dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was based on multiple presentations given at the  ASAS-NANP Symposium : " Mathematical Modeling in Animal Nutrition: Training the Future Generation in Data and Predictive Analytics for a Sustainable Development-Basic Training " at the 2021 Annual Meeting of the American Society of Animal Science held in Louisville, KY, July  14  to 17, with publications sponsored by the  Journal of Animal Science and the American Society of Animal Science. This work was partially supported by the  Data Science for Food and Agricultural Systems  (DSFAS;  2021-67021-33776 ) and the  Inter-Disciplinary Engagement in Animal Systems  (IDEAS;  2022-69014-36670 ) programs from the  U. S. Department of Agriculture (USDA), National Institute of Food and Agriculture (NIFA) , and by the National Research Support Project #9 from the  National Animal Nutrition Program  (https://animalnutrition.
This technique allows rapid identification proposals of molecules and their visualization and organization into clusters based on the similarity between their MS/MS fragmentation  [14] [15] [16]. Molecular networking is increasingly used for the tentative identification of natural substances by comparison of the experimental data with reference MS/MS fragmentation spectra  [17, 18]. In the present study, we combined fractionation of the MeOH extract of T. leiocarpa leaves with HPLC-PDA-HRMS/MS analysis and the use of molecular networking to identify the major compounds responsible for its anthelmintic activity. The anthelmintic activity was evaluated individually for identified major compounds commercially available, some of which were quantified by HPLC-PDA.
Molecular Networking and Major Compounds in the Most Active Fractions All fractions as well as the MeOH extract, were analyzed by HPLC-PDA-HRMS/MS in negative ion mode. The mass spectrometry data of all fractions (fraction 1 to 9) processed on MZmine 2. 5. 3 allowed the generation of a spectral alignment with 362 features. The molecular network was built with these 362 features on GNPS and is available on the link: http://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=a9c6688a76d54309817b5a61705 4b536 (accessed on 10 November 2022). The constructed molecular network shows the metabolites present in fractions 8 (green nodes) and 9 (red nodes) compared to those observed in fractions 1-7 (blue nodes) (Figure  1 ). We mainly focused on fractions 8 and 9 because of their stronger anthelmintic activity and lower cytotoxicity.
Compound (4) showed a deprotonated [M-H] - ion at m/z 173. 0456 and one of its major MS/MS fragments was observed at m/z 119. 0353. A comparison of the MS/MS data of compound  (4)  with the literature identified it as shikimic acid  [22]. To the best of our knowledge, this is the first time the presence of compound (4) has been reported in T. leiocarpa. Compound  (19) , eluting later, yielded a deprotonated molecular ion [M-H] -at m/z 300. 9978 that fragmented in MS/MS to give two main fragments at m/z 163. 0398 and m/z 169. 0144 (Table  2 ). Its correspondence to ellagic acid was confirmed by injection of a standard. Like compounds (3) and (  8 ), ellagic acid  (19)  was previously identified in T. leiocarpa extracts  [4, 7]. It was one of the major compounds in fractions 8 and 9 (Figures  2  and 3 , respectively) and can be visualized in the molecular network (Figure  1E ).
Their identification was confirmed by injection with the corresponding standards. Compounds (3) and  (8)  were previously identified in T. leiocarpa  [2, 3]. Compound (4) showed a deprotonated [M-H] -ion at m/z 173. 0456 and one of its major MS/MS fragments was observed at m/z 119. 0353. A comparison of the MS/MS data of compound (4) with the literature identified it as shikimic acid  [22]. To the best of our knowledge, this is the first time the presence of compound (4) has been reported in T. leiocarpa. Compound  (19) , eluting later, yielded a deprotonated molecular ion [M-H] -at m/z 300. 9978 that fragmented in MS/MS Compounds (  9 ), (  10 ), (  11 ), (  12 ), (  13 ), (  14 ),  (15) , and (  16 ), grouped into cluster 1C (Figure  1 ) were identified as derivatives of ellagic acid  (19).
The identification was confirmed by injection of the standard. The compound was previously identified in Pteleopsis suberosa (Combretaceae)  [27]. To the best of our knowledge, this is the first time this compound has been identified in T. leiocarpa where it seems to be present as the major compound of the leaves. The molecular network organized the flavonoids detected into two major groups. Cluster H consists of glycosylated galloylated flavonoids (including compounds 18 and 22) and cluster B contained O-glycosylated flavonoids (including compounds 17, 20, and 23) (Figure  1C, D ). Another major metabolite was eluted at 3. 44 min and showed a signal in the full scan spectrum at m/z 481. 0605 [M-H] -with a high MS/MS fragment at m/z 331. 0672. These spectral data are similar to those obtained by Fernandes et al.
HPLC-PDA-HRMS/MS Analysis T. leiocarpa fractions as well as MeOH extract were analyzed by HPLC-PDA-HRMS/MS to identify major compounds. HPLC-PDA (Thermo Scientific Accela LC Systems) coupled with mass spectrometry (Thermo Scientific LTQ orbitrap XL, Bremen, Germany) constituted the system used for analysis. Instruments were controlled using Thermo Scientific Xcalibur X software. HPLC separation was performed on a Luna C18 column, 250 × 4. 6 mm packed with 5 µm particles. The mobile phase consisted of water + 0. 1% formic acid (A) and 100% acetonitrile (B). The gradient used for elution was as follows: 0-10 min, 95% A; 10-40 min, 95-40% A; 40-45 min, 40% A; 45-50 min, 40-95% A, and 50-55 min, 95% A. Samples to be analyzed (10 mg/mL) were solubilized in MeOH and 20 µL was injected per fraction.
Data Processing on MZmine Raw mass spectrometry data of the fractions as well as that of MeOH extract of T. leiocarpa were pre-processed in the MZmine software (version 2. 5. 3). We only worked with the negative data as they were more sensitive and more informative in comparison with the positive mode. In summary, an ion list was generated by setting the noise level to 1. 5 ×10 5 and 1, respectively for MS1 and MS2. The ion list thus created was used to construct the chromatogram with the MZmine ADAP Chromatogram builder function. The minimum number of scans in the cluster was set to 5. The group intensity threshold and the highest minimum intensity were set to 1. 5 × 10 5. Deconvolution of the constituted ion list was performed using the wavelets (ADAP) algorithm. Deconvolution was performed by setting the main parameters as follows: S/N threshold: 6, SN estimator: intensity window S/N, minimum feature height: 120.
Dereplication on GNPS Exported MZmine files as well as the raw mass spectrometry data of the fractions were sent to the GNPS platform using Win SCP software (version 5. 21. 2). The m/z tolerance for MS1 and MS2 was set to 0. 02 Da by default. Molecular networking was created on the GNPS platform (http://gnps. ucsd. edu (accessed on 10 November 2022)), version 28. 2. Nodes were filtered to have a cosine score greater than 0. 7 and at least 6 matched peaks. Dereplication against GNPS libraries was set to a cosine score of 0. 7, with at least 6 matching peaks. The molecular network was finally processed and visualized on Cytoscape (version 3. 8. 2).
Quantification of Major Compounds The quantification of the major compounds from fractions 8 and 9 was performed using an HPLC-PDA system (Accela Thermo ScientificTM, Bremen, Germany) based on the UV absorbance of the compounds. The system was controlled using ChromQuest software (version 4. 2. 34). Separation of compounds was performed on a Luna C18 (250 × 4. 6 mm, 5 µm particles). The mobile phase consisted of water + 0. 1% formic acid (A) and 100% acetonitrile (B). The column was eluted in gradient mode: 0-10 min, 5-12% B; 10-20 min, 12-18% B; 20-45 min, 18-25% B; 45-46 min, 25-5% B; and 46-55 min, 5% B. This gradient is different from that used for LC-MS/MS analysis and was intended to allow for better separation of compounds to facilitate quantification. The quantification was performed with an injection of 20 µL and a flow rate of 700 µL/min.
Statistical Analysis Means ± standard deviation of the rate of inhibition of larval migration (H. contortus) and viability of young adult worms (C. elegans) were calculated for each fraction and control tested. The Pearson correlation coefficient between the metabolite intensities and anthelmintic activity of the fractions and the MeOH extract of T. leiocarpa was determined by the methodology developed by Nothias et al. [44]. Analysis was performed on RStudio software (Version 1. 4. 1717). The calculated correlation coefficient was used to identify on the molecular network, the metabolites whose intensities are significantly correlated (p < 0. 05) or not, with the anthelmintic activity of the fractions on C. elegans.
Bioinformatics and Data Analysis FASTQC was used to check the quality control of raw reads in FASTQ format and the Q20, Q30, and GC contents of the clean data were calculated. Transcript quantifications were made using the Salmon software (version 0. 9. 1)  (21). The index was built within Salmon using NCBI RefSeq reference transcript of the Capra hircus genome (assembly ARS1). The reads from each sample were mapped to the same index and quantified. Unix commands were used to obtain corresponding gene and transcript identifiers from the NCBI RefSeq annotation of the Capra hircus (ARS1). Using these identifiers, the tximport (version 1. 8. 0) package was used to import data into the R software (v3. 5. 1) and summarize the TPM estimates obtained from the Salmon tool of all samples at the gene level  (22).
This process produced a global count file on which the statistical analyses were performed. Genes with low expression levels were removed using a threshold of greater than or equal to 5 counts across samples. Read counts table were used to identify differentially expressed genes (DEGs) using the Bioconductor package DESeq2 within R  (23). Seven comparisons were performed; three comparing preinfected vs. naïve at day 0, 4, and 6 dpi, two comparing day 4 and 6 vs. 0 dpi in naïve kids, and another two comparing day 4 and 6 vs. 0 dpi in pre-infected kids. Genes were filtered using a Benjamini and Hochberg false discovery rate (FDR) of < 0. 001 to account for multiple testing. Final DEGs were determined on the basis of their fold change values to be log2 ≥ 1. 0 for upregulated genes and ≤ -1. 0 for downregulated genes.
Ability to re-calve In the context where it is not reasonable to use productive lifespan (i. e. because relevant datasets are not available or when within-lifetime evaluations are wanted), an easier reference measure for resilience could be the ability of a cow to re-calve  (Adriaens et al. , 2020). The term "ability to re-calve" is preferred because it makes explicit that we are looking to establish how prior resilience measures affect future reproductive performance. The advantage is that this information is available in each parity rather than at just one end-point, making it a trait that is easier to select for in breeding programmes. The assumption here is that poorer resilience will negatively impact reproductive performance. This is expected to hold for all types of environmental disturbances.
The case study presented here uses ability to re-calve as the reference measure against which different resilience proxy measures could be evaluated. This study was carried out in a multi-partner European project (GenTORE; www. gentore. eu) and the aim was to build a resilience ranking that could be readily replicated in the different countries, which could then be used to evaluate different measures of shortterm resilience. The resilience rank was developed via a series of discussions among experts and then evaluated using observational data from dairy herds. As the data were purely records of normal farm practise with no intervention, ethical approval for this work was not needed. The basis for the resilience ranking equation is the number of re-calvings, and within each lactation, bonus or penalty points are added or subtracted that allow distinguishing the resilience between animals with the same parity number.
g. 10 treatment days has a lower gold standard value than a first parity cow that has no recorded health events? This consideration should also take into account the ranges in the different component traits as well as the reliability of them. Ideally, the weights to be used could be informed by population level analyses that have quantified the hazard ratios for these different factors in epidemiological and survival analyses (e. g. Rostellato et al. , 2021). Examples of relative resilience rankings as proposed by six partners of GenTORE, including the weightings used, are given in Table  1. With one of the weightings in Table  1  (scheme 'B'), applied to a Dutch single-herd dataset with 1800 cows for which the culling date was known  (Ouweltjes et al. , 2019) , we showed that cows that reached the next parity had a higher resilience score than cows that were culled (Table  2 ).
Conflict of interest disclosure No conflicts of interest existed, all authors are members of state funded institutions or charities with the exception of those affiliated to RAFT Solutions Ltd. RAFT Solutions Ltd participated in the study as a member of the GenTORE project consortium, funded by the Horizon 2020 research and innovation program in accordance with European Commission rules. RAFT Solutions Ltd provided no financial support for this study and did not seek commercial advantage (patents or exclusive use) from the study. Ines Adriaens and Giulio Cozzi are PCI Anim Sci recommenders.
Examples of the tiered approach hazard and exposure framework The utility and practicality of the tiered approach has been assessed by a series of case studies. In each case, a chemical has been selected which has both extensive NAMs data and traditional data. The process of each evaluation started with deciding on some uses for the chemicals, which were not necessarily the ones it is actually used for, designed to cover both industrial and consumer situations and to give a range of potential exposure doses. The exposures were then estimated using the Targeted Risk Assessment (TRA) tool (ECETOC 2021b), which takes a range of uses and produces conservative estimates of exposure for a range of scenarios based on accepted models and using physicochemical data for the chemical. Higher tier exposure modelling using Advanced REACH Tool  (Fransman et al.
Example chemical 1 (EC1) Exposure assessment tier 1: The uses of EC1 were selected to include both industrial and consumer use, and then, relevant exposure scenarios from the ECETOC TRA were selected. The relevant physicochemical data were entered into the TRA and the exposures estimated using worst-case assumptions: • Use as a chemical intermediate: Category B. • Use in metal working fluids: Category B. • Use in glues for hobby use: Category B. Hazard assessment: Tier 0: The exposures were above the TTC limits. Tier 1 based on in silico assessment: EC1 would not be genotoxic on the basis of absence of alerts from the Derek Nexus software (Lhasa Ltd 2021). Tier 2 Hazard assessment based on in vitro assays for biological activity, genotoxicity, absorption, and metabolism: Non-genotoxic MoAs identified and adverse outcomes predicted: Androgen receptor blocker-male reproductive toxicity leading to infertility, hypospadias, delayed development, and Leydig cell tumours.
Example chemical 2 (EC2) Tier 1 usage and exposure assessment-The uses of EC2 were selected to include industrial and consumer use and the relevant exposure scenarios from the ECETOC TRA were selected. The relevant physicochemical data were entered into the TRA and the exposures estimated using worst-case assumptions: possibility of aerosol, no modifying factors such as gloves, 100% absorption via inhalation, and by dermal routes. Each exposure was assigned a category: • Uses requiring drum and small package filling (liquid or solid in liquid) Category A. • Use in consumer cleaning PC35: dish washing products, Laundry products, all purpose cleaners, and Trigger Sprays Category A.
Tier 1: in silico assessment EC3 is a preservative which can inhibit malate dehydrogenase in bacteria, which is part of the citric acid cycle and therefore conserved across most, if not all, species including  humans. Sequence homology was used to determine the level of sequence identity and similarity between the bacterial and human enzymes using CDOCKER module in Discovery Studio 2018 (ALTEM 2021). In silico tools were also used to predict the likely metabolism of EC3. The major metabolite was predicted using Meteor Nexus version 3. 1. 0 (Lhasa Ltd. ) and also assessed using the in silico tools with the same results as for EC3.
This analysis of Annex XI was undertaken with the intention of finding reasonable justification for the use of NAMs in REACH. We have shown that it should be possible to interpret the legislation in ways which can support the increased use of NAMs in REACH. However, it is clear that it is also possible to interpret Annex XI in other ways which do not fully support the introduction of NAMs in the ways proposed in this paper. We acknowledge that the legislation makes it difficult to accept the use of NAMs in ways that do not replicate the mandated studies which form the basis of CLP. The authors of Annex XI may have assumed that new methods would be developed in the future that would address reduced use of laboratory animals but through a study for study replacement rather than the integration of different data sources, for which is there is now a growing consensus.
Experimental design We conducted a study on 498 young Limousin breeding bulls. The reactions of the bulls in four types of reactivity tests to humans or handling, their daily activity recorded with 3D-accelometer collars, and their BW were measured on several occasions at the same bull testing station with standardised rearing conditions. The consistency of bull reactions or daily activity between testing or data collection periods and the correlation between the reactions during tests and activity or growth were analysed. The bulls in this study were part of the French genetic testing system of the Limousin breed. Young breeding bulls were sent by the farmers to be tested on the national station at Lanaud (Boisseuil, France). They arrived regularly each year from hundreds of farms, representing a large geographical distribution (France and Luxembourg).
The bulls were scored 1 when they accepted the contact, 2 when the final distance of the experimenter was 50 cm or less, 3 when that distance was between 1 m and 50 cm, and 4 when the distance was more than 1 m. The experimenter tested alternate bulls at the feed barrier. Docility test. The DO was adapted from that developed by Le  Neindre et al. (1995). Each bull was tested 3 weeks after its arrival at the Lanaud station. A bull was discarded if it scored 4 in the DO (see below). In the present study, 27 bulls were discarded being considered as too dangerous for humans, and so unfit for breeding. They were sent back to their original farm in the week following the DO and not considered in the final data set. A technician drove all the bulls from their home pen together to a 4 m Â 4 m handling pen, and then drove one bull through a 6 mlong lane to access a 4 m Â 4 m test pen where the bull was individually tested.
They took place 4 months after admission. Fig. 1  presents the data collection timeline. Forty-two bulls were discarded because of poor health or slow growth at the midterm of the evaluation process; they were returned to their original farms. Data from 498 bulls were included in the final data set. Due to organisational constraints, the behavioural tests were not always performed on all bulls. In all, 405 bulls were evaluated on the farm for MorF, 469 at the station for MorS, 497 and 491 for ADF1 and ADF2, and 496, 494, and 494 for CHUTE1, CHUTE2 and CHUTE3. Some data on daily activities were lost due to unclamped collars, while some were discarded because bulls were sick. In all, daily activities were available for 498 bulls in P1, 481 bulls in P2, and 469 bulls in P3.
Statistical analysis Data were analysed using R software version 3. 5. 1 (R Core Team, 2018). As missing data occurred throughout the data set, we visually verified their distribution among the other behavioural scores in the different tests. Correlations among behavioural scores were performed using Spearman rank correlations. To identify dimensions from behavioural scores, we then performed a varimax-rotated principal component analysis (PCA) after calculating the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (KMO threshold considered acceptable over 0. 5), using the package psych  (Revelle, 2020). As PCA is sensitive to missing data and as the animals were not all scored for all the behavioural tests, missing data were imputed with the regularised iterative PCA algorithm of missMDA package through the different test situations.
Factominer states that ''missMDA package imputes missing values in such a way that the imputed values have no weight (i. e. having no effect and the method is performed with only the observed values) on the PCA (or any other methods) results" (https://factominer. free. fr/missMDA/index. html). The consistency of daily activity data was calculated by Lin's concordance correlation coefficient using the epiR package  (Sergeant et al. , 2021 ). Lin's coefficient was considered as acceptable over 0. 5. The relationship between the individuals' coordi-nates on the PCA first two dimensions and the daily activity data was evaluated using linear mixed-effects models using lme4 and lmerTest packages  (Bates et al. , 2015; Kuznetsova et al. , 2017). The series, pen, and animal were added as nested random effects; the period was also a random effect.
Morphometric variables T1w MRI for volume calculation of brain, gray and white matters and cerebrospinal fluid T1-weighted images were first denoised using the DenoiseImage function of the Advanced Normalization Tools  (ANTs, Avants et al. , 2014; Manjon et al. , 2008). They were then cropped in all three dimensions to isolate the cerebral tissue as closely as possible from all non-brain tissue. To reduce the time required for manual brain mask segmentation, the original resolution (0. 4mm 3 isotropic) of the T1w images was linearly subsampled to 0. 8mm 3 isotropic using the resample-mm function of the Convert3D (ITK-SNAP, www. itksnap. org, Yushkevich et al. , 2006). The manual brain mask segmentation was performed with the ITK-SNAP software taking care to exclude the meninges, olfactory bulb and tentorium cerebelli (Figure  2 ).
Diffusion-parameters The DTI-images were corrected for geometric deformations due to inhomogeneities in the magnetic field using the Fieldmap tool from the Statistical Parametric Mapping toolbox. Eddy-current correction was performed with FSL (FMRIB Software Library v. 5. 0. 4). Using FSL, the FA-and RD-maps were calculated. The mean FA-and RD-values were calculated for GM, WM, and for specific WM-areas sampled by the segmentation of 3 (corpus callosum, optic chiasm and cerebellum; see Figure  3 ) or 4 (frontal, parietal, temporal and occipital lobes; see Figure  3 ) successive sections. This segmentation was done manually on the T1w images and controlled by experts in sheep neuroanatomy. The ROI were then re-sampled to the DTI-image resolution (2x2x2mm 3 ) and the mean FA-and RD-values were calculated.
Statistical Analysis Missing data Body weight data was not collected for 3 MC PA0. 86-2. 57w lambs. Due to a lack of T1w MR image contrast in a few individuals, the olfactory bulbs were only segmented for n=6 MC and n=6 MD lambs and the hippocampus for n=7 MC lambs. At the resolution of the DTIimages, some specific WM-areas, that had been segmented on T1w images, contained very few or even zero voxels for some individuals. We only include WM-areas of 3 voxels or more in the statistical analysis.