No abstract

Certain types of cancer exhibit downregulated expression of zonula occludens-1 (ZO-1), which serves an important function in tumor progression; however, the underlying molecular mechanisms that lead to this downregulation in cancer remain unclear. In the present study, the expression of ZO-1 in liver cancer (LC) tissues was investigated. Western blot and reverse transcription-quantitative polymerase chain reaction assays were used to detect the expression of ZO-1 protein and mRNA in LC tissues and paired adjacent non-tumorous tissues. The results indicated that, compared with non-tumorous tissues, the expression of ZO-1 was significantly downregulated at the protein (P<0.001) and mRNA (P=0.006) levels in LC tissue samples. In addition, various cellular and molecular methods were applied, including MTT, colony formation, flow cytometry and Transwell assays. The results indicated that overexpression of ZO-1 inhibited cell viability, proliferation and migration, and induced G 0 /G 1 phase arrest in vitro.

No abstract

No abstract

No abstract

Test sequencing is a binary identification problem wherein one needs to develop a minimal expected cost test procedure to determine which one of a finite number of possible failure states, if any, is present. In this paper, we consider a multimode test sequencing (MMTS) problem, in which tests are distributed among multiple modes and additional transition costs will be incurred if a test sequence involves mode changes. The multimode test sequencing problem can be solved optimally via dynamic programming or AND/OR graph search methods. However, for large systems, the associated computation with dynamic programming or AND/OR graph search methods is substantial due to the rapidly increasing number of OR nodes (denoting ambiguity states and current modes) and AND nodes (denoting next modes and tests) in the search graph. In order to overcome the computational explosion, we propose to apply three heuristic algorithms based on information gain: information gain heuristic (IG), mode capability evaluation (MC), and mode capability evaluation with limited exploration of depth and degree of mode Isolation (MCLEI). We also propose to apply rollout strategies, which are guaranteed to improve the performance of heuristics, as long as the heuristics are sequentially improving. We show computational results, which suggest that the information-heuristic based rollout policies are significantly better than traditional information gain heuristic. We also show that among the three information heuristics proposed, MCLEI achieves the best tradeoff between optimality and computational complexity.

Decision feedback (DF) is one of the most popular methods in multiuser detection due to its simplicity and outstanding performance. Despite the efficiency of the DF detector, there is usually a large performance gap between the DF detector and the optimal maximum likelihood detector. Rollout, an emerging technique from planning and optimisation, is employed to improve the performance of the decorrelator-based DF detector for synchronous code division multiple access channels. Simulation results show that the proposed algorithm significantly improves the joint error rate of the DF detector, and even outperforms the sequential group decision feedback detector for similar time complexities. Further, owing to the inherent parallel structure of the proposed algorithm, the method is particularly useful in applications where speed and accuracy are both important.

The recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. These advances facilitate remote monitoring, diagnosis and condition-based maintenance of automotive systems. With the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of the malfunction phenomena. Consequently, the current rule-based diagnostic systems are difficult to develop, validate and maintain. New intelligent model-based diagnostic methodologies that exploit the advances in sensor, telecommunications, computing and software technologies are needed. In this paper, we will investigate hybrid model-based techniques that seamlessly employ quantitative (analytical) models and graph-based dependency models for intelligent diagnosis. Automotive engineers have found quantitative simulation (e.g. MATLAB/SIMULINK) to be a vital tool in the development of advanced control systems. The hybrid method exploits this capability to improve the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on rule-based methods, enables remote diagnosis, and responds to the challenges of increased system complexity. The solution is generic and has the potential for application in a wide range of systems.