dataesr/scientific-paragraphs-categorization huggingface dataset:
All relevant studies identified, are further processed through data extraction, risk-of-bias assessment and data analysis. Subsequently, the certainty in the body of evidence that findings from a group of studies reflect the true relationship between exposure to a substance and effect is rated for each data type/evidence stream. Similar to approaches by the GRADE working group, OHAT has defined initial confidence ratings for the various data types based on study features, such as level of exposure control, that roughly corresponds to study designs. For example, experimental animal studies are initially rated to be of high confidence and cross-sectional human studies are initially rated as low (on a scale of very low, low, moderate, high). Bodies of evidence are then assessed for factors that can potentially increase confidence, such as effects size and dose-response, and factors that can potentially decrease confidence, such as risk-of-bias and indirectness.
The main purpose of the GRADE EtD framework is to structure evidence in clear and transparent way to inform recommendations and decisions in different contexts  (Parmelli et al. , 2017; Li et al. , 2018; Moberg et al. , 2018). It is structured in three main sections that reflect the main steps to go from evidence to a decision: formulating the question, summarizing and assessing the evidence, and drawing conclusions. To facilitate its use, the EtD framework has been implemented in the software GRADEPro 4. In the GRADE EtD, the criteria that are used to assess interventions or options, the judgments made by the panel for each criterion, and the research evidence and additional considerations used to inform each judgment are made transparent and explicit. The criteria of the GRADE EtD framework are listed in Table  2.
Moreover, frameworks need to shift the focus from adverse outcomes assessed via animal experiments, to AOP-structured information on pathway perturbations potentially providing more directly human relevant evidence. This approach also offers investigate broader questions going beyond the traditional focus of current chemical risk assessment, as demonstrated by and investigation of the potential role of plant protection products in the multifactorial human Parkinson's disease and childhood leukemia (EFSA PPR Panel, 2017). The discussion then addressed how this can be done. Regarding the construction of an AOP, the importance of reflecting the dose-and time-dependence was stressed, which is a pivotal determinant of the progression of perturbations along the pathway. NAM data on multiple chemicals that allow observations of common responses can be helpful for the identification of the early AOP events, particularly the molecular initiating events.
Acknowledgments We gratefully acknowledge funding by the  European Union 's  Horizon 2020 research and innovation programme  under the grant agreement No. 733032 HBM4EU. The authors would like to thank the  HBM4EU  Secretariat at the  German Environment Agency  for administrative support. The authors acknowledge all the participating and expert laboratories (Table A1, SM) that made the HBM4EU QA/QC programme possible as well as the Management and  Advisory Boards  of  HBM4EU.
Prediction of DE and ME values of complete feeds for pigs and poultry The DE and ME contents of diets can be obtained as the cumulative DE or ME contributions of the ingredients included in the complete feed. The energy values for the ingredients can either be obtained from feeding tables or by other techniques (see next section). This assumes there are no interactions between ingredients or among nutrients, a concept well-accepted for formulating pig and poultry diets. However, the actual ingredient   (Carr e et al. , 2013; Le Goff and Noblet, 2001; Noblet and Perez, 1993). As an example, the following equation was developed by Le  Goff and Noblet (2001)   As indicated above, the coefficient for ash is much higher than a simple energy diluting effect. The validity and the feasibility of such equations are dependent on the size and variability of the database used for their calculations and the accuracy and the statistical significance of each coefficient.
Unfortunately, some publications propose equations with redundant or correlated predictors (GE and fat or NDF and ADF, for instance) and no indication of the statistical significance of the coefficients of the equations. More importantly, as indicated above for pigs and poultry, the DE and ME values of a compound feed vary with the animal BW, its production stage and the technology of feed preparation. Such equations should then be established from measurements conducted according to a standardised methodology and, in theory, they should not be applied afterwards to conditions that differ from the original conditions and premises on which the measurements were taken and calculations based. Such an important prerequisite is often forgotten. Similarly, these equations have been obtained with complete feeds that often contain higher levels of ash than single ingredients and the mean chemical, and physical characteristics of fibre in the database values may differ markedly from those of a given ingredient.
, 2016)  than for complete feeds. As for DE or ME prediction from chemical indicators, the NIR method is not able to take into account the effect of technological preparation (pelleting, for instance) or the presence of supplements in the feed (enzymes, etc. ). Similarly, the prediction is valid only for the experimental conditions under which the in vivo measurements were taken. For instance, a prediction based on AMEn measured in adult cockerels is unable to predict the AMEs in broilers; likewise, that based on the DE values in growing pigs won't be able to predict DE in adult pigs accurately. In other words, an NIR prediction of DE or ME should be accompanied by the experimental conditions where in vivo data were collected for the calibration in the first place. This is particularly important for poultry with confusions related to adjustment for N balance, the age of the animals (adult vs broiler), and the ME types (AME vs TME).
This means that, in theory, there should be as many NE values of feeds as different physiological situations within a species; however, if HI/ME values in a series of feeds are ranked similarly for growing pigs, pregnant sows and lactating sows, for instance, the NE system developed for one stage of production can be applied to the other stages. That will be detailed later. Measuring NE of feeds responds to 3 main objectives: 1) quantification of NE value of a feed under variable animal (effect of BW or genotype, for instance) and environment conditions (ambient temperature, for instance); 2) estimation of the effect of manufacturing technologies and feed additives applied to a diet or a series of diets with a common methodological approach, and 3) building a database with NE values of different feeds obtained under the same animal and environment conditions, with the same methodologies in order to calculate relationships between characteristics of the diets and their NE values.
For the latter, linear regression models between dietary NE and some predictors are produced, the predictors being the digestible nutrient contents, the DE or ME values plus some chemical indicators  (Noblet et al. , 1994a; Wu et al. , 2019) , and the chemical characteristics or the NIR spectra of the diet. Some of these equations are presented in Table  5. One equation and, preferably, a group of equations, established from the same database with different regression models define what is termed an NE system  (Noblet et al. , 1994a; Wu et al. , 2019). It should also be noted that some NE prediction equations, and hence NE systems, do not arise directly from a data set obtained using strictly controlled experiments but from a compilation of literature equations (CVB, 2018) and/or biochemical and in vitro indicators  (Boisen and Verstegen, 1998).
In addition, it is reasonably constant over the successive days of an energy balance period. This means that RQ can be used as a reliable indicator of VO 2 and VCO 2 measurements, especially for detecting errors in VO 2 measurements. Energy gain in BW or as milk or as eggs can be obtained directly using the CST method and indirectly using the difference between ME intake and HP, whereas HP is measured directly in the direct calorimetry methods and indirectly from gaseous exchanges (HPcal). These methods have advantages and disadvantages as well as different domains of application. Furthermore, HP and RE obtained using various methods have been compared  (Gerrits et al. , 2015). First, direct and indirect calorimetry techniques when they are run simultaneously would produce comparable estimates of HP in humans  (Dauncey, 1980) ; but no data are available in pigs and poultry.
Practical considerations for NE measurement of feeds for pigs and poultry NE of a given quantity of daily feed to an animal or a group of animals is calculated as the addition of their daily RE and their daily FHP, with the latter quantity corresponding to the average metabolic body size over the fed period multiplied by the FHP per kilogram of metabolic body weight (FHP*). Daily data are further expressed as per kg of daily feed DM intake  (Noblet et al. , 1994a (Noblet et al. , , 1994b)). If the indirect calorimetry methods are used, the FHP* can be measured on each animal (or each group of animals) but, as explained above, FHP is obtained over a very short duration (i. e. , a few hours) and potentially rather inaccurate and variable between animals due to insufficient accuracy of the measurement technique as well as the impact of physical activity that is not quantified in most situations.
This can be explained by the limited size of our dataset and the constraints of the feeding behavior observations that could not be conducted all day. This is a limitation of the method, as sheep are able to modulate their grazing time according to sward quality  (Garcia et al. , 2003). Observations should thus be conducted at all times sheep eat, which is very difficult to implement. Therefore, by combining our results of fecal N and grazing observations and considering their limits, we consider that there may have been a moderate facilitation of grazing in the mixed treatments, which was anyhow not sufficient to significantly, and importantly, improve the diet of ewe lambs. In contrast, our results indicate a significant and strong effect of the treatments on the levels of strongyle infection, which were ~50% lower under mixed-grazing than under monospecific grazing.
Funding Our experiment was funded by the  INRAE scientific division "Animal Physiology and Livestock Systems" (PHASE) , the  PSDR4-Auvergne project new-DEAL  (cofounded by  INRAE, Irstea  and the  Auvergne-Rhone-Alpes region)  and  French government's national research agency (ANR)  and  its ''Investissements d'Avenir.
Genome sequencing and phylogenetic analysis We sequenced the genomes of 48 ponies and nine horses at an average depth of coverage of $11. 13 and combined them with 130 genomes previously characterized  7, 8  (Data S1A; STAR Methods). Our dataset includes 52 Chinese ponies from four breeds, 100 Chinese native horses from 12 breeds, 23 imported Falabella ponies, seven Thoroughbred horses, and five Przewalski's horses. Stringent quality filtering identified a total of 20,428,318 SNPs, 65. 1% of which were within introns and 20. 2% within intergenic regions (Data S1B; STAR Methods). Principal component analysis (PCA) and phylogenetic tree reconstruction revealed that all Chinese ponies clustered together in a single, monophyletic group (hereafter referred to as Southern ponies, SC; Figures  S1A  and S1B ).
Together, this suggested that the TBX3-EN1 and TBX3-EN2 SNPs may play a strong role in the regulation of TBX3 expression (Figure  1F ; Data S2P). We next measured the allelic frequency at the TBX3-EN1 and TBX3-EN2 SNPs in a comprehensive panel of 189 Chinese ponies and 574 Chinese horses using kompetitive allele specific PCR (KASP) assays (Data S3A-S3C; STAR Methods). Chisquare tests confirmed strong statistical association with the horse wither height, which was stronger for TBX3-EN2 (p = 2. 34eÀ39) than TBX3-EN1 (p = 8. 18eÀ29) (Figure  2 ). Our association model suggested that the variation at TBX3-EN2 (TBX3-EN1) could explain as much as $20. 3% ($15. 1%) of the height variation ($10 cm) in this panel (Figure  2A ). Interestingly, the A allele at TBX3-EN2 was most common in seven pony breeds (frequency = 65.
6%, N = 189) and the Przewalski's horses (frequency = 100%, N = 13). The G allele, however, reached a higher frequency in 18  The C allele is in blue and the T allele is in orange. TBX3-EN2 (p = 2. 34eÀ39) and TBX3-EN1 (p = 8. 18eÀ29) showed strong correlation with horse wither height (right y axis), explaining 20. 27% and 15. 05% of the phenotypic variation, respectively. The yellow and green bars represented the pony and horse breeds. Average wither's height within breed is indicated by the black line. Cartoon pictures of ponies and horses were generated according to the photos from the Animal Genetic Resources in China: horse, donkey, and camel (2011). See also Data S3A and S3B. breeds (71. 4%-90. 0%, wither height > 135 cm). Intermediate allelic frequencies were found in Tibetan horses (67.
Population structure Population structure among all modern horse and pony breeds was investigated using a total of 11,553,176 high-quality SNPs, following conversion of the VCF files into the PLINK format by the VCFTools,  31  and filtering for minor allele frequency (MAF) > 0. 05, call out rate > 0. 9 and hwe > 1e-6. Pruning was carried out within windows of 1,000 SNPs (step-size 5 SNPs), using the-indep-pairwise 1000 5 0. 5 parameters in the PLINK. This provided a total of 1,593,119 unlinked SNPs for PCA, ADMIXTURE and phylogenetic reconstruction. The PCA was conducted using the GCTA software,  32  the first two axes were plotted using with the R program (version 3. 5. 0). Individual ancestry profiles were inferred using 10,000 bootstrap replicates in the ADMIXTURE (version 1. 3. 0)  33 and assuming 2 to 4 ancestral populations (K).
Runs of homozygosity and linkage disequilibrium Runs of homozygosity (ROH) were identified for each breed/population applying the 'runs of homozygosity' function in the program PLINK v1. 9  48  to the unpruned matrix of high-quality variants. The analysis was run with the following options '-homozyg-window-kb 1000-homozyg-window-snp 50-homozyg-window-het 1-homozyg-snp 10-homozyg-kb 100-homozyg-density 10-homozyg-gap 100'. Patterns of linkage disequilibrium (LD) among different horse breeds were reconstructed from individual VCF files using the PopLDdecay 37 with default parameters, except that the maximum distance between two SNPs was set to 500 kb.
Those windows comprised within the top-1% quantile of all three statistics were considered as candidate selection targets and annotated using the genomic database search engine BioMart 36 (Data S2A-S2D). The selection detection analysis of F ST fixation was conducted in Falabella ponies versus Chinese horses and Thoroughbred horses. Functional enrichment for Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses was tested with goProfiler. 9 The enrichment significance was assessed correction for g:SCS'Set Counts and Sizes', which is more conservative than Benjamini-Hochberg False Discovery Rate but not as strict as Bonferroni correction. To investigate whether different genetic pathways may underpin the short stature of Chinese ponies and Western ponies, we downloaded whole genome sequencing data of 24 ponies and 40 horses from the European Nucleotide Archive (https://www.
ebi. ac. uk) and SRA (https://www. ncbi. nlm. nih. gov/sra/) (Data S2I). We then combined these data with FLBL ponies sequenced in this study to form a group of 'Western pony breeds' including FLBL (N = 23), Shetland pony (N = 18), German Riding pony (N = 2), Haflinger (N = 3) and Icelandic pony (N = 1), as well as a group of horses, including Akhal-Teke (N = 3), Arabian (N = 3), Hanoverian (N = 5), Holsteiner (N = 5), Olderburg (N = 3), Trakehner (N = 2), Franches-Montagnes (N = 15), Morgan (N = 1), Swiss Warmblood (N = 1), Dutch warmblood (N = 1) and Standardbred (N = 1). The methodology then followed the same analytical steps as described for the Selection scan analysis carried out between Chinese ponies and Chinese horses. The selection candidates identified among 'Western pony breeds' and the horse group are provided in Data S2J and S2K.
ACKNOWLEDGMENTS The project was supported by the  National Natural Science Foundation of China  ( 31972530 ,  32002144 ,  31772553 , and  31961143021 ), the  National Germplasm Center of Domestic Animal Resources , and the  Agricultural Science and Technology Innovation Program of China  ( ASTIP-IAS01 ). L. O. has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 681605 ). X. L. was supported by the  International Postdoctoral Exchange Fellowship Program  ( 20190102 ), the  ERC PEGASUS  attributed to L. O. ( 681605 ), and the  Marie Curie Individual Fellowship program  ( 101027750 ).
These differences make the results difficult to compare and generalise. If guidelines were harmonised and follow established standards, many datasets generated around the world could be better compared and more effectively combined, to produce new knowledge e. g. from meta-analyses. Harmonisation of guidelines would also minimise duplication of effort, save resources, and allow more information to be extracted from each study. Moreover, following commonly agreed and comprehensive guidelines will ensure that methods are updated to reflect new knowledge and promote high-quality experimental research. This requires common awareness amongst animal scientists, but would help to reduce the number of animals used in research by reducing the number of experiments as well as by reducing the number of animals per experiment, thereby following the Replacement, Reduction, and Refinement (3 R) principles.
Although it is time consuming, performing direct observations of the feeding choices of grazing herbivores remains the reference method because it does not require any destructive vegetation sampling and limits bias associated with the use of indirect markers. It is also the only method for characterizing the use of vegetation patches. These observations are usually performed during the diurnal period only; however, equids graze 16 h per day on average, with 20-50% of their grazing occurring during the nocturnal period. The objective of this paper was to test the representativeness of diurnal observations of horses' feeding choices to characterize their daily diet. We used an experimental dataset of 54 paired observations of the diurnal and daily (24-h period) diet composition in horses grazing a pasture with differing sward characteristics according to the stocking rate and season.
Research has improved our understanding of horse grazing behaviour  (Grace et al. , 2002; Edouard et al. , 2010; Collas et al. , 2015)  and impact on vegetation communities  (Nolte et al. , 2014; Fleurance et al. , 2016). Prior to performing the data analyses, we conducted a systematic search of the methods used to assess diet composition in grazing equids. This led us to select 65 references from the Web of Science in April 2022, using the words "equid* OR horse* OR donkey* OR ponies" AND "graz* OR forag* "; then refining our search by including "diet OR feed OR choice* OR selection OR bite* ". Most of these papers used direct observations (n = 31) and microhistology (n = 19) to characterize the diet composition of equids, while only a few of them used alkanes (n = 7), plant-based methods (n = 7) or diet-related metabolites in blood serum (n = 1).
Experimental design We used a dataset in which horses grazed a mesophile grassland of central France under contrasting stocking rates and over three grazing seasons  (Fleurance et al. , 2016). Two stocking rates were compared: a high stocking rate (1. 8 LU ha -1 ; 1 LU = 600 kg liveweight), which was designed to have most of the edible biomass consumed at the end of each grazing season; and a moderate stocking rate (1. 1 LU ha -1 ), which was assumed to benefit individual horse performance and sward biodiversity. Six 2. 7-ha plots were created within the pasture so that each treatment was replicated three times in a randomized block design. At the high stocking rate, five horses were continuously grazed in each plot from mid-April to early November each year, while at the moderate stocking rate, three horses grazed each plot during the same period.
Discussion Although horses graze during a significant part of the night  (Duncan, 1992) , only seven out of 31 published studies that have observed their feeding choices at pasture have included nocturnal observations. Here, we used the largest dataset among these seven studies  (Fleurance et al. , 2016) , where bites were characterized by sward height and vegetation stage, which are two key factors that affect horses' selection  (Edouard et al. , 2010). We demonstrated that observing horses' feeding choices during the diurnal period only makes it possible to predict their daily diet composition. The statistical test implemented here provides high confidence in our conclusion. First, the null hypothesis (H0) considered in the goodness-of-fit test is that all values fall within the specified distribution, while the alternative hypothesis (H1) implies that at least one value does not match the distribution.
Acknowledgements The data set was obtained with financial support from the  French Horse and Riding Institute  ( Institut Français du Cheval et de l'Equitation, IFCE, France ). The authors thank the staff of the IFCE experimental farm, namely,  Laurence Wimel ,  Patrice Dupuy ,  Claude Larry ,  Jean-Louis Larry ,  Jacques Boulanger , for their technical assistance;  Nadège Edouard ,  Francis Decuq  from  INRAE   UMR1213 ; and the students  Aurore Guiet ,  Sophie Steelandt ,  Alexia Mouchet ,  Laure Sallefranque  and  Marie Bourguet  for their help with behavioural measurements.
Fractionation of dichloromethane and ethyl acetate extracts DCME (560 mg) was submitted to silica gel column eluting with petroleum ether-ethyl acetate mixes of increasing polarity (100:0 to 0:100) and then with ethyl acetate-methanol (100:0 to 0:100) to yield 16 fractions (FrD1-FrD16) based on their TLC composition. Chrysophanol (4 mg) and physcion (7 mg) were isolated from FrD1 and FrD3 respectively (Fig. 1 ). The structure of these two anthraquinones were determined by comparing the chemical and spectral data (m. p. , UV, IR, 1 H-NMR and 13 C-NMR) with those reported in the literature  (Jibril et al. , 2017). EAE (8 g) was submitted to LH20 chromatography eluting with water-methanol solvent system of increasing polarity (80:20 to 0:100). The collected fractions were grouped into 17 major fractions (FrE1-FrE17) based on their TLC composition (Fig.
S1 , Table  S1 ). FrE13 (370 mg) was submitted to a CPC run, with Arizona G solvent system (n-heptane/EtOAc/MeOH/water, 1:4:1:4, v/v/v/v) in ascending mode at room temperature, with rotation speed of 1300 rpm, mobile phase flow rate of 8 ml. min À1. Back pressure at equilibrium was 22 bars; effluent was monitored at 215, 254, 280 and 365 nm. This fractionation led to 17 fractions (FrE13-A to FrE13-Q) (Fig. S2 ). Epiafzelechin (29 mg) and epicatechin (101 mg) were isolated from fractions FrE13-H and FrE13-M respectively. The structure of these two flavan-3-ol compounds were determined by comparing the chemical and spectral data (m. p. , UV, IR, 1 H-NMR and 13 C-NMR) with those reported in the literature  (Kpegba et al. , 2011 and Kashiwada et al. , 1990  respectively) (Fig. 1 ).