dataesr/scientific-paragraphs-categorization huggingface dataset:
IC 50 value calculations The IC 50 values of each of the derivatives against S. haematobium were calculated using the software CompuSyn 1. 0 (ComboSyn Inc, 2007) after 72 h of incubation with the following concentrations of each drug: 100, 50, 25, 12. 5 and 6. 25 μM. The equation to normalize the score of the treated worms to the controls and calculate the effect was: Effect ¼ 1 À Average score treatment =Average score control À Á The IC 50 values obtained for the drugs on S. mansoni are described elsewhere  [11]. by spines that are more or less uniformly distributed. In Fig. 2c  the oral and ventral suckers and the gynaecophoric canal of control worms are shown. Ridges are also present on females, but no tubercles or spines; instead, the teguments of the female look more porous and smooth (Fig.
Funding We gratefully acknowledge financial support from the  Swiss National Science Foundation  (Professorships No. PP00P2_133568  and  PP00P2_157545  to GG), the  University of Zurich (GG)  and the Swiss excellence scholarships for foreign students (ESKAS to VB). This work has received support under the program " Investissements d'Avenir " launched by the  French Government  and implemented by the  ANR  with the reference  ANR-10-IDEX-0001-02 PSL (GG ). JK and GG are grateful to the  European Research Council  ( ERC-2013-CoG 614739-A_HERO  to JK and  ERC-2015-CoG 681679-PhotoMedMet  to GG) for financial support.
CAD modalities were detected after 12-h (i-k), 24-h (a-e), or 48-h (f-h) co-culture of untreated (red) CMTMR-labeled cells with untreated (green) CMFDA-labeled cells (a, c, f, i, l) or of untreated (red) CMTMR-labeled cells with (green) CMFDA-labeled cells that have been irradiated with the indicated doses of γ-ionizing radiation (b, d, e, g, j, k, m, n). o-q Co-cultures of HCT116 WT cells were also performed during 24 h in the presence or absence of the indicated pharmacological death effector inhibitors. CAD modalities were determined as previously described for the untreated (red) CMTMR + HCT116 WT cells, for untreated (green) CMFDA + HCT116 WT cells, for treated (green) CMFDA + HCT116 WT cells, and for total cell population (CMTMR + or CMFDA + HCT116 WT cells). Representative dot plots (a, b) and quantitative data (c-q) are shown (means ± SEM, n = 3).
Brightfield, Annexin V-BV786, DRAQ7, CMFDA, CMTMR, and Hoechst 33342 stainings were detected using, respectively, channels for 420-480, 745-800, 642-745, 480-560, 595-642, and 430-505 nm. At least 1000 events of cells per sample were analyzed. Additional single-labeled controls were prepared to normalize fluorescent signal across different channels. Acquired data were analyzed using the IDEAS analysis software (v6. 1; Merck-Millipore). Gating strategy was the following. Cells were gated for focused cells using the Gradient RMS feature. Cells were gated for single cells using the aspect ratio and area features. For the cannibalism detection, cells were gated in the double positive CMFDA + and CMTMR + staining. The imaging flow cytometer FlowSight® provides the images of each events and thus allows to remove from the analysis the artifacts (such as cellular fragments or debris).
Flow cytometry and confocal fluorescent microscopy To detect PS exposure, plasma membrane permeability, and cell cycle progression, cells were after co-culture sequentially labeled with specific fluorescent probes (such as fluorescein isothiocyanate (FITC)-conjugated Annex-inV, propidium iodide (PI), and Hoechst 33342) and analyzed by flow cytometry. Both detached and adherent cells were collected and stained with Hoechst 33345 (10 ug/ml) during 1 h at 37 °C in warmed complete medium. After washing with PBS, HCT116 cells were suspended in 1× binding buffer supplemented with FITC-conjugated Annexin V (BD Biosciences) and PI (1 μg/mL) (Sigma), as per the manufacturer's instructions. Samples were then analyzed using LSRII flow cytometer (Becton Dickinson) and the FlowJo software v10. For confocal fluorescence microscopy, CMFDA-and CMTMR-labeled HCT116 cells were fixed after co-culture in 3.
Western blots Total cellular proteins were extracted in lysis buffer (containing 1% NP40, 20 mmol/L HEPES, 10 mmol/L KCl, 1 mmol/L EDTA, 10% glycerol, protease, and phosphatase inhibitor tablets). Protein extracts (30 µg) were run on 4-12% NuPAGE® Novex® Bis-Tris gels (Life Technologies) and transferred at 4 °C onto Immobilon polyvinyldifluoride membranes (Thermo Scientific). After blocking, membranes were incubated at 4 °C overnight with primary antibodies specific for: caspase-3 (#9662), cleaved caspase-3 (Asp175) (#9661), Myosin Light Chain 2 (MLC2) (#3672), phospho-MLC2 (Ser19) (#3675), LC3 A/B (#4108), and p-(S)-CDKs Substrate (#9477), obtained from Cell Signaling Technology. Antibodies against GAPDH (#MAB374) were purchased from Millipore. Horseradish peroxidase-conjugated goat anti-mouse or anti-rabbit (Southern Biotechnology) antibodies were then incubated for 1 h and revealed with the SuperSignal West Pico® reagent (Thermo Fisher Scientific) or the ECL TM Prime Western Blotting Detection System (GE Healthcare) using an ImageQuant LAS 4000 softwareassisted imager (GE Healthcare).
Acknowledgements This work was supported by funds from  La Ligue contre le Cancer Comité du Val-de-Marne ,  Agence Nationale de la Recherche (ANR) ,  Institut National du Cancer  ( INCA-2015-1-PLBIO-07-IGR-1 ),  Laboratory of Excellence (LabEx) LERMIT , the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ,  NATIXIS ,  Electricité de France  and  Fondation Gustave Roussy  (to J-L. P. ), and from the  Ligue contre le Cancer Comité de Charente-Maritime  (équipe labelisée),  Agence National de la Recherche (ANR)  - Projets  blancs,  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases, Association pour la Recherche sur le Cancer (ARC) ,  Cancéropôle Ile-de-France ;  Chancelerie des universités de Paris  ( Legs Poix ),  Fondation pour la Recherche Médicale (FRM) , a donation by Elior; the  European Commission (ArtForce) ; the  European Research Council (ERC) ,  Fondation Carrefour, Institut National du Cancer (INCa) ,  Inserm (HTE) ,  Institut Universitaire de France, LeDucq Foundation , the  LabEx Immuno-Oncology , the  RHU Torino Lumière , the  Searave Foundation , the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) , the  SIRIC Cancer Research and Personalized Medicine (CARPEM) , and the  Paris Alliance of Cancer Research Institutes (PACRI)  (to G.
K. ). I. M. is supported by the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE)  and  INCA  ( INCA-DGOS-INSERM 6043 ). S. Q. R is funded by  Higher Education Commission (Pakistan)  and by the  Laboratory of Excellence LERMIT  with a grant from  ANR  ( ANR-10-LABX-33 ) under the program " Investissements d'Avenir "  ANR-11-IDEX-0003-01. E. M. and M. T. are also supported by the  Laboratory of Excellence LERMIT  with a grant from  ANR  ( ANR-10-LABX-33 ) under the program " Investissements d'Avenir "  ANR-11-IDEX-0003-01. L. V. is funded by a grant from  INCA  ( INCA-2015-1-PLBIO-07-IGR-1 ). H. D and D. D are, respectively, recipients of PhD fellowships from  LabEx LERMIT and Fondation Philantropia. Q. W. is supported by  China Scholarship Council. The authors would like to thank  C.
The aim of this study was to construct celiac co-expression patterns at a whole genome level and to identify transcription factors (TFs) that could drive the gliadin-related changes in coordination of gene expression observed in celiac disease (CD). Differential co-expression modules were identified in the acute and chronic responses to gliadin using expression data from a previous microarray study in duodenal biopsies. Transcription factor binding site (TFBS) and Gene Ontology (GO) annotation enrichment analyses were performed in differentially co-expressed genes (DCGs) and selection of candidate regulators was performed. Expression of candidates was measured in clinical samples and the activation of the TFs was further characterized in C2BBe1 cells upon gliadin challenge. Enrichment analyses of the DCGs identified 10 TFs and five were selected for further investigation.
Microarray Results and Bioinformatics Analyses Raw data from two previous Human U133 Plus 2. 0 Array (Affymetrix, Santa Clara, CA, USA) experiments were downloaded from the EBI ArrayExpress database (http://www. ebi. ac. uk/ microarray-as/ae/; experiment numbers E-MEXP-1828 and E-MEXP-1823). Briefly, the long-term or chronic experiment consisted of duodenal biopsies from nine newly diagnosed, active CD patients and nine treated CD patients on GFD for more than 2 years. In the tissue-culture response experiment 10 biopsies from GFD-CD patients that had been divided into two portions and incubated with (or without) 10 µg/mL gliadin for 4 h were used  [14]. The workflow of the study is summarized in Figure  S1. Probes from the expression arrays were translated to gene names using the manifest of the array or using BLAST  [16]  of the probe sequences against the human reference genome Hg19.
If a gene was represented by more than one probe, the median of all probe intensities was taken as the expression level of the gene. To prioritize the most informative genes, only those genes whose expression was more variable than the median gene were selected for co-expression analyses, using the "varianceBasedfilter" function in the DCGL R package  [17]. Co-expression modules were defined using the weighted correlation network analysis available in the R package WGCNA  [18]. In addition, gliadin-induced changes in co-expression were also analyzed, considering differentially co-expressed genes (DCGs) as those genes that change significantly their relationships with the rest of the genes in their module, when comparing gliadin-stimulation (active CD or in vitro gliadin-challenge) to its respective basal situation, and viceversa.
Transcription Factor Binding Site and Gene Ontology Enrichment Analysis DCGs within each co-expression module were compared to the rest of the genome in order to detect significantly overrepresented transcription factor binding sites (TFBS), using the FatiGO tool  [19]  available in the Babelomics v4. 3 (http://v4. babelomics. org/) suite from the CIPF Research Center in Valencia, Spain  [20]. In summary, promoter regions of the scanned genes were interrogated for TF-specific motifs according to the TRANSFAC curated TFBS database  [21]. DCGs with enrichment for a TFBS as well as the complete original modules were compared to the rest of the genome in order to detect significantly overrepresented Gene Ontology (GO) terms (levels from 4 to 7) using the FatiGO tool available in the Babelomics v4.
Selection of Candidates Five TFs out of the significant ones in the enrichment analysis were selected for biological validation of our strategy based on the previous literature and the repetition of the terms in at least three different modules. For each TF, we selected up to three target genes from the DCGs sets with enrichment for that particular TF, prioritizing those with conserved binding sites and/or positive chromatin immunoprecipitation-sequencing (ChIP-seq) data at promoters and/or H3K27Ac-rich regions, according to the ENCODE data from the University of California Santa Cruz (UCSC) genome browser (https://genome. ucsc. edu/). Revision of the literature helped to further prioritize those genes that could be more relevant to CD pathogenesis, and CISD2, HDAC4 and WDR43 were chosen for IFN regulatory factor-1 (IRF1); AKTIP, NAMPT and TPK1 for CAMP Responsive Element Binding Protein 1 (CREB1); CRTAM, PLLP and RFX5 for ETS domain-containing protein (ELK1), and WNT11 for NFKB1.
Acknowledgments: The authors thank the technical and human support provided by  SGIker of the UPV/EHU. The work was funded by  ISCIII  Research Project Grants  PI13/01201  and  PI16/00258 , cofunded by the  European Union ERDF/ESF  "A way to make Europe" and by  Basque Department of Health  project  2011/111034  to JRB and  Basque Department  of  Health  project  2015/111068  to I. S. , N. F. -J. was supported by an  IARC Postodctoral Fellowship (FP7 Marie Curie Actions-People-COFUND ) and a  Postdoctoral Fellowship  from the  Basque Department of Education. I. R. -G. and A. J. -M. are supported by predoctoral fellowship grants from the  UPV/EHU  and the  Basque Department of Education , respectively.
| Participants and settings The International epidemiology Databases to Evaluate AIDS (IeDEA) is a collaborative network of ART programmes with sites in various regions throughout the world. Key clinical data are collected and collated within IeDEA generating large data sets to address high-priority HIV research questions. Participating cohorts include: Southern, East and West Africa (IeDEA-Africa), North America (NA-ACCORD), Caribbean, Central/South America (CCASAnet), and Asia-Pacific including Australia (IeDEA Asia-Pacific). All participating cohorts obtained local ethical committee approval to contribute data to the IeDEA collaboration. Countries were characterized as high, middle or low-income countries based on standard definitions using the gross national income per capita.
| Inclusion criteria and definitions Patients aged 18 years or older with a known date of ART initiation and a known first-line regimen who had not previously received ART (with the exception of ART used only for prevention of mother-to-child transmission (PMTCT) were included in the study for the period between 2003 and 2014. All data were de-identified for analysis. A switch in ART was defined as either a change in class of antiretrovirals or a change of at least two agents. An interruption in ART was defined as a documented treatment interruption (i. e. receipt of no antiretrovirals) of at least 30 days. Loss to follow-up (LTFU) was not considered as a treatment interruption in the main analysis as LTFU could be due to multiple reasons such as transfer out to other ART programmes, death or migration and may not necessarily imply an interruption.
However, rates of LTFU (defined as no documented visit for at least twelve months before the closing date of the database) were compared by sex and region. Patient level variables included sex, age, date of enrolment in cohort, date of last clinic visit, date of death, date of transfer, date of ART initiation, type of first-line regimen (EFV vs. NVP vs. PI-based regimen) and type of nucleoside reverse transcriptase inhibitor (NRTI) backbone (stavudine [d4T] vs. zidovudine [AZT] vs. TDF vs. other NRTI backbone). Pregnancy data were not routinely collected at all sites across the time period of the study or available for inclusion in the final analysis.
| Statistical analysis Descriptive statistics were used to describe first-line ART and CD4 count at ART initiation by sex, calendar year and region. To analyse time to switching in first-line ART and time to interruption in first-line ART by sex in each region, follow-up time started at the date of ART initiation and ended at either the first outcome or was censored at the end of available follow-up. We also assessed whether there was an interaction between region and sex variables. Cox regression models were used to adjust for the following variables: age and CD4 count at ART initiation and mode of exposure (injecting drug use vs. other) where available. Mode of exposure was only available for NA-ACCORD, CCASAnet and IeDEA Asia-Pacific. Given the heterogeneity between regions, we present the data individually by each region.
| RESULTS The combined cohort data set for this study comprised of 715,252 participants across seven regions including both low-and high-income settings. First-line ART varied according to setting and sex, with changes over time (Table  1 ). Efavirenz was more likely to be included in the first-line regimen in men compared with women but the proportion of women initiating efavirenz-based regimens increased in many regions from 2010 onwards. Protease inhibitors were rarely included in first-line regimens in low-income settings compared to high-income settings for both men and women. More specifically, in the Asia-Pacific region (including low-, middle-and high-income countries) nevirapine was the NNRTI more commonly prescribed in the first ART regimen in the earlier time period (2003 to 2005) for both men and women but this declined in the later time period (2010 to 2014) with efavirenz as the most commonly prescribed NNRTI.
However, it is striking that by 10 years of follow-up >75% of this cohort (Asia Pacific low/low middle income) had undergone a change in their first-line regimen. Data from Asia Pacific upper middle/high income sites demonstrated that switching was more likely in males compared to females. This data set has a significant contribution from Australia (17%) which comprises predominantly of men (90% of participant data from Australia to IeDEA Asia-Pacific is male). Hence, if genderspecific factors such as pregnancy are driving the earlier switch in ARTregimen, then this may explain inconsistent findings in this region compared to the African counterparts. This finding from a highincome setting in the Asia Pacific region was not a consistent finding in other high-income settings. In the North American cohort, women were more likely to undergo a switch than men, suggesting socioeconomic status of the region may not be a factor contributing to sexspecific differences in changes to ART therapy.
B+). In 2015, the WHO recommended lifelong ART for all pregnant and breastfeeding women regardless of CD4 count. Much of the data in our study included women prior to 2015. In summary, we have demonstrated that there are sex and regional variations in choice of initial ART, switching and treatment interruption. Given the massive scale up of HIV treatment that has occurred over the last decade, and the anticipated expansion in ART services in the next decade, it is essential to understand the drivers behind these differences. Drivers of treatment change such as pregnancy, toxicity, drug interactions and resistance need to be considered in more detail when exploring reasons for ART change or interruption, and in particular need to be compared between men and women. MLG conceived the project and contributed to manuscript writing.
Here, we present a DIA semi-automated process using Tissue Studio software (version 2. 6) to simultaneously discriminate, localize and quantify immune cell populations within distinct areas of a whole colon section. We chose the APC Min/+ mouse model to validate this approach, as it is a preclinical reference model for CRC  20, 21. Three areas were targeted: the colonic mucosa, lymphoid follicles and tumors, focusing on T cells, the significance of which is well-established in CRC  [5] [6] [7] 9, 18, 22, 23. We validat d the precision and robustness of our method on several colon slides and with different fluorescence patterns despite the tissue feature complexity and background. This pipeline allows precise characterization of the immune microenvironment in small tissue structures, such as lymphoid follicles, and was built to be used to other intestinal tissues and other gut diseases.
Results General DIA procedure based on Cognition Network technology (CNT). Immunostaining was performed using specific membrane fluorescent labelling associated with Tyramide Signal Amplification, as previously described  6, [24] [25] [26] [27]  (See Supplementary Fig. S1 ). This staining was completed with DAPI nuclear counterstaining for cell identification and numbering within tissues. We chose paraffin-embedding to preserve the colon structure and allow optimal sample quality for slide digitalization and image analysis processing. Moreover, the fluorescence signal to noise ratio must be managed to avoid signal saturation and allow accurate tissue and cell delimitation. To decipher the immune microenvironment in distinct regions of interest (ROIs) in the APC Min/+ colon, we had to develop the optimised DIA algorithm with Tissue Studio software (Definiens, Germany).
This established link thanks to selection, calibration and ordering of pre-coded rules in Tissue Studio catalogue, allowed the user to distinguish ROIs and cells on the colon section, as described in Fig. 1e. More precisely, we implemented different ROIs corresponding to each colonic structure and classified them using an automatic ROI classification step and manual refining as necessary, as shown in Fig. 1a. Up to 8 ROI classes can be defined with the software. Finally, we quantified immune cells by implementing cellular detection and classification based on the same CNT process using segmentation and classification iterations  28. We exported various parameters, such as the ROI area, mean fluorescence intensities and number of cells per ROI, for statistical comparison.
2  and Supplementary Fig. S2 ). All of these are described in detail below. (a) The procedure to quantify immune cells is based on a computer learning algorithm and elaborated using Tissue Studio software (Definiens, Munich, Germany). After optimal image acquisition, we evolved the algorithm using five sequential steps: global tissue detection, ROI (Region Of Interest) classification, ROI correction to discriminate the ROIs associated with APC Min/+ colon tissue, cellular detection and cellular classification to quantify positive membrane stained cells per ROI. (b-e) Particular blow up of a colon region is shown as an example of the digital image analysis process. Information is extracted from the picture using a hierarchy of image-objects, formed by grouping of pixels, after an evolutionary alternation of classification and segmentation steps.
3e : automatic ROI classification; 3f: ROI reclassification by algorithm; 3g: manual correction). To conduct this ROI classification, we chose to create three iROI classes and two other ROIs for non-interesting colon regions ("other tissue") and non-tissue areas ("background") (See Flowchart in Fig. 2c  and the results in Fig. 3e-g ). First we implemented a classical computer based learning step called "Training action" in Tissue Studio by recording a precise selection of representative FITC and DAPI labelled primitive objects for each ROI, as exemplified in Fig. 4a. In the first step, the software determined ROI-associated "primitive objects" for each distinct ROI, as represented in Fig. 4a, b. Mucosa, lymphoid follicle and background ROIs were correctly classified at the end of the computer based learning section (Figs  3e  and 4c ).
However, it failed to distinguish the mucosa from other tissue ROI (See Fig. 3e ) and the tumor from mucosa or lymphoid follicle (Figs  3e  and 4c ). In that case, the "primitive-objects" were similar between mucosa and other tissues, or between tumor and lymphoid follicle, and mucosa within a small area, respectively (See tumor-associated "primitive objects", in Fig. 4b , as an example). To solve this issue, we have added an ROI refining sub-step (named "Reclassification" in Tissue Studio) (See details in Fig. 4d-f ). It is based on the selection, organisation and calibration of up to eleven pre-coded conditional rules, from the software library (Summary of the possibilities in Supplementary Table  1 ). Each of these rules is built in the same fashion (See Fig. 4d, e ): it allowed the new classification of an incorrectly classified ROI object into its correct ROI class by selecting a specific characteristic of the object (named "Condition").