dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2011). A large range of apps (formerly called plugins) is readily available, providing a huge repertoire of visualization and analysis options, including the integration of information from other data sets and annotation sources, and statistical analyses  (Saito et al. , 2012; Lotia et al. , 2013). After the retrieval of functional information about a set of experimental genes, transcripts, or proteins, a further analytical step could be to deduce which of their properties or ontology terms are enriched relative to a background data set, thus giving an indication of which classes of gene or protein or biological systems are overrepresented and thus predominate within the data set. These analyses 1) free: publicly available, cross-platform, and open source, with database architecture and algorithms described in peer-reviewed publications; 2) comprehensive: able to draw on a wide variety of leading database resources; 3) updated: regularly, coordinated with releases of major sequence and functional databases; 4) smart: capable of automatically recognizing ID types and thus determining the relevant species and relationships between genes and their products; and 5) flexible: allowing a choice of analytical methods and output formats.
Continued increases in database comprehensiveness, usability, and integration can be expected in the future and are to be welcomed. So many genes and their products have undergone some degree of characterization, and so many biological processes have begun to be described in molecular terms, yet there remain a great many genes bearing the "uncharacterized" label. Thus, for researchers whose projects involve the discovery of genes, transcripts, or proteins and their functional characterization, with all the database resources available, paradoxically their most interesting hits may be the ones for which there is the least information to be found. represented by Pfam and reflected in the outputs of Gene3D, InterPro, CDD, Dasty, and Annie, with only the central Pfam domain appearing in ELM. Gene3D identifies two domains based on structure, but SMART only a "low-complexity region.
CONCLuSIONS ANd PERSPECTIVES Modern research biology increasingly relies on projects that use data derived from genome sequencing to make discoveries. The past decade saw a huge increase in the generation of sequence and experimental data, as well as in the number of databases relating to gene and protein sequence and function. A major challenge that has arisen is finding means of making optimal use of these resources to characterize and explore hits from larger-scale data sets in a way that makes sense to the research biologist and ultimately leads to discoveries of significance to the scientific community. The enhancement of plain data spreadsheets to generate hyperlinked and annotated results tables is a flexible and universally applicable approach that facilitates the exploration and characterization of experimental hits from discovery projects.
This procedure is relatively easily integrated as a last step in analytical workflows, using, for example, macros in Excel and online tools such as DAVID. For analytical service providers such as genomics, proteomics, and screening platforms, such enhancements give added value to the products they provide. For researchers, the presence of such links and annotations gives them the opportunity to easily categorize experimental hits on the basis of biological properties and allows them to pursue their curiosity as far as online resources allow. The rise in recent years of several independent database resources covering the same territory has in many cases led to increasing data standardization and exchange. This period has also seen the emergence of meta-databases, cross-database search engines, and overview sites providing unified portals for information retrieval-resources particularly beneficial for researchers exploring hits from larger-scale studies.
This being the case, support for expert-curated primary databases is still vital, as these remain the crucial points of contact with data-providing research teams and retain responsibility for data curation, quality control, and ensuring that connections are maintained between online data and peer-reviewed publications. Cross-database searches such as those from the NCBI and the EBI and the Bioinformatic Harvester can query dozens of resources, identifying hundreds of relevant entries. Internet-wide search tools can query thousands of sources, potentially retrieving billions of documents and data files. Evidently, as the rapid expansion of data continues, the danger increasingly looms of encountering a "too much information" situation. The challenge for developers of information-retrieval software will inevitably shift from enabling access to ever-larger data quantities to ensuring that data are delivered in a meaningful way: organized, categorized, and presented such that they can be interpreted and evaluated by researchers worldwide.
Although various programs are available for exploring the properties of sets of genes, transcripts, and proteins, the ideal software tool, in my opinion, has yet to be created. Such software would be ACkNOWLEdgmENTS I extend sincerest thanks to the numerous curators, developers, and support staff of bioinformatic resources who provided valuable information during the researching and writing of this article, in several cases implementing suggested improvements to their software. Grateful thanks also go to the many colleagues past and present who provided helpful information, advice, and comments on the manuscript. During the preparation of this article I worked in the laboratories of J. -M. Peters and M. Méchali, both of whom I gratefully acknowledge for their support and guidance. I was funded by the European Commission (FP6 Integrated Project LSHG-CT-2004-503464 MitoCheck), the Austrian Science Fund (Special Research Programme Chromosome Dynamics), the European Research Council (FP7/2007-2013 Grant 233339 ORICODE), a postdoctoral fellowship from La Fondation pour la Recherche Médicale, and the Centre National de la Recherche Scientifique.
It relies on an initial subdomain decomposition of the mesh. We manage the communications between the subdomains with the MPI library (coarse grain parallelism). In each domain, the computations of the local fluxes and source terms are accelerated through calls to OpenCL (fine grain parallelism). OpenCL is a recent software framework for programming multicore accelerators. Its main advantage on other tools, like CUDA, is its compatibility with GPU's of different brands. It is also able to address recent multicore processors.
MPI scalability The objective of this test case is to evaluate the MPI scalability of the computation. Left : triangular mesh. Rigth : hexaedral mesh obtain from srtuctured mesh. We consider a mesh made of 528, 840 elements with an order 2 approximation. This represents 14, 278, 680 degree of freedom. The mesh represent a generic airplane, see figure  4 , illuminated by a plane wave with Gaussian profile polarized in E y. Currently, we have not yet developed a tools which allow us to directly generate a mesh made of hexahedrons from a triangular surface mesh. So, we first generate a structured mesh from a triangular surface mesh. Then, we approximate this mesh by a hexahedrons mesh. We obtain a mesh which is not perfectly suited to CLAC software. But, here, we only want to evaluate the MPI scalability.
Summary of the proposed method In this paper we propose a method to segment ALS point clouds representing mixed scenes containing returns from terrain, vegetation, and standing archaeological remains using purely geometric measures. This method has been developed using ALS data at a nominal density of 8 pts/m2, a resolution likely to become increasingly available to researchers and managers through regional and national mapping schemes. In the proposed approach a point cloud representing a mixed scene containing terrain, vegetation and standing archaeological remains is selected for analysis. The point cloud was initially classified using a terrain-seeking algorithm implemented in TerraScan (TerraSolid software). TerraScan relies on an adaptation of Axelsson's algorithm, which discriminates between terrain and vegetation points using a series of adaptive TINs  [8].
In addition to presenting our core, original approach using multi-scale roughness, as presented at CIPA 2013  [9]  in this paper we demonstrate that the results obtained using Brodu and Lague et al. 's metric, as implemented in CANUPO  [16] , can be used as an alternative to roughness, and serve as an alternative input to the segmentation steps based on density and normal orientation. This is consistent with the point made below, that the ideal measure of dimensionality (how 1D, 2D, or 3D a geometry is) is not established. Presenting the use of an alternative metric to characterize dimensionality serves to emphasize that our approach is not strongly dependent on the specifics of the dimensionality metric used, or reliant on how it is calculated in a specific software program -the principle of segmentation based on dimensionality characterized at multiple scales combined with an opening operation is robust.
In the approach presented here in first instance we defined the kernel sizes based on the ALS data resolution, the scale of the remains studied, and by approximation guided by a series of tests. Density is calculated as the radius of the sphere that contains a point and its x number of neighbours. Here we use 27 neighbours, representing a 3x3x3 matrix around each point. Density can also be calculated as the number of points in a sphere or cube of a fixed radius. Because using a fixed radius requires testing to determine the appropriate radius to correctly represent local densities in the point cloud, and choosing an inappropriate value can lead to misleading results, we prefer the neighbours-based approach. Normal orientation is calculated at each point by selecting the closest neighbours and fitting a least squares plane on those points.
Alternative multi-scale spatial organization calculation using CANUPO The results of the CANUPO program developed by Lague and Brodu work well as an alternative dimensionality input (alternative to roughness) in our approach. In their metric for each point a spherical neighborhood is computed at each scale of interest S, and a Principal Component Analysis (PCA) is performed on the re-centered point coordinates. The proportions of the eigenvalues of the PCA are used to define a measure of local dimensionality per scale. The full feature space, taken over multiple scales S is used in defining a classifier by projecting the data into a plane of maximal separability and then separating the classes in that plane  [16].
Alternative calculations based on CANUPO To demonstrate the robustness of our approach to variations in the calculation of dimensionality, we classified the Bregille scene using CANUPO. Spatial scales between 1m and 10m were used, at 0. 5m intervals on training datasets for 'wall', 'vegetation' and 'terrain'. As with our original calculations using roughness, these scales are defined based on the resolution of the data, size of the objects in each class, and preliminary tests at various scales to determine which produced the best separation between classes (following the data driven approach used by Brodu and Lague). The reported balanced accuracies for the classifications were very good (ba>0. 98). The classification resulting from this step is in many ways similar to that from the roughness step in our standard approach, but we note edge effects at the limits of the dataset (figure  6a ).
Even in the cases of relatively steep local slopes, as seen at Montfaucon (figure  3 ) the normals on the sloping terrain are oriented more strongly toward the z-axis than the normals from architectural components. This allows for successful segmentation into connected components and discrimination based on dominant normal orientation. In conclusion, we emphasize the flexibility and robustness of the proposed approach, as demonstrated by the parallel tests in the Bregille and Montfaucon case studies in which we use both CANUPO and the traditional roughness metric. The approach is, as explained above, robust to the details of the metric used, which is beneficial as researchers may implement it in software with variations on algorithms and are not restricted to using the programs used here. The principles of combining metrics for dimensionality (roughness), density and normal orientation, and the opening operation within the multi-scale dimensionality calculation, are the core of the proposed method.
Chimera is a Bioconductor package that organizes, annotates, analyses and validates fusions reported by different fusion detection tools; current implementation can deal with output from bellerophontes, chimeraScan, deFuse, fusionCatcher, FusionFinder, FusionHunter, FusionMap, mapSplice, Rsubread, tophat-fusion and STAR. The core of Chimera is a fusion data structure that can store fusion events detected with any of the aforementioned tools. Fusions are then easily manipulated with standard R functions or through the set of functionalities specifically developed in Chimera with the aim of supporting the user in managing fusions and discriminating falsepositive results.
The second step is particularly critical, as each tool implements a different set of filters  (Beccuti et al. , 2013; Wang et al. , 2013) , and users have limited control on them. As each fusion detection tool has a specific combination of alignment and filtering approaches, results produced by each tool are only partially comparable with the others'  (Carrara et al. , 2013a,b). Thus, although the combination of the results derived from different tools can increase the number of true fusions detected, it also significantly increases the number of falsepositive results (see Supplementary Material). The Bioconductor package Chimera, reported in this note, provides a common framework to manipulate, analyze and filter fusion events detected by a variety of fusion detection tools. Chimera is based on typical R data structures, which allows its users to take advantage also of the full set of R functionalities.
DESCRIPTION The flow of analysis supported by Chimera is described in Figure  1 , and it is exemplified in the note's Supplementary Material. With the functions made available by Chimera, the user can import data from 11 different fusion detection tools, represented on top of Figure  1 , into a list of fusions. Each fusion stores the genomic break point and all the information that can be derived from the output of fusion detection tools (e. g. number of spanning and encompassing reads supporting the fusion junction, splicing pattern used and transcripts involved in the fusion). As each fusion detection tool might rely on different gene annotations, the import function recovers the HUGO  (Seal et al. , 2011)  symbols for the genes involved in the fusion, by overlapping the fusion break points to the genes' genomic location stored in the Bioconductor package  (Gentleman et al.
DISCUSSION To the best of our knowledge, Chimera is the only available software able to integrate and compare the data produced by different fusion detection tools. Thus, it represents an answer to the lack of standard data structure for fusion data representation. Furthermore, as the combination of results from more than one fusion detection tool enhances the probability to identify fusion events (see Supplementary Material), Chimera provides a common framework to manipulate, filter and prioritize fusion events. Moreover Chimera offers a validation procedure based on de novo assembly (thanks to the GapFiller tool) and enhances fusions' annotation through the exploitation of the Oncofuse results.
The workers performance will be measured before and after using the DFA in daily activities and in front of difficult tasks in order to evaluate their improvement using the DFA. Regarding the functional architecture of the DFA (figure  2 ), several interaction modes are preconized to provide data and knowledge capitalization and reuse. The first category of interactions concerns the communication between the user and the treatment module of the digital assistant throughout augmented reality and virtual reality interfaces. According to this interaction, user input his request and the DFA provide user useful assistance regarding his request, his profile and his working situation. The second category of interaction concerns data exchange between the DFA and other existing enterprise information systems through specific connectors.
For instance, in  [16] , an XML-based neutral file is defined by referring to PLM services for the building of PLM integrator as software that can exchange product, process and resource information between commercial PLM systems. The PLM integrator consists of the XML adapter and the PLM adapter to extract PPR (Product Process Resources) information from a commercial PLM system. PLM Services standard is used in another work by  [17]  to support data exchange between two different PLM systems via Internet; an implementation is fulfilled to support interoperability between two commercial PLM solutions: SmartPLM and DynaPLM. In this architecture, the product data inside the PLM system is translated to PLM Services reference server by the data transfer module. The data transfer module captures the PLM system data by using APIs and translates it into the format which PLM Services reference implementation can read and visualize.
For this need, the SOAP servelt invokes tasks execution on the SAK (Service Access Kit), which is an API facilitating the development of Java applications using of Info*Engine functions and features. During task execution, SAK interacts with the naming service in order to instantiate the required services. With the Naming Service, SAK can identify in the LDAP directory all network addresses and configuration properties. In the meantime, the client application has a direct connection to the SAK and the naming services to extract the services parameters and code interpretation respectively, which are required for the definition of the Webservice request.
Interactions process Based on the technical architecture presented above, a webservices based application is developed. Figure  5  illustrates the interactions schema between the user, the interoperability client application and Windchill, throughout the Info*Engine framwork. After the user identification by entering his Windchill login and password, the client application receives from Windchill a list of different objects that are associated to the user role in the database. At the right of the interface, a set of authorized actions are proposed to the user to perform on the Windchill server throughout the Info*Engine based framework. For instance, the user can obtain more details about the selected object, check out, modify or delete this object or also, creates link with other Windchill objects.
Conclusion and future work Windchill is one of the most popular PLM software, which proposes robust solutions to support communication with external information systems. The purpose of this paper is first to prospect the interoperability mechanisms offered by PTC editor as a solution to interoperability issue. Then, a new interoperability framework is proposed as a web-service based solution using the capacities embedded on the Windchill Info*Engine mechanisms and J2EE application technology. The first results of the interoperability tests performed on the proposed framework illustrate a high level of reactivity to different requests. The main advantages of the proposed architecture is that it offer a large possibilities to pilot the communication with Windchill server through an external third-party application while ensuring a coherence with internal mechanisms of Windchill, since it exploits directly the internal components of Windchill.
Acknowledgements. This work is financed by the  region of Pays de la Loire (France). The research project involves four teams from  Ecole Centrale de Nantes  ( IS3P  and PsyCoTec teams from  IRCCyN laboratory ,  GEM , and CERMA), and one virtual Reality applications integrator CLARTE. Particular thanks are addressed to the industrial partner, for accepting the validation of this scientific proposal.
RESULTS ICA Applied to the CIT Bladder Cancer Transcriptome Data Set: Interpretation of Components Based on Gene Projections ICA was applied to our reference data set, which was produced in the framework of the French national Cartes d'Identitedes Tumeurs (CIT) program from a series of 198 tumors and three normal samples (CIT series). These 198 tumors comprised both non-muscle-invasive tumors (n = 106) (deposited in Ar-rayExpress, accession number E-MTAB-1940) and muscle-invasive tumors (n = 92) (E-MTAB-1803,  Rebouissou et al. , 2014). Twenty independent components (ICs) were computed. The first interpretation of each component was based on analysis of the genes with the largest projections on the component (the contributing genes, the most strongly influenced by the pro-cess associated with this component; Table  S1 ).
ICA of Multiple Transcriptome Data Sets, Bladder, and Other Types of Cancer Allows Distinguishing Reproducible Cancer Type-Specific Components and Components Common to Different Cancer Types We investigated the reproducibility of the components retrieved from our reference bladder tumor set (CIT series), by applying ICA to seven other bladder cancer transcriptome data sets, six series of breast carcinomas and eight series of carcinomas of various types (colon, rectum, endometrium, kidney, lung squamous cell and lung adenocarcinoma, ovary, prostate). The data sets, their accession numbers, and gene projections for all data sets are given in Table  S4. We used a correlation graph approach to compare the ICs across the different data sets (see Supplemental Experimental Procedures) (Figure  2 ).
DISCUSSION We applied independent component analysis (ICA) to 21 transcriptome data sets, including eight data sets for bladder cancer and five for breast cancer. For one of the bladder cancer data sets (the CIT data set that was generated by our group), clinical, pathological, molecular, and sample processing data were available. The use of these additional data made it possible to assign several components to a biological process or to technical factors and allowed us to characterize subtypes of tumors and to identify molecular mechanisms associated with a component. Our analysis across different types of cancer identified both components common to some or all cancer types considered, and also components specific to a particular type of cancer. Three components common to all cancer types (B and T lymphocytes, cell cycle, myofibroblasts) probably correspond to the three multicancer gene signatures/attractors (lymphocyte-specific, mitotic chromosomal instability, and mesenchymal transition) identified very recently in breast, colon, and ovarian cancer by a different computational approach  (Cheng et al.
Bioinformatics Analysis To compute the independent components, we applied the FastICA algorithm  (Hyva ¨rinen, 1999)  to each data set using icasso  (Himberg et al. , 2004)  for selecting stable components. We computed 20 independent components (ICs), using pow3 nonlinearity and deflationary algorithm. We interpreted the genes associated with an IC (''most contributing genes'') by performing enrichment analysis with GOstats  (Falcon and Gentleman, 2007)  and ''GSEA Preranked'' module  (Subramanian et al. , 2005). We compared the contributions of predefined groups of tumors to the ICs in Wilcoxon rank-sum and Kruskal-Wallis tests. The ICs were compared across data sets using correlationbased graphs. Details of computational analyses are provided in Supplemental Experimental Procedures.