dataesr/scientific-paragraphs-categorization huggingface dataset:
Principal Results The Multilingual PubMed-French strategy allows the retrieval of French PubMed citations with high precision and high coverage. Despite more than 600,000 PubMed citations that were introduced into the CISMeF information system, the average server-side response time stayed below the threshold time (ie, 2 seconds), which is acceptable, in our experience, for the average health professional. The Multilingual PubMed-French search engine is now freely available on the Internet  [23]. Babel MeSH allows users to access PubMed database in French. It provides many results for low precision and is, therefore, of poor interest for French speakers. Moreover, the functioning of the search engine is not detailed anywhere; it is only possible to make rough free-term queries that are translated automatically by Babel MeSH.
Also, an interesting avenue for future development of Multilingual PubMed is to integrate bibliographic data from other sources. A partnership with Elsevier-Masson and 2 small French companies has now been launched. Elsevier will provide additional metadata for MEDLINE/PubMed citations (abstracts in French in particular), which currently are not yet available in the MEDLINE/PubMed database. Furthermore, Elsevier-Masson will provide metadata on journals not included in PubMed, but included in other bibliographic/bibliometric databases (eg, Web of Science, EMBASE, and BIOSIS). In addition to being more efficient than its competitors are, Multilingual PubMed is, in our opinion, more user friendly and provides more features. Nevertheless, there are still many interesting functionalities in PubMed (ie, related citations, history management) or in other tools  [8]  (graphical representation) that are not implemented in Multilingual PubMed.
Conclusion This feasibility study was carried out to create a multilingual search engine to query monolingual PubMed subsets. It was successful for the French language and will be extended to the other main European languages. Bibliographical references from non-English publications can now be searched in a native language-friendly interface using multilingual data from the PubMed database and MeSH vocabulary. Overall, this Multilingual PubMed tool could be of valuable interest for non-English-speaking health professionals who are unable to access PubMed.
Acknowledgments The authors would like to thank  Pablo Iriarte , Librarian at the  University Hospital CHUV in Lausanne, Switzerland , for his decisive impulse for starting the project. The authors are grateful to  Nikki Sabourin-Gibbs ,  Rouen University Hospital , for reviewing the manuscript in English. UMIT, Austria, Centre Tudor, Luxembourg, Hospital Italiano of Buenos Aires, Argentina , and the  Norwegian Electronic Health Library graciously translated some MeSH terms and some interface terms into German, German and Portuguese, Spanish and Italian , and  Norwegian , respectively.
[13], the Catalogue and Index of Online Health Resources in French (CISMeF=Catalogue et Index des Sites Médicaux de langue Française), a quality-controlled health gateway[13], describes and indexes using the MeSH thesaurus the main institutional health Web resources (documents and sites) available in French. A search engine, Doc'CISMeF, was developed in 2000 to allow querying of this gateway. In 2007, CISMeF began using several terminologies/ontologies to allow easier and more accurate indexing. The CISMeF team was prompted to develop a multiterminology portal (French/English) [14], which interoperated with the search engine. In 2012, a transition was made to establish a cross-lingual portal named Health Terminology/Ontology Portal (HeTOP) available in 23 different languages [15,16]. Recently, Doc'CISMeF was significantly updated in 2 ways: (1) it has become a generic tool able to integrate any metadata model and its related data; therefore, this tool is able to describe and index Web resources as well as PubMed citations and (2) the search engine has now been developed into a reliable multilingual tool, which is capable of searching by using multiple terminologies/ontologies in several languages.
In Vitro Characterization of the Putative G56S SMS Stabilizers The selected 51 compounds were purchased and tested experimentally. The goal of the in vitro experiments was to test the putative stabilizers for their ability to increase the G56S SMS activity via the homo-dimer stabilization. The measured activity of G56S SMS in presence of small molecules is shown in Figure  6. The activity in the presence of the previously tested 10 small molecules  [51]  is also given in Figure  6. It is seen that 31 molecules slightly increase the SMS mutant activity and 7 of them increase the activity of the G56S SMS by more than 10%. Unexpectedly, we discovered two molecules that decrease the mutant activity by 15% and 56%, respectively. One may speculate that these molecules affect the dimer formation or stability since they do not to contain scaffolds known to inhibit the SMS active site and are neither reactive nor frequent hitters that might result in false positive hits (we checked by our software FAFDrugs2, see in Methods for details).
In the putative binding area Pa (Figure  S4 ) the lowest docking energies were of 27. 95 and 26. 94 kcal/mol for E941-0318 and G796-1817, respectively. The docking experiments shown in Figure  9  suggest that the end of the propyl side chain, namely the first cycled amine in E941-0318 and the cycled amine in G796-1817 point toward the carboxylic group of D94. pKa calculations performed with MarvinSketch software (ChemAxon 2010) predicted that this cycled amine is protonated for both E941-0318 and G796-1817. Thus, the charged cycled amine of E941-0318 and G796-1817 forms a salt bridge with the carboxylic group of D94. In addition the amide NH of E941-0318 and G796-1817 forms a hydrogen bond with the carbonyl oxygen of D92. The aromatic cycles of both molecules are anchored in a deep cavity formed by R17 and H81, Y79.
I. In silico modeling Protein Structure. The X-Ray crystal 3D structure of wild type (WT) human SMS in complex with spermidine (SPD) and 5methylthioadenosine (MTA) (PDB ID: 3C6K) (Figure  1 ) was downloaded from the Protein Data Bank (http://www. rcsb. org) [64]. The crystallographic structure is made of four chains (chains A, B, C, and D) resulting in two homo-dimers in the asymmetrical unit cell. As pointed out in our previous work  [11, 12] , the homodimer formed by the A chain and B chain is not suitable for MD simulations because of significant van der Waals clashes. Due to this, in this work, we used the dimer formed by the C and D chains. The missing atoms and residues were rebuilt by ''profix'', a module in Jackal package (http://wiki. c2b2. columbia. edu/ honiglab_public/index. php/ Software:Jackal_General_Description).
The mutant G56S was created by the module SCAP  [65]  in the Jackal package. shows the WT 3D structure and the mutation site G56. In this paper, we kept the original residue number 56 according to previously published papers  [43, 45]  for the mutation site G56 (G71 in FASTA sequence) while the other residue numbers mentioned in this paper correspond to the protein sequence in FASTA file. The protonation states of the titratable groups were calculated with Multi Conformation Continuum Electrostatics (MCCE, version 2. 4)  [66] [67] [68]. The dielectric constant for MCCE was 8. 0. The results of pKa calculation suggested that several His (Table  S2 ) are neutral and the hydrogen atoms of these His (H d or H e ) were placed according to the obtained pKa values. Figure 1 Molecular Dynamics Simulations.
MD simulations were performed for the WT and the mutant homo-dimer structures using CHARMM program (Chemistry at HARvard Macromolecular Mechanics, version c35b1)  [69]. The substrates (SPD and MTA) in SMS complex were removed for the simulations since they are situated at the C-terminal domain (CTD) far from the mutation site G56S located at the N-terminal domain. The solvation was taken into account by the Generalized Born implicit solvent function FACTS  [70]. The WT and the mutant homodimer structures were initially minimized using 500 steps of a steepest descent algorithm followed by 500 steps of a conjugate gradient algorithm. Distances between heavy atoms and hydrogen atoms were constrained by the SHAKE algorithm allowing for a time step of 2 fs. The system was heated during 100 ps to reach 300 K and then equilibrated during 200 ps with a temperature window of 300610 K.
3 in the clusters. Tanimoto index of 0 means that there are no identical indices in either molecule and 1 means that both molecules are composed of identical sets of indices. The 3D structures of the remaining 273,226 molecules were generated using Corina program embedded in the Accelrys Pipeline Pilot v8. 5. The procedure was launched keeping a maximum of 2 stereocenters and a maximum of 4 stereoisomers per compound without generating multiple ring conformations. For chemical structural analysis of the identified bioactive compounds we used two clustering approaches. A first run was performed with the Cluster Molecule Protocol (Accelrys Pipeline Pilot v8. 5) and the MDL keys. A Tanimoto similarity index of 0. 6 was used to assess the similarity between all pair of compounds and 11 clusters were obtained.
A second clustering procedure was carried out with Stardrop (http://www. optibrium. com/) in order to define a final chemical series. It creates chemical space projections based on a combination of chemical structure and properties. Virtual Screening and Docking. Docking of the prepared 273,226 compounds from Asinex, ChemBridge, ChemDiv and LifeChemicals was performed into different protein binding pocket conformations using two software packages, Surflex  [71]  and AutoDock Vina  [78]. Surflex creates a protomol of chemical probes to which potential ligands are aligned by incremental construction based on the molecular similarity. In this work, we generated Surflex protomol based on the selected residues in the binding pockets. The residue lists are provided in Table  S4. In addition, the parameter ''proto_thresh'' was set to control the degree of burying (Table  S5  of supporting information) and the parameter ''proto_bloat'' was set to indicate how far the protomol should be expanded (Table  S5 ).
8 and an apparent n H of 0. 73 (Table  1 ). Because of its pH-dependent emission peak shift (Table  S1 ) and an incomplete quenching at low pH (Fig. S1 A ), this variant was not further pursued. pHuji: a red pH-sensitive FP with nearoptimal pK a and a high apparent n H For our next attempt at developing an improved red pH sensor with an emission peak well separated from that of SEP, we turned to mApple as a template  (Shaner et al. , 2008). Libraries were constructed by randomizing residues in close proximity to the chromophore, including positions  64, 70, 95, 97, 146, 148, 159, 161, 163, 177, and 197  (numbered according to mCherry crystal structure, PDB ID 2H5Q;  Shaner et al. , 2004). After initial screening, it was found that positions 161 and 163 were primarily responsible for modulating the pK a of mApple.
Two rounds of random mutagenesis using mApple K163Y as the template did not result in further improvements in pH sensitivity. Accordingly, mApple K163Y was designated as pHuji (pronounced Fuji, a common pHuji, a pH-sensitive red fluorescent protein • Shen et al. with TfR-pHoran4 and TfR-pHuji were only slightly lower than that obtained with TfR-SEP. In contrast, events detected in TfR-pHTomato-transfected cells were very rare (Fig. 4 D ). In 3T3 cells, scission events fall into two categories: terminal events, where the cluster visible at pH 7. 4 disappears <40 s after scission, and nonterminal events, where it remains visible  (Merrifield et al. , 2005; Taylor et al. , 2011). The proportion of terminal events measured in this dataset (40%) was very similar for SEP-, pHoran4-, and pHuji-tagged TfR (Fig.
pH buffer solutions from pH 3 to 11 were prepared according to the Carmody buffer system  (Carmody, 1961). pK a and apparent n H were determined by fitting the normalized data to the following equation: F n pK pH H a = + - 1 1 10 ( ). Cell culture and transfections NIH-3T3 (European Collection of Cell Cultures) and HeLa (gift from A. Echard, Pasteur Institute, Paris, France) cells were grown in DMEM supplemented with 10% fetal calf serum, 1% sodium pyruvate, and 1% Glutamax with (HeLa) or without (NIH-3T3) 1% penicillin/streptomycin and maintained at 37°C in 5% CO 2. Cells were subcultured every 2-4 d for maintenance. 24 h before imaging, cells were transfected with 1-3 µg of total DNA, complexed with either 10 µl Lipofectamine 2000 (Invitrogen; NIH-3T3) or 3-6 µl X-TREMEGene-HP (Roche; HeLa) in serum-free medium.
Quantification of fluorescence intensities in the other channel than the one used for detection was done using a set of x/y coordinates corrected for distortions caused by the Dual View system. This correction was calculated as a third order polynomial transformation using images of 0. 2-µm TetraSpeck beads (Invitrogen) taken before each experiment. Bleed-through from one channel to the other was not corrected. Protein enrichment in clusters or vesicles was assessed by computing randomized datasets as in  Taylor et al. (2011) , reflecting the overall background fluorescence of the cell over which fluorescence enrichment becomes significant. Events could be classified as terminal or nonterminal depending on whether the fluorescence in pH 7. 4 images 36 s after detection were <40% (terminal) or >60% (nonterminal) of the fluorescence in images at pH 7.
4 just before detection. We define objects (either pH 7. 4 clusters or pH 5. 0 scission events) as co-detected when they are independently detected in each channel <2 frames and 5 pixels (750 nm) apart. To determine the Euclidian distance between an object and its nearest neighbor in the other channel at a given frame, the x/y coordinates of one of the two datasets were corrected using the polynomial transformation described in the previous paragraph. Fluorescence intensity of each dot on the scatter plots of Figs. 5-8 and S5 is calculated as the intensity of an object (determined as described in the previous paragraph) at its time of detection. The lower detection limits of SEP and pHuji markers were estimated as the intensity of the least fluorescent detected object (Figs. 5-8 and S5, green and red lines).
Semi-automated detection of endocytic events The detection, tracking, quantification, and validation of endocytic events was performed using scripts in two ways. First, to analyze cells transfected with single label TfR (i. e. , single transfections and cotransfections with clc-GFP or dyn1-GFP), we used the algorithm developed previously in Matlab 7. 4 (Mathworks;  Taylor et al. , 2011)  with minor modifications. This method of detection had been characterized as missing 30% of all events but has a low percentage of false positives (20%;  Taylor et al. , 2011) , making it possible to work with large datasets. For these experiments, a quantitative detection of all endocytic events was not essential and we favored a fast and efficient data analysis. As shown in Fig. S10  of  Taylor et al. (2011) , the endocytic events not automatically detected are essentially similar to the detected ones.
We thank  Natacha Retailleau  for help with molecular biology and the  University of Alberta Molecular Biology Services Unit  and  Christopher W. Cairo  for technical support and access to instrumentation. The work was supported by the  Natural Sciences and Engineering Research Council of Canada  to  R. E. Campbell ; the  Fondation Recherche Médicale , the  Centre National de la Recherche Scientifique  ( Interface program ), and the  Agence Nationale pour la Recherche (CaPeBlE   ANR-12-BSV5-005 ) to  D. Perrais ; an  Alberta   Ingenuity Scholarship  to  Y. Shen ; and a  pre-doctoral fellowship  from the  University of Bordeaux  to  M. Rosendale. R. E. Campbell  holds a Tier II  Canada Research Chair in Bioanalytical Chemistry. The authors declare no competing financial interests. Submitted: 21 April 2014 Accepted: 7 October 2014.
Background: Electronic death certification was established in France in 2007. A methodology based on intrinsic characteristics of death certificates was designed to compare the quality of electronic versus paper death certificates. Methods: All death certificates from the 2010 French mortality database were included. Three specific quality indicators were considered: (i) amount of information, measured by the number of causes of death coded on the death certificate; (ii) intrinsic consistency, explored by application of the International Classification of Disease (ICD) General Principle, using an international automatic coding system (Iris); (iii) imprecision, measured by proportion of death certificates where the selected underlying cause of death was imprecise. Multivariate models were considered: a truncated Poisson model for indicator (i) and binomial models for indicators (ii) and (iii).
The death certificates are coded automatically by the international software Iris (Additional file 1: Iris software) in order to select the underlying cause of death (UCD); complex cases are reviewed by nosologists. The UCD is defined as "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury. " One single underlying cause is selected for each death, following the General Principle and rules described on the International Statistical Classification of Diseases (ICD) and Related Health Problems, 10 th revision  [2]  (Additional file 2: Rules for mortality coding). The poor quality and comparability of medical cause of death data are mainly due to the lack of training of certifiers.
Data sources The present study is a population-based study on routine death certificates. All electronic and paper death certificates received and coded by the CépiDc during year 2010 were taken into account, provided that at least one cause was mentioned. Neonatal death certificates regarding children deceased before 28 days of age were excluded because of their specificity. For each death, available data were birth date, death date, gender, place of occurrence, and region of death, type of certificate (electronic or paper), and all the causes of death reported by the physician certifier, coded with the tenth revision of the ICD (ICD-10). All the included certificates were coded with Iris software. Five automated coding systems (ACS) have been developed throughout the world. The United States was the first country to develop an ACS.
Sweden, France, and Hungary followed. All of these systems are compatible with the US system. They code the causes of death and select the underlying cause of death according to the ICD-10 rules and guidelines. However, these systems are dependent on the language used for the causes of death reporting. This is why Iris was developed: it provides a system both compatible with the US system and usable in any language. Iris is now used by several countries such as Sweden, France, Germany, Canada, South Africa, Israel, and Luxembourg. The CépiDc is one of the four institutions that participate in the Iris collaborative project. For each certificate, Iris documents the rules used to select the underlying cause of death through the Automated Classification of Medical Entities (ACME) system (Additional file 1: Iris Software,  [18, 19] ).
This method could be used to compare quality of data between countries using the same coding system (automated selection program). In France, electronic certification will regularly be evaluated during its development. This method could also potentially be used to evaluate more precisely the geographical and temporal distribution of the quality of death certificates or the effect of an educational intervention for medical students  [15, 25, 26]. The main limitation of the method used is that it did not take into account the whole coding process but only the choice of the UCD. Indeed, the preliminary coding phase is the attribution of an ICD code to a medical expression. The quality differential corresponding to this phase between electronic and paper certification was not evaluated. However, it is to be expected that the translation from a text to an ICD code is easier when no interpretation of the certifier's handwriting has to be done.
At low steam quality (low steam superficial velocity) the flow is expected to be intermittent, with large vapor slugs moving along the tube. At a higher quality, the flow is supposed to be annular, with vapor flowing at a high velocity in the center of the tube and liquid flowing in an annulus or being stratified at the bottom of the tube. The aim of the present work is to simulate evaporating two-phase flows in tubes using CFD commercial code Ansys Fluent 14. 5 and to compare the results to existing flow pattern maps. The intermittent adiabatic flow regime will be validated, and then a heat and mass transfer model will be developed and implemented.
A computer model simulating the multi scale hydro-structural functioning of the pedon, named Kamel®, was introduced by  Braudeau et al. (2009)  based on the pedostructure concept only, i. e. , without integrating the recent advances in soil water thermodynamics following application of the SREV concept to the pedostructure organization. The objective of the paper is to present the complete theory behind Kamel® updated with the recent thermodynamic development of its state equations of equilibrium. Specifically, we will introduce (i) the basic principles of the soil-water modeling framework represented by Kamel®, including the pedostructure concept, the SREV concept, and the new thermodynamics equations resulting from it; and (ii) the implementation of equations into the computer model using the Simile® software environment.
Overview of the OntoVIP medical image simulation ontology OntoVIP was developed to facilitate the sharing and automated processing of information managed within the VIP Virtual Imaging Platform. OntoVIP provides a coherent conceptual framework, grounded on the DOLCE (Descriptive Ontology for Language and Cognitive Engineering) foundational ontology  [13]. The ontology was built through intensive interviews with researchers involved in image simulation. It took almost a year to reach a consensual modeling and several incremental versions were produced. OntoVIP is publicly available through the BioPortal  6. It includes medical information object models (called for short 'Object models' in the following) whose sharing and reuse are essential since such models are hard to build from scratch, and can often be easily derived from existing ones.
They only involve domain-specific entities (vip-model, vipsimulation and vip-simulated-data prefixes of the OntoVIP ontology) whereas the Where clause of the query only involves OPM-O entities. These new statements semantically describe the nature of input parameters (medical-image-simulationobject-model, simulation-parameter-set) and simulated output medical images (PET-sinogram). In addition, these new annotations represent the nature of the simulation (i. e. Positron Emission Tomography) through the use of the PET-simulation class, and the relation between the medical object imaged and the produced simulated image (derives-from-model property). They are particularly useful because they involve medical imaging concepts and relations part of the OntoVIP ontology. • Lines 29 to 33 identify a process execution, its corresponding service description through an ?agent instance, and the achieved class of action through the iec:refers-to property.