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https://www.selleckchem.com/ Sleep staging is an important basis for diagnosing sleep-related problems. In this paper, an attention based convolutional network for automatic sleep staging is proposed. The network takes time-frequency image as input and predict sleep stage for each 30-s epoch as output. For each CNN feature maps, our model generate attention maps along two separate dimensions, time and filter, and then multiplied to form the final attention map. Residual-like fusion structure is used to append the attention map to the input feature map for adaptive feature refinement. In addition, to get the global feature representation with less information loss, the generalized mean pooling is introduced. To prove the efficacy of the proposed method, we have compared with two baseline method on sleep-EDF data set with different setting of the framework and input channel type, the experimental results show that the paper model has achieved significant improvements in terms of overall accuracy, Cohen's kappa, MF1, sensitivity and specificity. The performance of the proposed network is compared with that of the state-of-the-art algorithms with an overall accuracy of 83.4%, a macro F1-score of 77.3%, κ = 0.77, sensitivity = 77.1% and specificity = 95.4%, respectively. The experimental results demonstrate the superiority of the proposed network.Adipose tissue is an important organ in our body, participating not only in energy metabolism but also immune regulation. It is broadly classified as white (WAT) and brown (BAT) adipose tissues. WAT is highly heterogeneous, composed of adipocytes, various immune, progenitor and stem cells, as well as the stromal vascular populations. The expansion and inflammation of WAT are hallmarks of obesity and play a causal role in the development of metabolic and cardiovascular diseases. The primary event triggering the inflammatory expansion of WAT remains unclear. The present review focuses on the role of adipocyte progenitors (APS), wh

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