dataesr/scientific-paragraphs-categorization huggingface dataset:
This work used the platforms of the Grenoble Instruct-ERIC center (ISBG; UAR 3518  CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology (PSB ) supported by  FRISBI  ( ANR-10-INBS-05-02 ) and  GRAL , financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche)   CBH-EUR-GS  ( ANR-17-EURE-0003 ). The electron microscope facility is supported by the  Auvergne-Rhône-Alpes Region , the  Fondation pour la Recherche Médicale (FRM) , the fonds  FEDER  and the  GIS-Infrastructures en Biologie Santé  et.
For example, an addition of 37% of more eddies have been retrieved in eastern boundary upwelling systems  (Capet et al. , 2014). Using this upgraded satellite altimetry product, in this study we investigate: (a) the length-scales of the flow variability, (b) the eddy occurrence and their properties, and (c) the critical latitude at which eddy growth is halted in the tropical South-East Atlantic Ocean. The study has the following structure: In Section 2, we describe the different datasets and methods, including the eddy detection algorithm and the collocation methods applied to the altimetric identified eddies, Argo-float and satellite derived chlorophyll concentrations. The main results and findings are presented and discussed in Section 3 and the conclusions are summarized in Section 4.
Satellite Ocean-Color The satellite-based ocean color product used in this study has been derived from the Ocean Color Climate Change Initiative (OC CCI, https://esa-oceancolour-cci. org/) project  (Brewin, 2015; Lavender et al. , 2015). The project aims at providing the best ocean color essential climate variables (ECVs) data, based on a merging scheme of data retrieved from spaceborne optical sensors including: The MEdium Resolution Imaging Spectrometer (MERIS) on Envisat, operated by the European Space Agency (ESA), and both the MODerate resolution Imaging Spectroradiometer (MODIS), and the Sea-viewing Wide Field of View Sensor (SeaWIFS), operated by the National Aeronautics and Space Administration (NASA). The product is robust as it benefits from an improved bias correction, cloud masks algorithms, and more reliable automated quality assurance process.
Chlorophyll-a Within the Eddies Daily values of surface chlorophyll concentration were extracted from the daily OC CCI, using the Nansat, a Python toolbox for geospatial data  (Korosov et al. , 2016). To colocate the concentrations of chlorophyll within the altimetric eddies, the coordinates of ocean eddies were converted to pixel/line coordinates on the OC CCI products using Nansat  (Korosov et al. , 2016). The chlorophyll values were computed as averages of all valid pixels from the day corresponding to the altimeter-based eddy observation, located around the eddy from the center to the rim.
WOA18 The baroclinic Rossby radius of deformation used in this study was computed using the statistical and objectively analyzed in situ oceanic mean temperature  (Locarnini et al. , 2019) , and salinity  (Zweng et al. , 2019)  profiles, through the water column, derived from the World Ocean Atlas data set, released in the year 2018 (WOA18). WOA18 is quality controlled data set, covering a time period that spans from 1955 to 2017. The product includes annual, seasonal, and monthly climatologies. Temperature and salinity are gridded globally in a mesh size of 1/4° × 1/4°, in longitude and latitude dimensions; while the other parameters are gridded in a mesh size of 1° × 1°. The product has been regarded as being better than all its previous versions. It is freely available from the World Ocean Database center https://www.
e. , red dots) the spectra follows more closely the theoretical profile of k -3 (enstrophy cascade), whereas above the break points, the profiles follow more closely the slopes of k -5/3 (energy cascade). Important to mention that at scales smaller than 100 km, AVISO data set are very dissipative (the energy is much smaller than what would be expected from a direct energy cascade). This is a clear illustration of the low effective resolution of AVISO product. For a wavelength λ = 200 km, an eddy radius R = 50 km is expected. Notice that for each of the break points, the corresponding wavelengths λ have been specified in Figure  3. It increases equatorward: λ = 275 km (latitude band 25°S-20°S), λ = 300 km (latitude band 20°S-15°S), λ = 350 km (latitude band 15°S-10°S), and 454 km (latitude band 10°S-5°S).
Critical Latitude While investigating regime transition between waves and eddies in a quasi-geostrophic formulation of a two-layer shallow water model,  Theiss (2004)   = 1 a critical latitude can be determined. Klocker and Abernathey (2014)  proposed  Theiss (2004)  formulation r in a quadratic form: 𝑟𝑟 = ( 𝐿𝐿𝑅𝑅 𝑅𝑅𝐷𝐷 ) 2 (2) The relationship between L R , and R D are shown in Figures  4  and 5 -a. The deformation radius is portrayed with black contours and the Rhine scale is depicted in red color. Here R D is computed from hydrographic data from WOA18, obtained by solving the Sturm-Liouville eigenvalue problem. Comparison against global data set published by  Chelton et al. (1998) , freely available from http://www-po. coas. oregonstate. edu/, showed similar results. However, the use of WOA18 provides better representation, especially over the continental shelf, due to its relatively higher resolution of the data set.
Their relative proportions in terms of cyclonic and anticyclonic are presented in Table  1. The estimates were obtained by counting the number of all eddies within the domain in each time-frame. For each period presented in Figure  6 , a corresponding histogram of the eddy occurrence (in percentage %) by size (radius in km) is shown in the right panel. The dark-gray (light-gray) bars denote cyclonic (anticyclonic) eddies respectively. Because of the spatial limitations of the altimetric grid resolution of 1/4° × 1/4°, our analysis of the detected mesoscale eddy structures have discarded eddy features with radii smaller than 50 km, to prevent any potential influence of data artifacts. This may explain why H. M. A. Aguedjou et al. (2019)  found slightly more eddies in this region, because they have included features up to 30 km radii.
e. , 7, 30, and 120 days respectively). Blue represents cyclonic eddies and red anticyclonic. The background contours show the isobaths of  200, 500, 1, 500, 2, 500, 3, 500, and 4,  show the corresponding density and amplitude distribution of the eddies as function of their radii. Cyclones are represented by dark gray and anticyclones by light gray. In each panel the eddies nonlinearity parameters (γ) are represented (bue line for cyclones and red line for anticyclones). The eddies were tracked over a time period between 1 January 1993 and 31 December 2017. In view of the limited altimetric grid resolution (1/4° × 1/4°), eddy structures with radii smaller than 50 km have been discarded from the analysis to prevent any potential data artifacts. Schouten et al. , 2005) ; which are in agreement with our results presented in the previous paragraphs.
Data Availability Statement All the results of this study were generated using open access datasets. The data set are freely available at various institutional databases. The mean dynamic topography (MDT) product (CNES-CLS09) credited to  Rio et al. (2011)  used to map the streamlines of the flow field, can be accessed by registered users of the website using the link https://www. aviso. altimetry. fr/en/index. php?id=3622. The gridded maps of absolute dynamic topography derived from multi-mission satellite observations is identified with https://doi. org/10. 48670/moi-00148, and can be accessed by registed users of Copernicus Marine Environmental Monitoring Service (CMEMS) at the following weblink https://data. marine. copernicus. eu/product/. Temperature and salinity from the world ocean atlas (WOA2018) product used to compute the baroclinic Rossby radius can be found at https://www.
ncei. noaa. gov/archive/accession/NCEI-WOA18. The profiling Argo-floats used to assess the eddy hydrography was downloaded from http://www. argodatamgt. org/Access-to-data/Argo-data-selection. The ocean colour used is from Ocean Colour Climate Change Initiative (https://esa-oceancolour-cci. org/). If the weblinks to the data set appears not to work, alternatively copy the link and paste into your web browser. In addition we have centralized all the above listed data products for easy access at zenodo data repository name HALO_JGR_2022JC019050R_ DATASET (https://zenodo. org/record/7389301#. Y4nPU2Qzbow), version 1. The automatic eddy identification and tracking algorithm described by  Halo et al. (2014) , can be found at zonodo software repository name AUTO-MATIC EDDY DETECTION AND TRACKING ALGORITHM (http://zenodo.
org/record/3862545#. Y4Va83Z-By3A), version 2. The chlorophyll-a within the eddies were estimated using the Nansat algorithm  (Korosov et al. , 2016)  archived at zenodo (https://doi. org/10. 5281/zenodo. 59998, Licenced under GNU GPLv3, release 0. 6. 14). All figures were generated using a commercial license of Matlab software from Mathworks, Inc (http:// www. mathworks. com), R2021a.
Introduction: Chronic hepatitis B (CHB) is one of the world's major healthcare problems, especially in the Western Pacific regions. This study describes the prevalence, incidence, treatment profiles and clinical and economic burden of chronic hepatitis B patients in Japan using the Japan Medical Data Center (JMDC) Claims Database. Methods: This is a retrospective observational study. Prevalence cases were identified as patients with C 1 inpatient or C 2 outpatient CHB diagnoses and C 2 records for hepatitis B tests or C 1 prescription for CHB treatment between January 2010 and December 2019. Newly diagnosed CHB patients were defined as patients diagnosed from 2010 to 2018 with no history of the disease up to 2 years prior to the diagnosis. The index date is defined as the first CHB diagnosis day.
In addition to the clinical burden of the disease, CHB is associated with significant economic burden in Asian countries with direct cost being correlated with disease severity  [3, [5] [6] [7] [8]. Medical resources such as non-antiviral medications, laboratory tests and imaging examinations, especially in patients with cirrhosis and HCC, account for a significant fraction of direct medical costs  [8]. This study aims to provide real-world evidence about CHB epidemiology, disease burden, treatment patterns and real-life data on healthcare resource utilization (HCRU) and costs by key stages of disease progression in Japan using data for newly diagnosed CHB patients derived from the Japan Medical Data Center (JMDC) Claims database.
Data Source The study utilized 2008-2020 de-identified data from the JMDC Inc. (Japan) Claims database. Based on Ethical Guidelines for Epidemiological Research issued by the Japanese Ministry of Health, Labour and Welfare, ethics approval was not applicable to this study. This database included approximately 7. 3 million (as of April 2020) insured persons (approximately 5. 8% of the population) from multiple health insurance associations, comprised mainly of company employees and their family aged between 20 and 74 years  [9, 10]. The database includes monthly claims for health insurance reimbursement from medical institutions and pharmacies since 2005 and provides information on the beneficiaries [encrypted personal identifiers, age, sex, International Classification of Diseases 10th revision (ICD-10) procedure and diagnostic codes, and details of prescription]  [9, 10].
Study Design and Population This is a retrospective observational study on patients diagnosed with CHB derived from the JMDC Claims database. The primary outcomes of this study were the estimation of the prevalence of CHB and healthcare resource utilizations and costs and disease progression (health state) of the newly diagnosed CHB patients. The secondary outcome was the description of the newly diagnosed CHB patients in terms of demographic and clinical characteristics, and treatment patterns. CHB cases were identified based on a case selection algorithm developed with the hepatology expert in this study: patients with at least one inpatient or two outpatient claim for CHB diagnosis [ICD-10 code B18. 0 or B18. 1) between January 2010 and June 2020 and presence of either two records for HBV-specific tests [HBsAg, HBV DNA or hepatitis B virus e antigen (HBeAg)] or prescription for CHB treatment during this period.
Baseline Variables Baseline variables were analyzed for eligible patients of the incident cohort. Demographic data included age and sex at baseline. Comorbidities of interest in this study were hypertension, diabetes, hyperlipidemia, peptic ulcer, chronic kidney disease and arthrosis. The Charlson Comorbidity Index (CCI) score, a clinical comorbidity index used with administrative databases based on the presence of 17 comorbidities and their severity, was also calculated; the higher the score, the greater is the disease burden  [11]  (Supplementary Table  4 ). In this study, all diseases included in the CCI score were considered in the score calculation except for human immunodeficiency virus (HIV) data, which are not captured by the database. Also, we chose to exclude the CHB disease code from the score calculation since it defines the main pathology of the study sample and not a comorbidity.
Prevalence Of 4. 1 million beneficiaries in the JMDC claims database during the period of 2010-2019, 13,061 met the CHB case definition, yielding a crude 10-year prevalence of 0. 32%. The prevalence of CHB increased with age, with the highest age-specific prevalence (0. 95%) reported among adults 65-74 years old. The sex-specific prevalence showed a higher prevalence in men than women (0. 36% vs. 0. 27%) (Table  1 ).
DISCUSSION This large study aims to further characterize CHB patients, assessing their clinical profiles and management as well as understanding the clinical and economic burden associated with CHB in Japan. The prevalence of CHB obtained from this study (0. 32%) using 2010-2019 data can be considered a reflection of the prevalence of this disease in the last year (2019). Notably, it is lower than that estimated in the 2016 Polaris modeled study (0. 6%)  [2]. This discrepancy could be attributed to the nature of the database. The JMDC covers 6% of the Japanese population and older age groups ([ 75 years), and unemployed individuals are under-represented  [9, 10]. The highest prevalence in this study was among adults 65-74 and 55-64 years old (0. 95% and 0. 86%) and the lowest among children (\ 18 years) (0.
As expected, the demographics (old age) and comorbidities seen within the CHB patients appear to have translated into high healthcare resource utilization and costs. The results indicate that the liver disease sequelae of CHB have significant economic consequences, as DC and HCC both resulted in high management costs, a phenomenon that was already demonstrated elsewhere  [3, [5] [6] [7] 20]. In the present study, the total healthcare costs of CHB were lower than those estimated in the  Yotsuyanagi et   Lastly, as with many serious diseases, hospitalizations (inpatient costs) were the economic driver in most of these health states. This study used real-life data from the JMDC database, which, unlike other types of claims databases that are restricted to hospital data, allowed us to track patients longitudinally across different medical institutions.
The JMDC was used in various types of research studies including CHB infection among patients with liver-related diseases  [21] [22] [23]. In addition, the large sample size and long follow-up are important strengths of this study. However, the findings of this study should be considered within the context of several limitations besides the ones mentioned earlier. While a sizable proportion of CHB patients are elderly over 75 years of age, the limited representation of patients aged [ 75 years in the database could reduce the generalizability of findings to the Japanese older population. Despite age being a major limitation of the JMDC, it has been used as a main database for some research studies among elderly Japanese patients  [24, 25]. In a study published in 2020, authors showed that the results from the JMDC were comparable to data from the national database  [26].
To date, several studies have been performed on the epidemiology and economic burden of CHB in Japan  [27] [28] [29] , but we observed a considerable gap in this research area which needs the most recent and updated findings. Therefore, our study provides important evidence, i. e. , incidence of CHB at least in the working generation. Incidence of CHB in our study is mostly found to be more prevalent among individuals aged 40-60 years than in other countries. This age range was adequately covered within our study using the JMDC as a data source  [30] [31] [32] [33] [34]. In addition, a recent publication comparing the JMDC with the National Database of Health Insurance Claims and other publicly available data highlighted the similar distributions of these data sources  [10]. Information on laboratory data is very limited in the JMDC, restricting our ability to validate the CHB diagnosis and to identify the phase of the disease based on tests results such as ALT level and HBV viral markers.
However, to limit the inclusion of unconfirmed diagnosis, we included only those without suspicious flags in the database. Furthermore, incident case ascertainment was based on a 2-year period before the index during which a history of the disease was identified. Nevertheless, this period is deemed short to detect a history for a chronic disease that does not require regular care, especially in the early stages. Finally, HIV-related information is not available in the JMDC database for privacy protection as it is deemed sensitive information.
The study utilized de-identified data from the JMDC Claims database. Based on Ethical Guidelines for Epidemiological Research issued by the Japanese Ministry of Health, Labour and Welfare, ethics approval was not applicable to this study. Data Availability. Given the administrative nature of the data, patients did not provide informed consent for data sharing; however, all data are fully anonymized, and the risk of patient identification is low. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
ACKNOWLEDGEMENTS Funding. This study, including the journal's  Rapid Service  fee, was funded by  Janssen Asia Pacific. Cerner Enviza received funding from  Janssen Asia Pacific  during the conduct of the study and has no other funding, financial relationships or conflict of interest to disclose. Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole and have given their approval for this version to be published.
RNA sequencing and microarray datasets Raw RNA sequencing datasets from previously published studies were obtained from Gene Expression Omnibus (GEO) database of National Center for Biotechnology Information (NCBI) (http://www. ncbi. nlm. nih. gov/geo/) and European Genome-phenome Archive (EGA) (https://ega-archive. org/). Accession numbers were as follows: EGAS00001004827/ EGAD00001006621 , GSE140520 (PHH-D3, 1-3), GSE83518 (1HUH6HB, 2HUH6HB). Raw microarray data of gene expression in 53 HB tissue samples and 14 noncancerous liver tissue samples collected from the HB patients at the time of surgery were acquired from GEO, accession number GSE131329.
Identification of differentially expressed genes and differentially expressed metabolic genes RNA sequencing dataset files were analyzed with Chipster software (https://chipster. rahtiapp. fi/) (Kallio et al. , 2011). Reads were preprocessed using Trimmomatic and aligned to human reference genome (GRCh38) using HISAT2  (Kim et al. , 2015). Reads per genes were counted with HTseq  (Anders et al. , 2015). Differential expression analysis was conducted with the edgeR package  (Robinson et al. , 2009). Differentially expressed genes (DEGs) were then filtered using cut-off criteria adjusted to p-value <0. 05 and |log 2 FC|≥1. 0. Microarray data were analyzed with Chipster plus the normalization tool for Affymetrix gene arrays  (Li, 2001; Irizarry et al. , 2003). Statistical tests were conducted using the "Two group tests" tool (empirical Bayes as test and BH as p-value adjustment method)  (Smyth, 2004).
Protein-protein interaction (PPI) network construction PPI networks of differentially expressed metabolic genes in each cell line were constructed with the online tool of STRING database (Szklarczyk et al. , 2019) using 0. 7 as minimum required interaction score. Results were visualized with  Cytoscape (Shannon et al. , 2003). Highly interconnected areas (clusters) of these interaction maps were identified and scored using the Cytoscape plugin Molecular Complex Detection (MCODE)  (Bader and Hogue, 2003)  using the following criteria: degree cut-off: 2; haircut: yes; fluff: no; node score cut-off: 0. 2; K-core: 2; max. Depth: 100. Based on this score, highestranking cluster of each cell line was chosen to be investigated more thoroughly. PPI network of UBE2C was constructed using STRING with 0.
Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) pathway enrichment analyses Genes in each cell line's highest-scoring cluster were uploaded to Enrichr  (Chen et al. , 2013; Kuleshov et al. , 2016; Xie et al. , 2021). Results of KEGG pathway and GO Biological Processes term enrichment analyses were imported from the website. Results with adjusted p-value of <0. 05 were considered significant. The top 5 terms with lowest adjusted p-values from both KEGG and GO were chosen for each HB cell line. Python programming language (Python Software Foundation) with Matplotlib  (Hunter, 2007) , NumPy  (Harris et al. , 2020), and pandas (McKinney, 2010)  libraries were used for handling and plotting this data.
Statistical analysis of clinical variables Student's t-test, Mann-Whitney U test, and receiver operating characteristic (ROC) curves were used to analyze microarray gene expression data and clinical variables provided in GSE131329 dataset. Statistical significance was set to p-value <0. 05. Analyses were conducted with R software (v. 4. 0. 3) or GraphPad Prism (v. 8. 4. 2; GraphPad, San Diego, CA, United States).
Protein quantification was performed with Image Lab software (version 6. 0, Bio-Rad) by normalizing UBE2C band intensities to amount of total protein in corresponding lane utilizing stain-free technology  (Gürtler et al. , 2013). 2. 14 RNA sequencing of UBE2C silenced HUH6 cells HUH6 cells were cultured and treated with either UBE2C or non-targeting siRNAs. RNA and protein extraction were conducted as described above. Prior to sequencing, RNA concentration, quality, and integrity were assessed using Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA, United States) and the TapeStation system (Agilent, Glostrup, Denmark). After quality assessment, RNA libraries were constructed applying polyA selection, and Illumina compatible cDNA libraries were prepared by GENEWIZ (Leipzig, Germany).