dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Retrospective data on ASF outbreaks were obtained from the OIE disease database, veterinary services annual reports available on the South African Department of Agriculture, Land Reform and Rural Development (DALRRD) website, formerly Department of Agriculture, Forestry and Fisheries (DAFF), published articles (Web of Science, PubMed, Scopus and Google Scholar). The OIE diseases database comprised the year and month of outbreaks, the province, the state veterinary area, the district, the species, and the number of outbreaks, cases, dead and killed animals. For each outbreak, an individual manual correction was applied to control for possible double or multiple entries of the same information from multiple sources.
Data analysis Descriptive analysis was performed on ASF outbreaks in order to describe their spatial and temporal (annual and monthly) distribution. At the month and year levels, temporal aggregation was performed. The month is the precision utilised by the source that is the least precise. Because certain epidemics can last for several months, the year level was taken into account. This distinction allowed also to distinguish between seasonal patterns, available with the monthly aggregation, versus annual patterns, available at annual aggregation. The chi-square goodness-of-fit test for one sample known as the chi-squared test for given probabilities was used to compare proportions of outbreaks in the different months. Fisher's exact test was used to test the relationship between months and areas of occurrence (controlled or outside controlled areas).
2013; Ruiz-Moreno et al. 2010; Wang et al. 2020). For a given distance and time separations, D 0 (s,t) given the proportional increase in cases attributable to the interaction space-time  (Basáñez et al. 2009; Diggle et al. 1995). D 0 (s,t) is: D 0 (s,t) = D(s,t) K S (s)K t (t) Where: -K S (s) defines the K-function in space -K t (t) defines the K-function in time and -D(s,t) is the K-function difference defined as: D(s,t)=K(s,t)-K S (s)K t (t) The splancs package  (Rowlingson & Diggle 1993 ) adapted for use in R software was used to estimate the global space-time interaction. The confidence envelope was obtained through Monte Carlo simulation (number of simulations = 999) to perform hypothesis testing. The outbreak that occurred in Modimolle in October 1993 was the landmark for the spatial distribution.
It is the local municipality where the first outbreak recorded in the OIE database occurred. The retrospective space-time analysis scanning for clusters with high rates using the discrete Poisson model in SaTScan 9. 4. 6 was used to detect local clusters. The detection of high rates clusters of ASF consist of comparing cases from previous space-time window with a user-defined baseline. The temporal window is limited to one month and one year selected based on the size and length of outbreaks. The spatial window was based on default input parameters. The statistical significance of clusters was determined using Monte Carlo testing set at 999 replications  (Kulldorff et al. 2005; Mathes et al. 2017).
As a result, ASF outbreaks in this study include only those that were reported to the OIE and declared by DALRRD in their database, which may be an underestimation of the scale of the problem. The detection of spatial and spatiotemporal clustering represents a preliminary step for an in-depth analysis of ASF outbreaks. A series of ASF outbreaks has been observed in South Africa, especially in the controlled area  (Magadla et al. 2016). As spatial and temporal dimension to outbreaks interrelate, as well as with other risk factors, this study adds precision to this qualitative description. This type of analysis is well suited for working with historical data on outbreak incidence and allows a visual assessment of their development in time  (Kulldorff et al. 2005 ). However, the degree of detail and completeness of reports and histories could have affected the quality of outputs.
Acknowledgements We acknowledge the  South African Provincial Veterinary Services and DAFF  for making this research possible. The authors would like to thank the  Belgian Directorate General for Development Co-operation Framework Agreement  ( 3-III DGD/ITM 2014-2016 ) (project  920108 ; collaborative MSTAH  ITM/UP)  awarded to the  Department of Veterinary Tropical Diseases, Faculty of Veterinary Science, University of Pretoria.
However, note that the data in Figures  3 4 5 are presented as percent survival per dish. These figures are meant to provide the reader with more insight into the results, such as the variation in recovery rates, but no statistical analysis was performed on the continuous data. The average recovery rates were calculated, and the values are presented in the text, but not indicated in the figures. The comparison of the treatment efficacies between the lidocaine HCl with EtOH and clove oil at individual developmental stages was assessed using Fisher's exact test (on pass/fail frequencies), with Bonferroni adjusted significance levels.
In vivo reference data curation The set of in vivo reference data used to assess the DAs performance underwent extensive curation (OECD 2021b). For the available LLNA studies collected through different sources, a set of criteria for inclusion/exclusion were agreed upon. These criteria were based on the essential test method components of the LLNA Performance Standards for the validation of modifications to the traditional LLNA as described in OECD TG 429  (OECD, 2010). According to these criteria, the test chemical must be applied topically to both ears of the mice, lymphocyte proliferation must be measured during the induction phase of skin sensitisation and in the draining lymph nodes at the site of test chemical application, a vehicle control must be included in each study, and either individual or pooled animal data may be collected.
Negative study results were also accepted if a valid scientific reason (in accordance with TG 429) was provided explaining why the highest tested concentration was lower than 50%. When multiple LLNA test results were available for the same chemical, the Median Like Location Parameter (MLLP) approach to derive an overall reference classification was applied  (Hoffmann et al. , 2018). By applying these criteria, an unambiguous classification based on LLNA reference data was obtained for 168 of the chemicals, of which 135 were classified as sensitisers (UN GHS Skin Sens. 1) and 33 were not classified (NC). For 123 chemicals with an unambiguous classification as skin sensitisers, a UN GHS sub-categorisation could also be obtained. Of these, 38 were classified as Un GHS 1A and 85 as UN GHS 1B. The same rigorous approach was applied in the review of the available Human Predictive Patch Test (HPPT) data.
To that end, a database of HPPT studies was built starting from the information collated previously at the United States National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) and complemented with test results from the scientific literature, most of which were monographs on fragrance ingredients published by the Research Institute for Fragrance Materials (RIFM). The HPPT database, available via NICEATM's Integrated Chemical Environment (https://ice. ntp. niehs. nih. gov/), contains information on chemical identity, test design, and test results and provides bibliographic information on the original test reports. Review of the data included an analysis of the sources of variability and uncertainty, and assignment of relative reliability scores to the respective studies.
An additional analysis was performed to characterise to what extent the DAs correctly identify chemicals that need to be activated either through abiotic activation (pre-haptens) and/or through biotic (enzyme-mediated) mechanisms (prohaptens) to acquire skin sensitisation potential (OECD 2021b). In the dataset of 168 chemicals, there were 29 chemicals that are considered putative pre/pro haptens, and all of them were positive in the LLNA. Of these, only nine chemicals had human data available and all were found to be sensitisers in humans. Since all chemicals in the subset were positive in vivo, consequently no specificity or accuracy were calculated. ITSv1 and ITSv2 show excellent performance for pre/pro haptens as they predict essentially all chemicals correctly, with only one inconclusive result for ITSv2.
Data Processing Data were analysed using custom-written Matlab2020a (The MathWorks, Natick, MA, USA) scripts. Stance sequences were selected based on sequences of the cannon bones gyroscopic signal. Foot-on and foot-off were determined from cannon bones IMUs' signals thanks to the method developed by Hatrisse and al. [19]. The dorso-ventral acceleration signal was integrated twice to obtain displacement values, and was high-pass filtered using a fourth-order Butterworth filter with a cut-off frequency set to 1 Hz. The choice of.
Data Processing Data were analysed using custom-written Matlab2020a (The MathWorks, Natick, MA, USA) scripts. Stance sequences were selected based on sequences of the cannon bones gyroscopic signal. Foot-on and foot-off were determined from cannon bones IMUs' signals thanks to the method developed by Hatrisse and al. [19]. The dorso-ventral acceleration signal was integrated twice to obtain displacement values, and was high-pass filtered using a fourth-order Butterworth filter with a cut-off frequency set to 1 Hz. The choice of this cut-off frequency was made after checking that the high-pass filter suppresses the slow drift but does not distort the shape of the signal, following the recommendations of Serra Bragança et al. [20]. Vertical displacements of the head, withers and pelvis were segmented into strides.
Statistical Analysis To evaluate the repeatability and the inter-operator reproducibility, for each AI presented above and each condition (i. e. , straight line, left and right rein circle), the total variance, as well as the corresponding SD, and the variance attributable to the operator were calculated. To do this, a model of analysis of variance was computed with operators and horses as random effects  [22]. As the rank of repetition and the experience of the operator could also be a source of variation, the model was adjusted on these two factors as fixed effects. The statistical analysis was performed with the RStudio software 4. 0. 3 (RStudio Inc. , Boston, MA, USA).
Fecal sample collection and processing Fecal samples were collected two times per week for 6 months from African elephants (n ¼ 2), 4 months from greater one-horned rhinoceros (n ¼ 4), and 3 months from white rhinoceros (n ¼ 2) and hippopotamus (n ¼ 2). Samples from okapis (n ¼ 2) and giraffes (n ¼ 4) were collected three to four times per week for 3 months. Only one fecal sample per week could be collected from giant anteater (n ¼ 1) for 3 months. Keepers collected only fresh fecal samples (recently defecated) in freezer bags before being stored at À20 C until the extraction process. For all females (except those collected in blood), each fecal sample was divided into two aliquots. One was analyzed directly at the ZooParc de Beauval clinic with mini VIDAS® PRG and the second was sent to the endocrine laboratory of the Vetmeduni Vienna, Austria (Unit of Physiology, Pathophysiology and Experimental Endocrinology) for a fecal progestagen assay (okapi:  [11, 12] ; greater one-horned rhinoceros:  [13] ; data unpublished for common hippopotamus and giraffes).
Data analysis Statistical tests were performed on the Rstudio software. Data are presented as mean ± SEM. Parallelism test was carried out by comparing the standard curve with serial dilutions of fecal extracts from each species by a t-slope comparison test. A baseline progestagen was calculated using an iterative process  [15]. Thus, each value above mean plus 1. 5 standard deviations (SD) was discarded. The elimination process was repeated by recalculating the average until no value exceeded the mean plus 1. 5 SD. The beginning of the luteal phase (LP) was defined as the first point after an increase in values above the baseline for at least three consecutive values. The end of the LP and the beginning of the follicular phase (FP) was established as the first of the two values returning to the basal level  [15].
Estrous cycle length was calculated from the beginning of one luteal phase to the beginning of the next (or using the follicular phase if the kinetics start in the luteal stage). A period longer than twice the length of a normal follicular phase between two luteal phases was considered as anoestrus (8e14 weeks for African savannah elephant  [16, 17] ; 30 days for Southern white rhinoceros  [15, 18] ; 30 days for greater one-horned rhinoceros  [13, 19] ; 15 days for giraffe  [20, 21] ; 15 days for okapi  [11, 22] ; 20 days for giant anteater  [23] ; and not determined for common hippopotamus). The normality of fecal progestagen profiles was examined using the Shapiro-Wilk test. Significant differences in the non-parametric data were evaluated using the Mann-Whitney test for two groups (e. g.
Acknowledgments This work was supported by the  Beauval Nature association. We want to thank the  ZooParc de Beauval 's veterinary service and the animal keepers for blood sampling and fecal sample collection. We also thank  BioM erieux  for supplying the  VIDAS® Progesterone (PRG)  assay kits (7 boxes of 60 tests were offered). The mini VIDAS® used in this study was in the possession of the zoo. Finally, we thank the  endocrine laboratory of the Vetmeduni Vienna, Austria (Unit of Physiology, Pathophysiology and Experimental Endocrinology)  for its participation in the project.
Sows and ground-truth measurements Measurements were made on ten lactating sows from the Large White breed, on the GenESI INRAE experimental farm of Le Magneraud (France; doi: 10. 15454/1. 5572415481185847E12). Sows were studied up to two weeks after farrowing. Their behaviour was monitored with FMCW radars for a minimum of five hours. Video recordings were made in parallel to assess the reliability of the radar measurements. Postures were annotated manually from these recordings frame by frame every 100ms. The synchronisation between the radar and the video measurements was performed a posteriori by detecting manually the kneeling posture. This posture was chosen as reference because it was easily distinguished on the radar signal and was observed on all sows. We used five hours of annotated data per sow and considered five postures (Figure  1 ): standing; sitting; lying (i.
Radar signal analysis and electromagnetic clutter mitigation The raw data (radar signal) are available upon request to the corresponding author. To estimate the posture of the sows, we analysed the signal delivered by the two radars over time. We computed the beat frequency spectrum (i. e. , echo level magnitude as a function of the distance) by applying a Fourier transform  (Stove et al. 1992)  on the time signal. The result was a 2D raw data image of echo levels from which the posture of the sow could be estimated (Figure  2A ). However, potential electromagnetic clutter, for instance due to the electromagnetic backscattering from the crate or from the piglets close to the sow, may alter the estimation accuracy of the posture. In order to mitigate this clutter, we used time-gating and spatial filtering.
First, we applied a mean filter on the data across the slow time axis (Figure  2B ). The mean was estimated over 1s, which corresponds to 20 consecutive radar measurements. Next, we applied Gabor filters to reduce the impact of uncorrelated data over time  (Feichtinger and Strohmer 2012 ; see Figure  2C ). Finally, we used an average filter (Figure  2D ). The main advantage of using these filters is that they are simple to implement and suitable to real-time applications. In the final filtered 2D radar image (Figure  2D ), some distinguishable behaviours of the sow could be detected at the distance of 1m, such as the transition to the standing posture around 1300s, 2700s and 3400s. These images obtained from two radars were used as input data for the posture classifier (see below).
To build a model, data from each sow was separated into two subsets containing respectively the first 60% and the last 40% of each posture data. The first subset was used for the training of the CNN. The second subset was used to estimate the quality of the predictions by checking that the results on the training dataset were not due to overfitting. During the training phase, we used 32 annotated data between two adaptations of the neural network weights. The CNN weights were adjusted following the method described by  (Kingma and Ba 2014)  which used a stochastic gradient descent and momentum estimation to optimise the neural network weights. The cost function was the standard function used by  Murphy (2012)  for multi-class classification. This function computes the minimal cross entropy for each output class.
During the initial training, the five postures of interest were considered (Figure  1 ). After the training phase, each CNN was tested on the validation dataset. The performance of the classifier in terms of the number of sow posture images that were correctly classified as well as the misclassified cases for each class was assessed with the confusion matrices. In the multi-class classification task, the evaluation metrics are computed based on these confusion matrices. We used three evaluation metrics to estimate the quality of the classifier: the specificity, the sensitivity and the accuracy  (Fawcett 2006). The specificity (sp) indicates if the classifier gives negative results when the considered posture is not seen. The sensitivity (sb) indicates if the classifier gives positive results when the considered posture is seen.
Nursing and lying were confused by 10% on average. Kneeling had the lowest sensitivity (9. 1%), but a high specificity (99. 5%) and a high accuracy (99. 4%). Our method can thus detect four out of five postures with a high specificity, a high sensibility and a high accuracy. The pie charts of the ground-truth measurements and estimated measurements of sow postures obtained from the validation datasets are shown in Figure  5. The relative time spent by each sow in each posture over a 2-hours period (corresponding to the validation dataset) was correctly estimated, with an error of less than 10% for all postures except kneeling. The difficulty in correctly estimating kneeling is likely due to the lack of sufficient radar data to analyse this posture with a convolutional neural network. Kneeling lasted only a few seconds (mean: 16s, standard deviation: 7s), which is considerably less than the other postures that lasted several minutes or hours (Table  2 ).
New possible analyses Beyond being non-invasive, accurate and fast, the radar approach could be used to monitor sow carefulness when lying down  (Pokorná et al. , 2008). In the past, posture changes were rarely characterised by considering the passage through transient postures, i. e. , kneeling and sitting (but see  Pokorná et al. , 2008; Wischner et al. , 2010)  and if so, have involved small groups of animals. Automated monitoring with radars in a farrowing crate enables such accurate recording of whether the sow lies down carefully. This feature can be linked to piglet crushing events. Our data also indicate it is possible to detect piglets with radars. Prior to this study, preliminary measurements were performed in which the sow's postures were estimated and the standard deviation was calculated over 20min, while the sow remained in a given posture.
The use of radar with sows free of movements can be more challenging. Then, it should be possible to detect the sow by using a single 3D radar with a larger beam width, including position and posture simultaneously. To do so, the radar should be placed centrally on the ceiling above the pen to cover the entire pen with the radar beam. Finally, adding data would also make it possible to define a single classifier to estimate the posture for all sows and develop more detailed behavioural analyses  (Bonneau et al. , 2021). So far, the relevance of radars to detect sows that are injured (e. g. excessive time spent lying) or that are not careful towards their piglets (e. g. fast transition from standing to lying) has not been studied. Data recorded for longer periods on animals contrasted on these aspects could be collected with the radar technology.
Funding The Authors thank the  Region Occitanie Council (France)  for financial support through the  SIDIPAR  project. While writing, ML, DH and HA received funding from the  French National Research Agency (ANR)  to ML and  HA  ( ANR-19-CE37-0024-3DNaviBee ). AD and ML were also supported by a  European Research Council (ERC)   Consolidator grant  to ML ( GA101002644 -Bee-Move ).
For each sample, urine specific gravity (USG), urine Na (uNa) and K (uK) concentrations, torasemide, as well as urinary creatinine concentration (uCr) and aldosterone (uAldo) concentrations were assessed. Additionally, urinary Cl concentration (uCl) was measured in the baseline cystocentesis sample and the 36 to 48 hours samples. To correct for urinary dilution, uNa and uK were indexed to uCr. They also were used to calculate the uNa : uK ratio, a short-term marker of RAAS activation. 21 Similarly, uAldo was indexed to uCr to calculate the uAldo : C ratio, an additional indicator of RAAS activity. Plasma and urine concentrations of torasemide were determined using a high performance liquid chromatography (HPLC-MS/MS) method  10  and uAldo was determined by radioimmunoassay  23  (see supplementary material, Data S2).
| Pharmacokinetic and pharmacodynamic modeling Individual plasma data sets were used to perform a noncompartmental analysis using Phoenix WinNonlin software (version 6. 3). Classical pharmacokinetic parameters were calculated. Absolute bioavailability (F) was calculated using data obtained after the single PO and IV doses of 0. 2 mg/kg, using the following equation: F ¼ AUC INF PO=AUC INF IV ð Þ Â DIV=DPO ð Þ Â 100: where AUC INF is the area under the concentration-time curve extrapolated to infinity and D the actual administered dose. The individual dose-corrected C max and AUC INF values were obtained after the PO doses were tested for proportionality using analysis of variance followed by regression analysis. For C max and AUC INF to be proportional to the dose, the regression line obtained with the measured values was compared with the regression line obtained with inclusion of the 0 coordinates.
The slopes of both regression lines should not be significantly different. Using the urinary concentration of torasemide and the volume of urine excreted during the different time intervals, cumulative excreted amount and excretion rates were calculated. Using the actual administered doses, the fraction of the dose eliminated in urine (%D) as unchanged torasemide was calculated. Absolute bioavailability (F) also was calculated using urinary data obtained after the single PO and IV doses of 0. 2 mg/kg. The following equation was used: F ¼ X ∞ u PO=X ∞ u IV À Á Â DIV=DPO ð Þ , where X ∞ u is the total amount of torasemide eliminated in urine. The individual dose-corrected X ∞ u values obtained after the PO doses were tested for proportionality using the same procedure used for plasma data. Using both plasma and urine data, renal clearance (Cl R ) was calculated using the following equation: Cl R ¼ X ∞ u =AUC INF : Regarding pharmacodynamics, diuresis in mL/h was plotted against excretion rates using the midpoint of the collection time intervals (Figure  2 ).