Synchronized neural bursts are one of the most noticeable dynamic features of neural networks, being essential for various phenomena in neuroscience, yet their complex dynamics are not well understood. With extrinsic electrical and optical manipulations on cultured neural networks, we demonstrate that the regularity (or randomness) of burst sequences is in many cases determined by a (few) low-dimensional attractor(s) working under strong neural noise. Moreover, there is an optimal level of noise strength at which the regularity of the interburst interval sequence becomes maximal-a phenomenon of coherence resonance. The experimental observations are successfully reproduced through computer simulations on a well-established neural network model, suggesting that the same phenomena may occur in many in vivo as well as in vitro neural networks.
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http://dx.doi.org/10.1103/PhysRevE.92.042701 | DOI Listing |
J Integr Neurosci
December 2024
Federal State Budgetary Educational Institution, Institute of Theoretical and Experimental Biophysics, 142290 Pushchino, Russia.
Background: Long-term use of levodopa, a metabolic precursor of dopamine (DA) for alleviation of motor symptoms in Parkinson's disease (PD), can cause a serious side effect known as levodopa-induced dyskinesia (LID). With the development of LID, high-frequency gamma oscillations (~100 Hz) are registered in the motor cortex (MCx) in patients with PD and rats with experimental PD. Studying alterations in the activity within major components of motor networks during transition from levodopa-off state to dyskinesia can provide useful information about their contribution to the development of abnormal gamma oscillations and LID.
View Article and Find Full Text PDFJ Integr Neurosci
December 2024
Department of Neurology, Hainan West Central Hospital, 571799 Danzhou, Hainan, China.
Background: Ischemic stroke (IS) is the leading cause of mortality worldwide. Herein, we aimed to identify novel biomarkers and explore the role of C-type lectin domain family 7 member A () in IS.
Methods: Differentially expressed genes (DEGs) were screened using the GSE106680, GSE97537, and GSE61616 datasets, and hub genes were identified through construction of protein-protein interaction networks.
JACS Au
December 2024
Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States.
Understanding the origin and effect of the confinement of molecules and transition states within the micropores of a zeolite can enable targeted design of such materials for catalysis, gas storage, and membrane-based separations. Linear correlations of the thermodynamic parameters of molecular adsorption in zeolites have been proposed; however, their generalizability across diverse molecular classes and zeolite structures has not been established. Here, using molecular simulations of >3500 combinations of adsorbates and zeolites, we show that linear trends hold in many cases; however, they collapse for highly confined systems.
View Article and Find Full Text PDFHeliyon
December 2024
School of Mechanical Engineering, Institute of Technology, Wallaga University, P.O. Box 395, Nekemte, Ethiopia.
Turning AISI (American Iron and Steel Institute) D3 tool steel can be challenging due to a lack of optimal process parameters and proper coolant application to achieve high surface quality and temperature control. Machine learning helps in predicting the optimal parameters, whereas nanofluids enhance cooling efficiency while preserving both the tool and the workpiece. This work intends to utilize advanced machine learning approaches to optimize process parameters with the application of hybrid nanofluids (AlO/graphene) during the CNC turning of AISI D3.
View Article and Find Full Text PDFBioinform Adv
December 2024
Computer Science Department, Indiana University, Bloomington, IN 47408, United States.
Motivation: Microbial signatures in the human microbiome are closely associated with various human diseases, driving the development of machine learning models for microbiome-based disease prediction. Despite progress, challenges remain in enhancing prediction accuracy, generalizability, and interpretability. Confounding factors, such as host's gender, age, and body mass index, significantly influence the human microbiome, complicating microbiome-based predictions.
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