To discuss the application of complex networks models in preventing and controlling communicable disease, analyze and control the spread of infectious diseases by using the models and the software of complex networks based on its basic properties. Compared with conventional epidemiological approach, the complex networks theory, as a new theory, not only can describe the dynamic process of infections diseases spreading but also forecast the situation of infectious disease. The influence of the network's topology on the infections diseases transmission can be deeply understood through the research on disease spreading by its theory, so to control the spread of diseases. Complex networks theory approach can be used in epidemiological research for having much advantage compared with those conventional epidemiological approaches.
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PLoS Comput Biol
January 2025
Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, Rhode Island, United States of America.
Negotiating social dynamics among allies and enemies is a complex problem that often requires individuals to tailor their behavioral approach to a specific situation based on environmental and/or social factors. One way to make these contextual adjustments is by arranging behavioral output into intentional patterns. Yet, few studies explore how behavioral patterns vary across a wide range of contexts, or how allies might interlace their behavior to produce a coordinated response.
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January 2025
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Genes on the X chromosome are extensively expressed in the human brain. However, little is known for the X chromosome's impact on the brain anatomy, microstructure, and functional networks. We examined 1045 complex brain imaging traits from 38,529 participants in the UK Biobank.
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January 2025
Forest Entomology, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland.
Understanding how land use affects temporal stability is crucial to preserve biodiversity and ecosystem functions. Yet, the mechanistic links between land-use intensity and stability-driving mechanisms remain unclear, with functional traits likely playing a key role. Using 13 years of data from 300 sites in Germany, we tested whether and how trait-based community features mediate the effect of land-use intensity on acknowledged stability drivers (compensatory dynamics, portfolio effect, and dominant species variability), within and across plant and arthropod communities.
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January 2025
School of Information Science and Engineering, Xinjiang University, Urumqi, China.
Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly detection methods overlook the importance of local density, limiting their effectiveness in detecting anomaly objects in complex data distributions. To address this challenge, we introduce a generative adversarial local density-based anomaly detection (GALD) method, which combines the data distribution modeling capabilities of GANs with local synthetic density analysis.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
The "similarity of dissimilarities" is an emerging paradigm in biomedical science with significant implications for protein function prediction, machine learning (ML), and personalized medicine. In protein function prediction, recognizing dissimilarities alongside similarities provides a more detailed understanding of evolutionary processes, allowing for a deeper exploration of regions that influence biological functionality. For ML models, incorporating dissimilarity measures helps avoid misleading results caused by highly correlated or similar data, addressing confounding issues like the Doppelgänger Effect.
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