dataesr/scientific-paragraphs-categorization huggingface dataset:
g. milk yield 14 to 38 kg d -1 ) and behaviour (e. g. total eating time 441 to 742 min d -1 , total eating chews 31668 to 54174 n d -1 , grazing time 355 to 691 min d -1 , prehension bites 11037 to 40304 n d -1 , total eating chews at pasture 26225 to 48710 n d -1 ) were thinned out based on statistical and nutritional-linked criteria. For the final reduction of predictors in the models the best subset regression approach was applied (R Core Team (2016), package leaps). As the sample size was too small to retain an independent validation dataset, the bootstrap cross-validation method was chosen (package rms).
Results & discussion The 100 % of ewes were well-adapted after 2-3 wk of familiarization with the WoW device. Thus, ewes voluntarily crossed several times (~ 6-8/ ewe/ d) and, overall, more than 65% of BW values recorded by the WoW in the three experiments were useful for interpretation. The rest of the observations (around 35%) were considered as outliers and removed from the database to be interpreted. Interestingly, more the conditions were extensive more concordant were the WoW BW values when compared with static BW. The Figure  1  illustrate the excellent fit of static vs. WoW BW on pooled grazing ewes groups (i. e. concordance correlation = 0. 97).
Acknowledgement Funding for this work was provided by the Universities of Glasgow and Strathclyde. Figure  1  The relationship between the amplitude (size of the 3-D vector derived from accelerometer, reflecting the force of the contraction) and the period of contractions exhibited considerable noise but suggests that most of the likely true observations have periods of between 30 and 60 s. Introduction Novel in-paddock technologies, such as electronic feeders (EF), allow measuring individual supplement intake and feeding behaviour of molasses and mineral blocks offered as free-choice to group-fed cattle. EF might also help to capture temporal changes in supplement intake as a result of forage type and grazing management  (Titgemeyer et al. , 2004). We studied the variability in lick-block supplement intake of individual animals as they grazed paddocks with different forage quantity and quality.
Individual energy balance is difficult to determine or predict, because measuring energy input and output is laborious and current feeding standards do not take into account the individual variation in energy requirements. Less labour-intensive methods are needed to predict individual energy status of large number of dairy cows for selection of animals with high feed efficiency but without excessive negative energy balance. Further, predicted energy status may be used as a tool to monitor and optimize feeding of early lactation dairy cows. The aim of the study was to evaluate the potential to predict plasma non-esterified fatty acid (NEFA) concentrations of dairy cows based on concentrations of individual milk fatty acids. Materials & methods A database with 809 simultaneous measurements of plasma NEFA and milk fatty acid concentrations was built by collecting blood and milk samples from first and second parity cows at three research farms.
Materials & methods Milk protein and plasma free methionine (Met) data were used from 5 trials involving lactating Holstein cows fed corn-based, Met-deficient diets. Treatments were continuous infusion of increasing amounts of Met into the abomasum or feeding increasing amounts of Smartamine ® M. The dataset consisted of 28 treatments means involving 139 individual cow observations. The NRC (  2001 ) model was used to predict concentrations of Met in metabolizable protein (MP) for all treatments. Milk protein content and yield responses to supplemental Met were calculated as described in NRC  (2001). Milk protein responses were regressed on model predicted concentrations of Met in MP and on different measures of expression of plasma sulfur AA using PROC NLIN in  SAS (9. 4). Relationships between these variables were described using a segmented rectilinear model which provides an objective, mathematically determined optimum supply.
Results & discussion The described Met treatments resulted in predicted Met concentrations in MP that ranged from 1. 67 to 2. 75% (see x-axis in Figure  1 ), which were positively correlated (r 2 = 0. 751; graph not shown) with plasma Met concentrations that ranged from 19 to 54 µM. Regressions of milk protein content responses on Met in MP (Figure  1 ) resulted in a breakpoint estimate of 2. 29%, a value similar to that determined for NRC (2001) with a different and much larger dataset. Regressions of milk protein content responses on concentrations of plasma Met (Figure  2 ) and Met as a percentage of total EAA (Figure  3 ) demonstrate a correlation between plasma Met concentrations and milk protein responses and indicate that an 'ideal' plasma EAA profile for lactating cows might eventually be estimated.
Materials & methods The CM of  Conrad et al. (1964)   the mean of the physically and physiologically predicted DMI rather than the lowest value was retained as actual DMI. The eight CM were then evaluated using a dataset summarizing results from 51 studies conducted under (sub-)tropical conditions with 231 dietary treatments. The mean bias (MB), root mean squared error of prediction (RMSEP), relative prediction error (RPE), and concordance correlation coefficient (CCC) were used to evaluate the CM's adequacy (i. e. accuracy and precision)  (Tedeschi, 2006).
A quantitative review of the literature was carried out to obtain more generic relationships between IB, BW and BM, in order to provide useful and simple tools for monitoring nutrition and to better assess individual variations. Materials & methods This work was performed from a meta-analysis of published data. The database comprised 6 publications, 27 experiments and 59 treatments (44 vs 15 with cattle vs sheep). The regressions were calculated intra-experiment for studies where IB varied significantly.
Results& Discussion The IB(6. 3±1. 9, min=2. 7, max=8. 6 cm) is closely related to BW (327±214, min=26, max=618 kg): IB(cm)=0. 91 BW 0. 346  (n=59, nexp=27, RMSE=0. 27)  [1, Figure 1]  The allometry coefficient of this equation is <1 and is fairly similar to that of  Gordon et al (1996). Besides, there is a close intraexperiment regression between BM (0. 56±0. 07, min=0. 1, Max=1. 2 g DM/bite) and IB: BM (g) = 0. 023 IB 1. 71  (n=49, nexp=22, RMSE=0. 10) [2, Figure  2 ] The power coefficient is > 1, stressing the advantage of having a larger IB, propitious for forage acquisition for grazers. It could be seen as a systematic advantage of grazing cattle, compared with sheep (Figure  2 ). In the same dataset IB is also related to the theoretical diameter of the bite and therefore to the bite area, but not at all with the bite depth, which is more impacted by sward height in grazing conditions.
Data were randomly split in two subsets of 221 and 92 samples, respectively as training and validation sets, The training database was first submitted to a stepwise analysis, specifically conceived to select the subset of variables that best discriminate groups. The selected variables were then submitted to LDA. The effective separation between groups was measured with the Mahalanobis' distance and the corresponding Hotelling's Tsquared test  (Dimauro et al. , 2013). The Total Error Rate (TER) was expressed as the total number of misclassifications over the total sample size, estimated in both the training and validation set  (Biffani et al. , 2015). All statistical analyses were developed by using the R software (R version 3. 3. 2).
Conclusion The inclusion of field pea had minor effects on the chemical composition and fatty acid profile. However, the inclusion of 20% of field pea increased the cholesterol and total SFA compared with the other concentrates. Introduction Producing ruminant with adequate muscular and adipose tissues masses, i. e. lean-to-fat ratio, is an economic challenge for the bovine sector. The lean-to-fat ratio contribute to the animal adaptability, food efficiency and meat / carcass qualities. Despite numerous genomics studies that assayed how rearing factors affect the lean-to-fat ratio  (Ceciliani et al. , 2018)  there is no consensus on molecular biomarkers of the lean-to-fat ratio, perhaps due to the difficulty to compare large datasets produced with different quantitative protocols. One of the rare attempt to compile large datasets had merged micro-arrays data from a same quantitative protocol  (Baron et al.
Materials & methods For datasets comparisons, we used R software, first to merge identifiers over datasets and to perform descriptive statistics with the ade4 package. We then used the metaMA package to look for differentially abundant genes in each study and whatever the studies (named Merged Data), by performing Benjamini-Hochberg corrections at a significant level of 5%. This pipeline was bench tested with 4 datasets from 2 transcriptomics studies of bovine Longissimus muscle  (Qin et al. , 2011; Moisá et al. , 2013). These studies used different microarrays (UIUC Bos taurus 13. 2K 70-mer condensed oligoarray and Affymetrix Bovine Genome Array), and cattle differing by the breed (Chinese Red Steppe vs Angus X Simmental), diet (compensatory growth plane of nutrition vs standard diet) and sex (steers vs bulls).
Results & discussion Only 855 GenBank accessions over thousands were shared by the 4 datasets. One gene (Homeobox protein Hox-A5, HOXA5), was overexpressed during muscle growth in each study and in the Merged Data (Figure  1 ), highlighting HOXA5 as a potential marker of muscle accretion whatever the breed, diet and castration. The embryonic factor HOXA5 has been found at the adulthood involved in fat depot in adipose tissue  (Gesta et al. , 2006) , but was never related to post-natal muscle biology. In dataset C, D and Merged Data, 179 genes were differentially abundant and may be drivers of muscle (including marbling) accretion with age. Of these, genes involved in SMAD, TGF-β or activin signalling already known to modulate muscle hypertrophy underscored the meta-analysis method. Figure  1  Descriptive data and number of differentially abundant genes in datasets used to bench test the meta-analysis.
Meat quality is a complex phenotype that can be evaluated only after slaughtering and ageing. Previous research efforts have investigated the potential of muscle-derived markers to assess meat quality  (Picard et al. , 2015; Cassar-Malek and Picard, 2016). However, their evaluation requires post-mortem muscle sampling or biopsies in the living animals. Thus, the identification of low invasive biomarkers is an issue for the beef sector. In this study, we hypothesized that available published data may help identifying candidate plasma biomarkers for beef quality. Materials & methods Proteomic, transcriptomic and genetic muscle data related to beef tenderness (with significant correlation, differential abundance in extreme groups, or gene polymorphism) were collected from 54 publications including 1 GEO dataset (GSE9256) and computed according to  Bonnet et al.
(2016). Aggregation of the Gene Names (GN) from 3 datasets (proteomics, transcriptomics and genetics) were analysed using ProteINSIDE (http://www. proteinside. org/) to find biological information and predict secretion through a signal P sequence (SignalP score ≥ 0. 5; TargetP score ≤ 2) or through a non-classical pathway without signal P (TargetP score ≤ 2). The lists of secreted proteins (named SignalP, other secreted) and cytoplasmic proteins were compared to a list of bovine plasma proteins from publications and experimental data (n=1106). Results & discussion. The numbers of GN included in each dataset are shown in Table  1. Among the total 681 unique GN, 145 corresponded to secreted proteins, and 543 to cytoplasmic proteins. The comparison of the GO annotations revealed 73 common GO Biological Pathways (BP) between the datasets (oxidation-reduction process, response to oxidative stress, apoptotic process, proteolysis, lipid metabolic process, response to hypoxia, transport) consistent with tenderness mechanisms (Cassar-Malek and  Picard, 2016).
, 2017a)  and data from two farm-types with dairy and suckling herds in cruising condition from the INOSYS database  (Charroin et al. , 2005). Farm_1 is a medium size system with a summer mountain pasture and Farm_2 is a large size system with a fragmented plot and cereal crops. These two farms are representative of the mountain double herd cattle farms. The model was adapted to take into account geographical constraints and the heterogeneity of grassland plots based on the French typology of permanent grasslands  (Launay et al. , 2011). Simulations are based on the two different farms and five scenarios (described as the percentage of livestock units (LU) in the dairy (D) herd): 100D for a single dairy herd, 75D for a dominant dairy herd, 50D for balanced dairy and suckling herds, 25D for a dominant suckling herd and 0D for a single suckling herd.
Conclusion Generally, comparisons of canola seeds, meal or pellets in Canada, in China, and between Canada and China indicated the presence of some variations among different crusher plants and bio-oil processing products. Introduction Brewers grains is an agro-industrial by-product of beer production. According to the Feedipedia database  (Heuzé et al. , 2017) , it can be considered as a good source of protein (24%) and fibre (NDF: 62. 4%, ADF: 19. 7%). In Algeria this product is inexpensive and locally available (8. 37 €/quintal) compared to soybean meal (46 €/quintal). The aim of this trial was to study the possibility to produce acceptable rabbits carcasses with a feed containing high rate of brewers grains and measure its effect on economic efficiency and competition with humain food.
Conclusion Dietary supplementation with fat reduced methane yield when cows were fed a diet containing wheat and when the diet contained corn. Introduction Previous research has shown that confined beef steers with feed-efficient (low RFI) phenotypes have lower (P < 0. 01) frequency and duration of bunk visit (BV) and meal events  (Lancaster et al. , 2009) , and reduced (P < 0. 05) day-to-day variation of feeding behaviour patterns  (Parsons et al. , 2018)  than high-RFI steers. Partial least squares regression is a statistical method designed to handle multivariate datasets comprised of variables with substantial collinearity. The objective of this study was to calibrate and validate PLSR-based models to predict feed efficiency of finishing beef steers using feeding behaviour traits as independent variables.
Separate PLSR models were constructed for RFI and residual gain using cross-validation procedures (JMP; SAS Inst. , Cary, SC). Feeding behaviour traits with variable of importance (VIP) scores  < 0. 8 (Wold et al. , 2001)  were eliminated, and the optimal number of PLSR components used in each model based on the lowest predictive residual sum of squares. Model performance was evaluated using R 2 of cross-validation (R 2 CV), mean bias, mean squared error of prediction (MSEP), and Spearman's rank correlation. Test-set validation of the RFI model was conducted by iteratively using 2 trials as training and the remaining trial as validation datasets, with evaluation based on the mean R 2 of calibration (R 2 C) and validation (R 2 V) for all 3 combinations of training and validation datasets, respectively.
Results & discussion The RFI standard deviation (s. d. ) is 0. 47 kg/d while FI s. d. is 1. 12 kg/d. The heritability of 0. 26 for RFI shows that this trait can be genetically improved by selecting the best bulls. Berry and Crowley 2013 reported heritability of RFI from literature between 0. 14 to 0. 62. A selection on RFI will reduce the FI (Rg=0. 58, table  1 ) without strong effect on ADG (Rg=0. 15). For the GWAS analysis, 647,225 SNP remained after filtering. Several SNP are significantly associated with RFI (figure  1 ) and 16 QTL are defined. The strongest QTL is found on chromosome 14 with 105 markers inside, known to be associated with several milk or body traits but not with feed efficiency traits (AnimalQTLDatabase.
Acknowledgements Material & methods Red clover (Trifolium pratense cv. Betty) was grown on two replicate plots of 2 m 2 with one plot receiving labelled 15 NH 4 15 NO 3 fertiliser and the other unlabelled NH 4 NO 3. The harvest was preserved as formic acid treated (FAS) and untreated silage (UTS) or as oven dried hay (DRC; 60°C). The freeze-dried forages were milled through 1 mm screen and then separated into three N fractions: soluble N (SN), insoluble N (ISN) and neutral detergent-insoluble N (NDIN). Isolated N fractions were incubated in pre-incubated rumen fluid as mixed diets, containing isolated NDF or soybean meal and a carbohydrate mixture (pectin, starch and maltose) to achieve a similar concentration of N and NDF as in the intact forage. The ammonia N (AN) in in vitro bottles containing unlabelled samples was labelled with 15 N with ( 15 NH 4 ) 2 SO 4.
We made the hypothesis that computation of available omic data may enable the identification of plasma protein biomarkers for RFI in the prediction of tissue secretomes. Material & methods Transcriptomic data from liver, muscle, adipose tissue, rumen, duodenum, and pituitary were collected from 5 publications on cattle  (Chen et al. , 2011; Weber et al. , 2016; Tizioto et al. , 2015 Tizioto et al. , , 2016;; Kong et al. , 2016)  and 1 GEO dataset (GSE56705) (Table  1 ). Lists of unique Gene Names (GN) up-and down-regulated in low RFI groups were submitted to a custom analysis using ProteINSIDE. (http://www. proteinside. org/ ) to mine biological information and predict secreted proteins with Signal P sequence  (Bonnet et al. , 2016). The lists of proteins secreted thanks to a SignalP were compared to a list of 1106 plasma bovine proteins from publications and proteome analysis.
Results & discussion A PCA analysis of all data was applied to remove outliers (n=2/per RFI group) from further analysis. Then a differential abundance according to RFI was revealed for 51 proteins (P <0. 1) including 4 uncharacterised proteins. A correlation between plasma abundance and RFI values was detected for 24 proteins out of the 51 proteins (-0. 795 <r2 <+0. 806). Nine of the 51 differential proteins-5 of the proteins correlated to RFI-were detected in a repertoire of secreted proteins identified by computation of published RFI omic datasets (Cassar-Malek and Bonnet, this symposium). These proteins are candidate biomarkers for RFI. The top GO Molecular Functions of the differential proteins were: calcium ion binding, serine-type endopeptidase inhibitor activity, lipid binding, carbohydrate binding, growth factor activity, regulation of insulin-like growth factor receptor signaling pathway.
GO Biological Processes were mainly related to immunity (immune response, innate immune response), inflammatory response, complement and coagulation cascades, protein modification (protein folding, proteolysis, negative regulation of endopeptidase activity) and lipid metabolism (lipid transport, triglyceride and cholesterol homeostasis). These findings agree with the recent report that non-productive functions including immunity may contribute to inter-individual variations in feed efficiency in pig  (Gondret et al. , 2017)  and cattle  (Weber et al. , 2016; Alexandre et al, 2015). Examination of proteomic data according to the FCE index revealed 21 differentially abundant proteins and 7 correlated proteins (-0. 599< r2 < +0,637). Comparison of FCE and RFI datasets showed that 6 proteins out of the 9 identified above as candidate RFI biomarkers are specific for RFI while 3 of them are common to both RFI and FCE.
The aim of this study was to describe the more general potential of individual MFA as predictors of CH 4 emissions, based on individual data of cows fed a wide range of diets. Material & methods A total of 825 CH 4 emission measurements (g/day) from individual lactating Holstein dairy cows linked to milk samples characterized by fatty acid composition (% of total FA), were obtained from 34 in vivo experiments (Australia, Canada, Denmark, France). Dry matter intake, diet chemical composition (OM, NDF, starch, EE), milk yield, fat, protein and lactose contents, and animal characteristics (DIM, BW) were also gathered in the database. Outliers were removed (n=1) from the dataset and missing values on diet chemical compositions were estimated according to ruminant feeding tables  (INRA, 2007). The diets were mainly based on maize silage (297), grass silage (157), legume hay (157), or a mixture of forages (  215 ).
The database contained control diets (198) and CH 4 mitigating treatments including lipid supplementation (198), different forage or concentrate types (149; 140), probiotics (58), plant extracts (33), saponins (8), or tannins (4). Methane emissions were measured using open-circuit respiration chambers (  462 ) and the SF 6 method (363), while MFA were analysed by gas chromatography. Pearson's pairwise correlation coefficients were obtained between the 46 initially collected individual MFA with CH 4 emissions to determine the most correlated individual MFA per MFA family (saturated FA, branched FA, cis monounsaturated FA, trans monounsaturated FA and polyunsaturated FA). Pearson's pairwise correlation coefficients were also obtained for each pair of MFA. Principal component analysis (PCA) was performed, using the FactoMinR package in R, on the MFA significantly correlated to CH 4 production (r ≥ |0.
Conclusion Five MFA (C10:0, iso-C17:0 (+t9-C16:1), c11-C18:1, t10-C18:1, t11,c15-C18:2), are potential predictors of the daily CH 4 emissions from dairy cows. Furthermore, they are not correlated, thus suitable for inclusion in more complex prediction models. 1  Summary statistics of some variables included in the database and their correlation 1 with CH 4 emissions.
Thus, the objective of this study was to identify variables related to the MRT of particles and solutes of growing Saanen goats, using MFA. Material & methods Our dataset involved 103 individual records of castrated males (n = 36), females (n = 34), and intact males (n=33) growing Saanen goats slaughtered at 15, 22, 30, 37, and 45 kg BW. All animals were fed ad libitum with a similar diet (45% of forage in offered diet, dry matter (DM) basis; 191 ± 25. 5 and 379 ± 44. 7 g/kg of DM of crude protein and neutral detergent fibre (NDF), respectively). Variables evaluated in our dataset were BW (kg), DM intake (DMI, kg/day), NDF intake (NDFI, kg/day), MRT of particles (MRT p , h) and solutes (MRT s , h) in GIT segments, ratio of indigestible NDF (iNDF) and NDF (iNDF:NDF ratio) of ingested diet, total NDF digestibility (1 -[(iNDF:NDF ratio of ingested diet)/(iNDF:NDF ratio of colon-rectum (CR) digesta)]; NDF d ), wet weight of GIT tissues segments (kg), wet weight of digesta pool size of GIT segments (kg), and digesta DM concentration of GIT segments (g/kg of wet digesta).
The MRT was assessed by using the method of emptying the GIT compartments in slaughtered animals, adopting iNDF and Cr-EDTA as makers for particles and solutes, respectively. The evaluated GIT segments were reticulorumen (RR), omasum (O), abomasum (Ab), small intestine  (Si) , cecum (Ce), and CR. The statistical analysis was performed with the psych package  (Revelle, 2014)  in R version 3. 4. 2 (R Core Team). First, a Pearson correlation matrix was computed. Bartlett's test and Kaiser-Meyer-Olkin (KMO) tests were applied on the correlation matrix to assess the database adequacy to MFA (i. e. , identify if there is a latent structure of dependency among our dataset variables). Parallel analysis was used to choose the number of factors. Finally, the MFA was computed. Varimax as orthogonal rotation and maximum likelihood as factoring method were used in the parallel analysis and MFA.