dataesr/scientific-paragraphs-categorization huggingface dataset:
Host prediction Four computational host prediction strategies were used to identify virus-host interactions  [35]. (i) CRISPR spacers match: a clustered regularly interspaced short palindromic repeats (CRISPRs) spacer database was created for a set of microbial genomes using the MinCED tool  [108]. For metagenomics reads, Crass v1. 0. 1  [109]  was used with the default parameters to recover CRISPR spacers and repeat elements. The identified spacers were queried for exact sequence matches against all viral contigs using the BLASTn-short mode in the BLAST+ package  [110]. Match requirements were at least 95% identity over 95% spacer length, and only ≤ 1 mismatch was allowed. The corresponding CRISPR direct repeat types were connected to microbial genomes via BLASTn (with an e-value cut-off of 1e-10, 100% nucleotide identity)  [111].
(ii) Transfer RNA (tRNA) match: tRNAs were recovered from the microbial genomes and viral contigs using ARAGORN with the "-t" option  [112]. The identified tRNA sequences were compared using BLASTn, and only a perfect match (100% coverage and 100% identity) was considered indicative of putative host-virus pairs. (iii) Nucleotide sequence homology search  [113] : To link prophages with hosts, viral contigs were searched against microbial genomes using BLASTn with the following thresholds: 75% minimum coverage of the viral contig length, 70% minimum nucleotide identity, 50 minimum bit score, and 0. 001 maximum e-value. (iv) k-mer frequencies: WIsH v1. 0  [114]  was run with the default parameters against the host database. Connections were inferred when p < 0. 001. If multiple hosts were predicted for a vOTU, the one supported by different approaches was chosen as the one with the most confidence.
The host database employed for these prediction methods was composed of (i) all reference genomes from the Genome Taxonomy Database (GTDB), (ii) all MAGs (≥ 50% completeness, and ≤ 10% contamination) recovered from hydrothermal vent metagenomes (n = 581), (iii) all SAGs obtained from the CR hydrothermal vent (n = 440), and (iv) a custom collection of marine microbial genomes from the Marine Culture Collection of China (n = 1452).
Viral taxonomic assignment and network analysis The predicted open reading frames (ORFs) of the viral contigs were mapped against the NR protein database using DIAMOND v0. 9. 21  [115] , and their taxonomic affiliations were determined using the CAT v5. 0. 3 package  [116]  based on the last common ancestor (LCA) algorithm. Contig classification is based on a voting approach of all classified ORFs by summing up all the bit scores from ORFs supporting a specific classification. Protein-sharing network analysis of the hydrothermal vent vOTUs, the reference phage genomes (from NCBI Viral RefSeq version 97), the Global Oceans Viromes 2 (GOV 2. 0) datasets  [57] , and viral contigs from cold seeps  [58]  was performed using vConTACT v2. 0  [56]. Briefly, Prodigal v2. 6. 3  [117]  was used for ORF prediction from the vOTUs.
Construction of phylogenetic trees For phylogenetic trees, the deduced amino acid sequences of selected marker genes were aligned using the MUSCLE program  [118] , and the multiple alignments were trimmed with TrimAl v1. 2  [119]. IQ-TREE2  [102]  was used to infer the maximum-likelihood (ML) tree with the best substitution model selected by Mod-elFinder  [120] , and support for nodes in the ML trees was evaluated with 1000 ultrafast bootstrap replicates  [103]. The resulting trees were visualized and annotated using FigTree v1. 4. 4 (http:// tree. bio. ed. ac. uk/ softw are/ figtr ee/) and the iTOL v4 online tool  [104].
Identification of auxiliary metabolic genes Functional annotations on the ORFs in the viral contigs were conducted based on comparisons with the eggNOG v5. 0  [94]  database using eggNOG-mapper v2  [121]. Genes with a KEGG annotation falling under the "metabolic pathways" category or "sulfur relay system" were considered to be putative vAMGs, as previously defined in VIBRANT  [41]. Lastly, manual curation was performed to remove the metabolic genes involved in common viral functions. To generate the abundance profiles for AMGs, clean reads were mapped to the metagenomes using bowtie2  [122] , and the RPKM values of each gene were calculated. The sum of the RPKM values of genes with the same KO annotations was used to represent the relative abundance of each gene category.
In Vitro-and In Vivo-Matured Prepubertal Testicular Tissues Have Distinct Transcriptomic Profiles In order to identify the molecular mechanisms involved in the spermatogenic arrest during organotypic culture, bulk RNA sequencing analyses were conducted on in vitroand in vivo-matured testicular tissues. For each time point, three replicates were analysed, with three different testes for in vivo controls and twelve pooled cultured explants per replicate for the in vitro conditions. One 14. 5 dpp testicular tissue, which displayed a different transcriptomic profile compared to the other two replicates, was excluded from the downstream analyses. A PCA (Principal Component Analysis) revealed the biological relevance of the resulting transcriptomic dataset: indeed, the first component (or dimension), containing 84.
The In Vitro Spermatogenesis Process May Be Associated with an Inflammatory Process To identify a potential common regulator of these deregulated genes, the upstream regulator tool from Ingenuity Pathway Analyses (IPA) software was used. IPA analyses clearly demonstrated that zinc finger and BTB domains containing 17 (ZBTB17), tumor necrosis factor (TNF), and CCAAT/enhancer binding protein beta (CEBPB) were the top three predicted upstream regulators of the 594 deregulated genes between 14. 5 dpp and D28 cultured tissues, among the 600 candidate genes (Figure  7a , Table  S4 ). Most of the top 10 predicted upstream regulators were involved in inflammatory processes or immune cell recruitment. We hypothesized that the maturation arrest observed in vitro could be associated with a strong response of the local inflammatory system, probably generated by the resident macrophages, as previously described in cultures of mouse prepubertal testicular tissues  [49].
Then, IPA analyses performed on our dataset led to the identification of predictive upstream regulators. We found that the top 10 upstream regulators were involved in inflammation and in regulation of spermatogenesis. Among them, TNFα is partly produced by macrophages to promote the inflammatory process, and its expression is accompanied by the production of reactive oxygen species (ROS) or chemokines  [165]. In addition, TNFα has been shown to be activated by the NF-κB signaling pathway, which contributes to immune cell survival and proliferation, followed by the production of various cytokines  [166]. These results are consistent with a recent transcriptomic analysis that revealed the presence of a drastic and overwhelming immune reaction involving the TLR4-NF-κB signaling pathway in cultures of prepubertal mouse testicular tissues  [49].
After nuclear counterstaining with hematoxylin (Diapath, Martinengo, Italy), sections were dehydrated in successive ethanol baths of increasing concentrations, followed by two xylene baths and mounting with Eukitt (Sigma-Aldrich, Saint-Quentin Fallavier, France). Each in vivo replicate originated from different rats. For quantitative histological analyses, at least 2 levels of the testis/explant (separated by at least 30 µm) and 30 seminiferous tubules were analysed for each replicate. For each in vitro replicate, 2-3 testicular explants were analysed. In total, approximately 360 tubules were counted for in vivo controls and at least 720 tubules for the in vitro condition, per analysis. HES and γH2AX immunostaining were analysed with a DM4000B light microscope (Leica Microsystems, Wetzlar, Germany) equipped with Leica Application Suite software (LAS; Leica Microsystems, Wetzlar, Germany).
Confocal Microscopy Analysis Confocal images (1024 × 1024 pixels) of meiotic chromosome spreads were acquired using an upright fixed-stage Leica TCS SP8 CFS confocal microscope (Leica Microsystems, Wetzlar, Germany) equipped with diode lasers (Coherent, Les Ulis, France) at 488 nm to excite Alexa Fluor ® 488 and at 552 nm to excite Alexa Fluor ® 594 and a conventional scanner at 400 Hz. Using a 63× (1. 40, oil immersion), fluorescence emission was sequentially detected through a hybrid detector (Leica Microsystems, Wetzlar, Germany) in a photon counting mode with a specific band from 500 to 540 nm for Alexa Fluor ® 488 and 600 to 650 nm for Alexa Fluor ® 594. Z-stack images were acquired using an adapted step-size of 250 nm. 3D images (xyz) reconstruction and analysis were performed using Imaris software (Bitplane, Zurich, Switzerland).
Reads from each individual sample were aligned to the rn6 release of the rat genome with STAR (version 2. 5. 2a), [184]  using previously published approaches  [185] [186] [187] [188]. Briefly, the STAR program was first run for each fastq file, using the RefSeq transcript annotation (GTF format) of the rat genome (release rn6) downloaded in November 2017. Exon junction outputs were then added to a splice junction set. STAR was next run a second time, complemented by the splice junction dataset, to produce a final alignment file (BAM format) for each sample.
Statistical Filtration and Clustering Analysis The statistical filtration of the genes showing a differential expression (DE) across experimental samples was performed using AMEN  [191]. Out of 17,322 genes in the dataset, we first selected 11,701 "detectable" or "expressed" genes, defined as those for which abundance levels exceeded 0. 5 RPKM in at least one experimental condition (median value of sample duplicates). The D6 condition was removed due to the differences in cellular composition (Figure  S4 ). Next, we compared 14. 5 dpp testicular tissue samples to D16, D22, and D28 cultured tissue conditions and selected 1490 genes yielding at least one-fold change greater than or equal to 2. 0 (median values of sample duplicates). A linear models for microarray data (LIMMA) statistical test was used to identify 1240 genes with significant abundance variations across samples (F-value adjusted with the FDR method: p ≤ 0.
Functional Analysis AMEN  [191]  was used to calculate the Fisher exact probability, and the Gaussian hypergeometric test was used to identify significantly enriched biological processes (Gene ontology) and pathways (KEGG pathway) associated with the seven expression patterns (P1-P7). A specific term was considered enriched in a group of coexpressed genes if the p value was <0. 01 and the number of genes in this cluster showing this annotation was >5. Upstream regulator analysis was performed using the Ingenuity Pathway Analysis software (Ingenuity, Qiagen, Hilden, Germany).
Validation of the Transcriptome Data by RT-qPCR To check the accuracy of the transcriptome data, DEGs identified as important for the process of spermatogenesis were selected for RT-qPCR. Each reaction was performed using three technical replicates and three biological replicates for the robustness of our analysis. RNA extraction and integrity analysis were performed as previously described. 4. 13. 1. Reverse Transcription cDNA synthesis was performed using qScript™ cDNA SuperMix (Quanta Biosciences by VWR, Fontenay-sous-Bois, France) according to the manufacturer's instructions using the following program: 5 min at 25 • C, 30 min at 42 • C, and 5 min at 85 • C. 4. 13. 2. qPCR cDNA amplifications were carried out in a total volume of 13 µL containing 6 ng of cDNA templates, 6. 5 µL of SYBR Green (ThermoFisher Scientific, Saint-Aubin, France), and 300 nM of each primer.
Transfer quality was confirmed by Ponceau S staining. Membranes were washed in TBS and then blocked in 5% non-fat milk (Androgen receptor and CYP17A1) or 5% BSA (3βHSD and β-actin) in TBS + 0. 2% Tween-20 (TBST) for 30 min at 37 • C with a gentle rocking action. Membranes were incubated with primary antibodies (Table  S5 ) overnight at 4 • C with rocking, washed three times in TBST for 5 min, and incubated with the appropriate secondary antibody (Table  S5 ). Membranes were washed before incubation with ECL reagent (Clarity Western ECL Substrate, Biorad, Marne-la-Coquette, France) for 2 min and analysis with the Chemidoc imaging system (Biorad, Marne-la-Coquette, France) and the Image Lab TM Software (v6. 0. 1, Biorad, Marne-la-Coquette, France). B-actin was used to normalize proteins of interest.
Acknowledgments: The authors are grateful to  Damien Schapman  and  Ludovic Galas  for their technical advices during the confocal microscopy acquisitions. Confocal microscopy images and PCR data were obtained at PRIMACEN (http://primacen. fr,accessed on 1 May 2019), the  Cell Imaging Platform of Normandy, IRIB, Faculty of Sciences, Rouen Normandy University , 76281 Mont-Saint-Aignan. The authors also thank the team  U1096  for the access to their western-blot devices, and  Mariam Ouali  for her technical assistance.
Background: The nicotine products relative risk assessment estimates the relative risk of tobacco-related diseases due to use of 15 nicotine products. This update adds new data to the original analysis and creates separate categories for United States and rest of world varieties of smokeless tobacco, as well as bidi cigarettes. Methods: The PubMed®, MEDLINE and Clinicaltrials. gov databases were searched systematically. The study lists were exported, screened at the title, abstract and full-text level according to pre-defined inclusion/exclusion criteria. The study quality was assessed, and risk of bias was accounted for in the screening criteria. The extracted data was synthesized into a toxin emissions/content analysis for 12 Group 1 carcinogens, used to estimate lifetime cancer risk, and epidemiological meta-analysis of over 40 tobacco-related diseases.
S. varieties only. The full Population-Intervention-Comparison-Outcomes-Study type (PICOS) question addressed in this review is outlined in Table  1. Systematic searches were conducted using specific search terms pertaining to the health risks of nicotine products on May 9 th 2022 in the MEDLINE (Pubmed) and NIH clinical trials (ClinicalTrials. gov) databases (see extended data). Due to the broad scope of the searches, the most relevant literature was targeted by searching at the title and abstract levels. The publication lists returned by the searches were exported and screened at the title, abstract and full-text levels according to pre-defined inclusion and exclusion criteria (Table  2 ). The screening steps were completed by one researcher and.
Data extraction and harmonization The shortlisted studies were analyzed in detail to extract health risk data and relevant meta-data, including the nicotine product, brand, disease/symptom, methodology used, measurement and unit, error in measurement, significance of measurement, geographic location, sample size and conflict of interest. For the toxin emissions/content studies, only data points for Group 1 International Agency for Research on Cancer (IARC) carcinogens were extracted. Data were sought for NNN and NNK, formaldehyde, 2-amino-naphthalene, 4-aminobiphenyl, benzo(a)pyrene, 1,3-butadiene, benzene, vinyl chloride, ethylene oxide, arsenic, chromium-IV and cadmium for inhalable products, and NNN and NNK, benzo(a)pyrene, arsenic, chromium-IV and cadmium for ingestible products. In addition, studies that reported sample collection conducted before 1 st January 1990 were excluded due to lack of relevance to the current market.
For the epidemiological studies, only adjusted odds ratios pertaining to tobacco-related diseases were extracted and only data points that adjusted for smoking were included. Where stratification was available, data points for current users were prioritized over former/ever users, data points for both sexes were prioritized over single sex datasets (within the same study), and non-or never users of any other nicotine product were prioritized over dual users. If stratification was provided by consumption level, moderate consumption matching as closely as possible to the assumptions used in the LCR analysis were selected. In applying these additional selection criteria to the data, we aim to adjust for confounders, reduce the risk of bias and focus on risk associated with the same consumption levels across all analyses.
Lifetime cancer risk. The lifetime cancer risk (LCR) of each nicotine product was calculated separately for inhalable and smokeless products. The inhalable products LCR calculation was based on the methodology outlined by Stephens  9  , whereas the LCR of the smokeless products was determined using the methodology outlined by the FDA  10. The LCR of each inhalable nicotine product was calculated from the toxin emissions data by adjusting the OEHHA unit risk values  12. A total of 12 toxins are included in the inhalable nicotine product analysis. The toxins selected are all International Agency for Research on Cancer (IARC) Group 1 carcinogens ("known carcinogens") for which OEHHA unit risk values and emissions data were available  13. The protocol used in this study deviates slightly from that of Stephens.
This is contrary to the methodology outlined by Stephens, which takes unit risk values in their original form of risk per µg per m 3 and adjusts the smoke/vapour toxicants to match these units. For each carcinogen, the OEHHA unit risk values were sourced and converted from risk per µg per m 3 to risk per µg per breath by assuming the average breath volume of a healthy human is 500 mL  14. The toxin emissions of the nicotine products were reported in varying units. For instance, combustible cigarette studies reported toxin emissions as µg or ng per stick, whereas electronic cigarette studies reported toxin emissions as µg or ng per 150 puffs. Therefore, the toxin emissions data for each product were converted to per puff values. In order to make this conversion, the average number of puffs per product per session was extracted from puff topography studies in the scientific literature (see extended data  11  ).
Epidemiological data analysis. Risk ratios, odds ratios and hazard ratios were extracted from the epidemiological studies and a set of meta-analyses were performed to determine the relative risk of cardiovascular disease, respiratory disease, cancer and mortality in users of each nicotine product compared to non-users of any nicotine products. The epidemiological data extracted from the systematic literature searches was screened to include only relative risk values that compared current users of a single nicotine product to non-users of any nicotine product. Relative risk values were excluded if they were unadjusted for tobacco smoking. Where more than one study was available for a specific disease, the best available evidence was selected according to the Oxford Center for Evidence-based Medicine Level of Evidence Scale  20.
For instance, if a meta-analysis of prospective cohort studies and a single case-control study were available, the former would be included and the latter would be excluded. The remaining data after screening were grouped by disease type into cardiovascular disease, respiratory disease, cancer and mortality. The cancer category was further broken out into oral, other head and neck, lung, gastrointestinal and other. Meta-analyses of the relative risk data for each disease category were conducted using a random-effects model in the Comprehensive Meta-analysis (CMA) software Version 3. 3, with a statistical significance threshold of α = 0. 05. The meta-analysis could also be conducted in the open-access alternative, the metaphor package of R. A final meta-analysis was conducted to obtain overall relative risk ratios of cancer and non-cancer diseases for each nicotine product.
In the epidemiological data analysis, no data were available for the cut tobacco category and very few studies differentiated between factory-made and roll-your-own cigarette smokers. Therefore, it was assumed that the studies investigating cigarette smokers most likely included a mix of factory-made and roll-your-own, with both identifying as combustible cigarette smokers. Based on this assumption, as well as the assumption that the two products would carry comparable risk, we used the same data for the combustible cigarettes and cut tobacco categories. No other assumptions were made in the epidemiological analysis and missing data was simply recorded using the data completeness score. For each product, a data completeness score was derived which shows to what extent data points were available for each product and category of data.
Relative risk hierarchy The RRH combines the results of the lifetime cancer risk and epidemiological analysis, with a weighting system that accounts for the completeness of the dataset. In order to integrate these two analyses into a combined risk score for each nicotine product, an arbitrary scale from 0 to 100 was defined, with 0 representing non-users of nicotine products and 100 representing users of combustible cigarettes. Combustible cigarettes were selected as the top of the scale because they were the highest risk product in both of the analyses. Non-user groups were the control or baseline in both analyses, therefore no correction was required to the lower end of the scale. After converting both analyses onto a 100-point scale, a weighting of 68 was applied to the lifetime cancer risk analysis and 54 to the epidemiological analysis, according to the data completeness scores for each component.
Epidemiological analysis In total, 151 risk ratios across 12 categories of nicotine products were included in the epidemiological analysis, compared with 101 risk ratios across eight categories of nicotine products in the 2020 iteration. Heat-not-burn, non-tobacco pouches and cigarillos are not represented in the epidemiological analysis. In this update, new data has been added for NRT, electronic cigarettes, smokeless (rest of world), bidis, snus, U. S. chewing tobacco, U. S. dipping tobacco, water pipe tobacco and western pipe tobacco. Meta-analyses of cancer and non-cancer outcomes for each nicotine product were conducted and are presented in Table  6. The completeness of the epidemiological data ranges from 33% for smokeless (rest of world), water pipe tobacco and electronic cigarettes to 100% for combustible cigarettes, cut tobacco, cigars and snus.
More complete datasets are representative of the relative risk for a wider range of diseases, whereas less complete datasets generally represent a narrower range. While there is significant overlap in the diseases represented for most products, there remain significant gaps in the dataset for certain products. For cancer outcomes, the products with the highest risk ratios are smokeless (rest of world) (RR 3. 675, CI 95% 1. 166 -11. 585), combustible cigarettes (RR 2. 964, CI 95% 1. 579 -5. 565) and cut tobacco (RR 2. 964, CI 95% 1. 579 -5. 565), followed by bidis (RR 2. 884, CI 95% 1. 75 -4. 752), water pipe tobacco (RR 2. 639, CI 95% 1. 635 -4. 258), western pipe tobacco (RR 1. 924, CI 95% 1. 345 -2. 751) and cigars (RR 1. 672, CI 95% 1. 218 -2. 295) (Figure  3 ). In all cases, users of these products have a statistically significant risk of cancer compared to non-users of nicotine products.