Data Sharing Research Articles

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network. Objectives: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC). Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network. Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

Paths to Data Transfer: Exploring Routine Change for Compliant Data Sharing

Paths to Data Transfer: Exploring Routine Change for Compliant Data Sharing

Trusted access control mechanism for data with blockchain-assisted attribute encryption

In the growing demand for data sharing, how to realize fine-grained trusted access control of shared data and protect data security has become a difficult problem. Ciphertext policy attribute-based encryption (CP-ABE) model is widely used in cloud data sharing scenarios, but there are problems such as privacy leakage of access policy, irrevocability of user or attribute, key escrow, and trust bottleneck. Therefore, we propose a blockchain-assisted CP-ABE (B-CP-ABE) mechanism for trusted data access control. Firstly, we construct a data trusted access control architecture based on the B-CP-ABE, which realizes the automated execution of access policies through smart contracts and guarantees the trusted access process through blockchain. Then, we define the B-CP-ABE scheme, which has the functions of policy partial hidden, attribute revocation, and anti-key escrow. The B-CP-ABE scheme utilizes Bloom filter to hide the mapping relationship of sensitive attributes in the access structure, realizes flexible revocation and recovery of users and attributes by re-encryption algorithm, and solves the key escrow problem by joint authorization of data owners and attribute authority. Finally, we demonstrate the usability of the B-CP-ABE scheme by performing security analysis and performance analysis.

Inherit or innovate?–evidence from China’s auto industry’s new car series naming

The automotive industry introduces a sizable number of new automobile products annually. The naming of new products may have significant impact on their market performance. Diverging from prior research on the impact of product names, companies’ innovative consciousness endows them with a certain level of proactive positioning, thereby implementing independent naming strategies after considering product factors and consumer preferences passively. This paper analyzes the drivers of the market performance of sequential products on the naming of new products in Chinese automotive industry. We collected market share data and names of various car series from January 2007 to September 2022. Then, we calculated semantic distances between names and performed empirical tests. Our findings show that firms demonstrate a risk preference pattern contrary to traditional prospect theory when using the previous car series’ market share as a reference point. In the interplay between market orientation and risk preferences, the relationship between the market share of the last car series and the similarity in names between old and new car series follows an inverted U-shaped pattern. This study extends the discussion on risk preferences within the behavioral theory of the firm (BTOF) and proposes a decision mechanism for product naming that considers market and firm factors during the response phase of market orientation.

Spatiotemporal analysis of sand and dust emission point sources detected from satellite imagery in Syria, Jordan, and Iraq

This study utilized MODIS true color satellite imagery to analyse blowing sand and dust events dynamics in the Middle East from 2010 to 2021, focusing on Syria, Iraq, and Jordan. A total of 4923 dust point sources were detected, with a significant concentration (~90 %) located within the Tigris-Euphrates Basin (Nearest Neighbor Ratio = 0.41, р < 0.001). Land cover analysis revealed that bare land, comprising most of the study area, was the predominant source of dust emissions. Wetlands, though only constituting about 1 % of the area, showed the highest frequency of dust sources per unit area, highlighting their role as critical dust emission hotspots. The study emphasizes the impact of drought and anthropogenic factors, such as poor land management, on blowing dust intensity. It suggests the necessity of strategic land management practices, including re-vegetation of arid areas, reducing soil exposure, and implementing wind erosion control measures. To effectively address the transboundary nature of dust emissions, the findings underscore the importance of fostering regional cooperation through mechanisms such as shared environmental monitoring and data exchange platforms, joint management of cross-border natural resources, and collaborative policy making.

Digit and Mark Recognition Using Convolutional Neural Network for Voting Digitization in Indonesia

Digitization of voting results in Indonesia is essential to ensure the accuracy and integrity of the election process. This research introduces an innovative approach that uses Convolutional Neural Networks (CNN) for handwritten number recognition and tally mark recognition. This research uses a dataset obtained from Kaggle. The research process is conducted in several stages, namely Data Collection, Preprocessing, Data Sharing, Modelling, and Evaluation. The results showed that the proposed model achieved an accuracy of 98%. This research shows that using the CNN algorithm for handwritten number recognition and tally mark recognition can improve accuracy and efficiency in digitizing voting results. It is expected that this research can make a significant contribution to the development of a more reliable digital voting system. Future research is recommended to use a larger dataset to validate the strength of the model, which has been built on more varied data.

A Medical Data Storage and Sharing Model Based on Blockchain

A Medical Data Storage and Sharing Model Based on Blockchain

What Do We Mean by Sharing of Patient Data? DaSH - A Data Sharing Hierarchy of Privacy and Ethical Challenges.

Sharing of clinical data is common and necessary for patient care, research, public health, and innovation. The term "data sharing," however, is often ambiguous in its many facets and complexities-each of which involves ethical, legal, and social issues. To our knowledge there is no extant hierarchy of data sharing that assesses these issues. Develop a hierarchy explicating the risks and ethical complexities of data sharing with particular focus on patient data privacy. We surveyed the available peer-reviewed and gray literature, and with our combined extensive experience in bioethics and medical informatics, created this hierarchy. We present six ways data are shared and provide a tiered Data Sharing Hierarchy (DaSH) of risks, showing increasing threats to patients' privacy and to clinicians and organizations as one progresses up the hierarchy from data sharing for direct patient care, public health and safety, scientific research, commercial purposes, complex combinations of the preceding efforts, and among networked third parties. We offer recommendations to enhance benefits of data sharing while mitigating risks and protecting patients' interests by: improving consenting; developing better policies and procedures; clarifying, simplifying, and updating regulation to include all health-related data regardless of source; expanding the scope of bioethics for information technology; and increasing ongoing monitoring and research. Data sharing, while essential for patient care, is increasingly complex, opaque, and perhaps perilous for patients, clinicians and healthcare institutions. Risks increase with advances in technology and with more encompassing patient data from wearables and artificial intelligence database mining. Data sharing places responsibilities on all parties: patients, clinicians, researchers, educators, risk managers, attorneys, informaticists, bioethicists, institutions, and policy makers.

Maximizing the Utility of Alzheimer’s Disease Trial Data: Sharing of Baseline A4 and LEARN Data

BackgroundThe Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies were conducted between 2014 and 2023, with enrollment completed in 2017 and final study results reported in 2023. The study screening process involved the collection of initial clinical, cognitive, neuroimaging, and genetic measures to determine eligibility. Once randomized, enrolled participants were assessed every four weeks over a 4.5-year follow-up period during which longitudinal clinical, cognitive, and neuroimaging measures were collected. A large number of longitudinal fluid biospecimens were also collected and banked. Consistent with the NIH data sharing policy and the principles of Open Science, the A4/LEARN investigators aimed to share data as broadly and early as possible while still protecting participant privacy and confidentiality and the scientific integrity of the studies.ObjectivesWe describe the approach, methods, and platforms used to share the A4 and LEARN pre-randomization study data for secondary research use. Preliminary results measuring the impact of these efforts are also summarized. We conclude with a discussion of lessons learned and next steps.DesignThe materials shared included de-identified quantitative and image data, analysis software, instruments, and documentation.SettingThe A4 and LEARN Studies were conducted at 67 clinical trial sites in the United States, Canada, Japan, and Australia.ParticipantsThe A4 study screened (n=6763), enrolled, and randomized (n=1169) participants between the ages of 65 and 85 with a blinded follow-up period of 240 weeks followed by an open-label period of variable length. The LEARN study screened and enrolled individuals (n=538) who were ineligible for the A4 study based on nonelevated measures of amyloid accumulation using positron emission tomography imaging (amyloid PET).MeasurementsWe provide descriptive measures of the data shared and summarize the frequency, characteristics, and status of all data access requests submitted to date. We evaluate the scientific impact of the data-sharing effort by conducting a literature search to identify related publications.ResultsThe A4 and LEARN pre-randomization study data were released in December 2018. As of May 8, 2024, 1506 requests have been submitted by investigators and citizen scientists from more than 50 countries. We identified 49 peer-reviewed publications that acknowledge the A4/LEARN study.ConclusionsOur initial results provide evidence supporting the feasibility and scientific utility of broad and timely sharing of Alzheimer’s disease trial data.

Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation

We describe the following shared data from N = 103 healthy adults who completed a broad set of cognitive tasks, surveys, and neuroimaging measurements to examine the construct of self-regulation. The neuroimaging acquisition involved task-based fMRI, resting state fMRI, and structural MRI. Each subject completed the following ten tasks in the scanner across two 90-minute scanning sessions: attention network test (ANT), cued task switching, Columbia card task, dot pattern expectancy (DPX), delay discounting, simple and motor selective stop signal, Stroop, a towers task, and a set of survey questions. The dataset is shared openly through the OpenNeuro project, and the dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard.

Federated semi-supervised representation augmentation with cross-institutional knowledge transfer for healthcare collaboration

In the healthcare field, cross-institutional collaboration can fasten medical research progress. Vertical federated learning (VFL) addresses data heterogeneity across multiple medical institutions while ensuring medical data privacy, thereby enhancing the accuracy of disease diagnoses and treatments. However, traditional VFL only benefits from aligned samples, thereby limiting its applicability due to constrained sample sizes, and a large amount of non-aligned data remains untapped, resulting in wasted data. To exert full leverage on the value of all data obtained from medical institutions, this paper proposes a federated healthcare collaborative framework based on semi-supervised representation augmentation mechanism with cross-institutional knowledge transfer (CrossKT-FRA). Specifically, the developed method comprises three steps. First, the federated representations of shared data (aligned data) among medical institutions are extracted through efficient vertical federated representation learning (FRL) methods. Second, the federated knowledge contained in federated representations and potential labels derived through recurrent learning assist local shared data representations in performing supervised augmented learning. Finally, the federated knowledge is transferred indirectly from the representation augmentation module for shared data to the unsupervised representation augmentation module for local private data (non-aligned data). The experimental results show the effectiveness of the proposed knowledge transfer mechanism, whether applied independently or used to enhance VFL on medical datasets. Our findings contribute to a deeper theoretical understanding of VFL, further facilitating the utilization of high-value medical data. By promoting cross-institutional and cross-disciplinary collaboration in healthcare data sharing, our study enhances the quality efficiency of medical services, thereby accelerating the development of interdisciplinary medical research. Code is available at https://github.com/LieLieLieLieLie/CrossKT-FRA.

The IMPACT Score: Does Sex Matter?

ObjectiveThe Index for Mortality Prediction After Cardiac Transplantation (IMPACT) score is a quantitative risk index that predicts 1-yr mortality risk, derived from United Network for Organ Sharing (UNOS) data in which women are underrepresented. The validity of the IMPACT score in 1-yr mortality risk after OHT in women is unknown. The objective of this study is to assess differences in score performance by sex. We hypothesize that the IMPACT score is a poor predictor of 1-yr mortality risk after OHT in women. DesignExternal validation study including patients 18 years and older first-time single organ OHT recipients from The International Society of Heart and Lung Transplantation (ISHLT) data from 2009-2018. Demographic and clinical characteristics were compared by sex. IMPACT score was calculated and regression models constructed for the entire sample, and stratified by sex. Model discrimination was assessed with the area under the receiver operating characteristic curve (AUROC) and calibration was assessed graphically. SettingN/A InterventionsN/A ParticipantsPatients 18 years and older first time single OHT recipients from ISHLT registry from 2009-2018. Measurements and Main ResultsFor 1-yr mortality, the AUROC (95% CI) for the full sample was 0.59 (0.57-0.60); 0.58 (0.55-0.61) for women; 0.59 (0.58-0.61) for men. 1-yr mortality was 9.4% in the overall cohort, with no difference in mortality by sex (9.0% vs 9.6% women vs men, p=0.22). ConclusionsThe IMPACT score exhibited poor discrimination and calibration in the ISHLT 2009-2019 cohort, overall and by sex. There was no difference in 1-yr mortality between women and men.

Survival Analysis Without Sharing of Individual Patient Data by Using a Gaussian Copula.

Cox regression and Kaplan-Meier estimations are often needed in clinical research and this requires access to individual patient data (IPD). However, IPD cannot always be shared because of privacy or proprietary restrictions, which complicates the making of such estimations. We propose a method that generates pseudodata replacing the IPD by only sharing non-disclosive aggregates such as IPD marginal moments and a correlation matrix. Such aggregates are collected by a central computer and input as parameters to a Gaussian copula (GC) that generates the pseudodata. Survival inferences are computed on the pseudodata as if it were the IPD. Using practical examples we demonstrate the utility of the method, via the amount of IPD inferential content recoverable by the GC. We compare GC to a summary-based meta-analysis and an IPD bootstrap distributed across several centers. Other pseudodata approaches are also considered. In the empirical results, GC approximates the utility of the IPD bootstrap although it might yield more conservative inferences and it might have limitations in subgroup analyses. Overall, GC avoids many legal problems related to IPD privacy or property while enabling approximation of common IPD survival analyses otherwise difficult to conduct. Sharing more IPD aggregates than is currently practiced could facilitate "second purpose"-research and relax concerns regarding IPD access.

A double-masked, sham-controlled trial of rose bengal photodynamic therapy for the treatment of fungal and acanthameoba keratitis: Rose Bengal Electromagnetic Activation with Green Light for Infection Reduction (REAGIR) Study.

Infectious keratitis secondary to fungus or acanthamoeba often has a poor outcome despite receiving the best available medical therapy. In vitro Rose Bengal Photodynamic therapy (RB-PDT) appears to be effective against fungal and acanthamoeba isolates.22,23 In one published series RB-PDT reduced the need for therapeutic penetrating keratoplasty in severe bacterial, fungal, and acanthameoba keratitis not responsive to medical therapy. This international, randomized, sham and placebo controlled 2-arm clinical trial, randomizes patients with smear positive fungal and acanthameoba and smear negative corneal ulcers in a 1:1 fashion to one of two treatment arms: 1) Topical antimicrobial plus sham RB-PDT or 2) Topical antimicrobial plus RB-PDT. We anticipate that RB-PDT will improve best spectacle corrected visual acuity and also reduce complications such as corneal perforation and the need for therapeutic penetrating keratoplasty. This study will comply with the NIH Data Sharing Policy and Policy on the Dissemination of NIH-Funded Clinical Trial Information and the Clinical Trials Registration and Results Information Submission rule. Our results will be disseminated via clinicaltrials.gov website, meetings, and journal publications. Our data will also be available upon reasonable request. NCT, NCT05110001, Registered November 5, 2021. https://www.clinicaltrials.gov/study/NCT05110001.

Australian Attitudes Towards Waivers of Consent Within the Context of Genomic Data Sharing.

This research identifies the circumstances in which Human Research Ethics Committees (HRECs) are trusted by Australians to approve the use of genomic data - without express consent - and considers the impact of genomic data sharing settings, and respondent attributes, on public trust. Survey results (N = 3013) show some circumstances are more conducive to public trust than others, with waivers endorsed when future research is beneficial and when privacy is protected, but receiving less support in other instances. Still, results imply attitudes are influenced by more than these specific circumstances, with different data sharing settings, and participant attributes, affecting views. Ultimately, this research raises questions and concerns in relation to the criteria HRECs use when authorising waivers of consent in Australia.

Blockchain-Based Shared Data Integrity Auditing and Deduplication

Blockchain-Based Shared Data Integrity Auditing and Deduplication

Risk factors for mid- and long-term mortality in lung transplant recipients aged 70 years and older.

With increased lung transplantation in those aged 70 and older, limited literature addresses risk factors affecting their survival. Our study aims to identify independent factors impacting mid- and long-term mortality in this elderly population. This study analyzed lung transplant patients over 70 from May 2005 to December 2022 using United Network for Organ Sharing data. The 3- or 5-year cohort excluded multi-organ, secondary transplantation and loss to follow-up. Univariable Cox analysis was conducted to assess recipient, donor and transplant factors. Factors with a significance level of P < 0.2 were subsequently included in a multivariable Cox model to identify correlations with 3- and 5-year mortality in patients aged over 70. Multivariable analysis has identified key factors affecting 3- and 5-year mortality in elderly lung transplant patients over 70. Common notable factors include recipient total bilirubin, intensive care unit status at the time of transplantation, donor diabetes, Cytomegalovirus(CMV) mismatch and single lung transplantation. Additionally, Hispanic/Latino patients and ischaemia time of the transplant significantly impact the 3-year mortality, while recipient age, diabetes, nitric oxide use before transplantation and creatinine were identified as unique independent risk factors affecting the 5-year morality. The study identified several independent risk factors that impact the mid- and long-term survival of lung transplantation for individuals over 70 years. These findings can contribute to the optimization of lung transplant treatment strategies and perioperative management in elderly patients, thereby enhancing the survival rate of this age group.

HumanIslets: An integrated platform for human islet data access and analysis.

Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.

Composite expectile estimation in partial functional linear regression model

Recent research and substantive studies have shown growing interest in expectile regression (ER) procedures. Similar to quantile regression, ER with respect to different expectile levels can provide a comprehensive picture of the conditional distribution of a response variable given predictors. This study proposes three composite-type ER estimators to improve estimation accuracy. The proposed ER estimators include the composite estimator, which minimizes the composite expectile objective function across expectiles; the weighted expectile average estimator, which takes the weighted average of expectile-specific estimators; and the weighted composite estimator, which minimizes the weighted composite expectile objective function across expectiles. Under certain regularity conditions, we derive the convergence rate of the slope function, obtain the mean squared prediction error, and establish the asymptotic normality of the slope vector. Simulations are conducted to assess the empirical performances of various estimators. An application to the analysis of capital bike share data is presented. The numerical evidence endorses our theoretical results and confirm the superiority of the composite-type ER estimators to the conventional least squares and single ER estimators.

The association of non-severe COVID-19 infection and progression to frailty among robust older veterans

BackgroundStudies have shown that frailty was increased in hospitalized COVID-19 patients. However, it is not clear whether non-severe COVID-19 increases the risk for pre-frailty and frailty development. Our study aimed to determine the risk of developing frailty and pre-frailty in robust veterans who contracted non-severe COVID-19. MethodsWe conducted a retrospective cohort study to assess the association of SARS-CoV-2 infection with the development of pre-frailty and frailty status among robust U.S. veterans using VA COVID-19 Shared Data Resource. We included patients 55 years and older who had at least one SARS-CoV-2 testing between March 15, 2020, and November 30, 2020, had been active patients in the past 12 months, and had a VA frailty index of zero (robust status) at the time of testing. Cox proportional hazard model was used to assess the association between COVID-19 infection and developing frailty or pre-frailty and frailty. We also assessed the association by patients’ age groups, sex, and race. FindingsWe identified 82070 veterans mean age 68.3 ± 7.8, 74738 (91.1%) male, 53899 (65.7%) white, 7557 (9.2%) with mild COVID-19 infection. Over the follow up period of 36 months, testing positive for COVID-19 was associated with a 66% increase in the hazard of becoming frail (adjusted HR = 1.66, 95%CI: 1.32–2.08), and a 68% increase in the hazard of becoming pre-frail (adjusted HR = 1.68, 95%CI: 1.45–1.94). Among male patients, mild COVID-19 infection was associated with a 54% increase in the hazard of becoming frail (adjusted HR = 1.54, 95% CI: 1.21−1.96), while among female patients there was a 330% increase (adjusted HR = 4.30, 95% CI: 2.13−8.64). Conclusions and relevanceNon-severe COVID-19 infection that occurred in robust older adults increased the risk of developing frailty. Further multi-center prospective cohort studies evaluating the mechanism of action and clinical trials of treatment options for post-COVID frailty are indicated in Veterans to support clinical care.