191,876 research outputs found
Post-Transcriptional Mechanisms of Neuronal Translational Control in Synaptic Plasticity
The dynamic complexity of synaptic function is matched by extensive multidimensional regulation of neuronal mRNA translation which is achieved by a number of postâtranscriptional mechanisms. The first key aspect of this regulatory capacity is mRNA distal trafficking through RNAâbinding proteins, which governs the transcriptomic composition of postâsynaptic compartments. Small nonâcoding microRNA and associated machinery have the capacity to precisely coordinate neural gene networks in space and time by providing a flexible specificity dimension to translational regulation. This RNAâguided subcellular fineâtuning of protein synthesis is an exquisite mechanism used in neurons to exert control of synaptic properties. Emerging evidence also implicates brainâenriched long nonâcoding RNA and novel circular RNA in posttranscriptional regulation of gene expression through the modulation of both mRNA and miRNA functions, thereby exemplifying the complex nature of neuronal translation. Herein, we review current knowledge of these regulatory systems and analyse how these mechanisms of transcriptomic regulation may be linked together to achieve highâorder spatiotemporal control of postâsynaptic translation
Learning Compositional Visual Concepts with Mutual Consistency
Compositionality of semantic concepts in image synthesis and analysis is
appealing as it can help in decomposing known and generatively recomposing
unknown data. For instance, we may learn concepts of changing illumination,
geometry or albedo of a scene, and try to recombine them to generate physically
meaningful, but unseen data for training and testing. In practice however we
often do not have samples from the joint concept space available: We may have
data on illumination change in one data set and on geometric change in another
one without complete overlap. We pose the following question: How can we learn
two or more concepts jointly from different data sets with mutual consistency
where we do not have samples from the full joint space? We present a novel
answer in this paper based on cyclic consistency over multiple concepts,
represented individually by generative adversarial networks (GANs). Our method,
ConceptGAN, can be understood as a drop in for data augmentation to improve
resilience for real world applications. Qualitative and quantitative
evaluations demonstrate its efficacy in generating semantically meaningful
images, as well as one shot face verification as an example application.Comment: 10 pages, 8 figures, 4 tables, CVPR 201
Multi-channel Encoder for Neural Machine Translation
Attention-based Encoder-Decoder has the effective architecture for neural
machine translation (NMT), which typically relies on recurrent neural networks
(RNN) to build the blocks that will be lately called by attentive reader during
the decoding process. This design of encoder yields relatively uniform
composition on source sentence, despite the gating mechanism employed in
encoding RNN. On the other hand, we often hope the decoder to take pieces of
source sentence at varying levels suiting its own linguistic structure: for
example, we may want to take the entity name in its raw form while taking an
idiom as a perfectly composed unit. Motivated by this demand, we propose
Multi-channel Encoder (MCE), which enhances encoding components with different
levels of composition. More specifically, in addition to the hidden state of
encoding RNN, MCE takes 1) the original word embedding for raw encoding with no
composition, and 2) a particular design of external memory in Neural Turing
Machine (NTM) for more complex composition, while all three encoding strategies
are properly blended during decoding. Empirical study on Chinese-English
translation shows that our model can improve by 6.52 BLEU points upon a strong
open source NMT system: DL4MT1. On the WMT14 English- French task, our single
shallow system achieves BLEU=38.8, comparable with the state-of-the-art deep
models.Comment: Accepted by AAAI-201
BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings
In this paper, we propose a bidimensional attention based recursive
autoencoder (BattRAE) to integrate clues and sourcetarget interactions at
multiple levels of granularity into bilingual phrase representations. We employ
recursive autoencoders to generate tree structures of phrases with embeddings
at different levels of granularity (e.g., words, sub-phrases and phrases). Over
these embeddings on the source and target side, we introduce a bidimensional
attention network to learn their interactions encoded in a bidimensional
attention matrix, from which we extract two soft attention weight distributions
simultaneously. These weight distributions enable BattRAE to generate
compositive phrase representations via convolution. Based on the learned phrase
representations, we further use a bilinear neural model, trained via a
max-margin method, to measure bilingual semantic similarity. To evaluate the
effectiveness of BattRAE, we incorporate this semantic similarity as an
additional feature into a state-of-the-art SMT system. Extensive experiments on
NIST Chinese-English test sets show that our model achieves a substantial
improvement of up to 1.63 BLEU points on average over the baseline.Comment: 7 pages, accepted by AAAI 201
TITER: predicting translation initiation sites by deep learning.
MotivationTranslation initiation is a key step in the regulation of gene expression. In addition to the annotated translation initiation sites (TISs), the translation process may also start at multiple alternative TISs (including both AUG and non-AUG codons), which makes it challenging to predict TISs and study the underlying regulatory mechanisms. Meanwhile, the advent of several high-throughput sequencing techniques for profiling initiating ribosomes at single-nucleotide resolution, e.g. GTI-seq and QTI-seq, provides abundant data for systematically studying the general principles of translation initiation and the development of computational method for TIS identification.MethodsWe have developed a deep learning-based framework, named TITER, for accurately predicting TISs on a genome-wide scale based on QTI-seq data. TITER extracts the sequence features of translation initiation from the surrounding sequence contexts of TISs using a hybrid neural network and further integrates the prior preference of TIS codon composition into a unified prediction framework.ResultsExtensive tests demonstrated that TITER can greatly outperform the state-of-the-art prediction methods in identifying TISs. In addition, TITER was able to identify important sequence signatures for individual types of TIS codons, including a Kozak-sequence-like motif for AUG start codon. Furthermore, the TITER prediction score can be related to the strength of translation initiation in various biological scenarios, including the repressive effect of the upstream open reading frames on gene expression and the mutational effects influencing translation initiation efficiency.Availability and implementationTITER is available as an open-source software and can be downloaded from https://github.com/zhangsaithu/titer [email protected] or [email protected] informationSupplementary data are available at Bioinformatics online
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