dataesr/scientific-paragraphs-categorization huggingface dataset:
01 were displayed and the top 5 biological processes for each comparison were plotted. Analysis of canonical pathways and regulator effects were performed using Ingenuity pathway analysis (IPA) software (Ingenuity Systems, Redwood City, CA, USA) for DEG in each comparison. Faecal egg counts (FEC) were measured twice a week after infection from 21 to 36 dpi. The FEC variance was normalized using log transformation. PROC MIXED procedure (v. 9. 4, SAS Inst. Inc. , Cary, NC, USA, 2012) was used to test statistical differences. The differences were considered significant when p < 0. 05. The results are presented after back transformed.
Quantitative real-time PCR (qRT-PCR) validation To validate the results of the RNAseq analysis, the gene expression for a total of 9 genes (n = 6 for each comparison: resistant vs susceptible at 0, 15 and 35 dpi, and resistant and susceptible for 0 vs 8 dpi, 0 vs 15 dpi and 0 vs 35 dpi) was determined by qRT-PCR. The endogenous control for all reactions was goat ACTB (actin beta) gene whose expression remained stable among the samples. The cDNA was synthetize with a total of 2 µg of high quality total RNA (RIN > 7. 5) by using M-MLV Reverse Transcriptase (Promega, Charbonières, France) according to the manufacturer's instructions. All qRT-PCR reactions were carried out in 48-well plates in a Prime Pro 48 Real-Time PCR System and analyzed with the ProStudy Software v5. 2. 10 (Techne, Staffordshire, UK).
Phosphorus (P) is an essential nutrient in livestock feed but can pollute waterways. In order for pig production to become less of a threat to the environment, excreta must contain as little P as possible or be efficiently used by plants. This must be achieved without decreasing the livestock performance. Phosphorus and calcium (Ca) deposition in the bones of growing pigs must be optimised without affecting the muscle gain. This requires precision feeding based on cutting-edge techniques of diet formulation throughout the animal growth phase. Modelling and data mining have become important tools in this quest. In this study, a mechanistic model taking into account the distribution of P between bone and soft tissues was compared to the established factorial models (INRA (Jondreville and Dourmad, 2005)  and NRC (National Research Council, 2012)) that predict P (apparent total tract digestible, ATTD-P; or standardised total tract digestible, STTD-P) and Ca (total and STTD) requirements as a function of BW and protein deposition.
It should be noted that other models to predict soft tissue growth (e. g. NRC, 2012)  or user data can be easily incorporated. As recommended by  Van Milgen et al. , (2008) , model parameters related to feed intake and growth potential are adjusted by the model user and feed intake, and protein and lipid deposition are thereafter calculated daily. The daily feed intake is controlled by the net energy of the diet, and the net energy intake desired was calibrated with previous studies  (Pomar et al. , 2006; Langlois et al. , 2016a and 2016b; Gonzalo et al. , 2018). Potential protein deposition is described by a Gompertz function that can be changed by user, and actual deposition is determined as the lesser among the potential and the allowable deposition based on energy and amino acid supplies.
00, 2. 08 and 2. 16, with 2. 16 being the default value  (Nielsen, 1973; Crenshaw, 2001). For bone Ca potential deposition, variation was based on the difference between the default equation (equation (  1 )) and the individual animal with the highest bone mineralisation in the Lautrou, Pomar, Dourmad, Narcy, Schmidely and Létourneau-Montminy database  (Pomar et al. , 2006; Langlois et al. , 2016a and 2016b; Gonzalo et al. , 2018). All parameters were tested under three scenarios, namely, at BW of 30, 60 and 120 kg. Sobol indices  (Sobol, 1993)  were calculated to quantify the influence of each tested parameter on the outputs of the model. An objective of 100% bone mineralisation was used for the simulations. This method measures the contribution of each parameter variance to the variance of the outputs.
Sample size determination and Sampling The sample size was calculated in Open Epi, using the formula for the estimation of a proportion in a descriptive study. The sample size was determined using single population proportion formula considering expected proportion of skin NTDs of 50% as no previous similar studies were found. Besides, precision was taken at 4% and 95% confidence level. The resulting sample size was 601. The final sample size was found to be 661 considering 10% contingency to account for missing data or patient records. A total of 23,000 patient records were found and simple random sampling technique was employed. We made a list with all visits using Microsoft Office Excel software, assign a random number and total of 661 patients records were selected using simple random sampling.
Data collection and analysis The health management information system registry was used to list the chart numbers. The randomly selected chart numbers were retrieved. Data were collected by the first author using a structured data extraction tool, containing questions on the type of skin disease like cutaneous leishmaniasis, leprosy, scabies, dermatitis, fungal, bacterial, viral, pigmentary, papulosquamous and pilosebaceous skin conditions. Besides, socio-demographic data such as age and sex of patient were included. The diagnosis was done by trained dermatologists using the national guidelines clinically complemented with laboratory tests for some diseases like leprosy and CL. Epi Data software version 4. 4. 2 (EpiData Association, Odense, Denmark) was used for data storage and analysis. Descriptive analysis 18S entailed the calculation of absolute and relative frequencies.
Demographic characteristics and skin disease type A total of 661 patient records were reviewed and 656 complete records were included in the analysis. Five patient records were excluded for missing key data on the dependent variable. More than half 347 (52. 9%) were female; 171 (26. 1%) were aged less than 15 years (Table  1 ). One hundred thirteen (17. 2%) of patients presented with skin NTDs, 559 (83. 4%) patients were diagnosed with non-NTD skin diseases (Table  2 ). Sixteen patients had both NTDs and non-NTD skin diseases.
Acknowledgements This research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership coordinated by  TDR , The Special Program for Research and Training in Tropical Diseases hosted at the  World Health Organization. The training model is based on a course developed jointly by the  International Union against Tuberculosis  and  Lung Disease  and  Médecins Sans Frontières-Luxembourg (LuxOR). The specific SORT IT program that led to these publications included a joint implementing partnership between TDR the  Institute of Tropical Medicine Antwerp, Belgium , the  University of Gondar, Ethiopia , The WHO country office in Ethiopia,  Médecins Sans Frontières, Luxembourg (LuxOR)  and The  International Union Against Tuberculosis and Lung Disease ,  Paris France.
Data collection Since 2010 till 2017, we used camera-trapping methods to conduct bear activity monitoring during three seasons: spring (March 21st-Jun 20th), summer (Jun 21st-Sep 20th), and autumn (Sep 21st-Dec 20th). We defined two disturbance types according to the season: (1) intraspecific disturbance in spring, mainly through competition between dominant and vulnerable individuals during the mating season; and (2) human disturbance in summer and autumn, from touristic activities, extensive farming, and hunting within the bear distribution range. The monitoring was not continuous. We deployed an average of 48 camera traps (SE = 15) per year throughout the brown bear distribution area. Most of the cameras were active for 275 days/ year; however, due to technical issues, the number of camera traps and the trapping days varied over the sampling period, ranging between 34 and 275 trapping days per year.
Data analysis We analyzed bear activity pattern to determine the relationship with the following variables: daylight time interval; season (autumn, spring, and summer); sex class (female with offspring, solitary female, males), including adult and subadult individuals; and age class (adult: ≥ 4, subadult: < 4). We based the daylight time interval on solar time with the monthly daylight length differences and defined during equinox periods as night (1800-0559 h) and day (0600-1759 h). We estimated activity preferences at daily and seasonal scales. We conducted genetic analyses to identify the sex class and parentage assessment of the individuals, using microsatellite and SNPs markers from hair samples collected in the hairtraps. Further, we used the population genealogy to determine the age class of the detected individuals.
Consequently, the presence of new cubs was noted. In some cases, cubs were encountered in the second year after birth. We estimated their age based on the body size of the cub when first seen on the pictures. This annual monitoring has allowed constructing the genealogy of the population, including the determination of age and sex classes. We excluded individuals with undetermined both age and sex classes. Further, to control for the differences in camera detection (see below), we used data pooling. All the analyses were restricted to data sets with > 10 activity samples. We pooled the data when activity distributions were not different across the season, daily period, sex class, and age class. To control for the experiment-wise error rate, we used an alpha level of 0. 05 to determine statistical significance and applied a Bonferroni correction  (Zar 1999).
We assessed daily activity patterns of bears and compared its temporal overlapping among three class groups: sex group (female with offspring vs. male, female with offspring vs. solitary female), age group (adult vs. subadult), and sex-age group (male and female with/without offspring adult vs. male and female subadult). However, due to the lower number of occurrences of juveniles, we were not able to estimate the avoidance level among seasons for the sex-age group. To evaluate the daily activity pattern, we applied the Overlap package  (Meredith and Ridout 2018)  for each independent detection, calculating the probability density function using the kernel density, followed by pairwise comparisons by estimating the coefficient of overlap. Overlapping coefficient (Δ) is an estimator that determines the area under the curve formed by taking the minimum of the two density functions at each time point and ranges from 0 (no overlap) to 1 (complete overlap)  (Ridout and Linkie 2009; Linkie and Ridout 2011).
Acknowledgments We acknowledge the technical assistance of employees, students, and volunteers who helped with data collection, and  Brown bear Foundation (Fundación Oso Pardo, FOP) ,  Universitat de Lleida (UdL), and Forestal Catalana (FORCAT)  for their support. We thank the  Carpathian Brown Bear Project team from the Institute of Nature Conservation of the Polish Academy of Sciences in Poland  for the contributions to this manuscript, the advice in the statistical analysis and the helpful revisions offered.
Funding information This research was financially supported by the  Regional Government of Catalonia, Spain ;  French Agency of Biodiversity  ( Office Français de la Biodiversité ), France;  General Council of Aran, Spain; and Government of Aragon, Spain. The material and work was supported by the  Piros Life Project  from the  LIFE program  ( LIFE 13 NAT/ES/001394 ) during 2014-2017. AP was partly supported by the  BearConnect  project funded by the  National Science Centre, Poland , under the  BiodivERsA call  ( 2016/22/Z/NZ8/00121 ).
4 mmol/L. After incubation of the pleural effusion sample at a temperature of 37°C, numerous pale yellow, dry and rough colonies grew, wrinkled and encrusted in the agar. No growth was noted after incubation at 45°C. Gram staining of these colonies showed crooked, branching Gram-positive filaments (Fig. 1(B) ). The modified acid-fast stain was positive, suggesting nocardial infection. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (Microflex; Bruker Daltonics, Bremen, Germany) was carried out; results identified N. farcinica. PCR amplification of the 16S ribosomal RNA gene was then performed using primers A2 (5 0 -AGAGTTTGATCATGGCTCAG-3 0 ) and S15 (5 0 -GGGCGCTGTGTACAAGGCC-3 0 ). The sequences were compared with sequences published in the GenBank database using the BLAST algorithm; the closest sequence based on patristic distance was N.
Each growing-finishing pen contained a single place electronic feeder equipped with a weighing scale (Genstar, Skiold Acemo) to record individual feed intake and body weight of the animal at each visit to the feeder. At 115 kg body weight, pigs were fasted for 24 hr and then transported to the slaughterhouse. Animals were slaughtered in 89 slaughter batches of around 19 pigs. All pigs were issued from 171 sires representative of those used in the French LW collective breeding scheme, and each couple of full-sibs came from a different dam. Pigs that experienced health problems or injury during the test period, in equal proportion in the two diets, were discarded from the analysis. At this step, 1,663 pigs were kept in the data set with 880 pigs fed a CO dietary sequence and 783 pigs fed a HF dietary sequence.
| NIRS predictions of digestibility coefficients Digestibility coefficients of energy, organic matter and nitrogen were predicted based on the prediction protocol described by  Labussière et al. (2019). The prediction equations of the three DC were previously obtained using a calibration data set of 412 samples for DC of organic matter, 424 samples for DC of nitrogen and 423 samples for DC of energy, with R 2 higher than. 89, very low biases (-0. 01 to 0. 00) and regression coefficients between predicted and true values close to one. The prediction equations were assessed by comparison with values measured using the total collection technique in individual digestibility cage on 10 pigs that were fed the two diets. Then, three successive spectra were acquired for each sample, and the average spectrum was used for prediction of DC.
Usual criteria were used to individually assess the quality of NIRS predictions and detect outliers. When prediction values were lower or higher than the bounds of the model, samples were eliminated (131 samples). During the partial least squares regression, a Mahalanobis distance was calculated for each prediction with the OPUS/Quant2 software (Bruker) previously used to determine DC. The 99. 999% distribution bound of the calibration data set was used as a reference and multiplied by three to obtain a realistic prediction interval for the variability of an independent data set. Thus, if the Mahalanobis distance of a prediction was greater than the realistic prediction interval, samples were removed from the data set (39 samples). It represented a bound of 0. 36 for DC of organic matter, 0.
| Phenotypic comparison of digestibility coefficients across diets First, a statistical analysis was carried out to assess the differences of DC between diets. The model contained the fixed effects of the batch and diet, as well as the random effect of the pen nested within the diet and batch and the random effect of the sire. Because feed intake influences the DC of dry matter and crude protein  (Verschuren, et al. , 2019) , the individual DFI was also included in the models as a covariate nested within diet. A Levene test was used to evaluate the heterogeneity of residual variances among diets for each DC using the ANOVA procedure ((SAS, 2013) version 9. 4; SAS Institute Inc. ). In case of variance heteroscedasticity among diets, residual variances were estimated within each diet. Least squares means (LSMeans) of DC were determined for each diet using the MIXED procedure ((SAS, 2013) version 9.
n ~ N(0, I 2 n ) is the random effect of the pen effect nested within diet and batch, applied to all traits except primal cut ratios. W is an incidence matrix relating performances to the random effect n. q ~ N(0, I 2 q ) is the random effect of the slaughter dates applied only for upH. S is an incidence matrix relating performances to slaughter date effect. Finally, e ~ N(0, I 2 e ) is the residual random effect. Variance components were estimated by average information restricted maximum likelihood (AIREML) using the ASREML 3. 0 software  (Gilmour et al. , 2009). Then, bivariate analyses were carried out to estimate covariances between DC and other production traits within diets, and between diets for DC. In this study, heritability estimates were qualified as low below 0. 20, moderate from 0.
They also showed that, without indigestible markers, the accuracy of the DC predictions was higher when pigs were fed a diet with a high-fibre content, that is a diet that induced variability in digestive utilization. These authors also insisted on the importance of having connectedness between the calibration and predicted data set to ensure predictions of sufficient accuracy. For our study, the equations used to predict the DC of organic matter, energy and nitrogen had high R 2 , >. 89. In our study, different measures were taken to isolate the "animal factor of variation of digestibility," as termed by  Bastianelli et al. (2015) , and to limit the impact of other factors of variation on estimations of genetic parameters. All samples were collected at fixed time in the morning to get a large quantity of faeces (>50 g).
| Genetic and phenotypic variability of nitrogen, energy and organic matter digestibility Based on a large data set representative of a commercial pig population, heritabilities estimated in both diets confirm that digestive efficiency is a heritable trait, as suggested by  Noblet et al. (2013). Estimation of genetic variability suggests that the NIRS predictions make it possible to capture relevant genetic information from faecal samples. Moderate heritabilities were estimated for DC in the CO diet, and larger estimates were obtained in the HF diet. The range of values obtained for the three DC is of similar magnitude as estimations for other production traits. Heritabilities estimated for the DC of dry matter and energy were slightly larger than those estimated by  Hardie et al. (2014)  which is, to our knowledge, the only study reporting heritability for DC in pigs.
Statistical analyses The percentage of scans recorded for each behavioural indicator per period was calculated from the total number of observations per horse. The percentage of scans of aggressive behaviours towards humans when horses were in individual boxes, and that of the "withdrawn" and "alert" postures were analysed as continuous variables. However, stereotypies were processed as the proportion of horses expressing these indicators because of the high number of null values among the sample, as well as aggressive behaviours towards humans on pasture. Nonparametric tests were used for the four behavioural indicators because the data were not normally distributed. Intra-group comparisons (i. e. , differences in the expression of the four behavioural indicators over the five periods for the "Pasture" horses) were investigated first.
Acknowledgments This project was funded by the  IFCE and the "Fonds Eperon". This funding source had no role in the study design, data collection and analysis or in the preparation and submission of the manuscript. The authors are very grateful to the staff of the  French Horse and Riding Institute (IFCE)  and  INRAe (Nouzilly, France)  and in particular to  Patrick Galloux ,  Xavier Goupil ,  Isabelle Burgaud ,  Sophie Biau ,  Colonel Patrick Teisserenc  and all the animal keepers for their collaboration during the experiment. We would like to thank the  Springer Nature Author Services team  and  Sue Edrich  from  Interconnect  for correcting the English manuscript.
Funding: This study was supported by the  Health Service of the French Army  and the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection , the  National Research Agency  under the program " Investissements d'avenir ", reference  ANR-10-IAHU-03 , the  Région Provence-Alpes-Côte d'Azur and European funding FEDER PRIMI.
The area under the curve (AUC) was determined using the linear trapezoidal rule for increasing and the logarithmic trapezoidal model for decreasing plasma concentrations from Day 0 to the last time plasma drug concentrations were above the lower limit of quantitation (AUC last ). AUCs were extrapolated to infinity using the formula AUC inf = AUC last + C last /λ z. Dose proportionality of GAM following IM administration over the range 3-12 mg/kg BWT was assessed by analysing the dose-normalized average AUC inf at each dose level from Groups 3, 4 and 5 and also using a power model: Linear regression analysis was performed using Proc Reg in SAS 9. 0 (SAS Corporation, Cary, NC, USA) with weighting of (Dose)-1. The residuals were normally distributed, independent and randomly distributed around zero.
| Clinical evaluation and data analysis All animals removed from the study according to swine respiratory disease removal criteria and confirmed swine respiratory disease positive by necropsy were included in the efficacy analysis and considered as treatment failures. Animals removed for non-swine respiratory disease pathological conditions were not included in the data analysis. On Day 10, each remaining animal was assessed and as overall clinical evaluation considered as "Treatment Success" (defined as clinical cure) or "Treatment Failure" (= Depression Score ≥1 and Rectal Temperature >40. 0°C or Respiratory Score ≥1 and Rectal Temperature >40. 0°C). For analysis of individual clinical signs of swine respiratory disease, Depression Score and Respiratory Score were dichotomized as "Acceptable" (0 or 1) and "Not acceptable" (>1).
These reactions resolved rapidly within 1 day and no other injection site reactions were recorded in any of the study animals. Due to clinical manifestations of swine respiratory disease, three GAM-treated animals and five TIL-treated animals were removed from the study. For non-swine respiratory disease reasons, four animals were removed from the study: three GAM-treated animals on animal welfare grounds; one TIL-treated animal because of unintentional underdosing. Non-swine respiratory disease-related removals were excluded from the data analysis, whereas swine respiratory disease-related removals remained eligible for analysis. Consequently, a total of 301 pigs were included in the analysis, 150 GAM-treated animals and 151 TIL-treated animals. Results of the data analysis are summarized in Table  5.
-Control condition In the control condition, the videos used during the test were edited so that the experimenters and the horse were replaced by two ovals with the same surface area and colored with the main color of the experimenter and the actor horse as appropriate. The videos were edited semiautomatically using the image editing and analysis software ImageJ and Fiji  (Schindelin et al. 2012; Rueden et al. 2017 ). These ovals followed the movement pattern of the horse and the experimenter and copied the variations in their surface area (which was recalculated every 25 ms).