dataesr/scientific-paragraphs-categorization huggingface dataset:
High-throughput RNA-seq of human samples As previously reported, RNA-seq analysis was performed using the data from 206 snap-frozen biopsy samples from 206 patients diagnosed with NAFLD in France, Germany, Italy, and the UK and enrolled in the European NAFLD Registry (GEO accession GSE135251)  51, 52. Samples were scored for NAS by two pathologists  53. Alternate diagnoses were excluded, including excessive alcohol intake (30 g per day for males, 20 g for females), viral hepatitis, autoimmune liver diseases, and steatogenic medication use. Patient samples were grouped: NAFL (n = 51) and NASH with fibrosis stages of F0/1 (n = 34), F2 (n = 53), F3 (n = 54) and F4 (n = 14). Collection and use of data of the European NAFLD Registry were approved by the relevant local and/or national Ethical Review Committee  51.
Isolation of cells for scRNA-seq data analysis (human) Analyses used liver samples from patients undergoing bariatric surgery at the Department of Surgery at Heidelberg University Hospital (S-629/2013). Samples were preserved by FFPE for pathological evaluation and single cells were generated by mincing, using the Miltenyi tumour dissociation kit (130-095-929) per the manufacturer's instructions, filtering through a 70-μm cell strainer and washing. ACK lysis using the respective buffer (Thermo Fischer Scientific A1049201) was performed, and samples were stored in FBS with 20% DMSO until further processing (scRNA-seq analysis and mass cytometry). Cells were thawed in a 37 °C water bath, washed with PBS + 0. 05 mM EDTA (10 min, 300g at 4 °C), Fc receptor-block (10 min at 4 °C), stained with CD45-PE (3 μl, Hl30, 12-0459-42) and Live/Dead discrimination (1:1,000, Thermofischer, L34973), washed and sorted on a FACSAria FUSION in collaboration with the DKFZ FACS.
Library generation was performed according to the manufacturer's protocol (Chromium Next EM Single Cell 3′GEM, 10000128), and sequencing was performed on an Illumina NovaSeq 6000. De-multiplexing and barcode processing were performed using the Cell Ranger Software Suite (Version 4. 0. 0) and reads were aligned to human GRCh38  56. A gene-barcode matrix containing cell barcodes and gene expression counts was generated by counting the single-cell 3′ UMIs, which were imported into R (v4. 0. 2), where quality control and normalization were executed using Seurat v3  57. Cells with more than 10% mitochondrial genes, fewer than 200 genes per cell, or more than 6,000 genes per cell were excluded. Matrices from 10 samples were integrated with Seurat v3 to remove batch effects across samples. PCA analysis of filtered gene-barcode matrices of all CD3 + cells, visualized by UMAP (top 50 principal components), and identification of major cell types using the highly variable features and indicative markers were performed.
Article (5 min, room temperature). For intracellular staining, the FOXP3 intracellular staining kit from Miltenyi Biotec was used per the manufacturer's instructions, followed by staining for intracellular targets for 30 min at room temperature. Cells were washed, resuspended in 1 ml of iridium intercalator solution, and incubated for 25 min at room temperature. Cells were washed with CSM, PBS, and MilliQ water, adjusted to a final concentration of 7. 5 × 10 5 cells/ml and supplemented with 4-element EQ beads. The sample was acquired on a Helios mass cytometer and raw data were EQ-Bead-normalized using Helios mass cytometer and Helios instrument software (version 6. 7). Compensation was performed in CATALYST (v1. 86)  63  and FlowCore (1. 50. 0). De-barcoding and gating of single, live CD45 + cells were performed using FlowJo (v10.
Search strategy, selection criteria, and meta-analysis of phase III clinical trials The literature search was done through MEDLINE on PubMed, Cochrane Library, Web of Science, and clinicaltrials. gov, using the following searches: 'checkpoint inhibitors', 'HCC', 'phase III', between January 2010 and January 2020, and complemented by manual searches of conference abstracts and presentations. Single-centre, non-controlled trials, studies with insufficient data to extract HRs or 95% confidence intervals, and trials including disease entities other than HCC were excluded. As conference abstracts were not excluded, quality assessment of the included studies was not performed. Three studies  5, 10, 11  fulfilled the criteria and were included in the quantitative synthesis (Extended Data Fig. 10 ).
A validation cohort of patients with HCC treated with PD1-targeted immune checkpoint blockers A multi-institutional dataset that included 427 patients with HCC treated with immune checkpoint inhibitors between 2017 and 2019 in 11 tertiary-care referral centres specialized in the treatment of HCC was analysed. Clinical outcomes of this patient cohort have been reported elsewhere  67, 68. Inclusion criteria were: 1) diagnosis of HCC made by histopathology or imaging criteria according to American Association for the Study of Liver Disease and European Association for the Study of the Liver guidelines; 2) systemic therapy with immune checkpoint inhibitors for HCC that was not amenable to curative or loco-regional therapy following local multidisciplinary tumour board review; 3) measurable disease according to RECIST v1.
Statistical analyses No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. Data were collected in Microsoft Excel. Mouse data are presented as the mean ± s. e. m. Pilot experiments and previously published results were used to estimate the sample size, such that appropriate statistical tests could yield significant results. Statistical analysis was performed using GraphPad Prism software version 7. 03 (GraphPad Software). Exact P values lower than P < 0. 1 are reported and specific tests are indicated in the legends. + anti-PDL1 n = 6 mice). Scale bar, 100 μm. Arrowheads, positive cells. f, Tumour or lesion incidence in mice fed with CD-HFD for 15 months and untreated or treated for 8 weeks with anti-PDL1 antibodies (CD-HFD n = 19 tumours/lesions in 25 mice; CD-HFD + anti-PDL1 n = 7 tumours/lesions in 8 mice).
1% FDR was used for controlling at the peptide and protein level, with a minimum of two peptides needed for consideration of analysis. Gene set enrichment analysis was performed using ClusterProfiler (3. 18)51 and gene sets obtained from WikiPathway (wikipathways. org) and MSigDB (broadinstitute. org/msigdb)52-54. Isolation of RNA and library preparation for bulk RNA sequencing: RNA isolation23and library preparation for bulk 3'-sequencing of poly(A)-RNA was described previously55. Using the with the Feature Extraction software (11. 0. 1. 1, Agilent Technologies), gencode gene annotations version M18 and the mouse reference genome major release GRCm38 were derived from (https:// www. gencodegenes. org/). Dropseq tools v1. 1256 were used for mapping the raw sequencing data to the reference genome.
Resulting UMI filtered count matrix was imported into R v3. 4. 4. Prior differential expression analysis with Limma v3. 40. 657 sample-specific weights were estimated and used as coefficients alongside the experimental groups as a covariate during model fitting with Voom. T-test was used for determining differentially (p-value below 0. 05) regulated genes between all possible experimental groups. Gene set enrichment analysis was conducted with the pre-ranked GSEA method53 within the MSigDB Reactome, KEGG, and Hallmark databases (broadinstitute. org/msigdb). Raw sequencing data are available under the accession number PRJEB36747. Mass cytometry: The sample was acquired on a Helios mass cytometer and raw data were EQ-Bead-normalized using Helios mass cytometer and Helios instrument software (version 6.
7). Compensation was performed in CATALYST (v1. 86)70 and FlowCore (1. 50. 0). Debarcoding and gating of single, live CD45+ cells were performed using FlowJo (v10. 6. 2). Then, data of CD45+ cells were imported into Cytosplore 2. 3. 1 and transformed using the arcsinh(5) function. Major immune cell lineages were identified at the first level of a 2-level hierarchical stochastic neighbor embedding (HSNE) analysis with default perplexity and iteration settings. HSNE with the same parameters was run on CD3+ cells to identify T-cell phenotypes. Gaussian mean shift clustering was performed in Cytosplore and a heatmap of arcsinh(5)-transformed expression values of all antibody targets was generated. Cell type identification was based on the transformed expression values and clusters showing high similarity were merged manually.
Search strategy, selection criteria, and meta-analysis of phase III clinical trials: The literature search was done through MEDLINE on PubMed, Cochrane Library, Web of Science, and clinicaltrials. gov, using the following searches: "checkpoint inhibitors", "HCC", "phase III", between January 2010 and January 2020, and complemented by hand searches of conference abstracts/presentations. Single-center, non-controlled trials, studies with insufficient data to extract hazard ratios (HR), 95% confidence intervals, or trials including disease entities other than HCC were excluded. As conference abstracts were not excluded, quality assessment of the included studies was not performed. Three studies5,13,14 fulfilled the criteria and were included in the quantitative synthesis (Extended Data 10). The primary outcome of the meta-analysis was OS, defined as the time from randomization to death.
HRs and CIs related to OS were extracted from the papers/conference presentations5,13,14. Pooled HRs were calculated using the random-effects model (Der Simonian and Laird), and the generic inverse variance was used for calculating weights72. To evaluate heterogeneity among studies, Cochran's Q test and I2 index were used. A p-value < 0. 10 in the Q-test was considered to indicate substantial heterogeneity. I2 was interpreted as suggested in the literature: 0% to 40% might not represent significant heterogeneity; 30% to 60% may represent moderate heterogeneity, 50% to 90% may represent substantial heterogeneity, 75% to 100% represents considerable heterogeneity. All statistical pooled analyses were performed using the RevMan 5. 3 software. Statistical analyses: Data was collected in Microsoft Excel.
Mouse data are presented as the mean±SEM. Pilot experiments and previously published results were used to estimate the sample size, such that appropriate statistical tests could yield significant results. Statistical analysis was performed using GraphPad Prism software version 7. 03 (GraphPad Software). Exact p-values lower than p< 0. 1 are reported and specific tests are indicated in the legends. Survival analyses were performed using IBM SPSS Statistics version 25 (SPSS Inc. , Chicago, IL). For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Research guidelines for submitting code & software for further information.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability The proteomics data described in this article is available at ProteomeXchange Consortium via the PRIDE under the identifier XD017236 login/password: /37ScigLe. The bulk-RNA-seq data described in this article is available at PRJEB36747. The single-cell RNA-seq data described in this article is available at GEO Submission (GSE144635). The array of comparative genomic hybridization data described in this article is available at GEO Submission (GSE144875).
Teufel  for human cohort gathering;  K. Inoue  and  Z. Ammozgar  for help with the non-NASH mouse model;  T. O'Connor ,  C. Groth ,  M. Matter ,  L. Terracciano ,  R. Kaeser ,  T. Boettler ,  R. Thimme ,  M. Yabal ,  T. Longerich  and  B. Müllhaupt  for guidance and discussions; and  S. Prokosch ,  U. Rothermel ,  J. Janzen ,  J. Hetzer ,  C. Gropp ,  S. Jung ,  L. Beideck ,  S. Torrecilla ,  K. E. Lindblad ,  E. Rist  and  T. -W. Kang  for technical support. D. Pfister  was supported by the  Helmholtz Future topic Inflammation and Immunology. I. S. is funded by the  'Deutsche Forschungsgemeinschaft' (DFG, Bonn Germany)  through  Emmy Noether program  ( SI 2620/1-1 ). A. Sinha is supported by  EMBO LT  fellowship.
Article ( ALTF 539-2018 ). F. Meissner is supported in this project by  SFB 1335  and  BMBF 031B0686B. Q. M. A. , O. G. , D. T. , P. B. , S. C. , R. Y. , M. V. ,  F. Marra , J. M. S. , M. A. , E. B. , V. R. and A. K. D. are supported by the  EPoS (Elucidating Pathways of Steatohepatitis)  consortium funded by the  Horizon 2020 Framework Program  of the  European Union  under Grant Agreement 634413, the  LITMUS (Liver Investigation: Testing Marker Utility in Steatohepatitis ) consortium funded by the  Innovative Medicines Initiative  ( IMI2) Program  of the  European Union  under Grant Agreement  777377 , and are collaborators in the European NAFLD Registry. Q. M. A. is also supported by the  Newcastle NIHR Biomedical Research Centre. I. A. is supported by the  Chan Zuckerberg Initiative (CZI) , an  HHMI international scholar award, European Research Council consolidator grant  ( ERC-COG) 724471-HemTree2.
C9380/A26813 ), by the  European Commission (EC) / Horizon 2020 Program (HEPCAR, Ref. 667273-2 ),  US Department of Defense  ( CA150272P3 ),  NCI Cancer Center Support Grant ,  National Cancer Institute, Tisch Cancer Institute  ( P30-CA196521 ),  Samuel Waxman Cancer Research Foundation ,  Spanish National Health Institute  ( SAF2016-76390  and  PID2019-105378RB-I00 ) and the  Generalitat de Catalunya/AGAUR  ( SGR-1358 ). R. P. is supported by  HEPCAR  and  AECC. C. M. is supported by a  Rio Hortega grant  ( CM19/00039 ) from the  ISCIII  and the  European Social Fund. F. C. is supported by grant funding from  AECC. A. Weber  is supported by a grant from the  Swiss National Science Foundation  (SNF). M. H. was supported by an  ERC   Consolidator grant  (HepatoMetaboPath),  SFBTR179  Project -ID 272983813 ,  SFB/TR 209 Project-ID 314905040 ,  SFBTR1335  Project -ID 360372040 , the  Wilhelm Sander-Stiftung , the  Rainer Hoenig Stiftung , a  Horizon 2020 grant  (Hepcar),  Research Foundation Flanders (FWO)  under grant  30826052  ( EOS Convention MODEL-IDI ),  Deutsche Krebshilfe  projects  70113166  and  70113167 ,  German-Israeli Cooperation in Cancer Research (DKFZ-MOST)  and the  Helmholtz-Gemeinschaft, Zukunftsthema 'Immunology and Inflammation'  ( ZT-0027 ).
M. Q. , S. Y. and S. R. were supported by  German Cancer Aid  grants ( 70112720  and  70113167 ). J. -F. D. is supported by the  Swiss National Foundation  and the  Swiss Foundation against Liver Cancer. H. E. M. and A. R. S. received support from  DFG   Me3644/5-1  and the  Elke-Kröner-Fresenius foundation. This work was supported by the  Deutsche Forschungsgemeinschaft  ( FOR2314 ,  SFB-TR209 ,  Gottfried Wilhelm Leibniz Program ) and the  German Ministry for Education and Research (BMBF). Further funding was provided by the  DFG under Germany 's excellence strategy EXC  2180-390900677  ( Image Guided and Functionally Instructed Tumour Therapies (iFIT) ), the  Landesstiftung Baden-Wuerttemberg , the  European Research Council (CholangioConcept)  and the  German Cancer Research Center (DKTK).
B. B. , N. G. N. and E. F. are supported by the  Swiss National Science Foundation  (grants  733 310030_170320 ,  316030_150768  and  CRSII5_183478 ) and the  Swiss Cancer League. N. G. N. is a recipient of a  University Research Priority Program (URPP) postdoctoral fellowship. J. -P. M. is supported by  SNF  Project Grant  310030 182679 ,  Canica Holding Research Grant ,  Norwegian PSC Research Center, Stiftung zur Krebsbekämpfung ,  Bangerter-Rhyner Stiftung ,  Dangel Stiftung. Funding Open access funding provided by  Deutsches Krebsforschungszentrum (DKFZ)  ( 1052 ).
Data availability The proteomics data described in this article are available at the PRIDE database, under the identifier PXD017236 or through the dataset website (http://www. ebi. ac. uk/pride/archive/projects/PXD017236). The bulk RNA-seq data described in this article are available at the European Nucleotide Archive (ENA) under accession number PRJEB36747. The scRNA-seq data described in this article are available at GEO under accession GSE144635. The array of comparative genomic hybridization data described in this article is available at GEO under accession GSE144875. The results here are in whole or part based upon data generated by the TCGA Research Network (https://www. cancer. gov/tcga). The human scRNA-seq data described in this article are available at GEO under accession GSE159977.
There are two types of sample-level estimates of richness. Observed richness refers to the number of taxa observed in a sample. Asymptotic richness is obtained by adding an estimate of the number of unobserved taxa to the number of observed taxa. Approaches to estimate asymptotic richness vary, but often assume that relatively uncommon taxa are the most informative  28. Both types of richness estimates enable valid comparisons among macroecological communities  24, 25, [28] [29] [30]. However, direct application of the aforementioned richness estimates and comparisons to 16S microbiome data would ignore the plethora of uncommon and spurious taxa that inflate observed richness estimates in 16S survey data  27, [31] [32] [33] [34]. When this artificial inflation in observed richness is ignored, we find that differential abundance of detected taxa confounds current methods for differential richness inference.
Mock experimental communities are expected to represent a greater degree of homogeneity than real world communities as the latter may contain rare variants. Restricting analysis to experimental communities with single-and multiple-mock genera, we find that in eight out of nine datasets, over 50% of sub-genus taxa in over 50% of the mock genera replicated in less than 50% of the samples (Fig. S19 ). These results indicate poor within-study replicability of most sub-genus taxa. Finally, because we expect true taxa richness and evenness to vary along the taxonomic tree, we explored taxa accumulations for the various taxonomic levels (i. e. , family, order, class and phylum) in each study. Remarkably, the total number of observed taxa at any level of the taxonomic tree, was strongly predicted by recovered abundance alone and was not dependent on the taxonomic level considered (Fig.
2B, S8 ). Second, inferring differential richness between sample-groups (i. e. , sample-wide differential richness) are highly likely to be confounded by a net non-zero relative abundance fold change of detected genera. Straightforward simulations where spurious taxa are generated in an abundance dependent fashion illustrate this behavior (Fig. S6 ). Interestingly, illustrative examples of the same were rare in several 16S surveys, suggesting that spurious taxa accumulations are comparable at the sample-level. Indeed, in many datasets, the relative abundance log fold changes of member genera were symmetric and concentrated around zero (Figs. S9-S10 ). Nevertheless, exceptions with asymmetric relative abundance log fold change distributions exist and a case in point is offered by the long-term time series study discussed below (Fig.
1, we assume that most sub-genus taxa in 16S surveys are false. This allows us to exploit a 16S survey's overall sub-genus taxa accumulation trend, along with any systematic genus-specific effects, as a sampling effort dependent control for false taxa accumulation (Methods). This control is exploited within standard regression methods for differential richness inference. With a few 16S surveys, we illustrate the insights offered by the proposed procedure, Prokounter, in achieving genus-specific and sample-wide differential richness inferences. Unlike other estimators analyzed here (Chao1 29 , ACE  48  and Breakaway 49 ), the uncorrelatedness of Prokounter's richness statistics with genus-wide differential abundance statistics is clear in each dataset (Fig. 2B, C , Fig. S12 ). Breakaway's estimates were the most variable, often accompanied by wide confidence intervals.
Prokounter refines this result further by identifying several Firmicutes genera (Faecalibacterium, [Ruminococcus], Oscillospora) that are less rich post-infection. On the other hand, Dorea and Coprobacillus, members of Firmicutes, were found to have significantly increased richness in infection and post-infection samples. The genus Acinetobacter from the phylum Tenericutes was found to have significantly higher richness in samples collected during infection, while this was not the case post-infection. Thus, differential richness adds another state variable to the microbiome state specifications of the original study. In David et al's dataset, sample-wide inferences disagreed among the methods compared. Prokounter produced negative richness inferences for both infection and post-infection samples consistent with antibiotic exposure.
3 , S5). Our expectation was set in part by a mathematical model of cellular reproduction, where we tracked the probability distribution over substitutions, over generations (Supplementary Note 1). Second, in a manner similar to what we would expect of low probability errors, most sub-genus taxa in both controlled mock and real world datasets are rare and show poor replicability across samples (Fig. S19 ). Third, within-genus taxa accumulation patterns in several publicly available datasets, including those from single-and multi-genera mock experiments, appear remarkably regular as if most genera in 16S surveys have similar richness and taxa evenness (Fig. 2 3 , S1-S3, Tables 1-2, S2-S3). Fourth, the total number of taxa observed for any taxonomic level was strongly determined by the category's recovered abundance alone and was not dependent on the level itself (Fig.
The specification of (⋅) involves, in part, integrals of squared second order derivatives of the estimand over the range of   !" , thereby enforcing smoothness. Supplementary Note 4 offers more details on the model construction and an exact correspondence to example 2. 7 in Gu  81. Numerical optimization is performed using the R package gss  65. Supplementary figures S2 and S18 offer examples of the fits that result. The technical contribution to taxa growth is estimated as  + # (,  !" ) =  +  + & (  !" ) +  + ' (). Only the significant genus effects are retained after multiple testing correction with the Benjamini-Hochberg procedure. When the genera contributions are null or similar, as we observed empirically in several datasets (e. g. , Fig. S9 , S10),  + # (,  !" ) ∝  + & (  !" ). The latter observation inspires the following alternative strategy: estimate  + # (⋅) as a net average within-genus accumulation curve using the loess smoother.
Both options are made available in our software. As expected, inferences arising and the results in tables 1-2 are similar with both approaches. Fig. S3  offers examples of the fitted trends. The spline strategy does offer better control in the presence of systematic genus effects (Fig. S18 ). For consistency, in this paper, we have chosen the spline strategy. The fitted. can be used to control for false taxa accumulation in standard differential richness inference procedures. In Prokounter, we incorporate it through the models presented below.