Abstract-Over the Internet today, computing and communications environments are significantly more complex and chaotic than classical distributed systems, lacking any centralized organization or hierarchical control. There has been much interest in emerging Peer-to-Peer (P2P) network overlays because they provide a good substrate for creating large-scale data sharing, content distribution and application-level multicast applications. These P2P networks try to provide a long list of features such as: selection of nearby peers, redundant storage, efficient search/location of data items, data permanence or guarantees, hierarchical naming, trust and authentication, and, anonymity. P2P networks potentially offer an efficient routing architecture that is self-organizing, massively scalable, and robust in the wide-area, combining fault tolerance, load balancing and explicit notion of locality. In this paper, we present a survey and comparison of various Structured and Unstructured P2P networks. We categorize the various schemes into these two groups in the design spectrum and discuss the application-level network performance of each group.

Dioscin (DS) is a steroidal saponin present in a number of medicinal plants and has been shown to exert anticancer, antifungal and antiviral effects. The present study aimed to deternube the effects DS on the regulation of adipogenesis and to elucidate the underlying mechanisms. In vitro experiments were performed using differentiating 3T3-L1 cells treated with various concentrations (0-4 µM) of DS for 6 days. A cell viability assay was performed on differentiating cells following exposure to DS. Oil Red O staining and triglyceride content assay were performed to evaluate the lipid accumulation in the cells. We also carried out the following experiments: i) flow cytometry for cell cycle analysis, ii) quantitative reverse transcription polymerase chain reaction for measuring adipogenesis-related gene expression, and iii) western blot analysis to measure the expression of adipogenesis transcription factors and AMP-activated protein kinase (AMPK), acetyl-CoA carboxylase (ACC) and mitogen-activated protein kinase (MAPK) phosphorylation. In vivo experiements were performed using mice with obesity induced by a high-fat diet (HFD) that were treated with or without DS for 7 weeks. DS suppressed lipid accumulation in the 3T3-L1 cells without affecting viability at a dose of up to 4 µM. It also delayed cell cycle progression 48 h after the initiation of adipogenesis. DS inhibited adipocyte differentiation by the downregulation of adipogenic transcription factors and attenuated the expression of adipogenesis-associated genes. In addition, it enhanced the phosphorylation of AMPK and its target molecule, ACC, during the differentiation of the cells. Moreover, the inhibition of adipogenesis by DS was mediated through the suppression of the phosphorylation of MAPKs, such as extracellular-regulated kinase 1/2 (ERK1/2) and p38, but not c-Jun-N-terminal kinase (JNK). DS significantly reduced weight gain in the mice with HFD-induced obesity; this was evident by the suppression of fat accumulation in the abdomen. the present study reveals an anti-adipogenic effect of DS in vitro and in vivo and highlights AMPK/MAPK signaling as targets for DS during adipogenesis.

Twitter’s recent growth in the number of users has redefined its status from a simple social media service to a mass media. We deal with clustering techniques applied to Twitter network and Twitter trend analysis. When we divide and cluster Twitter network, we can find a group of users with similar inclination, called a “Community.” In this regard, we introduce the Louvain algorithm and advance a partitioned Louvain algorithm as its improved variant. In the result of the experiment based on actual Twitter data, the partitioned Louvain algorithm supplemented the performance decline and shortened the execution time. Also, we use clustering techniques for trend analysis. We use nonnegative matrix factorization (NMF), which is a convenient method to intuitively interpret and extract issues on various time scales. By cross-verifying the results using NFM, we found that it has clear correlation with the actual main issue.