dataesr/scientific-paragraphs-categorization huggingface dataset:
Other substances, for example often in the case of pharmaceuticals or pesticides, are 'selective' in interacting with biological targets and have a known biological mechanism. Information on toxicokinetics and toxicodynamics are important in either case. The safety assessment workflow developed here is based on the general SEURAT-1 conceptual framework. As stated earlier, applying the framework to an ab initio assessment at this point in time was a stretch goal aiming to highlight gaps for future development and illustrate overall progress made in SEURAT-1. It assists in structuring the information and provides guidance regarding what additional alternative data are needed to establish and then test a hypothesis. The assessment is based on gathering existing data and using information from alternative methods, as described in the guidelines for safety evaluation of cosmetic substances, which were developed and are regularly updated by the Scientific Committee on Consumer Safety  [78].
This initial tier includes exit points where the TTC approach or a read-across assessment based on chemical similarity could be applied. In cases where neither of these approaches is considered to be adequate, it is necessary to proceed with applying the workflow. In the following steps, high throughput or high content data from alternative methods are collected under Tier 1 to better understand possible MoA, while Tier 2 is targeted testing based on the hypothesis(es) set up under Tier 1. To illustrate this workflow a case study with a hypothetical exposure scenario was created for the substance x: a new ingredient introduced in a body lotion formulation, which is applied twice per day on skin (overall body surface).
Identify chemical of interest The molecular structure and structural variations (e. g. isomers) for the assessed chemical must be identified and if available with other chemical identifiers, such as CAS number, IUPAC name etc. It is important to identify and define the chemical structure fully and correctly. Free or commercial Software such as ChemSpider (www. chemspider. com) or ChemIDPlus are available as support. The use of chemical identifiers such as SMILES or InChiKeys are useful for identification and cheminformatics processing of the structure information. Questions such as whether there are impurities or degradation or biotransformation products, in amounts considered of concern for toxicity, also need to be addressed. It might be possible at this point to conclude by applying a TTC approach  [10, 65] , and not continue to a more refined assessment.
However, as in the case of cosmetics, the primary route of exposure might be dermal. Within the COSMOS project the TTC non-cancer dataset was enhanced with several hundred substances related to cosmetic ingredients  [40, 99]  and the applicability of the TTC approach was evaluated both for oral exposure  [98, 99]  and for dermal exposure, taking into account bioavailability differences between dermal and oral exposure  [97]. Chemical x falls into Cramer Class III (1. 5 µg/kg/day) and for the worst case scenario of 100% dermal absorption with 18. 1 mg/kg/day, the TTC is exceeded by far.
This will provide useful information on the potential biological activity profile of the molecule, and will give an indication of the potential for both selective a nonselective biological activity. In silico profilers can be used to identify active groups, such as functional groups that can bind to proteins or DNA. Selected physicochemical properties might also be requested as input to QSPR and QSAR modelling tools and where not available can be estimated or measured. Available software and databases can be used to obtain predicted (or existing measured) physicochemical properties, such as EPI suite  [28] , the CompTox dashboard (https:// comptox. epa. gov/dashboard) or the OECD QSAR Toolbox (https:// www. qsartoolbox. org) [70]. Furthermore, occurrence of toxicological active metabolites or detoxification by metabolism should be considered.
Chemical compounds such as reaction products and metabolites, identified as of relevance to the assessment, need also to be subject to literature and data searching. Existing animal data available for any of the compounds should be collected and evaluated, as well as any data on human experience, e. g. through poisoning centers, clinical testing, biomonitoring or epidemiology studies. If the data are not available or not complete and robust enough for being applied in a risk assessment as such, it might still be supporting evidence in the hypothesis building and as well used in a final weight of evidence assessment together with alternative data. In the case of chemical x, metabolism prediction with the OECD QSAR Toolbox v. 3. 0 pointed at the formation of metabolites with a potential to bind to proteins and DNA, as well as formation of possible reactive oxygen species (ROS).
Identify analogues, suitability assessment and existing data In particular, existing data for compounds similar to the considered substance should be collected. To this aim, it is necessary to understand the chemical features of the molecule and similarity to other chemicals. For the structural chemical similarity assessment, it is important to identify which features are relevant and to decide on a suitable similarity metric (e. g. Tanimoto index, Rogers and Tanimoto  [71] , Johnson and Maggiora  [47] ). Information regarding chemicals with similar structure is an important first screening in order to identify substances with potentially similar MoA. However, chemical similarity alone is not sufficient, the biological similarity, including both toxicodynamic and toxicokinetic aspects and (bio)transformations has to be taken into account.
A number of software tools are available to support finding suitable analogues, such as the OECD QSAR Toolbox, Analog Identification methodology (AIM), Toxmatch and AMBIT (see recent review in Patlewicz et al. [70] ). By grouping similar substances, it is possible to infer properties and toxicity from data-rich substances in the group (sources) to the datapoor ones (targets), or as in this case to a specific unknown target chemical, applying read-across. One of the key aspects of performing a read-across is the confirmation that the source and target substance belong to the same category and can be considered to be toxicologically similar. If in the weight of evidence later in the workflow additional proof substantiating the chemical and biological similarity and relevance of analogues is gathered, a read-across can still be considered and applied also in Tier 1 and 2 (see Fig.
'Omics techniques (e. g. transcriptomics, metabolomics, proteomics) collect large amounts of information, and the availability and interpretability of this data are rapidly growing. Alternative data sets are also becoming publicly available. Libraries are created with toxicity data for large sets of chemicals, as in ToxCast  [29]  or 'omics databases, e. g. TG gates  [46]  and DrugMatrix (http://ntp. niehs. nih. gov/results/dbsearch/). These data, coupled with existing information from traditional animal-based studies can help to better understand linkages to adverse outcomes and can enable identification of new data that might assist in the safety assessment. Screening of 'omics databases, results from in silico profilers and in vitro libraries (like ToxCast) assist in providing a biological profile of the chemical.
Since we assume that no information of adverse effects is available for the specific compound x, a very large number of specific models or assays are needed covering a broad biological space to predict the MIE, as can be seen by the large efforts invested into the ToxCast  [29]  project. Therefore, an alternative way to identify areas of possible concern is needed, which then can target more specific assays. 'Omics data provide a general overview of the molecular changes in a cellular system as a response to treatment with a chemical compound. Using bioinformatics and systems biology tools it should be possible to relate these changes to biological pathways and finally to possible adverse outcomes  [36]. 'Omics data for chemical x were generated within the FP6-carcinoGENOMICS project and are available in the Data Infrastructure for Chemical Safety warehouse (diXa).
The data represent three in vitro human hepatocytes models: HepaRG (human hepatoma-derived cells), HepG2 (hepatocellular carcinoma-derived cell line) and hES-DE-Hep (hepatocyte-like cells derived from embryonic stem cells). These models were treated with different concentrations of chemical x for 24 h or 72 h. For the pathway analysis, following partially the approach described previously by Kohonen et al. [52]  on doxorubicin, we applied the Kyoto Encyclopedia of Genes and Genomes (KEGG) approach using the 'Integrated analysis of Cross-platform MicroArray and Pathway data' (InCroMap) software in order to select the most relevant molecular pathways influenced by the treatment with x. We observed important differences between enriched pathways in the HepaRG and HepG2 cells and non-specificity in the hESC DE-Hep cells.
We also could observe dose-and time-dependent responses on all cellular models. The most enriched pathways (Q < 0. 05) were then transferred and analyzed for genes, diseases and chemicals associations in the Comparative Toxicogenomics Database (CTD). To identify adverse outcomes of chemical x, we initially followed a consensus approach by using the information of all three liver cellular models. The pathways enriched in at least two cell lines were selected and analyzed further using the CTD services in order to correlate the observed pathways with diseases showing that 'dermatitis-allergic contact' appeared as the most relevant adverse outcome. The skin sensitization would need to be further evaluated, but the indication of protein binding leading to skin sensitization also provide support for other repeat dose systemic toxicity, e.
g. the formation of a reactive metabolite or activation of an immune response could also lead to hepatotoxicity. Since only seven enriched pathways were seen in at least two cell lines and a large amount of information could be lost, in a second approach we analyzed each dataset separately and looked at the sum of the identified effects. By performing this analysis, we showed that transcriptomics data were able to identify drug-induced liver toxicity as one major adverse outcome of treatment with chemical x. To cover a broader biological space additional cell lines/types should be tested to provide a truly comprehensive screen. In order to support the identification or exclusion of target organs and the MoAs for human adversity and hazard characterization, a scan for possible effects can also be performed with in silico profilers, such as developed in the COSMOS project within the SEURAT-1 initiative.
Chemical x was also analyzed across the multiple ToxCast assays as part of the Phase I testing  [15]. This data have been made publically available through the Toxcast Dashboard https://actor. epa. gov/ dashboard2/). 92 active assays, out of the 700 examined, were identified based on the structure of chemical x. These showed activity across a broad concentration range from 0. 1 µM to 100 µM in cell free assay of different cell types. Further filtering has been applied to reduce this assay set to a subset of 26 assays believed to be most relevant to MoA determination. The selection was made on human relevance (removal of rat CYP NVS assays), removal of cytotoxicity assays and application of the cytotoxicity burst consideration as described in Judson et al. [49]. This defines a z-score derived from 3 median absolute deviations from the median cytotoxicity response.
Furthermore, from high throughput screening data available in Tox-Bank, there is an indication that there might be a concern for liver steatosis, due to lipid droplet accumulations. To assess the fibrotic potential, compound x was tested in repeated dose, on human hepatic organoids composed of HepaRG and human hepatic stellate cells (HSC)  [60]. Culture maintenance and exposure was performed as previously described in Leite et. al. Biomaterials  [60]. After a week of cultivation, the cells were exposed every second day to a series of concentrations of compound x (20-1620 μM) during 2 weeks (D8 to D21). Cell samples were collected 24 h after exposure on days 9, 11, 15 and 21 to check cell viability (ATP) and gene expression of HSC activation markers. Results show that the compound is barely toxic after 1 exposure (Fig.
The Virtual Cell Based Assay VCBA),  [103]  is a mathematical tool developed within the COSMOS/SEURAT-1 project to simulate the fate of the chemical in an in vitro assay and was applied to obtain the dissolved concentration of chemical x that could enter the cell. The VCBA model has similarities to the Armitage model  [4]  and similar as the one developed by Kramer et al. [54]. The relevant parameters for running the VCBA can be estimated based on in silico predictions, using tools like EPI suite  [28]  or databases like Chemspider (http://www. chemspider. com/), or the CompTox dashboard (https://comptox. epa. gov/ dashboard). The in vitro concentration response curve to optimize the VCBA model can be taken from experimental cytotoxicity testing. Information needed to perform the VCBA simulation for chemical x (Based on Zaldivar et al.
In other words, an assumption needs to be made regarding the "structure" of the toxicodynamic model, which could be based on C max , AUC, or on a combination of these two parameters. In the translation of the in vivo system to the in vitro model, it must be noted that the time scale might be completely different due to the limitations in metabolic activity in the in vitro system. Therefore the repeated challenge mimicked in a short time interval might be more relevant. Further refinements to the relative skin absorption may be possible using the tiered decision tree that was developed within the COSMOS project based on estimated skin exposure and dermal absorption  [97]. Based on this decision tree for TTC, with a percutaneous absorption of 2. 1% (calculated using J max ,  [42]  and Systemic Exposure Dose formulas), confirmed by in vivo literature data in the COSMOS skin permeability database for chemical x, a maximal systemic availability of 380 µg/kg/day is calculated.
Discussion This general workflow was developed as a means of structuring knowledge and data in a logical sequence for an integrated safety assessment relying specifically on alternative methods and specifically taking into account exposure considerations and kinetics. The safety assessment begins with Tier 0, where the exposure scenario and chemical identity are defined and data are collected. Two exit points where identified, i) applying the TTC approach, ii) performing a read-across assessment taking into account similar substances. When TTC or read across cannot be performed, the assessment continues to Tiers 1 and 2, which define the ab initio assessment. Exposure considerations and Physiologically Based Kinetic models are important to define the target organs and internal concentrations applicable as well as to set the appropriate concentrations for the targeted testing.
Data from alternative methods are collected under Tier 1 to better understand possible modes-of-action, while Tier 2 is targeted (in vitro) testing based on the MoA hypothesis set up under Tier 1. The case study selected using chemical x highlights the challenge in integration of multiple data streams for safety assessment. It provides progress on how to proceed to infer a mode-of-action using a combination of in silico, high throughput and high content data. The use of biological and chemical sub-structure similarity screens can provide some anchoring to build confidence and give clues as to adverse outcomes on the organism level. To provide confidence in the assessment, uncertainty should be identified and evaluated for the different steps of the workflow, the methods and data contributing.
g. in silico models for bioavailability; Partosch et al. [72] ) that can be used to convert the chemical specific external doses (i. e. NOAELs) in the TTC database to an estimated internal exposure. The impact of metabolism will be critical to understand in this context because it provides information regarding what is the toxicologically relevant chemical species (i. e. parent or metabolite) that should be represented in the TTC distribution. Internal TTC values could also be derived from a sufficiently large and representative dataset of relevant in vitro points of departure (e. g. lowest in vitro concentration active in a metabolically competent system) but most likely it will be a combination of the two, with the in vitro points of departure being used to corroborate the model predictions.
Acknowledgements We would like to thank the SEURAT-1 Scientific Expert Panel that provided support and advice as the ab initio case study was developed. The authors would like to acknowledge the following scientist that provided support to the ab initio case study:  Emilio Benfenati ,  Jos Bessems ,  Simona Kovarich ,  Elisabeth Joossens ,  Richard Judson ,  Alfonso Lostia ,  Tomislav Horvat ,  Christof Schwab ,  Vessela Vitcheva ,  Ivanka Tsakovska ,  Elmar Heinzle ,  Lucian Farcal ,  Barry Hardy ,  Chihae Yang  and  Joan Fisher.
Rearing Period One-hundred-ninety-two chicks from each incubation group were randomly assigned to 3 different rear-ing rooms. Thirty-two males and 32 females from I 0 , I 1 , and I 2 (Figure  2 ) were placed in the control temperature (T 0 ) room where the ambient temperature was gradually decreased until 21 d of age from 33 to 21 • C. In a second room, 32 males and 32 females from groups I 0 , I 1 , and I 2 were exposed to cyclical variations T 1 of temperature from 33 to 28 • C at d 0 to 21 • C at d 21 in a gradual decrease. In a third identical room, 32 males and 32 females from I 0 , I 1 , and I 2 groups were submitted to cooler ambient temperature T 2 , decreasing gradually on d 21 from 28 to 21 • C. The rearing temperature in each room was monitored by a temperature controller each min during the entire experimental period (Thermo Button, Proges Plus, Willems, France) and data were collected via the software Thermotrack PC for analyses (Figure  3 ).
Statistical Analysis Data were collected and analyzed by statistic software Statview (version 5. 0; SAS Institute, Cary, NC) in order to determine the effects of incubation group, postnatal condition, and their interaction on performance traits for each sex. The hatchability and mortality results were analyzed by using a Chi-square test. The responses to treatments and sex and their interactions concerning chick quality, body temperature, and weight were determined using analysis of variance (ANOVA) after having checked the normality of data distribution. The statistical significance of the difference was determined using a Student t test for the comparison of results from each group. Due to a significant heterogeneity of variance evaluated using Levene's test (P < 0. 001) on data of FCR, values for this parameter were analyzed by nonparametric tests, including the Kruskal-Wallis test followed by the Mann-Whitney test.
ACKNOWLEDGMENTS D. Nyuiadzi  is funded by  West Africa Agricultural Productivity Program from Togo (WAAPP-Togo)  and  Centre d'Excellence Régional en Sciences Aviaires (CERSA) / Lomé University  for realizing her PhD. This study was funded by  INRA (Department Animal Physiology and Livestock Systems ,  Eval Adapt  project) and carried out within Technical Mixed Unit (UMT) framework  Integrative Biology Research and Development (BIRD)  associating  ITAVI Poultry Technical Institute  and  INRA  on applied research projects. The authors thank  F. Mercerand ,  J. Delaveau ,  C. Rat ,  I. Grimaud-Jottreau ,  H. Rigoreau , and  C. Le Bourhis  from  Experimental Unit Pôle d'Experimentation Animale de Tours (PEAT) ,  INRA ,  37380 ,  Nouzilly, France , for helpful animal care,  S. Crochet ,  N. Couroussé ,  E.
Data analysis Statistical analyses were performed with the SAS version 9. 3 software (SAS Institute Inc. , Cary, NC, USA). The normality was evaluated with Shapiro-Wilk test. The effect of time on each parameter evaluated <8 h and at Day 1 of life was tested with Wilcoxon signed-rank test. Linear mixed models were performed to determine the variables affecting each monitored parameter, separately <8 h and at Day 1: Apgar score/glucose/lactate/ˇ-hydroxybutyrate/rectal temperature/urine specific gravity ∼ MIXED procedure (sex of the puppy (female/male), birth weight (g), breed size (small/medium/large), age of the dam (years) and litter size (total number of puppies born within a litter) + litter as random term + birth weight and litter size nested within breed size. Generalized linear mixed models (GLIMMIX procedures) with neonatal mortality as a binary outcome (logit transformation) were used to assess the effect of different monitored parameters (fixed effects): Mortality 0-1 day ∼ GLIMMIX procedure (Apgar score) + litter as random term.
The fastq files generated after Illumina sequencing were uploaded into Qiime software  [12]. Quality trimming criteria were set to a value of 19 and no mismatch in the MID sequences. Reverse reads were shortened to a length of 250 bp and forward and reverse sequences were joined. Chimeric sequences were predicted by the slayer algorithm and excluded from subsequent analysis. The resulting sequences were then classified by RDP Seqmatch with an OTU (operational taxonomic units) discrimination level set to 97% followed by UniFrac analysis. Principal coordinate analysis (PCoA) implemented in Qiime was used for data visualization. The raw sequence reads have been deposited in the NCBI Short Read Archive under the accession number SRP071000.
This work has been supported by the  EU   FP7   ProHealth  project (no. 613574 ),  RO0516  project of the  Czech Ministry of Agriculture  and  AdmireVet  project  CZ. 1. 05/2. 1. 00/01. 0006-ED0006/01/01from  the  Czech Ministry of Education. The commercial partner CCPA was a member of research consortium of  FP7   EU  project similar to the other co-authors. The CCPA company did not fund work presented in this manuscript at all. The funder provided partial support in the form of salaries for authors IR, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data recording Phenotypic measurements. All pigs were weighed (BW) from week 11 (BW11) to week 23 (BW23). The average daily BW gain (ADG, g/d) was calculated by dividing the BW gain from 11 to 23 weeks of age divided by the period duration in days. On week 23, the backfat thickness was measured ultrasonically (Agroscan, E. C. M, Angoulême, France) at 6 different sites, measured directly above the point of the elbow, last rib (P2 site) and last lumbar vertebra locations, respectively, and taken 5 cm off the midline on each side of the pig. The average back fat thickness (ABFT, mm) was the average value of these 6 measurements. Individual daily feed intake was recorded using electronic feeders (ACEMA 128, ACEMO, Pontivy, France) either only during test weeks 11-12, 15-16 and 19-20 (defined as period 1) for half of the pigs or only during test weeks 13-14, 17-18 and 21-23 (defined as period 2) for the other pigs.
During the remaining test weeks, pigs had free access to a conventional feed dispenser. We calculated the average daily feed intake (ADFI) over the whole trial based on these measurements, as proposed by  [15]. Rectal temperature (RT) and skin temperature (ST) were measured at 23 weeks of age on resting animals. RT was measured using a digital thermometer (Microlife Corp. , Paris, France), and ST was measured on the back at the P2 site using a skin surface thermocouple probe (type K, model 88002K-IEC, Omega Engineering Inc. , Stamford, ST, USA) connected to a microprocessor-based handheld thermometer (model HH-21, Omega Engineering Inc). Plasma sample preparation. From the 1298 pigs, data from 560 animals sampled for plasma at 23 weeks of age in TEMP climate were used in the present study.