Chronic disease risks and use of a smartphone application during a physical activity and dietary intervention in Australian truck drivers

- Objective This study examined chronic disease risks and the use of a smartphone activity tracking application during an intervention in Australian truck drivers (April-October 2014). - Methods Forty-four men (mean age=47.5 [SD 9.8] years) completed baseline health measures, and were subsequently offered access to a free wrist-worn activity tracker and smartphone application (Jawbone UP) to monitor step counts and dietary choices during a 20-week intervention. Chronic disease risks were evaluated against guidelines; weekly step count and dietary logs registered by drivers in the application were analysed to evaluate use of the Jawbone UP. - Results Chronic disease risks were high (e.g. 97% high waist circumference [≥94 cm]). Eighteen drivers (41%) did not start the intervention; smartphone technical barriers were the main reason for drop out. Across 20-weeks, drivers who used the Jawbone UP logged step counts for an average of 6 [SD 1] days/week; mean step counts remained consistent across the intervention (weeks 1–4=8,743[SD 2,867] steps/day; weeks 17–20=8,994[SD 3,478] steps/day). The median number of dietary logs significantly decreased from start (17 [IQR 38] logs/weeks) to end of the intervention (0 [IQR 23] logs/week; p<0.01); the median proportion of healthy diet choices relative to total diet choices logged increased across the intervention (weeks 1–4=38[IQR 21]%; weeks 17–20=58[IQR 18]%). - Conclusions Step counts were more successfully monitored than dietary choices in those drivers who used the Jawbone UP. - Implications Smartphone technology facilitated active living and healthy dietary choices, but also prohibited intervention engagement in a number of these high-risk Australian truck drivers.

Analysing the Impact of Learning Inputs - Application to Terrain Traversability Estimation

Data-driven approaches such as Gaussian Process (GP) regression have been used extensively in recent robotics literature to achieve estimation by learning from experience. To ensure satisfactory performance, in most cases, multiple learning inputs are required. Intuitively, adding new inputs can often contribute to better estimation accuracy, however, it may come at the cost of a new sensor, larger training dataset and/or more complex learning, some- times for limited benefits. Therefore, it is crucial to have a systematic procedure to determine the actual impact each input has on the estimation performance. To address this issue, in this paper we propose to analyse the impact of each input on the estimate using a variance-based sensitivity analysis method. We propose an approach built on Analysis of Variance (ANOVA) decomposition, which can characterise how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We apply the proposed approach to a terrain-traversability estimation method we proposed in prior work, which is based on multi-task GP regression, and we validate this implementation experimentally using a rover on a Mars-analogue terrain.

Location and scale mixtures of Gaussians with flexible tail behaviour: Properties, inference and application to multivariate clustering

The family of location and scale mixtures of Gaussians has the ability to generate a number of flexible distributional forms. The family nests as particular cases several important asymmetric distributions like the Generalized Hyperbolic distribution. The Generalized Hyperbolic distribution in turn nests many other well known distributions such as the Normal Inverse Gaussian. In a multivariate setting, an extension of the standard location and scale mixture concept is proposed into a so called multiple scaled framework which has the advantage of allowing different tail and skewness behaviours in each dimension with arbitrary correlation between dimensions. Estimation of the parameters is provided via an EM algorithm and extended to cover the case of mixtures of such multiple scaled distributions for application to clustering. Assessments on simulated and real data confirm the gain in degrees of freedom and flexibility in modelling data of varying tail behaviour and directional shape.

A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: Application to robust clustering

We propose a family of multivariate heavy-tailed distributions that allow variable marginal amounts of tailweight. The originality comes from introducing multidimensional instead of univariate scale variables for the mixture of scaled Gaussian family of distributions. In contrast to most existing approaches, the derived distributions can account for a variety of shapes and have a simple tractable form with a closed-form probability density function whatever the dimension. We examine a number of properties of these distributions and illustrate them in the particular case of Pearson type VII and t tails. For these latter cases, we provide maximum likelihood estimation of the parameters and illustrate their modelling flexibility on simulated and real data clustering examples.

Enrichment and identification of glycoproteins in human saliva using lectin magnetic bead arrays

Aberrant glycosylation of proteins is a hallmark of tumorigenesis, and could provide diagnostic value in cancer detection. Human saliva is an ideal source of glycoproteins due to the relatively high proportion of glycosylated proteins in the salivary proteome. Moreover, saliva collection is non-invasive, technically straightforward and the sample collection and storage is relatively easy. Although, differential glycosylation of proteins can be indicative of disease states, identification of differential glycosylation from clinical samples is not trivial. To facilitate salivary glycoprotein biomarker discovery, we optimised a method for differential glycoprotein enrichment from human saliva based on lectin magnetic bead arrays (saLeMBA). Selected lectins from distinct reactivity groups were used in the saLeMBA platform to enrich salivary glycoproteins from healthy volunteer saliva. The technical reproducibility of saLeMBA was analysed with LC-MS/MS to identify the glycosylated proteins enriched by each lectin. Our saLeMBA platform enabled robust glycoprotein enrichment in a glycoprotein- and lectin-specific manner consistent with known protein-specific glycan profiles. We demonstrated that saLeMBA is a reliable method to enrich and detect glycoproteins present in human saliva.

A note on sampling chironomids for RNA-based studies of natural populations that retains critical morphological vouchers

The rapid uptake of transcriptomic approaches in freshwater ecology has seen a wealth of data produced concerning the ways in which organisms interact with their environment on a molecular level. Typically, such studies focus either at the community level and so don’t require species identifications, or on laboratory strains of known species identity or natural populations of large, easily identifiable taxa. For chironomids, impediments still exist for applying these technologies to natural populations because they are small-bodied and often require time-consuming secondary sorting of stream material and morphological voucher preparation to confirm species diagnosis. These procedures limit the ability to maintain RNA quantity and quality in such organisms because RNA degrades rapidly and gene expression can be altered rapidly in organisms; thereby limiting the inclusion of such taxa in transcriptomic studies. Here, we demonstrate that these limitations can be overcome and outline an optimised protocol for collecting, sorting and preserving chironomid larvae that enables retention of both morphological vouchers and RNA for subsequent transcriptomics purposes. By ensuring that sorting and voucher preparation are completed within <4 hours after collection and that samples are kept cold at all times, we successfully retained both RNA and morphological vouchers from all specimens. Although not prescriptive in specific methodology, we anticipate that this paper will assist in promoting transcriptomic investigations of the sublethal impact on chironomid gene expression of changes to aquatic environments.

Nonradial directional performance measurement with undesirable outputs: An application to OECD and Non-OECD countries

The objective of this paper is to provide a more comprehensive e±ciency measure to estimate the performance of OECD and non-OECD countries. A Russell directional distance function that appropriately credits the decision-making unit not only for increase in desirable outputs but also for the decrease of undesirable outputs is derived from the proposed weighted Russell directional distance model. The method was applied to a panel of 116 countries from 1992 to 2010. This framework also decomposes the comprehensive efficiency measure into individual input/ output components' inefficiency scores that are useful for policy making. The results reveal that the OECD countries perform better than the non-OECD countries in overall, goods,labor and capital efficiencies, but worse in bad and energy efficiencies.

The PerCues framework and its application for sustainable mobility

This paper presents a framework, design and study of an ambient persuasive interface. We introduce a novel framework of persua sive Cues in Ambient Intelligence (perCues). Based on this framework we designed an application for mobile devices. The application aims to persuade people to abstain from using their cars and to use public mass transportation instead in order to reduce emissions. It contains a bus schedule and information about the pollution status. We evaluated the application in two successive studies regarding user acceptance, oppor tune moments of use and persuasive effects. The perCues received a high acceptance due to its benefit for the users. The results confirm the im portance of opportune moment and user acceptance for persuasion. The findings also indicate the persuasive potential of perCues.

Optimisation of in-cell accelerated solvent extraction technique for the determination of organochlorine pesticides in river sediments

Organochlorine pesticides (OCPs) are ubiquitous environmental contaminants with adverse impacts on aquatic biota, wildlife and human health even at low concentrations. However, conventional methods for their determination in river sediments are resource intensive. This paper presents an approach that is rapid and also reliable for the detection of OCPs. Accelerated Solvent Extraction (ASE) with in-cell silica gel clean-up followed by Triple Quadrupole Gas Chromatograph Mass Spectrometry (GCMS/MS) was used to recover OCPs from sediment samples. Variables such as temperature, solvent ratio, adsorbent mass and extraction cycle were evaluated and optimised for the extraction. With the exception of Aldrin, which was unaffected by any of the variables evaluated, the recovery of OCPs from sediment samples was largely influenced by solvent ratio and adsorbent mass and, to some extent, the number of cycles and temperature. The optimised conditions for OCPs extraction in sediment with good recoveries were determined to be 4 cycles, 4.5 g of silica gel, 105 ᴼC, and 4:3 v/v DCM: hexane mixture. With the exception of two compounds (α-BHC and Aldrin) whose recoveries were low (59.73 and 47.66 % respectively), the recovery of the other pesticides were in the range 85.35 – 117.97% with precision < 10 % RSD. The method developed significantly reduces sample preparation time, the amount of solvent used, matrix interference, and is highly sensitive and selective.

Quarantine and surveillance strategies for plant pathogen detection and control

Incursions of plant pests and diseases pose serious threats to food security, agricultural productivity and the natural environment. One of the challenges in confidently delimiting and eradicating incursions is how to choose from an arsenal of surveillance and quarantine approaches in order to best control multiple dispersal pathways. Anthropogenic spread (propagules carried on humans or transported on produce or equipment) can be controlled with quarantine measures, which in turn can vary in intensity. In contrast, environmental spread processes are more difficult to control, but often have a temporal signal (e.g. seasonality) which can introduce both challenges and opportunities for surveillance and control. This leads to complex decisions regarding when, where and how to search. Recent modelling investigations of surveillance performance have optimised the output of simulation models, and found that a risk-weighted randomised search can perform close to optimally. However, exactly how quarantine and surveillance strategies should change to reflect different dispersal modes remains largely unaddressed. Here we develop a spatial simulation model of a plant fungal-pathogen incursion into an agricultural region, and its subsequent surveillance and control. We include structural differences in dispersal via the interplay of biological, environmental and anthropogenic connectivity between host sites (farms). Our objective was to gain broad insights into the relative roles played by different spread modes in propagating an invasion, and how incorporating knowledge of these spread risks may improve approaches to quarantine restrictions and surveillance. We find that broad heuristic rules for quarantine restrictions fail to contain the pathogen due to residual connectivity between sites, but surveillance measures enable early detection and successfully lead to suppression of the pathogen in all farms. Alternative surveillance strategies attain similar levels of performance by incorporating environmental or anthropogenic dispersal risk in the prioritisation of sites. Our model provides the basis to develop essential insights into the effectiveness of different surveillance and quarantine decisions for fungal pathogen control. Parameterised for authentic settings it will aid our understanding of how the extent and resolution of interventions should suitably reflect the spatial structure of dispersal processes.

Reputation model based on rating data and application in recommender systems

This thesis introduced two novel reputation models to generate accurate item reputation scores using ratings data and the statistics of the dataset. It also presented an innovative method that incorporates reputation awareness in recommender systems by employing voting system methods to produce more accurate top-N item recommendations. Additionally, this thesis introduced a personalisation method for generating reputation scores based on users' interests, where a single item can have different reputation scores for different users. The personalised reputation scores are then used in the proposed reputation-aware recommender systems to enhance the recommendation quality.

Annual nitrogen dynamics and urea fertilizer recoveries from a dairy pasture using 15N; effect of nitrification inhibitor DMPP and reduced application rates

Direct nitrogen (N) losses from pastures contribute to the poor nitrogen use efficiency of the dairy industry, though the exact fate of applied N and the processes involved are largely unknown. Nitrification inhibitors such as DMPP can potentially increase fertilizer N use efficiency (NUE), though few studies globally have examined the effectiveness of DMPP coated urea in pastures. This study quantified the NUE of DMPP combined with reduced application rates, and the effect on N dynamics and plant–soil interactions over an annual ryegrass/kikuyu rotation in Queensland, Australia. Labeled 15N urea and DMPP was applied over 7 winter applications at standard farmer (45 kg N ha−1) and half (23 kg N ha−1) rates. Fertilizer recoveries and NUE were calculated over 13 harvests, and the contribution of fertilizer and soil N estimated. Up to 85% of the annual N harvested was from soil organic matter. DMPP at the lower rate increased annual yields by 31% compared to the equivalent urea treatment with no difference to the high N rates. Almost 40% of the N added at the conventional fertilizer application rate as urea was lost to the environment; 80 kg N ha−1 higher than the low DMPP. Combining the nitrification inhibitor DMPP with reduced fertilizer application rates shows substantial potential to reduce N losses to the environment while sustaining productivity in subtropical dairy pastures.

A distributed services based conference planner application using software agents, grid services and web services

This demonstration highlights the applications of our research work i.e. second generation (Scalable Fault Tolerant Agent Grooming Environment - SAGE) Multi Agent System, Integration of Software Agents and Grid Computing and Autonomous Agent Architecture in the Agent Platform. It is a conference planner application that uses collaborative effort of services deployed geographically wide in different technologies i.e. Software Agents, Grid computing and Web services to perform useful tasks as required. Copyright 2005 ACM.

A collaborative multi-agent based conference planner application using web and grid services

Distributed Collaborative Computing services have taken over centralized computing platforms allowing the development of distributed collaborative user applications. These applications enable people and computers to work together more productively. Multi-Agent System (MAS) has emerged as a distributed collaborative environment which allows a number of agents to cooperate and interact with each other in a complex environment. We want to place our agents in problems whose solutions require the collation and fusion of information, knowledge or data from distributed and autonomous information sources. In this paper we present the design and implementation of an agent based conference planner application that uses collaborative effort of agents which function continuously and autonomously in a particular environment. The application also enables the collaborative use of services deployed geographically wide in different technologies i.e. Software Agents, Grid computing and Web service. The premise of the application is that it allows autonomous agents interacting with web and grid services to plan a conference as a proxy to their owners (humans). © 2005 IEEE.

Managing uncertainty to improve the cost performance of complex infrastructure projects

There has been a recent spate of high profile infrastructure cost overruns in Australia and internationally. This is just the tip of a longer-term and more deeply-seated problem with initial budget estimating practice, well recognised in both academic research and industry reviews: the problem of uncertainty. A case study of the Sydney Opera House is used to identify and illustrate the key causal factors and system dynamics of cost overruns. It is conventionally the role of risk management to deal with such uncertainty, but the type and extent of the uncertainty involved in complex projects is shown to render established risk management techniques ineffective. This paper considers a radical advance on current budget estimating practice which involves a particular approach to statistical modelling complemented by explicit training in estimating practice. The statistical modelling approach combines the probability management techniques of Savage, which operate on actual distributions of values rather than flawed representations of distributions, and the data pooling technique of Skitmore, where the size of the reference set is optimised. Estimating training employs particular calibration development methods pioneered by Hubbard, which reduce the bias of experts caused by over-confidence and improve the consistency of subjective decision-making. A new framework for initial budget estimating practice is developed based on the combined statistical and training methods, with each technique being explained and discussed.