This work presents a discrete multidomain model that describes ionic diffusion pathways between connected cells and within the interstitium. Unlike classical models of impulse propagation, the intracellular and extracellular spaces are represented as spatially distinct volumes with dynamic/static boundary conditions that electrically couple neighboring spaces. The model is used to investigate the impact of nonuniform geometrical and electrical properties of the interstitial space surrounding a fiber on conduction velocity and action potential waveshape. Comparison of the multidomain and bidomain models shows that although the conduction velocity is relatively insensitive to cases that confine 50% of the membrane surface by narrow extracellular depths (> or =2 nm), the action potential morphology varies greatly around the fiber perimeter, resulting in changes in the magnitude of extracellular potential in the tight spaces. Results also show that when the conductivity of the tight spaces is sufficiently reduced, the membrane adjacent to the tight space is eliminated from participating in propagation, and the conduction velocity increases. Owing to its ability to describe the spatial discontinuity of cardiac microstructure, the discrete multidomain can be used to determine appropriate tissue properties for use in classical macroscopic models such as the bidomain during normal and pathophysiological conditions.
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http://dx.doi.org/10.1529/biophysj.108.137349 | DOI Listing |
J Phys Condens Matter
January 2025
Departamento de Física, Facultad de Ciencias, Universidad Nacional Autónoma de México, Apartado Postal 70542, Ciudad de México 04510, Mexico.
Magnetic fields can be introduced into discrete models of quantum systems by the Peierls substitution. For tight-binding Hamiltonians, the substitution results in a set of (Peierls) phases that are usually calculated from the magnetic vector potential. As the potential is not unique, a convenient gauge can be chosen to fit the geometry and simplify calculations.
View Article and Find Full Text PDFFront Comput Neurosci
November 2024
School of Information Science and Engineering, Linyi University, Linyi, China.
Background: The methods used to detect epileptic seizures using electroencephalogram (EEG) signals suffer from poor accuracy in feature selection and high redundancy. This problem is addressed through the use of a novel multi-domain feature fusion and selection method (PMPSO).
Method: Discrete Wavelet Transforms (DWT) and Welch are used initially to extract features from different domains, including frequency domain, time-frequency domain, and non-linear domain.
Genome Res
October 2024
Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, USA
Recently developed protein language models have enabled a variety of applications with the protein contextual embeddings they produce. Per-protein representations (each protein is represented as a vector of fixed dimension) can be derived via averaging the embeddings of individual residues, or applying matrix transformation techniques such as the discrete cosine transformation (DCT) to matrices of residue embeddings. Such protein-level embeddings have been applied to enable fast searches of similar proteins; however, limitations have been found; for example, PROST is good at detecting global homologs but not local homologs, and knnProtT5 excels for proteins with single domains but not multidomain proteins.
View Article and Find Full Text PDFPhys Med Biol
May 2024
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, Bangladesh.
Automatic medical image segmentation is crucial for accurately isolating target tissue areas in the image from background tissues, facilitating precise diagnoses and procedures. While the proliferation of publicly available clinical datasets led to the development of deep learning-based medical image segmentation methods, a generalized, accurate, robust, and reliable approach across diverse imaging modalities remains elusive.This paper proposes a novel high-resolution parallel generative adversarial network (GAN)-based generalized deep learning method for automatic segmentation of medical images from diverse imaging modalities.
View Article and Find Full Text PDFJ Theor Biol
September 2023
Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, USA. Electronic address:
The different active roles of neurons and astrocytes during neuronal activation are associated with the metabolic processes necessary to supply the energy needed for their respective tasks at rest and during neuronal activation. Metabolism, in turn, relies on the delivery of metabolites and removal of toxic byproducts through diffusion processes and the cerebral blood flow. A comprehensive mathematical model of brain metabolism should account not only for the biochemical processes and the interaction of neurons and astrocytes, but also the diffusion of metabolites.
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