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http://dx.doi.org/10.1126/science.aaz2170 | DOI Listing |
Glob Chang Biol
January 2025
School of Biological Sciences, The University of Hong Kong, Hong Kong, China.
Land use change threatens global biodiversity and compromises ecosystem functions, including pollination and food production. Reduced taxonomic α-diversity is often reported under land use change, yet the impacts could be different at larger spatial scales (i.e.
View Article and Find Full Text PDFPlant Dis
December 2024
Department of Plant Protection, Biotechnical Faculty, University of Montenegro, 81000 Podgorica, Montenegro.
Alzheimers Dement
December 2024
Oasis Diagnostics® Corporation, Vancouver, Washington, USA.
There is a pressing need for accessible biomarkers with high diagnostic accuracy for Alzheimer's disease (AD) diagnosis to facilitate widespread screening, particularly in underserved groups. Saliva is an emerging specimen for measuring AD biomarkers, with distinct contexts of use that could complement blood and cerebrospinal fluid and detect various analytes. An interdisciplinary, international group of AD and related dementias (ADRD) researchers convened and performed a narrative review of published studies on salivary AD biomarkers.
View Article and Find Full Text PDFEcol Lett
January 2025
Climate Impacts Research Centre, Department of Ecology and Environmental Sciences, Umeå University, Umeå, Sweden.
Empirical studies worldwide show that warming has variable effects on plant litter decomposition, leaving the overall impact of climate change on decomposition uncertain. We conducted a meta-analysis of 109 experimental warming studies across seven continents, using natural and standardised plant material, to assess the overarching effect of warming on litter decomposition and identify potential moderating factors. We determined that at least 5.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Computer Sciences and Industries, Universidad Católica del Maule, Talca, Chile.
Antimicrobial resistance (AMR) poses a significant global health challenge, necessitating advanced predictive models to support clinical decision-making. In this study, we explore multi-label classification as a novel approach to predict antibiotic resistance across four clinically relevant bacteria: E. coli, S.
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