dataesr/scientific-paragraphs-categorization huggingface dataset:
This includes that a larger involvement of regulators in such projects is required to advance the uptake of NAMs into regulatory processes. Additionally, the high level of standardization, validation and training that is needed when implementing NAMs into risk assessment has been highlighted. The storage and availability of data from EU projects to the regulatory community have been identified as critical aspects as well. Also, even though funding was sufficient to achieve the scope of the listed projects to fully implement NAM-based ab initio testing, more funding is needed. Consequently, the European Partnership for the Assessment of Risks from Chemicals (PARC) initiative, which kicked off in May 2022, addresses these critical points as not only major funding has been provided to foster innovation and to accelerate the regulatory acceptance of NAMs but also the inclusion of regulators in the management of the project, standardization, communication with policy, application of FAIR principles to data management and training have been addressed.
A guidance document (GD) on quality standards required for the scientific review of an AOP on the AOP-Wiki portal was recently published (OECD, 2021a), building the foundation for an increased confidence in AOP quality on which their widespread acceptance as an information source to guide interpretation, generation and application of data from NAMs depends. It should also be noted that it is not always necessary to address all KEs of an AOP by testing, if measuring certain KEs by an accepted and validated (set of) NAM(s) has proven to be sufficiently predictive of the apical effect in humans, including quantitative aspects. The combined employment of multiple NAMs, though, needs to effectively distinguish between perturbational effects of an adaptive, temporary nature and an actual adverse effect  (Dent et al.
This provides an approach to utilize NAMs in a protective way, rather than requiring them to be predictive of a specific toxicity endpoint. The respective PoDs are estimated based on biological pathway or cellular phenotype perturbation, or based on AOPs or organotypic assays/microphysiological systems, respectively  (Thomas et al. 2019). Dedicated software (e. g. BMD Express 2) or statistical packages (e. g. tcplfit2) have been developed that enable the calculation of quantitative PoDs by benchmark dose (BMD) modelling of omics data  (Phillips et al. 2019; Sheffield et al. 2022; Yang et al. 2007). Using qualitative NAM data within a margin of safety approach has been demonstrated for the risk assessment of cosmetic products by using PoDs based on in vitro assays together with exposure estimates  (Baltazar et al.
With that in mind, the exercise was recently repeated to establish an equivalent panel for metabolic biomarkers (MTox700+)  (Sostare et al. 2022). Under the Horizon2020 project PrecisionTox, the applicability of omics and molecular biomarkers will be further determined  (Malinowska et al. 2022). The OECD EAGMST and Working Party on Hazard Assessment (WPHA) have joined forces to accelerate the regulatory use of omics (https://www. oecd. org/ chemicalsafety/testing/omics. htm). Existing and newly developed NAMs should be collected and catalogued, clearly stating the information one can obtain from them including their individual strengths and limitations. Initiatives such as the Database on Alternative Methods (DB-ALM) produced by the EU Reference Laboratory on Alternatives to Animal Testing (EURL ECVAM) are a good starting point (European Commission 2019).
Currently, the OORF includes modules relevant to transcriptomics and metabolomics toxicology studies with flexibility to incorporate other omics data types (such as proteomics) in the future  (Harrill et al. 2021 ). More general approaches toward Good In Vitro Reporting Standards are on the way  (Hartung et al. 2019). Making use of standardized hazard reporting would allow regulators to quickly grasp methodological details and results to interpret data and draw conclusions on the quality and usefulness of the information, and scientists to effectively replicate them. Moreover, it supports the dissemination of new methods via structured databases. Ideally, standardized reporting and data management should follow the FAIR principles, i. e. Findable, Accessible, Interoperable and Reusable, to enable machine-actionability  (Wilkinson et al.
Acknowledgements The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the organizations they are affiliated with. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. We also would like to thank  Tara Barton-Maclaren  ( Health Canada, Canada ),  Richard Brown  ( World Health Organization (WHO), Switzerland ),  Sandrine Ellero-Simatos  ( TOXALIM Research Centre in Food Toxicology, France ),  Toru Kawanishi  ( Food Safety Commission of Japan (FSCJ), Japan ),  Stefano Monti  ( Boston University, USA ),  Katrin Schütte  ( DG Environment, Belgium ),  Anton Simeonov  ( National Center for Advancing Translational Sciences (NCATS ),  National Institutes of Health (NIH), USA ),  Tomasz Sobanski  ( European Chemicals Agency (ECHA), Finland ),  Weida Tong  ( United States Food and Drug Administration (US FDA), USA ),  Jörn Walter  ( Saarland University, Germany ) and  Clemens Wittwehr  ( Joint Research Centre (JRC), Italy ) for their tremendous and constructive input during the workshop and while conceiving this manuscript.
Nontoxic fluorescent cell tracker tools are efficient for schistosoma larvae staining To first determine whether different concentrations of CMAC and CMFDA can be used individually to detect live parasites by fluorescence microscopy, we repeated the same concentrations as in the initial toxicity assay while varied the exposure time from 5 to 60 minutes (data not shown). Furthermore, all live parasites fluoresced when visualized after CMAC or CMFDA uptake with corresponding microscopy imaging of the same miracidia samples providing visual confirmation of a physiological normal phenotype (Figure  2 , Video S01, S02, S03, S04). An optimal concentration of 10µM for 10 minutes was set to obtain maximal efficient dye retention for combining: i) minimum manipulation, ii) sort exposition, and iii) survival rate.
Acknowledgments Thanks to the technical staff of the  IHPE laboratory ,  Damien Pouzol  and  Olivier Portela  for the maintenance of mollusc strains and their expertise on animal experimentation. We are greatful to  Paradev Compagny  for have facilitated the parasite egg collection. We acknowledge the " Reśeau d'Histologie Expeŕimentale de Montpellier" -RHEM facility  supported by  SIRIC Montpellier Cancer  (Grant  INCa_Inserm_DGOS_12553 ), the  European regional development foundation  and the occitanian region ( FEDER-FSE 2014-2020   Languedoc Roussillon ) for expertise, especially  Dr Heĺène Hirbec  and  Anne Guillou-Duvoid  for advices on using vibratome. The authors also want to thank  Julien Portela  for his help with the video editing.
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study is set within the framework of the " Laboratoires d'Excellences (LABEX)" TULIP  ( ANR-10-LABX-41 ) and  CeMEB  ( ANR-10-LABX-04-01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. DD, PP and BG were supported by the  ANR AEROSNAIL  ( ANR-19-CE11-0016-01 ) from the  French National Research Agency (ANR). RA was supported by  ANR  ( ANR-22-CPJ1-0056-01 ) -Tropical diseases of today, European diseases of tomorrow: a systems biology approach to understand, predict and control their emergence.
Farmer Semi-Directive Interviews A short visit of the farm was conducted before the interviews. The interviewers (M. Me and J. C. or M. Ma. ) asked the farmers open questions, as described by  [29]. These questions had been pre-prepared in an interview guide, which was identical for all interviews (Supplementary Materials). The interview guide for the cooperative technician was adapted from the farmers' guide. The recorded interviews were transcribed into a Word text. Tropes (V8. 5) [30] speech analysis software was first used to process the data for the cognitive analysis of the interviews  [31] , which were then analyzed using the multivariate method  [32]  applied to the most frequently used words in the interview. Significant differences in the word's occurrence between farming types were assessed using Z score statistics for two populations; where the proportions were low (less than 4%), Fisher's exact test was applied to the number of occurrences for each word.
Statistical Analyses of Characteristics of Farms The relationship between EPG and farm or farmer management characteristics were conducted by one-way analysis of variance, ANOVA (qualitative data), or with nonparametric Spearman correlation for the quantitative ones, with the SPSS 11. 5 software. The EPG were Napierian log transformed prior to analysis due to their aggregated distribution. Cluster analysis based on centroid grouping and Gower distance (variables being binary, multistate, or quantitative) was conducted with MVSP software. Cluster analysis permitted relating all the variables at once.
Most importantly, starting with the year a female was subjected to this PZP treatment, its data were excluded from the analysis presented in this paper (see details in Table  A  in Suppl. Materials). Nevertheless, (a) for five of our focal females for which reproductive probabilities were assessed, mothers were treated with PZP when these females were still at the foal stage. (b) For another five of our focal females, mothers were treated with PZP prior to their pregnancy with those females, i. e. , the PZP treatment was obviously not always efficient in preventing pregnancies. However, our analyses revealed that the age-dependent reproductive probabilities of these females with PZP-treated mothers of conditions (a) and (b) did not differ significantly from females of untreated mothers (LMM: (a) 2 1 = 0.
Study period and sample size We explored the females' annual probability of parturition (events at the individual level; foaling: yes/no) starting at an age of 2 years, when Przewalski's horse females potentially start to reproduce  (Kerekes et al. 2021). We only considered females born inside the Pentezug reserve. In total, data for this analysis were collected during 20 years  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) , from a total of 146 females, born between 1998 and 2017. Females were between 2 and 16 years old when their reproductive activity was surveyed. This resulted in a sample size of n = 712 observations of presence (57. 4%, 409/712) or absence (42. 6%, 303/712) of annual parturitions.
Body condition scores of females We also analyzed weather and density effects on females' body condition based on a data set collected during 6 years; see below for details on sample sizes and a schema in Fig. 1b. Following the method developed by  Rudman and Keiper (1991)  for feral ponies (E. ferus caballus), we used a body condition score from 0 (very low body condition, although animals with such a low score were never observed during our study) to 5 (very high body condition), in steps of 0. 5. As the basis of this score, we assessed the shape of the horses' hind quarters from an observer position behind the animal, by the aid of binoculars, from a distance of around 20 to 40 m. Detailed drawings describing the method of quantification can be found in  Rudman and Keiper (1991) , and more details on the application of this method to Przewalski's horses are in  Kerekes et al.
Density of large herbivores (horses and cattle) The number of cattle in the Pentezug reserve was counted once per year, in November or December when all cattle were locked into a small and closed area for veterinary inspection. The number of horses was known due to regular surveys at the individual level; that is, individual compositions of all harem groups were known (see above in "Surveys of reproductive activity"). Foal mortalities and their survival times were known due to surveys at the daily to weekly basis (more details in:  Kerekes et al. 2021 ). For consistency with density data available from cattle, we used the density of horses assessed in November/December of each year. During the study period, November/December densities of horses varied from 0. 08 to 1. 37 individuals per 10 ha, and densities of cattle varied from 0.
Effects of different predictors on foaling probability Statistical analyses were done with the program R, version 4. 3. 0 (R Core Team 2023). We analyzed the effects of different predictor variables on females' probability of foaling (binary response variable) by generalized linear mixedeffects models (GLMM) for binomial data with a logit link, fitted by the Laplacian maximum likelihood approximation, using the R package lme4  (Bates et al. 2015). P-values were calculated by type-3 Wald chi-square tests  (Bolker et al. 2009). This analysis was based on a total of 712 observations (occurrence of annual foaling: yes/no) from 146 females (2-16 years old) over a period of 20 years. We included female identity as a random (intercept) factor, since the data set included repeated measurements of individual females across consecutive years.
Effects of different predictors on body condition The effects of the environmental variables D, P 1 , and P 2 on female body condition scores (response variable) were tested by multifactorial linear mixed effects models (LMM) using the R package lme4  (Bates et al. 2015 ) (Fig. 1b ). As the distribution of these equidistant body condition scores  (Rudman and Keiper 1991)  was unknown, we calculated p-values by parametric bootstrapping (1000 iterations) using the package afex  (Singmann et al. 2022). The available data set included n = 393 measurements of score values from 64 females (2-17 years old) collected all year round during 6 years  (2004-2007, 2018, 2019). Thus, the analysis was based on 6 different measurements of P 2 stemming from 6 years, although from 19 different measurements of P 1 (late-summer rain during first year of life), since the females included in this analysis were part of 19 annual birth cohorts.
Acknowledgements We are grateful to all colleagues of the  Hortobágy National Park Directorate  and students from different universities who contributed to the data collection. We are also grateful to  Cologne Zoo Germany  who supported the project from its founding up to the present, both financially and technically. We would like to thank the work of all  EAZA (European Association of Zoos and Aquariums)  coordinators during the last 25 years, supporting the project and the data collection. We also thank the three anonymous reviewers, whose constructive comments have contributed to improve our manuscript.
Immunoglobulins Serum IgA, IgG and IgM were quantified on the samples collected from the sows and their respective piglets. The Igs concentration was analysed using an immunoglobulin enzyme-linked immunosorbent assay (ELISA) procedure following the protocol described by Bosi et al. [25]. The serum samples of sows were diluted at 1:7000 for IgA, 1:300,000 for IgG, 1:12,000 for IgM. The serum samples of the piglets were diluted at 1:3200 for IgA, 1:100,000 for IgG and 1:12,000 for IgM. The reaction was quantified spectrophotometrically at an absorbance of 405 nm using a microplate reader (Multiskan FC Microplate Photometer -Thermo Fisher Scientific). The data regarding Igs concentrations were calculated using a four-point parametric curve and were expressed as milligram per millilitre (mg/mL).
Colostrum and milk analysis Colostrum and milk composition in terms of total fat, total proteins, caseins, lactose, urea, dry matter and SCC were analysed in triplicate, assayed using the infrared spectroscope Milkoscan FT2 (FOSS A/S, Padua, Italy). The concentrations of IgA, IgG and IgM were quantified using an immunoglobulin ELISA procedure following the protocol described by Luise et al. [26]. For the analysis, the colostrum samples were diluted at 40,000, 500,000 and 10,000 for IgA, IgG and IgM, respectively, and the milk samples were diluted at 20,000, 2400 and 4000 for IgA, IgG and IgM, respectively. The reaction was quantified spectrophotometrically at an absorbance of 405 nm using a microplate reader (Multiskan FC Microplate Photometer -Thermo Fisher Scientific). The data regarding the Igs concentrations were calculated using a four-point parametric curve and were expressed as milligram per millilitre (mg/mL).
QuantStudio Design and Analysis Software v2. 5 (Thermo Fisher Scientific, Waltham, MA, USA) was used for determining the gene expression cycle threshold (Ct) values. For each sample the Ct value of the HMBS gene was subtracted from the Ct value of the target gene (ΔCt). The average ΔCt value of the reference animals was then subtracted from the ΔCt value of all the samples (ΔΔCt). The expression of the target gene was given as fold change calculated by 2 -ΔΔCt.
Statistical and bioinformatic analysis Statistical analysis was carried out using SAS version 9. 3 (SAS Inst. Inc. , Cary, NC, USA) and R software (V 3. 6). The 4 dietary groups were considered to be arranged in a factorial manner with a 2 × 2 design, considering the two groups which were supplemented with these AAs (BCAA and BCAA + Arg) for the BCAA positive effect and, equally, the two groups which were supplemented with these AAs (Arg and BCAA + Arg) for the Arg positive effect. The CO diet was considered negative for both BCAA and Arg factors. The sows were also classified by parity as follows: 1 = parity 2 to 4, 2 = parity 5 to 7 and 3 = parity 8 to 10. A general linear model (GLM) procedure was used to fit measurements carried out on sows with a linear model including batch, class of parity order, BCAA and Arg supplementation, and their interaction, as factors.
The GENMOD procedure was used with a binomial distribution and a Logit link function was used for mortality rates in a model which included the effects of litter size at birth, batch, class of parity, BCAAs, Arg and their interaction. In addition, the following contrasts were carried out to assess the effect of BCAA and Arg supplementation: CO vs. Addition: (CO vs. BCAA, Arg and BCAA + Arg groups), alone vs. mixed addition (BCAA and Arg groups vs. BCAA + Arg group), BCAAs alone vs. Arg alone (BCAA vs. Arg group). The data are expressed as least-square means and standard error of the mean (SEM). A difference was declared at P < 0. 05 and P > 0. 05, and < 0. 10 was considered a tendency. Microbiota analysis was carried out using the DADA2 pipeline  [29] , and taxonomic categories were assigned using the Silva Database (release 138) as a reference  [30].
Alpha (Shannon, Chao1 and Simpson indices) and Beta (calculated as Bray Curtis distance matrix) diversity, as well as the abundance of taxonomic categories, were analysed with R software 3. 6, using the PhyloSeq  [31] , Vegan  [32] , lme4  [33]  and MixOmics  [34]  packages. The alpha diversity indices were analysed using an ANOVA model including batch, class of parity order, BCAA and Arg supplementation, and their interaction, as factors. The beta diversity was analysed using a PERMANOVA model (' Adonis' procedure) including batch, class of parity order, BCAA and Arg supplementation and their interaction as factors. The effect of BCAAs, Arg and parity class on Bray Curtis distance was visualised using a Non-Metric Multidimensional Scaling (NMDS) approach. The difference in the taxa abundancy of the Arg, BCAA and Arg + BCAA groups as compared with the CO group was analysed using the DESeq2 package, based on negative binomial generalised linear models and applying the Benjamini-Hochberg method for multiple testing correction  [35].
Case study 2. Differential smoothing model This example shows the use of a purely data-driven approach for the post-treatment of performance data under perturbations. Fig. 4  shows a hypothetical example, in which the theoretical performance is equal to 1, and the deviations from this value could be considered as perturbations. Data were smoothed using B-Spline bases of order 5 with the roughness penalty k ¼ 10 4 to estimate animal performance (https://quantanimal. github. io/). Three features are used to characterise the animal response to the perturbation i: the area between the theoretical trajectory and the perturbed curve (A i ), the maximum amplitude of deviation from the theoretical performance (h i ), and the duration of perturbation (DT i ). These criteria allowed comparison of the animal responses to each of the perturbations, and also, in the case of a larger number of animals, to rank animals for their adaptive capacity.
Case study 3. Known theoretical trajectory In this example, we show the use of state observers to quantify dynamic perturbations. An observer is an object that combines a mathematical model and on-line data to estimate unmeasured variables  (Dochain, 2003). For our case study, we aim to use realtime records of BW to extract information on the perturbation that an animal is facing. For that, we assume that the theoretical trajec- tory of growth follows a Gompertz function. This example is developed from the work of  Revilla et al. (2019). The following equation describes the dynamic changes in BW (y) in the presence of perturbations dy t ð Þ dt ¼ Àu:y þ y:p 1 :e Àp 2 :t ð4Þ The term u is assumed to be unknown and represents a dynamic perturbation factor that explains the deviations from the theoretical trajectory.
In this regard, (a)  Sun et al. (2010)  used the neural network for prediction of mastitis risk; (b)  Warner et al. (2020)  tested the usefulness of a machine learning approach based on decision tree induction for the detection of dairy herds at high risk of lameness; (c)  Morota et al (2018)  provides a complete overview on the use of machine learning to analyse big data in precision agriculture. The common point in such approaches is to provide a training dataset with enough data to enable robust predictions on the test dataset. To date, machine learning approaches enable the prediction of risks at individual scale (detection of perturbations). As the use of sensors in farms increases, and in the context of studying animal resilience capacity, we can expect more accurate information on the potential causes and timing of perturbated performance (eg.
Acknowledgment The idea of this manuscript was firstly discussed in the doctoral module ''robustness from a wooly concept to operational measures" supported by  French alliance Agreenium. MT is grateful to  Dr. Isabelle Veissier  for the stimulating discussions on the analysis of sensor data. The preprint of this article is deposited in Zenodo (https://zenodo. org/record/7755959#. ZBnD43aZOF6).
A challenging step in human risk assessment of chemicals is the derivation of safe thresholds. The Threshold of Toxicological Concern (TTC) concept is one option which can be used for the safety evaluation of substances with a limited toxicity dataset, but for which exposure is sufficiently low. The application of the TTC is generally accepted for orally or dermally exposed cosmetic ingredients; however, these values cannot directly be applied to the inhalation route because of differences in exposure route versus oral and dermal. Various approaches of an inhalation TTC concept have been developed over recent years to address this. A virtual workshop organized by Cosmetics Europe, held in November 2020, shared the current state of the science regarding the applicability of existing inhalation TTC approaches to cosmetic ingredients.