Flexibility of the N-Terminal mVDAC1 Segment Controls the Channel’s Gating Behavior

Since the solution of the molecular structures of members of the voltage dependent anion channels (VDACs), the N-terminal α-helix has been the main focus of attention, since its strategic location, in combination with its putative conformational flexibility, could define or control the channel’s gating characteristics. Through engineering of two double-cysteine mVDAC1 variants we achieved fixing of the N-terminal segment at the bottom and midpoint of the pore. Whilst cross-linking at the midpoint resulted in the channel remaining constitutively open, cross-linking at the base resulted in an “asymmetric” gating behavior, with closure only at one electric field´s orientation depending on the channel’s orientation in the lipid bilayer. Additionally, and while the native channel adopts several well-defined closed states (S1 and S2), the cross-linked variants showed upon closure a clear preference for the S2 state. With native-channel characteristics restored following reduction of the cysteines, it is evident that the conformational flexibility of the N-terminal segment plays indeed a major part in the control of the channel’s gating behavior

Enabling Early Audio Event Detection With Neural Networks

This paper presents a methodology for early detection of audio events from audio streams. Early detection is the ability to infer an ongoing event during its initial stage. The proposed system consists of a novel inference step coupled with dual parallel tailored-loss deep neural networks (DNNs). The DNNs share a similar architecture except for their loss functions, i.e. weighted loss and multitask loss, which are designed to efficiently cope with issues common to audio event detection. The inference step is newly introduced to make use of the network output for recognizing ongoing events. The monotonicity of the detection function is required for reliable early detection, and will also be proved. Experiments on the ITC-Irst database show that the proposed system achieves state-of-the-art detection performance. Furthermore, even partial events are sufficient to achieve good performance similar to that obtained when an entire event is observed, enabling early event detection

Recurrent Neural Network Based Early Prediction of Future Hand Movements

This work focuses on a system for hand prostheses that can overcome the delay problem introduced by classical approaches while being reliable. The proposed approach based on a recurrent neural network enables us to incorporate the sequential nature of the surface electromyogram data and the proposed system can be used either for classification or early prediction of hand movements. Especially the latter is a key to a latency free steering of a prosthesis. The experiments conducted on the first three Ninapro databases reveal that the prediction up to 200 ms ahead in the future is possible without a significant drop in accuracy. Furthermore, for classification, our proposed approach outperforms the state of the art classifiers even though we used significantly shorter windows for feature extraction

Early Prediction of Future Hand Movements Using sEMG Data

We study in this work the feasibility of early prediction of hand movement based on sEMG signals to overcome the time delay issue of the conventional classification. Opposed to the classification task, the objective of the early prediction task is to predict a hand movement that is going to occur in the future given the information up to the current time point. The ability of early prediction may allow a hand prosthesis control system to compensate for the time delay and, as a result, improve the usability. Experimental results on the Ninapro database show that we can predict up to 300 ms ahead in the future while the prediction accuracy remains very close to that of the standard classification, i.e. it is just marginally lower. Furthermore, historical data prior the current time window is shown very important to improve performance, not only for the prediction but also the classification task