Results 101 to 110 of about 92,175 (266)
Loss of AMBRA1 activates MAPK and angiogenesis signaling pathways in melanoma cells
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
Exploring Efficient Neural Architectures for Linguistic–Acoustic Mapping in Text-To-Speech
Conversion from text to speech relies on the accurate mapping from linguistic to acoustic symbol sequences, for which current practice employs recurrent statistical models such as recurrent neural networks. Despite the good performance of such models (in
Santiago Pascual +2 more
doaj +1 more source
Contextual Recurrent Neural Networks
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
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
Sliced Recurrent Neural Networks
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
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
The ability to predict seiches can help prevent the damage and mitigate the risks associated with these natural phenomena. This paper presents a novel approach for seiche prediction in Marsaxlokk, Malta, using Long Short-Term Memory (LSTM) neural network
Nicole Borg +4 more
doaj +1 more source
Myocardial infarction (MI) is a medical emergency for which the early detection of symptoms is desirable. The prevalence of portable electrocardiogram (ECG) devices makes frequent screening for MI possible. In this study, we develop an MI classifier that
Hin Wai Lui, King Lau Chow
doaj +1 more source
Value of MRI Outcomes for Preventive and Early‐Stage Trials in Spinocerebellar Ataxias 1 and 3
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
Experimental Study on Long Short-term Memory Networks for Identifying P-wave Primary Phase
Identifying primary phases of seismic waveforms is a routine task in seismic data processing. Owing to the low efficiency of manual identification and the influence of human subjective factors, many methods for the automatic identification of the primary
Tianzhe WANG +3 more
doaj +1 more source

