dataesr/scientific-paragraphs-categorization huggingface dataset:
Subsequent duodenal digestion of the 11 min pH 6. 5 gastric digest showed incomplete digestion with significant peanut protein remaining in the insoluble phase, which appeared to be gradually, but only partially, digested over the time course of duodenal digestion (Figure  1G, H ). No immunoreactive Ara h 1 was detected by immunoblotting of the soluble fraction (data not shown) and immunoblotting analysis only showed evidence of the acidic subunit of Ara h 3 in the undigested duodenal control sample and the 0. Subsequently, the effect of incurring the peanut flour into two food matrices on allergen digestibility was investigated. The first was a chocolate dessert matrix containing cocoa powder and comprising a cold swelling starch gel, which has been used for oral food challenges in the EuroPrevall project.
Significant amounts of undigested protein were observed in the insoluble fraction of the cookie digest performed at pH 2. 5 even after 120 min of digestion, a pattern reminiscent of that previously observed for bread digests. [28] Immunoblotting analysis of the soluble fraction of the digests indicated that whilst Ara h 1 and Ara h 3 immunoreactivity was lost, that of Ara h 2 and 6 was retained (Figure  S11  A-C, Supporting Information). Duodenal digestion of the pH 2. 5 11 min gastric time point was then undertaken, since it was again representative of the stable end-point of gastric digestion as indicated by SDS-PAGE analysis (Figure  3 ). No immunoreactivity towards Ara h 1 was observed (data not shown), although two faintly staining Ara h 3 reactive polypeptides of Mr ≈23 and 12 kDa persisted for 60 min (Figure  S12 , Supporting Information).
Acknowledgements This research was supported by a  Newton  fund project placement grant and a  Chinese Scholarship Council award  to HR. It was also supported by funding from the  European Union  through the iFAAM project: Integrated Approaches to  Food Allergen  and  Allergy Risk Management  (Grant Agreement  N°312147 ). F. S. was supported by a  Biological and Biological Sciences Research Council (BBSRC)  award ( BB/1006109/1 ) and  RLS a BBSRC Cooperative Award in Science and Engineering  ( BB/K501608/1 ).
Construction of the Genomic Dataset The whole collection of 141 non-human S. Derby strains previously published in 2018  (Sévellec et al. , 2018a)  was used in this study. This collection included food strains from pork (n = 85) and poultry (n = 56) sectors. Strains were isolated from raw material (carcass, offal, and fresh meat) or processed meat products (sausage) sampled at slaughterhouses, processing plants and retail markets. S. Derby strains from beef were excluded due to their very low frequency in the cattle sector (<1%)  (Leclerc et al. , 2016). Strains isolated from ovine meat were also excluded due to the low consumption of sheep and goat meat in France (<3%)  (Menard et al. , 2015). The 299 human strains corresponding to all the clinical cases that occurred in metropolitan France in 2014 and 2015 were collected by the National Reference Center (NRC) (Institut Pasteur).
These strains were identified as serovar Derby by glass slide agglutination, according to the White-Kauffmann-Le Minor scheme  (Grimont and Weill, 2007) , and their genomes were extracted and sequenced as previously described  (Ung et al. , 2019). Therefore, the total French dataset used for the source attribution and comparative genome studies comprised 440 genomes. Genome accession numbers, relevant epidemiologic data and sequence quality metrics for all the genomes used in this study are reported in Supplementary Table  S1.
Single Nucleotide Polymorphism (SNP) and Phylogenetic Analyses The SNP analysis of the 440 strains was conducted using the iVARCall workflow  (Felten et al. , 2017). The 2014LSAL02547 S. Derby reference sequence (NCBI accession number CP029486)  (Sévellec et al. , 2018a)  was used to generate the VCF files. Phylogenetic analyses were computed using RAXML software  (Stamatakis, 2014). The phylogenetic trees were constructed under the maximum likelihood criterion using the GTRgamma model of nucleotide evolution. The phylogenetic analyses were based on the pseudogenome obtained using GATK  (McKenna et al. , 2010). For each lineage, a subtree was constructed as described above. The genome of strain 2014LSAL02547 (NCBI no. CP029486)  (Sévellec et al. , 2018a)  was used as a reference for the ST40 and ST39 analysis (average breath coverage of 99.
Identification of Acquired Resistance Genes The collection of genomes in the dataset was analyzed by the ResFinder 2. 1 application  (Zankari et al. , 2012)  on the CGE server, as described previously  (Sévellec et al. , 2018b). The localization of resistance genes inside the assembled genomes was tested by running a blast on Bionumerics V. 7. 6. 1. The structure of the S. Derby class 1 integron (NCBI: HG314953. 2) was obtained from the whole database  (Moura et al. , 2009) , and the presence of this integron was tested against our collection using the BLAST tool. 1 https://cge. cbs. dtu. dk/services/MLST/ 2 https://itol. embl. de/.
In case of a higher diversity of sources in STs or a higher difference in relative meat consumption, it would have led us to apply frequency-matching source attribution models. Considering the strong association observed between STs and sources, the use of sophisticated source attribution models did not appear relevant here  (Mughini-Gras et al. , 2019). Interestingly, the S. Derby genomes of human origin displayed higher genomic diversity, with 11 ST profiles that were not present in our collection of food isolates. They were found only among these isolates and were genetically distant from the clades isolated from food. The origin of contamination by these rare STs could be either unreported history of travel or other animal sources that were not investigated in our datasets, such as bovine or ovine reservoirs or imported food products.
Although ST40 is responsible for most human infections in France, ST39 and ST682 also participate with 15 and 7% of human cases, respectively. Importantly, within the strains belonging to ST39 and ST682, a small number of human strains have been linked to food strains (Figures  4, 5 ). This can be explained by the number of ST39 and ST682 food strains in our dataset. The inclusion of more food strains from these two lineages and the use of a more accurate reference genome for the analysis of ST682 strains would probably allow a better understanding of the diversity of these lineages and the sources of human contamination. Nonetheless, two clusters group human and food strains within ST39 (cluster ST39a and ST39b; Figure  4 ). Remarkably, most of the S. Derby genomes without any antimicrobial resistance genes identified in France belong to ST682 (28/30 genomes) and ST71 (62/64 genomes).
The presence of a resistance gene for fosfomycin fosA7  (Rehman et al. , 2017)  was detected in all genomes from ST39 and a 97% identity variant is present in all the genomes of ST40. These genes were not reported in previous studies  (Sévellec et al. , 2018b)  as this gene was included only recently in the Resfinder database. This result demonstrates the importance of keeping track of the updates of public databases since some resistance genes may go unnoticed, depending on the database version. Resistance to fosfomycin was not tested in our antimicrobial susceptibility test, nor in previous publications to our knowledge. Further antimicrobial susceptibility tests should be performed to verify the functionality of the fosA7 gene both in ST39 and in ST40. Only 6 human cases were caused by strains from ST71.
1A ). Although this purification step drastically reduced the total numbers of iRBCs, it was essential to achieve the highest level of comparability within the dataset. Dual hybridizations comparing the global expression of parasites obtained from the different experimental infections (labeled with Cy5) with a reference pool PvSp-1 obtained from splenectomized monkeys from the Centers for Disease Control and Prevention (CDC) donated by John Barnwell, Malaria Branch, Division of Parasitic Diseases and Malaria, CDC, Atlanta, GA (labeled with Cy3) were performed using an Agilent custom-made array containing 5,038 P. vivax coding genes (one 60-base oligonucleotide at every 2 kb) as annotated  (10). Use of this reference pool from the CDC was needed due to the limited amounts obtained from our groups of monkeys Sp-1.
Of note, sample Sp-4 was not included in these analyses due to insufficient amount of total RNA obtained. All data are freely available through the Gene Expression Omnibus (GEO) database (GEO accession no. GPL6667). Two different statistical algorithms were used for expression analysis (SI Appendix, Fig. S1 ). In the first model, probe intensities were compared with negative controls. Average intensity of each probe was compared with the probability density function of the negative controls. If expression probability of negative controls was less than 0. 001 compared with the probe, this probe was classified as "on. " If it was greater than 0. 05, probe was considered "off. " In the second model, a 0. 98 quantile of negative controls intensity was used as a cutoff. Probes presenting average intensity at least 10 times greater than this cutoff were considered on, and the ones with average intensity below the quantile were classified as off.
5 (Agilent Technologies). Spike-in RNA (Agilent Technologies) was used as internal control. Slides were scanned with Agilent's G2565CA Microarray Scanner System. Dye normalized, background subtracted, and log ratios of sample to reference expression were calculated using Agilent's Feature Extraction Software version 9. 5. Dual hybridizations comparing the global expression of parasites obtained from the different experimental infections (labeled with Cy5) with a reference pool PvSp-1 obtained from splenectomized monkeys from the CDC (donated by John Barnwell; labeled with Cy3) were performed using Agilent's custom-made array containing 5,038 P. vivax coding genes (one 60-base oligonucleotide at every 2 kb) as annotated  (10). Of note, sample Sp-4 was not included in these analyses due to insufficient amount of total RNA obtained.
All data are freely available through the GEO NIH database repository (GEO accession no. GPL6667). Selection of Spleen-Dependent Genes Based on Microarray Analyses. Two distinct probabilistic models were proposed to identify P. vivax genes expressed only in monkeys with intact spleen (SI Appendix, Fig. S1 ), one considering each time point independently (Method 1) and the other considering the entire time series (Method 2). In the first model, probe intensities were compared with negative controls. negative control was considered as a realization of a random variable (l ctrl ), and a probability density function was modeled using straightforward Kernel Density Estimation  (52)  in R programming language's package stats  (53). Average intensity of each gene probe (a number, l probe ) was compared with the probability density function of the negative controls.
ACKNOWLEDGMENTS. We thank  John Barnwell  ( Malaria Branch ,  Division of Parasitic Diseases and Malaria, CDC ) for the gift of the reference RNA pool PvSp-1 obtained from splenectomized monkeys from the  CDC ,  Artur Scherf  ( Biology of Host-Parasite Interactions, Institut Pasteur ) for the gift of CHOcells expressing different receptors and  Marc Nicolau  for technical assistance. 13064 | www. pnas. org/cgi/doi/10. 1073/pnas. 1920596117Fernandez-Becerra et al. We also thank the anonymous reviewers of this manuscript whose criticisms and suggestions significantly improved its content and quality. This work was supported with funding from the  Cellex Foundation. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The  Barcelona Institute for Global Health (ISGlobal ) receives support from the  Spanish Ministry of Science, Innovation and Universities  through the " Centro de Excelencia Severo Ochoa 2019-2023" Program  ( CEX2018-000806-S ).
Purpose Little is known about the aetiology of renal cell carcinoma (RCC). Components of one-carbon (1C) metabolism, which are required for nucleotide synthesis and methylation reactions, may be related to risk of RCC but existing evidence is inconclusive. We conducted a systematic review and independent exposure-specific meta-analyses of dietary intake and circulating biomarkers of 1C metabolites and RCC risk. Methods Medline and Embase databases were searched for observational studies investigating RCC or kidney cancer incidence or mortality in relation to components of 1C metabolism and 12 eligible articles were included in the meta-analyses. We used Bayesian meta-analyses to estimate summary relative risks (RRs) and 95% credible intervals (CrIs) comparing the highest versus lowest categories as well as the between-study heterogeneity.
Study selection Medline and Embase databases were searched on April 5th, 2019 for records of observational studies related to RCC or kidney cancer incidence or mortality and 1C metabolism. A medical school librarian was consulted for selection of search terms. The exposures were intake or circulating biomarkers of riboflavin (vitamin B 2 ), vitamin B 6 , folate (vitamin B 9 ), vitamin B 12 , methionine, homocysteine, choline, or betaine. The full search strategy for Medline is provided in Online Resource 1. Results from the database searches were combined and duplicates were removed. Two investigators (JLC and AKH) independently screened the titles and abstracts with a webbased screening software and removed ineligible papers  [7]. Papers were excluded if the exposure data did not represent status prior to diagnosis.
Data extraction Data collected for each paper included: author, publication year, study name and location, study design, number of cases, cohort size or number of controls, years of follow-up, sex distribution, age range, measured exposures of interest, outcome (RCC or all kidney cancer), covariates included in the most comprehensively adjusted model, effect estimate and 95% confidence interval (CI), and criteria for the Newcastle-Ottawa Scale (NOS) assessment. The summary measure of interest was the relative risk (RR); however, RCC is a sufficiently rare disease to also include odds ratios (ORs), hazard ratios (HRs), and standardised incidence ratios (SIRs) as reasonable approximations of the RR. If an article reported pooled results from more than one study, only the individual study results were used.
Quality assessment After consideration of several quality assessment tools, the NOS scale was selected as the most appropriate for use with observational studies  [8]. In the NOS system, a maximum of 9 stars are awarded to each study, with stars deducted for suboptimal study design or reporting. Studies with fewer stars may be more prone to bias. We used a modified version of NOS which was tailored for use with dietary data and cancer outcomes and each study within each publication was rated separately. There are separate sets of questions for case-control versus cohort studies and nested case-control and case-cohort studies were rated on the cohort study version.
Statistical analysis Each exposure was modelled individually and dietary intake and biomarkers were assessed separately. All dietary components of 1C metabolism (riboflavin, vitamin B 6 , folate, vitamin B 12 , methionine, betaine, and choline) as well as homocysteine were analysed if data were available from at least two different studies (k ≥ 2). All effect estimates were log-transformed for analysis. A Bayesian approach was used for the meta-analyses to estimate summary RRs and 95% credible intervals (CrIs) for the highest versus lowest category of the exposures. A CrI is analogous to a confidence interval in frequentist analysis, but it has the advantage of a more intuitive definition in that it represents the probability that the population parameter lies within the specified range conditional on the model and the data  [9].
for use in a metaanalysis of a "major morbidity event" comparing non-pharmacological exposures  [11]. The prior was derived based on observed heterogeneity in binary outcome meta-analyses from the Cochrane Database of Systematic Reviews. This method allows for incorporation of prior knowledge to refine the estimated heterogeneity. Two other Bayesian models were run to assess sensitivity of the results to the prior distribution. The first uses the same distribution for µ and a half-normal distribution for τ, which has been shown to be an appropriate distribution in metaanalyses with a small number of studies  [12]. The second employs a very weak prior for both µ and τ and it is intended to test the impact of extreme alteration of prior distributions. We avoided using a uniform prior because placing equal density across all real values would give too much weight to implausible values.
Finally, we conducted analyses on dietary intake exposures for food consumption only, excluding supplement use, because of the differences in bioavailability and data collection methods. Funnel plots were visually inspected to check the risk of publication bias and heterogeneity was assessed with τ 2 and I 2 based on the posterior distribution of τ. I 2 is an intuitive measure of heterogeneity, indicating the percentage of total variation in the estimated associations due to betweenstudy heterogeneity. We have also reported τ 2 , indicating the between-study variance because, unlike I 2 , it is not dependent on the number or size of included studies  [13]. Meta-analyses were done using R 3. 6. 0 [14] , the bayesmeta package  [15] , and the metafor package  [16].
Literature search Figure  1  shows the results of the database search and study selection. The database searches produced 455 total records and 113 duplicates were removed, leaving 342 unique records. After title and abstract review, 317 records were deemed ineligible, leaving 25 records for full-text review. Of these 25, 9 were excluded for no exposure of interest, 1 was excluded for exposure measured after diagnosis, 2 were excluded for no outcome of interest, and 1 was excluded due to multiple publications from the same study population. The 12 remaining records were included in the review. Additionally, the references and forward citations of these 12 records were searched and 1 additional record was identified. Therefore, a total of 13 records were included in the systematic review. One of these papers examined B-complex vitamins rather than individual nutrients and was excluded from the meta-analyses, therefore 12 records were included in the meta-analyses.
Of these 12, 8 examined incident RCC risk and 4 examined risk for any type of kidney cancer. Two of the eight RCC outcome publications included data from two studies each, so a total of ten studies were included in the RCC meta-analyses. Of the ten studies, three included riboflavin intake, five included vitamin B 6 intake, six included folate intake, three included vitamin B 12 intake, two included methionine intake, two included choline intake, and two included betaine intake. For biomarkers, analyses from the same two studies, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Alpha-Tocopherol Beta-Carotene Cancer Prevention Study (ATBC), included riboflavin, folate, vitamin B 12 , and homocysteine, and these two studies plus the Melbourne Collaborative Cohort Study (MCCS) included vitamin B 6.
Unlike the RCC-specific estimate, the biomarker estimate including any kidney cancer (RR = 1. 07) was in the same direction as the intake estimate; however, there is considerable between-study heterogeneity for this estimate largely driven by the Arendt 2013 study. This study differed from the others in that the median follow-up time after blood draw was only 3. 5 years, so their results may be more strongly affected by reverse causation. Three exposures (methionine, choline, and betaine) only included data from the NHS and HPFS studies within the Cho 2013 paper, which also presented pooled results from the two studies. The estimates found from our model were very similar to those presented by Cho et al. [22]. Our analysis included three more RCC-specific studies than the most recently published meta-analysis, and also assessed additional key 1C metabolites choline and betaine.
We used a custom bioinformatics pipeline to process fastq files and generate single-cell gene expression matrices and TCR sequence files, as described  30. Briefly, the pipeline to obtain gene expression matrices was adapted from the Drop-seq pipeline  31  , relied on extracting the cell barcode and UMI from Read2 and aligning Read1 on the reference genome using STAR and HTSeqCount. For TCR (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted January 7, 2020. ; https://doi. org/10. 1101/2020. 01. 06. 895938 doi: bioRxiv preprint sequence reconstruction, we used Trinity for de novo transcriptome assembly for each cell based on Read1 sequences, then filtered the resulting isoforms for productive TCR sequences using MigMap, Blastn and Kallisto.
Quality control was performed on each scRNAseq batch independently to remove poor quality cells. Cells with less than 250 genes detected were removed. We further excluded cells with values below 3 median absolute deviations (MADs) from the median for UMI counts, for the number of genes detected, or for ERCC accuracy, and cells with values above 3 MADs from the median for ERCC transcript percentage, as described  30. The number of cells passing quality control, mean sequencing depth, and mean gene detection for each batch is shown in Extended Table  1. All cells passing quality control were analyzed together with custom scripts in the R programming language (available upon request). For each cell, gene expression UMI count values were log normalized with Seurat v3. 0. 0. 9  32  NormalizeData with a scale factor of 10,000.
Cells with both TCRα and TCRβ sequences were grouped into TCRαβ clonotypes if they were of the same TCRα and TCRβ clonotypes. Cells for which only one of the two sequences was available were joined to a TCRαβ clonotype based only on its TCRα or TCRβ clonotype identity. Clonotype identity for each cell was added as a metadata to the Seurat scRNAseq dataset for visualization in tSNE embeddings.
Unsupervised flow cytometry analysis Flow cytometry data from 22 patients (11 active AIH and 11 NASH) were generated the same day and first analyzed using FlowJo 10. 6. 0 (TreeStar Inc) to extract the same number of viable lymphoid CD3+ cells (27. 10 3 per patients; total of 594. 10 3 ). New FCS folders were generated with identical CD3+ T cell number to performed unsupervised clustering analysis. FlowSOM package on R was used to generate (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted January 7, 2020. ; https://doi. org/10. 1101/2020. 01. 06. 895938 doi: bioRxiv preprint 400 clusters. Metaclustering was performed by using WGCNA package. Heat-maps were generated by using complex heat map package.
The PD-1 + CXCR5 -CD4 T cell population is the reservoir of auto-reactive CD4 T cells in AIH. Given the B-helper molecular profile and PD-1 + CXCR5 -surface phenotype of ex vivo activated SLA-specific CD4 T cells, we set out to more precisely identify the phenotypic niche of autoreactive CD4 T cells in the peripheral blood of AIH patients. Bulk TCRβ sequencing was performed on sorted memory PD-1 + CXCR5 -CCR6 -, PD-1 -CXCR5 -CCR6 + and PD-1 -CXCR5 -CCR6 -CD4 T cell subsets sampled at three different time points from the same patient (Extended Data Figure  2 ). SLA-specific TCRβ clonotypes identified at the single-cell level were then tracked in the bulk datasets and found to be restricted to the PD-1 + CXCR5 -CCR6 -CD4 T cell population (Figure  3a ). In contrast, C. Alb-specific TCRβ clonotypes were restricted to the PD-1 -CXCR5 -CCR6 + CD4 T cell population (Figure  3b ).