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There are various shortages of the traditional data center in cost, resource utilization, power consumption and operation. This paper studies the development trend of the virtual data center and its technical advantages, proposes a scheme of virtual system of smart transportation data center based on VMware vSphere. At the same time, oriented on requirements of traffic data stream management in city smart transportation system under the environment of cloud computing, aimed at massive, multi-source, real-time, dynamic uncertain data stream sent back from all kinds of cross regional intensive control perception device, this paper analyses the characteristics and correlation of actual city traffic operation and traffic data flow, researches evolution mechanism of uncertain data; construct traffic data flow model based on ontology, core metadata and theory of constraints. And on this basis, this paper uses virtualization and large data sets of parallel processing, considers load balancing and adaptive mechanism, combines fuzzy theory and dynamic multi object, multi constrained decision theory, to seek the efficient query algorithm for dynamic, complex and continuous transportation data stream.

Based on efficient continuous parallel query series algorithm supporting multi-objective optimization, by using visual graphics technology for traffic data streams for efficient real-time graphical visualization, it improve human-computer interaction, to realize real-time and visual data analysis and to improve efficiency and accuracy of the analysis. This paper employs data mining processing and statistical analysis on real-time traffic data stream, based on the parameters standards of various data mining algorithms, and by using computer graphics and image processing technology, converts graphics or images and make them displayed on the screen according to the system requirements, in order to track, forecast and maintain the operating condition of all traffic service systems effectively.

Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.

Background Myeloid cell leukemia-1 (Mcl-1), an anti-apoptotic member of the B cell lymphoma/leukemia-2 (Bcl-2) family, has been shown to be involved in apoptosis and the cell cycle. Mcl-1 is overexpressed in many malignancies, including pancreatic cancer. The aim of this study was to investigate the effect of siRNA targeted against Mcl-1 on the radiosensitivity of human pancreatic carcinoma cells. Methods In 3 pancreatic cancer cell lines, the expression of Mcl-1 mRNA was determined by RT-PCR assay, and the dose-dependent cytotoxicity of radiation was also assessed. Furthermore, the effects of adenovirus-mediated siRNA targeted against Mcl-1 on radiosensitivity of PANC-1 cells both in vitro and in vivo were evaluated. Results Pancreatic cancer cells showed various levels of Mcl-1 mRNA that were correlated with the radiosensitivity of tumor cells. AdU6/shMcl-1 significantly downregulated the expression of Mcl-1 gene in PANC-1 cells, the most radioresistant cell line. Furthermore, Mcl-1 downregulation could significantly enhance radiosensitivity of PANC-1 cells in vitro and in vivo. The mechanism might be correlated with apoptosis enhancement by activating celluar caspase-3. Conclusion Mcl-1 might be a therapeutic target for radiosensitization of pancreatic carcinoma cells. Adenovirus-mediated siRNA targeting of Mcl-1 could enhance the radiosensitivity of pancreatic cancer cells both in vitro and in vivo, and thus might be a potential strategy for ameliorating cancer.

This paper reviews the status of research in modeling and simulation of single-event effects (SEE) in digital devices and integrated circuits. After introducing a brief historical overview of SEE simulation, different level simulation approaches of SEE are detailed, including material-level physical simulation where two primary methods by which ionizing radiation releases charge in a semiconductor device (direct ionization and indirect ionization) are introduced, device-level simulation where the main emerging physical phenomena affecting nanometer devices (bipolar transistor effect, charge sharing effect) and the methods envisaged for taking them into account are focused on, and circuit-level simulation where the methods for predicting single-event response about the production and propagation of single-event transients (SETs) in sequential and combinatorial logic are detailed, as well as the soft error rate trends with scaling are particularly addressed.

To identify quantitative trait loci (QTL) associated with southern leaf blight (SLB) in sweet corn, a population consisting of 330 F2:3 families was developed from a T14 (resistant) × T4 (susceptible) cross. The genetic linkage map was constructed based on the F2 population, with 192 SSR markers covering a total map distance of 1260 cm, and an average interval length of 6.56 cm. These 330 F2:3 families were evaluated in 2009 and 2010 for SLB resistance following inoculation at the seedling stage and milk stage. Using composite interval mapping (CIM) in QTL Cartographer v.2.5, 7 QTL out of 18 total QTL were identified at the seedling stage on chromosomes 3, 4, 6 and 9, and the phenotypic variance explained by each QTL ranged from 4.02 to 35.4%. The 11 other QTL were identified at the milk stage on chromosomes 3, 4, 6, 8, 9 and 10 and accounted for 3.50 to 13.65% of the phenotypic variance. Two QTL were detected in both years; these were located on chromosome 3 and 4 in the marker intervals umc2275 to umc2008 and umc2287 to bnlg2162, respectively. One QTL, located on chromosome 3 between umc2275 and umc2008, was detected at both developmental stages. The findings in this study may provide useful information for marker-assisted selection (MAS) and with further genetic and breeding studies for maize SLB. Key words: Sweet corn, southern leaf blight (SLB), composite interval mapping (CIM), quantitative trait loci(QTL).

Background X-linked inhibitor of apoptosis protein (XIAP) is a member of the inhibitor of apoptosis family of proteins and deregulation of XIAP can result in tumorigenicity. The objective of this study was to evaluate the prognostic significance of XIAP expression in colorectal cancer (CRC). Methods RT-PCR was performed to detect the expression of XIAP mRNA in CRC cells and tissues. The expression of XIAP protein in tissues was measured by immunohistochemistry. The correlation of XIAP expression with clinicopathologic factors and prognosis of CRC patients was evaluated. Results CRC cells showed significantly higher levels of XIAP mRNA expression than normal human intestinal epithelial cell. The expression level of XIAP mRNA in CRC samples was significantly higher than that in corresponding non-tumor samples. XIAP staining was positive in the cytoplasm of CRC cells. Higher XIAP protein expression was significantly correlated with tumor differentiation (P = 0.016), venous invasion (P = 0.039), and Duke's staging (P = 0.002). Moreover, XIAP-high group showed lower disease-free (P = 0.0136) and overall survival (P = 0.0084) rates than XIAP-low group. Multivariate analysis indicated that the status of XIAP expression was an independent prognostic factor for CRC (P = 0.0206; HR: 2.730; 95% CI: 1.226–5.445). Conclusion The status of XIAP expression might become an independent prognostic marker for CRC. J. Surg. Oncol. 2009;100:708–712. © 2009 Wiley-Liss, Inc.