dataesr/scientific-paragraphs-categorization huggingface dataset:
How is it then possible to group chemicals according to common characteristics correlated with similar properties, including above all toxicological properties? The process we observed when studying scientists developing QSAR for nanomaterials comprised: • the choice of a set of reference substances (in the project we observed, as many as 45 different nano-substances, belonging to different chemical families such as Zinc oxides, Nickel oxides, or Boehmite); • the definition of a list of "descriptors" such as the morphology (shape) or the size of chosen compounds, whether they come in filaments, aggregates, etc. ; • the definition of a list of "endpoints" linked to experimental test data on cell cultures in the laboratory, mostly so as to predict rates of reproduction or cell defects; • the production of statistical correlations between descriptors and endpoints, which led to the refining of both lists.
If she observed "no answer" from a series of descriptors, that is, that they did not impact the value of the endpoints in statistically significant ways, then she would deduce that they were not relevant. She would eliminate them, thereby reducing an initial long list to just a few parameters. These considerations show that the practices of QSAR modelling are not stabilized, but partly re-invented for each dataset of chemicals used to build models. For QSAR practitioners, the objective is to build models accurate enough. To do so, these practitioners process by trial and error, concerning the list of descriptors, the list of endpoints, and the calculation of statistical correlations. Thus, in QSAR modelling, accuracy is negotiated. As sociologists of science and technology have demonstrated, constructing accuracy is part and parcel of the making of technological systems, and impacts on / is impacted by the larger choices about their objectives and modes of functioning  (MacKenzie, 1993).
The lack of such a framework led to the widespread recognition of the need for an internationally-agreed set of principles for (Q)SAR validation. The development of a set of agreed principles was considered important, not only to provide regulatory bodies with a scientific basis for making decisions on the acceptability (or otherwise) of data generated by (Q)SARs, but also to promote the mutual acceptance of (Q) SAR models by improving the transparency and consistency of QSAR reporting. (OECD, 2007: 15)  In this quote, "the development of a set of agreed principles" can be read in the terms of regulatory objectivity. It proposes international coordination for producing conventions. Within the international organization, this objective is directly connected to a boundary work, between internationally harmonized procedures that could guarantee the validity of the modelling approach, and the technical content of the model, which could be adapted to local situations according to regulatory choices  (Thoreau, 2016).
While the OECD principles only considered the validation of QSAR models in general terms so that the international organization would not enter the perimeter of states' regulatory actions, the European institutions undertook an explicit reflection about whether and how QSAR models could be validated. Validating QSAR models can be carried out by processing the data that have been used to construct the statistical correlations (this is described as "internal validation"), or other data (e. g. chemicals of known risks, on which the model will be run, and its predictions checked against the known risks of the tested chemicals). The latter approach is called "external validation" and is deemed more robust for regulatory choice by QSAR specialists  (Gramatica, 2007). Yet external validation also requires additional data, and additional testing to check whether the predictions according to the model are correct, and yet another validation process for the choice and use of these additional data.
This means that QSAR models cannot be grounded on mechanical objectivity and the accompanying 'view from nowhere', or on regulatory objectivity and the coordinated approach on which it relies. How then can we understand the type of expert judgment at play when QSAR models are used? Another OECD initiative, the "QSAR toolbox" developed in partnership with the European Chemical Agency (ECHA), can help us to understand how experts working in public agencies are expected to use QSAR models. Developed at the OECD and supported by ECHA since 2008, the QSAR toolbox is a free software application designed to "identify and fill (eco)toxicological data gaps for chemicals hazard assessment"  (ECHA, 2011). It is intended to be used by private companies seeking to evaluate the hazard of the substances they produce, by experts working in public agencies and in charge of eval- Thus, QSAR models are one component of a more general platform.
This platform is fed with experimental data, some of which are related to the physical causality between "structural characteristics" and hazards (second bullet point in the previous quote), and comprise modelling tools, some quantitative (as QSAR models are), and others based on statistical approaches that do not use quantitative predictive modelling. An example of the latter in the quote above is "readacross", which consists in using available empirical data to estimate the missing ones. The QSAR toolbox does not attempt to deliver ready-made risk assessments for a user (whether a regulator or a scientist) eager to know the toxicity of a given chemical. Rather, it offers a way "to systematically group chemicals into categories according to the presence or potency of a particular effect for all members of the category.
e. "referring to a specific effect within a specific tissue/organ under specified conditions"  (OECD, 2007: 92). One sees here that an active uptake about the very purpose of choosing the endpoint will affect its relevance. Thus, the QSAR toolbox can only be used by an informed user, who has particular regulatory objectives in mind. This informed user is able to identify the scope of the evidence provided, and its limitations. This means that the QSAR toolbox can in no way be mobilized as a black-boxed instrument that could be used without opening up its inner mechanism. It follows that the concern for the transparency of the platform is constant among both the designers and users of the QSAR toolbox. Allowing regulators to access the characteristics of databases has become a necessary condition for the platform to function, as an OECD official told us during an interview: What we're also going to develop in the new version is to have a kind of reliability score related to the database and the profile so that at least they are all well documented.
(. ) we are very transparent on how these databases or profiles are constructed, what kind of chemicals have been used to develop -which are included in the database. So, if you go to the Toolbox, you also have an "about" section. You select a database and click on the "about" section then you will get information on the database. (interview, OECD) Being transparent about the toolbox is about making its inner mechanism visible. It is also about making it possible for users to contribute, by providing new experimental data that could refine the existing correlations. The toolbox is indeed designed to be fed on an on-going basis with new experimental data and refined statistical correlations. Such a development implies enrolling more and more users, so as to ensure both the collective legitimacy and the technical validity of the instrument.
Datasets to inform the models are compiled by many different actors, including scientists for knowledge-production purposes, but not only. Many statistical techniques or mathematical models can be tailored to the creation of a particular QSAR. Various heuristic tools and different classes of algorithms are designed as a means to browse through the diversity of data and gather different sorts of results, including a wealth of machine-learning techniques  (Lavecchia, 2015). Second, the nature of expert intervention evolves, as neither the delegation to a trusted instrument (as in a regime characterized by mechanical objectivity) nor the mobilization of collectively produced conventions (as in a regime of regulatory objectivity) are possible. When assessing the dossiers submitted by companies to apply for the registration of chemicals, officials at ECHA will examine the models by opening them up, and comparing them with experimental data, as one of them told us during an interview: If I know that for example the prediction is backed up by some solid hypothesis which is confirmed by for example different in vitro observations or other observations in vitro from similar substances, this is for me something much more important than just predictions generated by super duper fancy logic, for example neural networks.
uating companies' propositions, and by other stakeholders 2. Contrary to what its name seems to indicate, the QSAR toolbox does not provide a ready-made QSAR model fit for application on any given chemical. Rather, it brings together: • databases with results from experimental studies; • accumulated knowledge for structural char- acteristics (alerts) that can indicate the pres- ence of hazards and other properties, and • tools to estimate missing experimental values by read-across, by trend analysis (i. e. inter- polating [preferred] or extrapolating from a trend [increasing, decreasing, or constant] from tested to untested chemicals within a category) and/or by (Q)SAR models. (ECHA, 2011).
1) (Fig. 2 ), a newly described species of the Mycobacterium terrae complex  6. These values being equal of cut-off value 97% previously proposed to delineate different species among the genus Mycobacterium 9 , enforced the suggestion that isolate P7213 was representative of a new species Phenotypic characterization. Isolate P7213 displayed smooth and non-pigmented colonies growing at a temperature range of 25 °C to 37 °C after 7-day incubation on Middlebrook 7H10 agar medium supplemented with 10% oleic acid-albumin-dextrose-catalase (OADC) (Becton Dickinson, Sparks, MD, USA). The observation of colonies by electron microscopy showed rod-shaped bacilli measuring 1. 15 µm long and 0. 38 µm large (Fig. 3 ). However, isolate P7213 reproducible matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS) profile did not match any of the profiles entered in the Bruker database (Supplementary Fig.
S1 ). Currently, Bruker database does not contain the spectrum of M. virginiense. In vitro susceptibility testing was performed using broth microdilution method with MTT test (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide), the MIC values found for isolate P7213 are: rifampicin [minimal inhibitory concentration (MIC) < 8. 2 µg/mL], rifabutin (MIC < 8. 2 µg/mL), ethambutol (MIC = 4 µg/mL), isoniazid (MIC = 2. 3 µg/ mL), doxycycline (MIC = 8. 8 µg/mL) and kanamycin (MIC = 8 µg/mL), trimethoprim (MIC = 25 µg/mL) and sulfamethoxazole (MIC = 29 µg/mL) (Table  1 ). Using Biolog ® Phenotype MicroArray testing (Hayward, California, United States of America) as previously described  10  , isolate P7213 grew under 15 inhibitory chemical conditions including fusidic acid, troleandomycin, minocycline, lincomycin, guanidine HCl, niaproof 4, aztreonam, vancomycin, nalidixic acid, tetrazolium violet, tetrazolium blue, potassium tellurite, sodium butyrate  and sodium bromate (Supplementary Table  S1 ).
Genomic characterization. Full genome sequencing of isolate P7213 yielded 134 scaffolds corresponding to 4,511,948-pb with a 67. 6% GC content (EBI accession number GCA_900604305). Within the complex M. terrae, the genome size of P7213 is larger than that of M. hiberniae (4. 20 Mb) and M. minnesotense (4. 05) and smaller than that of M. nonchromogenicum, M. heraklionense, M. virginiense, M. longobardum, M. terrae, M. arupense, M. hiberniae, M. engbaekii and "M. sinense". Moreover, the GC % content of isolate P7213 is lower than that M. hiberniae, M. kumamotonense, M. algericum, M. engabaekii, M. terrae, M. longobardum, M. heraklinonense, M. nonchromogenicum, M. senuense and "M. sinense" and higher than that of M. virginiense, M. icosiumassiliensis, M. arupense and M. minnesotense (Table  3 ).
42% bewteen M. algericum and M. arupense and the maximum value was of 97. 69% between M. algericum and "M. sinense". The pangenome of this complex contained 41. 095 genes including 278 conserved genes, 3. 583 shell genes shared by several species and 37. 234 cloud genes unique to one species. Pangenome study showed divergence between all the members of this complex: we found one large cluster comprising of seven species: isolate P7213, M. virginiense, M. icosiumassiliensis, M. heraklionense, M. nonchromogenicum, M. arupense and M. minnesotense; two small clusters each comprising of two species; and four unclustered species (Fig. 5 ). Whole genome sequence-based fishing in the databases. Fishing NCBI database using the P7213 strain hsp65 partial sequence as probe, we observed 99. 05%, 99. 05%, 99.
25%, 97. 24% and 97. 01% sequence similarity with M. terrae strain variant MS699 isolated in Canada, Mycobacterium sp. FI-11038 isolated from Italy, Mycobacterium sp. FI-7105 isolated from Italy, M. engbaekii strain FI-06247 isolated from Italy and Mycobacterium virginiense, respectively. Moreover, phylogenetic tree based on hsp65 gene with 50 Max target sequences showed that strain P7213 was away to M. virginiense but grouped with M. sp. FI-11038, M. terrae strain variant MS699 and Mycobacterium. sp. FI-7105 (Supplementary Fig. S2 ). While the complete rpoB sequence (extracted using Prokka software) showed 97%, 94% and 94% sequence similarity with M. virginiense, M. terrae strain NCTC10856 unknown origin and "M. sinense" strain JDM601 isolated in China, respectively. But, blastn against NCBI database with 724-bp partial rpoB gene (extracted by Blastn using reference partial rpoB) of M.
In particular, M. terrae and M. nonchromogenicum which have historically founded the M. terrae complex are scattered apart with M. terrae remaining apart from any other species in the complex. Exploiting P7213 whole genome sequence to probe the large nucleotide database NCBI using rpoB and hsp65 partial gene sequences, we detected three additional strains previously isolated from Italy and one from Canada, as belonging to the same new species as P7213, expanding the probable geographical repartition of this new species. In order to clarify the on-going taxonomy of this group of microorganisms, we propose to name Mycobacterium mephinesia complex the largest cluster including M. mephinesia, M. virginiense, M. icosiumassiliensis, M. heraklionense and M. nonchromogenicum and characterized by DHH values higher than 30 and OrthoANI values higher than 85 among members of this cluster.
MALDI-TOF-MS. One colony was picked on a Middlebrook 7H10 solid-medium and put directly on the MALDI-TOF plate. Next, 1. 5 µL of a matrix solution (saturated α-cyano-4hydroxycinnamic acid in 50% acetonitrile and 2. 5% trifluoroacetic acid) (Bruker Daltonics) was used to cover the sample. Then, when the matrix is dry, the plate was loaded into the Microflex LT (Bruker Daltonics) mass spectrometer. The spectra were recorded according to the parameters as previously described  39. All signals with resolution ≥400 were automatically acquired using AutoXecute acquisition control in flexControl software version 3. 0 and the identifications were obtained by MALDI Biotyper software version 3. 0 with the Mycobacteria Library v2. 0 database.
Acknowledgements This work was supported by the  French Government  under the  « Investissements d'avenir » (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). This work was supported by  Région Provence Alpes Côte d' Azur and European funding FEDER PRIMI. Jamal Saad benefits from a  Foundation Méditerrannée Infection PhD grant. The authors also acknowledge  Nicolas Armstrong  for the technical assistance in HPLC experiments.
Type strains Accession number Size (Mb) GC % No. of contigs Gene content M. mephinesia GCA_900604305 4. 30 67. 6 134 4240 M. heraklionense NZ_LDPO01000001. 1 4. 98 67. 9 75 4798 M. arupense NZ_LASW02000001. 1 4. 31 67. 3 173 4230 M. hiberniae NZ_LQOZ01000036. 1 4. 20 68. 5 48 4078 M. longobardum NZ_LQPG01000007. 1 4. 66 67. 9 59 4660 M. terrae NZ_LT906469. 1 4. 38 68. 4 1 4240 M. engbaekii NZ_LQOT01000069. 1 4. 38 68. 5 79 4192 M. virginiense NZ_PUEV00000000. 1 4. 70 67. 2 128 4675 M. nonchromogenicum NZ_LQPI00000000. 1 4. 32 67. 8 91 4154 M. algericum NZ_MVHC00000000. 1 4. 48 68. 3 107 4126 M. icosiumassiliensis NZ_FJVP00000000. 1 4. 69 66. 8 79 4758 M. kumamotonense NZ_MVHU00000000. 1 4. 67 68. 1 128 4602 M. senuense NZ_LQPS00000000. 1 4. 39 68. 7 54 4271 M. tuberculosis H37Rv NC_000962.
Material and methods The present study is based on data from 74 pig farms in eight European countries (Austria: 16 farms; Switzerland 9; Czech Republic 1; Germany 16; Denmark 11; France 4; Italy 9; United Kingdom 8), which were collected during summer/autumn 2012 (all countries), as well as over the winter 2012/13 (Denmark, United Kingdom, Germany). The winter recordings comprised all three husbandry systems, thus not introducing a seasonal bias. Organic pig farmers were recruited via organic farming advisors, producer associations, agricultural journals or personal contacts. Farms had to be certified organic for at least two years and combined farrow-finish farms with more than 20 sows in the herd and 100 finishing places were chosen when available. Additionally, special needs persons' farms, research and teaching farms were excluded.
Assessment of animal health and welfare Based on literature  (Welfare Quality® Consortium, 2009; Dippel et al. , 2014; Bernardi, 2015)  and expert knowledge, a standardised on-farm assessment protocol was developed. The final protocol consisted of (1) an interview: management and husbandry procedures, (2) evaluation of records: productivity and treatment data and, (3) direct assessment of health and welfare parameters in weaners (WE): pigs from weaning until transfer to the fattening unit at around 35 kg, fatteners (FA): pigs weighing more than 35 kg), and sows (SO): dry or pregnant sows or gilts from first insemination onwards). Due to safety considerations, lactating sows and their piglets were not directly assessed for clinical parameters, although data on treatments (Mastitis -Metritis -Agalactia Syndrome (MMA), suckling piglet diarrhoea) and productivity (mortality) were collected.
Per country, all assessments were carried out by one trained observer during a one-day visit to each of the farms (details of observer training, standardisation procedures and inter-observer repeatability assessments are given in Supplementary Material S1 and Supplementary Table  S1 ). Treatment records and productivity data were assessed during the farm visit on the basis of recordings from the farmer, the veterinarian and slaughter protocols covering the 12 months prior to the farm visit. If possible, assessments were carried out in all pens/paddocks of a given farm. If this was not possible, the following sampling strategy adapted from the 'Real Welfare' scheme  (Pandolfi et al. , 2017)  was applied:  <10 pens/paddocks: full sampling  10-25 pens/paddocks: 10 pens/paddocks (as random as possible choice of pens across fields/buildings/animal categories)  >25 pens/paddocks: 15 pens/paddocks (as random as possible choice of pens across fields/buildings/animal categories) The number of animals assessed per pen was based on the following sampling strategy:  <25 animals in pen/paddock: full sampling  25-100 pigs in pen/paddock: 25 animals (randomly 5 pigs in 5 different places)  >100 pigs in pen/paddock: 50 animals (randomly 5 pigs in 10 different places).
Statistical analysis All calculations were performed at farm level with SAS 9. 2 and 9. 3. For this purpose, clinical parameters were aggregated by calculating the median of pen prevalences (count measures at pen level such as tail lesions) or by calculating the prevalence of affected groups (e. g. diarrhoea). Treatments were described as incidence per 100 animals per year. Productivity data were also expressed for a one-year period. Farm level values of behavioural measures were calculated as median percentage of active (standing or sitting) animals performing the respective behavioural category. In order to explore differences in animal health and welfare between the three husbandry systems, a non-parametric Kruskal-Wallis test was used, as data did not meet the requirements for parametric analysis (non-normal distribution with a high prevalence of zero values), which was also not achieved through transformation of data.
Acknowledgements The results presented in the paper have been obtained as part of  Gwendolyn Rudolphs ' PhD thesis within the project " ProPIG ". The authors would like to thank all involved farmers for their hospitality and openness and all National Agencies for funding within the framework of the 1st call on Research within Core Organic II. Gudrun Illmann was supported by the  Ministry of Agriculture of the Czech Republic , institutional support  MZE-RO0718.
Materials and Methods Between October 2011 and December 2013, a prospective cohort study was conducted in the Akonolinga Health District, central Cameroon  [12]. The funder, Médecins Sans Frontières, had a role in the study design and data collection. All individuals presenting at the Akonolinga District Hospital with a suspected cutaneous new BU lesion (defined as either a nodule, plaque, localized swelling and/or an ulcer in an individual residing or having spent at least 1 night in a known endemic area for M. ulcerans) were included consecutively. A dermatologist from Yaounde, Central Hospital, visited the project every 2 weeks and saw a subset of the patients. Photographs were taken of all suspected lesions. Two dermatologists reviewed the photographs of all cases independently; the first dermatologist was from Yaounde and the second was an expert in tropical skin diseases in the University Hospital of Geneva in Switzerland (Fig.
Animals Animals were produced by the three French breeding companies of the Alliance R&D group (Axiom, Choice Genetics France, Nucléus, IFIP) involved in the UtOpIGe project ANR-10-GENOM_BTV-015. A number of 636 purebred Piétrain (PB) and 720 crossbred Piétrain × Large White (CB) entire male piglets were produced on selection and multiplication farms and tested at a single test station. The PB and CB animals entered the test station facilities of Le Rheu (France) at approximately 4 weeks of age and were slaughtered at a fixed BW of 112 kg (at 5 to 6 months of age).
Model comparison Reduced models that were not accounted for dominance genotypic effects were implemented for all traits in both populations to be compared to the complete models that included dominance to statistically assess the relevance of including a dominance genetic effect in the models. Under a frequentist approach, restricted maximum likelihoods of the reduced and the complete models were obtained using REMLF90 software  (Misztal, 1999)  and a restricted likelihood ratio test was performed. Because the model comparison corresponded to a test of a parameter on the boundary of parameter space (i. e. testing for zero dominance variance), the distribution of the test statistic under the null hypothesis was a 50:50 mixture of Chi2-0df and chi2-1df distributions  (Morrell, 1998).