dataesr/scientific-paragraphs-categorization huggingface dataset:
Since juveniles are not yet reproductively active, they also likely display different endocrine profiles than adults  (Dantzer et al. 2010). Finally, adult roe deer habituate faster than juveniles to stress, which can in turn affect the relationship between FGM and fitness-related trait  (Bonnot et al. 2018). Body mass varies with age, and at a given age, varies between sexes and according to environmental conditions at birth (i. e. cohort effects,  Hamel et al. 2016). We thus tested the relationship between FGMs measured in a given year and individual body mass relative to the average body mass of all individuals of the same age, population and sex, considering only ages for which we had data from at least 3 individuals. Therefore, the average body mass was calculated up to 10 and 15 years old in males and females at CH, respectively, and up to 12 and 13 years old in males and females at TF, respectively.
For juveniles, we analysed the relationship between FGMs and body mass measured the same year on 368 juveniles (78 females and 96 males in CH, 91 females and 103 males in TF). The response variable was the mass corrected for the date of capture (see above) and fixed explanatory variables included FGMs (corrected for the date of capture and the population), cohort quality, population, sex and the two-way interactions between FGMs and the other covariables. Cohort quality was expressed as a relative cohort quality (i. e. the difference between mean cohort quality in each population and an individual cohort quality), so there is no redundancy with the population variable. The year of birth of the individuals (i. e. cohort) was included as a random effect. For individuals aged 2 years or older, the dataset included 655 observations on 377 individuals: 104 females (218 observations) and 83 males (136 obs.
The relative mass was calculated as the difference between the mass of an individual at age ‫,ݔ‬ and the mean mass of all individuals of age t of the same sex and the same population. The change in relative body mass was calculated as the difference between the relative mass at age ‫ݐ‬ 1 and the relative mass at age ‫. ݐ‬ Fixed effects included relative cohort quality, population, sex, FGMs (either measured at age ‫ݐ‬ or considered as the average of the FGM values measured at age t and age ‫ݐ‬ 1), body mass at age ‫ݐ‬ (i. e. initial body mass) and all two-way interactions between FGMs and the other covariables. Random effect of the cohort was included, and for late-growing and prime-aged individuals, random effects of the year of capture and individual ID were also included. For growing individuals, the dataset included 99 individuals with FGMs measured as juveniles, and 67 individuals when including FGMs measured during the first and second years of life to calculate the mean value.
For both late-growing individuals and adults, we kept a single observation per individual since the inclusion of individual ID as a random effect created singularities. For individuals with several observations, we kept the one for which the individual's age was the closest to the mean age of all individuals with a unique observation according to sex and population, among observations with complete data. The final dataset comprised 85 individuals in their late-growth period and 90 prime-aged adults with FGMs measured at age t, and 63 late-growing individuals and 71 prime-aged adults for which we had measurements at age t and t+1.
Late-growing individuals We found a negative relationship between body mass gain and initial mass (β mass = -0. 15 [-0. 25, -0. 05], Table  1 , SI) for the dataset accounting for FGMs measured in juveniles. With the dataset including the mean FGM value between FGMs measured at age t and age t+1, we also found a negative relationship between late growth and body mass measured at age t (β mass = -0. 17 [-0. 27, -0. 07], Table  1 , SI). However, in both cases, we found no support for a relationship between FGMs and change in relative body mass (Figure  2 (b), Table  1 , SI). Models resulted in singularities when the random effect of the cohort was included so that only the year of capture was included as a random effect in the models.
Data collection and calculations Feeding and drinking behaviours were automatically recorded using automatons. Daily time spent at the feeders, the number of visits with ("nutritive visit") or without ("non-nutritive visit") feed consumption, and the amount of feed consumed were determined based on ESF records. Daily time spent at the troughs, the number of visits and water consumption were determined based on the electronic drinker records. The sow order of visits to the feeder for the first nutritive visit of the day was used as an indicator of hierarchical status within the group, with the method developed by  Lanthony et al. (2022). The social ranking was initially identified based on the data obtained during the entire gestation. Three categories were created according to the first and third quartiles: low (LR), intermediate (IR) and high (HR) ranking sows.
Only skin lesions without sign of healing process (thick, solid crust, no bloody or weeping, no redness/heat or open wounds) were counted, to avoid old lesions from the previous week. The data collected on Monday were linked to the previous period to see the effect over the time. Twenty-five sows (12 in group A and 13 in group B; 7 in LR sows, in IR sows and 8 in HR sows) were equipped with a randomly functional accelerometer fixed on an ear tag (RF-Track, Rennes, France) in order to continuously record the number of position changes, the daily time spent lying down (flank fully on the ground or not), standing (all 4 legs standing still), or walking (1 or more legs active) and therefore to recalculate ex-post the ME requirements for maintenance. The accelerometer's specificity and sensitivity measured in similar straw-bedding conditions were 75.
Statistical analysis The statistical analyses were carried out using R studio software (version 4. 1. 2, R Foundation, Vienna, Austria). Due to the lower number of primiparous, the hierarchy ranking of every sow were used instead of the parity for the rest of the statistical analysis to obtain three balanced groups. To verify that the hierarchy ranking is indeed linked to the age of the sow, correlations of Pearson between the different hierarchy rankings, the age and parity of the sows were calculated using the "cor. test" function of the "stats" package  (Best and Roberts, 1975). Linear mixed-effects models were used with the "lme" function of the "nlme" package  (Pinheiro et al. , 2022). The models included the fixed effects of the treatment (C. vs. R. ), the period (1 vs. 2), group (A vs.
B), social ranking (LR vs. IR vs. HR), their two-way interaction with the treatment and the random effect of each sow. The feeding and drinking behaviours were analysed as a daily average, over the 5 experimental days. The occupation, location, and social behaviours were analysed as an average of the first 36 h of the experiment. The physical activity data were analysed as a daily average over the 5 experimental days. The nutritional requirements were analysed as a daily average of the experimental days. The "performance" package  (Lüdecke et al. , 2022)  made it possible to choose the best model, by potentially removing some non-significant interactions or factors, based on multiple indices of model quality and goodness of fit (r-squared, root-mean-square error (RMSE), intraclass correlation coefficient).
Revised recommendations Eight of the ten recommendations provided in August 2021 are still valid today, while two (recommendations n. 3 and n. 7) need to be revised according to new information. All recommendations are again presented below, and a brief discussion is proposed for those that have been revised. 1. In patients with previous history of breast cancer, vaccine injection (both doses for two-doses vaccines) should be performed in the contralateral arm or in the anterolateral thigh. This recommendation has not been revised. 2. COVID-19 vaccination data (vaccination status, date, dose, injection site) of all patients presenting for breast imaging with any modality should be collected and made available to radiologists, including the cases of breast imaging performed for cancer staging and of follow-up imaging examinations.
Detection and Quantification of Mycobacteria by Quantitative PCR (qPCR) Bacterial genomic DNA was detected by quantitative PCR (qPCR) in a final volume of 25 µL including 5 µL of extracted DNA, 12. 5 µL of TaqManTM Fast Advanced Master Mix with UNG (Applied Biosystems, Thermo Fischer Scientific, Vilnius, Lithuania), 2. 5 µL of primer mix (Table  2 ) at 3 µM each and probed at 2. 5 µM (table below) and 5 µL of ultrapure water. Experiments were carried out using a BIO-RAD CFX96 thermocycler (BIO-RAD), consisting of polymerase activation at 50 • C for 2 min, followed by an initial denaturation step at 95 • C for 10 min, followed by 40 cycles of denaturation at 95 • C for 15 s, elongation at 60 • C for 1 min. The Ct is calculated with the CFX Manager software (BIO-RAD CFX Manager 3. 1). The primers used are listed in Table  2.
10, birds in half of replicates per diet were orally challenge with 1 mL of E. acervulina and E. maxima culture and the other half equal volume of saline. Excreta samples were collected from d 12 to 14. With exception of AR of Ca and P, there was no interaction (P > 0. 05) between Eimeria and diet on AR of dry matter, crude fat (CF), crude protein and gross energy and AMEn of ingredients. Eimeria reduced AR of CF (P = 0. 01) and had a tendency to reduce AR of DM (P = 0. 09) and AMEn (P = 0. 063) of ingredients. The data demonstrated exposure to Eimeria impacted nutrient retention and energy utilization irrespective to diet composition.
(2022)   2021 ) using the following equation: AMEti ¼ AMEtd À AMEnfd Â poeyi ð Þ ½ =poti; where AMEti is AME of the test ingredient (corn, wheat, SBM, or PM) and AMEnfd is AME of the NFD diet. The AMEtd and AMEnfd were calculated according to  Woyengo et al. (2010). T a g g e d E n d T a g g e d P The data were subjected to a 6 £ 2 for AR of components or 4 £ 2 for AMEn factorial arrangement using mixed procedures of SAS version 9. 4 (SAS Institute Inc. , Cary, NC). The model included diet, challenge, and associated interaction as fixed factors. An alpha level of 0. 05 was used as the criterion for statistical significance and treatments were compared using Tukey test. T a g g e d E n d.
The use of these sensors seems to be economically advantageous  [23]  and may in the future be integrated with physiological data to target cows more individually for even higher reproductive performance  [24]. Most parameters around an individual dairy cow or the herd can be monitored by sensors. One biological parameter may be measured by different sensor types. Rumination monitoring may serve as an example: it may be captured using accelerometers  [25] , pressure sensors at the head [26] or microphones  [27]. Scheurwater et al. [28]  showed the possibility to assess rumination or other activities using an intrareticular pressure sensor. The device was able to identify rumination with an accuracy of 0. 92, a sensitivity of 0. 97 and a specificity of 0. 90. Various sensors are currently available and regularly connected to data systems-they can be understood as the "Internet of Things" (IoT) application to animal husbandry (Figure  2 ).
(2) The algorithm itself identifies relevant characteristics in sensor data and defines the event; the algorithm thus identifies conspicuous patterns in the data that can retrospectively be linked to an event of interest. This process can therefore be defined as either "supervised" or "unsupervised" learning  [10]. Dittrich et al. [38]  describe the structure of algorithms for recognizing events. Numerous clinical studies have identified measurable behavioral changes in animals with certain diseases. For example, cows with clinical mastitis show different patterns in lying and standing times, and their patterns of activity and feeding behavior change. These changes are captured by sensors and will be analyzed by algorithms using the recorded patterns of healthy animals as a standard. Deviations from this standard point to the occurrence of clinical mastitis.
In a second step, the thus trained algorithm was tested for repeatability of motion pattern recognition in a new data set by comparing sensor labels with visual observation. The study shows a highly correlated agreement between visual observation and recognition by the sensor. For example, the sensor identified an average of 1508 s spent ruminating per hour of resting phases; observation by a human recorded 1492 s of rumination, a mere difference of 16 s per hour spent by the animal resting. It is usually not known how exactly an algorithm in a commercially available system is built. The algorithms are typically not accessible in publications as this is commercially used intellectual property and therefore sensitive information. Some studies, however, illustrate the development of an algorithm.
An example is the study by Carslake et al. [39] , demonstrating the practical implementation of algorithms on the example of the behavior of calves. The authors describe the "training" of a sensor from mere measuring activity up to the interpretation of captured data. In this study, the activity and behavior of 13 calves were captured by an activity sensor and at the same time recorded on video. The aim was to correlate both sensor and visual information. The incoherent raw sensor information on activity was therefore "mapped" with labels derived from the visual information captured on video. As a result, it was possible to identify activities such as licking, suckling or playing in the sensor data set. This would allow for recognition of these activities in the field; changes in the behavior could then be identified, this possibly indicating disease.
Applications Sensors and their respective algorithms are an important element of PLF. These components produce data, either raw or processed, and this data has to be made accessible and meaningful. Applications achieving this are often computer systems installed on the farm; they form a database that contains farm-related information, e. g. , production and fertility data or milk production. They can also serve as a platform for sensor-related applications and directly provide communication between these components, serving as an interface between farm production data and sensor-related information. Herd management programs, originally designed for storage and analysis of farm production data, are increasingly serving as exchange platforms combining data originating from the farm. This is developing into a two-way process because alarms from a sensor system are useful to be recorded and, inversely, a sensor-related application will benefit from farm records such as insemination, calving dates and alike.
Interfaces Connecting data originating from different sources needs interfaces; they link electronic farm-specific data (e. g. , insemination dates) with external data (e. g. , milk recording data) and data from sensor systems, which may originate from different manufacturers. However, there is a tendency towards closed "ecosystems", in which components from a single manufacturer are combined. The exchange of information between components of different manufacturers may be difficult to establish. Bypassing the ecosystem barrier can be achieved externally by a so-called "warehouse". Relevant data from various sources on one farm is uploaded onto a server; there, it is collected, cleaned and sorted. Relevant information for certain analyses is then extracted from the warehouse and transformed to answer the questions regarding an individual or a group of animals, an individual farm or a cluster of farms  [44].
Motion Capture Data Processing First, the raw motion capture data needed the preliminary step of labelling. With the help of the manufacturer's dedicated software (Nexus 2. 14. 0, Oxford Metrics Ltd. , Oxford, UK), the 3D coordinates for each reflective marker of interest were recovered. Then, the angle of flexion/extension between the withers, thoracolumbar (T18), and pelvis markers was calculated. A fourth-order Butterworth low-pass filter with a 5 Hz cut-off frequency was applied to the 3D data of the withers, thoracolumbar, and pelvis markers. From the filtered data, the angle of flexion/extension was obtained using vectors (Equations (  1 ) and (  2 )), vectors norms (Equations (  3 ) and (  4 )), and scalar products (Equation (  5 )). The final angle (AngleMocap) was obtained from the (Equation (  6 )).
IMU Data Processing The tri-axial accelerations were obtained from the raw IMU data in each IMU reference frame. The IMU acceleration data were corrected from a motionless frame (a standing square), using a rotation matrix in order to align the Z axis with the direction of the gravity vector (Equation (  7 )). We assumed that the rotation of each IMU reference frame around its own Z axis was zero at standing square. Then, the orientation correction angles α and β (regarding the terrestrial X-axis and floating Y-axis, respectively) were obtained for each IMU by solving the system (Equation (  8 )). Thus, assuming that the norm of the acceleration vector is equal to 1 g motionless, the acceleration values of each IMU at each instant in the same terrestrial reference could be found by multiplying the rotation matrix (with its associated angles α and β) by the raw acceleration values (Equation (  9 )).
Ordinary Least Product Regression To determine the constant and proportional bias, the ordinary least product regression method was used  [33] [34] [35] [36] [37]. The biases were calculated using the Matlab software functions "fitlm"  [40]  and "coefCI"  [41]  (Matlab R2021b, The MathWorks Inc. , Natick, MA, USA). The degree of constant bias was calculated using the 95% confidence interval (95% CI) of the y-intercept. If the 95% CI for the intercept included the value of zero, there was no constant bias. The proportional bias was determined from the 95% CI of the slope. If the 95% CI of the slope included the value of 1. 0, there was no proportional bias.
Correlation To study the link between both methods, linear regression curves  [38]  were plotted. Pearson's correlation coefficient (Pearson's r)  [39]  was calculated using the Matlab software function "corr" (Matlab R2021b, The MathWorks Inc. , Natick, MA, USA). Pearson's correlation coefficient was compared to the thresholds defined by  Cohen (1992)  to evaluate the relationship's strength (0-0. 1: none; 0. 1-0. 3: small; 0. 3-0. 5: medium; >0. 5: large)  [42].
The IMU method showed a mean bias of 0. 8 • (±1. 5 • ) for flexion movements and a mean bias of 0. 8 • (±1. 4 • ) for extension movements, all gaits combined, in comparison with the MOCAP method using Bland-Altman's comparison. In a previous study  [27] , the mean bias of the thoracolumbar angles between the IMU and MOCAP methods was 0. 65 • (±0. 47 • ) during trotting and 0. 63 • (±0. 44 • ) during galloping. In this study, the data were recorded only using one horse on a treadmill involving a stabilized gait and more repeatable strides between themselves. In our study, four horses were practiced in real conditions involving variable speeds and strides. The range of flexion and range of extension were calculated separately in our study. We noticed a mean difference between the range of flexion and extension of 0.
Absolute bias should not be a major problem as long as it is constant. This study presents some limitations. First, only four sound horses were included in the study. This small amount of collected data does not present significant population diversity. However, analyzing the variability of back mobility in a large population of horses was not the purpose of the study. In this study, the first objective was to validate a method with a sufficient number of back movements. We were able to analyze 340 movements of flexion/extension, all gaits confused, and all horses combined. Another limitation of our study is the prerequisites to use this method. Indeed, we need to know the distance between the IMU placed on the withers, the thoracolumbar region, and the distance between the IMU placed on the thoracolumbar region and the pelvis.
Individual pigs in the pen were scored as 'active' if they were standing, walking, or sitting, as 'lying active' if they were interacting with the environmental enrichment or a pen mate while lying, or as 'inactive' if they were lying without performing any other described behaviour. This resulted in 12 scans per pig per week of observation. Distribution of the 10-min observation blocks over the day and over the two observation days was balanced for experimental treatment and observer. Four observers were trained by the same person before behavioural observations and inter-observer reliability was regarded as 'substantial' (Cohen's kappa > 0. 75)  28. All observations were done using the program Observer 14. 2 (Noldus Information Technology B. V. , Wageningen, The Netherlands) installed on a tablet.
Data processing Individual body weights, ADG, ADFI per pig, G:F per pig, and hair cortisol concentrations were averaged per pen. Activity, behavioural observations, tail position, and body damage scores were averaged per pen and per growth phase, i. e. pen was the experimental unit. Behavioural frequencies were calculated as frequency/pig/hour Table  1. Composition of the starter experimental diets (as-fed basis). 1 NP = normal protein diet; LP = low protein diet; LP + = low protein diet with supplemented indispensable amino acids (IAA). 2  Based on chemical composition, digestibility, and energy values for pigs from the Centraal Veevoeder Bureau Nutrient requirements and feed ingredient composition for pigs 25. 3  Analysed according to ISO 5983:2005  26. 4  Analysed according to ISO 15914:2004  27.
Data analysis Data were analysed using the statistical software R 4. 2. 2 (R Core Team, Vienna, Austria) and SAS 9. 4 (SAS Institute Inc. , Cary, NC, USA). General linear (mixed) model residuals were checked for normality. Preliminary analyses revealed that interactions with body weight block were not significant. Therefore, only the main effect of body weight block was used in all final models. Performance data was analysed in a linear mixed model (with 'lmer' function from the R package 'lme4') including a fixed effect of treatment, phase, interaction treatment × phase, body weight block, and batch, and a random effect of pen. Performance over the whole experimental period, initial and final BW, and hair cortisol concentration at the start of the experiment (baseline) were analysed in a linear model (with 'lm' function from the R package 'stats') including a fixed effect of treatment, body weight block, and batch.
Treatment, phase, the interaction treatment × phase, body weight block, and batch were included as fixed effects, and pen as random effect. In case of a significant treatment × phase interaction, the models were run per phase with treatment, body weight block, and batch as fixed effects to clarify within-phase treatment effects. The proportion of pigs which had at least once a tucked tail during the growing-finishing period was analysed with a GLIMMIX procedure with binary distribution and Logit function in SAS with treatment, body weight block, and batch as fixed effects, and pen as random effect. An ordinal logistic regression was performed to evaluate the effect of treatment on the severity of tail damage (with 'polr' function from the R package 'MASS'). To facilitate comparison with other studies, also the effect of treatment on the occurrence of medium wounds or worse (i.