dataesr/scientific-paragraphs-categorization huggingface dataset:
Primers and probes The primers and probes used to detect the tox gene and rpoB genes for C. diphtheriae and C. ulcerans/C. pseudotuberculosis species identification were as described by De Zoysa et al. [26]. For this study we introduced a conserved fragment of the 16S rRNA gene instead of the fragment of gfp gene  [28]  as the internal process control (IPC). The two primers and probe (u-16S) used to detect the 16S rRNA gene were designed with the software LC probe design2 (Roche). Then, 16S rRNA gene sequences of known pathogenic or commensal species of the respiratory tract were aligned and a final selection of primers and a probe was accomplished according to their universality (Fig. S1 , available in the online version of this article). The sequences of primers and probes are given in Table  2.
The expression of the other genes was not statistically different (Student's t-test) between the two conditions ( ±IPTG) (n = 3). Source data are provided as a Source Data file. d Survival of GcrA-depleted strain in the presence of in vitro alkylating stress. Bacteria were cultured in liquid medium supplemented or not with IPTG and in the presence or absence of 5 mM of MMS. Samples were taken after 0, 3, 6, 24, and 48 h of culture and plated on rich medium supplemented with IPTG. Colony-forming units were counted to evaluate survival. Error bars represent standard deviation (n = 3). Source data are provided as a Source Data file environment. Indeed, alkylating stress is generated by those modified metabolites, and not by N-nitrosation per se  66. It has been proposed that the eBCV is deficient in metabolites  67, 68  , which could explain why host cells do not produce detectable exogenous alkylating agents.
05 (*) and p < 0. 001 (***). Source data are provided as a Source Data file B. abortus deletion strains were constructed by allelic exchange, via pNPTS138 vectors (M. R. K. Alley, Imperial College of Science, London, UK) carrying a kanamycin resistance cassette and a sucrose sensitivity cassette  27. Briefly, we selected bacteria which had integrated the plasmid containing upstream and downstream sequences (about 750 bp each) of our target gene by plating them on kanamycin-containing agar plates, then performed a counterselection on 5% sucrose-containing agar plates without kanamycin to allow plasmid curing. To confirm gene deletion, PCR was performed on colonies that were kanamycinnegative and sucrose-resistant with primers targeting sequences upstream and downstream of the plasmid-containing sequences.
The FITC channel (1 s) was used to detect either the Nnitrosation-sensitive probe or the GFP signal of the reporter system. LampI proteins were detected with the APC channel (800 msec). Pictures were encoded with NIS-element software and analyzed with the plug-in MicrobeJ in ImageJ  79. For bacteria on pads, mean fluorescence intensities (MFI) were obtained as the "mean_c" values with MicrobeJ for individual bacteria. For intracellular bacteria, MFI were obtained by subtracting the background fluorescence (defined here as the average value of fluorescence given by the Pixel Inspection Tool on three points randomly chosen around a bacterium) to the "mean" value of fluorescence obtained for each bacterium with MicrobeJ (see Supplementary Note 3 for a detailed protocol). Note that MFI values were considered as positive for bacteria labeled with the N-nitrosation-sensitive probe when they reached an arbitrary threshold of MFI = 100, as this value is above 86% of the MFI values on labeled bacteria (in blue in Supplementary Fig.
Illumina MiSeq was used to sequence immunoprecipitated DNA. Data consisted of a number of reads per nucleotide. A Z-score for each base pair (i. e. the number of standard deviations from the average) was calculated based on average and variance in a window of 1 million base pairs. A threshold of Z-score above 4 was set to consider genomic regions as bound by GcrA. These sequences were mapped to the genome of B. abortus 2308 (available at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc = GSE136733). The GcrA-binding peaks (. txt files) can also be visualized on Artemis (freely available at http://www. sanger. ac. uk/science/ tools/artemis) with the genomic sequences (. gb files) available at https://figshare. com/s/0e580305b65f67619d36. To calculate the number of GANTC sequences in ChIP-seq peaks, we extracted peak sequences online with Emboss-extractseq (http://emboss.
Samples were sequenced with the Illumina sequencing technique using NextSeq500 run Mid PE150 after preparing a TruSeq DNA library (performed by Genomics Core Leuven, Belgium). Sequencing hits were mapped on the genome of B. abortus 544 and, for each colony, all the nucleotides that were different from the reference strain were compiled in an Excel table (performed by Genomics Core Leuven, Belgium; data available at https:// figshare. com/articles/Summary_of_whole_genome_sequencing_for_B_abortus/ 9747653). Mutations were counted for each isolated colony by excluding regions corresponding to microsatellites and with less than 10 reads. Reverse transcription followed by quantitative PCR. Bacterial cultures were grown in rich medium to exponential phase (OD 600 0. 3), washed twice in PBS, and allowed to grow in rich medium supplemented or not with IPTG and/or MMS 2.
5 mM (Sigma) for 5 h. Bacteria were washed twice in PBS, then collected by centrifugation, and immediately frozen and stored at -80 °C until processing. RNA was extracted using TriPure isolation reagent (Roche) according to the manufacturer's instructions. Samples were treated with DNase I (Fermentas), then RNA was reverse transcribed with specific primers (Supplementary Data 1), using the High capacity cDNA Reverse Transcription kit (Applied Biosystems). Specific cDNAs were amplified on a LightCycler 96 Instrument (Roche) using FastStart Universal SYBR Green Master (Roche). Results were normalized using 16S RNA as a reference with the E -ΔΔCt calculation method  81  , where E corresponds to the efficiency of the primers (determined by serial dilution). Colony-forming units counts. For CFU counts after infection, RAW 264.
Acknowledgements We thank members of the  URBM  and  I. Matic  for stimulating discussions. We also thank  Y. Ashhab  for his help regarding ChIP-seq analysis. We thank  H. Lavender  and  C. M. Tang  for their careful reading of our manuscript. We thank  UNamur  (https://www. unamur. be/)for financial and logistic support. This work was funded by The  Fédération Wallonie-Bruxelles  ( ARC 17/22-087 ) and by the  FRS-FNRS "Brucell-cycle"  ( PDR T. 0060. 15 ). K. P. , A. M. , and G. P. were supported by a  FRIA Ph. D. fellowship. A. R. and N. F. held an  Aspirant fellowship  from  FRS-FNRS. This work was also supported by the  French Agence Nationale de Recherche  ( ANR-JCJC-2011-Castacc ) (http://www. agence-nationale-recherche. fr/) and the  Region Pas-De-Calais  (http://www. nordpasdecalais.
Data availability ChIP-seq data are available at the GEO database under accession code GSM4056904. The list of mutations for whole genome sequencing experiments is available at figshare [https://figshare. com/articles/Summary_of_whole_genome_sequencing_for_B_abortus/ 9747653]. The source data underlying Figs. 2b, 4, 5, 7 , and 8 and Supplementary Fig. d, 7 , and 9 are provided as a Source Data file. All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information file or from the corresponding author upon request.
bmj. com/ info/us/toolkit) with a section on how to use GRADE, and various electronic databases and journals that summarize evidence are also available to clinicians. In a recent development, the GRADE Working Group has begun to explore how to rate evidence from nonclinical animal studies, and the first attempt to implement GRADE in the nonclinical space has successfully been performed on a sample of systematic reviews and examples, with further efforts planned  (Hooijmans et al. 2018). In contrast, with the exception of those who also have medical training or clinical research experience, most scientists are unaware of the guiding principles of EBM and are unfamiliar with formal decision-enabling algorithms. At least in part due to the diversity of nonclinical experiments, systematic reviews and metaanalyses are far less common in nonclinical phases than in clinical ones, and there are very few broadly accepted tools with which to assess nonclinical data quality  (Hooijmans et al.
Collaboratively building a study plan is a good opportunity to share knowledge, ensure that a study is conducted and reported according to expectations, and work to identify and reduce conscious and unconscious biases. Across all sectors, planning ahead for in vivo pharmacology studies is more elaborate than for in vitro experiments, due to animal ethics requirements and the logistics of animal care and welfare. However, nonclinical study plans are not normally published, whereas clinical trial protocols are available in online databases such as the EU (https://www. clinicaltrialsregister. eu) and US (https://clinicaltrials. gov/) registers. A few initiatives, such as OSF's "preregistration challenge" (Open Science Foundation, Preregistration Challenge, Plan, Test, Discover, https://osf. io/x5w7h), have begun to promote formal preregistration of nonclinical study protocols, as a means to improve research quality  (Nosek et al.
2. 7 Data Integrity, Reporting, and Sharing Notwithstanding the existence of vast amounts of electronic storage space and sophisticated software to ensure file integrity, retaining, and potentially sharing, original datasets and protocols is not yet straightforward. Barriers to widespread data sharing are slowly being overcome, but there remains a need for long-term funding, and the ability to browse data long after the software used to generate or store them has become obsolete. In biopharma companies and CROs, it is customary to retain all original individual and transformed data, with information on how a study was performed, in laboratory notebooks and annexes. Scientists working in industry are all aware that the company owns the data; one does not lose or inadvertently misplace or destroy the company's property, and in audits, quality control procedures, preparation for regulatory filings, or patent litigation cases, to name a few, original data must often be produced.
A summary of the data is included in the nonclinical overview of the application dossiers and in the Investigator's Brochure. From these documents it is often difficult to assess the quality of the evidence, since they contain relatively little experimental or study information  (Wieschowski et al. 2018) ; study design features are more likely to be found in the study reports, although there are no explicit guidelines for these  (Langhof et al. 2018 ). The study reports themselves are confidential documents that are usually only disclosed to health authorities; they are intended to be factual and include study plans and results, statistical plans and analyses, and individual data. In academia, publishing is the primary goal; publication standards and content are set by guidelines from funders, institutions, partners, peer reviewers, and most importantly by journals and editorial policies.
Collective efforts have produced consensus recommendations, based on the critical appraisal of published and unpublished data, in fields such as stroke  (Macleod et al. 2009 ) and pain  (Knopp et al. 2015; Andrews et al. 2016). In IMI Europain (home page: www. imieuropain. org), the group of scientists and clinicians working on improving and refining animal models of chronic pain, addressing the clinical relevance of endpoints used in animal models and methodologies to reduce experimental bias, held teleconference meetings roughly 10 times a year over 5 years, which represents a substantial amount of shared data and expertise. Leveraging this combined expertise and aiming to develop a novel, non-evoked outcome measure of pain-related behavior in rodents, IMI Europain partners from both academia and industry accomplished a multicenter nonclinical study  (Wodarski et al.
2016) , in the spirit of a phase 3 multicenter clinical trial. One of the important lessons learned during this study was that absolute standardization should not be the goal, since circumstantial differences such as site location cannot be erased, leading to pragmatic accommodations for local variations in laboratory practice and procedures. An effort to uncover evidence-based drivers of reliability in other subfields of neuroscience is ongoing in IMI EQIPD (home page: https://quality-preclinical-data. eu), with the overarching goal of building broadly applicable tools for managing nonclinical data quality. Discussions on emerging pathways of neurodegenerative disease within the IMI neurodegeneration strategic governance group led to a single, collectively written article describing independent attempts that failed to reproduce or extend the findings of a prominent publication  (Latta-Mahieu et al.
Parametrisation: stage 1 Input was derived from various data sources: herd recordings, literature and information directly obtained from the farmer. Therefore, parameterization of the tool occurred in two stages: 1) prior to the on farm application by the use of official herd recordings data and literature data and 2) during the actual on farm application by means of information directly obtained from the farmer. Official herd recordings included: treatments and disorder incidence levels, and test-day milk recordings, and was initially collected for the purpose of other research aims within the IMPRO project and described and discussed by  Krieger et al. (2017)  and used as primary input for this study. Data derived from the official herd recordings was only used for the performance related input.
Herd recordings that could be entered directly in the tool were (stage 1 of parametrization): the number of cases of clinical mastitis and the number of dairy cows in the different lameness classes (non, moderate or severe). Lameness scores for each herd were available from the preceding farm visits and performed following the Welfare Quality ® Protocol (2009) and clinical mastitis incidence was derived from herd health recordings  (Krieger et al. , 2017). The remaining herd recorded data needed to be processed first. The milk production data were aggregated at the farm level such that the average milk yield per cow per year was entered in the tool. Based on each test-day-date record the average number of lactating dairy cows on the farm during the last year was estimated. For each test-daydate record the distribution among lactating dairy cows in any of the following somatic cell count (SCC) categories was determined: <50,  50-100, 100-200, 200-300, 300-400  and >400 x1,000 cells per ml.
Each of these parameters were administered to the tool as part of stage 1 of the parametrization. Due to the specific nature of each disorder specific changes to the framework were required for some cost components. Milk production losses associated with ketosis were estimated for cows with a disturbed FPR which was assumed to include clinical and subclinical cases of ketosis, as the provided data was on herd level. Additional milk production losses associated with farmers identified clinical ketosis cases were excluded to prevent overestimation of milk production losses associated with ketosis (Table  3 ). The farmers identified cases were used to calculate the economic losses of herd removal and treatment. Production loss estimates of mastitis, included both clinical and subclinical production losses which is due to the specific nature of both disorders and moment of occurrence  (Barkema et al.
Parametrisation: stage 2 Data missing or not-available from either official herd recordings or literature were asked directly from the dairy farmer during the application of the tool and was part of stage 2 of tool parametrization. For this purpose, a postal questionnaire was send out preceding the farm visits. Farmers were asked to complete the questionnaire before the farm visit to facilitate the data entry during the visit. This preliminary questionnaire included questions regarding price levels, herd removal, disorder incidence levels and applied treatments. More specific, farmers were requested to indicate how many of their cows were treated for ketosis (incidence of clinical ketosis), early metritis (incidence of metritis) and late metritis (incidence of late metritis). Furthermore, farmers were asked to indicate the number of cows culled for mastitis, lameness, ketosis and metritis, separately, and to do the same for the number of cows dead on the farm.
On farm tool application. The farm visits were performed by native speaking researchers. The researchers received a training on how to work with the tool and to address potential questions that may arise during the visits. Moreover, since multiple research aims were performed during each visit a visiting protocol was set up to ensure that each farm visit proceeded in a similar manner. Furthermore the farm's veterinarian was present to ensure validation of input data. Based on the collected herd record data (Table  2 ) and literature data (Tables  2)  and  assumed price levels (Table  4 ) the tool was partly parametrised prior to the farm visit, stage 1 of parametrization. During the farm visits these prepared inputs were presented and discussed with the farmer. After presenting the collected herd record data, the replies on the preliminary questionnaire were collected and administered in the tool, stage 2 of parametrization.
Statistical analysis A statistical analysis was performed on the variables FC mastitis, ketosis, lameness and metritis (€ per cow per year) using SAS/ STAT® software (SAS Institute Inc. , Cary, NC, USA). FC estimates are expressed in € per cow per year to correct for any effect of farm size on the outcome. Assumptions of normality of the FC was based on a graphical display of the farm-specific FC and a Shapiro-Wilk test. Depending on whether normality could be assumed, either parametric or non-parametric tests were performed. It was tested whether the FC for each disorder varied among countries. Furthermore, the relative ranking of disorders within a country was tested to determine whether the relative order varied among countries. Finally, a correlation analysis was performed to explore how the FC of one disorder may affect in-or decrease of other FCs.
However, the aim of this study was to estimate the farm specific failure costs by using readily available farm data, which justified the assumption on average technical input parameters. For the parameterisation of technical input values insights from studies based on data from conventional farms were used. Biological herds, however, may have different breeds and consequently have different herd milk production levels than conventional herds. To account for this breed aspect production losses were defined as a relative decrease in average herd production level. Meaning that low production herds had lower absolute milk production losses that high yielding farm, reflecting the current believe that production losses are higher in high yielding herds. The herd removal behaviour of the farmers regarding the various production disorders was not recorded which could have led to an overestimation of the failure costs in cases were affected animals were immediately removed upon diagnosis, to prevent the occurrence of production losses and treatment costs.
It should be noted that better indicators may become present over time e. g. routinely collected claw health data derived from claw trimmers recordings or BHBA levels in milk, urine or blood from dairy cows as an indicator for subclinical ketosis. At the moment these data were not routinely collected. Furthermore, the availability of information among countries differs and may be more evolved, such as the use of Bulk tank SCC vs. individual reports on SCC. To omit reporting bias, the current farm information systems should adapt to include more routinely collected animal health related data, e. g. as part of new developed precision livestock farming technologies  (Rutten et al. , 2013) , which may subsequently lead to more accurate FC estimates. Recent simulation model studies on the FC of ketosis reported values of $289 or $203 per case of subclinical ketosis  (Gohary et al.
, 2017)  which exceeds our own estimates. The study performed by  Liang et al. included  increased costs caused by secondary disorders (e. g. ketosis, mastitis). Metritis incidence in our study was reported by the farmer only and could not rely on any official recorded data. Similar levels of variation in FC of mastitis and a slightly higher average reported FC of mastitis were found in  van Soest et al. (2016)  in which also farm specific FC of mastitis were estimated for conventional Dutch dairy farms using a more systematic and precise data collection. The variation in FC estimates, found in our study, was larger than those found in the other studies. It could be stated that the average farm specific FC estimated in our study are at least comparable to those obtained by more complex estimation methods.
For Brucella, a number of genotyping methods have been proposed including AMOS-PCR, Bruceladder, tandem-repeats polymorphism [multiple-locus VNTR analysis (MLVA)] and whole genome SNP analysis (wgSNP). wgSNP analysis is by far the most powerful and robust approach, but its current cost does not allow yet to use it as a routine first line assay  (Ledwaba et al. , 2019). MLVA typing is a compromise in terms of discriminatory power, cost and speed to provide an overview of genetic diversity and assist the selection of strains for whole genome sequencing  (Scholz and Vergnaud, 2013). Availability of freely accessible databases with MLVA genotypes allow to track the spreading of specific Brucella biovars globally  (Kattar et al. , 2008; Kilic et al. , 2011; Ferreira et al. , 2012; Vergnaud et al.
Size analysis of VNTR repeats was performed using GeneMapper 4. 1 (Applied Biosystems). To visualize clustering relations, maximum parsimony analysis dendrograms were constructed using BioNumerics 7. 6 (Applied Maths, Sint-Martens-Latem, Belgium). The Hunter and Gaston diversity index (HGDI) was used to describe discriminatory capacity of each locus, as well as of the panels MLVA8, MLVA11, and MLVA16  (Hunter and Gaston, 1988). Computations were performed using the Internet resources at www. hpa-bioinfotools. org. uk/cgi-bin/DICI/DICI. pl. Geographic coordinates (longitude, latitude) were deduced from available geographic origin data using the BioNumerics geographical plugin. The geographic location map was drawn using QGIS 3. 4. 8-Madeira (QGIS Geographic Information System. Open Source Geospatial Foundation) 1.
Among 61 medium-prevalence genotypes (e. g. , found in four to nine samples), geographic linkage was established for ten genotypes: four genotypes are confined to Almaty region, five genotypes to East Kazakhstan, and one to West Kazakhstan. Figure  5  shows the diversity of genotypes observed in humans, together with diversity previoulsy reported in animals  (Shevtsov et al. , 2015). Clustering analysis using all 16 VNTR loci (MLVA16) was conducted including B. melitensis East Mediterranean data from MLVAbank. Shared genotypes were found for 83 (among 313) genotypes representing more than 57% of the isolates (Figure  6 ). The highest number of shared genotypes was found for strains circulating in China and Kazakhstan. Fifty-four MLVA16 genotypes are common between 530 isolates from Kazakhstan and 172 strains from China.