dataesr/scientific-paragraphs-categorization huggingface dataset:
One obvious limitation of our approach is the data requirement. As with all deep learning approaches, learning is limited by the data available in the training set, and enough data should also be available for validation and test sets. The whole dataset we gathered here, despite being rather large (> 1500 trajectories) compared to movement datasets currently analyzed in ecology, is likely close to the minimal size required to obtain a robust model and avoid severe overfitting issues. Currently, there are probably very few, if any, single-species datasets large enough to fit this model. For this reason, we aggregated data from numerous species; as a benefit, this allowed us to demonstrate that comparative analyses could be conducted with the model, for instance by comparing the distribution of relevant context lengths between species or higher-order taxa.
Acknowledgments This work was supported by the LabEx NUMEV (ANR-10-LABX-0020) and the REPOS project, both funded by the I-Site MUSE (ANR-16-IDEX-0006). Computations were performed using HPC/AI resources from GENCI-IDRIS (Grant AD011012019R1). We would like to thank all authors who made their data available through MoveBank under Creative Commons licenses (see Table  4  in the appendix for the list of datasets used in this work).
A Appendix Table  4 : The list of all Movebank datasets used in this work. MPIAB Lake Constance Mallards GPS  [13]  CC BY Vultures Acopian Center USA GPS  [14] [15] [16] [17]  CC BY Kruger African Buffalo, GPS tracking, South Africa  [18] [19] [20] [21]  CC0 Galapagos Tortoise Movement Ecology Programme  [22] [23] [24] [25] [26] [27] [28]  CC BY Navigation experiments in lesser black-backed gulls (data from  Wikelski et al. 2015)   [29, 30]  CC0 MPIAB PNIC hurricane frigate tracking  [127]  CC BY HUJ MPIAB White Stork GSM E-Obs  [128]  CC BY MPIAB Argos white stork tracking (1991-2018)  [31]  CC BY Dunn Ranch Bison Tracking Project  [129]  CC BY LifeTrack -Great Egrets  [130]  CC0 HUJ MPIAB White Stork E-Obs  [131]  CC BY LifeTrack White Stork Uzbekistan  [132]  CC BY Egyptian vultures in the Middle East and East Africa  [32] [33] [34] [35]  CC BY LifeTrack White Stork Tunisia  [133]  CC BY Pandion haliaetus Osprey -SouthEast Michigan  [134]  CC0 LifeTrack White Stork Armenia  [36] [37] [38]  CC BY LifeTrack White Stork Loburg  [135]  CC BY LifeTrack White Stork Greece Evros Delta  [36] [37] [38]  CC BY HUJ MPIAB White Stork GSM 2013  [136]  CC BY LifeTrack White Stork Moscow  [36] [37] [38]  CC BY LifeTrack White Stork Poland  [37]  CC BY Hooded Vulture Africa  [137]  CC BY Turkey vultures in North and South America (data from Dodge et al.
Formally, we consider the dataset as composed of trajectories, where a trajectory 6 ξ 1. N of length N consists of locations x 1. N , corresponding to timestamps t 1. N , and any associated variables z 1. N , i. e. ξ n = (x n , z n , t n ) as described above. Our main goal is to estimate a model for the next-step prediction task, i. e. for any given n ∈ {1,. , N }, predict the next location x n+1 from the trajectory prefix ξ 1. n and the next timestamp t n+1.
Formally, we consider the dataset as composed of trajectories, where a trajectory 6 ξ 1. N of length N consists of locations x 1. N , corresponding to timestamps t 1. N , and any associated variables z 1. N , i. e. ξ n = (x n , z n , t n ) as described above. Our main goal is to estimate a model for the next-step prediction task, i. e. for any given n ∈ {1,. , N }, predict the next location x n+1 from the trajectory prefix ξ 1. n and the next timestamp t n+1. (resolution: ∼1 km); we normalize the values between 0 and 1 and sample them with bilinear in- terpolation; • 19 bioclimatic variables from WorldClim 2. 1 [169] (resolution: ∼1 km); we standardize the values (zero mean, unit variance) and use nearest neighbor interpolation; • land cover classification (23 classes) from Copernicus Global Land Service, version 3.
expasy. org)  62 and displayed with PyMol v. 2. 0 63 b The presence of the 3Flag in LptD was assessed by western blot (WB) in the WT and in the gmd mutant with polyclonal antibodies directed against LptD and a mAb recognizing the 3Flag. Omp10 was used as a loading control for the blot using α-Flag antibody. All the WB were performed in triplicates with similar results. Source data are provided as a Source Data file. c Representative scanning electron microscopy picture for one replicate of gmd mutant with 3Flag::lptD fusion. 3Flag-LptD was detected using a mAb against 3Flag, followed by secondary antibody coupled with gold particles, each white focus corresponds to a single gold particle with the expected size (18 nm) (n = 3). Scale bar represents 100 nm. Source data are provided as a Source Data file.
Indeed, it was already suggested that O-antigen ligases display a so-called "relaxed specificity" towards O-antigen and would rather be specific to the core structure of the lipid A-core than to the O-antigen  51, 52. In that regard, displaying both GTase and O-antigen activity on one protein would increase the probability for a successful gene transfer. Indeed, the GTase activity in the cytoplasm, acting on a very conserved KDO residue, prepares the substrate for the O-antigen ligase activity in the periplasm. Interestingly, many genes involved in the O-antigen synthesis in Brucella were already suggested to be acquired by horizontal gene transfer, as their GC content is much lower (about 48%) as compared to the rest of the genome (58%), and wadA displays a GC content of 51%  21, 53. Although few WadA homologs were identified in available databases, this bifunctional action mode could be found in other O-antigen ligases as well.
Microscopy and analysis Exponential phase bacterial cultures were washed two times and resuspended in PBS. Two µl of the bacterial suspension were spotted onto 1% agarose pads. Images were acquired using a Nikon Eclipse Ti2 equipped with a phase-contrast objective Plan Apo λ DM100XK 1. 45/ 0. 13 PH3 and a Hamamatsu C13440-20CU ORCA-FLASH 4. 0. Fluorescence images were analyzed using FIJI v. 2. 0. 0 58 , a distribution of ImageJ. Look-up tables (LUT) were adjusted to the best signal-background ratio. For demograph analysis, bacteria were detected and analyzed using MicrobeJ, a plugin of ImageJ software  59. Only isolated bacteria were taken into account, aggregates were excluded from the analysis. If necessary, small aggregates or incorrectly selected bacteria were manually removed from the analysis.
Both antibodies were diluted in PBS supplemented with 0. 05% Tween-20 and 0. 5% milk, and the membranes were washed as described after both incubations. The membranes were revealed using Clarity ECL Substrate (Bio-Rad) solutions and images were acquired using GE Healthcare Amersham Imager 600. Unprocessed scans of Western Blots are available in the Source Data files. Replicates of western blots for Figs. 1b, 7a, and S6b are available on Figshare (https://doi. org/10. 6084/m9. figshare. c. 6383685. v1).
Acknowledgements We are grateful to  Anke Becker  and  Elizaveta Krol  for the discussion and help regarding RgsE. We thank the  Electron microscopy facility of UNamur. We thank  Raquel Conde-Alvarez  and  Jean Michel Jault ,  Jean-François Collet  and  Jean-Yves Matroule  for their insightful discussions and advices, as well as  Adrien Ducret  for his help with  MicrobeJ. Marcela Suárez-Esquivel  is also acknowledged for providing an annotation of B. abortus 2308 genome with PFAM domains prediction. We also thank  Neeraj Dhar  for advices regarding mNeonGreen and  Mathieu Waroquier  and  Françoise Tilquin  for their technical assistance. This research has been funded by grants from the  Fonds de la Recherche Scientifique-Fonds National de la Recherche Scientifique (FRS-FNRS , http://www. fnrs.
MATERIALS AND METHODS All procedures involving animals were conducted under the supervision and approval of the Animal Care Committee of the Institute of Agrifood Research and Technology  (Barcelona, Spain; expedient number: 11456). Forty-five lactating (multiparous) cows were randomly split into 3 treatments (n = 15). Randomization was conducted using a random generator script in Python written by author AB (2020). Sample size was defined for milk fat yield as main outcome variable (as it was expected to collect the potential effects of DMI, milk yield, and rumen changes) based on a power analysis for repeated measures conducted with GLIMMPSE (www. glimmpse. samplesizeshop. org) and a Hotelling-Lawley trace test  (Akbari et al. , 2013)  with power = 0. 80, α = 0. 05, and an expected standard deviation of 0.
18); thus, all data were averaged by cow, treatment, and hour, and a mixed-effects model that accounted for the fixed effects of treatment, hour, and their 2-way interaction was run, including cow within treatment as a random effect. Beta diversity differences among treatments were assessed by permanova (a nonparametric multivariate statistical analysis using permutations to avoid bias) on Bray-Curtis dissimilarity matrices performed with 1,000 permutations. Differential relative abundance among treatments was assessed using a multivariate ANOVA with the Limma package  (Ritchie et al. , 2015)  of R (Version 4. 1. 3; R Core Team, 2022) using centered, log-transformed data as input and correcting for false discovery rates  (Benjamini and Hochberg, 1995). False discovery rate-adjusted P-values are shown throughout the manuscript.
Materials and Methods A 15 member technical working group (TWG) was formed to develop a Biosafety Research Roadmap (BRM) with the goal of supporting the application of laboratory biological risk management and improving laboratory sustainability by providing an evidence base for biosafety measures. The TWG conducted a gap analysis for a selected list of priority pathogens on procedures related to diagnostic testing and associated research for those pathogens, including but not limited to sample processing, testing, animal models, tissue processing, necropsy, culture, storage, waste disposal and decontamination. To achieve this, the TWG screened databases, websites, publications, reviews, articles, and reference libraries for relevant data. The main research domains used to perform the literature searches were the ABSA database, Belgian Biosafety Server, CDC reports, WHO reports, PubMed, and internet searches for terms related to biosafety matters, including, for example, inactivation, decontamination, laboratory-acquired infections, laboratory releases and modes of transmission.
Levels of evidence Following the draft development of the COAST Group treatment recommendations, a new therapeutic class (a canine monoclonal antibody targeting Nerve Growth Factor) was introduced for dogs with osteoarthritis, prompting a supplementary evaluation of treatment options and an update to the draft consensus (June 2022). Relative to other treatment options evaluated, more complex user scenarios could not be clearly defined due to the finite amount of data available (as for any new product) as well as the clinical experience being limited by geographic availability and the product's relatively short time in the market. As a result, the current COASTeR stage-specific treatment recommendations for the anti-nerve growth factor monoclonal antibody simply reflect the approved indications for use.
Due to the possibility of utilizing options with a lower strength of evidence when needed, dogs receiving a long-term management plan and advancing to the next COASTeR Stage of OA may already be receiving the treatments unanimously recommended for that new stage. This situation necessitates the immediate consideration of options with weaker evidence within that new stage. Similarly, complex or challenging cases are likely to require treatment selection from a more expansive list of recommendations, even if evidence for use is not as strong. The COAST group consensus also supports the adaptation of previous protocols (e. g. , a dosing increase or decrease), according to the patient's needs. Products approved for use in dogs with OA tend to have the most robust data supporting efficacy and safety, so the COAST approach aligns with other risk-based decision approaches that allow the use of clinical judgment when authorized products are not available.
• Pharmaceuticals or Biologics Two different major classes of therapeutics (NSAIDs and an anti-NGF mAb) are approved in many countries for the control of pain in dogs with clinical signs of OA. Both classes of therapeutics have been evaluated for efficacy in similar populations of dogs, are supported by comparable datasets, and currently have similar approvals for use, resulting in the COAST Development Group unanimously recommending the use of either medicine for dogs with clinical signs of OA (Stage 2, 3 or 4). All members of the COAST group agreed on either NSAIDs or anti-NGF as first-line therapy for COAST Stage 3 or 4; 3 of the 9 COAST group members considered an NSAID as first-line therapy for COAST stage 2 dogs, with 6 out of 9 members considering either NSAIDs or anti-NGF as appropriate first-line options for COAST Stage 2.
These values were log transformed and compared using a non-paired t-test. Within and between individual variability, as well the effect of age and sex on individual HC levels from samples in Study 2 (log transformed), was estimated using linear mixed effects models (LME) with Individual as a random effect. Models with differing fixed effects (age and sex) were compared using the Akaike Information Criterion adjusted for small sample sizes (AICc  [3]  ). LMEs were implemented using the 'lmer' function in the 'lme4' R package  [5]. Intra-class correlation (ICC) and coefficient of variation (CV) were extracted from the best fitting model using respectively the 'icc' and 'cv' functions in the 'sjstats' R package  [34]. Estimates of individual random effects ± 95 % confidence intervals have been simulated from the distribution of each of the random effects obtained for the best fitting LME model using the 'REsim' function in the 'merTools' R Package  [30].
Acknowledgments This work was supported by the  French National Research Agency  (  ANR-13-JSV7-0005  ). The  Department of Biological Sciences  at the  University of Chester  contributed assay reagents and laboratory facilities. The ball mill was provided by the  Faculty of Science and Engineering  and guidance was kindly provided by  Dr James Nicholson  and  Piotr Sanaowicz. The authors are grateful for technical assistance provided by  Dan Baldock  and  Kate Peters  and the comments provided by an anonymous reviewer. The photograph of the marmot in the graphical representation was taken by  Rebecca Harris. We also thank  Sylvia Pardonnet  and all the students who participated in the marmot captures.
Acknowledgements We are grateful to all members of the  INRAE  EASM unit, especially the animal caretakers, for their help with animal care. We thank all members of the  INRAE PEAT  unit and the  INRAE BOA  unit for their help at the slaughterhouse. We thank  Alexandre Barbin  and  Nina Kruger  for their help with data collection,  Catherine Hurtaud  for her help with ATOL ontology, and  Pierre Blavy  for his advice on statistical analysis and we are grateful to  Julie Collet  for her statistical support and reviewing. We also thank the breeding companies for providing 1-day-old chicks. This study was funded by the  PPILOW  project and the  French National Research Institute for Agriculture, Food, and Environment (INRAE). The  PPILOW  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No  816172.
Data availability The data were deposited in an o cial repository entrepot. recherche. data. gouv. fr/dataverse/inrae. The data-sets generated and analysed during the current study are available on this website: https://entrepot. recherche. data. gouv. fr/dataverse/inrae. The data-sets digital object identi er ishttps://doi. org/10. 57745/JS5HLR.
Material & Methods Literature search. We systematically searched for all peer-reviewed journal articles studying Buruli ulcer and/or M. ulcerans using the Web of Science and Google Scholar until 31 July 2020. We selected articles from the period 1996-2020 because the first PCR tests targeting M. ulcerans DNA were proposed from 1996 onwards. We excluded conference papers, preprints, PhD theses and papers that were not available online. We used the following keywords: (Buruli ulcer* OR Mycobacterium ulcerans*) AND (Mycobacterium ulcerans DNA OR detection) AND (PCR IS2404 OR PCR IS2606 OR PCR KR OR qPCR OR VNTR). We obtained a dataset containing 175 studies of which 89 were relevant in that they focused on M. ulcerans DNA detection or genetic characterization using molecular tests. We decided to exclude nested-PCR amplifications from our analysis due to their low use in the literature and their high potential to generate non-specific amplifications.
Also, studies focusing on loop-mediated isothermal amplification (LAMP) were not included  (Ablordey et al. , 2012; Njiru et al. , 2012). See "Studies" in Supplementary Information. In silico evaluation of M. ulcerans detection primers. Thirty-one primer sets from nine previous reference studies were screened against M. ulcerans genome and plasmid sequences to assess the efficiency of M. ulcerans detection methods. The Primer-Blast  (Ye et al. , 2012)  and Primer3Plus  (Untergasser et al. , 2007)  tools were used for this purpose. The complete genome sequence of M. ulcerans Agy99 (NC_008611. 1) and the complete sequence of the Agy99 plasmid pMUM001 (NC_005916. 1) were used as the database for the screening. Only primers with 100% alignment coverage and specific amplification (expected position and size) were considered effective.
VNTR-based identification key. Ten VNTR loci have been tested to construct the identification key. First, only 8 VNTRs (see "MIRU-VNTR" in the Supplementary Information) amplified by the primers validated in the in silico evaluation step were retained. Then, two additional VNTRs (MIRU33 and VNTR18) for which the VNTR sequences are available and aligned against the Agy99 genome were added to the set. The VNTR sequences were extracted from the Agy99 genome based on primers positions using an in-house extraction script. Next, the VNTRs sequences were aligned against a MPM genome variants database consisting of nine variants plus two outgroups, M. marinum and M. gilvum (Figure  S1 ), using blastn implemented in BLAST+ suite  (Camacho et al. , 2009). To count the number of VNTRs in each genome, only matches with at least 99% alignment coverage were counted.
The identification key was constructed with the VNTRs loci identified as the most discriminating for each variant. First, the loci were classified hierarchically according to the highest copy number variation (CNV) per locus. Then, the best combination of VNTR loci discriminating all variants is selected to finally classify the variants according to their CNV profile on the selected VNTRs. Copy number variation of IS2404, IS2606 and KR. The target sequences for IS2404, IS2606 and KR amplification were extracted from plasmid pMUM001 according to primer positions using an inhouse extracting script. Next, the sequences were aligned against the MPM genomes and plasmids database consisting of nine variants plus two outgroups, M. marinum and M. gilvum (Figure  S1 ), using blastn implemented in the BLAST+ suite  (Camacho et al.
Our new method focussed on compounding behavioural measures observed during trips with measures of trip speed or duration in order to evaluate motivation. We collected data in three trials held during a different season each, and with each trial presenting different constraints during outing (i. e. , the process of letting the cows out to and from the exercise yard). We hypothesized that cows which are more motivated to access the exercise yard would complete the go-out trip in shorter times than less motivated individuals and would require fewer interventions from handlers, and would exhibit slower speed upon the go-in (or return) trips. Through our assessment of go-out and go-in (return) trips, we also assessed whether the amount of space provided in the paddock and the duration of the outing affected the motivation of cows.
Statistical Analysis Before statistical analysis, we calculated the frequency by second for each behaviour, by dividing the number of occurrences by the duration of the trip. Normality of the data or residuals was assessed graphically using Q-Q Plot. For each trial, we first calculated the difference between the duration of go-in and go-out trips for each day. We compared this difference to 0 by means of a t-test with a Bonferroni adjustment to account for the multiple comparisons. This adjustment yielded the following Pvalue thresholds for the three trials: 0. 0033 for Winter, 0. 005 for Summer, and 0. 0021 for Fall. We computed a principal component analysis (PCA) for each trial with the frequencies of behaviours, duration, and speed of cows for each trip, excluding data from the habituation phases.
Acknowledgements We would like to thank  Sirine El Hamdaoui ,  Catherine Lussier ,  Nicolas Gafsi ,  Rachel van Vliet  and  Jordan Tonooka  for their assistance with the animals and with the collection of the study data. In addition, we would like to thank  Rachel van Vliet  with her help editing the manuscript. We would also like to acknowledge the staff of the  McGill University Macdonald Campus Dairy Complex (Sainte Anne de Bellevue, QC Canada)  for their animal husbandry and management. We would like to thank the  French government IDEX-ISITE  initiative  16-IDEX-0001.
Shortcomings of traditional animal testing in chemical risk assessment The current approach of chemical safety assessment is confronted with a number of challenges. In Europe, Regulation (EC) 1907/2006 on the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) entered into force in June 2007; 16 years later, it has led to the creation of the most comprehensive chemical safety-related database in the world. However, for a large share of the thousands of chemicals on the European market, data relevant for a meaningful and sound risk assessment are still lacking or incomplete. For higher-tonnage chemicals with more stringent information requirements, this can often be attributed to a combination of inadequate compliance on the side of the registrants, insufficient understanding of the concept and pre-requisites of read-across or a lack of assessment and enforcement capacities on the authorities' side  (ECHA, 2019; ECHA, 2020a; ECHA, 2021a).
At the same time, a comprehensive analysis of the REACH registration by means of machine learning technology allowed the identification of reproducibility issues of the eight most common acute and topical OECD guideline tests  (Luechtefeld et al. 2018 ). In addition, for chemicals marketed at a low annual tonnage by individual registrants (which may nevertheless account for a higher marketed tonnage overall), the regulatory information requirements in place only include a basic set of indicators for judging whether registered uses might pose a risk. Putting aside ethical concerns as well as questions about the scientific validity of the current paradigm, it would likely take a decade or more as well as a vast amount of testing resources/capacities to fully complete the REACH database according to the current information requirements of the REACH Annexes VII -XI.