dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the staff of  Doñana Biological Reserve (DBR-ICTS) ,  Doñana National Park , and  Los Alcornocales Natural Park , especially  D. Paz ,  F. Ibáñez ,  P. Bayón ,  M. Malla  and  D. Ruiz  for logistic facilities for the field work and permissions (2019107300000904/IRM/MDCG/mes) to collect cattle dung and dung beetles. We are grateful to  J. Castro  and  A. Rascón  for technical assistance. We also thank  A. V. Giménez-Gómez  for her technical assistance in the laboratory work. We thank also  F. -T Krell  and the two anonymous reviewers for their constructive comments. Financial support was provided by the project  CGL2015-68207-R  of the  Secretaría de Estado de Investigación-Ministerio de Economía y Competitividad.
Overall experimental design Dogs naïve to clicker training were recruited through the Clever Dog Lab (University of Veterinary Medicine, Vienna) database. The study consisted of three consecutive stages, performed on different days (see Table  1 ). In Stage 1, dogs were prepared for the "Cognitive Bias test" by being trained to discriminate between two different locations: one positive, where food was always present, and one negative, always empty (training phase). In Stage 2, dogs were included in a training procedure ("Clicker training", divided into an association phase + training phase) where they were taught to put two paws on a target (i. e. , wooden board) for at least two seconds. Once the subjects had reached the criterion of reliably putting the two front paws on the target, they were 1 3 invited to a final stage (Stage 3) consisting of a Cognitive Bias refreshment (to be sure they still reliably discriminated between the positive and negative location), followed by a Clicker training (testing phase, one session of max.
69, p < 0. 05) or excellent ("E Rejection": ICC = 0. 93, F (9, 9. 11) = 25. 4, p < 0. 001) depending on the variable. Moreover, videos of the Cognitive Bias test were coded by an observer blind to group allocation. Sixteen percent of the videos from the Cognitive Bias test sessions (in total 260 session) were independently coded by a second person who was also blind to group allocation. Inter-rater reliability between the main coder and the second one was excellent ("Latency to reach the bowl": ICC = 0. 83, F (256, 253) = 11. 2, p < 0. 001). Videos were either scored by entering data on an excel sheet (Clicker training) or coded using Somolon Coder (@ András Péter) (Cognitive Bias test).
The identity of the subject was included as random effect. For all models conducted on the behavioral variables coded during the Cognitive Bias test, we used a likelihood ratio test to determine whether the full model differed from a null one only containing the three E behavioral variables as well as dog's age and sex. For all models, we z-transformed all continuous predictors (i. e. , age, "E Affiliation", "E Rejection", "E Reactivation", "Positive Activation", "Negative Activation") to a mean of zero and a standard deviation of one to obtain more easily interpretable model estimates  (Schielzeth 2010). Models' assumptions were checked by plotting residuals vs. fitted values (homogeneity of variance) and by means of qqplots of the models' residuals (normality). Model stability was determined by removing individual cases one at a time (model 1) or individual dogs (models 2, 3 and 4) and comparing model estimates obtained from each subset to those obtained for the full dataset.
Confidence intervals were determined based on parametric bootstrapping (n = 1000 bootstraps) based on random sampling (with replacement) of the individual subjects, allowing to define whether potential negative results could be due to the small sample size. The PCA was conducted using SPSS v. 25 (IBM) while the models were fitted in R 3. 6. 2 (R Development Core Team 2019) using the function lmer of the package lme4  (version 1. 1-21, Bates et al. 2015).
Data Set and Radiographic Screening A total of 80 skulls of sexually mature domestic rabbits (Oryctolagus cuniculus) were sampled for the present study. Smaller breeds reach sexual maturity around 4-5 months of age, larger breeds at 6 months of age. We chose to focus not on a specific breed, but on the most commonly recorded group called 'domestic' in the veterinary clinical records. The breed status in these animals is not known due to frequently loose records or may even be unknown by the owner or veterinary personnel  [1]. Our sample represents the typical diversity of pet rabbits in a clinical population, excluding animals with congenital brachygnathia or extremely giant breeds. The animals' body mass ranged from 0. 9 to 6. 3 kg. The specimens were radiographed for medical reasons and not for the purpose of this study.
Linear Morphometrics Six linear measurements and three angles were obtained to evaluate the diversity in cranial morphology (Figure  2A , Table  1 ). Maximal cranial length was used to size-normalize the data (see below) and, thus, not further included in the statistical analysis. An additional angle, the occlusal angle, was analyzed separately because it is not independent of the facial tilt and palatal angle. Each measurement was taken three times using the software ImageJ v. 1. 48 (National Institutes of Health, Bethesda, MD, USA)  [15]  and the mean value was then calculated. below) and, thus, not further included in the statistical analysis. An additional angle, the occlusal angle, was analyzed separately because it is not independent of the facial tilt and palatal angle. Each measurement was taken three times using the software ImageJ v.
Geometric Morphometrics Thirteen anatomically defined landmarks and 35 sliding semilandmarks (representing five curves) were used to quantify the cranial morphology (Figure  2B , Table  2 ). The homologous points were digitized onto laterolateral radiological images to obtain the two-dimensional (2D) coordinates using Vet. Sci. 2020, 7, 182 6 of 17 the software tpsDig2 (New York, NY, USA)  [17]. The digitalization of the landmarks was performed by a single author (E. B. ) in order to prevent interobserver measurement errors. The placement of the landmarks was repeated three times for each individual. The assessment of intraobserver variance revealed that the error is low ensuring reproducibility of the measurements. The collected data were imported in the software R version 3. 6. 3 (Vienna, Austria)  [18]  using the 'geomorph' package  [19].
Statistical Analyses All subsequent statistical analyses were performed using the software R version 3. 6. 3 (respective packages are indicated below). Principal components analyses (PCAs) were used to reduce the multidimensionality of the collected variables and to visualize the distribution of the specimens in the morphospace delimited by the PCs. For the linear morphometric data, the PCA was performed using the 'prcomp' function in R ('stats' package). The 'factoextra' package was used to extract and visualize the output of the PCA  [22]. For the geometric morphometric data, the PCA was conducted using the 'plotTangentSpace' function in R ('geomorph' package). The shape differences were visualized with point displacements ('plotRefToTarget' function in R). The plots display the landmarks and semilandmarks of the target specimen overlying those of the reference specimen.
A K-means cluster analysis was performed to reveal the grouping of the studied specimens based on the linear morphometric data ('kmeans' function in R package 'factoextra'). The number of clusters (K) was specified as two (corresponding to the number of groups Y and N, respectively). The correlation coefficient (Pearson's R) between the linear measurements was determined to measure the strength of linear association between two variables ('cor. test' function in R package 'stats').
A retrospective analysis was performed to determine mortality and morbidity rates for elective and emergency cases in an equine university teaching hospital. It investigated the effect of horse-, anesthetic-, timing, and clinician experience-related variables on anesthetic complications. In total, 1,161 horses undergoing general anesthesia between January 2012 and December 2016 were included in the study. Patient information and details of the anesthetic, recovery period and immediate complications were retrieved from an archival database. Statistical analysis of qualitative and quantitative factors affecting anesthetic complications was performed using an univariable and multivariable ordinal logistic regression. Odds ratio of variables primarily affecting mortality and complications were calculated.
This result is in accordance with Johnston et al. (2). This can be explained by the length of these surgeries that are often long, the urgency, that recoveries are complicated (with a significant risk of fracture), that the ASA score is increased. In addition, the surgeon's seniority was identified as a risk factor in our study, yet a senior surgeon is often involved in orthopedic surgery. Colic and fracture surgeries are performed urgently in horses having a high ASA score. It is therefore natural to find a higher mortality rate for emergency surgeries as Mee et al. also showed in 1998  (13). The duration of anesthesia increased the risk of anesthetic mortality and systemic complications. Our data demonstrated that anesthesia lasted longer when the ASA score increased, the latter being a variable in the final model of anesthetic mortality and systemic complications.
For reasons linked to sample size, in subsequent analyses subadults and adults were grouped in the same age-class termed 'adult'. Harems and/or bachelor groups regularly aggregate with others in larger groups (referred to as 'herds' hereafter), defining a multi-layered social system  (Klingel 1969). Stability of herds is low, likely a few hours or less (pers. obs. and unpublished data from GPS collars). Stability of lower social layers, i. e. bachelor groups or harems, is greater. Some bachelors appear to spent months, if not years, together (unpublished data). Size and composition of bachelor groups had however not been consistently recorded during the historical demographic monitoring we based our analysis on, and we therefore focused our study on harems. Harem size is highly stable over time.
Collection and analysis of behavioral data We collected data on the behavior of zebras during 2 wet and 2 dry seasons (in 2018 and 2019). We conducted direct observations, videotaped for later analysis, during daytime using a car as a hide. We considered individuals to be part of the same group when the distance between them was below 50 m: in the field, this distance threshold (also used by  Barnier et al. 2016 ) is adequate to separate isolated harems or groups that are in close contact from other groups of individuals located further away, and is thus relevant to study group-level dilution and detection effects. WErreur ! Signet non défini. e focused on vigilance, foraging, and agonistic behaviors. We studied individual vigilance and foraging using a focal-sampling approach  (Altmann 1974 ).
An individual was considered to be vigilant when it was stationary with the head raised above the shoulder and scanning the surroundings, and to be foraging when it was grazing. Based on the scanning direction of individuals, we focused on anti-predator vigilance and avoided conducting a focal observation if the animal was vigilant towards conspecifics. We studied only individuals in groups whose main activity was foraging to prevent movement or change in group size or composition during focal samples. To obtain a large sample size within the limited time available, we only recorded, for each focal harem, data on the stallion and on one female, whose reproductive status (lactating or not) was recorded. A female was considered to be lactating if she had a foal (i. e. from birth to 1 y. o. ).
If a least one non-lactating and one lactating female were present in the group, we recorded data on one female of each status. As we conducted our research in the same study area during the whole study period, we could not avoid to occasionally repeating observations on the same individuals (number of focal per individual: min=1, max=11, median=1, mean=2. 6). For each studied individual, we measured the duration of one bout of vigilance and the duration of the foraging bouts that immediately preceded and followed this vigilance bout. We only focused on active vigilance (i. e. head above shoulder), differentiating between free (i. e. while chewing) and costly vigilance (i. e. scanning exclusively). We recorded whether vigilance shifted from free to costly vigilance or whether foraging shifted to vigilance in response to a sudden external stimulus or to agonistic interactions (i.
e. threat, push, chase or attack behaviors). This allowed us to (1) remove from the data the variability linked to external stimuli, by removing the affected bouts from the analyses (4 of the 992 bouts recorded), and (2) get an estimate of the proportion of foraging bouts interrupted by agonistic interactions, which partially reflects the strength of intragroup interference in the access to food resources. As costly vigilance bouts were rare (35 of the 352 bouts of vigilance recorded), we choose to analyze only focals during which the individual was in free vigilance. We collected data to control for the multiple factors that could influence antipredator vigilance and could either mask or reinforce the group size effect  (Elgar 1989 , Beauchamp 2015). We systematically recorded individual variables (i.
We recorded wind strength using Beaufort number given by an anemometer ('none' (0 km h -1 ), 'low' (0 -1. 8 km h -1 ), 'medium' (1. 8 -7. 4 km h -1 ), and 'strong' (> 13 km h -1 ) as zebras could be more vigilant in windy conditions that may affect the perception of predator cues. In total, we analyzed 282 focal observations (122 for males, 89 for nonlactating females and 71 for lactating females). In 2018, we collected 138 and 35 focal samples in the wet and dry season respectively, and in 2019, we collected 49 and 60 focal samples in the wet and dry season respectively. We tested the effect of group size on the duration of vigilance and foraging bouts by fitting generalized linear mixed-effects models using the R package 'lme4'  (Bates et al. 2015). We first built full models with the duration of vigilance or foraging bouts as response variable (we assumed Poisson-distributed errors), and the main and interactive effects of group size, season, and a variable coding for sex and reproductive status (male, lactating female, nonlactating female) as fixed effects.
From these full models, we conducted variable selection using the Akaike Information Criterion corrected for small sample sizes (AICc), considering only informative parameters  (Arnold 2010) , i. e. those reducing AICc by >2. Variable selection was performed using the R package 'MuMIn'  (Barton 2019 ). We studied the potential for collective vigilance using the scan-sampling approach (i. e. instantaneous sampling on groups)  (Altmann 1974 ). We focused only on groups whose main activity was foraging. For each group studied, and every minute for 5 min, we performed a scan on the whole group and counted the number of individuals vigilant in the group (without considering foals which we assumed naïve regarding predation risk). We could thus compute the proportion of time (i. e. proportion of scans) when at least one individual in the group was vigilant, which is a metric of collective vigilance  (Pays et al.
Collection and analysis of demographic data We estimated the relationship between harem size and individual survival using data from the long-term sight-resight study. Twice a year, during approximately one month in the wet season and three weeks in the dry season, groups whose individuals were known were sought. When found, harem size and identity of the individuals were recorded. The age of previously unobserved young individuals was estimated as in  Grange et al. (2015)  and suckling used to determine motherhood. Further details on the monitoring of this population can be found in  Grange et al. (2015). Data collected between January 2008 and March 2017 were used here, corresponding to 19 capture occasions (10 wet and 9 dry seasons) over 10 years. We used multistate CMR models to estimate adult survival  (Lebreton et al.
We added a fourth state in the male analysis to represent their presence in bachelor group (whose group size was usually not recorded). A total of 143 females and 105 males were retained for analysis. The U-CARE software  (Choquet et al. 2009)  was first used to perform goodness-of-fit tests to identify any issues in the structure of our datasets  (Pradel et al. 2003). Models were fitted using the R package 'RMark'  (Laake 2013) , starting from a full model including an interactive effect of group size and season on survival probability, an additive effect of group size and season on detection probabilities, and a group size effect on transition probability. As our goal was to see how group size affects survival, we created only three simpler models from this model, which included either a season effect, a group size effect, or an additive effect of season and group size on the survival probability of adults.
We used AICc to select the most parsimonious model, considering only informative parameters  (Arnold 2010). Estimates were corrected for unequal time intervals between capture occasions. Each interval was defined as the time from the start of the field session until the start of the following field session. For foals (less than 1 y. o. ), we used the fact that they are dependent on their mother. If the mother was observed, the detection probability of the foal being alive is 1, and the observation of a mare without her foal is reliable evidence that the foal had not survived. We therefore used standard Cox proportional-hazards models, fitted with the R package 'survival'  (Therneau 2020). A total of 114 foals were retained for the analysis. Due to small sample sizes, we could not test for the effect of season on foal survival, so we restricted our analysis to two models which included either an effect of age-at-entry (i.
1 (Bruker Daltonik). LC-MS/MS data were acquired using the PASEF method with a total cycle time of 1. 31 s, including 1 TIMS MS scan and 10 PASEF MS/MA scans. The 10 PASEF scans (100 ms each) containing, on average, 12 MS/MS scans per PASEF scan. Ion mobility-resolved mass spectra, nested ion mobility vs. m/z distributions, as well as summed fragment ion intensities were extracted from the raw data file with DataAnalysis 5. 1 (Bruker Daltonik GmbH, Bremen, Germany).
Quantification of Proteins, Identification of Embryo-Interacting Proteins and Statistical Analysis All proteins with more than two peptides identified were considered for protein quantification. Protein quantification was based on a label-free approach using spectral counting, as previously described  [31]. Scaffold Q+ software (version 4. 9, Proteome Software; www. proteomesoftware. com) was used using the Spectral Count quantitative module. Peptide identifications were accepted if they could be established with greater than 95. 0% probability as specified by the Peptide Prophet algorithm  [53]. Peptides were considered distinct if they differed in sequence. Protein identifications were accepted if they could be established with greater than 95. 0% probability as specified by the Protein Prophet algorithm  [54]  and contained at least two identified peptides (false discovery rate (FDR) < 0.
Functional Analyses of Interacting Proteins To predict the secretion ways of embryo-interacting proteins, the online tools SignalP 5. 0 (http: //www. cbs. dtu. dk/services/SignalP/) and SecretomeP 2. 0 (http://www. cbs. dtu. dk/services/SecretomeP/) were used. Proteins predicted as possessing a standard secretory signal peptide and a peptide cleavage site in their N-terminal sequence were considered as secreted in a conventional way. In addition, proteins predicted as being targeted to a non-classical pathway in the mammalian dataset of SecretomeP were considered as potentially undergoing non-conventional secretion. A cutoff NN-score value of 0. 6 was applied. To go further in the non-conventional secretion pathway, we looked for the presence of the protein among those reported in bovine  [9]  and feline  [30]  oviductal EVs.
The online tool Proteomaps 2. 0 (https://bionic-vis. biologie. uni-greifswald. de) was used for the functional annotation of embryo-interacting proteins using NWS values and Gene IDs of interacting proteins, and the Bos taurus dataset. 4. 6. Immunolocalization of ANXA1, OVGP1 and PYGL By western blotting, the primary antibodies used gave one band at the expected molecular weight in bovine post-ovulatory oviduct epithelial cells and OF (Figure  S2 ). For immunostaining, embryos of normal morphology at Day 3 were used. Embryos were incubated or not (controls) in OF and washed in Tris-sucrose as described above. Embryos were then fixed for 30 min in 4% paraformaldehyde at 35 • C then washed three times in PBS supplemented with 0. 1% (w/v) BSA (PBS-BSA). For blocking, embryos were incubated for 40 min at ambient temperature in PBS-BSA supplemented with 10% (v/v) serum from the same host species as the secondary antibody (donkey for OVGP1 and PYGL; goat for ANXA1).
Name Protein Name Accession Number (UniprotKB) Molecular Weight (kDa) 4-6 Cell OF-Treated: Control Ratio Morula OF-Treated: Control Ratio ANXA8 Annexin A8 Q95L54 37 T 1 T 1 Anterior gradient 2. protein AGR2 disulphide isomerase family F1N3J3 20 T 1 T 1 member CAPS Calcyphosin Q0VCC0 21 T 1 61 CD109 CD109 molecule F1MPE1 161 T 1 T 1 CKB Creatine kinase B-type Q5EA61 43 T 1 4. 1 CNDP2 Cytosolic non-specific dipeptidase Q3ZC84 53 T 1 17 EPHX2 Epoxide hydrolase 2 F6QS88 63 T 1 T 1 PYGL Glycogen phosphorylase. liver form Q0VCM4 97 T 1 T 1 LRBA LPS responsive beige-like anchor protein E1BND6 316 T 1 8. 2 DCXR l-xylulose reductase Q1JP75 26 T 1 T 1 OVGP1 Oviduct-specific glycoprotein Q28042 60 T 1 71 PTGR2 Prostaglandin reductase 2 Q32L99 38 T 1 T 1 SEPTIN2 Septin-2 Q2NKY7 41 T 1 4. 2 SARS Serine-tRNA ligase.
Name Protein Name Accession Number (UniprotKB) Molecular Weight (kDa) 4-6 Cell OF-Treated: Control Ratio Morula OF-Treated: Control Ratio SHTN1 Shootin 1 F1MUA7 63 5. 3 4 ALDH9A1 4-trimethylaminobutyraldehyde dehydrogenase Q2KJH9 54 4. 2 2. 9 ANXA2 Annexin A2 P04272 39 3. 6 2. 2 CLIC1 Chloride intracellular channel protein 1 Q5E9B7 27 2. 1 3 1 Protein detected only in OF-treated embryos.
Tear Staining Photographs of the left eye of each piglet were taken 11 times during the experiment to measure tear-staining: 24 to 48 h (d-2) before transport and on d2,  7, 15, 21, 28, 34, 47, 84, 110 , and 146 after transport. Measurements were made on photographs by a single experienced person, blind to treatment, using the ImageJ software  (38)  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  (34). The variable analyzed was the cumulative area covered by the stain.
Weighing Procedures Once in the grow-finish barn, pigs were weighed every 3 weeks. Three of these handling procedures were videotaped on d55, 96, and 138. The gate of the pen was opened for 15 s with no human handling, and the number of pigs exiting the pen voluntarily was counted. After this period, a single experimenter, blind to treatment, entered the pen to bring out the remaining pigs and drive the group to the scale. The variables analyzed were: the number of pigs exiting the home pen voluntarily and the duration that it took the handler to remove all pigs from the pen ending with the rear feet of the last pig crossing the pen threshold "All out duration. " Video recordings were analyzed using the XP Observer 14 software (Noldus, The Netherlands) by a single experienced trained observer, blind to treatment.