Excretory and secretory products (ESPs) of nematode contain various proteins which are capable of inducing the instigation or depression of the host immune response and are involved in the pathogenesis of the worms. In the present study, Haemonchus contortus excretory and secretory products (HcESPs) were collected from the adult worms. Binding of HcESPs to goat peripheral blood mononuclear cells (PBMCs) was confirmed by immune-fluorescence assay. Effects of the HcESPs on cytokine production, cell proliferation, cell migration and nitric oxide (NO) production of PBMCs were checked by co-incubation of HcESPs with goat PBMCs. The results indicated that the production of IL-4 and IFN-γ were significantly decreased by HcESPs in dose dependent manner. On the contrary, the production of IL-10 and IL-17 were increased. Cell migration was significantly enhanced by HcESPs, whereas, HcESPs treatment significantly suppressed the cell proliferation and NO production. These results indicated that the HcESPs played important suppressive regulatory roles on PBMCs and provided highlights to the understanding of the host-parasite interactions.

This paper describes a post-processing method for Chinese and Japanese character recognition based on dictionary. By the analysis results of recognition in the processing of OCR, we can find some segmentation and recognition errors do not conform to the rules of lexical and just recognized as the characters which its fonts approach to the scanned texts. For these errors we can deal with them by the Fix Length Segmentation Matching based on Dictionary and the Glyph Code Matrix Transforming. Through the above processing, most of the inaccurate recognitions can be corrected and by the experimental results, it can be proved that this method is an effective way to improve the recognition rate of Chinese and Japanese Character.

In this paper, firstly we try to look for ways to avoid the camera parameters in order to reconstruct 3D model. We attempt to use the parallel stereo visual system and carry out the mathematical derivation of argumentation. Then we use epipolar geometry to solve this problem. And compare the computation algorithms of fundamental matrix. Then for the algorithm, we propose some improvement to compute the fundament matrix more precisely so that the algorithm is more stable and the robustness is stronger.

This paper briefly introduced the development of video face detection and point out the shortage of current face detection system that may produce much of false alarms. Then we detail the classic Viola face detector which using integral image, Haar-like features and AdaBoost algorithm for training. Compared with Viola face detector, we proposed an available multi-model fusion method to reduce false alarms in video face detection that is combining head-shoulder detector with HOG features. After introduced the related knowledge of HOG features, we proposed a fusion detector structure which can improve the accuracy and efficiency of detection.

Character Classification technology is the key link in OCR system. Most classification methods require abundant marked samples training to get classifier. In the real OCR application, there are so many classes, to label these samples are often waste time and energy, especially for unacquainted language, such as Arabic and Uygur, many characters are difficult to differentiate, so it even needs the help of professional guidance. This paper proposed a novel character classification with semi-supervised learning based on information entropy, introduced discrete event probability estimation theory of information entropy, active to select the optimization character samples, got the new parameters to train the classifier again, choose the most conducive to the classifier performance samples, iteration until the unlabeled samples set is empty. The experiment results show that this method achieves high performance in specific condition.