dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgment We thank  Relief International, Pakistan , for providing funding for conducting this study on the prevalence of leishmaniasis in Waziristan, Pakistan. This study was conducted in the following institutions: Vector Borne Diseases Management Center,  Department of Microbiology, Kohat University of Science and Technology, Kohat, KP, Pakistan; ANSES-French Agency for Health and Safety, Animal Health Laboratory, Leishmaniasis  and  Sandflies Team ,  Maisons-Alfort , France; and  US Naval Medical Research Unit  ( NAM-RU3 ), Cairo, Egypt.
Molecular investigation Whole-genome sequencing (WGS) was performed in the Associated laboratory for gonococci of the French National Reference Centre for bacterial STI, Paris, France. DNA extraction was conducted using Wizard Genomic DNA Purification kit (Promega), as previously described  [7]. Multiplexed DNA libraries were prepared with the Nextera XT construction protocol (Illumina, San Diego, CA, USA). Paired-end, 1. 547. 414 150-bp indexed reads with an average depth of 151 were obtained on a MiSeq platform (Illumina). De novo assembly was performed using SPAdes 3. 11. 1 software  [8]. QUAST software  [9]  revealed that the assembly provided 106 contigs with an average length of 20,394 nucleotides, a N50 of 47,830 nucleotides. The contigs covered 93. 7% of the genome of NG reference strain FA1090.
The genome annotation was performed using the MicroScope platform (http://www. genoscope. cns. fr/ agc/microscope)  [10]. The whole nucleotide sequence of the penA gene of F90 is available on GenBank under accession number MH172152. Using the WGS data, NG antimicrobial resistance determinants were determined in silico with the NG FA1090 genome as reference. Sequence types (ST) were also determined in silico from the WGS data using the NG multi-antigen sequence typing (NG-MAST) online database (http://www. ng-mast. net) [11] , the multilocus sequence typing (MLST) from the PubMLST database  [12] , and the NG Sequence Typing for Antimicrobial Resistance (NG-STAR)  [13]. F90 was assigned as MLST 1903 , NG-MAST ST 3435 , and NG-STAR 233. Regarding ceftriaxone resistance determinants, the isolate harboured a mosaic penA-60.
Thus, the HIV-2 WB performed at V1 was indeterminate, although consistent with an HIV-2 primary infection, whereas the WB performed at V2 and V4 fulfilled the criteria for HIV-2 positivity. C, A consensus phylogenetic tree was built by maximum of likelihood method using PhyML v3. 1, using a data set composed of protease and reverse transcriptase sequences obtained from the patient and from Los Alamos HIV Database, including HIV-2 groups A and B, as well as SIV viruses. Robustness of the tree was assessed by bootstrapping with 1000 replicates. The tree was edited using FigTree v1. 4. 3 (http://tree. bio. ed. ac. uk/software/figtree/). detects antibodies directed against HIV-2 Env (gp36, gp105, and gp140), Gag (p56, p26, and p16), and Pol (p55 and p34) proteins. At V1, only weak anti-gp140, anti-gp105, anti-p26 bands and faint anti-p16 bands were present (Figure  1 ).
Genotypic resistance testing was performed on samples collected at V1 and V2. HIV-2 protease, reverse transcriptase, integrase, and the V3 loop of the gp105 regions were sequenced by Sanger technology, as described by the Agence Nationale de Recherche sur le Sida et les Hépatites Virales (ANRS)  [1]. Phylogenetic analyses identified this virus as belonging to HIV-2 group A, which is the main epidemic group of HIV-2. No mutations on residues associated with drug resistance were identified, and the predicted viral tropism was CCR5  [2, 3]. The protease, reverse transcriptase, integrase, and the V3 loop of gp105 region sequences were deposited in GenBank under accession numbers MG878085, MG878086, MG878087, and MG878088. The HIV-2 cellular reservoir was estimated by quantifying HIV-2 total DNA in peripheral blood mononuclear cells (PBMCs) using a recently developed technique with a quantification threshold of 6 HIV-2 total DNA copies per polymerase chain reaction (PCR)  [4].
At V4, the patient was receiving antiretroviral therapy. The criteria for HIV-2 positivity are the presence of at least 1 band for each gene (Env, Gag, and Pol). Thus, the HIV-2 WB performed at V1 was indeterminate, although consistent with an HIV-2 primary infection, whereas the WB performed at V2 and V4 fulfilled the criteria for HIV-2 positivity. C, A consensus phylogenetic tree was built by maximum of likelihood method using PhyML v3. 1, using a data set composed of protease and reverse transcriptase sequences obtained from the patient and from Los Alamos HIV Database, including HIV-2 groups A and B, as well as SIV viruses. Robustness of the tree was assessed by bootstrapping with 1000 replicates. The tree was edited using FigTree v1. 4. 3 (http://tree. bio. ed. ac. uk/software/figtree/).
Acknowledgments Financial support. No specific funding was received for this work. Potential conflicts of interest. C. N. , L. H. Q. , A. B. , D. S. P. , G. L. , G. S. , D. F. , V. B. , C. C. , S. A. , R. R. S. , and D. D. : no conflicts. All authors have submitted the  ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
HIV-1 integration hotspots are within genes delineated with super-enhancers We assembled a list of 4,993 HIV-1 integration sites from primary CD4 + T cells infected in vitro ( 30 and this study) and 9,519 insertion sites from six published studies from HIV-1 patients  18, 19, [31] [32] [33] [34]  ( Additional file 1 ). 11,613 integrations were in gene bodies (85. 5% for patient studies and 86% for in vitro infection studies), targeting a total of 5,824 different genes ( Supplementary Figure  1A  ). This insertion data set is not saturating ( Supplementary Figure  1B  ), but we found that a subset of genes are recurrent HIV-1 targets, consistent with our previous findings  6. We thus defined Recurrent Integration Genes (RIGs) as genes with HIV-1 insertions in at least two out of eight datasets, yielding a total of 1,831 RIGs ( Supplementary Figure  1C  ).
To characterize RIGs, we extracted genes without HIV-1 insertions in any dataset (called non-RIGs in the analysis; 51,939 genes in total) and compared their ChIP-Seq features in primary CD4 + T cells ( Figure  1A  ). We observed higher levels of H3K27ac, H3K4me1 and H3K4me3, as well as BRD4 and MED1 at transcription start sites of RIGs vs non-RIGs. Histone profiles of H3K36me3 and H4K20me1 were higher throughout gene bodies of RIGs, while the repressive transcription mark H3K27me3 was lower on the gene bodies of RIGs vs non-RIGs. Of note, the mark of facultative heterochromatin H3K9me2 was depleted at transcription start sites of RIGs but remained unchanged throughout the gene body of RIGs vs non-RIGs. In order to test the specificity of chromatin signatures of HIV-1 integration sites, we adapted the receiver operating characteristic (ROC) analysis  35, 36.
We used controls sites matched according to the distance to the nearest gene (see Methods) and confirmed significant enrichment reproducible over all datasets of the following genomic features: H3K27ac, H3K4me1, BRD4, MED1, H3K36me3 and H4K20me1 ( Figure  1B  ). The marks H3K27ac, H3K4me1 and H3K36me3, characteristic of active enhancers  37  , cell type specific enhancers  38  , and bodies of transcribed genes  39  , respectively, were the most enriched in the proximity of insertion sites. Consistent with the presence of H3K27ac and H3K4me1, we also found significant enrichment of BRD4, a constituent of super-enhancer genomic elements  10, 12  ( Figure  1B  ). On average, 60% of insertion sites were significantly enriched in these chromatin marks (not shown) while we observed depletion of H3K27me3 and H3K9me2 in the proximity of insertion sites.
Interestingly, we did not observe a statistically significant enrichment of H3K4me3 in the proximity of insertion sites. We therefore identified super-enhancers in activated CD4 + T cells using H3K27ac ChIP-Seq and merged them with the super-enhancers in activated CD4 + T cells from dbSuper  40, 41. We obtained 2,584 super-enhancers, intersecting 360 RIGs out of 1,813 (34. 41%, Supplementary Figure  1D  ). In addition, the more a RIG is targeted by HIV-1 ( i. e. the higher the number of datasets where HIV-1 insertions are found in the gene), the closer it lies to super-enhancers on average ( Figure  1C  ). In contrast, the insertion sites of the retrovirus HTLV-1  42  (Human T Lymphotropic Virus type 1) were not enriched in super-enhancer marks ( Supplementary Figure  1E  ). Figure  1D  shows the integration biases at gene scale on FOXP1 , STAT5B and BACH2 , three highly targeted RIGs involved in T cell differentiation and activity.
Hi-C on uninfected Jurkat cells yielded ~1. 5 billion informative contacts. Topologically Associating Domains (TADs) and loop domains are clearly visible on the raw Hi-C map in 5 kb bins ( Figure  4C  ), showing that the experiment captures the basic structural features of the Jurkat genome. We also verified that the A and B compartments are well defined and that they correspond to the regions of high and low gene expression, respectively (data not shown). To our knowledge, this dataset constitutes the highest resolution Hi-C experiment presently available in Jurkat cells. If the insertion pattern of HIV-1 reflects a particular organization of the genome, one predicts that the insertion hotspots occupy the same nuclear space and thus cluster together in 3D. We tested this hypothesis by measuring the amount of inter-chromosomal Hi-C contact densities among different classes of HIV-1 insertion sites ( Figure  4D  ).
We assessed the 3D nuclear position of two RIGs with super-enhancer elements in resting T cells and compared them with T cells treated with JQ1 prior to the cellular activation with CD3/CD28 antibodies. Although the activation status of cells pretreated with JQ1 does not differ from the normally activated T cells (data not shown), we observed that two of the super-enhanced RIGs retain their position in the center of the nucleus, i. e. do not shift towards the periphery ( Supplementary Figure  6F  ). As we find that seven out of nine RIGs delineated with super-enhancers change radial positioning in activated CD4 + T cells we conclude that radial positioning of genes with super-enhancers changes with the activation status of CD4 + T cells. Overall, our results indicate that during T cell activation, super-enhancers poise the genome into a particular structure that defines the insertion biases of HIV-1.
DISCUSSION The integration of the viral DNA into the host cell genome is responsible for the long term persistence of HIV-1 in cellular reservoirs  46. The persistence of HIV-1 is influenced by the chromosomal context at the sites of integration, with a strong impact on the outcome of viral infection  9. Here, we characterized the genomic features of integration sites identified from patients  18, 19, [31] [32] [33] [34]  and from in vitro infections of CD4 + T cells ( 30 and this study). By analyzing these large datasets we confirmed that HIV-1 repeatedly integrates into a subset of active cellular genes, which we found to be delineated with super-enhancer genomic elements ( Figure  1C  ). Yet, neither the activity of super-enhancers, nor their effect on gene expression alone explain the integration process ( Figure  3A  ).
activated or DMSO vs. JQ1). (2) High-throughput imaging (HTI) and image analysis of dual-color FISH Images were acquired with a spinning disk Opera Phenix High Content Screening System (PerkinElmer), equipped with four laser lines (405 nm, 488 nm, 568 nm, 640 nm). Images of FISH experiments to calculate 3D distances were acquired in confocal mode using a 40X water objective lens (NA 1. 1) and two 16 bit CMOS cameras (2160 by 2160 pixels), with camera pixel binning of 2 (corresponding to 299 nm pixel size). For each sample, 11 z -planes separated by 0. 5 µm were obtained for a total number of at least 36 randomly sampled fields, which acquired per condition a minimum of 16x10 3 cells. Image analysis was performed using the Harmony high-content imaging and analysis software (version 4. 4, Perkin Elmer), using custom made image analysis building blocks.
Nuclei were segmented based on the Hoechst nuclei staining signal of maximum projected images using the algorithm B and cells in the periphery of the image were excluded from further analysis. FISH probe detection was performed by using the spot detection algorithm C and custom made scripts were used to calculate the Euclidean distances between all the different colored probes per cell. Single cell level data were then exported and custom made R scripts were used to select the minimum distance between the different FISH probes per allele basis. To exclude spurious spot detection events from the analysis, only the distances of cells with two FISH probes detected per channel were calculated and plotted (Graph Pad, Prism).
Quantitative real-time PCR (qPCR) Up to 5x10 6 CD4 + T cells were used for RNA extraction with InviTrap Spin Kit (Stratec Biomedical) according to the manufacturer's instructions and up to 500 ng of RNA was retro-transcribed using Moloney murine leukemia virus reverse transcriptase (M-MLV RT) from Invitrogen according to the manufacturer's instructions. Gene expression analysis were performed in duplicates using IQ supermix from Biorad in CFX96/C1000 Touch Real-Time PCR system, as described in Lusic et al. , 2013. Statistical analysis of qPCR data was performed using Graphpad. Taqman assays used: for MYC Taqman Hs00153408_m1 FAM/MGB, and for GAPDH 4310884E VIC/TAMRA. Western blotting 5x10 6 cells were harvested and homogenized in lysis buffer (20 mM Tris-HCl, pH 7. 4, 1 mM EDTA, 150 mM NaCl, 0.
ChIP-Seq data analysis We analysed ChIP-Seq datasets obtained from this study (H3K4me3, H3K36me3, H3K27Ac, H4K20me1 and H3K9me2) and publicly available data sets from University of Washington Human Reference Epigenome Mapping Project H3K4me1 (SRA accession number: SRX342315), H3K27me3 (SRX342313), input for CD4 primary T cells (SRX252742). Datasets for BRD4 (SRR2971477), MED1 (SRR2971478) and corresponding Input (GSM1527712) were downloaded from 70. ChIP seq reads were mapped to human genome (GRCh37) using Bbmap(  71 ) with parameters minid=0. 98, qtrim=lr, minavgquality=20. Resulting bam files belonging to same experiments were merged and sorted using bamtools. Average binding profiles in reads per million across sets of genes were made using ngsplot 72. Peaks were called using MACS2 73 , for every dataset versus its matching input, with parameters --broad --broad-cutoff 0.
1 -p 1e-9 -g 2. 7e9 -B. All results were transformed to RPKM for downstream analysis and visualization. For Jurkat cells, ChIP-Seq reads were mapped to hg19 using BWA-mem. BWA options were as follows: '-k17 -r1. 3 -B2 -O4 -T22' for read lengths less or equal to 30 nt, '-k18 -B3 -O5 -T28' for read lengths less or equal to 40 nt and default options for longer reads. ChIP-Seq enriched regions were discretized using Zerone  74  with mapping quality cutoff 20 and enrichment confidence 0. 99. We used publicly-available ChIP-Seq profiles for Jurkat cell line. All datasets were obtained from NCBI Gene Expression Omnibus with the following series accessions: ERG and GABPA (GSE49091)  75  ; H3K27Ac, CDK7 and PolII (GSE50622, GSE60027)  76  ; H3K36me3, H3K79me3, H3K4me1, H3K9me3, H3K27Ac, H3K4me3, PolII, S5P and S2P (GSE65687)  77  ; NRSF (GSE53366)  78  ; PolII and CDK12 (GSE72023)  79  ; ETS1, CBP and RUNX (GSE17954)  80  ; H3K27Ac (GSE51522)  81  ; PolIII (GSE20309)  82  ; H3K27Ac and H3K27me3 (GSE59257)  83  ; H3K4me3, H3K27me3, H3K79me2 and PolII (GSE23080); PHF6 (GSE45864); KDM2B (GSE70624)  84  ; RUNX1, GATA3, TAL1, LMO1, TCF3 and TCF12 (GSE29181) 85 ; H3K27Ac, MED1 and MYB (GSE59657)  86  ; H3K4me3 (GSE35583)  87  ; Lamin (DamID, GSE94971)  88  ; RUNX1 (GSE42575)  89  ; TAL1 (GSE25000)  90  ; H3K4me3 and H3K79me2 (GSE60104)  91  ; PolII (GSE25494)  92  ; RUNX1, GATA3, H3K27Ac and CTCF (GSE68976)  93  ; MYC, BRD4 and CDK7 (GSE83777); RUNX1 and GATA3 (GSE76181)  94  ; CTCF (GSE12889) 95   ; H2AX (GSE25577) 96   ; UTX (GSE72300)  90  ; YY1 (GSE99521) 97.
Super-enhancer calling We used super-enhancer data for all activated CD4 + cell types CD4p_CD25-_Il17p_PMAstim_Th17 and CD4p_CD25-_Il17-_PMAstim_Th from dbSuper  41. To define super-enhancers using our own ChIP-Seq data we followed the same procedures as in dbSuper. Thus, we defined super-enhancers using HOMER software  40, 41  findPeaks with default parameters ('-style super') on our H3K27Ac peaks (peak finding described above). Briefly, peaks found within a distance of 12. 5 kb were stitched together into larger regions. Super-enhancer signal of each region was determined by the total normalized number of reads subtracted by normalized number of reads in the input peaks. Regions are sorted by score and super-enhancers are identified as regions with score higher than that defined by slope greater than 1.
Hi-C contacts Hi-C reads were mapped using BWA-MEM with the following options: '-P -k17 -U0 -L0,0 -T25'. Each read end was mapped independently. Genuine Hi-C contacts were validated with the Hi. C pipeline ( https://github. com/ezorita/hi. c ), using the following discard filters: (i) contact pairs with mapping quality below 10, (ii) self-circularized molecules and (iii) reads with inferred insert size greater than 2000 bp after digestion and ligation. Hi-C contacts were then binned at 5kb resolution and stored in HDF5 format using Cooler ( https://github. com/mirnylab/cooler ).
This work was supported by German Center for Infection Research (DZIF) Thematic Translational Unit HIV-1 04. 704 Infrastructural Measure to M. L, and to the Hector Grant "HiPNose: HiV Positioning in the Nuclear Space" to M. L and M. S. 101. Love, M. I. , Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). 102. Love, M. I. , Anders, S. , Kim, V. & Huber, W. RNA-Seq workflow: gene-level We acknowledge the financial support of the Spanish Ministry of Economy and exploratory analysis and differential expression. F1000Res. 4, 1070 (2015). Competitiveness ('Centro de Excelencia Severo Ochoa 2013-2017', Plan Nacional BFU2012-37168), of the CERCA (Centres de Recerca de Catalunya) Programme / 103. Chen, H. -C. , Martinez, J.
Inclusion/Exclusion Criteria 1,274 adult patients were identified in the CIBMTR registry who had undergone allo-HCT from 2000-2013 for CLL. After excluding patients who had undergone cord blood transplant (n=79) or unknown graft source (n=2), as well as those who had undergone syngeneic, other related, or partially matched donor transplants (n=229), or received GVHD prophylaxis including ex vivo T-cell depletion, CD34 selection or post-transplant cyclophosphamide (n=32), there were 932 patients remaining for analysis. An additional 112 were excluded due to missing HLA, graft source or follow-up data and 256 patients were excluded due to missing disease specific data regarding CLL. This left a total of 564 patients for the final analysis of whom 83 were identified to have the HLA combination of HLA-A1, non-A2 and non-B44 and 481 with other HLA allele combinations.
Sequence reads were mapped to the reference genome of ZIKV PE243 (GenBank accession: KX197192. 1). Reads aligning to the reference genome with zero or one mismatch and alignment length from 18 to 35 bp were selected for further analysis. Based on the orientation of alignment, mapped reads were categorized into two groups, mapping to the genome and antigenome. They were further aggregated according to length, and we plotted their distribution. We did not observe any difference in ZIKV-specific small RNAs between samples obtained from ZIKV-infected or mock-infected mosquitoes (Fig. 1 ). With the exception of a few isolated spots, no 21 nt ZIKV-specific vsiRNAs mapping across the ZIKV genome were detected at 3 and 7 days p. i. These few spots are most likely false positives, as they occurred in both mock-infected and virusinfected mosquitoes.
Briefly, small RNA libraries were submitted to quality control, adaptors were removed and the libraries filtered to remove reads containing ambiguous nucleotides. Small RNAs mapping to the genome reference of Cx. quinquefasciatus (version CpipJ2) were removed. Remaining reads greater than 15 nt were used to assemble longer contiguous sequences. Contigs larger than 50 nt were characterized by sequence similarity searches against GenBank followed by analysis of the size profile of small RNAs. Our analysis was performed with all 24 libraries (described above) obtained from Culex mosquitoes  [55]. In total, 46 039 contigs that did not map to the Culex genome were obtained from the 24 libraries (Table  S1 ). The large majority of contigs did not show any similarity to known sequences available in GenBank.
Funding information This study was partially supported by the European Union's Horizon 2020 research and innovation programme under ZIKAlliance grant agreement no. 734548 (A. K. , A. B. F. , J. M. , R. L. O. ) and under Marie Skłodowska-Curie grant agreement no. 661232 (M. V. ). Moreover, the project received support under the CAPES-COFECUB programme grant agreement no, 799-14 (A. B. F. , R. L. O. ), and the Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro grant agreement no. E-26/201. 335/2016 (R. L. O. ). This work was also funded by the UK Medical Research Council (MC_UU_12014) (A. K. ).
ACKNOWLEDGMENTS This work has been supported by ERA-NET NEURON, project "MICRO-MET" to PG and CV; Merck Serono Grant for Multiple Sclerosis Innovation (GMSI) to CV, and PG and MF as partners; Fondazione Cariplo, grant no. 2015-0910 to MF, Università degli Studi di Milano, piano di sostegno alla Ricerca 2015-2017 to MF; Fondation Roger de Spoelberch and Fondation Grace de Monaco to PG. The funders had no role in decision to publish, or preparation of the manuscript. The authors thank Dr Myriam Baes (KU Leuven) and Dr.
Patients and ethical aspects We studied a cohort of 16 patients (summarized in Table  1 ) with typical manifestations of ALPS (adenopathy, lymphadenopathy, anemia, thrombopenia, and splenomegaly). The patients carried various heterozygous germline TNFRSF6 mutations affecting the intracellular domain of the protein and were all registered in the French national primary immunodeficiency database (CEREDIH, Paris, France)  (16). As indicated in Table  1 , some patients were being treated with immunosuppressive treatments. Written informed consent (parental consent, in case of.
European Research Council (ERC PIDIMMUN no. 249816 to AF), Agence Nationale de la Recherche (ANR-14-CE14-0026-01 "Lumugene" to FR-L), Institut National du Cancer (INCa 2014-1-PL BIO-10-INSERM 5-1 to FR-L), Centre de Référence des Déficits Immunitaires Héréditaires (CEREDIH, to FR-L), Ligue National Contre le Cancer (To AM-C), and Fondation Maladies rares (to AM-C and to FR-L). This program has received a state subsidy managed by the National Research Agency under the "Investments for the Future" program bearing the reference ANR-10-IAHU-01 and IDEX Sorbonne Paris Cité (SPC/JFG/2013 -031 to FR-L).
the European Research Council (ERC PIDIMMUN no. 249816 to AF), Agence Nationale de la Recherche (ANR-14-CE14-0026-01 "Lumugene" to FR-L), Institut National du Cancer (INCa 2014-1-PL BIO-10-INSERM 5-1 to FR-L), Centre de Référence des Déficits Immunitaires Héréditaires (CEREDIH, to FR-L), Ligue National Contre le Cancer (To AM-C), and Fondation Maladies rares (to AM-C and to FR-L). This program has received a state subsidy managed by the National Research Agency under the "Investments for the Future" program bearing the reference ANR-10-IAHU-01 and IDEX Sorbonne Paris Cité (SPC/JFG/2013 -031 to FR-L).