dataesr/scientific-paragraphs-categorization huggingface dataset:
Fingerprinting of the dominant microbiota Sequence-specific fingerprints were obtained with Temporal Temperature Gradient Gel Electrophoresis (TTGE) as previously described. 19 TTGE profiles were analysed with Gel Compar software V. 2. 0 (Applied Maths, Kortrijk, Belgium). Similarity indexes (Pearson correlation method with unweighted pair group method with arithmetic mean (UPGMA) method) were calculated for each pair of profiles. Means were compared using paired Student's t test when the variances were equal, and otherwise using Wilcoxon's test.
5. 2. 32) pipeline. 23 perational taxonomic units (OTUs) with abundance lower than four reads were discarded. Chimera detection and removal were assessed using GOLD reference database and uchime. 23 TUs were assigned to closest taxonomic neighbours using SEQMATCH and up to date 16S rRNA gene RDP database (release 10, update 30). 24 Alpha diversity (Shannon index) was analysed on the OTU table rarefied to 1000 reads. All statistical analyses were performed using R program, 25 ade4 26 and igraph packages. 27 Statistics was assessed using either a Wilcoxon's test or the analysis of variance ( p<0. 05). If required, p values were adjusted for multiple testing effects using the Bonferroni correction. Principal component and between-class analyses were performed to map each sample based on their overall microbial composition and to assess similarities between individual's microbiota.
Acknowledgements The authors thank the  GETAID ,  Eduardo Schiffrin  and  Stephanie Blum  for critical construction of the clinical study design;  Anne Berger ,  Pierre Cattan ,  Simon Msika  and  Yves Panis  for providing rapid access to the surgical specimen,  Karine Le Roux  and  Céline Monot  for technical support and  Nestlé Research  for sponsorship. Contributors SM and PL contributed equally. PL, PS, PP, JD and PM designed the Research. All authors contributed to data acquisition, analysis and results interpretation. SM, PL and PM wrote the manuscript. All authors revised and approved the manuscript.
In a second step, the intracellular labeling against Foxp3 was performed using a staining kit according to the manufacturer's protocol (eBioscience). For nuclear transcription factors (T-bet, GATA-3, ROR-ct) staining monoclonal antibodies conjugated with PE or FITC were used (eBiosciences). Cell proliferation was evaluated staining nuclear Ki-67 using anti-Ki-67 kit (BD Pharmingen) and cell death was stimated by Annexin V Apoptosis Detection Kit I (BD Pharmingen). Cell acquisition was performed on a FACS ARIA II using FACSDiVa software (BD Biosciences). Living cells were gated on the basis of forward-and side cell-scatter. Background staining values obtained with fluorochrome conjugate isotype were subtracted. For each sample, 100,000 events were collected. Data were analyzed using DiVa or FlowJo Version 9.
Cell acquisition was performed on FACS ARIA II flow cytometer (Becton Dickinson, San Jose, CA, USA) operating FACSDiVa software (BD Biosciences). For each sample, at least 200,000 events were collected. Living cells were gated on the basis of forwardand side cell-scatter. Background staining values obtained with fluorochrome conjugate isotype were subtracted. Data were analyzed using DiVa or FlowJo Version 9. 4. 4 (TreeStar) softwares.
RNA isolation, cDNA synthesis and qPCR Spleens were obtained at different days pi. Total RNA was isolated from cells using TRIzol (Invitrogen) according to the manufacture's recommendations. mRNA levels were determined by RT-qPCR. cDNA was synthesized from 1 lg of total RNA using Superscript III reverse transcriptase (Invitrogen) and specific primers for murine IL-2, IL-6, IL-12p40, IL-23p19, IL-27p28, IL-10 and TGF-b (Invitrogen). PCR reactions were performed in a StepOne™/Plus Real-Time PCR System (Life Technologies) using SYBRGreen I (Roche) to monitor dsDNA synthesis. Data was normalized using GAPDH cDNA quantification. Primers sequences were: IL-2 F 5 0 -CCTGAGCAGGATGGAGAATTACA-3 0 IL-2 R 5 0 -TCCAGAACATGCCGCAGAG-3 0 IL-6 F 5 0 -GAGGATACCACTCCCAACAGACC-3 0 IL-6 R 5 0 -AAGTGCATCATCGTTGTTCATACA-3 0 IL-12 p40 F 5 0 -GGAAGCACGGCAGCAGAATA-3 0 IL-12 p40 R 5 0 -AACTTGAGGGAGAAGTAGGAATGG-3 0 IL-23 p19 F 5 0 -TGCTGGATTGCAGAGCAGTAA-3 0 IL-23 p19 R 5 0 -GCATGCAGAGATTCCGAGAGA-3 0 IL-27 p28 F 5 0 -GGCCATGAGGCTGGATCT-3 0 IL-27 p28 R 5 0 -AACATTTGAATCCTGCAGCCA-3 0 IL-10 F 5 0 -GGTTGCCAAGCCTTATCGGA-3 0 IL-10 R 5 0 -ACCTGCTCCACTGCCTTGCT-3 0 TGF-b F 5 0 -TGACGTCACTGGAGTTGTACGG-3 0 TGF-b R 5 0 -GGTTCATGTCATGGATGGTGC-3 0 GADPH F 5 0 -AGCAATGCATCCTGCACCACCA-3 0 GADPH R 5 0 -ATGCCAGTGAGCTTCCCGTTCA-3 0.
Statistical analysis Results are expressed as mean ± SEM or as median/range according to the variable distribution. Data were analyzed using nonparametric tests (Kruskall-Wallis test for k samples followed by Mann-Whitney U-test for comparisons between two samples). Kaplan Meier and Long Rank test were used to evaluated survival curves. The GraphPad Instat 4. 0 software (GraphPad, California, USA) was applied for statistical analyses, and differences were considered significant was when p value was < 0. 05.
Proliferation and death indexes were calculated as follows: the percentage of Ki-67 positive cells or Annexin V positive cells recorded in each infected mouse divided by the mean percentage recorded in control mice. Index graphs show these data as mean ± SEM. Results shown in figures a, b and c are representative of as least three independent experimental rounds (n = 4-5/group/day). Differences between Treg vs. Teff cells at the same day: ⁄ p < 0. 05 and ⁄⁄ p < 0. 01. In another set of experiments, animals were treated with PC61 or PBS on day before infection (n = 6-8/group): (d) CD25 + proportions among CD4 + cells one days after PC61 or PBS inoculation (day 0 pi) and 10 and 17 days pi; (e) Foxp3 + expression level in PC61 or PBS treated animals at day 0, 10 and 17 days pi; (f) Treg (CD4 + Foxp3 + )/Teff (CD4 + Foxp3 À ) cell ratio and, (g) Treg and Teff absolute number determined along the infection in PC61 or PBS treated animals; (h) blood parasite burden in mice treated with PC61 or PBS one day before to infection; (i) survival curve of infected mice (n = 6).
Acknowledgments We thank  Eva Acosta-Rodriguez  and  Marta Toscano  who kindly provided some reagents and  Eduardo Roggero  for her technical assistance. This work was supported by grants from  ANPCyT-FON-CyT  ( PICT 2008-0980 ), as well as EcosSUD ( A10S01 ). The funders had no role in study design, data collection, analysis, decision to publish or preparation the manuscript.
RESULTS AND DISCUSSION This workflow of this study started with a whole genome microarray study of silymarin versus DMSO solvent control treated human hepatoma Huh7. 5. 1 cells for four, eight, and 24 h. These time points were chosen in order to capture the earliest transcriptional changes in a cell following exposure to silymarin. Preliminary whole genome microarray studies were also performed following one h and four h of silymarin exposure. Unsupervised hierarchical clustering of differentially expressed transcripts revealed that silymarin-specific clustering only occurred with the 4h treatment (data not shown). Thus, four h was chosen as the earliest time point. Gene expression data were then analyzed by various bioinformatics software to identify key genes and pathways modulated by silymarin.
Silymarin Induces Endoplasmic Reticulum Stress The first focus was to determine the cell signaling events behind the initial and sustained induction of gene expression. Bioinformatics analyses using IPA software (Figure  S9 , Supporting Information) revealed that genes associated with endoplasmic reticulum (ER) stress were significantly induced at four and eight h post-silymarin treatment, with many ER stress genes remaining significantly up-regulated at the 24-h time point. IPA predicted that ATF4, a key transcription factor involved in ER stress, was directly involved in the rapid induction of gene expression following silymarin treatment. Twelve ATF4-regulated mRNAs were induced at four h (Figure  1A ), and most ATF4-regulated genes remained induced at eight and 24 h post-silymarin treatment in Huh7.
Microarray Analyses Microarray data were background corrected, normalized and summarized using a robust multi-array average (RMA),  65  implemented in the Bioconductor oligo package. 66 Data were summarized at the transcript level. Comparisons at each time point were made by first fitting a weighted one-way analysis of variance (ANOVA) model,  67  and then computing empirical Bayes adjusted contrasts, using the Bioconductor limma package. 68 Transcripts were chosen with an unadjusted p-value < 0. 05 and an absolute fold change > 1. 5. Various quality control plots were generated to ensure that both raw and summarized data fulfilled expectations. Density plots of the raw data indicated that the raw data were all distributed similarly, so a quantile normalization was warranted. False color images of each array showed no gross defects on the arrays, nor any entrapped bubbles.
Bioinformatics Analyses, Protein Validation, and Signaling Studies Ingenuity Pathway Analysis (IPA) software was used to predict and generate plausible regulatory networks (e. g. transcription factors or signaling kinases) to help explain changes observed in this study's gene expression profile. Potential up-and down-regulators induced by silymarin-treatment were based on relationships in the molecular pathways (networks) represented by published and experimentally observed gene expression, transcription events, and function annotation data, as well as the statistical likelihood of their association (i. e. , p<value). To target regulators of interest, Z-scores were used to score and prioritize potential up-or down-regulators. Specifically, priority was given to Z-scores ≤ -2 and ≥ 2 of silymarin-induced gene expression data.
Metabolomics Analyses For metabolomics studies, 1. 5×10 6 Huh7TLR3 cells were plated in 60 mm dishes. After 2 d of cell growth, medium containing DMSO or 80 μM of silymarin was added to cells (n = 4, each). For harvesting, a quenching solution (60% methanol, 0. 85% ammonium bicarbonate, stored at -80°C before use) was added to the dish for 1 min to quench metabolism. The quenching solution was then removed, 150 mM ammonium bicarbonate was added, and cells were scraped and moved into a 1. 5 mL siliconized tube, which was then snap frozen in liquid nitrogen and stored at -80°C. [71] [72]  The integrated peak areas of identified metabolites were z-score transformed and visualized as a heat-map using InfernoRND (http://omics. pnl. gov/software/infernordn). 73, 74  The GC-MS data were log 10 transformed and processed similarly to remove metabolites with inadequate information for statistical analyses and identify outlier GC-MS analyses.
Metabolites were removed if they did not meet the minimum requirements for a t-test. 75 owever, the data processing of the GC-MS raw files did not return any metabolites with inadequate data for statistics in either control or silymarin-treated cells at any time point. Extreme behavior in GC-MS datasets (outliers) was identified using a combination of correlation, principal component analysis (PCA), and an approach based on a robust Mahalanobis distance (rMd) to assess the reproducibility of the distribution of metabolite abundance values across biological replicates. 76 Strategies for normalization of the identified metabolite and unidentified metabolite feature abundances in each data matrix were evaluated using the Statistical Procedure for the Analyses of Normalization Strategies (SPANS) protocol.
77 In particular, a Rank Invariant set as defined by a non-parametric test at a threshold of 0. 05 was utilized to derive median values, and the data was subsequently median centered. Comparisons across time were performed by Analysis of Variance (ANOVA) with a Dunnett test correction to determine significance of each time point from the 1 h time point. Comparisons between DMSO and silymarin at a given time point were compared with a standard two-sample t-test. Silymarin suppresses inflammatory signaling. A, IPA predicted silymarin treatment to be involved in the inflammatory response based on the down-regulation of many inflammatoryrelated genes. B, Silymarin treatment results in the significant down-regulation of NF-κBrelated genes. C, Many of the genes significantly regulated with silymarin treatment in Huh7.
Acknowledgments We thank  Amanda Milstein  and  Abigail Payne  for technical assistance,  Tyler Graf  for preparation of single use silymarin aliquots,  Benoit Viollet  for AMPK wild type and knock out cells, and  Leif W. Ellisen  for DDIT4 wild type and knock out MEFs. We thank  Nadja Cech  and  Edward Calabrese  for insightful discussions.
ACKNOWLEDGEMENTS  M. V. Tuttolomondo  is grateful for her posdoctoral fellowship granted by the  National Research Council (CONICET). The authors would like to acknowledge the support of grants from the  University of Buenos Aires   UBACYT 20020110100081 , from  CONICET   PIP 11220120100657CO  and from  Agencia Nacional de Investigaciones Cientificas y Técnicas   PICT 2012-1441  (to M. F. M. F. D. and T. C. thank the  Argentina-France MINCYT-ECOS-Sud  (project  A12S01 ) and  CONICET-CNRS  programs for financial support of their collaboration. The authors would like to thank  J. Nesterzak  for his technical assistance. Sylvie Noinville  ( MONARIS, UPMC ) is kindly acknowledged for her collaboration with the FTIR measurements.
Acknowledgements. This paper is dedicated to  Gunnar Østrem  (PhD Stockholm Univ. , 1965) a tireless pioneer in the study of mountain and Arctic glaciers across  Canada , who surveyed GRIC in the early 1990s. Charles Papasodoro acknowledges support from the  Fond Québécois de Recherche en Nature et Technologies (FQRNT) fellowship program  and the  Centre d'Études Nordiques (CEN)  for an internship at LEGOS (Toulouse, France). The 2003-04 field surveys on GRIC were conducted with the able assistance of  J. C. Lavergne  and  C. Kinnard , and logistical support from the  Geological Survey of Canada , the  Polar Continental Shelf Project , and the  Nunavut Research Institute. D. Scott ,  F. Savopol ,  C. Armenakis  and  P. Sauvé  ( Geomatics Canada ) assisted with the GPS data reduction back in 2004.
Understanding the drivers of the spatio-temporal organization of communities at the scale of the forest is essential to understanding and conserving tropical arthropod biodiversity  [13]. If beta-diversity scaling relationships differ among disparate organisms, then conservation strategies aimed at preventing the loss of species will have to consider the requirements of multiple taxa at multiple spatial scales  [13]. To date, most studies have focused on a few assemblages only (e. g. butterflies, beetles:  [14] [15] [16] [17] ) and have rarely been designed to allow the explicit partitioning of components of arthropod diversity. Here, we consider a uniquely large dataset for the San Lorenzo forest in Panama. Within this forest, we surveyed the full phylogenetic breadth of arthropod taxa using 14 structured protocols targeting microhabitats from the soil to the upper canopy, and replicated across seasons in 2003 and 2004.
We use this dataset to explore the relative contributions of horizontal, vertical and seasonal variation to the total distribution in space and time of arthropods in this forest. In lowland tropical rainforests, one of the obvious spatial gradients of species change (hereafter referred to as 'dimension', so as not to assume the existence of an autocorrelated gradient per se) is related to geographic distance, as tropical rainforests are notoriously heterogeneous environments  [18]. Faunal changes in this horizontal dimension may be driven by multiple factors, including forest dynamics  [19] , the presence of host-plants and their relatives for herbivores  [20] , soil properties affecting the quality of host-plants for herbivores  [21] , distance from forest edge  [22] , or even neutral processes that incorporate distance-decay of dispersal  [18].
Arthropod data and overall rationale of statistical analyses We considered three major spatio-temporal dimensions: spatial turnover or difference in species composition (a) among sites (horizontal dimension), (b) within the vertical dimension (vertical dimension), and (c) temporal variation among repeated sampling intervals through time (seasonal dimension). We evaluated the effects of horizontal, vertical and seasonal dimensions for four main categories of arthropod variables: (1) abundance, (2) observed and estimated species richness, (3) species composition and faunal similarity and (4) guild structure. Because of the great number of protocols used at the different sites, within different habitats and surveys, a full factorial design (sites x habitats x surveys) allowing the partitioning of insect variables among the three dimensions, was not always possible.
In addition, some protocols were only valid for particular habitats (e. g. , Winkler extractions targeting the litter fauna). There were also obvious physical limitations as, for example, bulky flight-interception and light traps could rarely be placed in the upper canopy, as opposed to small and light sticky traps. When partitioning the insect variance in the horizontal, vertical and seasonal dimensions, we opted to include the datasets best balanced in the dimension of relevance for the analysis, as explained below and in S1 Text. We also restricted our data to protocols that targeted multiple taxa (i. e. , we excluded single taxon bee and termite protocols,  [30] ). This limited our analyses to the 10 following protocols, listed in order of decreasing number of individuals collected: Berlese-Tullgren, flight-intercept trap (FIT), ground flight-intercept trap, light trap, fogging, Malaise trap, Winkler, sticky trap, pitfall trap and beating.
Arthropod abundance and species richness For analyses of arthropod abundance and species richness we restricted the dataset to species that were collected with at least a probability of being present as a single individual at all study sites (i. e. , minimal number of individuals collected 12). This entire dataset so defined included 98,793 individuals representing 1,041 species distributed among 12 sites, four habitats along vertical gradients, and four seasonal surveys, with more specific subsets used in given analyses (see below and S1 Text). To evaluate the effect of horizontal, vertical and seasonal dimensions on arthropod abundance and species richness we used Kruskal-Wallis tests, considering protocols that maximized replicates among sites, habitats or surveys, or were more comprehensive (e.
Partitioning of arthropod species richness and diversity For analyses other than those related to abundance and species richness, we used, as far as possible, the full dataset that included 113,952 individuals representing 5,858 species. There are currently two main frameworks, additive and multiplicative, to partition species richness or diversity, and both are complementary for the interpretation of a robust estimation of biodiversity  [31]. Following Marcon et al. [31] , we call them the additive decomposition of species richness and multiplicative partitioning of species diversity, respectively. The first analysis refers more particularly to changes in species richness irrespective of the relative abundance of species, whereas the second one takes into account the relative commonness and rarity of species, and further minimizes the influence of sample size  [32].
We partitioned species richness for different data sets, including all species collected using the 10 principal protocols, all species collected with FITs, the estimated number of species present in the study system collected either with the 10 protocols or with FITs, common and rare species, and arthropod guilds. The Chao2 estimator was used to estimate species richness and was calculated using EstimateS 8. 20  [36]. Multiplicative partitioning of species diversity was calculated with Hurlbert's effective number of species and as  [32, 33] : g ¼ axbTxbHxbV Methods are detailed in S1 Text, as well as other aspects related to the partitioning of arthropod species richness and diversity.
In the seasonal dimension, we considered litterfall data for flowers only (S1 Text), to quantify the availability of reproductive units. We compared effect sizes for arthropods and trees in the horizontal, vertical and seasonal dimensions. Additive decomposition of species richness and multiplicative partitioning of species diversity was performed with data for flowering trees during surveys 1-4. Variation partitioning was performed on these data in a fashion similar to that for arthropods, with the exception that seasonal variables included total rainfall, average temperature, wind speed and radiation during Surveys 1-4. Floristic turnover was estimated as for arthropods, with either all tree species occurring within study sites, or with only tree species that were flowering during Surveys 1-4.
The model fitted tree species distribution well from 0. 2 to 50 km for datasets originating from Panama and Ecuador. We observed a distance decay of similarity in our tree data, but not in our arthropod data. Hence, it is unlikely that this 'neutral' tree model will be useful to predict the distribution of arthropods within the San Lorenzo forest. This is hardly surprising considering that trees and arthropods may represent two extremes of life-histories: large, sedentary and long-lived organisms vs. small, mobile and short-lived organisms, respectively. As discussed previously, phytophages may represent an exception to this rule because most species are strongly associated with their hosts. The one common denominator we identified for rainforest trees and arthropods is their dependence on factors related to the vertical dimension, particularly when considering reproductive units, and we believe that this area of research deserves more attention.
Acknowledgments  IBISCA-Panama is an initiative of Pro-Natura International ,  Océan Vert , the  universities Blaise Pascal and of Panama , and the  Smithsonian Tropical Research Institute (STRI). J. Herrera ,  E. Andrade ,  M. Samaniego ,  S. J. Wright ,  N. Baiben ,  S. Bechet ,  J. Belleguic ,  T. Aubert ,  K. Jordan ,  G. Ebersolt ,  D. Cleyet-Marrel ,  L. Pyot ,  O. Pascal ,  P. Basset , and  E. Bauhaus  helped with logistics in the field. A. Barba ,  R. Cabrera ,  A. Cornejo ,  I. Díaz ,  A. F. R. do Carmo ,  I. C. do Nascimento ,  E. A. dos Santos ,  M. González ,  A. Hernandez ,  M. Manumbor ,  M. Mogia ,  S. Pinzón ,  B. Pérez ,  L. S. Ramos-Lacau , and  O. Valdez  helped with initial sorting of the arthropod and plant material. Further taxonomic help for the arthropod material is acknowledged in [1].
Core funding was provided from  SolVin-Solvay SA, STRI , the  United Nations Environment Programme , the  Smithsonian Institution (Walcott Fund) , the  European Science Foundation , and the  Global Canopy Programme. YB and HB are members of the  Sistema Nacional de Investigación of the SENACYT in Panama , and acknowledge the help of the  Smithsonian Tropical Research Institute and Maestría Centroamericana de Entomología, Universidad de Panamá , during the preparation of the studies  IBISCA-Panama  project. YB was supported by  Czech Science foundation GAČR  grant  14-36098G ,  European Social Fund and Czech Ministry of Education   CZ. 1. 07/2. 3. 00/20. 0064and U. S. National Science Fundation DEB-0841885. RLK acknowledges the  Queensland Government  for provision of a visiting fellowship under its  Queensland/Smithsonian Institution Scheme.
RKD was supported by an  Australian Research Council   Future Fellowship   FT100100040. SPR was supported by  CNPq. JO was supported by a '' Investissement d'Avenir '' grant managed by the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ). FØ's research was funded by the  Norwegian Research Council. VN was supported by the  Grant Agency of the Czech Republic  ( 14-36098G ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.