SNUAGE: an efficient platform-as-a-service security framework for the cloud

In this paper we present SNUAGE, a platform-as-a-service security framework for building secure and scalable multi-layered services based on the cloud computing model. SNUAGE ensures the authenticity, integrity, and confidentiality of data communication over the network links by creating a set of security associations between the data-bound components on the presentation layer and their respective data sources on the data persistence layer. SNUAGE encapsulates the security procedures, policies, and mechanisms in these security associations at the service development stage to form a collection of isolated and protected security domains. The secure communication among the entities in one security domain is governed and controlled by a standalone security processor and policy attached to this domain. This results into: (1) a safer data delivery mechanism that prevents security vulnerabilities in one domain from spreading to the other domains and controls the inter-domain information flow to protect the privacy of network data, (2) a reusable security framework that can be employed in existing platform-as-a-service environments and across diverse cloud computing service models, and (3) an increase in productivity and delivery of reliable and secure cloud computing services supported by a transparent programming model that relieves application developers from the intricate details of security programming. Last but not least, SNUAGE contributes to a major enhancement in the energy consumption and performance of supported cloud services by providing a suitable execution container in its protected security domains for a wide suite of energy- and performance-efficient cryptographic constructs such as those adopted by policy-driven and content-based security protocols. An energy analysis of the system shows, via real energy measurements, major savings in energy consumption on the consumer devices as well as on the cloud servers. Moreover, a sample implementation of the presented security framework is developed using Java and deployed and tested in a real cloud computing infrastructure using the Google App Engine service platform. Performance benchmarks show that the proposed framework provides a significant throughput enhancement compared to traditional network security protocols such as the Secure Sockets Layer and the Transport Layer Security protocols.     

A statistical framework for genomic data fusion

MOTIVATION: During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data. RESULTS: This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins--membrane proteins and ribosomal proteins--performs significantly better than the same algorithm trained on any single type of data. AVAILABILITY: Supplementary data at http://noble.gs.washington.edu/proj/sdp-svm     

A statistical framework for testing modularity in multidimensional data

Modular variation of multivariate traits results from modular distribution of effects of genetic and epigenetic interactions among those traits. However, statistical methods rarely detect truly modular patterns, possibly because the processes that generate intramodular associations may overlap spatially. Methodologically, this overlap may cause multiple patterns of modularity to be equally consistent with observed covariances. To deal with this indeterminacy, the present study outlines a framework for testing a priori hypotheses of modularity in which putative modules are mathematically represented as multidimensional subspaces embedded in the data. Model expectations are computed by subdividing the data into arrays of variables, and intermodular interactions are represented by overlapping arrays. Covariance structures are thus modeled as the outcome of complex and nonorthogonal intermodular interactions. This approach is demonstrated by analyzing mandibular modularity in nine rodent species. A total of 620 models are fit to each species, and the most strongly supported are heuristically modified to improve their fit. Five modules common to all species are identified, which approximately map to the developmental modules of the mandible. Within species, these modules are embedded within larger "super-modules," suggesting that these conserved modules act as building blocks from which covariation patterns are built.     

A knowledge-driven agent-centred framework for data mining in EMG

In this paper, we present a multi-agent framework for data mining in electromyography. This application, based on a web interface, provides a set of functionalities allowing to manipulate 1000 medical cases and more than 25,000 neurological tests stored in a medical database. The aim is to extract medical information using data mining algorithms and to supply a knowledge base with pertinent information. The multi-agent platform gives the possibility to distribute the data management process between several autonomous entities. This framework provides a parallel and flexible data manipulation.     

qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data

Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data.     

A framework for how environment contributes to cancer risk

Evolutionary theory explains why metazoan species are largely protected against the negative fitness effects of cancers. Nevertheless, cancer is often observed at high incidence across a range of species. Although there are many challenges to quantifying cancer epidemiology and assessing its causes, we claim that most modern‐day cancer in animals – and humans in particular – are due to environments deviating from central tendencies of distributions that have prevailed during cancer resistance evolution. Such novel environmental conditions may be natural and/or of anthropogenic origin, and may interface with cancer risk in numerous ways, broadly classifiable as those: increasing organism body size and/or life span, disrupting processes within the organism, and affecting germline. We argue that anthropogenic influences, in particular, explain much of the present‐day cancer risk across life, including in humans. Based on a literature survey of animal species and a parameterised mathematical model for humans, we suggest that combined risks of all cancers in a population beyond c. 5% can be explained to some extent by the influence of novel environments. Our framework provides a basis for understanding how natural environmental variation and human activity impact cancer risk, with potential implications for species ecology.     

XplorSeq: A software environment for integrated management and phylogenetic analysis of metagenomic sequence data

Background  

Advances in automated DNA sequencing technology have accelerated the generation of metagenomic DNA sequences, especially environmental ribosomal RNA gene (rDNA) sequences. As the scale of rDNA-based studies of microbial ecology has expanded, need has arisen for software that is capable of managing, annotating, and analyzing the plethora of diverse data accumulated in these projects.     

A stepwise framework for the normalization of array CGH data

Background  

In two-channel competitive genomic hybridization microarray experiments, the ratio of the two fluorescent signal intensities at each spot on the microarray is commonly used to infer the relative amounts of the test and reference sample DNA levels. This ratio may be influenced by systematic measurement effects from non-biological sources that can introduce biases in the estimated ratios. These biases should be removed before drawing conclusions about the relative levels of DNA. The performance of existing gene expression microarray normalization strategies has not been evaluated for removing systematic biases encountered in array-based comparative genomic hybridization (CGH), which aims to detect single copy gains and losses typically in samples with heterogeneous cell populations resulting in only slight shifts in signal ratios. The purpose of this work is to establish a framework for correcting the systematic sources of variation in high density CGH array images, while maintaining the true biological variations.     

A general framework for modeling population abundance data

Time-series data resulting from surveying wild animals are often described using state-space population dynamics models, in particular with Gompertz, Beverton-Holt, or Moran-Ricker latent processes. We show how hidden Markov model methodology provides a flexible framework for fitting a wide range of models to such data. This general approach makes it possible to model abundance on the natural or log scale, include multiple observations at each sampling occasion and compare alternative models using information criteria. It also easily accommodates unequal sampling time intervals, should that possibility occur, and allows testing for density dependence using the bootstrap. The paper is illustrated by replicated time series of red kangaroo abundances, and a univariate time series of ibex counts which are an order of magnitude larger. In the analyses carried out, we fit different latent process and observation models using the hidden Markov framework. Results are robust with regard to the necessary discretization of the state variable. We find no effective difference between the three latent models of the paper in terms of maximized likelihood value for the two applications presented, and also others analyzed. Simulations suggest that ecological time series are not sufficiently informative to distinguish between alternative latent processes for modeling population survey data when data do not indicate strong density dependence.     

A cross-validation statistical framework for asymmetric data integration

The proliferation of biobanks and large public clinical data sets enables their integration with a smaller amount of locally gathered data for the purposes of parameter estimation and model prediction. However, public data sets may be subject to context-dependent confounders and the protocols behind their generation are often opaque; naively integrating all external data sets equally can bias estimates and lead to spurious conclusions. Weighted data integration is a potential solution, but current methods still require subjective specifications of weights and can become computationally intractable. Under the assumption that local data are generated from the set of unknown true parameters, we propose a novel weighted integration method based upon using the external data to minimize the local data leave-one-out cross validation (LOOCV) error. We demonstrate how the optimization of LOOCV errors for linear and Cox proportional hazards models can be rewritten as functions of external data set integration weights. Significant reductions in estimation error and prediction error are shown using simulation studies mimicking the heterogeneity of clinical data as well as a real-world example using kidney transplant patients from the Scientific Registry of Transplant Recipients.     

A general framework for biclustering gene expression data

A large number of biclustering methods have been proposed to detect patterns in gene expression data. All these methods try to find some type of biclusters but no one can discover all the types of patterns in the data. Furthermore, researchers have to design new algorithms in order to find new types of biclusters/patterns that interest biologists. In this paper, we propose a novel approach for biclustering that, in general, can be used to discover all computable patterns in gene expression data. The method is based on the theory of Kolmogorov complexity. More precisely, we use Kolmogorov complexity to measure the randomness of submatrices as the merit of biclusters because randomness naturally consists in a lack of regularity, which is a common property of all types of patterns. On the basis of algorithmic probability measure, we develop a Markov Chain Monte Carlo algorithm to search for biclusters. Our method can also be easily extended to solve the problems of conventional clustering and checkerboard type biclustering. The preliminary experiments on simulated as well as real data show that our approach is very versatile and promising.     

A microarray data analysis framework for postmortem tissues

This paper will give a complete methodological approach to the processing of oligonucleotide microarray data from postmortem tissue, particularly brain matter. Attention will be drawn to each of the important stages in the process; specifically the quality control, gene expression value calculation, multiple hypothesis testing and correlation analyses. We shall initially discuss the theoretical foundations of each individual method and subsequently apply the ensemble to a sample data set to illustrate and visualise important points.     

A cluster validity framework for genome expression data

This paper presents a method for the assessment of expression cluster validity.     

A framework for actualising normalisation data in LCA: Experiences in the Netherlands

In LCA, normalisation is applied to quantify the relative size of the impact scores. Several sets of normalisation data exist in the Netherlands, which all have a certain degree of unreliability. The purpose of this study is to actualise Dutch normalisation data and to make a framework for deriving these data. In this study normalisation data are calculated for three different levels in order to give the LCA practitioner a more extended basis for preparing the interpretation process. The first level of normalisation contains all impacts relating to activities that take place within the Dutch territory. The second level is based on the Dutch final consumption, which means that import and export are taken into account. The third level is an attempt to estimate impacts in Europe based on European data if possible, and otherwise based on extrapolation from the Dutch situation.     

A biogeochemical framework for bioremediation of plutonium(V) in the subsurface environment

Accidental release of plutonium (Pu) from storage facilities in the subsurface environment is a concern for the safety of human beings and the environment. Given the complexity of the subsurface environment and multivalent state of Pu, we developed a quantitative biogeochemical framework for bioremediation of Pu(V)O(2) (+) in the subsurface environment. We implemented the framework in the biogeochemical model CCBATCH by expanding its chemical equilibrium for aqueous complexation of Pu and its biological sub-models for including Pu's toxicity and reduction reactions. The quantified framework reveals that most of the Pu(V) is speciated as free Pu(V)O(2) (+) ((aq)), which is a problem if the concentration of free Pu(V)O(2) (+) is ≥28?μM (the half-maximum toxicity value for bacteria able to reduce Pu(V) to Pu(III)PO(4(am))) or ≥250?μM (the full-toxicity value that takes the bioreduction rate to zero). The framework includes bioreduction of Fe(3+) to Fe(2+), which abiotically reduces Pu(V)O(2) (+) to Pu(IV) and then to Pu(III). Biotic (enzymatic) reduction of Pu(V)O(2) (+) directly to Pu(III) by Shewanella alga (S. alga) is also included in the framework. Modeling results also reveal that for formation of Pu(III)PO(4(am)), the desired immobile product, the concentration of coexisting model strong ligand-nitrilotriacetic acid (NTA)-should be less than or equal to the concentration of total Pu(III).     

A life scientist's gateway to distributed data management and computing: the PathPort/ToolBus framework

The emergent needs of the bioinformatics community challenge current information systems. The pace of biological data generation far outstrips Moore's Law. Therefore, a gap continues to widen between the capabilities to produce biological (molecular and cell) data sets and the capability to manage and analyze these data sets. As a result, Federal investments in large data set generation produces diminishing returns in terms of the community's capabilities of understanding biology and leveraging that understanding to make scientific and technological advances that improve society. We are building an open framework to address various data management issues including data and tool interoperability, nomenclature and data communication standardization, and database integration. PathPort, short for Pathogen Portal, employs a generic, web-services based framework to deal with some of the problems identified by the bioinformatics community. The motivating research goal of a scalable system to provide data management and analysis for key pathosystems, especially relating to molecular data, has resulted in a generic framework using two major components. On the server-side, we employ web-services. On the client-side, a Java application called ToolBus acts as a client-side "bus" for contacting data and tools and viewing results through a single, consistent user interface.     

A conceptual framework for the spatial analysis of landscape genetic data

Understanding how landscape heterogeneity constrains gene flow and the spread of adaptive genetic variation is important for biological conservation given current global change. However, the integration of population genetics, landscape ecology and spatial statistics remains an interdisciplinary challenge at the levels of concepts and methods. We present a conceptual framework to relate the spatial distribution of genetic variation to the processes of gene flow and adaptation as regulated by spatial heterogeneity of the environment, while explicitly considering the spatial and temporal dynamics of landscapes, organisms and their genes. When selecting the appropriate analytical methods, it is necessary to consider the effects of multiple processes and the nature of population genetic data. Our framework relates key landscape genetics questions to four levels of analysis: (i) node-based methods, which model the spatial distribution of alleles at sampling locations (nodes) from local site characteristics; these methods are suitable for modeling adaptive genetic variation while accounting for the presence of spatial autocorrelation. (ii) Link-based methods, which model the probability of gene flow between two patches (link) and relate neutral molecular marker data to landscape heterogeneity; these methods are suitable for modeling neutral genetic variation but are subject to inferential problems, which may be alleviated by reducing links based on a network model of the population. (iii) Neighborhood-based methods, which model the connectivity of a focal patch with all other patches in its local neighborhood; these methods provide a link to metapopulation theory and landscape connectivity modeling and may allow the integration of node- and link-based information, but applications in landscape genetics are still limited. (iv) Boundary-based methods, which delineate genetically homogeneous populations and infer the location of genetic boundaries; these methods are suitable for testing for barrier effects of landscape features in a hypothesis-testing framework. We conclude that the power to detect the effect of landscape heterogeneity on the spatial distribution of genetic variation can be increased by explicit consideration of underlying assumptions and choice of an appropriate analytical approach depending on the research question.     

A practical framework for ethics assessment in wildlife management decision-making

Wildlife professionals lack a framework and process for incorporating ethical considerations in a systematic and transparent way, along with ecological and social science, to support wildlife management decision-making. We provide such a framework and process based on 3 of the major theoretical branches ethicists have developed in Western culture: consequentialist moral theory, which focuses on consequences and outcomes; principle- and rule-based approaches that deal with what is considered right or wrong; and virtue ethical theory, which considers factors such as character, virtue, and aesthetics. The framework can be used to anticipate the ethical consequences of alternative courses of action or taking no action. If wildlife professionals use this framework as an assessment tool to provide input into decision-making, resulting decisions will be more transparent, better understood by stakeholders, and more consistent with public trust responsibilities.