dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA sequencing data processing and clustering analysis in the discovery series The fragments per kilobase million (FPKM) counts selected from the TCGA-LIHC dataset were processed for normalization due to non-linear bias induced by genomic screening. Log 2 transformation was then applied after adding 1 to the FPKM matrix. The Z-score approach was chosen for gene-wise standardization. We further aimed to investigate the 6 immune gene signatures that were previously shown to be associated, in patients with advanced HCC, with improved response and survival rates after treatment with the anti-PD-1 monoclonal antibody nivolumab: "6-Gene Interferon Gamma" (6G IFNg) (CXCL10, CXCL9, HLA-DRA, IDO1, IFNG, STAT1),  17  "Gajewski 13-Gene Inflammatory" (Gajewski 13G) (CCL2, CCL3, CCL4, CD8A, CXCL10, CXCL9, GZMK, HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, ICOS, IRF1, CCL3 was not included in the Nanostring Panel so we also removed it from the discovery series),  18  "Inflammatory" (CD274/PD-L1, CD8A, LAG3, STAT1),  9  "Interferon Gamma Biology" (IFNg Biology) (CCL5, CD27, CXCL9, CXCR6, IDO1, STAT1),  17  "Ribas 10-Gene Interferon Gamma" (Ribas 10G) (CCR5, CXCL10, CXCL11, CXCL9, GZMA, HLA-DRA, IDO1, IFNG, PRF1, STAT1)  17  and "T-cell Exhaustion" (CD274/ PD-L1, CD276, CD8A, LAG3, PDCD1LG2, TIGIT) 17 signatures.
Slide preprocessing and tessellation, staining conversion, color normalization and data augmentation Slides from the discovery series were stained with hemateineosin and encoded in svs format while slides from the validation series were stained with hematein-eosin-saffron and encoded in ndpi format. Tissue regions were then exhaustively split into patches of 256×256 pixels (without overlapping) at 20x using the OpenSlide library in Python. Tumor regions were annotated by an expert pathologist (JC) in polygonal regions of interest using the open source QuPath software. 21 Patches were extracted from the intersection area of the detected tissue regions and the annotated tumor regions. Staining conversion, color normalization and data augmentation protocols were assessed (supplementary materials and methods).
22 or both strategies, the first stage involves feature extraction using a modified ResNet50 model pre-trained on ImageNet (Fig. 3 ). Each of the N i patches from a given WSI was encoded as a 1,024-dimensional feature, allowing the possibility to load all the patches of the WSI into the GPU memory simultaneously. For the classic MIL approach, we used the implementation in the CLAM work (code available at https://github. com/mahmoodlab/CLAM). The first fully connected (FC) layer further reduced the features to 512 dimensions, and the second FC layer was used as a classifier to generate 2-class scores for each patch. A max-pooling function was then applied on the Cluster High class to select the top-1 patch and normalize its scores to WSI-level probabilities by softmax. CLAM is a recently reported more sophisticated MIL approach specifically designed for digital pathology (code available at https://github.
Results Unsupervised hierarchical clustering of samples from the discovery series A total of 336 cases from the TCGA-LIHC dataset met our inclusion criteria (349 slides from 336 tumors). The main clinical, biological and pathological features of the patients and tumors are presented in Table  S2. They were common for a series of patients with HCC treated by surgical resection. The median age at surgery was 61 years and a male predominance was observed (sex ratio 68%, 228/336). The main risk factors were alcohol consumption (35%, 111/318) and hepatitis B virus (HBV) (32%, 101/318). Microvascular invasion was identified in 29% of the tumors (83/285). As expected in a series of patients treated by surgery, a low rate of significantly fibrotic livers was observed (Ishak score 5-6, 37%, 74/198).
Although this type of model is designed to reduce the need for manual annotations, we show that tumor area delineation by a pathologist results in a significant improvement for all gene signatures investigated. We may hypothesize that the negative impact of irrelevant noise/ patterns from the adjacent non-tumor parenchyma was avoided by the use of expert-driven annotations. These findings underscore the importance of human-machine interactions for the development of highly efficient AI-based pathology. The processing of surgical and biopsy samples is complex with several critical steps (embedding, cutting, staining, and scanning) that may impact the overall quality of the WSIs. Models can easily overfit and thus learn noise or fluctuations that are specific to the training dataset, explaining why they most often do not generalize well on external datasets.
Technical protocols for gene expression analyses also include multiple steps that are impacted by experimental conditions (RNA extraction, reverse transcription, and amplification, for example) and may introduce non-linear effects. The identification of robust tumor subgroups across different technological platforms thus remains challenging. These issues explain why, although numerous studies using deep learning on WSIs have recently been published, the majority of the proposed models were not validated on true independent series of samples processed by different histological and molecular protocols. We thus believe that one of the strengths of our work is the validation to which the various models investigated have been subjected. We were indeed able to validate our different models on a completely independent dataset that included i) patients treated in a different center, ii) slides stained with different protocols and encoded in a different format and iii) gene expression experiments performed using a different technology.
0)  [50]  was chosen, increasing the taxonomic resolution with which the molecular species in sorted bacterial populations were delineated. Sequence alignment and taxonomic classification was conducted with SINA v1. 6. 1, using the taxonomy within the SILVA release 128  [51]. Downstream analysis was performed in the R programming environment using Rhea (https:// lagko uvard os. github. io/ Rhea/)  [52]. Normalization of OTU and ZOTU tables was performed by dividing through sample size and subsequent multiplication by the number of reads in the smallest sample, to account for differences in sequencing depth.
Identification of cultured isolates To distinguish potentially different strains isolated from the same sample, Random Amplified Polymorphic DNA (RAPD) analysis was conducted. Briefly, candidate F. prausnitzii or C. minuta colonies were subcultivated on mGAM plates and then several colonies were resuspended in 100 μl DNA-free water and DNA was isolated using InstaGene ™ Matrix (BioRad) kit according to the manufacturer guidelines. This DNA preparation served as a template for PCR reaction using primer D9355 for F. prausnitzii candidates and primer D14307 for C. minuta candidates, and a PCR program described by Akopyanz et al. [25]. One isolate corresponding to each specific RAPD profile was then chosen for further identification by sequencing the almost complete 16S rRNA-encoding genes. For phylogenetic analysis, 16S rRNA-encoding genes were aligned using Muscle  [55]  integrated in MEGA7  [56]  with default parameters.
Funding This work has received, through BIOASTER investment, funding from the  French Government  through the  Investissement d' Avenir program  (Grant No. ANR-10-AIRT-03 ). TC received funding from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ), Project-ID  403224013 -SFB 1382  " Gut-liver axis. " Part of this work corresponds to contract research established between BIOASTER and  YSOPIA Bioscience  to isolate C. minuta strains.
Availability of data and materials 16S gene amplicon sequencing data generated from fecal samples collected from healthy volunteers HV5-HV17 and from sorted fractions were deposited in Sequence Read Archive under accession number PRJNA748004. Partial 16S-rRNA encoding gene sequences of the new F. prausnitzii and C. minuta isolates described in this study are available under GenBank accession numbers MZ577583 to MZ577588, and MZ577589 to MZ577595, respectively.
Preparation of Extract The fresh and mature stem bark of plant L. glutinosa was collected and authenticated from Bangladesh National Herbarium (BNH) during the autumn season (October 2018) in Mirpur, Dhaka, Bangladesh, with the DACB accession number 48,288. After washing and drying (shed), the bark was ground to a coarse powder and preserved until use at 4 • C. Approximately 0. 09 kg powder of bark was soaked in 500 mL of methanol solvent to obtain approximately 0. 045 kg of crude methanolic extract, indicated as LGBME. The collection of plant material and all the experimental research and field studies on plants complied with the relevant institutional, national, and international guidelines and legislation.
Histopathologic Evaluation Livers and kidneys were collected from control (2% DMSO) and treated (dose 150 mg/kg. bw/day) mice after five days of treatment  [49] , immediately fixed in 10% neutral buffered formalin for at least 24 h, processed by paraffine-embedding technique, stained with hematoxylin and eosin  [52] , and examined under Motic Advanced system microscope (B, series) with the help of Motic J1. 0 software on a Macintosh computer for histopathological changes.
Statistical Analysis All the analyses were performed in triplicates, and data were expressed as mean ± SD (Standard deviation). Statistical analyses were performed by one-way analysis of variance (ANOVA) followed by Dunnett's T3-test for evaluating the anticancer activity of LGBME using Statistical Package for Social Science (SPSS) statistical software, v16. A p-value < 0. 05 was considered to be statistically significant. Levels of significance were tested at 5% (* p-value < 0. 05), 1% (** p-value < 0. 01), and 0. 1% (*** p-value < 0. 001), respectively.
Exhausitvely and concisely calling variants relative to a reference genome in a large dataset of over 10,000 genomes is not trivial  [80]. An additional benefit of analysing oligopeptides (versus oligonucleotides) is to pool signals of association across nucleotides that encode the same amino acid. Since we expected most resistance-conferring mutations to affect coding sequences, we anticipated that this should improve power and interpretability for most associations. A combination of strong linkage disequilbrium and lower diversity at the protein versus nucleotide level meant that, despite testing all 6 reading frames, the Bonferroni thresholds for the oligopeptide analyses were 2-fold less stringent than for the oligonucleotide analyses. For 8/13 drugs with previously catalogued resistance determinants, the most significant GWAS signal in CRyPTIC was a previously catalogued variant.
The combination of a very large dataset exceeding 10,000 isolates and quantification of resistance via MIC enabled the CRyPTIC study to attribute a large proportion of fine-grained variability in antimicrobial resistance in M. tuberculosis to genetic variation. Compared to a parallel analysis of binary resistance phenotypes in the same samples, we observed an increase in sample heritability of 26. 1% to 37. 1% for the new and repurposed drugs bedaquiline, clofazimine, delamanid, and linezolid. The improvement was most striking for delamanid, whose heritability was not significantly different to zero for the binary resistance phenotype. In the case of delamanid, the MIC analysis detected a surprisingly large number of signals. Since few isolates were strongly delaminid resistant, this indicates we were picking apart fine-grained differences in MIC, a phenotype which may be more polygenic than binary resistance/sensitivity.
A total of 15,211 isolates were included in the initial CRyPTIC dataset with both genomes and phenotype measurements after passing genome quality control filters  [31, 78] ; however, some plates were later removed due to problems identified at some laboratories with inoculating the plates  [31]. Genomes were also excluded if they met any of the following criteria, determined by removing samples at the outliers of the distributions: (i) no high-quality phenotypes for any drugs; (ii) total number of contigs > 3,000; (iii) total bases in contigs < 3. 5 × 10 6 or > 5 × 10 6 ; (iv) number of unique oligonucleotides < 3. 5 × 10 6 or > 5 × 10 6 ; and (v) sequencing read length not 150/151 bases long. This gave a GWAS dataset of 10,422 genomes used to create the variant presence/absence matrices. We used Mykrobe  [78] [79] [80]  to identify Mycobacterium genomes not belonging to lineages 1 to 4 or representing mixtures of lineages.
This led to the exclusion of 193 genomes, which were removed from GWAS by setting the phenotypes to NA. The number of genomes with a high-quality phenotype for at least 1 of the 13 drugs was therefore 10,228. Of these, 533 were lineage 1; 3,581 were lineage 2; 805 were lineage 3; and 5,309 were lineage 4. Due to rigorous quality control described above, only samples with high-quality phenotypes were tested for each drug, resulting in a range of 6,388 to 9,418 genomes used in each GWAS. The log 2 MIC phenotypes used in the study are available in S2 Data, and the data is publicly available for download via an FTP site at the European Bioinformatics Institute (https://ftp. ebi. ac. uk/pub/databases/cryptic/release_june2022/pubs/ gwas2022).
Phylogenetic inference A pairwise distance matrix was constructed for the full CRyPTIC dataset based on variant calls  [78]. For visualisation of the dataset, a neighbour-joining tree was built from the distance matrix using the ape package in R and subset to the GWAS dataset. Negative branch lengths were set to zero, and the length was added to the adjacent branch. The branch lengths were square rooted and the tree annotated by lineages assigned by Mykrobe  [79].
A total of 11 amino acid long oligopeptides were counted in a 1 amino acid sliding window from these translated contigs. The 31-bp nucleotide oligonucleotides were also counted from the assembled contigs using dsk  [82]. For both oligonucleotide and oligopeptide analyses, a unique set of variants across the dataset was created, with the presence or absence of each unique variant determined per genome. An oligonucleotide/oligopeptide was counted as present within a genome if it was present at least once. This resulted in 60,103,864 oligopeptides and 34,669,796 oligonucleotides. Of these, 10,510,261 oligopeptides and 5,530,210 oligonucleotides were variably present in the GWAS dataset of 10,228 genomes.
Oligonucleotide/oligopeptide alignment We used the surrounding context of the contigs that the oligopeptides/oligonucleotides were identified in to assist with their alignment. First, we aligned the contigs of each genome to the H37Rv reference genome  [83]  using nucmer  [84] , keeping alignments above 90% identity, assigning an H37Rv position to each base in the contig. Version 3 of the H37Rv strain (NC_000962. 3) was used as the reference genome throughout the analysis. All numbering refers to the start positions in the H37Rv version 3 GenBank file. This gave a position for each oligonucleotide identified in the contigs, and after translating the 6 possible reading frames of the contig, each oligopeptide too. Each oligonucleotide/oligopeptide was assigned a gene or intergenic region (IR) or both in each genome.
Testing for locus effects We performed association testing using LMM analyses implemented in the software GEMMA to control for population structure  [32]. Significance was calculated using likelihood ratio tests. We computed the relatedness matrix from the presence/absence matrix using Java code that calculates the centred relatedness matrix. GEMMA was run using no MAF cutoff to include all variants. When assessing the most significant regions for each drug, we excluded oligopeptides below 0. 1% MAF. To understand the full signal at these regions, oligopeptides and nucleotides were visualised in alignment figures to interpret the variants captured. When assessing the gene highlighted for each drug, we assessed the LD (r 2 ) of the most significant oligopeptide or nucleotide in the gene with all other top oligopeptides or nucleotides for the top 20 genes for the drug.
The spoU stop codon is highlighted in red in the alignment plots. EMB, ethambutol; MAF, minor allele frequency; MIC, minimum inhibitory concentration; RIF, rifampicin. oligopeptides present in at least 1 genome in the full GWAS dataset. The black dashed lines indicate the Bonferroni-corrected significance thresholds. In the Manhattan plots, oligopeptides are coloured by the reading frame that they align to, black for the correct reading frame for Rv1219c. Oligopeptides and nucleotides assigned to the region but did not align using BLAST are shown in grey on the right-hand side of the plots. In the oligopeptide alignment plots, the H37Rv reference codons are shown at the bottom of the figure, grey for an invariant site, coloured at variant site positions. The oligopeptides that aligned to the region are plotted from least significant at the bottom to most significant at the top.
(PDF) S8 Fig. Variants in Rv1219c associated with isoniazid MIC. Manhattan plots showing the association results for the Rv1219c coding region for the A oligopeptides and B oligonucleotides, and oligopeptide alignment plots showing close-ups of the significant region in Rv1219c for C oligopeptides present in 5 or more genomes in the full GWAS dataset and D.
L. G. was supported by the Wellcome Trust (201470/Z/16/Z), the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number 1R01AI146338, the GOSH Charity (VC0921) and the GOSH/ICH Biomedical Research Centre (www. nihr. ac. uk). A. B. is funded by the NDM Prize Studentship from the Oxford Medical Research Council Doctoral Training Partnership and the Nuffield Department of Clinical Medicine. D. J. W. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant No. 101237/Z/13/B) and by the Robertson Foundation. A. S. W. is an NIHR Senior Investigator. T. M. W. is a Wellcome Trust Clinical Career Development Fellow (214560/ Z/18/Z). A. S. L. is supported by the Rhodes Trust. R. J. W. receives funding from the Francis Crick Institute which is supported by Wellcome Trust, (FC0010218), UKRI (FC0010218), and CRUK (FC0010218).
Acknowledgments We thank  Faisal Masood Khanzada  and  Alamdar Hussain Rizvi  ( NTRL, Islamabad, Pakistan ),  Angela Starks  and  James Posey (Centers for Disease Control and Prevention, Atlanta, USA) ,  Juan Carlos Toro  and  Solomon Ghebremichael  ( Public Health Agency of Sweden, Solna, Sweden) , and  Iñaki Comas and A ´lvaro Chiner-Oms  ( Instituto de Biologı ´a Integrativa de Sistemas, Valencia, Spain; CIBER en Epidemiologı ´a y Salud Publica, Valencia, Spain ;  Instituto de Biomedicina de Valencia, IBV-CSIC, Valencia, Spain). Computation used the  Oxford Biomedical Research Computing (BMRC) facility , a joint development between the  Wellcome Centre for Human Genetics  and the Big Data Institute supported by  Health Data Research UK  and the  NIHR Oxford Biomedical Research Centre.
T. C. has received grant funding and salary support from US NIH, CDC, USAID and Bill and Melinda Gates Foundation. The computational aspects of this research were supported by the Wellcome Trust Core Award Grant Number 203141/Z/16/Z and the NIHR Oxford BRC. Parts of the work were funded by the German Center of Infection Research (DZIF). The Scottish Mycobacteria Reference Laboratory is funded through National Services Scotland. The Wadsworth Center contributions were supported in part by Cooperative Agreement No. U60OE000103 funded by the Centers for Disease Control and Prevention through the Association of Public Health Laboratories and NIH/NIAID grant AI- 117312. Additional support for sequencing and analysis was contributed by the Wadsworth Center Applied Genomic Technologies Core Facility and the Wadsworth Center Bioinformatics Core.
Diet, BMI, and Covariate Data Detailed information on dietary, lifestyle, and anthropometric data was gathered at recruitment, previously described in Riboli et al. [7]. In order to determine usual dietary intakes, center-or country-specific validated dietary questionnaires covering the previous 12 months were used  [13]. The dietary variables (continuous, consumption in g/day) investigated in this study were intakes of total dairy (sum of milk, cheese, and yogurt), milk, cheese, yogurt, eggs, red meat, poultry, processed meat, total fish products (refers to fish and shellfish combined), total fish (subset of total fish products), fatty fish (subcategory of total fish), lean fish (subcategory of total fish), fats and oils (sum of butter, margarine, and vegetable oils), butter, margarine, vegetable oils, total vegetables (sum of leafy, root, and fruiting vegetables), leafy vegetables, root vegetables, fruiting vegetables, total fruit, cereals and cereal products, and alcohol.
Individual Metabolite Analysis A supplementary analysis was conducted of dietary exposures and BMI with the individual metabolites that contributed to metabolite patterns with which they were robustly associated (Supplementary Table  S3 ). Models were adjusted as described above for the main analysis. Individual metabolite values were log-transformed. Linear regressions were run in the overall dataset (n between 2136 and 3042, depending on exposure). To account for multiple testing, dietary and lifestyle correlates of metabolites that passed the FDR of 0. 05 were determined to be statistically significant.
Potential heterogeneity in metabolite concentrations, such as between the four sub-studies, was addressed by applying a dedicated pipeline to the data prior to statistical analyses  [12] , and the analytical protocol used has demonstrated high reproducibility between instruments  [57]. To assess dietary intakes, food frequency questionnaires were used in most EPIC centers, which can result in some measurement error due to the misreporting of food consumption, recall bias, or errors related to the food composition tables used. Despite this, numerous pilot and cross-sectional studies have supported the reproducibility and validity of the food frequency questionnaire method  [27, 58]. Finally, this study was conducted on a European population, and while there is limited information on racial and ethnic diversity of the participants, it is expected that the participants are primarily of European ancestry.
Acknowledgments: The authors thank the participants in the EPIC study, and  Carine Biessy and Bertrand Hemon at IARC  for their expertise in data handling. We would also like to thank the  Julius Center for Health Sciences  and  Primary Care ,  University Medical Center Utrecht, Utrecht University, Utrecht , The Netherlands, as well as the  National Institute for Public Health  and the  Environment (RIVM) ,  Bilthoven , the  Netherlands , for their contribution and ongoing support to the  EPIC Study.
Analysis of the amperometric recordings was performed as previously described with a macro (laboratory of Dr. R. Borges; http://webpages. ull. es/users/rborges/) written for Igor software (WaveMetrics), allowing automatic spike detection and extraction of spike parameters  [21]. Only cells containing at least 5 spikes are included in the analysis. The spike parameters analysis was restricted to spikes with amplitudes higher than 5 pA, which were considered as exocytic events  [13, 22]. All spikes identified by the program were visually inspected. Overlapping spikes and spikes with aberrant shapes were counted in the total number of spikes and then discarded for parameters analysis. Quantal size (spike charge, Q) of each individual spike was measured by calculating the spike area above the baseline, the spike area being defined as the time integral of each transient current.