Plasmacytoid dendritic cells (pDCs) were considered to be the major IFNα source in systemic lupus erythematosus (SLE) but their phenotype and function in different disease status have not been well studied. To study the function and phenotype of pDCs in lupus-prone mice we used 7 strains of lupus-prone mice including NZB/W F1, NZB, NZW, NZM2410, B6.NZMSle1/2/3, MRL/lpr and BXSB/Mp mice and C57BL/6 as control mice. Increased spleen pDC numbers were found in most lupus mice compared to C57BL/6 mice. The IFNα-producing ability of BM pDCs was similar between lupus and C57BL/6 mice, whereas pDCs from the spleens of NZB/W F1 and NZB mice produced more IFNα than pDCs from the spleens of C57BL/6 mice. Furthermore, spleen pDCs from MRL-lpr and NZM2410 mice showed increased responses to Tlr7 and Tlr9, respectively. As the disease progressed, IFN signature were evaluated in both BM and spleen pDC from lupus prone mice and the number of BM pDCs and their ability to produce IFNα gradually decreased in lupus-prone mice. In conclusion, pDC are activated alone with disease development and its phenotype and function differ among lupus-prone strains, and these differences may contribute to the development of lupus in these mice.

Although the traditional CVaR-based portfolio methods are successfully used in practice, the size of a portfolio with thousands of assets makes optimizing them difficult, if not impossible to solve. In this article we introduce a large CVaR-based portfolio selection method by imposing weight constraints on the standard CVaR-based portfolio selection model, which effectively avoids extreme positions often emerging in traditional methods. We propose to solve the large CVaR-based portfolio model with weight constraints using penalized quantile regression techniques, which overcomes the difficulties of large scale optimization in traditional methods. We illustrate the method via empirical analysis of optimal portfolios on Shanghai and Shenzhen 300 (HS300) index and Shanghai Stock Exchange Composite (SSEC) index of China. The empirical results show that our method is efficient to solve a large portfolio selection and performs well in dispersing tail risk of a portfolio by only using a small amount of financial assets. AbstractAlthough the traditional CVaR-based portfolio methods are successfully used in practice, the size of a portfolio with thousands of assets makes optimizing them difficult, if not impossible to solve. In this article we introduce a large CVaR-based portfolio selection method by imposing weight constraints on the standard CVaR-based portfolio selection model, which effectively avoids extreme positions often emerging in traditional methods. We propose to solve the large CVaR-based portfolio model with weight constraints using penalized quantile regression techniques, which overcomes the difficulties of large scale optimization in traditional methods. We illustrate the method via empirical analysis of optimal portfolios on Shanghai and Shenzhen 300 (HS300) index and Shanghai Stock Exchange Composite (SSEC) index of China. The empirical results show that our method is efficient to solve a large portfolio selection and performs well in dispersing tail risk of a portfolio by only using a small amount of financial assets.

Using the slacks-based measure (SBM) directional distance function and constructing the Luenberger productivity index, we measure the green total factor productivity (GTFP) of China’s provinces under resource and environmental restrictions. At the same time, based on the provincial panel data, the threshold regression method is used to empirically analyze the impact of financial development on green total factor productivity and its threshold effect. The study explores how technological innovation, foreign direct investment (FDI), and environmental governance affect green total factor productivity, as well as how financial development plays a role in the direction and intensity of the impact, with a view to providing policy recommendations for promoting green economic development. The results show that: (1) during the sample period, China’s green total factor productivity had an overall upward trend, and pure technological progress was the main reason for the growth in the green all-factor growth rate; (2) taking financial development as a threshold dependent variable, financial development had a nonlinear, double-threshold effect on green total factor productivity and diminishing marginal efficiency; (3) the increase in financial development will help attract high-quality and low-pollution FDI inflows, and can exert a technology spillover from FDI to green total factor productivity; (4) the impact of technological innovation on green total factor productivity has a nonlinear feature, with significant positive and increasing marginal efficiency; and (5) there is a positive “U” relationship between environmental governance and green total factor productivity.

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Traffic measurement is a critical function in transportation engineering. We consider privacy-preserving point-to-point traffic measurement in this paper. We measure the number of vehicles traveling from one geographical location to another by taking advantage of capabilities provided by the intelligent cyber-physical road systems that enable automatic collection of traffic data. The challenge is to allow the collection of aggregate point-to-point data while preserving the privacy of individual vehicles. We propose a novel measurement scheme which utilizes bit arrays to collect "masked" data and adopts maximum likelihood estimation (MLE) to obtain the measurement result. Both mathematical proof and simulation demonstrate the practicality and scalability of our scheme. Roadside Equipment Rx Roadside Equipment RyCentral Server 0018-9545 (c)