dataesr/scientific-paragraphs-categorization huggingface dataset:
Dedicated tool for communication cost measurement The last piece of the puzzle that we implemented to set up an effective benchmarking environment in NEMO is to collect and summarizes information related to the performances of the MPI part in the code. In NEMO, MPI communications are wrapped in a small number of high level routines co-located in a single Fortran file (lib_mpp. F 90). These routines provide functionalities for horizontal halo exchange and global averages or min/max operations between all horizontal subdomains. With very little code changes in this file, it is possible to identify and characterise the whole MPI communication pattern. This instrumentation does not replace external solutions, based on automatic instrumentation, e. g. with "Extrae-Paraver"  (Prims et al. , 2018)  or "Intel Trace Analyzer and Collector"  (Koldunov et al.
Reducing or removing unnecessary MPI communications This third section presents the code optimizations that were done following the implementation of the benchmarking environment described in the second section. In a former study  (Maisonnave and Masson, 2019) , we relied on the measurement tool (section 2. 3) to assess the performance of the BENCH configuration (section 2. 2). In this work, they focused only on global configurations with grid sizes equivalent to 1 • to 1/12 • horizontal resolution, including or not sea-ice and bio-geo-chemistry modules. Our analysis revealed that the global configurations scalability was limited by a major load imbalance due to the special halo exchange required by the north fold treatment. A fine study of the north fold communications pattern revealed that it was possible to further reduce the number of array lines involved in these specific communications, achieving a speed up of x1.
As evidenced by  Tintó et al. (2019) , the MPI efficiency in NEMO is not limited by the volume of data to be transferred between processes but by the number of communications itself. Regrouping the communications that cannot be removed is therefore a good strategy to improve NEMO performances. Our goal is thus to find the parts of the code which do the most of communications and next figure out how we can reduce this number either by removing communications that appear to be useless or by regrouping as much as possible the indispensable communications. Note that following the development of  Tintó et al. (2019)  a Fortran generic interface has been added to NEMO and makes the grouping of multiple communications extremely easy for the user. In the configuration we are testing (BENCH with no periodicity, open boundaries and no sea-ice), almost 90% of the number of communications are due to the surface pressure gradient computation (44%) and the open boundary conditions (45%).
Conclusions We presented in this paper the new HPC optimisations what have been implemented in NEMO 4. 0, the current reference version of the code. The different skills we gathered among the co-authors allowed us to improve NEMO performances while facilitating its use. The automatic and optimized domain decomposition is a key feature to perform a proper benchmarking work but it also benefits to all users by selecting the optimum use of the available resources. This new feature also points out possible waste of resources to the users, making them aware of the critical impact of the choice of the domain decomposition on the code performance. The new BENCH test case results from a close collaboration between ocean physicist and HPC engineers. We distorted the model input, boundary and forcing conditions in such way that the model is stable enough to do benchmark simulations in any configuration with the less possible input files as possible (basically the namelist files).
In case potentially lethal VT/VF episodes occur, up to five treatment shocks can be applied per treatment sequence by electrodes integrated in the garment. The time between the detection of an arrhythmia and the delivery of the treatment shock is generally less than 1 min. Recorded episodes as well as patient compliance reports are stored on a web-based server (LifeVest Network) which can be accessed by the treating physician via personalized login data. Instead of inpatient monitoring with a manual defibrillation option, the WCD can effectively and systematically protect patients from SCD outside of the hospital. [6] [7]  Despite proven safety and efficacy, there is still little data on the economic impact of WCD use, especially in patients following ICD explant due to infection. We therefore decided to perform a literature review to summarize the evidence related the efficacy, safety, and compliance of the WCD in patients after ICD explantation and to perform a cost-minimization analysis to assess the economic impact associated with WCD use compared to the standard therapy.
| Literature search The EUnetHTA health technology assessment (HTA) core model (EUnetHTA 2015) was used as a guideline for the literature search. Clinical, epidemiological, and economic aspects were considered. Electronic databases (Medline, Pubmed, and Web of Science) were used for the literature search. Clinical and economic keywords related to ICD explantation, its treatment options, health outcomes, consequences for health-related quality of life (HRQoL), and its economic implications were used. The search was performed using index/ MeSH (Medical Subject Heading) and strings. Studies were selected based on the included indications, the age of the subjects, the year of publication (2008 and later) and the type of publication. We considered retrospective and prospective studies, randomized controlled trials, reviews, guidelines, and practice guides.
They were established in Italy to meet the increasing need of long in-hospital stay, which was previously managed in acute hospitals, and are used for patients that do not need an acute management. The choice of a cost-minimization analysis was made to perform a conservative analysis. The assumption of same efficacy, which is required to perform a cost-minimization analysis instead of a costeffectiveness analysis, was considered conservative due to the evidence available that suggests a possible higher efficacy of WCD. 10  Hospitalization in a low-intensity hospital does not guarantee that patients are adequately protected from SCD unless they are in a monitor bed or on an intensive care unit. To perform the cost-minimization analysis, we retrieved data performing an extensive literature review.
| Data input: Clinical data Clinical data are reported in Supplementary Table  4. Based on the conservative approach of the cost-minimization analysis, we assumed a comparable WCD effectiveness to the standard therapy in Italy. The effectiveness of the intervention was presented based on the incidence of SCA in the first month after ICD extraction and WCD event survival rates of 85. 5%. Event survival rate was defined as SCA rate due to VT/VF events in the context of all SCA events including, for example, asystole, with a 100% termination-success rate of VT/VF events by the WCD. 10, 14 Cardiac and noncardiac deaths were presented and analyzed separately for the assessment of long-term mortality after ICD reimplantation, in accordance with Woo et al. 15  Furthermore, the mortality risk was split and separately assessed according to general (noncardiac) causes of death, which increase with age, and specific mortality risks, which are particularly present in patients with heart disease.
Data on general population mortality were taken from the Italian Institute of Statistics (ISTAT). In order to assess excess mortality in patients with ICDs, total mortality in the ICD group and the prevalence of SCD were estimated from the SCD-HeFT (Sudden Cardiac Death in Heart Failure) and MADIT-II (Multicenter Automatic Defibrillator Implantation Trail) studies. 16, 17 During the simulation of the model, excess cardiac mortality remained constant, allowing the mortality of the general population to increase with age. Thus, the proportion of SCD in the overall mortality risk decreased over time. [20] [21] [22] [23].
14, 21 Thus, most of the IARC GLOBOCAN estimates for the CA-4 region have been imputed from neighboring countries. 22 nduras is rapidly growing with the third highest fertility rate in the Americas (2. 46 births per woman). The initial effort to establish a PBCR in Honduras in 2005 was short-lived because of funding and shortage of trained personnel, similar to Guatemala. 23, 24 In 2002, a registry of gastric cancer cases in Western Honduras launched through academic collaboration, using endoscopy and pathology databases, named the Western Honduras Gastric Cancer Prevention Initiative (WHGCI). 16 This served as the foundation for the multi-institutional collaboration of the Western Honduras PBCR (Cop án-PBCR): principal public hospital and clinic system (Hospital de Occidente, HdO), local academic institutions (Universidad Nacional Aut ónoma de Honduras), the Honduran Ministry of Health, and international universities (University of Alabama at Birmingham, Vanderbilt University, and Universidad del Valle, Cali, Colombia).
All incident cases were recorded from January 1, 2013, to December 31, 2017. Mortality data are not available at the regional nor national levels (eg, National Population Registry [RNP]), as death certificates in Honduras are incomplete and of low quality. Residents of the state of Cop án were included with address verification (RNP) and records from the diagnostic centers. Protocols were in place to maintain confidentiality and protect personal health information, per international standards. The cancer case definition was any incident invasive malignant tumor from any anatomical location confirmed regardless of treatment status. The diagnosis was either microscopic (fluid cytology, peripheral blood and bone marrow, histology of primary tumors, or autopsy) or nonmicroscopic (clinical, surgical, or imaging).
The most recent national census was conducted in 2013 by the Honduras National Statistics Institute (INE), with official estimates for subsequent years (eg, 2014-2017). The census represents the geographical strata of the local population with a limited censal omission of 10. 3%. 25  Innovations included the use of an alternate data management platform and leverage of Health Ministry personnel resources. We designed the PBCR database using the webbased Research Electronic Data Capture (REDCap) platform, which required minimal training. The online database facilitated collaboration with real-time access to deidentified registration data for quality checks and for feedback. In the pilot phase, we noted improved efficiency and quality measures when compared with local use of CanReg5 (data not shown).
DISCUSSION We present the initial cancer incidence estimates from the Cop án-PBCR over a 5-year period, in an effort to advance cancer control in Honduras and the CA-4 region. The ASRs for both sexes were 124 and 112. 2 per 100,000 including and excluding NMSC (C44), respectively. The gender difference (1:2. 4 male-female ratio) was driven by cervical cancer. The highest ASRs were cervical cancer (43. 6/100, 000) and breast cancer (14. 6/100,000) among females and stomach cancer in males (26/100,000). The rates in the major cancers did not significantly vary during the 5year period. There was modest geographic variation, with lower rates observed in more remote municipalities, suggesting that access to diagnostic services may play an important role. Innovations included the use of the REDCap software platform with online and offline secure data entry and the inclusion of recently trained physicians in their required year of rural national service for network support.
39, 40 ere is a substantial gap in IARC CI5 coverage between low-and middle-HDI countries (5%-10% coverage in South America and no coverage in the CA-4) and high-HDI countries (35% coverage in United States). 27 In South and Central America, only 11 countries submitted cancer data to the Cancer in Five Continents Report. Specifically, for Central America, only Costa Rica provided data in the past 5 years. 10, 20 e design and implementation of the PBCR faced several challenges with lessons learned: (1) Using CanReg5 for data collection in public and private institutions and pathology laboratories was difficult to coordinate. The RED-Cap platform permitted straightforward training of personnel and efficient data entry available online and offline. (2) The Honduras National Population (Census) Registry has under-registration of residents in Western Honduras, and cause of death is not recorded.
Background: The Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open source community specifications and software tools for enabling discovery, exchange, and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab-a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, the JSON serialization ISA-JSON was developed. Results: In this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python object classes.
Background There are many data models and frameworks for describing entities and artefacts of scientific research. The life sciences have pioneered the development and application of ontologies, data standards, and minimum annotation checklists for reporting research results. More than ever, the importance of making scientific data FAIR (Findable, Accessible, Interoperable, and Reusable) is at the forefront of discourse in the research community  [1]. The ISA Metadata Framework, or simply ISA-so named after its constituent key concepts: "Investigation" (the project context), "Study" (a unit of research), and "Assay" (analytical measurement), was born out of initial efforts to create an "exchange network test bed" for a diverse set of digital resources in 'omics studies. These included public and proprietary databases, as well as free and open source software tools and commercially developed systems.
Because the original ISA software suite does not provide a programmatic interface, we have developed the ISA API to position the ISA framework as an interoperable and open platform  [18]. We also recognized a trend of growing enthusiasm by end users for writing software code. This trend is perhaps most evident in statistical programming platforms such as R, Python, and MatLab, and also greatly helped with the uptake of interactive programming environments such as Project Jupyter  [19]. The aim of the ISA API is to reproduce much of the userfacing functionality afforded by the ISA software suite and to additionally enable interoperation between software systems that produce and consume ISA formats and other machine-readable data formats, as illustrated in Fig. 2. The ISA API is written in Python, which has a high uptake by non-programmers and a rich open source community ecosystem of supporting software packages, including many statistical and bioinformatics ones.
Related work Developed in early 2011, the Bio-Parser-ISATab project  [25]  is, to our knowledge, the first effort to create an ISA-Tab parser outside of the ISA tools ecosystem for programmatic manipulation of ISA metadata. While Bio-Parser-ISATab is written in the Perl programming language, the first Python project to implement an ISA-Tab parser with scripts found in the biopy-isatab project  [26]  was developed as part of an early version of bcbio  [27]    Recent efforts to create ISA programming libraries have focused on reading and writing of ISA-Tab files: AltamISA  [28]  was developed in Python with the aim for strict validation and error handling when handling ISA-Tab files; and a Java library, isa4j  [29] , has been developed for high-performance writing of ISA-Tab metadata. ISA API provides a reference implementation of ISA-Tab support and additionally supports other formats and features as described in the following section.
Implementation The ISA API is written in the Python 3 programming language and therefore part of the wider Python ecosystem of software tooling that is very popular in the bioinformatics and data science communities  [30]. The ISA API can immediately be imported and used alongside other Python packages in custom Python programs, or it can be used interactively via iPython's interactive shell  [31]  and in Jupyter Notebook web applications. It can be used in cloud infrastructure that supports Docker through the container image isatools/isatools published in DockerHub. The ISA API can be also exposed through RESTful APIs as demonstrated by the MetaboLights Labs Web service interface and the MetaboLights Online Editor Web service  [32].
Support for all ISA formats The ISA API provides the solution to simplify access and generation of ISA metadata now that the ISA framework supports >1 on-disk data format-i. e. , ISA-Tab and ISA-JSON. By using the ISA API, software can support full read, write, and validation transparently on all current and future ISA formats without any additional complexity. The ISA-Tab and ISA-JSON document validators included in the ISA API provide reference implementations by which to also validate documents generated by other systems that claim ISA compatibility  [17].
Support for other bioinformatics formats The ISA API also supports related domain formats used by bioinformatics communities and which are usually technologycentric (e. g. , mass spectrometry, sequencing, DNA microarrays). This feature is a consequence of the ISA framework's support for multi-modality datasets (e. g. , multi-omics datasets). The ISA API supports the import and export of MAGE-TAB  [33, 34]  and SampleTab  [35, 36] , import of metadata from mzML  [37, 38]  and nmrML  [39, 40] , and exports to SRA-XML  [41, 42]  and Work-flow4metabolomics  [43].
Export metadata for submission to public repositories The ability to export metadata to a range of formats means that software using the ISA API can prepare metadata submissions to public data repositories. The ISA API supports export of ISA metadata to submission-ready formats for public repositories such as the EMBL-EBI ArrayExpress  [44, 45] , European Nucleotide Archive  [46, 47] , and MetaboLights  [48, 49]. Support for submission to additional databases will be developed in future.
Extensible and coherent object-oriented class model Metadata objects are represented in the ISA API using an objectoriented class model, providing a format-agnostic in-memory representation. This in-memory model is based on the ISA Abstract Model, which was extracted from the original ISA-Tab specification to formalize the concepts used in the ISA framework. It describes the steps in an experimental workflow as a directed acyclic graph (DAG), which in the ISA API is expressed naturally as connected Python class objects. For example, metadata relating to the assay description is held in an Assay object that is in turn contained by a Study object and its relevant attributes, which is finally contained by an Investigation object-thus reflecting the hierarchical nature of the ISA framework concepts.
All ISA concepts are modelled as classes at a granular level, giving software developers full control to extend the API's capabilities and build new features. Details of the class model used in the ISA API are published in the ISA Model and Serialization Specifications 1. 0  [4]. This functionally rich object-based representation completely avoids many of the complexities associated with handling ISA metadata as tables-e. g. , joining data from the sample and assay tables, handling multiple table rows per edge of the DAG. While in memory, the experimental metadata can be programmatically processed and manipulated. Moreover, the ISA API's object-based representation is a powerful decoupling tool. First, it decouples the client application from the specific ISA file format being handled. Second, it decouples the input and output metadata formats.
When metadata are read, the format-specific parser creates the standardized in-memory representation, which can be edited and manipulated; when writing, a format-specific serialization routine traverses the objects to extract the data structure according to the target ISA format. Writing the ISA API object model to ISA-Tab requires converting experimental workflow graphs to tabular formats. Such work is described in the graph2tab library by Brandizi et al. [50]. However, in that work, the authors only describe cases of unidirectional transformations from graphs to tables; ISA API implements bidirectional transformations between each of the 2 ISA formats and ISA content expressed as Python objects that includes a representation of DAGs. ISA content can also be authored by directly creating Python objects, a brief example of which is shown in Figs  3  and 4.
Querying over ISA content Common use cases for ISA metadata involve gathering samples and data files produced by assays that satisfy certain selection criteria. The criteria are objects based on sample characteristics, processing parameters, and study factor combinations (treatment groups). When ISA metadata are loaded as inmemory model objects, this representation can be queried using native Python code, e. g. , by filtering using list comprehensions with conditional logic. The ISA API also provides helper functions, in the isatools. utils package, to query and fix possible encoding problems in ISA content. For example, in ISA-Tab files, a common issue is when ISA content is encoded in such a way that some branches in experimental graphs converge unexpectedly, which represents the intended workflow incorrectly.
Semantic markup An important feature that is embedded into the class model is the support for ontology annotation objects to better qualify metadata elements, contributing to making them FAIR  [56]. The ISA API implements the OntologyAnnotation class that is used extensively throughout. To express quantitative values, units are implemented with the Unit class, which itself can be qualified with an OntologyAnnotation object. Populating annotations is aided by a network package in the ISA API for connecting to and querying the Ontology Lookup Service (OLS)  [9]. JSON-LD context files complementing the ISA-JSON schemas mapping into schema. org [57]  or OBO Foundry-based resources  [58]  are also available.
Assisted curation of study metadata Studies published using ISA formats are increasingly commonplace, many of which are produced in ISA-Tab using the ISAcreator (e. g. , NASA GeneLab  [59, 60]  and MetaboLights  [48] ), and many others produced either by hand (using a spreadsheet or text editor) or output by third-party systems (e. g. , COPO  [61] , FAIRDOM/SEEK  [62, 63] , and SCDE  [64, 65] ). There are sometimes cases of invalid ISA-formatted content appearing in public databases or reported via bug reports to the ISA development team. To help with this, the ISA API includes features to assist with metadata curation of ISA-formatted studies. Beyond using the validators contained in the API, additional features include checking for possible structural problems in the DAGs that describe the provenance of samples and data files (e.