dataesr/scientific-paragraphs-categorization huggingface dataset:
3 and 1. 65) respectively. The much higher oral bioavailability of BPS in comparison with that of BPA was rather unexpected due to the close chemical structure of the compounds. Using a database for 309 drugs in humans,  Varma et al. (2010)  showed that although lipophilicity favors absorption, the fractions of drugs escaping gut-wall and hepatic presystemic metabolism were decreased by lipophilicity, with compounds having logP values greater than 3 demonstrating the higher gut and hepatic extraction. This single physicochemical descriptor is however insufficient to explain the highly different extents of first-pass extraction of BPS in comparison with those of BPA. All of the five examined BPA analogs, except BPS,  were predicted to be CYP2D6 substrates based on the in silico structure activity relationship  (Rosenmai et al.
Toxicokinetic parameters of BPA and BPS (mean ± SD) estimated by noncompartmental analysis after BPA and BPS IV dosing. : The toxicokinetic parameters after IV dosing were estimated from datasets obtained after a single IV administration of BPA at 21:9 lmol=kg (Exp. 1-2, n = 11), a single IV administration of BPS at 20 lmol=kg (Exp. 3-4, n = 12), and simultaneous IV administrations of BPA and BPS at the same dosage (Exp. 5, n = 4). AUC 0-tlast =dose Dose scaled area under the plasma concentration-time curve from dosing time to the time of the last measurable plasma concentration;BPA, Bisphenol A; BPAG, Bisphenol A glucuronide; BPS, Bisphenol S; BPSG, Bisphenol S glucuronide. ; BW, Body weight; Cl, Clearance; Cl_F, Apparent clearance; Cmax/dose, Dose scaled maximal plasma concentration; IV, Intravenous; NA, not applicable; Tmax, Time of Cmax.
Acknowledgments The authors thank  N. Deschamps ,  C. Lacassagne , and  J. P. Gau , for their assistance and involvement in animal care. This work was supported by the  French National Research Program for Environmental and Occupational Health of Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES)  (grant no. 2017/1/174 ).
were included if they were diagnosed with CKD and had a culture performed on urine collected by cystocentesis. The PUC frequency was calculated, multivariate analysis was performed to identify risk factors, and associations with clinicopathologic data were investigated. Results: Sixty-five dogs (32%) with CKD had PUC, including 8 (28%) in International Renal Interest Society (IRIS) stage 1; only 8% showed signs of a urinary tract infection. Escherichia coli was the most common isolate (67%). A PUC was more likely in females (odds ratio [OR], 3. 22; 95% confidence interval [CI], 1. 67-6. 37; P <. 001) than males and in dogs with isosthenuria (OR, 2. 48; 95% CI, 1. 24-5. 03; P =. 01) than in dogs with urine-specific gravity 1. 013-1. 024. A positive leukocyte esterase test and microorganisms found by urine sediment analysis were significantly associated with PUC (both P <.
10 A PUC was found in 75% of them, and pyuria was highly associated with the presence of a PUC, whereas fever was unusual. Previous guidelines for diagnosing dogs with UTIs included inappropriate urination, dysuria, gross hematuria, stranguria, pollakiuria, and significant bacteriuria (>1. 10 3 colony-forming units per milliliter [cfu/mL] for urine collected by cystocentesis) determined by a quantitative urine culture. 11, 12  contrast to cats, no clinical studies have evaluated the frequency of PUCs in dogs with CKD. We hypothesized that dogs with CKD would have a higher frequency of PUC than that indicated by data published for healthy dogs. The initial aim was to determine the frequency of PUC in a cohort of dogs with CKD and identify those with clinical signs in order to distinguish dogs with UTIs from dogs with subclinical bacteriuria.
| Case selection criteria and data collection Medical records from each dog were reviewed, and the following data were recorded: relevant history findings, signalment, presenting complaint, clinical signs (eg, stranguria, pollakiuria, dysuria, gross hematuria, polyuria/polydipsia), imaging findings, and urinalysis, urine culture, and blood analysis results. Clinical signs of lower urinary tract disease (LUTD) were defined as the presence of at least 1 of the following: dysuria, stranguria, pollakiuria, or gross hematuria, regardless of etiology. Ultrasonographic signs of cystitis were defined as the presence of irregular or regular thickening of the cranial bladder wall. A CBC and serum biochemistry profile were performed in both hospitals in a standard fashion. Urinalysis, on urine collected by cystocentesis, consisted of USG measurement using a manual calibrated standard refractometer, semiquantitative analysis using a urinary dipstick, sediment evaluation, and bacterial culture.
When multiple urine cultures were performed in the same dog, data were collected at the time of the first PUC. Multidrug resistance was defined as resistance to 1 agent in at least 3 separate antimicrobial categories in which the wild-type bacteria would normally be susceptible. Possible extreme drug resistance was defined as resistance to all except ≤2 antimicrobial categories tested. Possible pan-drug resistance was defined as resistance to all tested antimicrobials. 14 The term "possible" was used because only a subset of antimicrobial agents was tested compared to the full list of antimicrobial categories proposed for each bacteria. 14.
| Data representativeness To assess the representativeness of our results in the general population of dogs with CKD of both hospitals, we evaluated at the percentage of dogs presenting with CKD and having at least 1 urine culture performed. The electronic medical record database was searched using the words "renal" and "CKD" during the same study period. Records containing 1 of these words in the conclusion were reviewed to confirm that the dogs were diagnosed with CKD, using the criteria defined above. Records then were searched to determine whether these dogs had a urine culture performed at least once, and the percentage of dogs with CKD having a urine culture performed was calculated.
| Statistical analysis The PUC frequencies and 95% confidence intervals (CIs) were calculated. The frequencies of PUC in each hospital were compared using the Chi-squared test. The normality of continuous variables (age, weight) was investigated using the Shapiro-Wilk test. The median and interquartile range (IQR, first and third quartiles) are provided for all quantitative variables, because some variables were not normally distributed. Categorical data included age (juvenile, adult, senior), sex (male or female), neuter status (yes or no), body condition score (<4/5 or ≥4/5), presence of clinical signs of LUTD (yes or no), presence of polyuria/polydipsia (yes or no), IRIS stage (1-4 and unclassified), USG (≤1. 012; 1. 013-1. 024, or ≥1. 025), urine pH (<7. 5 or ≥7. 5), positive leukocyte esterase test (PLET; yes or no), presence of microorganisms determined by routine urine sediment analysis (yes or no), presence of leukocytosis (yes or no), and presence of signs of cystitis on ultrasound (yes or no).
Glomerular filtration rate was estimated using a noncompartmental approach with extrapolation to infinity. The clearance was equal to the dose divided by the area of the concentration versus time curve. The borderline low GFR cutoff value was defined as 1. 9 mL/min/kg and the low GFR cutoff value as 1. 4 mL/min/kg based on previous data from our research group. 9.
In detail, when using rough normalization procedures for data analyses, information from samples with a scarce number of sequences is excluded from the dataset. In specific cases, such as healthy skin samples, the exclusion of this information may conduct to neglect the healthy nature of the sample which should have low numbers of sequences in comparison to diseased samples. The within cow design of this study offered the possibility of exploring the dynamics of microbiotas according to the clinical evolution of each animal. Indeed, the simple comparison between microbiotas from DD lesions obtained from different animals at diverse states may underestimate or neglect the impact of individual and environmental factors (i. e. foot conformation or barn hygiene) on the clinical outcome of lesions and their microbial configuration.
The V4 region of the bacterial 16S rRNA gene was amplified by PCR, using Illumina Nextera XTIndex Primer 1 (N7xx) and Primer 2 (S5xx), where barcodes were unique to each sample. The PCR protocol consisted of an initial denaturation at 95 C for 10 minutes followed by 40 cycles of 95 C for 30 seconds and 60 C for 1 minute and the real-time fluorescence data acquisition occurred at the end of each annealing/extension phase. All PCR assays use the same PCR cycling conditions allowing parallel testing of all PCR assays. The amplicons were extracted and purified from the gel using the Zymo gel extraction kit according to the manufacturer's instructions and quantified using the Quant-iT dsDNA Assay Kit (Thermo Fisher Scientific, MA, USA). Finally, the purified amplicons were sequenced using the MiSeq reagent kit v2 (13 cycles) on the MiSeq platform (Illumina, Inc.
Sequencing data processing The generated 16S rRNA raw gene sequences were preprocessed to remove any remaining, barcodes or adapters using Trimmomatic v0. 38  31. Raw reads were then processed using R  32  , implementing the established DADA2 workflow  33. The sequence variants in each sample were inferred using the "divisive amplicon denoising algorithm", a parameterized model of substitution errors to distinguish sequencing errors from real biological variation  34. The inferred forward and reverse sequences were then merged, removing sequences that did not perfectly overlap and subsequently chimeras were removed. Thereafter, to these ASV taxonomic labels were assigned through a Bayesian classifier using the Ribosomal Database Project (RDP) v16 training set  35, 36. The phylogenetic relatedness of the ASV identified was preprint (which was not certified by peer review) is the author/funder.
T) over time by using the Mann-Whitney U test, and within the group over time by using the Wilcoxon signed-rank test. Likewise, all microbial proxies were compared between the clinical variables studied using Kruskal-Wallis and pairwise Mann-Whitney U tests. The impact of the clinical variables on the structure of microbiotas over time was visualized using the Bray-Curtis distances and nonmetric multidimensional scaling ordination (NMDS). In the scatter plots created, high dissimilarity of microbiotas was represented by long distances between dots, whereas microbiotas with a similar composition were clustered together. Finally, the dynamic on DD microbiota composition was explored by identifying specific ASV changing over time and according to the allocation group through a differential abundance analysis using the R package DEseq2  23.
DEseq2 uses a negative binomial distribution and implement the variance stabilization transformations of the ASV read counts identified in the raw data set to account for overdispersion. A single model was fitted including the main effects of the allocation group and the sample time. Within the allocation groups, a code for animal identification was created and included as a fixed effect in the model to account for dependencies between the multiple samples within the same cow. For each allocation group, the noticeable effects (p-value ≤ 0. 05) on the counts of ASV were investigated between the microbiotas of: i) healthy skin Vs. of DD lesions at day 0, ii) DD lesions at day 0 Vs. DD lesions after 15 days, iii) DD lesions at day 0 Vs. DD lesions after 30 days and, iv) DD lesions at day 0 Vs.
Acknowledgments The authors acknowledge the farmers and veterinarians who have contributed to this study. Additionally, we thank  G. Puel  ( Oniris, Nantes, France ) for helpful participation as investigator, for his time and motivation. This study was supported as part of a Ph. D. studentship by  Qalian (Neovia group, Segré, France)  and the  "Association Nationale de la Recherche et de la Technologie" (ANRT, Paris, France).
Study population A total of 122 organic dairy farms were recruited. Sufficient data were only available from 119 farms in France (27), Germany (59) and Sweden (33). All study farms were taking part in the FP7-funded research project IMPRO (Impact matrix analysis and costbenefit calculations to improve management practices regarding health status in organic dairy farming, www. impro-dairy. eu). Farms were selected based on the following inclusion criteria: participation in an official milk recording scheme since January 2012, official certification as an organic farm for at least one year before the start of the study, expected to be in operation for the coming year, and herd sizes reflecting the farm demography of the country (as regards range and mean). Farms were recruited by mail or phone in Sweden.
Participatory approach and impact matrix analysis As part of the herd health planning, actions to improve animal health were identified, using a structured participatory approach. Farm visits were performed between November 2013 and April 2014 as described in detail by  Krieger et al. (2017b). Briefly, each farm visit was attended by the farmer, the herd veterinarian and/or an advisor, and a researcher facilitating discussions. The advisors' speciality varied between farms. Baseline data, from the official milk recording schemes, breeding companies and animal movement databases, on the animal health status (e. g. calf mortality, somatic cell count, cow mortality, milk yield) of the individual farm were presented as an input for the discussion. Thereafter an impact matrix analysis  (Krieger et al.
Data collection Three time periods were defined: a) baseline, refers to data from the 12 months prior to the visit; b) follow-up 1, refers to data from 1 month to 13 months after the visit; c) follow-up 2, refers to data from 6 months to 18 months after the visit (Figure  1 ). Data from the national recording systems were retrieved as relevant for each country. All countries had access to data from the official milk recording schemes, databases of artificial insemination or natural service information and data from the animal identification and registration databases. The different databases were in most cases separate entities, except in Sweden where all the information is maintained in a common database for dairy herds that participate in the official milk recording scheme. In all countries permission from the participating farmers and database managers was obtained before data collection.
The national recording systems are not harmonized and the method of record-keeping, as well as the amount of information recorded, differ. For the purpose of this study, only data that were available in all participating countries were used, and transformed into a common structure. Variables derived from data in the national recording systems, and calculated for baseline and follow-up 1 and 2, were: a) Cow mortality, defined as on-farm mortality of cows, i. e. the number of cows that died or were euthanized on-farm divided by the sum of their days at risk of dying. Animals that were sold were censored on the day of leaving the herd; b) Calf mortality, defined as the number of calves that died between birth and 30 days of life divided by the sum of their days at risk of dying. Animals that were sold were censored on the day of leaving the herd; c) Proportion of prolonged calving intervals, used as a proxy for reproductive health, defined as the proportion of all individual calving intervals exceeding 400 days length  (LeBlanc et al.
5, as indicator of ketosis, SCC > 200' = Proportion of milk-tests with somatic cell count in milk over 200 000 cells/mL. Figure captions Figure  1  Illustrates the timeline of data collection. Baseline data pertains to 12 months before the farm visit, on organic dairy cattle farms, when the participatory approach with the Impact Matrix was performed. Follow-up 1 pertains to data from 1 month until 13 months after the visit and follow-up 2 pertains to data from 6 months until 18 months after the visit.
The 16M B. melitensis reference strain was included as a control since each VNTR locus size is known. Brucella reference strains that have already been characterized using the MLVA16 markers panel 1, panel 2A and panel 2B were included to ensure accurate evaluation of field strain genotypes. For Panel 1 VNTRs, 2% agarose gel was used with Gen-eRuler 100 bp plus DNA ladder (Thermo Scientific). For panel 2 VNTRs, 3% standard agarose gel and low molecular weight DNA ladder 766-25 bp (New England Biolabs) were used. The ethidium bromide stained gels were visualized by UV light. Genotype was scored by visual analysis of the gel images or BioNumerics software version 6. 6 (Applied-Maths).
Data analysis Band size estimates were converted to repeat units following the published allele numbering system version 3. 6  [45]  (S1 Table ). MLVA data were analysed as a character data set within BioNumerics software (version 6. 6) (Applied Maths). Clustering analysis was performed using the categorical coefficient and UPGMA (unweighted pair group method using arithmetic averages). A different weight was given to the markers depending on their panel: Panel 1 markers were assigned an individual weight of 2 (total weight for panel 1: 16), panel 2A markers a weight of 1 (total weight for panel 2A: 3), and markers of panel 2B a weight of 0. 2 (total weight for panel 2B: 1)  [20]. The MLVA16 results were compared with MLVA16 published data of Brucella reference and other strains  [20, 31, 44]  (S1 Table ).
Whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) analyses Zimbabwean B. suis strains ZW043 (GenBank accession CP009094. 1 and CP009095. 1) and ZW046 (GenBank accession CP009096. 1 and CP009097. 1) and B. abortus strain ZW053 (Gen-Bank CP009098. 1 and CP009099. 1) [47]  were selected for WGS since these strains were isolated from cattle in different regions of Zimbabwe and represented different MLVA genotype subclades. In addition, 23 B. abortus and 17 B. suis complete genomes were retrieved from GenBank and used for comparison and phylogenetic analyses (S2 Table ). Sequenced reads from B. abortus and B. suis strains were aligned to B. abortus str. 9-941 (Accession no: NC_006932. 1, NC_006933. 1) and B. suis 1330 (Accession no: NC_017251, NC_017250) respectively, using Burrows-Wheeler Aligner (BWA)  [48].
MLVA Due to lack of sufficient DNA, strains ZW002 and ZW005 (both B. ovis), and ZW248 and ZW283 (both B. abortus) could not be genotyped using MLVA16. MLVA data derived from the seven reference strains were as expected from previously published data, with the exception of reference strains 16M, 63/290 and RM 6/66. The 16M strain used in the present study differs from the B. melitensis reference 16M strain at locus Bruce07, which is not unexpected due to high variability at this locus  [25]. The reference strain RM 6/66 we used differed from B. canis reference RM 6/66 strain at Bruce07, Bruce09 and Bruce16 loci whereas 63/290 differs at loci Bruce09 and Bruce16. The difference between control strains used in this study and the reference strains may be due to amplification of the reference DNA using Genomiphi (GE Healthcare Life Sciences AEC-Amersham) due to low quantities of DNA from these strains available in our study.
suis bv. 1 reference strain 1330  [37]  by only five SNPs. A third strain, identified as B. abortus bv. 1 was shown by WGS-SNP analysis to be closest to a strain independently recovered from Zimbabwe. A previous study  [24]  compared the AMOS, Bruce-ladder and MLVA8 assays for typing of Brucella species and found only Bruce-ladder correctly identified all tested Brucella strains as MLVA8 does not resolve the very closely related B. canis and B. suis bv. 4. Both MLVA11 and MLVA16 resolve the two species however and also allows comparison to a worldwide Brucella MLVA dataset  [53]. As shown in previous studies  [39, 40, 41] , WGS-SNP analysis provides better resolution than MLVA16, and much stronger phylogenetic support although there are still fewer strains from more limited geographic areas available for comparisons as compared to the MLVA database.
Importantly the number of public whole genome sequences, particularly sequence reads archives, is rapidly growing with already more than 1000 datasets available. The status of B. suis as a single species has been questioned in light of a broader host specificity  [54]. Isolation of B. suis bv. 1 from bovines in Zimbabwe was first reported in 2014  [47]. The present study further emphasizes the occurrence of B. suis bv. 1 in cattle and pigs. There are several reports of isolation of B. suis bv. 1 from cattle  [55, 56]  in which the infection appears to be noncontagious with limited induced pathology and no induction of abortions  [19, 54]. The presence of B. suis bv. 1 in pigs and bovines in Zimbabwe could be due to the predominance of smallholdings with mixed populations of livestock  [57].
Considering affordability and reproducibility; Bruce-ladder can be used as it allows identification of all known Brucella species including the vaccine strains simultaneously in one run. This study has confirmed that species differentiation can be correctly deduced from both MLVA16 and Bruce-ladder analysis. These PCR assays can therefore add to the control and eradication of brucellosis, since B. ovis, B. abortus, B. suis and B. canis could be identified. The latter two species are reported for the first time in Zimbabwe. Additionally, more strains, whole genome sequences, and epidemiological data from Zimbabwe are needed to accurately draw conclusions on the clustering and circulation of strains.
Results The results present the pain control in dogs after induction of a transient joint arthritis (see Methods). The acute pain was obtained after intra-articular injection of 1 mL of a sodium urate crystal suspension into the femorotibial joint using a 30 mm by 1 mm sterile needle (Experiment 1right joint; Experiment 2left) in anaesthetized dogs  [5]. All dogs were fully awake by the time of the start of the assessments (beginning 1. 5 h after anaesthesia). In Experiment 1, the force plate and video recordings were available for 5 of the 6 dogs in the untreated control group because one dog did not show any sign of lameness or pain at any time point, indicating that arthritis induction had failed, and so data from that control dog were not included in lameness calculations (i. e. 5 control dogs, 6 fipronil treated and 6 grapiprant treated dogs included in Experiment 1).
Arthritis induction was successful in this control dog during the Experiment 2. In experiment 1, for one firocoxib treated dog, the values were missing at 7 h post arthritis induction (PAI) because of a computer error; therefore the statistical analysis included 5 fipronil treated dogs at 7 h PAI, and 6 at the other time-points. In Experiment 2 a computer error in the force plate and video recordings for one dog in the firocoxib group at the final time point assessment (10 h PAI, i. e. 24 h post-treatment) prevented inclusion of data from that dog at that particular time-point. Other than for the induced lameness, no abnormal clinical signs were recorded during the study in any group.
Statistical analysis Statistical analyses were performed using SAS© software version 9. 4 (SAS Institute Inc. , Cary, NC, USA) under Microsoft Windows© OS. A two-way analysis of the variance (ANOVA) with repeated measurements on Time was performed to analyse the Lameness ratios with the factors: Group (Control, Firocoxib, Grapiprant) and Time (1. 5 h, 3 h, 5 h, 7 h, 10 h) as fixed effects; Group*-Time as an interaction; and Dog ID (Group) as random effect for repeated measurement over Time. Estimated least squares means and standard error of the mean were calculated. The Visual Lameness Score parameters data were analysed through ANOVA Test involving the two fixed effects Group and Time. A significance level of α = 0. 05 tested statistical significance of fixed effects.