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The performance-based payment structure has been widely used in infrastructure PPP projects. However, existing research has been absent on the structural unbalance problem of performance-based payment in the current infrastructure PPP projects. This study aims to construct an optimal proportion of the performance-based payment in the total payment, and then design a performance-based payment structure for infrastructure PPP projects. Firstly, the definition of the performance-linked rate is introduced to characterize the proportion of the performance-based payment. Secondly, based on the different objectives of the maximum social benefit and the minimum cost for the public and private sectors, a multi-objective optimization model is constructed to obtain a reasonable value range of the performance-linked rate. Thirdly, the impacts on different parameters in the performance-linked rate are revealed using simulation methods. Finally, the numerical and simulation results show that, for the weak social average ability, the social cost is also high, and a large performance-linked rate should be set. Conversely, when the social average ability is strong, the social average cost is relatively reduced, and a relatively low performance-linked rate should be set. Consequently, the results can guide the contract design in PPP projects.

Effective measurement and monitoring of the quality of services from the private sector, in the terms of performance in water environment treatment public-private partnership projects (WETPs-PPP), is one of the keys to the success of these projects. Based on the deficiencies of performance monitoring and evaluation theory and the specific characteristics of WETPs-PPP, this study developed a performance monitoring and evaluation model for WETPs-PPP. First, through a literature analysis, a performance monitoring and evaluation index system of WETPs-PPP was constructed from eight dimensions including river embankment, bridges, river water, environment, garden facilities, garden plants, special purpose vehicle, hydraulic structure, and public satisfaction. Second, by combining the adaptive weighted fusion algorithm and Intuitionistic Fuzzy (IF) average operators, multi-source, multi-dimensional, and multi-spatio-temporal data were aggregated. Third, to improve the robustness of the performance monitoring results, the MULTIMOORA evaluation method with a defuzzification procedure under the IF environment was developed to conduct performance monitoring and evaluation for WETPs-PPP. Finally, a water environment treatment and ecological restoration project was used as an example to illustrate the feasibility and effectiveness of the proposed theoretical model.

The water channel aquaporin-2 (AQP2) is a major regulator of water homeostasis in response to vasopressin (VP). Dynamic trafficking of AQP2 relies on its close interaction with trafficking machinery proteins and the actin cytoskeleton. Here, we report the identification of ezrin, an actin-binding protein from the ezrin/radixin/moesin (ERM) family as an AQP2-interacting protein. Ezrin was first detected in a co-immunoprecipitation (co-IP) complex using an anti-AQP2 antibody in a proteomic analysis. Immunofluorescence staining revealed the co-expression of ezrin and AQP2 in collecting duct principal cells, and VP treatment caused redistribution of both proteins to the apical membrane. The ezrin-AQP2 interaction was confirmed by co-IP experiments with an anti-ezrin antibody, and by pulldown assays using purified full-length and FERM domain-containing recombinant ezrin. By using purified recombinant proteins, we showed that ezrin directly interacts with AQP2 C-terminus through its N-terminal FERM domain. Knocking down ezrin expression with shRNA resulted in increased membrane accumulation of AQP2 and reduced AQP2 endocytosis. Therefore, through direct interaction with AQP2, ezrin facilitates AQP2 endocytosis, thus linking the dynamic actin cytoskeleton network with AQP2 trafficking.

To investigate effects of ( Z )-3-chloromethylene-5-methyl-thiochroman-4-ketone (CMTK) on apoptosis in human breast cancer MCF-7 cells in vitro , cultured MCF-7 cells were treated with different concentrations of CMTK. The MTT method was used to study inhibition rates; the trypan blue method for cell viability and to construct growth curves; flow cytometry for cell cycles; the TUNEL assay and flow cytometry to assess proportion of apoptotic cells; Ac-IETD-pNA caspase-8/Ac-LEHD-pNA caspase 9 Colorimetric Assay Kit for caspase-8 and caspase-9 activities; and ELISA assays to detect DR3, TNFR1, TNF-α and caspase-3 proteins. Results showed that CMTK had dramatic anti-tumor activities at low concentrations (IC 50 : 14.24 ± 0.12 μmol L -1 ). Importantly, the IC 50 of CMTK on MCF-7 cells was much higher than that of cisplatin. Growth curves show CMTK can inhibit cell proliferation in time- and dose-dependent manners. In cell-cycle analysis, proportions of MCF-7 cells in G 0 /G 1 phase after 12-h and 24-h treatment with CMTK were significantly different from the control group. In contrast, the proportion of cells in S phase tended to decrease, but not significantly. Use of TUNEL and flow cytometry indicated that CMTK induces apoptosis. In MCF-7 cells, CMTK induces TNFR1 production, activates caspase-8 and caspase-9, and enhances caspase-3 levels in a concentration-dependent way; however, CMTK does not affect production of DR3 and TNF-α. These results suggest that the anti-tumor mechanism of CMTK involves inducing apoptosis via caspase-8 activation by death receptor, and caspase-9 activation by the mitochondrial-dependent pathway. Our results provide preliminary data for further investigation of the apoptotic mechanism.

Compressed sensing seeks to recover an unknown sparse signal with entries by making far fewer than measurements. The restricted isometry Constants (RIC) has become a dominant tool used for such cases since if RIC satisfies some bound then sparse signals are guaranteed to be recovered exactly when no noise is present and sparse signals can be estimated stably in the noisy case. During the last few years, a great deal of attention has been focused on bounds of RIC, see, e. g., Candes (2008), Foucart et al (2009), Foucart (2010), Cai et al (2010), Mo et al (2011), Ji et al (2012). Finding bounds of RIC has theoretical and applied significance. In this paper, we obtain a bound of RIC. It improves the results by Cai et al (2010) and Ji et al (2012). Further, we discuss the problems related larger bound of RIC, and give the conditional maximum bound.

We consider the recovery of high-dimensional sparse signals via ${\ell }_{1}$ -minimization under mutual incoherence condition, which is shown to be sufficient for sparse signals recovery in the noiseless and noise cases. We study both ${\ell }_{1}$ -minimization under the ${\ell }_{2}$ constraint and the Dantzig selector. Using the two ${\ell }_{1}$ -minimization methods and a technical inequality, some results are obtained. They improve the results of the error bounds in the literature and are extended to the general case of reconstructing an arbitrary signal.

Image texture classification and segmentation is a main topic in the analysis of many types of images. People usually use the least squares estimation (LSE) for analyzing SAR textures. But we find that the LSE is unstable in practical computation. Therefore, in this paper we present regularization methods for image texture classification and segmentation. Regularization is such a technique which can successfully suppress the instability due to noise or truncation error when computing. Several regularization techniques, including standard regularization (SR), penalized regularization (PR) and total variation based regularization (TVR), are exhibited to reduce instability in texture extraction. Experiment results demonstrate that the regularization methods are superior to LSE and seem to be promising in practical applications.