dataesr/scientific-paragraphs-categorization huggingface dataset:
PCR amplification was carried out at the genomic platform of the Institut Jacques Monod in a 384-well plate using 5 μL of SYBR Green I Master Mix 1x preheated to 37°C and containing 0. 5 μM of each primer and 2 μL of DNA, distributed by an epMotion 1 5070 liquid handling robot (Eppendorf, Hamburg, Germany). A cycling program of 8 min of activation at 95°C, followed by 60 cycles at 95°C for 10 sec and 60°C for 1 min was used, with a melting curve established at the end of the run through one melting step (95°C for 5 sec, then 60°C for 1 min and a temperature increase of 0. 11°C/sec with continuous fluorescence measurement). The performance of the primer pairs was tested in silico and in vitro and primer pairs were required to have an efficiency superior to 90% and not to yield primer-dimers before 40 cycles.
Multiplex PCR We made use of in silico prediction for possible dimer formation in multiplex PCRs with the software PriDimerCheck (http://biocompute. bmi. ac. cn/MPprimer/primer_dimer. html). Based on the output of the program, 16 primer pairs were divided into 4 different multiplex reactions to minimize the predicted stability of the possible 3'-annealed dimers. Each multiplex was optimized by testing different MgCl 2 concentrations (3 mM, 4 mM and 5 mM) and primer concentrations (0. 08 μM, 0. 15 μM, 0. 2 μM, 0. 25 μM and 0. 3 μM). Each multiplex reaction was mixed in the high containment laboratory of the Institut Jacques Monod using a 4 μL mixture of each targeted DNA at 2. 5 pg/μL, in a total volume of 30 μL of 50 mM Tris/HCl pH 8. 3 (at 25°C), 10 mM KCl, 5 mM (NH 4 ) 2 SO 4 , 3-5 mM MgCl 2 , 1 mg/mL BSA (bovine serum albumin), 0.
08-0. 3 μM of each primer, 0. 25 mM dA/G/CTPs and 0. 50 mM dUTP, 2 U of Fast Start DNA polymerase (Roche Applied Science, Penzberg, Germany), and 0. 5 U of cod uracil-N-Glycosylase (UNG, Arcticzymes, Tromsø, Norway). MgCl 2 solution, reaction buffer and BSA were decontaminated by exposing the solutions in UV-pervious tubes (Qubit1, Life Technologies, Carlsbad, CA) to UV light for 10 min at a short distance as described  [21]. A negative control with no template was performed for each multiplex reaction, processed in the same way as the samples throughout the whole experimental procedure. The cycling program consisted of 15 min at 37°C (carry-over contamination prevention through digestion by UNG of dUTPlabeled amplicons), 95°C for 10 min (inactivation of UNG and activation of the Fast Start DNA polymerase), followed by 40 cycles at 95°C for 10 sec and 60°C for 1 min and a final extension step at 72°C for 10 min.
Data analysis Using the software provided in the Torrent suite on the Ion Torrent server, sequencing reads were demultiplexed and mapped with the Torrent Mapping Alignment Program (TMAP) to a reference fasta sequence created by concatenating the various PCR product reference sequences, each separated by a series of Ns. The sequence corresponding to the primers described herein is provided in S1 File. Following mapping, the. bam and. bai files were grouped in a single directory on a Unix-based computer to be further processed. First, files were renamed to allocate simple descriptive names, using bash commands described in the introduction of the analysis bash script in S4 File. The analysis bash script is dependent on the following programs (and tested with the versions indicated between brackets): featureCounts (Subread package 1.
"Samtools mpileup" is used in combination with Varscan to generate a list of the variants detected in each sample. This list is found in the resulting. varscan files in the Varscan directory and allows the identification of parasites at the species level using the information recapitulated in S2 and S3 Tables. Finally, the combination of the "samtools mpileup", "bcftools call", and vcfutils. pl vcf2fq is used to generate a consensus sequence from the concatenated PCR products obtained for each sample. The consensus sequences in fasta format found in the Fasta directory within the Consensus directory can be imported into a software environment for multiple alignments to analyze the data in more detail. We used Geneious  [20]  to import both the consensus fasta and the initial bam files. We performed a multiple sequence alignment of the fasta files in Geneious using MAFFT 7  [25] , with manual editing when necessary.
For each genus, the analysis of three samples containing distinct DNA quantities was deduced from the DNA In order to achieve maximal sensitivity of parasite DNA detection, it is thus advisable to perform several replicates to identify situations where the DNA quantity may not be sufficient to ensure detection with a single PCR. We consider that three to five replicates are a manageable number for the study of archaeological samples. We compared both strategies, (1) sequencing independently five replicates and (2) pooling these five replicates prior to barcoding for the analysis of a larger number of samples per sequencing run, which reduces the costs of the analysis. For most samples and primer pairs where detection was not achieved in all replicates, the pool of these replicates was able to summarize the result with a single barcode (Pr1-5 in Fig  2 ).
Acknowledgments We thank  Coralie Martin ,  Robert Nicol ,  Abdelillah Brahim ,  Solène Le Gal ,  Teresa Garate ,  Keesom Eom ,  Martha Betson ,  Akira Ito ,  Minoru Nakao ,  Joong-Ki Park ,  Bruno Gottstein ,  Jenny Knapp ,  Jan Brabec ,  Ralf Bialek ,  Angela Di Cesare  and  Cristina Cutillas  for providing modern parasite samples. We thank  Maria Saña ,  Michaël Landolt ,  Stéphane Buttner ,  Hervé Pomarèdes ,  Christophe Tardy ,  Franck Gama ,  Benjamin Dufour ,  Tristan Moriceau ,  Séverine Hurard ,  Nicolas Cayol ,  Thomas Romon ,  Sacha Kacki ,  Annelise Binois ,  Corinne Thévenet ,  Thomas Stöllner ,  Abozfal Aali ,  Nicole Boenke ,  Masoud Nezamabadi ,  Jean-Marc Fémolant ,  Sébastien Lefèvre , and  Tetsuya Yanagida  for providing archeological samples.
Nathalie M. L. Côté  was supported by a PhD fellowship from the  Franche-Comte region. The financial support for the project was obtained from the  French National Research Center (CNRS, TG, EMG) , the  Faculty of Sciences and Techniques of the Franche-Comte University, Besançon (MLB, NC) , and the  Franche-Comte region, France (MLB, NC). The sequencing facility of the  Jacques Monod Institute, Paris , is supported by grants to TG from the  University Paris Diderot , the  "Fondation pour la Recherche Médicale " ( DGE20111123014 ), and the  "Région Ile-de-France " (grant  11015901 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Cross sections of 6-8 µm thick were cut using a microtome, mounted on glass slides and stained with Harris haematoxylin and eosin containing phloxine B as previously described for the diagnosis of tissue fragmentation, necrosis and the identification of invasive organisms  (Sudek et al. , 2012; Work & Aeby, 2011). Serial sections were examined under a light microscope and photographed using NIS Element software (Nikon c ).
Genomic DNA was then extracted from each cyanobacterial isolate by boil lysing in 100 µL of 5 mM Tris/HCl at 100 • C for 5 min. PCR amplification was carried out in a volume of 25 µl GoTaq R Hot Start Green Master Mix (Promega, Madison, WI) containing 0. 5 mM of 16S rRNA gene cyanobacterial primers CYA781R/CYA106F  (Nübel, Garcia-Pichel & Muyzer, 1997; Rasoulouniriana et al. , 2009; Sussman, Bourne & Willis, 2006)  and 10 ng of template DNA. Amplification conditions for the PCR included an initial denaturing step of 5 min at 94 • C, followed by 35 cycles at 94 • C for 60 s, 60 • C for 60 s, 72 • C for 60 s, and a final extension step of 5 min at 72 • C. Sequences obtained for each cyanobacterial strain were examined for error and edited using GENEIOUS TM Pro (V. 5. 6. 6) sequencing software  (Kearse et al.
, 2012). All consensus sequences were submitted to BLAST at the National Centre for Biotechnology Information (NCBI, www. ncbi. nlm. nih. gov) and compared with published sequences. The 16S rRNA sequences were aligned with reference sequences from closely related to known cyanobacterial strains available in genbank. A phylogenetic tree was built by neighbourjoining in GENEIOUS TM Pro (V. 5. 6. 6) with bootstrap values based on 1,000 replicates.
Sequence analyses All 454 GSFLX sequences were sorted by MID identification using GENEIOUS TM Pro (V. 5. 6. 3). All generated reads were analysed using the SILVA online NGS tool (available online at www. arb-silva. de/ngs). Raw sequence reads were aligned with a gap extension penalty of 2 and a gap penalty of 5. Reads were filtered based on the following quality criteria: minimum length-200 bp, minimum quality score-30, maximum percent ambiguities-1%, minimum base pair score-30 and maximum percent repetitive-2%. Remaining reads were clustered into operational taxonomic units (OTUs) at a threshold sequence identity of 99%. OTUs were classified by BLAST score comparison against the SILVA rRNA database version 115, with a classification similarity threshold of 93%. Data from the SILVA classification were exported for further processing in MEGAN software version 5.
0. 78 beta  (Huson et al. , 2007)  using the lowest common ancestor (LCA) algorithm with all parameters kept at default values (min support, 5; min score, 35; top percent, 10. 0; win score, 0. 0). Cyanobacterial 16S rRNA gene sequences are accessible through the NCBI GeneBank database under accession numbers KF957835-KF957838. Raw 454-pyrosequencing reads were submitted in Study (BioProject PRJNA231011) to the NCBI Sequence Read Archive (SRA).
Statistical analysis Prevalence of PorBBD was calculated per transect and for each site. Data were tested prior to analysis for homoscedasticity (Levene's test) and normality of variance (Kolmogorov-Smirnov and Lilliefors tests) and were then log transformed [log 10 (X)] for analysis of variance (ANOVA). Variations in the prevalence of PorBBD over the two survey years in the consecutive summers and winters and across reef zones (reef slope vs. reef flat) were tested using Factorial ANOVA (STATISTICA 8). Fisher tests were performed for post hoc multiple comparison. Rarefaction curves were performed on bacterial populations associated with each tissue category. Principal coordinate analysis (PCoA) and cluster analysis were performed using the Bray-Curtis similarity coefficient to compare the bacterial community structures of the different tissue categories and the Simpson's and Shannon's diversity indices were calculated.
Western blotting Equal proteins aliquots derived from cell cultures and murine or human BAL fluids were resolved on 12 % SDS-polyacrylamide gels and transferred to polyvinylidene difluoride membranes (Millipore Corp. , Bedford, MA). Of note, unlike murine BAL fluids, human BAL (5 ml) were concentrated by lyophilisation prior to determination of protein concentration  [35]. Blots were incubated with the indicated antisera specific to extracellular or cytoplasmic E-cad domains or NE (dilution for E-cad antibodies 1:1000 and dilution for NE 1:2500)  [35]. Membranes were subsequently incubated with an appropriate dilution of HRP-linked secondary IgG (dilution 1:2000) in blocking buffer. Immunoreactive fragments were visualized by enhanced chemiluminescence (ECL, Amersham Biosciences). Labelled proteins or fragments were detected with the Molecular Imager ChemiDoc XRS System and quantified using Quantity One 1-D Analysis Software (Biorad, Marnesla-Coquette, France).
Acknowledgements We thank  D Ferrandon ,  D Louvard ,  T Mayadas ,  J-M Reichhart ,  B Reina  and  L Sorokin  for critical reading of the manuscript,  A Klein  and  C Arnold  for technical assistance,  C Ebel  for help with FACS analysis,  J-P Ghnassia  for anatomopathogical analysis, the IGBMC-histology platform for technical support,  J Pontabry  for CD4-SLC26A3 image analysis,  L Cao  ( Inviscan SAS ) for PET scan image reconstruction, M-F Champy for cytokine multiplex measurements,  C Thibault-Carpentier  and  V Alunni  for microarray analysis,  A Van Es  and the  IGBMC animal facility  staff for help and advice in animal experimentation,  D Metzger ,  P Chambon ,  M Li ,  S Chan  and  T Sasaki  for gift of reagents and  S Bour  for help in figure preparation. Contributors EG-L conceived and supervised the project until mid-2012; ADA and ML supervised the project afterwards.
Funding This work was supported by funds from  CNRS ,  Inserm  and  Université de Strasbourg , by grants from the  Association pour la Recherche sur le Cancer and Ligue Régionale contre le Cancer (EG-L) , the  Fondation pour la Recherche Médicale  ('Equipe FRM 2009' to MC), the  Institut National du Cancer  (Appel à projets 'Formes précoces du cancer colorectal' to EG-L and PS-A and to EG-L and MC),  Agence Nationale pour la Recherche  (EG-L and ML) and  LabEx INRT (IGBMC, UdS ). SN was a recipient of a  Postdoctoral Fellowship  from the  Ligue contre le Cancer.
Materials and methods: From January 2014 to September 2015, 1,500 patients infected with HCV genotype 1, 3 and 4 were exposed to NS5A inhibitors (LDV/SOF, DCV/SOF, DCV/ASU). In the 4 French referent liver centers: Marseille, Lyon, Grenoble, St Laurent du Var, 22 (1. 5%) of them relapsed. Prevalence of NS5A RAVs/polymorphisms was analyzed using Sanger sequencing. HCV subtypes and drugresistance interpretation were identified using DeepChek-HCV software. Results: Numbers and characteristics of NS5A class RAVs (variants at any position associated with resistance to any NS5A inhibitors) from different genotypes are shown in Table  O2. 1. Main G1a RAVs detected were M28A, Q30H/K/R, L31M, H58D, Y93F/H/N; main G1b RAVs detected were L31F/M/V and Y93H; main G3a RAVs detected was Y93H; and main G4d RAVs detected was L28V.
Material and methods The Matching Validation Subcommittee defined three matching validation tasks (MVTs). For each task, a new matching validation data set (MVS) comprising the HLA typings of 1000 patients and 10,000 donors was created. The 10 million possible patient-donor pairs of these three MVSs had to be used by the participants as input data for their own HMA. The complexity of the computational tasks with regard to algorithmic requirements and considered data increased from MVT 1 to 3. All materials and results necessary to run the experiments for newly developed or modified algorithms and to analyze and validate match results are made available for download as part of the Supporting Information. In the following, references to tables and figures in the Supporting Information are denoted with 'S'.
Simulation of ambiguous HLA genotypes The genotypes of the patients and the donors for the three tasks of this study were all separately generated by independently drawing two haplotypes from the set of high-resolution US Caucasoid haplotypes with a probability corresponding to their frequencies given in Maiers et al. (19). For the purpose of having patient and donor genotypes at varying levels of typing resolution, the HLA used in the tasks corresponds to what the known HLA type would have been if those individuals had been typed using representative methods and typing kits. The ambiguities thus introduced are based on IPD-IMGT/HLA Database (20) releases v2. 16. 0 for MVS 1 and MVS 2 and v3. 4. 0 for MVS 3. Patient and donor HLA genotypes are encoded using genotype list (GL) string syntax  (21)  and multiple allele codes are used to reflect typing ambiguities  (22).
txt (25)  according to IPD-IMGT/HLA Database release v3. 6. 0 have been used. To ensure consistency with World Health Organization (WHO)-assigned antigen mappings it was decided to disregard the expert-assigned values of the WMDA mapping file in this experiment. The differences observed in a first round of analysis for MVT 3 showed the need for an even stricter rule set to achieve comparable results. In summary, the requirements to achieve a 'baseline' MVT 3 result are: R1 ARD level matching has to be used. R2 Mismatches have to be counted according to Table  S3. R3 WHO HLA nomenclature with WMDA extensions has to be used  (24). R4 The reported matching probabilities have to utilize simple rounding half up to integers and range between 0 and 100. R5 Possible values for the locus-specific match grade characters are A (allele-level match), P (potential allele-level match) and M (mismatch).
Comparison and statistics In the central analyses for MVT 1 and 2, the overall consistency of the data provided and their compliance with the specification was initially checked. Then, for each patient-donor pair the reported numbers of match differences from the submitted result files were compared separately by means of Perl  (27)  scripts in which the redundant cases per locus were only considered once. In case of disparity, the values provided by the participants were collated in dedicated spreadsheets in which the cells containing values deviating from the majority (if any) were displayed in color. The discrepancies were manually inspected and categorized. These preliminary findings were discussed and, when necessary, clarified and adjusted in the meetings of the Matching Validation Subcommittee in order to compile a consensus result.
For MVT 3, a two-step analysis approach was used. First, an overview comparison between the results of every pair of (B) The total number of differences (allele or antigen) for the loci HLA-A, -B and -DRB1 as well as the number of antigen differences had to be reported (6 values). The valid range for the differences is 0 to 2. For each locus the total number of differences must be greater or equal to the number of antigen differences. participants was made in which only the number of discrepant results concerning the three areas 'overall 10/10 and 9/10 matching predictions', 'locus-specific 2/2 matching predictions' and 'locus-specific match grade characters' were counted. Second, for the detailed analysis Perl scripts were used to collect the specific result items for all discrepant cases in suitably structured spreadsheets including the observed differences and the number of possible diplotypes as indicator for the complexity of the considered patient-donor pair.
mammalian host adaptation, remains a key gap in the field. Infectious Leptospira, comprised of pathogenic and intermediately pathogenic Leptospira, evolutionarily diverged from non-infectious, saprophytic Leptospira, as demonstrated by the following computational biology analyses: 1) the definitive taxonomy and evolutionary relatedness among all known Leptospira species; 2) genomically-predicted metabolic reconstructions that indicate novel adaptation of infectious Leptospira to mammals, including sialic acid biosynthesis, pathogen-specific porphyrin metabolism and the first-time demonstration of cobalamin (B12) autotrophy as a bacterial virulence factor; 3) CRISPR/Cas systems demonstrated only to be present in pathogenic Leptospira, suggesting a potential mechanism for this clade's refractoriness to gene targeting; 4) finding Leptospira pathogen-specific specialized protein secretion systems; 5) novel virulence-related genes/gene families such as the Virulence Modifying (VM) (PF07598 paralogs) proteins and pathogen-specific adhesins; 6) discovery of novel, pathogen-specific protein modification and secretion mechanisms including unique lipoprotein signal peptide motifs, Sec-independent twin arginine protein secretion motifs, and the absence of certain canonical signal recognition particle proteins from all Leptospira; and 7) and demonstration of infectious Leptospira-specific signal-responsive gene expression, motility and chemotaxis systems.
Phylogenetic analysis 16S rRNA trees were generated by first creating a multiple sequence alignment to the bacterial 16S rRNA reference alignment using Ribosomal Database Project release 10 (RDP-X)  [45]. The aligned FASTA sequences were downloaded and trimmed to remove gapped columns using Belvu (v2. 31)  [46]. Based on the alignment, a bootstrapped Maximum-likelihood tree was subsequently inferred using phylipFasta, an in-house wrapper script  [47]  for the Phylip program  [48, 49]. SecY trees were created by first aligning secY nucleotide sequences using Clustal Omega  [50]  with 100 combined guide-tree/HMM iterations. The multiple sequence alignment was trimmed to remove gapped columns and a bootstrapped Maximum-likelihood tree was inferred as was done for the 16S rRNA trees. The nucleotide sequences of 7 MLST housekeeping genes were extracted from the 20 genomes, and sequence types (STs) assigned using the MLST website (http://leptospira.
Genomic location and genetic organization of vitamin B12 biosynthesis -related gene clusters in Leptospira PanOCT data were used to identify the cob I/III and cob II gene clusters in infectious and noninfectious Leptospira (when present), which encode proteins predicted to participate in B12 transport or synthesis. To determine the genomic locations of the btuB and cob II and I/III clusters, a custom Postgresql database was created using the annotated genomes of 20 species. Orthologs were identified with blastp (-F "m S" -s T) and conserved genomic neighborhood using the Prokaryotic Sequence homology Analysis Tool (PSAT)  [77].
The first glycosyltransferase in this cassette, LEP1GSC185_2122 (GenBank EIE02925) in L. licerasiae and LEP1GSC061_3728 (GenBank EPG64090) in L. wolffii, are highly conserved and would be a useful marker for this extremely small O-antigen gene cluster. No other protein in the replacement six-gene cassette is conserved across the different variants. Genes in these regions have no close homologs in any other Leptospira, in the O-antigen region or anywhere else, supporting the notion that these cassettes provide unique carbohydrate chemistry and serology, and is not simply an unusual gene neighborhood for otherwise common leptospiral enzymes. Lipid A biosynthesis. The lipid A of leptospiral LPS is not as potent an endotoxin as lipid A moieties of other bacteria such as the Enterobacteriaceae or Neisseria spp.
, and most of the 79 predicted ORFs display no similarity to known ORFs, but 21 ORFs appeared to be organized in clusters that might encode head and tail structural proteins and immunity repressor proteins  [130]. Next generation sequencing and refinement of computational methods have allowed comparative genome analysis to discover new prophage and genomic islands  [131]. A few phage related genomic islands have thus been characterized in L. interrogans and L. licerasiae  [122, 132, 133]  and it was previously shown that one of these genomic islands can excise from the L. interrogans chromosome  [133]. To determine the distribution of prophages within the Leptospira genus, Phage_Finder  [60]  was run under both strict (-S) and non-strict modes to identify predicted prophage regions. Phage_Finder predicted a total of 14 major prophage regions across the 20 genomes, most of which were found to be shared between the Leptospira species (Table  4 ).
meyeri, L. wolbachii, L. vanthielii, L. yanagawae, and L. terpstrae-were CRISPR/Cas systems detected, suggesting that these species rely on some other mechanism for escaping phage/plasmid attack. In these saprophytes, we were also unable to detect sequences encoding prophage, while CRISPR systems and prophage occurred together in several, but not all, representative pathogenic and intermediate strains. When present, between one and six CRISPR repeat arrays were detected, containing between three and 25 spacer sequences (Table  5 ). Since CRISPR spacer sequences in other organisms are known to target phage sequences for destruction, we wondered if any of the 239 predicted spacer sequences targeted any of the known Leptospira spp. phage or predicted prophages. A database containing the nucleotide sequences of the 19 predicted prophages from this study plus the LE1 phage and the prophage from Qin et al.