The design and evaluation of a virtual distributed computing environment

Current advances in high-speed networks such as ATM and fiber-optics, and software technologies such as the JAVA programming language and WWW tools, have made network-based computing a cost-effective, high-performance distributed computing environment. Metacomputing, a special subset of network-based computing, is a well-integrated execution environment derived by combining diverse and distributed resources such as MPPs, workstations, mass storage, and databases that show a heterogeneous nature in terms of hardware, software, and organization. In this paper we present the Virtual Distributed Computing Environment (VDCE), a metacomputing environment currently being developed at Syracuse University. VDCE provides an efficient web-based approach for developing, evaluating, and visualizing large-scale distributed applications that are based on predefined task libraries on diverse platforms. The VDCE task libraries relieve end-users of tedious task implementations and also support reusability. The VDCE software architecture is described in terms of three modules: (a) the Application Editor, a user-friendly application development environment that generates the Application Flow Graph (AFG) of an application; (b) the Application Scheduler, which provides an efficient task-to-resource mapping of AFG; and (c) the VDCE Runtime System, which is responsible for running and managing application execution and for monitoring the VDCE resources. We present experimental results of an application execution on the VDCE prototype for evaluating the performance of different machine and network configurations. We also show how the VDCE can be used as a problem-solving environment on which large-scale, network-centric applications can be developed by a novice programmer rather than by an expert in low-level details of parallel programming languages. This revised version was published online in July 2006 with corrections to the Cover Date.     

Prediction of Cancer Class with Majority Voting Genetic Programming Classifier Using Gene Expression Data

In order to get a better understanding of different types of cancers and to find the possible biomarkers for diseases, recently, many researchers are analyzing the gene expression data using various machine learning techniques. However, due to a very small number of training samples compared to the huge number of genes and class imbalance, most of these methods suffer from overfitting. In this paper, we present a majority voting genetic programming classifier (MVGPC) for the classification of microarray data. Instead of a single rule or a single set of rules, we evolve multiple rules with genetic programming (GP) and then apply those rules to test samples to determine their labels with majority voting technique. By performing experiments on four different public cancer data sets, including multiclass data sets, we have found that the test accuracies of MVGPC are better than those of other methods, including AdaBoost with GP. Moreover, some of the more frequently occurring genes in the classification rules are known to be associated with the types of cancers being studied in this paper.     

Load balancing on temporally heterogeneous cluster of workstations for parallel simulated annealing

Simulated annealing (SA) is a general-purpose optimization technique widely used in various combinatorial optimization problems. However, the main drawback of this technique is a long computation time required to obtain a good quality of solution. Clusters have emerged as a feasible and popular platform for parallel computing in many applications. Computing nodes on many of the clusters available today are temporally heterogeneous. In this study, multiple Markov chain (MMC) parallel simulated annealing (PSA) algorithms have been implemented on a temporally heterogeneous cluster of workstations to solve the graph partitioning problem and their performance has been analyzed in detail. Temporal heterogeneity of a cluster of workstations is harnessed by employing static and dynamic load balancing techniques to further improve efficiency and scalability of the MMC PSA algorithms.     

A survey of application: genomics and genetic programming, a new frontier

The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP). Particular emphasis is placed on the application of GP to genomics. First, the basic methodology of GP is introduced. This is followed by a review of applications in the areas of gene network inference, gene expression data analysis, SNP analysis, epistasis analysis and gene annotation. Finally this paper concluded by suggesting potential avenues of possible future research on genetic programming, opportunities to extend the technique, and areas for possible practical applications.     

Harnessing networked workstations as a powerful parallel computer: a general paradigm illustrated using three programs for genetic linkage analysis

It is widely accepted that parallel computers, which have theability to execute different parts of a program simultaneously,will offer dramatic speed–up for many time–consumingbiological computations. The paper describes how the use ofthe machine–independent parallel programming language,Linda, allows parallel programs to run on an institution^'s networkof workstations. In this way, an institution can harness existinghardware, which is often either idle or vastly underutilized,as a powerful ‘parallel machine’ with supercomputingcapability. The paper illustrates this very general paradigmby describing the use of Linda to parallelize three widely usedprograms for genetic linkage analysis, a mathematical techniqueused in gene mapping. The paper then discusses a number of technical,administrative and social issues that arise when creating sucha computational resource.     

Dynamics of lipid domain formation: Fluctuation analysis

Scanning-fluctuation correlation spectroscopy was used to detect subresolution organizational fluctuations in the lipid liquid-crystalline phase for single lipid model systems. We used the fluorescent probe Laurdan which is sensitive to the amount of water in the membrane to show that there is a spatial heterogeneity on the scale of few pixels (the size of the pixel is 50 nm). We calculated the pixel variance of the GP function and we found that the variance has a peak at the phase transition for 3 different samples made of pure lipids. The pixel variance has an abrupt change at the phase transition of the membrane and then it slowly decreases at higher temperature. The relatively large variance of the GP indicates that the liquid phase of the membrane is quite heterogeneous even several degrees higher than the phase transition temperature. We interpreted this result as evidence of an underlying microscale structure of the membrane in which water is not uniformly distributed at the micron scale. Imaging of these microstructures shows that the pixels with different GP tend to concentrate in specific domains in the membrane. In the case of single lipid membrane, the statistical and fluctuation analysis of the GP data shows that even such simple lipid systems are capable of generating and maintaining stable structural and organizational heterogeneities.     

Parallel FFT on ATM‐based networks of workstations

We consider parallel computing on a network of workstations using a connection-oriented protocol (e.g., Asynchronous Transfer Mode) for data communication. In a connection-oriented protocol, a virtual circuit of guaranteed bandwidth is established for each pair of communicating workstations. Since all virtual circuits do not have the same guaranteed bandwidth, a parallel application must deal with the unequal bandwidths between workstations. Since most works in the design of parallel algorithms assume equal bandwidths on all the communication links, they often do not perform well when executed on networks of workstations using connection-oriented protocols. In this paper, we first evaluate the performance degradation caused by unequal bandwidths on the execution of conventional parallel algorithms such as the fast Fourier transform and bitonic sort. We then present a strategy based on dynamic redistribution of data points to reduce the bottlenecks caused by unequal bandwidths. We also extend this strategy to deal with processor heterogeneity. Using analysis and simulation we show that there is a considerable reduction in the runtime if the proposed redistribution strategy is adopted. The basic idea presented in this paper can also be used to improve the runtimes of other parallel applications in connection-oriented environments. This revised version was published online in July 2006 with corrections to the Cover Date.     

Load balancing and optimization of distributed proximity effect correction on a temporally heterogeneous cluster

Grayscale electron-beam lithography is a technique widely used in transferring three-dimensional structures onto the resist layer or substrate. The proximity effect caused by electron scattering in the resist imposes a severe limitation on the ultimate spatial resolution attainable by e-beam lithography. Therefore, correction of the proximity effect is essential particularly for the fine-feature, high-density circuit patterns. However, the proximity effect correction is very time-consuming due to the intensive computation required in the correction procedure and a large size of circuit data to be processed. Hence, it is an ideal candidate for distributed computing where the otherwise-unused CPU cycles of a number of computers on a network (cluster) can be efficiently utilized. One of the characteristics of such a cluster is its heterogeneity, i.e., the available computing power varies with computer and/or time. This variation may degrade the performance of distributed computing significantly. In this paper, efficient distributed implementations of grayscale proximity effect correction on a temporally heterogeneous cluster are described with the main emphasis on static and dynamic load balancing schemes and their optimization through effective task partitioning methods. The experimental results obtained on a cluster of Sun workstations shared by multiple users are presented with detailed discussion.     

High-efficiency and multilocus targeted integration in CHO cells using CRISPR-mediated donor nicking and DNA repair inhibitors

Efforts to leverage clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) for targeted genomic modifications in mammalian cells are limited by low efficiencies and heterogeneous outcomes. To aid method optimization, we developed an all-in-one reporter system, including a novel superfolder orange fluorescent protein (sfOrange), to simultaneously quantify gene disruption, site-specific integration (SSI), and random integration (RI). SSI strategies that utilize different donor plasmid formats and Cas9 nuclease variants were evaluated for targeting accuracy and efficiency in Chinese hamster ovary cells. Double-cut and double-nick donor formats significantly improved targeting accuracy by 2.3–8.3-fold and 19–22-fold, respectively, compared to standard circular donors. Notably, Cas9-mediated donor linearization was associated with increased RI events, whereas donor nicking minimized RI without sacrificing SSI efficiency and avoided low-fidelity outcomes. A screen of 10 molecules that modulate the major mammalian DNA repair pathways identified two inhibitors that further enhance targeting accuracy and efficiency to achieve SSI in 25% of transfected cells without selection. The optimized methods integrated transgene expression cassettes with 96% efficiency at a single locus and with 53%–55% efficiency at two loci simultaneously in selected clones. The CRISPR-based tools and methods developed here could inform the use of CRISPR/Cas9 in mammalian cell lines, accelerate mammalian cell line engineering, and support advanced recombinant protein production applications.     

Optimal breeding-value prediction using a sparse selection index

Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a sparse selection index (SSI) that integrates selection index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-Best Linear Unbiased Predictor (G-BLUP) (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in 10 different environments) that the SSI can achieve significant (anywhere between 5 and 10%) gains in prediction accuracy relative to the G-BLUP.     

Sporophytic self-incompatibility in Senecio squalidus (Asteraceae): S allele dominance interactions and modifiers of cross-compatibility and selfing rates

Understanding genetic mechanisms of self-incompatibility (SI) and how they evolve is central to understanding the mating behaviour of most outbreeding angiosperms. Sporophytic SI (SSI) is controlled by a single multi-allelic locus, S, which is expressed in the diploid (sporophyte) plant to determine the SI phenotype of its haploid (gametophyte) pollen. This allows complex patterns of independent S allele dominance interactions in male (pollen) and female (pistil) reproductive tissues. Senecio squalidus is a useful model for studying the genetic regulation and evolution of SSI because of its population history as an alien invasive species in the UK. S. squalidus maintains a small number of S alleles (7–11) with a high frequency of dominance interactions. Some S. squalidus individuals also show partial selfing and/or greater levels of cross-compatibility than expected under SSI. We previously speculated that these might be adaptations to invasiveness. Here we describe a detailed characterization of the regulation of SSI in S. squalidus. Controlled crosses were used to determine the S allele dominance hierarchy of six S alleles and effects of modifiers on cross-compatibility and partial selfing. Complex dominance interactions among S alleles were found with at least three levels of dominance and tissue-specific codominance. Evidence for S gene modifiers that increase selfing and/or cross-compatibility was also found. These empirical findings are discussed in the context of theoretical predictions for maintenance of S allele dominance interactions, and the role of modifier loci in the evolution of SI.     

Adaptive resource utilization and remote access capabilities in high‐performance distributed systems: The Open HPC++ approach

Development of high-performance distributed applications, called metaapplications, is extremely challenging because of their complex runtime environment coupled with their requirements of high-performance and Quality of Service (QoS). Such applications typically run on a set of heterogeneous machines with dynamically varying loads, connected by heterogeneous networks possibly supporting a wide variety of communication protocols. In spite of the size and complexity of such applications, they must provide the high-performance and QoS mandated by their users. In order to achieve the goal of high-performance, they need to adaptively utilize their computational and communication resources. Apart from the requirements of adaptive resource utilization, such applications have a third kind of requirement related to remote access QoS. Different clients, although accessing a single server resource, may have differing QoS requirements from their remote connections. A single server resource may also need to provide different QoS for different clients, depending on various issues such as the amount of trust between the server and a given client. These QoS requirements can be encapsulated under the abstraction of remote access capabilities. Metaapplications need to address all the above three requirements in order to achieve the goal of high-performance and satisfy user expectations of QoS. This paper presents Open HPC++, a programming environment for high-performance applications running in a complex and heterogeneous run-time environment. Open HPC++ provides application level tools and mechanisms to satisfy application requirements of adaptive resource utilization and remote access capabilities. Open HPC++ is designed on the lines of CORBA and uses an Object Request Broker (ORB) to support seamless communication between distributed application components. In order to provide adaptive utilization of communication resources, it uses the principle of open implementation to open up the communication mechanisms of its ORB. By virtue of its open architecture, the ORB supports multiple, possibly custom, communication protocols, along with automatic and user controlled protocol selection at run-time. An extension of the same mechanism is used to support the concept of remote access capabilities. In order to support adaptive utilization of computational resources, Open HPC++ also provides a flexible yet powerful set of load-balancing mechanisms that can be used to implement custom load-balancing strategies. The paper also presents performance evaluations of Open HPC++ adaptivity and load-balancing mechanisms. This revised version was published online in July 2006 with corrections to the Cover Date.     

Cytogenetic analysis in prenatal diagnosis.

Chromosome analysis is the single most frequent test used in laboratory prenatal diagnostic studies. I summarize the current status of the field, including diagnostic problems in the laboratory and the clinical problems associated with communicating unexpected laboratory findings. I explore the effect of molecular genetics on these issues and its possible future effects on the entire practice of prenatal diagnosis as it relates to the risk for chromosome nondisjunction (trisomy). I also discuss the use of cytogenetic analysis in the prenatal diagnosis of certain inherited genetic diseases.     

An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.     

ddRAD‐Seq reveals evolutionary insights into population differentiation and the cryptic phylogeography of Hyporhamphus intermedius in Mainland China

Species differentiation and local adaptation in heterogeneous environments have attracted much attention, although little is known about the mechanisms involved. Hyporhamphus intermedius is an anadromous, brackish‐water halfbeak that is widely distributed in coastal areas and hyperdiverse freshwater systems in China, making it an interesting model for research on phylogeography and local adaptation. Here, 156 individuals were sampled at eight sites from heterogeneous aquatic habitats to examine environmental and genetic contributions to phenotypic divergence. Using double‐digest restriction‐site‐associated DNA sequencing (ddRAD‐Seq) in the specimens from the different watersheds, 5498 single nucleotide polymorphisms (SNPs) were found among populations, with obvious population differentiation. We find that present‐day Mainland China populations are structured into distinct genetic clusters stretching from southern and northern ancestries, mirroring geography. Following a transplant event in Plateau Lakes, there were virtually no variations of genetic diversity occurred in two populations, despite the fact two main splits were unveiled in the demographic history. Additionally, dorsal, and anal fin traits varied widely between the southern group and the others, which highlighted previously unrecognized lineages. We then explore genotype–phenotype‐environment associations and predict candidate loci. Subgroup ranges appeared to correspond to geographic regions with heterogeneous hydrological factors, indicating that these features are likely important drivers of diversification. Accordingly, we conclude that genetic and phenotypic polymorphism and a moderate amount of genetic differentiation occurred, which might be ascribed to population subdivision, and the impact of abiotic factors.     

Applying genetic programming to the prediction of alternative mRNA splice variants

Genetic programming (GP) can be used to classify a given gene sequence as either constitutively or alternatively spliced. We describe the principles of GP and apply it to a well-defined data set of alternatively spliced genes. A feature matrix of sequence properties, such as nucleotide composition or exon length, was passed to the GP system "Discipulus." To test its performance we concentrated on cassette exons (SCE) and retained introns (SIR). We analyzed 27,519 constitutively spliced and 9641 cassette exons including their neighboring introns; in addition we analyzed 33,316 constitutively spliced introns compared to 2712 retained introns. We find that the classifier yields highly accurate predictions on the SIR data with a sensitivity of 92.1% and a specificity of 79.2%. Prediction accuracies on the SCE data are lower, 47.3% (sensitivity) and 70.9% (specificity), indicating that alternative splicing of introns can be better captured by sequence properties than that of exons.     

Distributed volume morphing

3D morphing is a popular technique for creating a smooth transition between two objects. In this paper we integrate volume morphing and rendering in a distributed network environment to speed up the computation efficiency. We describe our proposed system architecture of distributed volume morphing and the proposed algorithms, along with their implementation and performance on the networked workstations. A load evaluation function is proposed to partition the workload and the workstation cluster for better load balancing and then to improve the performance under highly uneven load situation. The performance evaluation for five load balancing strategies are conducted. Among them, the strategy ‘Request’ performs the best in terms of speedup. This revised version was published online in July 2006 with corrections to the Cover Date.     

Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator.

We have devised a Cartesian combination operator and coding scheme for improving the performance of genetic algorithms applied to the protein folding problem. The genetic coding consists of the C alpha Cartesian coordinates of the protein chain. The recombination of the genes of the parents is accomplished by: (1) a rigid superposition of one parent chain on the other, to make the relation of Cartesian coordinates meaningful, then, (2) the chains of the children are formed through a linear combination of the coordinates of their parents. The children produced with this Cartesian combination operator scheme have similar topology and retain the long-range contacts of their parents. The new scheme is significantly more efficient than the standard genetic algorithm methods for locating low-energy conformations of proteins. The considerable superiority of genetic algorithms over Monte Carlo optimization methods is also demonstrated. We have also devised a new dynamic programming lattice fitting procedure for use with the Cartesian combination operator method. The procedure finds excellent fits of real-space chains to the lattice while satisfying bond-length, bond-angle, and overlap constraints.     

Ultrathin Few‐Layer GeP Nanosheets via Lithiation‐Assisted Chemical Exfoliation and Their Application in Sodium Storage

Ultrathin few‐layer materials have attracted intensive research attention because of their distinctive and unique properties. Few‐layer GeP (FL‐GP) is potentially interesting for application in electronics and optoelectronics because of its appropriate band gap and good stability under ambient conditions. Nevertheless, it is a challenge to achieve ultrathin few‐layer or single layer GeP from exfoliation of bulk crystals. Here, a lithiation‐assisted chemical exfoliation technique is employed to achieve FL‐GP, in which the interlayer spacing can be efficiently enlarged after a preliminary lithium ion intercalation, allowing the bulk crystal to be readily exfoliated in a following ultrasonication. As a result, ultrathin FL‐GP is obtained. In a demonstration, the FL‐GP/reduced graphene oxide (rGO) demonstrates remarkable sodium storage performance. The FL‐GP with a two‐dimensional structure shortens the ion transport pathways and alleviates the volume variation during sodiation. Meanwhile, the rGO in the composite improves the conductivity of the whole electrode. The as‐prepared FL‐GP/rGO electrode exhibits a high capacity of 504.2 mAh g^?1 at 100 mA g^?1, remarkable rate performance, and superior cycling stability in the half cells. FL‐GP/rGO//Na₃V₂(PO₄)₃ full cells are also assembled and demonstrated satisfactory electrochemical performance, indicating potential application of the as‐prepared anode materials.