dataesr/scientific-paragraphs-categorization huggingface dataset:
Availability of Data Data used in this manuscript can be accessed as follows: (1) AT Illumina microarray data with Gene Expression Omnibus (GSE112307), (2) serum metabolomics NMR data with Metabolights (pending accession code), (3) urine metabolomics NMR data with Metabolights (pending accession code), (4) fecal metabolomics GC-MS data with Metabolights (MTBLS653), and (5) metagenomics raw solid read data with the European Bioinformatics Institute European Nucleotide Archive (ERP003699).
The data reduction approach led to focus on the following features: three nutrients (carbohydrate in % of energy intake, fiber in grams, and alcohol in grams), MGS (three species with highest prevalence in subjects, namely GU:41 from the Clostridiales order, GU:84 from the Alistipes genus, and GU:228 which is not annotated but whose strongest hit from the gene catalog is Faecalibacterium sp. ), 22 serum metabolic features, 19 urine metabolic features, 24 fecal metabolic features, the markers of IS, adipocyte diameter, NEFA, and 32 sAT genes. ScaleNet thus established statistical dependencies between these variables based on the consensus taken from different network reconstruction approaches. The dependencies derived through ScaleNet are therefore more robust than if any one single network reconstruction method were used.
DISCUSSION In this study, we have identified novel links between host glucose homeostasis, lifestyle factors and gut microbiota in the context of a 6-week CR intervention. This is to our knowledge the first study integrating information from lifestyle factors, gut microbiota composition, clinical factors, sAT gene expression, and metabolomics from serum, urine and feces, to investigate their relationship with IS changes after CR. The variables that were found to be more strongly associated with changes in IS should be explored in future studies as potential biomarkers or predictors of individual responses to this kind of intervention. The analytical approach presented herein has led to: (1) integration and visualization of multiple associations between changes in gut microbiota, human omics data, lifestyle factors, and IS; (2) reduction of data dimensionality to highlight key variables of interest (from 9,986 variables to 115 variables included in the ScaleNet reconstruction); (3) identification of a robust inverse association between changes in circulating BCAA with IS during CR; and (4) identification of new links between IS and different elements such as those seen with metabolic features in urine and feces, sAT genes and gut microbiota composition.
, 2010; Roberts et al. , 2014). Our data integration and reduction approach led to the identification of robust dependencies between the IS markers/BCAA cluster in the ScaleNet network with DDRGK1, a gene involved in protein ubiquitination. The relationship between DDRGK1 and IS was confirmed in an independent group of overweight/obese subjects following a different dietary intervention setting. Indeed, this second group was explored during a weight maintenance phase after a period of weight loss. Thus the fact that AT gene expression profiles greatly vary depending on the stage of weight loss and regain  (Capel et al. , 2009; Piening et al. , 2018)  could account for the difference in the direction of correlation between this study population and the independent clinical group. DDRGK1 has been shown to become activated and bind to UFM1 in pancreatic beta cell lines for protection against ER stress-induced apoptosis  (Lemaire et al.
Challenges in Integration of Data With High Dimensionality A limitation of this study is the small sample size, an issue generally encountered in studies using big data and deep phenotyping. To circumvent this issue, we have used stringent approaches in the different steps of the analysis, including high thresholds for selection of relevant variables and methods adapted to high dimensionality such as the local reconstruction aspect of ScaleNet. The advantage of this method is that it goes far beyond pairwise associations of variables, taking into account all long-range associations within subsets of variables, considering both linear and non-linear relationships. This method is adapted to high-dimensional settings since each reconstruction method uses many local reconstructions based on a spectral setting.
However, it is important to note that the links we have identified need to be corroborated by future research, including comparable study designs and similar population characteristics. In fact, this analytical approach will be applied to the Metacardis study population 1. This study included only three men, which prevented the assessment of sex differences in our outcomes. Future studies should be balanced for gender to identify potential sex-specific effects relevant in the treatment of obesity. Another limitation is that some metabolic features included in this analysis could not be annotated due to lack of knowledge in public databases and/or limitations in the identification of low intensity metabolites, so interpretation of these results could evolve depending on the depth of our abilities to annotate further features.
ACKNOWLEDGMENTS We thank the subjects for their participation in this study. We also thank  Christine Baudoin  who contributed to the clinical investigation study,  Soraya Fellahi  and  Jean-Philippe Bastard  ( Department of Biochemistry and Hormonology, Tenon hospital ) for analyses of biological markers,  Dominique Bonnefont-Rousselot  and  Randa Bittar  ( Department of Metabolic Biochemistry, Pitié-Salpêtrière Hospital ) for help with the analysis of plasma lipid profile.
FUNDING This work was supported by  Agence Nationale de la Recherche  ( ANR MICRO-Obes  and  ANR-10-IAHU-05 ) and the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement  HEALTH-F4-2012-305312  (METACARDIS) and  Fondation Leducq  (to KC's team). The clinical work received support from  KOT-Ceprodi ,  Danone Nutricia Research and the Foundation Coeur et Artère. LH was in receipt of an  MRC   Intermediate Research Fellowship in Data Science  (Grant No. MR/L01632X/1 ) and  Heart and Stroke Foundation of Canada.
The NNPDF3. 0 lo as 0130 and NNPDF3. 0 nlo as 0118  [34, 35]  parton distribution function (PDF) sets are used for the LO and NLO simulations, respectively. The cross sections used to normalize the simulated predictions are calculated at the highest orders of perturbative QCD currently available: next-to-next-to-leading order (NNLO) for W+jets and Z+jets  [36] ; approximate NNLO for single top quark production in the tW channel  [37] ; and NLO for diboson  [38]  and tt + W/Z  [39]. The tt simulation is normalized to a cross section of 831. 8 +19. 8 -29. 2 (scale) ± 35. 1 (PDF + α S ) pb (where α S is the strong coupling constant), calculated with the TOP++2. 0 program  [40]  at NNLO, including resummation of next-to-next-to-leadinglogarithmic soft-gluon terms and assuming m t = 172. 5 GeV.
The total trigger uncertainty is derived by taking the maximum deviation produced by the two variations in each unfolded bin. The SFs for the lepton identification and isolation efficiencies are determined with a tag-andprobe method using Z+jets event samples  [50, 53]. Measured in bins of η and p T , the SFs are generally within 10% of unity for electrons, and consistent with unity for muons. The lepton identification and isolation uncertainty is estimated by varying the SFs within their uncertainties. The efficiency of the kinematic reconstruction of the top quarks is found to be consistent between data and simulation within around 0. 2%. An associated uncertainty is derived by varying the corresponding SFs by ±0. 2%. The uncertainty from the modeling of the number of pileup events is obtained by changing the inelastic pp cross section assumed in simulation by ±4.
Summary Measurements of the top quark polarization and tt spin correlations have been presented, probing all of the independent coefficients of the top quark spin-dependent parts of the tt production density matrix for the first time in proton-proton collisions at √ s = 13 TeV. Each coefficient was extracted from a normalized differential cross section, unfolded to the parton level and extrapolated to the full phase space. The measurements were made using a data sample of events containing two oppositely charged leptons (e + e -, e ± µ ∓ , or µ + µ -) and two or more jets, of which at least one was identified as coming from the hadronization of a bottom quark. The data were recorded by the CMS experiment in 2016 and correspond to an integrated luminosity of 35. 9 fb -1. The measured normalized differential cross sections and coefficients were compared with standard model predictions from simulations with next-to-leading order (NLO) accuracy in quantum chromodynamics (QCD) and from NLO QCD calculations including electroweak corrections.
Splicing data were also collated from published studies of nine different tissues. The impact of the findings for PVS1 annotation was assessed for truncating and splice site variants. Results: We identified 62 naturally occurring alternative spliced BARD1 splicing events, including 19 novel events found by next generation sequencing and/or reverse transcription PCR analysis performed for this study. Quantitative analysis showed that naturally occurring splicing events causing loss of clinically relevant domains or nonsense mediated decay can constitute up to 11. 9% of overlapping natural junctions, suggesting that aberrant splicing can be tolerated up to this level. Nanopore sequencing of whole.
, 2007; Lombardi et al. , 2007; Sporn et al. , 2011; Bosse et al. , 2012; Zhang et al. , 2012; Pilyugin and Irminger-Finger, 2014; Davy et al. , 2017). However, despite previous published reports of BARD1 splicing, current catalogues of alternatively spliced events (e. g. , Ensembl-ENSG00000138376) only account for a fraction of transcripts associated with this gene. We present the most comprehensive assessment of BARD1 mRNA splicing generated by both RT-PCR and RNA-seq (longread and short-read) platforms across multiple tissue types. Furthermore, we also utilize American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines  (Richards et al. , 2015) , bioinformatic splicing, and population frequency data to evaluate potential pathogenicity of BARD1 variants located at canonical splice sites.
Ribonucleic Acid Samples RNA samples assessed in this study were isolated from different tissue types, including 47 human lymphoblastoid cell lines (LCLs) derived from female healthy controls, an epithelial enriched area of nine healthy breast samples from women with breast tumors (SCAN-B study, ClinicalTrials. gov identifier: NCT02306096), two normal fimbria tissues obtained from prophylactic oophorectomies performed in post-menopausal women without cancer, commercially available RNA from one non-malignant breast tissue (Clontech 636576), and one pool of three nonmalignant ovarian tissues (Clontech 636555) (Supplementary Figure  S1 ).
Laboratory 1 The Oxford Nanopore MinION Genomic DNA sequencing of LCL RNA was carried out as previously described  (de Jong et al. , 2017). Briefly, PCR products were prepared for sequencing using the Nanopore Sequencing Kit SQK-NSK007 (R9 Version). Primer sequences for BARD1 exons 1 and 11 are as follows: 5'-CTCGACCGCCTGGAGAAG-3' and 5'-CTGGCTTGGGCTTTCTACTG-3. ' The raw electrical signal was uploaded to Metrichor (version 1. 107), using the 2D Basecalling RNN for SQK-NSK007. Full-length alternative isoform analysis of RNA (FLAIR; https://github. com/BrooksLabUCSC/flair) was used to identify novel and known isoforms of BARD1. Sequence reads in FASTA format were aligned to the GRCh38 using the align module, which implements minimap2 with the splice option. Aligned reads were then corrected and collapsed using the respective modules of FLAIR with default settings.
Annotation of Alternative Splicing events Alternative splicing events were annotated according to the Human Genome Variation Society (HGVS) guidelines, using the Ensembl transcript ENST00000260947. 8 (NCBI RefSeq NM_000465. 3) as a reference. Splicing events were also coded as described previously (Lopez-Perolio et al. , 2019) using the following symbols: Δ (skipping of reference exonic sequences), ▼ (inclusion of reference intronic sequences), E (exon), I (intron), p (acceptor shift), q (donor shift), and int (interstitial deletion within an exon). Where possible, the exact number of nucleotides skipped (or retained) is indicated. All BARD1 alternative splicing events reported are predicted to alter the encoded protein. To decide if the truncated/altered region is critical to protein function, we considered the RING, ARD (ankyrin repeat domain), and BRCT domains as shown in Supplementary Table  S2  and Figure 1.
We thank the  Cancer Society of New Zealand Canterbury/West Coast Division  for funding. LW was supported by the  Royal Society of New Zealand Rutherford Discovery Fellowship. VL was supported by the  Mackenzie Familial Breast Cancer Post-doctoral Fellowship. MH have received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  634935   MH , and from  Spanish Instituto de Salud Carlos III  (grants  PI15/00059 ). We thank the  Jim and Mary Carney Charitable Trust (Whangarei, New Zealand ) for support (MK, LJ). AS is supported by an  NHMRC   Senior Research Fellowship  ( ID1061779 ). We wish to thank  Heather Thorne ,  Eveline Niedermayr , all the kConFab research nurses and staff, the heads and staff of the  Family Cancer Clinics , and the  Clinical Follow Up Study  (which has received funding from the  NHMRC , the  National Breast Cancer Foundation ,  Cancer Australia  and the  National Institute of Health (USA))  for their contributions to this resource, and the many families who contribute to kConFab.
Transplant Aspects. All transplants were performed with a terminoterminal choledochocholedochostomy with T-tube placement. The immunosuppressive protocol was based on a triple therapy with methylprednisolone, mycophenolate mofetil, and calcineurin inhibitor (cyclosporine=40 patients; tacrolimus=166 patients). Methylprednisolone was rapidly tapered. Donor and recipient data were prospectively collected using an in-house database and retrospectively reviewed; donor information was supplemented by data held in the National Transplant Center database. Initial Poor Graft Function (IPGF) was defined according to Nanashima et al. [20]. Early allograft dysfunction (EAD) was defined according to Olthoff et al. [21]. Causes of graft loss were reported and classified as liver-related or liver-unrelated according to the European Liver Transplant Registry  [22].
Liver Biopsies. Permanent histological sections were prospectively collected from allograft preischemia liver wedge biopsies performed on the left hepatic lobe. The liver tissue was immediately fixed in 10% formalin and within few days was embedded in paraffin and then stained with hematoxylin and eosin, to assess hepatic steatosis in all transplants. To grade the severity of ischemia-reperfusion injury (IRI), permanent histological sections in the recipient within 1 hour after complete revascularization of the allograft (postreperfusion biopsy) were obtained in 134 cases (79 HCV-and 55 HCV+ patients, respectively) with the same procedure. Frozen-section evaluation was performed in selected cases based on gross appearance of the graft only to decide whether to discard the graft. Two expert pathologists (AC and MR), blinded to clinical data and to the frozen-section evaluation, retrospectively reviewed and scored all the preischemia liver samples for steatosis, defined according to the Brunt classification  [11, 14]  (Figure  2 ) as follows: Ld-MaS, as one or few large vacuoles in the cytoplasm with eccentric nuclear displacement; Sd-MaS, as few and discrete fat vacuoles that were smaller than half of the cell and did not displace the nucleus.
The aim of the present study was to map the risk of CL based at two different scales in the Amazonian forest and surrounding Neotropical moist forest ecosystems. This geographic area allows working at higher spatial resolution than previously published studies, controlling the influence of bioclimatic factors previously identified as disease occurrence drivers  [14, 38, 39]  and likely highlighting a putative role of more local bioecological drivers. We used maximum entropy implemented with the MaxEnt software  [42] , based on a presence-background ENM, identifying non-linear responses of CL cases to different fine-resolution biotic and abiotic variables at both the Amazonian and French Guianan scales. These two models were run independently and are not assumed to validate each other, but instead are expected to show the extent to which the geographic grain influences the relative importance of contributory variables for the spatial prediction of the disease risk.
We used only the official human CL epidemiological records as input data to predict the risk of leishmaniasis occurrence. The cases were geolocated in the health centres, resulting in uncertainty as to the contamination area and geographybiased case reports for this sylvatic disease. To stay within the BAM reasoning framework, we attempted to adapt the model to the real ecological conditions of the CL cycle. To reflect the most likely places of contamination and properly handle the field realities, we randomly distributed the occurrence of cases outside urban centres. By eliminating areas where one is unlikely to find autochthonous CL cases, we succeeded in integrating the movement (M) of the BAM framework. Several redistribution methods made it possible to control the sampling biases related to the uncertainty of case geolocation.
Parameters in the models All data were processed in ArcGis 10. 4  [46]. All variables were used at the resolution of 30 arcseconds (~1 km 2 ) for the Amazonian and French Guiana models. Geographic variables available at another resolution and vectorized variables were resampled at 30 arc-seconds using the nearest neighbour joining method, implemented with ArcGis 10. 4. The bioclimatic, environmental and anthropogenic variables are given in Table  1 , with their initial resolution. In total, 26 variables were used for the Amazonian model including 19 bioclimatic variables from WorldClim2, three anthropogenic variables with the population density, the human poverty and the human footprint (HFP) and four environmental variables: the biomass aboveground, elevation, forest canopy height and species richness in mammals.
Model implementation and processing Using MaxEnt. The aim was to identify the response curves of the likelihood of case occurrence in relation to the most contributing explanatory variables and then to highlight areas with the most favourable predictors for disease occurrence and risk. To examine how our range of environmental and anthropogenic factors may influence leishmaniasis occurrence, we used a generalised additive model (GAM) to model the occurrence of human CL cases as input data, under the software MaxEnt, version 3. 3. 3k [42]. The main benefit of GAMs over techniques such as logistic regression and other generalised linear models is that they use regression splines to estimate the response curves of different predictor variables and can thus learn their non-linear contributions to the dependent variable.
Data preparation for modelling Weighting cases. Since the geographically close occurrences are likely to be spatially autocorrelated, MaxEnt requires rarefaction of input data when data sets are large, minimizing the number of cases per cell (i. e. 1 km 2 in our study) before the model is implemented. The resampling strategy thus allowed mitigating the model's over-learning in oversampled areas. For the Amazon, we averaged the number of cases in each locality recorded yearly. Then we arbitrarily created three classes of weighted occurrences: localities with one to ten cases (n = 1,115) were represented by one point, localities with 11-100 cases were weighted with two points (n = 266) and localities with more than 101 cases (n = 102) with four points. A total of 149,368 cases for the Amazonian model were weighted, giving a final 4,280 CL cases to use for model development.
Results The three different methods for Amazonian and French Guiana models show only very few differences in their respective AUCs, and the occurrence of CL cases is explained by the same set of environmental and anthropogenic variables (S1 Table ). Test omission rates are null at the minimum training presence threshold for training datasets (rate = 0. 000 for all methods 1, 2 and 3) and very low for the test omission (rate method 1 = 0. 0005, rate method 2 = 0. 0017, rate method 3 = 0. 0008). For the 10th percentile training presence threshold the training rate = 0. 099 and the test rate = 0. 11 for the three methods. Pair-wise comparisons, using nonparametric tests, show no deviation from the null hypothesis of differences across the three Amazonian ENM methods (for latitude, Z [0. 688; 0.
In Colombia, the states in the south-east did not come out as a potentially high-risk area. This result seems to contradict the recent study conducted by Herrera et al. [43] , which indicated that these states had the highest incidence and number of cases in the country. This failure may be explained by the limit of spatial ENM when working with quantitative data. Despite a very high number of reported CL cases in this region, the number of cities and the population density remain very low. However, ENMs handle quantitative data such as prevalence, because the information is retained at the pixel scale and whatever the number of cases in one pixel, it is saturated with the first reported case. Despite our procedure to create a buffer zone to randomly disperse cases, cases and substantially increase the number of available pixels to distribute the cases, the model still gives greater importance to areas where the spatial occurrence of cases is widely distributed.
Limits The methodology of this study is based on satellite imagery and correlative analyses, but it remains a visual assessment. It also excludes that the cycles could occur in anthropised and highly disturbed habitats. Indeed, in Colombia CL is linked to the urban cycle  [34]  and in the largest Amazonian cities such as Belem, CL is associated with small forest fragments surrounded by an urban area and where (phlebotomine) putative vectors may sustain  [64]. Consequently, it may be interesting to retain relatively high values of HFP in order not to completely obscure the likelihood of local peri-domestication of CL. Another limitation of our study is that some areas of the Amazon biome are not considered at risk while we do know the existence of CL cases, as in Peru and Bolivia. Heterogeneity in the availability of our data increases the models' omission rate, but we favoured data that were reliable and retrieved directly from the public health database for each country.
Unfortunately, it was possible to find this kind of data for only two countries, i. e. , Colombia and Brazil, and for the French Guiana region. We also attempted to obtain the most updated variables for the Amazonian model, but some are not updated over the period when the cases occurred, so the environmental data are not necessarily concomitant with the case occurrence period. In addition, we are aware that the models are highly dependent on the input variables and spatial scaling, so risk maps produced with large-scale data and models should not be extrapolated for more restricted geographical areas; risk maps are first context-and space-dependent.