dataesr/scientific-paragraphs-categorization huggingface dataset:
Deployment View The deployment view describes how the application is being deployed by representing the executables of the software component, the software dependencies and the physical environment required for running the application. Figure  9. 1 shows an example of the deployment view of the component "C16. 01: OpenVA (VTT)". As shown by the figure, OpenVA is packaged as JAR Java Package (openva. jar) which is run as a server via Java Runtime Environment (JRE). The database is handled by the Database Management System (DBMS) PostgreSQL 9. 6. OpenVA server depends on two applications: DeployR and OpenCPU. DeployR is an open source application that turns R scripts into web services, so R code can be executed by applications running on a secure server. The OpenCPU server provides an HTTP API for data analysis for running R scripts on the server.
Pipeline View Pipeline Views illustrate the connections between the different components and the interfaces that allow them to interact together. Figure  9. 4 illustrates the Pipeline View of the fishery pilot "Oceanic tuna fisheries immediate operational choices"  [8]. In this pilot, measurements from the ship engines are recorded continuously and are then uploaded to the ship owner server. These measurements are processed and analysed by three major components: "C16. 01: OpenVA (VTT)", "C34. 01: EXUS Analytics Framework (EXUS)" and "C19. 01: Proton (IBM)". Each of these components offers a web interface for interacting with users and visualizing data via dashboards. "C19. 01: Proton" is an event processing engine that processes events from different sources such as reading from files or from RESTful API.
Lifecycle View The lifecycle view shows the different tasks accomplished by each component along the data value chain according to the Big Data Value Reference Model  [6, 9]. Figure  9. 5 illustrates the Lifecycle View of the same fishery pilot as above "Oceanic tuna fisheries immediate operational choices". In this figure, we can see that the "Ship legacy system" is responsible for collecting raw sensor data. Then, custom tools and specific scripts are applied for data preparing (cleaning and transforming data) before executing the analytics tools. Finally the three major tools "C16. 01: OpenVA (VTT)", "C34. 01: EXUS Analytics Framework (EXUS)" and "C19. 01: Proton" are used for data analytics and data visualisation.
Models Quality Metrics The DataBio ArchiMate models are structured in five so-called projects: three projects for describing the pilots of agriculture, forestry and fishery, one project for modelling software and IoT system components and one project for modelling Fig. 9. 5 "Oceanic tuna fisheries immediate operational choices" pilot-Lifecycle View "Earth Observation" data services. These projects are monitored by "Measure Platform"  [10] , which is a monitoring platform that allows to collect periodic measurements on monitored projects. In this case, these measurements are obtained via the model indexing tool "Hawk"  [11] , which processes the queries of ArchiMate models. For each metric, we define a query for Hawk to interrogate from the models. After this, we store and visualize the collected measurement via the Measure Platform  [12].
Conclusion and Future Vision The modelling of DataBio components and data pipelines provided more clarity to the project and helped to understand the architecture of the used software components and their integration in the pilots workflows. Moreover, the created models have also contributed to the process of requirements elicitation throughout the project period and to the efficient writing of the documentation. In order to monitor the quality of the models, we have defined a metric that evaluates the efficiency of the modelling process, the comprehensibility of the models and the model size. The metric discussed here could be applied also in other projects, where the modelling tool Modelio or the modelling language ArchiMate are in use  [12]. The proposed metric indicates that the quality level in DataBio is acceptable as comes to the efficiency of the modelling process and the comprehensibility of the models.
However, we note that there are some areas to be improved such as the cohesion and the completeness of the models. The analysis showed that the models are lacking a more holistic view of the DataBio project, where there is a big data platform or environment offering services and components to the different pilots. Hence, we aim at finding more metrics for evaluating the cohesion of the models and expressing the interdependency between elements and diagrams inside the project. Moreover, our analysis showed that there are many incomplete and undetailed diagrams and we need therefore a metric that expresses the completeness and the maturity of the diagrams. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons.
3  shows an example of the interface view of the component "C16. 01: OpenVA (VTT)", which offers a web user interface for accessing OpenVA via a browser. OpenVA is also accessible via interfaces that can be provided by other components such as JDBC interface for accessing OpenVA database, Sqoop export tool for moving a set of files from HDFS (Hadoop Distributed File System) to RDBMS (Relational DataBase Management System).
Acknowledgements The authors would like to thank  Antonio Garcia-Dominguez  from  School of Engineering and Applied Science at Aston University, Birmingham, UK  for his support with the Hawk tool. We would like also to acknowledge the effort of  ITEA3  project  14009 ,  MEASURE  Project and thank their consortium team members for their feedback on this work. Furthermore, the research leading to these results has received funding from the  European Union   Horizon 2020 research and innovation program  under grant agreement No. 732064 , Databio Project.
Introduction The CHIMERE chemistry-transport model has been developed and distributed since 1999. It is used for analysis, forecast, and scenarios in the framework of regional air pollution. Since its first version, it has evolved a lot and is now able to accurately model gas and aerosols pollutants, at spatial scales ranging from urban to hemispheric and within the whole troposphere. The number of users is difficult to estimate, but more than 300 different institutes are registered in the users mailing list. Originally, more prominent in France and Europe, the CHIMERE users are now spread all around the world, as displayed in Figure  1. The previous version, called v2017r5 and distributed in 2017, was the last offline version. Its novelties were the code structure, the update or addition of parameterizations, and the possibility to use the model with a hemispheric domain,  (Mailler et al.
, 2017). The new version presented in this article is called v2020r1 (for the year of distribution of this first release). All databases used to run simulations were also selected for their open data status. In addition to the CHIMERE code itself, this new version is also distributed with the OASIS3-MCT and WRF models. We can group and summarize these developments as: 1. The online coupling with WRF via OASIS3-MCT, to calculate the possible retroactions of aerosols on radiation (direct effect) and cloud formation (indirect effect),  (Briant et al. , 2017; Tuccella et al. , 2019). 2. Natural emissions: the addition of dimethylsulfide, new schemes for sea-salt, improvements of the mineral dust and biomass burning, NO x by lightning,  (Cholakian et al. , 2018; Menut et al. , 2018; Menut, 2018; Turquety et al.
Numerics • accurmet: Enables to select what meteorological variables are written in the out file. Possible values are low, medium or full. • imethod and splitcfl: These flags are added to manage the pollutants' concentrations calculation. Historically, CHIMERE uses a method integrating all processes with a production and loss term. Many models are also using operator-splitting: this later possibility was added in CHIMERE. The flag splitcfl is here to separate the time-step calculation between meteorology and chemistry when the operator splitting technique is chosen. Details are presented in section 10. 2. • timeverb: A flag to have, verbose, on screen, the time spent in each routine or part of the model. It is mainly for developers, i. e. if you decide to add a new parameterization in the code and want to check the time used.
• outloc: A flag to write output results in the same directory as the executable file. Sometimes, depending on the computer architecture, to write output files on remote disks may cause instabilities and bus errors. To avoid this problem, output files are written locally, then moved to remote disks at the end of the simulation only. • imegan: It enables to select the landuse database used by MEGAN to calculate the biogenic emission fluxes. Two datasets are available in the CHIMERE distribution: • imegan=1 for the dataset with a resolution of 2. 5 min, with monthly LAI. • imegan=2 with a resolution of 30 seconds and with 8-days LAI,  (Sindelarova et al. , 2014). • iusedms: This flag is whether to take into account DMS emissions or not. The option 0 is for 'no emissions' and the options 1, 2, 3, or 4 are to select a parameterization such as:  (Liss and Merlivat, 1986)  • iusedms=2,  (Wanninkhof, 1992)  • iusedms=3,  (Nightingale et al.
The observations used for validation The model was validated by using a vast dataset of atmospheric and meteorological in-situ measurements. simulations and all the observations, this tool provides a statistical comparison between the aforementioned simulations and the provided measurements. The tables and images presented in this paper are all produced by this tool. 4 Improvement of v2020r1 compared to v2017r5 In order to quantify the quality of this new version, two simulations are performed, one with v2017r5 and one with v2020r1. The meteorological forcing is ECMWF. The new version is used in offline mode to be homogeneous with the v2017r5 version. 4 Results are presented in Table  2  for the daily mean and maximum values of O 3 , NO 2 , PM 10 , PM 2. 5 , and Aerosol Optical Depth (AOD).
In general, global, and regional models use the dominant landuse and soil properties in their domain cells. For each cell, there is one characteristic and the emissions flux is thus calculated once. Using high resolution (30s) USGS databases, we implemented a matrix, providing for each model cell the percentage of each soil type, and for each one, the percentage of each landuse type. The USGS database has 16 soil categories and 24 landuse categories, the dust flux is calculated for a maximum of 384 combinations. The values are then accumulated to have the mean flux in the cell.
The main improvement included in version 2  (Turquety et al. , 2020)  is related to temporal variability, with the possibility to: (i) redistribute the total monthly burned area (MCD64 data) according to the FRP value (MOD14 data) so that higher emissions will correspond to enhanced fire intensity; (ii) redistribute daily emissions using the diurnal variability of the FRP in the geostationary observations (SEVIRI above Europe and Africa). Small fires that may be observed with the active fire detection (FRP), but that are not present in the burned area product, may also be included. The flexibility of the use of input datasets for fire activity or vegetation type burned allows for the calculation models in an ensemble that provide information on the uncertainties related to these choices. The analysis of a case study in Portugal during the summer of 2016 shows an average dispersion of ∼ 75 % of daily carbon emissions.
To achieve this 1-way nesting procedure the ndown. exe WRF executable is used, permitting the use of meteorological outputs coming from the above domain to drive the nested domain. Therefore, for a given level of nesting, WRF and CHIMERE are always run using the downscaled information from the level above and never benefiting from the interior level, if it exists. A nested domain can use different numbers of vertical levels, as long as the top height of the nested domain is less than the top height of the parent domain. The CHIMERE-WRF coupled suite can use the WRF nudging option to avoid strong divergences inside the models. This used in coarse model simulations (typically above 25km resolution) that are driven by global scale simulations, commonly issued from GFS or ECMWF datasets that have been re-analysed with synoptic observations.
At higher resolutions with nested domains, model nudging should be not necessary to allow the model to accurately develop the local physics and dynamics. For the chemistry, model nudging is not used, but the assimilation of observation data (from satellite and ground stations) is an area of interest that is not yet fully operational in this version of CHIMERE. 12 Computing performances Computing performances and scalability are studied with a series of 1 day long run performed on the Irene Joliot-Curie machine of TGCC with varying number of cores dedicated to WRF and CHIMERE for the coupling case (cpl_case) 1 and 4. To increase the number of points and therefore allow the use of more cores, the MED20 domain (same domain as MED60 but at 20 km horizontal resolution) is used. The effective computational times spent in both components of the coupled system (WRF and CHIMERE) are obtained with the LUCIA tool of the OASIS3-MCT coupler  (Maisonnave and Caubel, 2014 ) which enables separation of computational time and waiting time.
It contributes to cloud condensation nuclei (CCN) and has thus an impact on cloud formation. DMS atmospheric emission fluxes are parameterized and correspond to a fraction of aqueous DMS sea surface concentrations (nM). These concentrations are extracted from the global database of Lana et al. (2011). The global database contains monthly mean global fields of https://doi. org/10. 5194/gmd-2021-96 Preprint. Discussion started: 21 April 2021 c Author(s) 2021. CC BY 4. 0 License. oceanic DMS with a horizontal resolution of 1 o ×1 o. The aqueous DMS is produced by plankton under light exposure and concentrations are thus sensitive to the time of the year, the latitude, and the sea surface temperature.
v2020r1, for both the correlation and bias. Code availability. The CHIMERE v2020 model is available on its dedicated web site https://www. lmd. polytechnique. fr and for download at https://doi. org/10. 14768/8afd9058-909c-4827-94b8-69f05f7bb46d. Data availability. All data used in this study, as well as the data required to run the simulations, are available on the CHIMERE web site download page https://doi. org/10. 14768/8afd9058-909c-4827-94b8-69f05f7bb46d.
Alcian blue and safranin-O staining of alginate beads After differentiation of human WJ-MSCs and IL-1β induction, 3 beads in each group were harvested and fixed in 10% buffered paraformaldehyde at room temperature. Then, these beads were rinsed with phosphate-buffered saline (PBS), serially dehydrated, infiltrated with arnyl acetate, paraffin embedded, and sectioned at 5-μm thickness for staining  [42]. In detail, the sections were rinsed with PBS and then stained overnight with 1% Alcian blue dye at pH 1. 0 or 0. 1% aqueous safranin-O for 10 min at room temperature. Images were captured with an Olympus AH-2 light microscope (Olympus, Tokyo, Japan) and quantitatively analyzed with ImageJ software (National Institutes of Health, Bethesda, MD) using methodology as previously described  [43, 44].
The input values were compared to the immunoprecipitated samples, with the IgG negative controls values subtracted as background. The calculated errors in all of the graphs depicting ChIP data represent the standard deviations for three replicate RT-qPCRs for precipitated chromatin,   [19] [20] [21] [22] , while the control group was given the same volume of distilled water. On GD20, 8 randomly selected pregnant rats with 10-14 live fetuses from each group were anesthetized. The male fetuses were quickly removed, weighed, and IUGR was diagnosed when the body weight of a fetus was two standard deviations less than the mean body weight of fetuses in the control group. Fetal knee joints were separated under a dissecting microscope and collected from each littermate were pooled together and immediately frozen in liquid nitrogen, followed by storage at -80 °C for analyses.
01, Fig. S3 ), which was consistent with the result reported by Mericq et al. [48]. Taking the reported data and our present results into account, we chose 300 nM cortisol as the physiological concentration and 600 nM and 1200 nM as a series of pathological concentrations in vitro. Then, the chondrogenic potential of WJ-MSCs treated with different concentrations of cortisol and the subsequent susceptibility to an osteoarthritis-like phenotype were evaluated. Compared with the 300 nM cortisol group, the cell viability in the 600 and 1200 nM cortisol groups had no significant changes on 0 day and 21th day after chondrogenic differentiation (Fig. S2B ), while the glycosaminoglycan staining in the 1200 nM cortisol group was significantly decreased (P < 0. 01, Fig. 2a-c ). The mRNA expression levels of COL2A1 and ACAN in the 600 and 1200 nM groups were substantially reduced (P < 0.
Patient Population We retrospectively reviewed the echocardiographic and CMR studies of 106 patients with AS and preserved LV ejection fraction (LVEF) who were prospectively recruited in the PROGRESSA study (NCT01679431) between 2008 and 2014. All patients underwent comprehensive Doppler echocardiographic studies and CMR within a period of #3 months. Detailed inclusion and exclusion criteria were previously reported. 20 Briefly, patients $18 years of age with peak aortic velocity >2. 0 m/sec were included. Patients were excluded if they had symptomatic AS, moderate to severe aortic regurgitation or mitral valve disease (mitral stenosis or regurgitation), or LVEF < 50% or if contraindications to contrast-enhanced CMR were present. The study was approved by the ethics committee of the Quebec Heart and Lung Institute, and patients provided written informed consent at the time of inclusion.
Doppler Echocardiography All Doppler echocardiographic examinations were acquired using commercially available ultrasound machines and according to current recommendations of the ASE. 9, 10, 21 Images were analyzed offline in a core laboratory using a commercially available software (TomTec Imaging Systems, Bayern, Germany) by experienced readers blinded to clinical and CMR data. Minor-axis dimensions (LV internal diameter [LVID] at end-diastole and end-systole and interventricular septal [IVS] thickness and PW thickness) were measured in parasternal long axis using 2D images at three different levels: (1) the BL (i. e. , at the level of the mitral valve leaflet tips), (  2 ) BSB (just apical to the septal bulge), and (3) the ML (Figure  1 ). The traditional BL was visually identified at the mitral leaflet tips.
Cardiovascular Magnetic Resonance Cardiovascular magnetic resonance examinations were performed using a 1. 5-T system (Achieva; Philips Healthcare, Best, the Netherlands). Standard cine images of cardiac morphology and function were acquired. Image analysis was performed offline in a core laboratory using standardized approaches and dedicated software (CMR 42 ; Circle Cardiovascular Imaging, Calgary, AB, Canada), blinded to clinical and transthoracic echocardiographic data. LV volume, LV mass, and LVEF were measured by contour analysis of end-diastolic and end-systolic phases of complete short-axis stacks. Papillary muscles and trabeculations were included when measuring mass (equivalent to weighting the left ventricle) and excluded when measuring volumes (equivalent to blood-pool techniques).
Statistical Analysis Normal distribution of continuous variables was assessed using the Shapiro-Wilk test. Continuous data are expressed as mean 6 SD or median (interquartile range) according to their distribution and were compared using one-way analysis of variance or the Kruskal-Wallis test as appropriate. Categorical variables are expressed as percentages. Correlation and agreement (95% CIs) between transthoracic echocardiographic measurements compared with the referent method (CMR) were assessed using Spearman correlations and Bland-Altman comparisons, respectively. A paired Student's t test was used to test for any overestimation or underestimation. Comparisons between continuous variables were made using repeated-measures analysis of variance with post hoc Bonferroni.
Alzheimer's disease (AD) is the leading cause of dementia in aging individuals. However pathophysiological processes involved in the brain are still poorly understood. Among numerous strategies, a comprehensive overview of gene expression alterations in the diseased brain has been proposed to help for a better understanding of the disease processes. In this work, we probed the differential expression of genes in different brain regions of healthy and AD adult subjects using data from three large studies: Mayo Clinic; Mount Sinai Brain Bank (MSBB) and ROSMAP. Using a combination of differential expression of gene and isoform switch analyses we provide a detailed landscape of gene expression alterations in the temporal and frontal lobes, harboring brain areas affected at early and late stages of the AD pathology, respectively.
transcript usage (DTU), enables identification of alternative splicing and isoform switches with prediction of functional consequences  (Anders et al. , 2012; Vitting-Seerup & Sandelin, 2019). Therefore, important gene expression modifications in the AD brain could occur at the transcript level and be overlooked in classical DEG analyses. Here, we took advantage of three available RNAseq datasets, generated using samples from different brain regions, to systematically probe gene expression changes (DEG and DTU) in AD. In the Mayo's clinic study, both the temporal cortex and cerebellum were used to obtain bulk RNAseq  (Allen et al. , 2016). In the Religious Orders Study (ROS) and Memory and Aging Project (MAP), henceforth called ROSMAP, the dorsolateral prefrontal cortex was used  (De Jager et al.
Regional gene expression alterations in the AD brain correlates with pathological progression Several consortia have generated RNAseq data from brains of individuals with a clinical and/or pathological diagnostic of AD  (Allen et al, 2016; Wang et al, 2018; De Jager et al, 2018). Considering the regional progression of AD pathology  (Braak and Braak, 1991) , we set out to identify and compare differentially expressed genes (DEG) in the temporal lobe (TL), encompassing brain regions affect at early stages of the AD such as the hippocampus and entorhinal cortex, and in the frontal lobe (FL), affect at more advanced stages of the pathology (Figure  1 ). Comparisons between control and AD individuals were performed independently for each dataset and only genes with fold change > 1. 3 and FDR > 0.
01 were considered as DEGs. We found 3,348 (1244 downand 2104 up-regulated genes) and 2,172 (1170 down and 999 up-regulated genes in BM22 and BM36; 3 genes regulated in opposite directions in these two areas) DEGs in the TL of AD individuals compared to their respective controls in the MSBB_TL and Mayo datasets, respectively (Figure  2A -B; Supplementary table 1). Of those DEGs, 734 genes (145 down and 520 up) were commonly regulated in both Mayo and MSBB_TL (88. 4% of genes altered in the same direction; 15,33% of overlap; p= 8. 56 x 10 -59 , hypergeometric test). In contrast, only 327 (113 down and 214 up) and 209 (97 down and 112 up) DEGs were detected in the MSBB_FL and ROSMAP, respectively. Of those, 31 genes (18 down and 13 up) were found in both datasets (7,34% of overlap; p = 1. 67 x 10 -14 , hypergeometric test) (Figure  2A -B; Supplementary table  1 ).