Network-based models of epidemic spread have become increasingly popular in recent decades. Despite a rich foundation of such models, few low-dimensional systems for modeling SIS-type diseases have been proposed that manage to capture the complex dynamics induced by the network structure. We analyze one recently introduced model and derive important epidemiological quantities for the system. We derive the epidemic threshold and analyze the bifurcation that occurs, and we use asymptotic techniques to derive an approximation for the endemic equilibrium when it exists. We consider the sensitivity of this approximation to network parameters, and the implications for disease control measures are found to be in line with the results of existing studies.
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http://dx.doi.org/10.1007/s11538-021-00907-2 | DOI Listing |
J Neural Eng
January 2025
Department of Neuroscience, Northwestern University, 303 East Chicago Ave, Chicago, Illinois, 60611, UNITED STATES.
Objective: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Many human diseases result from a complex interplay of behavioral, clinical, and molecular factors. Integrating low-dimensional behavioral and clinical features with high-dimensional molecular profiles can significantly improve disease outcome prediction and diagnosis. However, while some biomarkers are crucial, many lack informative value.
View Article and Find Full Text PDFNature
January 2025
Department of Brain and Cognitive Sciences and McGovern Institute, MIT, Cambridge, MA, USA.
Hippocampal circuits in the brain enable two distinct cognitive functions: the construction of spatial maps for navigation, and the storage of sequential episodic memories. Although there have been advances in modelling spatial representations in the hippocampus, we lack good models of its role in episodic memory. Here we present a neocortical-entorhinal-hippocampal network model that implements a high-capacity general associative memory, spatial memory and episodic memory.
View Article and Find Full Text PDFArXiv
December 2024
Department of Chemistry and James Franck Institute, University of Chicago, Chicago, Illinois 60637, United States.
Identifying informative low-dimensional features that characterize dynamics in molecular simulations remains a challenge, often requiring extensive manual tuning and system-specific knowledge. Here, we introduce geom2vec, in which pretrained graph neural networks (GNNs) are used as universal geometric featurizers. By pretraining equivariant GNNs on a large dataset of molecular conformations with a self-supervised denoising objective, we obtain transferable structural representations that are useful for learning conformational dynamics without further fine-tuning.
View Article and Find Full Text PDFSci Adv
January 2025
Key Laboratory for the Physics and Chemistry of Nanodevices and Center for Carbon-Based Electronics, School of Electronics, Peking University, Beijing 100871, China.
Multi-valued logics (MVLs) offer higher information density, reduced circuit and interconnect complexity, lower power dissipation, and faster speed over conventional binary logic system. Recent advancement in MVL research, particularly with emerging low-dimensional materials, suggests that breakthroughs may be imminent if multistates transistors can be fabricated controllably for large-scale integration. Here, a concept of source-gating transistors (SGTs) is developed and realized using carbon nanotubes (CNTs).
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