A Framework for Adaptive Cluster Computing Using JavaSpaces

Heterogeneous networked clusters are being increasingly used as platforms for resource-intensive parallel and distributed applications. The fundamental underlying idea is to provide large amounts of processing capacity over extended periods of time by harnessing the idle and available resources on the network in an opportunistic manner. In this paper we present the design, implementation and evaluation of a framework that uses JavaSpaces to support this type of opportunistic adaptive parallel/distributed computing over networked clusters in a non-intrusive manner. The framework targets applications exhibiting coarse grained parallelism and has three key features: (1) portability across heterogeneous platforms, (2) minimal configuration overheads for participating nodes, and (3) automated system state monitoring (using SNMP) to ensure non-intrusive behavior. Experimental results presented in this paper demonstrate that for applications that can be broken into coarse-grained, relatively independent tasks, the opportunistic adaptive parallel computing framework can provide performance gains. Furthermore, the results indicate that monitoring and reacting to the current system state minimizes the intrusiveness of the framework.     

A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses

Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametric-based methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only within-family information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., between-family information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.     

A New Statistical Method for Mixed Variates Analysis in Epidemiological Research

Epidemiologically oriented research often may not do without observational or only partially controlled studies. In many such situations both qualitative characteristics and quantitative ones are observed. In literature there are different methods of handling such problems. The paper presents a method for analyzing dependencies resp. associations between random variates of any kind. The model concerned fullfills the whole field between analysis of variance, analysis of covariance and contingency table analysis. The method is named MIVA or mixed variates analysis, bases on the class of Conditional Gaussian Distributions of the exponential family and results in a unique system of mixed and unmixed measures of association–of pairwise, partial, multiple and global type. These measures are easy to be estimated and tested on significant deviation from zero. They may be used describing or analyzing dependence structures in many epidemiological studies but also in other fields.     

A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads

The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial community. Multiple genomes contained in a metagenomic sample can be identified and quantitated through homology searches of sequence reads with known sequences catalogued in reference databases. Traditionally, reads with multiple genomic hits are assigned to non-specific or high ranks of the taxonomy tree, thereby impacting on accurate estimates of relative abundance of multiple genomes present in a sample. Instead of assigning reads one by one to the taxonomy tree as many existing methods do, we propose a statistical framework to model the identified candidate genomes to which sequence reads have hits. After obtaining the estimated proportion of reads generated by each genome, sequence reads are assigned to the candidate genomes and the taxonomy tree based on the estimated probability by taking into account both sequence alignment scores and estimated genome abundance. The proposed method is comprehensively tested on both simulated datasets and two real datasets. It assigns reads to the low taxonomic ranks very accurately. Our statistical approach of taxonomic assignment of metagenomic reads, TAMER, is implemented in R and available at http://faculty.wcas.northwestern.edu/hji403/MetaR.htm.     

A Statistical Framework for Improving Genomic Annotations of Prokaryotic Essential Genes

Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the essential gene annotations are derived from whole-genome transposon mutagenesis (TM), the most frequently used experimental approach for determining essential genes in microorganisms under defined conditions. However, there are substantial systematic biases associated with TM experiments. In this study, we developed a novel Poisson model–based statistical framework to simulate the TM insertion process and subsequently correct the experimental biases. We first quantitatively assessed the effects of major factors that potentially influence the accuracy of TM and subsequently incorporated relevant factors into the framework. Through iteratively optimizing parameters, we inferred the actual insertion events occurred and described each gene’s essentiality on probability measure. Evaluated by the definite mapping of essential gene profile in Escherichia coli, our model significantly improved the accuracy of original TM datasets, resulting in more accurate annotations of essential genes. Our method also showed encouraging results in improving subsaturation level TM datasets. To test our model’s broad applicability to other bacteria, we applied it to Pseudomonas aeruginosa PAO1 and Francisella tularensis novicida TM datasets. We validated our predictions by literature as well as allelic exchange experiments in PAO1. Our model was correct on six of the seven tested genes. Remarkably, among all three cases that our predictions contradicted the TM assignments, experimental validations supported our predictions. In summary, our method will be a promising tool in improving genomic annotations of essential genes and enabling large-scale explorations of gene essentiality. Our contribution is timely considering the rapidly increasing essential gene sets. A Webserver has been set up to provide convenient access to this tool. All results and source codes are available for download upon publication at http://research.cchmc.org/essentialgene/.     

A Decision Support Framework for Sustainable Waste Management

This article describes a decision support framework for the evaluation of scenarios for the integrated management of municipal solid waste within a local government area (LGA).
The work is initially focused on local government (i.e., municipal councils) in the state of Queensland, Australia; however, it is broadly applicable to LGAs anywhere. The goal is to achieve sustainable waste management practices by balancing global and regional environmental impacts, social impacts at the local community level, and economic impacts. The framework integrates life-cycle assessment (LCA) with other environmental, social, and economic tools. For this study, social and economic impacts are assumed to be similar across developed countries of the world. LCA was streamlined at both the life-cycle inventory and life-cycle impact assessment stages.
For this process, spatial resolution is introduced into the LCA process to account for impacts occurring at the local and regional levels. This has been done by considering social impacts on the local community and by use of a regional procedure for LCA data for emissions to the environment that may have impacts at the regional level.
The integration follows the structured approach of the pressure-state-response (PSR) model suggested by the Organisation for Economic Cooperation and Development (OECD). This PSR model has been extended to encompass nonenvironmental issues and to guide the process of applying multiple tools.
The framework primarily focuses on decision analysis and interpretation processes. Multiattribute utility theory (MAUT) is used to assist with the integration of qualitative and quantitative information. MAUT provides a well-structured approach to information assessment and facilitates objective, transparent decisions. A commercially available decision analysis software package based on MAUT has been used as the platform for the framework developed in this study.     

Improving Environmental Sanitation, Health, and Well-Being: A Conceptual Framework for Integral Interventions

We introduce a conceptual framework for improving health and environmental sanitation in urban and peri-urban areas using an approach combining health, ecological, and socioeconomic and cultural assessments. The framework takes into account the three main components: i) health status, ii) physical environment, and iii) socioeconomic and cultural environment. Information on each of these three components can be obtained by using standard disciplinary methods and an innovative combination of these methods. In this way, analyses lead to extended characterization of health, ecological, and social risks while allowing the comprehensive identification of critical control points (CCPs) in relation to biomedical, epidemiological, ecological, and socioeconomic and cultural factors. The proposed concept complements the conventional CCP approach by including an actor perspective that considers vulnerability to risk and patterns of resilience. Interventions deriving from the comprehensive analysis consider biomedical, engineering, and social science perspectives, or a combination of them. By this way, the proposed framework jointly addresses health and environmental sanitation improvements, and recovery and reuse of natural resources. Moreover, interventions encompass not only technical solutions but also behavioral, social, and institutional changes which are derived from the identified resilience patterns. The interventions are assessed with regards to their potential to eliminate or reduce specific risk factors and vulnerability, enhance health status, and assure equity. The framework is conceptualized and validated for the context of urban and peri-urban settings in developing countries focusing on waste, such as excreta, wastewater, and solid waste, their influence on food quality, and their related pathogens, nutrients, and chemical pollutants.     

A Systematic Review Exploring the Social Cognitive Theory of Self-Regulation as a Framework for Chronic Health Condition Interventions

Background

Theory is often recommended as a framework for guiding hypothesized mechanisms of treatment effect. However, there is limited guidance about how to use theory in intervention development.

Methods

We conducted a systematic review to provide an exemplar review evaluating the extent to which use of theory is identified and incorporated within existing interventions. We searched electronic databases PubMed, PsycINFO, CENTRAL, and EMBASE from inception to May 2014. We searched clinicaltrials.gov for registered protocols, reference lists of relevant systematic reviews and included studies, and conducted a citation search in Web of Science. We included peer-reviewed publications of interventions that referenced the social cognitive theory of self-regulation as a framework for interventions to manage chronic health conditions. Two reviewers independently assessed articles for eligibility. We contacted all authors of included studies for information detailing intervention content. We describe how often theory mechanisms were addressed by interventions, and report intervention characteristics used to address theory.

Results

Of 202 articles that reported using the social cognitive theory of self-regulation, 52% failed to incorporate self-monitoring, a main theory component, and were therefore excluded. We included 35 interventions that adequately used the theory framework. Intervention characteristics were often poorly reported in peer-reviewed publications, 21 of 35 interventions incorporated characteristics that addressed each of the main theory components. Each intervention addressed, on average, six of eight self-monitoring mechanisms, two of five self-judgement mechanisms, and one of three self-evaluation mechanisms. The self-monitoring mechanisms ‘Feedback’ and ‘Consistency’ were addressed by all interventions, whereas the self-evaluation mechanisms ‘Self-incentives’ and ‘External rewards’ were addressed by six and four interventions, respectively. The present review establishes that systematic review is a feasible method of identifying use of theory as a conceptual framework for existing interventions. We identified the social cognitive theory of self-regulation as a feasible framework to guide intervention development for chronic health conditions.     

A Framework for Information Systems in Life-Cycle-Oriented Environmental Management

Although business firms have improved their environmental performance, a variety of forces are pushing businesses toward adopting environmental management throughout the entire life cycle of their products and processes. In this article we discuss the information systems elements of an environmental management approach we call "life-cycle-oriented environmental management" (LCOEM).This approach requires the firm to manage the effects of its processes from the creation of inputs to the final disposal of outputs, that is, from cradle to grave. We present a framework of the classes of information systems needed, describe their use in an LCOEM setting and define their inter relationships. We conclude with a discussion of the implications of LCOEM information systems.     

A Proposed Framework for the Management of Water Quality in Arid-Region Lakes

For arid-region lakes, management conflicts are likely to occur between quantity and quality of water supplied: increasing quantity of water supply can lead to water quality deterioration. Such conflicts can best be resolved within an effective management program based on awareness and cooperation at all levels of water management from policy makers to experts. We propose a general framework for designing effective water resource management programs for lakes based on concrete definitions of management criteria such as water quality. The proposed system requires close interaction between policy makers, water resource managers, water suppliers and users, hydrological engineers and limnologists. The significance of mathematical modeling as a self-organizing tool of the management program is emphasized, especially with regards to designing limnological investigations directed toward lake management. We illustrate the application of this approach to water resource management in arid-region lakes (Lake Kinneret, Israel and Lake Sevan, Armenia), where artificial variability of lake morphometry due to water use is a forcing function affecting water quality.     

A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.     

Toward the Quantification of a Conceptual Framework for Movement Ecology Using Circular Statistical Modeling

To analyze an animal’s movement trajectory, a basic model is required that satisfies the following conditions: the model must have an ecological basis and the parameters used in the model must have ecological interpretations, a broad range of movement patterns can be explained by that model, and equations and probability distributions in the model should be mathematically tractable. Random walk models used in previous studies do not necessarily satisfy these requirements, partly because movement trajectories are often more oriented or tortuous than expected from the models. By improving the modeling for turning angles, this study aims to propose a basic movement model. On the basis of the recently developed circular auto-regressive model, we introduced a new movement model and extended its applicability to capture the asymmetric effects of external factors such as wind. The model was applied to GPS trajectories of a seabird (Calonectris leucomelas) to demonstrate its applicability to various movement patterns and to explain how the model parameters are ecologically interpreted under a general conceptual framework for movement ecology. Although it is based on a simple extension of a generalized linear model to circular variables, the proposed model enables us to evaluate the effects of external factors on movement separately from the animal’s internal state. For example, maximum likelihood estimates and model selection suggested that in one homing flight section, the seabird intended to fly toward the island, but misjudged its navigation and was driven off-course by strong winds, while in the subsequent flight section, the seabird reset the focal direction, navigated the flight under strong wind conditions, and succeeded in approaching the island.     

A Bayesian Mutation–Selection Framework for Detecting Site-Specific Adaptive Evolution in Protein-Coding Genes

In recent years, codon substitution models based on the mutation–selection principle have been extended for the purpose of detecting signatures of adaptive evolution in protein-coding genes. However, the approaches used to date have either focused on detecting global signals of adaptive regimes—across the entire gene—or on contexts where experimentally derived, site-specific amino acid fitness profiles are available. Here, we present a Bayesian site-heterogeneous mutation–selection framework for site-specific detection of adaptive substitution regimes given a protein-coding DNA alignment. We offer implementations, briefly present simulation results, and apply the approach on a few real data sets. Our analyses suggest that the new approach shows greater sensitivity than traditional methods. However, more study is required to assess the impact of potential model violations on the method, and gain a greater empirical sense its behavior on a broader range of real data sets. We propose an outline of such a research program.     

Statistical and Economic Techniques for Site-specific Nematode Management

Recent advances in precision agriculture technologies and spatial statistics allow realistic, site-specific estimation of nematode damage to field crops and provide a platform for the site-specific delivery of nematicides within individual fields. This paper reviews the spatial statistical techniques that model correlations among neighboring observations and develop a spatial economic analysis to determine the potential of site-specific nematicide application. The spatial econometric methodology applied in the context of site-specific crop yield response contributes to closing the gap between data analysis and realistic site-specific nematicide recommendations and helps to provide a practical method of site-specifically controlling nematodes.     

CorSig: A General Framework for Estimating Statistical Significance of Correlation and Its Application to Gene Co-Expression Analysis

With the rapid increase of omics data, correlation analysis has become an indispensable tool for inferring meaningful associations from a large number of observations. Pearson correlation coefficient (PCC) and its variants are widely used for such purposes. However, it remains challenging to test whether an observed association is reliable both statistically and biologically. We present here a new method, CorSig, for statistical inference of correlation significance. CorSig is based on a biology-informed null hypothesis, i.e., testing whether the true PCC (ρ) between two variables is statistically larger than a user-specified PCC cutoff (τ), as opposed to the simple null hypothesis of ρ = 0 in existing methods, i.e., testing whether an association can be declared without a threshold. CorSig incorporates Fisher''s Z transformation of the observed PCC (r), which facilitates use of standard techniques for p-value computation and multiple testing corrections. We compared CorSig against two methods: one uses a minimum PCC cutoff while the other (Zhu''s procedure) controls correlation strength and statistical significance in two discrete steps. CorSig consistently outperformed these methods in various simulation data scenarios by balancing between false positives and false negatives. When tested on real-world Populus microarray data, CorSig effectively identified co-expressed genes in the flavonoid pathway, and discriminated between closely related gene family members for their differential association with flavonoid and lignin pathways. The p-values obtained by CorSig can be used as a stand-alone parameter for stratification of co-expressed genes according to their correlation strength in lieu of an arbitrary cutoff. CorSig requires one single tunable parameter, and can be readily extended to other correlation measures. Thus, CorSig should be useful for a wide range of applications, particularly for network analysis of high-dimensional genomic data.

Software Availability

A web server for CorSig is provided at http://202.127.200.1:8080/probeWeb. R code for CorSig is freely available for non-commercial use at http://aspendb.uga.edu/downloads.     

An Integrated Framework for Risk Management and Population Health

The traditional medical model of health and health policy development has focused on individuals and the role of medical care in preventing and treating disease and injury. Recent attention to health inequities and social determinants of health has raised the profile of population heath and evidence-based strategies for improving the health of whole populations. At the same time, risk science has emerged as an important new discipline for the assessment and management of risks to health. This article reviews historical developments in the fields of risk management and population health and proposes a joint population health risk management framework that integrates the key elements of both fields. Applying this integrated approach to managing population health risks will facilitate the development of evidence-based health policy. It will encourage a more systematic and comprehensive evaluation of population health issues and promote the use of a broader suite of interventions to reduce health risks and enhance population health status.     

关于iFlora创建工作框架的建议

iFlora是依据传统植物分类学及相关学科的研究基础,融入现代DNA测序技术,应用高速发展的信息、网络技术及云计算分析平台,收集、整合和管理植物物种相关信息,以建成智能物种鉴定和数据提取的开放应用系统（智能装备）。通过与该系统的双向交流,一方面,可以不断整合新的数据和技术充实iFlora的内容和功能;另一方面,可以通过该系统的多种鉴定途径实现快速、准确和方便的物种鉴定,获取所需物种的相关信息,满足专业机构和公众对物种和生物多样性的认知要求。本文重点介绍了构成iFlora的应用装置和支撑该装置的实物库（凭证标本、分子材料和DNA库）的建设及其重要性;阐述了构成iFlora各单元的高度整合和集成的特点,以及基于计算机技术的物种信息数字化和开放的云计算数据分析处理服务平台的枢纽作用;并讨论了iFlora创建过程所面临的困难和挑战,以及拟研发的智能装备的框架和应用前景。