dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of the Isolates-rDNA Region Sequencing Identification of the isolates was conducted through sequencing and blasting against GenBank database of a genomic DNA region covering 5. 8S rDNA gene, a fragment of 18S rDNA gene, and the two ITS regions, altogether of ~2 kbp  [35]. Prior to amplification, the cells were disrupted by 10 min incubation at 95 • C, in STET buffer (1% Triton X-100, 20 mM Tris-HCl, 2 mM EDTA, pH 8. 0), followed by 10 min centrifugation at 4 • C, 22,639× g (Biofuge R, Hareus, Hanau, Germany). The following PCR conditions were applied: primers: NS3: GCAAGTCTGGTGCCAGCAGCC, ITS4: CTCCGCTTATTGATATGC; Pwo DNA polymerase WALK (A&A Biotechnology, Gdynia, Poland), the reaction mixtures were prepared according to the manufacturer's recommendation; temperature profile: 94 • C for 5 min, (94 • C for 30 s, 55 • C for 1 min, 72 • C for 2 min) × 25, 72 • C for 5 min, 4 • C hold.
Amplicons were verified through 1% agarose gel electrophoresis in TBE buffer. Provided the correct length of the amplicons and a single band occurrence, the remaining volume of PCRs was purified using Clean-Up kit (A&A biotechnology, Gdynia, Poland), and the purified fragments were sequenced (Genomed sequencing facility, Warsaw, Poland), blasted against GenBank Nucleotide database using BLASTn tool  [36].
Acknowledgments: This study was co-funded by the  European Union  via the  European Regional Development Fund  within the framework of the  Innovative Economy Operational Program 2007-2013  (No. 01. 01. 02-00-074/09 ), and was also financially supported by the  Ministry of Science and Higher Education  Project entitled " Exploitation of biotechnological processes in production of safe food commodities and chemical compounds " (No. 508. 864. 00 ).
Real time PCRs were performed in 96 well plates using the IQ SYBR Green Supermix (BioRad). Data were quantified by ΔΔ-Ct method and normalized to 36b4 expression. Sequences of the primers used in this study are shown on Supplementary Table  S1. All genes studied here have been defined as belonging to the ascribed categories (positively or negatively responding to LSD1 and/or ERRα) in our previous publication  15. ChIP. 10 × 10 6 cells were cross-linked with 1% formaldehyde and quenched for 5 min in 1 M Glycine. After centrifugation, cell pellets were resuspended in lysis buffer (1% SDS, 50 mM Tris-HCl pH8, 10 mM EDTA). Sonication was performed with Bioruptor (Diagenode). Lysates from 6 × 10 6 cells were processed with the iDEAL ChIP kit (Diagenode) according to the manufacturer's recommendations using 5 μg of antibody.
Acknowledgements This work was funded by  Ligue contre le Cancer (comité Rhône) ,  JoRiss/ENS research program  and  Région Auvergne Rhône-Alpes. LZ is funded by the  Chinese Scholarship Council (CSC)  and  Région Auvergne Rhône-Alpes. JC has been funded by  Association pour la Recherche sur le Cancer.
, 2012). All patients had a systematic follow-up visit 1 month later. The following clinical data were recorded at the time of sampling by clinicians: demographic data, diagnosis at admission, PEDIS grade of wound severity, topography, number, ulcer size (including surface area and depth of the wound), diabetes type, duration of diabetes, HbA1C value, previous history of DFU, underlying diseases and their severity according to the Charlson score, and antibiotic therapy in the 14 previous days. When patients had several wounds, the sample was taken from the most severe wound. DFI management was performed according to international guidelines  (Lipsky et al. , 2012) , independently of our study results. At the follow-up visit, the trained diabetologist noted the evolution of the most severe wound (PEDIS grade, size, and depth) and the antibiotics used.
Statistical Analysis Descriptive statistics included percentages for the categorical variables and medians with interquartile ranges (IQR) for the non-normally distributed continuous variables. The comparisons were performed using the Chi-square test or Fisher's exact test for the categorical variables. The correlation analysis was performed by assessing the Spearman's rho coefficient of correlation. A P-value < 0. 05 was considered to be statistically significant. Statistical analysis was performed using SPSS statistics 2016 (IBM, NY, USA). The principal component analysis was performed with XLSTAT 2017 (Addinsoft, Paris, France). Because richness (defined as the number of different species) does not take into account the abundance of species, we used the Shannon index to compare diversity (taking abundance into account), as previously described  (Wang et al.
Clinical Data In total, 43 patients were hospitalized for an episode of DFI and were enrolled into the study. The demographic and clinical characteristics of these patients are presented in Table  S2. Most of the included patients were male (69. 7%) with a median age of 67. 5 years (27-90). Thirty-eight patients (88. 4%) had Type 2 diabetes. Twenty (46. 5%) of the DFI were located on the plantar forefoot. Overall, the mean hemoglobin A1C was 7. 16% (±1. 13), the mean ulcer surface area was 1. 7 cm (±1. 9), and the mean ulcer depth was 1. 22 cm (±1. 36). Eighteen (58. 1%) of the DFI were classified as grade 2 (PEDIS score), while the remainder were classified as above grade 2. For 31 patients (72%), the current wound was the first episode of DFI and 12 (27. 9%) were included during a relapse of a previous DFU or DFI.
FUNDING This work was supported by the  French Government  under the  Investissements d'avenir (Investments for the Future) program  managed by the  French Agence Nationale de la Recherche  ( ANR, National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). This work was also supported by  Region Provence Alpes Cote d'Azur and European funding FEDER PRIMI.
The first part included a medical interview, a retrospective chart review and medical examinations; all carried out by trained researchers following standard operation procedures. The second part of the study consisted of the patient reported outcome (PRO) questionnaire. The PRO was administered as an online version, accessible only with a secure password on the recruitment centres; if needed, a paperpencil version was provided as well. The PRO included sociodemographic data (including age, the ESISCED as an European standardised education measurement, feeling about household income, relationship status with having a partner, being single or living with parents, and information about the participants health status with the general question 'How is your health in general? Would you say it is: (very) bad to (very) good.
Reference data: Comparison data for each participating country France Dsd-LIFE participants from France were compared to an adult sample of 16,450 randomly selected people, 18-75 years old, drawn from the National Health Barometer 2005, a periodic study by the French National Institute for Preventive and Health Education  [20]. The study population included 6808 male and 9584 female participants. 1447 were young adults (18 to 24) and 2313 elderly people (65-75 years old). Self-reported chronic physical or mental disease was described in 4192 participants. For economical reasons the environment domain was not assessed in the National Health Barometer. The survey used a computerassisted telephone interview system.
Germany Dsd-LIFE participants from Germany were compared to a representative urban sample of 2073 adults (≥18 years) of the general population  [21]. For 2055 participants data was included in the analysis: 927 males and 1128 females participated; 240 were young adults (18 to 25 years) and 393 elderly people (≥66 years). Additionally 359 patients with physical (n = 261) or mental (n = 98) chronic diseases were investigated in two university hospitals; one in the Eastern and one in the Western part of Germany. The German reference population was part of the first evaluation study of the WHOQOL-BREF  [5].
The Netherlands Dsd-LIFE participants from the Netherlands were compared to a sample of non-healthy persons with a mental chronic disease and their healthy control group, matched for age and sex ratio  [22]. 410 psychiatric outpatients of one community mental health centre completed the WHOQOL-BREF during the study period from March 2001 to March 2002. No persons with severe mental illness or mental retardation and some further exclusion criteria took part. All participants were of Dutch ethnic origin and between 21 to 50 years of age. 41% of the participants were male with a mean age of 34. 8 years, 59% female with a mean age of 32. 5 years. The matched reference group was taken from a pooled data set based on Dutch general population studies  (1999) (2000) (2001) (2002).
UK Dsd-LIFE participants from the UK were compared to an assessment about the psychometric properties and results of the WHOQOL-BREF in healthy and non-healthy people all over the UK. The sample included convicted individuals in prisons and a sample undergoing plastic or lifestyle surgery which was excluded from our reference group  [24]. We compared our data to 1328 healthy people and 1864 people with various physical and mental disorders, treated at multiple settings all over UK. The healthy people included students and student nurses. Age range for the whole cohort was 16-105 years with a mean age of 45 years; overall 64% females took part in the study.
Acknowledgements We are grateful to the persons who participated in dsd-LIFE and to all of the study centers for their enthusiasm and dedication to contacting potential participants and collecting high-quality data. We especially thank the support groups in the different countries that have supported the study. For an overview of all contributors we refer our study protocoll [18]. Funding dsd-LIFE was funded by the  European Seventh Framework Program  ( FP7/ 2007-2013)  under grant agreement  n°305373.
Collection of samples This study was conducted between January 2012 and January 2014 on 13 sheep flocks in Ksar el Boukhari of Médea Province (No. 1), and 18 goat flocks from seven localities in the provinces of Algiers (Nos 2, 3, 4, 5) and Boumerdes (Nos 6, 7, 8) (Fig. 1 ). A total of 92 and 62 fecal samples were collected directly from the rectum of goat kids and lambs, respectively. Only animals aged 4 weeks or younger were sampled. Fecal consistency and demographic data on the animals were recorded at the site of sample collection. The samples were transported to the laboratory in ice boxes and preserved in 2. 5% potassium dichromate at 4 °C until molecular analysis.
DNA sequence analysis To confirm the identification of C. ubiquitum and C. xiaoi, the secondary PCR products of the SSU rRNA gene from the two Cryptosporidium species were sequenced in both directions on an ABI 3130 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). The SSU rRNA gene products of C. parvum were not sequenced because it has a well-known SspI and MboII RFLP pattern. In addition, all PCR products of the gp60 gene were sequenced to identify C. parvum and C. ubiquitum subtypes. The generated sequences were assembled using the ChromasPro v. 1. 5 software (http://www. technelysium. com. au/ChromasPro. html) and aligned with each other and reference sequences downloaded from GenBank using ClustalX (http://www. clustal. org/). Representative sequences generated in the study were submitted to GenBank under accession numbers LC414387-LC414393 for the SSU rRNA gene and LC414394 and JX412917 for the gp60 gene of C.
Cryptosporidium species and subtypes The RFLP analysis of the SSU rDNA PCR products identified two Cryptosporidium species in goat kids, including C. xiaoi in 6 of 8 Cryptosporidium-positive samples and C. ubiquitum in 2 of 8 Cryptosporidium-positive samples (Fig. 2 ). Cryptosporidium xiaoi was detected in Rouiba (Algiers) and Zemouri (Boumerdes), while C. ubiquitum was seen in Khemis-elkhechna (Boumerdes). In lambs, C. parvum was present in 3 of 9 Cryptosporidium-positive and C. xiaoi was identified in the 6 of 9 Cryptosporidium-positive samples from Ksar-elBoukhri (Médea). DNA sequencing of SSU rDNA PCR products confirmed the detection of C. ubiquitum and C. xiaoi in these samples. Data from 12 samples of C. xiaoi (six each from sheep and goats) generated two sequence types. The first type was represented by six sequences from sheep and was identical to one C.
Discussion In the present study, the occurrence and genotype and subtype identity of Cryptosporidium spp. in goat kids and lambs in Algeria were examined. The overall infection rate of Cryptosporidium spp. was 11. 0% (8. 7% in goat kids and 14. 5 % in lambs). Previous studies reported Cryptosporidium infection rates of 5. 1-82. 0% in sheep and 7. 1-93. 0% in goats in industrialized nations  [4, 10, 14, 16, [29] [30] [31] [32] [33] [34] [35]. Few comparable data are available from C. xiaoi (6), C. ubiquitum (0) 22-28 Diarrheic 2 0 (0) Non-diarrheic 16 0 (0) Total 92 8 (8. 7) developing countries, but infection rates of 2. 5-67. 5 and 2. 9-72. 5% have been reported in sheep and goats, respectively, in Zambia, Egypt, China, Bangladesh, Iran, Argentina and México  [11-13, 17, 36-41]. Variations in infection rates among studies could be attributed to the differences in animal age, diagnostic methods, sample sizes, animal management and climates.
In the presence study, two types of SSU rDNA sequences were obtained from C. xiaoi, with sheep and goats having different types. Both sequence types, however, have been observed in both sheep and goats in previous studies based on BLAST analysis of GenBank sequences. As sheep and goat samples from the present study were collected from  Cryptosporidium parvum was seen in three sheep among the small number of animals examined. These findings are in agreement with previous common findings of the pathogen in sheep in European countries and Australia  [4, 10, 18, 31, 36, 46]. Among developing countries, a small number of C. parvum infections have been reported in goats and sheep from Asia, including China  [17, 47, 48] , India  [49] , Jordan  [50]  and Turkey  [51]. Our results, however, are in contrast to those from studies conducted in most African countries including Egypt  [37] , Tunisia  [52]  and Ethiopia  [53] , where C.
Simulations We used the NAMD 2. 10 software on GPU/CPU nodes to run the molecular dynamics (MD) simulations in explicit solvent  (Phillips et al. , 2005). The CHARMM36 forcefield was employed for the protein, together with the TIP3P-CHARMM water model  (Huang and MacKerell, 2013). The system was neutralized with sodium and chloride ions. Starting from the crystal structure of the complex (pdb id: 1I7X) that contained an incomplete dimer of the complex, we selected the chain A (for bcat) and chain B (for E-cadherin), completing a missing loop in chain A between residues 553 and 560 with the Modloop online software (https://modbase. compbio. ucsf. edu/modloop/). Chain B was truncated at residue 648 on the N-terminal side, as the corresponding portion of the chain would be extended in vivo and not interacting with b-cat (as observed in the crystal structure).
FRET experiments FRET experiments were performed on an inverted 2-photon Leica TSC SP8 NLO SMD (Picoquant module) microscope system or on a spinning disc CSU10 head (Yokogawa) mounted on a Nikon TE2000 microscope, both equipped with a Lifa module (Lambert Instruments) by measuring the lifetime of the donor, so called FLIM-FRET. The FRET efficiency was thereby measured as a shortening of the donor lifetime. Lifetime measurements were analysed using either the SymphoTime software (Leica/Picoquant system) or the FLIMfit 4. 12. 1 (software tool developed at Imperial College London), with a monoexponential fit. For this, the lifetime of the donor alone was first determined for calibration of the system. Afterwards the lifetime of the donor in the presence of the acceptor in the mesoderm and ectoderm region was analysed at the level of the adherens junctions in the lifetime images.
, and on the high conservation of the sequence of E-cad around D665 between Drosophila and mice (Figure 1-figure supplement 1e). The JCAb20 antibody targets an E-cad loop at 1143-1158 (in Drosophila) neighbouring directly the alpha helix of region II. The latter contains the D665 (D1170 in Drosophila), which interacts with the Y654 site of b-cat (Y667 in Drosophila), and is directly labelled with Alexa 555 (in orange in Figure 1figure supplement 1c and Figure 1-figure supplement 1a, from Protein Data Bank and Huber and Weis, 2001, with accession code 1I7X, schematized in red Figure.
Acknowlegements This research was funded by the  ANR  (grant #  11 BSV5014-01 ), the  AVIESAN Cell Biology, Development and Evolution (CNRS, INSERM, INSTITUT CURIE co-funding of the dedicated Magneto-Spinning disk) , and the  Labex DYNAMO  ( ANR-11-LABX-0011-01 ). EF lab is funded by the  FRM  (grant #  DEQ20150331702 ). JCR was funded by the  Institut Curie post-doc starting grant, the Marie Sklodowska Curie Fellowship  ( PIEF-GA-2012-332422 ), the  Labex CelTisPhyBio  (post-doc grant #  11-LBX-0038 ). The authors are very grateful to  Christine Me  ´nager for providing the UMLs required for magnetic manipulation. The PICT-Cell and Tissue Imaging Facility is a member of the  French National Research Infrastructure France-BioImaging  ( ANR-10-INBS-04 ).
Motion Model. The motion estimation process uses the measurements of proprioceptive sensors to predetermine the localization vector [X  ]. EKF use a probabilistic motion model to fuse sensor data  [30]. We make an extension of this model to obtain an interval based model defined by Seignez et al. [18] : [X 𝑘 ] = 𝑓 [] ([X 𝑘-1 ] , [U 𝑘 ]) (18) ( [𝑥 𝑘 ] [𝑦 𝑘 ] [𝜃 𝑘 ] ) = ( [𝑥 𝑘-1 ] + [𝛿𝑠 𝑘 ] cos [] ([𝜃 𝑘-1 ] + [ 𝛿𝜃 𝑘 2 ]) [𝑦 𝑘-1 ] + [𝛿𝑠 𝑘 ] sin [] ([𝜃 𝑘-1 ] + [ 𝛿𝜃 𝑘 2 ]) [𝜃 𝑘-1 ] + [𝛿𝜃 𝑘 ] ) (19) where [U 𝑘 ] = ([𝛿𝑠 𝑘 ], [𝛿𝜃 𝑘 ] )  is the input vector. and   represent, respectively, the elementary displacement and rotation. The elementary movements can be deduced from raw odometric data (odometer and gyro) using an interval based static fusion method: [𝛿𝑠 𝑘 ] = [𝜋] ([𝜔 𝑙 ] [𝛿𝑝 𝑙 ] + [𝜔 𝑟 ] [𝛿𝑝 𝑟 ]) [𝑃] [𝛿𝜃 𝑘 ] = [𝜋] ([𝜔 𝑙 ] [𝛿𝑝 𝑙 ] -[𝜔 𝑟 ] [𝛿𝑝 𝑟 ]) [𝑒] ⋅ [𝑃] ∩ [𝛿𝜃 𝑔𝑦𝑟𝑜 ] (20) where   and   are the odometer measurements of the left and right wheels.
(ii) Gyro (Melexis MLX90609-N2): it measures the rotational speed in an inertial reference system. Vibrating silicon structures, that use the Coriolis force, output the yaw rate from which we can deduce the elementary rotation. (iii) Camera (FOculus FO432B camera + Pentax C418DX lens): it provides information about the perceived environment. We use the camera which located in the left-front of the vehicle and looking ahead. (iv) RTK-GPS: the data acquired with an embedded ProFlex500 RTK-GPS receiver from NavtechGPS coupled with a Sagitta Magellan GPS base station are considered as the ground truth. This system provides an accurate absolute localization measurement to within 2 cm (±1 cm).
III. SPH VALIDATION To our knowledge, there are quite few investigations on the present flow case, which can be compared with the analytical solution. 32 In order to quantitatively verify that, a numerical run is performed by using the open-source code SPHyscis. 33, 34 As the original SPHyscis code can only consider the flow with constant number of particles, in the present work a jet condition is additionally implemented for simulating the present 2D free-surface flow case, with the following algorithm procedure: (1) Determining the number and position of added particles; (2) Adding the particles with reasonable value of necessary physical quantities, e. g. , position, velocity, mass, pressure, density etc.
When engineering a language (and its compiler), it is convenient to use widespread and easy-to-use MDE frameworks like Xtext that automatically generate a compiler infrastructure, and even a full-featured IDE. At the same time, a formal workbench such as a proof assistant is helpful to ensure the language specification is sound. Unfortunately, the two technical spaces hardly integrate. In this paper, we propose a transformation from Ecore's metametamodel to Coq's language named Gallina/Vernacular. The structural fragment of Ecore is fully handled. At the cost of not being bijective, our transformation has relaxed constraints over the input metamodel, in comparison to previous state of the art. To validate, we have used the proposed transformation with a complete and representative test suite, as well as a proof-carrying code type checker.
INTRODUCTION In the context of designing a formal architecture description language for system of systems engineering, named SosADL  (Oquendo et al. , 2016) , the work presented in this paper is specifically related to the implementation of supporting tools for this language. On the one side, model-driven engineering provides effective tools like ASF+SDF  (Klint, 1993) , Xtext  (Bettini, 2013)  or MPS  (Voelter, 2013)  that ease the creation of a language and its supporting infrastructure. From a combined description of a concrete and/or abstract syntax, a complete editing environment is generated, which includes syntaxhighlighting, auto-completion, error reporting. These tools often come with a compilation or interpretation framework, which is designed to smoothly interact with the generated editing environment.
On the other side, language theory promotes principled language design by means of well-established techniques to specify a language in terms of, e. g. , semantics and type system, then to study this specification, e. g. , proving a type soundness theorem. Existing literature hints at relevant properties, proof techniques, and mechanization approaches by means of proof assistants like Coq  (Bertot and Castran, 2010)  or Isabelle/HOL  (Nipkow et al. , 2002). In our project, we expect to benefit from the two fields. But model-driven engineering tools like Xtext or MPS hardly integrate with proof assistants. For instance, often, the former use a graph-based formalism while the latter rely on inductive data types, despite some exceptions such as Rascal  (Klint and van der Storm, 2016). Furthermore, the question arises whether the (informal) implementation, e.
g. , in the Java technical space conforms to the (formal) specification, e. g. , in the Coq technical space. To address this issue, we consider the proof-carrying code approach  (Necula, 1997). Figure  1  summarizes this context. From a concrete grammar, Xtext generates a metamodel, and an editor, a parser and a compilation infrastructure. The parser transforms the textual source into an object-oriented model, which is an instance of the metamodel. Thanks to the compilation infrastructure, we develop a compiler that transforms the model into output artifacts. Proof-carrying code appears in the lowest part of the figure: the compiler also uses a proof generation infrastructure to produce a proof, which is an instance of the language's specification, i. e. , of the type system or semantics. The proof contains terms, which are instances of abstract syntax type.
More recently, Xtext  (Bettini, 2013)  generates a fullfeatured text editor and a compilation framework from the combined definition of concrete and abstract syntax. MPS  (Voelter, 2013 ) introduces a projectional editor, i. e. , edition is made directly at the level of the abstract syntax. MPS also provides a declarative language for executable type systems. But none of them allow formal specification and study of languages. There is therefore a need for bridging the gap between model-driven engineering and formal methods. Most of the works in this topic, e. g. ,  (Meyer and Souquières, 1999; Lano et al. , 2004; Barbier and Cariou, 2012; Cabot et al. , 2014)  focus on a different issue: verification of properties of metamodels. Inhabitation (or consistency) is for instance a widely-studied problem, which aims at verifying that a metamodel is contradiction-free, i.