Multistaged discharge constructing heterostructure with enhanced solid-solution behavior for long-life lithium-oxygen batteries.

Inferior charge transport in insulating and bulk discharge products is one of the main factors resulting in poor cycling stability of lithium-oxygen batteries with high overpotential and large capacity decay. Here we report a two-step oxygen reduction approach by pre-depositing a potassium carbonate layer on the cathode surface in a potassium-oxygen battery to direct the growth of defective film-like discharge products in the successive cycling of lithium-oxygen batteries. The formation of defective film with improved charge transport and large contact area with a catalyst plays a critical role in the facile decomposition of discharge products and the sustained stability of the battery. Multistaged discharge constructing lithium peroxide-based heterostructure with band discontinuities and a relatively low lithium diffusion barrier may be responsible for the growth of defective film-like discharge products. This strategy offers a promising route for future development of cathode catalysts that can be used to extend the cycling life of lithium-oxygen batteries

Expression and Clinical Significance of Antiapoptotic Gene (Survivin) in NB4 and Acute Promyelocytic Leukemia Cells

To study survivin gene expression in APL cells and to explore its correlation with clinical manifestations. PML/RARα and survivin mRNA expression were analysed using RT-PCR. By treatment of ATRA, the survivin mRNA expression in NB4 cells gradually decreased with time and was almost undetectable in the 72th hour. Survivin was expressed in 67% of the 36 APL cases (de novo and relapse patients) with PML/RARα fusion gene expression. However, in 22 cases of remission stage patients without PML/RARα fusion gene expression, survivin was expressed in 36%. The survivin mRNA expression positive rate in de novo and relapse groups, and PML/RARα fusion gene L-type positive groups, was obviously higher than those in remission period groups and was significantly lower than those in acute leukemia groups. In 36 cases of de novo and relapse APL patients, all cases could obtain complete remission, irrespective of the survivin expression. APL patients expressed with survivin mRNA had DIC and serious infection (one patient died). The clinical symptom included slight skin or mucosa bleeding, fever, and asthenic for patients without the survivin mRNA expression. Later, two cases of APL patients with the survivin mRNA expression were treated by ATRA, induction differentiation sign in their peripheral blood and bone marrow figure was not obvious. It was concluded that the survive gene expression was lower in APL than those in any other types of leukemia, thus closely associated with clinical manifestation

Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network

High accuracy video label prediction (classification) models are attributed to large scale data. These data could be frame feature sequences extracted by a pre-trained convolutional-neural-network, which promote the efficiency for creating models. Unsupervised solutions such as feature average pooling, as a simple label-independent parameter-free based method, has limited ability to represent the video. While the supervised methods, like RNN, can greatly improve the recognition accuracy. However, the video length is usually long, and there are hierarchical relationships between frames across events in the video, the performance of RNN based models are decreased. In this paper, we proposes a novel video classification method based on a deep convolutional graph neural network(DCGN). The proposed method utilize the characteristics of the hierarchical structure of the video, and performed multi-level feature extraction on the video frame sequence through the graph network, obtained a video representation re ecting the event semantics hierarchically. We test our model on YouTube-8M Large-Scale Video Understanding dataset, and the result outperforms RNN based benchmarks.Comment: ECCV 201