ALBERT

Description: We sought to explore the treatment effects and the repair mechanisms of bone marrow derived mesenchymal stem cells (MSCs) during HCl-induced acute lung injury (ALI). MSCs were delivered through the tail veins of rats 24h after intranasal instillation of HCl. The results showed that MSCs did not ameliorate the histopathologic changes of ALI and pulmonary fibrosis. We found that the activated Wnt/β-catenin signaling may regulate the differentiation of MSCs and is associated with lung fibroblasts activation, pulmonary fibrosis and tissue repair process in ALI rats. Immunofluorescence and histology analysis indicated that activated canonical Wnt/β-catenin signaling induced most MSCs to differentiate into myofibroblasts or fibroblasts in vivo. However, inhibition of Wnt/β-catenin signaling by Dickkopf-1 (DKK1) promotes epithelial differentiation of MSCs induced by native alveolar epithelial cells which are beneficial to repair the injured lung epithelium. Inhibition of Wnt/β-catenin signaling after MSCs transplantation ameliorated pulmonary fibrosis and improved pulmonary function which attenuated the lung injury. In vitro study, activation of the Wnt/β-catenin signaling stimulated MSCs to express myofibroblasts markers, which was attenuated by DKK1. Furthermore, Wnt3α activated Wnt/β-catenin signaling in lung fibroblasts to enhance the expression of collagenl, vimentin and α-smooth muscle actin, but DKK1 attenuated these proteins expression. These findings demonstrated that canonical Wnt/β-catenin signaling plays a key role in regulating differentiation of MSCs in vivo or in vitro and the pathogenesis of fibrotic diseases. Our study suggested that inhibition of abnormal activated Wnt/β-catenin signaling would promote MSCs epithelial differentiation to repair lung injury and reduce pulmonary fibrosis. J. Cell. Physiol. © 2013 Wiley Periodicals, Inc.

Description: The fraud detection of cargo theft has been a serious issue in ports for a long time. Traditional research in detecting theft risk is expert- and survey-based, which is not optimal for proactive prediction. As we move into a pervasive and ubiquitous paradigm, the implications of external environment and system behavior are continuously captured as multi-source data. Therefore, we propose a novel data-driven approach for formulating predictive models for detecting bulk cargo theft in ports. More specifically, we apply various feature-ranking methods and classification algorithms for selecting an effective feature set of relevant risk elements. Then, implicit Bayesian networks are derived with the features to graphically present the relationship with the risk elements of fraud. Thus, various binary classifiers are compared to derive a suitable predictive model, and Bayesian network performs best overall. The resulting Bayesian networks are then comparatively analyzed based on the outcomes of model validation and testing, as well as essential domain knowledge. The experimental results show that predictive models are effective, with both accuracy and recall values greater than 0.8. These predictive models are not only useful for understanding the dependency between relevant risk elements, but also for supporting the strategy optimization of risk management.

Description: In recent years, the number of artificial light sources has tremendously increased with the development of lighting technology and the economy. Nighttime light pollution has been an increasing environmental problem, resulting in negative impacts on human health and the ecological environment. Detailed knowledge of light pollution is important for the planning and management of urban lighting. In this study, light pollution in Nanjing, China was monitored and analyzed using field observations and a 130-m resolution Luojia 1-01 nighttime light imagery. Combined with in situ observations and satellite imagery, a variety of empirical models were established for estimating ambient illuminance at night. Cross-validation was employed to assess the performance of these models, indicating that the third-degree polynomials model had the best performance (MAE = 5.06 lx, R2 = 0.81). The developed third-degree polynomial model was then applied to the Luojia 1-01 image to map the nighttime illuminance in Nanjing. The nighttime illuminance depicted the spatial pattern of the light environment over Nanjing and also indicated some heavily light-polluted areas. Some lit areas were residential areas, whose high brightness had negative effects on residents and need particular attention. This study provides a quantitative and objective reference for the light pollution management in Nanjing, and also a reference for light pollution survey in other regions.