The impact of cognitive training on cerebral white matter in community-dwelling elderly : one-year prospective longitudinal diffusion tensor imaging study

It has been shown that cognitive training (CogTr) is effective and recuperative for older adults, and can be used to fight against cognitive decline. In this study, we investigated whether behavioural gains from CogTr would extend to white matter (WM) microstructure, and whether training-induced changes in WM integrity would be associated with improvements in cognitive function, using diffusion tensor imaging (DTI). 48 healthy community elderly were either assigned to multi-domain or single-domain CogTr groups to receive 24 sessions over 12 weeks, or to a control group. DTI was performed at both baseline and 12-month follow-up. Positive effects of multi-domain CogTr on long-term changes in DTI indices were found in posterior parietal WM. Participants in the multi-domain group showed a trend of long-term decrease in axial diffusivity (AD) without significant change in fractional anisotropy (FA), mean diffusivity (MD) or radial diffusivity (RD), while those in the control group displayed a significant FA decrease, and an increase in MD and RD. In addition, significant relationships between an improvement in processing speed and changes in RD, MD and AD were found in the multi-domain group. These findings support the hypothesis that plasticity of WM can be modified by CogTr, even in late adulthood

Structural Basis for Recognition of Human Enterovirus 71 by a Bivalent Broadly Neutralizing Monoclonal Antibody

Enterovirus 71 (EV71) is the main pathogen responsible for hand, foot and mouth disease with severe neurological complications and even death in young children. We have recently identified a highly potent anti-EV71 neutralizing monoclonal antibody, termed D5. Here we investigated the structural basis for recognition of EV71 by the antibody D5. Four three-dimensional structures of EV71 particles in complex with IgG or Fab of D5 were reconstructed by cryo-electron microscopy (cryo-EM) single particle analysis all at subnanometer resolutions. The most critical EV71 mature virion-Fab structure was resolved to a resolution of 4.8 Å, which is rare in cryo-EM studies of virus-antibody complex so far. The structures reveal a bivalent binding pattern of D5 antibody across the icosahedral 2-fold axis on mature virion, suggesting that D5 binding may rigidify virions to prevent their conformational changes required for subsequent RNA release. Moreover, we also identified that the complementary determining region 3 (CDR3) of D5 heavy chain directly interacts with the extremely conserved VP1 GH-loop of EV71, which was validated by biochemical and virological assays. We further showed that D5 is indeed able to neutralize a variety of EV71 genotypes and strains. Moreover, D5 could potently confer protection in a mouse model of EV71 infection. Since the conserved VP1 GH-loop is involved in EV71 binding with its uncoating receptor, the scavenger receptor class B, member 2 (SCARB2), the broadly neutralizing ability of D5 might attribute to its inhibition of EV71 from binding SCARB2. Altogether, our results elucidate the structural basis for the binding and neutralization of EV71 by the broadly neutralizing antibody D5, thereby enhancing our understanding of antibody-based protection against EV71 infection. © 2016 Ye et al

Mastering Complex Control in MOBA Games with Deep Reinforcement Learning

We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state and action spaces than those of traditional 1v1 games, such as Go and Atari series, which makes it very difficult to search any policies with human-level performance. In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our system is of low coupling and high scalability, which enables efficient explorations at large scale. Our algorithm includes several novel strategies, including control dependency decoupling, action mask, target attention, and dual-clip PPO, with which our proposed actor-critic network can be effectively trained in our system. Tested on the MOBA game Honor of Kings, our AI agent, called Tencent Solo, can defeat top professional human players in full 1v1 games.Comment: AAAI 202

Evolutionary modeling for streamflow forecasting with minimal datasets: a case study in the West Malian River, China

A large data set is generally needed when modeling hydrological processes. However, for developing countries such as China, data sets are often unavailable in remote areas. An attempt to apply a novel genetic programming (GP) technique was made to model the relationship between streamflow of the West Malian River and the impact of climate change in the northeastern part of China. Available annual streamflow and climatic data were used for training and testing of the GP model. Data from the years between 1982 and 2002 were used for automatic selection of the model relationship. Prediction of the model was undertaken for the period 2003–2006 and the results were compared with measured data. Predicted annual streamflow of the West Malian River agreed with measured data to an acceptable degree of accuracy even with a small amount of data set. For comparison, a multilayer perceptron method with back propagation algorithm, a gray theory model, and a multiple linear regression model were selected to conduct the prediction with the same data set. Results showed that the performance of GP method was generally better than other statistical methods such as multilayer perceptron, gray theory model, and multiple linear regression model. Further, the results also showed that the GP method is a useful tool for water resource management, especially in developing countries, to evaluate the potential impacts of climate change on the streamflow when large data sets are unavailable

Nonlinear Vibration Signal Tracking of Large Offshore Bridge Stayed Cable Based on Particle Filter

The stayed cables are key stress components of large offshore bridge. The fault detection of stayed cable is very important for safe of large offshore bridge. A particle filter model and algorithm of nonlinear vibration signal are used in this paper. Firstly, the particle filter model of stayed cable of large offshore bridge is created. Nonlinear dynamic model of the stayed-cable and beam coupling system is dispersed in temporal dimension by using the finite difference method. The discrete nonlinear vibration equations of any cable element are worked out. Secondly, a state equation of particle filter is fitted by least square algorithm from the discrete nonlinear vibration equations. So the particle filter algorithm can use the accurate state equations. Finally, the particle filter algorithm is used to filter the vibration signal of bridge stayed cable. According to the particle filter, the de-noised vibration signal can be tracked and be predicted for a short time accurately. Many experiments are done at some actual bridges. The simulation experiments and the actual experiments on the bridge stayed cables are all indicating that the particle filter algorithm in this paper has good performance and works stably