We have previously reported that astrocyte elevated gene-1 (AEG-1) was upregulated in human breast cancer. However, the biological function of AEG-1 in the development and progression of breast cancer remains to be clarified. In this study, we examined the effect of AEG-1 on cell proliferation and found that AEG-1 upregulation was significantly linked to increased Ki67 (Po0.001). Ectopic expression of AEG-1 in MCF-7 and MDA-MB-435 breast cancer cells dramatically enhanced cell proliferation and their ability of anchorage-independent growth, whereas silencing endogenous AEG-1 with shRNAs inhibited cell proliferation and colony-forming ability of the cells on soft agar. Furthermore, these proliferative effects were significantly associated with decreases of p27 Kip1 and p21 Cip1 two key cell-cycle inhibitors. Moreover, we further demonstrated that AEG-1 could downregulate the transcriptional activity of FOXO1 by inducing its phosphorylation through the PI3K/Akt signaling pathway. These observations were further confirmed in clinical human primary breast cancer specimens, in which high-level expression of AEG-1 was inversely correlated with the expression of FOXO1. Taken together, our results provide the first demonstration of a novel mechanism by which AEG-1 induces proliferation of breast cancer cell, and our findings suggest that AEG-1 might play an important role in tumorigenesis of breast cancer.

Abstract. Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively.

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Due to the increasing variations in raw materials and manufacturing processes, composite manufacturing processes have more part-to-part variations compared with the metal manufacturing processes. To improve part quality consistency, tooling design optimisation is an imperative step for addressing the stochastic behaviour of composite manufacturing processes. This paper presents an optimisation approach for the typical composite manufacturing technique of resin transfer moulding (RTM), which minimises the sensitivity of the mould design to uncertain material properties by choosing appropriate locations of injection gates and vents. This paper proposes a stochastic simulation based approach for the RTM processes. Normal distribution and Weibull distribution were utilised as the statistical models for representing the permeability values for the main region and race-tracking, respectively. Based on the statistical properties of the permeability, a graph-based two-phase heuristic (GTPH) was adopted to minimise the flow dispersion value (a quantitative measure for part quality consistency) such that the process design is not sensitive to the material and process parameter variations.

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