dataesr/scientific-paragraphs-categorization huggingface dataset:
To our knowledge, this is the first demonstration that a 21-bp DGE tag can be efficiently used to target transcripts that are expressed and conserved between species. Moreover, the proportion of conserved intergenic tags was smaller than that of conserved protein-coding tags, in agreement with the lower conservation of lncRNAs compared with proteincoding genes  (9, 10). As expected, annotated lincRNAs were in intergenic regions, and by mining 'TranscriRef', we could detect 50% of the annotated Ensembl data set. However, this DGE data set includes only polyA RNA+, while many non-coding transcripts have also been found using total RNA  (8). It could be interesting to determine whether the lincRNAs described by the ENCODE project are present in our data set as well  (9). A common feature of noncoding transcripts, and particularly of lincRNAs, is their low expression level.
Introduction For about 15 years at Onera, the elsA software is simultaneously a basis for Computational Fluid Dynamics (CFD) research, a software package capitalizing on the innovative results of research over time, a tool allowing investigation and understanding of flow physics, and a multi-purpose tool for applied CFD and multiphysics  [1] [2] [3] [4] [5]. The range of aerospace applications covered by elsA is very wide  [6] : aircraft, helicopters, tilt-rotors, turbomachinery, counter-rotating open rotors (CROR), missiles, unmanned aerial vehicles (UAV), launchers. As a matter of fact, the aerodynamic analysis and design in aerospace today require high levels of accuracy and reliability which result from studies in several domains: physical modelling, numerical methods, software engineering, efficiency on rapidly evolving hardware and extensive validation by comparison with experimental databases.
The capitalization of various research results in the elsA multi-purpose code allows in the first place the sharing of common CFD features for simulating external flows around airframes or internal flows in turbomachinery. In the second place, it allows the selection over the wide range of capabilities, of the features which are best suited to the application, since there is no universal CFD method answering all of the problems. The research studies, software development and validation activities, dealing with elsA, rely on a project approach necessary to cope with the complexity of today's CFD. This project approach is coordinated by Onera and a Corresponding author: Laurent. Cambier@onera. fr involves input from other research laboratories and feedback from aeronautical industry. The objective of the paper is to show recent outstanding accomplishments both from the research side and from the industry side.
General description of elsA First, let us briefly recall the main features of the elsA software (see a more detailed overview in  [5] ). The elsA multi-application CFD simulation platform deals with internal and external aerodynamics from the low subsonic to the high supersonic flow regime and relies on the solving of the compressible 3-D Navier-Stokes equations. elsA allows the simulation of the flow around moving deformable bodies in absolute or relative frames. A large variety of turbulence models from eddy viscosity to full differential Reynolds stress models (DRSM) are implemented in elsA for the Reynolds averaged Navier-Stokes (RANS) equations  [7, 8]. Laminar-turbulent transition modelling relies either on criteria, or on solving additional transport equations  [7]. Various approaches for Detached Eddy Simulations (DES)  [9, 10]  and Large Eddy Simulations (LES) are also available.
The convergence is accelerated by the use of multigrid techniques for steady flows. The implicit Dual Time Stepping (DTS) method or the Gear scheme is employed for time accurate computations. elsA also includes an "aeroelasticity module"  [16, 17]  offering a framework for aeroelastic simulations and an "optimization module" for calculation of sensitivities by linearized equation solution or by adjoint solver techniques  [18, 19]. elsA is based on an Object-Oriented (OO) design method and is coded in three programming languages: C++ as main language for implementing the OO design, Fortran for CPU efficiency of calculation loops, Python for the user interface. A good CPU and parallel efficiency is reached on a large panel of computer platforms.
Research partners and industry users The development and the validation of the elsA software benefit by inputs from research partners and feedback from industry users. By first considering the research side, the Cerfacs organization (Toulouse) is an important elsA partner since 2001 and has been participating over the last decade to research studies and software development dealing in particular with mesh strategies  [13] , numerical methods  [20] [21] [22] [23] [24]  and CPU efficiency  [25]. Other main research partners are the Fluid Mechanics and Acoustics lab (LMFA, École Centrale de Lyon)  [26]  and Cenaero (Belgium)  [27]  for turbomachinery flow simulation, and the Dynfluid lab (Arts et Métiers ParisTech) for high accuracy numerical schemes  [28, 29]  (see Sect. 5 for activities about elsA by Cerfacs, LMFA, Cenaero and Dynfluid).
The Von Karman Institute also uses elsA for turbomachinery flow simulation (see the validation study of the transport equation transition model in Ref. [30] ). Whereas it is rather unusual for a CFD software package to deal both with external flows around aircrafts or helicopters and with internal flows in turbomachinery, elsA is today used as a reliable tool by Airbus for transport aircraft configurations, by Safran group for turbomachinery flow simulations and by Eurocopter for helicopter applications (see Sect. 4). Among other users, let us mention MBDA for missile configurations and Électricité de France for steam turbine applications  [31]. A two-day workshop gathering simultaneously research partners and industry users is organized every two years to share experience. The results presented in the paper either are issued from the last elsA workshop, or correspond to more recent accomplishments.
Turbomachinery results Figure  1  provided by Snecma company (Safran group) shows some elsA results for modern fans. The two curves illustrate the contribution of the Onera CFD software (elsA in recent years) to the simultaneous progress of the efficiency of the conventional fans (more than 10 points in 30 years) and the reduction of the blade number (twice less blades). Examples of simulations are shown for a conventional fan, a counter rotating fan and a CROR. The elsA software has been strongly validated by Onera and Safran for various turbomachinery configurations and is today intensively used for design studies which rely for a part on isolated row simulations, and for the major part on multi-stage applications. Best practice on space and time numerical schemes, turbulence and transition modelling, boundary conditions have been defined through comparisons with experiment and with legacy code results.
The number of mesh points for the 4-stage low pressure turbine configuration of Figure  2  is about 10 million. Numerical settings for the steady flow simulation are the following: second order Jameson scheme, backward Euler implicit time integration scheme, LU-SSOR implicit technique, multigrid convergence acceleration. Turbulence modelling relies on Wilcox (k, ω) model. Converged solution is obtained on Snecma computer in less than one day. Figure  2  presents the flow field in the complete 8-row computational domain. Numerical results show a good comparison with measured static pressure on the vane walls. The comparison of the isentropic Mach number on one of the distributors for different span locations (here shown at mid-span) is also satisfactory. elsA software is also used by other turbomachinery manufacturers of the Safran group: Turbomeca for helicopter engines and Techspace Aero for fans and boosters.
Thanks to the use of Cassiopée pre-processing tools  [11, 32]  delivered with the elsA software suite, these calculations illustrate the possibilities of the Chimera technique to deal with such configurations. These results are considered by Airbus of utmost importance for performance prediction of the landing configurations. Thanks to elsA Chimera features, Airbus is also able to simulate the unsteady flow in a representative shape of the landing gear cavity of a transport aircraft (Fig. 4 ). A first mesh is defined for the basic configuration including four Chimera domains respectively for the cavity, for the fuselage, for the wheel and pylon and for a triangle added plate. Then, to study the influence of an internal spoiler, which was proposed in order to reduce the rear door loads, a second Chimera mesh is defined by adding to the first mesh an internal spoiler Chimera domain.
The full 360 • 11 blade generic propeller configuration (Fig. 20 ) is meshed, leading to a 121 structured block grid, including about 14 million cells. A view of the static pressure field computed by elsA on the rigid propeller at cruise conditions (M ∞ = 0. 73 and α ∞ = 1 • ) is shown. In this case, due to incidence, the flowfield is unsteady, and produces a 1/rev periodic loading on each blade which is likely to induce periodic forced response of soft bladings, as shown below. In a second step, after rigid blade computations, aeroelastic dynamic fluid-structure coupled computations have been carried out using the Ael aeroelasticity module of elsA, in order to study the forced response of the soft model, due to an incidence effect. A modal structural model has been implemented, including first and second bending and first torsion modes at nodal diameter 1, likely to be excited by the diameter 1 loading due to incidence.
During the transient computation, several pieces of information are collected: generalized coordinates and forces histories, global loads history such as thrust or vertical force. These data are post-processed in order to extract the frequency response in terms of blade deformation and forces: 1/rev forced response deformation and force levels are extracted, as well as aeroelastic modal dampings. Such an analysis, performed at the same cruise conditions, is presented in Figure  21. The 1/rev forced response peak is visible, as well as broader peaks at modal frequencies, illustrating damped modes. Fluid-structure dynamic coupling simulations are conducted with elsA/Ael over 10 full propeller rotation cycles, using the Spalart-Allmaras turbulence model. The DTS scheme is used. Simulations are run in parallel on a Linux Xeon cluster using 44 processors, for about 5 days.
Acknowledgements. We want to acknowledge the following contributions to the paper: Snecma ( Safran group ) for Section 4. 1, Airbus for Section 4. 2,  C. François  ( Onera ) for the landing aircraft simulation, Eurocopter for Section 4. 3,  G. Ngo Boum  ( LMFA ) for the EARSM turbine simulation,  V. Brunet  ( Onera ) for the ZDES jet simulation,  M. de la Llave Plata  and  M. -C. Le Pape  ( Onera ) for the simulations in hybrid grids,  T. Renaud  ( Onera ) for the Cartesian/curvilinear rotor simulation,  B. Michel  (Onera) and  P. Cinnella  ( Dynfluid ) for RBC results,  H. Deniau  ( Cerfacs ) for the high order LES jet noise simulation,  F. Thirifay  ( Cenaero ) for the blisk forced response and  A. Dugeai  ( Onera ) for the propeller forced response. Many thanks also to all those who have made elsA software project so successful for many years!This paper is a revised version of a paper entitled "The Onera elsA CFD software: input from research and feedback from industry" presented at the 28th  International Congress of the Aeronautical Sciences, Brisbane , 23rd-28th September 2012.
Design of Primers and Probes for the Real-Time PCR Systems We used a new strategy to design primers and probes for realtime PCR systems targeting the three major lineages of mimivi-ruses, their virophages, and marseilleviruses ( fig. 2  ). First, we divided the genome of one representative member of each of the viral groups (mimivirus lineages A, B and C; marseilleviruses; virophages) into windows of 140 nucleotides with steps of 20 nucleotides, which are in the order of magnitude of usual sizes in real-time PCR assays of amplified fragments and PCR oligos, respectively. Genomes that were analyzed were those of amoebaeassociated viruses previously described  [4, 8, 13, 49, 50]  and those sequenced from giant viruses isolated in our laboratory  [3, 10]. Next, we created a database of all genomes of each of the viral groups and performed a local BLASTn search for 140-nucleotide-long fragments against this genome database.
primers for assays for lineage B mimiviruses, marseilleviruses and Sputnik virophages, or 1 μl (0. 4 μ M ) of forward and reverse primers for assays for lineage A and C mimiviruses. PCR reactions were adjusted to a final volume of 25 μl by adding RNAse/DNAse-free water. PCR reactions were performed on a CFX96TM real-time system instrument (Bio-Rad, Hercules, Calif. , USA). forward and reverse Representative member of a given viral group Fragmentation (window 140, step 20): 140 nt 20 nt Database of genome fragments. BLASTn searches All mimiviruses of lineage A All mimiviruses of lineage B All mimiviruses of lineage C CroV Fig. 2. Schematic diagram of the strategy to Selection design primers and probes for real-time BLASTn hits with nucleotide identity >97% over 100 nucleotides PCR systems targeting the three major lin- for all genomes of a given viral group but none of those of other viral groups eages of mimiviruses, their virophages and marseilleviruses.
This platform should facilitate access to models and physical parameters, to simulators of different modalities, and to appropriate computing power and storage. This paper describes VIP, an openly-accessible online platform sharing models and simulators, and supporting the execution of heavy simulations. It extends the summary presented in  [4]  with a more detailed platform description and additional simulations illustrating different image modalities. The manuscript is organized as follows. After a review of related work in Section II, we describe in Section III how ontologies were created to describe models in VIP. In Section IV we show how a workflow-based template was used to integrate simulators of 4 different imaging modalities and deploy them on a DCI. Methods and techniques employed to support simulation execution are described in Section V, highlighting computation management, data provenance recording, storage of models and simulated data, and interface.
II. RELATED WORK Several databases of simulated images are available for whole-body or brain PET imaging  [5] ,  [6] ,  [7] ,  [8] , and for brain MRI, e. g. Brainweb  [9] , offering an online simulation service for MRI. The platform in  [10]  provides simulationbased interactive tutorials on imaging modalities. Simulators are usually dedicated to a particular modality, for instance SPECT  [11] , CT  [12] , mammography  [13] , MRI  [14] , or US  [15] ,  [16] ,  [17] , with the notable exception of GATE  [18] , which was validated for PET, SPECT, CT, and even radiation therapy. VIP targets a multi-modality online simulation service relying on existing codes. The sharing of object models is important for initiatives related to the Virtual Physiological Human (VPH) program. FieldML  [19]  was developed for this program as a standard to represent information contained in models.
It can structure repositories and databases such as those available in eu-Heart  [20] , focusing on the modeling of the heart. Several ontologies are available to describe human anatomy  [21] , pathology  [22] , qualities  [23] , and radiological terms  [24] , but none can completely describe the exhaustive list of physical and biological parameters targeted for VIP models. A specific development is therefore needed. One approach to medical image simulation is to simulate images from geometrical models parametrized with distributions of physical properties such as magnetic properties, echogenicity, radioactivity, or chemical composition. This approach is flexible since geometrical models can be deformed to a particular individual  [25] ,  [26]. However, obtaining realistic simulations is challenging because parameter distributions are not easily estimated and human morphology is difficult to model from geometrical objects alone.
An alternate  [27]  is to create a digital object from a real acquisition by extracting object geometry and physical parameters from signal properties. Resulting images usually look very realistic, but this method requires an in-vivo acquisition for each simulation, which constrains investigation. Both model-based and imagebased approaches are targeted in VIP. Some online platforms, commonly called scientific gateways, integrate medical imaging software with access to computing and storage resources. neuGRID  [28] , CBRAIN 1  , and LONI pipeline  [29]  (Laboratory Of Neuro Imaging) target neuroimaging data sharing and analysis using DCIs. In neuGRID, users can start remote desktop sessions on machines where image analysis tools and clients are pre-installed to access resources of the European Grid Initiative (EGI)  2.
CBRAIN is a web portal where neuroscientists can launch data processings such as segmentation tools on several clusters. The location of files and executions is controlled from the interface. In the LONI pipeline, users describe their own image processing pipelines executed on local resources. Neurolog  [30]  focuses on the sharing and reuse of heterogeneous data and tools produced by distributed sites. The OntoNeuroLOG ontology describing involved datasets and entities is used to harmonize database schemas, and annotate tools. e-bioinfra  [31]  is a web portal for executing image processing tools on distributed computing resources, e. g. Freesurfer and the Functional MRI of the Brain Software Library (FSL 3  ). Some scientific gateways such as EUMEDGrid 4  , DECIDE  5  , and GISELA  [32]  are built from a set of reusable portlets providing basic functions such as authentication and data transfer.
III. MODEL INTEGRATION We chose ontologies to share model semantics in the platform. Apart from structuring the model repository, two additional roles of ontologies are interesting  [33] : (1) they provide a standard vocabulary to refer to conceptual entities, and (2) they model conceptual entities formally, using axioms expressed in a logical language. In VIP, model files describing geometrical objects (represented as voxel maps or meshes) are annotated with concepts defined in our application ontology called OntoVIP (described below). Annotated models are stored in the repository shown on Fig. 1.
The above components were imported as modules into OntoVIP following the integration approach used in  [34]. This integration relies on the foundational ontology of Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE  [35] ) and on other core ontologies guaranteeing overall consistency. Models are described using two basic entities: (1) values-layers describe data files associating each point of a 3D map with a particular value, e. g. the activity of a radiopharmaceutical; (2) object-layers describe data files containing labels associated with object-layer-parts linked to anatomical, pathological, foreign-body or externalagent objects. Each object can be associated with a mathematical-distribution, to specify the range of an associated physical parameter, e. g. , T1 values.
Queries may use terms extracted from a relational database, but the ontology brings flexibility with the desired specificity. For example, a lesion may be annotated using the generic term neoplasm or the more specific term glioma. Thanks to relations in the ontology, searching for models containing neoplasm returns models annotated with neoplasm and glioma. Other inferences could be made from part-whole relationships, such as FMA's constitutionalPart / constitutionalPartOf, to enable selecting entities that are part of a structure of interest. The second motivation to ontological modeling is their reasoning capabilities. For instance, rules were implemented to check the compatibility of models with modalities and simulators (see top bar on Fig. 1 ). A model is compatible with a simulator if it has all the required physical parameters.
B. Semantic model repository Models can be uploaded to VIP in the form of data files bundled with OntoVIP annotations expressed as Resource Description Framework (RDF 7  ) triplets. For instance, in the mymodel rdf:type ontovip:static-model triplet, ontovip:static-model denotes the ontology class modeling static models. During model import, the consistency of assertions is checked before being added to the repository. A standalone software currently assists users with model annotation. Knowledge stored in OntoVIP is leveraged to enrich annotations with limited manual effort from the user. For example, when a user annotates a data file with the term Brain, the software automatically detects that it is an anatomical-object using subsumption information in OntoVIP. This annotation software is progressively integrated in the online platform.
IV. SIMULATION WORKFLOWS VIP integrates simulators based on their workflow description, and without modifying their code so that their validation remains under the responsibility of their developers. The workflow representation was chosen because it provides structured, graph-based representations of applications, which is useful for several reasons. Firstly, graph representations are parallel languages, which facilitates workflow deployment on distributed computing infrastructures. Workflows also facilitate data annotation because dependencies between data files, parameters, programs and results (a. k. a data provenance) are clearly expressed, which structures output data. Finally, workflows foster reusability of software components among simulators. Ultimately, it is envisioned that the platform could facilitate simulator integration by suggesting workflow components based on existing tools (e.
g. format converters). Integrating image simulators in VIP with minimal software refactoring is difficult due to the variety of their interfaces, parameters and characteristics related to (i) the format and in- formation required in the models; (ii) the acquisition protocols and imaging sequence and (iii) the computing model and its parallelization. To facilitate simulator integration, we proposed in  [37]  a workflow template composed of the components presented in Fig. 3. Object preparation converts a repositoryannotated model into the native format suitable for the simulator. It combines the different model layers, defines the scene geometry from the geometrical transformations produced from the 3D interface, splits dynamic models into multi-static models, and adjusts physical parameters if needed.
Parameter generation generates simulator parameter files from parameter values. Depending on the type of users, some parameters may be pre-set and hidden. Core simulation invokes the simulation code. It splits the simulation into independent chunks of data on which the code is iterated. Finally, post-processing performs image reconstruction and format conversion. To facilitate the integration of simulators, the Conceptual Workflows formalism was developed  [38]. Conceptual Workflows represent a family of workflows complying with a given template as illustrated on Fig. 3  for VIP. This formalism not only provides a frame for the design of new workflows, but it also assists users in workflow creation and validation. Following this formalism, image simulators of 4 modalities were integrated using the Gwendia (Grid Workflow Efficient Enactment for Data Intensive Applications) workflow language  [39] : SIMRI  [40]  for MRI, FIELD-II  [41]  for US imaging, SORTEO  [6]  for PET, and SINDBAD  [42]  for CT.