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Multifunctional Conductive Hydrogel for Sensing Underwater Applications and Wearable Electroencephalogram Recording.
ACS Appl Mater Interfaces
January 2025
School of Chemical Engineering and Light Industry, Guangdong University of Technology, Guangzhou 510006, P. R. China.
Flexible electronics have been rapidly advancing and have garnered significant interest in monitoring physiological activities and health conditions. However, flexible electronics are prone to detachment in humid environments, so developing human-friendly flexible electronic devices that can effectively monitor human movement under various aquatic conditions and function as flexible electrodes remains a significant challenge. Here, we report a strongly adherent, self-healing, and swelling-resistant conductive hydrogel formed by combining the dual synergistic effects of hydrogen bonding and dipole-dipole interactions.
View Article and Find Full Text PDFProtect or prevent? A practicable framework for the dilemmas of COVID-19 vaccine prioritization.
PLoS One
January 2025
Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, United States of America.
Determining COVID-19 vaccination strategies presents many challenges in light of limited vaccination capacity and the heterogeneity of affected communities. Who should be prioritized for early vaccination when different groups manifest different levels of risks and contact rates? Answering such questions often becomes computationally intractable given that network size can exceed millions. We obtain a framework to compute the optimal vaccination strategy within seconds to minutes from among all strategies, including highly dynamic ones that adjust vaccine allocation as often as required, and even with modest computation resources.
View Article and Find Full Text PDFCommunity responses to a novel house design: A qualitative study of "Star Homes" in Mtwara, southeastern Tanzania.
PLoS One
January 2025
Amsterdam University Medical Centres, University of Amsterdam, Department of Global Health, Amsterdam, The Netherlands.
Introduction: To evaluate the impact of a novel design "Star Home" on the incidence of malaria, respiratory tract infections and diarrheal diseases among children, randomly selected households in Mtwara, Tanzania were offered a free, new Star Home. Drawing on longitudinal qualitative research that accompanied the Star Homes study, this article describes the experiences of residents and the wider community of living with these buildings.
Methods: A total of four rounds of face-to-face interviews were undertaken with residents of Star Homes (n = 37), control (wattle/daub) homes (n = 21), neighboring households n = 6), community members (n = 17) and community leaders (n = 6).
Optimal sigmoid function models for analysis of transspinal evoked potential recruitment curves recorded from different muscles.
PLoS One
January 2025
Klab4Recovery Research Program, The City University of New York, Staten Island, New York, United States of America.
Recruitment input-output curves of transspinal evoked potentials that represent the net output of spinal neuronal networks during which cortical, spinal and peripheral inputs are integrated as well as motor evoked potentials and H-reflexes are used extensively in research as neurophysiological biomarkers to establish physiological or pathological motor behavior and post-treatment recovery. A comparison between different sigmoidal models to fit the transspinal evoked potentials recruitment curve and estimate the parameters of physiological importance has not been performed. This study sought to address this gap by fitting eight sigmoidal models (Boltzmann, Hill, Log-Logistic, Log-Normal, Weibull-1, Weibull-2, Gompertz, Extreme Value Function) to the transspinal evoked potentials recruitment curves of soleus and tibialis anterior recorded under four different cathodal stimulation settings.
View Article and Find Full Text PDFTherapeutic gene target prediction using novel deep hypergraph representation learning.
Brief Bioinform
November 2024
Division of Artificial Intelligence, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, South Korea.
Identifying therapeutic genes is crucial for developing treatments targeting genetic causes of diseases, but experimental trials are costly and time-consuming. Although many deep learning approaches aim to identify biomarker genes, predicting therapeutic target genes remains challenging due to the limited number of known targets. To address this, we propose HIT (Hypergraph Interaction Transformer), a deep hypergraph representation learning model that identifies a gene's therapeutic potential, biomarker status, or lack of association with diseases.
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