dataesr/scientific-paragraphs-categorization huggingface dataset:
Metabolomics analysis using 1 H-nuclear magnetic resonance The 1 H NMR analysis of saliva samples was described previously by Goudet and collaborators  (Goudet et al. , 2019a). Briefly, saliva samples were thawed at room temperature and 150 μl were added to 100 μl of 0. 2 M potassium phosphate buffer in deuterium oxide (D 2 O) 99%. Eight μl of 3-trimethylsilylpropionic acid (0. 05% wt in D 2 O) were added to samples as an internal reference. The 1 H NMR spectra were obtained with a DRX-600 Bruker spectrometer (Bruker, Sadis, Wissembourg, France) and post-processed as previously described  (Goudet et al. , 2019a). The NMR assignment was done using spectra online databases as HMDB (http://www. hmdb. ca) and ChenomX NMR Suite 8. 1 evaluation edition  (ChenomX, Inc. , Edmonton, Canada).
Statistical analysis The normality of the data was checked with the Kolmogorov-Smirnov test. The comparison of the absolute concentrations of steroids and metabolites between receptive and non-receptive gilts was performed using repeated measures two-way ANOVA, followed by a post-hoc Fisher multiple comparisons test, using GraphPad Prism version 6 (GraphPad Software, La Jolla California, USA) to unveil potential differences at each time point. A difference between receptive and non-receptive gilts was considered to be significant if P < 0. 05 and to tend to be different if P < 0. 1.
Here, we investigated whether the good intra-laboratory reproducibility observed in Phase 0 and 1 with a small number of compounds  (21)  could be confirmed with a larger dataset, in addition to further evaluating the predictive capacity of the RSMN. Ultimately, we aimed to establish its usefulness as a 'tier 2' assay that can, alongside the RS Comet assay  (1) , be used to follow-up on positive results from the standard in vitro test battery.
05 was considered to be indicative of a statistically significant positive response, i. e. without accounting for multiple comparison. To help judge the dose dependence of the effect of experiments with equivocal results, a Cochran-Armitage (CA) trend test (P < 0. 05) was used in the overall judgement within each laboratory. In the overall analysis after finalisation of all experimental work, the trend test from the freely available R package 'CAATexact', version 0. 1. 0 (available at http://CRAN. R-project. org/package=CATTexact) (or equivalent) was used, which we also recommend to all other users going forward. In addition, the biological relevance of the findings was considered taking into account dose dependence and strength of the effect in relation to the 95% control limit of the historical SC data as well as reproducibility between tissues within and between experiments.
However, the increase in the % MN-BN was not duplicated in the repeat experiment, possibly due to higher levels of cytotoxicity (Supplementary Figure  S4G ). Mixed results were also found in Phase 2c testing by Lab C. CdCl 2 was negative in the 48-h protocol and a follow-up 72-h experiment (Supplementary Figure  S4H  and I ). In Phase 1, each chemical was tested in three laboratories  (21). Subsequently, in Phases 2a-2d, chemicals were each tested in 1-3 laboratories each to expand the chemical space tested in the RSMN assay or to expand the overlap with chemicals tested in the RS Comet assay. Cat, Category, i. e. expected outcome based on historical in vitro and in vivo genotoxicity or carcinogenicity data as provided in Supplementary Table  S1 ; MP, misleading positive; TN, true negative; TP, true positive; Pos, positive study outcome (printed in bold), DNA damage detected; Neg, negative; Equiv, equivocal; BLR, between-laboratory reproducibility.
Historical database For all laboratories participating in Phase 2, descriptive statistics were performed for the percentage of MN-BN for the SC (Table  2 ). These data were used to obtain information on the 95% quantile of the mean of the historical SC controls for the % MN-BN which was determined at the end of each test phase (2a-2d) for the 48-(two-treatment) and 72-h (three-treatment) protocols for each of the four participating laboratories. While variable among laboratories, each one (A, B, C, D) showed a low and reproducible level of the % MN-BN for the SC (as well as a clear induction of MN by the PC (MMC) across the various testing phases (2a-2d), demonstrating the robustness of the method over time. The acceptance criterion for the PC (a significant increase in % MN-BN above SC) was also fulfilled in all main experiments in Phase 2.
Assessment of intra-and inter-laboratory reproducibility In order to assess intra-and inter-laboratory reproducibility, all data generated within the coded validation effort were tabulated from Phase 1 of the validation study published previously  (21)  and from Phase 2 of the present study (Table  1  and Supplementary Table  S2 ). The reproducibility of the RSMN assay within a laboratory over time was assessed by comparing the outcome of individual experiments when more than one experiment was performed under identical conditions (using either the 48-or 72-h protocol) in the same laboratory. Across all four laboratories, 69 double and triple experiments could be identified and counted towards assessing the concordance of classification (see Table  3  and Supplementary Table  S2 ). The overall within-laboratory reproducibility for the validation exercise was 84% (Table  3 ), with values between 74 and 93% for the individual laboratories that participated in the validation.
Predictive capacity of the RSMN assay The predictive capacity of the RSMN assay was calculated using the final calls, per laboratory, for all available experimental data from all phases of the validation exercise. Applying the approach described in the Materials and methods section reveals an overall accuracy of the RSMN assay of 80%, with a sensitivity of 75% and a specificity of 84% (Table  4 ) (for detailed results see Supplementary Table  S3 ). When the RSMN assay is used in combination with the RS Comet assay as intended, which is as a follow-up to positive results from the standard in vitro testing battery with a choice of RSMN or RS Comet, or both, depending on the genotoxicity endpoint(s) affected  (17, 52, 53) , the sensitivity increases to 89% while the specificity remains high. A manuscript with a detailed analysis of the predictive capacity of these two assays in a 'battery' approach is in preparation.
Acknowledgments We would like to dedicate this paper to an amazing colleague,  Brenda Barnett , who recently lost her battle with cancer. Brenda was instrumental in the RSMN assay work presented here and is the person who has performed and counted more RSMN studies than anyone in the world. We would like to acknowledge not just her great work but also her wonderful attitude to life and her bravery. She will be missed. The presented work is the result of a broad joint research project. Due to limited space, not all colleagues that contributed to this study could be added to the author list and their excellent contributions are therefore acknowledged here. These are  Brigitte Faquet  at l'Oréal and  Nathan Wilt  at IIVS. In addition, we thank  Covance Laboratories (UK) ,  Integrated Laboratory Systems, USA and VitroScreen, Italy , for their support in ordering, coding and shipping of chemicals at different stages of the project.
RNA was then purified using the Qiagen RNeasy mini kit and eluted in 42μl of ultrapure water. Quality and concentration of the RNA was assessed by spectrophotometry with the Agilent 2100 Bioanalyzer system and using the Agilent RNA 6000 nano kit. Further details are available at Environmental and Evolutionary Epigenetics Webpage (http://methdb. univ-perp. fr/epievo). Illumina library construction and high-throughput sequencing. cDNA library construction and sequencing were performed at the Genome Quebec platform. The TruSeq stranded mRNA library construction kit (Illumina Inc. , USA) was used following the manufacturer's protocol on 300 ng of total RNA per sample. Sequencing of the 24 samples was performed in 2x100 bp paired-end on a Illumina HiSeq 4000 (S1 Table ). Sequencing data are available at the NCBI-SRA under the BioProject PRJNA491632.
Transcriptomic analysis of hetero-specific pairing versus homo-specific pairing. Raw sequencing reads were analysed on the Galaxy instance of the IHPE laboratory  [57, 58]  First, raw reads were subjected to quality assessment and sequence adaptor trimming. We used the set of tools based on the FASTX-toolkit  [59] , as well as Cutadapt program (Galaxy Version 1. 16. 1) to remove adapter sequences from Fastq files  [60]. Finally, paired end reads were joined in a single fastq file using the FASTQ interlacer/de-interlace programs (Galaxy Version 1. 1). Processed reads were mapped using RNA-star Galaxy Version 2. 6. 0b-1 [61]  to the S. haematobium reference genome  [62]  downloaded from the Schistosoma Genomic Resources website SchistoDB (http://schistodb. net/common/downloads/Current_Release/ShaematobiumEgypt/ fasta/data/).
Exon-intron structure was thereafter reconstructed for each mapping BAM file using Cufflinks transcript assembly Galaxy Version 2. 2. 1. 2, by setting the max intron length at 50000, but without any correction parameters  [63]. Finally, in order to create a reference transcriptome representative of S. haematobium and S. bovis male and female reads, we merged all cufflinks data with Cuffmerge Galaxy Version 2. 2. 1. 2 [63]  without using any guide or reference. This enabled us to create a representative reference transcriptome of both species and both sexes using the same reference genome. The Genomic DNA intervals of all newly assembled genes of this reference transcriptome were extracted from the S. haematobium reference genome and converted into a Fasta file. The number of reads per transcript for each sample (i.
Functional annotation. Using our BLAST local server, we annotated the entire de novo assembled transcriptome by Blastx search against the non-redundant database of the NCBI. We conserved only the longest unique transcript (TCONS) of each representative gene (XLOC) for Blastx search and subsequent analysis. Output XML files were used for gene ontologies (GO) mapping and annotation using Blast2Go version 4. 1. 9 [67]. Finally, enrichment Fisher's exact tests were performed on up and down regulated sets of genes focusing on biological process (BP) ontology terms. The P-value for significance was set to 5% False Discovery Rate (FDR).
The heatmap of the sample-to-sample distances as well as the principal component analysis plot are presented in Fig 3. A total of 1,277 genes (~7% of the 18,648 genes present in the reference transcriptome) were differentially expressed in at least one of the four homo-versus hetero-specific comparisons with a FDR <5% (Sheet G in S1 File). Of these, 1,234 (97%) had a match using Blastx against the non-redundant database of the NCBI and 1,088 (85%) were mapped and successfully annotated with at least one GO term using Blast2GO  [67]  (Sheet G in S1 File). Most of the differentially expressed genes (DEGs) were identified in S. haematobium males, with 1,166 DEGs between the hetero-specific and homo-specific pairing combinations (734 over-expressed and 432 under-expressed in hetero-specific paired males compared to homospecific ones).
Acknowledgments We would like to acknowledge  Genome Quebec and University McGill innovation center as well as Roscoff Bioinformatics platform ABiMS  (http://abims. sb-roscoff. fr)and  NGS  facility at the bio-environment platform ( University of Perpignan ). This study is set within the framework of the  "Laboratoires d'Excellence (LABEX) "  TULIP  ( ANR-10-LABX-41 ).
This work has been funded by the  French Research National Agency  (project  HySWARM , grant no. ANR-18-CE35-0001 ). SM was supported by the  Occitania region  (project  MOLRISK , award no. NREST 2019/1/059 ), the  European "Fonds Europeen de Developpement Regional" (FEDER)  and  MRG  by the  Fellowship of "Estancias breves " (linked to the  Programa de Ayudas de Formacion de Profesorado Universitario 2015, Ministerio de Ciencia, Innovacion y Universidades, Spain , https:// www. educacionyfp. gob. es/servicios-al-ciudadano/catalogo/general/20/200487/ficha/200487-2017. html# dc1 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Two sets of thresholds were derived: general HB-TTCs as preliminary screening levels for compounds with no eco-and toxicological information, and compound-specific HB-TTCs for chemicals with known hazard assessment, in terms of Predicted No effect Concentration (PNEC) values for freshwater ecosystems and acceptable doses for human health. The proposed strategy is based on freely available public data and tools to characterize and group chemicals according to their toxicological profiles. Five generic HB-TTCs were defined, based on the ecotoxicological profiles reflected by the Verhaar classes, and compound-specific thresholds for more than 400 organic chemicals with complete eco-and toxicological profiles. To complete the strategy, the use of in silico models is proposed to predict the required toxicological properties and suitable models already available on the VEGAHUB platform are listed.
Preliminary Integrated Profile for Human and Ecological Risk Assessment The use of profilers allowed the grouping of the substances according to their preliminary eco-and toxicological-profile (Figure  1 ). The classification of chemicals is the first step to derive screening values for chemicals of interested without performing any additional toxicity testing. We retrieved structural information and PNEC data for 3728 organic chemicals from the freely available EnviroTox platform of the Health and Environmental Sciences Institute (HESI)  [20, 25, 26]. According to the Verhaar classification  [27, 28] , most of the chemicals in this dataset cannot be classified (class 5) while narcosis (class 1) and unspecific reactivity (class 3) are the two most common modes of action (Figure  1A ). Focusing on human toxicology, more than two thirds of chemicals fell into Cramer class III (Figure  1B )  [29].
Proposed Eco-TTCs and Quality Standards from hTTCs. Profiling the dataset for Verhaar classification was necessary to group chemicals according to their mode of action on aquatic organisms. This was fundamental for the derivation of eco-TTCs. We selected the Verhaar classification because it is a consolidated approach and because it is possible to classify the chemicals of concern automatically with public available profilers such as Toxtree or VEGAHUB implementing the rationale behind the classification. The eco-TTC of each subset based on the Cramer classification was calculated as the fifth percentile of the log10-transformed PNEC distribution. Empirical, normal fitted and logistic fitted distributions were calculated (Table  1 ).
Proposed Eco-TTCs and Quality Standards from hTTCs. Profiling the dataset for Verhaar classification was necessary to group chemicals according to their mode of action on aquatic organisms. This was fundamental for the derivation of eco-TTCs. We selected the Verhaar classification because it is a consolidated approach and because it is possible to classify the chemicals of concern automatically with public available profilers such as Toxtree or VEGAHUB implementing the rationale behind the classification. The eco-TTC of each subset based on the Cramer classification was calculated as the fifth percentile of the log10-transformed PNEC distribution. Empirical, normal fitted and logistic fitted distributions were calculated (Table  1 ). Safer concentrations for humans were defined according to the Water Framework Directive and selecting the water abstracted for drinking purpose as exposure route for humans.
HB-TTCs Specific for Each Chemical The generic HB-TTCs listed in Table  3  can be applied to chemicals with no eco-and toxicological data as benchmarks for preliminary screening. Compound-specific HB-TTC can be obtained when experimental data relating to the toxicological and ecotoxicological potential are available or, as we will see later, when predictions are available for the properties of interest. We derived 410 HB-TTCs combining data from the EnviroTox database, the No Observed Adverse Effect Level (NOAEL) dataset collected from the literature, and data for carcinogenic compounds (see Supplementary Material SM3). Values range from 2. 996 × 10 -8 to 20. 955 mg/L and the distribution in fixed intervals is shown in Figure  2A. Focusing on the types of chemical, most of the compounds are non-carcinogenic (344, ~84%) (Figure  2B ).
erties of interest. We derived 410 HB-TTCs combining data from the EnviroTox database, the No Observed Adverse Effect Level (NOAEL) dataset collected from the literature, and data for carcinogenic compounds (see Supplementary Material SM3). Values range from 2. 996 × 10 -8 to 20. 955 mg/L and the distribution in fixed intervals is shown in Figure  2A. Focusing on the types of chemical, most of the compounds are non-carcinogenic (344, ~84%) (Figure  2B ).
The Proposed Eco-TTCs An essential step in the proposed strategy for generic HB-TTCs is to define eco-TTCs as generic safe concentrations for freshwater organisms, according to their common mode of action. To do this, we profiled the organic chemicals in the HESI EnviroTox database, which includes PNEC values for freshwater organisms. Chemicals were analyzed with the VEGA implementation of the Verhaar scheme as it appears in the Toxtree software, and grouped in 5 sets according to the defined classes. The Verhaar classification is a consolidated approach used worldwide to classify substances according to their ecotoxicological mode of action  [27, 28]. Then the fifth percentile of the log10-transformed PNEC distribution of each set was calculated and selected as eco-TTCs for the class, similarly to what is done for the definition of human TTC  [1] [2] [3] [4] [5].
Use of VEGA in Silico Models for the Predictions of Eco-and Toxicological Properties of Substances with Partial Toxicological Information In the present work, we propose two strategies for the definition of HB-TTCs for organic chemicals, with a general or compound specific approach. For substances other than those listed in Supplementary Material SM3, it is always possible to derive the general HB-TTCs by profiling with profilers already available and widely used, while the compound-specific approach requires more data, not always available. When data is lacking, one can use in silico models to predict the eco-and toxicological properties of interest  [33] [34] [35]. These data will then be integrated with any experimental data available to complete the toxicological assessment. The currently available in silico ecotoxicological models are not able to provide predictions for the PNEC directly but these tools predict single endpoint for each of the three trophic levels considered.
Endpoints Available Models NOAEL  [36] [37] [38]  Algae acute toxicity  [39] [40] [41] [42] [43] [44] [45] [46]  Algae chronic toxicity  [47, 48]  Focusing on VEGAHUB, several models are already freely available for the properties listed in our strategy and can be used for organic chemicals only. The NOAEL model is a QSAR model based on data from repeated dose 90-day oral toxicity studies in rodents and the graph of atomic orbitals by the Monte Carlo method, using CORAL software  [61]. A combined approach for carcinogenicity based on oral slope factors has been recently uploaded on the platform. It includes a classification model to distinguish between carcinogens and non-carcinogens and a regression model for the quantitative prediction of the oral slope factor  [62]. The algae toxicity models are a Tree Ensemble Random Forest built respectively with 72 h EC50 data based on growth rate and 72 h NOEC values from the Japanese Ministry of Environment, respectively for the acute and chronic models.
The two models for acute toxicity with Daphnia magna are both based on 48 h EC50 data from studies following the OECD 202 guideline: the DEMETRA model is a hybrid model based on multiple linear regression and the IRFMN model is a Tree Ensemble Random Forest. The Daphnia Chronic toxicity model is a Tree Ensemble Random Forest based on experimental 21 d NOEC data retrieved from the Japanese Ministry of Environment and selected according to the OECD TG 211 guideline. Several models for acute fish toxicity are implemented in VEGAHUB. For the purpose of this work, we suggest the following models: - The Finally, the fish chronic toxicity model is a Tree Ensemble Random Forest built with data from the early-life stage toxicity test (OECD TG 210) and juvenile growth test (OECD TG 215) collected by the Japanese Ministry of Environment.
Data Collection Ecotoxicological data for freshwater species were collected from the EnviroTox database by the Health and Environmental Sciences Institute (HESI)  [20, 26]. The collection includes experimental data from acute and chronic aquatic toxicity tests on algae, invertebrates, and fish, associated to Canonical SMILES for each chemical. Predicted No-Effect Concentration (PNEC) values were extracted automatically, applying the PNEC Calculator tool included in the platform on the entire downloadable database: briefly, for each chemical, the tool calculates the geometric mean of multiple data for the same endpoints (the combination of acute or chronic and algae, invertebrates, and fish) and then the PNEC is calculated considering the European chemical assessment conditions implemented in EnviroTox  [26].
Data Available and Combination Assessment Factors 1 trophic acute level 10,000 2 trophic acute levels 5000 on the most sensitive taxon 3 trophic acute levels 1000 on the most sensitive taxon 3 trophic acute levels and 1 chronic data not on the most sensitive acute taxon 1000 3 trophic acute levels and 1 chronic data on the most sensitive acute taxon 100 3 trophic acute levels and 2 chronic data including most sensitive acute taxon 50 3 trophic acute levels and 3 chronic levels 10 More than 10 chronic toxicity data or microcosm/mesocosm studies 1 to 5 No Observed Adverse Effect Level (NOAEL) data from repeated dose 90-day oral toxicity studies in rats were collected from several public repositories, in line with their adherence to the OECD 408 guideline  [65]. The sources of data include the Munro database and the Hazard Evaluation Support System (HESS) database, both available from the OECD QSAR Toolbox  [66, 67] , the Integrated Risk Information System (IRIS) by the U.
Data Curatiom and Profiling Inorganic and organometallic compounds, polymers, isomeric mixtures, and data relating to mixtures of chemicals were removed from the ecotoxicological and NOAEL datasets, as previously reported  [72]. Where necessary, structures were desalted manually using the Marvin suite by Chemaxon  [73]. From the data curation, a final dataset with ecotoxicological data relating to 3728 chemicals (curated EnviroTox DB) and a NOAEL dataset of 746 compounds were obtained. Chemicals in the curated EnviroTox DB were first analyzed with the Cramer rules module of Toxtree  [3, 29]  as recently implemented in the VEGAHUB platform (www. vegahub. eu (accessed on 25 February 2021)). This decision tree estimates the human TTC (hTTC) for a substance based on its chemical structure. As a result, the substance is classified in one of the three Cramer classes where class I is for chemicals with low oral toxicity while class III includes chemicals with the most severe toxic hazard (Table  7 ).
The curated EnviroTox DB were then profiled with the VEGA implementation of the Verhaar scheme in Toxtree software (version 3. 2, Joint Research Center, European Commission, Ispra, Italy)  [27, 28]. The profiler classifies substances in 5 classes according to their Mechanism of Action (MoA) of substances for fish acute toxicity (Table  8 ). Compounds and groups acting by a specific mechanism 5 Not possible to classify according to these rules (unclassified).