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Geosocial Media's Early Warning Capabilities Across US County-Level Political Clusters: Observational Study.
JMIR Infodemiology
January 2025
Salzburg University of Applied Sciences, Puch/Salzburg, Austria.
Background: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises.
View Article and Find Full Text PDFGlobal natural history infrastructure requires international solidarity, support, and investment in local capacity.
Proc Natl Acad Sci U S A
February 2025
Museum of Natural History, University of Colorado-Boulder, Boulder, CO 80309.
Amid global challenges like climate change, extinctions, and disease epidemics, science and society require nuanced, international solutions that are grounded in robust, interdisciplinary perspectives and datasets that span deep time. Natural history collections, from modern biological specimens to the archaeological and fossil records, are crucial tools for understanding cultural and biological processes that shape our modern world. At the same time, natural history collections in low and middle-income countries are at-risk and underresourced, imperiling efforts to build the infrastructure and scientific capacity necessary to tackle critical challenges.
View Article and Find Full Text PDFA framework for managing infectious diseases in rural areas in low- and middle-income countries in the face of climate change-East Africa as a case study.
PLOS Glob Public Health
January 2025
Global Health Program, Smithsonian Conservation Biology Institute, Washington, DC, United States of America.
Climate change is having unprecedented impacts on human health, including increasing infectious disease risk. Despite this, health systems across the world are currently not prepared for novel disease scenarios anticipated with climate change. While the need for health systems to develop climate change adaptation strategies has been stressed in the past, there is no clear consensus on how this can be achieved, especially in rural areas in low- and middle-income countries that experience high disease burdens and climate change impacts simultaneously.
View Article and Find Full Text PDFCoevolutionary dynamics in the grass-livestock social-ecological system of China's alpine pastoral areas: A case study of the Qilian Mountains region in China.
PLoS One
January 2025
Department of Geography, University College London, London, England, United Kingdom.
Evaluating the dynamic co-evolution and feedback mechanisms within socio-ecological systems is crucial for determining the resilience and sustainability of environmental governance strategies. The grass-livestock system, as a complex entity encompassing livestock nutrition, foraging behavior, vegetation ecology, pastoralists' economic income, and policy interventions, indicates that any change in a single element may trigger a chain reaction within the system. This paper uses a system dynamics approach to construct a simulation model of the grass-livestock system in alpine pastoral areas, simulating the long-term dynamic co-evolution of the socio-ecological system in the Qilian Mountains region of China.
View Article and Find Full Text PDFASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.
PLoS Comput Biol
January 2025
School of Software, Taiyuan University of Technology, Taiyuan, China.
Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to these challenges, this study introduces the Adaptive Sparse Graph Contrastive Learning Network (ASGCL), an innovative approach to unraveling latent interactions in the complex context of cancer cell lines and drugs.
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