dataesr/scientific-paragraphs-categorization huggingface dataset:
This resulted in 4250 cases where at least five SBP measurements were known to have been made and the duration of SBP measurement was recorded, and this population was used for analysis of factors affecting the duration of SBP measurement (Figure  2 ). The demographics of the 8884 cats in the final dataset are shown in Table  1  and Figure  3. Database entries were received from a total of 811 clinics from 16 countries. The median number of entries per clinic was 6. 0 (range 1-162, interquartile range [IQR] 2. 0-16. 0). The age of the cats (n = 8884) ranged from 7 to 26 years old (median 13. 0; IQR 10. 0-16. 0). Data on body weight were available from 8517 cats, but the weights of seven cats were excluded as outliers and potentially unreliable based on published data,  [45] [46] [47] [48] [49] [50] [51] [52]  three weighing >15 kg (range 16-59 kg) and four <1.
Discussion This study represents the largest published data of feline SBP measurements to date, and the first to look at measurements conducted in primary care veterinary clinics across numerous countries. The initial aim was to collect data from only European countries, but 26 entries were received from three South American countries (Table  1  and Figure  3 ) and were also included in the final analyzed dataset, which comprised nearly 9000 unique cat entries. The study was designed to capture data from cats ⩾7 years of age where SBP was already being measured as part of their clinical evaluation. An age cutoff of 7 years was applied as hypertension is more common in older cats,  5, 10, 17, 18  which is also in line with the International Society of Feline Medicine recommendations for routine measurement of SBP in cats.
In our study, analysing data for the 4250 cases where five individual SBP values had been entered (Table  5 ), the time taken to assess SBP was reported to be ⩽10 mins in 89. 7% (n = 3812) and <5 mins in 43. 2% (n = 1837). These findings are comparable to a previous study, which reported that five Doppler SBP readings were obtained in <5 mins from 37. 5% of cats. 23 Interestingly, in that study,  23  one specific oscillometric device was also evaluated and it was reported that five oscillometric readings could only be obtained from 5% of cases in <5 mins and that 55% of cases took >10 mins. In our study, although the specifics of the oscillometric devices used were largely unknown, we found no significant difference in the overall time taken between Doppler and oscillometric devices (Tables  5  and 6 , Figure  11 ).
Sets of simulations Various sets of simulations were considered. These different alternatives were obtained by considering: Different proportions of pen and individual disturbances (only pen disturbance, only individual disturbance, or both disturbance types), Different correlations (negative, neutral, or positive) between the production traits and the two components of the robustness (resistance and resilience), Different correlations (negative, neutral, or positive) between resistance and resilience, Different heritabilities for resistance and resilience (moderate or low). The various situations listed above are summarized in Table  2 , and they led to 54 different alternatives. For each alternative, 1 000 independent datasets were generated by Monte Carlo simulations. For simplicity, in the rest of the document, the term 'robustness' will refer to resistance and resilience.
Baseline cardiovascular variables (all dogs) The baseline data for cardiovascular variables for all dogs is shown in Supplement Tables  1  and 2. N-terminal Pro B-type Natriuretic Peptide (NT-proBNP) was below the laboratory reference range of 900 pmol/L in all dogs at baseline (median 285 pmol/L, IQR 250-377) excluding significant increased myocardial wall stress. Baseline high-sensitivity cardiac troponin I (hs-cTnI) was missing for one dog; one dog had a moderately increased hs-cTnI at baseline (1. 5 ng/mL, reference interval [RI]: < 0. 07), echocardiography of this dog showed stage B1 myxomatous mitral valve disease (MMVD), mild hypertrophy of the IVS and LVFW and subjective volume depletion; Doppler blood pressure was 180 mmHg (assumed to be stress induced due to patient anxiety). Cardiac troponin I for all other dogs at baseline was normal (median 0.
Weight reduction regimen Details of the weight reduction programme have been previously described  [33, 34]. In brief, dogs were determined to be clinically well with no systemic diseases that might affect the ability to achieve weight reduction (based on physical examination, haematology, serum biochemistry, urinalysis and free thyroxine performed during initial visit). Body condition score was assessed using a nine-point scale as previously described  [1, 35]. At this initial visit, body composition was analysed by fan-beam DEXA, as previously described  [36]. Briefly whole-body DEXA was performed under sedation, with dogs in dorsal recumbency, providing measurements of fat mass, lean mass and bone mineral content. These data were used to create individualised weight reduction plans for each dog, establishing both an ideal and a target weight (which considered age and severity of obesity).
For LV wall thickness, comparison pre-and post-weight-reduction was made between the non-normalised values, as well as those derived from allometric scaling (both to the actual and the target bodyweights). Effect size was calculated as: r = Z / √N; r = 0. 1 was considered small effect, r = 0. 3 medium effect and r ≥ 0. 5 large effect  [46]. Missing data sets included DEXA for one dog, hs-cTnI for one dog at inclusion, cardiac biomarkers for one dog post weight reduction and SBP for three dogs post weight reduction, these were not accounted for statistically but were taken into consideration when interpreting the results. The level of statistical significance was set at P < 0. 05, for two-sided analyses.
The recent advances in sensor technologies and data analysis could improve our capacity to acquire long-term and individual dataset on animal behavior. In livestock management, this is particularly interesting when behavioral data could be linked to production performances, physiological or genetical information, with the objective of improving animal health and welfare management. In this study, we proposed a framework, based on computer vision and deep learning, to automatically estimate animal location within pasture and discuss the relationship with the risk of gastrointestinal nematode (GIN) infection. We illustrated our framework for the monitoring of goats allowed to graze an experimental plot, where feces containing GIN infective larvae were previously dropped in delimited areas. Four animals were monitored, during two grazing weeks on the same pasture (week 1 from April 12 to 19, 2021 and week 2, from June 28 to July 5, 2021).
Two different CNNs were also tested, resNet-50, and inceptionV3. In each case, only the parameters of the last 10 layers were retrained. When labeling the training images for YOLO, the color of the animals was also labeled. Thus, the 3,200 training images labeled for YOLO were used, to extract 12,236 images with color labels. In total, approximately 3,400 images were available for the white goat and 2,900 images for the other goats. A total of 70% of the dataset were used to retrain the CNNs and 30% to evaluate their performance. resNet-50 provided higher sensitivity and precision values and was used for the analysis. Compared to other image classification problem, an extra information was available: two detections cannot be in the same class. Instead of using the prediction of the CNN directly, we used it to compute the probability of each bounding box being from an animal of the four different colors.
Evaluation To evaluate the capacity of the method to detect and identify animals, a MATLAB application was designed to select randomly an image on the data bank and displayed the detected animals with their estimated color. For each color (i. e. , white, brown, black, and red), the user first selected if the animal was detected, non-detected, or absent (i. e. , inside the resting area). When the animal was detected, the user also had to select the estimated color. A second script was designed to manually record the location of the missed detection. We ran the application to control more than 600 images for each week. To assess the capacity of the method to detect the animals, we computed the percentage of detected animals. For each color, the percentage of detection is equal to 100 * nbD nbD+nbND.
Statistical Analyses All the statistical analyses were performed using MATLAB R2021a. The correlation between the individual LAF obtained on week 1, denoted LAF i for animal i = 1,. , 4, and the increase in the weekly avoidance on week 2, denoted AV i , was studied using the Pearson's correlation coefficient. It is equal to: 1 3 4 i=1 LAF i -µ LAF σ LAF AV i -µ AV σ AV , where µ LAF and σ LAF are the mean and standard deviation of the individual LAF obtained on week 1. In addition, µ AV and σ AV are the mean and standard deviation of the increase in the weekly avoidance on week 2. We also compared the avoidance index of each animal between weeks 1 and 2, to detect changes in the avoidance capacity using a Wilcoxon rank test, with 5% significance level. More precisely, if av w i = av w i1 ,.
Funding statement Partial financial support for this investigation was provided by the subventions granted to AA by the  French Embassy in Cuba  and to AAV by  Institut de Recherche pour le Développement  ( BEST grant ). This study is set within the framework of the  "Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ). BG was supported by  ANR  JCJC  INVIMORY  (number  ANR 13-JSV7-0009 ) from the  French National Research Agency (ANR).
Acknowledgements This research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership coordinated by  TDR , The Special  Programme for Research and Training in Tropical Diseases  hosted at the  World Health Organization. The training model is based on a course developed jointly by the  International Union Against Tuberculosis and Lung Disease  and  Médecins Sans Frontières-Luxembourg (LuxOR). The specific SORT IT program that led to these publications included a joint implementing partnership between TDR the  Institute of Tropical Medicine Antwerp, Belgium , the  University of Gondar, Ethiopia , The WHO country office in Ethiopia,  Médecins Sans Frontières, Luxembourg (LuxOR) , and The  International Union Against Tuberculosis and Lung Disease, Paris France.
020. P. 0003; RMCA 2022. 020. P. 0004) and three others at the Royal Belgian Institute of Natural Sciences (RBINS 952; RBINS 953; RBINS 954). Sequences of C. guineensis were deposited in GenBank from a previous study  [62]  (Sebkha Imlili: MG755500; MG755474 and Oued Aabar: MK955801). Some fish were dissected fresh in the field, and others were frozen prior to dissection. Collection of the parasites was carried out by examination of the intestine and the body cavity under a dissecting microscope, and squashes of kidney, mesentery, spleen, gonads and skeletal muscles of the fish under a compound microscope. Gills were also examined under a dissecting microscope for a subsample of fish (N = 132). When fish were dissected fresh, acanthocephalans were relaxed in bottled (drinking) water for about 15 min at ambient temperature prior to fixation.
Light and scanning electron microscopy (SEM) Acanthocephalans fixed in 96% EtOH were rinsed in water and examined in wet mounts to determine the sex ratio and stage of maturity of females. Measurements of morphological features were taken using a microscope (Leica DM 2500) equipped with a digital camera (Leica DMC 4500) and LAS version 4. 12. 0 software (all from Leica Microsystems GmbH, Wetzlar, Germany). A subset of specimens fixed in NBF was stained in acetocarmine or Meyer's hematoxylin, dehydrated in a series of EtOH, and mounted in Canada balsam or Kleermount. For SEM, acanthocephalans and excysted metacercariae fixed in NBF were dehydrated in an EtOH series and chemically dried overnight using hexamethyldisilazane (HMDS), coated with gold using a sputter coater (JEOL JFC-2300HR, Japan), and observed with a SEM JEOL JSM IT 100 (Japan) under 10 kV.
Molecular study DNA from parasites fixed in 96% EtOH was isolated using a DNeasy Blood and Tissue kit (Qiagen, Germantown, MA, USA), following the manufacturer's protocol except for decreasing the elution volume to 100 lL. Based on the results of the morphological analysis for the acanthocephalan, we chose three nuclear markers for molecular identification of parasites based on sequences available in GenBank (Table  1 ): portions of the large (28S) and small (18S) subunit and the internal transcribed spacer (ITS) region of the ribosomal RNA (rRNA) gene. For Table  1. Acanthogyrus (Acanthosentis) species and sequences used in this study, including host(s), locality, GenBank accession numbers, and sequence lengths. If only one sequence length is listed for multiple accession numbers, the sequences are the same number of base pairs; otherwise, ranges are reported.
Accession numbers in bold are from this study. There are mitochondrial cytochrome c oxidase I (mtCOI) sequences in GenBank for Acanthogyrus cheni (KX108947) and A. kenyirensis (MN833316), but our efforts to amplify mtCOI for A. (A. ) cf. tilapiae were unsuccessful. *As of this submission, the species names were not updated in GenBank. 28S = large subunit ribosomal RNA (rRNA) gene, 18S = small subunit rRNA gene, ITS = the internal transcribed spacer region of the rRNA gene.
For the 18S rRNA gene region, we designed two internal sequencing primers using Primer-BLAST  [102]  in order to generate bidirectional sequence for this marker (Table  2 ). Complementary sequences were assembled, compared to their chromatograms, and edited accordingly using Sequencher version 5. 4 (Gene Codes Corp. , USA). Resulting sequences were compared to those in GenBank using BLASTN (Basic Local Alignment Search Tool  [6] ) and deposited in GenBank. All sequences from each marker in this study were then aligned with one another to examine any differences among specimens. The longest sequence for each respective marker was then aligned with sequences from GenBank (see Table  1  for acanthocephalans; see below for digeneans) except for the digenean ITS rDNA sequences for which the BLASTN queries resulted in an identical match to an existing GenBank sequence.
Clus-talW was used to generate acanthocephalan 18S rDNA and digenean 28S rDNA sequence alignments in MEGA11  [95]. NGPhylogeny. fr webservice  [61]  was used to implement MAFFT  [54]  for acanthocephalan 28S and ITS rRNA gene sequence alignments. Multiple alignments of the 28S rDNA data were generated in order to include as many nucleotides as possible in p-distance calculations given the high variation in length of GenBank sequences. NGPhylogeny. fr webservice  [61]  was also used to implement Gblocks  [27]  for selection of conserved regions of the resulting acanthocephalan ITS rRNA gene sequence alignment for use in subsequent p-distance calculations. All alignments were trimmed to remove any gaps on the terminal ends, and p-distances were calculated in MEGA11  [95].
Molecular identification of the acanthocephalan Partial 28S rRNA gene sequences (n = 6 from C. guineensis, n = 2 from O. aureus; 686-2649 bp) from the acanthocephalan were 99. 9% similar to one another where there was overlap, and 92-96% similar (98% BLAST query coverage) to sequences from A. (A. ) bilaspurensis Chowhan et al. 1987  [29]  collected from carp Cyprinus carpio in Pakistan (OM333893, OM333899). The A. (A. ) maroccanus Dollfus, 1951  [33]  sequence collected from a specimen found in barb Luciobarbus callensis from Algeria (MK953673  [70] ) only encompasses the D1-D3 regions of the 28S rRNA gene and differed by 16% based on a 757-bp alignment. The 28S rDNA sequence in GenBank of A. (A. ) tilapiae collected from an unknown host in the "Atlantic Ocean" (U53000) is very short (311 bp containing the D7 region;  [28] ) and was only 0.
However, the types, although listed in the original manuscript as having been deposited at the Museum of Natural History in Paris, France, are non-existent and our efforts to obtain acanthocephalan specimens from this area were fruitless. Further, there are no sequencing data for A. (A. ) nigeriensis, A. (A. ) malawiensis, or A. papilio in GenBank. The fifth species, A. (A. ) tilapiae  [19] , has a broad distribution throughout continental Africa and Madagascar and is reported from over 10 species of tilapia (e. g. ,  [7, 10, 66, 68] ) but also in the pufferfish Tetraodon fahaka (see  [98]  in  [10] ) and bagrid Labeo cylindricus  [10]. Recent studies using SEM have brought up new morphological details  [9, 68] , in particular regarding body spine distribution, size, and shape. Given our data and the very little information available on A.
ITS rRNA gene sequences (n = 6, 353-369 bp long) were identical to one another and to that from a specimen of Pygidiopsis genata Looss, 1907 (AY245710) collected from cormorant Phalacrocorax carbo in Israel  [36]. Our partial 28S rRNA gene sequences (n = 6, 862-877 bp) were identical to one another, and when aligned with 28S rRNA gene sequences from the only two species of Pygidiopsis in GenBank, P. summa Onji and Nishio, 1916 obtained from an experimental infection of an unknown host (AF181885) and P. macrostomum Travassos, 1928 from experimentally-infected hamster Mesocricetus auratus in Brazil (MF972527-MF972531, KT877409) and from greater bulldog bat Noctilio leporinus in Mexico (MW332629), p-distances were 9. 5% (243-bp alignment) and 12. 5% (816 bp-alignment), respectively. Host Unidentified metacercaria (Fig.
It is also plausible for such a marine parasite to have persisted over a long period of time in an environment where its intermediate hosts would already be present, as all organisms associated with the Sebkha are coastal or estuarine and known from other areas in Morocco  [48]. To add to the argument, there is no host detail for the sole 28S rDNA sequence of A. (A. ) tilapiae available in GenBank  [28]  other than the notation that it came "from the Atlantic Ocean and [was] provided by Dr. Mattei" (p. 579-580), who was a researcher in Dakar, Senegal (https://en. wikipedia. org/wiki/Xavier_Mattei#Career). Hence, it is likely that this specimen, identified as A. (A. ) tilapiae  [13] , was from the Dakar areathe area where A. papilio was described (Joal-Fadiouth, Senegal  [99] ) and where C.
guineensis also occurs  [57]. In short, it is likely that the short GenBank sequence could as well have been from specimens of A. papilio. The 99. 9-100% similarities among sequences (using all three markers) that we generated from acanthocephalan specimens taken from C. guineensis and O. aureus would imply that A. papilio is found in both marine and freshwater systems. This conundrum can be reconciled if we question the validity of A. papilio, as its description is based only on immature specimens and overlaps somewhat with that of A. (A. ) tilapiae. We should also consider that the acanthocephalan species could be an altogether different and cryptic species because it has been isolated for a long period of time, and given that the Coptodon from the Sebkha Imlili is possibly an "incipient" species of tilapia  [2].
7orpho-anatomical metrical data for female specimens (immature with or without ovarian balls, and gravid) and male specimens (all stages of development combined) of Acanthogyrus (Acanthosentis) cf. tilapiae from Coptodon guineensis at Sebkha Imlili. Data are averages in lm, followed by ranges in parentheses and number of specimens studied in italics. L = length; W = width; na = data not available. region28S rDNA GenBank sequences for Acanthogyrus spp. in GenBank are unpublished (OP476684-OP476687), and pdistances ranged from 6. 3% to 11. 1% based on the 327-bp alignment (and 6. 7-11. 0% based on a 1630-bp alignment). Partial 18S rRNA gene sequences (n = 6 from C. guineensis, n = 2 from O. aureus; 1005-1703 bp) were 100% similar to one another and were again most similar to A. (A. ) bilaspurensis sequences (98.
: Coptodon guineensis (Günther, 1862) Sites of infection: surface of stomach and liver, kidney, ovaries, more rarely muscle Locality: Sebkha Imlili, Morocco (23°16 0 35. 21 00 N, 15°5 4 0 55. 47 00 W; 23°16 0 21. 35 00 N, 15°55 0 17. 42 00 W) Other hosts and localities: Oreochromis aureus (Stein- dachner, 1864) at Guelta Mrimima, Drâa Valley (MK955803), Morocco (29°47 0 03. 6 00 N, 07°10 0 20. 1 00 W) Date of collection: July 2019 GenBank accession numbers: 28S rDNA from host C. guineensis: OP498346-OP498349 / O. aureus: OP498350- OP498351. ITS region rDNA from C. guineensis: OP498336- OP498338, OP481215 / O. aureus: OP498334-OP498335.
g. snort, snore and blow in horses:  Stomp et al. , 2018;  sneeze in African wild dogs:  Walker et al. , 2017). We observed both single-and multi-unit sounds in our dataset consisting of 2656 sounds, thus we coded all sounds at the unit level, in terms of unit types and number of units. As transitions between expiratory and inspiratory phases have been estimated to be quite short in cheetahs, with durations in the range of 50-200ms in purrs for example  (Eklund et al. , 2010) , we set up the threshold for two units (of any type) to be considered as parts of the same sound to ∆=200ms. This way, we could code for single-type sounds, including either one (Single) or several (Repeated, in series) units of the same type, as well as multi-type sounds made of a mix of several units of different types, either produced joined (Transitions, with ∆=0ms) or juxtaposed (Combinations, with 0<∆≤200ms).
glmer' functions in R RVAideMemoire package, respectively). To test for the significance of the fixed factors and their interactions, we applied a type III ANOVA to the model (using 'Anova' function in R car package). We additionally verified the goodness of fit of the model (using 'r. squaredGLMM' in R MuMIn package that computes the pseudo-R-squared for GLMM), and conducted comparisons with null models including either a constant value or including only sound type as a fixed factor (using 'anova' function in R stats package). Then, we conducted post-hoc pairwise comparisons between the different categories of individuals (females vs males, early vs late-reared) within each sound type (using 'lsmeans' function in R lsmeans package), with p-values adjusted for multiple comparisons (Sidak method).
Non-vocal sound types The four non-vocal (unvoiced) sounds could be distinguished based on their production source: the airflow was being forced through either the mouth or the nose (Figure  2e ). 2. 1. From the mouth -Spits were explosive sounds, whereas Hisses were aperiodic sounds corresponding to a harsh blow with the mouth open. 2. 2. From the nose -Snuffs and Sneezes were abrupt blowing nasal sounds, Sneeze being characterised by an additional introductory unit (either a short tonal unit, or a short series of pulses). --Figure  1  ---Figure  2  -Single-type sounds: Single and Repeated Although they were mostly produced as isolated utterances (Single = 79. 4% of the dataset, N=2108), sounds could also be made of a series of units of the same type (Repeated = 10. 9%, N=289). Purrs were the most frequent Repeated pattern, as they were produced mostly as long series of expired units (94.