Results 121 to 130 of about 99,773 (316)

Loss of AMBRA1 activates MAPK and angiogenesis signaling pathways in melanoma cells

open access: yesFEBS Open Bio, EarlyView.
Loss of AMBRA1 in melanoma cells activates multiple oncogenic pathways associated with tumor progression. Transcriptomic and protein network analyses revealed that AMBRA1 depletion enhances MAPK/ERK signaling, angiogenesis, TGF‐β/EMT signaling, and Wnt/axon guidance pathways.
Milad Ibrahim   +4 more
wiley   +1 more source

Super‐Refractory Status Epilepticus (SRSE) in a Patient With Compound Heterozygous OPA1 Variants: Case Report and Literature Review

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
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

Contextual Recurrent Neural Networks

open access: yesCoRR, 2019
There is an implicit assumption that by unfolding recurrent neural networks (RNN) in finite time, the misspecification of choosing a zero value for the initial hidden state is mitigated by later time steps. This assumption has been shown to work in practice and alternative initialization may be suggested but often overlooked.
Sam Wenke, Jim Fleming
openaire   +2 more sources

Robust stability for stochastic Hopfield neural networks with time delays

open access: yes, 2006
This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.In this paper, the asymptotic stability analysis problem is considered for a class of uncertain stochastic ...
Liu, X, Wang, Z, Shu, H, Fang, J
core   +1 more source

08041 Summary – Recurrent Neural Networks - Models, Capacities, and Applications [PDF]

open access: yes, 2008
The seminar centered around recurrent information processing in neural systems and its connections to brain sciences, on the one hand, and higher symbolic reasoning, on the other side. The goal was to explore connections across the disciplines and to
Maass, Wolfgang   +3 more
core   +1 more source

Diffusion Spectrum Imaging Maps Early Axonal Loss and a Unique Progressive Signal in Neuronal Intranuclear Inclusion Disease

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To delineate specific in vivo white matter pathology in neuronal intranuclear inclusion disease (NIID) using diffusion spectrum imaging (DSI) and define its clinical relevance. Methods DSI was performed on 42 NIID patients and 38 matched controls.
Kaiyan Jiang   +10 more
wiley   +1 more source

Sliced Recurrent Neural Networks

open access: yesCoRR, 2018
12 pages (including references), 2 figures, 3 tables, conference: The 27th International Conference on Computational Linguistics (COLING 2018)
Zeping Yu, Gongshen Liu
openaire   +3 more sources

Recurrent Neural Networks and Soft Computing [PDF]

open access: yes, 2012
New applications in recurrent neural networks are covered by this book, which will be required reading in the field. Methodological tools covered include ranking indices for fuzzy numbers, a neuro-fuzzy digital filter and mapping graphs of parallel ...

core   +1 more source

Value of MRI Outcomes for Preventive and Early‐Stage Trials in Spinocerebellar Ataxias 1 and 3

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To examine the value of MRI outcomes as endpoints for preventive and early‐stage trials of two polyglutamine spinocerebellar ataxias (SCAs). Methods A cohort of 100 participants (23 SCA1, 63 SCA3, median Scale for the Assessment and Rating of Ataxia (SARA) score = 5, 42% preataxic, and 14 gene‐negative controls) was scanned at 3T up ...
Thiago J. R. Rezende   +26 more
wiley   +1 more source

Brain-inspired, interpretable, resonant recurrent neural networks

open access: yesPhysical Review Research
Traditional artificial neural networks consist of nodes with nonoscillatory dynamics. Biological neural networks, on the other hand, consist of oscillatory components embedded in an oscillatory environment. Motivated by this feature of biological neurons,
Mark A. Kramer
doaj   +1 more source

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