By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation.
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http://dx.doi.org/10.3390/s23136181 | DOI Listing |
Neuropsychobiology
January 2025
Introduction: Bipolar 2 disorder (BD2) is an independent disease with specific familial aggregation, significant functional impairment, specific treatment challenges and several distinctive clinical features. However, unlike bipolar 1 disorder, studies investigating causal and functional genes are lacking. This study aims to identify and prioritize causal genetic variants and genes for BD2 by analyzing brain-specific gene expression markers, to improve the understanding of its genetic underpinnings and support advancements in diagnosis, treatment and prognosis.
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January 2025
Global Alliance for Improved Nutrition, Geneva, Switzerland.
Food fortification (i.e., industrial fortification and biofortification) increases the micronutrient content of foods to improve population nutrition.
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December 2024
Health Policy Research Center, Guangxi Medical University, Nanning, Guangxi, China
Objective: The purpose of this study is to analyse the changes in the equity of intensive care unit (ICU) bed allocation in 14 cities in China's Guangxi Zhuang Autonomous Region from 2018 to 2021, to identify the problems in the process of ICU bed allocation in China's ethnic minority regions.
Design: The Gini coefficient, Theil index, health resource density index, and spatial correlation analysis were used to analyse the current status of ICU bed resource allocation and allocation equity in Guangxi, China, on two dimensions: geography, and population.
Setting: The Guangxi Zhuang Autonomous Region.
Nat Commun
January 2025
Division of Virology, Institute of Medical Science, University of Tokyo, Tokyo, Japan.
Microthrombus formation is associated with COVID-19 severity; however, the detailed mechanism remains unclear. In this study, we investigated mouse models with severe pneumonia caused by SARS-CoV-2 infection by using our in vivo two-photon imaging system. In the lungs of SARS-CoV-2-infected mice, increased expression of adhesion molecules in intravascular neutrophils prolonged adhesion time to the vessel wall, resulting in platelet aggregation and impaired lung perfusion.
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January 2025
Artificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gated CT or PET/CT scans. Traditional CAC assessment requires gated CT scans, leading to increased radiation exposure and the need for specialized personnel. This study aims to develop an artificial intelligence (AI) method to automatically detect CAC from non-gated, freely-breathing, low-dose CT images obtained from positron emission tomography/computed tomography scans.
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