MVAR: A Mouse Variation Registry.

The Mouse Variation Registry (MVAR) resource is a scalable registry of mouse single nucleotide variants and small indels and variant annotation. The resource accepts data in standard Variant Call Format (VCF) and assesses the uniqueness of the submitted variants via a canonicalization process. Novel variants are assigned a unique, persistent MVAR identifier; variants that are equivalent to an existing variant in the resource are associated with the existing identifier. Annotations for variant type, molecular consequence, impact, and genomic region in the context of specific transcripts and protein sequences are generated using Ensembl's Variant Effect Predictor (VEP) and Jannovar. Access to the data and annotations in MVAR are supported via an Application Programming Interface (API) and web application. Researchers can search the resource by gene symbol, genomic region, variant (expressed in Human Genome Variation Society syntax), refSNP identifiers, or MVAR identifiers. Tabular search results can be filtered by variant annotations (variant type, molecular consequence, impact, variant region) and viewed according to variant distribution across mouse strains. The registry currently comprises more than 99 million canonical single nucleotide variants for 581 strains of mice. MVAR is accessible from https://mvar.jax.org.

Diseño de una base de datos para la Revista Cubana de Tecnología de la Salud

Introducción: la Revista Cubana de Tecnología de la Salud, requiere un sistema de base de datos para el tratamiento de la información concerniente a los diferentes procesos de la gestión editorial. Esto es necesario en la automatización y a la vez humanización de la recolección, organización, procesamiento y presentación de los datos relacionados con los indicadores de evaluación de la calidad, los bibliométricos y los referentes con la indexación, por citar ejemplos. Objetivo: diseñar una base de datos para la Revista Cubana de Tecnología de la Salud. Métodos: se realizó una investigación aplicada de innovación tecnológica, en la que diseñó una base de datos relacional. Se emplearon métodos a nivel teórico y empíricos; entre ellos la entrevista para obtener una descripción detallada de las necesidades de la revista, y la modelación en la representación a través de modelos lógicos y conceptuales del universo de discurso. Resultados: la base de datos se diseñó mediante el modelo Entidad-Relación. Se obtuvieron el diagrama de entidad-relación y el esquema relacional de la base de datos. Se aplicó la teoría de la normalización para eliminar las redundancias. Conclusiones: quedó descrito el proceso de diseño de la base de datos con la cual la Revista Cubana de Tecnología de la Salud, podrá gestionar los datos de una forma centralizada y eficaz, de manera que se puedan aplicar en la evaluación de la gestión editorial y el desarrollo de estudios bibliométricos de la producción científica de la revista.

Introduction: The Cuban Journal of Health Technology requires a database system for the treatment of information concerning the different editorial management processes. This is necessary in the automation process and, at the same time, in the humanization of the collection, organization, processing, and presentation of the data related to the quality evaluation and bibliometric indicators, as well as those related to the indexing, to cite some examples. Objective: To design a database for the Cuban Journal of Health Technology. Methods: An applied research on technological innovation was carried out, in which a relational database was designed. Theoretical and empirical methods were used; among them, we can mention the interview to obtain a detailed description of the needs of the journal, and the representation modeling through logical and conceptual models of the universe of discourse. Results: The database was designed using the Entity-Relationship model. The entity-relationship diagram and the relational schema of the database were obtained. Normalization theory was applied to eliminate redundancies. Conclusions: The design process of the database that will allow the Cuban Journal of Health Technology to manage data in a centralized and efficient way was described, so that it can be applied to the evaluation of the editorial management and the development of bibliometric studies of the scientific production of the journal.

A comparison of approaches to accessing existing biological and chemical relational databases via SPARQL.

Current biological and chemical research is increasingly dependent on the reusability of previously acquired data, which typically come from various sources. Consequently, there is a growing need for database systems and databases stored in them to be interoperable with each other. One of the possible solutions to address this issue is to use systems based on Semantic Web technologies, namely on the Resource Description Framework (RDF) to express data and on the SPARQL query language to retrieve the data. Many existing biological and chemical databases are stored in the form of a relational database (RDB). Converting a relational database into the RDF form and storing it in a native RDF database system may not be desirable in many cases. It may be necessary to preserve the original database form, and having two versions of the same data may not be convenient. A solution may be to use a system mapping the relational database to the RDF form. Such a system keeps data in their original relational form and translates incoming SPARQL queries to equivalent SQL queries, which are evaluated by a relational-database system. This review compares different RDB-to-RDF mapping systems with a primary focus on those that can be used free of charge. In addition, it compares different approaches to expressing RDB-to-RDF mappings. The review shows that these systems represent a viable method providing sufficient performance. Their real-life performance is demonstrated on data and queries coming from the neXtProt project.

Validation of an Automated System for the Extraction of a Wide Dataset for Clinical Studies Aimed at Improving the Early Diagnosis of Candidemia.

There is increasing interest in assessing whether machine learning (ML) techniques could further improve the early diagnosis of candidemia among patients with a consistent clinical picture. The objective of the present study is to validate the accuracy of a system for the automated extraction from a hospital laboratory software of a large number of features from candidemia and/or bacteremia episodes as the first phase of the AUTO-CAND project. The manual validation was performed on a representative and randomly extracted subset of episodes of candidemia and/or bacteremia. The manual validation of the random extraction of 381 episodes of candidemia and/or bacteremia, with automated organization in structured features of laboratory and microbiological data resulted in ≥99% correct extractions (with confidence interval < ±1%) for all variables. The final automatically extracted dataset consisted of 1338 episodes of candidemia (8%), 14,112 episodes of bacteremia (90%), and 302 episodes of mixed candidemia/bacteremia (2%). The final dataset will serve to assess the performance of different ML models for the early diagnosis of candidemia in the second phase of the AUTO-CAND project.

Diseño de base de datos para el departamento de Sistemas de Información en Salud, La Habana 2021

Introducción: La Facultad de Tecnología de la Salud dispone de una base de datos para el tratamiento de la información concerniente a los estudiantes matriculados, sin embargo, esta base de datos no cubre las necesidades propias del departamento de Sistemas de Información en Salud. Se necesita para la elaboración de estrategias organizativas por parte del departamento un sistema de gestión de información ajustado a las características del colectivo docente. Objetivo: Diseñar una base de datos para el departamento de Sistema de Información en Salud en aras de una mejor gestión de la información departamental. Método: Se realizó una investigación aplicada de innovación tecnológica en el período noviembre de 2020 - febrero de 2021, en la Facultad de Tecnología de la Salud, La Habana, en la que diseñó una base de datos relacional. Se emplearon métodos a nivel teórico y empíricos; entre ellos la entrevista para obtener una descripción detallada de las necesidades del departamento, y la modelación para representar a través de modelos lógicos y conceptuales del universo de discurso. Se empleó el Microsoft Access 2019 para la implementación de la base de datos diseñada. Resultados: La base de datos se diseñó mediante el modelo Entidad-Relación. Se obtuvieron el diagrama de entidad-relación y el esquema relacional de la base de datos. A través de Microsoft Access se crearon las tablas con sus respectivas relaciones, las consultas, los formularios e informes de la base de datos. Conclusiones: Quedó descrito todo el proceso de diseño e implementación de la base de datos que satisface los requisitos funcionales con la cual el departamento de Sistema de Información en Salud podrá gestionar sus datos de una manera centralizada y eficaz.

Introduction: The Facultad de Tecnología de la Salud has a database in use for gathering information concerning enrolled students; however, this database does not meet the needs of the department of Sistemas de Información en Salud, it is very important for professors to have a properly information management system helps them for performing the organizational strategies required. Objective: To design a database to be use in the department of Sistemas de Información en Salud for a better information management. Method: An applied research in technological innovation was conducted at the Facultad de Tecnología de la Salud, La Habana, in the period November 2020 - February 2021. A relational database was designed for practical usage. Theoretical and empirical methods were used, including the use of the interview methods focused on obtaining a better description of the department's needs, designing properly through logical and conceptual models the proposed databases. Microsoft Access database engine 2019 was used to build and implemented the databases. Results: The database was developed using the Entity-Relationship model. The entity-relationship diagram and a relational database schema were obtained. By using the Microsoft Access software were built different tables with their respective links, queries, forms and reports. Conclusions: It was designed and implemented a database that satisfies the functional requirements with which the department of Sistemas de Información en Salud will be able to manage its data, in a centralized and efficient manner. The designing and implementation processes of the database were described.

Introdução: A Facultad de Tecnología de la Salud possui um banco de dados para o tratamento das informações dos alunos matriculados, porém esse banco de dados não atende às necessidades do Departamento de Sistemas de Información en Salud. Para o desenvolvimento de estratégias organizacionais por parte do departamento, é necessário um sistema de gestão da informação ajustado às características do grupo docente. Objetivo: Projetar um banco de dados para o departamento de Sistemas de Información en Salud para melhor gerenciar as informações do departamento. Método: Foi realizada uma pesquisa aplicada sobre inovação tecnológica no período de novembro de 2020 a fevereiro de 2021, na Facultad de Tecnología de la Salud, Havana, na qual foi projetada uma base de dados relacional. Foram utilizados métodos teóricos e empíricos; entre eles a entrevista para obter uma descrição detalhada das necessidades do departamento, e a modelagem para representar por meio de modelos lógicos e conceituais o universo do discurso. O Microsoft Access 2019 foi utilizado para a implementação do banco de dados projetado. Resultados: O banco de dados foi desenhado usando o modelo entidade-relacionamento. Obteve-se o diagrama entidade-relacionamento e o esquema relacional do banco de dados. Através do Microsoft Access, foram criadas as tabelas com seus respectivos relacionamentos, as consultas, os formulários e relatórios do banco de dados. Conclusões: Foi descrito todo o processo de desenho e implementação da base de dados que satisfaça os requisitos funcionais com os quais o departamento do Sistemas de Información en Salud poderá gerir os seus dados de forma centralizada e eficiente.

A Wide Database for a Multicenter Study on Pneumocystis jirovecii Pneumonia in Intensive Care Units.

Pneumocystis jirovecii pneumonia (PJP) is an opportunistic fungal infection that may affect patients with immunosuppression. In order to improve the diagnosis accuracy for PJP, facilitating the collection of data across Europe to reliably assess the performance of diagnostic tests for PJP is essential to improve the care of critically ill patients developing this severe condition. Such large data can be collected thanks to the contribution of several European hospitals in the compilation of a dedicated electronic Case Report Form (eCRF). The main focus of this work is to create an interface with high ergonomics both in the compilation and in the subsequent validation of the records.

Advancing genomic epidemiology by addressing the bioinformatics bottleneck: Challenges, design principles, and a Swiss example.

The SARS-CoV-2 pandemic led to a huge increase in global pathogen genome sequencing efforts, and the resulting data are becoming increasingly important to detect variants of concern, monitor outbreaks, and quantify transmission dynamics. However, this rapid up-scaling in data generation brought with it many IT infrastructure challenges. In this paper, we report about developing an improved system for genomic epidemiology. We (i) highlight key challenges that were exacerbated by the pandemic situation, (ii) provide data infrastructure design principles to address them, and (iii) give an implementation example developed by the Swiss SARS-CoV-2 Sequencing Consortium (S3C) in response to the COVID-19 pandemic. Finally, we discuss remaining challenges to data infrastructure for genomic epidemiology. Improving these infrastructures will help better detect, monitor, and respond to future public health threats.

Pladias platform: Technical description of the database structure.

Background: Digitising and aggregating local floristic data is a critical step in the study of biodiversity. The integrative web-based platform Pladias, designed to cover a wide range of data on vascular plants, was recently developed in the Czech Republic. The combination of occurrence data with species characteristics opens many opportunities for data analysis and synthesis. New information: This article describes the relational structure of the Pladias database service (PladiasDB) and the context of the platform architecture. The structure is relatively complex, as our goal was to cover: (i) species occurrence records, including their management, validation and export of revised species distribution maps, (ii) data on species characteristics with quality control tools using defined data types and (iii) separate user interfaces (UI) for professionals and the general public. We discuss the approaches chosen to model individual elements in PladiasDB and summarise the experience gained during the first five years of operation of the Pladias platform.

The need for data management standards in public health nursing: A narrative review and case study.

BACKGROUND: Data management is the key to the success of all projects and research. The ability to safely store, manipulate, and decipher data in real time is invaluable. Currently data management standards in public health are non-existent. Since the invention of computers real-time data retrieval and analysis has been possible but underutilized by researchers in the field. Historically, most small research studies and field-based projects have utilized spreadsheets for data management, which often proves problematic as the project grows. However, a viable and superior alternative exists in relational databases, such as REDCap. Relational databases allow for easier concatenation of multiple legacy datasets, facilitate data entry with surveys that incorporate branching logic, and allow for real time data entry in the field without the need for WIFI. METHODS: One example of a public health project being transitioned from spreadsheet data management to a relational database is the Farmworker Family Health Program based out of the Lillian Carter Center for Global Health & Social Responsibility at Emory University's Nell Hodgson Woodruff School of Nursing. The data management transition from spreadsheets to REDCap has provided the team with unique insight into the data that has been collected in the 30 years the program has been running. CONCLUSION: Through this case study, we identify the need for and recommend that those in public health nursing utilize relational databases when collecting data during research studies or as electronic medical records for field clinics.

Multi-omic molecular profiling guide's efficacious treatment selection in refractory metastatic breast cancer: a prospective phase II clinical trial.

This prospective phase II clinical trial (Side Out 2) explored the clinical benefits of treatment selection informed by multi-omic molecular profiling (MoMP) in refractory metastatic breast cancers (MBCs). Core needle biopsies were collected from 32 patients with MBC at trial enrollment. Patients had received an average of 3.94 previous lines of treatment in the metastatic setting before enrollment in this study. Samples underwent MoMP, including exome sequencing, RNA sequencing (RNA-Seq), immunohistochemistry, and quantitative protein pathway activation mapping by Reverse Phase Protein Microarray (RPPA). Clinical benefit was assessed using the previously published growth modulation index (GMI) under the hypothesis that MoMP-selected therapy would warrant further investigation for GMI ≥ 1.3 in ≥ 35% of the patients. Of the 32 patients enrolled, 29 received treatment based on their MoMP and 25 met the follow-up criteria established by the trial protocol. Molecular information was delivered to the tumor board in a median time frame of 14 days (11-22 days), and targetable alterations for commercially available agents were found in 23/25 patients (92%). Of the 25 patients, 14 (56%) reached GMI ≥ 1.3. A high level of DNA topoisomerase I (TOPO1) led to the selection of irinotecan-based treatments in 48% (12/25) of the patients. A pooled analysis suggested clinical benefit in patients with high TOPO1 expression receiving irinotecan-based regimens (GMI ≥ 1.3 in 66.7% of cases). These results confirmed previous observations that MoMP increases the frequency of identifiable actionable alterations (92% of patients). The MoMP proposed allows the identification of biomarkers that are frequently expressed in MBCs and the evaluation of their role as predictors of response to commercially available agents. Lastly, this study confirmed the role of MoMP for informing treatment selection in refractory MBC patients: more than half of the enrolled patients reached a GMI ≥ 1.3 even after multiple lines of previous therapies for metastatic disease.

Visualizing risk factors of dementia from scholarly literature using knowledge maps and next-generation data models.

Scholarly communication of knowledge is predominantly document-based in digital repositories, and researchers find it tedious to automatically capture and process the semantics among related articles. Despite the present digital era of big data, there is a lack of visual representations of the knowledge present in scholarly articles, and a time-saving approach for a literature search and visual navigation is warranted. The majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation, storage, and dynamic visualization. To address this limitation, the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital repositories. Contemporary topics of research and practice, including modifiable risk factors leading to a dramatic increase in Alzheimer's disease and other forms of dementia, warrant deeper insight into the evidence-based knowledge available in the literature. The goal is to provide researchers with a visual-based easy traversal through a digital repository of research articles. This paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge, such as dementia risk factors. The model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research articles. It also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly articles. Further, it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations. In this study, the implementation of the proposed model in the Neo4j graph data repository, along with the results achieved, is presented as a proof of concept. Using scholarly research articles on dementia risk factors as a case study, automatic knowledge extraction, storage, intelligent search, and visual navigation are illustrated. The implementation of contextual knowledge and its relationship for a visual exploration by researchers show promising results in the knowledge discovery of dementia risk factors. Overall, this study demonstrates the significance of a semantic visualization with the effective use of knowledge maps and paves the way for extending visual modeling capabilities in the future.

A Wide Database for Future Studies Aimed at Improving Early Recognition of Candidemia.

Invasive candidiasis is associated with high morbidity and mortality in critically ill patients, i.e. patients admitted to Intensive Care Units (ICUs) or in surgical wards. There are no clinical signs or specific symptoms and even though early diagnosis risk scores and rapid tests are available, none of such strategies has an equally-optimal level of sensitivity and specificity. In the era of Electronic Health Records (EHRs), several clinical studies exploited Machine Learning (ML) models and large database of features to improve the diagnosis accuracy. The main aim of this work is to build a wide dataset which can be exploited to apply ML models to further improve the early recognition of candidemia at the bedside of patients with compatible signs and symptoms.

Conceptual Design and Prototyping for a Primate Health History Knowledge Model.

Primate models are important for understanding human conditions, especially in studies of ageing, pathology, adaptation, and evolution. However, how to integrate data from multiple disciplines and render them compatible with each other for datamining and in-depth study is always challenging. In a long-term project, we have started a collaborative research endeavor to examine the health history of a free-ranging rhesus macaque colony at Cayo Santiago, and build a knowledge model for anthropological and biomedical/translational studies of the effects of environment and genetics on bone development, aging, and pathologies. This paper discusses the conceptual design as well as the prototyping of this model and related graphical user interfaces, and how these will help future scientific queries and studies.

WingBank: a wing image database of mosquitoes

Mosquito-borne diseases affect millions of people and cause thousands of deaths yearly. Vaccines have been hitherto insufficient to mitigate them, which makes mosquito control the most viable approach. But vector control depends on correct species identification and geographical assignment, and the taxonomic characters of mosquitoes are often inconspicuous to non-taxonomists, which are restricted to a life stage and/or even damaged. Thus, geometric morphometry, a low cost and precise technique that has proven to be efficient for identifying subtle morphological dissimilarities, may contribute to the resolution of these types of problems. We have been applying this technique for more than 10 years and have accumulated thousands of wing images with their metadata. Therefore, the aims of this work were to develop a prototype of a platform for the storage of biological data related to wing morphometry, by means of a relational database and a web system named “WingBank.” In order to build the WingBank prototype, a multidisciplinary team performed a gathering of requirements, modeled and designed the relational database, and implemented a web platform. WingBank was designed to enforce data completeness, to ease data query, to leverage meta-studies, and to support applications of automatic identification of mosquitoes. Currently, the database of the WingBank contains data referring to 77 species belonging to 15 genera of Culicidae. From the 13,287 wing records currently cataloged in the database, 2,138 were already made available for use by third parties. As far as we know, this is the largest database of Culicidae wings of the world

Data Block and Tuple Identification Using Master Index.

Relational databases are still very often used as a data storage, even for the sensor oriented data. Each data tuple is logically stored in the table referenced by relationships between individual tables. From the physical point of view, data are stored in the data files delimited by the tablespaces. Files are block-oriented. When retrieving data, particular blocks must be identified and transferred into the memory for the evaluation and processing. This paper deals with storage principles and proposes own methods for effective data block location and identification if no suitable index for the query is present in the system. Thanks to that, the performance of the whole system is optimized, and the processing time and costs are significantly lowered. The proposed solution is based on the master index, which points just to the blocks with relevant data. Thus, no sequential block scanning is necessary for consuming many system resources. The paper analyzes the impact of block size, which can have a significant impact on sensor oriented data, as well.

Linking administrative data sets of inpatient infectious diseases diagnoses in far North Queensland: a cohort profile.

PURPOSE: To design a linked hospital database using administrative and clinical information to describe associations that predict infectious diseases outcomes, including long-term mortality. PARTICIPANTS: A retrospective cohort of Townsville Hospital inpatients discharged with an International Classification of Diseases and Related Health Problems 10th Revision Australian Modification code for an infectious disease between 1 January 2006 and 31 December 2016 was assembled. This used linked anonymised data from: hospital administrative sources, diagnostic pathology, pharmacy dispensing, public health and the National Death Registry. A Created Study ID was used as the central identifier to provide associations between the cohort patients and the subsets of granular data which were processed into a relational database. A web-based interface was constructed to allow data extraction and evaluation to be performed using editable Structured Query Language. FINDINGS TO DATE: The database has linked information on 41 367 patients with 378 487 admissions and 1 869 239 diagnostic/procedure codes. Scripts used to create the database contents generated over 24 000 000 database rows from the supplied data. Nearly 15% of the cohort was identified as Aboriginal or Torres Strait Islanders. Invasive staphylococcal, pneumococcal and Group A streptococcal infections and influenza were common in this cohort. The most common comorbidities were smoking (43.95%), diabetes (24.73%), chronic renal disease (17.93%), cancer (16.45%) and chronic pulmonary disease (12.42%). Mortality over the 11-year period was 20%. FUTURE PLANS: This complex relational database reutilising hospital information describes a cohort from a single tropical Australian hospital of inpatients with infectious diseases. In future analyses, we plan to explore analyses of risks, clinical outcomes, healthcare costs and antimicrobial side effects in site and organism specific infections.

Quantitatively Measuring Privacy in Interactive Query Settings Within RDBMS Framework.

Little attention has been paid to the measurement of risk to privacy in Database Management Systems, despite their prevalence as a modality of data access. This paper proposes PriDe, a quantitative privacy metric that provides a measure (privacy score) of privacy risk when executing queries in relational database management systems. PriDe measures the degree to which attribute values, retrieved by a principal (user) engaging in an interactive query session, represent a reduction of privacy with respect to the attribute values previously retrieved by the principal. It can be deployed in interactive query settings where the user sends SQL queries to the database and gets results at run-time and provides privacy-conscious organizations with a way to monitor the usage of the application data made available to third parties in terms of privacy. The proposed approach, without loss of generality, is applicable to BigSQL-style technologies. Additionally, the paper proposes a privacy equivalence relation that facilitates the computation of the privacy score.

Investigation of Content Overlap in Proprietary Medical Mappings.

To manage medical information semantic interoperability is essential. Mapping of concepts and mapping of terminologies are two objectives to reach semantic interoperability. Russian proprietary health information system and FHIR overlaps (60%) were calculated to estimate possibility of standardization. Russian terminology directories and FHIR overlaps were calculated to estimate possibility of use Russian terminologies and codifications in FHIR-based information system. The result is promising, however, requires more wide investigation using automatic tools.

FisOmics: A portal of fish genomic resources.

An online portal, accessible at URL: http://mail.nbfgr.res.in/FisOmics/, was developed that features different genomic databases and tools. The portal, named as FisOmics, acts as a platform for sharing fish genomic sequences and related information in addition to facilitating the access of high-performance computational resources for genome and proteome data analyses. It provides the ability for quarrying, analysing and visualizing genomic sequences and related information. The featured databases in FisOmics are in the World Wide Web domain already. The aim to develop portal was to provide a nodal point to access the featured databases and work conveniently. Presently, FisOmics includes databases on barcode sequences, microsatellite markers, mitogenome sequences, hypoxia-responsive genes and karyology of fishes. Besides, it has a link to other molecular resources and reports on the on-going activities and research achievements.