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Infection with Epstein-Barr virus (EBV) usually leads to a latent state in B lymphocytes. The virus can be reactivated through two viral transactivators, Zta and Rta, leading to a cascade of gene expression. An EBV DNA array was generated to analyze the pattern of transcription of the entire EBV genome under various conditions. Firstly, a complete set of temporal expression clusters of EBV genes was displayed by analyzing the array data of anti-IgG-induced Akata cells. In addition to assigning genes of unknown function to the various clusters, increasing expression of latent genes, including EBNA2, EBNA3A and EBNA 3C, was observed during virus replication. Secondly, gene expression independent of viral DNA replication was analyzed in PAA blocked Akata cells and in chemically induced Raji cells. Several genes with presumed late functions were found to be expressed with early kinetics and independent of viral DNA replication, suggesting possible novel functions for these genes. Finally, the EBV array was used to identify Rta responsive gene expression in Raji cells, and in the EBV-positive epithelial cells NA, using a Zta siRNA strategy. The array data were confirmed by Northern blotting, RT-PCR and reporter assays. All the information here thus provides a better understanding of the control of EBV lytic gene expression.

Rta, an Epstein-Barr virus (EBV)-

The objective of this article is to develop methods for extracting trends from long-term structural health monitoring data and try to set an early warning threshold level based on the results of analyses. The long-term monitoring data in this study is the continuous monitoring of the dam static deformation. Two different approaches were applied to extract features of the long-term structural health monitoring data of the static deformation of the Fei-Tsui Arch Dam (Taiwan). The methods include the singular spectrum analysis with auto regressive model (SSA-AR) and the nonlinear principal component analysis (NPCA) using auto-associative neural network method (AANN). The singular spectrum analysis is a novel nonparametric technique based on principles of multi-variance statistics. An AR model is optimized for each of the principal components obtained from SSA, and the multi step predicted values are recombined to make the time series. Different from SSA method the NPCA-AANN method is also used to extract the underlying features of static deformation of the dam. By using these two different methods, the residual deformation between the estimated and the recorded data was generated, through statistical analysis, the threshold level of the dam static deformation can be determined. Discussion on the two proposed methods to the static deformation monitoring data of Fei-Tsui Arch Dam (Taiwan) is discussed.

Damage detection in structural health monitoring should accommodate the variation caused by varying environmental conditions such as temperature, humidity, loading, and boundary conditions. A structural damage detection technique is proposed to deal with the continuous monitoring data of a structural system subjected to the complex nonlinear behavior caused by varying environmental conditions. Based on the identified or measured target features of the structural system under varying environmental conditions, e.g. stiffness of the structural components, the nonlinear principal component analysis (NLPCA) using autoassociative neural network is performed to extract the underlying environmental factors. Then a prediction model for NLPCA is proposed to estimate the damage extent. This proposed technique is capable of dealing with not only the non-increasing features, e.g. stiffness, but also the non-decreasing features, e.g. damage index, after damage is introduced without measuring the environmental factors directly. A numerical study is performed to demonstrate the advantages of the proposed technique over the technique using principal component analysis. A synthetic bridge model is simulated with the consideration of a specific element stiffness reduction together with the change due to environmental conditions including temperature, gradient of temperature, humidity, and frozen supports. Results show that the extent of stiffness loss can be quantified accurately and promptly after the damage is introduced.

Latent membrane protein 1 (LMP1) of Epstein-Barr virus (EBV) is a transforming protein that affects multiple cell signaling pathways and contributes to EBV-associated oncogenesis. LMP1 can be expressed in some states of EBV latency, and significant induction of full-length LMP1 is also observed frequently during virus reactivation into the lytic cycle. It is still unknown how LMP1 expression is regulated during the lytic stage and whether any EBV lytic protein is involved in the induction of LMP1. In this study, we first identified that LMP1 expression is associated with the spontaneous virus reactivation in EBV-infected 293 cells and that its expression is a downstream event of the lytic cycle. We further found that

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