Type 1 diabetes is a chronic autoimmune disease in which pancreatic beta cells are killed by the infiltrating immune cells as well as the cytokines released by these cells. Many studies indicate that inflammatory mediators have an essential role in this disease. In the present study, we profiled the transcriptome in human islets of langerhans under control conditions or following exposure to the pro-inflammatory cytokines based on the RNA sequencing dataset downloaded from SRA database. After filtered the low-quality ones, the RNA readers was aligned to human genome hg19 by TopHat and then assembled by Cufflinks. The expression value of each transcript was calculated and consequently differentially expressed genes were screened out. Finally, a total of 63 differentially expressed genes were identified including 60 up-regulated and three down-regulated genes. GBP5 and CXCL9 stood out as the top two most up-regulated genes in cytokines treated samples with the log2 fold change of 12.208 and 10.901, respectively. Meanwhile, PTF1A and REG3G were identified as the top two most down-regulated genes with the log2 fold change of -3.759 and -3.606, respectively. Of note, we also found 262 lncRNAs (long non-coding RNA), 177 of which were inferred as novel lncRNAs. Further in-depth follow-up analysis of the transcriptional regulation reported in this study may shed light on the specific function of these lncRNA.

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Peer-to-peer (P2P) architectures have been proposed as an efficient and truly scalable solution for distributed virtual environments (DVEs). However, heavy and unbalanced network load has restricted the development of large scale DVEs. To solve this problem, this paper attempts to apply the mobile agent technology in DVEs. First, the virtual environment space was divided into a number of adjacent sub-spaces. Then, using the agent mobility, entities models moved themselves to the adjacent sub-space, and completed interactions with other entities in the sub-space. As a result, a significant part network load is transformed into local calculation load. The theoretical analysis results show that it is feasible and effective to ease the network communications bottleneck in the expansion of the DVEs.

Web crawlers have the ability to automatically extract web page information, but there exists the issue that some pages reuse keywords to improve their search rankings. Therefore, we propose an adaptive Page-rank algorithm to build a crawler system to resolve the issue mentioned above. Specifically, we generate a relationship matrix based on the crawled web page access relationships, and then an probability matrix based on the number of web pages is generated iteratively, and finally the web pages crawled are displayed in descending order of calculated weights. Besides, we propose to control the iterative process in Page-rank with the coherence of anchor texts. The system uses Python language to realize the functions of web crawling. Experimental results demonstrate that this system has a high speed in data collection. Comparing with Hints and classical Page-rank crawler systems, The results show that the proposed method outperforms in precision and recall.

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