Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding.

Decoding motor imagery (MI) electroencephalogram (EEG) signals for brain-computer interfaces (BCIs) is a challenging task because of the severe non-stationarity of perceptual decision processes. Recently, deep learning techniques have had great success in EEG decoding because of their prominent ability to learn features from raw EEG signals automatically. However, the challenge that the deep learning method faces is that the shortage of labeled EEG signals and EEGs sampled from other subjects cannot be used directly to train a convolutional neural network (ConvNet) for a target subject. To solve this problem, in this paper, we present a novel conditional domain adaptation neural network (CDAN) framework for MI EEG signal decoding. Specifically, in the CDAN, a densely connected ConvNet is firstly applied to obtain high-level discriminative features from raw EEG time series. Then, a novel conditional domain discriminator is introduced to work as an adversarial with the label classifier to learn commonly shared intra-subjects EEG features. As a result, the CDAN model trained with sufficient EEG signals from other subjects can be used to classify the signals from the target subject efficiently. Competitive experimental results on a public EEG dataset (High Gamma Dataset) against the state-of-the-art methods demonstrate the efficacy of the proposed framework in recognizing MI EEG signals, indicating its effectiveness in automatic perceptual decision decoding.

Two-stage transcriptional reprogramming in Saccharomyces cerevisiae for optimizing ethanol production from xylose.

Conversion of lignocellulosic material to ethanol is a major challenge in second generation bio-fuel production by yeast Saccharomyces cerevisiae. This report describes a novel strategy named "two-stage transcriptional reprogramming (TSTR)" in which key gene expression at both glucose and xylose fermentation phases is optimized in engineered S. cerevisiae. Through a combined genome-wide screening of stage-specific promoters and the balancing of the metabolic flux, ethanol yields and productivity from mixed sugars were significantly improved. In a medium containing 50g/L glucose and 50g/L xylose, the top-performing strain WXY12 rapidly consumed glucose within 12h and within 84h it consistently achieved an ethanol yield of 0.48g/g total sugar, which was 94% of the theoretical yield. WXY12 utilizes a KGD1 inducible promoter to drive xylose metabolism, resulting in much higher ethanol yield than a reference strain using a strong constitutive PGK1 promoter. These promising results validate the TSTR strategy by synthetically regulating the xylose assimilation pathway towards efficient xylose fermentation.