Zorilla: a peer‐to‐peer middleware for real‐world distributed systems

The inherent complex nature of current distributed computing architectures hinders the widespread adoption of these systems for mainstream use. In general, users have access to a highly heterogeneous set of compute resources, which may include clusters, grids, desktop grids, clouds, and other compute platforms. This heterogeneity is especially problematic when running parallel and distributed applications. Software is needed which easily combines as many resources as possible into one coherent computing platform. In this paper, we introduce Zorilla: peer‐to‐peer (P2P) middleware that creates a single distributed environment from any available set of compute resources. Zorilla imposes minimal requirements on the resource used, is platform independent, and does not rely on central components. In addition to providing functionality on bare resources, Zorilla can exploit locally available middleware. Zorilla explicitly supports distributed and parallel applications, and allows resources from multiple sites to cooperate in a single computation. Zorilla makes extensive use of both virtualization and P2P techniques. We will demonstrate how virtualization and P2P combine into a simple design, while enhancing functionality and ease of use. Together, these techniques bring our goal a step closer: transparent, easy use of resources, even on very heterogeneous distributed systems. Copyright © 2011 John Wiley & Sons, Ltd.     

Multi-Objective Optimization using Grid Computing

This paper analyzes some technical and practical issues concerning the use of parallel systems to solve multi-objective optimization problems using enumerative search. This technique constitutes a conceptually simple search strategy, and it is based on evaluating each possible solution from a given finite search space. The results obtained by enumeration are impractical for most computer platforms and researchers, but they exhibit a great interest because they can be used to be compared against the values obtained by stochastic techniques. We analyze here the use of a grid computing system to cope with the limits of enumerative search. After evaluating the performance of the sequential algorithm, we present, first, a parallel algorithm targeted to multiprocessor systems. Then, we design a distributed version prepared to be executed on a federation of geographically distributed computers known as a computational grid. Our conclusion is that this kind of systems can provide to the community with a large and precise set of Pareto fronts that would be otherwise unknown.     

微流控DNA计算的研究进展及展望

DNA计算机具有超强的并行运算能力和巨大的数据存储能力,被认为有望解决电子计算机所面临的瓶颈问题。微流控技术提供了一个可实现自动化操作、通用型DNA计算机的支持平台。借助于微流控技术,将DNA计算相关的生化反应有机地集成在芯片平台上加以实现,进一步提高了DNA计算的可靠性、减少了实验过程的手工操作和反应时间。在介绍DNA计算机的基本概念和微流控技术基础上,围绕微流控DNA计算机的原理、模型和应用等关键问题,分析了微流控DNA计算机的体系结构及设计方法,讨论了微流控DNA计算机未来可能的发展方向。     

一种带混合进化机制的膜聚类算法

膜计算(也称为P系统或膜系统)是一种新颖的分布式、并行计算模型.为了处理数据聚类问题,提出了一种采用混合进化机制的膜聚类算法.它使用了一个由3个细胞组成的组织P系统,为一个待聚类的数据集发现最优的簇中心.其对象表示候选的簇中心,并且这3个细胞分别使用了3种不同的进化机制:遗传算子、速度-位移模型和差分进化机制.然而,所使用的速度-位移模型和差分进化机制是结合了这个特殊膜结构和转运机制所提出的改进版本.这种混合进化机制能够增强系统中对象的多样性和改善收敛性能.在混合进化机制和转运机制控制下,这种膜聚类算法能够确定一个数据集的良好划分.所提出的膜聚类算法在3个人工数据集和5个真实数据集上被评估,并与k-means和几种进化聚类算法进行比较.统计显著性测试建立了所提出的膜聚类算法的优势.     

Homogeneous spiking neural P systems working in sequential mode induced by maximum spike number

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes, where neurons work in parallel in the sense that each neuron that can fire should fire at each computation step, and neurons can be different in the sense that they can have different sets of spiking rules. In this work, we consider SN P systems with the restrictions: (1) all neurons are homogeneous in the sense that each neuron has the same set of rules; (2) at each step the neuron with the maximum number of spikes among the neurons that are active (can spike) will fire. These restrictions correspond to the fact that the system consists of only one kind of neurons and a global view of the whole network makes the system sequential. The computation power of homogeneous SN P systems working in the sequential mode induced by the maximum spike number is investigated. Specifically, it is proved that such systems are universal as both generating and accepting devices.     

An integrated microfluidic system for live bacteria detection from human joint fluid samples by using ethidium monoazide and loop-mediated isothermal amplification

Periprosthetic joint infection (PJI) is one of the severe complications of prosthetic joint replacement. Delayed PJI diagnosis may anchor bacteria in periprosthetic tissues, and removal of the prosthesis might be inevitable. The diagnosis of PJI depends on the identification of microorganisms by standard microbiological cultures or more advanced molecular diagnostic methods for detection of bacterial genes. However, these methods are relatively time-consuming, labor-intensive and not human error-free. Moreover, it is challenging to distinguish live from dead bacteria by using DNA-based molecular diagnostics since bacterial DNA will be remained in the tissue even after the death of the bacteria. In this work, an integrated microfluidic system has been developed to perform the entire molecular diagnostic process for the PJI diagnosis in a single chip. We combined the loop-mediated isothermal amplification (LAMP) with ethidium monoazide (EMA) in an integrated microfluidic system to identify live bacteria with reasonable sensitivity and high specificity. All the diagnostic processes including bacteria isolation, cell lysis, DNA amplification and optical detection can be automatically performed on the integrated microfluidic system by using a compact custom-made control system. The integrated system can accommodate four primers complementary to six regions of the target genes and improve the detection limit by using LAMP. The limit of detection in this multiple EMA-LAMP assay could be as low as 5 fg/reaction (~1 CFU/reaction) when choosing an optimized primer set as we demonstrated in mecA gene detection. Thus, the developed system for PJI diagnosis has great potential to become a point-of-care device.     

A two-stage electrophoretic microfluidic device for nucleic acid collection and enrichment

Sample purification and enrichment is an important and usually time-consuming step for on-chip nucleic acid detection and analysis. This paper presents an electrophoretic DNA focusing method in microfluidic devices to enrich nucleic acid concentration by around 2700-fold. The electrical waveforms applied to five individual electrodes are such designed that DNAs move successively to the collection electrodes at high speed, while the interferences from bubbles due to electrohydrolysis are minimized. In a spiral channel with a total length of 48 cm, 1 ml DNA sample is purified and enriched by 57 times at a flow rate of 30 μl/min at first. The captured DNAs are then released and transported to the second microfluidic chamber where DNAs are collected and concentrated by 49 times. Thus, in about 40 min, the two-stage device can extract DNAs from 1 ml sample volume and enrich its concentration by 2790-fold. A trade-off exists between the process throughput and the DNA collection efficiency. A DNA capture efficiency of 99.7 % is reached when the flow rate is 1 μl/min, and the maximum DNA capture throughput is achieved at a flow rate of 30 μl/min. As a platform technology, the device can be integrated into bio-sensing and genetic analysis assays for DNA extraction and pre-concentration.     

Dense Linear System: A Parallel Self-verified Solver

This article presents a parallel self-verified solver for dense linear systems of equations. This kind of solver is commonly used in many different kinds of real applications which deal with large matrices. Nevertheless, two key problems appear to limit the use of linear system solvers to a more extensive range of real applications: solution correctness and high computational cost. In order to solve the first one, verified computing would be an interesting choice. An algorithm that uses this concept is able to find a highly accurate and automatically verified result providing more reliability. However, the performance of these algorithms quickly becomes a drawback. Aiming at a better performance, parallel computing techniques were employed. Two main parts of this method were parallelized: the computation of the approximate inverse of matrix A and the preconditioning step. The results obtained show that these optimizations increase significantly the overall performance.     

Pneumatic mold-aided construction of a three-dimensional hydrogel microvascular network in an integrated microfluidics and assay of cancer cell adhesion onto the endothelium

A pneumatic microchannel network (PμCN) fabrication method for producing a three-dimensional microvascular system in an integrated microfluidic device is described. The spatial dynamics of the PμCN profile is systematically characterized and quantitatively analyzed. A microvessel network-embedded hydrogel scaffold is constructed using in situ pneumatic actuation of the PμCN and collagen polymerization. The endothelium-containing microvasculature, which has high cell viability and typical vascular features, was formed by seeding and cultivating human umbilical vein endothelial cells (HUVEC-C). Furthermore, a quantitative investigation of the adhesive interactions between breast cancer cells and endothelial cells was performed with vascular tissue-mimicry in the hydrogel-supported endothelial network using human breast cancer cells (MDA-MB-231) and HUVEC-C cells. The results show signal-promoted and region-preferred adhesion of cancer cells in the established microvascular network. The PμCN can be applied as an active microfluidic molding component for convenient and robust reproduction of microvasculature in vitro. PμCN application can be valuable in monitoring and investigating blood vessel-involved physiologic/pathologic processes. Moreover, this method will facilitate controllable parenchymal tissue organization and construction for tissue engineering as well as subsequent applications for clinical medicine.     

Constructing large suffix trees on a computational grid

The suffix tree is a key data structure for biological sequence analysis, since it permits efficient solutions to many string-based problems. Constructing large suffix trees is challenging because of high memory overheads and poor memory locality. Even though efficient suffix tree construction algorithms exist, their run-time is still very high for long DNA sequences such as whole human chromosomes. In this paper, we are using a hierarchical grid system as a computational platform in order to reduce this run-time significantly. To achieve an efficient mapping onto this type of architecture we introduce a parallel suffix tree construction algorithm that makes use of a new data structure called the common prefix suffix tree. Using this algorithm together with a dynamic load balancing strategy we show that our distributed grid implementation leads to significant run-time savings.