A state-dependent dynamic network is a collection of elements that interact through a network, whose geometry evolves as the state of the elements changes over time. The genome is an intriguing example of a state-dependent network, where chromosomal geometry directly relates to genomic activity, which in turn strongly correlates with geometry. Here we examine various aspects of a genomic state-dependent dynamic network. In particular, we elaborate on one of the important ramifications of viewing genomic networks as being state-dependent, namely, their controllability during processes of genomic reorganization such as in cell differentiation.
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http://dx.doi.org/10.1073/pnas.1113249108 | DOI Listing |
J Neurosci
January 2025
Department of Integrative Anatomy, Nagoya City University Graduate School of Medicinal Sciences.
Neurons in the cerebral cortex and hippocampus discharge synchronously in brain state-dependent manner to transfer information. Published studies have highlighted the temporal coordination of neuronal activities between the hippocampus and a neocortical area, however, how the spatial extent of neocortical activity relates to hippocampal activity remains partially unknown. We imaged mesoscopic neocortical activity while recording hippocampal local field potentials in anesthetized and unanesthetized GCaMP-expressing transgenic mice.
View Article and Find Full Text PDFNeuroimage
January 2025
Center for Mind/Brain Sciences (CIMeC), University of Trento, 38068 Rovereto (TN), Italy.
Transcranial magnetic stimulation (TMS) has the potential to yield insights into cortical functions and improve the treatment of neurological and psychiatric conditions. However, its reliability is hindered by a low reproducibility of results. Among other factors, such low reproducibility is due to structural and functional variability between individual brains.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
School of Computer Science, Peking University, Beijing 100871, China.
Multi-agent systems often face challenges such as elevated communication demands, intricate interactions, and difficulties in transferability. To address the issues of complex information interaction and model scalability, we propose an innovative hierarchical graph attention actor-critic reinforcement learning method. This method naturally models the interactions within a multi-agent system as a graph, employing hierarchical graph attention to capture the complex cooperative and competitive relationships among agents, thereby enhancing their adaptability to dynamic environments.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Department of Mathematics, University of Wisconsin-Madison, Madison, WI 53706, USA.
The conditional Gaussian nonlinear system (CGNS) is a broad class of nonlinear stochastic dynamical systems. Given the trajectories for a subset of state variables, the remaining follow a Gaussian distribution. Despite the conditionally linear structure, the CGNS exhibits strong nonlinearity, thus capturing many non-Gaussian characteristics observed in nature through its joint and marginal distributions.
View Article and Find Full Text PDFJ Gen Physiol
March 2025
Department of Biomolecular Sciences, School of Pharmacy, University of Mississippi, Oxford, MS, USA.
Voltage-gated sodium (Nav) channels are pivotal for cellular signaling, and mutations in Nav channels can lead to excitability disorders in cardiac, muscular, and neural tissues. A major cluster of pathological mutations localizes in the voltage-sensing domains (VSDs), resulting in either gain-of-function, loss-of-function effects, or both. However, the mechanism behind this functional diversity of mutations at equivalent positions remains elusive.
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