Carpenter Syndrome: Extended RAB23 Mutation Spectrum and Analysis of Nonsense-mediated mRNA Decay

Carpenter syndrome, a rare autosomal recessive disorder characterized by a combination of craniosynostosis, polysyndactyly, obesity, and other congenital malformations, is caused by mutations in RAB23, encoding a member of the Rab-family of small GTPases. In 15 out of 16 families previously reported, the disease was caused by homozygosity for truncating mutations, and currently only a single missense mutation has been identified in a compound heterozygote. Here, we describe a further 8 independent families comprising 10 affected individuals with Carpenter syndrome, who were positive for mutations in RAB23. We report the first homozygous missense mutation and in-frame deletion, highlighting key residues for RAB23 function, as well as the first splice-site mutation. Multi-suture craniosynostosis and polysyndactyly have been present in all patients described to date, and abnormal external genitalia have been universal in boys. High birth weight was not evident in the current group of patients, but further evidence for laterality defects is reported. No genotype-phenotype correlations are apparent. We provide experimental evidence that transcripts encoding truncating mutations are subject to nonsense-mediated decay, and that this plays an important role in the pathogenesis of many RAB23 mutations. These observations refine the phenotypic spectrum of Carpenter syndrome and offer new insights into molecular pathogenesis. (C) 2011 Wiley-Liss, Inc.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

The use of correspondence analysis to develop a scale to measure workplace morale from multi-level data

This paper focuses on the issue of comparing social groups or collectivities using measures derived from individual-level multivariate data. In this case, groups need to be differentiated such that: (a) between-group differences are maximized; (b) within-group differences are minimised; and (c) `differences' are calibrated to a scale that reflects a set indicators or observed variables.This paper demonstrates empirically how correspondence analysis can achieve this. It presents a scale of `workplace morale' derived from the responses of employees in a large sample of workplaces to questions concerning satisfaction with various facets of their job and their workplace. The scale derived through correspondence analysis is shown to achieve the three criteria described above.

Convergence analysis of multi-stage PIC detectors

This paper analyses the convergence behaviour of the parallel interference cancellation (PIC) detector in code division multiple access (CDMA) systems. Using the results from previous stability analysis of an iterated-map neural network, the paper derives a general condition from which the sufficient condition for convergence of the PIC detector with tentative decision functions that are monotonically increasing at a sublinear rate can be calculated. As examples, the paper derives the sufficient conditions for convergence of the PIC detector with the clip decision and the hyperbolic tangent decision functions. The paper also examines the convergence behaviour of the PIC detector with hyperbolic tangent decision function via computer simulation and compares it with the analytical results.

Multi-cluster computing interconnection network performance modeling and analysis

The overall performance of a distributed system is often depends on the effectiveness of its interconnection network. Thus, the study of the communication networks for distributed systems is very important, which is the focus of this paper. In particular, we address the problem of fat-tree based interconnection networks performance modeling for multi-user heterogeneous multi-cluster computing systems. To this end, we present an analytical model and validate the model through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Analysis of forest policy using multi-attribute value theory

Contents:
1. Role of multi-criteria decision making in natural resource management /​ Gamini Herath and Tony Prato
2. Analysis of forest policy using multi-attribute value theory /​ Jayanath Ananda and Gamini Herath
3. Comparing Riparian revegetation policy options using the analytic hierarchy process /​ M. E. Qureshi and S. R. Harrison
4. Managing environmental and health risks from a lead and zinc smelter : an application of deliberative multi-criteria evaluation /​ Wendy Proctor, Chris McQuade and Anne Dekker
5. Multiple attribute evaluation of management alternatives for the Missouri River System /​ Tony Prato
6. Multi-criteria decision analysis for integrated watershed management /​ Zeyuan Qiu
7. Fuzzy multiple attribute evaluation of agricultural systems /​ Leonie A. Marks and Elizabeth G. Dunn
8. Multi-criteria decision support for energy supply assessment /​ Bram Noble
9. Seaport development in Vietnam : evaluation using the analytic hierarchy process /​ Tran Phuong Dong and David M. Chapman
10. Valuing wetland aquatic resources using the analytic hierarchy process /​ Premachandra Wattage and Simon Mardle
11. Multiple attribute evaluation for national park management /​ Tony Prato
12. The future of MCDA in natural resource management : some generalizations /​ Gamini Herath and Tony Prato.

Multi-level semantic analysis for sports video

There has been a huge increase in the utilization of video as one of the most preferred type of media due to its content richness for many significant applications including sports. To sustain an ongoing rapid growth of sports video, there is an emerging demand for a sophisticated content-based indexing system. Users recall video contents in a high-level abstraction while video is generally stored as an arbitrary sequence of audio-visual tracks. To bridge this gap, this paper will demonstrate the use of domain knowledge and characteristics to design the extraction of high-level concepts directly from audio-visual features. In particular, we propose a multi-level semantic analysis framework to optimize the sharing of domain characteristics.

Performance analysis of interconnection networks for multi-cluster systems

With the current popularity of cluster computing systems, it is increasingly important to understand the capabilities and potential performance of various interconnection networks. In this paper, we propose an analytical model for studying the capabilities and potential performance of interconnection networks for multi-cluster systems. The model takes into account stochastic quantities as well as network heterogeneity in bandwidth and latency in each cluster. Also, blocking and non-blocking network architecture model is proposed and are used in performance analysis of the system. The model is validated by constructing a set of simulators to simulate different types of clusters, and by comparing the modeled results with the simulated ones.

A performance model for analysis of heterogeneous multi-cluster systems

This paper addresses the problem of performance modeling for large-scale heterogeneous distributed systems with emphases on multi-cluster computing systems. Since the overall performance of distributed systems is often depends on the effectiveness of its communication network, the study of the interconnection networks for these systems is very important. Performance modeling is required to avoid poorly chosen components and architectures as well as discovering a serious shortfall during system testing just prior to deployment time. However, the multiplicity of components and associated complexity make performance analysis of distributed computing systems a challenging task. To this end, we present an analytical performance model for the interconnection networks of heterogeneous multi-cluster systems. The analysis is based on a parametric family of fat-trees, the m-port n-tree, and a deterministic routing algorithm, which is proposed in this paper. The model is validated through comprehensive simulation, which demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Performance analysis of heterogeneous multi-cluster systems

When building a cost-effective high-performance parallel processing system, a performance model is a useful tool for exploring the design space and examining various parameters. However, performance analysis in such systems has proven to be a challenging task that requires the innovative performance analysis tools and methods to keep up with the rapid evolution and ever increasing complexity of such systems. To this end, we propose an analytical model for heterogeneous multi-cluster systems. The model takes into account stochastic quantities as well as network heterogeneity in bandwidth and latency in each cluster. Also, blocking and non-blocking network architecture model is proposed and are used in performance analysis of the system. The message latency is used as the primary performance metric. The model is validated by constructing a set of simulators to simulate different types of clusters, and by comparing the modeled results with the simulated ones.

Analysis of interconnection networks in heterogeneous multi-cluster systems

The study of interconnection networks is important because the overall performance of a distributed system is often critically hinged on the effectiveness of its interconnection network. In the mean time, the heterogeneity is one of the most important factors of such systems. This paper addresses the problem of interconnection networks performance modeling of large-scale distributed systems with emphases on heterogeneous multi-cluster computing systems. So, we present an analytical model to predict message latency in multi-cluster systems in the presence of cluster size heterogeneity. The model is validated through comprehensive simulation, which demonstrates that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Analysis of multi-cluster computing systems with processor heterogeneity

This paper addresses the problem of performance modeling of heterogeneous multi-cluster computing systems. We present an analytical model that can be employed to explore the effectiveness of different design approaches so that one can have an intelligent choice during design and evaluation of a cost effective large-scale heterogeneous distributed computing system. The proposed model considers stochastic quantities as well as processor heterogeneity of the target system. The analysis is based on a parametric fat-tree network, the m-port n-tree, and a deterministic routing algorithm. The correctness of the proposed model is validated through comprehensive simulation of different types of clusters.