Determination of NAD+ and NADH level in a single cell under H2O2 stress by capillary electrophoresis

A capillary electrophoresis (CE) method is developed to determine both NAD+ and NADH levels in a single cell, based on an enzymatic cycling reaction. The detection limit can reach down to 0.2 amol NAD + and 1 amol NADH on a home-made CE-LIF setup. The method showed good reproducibility and specificity. After an intact cell was injected into the inlet of a capillary and lysed using a Tesla coil, intracellular NAD + and NADH were separated, incubated with the cycling buffer, and quantified by the amount of fluorescent product generated. NADH and NAD + levels of single cells of three cell lines and primary astrocyte culture were determined using this method. Comparing cellular NAD+ and NADH levels with and without exposure to oxidative stress induced by H2O2, it was found that H9c2 cells respond to the stress by reducing both cellular NAD+ and NADH levels, while astrocytes respond by increasing cellular NADH/NAD+ ratio

Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks

Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions. Considering that recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) can learn feature representations and model long-term temporal dependencies automatically, we propose an end-to-end fully connected deep LSTM network for skeleton based action recognition. Inspired by the observation that the co-occurrences of the joints intrinsically characterize human actions, we take the skeleton as the input at each time slot and introduce a novel regularization scheme to learn the co-occurrence features of skeleton joints. To train the deep LSTM network effectively, we propose a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons. Experimental results on three human action recognition datasets consistently demonstrate the effectiveness of the proposed model.Comment: AAAI 2016 conferenc

NRPA: Neural Recommendation with Personalized Attention

Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, different users have different preference and different items have different characteristics. Thus, the same word or similar reviews may have different informativeness for different users and items. In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews. We use a review encoder to learn representations of reviews from words, and a user/item encoder to learn representations of users or items from reviews. We propose a personalized attention model, and apply it to both review and user/item encoders to select different important words and reviews for different users/items. Experiments on five datasets validate our approach can effectively improve the performance of neural recommendation.Comment: 4 pages, 4 figure