Results 51 to 60 of about 99,773 (316)

Reciprocal control of viral infection and phosphoinositide dynamics

open access: yesFEBS Letters, EarlyView.
Phosphoinositides, although scarce, regulate key cellular processes, including membrane dynamics and signaling. Viruses exploit these lipids to support their entry, replication, assembly, and egress. The central role of phosphoinositides in infection highlights phosphoinositide metabolism as a promising antiviral target.
Marie Déborah Bancilhon, Bruno Mesmin
wiley   +1 more source

Grounded Recurrent Neural Networks

open access: yesCoRR, 2017
In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process "grounding"). The approach is particularly well-suited for extracting large numbers of concepts from text.
Ankit Vani   +2 more
openaire   +2 more sources

NLOS Identification in WLANs Using Deep LSTM with CNN Features

open access: yesSensors, 2018
Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for ...
Viet-Hung Nguyen   +3 more
doaj   +1 more source

Organ‐specific redox imbalances in spinal muscular atrophy mice are partially rescued by SMN antisense oligonucleotides

open access: yesFEBS Letters, EarlyView.
We identified a systemic, progressive loss of protein S‐glutathionylation—detected by nonreducing western blotting—alongside dysregulation of glutathione‐cycle enzymes in both neuronal and peripheral tissues of Taiwanese SMA mice. These alterations were partially rescued by SMN antisense oligonucleotide therapy, revealing persistent redox imbalance as ...
Sofia Vrettou, Brunhilde Wirth
wiley   +1 more source

Multistability in Recurrent Neural Networks [PDF]

open access: yesSIAM Journal on Applied Mathematics, 2006
Stable stationary solutions correspond to memory capacity in the application of associative memory for neural networks. In this presentation, existence of multiple stable stationary solutions for Hopfield-type neural networks with delay and without delay is investigated. Basins of attraction for these stationary solutions are also estimated.
Chang-Yuan Cheng   +2 more
openaire   +1 more source

A delay-dependent LMI approach to dynamics analysis of discrete-time recurrent neural networks with time-varying delays

open access: yes, 2007
This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this Letter, the analysis problem for the existence and stability of periodic solutions is investigated for
Zidong Wang   +3 more
core   +1 more source

The role of recurrent networks in neural architectures of grounded cognition: learning of control [PDF]

open access: yes, 2008
Recurrent networks have been used as neural models of language processing, with mixed results. Here, we discuss the role of recurrent networks in a neural architecture of grounded cognition.
Van der Velde, Frank, de Kamps, Marc
core   +1 more source

Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system

open access: yesFEBS Letters, EarlyView.
Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley   +1 more source

Pixel Recurrent Neural Networks

open access: yesCoRR, 2016
Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel
Aäron van den Oord   +2 more
openaire   +3 more sources

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