzu0kDvzt52wqlxOVqEQlKlHNz063slnc9uKOO+76g9/Yf8zVddfesigXu89YOxOtrt59zWwbiGP+Yu+N5bSrxZc+/jnYypKcEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJtmrrrJyW215gkahEJSpRiUpUohKVqEQlKlGJSlSiEpWoxvGPPPed42X4M+/58Ox9rL3euYFcbhx//WP+YSwanv6o2U1sTnj6WzaQy4lKVOP497/+L+NXHPbwF24qlxOVqEQlKlHNzx768BeMqx3XvChf/MwTxlwdf/Qli3KxtYEWEq2u3nH59WPwY5/who3liU9582Bf+NhnYMtySlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQl2qqts3JabnuBRaISlahEJSpRiUpUohKVqEQlKlGJSlSiGsePe9zrZ+9zfGR2f+LxeAO53Dj+5N+abeD+zhcdO55+9J6v3kAuJypRjeP/cttsv675DRM3kMuJSlSiEpWo5menPxpgX7GjDvnkmKtXPu/kRbnY2kALiVZXb7vki2Pw45/y9xvLk5/1D4NdfebFsGU5JSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISbdXWWTktt73AIlGJSlSiEpWoRCUqUYlKVKISlahEJSpRjeOHP+Il42X4a5+/bvw8/JEv3UAud9dddz/8gf84Fg0XHH/hePqhf/iiXUmHK1GJahz/xvW3j1/x/j/be1O5nKhEJSpRiWp+9v17vmZc7bjmRXn1lTfO/tzyt/6j+93f/InZvQ5PeObbNpanPG/HYFecciFsWU6JSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqERbtXVWTsttL7BIVKISlahEJSpRiUpUohKVqEQlKlGJSlTj+AEPnm0x+v1vzD6AG483kMtNd9l75B5vu+Xar4ynv/s3nrEr6XAlKlGt7twQ9YP/+02byuVEJSpRiUpU87MffMIbx9WOa0ZOmzX8B+/YeMPHLhuDn7TXOzaWH33J/oNd9qHzYMtySlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQl2qqts3JabnuBRaISlahEJSpRiUpUohKVqEQlKlGJSlSi2nlXu6et7vzDt3FEVA5X+73t3LFcePKDXjPAdLOdO796h6gcrkQlqnH89s/M7slz8jPfvqlcTlSiEpWoRDU/O65zXO3tl16LfMveZ4wZ2/8d560NtNCinCdaXb3+zM+MwT/8wh0by7NeedBgnz3ubNiynBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRibZq66ycltteYJGoRCUqUYlKVKISlahEJSpRiUpUohKVqL63MtsTfPrm+L3fy/7nb4vK4eop//PIsVzY+7deMcBBD5vdmOWWL31ZVA5XohLV6s4d5097wb6byuVEJSpRiUpU87OnvmCfcbXjmpFnnHLF7G8C/uKYtYEWWpTzRKur154y+8LcKS/bf2N57mvfO9hnjv4obFlOiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEW7V1Vk7LbS+wSFSiEpWoRCUqUYlKVKISlahEJSpRiUpU045K0xebpq8NjSOicrjV1e9958cPfeA+D93jXYc+ZrYh05GPfcWOdl3SlcOVqES1uvNtm7NeceCmcjlRiUpUohLV/Oy4znG11591KfK2W+/4vV/Z96EP2ud73/3x2lg7W5TzRKur15w4+8bbR//2wI3lBW86arCL33sKbFlOiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEW7V1Vk7LbS+wSFSiEpWoRCUqUYlKVKISlahEJSpRiUpU09+jTZ+73fup1me+JCqHW1298JyvPGSPHf9rj7877klvHGD83NHfrOnK4UpUohrHv3jyJ3a0X9emcjlRiUpUohLV/Oy0r9i6tx7a64nHjXkbs7c21s6QU6LV1SuPPXcMfubrDt1Yfvxt7x/sooNOgi3LKVGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaKu2zsppue0FFolKVKISlahEJSpRiUpUohKVqEQlKlGJavFtoTNf3psuZ66zbdLKemO+7bWzL2A9d4+XffgF7xrg5OfP9oL/7HFn68rhSlSiGsev+sDHxvhj0bCpXE5UohKVqEQ1P/vxt84WN+Oal+Uh+1845m3M3tpYO0NOiVZXLz/yjDH42W86YmN58bs+ONiF+8/uX7SxnBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRibZq66ycltteYJGoRCUqUYlKVKISlahEJSpRiUpUohKVqKb3QsZqYHVhTSAqh1td/R8PP3QsFN6wx7PO2PuQAcbP8fRPHnyyrhyuRCWqcfyy954+xv/UviduKpcTlahEJSpRzc+O6xxXe9nhH12Wn/vM9WPexuytjbUz5JRodfWzh5w6ezPv7ffe1nBX8pJ3nzzY+e86FrYsp0QlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUoq3aOiun5bYXWCQqUYlKVKISlahEJSpRiUpUohKVqEQlqk+/+6TZ206HnDrApQefMh6PV2VRMdpNX/nmWCU86sH7zl7C33nsAOf942x99rG3HY2ccrgSlahWdy4XPnvo7FI3lsuJSlSiEpWo5menNdAlB8yWm6KVlcc87KAxezff8M214UpXmNH0X+2Cd31gYznmavovAluWU6ISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0VZtnZXTctsLLBKVqEQlKlGJSlSiEpWoRCUqUYlKVKIS1flvnH0P+poTLhhg/Jytlt54lKgY7bijLh9LhL0etd94ysWHfWSA8XM8Pv1Va19CX8zhSlSiGscv+sfjxvhXHL32EaSo1gZaSFSiEpWoRDU/+/n3nTWudlzzuvJ1LztlzN6Yw7XhSleY0SffNbs/98f3P2FjeflR/T4wZQAAgABJREFUZw52ztInictySlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQl2qqts3JabnuBRaISlahEJSpRiUpUohKVqEQlKlGJSlSiOv3Fs70ob/jYZas7t6/86Ev84/8pRnvR008aS4S37PmP4ymXnTj7VHH83NHul8gphytRiWocv3cteOKFm8rlRCUqUYlKVPOz0x/6XfCmo9aVH/7g58bsvfgZJ60NV7rCrO78MPeTB/teI2z61toZr519gLuxnBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRibZq66ycltteYJGoRCUqUYlKVKISlahEJSpRiUpUohKVqKb7Ad9x+fUDfO3y2d1yxhFRLQ51z49++t9+db+HPmifw//yDeMpXzh3dvfia8799Hh87F/N7ma4nMOVqEQ1jp/96tl3vK47/dObyuVEJSpRiUpU87P/dPqnZu8evXr2RSvRyspNN3z1oQ/cZ8zhmMm1EdeTK+v99mmt+anD3X8B9oUPzRZ5p7/K3RyW5ZSoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrRVW2fltNz2AotEJSpRiUpUohKVqEQlKlGJSlSiEpWoRPW+x75yvAZ/66bZ9uvj53h89GNnm1ottzjUJRfdMvv7wSd88PBHv2w85cufvXqA8XM8PuLRf7Mo5zlciUpUqztv/HLDeWsfq4lqbaCFRCUqUYlKVPOz87cDdyWf84TjxxyOmVwbcRdyEUydu/dhY/BLlnYQhV176sWDnbq0H+mynBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRibZq66ycltteYJGoRCUqUYlKVKISlahEJSpRiUpUohKVqA56yF7jNfhH3/7uAD/81nfH44N/969FtTjUPm+ZbTRw5EGXvue3nzOectv1Nw5w63U3jscH/s5zF+U8hytRiWoc/8hzZh9H3vqpazaVy4lKVKISlajmZ8d1jqv9yHPfuSs5Zm/M4T5vXfvEc1dyEUxNu5h+9vhzNpbXn3XpYNMmGhvLKVGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVaKu2zsppue0FFolKVKISlahEJSpRiUpUohKVqEQlKlGJVlbuvuvufR74pB0PeNIvfvbzAcbP8XifBz15HJfed8y/fNQRY3Fw7dV3TU+/+867Bhg/p6f/4ue/WMRTDleiEtU4fu+nmZ9fuxWPqNYGWkhUohKVqEQ1P3vH5dfv6PPWXclrr7l7zOHj/vjItRF3IRfB1PTNuctP8i7OsBvOu3ywDz3Xe0IvyylRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWirts7KabntBRaJSlSiEpWoRCUqUYlKVKISlahEJSpRiVZWbv/KzbznNB6PI7ffcLN0Ycw7v/adsTL449858Iff/t7AB/zWs+dmPB5H7vnO9+d43sJIa4lKVOP4B/7y9WPwf7527TM1Ua0NtJCoRCUqUYlqfnZc57jacc27kj//+S/GHI6ZHPO5m2POO+WvZ3u6XnHqxzeWt1x01WAnPOOtsGU5JSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISbdXWWTktt73AIlGJSlSiEpWoRCUqUYlKVKISlahEJSrRyspXLvvCeAE+6r+/Ym6O+pOXjyPjuHRhzA8ff/Vsf9G/OfPbt9w18Hsf9dK5ee8jXzKOjONzPG9hpLVEJapx/N6vi91856ZyOVGJSlSiEtX87LjOHX2hbQM55nDM5Ec+eL8/9JxuanT1WZ/aWN7+mS8NdtyT3wRbllOiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtFWbZ2V03LbCywSlahEJSpRiUpUohKVqEQlKlGJSlSiEq2sfPG82X1yTnjim+ZmPB5Hvnj+pdKFMV/5vNkeTmedeu2dV3x54Pf/5WvnZjweR+688v/+XfZWdy3f+0ez1dv37lrbnFNUawMtJCpRiUpUopqf/e6d35ytPv/oJRvIM0+5dszkq55/v7dOPeHJs09Lx3++jeUdn5/9Bzr2r14PW5ZTohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRVm2dldNy2wssEpWoRCUqUYlKVKISlahEJSpRiUpUohKtrFx+0nnjBfi0F+47N6e9YLYz++Unny/dOeZPf/qzP3zwu8ey4Fvf+MGN58++4nPis98+Nyc+a/bOyo0XfH4+4LyFkdYSlajG8Xu/j/8v39tULicqUYlKVKKanx3XuaO/GNhAfvPrPxgz+UcPPmDM6u6MOe+4x892x7j2E5dtLO/+wk2DHfPne8OW5ZSoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrRVW2fltNz2AotEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCsrnzpidiuV895wxNyMx+PIOC7dOeYpJ8w+H3zco2dP+cJJH5+tz15xwNyMx+PIOD4fcN7CSGuJSlTj+Lt/4xlj8J/+6CebyuVEJSpRiUpU87PjOsfVvvvBz9hYPv7Rsz8XOPXEez8lFNV8zHnv3/M1Y/DrP33lxvIb198+2JF/+krYspwSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYm2auusnJbbXmCRqEQlKlGJSlSiEpWoRCUqUYlKVKISlWhl5YJ3fWC2nNrvQ3MzHo8jF+w4TrpzzGc/brZ707MeN7sDzOcOPW3gc96ydmud8Xgc+dxhp88HnLcw0lqiEtXqL36x4wFPGoOPB5vI9RKVqEQlKlGtnR4XvMcTxzVvLKf5HD+n/yuq+ZDz3veY2dfRvvI5vyoH+/bNsy/JHf7ol8GW5ZSoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrRVW2fltNz2AotEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCsrZ75+tl/lFcecMzdXHH32ODKOS3eO+RePmL3jctqJXxiPP/EPs/XZJ97zobn5xAEnzo78wwfmA85bG2ghUYnqp/fM3hDa/788fVO5COaJSlSiEpWoFsG42nHN//7jfxPVZE498Zoxn2NWp/8rqrURd3bEI2f7u9541bUby+/c8fXBDvvDF8OW5ZSoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrRVW2fltNz2AotEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCsrH37RPjvue/OZ8Xgc+ciL95E25t13fnesBh75f77nZ+2bddarDh740vefOTfj8Thy9t8ePB9w3sJIa4lKVPf86/fHyAc9ZK9N5SKYJypRiUpUoloEB/3O7Etj93zn+6KazJjJMZ9jVsfc7s6YU4c9/IVj5Fuu/crG8vtf/5fBDn7Y82DLckpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJdqqrbNyWm57gUWiEpWoRCUqUYlKVKISlahEJSpRiUpUopWV45/y9+MF+NZPz96Ompr2Hz/+qX8vbcyPfHD2dsvrXnbGhD/87HcMfOVHL5qbK0//xDjy4eesc7/nhZHWEpWovnf3t8bI7/3DF28qF8E8UYlKVKIS1SJ47x+8aFzzuHJRzdmYz/lmDaJaG3Fn0/f9b/+K25XBpi/av+e3nwNbllOiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtFWbZ2V03LbCywSlahEJSpRiUpUohKVqEQlKlGJSlSiEq2sHPU/XrXjvlt3TttjjuPSxnz5XrMNGs4+7Z8mfOyfzzZl+KdPXj434/E4Mo7PB5y3MNJaohLV9HWi9z3mlZvKRTBPVKISlahEtQimb0p9+5bZNvfLzdlZp842axhzu7obY04d8F+fNUb+2i23bSx/8oN7dsy+aP9M2LKcEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJtmrrrJyW215gkahEJSpRiUpUohKVqEQlKlGJSlSiEpVoZeWQ//b82VssCztLjcfjyCEPf4F0ZeUnP/733/8v+//uA3b8y7d/NOHpHZqbr7lubsbj5feZphZGWktUovrnf7p1jPyBv3jdpnIRzBOVqEQlKlEtgmkBOq5cVHP2L9/64ZjVMbdjhkW1MOS97ferTx0j33XHnRvLn/3bTwfb91efCluWU6ISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0VZtnZXTctsLLBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISrazs9+tPGy/AP71nbeODaXOBcVy6snLpxbc8ZI8dz+zvB6emL3HfcdtX5+aOW7+6Y+mr6FMLI60lKlFNm5qe8OTZrf02lotgnqhEJSpRiWoRnPCk2Sew48pFtSjHrI65HTMsqkU5a/r7xD2eqPuPyBKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRibZq66ycltteYJGoRCUqUYlKVKISlahEJSpRiUpUohIVZleLoeVl09SON18wFgFHHnTpxHa1FFtetE3BpjBTorr107O7+vDtLlEtgnmiEpWoRCWqRTCudlzzbZ/+oqgW5ZEHfmbM7ZhhUS3K1fv5vtRuvtc1hZkSlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0VZtnZXTctsLLBKVqEQlKlGJSlSiEpWoRCUqUYlKVKLC3HT1+h/nLX/wN/Vnf/TesQi4/kv3Dj7dCmb5w8Tljx2nYFOYKVFNu8af/uL9N5WLYJ6oRCUqUYlqEZz+ov3GNY8rF9WiHLM65vbPH3G4qBbl6v38ZtVufltrCjMlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUoq3aOiun5bYXWCQqUYlKVKISlahEJSpRiUpUohKVqESF2dUX0pe/uj66/HNfHiuAxzzskPk2n7v6OvzyF+enYFOYKVFd99FLdixtACGqRTBPVKISlahEtQjG1Y5rHlcuqkU5ZvUxDz14zPCYZ93Sb79ffxu4m39vOIWZEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKtFWbZ2V03LbCywSlahEJSpRiUpUohKVqEQlKlGJSlSiwuxqS4Xpg63FzRdGh+x/4Xj5f+ve587ZrjZ0GEd23HfrhynYFGZKVF/40IVj2PP/7shN5SKYJypRiUpUoloE42p3dOMgUS3K0Vtec86Y4THPuiV5v3a32s0ds6YwU6ISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCXaqq2zclpue4FFohKVqEQlKlGJSlSiEpWoRCUqUYlKVJhdbQq6vH3o6NlPOHa8/H/83K/M2a62JF3evHQKNoWZEtUVx5wzhr3oHR/YVC6CeaISlahEJapFMO10P65cVItyNOZ2zPBznvAB3ZK8X/uz7+ae71OYKVGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohJt1dZZOS23vcAiUYlKVKISlahEJSpRiUpUohKVqEQlKsyubmuzfAOcm2/66sMetO/43w++v/bV9V3dVGf59jtTsCnMlKg+e+jsvtSffvdJm8pFME9UohKVqES1CMbVjmv+3KGniWpRjsbcTpM8Zntjeb/uMPi+x+7WXQunMFOiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQl2qqts3JabnuBRaISlahEJSpRiUpUohKVqEQlKlGJSlSYXd2YefkWzh854XMP2WPHC576oUW2q9tCL99Aego2hZkS1fTruFpRLYJ5ohKVqEQlqkUwrna2KNz/Q6JalFPPf+qJsy3dT/jcxvIb198+hj3yT18JW5aj9+/5moGv//SVm8rV3fgXzROVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJdqqrbNyWm57gUWiEpWoRCUqUYlKVKISlahEJSpRiUpUmNNeccCOviEEG0fG8XF2Ll/70tkG7scddfkiO+8NR8wWUkecujDkrHFkHB9nF/Hq0m+fwkyJ6hNvP3YMe+Wxa18C25VcBPNEJSpRiUpUi+DK988+1hxXLqpFOTVm+CGzOxE5pbC7v3DTGPaYP98btixHxz3+DQNf+4nLNpWru/EvmicqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSrRVW2fltNz2AotEJSpRiUpUohKVqEQlKlGJSlSiEpWoMCc+6+3jpffGCz4PG0fG8ROf/faJ3X33yp885MDxwn/rzd9aZKe9YN/BLj/5/PuOujKOzNZnL9x3Ea8u/fYpzJSopvXcF0/6xKZyEcwTlahEJSpRLYJxtbO15t8dIapFOTVmeMzzn/zuQWPON5B3fH625+qxf/X6+6D15OiEJ795Nm/nfWZTubob/6J5ohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEW7V1Vk7LbS+wSFSiEpWoRCUqUYlKVKISlahEJSpRiQrz/r+cbcdw55Vr31ufmvZMH2cn9umLZnfK+19/cCjshCe+afbKff6l9x11ZbyWj+PjLB42hZkS1VmvPGgMe/2Zn9lULoJ5ohKVqEQlqkVw/RmzrSXOetXBolqU88Y8j9kec76BvP0zXxrDHvfkNy2adeXo5GfOltFXn/WpTeXqbvyL5olKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEm3V1lk5Lbe9wCJRiUpUohKVqEQlKlGJSlSiEpWoRCUqzHsf+ZId3YoYNo6M4+991Esn9u53nDde8t/8mo/CjvqTlw/2lcv89vQ4Mo4f9d9fgYdNYaZEddoLZ2+Y3XThFZvKRTBPVKISlahEtQim9wLHlYtqUc4b8zxme/93nLeBvOWiq2ZL2Ge8ddGsK0en/PW7Br7iVLeK0BVmSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqERbtXVWTsttL7BIVKISlahEJSpRiUpUohKVqEQlKlGJCnPAbz17vPTe853vw+751++P4+PsxJ6y59HjJf+s0+6zshkd+DvPHez2G9zB8vav3DyOj7N42BRmSlQnP+sfxrC3XfLFTeUimCcqUYlKVKJaBONqxzV/+NnvENWinDfmecz2mPMN5A3nzXaI/dBz1xkWOTr9xfsPfPlJ91mxrStXd+NfNE9UohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlWirts7KabntBRaJSlSiEpWoRCUqUYlKVKISlahEJSpRLYK77rxzxwOetM+DnvyLn+/cmn1n48g+D3zSOHv3nXd9+bpbf/cBOx7+6/t99fY7F83Pf/az6el333XfrwutrIwj09N/8bOfLz4FNrUI5olq+kTyzqtu2FQugnmiEpWoRCWqRXDnlV+ZvdX0xHU+y1vZxZhjnsdsjzn/8vVrN7fBXH/WpbN12wvetTDY+nJ01isOHPizx7sXl64wU6ISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCXaqq2zclpue4FFohKVqEQlKlGJSlSiEpWoRCUqUYlKVIvg1utuXPd9pqnp3anbrr/x+KMvecgeO1749BMY84ff+u4AB//uXy+OOW8cH2d/9O3vLj5FVItgnqiO/bPZN8a+fv1tm8pFME9UohKVqES1CL5+3W3jmseVi2pRzhvHx2yPOf/gMWtfS8dce+rFY9hTX7b/2li7kKNz955tS3bJ0R/dVK7uxr9onqhEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUq0VZtnZXTctsLLBKVqEQlKlGJSlSiEpWoRCUqUYlKVKJaBF/+7NXjdfeIR/+NqA5/9Gz772Fe8byTx4v9+w69mDG/ddMdAxz9WG9EODVtbjnM4lNEtQjmiWr6yte/3Hr3pnIRzBOVqEQlKlEtgnG1O/o6mqgW5bxxfMz2mPMx87uS012DTn/VgWtj7UKOzn/jbLezTx1+yqZydTf+RfNEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYm2auusnJbbXmCRqEQlKlGJSlSiEpWoRCUqUYlKVKIS1SK45tzZjW6O/avXi2ocH2evPvvTj/jNA8aL/RevmX3RahF87fLrBjjxKW9eHHPeOD7O3nH59YtPEdUimCeqw37/RWPM7//ztzeVi2CeqEQlKlGJahF8b+Xb45rHlYtqUc4bx79w9U1jzsfM33nnXevKqz7wsTHsGa895D7DrSdHH3/r+wf+5MFry7VdydXd+BfNE5WoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQl2qqts3JabnuBRaISlahEJSpRiUpUohKVqEQlKlGJSlSL4LITZy/SJ+3lnZ6nPvTc2f2e93/V7LOqPf/g3pvhLIIbPnbZAB99yTqfUo2mL1YPs/gUUS2CeaKaPrX88Xd+sKlcBPNEJSpRiUpUi2Bc7Y4+hxXVopw3ndrzDw8bM3/sEZ9eV15+1Owmkue8aZ3ttZCjT77r+IEv3P+Dm8rV3fgXzROVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJdqqrbNyWm57gUWiEpWoRCUqUYlKVKISlahEJSpRiUpUi+Diwz6yo4+ZRDWOj7OPe/hsf9Gn/q97/6JtEVxzwgUDnP/GtdvpLDZ9LDXM4lNEtQjmiWq/X3vaGPPff/LTTeUimCcqUYlKVKJaBONqZ+vUX3+aqBblvOnU9Jeb878lxEy3ZfzY2+7zl4brytWd90O84F3eQ1pXmClRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISbdXWWTktt73AIlGJSlSiEpWoRCUqUYlKVKISlahEJapFcN4/zj45Gi/Sojr3be8bZ//4wfuOl/njj7pkecxLDz5lgEve7QdPU9OL+mcPOXXxKaJaBPNE/WXiGHCfBz5pU7my22Ou/FLk7A8q93ji8t9aLsup6dQHjvzUmPnHPuzeHUoxY9pnq9t3Hbs21s6Qo/FfYfYf+u2uxnSFmRKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRVm2dldNy2wssEpWoRCUqUYlKVKISlahEJSpRiUpUoloEZ+x9yI6+miOqiw466fV7PGu8xv/3hx50187FwSKYvtlz1Qc+tjjmvCuPPXecHWbxKaJaBPNEKytfu2X253gHPPiZm8qV3R5z5ZcixzWPK//arbfrluTUdGrM+Zj5Mf+fveT6ZXnxuz64Y71P/Zbl6PIjzxj47KXPE3WFmRKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRVm2dldNy2wssEpWoRCUqUYlKVKISlahEJSpRiUpUoloEJz9/tsH3Z487W1Tj+DP3eMV4gX/T3679ef8iOPPls88Qrz/TO9xNjePj7FmvuM/nj6JaBPNEOzeVOPihz9tUruz2mCu/FDmueUcbXuiW5NT87BtfdfqY/3e3pTvm42+brW7HInhtrJ0hR9Ni98zXHbqpXN29f9GUqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRVm2dldNy2wssEpWoRCUqUYlKVKISlahEJSpRiUpUoloExz3pjeN19+ozLxbVVWd88tF7vGW8wJ97xlXzpyyC6Q53t3/mSwtDrjXtYD7M4lNEtQjmiVZWbrrqn8aAhz/ypZvKld0ec+WXIg9/xOx+ROP6dUtyan72nI9eNeb/CY85clle8KbZV9wufq87LyzL0TUnzvZ0+OjfuqeDrjBTohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJdqqrbNyWm57gUWiEpWoRCUqUYlKVKISlahEJSpRiUpUi+DIx75ivO5e92lvgDN10Smz7wD9/gPfeccd9+4UsHLfMd+/52t2tO3nwpBrjePj7DCLTxHVIpgnWlm5/tLZrl1H/+nfbipXdnvMlV+KHNe8ox3FdEtyan52zPwjfvPd47/C1VfeiDn3te8dY35mae/QlfXGvPaUTw58ytKupLrCTIlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpVoq7bOymm57QUWiUpUohKVqEQlKlGJSlSiEpWoRCUqUS2Cgx4222z9li99WVQHvO2s8dL+pF9/4+JTFsGhD3/BjnalWgTz7t3/6eEvXHyKqBbBPNHKyrWfmO0Kcdzj37CpXNntMVd+KfK4x812FLv2ost1S3JqEbz6RR8e/xUO2f9CzFmvPGhHH+8u4inkaPq49sMv3LGpXN29f9GUqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSrRVm2dldNy2wssEpWoRCUqUYlKVKISlahEJSpRiUpUoloE+/7KU8br7p1fvc9m6/Oe9mezbQJe/qD77JC5CPb71afuaNOERTBv2p5gv1972uJTRLUI5ol2bov6oae+dVO5sttjrvxS5IlPfcu48nH9uiU5tQhOPeny8V/h6X9+DOajL5ltM3bZh7x/88p6Y047lp20l3eG1hVmSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUohKVqERbtXVWTsttL7BIVKISlahEJSpRiUpUohKVqEQlKlGJan72qzfPPsJ79288Y135za//4HcfsONhD3jnP+7x5K/dss4th3/yg3t27PybvvnZxVZ3/vXcv/3wnvmzRDU/u5hoZeWKUz8+Rjtlr3duKld2e8yVX4oc1zyu/IrTPqFbklOL4Pbb7phu/Pytb9xne9VTnrdjNuYpFy7iqUU2dfMnrhr4hGe+bVO5unv/oilRiUpUohKVqEQlKlGJSlSiEpWoRCUqUYlKVKISlahEJSpRiUpUovq/2LvzL6mqe2Hj/0tr5uQmcYqamBhvTNRcc/NqYhwTNQ5xjMYpRo0aE20GARkEB5BJUUARBRVQcEQERMEREEWQMiY3xpgYjQP1fs+z6d27n1NNY7uWv1Q9ix+a2p/ap6pOr3X2qq46x4iMyIiMyIiMyIiMyIiMyIiMyIiMyIiM2rUWK6d6nQWWMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjyqNrV66Jg+7Yb1VvUBk1mzOnrdq1q/v/ffGSMCHrc/5905+7ez5ynkfLmj0f7g6Z72VEebTMqNFYOr36Ktydp48YUDZ2eM7GpyLnnHZ1PPJ4/HY1mZI57Rc3x76YNX1VaW79+RXdfEFBuNFqzvSFg6mHXzKgbNa2njIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzJq11qsnOp1FljKiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIwoj65etDQOujf+8JyW8pRjqgs8n/+t6muGIetzbnlqXQxN+XF1HcM8Wtbs+exRyHwvI8qjZUaNxiMTq5M5zTt37ICyscNzNj4VOe+csfHI4/Hb1WRKZvqkR2JfxB4pTbrU41P3tDhHRslSm5Y9F/imQy8cUDZrW08ZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZtWstVk71OgssZURGZERGZERGZERGZERGZERGZERGZERGlEeXz6q+uj/9qEvr8h9vvbvHZ4bGvxsOuyzM8tmL6nO+9ED1V6f0B7s8Wha3zzyu+tPYS4ufzPcyojxaZtRoLBl7W8y24JIbBpSNHZ6z8anIeMzxyJeMm2lXkymZdWs3pt0R+yWbqYf8TmvfXDHTtrasqlbDE3903oCyWdt6yoiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyKhda7FyqtdZYCkjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMqI8+vCNc6oV0mnD6nLeHc/s2tX9y59NmXXqsDAPF+cCyGbN7CX5/aQ8WlbNwzs3IfO9jCiPlhk1GouGT4/ZFl81dUDZ2OE5G5+KXHxlda3lePx2NZkyajSOP2xKdUKyO57NZtL/nNfNKTZMW82ZTplx/Q/OHlA2W2290ZG1jMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMioXWuxcqrXWWApIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzKiPLpweHWtlbsvGl+X55x8exzObxy3JEa1Mshm2fVzq+XOldVyJ4+WNXsWFk9cf1e+lxHl0TKjnisnPjLytgFlY4fnbHwqMh5zPPJ4/HY1mTJqNGJfxB6J/ZLNDd8/K+Z8fukq01Zz/nX95sAT9jtjQNlstfVGR9YyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIqN2rcXKqV5ngaWMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIjy6N0XT4iD7sJh1cXsSvDuv9/f+4vDd9up+9k1Ly0cWq2QymvYZbaku3o/6fEJdzb73/rj46s3yULmexlRHi0z6nnAaYvbl40dnrPxqch4zPHI513iSwHWZcqo0Xh2zYbYI7FfYu8kM2Hf02POtSt6v3+Q6ztZ1d9frb6RMG6fUwaUzVZbb3RkLSMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyatdarJzqdRZYyoiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMKI/OOr06kdLD198h+cB9a3ft6j7u0GrhFaPdfc8Anlk6k/jq2x5o9r/1GA0TMt/LiPJomVGjMee80THbk1PuHVA2dnjOxqciV065Jx55PH67mkwZ0bGHTo39EnsnmXF7/6qbk8TatZrz7derk76O/saJA8pmP1s3IiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyatdarJzqdRZYyoiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMKI9OP+byOOgun1mdSKkEF59dncB9yvXVacefmLmguzp/0mX1OdN5mNYvWtHsf+vrFi0PM/vE7nwvI8qjZUY9K8LVMxcPKBs7PGfjU5HxmKvX4QxfqaYuU0YUeyT2yyW/2XZl7tG7VWfhf/n5dXat5nzn/94KPGqX4waUzX62bkRGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZNSutVg51esssJQRGZERGZERGZERGZERGZERGZERGZERGVEenfij6lPS6WtoefSDDz76zldHxoF886t/j9tXL3ysu++3z7Kcemj1LbbXVlVvqOTRsrj9tSdfDBMy38uI8miZUaNx6wnVdxKfn/fYgLKxw3M2PhX5/N2PxiO/9cSr7GoyZUSbNr4Z+2Xfr42MfRRm5H/9PObcuL7FhSA9XbP53tvvBB7xlWMGlM1+tm5ERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmTUrrVYOdXrLLCUERmRERmRERmRERmRERmRERmRERmRERlRHh23zylx0F27fHUplz70chzFf3ZgdWGWuD1Gu/t+difL9BmgN1+pbsmjZXF7jIa57rtn5HsZUR4tM6recqvOGbH+/pUDysYOz9n4VOT6RSu6+74R2J9MGVHcHvsl9s7jD78cPw/7/JHVAvfVTXat5uy5bNERA8pm/1uvZ0RGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERG7VqLlVO9zgJLGZERGZERGZERGZERGZERGZERGZERGZER5dERvAXy6roNpfzTRffFIfy6UY8kuXHthuqdj//6eX3OkV/9RQy9949/lXOWxe3vvvWvMCHzvYwoj5YZNRpTflqd9XTjY2sGlI0dnrPxqch4zPHIJ/+0z2WzW8qUEcXtE0ZWZxyNfRQ/d+90WMzZ2LLFruWcW7cGjrsMLPvfej0jMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMmrXWqyc6nUWWMqIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjCgNvbap+t7+0M8cno7Qaeijj7Z+/xtj4hC+9vk3tsktW8JU75Rs2lzO+eH7H6S7x5E7z6kqt3VruvtHH3xYbl2lIWXU8zfNLat6zwvfn2zs8JyNT0W+tmptPPKJB/s8n3WZMqK4PfZL7J3YR++nd6Q+53ekUp6O0oW9837cjixBzoiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyKhda7FyqtdZYCkjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMqI0tGFN9emoa3b/ZSnvnr0mjt8H7j2ulNfsdnzIl9a8UMp//vnNuHHMN04spUpDY/Y4IeQ/33hzQKmMGo0J+/86pvrLCxsHlI0dnrPxqcg3nt8Yjzwev11NpowoDR2417jYR3NuqU6jP+LL/kxVqu9k27r6S0fHXV7d8MqAsgQ5IzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIio3atxcqpXmeBpYzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiNLQ849UR+jreg75aeiog2+Kg/dRP5pcyuu+d2bI8KWMVU71lsyB55RSpaGJB/ymXBUZURpSRj0fGntz4+sDysYOz9n4VGQ85u5Wp6Gqy5QRpaGjDp4c++jIgyZW6+NdjjeivpNta+TXjo27vPLi+gFlCXJGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERG7VqLlVO9zgJLGZERGZERGZERGZERGZERGZERGZERGZERpaFVdz8Yh9vJP/ldlh9++NG+Xx8VB+975j5XyjAhn7z7wXLO9KGiW468rJQqDYUJuXHpMwNKZdRojO77Zth2ZGOH52x8KjK94ReP364mU0aUhmLvVN8l/OrIq7oOG7vXSUbUd7JtXbNHdVqHDWteHFCWIGdERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmTUrrVYOdXrLLCUERmRERmRERmRERmRERmRERmRERmRERlRGlo6fX53deKAK7N86P71ceQ+7IDq+4OlTCdHCJ9l9Py86vQNd54xopQqDd15+oju4twKRpSGlFHPp/Lfe/udAWVjh+dsfCpy21kSiu8K9CdTRpRHf/qDG2NPndl1wvjvnGZExUy9jf1WdWLSdSufGVCWIGdERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmTUrrVYOdXrLLCUERmRERmRERmRERmRERmRERmRERmRERlRGpp7fnUZ5pt/cUWW5506Z1fOLyp587F/CDntyEvv/eOk+PfIqJnxb+Zx1arr9pOHlVKlodtPHhry/j/0+bOjSkPKqNHQ5+W3Ixs7PGfjU5HxmOORD/3s4XY9csVN828+/NL5541LL296qWefNvymQy5cPGpGKaMp11VnHP3frgtu2P+svpNtK8uy8d+tTqvxwuNP1eXH2npZOVXOiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIqF1rsXKq11lgKSMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyojQUx8443N506LY/Ef7jrXf3+vyw3T8z5C9//mdL2fLfLUcM/CfCOHJXK7kdkEpm29ceP3u4XU2mjMiIjMiIjMiIjJpNfRlTMr1KLf/ddOiFpYxiH+2+85A9uq689oAWX0tstNp6lL4i8NzD2z5LV8qPtfWycqqcERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRUbvWYuVUr7PAUkZkREZkREZkREZkREZkREZkREZkREZkRGnoll9cEYfPuReMS/+dffOqXbu6Tz/2trpcPGrG5EMvnH368Hv/ODH+PXrNzPh3z3njYnW18qZ7SqnS0P2X3xQbuv1XA7/XpWReXVedkas8pVZOMmVERmRERmRERmTUc9qwjZx1rC7T+3yzjr8yvbzppZ52JGvT4l3G3AmHVF9HOPfbl/adbFulzN34w3NitjUPVFdAkvy4W8+VU+WMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiM2rUWK6d6nQWWMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjSkPbPlk1bdsnq9KFhO/t+Xh7KVUJckaUhrZ9Wuv0gT+tpWTSeSXG7nmSXU2mjMiIjMiIjMiIjJrNeOTdxdkuJNMn1V6YtzT9Nw1t+5zcCVeWMjV9xKLYXwd/ZduQKmVu0o8viNmevu/Ruvy4W8/lecqMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiM2rUWK6d6nQWWMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjSkOTuZJg+m7gyuVr42i9z1dGvPfuB3WpSpAzojS0cekz3Tv2x0Qls3ZF9b3F8qo7/cmUERmRERmRERmRUc+VheJZtJTx+nTXvmv5ZPqmZ8+fcXvnajZffOCpb3T9KfZa7LveuXoqZW4y58Ff1fNt0FJ+3K3neicqMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMmrXWqyc6nUWWMqIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjCgNTfjv6uxWz3F2q6GX3xOH6ssv2Pb3PkklkzKiNPSXF1+NDU084DcDSiXz3MPVibsmHnSuXU2mjMiIjMiIjMiIjJrNeOTdxenEJG/8AWcLe/HV9N809Fw6V9n3zixl6qXFT/6k69zYa7HveufqqZS56UdVf/Jbfvv9dflxt57rnajIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIqF1rsXKq11lgKSMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyojQ0atfq/Owbnnlxy5bGgXtXl8d5ctmmllLJpIwoDf3rL3+PDY3Z44QBpZJZvWhpzDPt0IvsajJlREZkREZkREZk1GxO5S3D1QuXtpSjd69O8RWvVfpvGoq9Ezdes9u2s4n2ztVsrr1v2Tldx+9anXZ/TP1qhKXM3XJs9am7ZbfeV5cfd+u53omKjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjNq1Fiunep0FljIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiI6oGtmwZ8pmfde902GubNy+YX328/eBvj+eigjVZy4iMKA2lMxTE5ga4amEtmXRm1BlH/8GuJlNGZERGZERGZERGzeaMoy7vrv2FbpvcujXtjo8+7HPFxvTFyRgqrxeZev7uR6/qOuy/vzQk9t3C+X3OvCCZu/XEq7qrT93Ns/z4W8+VU+WMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiM2rUWK6d6nQWWMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjits3vvhSHDtHfLU67+UFp8+Kg/T4EQ/bfcw56+XRdJGWd/9enQDCiHonKpJZPnNhTDL7xG67mkwZkREZkREZkREZNZvxyOPxL5+1qC7jlYmhUV8/NuM8ejXnVo39lWTumdur5eZvDhwR+y72YO90VMrcrNOGxV0emXin5CC2nuudqMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMioXWuxcqrXWWApIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzKiuP3FZU/HgfPa75z6ysubvvXlq+Mg/eorvv5MkvWMyIjy6HX7VZ/3+tuGLQPKMpml0+bFJHPPHGVXkykjMiIjMiIjMiKjZnPuGSPrbyAlGa9MDF3/32dmnEfHffvUGIr9lWTuqRmL4vbpZ4yPfRd7MPZj74ytth7dcVb1AB6cMFtyEFvP9U5UZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZNSutVg51esssJQRGZERGZERGZERGZERGZERGZERGZERGVHc/vR9j8aBc+KPzr9l8mNxhD7q4N7L45R5OjIiI8qj035ycWxu84oXBpRlMg/fMCcmueeCa+1qMmVERmRERmRERmTUbM4//9p4/PEs6jJemRiKVynjPBr7qLvn3Aq9czWbK6fcE7cvumxS7LvYgzOmPNY7Y6utR3POGx13WTzmVslBbD3XO1GRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRUbvWYuVUr7PAUkZkREZkREZkREZkREZkREZkREZkREZkRHH7slvviwPnzT//wwmHV6e/mnjtEiPydGRERpRHbz+pOo/luoXLB5RlMrEyiEkWXtZiLSiZMiIjMiIjMiIjMmo245HH418y5ra6XLvgiRiKVynjPBr7KIaeuG1Bkrknrr+rWi1dNTX2XezB2I+9M7baenT3ReOrNdnw6ZKD2Hqud6IiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIio3atxcqpXmeBpYzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiOL2ByfMjgPn5JNH7L7zkD0/N3Td2o1G5OnIiIwoj9574YTqjZAZ9w8oy2QWDpsWkzw4ZLpdTaaMyIiMyIiMyIiMms0l3dOrBeKwaXWZ/t4Xr1LGefT2X1enAH1owu1J5paOvb26/eoZse9iD8Z+fHZN7zniS5mbf9n1cZcF3VMyS3IQW8/1TlRkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZk1K61WDnV6yywlBEZkREZkREZkREZkREZkREZkREZkREZUdy+4KrJceC88Cejd+3qPvOX1dV8jcjTkREZUR59cOjNsbml4+4YUJbJ3HtF9Q7Qo6Nn2dVkyoiMyIiMyIiMyKjZjEderWOumFiXabX04LBbMs6j835/XV4V9c7VbD48ono/77Ex1QeqYg/GfhwzfGE5Z717/zSp/gCag9p6rneiIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIqN2rcXKqV5ngaWMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIji9rkXjI0D50G7VR9vv3PW8u3IekZkRHl0+Y13x+buv2LygLJMJv2F64nr77KryZQRGZERGZERGZFRzx/17r54fF3e/4fqco3LJ87LOI/eP6Jam879bXXJyN65et4PW3Zd9ZXA2IOxHw/53nXlnPXSW4DzLpmQWZKD2Hqud6IiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIio3atxcqpXmeBpYzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiOL223415Iyuk+KovO9XR27e/Np2ZD0jMqI8+uwdD8Vx+q6zRw8oy2TmnHNNTLJq+gK7mkwZkREZkREZkREZNZurplWfe4tnUZfxysTQs3N6z5SRRx+dNDeGYn8lmUurohWTqu8kxh7c96vV+Rpmz9h2IedS5h4YdUteLeWag9p6rneiIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIqN2rcXKqV5ngaWMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIji9qmHX7Jf16VxSD50/21vexhRn7l6MiIjyqMbHnqqOk7/4o8DyjKZWadWp3F65vYH7WoyZURGZERGZERGZNRz5qpZpw2ry3hlYihepYzz6Io5D8RQ7K8kcwsurj5QFcvNxGI/Drg3l4ybWV/hNQe19VzvREVGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERG7VqLlVO9zgJLGZERGZERGZERGZERGZERGZERGZERGZERxe1jvnfO7l1X7tbVPee2J7Yv6xmREeXR15/ZEMfpm/7n/AFlmcyMX/4pJnlh/lK7mkwZkREZkREZkREZNZvxyOPx33rClXUZr0wMxauUcR59Zkn1Fb/rf3B2krl7LqhO+rB65uLE7rjtidibe31h2Lq1GyVz6TwXs88YnidPchBbz/VOVGRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmTUrrVYOdXrLLCUERmRERmRERmRERmRERmRERmRERmRERlR3H7SV87btav7l4dMGlDWMyIjyqP/2PLXOE5f+81fDSjLZNK1il9assquJlNGZERGZERGZERGXJ45Hn88i7oct/evYihepYzz6PqnnouhMXuemGQu/V3vuTsfzvKkI6ZVH3UfVn3UvZS5xyZXHwJLf+/LNQe19VzvREVGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERG7VqLlVO9zgJLGZERGZERGZERGZERGZERGZERGZERGZERbd782re6roiD8V0zt31kpz9ZzNSbERlRHn3/3+/FcXr4F44cUJbJ3HTohTHJq8uetavJlBEZkREZkREZkVGzGY88Hv/kQ39Xl8M+f2QMffDufzLOo5te3hhDwz5/RJK5OaddHbe/eM/jWc6bszL26Q/2HBP7t5S5pdPnx11m/PJP+S6NwW491ztRkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkVG71mLlVK+zwFJGZERGZERGZERGZERGZERGZERGZERGZEQ3T3wwjsT77Xw51/DdnuwdLjIiIyrB1V86Og7V/3nnXSMqZU7mxh+eEzM0Vr9kV5MpIzIiIzIiIzIio2BPr4/Hf+P/nCv5n3/9O26/+stH98FFw794VLWy3PBKCdKVDdffvzKz2Js/3q+6cs5t05eWMpcu5jj9mMuKuQe59Vwpc0ZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREbtWouVU73OAksZkREZkREZkREZkREZkREZkREZkREZkREdfsCEOAyf8fU+R3ojKkHOiIyoBNfuU13V7q1NbxhRKXMyE/67uqDhX9dtsqvJlBEZkREZkREZkVGz+Ze1m+Lxx7OQjNckbh//7VNLXIIxe58cYN3KZ0qQPpn+8sPVVQJzk8ZXS+ejftTiZPfRyjmL4y5Tj6g+sZ4b3NZzpcwZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZtWstVk71OgssZURGZERGZERGZERGZERGZERGZERGZERGzeYzT22JY/BeXX+ccOCnusCa/L+/jeN04+n1RlTKnMzYb1ZH+lgQ2NVkyoiMyIiMyIiMyKjZTEuZsd/6lSnvbE358W9LXI7fcFD11t2a+x8vwc1HVJ9I27TsuVK+8vKm73ytOl9D7OUSp1bNr06W4b9RDmrruVLmjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjNq1Fiunep0FljIiIzIiIzIiIzIiIzIiIzIiIzIiIzIio2bzwjPnVm9ydJ017chLty+bOzxncwfkzOOujOP0S4ufNKJS5mRG7Xp8zPDO/71lV5MpIzIiIzIiIzIio2bzX399Kx5/PAvJ9OH3eH1KXILYUwGWz15UgqmH/i5u3LJqXSmjq34/L/Zv7OUSp1Yvqr7GOPHg80o/uK3nSpkzIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyatdarJzqdRZYyoiMyIiMyIiMyIiMyIiMyIiMyIiMyIhk3nj97T0+M3T3nYZc2nXkrFP7nJBJMlWCnBEZUQnmnVOdO/6Z2x80olLmZPKnuOxqMmVERmRERmRERmTUbG77tNOXjpZcM3tJ3B6vT4lLkE4A9sjEO0uQzq3w52dfLmX0zOqXYhfHv9jXpY+efXB53OX6A6pzLuQGt/VcKXNGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERG7VqLlVO9zgJLGZERGZERGZERGZERGZERGZERGZERGZHMmKHVZ3SO+351SvS7L+pzzRbJVAlyRmREJVh85dRuLnRjRKXMyQzZ+bCYYetHW+1qMmVERmRERmRERmTUbMYjj8cfz0Jy2fXV2dLj9SlxCdI1ghYN73OV6xu/f3Y3n0grZeqsE2+tztcw9MHSR88/Vr1ZpQ+BDW7ruVLmjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjNq1Fiunep0FljIiIzIiIzIiIzIiIzIiIzIiIzIiIzKiErz77/e/u8uoOPRO+FX1ZtLCYdP6k7kS5IzIiErw+PjqLJdLuqcbUSlzJdi8sfqE+LDPH2FEpcwZkREZkREZkREZ0dDPHRHPIp5LKZdcVV0i8PEJfd4iKsHCodXa9O6LJ5Tguu+eETe+ufH1UqaWLFodezn29XvvflDeZe2KNXGXcfuc0gcPauu5UuaMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiM2rUWK6d6nQWWMiIjMiIjMiIjMiIjMiIjMiIjMiIjMqIS3H7LU3HcPfbQqbNOHx6HzIdvmNOfzJUgZ0RGVILVt1UXXZl//rVGVMpcCTa++FLcfcTXfmFEpcwZkREZkREZkREZUTz+eBbxXEoZr0k352QvZQkevv6OALHXSnDtNzk7aOP/SpmLvRz7OvZ4eZf1Tz/f3XPW0Nzgtp4rZc6IjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMioXWuxcqrXWWApIzIiIzIiIzIiIzIiIzIiIzIiIzIiI8qjW7c2f/L9G+Kgu2De89OPuTwOmctnLWopy0qQMyIjKsH6RSuq4/QJ3UZUylwJ0uJg9DdONKJS5ozIiIzIiIzIiIwoHn88i5dWP1/KeE26OaNVKUvQc/6qy0swevcTuvnIfylzsZdjX8ce31r8QfXl59Z21z5lP7it50qZMyIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMmrXWqyc6nUWWMqIjMiIjMiIjMiIjMiIjMiIjMiIjMiI8ujSh16OI+5B37z2ww8/mnjwedWbFouWtpRlJcgZkRGV4LVV1dF96iE+j3mqlLkSvLjs6bj7tfueZkSlzBmRERmRERmRERnRtd+pTgYWz6WU8ZrEjfH6lLIE+dt/JRj51erNsPfefqeUudjLB+49LvZ47Pd8l1fXVVeEHPFfPy/l4LaeK2XOiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIqF1rsXKq11lgKSMyIiMyIiMyIiMyIiMyIiMyIiMyIiPKo2ccOzMOt5MnVGczGvetU+KQuXbFmpayrAQ5IzKiEry58fXY6IR9TzeiUuZKsO0bcD8424hKmTMiIzIiIzIiIzKiePzxLJ59aHkpJ3zn9G4+TVXKEuTPTpVg+Be4vs1775cyFyD2dezx2O/5LptfeTXuMuwLR5ZycFvPlTJnREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZk1K61WDnV6yywlBEZkREZkREZkREZkREZkREZkREZkRGloWWPvrJbV/feXxz+j7eqcxxc/eVj4pC5cd2GulQlyBmREZXgvbffiY2O7Pv2Sa6UuRI8fd+jcfdJ/3u+EZUyZ0RGZERGZERGZETx+ONZPL3gsVKO+MrPu3kvqpQl2JjeefrKMSXo3qn6TmVz69ZS5gL84+/vxh6PNVbs/XSXLa9t6a59jXFwW8+VMmdERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmTUrrVYOdXrLLCUERmRERmRERmRERmRERmRERmRERmREaWhQ79Xffrq0P1viJ8//M/7cbwc+tnDW0olkzIiI5KJjcamX9u02a4mUyVYeSeXeTn8EiMqZc6IjMiIjMiIjMiIpvzs4ngW8Vwye+3V6tuR8cpIFjNVpZfuw/e3fSvwow8+zPeSTCV2yP7s9+9dn+cc+hl2weZtu2BwW9+OTBmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERm1ay1WTvU6CyxlREZkREZkREZkREZkREZkREZkREZkRHH7a5v+vsfOQ3bbaUh6J+PtP/8tDpbX7PHLuqwnkzIiI5IZu+dJsemX1rxgV5OpEiy79b64782/+IMRlTJnREZkREZkREZkRPH441k8cduCzOLViFvG7nWSZDFT1eg9qo+0x75Lo/95593unis0S6YSq9653GnI7jsPjd+BJId/qbpy86YNGxMb3Na3I1NGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERG7VqLlVO9zgJLGZERGZERGZERGZERGZERGZERGZERGVHcfvn583ft6r78gnsSe+O5V+Jged33f12X9WRSRmREMhMPOjc2/dxDK+xqMlWCxybfFfe97VdDjKiUOSMyIiMyIiMyIiO67eTqK3uPTbk7s3g14pZJPzxXspipasL+vw4W+y6N/vvvb8d/R+1yXF2m8jzb9v7585PUeSIGt/UyyZQRGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZFRu9Zi5VSvs8BSRmRERmRERmRERmRERmRERmRERmRERlS9fcWFU9J7GNErj6yOg+Xkn14k2XeybcmkjMiIZGYcXb1/8+TcJXY1mSrBQxNuj/veftYIIyplzoiMyIiMyIiMyIhu//WIeBbxXDKLVyNuiVdGspipKvZXsNh3afSfb7wZ/x2750l1mcrzlL8AjZ73ovKbiIPb+nZkyoiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyKhda7FyqtdZYCkjMiIjMiIjMiIjMiIjMiIjMiIjMiIj0ttX0XNzH4mD5a0nXiVZzNSbTMqIjEhm7pmjYtOPTZ1nV5OpEjww6pa479wL+lwyL1fKnBEZkREZkREZkRHF449n8cA1MzJ7bMrd1fM6c5RkMVNV7K9gz931aBp9a/Nf4r/jv31qXabKqfKbWHH72G+eHHdc9+SziQ1u69uRKSMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIqN2rcXKqV5ngaWMyIiMyIiMyIiMyIiMyIiMyIiMyIiMGo1VK9fq7atoxU3z42B553ljhIuZepNJGZERySy6bFJsevHoW+1qMlWCBd1T4r7zL9v2eW1VypwRGZERGZERGZERzbv0+ngW8Vwyi8VW3BKvjGQxU1Xsr2ArJ29bKP9tQ/V9wOu/9+u6TJVT5Tex4vfh2n1P6y5OxDW4rW9HpozIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzatRYrp3qdBZYyIiMyIiMyIiMyIiMyIiMyIiMyIiMyajQuOH2W3r6KHrq6Oqbed+VNwqXJyaSMyIhkHh09KzZ9z+U32tVkqgTzL7+hWppcNdmISpkzIiMyIiMyIiMyovuuuqlaJl5+Q2bpeT02ZrZkMVPVvX+q1qYPX31rGv3LCxvjvxMPqj47JZnqM1fPm1jx+3Adn6Z6/pGViQ1u69uRKSMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIqN2rcXKqV5ngaWMyIiMyIiMyIiMyIiMyIiMyIiMyIhkWr59Fd13UfUmypJrZ8mXJieTMiIjklk1fUFs+o6zR9nVZKoEc387Lu57/8hbjKiUOSMyIiMyIiMyIiOKxx/P4q4Lx2UWr0bcsurmhZLFTFVLxs2slsUX9ZxwYXV1ZcYpP/5tXab6zFW8iXX1/tWJuNY8sCyxwW19OzJlREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZk1K61WDnV6yywlBEZkREZkREZkREZkREZkREZkREZkUzLt6+iO06prvT8+C33youlZFJGZEQyL97zeGz6luP+aFeTqRLccdbIuO+DE/xeS6qUOSMyIiMyIiMyIiN6cHz1Hl48l8zi1YhbXrx3mWQxU9XjN99T3fGUbVdc3ryiOr3C9MOqs4JJpvrMRelNrKN3rS5Dmc90Oritb0emjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjNq1Fiunep0FljIiIzIiIzIiIzIiIzIiIzIiIzIiIypBf29fRdN/9vs4WD41/+HSN3ZgzpwRGZHMpmXPxaYn/b/f2tVkqgQzTxkS9330pruMqJQ5IzIiIzIiIzIiI4rHH88inktm8WrELfHKSBYzVcX+qlZUP/t9Gt34WHX5mvTtP8lUn7kovYm1e1f3JV1HP3n3g4kNbuvbkSkjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiKjdq3FyqleZ4GljMiIjMiIjMiIjMiIjMiIjMiIjMiISpDevrrgjBbv99yw/1lxsHx+6arSN3ZgzpwRGZHMX9dvjk2P36/F5QglUyW45dgr4r7LZtxrRKXMGZERGZERGZERGVE8/u6+7+GN/251KcB4ZSSLmapifwW74ftnpdGXllT/nXX8lXWZ6jNXT+m34idd56644/7EBrf17ciUERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRUbvWYuVUr7PAUkZkREZkREZkREZkREZkREZkREZkRHk0v30VPxg1m9fscnwcLF9+fl0xX5UdyaSMyIhk3vnbP2LTI79+rF1Npkow9YhL4r4r5yw2olLmjMiIjMiIjMiIjGjFnAfiWcRzyWzk146NW+KVkSxmqor9FeyaXY9Po2sXPBH/vf3koXWZ6jNXT/H7sHtX925dV80ff1dig9v6dmTKiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIqF1rsXKq11lgKSMyIiMyIiMyIiMyIiMyIiMyIiMyojya376qy60ffjRk58Pi35bXtvROR5IpmZQRGZHMJ9n6pB9fEMf4p+99xIhKmTMiIzIiIzIiIzKiePzdxR9Jt2x+Lb0gWz/aKtl3Msvn5z0W88w9Y2RdpjRbKm4/9ptXxe/GaYde16jNKVm24zIlkzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIio3atxcqpXmeBpYzIiIzIiIzIiIzIiIzIiIzIiIzIiNLQ4kWrufzckFUr19blJ3kPKWdERmT0Cd4/u/6As+OOzz643IhKmTMiIzIiIzIiIzKiZ5ZU7zzdcOBvkvlY7wyV7zY9c8eD8fO8c6rTrtqRp6O4/Ybjhu7addVuXd0PLHx60Fvfvmz0v/V6RmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERu1ai5VTvc4CSxmRERmRERmRERmRERmRERmRERmREaWhA/Yes2tX9w/32XYiAJlP8imonBEZkdEn+ARY+rTQC48/ZUSlzBmRERmRERmRERnRC0ufql7/7257/T/WZ5vKz0s9PeP++Pne301oKRv9bD1uv/2sEd/pujx+Qw7Ya8ygt7592eh/6/WMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiM2rUWK6d6nQWWMiIjMiIjMiIjMiIjMiIjMiIjMiIjittnz1gWx849PjtsyaI1LeUn+R5fzoiMyOgTfIdxzJ4nxh3XP+Xvu6VKmTMiIzIiIzIiIzKiePzxLMbsdVIyH+vbeeU3/p6cUn1YfuFlE1vKRj9bb1TnZB99Xtexe+w8JH5PbrzyzsFtffuy0f/W6xmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERm1ay1WTvU6CyxlREZkREZkREZkREZkREZkREZkREa0ft3G/fcYXR04xy3pT36SM1HljMiIjD7BWbjSn6heeXG9EZUyZ0RGZERGZERGZESvvLC+u/gT7cc6v1R5zqonbqhO97D4yqktZaOfrcftd/3u2rjjFcdNiN+T/b529RVdhw9i69uXjf63Xs+IjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMioXWuxcqrXWWApIzIiIzIiIzIiIzIiIzIiIzIiIzKii86+ozqT5P9O2lJ8glzmk5xLPWdERmT0Cc4jP+wLR8YdN7/yqhGVMmdERmRERmRERmREm16uLnETzyWZj3WG9PKs60vH3RE/PzT8lpay0c/W4/Z8McT4PYnflp92nTOIrW9fNvrfej0jMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiKjdq3FyqleZ4GljMiIjMiIjMiIjMiIjMiIjMiIjMio2Xxqxebddur+xmeHPrH0he3IT3I1wJwRGZHRoK+EuKW6tnH3TocNLIuMyIiMyIiMyIiMqHoiOx0Wz6XBKvhjXeOvvG7gIyNvi59jb7aUjf62Hlv848S4471/nLRs6fN77Dxkt66rJp872a7VnDt41cKUTMqIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjNq1Fiunep0FljIiIzIiIzIiIzIiIzIiIzIiIzIimf/858ND979h167uq/84wJ/eFl1WHdEXj75VrC5TRmRERmTUbK64aX48gDvPGzOgbBZzbtpQvfEz/EtHDSjLjMiIjMiIjMiIjChuH/7Fo+K5xDNqVJ+IGhM/r5zsyxklqR64plqbxh6M0QeHTI+fH59wZ0vZ6H/rC4dOjTvO+311mobf/PDq6vsQe4x6//0P61KVW9++bPS/9XpGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERGZERG7VqLlVO9zgJLGZERGZERGZERGZERGZERGZERGZHMhJGPxJHyx/uN37Tpte3LuWdWf+h5bOo8sbpMGZERGZFRs/nc3OpEULeeeNWAslnMmU4oMGqX4waUZUZkREZkREZkREYUt8ez6O45UUW8DvHzc3c9atdqzsem3B049mCM3n/F5Ph5+cR5LWWj/62XV5u++YSr9u26LH5zrhvls4t5ur5b375s9L/1ekZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREbtWouVU73OAksZkREZkREZkREZkREZkREZkREZUQk2rP/rnp8btttO3fff97Rdbc4ZR/8hDpNPzl1iV5MpIzIiIzJqNl95ZHU8gMk/vWhA2SzmXPfks3GvMXufPKAsMyIjMiIjMiIjMqK4PZ5FPJf1q56Nn+N1iJ/jNbFrNWfste6e6w8uuKT6g92qafe1lI3+t75kbPW3xTnnjm6w9XO7jovfnPj9id8iSVVuffuy0f/W6xmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERmRERm1ay1WTvU6CyxlREZkREZkREZkREZkREZkREZkRHl069bmcT+ZtmtX95UXLzCiYqaqiQedW71l8tAKu5pMGZERGZFRs/nGc6/EA7ju+78eUDaLOdMZmybsd8aAssyIjMiIjMiIjMiI4vbx+1UnlEpnApuw/6/j53hN7FrNGXst8KQfnhuj9/x2fPy8+rYHWspG/1t/+Prq0/Gzz7y60bP1i0+tzvt//E+nby1O0u7p+m69zI5kUkZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZkREZk1K61WDnV6yywlBEZkREZkREZkREZkREZkREZkRHl0ZnTVnHSyLH/fPs9Iypmqhq750lxmHxpTZ8PwreUKSMyIiMyajbf/vPf4gFcs8cvB5TNYs41DyyLe91w0DkDyjIjMiIjMiIjMiIjitvjWcRzeWbxsvh59B4nxM/xmti1mjP2WuCxe50Uo3f/ZnT8/Oych1vKRv9bf/Sm6vwOM08Z0ujZ+usvvR6/P/FbNGv6qlKqcutldiSTMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiKjdq3FyqleZ4GljMiIjMiIjMiIjMiIjMiIjMiIjCgNrX3hjW98dlgcGhcvqC7qbER95mo2h3728DhMvrZps11NpozIiIzIqNn88D/vxwOIhzGgbBZzphNy3nRIdX5UI+qdqMiIjMiIjMiIjMiI4vZ4FvFcnrrnkfg57Y4P3//ArtWcr726Kb10MXrn6SPi5xfmL20pG/1vfem06ksGt55wZaPY+gP3rY3fom98bmj8RmWpyq2X2ZFMyoiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiMyIiM2rUWK6d6nQWWMiIjMiIjMiIjMiIjMiIjMiIjMqK4/d1/v7/fLtfEcfHAvaoL0m1H5t77x7/iGDnyv35uRKXMGZERGZERXf3lY+JhbFy3YUCZR1fcUV0ZZtqRlw4oy4zIiIzIiIzIiIwobp92RHUu+3hGr67bED+M+MoxRuTpaMRXfh53ee/td2afNCR+WL9oRX/S01Hc/sTM6kRo039+ubZ+4N7Vm1j77XpN/F4lWS9vXXPWK0HOiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzatRYrp3qdBZYyIiMyIiMyIiMyIiMyIiMyIiMyoo8+2nr2SbPjiLjPl69e98JftiPLqd7c+HocICfs2+JChI2Ps3UjMiIjGvetU+JhrF2x7WI+25F5NJ3xfMbx1Qnojah3oiIjMiIjMiIjMiIjitvjWXRzLvt4BeKHcfucYkSejiZ8p/r8VuzH246tJnn5oaf7k56O4vYVcx6olqpH/F5bX/vCG/t8eUT8RsXvVfx2eTrKW9ec9UqQMyIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiIjMiKjdq3FyqleZ4GljMiIjMiIjMiIjMiIjMiIjMiIjGjIpQur9xt2uebVl3s/vmNExUzN11atjQPk1EN+Z0SlzBmRERmREU08+Lx4GKsXLR1Q5tFHJlVXzZt16rABZZkRGZERGZERGZERxe0zTx0az+XRSXPjFYgf4tUwIk9Hse/iLrEfbznisvjh1WXP9ic9HcXtq+Y9FHec/JPf1be+ccPfvrvLqPi9it8uT0d568WUH2/r9YzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzIiIzatRYrp3qdBZYyIiMyIiMyIiMyIiMyIiMyIiMyajTGDl8UR8G9vjD86ZWbty8bfedcv6j6ItisE7qNqJQ5IzIiIzKi6cdcHg9j+cw+R3EjyqNLrq1OQD/nN9UVfoyod6IiIzIiIzIiIzIiI2r0XHMmnlG8AvFDvBpG5On+f3t34i1XVSd6PH9L1Ce2CvZqByIaWlQGW7BbJkGJIiCItD4HWN2IojaGBDIIIZBAMJAQZIqoBJRBJSCRAD6mRoYAMqZX61Pbbm3tp3DfPt+Te+6+3/M751RurmPtz6rFquzz3edU1T2V2tzcW4X0tVvEvwyuP7h6f4d6reMI3h3S+P033VGtqw46JTz6vXc/O2+36qf60jnmPWZHz6c4Qh40HMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMHRuApWTm1lgSWO4AiO4AiO4AiO4AiO4AiO4Ahqrly/5bUvWZQuN1//cH9Zy4P7r6z+eej6U853hLxsOIIjOIIjXH1S9XnPt1107WDZbL15afXG5d84tbrljjC1o4wjOIIjOIIjOIIjbJ/8uOVblm2o3zEhPRqO4N0hfe3SlPuv+s7aA09JV/7twSe6Su8OafzB725NEy864BNdR0/nVX2CXXXZFu2zOXreq6nlQcMRHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHI2rYOXUVhZY4giO4AiO4AiO4AiO4AiO4AiOkAe3fOu+PV9+1t/MXbT+oq3uRtjn9y+4Nr1AfnfRZY6Qlw1HcARHcIRNp61KN+Oms9cPls3Wby1cm6bc8IXqIxQdYWpHGUdwBEdwBEdwBEdI4zd8fs0iPo2x/tSa605b5QjeHb575vpFfELOmv0+ka78+NFnukrvDmn8oe/9IE1c/baP9hx93YVb0zm2525n661rm6PncR408qDhCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7GVbByaisLLHEER3AER3AER3AER3AER3AER2i23n3nI3u/pvox5M+efK0jZHuakgff/uKl6QVy64XfcIS8bDiCIziCI9y0pHqdvu7TFwyWzdZNn11drcnOWjdY5hzBERzBERzBERwhjd+0uPoqpHtUfyvr5iWXOYJ3hzsv/Hqa8p2F61bv89F05Wc/qo7iCN4d0vijd1XvpL9y/on9Rz/95K+lMy2db/dsfaTZZ3P0vJw6ZCYPGo7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gaFwFK6e2ssASR3AER3AER3AER3AER3AER3CEetOdWx5+4yuXpNe8ExdseO655x1h+s52yINNnzwvvUA+uPFWR8jLhiM4giM4wm1rqm+k1T+x3l82W+sPRa4/o9oRpnaUcQRHcARHcARHcIQ0nu5Fui9fO2VFegTSldsvnvYNoYZ3hweuqT6vJn0dz+c3MX/xfPX5No7g3SGNb7v3oTRxxbwP9R89nWMfXnBZOt/2euXSdO7V+2yOnpfTjjopDxqO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4GhcBSuntrLAEkdwBEdwBEdwBEdwBEdwBEdwhDT+w4eefPPuS9Or3d++ZvmPnnymp2zLg6uOXpheIB//bvUhLW152XAER3AER7jrmpsXTb6pVX/ZbL3mo0vTlNsu/OpgmXMER3AER3AER3CENL559cZFfFhNegTSlbs23uII3h0e/071D3zp67iCN2H/5U/+o6v07pDGn/jX6jdJz3ntBwePns60dL5VbwXy6qUP//DJ7dnRVbblQcMRHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHI2rYOXUVhZY4giO4AiO4AiO4AiO4AiO4AiOkFZX//DWVel1bv7uy+78/o7vJTjC9J3tkAeXvKt6D/Ht921zhLxsOIIjOIIjPHDL9xdNfu5Nf9lsvfJDZ6Ypd6zbNFjmHMERHMERHMERHCGNp3uR7ku6R/Vn5qRHwxG8u9p929KU9HVc/poPpCu/+cUvu0rvDmn8qUer9xdduvuCUY5+55aH0+oqnXvpDKzWWJNHb5eSBw1HcARHcARHcARHcARHcARHcARHcARHcARHcARHcARHcARHcARHcARHcARHcARHcARHcARHcARHcARHcARHcDSugpVTW1lgiSM4giM4giM4giM4giM4giM4mpj46f/9Vb26evfbVtffRegqJ0bY5/lvPjG9QP7HM//uCHnZcARHcARHeOyeB9PNOG+v4wfLZutlC6p3dth6VfWB1o4wtaOMIziCIziCIziCI2yffC/1DQu+sOKNx6cr6dFwBO8O6WuXplww/8Qlf/XedOW3v/6frtK7Qxp/5smn0sQ0fcSjp7OuPgPTf5964On66GGZy4OGIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIzgaV8HKqa0ssMQRHMERHMERHMERHMERHMER1KTV1aH7rWmvrtplLQ8aebD0le9LL5D/86tfO0JeNhzBERzBEZ5+4kfVa/wr3ztYNlsvPax626cffKP66TFHmNpRxhEcwREcwREcwRHS+A++Xv0kU7pH9SIpPRqO4N0hfe3SlKWvet/ilx6Wrrz4wotdpXeHNP78s8+liYtfdvjoR2/WWIe8/aLPzz0iHb2rbORBwxEcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcjatg5dRWFljiCI7gCI7gCI7gCI7gCI7gCHnQs7pS2VBTa7b+v//+TbWsecWRg2XOERzBERwhjZ+92xHpxjz7o6cHy9qON3+/6Y7BMucIjuAIjuAIjuAI2yff6nPNOz+V/pseh56yLY2nr2CamC5nvWzH5y47wrR9Tao3pdVVvYfRj96ssfZ5yefSGqv+zllY1rI9TXEER3AER3AER3AER3AER3AER3AER3AER3AER3AER3AER3AER3AER3AER3AER3AER3AER3AER3AER3AER3AER+MqWDm1lQWWOIIjOIIjOIIjOIIjOIIjOEKztVldHbb/xe3VVV7mHKHZ+ovnf5JeU8/f64TBMucIjuAIjpDGz33DcenGbLv3ocGytnrfj6X+oc13D5Y5R3AER3AER3AER0jj6V6k+3LBW/8x/XfFnsf1lG1pfOUbT6iXR813khxh2r4m1Zvq713t7NHTeVifk2+Ze/rTD019hIA7ZHua4giO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiOxlWwcmorCyxxBEdwBEdwBEdwBEdwBEdwhHrT44/+ZP4e1W9vpdXVz376K0eYvrMdHKHZ+m8PVj/dvPbAUwbLnCM4giM4Qhq/cL+Ppxvz4He3Dpa1lfOrnx579K77B8ucIziCIziCIziCI6TxR7ZW70R17rwPpf9euP/He8q2NL72ndV7uKfLl/76A/1lW71p2R4L6j3s7NHT2fj2V3yx+r3CVy194rHqHSK6yuk728ERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHI2rYOXUVhZY4giO4AiO4AiO4AiO4AiO4AhpfPMt2/bkw+D23uNL6fWsp2xzhGbrE5vvTa+pV77/jMEy5wiO4AiOkMbXvecz6cbcfe23B8vaOa8/JvWPP1D9EqUjNGXOERzBERzBERzBEdJ4uhfpviz/6+rXANPj0FO2pfH0Fdzx/ac3HNdfttWbznld9WDO7OhrjzzjDXOrNVY6S9O52lO2OYIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIKjcRWsnNrKAkscwREcwREcwREcwREcwREcbd/+1I+ePfMzN6aXrnTZ9w0rZv07BP/61c3pNfUbHz93sMw5giM4giOk8StPWJxuzPfWTr2nvCM0W5fuXn3T5enHnhgsc47gCI7gCI7gCI6wffKNEup/p0uPQ0/ZlsbTV7BeHp3/5h2/zecI0/Y1qd5U//7gjI9+6twF+/CJAumSzth03rrbyX22OYIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIIjOIKjcRWsnNrKAkscwREcwREcwREcwREcwRHU3HHbQ3+/zwXpFWvebmdvuPiuF6tfDovL2tTmjCM0W+9ac116Tb3lXy4ZLHOO4AiO4Ahp/Ov/tLK6McsvHyxrZ/2v96T+uWeq13JHaMqcIziCIziCIziCI6Tx555+Jt2XxS+tftI8PQ49ZVsaT1/Benl04Vs/2l+21ZvO37v699ZdOfrWNZvSWTqPb7X+wz6r0tnbLtvU1BzBERzBERzBERzBERzBERzBERzBERzBERzBERzBERzBERzBERzBERzBERzBERzBERzBERzBERzBERzBERyNq2Dl1FYWWOIIjuAIjuAIjuAIjuAIjtBsff757Rcsv6X+COdD913z6A//vavMqak5QrP11rM2pJfGLSu/OljmHMERHMER0viNZ1av05tOv3CwTJ579tlqRfKywwdLcQRHcARHcARHcIR6U/N7fDcuurS/lDS+5bzqjeDT5eIDPtlfttWbVr+t+hzDXTn6rWdfnq6nczWdsdU/F778rHQOpzM5L9umNmccwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcjatg5dRWFljiCI7gCI7gCI7gCI7gCI5Qb7rphnv3m7ei/teWz37q2t/8+rfudn6f0mz95j+vSi+N932l+mgUR5jaUcYRHMERHCGN37rqmnRjNn5s2WCZPLXtyRQve/VRg6U4giM4giM4giM4Qr1p6auOqpc4m1dt7C8ljd/7leqDhhZl76juCNP2NanedNEBn9jFo6dzqc7SGZvO2/oE3m/PFTffcG9Ttk3f2Q6O4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiO4AiOxlWwcmorCyxxBEdwBEdwBEdwBEdwBEcTEy+88OLXrr7rg4ddWr8yzdvt7PTHsJwYeZ8TQ+XGD52VXhofu+muwTLnCI7gCI6Qxu+84luLeDfzwTJ54sFHUnzO644ZLMURHMERHMERHMER6k3nvPaD9RJn65U7/d70j964tZ572aGn9Zdt9aaLD9rxe4gzPno6l/Iynb3zXlH9c2G6pLM6/fEF3gFVvDs4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4GlfByqmtLLDEERzBERzBERzBERzBEfLgP3/xm8vW3HXQ3tV7NqbLG/9qyYHzz7/5hvvaZWP6znZwBEdotq4/5LT00vjs3Q8PljlHcARHcIQ0ft+3vpduTHqlHyyTR+9+IMUr3/ThwVIcwREcwREcwREcod6U7ku9xEmPQ38paTx9Beu5X3nv5/vLtnrTJYecuotHT+eSyptvuPed81em87k+sdMZns7z//rP30ztsXufbY7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gaFwFK6e2ssASR3AER3AER3AER3AER6g3bf72tsP2W7PX5CvQ371p5QXLb9n22FPtUvKg4QiO0GxdvU/1kzc/feL5wTLnCI7gCI6Qxh+58750Y87f+8TBMnno9ntSvPrtHxssxREcwREcwREcwRHqTc1PQaXHob+UNJ6+gvXcq45e2F+21ZvWH/HZXTx68/P1KtP5nM7qdG43//+Qzvnbvv14u2w0+8k5giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4gqNxFayc2soCSxzBERzBERzBERzBERxt3/7cc89vvmXbh4+6on7JSZcTF1yRRtK405H3uX1G5fLXVG+89Ouf/9dgmXMER3AER0jjTz3yeLoxy/ZYMFgm99+8JcUXH3jyYCmO4AiO4AiO4AiOUG+qP/knXdLj0F9KGk9fwXpu8+90jjBtX5PqTRsWfGEXj968x2lY1ud8Os+bcz6d/3+Ucz7nCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7gCI7GVbByaisLLHEER3AER3AER3AER8iDbY89ff6yP5X/m3/ht79bxC/iTfAOEI4wbV+THMERHMERqg3PP1/9Jt1LDnvu2R3vouQI9aYfXHdruvGXHHLqYCmO4AiO4AiO4AiOUG+65GD+ke6lh1W/RNpbSrXhxRcX8UnP1560dKBsqTddcewXd+Xo9Rfuhd/9bqCcmEhnu75rm54Rf8jv2uYcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwREcwdG4ClZObWWBJY7gCI7gCI7gCI7gCA//8Mnv3Hz/uWfddPg7Lmp+7PegvVdtuPiP/PMov/zxz9Nr6orXHztYiiM4giM4Qr3pS39T/aD3Ew8+MljedfVNqbzsqM8PluIIjuAIjuAIjuAI9aZ1h1U/D7ds9x3fwHOEqR1l6k3Ld39/tcD6yAwXWF85plpgzfi3Ms993bFpejqjBstaOvPT+d/83OG8Vyw5/ICL0nMkPVPS8yUvG94dHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHMERHI2rYOXUVhZY4giO4AiO4AiO4Ahp/Le/feHytfccffD6jyy44n0HXbI3HyCYX445/E/lN6p+/MjT6UXx4v0/MViKIziCIzhCvWnVW6ufQ3ro9nsGyy2XXZ/KK45bOFiKIziCIziCIziCI9Sb1h1RfVJQ/VnL/aXUm87bs/ocw2tPXDJYSr3p8qOrD9s59/XHDpZSb1qzX/UuD+mMGixz9W/OpmeEniPpWXPUuy45ccGV6Xl0+SX3pOfUxMj7nChliyM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4giM4gqNxNW3lNGdSPliPa2RiZx5WR3AER3AER3AER3AER3AER3AER3AER3AENQ/ct+3Lq2795PEb3/zqHR8Y0lzm7770Pe+46Pj3Xnb4Oy7asPaOuvfuMH2XOziCIziCI9SbntryYHpRvPyIzw2W4giO4AiOUG+q/5nsB9fdOljevubaVF5z0pLBUhzBERzBERzBERyh3rSOHzO/4G9PGiyl3lT/vsLG42f6M1jvr34G6/z5O37DwBGmdpSpN6WzKE1PZ9RgKfWm9Lw4/IDqOZKeKfN39zMoPac+dcJX167anJ5l03fZt09xBEdwBEdwBEdwBEdwBEdwBEdwBEdwBEdwBEdwBEdwBEdwBEdwBEdwBEdwBEdwBEdwBEdwBEdwNK6mVk75KkorqrLAEkdwBEdwBEf42U9/df//ee68s2897j0bPn/K9au/dHu6LD3jm+ly6sc2HvWuL79jrx0/U1VfDtx71dGHrL/wnO+lWT//2X97d/CB4QiO4AiO4Aj1ph9uuiO9KH7tpGWDpTiCIziCI9Sbrjh2YbpJW9ZfP1h+Z8WV1Y0/+dzBUhzBERzBERzBERyh3nQpn3V94f4fHyyl3vTlv6sz7nSJAAAK+ElEQVR+Rv7qYxYNllJvWv++06uj7/u/B0upN6WzKE1/eNOWwVIcIT1r0nMnPYOOPnh988+I9eUdb1qZnnHpeVc/Aesn4+dOuT49N887e3OalZ6t3h18YDiCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCIziCo3EVL6rCBdYNazd/+ojVzeXkg1ekywfmLz5o9zOOfvPi+o/1pmPfsuTv91h47FuWlnIG5YGvPuPdr1t80OuW7rXbYv3/dHiZ9/LFR71l+VnHXrzpjMs3nXzehiM+d/3JK2//0lXp8s0zvpwuV5909tqDT736I0vqP9ab/rjlVUdXq5mNx59dn2B+diI/AxuO4AiO4Aj1pg0fqL6Psv7I0wfv0aWHVz+xdOVxZw7uUxzBERzBERzBERyh3rTu0OoerZl8GzBHmNpRpt607t3VN/+ueP8Zg6XUm9bxeK6Z6W9lbjy+esfaqz545uBZF341+8v0nErPrPT8Ss+y9lOvfUnP2YNev6R6/lbP97NGeb6P/jdDKUv551XWl2+uvW36s3mMxIsqLbA24cP7Lmz/hVIuv9fLvLlnvH3uZw6Ye9r+c087eO7JR839WHM5dO6n9p172jFzP7JwbvU7XH+OlxX7/mN9av0pSDemfQt7Liv2O8m7+LNV3/cZfzl2TN9v16bv2tF/35f0LDt27kn7zv1Met7lT8P0rNx/7qcPmPvp9Dzdc+4X20/hcimXMb+csN9CP2n/suSLJYkXVeG/CaZ16KePXK3LsW9Z9q7XsJgtg7MyuM+yd+2x8BMHLF//z+tuXHzF9865Kl1uOHnl5Ueenv5/uv7jX8jgEZ+7Z+0NPsn+eO5Ze/3lR5x+/Smt2xne+CNPT7138WeL+z7zL0f90O3a9F09+uhfuN/rYHrOpmfuJw9YXj219xnh+V4Gy+Bf7mB9Kd/B4trQAqsoiqIoiqIYRbyoKgusoiiKoiiKGYsXVWWBVRRFURRFMWPTFlJzJuWDRVEURVEUxU7ZibXUiMuvJhssGyOWI+52xEzCPhwMdZVd46KsuQu1fJN0ZeFgWzZ7qgwHQz1l13gum/2ncuPzwXy8rSsLB3u0y/ZIjzAOB0N52dzyWlZZV9keCWVTp+L2SJe8VBwOSjZ1Wtke6aG4+WMz0k9xNntqsEe7bI+EmqwdD86d6J1eb9WIhNPDwS5hFg6G2mUzkg926Sq7xiXMwsFQu2xG8sEu7TKbPW08FGbhYJEb9aHJH8T+B3T0sjbKV2iUprazR6+F+w8HQ11l17j0Zz2bJjrubzgYCstwMBSW/Xcn1zW9PRgKy3AwFJb9U3KD00fZ1Rz0j/QI43Aw1FN2jdfCrflgGDTCrbs+PRxvC7OdOno7mMXp/Qant7d20azRJ9bU7+wewjgcbIR3MxwMhWU4GArL0e911/T2YCgsw8FQWPZPyQ1OH31X42bUx2Vmj+Yo5Rx4dLrBoDGD21lnisPBUE85+l3rykbZQ6Mp8ykzmD44GAqPPrpw+ui7CstwMBQefXTh9FF2NQf5H5v/jkLTewZDXWXXeCPcmg+GQaN/68RQEG4NB0NhOSY3vjEHzfXmvyPKp3eN9AjjcDCXb22uh4OhwTIcbITT+6fkBqf372qwDAcb4fT+KbnB6aPvatyM+rjM7NEcLOtglKzhbdM1zWApYR8OhtrlTt2MrqxrPNTE+azR99AumwdzFPnRG9OTPvn09mC/rmOFg2359Pp61w5D7en1lWlRpKtsj4TC6eFgqKcMB3P13azlg+H1tnC6tno0E04PB0Nh2XW9LZye6xqvhdPDwVBYNn8cnN5ol+2RHop3/ehdg7nwbnZdbwunN5qtXcLp9eDg3Inu6eH1tsEyHGyE0+ubVGu2hnqma2shoz404UPcbzDLv1TTt9hOHb3+wg9mEvbhYEjl6Het1pV1jbd1PUQj7qF9+2v5YI+uI464h64pg9N7bmc4KD3TJ0bYQzg9HGxrgnbZHmkLp4eDof4yHOwS7mr0PWhWLds+YLaOPvr0/rI9Iv3TuwYbXdOrRw3NSL922R7pEd6M0fcQluGgtO9m1/VQe3rXYKhddl0P7cr0/rI9Iv3TuwYbXdMn71Df3DE36kPT9RB32almMB796KOXEsbhYEjl6HetFmbhYKjr6Lrepafp2dSY9aPPyWhTl57bMIqwDwdDTZlP6Z8eTukaaQunh4OhnrI90i/c1eg7CctwMDSLR5+TyRLrOVD/xFrP9J7BRjg9HOwRNuFgqOtej7iHMAsHJbyb4WBosAwHG+H0cDDUVc7JNIOhrnJwYq1rerPVQ9O1p+dTBqePrVEfl516NAeDielfsIajSfmmnmxiZ0oJ43AwpOO2ZW0gDMLBtnaWj7S3Sn/Qv3UiCv6QR2/s1EHbwinhYKgpR7wZcyL51qwNeGY3z4Qj5FuzdljTz2wnYRkOhv7wR+86UP+sxuCUcLARTg8He4RNOBjS4dqyNhAG4aDkTXM9HAwNluFgI5weDoYGy3AwNLirfuGUcDA0g/s+zkZ9XEZ/NPu3hganzOzo/aWEcTgY6iq7xqWdtUdCYTb6g9C/dWIoCLfO1tH7t0oT79SsRjh99F3tyvR21h7pEcbhYEjliBPDuxkOhgbLcLARTg8HQ/1le0TC6YOzGoPT+3cVluFgl66ga1x6sp5NjbAJB9vCuxkOhgbLcLARTg8HQ/1le6RL/34GdU0fcVddU0acPoZ24nGZM8kbpmuyUeLaKNmIO5w68FApYR8OhrrKrnFpZ+2RUH5/8yntkVA2dSpuj3TJyzxuj4SyqVOx/tgjmxocWuNtYRYOhsIyHOzXLtsjPcI4HAypHHFi190MB9uy2TN56LrKcLAtmx0cPQs7teNmROOhdpZNHZg7MRvTPYSucenJejY1wiYcbOu6m+FgWzZ7Jg9dVxkOtmWzg6NnYSwsm8H2JgmbbGrf3Ildnj7OykNTFEVRFEUxy8oCqyiKoiiKYpaVBVZRFEVRFMUsKwusoiiKoiiKWVYWWEVRFEVRFLOsLLCKoiiKoihmWVlgFUVRFEVRzLKywCqKoiiKophlZYFVFEVRFEUxy8oCqyiKoiiKYpaVBVZRFEVRFMUsKwusohhr4aeJhYOzYuoDzFqH6BrfRTPbW9esZrx9O3s2FUUxhsrfAkUx1sLVQDi463r2mW/qyWZgZnsLZ7VvZDOiPpxeFMVYKX8LFMVY00Kha6TWHtFgGOdbQz2bGu3dhiMab67Xf5zxrLzMrzcjKsORoijGSvkroCjGmhYK7ZH2JsnLcFbXSKNnU60dhLcqvAFhmV8fnNVoT2/+2wjjoijGUPkroCjGWr5Q6L+i6+3B/lJba+1NoXbQ3lt7UFe6rg8ONtqlDt0OiqIYW+WvgKIYa1pStFcY9Rqi0T9YX9H1wZH2JmkHGqn/GN4ADebCoD3YjKis/9j+bxM014uiGEPlr4CiGGtaUrRXGOFCIVxJhIOjjLQ3STvQsXTj8+vhYC4MBkuN9NyAoijGU/kroCjGWr5ECNcH4UIhnBVOz2lwsG+0t4ZzRx/MhcFgqZH6SriroijGU/kroCjGWteaQNcbGsnL5oqu55qJzfRwUz6urf0jXdebPzaz8pG8zK/nf6xHuv6ofWprURRjqPwVUBRFMZLR10yjl0VR/KUqfwsURVGMapSVk76VVRTFeCp/CxRFURRFUcyyssAqiqIoiqKYZWWBVRRFURRFMcv+PzqfxURT7G7iAAAAAElFTkSuQmCC" alt="Per sequence GC content graph"/></p></div><div class="module"><h2 id="M6"><img src="data:image/png;base64,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" alt="[OK]"/>Per base N content</h2><p><img class="indented" 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" alt="N content graph"/></p></div><div class="module"><h2 id="M7"><img src="data:image/png;base64,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" alt="[OK]"/>Sequence Length Distribution</h2><p><img class="indented" src="data:image/png;base64,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" alt="Sequence length distribution"/></p></div><div class="module"><h2 id="M8"><img src="data:image/png;base64,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" alt="[OK]"/>Sequence Duplication Levels</h2><p><img class="indented" src="data:image/png;base64,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" alt="Duplication level graph"/></p></div><div class="module"><h2 id="M9"><img src="data:image/png;base64,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" alt="[FAIL]"/>Overrepresented sequences</h2><table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>CCTTTCGCCATCAACTAACGATTCTGTCAAAAACTGACGCGTTGGATGAG</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>TGGCGCTCTCCGTCTTTCTCCATTTCGTCGTGGCCTTGCTATTGACTCTA</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>ACCATAAACGCAAGCCTCAACGCAGCGACGAGCACGAGAGCGGTCAGTAG</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>TGTTTTCCGTAAATTCAGCGCCTTCCATGATGCGACAGGCCGTTTGAATG</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>CTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGGT</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>TCTGCGTTTGCTGATGAACTAAGTCAACCTCAGCACTAACCTTGCGAGTC</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>CCATACAAAACAGGGTCGCCAGCAATATCGGTATAAGTCAAAGCACCTTT</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>TAAGCATTTGTTTCAGGGTTATTTGAATATCTATAACAACTATTTTCAAG</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>CAAATTAGCATAAGCAGCTTGCAGACCCATAATGTCAATAGATGTGGTAG</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>GCGTTAAGGTACTGAATCTCTTTAGTCGCAGTAGGCGGAAAACGAACAAG</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>CTGAATGGAATTAAGAAAACCACCAATACCAGCATTAACCTTCAAACTAT</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>GCGACCATTCAAAGGATAAACATCATAGGCAGTCGGGAGGGTAGTCGGAA</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>GTGAAATTTCTAGGAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAG</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>CTCAAATCCGGCGTCAACCATACCAGCATAGGAAGCATCAGCACCAGCAC</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>TTCTGGTGATTTGCAAGAACGCGTACTTATTCGCCACCATGATTATGACC</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>CTCGCGATTCAATCATGACTTCGTGATAAAAGATTGAGTGTGAGGTTATA</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>TTAGGTGTGTGTAAAACAGGTGCCGAAGAAGCTGGATTAACAGAATTGAG</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>GCGGTATTGCTTCTGCTCTTGCTGGTGGCGCCATGTCTAAATTGTTTGGA</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>TTTCGGATATTTCTGATGAGTCGAAAAATTATCTTGATAAAGCAGTAATT</td><td>1</td><td>5.0</td><td>No Hit</td></tr><tr><td>CTCGCCAAATGACGACTTCTACCACATCTATTGACATTATGGGTCTGCAA</td><td>1</td><td>5.0</td><td>No Hit</td></tr></tbody></table></div><div class="module"><h2 id="M10"><img src="data:image/png;base64,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" alt="[OK]"/>Adapter Content</h2><p><img class="indented" 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" alt="Adapter graph"/></p></div></div><div class="footer">Produced by <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc/">FastQC</a> (version 0.12.1)</div></body></html> \ No newline at end of file
--- a/test-data/fastqc_report_kmer.html Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -<html><head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <title> 1000trimmed_fastq - report </title><link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous"><link href="https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-wvfXpqpZZVQGK6TAh5PVlGOfQNHSoD2xbE+QkPxCAFlNEevoEH3Sl0sibVcOQVnN" crossorigin="anonymous"><style type="text/css"> @media screen { div.summary { width: 18em; position:fixed; top: 4em; margin:1em 0 0 1em; } div.main { display:block; position:absolute; overflow:auto; height:auto; width:auto; top:4.5em; bottom:2.3em; left:18em; right:0; border-left: 1px solid #CCC; 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list-style-type:none; } div.summary ul li img { margin-bottom:-0.5em; margin-top:0.5em; } div.main { background-color: white; } div.module { padding-bottom:3em; padding-top:3em; border-bottom: 1px solid #990000 } div.footer { background-color: #EEE; border:0; margin:0; padding: 0.5em; font-size: 100%; width:100%; } h2 { color: #2a5e8c; padding-bottom: 0; margin-bottom: 0; clear:left; }table { margin-left: 3em; text-align: center; } th { text-align: center; background-color: #000080; color: #FFF; padding: 0.4em;} td { font-family: monospace; text-align: left; background-color: #EEE; color: #000; padding: 0.4em;}img { padding-top: 0; margin-top: 0; border-top: 0;} p { padding-top: 0; margin-top: 0;}.pass { color : #009900;}.warn { color : #999900;}.fail { color : #990000;}</style><script src="https://cdn.plot.ly/plotly-latest.min.js"></script></head><body><div class="header"> <div id="header_title">Report</div> <div id="header_filename">Mon May 27 16:59:30 2024 -<br/> 1000trimmed_fastq </div></div><div class="summary"><h2>Summary</h2><ul> <li><a class="pass" href="#basicstatistics"> Basic Statistics </a></li> <li><a class="pass" href="#perbasesequencequality"> Per base sequence quality</a></li> <li><a class="fail" href="#pertilesequencequality">Per tile sequence quality</a></li> <li><a class="pass" href="#persequencequalityscores">Per sequence quality scores</a></li> <li><a class="fail" href="#perbasesequencecontent">Per base sequence content</a></li> <li><a class="warn" href="#persequencegccontent">Per sequence GC content</a></li> <li><a class="pass" href="#perbasencontent">Per base N content</a></li> <li><a class="warn" href="#sequencelengthdistribution">Sequence Length Distribution</a></li> <li><a class="pass" href="#sequenceduplicationlevels">Sequence Duplication Levels</a></li> <li><a class="warn" href="#overrepresentedsequences">Overrepresented sequences</a></li> <li><a class="pass" href="#adaptercontent">Adapter Content</a></li> <!-- <li><a class="{{passkmercontent}}" href="#kmercontent">{{kmercontentname}}</a></li> --></ul></div><div class="main"><div class="module"> <h2 class="pass" id="basicstatistics"> Basic Statistics: pass </h2> <table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>1000trimmed_fastq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4905</td></tr><tr><td>Sequences Flagged As Poor Quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1 - 108</td></tr><tr><td>%GC:</td><td>41</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="perbasesequencequality"> Per base sequence quality: pass</h2> <div id="seqbasequalityboxplot"></div></div><div class="module"> <h2 class="fail" id="pertilesequencequality"> Per tile sequence quality : fail </h2> <div id="tilequalityheatmap"></div></div><div class="module"> <h2 class="pass" id="persequencequalityscores"> Per sequence quality scores : pass </h2> <div id="seqqualitylineplot"></div></div><div class="module"> <h2 class="fail" id="perbasesequencecontent"> Per base sequence content : fail </h2> <div id="basesequencecontentlineplot"></div></div><div class="module"> <h2 class="warn" id="persequencegccontent"> Per sequence GC content: warn </h2> <div id="sequencegccontentlineplot"></div></div><div class="module"> <h2 class="pass" id="perbasencontent"> Per base N content : pass </h2> <div id="basencontentlineplot"></div></div><div class="module"> <h2 class="warn" id="sequencelengthdistribution"> Sequence Length Distribution : warn </h2> <div id="sequencelengthdistributionlineplot"></div></div><div class="module"> <h2 class="pass" id="sequenceduplicationlevels"> Sequence Duplication Levels : pass </h2> <div id="seqduplevelslineplot"></div></div><div class="module"> <h2 class="warn" id="overrepresentedsequences"> Overrepresented sequences : warn</h2> <table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT</td><td>33</td><td>0.672783</td><td>No Hit</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="adaptercontent"> Adapter Content : pass </h2> <div id="adapterlineplot"></div></div><!--<div class="module"> <h2 class="{{passkmercontent}}" id="kmercontent"> {{kmercontentname}} : {{passkmercontent}} </h2> <div id="kmerlineplot"></div></div>--></div><div class="footer">Falco 1.2.2</div></body><script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1"crossorigin="anonymous"></script><script 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--- a/test-data/fastqc_report_min_length.html Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -<html><head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <title> 1000trimmed_fastq - report </title><link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous"><link href="https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-wvfXpqpZZVQGK6TAh5PVlGOfQNHSoD2xbE+QkPxCAFlNEevoEH3Sl0sibVcOQVnN" crossorigin="anonymous"><style type="text/css"> @media screen { div.summary { width: 18em; position:fixed; top: 4em; margin:1em 0 0 1em; } div.main { display:block; position:absolute; overflow:auto; height:auto; width:auto; top:4.5em; bottom:2.3em; left:18em; right:0; border-left: 1px solid #CCC; padding:0 0 0 1em; background-color: white; z-index:1; } div.header { background-color: #EEE; border:0; margin:0; padding: 0.2em; font-size: 200%; position:fixed; width:100%; top:0; left:0; z-index:2; } div.footer { background-color: #EEE; border:0; margin:0; padding:0.5em; height: 2.5em; overflow:hidden; font-size: 100%; position:fixed; bottom:0; width:100%; z-index:2; } img.indented { margin-left: 3em; } } @media print { img { max-width:100% !important; page-break-inside: avoid; } h2, h3 { page-break-after: avoid; } div.header { background-color: #FFF; } } body { color: #000; background-color: #FFF; border: 0; margin: 0; padding: 0; } div.header { border:0; margin:0; padding: 0.5em; font-size: 200%; width:100%; } #header_title { display:inline-block; float:left; clear:left; } #header_filename { display:inline-block; float:right; clear:right; font-size: 50%; margin-right:2em; text-align: right; } div.header h3 { font-size: 50%; margin-bottom: 0; } div.summary ul { padding-left:0; list-style-type:none; } div.summary ul li img { margin-bottom:-0.5em; margin-top:0.5em; } div.main { background-color: white; } div.module { padding-bottom:3em; padding-top:3em; border-bottom: 1px solid #990000 } div.footer { background-color: #EEE; border:0; margin:0; padding: 0.5em; font-size: 100%; width:100%; } h2 { color: #2a5e8c; padding-bottom: 0; margin-bottom: 0; clear:left; }table { margin-left: 3em; text-align: center; } th { text-align: center; background-color: #000080; color: #FFF; padding: 0.4em;} td { font-family: monospace; text-align: left; background-color: #EEE; color: #000; padding: 0.4em;}img { padding-top: 0; margin-top: 0; border-top: 0;} p { padding-top: 0; margin-top: 0;}.pass { color : #009900;}.warn { color : #999900;}.fail { color : #990000;}</style><script src="https://cdn.plot.ly/plotly-latest.min.js"></script></head><body><div class="header"> <div id="header_title">Report</div> <div id="header_filename">Mon May 27 17:01:42 2024 -<br/> 1000trimmed_fastq </div></div><div class="summary"><h2>Summary</h2><ul> <li><a class="pass" href="#basicstatistics"> Basic Statistics </a></li> <li><a class="pass" href="#perbasesequencequality"> Per base sequence quality</a></li> <li><a class="fail" href="#pertilesequencequality">Per tile sequence quality</a></li> <li><a class="pass" href="#persequencequalityscores">Per sequence quality scores</a></li> <li><a class="fail" href="#perbasesequencecontent">Per base sequence content</a></li> <li><a class="warn" href="#persequencegccontent">Per sequence GC content</a></li> <li><a class="pass" href="#perbasencontent">Per base N content</a></li> <li><a class="warn" href="#sequencelengthdistribution">Sequence Length Distribution</a></li> <li><a class="pass" href="#sequenceduplicationlevels">Sequence Duplication Levels</a></li> <li><a class="warn" href="#overrepresentedsequences">Overrepresented sequences</a></li> <li><a class="pass" href="#adaptercontent">Adapter Content</a></li> <!-- <li><a class="{{passkmercontent}}" href="#kmercontent">{{kmercontentname}}</a></li> --></ul></div><div class="main"><div class="module"> <h2 class="pass" id="basicstatistics"> Basic Statistics: pass </h2> <table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>1000trimmed_fastq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4905</td></tr><tr><td>Sequences Flagged As Poor Quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1 - 108</td></tr><tr><td>%GC:</td><td>41</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="perbasesequencequality"> Per base sequence quality: pass</h2> <div id="seqbasequalityboxplot"></div></div><div class="module"> <h2 class="fail" id="pertilesequencequality"> Per tile sequence quality : fail </h2> <div id="tilequalityheatmap"></div></div><div class="module"> <h2 class="pass" id="persequencequalityscores"> Per sequence quality scores : pass </h2> <div id="seqqualitylineplot"></div></div><div class="module"> <h2 class="fail" id="perbasesequencecontent"> Per base sequence content : fail </h2> <div id="basesequencecontentlineplot"></div></div><div class="module"> <h2 class="warn" id="persequencegccontent"> Per sequence GC content: warn </h2> <div id="sequencegccontentlineplot"></div></div><div class="module"> <h2 class="pass" id="perbasencontent"> Per base N content : pass </h2> <div id="basencontentlineplot"></div></div><div class="module"> <h2 class="warn" id="sequencelengthdistribution"> Sequence Length Distribution : warn </h2> <div id="sequencelengthdistributionlineplot"></div></div><div class="module"> <h2 class="pass" id="sequenceduplicationlevels"> Sequence Duplication Levels : pass </h2> <div id="seqduplevelslineplot"></div></div><div class="module"> <h2 class="warn" id="overrepresentedsequences"> Overrepresented sequences : warn</h2> <table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT</td><td>33</td><td>0.672783</td><td>No Hit</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="adaptercontent"> Adapter Content : pass </h2> <div id="adapterlineplot"></div></div><!--<div class="module"> <h2 class="{{passkmercontent}}" id="kmercontent"> {{kmercontentname}} : {{passkmercontent}} </h2> <div id="kmerlineplot"></div></div>--></div><div class="footer">Falco 1.2.2</div></body><script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1"crossorigin="anonymous"></script><script 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--- a/test-data/fastqc_report_nogroup.html Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -<html><head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <title> 1000trimmed_fastq - report </title><link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous"><link href="https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-wvfXpqpZZVQGK6TAh5PVlGOfQNHSoD2xbE+QkPxCAFlNEevoEH3Sl0sibVcOQVnN" crossorigin="anonymous"><style type="text/css"> @media screen { div.summary { width: 18em; position:fixed; top: 4em; margin:1em 0 0 1em; } div.main { display:block; position:absolute; overflow:auto; height:auto; width:auto; top:4.5em; bottom:2.3em; left:18em; right:0; border-left: 1px solid #CCC; padding:0 0 0 1em; background-color: white; z-index:1; } div.header { background-color: #EEE; border:0; margin:0; padding: 0.2em; font-size: 200%; position:fixed; width:100%; top:0; left:0; z-index:2; } div.footer { background-color: #EEE; border:0; margin:0; padding:0.5em; height: 2.5em; overflow:hidden; font-size: 100%; position:fixed; bottom:0; width:100%; z-index:2; } img.indented { margin-left: 3em; } } @media print { img { max-width:100% !important; page-break-inside: avoid; } h2, h3 { page-break-after: avoid; } div.header { background-color: #FFF; } } body { color: #000; background-color: #FFF; border: 0; margin: 0; padding: 0; } div.header { border:0; margin:0; padding: 0.5em; font-size: 200%; width:100%; } #header_title { display:inline-block; float:left; clear:left; } #header_filename { display:inline-block; float:right; clear:right; font-size: 50%; margin-right:2em; text-align: right; } div.header h3 { font-size: 50%; margin-bottom: 0; } div.summary ul { padding-left:0; list-style-type:none; } div.summary ul li img { margin-bottom:-0.5em; margin-top:0.5em; } div.main { background-color: white; } div.module { padding-bottom:3em; padding-top:3em; border-bottom: 1px solid #990000 } div.footer { background-color: #EEE; border:0; margin:0; padding: 0.5em; font-size: 100%; width:100%; } h2 { color: #2a5e8c; padding-bottom: 0; margin-bottom: 0; clear:left; }table { margin-left: 3em; text-align: center; } th { text-align: center; background-color: #000080; color: #FFF; padding: 0.4em;} td { font-family: monospace; text-align: left; background-color: #EEE; color: #000; padding: 0.4em;}img { padding-top: 0; margin-top: 0; border-top: 0;} p { padding-top: 0; margin-top: 0;}.pass { color : #009900;}.warn { color : #999900;}.fail { color : #990000;}</style><script src="https://cdn.plot.ly/plotly-latest.min.js"></script></head><body><div class="header"> <div id="header_title">Report</div> <div id="header_filename">Sun Sep 1 15:40:19 2024 -<br/> 1000trimmed_fastq </div></div><div class="summary"><h2>Summary</h2><ul> <li><a class="pass" href="#basicstatistics"> Basic Statistics </a></li> <li><a class="pass" href="#perbasesequencequality"> Per base sequence quality</a></li> <li><a class="fail" href="#pertilesequencequality">Per tile sequence quality</a></li> <li><a class="pass" href="#persequencequalityscores">Per sequence quality scores</a></li> <li><a class="fail" href="#perbasesequencecontent">Per base sequence content</a></li> <li><a class="warn" href="#persequencegccontent">Per sequence GC content</a></li> <li><a class="pass" href="#perbasencontent">Per base N content</a></li> <li><a class="warn" href="#sequencelengthdistribution">Sequence Length Distribution</a></li> <li><a class="pass" href="#sequenceduplicationlevels">Sequence Duplication Levels</a></li> <li><a class="warn" href="#overrepresentedsequences">Overrepresented sequences</a></li> <li><a class="warn" href="#adaptercontent">Adapter Content</a></li> <!-- <li><a class="{{passkmercontent}}" href="#kmercontent">{{kmercontentname}}</a></li> --></ul></div><div class="main"><div class="module"> <h2 class="pass" id="basicstatistics"> Basic Statistics: pass </h2> <table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>1000trimmed_fastq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4905</td></tr><tr><td>Sequences Flagged As Poor Quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1 - 108</td></tr><tr><td>%GC:</td><td>41</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="perbasesequencequality"> Per base sequence quality: pass</h2> <div id="seqbasequalityboxplot"></div></div><div class="module"> <h2 class="fail" id="pertilesequencequality"> Per tile sequence quality : fail </h2> <div id="tilequalityheatmap"></div></div><div class="module"> <h2 class="pass" id="persequencequalityscores"> Per sequence quality scores : pass </h2> <div id="seqqualitylineplot"></div></div><div class="module"> <h2 class="fail" id="perbasesequencecontent"> Per base sequence content : fail </h2> <div id="basesequencecontentlineplot"></div></div><div class="module"> <h2 class="warn" id="persequencegccontent"> Per sequence GC content: warn </h2> <div id="sequencegccontentlineplot"></div></div><div class="module"> <h2 class="pass" id="perbasencontent"> Per base N content : pass </h2> <div id="basencontentlineplot"></div></div><div class="module"> <h2 class="warn" id="sequencelengthdistribution"> Sequence Length Distribution : warn </h2> <div id="sequencelengthdistributionlineplot"></div></div><div class="module"> <h2 class="pass" id="sequenceduplicationlevels"> Sequence Duplication Levels : pass </h2> <div id="seqduplevelslineplot"></div></div><div class="module"> <h2 class="warn" id="overrepresentedsequences"> Overrepresented sequences : warn</h2> <table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT</td><td>33</td><td>0.672783</td><td>No Hit</td></tr></tbody></table></div><div class="module"> <h2 class="warn" id="adaptercontent"> Adapter Content : warn </h2> <div id="adapterlineplot"></div></div><!--<div class="module"> <h2 class="{{passkmercontent}}" id="kmercontent"> {{kmercontentname}} : {{passkmercontent}} </h2> <div id="kmerlineplot"></div></div>--></div><div class="footer">Falco 1.2.3</div></body><script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1"crossorigin="anonymous"></script><script 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--- a/test-data/fastqc_report_reverse_complement.html Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -<html><head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <title> 1000trimmed_fastq - report </title><link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous"><link href="https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-wvfXpqpZZVQGK6TAh5PVlGOfQNHSoD2xbE+QkPxCAFlNEevoEH3Sl0sibVcOQVnN" crossorigin="anonymous"><style type="text/css"> @media screen { div.summary { width: 18em; position:fixed; top: 4em; margin:1em 0 0 1em; } div.main { display:block; position:absolute; overflow:auto; height:auto; width:auto; top:4.5em; bottom:2.3em; left:18em; right:0; border-left: 1px solid #CCC; padding:0 0 0 1em; background-color: white; z-index:1; } div.header { background-color: #EEE; border:0; margin:0; padding: 0.2em; font-size: 200%; position:fixed; width:100%; top:0; left:0; z-index:2; } div.footer { background-color: #EEE; border:0; margin:0; padding:0.5em; height: 2.5em; overflow:hidden; font-size: 100%; position:fixed; bottom:0; width:100%; z-index:2; } img.indented { margin-left: 3em; } } @media print { img { max-width:100% !important; page-break-inside: avoid; } h2, h3 { page-break-after: avoid; } div.header { background-color: #FFF; } } body { color: #000; background-color: #FFF; border: 0; margin: 0; padding: 0; } div.header { border:0; margin:0; padding: 0.5em; font-size: 200%; width:100%; } #header_title { display:inline-block; float:left; clear:left; } #header_filename { display:inline-block; float:right; clear:right; font-size: 50%; margin-right:2em; text-align: right; } div.header h3 { font-size: 50%; margin-bottom: 0; } div.summary ul { padding-left:0; list-style-type:none; } div.summary ul li img { margin-bottom:-0.5em; margin-top:0.5em; } div.main { background-color: white; } div.module { padding-bottom:3em; padding-top:3em; border-bottom: 1px solid #990000 } div.footer { background-color: #EEE; border:0; margin:0; padding: 0.5em; font-size: 100%; width:100%; } h2 { color: #2a5e8c; padding-bottom: 0; margin-bottom: 0; clear:left; }table { margin-left: 3em; text-align: center; } th { text-align: center; background-color: #000080; color: #FFF; padding: 0.4em;} td { font-family: monospace; text-align: left; background-color: #EEE; color: #000; padding: 0.4em;}img { padding-top: 0; margin-top: 0; border-top: 0;} p { padding-top: 0; margin-top: 0;}.pass { color : #009900;}.warn { color : #999900;}.fail { color : #990000;}</style><script src="https://cdn.plot.ly/plotly-latest.min.js"></script></head><body><div class="header"> <div id="header_title">Report</div> <div id="header_filename">Sun Sep 1 15:41:08 2024 -<br/> 1000trimmed_fastq </div></div><div class="summary"><h2>Summary</h2><ul> <li><a class="pass" href="#basicstatistics"> Basic Statistics </a></li> <li><a class="pass" href="#perbasesequencequality"> Per base sequence quality</a></li> <li><a class="fail" href="#pertilesequencequality">Per tile sequence quality</a></li> <li><a class="pass" href="#persequencequalityscores">Per sequence quality scores</a></li> <li><a class="fail" href="#perbasesequencecontent">Per base sequence content</a></li> <li><a class="warn" href="#persequencegccontent">Per sequence GC content</a></li> <li><a class="pass" href="#perbasencontent">Per base N content</a></li> <li><a class="warn" href="#sequencelengthdistribution">Sequence Length Distribution</a></li> <li><a class="pass" href="#sequenceduplicationlevels">Sequence Duplication Levels</a></li> <li><a class="warn" href="#overrepresentedsequences">Overrepresented sequences</a></li> <li><a class="warn" href="#adaptercontent">Adapter Content</a></li> <!-- <li><a class="{{passkmercontent}}" href="#kmercontent">{{kmercontentname}}</a></li> --></ul></div><div class="main"><div class="module"> <h2 class="pass" id="basicstatistics"> Basic Statistics: pass </h2> <table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>1000trimmed_fastq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4905</td></tr><tr><td>Sequences Flagged As Poor Quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1 - 108</td></tr><tr><td>%GC:</td><td>41</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="perbasesequencequality"> Per base sequence quality: pass</h2> <div id="seqbasequalityboxplot"></div></div><div class="module"> <h2 class="fail" id="pertilesequencequality"> Per tile sequence quality : fail </h2> <div id="tilequalityheatmap"></div></div><div class="module"> <h2 class="pass" id="persequencequalityscores"> Per sequence quality scores : pass </h2> <div id="seqqualitylineplot"></div></div><div class="module"> <h2 class="fail" id="perbasesequencecontent"> Per base sequence content : fail </h2> <div id="basesequencecontentlineplot"></div></div><div class="module"> <h2 class="warn" id="persequencegccontent"> Per sequence GC content: warn </h2> <div id="sequencegccontentlineplot"></div></div><div class="module"> <h2 class="pass" id="perbasencontent"> Per base N content : pass </h2> <div id="basencontentlineplot"></div></div><div class="module"> <h2 class="warn" id="sequencelengthdistribution"> Sequence Length Distribution : warn </h2> <div id="sequencelengthdistributionlineplot"></div></div><div class="module"> <h2 class="pass" id="sequenceduplicationlevels"> Sequence Duplication Levels : pass </h2> <div id="seqduplevelslineplot"></div></div><div class="module"> <h2 class="warn" id="overrepresentedsequences"> Overrepresented sequences : warn</h2> <table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT</td><td>33</td><td>0.672783</td><td>No Hit</td></tr></tbody></table></div><div class="module"> <h2 class="warn" id="adaptercontent"> Adapter Content : warn </h2> <div id="adapterlineplot"></div></div><!--<div class="module"> <h2 class="{{passkmercontent}}" id="kmercontent"> {{kmercontentname}} : {{passkmercontent}} </h2> <div id="kmerlineplot"></div></div>--></div><div class="footer">Falco 1.2.3</div></body><script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1"crossorigin="anonymous"></script><script 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--- a/test-data/fastqc_report_reverse_complement.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/fastqc_report_reverse_complement.txt Fri Sep 27 17:41:40 2024 +0000 @@ -1,4 +1,4 @@ -##Falco 1.2.3 +##Falco 1.2.4 >>Basic Statistics pass #Measure Value Filename 1000trimmed_fastq @@ -1597,114 +1597,114 @@ #Sequence Count Percentage Possible Source ATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCAT 33 0.672783 No Hit >>END_MODULE ->>Adapter Content warn +>>Adapter Content pass #Position Illumina Universal Adapter Illumina Small RNA 3' Adapter Illumina Small RNA 5' Adapter Nextera Transposase Sequence PolyA PolyG 1 0 0 0 0 0.0203874 0 -2 0 0 0 0 0.0815494 0 -3 0 0 0 0 0.142712 0 -4 0 0 0 0 0.183486 0 -5 0 0 0 0 0.285423 0 -6 0 0 0 0 0.38736 0 -7 0 0 0 0 0.489297 0 -8 0 0 0 0 0.591233 0 -9 0 0 0 0 0.672783 0 -10 0 0 0 0 0.754332 0 -11 0 0 0 0 0.835882 0 -12 0 0 0 0 0.917431 0 -13 0 0 0 0 1.01937 0 -14 0 0 0 0 1.1213 0 -15 0 0 0 0 1.24363 0 -16 0 0 0 0 1.34557 0 -17 0 0 0 0 1.46789 0 -18 0 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--- a/test-data/fastqc_report_reverse_complement_summary.txt Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,11 +0,0 @@ -PASS Basic Statistics 1000trimmed_fastq -PASS Per base sequence quality 1000trimmed_fastq -FAIL Per tile sequence quality 1000trimmed_fastq -PASS Per sequence quality scores 1000trimmed_fastq -FAIL Per base sequence content 1000trimmed_fastq -WARN Per sequence GC content 1000trimmed_fastq -PASS Per base N content 1000trimmed_fastq -WARN Sequence Length Distribution 1000trimmed_fastq -PASS Sequence Duplication Levels 1000trimmed_fastq -WARN Overrepresented sequences 1000trimmed_fastq -WARN Adapter Content 1000trimmed_fastq
--- a/test-data/fastqc_report_subsample.html Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -<html><head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <title> 1000trimmed_fastq - report </title><link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous"><link href="https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-wvfXpqpZZVQGK6TAh5PVlGOfQNHSoD2xbE+QkPxCAFlNEevoEH3Sl0sibVcOQVnN" crossorigin="anonymous"><style type="text/css"> @media screen { div.summary { width: 18em; position:fixed; top: 4em; margin:1em 0 0 1em; } div.main { display:block; position:absolute; overflow:auto; height:auto; width:auto; top:4.5em; bottom:2.3em; left:18em; right:0; border-left: 1px solid 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1000trimmed_fastq </div></div><div class="summary"><h2>Summary</h2><ul> <li><a class="pass" href="#basicstatistics"> Basic Statistics </a></li> <li><a class="pass" href="#perbasesequencequality"> Per base sequence quality</a></li> <li><a class="fail" href="#pertilesequencequality">Per tile sequence quality</a></li> <li><a class="pass" href="#persequencequalityscores">Per sequence quality scores</a></li> <li><a class="fail" href="#perbasesequencecontent">Per base sequence content</a></li> <li><a class="warn" href="#persequencegccontent">Per sequence GC content</a></li> <li><a class="pass" href="#perbasencontent">Per base N content</a></li> <li><a class="warn" href="#sequencelengthdistribution">Sequence Length Distribution</a></li> <li><a class="pass" href="#sequenceduplicationlevels">Sequence Duplication Levels</a></li> <li><a class="pass" href="#overrepresentedsequences">Overrepresented sequences</a></li> <li><a class="pass" href="#adaptercontent">Adapter Content</a></li> <!-- <li><a class="{{passkmercontent}}" href="#kmercontent">{{kmercontentname}}</a></li> --></ul></div><div class="main"><div class="module"> <h2 class="pass" id="basicstatistics"> Basic Statistics: pass </h2> <table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>1000trimmed_fastq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4905</td></tr><tr><td>Sequences Flagged As Poor Quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1 - 108</td></tr><tr><td>%GC:</td><td>41</td></tr></tbody></table></div><div class="module"> <h2 class="pass" id="perbasesequencequality"> Per base sequence quality: pass</h2> <div id="seqbasequalityboxplot"></div></div><div class="module"> <h2 class="fail" id="pertilesequencequality"> Per tile sequence quality : fail </h2> <div id="tilequalityheatmap"></div></div><div class="module"> <h2 class="pass" 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end of file
--- a/test-data/fastqc_report_subsample.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/fastqc_report_subsample.txt Fri Sep 27 17:41:40 2024 +0000 @@ -1,4 +1,4 @@ -##Falco 1.2.3 +##Falco 1.2.4 >>Basic Statistics pass #Measure Value Filename 1000trimmed_fastq @@ -1593,110 +1593,110 @@ #Position Illumina Universal Adapter Illumina Small RNA 3' Adapter Illumina Small RNA 5' Adapter Nextera Transposase Sequence PolyA PolyG 1 0 0 0 0 0 0 2 0 0 0 0 0.0203874 0 -3 0 0 0 0 0.0407747 0 -4 0 0 0 0 0.0407747 0 -5 0 0 0 0 0.0407747 0 -6 0 0 0 0 0.0407747 0 -7 0 0 0 0 0.0407747 0 -8 0 0 0 0 0.0407747 0 -9 0 0 0 0 0.0407747 0 -10 0 0 0 0 0.0407747 0 -11 0 0 0 0 0.0407747 0 -12 0 0 0 0 0.0407747 0 -13 0 0 0 0 0.0407747 0 -14 0 0 0 0 0.0407747 0 -15 0 0 0 0 0.0407747 0 -16 0 0 0 0 0.0407747 0 -17 0 0 0 0 0.0611621 0 -18 0 0 0 0 0.0815494 0 -19 0 0 0 0 0.0815494 0 -20 0.0203874 0 0 0 0.0815494 0 -21 0.0203874 0 0 0 0.0815494 0 -22 0.0203874 0 0 0 0.0815494 0 -23 0.0203874 0 0 0 0.0815494 0 -24 0.0203874 0 0 0 0.0815494 0 -25 0.0203874 0 0 0 0.0815494 0 -26 0.0203874 0 0 0 0.0815494 0 -27 0.0203874 0 0 0 0.0815494 0 -28 0.0203874 0 0 0 0.0815494 0 -29 0.0203874 0 0 0 0.0815494 0 -30 0.0203874 0 0 0 0.0815494 0 -31 0.0203874 0 0 0 0.0815494 0 -32 0.0203874 0 0 0 0.0815494 0 -33 0.0203874 0 0 0 0.0815494 0 -34 0.0203874 0 0 0 0.0815494 0 -35 0.0203874 0 0 0 0.0815494 0 -36 0.0203874 0 0 0 0.0815494 0 -37 0.0203874 0 0 0 0.0815494 0 -38 0.0407747 0 0 0 0.0815494 0 -39 0.0611621 0 0 0 0.0815494 0 -40 0.0611621 0 0 0 0.0815494 0 -41 0.0611621 0 0 0 0.0815494 0 -42 0.0611621 0 0 0 0.0815494 0 -43 0.0611621 0 0 0 0.0815494 0 -44 0.0611621 0 0 0 0.0815494 0 -45 0.0611621 0 0 0 0.0815494 0 -46 0.0611621 0 0 0 0.0815494 0 -47 0.0611621 0 0 0 0.0815494 0 -48 0.0611621 0 0 0 0.0815494 0 -49 0.0611621 0 0 0 0.0815494 0 -50 0.0611621 0 0 0 0.0815494 0 -51 0.0611621 0 0 0 0.0815494 0 -52 0.0611621 0 0 0 0.0815494 0 -53 0.0611621 0 0 0 0.0815494 0 -54 0.0611621 0 0 0 0.0815494 0 -55 0.0611621 0 0 0 0.0815494 0 -56 0.0611621 0 0 0 0.0815494 0 -57 0.0611621 0 0 0 0.0815494 0 -58 0.0611621 0 0 0 0.0815494 0 -59 0.0611621 0 0 0 0.0815494 0 -60 0.0611621 0 0 0 0.0815494 0 -61 0.0611621 0 0 0 0.0815494 0 -62 0.0611621 0 0 0 0.0815494 0 -63 0.0611621 0 0 0 0.0815494 0 -64 0.0611621 0 0 0 0.0815494 0 -65 0.0611621 0 0 0 0.0815494 0 -66 0.0611621 0 0 0 0.0815494 0 -67 0.0611621 0 0 0 0.0815494 0 -68 0.0611621 0 0 0 0.0815494 0 -69 0.0611621 0 0 0 0.0815494 0 -70 0.0611621 0 0 0 0.0815494 0 -71 0.0611621 0 0 0 0.0815494 0 -72 0.0611621 0 0 0 0.0815494 0 -73 0.0611621 0 0 0 0.0815494 0 -74 0.0815494 0 0 0 0.0815494 0 -75 0.0815494 0 0 0 0.0815494 0 -76 0.0815494 0 0 0 0.0815494 0 -77 0.0815494 0 0 0 0.0815494 0 -78 0.0815494 0 0 0 0.0815494 0 -79 0.0815494 0 0 0 0.0815494 0 -80 0.0815494 0 0 0 0.0815494 0 -81 0.0815494 0 0 0 0.0815494 0 -82 0.0815494 0 0 0 0.0815494 0 -83 0.0815494 0 0 0 0.0815494 0 -84 0.0815494 0 0 0 0.0815494 0 -85 0.0815494 0 0 0 0.0815494 0 -86 0.0815494 0 0 0 0.0815494 0 -87 0.0815494 0 0 0 0.0815494 0 -88 0.0815494 0 0 0 0.0815494 0 -89 0.0815494 0 0 0 0.0815494 0 -90 0.0815494 0 0 0 0.0815494 0 -91 0.0815494 0 0 0 0.0815494 0 -92 0.101937 0 0 0 0.0815494 0 -93 0.122324 0 0 0 0.0815494 0 -94 0.122324 0 0 0 0.0815494 0 -95 0.122324 0 0 0 0.0815494 0 -96 0.122324 0 0 0 0.0815494 0 -97 0.122324 0 0 0 0.0815494 0 -98 0.122324 0 0 0 0.0815494 0 -99 0.122324 0 0 0 0.0815494 0 -100 0.122324 0 0 0 0.0815494 0 -101 0.122324 0 0 0 0.0815494 0 -102 0.122324 0 0 0 0.0815494 0 -103 0.122324 0 0 0 0.0815494 0 -104 0.122324 0 0 0 0.0815494 0 -105 0.122324 0 0 0 0.0815494 0 -106 0.122324 0 0 0 0.0815494 0 -107 0.122324 0 0 0 0.0815494 0 -108 0.122324 0 0 0 0.0815494 0 +3 0 0 0 0 0.0203874 0 +4 0 0 0 0 0.0203874 0 +5 0 0 0 0 0.0203874 0 +6 0 0 0 0 0.0203874 0 +7 0 0 0 0 0.0203874 0 +8 0 0 0 0 0.0203874 0 +9 0 0 0 0 0.0203874 0 +10 0 0 0 0 0.0203874 0 +11 0 0 0 0 0.0203874 0 +12 0 0 0 0 0.0203874 0 +13 0 0 0 0 0.0203874 0 +14 0 0 0 0 0.0203874 0 +15 0 0 0 0 0.0203874 0 +16 0 0 0 0 0.0203874 0 +17 0 0 0 0 0.0407747 0 +18 0 0 0 0 0.0407747 0 +19 0 0 0 0 0.0407747 0 +20 0.0203874 0 0 0 0.0407747 0 +21 0.0203874 0 0 0 0.0407747 0 +22 0.0203874 0 0 0 0.0407747 0 +23 0.0203874 0 0 0 0.0407747 0 +24 0.0203874 0 0 0 0.0407747 0 +25 0.0203874 0 0 0 0.0407747 0 +26 0.0203874 0 0 0 0.0407747 0 +27 0.0203874 0 0 0 0.0407747 0 +28 0.0203874 0 0 0 0.0407747 0 +29 0.0203874 0 0 0 0.0407747 0 +30 0.0203874 0 0 0 0.0407747 0 +31 0.0203874 0 0 0 0.0407747 0 +32 0.0203874 0 0 0 0.0407747 0 +33 0.0203874 0 0 0 0.0407747 0 +34 0.0203874 0 0 0 0.0407747 0 +35 0.0203874 0 0 0 0.0407747 0 +36 0.0203874 0 0 0 0.0407747 0 +37 0.0203874 0 0 0 0.0407747 0 +38 0.0407747 0 0 0 0.0407747 0 +39 0.0611621 0 0 0 0.0407747 0 +40 0.0611621 0 0 0 0.0407747 0 +41 0.0611621 0 0 0 0.0407747 0 +42 0.0611621 0 0 0 0.0407747 0 +43 0.0611621 0 0 0 0.0407747 0 +44 0.0611621 0 0 0 0.0407747 0 +45 0.0611621 0 0 0 0.0407747 0 +46 0.0611621 0 0 0 0.0407747 0 +47 0.0611621 0 0 0 0.0407747 0 +48 0.0611621 0 0 0 0.0407747 0 +49 0.0611621 0 0 0 0.0407747 0 +50 0.0611621 0 0 0 0.0407747 0 +51 0.0611621 0 0 0 0.0407747 0 +52 0.0611621 0 0 0 0.0407747 0 +53 0.0611621 0 0 0 0.0407747 0 +54 0.0611621 0 0 0 0.0407747 0 +55 0.0611621 0 0 0 0.0407747 0 +56 0.0611621 0 0 0 0.0407747 0 +57 0.0611621 0 0 0 0.0407747 0 +58 0.0611621 0 0 0 0.0407747 0 +59 0.0611621 0 0 0 0.0407747 0 +60 0.0611621 0 0 0 0.0407747 0 +61 0.0611621 0 0 0 0.0407747 0 +62 0.0611621 0 0 0 0.0407747 0 +63 0.0611621 0 0 0 0.0407747 0 +64 0.0611621 0 0 0 0.0407747 0 +65 0.0611621 0 0 0 0.0407747 0 +66 0.0611621 0 0 0 0.0407747 0 +67 0.0611621 0 0 0 0.0407747 0 +68 0.0611621 0 0 0 0.0407747 0 +69 0.0611621 0 0 0 0.0407747 0 +70 0.0611621 0 0 0 0.0407747 0 +71 0.0611621 0 0 0 0.0407747 0 +72 0.0611621 0 0 0 0.0407747 0 +73 0.0611621 0 0 0 0.0407747 0 +74 0.0611621 0 0 0 0.0407747 0 +75 0.0611621 0 0 0 0.0407747 0 +76 0.0611621 0 0 0 0.0407747 0 +77 0.0611621 0 0 0 0.0407747 0 +78 0.0611621 0 0 0 0.0407747 0 +79 0.0611621 0 0 0 0.0407747 0 +80 0.0611621 0 0 0 0.0407747 0 +81 0.0611621 0 0 0 0.0407747 0 +82 0.0611621 0 0 0 0.0407747 0 +83 0.0611621 0 0 0 0.0407747 0 +84 0.0611621 0 0 0 0.0407747 0 +85 0.0611621 0 0 0 0.0407747 0 +86 0.0611621 0 0 0 0.0407747 0 +87 0.0611621 0 0 0 0.0407747 0 +88 0.0611621 0 0 0 0.0407747 0 +89 0.0611621 0 0 0 0.0407747 0 +90 0.0611621 0 0 0 0.0407747 0 +91 0.0611621 0 0 0 0.0407747 0 +92 0.0611621 0 0 0 0.0407747 0 +93 0.0611621 0 0 0 0.0407747 0 +94 0.0611621 0 0 0 0.0407747 0 +95 0.0611621 0 0 0 0.0407747 0 +96 0.0611621 0 0 0 0.0407747 0 +97 0.0611621 0 0 0 0.0407747 0 +98 0.0611621 0 0 0 0.0407747 0 +99 0.0611621 0 0 0 0.0407747 0 +100 0.0611621 0 0 0 0.0407747 0 +101 0.0611621 0 0 0 0.0407747 0 +102 0.0611621 0 0 0 0.0407747 0 +103 0.0611621 0 0 0 0.0407747 0 +104 0.0611621 0 0 0 0.0407747 0 +105 0.0611621 0 0 0 0.0407747 0 +106 0.0611621 0 0 0 0.0407747 0 +107 0.0611621 0 0 0 0.0407747 0 +108 0.0611621 0 0 0 0.0407747 0 >>END_MODULE
--- a/test-data/limits.txt Tue Sep 10 19:02:42 2024 +0000 +++ b/test-data/limits.txt Fri Sep 27 17:41:40 2024 +0000 @@ -1,81 +1,81 @@ -# For each of the modules you can choose to not run that -# module at all by setting the value below to 1 for the -# modules you want to remove. -duplication ignore 0 -kmer ignore 1 -n_content ignore 0 -overrepresented ignore 0 -quality_base ignore 0 -sequence ignore 0 -gc_sequence ignore 0 -quality_sequence ignore 0 -tile ignore 0 -sequence_length ignore 0 -adapter ignore 0 - -# For the duplication module the value is the percentage -# remaining after deduplication. Measured levels below -# these limits trigger the warning / error. -duplication warn 70 -duplication error 50 - -# For the kmer module the filter is on the -log10 binomial -# pvalue for the most significant Kmer, so 5 would be -# 10^-5 = p<0.00001 -kmer warn 2 -kmer error 5 - -# For the N module the filter is on the percentage of Ns -# at any position in the library -n_content warn 5 -n_content error 20 - -# For the overrepresented seqs the warn value sets the -# threshold for the overrepresented sequences to be reported -# at all as the proportion of the library which must be seen -# as a single sequence -overrepresented warn 0.1 -overrepresented error 1 - -# The per base quality filter uses two values, one for the value -# of the lower quartile, and the other for the value of the -# median quality. Failing either of these will trigger the alert -quality_base_lower warn 10 -quality_base_lower error 5 -quality_base_median warn 25 -quality_base_median error 20 - -# The per base sequence content module tests the maximum deviation -# between A and T or C and G -sequence warn 10 -sequence error 20 - -# The per sequence GC content tests the maximum deviation between -# the theoretical distribution and the real distribution -gc_sequence warn 15 -gc_sequence error 30 - -# The per sequence quality module tests the phred score which is -# most frequently observed -quality_sequence warn 27 -quality_sequence error 20 - -# The per tile module tests the maximum phred score loss between -# and individual tile and the average for that base across all tiles -tile warn 5 -tile error 10 - -# The sequence length module tests are binary, so the values here -# simply turn them on or off. The actual tests warn if you have -# sequences of different length, and error if you have sequences -# of zero length. - -sequence_length warn 1 -sequence_length error 1 - -# The adapter module's warnings and errors are based on the -# percentage of reads in the library which have been observed -# to contain an adapter associated Kmer at any point - -adapter warn 5 -adapter error 10 +# For each of the modules you can choose to not run that +# module at all by setting the value below to 1 for the +# modules you want to remove. +duplication ignore 1 +kmer ignore 1 +n_content ignore 1 +overrepresented ignore 1 +quality_base ignore 1 +sequence ignore 1 +gc_sequence ignore 1 +quality_sequence ignore 1 +tile ignore 1 +sequence_length ignore 0 +adapter ignore 1 + +# For the duplication module the value is the percentage +# remaining after deduplication. Measured levels below +# these limits trigger the warning / error. +duplication warn 70 +duplication error 50 + +# For the kmer module the filter is on the -log10 binomial +# pvalue for the most significant Kmer, so 5 would be +# 10^-5 = p<0.00001 +kmer warn 2 +kmer error 5 + +# For the N module the filter is on the percentage of Ns +# at any position in the library +n_content warn 5 +n_content error 20 + +# For the overrepresented seqs the warn value sets the +# threshold for the overrepresented sequences to be reported +# at all as the proportion of the library which must be seen +# as a single sequence +overrepresented warn 0.1 +overrepresented error 1 + +# The per base quality filter uses two values, one for the value +# of the lower quartile, and the other for the value of the +# median quality. Failing either of these will trigger the alert +quality_base_lower warn 10 +quality_base_lower error 5 +quality_base_median warn 25 +quality_base_median error 20 + +# The per base sequence content module tests the maximum deviation +# between A and T or C and G +sequence warn 10 +sequence error 20 + +# The per sequence GC content tests the maximum deviation between +# the theoretical distribution and the real distribution +gc_sequence warn 15 +gc_sequence error 30 + +# The per sequence quality module tests the phred score which is +# most frequently observed +quality_sequence warn 27 +quality_sequence error 20 + +# The per tile module tests the maximum phred score loss between +# and individual tile and the average for that base across all tiles +tile warn 5 +tile error 10 + +# The sequence length module tests are binary, so the values here +# simply turn them on or off. The actual tests warn if you have +# sequences of different length, and error if you have sequences +# of zero length. + +sequence_length warn 1 +sequence_length error 1 + +# The adapter module's warnings and errors are based on the +# percentage of reads in the library which have been observed +# to contain an adapter associated Kmer at any point + +adapter warn 5 +adapter error 10
--- a/test-data/summary.txt Tue Sep 10 19:02:42 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,11 +0,0 @@ -PASS Basic Statistics 1000trimmed_fastq -PASS Per base sequence quality 1000trimmed_fastq -FAIL Per tile sequence quality 1000trimmed_fastq -PASS Per sequence quality scores 1000trimmed_fastq -FAIL Per base sequence content 1000trimmed_fastq -WARN Per sequence GC content 1000trimmed_fastq -PASS Per base N content 1000trimmed_fastq -WARN Sequence Length Distribution 1000trimmed_fastq -PASS Sequence Duplication Levels 1000trimmed_fastq -WARN Overrepresented sequences 1000trimmed_fastq -PASS Adapter Content 1000trimmed_fastq