dataesr/scientific-paragraphs-categorization huggingface dataset:
Fort Michilimackinac, constructed in 1713 at the northern tip of Michigan as a base for French fur trade enterprises, was occupied by the English in 1761 and held for the remainder of the French and Indian War  (Cleland 1970). Despite its location on a major waterway, Fort Michilimackinac suffered from shortages in supply resulting from difficulties in transporting provisions up the   2 ). French-held Saint Lawrence River, and across the Great Lakes  (Cleland 1970; Scott 1985). Number of Identified Specimen (NISP) and Minimum Number of Individuals (MNI) data were the only measures widely available in the published sources and therefore serve as the units of measure in this analysis. ANOVA analysis performed in R was used to test for correlations between site accessibility and the abundance of domestic livestock recorded in faunal assemblages.
The relative percentage of the total assemblage comprised by domestic livestock and key individual species (%NISP; %MNI) were used as measures of species' dietary importance. Since NISP values were not available for Fort Stanwix  (Hanson and Hsu 1975) , it was included only in MNI-based calculations. Finally, the Simpson's Diversity and Evenness was calculated in PAST to assess species diversity and the abundance of those species relative to others at individual sites as a further investigation of dietary patterning in this dataset.
Historic documentation indicates that French and Indian War soldiers were provisioned with daily meat rations composed largely of beef and pork  (Anderson 1984; Beattie 1986; Knox 1769; Parker 1970). Domestic livestock are expected to be more abundant in faunal assemblages from forts located in developed areas or accessible by roads and navigable rivers and lakes, than those which are hard to reach. To test whether the abundance of domestic livestock in the diets of soldiers at French is influenced by site accessibility, faunal material from Fort Shirley was analyzed (Table  3 ) and integrated into a dataset of French and Indian War fortifications (Tables  2  and 4 ). ANOVA analysis was used to test for statistically significant differences in the abundance of domestic livestock on forts associated with developed infrastructure and reliably navigable rivers, and those which were not.
Measurements taken on the distal end and shaft of a radius was used to generate a minimum shoulder height of 60. 09 cm  (Harcourt 1974) , approximately the size of a modern large German shepherd. Epiphyseal fusion data indicate deer ranged in age from less than 5 months to over 3 years, with the majority around 2. 5 years  (Purdue 1983). Similarly, cattle butchered at Fort Shirley were between 3. 5 and 5 years of age  (Silver 1969). Pigs were likely younger than 3. 5 years when they were slaughtered  (Silver 1969). No age estimates were calculated for the single horse and sheep specimens. Only 48 fragments could be identified to species from material recovered from Aughwick Old Town, the Mingo Seneca refuge village constructed west of Croghan's fort (Table  1 ). White-tailed deer again comprise the largest single contributor to the assemblage (66.
Acknowledgements Thanks are due to  Penn State  undergraduate students  Kristi Bartolome ,  Grace Bratlee ,  Paige Lynch , and zooarchaeology lab volunteer  Joanne Hughes  in addition to the staff and students of the  Penn State Archaeological Field School  ( 2009-2014 ). We would also like to thank  Terrance Martin  ( Illinois State Museum ),  Chantal Dussault  ( Canadian Museum of Nature ), and the many others who provided copies of unpublished reports employed in this analysis.
6a, c ). To test more broadly for signals of immune activation, we performed GSEA with gene sets from the KEGG immunity collection. We found that some pathways associated with adaptive immune signaling (T Cell Receptor, B Cell receptor, Th1, and Th2) were enriched in both GU and GU1N relative to WT infection (Fig. 6d ). Histological analyses (see 'Histological and pathological findings at necropsy') detected virally infected cells in nonviremic SIVagm GU exposed AGMs. Thus, persistent viral gene expression in lymphoid tissues of these animals might induce some immune activation. However, several pathways associated with innate immunity (NLR-, RLR-, TLR-signaling, and cytosolic DNA sensing) were only enriched in the GU1N group (Fig. 6d ). Collectively, these data suggest that infection of AGMs with the GU1N virus induces transcriptional pathways associated with immune activation above the levels seen in WT SIVagm infection.
For immunoblot analysis, we employed anti-AU1 (1:10,000, AU1, Covance, Cat# MMS-130R), anti-GFP (1:3,000, polyclonal, Abcam, Cat# ab290), anti-GAPDH (1:200, polyclonal, BioLegend, Cat# 631401) and secondary anti-mouse or anti-rabbit IRDye Odyssey antibodies (1:20,000, LI-COR, Cat# 926-32220, Cat# 926-32211 or Cat# 926-32221) or alkaline phosphatase-conjugated anti-human IgG (1:1,000, Jackson ImmunoResearch, Cat# 109-055-098). For histological analysis, anti-human IL-18 (1:500, polyclonal, Atlas antibodies, Cat# HPA003980), goat anti-rabbit Alexa Fluor 568 (1:800, Abcam, Cat# ab175695), and DAPI (0,05 µg/ml, Sigma Aldrich, Cat# D9542) were used. Flow cytometry. A volume of 50 µl whole blood, 500,000 PBMCs, or 300,000 mononuclear cells (MNCs) purified from BAL or duodenum  63  were stained for 30 min at room temperature in the dark with mixtures of pre-titrated antibodies.
For intracellular staining, a Fix and Perm kit (Biozol) was used according to manufacturer's instructions. Briefly, the cells were washed and fixed after staining of the surface receptors with Fixing Solution A. The cells were then washed and incubated with the Permeabilization Solution B containing the antibody used for intracellular staining for 30 min at 4 °C. FACS analyses was performed with a LSR II flow cytometer or a FACSCantoII (BD Biosciences) and data were analyzed using the BD FACSDIVA™ software. Determination of CD4+ T cell counts. CD4+ T cell proportions were determined by staining whole-blood leukocytes with a pre-titrated antibody cocktail comprising anti-CD11a-allophycocyanin (APC), CD3-Alexa700 and CD4-Horizon V450. Following staining with the antibody mixture, red blood cells were lysed with BD FACS lysing solution and labeled lymphocytes were analyzed for their expression of cell surface markers by flow cytometry on an LSR II flow cytometer (BD Biosciences).
Lymphocyte populations were gated based on forward and side scatter characteristics and then exclusion of doublets and expression of CD11a, followed by that of CD3. Data were generated with BD FACS Diva 6. 1. 3 Software before analysis with FlowJo 8. 8 Software (Treestar). Generation of HIV-1 like SIVagm constructs. To insert the SIVgsn vpu into the SIVagmSab92018 molecular clone  24  , the AflII and PstI restriction sites at nucleotide positions 4160 and 4520 were eliminated by splice overlap extension (SOE)-PCR. A chemically synthesized 1041 bp fragment containing parts of tat, the SIVgsn vpu gene, and the 5′ end of env flanked by PstI and AflII sites was cloned into the unique AflII and PstI restriction sites of the modified SIVagm Sab92018ivTF construct. To replace nef, the original AGM nef start codon was mutated, thereby eliminating the overlap between the env and nef reading frames.
1% NP40, 500 µM Na 3 VO 4 , 500 µM NaF, pH 7. 5). Proteins were separated on a 4-12% NuPAGE Novex Bis-Tris precast gel (Invitrogen) and transferred onto a nitrocellulose membrane (Millipore, Schwalbach) or an Immobilon-FL PVDF membrane (Merck Millipore) using a Trans-Blot SD Semi-Dry electrophoretic Transfer Cell (BioRad, Munich). The transferred membranes were blocked for 1 h with 10% milk solution and stained with anti-AU1, anti-GFP, and anti-GAPDH antibodies. Secondary anti-mouse or anti-rabbit IRDye Odyssey antibodies (1:20,000, LI-COR) were used and the fluorescent signal detected using a LI-COR Odyssey scanner and the software LI-COR Image Studio Lite Version 3. 1. Viral Env expression was detected using serum from an AGM infected with SIVagmsab92018ivTF probed with horseradish peroxidase-conjugated goat anti-human immunoglobulin G (γchain-specific) secondary antibody (Sigma).
Qualitative and quantitative analyses were performed on the Bioanalyzer and NanoDrop respectively to assess the size distribution of the amplified DNA and quantity. Approximately 4 µg of the amplified DNA was used for biotinylation and fragmentation using the NuGEN Ovation Encore Biotin Module (Nugen, San Carlos, CA, USA). All samples were hybridized to Affymetrix GeneChip® Rhesus Macaque Genome Arrays (Affymetrix, Santa Clara, CA, USA), which contains over 52,000 individual probe sets that assay over 47,000 transcripts. The probe arrays were washed, stained, and scanned as described in the Affymetrix GeneChip® Expression Analysis Technical Manual. CEL files were extracted from the raw scanned images using the Affymetrix GeneChip® command console Software. Quality control metrics were monitored on the Affymetrix Expression console software; discordant arrays were excluded from further downstream analyses.
Microarray transcript differential expression analyses. Transcript expression values were modeled and normalized using the Robust Multi-array average (RMA) method, and log 2 transformed (AffyPLM, R Bioconductor). Transcripts differentially expressed between animals infected with different viral constructs were identified by a moderated Student's t-test (limma, R Bioconductor). Only one transcript, MmugDNA. 39120. 1. S1_s_at (LOC100293771), was differentially expressed at p ≤ 0. 05 with Benjamini-Hochberg multiple testing correction. Gene set enrichment analysis. To identify pathways differentially modulated by the different viral constructs, gene set enrichment analysis  39  was performed as follows: For each contrast, transcripts were ranked by differential expression using the Signal2Noise metric.
The "Chronic SIV immune activation" gene set was generated from a microarray analysis of SIV infection of sooty mangabeys and rhesus macaques  38  and is described in detail by Rotger and colleagues  40. The "inflammation and immune activation" set is based on Micci and colleagues  41. In addition to these custom gene sets, 19 immunity-related gene sets from the KEGG collection (#4062, 4610, 4611, 4612, 4620, 4621, 4622, 4623, 4640, 4650, 4657, 4658,  4659, 4660, 4662, 4664, 4666, 4670, and 4672) were obtained from the Molecular Signatures Database (http://software. broadinstitute. org/gsea/msigdb/ collections. jsp. ). GSEA was performed using the desktop module available from the Broad Institute (www. broadinstitute. org/gsea/). GSEA was performed on the ranked transcript lists using 1000 phenotype permutations, collapse of duplicates to Max_probe, and random seeding.
Images were acquired on a Confocal Laser Scanning Microscope Leica TCS SP8, running LAS AF 3 (Leica Application Suite Advanced Fluorescence). Individual optical slices were collected at 1024 × 1024 pixel resolution. Image J software was used to assign colors to the channels collected. Immunohistochemical detection of IL-18 production. Frozen sections of 10 μm were stained using a cross-reactive unconjugated primary antibody anti-human IL-18 (Atlas antibodies) followed by the appropriate secondary antibody conjugated to Alexa 568 (red). Prior to staining, slides were fixed in ice-cold methanol, allowed to rest for 10 min then washed 3 times with PBS. Slides were permeabilized with Tween20 and blocked with serum to avoid unspecific binding of the antibody. Confocal microscopy acquisition was performed with the Cellvoyager CV-1000 confocal microscope (Yokogawa, Japan) and images were analyzed using Image J software.
Computer programs and data analyses. For the analysis of nucleotide and amino acid sequences following programs were used: (i) Sequence reverse complementor (http://bioinformatics. org/ sms/ rev_comp. html) 68 ; (ii) MultiAlin V5. 4. 1 (http:// prodes. toulouse. inra. fr/multalin/) 69 ; (iii) DNA/amino acid program Expasy-tool (http://www. expasy. org/tools/dna. html)  70 ; (iv) Gene construction Kit V2. 0 program (http://www. textco. com/gene-construction-kit. php); (v) Sequence analysis program Chroma 1. 62 (http://en. bio-soft. net/format/chroma. html); and (vi) Phylogenetic tree construction by the Bayesian method using the general reversible (GTR) model of evolution. Quantification and statistical analysis. Statistical analyses were performed with GraphPad PRISM (GraphPad Software) and Microsoft Excel.
Acknowledgements We thank  K. Regensburger ,  M. Mayer ,  R. Linsenmeyer ,  S. Engelhart ,  D. Krnavek ,  S. Heine , and  J. Hampe  for technical assistance,  S. Sopper ,  K. Töpfer , and  T. Schultheiss  for support with flow cytometry and  Nirav Patel  and  Greg Tharp  of the  Yerkes NHP Genomics Core  for microarray analysis. This work was supported by  NIH  grants to B. H. H. ( R37 AI50529 ,  R01 AI 120810 ,  R01 AI 114266 ), G. S. ( R37 AI66998 ), and  YYY  ( R01 AI 120860 ), and the  BEAT-HIV Delaney Consortium  ( UM1 AI 126620 ),  DFG CRC 1279  and  SPP 1923 , an Advanced  ERC investigator grant  to F. K. , and a grant from  Sidaction  to M. M. -T. N. H. was supported by  VRI  ( Créteil, France ) and T. G. -T. by the  Pasteur-Paris University PhD program. We thank the  IDMIT center  for access to the stellar Imaging platform and acknowledge the  Imagopole France-BioImaging infrastructure , supported by the  French ANR  ( 10-INSB-04-01 , Investments for the Future), for advice and access to the CV1000 system.
Data availability. Microarray results have been deposited in the Gene Expression Omnibus database; the accession number is GSE103072. Sequences derived from AGM infected with WT or chimeric SIVagm constructs have been submitted to GenBank and are available under accession numbers MF774879 to MF775120. The authors declare that all other data supporting the findings of this study are available within the article and its Supplementary Information files, or are available from the authors upon request.
Participants. A minimum target of 20 TD adolescents and 20 adolescents with ASD was fixed a priori based on our past experience with social cognition experiments. In particular, a previous experiment conducted in our team on a sample of the same size  60  successfully applied statistical and computational analyses to highlight differences and similarities in social stimuli processing between TD adolescents and adolescents with ASD. The exact number was determined by scheduling constraints. A final number 22 TD adolescents (6 females) and 22 adolescents with ASD (4 females), aged between 12 and 17 years old (TD: M = 13. 70 ± 0. 61; ASD: M = 14. 45 ± 0. 89; Table  1 ), participated in this study. The experiment was approved by INSERM and by the local research ethics committee (ClinicalTrials.
gov Identifier: NCT02628808, Protocol Study ID: 2008-A00019-46) and was performed in accordance with the Declaration of Helsinki. The TD adolescents were recruited from a mainstream school and   73  ). b Wechsler Abbreviated Scale of Intelligence (four subsets form  74  ). c Kazdin's Pleasure Scale  41. the adolescents with ASD were recruited from the University Hospital Robert Debré (Paris, France). The adolescents with ASD had received an official diagnosis of autism by an independent clinician according to the criteria of the Diagnostic and Statistical Manual for mental disorders-IV TR (DSM IV TR  69  ). The Autism Diagnostic Interview Revised (ADI-R  70  ) and the Autism Diagnostic Observational Schedule (ADOS  71  ) were used to further assess the ASD group. The mean ADOS score for the ASD group was 13.
Therefore, higher scores to this scale indicates higher levels of anhedonia. The three scales were thus reverse-coded in order to reflect participants' levels of motivation. The Face Evaluation Task. The experiment was programmed on ePrime (Psychology Software Tools, 2002)   and lasted approximately 15 minutes. 30 faces varying parametrically on dominance and trustworthiness were generated using FaceGen 3. 1 (http://www. facegen. com). Previous research has shown that these faces elicit dominance and trustworthiness judgments both at the explicit and the implicit level  39, 40. Following Oosterhof and Todorov's methodology  46  , the questions bearing on the three traits of interest, i. e. trustworthiness, dominance or likeability, were presented in separate blocks. The three-block sequence and the sequence of trials within each block were randomized between blocks and between participants.
Data analysis. Cues decoding. To measure the influence of social motivation on participants' ability to decode trustworthiness and dominance cues, we ran mixed linear regressions on trustworthiness and dominance ratings, taking avatars' levels of trustworthiness/dominance as well as participants' level of social motivation as regressors and participants' ID as a random factor (R freeware, nlme package  78  ). Likeability evaluations. To investigate the impact of social motivation on the composition of likeability judgments, we ran a mixed linear regression on likeability evaluations, taking social motivation, ratings of dominance and ratings of trustworthiness as predictors and participants' ID as a random factor. Following Todorov et al. 62  , this model included linear and quadratic interaction effects of perceived trustworthiness and perceived dominance as well as interaction terms between these two factors (R freeware, nlme package  78  ).
ABSTRACT The smallest members of the sunlit-ocean microbiome (prokaryotes and picoeukaryotes) participate in a plethora of ecosystem functions with planetary-scale effects. Understanding the processes determining the spatial turnover of this assemblage can help us better comprehend the links between microbiome species composition and ecosystem function. Ecological theory predicts that selection, dispersal and drift are main drivers of species distributions, yet, the relative quantitative importance of these ecological processes in structuring the surface-ocean microbiome is barely known. Here we quantified the role of selection, dispersal and drift in structuring surface-ocean prokaryotic and picoeukaryotic assemblages by using community DNA-sequence data collected during the global Malaspina expedition.
Selection acting on the microbiome We investigated the abiotic agents exerting selection in the ocean microbiome by analysing the compositional differences between communities (ß-diversity) together with a set of environmental variables considered in the Meta-119 dataset (See Supplementary Information). We used different ß-diversity indices (Bray-Curtis, TINAw, PINAw, gUniFrac; See Methods), as each captures distinct features of community differentiation. Water temperature was the most important driver of selection on prokaryotes (Fig. 2 ). Furthermore, water temperature appeared to affect prokaryotic associations, given that the association-aware ß-diversity index TINAw  (35)  explained ~50% of community variance (PERMANOVA R 2 ) [Fig. 2 ], while other ßdiversity indices tested that do not consider species associations explained considerably lower proportions (Fig.
2 ). In contrast, temperature had limited effects on picoeukaryotic species associations (Fig. 2 ). Our results were further confirmed by independent data from the global surface-ocean collected during the TARA Oceans expedition  (36) , as both the Malaspina and TARA Oceans datasets displayed stronger positive correlations between TINAw and water-temperature differences in prokaryotes than in picoeukaryotes [Fig. 3 ]. This indicates that locations with similar temperatures feature co-occurring prokaryotic species, with this pattern disappearing as the temperature difference between stations increases. Further analyses, exploring 17 environmental variables from 57 stations (Supplementary Information, Fig. S4 ), showed that fluorescence (a proxy for Chlorophyll a concentration) explained 31% of PINAw-based prokaryotic community variance (PERMANOVA R 2 ), while it was not significant for picoeukaryotes (Fig.
Chlorophyll measurements were obtained from Estrada et al. (86). In specific samples nutrient concentrations were estimated using the World Ocean Database (88-91) due to issues with measurements. Since not all environmental parameters were available for all stations, two contextual datasets were generated: Meta-119, including 119 stations (Fig. S1 ), 5 environmental parameters and 5 spatial features and Meta-57 (Fig. S4 ), including 57 stations and 17 environmental parameters. See Supplementary Information for further details.
(97). Corrected reads were merged with PEAR (98) and sequences >200bp were quality-checked and de-replicated using USEARCH (99). Operational Taxonomic Units (OTUs) were delineated at 99% similarity using UPARSE V8. 1. 1756 (100). To obtain OTU abundances, reads were mapped back to OTUs at 99% similarity. Chimera check and removal was performed both de novo and using the SILVA reference database (101). After our stringent quality control (see Supplementary Information), a total of 42,505 picoeukaryotic and 10,158 prokaryotic OTUs were obtained. Taxonomic assignment of picoeukaryotic and prokaryotic OTUs was generated by BLASTing (102) representative sequences against different reference databases. Metazoan, Charophyta, nucleomorphs, Chloroplast and mitochondrial OTUs were removed from the OTU tables.
General analyses and phylogenetic inferences Both picoeukaryotic and prokaryotic datasets were sub-sampled to 4,060 reads per sample using rrarefy in Vegan (103), resulting in sub-sampled tables containing 18,881 picoeukaryotic and 7,025 prokaryotic OTUs. All OTUs with mean relative abundances above 0. 1% and below 0. 001% were defined as regionally abundant or rare respectively (104). Phylogenetic trees were constructed for both the 16S and 18S datasets using OTU-representative sequences. Reads were aligned against an aligned SILVA template using mothur (105). Afterwards, poorly aligned regions or sequences were removed using Trimal (106). A phylogenetic tree was inferred using FastTree v2. 1. 9 (107). Most analyses were performed in the R statistical environment (108) using adespatial  (109) , APE  (110) , ggplot2  (111) , gUniFrac  (112) , Maps  (113) , Mapplots  (114) , Picante  (115)  and Vegan (103).
The randomization was run 9,999 times and only OTUs with >1,000 reads over the entire dataset were considered in order to prevent any bias due to potential under sampling. RCbray values between -0. 95 and +0. 95 point to a community assembly governed by drift. On the contrary, RCbray values > +0. 95 or < -0. 95 indicate that community turnover is driven by dispersal limitation or homogenizing dispersal respectively  (116). The previous framework was applied as following: First, we determined the proportion of community pairwise comparisons displaying a |bNTI| > 2, which points to the action of selection. Subsequently, for the pairwise comparisons that did not indicate the action of selection, we calculated the proportion of total comparisons that could be assigned to dispersal limitation, homogenizing dispersal or drift according to their RCbray values.
Associations between taxa and environmental parameters We analysed whether OTUs had differential associations with environmental parameters as well as between themselves using different algorithms. Firstly, we used the Maximum Information Coefficient (MIC) which captures diverse relationships (including non-linear and non-functional) between two pairs of variables  (38). The Malaspina dataset consisted of 119 stations and 17 environmental parameters (See Supplementary Information for extra details). In the TARA Oceans dataset, prokaryotes were analysed across 63 surface stations (including 8 environmental parameters), while microbial eukaryotes were analysed across 40 surface stations (including 6 environmental parameters) [see Supplementary Information]. In both datasets, MIC analyses were run using CV=0.