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Ultraviolet C (UVC) has been applied to treatment of infections in wounds for at least the last two decades, however, cells being treated can be damaged if exposure is prolonged, which calls for protective measures, such as drug or herbal pre-treatment, to minimize damage. Ocimum gratissimum contains plant polyphenols such as isoflavones and caffeic acid, which have antioxidant effects. We hypothesize that Ocimum gratissimum aqueous extracts (OGE) can inhibit UVC-induced oxidative damage on skin cells. In this study, HaCaT skin cells are used to test the protective effects of OGE on cell proliferation and migration after exposure to UVC radiation. Pretreatment with OGE (50~150μg/mL) before 40 J/m 2 UVC exposure was able to restore survival from 32.25% to between 46.77% and 68.00%, and 80 J/m 2 UVC exposure from 11.49% to between 19.07% and 43.04%. Morphological observation of primarily apoptotic cell death confirms the above findings. The flow cytometry analysis revealed that UVC increased the number of cells at the sub-G1 phase in a dose dependent manner, and when pre-treated with OGE the changes were partially reversed. Moreover, the wound healing test for observing migration showed that UVC 40-80 J/m 2 decreased cell migration to 47-28% activity and 100 μg/mL OGE was able to restore cell activity to81-69% at day 3. Based on the above results, we suggest that OGE has a protective effect on UVC-induced inhibition of cell proliferation and migration of skin cells and thus has potential application in wound care.

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Hand gesture recognition is a topic in artificial intelligence and computer vision with the goal to automatically interpret human hand gestures via some algorithms. Notice that it is a difficult classification task for which only one simple classifier cannot achieve satisfactory performance; several classifier combination techniques are employed in this paper to handle this specific problem. Based on some related data at hand, AdaBoost and rotation forest are seen to behave significantly better than all the other considered algorithms, especially a classification tree. By investigating the bias-variance decompositions of error for all the compared algorithms, the success of AdaBoost and rotation forest can be attributed to the fact that each of them simultaneously reduces the bias and variance terms of a SingleTree's error to a large extent. Meanwhile, kappa-error diagrams are utilized to study the diversity-accuracy patterns of the constructed ensemble classifiers in a visual manner.

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