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http://dx.doi.org/10.1126/science.279.5347.40 | DOI Listing |
Sci Rep
January 2025
Department of Electronics and Communication Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, Tamilnadu, India, 641010.
The global spread of COVID-19, particularly through cough symptoms, necessitates efficient diagnostic tools. COVID-19 patients exhibit unique cough sound patterns distinguishable from other respiratory conditions. This study proposes an advanced framework to detect and predict COVID-19 using deep learning from cough audio signals.
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January 2025
Universities Space Research Association, Washington, DC, USA.
During the COVID-19 pandemic changes in human activity became widespread through official policies and organically in response to the virus's transmission, which in turn, impacted the environment and the economy. The pandemic has been described as a natural experiment that tested how social and economic disruptions impacted different components of the global Earth System. To move this beyond hypotheses, locally-resolved, globally-available measures of how, where, and when human activity changed are critically needed.
View Article and Find Full Text PDFPoult Sci
January 2025
Ploufragan-Plouzané-Niort Laboratory, Epidemiology Health and Welfare Unit, French Agency for Food, Environmental and Occupational Health and Safety (ANSES), BP53 22440 Ploufragan, France. Electronic address:
Appropriate disposal of dead farming animals is required to guarantee effective disease control while protecting the environment. In crisis situations, alternatives to rendering can be used, including on-farm burial. The objectives of this study were to: (i) describe the burial and monitoring protocols used on poultry farms in France in response to major avian influenza outbreaks; (ii) assess the effectiveness of the burial protocol, in terms of both technical and biosecurity aspects, and microbiological, physical and chemical changes of the buried materials and the environment over time; (iii) provide recommendations for future burial and follow-up protocols.
View Article and Find Full Text PDFSimulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding.
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