The pathogenesis of venous thromboembolism in cancer: emerging links with tumour biology.

Venous thromboembolism (VTE) is a frequent complication in individuals with cancer and is considered to be a cause of substantial mortality. Epidemiological studies identify malignancy as an independent VTE risk factor and show that cancer patients are at increased risk of both initial and recurrent VTE events. The risk due to cancer is compounded by the effects of chemotherapy and other treatments. The pathogenesis of cancer-associated VTE is complex involving multiple interactions between tumours and various components of haemostasis. The development of a systemic hypercoagulable state is considered a key pathogenetic feature and is attributed to tumour expression of tissue factor and other procoagulants, activation of vascular cells by tumour-derived cytokines and adhesive interactions between tumour cells and host cells. An increasing body of evidence indicates that the activation of haemostasis in malignant disease contributes to tumour growth and progression by stimulation of intracellular signalling pathways. The interaction of tissue factor, thrombin and other coagulation factors with protease activated receptor (PAR) proteins expressed by tumour cells and host vascular cells leads to the induction of genes related to the processes of angiogenesis, cell survival and cell adhesion and migration.

One-way quantum computing in a decoherence-free subspace

We introduce a novel scheme for one-way quantum computing (QC) based on the use of information encoded qubits in an effective cluster state resource. With the correct encoding structure, we show that it is possible to protect the entangled resource from phase damping decoherence, where the effective cluster state can be described as residing in a decoherence-free subspace (DFS) of its supporting quantum system. One-way QC then requires either single or two-qubit adaptive measurements. As an example where this proposal can be realized, we describe an optical lattice set-up where the scheme provides robust quantum information processing. We also outline how one can adapt the model to provide protection from other types of decoherence.

Learning soft computing control strategies in a modular neural network architecture

Modelling and control of nonlinear dynamical systems is a challenging problem since the dynamics of such systems change over their parameter space. Conventional methodologies for designing nonlinear control laws, such as gain scheduling, are effective because the designer partitions the overall complex control into a number of simpler sub-tasks. This paper describes a new genetic algorithm based method for the design of a modular neural network (MNN) control architecture that learns such partitions of an overall complex control task. Here a chromosome represents both the structure and parameters of an individual neural network in the MNN controller and a hierarchical fuzzy approach is used to select the chromosomes required to accomplish a given control task. This new strategy is applied to the end-point tracking of a single-link flexible manipulator modelled from experimental data. Results show that the MNN controller is simple to design and produces superior performance compared to a single neural network (SNN) controller which is theoretically capable of achieving the desired trajectory. (C) 2003 Elsevier Ltd. All rights reserved.

2-D R-matrix propagation: A large scale electron scattering simulation dominated by the multiplication of dynamically changing matrices

We examine the computational aspects of propagating a global R-matrix, R, across sub-regions in a 2-D plane. This problem originates in the large scale simulation of electron collisions with atoms and ions at intermediate energies. The propagation is dominated by matrix multiplications which are complicated because of the dynamic nature of R, which changes the designations of its rows and columns and grows in size as the propagation proceeds. The use of PBLAS to solve this problem on distributed memory HPC machines is the main focus of the paper.