dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical and radiological assessment. Assessment complied with the 1964 Declaration of Helsinki and MR-003 reference methodology of the French CNIL data protection commission, and the study was registered in the CNIL database (n° 2216205 v 0). All patients and legal representative were informed ahead of data collection and analysis. Patients were followed in consultation with clinical and radiological work-up at 6 weeks, 3 months, 6 months and 1 year post-trauma. In case of incomplete recovery by 1 year, follow-up continued until consolidation was achieved. The main endpoints were associated lesions at time of trauma, and range of pronation-supination at last follow-up. Other data comprised demographics (including age at trauma, gender), history (including lesion mechanism), treatment (operative/non-operative; percutaneous/open), and follow-up (complications, observed immobilization time, rehabilitation, time to return to sport, and time to consolidation).
78, -0. 53 and -0. 62, -0. 36, respectively) in Scottish Blackface lambs exposed to natural mixed infection  47. It was suggested that parasite development such as worm growth and fecundity in sheep can be regulated via an IgA response, possibly in conjunction with eosinophils  11, 41. In this paper, we have collected data from previous studies that measured correlations between FEC and blood IgA levels against different larval stages. We also calculated correlations using raw data for FEC and blood IgA level from these studies before re-analyzing all the data. Figure  4  shows the correlations between FEC and blood IgA levels against different larval stages from different studies. The overall correlation between IgA in blood and FEC was negative (r = -0. 36, 95% CI = -0. 46, -0. 26). Correlations between FEC and blood IgA activity against L3 or adult were -0.
Material and Methods The study methodology followed the guideline of "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement"  91. The literature search had been conducted using electronic databases. Web of Science and PubMed were used as databases to cover the literature on small ruminants and parasites. Besides, the reference lists from five literature reviews, all published after 2008, were additionally searc hed  24, 28, 29, 89, 92. Studies were closely evaluated and selected for inclusion if they measured at least one immunoglobulin type and the data were extractable. The literature review revealed 41 studies which measured at least one immunoglobulin: 35 focused on lamb immune response (18 used non-naïve lambs) and 7 on yearlings. Overall, the methodological quality of included studies was evaluated qualitatively with an 9-point scoring system based on SYRCLE's risk of bias tool 93 : (1) Statement on ethical approval or guidelines followed for using animals in the study clearly mentioned, (2) Adequate allocation sequence generation, (3) similar groups at baseline, (4) Allocation concealment, (5) Random housing, (6) Random outcome assessment, (7) Outcome assessment blinded and (8) incomplete outcome adequately addressed (Fig.
1 ). Information extracted from each study included nematode genus, host (sheep or goat), infected breed, host classification (resistant or susceptible), host sex, host age (months), age at weaning (weeks), infection period (days post infection), sample tissue, infection type (artificial or natural), number of animals per group, immunological state (naïve or non-naïve), total number of infected larvae, larval details and infection method for artificial infection (single or trickle). Measured traits of interest included both parasitological and immunological parameters. If data were provided in graphical form, traits means were extracted using WebPlotDigitizer (version 3. 8)  93. These summary measures beside the study information previously mentioned were entered into an electronic spreadsheet in Microsoft Excel and a dataset was built.
Data From 1997 to 2017, 40,521 standard radiographs of extended hindlimbs submitted by breeders or owners in 195 different breeds were evaluated independently by the same examiner (JPG) for HD assessment. All the data were computerized and recorded in a single electronic database to allow further analysis. For each breed, the incidence of each of the 5 scoring classes was extracted from the database for each year covered in this retrospective study. Breeds were excluded if the creation of 3 homogeneous cohorts of 7 years was not possible or if the total number of radiographs read per breed and per period was insufficient (i. e. <200). Breeds selected were analysed without exclusion nor selection in the database. The same single panelist evaluated all included breeds from 1997 to 2017.
In a 1993-2002 survey  [22] , it was demonstrated that in France, only 2 to 19% of the dogs were screened for HD. Although the number of screened dogs has increased since this period, it is likely that, depending on the breed, a small fraction of all breeding dogs undergo a hip radiograph. Moreover, there is an unknown proportion of veterinarian (or owner) prescreening of the radiographs with obvious hip dysplasia, leading to the lack of presentation of the "worst" radiographs for official screening. Therefore, our data reflect only those dogs whose owners and breeders submitted radiographs for analysis. This proportion varies from 20 to 40% in Switzerland  [23]. In Scandinavian countries, all breeding animals in control-program breeds are screened, and both dam and sire hip radiographs need to be submitted for screening to be registered by the Kennel Club  [13, 17, 18].
The aim was to assess the efficiency of gaits characteristics in improving jumping performance of sport horses and confront accelerometers and judge scores for this purpose. A sample of 1,477 young jumping horses were measured using accelerometers for walk, trot, and canter. Of these, 702 were genotyped with 541,175 SNPs after quality control. Dataset of 26,914 horses scored by judges in breeding shows for gaits and dataset of 142,682 horses that performed in jumping competitions were used. Analysis of accelerometric data defined three principal components from 64% to 89% of variability explained for each gait. Animal mixed models were used to estimate genetic parameters with the inclusion to up 308,105 ancestors for the relationship matrix. Fixed effects for the accelerometric variables included velocity, gender, age, and event.
Accelerometry, gait and conformation judge scoring, jumping competitions, and genotyping data During the spring of both 2015 and 2016, 1,477 young sport horses aged four (42%) and five (58%) years were recorded with an Equimetrix 1 accelerometer  [14]  at walk, trot, and canter under saddle. These horses were 50% females, 35% geldings, and 15% males. Their breed was 84% Selle Francais (SF), while for the remaining 16% (10% foreign sport horses, 2% Anglo-Arabs, and 4% unregistered horses), 39% had at least one SF parent. Acceleration of the horses was recorded with the Equimetrix 1 accelerometer device attached to the girth. Data were recorded at competition events for young jumping horses after the competition was over. Riders were asked to perform a quick gait test including the three gaits in an arena with diagonal lines of 60 meters in order to have a sufficiently long measurement time.
For trot and canter, two diagonals were measured: one performed at working gait and the other one at medium gait according to the rider's feeling. The difference between the two gaits was mostly specific to the horse and lead to two different velocities for each gait. Velocity was measured using a chronometer on this diagonal. A sample of 10 seconds for each gait was extracted for the gait measurement calculations. The raw data were the acceleration recorded at 100 Hz per axis in the dorsoventral, longitudinal, and lateral axes of the horse. Five gaits were distinguished: walk (W), working trot, medium trot, working canter, and medium canter. Eight measurements were then calculated: • velocity (V), • stride frequency (SF), • regularity (R), • symmetry (S, except for canter), • dorsoventral displacement (DVD), • dorsoventral power or activity (DVA), • longitudinal activity (LGA), • lateral activity (LTA).
The calculation methods are described in  [14] [15] [16]. Logarithm transformation was applied to normalize the symmetry and the activity measurements (except for dorsoventral at trot) in order to obtain better Gaussian distributions. Thus, the five gaits of each horse were characterized by a total of 38 measurements. Thirty-two percent of the horses were recorded in 2015 and the remainder in 2016. Combination of the locations and the day of the recording resulted in 26 levels of "event" effects. Height at withers was routinely measured for 97% of these horses (mean 166. 75 cm, SD 4. 16). Nearly all of these horses (94%) performed in jumping competitions between 2014 and 2019. They spent between one and six years in competition, with an average of 4. 0. Analysis of the jumping competition data was performed with the complete dataset of official jumping competitions from 2002 to 2019 since the birth year 1998, including 142,682 horses, and 612,449 annual performances.
All starters in an event earned points. Finally, points earned by the horse in one event were the product of points allocated to the event and point allocated to the rank. The points were summed over one year and log transformed to ensure a normal distribution between horses. Only twenty nine percent of the horses measured for accelerometry also participated in breeding shows where conformation and gaits were scored. The analysis of the judges' scores included all of the data recorded by the Stub Book Selle Francais (SBSF) during regular breeding shows for young horses (two to three years old) from 2005 to 2018. They involved nine conformation traits and three gait traits judged in hand for the walk and during free presentation for trot and canter. There were 26,914 horses that were recorded once (for 52% of the horses), twice (37% of the horses), and up to a maximum of nine times for the same horse in 1,540 local, regional, and national shows.
X and Z are incidence matrices. The variance-covariance matrices of random effects were: VðuÞ ¼ Is 2 u and VðeÞ ¼ Is 2 e. The principal component analysis was performed using the FactoMineR package  [18]  with as variables the û estimates for trot and canter and y À X b for walk. In the rest of the study, the eigenvectors of the three first principal components (PC) of each gait (walk, trot, and canter) were used to compute three linear combinations of the measurements for each gait resulting in nine synthetic gait traits. Genome-wide association study. We performed a genome-wide association study (GWAS) for the nine PCs. Scores for each of the three principal components of each gait were computed for each horse using the linear combination given by eigenvectors obtained by the principal component analysis.
A mixed model including both the fixed effect of the SNP and the random genetic value of the horse would be the correct model to analyze these scores. The GWAS was performed using the GenABEL package  [19]. GenABEL used a slightly simplified model. The GRAMMAR model  [20]  was a two steps model. It performed only once the estimation of variance components and effects without the SNP effect and then used the residuals of the model to estimate each SNP effect. The power of this method is very closed to the use of the one step full model and avoid in the same way problems due to the genetic structuration  [21]  of the data. Residuals in case of repeated measurement (for trot and canter) can be replaced by the estimate of the permanent environment effect ðpÞ. Then, in the first step, the model used was a multiple trait animal model: y ¼ Xb þ Za þ Zp þ e; ð3Þ with y representing the vector one of the nine PCs, b the vector of fixed effects, which included gender (female, male, or gelding), age (four or five years), velocity (covariate), event (26 levels), a the random polygenic effect (10,907 horses including ancestors over four generations), p the random permanent environmental effect (for trot and canter repeated two times), and e the vector of residuals.
The model included the already mentioned fixed effects, genetic values, and permanent environmental effects. The residual variance-covariance matrix included the covariance residuals between the scores obtained in the same event. The pedigree restricted to horses measured in breeding shows included 75,528 horses. Genetic correlations between jumping competitions and judges' scores using estimated breeding values. Estimated Breeding Values (EBV) for the jumping competition trait are available to the public for all horses in France since birth (https://www. ifce. fr/). We calculated the EBV for all horses judged in breeding shows obtained the year of the show, deleting results in competitions obtained after this date from the whole dataset (15 evaluations for last year of performance from 2004 to 2018).
Acknowledgments Authors wish to thank all the owners, riders, lads, and show organizers who made the data collection possible. We thank the team who measured the horses:  E. Bezombes ,  D. Billerey  le men,  P. Bleteau ,  M. Chassot ,  P. Fontaine ,  N. Garcia-Marinez ,  L. Gosselin ,  F. Guillot ,  N. Marechal,  C. Ruhlmann ,  JM. Yvon.
Behavioral Observations and Analyses The tests were video recorded by a camera (Bosh, Box 960H-CDD) and a recorder (Noldus Mpeg recorder 2. 1. , The Netherlands), linked to a LCD monitor (DELL 48 model 1907FPc) which allowed us to visualize the experimental room from an adjacent room. The location of the pigs was monitored directly during the tests and the other behaviors later from videos, both using The Observer XT 14. 0 software (Noldus R , The Netherlands). All behaviors recorded are indicated in Table  1  and correspond to the behavior code numbers: 2-7, 11, 14 (restricted to stimulus zone) and 16.
Acoustic Monitoring and Analyses Vocalizations produced during the Isolation/Reunion test were recorded with a C314 microphone (AKG, Austria) placed in the center of the room at a height of one meter, connected to a MD661MK2 recorder (Marantz, USA). The vocal types were then manually annotated (grunt, squeak, bark, scream, and mixed calls), after visual inspection of spectrograms on Praat R software, by an expert [call types in pigs have been described and grunts are particularly typical low frequency and noisy calls  (27, 28) ]. Only grunts were subsequently acoustically analyzed as they represented the most frequent call type that constituted a dataset of 5,766 calls. A spectro-temporal analysis was performed with custom-written codes using the Seewave R package  (29)  implemented in R  (30).
Parameters quantifying total duration or numbers were standardized per minute. Table  6 ). Only scores from day 1 were used, to minimize habituation effects that could occur on day two. For the human, HproxPC1 explained 63% of data variability and, for the object, OproxPC1 explained 47%. For further analyses, the score toward each stimulus was matched accordingly to the type of reunion the pig was experiencing (Human vs. Object): when reunited with the human, the behavioral proximity score toward the human (HproxPC1) was used, whereas when reunited with the object, the behavioral proximity scores toward the object (OproxPC1) was used.
To increase interpretability, all continuous variables (interval index and behavioral proximity scores) were scaled, so the Z score is presented every time  (32) , the individual was put as a random factor to take into account multiple tests on the same pig. On this full model, a model selection was performed with the "dredge" function of the "MuMIn" R package  (33) , which compares all possible models built using subsets of the initial explanatory variables of the full model, including the null model. Models were compared using Akaike Information Criteria corrected for small sample size (AICc). Significant models were selected when delta AICc was below two  (34) , the weight of remaining explanatory variables was evaluated by calculating the presence or absence of the term in the remaining models ("importance" function).
Tests and Validation of All Models and Model Selection All linear models were validated by visual inspection of the symmetrical and normal distribution of the residuals ["plotresid" in "RVAideMemoire" R package  (35) ]. For generalized models, overdispersion was tested using the "overdisp. glmer" function ("RVAideMemoire" R package); when overdispersed, a correction with the line number as random factor was used. Anovas were computed on models to test for significant effects of explanatory variables ("car" R package), effects were considered significant when the p-value was below 0. 05. Model estimates and pairwise post hoc tests were computed using Tukey correction for multiple testing ["lsmeans" R package (36) (models 1-3)]. A complete report of statistics is available as Supplementary Tables  4 5 6.
ACKNOWLEDGMENTS We acknowledge all the technical staff at UEPR: especially  Patrick Touanel , who participated greatly in handling the pigs. We thank  Eric Siroux  who helped in building the acoustic chamber at the beginning of the experiment and  Remi Resmond  for great discussion about statistics. The authors would like to thank  Sandra Edwards  for English proofing the manuscript. The authors would like to acknowledge  Camille Noûs , a symbol of scientific collaboration (https://www. cogitamus. fr/indexen. html).
Toxicokinetic analyses The plasma mass concentrations were converted into molar concentrations before analysis. The cumulated molar quantities of excreted BPS (BPSG) in urine were calculated by multiplying the molar concentrations of BPS (BPSG) by the volume of urine at each sampling time. Plasma concentration-time profiles of BPS were analyzed using Phoenix (version 8. 0 Pharsight Corporation) according to a non-compartmental approach (NCA) involving a sparse data option. Sparse data methodology consists of calculating TK parameters based on the mean profile of all the subjects in the dataset and is appropriate when no rich data series can be obtained, as in the present experiment. The AUC and the Area under the first moment curve from the time of dosing to the last measurable concentration, AUClast and AUMClast were calculated using the linear trapezoidal rule.
Eq1 where a and b are the allometric coefficient and the exponent of the allometric equation, respectively. The Cl and BW were transformed logarithmically and fitted to Equation 2 by linear regression analysis using Systat R (Version 11, Systat Software, Inc. , San Jose California USA). 𝑙𝑜𝑔𝐶 𝑙 = 𝑙𝑜𝑔𝑎 + 𝑏 × 𝑙𝑜𝑔𝐵𝑊.
Main results Six trials met all our eligibility criteria and provided extractable data in the previous version. Following our updated search, one new RCT was identified, meaning that seven trials were included in this updated review. In addition, one previously included trial provided fully published data out to 10 years, and another previously included trial provided fully published data out to 22 years of follow-up. Four trials were at low risk of bias, one trial was at unclear risk, and two trials were at high risk of bias. Six trials were conducted in Asian populations. In preventing development of subsequent gastric cancer, H. pylori eradication therapy was superior to placebo or no treatment (RR 0. 54, 95% confidence interval (CI) 0. 40 to 0. 72, 7 trials, 8323 participants, moderate certainty evidence).
Data extraction and management The lead review author extracted data and recorded it on to specially developed forms. The second review author did a blinded check on this, and any discrepancies were resolved by consensus. Data entry into RevMan 2014 was also double-checked. Due to the outcome of interest under study, several groups of trial investigators followed-up trial participants at more than one time point. Where we found multiple articles for a single study, we extracted only data from the latest publication from each eligible study. We recorded the following characteristics for each trial: • geographical location In addition, as some of the trials we identified performed upper gastrointestinal endoscopy and obtained gastric biopsy specimens in all recruited individuals, we were able to obtain the number of participants in these RCTs with preneoplastic lesions at baseline (defined as presence of gastric atrophy, intestinal metaplasia, or dysplasia).
We extracted data using a modified intention-to-treat (ITT) analysis. In this, we excluded from the analysis individuals found to be ineligible a er randomisation, and those who did not receive the intervention to which they were assigned. Due to the relatively rare nature of the primary outcome of interest, we assumed that all participants lost to follow-up had not developed gastric cancer, but kept them in the denominator for the study. We did this because the shortest duration of follow-up in the studies we identified was greater than or equal to four years, and therefore drop-out rates were relatively high. We also performed a complete case analysis, as a sensitivity analysis, where we excluded all participants for whom data were missing or unavailable from the analysis altogether (Akl 2013).
Incomplete outcome data (attrition bias) We assessed attrition bias for H. pylori eradication and adverse events. • Low risk, if there were no missing outcome data; reasons for missing outcome data were unlikely to be related to true outcome; missing outcome data were balanced in numbers across intervention groups, with similar reasons for missing data across groups; the proportion of missing outcomes compared with the observed event risk was not enough to have a clinically relevant impact on the intervention e ect estimate; missing data were imputed using appropriate methods.
Cochrane Database of Systematic Reviews • Unclear risk, if insu icient reporting of attrition or exclusions to permit judgement of low risk or high risk (e. g. no reasons for missing data provided). • High risk, if reasons for missing outcome data were likely to be related to true outcome, with either imbalance in numbers or reasons for missing data across intervention groups; the proportion of missing outcomes compared with observed event risk were enough to induce clinically relevant bias in intervention e ect estimate; per protocol analysis done with substantial departure of the intervention received from that assigned at randomisation; potentially inappropriate application of simple imputation.
Dealing with missing data Where possible, we recorded completeness of follow-up, with dropout rates by group. We attempted to contact authors for missing data. If no outcome data were available, we used the modified ITT approach, which included all eligible and randomised participants in the analysis, but we did not consider participants who were found to be ineligible a er randomisation in the ITT analysis in the primary analyses. Correa 2000 -Correa 2001  and You 2006 -Li 2019 both excluded participants from the analyses a er randomisation (as they were subsequently found to be ineligible or did not take the treatment). We included these ineligible participants in the sensitivity analyses. Since the incidence of gastric cancer is low, we did not presume that missing participants had developed subsequent gastric cancer (worst-case scenario).
Assessment of heterogeneity We pooled data using a random-e ects model to give a more conservative estimate of the e ect of H. pylori eradication therapy on the subsequent occurrence of gastric cancer, allowing for any heterogeneity between studies (DerSimonian 1986). We assessed heterogeneity using both the I 2 statistic with a cuto of greater than or equal to 50%, and the Chi 2 test with a P value less than 0. 10 used to define a significant degree of heterogeneity (Higgins 2003).