dataesr/scientific-paragraphs-categorization huggingface dataset:
In vitro assays In vitro assays covering a wide range of endpoints and including AOPrelated assays and the Tox21/CAST high throughput assays database, are available in the ChemTunes database, including, but not limited to, additional assay results from the Cosmetics Europe LRSS activities, e. g. , Caco-2 cell permeability and in vitro skin sensitization assays. The in vitro ToxCast/Tox21 database is available for 9,298 substances and 1,569 assays developed from ToxCast/Tox21 and their collaborators  [37]. Due to the sparse nature of this database (i. e. , many missing values), the signals from related assays were aggregated to increase sensitivity. Each assay result was transformed to a binary response (active/inactive) so that the data are represented as a compound-assay array, where each compound is represented as a vector of 0/1 values across all in vitro assays, with missing values assigned denoted "NaN".
Chemotype profilers Chemotype profiling attempts to identify structural (sub-)fragments that are associated with some activity, property or PK effects. Three types of structure profiling methods are available in ToxGPS, ranging from general feature mapping to endpoint-specific rules coded with sensitivity and specificity statistics derived from testing the rules against knowledgebase datasets. These chemotype profilers are coded in Chemical Structure and Reaction Mark-Up Language (CSRML), in which electronic, atomic, and bond properties can be encoded in addition to molecular connectivity  [42].
Profilers. In ToxGPS, the term "profilers" is used in general where chemotypes are applied to associate structural features with chemical binding or reactivity towards biological target molecules. Profilers are not specifically intended to reflect endpoint associations, nor the likelihood of such events. Examples are DNA and/or protein binders  [43] [44] [45]. ToxPrint chemotypes are offered only to allow visualization of each chemotype defined for fingerprinting structures. Although ToxPrint chemotypes cover the chemical space of large diverse toxicity databases and are designed with toxicity modes of action in mind, these profilers should not be interpreted as endpoint-related alerts unless this is justified by further analysis against a training set to demonstrate a sufficiently strong statistical association  [46].
Biotransformation rules. Liver Metabolism Rules: Liver BioPath rules consist of a total of 144 human-relevant biotransformation rules that represent enzymatic reaction sites similar to the public Systematic Generation of potential Metabolites (SyGMa) rule definitions  [56]. The top transformation categories include cleavage, conjugation, hydrolysis, hydroxylation, lactam formation, lactone formation, oxidation/reduction, and rearrangement. The rules are represented in chemotypes embedded with physicochemical properties according to the CSRML syntax  [42]. The rules are used as profilers and fingerprints to allow comparison of metabolic reactivities between chemical structures. Within the ChemTunes Metabolism database, these human-based transformations can be explored quickly against other species.
In silico predictions 2. 3. 4. 1. Machine learning (ML) hybrid rules. The CSRML methodology allowed development of novel hybrid rules combining expert-guided knowledge with machine learning (ML) approaches. During the expert-driven rule extraction stage, a highly curated training set containing target response data (binary, multinomial, or continuous) about a clearly defined endpoint is constructed. Initial chemotypes are first selected by substructure searches within the training set using the ToxPrint chemotypes. Associations of these initial chemotypes with the target endpoint are explored and the chemotypes are further refined to enhance the SAR (structure-activity relationship) and enrich target association. These enriched chemotypes are then used to subset the datasets into more homogeneous target responses by splitting the set using compound properties based on machine-learning techniques, e.
Structure similarity and measures Various similarity searching methods are available in Chem-Tunes•ToxGPS®. From the main query builder, similar structures can be queried against the whole database, which can be further constrained to include particular endpoints in the database. Within the ToxGPS workflow, this first query result can also be augmented by analogue searching tools that constrain search hits based on a specified similarity threshold and, if desired, substructure scaffolds representing relevant biology. In the Express service, finding analogues via ensemble searching of nearest neighbors from a specific endpoint dataset (NOAEL or EC3 datasets) is also possible. ToxGPS provides a number of fingerprinting schemes for quantifying structure-based similarity, including both extended connectivity fingerprints (RDKit molecular topological and Morgan  [15, 74, 75] ) and the pre-defined features (ToxPrints  [42, 46]  or public 166 MACCS keys  [76] ).
The capability of evaluating similarity with multiple fingerprinting schemes is important because different schemes may capture different perspectives. Once the structures have been fingerprinted, Tanimoto  [77]  and Dice indices  [78]  are applied to calculate pairwise similarities. Based on similarity calculations for all structure pairs in the full ChemTunes database using the above four fingerprints, a similarity index of approximately 0. 7 is recommended as a reasonable threshold for defining similar structures  [32]. The issue over the values of similarity indices such as Tanimoto and Dice is important and must be acknowledged when applying such a method. Mellor et al. [79]  undertook an analysis of various calculation methods for molecular similarity. The study demonstrated that similarity indices are dependent on the fingerprint (or other information) that they are computed from.
Equation (  1 ) thus represents a robust improvement for property-or assay-based similarity. It is important to note that when calculating property or assay-based similarity values, the variables must first be scaled by standardization or some other appropriate normalization method since the original variables have different units and may vary widely in magnitude. Before computing the profile similarity for properties, the original values were therefore standardized using means and standard deviations computed from a large random sample of approximately 50,000 structures in the ChemTunes database so that robust and reproducible pairwise similarities can be calculated. For bioavailability properties such as Caco-2 cell permeability, skin permeability, hepatic clearance, and fraction unbound, ChemTunes provides large datasets so that the smaller datasets can be standardized in a systematic and reproducible manner.
Profile comparisons of assays. Biological similarity can also be quantified based on data from activity or screening assays. For example, patterns for binding activities related to EDSP assays were selected including the 21 estrogen (ER), 12 androgen (AR), 13 progesterone (PR), 6 aryl hydrocarbon receptor (AhR), and 12 peroxisome-proliferatoractivity (PPAR) assay categories  [82]. In this analysis, only the assays having data for at least 30% of the structures in the database were included. The Endocrine Disruption Screening Assays and others selected in this analysis contained a large number of data points: AR_BLA Activity (8,306), ER_Era_BLA Activity (8,305), PR_BLA Activity (7,871), Ahr Activity (8,887), and PPARg Activity (9,179). For each substance, all available assay results within each group of aggregated assays are averaged and normalized from -0.
5 to +0. 5. Each substance can then be visualized with a skyline plot where each bar represents a different assay group  [65]. Fig. 6  illustrates the 23 aggregated assay categories. Pooling individual assays in this manner increases the signals from the above 64 assays to 33 assay categories (6 AR, 14 ER, 2 AhR, 5 PR, 6 PPAR assays). The values above the horizontal axis depict the assays whose activities were greater than the mean of the available database substances. The EDSP assay categories are grouped by AR, ER, AhR, PR, and PPAR. The assay-based similarity between any two substances can then be calculated from the respective profiles in the same way as was done for property-based similarity, using either Pearson or Euclidean similarity measures. When the activities are binned into categories, variability among the observations with many missing values is minimized and Euclidean similarity is a better choice because it depends on absolute distance, not simply correlation.
Problem formulation: safety assessment of the essential oil of Perilla frutescens This investigation and associated use cases attempt to demonstrate how molecular informatics, implemented through a software platform, can assist elements of Tier 0 in the NGRA of a botanical used in a cosmetic product. Specifically, these use cases considered a hypothetical use case of a leave-on hand cream containing P. frutescens at 0. 01%. In this instance, the substance was characterized in the initial step (otherwise considered to be Steps 2 or 3 of the NGRA) such that the exposure to individual components could be considered (Section 3. 2). P. frutescens largely consists of naturally occurring monoterpenes and monoterpenoids. One of its constituents is perillyl alcohol, a monoterpene derived from the volatile components of the essential oil of many botanicals including lavender, peppermint, cherries, sage, lemongrass as well as P.
Thresholds of toxicological concern For substances whose exposures are sufficiently low, a substancespecific data waiver, such as the use of TTC may be appropriate at Steps 2 and 3 of Tier 0 of NGRA. TTC was performed on the identified constituents of P. frutescens according to the method described in Section 2. 3. 3. A component-based approach was applied by assessing all welldefined components individually for their genotoxic potential  [64]. The outcome of the TTC analysis along with the genotoxic outcomes is summarized in Table  1. Two constituents were found to be associated with genotoxic potential according to predictions from Ames mutagenicity, in vitro chromosome aberration, and in vivo micronucleus QSAR models and rule-base outcomes in ToxGPS as well as experimental data, where available, in the ChemTunes database and literature.
Quantitative read-across (qRAX) Botanicals in general are data-poor substances whose chemical structures and compositions are often ill-defined. The case of P. frutescens is fortunate since structure and composition data have been published. As shown in Fig. 8 , further characterization of the constituents into several structurally similar clusters facilitated the read-across approach for substance-specific assessment. We selected three clusters for the use-case: 1) alicyclic alkenes; 2) aliphatic chain alcohols; 3) terpenoid alcohol. The structures in three clusters and their data profile are listed in Table  2. To establish a broad profile of data availability, all constituents of P. frutescens were searched for similar structures in the ChemTunes database (over 100,000 structures), selecting structures with pairwise similarity >0.
70 (Tanimoto coefficients) against the constituent of interest. From this query, additional structures were identified including p-menthane-3,8-diol (CMS-7392), dihydro alpha-terpineol (CMS-9118), 4-terpineol (CMS-10854), perillic acid (CMS-59861), menthadienol (CMS-14225), and isocyclogeraniol (CMS-2635). When the similarity searching is constrained to the ToxCast in vitro assay database of 9,298 substances, most structures in Cluster-2 (aliphatic chain alcohols) had data on endocrine disruption screening, DNA binding/damage/repair, liver/biliary metabolism as well as immune system/inflammation assays. Other clusters also return data although not necessarily for all substances in the cluster. As described in Section 2. 4. 2. 3, these assays were used as a metric for biological similarity whenever possible.
Structural similarity was calculated based on both RDKIT molecular fingerprints and ToxPrint chemotypes, while property-based similarity was calculated using the same 12 properties depicted in Fig. 5. For the purpose of this use-case, and in order to present a stepwise demonstration of qRAX, each cluster was assigned to a major target endpoint, based on the data availability in the areas of genetic toxicity, repeated-dose, DART and skin sensitization. Toxicity and safety data were compiled from the ChemTunes database and augmented with additional public data from ECHA, EFSA, HESS, SCCS, US Cosmetics Ingredients Review (CIR)  [116] , US EPA, and open literature. The toxicity data availability is summarized in Table  2  and more details are described in the Supplementary Information Table  S7.
Use case 1: read-across to fill a data gap for genetic toxicity potential of beta-elemene Constituents in the Cluster-1 including beta-elemene (T-1) are monoterpenes that are found in many herbs and botanicals. They are mostly data-poor with one exception, i. e. , various (d-, l-, racemic) forms of limonene. Conducting a similarity searching against ChemTunes database, trivinylcyclohexane (A-1. 2) was identified as an analogue Experimental data not found 1 The composition was taken from the literature  [89]. 2 Toxtree v3. 1. 0 with revised Cramer decision rules was used. TTC values used the SCCS  [60]  recommendation (values in parenthesis are Munro original). 3 Expected daily intake was estimated in the section 3. 2. 4 Predicted by ToxGPS models and alerts for Ames mutagenicity, in vitro chromosome aberration, and in vivo micronucleus.
6 against the full chronic NOAEL dataset. The similar structures were also constrained to contain the scaffold 3-butene-2-ol, thus limiting search candidates to tertiary or secondary alcohols. The NOAEL bounds estimation service identified additional nearest neighbors, e. g. , 2-methyl-ebuten-2-ol (CMS-11757) and linalyl isobutyrate (CMS-11771). Chronic NOAEL bounds were based on 5 observations from repeated-dose toxicity studies. This additional investigation narrowed the 95% confidence interval for the target's NOAEL to 5. 1-353 mg/kg-bw/day, a tighter interval than the initial range of 3-765 obtained in the qRAX process. We can say with 95% confidence that this interval contains the true mean chronic NOAEL value for the target. This estimation method allows the refinement of the NOAEL bounds by combining the data from ChemTunes NOAEL dataset with the user provided data, which is the ultimate result of the read-across for repeated-dose toxicity.
Although there are no assays directly related to hapten formations or dendritic cell interactions, several assay categories were related to cell surface, growth factors, immune responses, inflammation (e. g. , interlukin-2), and histone modifications. These assay data were available for target T-3 and the two analogues (A-3. 1 and A-3. 2). Pairwise biological similarity was estimated by Euclidean similarity over the assay vectors when data were available. Table  2  shows that assay activity patterns of the target analogues were similar and tend to be lower than the average of the whole in vitro dataset. Decisions on the analogues also depended on the availability of reliable local lymph node assay (OECD Test Guideline (TG) 429) data although the AOP-related assays such as DPRA, KeratinoSens (or LuSens) and/or hCLAT assays were also considered.
Each read-across was then completed by exploring combinations of the available evidence to estimate outcomes, with examples presented for genotoxic calls, systemic/target organ findings with estimated NOAEL bounds, and skin sensitization potential with estimated EC3 bounds. Implementation of these workflows is enabled by computational methods available in the ChemTunes•ToxGPS® platform. This paper will be supported by the findings of a further study with use cases in which bioavailability and metabolite generation must be considered. In summary, this study has confirmed the possibility of integration informatics capabilities to support NGRA into a single software platform.
Statistical analysis The statistical analyses aimed to determine the indicator factors (early-life indicators and behavioural reactivity) in lamb survival and growth rate until weaning. The analyses were performed on three datasets: 1) neonatal measures taken on all 567 lambs for survival analysis, 2) 501 lambs that survive until weaning for growth analysis and 3) the 202 lambs that were submitted to the reactivity test at 21 days also for growth analysis.
Factors reflecting lamb reactivity at 21 days of age When a variable was recorded in phases 1 and 2, it was analysed as a repeated measure with a mixed linear model using the lme4 package with test phase, lamb sex and order within the test as fixed effects and the lamb as random effect. The two-way interaction phase*sex being non-significant was not kept in the model. We graphically checked the   normality of the residuals. As for the variables 'proximity' and 'time in human zone' the plot of residuals was not equivalent for phase 1 and 2, and that for these 2 variables, the random effect of animal was not significant, we performed a robust' regression with 'robustbase' package. To integrate the behavioural variables recorded during the two phases of the reactivity test, we carried out a principal component analysis (PCA) for each phase of the test in order to obtain synthetic reactivity variables.
Analysis of lamb survival A Chi 2 test was performed on autopsy data to test whether the causes of death differed between the first week of life (neonatal stage) and the rest of the rearing period. To determine the effect of several indicators on the odds of survival of the lambs, we conducted a survival analysis using a Weibull model. Given the low mortality rate found in the present study, in the first week of life, we performed a global analysis running up until the end of the study, i. e. one week after weaning. For lambs that died during this period, age at death was used as the time variable in the Weibull regression. Lambs that were alive a week after weaning were censored and their current age was used as the time variable. Survival analysis tested all 10 early-life indicators (dam age, BCS and litter size; stockperson recording the scores; lamb sex, birth weight, rectal temperature, handling score, vigour score, and sucking score).
The third step consisted in simplifying each of the full models by bidirectional stepwise elimination according to AIC. The simplification procedure was stopped once no indicator that would decrease the AIC value could be added or removed  (Burnham et al. , 2011). We also only kept indicators that have potential effect (i. e. p-value < 0. 10). Calculations were done using R 3. 6. 0 (R Core  Team, 2018). The vcd package  (Meyer et al. , 2017)  was used to calculate Cramér's V intercorrelation coefficient. The survival package  (Therneau and Grambsch, 2000)  and the SurvRegCensCov package  (Hubeaux and Rufibach, 2015)  were used to compute the survival curves.
Analysis of lamb growth rate ADG between birth and weaning was modelled by linear regression. We built the model in three steps, in the same way as for lamb survival with the same initial indicators (see above). Normality of the residuals was checked by quantile-quantile plot, and homogeneity of the variance was checked graphically (the plot of residuals vs. fitted values and the plot of square root of the residuals vs. fitted values). For the subpopulation of lambs that also underwent the reactivity test, we added PCA1-P1 and PCA1-P2 to the set of early-life indicators in the regression. Calculations were done using R 3. 6. 0 (R Core  Team, 2018). The car package  (Fox and Weisberg, 2019)  was used for graphical checks. The vcd package  (Meyer et al. , 2017)  was used to calculate Cramér's V intercorrelation coefficient.
Acknowledgements This work was supported by a grant from the  Centre-Val de Loire Regional Council  for the ' Ovin2A ' project. We thank the staff at the  INRAE experimental farm (UE 0332, Bourges)  for their committed animal care and  E Delval  and  C Mallet  for performing the behavioural reactivity tests. We thank  Metaform Langues  for English-language proofediting support.
Methodology/Principal findings Approximately 24 million reads obtained by amplicon sequencing of the mHVR were analyzed for 62 strains belonging to the six main T. cruzi DTUs. To build reference databases of mHVR diversity for each DTU and to evaluate this target as a typing tool. Strains of the same DTU shared more mHVR clusters than strains of different DTUs, and clustered together. Different identity thresholds were used to build the reference sets of the mHVR sequences (85% and 95%, respectively). The 95% set had a higher specificity and was more suited for detecting co-infections, whereas the 85% set was excellent for identifying the primary DTU of a sample. The workflow's capacity for typing samples obtained from cultures, a set of whole-genome data, under various simulated PCR settings, in the presence of co-infecting lineages and for blood samples was also assessed.
Building the reference datasets The raw reads were pre-processed, trimmed, P-E merged, and filtered as described in detail by  [21]. Then, sequences were clustered at different pairwise identity percentages ranging from 85% to 97. 5% every 2. 5% increment. This was made by using "pick_de_novo_otus. py" script from QIIME v1. 9. 1  [35]. The parameters were used by default to cluster the sequences according to the two identity thresholds. The outputs (seqs_otus. txt and the otu table) were filtered using "filter_otus_from_otu_table. py" script from QIIME v1. 9. 1 to discard those mHVR clusters with low abundance and conserving those that were observed more than five times. The datasets were first evaluated based on their ability to cluster strains of the same DTU. The most abundant sequence in each mHVR cluster was selected as the representative sequence using the "pick_rep_set.
Using the reference datasets The reference sets can be used for typing unknown samples. A DTU-tag was assigned to each representative sequence in the reference set according to the DTU in which the mHVR cluster was observed. If an mHVR cluster is shared by strains of different DTUs, the tag is assigned based on the DTU of the strain, with more reads for that specific mHVR cluster. Following the mHVR sequencing of the sample(s) to be typified, the processed reads -according to the aforementioned procedure-and one of the reference sets are used to run the "pick_closed_reference_otus. py" algorithm available in QIIME 1. 9. 1 or a Google Colaboratoy notebook implementing the USearch algorithm  [36]. The result is a table of mHVR clusters containing each sample. DTU assignment to each sample is based on the following rules: 1.
Reference datasets availability and online typing tool The two reference datasets of mHVRs, generated at 85% and 95% identity thresholds, are accessible at https://ntomasini. github. io/cruzityping. The methodology outlined in the preceding section was automated through a Google Colaboratory notebook, also available at the aforementioned link. This notebook is configured to accept raw data input, execute the described workflow automatically using reference datasets, and generate various graphical representations. An accompanying tutorial was provided to aid users in navigating this too.