dataesr/scientific-paragraphs-categorization huggingface dataset:
A principal component analysis (PCA) including inflammatory indicators (numbers of granulocytes and lymphocytes, rectal temperature, CRP and haptoglobin in plasma, and salivary cortisol measured on Day 0 and Day 27, all previously transformed for normality) followed by a clustering procedure was performed to identify groups of pigs with different inflammatory status. First, three missing values in health parameters (two for CRP Day 27 and one for haptoglobin Day 27) were imputed with a PCA (10 first dimensions kept for the imputation) with the function imputePCA of the R package missMDA  (Josse and Husson, 2016). Thereafter, the functions PCA and hierarchical clustering on principal components (HCPC) of the R package FactoMineR  (Le et al. , 2008)  were successively used. For HCPC, five dimensions were kept (eigenvalue >1) and two clusters were selected after considering the overall shape of the tree and the bar plot of the gain in inertia  (Le et al.
Comparison between clusters, for normal/ normalized variables with repeated measures, was performed with the function lmer from the R package lme4 (Bates et al. , 2015), with the animal being included as a random effect. The time (Day 0, Day 27, Day at slaughter) and the interaction between cluster and time were tested and removed when P > 0. 05. The lsmeans function from the R package lsmeans  (Lenth, 2016)  was used to perform pairwise comparisons with the Scheffe's test when the interaction was significant. For variables that could not be normalized (androstenone and organ lesion scores), the nonparametric Kruskal-Wallis test was performed.
Acknowledgements The authors are extremely grateful to the personnel of the  Experimental Pig Unit of PEGASE , especially  J. Delamarre ,  P. Knapen  and  P. Roger  for taking care, performing measures and sampling on live pigs,  M. Alix ,  J. Liger  and  J. -F. Rouaud  of the slaughterhouse, as well as to  R. Comte ,  S. Jaguelin ,  A. Lecorgne  and  F. Thomas  of the  PEGASE Research Unit  for laboratory analyses.
Statistical analyses The only variable recorded during the study was the percentage of inhibition (PI) of optical density assessed by competitive ELISA. The non-inferiority of BS vs. RC for the PI values was assessed using 95% confidence intervals (CI) of the difference between the former vaccine and the latter. As the assumption of normality does not hold for inhibition, the 95% CI of the difference between treatment groups was estimated with the bootstrap method. The bootstrap was performed with repeated samples with the same size as the original one and was done with replacement, using 5000 bootstrap replications (SAS 9. 3 software). To calculate the 95% CI with the bootstrapped data, the bootstrap estimates corresponding to the 2. 5th percentile and 97. 5th percentile were selected. The non-inferiority of BS compared with RC was inferred if the lower bound of the 95% CI of the difference between groups at the end of the study was greater than the non-inferiority margin Δ = -10%.
Cross sectional area (CSA) of the fillet and histological observations were performed on the left half-breasts. CSA data were obtained cutting transversely the cranial portions and photographing the views with Canon EOS 500D camera with 18-200 mm/3. 5-5. 6 lens; images were subsequently processed with digital image software (Carl Zeiss, Model Axiovision 4. 6. 3. 0). Breast samples (15 N and 15 WB) were taken from the central-external part sampling both the surface and the deep layers of Pectoralis major, fixed in 10% formalin, processed by conventional methods and embedded in paraffin wax. Then, samples were cut at a 4-µm thickness for hematoxylin-eosin (HE) staining (to estimate average normal fibres and giant fibres number and size) and for Masson's trichrome staining (to measure collagen and empty spaces percentages).
Computerized image analysis  (Buche 1990 ) was used to determine the fibre mean CSA (of both normal and giant fibres), the collagen and empty space area, and to count the average frequency of giant fibres per unit size of muscle section. Breast quality traits, CSA, weight and histological data were analyzed through one-way ANOVA of the SAS software (Statistical Analysis System, Version 9. 3, 2004), considering treatment (N vs WB) as independent variable. Descriptors were examined by χ 2 and z tests.
40 • horizontally and 23. 99  vertically. In order to minimize peripheral visual information, opaque black curtains were placed parallel to the rider's line of vision on either side of the experimental set-up from the edges of the projection screen to the back of the simulator. SimPiste software (Persival Industrie, Saumur, France) was used to create a computer-generated movie projected on the screen. The resulting 3D animated scene represented a classic situation of a ride in a show jumping arena from the rider's viewpoint and was synchronized with the simulator's movements. The visual environment included several fences around which the horserider pair moved at a gallop. Thus, the visual scene simulated displacement around the fences and the mechanical movements of the simulator maintained a gallop pace with no jumps.
Data Analysis The raw position data collected by the magnetic tracking system were processed and analyzed using Matlab software (R2009b, The MathWorks Inc. , Natick, MA, USA). In order to eliminate the initiation of the simulator's motion and participants' adaptation phase to it, we considered position data from the fifth second to the end of each trial. Two types of dependent variables were computed from the position data. First, the variability of the displacement of each segment (head, C7, L3) was quantified by computing the standard deviation of the position along the anteroposterior (SD AP ), mediolateral (SD ML ), and vertical (SD V ) axes. These measurements were used to quantify the degree of stability of the head, the cervical segment (upper trunk) and the lumbar segment (lower trunk) in space (the lower the standard deviation of position, the more stable the segment is in space).
Second, we evaluated the coordination modes the riders used to stabilize their posture. Because the control of the hips, trunk and neck impacts mainly the displacements of the upper body in the AP axis, we analyzed postural coordination along this axis. Coordination modes were computed from position data in the AP axis using the mean relative phases φ L-C , φ L-H , and φ C-H between the lumbar and the cervical segments, the lumbar segment and the head, and the cervical segment and the head, respectively (Figure  2A ). The standard deviations of the mean relative phases (SDφ L-C , SDφ L-H , SDφ C-H ) were computed as measurements of the within-participants dispersion around the mean relative phase. Prior to relative phase computation, position data were filtered with a recursive second-order Butterworth filter with a cutoff frequency of 2 Hz.
A given effect was considered for inclusion in the model if its significance was demonstrated by at least half of the selected studies conducted on it. Next, quantitative estimates of the selected effects were chosen for use in the model. These estimates were preferentially taken from studies conducted in France. When no studies done in France was identified, quantified estimates obtained in other countries where used. Similarly, estimates of BRD incidence were taken from surveys conducted in France. Current production parameters of the French cattle system and veterinary costs linked to BRD were derived from results of national census or national cattle movements and slaughter databases. Product market prices and variable farming costs were found in online national market records. Sources of the data are detailed in S1-S5 Tables.
The sampling followed a random Latin Hypercube Sampling approach, using the R package "lhs"  [72]. For each iteration, corresponding changes in productivity in response to given changes in BRD incidence rates were estimated. The variability of market values (livestock products prices and additional farming costs) was also accounted for using the same method. As the true probability distributions of market values are unknown, uniform probability distributions bounded by minimum and maximum values, corresponding to a decrease or increase of 5% of these market values respectively, were simulated. Current production parameters of the French cattle system were obtained from results of national census or estimates made on large cattle populations. Therefore, it was considered that the uncertainty on these parameters is weak and their probability distribution was not modelled.
Statistical analysis Statistical analyses were performed with the software R  [34]. The two variables of the tonic immobility test (number of inductions and total duration) were normalized by logarithmic transformation. Other variables were normal without transformation. In all the statistical analysis, p < 0. 05 was considered statistically significant, and 0. 05 ≤ p < 0. 1 as a trend. Mixed effects model for repeated measures with the treatment, the genetic line, the day, all the double interactions as fixed effects and the animal being included as a random effect were used to test the effect of treatment on memory traits (total duration, number of visits, global rate of error). These analyses were done with the function lme from the R package nlme  [35]. The effects of the line and the day (from 1 to 3), as well as the interactions were removed from the model when p > 0.
The aim of this article is to describe this novel system and display the EEG recordings obtained. We also describe here an original approach for analyzing the EEG data through an evaluation of the relative proportion of the different types of waves. This approach allows to examine intra-and interindividual variations in the "EEG profile" within a given situation and intraindividual variations between situations. Here, we show EEG recordings obtained from five relaxed standing adult sport horses that were freely moving in their home stall over two sessions performed on two successive days.
To obtain the EEG signals, we used a classical FM receiver associated with an envelope demodulator. The demodulation of the signal received gives an audio signal chopped at 1 kHz, and to obtain the EEG signals, we made an envelope demodulation of this audio signal. The EEG signals were then recorded in a computer using a digital oscilloscope "Picoscope 2200 Series" (pico R Technology) and the Picoscope Oscilloscope Software. The sampling rate was 1 kHz.
Data Analysis We processed all data using FieldTrip (  17 ), a free toolbox of Matlab 7. 0 (MathWorks, Natick, MA). For each horse, the videos were screened, and the periods when the animal was standing relaxed were identified. EEG signals during these time periods were visually inspected, and large artifacts, due to movements (body, head, ears), were removed. The data were then segmented into 500 ms epochs, and a second visual artifact rejection procedure was performed to exclude potential remaining artifacts. There were an average of 86. 45 ± 9. 05 epochs/horse/session. Then, a complex fast-Fourier transform was applied on these 500 ms epochs in which, for each frequency, outlying values ±3SD were identified, and the corresponding epoch was removed from the analysis. The FFT values for each horse were averaged separately for each session.
AcKnoWleDgMenTS The study was funded by the IFCE (Institut français du cheval et de l' équitation), the University of Rennes 1, the CNRS (Centre National de la Recherche Scientifique), and the French Ministry of Research. The authors are grateful to the director of the National School of Equitation and especially Mrs. S. Biau, as well as the school staff who made this study possible. The authors also thank Melissa Sebilleau for her technical help.
Data collection and statistical analysis For both training phases (colored cards and images of faces), learning speed, i. e. , the number of sessions needed to reach the learning criterion, was recorded for each sheep. For every run of the training and test phases, the outcome (success or error), the time from the sheep's entry into the maze (two front feet inside the maze) to its choice (gate arm closed behind the sheep) were recorded (LatChoice, seconds) from video files using The Observer 5. 0 (Noldus Information Technology, Netherlands). All analyses with non-parametric tests were conducted in Minitab 17 (Minitab Inc. , PA, USA). Mixed models were run in GenStat 16th edition (VSN International Ltd. , UK). Significance level was set at P = 0. 05. Due to the small number of animals (16 sheep were included in the second training phase, four for each type of image rewarded, see Section "Results" for more details), the two types of negative images (SI and Aggr) and the two types of neutral images (NSI, when the second image was SI, NAggr when the second image of the pair was Aggr) were grouped under "negative images" and "neutral images, " respectively, for analysis of learning speed.
However, sheep actually learned the task more quickly when their rewarded image was one of the two negatives images (SI or Aggr). It is of course possible that sheep learned to avoid the unrewarded image rather than to approach the rewarded one; however, this would not impact our findings regarding the ability of sheep to discriminate between images of faces. In humans, negative stimuli (colored images of beetles, negatively valenced images from the International Affective Picture System database) induce stronger and faster responses (e. g. , higher amplitude and shorter latency of electrophysiological markers, shorter response time in key pressing) than positive or neutral stimuli (colored images of buildings, neutral or positively valence images from the International Affective Picture System database  (35, 36) ).
acKnOWleDgMenTs We are grateful to I. Nevison from Biomathematics and Statistics Scotland for his statistical advice. This project was funded by Scotland's Rural College (SRUC, UK) and Institut National de la Recherche Agronomique (France) as part of Lucille Bellegarde's Ph. D. project. SRUC receives core funding from RESAS (Rural and Environment Science and Analytical Services), a division of the Scottish Government.
Previous video validation showed 100% detection rate, but the system also registered 'false' readings such as a bird jumping onto the antenna and jumping back off, triggering only one set of sensor beams  (Hinch and Lee, 2014). These false readings were filtered out with custom-designed software (see 'Data and statistical analyses'). The RFID systems were placed in the indoor pens 2 weeks before pop holes first opened and RFID tracking occurred daily from 22 to 36 weeks (1 week permitted to acclimate to the pop hole passageways and the range). However, due to having three RFID systems only, one replicate from each stocking density was recorded for 2 weeks, then the three RFID units were swapped to record the second replicate for 2 weeks. In total (excluding days of system failure), daily tracking data were recorded for each stocking density as follows: 2000 hens/ha: 91 days; 10 000 hens/ha: 91 days; 20 000 hens/ha: 88 days.
The program then summarised the daily data per hen from within the three sampling time blocks across the experimental period to provide the daily time spent outdoors, the number of daily visits, maximum time per visit, and overall, how many days the range was accessed by each tagged hen (converted to percentage of total available days that each hen visited the range). The daily time outdoors, and maximum time per visit were log 10 transformed to normality with number of daily visits square-root transformed. Data were analysed in JMP 12. 1. 0 (SAS Institute Inc. , Cary, NC, USA) using GLM with α set at 0. 05. GLMs were first used to compare the changes in rangeuse parameters between the three sampling week periods by all stocking densities combined. GLMs were then used to compare the effects of individual hens nested within pen replicate nested within stocking density, pen replicate nested within stocking density and stocking density on range-use parameters (daily hours outside, daily visits, maximum time per visit), separately within each sampling time period (weeks 22 to 26, weeks 27 to 31 and weeks 32 to 36).
Where significant differences were present between stocking densities, Student's t tests were applied to the least squares means with a Bonferroni correction applied to the α level to account for multiple post-hoc comparisons. Data from video decoding were also compiled graphically (only) to show the average percentages of hens out on the range within the three different range divides (front, middle, back) at each hour across the day, averaged across all sampling weeks, separately for each stocking density.
Acknowledgements This research was conducted within the Poultry  CRC , grant  1. 5. 6 ,established and supported under the  Australian Government's Cooperative Research Centres Program. L. Warin was supported by a  Universities Federation for Animal Welfare Small Project  and  Travel Award. The authors thank  Sue Belson ,  Jonathan Campbell ,  Grahame Chaffey ,  Mandy Choice ,  Andrew Cohen-Barnhouse ,  Bradley Dawson ,  Troy Kalinowski ,  Jim Lea ,  Barry Lockwood ,  Greg Mills ,  Dominic Niemeyer ,  Hélène Pecourt ,  Mark Porter ,  Samantha Robinson ,  Varun Uthappa  and  Lindsay Wittig  for technical assistance and advice. Thank-you to anonymous reviewers for their comments that improved earlier versions of this manuscript.
Data analysis In study 1, the response to treatment was assessed as the change from baseline to each examination day for parasite and lesional scores. Wilcoxon signed-rank tests were used to compare data obtained on each examination day with the D0 baseline. In study 2, the percentage of protection was calculated using Abbott formula: % of protection ¼ mean number of lice in non À treated horses ð Þ À mean number of lice in treated horses ð Þ mean number of lice in non À treated horses A bilateral Mann-Whitney test was used to compare both samples. Significance was defined as P<0. 05. All statistical analysis was performed using XLSTAT 2017-02 (Addinsoft SARL, Paris, France).
Statistical analyses The XLSTAT software (Addinsoft Software, Paris, France) was used to analyse the data. Comparisons between groups were made using Kruskal-Wallis tests followed by Dunn tests by pair in case of significance or tendency. Spearman rank correlations were calculated between the parameters recorded during the personality tests and learning performance. The behaviour observed during the inter-trial intervals was analysed only during the first three learning sessions by summing the number of observed behaviours in the entire period. These sessions were chosen because they covered the period during which all the horses were present during learning. Results are presented as median value and interquartile range. The level of statistical significance was set at P 0. 05 and the level of tendency at P 0.
Acknowledgments The first author was supported by a Ph. D. grant from the ' Region Centre ' and from the  French Horse and Riding Institute (IFCE). This institute also funded this experiment. We thank  Guy Duchamp  and  Fabrice Reigner  and their staff ( INRA, Nouzilly, France ) for allowing the use of the animals and facilities. We also thank  Jean-Marie Yvon  and  Rejane Monteville  for their assistance in data collection and  Ludovic Calandreau  for his help throughout this project. We are also very grateful to three anonymous reviewers for providing valuable comments on an earlier version of the manuscript.
Study Population and Data Collection Medical records of all the dogs that had both an ionized calcium concentration measurement and a serum biochemistry panel (creatinine, blood urea nitrogen, total protein, albumin, globulin, tCa, phosphorus, sodium, potassium, chloride, glucose, alkaline phosphatase [ALP], alanine transferase, gamma-glutamyltransferase, total bilirubin, cholesterol, triglycerides, and bicarbonate) performed at the University of Illinois Veterinary Teaching Hospital between 2010 and 2016 were retrospectively identified by computerized database search. Ionized calcium concentration was measured on whole blood. A volume of 0. 4 mL of blood was immediately placed into a lithium heparin-coated plastic screw-top tube containing <15 USP units of heparin/mL of blood. The tube was immediately inverted 8-10 times to assure anticoagulation, according to the manufacturer's recommendations.
The tubes remained closed in an upright position at room temperature until the time of sample analysis, which was routinely performed within 15 minutes of collection. Immediately before analysis, the tube was gently inverted several times to ensure adequate mixing of the sample. The tube was opened and the sample was drawn into a syringe for ionized calcium concentration measurement using a stat blood gas analyzer, a which uses ion-selective electrode technology. The unadjusted ionized calcium concentration was reported. The biochemistry panel was obtained from 2 chemistry analyzers (1 b from February to August 2010 and another c from August 2010 to February 2016). Patient data were included in the study only if the ionized calcium concentration measurement and the biochemistry panel were performed within 24 hours of each other.
Patients were excluded if any laboratory results were missing. To guarantee independence of observations, an individual could not be included more than once; if the patient was evaluated several times, only the first evaluation was included. All biochemistry results and the measured ionized calcium concentration (miCa) were recorded for every patient. Additional data obtained from the medical records included age, breed, sex, body weight, and body condition score. When available, information regarding drug therapy that could influence calcium concentrations (eg, calcitriol, calcium supplementation, prednisone, nonsteroidal anti-inflammatory drugs, phenobarbital, diuretics) and final diagnosis, particularly with regard to renal disease or calcium disorders, also was collected. A patient was considered to have parenchymal renal disease if the combination of a serum creatinine concentration >2.
Study Population Two thousand dogs met the criteria for inclusion in the study and 281 were excluded, leaving 1,719 included dogs. The training set was composed of the data from 1,200 (69. 8%) dogs, whereas the remaining data from 519 (30. 2%) dogs were used for the test set. In total, 135 breeds were represented, with the majority being mixed-breed dogs (23. 09%), and lesser numbers of Labrador Retrievers (8. 14%), Dachshunds (5. 22%), Yorkshire Terriers (3. 72%), Golden Retrievers (3. 14%), Shih Tzus (2. 73%), German Shepherds (2. 67%), Boxers (2. 55%), Beagles (2. 21%), and <2% of each remaining breed. The differences in patient characteristics among the total group, test set, and training set are outlined in Table  1. There were no significant differences regarding age, weight, and sex between the training set and test set (P = 0.
Second, dogs included between February and August 2010 had a biochemistry profile that was obtained with a different analyzer than the other dogs included in the study. This subset represented 3% and 4% of the training and test sets, respectively. Although the different analyzers were unlikely to have the exact same reference intervals and measurements, the directions toward which the biochemistry results tended to change were likely similar for both analyzers. The way our MARS model was selected, based on cross-validation statistics, allowed it to capture the underlying reproducible structure in the data in order to sense how changes in biochemistry variables influence the ionized fraction of calcium rather than trying to stringently fit a specific biochemistry result with a particular miCa value.