dataesr/scientific-paragraphs-categorization huggingface dataset:
The reads were aligned to the EquCab3. 0 reference genome (84) (GCF_002863925. 1)using BWA-MEM 0. 7. 17. SAM files were converted to BAM files using SAMtools, and Picard 2. 10. 3 was used to sort the BAM files and remove potential PCR duplications. The BAM files were recalibrated in two steps using BaseRecalibrator and PrintReads. Variant calling was performed using HaplotypeCaller with a list of known variants in horses downloaded from the NCBI web page annotation release 103 (https://www. ncbi. nlm. nih. gov). Structural variants were called using default parameters in FindSV comprising the software programs Manta, TIDDIT and CNVNator (https://github. com/J35P312/FindSV). Next, as input for Oxford Nanopore sequencing (ONT, Oxford, UK), a 25 kb PCR product was designed to span the 19. 6 kb sweep region and adjacent repeats.
Indwelling Rumen pH Measurements On d 35, all 48 calves were orally dosed with a small ruminant pH logger (SRL; Dascor Inc. ) to measure rumen pH every 2 min until the end of the trial (i. e. , harvest) to assess rumen pH before, during, and after weaning. The protocol was based on previous studies by  Laarman et al. (2012)  and  Wood et al. (2015) , where the SRL was calibrated before rumen insertion and then dosed while calves were held in a reclining position to allow passage into the rumen. At harvest, the SRL were retrieved and recalibrated, and the raw data were downloaded for further analysis. Average, minimum, and maximum pH were calculated on a daily basis, and further processing of the raw rumen pH data was based on the process described by  Villot et al. (2018). Briefly, raw data were organized in Microsoft Excel, and a Visual Basic for Applications program was used to create a baseline pH measurement, which was calculated from the data taken before weaning (d 35 to 42); subsequent calculations were then made to compare the data obtained during weaning (d 43 to 49) and after weaning (d 50 to harvest) back to the baseline measurement to allow for assessing minutes per day that pH was 0.
Statistical Analysis Average daily gain data were used for sample size determination, as this was part of a larger study  (Yohe et al. , 2022) , with the power analysis utilizing a power of 80%, an α-level of 0. 05, and a minimum difference in BW gain of 100 g/d showing the minimal sample size of 8 calves per treatment. Absolute and relative rumen pH, GIT dissection pH, fecal composition, apparent nutrient digestibility, fecal and rumen VFA, and systemic inflammatory marker measurements were analyzed using the MIXED procedure of SAS 9. 4 (SAS Institute Inc. ). The study was designed as a complete randomized design with 2 × 2 factorial arrangement of treatments with the model including the fixed effects of MRA and SPS with repeated measures data including week (WK) and their interactions. The random effect was calf within dietary treatment, and calf was the experimental unit.
DISCUSSION The goal of this study was to examine whether feeding a higher amount of MR and a higher-starch starter would lead to an increased inflammatory response due to a buildup of nutrient fermentation end products and subsequent decrease in GIT pH during and after weaning. These data are from a larger study that has previously reported intake, growth, and visceral tissue growth and development from the same calves  (Yohe et al. , 2022). Our findings indicate limited effects at the individual treatment levels but multiple differences in nutrient fermentation and subsequent pH and systemic inflammation at the main effects and interactions of MRA, SPS, and WK. One of the most important aspects for proper rumen function is pH, which was measured using an indwelling bolus around the time of weaning.
ACKNOWLEDGMENTS The authors thank the funding agencies for the financial support provided for this study:  Provimi North America (Lewisburg, OH) , a division of  Cargill Animal Nutrition, Mitacs (Toronto, ON, Canada ),  Natural Sciences and Engineering Research Council of Canada (Ottawa, ON) ,  Alberta Milk  ( Edmonton, AB, Canada), Westgen (Abbotsford, BC, Canada), BC Dairy Association (Burnaby, BC, Canada), SaskMilk (Regina, SK, Canada ), and the  Dairy Farmers of Manitoba (Winnipeg, MB, Canada ). The authors thank the staff at the  Provimi Nurture Research Center (Lewisburg, OH)  for daily care and husbandry of calves, as well as everyone from the laboratories of  M. A. Steele ,  J. H. C. Costa , and  A. H. Laarman  who helped during the harvesting procedures. The authors have not stated any conflicts of interest.
The 95% CI of relative sensitivity was estimated by: Se ± 1. 96 √ Se (1 -Se) n , where n was the total number of calves. In addition, inter-test agreements between scores attributed with the reference technique and the two alternative techniques were assessed using the weighted κ ( W ). The symmetry of consolidation lesion distribution between the right and left sides of the thorax and the performances of different unilateral ultrasonography techniques were described and discussed in Supporting Information 1. All analyses were carried out using the κ function of the R software 'vcd' package (R Foundation for Statistical Computing, Vienna, Austria, http://www. R-project. org).
These two durations were estimated by carrying out a series of LUS examinations on another farm including calf handling and restraint, clipping, examination and data recording. The authors prefer to clip the thorax before LUS examination considering it has the advantage of improving probe contact and saving time afterwards for the LUS itself. However, this step was not performed in most of the recent studies because it is time consuming and seems not to impact the ability to identify ultrasonography lesions  (Buczinski et al. , 2014; Buczinski et al. , 2013; Dunn et al. , 2018; Ollivett et al. , 2015; Pravettoni et al. , 2021; Teixeira & McArt, 2017; Timsit et al. , 2019). If it is preferred not to clip the calves, the alternative techniques are even faster.
Data Collection Throughout the data acquisition period (from 5 April 2016 to 22 June 2016), we observed the bison herd from 09:30 to 16:30, for an average of 4 h per day depending on the climatic conditions as well as the time required to locate the group in the area. The total observation time for the entire study period is 156 h. We observed the bison at sufficient distance, minimum distance of 50 metres, which sometimes decreased slightly due to terrain geography or herd movement direction, to avoid setting off stress behaviours and ensure that our presence did not interfere with natural activity patterns and the welfare of the herd. We recorded social relationships of individuals, dominance interactions, movement initiations (leadership) and collected faeces in order to obtain measure stress via glucocorticoids.
Social and dominance data were collected using CyberTracker 3. 0 Software  (Steventon et al. , 2011) , installed on a Samsung Galaxy Tab3 Lite. Leadership data was recorded with a HDR-CX320E Sony camera recorder. Social relationships: Each 5 minutes, the identity of the nearest neighbour for each individual was recorded by the instantaneous sampling method  (Altmann, 1974). When it was not possible for the observers to distinguish only one nearest neighbour (focal individual placed at the same distance of several individuals), the identities of these different neighbours were recorded. We used the eigenvector centrality as a measure of social centrality. This index was calculated using the nearestneighbour matrix implemented in SocProg 2. 7  (Whitehead, 2009). More details are given in  Ramos et al.
(2019 Ramos et al. ( , 2018)). Dominance interactions: All unidirectional agonistic interactions were recorded using the continuous sampling method  (Altmann, 1974). For each interaction (see  Ramos et al. 2019)  for details), the identity of the emitter and the receiver were noted. Agonistic interactions were carried over an asymmetrical matrix to calculate dominance scores of individuals. This was analysed using SocProg 2. 7 Software to measure the linearity of the hierarchy (h′ = 0. 949, P < 0. 0001, 10,000 permutations; de  Vries et al. 2006)  and to obtain a dominance index for each animal, the Modified David's Score (MDS). From these MDS, a dominance score, ranging from 1 (most dominant) to 14 (least dominant), was attributed to each bison. The dominance never changed during the observation period.
, 2001; Ramos et al. , 2019; Sosa et al. , 2019; Sueur et al. , 2017). Subsequently, multifactorial linear models (LM) were applied as follows: - We tested leadership as influenced by the eigenvector centrality and not the converse, as usually done in studies on leadership  (Fischhoff et al. , 2007; Jacobs et al. , 2008; Van Belle et al. , 2013) , by using the function lm() from the R package car  (Fox et al. , 2007). P-values for LM were calculated by Monte Carlo sampling with 10 000 permutations, using the function PermTest() of the R package pgirmess  (Giraudoux et al. , 2018). Permutation tests for LM are well adjusted for moderate sample size and do not require normal distribution of model residuals  (Good, 2013). We repeated three times the tests to check for the stability of the statistics (α = 0.
Acknowledgements We are very grateful to all the teams at  Zuid-Kennemerland National Park, PWN, ARK Nature  and all the partners of the  Kraansvlak Bison Project  for granting us access to the bison herd, for their valuable help during data collection and for their contributions during the various stages of the study. We thank  Professor Rupert Palme  and the  Unit of Physiology, Pathophysiology, and Experimental Endocrinology  from the  University of Veterinary Medicine, Vienna  for analysing the faeces cortisol data. Thanks also to Editage for reading and correcting the final version of this paper.
Next, the slides were incubated with an antivimentin antibody (1:100) at 4 °C overnight. The next day, the slides were incubated with secondary antibody goat anti-rabbit-FITC (1:100) dilutions at room temperature for 1 h. Lastly, cells were stained with DAPI (Invitrogen; Thermo Fisher Scientific, Inc. ) and washed with PBS. Stained cells were analyzed by a flow cytometer (FACSCanto II, Becton Dickinson, San Jose, CA) accompanied with the BD FACSDIVA™ software for data analysis. The cell-associated fluorescence of 5,000 cells per sample was measured as mean fluorescence intensity (MFI) in the FL1 channel. The MFI values were corrected for unspecific staining by substracting the fluorescence of cells unstained (negative control).
In vitro scratch wound healing assay To evaluate the effect of 9f on epithelial motility, we performed the scratch assay as described by Cardoso and coworkers  [53]. Cells were maintained in 24 well plates until they reached 90% confluency. Thereafter, a vertical stripe on the cell monolayer was made using a sterile pipette (200 μl) tip. The wells were washed with PBS to remove dead cells and debris, and then 9f was added at concentration of 7 µM. As a control, the cells were treated with cell culture medium. Photographs were captured by a digital camera connected to inverted microscope (Olympus IX70) at 0 and 24 h after scratch. The migration gap area of the cells was measured by ImageJ software (https://imagej. nih. gov/ij/; Center for Information Technology, National Institute of Health, Bethesda, MA, USA).
Acknowledgements This work was supported by the  Federal University of Alagoas  (UFAL),  State University of Paraiba (UEPB) , by the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  [grant numbers  308590/2017-1  and  306798/2020-4 ]. This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil  ( CAPES) -Finance Code 001  and by  Paraiba State Research Foundation (FAPESQ)  grant number  301724/2021-0  through concession of scholarship to R. S. A. A.
Purification was performed using Agencourt RNAClean XP (Agencourt Bioscience Corporation, Beverly, MA, USA). Dye incorporation and cRNA yield were checked using the DropsenseTM 96 UV/VIS droplet reader (Trinean, Gent, Belgium). Cy3-labeled cRNA (600 ng) was hybridized on the microarray slides following the manufacturer's instructions. Immediately after washing, the slides were scanned on an Agilent G2505C Microarray Scanner with Agilent Scan Control A. 8. 5. 1 software. The fluorescence signal was extracted using Agilent Scan control v10. 10. 1. 1 with default parameters. All subsequent data analyses were performed using R (www. rproject. org (last accessed on 21 January 2022)), with packages from Bioconductor (www. bioconductor. org (last accessed on 21 January 2022)). Raw data (median signal intensity) were filtered, log 2 transformed, and normalized using Smooth quantile normalization.
Bioinformatic Data Analysis Venn analysis was performed to identify common or specific genes with modulated expression levels in different organs. Data were processed using R (www. r-project. org (last accessed on 21 January 2022)). Gene ontology enrichment of biological processes was performed using either Enrichr online tools  [40, 41]  or Metascape online tools  [42]  (accounting for the microarray background list of genes for a better analysis) to determine common and specific regulated biological processes, respectively.
To go further in the analysis, a pairwise comparison was made using the estimated marginal means model. The footpad dermatitis scores assessed on the farm were analysed with two GLMs: one distinguished score c from scores a + b to evaluate the severity of footpad dermatitis, and the other distinguished score a from scores b + c to evaluate the prevalence of lesions, whatever their severity. The hock burn scores assessed on the farm were analysed with a GLM that distinguished score a from score b (there being no or very few c scores observed). As only one data point was available per treatment (pens were not distinguished at slaughter), footpad dermatitis and hock burn at the slaughterhouse were analysed with a chi-square test for each severity score between treatments.
Data Acquisition Horses were equipped with seven synchronized IMUs (Figure  1 ) (ProMove-mini, Inertia Technology BV, Enschede, the Netherlands) with a full-scale range of 16 g, 2000 • /s, 16 bits, sampling at 200 Hz. IMUs were positioned on the withers and in the center of the dorsal side of the third metacarpal bone (cannon bone) of the four limbs, and the last two IMUs were mounted on the lateral quarter of the right front and hind hoof of the horse (Figure  1 ). The withers IMU was used to help us detect the trot gait. The four cannon bone IMUs were used to develop and test our foot-on and heel-off events detection methods. The hoof IMUs were used to obtain the foot-on and heel-off event references. Unfortunately, we did not have more IMUs available to equip the four horse hooves. The presented results were therefore only focused on the right limbs.
Heel-Off In the processing window, the first positive peak, after the plateau was identified, corresponding to the ImuCannonHeelOff (i, "exercise", "ground", "limb") (Figure  4 ). The peak of ImuCannonHeelOff (i, "exercise", "ground", "limb") was followed by a negative peak, corresponding to the foot-off event (complete hoof lift-off), itself detected with a threshold of 85% of the maximum value of the cannon bone x-axis acceleration. For the ImuCannonHeelOff (i, "CircleIn", "ground", "Fore"), the curve did not always follow the same curve shape as in other conditions. The signal sometimes showed only one large positive peak after the plateau (Figure  5 ). The ImuCannonHeelOff (i, "CircleIn", "ground", "Fore") was obtained by detecting a change of curve variation from a derivative subsampled signal, with the Piecewise Cubic Hermite Interpolating Polynomial Matlab function.
All calculations were performed by self-made functions with Matlab software (Matlab R2021a, The MathWorks Inc. , Natick, MA, USA). For each condition and both limbs, the stride duration and stance duration were calculated with these equations: Stride Duration ImuCannon (i) = ImuCannonFootOn (i + 1) -ImuCannonFootOn (i), (1) Stance Duration ImuCannon (i) = ImuCannonHeelOff (i) - ImuCannonFootOn (i), (2) Stride Duration ImuHoof (i) = ImuHoofFootOn (i + 1) -ImuHoofFootOn (i), Stance Duration ImuHoof (i) = ImuHoofHeelOff (i) - ImuHoofFootOn (i), (4) The stance duration was normalized with the stride duration to obtain information on the percentage of stride duration, with the following equation: Stance Duration (i)% stride duration = (Stance Duration (i)/Stride Duration (i)) × 100, ( 5 ) ways follow the same curve shape as in other conditions.
The signal sometimes showed only one large positive peak after the plateau (Figure  5 ). The ImuCannonHeelOff (i, "Cir-cleIn", "ground", "Fore") was obtained by detecting a change of curve variation from a derivative subsampled signal, with the Piecewise Cubic Hermite Interpolating Polynomial Matlab function. All calculations were performed by self-made functions with Matlab software (Matlab R2021a, The MathWorks Inc. , Natick, MA, USA). For each condition and both limbs, the stride duration and stance duration were calculated with these equations: Stride Duration ImuCannon (i) = ImuCannonFootOn (i + 1) -ImuCannonFootOn (i),  (1)  Stance Duration ImuCannon (i) = ImuCannonHeelOff (i) -ImuCannonFootOn (i),  (2)  Stride Duration ImuHoof (i) = ImuHoofFootOn (i + 1) -ImuHoofFootOn (i), Stance Duration ImuHoof (i) = ImuHoofHeelOff (i) -ImuHoofFootOn (i), (3) The stance duration was normalized with the stride duration to obtain information on the percentage of stride duration, with the following equation: Stance Duration (i)% stride duration = (Stance Duration (i)/Stride Duration (i)) × 100, (5).
For all these results (Tables  2 3 4 5 ), the Bland-Altman graphs are available in the supplementary data attached document. Table  2. Bland-Altman comparison (in percentage of stride duration%) of the reference foot-on event and the foot-on event detected with the developed method from the cannon bone data. Accuracy (bias), precision (SD), and upper/lower limits of agreement (ULA and LLA) were indicated for each condition: straight line (SL), left circle (LC), and right circle (RC), for both ground conditions-hard (H) and soft (S)-and for the right forelimb and right hindlimb. For the left-circle condition, the equipped limb was the one outside the circle. For the right-circle condition, the equipped limb was the one inside the circle. Results are in percentage of stride duration.
[9]nd-Altman comparison (in milliseconds)of the reference foot-on and heel-off events and the foot-on and heel-off events detected with the method from Briggs and Mazza[9]and our method from the cannon bone data. Accuracy (bias), precision (SD), and upper/lower limits of agreement (ULA and LLA) were indicated for the straight-line-on-hard-ground condition, and for the right forelimb and right hindlimb. For both events (foot-on and heel-off), the table shows results obtained from the same dataset with our method (Hatrisse & al. ) and the Briggs and Mazza method[9].
Strains This study included a total of 86 strains comprising of 47 Candida spp. , 29 Aspergillus spp. , and 10 Rhizopus spp. , strains. Candida strains comprised of 10 Candida albicans, 15 Candida auris, five Candida glabrata, three Candida kefyr, five Candida krusei, four Candida parapsilosis and five Candida tropicalis. All Candida strains are clinical stains either obtained from the Westerdijk Fungal Biodiversity Institute collection (C. auris), or from the Department of Microbiology of the University Hospital Marburg and were identified to the species level by sequencing of the complete ITS1-5. 8S-ITS2 region as described elsewhere  [32]. Sequences were deposited at GenBank under the accession numbers OL351325 to OL351356. Aspergillus strains comprised of five Aspergillus flavus, 10 Aspergillus fumigatus, four Aspergillus nidulans, five Aspergillus niger, and five Aspergillus terreus.
This surface was then compared to the experimental surface and the synergy distribution was calculated. All calculations were performed by the Combenefit software (Windows v2. 02)  [37]. The synergy distribution was evaluated using three metrics: the SYN-SUM, the ANT-SUM, and the SUM-SYN-ANT. This later metric consists of the sum of synergy and antagonism observed by comparison of the two surfaces. To determine the threshold of the metric, a response surface with an indifferent interaction was determined experientially. Therefore, the combination of an antifungal with itself (amphotericin B + amphotericin B) was tested by checkerboard in triplicate. Based on the results of the experimental plates, synergy was assumed when the SUM-SYN-ANT was ≥43. 8%, and antagonism was assumed when ≤-43. 8%.
tropicalis), which would be in the range of peak serum levels reported in patients with cystic fibrosis  [51] , and critically ill patients  [52]. The geometric mean MIC of colistin in combination with the synergistic C. auris isolates was slightly higher (5. 28 µg/mL), but was still in the range of the achievable serum levels. In this study, we analyzed the checkerboard data of Candida species by interpretation of the results by FICI, or response surface analysis. One of the disadvantages of the FICI technique is its dependence on the MIC endpoints. Another problem is the definition of the endpoint itself, as 50% or 90% of growth inhibition compared to the growth control can be used, using either can lead to completely different conclusions  [53, 54]. For combination studies, no standardized methods exist, especially if one of the partners belongs to another drug class (in our case antifungal and antibiotic).
Alternatively, the use of live attenuated or nonattenuated vaccines to stimulate the immune system to produce antibodies against Eimeria antigens has been shown to be quite successful  (Williams, 2002). However, the nature of vaccines is to mimic their natural virulence through the exposure of controlled doses  (Williams, 2002; Price, 2012). Thus, the vaccine strains still possess the ability to undergo their replicative life cycle in the host intestines and cause varying levels of tissue damage, which may negatively affect nutrient digestibility and weight gain  (Gautier and Rochell, 2020). It is standard practice to use SID values of individual ingredients for accurate feed formulation  (Adedokun et al. , 2012 (Adedokun et al. , , 2016)). However, the industry relies on compiled databases (e.
6 Wheat-soybean meal-pork meal. N=6. a-d Means assigned different letters within a response criterion are significantly different, P < 0. 05. The equation for SID was: SID, % = AID + (basal EAAL/ AA diet ) £ 100. The data was subjected to a 6 £ 2 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. The data for BEL was analyzed using a one-way ANOVA with challenge as a fixed effect. An alpha level of 0. 05 was used as the criterion for statistical significance.
Furthermore, using the test time augmentation technique is proposed to improve the model performance. Experiments are conducted on two online pig detection datasets; the network surpasses state-of-the-art results for both datasets. It is also found that the proposed method outperforms the latest anchor-free techniques commonly used in crowded scenarios. The method can detect pigs individually, even if their bounding boxes overlap strongly or occlude each other. Moreover, the realtime system achieves an improvement of 10% in F measure when testing in unconstrained real-world conditions.