Striatal Volume Predicts Level of Video Game Skill Acquisition

Video game skills transfer to other tasks, but individual differences in performance and in learning and transfer rates make it difficult to identify the source of transfer benefits. We asked whether variability in initial acquisition and of improvement in performance on a demanding video game, the Space Fortress game, could be predicted by variations in the pretraining volume of either of 2 key brain regions implicated in learning and memory: the striatum, implicated in procedural learning and cognitive flexibility, and the hippocampus, implicated in declarative memory. We found that hippocampal volumes did not predict learning improvement but that striatal volumes did. Moreover, for the striatum, the volumes of the dorsal striatum predicted improvement in performance but the volumes of the ventral striatum did not. Both ventral and dorsal striatal volumes predicted early acquisition rates. Furthermore, this early-stage correlation between striatal volumes and learning held regardless of the cognitive flexibility demands of the game versions, whereas the predictive power of the dorsal striatal volumes held selectively for performance improvements in a game version emphasizing cognitive flexibility. These findings suggest a neuroanatomical basis for the superiority of training strategies that promote cognitive flexibility and transfer to untrained tasks.United States. Office of Naval Research (grant number N00014-07-1-0903

An Effective Multi-Task Two-Stage Network with the Cross-Scale Training Strategy for Multi-Scale Image Super Resolution

Convolutional neural networks and the per-pixel loss function have shown their potential to be the best combination for super-resolving severely degraded images. However, there are still challenges, such as the massive number of parameters requiring prohibitive memory and vast computing and storage resources as well as time-consuming training and testing. What is more, the per-pixel loss measured by L2 and the Peak Signal-to-Noise Ratio do not correlate well with human perception of image quality, since L2 simply does not capture the intricate characteristics of human visual systems. To address these issues, we propose an effective two-stage hourglass network with multi-task co-optimization, which enables the entire network to focus on training and testing time and inherent image patterns such as local luminance, contrast, structure and data distribution. Moreover, to avoid overwhelming memory overheads, our model is capable of performing real-time single image multi-scale super-resolution, so it is memory-friendly, meaning that memory space is utilized efficiently. In addition, in order to best use the underlying structure and perception of image quality and the intermediate estimates during the inference process, we introduce a cross-scale training strategy with 2×, 3× and 4× image super-resolution. This effective multi-task two-stage network with the cross-scale strategy for multi-scale image super-resolution is named EMTCM. Quantitative and qualitative experiment results show that the proposed EMTCM network outperforms state-of-the-art methods in recovering high-quality images

A Facile Approach towards Fluorescent Nanogels with AIE-Active Spacers

A facile and efficient approach for design and synthesis of organic fluorescent nanogels has been developed by using a pre-synthesized polymeric precursor. This strategy is achieved by two key steps: (i) precise synthesis of core–shell star-shaped block copolymers with crosslinkable AIEgen-precursor (AIEgen: aggregation induced emission luminogen) as pending groups on the inner blocks; (ii) gelation of the inner blocks by coupling the AIEgen-precursor moieties to generate AIE-active spacers, and thus, fluorescent nanogel. By using this strategy, a series of star-shaped block copolymers with benzophenone groups pending on the inner blocks were synthesized by grafting from a hexafunctional initiator through atom transfer radical copolymerization (ATRP) of 4-benzoylphenyl methacrylate (BPMA) or 2-(4-benzoylphenoxy)ethyl methacrylate (BPOEMA) with methyl methacrylate (MMA) and tert-butyldimethylsilyl-protected 2-hydroxyethyl methacrylate (ProHEMA) followed by a sequential ATRP to grow PMMA or PProHEMA. The pendent benzophenone groups were coupled by McMurry reaction to generate tetraphenylethylene (TPE) groups which served as AIE-active spacers, affording a fluorescent nanogel. The nanogel showed strong emission not only at aggregated state but also in dilute solution due to the strongly restricted inter- and intramolecular movement of TPE moiety in the crosslinked polymeric network. The nanogel has been used as a fluorescent macromolecular additive to fabricate fluorescent film