dataesr/scientific-paragraphs-categorization huggingface dataset:
com/carlobaldassi/ GaussDCA. jl. Funding: AP is partially supported by EU Marie Curie 439 Training Network NETADIS, (FP7 Grant 290038). GU was partly funded by the Agence Nationale de la Recherche: project COEVSTAT (ANR-13-BS04-0012-01). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Analysis (DCA)  [13] [14] [15] , (ii) pseudo-likelihood maximization (PlmDCA),  [16] [17] [18] , (iii) Multivariate Gaussian Modeling (MGM),  [19, 20]. All these methods rely on the inference of a generative probabilistic model for sequences in the presence of selective pressure. This feature makes this kind of analytic techniques particularly suited for the study of Ab affinity maturation. In fact, this process closely resembles a Darwinian evolutionary framework where B-cell clones compete for the antigen in the germinal centers, and it is now widely accepted that the affinity for the target antigen represents the main contribution to the fitness in this evolutionary scenario.
Affinity predictions Wu and coworkers  [1]  used 70 sequenced heavy chain variable regions, which originated mostly from immunoglobulins using the IGHV1-2 gene, for constructing chimeric antibodies by combining them with the light chain of VRC-PG04. Among these, 45 have been tested for their neutralization power against 20 HIV-1 mutations. When included in the sequencing data set and used as input for the clustering procedure, 30 of these 45 tested Abs are found to belong to the hypermutated cluster. The remaining 15 (none of which was found to be neutralizing) belong to the germline cluster. Although in general the neutralization power depends on both the light and the heavy chain sequences (cf. Fig. 4A  in  [1] ), the light chain plays only a minor role in the interaction (most notably steric contacts with its CDR1 and CDR3 regions) here, as visible from the solved structure of VCR-PG04 (PDB code 3SE9).
We therefore will make the simplifying assumption that the neutralization measurements on chimeric Abs depend on the heavy chain contribution alone. Under the assumption that the hypermutated cluster is a statistically representative sample of the Abs that underwent affinity maturation against gp120, we can use the statistical properties of this set of sequences to construct a predictor for the Abs neutralization power. We thus inferred an MGM on the MSA of this cluster and used the MGM-score of the inferred model sequences from  [1]. Dark green boxes represent sets of sequences that are obtained after the bioinformatics analyses: the first step consists in the identification of primers and conversion to the reverse complement; then, after amino acid translation, non-productive sequences are filtered out; finally, through the use of the IgBLAST software, germline genes of origin are inferred and different subsets of sequences are selected.
We start from the multiple sequence alignment of the heavy chain variable region sequences belonging to the hypermutated cluster and define a summary statistics of the data set in terms of the single residue frequency counts (means) x and the covariance matrix C calculated after pseudo-count correction (see  [20] ). These quantities define the maximum-likelihood MGM, a multivariate Gaussian distribution whose parameters are the mean and the covariance. The exponent of the Gaussian distribution is the MGM-score function (z is the amino acid sequence of the Ab to be scored) which is used as a proxy of the binding affinity toward gp120. A set of 30 IGHs is used to test the accuracy of the model in predicting the IGH contribution to the neutralization power. In fact, in  [1] , these IGHs sequences were used to produce chimeric Abs with the IGL of a known bnAb (VRC-PG04), which were eventually tested for neutralization power against 20 HIV viruses.
Data of this MSA reduction analysis are reported in S1 Text (see Section "Affinity predictions"). Our predictor was also compared with a structure-based method: we produced structural models for all the 45 antibody/antigen complexes for which the IC 50 was measured and predicted their binding affinity using FoldX (see Methods for details). The results of this structural method show no significant correlation (r = -0. 23, p-value = 0. 13) with the experimental data. Taken together, our findings indicate that: (i) MGM inferred on the proper set of clonally expanded sequences contains enough information to predict the neutralization power of Ab sequences. This suggests that the procedure can be used as a tool to generate new and highly.
These antibodies have been hypothesized  [1]  to have matured from subsequent expansions of an original clone expressing the IGHV1-2 Ã 01 and IGHJ Ã 02 germline genes-see  [26]  for information on (IMu-noGenTics, IMGT) IGH genes nomenclature scheme. Therefore, in order to identify the ensemble of bnAbs from the whole donor Ab repertoire, we used the IgBLAST platform  [27]  to assess both germline gene of origin and productivity. We first selected sequences with productive amino acid translation and then, in a subsequent step, we screened sequences of those Abs that mutated from these particular germline genes. Data are available from National Center for Biotechnology Information Short Reads Archives (SRA) under accession number SRP006992. Multiple sequence alignments. Ab sequences have been aligned by taking advantage of a custom Hidden Markov Model (HMM).
A posteriori, this insertion was confirmed by the analysis of the solved structure of VRC-PG04 in complex with gp120 (PDB code 3SE9), in which the insertion is located at residue at position 59 of the heavy chain following the original PDB file numbering. The same position corresponds to an insertion between residue 59 and 60 in the Kabat-Chothia numbering scheme. Accordingly, we modified the alignment originally used to generate the HMMs by introducing such insertions and used them to produce the final multiple sequence alignments, whose characteristics are resumed in Table  2. Crystallographic structure. The crystallographic structure of the broadly neutralizing antibody VRC-PG04 in complex with gp120 described in  [1]  is available in the Protein Data Bank under the identification 3SE9.
Structural analysis The structure of VRC-PG04 in complex with gp120 (PDB-id 3SE9) has been subjected to both visual inspection and quantitative predictions to assess the importance of each somatic mutation observed in the antibody to the binding affinity towards the antigen. Somatic mutations were retrieved using the IMGT database  [34]. We used the FoldX software  [35]  to predict the difference in binding energy (ΔΔG) of the actual antibody with all the mutants obtained reverting each single somatic mutation to the original residue observed in the germline gene IGHV1-2.
Using hidden Markov Models to predict binding affinities. In order to compare the results obtained by MGM modeling with another sequence-based technique, we used a Hidden Markov Model based strategy based on the HMMER suite (v3. 1b2)  [37, 38]. From the multiple sequence alignment built on the set of sequences belonging to the hypermutated cluster, we first extracted the HMM with the command hmmbuild. We then used the program hmmsearch to produce a score for each of the 45 Abs. All programs were run with parameters. There are two different scores produced by hmmsearch: the E-value (we considered the negative log transform of this quantity) and the so-called hmm-score. The Pearson correlation coefficient of these two scores with the measured IC 50 is the same within error bars. For this reason, we will only report the correlation with the hmm-score.
Acknowledgments We acknowledge  Antonio Lanzavecchia  and  Giorgio Parisi  for inspiring this work by addressing us to reference [1],  Emanuela Giombini  for the help provided with Deep Sequencing data,  Marco Zamparo ,  Carlo Baldassi  for many interesting discussions on the theoretical aspects of the inference problem,  William Bialek  for some precious hints and  Peter D. Kwong ,  Lawrence Shapiro ,  Jiang Zhu  and  Xueling Wu  for helpful clarifications on their experimental work. We are deeply indebted with  Christoph Feinauer  for his critical reading of the manuscript.
Representative data are shown in Fig. 3b. The quality of subcellular fractionation was not affected by viral infection (Fig. 3b ). Strand-specific RT-qPCR was used to monitor the levels of NP and NA vRNAs in the cytoplasmic and nuclear fractions of CHX-treated cells, as well as the level of primary transcription from these incoming vRNA templates. The proportion of nuclear vRNAs (about 50%) was similar in DDX19-silenced or control cells (Fig. 3c ), thus indicating that the nuclear import of parental vRNAs was not impaired. As shown in Fig. 3d , mRNA/vRNA ratios were reduced after DDX19 silencing in CHX-treated cells. However the differences were not statistically significant, indicating that DDX19 has, if any, a limited effect on primary transcription. Altogether, these results demonstrate that DDX19 does not have a critical function in the earliest steps of the influenza viral cycle.
5a ). Upon DDX19 immunoprecipitation (Fig. 5a , upper panel), the PB2-Strep protein was specifically co-immunoprecipitated in conditions where RNA integrity was preserved (Fig. 5a , middle panel, -RNase) or not (Fig. 5a , middle panel, + RNase). RNAs from cell lysates and eluates were extracted and the viral NP mRNAs and vRNAs were quantified by strand-specific RT-qPCR. They could not be detected in RNase-treated samples (data not shown), thus demonstrating the efficiency of RNase treatment. In RNase-free samples, a higher proportion of NP mRNAs was co-immunoprecipitated with DDX19 (8. 6 × 10 6 copies -0. 79% of the input NP mRNAs) compared to NP vRNAs (4. 8 × 10 6 copies -0. 10% of the input NP vRNAs) (p = 0. 0465) (Fig. 5b ). To confirm the specificity of viral mRNAs association with DDX19, we determined how efficiently they were co-purified with a recombinant Strep-tagged wild-type DDX19B protein over its RNA-binding and ATPase defective mutant counterpart (the R372A-V386N-R428Q and E243Q mutant) (Fig.
The chemiluminescence signals were acquired using G-Box and the GeneSnap software (SynGene). siRNA-based assays. Small interfering RNAs (siRNAs) were purchased from Dharmacon (ON-TARGETplus SMARTpools or individual siRNAs and Non-targeting Control pool). Cells were transfected with 25 nM of siRNA using the DharmaFECT1 transfection reagent (Dharmacon). Cell viability was determined using the CellTiter-Glo Luminescent Viability Assay kit (Promega). To control the efficiency of siRNAs targeting DDXn genes, siRNA-treated A549 cells were transfected with plasmids encoding a DDXn protein fused with the full-length Gaussia luciferase (pGlucFL-DDXn) using polyethylenimine PEI (Polysciences Inc). The luciferase activity was measured 24 h later in cell lysates using the Renilla luciferase assay reagent (Promega) and a Berthold CentroXS luminometer.
Co-immunoprecipitated to input copy ratios between NP mRNAs and vRNAs differed significantly (multivariate linear model with an interaction term between sample type and RNA type, p = 0. 0465). (c,d) HEK-293T cells expressing Strep-tagged wild-type DDX19B, mutant DDX19B or NXF1 were infected with WSN (5 pfu/cell). At 6 hpi, Strep-tagged proteins were purified, and the levels of co-purified mRNAs were determined (c). For wild-type DDX19B and NXF1, co-purified/total mRNAs ratios were normalized to the one obtained with the DDX19 mutant. The data represent the mean ± SEM of three independent experiments. Statistical analysis (multivariate linear model) did not demonstrate significance. (d) Lysates and eluates were analyzed by immunoblot using Strep-Tactin. The dashed line indicates the juxtaposition of non-adjacent lanes from the same immunoblot.
Acknowledgements We are grateful to  Y. Jacob ,  C. Demeret ,  P. Cassonnet  and  E. Biquand  for providing materials and for helpful discussions. We thank  H. Varet  and  M. Rotival  for assistance with statistical analysis and  R. Weil  for advice on subcellular fractionation experiments. We thank  Y. Jacob ,  C. Demeret ,  C. Barbezange  and  C. Isel  for critical reading of the manuscript. We thank the  Center for Cancer Systems Biology (CCSB)  at the  Dana-Farber Cancer Institute  for providing the DDXs entry clones from the human ORFeome resource. G. Fournier  and  J. Magnus  were funded by the  EU   FP7   PREDEMICS  project (Grant Agreement No. 278433 ).
LPS was removed from toxin solutions by affinity chromatography with Detoxi-GEL endotoxin Gel R (Pierce, Rockford, IL, USA). The QCL-1000 Limulus amebocyte lysate assay R (Cambrex-BioWhittaker, Walkersville, MD, USA) showed that the endotoxin content of the recombinant SSAg solutions was less than 0. 005 units/mL. Toxins activities were assessed by measuring CD69 surface expression by T cells upon toxin challenge (data not shown).
Cell viability data are expressed as ratio of OD 540 of treated cells /untreated cells. For [ 3 H]TdR assay, 5 µ Ci of [ 3 H]TdR (1Ci/mM, CEA, Sacaly, France) was added during 48h to each well. After three washes with phosphate buffered saline (PBS) without Ca 2+ and Mg 2+ , cells were collected on glass fibber filters and [ 3 H]TdR incorporation was measured in Beckman scintillation counter. Cell viability data are expressed as the percentage of the mean value obtained for untreated cells.
2 (BD Biosciences) as described  (Nougayrède et al. , 2005). Data were collected from 20,000 cells and analysis was performed with CFlow software. In order to synchronize the cells at the G1/S border, the commonly used double thymidine block (DTB) protocol was employed as described  (Deplanche et al. , 2015). Briefly, HeLa cells were grown in a 25-mL flask up to 30% confluence. After washing with PBS, the cells were cultured in DMEM containing 10% of FCS supplemented with 2 mM thymidine (DMEM-T) for 18 h. Thymidine was then removed by washing with PBS and the cells were cultured for 9 h to release cells. The cells were then cultured in DMEM-T for 17 h, followed by DMEM containing 10% of FCS. The detached cells were then combined with adherent cells and fixed in 70% ethanol overnight. Cells were then stained with PI and analyzed with an Accuri C6 flow cytometer (BectonDickinson, Le Pont de Claix, France) as described previously  (Deplanche et al.
A total of 56 His+ colonies were selected from a medium lacking tryptophan, leucine and histidine. The prey fragments of the positive clones were amplified by PCR and sequenced at their 5 and 3 junctions. The resulting sequences were used to identify the corresponding interacting proteins in the GenBank database (NCBI) using a fully automated procedure. A confidence score (PBS, for Predicted Biological Score) was attributed to each interaction as previously described  (Formstecher et al. , 2005).
Statistical Analysis T-tests were performed to compare level of MTT and incorporation [ 3 H]TdR by cell lines challenged or not by toxins. The differences among the cell cycle phases were assessed by analysis of variance (ANOVA). Tukey's Honestly Significant Difference test was applied for comparison of means between groups. The level of statistical significance was set at 0. 05. The tests were carried out with SPSS Statistics R version 22 software (IBM France, Bois Colombes, France).
Isotype-matched antibodies of irrelevant specificities were used as a negative control. Cells were incubated with mAbs for 30 min on ice and washed twice in phosphate-buffered saline (PBS) containing 0Á1% (w/v) NaN 3 (Sigma). After surface staining, the cells were fixed with 2% (w/v) paraformaldehyde (Sigma) for 30 min at 48C and then treated with a permeabilization solution (BD Biosciences) for 10 min at room temperature and stained with antibodies for intracellular antigens for 30 min at 48C. Cells were acquired by a fluorescence activated cell sorter (FACS) Canto II flow cytometer (BD Biosciences) and analysis was performed using FlowJo analysis software (TreeStar Inc. , Ashland, OR, USA). The gating strategy involved progressively measuring total cells, viable cells only, lymphomonocytes and specific cell types.
Statistical analysis Statistical analyses were performed using GraphPad Prism software (version 5. 0; GraphPad Software, La Jolla, CA, USA). Multiple groups were analysed using one-way analysis of variance (ANOVA) and pairwise comparisons using the Mann-Whitney U-test, with Bonferroni's correction to adjust significance levels for multiple comparisons where appropriate. Correlation analyses were performed using the Spearman's rank correlation coefficient. P-values less than 0Á05 were considered significant.
Flow cytometry data were analyzed in FlowJo (TreeStar, Ashland, OR)  (22). Staining for the LIP cohort was performed in the same flow cytometry facility at the same core facility (IRS-UN, CYTOCELL, Nantes, France) with the use of the same protocol, and the same instruments (similar voltages and cytometer configuration) and antibodies as the control groups. CD4 + CD45RA À Foxp3 hi memory Tregs are reported as a relative expression of the total CD4+ cell frequency.
Acknowledgments This article was accepted in the "best abstract session" at the  International Small Bowel Transplant Symposium (ISBTS) in Buenos Aires, Argentina  ( June 10-13 , 2015) for which the first author of this manuscript was granted the  Young Investigator Award by The Transplantation Society (TTS)  and the  Intestinal Transplant Association (ITA). We would like to acknowledge the clinical efforts of our colleagues of the Leuven We are grateful to the  experimental surgery laboratory at the University of Li ege, Belgium ; the transplant surgery laboratory at the  University of Minnesota, Minneapolis ; and the laboratories of experimental transplant surgery at  Birmingham University, United Kingdom , where part of the preclinical experimental work was performed. The authors thank the  Cytocell platform of Nantes , especially  Juliette Desfranc ßois , for assistance with the flow cytometry data.
Laurens Ceulemans received research grants from the  Belgian Federation for Gastroenterology , the  Flemish Society for Gastroenterology , and the  European Society for Organ Transplantation. Jacques Pirenne  received grants from  University Hospitals Leuven , the  Flemish Fund for Scientific Research (FWO) , and unrestricted educational grants from  Roche  and  Astellas. Jacques Pirenne ,  Diethard Monbaliu , and  Ina Jochmans  are holders of a CAF chair for abdominal transplant surgery research. Partial data analysis of this work was carried out in the context of a CENTAURE foundation grant and under the auspices of the  IHU-Cesti  project, which received French government financial support managed by the  National Research Agency  via the " Investment into the Future" program   ANR-10-IBHU-005.
Management implications Parasites such as S. destruens cause chronic mortalities, which are extremely difficult to detect in the wild  (Gozlan, 2012)  in the absence of long term monitoring as evidenced in  Ercan et al. (2015). Under the Habitats Directive, the health of riverine fish populations is assessed every 5 years, which does not allow the regular monitoring of fish populations to identify and respond to observed declines (European Topic Centre on Biological Diversity, Eionet). Although, bringing together the national fish population assessments in theory is possible, in many cases a combination of missing data or incompatible data (e. g. population sizes reported in different units) makes this exercise impossible. In such cases, the Eionet assessments of conservation status are determined as the proportion of the species in each country and then evaluated.
However, it is still not recorded on the Eionet's conservation list, most likely due to data deficiency in Member States assessments. A number of recommendations are made to policy makers both at the European level and a local level. The potential risk posed by S. destruens needs to be urgently re-evaluated in light of the results presented here and an extensive epidemiological survey should be performed by primarily focusing on aquaculture facilities and fisheries where P. parva have been reported. Pseudorasbora parva need to be screened for infection with S. destruens by following a specific sampling strategy involving the sampling of a minimum of 30 fish and the use of molecular techniques to detect the parasite using at least the kidney and liver as the organs of choice. Samples for both molecular analysis and histology should be collected for individual fish.
Methods and Results Vidjil is an open-source platform for the interactive analysis of high-throughput sequencing data from lymphocyte recombinations. It contains an algorithm gathering reads into clonotypes according to their V(D)J junctions, a web application made of a sample, experiment and patient database and a visualization for the analysis of clonotypes along the time. Vidjil is implemented in C++, Python and Javascript and licensed under the GPLv3 open-source license. Source code, binaries and a public web server are available at http://www. vidjil. org and at http://bioinfo. lille. inria. fr/vidjil. Using the Vidjil web application consists of four steps: 1. uploading a raw sequence file (typically a FASTQ); 2. running RepSeq analysis software; 3. visualizing the results; 4. annotating the results and saving them for future use.
Introduction The immunological diversity of the lymphocytes mainly comes from the V(D)J recombinations. These recombinations are also useful markers of pathologies, and in leukemia, are used to quantify the minimal residual disease (MRD) during patient follow-up  [1]. High-throughput sequencing (HTS) now enables the deep sequencing of a lymphoid population, analyzing and quantifying the clones that could come either from an immune response or from a pathology. Dedicated Repertoire Sequencing (RepSeq) methods and software  [2]  are necessary to deal with the specificity of V(D)J recombinations, that is handling small recombinations, somatic hypermutations, and short insertions. Many tools for the in-depth analysis of V(D)J recombinations were developed by IMGT  [3, 4]. Recently, new software able to deal with up to millions of sequences have appeared:  [5] , IgBlast  [6] , Decombinator  [7] , miTCR  [8] , TCRKlass  [9] , MiXCR  [10] , IMSEQ  [11].
At the heart of these programs is optimized comparison of the reads against germline databases to detect and quantify clonotypes, whose definitions are supposed to overlap as much as possible the definition of biological clones. IgGalaxy has the purpose to provide an easy-to-use interface to IgBlast and IMGT/HighV-QUEST results  [12]. Some other programs enable to further analyze or visualize statistics on the whole lymphocyte population, such as ARResT/Interrogate  [13] , tcR  [14] , ImmunExplorer  [15]  or VDJviz  [16]. The programs that are only focused on the algorithmics part are difficult to use for biologists or clinicians. There is the need to have easy-to-use interfaces for labs without bioinformatics expertise, suitable for daily clinical or research job in RepSeq studies, allowing to easily navigate between statistical informations on the clonotypes and detailed view of some sequences and enabling user annotations.