dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatics pipelines (mutation and DNA copy number pipelines) process each molecular profile and are responsible for raw data storage and traceability within the KDI core database. The summarized results are structured in the BIRD (Biological Results Database) application. The last step of the data integration workflow is the generation of the bioinformatics reports. Two reports are required in the context of the SHIVA clinical trial at two different time points. A first report is generated by the system after the processing of the DNA copy number profile in order to request an IHC validation if needed. A second report is generated by the system and sent to the MBB for the final therapeutic decision. All reports, data and analysis results for each patient are gathered within the KDI modules (KDI core database, Clini-calDB, BIRD).
The configuration management is delegated to a SVN repository where all the source codes of KDI system are regularly committed. The unit testing is strongly recommended for all programming languages involved in the system (X-Unit) and part of our continuous integration server based on Jenkins software. This system allows to check weekly that all the tests parameterized for all applications are successfully passed. This control ensures that the analysis pipelines provide the expected results, identical to a reference analysis which is considered as a gold standard. Second, we pay attention to the availability of KDI system. All web applications are monitored with Nagios software in order to be able to detect in real-time any disorder on the system and therefore take immediately all necessary actions (log analysis, server restart, update configuration, etc.
) to restore initial and nominal state if needed. In order to reach this high quality service expectations, three different informatics environments (meaning three different instances of all applications, three different web servers, three different database servers and three dedicated file systems) have been set up. An update on any environment is always linked with Each developer, after doing unit testing on his local workspace is allowed to install a new version of his components on this environment. It results that the D-env can be temporarily unstable and this is assumed. The second environment is the validation (V-env) which must be stable at every time. Integration testing is performed on this environment to validate the candidate release of the KDI system. The V-env can be seen as a pre-production environment.
Thus, the difference between biotechnological and informatics capacities grows exponentially. Entering the era of big data in cancer research implies a breakthrough at the informatics level. First, the scalability of the infrastrure (Input/Ouput performance and computing power) is required to allow the management and analysis of ever-growing data. Second, bioinformaticians must be trained to the use of low-level programming languages for parallel computing such as Message Passing Interface (MPI), Open MultiProcessing (OpenMP), or MapReduce  (Dean and Ghemawat, 2008)  and to the algorithm analysis. Developing these new skills will be essential in order to improve the efficiency of software used in downstream analysis to deliver results as quick as possible to meet deadline expected in the clinical practice.
To this aim, the Resource Description Framework (RDF) data format along with SPARQL query language provide the technical framework to describe, share, interact and query semantic data. While the technical solutions exist to support data exchange and linking, the definition of ontology, their choice and their use in practice for healthcare and biomedical data is still an issue. In order to tackle these challenges and to promote the use of standards and ontologies in the biomedical field, many European initiatives supported by the European Community (FP6 and FP7 programs) are involved in the definition and harmonization of standards: 1. SemanticHEALTH FP6 5  focused on semantic interoperability issues of electronic health systems and infrastructures and provided a number of relevant definitions, standards, and application domains for semantic interoperability  (Stroetman et al.
To this aim, we will have to capitalize on large collection of public datasets such as data from The Cancer Genome Atlas (TCGA 8  ,  Kandoth et al. , 2013)  or International Cancer Genome Consortium (ICGC 9  ) and also pathway databases for gene regulatory network, signaling pathway, metabolic pathway, Protein-Protein Interaction network and protein-compound network (e. g. , DIP, HPRD, KEGG, Reactome to name only a few). The TCGA has initiated a pan-cancer analysis project  (Cancer Genome Atlas Research Network et al. , 2013)  on the first 12 tumor types profiled by the consortium where the goal is to characterize molecular alterations and their functional impact across tumor type in order to promote the development of new therapies to fight cancer.
ACKNOWLEDGMENTS We would like to greatly thank  Dr. Ivan Bièche ,  Dr. Céline Callens ,  Dr. Olivier Delattre ,  David Gentien ,  Dr. Thomas Rio-Frio ,  Dr. Gaelle Pierron ,  Dr. Etienne Rouleau ,  Dr. Xavier Poaletti ,  Dr. Claude Houdayer ,  Dr. Marc-Henri Stern ,  Eric Voirin , and  Dr. Dominique Stoppa-Lyonnet  from the  Institut Curie (Paris France)  for their fruitful advices. This work is supported by the grant  ANR-10-EQPX-03  from the  Agence Nationale de la Recherche  ( Investissements d'avenir ) and grant  INCa-DGOS-4654 SiRIC (Site de Recherche Intégré contre le Cancer ) from the  Institut National du Cancer. High-throughput sequencing has been performed by the  NGS platform of the Institut Curie , supported by the grants  ANR-10-EQPX-03  and  ANR10-INBS-09-08  from the  Agence Nationale de la Recherche  (investissements d'avenir) and by the  Canceropôle Ile-de-France.
Monte Carlo simulations In the present work we compare data with Monte Carlo simulations, using GEANT4, release 4. 9. 5. p01, interfaced with VMC (Virtual Monte Carlo) software, release v2-13c  [21]. Two particular GEANT4 models were studied, CHIPS (QGSP BERT) and FTFP (FTFP BERT TRV). The CHIPS (CHiral Invariant Phase Space) model  [22]  is a 3D quark-level event generator for the fragmentation of excited hadronic systems into individual hadrons, whereas the FTFP -3 - model  [23]  relies on a string model to describe the interactions between quarks.
Since data were taken with two different degrader configurations, the datasets were studied in order to verify whether there was enough statistically significant difference to justify a separate analysis. We separated the events collected with 2 and 5 µm degrader, and the corresponding energy spectrum can be seen in figure  19. The overall distribution of the energy of the annihilation clusters is very similar for both degraders. Small statistically significant differences can be observed for only a few bins, probably related to the differences in the penetration depth (and hence the deposited kinetic energy) in silicon for the two degraders configuration. However, given the non-systematic nature of the difference, we decided to consider the two datasets together, thus improving significantly the statistics.
Comparison with Monte Carlo simulations The Monte Carlo samples were generated separately for CHIPS and FTFP and consist of three million events each. The entire flight path of the antiprotons was simulated, starting with the -16 - Figure  23  and 24 show the total energy distribution and the particle composition of clusters for the two simulation models. For CHIPS one expects higher cluster energies and a broader distribution containing more alpha particles and protons and less heavy nuclei than for FTFP. The signal in single pixels was obtained from the ionizing energy deposited by particles in the geometrical volume covered by the pixel cell. The clustering algorithm developed for the data analysis was also implemented in the simulations. Random gaussian noise was included as well, with the same RMS obtained from the data.
Acknowledgments We would like to thank the  Research Council of Norway  and the  Bergen Research Foundation  for their support. We would like to thank the  CERN SSD lab , especially in the persons of  Michael Moll  and  Maurice Glaser , for having provided the instrumentation and infrastructures used for laser calibration measurements. We would also like to thank  Alberto Ribon  for his help with the fine tuning of GEANT4 with antiprotons as well as with the interpretation of the results.
On nautical charts, undersea features are portrayed by sets of soundings (depth points) and isobaths (depth contours) from which map readers can interpret landforms. Different techniques were developed for automatic soundings selection and isobath generalisation from a sounding set. These methods are mainly used to generate a new chart from the bathymetric database or from a large scale chart through selection and simplification however a part of the process consists in selecting and emphasising undersea features on the chart according to their relevance to navigation. Its automation requires classification of the features from the set of isobaths and soundings and their generalisation through the selection and application of a set of operators according not only to geometrical constraints but also to semantic constraints.
The objective of this paper is to define an ontology formalising undersea feature representation and the generalisation process achieving this representation on a nautical chart. The ontology is built in two parts addressing on one hand the definition of the features and on the other hand their generalisation. The central concept is the undersea feature around which other concepts are organised. The generalisation process is driven by the features where the objective is to select or emphasise information according to their meaning for a specific purpose. The ontologies were developed in Protégé and a bathymetric database server integrating the ontology was implemented. A generalisation platform was also developed and examples of representations obtained by the platform are presented. Finally, current results and on-going research are discussed.
INTRODUCTION Nautical charts provide a schematic representation of the seafloor where features are portrayed by isobaths and soundings. In order to address navigation requirements, navigation hazards on the seafloor such as reefs must be emphasised and relevant fairways and berths must be highlighted. Furthermore, the navigator does not see the seafloor and a precise description is not required. As a consequence, nautical charts provide a more schematic representation of the relief than topographic maps. Undersea features are portrayed on the chart based on their relevance for navigation and their meaning has to be taken into account in designing an automatic generalisation process. Starting from the seafloor modelled by a set of soundings and isobaths extracted from the bathymetric database, a first part consists in simplifying the seafloor representation according to the scale of the chart  (Peters, 2012).
Both parts form an ontology where the central concept is the undersea feature around which other concepts are organised so that the generalisation process is not driven by geometrical constraints but by the semantic of the chart where the objective is to select the information according to their meaning for a specific purpose, navigation. The ontologies were developed in Protégé and a triplestore database server integrating the ontologies was implemented. The database stores both the knowledge inferred from the ontology and the bathymetric data. A generalisation platform was also developed accessing the database to retrieve bathymetric data but also to classify the features and evaluate the cartographic constraints. This work contributes at providing a new approach for the development of a cartographic tool for chart.
First, the ontology is used to design an enriched bathymetric database storing soundings and isobaths and also features as characterised from the data. Second, from the knowledge gathered in the database, a generalisation process could be designed for the automatic construction and update of nautical charts. The paper is organised as follows: the next section reviews existing development on cartographic ontologies describing geographical information and formalising the generalisation process as well as issues related to the representation of landforms. Section 3 introduces the framework of the new ontology and describes concepts involved in the feature representation and generalisation process. The database implementation and examples of representations obtained on the platform are presented in section 4.
ONTOLOGIES FOR CARTOGRAPHIC REPRESENTATION Existing works on ontologies in cartography are of two kinds and were mainly conducted by national mapping agencies. On one hand, domain ontologies describing the knowledge portrayed on the map were designed by the Spanish mapping agency (IGN-E)  (Gómez-Pérez et al. , 2008)  and the Ordnance Survey in the UK 1. Both ontologies rely on several ontologies including a topographic ontology and a hydrological ontology. They provide a taxonomy of geographical feature types. Their objective is to deal with the heterogeneity of data sources and with large amount of cartographic data. They do not address processes involved in the construction or techniques for representation of geographical objects on the map. Furthermore, they focus on topographical maps and so do not address requirements of nautical charts.
In these works, concepts describing objects portrayed on the map relate to objects stored in the topographic database without ambiguity. For example, in the expression "the river circles the building", the river and the building already have properties including a geometry explicitly defined in the database. This does not apply to undersea features which are not modelled in the bathymetric database. One supplementary requirement is the characterisation of undersea features. As said by  (Janowicz et al. , 2013) , "the definitions of feature types are a product of human perception, cognition, current state of knowledge and social agreement" and so are often domain-specific. In hydrography, a nomenclature is provided by the IHO  (IHO, 2008)  and used in the GEBCO gazetteer of undersea feature names 2.
As each feature type is defined by one or two sentences, a semantic model is required to capture the meaning into a set of concepts and relationships. On one hand, such model leads to an object-based representation where each feature is a part of the seafloor corresponding to common perception but whose properties including its boundaries are not clearly defined  (Smith and Mark, 2003). On the other hand, the bathymetry is defined by a field model (and stored in the database by a discrete set of soundings and isobaths), leading to what  (Mark and Smith, 2001)  called the "qualitative-quantitative divide". The ability to integrate field and object views of the landscape is essential for rastervector data-layer integration. Ontologies have been commonly used as a tool to capture the semantic meaning behind different types of landforms and extract them from terrain models.
The latter describes activities that require or analyse seafloor information. In this context, the task ontology relates to navigation and its definition is beyond the scope of this paper. The phenomenological domain ontology (PDO) deals with the representation of undersea features on the chart. The method ontology focuses on representation techniques and includes elements on the chart (soundings, isobaths). It is composed of a cartographic representation ontology which includes operations matching features from the ADO to the PDO and of a generalisation process ontology where generalisation constraints and their evaluations are formalised. Finally, the measurement ontology refers to data collection techniques (e. g. echo sounding) and, like the task ontology, is out of the scope of this work.
For example, the Seamount and the Hill defined in the IHO terminology are generalised as Peak Feature which gathers all features whose vertical profile is a peak and, at the next level, the Peak Feature and Ridge Feature are generalised into Eminence Feature vertical profile is an eminence. By this way, the granularity can be adjusted according to the accuracy of input data or the requirements of the application. For a full review of the feature description and hierarchy, the ontology is accessible online 3.
The cartographic ontology The method ontology formalises the way undersea features are represented on the chart. Two categories of concepts are considered to describe the information portrayed on the map: the graphical elements such as soundings and isobaths which are effectively drawn on the chart and the visual elements which are perceived by users based on their interpretation. These include morphometric features translating undersea features of the ADO and other characteristic features such as critical lines and areas defined by different sounding densities. These concepts are represented in Figure  6. The chart concept includes metadata such as the chart scale and the symbology. The method ontology also describes the processes classifying the features. They correspond to functions translating the different feature properties presented in the ADO into functions that apply to features defined by isobaths and soundings such as the area and the height of features.
System design Both ADO and PDO ontologies were designed in Protégé 4. 2 and integrated in a Virtuoso triplestore database server. Virtuoso stores all the concepts and relationships of the ontology, forming an ontology database and is also used to store the bathymetric database from which chart data are extracted. The schema of the bathymetric database is directly generated from the ontology database. Knowledge is defined as triples in the form of subjectpredicate-object expressions so that predicates connect data with concepts, e. g. isobath I -is an instance of -Isobath concept, and data together (e. g. isobath I -is part of -feature F). Constraints and evaluation are also defined as concepts in the ontology and connected by predicates to cartographic elements. Examples of triples in the ontology database are Area conflict -is a -Legibility constraint, Area measure -evaluates -Area conflict and Area measure -applies to -Feature.
The database server was connected to an existing generalisation platform which was developed in C++ using Qt and CGAL libraries. The platform relies on previous works from  (Guilbert, 2013)  for the identification of features where only isobaths were considered and isobath generalisation operations are implemented. Therefore, although all feature types are defined in the ADO, only features delineated by isobaths and forming prominences or depressions are considered in the representation. At its creation, the bathymetric database is populated only by isobaths and soundings. Topological relationships between isobaths and soundings are first added with a constrained Delaunay triangulation. Shape properties of each features are computed from their soundings and isobaths and topological relationships between the features are extracted into a feature tree.
Introduction Structure determination of large macromolecular assemblies embedded in vitreous ice using electron microscopy (EM) is becoming increasingly popular as evidenced by the steady increase in the number of structure depositions into the EM databank (EMDB)  (Lawson et al. , 2011). Depending on the molecular weight and order of the assembly, a series of three-dimensional (3D) structures at near-atomic resolution have become available in the past decade. Pioneering work on highly symmetric structures derived from two-dimensional arrays, helical or icosahedral assemblies  (Henderson et al. , 1990; Unwin, 2005; Zhang et al. , 2008)  have demonstrated the potential of electron cryomicroscopy (cryo-EM) based structure determination. Historically, the first 3D reconstructions were computed from electron micrographs of helical assemblies  (De Rosier and Klug, 1968 ).
, 1999; Sachse et al. , 2008). Despite the previous successes of helical structure determination, a simple standardized workflow for 3D helical reconstruction is still lacking. The most widely used approach is the implementation of the iterative helical real-space reconstruction (IHRSR) based on the SPIDER package  (Frank et al. , 1996)  and additional tools for helical symmetry determination and imposition  (Egelman, 2000). In the meantime, other packages such as SPARX have adapted the IHRSR algorithm  (Behrmann et al. , 2012). Moreover, several structures have been determined by extending and modifying the original IHRSR approach significantly with additional SPIDER operations  (Sachse et al. , 2007). Using a full correction of the contrast-transfer function, alignment restraints and an adapted 3D symmetrization procedure, a series of structures were determined  (Bharat et al.
, 2012; Korkhov et al. , 2010; Low et al. , 2009; Sachse et al. , 2007; Sachse et al. , 2008). In order to condense the adapted procedures into a generally usable workflow, we describe here a package for single-particle based helical reconstruction termed SPRING (Single particle reconstruction from images of known geometries). We demonstrate the full functionality of the package by processing a subset of previously published micrographs of Tobacco Mosaic Virus (TMV)  (Sachse et al. , 2007)  (http://grigoriefflab. janelia. org/datadownload). SPRING contains programs that determine the microscope parameters, analyze and classify the segmented helices, explore helical symmetry at low resolution, refine high-resolution symmetry and determine the 3D structure.
The beginner level reduces the complexity of the input parameters by using sensible default values. As their familiarity with the processing operations increases the user can choose to add more parameters. In the current implementation of SPRING, a significant effort was invested to streamline analysis and diagnosis of the obtained results in a user-friendly manner. Where possible, either condensed graphical plots are generated or more complex data representations can be browsed interactively (Fig. 2 ). The SPRING package is entirely written in object-oriented python and uses EM-related libraries and functions from SPARX and EMAN2  (Hohn et al. , 2007). Microscope parameters are determined by CTFFIND and CTFTILT  (Mindell and Grigorieff, 2003). In addition, scientific computing tasks are performed by Numpy and Scipy functions (numpy.
scipy. org). For parameter storage sqlite3 databases are used and interfaced by SQLAlchemy (www. sqlalchemy. org). Interactive and diagnostic plots were made with the plotting libraries of matplotlib (http://matplotlib. sourceforge. net). SPRING's GUI has been built using PyQT libraries. SPRING is optimized to run in a multi-CPU environment on high-performance computer cluster implemented by Mpi4Py (http://mpi4py. scipy. org). Python can be used as a scripting language as well as a structured programming language. Both of these features make the usage of isolated existing functions in a new processing context and the easy modification of SPRING possible. In addition, the widespread use of python as a programming language and the excellent interfaces to scientific computing libraries such as Numpy and Scipy are a great advantage for prototyping any numerical computations and thus promoting further development of the package.