dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequence preprocessing The first step performed with the fastq data was the phiX control library cleaning, as it was spiked into every run. Each demultiplexed pool was mapped to an Enterobacteria phage phiX174 reference genome (NC_001422. 1) using bowtie2 with default parameters. All read pairs that mapped concordantly were removed from the sample. Subsequently, PEAR v. 0. 9. 11  (Zhang, Kobert, Flouri, & Stamatakis, 2014)  was used to merge the paired-end reads (parameters: -v 30 -n 50). The RAD_digestion. py script was used to redigest the samples according to ddRAD (options: --dd --rad -q) to account for any undigested or chimeric sequences. To remove non-targeted loci derived from star activity of the enzymes, sequences with incorrect restriction sites at either end of the locus (MseI and EcoRI, respectively) were discarded using the script checkRestrictionSites.
Definition of the R2SCO term We have designed a locus catalog pipeline that is based on two of the most utilized pipelines for RAD data -Stacks and ipyrad -but that incorporates information about coverage from merged sequences based on fragment size and uses the entire sequence as a putative allele. At the end of this pipeline, loci with accepted genotypes are considered as the set of reduced-representation single-copy orthologs (or R2SCOs, pronounced "Artuscos"). The idea to form a set of R2SCOs was inspired on BUSCO (Benchmarking Universal Single-Copy Orthologs,  Seppey, Manni, & Zdobnov, 2019) , but instead of representing orthologous single-copy coding genes among species, an R2SCO represents a set of orthologous loci produced by a certain combination of enzymes and a specific size range.
Reference genome evaluation CheMyd_1. 0_DNAzoo was used in different types of tests. First, the genome was digested using the RAD_digestion. py script and the sequence fragments were size-selected according to the R2SCO pipeline. Size-selected sequences were compared pairwise and clustered using Tances, as described above for the empirical data. Any sequences forming clusters represent putative paralogs in the genome considering only the ddRAD simulated and size-selected sequences (not the entire genome). The putative single-copy and paralogous loci were compared with the locus classification obtained through the R2SCO pipeline for the two individuals of C. mydas (Cm1 and Cm2). The clusters obtained for the genome were also used to evaluate the identity among potential paralogs, obtained via the pairwise identity comparison performed using vsearch v.
2. 8. 6 (option: --allpairs_global; Rognes, Flouri, Nichols, Quince, & Mahé, 2016) with an identity defined by Tances (here 0. 90). Statistical analysis and visualization was performed in Python v. 2. 7. 13  (Rossum, 1995)  using the biopython library v. 1. 68 (Cock et al. , 2009)  and in R v. 3. 6. 1  (Team & Others, 2013)  using the packages ggplot2  (Wickham, 2016) , gplots  (Warnes et al. , 2015) , circlize  (Gu, Gu, Eils, Schlesner, & Brors, 2014)  and gridExtra  (Auguie, Antonov, & Auguie, 2017).
S8 ). The putative alleles are compared to each other exhaustively (i. e. pairwise for all possible combinations), and a new script was designed to build clusters based on the pairwise alignments above the ancestral threshold. The exhaustive search for identity connections guarantees reproducibility of alleles clusterization within and between individuals. Since the data was extremely reduced to a set of high-quality and high-coverage sequences, the extended computational time demanded for exhaustive searches became a minor issue. We also performed the pairwise comparisons including putative alleles from all 10 individuals at the same time, and the alignment results were used for all intra-individual, intraspecific and interspecific analyses later on. We have also selected identity and coverage thresholds in an innovative way, using the identities generated by the pairwise comparisons to help establish an ancestral as well as intraspecific thresholds.
Possible adjustments for future libraries and analysis limitations Our data revealed significant carryover of small fragments in the size selection attempted in the lab, as also seen by  daCosta & Sorenson (2014). We believe that this is due to a combination of enzyme choice and the protocol used. MseI-EcoRI generates high amounts of small fragments (Fig. S4 ), mostly due to the fact that MseI is a 4-bp cutter rich in AT (recognition site: AATT) and cuts the genome with a very high frequency. Moreover, we have performed the indexing PCR before size selection, therefore including all digested fragment sizes in the 10-cycle-amplification. As amplification of sequencing libraries tends to favour smaller fragments  (Dabney & Meyer, 2012) , we believe that the number of small fragments increased so dramatically for some libraries that even precise machines like the PippinPrep did not manage to remove them all.
, 2014). Green et al. (2014)  found low levels of heterozygosity in all three crocodilian genomes they analyzed when compared to avian and mammalian genomes. The least variable species, the American alligator Alligator mississippiensis, presented heterozygosity levels around 0. 01%, which is at least three times higher than what we found for L. olivacea and D. coriacea, and comparable to E. imbricata, which presented the second highest heterozygosity levels in our study (Figure  11 ). The reduced genetic diversity observed for L. olivacea and D. coriacea compared to other species is concordant with their lower haplotypic diversity based on mitochondrial control region data for worldwide populations  (Reid, Naro-Maciel, Hahn, FitzSimmons, & Gehara, 2019). Assuming that the consistency across locus numbers and heterozygosity levels in our dataset make values comparable across species, we can evaluate the genetic distances among sea turtle species.
Acknowledgements This work was supported financially by the  Alexander von Humboldt Foundation , the  Leibniz Institute for Zoo and Wildlife Research  and the  ANTIDOT  project ( Pépinière Interdisciplinaire Guyane, Mission pour l'Interdisciplinarité, CNRS ). All sequencing and computer infrastructure utilized was part of the  BeGenDiv Consortium. We are grateful to the  Brazilian sea turtle conservation program Projeto TAMAR  for collecting and providing part of the samples. We are similarly grateful to  Prof. Fabricio Santos , who provided some of the Brazilian samples. We are also grateful for the sequencing lab support of  Susan Mbedi.
0001] versus 17% [P ϭ 0. 0045] for 5-25), but not with the isotype control (Fig. 3B ). In agreement with this, overlapping virus-antibody fluorescent signals were more frequently detected with 3BNC117 than with 5-25 (45% versus 16% [P ϭ 0. 0077]) and in less than 1% with the IgG control (Fig. 2B ). As for HIV-1 and antibodies alone, virions in complex with 5-25 and 3BNC117 were located in the cytoplasm and distributed across the endothelial cell depth (Fig. 3A  and C ). As we observed intracellular fluorescent clusters of HIV-1 with anti-gp160 IgGs by confocal microscopy, we next determined whether HIV-1 transcytosed alone and in the presence of antibodies was still infectious using TZM-bl reporter cells. Infectivity data normalized for virus input by p24 content revealed that the viral transit through hCMEC/D3 endothelial monolayers substantially decreased HIV-1 infectivity compared to virions that only crossed the membrane without hCMEC/D3 cells, with an average drop of 1.
z-stack confocal acquisitions were performed on a Leica TCS SP5 microscope with a HCXPLAPO 63ϫ oil objective, NA 1. 4, at the UTechS PBI platform (Imagopole, Institut Pasteur). Channel alignments were performed with 0. 1-mm tetraSpeck microspheres (blue, green, orange, and dark red; Thermo Fisher Scientific) to control and correct for the chromatic shift. Data were then analyzed after deconvolution using the object analyzer advanced modules of the Huygens Professional software (v14-10; SVI) at the PBI platform (C2RT, Institut Pasteur). For three-dimensional (3D) reconstitutions, Imaris 64 software (v 9. 2. 0; Bitplane) was used at the Image Analysis Hub (Institut Pasteur). In vitro HIV-1 infectivity assay. Infectivity of transcytosed HIV-1 virions in the presence or absence of anti-gp160 or isotypic control IgG antibodies was measured using the TZM-bl cell assay.
ACKNOWLEDGMENTS We thank the  NIH AIDS Reagent Program  ( Division of AIDS, NIAID, NIH ) for contributing reagents. We also thank the  UTechS Photonic BioImaging/C2RT  supported by the  French National Research Agency  ( ANR-10-INSB-04-01 , Investments for the Future), by  Conseil de la Région Ile-de-France  ( Domaine d'Intérêt Majeur   DIM1HEALTH ), and by  Fondation Française pour la Recherche Médicale  (Program Grands Equipements), and the  Image Analysis Hub/C2RT. We thank the  Agence National de Recherche sur le SIDA et les hépatites virales (ANRS)  for an equipment grant. H. M. received core funding from the  Institut Pasteur , the  INSERM  and the  Milieu Intérieur program  ( ANR-10-LABX-69-01 ). This work was funded by the  European Research Council (ERC) - Seventh Framework Program  ( ERC-2013-StG 337146 ), and  Gilead Sciences -HIV Cure Grants  (no.
PubMed, SciELO, and Google Scholar databases were queried for any article published between January 1, 2014, and December 31, 2019, with keywords "Malaria," "gold," "mine," "Amazon," and "America. " This search was then completed with data from the National Surveillance Systems, when available (sources: WHO, PAHO, Ministries of Health (MoH)). All countries and territories belonging to the Amazon region that reported malaria cases in gold mining areas over the same time period were considered, namely Brazil, Colombia, Guyana, French Guiana, Peru, Suriname, and Venezuela, even though the mining areas themselves might not all be located strictly in the Amazon forest.
SARS-CoV-2 induces activation and diversification of primary human pDC In order to efficiently recapitulate SARS-CoV-2-pDC interactions, we used two strains of SARS-CoV-2 primary isolates. Their viral genome sequences were nearly identical with 99,98% identity. Sequence comparison with reference strain Wuhan-Hu-1 (NCBI accession number NC_045512. 2) showed that both strains contain a subset of mutations (C241T; C30307T; A23403G and G25563T), characteristic of the GH clade based on GISAID nomenclature. Human primary pDC were purified from healthy donor (HD) peripheral blood mononuclear cells (PBMC) by cell sorting. First, we asked whether SARS-CoV-2 was able to induce pDC activation, and diversification into IFN-producing and/or T cell stimulating effectors, as we previously described for influenza virus A (Flu)  (Alculumbre et al.
Flow cytometry analysis To sort pDC, cells were stained with zombie violet or BUV fixable viability dye For ACE2 cell surface expression, indicated cells were incubated with a goat antihuman ACE2 polyclonal Ab (5 μg/ml; AF933 Biotechne) in 100 μl of PBS with 0. 02% NaN3 and 5% FBS for 1 h at 4°C. Cells were then washed and incubated with an Alexa 647-conjugated secondary antibody (Jackson ImmunoResearch) for 30 min at 4°C. Acquisition was performed on an Attune NxT Flow Cytometer (Thermo Fisher Scientific) or a LSR Fortessa (BD Biosciences), and analysis was done by using FlowJo software (Tree Star). Flow cytometry analyses were performed at flow cytometry core facility of IRSL (Paris, France).
Secreted inflammatory cytokines measurement PDC cytokine production of IFN-α2, IL-8, IL-6, IP-10 and TNF-α, was measured in culture supernatants using BD cytometric bead array (CBA), according to the manufacturer's protocol, with a 20pg/ml detection limit. Acquisitions were performed on a LSR Fortessa (BD Biosciences), and cytokine concentrations were determined using FCAP Array Software (BD Biosciences). The concentration of secreted IFN-λ1 was measured by enzyme-linked immunosorbent assay (ELISA) (R&D Systems, DuoSet DY7246), according to the manufacturer's instructions. The manufacturer reported neither cross-reactivity nor interference with IFN-α, IFN-β 1a, IL-10Rβ, IFN-λ2 and λ3, and IL-28Rα. The optical density value (OD) of the supernatants was defined as its absolute OD value, minus the OD Absorbance from blank wells.
Samples were centrifugated at 4,000 x g for 10 min then filtered using a 0. 45 μM filter, and diluted 1:1 with DMEM-4% (DMEM supplemented with 4% FBS, 1% penicillin-streptomycin, 1% GlutaMAX and 25 mM Hepes). Vero E6 cells were seeded in 96-well cell culture plate (15,000 cells/well), and incubated at 37°C with 200μl of inoculum and observed daily for cytopathogenic effects (CPE) by light microscopy. Substantial CPE were seen at 72-96 hours post inoculation. Culture supernatants were then collected, clarified by centrifugation, filtered using a 0. 45 μM filter and kept at -80°C. We confirmed SARS-CoV-2 replication by RT-qPCR and whole viral genome sequences were obtained by next generation sequencing using Illumina MiSseq sequencers. Strains sequences have been deposited in the Global Initiative of Sharing All Influenza Data (GISSAID) database with accession ID EPI_ISL_469284 (220_95) and EPI_ISL_469283 (211_61).
Acknowledgments Ali Amara 's lab received fundings from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant n°ANR-10-LABX-62-IBEID), the  Fondation pour la Recherche Medicale  (grant  FRM -EQU202003010193 ), the  Agence Nationale de la Recherche (ANR-FRM  FLASH  COVID  project  IDISCOVR ),  University of Paris (Plan de Soutien  Covid-19:  RACPL20FIR01-COVID-SOUL ). Vassili Soumelis ' team was supported by  Agence Nationale de la Recherche  ( ANR-17-CE15-0003 ,  ANR-17-CE15-0003-01 ), and by  Université de Paris « PLAN D'URGENCE   COVID19  ». The.
To capture the molecular events induced by TCR engagement in a hypothesis-free and site-specific fashion, briefly expanded T cells (as described above) were left unstimulated or stimulated via their TCR for 15, 30, 120, 300, or 600 s prior to phosphoproteomic analysis (Fig  1A ). Four independent experiments were performed in which phosphopeptides were enriched using TiO 2 beads and analyzed by LC-MS. In three of these experiments, an additional enrichment step using phosphotyrosine immunopurification (pY-IP) was introduced to achieve a better mapping of pY peptides. Therefore, the TiO 2 data set and the pY-IP data set correspond, respectively, to 24 and 18 independent samples, each analyzed in triplicate nano-LC-MS runs. These led to the identification of 13,009 unique phosphorylated peptides corresponding to 9,702 and 560 phosphosites-or combination of sites in the case of multiple phosphorylated peptides-in the TiO 2 and the pY-IP data set, respectively  (Fig EV1B).
From these peptides, we were able after filtering (PEP value > 0. 01, localization score > 75%, elimination of sites with high number of missing values; see Materials and Methods) to relatively quantify 7,180 phosphorylation sites or combinations of sites (Dataset EV1). These correspond to 6,984 unique, non-redundant sites (Fig  1B  and C ), including 5,659 phosphoserines (pS), 1,070 phosphothreonines (pT), and 254 pY present on 2,118 proteins in primary T cells. The analysis of the number of phosphosites present per protein indicated that although some proteins were phosphorylated on more than ten residues (e. g. , ACIN1, ADD3), 77% of phosphoproteins displayed between one and three phosphosites  (Fig EV1C). We checked whether the probability of detection of phosphopeptides could be affected by the corresponding protein abundance or protein length.
For example, multiple phosphorylations were detected on the ARFGEF1/2 proteins involved in vesicular trafficking and maintenance of the Golgi structure. As highlighted by annotations, high occurrences of constitutive phosphorylations were also observed on transcriptional repressors (NCOR1/2, SIN3A, HDAC1/ 4/5) involved in methylation and histone deacetylation leading to the formation of repressive chromatin structures. This data set contains more than 1,000 phosphosites that were not previously reported in the literature or in the dedicated database Phos-phoSitePlus (www. phosphosite. org), accounting for 14% of our global data set (Fig  1B  and C ). Collectively, these results constitute an extensive and quantitative resource mapping phosphosites specific to primary CD4 + T cells.
Identification of TCR-regulated phosphorylation sites We next sought to identify sites whose phosphorylation was regulated by TCR stimulation. With this aim, we set up a workflow based on label-free relative quantification of intensities across all time points, followed by an ANOVA-based statistical analysis (see Materials and Methods). In brief, the phosphosites presenting wellresolved kinetics (a minimum of two biological replicates with three common time points) were considered regulated when they presented an absolute fold change ≥ 1. 75 associated with a corrected P-value ≤ 0. 05 between two time points (Dataset EV1). With this approach, we identified 731 phosphorylation sites (or combination of sites) that were regulated in the course of TCR stimulation, including 611 pS, 77 pT, and 52 pY (Fig  2A  and B ).
A Experimental workflow to quantify phosphorylations occurring at the early time points after TCR stimulation. Digested peptides were analyzed by MS before further enrichment to control the impact of TCR stimulation on protein abundances (proteome). Further steps of TiO 2 enrichment and phosphotyrosine immunoprecipitation (pY-IP) were conducted to quantify changes in the phosphorylated serines and threonines (pST phosphoproteome), as well as phosphorylated tyrosines (pY phosphoproteome), respectively (see the Materials and Methods section for more details). Schematic representations of the measured phosphokinetics are presented on the right. B, C Phosphosites identified after TiO 2 enrichment (B) and phosphotyrosine immunoprecipitation (pY-IP) (C) Left panel: pie chart presenting the proportion of identified phosphorylated serines (pS), threonines (pT), and tyrosines (pY) in the two data sets.
Right panel: cumulative number of unique identified phosphosites across the biological replicates (3 and 4 independent experiments for the TiO 2 and pY-IP, respectively). Phosphorylated serines, threonines, and tyrosines are presented individually next to the total number of identified sites (pSTY). The phosphosites that were not previously reported in any species in the PhosphoSitePlus database (www. phosphosite. org) are reported in isolation in the graph on the right. activation at these early time points  (Figs 1A and EV2B, and Dataset EV2)  and that changes in phosphopeptide quantities directly reflected phosphorylation or dephosphorylation events. The analysis of annotations (UniProt keywords) enriched within the set of regulated phosphosites compared to the set of phosphosites of the total data set revealed that proteins associated with cytoskeletal organization ("Cytoskeleton", "Actin-binding", and "Synapse"), ion transport ("Calcium transport", "Chloride channel") and with specific protein domains ("SH3 domain", "SH2 domain") were impacted by TCR stimulation  (Fig EV2C).
Noticeably, among the 730 phosphorylation sites that we identified as significantly regulated according to the criteria described above, some exhibited very strong regulation, with fold changes of high amplitude (shown as a list in Dataset EV1). Additionally, Fig  2C  shows a schematic representation of phosphosites found to be significantly regulated following TCR stimulation in this study, and known from the literature to be functionally related to this pathway (also see Dataset EV1). It covers many of the major previously reported regulators of T-cell activation.
Profiling kinase activities in the course of TCR stimulation To gain insights into the dynamics of kinase activities underlying TCR-mediated cell activation, we mapped kinase-substrate relationships from the PhosphoSitePlus database, which provides a data set containing experimentally determined substrates, sequences, cognate kinases, and metadata curated from the literature (www. phosphosite. org/). We restricted our analysis to kinases that could be identified in our proteome of CD4 + T cells  (Voisinne et al, 2019). Analysis of substrate enrichment per cluster revealed significant associations between activity of kinases and specific clusters, thereby reflecting the temporal dimension in which kinases operate (Fig 4A ). For example, substrates corresponding to ERK and AKT ◀ Figure  3. Enrichment and dynamics of cellular processes triggered by early TCR signals.
D Similar representation as in (C) for the terms "Tyrosine-protein kinase", "Serine/threonine-protein kinase", and "Receptor". E Illustration of the transcription factors for which activation through nucleus-cytoplasmic shuttling is regulated by phosphorylation. The normalized dynamics/ intensities of the corresponding phosphosites are indicated on the right panel (as in C, D). More specifically, we identified phosphorylation of STMN1, CEP55, DLGAP5 and ACLY, DNMT1, and FOXO3, known to be specific to the ERK and AKT signaling axis, respectively, although some of these kinase-substrate relationships have yet to be confirmed in primary T cells. Interestingly, phosphorylation of Pdcd4-S457, Bad-S155, and Rps6-S236/379 is indistinctly attributed to the AKT or the ERK axis in the literature and databases  (Fig 4C).
This observation can reflect a crosstalk between the two signaling pathways or alternatively, may be due to the diversity of cell types or engaged receptors analyzed and reported in public databases. To better characterize these sites and refine the specificity of the signals associated with TCR engagement, we treated T cells with specific MEK (U0126) or AKT1/2 (AKTi VIII) inhibitors and analyzed phosphorylation of their substrates  (Fig 4D). As expected, phosphorylation on specific sites of MEK substrates such as ERK1/2-Y204/T202 and RSK1-S369/S377 was selectively inhibited by U0126, while treatment with AKTi VIII specifically reduced FOXO3-S252 and AKT-T308 phosphorylations. Interestingly, in these inhibitory conditions, phosphorylation of PDCD4-S457 was strongly reduced only by U0126, indicating a predominant dependence on the ERK signaling pathway for this site upon TCR activation in primary T cells  (Fig 4D).
The ITSN-short (ITSN-S) is composed of two Eps15 homology (EH) domains and a central coiled-coil domain followed by a series of SH3 domains. The longer form (ITSN-L) possesses an extended C-terminal part encoding for an exchange factor (DBL), a pleckstrin (PH), and a C2 domain. Although ITSN2 is ubiquitously expressed, the exploration of mouse public databases showed that it is preferentially expressed in B and T lymphocytes (www. immgen. org). In B cells, ITSN2 is required to promote germinal center formation and antibody production to influenza virus infection  (Burbage et al, 2018). In addition, we also detected this protein in the interactomes of the CBL and CBL-B ubiquitin ligases in primary T cells, suggesting its involvement within the TCR signaling pathway  (Voisinne et al, 2016).
5. IQR to Q3 + 1. 5 IQR. Outliers are shown as black dots. C Proliferation of OT-I Cas9-EGFP CD8 + T cells transfected with EGFP or with ITSN2 sgRNA activated for 48 h with N4 peptide MHC tetramers (0. 01-0. 1 nM) in the presence or absence of soluble anti-CD28 antibody or with PMA and ionomycin (PMA/Iono) or with IL-7. Data are presented as mean AE SD from four proliferative measures of two independent sgEGFP and sgITSN2 transfections. Data are representative of three independent experiments. D IFN-c production of cells treated as in (C) was assessed by intracellular staining after 24 h of stimulation. E Proliferation of similar cells as in (C) activated for 48 h in vitro with a range of increasing doses of N4 peptide MHC tetramers in the presence of soluble anti-CD28 antibody. Data information: For panels (C) and (E): Data are representative of at least three independent experiments, and comparison between sgEGFP and sgITSN2 conditions was performed using a paired t-test (*P ≤ 0.