（1）主 题：A convex graph regularized model for hyperspectral unmixing
Hyperspectral unmixing has received considerable consideration in analyzing hyperspectral images, which estimates the fractional abundances of pure spectral signatures in each mixed pixel. Recently, many sparse unmixing methods have achieved promising performance. However, most existing methods fail to consider the latent spatial structure of the hyperspectral data. In order to utilize the intrinsic structure of the hyperspectral data, we present a semisupervised graph-regularized sparse model for hyperspectral unmixing and prove the convexity of it. To solve this model efficiently, we develop a fast implementation by using symmetric alternating direction method of multipliers technique. The proposed model takes the advantage of the close link between the hyperspectral images and the material abundance maps, thus exploiting the spatial information. As the proposed model is a convex model, the convergence of our scheme is guaranteed. The experimental results on synthetic data sets demonstrate the superiority of our method compared with some sparse based approaches.
Si Wang received the Bachelor degree and Ph.D. degree from University of Electronic Science and Technology of China, in 2010 and 2016, respectively. She currently works as a Postdoctoral Fellow in the School of Mathematical Science of UESTC. Her main interests lie in image processing, such as deblurring, denoising, and inpainting. Her research focuses on translation image restoration problems into mathematical models, and development of numerical algorithms to solve the models. Another important aspect of her research is analysis of the mathematical properties of the models and algorithms, including existence, uniqueness and convergence.Now she is working on the hyperspectral image unmixing.
（1）主 题：Adaptive Radar Target Detection in Clutters
The target detection ability of radar is being challenged by the various and complex environments. The targets are commonly overwhelmed by clutters, and the utilization of adaptive technique makes the detectors match the clutter environment better, which is the key to improve target detection performance. In this presentation, the key problem of adaptive target detection algorithm in clutter, covariance matrix estimators with limited secondary data, will be discussed.
In order to solve the problem of limited secondary data, three new covariance matrix estimators will be discussed: (1) a class of covariance matrix estimators based on the geometric barycenters; (2) covariance matrix estimators based on multiple a priori spectral models; (3) maximum a posteriori (MAP) estimator based on a priori distribution knowledge of the clutter covariance matrix.
Na Li received the Ph.D. degree with the School of Electronic Engineering, University of Electronic Science and Technology of China in 2016. From September 2013 to August 2015, she was a Visiting Researcher with the Department of Electrical Engineering, Duke University, under the financial support from the China Scholarship Council. Now she is a post-doctoral in the School of Electronic Engineering, University of Electronic Science and Technology of China. Her research interests include radar signal analysis and processing.
（1）主 题：The antidepressant-like effects of pioglitazone in a chronic mild stress mouse model are associated with microglia-mediated neuroinflammatory response
Major depressive disorder (MDD) is one of the most frequently occurring mental disorders and has a considerable rate of mortality. Discoveries that microglia-mediated neuroinflammation is involved in the pathological process of depression provided a new strategy for novel antidepressant therapy. Pioglitazone is a highly selective agonist for PPARγ, which causes the transcription of several genes involved in glucose and lipid metabolism as well as with the production of inflammatory mediators. Chronic mild stress (CMS) treatment was performed on C57BL/6 mice for 6 weeks. After 3 weeks with the CMS procedure, depressive-like behaviors were evaluated. Pioglitazone was administered intragastrically once per day for 3 weeks, then confirmed the expression of neuroinflammatory cytokines and activated microglial state. It was demonstrated that pioglitazone ameliorated depression-like behaviors in CMS-treated mice, as indicated by body weight, sucrose preference, tail suspension test, forced swimming test, and locomotor activity test. The amelioration of the depression was blocked by GW9662. The expression of classical activation (M1 phenotype) markers increased, and the gene expression of alternative activation (M2 phenotype) markers decreased in the stress treated mice. Pioglitazone significantly inhibited the increased numbers and morphological alterations of microglia, reduced the elevated expression of microglial M1 markers, and increased the downgraded expression of microglial M2 markers in C57BL/6 mice exposed to CMS. In an in vitro experiment, pioglitazone reversed the imbalance of M1 and M2 inflammatory cytokines, which is correlated with the inhibition of nuclear factor kB activation and is expressed in LPS-stimulated N9 microglial cells. In conclusion, we showed that pioglitazone administration induce the neuroprotective phenotype of microglia and ameliorate depression-like behaviors in CMS-treated C57BL/6 mice. These data suggested that the microglia modulating agent pioglitazone present a beneficial choice for depression.
Qiuying Zhao is a postdoctoral fellow in the School of Life Science and Technology, University of Electronic Science and Technology of China. She obtained the PhD degree in biomedical engineering advised by Prof. Zili You in UESTC, 2016. Her research interests include maternal stress and microglial activated phenotypes, depression and neuroinflammatory response.
编辑：胡武辉 / 审核：罗莎 / 发布者：林坤