dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank colleagues mentioned in the text for their gifts of material. We are grateful to  Julia Chamot-Rooke  and  Mariette Matondo  from the proteomic platform of the  Pasteur Institute  for the availability of the Orbitrap instrument and to  Odile Sismeiro  and  Jean-Yves Coppée  from the  Biomics pole of the Pasteur Institute  for running the sequencing libraries on the Illumina machine. We thank the members of the lab, notably  Cosmin Saveanu  and  Gwenael Breard  for reading the manuscript and particularly  Maïté Gomard  who recently joined the team. E. Z and E. D were supported by  ANR-14-CE-10-0014-01  and  ANR-17-CE11-0049-01  grants from the  Agence Nationale de la Recherche  ( ANR ) respectively. This work was supported by  ANR-14-CE-10-0014-01  and  ANR-17-CE11-0049-01  grants, the  Pasteur Institute  and the  Centre National de la Recherche Scientifique.
5 μg per sample. The expression of the gene encoding IFN-γ and the genes encoding receptors on the surface of T cells (CD3, CD4 and CD8) was determined by realtime PCR. The reaction mixture for all analyzed genes had the following composition: 10 μL of the Power SYBR® Green PCR Master Mix (Life Technologies, USA), 1. 8 μL of each 10 μM primer, 4. 4 μL of RNase-free water, and 2 μL of cDNA. The primer sequences and the accession numbers of gene sequences used for designing the primers are presented in Table  2. The reaction was carried out under the following conditions: polymerase activation at 95 °C for 10 min, followed by 40 two-stage cycles: denaturation at 95 °C for 30 min, primer annealing and chain elongation at 60 °C for 60 s. The relative expression of each gene was calculated using the 2 -ΔΔCt method  [25]  normalized to efficiency corrections, expression levels of reference gene coding glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and reference groups (K and K1) in GenEx 6.
Flow cytometric analysis Flow cytometry data are presented in Fig. 3. No significant differences in the percentage of IgM + B cells were found between experimental groups or between sampling dates. The highest percentage of IgM + B cells was noted at 7 dpi in groups A, B and D (34. 4%, 34. 67% and 37. 72%, respectively). In comparison, in control group birds at 7 dpi, the percentage of IgM + B cells was determined at 28. 14% (group K) and 27. 23% (group K1). The percentage of IgM + B cells was lowest in groups A1, B1 and C1 at 7 dpi (22. 30%, 24. 33% and 26. 05%, respectively) and in groups.
X-ray Absorption Measurements The iron speciation in the samples was determined using synchrotron-based techniques, such as X-ray Absorption Near-Edge Structure (XANES) spectroscopy and microscopic X-ray fluorescence measurements at LISA (Linea Italiana per la Spettroscopia di Assorbimento di raggi X), the BM08 beamline of the European Synchrotron Radiation Facility (ESRF). The energy has been selected with a double crystal monochromator using a Si(111) crystal pair and two Si-coated mirrors for harmonics rejection. Further details about the beamline and the technical specifications are available elsewhere  [24, 25]. We worked at room temperature, and, due to the flux available on the bending magnet beamline, we never observed radiation damage to our samples. The preliminary Fe K-edge and XRF data results indicate that they can be used to trace the variability of mineralogy on snow, ice, and solid samples with different provenances.
Energy was calibrated by setting to 7112 eV the energy position of the first inflection point in the K-edge of a Fe foil recorded in a double transmission setup. For each snow sample and solid sample, one to four scans were recorded, depending on the sample content and Fe concentration. Spectra have been collected under vacuum (10-2 mbar) at ambient temperature in fluorescence mode. We used a four-channel SDD (Solid State Detector) detector with an energy resolution of ~180 eV at the Fe kα emission line (~6400 eV). In the case of filters, due to the large dimensions and the inhomogeneity, an unfocused beam of ~2 mm 2 was used to probe these samples. Data were averaged if necessary and normalized using the Athena  [26]  software. EXAFS spectra were then background subtracted from these normalized data using the XAFS code  [27].
Linear Combination Fitting The k 2 -weighted Fe K-edge XANES spectra of all samples were analyzed by a principal component analysis (PCA)  [20]  to determine the number of reference model spectra needed to simulate the experimental data. Based on the PCA results, a target transformation (TT) was further performed in order to select the reference standard substances that were most likely present in these samples  [31]. After that, based on the results from the PCA/TT, a linear combination fitting (LCF) analysis was performed using the software Athena to calculate the proportion of each iron reference in the soil, moraine, and snow samples. Several standard substances were selected to represent the possible iron compounds that were potentially present in the LHG samples. The PCA/TT result shows that Fe2O3, Fe3O4, biotite, and ferrous oxalate dihydrate (FOD) are the major standard substances in the soil samples, moraine samples, and snow samples.
Linear Combination Fitting The k 2 -weighted Fe K-edge XANES spectra of all samples were analyzed by a principal component analysis (PCA)  [20]  to determine the number of reference model spectra needed to simulate the experimental data. Based on the PCA results, a target transformation (TT) was further performed in order to select the reference standard substances that were most likely present in these samples  [31]. After that, based on the results from the PCA/TT, a linear combination fitting (LCF) analysis was performed using the software Athena to calculate the proportion of each iron reference in the soil, moraine, and snow samples. Several standard substances were selected to represent the possible iron compounds that were potentially present in the LHG samples. The PCA/TT result shows that Fe 2 O 3 , Fe 3 O 4 , biotite, and ferrous oxalate dihydrate (FOD) are the major standard substances in the soil samples, moraine samples, and snow samples.
An entirely new Virtual Access Service, "Planetary Space Weather Services" (PSWS, http://planetaryspaceweather-europlanet. irap. omp. eu/) has therefore been included in the Europlanet H2020 Research Infrastructure funded by the Euroepan Union Framework Programme for Research and Innovation. A variety of tools (in the form of web applications, standalone software, or numerical models in various degrees of implementation) are available for tracing propagation of 1) planetary or 2) Solar events through the Solar System and modelling the response of the planetary environment (surfaces, atmospheres, ionospheres, and magnetospheres) to those events. As these tools were usually not designed for 1) planetary event prediction and 2) space weather applications, additional research and tailoring is required to apply them for these purposes.
The Planetary Space Weather Services will provide 12 services distributed over 4 different service domains -Prediction, Detection, Modelling, Alerts -having each its specific groups of end users. The PSWS portal (http://planetaryspaceweather-europlanet. irap. omp. eu/) gives access to an initial presentation of PSWS activities. Section 2 gives an overview of the foreseen services. Each service will be implemented through a combination of data products, software tools, technical reports, and tutorials. Section 3 describes how the services will comply with Virtual Observatory (VO) methods and standards. Section 4 illustrates some of the VO-compliant functionalities already implemented in some services that are already operational. Section 5 summarizes the status of the project and lists a few perspectives for PSWS services in the VO context and beyond.
1D MHD Solar Wind Prediction Tool The Centre de Données de Physique des Plasmas (CDPP) within the Institut de Recherche en Astrophysique et Planétologie (IRAP/CNRS) will provide real time and archive access to propagated solar wind parameters at various planetary bodies (Mercury, Venus, Mars, Jupiter, Saturn,…) and spacecraft (Rosetta, Juno, Maven,…) using a 1D magnetohydrodynamic (MHD) code available through the CDPP/AMDA tool (http://amda. cdpp. eu) initially developed by Chihiro Tao  (Tao et al. , 2005).
VO protocols of interest for PSWS We have identified the three VO protocols below for implementation within PSWS services: • SAMP  (Taylor et al. , 2015) , the Simple Application Messaging Protocol is developed now for many years in the frame of the International Virtual Observatory Alliance (IVOA). It enabled easy communication and interoperability between astronomy software, stand-alone and web-based, before being rapidly and largely adopted by the planetary sciences and space physics community. Its attractiveness is based, on one hand, on the use of common file formats for exchange and, on the other hand, on established messaging models. • EPN-TAP  (Erard et al. , 2014; Erard et al. , this issue)  is a specific protocol developed by the Europlanet H2020 Research Infrastructure that enables to handle the communication between clients and data providers.
Using EPN-TAP, data are accessed in two steps. The first one consists in searching for available EPN-TAP services registered in the IVOA registries, while the second step consists in sending a query searching for data according to specific values of the parameters contained in a table in order to filter the database contents. • VOEvent  (Cecconi et al. , this issue)  is a standardized language used to report observations of astronomical events; it was officially adopted in 2006 by the IVOA. Although most VOEvent messages currently issued are related to supernovae, gravitational microlensing, and gamma-ray bursts, they are intended to be general enough to describe all types of observations of astronomical events.
1D MHD Solar Wind Prediction Tool The lack of solar wind monitoring just upstream of Solar System bodies can be overcome by using simulations that propagate the solar wind from in situ observations obtained elsewhere in the Solar System (e. g. ,  Zieger and Hansen, 2008). We have implemented in the CDPP tools AMDA (Automated Multi-Dataset Analysis,  Génot et al. , 2010) , 3DView  (Génot et al. , this issue) , and Propagation Tool  (Rouillard et al. , this issue) , a one-dimensional (1D) magnetohydrodynamic (MHD) simulation of the solar wind propagation developed by  Tao et al. (2005). The simulation uses time-varying boundary conditions at one Astronomical Unit (AU) obtained either from hourly solar wind data observed near the Earth made available by OMNI (http://omniweb. gsfc. nasa. gov/), or real-time ACE observations.
Within PSWS, such software (http://pvol2. ehu. eus/psws/jovian_impacts/) will be further enhanced in order to improve their usability and reach an even wider participation by amateurs. It will be connected to the Planetary Virtual Observatory and Laboratory (PVOL,  Hueso et al. , this issue ) that provides a searchable database of ground-based amateur observations of solar system planets (http://pvol2. ehu. eus/pvol2/).
2 log 2 fold change). BP3831 is annotated as a periplasmic amino acid-binding protein of a putative ABC transporter within the B. pertussis genome  (Parkhill et al. , 2003). Microarray data suggested that presence of RgtA would display a negative effect on BP3831 transcript levels and consequently, on BP3831 protein levels. To verify this hypothesis the levels of BP3831 protein were examined by immunoblotting. Western blot analysis with protein samples isolated from cells of the wild type, ΔrgtA, ΔbvgA and ΔbvgAΔrgtA strains revealed that when compared to ΔbvgA strain (increased expression of RgtA RNA), the amounts of BP3831 protein in ΔbvgAΔRgtA (lack of RgtA expression) were significantly increased (Fig. 4A ). There was no significant difference between the wild type and ΔrgtA strain most probably due to overall lower production of RgtA in the wt strain (see Fig.
2C ). This result was in agreement with our microarray data and confirmed the negative effect of the RgtA RNA on the BP3831 protein production. In many cases, the trans-encoded sRNAs exhibit their negative effects on translation by blocking the ribosome loading. We searched in silico for possible interactions between the RgtA sRNA and the BP3831 mRNA. Using the program CopraRNA (http://rna. informatik. uni-freiburg. de/CopraRNA/Input. jsp) (Wright et al. , 2014)  we identified a 13-nt region within the RgtA sequence which shared partial complementarity to the region immediately upstream of the BP3831 start codon and which also included the Shine and Dalgarno sequence (Fig. 4B ). As depicted in the Supplementary Fig. S2 , this 13-nt region within the RgtA is predicted by mfold RNA folding server  (Zuker, 2003)  to be singlestranded.
BP3831 is presumably involved in glutamate uptake BP3831 is tentatively annotated as a putative periplasmic amino acid-binding protein of an ABC transporter in the B. pertussis genome. BLAST search unveiled that the BP3831 protein exhibits high homology with the entry 4Z9N in the PDB protein database (10. 2210/pdb4Z9N/pdb). This entry contains X-ray diffraction data of an ABC transporter periplasmic binding protein from Brucella ovis which had been co-crystallized with glutathione. Interestingly, the binding pocket of the B. ovis protein shares 9 of 11 amino acids with BP3831 transporter (data not shown). Glutathione (GSH) is a tripeptide consisting of three amino acids glutamate, cysteine and glycine and in the SS medium serves as an important antioxidant due to its reducing capacity. Nevertheless, glutathione was recently demonstrated as a suitable source of sulphur for B.
DISCUSSION In this work we present the first fully characterized regulatory sRNA RgtA in the human pathogen B. pertussis. Furthermore, we identified its putative target gene as well as the possible RgtA-dependent regulatory mechanism and we propose that this sRNA is involved in control of transport of glutamate, an important source of carbon and nitrogen. Importantly, RgtA displays several characteristics typical for a trans-encoded sRNA as it is a) encoded within the intergenic region between genes BP2735 and BP2736, b) its size is 174 nt and c) its expression is Hfq-dependent. Primer extension and Northern blot analyses revealed that RgtA is transcribed from the negative strand and that the 5´end of RgtA starts at nt 2,903,162 in the B. pertussis Tohama I genome (Accession number: NC 002929.
Oligonucleotide probes were designed for 3554 annotated coding sequences of Bordetella pertussis Tohama I strain (BX470248. 1, GI:33591069) and were spotted in duplicate (non-adjacent) on aldehydsilane coated glass slides (Nexterion Slide AL, Schott) using a QArray II spotter (Genetix) equipped with 12 ArrayHit 946MP3 pins. After hybridization, slides were subsequently washed at room temperature in 2× SSC, 0. 2% SDS for 5 minutes, in 0. 5× SSC for 10 minutes, in 0. 05× SSC for 5 minutes, and 0. 01× SSC for 2 minutes before being dried and scanned using InnopScan700 (Innopsys). Background correction as well as within-and between-array normalization  (Yang et al. , 2002)  was performed from raw data using LIMMA (Linear Models for Microarray Data) R package  (Smyth et al. , 2003). After normalization, identification of genes with significantly modulated expression was performed using moderated Student's t-test with empirical Bayes shrinkage of standard errors  (Lonnstedt & Britton, 2005).
ACKNOWLEDGEMENTS We gratefully acknowledge  Scott Stibitz  ( FDA, Bethesda, Maryland ) for the kind gift of the pSS4245 plasmid and  Rudi Antoine  ( Institute Pasteur, Lille ) for stimulating discussions. This work was supported by the  Czech Science Foundation  (www. gacr. cz)(grant number  16-34825L  to B. V. ), by the  Czech Health Research Council  (www. azvcr. cz/)(grant number  16-30782A  to B. V. ) and by funding from  RVO61388971. This work was also supported by the  Ministry of Education, Youth and Sports of the Czech Republic projects  (grant numbers  CZ. 1. 07/2. 3. 00/20. 0055  to B. V. and I. B. ,  CZ. 1. 07/2. 3. 00/30. 0003  to B. V. and K. K. ). This work was further funded by the  Austrian Science Fund (FWF)  project  SFB F43  to F. A.
We propose the use of the Eclipse Modeling Framework (EMF). Indeed, EMF is a modeling framework and code generator for building tools and other applications based on a structured data model  (Steinberg et al. , 2009). • Generator development: this activity aims at developing the generator code that will take in charge the adaptive generation of the different scenarios. This development can be partially eased by using the EMF environment. Indeed, from our metamodel specification, EMF provides tools and runtime support to produce a set of Java classes for the models, along with a set of adapter classes that enable viewing and command-based editing of the model. Thus, the generator code can use the generated Java classes to import the input models, handle them to produce the scenario model, and then to serialize the output scenario.
Development of the Generator We developed the generator as a Java, console-userinterface program in order to benefit from the Java Model code and other code facilities generated from the metamodel. Nevertheless, the generation rules still have to be considered. Those corresponding to levels 1 and 3 (cf. Table  1 ) are hard-coded with aleatory procedures when several possibilities occur. We used the Java Choco-Solver-4 library to specify level 2 (scenes and themes selection) rules as constraints-based programs. As mentioned in previous sections we do not yet tackle the domain rules relative to the child's profile for levels 2 and 3. The generator allows the successive generation of the 3 scenarios. The resulting final scenario is persisted as a model that can be displayed and handled by the EMF-generated model editor.
Data Collection A pair of specifically trained investigators (a professional lay interviewer and a clinical psychologist) interviewed each person for an average time of one hour. First, a detailed questionnaire about personal and family history, past and present social situation, housing and living conditions, and health and use of healthcare services at the time of the survey was administered. A section on addictions (alcohol, drugs, medications abuse) used the Alcohol Use Disorders Identification Test (AUDIT)  [19]  and the Assessment and Screening of Assistance Needs (ASAN) drugs questionnaire  [20] , followed by questions on intake methods in the absence of housing. Psychiatric disorders were investigated through a structured clinical interview (MINI plus v 5. 0  [21] ) integrated into the questionnaire, which generated diagnoses according to the Diagnostic and Statistical Manual of Mental Disorders (DSM, 4th revision).
Acknowledgments: The Samenta study has been funded by the following national and regional institutions: Direction générale de la santé,  Haut-Commissaire  aux solidarités actives contre la pauvreté et à la jeunesse,  Observatoire national de la pauvreté et de l'exclusion sociale, Institut de veille sanitaire, Institut national de prévention  et d'éducation pour la santé, région  Ile-de-France ,  Préfecture de Paris ,  Mairie de Paris. The authors thank  Lauren Dalecky  for the English editing and revision of the manuscript. Author Contributions:  Anne Laporte  conceived and directed the SAMENTA survey;  Anne Laporte ,  Erwan Le Mener  and  Pierre Chauvin  created the questionnaire;  Yann Le Strat  designed the sampling method;  Anne Laporte ,  Stéphanie Vandentorren ,  Marc-Antoine Desprez  and  Caroline Douay  performed the analysis;  Anne Laporte ,  Marc-Antoine Desprez ,  Caroline Douay  and  Erwan Le Mener  supervised the data collection; the Samenta research group gave its expertise at all the steps of the project;  Anne Laporte ,  Stéphanie Vandentorren  and  Pierre Chauvin  wrote the manuscript.
INTRODUCTION The "Optical Interferometry Image Reconstruction Contest" is organised every two years  [1] [2] [3] [4] [5] [6] [7]  on the same principles since its first edition in 2004: one or several datasets are produced in the OIFITS format and provided to teams which will attempt to reconstruct images. The contest aims at not only comparing different image reconstruction algorithms, but also to encourage the use of the OIFITS format for optical interferometric data. The contest was announced on March 13th, 2018, on the OLBIN email list *.
DATASET This year, only one dataset was provided using the following method. First, OIFITS2  8  were generated using the Aspro software. 9 Since the contest intended to demonstrate the complementarity of the spatial frequency coverage (u,v coverage) of the VLTI and CHARA Array, we chose four different telescope configurations: CHARA 6T (that is, using all the available telescopes) as well as the 3 different 4 Auxiliary Telescopes configurations currently offered at VLTI. The declination was chosen to be +9 degrees and realistic observabilities were chosen. Aspro's simulator estimated we could have pointed the object 4 times with CHARA and 8 times with VLTI (on each of the AT configuration) within the observability window (limited by delay line range, object's elevation or domes' shadowing -for VLTI-).
1 mas and the image is made of 256×256 pixels. For each step we took a different fixed model and the image reconstruction was therefore made on different component of the object. We tuned the regularisation weight at each step in order to retrieve an image of a good quality (10 8 , 10 9 and 10 4 for each step respectively). Here we describe the 3 steps: 1. model = point source. Given the dataset and the instructions we assumed that there is a non-resolved star in the object. We modelled it as a point source and performed an image reconstruction of its environment. We fitted the flux of the point source bu performing a grid with different flux ratios and choosing the one that has the smallest value of the global cost function (f ) consisted in assuming that there is an unresolved star, in order to reconstruct the environment.
Submission from É. Thiébaut Reconstruction were carried out with MiRA 13 † , using the power spectrum (OI-VIS2) and phase closure data (OI-T3PHI) of the provided dataset. The image reconstruction settings were as follows: pixel size is δθ = 0. 1 mas, synthesized field of view Ω = 20 mas (hence restored images are 200 × 200 pixels). The maximum sampled frequency is about 200 Mega cycles which corresponds to an angular resolution of 0. 5mas; hence images have a fair amount of super-resolution. Positivity and normalisation constraints were applied (such that the sum of pixel values is equal to 1). The different reconstructions were all started with an initial image given by an unresolved source centered in the field of view. An hyperbolic edge preserving smoothness  16  was used as regularisation term with transition threshold parameter τ and relative weight µ.
Submission from J. Sanchez-Bermudez, A. Alberdi and R. Schödel In order to recover the morphology from the delivered MIRC and PIONIER observables, we used two of the software available to the community: BSMEM and SQUEEZE. Our reconstruction process first consisted in combine all the data sets into a single OIFITS file. In order to have the same wavelength information for both MIRC and PIONIER, we performed a wavelength interpolation in the MIRC data set at PIONIER wavelengths. Since the source is considered a grey body (i. e. , the morphology is the same at all the sampled wavelengths) we did not performed a chromatic reconstruction of the delivered data set. After the combination, we used SQUEEZE to perform the first reconstruction. This is a Monte Carlo -Markov Chain software that allows us to compute parallel reconstruction chains with a large variety of regularisers.