dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by the  Medical Research Council UK  ( MR/K501293/1 ) and the  British Heart Foundation  ( PG/17/54/32981 ). A  Wellcome Trust Institutional Strategic Support Fund  to S. V. supported the parallel flow chamber work. H. E. C. was supported by a  Biotechnology and Biological Sciences Research Council  studentship and M. M. was supported by a  Biotechnology Biological Sciences Research Council-CASE studentship with GlaxoSmithKline  ( BB/R505651/1 ). A. G. R. and J. M. F. were supported by the  Medical Research Council UK  ( MR/K013386/1 ). L. M. C. is funded by  Cancer Research UK  core funding ( A23983 ). Lung slice microscopy was performed in the  Beatson Advanced Imaging Resource , supported by  Cancer Research UK  core funding ( A17196 ). T. S. is funded by  Japan Society for the Promotion of Science  Grants  JP15H05897 ,  JP15H05899 ,  JP17H03980 , and  JP19K22508  and  Japan Agency for Medical Research and Development  Grant  18gm0710002h0706.
01, Student's t test), and GAPDH intracellular staining in CD4 + mPTCL and healthy splenocytes analyzed by FACS. (G) mRNA expression levels (RNA-seq data) of GAPDH in LN from AITL and healthy donors (box plots represent the 25th and 75th percentiles with midlines indicating the median values; whiskers extend to the maximum and minimum values of the data samples; ***p < 0. 001, Student's t test). (H) Metabolic pathway analysis from RNA-seq data of the plck-GAPDH lymphoma versus WT splenocytes was performed using the KEGG database. *p < 0. 05; ns, not significant. See also Figures  S3  and S4 ; Tables  S1  and S2. (B) Active RHOA (Rho-GTP) was compared using a GST-rhotekin RBD pull-down assay using lysates of HEK293T transfected with WT and different RHOAinactivating mutants (T37M, T37A, T19N) or an RHOA-activating mutant (Q36L).
ACKNOWLEDGMENTS We acknowledge animal and confocal facilities at the  C3M (INSERM U1065, Nice, France). This work was supported by the  'Fondation ARC pour la Recherche sur le Cancer' and the 'Agence Nationale de la Recherche' (Labex SIGNALIFE   ANR-11-LABX-0028-01 ), the  Canceropole PACA, Institut National Du Cancer (INCA)  and  Conseil Regional PACA , and the  French Ministry of Research. L. M. was supported by  'la Fondation pour la Recherche Medicale (FRM) ' and ' Aides individuelles aux jeunes chercheur ville de Nice '. R. M. was supported by a PhD fellowship from the  French Ministry of Research. RNA-seq experiments were performed at the  UCAGenomiX (IPMC), Sophia-Antipolis, Nice, France. Sequencing at UCAGenomiX (IPMC) , a partner of the National Infrastructure France Genomique, was supported by  Commissariat aux Grands Investissements  ( ANR-10-INBS-09-03 ,  ANR-10-INBS-09-02 ) and  Canceropole PACA.
AITL Patient Data Public raw data AITL transcriptomes using Affymetrix HGU133 Plus 2. 0 microarrays were downloaded from GEO dataset (GSE58445  (Iqbal et al. , 2014) , GSE3526  (Roth et al. , 2006) : GSE7307; https://www. ncbi. nlm. nih. gov/gds) and ArrayExpress (E-TABM-783  (de Leval et al. , 2007) ; https://www. ebi. ac. uk/arrayexpress/), normalized together (RMA) and collapsed to HUGO gene symbols using chipset definition files. Expression data were normalized with z-score methods when specified and illustrated with heatmaps using R software (3. 3. 2). Statistical differences were verified using an unpaired two-tailed Wilcoxon signed rank test versus the specified controls. Metabolic pathway analysis was performed using the data base KEGG: https://www. ncbi. nlm. nih. gov/pubmed/10592173.
As noted above, several bacterial genes potentially involved in host resistance to stress conditions, adaptation and colonization have been identified close to theses insertion sites. We investigated the possible co-transcription of prophage and bacterial genes, by performing an in silico analysis with BPROM software, to detect promoters in the prophage ORFs directly upstream from the bacterial ORFs, or in the bacterial ORFs upstream from the prophage ORFs  (Solovyev and Salamov, 2011). This analysis revealed short intergenic regions (38-249 bp long) between the last prophage ORF (encoding a putative Tn3 transposon revolvase for IS A1, IS A3 and IS A4, and pknB for IS A2) and the first bacterial ORF downstream from the prophage (encoding a putative phosphatase (IS A1), ybjl (IS A3 and IS A4), or a hypothetical protein (IS A2), respectively).
ACKNOWLEDGMENT We are grateful to  Renaud Mahieux  and  Carole Passone  for critical reading of this review. This work was supported by grants from  INSERM ,  CNRS, Laboratoire d'Excellence consortium Inflamex   ANR-11-IDEX-0005-02  and the  Fondation pour la Recherche Médicale  (FRM grant number  DEQ20140329520  to A. L. ),  EFSD/JDRF/Lilly  and  EFSD/Lilly  to A. L. ,  Fondation Francophone pour la Recherche sur le Diabète  to A. L. ,  Agence National de la Recherche (Provide  and  Diab1MAIT  to A. L. ) and  Servier Medical Art.
Immunological Targeting of Human Embryos By confocal microscopy we observed targeting of healthy human embryos by complement. Auto-activation is the default of the alternative pathway of complement activation, thus making the expression and recruitment of regulators essential for human health. While the potent inflammatory role of complement has mainly been studied in the context of serum, it is well-established that complement components are found in mucosal secretions, e. g. , from cervix, uterus, and fallopian tubes  (22, (38) (39) (40). The presence of complement in the uterine compartment, alongside the data presented here, indicates that human embryos are targeted by complement in a physiological setting. This makes the observed expression of surface inhibitors and the recruitment of soluble regulators essential for the survival of the embryo already prior to implantation.
Reanalysis of Single-Cell RNA Sequencing Data on Human Oocytes and Blastomeres in the Pre-implantation Embryos Initial transcriptomics data were generated earlier  (26). Embryos utilized for single cell-RNA sequencing were collected and cultured as described. Individual blastomeres were obtained by laser-assisted biopsy, the ZP was removed and cells were placed in lysis buffer in individual wells of a 96-well plate. For singlecell RNA sequencing STRT was applied  (48). Three individual libraries were prepared from the total number of single cells; oocytes: 19 cells, zygotes: 29 cells, 4-cell stage blastomeres: 30 cells and 8-cell stage blastomeres: 21 cells, as described  (26). Expression levels were correlated to eight synthetic spike-in RNAs (ArrayControl RNA spikes Ambion, cat. no. AM1780)  (49).
Following amplification, the synthesized cDNA was sequenced on the Illumina platform, filtered, demultiplexed and trimmed as described  (26). Estimation of the ratio of transcripts per cell was done by comparing total reads to total spike-in RNA associated reads (all with sample-specific barcodes). Following pre-processing, the reads were aligned to human UCSC genome hg19, ArrayControl RNA spikes and human ribosomal DNA complete repeat unit (GenBank: U13369) by TopHat version 2. 0. 6  (45)  and annotated by genomic features. The aligned STRT reads were assembled by sample types using Cufflinks  (45)  and counted as TFEs, as described  (26).
Complement Gene-Expression Analysis A list of genes relevant to complement function was generated based on an exhaustive analysis of functional studies in the field  (1, 2, 27). The method of TFE-based quantification is implemented as open-source software (https://github. com/ shka/STRTprep). This method was used to identify expressed complement genes. In brief, the TFEs were defined by STRT RNAseq reads, which correspond to the 5 ′ -end of polyA-tailed RNAs. Therefore, TFEs not at the 5 ′ -UTR or the proximal upstream were excluded from the investigation of known protein-coding genes, as mRNAs suggested by these TFEs were less likely to have a methionine for translation of functional protein. Furthermore, TFE-based quantitation provides an advantage over normal sequencing approaches particularly in studies of pre-implantation development, where the gene-based quantitation methods also sum promiscuous (zygotic) RNAs and degraded (maternal) RNAs, which are less likely to translate into proteins  (29).
Confocal Image Processing Confocal images were processed using Fiji (http://fiji. sc) and Imaris (BitPlane, Oxford Instruments). Depending on the dataset, preprocessing consisted of applying a sliding-window averaging in the z dimension, denoising with Rolling Ball and smoothing by applying either a Gaussian filter or a 3D-Median filter (kernel 1). When necessary, DAPI signal was isolated from the background by applying a binary mask (obtained by local maxima functions) to the raw image to specifically render nuclei only. Fluorescence intensity profiles were obtained by averaging lines cropped in the inset (to smooth the noise) and are plotted as normalized to the maximum peak. 3D renderings were obtained using Imaris. and Sweden. All analyses were performed in Sweden. The full protocol was approved by the ethical committees in Switzerland (authorization CE2161 of the Ticino ethical committee, Switzerland) and in Sweden (Dnr 2010/937-31/4 of the Regional Ethics Board in Stockholm).
The spread of the main HIV-1 group M lineages to East Africa To quantify the evolution and dispersal patterns of the dominant HIV-1 lineages out of the DRC and into East Africa, we collated additional HIV-1 genetic data from 20 cities and villages across Burundi, Kenya, Rwanda, Tanzania, and Uganda, collected between 1996 and 2011 (Tables  1  and S3 ). For comparative purposes, we focused here on central and East African (CEA) data sets of unambiguously typed subtype C, A1 and D sequences (see the Materials and Methods section). Together, these subtypes account for a total of >84% of all HIV-1 infections in East Africa  [13]. All CEA data sets tested negative for inter-as well as intra-subtype recombination using the pairwise homoplasy index test (p-value > 0. 95). Of note, maximum likelihood (ML) phylogenies with available sub-Saharan sequences and the DRC sequences reported here (subtype C = 5304, subtype A1 = 2187, subtype D = 1210) show that the DRC sequences are predominantly basal to all sub-Saharan diversity (S2-S4 Figs).
Discussion Whether the rapid expansion of HIV-1 subtype C has been the result of ecological or evolutionary circumstances has been a matter of debate. We investigated the evolutionary origins of the HIV-1 group M diversity by analysing pol data from the DRC with data from eastern Africa. We find that the emergence of HIV-1 subtypes resulted from two distinct scenarios (i) where ancestral lineages from Kinshasa seeded epidemics elsewhere, e. g. , subtype C in Mbuji-Mayi and subtype H in Matadi, and (ii) the emergence and circulation of subtypes within Kinshasa such as sub-subtype A1, and subtypes D, G and F1 (Fig  1 ). For scenario (i), two contrasting epidemiological outcomes seem to occur, depending on the remoteness/accessibility of the location where different lineages first emerged. For example Mbuji-Mayi, where subtype C emerged, was a thriving, well-connected, economic hub with the largest production of diamonds in the world, which attracted migrants from Lubumbashi and from other countries, such as Zambia and Zimbabwe  [28].
These connectivity patterns agree with the strongly supported virus lineage migration events we observe along the northern highway. Moreover, we estimated that the epidemic wavefront of subtype C travelled faster compared to other co-circulating lineages. These findings were robust to the type of model-based phylogeographic reconstruction. Our posterior estimates are directly comparable among subtypes because sequences were sampled across the same geographic range. However, we note that these estimates are dependent of the scale of sampling  [36]  and it may be more problematic to compare these spatial invasion rates to HIV-1 datasets collated from different geographic regions and sampling regimes. Our study represents the largest survey of group M's pol gene sequence diversity in the DRC and our results provide further support for an origin of group M in Kinshasa  [2].
Estimating temporal and spatial signal Multiple sequence alignment was performed using MAFFT v7  [90]  and manually curated using Se-Al (http://tree. bio. ed. ac. uk/software/seal). Maximum likelihood (ML) phylogenies were reconstructed in FastTree v. 2 using the GTR+4Г nucleotide substitution model  [91]. A regression analysis  [92]  was used to determine the correlation between sampling dates and divergence to the root of midpoint rooted maximum likelihood (ML) CEA phylogenies (S6 Fig). To assess whether virus populations were structured per country compartmentalization analyses were performed using tree-based methods such as the association index estimated using a posterior distribution of phylogenies in BaTS  [93]  and estimated using maximum likelihood Simmond's Association Index (AI)  [94]  implemented in HyPhy  [95].
Checking for recombination To check for inter-and intra-subtype recombination, we applied the F-test  [96]  implemented in the program SplitsTree 4  [97]. The F-test is based on a pairwise homoplasy index (PHI), which is a measure of the similarity between closely linked sites. In the test, the level of significance of this statistic is tested by permuting the sites. The rationale behind this permutation procedure is that under the null hypothesis of no recombination, the genealogical correlation between sites is not altered by such permutations because all sites are linked and share the same evolutionary history  [96].
We jointly estimated the expected number of country and location changes along the branches of the posterior tree distribution using a "robust counting" approach implemented in BEAST v1. 8. 4  [32] [33] [34]. Specifically, we inferred on a branch-by-branch basis the history of virus movement between each pair of countries and each pair of locations. We used the R package "circlize"  [108]  to summarise the estimated number of migrations between countries in the form of circular plots. We also used an in-house script to estimate the proportion of within-country virus lineage movement over time. The script takes an input the "robust counting" files from BEAST v1. 8. 4, i. e. a tab-delimited file with three columns (source location, sink location, estimated date of virus migration), and a tab delimited file containing 2 columns (location, country), and generates the proportion of virus lineage movement within a single country over time (available from the authors upon request).
Framework Programme (FP7/2007-2013) / ERC grant agreement no. 268904 -DIVERSITY. The research leading to these results has received funding from the European Research Council under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 725422-ReservoirDOCS). MAS and PL acknowledge funding from the Wellcome Trust Collaborative Award, 206298/Z/17/Z and the European Research Council award ReservoirDOCS. MAS acknowledges funding from the National Science Foundation through grant DMS 1264153 and the National Institutes of Health through grants AI107034 and AI135995. ACPP was supported by European Funds through grant 'Bio-Molecular and Epidemiological Surveillance of HIV Transmitted Drug Resistance, Hepatitis Co-Infections and Ongoing Transmission Patterns in Europe (BEST HOPE) (project funded through HIVERA: Harmonizing Integrating Vitalizing European Research on HIV/Aids, grant 249697).
AJT is supported by funding from the Bill & Melinda Gates Foundation (OPP1182408, OPP1106427, 1032350, OPP1134076), the Clinton Health Access Initiative, National Institutes of Health, a Wellcome Trust Sustaining Health Grant (106866/Z/15/Z), and funds from DFID and the Wellcome Trust (204613/Z/16/Z). TFRW received funding from NOW-WOTRO and the Netherlands Regional AIDS Program in Southern Africa. NN received funding from Abbott (ISR#212620). GB acknowledges support from the Interne Fondsen KU Leuven / Internal Funds KU Leuven under grant agreement C14/18/094, and the computational resources and services provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation -Flanders (FWO) and the Flemish Government -department EWI. OGP received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007 -2013)/ERC grant agreement no.
614725 -PATHPHYLODYN. PL acknowledges support by the Special Research Fund, KU Leuven (Bijzonder Onderzoeksfonds, KU Leuven, OT/14/115), and the Research Foundation -Flanders (Fonds voor Wetenschappelijk Onderzoek -Vlaanderen, G066215N, G0D5117N and G0B9317N). SD was supported by the Wiener-Anspach Foundation and the Fonds Wetenschappelijk Onderzoek (FWO, Belgium), and is currently funded by the Fonds National de la Recherche Scientifique (FNRS, Belgium). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: Rebecca Rose is employed by a commercial company, Bioinfoexperts, LLC.
Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. 268904 -DIVERSITY. The research leading to these results has received funding from the European Research Council under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 725422-ReservoirDOCS). MAS and PL acknowledge funding from the Wellcome Trust Collaborative Award, 206298/Z/17/Z and the European Research Council award ReservoirDOCS. MAS acknowledges funding from the National Science Foundation through grant DMS 1264153 and the National Institutes of Health through grants AI107034 and AI135995. ACPP was supported by European Funds through grant 'Bio-Molecular and Epidemiological Surveillance of HIV Transmitted Drug Resistance, Hepatitis Co-Infections and Ongoing Transmission Patterns in Europe (BEST HOPE) (project funded through HIVERA: Harmonizing Integrating Vitalizing European Research on HIV/Aids, grant 249697).
The data for the latter amide have been previously presented  [23]. The structure of the compounds was confirmed by NMR spectroscopy. The 13 C-NMR spectra of azepanes 2, 5 and 8 showed signals of C-3 at dC 62-63 ppm. For aldimines 7-9, the signals of the -CH=N-function were detected at δ 169-173 ppm ( 13 C-NMR) and at δ 7. 3-7. 8 ppm ( 1 H-NMR). In the spectra of compound 10, the signal of amide function C(O)NH was detected at δ 178. 9 ppm ( 13 C-NMR) and at δ 6. 61 ppm ( 1 H-NMR). Finally, the presence of polyamine moieties at position C-28 of the triterpene core in the structure of compounds 7-11 was confirmed by the characteristic signals of the aminomethylene groups -CH2NH-at δ 2. 58-3. 70 ppm ( 1 H-NMR).
Flow cytometry analysis A phenotypic analysis was performed during cell sorting. Additional phenotypic analyses of co-receptors and restriction factors were performed ex vivo on thawed PBMC from 7 HIV-2 infected individuals and donors either directly after thawing (D0) or after cell culture. The D0 cell surface staining was performed on thawed cells with: Fixable Viability-Stain-780, anti-CD3-BV510, -CD4-BUV395, -CD45RA-PE, -CCR7-PE-Cy7, -CD27-BUV737, -CD25-BV786, -HLA-DR-BV786, -CD69-BV786, -PD1-APC, -CXCR5-APC-R700, -CCR5-PE for expression on total CD4 cells or -CCR5-BV650 for expression on cell subsets (Becton-Dickinson). Cells were analyzed on a Fortessa flow cytometer (Becton-Dickinson) using the FacsDIVA version 6. 1. 3 software. The CD32, CXCR6 and TRIM5α expression was additionally studied at D0 after staining with: Viability FSV620, anti-CD3-APC-H7, -CD4-BV510, -CD45-RA-FITC, -CD27-PE-Cy7, -CCR7-APC-R700, -PD1-BB700, -CXCR5-AF647, -CXCR6-BV421, -CD32-PE (Becton-Dickinson).
Sample premix (Fast EvaGreen-Supermix; Bio-Rad, Marnes la Coquette, France) and assay premix were prepared for 96. 96 Fluidigm Dynamic arrays (Fluidigm, San Fransisco, CA, USA). Two 96. 96 Dynamic Array primed chips were then loaded and the preamplified prediluted cDNA samples. A real time-PCRs was run on a BioMark HD System for Genetic Analysis according to the Fluidigm Protocol. Invalid reactions, determined using the Real-time PCR-Analysis Software (Fluidigm), were treated as missing data. Data analysis of raw-Cq values were processed with Partek Genomics-Suite (Partek, Saint-Louis, Missouri, USA) as follows: i) principal-component analysis (PCA) was performed; ii) data normalization was completed by subtracting the value of the house-keeping gene RPS14 to the value of the target genes, iii) comparisons between TN, TCM, TTM and TEM subsets were done.
Acknowledgments We would like to thank the members of the French HIV-2 cohort ( ANRS CO5 HIV-2 ), which is supported by a grant from the  ANRS  (" France Recherche Nord & Sud Sida-HIV Hepatites ") and sponsored by  INSERM-ANRS. We would like to thank the members of  ANRS CO5 IMMUNOVIR-2 Study Group :  Victor Appay ,  Brigitte Autran ,  Francoise Brun-Vezinet ,  Nathalie Chaghil ,  Charlotte Charpentier ,  Remi Cheynier ,  Diane Descamps ,  Anne Hosmalin ,  Gianfranco Pancino ,  Nicolas Manel ,  Lucie Marchand ,  Sophie Matheron ,  Fideline Collin ,  Livia Pedroza-Martins ,  Asier Sàez-Cirion ,  Assia Samri ,  Rodolphe Thiebaut , and Vincent Vieillard We wish to thank participants. We are grateful to  Ioannis Theodorou ,  Sabine Canivet  and  Marie-Line Moussalli  for HLA typing. We thank  Benedicte Charmeteau ,  Suzanne Figueredo  and  Stephane Isnard  for sample management.
The IL-15Rα chain is a single transmembrane protein, whose distal domain, also called sushi domain, is required for IL-15 binding  [9]. IL-15Rα can be expressed depending on the activation status of the cells. Thus, IL-15 can bind to the IL-2Rβ/γ c dimeric receptor as well as to a high affinity IL-15Rα/IL-2Rβ/γ c trimeric receptor. IL-15Rα can also be expressed by an IL-15 producing cell (macrophages, dendritic cells, epithelial cells). In that context, IL-15Rα binds and presents in trans IL-15 to a neighboring cell expressing the IL-2Rβ/γ c complex  [2]. At the 3D level, crystallographic data (PDB codes: 2Z3Q and 4GS7) of the IL-15/IL-15Rα complex exist, respectively with resolutions of 1. 8 and 2. 3 Å  [10, 11]. These data revealed that IL-15 residues located on B helix and AB and CD loops established key interactions with IL-15Rα.
Both IL-2 and IL-15 bind heterodimers formed by the IL-2Rβ and the γc chains, inducing the activation of the JAK/Stat pathways  [8]. Each of the cytokines has its own specific α-receptor subunit, namely, IL-2Rα and IL-15Rα, respectively. The IL-15Rα chain is a single transmembrane protein, whose distal domain, also called sushi domain, is required for IL-15 binding  [9]. IL-15Rα can be expressed depending on the activation status of the cells. Thus, IL-15 can bind to the IL-2Rβ/γc dimeric receptor as well as to a high affinity IL-15Rα/IL-2Rβ/γc trimeric receptor. IL-15Rα can also be expressed by an IL-15 producing cell (macrophages, dendritic cells, epithelial cells). In that context, IL-15Rα binds and presents in trans IL-15 to a neighboring cell expressing the IL-2Rβ/γc complex  [2]. At the 3D level, crystallographic data (PDB codes: 2Z3Q and 4GS7) of the IL-15/IL-15Rα complex exist, respectively with resolutions of 1.
Structure Preparation The initial coordinates for the molecular modeling study were extracted from the crystallographic data available on the Protein Data Bank for the IL-15/IL-15Rα binary complex (PDB ID: 2Z3Q)  [10]  and for the IL-15/IL-15Rα/IL-2Rβ/γc quaternary complex (PDB ID: 4GS7)  [11]. Starting from these structures, four relevant combinations of IL-15 complexed with its receptors were built, in order to assess the influence of the different receptors on the overall IL-15 conformation. These systems were: the monomeric IL-15 form, the IL-15/IL-15Rα binary complex, the IL-15/IL-2Rβ/γc ternary complex and the full IL-15/IL-15Rα/IL-2Rβ/γc quaternary complex. Apart from the model consisting of IL-15 and its IL-15Rα, which was built starting from the 2Z3Q PDB file, all the other models were built starting from the 4GS7 PDB tetrameric structure.