71,543 research outputs found
High-dimensional sequence transduction
We investigate the problem of transforming an input sequence into a
high-dimensional output sequence in order to transcribe polyphonic audio music
into symbolic notation. We introduce a probabilistic model based on a recurrent
neural network that is able to learn realistic output distributions given the
input and we devise an efficient algorithm to search for the global mode of
that distribution. The resulting method produces musically plausible
transcriptions even under high levels of noise and drastically outperforms
previous state-of-the-art approaches on five datasets of synthesized sounds and
real recordings, approximately halving the test error rate
The genome and transcriptome of Trichormus sp NMC-1: insights into adaptation to extreme environments on the Qinghai-Tibet Plateau
The Qinghai-Tibet Plateau (QTP) has the highest biodiversity for an extreme environment worldwide, and provides an ideal natural laboratory to study adaptive evolution. In this study, we generated a draft genome sequence of cyanobacteria Trichormus sp. NMC-1 in the QTP and performed whole transcriptome sequencing under low temperature to investigate the genetic mechanism by which T. sp. NMC-1 adapted to the specific environment. Its genome sequence was 5.9 Mb with a G+C content of 39.2% and encompassed a total of 5362 CDS. A phylogenomic tree indicated that this strain belongs to the Trichormus and Anabaena cluster. Genome comparison between T. sp. NMC-1 and six relatives showed that functionally unknown genes occupied a much higher proportion (28.12%) of the T. sp. NMC-1 genome. In addition, functions of specific, significant positively selected, expanded orthogroups, and differentially expressed genes involved in signal transduction, cell wall/membrane biogenesis, secondary metabolite biosynthesis, and energy production and conversion were analyzed to elucidate specific adaptation traits. Further analyses showed that the CheY-like genes, extracellular polysaccharide and mycosporine-like amino acids might play major roles in adaptation to harsh environments. Our findings indicate that sophisticated genetic mechanisms are involved in cyanobacterial adaptation to the extreme environment of the QTP
Progress in the use of adeno-associated viral vectors for gene therapy
The development of safe and efficient gene transfer vectors is crucial for the success of gene therapy trials. A viral vector system promising to meet these requirements is based on the apathogenic adeno-associated virus (AAV-2), a member of the parvovirus family. The advantages of this vector system is the stability of the viral capsid, the low immunogenicity, the ability to transduce both dividing and non-dividing cells, the potential to integrate site specifically and to achieve long-term gene expression even in vivo, and its broad tropism allowing the efficient transduction of diverse organs including the skin. All this makes AAV-2 attractive and efficient for in vitro gene transfer and local injection in vivo. This review covers the progress made in AAV vector technology including the development of AAV vectors based on other serotypes, summarizes the results obtained by AAV targeting vectors and outlines potential applications in the field of cutaneous gene therapy. Copyright (C) 2004 S. Karger AG, Basel
Modeling Evolution of Crosstalk in Noisy Signal Transduction Networks
Signal transduction networks can form highly interconnected systems within
cells due to network crosstalk, the sharing of input signals between multiple
downstream responses. To better understand the evolutionary design principles
underlying such networks, we study the evolution of crosstalk and the emergence
of specificity for two parallel signaling pathways that arise via gene
duplication and are subsequently allowed to diverge. We focus on a sequence
based evolutionary algorithm and evolve the network based on two physically
motivated fitness functions related to information transmission. Surprisingly,
we find that the two fitness functions lead to very different evolutionary
outcomes, one with a high degree of crosstalk and the other without.Comment: 18 Pages, 16 Figure
The rational development of molecularly imprinted polymer-based sensors for protein detection.
The detection of specific proteins as biomarkers of disease, health status,
environmental monitoring, food quality, control of fermenters and civil defence
purposes means that biosensors for these targets will become increasingly more
important. Among the technologies used for building specific recognition
properties, molecularly imprinted polymers (MIPs) are attracting much attention.
In this critical review we describe many methods used for imprinting recognition
for protein targets in polymers and their incorporation with a number of
transducer platforms with the aim of identifying the most promising approaches
for the preparation of MIP-based protein sensors (277 references)
Understanding the Dynamics of Gene Regulatory Systems : Characterisation and Clinical Relevance of cis-Regulatory Polymorphisms
Peer reviewedPublisher PD
Attend and Diagnose: Clinical Time Series Analysis using Attention Models
With widespread adoption of electronic health records, there is an increased
emphasis for predictive models that can effectively deal with clinical
time-series data. Powered by Recurrent Neural Network (RNN) architectures with
Long Short-Term Memory (LSTM) units, deep neural networks have achieved
state-of-the-art results in several clinical prediction tasks. Despite the
success of RNNs, its sequential nature prohibits parallelized computing, thus
making it inefficient particularly when processing long sequences. Recently,
architectures which are based solely on attention mechanisms have shown
remarkable success in transduction tasks in NLP, while being computationally
superior. In this paper, for the first time, we utilize attention models for
clinical time-series modeling, thereby dispensing recurrence entirely. We
develop the \textit{SAnD} (Simply Attend and Diagnose) architecture, which
employs a masked, self-attention mechanism, and uses positional encoding and
dense interpolation strategies for incorporating temporal order. Furthermore,
we develop a multi-task variant of \textit{SAnD} to jointly infer models with
multiple diagnosis tasks. Using the recent MIMIC-III benchmark datasets, we
demonstrate that the proposed approach achieves state-of-the-art performance in
all tasks, outperforming LSTM models and classical baselines with
hand-engineered features.Comment: AAAI 201
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