Alzheimer's disease (AD) is a progressive neurodegenerative disorder and is the most common form of dementia among the aging population. Although the incidence of the disease continues to increase, no cure has been developed. Effective treatment is restricted not only due to the lack of curative medicine, but also due to limited understanding of the underlying mechanisms and the diffculties in accurately diagnosing AD in its earliest stages prior to clinical symptoms. Micro (mi) RNAs (miR) have gained increasing attention in the investigation of neurodegenerative diseases. Previous reports have demonstrated that deregulation of miR-146a-5p is associated with the pathogenesis of human AD. In the present study, the coding region of primary (pri)-miR-146a in patients with AD was scanned and the rare C allele of rs2910164 was found to be associated with AD. Using reverse transcription quantitative polymerase chain reaction, it was demonstrated that site variation reduced the expression of mature miR-146a-5p. Notably, a reduction in the expression of miR-146a-5p led to less effcient inhibition of target genes, including Toll-like receptor (TLR)2, which is important in the pathogenesis of AD. Biological function investigations in RAW264.7 cells indicated that, compared with the G allele, the rare C allele upregulated the expression of tumor necrosis factor-α following stimulation with β-amyloid. These findings suggested that one common polymorphism in pri-miR-146a may contribute to the genetic predisposition to AD by disrupting the production of miR-146a-5p and affecting the expression and function of TLR2.
Parkinson disease (PD) is a common neurodegenerative disease. Most people with PD are idiopathic, with no specific known cause. Recently, several studies have indicated small proportion of PD cases may result from a mutation in some specific genes. However, the involved pathways of these genes and the co-expression patterns of associated pathways still remain unclear. Here, we aimed to systematically investigate PD related pathways by using microarray dataset GSE7621 from the public database library of gene expression omnibus and gene set enrichment analysis on the datasets. Furthermore, candidate transcription factors were also explored by distant regulatory elements software. As a result, 11 up-regulated pathways (such as glycosaminoglycan degradation) and 24 down-regulated pathways (such as ErbB signaling pathway and Long-term depression) were identified as PD related. Most of them were classified into the maps of human diseases, organismal system, and metabolism with no previous reports. Finally, we constructed co-expression networks of related pathways with the significant core genes and transcription factors, such as OCT and HNF3. All of these may be helpful to better understand the molecular mechanisms of human PD in genome wide.
In a traditional network, server load balancing is used to satisfy the demand for high data volumes. The technique requires large capital investment while offering poor scalability and flexibility, which difficultly supports highly dynamic workload demands from massive mobile users. To solve these problems, this paper analyses the principle of software-defined networking (SDN) and presents a new probabilistic method of load balancing based on variance analysis. The method can be used to dynamically manage traffic flows for supporting massive mobile users in SDN networks. The paper proposes a solution using the OpenFlow virtual switching technology instead of the traditional hardware switching technology. A SDN controller monitors data traffic of each port by means of variance analysis and provides a probability-based selection algorithm to redirect traffic dynamically with the OpenFlow technology. Compared with the existing load balancing methods which were designed to support traditional networks, this solution has lower cost, higher reliability, and greater scalability which satisfy the needs of mobile users.
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