dataesr/scientific-paragraphs-categorization huggingface dataset:
0 ST arrays (Affimetrix, Cleveland, OH, USA), according to the manufacturer's instructions. Data analyses. The scanned raw data were processed with the apt-probe-set summarise program from Affymetrix Power Tools (v1. 10. 2) for background correction and normalisation. The RMA (Robust Multichip Analysis) algorithm  (Irizarry et al, 2003) , with full quintile normalisation, was applied, with array design library files V1 release 4, and normalised values were expressed on a log base 2 scale. Transcripts and genes were annotated with the Affymetrix release 29 annotation file based on the human genome version 18 assembly. Data were analysed with the R Bioconductor LIMMA package  (Smyth, 2005). The microarray data have been deposited in the NCBI Gene Expression Omnibus  (Edgar et al, 2002)  and are accessible through the GEO Series accession number GSE37911 (http://www.
Immediately after surgery, the tumour samples were frozen in liquid nitrogen, in which they were stored until RNA extraction. The characteristics of the patients are shown in Table  1. We determined the oestrogen receptor, progesterone receptor and ERBB2 status of the tumours at the protein level, by biochemical methods (enzymatic immunoassay), with confirmation by real-time PCR. The malignancy of infiltrating carcinomas was scored according to Scarff Bloom and Richardson's histoprognostic system  (Bloom and Richardson, 1957). In silico analyses. We identified genes involved in breast carcinogenesis by analysing genes expressed differentially in the V165 and V189 clones (V165 vs V189) or in 165M and 189M lung metastases (165M vs 189M), on Oncomine (www. oncomine. org), with the Richardson breast 2 cancer data set in particular.
This data set corresponds to 40 ductal breast carcinomas and seven normal breast samples that were analysed on Affymetrix U133 Plus 2. 0 microarrays  (Richardson et al, 2006 ) (http://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE3744s). For each gene, we checked the expression level (fold change) and the corresponding P-value (Po0. 05 considered significant) in the Richardson breast 2 data set. We took into account only the genes considered to be significantly misregulated. We then analysed those misregulated genes with BRB-ArrayTools, version 3. 3b3 (Rockville, MD, USA), developed by Dr R. Simon and Dr A. Peng Lam on a series of human breast tumours (n ¼ 344) described in detail elsewhere  (Wang et al, 2005). We used a filter to refine the metastasis-related gene set. The Cox proportional hazards model and Wald statistics were used for univariate analysis.
Metastasis-free survival and P-valueso0. 05 were considered significant in each univariate test. Only significantly misregulated genes were taken into account in subsequent real-time PCR analysis. Statistical analyses. The graphs show the means and the standard errors of the means. We used a suitable two-way ANOVA multiple comparison test including the Kruskal-Wallis test followed by the Dunn post-test. The statistical analyses were performed using GraphPad Prism 5. 01 software (La Jolla, CA, USA). *Po0. 05. To analyse the prognostic value of the genes tested by RT-PCR in the series of 120 human breast tumours, the optimal cutoff for each gene was determined by calculating the area under the curve for a comparison of tumours without metastases (n ¼ 60) and tumours giving rise to metastases (n ¼ 60).
Transcriptomic profiles of the V189 vs the V165 cell line and of the 189M vs the 165M lung metastatic cell line. We compared the transcriptomic profiles of the V165 and V189 cell lines (Supplementary Data S4). We identified a subset of 149 genes with significantly different expression levels in the two cell lines (V165 vs V189) (Po0. 05 and |Log FC|41) (Supplementary Data S4). We reduced the number of genes for further analysis, by comparing our 149 deregulated genes with a list of human genes known to be specifically involved in mammary carcinogenesis. We used the Richardson breast 2 database (oncomine. org), which compared the expression of 19 514 human genes between 40 breast tumours and seven normal breast tissue samples. We found that 36 genes expressed differentially between V165 and V189 cells (V165 vs V189 list) were also included in the Richardson breast 2 database (Figure  4A ).
We then analysed the expression profiles of the 36 genes identified with BRB-ArrayTools and a metastasis potential event selection filter, and identified 11 genes expressed significantly (Figure  4A ). As described above, animals developed distant lung colonisation after intracardiac injection of the V165, V189 and cV clones. We applied the same protocol to these lung metastatic cell lines. We first identified a subset of 150 genes displaying significant differential expression between 165M and 189M cells (165M vs 189M) (Po0. 05 and |Log FC| 41) (Figure  4B ; Supplementary Data S5). After the specific selection of genes involved in metastatic processes, from the Richardson breast 2 database and BRB ArrayTools, respectively (Figure  4B ), we identified seven genes expressed significantly (Figure  4B ).
ACKNOWLEDGEMENTS We thank  P Balaguer  ( INSERM U896, Montpellier, France ) for providing the plasmid expressing the luciferase gene and the hygromycin resistance gene, and  E. Savariau  for micrografts. We also thank  J-P Jais  ( Bioinformatics platform, Necker Institute, Paris ) for transcriptomic analysis. Aurore Toullec PhD was supported by a grant from the  Ile-de-France Region. We thank  Alexandra Becquet, PhD , for having proofread and edited the English language in our manuscript. This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 4. 0 Unported License.
Transcriptomic data analysis Paired-end RNA-seq data were mapped to the T. vivax Y486 reference strain  [11]  (downloaded from TritrypDB release 6. 0) using Bowtie2  [31]  under the default parameters and within the Galaxy bioinformatics platform  [32]. Transcript abundance for each replicate was estimated across the genome using Cufflinks  [33]  and measured in Fragments Per Kilobase Mapped (FPKM). The option for quartile normalization within Galaxy was applied to maximize our ability to detect preferential expression of low abundance transcripts against the background of highly abundant species. The option for bias detection and correction was enforced. The option for multi-read correction was applied because some of our genes of interest are multi-copy and may map to multiple locations.
Fold change in transcript abundance, and significance of differential expression, was estimated using Cuffdiff  [33]  for three pairwise comparisons of T. vivax life stages, combining all replicates in each case. Cuffdiff applies the Benjamini-Hochberg correction for multiple testing when assessing the significance of fold changes. To ensure accurate assessment of differential expression, transcript abundance was corroborated using a second method, edgeR  [34]. Correlations for fold change in transcript abundance returned by Cufflinks and edgeR displayed high congruence when comparing life stages (r 2 = 0. 89-0. 91). Significant differences in transcript expression were defined as at least 2-fold enrichment between conditions and q < 0. 05, where q is the p value corrected for false discovery rate (FDR).
Proteomic data analysis Thermo RAW files were imported into Progenesis LC-MS (version 4. 1, Nonlinear Dynamics, UK). Runs were time aligned using default settings and using an auto selected run as reference. Peaks were picked by the software and filtered to include only peaks with a charge state of between +2 and +6. Peptide intensities were normalized against the reference run by Progenesis LC-MS and these intensities are used to highlight differences in protein expression between control and treated samples with supporting statistical analysis (ANOVA and q-values) calculated by the Progenesis LC-MS software. Spectral data were transformed to mgf files with Progenesis LC-MS and exported for peptide identification using the Mascot (version 2. 3. 02, Matrix Science) search engine. Tandem MS data were searched against a custom database that contained the common contamination and protein sequences predicted for the T.
Data accessibility All cDNA sequence data are available from the European Nucleotide Archive (http://www. ebi. ac. uk/ena), accession number ERP001753. Details of the transcriptomic experiments are also available from the Array Express website (https://www. ebi. ac. uk/arrayexpress/), accession number E-ERAD-100. The mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium via the PRIDE partner repository (http://www. ebi. ac. uk/pride/ archive/) with the dataset identifier PXD001617.
Estimation of transcript and peptide abundance By exploiting new protocols for the in vitro cultivation of T. vivax epimastigote and metacyclic forms, we have produced comparative transcriptomic and proteomic data for the whole T. vivax life cycle using RNAseq and LC-MS/MS approaches respectively. Transcripts were detected for 10116 T. vivax Y486 genes (85. 1% of all genes); 8994 of these transcripts (88. 9%) were observed with at least 10 FPKM. The abundance of each transcript, as estimated using Cufflinks  [33] , is described in S1 Table. The most abundant transcripts in the bloodstream form were derived from tubulins, diverse ribosomal proteins and VSG-like sequences TvY486_0009580 (17331 FPKM) and TvY486_0018880 (9669 FPKM), which are assumed to have been the active VSG at the time of sequencing.
vivax, and which might contribute to its unique phenotypes. Subset c contains proteins that are significantly more abundant in the insect stages of T. brucei and T. congolense than the vertebrate stage, but preferentially expressed in T. vivax bloodstream forms. In the larger dataset of Fig  3 , this cohort is expanded to 714 proteins by excluding T. congolense (for which proteome coverage is lowest); the number of proteins falling into subset c is increased to 27 and these are listed in Table  1. The expression profile of these proteins in T. vivax is not simply a lack of regulation or low expression generally, since many are highly abundant. Ten of the proteins in Table  1  appear in the top 10% of our proteome when ranked by abundance. Analysis of the GO terms associated with these proteins shows enrichment for succinate-CoA ligase (GDP-forming) activity (GO:0004776; FDR = 1.
All cDNA sequence data are available from the European Nucleotide Archive (http://www. ebi. ac. uk/ena), accession number ERP001753. Details of the transcriptomic experiments are also available from the Array Express website (https://www. ebi. ac. uk/arrayexpress/ ), accession number E-ERAD-100. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http:// proteomecentral. proteomexchange. org) via the PRIDE.
Experimental (i) Àx þ 1; Ày þ 1; Àz þ 2; (ii) Àx þ 1 2 ; y À 1 2 ; Àz þ 3 2. Data collection: APEX2  (Bruker, 2006) ; cell refinement: SAINT  (Bruker, 2006) ; data reduction: SAINT; program(s) used to solve structure: SIR92  (Altomare et al. , 1993) ; program(s) used to refine structure: SHELXL2014  (Sheldrick, 2015) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012) ; software used to prepare material for publication: WinGX  (Farrugia, 2012).
A meta-analysis using the NCBI HIV Interaction Database revealed a set of Th17-specific HIV dependency factors (HDFs): PARG, PAK2, KLF2, ITGB7, PTEN, ATG16L1, Alix/AIP1/PDCD6IP, LGALS3, JAK1, TRIM8, MALT1, FOXO3, ARNTL/BMAL1, ABCB1/MDR1, TNFSF13B/BAFF, and CDKN1B. Functional studies demonstrated an increased ability of Th17 versus Th1 cells to respond to TCR triggering in terms of NF-κB nuclear translocation/DNA-binding activity and proliferation. Finally, RNA interference studies identified MAP3K4 and PTPN13 as two novel Th17-specific HDFs.
These subsets were identified based on the differential expression of the well-established surface markers CCR4, CCR6, and CXCR3, as previously described  [13, 15, 37]  and illustrated in Fig. 1a : Th1 (CXCR3 + CCR4 -CCR6 -), Th2 (CXCR3 -CCR4 + CCR6 -), Th17 (CXCR3 -CCR4 + CCR6 + ), and Th1Th17 (CXCR3 + CCR4 -CCR6 + ). Total mRNA extracted from each subset was hybridized onto the Illumina HumanHT-12 v4 Expression BeadChip (GEO access number GSE70396) and transcripts up-and down regulated in Th17 compared to Th1, Th2, or Th1Th17 were identified based on p values (p < 0. 05) or adjusted p values (adj. p < 0. 05) and fold change (FC) expression ratios (cut-off 1. 3-fold) (Additional file 1: Table  S1 ; Additional file 2: Table  S2 ). The most robust differences in gene expression were observed between Th17 versus Th1 (Fig.
We also performed a meta-analysis to identify transcripts enriched in Th17 versus Th1 that overlapped with those listed on the National Center for Biotechnology Information (NCBI) interaction database in the categories "HIV-1 proteins enhanced by expression of human genes" (Fig. 3 ) and "HIV-1 proteins interact with" (Additional file 6: Figure  S3 ). Such transcripts included PTEN (a negative regulator of the mTOR pathway  [75] ), KLF2  [76] , ITGB7 (a gut-homing molecule and an alternative HIV-binding receptor  [77] ), ATG16L1 (a regulator of autophagy, a pathway associated with HIV intracellular degradation  [78, 79] ), PDCD6IP (AIP1/Alix, a component of the HIV budding machinery  [80] ), JAK1 (a tyrosine kinase that phosphorylates STAT3  [60] ), TRIM8 (a regulator of NF-κB and STAT3-dependent signaling cascades  [81, 82]  and a documented HDF  [57] ), LGALS3 (an HIV Tat-induced glycoprotein recently reported to promote HIV budding by association with Alix  [79, 83] ), FOXO3  [84] , ARNTL  [84]  (Fig.
3  Meta-analysis using the NCBI HIV interaction database for the identification of HIV-1 proteins enhanced by expression of human genes enriched in Th17 cells. Differentially expressed genes between Th17 and Th1 subsets (p < 0. 05, FC cut-off 1. 3) were matched to the lists of human genes known to interact with HIV-1 proteins. Shown are significant probes with the smallest p value for each overlapping gene. Heatmap cells are scaled by the expression level z scores for each probe individually new transcripts that may contribute to Th17 when compared to Th1 lineage differentiation fate and/or HIV permissiveness at post-entry levels.
Th17 versus Th1 express higher Lck and ZAP-70 levels HIV replication is restricted in resting CD4 + T-cells  [88] [89] [90]  through mechanisms that are counteracted upon activation by the TCR and/or cytokines  [60, 91, 92, 93, 94, 95]. Ingenuity Pathway Analysis (IPA) performed on differentially expressed genes (p < 0. 05; FC cut-off 1. 3) was used to illustrate transcripts enriched in Th17 versus Th1 that are linked to TCR signaling (Additional file 7: Figure  S4 ). The later transcripts included Lck and ZAP-70, two kinases associated with CD4 and CD3zeta, respectively, that play critical roles in the TCR downstream signaling cascade  [67, 70]. ZAP-70 was reported to be essential for HIV-1 cell-to-cell transmission  [71]  and HIV-Nefmediated effects  [96]. The Lck and ZAP-70 mRNA levels were confirmed by RT-PCR as being significantly higher in Th17 versus Th1 (Fig.
Upon receptor-mediated entry, HIV-1 uses the hostcell molecular machinery to ensure its reverse transcription, integration and transcription  [59, 60]. Large siRNA screens performed on cell lines identified networks of HDFs acting at different levels of the viral life cycle  [56, 57, 58, 84]. Very few HDF identification studies were performed on primary cells [127-129]. JAK1 is one of the very few HDFs identified in more than two siRNA screens that is also included in the NCBI HIV interaction database  [60]. Of particular importance, our microarrays revealed an up-regulation of JAK1 transcripts in Th17 versus Th1. JAK1 is a tyrosine kinase associated with the signaling through the receptors of type I and II cytokines; activation of JAK1 induces STAT3 phosphorylation in response to IL-21 stimulation [130].
Slides were mounted using ProLong Gold Antifade medium with the nuclear dye DAPI (Invitrogen, Molecular Probes). Epifluorescent and Spinning Disc confocal microscopy images were acquired out on an automated Cell Observer Z1 ® microscope (Carl Zeiss) using the AxioVision 4. 8. 2 software (Carl Zeiss). For the analysis of protein cellular localization, spinning disc confocal images were acquired using the 100× oil immersion objective (numerical aperture, NA: 1. 46) and maximum intensity projection of 0. 2 μm z-stack sections were realized using ImageJ software (NIH) after background subtraction. For statistical analysis of protein expression, random epi-fluorescent images were acquired with the 40× oil immersion objective (NA: 1. 3). All acquisitions between the different T-cell subsets were performed with the same illumination status in the same run.
Acknowledgements The authors thank  Sylvain Gimmig ,  Laurence Lejeune , and  Dr. Dominique Gauchat  for expert technical support in FACS analysis and cell sorting (the  CHUM-Research Centre FACS Core Facility) ,  Anne Vassal  and  Mario Legault  ( HIV/AIDS  and infectious diseases network of the  Fonds de Recherche Québec -Santé (FRQ-S) ) for help with ethical approvals and informed consents,  Dr. Mohamed-Rachid Boulassel  and  Véronique Lafontaine  for sample management,  Dr. Dana Gabuzda  ( Dana-Farber Cancer Institute, Boston, MA, USA ),  Dr. Chris Aiken  ( Vanderbilt University, Nashville, TN, USA ),  Heinrich Gottlinger  ( University of Massachusetts, Worchester, MA, USA ) for access to HIV-1 molecular clones, and  Nadia Ketaf  for technical assistance with the Illumina beads arrays technology.
These latter two are defined by the combinations of individual KIR genes that are present on a haplotype. We developed and validated KIR*IMP by using data from two cohorts of European individuals typed for both SNP genotypes and KIR copy number. A cohort of UK families formed our primary dataset; resolving phase and familial relationships resulted in a reference panel of 479 unrelated haplotypes (''UK reference panel''). We used this for training a statistical model on the basis of the random-forests algorithm. A separate panel of 1,338 unrelated and unphased Norwegian and German individuals was prepared for validation (''NG validation panel''). Both cohorts were typed for copy number at 17 KIR loci. For the UK reference panel, this also served as a basis for a fine-scale classification of KIR haplotypes (KIRhaplotype) and a standard broad classification of A and B KIR haplotypes (AvsB), giving us a total of 19 KIR types for this panel.
DNA Samples Our primary dataset, the UK cohort, comes from the UK DNA Banking Network (DBN), as described in Jiang et al. 9  It consists of families of individuals ascertained for having either asthma or atopic dermatitis. All subjects are of self-reported European ancestry. Of the 1,768 individuals reported in Jiang et al. , 149 were removed because of missing data and haplotype ambiguity, leaving 1,619 individuals in 419 families. All of these individuals were typed for KIR copy number at all loci. SNP genotypes were obtained for 998 individuals from 449 families, which partially overlapped the above set. For logistical reasons, we were not able to obtain both KIR and SNP types for exactly the same individuals. The overlap between the two sets was 698 individuals from 343 families; these individuals were considered for inclusion in the UK reference panel, but all individuals were used for phasing and filtering (see below).
Producing the UK Reference Panel The UK dataset consisted of a combination of individuals, some of whom had missing KIR types and some of whom had missing SNP data. Furthermore, because of the family structure, this dataset contained haplotypes that were identical by descent. To create an imputation model, we needed a reference panel with both SNP and KIR data that consisted of unrelated haplotypes. We produced this as follows. First, we determined the parent of origin for each haplotype where it was unambiguous. We did this separately by using the KIR types and the SNP genotypes (on the respective subsets of the dataset where these data were available). For KIR types, we compared the types directly and required an exact match. For SNP genotypes, we used all SNPs across 19q13. 42 and required fewer than 20 mismatches to declare that the haplotypes matched.
Producing the NG Validation Panel Of the individuals in the Norwegian-German cohort, we took only those for whom we had both KIR and SNP types. This sample consisted of unrelated individuals, so no further filtering of individuals was required. For this dataset, we phased the SNPs; however, the KIR types were not phased (unlike the scenario for the UK cohort, for which we had family relationships that allowed us to phase the KIR types as well). Therefore, we used this as a validation sample. We checked that the KIR copy-number distribution was consistent with that of the UK reference panel under the assumption of Hardy-Weinberg equilibrium (the comparison is not shown, although this can be reproduced from the data in Table  S3 ). To make it compatible with the UK reference panel, we excluded SNPs that were not present in both sets.
The trees are constructed iteratively with standard algorithms for this purpose,  23  such that new SNP loci are selectively added to the tree, leading the resulting haplotypes to be informative for the target variable. A random forest consists of a large number of such trees, referred to as an ''ensemble. '' Because the tree-fitting algorithms are deterministic, two techniques are used to create variation among the fitted trees. First, a random subsample (with replacement) is taken from the reference panel and is used as training data for each individual tree (in other words, each tree is based on a slightly different training dataset). This is known as ''bootstrap aggregation'' or ''bagging. ''  24 Second, during the tree-fitting algorithm, only a random subset of the SNPs are considered candidates for inclusion as an internal node.
Thus, we can view random forests as a sophisticated multi-marker approach, in which the bagging and subsetting operations add substantial robustness. We used the UK reference panel as the training set for all analyses and the KIR types as the target variable and fitted a separate model for each KIR locus. We used either all SNPs in the reference panel or a subset, depending on the analysis of interest. Using a fitted model for imputation involves applying each tree to a new dataset of SNP alleles and taking the set of predictions across the ensemble, which can be summarized as a probability distribution. To obtain a single predicted type, the simplest approach is to take the one with the highest probability. We did this for most analyses we present. In addition, we also explored the use of probability thresholds, as is often done in practice for association analyses.
This mimics cross-validation but is computationally more convenient. We found that it was an adequate proxy for full cross-validation (Figure  S4 ) and thus chose to use it for some analyses. To assess imputation accuracy for particular alleles, we calculated the ''sensitivity'' and the ''positive predictive value'' (PPV). These are, respectively, the proportion of correctly imputed types among all haplotypes with that allele and the proportion of correctly imputed types among all haplotypes imputed with that allele. To assess the relative contribution of each SNP to the prediction accuracy of the model, we used the permutation strategy described by Breiman. 23 For each tree, the OOB accuracy is calculated for two versions of the data: (1) the actual data and (2) the data after the alleles for a particular SNP are permuted across all samples.