[This corrects the article DOI: 10.1039/D4NA00383G.].
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11302095 | PMC |
http://dx.doi.org/10.1039/d4na90079k | DOI Listing |
Microsc Res Tech
December 2024
Department of Electronics and Communication Engineering, Annamacharaya Institute of Technology and Sciences, Tirupati, Andhra Pradesh, India.
The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, interpretability, ethical integration, population variety, and picture standardization must be addressed using deep learning for the functional magnetic resonance imaging (MRI) classification of AD in order to guarantee a trustworthy and practical therapeutic application. In this manuscript Classifying AD using a finite basis physics neural network (CAD-FBPINN) is proposed.
View Article and Find Full Text PDFSpine Deform
December 2024
Department of Orthopedic Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
Bone Joint J
December 2024
Nottingham University Hospitals NHS Trust, Nottingham, UK.
Sci Rep
November 2024
Département Neurosciences et Sciences Cognitives, Institut de Recherche Biomédicale des Armées, Brétigny-sur-Orge, France.
Studies have shown that adaptation to a virtual reality driving simulator takes time and that individuals differ widely in the time they need to adapt. The present study examined the relationship between attentional capacity and driving-simulator adaptation, with the hypothesis that individuals with better attentional capacity would exhibit more efficient adaptation to novel virtual driving circumstances. To this end, participants were asked to steer in a driving simulator through a series of 100 bends while keeping within a central demarcated zone.
View Article and Find Full Text PDFStat Methods Med Res
October 2024
Department of Economics, Massachusetts Institute of Technology, Cambridge, MA, USA.
We describe a novel approach for recovering the underlying parameters of the SIR dynamic epidemic model from observed data on case incidence. We formulate a discrete-time approximation of the original continuous-time model and search for the parameter vector that minimizes the standard least squares criterion function. We show that the gradient vector and matrix of second-order derivatives of the criterion function with respect to the parameters adhere to their own systems of difference equations and thus can be exactly calculated iteratively.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!