Results 91 to 100 of about 4,958,442 (285)
A Model for Programmability and Virtuality in Dynamical Neural Networks [PDF]
In this dissertation a fixed-weight architecture for Continuous Time Recurrent Neural Networks (CTRNNs) is proposed in order to give an account for biological phenomena, controlled by neuronal activity, in which changes of behavior occur so fast that ...
Donnarumma, Francesco
core +1 more source
Performance Estimate of Inverse Rashba–Edelstein Magnetoelectric Devices for Neuromorphic Computing
We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses. Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows natural emulation of the behavior of an ideal cellular network.
Andrew W. Stephan +2 more
doaj +1 more source
RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease
ABSTRACT Objective Mitochondrial diseases are the most common inherited metabolic disorders, characterized by pronounced clinical and genetic heterogeneity that complicates molecular diagnosis. Although DNA‐based sequencing approaches have become standard in genetic testing, up to half of patients remain without a definitive diagnosis.
Zhimei Liu +21 more
wiley +1 more source
ABSTRACT Objective Variants in SLC6A1, encoding the GABA transporter 1 (GAT‐1), cause epilepsy, autism spectrum disorder, and developmental delay via loss of GABA uptake, impaired trafficking, and ER retention. We previously found that 4‐Phenylbutyrate (PBA), an FDA‐approved drug, restores GABA uptake and reduces seizures in SLC6A1‐related disorders ...
Melissa B. DeLeeuw +5 more
wiley +1 more source
Uncovering G Protein‐Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis
ABSTRACT Background Low‐grade gliomas (LGG) exhibit significant heterogeneity and recurrence risk. G protein‐coupled receptors (GPCR) contribute to glioma malignant progression, but their prognostic value remains unclear. This work attempts to formulate a GPCR‐based outcome‐predicting model for LGG. Methods Based on TCGA LGG data, the enrichment scores
Jun Yang +4 more
wiley +1 more source
Attack and Defense in Cellular Decision-Making: Lessons from Machine Learning
Machine-learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signaling, like in early immune recognition.
Thomas J. Rademaker +2 more
doaj +1 more source
ABSTRACT Objective To evaluate the expression of nine blood RNA biomarkers in a clinical trial based on genes previously identified in an experimental monkey model of stroke for diagnosis feasibility and prognostication. Methods IBIS‐CT1 was a prospective longitudinal study enrolling patients with ischemic stroke (IS) or intracerebral hemorrhage (ICH ...
Salomé Retailleau +11 more
wiley +1 more source
EXTRACTION OF FACIAL FEATURES USING GENETIC CELLULAR NEURAL NETWORKS
This paper presents proper CNN templates to locate face region consisting of eyes,eyebrows, nose and mouth in frontal face images. Training of CNN is done by using genetic algorithms. At the begining, 72 random genes are selected.
Osman Nuri UCAN +2 more
doaj +2 more sources
Extended LaSalle's Invariance Principle for Full-Range Cellular Neural Networks
In several relevant applications to the solution of signal processing tasks in real time, a cellular neural network (CNN) is required to be convergent, that is, each solution should tend toward some equilibrium point. The paper develops a Lyapunov method,
Mauro Di Marco +3 more
doaj +1 more source
Global Rather Than Vertical‐Selective Saccadic Abnormalities in Progressive Supranuclear Palsy
ABSTRACT Objective To test whether vertical saccades are preferentially affected in Progressive Supranuclear Palsy (PSP). Methods PSP patients (n = 24) were compared to age‐matched controls (n = 94) and two degenerative groups (Alzheimer's disease, n = 20; Lewy body disease, n = 50).
Duy Duan Nguyen +6 more
wiley +1 more source

