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

Cell entry and trafficking of human adenovirus bound to blood factor X is determined by the fiber serotype and not hexon: heparan sulfate interaction

Human adenovirus serotype 5 (HAdV5)-based vectors administered intravenously accumulate in the liver as the result of their direct binding to blood coagulation factor X (FX) and subsequent interaction of the FX-HAdV5 complex with heparan sulfate proteoglycan (HSPG) at the surface of liver cells. Intriguingly, the serotype 35 fiber-pseudotyped vector HAdV5F35 has liver transduction efficiencies 4-logs lower than HAdV5, even though both vectors carry the same hexon capsomeres. In order to reconcile this apparent paradox, we investigated the possible role of other viral capsid proteins on the FX/HSPG-mediated cellular uptake of HAdV5-based vectors. Using CAR- and CD46-negative CHO cells varying in HSPG expression, we confirmed that FX bound to serotype 5 hexon protein and to HAdV5 and HAdV5F35 virions via its Gla-domain, and enhanced the binding of both vectors to surface-immobilized hypersulfated heparin and cellular HSPG. Using penton mutants, we found that the positive effect of FX on HAdV5 binding to HSPG and cell transduction did not depend on the penton base RGD and fiber shaft KKTK motifs. However, we found that FX had no enhancing effect on the HAdV5F35-mediated cell transduction, but a negative effect which did not involve the cell attachment or endocytic step, but the intracellular trafficking and nuclear import of the FX-HAdV5F35 complex. By cellular imaging, HAdV5F35 particles were observed to accumulate in the late endosomal compartment, and were released in significant amounts into the extracellular medium via exocytosis. We showed that the stability of serotype 5 hexon:FX interaction was higher at low pH compared to neutral pH, which could account for the retention of FX-HAdV5F35 complexes in the late endosomes. Our results suggested that, despite the high affinity interaction of hexon capsomeres to FX and cell surface HSPG, the adenoviral fiber acted as the dominant determinant of the internalization and trafficking pathway of HAdV5-based vectors

Striatal molecular signature of subchronic subthalamic nucleus high frequency stimulation in parkinsonian rat

International audienceThis study addresses the molecular mechanisms underlying the action of subthalamic nucleus high frequency stimulation (STN-HFS) in the treatment of Parkinson's disease and its interaction with levodopa (L-DOPA), focusing on the striatum. Striatal gene expression profile was assessed in rats with nigral dopamine neuron lesion, either treated or not, using agilent microarrays and qPCR verification. The treatments consisted in anti-akinetic STN-HFS (5 days), chronic L-DOPA treatment inducing dyskinesia (LIDs) or the combination of the two treatments that exacerbated LIDs. STN-HFS modulated 71 striatal genes. The main biological processes associated with the differentially expressed gene products include regulation of growth, of apoptosis and of synaptic transmission, and extracellular region is a major cellular component implicated. In particular, several of these genes have been shown to support survival or differentiation of striatal or of dopaminergic neurons. These results indicate that STN HFS may induce widespread anatomo-functional rearrangements in the striatum and create a molecular environment favorable for neuroprotection and neuroplasticity. STN-HFS and L-DOPA treatment share very few common gene regulation features indicating that the molecular substrates underlying their striatal action are mostly different; among the common effects is the down-regulation of Adrb1, which encodes the adrenergic beta-1-receptor, supporting a major role of this receptor in Parkinson's disease. In addition to genes already reported to be associated with LIDs (preprodynorphin, thyrotropin-releasing hormone, metabotropic glutamate receptor 4, cannabinoid receptor 1), the comparison between DOPA and DOPA/HFS identifies immunity-related genes as potential players in L-DOPA side effects

Tunable magnetic states in h-BN sheets

Magnetism in 2D atomic sheets has attracted considerable interest as its existence could allow the development of electronic and spintronic devices. The existence of magnetism is not sufficient for devices, however, as states must be addressable and modifiable through the application of an external drive. We show that defects in hexagonal boron nitride present a strong interplay between the the N-N distance in the edge and the magnetic moments of the defects. By stress-induced geometry modifications, we change the ground state magnetic moment of the defects. This control is made possible by the triangular shape of the defects as well as the strong spin localisation in the magnetic state.Comment: 10 pages, 3 figures, published in AP

EmoNets: Multimodal deep learning approaches for emotion recognition in video

The task of the emotion recognition in the wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies. The videos depict acted-out emotions under realistic conditions with a large degree of variation in attributes such as pose and illumination, making it worthwhile to explore approaches which consider combinations of features from multiple modalities for label assignment. In this paper we present our approach to learning several specialist models using deep learning techniques, each focusing on one modality. Among these are a convolutional neural network, focusing on capturing visual information in detected faces, a deep belief net focusing on the representation of the audio stream, a K-Means based "bag-of-mouths" model, which extracts visual features around the mouth region and a relational autoencoder, which addresses spatio-temporal aspects of videos. We explore multiple methods for the combination of cues from these modalities into one common classifier. This achieves a considerably greater accuracy than predictions from our strongest single-modality classifier. Our method was the winning submission in the 2013 EmotiW challenge and achieved a test set accuracy of 47.67% on the 2014 dataset