Results 101 to 110 of about 911,793 (268)
Insight mixed deep neural network architectures for molecular representation
Learning molecular representation is a crucial task in the field of drug discovery, particularly for various specific applications such as predicting molecular properties.
Tianze Zhao +4 more
doaj +1 more source
Functional Connectivity Linked to Cognitive Recovery After Minor Stroke
ABSTRACT Objective Patients with minor stroke exhibit slowed processing speed and generalized alterations in functional connectivity involving frontoparietal cortex (FPC). The pattern of connectivity evolves over time. In this study, we examine the relationship of functional connectivity patterns to cognitive performance, to determine ...
Vrishab Commuri +7 more
wiley +1 more source
Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement
The structure of the deep artificial neural network is similar to the structure of the biological neural network, which can be well applied to the 3D visual image recognition of aerobics movements.
Wenhua Fan, Hyun Joo Min
doaj +1 more source
Spatiotemporal diffractive deep neural networks
A spatiotemporal diffractive deep neural network (STD2NN) is proposed for spatiotemporal signal processing. The STD2NN is formed by gratings, which convert the signal from the frequency domain to the spatial domain, and multiple layers consisting of spatial lenses and space light modulators (SLMs), which conduct spatiotemporal phase modulation.
Junhe Zhou, Haoqian Pu, Jiaxin Yan
openaire +2 more sources
Discovery and Targeted Proteomic Studies Reveal Striatal Markers Validated for Huntington's Disease
ABSTRACT Objective Clinical trials for Huntington's disease (HD) enrolling persons before clinical motor diagnosis (CMD) lack validated biomarkers. This study aimed to conduct an unbiased discovery analysis and a targeted examination of proteomic biomarkers scrutinized by clinical validation. Methods Cerebrospinal fluid was obtained from PREDICT‐HD and
Daniel Chelsky +8 more
wiley +1 more source
Spatial deep convolutional neural networks
Spatial prediction problems often use Gaussian process models, which can be computationally burdensome in high dimensions. Specification of an appropriate covariance function for the model can be challenging when complex non-stationarities exist. Recent work has shown that pre-computed spatial basis functions and a feed-forward neural network can ...
Qi Wang, Paul A. Parker, Robert Lund
openaire +3 more sources
Developmental, Neuroanatomical and Cellular Expression of Genes Causing Dystonia
ABSTRACT Objective Dystonia is one of the most common movement disorders, with variants in multiple genes identified as causative. However, an understanding of which developmental stages, brain regions, and cell types are most relevant is crucial for developing relevant disease models and therapeutics.
Darren Cameron +5 more
wiley +1 more source
ABSTRACT Objective Super‐Refractory Status Epilepticus (SRSE) is a rare, life‐threatening neurological emergency with unclear etiology in many cases. Mitochondrial dysfunction, often due to disease‐causing genetic variants, is increasingly recognized as a cause, with each gene producing distinct pathophysiological mechanisms.
Pouria Mohammadi +2 more
wiley +1 more source
ABSTRACT Objective The Gold Coast criteria permit diagnosis of amyotrophic lateral sclerosis (ALS) even without upper motor neuron (UMN) signs. However, whether ALS patients with UMN signs (ALSwUMN) and those without (ALSwoUMN) share similar characteristics and prognoses remains unclear.
Hee‐Jae Jung +7 more
wiley +1 more source
Deep Multi-Component Neural Network Architecture
Existing neural network architectures often struggle with two critical limitations: (1) information loss during dataset length standardization, where variable-length samples are forced into fixed dimensions, and (2) inefficient feature selection in ...
Chafik Boulealam +4 more
doaj +1 more source

