dataesr/scientific-paragraphs-categorization huggingface dataset:
18, P=1. 5×10 -4 ) which was initially reported on a subset of the studies in stage 1 (OR=1. 36, P=1. 46×10 -7 )  8. Previously, an InterLymph study of ~1,800 DLBCLs and ~6,500 controls reported a strong signal for a dinucleotide haplotype in the LTA/TNF locus (LTA 252A>G/ TNF-308G>A) at 6p21. 3 (OR=1. 31, P=2. 9 x10 -8 )  5. Although nearly all of the cases from the previous publication were included in our current GWAS, the signal we observed overall was weaker (OR=1. 15, P=8. 5×10 -4 ). The attenuation was not explained by study design (case-control, cohort) or adjustment for population substructure (data not shown), but could be due to population sampling differences, heterogeneity, or chance. To explore the heritability of DLBCL, we estimated the contribution of all common SNPs to the variance explained by fitting all genotyped autosomal SNPs simultaneously using the method proposed by Yang et al  41  in the Stage 1 dataset.
ONLINE METHODS Stage 1: DLBCL-GWAS As part of a larger initiative, we conducted a genome-wide association study (GWAS) of diffuse large B-cell lymphoma (DLBCL) using cases and controls of European descent from 22 studies of non-Hodgkin lymphoma (NHL) (Supplementary Table  1 ), including nine prospective cohort studies, eight population-based case-control studies, and five clinic or hospital-based case-control studies. All studies were approved by their respective Institutional Review Boards, and informed consent was obtained for all participants. Cases were ascertained from cancer registries, clinics or hospitals, or through self-report verified by medical and pathology reports. To determine NHL subtype, phenotype data for all NHL cases were harmonized to the hierarchical classification proposed by the InterLymph Pathology Working Group  42, 43  based on the World Health Organization (WHO) classification  44.
All DLBCL cases with sufficient DNA (n=2,878) and a subset of controls (n=2,854) frequency matched by age, sex, and study to the entire group of NHL cases, along with 4% quality control duplicates, were genotyped on the Illumina OmniExpress at the NCI Cancer Genomic Research Laboratory (CGR). Genotypes were called using Illumina GenomeStudio software, and quality control duplicates showed >99% concordance. Monomorphic SNPs and SNPs with a call rate of <95% were excluded. Samples with a call rate of ≤93%, mean heterozygosity <0. 25 or >0. 33 based on the autosomal SNPs, or gender discordance (>5% heterozygosity on X chromosome for males and <20% heterozygosity on the X chromosome for females) were excluded. Furthermore, unexpected duplicates (>99. 9% concordance) and first-degree relatives based on identity by descent (IBD) sharing with Pi-hat>0.
40 were excluded. Ancestry was assessed using the GLU struct. admix module based on the method by Pritchard et al. 45  and participants with <80% European ancestry were excluded (Supplementary Figure  6 ). After exclusions, 2,661 (92. 5%) cases and 2,685 (94. 1%) controls remained (Supplementary Table  2 ). Genotype data previously generated on the Illumina Omni2. 5 from an additional 3,536 controls from three of the studies (ATBC, CPSII, and PLCO) were also included  11  , resulting in a total of 2,661 cases and 6,221 controls for the stage 1 analysis. Of these additional 3,536 controls, 703 (~235 from each study) were selected to be representative of their cohort and cancer-free  11  , while the remainder were cancer-free controls from an unpublished study of prostate cancer in the PLCO.
SNPs with call rate <95%, with Hardy-Weinberg equilibrium P-value<1×10  -6  , or with a minor allele frequency <1% were excluded from analysis, leaving 611,844 SNPs for analysis (Supplementary Table  4 ). To evaluate population substructure, a principal components analysis (PCA) was performed using the Genotyping Library and Utilities (GLU), version 1. 0, struct. pca module, which is similar to EIGENSTRAT  46. Plots of the first five principal components are shown in Supplementary Figure  7. Association testing was conducted assuming a log-additive genetic model, adjusting for age, sex, and four significant principal components. All data analysis and management was conducted using GLU.
Stage 2: In Silico Analysis of Three Independent DLBCL GWAS Three independent DLBCL GWAS provided genotyping data for a meta-analysis, (Supplementary Table  1 ), which included data generated with the following commercial, SNP microarrays: Illumina HumanHap 660W for Mayo (393 DLBCL and 172 controls), HumanCNV370-Duo for UCSF  7  (254 DLBCLs and 748 controls), and HumanHap 610K for GELA (549 cases). In all studies, subjects with a genotyping call rate <95%, duplicates, related individuals, and SNPs with a call rate <95% were removed prior to imputation (Supplementary Table  4 ). The GELA study was conducted on cases only; controls were drawn from a pool of 928 individuals from the French component of the EPIC cohort, who were previously scanned on Illumina HumanHap 660W or 610K  47, 48. We subsequently chose a subset of 525 individuals with matched ancestry as determined from the principal components analysis.
HLA imputations and analysis We imputed dense SNPs as well as classical HLA alleles (A, B, C, DRB1, DQA1, DQB1) and coding variants across the HLA region (chr6:29. 5-33. 2Mb, hg19) in the stage 1 (NCI) and stage 2 (MAYO, USCF2 and GELA/EPIC) studies using SNP2HLA  36. The imputation was based on a reference panel from the Type 1 Diabetes Genetics Consortium (T1DGC), and consisted of genotypes from 5,225 individuals of European descent who were typed for HLA-A, B, C, DQA1, DQB1, DRB1, DPA1, DPB1 4 digit alleles. To assess imputation accuracy, we compared the imputed HLA alleles to HLA sequencing data (to 4 digits) available on a subset of samples from the NCI GWAS  14  , and found high concordance rates for HLA-A (97. 3%), B (98. 5%), C (98. 1%) and DRB1 (97. 5%). Due to the limited number of SNPs (N=7,253) in the T1DGC reference set, imputation of HLA SNPs was conducted with IMPUTE2 and the 1000 Genomes reference set as described above.
Expression quantitative trait loci (eQTL) analysis To evaluate the effect of our top loci (and SNPs in LD based on r 2 >0. 8 in HapMap-CEU release 28) on gene expression, we conducted an eQTL analysis on lymphoblastoid cell lines using two independent datasets: Childhood asthma  50  and HapMap  51. For the childhood asthma dataset  50  , peripheral blood lymphocytes were transformed into lymphoblastoid cell lines for 830 parents and offspring from 206 families of European ancestry. Using extracted RNA, gene expression was assessed with the Affymetrix HG-U133 Plus 2. 0 chip. Genotyping was conducted using the Illumina Human1M Beadchip and Illumina HumanHap300K Beadchip, and imputation was performed using data from the 1kGP. All SNPs selected for replication were tested for cis associations (defined as gene transcripts within 1 Mb), assuming an additive genetic model, adjusting for non-genetic effects in the gene expression value.
To gain insight into the relative importance of associations with our SNPs compared to other SNPs in the region, we also conducted conditional analyses, in which both the DLBCL SNP and the most significant SNP for the particular gene transcript (i. e. , peak SNP) were included in the same model. Only cis associations that reached P<6. 8×10  -5  , which corresponds to a false-discovery rate (FDR) of 1%, are reported (Supplementary Table  10 ). The HapMap dataset consisted of a publicly available RNAseq dataset  51  from transformed lymphoblastoid cell lines from 41 CEPH Utah residents with ancestry from northern and western Europe (HapMap-CEU), samples available from the Gene Expression Omnibus (GEO) repository (http://www. ncbi. nlm. nih. gov/geo) under accession number GSE16921. Genotyping data for the same HapMap-CEU individuals were directly downloaded from HapMap (www.
Bioinformatics: ENCODE and Chromatin State Dynamics Using 1000 Genomes data, we identified SNPs with r 2 ≥0. 8 with our sentinel SNP that were reported to be non-synonymous or nonsense variants. We utilized HaploReg v2  15  , which is a tool for exploring non-coding functional annotation using ENCODE data, to evaluate the genome surrounding our SNPs (Supplementary Table  12 ). To assess chromatin state dynamics, we used Chromos  16  , which has pre-computed data from ENCODE on 9 cell types based on Chip-Seq analyses  17. These pre-computed data have genome-segmentation performed using multivariate hidden Markov-model to reduce the combinatorial space to a set of interpretable chromatin states. The output from Chromos separates data into 15 chromatin states corresponding to repressed, poised and active promoters, strong and weak enhancers, putative insulators, transcribed regions, and large-scale repressed and inactive domains (Supplementary Figure  5 ).
Heritability analyses To estimate the contribution of all common SNPs to the variance explained, we used the method proposed by Yang et al  41  , which was extended to dichotomous traits  52  and implemented in the Genome-wide Complex Trait Analysis (GCTA) software  53. The genetic similarity matrix was estimated from our stage 1 data using all genotyped autosomal SNPs with a minor allele frequency >0. 01. We used restricted maximum likelihood (REML), the default option for GCTA, to fit the appropriate variance components model that included the top 10 eigenvectors as covariates. The final estimate of heritability on the underlying liability scale assumed that the lifetime risk of DLBCL was 0. 0074  54.
Estimate of recombination hotspots To identify recombination hotspots in the region we used SequenceLDhot  55  , a program that uses the approximate marginal likelihood method  56  and calculates likelihood ratio statistics at a set of possible hotspots. We tested five unique sets of 100 control samples. PHASE v2. 1 program was used to calculate background recombination rates  57, 58  and LD heatmap was visualized in r2 using snp. plotter program  59.
Acknowledgments We thank  C. Allmer ,  E. Angelucci ,  A. Bigelow ,  S. Buehler ,  K. Butterbach ,  A. Chabrier ,  J. M. Conners ,  M. Corines ,  M. Cornelis ,  K. Corsano ,  H. Dykes ,  L. Ershler ,  A. Gabbas ,  R. P. Gallagher ,  R. D. Gascoyne ,  P. Hui ,  L. Irish ,  L. Jacobus ,  L. Klareskog ,  A. S. Lai ,  J. Lunde ,  M. McAdams ,  R. Montalvan ,  L. Padyukov ,  M. Rais ,  T. Rattle ,  L. Rigacci ,  K. Snyder ,  G. Specchia ,  M. Stagner ,  G. Thomas ,  C. Tornow ,  G. Wood , and  M. Yang. The overall  GWAS  project was supported by the intramural program of the  US National Institutes of Health/ National Cancer Institute. A list of support provided to individual studies is provided in the Supplementary Note.
In the constructs pCqc9 and pCqc12 three, respectively, four nucleotides of the stem part were exchanged to destabilize the putative terminator structure (see Fig. 3 ). Both constructs replicated successfully suggesting that the putative terminator structure is not of relevance concerning the replication of pRN1. The second possible reason why the region downstream of orf904 is essential for plasmid replication could be that the origin of replication is situated in this region. We searched for secondary structures and identified a large stem-loop structure starting 54 bp downstream of orf904 (Fig. 3 ) comprising about 100 bp. The DNA folding program Mfold  (Zuker 1989 ) predicted an additional paired stem at 37°C but at 75°C only a 21 nt stem and a 58 nt loop are predicted. The constructs with the deletion closest to the stop codon of orf904 that still replicate, pCdel7 and pGdel7, are truncated just downstream of this large stemloop structure, whereas in the nonreplicating deletion constructs -pCdel13, pCdel6, and pCdel12the structure is at least partially affected by the deletions (Figs.
The large conserved region comprises the 100 bp stem-loop structure which was identified as the putative pRN1 origin region. Plasmid pRN2 contains a structurally similar stem-loop of 97 bp (position 61-157 in pRN2, NCBI accession number NC_002101) downstream the open reading frame coding for the replication protein of pRN2. However, the sequence stretch corresponding to the stem-loop in pRN1 and pRN2 exhibits a considerably lower degree of conservation than the flanking regions of the stem-loop (Fig. 5 ). Given the high similarity of their putative origins and their same set of replication proteins it appears that both plasmid replicate using the same replication mechanism. Nevertheless, subtle sequence differences, for example, in DNA binding proteins and in the nucleotide sequences might allow both plasmids to replicate independently of each other.
We therefore searched the Aclame collection of mobile genetic elements  (Leplae et al. 2010)  for the presence of primases. We first retrieved from the Conserved Domain database domains with known primase activity and selected one representative protein from each conserved domain. We then queried the Aclame collection using the representative protein, that is, the primase domain of the pRN1 replication protein, the primase domain of the RSF1010 replication protein RepB', and Herpes virus primase as well as the cellular primases of the archeaon Pyrococcus horikoshii (small subunit) and Methanobrevibacter smithii (large subunit), the bacterium Synechococcus elongates and the human Prim/Pol-Protein. Although the Aclame collection contains over 122,000 proteins from 2300 mobile genetic elements, we found only a very limited number of proteins with highly significant (E = 0.
Figure5. Comparison of the downstream regions of the replication genes. Sequences downstream of the replication gene from the episomal and integrated pRN plasmids were withdrawn from the databases and aligned. Strong sequence conservation was found between all pRN episomal plasmid except pDL10 from Acidianus ambivalens and with pST3 integrated into S. tokodaii. The sequences were first aligned with T-Coffee. This alignment showed a region of about 100 nucleotides with low sequence conservation flanked by regions of high sequence conservation. The region with low sequence conservation was then analyzed for conserved secondary structure elements with Locarna and structurally realigned. This analysis revealed a highly conserved extended stem-loop structure in all sequences despite its very low sequence conservation.
5uC increment steps measuring fluorescence at each temperature for a period of 10 s. All reactions were carried out in duplicate for each sample. Using the Bio-Rad software, the threshold (C t ) at which the cycle numbers were measured was adjusted to areas of exponential amplification of the traces. The DDC t method was used to compare expression levels of two samples by applying the formula 2  (A) Immunoblots demonstrating tyrosine phosphorylation and expression levels of STAT1 in differentially treated HFF (left) and A549 cells (right) using antibodies against tyrosine-phosphorylated STAT1 (apY-STAT1) and, after stripping off of bound immunoreactivity from the membranes, pan-STAT1 antibody (aSTAT1). Cells were either left untreated (lane 2), stimulated for 18 h with 5 ng/ml IFNc (lane 3) or challenged for 18 h with T.
With the exception of adam19 used as a negative control, all tested genes up-regulated by parasite infection showed a titer-dependent increase (Fig. 3C ). Similar results were obtained in A549 cells (Fig. S3  in File S1). These data confirmed that STAT1 participates in the regulatory effects of IFNc on T. cruzi parasitism. Next, the action of the JAK inhibitor AG-490, also termed tyrphostin B42, on T. cruzi-mediated STAT1 activation was studied by measuring its effect on tyrosine and serine phosphor-ylation. As shown in Figure  4 , incubation of either HFF cells or A549 cells for 18 h with AG-490 at a concentration of 50 mM resulted in a significant reduction of both tyrosine-and serinephosphorylated STAT1 as well as the total cellular amount of STAT1 (Fig. 4A, B ). The impaired phosphorylation status was particularly observed in cells infected with parasites at an MOI of 15.
Statistics Parasite viability values of treated and untreated worms obtained from microscopic evaluation were averaged (means (+/-standard deviation)) using Microsoft Excel software. The Kruskal-Wallis test was applied for in vivo studies, comparing the medians of the worm counts of the treatment and control groups. A difference in median was considered to be significant at a significance level of 5% (StatsDirect statistical software, version 2. 7. 2. ; StatsDirect Ltd. , United Kingdom).
Acknowledgments This work was financially supported by the  European Research Council  ( ERC-2013-CoG 614739-A_HERO  to J. K. ), the  Swiss National Science Foundation  ( SNSF Professorship   PP00P2_133568  to G. G. ), the  University of Zurich  (G. G. ), the  Stiftung für Wissenschaftliche Forschung of the University of Zurich  (G. G. ), and the  Novartis Jubilee Foundation  (R. R. and G. G. ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Dr. Faustine Dubar  is acknowledged for the synthesis of RQ.
Development of a qPCR Assay for Generic Detection of Orthopoxviruses and Specific Detection of Smallpox The qPCR assay developed for the detection of clinically relevant orthopoxviruses was a modification of the assay described by Scaramozzino  [19]. The modification was aimed at developing a more straightforward assay with respect to data analysis. In brief, after multiple alignments of available orthopoxviruses 14-kilodaltons protein genes (Figure  2 ), a couple of consensus primers (orthopox-FOR: 59-ccagagatatcatagccgctctt-39; and orthopox-REV: 59-gaaactctcaaacaacgrctaact -39) were designed to generically amplify a 157-bp fragment of the clinically relevant orthopoxviruses 14-kilodaltons (14-kDa) gene (accession number AF380138. 1). The reverse primer was 59-biotinylated in order to allow subsequent amplicon capture on streptavidin-coated beads for subsequent pyrosequencing assay.
A pan-orthopoxvirus 59 nuclease probe (Pox-probe) (59-tttgttcaaactttgttgtta-39) exactly matching a consensus sequence within the 157-bp 14 kDa amplicon was designed for detecting the whole clinically relevant orthopoxvirus genus whereas a second 59-nuclease probe (59taaatagaacgtcatcatt-39) encompassing a smallpox-specific SNP (Var-probe) was designed for specific detection of smallpox virus. Specificity of primers and probes was assessed in silico against all publicly available nucleotide sequence databases by using BLASTN  [20]. The Pox-and the Var-probes were respectively labeled with VIC and FAM reporters at their 59-end. Both probes were labelled at their 39-end with a MGB group and a nonfluorescent quencher (NFQ). Primers were purchased from Eurogentec (Ougree, Belgium), whereas probes were purchased from Applied Biosystems (Carlsbad, CA, USA).
Each qPCR assay was carried out in 25 mL of a reaction mixture containing 2. 5 mL of extracted DNA as template, 300 nM of each primer and 100 nM of each probe and 12. 5 mL of Universal PCR Master Mix (Applied Biosystems, Foster City, USA). The reaction was initiated at 50uC for 2 min, and 95uC for 10 min followed by 45 cycles of denaturation at 95uC for 15 s and annealing/extension at 60uC for 1 min and data were recorded as Cycle threshold (Ct) on a StepOnePlus Real-time PCR system (Applied Biosystems, Foster City, USA), using the analytical software from the same manufacturer. The orthopoxvirus duplex qPCR assay was first assayed on positive controls, which consisted in recombinant pGEM-T Easy plasmids carrying an insert the 14-kDa fusion protein gene of variola major virus (pVARV-A30L), camelpoxvirus (pCAMV), cowpoxvirus (pCPXV-V162), and MPXV (pMPXV-A29L), as described by Scaramozzino  [19].
The aim was to test the samples for potential false-negative and -positive results. Regarding the latter issue, attention was paid to each sample defined on-site as MPXV-positive according to the OPV fluorescence signal by qPCR and the MPXV-specific banding pattern by PCR-RFLP. The assay aimed at identifying the MPXV-specific CRT substitution in the 14 kDa gene at position 139716 of the MPXV genome (accession number AF380138. 1). Accordingly, the 157-bp 14 kDa biotinylated PCR amplicon were first immobilized on streptavidin sepharose beads, annealed with the MPXV sequencing primer and subjected to pyrosequencing using a pyrosequencer PSQ 96MA (QIAGEN Benelux B. V. , Venlo, The Netherlands) according to the manufacturer's instructions. Following enzyme and substrate incorporation, nucleotides were dispensed in the following order (G  , T, C, G, A, T, T, A, G, T, A, A, T ) that was derived from the orthopoxvirus sequence alignment (see Figure  2 ).
Acknowledgments This work was funded by the  Department Management of Scientific and Technological Research of Defense  (grants  MED 05  and  MED 08 ). We would also like to thank our Congolese partners in  Kasai-Occidental , especially  Dr Charles Kabasele ,  Dr Moı ¨se Tshiombe  and the first responders for their outstanding contribution to this work. We also acknowledge  Jean-Francois Durant  ( Universitecatholique de Louvain ),  Jasmine Minguet , and  Yann Deccache  ( IRSD-RSTD ) for their contribution.
4C ). Based on the quantification of viral particle RNAs and on their infectivity, we then calculated the specific infectivity of viral particles harboring 3M and DIII heterodimer and confirmed that, similar to HCVpp infectivity, HCVcc harboring the 3M or DIII heterodimers presented a restored cell entry function (Fig. 4D ). These observations were confirmed by HCVcc infection assays using equal amounts of viral particles (data not shown). Altogether, these data demonstrate that the identified amino acids in E1 and DIII in E2 are important for the functionality of E1E2 complex during HCVcc entry. Identified Amino Acids in E1 Are Involved in a Common Crosstalk With E2. To address whether the three amino acids identified in E1 and DIII of E2 are partners of a crosstalk, we constructed H77 E1E2 mutants by substituting the three identified E1 amino acids (3M), the domain III of E2 (DIII), or both, by those of A40 E1E2 sequence.
Acknowledgment: We thank  R. Bartenschlager , J. McKeating,  H. Greenberg ,  J. Dubuisson ,  M. Law ,  D. Burton ,  C. Rice , and  T. Wakita  for providing reagents. Cytometry analyses were performed at the  SFR Bio-Sciences Gerland-Lyon Sud  ( US8/UMS 3444 ). We thank our colleagues of the EVIR team at  CIRI  and members of  UMR754   UCBL INRA EPHE  and  UMR 5557  CNRS for encouragement and advice.
Supported by the  French " Agence Nationale de la Recherche sur le Sida et les h epatites virales" (ANRS) , the FINOVI foundation, and by the  European Research Council  ( ERC-2008-AdG-233130-HEPCENT ) and the  LabEx HEPSYS  ( ANR-10-LAB-28 ). F. D. was supported by a fellowship from the  French Ministry of Research (MESR). Present address for F. Douam and D.
Flow cytometry Surface staining for flow cytometry was performed after blocking with mouse IgG (Jackson ImmunoResearch, Suffolk, UK) using the following reagents (all eBioscience, Hatfield, UK): anti-CD4-APC and -APC-H7 (RM4-5), anti-CD25-PerCP-Cy5. 5 and -PeCy7 (PC61. 5), anti-CD44-APC (1M7), anti-CD62L-PE-Cy7 (MEL-14) and anti-Foxp3-APC (FJK-16s). Following surface staining, cells were treated with the Foxp3 staining kit (eBioscience) before staining with anti-Foxp3-APC (FJK-16s) and anti-Ki-67-PE (SolA15). For staining activated caspase-3, cells were plated at 10 6 cells per 50 µl and well in 96-well U-bottom tissue culture plates in RPMI/10% fetal calf serum and incubated for 1 h at 37 °C with FITC-DEVD-FMK (Abcam, Cambridge, UK) before continuing with surface staining. Data were collected on a FacsCantoII flow cytometer (Becton Dickenson, Erembodegem, Belgium) and analysed using Flowjo (Treestar, Ashland, OR, USA) software.
ACKNOWLEDGEMENTS We would like to thank  S Schoenefeldt  and  J Haughton  for mouse colony support. We declare they have no conflicting financial interest. The research has been supported by the  FWO  (grant  92819 ) and the  German Research Foundation  ( SFB738  to JH). SMS is supported by the  FWO. MAL has been supported by the  Biotechnology and Biological Sciences Research Council.