dataesr/scientific-paragraphs-categorization huggingface dataset:
The average values of the transmittance, reflectance and absorption spectras of the Na 2 WO 4 sample measured and calculated by SE measurements are in the spectral range of about 200 nm to 2200 nm. These results are obtained with the DELTAPSI2 software. As one can see, the average measured and fitted transmission value in the visible region for the Na 2 WO 4 sample are about 0. 9% and 1. 3% respectively, whereas the average reflectance value is about 68% and 70%, respectively. The average absorptance value is about 31% and 29%, respectively. Thus, a    Based on the transmission and reflection curves, measurements have been obtained from the new amorphous model, the other optical parameters such as n, k, E g , ε r , ε i of the Na 2 WO 4 sample were investigated experimentally and theoretically.
Acknowledgements. Authors are grateful to  Eric Levillain  and  Olivier Aleveque ,  Nicolas Mercier ,  Magali Allain  and  Sylvie Dabos Seignon  from  MOLTECH-Anjou laboratory  for spectrophotometry measurements, providing the necessary facilities for XRD studies and for AFM image respectively. Also, thanks go to  Romain Mallet  from  SCIAM-Microscopy  for EDSX, SEM and TEM measurements. We would like to thank  Jean-Paul Gaston  and  Celine Eypert  from  JobinYvon Horiba Company  for the spectroscopic ellipsometry measurements and to  Cecile Mézière ,  Valerie Bonnin  for the help with the chemicals and corresponding equipment.
Afterwards, studies using cloud shading retrievals were performed. Lipton (1993)   Also in the 1990s,  Macpherson et al. (1996)  applied the DA system MOPS (Moisture Observation Preprocessing System) and the UKMO (UK Met Office) mesoscale model, testing the assimilation of Meteosat IR imagery together with other sources of observations. The observations were used to generate a three-dimensional analysis of cloud fraction that has been converted into synthetic humidity profiles that are assimilated the same way as radiosondes. The concept of generating a threedimensional cloud analysis from several observation sources had also been applied operationally with the limited-area model NAE (North Atlantic/European) using a 4D-Var DA system  (Renshaw and Francis, 2011; Taylor et al. , 2008). Another often-cited cloud analysis scheme that makes use of several sources of observations is the ARPS (Advanced Regional Prediction System) Data Analysis System (ADAS).
Previously mentioned examples are the systems or models ARPS, LAPS, MMR and NAE. Two operational state-of-the-art systems that have not been mentioned yet are the Rapid Refresh (RAP) model and the AROME (Application de la Recherche à l'Opérationnel à Méso-Échelle) model. For short-term forecasting with a forecast length of 18 hours NCEP operationally runs RAP, which replaced the Rapid Update Cycle (RUC) model in 2012 (NOAA NCEI 2017). Rapid Refresh is an assimilation/modelling system, based on RUC, WRF and GSI  (Benjamin et al. , 2016b)  that uses a hybrid 3D-Var/Ensemble data assimilation approach. GOES-derived cloud-top height temperature are incorporated as it had already been the case in the RUC system  (Benjamin et al. , 2004). Hu et al. (2007)  and  Weygandt et al. (2006)  provide further information about the cloud analysis scheme of RUC/RAP.
New generations of geostationary satellites like Himawari-8, MTG and GOES-R bring more channels and higher temporal and spatial resolution. This imposes new challenges and brings more opportunities for data assimilation methods and NWP. It should lead to an increased exchange between researcher communities and institutions in order to use the newly available observations as efficiently as possible with the diverse LAMs that exist. This review shows that both the assimilation of radiances and cloud properties in regional NWP offer great potential for short-term forecasting of cloudiness. The reason is that the topic comes along with diverse problems and challenges to be overcome, e. g. the choice of satellite channels or retrieved cloud properties and their optimal processing, the non-linearity of cloud processes, observation quality control, bias correction and subsampling, cloud classification and localisation, observation and background error estimation, computational efficiency, domain size, domain location and grid spacing optimisation.
It is difficult to design the engine intake manifold in normal method because of the complex structure, and the real situation of fluid movement in the manifold cannot be obtained through the steady flow test, so the optimized design is proposed about the engine intake manifold based on reverse engineering. A3D model of the intake manifold is build according to the CATIA software; fluid mathematical is set up by computational fluid dynamics method. The 3D geometry model of the intake manifold is grid with the Gambit software and the divided grid data is imported into the Fluent. These are performed about the simulation of the fluid pressure field, velocity field, flow field, and local flow field in the manifold. Key parameters design of the intake manifold relies on the results. It proves that the reverse technology is reasonable for the intake manifold design through the theory and simulation analysis; it can ensure sufficient air intake and reduce the intake resistance and energy loss of the air, and can improve the air intake uniformity.
Simulation theory The simulation technology is mature for the air movement in the manifold of an engine with the rapid development of the computer technology. The κ-ε models and unstructured grids have been introduced and used, as well as the availability of computational fluid dynamics (CFD) software, such as CFX, FLUENT, STAR-CD, etc. , which have greatly optimized the intake manifold design process and accuracy. CFD has gradually become the most effective tool for analyzing the air movement of the engine intake system.
Again, calculation procedures and operations is to be done. It includes grid, setting boundary conditions or initial conditions, and inputting control parameters, etc. Finally, the calculation results are displayed. The results are usually expressed using graphs or tables, which is of great significance for analyzing and judging the accuracy of calculation results. Usually, the practical problems can be solved through CFD software. However, if the problems are more complicated and CFD software is difficult to meet the calculation requirements, users need to write their own calculation programs. The general analysis process is the same regardless of the method.
FLUENT software A calculation program is basically written by user in order to complete the simulation calculation before the CFD software is applied. The degree of correlation is limited among the program written by user due to the diversity of computer hardware and the complexity of computational fluid dynamics, resulting in a waste of resources. The CFD is easily edited into generic software because of its systematicity and regularity. FLUENT is very versatile to perform numerical conversions  [5]. The various problems can be solved for each field if it can be combined with other CFD software. And it can also link with various drawing software. Automate drawing cannot be done by the FLUENT software because the model is relatively complex. You can import the 2D and 3D models drawn by CATIA into the preprocessor GAMBIT for grid.
The conditions of the initial and boundary are set to solve the model, thus a lot of time can be saved. It is more convenient to draw a simple model directly from GAMBIT. The grid model with well-defined boundary conditions is imported into the solver. The appropriate solution method is selected by FLUENT based on the specific flow problem, and the solving equation is determined accordingly, which make the calculation process stable and the result more accurate. Finally the basic information is displayed for the flow field through the post-processing module, such as the speed vector, pressure cloud map, kinetic energy cloud map, and the other information required by the user, such as contour maps (including streamline maps, isobaric maps, etc. ). The XY-PLOT function can be also used to represent the values of various physical quantities (such as pressure, velocity, temperature, etc.
If the flow field is not affected by the variable Φ, the velocity field should be solved after convergence. (7) A new coefficient of the momentum discrete equation will be calculated after the velocity field is improved, then the calculated value of the pressure field can be used as the initial value of the next layer of calculation. Repeat the above steps until you get a converged solution. 3 Simulation method of air flow in the engine intake manifold The solid model has been obtained through the reverse method before the value is analyzed for the engine intake manifold. At the same time, the simplified Gambit model is used to divide the solid model of the grid so that the analysis can be fully satisfied for the CFD application software. Boundary condition is determined to prepare simulation.
Boundary condition settings In order to facilitate the air intake comparison of each manifold, the air intake and an air outlet are set for the boundary of the cylinder in the calculation. The other boundary conditions are set as the wall conditions. All calculations use the same boundary conditions. The calculations are performed for the outlet 1, the outlet 2, the outlet 3, and the outlet 4, respectively. The flow calculation scheme is listed in Table  1. A grid analysis is performed on the manifold model by using the Gambit software, and import the partitioned grid into FLUENT. When the result converges, it will actively pause the calculation and display the hint on the monitor; if the calculation cannot be stopped in the example, convergence value is set at 0. 001for pressure, momentum, turbulent kinetic energy, and dissipative rate.
Optimization method of intake manifold structure According to the previous analysis, the curvature of the original manifold is small, and the intake resistance is reduced by increasing the curvature of the manifold, which makes the intake air smooth. At the same time, the volume of the stable tank is increased appropriately to reduce the eddy current intensity formed in the manifold. The GAMBIT software is used to grid the improved intake manifold to generate a calculation domain  [19]. The grid model is shown in Fig. 7.
Conclusion In this paper, the 3D model of the intake manifold is reconstructed using the inverse method and CATIA software. The applicability and authenticity of the model is ensured due to the actual shape of the engine intake manifold. The internal flow field of the manifold is analyzed by combining CAD, CATIA, and CFD software with FLUENT; in addition, the equation is established on the air flow in the manifold and used in the SIMPLE algorithm. The calculation speed is accelerated through combining GAMBIT and FLUENT software, dividing the internal flow field of the intake manifold, and setting the boundary conditions. The results show that the optimized flow field is reasonable, the air flow is higher, and the pressure is reduced to varying degrees in the manifold, which effectively reduces the intake resistance and helps to increase the intake manifold, especially in the main optimization position by comparing the internal flow field of the engine intake manifold and the optimized intake manifold.
Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module.
Visualization tools  (4, (7) (8) (9) , enabling the location of biological entities of interest in a network, over-representation tools  (10) , aiming to identify metabolic pathways with a higher representation in a dataset, and flux balance modelling tools  (11, 12) , allowing hypothesis generation and testing of system responses to genetic or environmental changes, present a large range of features. However, most of the available tools are dedicated solely to one specific analysis and a single type of W496 Nucleic Acids  Research, 2018, Vol. 46 , Web Server issue data. One of the main advantages of MetExplore is that it provides an end-to-end solution for such analyses. Me-tExplore thus constitutes a flexible solution to build tailored metabolic networks, either from scratch or by curating existing networks.
It greatly facilitates this curation process by providing collaborative tools to curate and manage networks. MetExplore also constitutes a comprehensive solution for the interpretation of omics data into the context of metabolism, by contextualizing biological entities of interest in the metabolic network and by switching between a global overview (entire network) and detailed (sub-networks) analyses. To facilitate this data mining loop, MetExplore provides a modular environment enabling simultaneous network representations in the form of grids (spreadsheet-like views of reactions, genes, metabolites. ) and interactive network visualization, seamlessly propagating user interactions from one view to another, and allowing focusing on parts of interest using data-based filters. MetExplore project was first released in 2010 (  13 ) and has undergone regular improvements in its architecture, database and functionalities.
Importing a metabolic network The exchange format commonly used for metabolic networks is the Systems Biology Markup Language (SBML)  (22). Many network repositories like BIGG  (23)  or BioModels  (24)  offer to download metabolic networks in that format. However, the generated SBML files can be formatted in significantly different ways depending on the SBML version used. Furthermore, metabolic elements such as pathways or gene-reaction links may be described in nonstandard data fields which vary from one SMBL provider to another one. MetExplore thus provides a very versatile SBML import web interface which allows importing the latest SBML versions with the Flux Balance Constraints (FBC) package as well as SBML files with specific annotation. Patterns corresponding to the format of the information stored in the SBML can thus be defined by the user (see Supplementary Figure  S1 ).
Visualization and subnetwork extraction Network visualization is one of the main features in MetExplore. It is possible to create a graphical representation of a network based on any selection of reactions. For instance, one can visualize all network reactions at once or select the reactions involved in a combination of pathways and visualize how they interconnect. Representation is achieved using MetExploreViz web module  (29)  which allows data mapping on reactions and metabolites, side compound duplications (e. g. water, ATP), compartments and pathways highlighting, edition, graphical exports (e. g. SVG, png, jpeg), graph format exports (e. g. gml). Visual data analysis in the context of large networks is a challenging task when networks contain hundreds of elements (reactions and metabolites).
Network exploration to decipher Yeast metabolic response to Cadmium exposure MetExplore is largely surveyed as a key server for metabolomics data analysis  (10, (34) (35) (36) , in particular to biochemically interpret metabolic fingerprints (list of significant metabolites for a given experimental condition). To highlight this feature, we will use a dataset extracted from a paper by Madalinski et al. (37). The aim of the experiment described in this article was to decipher yeast metabolic modulations induced by cadmium (toxicological metal) exposure. The list of metabolites, obtained using direct infusion high resolution mass spectrometry, contains 10 metabolites for which concentrations have significantly increased or decreased between control and exposed cells. Following this metabolomics study, MetExplore has been used to make sense of this list in terms of metabolic modulations.
As for mapping, since version 2. 0, flux analyses can be launched on the whole network and results displayed only on a sub-network, which allows the user to focus on specific metabolic processes. Filtered grids with additional columns corresponding to analysis results can be exported in tabulated files and then easily shared or published. MetExplore is designed to allow users with no coding skills to perform omics data analysis. By providing an export in tabulated format, MetExplore offers the opportunity to perform more advanced data analysis methods using other platforms like R. The mapping functions of MetExplore depend on identifiers provided by the metabolic databases. The lack of standardization of identifiers makes this task sometimes difficult. For metabolite mapping, to overcome the lack of a unified identifier system shared by all databases, the mapping can be performed in MetExplore using InChI strings or InChI keys, which provide a unique identifier for each metabolite, based on chemical structures.
This is also true for genes, which can be identified by identifiers from different databases such as KEGG or NCBI. Globally, the standardization of identifiers for metabolic network identifiers is certainly a great challenge and we think that an effort of the whole community should be made to tackle this issue, which is often a limiting step in omics data interpretation. The collaborative curation and annotation functionalities of Met-Explore, introduced in version 2. 0, will help metabolic reconstruction to be organised between several collaborators. Until now, a thousand private networks, about 60 projects and more than 500 user accounts have been created in Me-tExplore. MetExplore makes available 285 metabolic networks coming from different metabolic databases: Micro-Cyc  (33) , BioCyc  (41) , BIGG  (23) , KEGG (4), BioModels (24) or publications.
However, the objective of MetExplore is not to become a model repository, such as BioModels or BIGG. Once the metabolic curation is performed, metabolic networks can be exported in SBML format and then, uploaded in such model repositories. Automatic reconstruction from genomic data is also not in the scope of MetExplore. Indeed, there are already many efficient automatic tools to automat-ically reconstruct metabolic networks  (5, 18, 42)  but it was clearly lacking an effective tool for curation steps which are essential for the quality of a reconstruction and the results that will be based on its analysis  (6). At last, programmatic access (web service), introduced in version 2. 0, allows MetExplore functionalities to be integrated in other websites, like it was done in PiMP  (43)  or Metabolights  (44).
MetExplore is a sustainable project maintained by a group of permanent scientists. New features, methods and improvements will be included. Complex queries, as proposed in BioCyc, could be incorporated to filter metabolic data in an 'expert' mode. Graph analyses could complete flux analyses by computing topological indices, as proposed by the web server NeAT  (45). Over-representation analysis could be also refined by taking into account the values associated to the mapped elements rather than just their presence. Besides, current cutting-edge sequencing techniques now allow the reconstruction (at least semi-automatically) of hundreds of metabolic networks  (46). The annotation and curation tools will have to adapt to this change of scale. Easy Nucleic Acids Research, 2018, Vol. 46, Web Server issue W501 propagation of annotations to several related metabolic networks could speed up the manual curation of such a large number of networks.
CONCLUSION Thanks to the integration of several original refinement and metabolic network exploration tools, MetExplore offers a powerful and easy-to-use solution to many biologists without advanced bioinformatic skills who would like to study and build their own metabolic networks. While initially intended for metabolomics data analysis, MetExplore's usage has now broadened to other omics data analyses (e. g. transcriptomics, fluxomics. ). To better accompany this large community interested by MetExplore's functionalities and its ease of use, the MetExplore team has organised numerous trainings for both students and principal investigators. Thus, since 2012, 401 users have been trained in 25 sessions in Europe, South America or Africa. MetExplore is in fact a very versatile and easy to use solution to grasp the concepts of metabolic networks and how they can greatly enhance multi-omics data analysis.
FUNDING French Ministry of Research and National Research Agency  as part of the  French MetaboHUB; National Metabolomics and Fluxomics Infrastructure  [ ANR-INBS-0010 ];  PhenoMeNal  project,  European Commission 's  Horizon 2020  programme [654241]; the development of the curation features were supported by the project Agromics from the  Centre National de la Recherche Scientifique ; Y. G. work was carried out at Glasgow Polyomics and supported by the  Wellcome Trust  [ 105614/Z/14/Z ]. Funding for open access charge:  INRA.
Human Cancer Datasets The human datasets of lung cancer (LUNG), stomach adenocarcinoma (STAD), nonalcoholic liver hepatocellular carcinoma (LIHC), esophageal carcinoma (ESCA), cervical carcinoma (CESC), untreated primary glioblastoma multiforme (GBM), cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (HNSC) and kidney clear cell carcinoma (KIRC) were part of The Cancer Genome Atlas (TCGA)  (25)  programme and downloaded from the UCSC Cancer Genomic Browser  (26). Gene expression profiling was performed on fresh or frozen tissue biopsies using the Illumina HiSeq 2000 RNA Sequencing platform. The estimates of GADD45B expression levels were derived from the normalised values in the UCSC Cancer Genomic Browser. The datasets of colon adenocarcinoma (COAD; GSE39582), bladder carcinoma (BLCA; GSE13507) and ovarian cancer (OV; GSE9891) were deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (www.
ncbi. nlm. nih. gov/geo). The COAD dataset was from the French national Cartes d'Identité des Tumeurs (CIT) programme and generated using the Affymetrix Human Genome U133 Plus 2. 0 Array platform  (27). The BLCA dataset was from the Chungbuk National University Hospital and generated using the Illumina human-6 v2. 0 expression beadchip platform  (28). The OV dataset was from the Australian Ovarian Cancer Study, Royal Brisbane Hospital, Westmead Hospital, and Netherlands Cancer Institute and generated using the Affymetrix Human Genome U133 Plus 2. 0 Array platform  (29). The relative GADD45B mRNA expression levels in these datasets were derived from the normalised values present in the GEO database. The breast carcinoma dataset (BRCA) was from the tumour banks in the UK and Canada, and gene profiling data were generated using the Illumina HumanHT-12 V3 platform and deposited on Oncomine® Research Premium Edition  (30).
The gene profiling data from each dataset were downloaded together with the accompanying clinical information. Where possible, the series were obtained from patients at an early disease stage and datasets with a sufficient number of patients with Recurrence Free Survival (RFS) information. In the other cases, where this was not possible, the series consisted of Overall Survival (OS) data and/or the entire patient dataset. Patients were stratified into two groups on the basis of the GADD45B mRNA expression levels. In each case, quintiles, quartiles, tertiles and 95 th percentiles were used as thresholds, and the best fits are reported in Figure  1A-1M.