dataesr/scientific-paragraphs-categorization huggingface dataset:
Investigating the frequency of the MS499 genome within the B1 and ExPEC groups To investigate the association between the MS499 genome and ExPEC or phylogroup B1 groups, we used our phylogenetic analysis to identify 365 genomes in phylogroup B1 and 414 extra-intestinal pathogenic E. coli within phylogroups B2, D and F (Additional file 2), and each set of genomes were formatted into BLAST databases. The MS499 chromosome and plasmid were split into 1000 bp sequential windows, and each window used as the query sequence against both BLAST databases so that the number of genomes in each group which contained a subject sequence which matched the query sequence at a BLAST e-value of zero was determined. The percentage frequency of the window sequences in the groups was calculated, and the difference in frequency determined as the percentage frequency in the ExPEC group minus the percentage frequency in the B1 group.
GO and psortb annotation comparison To compare the relative abundance of biological process (BP) GO annotations for the ExPEC genes versus the non-ExPEC genes in the MS499 genome, the BP GO annotations for each gene were traced back to their respective level 2 classification using the GOstats package within Bioconductor in R  [63]. Since any given gene may have more than one GO annotation, and the respective level 2 classification of each of the annotations may be the same or different, we ensured that no level 2 classification was present in duplicate for any gene. To calculate the relative abundance of each GO category, we determined the abundance of each GO category in each group (ExPEC genes or non-ExPEC genes) and compared the abundance of each category between the two groups. The same method was used for comparing the prediction for subcellular localisation using pSORTb  [47].
Detailed sequence comparison of ExPEC genes To perform a detailed analysis of the relationships between ExPEC-associated genes found in the MS499 genome, we probed for, and extracted, the sequence of these genes from 414 ExPEC genomes via blastn  [53] , using the nucleotide sequence for the gene present in MS499 as the query. The sequences were aligned by Muscle  [54] , and maximum likelihood trees for each gene constructed under the GTR + g model via PhyML  [55]. Following this, matrices for the phylogenetic distances between the MS499 gene sequences and those found in other ExPECs were calculated via the 'cophenetic' function from the APE package  [57]  launched within R  [60]. Missing genes was given a penalty distance of 100, and these distances were used to calculate which ExPEC genome encoded the most similar genes to those found in MS499.
Acknowledgements We would like to thank  Martin Goldstone  for assistance in script writing and  Neil Inglis  for careful reading of the manuscript and helpful comments. RJG and DGES are grateful for the funding by  BBSRC  through the  EMIDA ERA-NET initiative , project reference  BB/1017240/1  and  BB/1017283/1. RP is funded by a  Wellcome Trust   CIIE fellowship , no. 095831. 2  Centre for Immunity, Infection and Evolution, School of Biological Sciences, University of Edinburgh , Edinburgh  EH9 3JT ,  UK. 3 Immunology Unit, University of Veterinary Medicine, Bischofsholer Damm 15, 30173 Hannover, Germany. 4  INRA ,  UMR1198  Biologie du Développment et Reproduction, Jouy-en-Josas, France. 5 ENVA, Maisons Alfort, France. 6Institute for Life Sciences, School of Medicine, Swansea  SA2 8PP , UK.
Acknowledgements We thank  Massimo Gervasi ,  Rene Ong ,  Philip von Doetinchem  and  Tsuguo Aramaki  for useful communications concerning Ams-02 and Gaps. We also thank  Paolo Panci  for interesting discussions. This work is supported by the  European Research Council (Erc)  under the  EU   Seventh Framework Programme  (FP7/2007-2013) /  Erc Starting Grant  (agreement n. 278234 -' NewDark ' project). The work of M. C. is also supported in part by the  French national research agency Anr  under contract Anr  2010 Blanc 041301  and by the  EU ITN network Unilhc. N. F. and A. V. are supported by the research grant Theoretical Astroparticle Physics number  2012CPPYP7  under the program Prin 2012 funded by the  Ministero dell'Istruzione, Università e della Ricerca (Miur)  and by the research grant  TAsP (Theoretical Astroparticle Physics)  funded by the  Istituto Nazionale di Fisica Nucleare (Infn).
N. F. and A. V. are also supported by the  Strategic Research Grant: Origin and Detection of Galactic and Extragalactic Cosmic Rays  funded by  Torino University and Compagnia di San Paolo. N. F. acknowledges support of the spanish  Micinn Consolider Ingenio 2010 Programme Multidark   CSD2009-00064. M. C. , M. T. and A. V. acknowledge the hospitality of the  Institut d'Astrophysique de Paris (Iap)  and of the Theory Unit of Cern where part of this work was done.
Trial design This was a phase I, randomized, double-blind, placeboand positive-controlled, four-period crossover trial conducted at a clinical pharmacology unit (CPU) in France (NCT01552928). The study included four treatment periods, with a 96-h washout period between treatments, during which subjects remained in the CPU for 16 days (15 nights). A follow-up clinical evaluation was performed 7 days (AE2 days) after the last dose of investigational product. The study was conducted in accordance with the Declaration of Helsinki and ICH Good Clinical Practice guidelines, and according to local ethical and legal requirements. Prior to study initiation, the study protocol, protocol amendments, informed consent forms, and other documents were approved by the Independent Ethics Committee, Comit e de Protection des Personnes Ile de France VIII (study identification code: 12 03 24); and regulatory agency, Agence Nationale de S ecurit e du M edicament et des produits de sant e (study identification code: A120221-27), formerly Agence Franc ßaise de S ecurit e Sanitaire du M edicament et des produits de sant e.
On day -1 and day 1 of each period, continuous ECG data were recorded using a Holter monitor from 60 min before the anticipated dose time on day 1 until approximately 24 h later. Three 10-second, 12-lead ECG waveforms were extracted when possible from the continuous ECG recording within the 60 min prior to dosing and up to 10 min before each of the following time points: 0. 5, 1, 1. 5, 2, 2. 5, 3, 4, 8, and 12 h post dose. To account for the anticipated increase in HR associated with the PDE III inhibitory effect of anagrelide, subjects participated in 10 min of light exercise (i. e. , brisk walking) 1. 5-2 h after the nominal dose time (corresponding to the time taken to achieve maximal anagrelide plasma levels [t max ]) during the baseline day. This was performed to provide a drug-free HR range of time-matched baseline ECG tracings for postdose comparisons.
Endpoints The primary endpoint was QTcNi, defined as the QT interval corrected for heart rate using a subject-specific method. The QTcNi correction was derived from the individual subjects based on their index dataset. Each index dataset comprised the pooled day -1 ECG data from all four treatment periods plus the ECG data from day 1 of the placebo treatment (total 50 observations). Pharmacodynamic endpoints included changes from baseline (time-matched baseline from day -1 of the corresponding treatment period) in HR (and the corresponding RR interval), and the PR, QRS, QT, QTcNi, Fridericia-corrected QT (QTcF) and Bazett-corrected QT (QTcB) intervals. The pharmacokinetic profile of anagrelide and 3-hydroxy-anagrelide was assessed and the relationship between pharmacokinetic and pharmacodynamic parameters was explored.
Statistical methods Pharmacokinetic parameters were determined from the plasma concentration-time data by non-compartmental analysis. Pharmacokinetic analyses were performed using WinNonlin Version 5. 1. 1 (Pharsight Corporation, Mountain View, CA). Plasma concentrations of anagrelide, 3hydroxy-anagrelide, and moxifloxacin were determined using sensitive and specific, validated HPLC-MS/MS methods at York Bioanalytical Solutions (York, UK). For anagrelide and 3-hydroxy-anagrelide, the assay was linear over the range of 0. 05-20 ng/mL with a lower limit of quantification of 0. 05 ng/mL for both compounds. The interassay accuracy (%-bias) and precision (% co-efficient of variation [CV]) data for the quality control samples at 3 concentrations across all analytical batches were <10%, and all assay runs met the predefined acceptance criteria.
The secondary objective comprised analysis of the pharmacodynamic variables at subject-specific t max with an analysis of covariance framework using a mixedeffects model. For 12-lead ECG data, baseline was defined as the mean of the three measurements on day -1 of each period. When triplicate readings were completed for a time point, the average of the three values was used to summarize continuous variables. If triplicate readings could not be made, the mean of the available values was used. All demographic and baseline characteristic data, pharmacodynamic and safety data analyses were performed using SAS â Version 9. 2 (SAS Institute, Cary, NC).
QTc interval The QT versus RR regression slopes before (QT) and after subject-specific correction (QTcNi) for each individual subject are displayed in Figure  3A. Women had slightly higher QT regression slopes than men, although there is considerable overlap between the regression slopes in men and women. After correction, the regression slopes for QTcNi versus RR were almost zero for all subjects, demonstrating that the correction for HR worked well for the index dataset. However, a slight negative population correlation between RR and both QTcNi (r = À0. 15) and QTcF (r = À0. 02) was observed. Time-matched differences in QTcNi and QTcF intervals compared with placebo are shown in Figures  3B  and C  respectively. After administration of anagrelide 0. 5 mg, the maximum mean prolongation of QTcNi and QTcF did not exceed 7.
Acknowledgements The study was funded by  Shire Development LLC. The authors thank the subjects and investigators who participated in this study, including  Nicolas Fauchoux MD , principal investigator, Biotrial, Paris, France. The authors would also like to thank  Roger Secker  for providing data from the anagrelide dog cardiovascular studies. Under the direction of the authors,  Emma Burke , employee of iMed Comms, Macclesfield, UK, provided medical writing assistance for this publication. Editorial assistance in formatting, proofreading, copy editing, and fact checking was also provided by iMed Comms. iMed Comms was funded by  Shire  for support in writing and editing this manuscript.
Low-temperature phosphorescence measurements were undertaken to elucidate the energies of the pertinent states and relative energy levels of 1 and 2. At 77 K, MLCT emission (λ em max = 572 nm) could be observed from 1, while emission of bichromophoric 2 was located further to the red (λ em max = 595 nm) and is ascribed to pyrene emission from this lower-lying state. 3, 6 Assuming similar 3 MLCT levels in both complexes (at RT, they show similar emission spectra), only a small energy gap is expected between the pyrene triplet and the 3 MLCT state in 2, which could enable reversible interchromophore electronic energy transfer at room temperature. Because such a small energy gap cannot be measured with sufficiently high precision in this fashion, the energy gap was determined using analysis of data from time-resolved spectroscopies (vide infra), which also gave unambiguous evidence for this unusual phenomenon.
The authors acknowledge the  CNRS, UniversitéBordeaux, Reǵion Aquitaine , the  European Union  ( HetIridium, Grant   CIG322280 ), for financial support, the  School of Chemistry, University of Birmingham , for a studentship, and the  EPSRC  for underpinning support. This study has been carried out in the framework of "the Investments for the future"  Programme IdEx BordeauxLAPHIA  (Grant  ANR-10-IDEX-03-02 ).
CD44 expression evaluation by flow cytometry Each cell line (10 6 OVCAR-3, IGROV-1, SKOV-3, HeLA or REH lymphoblast cells) was incubated during 45 minutes with a primary antibody directed against CD44 (1/100, [F10-44-2] (ab6124), Abcam, Paris, France), or against a control isotype (κ isotype Ctrl PE Mouse IgG1, BioLegend). Cells were then washed with PBS, centrifuged 5 minutes at 300 g, then incubated 45 minutes with an anti-mouse secondary antibody (1/50, BD Biosciences, Le pont de Claix, France). Cells were washed with PBS and the fluorescence was measured with a FACS Calibur cytometer and analyzed with Cell Quest Pro software.
Statistics Quantitative variables were presented by median and range and qualitative data by frequency and percent. Group comparisons were made using the Wilcoxon signed rank test (matched) or Mann-Whitney test (independent) for non-parametric data and by the Student's t test for parametric ones. For this entire study, statistical significance was reached for p < 0. 05. In vivo, with a standard deviation of 0. 1, to reach a statistical difference mean of 15% in terms of tumor proliferation with a statistical power of 80% and α risk of 5%, 22 mice per experimentation were necessary. All analyses were performed using STATA software (Version 12. 0; Stata Corporation, College Station, TX, USA).
Detailed comparison of two popular variant calling packages for exome and targeted exon studies The Genome Analysis Toolkit (GATK) is commonly used for variant calling of single nucleotide polymorphisms (SNPs) and small insertions and deletions (indels) from short-read sequencing data aligned against a reference genome. There have been a number of variant calling comparisons against GATK, but an equally comprehensive comparison for VarScan not yet been performed. More specifically, we compare 1) the effects of different preprocessing steps prior to variant calling with both GATK and VarScan, 2) VarScan variants called with increasingly conservative parameters, and 3) filtered and unfiltered GATK variant calls (for both the UnifiedGenotyper and the HaplotypeCaller). Variant calling was performed on three datasets (1 targeted exon dataset and 2 exome datasets), each with approximately a dozen subjects.
In most cases, pre-processing steps (e. g. , indel realignment and quality score base recalibration using GATK) had only a modest impact on the variant calls, but the importance of the pre-processing steps varied between datasets and variant callers. Based upon concordance statistics presented in this study, we recommend GATK users focus on "high-quality" GATK variants by filtering out variants flagged as low-quality. We also found that running VarScan with a conservative set of parameters (referred to as "VarScan-Cons") resulted in a reproducible list of variants, with high concordance (>97%) to high-quality variants called by the GATK UnifiedGenotyper and HaplotypeCaller. These conservative parameters result in decreased sensitivity, but the VarScan-Cons variant list could still recover 84-88% of the high-quality GATK SNPs in the exome datasets.
One study indicated that VarScan was less accurate than the other variant callers  (Cheng et al. 2014). Another study showed VarScan as being more similar to the other variant callers but still ranked GATK as the best option  (Yi et al. 2014 ). However, VarScan has been used for variant calling in a large number of studies  (Worthey 2013 ), and we hypothesize that the simple, intuitive parameters can be helpful in establishing an optimal set of variants for a given dataset. Also, as emphasized in this study, the run-time for VarScan should typically be shorter than GATK. Therefore, we wished to determine if 1) the previous VarScan benchmarks can be reproduced in our own analysis and 2) if use of non-standard parameters can improve the quality of the VarScan variant calls. This study also examines the relative impact of pre-processing steps in GATK (specifically, the indel realignment and quality score base recalibration steps).
2013) , and dbSNP  (Sherry et al. 2001 )) in the variant list, and the reproducibility of variant calls using different technologies. A limited number of additional comparisons are also performed in order to help illustrate how these metrics can be used to select the optimal variant caller for a given dataset. This analysis demonstrates that a conservative set of parameters (referred to as "VarScan-Cons") can be used to produce a reproducible list of variants from VarScan, and there is limited evidence that VarScan-Cons has a lower false discovery rate among novel variants. This study also presents evidence that the GATK HaplotypeCaller may have a higher false discovery rate in calling indels compared to the GATK UnifiedGenotyper.
Sample Selection All datasets were downloaded as. fastq files from the European Nucleotide Archive  (Leinonen et al. 2011). Illumina exome samples were downloaded from SRP019719  (O'Rawe et al. 2013). 1000 Genomes Project (The 1000 Genomes Project Consortium 2012) data, abbreviated as 1KG in this manuscript, was selected on the basis of having 1) exome data, 2) targeted exome data, 3) Omni SNP chip data, and 4) validated SNPs. Among samples meeting those criteria, 12 samples were selected based upon 1) their presence in disparate populations (CEU: Northern and Western European Ancestry, CHB: Han Chinese, JPT: Japanese, and YRI: Yoruba/African) and 2) maximum number of validated SNPs within each of the four selected populations. 1000 Genomes validated SNPs and Omni SNP chip. idat files were downloaded from the 1000 Genomes FTP site (ftp://ftp.
1000genomes. ebi. ac. uk/vol1/ftp/). Likewise, target design files (for targeted exon and exome samples) were downloaded from this site. More specifically, the phase 3 design files were used to calculate coverage statistics for the exome samples, and validated variants come from  ALL. chr20. exome_consensus_validation_454. 20120118. snp. exome. sites. vcf. gz (pooled 454 PCR sequencing data). At the time this dataset was downloaded, the hg19 reference location for validated SNPs was off-set by one (similar to indels in. vcf files), and this was taken into consideration during analysis. Targeted gene co-ordinates (for hg18) from P3_consensus_exonic_targets. bed were converted to hg19 coordinates using the LiftOver function in Galaxy  (Blankenberg et al. 2001; Giardine et al. 2005; Goecks et al.
There are three additional pre-processing pipelines that were considered for VarScan and GATK comparisons. "Realign Only" runs an indel realignment using GATK (using the RealignerTargetCreater and IndelRealigner functions). "Recalibrate Only" uses GATK to recalibrate quality scores (using the BaseRecalibrator and PrintReads functions). "Full Pipeline" runs the indel realignment functions and then performs base recalibration. MiSeq amplicon data (for individual K8108-49685s in the SRP019719 dataset) was processed in a similar pipeline as the array-based targeted sequencing data except that reads were first trimmed to 150 bp and PCR duplicates were not removed (since all reads were PCR duplicates). Also, unlike the SRP019719 exome data, GATK did not detect miscoded quality scores in the SRP019719 amplicon data (so, the variant calling steps match the 1000 Genomes sample commands in Supplemental Text S1).
pl) is provided at https://sites. google. com/site/cwarden45/scripts. Additional variant callers were also applied to the "No Preprocessing" alignments and the "Full Pipeline" alignments. Freebayes  (Garrison & Marth 2012)  was applied to the 1000 Genomes Exon Targeted samples, using default parameters. The Bayesian variant caller in the bcftools function (in the samtools package  (Li et al. 2009 ) was applied to all three datasets, using default parameters (followed by applying vcfutils. pl with a maximum read depth of 200). Unlike GATK and VarScan, samtools has a unique indel format to represent ambiguous indels. Although the ANNOVAR file conversion program can remove all nucleotides that are not part of the indel, the genomic position used to represent this indel is not necessarily the same as GATK and VarScan.
For this reason, we only present the samtools SNP results in this manuscript. All analysis was performed on a shared Linux server with concurrent usage (x64, CentOS 5. 10, 256 GB RAM, 4 CPU, 8 cores each, 6 x 2. 27 GHz processors). Because a publication on the 1000 Genomes exome and targeted exon datasets has not yet been published, we are only reporting variant frequencies for a single chromosome (chr20) in order to comply with 1KG publication requirements. Thus, only 1,140,996 base pairs of targeted sequence is considered for variant call benchmarks in the 1000 Genomes exome datasets, and only 35,309 base pairs are considered for variant call benchmarks in the 1000 Genomes exon targeted samples. In contrast, the SRP019719 comparisons are genome-wide (with a targeted design covering 46,401,093 base pairs).
Unfortunately, this is a limited number of validated SNPs, so it is difficult to say how closely these are tied to the true sensitivity rates. SNP chips can also be used to assess sensitivity for variant lists. However, it should be noted that these will mostly be common SNPs, which importantly means that recovery of SNP array variants may not represent the accuracy of rare variant calls. Nevertheless, it is useful to see how the SNP chip recovery compared to the validated SNPs and the total SNP calls. In most cases, all variant calling algorithms could recover >85% of Omni SNP chip variants from the 1000 Genomes exome samples (Figure  2A ). Similarly, we compared variant calls for a subject from the SRP019719 dataset that had both Illumina exome and SNP chip data: again, the majority (88-96%) of SNP chip variants were recovered in the paired exome dataset (Figure  2B , Table  S7 ).
Reviewing Manuscript Accordingly, the recovery rate was extremely low for novel variants called using VarScan with default parameters (Table  S8 ). There were no novel VarScan-Cons variants called within the coding regions of chromosome 20, but it was likely difficult to call true novel variants given that variants called by the 1000 Genomes Project are defined as previously observed in our analysis. The recovery rate was also relatively low for other variant callers, but there are only a small number of novel variants occurring within targeted regions of chr20 for most of the lists of variant calls. One sample in the SRP019719 dataset underwent exome sequencing as well as amplicon sequencing for a selected number of variants. Although limited to a single sample, this comparison was important because the amplicon dataset contained more novel variants than present within 1000 Genomes targeted exon genes on chromosome 20.
The amplicon comparison showed a universal decrease in the recovery of novel variants (Figure  S3 ), although it should be noted that the overall recovery rate was also lower than the array-based enrichment samples. This is most likely because duplicate reads were not removed, and errors introduced during PCR amplification increased the total number of false positives in the amplicon dataset. For example, the targeted variants were located towards the middle of each amplicon, but sequencing errors may be more frequent towards the end of the amplicons. This expectation of an increased false discovery rate is supported by the fact that the number of SNPs called in the amplicon dataset (Table  S9 ) is much higher than the number of variants that were targeted for validation  (O'Rawe et al. 2013).