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Recently, deep clustering methods have achieved perfect clustering performances, which simultaneously perform clusters assignment and features representation learning. However, the performances greatly degenerate with the decreasing of images quality such as noisy image. To this end, a novel deep clustering method DDC (deep denoising clustering) is proposed. A deep convolutional denoising auto-encoder is employed to learn the robust features representation from noisy image, and self-attention mechanism improves the ability of capturing local features. End-to-end jointly training obtains features more suitable to clustering tasks and then completes clustering assignment. The similarities between feature embeddings and cluster-centers are weighted by different coefficients to enlarge the differences between intra-clusters and inter-clusters. The experimental results on the public datasets demonstrate that the proposed DDC can provide better clustering performances. And compared with other deep clustering algorithms, for example, the clustering accuracy of DDC is 0.803 while DEC (deep embedding clustering) is 0.597 on the COIL-20 dataset. Overall, DDC algorithm with the help of deep convolutional denoising auto-encoder and self-attention can efficiently group noisy images, and further enlarges the application range of deep clustering analysis.

Objective: Studies have shown that chemokines can stimulate the migration and activation of microglia to cause chronic post-surgical pain (CPSP). However, the involvement of C-X-C motif chemokine receptor 2 (CXCR2) as a new chemotactic factor in regulating CPSP and its underlying mechanism remains unclear. This study is to investigate the role of CXCR2 in the development of CPSP and reveal the underlying mechanism. Material and Methods: A rat model of skin/muscle incision and retraction was established, and treated with or without SB225002 (a selective inhibitor of CXCR2). In addition, the primary microglia cells induced by lipopolysaccharide were applied as an in vitro model for CPSP and treated individually with si-negative control (NC), si-CXCR2, si-CXCR2+Interleukin (IL)-6 (an agonist of the janus kinase (JAK)/signal transducers and activators of transcription (STAT)3 signaling pathway), si-CXCR2+IL-6+si-NC, or si-CXCR2+IL-6+si-exchange protein 1 directly activated by cAMP (EPAC1). Results: Results from the database analysis showed that CXCR2 and JAK/STAT3 signaling pathway-related genes, including JAK1, STAT3, and EPAC1, were mainly involved in the development of CPSP. Inhibition of CXCR2 expression not only inhibited the reduction of foot pain threshold in CPSP models but also led to a decreased expression of CXCR2 and the phosphorylation levels of JAK and STAT3 in both animal and cell models. Furthermore, inhibition of EPAC1 expression can hinder the regulatory function of CXCR2. Conclusion: This study indicated that the high expression of CXCR2 activates the JAK1/STAT3 signaling pathway, enhances EPAC1 activation in microglial cells, and exacerbates CPSP. [ABSTRACT FROM AUTHOR]

Wildfires are increasing in frequency, intensity, and extent globally due to climate change and they can alter forest composition, structure, and function. The destruction and subsequent regrowth of young vegetation can modify the ecosystem evapotranspiration and downstream water availability. However, the response of forest recovery on hydrology is not well known with even the sign of evapotranspiration and water yield changes following forest fires being uncertain across the globe. Here, we quantify the effects of forest regrowth after catastrophic wildfires on evapotranspiration and runoff in the world's tallest angiosperm forest (Eucalyptus regnans) in Australia. We combine eddy covariance measurements including pre‐ and post‐fire periods, mechanistic ecohydrological modeling and then extend the analysis spatially to multiple fires in eucalypt‐dominated forests in south‐eastern Australia by utilizing remote sensing. We find a fast recovery of evapotranspiration which reaches and exceeds pre‐fire values within 2 years after the bushfire, a result confirmed by eddy covariance data, remote sensing, and modeling. Such a fast evapotranspiration recovery is likely generalizable to tall eucalypt forests in south‐eastern Australia as shown by remote sensing. Once climate variability is discounted, ecohydrological modeling shows evapotranspiration rates from the recovering forest which reach peak values of +20% evapotranspiration 3 years post‐fire. As a result, modeled runoff decreases substantially. Contrary to previous research, we find that the increase in modeled evapotranspiration is largely caused by the aerodynamic effects of a much shorter forest height leading to higher surface temperature, higher humidity gradients and therefore increased transpiration. However, increases in evapotranspiration as well as decreases in runoff caused by the young forest are constrained by energy and water limitations. Our result of an increase in evapotranspiration due to aerodynamic warming in a shorter forest after wildfires could occur in many parts of the world experiencing forest disturbances. [ABSTRACT FROM AUTHOR]