dataesr/scientific-paragraphs-categorization huggingface dataset:
The cut-off for animal sample size was based on the median of the animal sample size for the identified studies. The median was 1,237, the cut-off was defined at 1,230. The choice of the farm number was based on the median of the farm sample size for the identified studies. The median was 4, and the farm sample size cut-off was defined as 5. Given the increasing awareness to the lameness issue through time the years of the start of data collection was used to create five different variables. Different cut-offs were defined  : 1995, 2000, 2005, 2008 , and 2010. The last 2 years reflect the adoption of the AHDB Dairy mobility scoring system as the dairy industry standard and the implementation of the AHDB Dairy Healthy Feet program, respectively  (49). The variables were named Start of data collection (year) as to indicate that the cut-off refers to the year data collection was initiated.
The five variables were numbered from 1 to 5 with respect to the chronology of the cutoff: 1 would stand for the year 1995 as the cut-off and 5 for the year 2010. As the incidence rate of lameness was reported in different time units, the incidence data were extracted and standardized for 100 animal-years. To explore the underlying causes of lameness the following grouping of lesions was defined, based on Griffiths et al. (50) : • White Line Diseases (WLD) and Abscess → White Line Disease. of studies identified by the systematic review and their selection process and inclusion for the meta-analysis on lameness frequency levels in British dairy cattle (*short communications, letters, self-assessments, and review articles were excluded, **if lameness frequency levels were reported but no information on population at risk/denominator was provided/retrievable the paper was excluded).
Analysis The primary outcome measure was incidence rate or prevalence of lameness in British dairy cattle. Analysis was conducted using RStudio statistical software (version-1. 2. 1335; R Foundation for Statistical Computing, Vienna, Austria) using the meta and metafor packages  (53). The metaprop function was used to conduct the meta-analysis on the prevalence data and the metarate function for the incidence data. The fixed-effect model disregards the between-study variance and assumes that the methods and underlying population from which the sample was drawn are equal between the different studies. These assumptions did not seem to fit well given the heterogeneity of the methods and sample population between the identified studies. For that reason a random effects model was chosen over a fixed-effect model  (54, 55).
Data were assessed for skewedness. As it was not normally distributed, data were transformed using arcsine transformation. A sensitivity analysis was conducted comparing the results obtained using the arcsine transformation with those obtained when using other available data transformation methods  (56). The GLM model was only used for the prevalence data. With the exception of the GLM model, all models used the inverse variance method for pooling the estimate of the lameness frequency level. Confidence intervals for individual studies were estimated through the normal approximation interval based on the summary measure (pooled lameness prevalence for studies reporting lameness prevalence and pooled lameness incidence for studies reporting lameness incidence). The DerSimonian-Laird (DL) estimate was used to calculate the between-study variance τ 2 in all models but the GLMM  (57, 58).
Factors providing a P-value of 0. 1 or below in the test for moderators were considered moderators and added to the multiple meta-regression model. The model for the multiple meta-regression was developed using the glmulti package, and according to the multimodel inference method in which all possible combinations of the identified predictors are explored and parsimony is rewarded  (60). The second step was only conducted if there were at least 10 papers, and if there were at least 5 papers per subgroup  (60). Risk factors for lameness were explored in the moderator analysis (breed, grazing regime, calving pattern, housing system, and milking system). Additionally, factors that could have an influence on the reported levels of lameness were also considered in the moderator analysis: lameness data source (records vs.
Chronological Overview of Lameness Classification Methods Out of the 151 papers that were considered for full-text screening, 70 papers were eligible for the chronological analysis. One paper  (61)  had been published sometime in the past when compared with the other studies and for this reason was excluded from the analysis. Overall 17 different lameness data sources were identified, among records and mobility scoring systems (MSS). Up until the year 2000 only 3 distinct MSS had been used in lameness research in British dairy cattle. From 2000 onwards another 10 MSS were used in publication regarding the study of lameness in the same population (Figure  2 ). Just over 20% of all the papers used the 9-point scale  Manson and Leaver 1988  to collect lameness data, being the most commonly used MSS.
The 4-point scale AHDB Dairy 2008 ranked second with about 10% of the papers making use of this MSS. Despite the existence of different mobility scoring tools for assessing lameness in dairy cattle, farm records are still a commonly used lameness data source by research (Table  1 ). Five out of the eight papers making use of data that started being collected from 2014 onwards sourced their lameness data from farm records. Only one paper made use of automated lameness detection systems (ALDS) for collecting data (Figure  2 ).
The pooled estimates between subgroups when papers were grouped per variables Start of data collection (year) 1, Start of data collection (year) 2, Start of data collection (year) 3, Start of data collection (year) 4, and Start of data collection (year) 5 were statistically different (p-value for the test of moderator < 0. 1) and therefore were considered as moderators for the meta-regression (Table  4 ). As there were <5 papers in one of the categories for variable Milking System no sub-group analysis was conducted on this factor. The five identified predictors were used in the multiple meta-regression model. The model with the moderator Start of data collection (year) 2 (year 2000 as cut-off) was the most parsimonious model. The pooled estimate for papers published from 2000 onwards was 34.
Meta-Analysis on Lameness Incidence Rate Levels Lameness incidence rate data was extracted from thirty-six studies, thirty-one of which reported the measure at cow level. One paper reported incidence per culled cow whereas the remaining four reported at heifer level (Supplementary Table  5 ). Additionally, data on the underlying cause of lameness were extracted from papers that reported it and a meta-analysis conducted. The results presented are based on incidence rate data from papers reporting cases at the cow level. Two studies were identified as outliers and removed from the analysis. After the removal of the outliers the overall pooled estimate for all-causes incidence rate in British dairy cattle was 36. 8 cases per 100 cow-years (95% CI 29. 3-45. 3 and 95% PI 5. 6-95. 5). Heterogeneity was present and extensive (Table  5  and Figure  5 ).
Other authors have raised this problem  (2, 30, 34, 35, 37, 74, 75) , there is a need for greater standardization in lameness data collection methods and case definition. This would allow for a more accurate understanding of lameness trends through time and inform the interested parties as to the effectiveness of the adopted approaches for tackling the problem. Despite these problems the analysis has provided great insight into the scientific documentation of lameness and its potential limitations in the estimates of the economic burden of this problem and its serious implication in animal welfare. We decided to group papers according to the study unit at which lameness was reported, and to concentrate the analysis and report results on cows rather than heifers and culled cows. Heifers are known to have lower incidence of lameness than older cows  (76).
Additionally culling records are not a good indicator of lameness frequency in dairy cattle, reporting low levels of the ailment  (16, 77)  when compared with evaluations from independent observers  (28, 50). Pooling lameness levels from papers reporting it at heifer and culled cow level with those reporting it at cow level would probably underestimate our results. The most parsimonious model for the analysis of prevalence data had Start of data collection (year) 2 as the only moderator. The pooled prevalence for studies for which data began being collected before the year 2000 was 20. 0% (95% CI 16. 3-24. 0%). This is in line with what Clarkson reported (20. 6%) in 1996  (78). The two most recent lameness prevalence studies in British dairy cattle reported higher levels of lameness. Griffiths et al.
The moderator Sample Size a (1,230 cut-off) retrieved the most parsimonious model when analyzing the lameness incidence data set. The estimated pooled incidence for the studies with more than 1,230 animals indicated 24. 5 cases of lameness per 100 cow-years (95% CI 17. 1-33. 3) 78 ) estimated-54. 6 cases per 100 cow-years. It must be noted that most studies with a sample size of more than 1,230 animals (12 out of 17) were based on farm records, a data source prone to under-reporting  (28, 29). On the other hand 2 out of every 3 studies with a sample size of less 1,230 animals relied on mobility scoring methods to assess lameness. Part of the observed difference could result from the different methods that were used for collecting lameness data. The pooled estimate for incidence rate for studies with <1,230 animals was 60.
However, the pooled estimate of the papers with lameness record-base data was not statistically significantly different from the pooled estimate of the papers with lameness data collected through MSS and/or ALDS. The authors acknowledge that the search terms used in the systematic literature review were somewhat narrow and that some references focusing on lameness in British dairy cattle might have been missed with the search strategy. Yet considering the research question the search terms seem to be adequate when identifying publications focused on reporting lameness frequency levels in British dairy cattle. Limiting the search to research conducted in British dairy cattle was strategic as the results from this analysis are intended to be used in an economic assessment of lameness in this population.
The literature search was restricted to six scientific databases. There is a possibility that some references were not captured in the search. However, these databases were chosen for their extensive coverage of veterinary science journals  (94)  and thus should have reduced the odds of missing a relevant paper. Although there was no restriction regarding year of publication the retrospective nature of the study might have conditioned data retrieve. Even if authors were still reachable databases were sometime no longer available. Nine papers were not accessible through our methodology. Additionally data could not be retrieved in twelve papers. This could have introduced bias into our analysis, but we do not know the direction of the bias nor the extent due the information lacking.
ACKNOWLEDGMENTS The authors would like to acknowledge the valuable contribution of  Bethany Griffins , for her participation in validating the adapted QUADAS-2 tool. Additional the authors would like to thank  Ben Huntington  and  Siv Nygaard  for their valuable comments on the document. Lastly a word of appreciation to  Ivo Fins  for his help retrieving some references. JA was grateful to the  Institute of Infection and Global Health, University of Liverpool  for supporting his Ph. D. research.
CGF40 Typing DNA was extracted using InstagenMatrix ® (Biorad, Marnes-la-Coquette, France) according the supplier recommendations. The forty accessory genes for CGF  [20]  were amplified according to previously published experimental conditions  [17]. The CGF fingerprints were visualized using a standard gel electrophoresis containing 2% of agarose colored with GelRed ® (Interchim, Montluçon, France) in accordance with supplier recommendations. DNA from the reference strain NCTC11168, known to contain the 40 assay genes, was used as positive control in all assays. The PCR results were converted into binary data corresponding to the absence (0) or the presence (1) of the marker in bacterial genomes and were stored into BioNumerics ® software (v6. 5, Applied Maths, Belgium). A dendrogram was built using the simple matching distance coefficient and unweighted-pair group method using average linkages (UPGMA) of clustering in BioNumerics ® as previously described  [20]  and using 100% of similarity for cluster definition to describe the population structure.
Data Analysis The Campylobacter status of the animals has been assessed with three main factors: (i) the kind of pets (cats or dogs), the season of sampling (spring, summer or fall) and the pet living condition (inside or outside the house or both). A multi-factor logistic regression was performed using the glm function of the R software. To assess the statistical difference between the carriage of Campylobacter spp. within pets according to their age, Fisher exact tests were used. In addition, the genetic diversity was assessed within C. jejuni isolates using the Simpson's diversity index calculated with the online tool "Comparing Partitions" from the website http://www. comparingpartitions. info/  [23]. The calculation of the confidence intervals for clustering agreement measures at 95% was performed using the resampling technique jackknife  [24].
Campylobacter Isolation from Pet Feces and Species Identification In this study, 304 feces samples from cats and dogs were collected: 234 samples were from dogs and 70 from cats. One hundred seventeen samples were collected in spring (89 dogs and 28 cats), 127 in summer (109 dogs and 18 cats), and 60 (36 dogs and 24 cats) in fall. According to the completed questionnaire, the age of animals ranged from 3 weeks to 17 years in dogs, and from 3 months to 15 years in cats, but no age information was collected for 5 dogs and 2 cats. Health status regarding diarrhea was also recorded, with only 3 dogs and 1 cat showed diarrhea symptoms at the sampling date. Among the 304 animals, 192 lived outdoor and indoor alternatively (156 dogs and 36 cats), 67 exclusively outdoor (65 dogs and 2 cats), 41 exclusively indoor pets (32 cats and 9 dogs), and no data were available for 4 dogs.
Within the 304 pet feces samples collected, thermophilic Campylobacter spp. were isolated from 89 dogs and 7 cats, which correspond to a presence in 38 and 10% of the animals, respectively (Table  1 ; Figure  1 ). Dogs carried significantly more thermophilic Campylobacter spp. than cats (p < 0. 0001), as well as puppies (dogs ≤ 1-year-old) compared with adult dogs (p < 0. 0001) (Figure  1 ). Indeed, 63. 6% (35/55) of puppies carried thermophilic Campylobacter spp. , in comparison with 30. 5% of adult dogs (53/174), while these bacteria were isolated from 20% (1/5) of dogs without age information (Table  1 ). No significant differences were observed in the carriage of thermophilic Campylobacter spp. in cats according to their age (Table  1 ; Figure  1 ). Campylobacter was not isolated from feces of animals showing diarrhea (n = 4) at the sampling date (data not shown).
Materials and Methods Twenty female riders and their horses (6 mares, 12 geldings and 2 stallions) were included in the study (Table  1 ). All horses were active riding and competition horses. We initially aimed to include an equal number of horses and ponies but due to safety issues with attaching lead weight to the upper body of children, we had to cut down on the number of participating ponies. The initial average rider:horse bodyweight ratio was 15. 3% ± 0. 4 (range 12-19%); with added 15%: 17. 2% ± 0. 5 (range 14-21%); and with added 25%: 18. 5% ± 0. 5 (range 15-23%). Rider weight included tack for the calculation of rider:horse bodyweight ratios. The 20 equipages were tested in two blocks of 10, with each block lasting four days (two successive weekends). All days followed the procedure illustrated in Figure  1 , except the first day, where baseline data were collected and the horses were habituated to the experimental set-up and equipment.
Behavior and Heart Rate The riding tests were recorded on video for later behavioral analysis (Table  3 ). The riders rode with the empty weight vest in the control treatment to hide the treatment to the video observer. Heart rate (R-R-recordings) was recorded with Polar Equine RS800 CX, consisting of two electrodes, a transmitter and a wristwatch receiver. Water and gel were used to enhance conduction and the electrodes were fitted under the saddle and girth while the transmitter and watch were attached to the saddle. Data were downloaded from the receiver to a computer, using the software Polar ProTrainer 5, Equine Edition. Artefacts in the data were rare, but if they occurred (i. e. , apparent as high spikes in the data graph), they were corrected using the error correction function in the software program.
Data Analysis The response variables (cortisol concentration; HR (mean), HRV (RMSSD, LF/HF ratio), frequency of conflict behavior (head, mouth, body, tail) and gait symmetry (number of lame legs, total asymmetry score)) were analyzed for an effect of treatment (control (0%), +15% and +25%) in a one-way repeated measures ANOVA (RM ANOVA, SigmaPlot14). A two-way ANOVA was used when appropriate, e. g. , to investigate the effect of sampling time and treatment on cortisol responses. Normality of the data was assessed using the Shapiro-Wilk test and variance homogeneity using the Brown-Forsythe test. If data did not meet the assumptions for the model, the Friedman RM ANOVA on ranks was used. In case of significance, the post-hoc tests Bonferroni t-test or Tukey test were used for pairwise multiple comparisons.
Expression of recombinant Omp25 and Omp19 Brucella proteins Recombinant U-Omp19 was produced as previously described  (Pasquevich et al. , 2009)  and finally stored in following buffer: 50 mM NaH2PO4, 300 mM NaCl pH 7. 4. To produce Omp25, the complete sequence of B. abortus omp25 gene (GenBank_X79284. 1) (de  Wergifosse et al. , 1995)  was synthetized and subcloned into pET22(b)+ (Novagen) in frame with 6×His-tag (Genscript). Expression and purification was performed as reported  (Goel and Bhatnagar, 2012). Recombinant purified Omp25 was finally refolded and stored at -20 ºC in refolding buffer (50 mM TrisHCl, 1 M NaCl, 0. 2 mM DTT, 0. 1 mM EDTA, 0. 5 M L-Arginine-HCl, 10% Glycerol, 0. 15 M Urea, 3. 6 mM Imidazole).
RNA extraction and RT Total RNA were extracted from infected BMDC using RNeasy Mini Kit (Qiagen) following the manufacturer's instructions. cDNAs were generated with 300 ng of RNA as a template using Quantitech Reverse Transcription Kit (Qiagen) according to manufacturer's recommendations. qPCR 2 µl of cDNA corresponding to 6 ng of starting total RNA were used for qPCR. Amplification reactions were performed in duplicates with SYBR Green (Takara) in 7500 Fast Real-time PCR (Applied Biosystem). Hprt was used as a housekeeping gene to normalize expression among samples and determine ΔCt. Data are presented as fold increase versus Mock treated cells, put arbitrarily at 1.
Flow cytometry Cells were stained for 20 min at 4°C with the antibodies listed in Table  S2 , then washed once in 2% FBS in 1xPBS and once in 1xPBS. Infected cells were then fixed for 20 min in 3 % PFA at 22°C. Events were collected by flow cytometry using a FACS LSRII UV or Fortessa (BD Biosciences) and analysis was performed on FACS DIVA and FlowJo softwares. For BMDC experiments, at least 100,000 CD11c + (DC marker) cells were analysed for MFI measurements. For in vivo experiments, DC subset identification followed the following strategy: after exclusion of neutrophils, NK cells, B cells, and T cells from live singlet cells, CD11c pos cells were divided into CD11c int CD11b -Bst2 + SiglecH + plasmacytoid DC (pDC) and CD11c hi cells, further identified as XCR1 + SIRPα -conventional DC type (cDC)1 and XCR1 - SIRPα + CD24 -cDC2 cells as described  (Mattiuz et al.
Acknowledgments We are especially grateful to  Y. Yanagi  ( Kyushu University, Japan ) for providing us with the Slamf1 -/-mice. We also thank  I. López-Goñi  ( Universidad de Navarra, Pamplona, Spain ) for his kind gift of the Omp25-deficent B. abortus strain. We are also thankful to  X. De Bolle  ( Namur University, Belgium ) for the E. coli S17 λpir strain,  C. Grégoire  ( CIML ) for the CD90 cDNA,  A. Cloeckaert  ( INRA, Tours, France ) for the rabbit anti-Omp25 and -Omp19 antibodies,  L. Chasson  and  C. Laprie  ( CIML histology facility ) for their help. We acknowledge the  CIML PICSL imaging facility (ImagImm, member of the national infrastructure France-BioImaging ), the  CIML cytometry facility , and  M. Sieweke  and  I. Moryión  for helpful discussions. This work was supported by the  French National Research Agency  through the  "Investments for the Future" program  ( France-BioImaging ,  ANR-10-INBS-04-01 ) and the  "Laboratories of excellence" program  ( Labex INFORM ,  ANR-11-LABX-0054 ), the  Fondation pour la Recherche Médicale  (grant FRM-CS), the  Excellence Initiative of Aix-Marseille University (AMU) -A*MIDEX , a  French "Investissements d'Avenir" programme , and institutional grants from the  CNRS, INSERM  and  AMU  to the CIML.
Tandem Mass Spectrometry Analysis Protein samples were partially air-dried in a speed-vac device, reconstituted in 1× final Laemmli buffer, and loaded on a onedimensional SDS-PAGE gel. The electrophoretic migration was stopped as soon as the proteins entered the separating gel, in order to isolate all proteins in a single gel band. This band was washed, subjected to in-gel tryptic digestion and analyzed by nanoLC-MS/MS using an UltiMate 3000 RSLCnano system (Dionex, Amsterdam, The Netherlands) as previously described  (Martinez-Pinna et al. , 2014)  except that peptides were eluted using a 5-50% gradient of solvent B (80% acetonitrile, 0. 2% formic acid) during 5 h at 300 nL/min flow rate. The nanoLC system was coupled to an Orbitrap Fusion TM Tribrid TM Mass Spectrometer (Thermo Scientific, Bremen, Germany) operated in a data-dependent acquisition mode with the XCalibur software.
Survey scan MS were acquired in the Orbitrap on the 300-2,000 m/z range with the resolution set to a value of 120,000. During data dependent acquisition, Orbitrap survey spectra were scheduled for execution at least every 3 s, with the embedded control system determining the number of MS/MS acquisitions executed during this period. Dynamic exclusion was employed within 60 s to prevent repetitive selection of the same peptide. Quadruplicate technical LC-MS measurements were performed.
Bioinformatic Processing of Mass Spectrometry Data Raw mass spectrometry files were processed with the MaxQuant software (version 1. 5. 0) for database search with the Andromeda search engine and for quantitative analysis. Data were searched for against M. abscessus entries of the Uniprot-Swissprot-TrEMBL protein database (October 2019 version). Carbamidomethylation of cysteines was set as a fixed modification, whereas oxidation of methionine, protein Nterminal acetylation was set as variable modifications. Specificity of trypsin digestion was set for cleavage after K or R, and two missed trypsin cleavage sites were allowed. The precursor mass tolerance was set to 20 ppm for the first search and 5 ppm for the main Andromeda database search. The mass tolerances MS/MS mode was set to 0. 6 Da.