dataesr/scientific-paragraphs-categorization huggingface dataset:
High purity carbon dioxide (99. 99%) was passed into the cabinet, and the flow rate was calibrated by checking the recovery of carbon dioxide gas  (18). The recovery was 98 -100%. Before the start of each period, CH 4 and O 2 of known concentrations were used to calibrate the analyzers (0-884 ppm for CH 4 ; 21. 46% for O 2 ). Data were collected using a data logger (Kooland Company Ltd. , Beijing, China), and were recorded every 25 min.
Background and Aims: Vitamins are bioactive compounds naturally found in many different types of food and required by the human body for many biological functions and enzymatic activities. Due to their antioxidant properties, certain vitamin derivatives have been synthesized for inclusion in many cosmetics, thus leading to an increasing incidence of allergic contact dermatitis (ACD) cases. Therefore, the present review may be helpful to provide an insight into the sensitizing role of at least certain vitamins and may also offer possible patch test alternatives for definitive diagnosis. Methods: This study was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Literature search regarding ACD cases to vitamins was performed using the Medline, PubMed, Scopus, EMBASE, and Google Scholar databases from January 1940 up to June 2021.
| METHODS This study was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Literature search was performed using the Medline, PubMed, Scopus, EMBASE, and Google Scholar databases from January 1940 up to June 2021, inserting the following search terms: allergic contact dermatitis; contact hypersensitivity; photoallergic contact dermatitis; photoallergic contact dermatitis; patch test; photopatch test; fat-soluble vitamins; hydro-soluble vitamins; multivitamins; vitamins; vitamin B complex; vitamin a; vitamin b; vitamin c; allergy; hypersensitivity.
A pluridisciplinary scientific expertise This paper summarises the main lessons regarding the quality of organic animal products from collective scientific expertise dedicated to the quality of animal-source foods  (Prache et al. , 2020a and b). This expertise was carried out by INRAE, at the request of the French Ministry of Agriculture and FranceAgriMer (public agency dedicated to trends and challenges in agriculture). Twenty public scientists were involved including specialists in cattle, sheep, pig, poultry and fish farming, food processing, economy, sociology, law, human nutrition, toxicology, epidemiology, microbiological and chemical food safety. The bibliographic collection was compiled by searching the Web of Science TM and PubMed databases. The collection was assembled from the initial research and monthly literature watch conducted by two librarians as well as references from the experts.
The location sampled was cleaned with betadine soap before the biopsy. Between each pig, the needle of the gun was cleaned with a solution of 70% ethanol. After the biopsy was done, the wound was sprayed with betadine to prevent infection. The wound healing process was followed by measuring the perimeter of the wound from photographs taken, without restraining the pigs, by a digital camera daily for 10 days and on day 15 after the biopsy. If needed, the wound was cleaned with water before taking a picture. Measurements were made by a single trained person, blind to treatment, using the ImageJ software  39  to delimit the wound perimeter. The length of a sticker placed close to the wound was used as a scale to standardize the measurements. Indicators measured during the challenges. Weights.
Measurements were made on photographs by a single experienced person, blind to treatment, using the ImageJ software  39  to delimit the tear perimeter. The length of the iris was used as a scale to standardize the measurements. All the brownish areas on the direct periphery of the eye (bottom of the upper eyelid, top of the lower eyelid, internal and external corners) were recorded  42. The variable analysed was the cumulative area covered by the stain. Pathological examination at slaughter. At the age of 140. 0 ± 1. 9 days (weight: 95. 2 ± 8. 5 kg), the 96 focal pigs were exsanguinated after an electrical stunning for dissection purpose. The judging of pathological changes was done blind to the housing treatment of the pigs. The heart was examined for pericarditis, the lungs for pneumonia and pleurisy, and the stomach for lesions and ulcers.
Statistical analyses. Statistical analyses were performed with the software R 4. 0. 3. 46. The variables tear staining, serum NEFA concentration and hair cortisol concentration were normalized by logarithmic transformation. Areas under the recovery curves (AUC) were approximated from repeated measurements using the trapezoidal rule N k=0 f (xk-1)+f (x k ) 2 × �xk , where f(x) is a function and x k is the length of the k-th subin- terval. The resilience indicator (ln(variance) based on body weight deviations was estimated using the same formulas as in the paper of Berghof et al. 6. On focal pigs only, linear mixed models were used for all variables measured once (relative growth, tear and skin lesions measured 24 h after the biopsy and before slaughter, post-mortem examinations, AUC, latencies of behaviours, total numbers of aggressive acts and posture changes during the transport challenge) with the function lmer from the R package "lme4", except for frequencies of behaviours and scores of skin lesions and postmortem measurements.
For these variables, generalized mixed models with a Poisson distribution and Log link function were used and the function glmer from the R package "lme4". In these models, Housing and Sex (boar versus gilt) were fixed effects, and Pen and Batch were random effects. For the repeated variables, repeated linear mixed models or repeated generalized mixed models with Housing, Sex, Time and the housing × time interaction as fixed effects, Pen and Batch as random effects, and Pig as a repeated variable were used. For hair cortisol, the Side of shaving and the Status of the animal (focal versus companion) were also included as fixed effects. For the measurements of the heat challenge at the group level, Housing, Time period and housing × time period were used as fixed effects and Batch as a random effect.
Acknowledgements The authors would like to thank the farm staff at the  Swine Innovation Centre (Sterksel, The Netherlands)  and at Carus research facilities (Wageningen, the  Netherlands ) for daily animal care and the help in the data collection. They would also like to address a special thanks to the technicians and laboratory persons from the  Adaptation Physiology group :  Joop Arts ,  Rudie Koopmanschap ,  Bjorge Laurenssen ,  Manon van Marwijk  and  Monique.
Using a simulation approach based on farm databases  Gaillard et al. (2020)  evaluated the potential of such a precision feeding (PF) strategy for gestating sows (individual and daily mixing of two diets with different AA content) compared to a conventional feeding (CF) strategy (a single diet with a fixed AA content). Simulations indicated that PF appeared to be relevant to better meet the AA requirement and that, compared to a CF strategy, it reduced feed costs by 3. 6%, lysine intake by 30%, and nitrogen and phosphorus excretion by 17% and 15%, respectively  (Gaillard et al. , 2020). However, these simulations results have not been verified experimentally. Moreover, they do not allow to evaluate the effects of such a PF strategy on reproductive performance or feeding behaviors. Indeed, animals housed in group with feeding stations are subjected to feed competition.
Thus, finally 131 gestations from 97 multiparous and 34 primiparous were fully followed and their data analyzed. Each of the eight groups of gestating sows was housed in a pen of 7. 5 × 8. 0 m, with free access to water (2 drinking troughs), and to 2 automatic feeders (Gestal, JYGA Technologies Inc. , Québec, Canada) allowing the individual mixture of two diets. Every morning fresh straw was delivered in the resting areas of the pen.
A third linear mixed-effects model was used to evaluate the effect of the feeding strategy, parity, and period on the feeding behaviors (number of visits to the feeder and daily time spent at the feeder). Finally, a last linear mixed-effects model was used to evaluate the effect of the feeding strategy, parity, and the hour of the day (from 0 to 24) on the hourly feeding behaviors. For all these models, the double interactions between each fixed effect were considered unless for the reproductive parameters for which the model was simplified as the interactions were never significant. These models were applied on R (version 3. 4. 2) using the LME function from the NLME package  (Pinheiro et al. , 2018). All these models took into account the random effect of the sow. The sow was the experimental unit.
Sample sizes On farm 1, 40 cow-calf couples were involved in two trials of 20 couples each. Six calves died (all male, one BF calf and five MS calves). Data of dead calves was excluded when they lived less than 4 weeks (applicable to 4 calves: 1 BF, 3 MS), additionally one dwarf BF calf was excluded from the trial. This reduced observations to a maximum of 35 calves for data on behaviour, clinical scores and medical treatment (n: BF=18, MS=17), and due to one missing value to 34 calves (BF=18, MS=16) for data on weight gain. Milk and blood sampling started later in trial 1 and samples were available for 23 cows (milk, n: without calf contact= 12, with calf contact= 11) and 24 calves (blood, n: BF=13, MS=11), respectively. On farm 2, 23 calf-cow couples were involved in the study for all traits, of which 12 calves were BF and 11 were MS calves.
Observer training and inter-observer reliability assessment For assessment of inter-observer reliability regarding behaviour of calves a set of 26 videos (duration between 0:16 and 8:26 min:sec) with 4-7 calves per pen (in total 157 datasets on calf level) originating from a Farm 1: milk intake estimated for weeks 1 and 2; weekly weighing of animals started in week 3; for mother suckling calves weighing was realised before and after drinking, Farm 2: Milk intake for mother suckling calves estimated through weighing in weeks 1, 2, 3, 4, 8, 12, 16; milk intake for weeks in between were estimated by averaging the neighbouring weeks. five farms was used. A Pearson's correlation coefficient of r ≥ 0. 7 between each observer and the standard was defined as mandatory prior to admission as an observer in the trials.
Recording of medical treatments Health state and medical treatments were documented daily by the farmer. Documented treatment reasons were intestinal infections (diarrhoea), respiratory infections, umbilical hernia, locomotion problems, ear infections, reduced vitality and elevated body temperature. Medical treatment data was aggregated at animal level and counted as number of medical treatment cycles. Medical treatments of up to 7 consecutive days for the same reason were counted as one medical treatment cycle (i. e. 1-7 treatment days= 1 cycle, 8-14 treatment days= 2 cycles and so on). If the medical treatment was interrupted for at least seven days, a subsequent medical treatment was counted as a new medical treatment cycle. Administration of analgetics after routine zootechnical procedures (dehorning and / or castration, both only practiced on farm 2) was not counted.
0-6,  Fox and Weisberg, 2019). Feeding treatment was always kept as fixed effect and in case of repeated measurements at least animal was kept as random effect. For the following dependent variables the random effect calving year was removed from the final model obtained through model selection procedures: vitality, body condition score, cough, ocular discharge, total protein, immunoglobulin G content in milk, lactoferrin content. The differences between least square means of the fixed effects or in case of relevance of the interactions were verified by Tukey post hoc tests using the "emmeans" package (version 1. 4. 5,  Lenth, 2020)  for the respective final model. Apart from binary coded health scores for which we report odd ratios which are back-transformed from the logit scale in "emmeans", we present least square means and standard errors (LSM ± SE).
Acknowledgements The authors acknowledge the financial support for the  ProYoung-Stock  project provided by the  Federal Office for Agriculture FOAG  as the Swiss transnational funding body (contract number:  627001052 ) and  Institut National de Recherche pour l ′ Agriculture , l ′ Alimentation et l ′ Environnement as the French transnational funding body, being partners of the  H2020 ERA-net project ,  CORE Organic Cofund , and the cofund from the  European Commission. The authors acknowledge the financial support from the  French government IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ) and from  LiDL Switzerland. We thank the farmers of the two farms where we carried out the study for their commitment and interest in the questions of the study and their professional exchange, their careful data collection and their hospitality during our data collection.
Animals and data The data used for this analysis cover the first 10 generations of an experiment of divergent selection for RFI in growing pigs. The detailed selection process is in Gilbert et al. [16]. Information from 1100 sows (527 and 573 from the high and low RFI lines, respectively) was recorded on two farms from 2000 to 2015. From generations 1 to 4, only third-parity batches were inseminated with Piétrain boars, and starting from generation 5 only first-parity batches were inseminated with Piétrain boars. All other batches were inseminated with RFI boars from the own line. Details on how the lactation traits were recorded are in Gilbert et al. [4]. Sow weight was recorded at entrance to maternity in the farrowing house (SW E ) and at exit from maternity on weaning day (SW W ), with an average duration of farrowing to weaning of 28.
Sow metabolic body weight ( SMBW ) was computed as: The total number of piglets born, dead or alive and the number of cross-fostered piglets (i. e. , number of piglets transferred to another sow or adopted) were also recorded. Only records from litters in which crossfostering was carried out within 2 days after farrowing were included in the analysis. All piglets were weighed at farrowing and at weaning. This information was used to compute litter weight at farrowing, following crossfostering ( LW F ), and litter weight at weaning ( LW W ). Growth rate of the piglets during lactation was calculated to detect outliers. We considered a data point as an outlier when it was located outside the whiskers of the boxplot for the trait (i. e. outside 1. 75 times the interquartile range above the upper quartile or below the lower quartile ( Q1 -1.
e. from birth to day 21 of life or to death, if death occurred before day 21). Only information from lactations longer than 26 days and shorter than 38 days was included in the analysis. The total feed intake of the sow (FI) was calculated as the sum of daily feed intake records available from farrowing until weaning. Feed intake data were missing for some days and only 27% of the lactations had complete records. The average number of days with missing intake records during lactation was 0. 65 ± 3. 03. Most missing days were 1 or 2 days after farrowing. Daily lactation feed intake (dLFI) was computed by dividing FI by the number of daily records available. The traits dLFI, dSWB, dLWG, dBFTB, and SMBW were considered as the main components of LFE. After removing records with missing values for three or more of these traits, the dataset used for the analyses contained information on 1342 farrowings from 576 sows.
Therefore, the covariance matrices G * 0 = [I 5 -] -1 G 0,S [I 5 -] ′ -1 , P * 0 = [I 5 -] -1 P 0,S [I5 -] ′ -1 , B * 0 = [I 5 -] -1 B 0,S [I5 -] ′ -1 , and R * 0 = [I 5 -] -1 R 0,S [I 5 -] ′ -1 are those of the total additive genetic effects ( G * 0 ), total permanent environmental effects ( P * 0 ), total contemporary group effects ( B * 0 ), and total residuals ( R * 0 ). Prior distributions of SEM were the same as those for the MTAM but p R 0,S ∼ 5 j=1 P(σ 2 j ) and p( ) ∼ k(constant). All analyses were performed with the Gibbs sampling algorithm using the Gibbsf90 software  [21]. Single sampling processes of 2,000,000 iterations were run for all models, discarding the first 300,000 iterations based on visual inspection of trace plots of each chain and saving 1 of every 100 samples. Checking for convergence of the sampling process after 300,000 iterations according to Geweke's criterion  [22]  was performed using the"boa" R package  [23].
73 ± 0. 06 to 0. 90 ± 0. 08). Estimates of genetic correlations of R2 and R3 with backfat depth and raw FCR (not standardized between starting and finishing weights) were moderate and unfavorable (0. 20 ± 0. 13 to 0. 46 ± 0. 20). A part of these genetic correlations, that are of low precision due to the number of data available, have to be confirmed on larger datasets. The results showed the interest of using routine phenotypes collected on farm to build simple robustness indicators that can be applied in breeding.
Materials and Methods Specific Experimental Animal Care and Use Committee approval was not needed because all the data used in this study were obtained from preexisting databases provided by AXIOM. The data used were from animals raised under commercial conditions that were cared for according to EU-Council directive 2008/120/EC of 18 December 2008 laying down minimum standards for the protection of pigs (http://data. europa. eu/eli/dir/2008/120/oj).
Robustness traits Three synthetic phenotypes to characterize the robustness of the candidates were defined from the measurements performed during the individual test, and from the medical treatments recorded during the testing period (Table  1 ). The objectives of these synthetic traits were to describe the ability of the animal to be measured at the end of the individual testing present, that is, to be alive and weighing at least 70 kg, and to be in good health without observable defects. The trait R1 corresponded to the distinction used at present in the AXIOM testing protocol to differentiate candidates that can be tested (Note = 1) from those that are dead or weighing less than 70 kg on the day of the individual test (Note = 0). Individual mortality was not available in the database. Consequently, it was not possible to analyze directly this trait.
We did not include symptoms in the trait definition due to the subjectivity of the observations. In addition, the area between curves (ABC) index estimated during fattening period, developed by  Revilla et al. (2022)  which the authors called a resilience phenotype, was also calculated using weight measured by AFS for each animal alive at the end of the fattening period. The datasets analyzed by  Revilla et al. (2022)  were collected on the same farm from September 2015 to July 2019. The trait ABC was the accumulated difference of area between the unperturbed growth curve and the perturbed curve. The ABC index had no unit. The unperturbed growth curve of each animal was modeled using the Gompertz equation. The perturbed curve was constructed using linear interpolation of body weight measurements recorded by AFS.
In comparison to the method proposed by Revilla et al. (  2022 ), some modifications have been made to the data pretreatment when analyzing all data from each AFS within fattening group. This was done to detect inconsistencies linked to the AFS machine. A linear regression of weight on the number of days of test was applied. The standard deviation of the residual value was calculated for each day for each AFS within fattening group. If more than 20% of the weights measured on AFS in a fattening group were greater or less than 4 standard deviations, then records of the AFS within the fattening group were removed from the data set. Animals with less than 20 d of measurements in total were removed from the analysis. The ratio of the number of animals after cleaning procedure and the raw dataset was 0.
Genetic parameters estimation Each trait was analyzed with ASREML 3. 0 software  (Gilmour et al. , 2009) , using the restricted maximum likelihood (REML) method. Each line was analyzed separately. Firstly, to select fixed and random effects, all traits were analyzed using a single trait model. The global linear mixed model was defined as: y = µ + Xβ + Vb + Wl + Zu + e where y was the vector of phenotypes for the considered trait (R1, R2, and R3 considered as continuous phenotypes, IBW, ADG, LD100, BF100, FCR, DFI, and ABC); µ is the overall mean, β was the vector of fixed effects; b was the vector of random fattening group effect, with ∼ N(0, I b σ 2 b ), where Iwas the identity matrix of appropriate size; l was a vector of the common litter random effects with ∼ N(0, I l σ 2 l ); u was the vector of additive genetic random effects with ∼ N(0, Aσ 2 u ), where A was the pedigree-based relationship matrix; X, V, W, and Z were the known incidence matrices for fixed, fattening group effects, litter effect, and animal genetic, respectively; and e is the vector of residual random effects with ∼ N(0, I e σ 2 e ).
For all estimated traits, the fixed effects tested at an α-risk of 5% using the Wald F statistic of ASReml  (Gilmour et al. , 2009)  were the birth farm for Pie and Pie NN and halothane-sensitivity gene status for Pie line. The significance of common litter random effect was tested by using likelihood ratio test with R statistical software (R Core Team, 2018) from log-likelihood values estimated on ASReml software  (Gilmour et al. , 2009). In the two lines, the fattening group effect and common litter effect were significant (P < 0. 05) for all tested traits expected for traits ABC, FCR, and RFI with the common litter effect in Pie NN line. In the second step of the analysis, to follow the assumption of the BLUP method, which should be applied to a nonselected base population, and to estimate the covariance between traits, a series of multi-traits models including the four traits under selection (ADG, BF100, LD100, and FCR) and the nonselected traits to be estimated (R1, R2, R3, IBW, DFI, RFI, and ABC) were applied to the data.
Variance-Covariance components The heritability estimates for robustness traits R1, R2, and R3 were low and in the same range for the two lines, ranging from 0. 03 ± 0. 01 to 0. 09 ± 0. 02 (Table  3 ). Heritability estimates for R2 and R3 tended to be slightly higher than for R1 in each line. Heritability estimates for the ABC index were low for both Pie (0. 09 ± 0. 03) and Pie NN (0. 06 ± 0. 03). Heritability estimates were low to moderate in the Pie and Pie NN lines for the traits under selection (ADG, FCR, BF100, and LD100), and also for IBW, DFI, and RFI, ranging from 0. 13 ± 0. 03 to 0. 34 ± 0. 05. The higher standard errors in Pie NN were due to the smaller dataset for this line. The fattening group effect ranged from 0. 02 ± 0. 01 to 0. 38 ± 0. 07 for the studied traits, with the highest estimates being for LD100 in both lines.
Among the robustness traits, R2 was the trait with the highest heritability estimate in the two lines. It was highly genetically correlated (≥0. 92 ± 0. 06) with R3 but required less information in order to be calculated. Thus, R2 meets the objective of finding an operational trait to select in order to have live and healthy animals at the end of the period. A limit of this robustness trait is the difficulty of estimating the impact of the genetic evolution of the synthetic trait on each of its underlying traits. As such further investigation on the impact of the improvement of this robustness trait on mortality or on disease occurrence could be useful. The use of these types of additional information, not currently available in the databases, would require improved data management systems.