CNV and nervous system diseases - what's new?

Several new genomic disorders caused by copy number variation (CNV) of genes whose dosage is critical for the physiological function of the nervous system have been recently identified. Dup(7)(q11.23) patients carry duplications of the genomic region deleted in Williams-Beuren syndrome, they are characterized by prominent speech delay. The phenotypes of Potocki-Lupski syndrome and MECP2 duplication syndrome were neuropsychologically examined in detail, which revealed autism as an endophenotype and a prominent behavioral feature of these disorders. Tandem duplication of LMNB1 was reported to cause adult-onset autosomal dominant leukodystrophy. PAFAH1B1/LIS1 and YWHAE, which were deleted in isolated lissencephaly (PAFAH1B1/LIS1 alone) and Miller-Dieker syndrome (both genes), were found to be duplicated in patients with developmental delay. Finally, two novel microdeletion syndromes affecting 17q21.31 and 15q13.3, as well as their reciprocal duplications, were also identified. In this review, we provide an overview of the phenotypic manifestation of these syndromes and the rearrangements causing them. Copyright (C) 2009 S. Karger AG, Base

Joint effect of lattice interaction and potential fluctuation in colossal magnetoresistive manganites

Taking into account both the Jahn-Teller lattice distortion and the on-site electronic potential fluctuations in the orbital-degenerated double-exchange model, in which both the core-spin and the lattice distortion are treated classically, we investigate theoretically the metal-insulator transition (MIT) in manganites by considering the electronic localization effect. An inverse matrix method is developed for calculation in which we use the inverse of the transfer matrix to obtain the localization length. We find that within reasonable range of parameters, both the lattice effect and the potential fluctuation are responsible to the occurrence of the MIT. The role of the orbital configuration is also discussed.Comment: 4 figure

Fast Monte Carlo Simulation for Patient-specific CT/CBCT Imaging Dose Calculation

Recently, X-ray imaging dose from computed tomography (CT) or cone beam CT (CBCT) scans has become a serious concern. Patient-specific imaging dose calculation has been proposed for the purpose of dose management. While Monte Carlo (MC) dose calculation can be quite accurate for this purpose, it suffers from low computational efficiency. In response to this problem, we have successfully developed a MC dose calculation package, gCTD, on GPU architecture under the NVIDIA CUDA platform for fast and accurate estimation of the x-ray imaging dose received by a patient during a CT or CBCT scan. Techniques have been developed particularly for the GPU architecture to achieve high computational efficiency. Dose calculations using CBCT scanning geometry in a homogeneous water phantom and a heterogeneous Zubal head phantom have shown good agreement between gCTD and EGSnrc, indicating the accuracy of our code. In terms of improved efficiency, it is found that gCTD attains a speed-up of ~400 times in the homogeneous water phantom and ~76.6 times in the Zubal phantom compared to EGSnrc. As for absolute computation time, imaging dose calculation for the Zubal phantom can be accomplished in ~17 sec with the average relative standard deviation of 0.4%. Though our gCTD code has been developed and tested in the context of CBCT scans, with simple modification of geometry it can be used for assessing imaging dose in CT scans as well.Comment: 18 pages, 7 figures, and 1 tabl

'BioNessie(G) - a grid enabled biochemical networks simulation environment

The simulation of biochemical networks provides insight and understanding about the underlying biochemical processes and pathways used by cells and organisms. BioNessie is a biochemical network simulator which has been developed at the University of Glasgow. This paper describes the simulator and focuses in particular on how it has been extended to benefit from a wide variety of high performance compute resources across the UK through Grid technologies to support larger scale simulations