dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogeny Reconstructions Protein sequences were aligned using the Muscle program  (Edgar 2004 ). Multiple sequence alignment trimming was done for the analysis of metagenomic sequences using the TrimAL program with the gappyout command, which calculates the gap percentage for the whole sequence alignment  (Capella-Gutierrez et al. 2009). Phylogenetic reconstructions were performed using the maximum likelihood method with the Whelan and Goldman (WAG) substitution model. Confidence values were calculated by the Shimodaira-Hasegawa test using FastTree  (Price et al. 2010). Phylogenetic trees were visualized by FigTree (http://tree. bio. ed. ac. uk/software/figtree/, last accessed May 1, 2014).
Ancestral Sequence Reconstructions Ancestral sequences were reconstructed for sequences from Archaea, Bacteria, Eukarya, and the proposed order Megavirales using the maximum likelihood method including the WAG substitution model and conducted by the MEGA5 software  (Tamura et al. 2011). Comparative Analyses of p Distances between Reconstructed Ancestral Sequences for RNAP2 from Archaea, Bacteria, Eukarya, and Proposed Order Megavirales and These RNAP Sequences, and between These RNAP Sequences p distances, that is, the proportions of amino acid sites at which two sequences to be compared are different, were obtained using the MEGA5 software  (Tamura et al. 2011). Comparisons of mean p distances were performed using the OpenEpi software (www. openepi. com, last accessed May 1, 2014) with the analysis of variance test or the nonparametric Mann-Whitney U test when appropriate.
RNAP Homologous Sequence Detection from Metagenomic Databases Sequences homologous to RNAP2 were searched for in environmental metagenomes by BLASTp against the NCBI GenBank environmental protein sequence database (env_nr) using LUCAR2 as query sequence, 70% query length coverage and an e-value cutoff of 1e-10 as stringent parameters and considering only RNAP homologs larger than 400 amino acids. Circular Representations of BLAST Matches between Protein Sequences of H. magnipapillata, Marine Group II Euryarchaeote SCGC AB-629-J06 and Phy. parasitica, and Mimiviruses and Other Megavirales Members Putative protein sequences from scaffold 39305 (GenBank accession no. GL020074. 1) of the draft genome of H. magnipapillata were predicted using the GeneMarkS software  (Besemer and Borodovsky 2005) ; 337 ORFs were predicted including 186 equal to or larger than 100 amino acids in size.
Protein sequences annotated from the draft genomes of marine group II euryarchaeote SCGC AB-629-J06 and Phy. parasitica were downloaded from the NCBI GenBank protein sequence database. The gene repertoires from all available Megavirales members were compiled into an in house database. BLASTp searches were run using protein sequences for each of the three organisms against the NCBI GenBank nonredundant protein sequence database to identify those having viral sequences as best hits  (Altschul et al. 1990 ). Representations were created using DNAPlotter (http://www. sanger. ac. uk/resources/software/dnaplotter/, last accessed May 3, 2014).
Transcriptional profiling by cDNA microarray and quantitative PCR Total RNA was purified from cell lysates using the RNEasy mini kit (Qiagen). Samples were processed for Agilent microarrays, and data were normalized, as previously described  (56). Microarray data are available in the ArrayExpress database (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-1541. For quantitative PCR (qPCR), cDNAwas synthesized using the qScript cDNA Supermix kit (Quanta BioSciences), and qPCR of selected genes was performed using the following inventoried TaqMan assays (Applied Biosystems): IL23 (Hs00372324_m1), IL1b (Hs01555410_m1), IL6 (Hs00985639_m1), and TNFa (Hs00174128_m1). PTSG2 expression was quantified using the following: forward primer, 59-CGGTCCTGG-CGCTCAG-39; reverse primer, 59-CCGGGTACAATCGCACTTATACTG-39; and probe, 59-CCATACAGCAAATCCTT-39 (Applied Biosystems).
However, non-TLR2 transcriptional responses were largely comparable in MDMs stimulated with either the Dlgt or TIGR4 strains. The genes showing the greatest differences in expression between MDMs stimulated with Dlgt and TIGR4 strains included the cytokines IL-23, IL-6, and IL-1b, and the chemokines CXCL1, CXCL2, and CXCL3 (Supplemental Fig. 2A ), and transcriptional factor binding site analysis showed enrichment for regulation by NF-kB (Table  I ) in keeping with the data shown in Fig. 5  and Supplemental Fig. 1. Selected differences in expression of prototypic inflammatory genes identified by microarray were validated by qPCR. IL-23, IL-6, and PTGS2 expression were all significantly lower in macrophages stimulated with the Dlgt strain compared with WT TIGR4, confirming the transcriptome results (Supplemental Fig.
The list of TLR2-dependent genes has a striking preponderance for genes encoding important proinflammatory proteins that bioinformatic analysis suggests are largely activated by the NF-kB pathway, in keeping with the data showing reduced  NF-kB activation in response to the Dlgt strain. Our data contrast with those showing that pneumolysin induced macrophage expression of very few genes encoding chemokines or cytokines  (68). The potential clinical relevance of S. pneumoniae lipoproteindependent inflammation was assessed in a mouse model of pneumonia, and using PBMCs from individuals with IRAK-4 deficiency. We were able to demonstrate that the rapid TNF-a and IL-1b responses during early lung infection with S. pneumoniae were reduced in response to the Dlgt strain. Despite the effect of lipoproteins on the IL-6 response in vitro, BALF IL-6 responses were similar for mice infected with the TIGR4 or Dlgt strain.
In pull-down assays, the IAA12 positive face (IAA12m1) or its negative face (IAA12m2), reduced complex formation without abolishing it (Fig. 4b ). Similarly, IAA12 proteins mutated on a single face were still able to interact with ARF5 in Y2H assays (Fig. 4c ). Taken together, these data strongly suggest that IAA12 can indeed interact with ARF5 both via its negative and positive faces. According to this curve, ARF5 is eluted as a dimer, IAA12 as a trimer and ARF5m1, ARF5m2, ARF5m3, IAA12m1, IAA12m2 and IAA12m3 are eluted as monomers. Isolated domains III/IV were used for ARF and IAA proteins. All proteins (ARF, IAA and calibration standards) were loaded in the solubilization buffer (see Methods). In planta validation of interaction surfaces. To validate our findings in vivo, we tested the effect of wild-type and mutant ARF7 (another ARF activator), IAA17 and IAA19 on the expression of the DR5::GUS auxin-inducible synthetic reporter in plant protoplasts (Fig.
fr/htxlab). Crystals were grown by the hanging-drop vapour diffusion method. Purified protein was diluted to 1. 25 mg ml À 1 in Tris-HCl 5 mM pH 8. 0 containing 0. 5 mM of reduced and oxidized glutathione and combined with crystallization solution (12% dioxane) in a 3:1 ratio. Native crystals were harvested, swept through cryoprotection solution (Tris-HCl 5 mM pH 8. 0, 12% dioxane, 0. 5 mM GSH/GSSG and 25% glycerol) and then plunge-frozen in liquid nitrogen. Data collection and refinement. Data from native crystals were collected on the ESRF microfocus beamline ID23-EH2 (ref. 18) at 0. 873 Å in a gaseous nitrogen cryostream at 100 K. Data were collected using a helical trajectory to distribute radiation damage throughout a larger area of the crystal. Crystals were derivatized by soaking native crystals in 2 mM ethyl mercury thiosalicylate for 2 min, followed by back-soaking into cryoprotection solution and freezing in liquid nitrogen.
In subunit H, residues Gly885 to Asp896 were ordered and visible, probably because this segment makes crystal contacts with subunit C and F. The final model has a clash score of 1. 87 and Ramachandran values (computed with PROCHECK 24 ) of 91. 5% core, 8. 5% allowed, 0. 0% generously allowed and 0. 0% disallowed. Data collection and refinement statistics are shown in Supplementary Table  2. Y2H analysis and protoplasts assays. For the Y2H analysis, yeast strains (AH109 and Y187) were transformed by the LiAc method  3. The Y2H analysis was performed after mating of yeast from the two strains. Interactions were assessed using an HIS3 test, where HIS3 activity resulting from protein-protein interactions is detected by the restoration of growth in the absence of histidine. To do so, OD 600nm were measured during the exponential growth phase in a base medium (SD-Leu/Trp) with or without histidine.
Acknowledgements We thank  Jonathan Legrand  ( ENS, Lyon ) and  Hicham Chahtane  ( PCV, Grenoble ) for help with statistical analysis and  Chloe Zubieta  ( PCV, Grenoble ) for proofreading the manuscript. R. D. , F. P. T. V. -P. , G. B. , S. L. and T. V. were supported by the  ANR-12-BSV6-0005  grant (AuxiFlo), R. D. , F. P. and M. H. N. by the  GRAL Labex, S. W. by 'Program for Introducing Talents to Universities  ( B07017 ) and a startup fund from  Northeast Normal University  (www. nenu. edu. cn)' and T. G. by the  University of Missouri   F21 Program.
All centers used a common protocol and applied the standardised Brighton Collaboration GBS case definition for case classification  [9]. Implementation of the protocol and data collection differed per country based on ethical requirements and the healthcare structure. Data harmonization, transformation, and pooling used methods and tools derived from the EU-ADR (Exploring and Understanding Adverse Drug Reactions) project  [15]. Centers created harmonized input files according to well-defined instructions. These data files were generated directly from automated resources or manually using customized electronic case report forms. The harmonized input files were transformed using a standardized JAVA-based program (JerboaH version 2. 6. 0, September 2010, Erasmus University Medical Center, Rotterdam, Netherlands).
Source and study population The total source population exceeded 50 million (M) subjects, with most countries recruiting cases on a national level (NO (4. 4 M), SE (9 M), FI (5. 5 M), DK (5 M), NL (16 M)). In the UK, the General Practice Research Database (GPRD) (5 M) was used and in France specialized hospitals with a large but undefined catchment area participated. Case recruitment started on 1st November 2009 and lasted maximally until 1st November 2010. The study population encompassed all cases with GBS or its variant Miller Fisher syndrome with onset of disease during the study period. Case recruitment-procedures are described in Table  1. Completeness of recruitment was verified retrospectively at the end of the study period by comparing recruited cases with diagnosed case lists (see Table  1 ).
Covariates Information on several time varying risk factors for GBS was collected during follow-up including seasonal influenza vaccination, influenza-like illness (ILI), upper respiratory tract infections (URTI), and gastrointestinal infections (GI). Each of these covariates was assigned a 42-day risk period. The risk period began on day one of onset of ILI, URTI, or GI or of seasonal influenza vaccine receipt and ended 42 days after onset or exposure. Covariate data were not collected in DK and FI. In FR, covariate data were collected from neurologists at case occurrence for the period prior to GBS only, whereas in SE data on covariates were collected by interview at the end of follow-up. In the UK, NL, and NO general practitioner records were used to collect information on covariates throughout the follow up period; NO also assessed covariates reported by neurologists at the time of case data collection, leading to a potential for differential data collection over time.
The use of common methods and subsequent pooling reaches far beyond the traditional approach of meta-analyses where rather heterogeneous estimates resulting from different designs, methods, and settings are being pooled. The data from this VAESCO study are in line with other results from Europe with studies from FR (RI 0. 9, 95% CI: 0. 1 to 7. 6)  [16] , SE (RI 1. 1 95% CI: 0. 6 to 1. 9)  [17] , and the UK (RI 1. 05, 95% CI: 0. 37-2. 24) [18] , all showing no association. In contrast, a recent report from Germany, where AS03 adjuvanted vaccine was used, showed an increased risk of GBS after vaccination (RI 4. 65, 95% CI: 2. 17 to 9. 98)  [19]. German investigators had already started a separate SCCS study and thus elected not to participate in VAESCO. They did not adjust for infections or calendar-time and selection bias could not be excluded since cases originated from a reporting network.
Pooling of calendarmonth adjusted RI estimates with the VAESCO study would be possible through meta-analysis to enlarge the scale of the current EU based study. Five studies from the US, where non-adjuvanted influenza A(H1N1)pdm09 vaccines were used, have recently been published. Each of the initial observational studies found an increased RI ranging from 1. 6 (95% CI: 1. 0 to 2. 2)  [20] , to 2. 1 (95% CI: 1. 2 to 3. 5)  [21] , to 2. 5 (95% CI: 0. 42 to 15. 0)  [22] , and to 4. 4 (95% CI: 1. 3 to 14. 2)  [23]. Three studies used self-controlled designs but without further adjusting for time-varying confounders  [21] [22] [23]. The study assessing the lowest RI (1. 6 (95% CI: 1. 0 to 2. 2)) was a cohort study adjusting for age and sex  [20]. The highest RI of 4. 4 (95% CI: 1. 3 to 14. 2) was based on data from the US Vaccine Safety Datalink (VSD) project, which was based on 13 vaccinated cases  [23].
However, the association between GBS and antecedent infection was strongly elevated. The effect of infections on the risk of GBS and the potential preventive effect of vaccination on the risk of GBS by preventing influenza was recently discussed by Stowe and Poland  [27, 28]. This recent evidence underlines the need to adjust for infections as we could do in part of the countries in our analyses. Owing to its observational nature, our study suffers from limitations that should be considered when interpreting data. In NL and SE, where reporting networks were used, completeness of recruitment was verified by retrospectively comparing included cases with claims made for GBS. In FR and SE informed consent was required which could be another reason for non-inclusion. Finally, since cases were included only if charts/medical records could be reviewed, lack of data could be another source of selection bias.
Acknowledgments National Institute for Health and Welfare (THL), Helsinki, FI:  Jukka Ollgren ;  National Institute for Public Health and the Environment (RIVM), Bilthoven, NL: Hester de Melker. Haukeland University Hospital, Bergen , NO:  Anette Storstein ,  Christian Vedeler. All information and results concerning the UK in this study is based in part on data from the Full Feature General Practice Research Database obtained under licence from the  UK Medicines and Healthcare products Regulatory Agency  and covers the data collection time period up to February 2011. However, the interpretation and conclusions contained in this report are those of the authors alone.
Recruitment was incomplete because of delays in consent and non-consent. It was not possible to assess whether this non-response differed by vaccination status and hence selection bias cannot be excluded. By interview at end of follow-up, recall bias cannot be excluded. By interview for cases at the end of follow up. change in region over time. Should not be used for adjustment Consent required, potential selection bias. Recall bias (differential recall over time)UKEach case was identified in the General Practice Research Database by using appropriate READ codes (F370. 00, F370000, F370100, F370200, F370z00). Case verification was done using any hospital letters, discharge summaries and GPs' notes recorded as free text. No major selection to be expected doi:10. 1371/journal. pone. 0082222.
IMGT ® comprises seven databases, 15,000 pages of web resources and 17 tools. IMGT ® tools and databases provide a high-quality analysis of the IG from fish to humans, for basic, veterinary and medical research, and for antibody engineering and humanization. They include, as examples: IMGT/V-QUEST and IMGT/JunctionAnalysis for nucleotide sequence analysis and their high-throughput version IMGT/HighV-QUEST for next generation sequencing, IMGT/DomainGapAlign for amino acid sequence analysis of IG domains, IMGT/3Dstructure-DB for 3D structures, contact analysis and paratope/epitope interactions of IG/antigen complexes, and the IMGT/mAb-DB interface for therapeutic antibodies and fusion proteins for immunological applications (FPIA).
IMGT ® : The Birth of Immunoinformatics IMGT ® , the international ImMunoGeneTics information system ®  [1, 2] , was created in 1989 by Marie-Paule Lefranc at Montpellier, France (CNRS and Montpellier University). The founding of IMGT ® marked the advent of immunoinformatics, a new science, which emerged at the interface between immunogenetics and bioinformatics. For the first time, immunoglobulin (IG) or antibody and T cell receptor (TR) variable (V), diversity (D), joining (J) and constant (C) genes were officially recognized as "genes" as well as the conventional genes  [3] [4] [5] [6]. This major breakthrough allowed genes and data of the complex and highly diversified adaptive immune responses to be managed in genomic databases and tools. The adaptive immune response was acquired by jawed vertebrates (or gnathostomata) more than 450 million years ago and is found in all extant jawed vertebrate species from fishes to humans.
IMGT ® provides a common access to standardized data from genome, proteome, genetics, and two-dimensional (2D) and three-dimensional (3D) structures. IMGT ® is the acknowledged high-quality integrated knowledge resource in immunogenetics for exploring immune functional genomics. IMGT ® comprises seven databases (for sequences, genes, and 3D structures)  [7] [8] [9] [10] [11] [12]  and 17 online tools  [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28]  (Figure  1 ), as well as more than 15,000 pages of web resources (e. g. , IMGT Scientific chart, IMGT Repertoire, IMGT Education > Aide-mémoire  [29] , The IMGT Medical page, The IMGT Veterinary page, The IMGT Biotechnology page, The IMGT Immunoinformatics page)  [2]. IMGT ® is the global reference in immunogenetics and immunoinformatics  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45].
(NUMEROTATION). With a focus on IG, we first review the fundamental information generated from these IMGT-ONTOLOGY concepts which led to the IMGT Scientific chart rules. The major IMGT ® tools and databases used for IG repertoire analysis, antibody engineering and humanization, and IG/Ag structures are then briefly presented: IMGT/V-QUEST  [13] [14] [15] [16] [17] [18]  for the analysis of rearranged nucleotide sequence with the results of the integrated IMGT/JunctionAnalysis  [19, 20] ; IMGT/Automat  [21, 22]  and IMGT/Collier-de-Perles tool  [27] ; IMGT/HighV-QUEST, the high-throughput version for Next-Generation Sequencing (NGS)  [23, 24] ; IMGT/DomainGapAlign  [10, 25, 26]  for amino acid sequence analysis; IMGT/3Dstructure-DB for 3D structures  [9] [10] [11] ; and its extension, IMGT/2Dstructure-DB (for antibodies and other proteins for which the 3D structure is not available).
IMGT ® tools and databases run against IMGT reference directories built from sequences annotated in IMGT/LIGM-DB  [7] , the IMGT ® nucleotide database (176,806 sequences from 346 species in October 2014), and from IMGT/GENE-DB  [8] , the IMGT ® gene database (3464 genes and 5118 alleles from 21 species, of which there were 710 genes and 1439 alleles for Homo sapiens and 868 genes and 1318 alleles for Mus musculus in October 2014). An interface, IMGT/mAb-DB  [12] , has been developed to provide an easy access to therapeutic antibody amino acid sequences (links to IMGT/2Dstructure-DB) and structures (links to IMGT/3Dstructure-DB, if 3D structures are available). IMGT/mAb-DB data include monoclonal antibodies (mAb, INN suffix -mab) (a -mab is defined by the presence of at least an IG variable domain) and fusion proteins for immune applications (FPIA, INN suffix -cept) (a -cept is defined by a receptor fused to an Fc) from the WHO-INN programme  [48, 49].
This database also includes a few composite proteins for clinical applications (CPCA) (e. g. , protein or peptide fused to an Fc for only increasing their half-life, identified by the INN prefix ef-) and some RPI used, unmodified, for clinical applications. The unified IMGT ® approach is of major interest for bridging knowledge from IG repertoire in normal and pathological situations  [70] [71] [72] [73] [74] [75] , IG allotypes and immunogenicity  [76] [77] [78] , NGS repertoire  [23, 24] , antibody engineering and humanization  [33, [40] [41] [42] [79] [80] [81] [82] [83] [84] [85] [86].
IDENTIFICATION: IMGT ® Standardized Keywords More than 325 IMGT ® standardized keywords (189 for sequences and 137 for 3D structures) were precisely defined  [57]. They represent the controlled vocabulary assigned during the annotation process and allow standardized search criteria for querying the IMGT ® databases and for the extraction of sequences and 3D structures. They have been entered in BioPortal [87] at the National Center for Biomedical Ontology (NCBO) in 2010. Standardized keywords are assigned at each step of the molecular synthesis of an IG. Those assigned to a nucleotide sequence are found in the "DE" (definition) and "KW" (keyword) lines of the IMGT/LIGM-DB files  [7]. They characterize, for instance, the gene type, the configuration type and the functionality type  [57]. There are six gene types: variable (V), diversity (D), joining (J), constant (C), conventional-with-leader, and conventional-without-leader.
DESCRIPTION: IMGT ® Standardized Labels More than 560 IMGT ® standardized labels (277 for sequences and 285 for 3D structures) were precisely defined  [58]. They are written in capital letters (no plural) to be recognizable without creating new terms. Standardized labels assigned to the description of sequences are found in the "FT" (feature) lines of the IMGT/LIGM-DB files  [7]. Querying these labels represent a big plus compared to the generalist databases (GenBank/European Nucleotide Archive (ENA)/DNA Data Bank of Japan (DDBJ)). Thus it is possible to query for the "CDR3-IMGT' of the human rearranged productive sequences of IG-Heavy-Gamma (e. g. , 1733 CDR3-IMGT obtained, with their sequences at the nucleotide or amino acid level). The core labels include V-REGION, D-REGION, J-REGION, and C-REGION which correspond to the coding region of the V, D, J and C genes.
IMGT structure labels for IG chains and domains are illustrated with the example of an human IgG1 (Figure  2 ). Correspondence between human IG structure labels and sequence labels are provided in Table  1. These labels are necessary for a standardized description of the IG sequences and structures in databases and tools  [58]. Highly conserved amino acids at a given position in a domain have IMGT labels  [58]. Thus three amino acid labels are common to the V and C domains: 1st-CYS (cysteine C at position 23), CONSERVED-TRP (tryptophan W at position 41), and 2nd-CYS (C at position 104)  [60] [61] [62] [63] 65]. Two other labels are characteristics of the V-DOMAIN and correspond to the first amino acid of the canonical F/W-G-X-G motif (where F is phenylalanine, W tryptophan, G glycine, and X any amino acid) encoded by the J-REGION: J-PHE or J-TRP (F or W at position 118)  [60] [61] [62] 65].