dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, to test the robustness of GSEA the results to the choice of database, we performed GSEA on the top correlated genes with Dimensions 1 and 2 using the STRING database  [23]  v11 (Table  S6 ). From the top 300 genes correlated with Dimension 1, 66 Biological Process GO terms were enriched with vascular pathways, with the top 10 including: "regulation of vasculature development" (GO:1901342), "positive regulation of angiogenesis" (GO:0045766), and "artery development" (GO:0060840). Interestingly, the "cell adhesion" (GO:0007155) and "regulation of cell migration" (GO:0030334) GO terms were also in this top 10, suggesting that molecular pathways involved in the epithelial mesenchymal transition are also captured by Dimension 1. We performed the same analysis with the top 300 genes correlated with Dimension 2 coordinates and found 312 Biological Process GO terms enriched for a large majority of pathways related to the immune system.
Survival analysis Median survival times were estimated using the Kaplan-Meier nonparametric estimator. Survival predictions were tested using Cox proportional hazards models (R package survival v. 2. 42-6)  [24]. Goodness of fit was assessed using three diagnoses (following Bradburn and colleagues  [25] ): (i) to assess the proportional hazards assumption, we computed the Schoenfeld residuals test for each variable, using rank transformation for survival time (function cox. zph from package survival); (ii) to assess the leverage of each observation, we computed the change in regression coefficients when removing each observation (function ggdiagnostics with option "dfbeta" from package survminer); and (iii) to assess the general goodness of fit of the model, we plotted the deviance residuals as a function of linear predictions (function ggdiagnostics with option "deviance" from package survminer).
We also assessed the functional forms for continuous variables using plots of martingale residuals against the values of the focal variable; because the percentage of sarcomatoid, PC1 and PC2 presented non-linear functional forms, we modelled them using smoothing splines with four degrees of freedom. Diagnostics and functional forms were assessed using R package survminer, v. 0. 4. 3. We compared model fits using the time-dependant Area Under the ROC Curve (AUC) and its integral (iAUC; Section 3. 3 of Chambless and Diao, 2006; R package survAUC, v. 1. 0-5)  [26] , computed using leave-one-out cross-validation  [27]  (Table  S7 ). Timedependent AUC estimates the ability of a model to predict patients with a survival higher or lower than a given threshold, and iAUC integrates the results of time-dependent AUC over the threshold value, providing an interpretation similar to that of classical AUC.
Differential protein expression analysis from IHC data We performed differential gene-expression analyses from RNAseq data on the discovery cohort (data from Bueno and colleagues and the TCGA) using univariate independent two-sample Wilcoxon U tests between a long-survival epithelioid group (47 samples from the cohort with survival >30 months), a short-survival epithelioid group (58 samples with survival <10 months), and a sarcomatoid group (eight samples with survival <10 months) as defined in Section 2. 2 of Materials and Methods; because we had no a priori hypotheses about the direction of the effects of the groups on gene expression, we used two-sided tests in the discovery cohort. For the replication cohort, we conducted both univariable tests of differential protein expression between the matched sets (shortsurvival epithelioids, long-survival epithelioids, and sarcomatoids; paired two-sample Wilcoxon T-tests) and between epithelioid subtypes (subtypes and stromal variants; Kruskal-Wallis tests).
Data sharing TCGA RNA-seq data are available from the GDC portal (TCGA-MESO cohort) and the RNA-seq data from the Bueno and colleagues cohort are available from the European Genome-phenome Archive, EGA:EGAS00001001563. An interactive version of the PCA in Fig. 1a  is available for further exploration in https://tumormap. ucsc. edu/ under the project MESOMICS.
Upper panels: Hematoxylin Eosin Saffron (HE) stain at 7× magnification, where white and black arrows show tumour cells and TILS, respectively. Lower panels: corresponding staining with PDL1 rabbit monoclonal antibody (cl SP263; VENTANA) at 7× magnification, where white and black arrows show positive staining of tumour cells and TILS, respectively. e) Protein expression level of VISTA in the replication cohort when considering epithelioid subtypes, independently of the sample set (upper panel) and in addition to the sample set (bottom panel). Data used correspond to n = 63 samples from the replication cohort with available data for all protein markers. In the boxplot representation, centre line represents the median and box bounds represent the inter-quartile range (IQR). The whiskers span a 1.
Funding sources This work has been funded by the  French National Cancer Institute (INCa ,  PRT-K-15-039  to LFC), and the  Ligue Nationale contre le Cancer  ( LNCC 2017  to LFC). LM has fellowships from the  LNCC. The funding sources had no involvement in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.
, 2018). Here, we showed that this integration of visual cues and distance information in V1 may also happen throughout the trajectory of the animal in the corridor: in our dataset, V1 neurons exhibited a shift in their response when the distance changed, whether they fired preferentially at the reward position or elsewhere. The network of connections that could explain a top-down influence of the navigation system on visual cortex is unknown. Several intermediate cortical areas may be involved, such as the anterior cingulate cortex  (Fiser et al. , 2016) , or retrosplenial cortex  (Mao et al. , 2017; Vélez-Fort et al. , 2018). Feedback signals from the navigation system might also influence visual processing in thalamic neurons upstream to V1  (Busse, 2018; Hok et al. , 2018). A promising way to probe these connections may be during sleep, when V1 and CA1 tend to activate together, potentially replaying sequences of activation observed during active exploration  (Ji and Wilson, 2007).
Surgical procedure Data were collected from ten C57BL/6J male mice. The surgical procedure is similar to that described previously  (Saleem et al. , 2018). In brief, mice were implanted on their left hemisphere with a 4-mm diameter chamber at 4-10 weeks of age, under deep isoflurane anesthesia. Mice were left to recover for 3 days during which they received antiinflammatory drug (Carprofen/Rymadil, oral administration). After recovery, mice were waterrestricted and moved to light-shifted conditions (9 a. m. light off, 9 p. m. light on). Mice were then trained once a day during their dark cycle, approximately at the same hour of the day, for several weeks. After they reached sufficient performance in the task, we performed two 1-mm craniotomies under deep isoflurane anesthesia: one over CA1 (1.
For low and high gain conditions, data were sliced according to the same absolute speed ranges as in the medium gain condition. We used the average response profiles computed from medium gain trials to decode positions from the spike counts measured on low or high gain trials. The mean decoded probability (Figure  1E  and S3E-S3F) was computed by averaging the posterior probability for each position of the mouse. The resulting distribution was smoothed with a 2d-Gaussian window (2 x 2-cm s. d. ) and the mean decoded trajectory was estimated by computing the weighted circular average of this probability distribution for every position of the animal. To estimate the decoding bias between gain conditions, we measured the difference between the mean decoded trajectory computed in the low or high gain condition with the mean decoded trajectory computed in the medium gain condition.
Acknowledgements We thank  C. Reddy  for help with surgeries and  C. Barry  for discussions. This work was funded by  Human Frontier Science Program  (fellowship LT001022/2012-L to J. F. ) and  EC Horizon 2020  (fellowship 709030 to J. F. ), by  Wellcome Trust/Royal Society  ( Sir Henry Dale Fellowship   200501  to A. B. S. ), by  EPSRC  (PhD award  F500351/1351  to E. M. D. ), by the  Wellcome Trust  (grant  205093  to M. C. and K. D. H. ), and by the 8 Simons Collaboration on the  Global Brain  (grant  325512  to M. C. and K. D. H. ). M. C. holds the  GlaxoSmithKline/Fight  for Sight Chair in Visual Neuroscience.
Event and track selection The Pb+Pb data in this analysis were selected using two mutually exclusive minimum-bias triggers. Events with semi-central and central collisions were selected if the scalar sum of transverse energy in the entire ATLAS calorimeter system exceeded 50 GeV. Peripheral events, i. e. those with large impact parameter of the colliding Pb nuclei, fail the 50 GeV selection and were instead selected by requiring a deposition in the ZDC corresponding to at least one neutron and by requiring at least one track reconstructed in the HLT. Data in this analysis are required to come from periods when the entire detector was functioning normally. The events are required to have a reconstructed vertex within 100 mm of the nominal interaction point. The contribution from events containing more than one inelastic interaction (pile-up) is studied by exploiting correlations between the transverse energy measured in the FCal (ΣE FCal T ) with the estimated number of neutrons in the ZDC, and with the number of tracks associated with a primary vertex  [27, 35].
Six combinations of thresholds were used to optimise data-taking during periods with different luminosities. Samples of events collected by these triggers are combined by applying event weights to reproduce the charged-particle multiplicity distribution of the minimum-bias trigger. Further details of the data selection are given in Refs. [22, 36]. The average pile-up probability in the p+Pb dataset is approximately 3% but can be significantly larger in high-multiplicity events. Events with more than one reconstructed vertex are removed from the sample. Similarly to the Pb+Pb dataset, to remove events where the two interaction vertices are too close to resolve as independent ones, the ZDC signal on the Pb fragmentation side is used. The distribution of the number of neutrons, which is broader in events with pile-up than that for the events without pile-up is exploited for that purpose  [36].
64 × 10 8 events enter the analysis. The charged-particle tracks reconstructed in the ID are required to satisfy selection criteria in order to suppress the contribution of incorrectly reconstructed tracks and secondary products of particle decays. The selection criteria include the requirement that the number of hits in the pixel and SCT detectors should be greater than two and eight, respectively, for the Pb+Pb data and greater than one and six for the p+Pb data. The track impact parameters relative to the collision vertex in the transverse direction, |d 0 |, and longitudinal direction, |z 0 sin θ|, are required to be less than 1 mm for tracks in the Pb+Pb data sample and less than 1. 5 mm in the p+Pb sample. In addition, in p+Pb collisions, the track impact parameter significances must satisfy |d 0 /σ d 0 | < 3 and |z 0 sin θ/σ z | < 3, where σ d 0 and σ z are the uncertainties in d 0 and z 0 sin θ determined from the covariance matrix of the track fit.
The p+Pb data were taken with higher pile-up than the Pb+Pb data. To estimate the impact of contamination by residual pile-up events, p+Pb results were obtained with only the vertex criteria applied. The variation covers the estimated residual pile-up fraction in events of the highest track multiplicity  [36]. Centrality selection. The minimum-bias trigger is fully efficient for the 0-85% centrality interval. However, the total fraction of inelastic Pb+Pb events selected is known only to 1% accuracy due to trigger inefficiency and possible sample contamination in more peripheral interactions. The centrality is estimated using the ΣE FCal T distribution  [6, 10]  and the Glauber model  [38]  to obtain the mapping from the observed ΣE FCal T to the number of nucleons participating in the collision, N part.
Summary The first measurement of the modified PCC ρ(v n {2} 2 , [p T ]), which quantifies the correlation between the flow harmonics and the mean transverse momentum, is performed by ATLAS experiment at the LHC. The measurement uses 22 µb -1 of Pb+Pb data and 28 nb -1 of p+Pb data at the same centre-of-mass energy per nucleon pair of 5. 02 TeV. The correlation coefficient for several charged-particle p T ranges is measured as a function of the number of charged particles N ch and, in Pb+Pb collisions, the average number of nucleons participating in the collision, N part. For the 2 nd -, 3 rd -, and 4 th -order harmonics, the measured quantities exhibit a dependence on the choice of charged-particle p T range. Measurements with an upper limit of 5 GeV on p T indicate a stronger correlation than those with an upper limit of 2 GeV.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRT, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
In addition, individual groups and members have received support from  BCKDF ,  CANARIE ,  Compute Canada  and  CRC ,  Canada ;  ERC ,  ERDF ,  Horizon 2020 ,  Marie Skłodowska-Curie Actions  and  COST ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex  and  ANR, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales.
org/), developed by Andy Shapiro et al. in 2014 in collaboration with EPA National Center for Environmental Assessment (EPA NCEA) and National Institute of Environmental Health Sciences/National Toxicology Program (NIEHS/NTP) (Shapiro, HAWC provides transparency throughout the entire assessment process, from literature search and review, data extraction and evidence synthesis, endpoint analysis, risk of bias assessment, data visualisation, and even meta-analysis, should that be performed. This protocol has been developed in accordance with the reporting guidelines established by the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Protocols (PRISMA-P) 2015 and PRISMA for Abstracts (PRISMA-A)  (Moher et al. , 2015) , and registered in the International Prospective Register of Systematic Reviews on July 17th 2018 (PROSPERO) (registration number CRD42018102618).
Study selection Two reviewers (KM and GCS) will independently perform the study selection, and conflicting results will be discussed with a third reviewer (JPA). Study selection will occur in two phases. Phase I will consist of an initial screening of title and abstracts, according to the pre-established inclusion/exclusion criteria (Table  1 ). Studies whose titles/abstracts do not provide enough information to decide or are otherwise unclear will be automatically moved to the next phase for full text assessment. Retained articles from Phase I will be exported into a Research Information Systems (RIS) file for transparency and uploaded into the HAWC Literature Review module for Phase II. Phase II will consist of a comprehensive full-text review of all the articles retained from Phase I. The full text of retained records will be gathered in print or electronic pdf and linked within HAWC for ease of accessibility.
Data extraction The data extraction method will be piloted in duplicate by two reviewers (KM and GCS) to calibrate consistency using comprehensive data forms specifically designed for animal and in vitro studies available in HAWC, adapted from the NTP/OHAT Protocol (OHAT, 2015a). Complete data forms are available in Supplemental Table  4. The following items will be extracted from the studies: -Study identification information (i. e. Title, Author, Journal, Date of Publication) -Funding Source and Conflicts of Interest -Type of Study (i. e. Animal Bioassay, In vitro) -Exposure (i. e. Chemical name, class, purity, vehicle, etc. ) -Population (i. e. Animal species, strain, sex, source, age; Cell line, tissue culture, source, etc. ) -Dosing methodology (i. e. Route of exposure, duration, frequency, dose groups, vehicle, etc.
) -Controls (positive, negative, vehicle) -Randomisation or blinding procedure(s) -Endpoint (e. g. onset of endometriosis, severity of endometriosis, lesion diameter, nuclear receptor expression levels, changes in gene expression, effect sizes, etc. ) -Diagnostic or method to measure endpoint (e. g. laparoscopy; assay name, source, etc. ) -Statistical methods (e. g. significance levels) Data provided only on plots will be extracted directly from the image using the WebPlotDigitizer (version 4. 0 PLOTCON 2017 -Oakland, CA), a semi-automated web-based tool. Whenever relevant information is missing, the authors will be contacted to provide further details.
Were experimental conditions identical across study groups? 4. Were the research personnel blinded to the study group during the study? Attrition bias 5. Were outcome data incomplete due to attrition or exclusion from analysis? Detection bias 6. Can we be confident in the exposure characterisation? 7. Can we be confident in the outcome assessment? Selective reporting bias 8. Were all measured outcomes reported? Other 9. Were there any other potential threats to internal validity? Each question within the tool will receive one of five possible responses: "Definitely Low Risk of Bias", "Probably Low Risk of Bias", "Probably High Risk of Bias", "Definitely High Risk of Bias" or "Not Reported", with a statement of justification based on the study text. Reponses will be determined by two independent assessors (KM and GCS) following the pre-specified criteria detailed in Suppl.
Mat. 1 Section 3. 2. 1. 1: Risk of Bias Response Criteria, which are specific to each type of evidence stream. Discrepancies will be compared and discussed to reach a consensus, and, if needed, other members of the review team will be consulted to resolve remaining disagreements. In case of incomplete data or dubious reporting, study authors will be contacted for clarification. Risk of bias will be evaluated at individual and outcome level, meaning that one individual study subject to different outcome-based experiments will receive multiple RoB evaluations. Summaries of RoB will be presented through heatmaps for each stream of evidence and specific outcome. The 3-Tier approach proposed by the NTP/OHAT Handbook will be used to synthesise RoB evaluations across studies included in the same outcome.
The tiered approach establishes a Tier for each study considering the RoB responses of every question, from Tier 1 (Most answers including from the key questions, rated as "Definitely" or "Probably" low) to Tier 3 (Most answers including from the key questions, rated as "Definitely" or "Probably" high) (See details in Suppl. Mat. Section 3. 2. 1: Risk of Bias). The "Key Questions" are those domains which have a greater impact on the overall bias. Randomisation bias, outcome detection bias and performance bias related to blinding of assessors comprise the Key Questions for RoB assessment. The application of the tiered approach here does not seek to exclude Tier 3 (High Risk of Bias) studies but to support decisions for sub-group analysis based on RoB. The entire assessment will be performed and registered within the Risk of Bias Module in the HAWC platform to ensure the transparency.
Data synthesis A narrative synthesis will be performed for data from both in vivo and in vitro studies. Data from each stream of evidence will be synthesised separately, supported by tables and plots summarising the main direction of effects using visualisation tools available in HAWC. Subgroup analyses will be performed as data deems fit. Though exact subgroups depend on the nature of extracted data and thus cannot be precisely anticipated, potential subgroups may include but are not limited to: animal model (e. g. primate, rodent-autologous, rodent-heterologous), chemical exposure (e. g. dioxins and dioxin-like chemicals, non-dioxin-like chemicals), experimental design (e. g. acute, subchronic, chronic, developmental), dosing regime (e. g. dosing levels, frequency, timing, duration, route of administration), variation in study risk of bias level (e.
g. high risk of bias, low risk of bias) for specific domains or overall ratings. Subgroups will be analysed for their impact on not only degree of effect but also direction of effect. An emphasis will be placed on the primary and secondary outcomes of in vivo studies, using the evidence provided by in vitro studies as support. Meta-analysis will be conducted only on primary outcome measures when at least two studies provide quantitative and commensurate data of the same endpoint and exposure that may be combined. The main objectives of the meta-analysis will be to evaluate the consistency between studies, to estimate of the overall size and direction of associations and to identify sources of heterogeneity. If a quantitative synthesis is deemed appropriate, for dichotomous outcomes (e. g. spontaneous endometriosis onset in non-human primates), we will build contingency tables and calculate odds ratios.
, 2010; Leyval and Berthelin, 1991; Djihad Oulkadi et al. , 2014). The genus of each bacterial strain was determined by amplifying and sequencing 16S rRNA genes. Polymerase chain reactions were performed using a universal set of primers in a total reaction volume of 50 µl containing 1x PCR mastermix (Eppendorf ®), 0. 1 µM of primers (966 F and 1401 F) and 1 µl of cell extract (Fleske 1998). Prior to being sequenced by the MWG Biotech Company (Courtaboeuf, France), the PCR products (PCR product size: 433 base pairs) were purified and concentrated using mini-columns (High PureTM PCR Product Purification Kit, Roche diagnostic). A blast program was then used to compare the sequences with those of the GenBank databases (www. nbci. mlm. nih. gov. blast). The following identifications were made, with a maximum identification of approximately 99 %: Rhanella aquatilis (RA1) and Pantoae agglomerans (PA1), which are both anaerobic facultative bacteria, and Agrobacterium radiobacter (AR1) and Pseudomonas sp.
The detection limit for glucose concentration was 0. 1 ppm. The concentration of planktonic bacteria was typically determined by measuring the absorbance of the cell suspension at a wavelength of 600 nm. A correlation between the number of cells and the absorbance was realized by directly enumerating the number of cells with a Thoma cell under microscopic observation as a function of the optical density at 600 nm. For each bacterial strain, an equation was obtained to approximate the number of cells in suspension as a function of optical density (data not shown).