In the present study, we have investigated the bee venom (BV) and melittin (a major component of BV)-mediated antiproliferative effect and defined its mechanisms of action in cultured rat aortic vascular smooth muscle cell(s) (VSMC). BV and melittin (ϳ0.4 -0.8 g/ml) effectively inhibited 5% fetal bovine serum-induced and 50 ng/ml platelet-derived growth factor BB (PDGF-BB)-induced VSMC proliferation. The regulation of apoptosis has attracted much attention as a possible means of eliminating excessively proliferating VSMC. In the present study, the treatment of BV and melittin strongly induced apoptosis of VSMC. To investigate the antiproliferative mechanism of BV and melittin, we examined the effect of melittin on nuclear factor B (NF-B) activation, the PDGF-BB-induced IB␣ phosphorylation, and its degradation were potently inhibited by melittin and whether DNA binding activity and nuclear translocation of NF-B p50 subunit in response to the action of PDGF-BB were potently attenuated by melittin. In further investigations, melittin markedly inhibited the PDGF-BBinduced phosphorylation of Akt and weakly inhibited phosphorylation of extracellular signal-regulated kinase 1/2, upstream signals of NF-B. Treatment of melittin also potently induced proapoptotic protein p53, Bax, and caspase-3 expression but decreased antiapoptotic protein Bcl-2 expression. These results suggest the antiproliferative effects of BV and melittin in VSMC through induction of apoptosis via suppressions of NF-B and Akt activation and enhancement of apoptotic signaling pathway.

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This paper presents an algorithm for compositing a high dynamic range (HDR) image from multi-exposure images, considering inconsistent pixels for the reduction of ghost artifacts. In HDR images, ghost artifacts may appear when there are moving objects while taking multiple images with different exposures. To prevent such artifacts, it is important to detect inconsistent pixels caused by moving objects in consecutive frames and then to assign zero weights to the corresponding pixels in the fusion process. This problem is formulated as a binary labeling problem based on a Markov random field (MRF) framework, the solution of which is a binary map for each exposure image, which identifies the pixels to be excluded in the fusion process. To obtain the ghost map, the distribution of zeromean normalized cross-correlation (ZNCC) of an image with respect to the reference frame is modeled as a mixture of Gaussian functions, and the parameters of this function are used to design the energy function. However, this method does not well detect faint objects that are in low-contrast regions due to over-or under-exposure, because the ZNCC does not show much difference in such areas. Hence, we obtain an additional ghost map for the low-contrast regions, based on the intensity relationship between the frames. Specifically, the intensity mapping function (IMF) between the frames is estimated using pixels from high-contrast regions without inconsistent pixels, and pixels out of the tolerance range of the IMF are considered moving pixels in the low-contrast regions. As a result, inconsistent pixels in both the low-and high-contrast areas are well found, and thus, HDR images without noticeable ghosts can be obtained.

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