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Open source Geographic Information System (GIS) have been fostering spatial data research such as Earth observation and environmental monitoring for more than 30 years. More recently, globally available geospatial information combined with web technologies are providing new environments and tools for data handling. Thus, binding the mapping and processing capabilities of traditional GIS to the accessibility and reliability of web-based data providers can bring new opportunities for research. In this paper, we built a QGIS plugin to explore the integration of different public data providers in Brazil along with field data produced by the BONDS project. The biOdiversity conservatioN with Development in Amazon wetlandS project (BONDS) proposes to develop biodiversity scenarios for the Amazonian floodplains aiming to support solutions to preserve biodiversity and ecosystem services. The use of web services enabled dynamic and fast access to several products ranging from remote sensing images, land use and land cover, territorial cartography, water quality, to COVID-19 health data, and more. © 2022 National Institute for Space Research, INPE. All rights reserved.
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The ability of a health information exchange (HIE) to consolidate information, collected from multiple, disparate information systems, into a single, person-centric health record can provide a comprehensive and longitudinal representation of an individual's medical history. Shared, longitudinal health records can be leveraged to enhance the delivery of individual clinical care and provide opportunities to improve health outcomes at the population level. This chapter describes the clinical benefits imparted by the shared health record (SHR) component an HIE infrastructure. It also characterizes the potential public health benefits of the aggregate level, population health indicators calculated, stored, and distributed by a health management information system (HMIS) component. Tools for visualizing health indicators from the HMIS, including disease surveillance systems developed during the COVID-19 pandemic, are also described. Postpandemic components such as the SHR and HMIS will likely play critical roles in strengthening health information infrastructures in states and nations. © 2023 Elsevier Inc. All rights reserved.
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Health information exchange (HIE) now exists in diverse forms within and across countries. However, our HIE infrastructure is fragmented, which impedes the ability to meet the needs of varied data sharing use cases—particularly public health data needs that became evident during the COVID-19 pandemic. In response, several efforts—some within the United States and some outside the United States—have started to undertake work to help tie existing HIE approaches together into a more seamless whole. While the societal benefits of doing so are clear, there are substantial cost and complexity involved, leaving it an open question as to how successful they will be. This chapter describes three major efforts underway to advance HIE infrastructure at scale—the Trusted Exchange Framework and Common Agreement (a US policy strategy), the Joint Action Towards the European Health Data Space (an EU initiative), and the emerging concept of health data utility models as more comprehensive repositories of health data with strong government requirements for participation. For each, we describe the effort as well as discuss potential challenges to implementation and success in achieving the intended outcomes. We also discuss a complementary issue related to health data integration and usability of exchanged health information that will become more acute as efforts to advance data sharing at scale are pursued. © 2023 Elsevier Inc. All rights reserved.
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HealthECCO is the driving force behind the COVID-19 knowledge graph spanning multiple biomedical data domains. One way to access CovidGraph is SemSpect, an interface designed for data exploration in graphs. To showcase the possibilities that arise from integrating a variety of COVID-19 related data sources over the last three years, we present three use cases from the (bio-)medical domain. Availability: The project is open source and freely available from: https://healthecco.org/covidgraph/. The source code and documentation are available on GitHub: https://github.com/covidgraph.
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COVID-19 , Humans , Software , DocumentationABSTRACT
Complexity, heterogeneity, schemaless-ness, data visualization, and extraction of consistent knowledge from Big Data are the biggest challenges in NoSQL databases. This paper presents a general semantic NoSQL Application Program Interface that integrates and converts NoSQL databases to semantic representation. The generated knowledge base is suitable for visualization and knowledge extraction from different Big Data sources. The authors use a case study of the COVID-19 pandemic prediction and other weather occurrences in various parts of the world to illustrate the suggested API. The Authors find a correlation between COVID-19 spread and deteriorating weather. According to the experimental findings, the API's performance is enough for heterogeneous Big Data.
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The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.
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Genomics , Neoplasms , Humans , Genomics/methods , Proteomics/methods , Multiomics , Metabolomics/methodsABSTRACT
Financial sustainability is one of the crucial operations of many higher education institutes. Though since late 2019, the inevitable disruption and significant changes in the higher education system have continued after the increasing in COVID-19 transmissions. These affect the operations of higher education institutions in numerous ways, such as students' admission, financial management and teaching strategies. The purpose of this study is to present a data integration aspect of the analysis of financial data from academic income. Such data integration relates to the data from enrollment, admission, and research from many heterogeneous sources within the institution. In addition, the k-mean clustering approach is applied to group academic programs for further analysis. In the future, the institution's financial and risk management, research enhancement, and reputation and positioning will employ this analytics to support and shape the institution's operations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Conducting epidemiologic research usually requires a large amount of data to establish the natural history of a disease and achieve meaningful study design, and interpretations of findings. This is, however, a huge task because the healthcare domain is composed of a complex corpus and concepts that result in difficult ways to use and store data. Additionally, data accessibility should be considered because sensitive data from patients should be carefully protected and shared with responsibility. With the COVID-19 pandemic, the need for sharing data and having an integrated view of the data was reaffirmed to identify the best approaches and signals to improve not only treatments and diagnoses but also social answers to the epidemiological scenario. This paper addresses a data integration scenario for dealing with COVID-19 and cardiovascular diseases, covering the main challenges related to integrating data in a common data repository storing data from several hospitals. Conceptual architecture is presented to deal with such approaches and integrate data from a Portuguese hospital into the common repository used to explore data in a standardized way. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This study has produced an improved percentile and seasonal (median) trend estimate of free tropospheric ozone above western North America (WNA), through a data fusion of ozonesonde, lidar, commercial aircraft, and field campaign measurements. Our method combines heterogeneous data sets according to the consensus data characteristics and inherent uncertainty in order to produce our best fused product. In response to different data collection environments (in situ or ground‐based), we investigate the ozone variability based on a wide range of percentiles, which is preferable for trend detection due to tropospheric ozone's high degree of heteroscedasticity (i.e., inconsistent trends and variability between different ozone percentiles). We then compare the ozone trends and variability above the California sub‐domain to the full WNA region for better understanding of the correlations between different regional scales. In California, the 1995–2021 percentile (from the 5th to 95th) and seasonal trends are clearly positive in terms of high signal‐to‐noise ratios. The magnitude of the trends is generally weaker over WNA compared to California, but reliable positive trends can still be found between the 10th and 70th percentiles, as well as winter and summer, whereas autumn shows a negative trend over the same period. In addition, dozens of rural surface sites across the region are selected to represent the boundary layer variability. In contrast to increasing free tropospheric ozone, we find overall strong negative surface trends since 1995, with the greatest divergence found in summer. Throughout the analysis implications of the COVID‐19 economic downturn on ozone variability are discussed.Alternate :Plain Language SummaryFree tropospheric ozone above western North America has increased since the mid‐1990s. Despite an observed drop of ozone in 2020 due to the COVID‐19 economic downturn, this observation‐based study shows the overall free tropospheric ozone trends have not been offset and continued to increase over 1995–2021, mainly driven by strong positive trends in winter and summer. In combination with the strong negative trends observed at rural surface sites over the same period, this study adds to the growing body of evidence that surface trends are frequently disconnected from the general increases observed in the free troposphere.
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Profiling immune responses across several dimensions, including time, patients, molecular features, and tissue sites, can deepen our understanding of immunity as an integrated system. These studies require new analytical approaches to realize their full potential. We highlight recent applications of tensor methods and discuss several future opportunities.
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Communicable Diseases , Immunity , HumansABSTRACT
PurposeIn this paper, the authors introduce supply disruption ambiguity as the inability of a sourcing firm to attach probability point estimates to the occurrence of and to the magnitude of loss from supply disruptions. The authors drew on the "ambiguity in decision-making” literature to define this concept formally, connected it to relevant supply disruption information deficit, positioned it relative to supply chain risk assessment and hypothesized and tested its negative associations with both supply base ties and inventory turnover.Design/methodology/approachThe authors analysed survey data from 171 North American manufacturers and archival data for a subset (88 publicly listed) of these manufacturers via Ordinary Least Squares (OLS) estimation after ensuring that methodological concerns with survey research have been addressed. They used appropriate controls and employed the heteroskedasticity-based instrumental variable (HBIV) approach to ensure that inferences from our results are not unduly influenced by endogeneity.FindingsStrong supply base ties decrease supply disruption ambiguity, which, in turn, increases inventory turnover. Moreover, strong supply base ties and data integration with the supply base have indirect and positive effects on inventory turnover. As sourcing firms strengthen ties and integrate data exchange with their supply base, their inventory turnover improves from access to information relevant to detect and diagnose supply disruptions effectively.Originality/valueResearch on supply disruption management has paid more attention to the "disruption recovery” stage than to the "disruption discovery” stage. In this paper, the authors add novel insights regarding the recognition and diagnosis aspects of the "disruption discovery” stage. These novel insights reveal how and why sourcing firms reduce their overall ambiguity associated with detecting and assessing losses from supply disruptions through establishing strong ties with their supply base and how and why reducing such ambiguity improves inventory turnover performance.
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Convergence, and its various configurations, is an established topic in technology and innovation management literature. This article contributes to the extant literature about industrial convergence by conducting an explorative analysis of the industrial crisis caused by the Covid-19 pandemic. In this article, we aim to explore how industrial convergence affects the business dynamics of the healthcare markets. To fulfill our research purpose, we perform a qualitative study by exploring retrospectively the case study of precision medicine. Thus, we use a case study approach based on the triangulation of the multiple sources of evidence gathered. We propose a conceptual framework attesting to the continued recourse to digitalization for the need for data integration and the creation of hybrid figures within healthcare markets during the industrial crisis. This article proposes various implications for researchers and practitioners dealing with the management and development of innovation in convergent sectors.
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Severe manifestations of coronavirus disease 2019 (COVID-19) and mortality have been associated with physiological alterations that provide insights into the pathogenesis of the disease. Moreover, factors that drive recovery from COVID-19 can be explored to identify correlates of protection. The cellular metabolism represents a potential target to improve survival upon severe disease, but the associations between the metabolism and the inflammatory response during COVID-19 are not well defined. We analyzed blood laboratorial parameters, cytokines, and metabolomes of 150 individuals with mild to severe disease, of which 33 progressed to a fatal outcome. A subset of 20 individuals was followed up after hospital discharge and recovery from acute disease. We used hierarchical community networks to integrate metabolomics profiles with cytokines and markers of inflammation, coagulation, and tissue damage. Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) promotes significant alterations in the plasma metabolome, whose activity varies according to disease severity and correlates with oxygen saturation. Differential metabolism underlying death was marked by amino acids and related metabolites, such as glutamate, glutamyl-glutamate, and oxoproline, and lipids, including progesterone, phosphocholine, and lysophosphatidylcholines (lysoPCs). Individuals who recovered from severe disease displayed persistent alterations enriched for metabolism of purines and phosphatidylinositol phosphate and glycolysis. Recovery of mild disease was associated with vitamin E metabolism. Data integration shows that the metabolic response is a hub connecting other biological features during disease and recovery. Infection by SARS-CoV-2 induces concerted activity of metabolic and inflammatory responses that depend on disease severity and collectively predict clinical outcomes of COVID-19. IMPORTANCE COVID-19 is characterized by diverse clinical outcomes that include asymptomatic to mild manifestations or severe disease and death. Infection by SARS-CoV-2 activates inflammatory and metabolic responses that drive protection or pathology. How inflammation and metabolism communicate during COVID-19 is not well defined. We used high-resolution mass spectrometry to investigate small biochemical compounds (<1,500 Da) in plasma of individuals with COVID-19 and controls. Age, sex, and comorbidities have a profound effect on the plasma metabolites of individuals with COVID-19, but we identified significant activity of pathways and metabolites related to amino acids, lipids, nucleotides, and vitamins determined by disease severity, survival outcome, and recovery. Furthermore, we identified metabolites associated with acute-phase proteins and coagulation factors, which collectively identify individuals with severe disease or individuals who died of severe COVID-19. Our study suggests that manipulating specific metabolic pathways can be explored to prevent hyperinflammation, organ dysfunction, and death.
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BACKGROUND: Journey maps are visualization tools that can facilitate the diagrammatical representation of stakeholder groups by interest or function for comparative visual analysis. Therefore, journey maps can illustrate intersections and relationships between organizations and consumers using products or services. We propose that some synergies may exist between journey maps and the concept of a learning health system (LHS). The overarching goal of an LHS is to use health care data to inform clinical practice and improve service delivery processes and patient outcomes. OBJECTIVE: The purpose of this review was to assess the literature and establish a relationship between journey mapping techniques and LHSs. Specifically, in this study, we explored the current state of the literature to answer the following research questions: (1) Is there a relationship between journey mapping techniques and an LHS in the literature? (2) Is there a way to integrate the data from journey mapping activities into an LHS? (3) How can the data gleaned from journey map activities be used to inform an LHS? METHODS: A scoping review was conducted by querying the following electronic databases: Cochrane Database of Systematic Reviews (Ovid), IEEE Xplore, PubMed, Web of Science, Academic Search Complete (EBSCOhost), APA PsycInfo (EBSCOhost), CINAHL (EBSCOhost), and MEDLINE (EBSCOhost). Two researchers applied the inclusion criteria and assessed all articles by title and abstract in the first screen, using Covidence. Following this, a full-text review of included articles was done, with relevant data extracted, tabulated, and assessed thematically. RESULTS: The initial search yielded 694 studies. Of those, 179 duplicates were removed. Following this, 515 articles were assessed during the first screening phase, and 412 were excluded, as they did not meet the inclusion criteria. Next, 103 articles were read in full, and 95 were excluded, resulting in a final sample of 8 articles that satisfied the inclusion criteria. The article sample can be subsumed into 2 overarching themes: (1) the need to evolve service delivery models in health care, and (2) the potential value of using patient journey data in an LHS. CONCLUSIONS: This scoping review demonstrated the gap in knowledge regarding integrating the data from journey mapping activities into an LHS. Our findings highlighted the importance of using the data from patient experiences to enrich an LHS and provide holistic care. To satisfy this gap, the authors intend to continue this investigation to establish the relationship between journey mapping and the concept of LHSs. This scoping review will serve as phase 1 of an investigative series. Phase 2 will entail the creation of a holistic framework to guide and streamline data integration from journey mapping activities into an LHS. Lastly, phase 3 will provide a proof of concept to demonstrate how patient journey mapping activities could be integrated into an LHS.
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BACKGROUND: Previous literature showed significant health disparities between Native American population and other populations such as Non-Hispanic White. Most existing studies for Native American Health were based on non-probability samples which suffer with selection bias. In this paper, we are the first to evaluate the effectiveness of data integration methods, including calibration and sequential mass imputation, to improve the representativeness of the Tribal Behavioral Risk Factor Surveillance System (TBRFSS) in terms of reducing the biases of the raw estimates. METHODS: We evaluated the benefits of our proposed data integration methods, including calibration and sequential mass imputation, by using the 2019 TBRFSS and the 2018 and 2019 Behavioral Risk Factor Surveillance System (BRFSS). We combined the data from the 2018 and 2019 BRFSS by composite weighting. Demographic variables and general health variables were used as predictors for data integration. The following health-related variables were used for evaluation in terms of biases: Smoking status, Arthritis status, Cardiovascular Disease status, Chronic Obstructive Pulmonary Disease status, Asthma status, Cancer status, Stroke status, Diabetes status, and Health Coverage status. RESULTS: For most health-related variables, data integration methods showed smaller biases compared with unadjusted TBRFSS estimates. After calibration, the demographic and general health variables benchmarked with those for the BRFSS. CONCLUSION: Data integration procedures, including calibration and sequential mass imputation methods, hold promise for improving the representativeness of the TBRFSS.
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Health Status , Smoking , Humans , United States , Behavioral Risk Factor Surveillance System , Selection Bias , American Indian or Alaska Native , Population Surveillance/methodsABSTRACT
PURPOSE: Coronavirus disease 2019 (COVID-19) continues to threaten public health globally. Severe acute respiratory coronavirus type 2 (SARS-CoV-2) infection-dependent alterations in the host cell signaling network may unveil potential target proteins and pathways for therapeutic strategies. In this study, we aim to define early severity biomarkers and monitor altered pathways in the course of SARS-CoV-2 infection. EXPERIMENTAL DESIGN: We systematically analyzed plasma proteomes of COVID-19 patients from Turkey by using mass spectrometry. Different severity grades (moderate, severe, and critical) and periods of disease (early, inflammatory, and recovery) are monitored. Significant alterations in protein expressions are used to reconstruct the COVID-19 associated network that was further extended to connect viral and host proteins. RESULTS: Across all COVID-19 patients, 111 differentially expressed proteins were found, of which 28 proteins were unique to our study mainly enriching in immunoglobulin production. By monitoring different severity grades and periods of disease, CLEC3B, MST1, and ITIH2 were identified as potential early predictors of COVID-19 severity. Most importantly, we extended the COVID-19 associated network with viral proteins and showed the connectedness of viral proteins with human proteins. The most connected viral protein ORF8, which has a role in immune evasion, targets many host proteins tightly connected to the deregulated human plasma proteins. CONCLUSIONS AND CLINICAL RELEVANCE: Plasma proteomes from critical patients are intrinsically clustered in a distinct group than severe and moderate patients. Importantly, we did not recover any grouping based on the infection period, suggesting their distinct proteome even in the recovery phase. The new potential early severity markers can be further studied for their value in the clinics to monitor COVID-19 prognosis. Beyond the list of plasma proteins, our disease-associated network unravels altered pathways, and the possible therapeutic targets in SARS-CoV-2 infection by connecting human and viral proteins. Follow-up studies on the disease associated network that we propose here will be useful to determine molecular details of viral perturbation and to address how the infection affects human physiology.
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In the last years, Learning Management systems (LMSs) are acquiring great importance in online education, since they offer flexible integration platforms for organising a vast amount of learning resources, as well as for establishing effective communication channels between teachers and learners, at any direction. These online platforms are then attracting an increasing number of users that continuously access, download/upload resources and interact each other during their teaching/learning processes, which is even accelerating by the breakout of COVID-19. In this context, academic institutions are generating large volumes of learning-related data that can be analysed for supporting teachers in lesson, course or faculty degree planning, as well as administrations in university strategic level. However, managing such amount of data, usually coming from multiple heterogeneous sources and with attributes sometimes reflecting semantic inconsistencies, constitutes an emerging challenge, so they require common definition and integration schemes to easily fuse them, with the aim of efficiently feeding machine learning models. In this regard, semantic web technologies arise as a useful framework for the semantic integration of multi-source e-learning data, allowing the consolidation, linkage and advanced querying in a systematic way. With this motivation, the e-LION (e-Learning Integration ONtology) semantic model is proposed for the first time in this work to operate as data consolidation approach of different e-learning knowledge-bases, hence leading to enrich on-top analysis. For demonstration purposes, the proposed ontological model is populated with real-world private and public data sources from different LMSs referring university courses of the Software Engineering degree of the University of Malaga (Spain) and the Open University Learning. In this regard, a set of four case studies are worked for validation, which comprise advance semantic querying of data for feeding predictive modelling and time-series forecasting of students' interactions according to their final grades, as well as the generation of SWRL reasoning rules for student's behaviour classification. The results are promising and lead to the possible use of e-LION as ontological mediator scheme for the integration of new future semantic models in the domain of e-learning. • e-LION semantic approach is proposed for e-learning data source integration. • An OWL Ontology is designed for e-learning, including SWRL reasoning rules. • The proposal is validated with four real-world (Moodle) and academic cases study. • Obtained semantised data successfully feed predictive machine learning models. • We provide actual e-learning users with a model to enhance their analytics. [ FROM AUTHOR]
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The COVID-19 pandemic is an acute and rapidly evolving global health crisis. To better understand this disease's molecular basis and design therapeutic strategies, we built upon the recently proposed concept of an integrated cell, iCell, fusing three omics, tissue-specific human molecular interaction networks. We applied this methodology to construct infected and control iCells using gene expression data from patient samples and three cell lines. We found large differences between patient-based and cell line-based iCells (both infected and control), suggesting that cell lines are ill-suited to studying this disease. We compared patient-based infected and control iCells and uncovered genes whose functioning (wiring patterns in iCells) is altered by the disease. We validated in the literature that 18 out of the top 20 of the most rewired genes are indeed COVID-19-related. Since only three of these genes are targets of approved drugs, we applied another data fusion step to predict drugs for re-purposing. We confirmed with molecular docking that the predicted drugs can bind to their predicted targets. Our most interesting prediction is artenimol, an antimalarial agent targeting ZFP62, one of our newly identified COVID-19-related genes. This drug is a derivative of artemisinin drugs that are already under clinical investigation for their potential role in the treatment of COVID-19. Our results demonstrate further applicability of the iCell framework for integrative comparative studies of human diseases.
Subject(s)
COVID-19 , Humans , COVID-19/genetics , Molecular Docking Simulation , Pandemics , Drug RepositioningABSTRACT
Complexity, heterogeneity, schemaless-ness, data visualization, and extraction of consistent knowledge from Big Data are the biggest challenges in NoSQL databases. This paper presents a general semantic NoSQL Application Program Interface that integrates and converts NoSQL databases to semantic representation. The generated knowledge base is suitable for visualization and knowledge extraction from different Big Data sources. The authors use a case study of the COVID-19 pandemic prediction and other weather occurrences in various parts of the world to illustrate the suggested API. The Authors find a correlation between COVID-19 spread and deteriorating weather. According to the experimental findings, the API's performance is enough for heterogeneous Big Data.
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Proper territorial data management is critical for territorial planning projects, research, innovation, and the appropriate follow-up to act for the well-being of populations. A multidisciplinary team of professionals established a pilot project named Cortes Data Hub (Centro de Datos de Cortés). It presents several dashboards that show official statistics on the energy sector, mapping the region's energy demand, data on COVID-19 cases and vaccination rates by municipality or department, and a project using Google Earth that combines post-Eta and Iota observations and a social media campaign for disaster awareness and for the promotion of activities to develop tourism in the San Manuel Municipality. This pilot project shows the importance to observe and monitor various key environmental, health, and socioeconomic data. This will help improve initiatives for local development, disaster prevention and control, and the promotion of the One Health approach. The challenges to overcome are the quality and timing of data. Training more academics, government teams, and decision-makers in the use of new tools for data integration with earth observations are important for the Cortés department's development. © 2022 IEEE.