21 resultados para HYDROBENZOFURAN SKELETON


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Structured parallel programming is recognised as a viable and effective means of tackling parallel programming problems. Recently, a set of simple and powerful parallel building blocks RISC pb2l) has been proposed to support modelling and implementation of parallel frameworks. In this work we demonstrate how that same parallel building block set may be used to model both general purpose parallel programming abstractions, not usually listed in classical skeleton sets, and more specialized domain specific parallel patterns. We show how an implementation of RISC pb2 l can be realised via the FastFlow framework and present experimental evidence of the feasibility and efficiency of the approach.

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This paper presents a new programming methodology for introducing and tuning parallelism in Erlang programs, using source-level code refactoring from sequential source programs to parallel programs written using our skeleton library, Skel. High-level cost models allow us to predict with reasonable accuracy the parallel performance of the refactored program, enabling programmers to make informed decisions about which refactorings to apply. Using our approach, we demonstrate easily obtainable, significant and scalable speedups of up to 21 on a 24-core machine over the sequential code.

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Metastasis is the predominant cause of death from cancer yet we have few biomarkers to predict patients at increased risk of metastasis and are unable to effectively treat disseminated disease. Analysis of 448 primary breast tumors determined that expression of the hylauronan receptor CD44 associated with high grade (p = 0.046), ER- (p = 0.001) and PR-negative tumors (p = 0.029), and correlated with increased distant recurrence and reduced disease-free survival in patients with lymph-node positive or large tumors. To determine its functional role in distant metastasis, CD44 was knocked-down in MDA-MB-231 cells using two independent shRNA sequences. Loss of CD44 attenuated tumor cell adhesion to endothelial cells and reduced cell invasion but did not affect proliferation in vitro. To verify the importance of CD44 to post-intravasation events, tumor formation was assessed by quantitative in vivo imaging and post-mortem tissue analysis following an intra-cardiac injection of transfected cells. CD44 knock-down increased survival and decreased overall tumor burden at multiple sites, including the skeleton in vivo. We conclude that elevated CD44 expression on tumour cells within the systemic circulation increases the efficiency of post-intravasation events and distant metastasis in vivo, consistent with its association with increased distant recurrence and reduced disease-free survival in patients.

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A three-dimensional (3D) graphene-Co3O4 electrode was prepared by a two-step method in which graphene was initially deposited on a Ni foam with Co3O4 then grown on the resulting graphene structure. Cross-linked Co3O4 nanosheets with an open pore structure were fully and vertically distributed throughout the graphene skeleton. The free-standing and binder-free monolithic electrode was used directly as a cathode in a Li-O2 battery. This composite structure exhibited enhanced performance with a specific capacity of 2453 mA h g-1 at 0.1 mA cm-2 and 62 stable cycles with 583 mA h g-1 (1000 mA h gcarbon-1). The excellent electrochemical performance is associated with the unique architecture and superior catalytic activity of the 3D electrode. 

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We propose a novel skeleton-based approach to gait recognition using our Skeleton Variance Image. The core of our approach consists of employing the screened Poisson equation to construct a family of smooth distance functions associated with a given shape. The screened Poisson distance function approximation nicely absorbs and is relatively stable to shape boundary perturbations which allows us to define a rough shape skeleton. We demonstrate how our Skeleton Variance Image is a powerful gait cycle descriptor leading to a significant improvement over the existing state of the art gait recognition rate.

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Structured parallel programming, and in particular programming models using the algorithmic skeleton or parallel design pattern concepts, are increasingly considered to be the only viable means of supporting effective development of scalable and efficient parallel programs. Structured parallel programming models have been assessed in a number of works in the context of performance. In this paper we consider how the use of structured parallel programming models allows knowledge of the parallel patterns present to be harnessed to address both performance and energy consumption. We consider different features of structured parallel programming that may be leveraged to impact the performance/energy trade-off and we discuss a preliminary set of experiments validating our claims.