Jagged/Notch signalling is required for a subset of TGFβ1 responses in human kidney epithelial cells.

The Jagged/Notch pathway has been implicated in TGFß1 responses in epithelial cells in diabetic nephropathy and other fibrotic conditions in vivo. Here, we identify that Jagged/Notch signalling is required for a subset of TGFß1-stimulated gene responses in human kidney epithelial cells in vitro. TGFß1 treatment of HK-2 and RPTEC cells for 24 h increased Jagged1 (a Notch ligand) and Hes1 (a Notch target) mRNA. This response was inhibited by co-incubation with Compound E, an inhibitor of ?-secretase (GSI), an enzyme required for Notch receptor cleavage and transcription regulation. In both cell types, TGFß1-responsive genes associated with epithelial–mesenchymal transition such as E-cadherin and vimentin were also affected by ?-secretase inhibition, but other TGFß1 targets such as connective tissue growth factor (CTGF) and thrombospondin-1 (THBS1) were not. TGFß1-induced changes in Jagged1 expression preceded EMT-associated gene changes, and co-incubation with GSI altered TGFß1-induced changes in cell shape and cytoskeleton. Transfection of cells with the activated, cleaved form of Notch (NICD) triggered decreased expression of E-cadherin in the absence of TGFß1, but did not affect a-smooth muscle actin expression, suggesting differential requirements for Notch signalling within the TGFß1-responsive gene subset. Increased Jagged1 expression upon TGFß1 exposure required Smad3 signalling, and was also regulated by PI3K and ERK. These data suggest that Jagged/Notch signalling is required for a subset of TGFß1-responsive genes, and that complex signalling pathways are involved in the crosstalk between TGFß1 and Notch cascades in kidney epithelia.


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Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

China's Sovereign Wealth Funds: Problem or Panacea?

China’s impressive economic growth has led to the accumulation of massive financial assets. The emergence of sovereign wealth funds (SWFs), as a governmental investment device for its excessive foreign reserves, symbolizes a major rebalancing of economic power. With its investment portfolios drastically diversified for well-established financial institutions as well as some strategic sectors, a seminal debate seems centered on whether China’s SWFs are in furtherance of purely commercial or geopolitically strategic purposes. Under the sophisticated hard laws associated with international initiatives, it is unlikely that the SWFs-related investment would distort the global financial system, and genuinely threaten national security, which assumption may only exist at a hypothetical level. The potential protectionism would inevitably retard the world economy’s recovery, were it not to be proportionately addressed. A most significant necessity appears to be to strike a proportionate balance between sustaining the credibility of open investment environment and efficiently minimizing implications of SWFs political arenas.

Robust Dynamic Pricing Over Infinite Horizon in The Presence of Model Uncertainty( 2* Journal)

This paper studies a problem of dynamic pricing faced by a retailer with limited inventory, uncertain about the demand rate model, aiming to maximize expected discounted revenue over an infinite time horizon. The retailer doubts his demand model which is generated by historical data and views it as an approximation. Uncertainty in the demand rate model is represented by a notion of generalized relative entropy process, and the robust pricing problem is formulated as a two-player zero-sum stochastic differential game. The pricing policy is obtained through the Hamilton-Jacobi-Isaacs (HJI) equation. The existence and uniqueness of the solution of the HJI equation is shown and a verification theorem is proved to show that the solution of the HJI equation is indeed the value function of the pricing problem. The results are illustrated by an example with exponential nominal demand rate.

Enabling Free On-line Access to UK Law Reports: The Copyright Problem

The history of publishing legal decisions (law reporting) in the UK has been that of a privatised system since its inception, and that history has encompassed several hundred years. The privatised nature of this has meant that the product (the law report) has been, except in limited cases, viewed as the property of the publisher, rather than the property of the court or public. BAILII is an open access legal database that came about in part because of the copyrighted, privatised nature of this legal information. In this paper, we will outline the problem of access to pre-2000 judgments in the UK and consider whether there are legal or other remedies which might enable BAILII to both develop a richer historic database and also to work in harmony, rather than in competition, with legal publishers. We argue that public access to case law is an essential requirement in a democratic common law system, and that BAILII should be seen as a potential step towards a National Law Library.

C/EBP alpha Is Up-regulated in a Subset of Hepatocellular Carcinomas and Plays a Role in Cell Growth and Proliferation

BACKGROUND & AIMS: C/EBP alpha (cebpa) is a putative tumor suppressor. However, initial results indicated that cebpa was up-regulated in a subset of human hepatocellular carcinomas (HCCs). The regulation and function of C/EBP alpha was investigated in HCC cell lines to clarify its role in liver carcinogenesis. METHODS: The regulation of C/EBP alpha expression was studied by quantitative reverse transcription-polymerase chain reaction (qRT-PCR), Western blotting, immunohistochemistry, methylation-specific PCR, and chromatin immunoprecipitation assays. C/EBP alpha expression was knocked-down by small interfering RNA or short hairpin RNA. Functional assays included colony formation, methylthiotetrazole, bromodeoxyuridine incorporation, and luciferase-reporter assays. RESULTS: Cebpa was up-regulated at least 2-fold in a subset (approximately 55%) of human HCCs compared with adjacent non tumor tissues. None of the up-regulated samples were positive for hepatitis C infection. The HCC cell lines Hep3B and Huh7 expressed high, PLC/PRF/5 intermediate, HepG2 and HCC-M low levels of C/EBP alpha, recapitulating the pattern of expression observed in HCCs. No mutations were detected in the CEBP alpha gene in HCCs and cell lines. C/EBP alpha was localized to the nucleus and functional in Hep3B and Huh7 cells; knocking-down its expression reduced target-gene expression, colony formation, and cell growth, associated with a decrease in cyclin A and CDK4 concentrations and E2F transcriptional activity. Epigenetic mechanisms including DNA methylation, and the binding of acetylated histone H3 to the CEBP alpha promoter-regulated cebpa expression in the HCC cells. CONCLUSIONS: C/EBP alpha is up-regulated in a subset of HCCs and has growth-promoting activities in HCC cells. Novel oncogenic mechanisms involving C/EBP alpha may be amenable to epigenetic regulation to improve treatment outcomes.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.

A hybrid metaheuristic approach to the university course timetabling problem

This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions to be accepted based on some given upper boundary or ‘level’. In this paper, the dynamic force calculated from the attraction-repulsion mechanism is used as a decreasing rate to update the ‘level’ within the search process. The proposed method has been applied to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature, demonstrating that the method is able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches and the ability of the resultant algorithm to converge all solutions at every search process.