Community structure implies some features in various real-world networks, and these features can help us to analysis structural and functional properties in the complex system. It has been proved that the classic k-means algorithm can efficiently cluster nodes into communities. However, initial seeds decide the efficiency of the k-means, especially when detecting communities with different sizes. To solve this problem, we improve the classic community detection algorithm with Principal Component Analysis (PCA) mapping and local expansion k-means. Since PCA can preserve the distance information of every node pairs, the improved algorithm use PCA to map nodes in the complex network into lower dimension European space, and then detect initial seeds for k-means using the improved local expansion strategy. Based on the chosen initial seeds, the k-means algorithm can cluster nodes into communities. We apply the proposed algorithm in real-world and artificial networks, the results imply that the improved algorithm is efficient to detect communities and is robust to the initial seed of K-means.
Microblogging filtering is intended to filter out irrelevant content, and select useful, new, and timely content from microblogs. However, microblogging filtering suffers from the problem of insufficient samples which renders the probabilistic models unreliable. To mitigate this problem, a novel method is proposed in this study. It is believed that an explicit brief query is only an abstract of the user's information needs, and its difficult to infer users' actual searching intents and interests. Based on this belief, a filtering model is built where the multi-sources query expansion in microblogging filtering is exploited and expanded query is submitted as users interest. To manage the external expansion risk, a user filter graph inference method is proposed, which is characterized by combination of external multi-sources information, and a risk minimization filtering model is introduced to achieve the best reasoning through the multi-sources expansion. A series of experiments are conducted to evaluate the effectiveness of proposed framework on an annotated tweets corpus. The results of these experiments show that our method is effective in tweets retrieval as compared with the baseline standards.
Generation of reactive oxygen species significantly contributes to the pathogenesis of acute renal failure (ARF) induced by myoglobin release. Ginsenosides (GS), the principal active ingredients of ginseng, is considered as an extremely good antioxidative composition of Chinese traditional and herbal drugs. The purpose of the present study was to investigate the protective effect of ginsenoside in rats with ARF on the changes of cholinergic nervous system in the kidney as well as on the involvement of mitogen-activated protein kinases (MAPK) in the hypothalamic paraventricular nuclei (PVN). In our assay, glycerolinduced acute renal failure in rats was employed to study the protective effects of ginsenoside. Our results indicated that the treatment of ARF rats with ginsenosides for 48 h significantly reduced lipid peroxidation, restored the superoxide dismutase (SOD) level. Meanwhile, the obvious increase of choline acetyltransferase-immunoreactivity (ChAT-IR) in the proximal convoluted tubular cells (PCT) was observed by immunohistochemistry in ARF+GS group. The same effect was also observed in the changes of p-ERK1/2-IR in the hypothalamic paraventricular nuclei. Our results suggest that ginsenoside administered orally may have a strong renal protective effect against glycerol-induced ARF, reduce the renal oxidative stress, and ginsenoside can also activate the cholinergic system in PCT, simultaneously MAPK signal pathway in the PVN was also activated., J. Zhou, H. A. Zhang, Y. Lin, H. M. Liu, Y. M. Cui, Y. Xu, N. Zhao, J. M. Ma, K. Fan, C. L. Jiang., and Obsahuje bibliografii