dataesr/scientific-paragraphs-categorization huggingface dataset:
The '-' sign means a not expected scenario. doi:10. 1371/journal. pone. 0163936. t009 it possible that reliable copy number data could be obtained for DAZ4 in samples Ydel_06, Ydel_07, Ydel_08, Ydup_01 and Ydup_05 and for DAZ3 in Ydup_01 and Ydup_02. Preferably, the copy number should be determined for all DAZ family members separately, i. e. independently of the copy number of the other DAZ genes. In this point, we could assign a copy number to 48 out of the 52 (92. 31%) DAZ family members in our 13 partially rearranged samples at stage 3 analysis. In four samples (Ydel_09, Ydel_10, Ydup_03, Ydup_04), the DAZ3 copy number was still ambiguous. Summary of the results of subtyping. Our deletion and duplication samples were evaluated by subjecting them to stage 3 analysis in order to determine the samples' deletion or duplication subtype (i.
5/ Amplification of selected regions to get Fragment I in control mixtures. 6/ Sequencing Fragment I in control mixtures. 7/ Measuring AUCs by ImageJ software and calculating the AUC ratio at each SFV position in every control mixture. 8/ Correlating the AUC ratios measured in control mixtures with known variant ratios (S5 Table). 9/ Measuring AUCs and calculating the AUC ratio at each SFV position in all individual samples. 10/ Plotting AUC ratios throughout all samples for each studied position to visualize AUC ratio clustering (Fig3). 11/ Assigning a variant ratio to each SFV position in all individual samples. 12/ Determining the horizontal variant ratio distribution in all individual samples. 13/ Grouping samples according to AZFc partial deletion/duplication status based on their horizontal variant ratio distribution (Tables1-4).
This work was funded by: 1) Research Grants  KTIA-OTKA CK-80745  (http://www. nih. gov. hu/palyazatok-eredmenyek/otka-programok/cikkcime) received by EO; 2) Research Grants  OTKA K-112228  (http://nyilvanos. otka-palyazat. hu/index. php?menuid=930&num=112228&lang=HU) received by EO; 3)  Norwegian Financial Mechanism's Norway Grant   HU-0115/NA/2008-3/ O ¨P-9  (http://eeagrants. org/,http://www. oncol. hu/site/en/norway_grants. html);4) the  Istituto Toscano Tumori  grant proposal to CK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These studies have failed to clarify the pathogenic pathways associated with HCC. One study was negative  [21] , a second study found intergenic variants outside any pathogenic pathway  [22]  and the third reported an association with a genetic variant on a gene involved in neurotransmission  [20]. Furthermore, no GWAS has been performed in Europeanor in African-ancestry populations. We hypothesized that the inconclusive studies on the association between genetic variants in DNA repair genes and HCC risk may reflect limitations in study design and genetic variant selection. Using data from cirrhotic patients with and without HCC and non-cirrhotic patients with HCV-and/or HBV-related disease, we assessed the association between genetic variants on DNA repair genes and HCC risk using an in-house designed array.
2196, which was highly suggestive of directional selection (upper outlier threshold, 0. 0466) (Supplemental Table  S4 ). Consistently, in the Exome Aggregation Consortium (ExAC; http:// exac. broadinstitute. org/) database on 60,706 unrelated individuals, the 'A' allele of rs4986765 had a frequency of 33. 52% in Europeans (Non-Finnish) versus only 9. 52% in Africans. Hence, the data from ExAC support our findings in control populations from both derivation and validation studies. Second, the genomic coverage of hypothesisfree tag-SNP approach used in GWAS genotyping arrays do not cover all common variants, and thus leading to false-negative results  [45]. At the opposite, the custom array used in our study intentionally focused on DNA repair genes, thus increasing the a priori probability of identifying genetic variants associated with risk of HCC on DNA repair genes.
Briefly, biotinylated DNA was immobilized on paramagnetic beads and the pooled SNP-specific oligonucleotides in the OPA were annealed on the DNA. This hybridization step is followed by an extension and ligation step, connecting one of the allele-specific oligo with the locus-specific oligo to generate DNA templates which were amplified using universal fluorescently-labelled primers. PCR products are annealed to an array composed of beads carrying oligonucleotides complementary to the IllumiCode sequences in the locus-specific oligos that are used for recognition of each SNP site. The fluorescence of an array matrix carrying Cy3-and Cy5-labeled beads were generated with the two-channel scanner. Hybridization data intensity processing, clustering and genotype calling were performed using the genotyping module in the BeadStudio 2 software (Illumina, USA).
We assessed duplicated samples and those showing cryptic relatedness by calculating identity-bydescent (IBD). By estimating the probability of sharing zero, one, or two alleles for any two individuals as P(Z=0), P(Z=1), and P(Z=2), respectively, a proportion of IBD was calculated using the following formula: PI-HAT = PI = P(Z=1)/2 + P(Z=2). PI-HAT value >0. 99 was considered to be indicative of duplicate samples and PI-HAT threshold >0. 2 was used to suggest cryptic relatedness. No sample was removed after IBD quality control. In order to identify ancestry outliers and population stratification, PCA on genomic data was performed on the study samples merged with 1kG project populations (AFR, AMR, ASN, and EUR) as reference populations  [54]. Eigenvalues were calculated assuming an additive genetic model.
Significant genetic variants were tested for their association with HCC risk in multivariate analysis using logistic regression and Cox proportional-hazards regression. Results were expressed as odds ratios (ORs) and hazard ratios (HRs) with their 95% CI, respectively. Multivariate analyses were performed using MedCalc for Windows, version 16. 1 (MedCalc Software, Ostend, Belgium) on the basis of a two-sided type I error with an alpha level of 0. 05. All quality control and genotypic analyses were performed using SNP & Variation Suite (SVS) 8. 4. 3 (Golden Helix, Inc. Bozeman, MT, USA).
ACKNOWLEDGMENTS The authors would like to thank the  Exome Aggregation Consortium  and the groups that provided exome variant data for comparison. A full list of contributing groups can be found at http://exac. broadinstitute. org/about. †The CiRCE Study Group includes: (1) CiRCE Coordination France:  J. P. Bronowicki ,  V. Di Martino ,  M. Doffoël ,  P. Hillon  (Study coordinator), and  G. Thieffin ; CiRCE Coordination China:  H. Wen ,  F. P. He , and  X. M. Lu ,  P. Hillon  and  D. Vuitton ; (2)  CiRCE Scientific Board :  J. Faivre  (President),  J. P. Cercueil ,  V. Cottet ,  D. Delmas ,  P. Ducoroy ,  L. Duvillard ,  M. Guenneugues ,  J. L. Guéant ,  F. Habersetzer ,  N. Latruffe ,  M. Manfait ,  P. Oudet , and  G. Sockalingum ; (3) CiRCE pharmacologists:  P. Trechot ,  M. B. Valnet-Rabier ,  T.
Phytochemical investigation The fractionation of the dichloromethane extract of A. ataxacantha using silica gel column chromatography led to the isolation of three compounds (1-3) identified as lupeol, betulinic acid and betulinic acid-3-trans-caffeate (Fig. 1 ). Compound 1: White powder, 1   in the molecule. 1 H and 13 C NMR spectral data were resumed in Table  1. The 2D experiments (COSY, NOESY, HSQC and HMBC) were performed using standard Bruker programs. The compound 3 was inferred to be a triterpene, based on the 1 H NMR spectrum with a broad range of aliphatic signals including six methyl singlets and the 13 C NMR spectrum with 30 carbons (Table  1 ). Further investigation of 13 C NMR spectrum revealed the characteristic signals for carboxylic acid (δ C 177. 8), vinyl carbons (quaternary C at δ C 150.
The fit is performed over all data, and over the full t range available, yielding a χ 2 /NDF = 4. 00. This overall figure covers a considerable variation: for many datasets the fit quality is acceptable (χ 2 /NDF ∼ 1) but there are some features of some datasets where the model and the data systematically disagree in terms of the statistical errors, which are, particularly at low t in the peak, sometimes very small. The resulting fit parameters and uncertainties given by MINUIT are given in table  3. We note the two Pomeron intercepts are both soft (ε 0 and ε 1 ) -in essence we started off with a hard and a soft Pomeron and the fit to the data pushed them together, leaving a single soft Pomeron  [25, 26]  with a complicated t dependence.
The data are quoted with statistical and systematic errors. The former are due to Poisson statistics on the number of particles counted. The latter are dominated by uncertainties in the acceptance, and are common to all measurements made by a given experiment. These errors are strictly multiplicative but they are small enough in practice to be taken as additive, greatly simplifying the analysis. If the systematic errors are not considered, the quantity to be minimised is χ 2 = ∑ i N i ∑ j=1 ( f i j -d i j ) 2 σ 2 i j , where i is the number of the experiment, j the measurement within that experiment's dataset, and f i j , d i j and σ i j are respectively the fitted function, the measured cross section, and the quoted statistical error. If the experiment also quotes a systematic uncertainty on the acceptance of α i , so that each measurement has an error S i j = α i d i j , it is not possible to merely replace σ 2 i j in the denominator by σ 2 i j +S 2 i j , because although this correctly expresses the variance of an individual point, it does not take into account the correlation between points.
5 Loss maps simulation using MERLIN code MERLIN  [5] [6] [7] [8]  is a C++ accelerator physics library, which has been extended to be used for large scale proton collimation simulations  [5] [6] [7] , with the aim of providing an accurate simulation of the Large Hadron Collider (LHC) collimation system. MERLIN is used to simulate the nominal optics at 7 TeV of the LHC in order to generate loss maps, using a thick lens tracking model. These can be generated for different optics configurations, e. g. the β -function at the interaction points (β *) and beam crossing angles. The collimation process simulates all proton-collimator interactions and performs all aperture checking. If a proton undergoes an inelastic interaction inside the collimators or touches the beam pipe it is considered lost.
ACKNOWLEDGMENT The authors wish to thank  Prof. Donnachie  for his support, for assisting us during the development of the scattering models and for being always available to discuss and im-prove the work. In particular he was responsible for the form of the model in the low mass region of the diffractive scattering. We also thanks  Stefano Redaelli  and  Roderick Bruce  for providing support for calculations relating to the LHC collimation system. The  HiLumi LHC Design Study is included in the High Luminosity LHC  project and is partly funded by the  European Commission  within the  Framework Programme 7 Capacities Specific Programme , Grant Agreement 284404.
Results expressed as CI, were normalized with RTCA Software (Roche, Applied Science, Mannheim, Germany) (nCI) and are expressed as % specific lysis:% lysis = (nCI (no effector) -nCI (effector))/nCi (no effector) × 100. In some cocultures, NK cells were pretreated with anti-NKp46, anti-NKp30, anti-NKG2D, or anti-DNAM-1 mAbs (R&D System).
Flow cytometry Direct immunofluorescence was performed on PBMC or NK cells using specific mAbs and isotype-matched mAbs. Cells were washed with PBS and stained for 30 min at 4°C. NK cells, gated on CD3 -CD56 + cells in the lymphocyte FSC/SSC subset were analyzed for the expression of CD16, NKp46, NKp30, NKp44, NKG2D, DNAM-1, CD161, CD94, CD8, NKG2A, CD85j, CD158a, and CD158b (PE-conjugated antibodies, BD Biosciences). mAbs and respective clones are listed in Table  S1  in Supplementary Material. Cells were collected on a FACSCalibur flow cytometer (BD Biosciences). These data were analyzed using FlowJo software. elisa Plasma samples were tested for the presence of HLA-E and HLA-G soluble molecules by home-made ELISA tests. TGF-β content in human plasma was evaluated using a human TGF-β 1 ELISA kit (BD) following the manufacturer's instructions.
soluble TgF-β is augmented in crc Patients Several soluble molecules were associated with receptor expression modulation and the impairment of NK cell activity  (30, 31). To determine whether CRC patients presented augmentation of NK cell suppressive molecules, we performed ELISA assays from plasma patients. Soluble HLA-E molecules (>5 pg/ml) were detected in 7/35 patients (mean 22. 9 ± 53. 4; range 0-187. 4 pg/ ml), and soluble HLA-G molecules (>5 ng/ml) were detected in the plasma of 6/43 patients (mean 1. 8 ± 5. 1; range 0-23. 0 ng/ml). Non-significant differences were found as compared with plasma samples from HD (data not shown). However, the amount of total circulating TGF-β in the plasma of CRC patients, both early and late stages, was almost fivefold higher than in HD (Figure  5 ).
In an effort to reach an efficient NK cell status, a combination of therapies from early stages of disease could be developed, such as stimulation by cytokines and administration of mAbs to facilitate ADCC of NK cells. We did not observe any potentiating effect by lenalidomide in vitro, an immune-stimulating drug reported to activate T lymphocytes to secrete IL-2 that would, at the same time, stimulate NK cells  (57). However, it would be important to study the effect of lenalidomide in in-vivo models. By targeting EGFR with the antibody cetuximab, administered to treat metastatic K-RAS wild-type CRC patients  (58) , our data indicate that exhausted NK cells could be activated in presence of IL-2 or IL-15. Currently, several clinical trials are evaluating the uses of mAbs and cytokines (NCT02507154, NCT00569296 and NCT00625729 among others; https://clinicaltrials.
toulouse. inra. fr/bacteria/annotation/site/prj/T3Ev2/) (11)  for T3E identification and rip-based annotation. The phylotype I strains contain 67 to 73 effector genes and two to seven pseudogenes, which are in the range of those in the reference phylotype I genome GMI1000  (11). Conversely, the phylotype III RUN39 contains only 55 T3Es and seven pseudogenes, which constitutes a T3E repertoire smaller than that of the phylotype I and phylotype III reference strain CMR15  (12). We did not find a clear association between T3E repertoire and virulence on AG91-25. However, the prevalence of the genes ripAX2, ripC2, ripE2, ripS6, and ripP2 was different between avirulent and virulent strains. Nucleotide sequence accession numbers. The nine genome sequences have been deposited in the European Nucleotide Archive (ENA) under the study accession number PRJEB11298.
ACKNOWLEDGMENTS This work was financially supported by the  European Union  ( FEDER  project) and the  Conseil Régional de la Réunion , the  French Ministry of Agriculture  (Project  CASDAR C-2013-09 "RESAUBER" ), and the  National Science Foundation  ( IOS-1354215 ). J. Guinard is supported by a Ph. D. grant from  CIRAD  and the  University of Reunion Island.
Cell cycle and immunophenotypic analysis Cycling cells were identified by DAPI staining (Sigma-Aldrich) and cells in the S phase by incubation with bromodeoxyuridine (BrdU) for 1 hour followed by labeling with an anti-BrdU antibody (APC BrdU flow kit, BD Biosciences) according to the manufacturer's instructions. For immunophenotypic analysis, cells were stained with a combination of 4 to 7 mAbs conjugated to different fluorochromes, as indicated  [22]. The Cytofix/Cytoperm kit (BD Biosciences) was used for intracellular staining of IgM, IgA, IgG, IRF-4 or Ki67 antigen  [19]. Flow cytometry analysis was performed with a FACSAria cytometer using FACSDiva 6. 1 (Becton Dickinson, San Jose, CA, USA) and with a Cyan ADP cytometer driven by the Summit software (Beckman Coulter). The Kaluza software (Beckman Coulter) was used for data analysis.
CFSE labeling Cell division was assessed by CFSE labeling as previously described  [31]. Briefly, purified MBCs were washed and re-suspended at a concentration of 10 6 cells/ml in PBS/0. 1% BSA with 10 µM CFSE (Molecular Probes, Eugene, OR, USA), incubated at 37°C for 10 minutes and extensively washed before culture. At day 4 of culture, cells were washed and labeled with anti-PB-CD20 and anti-PerCP-Cy5. 5-CD38 for flow cytometry analysis. Cell divisions were quantified using the ModFit LT software (Verity Software House, Topsham, ME, USA).
The primary antibodies were visualized with peroxidase-conjugated goat antirabbit (Sigma-Aldrich) or goat anti-mouse (Jackson ImmunoResearch, West grove, PA, USA) antibodies and an enhanced chemiluminescence detection system. Western blots were quantified by densitometry using the NIH ImageJ software (National Institutes of Health, Bethesda, MD, USA) and protein levels were normalized according to those of β-actin.
Detection of malignant and normal PCs in BM of patients with MM Malignant and normal PCs were counted using the multi-parameter flow cytometry technique reported previously  [29]. Briefly, erythrocyte-lysed BM samples were labeled with anti-CD19, CD20, CD38 and CD45 monoclonal antibodies (mAbs) in association with anti-CD138, CD27, CD56, CD117, CD200 mAbs or isotype controls. Cells were then fixed and permeabilized with the Cytofix/Cytoperm kit (BD Biosciences, Le Pont De Claix, France), and labeled with anti-Ig Kappa and anti-Ig Lambda mAbs. Data were acquired with a Cyan flow cytometer, driven by the Summit 4. 3 software (Beckman Coulter, Fullerton, CA, USA). B-lymphocytes were defined as [CD19 + CD20 + CD45 + CD38 -/+ and (Kappa + or Lambda + )] cells and PCs as [CD38 high and (Kappa + or Lambda + ) and (not B lymphocytes)] cells.
MMCs were identified based on the monoclonal expression of Kappa or Lambda light chains together with the aberrant expression of one or several myeloma markers: CD20 and/or CD56 and/or CD117 and/or CD200, lack of CD19, and lack/ weak CD27 and/or CD45 expression. [32] [33] [34] [35]  Healthy PCs were identified based on the polyclonal expression of Kappa or Lambda light chains together with the expression of CD19, CD27. The acquisition of at least 100 events was required to define MMCs or healthy PCs as detectable  [36]. Data were analyzed with the FlowJo 9. 1 software (Tree star, Ashland, OR).
4A to G ). Annotation and analysis of the mobile elements within these plasmid sequences revealed that the plasmids can be unambiguously placed into a branched evolutionary pathway that involves five transposition events and two small base pair insertions/deletions (Fig. 4H ). The smallest plasmid in the group is pEA1509_B (Fig. 4C ), which is ~9 kb long. It was previously identified in an Enterobacter aerogenes clinical isolate  (14)  and differs only by 3 SNVs from Citrobacter koseri ATCC BAA-895 plasmid pCKO3, isolated in Maryland from a neonatal meningitis infection in 1983 indicating its long history (GenBank accession no. CP000823. 1). This small plasmid then served as the target for insertion of a ~6-kb nested transposon, Tn1331 containing a copy of IS26 (designated Tn1331⌬::IS26 [Fig.
MATERIALS AND METHODS Plasmid sequences of longitudinally carried strains in patients 15 and 16 as well as the sequences of the 20 NIH CC isolates in this paper are from the NCBI public database (lists shown in Tables  S1  and S2  in the supplemental material). Full annotation of the plasmid mobilome (the entire mobile genetic element content) was carried out using the ISfinder reference database (the curated database of prokaryotic insertion sequences [IS])  (25)  and its associated annotation software, ISsaga (http://issaga. biotoul. fr/ ISsaga2/issaga_index. php)  (26). SnapGene 3. 1. 2 was used to visualize the annotation results.
(C) pKPC-47e has two diagnostic SNVs relative to the parent pKPC-629; these SNVs occur in traB (S1) and in a noncoding region (S2). Tn4401 has been inactivated because IRR was destroyed. (D) Relative to pKPC-47e (C), pKPC-860 has received a Tn3-like transposon insertion. When this transposon sequence was subjected to a BLAST search against either the outbreak collection pool or the entire NCBI database, an identical copy without SNVs could be found only in other replicons from the same strain (ECNIH4). (E) Relative to pKPC-47e (C), pKPC-56a has received a Tn5403 insertion; identical copies of this transposon were found only in other plasmids within strain PSNIH2. In addition to the SNVs observed in pKPC-47e, (Continued).
ACKNOWLEDGMENTS We thank  Sean Conlan  and  Julie Segre  for sharing the plasmid sequences of longitudinally carried strains from patients  15  and  16  with us prior to publication and for useful comments on the manuscript. S. H. , M. C. , A. B. H. , J. P. D. , and F. D. contributed to project design and cowrote the paper. S. H. and M. C. carried out genome IS annotations and data analysis. A. M. V. adapted the ISsaga annotation tool to allow batch genome analysis.
FUNDING INFORMATION This work, including the efforts of Alison B. Hickman, was funded by  HHS | National Institutes of Health (NIH). This work, including the efforts of Fred Dyda, was funded by  HHS | National Institutes of Health (NIH). This work, including the efforts of Susu He, was funded by  HHS | National Institutes of Health (NIH). This work, including the efforts of  Alessandro M. Varani , was funded by  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). This work, including the efforts of Michael Chandler, was funded by  Centre National de la Recherche Scientifique (CNRS). This work, including the efforts of  John P. Dekker , was funded by  HHS | NIH | NIH Clinical Center. This work was partially supported by the  Intramural Program of the National Institute of Diabetes and Digestive and Kidney Diseases (S.
Cytotoxicity analysis Cells were observed with a fluorescence microscope (Nikon). Multi-channel pictures randomly covering the surface of each well were taken using NIS-Elements BR 3. 1 software (Nikon). Variable number of pictures were taken for each patient, to provide sufficient number of cells (at least 1000) for accurate results. Live, dead and overall cell populations were counted and percentage of dead cells was determined for each condition. Finally, for each patient, ratios of death percentages for treated cells to death percentages for untreated cells were computed. Whole endpoint analysis was solely performed by one person (CBMP). Results were not communicated to clinicians.
Results categorization and statistical analysis After each clinical evaluation (variable time interval, usually 2-4 months), patients were categorized into responders (complete or partial response, stable disease) and non-responders (progressive disease) to treatments according to RECIST 1. 1. Results were not communicated to Oncogramme reader until end of study. Oncogramme results were categorized according to the method of percentiles  [34] : patients highly sensitive to treatments were those for which ratios were above 75th percentile; intermediate sensitive patients included patients for which ratios were between 25th and 75th percentiles; resistant patients included patients for which ratios were below 25th percentile. After data verification, database was frozen and statistical analysis was computed.