No abstract

Heroin has been shown to elevate dopamine (DA) level. It is well known that an increase in DA oxidative metabolism leads to increased reactive oxygen species (ROS) formation, and thus, ROS have been frequently associated with neuronal cell death due to damage to carbohydrates, amino acids, phospholipids, and nucleic acids. This study investigated whether there are oxidative stress and effects of exogenous antioxidants in heroin-administered mice. The heroin-dependent mice model was made via intraperitoneal injection. Oxidative damage of DNA, protein, and lipid was measured by analysis of single cell electrophoresis, the 2,4-dinitrophenylhydrazine method, and thiobarbituric acid method respectively. The activities of antioxidative enzymes and total antioxidant capacity were assayed by spectrophotometry. After administration with heroin, the mice not only showed decrease of total antioxidant capacity in serum and antioxidant enzymes such as superoxide dismutase, catalase, and glutathione (GSH) peroxidase in brain, but also exhibited the oxidative damages of DNA, protein and lipid. On the other hand, exogenous antioxidants could restrain the oxidative stress, even alleviate withdrawal syndrome in heroin-administered mice. Our results also imply a possibility that ROS may participate in the whole process of dependence and withdrawal of heroin. Therefore, strategies of blocking oxidative stress may be useful in the development of therapy for opiate abuse.

Micro-expression is one of important clues for detecting lies. Its most outstanding characteristics include short duration and low intensity of movement. Therefore, video clips of high spatial-temporal resolution are much more desired than still images to provide sufficient details. On the other hand, owing to the difficulties to collect and encode micro-expression data, it is small sample size. In this paper, we use only 560 micro-expression video clips to evaluate the proposed network model: Transferring Long-term Convolutional Neural Network (TLCNN). TLCNN uses Deep CNN to extract features from each frame of micro-expression video clips, then feeds them to Long Short

Superpixels are perceptually meaningful atomic regions that can effectively capture image features. Among various methods for computing uniform superpixels, simple linear iterative clustering (SLIC) is popular due to its simplicity and high performance. In this paper, we extend SLIC to compute content-sensitive superpixels, i.e., small superpixels in content-dense regions with high intensity or colour variation and large superpixels in content-sparse regions. Rather than using the conventional SLIC method that clusters pixels in , we map the input image to a 2-dimensional manifold , whose area elements are a good measure of the content density in . We propose a simple method, called intrinsic manifold SLIC (IMSLIC), for computing a geodesic centroidal Voronoi tessellation (GCVT)-a uniform tessellation-on , which induces the content-sensitive superpixels in . In contrast to the existing algorithms, IMSLIC characterizes the content sensitivity by measuring areas of Voronoi cells on . Using a simple and fast approximation to a closed-form solution, the method can compute the GCVT at a very low cost and guarantees that all Voronoi cells are simply connected. We thoroughly evaluate IMSLIC and compare it with eleven representative methods on the BSDS500 dataset and seven representative methods on the NYUV2 dataset. Computational results show that IMSLIC outperforms existing methods in terms of commonly used quality measures pertaining to superpixels such as compactness, adherence to boundaries, and achievable segmentation accuracy. We also evaluate IMSLIC and seven representative methods in an image contour closure application, and the results on two datasets, WHD and WSD, show that IMSLIC achieves the best foreground segmentation performance.

The multiple light scattering of nanoporous (NP) GaN was systematically studied and applied to the color downconversion for micro-light-emitting diode (LED) display applications. The transport mean free path (TMFP) in NP GaN is 660 nm at 450 nm (light wavelength), and it decreases with a decreasing wavelength. It was observed that the short TMFP of the NP GaN increased the light extinction coefficient at 370 nm by 11 times. Colloidal QDs were loaded into a half 4″ wafer scale NP GaN, and 96 and 100% of light conversion efficiencies for green and red were achieved, respectively. By loading green and red QDs selectively into NP GaN mesas, we demonstrated the RGB microarrays based on the blue-violet pumping light with green and red color converting regions.