A distributed computing framework for all-to-all comparison problems

Distributed computation and storage have been widely used for processing of big data sets. For many big data problems, with the size of data growing rapidly, the distribution of computing tasks and related data can affect the performance of the computing system greatly. In this paper, a distributed computing framework is presented for high performance computing of All-to-All Comparison Problems. A data distribution strategy is embedded in the framework for reduced storage space and balanced computing load. Experiments are conducted to demonstrate the effectiveness of the developed approach. They have shown that about 88% of the ideal performance capacity have be achieved in multiple machines through using the approach presented in this paper.

Modelling the intention to adopt cloud computing services : a transaction cost theory perspective

This paper uses transaction cost theory to study cloud computing adoption. A model is developed and tested with data from an Australian survey. According to the results, perceived vendor opportunism and perceived legislative uncertainty around cloud computing were significantly associated with perceived cloud computing security risk. There was also a significant negative relationship between perceived cloud computing security risk and the intention to adopt cloud services. This study also reports on adoption rates of cloud computing in terms of applications, as well as the types of services used.

Choosing an appropriate modelling framework for analysing multispecies co-culture cell biology experiments

In vitro cell biology assays play a crucial role in informing our understanding of the migratory, proliferative and invasive properties of many cell types in different biological contexts. While mono-culture assays involve the study of a population of cells composed of a single cell type, co-culture assays study a population of cells composed of multiple cell types (or subpopulations of cells). Such co-culture assays can provide more realistic insights into many biological processes including tissue repair, tissue regeneration and malignant spreading. Typically, system parameters, such as motility and proliferation rates, are estimated by calibrating a mathematical or computational model to the observed experimental data. However, parameter estimates can be highly sensitive to the choice of model and modelling framework. This observation motivates us to consider the fundamental question of how we can best choose a model to facilitate accurate parameter estimation for a particular assay. In this work we describe three mathematical models of mono-culture and co-culture assays that include different levels of spatial detail. We study various spatial summary statistics to explore if they can be used to distinguish between the suitability of each model over a range of parameter space. Our results for mono-culture experiments are promising, in that we suggest two spatial statistics that can be used to direct model choice. However, co-culture experiments are far more challenging: we show that these same spatial statistics which provide useful insight into mono-culture systems are insuffcient for co-culture systems. Therefore, we conclude that great care ought to be exercised when estimating the parameters of co-culture assays.

Building energy simulation and parallel computing : opportunities and challenges

Increased focus on energy cost savings and carbon footprint reduction efforts improved the visibility of building energy simulation, which became a mandatory requirement of several building rating systems. Despite developments in building energy simulation algorithms and user interfaces, there are some major challenges associated with building energy simulation; an important one is the computational demands and processing time. In this paper, we analyze the opportunities and challenges associated with this topic while executing a set of 275 parametric energy models simultaneously in EnergyPlus using a High Performance Computing (HPC) cluster. Successful parallel computing implementation of building energy simulations will not only improve the time necessary to get the results and enable scenario development for different design considerations, but also might enable Dynamic-Building Information Modeling (BIM) integration and near real-time decision-making. This paper concludes with the discussions on future directions and opportunities associated with building energy modeling simulations.

The biology of an isolated population of American Flamingo Phoenicopterus ruber in the Galapagos Islands

A genetically and morphologically divergent population of c.500 American Flamingos, isolated from the parental Caribbean stock of Phoenicopterus ruber, occurs in the Galapagos archipelago. Based primarily on data from a 3-year study, we provide the first description of the feeding and breeding biology of this population. Galapagos provides a suitable habitat comprising lagoons on a number of islands, among which the flamingos travel in response to food and nest site availability. The occurrence and qualnity of some food species was associated with the chlorosity of lagoon water, as was the distribution of flamingos. They bred opportunistically at five lagoons on four islands, sometimes simultaneously on more than one island. Group display usually involved approx 20 birds and colonies contained as few as three nests. Laying occurred during nine months of the year... We review potential dangers to this unique population and suggest conservation measures.

Comparative genomics of koala, cattle and sheep strains of Chlamydia pecorum

Background Chlamydia pecorum is an important pathogen of domesticated livestock including sheep, cattle and pigs. This pathogen is also a key factor in the decline of the koala in Australia. We sequenced the genomes of three koala C. pecorum strains, isolated from the urogenital tracts and conjunctiva of diseased koalas. The genome of the C. pecorum VR629 (IPA) strain, isolated from a sheep with polyarthritis, was also sequenced. Results Comparisons of the draft C. pecorum genomes against the complete genomes of livestock C. pecorum isolates revealed that these strains have a conserved gene content and order, sharing a nucleotide sequence similarity > 98%. Single nucleotide polymorphisms (SNPs) appear to be key factors in understanding the adaptive process. Two regions of the chromosome were found to be accumulating a large number of SNPs within the koala strains. These regions include the Chlamydia plasticity zone, which contains two cytotoxin genes (toxA and toxB), and a 77 kbp region that codes for putative type III effector proteins. In one koala strain (MC/MarsBar), the toxB gene was truncated by a premature stop codon but is full-length in IPTaLE and DBDeUG. Another five pseudogenes were also identified, two unique to the urogenital strains C. pecorum MC/MarsBar and C. pecorum DBDeUG, respectively, while three were unique to the koala C. pecorum conjunctival isolate IPTaLE. An examination of the distribution of these pseudogenes in C. pecorum strains from a variety of koala populations, alongside a number of sheep and cattle C. pecorum positive samples from Australian livestock, confirmed the presence of four predicted pseudogenes in koala C. pecorum clinical samples. Consistent with our genomics analyses, none of these pseudogenes were observed in the livestock C. pecorum samples examined. Interestingly, three SNPs resulting in pseudogenes identified in the IPTaLE isolate were not found in any other C. pecorum strain analysed, raising questions over the origin of these point mutations. Conclusions The genomic data revealed that variation between C. pecorum strains were mainly due to the accumulation of SNPs, some of which cause gene inactivation. The identification of these genetic differences will provide the basis for further studies to understand the biology and evolution of this important animal pathogen. Keywords: Chlamydia pecorum; Single nucleotide polymorphism; Pseudogene; Cytotoxin

What patient information allows us to make accurate predictions of outcome?

Only some of the information contained in a medical record will be useful to the prediction of patient outcome. We describe a novel method for selecting those outcome predictors which allow us to reliably discriminate between adverse and benign end results. Using the area under the receiver operating characteristic as a nonparametric measure of discrimination, we show how to calculate the maximum discrimination attainable with a given set of discrete valued features. This upper limit forms the basis of our feature selection algorithm. We use the algorithm to select features (from maternity records) relevant to the prediction of failure to progress in labour. The results of this analysis motivate investigation of those predictors of failure to progress relevant to parous and nulliparous sub-populations.

Biology Aotearoa : Unique Flora, Fauna and Fungi

As a large, isolated and relatively ancient landmass, New Zealand occupies a unique place in the biological world, with distinctive terrestrial biota and a high proportion of primitive endemic forms. Biology Aotearoa covers the origins, evolution and conservation of the New Zealand flora, fauna and fungi. Each chapter is written by specialists in the field, often working from different perspectives to build up a comprehensive picture. Topics include: the geological history of our land origins, and evolution of our plants, animals and fungi current status of rare and threatened species past, present and future management of native species the effect of human immigration on the native biota. Colour diagrams and photographs are used throughout the text. This book is suitable for all students of biology or ecology who wish to know about the unique nature of Aotearoa New Zealand and its context in the biological world.

Model refinement through high-performance computing: an agent-based HIV example

Background Recent advances in Immunology highlighted the importance of local properties on the overall progression of HIV infection. In particular, the gastrointestinal tract is seen as a key area during early infection, and the massive cell depletion associated with it may influence subsequent disease progression. This motivated the development of a large-scale agent-based model. Results Lymph nodes are explicitly implemented, and considerations on parallel computing permit large simulations and the inclusion of local features. The results obtained show that GI tract inclusion in the model leads to an accelerated disease progression, during both the early stages and the long-term evolution, compared to a theoretical, uniform model. Conclusions These results confirm the potential of treatment policies currently under investigation, which focus on this region. They also highlight the potential of this modelling framework, incorporating both agent-based and network-based components, in the context of complex systems where scaling-up alone does not result in models providing additional insights.

High-performance computing for data analytics

One of the main challenges in data analytics is that discovering structures and patterns in complex datasets is a computer-intensive task. Recent advances in high-performance computing provide part of the solution. Multicore systems are now more affordable and more accessible. In this paper, we investigate how this can be used to develop more advanced methods for data analytics. We focus on two specific areas: model-driven analysis and data mining using optimisation techniques.

Performance evaluation of cloud-based parallel computing

As computational models in fields such as medicine and engineering get more refined, resource requirements are increased. In a first instance, these needs have been satisfied using parallel computing and HPC clusters. However, such systems are often costly and lack flexibility. HPC users are therefore tempted to move to elastic HPC using cloud services. One difficulty in making this transition is that HPC and cloud systems are different, and performance may vary. The purpose of this study is to evaluate cloud services as a means to minimise both cost and computation time for large-scale simulations, and to identify which system properties have the most significant impact on performance. Our simulation results show that, while the performance of Virtual CPU (VCPU) is satisfactory, network throughput may lead to difficulties.

Transdisciplinary approaches to urban computing

The research field of urban computing – defined as “the integration of computing, sensing, and actuation technologies into everyday urban settings and lifestyles” – considers the design and use of ubiquitous computing technology in public and shared urban environments. Its impact on cities, buildings, and spaces evokes innumerable kinds of change. Embedded into our everyday lived environments, urban computing technologies have the potential to alter the meaning of physical space, and affect the activities performed in those spaces. This paper starts a multi-themed discussion of various aspects that make up the, at times, messy and certainly transdisciplinary field of urban computing and urban informatics.