dataesr/scientific-paragraphs-categorization huggingface dataset:
'3D' skin models in genotoxicity testing The current status of the validation efforts for the reconstructed skin micronucleus test (RSMN) and RS comet assay was presented. Experimental data were shown from an international validation effort that started in 2006, with support from Cosmetics Europe (CE) and the German Federal Ministry for Education and Research (BMBF). This validation project is part of a strategy of the CE Genotoxicity Task Force towards developing an in-vitro-only genotoxicity testing strategy for cosmetic ingredients  [10, 11]. This project was initiated as a result of the 7 th Amendment to the EU Cosmetics Directive, which bans in vivo genotoxicity testing for cosmetics, effective since 2009  [12]. Addressing limitations of the skin assays discussed at the 5 th IWGT in Basel  [4] , the CE project has added more coded chemicals to the validation dataset and also has investigated the metabolic competency of commercially available 3D human RS skin models, specifically EpiDerm™  [13]  and Phenion® FT  [14, 15].
With respect to the positioning of 3D assays, the following three scenarios were discussed and agreed: • in vitro clastogenicity/aneugenicity positives lead to 3D MN testing, • in vitro mutagenicity positives lead to 3D comet • in vitro mutagenicity & clastogenicity/aneugenicity positives lead to both 3D comet and 3D MN. In this context, it was emphasized that the more frequent scenario likely will address a single positive from the standard battery. It is recognized by all WG members that there are other assays/readout methods available that could potentially be used in 3D organoid models (e. g. , a gene mutation readout such as Pig-a)  [40]  and could serve as a follow-up of positive results from the standard in vitro battery. The focus on the comet and MN assays was triggered by the existing experimental datasets for the 3D skin, airway and liver tissues, which was limited to these endpoints.
[51]. Based on the data presented at the Tokyo IWGT meeting, which reflect the outcome of a multi-year international validation exercise  [17, 18] , the WG concluded that 'following extensive validation and practice (in use for over 10 years) we are now in the position to define standard protocols for the 3D skin comet and RSMN assays'. Furthermore, it was concluded that 'transferability of the assays to a large number of laboratories across 3 continents has been demonstrated', and it was emphasized that 'the assays are now available at several Contract Research Organisations (CRO) and are performed under GLP (Good Laboratory Practice)'. There was considerable discussion specific to the RSMN protocol, since initial work for this assay used a 48 h treatment protocol (two exposures with a 24 h interval), whereas there is growing evidence from the validation dataset  [18]  as well as CRO experience  [31]  that a 72 h protocol with three exposures is superior since it increased the sensitivity of the assay.
Funding provided by Centro de Investigação Interdisciplinar em Sanidade Animal -Faculdade de Medicina Veterinária -Universidade de Lisboa, UIDB/00276/2020 (funded by FCT). The authors thank Centro de Investigação Interdisciplinar em Sanidade Animal for the support and funding of this project (Internal Project MIMV 04/19) and to the clinical pathology team of Laboratorio Professor Doutor Braço Forte, Faculdade de Medicina Veterinária -Universidade de Lisboa for the technical support. Part of this work was presented as a poster at ECVIM-CA Online Congress 2020. CONFLICT OF INTEREST DECLARATIONAuthors declare no conflict of interest. OFF-LABEL ANTIMICROBIAL DECLARATIONAuthors declare no off-label use of antimicrobials. INSTITUTIONAL ANIMAL CARE AND USE COMMITTEE (IACUC) OR OTHER APPROVAL DECLARATIONApproved by the local welfare and ethical committee Comissão de Ética e Bem-Estar Animal (CEBEA) -Faculdade de Medicina Veterinária -Universidade de Lisboa.
| Data A cohort of 704 FT has been recruited from their trainers during 2016, 2017 and the beginning of 2018. The trainers responded to a questionnaire for each horse that included 13 trot characteristics. The questionnaire was completed for 83% of these horses (586). From this cohort, 90% (637) horses were genotyped using the Affymetrix Axiom Equine genotyping array with 670,806 single nucleotide polymorphisms (SNPs). The horses of this sample were born between 2001 and 2016, with the majority born between 2009 and 2013 (87%). Among the horses, 43% were females, 41% were geldings and 16% were males. From earlier studies  (Brard & Ricard, 2015; Ricard, 2015) , a sample of 686 horses had been previously genotyped with Illumina Equine SNP50 chip  (54, 602 SNPs). This cohort of horses was assessed with a protocol which aimed to identify osteochondrosis markers.
It consisted of 98 sires born before 2003 and 588 progeny tested for osteochondrosis and born mostly from 2002 to 2008. They were 39% females, 31% geldings and 30% males. The final sample, genotyped or gait surveyed, contained 1,390 FT, from which 1,323 were genotyped (95%) and 586 had a completed questionnaire (42%). Ultimately, 519 horses (37%) were both genotyped and assessed with a questionnaire. Table  1  summarized the data. LeTrot, the French association in charge of the development of trotting races in France, and of the protection of the FT breed in its specificity, provided performance data in races for FT. Performances included all results in races from 1996 to 2018. There were 252,368 FT-born from 1994 to 2015. Among them, 38% were qualified and, therefore, authorized to participate in races.
| Model for racing performances Racing performances were analysed using all the data for horses born from 1994 to 2015, including the 1,390 horses of the studied sample. Racing performance traits of horses used in the study were the same as previously described  (Ricard, 2015) : • Qualification status (Q), which is obtained once during the horse's life, is a binary variable (0 = unqualified, 1 = qualified in the qualification test before entering races). Qualification test is a special short race of 2000 m which must be accomplished in less than a given time according to age (from 2 to 5) and year, changing with the improvement of the population. For example, qualification time was 1′20′′ for horses born from January to May 2017 at the age of 3 in 2020. • The logarithm of annual earnings, divided by the annual number of finished races (i.
A "probit" transformation was applied to the number of races started because its distribution was far from normal (SH3, SH4, SH5-10). A mixed linear animal model was used to analyse racing performances. Fixed effects included a combination of year of birth and gender effect (44 levels) and a cohort effect (3 levels: horses recruited for osteochondrosis analysis, horses recruited for questionnaire data and other horses). Random effect included the animal genetic value. The relationship matrix between genetic values was constructed using both genealogy data and genomic data in a single-step GBLUP method  (Aguilar et al. , 2010; Christensen & Lund, 2010; Fernando et al. , 2014; Legarra et al. , 2014)  and using the rules outlined by  VanRaden (2008)  for genomic relationship matrix. For FH3, FH4 and FH5, the performance was repeated several times.
Therefore, a random permanent environmental effect was added to the model. The software used was BLUPF90  (Aguilar et al. , 2018). To detect significant SNPs, single-trait analysis was performed using previously estimated variance components  (Ricard, 2015). GWAS was performed with back solutions of SNP effect, and p-value was calculated as described in  Aguilar et al. (2019). Significant SNPs were identified using p-values. Genomic control  (Devlin & Roeder, 1999)  was applied to the p-values to prevent incorrect distribution of the test statistics. The effective number of independent tests in our data was calculated as the inverse of the mean of linkage disequilibrium (r 2 ) between all available pairs of SNPs by chromosome  (Li et al. , 2012). Linkage disequilibrium between chromosomes was assumed to be negligible.
To consider all performances, phenotypes were computed as deregressed proofs calculated from the estimated breeding values and reliabilities obtained with the first full model without genomic data. Proofs were calculated according to  Ricard et al. (2013). Then, genomic best linear unbiased prediction (gBLUP)  (VanRaden, 2008)  was used with ASReml software  (Gilmour et al. , 2014) , which provided the Wald F statistics for the fixed effects, that is the genotypes. To perform gBLUP with ASReml, the inverse of the genomic matrix calculated with the BLUPF90 program was used. Results are the solutions of fixed effects and Wald F statistics for all QTNs and traits. A cross-validation was used to compare different strategies of genetic evaluation to predict racing performances. The training set included all horses born until 2010  (n = 192,167)  and their performance results until 2012 (first age for qualification of horses born in 2010).
| 207 RICARD AnD DULUARD performance results until 2018. Three genetic evaluations were calculated as follows: • Genetic evaluation with only genealogy data to compute the relationship matrix. Fixed effect included the combination of year and sex (34 levels) effect and a cohort effect. • Genomic evaluation using ssGBLUP with genealogy and genomic data to compute the relationship matrix. Fixed effect included the combination of year and sex (34 levels) effect and a cohort effect. • Genomic evaluation using ssGBLUP with genealogy and genomic data to compute relationship matrix. Fixed effect was the combination of year and sex (34 levels) effect, a cohort effect and genotypes of significant QTNs. For the cross-validation, multitrait analyses were used. To reduce the size of the system of equations, several multitraits were used, including the following: (a) Q, LnEH3, LnEH4, LnEH5-10; (b) LnEH3, FH3, SH3; (c) LnEH4, FH4, SH4; (d) LnEH5-10, FH5-10, SH5-10; and (e) LnEH3, LnEH4, LnEH5-10, LnES3, LnES4, LnES5-10.
| Model for trotting technique questionnaires The questionnaire used included 13 questions about each of the horses. Questionnaire responses were converted into binary variables (0/1), with 0 representing the absence of default or characteristic and 1 representing its presence. They were as follows: • Pacer • Defective gait: pace A mixed linear animal model was used to estimate heritability and the environmental and genetic effects on questionnaire responses. Fixed environmental effects included gender, birth year and trainer effect (23 levels). Random effects included the animal's genetic value. The relationship matrix between genetic values was constructed using genealogy data (9,092 ancestors over 6 generations). Probit transformation was used to transform binary variables. The software used was BLUPF90  (Aguilar et al.
, 2018). From this analysis, we constructed residual trotting technique variables equal to probit variable response to questionnaire, minus the estimated effect of gender, year of birth and trainer. Principal component analysis was performed on the residual trotting technique variables using the FactoMineR package  (Lê et al. , 2008). The results led to the creation of three synthetic trotting technique variables to summarize the characteristics of each horse's trotting technique as measured by the questionnaire. The synthetic variable was constructed from the grouping of elementary variables. The synthetic variable was the sum of elementary binary variables capped at 1, 2 and 3 respectively. Elementary variables were regrouped as follows: • Pacer, defective gait: pace (2 variables). • Defective gait: gallop, heavy trot, bad mouth (3 variables) • Other defects (8 variables).
Synthetic variables were used for GWAS analysis with the same methodology described for racing performance variables. A mixed linear animal model was used. Fixed environmental effects included gender, birth year and trainer effect (23 levels). Random effects included the animal's genetic value. The relationship matrix between genetic values was constructed using genealogy data, and genomic data in a single-step GBLUP method and back solutions of SNP effect and p-value were calculated as described in  Aguilar et al. (2019). Significant SNPs were identified using p-values after genomic control. We tested the capacity of the trotting technique questionnaire to predict the genotype of the horse for the DMRT3 mutation. We used the random forest classification analysis using all questionnaire binary variable and the genotype at DMRT3 on the 519 horses, with both questionnaire and genotype using the "randomForest" function of the "randomForest" package in R  (Breiman, 2001; Liaw & Wiener, 2018).
The default parameters were used, that is the number of trees was 500 and the number of variables tried at each split was ( √ n = 3). The training set included two-thirds of the sample chosen at random, and the remainder was used as the validation set. We performed 100 resamplings. We tested the capacity of trotting technique questionnaire to predict racing performances. We used the SAS software SAS/GLM (SAS/STAT, 2019) to perform general linear model analysis on racing performances. The dependent variables were residuals of performances, that is racing performance variables, minus the estimated effect of the combination of the year and gender and of the cohort (see above for the full model used to analyse racing performance). The independent variables were the three synthetic trotting technique variables defined after the principal component analysis.
Data Collection This study employed an across-method triangulation, i. e. , it combined qualitative and quantitative methods of observational research. Drawing on multi-stakeholder consultation approaches, as described by Häring et al. [20] , a variant of the focus group technique was used. Morgan et al. [21]  define focus groups as a research technique that collects (qualitative) data through group discussion on a topic determined by a researcher. The focus groups in this study fulfilled all three essential aspects of this definition by (i) their primary purpose being research, (ii) allowing interactive discussions between participants and (iii) employing an interviewer (facilitator) who directed the discussion according to the research goals. Complementarily to the focus groups, and upon their conclusion, a quantitative survey was conducted (with the same stakeholders), collecting data to ensure completeness of coverage and to identify any collective similarities and differences.
Summaries of answers were collected on moderation cards and arranged on pin boards (with picture protocols taken). When all ideas related to the first set of guiding questions were exchanged and nothing new was being added to the discussion, the facilitator wrapped up and moved on to the next agenda item. This involved participants being presented with data on the health status of organic dairy animals in each study country (see  [23] ) and asked for their reaction to these data, for example by asking "Did you expect these results and did you find them satisfactory?". Further questions were used to guide the discussion of this issue and to encourage participants to express their ideas and make suggestions in relation to policy options for further improving animal health on organic dairy farms.
Data Editing and Analysis All hand-written notes were cleaned, completed, and translated into English by the respective note-takers, and sent to the first author, together with the above-named recordings and the survey results. Thematic analysis was performed on the qualitative data using a deductive approach, i. e. , the identification of preconceived themes based on theory or existing knowledge. Verbatim text extracts providing interesting points of view or supporting arguments were organised in a separate protocol according to the pre-defined themes. If possible, responses to focus group questions were linked to the associated survey questions. The quantitative survey data were entered into Excel 2016 (Microsoft, Redmond, WA, USA) and analysed using the open-access analytics package R  [24].
Measuring Current Health Status The effective measurement of animal health status requires indicators that are both reliable and easy to measure. Three sources of such indicators were identified by stakeholders: (a) information already available in routinely collected databases, e. g. , somatic cell counts in milk recording data, (b) information obtainable from the farmer by self-reporting, e. g. , treatment records, and c) information that would need to be collected through farm inspections, e. g. , lameness scores. These indicators differ in terms of their validity, reliability, and feasibility of collection. Most stakeholders regarded indicators available in extant databases as the most reliable and trustworthy. However, the limited availability of such data in some countries represents a major constraint to the EU-wide roll-out of health requirements based on the use of these indicators.
To illustrate, only in Sweden are all commercially and publicly collected herd data related to performance and animal health combined into one central register, i. e. , the National Cattle Database. In the other study countries, there is considerable heterogeneity in the data types and sources, and this heterogeneity will most certainly also be mirrored across the rest of the EU. There is no need for action. 2. There should be minimum requirements for animal health. Even where particular types of useful data are collected, this may not happen on all farms. For example, not all organic dairy farms participate in national or commercial milk recording schemes. Data structures will also vary between datasets and countries, and data may not be freely available to use for all actors with an interest in animal health, because of ownership constraints.
To overcome these limitations, existing datasets must be supplemented with data collected directly from the farmer, for example, from foot trimming protocols and treatment records. The least feasible source of health data would be direct on-farm observations by trained assessors, for example, through on-site visits to record lameness scores. The use of such supplementary sources, some stakeholders argued, should only occur when absolutely necessary to fill in key data gaps. With continued market failure, there may be a need for regulatory action by government to ensure both the collection of, and access to, harmonised and relevant data. Also lacking, from the perspective of some stakeholders, is a standardised way of using these metrics to express either regulatory minima or societal aspirations for health standards such that farmers, or their advisors, can use them (i.
Acknowledgments Crispin Zavala-Alvarado  is part of the  Pasteur -Paris University (PPU) International PhD Program. Our laboratory  has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  665807  and from  Fondation Etchebès-Fondation de France. We would like to thanks  Robert Anthony Gaultney  for his suggestions on this manuscript.
Representative nematodes for mitochondrial 12S and 16S rRNA gene sequences We used representative nematodes of each clade (I, III, IV and V) that had previously been collected from humans and animals in various locations of Thailand. All nematodes were preserved in 70% ethanol and stored at -20 °C as archived specimens in the Department of Helminthology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand. Individual nematodes were identified, primarily to species level, based on morphological characteristics and host species using nematode taxonomic keys  [2, 5, 6]. Full-length mitochondrial 12S and 16S rRNA gene sequences were mined from the complete mitochondrial genomes of each nematode species on the NCBI database (http://www. ncbi. nlm. nih. gov). A total of 120 sequences from 75 species were used for the analysis together with our sequence data.
Representative nematode sequences of nuclear ribosomal ITS1 and ITS2 regions, 18S and 28S rRNA genes, and mitochondrial cox1 gene Full length and partial sequences of nuclear ribosomal ITS1 and ITS2 regions, 18S and 28S rRNA genes, and the mitochondrial cox1 gene were mined from the NCBI database. Sequences were obtained from different genetic markers for comparison and to evaluate the suitability of mitochondrial 12S and 16S rRNA genes as genetic markers for molecular systematics. Each gene was evaluated using the following criteria: nucleotide substitution saturation; clade arrangement; presence of indels; and availability of full-length sequences in the NCBI database.
Primer design Phylum-wide primers for nematode mitochondrial 12S and 16S rRNA genes were designed using DNA sequences obtained from NCBI. Sequences were aligned using ClustalX 2. 1  [22] , and conserved regions were manually checked with Bioedit 7. 0  [23]. Primers were designed at the conserved region of the 12S and 16S rRNA genes. The properties of oligonucleotide primers, including GC content, amplicon size, melting temperature, and hairpin formation, were calculated and predicted by OligoCalc version 3. 27 (http://bioto ols. nubic. north weste rn. edu/Oligo Calc. html)  [24]. A gradient PCR was performed to optimize the annealing temperature for each primer set. DNA sequences and NCBI accession numbers used for primer design are provided in Additional file 2: Table  S2. Clade II nematodes which consist of plant-parasitic and free-living, as well as plantparasitic nematodes belonging to clade IV were excluded from the primer design as this study only focused on human and animal parasitic nematodes.
Sequence alignment and phylogenetic analysis Analysis of representative nematodes for mitochondrial 12S and 16S rRNA gene sequences Electropherograms of the partial mitochondrial 12S and 16S rRNA gene sequences for all representative nematodes were manually checked using Bioedit 7. 0  [23]. To generate a dataset of concatenated DNA sequences, the two partial gene sequences were manually merged. Each gene sequence was then aligned with the sequences mined from the NCBI database listed in Additional file 1: Table  S1  using ClustalX 2. 1  [22, 23]. The aligned sequences were manually checked and edited for ambiguous sites, and gaps were removed before phylogenetic analysis by maximum likelihood (ML) and Bayesian inference (BI). ML analysis was performed using MEGA 6. 0 with the best fit nucleotide substitution model and with 1000 bootstrap replicates  [25].
Analysis of nuclear ribosomal ITS1 and ITS2 regions, 18S and 28S rRNA genes, and mitochondrial cox1 gene sequences We aimed to compare the robustness of the phylogenetic relationships obtained from the previously used genetic markers with the results of the mitochondrial 12S and 16S rRNA genes. DNA sequences of nuclear ribosomal ITS1 and ITS2 regions, 18S rRNA and 28S rRNA genes, and the mitochondrial cox1 gene that were mined from the NCBI database were aligned using ClustalX 2. 1  [22, 23]. The aligned sequences were manually checked, and phylogenetic analysis of each gene was performed using the two methods mentioned above  [23, [25] [26] [27]. Assessment of nucleotide substitution saturation was performed using DAMBE 6 as the criterion for genetic marker evaluation  [29]. Saturation was based on the values of Iss and Iss.
Evaluation of potential genetic markers for molecular systematics of nematodes DNA sequences from the NCBI database were obtained for nuclear ribosomal regions ITS1 and ITS2, 18S and 28S rRNA genes, and mitochondrial genes of cox1 and 12S and 16S rRNA. The DNA sequences from NCBI are provided in Additional file 1: Table  S1 , and a summary of each genetic marker with the respective criteria is provided in Table  2.