dataesr/scientific-paragraphs-categorization huggingface dataset:
(2010b). Ammonia emissions from the building and manure storage were calculated according to emission factors developed by  Rigolot et al. (2010b). N 2 O and NO x emissions from manure storage were calculated according to  IPCC (2006)  and  Dammgen and Hutchings (2008) , respectively. During field application, NH 3 emissions were based on  Andersen et al. (2001) , N 2 O emissions on  IPCC (2006)  and NO x emissions on  Nemecek and Kägi (2007). Potential NO 3 and phosphate (PO 4 ) leaching rates came from  Nguyen et al. (2012). We calculated Cadéro, Aubry, Brossard, Dourmad, Salaün and Garcia-Launay environmental impacts using the CML 2001 method and total cumulative energy demand V1. 08 (non-renewable fossil + nuclear) as implemented in Simapro software, version 8. 05 (PRé consultants, Amersfoort, the Netherlands).
45, b = 0. 0216/kg, PDm = 143 g/day, BGompertz = 0. 0171/day) and one for barrows (BW70 = 30 kg, a = 5. 02, b = 0. 0189/kg, PDm = 150 g/day, BGompertz = 0. 0194/day) for the Varsimulations, and to generate the structure of the pig population for the Var + simulations  (Vautier et al. , 2013). The average performance per simulation were calculated for the Varsimulations whereas for the results obtained from the Var + simulations a variance analysis was performed using a linear mixed model with fixed effects of feed sequence plan, feed rationing plan, and their interaction, and random effects of the animal profile within the batch. According to this model, the experimental unit was the batch (n = 5). For this analysis, we used the lme4 package (V1. 1, Douglas et al. , 2015) and the lmer function of R software (R Development Core  Team, 2015).
Acknowledgements The authors thank  Alain Valancogne  for advice during integration of the InraPorc model in MOGADOR. This study was supported by  CIFRE Ph. D. funding (agreement in industry for training by research) of the  ANRT (National Agency for Research and Technology)  with co-funding from the call for projects 'Targeted research and innovation' of the  French Ministry of Agriculture, Agrifood and Forestry.
This was desirable, as consumers are not concerned by how palatability scores are derived, rather, that the palatability of the meat matches its description. The initial MSA model was developed using consumer scores and production and processing data from 12 700 beef samples. The current 2017 version utilises data from over 100 000 consumers and 70 000 beef cuts (Meat and Livestock Australia 2017), and has undergone extensive international testing. The system is regularly updated to reflect current consumer tastes and production methods, which is essential to ensure the quality rating remains relevant and accurate for consumers. For example, the weight of the four sensory scores (tenderness, juiciness, flavour liking, overall liking) used to determine eating quality (MQ4) were modified to apply equal weightings to flavour liking and tenderness, in order to reflect changing consumer preferences.
2008a). Through this mechanism, there was an estimated AUD$153 million returned to producers from retail premiums for 3. 1 million MSA graded carcasses in the 2015/2016 financial year. This added value can be further broken down to an average of AUD$66 per head for grass fed and AUD$45/head for grain fed cattle. The last extensive review of the MSA system was performed in 2016 where the industry impact and benefit:cost ratio was calculated as AUD$679 million and 12. 5:1 in a 5-year performance review between 2010 and 2015 (Meat and Livestock Australia 2016a). Of most interest to livestock producers is the pricing of the carcass based on the summation of the weight of individual cuts by price according to their eating quality (Meat and Livestock Australia 2011). To determine this, the economic weights for the various quality traits were calculated from a dataset of 423 carcasses  (Doljanin 2013 ).
Given differences in quality and yield, the value of the carcasses at retail when expressed in AUD$ kg -1 hot carcass weight, ranged from AUD$2. 15 to 3. 16 kg -1. From this data set, it was calculated that tenderstretch resulted in an increase of AUD$0. 27 kg -1 , and as expected, this could be completely attributed to improvements in eating quality, with no impact of carcass yield. Additionally, the economic weight of the traits that the producer can either influence by management decisions or breed were calculated. For this data set, an increase in 100 units of ossification score resulted in AUD$0. 15 kg -1 decrease in value, and again, this effect was independent of carcass yield. For marbling, a 100 unit increase in marbling score resulted in a AUD$0. 07 kg -1 increase in carcass value and this increased to AUD$0.
For instance, men scored beef about 2% higher than women in most countries  (Bonny et al. 2017). The overall conclusion that can be drawn from this work is that consumers from many different countries and cultures have similar responses for the assessment of beef quality when the MSA system is used to assess preferences. However, some minor adjustments are sometimes required to reflect, with a better accuracy, preferences of consumers in some countries and more importantly, to predict more accurately beef eating quality from young bulls and from the different breed types. Therefore, scientists from six countries have decided to merge all the data obtained with the MSA consumer testing protocols to develop a global database. To achieve this goal, a Foundation under the name, "International Meat Research 3G Foundation" was established in 2017.
This is effective as the important aspect for consumers is that the expected quality matches their eating experience, as long as handling of the animal/ carcass along the red meat supply chain meets welfare and food safety standards. This also gives scope for branded products to use the grading system, while still retaining the ability to differentiate themselves in the market place with particular methodologies, for example "Certified Angus Beef" or "Prime Dorper Lamb". There are now plans in place to create an international database combining national MSA beef datasets from all of the different countries where it has been tested. The key to the financial success of the MSA system will be to use it to underpin value based trading for beef. Research is in progress to recalculate carcass value by summing value of all cuts in the carcass.
Data Collection All patients were assessed clinically by a triage nurse at the participating CDTUBs or the supervising nurse at the health posts. The nurses' classification as BU or non-BU based on the WHO diagnostic criteria (young age, residence in an endemic area, undermined edges, location on limbs, necrosis, absence of pain, adenopathy or fever, and hyperpigmented edges) was verified by a clinician, based on a combination of the WHO criteria and clinical experience  [3]. Samples for mycobacterial analysis were collected at the CDTUB sites. From each ulcerated lesion, 2 swabs were taken from the undermined edges. From nonulcerative lesions, 2 fine-needle aspirates were taken from the center of the lesion. One sample was processed immediately for DSE after Ziehl-Neelsen (ZN) staining. The second sample was placed in a semisolid transport medium  [11] , and stored at 4°C until weekly shipment to the Mycobacteriology Reference Laboratory (LRM) in Cotonou, Benin.
At the LRM, technicians performed DSE after auramine staining using fluorescence microscopy  [20] , IS2404 real-time PCR  [21] , and in vitro culture for M. ulcerans  [11, 16]. One 4-mm punch biopsy was taken from every lesion and stored in 10% formalin until embedding in paraffin. One section was stained by hematoxylin-eosin and 1 section by ZN. Histopathological reading was done at the University of Parakou (Benin) and the hospital of Chambéry (France). Both histopathologists were blinded to clinical information (except for age, gender, and type and localization of the lesion) and the results of other diagnostic tests. A standardized reading form was used to score histological changes (Supplementary Data 1). Based on the score, specimens were classified as probable BU (score 7-15), compatible with BU (score 4-6), or not compatible with BU (score ≤3).
Three of the characteristics that make up the Buruli score (yellow and green color, and lesion hyposensitivity) were not available in our dataset. The Buruli score was only tested on ulcerative lesions as 2 of its components (characteristic smell and undermined edges) are unavailable for nonulcerative lesions. Next, since diagnostic accuracy estimates can vary across patient subgroups, we evaluated effect modification by study site, type of lesion, recruitment type, time since start of study, availability of photographs, patient delay before consultation, transport time of samples to the LRM, and HIV status.
Participants Between March 2012 and March 2015, 260 eligible patients presented at the study sites: 166 with symptoms or signs clinically compatible with BU and 94 with lesions clinically not compatible with BU (Figure  1 ). Of the 166 patients clinically suspected to have BU, 133 were enrolled. The 33 not recruited did not differ demographically nor clinically from the study participants, suggesting that, at least among those clinically suspected to have BU, there was no overt selection bias (Supplementary Data 4). The total number of eligible patients with lesions clinically not compatible with BU was not documented. Among the 227 participants recruited, 205 had complete test results and were included in the analysis (Figure  1 ). Their demographic and clinical characteristics are summarized in Table  1.
However, if the burden of BU would continue reducing, it would virtually never be missed by the clinicians, but a considerable proportion of clinical suspects would not have BU (Supplementary  Data 13). The sensitivity of PCR (65% [95% CI, 56%-73%]) in the present study was lower than generally reported in studies using a variety of reference standards (85. 4%  [13] , 86. 0%  [28] , 87. 5%  [14] , 100%  [15] ). Clinical characteristics were included into the expert panel approach which resulted in a considerable proportion of PCR-negative patients being classified as BU and therefore more false-negative PCR results than in studies using only microbiological assays as reference standards. However, the addition of PCR to DSE resulted in an increased sensitivity (69% [95% CI, 60%-77%]) while the combined specificity remained high (93% [95% CI, 85%-98%]).
In those cases, the selection of the method may be constrained by the accessibility to the animals, and the inherent requisite resources. Subsequently several studies have previously used different methods in parallel or consecutively to gather complementary or preliminary data at the interface  (31, 32). In this study, we investigated an a priori interface between domestic pigs and bushpigs in a farmland located in the vicinity of the largest national park in Uganda and where both species are known to raid crop fields for feed  (16). ASF is endemic in domestic pigs in the area and has been detected occasionally also in bushpigs  (9, 15). We estimated the frequency of visits of bushpigs to the crop fields as an indirect measure of the potential for interaction with free ranging pigs. We hypothesized that the season, the type of crop, the proximity of the forest, river, and protected area can influence the occurrence of bushpigs in the fields and thus the potential for direct or indirect interactions with domestic pigs.
Design and Implementation The questionnaire, designed for individual interviews, consisted of 115 questions. The questions were designed to collect data related to crop raiding by wild and domestic animals. It was reviewed by a local and international team of epidemiologists and social scientists and uploaded in the KoBo toolbox (KTB) online platform (http://www. kobotoolbox. org/). The interviews were administered in English and translated simultaneously into the local language, Luo, by a trained facilitator fluent in both Luo and English. In order to evaluate the understanding and appropriateness of the questions by all the stakeholders, a pilot trial of seven interviews was carried out in the vicinity of the study area. Subsequently appropriate changes were made in the questionnaire after the trial.
The responses and household geolocations were collected using a tablet device (Nexus 9, HTC Corp. ). The questionnaire survey was implemented from November 2016 to January 2017 and farmers were inquired about data corresponding to the dry and rainy seasons pertaining to the year 2016. After having described their farms in terms of crops grown, they were asked about the frequency of visits from wildlife and domestic animals and the season when the visits occurred. We also asked them to rank the species in descending order from the one causing most damage to the one causing the least. Complementary data on the geographical characteristics of the farms were gathered using QGis 2. 10. Pisa (Table  3 ). A total of 41 crop fields were selected (mean: 1. 46 ± 0. 58 crop fields per farm). For each selected crop field, one camera was placed in a site where tracks of wildlife were previously observed by the track observer (see next paragraph).
Statistical Analysis From the questionnaire, a descriptive analysis conveying the responses dealing with bushpig visits was carried out. From the CT survey data, we calculated the number of bushpig visits per night per session and the mean and standard error of the visit frequency. From the track observations, we calculated the number of sessions in which the bushpig tracks were observed. Secondly, we analyzed how the three response variables varied among the type of crop, the distance of the crop field to the forest, park boundary and river; and also between the dry and rainy seasons. We did this by using generalized linear mixed models (GLMM) to take into account the likely dependence within one farm. For the questionnaire and CT survey data sets, we used a Poisson link as usually used for count data having Poisson distribution  (36).
The significance of each variable included in the model was assessed using likelihood ratio tests (LRT). The significance of contrasts between categories was assessed using Wald tests. We checked whether overdispersion was present in the residuals of the selected model by calculating the ratio between the sum of squared Pearson residuals and the degrees of freedom. The goodness of fit was assessed by calculating the pseudo R squared giving the conditional (interpreted as variance explained by both fixed and random factors) and marginal (representing the variance explained by fixed factors) coefficient of determination for generalized mixed-effect models. All analyses were performed by using lme4 and MuMIn (37) packages in R 3. 4. 2 software  (38).
Comparison Among the Different Methods In order to compare the results from the questionnaire, for which the sampling unit was the farm, with the CT and track survey for which the sampling unit was the crop field, we extracted questionnaire data corresponding to the crop fields monitored by the two other methods. We also transformed the variables corresponding to the visit frequencies given by CT and the questionnaires into dichotomic responses (presence/absence of bushpigs) for each crop field and each season. We evaluated the degree of agreement between the results obtained by the different methods by computing the Kendall coefficient of concordance for each season, using the irr R package  (39). We also performed a GLMM to investigate the ability of the three methods to detect the bushpig presence in relation to the drivers highlighted by the models selected for each method separately.
It is noteworthy that combining the results of the three methods allowed us to obtain a more powerful dataset with better estimation of effects. Although CT was the most specific method allowing to identify the species with more certainty, visualize the number of individuals and their behavior and provide a quantitative estimation of the bushpig visits, it was also the least sensitive in detecting their presence. The number of visits by bushpigs drawn by the CT might have been underestimated for several reasons. First, we used only one camera per crop field and could have missed bushpig incursions occurring outside the field of view of the camera. Secondly, animals passing rapidly in front of the motion sensor without staying in the field of view may trigger the camera but could not be "captured" even if the trigger speed was set to minimum (1 s), leading to "false negative" results  (43).
This practice may reduce the potential for interactions with bushpigs since we showed that they forayed into crops mostly during the rainy season. Pigs which are not tethered during this season are much more at risk to interact with bushpigs, particularly in cassava fields, which are attractive for the two species. The only observation of consecutive visits by the two species in the same field within a short time interval (3 days) was collected by CT in a cassava field during the rainy season. Our data also showed that domestic pigs might visit crop fields at night, increasing the potential for direct contact with bushpigs. The use of GPS or VHF technology would be of great value to investigate these shared habitats between both species. This tool allows to monitor animal movements at regular and possibly very close intervals, providing a clear picture of the animal's home range and use of the habitat  (26-28, 31, 32).
ACKNOWLEDGMENTS We acknowledge all the farmers who took part in the study and made that work possible. We also thank  Erika Chenais  from  SVA  for her valuable comments on the questionnaire,  David Chavernac  from  CIRAD  for his help in using KoBo toolbox  Christophe Ferrier  from  ONCFS  for his help with the figure and  Esther Kukielka  from  UC Davis  for her help with the data. We are grateful to Raymond who drove us and took part into the field work,  Philip, Brenda , and  Kevin , the team in Lutuk, who help with the logistics.
SD cards collection, 1. Data sheets filling from the data Kobo software. pictures downloading, 2. Entering data in collection to the Extraction and recoding visualization and database building of the of the data of interest filtering database 2. data of interest entered in a database Variable used to Number of reported BP Number of detected Absence or presence quantify the visit visits in the farm's crop BP visit per day in the of BP track in the crop of BP fields per year crop field field per session Categorical variable Quantitative variable Qualitative variable Characteristics of the data collected are also provided.
mayottensis, may be at least in part responsible for this contrasted regional epidemiology. The genomes presented herein will accelerate comparative genomic approaches aimed at delineating the main genomic features involved in Leptospira virulence. Data availability. Genome sequences were deposited in DDBJ/EMBL/GenBank under the accession numbers CP030142 and CP030143, CP030144 to CP030146, and CP030147 to CP030149 for strains VS2413, MDI222, and MDI272, respectively, as well as BioProject number PRJNA477299. Raw sequence data are available from the NCBI Sequence Read Archive (accession number SRP154442).
ACKNOWLEDGMENTS We are thankful to  Pierre Lefeuvre ,  Frédéric Pagès , and  Claire Valiente-Moro  for fruitful discussions. This work was financed by  European Regional Development Funds ERDF PO INTERREG V ECOSPIR  number  RE6875  and was carried out in the framework of  Colette Cordonin's Ph. D. degree , financed by the  French Ministry of Higher Education, Research and Innovation. We thank members of the  IRD i-trop Plantes Santé bioinformatics platform  for providing HPC resources and support.
The objective of the present study was to assess genetic diversity of Brucella spp. by MLVA on a larger scale. Thus, 1404 animal or human isolates covering all parts of the world over a period of 32 years , hosted by INRA in the Brucella Culture Collection Nouzilly (BCCN), were investigated. Data were compared for congruency with those of MLST when available, to try to determine the advantages and complementarity of each methodology. This was facilitated by taking advantage of the availability of whole genome sequence data from which both MLVA and MLST genotypes could be deduced by in silico analysis.
MLST and MLVA in Silico Analysis of Available Genome Sequence Data Multilocus Sequence Typing codes of complete genomes or de novo assemblies from reads archives were deduced in silico using the BioNumerics (Applied-Maths) version 7. 6. 2 tools. The scheme for MLST coding was recovered from the Brucella MLST database at https://pubmlst. org/brucella/. The scripts used to deduce MLVA codes of complete genomes or draft assemblies are deposited at https://github. com/dpchris/ MLVA. In order to estimate the read length necessary for a correct reconstruction of tandem repeats length from read archives, artificial reads data were produced from complete genome sequences using artificial fastq generator  (Frampton and Houlston, 2012). The reads were then assembled using SPAdes version 3. 9  (Bankevich et al.
Whole Genome SNP Analysis Sequencing reads were mapped on a reference genome using BioNumerics version 7. 6. 2. The B. melitensis 16M assembly GCA_000740415. 1 was used as reference after concatenating chromosome I accession number CP007763. 1 and chromosome II accession number CP007762. 1 in a single file. SNPs were called within BioNumerics using the strict closed dataset option. Minimum spanning trees were produced with the hypothetical missing links option.
, 2012). The four groups of rodent strains and human isolate BO2 are clearly separated with no apparent epidemiological link. As a consequence, from these and previous molecular data provide no clue regarding the animal or environmental reservoir for the human Brucella sp. isolate BO2. Although only a limited number of rodent strains are currently available (n = 37 in Figure  5 ), intra-species diversity is observed by MLVA within each species represented, except B. suis biovar 5. Regarding host diversity only B. microti appears not restricted to rodents (Figure  5C ).
Taking Advantage of Available Whole Genome Sequence Data to Link MLVA Clusters Onto MLSA-Based Phylogeny MLVA is an efficient clustering tool but with moderate phylogenetic value on its own. Phylogeny can be recovered indirectly by anchoring MLVA genotypes on a phylogenetic framework, as provided by whole or partial (as in MLST) genome analysis. This can be done if a representative set of strains has been typed with both methods as described by  Riehm et al. (2012). In theory, both MLVA and MLST data can be deduced from whole genome sequence data. We recovered fifty-four complete Brucella genomes from the EBI-ENA public databases. Seventeen correspond to 14 reference strains (strains B. abortus 870, B. melitensis 16M, and B. suis 1330 were sequenced twice independently), which we have also typed in vitro by MLVA via electrophoresis of PCR amplification products.
For example, Illumina MiSeq 250 bp paired ends files allow to confidently reconstitute all VNTR loci, whereas reads archives with shorter reads are used to reconstruct only some loci as deduced from Supplementary Table  S3. From these assemblies, MLVA and MLST data were extracted in silico. Full MLVA11 and MLST9 (according to  Whatmore et al. , 2007)  genotypes could be recovered for 355 datasets including the complete genome sequences.