Assessment of the effects of maternal exposure to heatwave on birth outcomes in Brisbane, Australia

Even though heatwave events have become more frequent and intense in most regions around the world, little is known about the impact of heatwave on birth outcomes. This thesis uses a population-based study design to investigate the relationship between maternal heatwave exposure and adverse birth outcomes in Brisbane, Australia. This study found that heatwave exposure at any stage of pregnancy can be harmful to fetal growth, and further increase the risk of adverse birth outcomes. Both short- and long-term effects of heatwave on adverse birth outcomes were found. The findings in this thesis may have significant public health implications.

Approximate Bayesian Computation for astronomical model analysis : a case study in galaxy demographics and morphological transformation at high redshift

Approximate Bayesian Computation’ (ABC) represents a powerful methodology for the analysis of complex stochastic systems for which the likelihood of the observed data under an arbitrary set of input parameters may be entirely intractable – the latter condition rendering useless the standard machinery of tractable likelihood-based, Bayesian statistical inference [e.g. conventional Markov chain Monte Carlo (MCMC) simulation]. In this paper, we demonstrate the potential of ABC for astronomical model analysis by application to a case study in the morphological transformation of high-redshift galaxies. To this end, we develop, first, a stochastic model for the competing processes of merging and secular evolution in the early Universe, and secondly, through an ABC-based comparison against the observed demographics of massive (Mgal > 1011 M⊙) galaxies (at 1.5 < z < 3) in the Cosmic Assembly Near-IR Deep Extragalatic Legacy Survey (CANDELS)/Extended Groth Strip (EGS) data set we derive posterior probability densities for the key parameters of this model. The ‘Sequential Monte Carlo’ implementation of ABC exhibited herein, featuring both a self-generating target sequence and self-refining MCMC kernel, is amongst the most efficient of contemporary approaches to this important statistical algorithm. We highlight as well through our chosen case study the value of careful summary statistic selection, and demonstrate two modern strategies for assessment and optimization in this regard. Ultimately, our ABC analysis of the high-redshift morphological mix returns tight constraints on the evolving merger rate in the early Universe and favours major merging (with disc survival or rapid reformation) over secular evolution as the mechanism most responsible for building up the first generation of bulges in early-type discs.

Species differences in the glutathione transferase GSTT1-1 activity towards the model substrates methyl chloride and dichloromethane in liver and kidney

Glutathione transferase (GST) GSTT1-1 is involved in the biotransformation of several chemicals widely used in industry, such as butadiene and dichloro methane DCM. The polymorphic hGSTT1-1 may well play a role in the development of kidney tumours after high and long-term occupational exposure against trichloroethylene. Although several studies have investigated the association of this polymorphism with malignant diseases little is known about its enzyme activity in potential extrahepatic target tissues. The known theta-specific substrates methyl chloride (MC) dichloromethane and 1,2-epoxy-3-(p-nitrophenoxy)propane (EPNP) were used to assay GSTT1-1 activity in liver and kidney of rats, mice, hamsters and humans differentiating the three phenotypes (non-conjugators, low conjugators, high conjugators) seen in humans. In addition GSTT1-1 activity towards MC and DCM was determined in human erythrocytes. No GSTT1-1 activity was found in any tissue of non-conjugators (NC). In all organs high conjugators (HC) showed twofold higher activity towards MC and DCM than low conjugators (LC). The activity in human samples towards EPNP was too close to the detection limit to differentiate between the three conjugator phenotypes. GSTT1-1 activity towards MC was two to seven-times higher in liver cytosol than in kidney cytosol. The relation for MC between species was identical in both organs: mouse > HC > rat > LC > hamster > NC. In rats, mice and hamsters GSTT1-1 activity in liver cytosol towards DCM was also two to seven-times higher than in the kidney cytosol. In humans this activity was twice as high in kidney cytosol than in liver cytosol. The relation between species was mouse > rat > HC > LC > hamster > NC for liver, but mouse > HC > LC/rat > hamster/NC for kidney cytosol. The importance to heed the specific environment at potential target sites in risk assessment is emphasized by these results.

Simultaneous underwater visibility assessment, enhancement and improved stereo

Vision-based underwater navigation and obstacle avoidance demands robust computer vision algorithms, particularly for operation in turbid water with reduced visibility. This paper describes a novel method for the simultaneous underwater image quality assessment, visibility enhancement and disparity computation to increase stereo range resolution under dynamic, natural lighting and turbid conditions. The technique estimates the visibility properties from a sparse 3D map of the original degraded image using a physical underwater light attenuation model. Firstly, an iterated distance-adaptive image contrast enhancement enables a dense disparity computation and visibility estimation. Secondly, using a light attenuation model for ocean water, a color corrected stereo underwater image is obtained along with a visibility distance estimate. Experimental results in shallow, naturally lit, high-turbidity coastal environments show the proposed technique improves range estimation over the original images as well as image quality and color for habitat classification. Furthermore, the recursiveness and robustness of the technique allows implementation onboard an Autonomous Underwater Vehicle for improving navigation and obstacle avoidance performance.

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods which have been recently employed to analyse PNSD data, however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K-means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K-means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K-means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectra to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

Development and parameterization of a rain- and fire-driven model for exploring elephant effects in African savannas

We describe the development and parameterization of a grid-based model of African savanna vegetation processes. The model was developed with the objective of exploring elephant effects on the diversity of savanna species and structure, and in this formulation concentrates on the relative cover of grass and woody plants, the vertical structure of the woody plant community, and the distribution of these over space. Grid cells are linked by seed dispersal and fire, and environmental variability is included in the form of stochastic rainfall and fire events. The model was parameterized from an extensive review of the African savanna literature; when available, parameter values varied widely. The most plausible set of parameters produced long-term coexistence between woody plants and grass, with the tree-grass balance being more sensitive to changes in parameters influencing demographic processes and drought incidence and response, while less sensitive to fire regime. There was considerable diversity in the woody structure of savanna systems within the range of uncertainty in tree growth rate parameters. Thus, given the paucity of height growth data regarding woody plant species in southern African savannas, managers of natural areas should be cognizant of different tree species growth and damage response attributes when considering whether to act on perceived elephant threats to vegetation. © 2007 Springer Science+Business Media B.V.

Real-time assessment of GNSS observation noise with single receivers

Stochastic modelling is critical in GNSS data processing. Currently, GNSS data processing commonly relies on the empirical stochastic model which may not reflect the actual data quality or noise characteristics. This paper examines the real-time GNSS observation noise estimation methods enabling to determine the observation variance from single receiver data stream. The methods involve three steps: forming linear combination, handling the ionosphere and ambiguity bias and variance estimation. Two distinguished ways are applied to overcome the ionosphere and ambiguity biases, known as the time differenced method and polynomial prediction method respectively. The real time variance estimation methods are compared with the zero-baseline and short-baseline methods. The proposed method only requires single receiver observation, thus applicable to both differenced and un-differenced data processing modes. However, the methods may be subject to the normal ionosphere conditions and low autocorrelation GNSS receivers. Experimental results also indicate the proposed method can result on more realistic parameter precision.

A comparative study of prognostic model for bearing failure

An effective prognostics program will provide ample lead time for maintenance engineers to schedule a repair and to acquire replacement components before catastrophic failures occur. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique. For comparative study of the proposed model with the proportional hazard model (PHM), experimental bearing failure data from an accelerated bearing test rig were used. The result shows that the proposed prognostic model based on health state probability estimation can provide a more accurate prediction capability than the commonly used PHM in bearing failure case study.

Non-destructive structural integrity assessment of a decommissioned rail wagon system

This paper presents a multi-criteria based approach for nondestructive diagnostic structural integrity assessment of a decommissioned flatbed rail wagon (FBRW) used for road bridge superstructure rehabilitation and replacement applications. First, full-scale vibration and static test data sets are employed in a FE model of the FBRW to obtain the best ‘initial’ estimate of the model parameters. Second, the ‘final’ model parameters are predicted using sensitivity-based perturbation analysis without significant difficulties encountered. Consequently, the updated FBRW model is validated using the independent sets of full-scale laboratory static test data. Finally, the updated and validated FE model of the FBRW is used for structural integrity assessment of a single lane FBRW bridge subjected to the Australian bridge design traffic load.

Novel loci affecting iron homeostasis and their effects in individuals at risk for hemochromatosis

Variation in body iron is associated with or causes diseases, including anaemia and iron overload. Here, we analyse genetic association data on biochemical markers of iron status from 11 European-population studies, with replication in eight additional cohorts (total up to 48,972 subjects). We find 11 genome-wide-significant (P<5 × 10−8) loci, some including known iron-related genes (​HFE, ​SLC40A1, ​TF, ​TFR2, ​TFRC, ​TMPRSS6) and others novel (​ABO, ​ARNTL, ​FADS2, ​NAT2, ​TEX14). SNPs at ​ARNTL, ​TF, and ​TFR2 affect iron markers in ​HFE C282Y homozygotes at risk for hemochromatosis. There is substantial overlap between our iron loci and loci affecting erythrocyte and lipid phenotypes. These results will facilitate investigation of the roles of iron in disease.

Ergonomic assessment of hospital bed moving using DHM Siemens JACK

While the indirect and direct cost of occupational musculoskeletal disorders (MSD) causes a significant burden on the health system, lower back pain (LBP) is associated with a significant portion of MSD. In Australia, the highest prevalence of MSD exists for health care workers, such as nurses. The digital human model (DHM) Siemens JACK was used to investigate if hospital bed pushing, a simple task and hazard that is commonly associated with LBP, can be simulated and ergonomically assessed in a virtual environment. It was found that while JACK has implemented a range of common physical work assessment methods, the simulation of dynamic bed pushing remains a challenge due to the complex interface between the floor and wheels, which can only be insufficiently modelle

Deathbed instructions and the assessment of capacity: A practical perspective

The assessment of capacity in the context of wills and advance care planning is a challenging task for modern practitioners with the increase in mentally disabling conditions, such as acquired brain injury. This challenge is only heightened in the situation where the assessment occurs at the client’s deathbed as there are the added elements of urgency, and emotional upheaval at the impending death of a loved one. This situation poses a minefield of problems a legal practitioner should be aware of, involving not only the assessment process but also potential professional liability issues. This presentation will address the practical considerations a legal professional should take into account when in this situation. We will focus on two discrete situations and the issues arising from them: first, where there has been an acquired brain injury and legal capacity is in question; and second, where a spinal injury has occurred which does not affect capacity, rather results in the inability to use conventional forms of communication to communicate his/her instructions. We will examine these case studies with a view to proposing a best practice model for legal practitioners when assessing capacity in this context.

A mouse with an N-ethyl-N-nitrosourea (ENU) induced Trp589Arg Galnt3 mutation represents a model for hyperphosphataemic familial tumoural calcinosis

Mutations of UDP-N-acetyl-alpha-D-galactosamine polypeptide N-acetyl galactosaminyl transferase 3 (GALNT3) result in familial tumoural calcinosis (FTC) and the hyperostosis-hyperphosphataemia syndrome (HHS), which are autosomal recessive disorders characterised by soft-tissue calcification and hyperphosphataemia. To facilitate in vivo studies of these heritable disorders of phosphate homeostasis, we embarked on establishing a mouse model by assessing progeny of mice treated with the chemical mutagen N-ethyl-N-nitrosourea (ENU), and identified a mutant mouse, TCAL, with autosomal recessive inheritance of ectopic calcification, which involved multiple tissues, and hyperphosphataemia; the phenotype was designated TCAL and the locus, Tcal. TCAL males were infertile with loss of Sertoli cells and spermatozoa, and increased testicular apoptosis. Genetic mapping localized Tcal to chromosome 2 (62.64-71.11 Mb) which contained the Galnt3. DNA sequence analysis identified a Galnt3 missense mutation (Trp589Arg) in TCAL mice. Transient transfection of wild-type and mutant Galnt3-enhanced green fluorescent protein (EGFP) constructs in COS-7 cells revealed endoplasmic reticulum retention of the Trp589Arg mutant and Western blot analysis of kidney homogenates demonstrated defective glycosylation of Galnt3 in Tcal/Tcal mice. Tcal/Tcal mice had normal plasma calcium and parathyroid hormone concentrations; decreased alkaline phosphatase activity and intact Fgf23 concentrations; and elevation of circulating 1,25-dihydroxyvitamin D. Quantitative reverse transcriptase-PCR (qRT-PCR) revealed that Tcal/Tcal mice had increased expression of Galnt3 and Fgf23 in bone, but that renal expression of Klotho, 25-hydroxyvitamin D-1α-hydroxylase (Cyp27b1), and the sodium-phosphate co-transporters type-IIa and -IIc was similar to that in wild-type mice. Thus, TCAL mice have the phenotypic features of FTC and HHS, and provide a model for these disorders of phosphate metabolism. © 2012 Esapa et al.

Development and validation of a multilevel model for predicting workload under routine and nonroutine conditions in an air traffic management center

Objective: The aim of this study was to develop a model capable of predicting variability in the mental workload experienced by frontline operators under routine and nonroutine conditions. Background: Excess workload is a risk that needs to be managed in safety-critical industries. Predictive models are needed to manage this risk effectively yet are difficult to develop. Much of the difficulty stems from the fact that workload prediction is a multilevel problem. Method: A multilevel workload model was developed in Study 1 with data collected from an en route air traffic management center. Dynamic density metrics were used to predict variability in workload within and between work units while controlling for variability among raters. The model was cross-validated in Studies 2 and 3 with the use of a high-fidelity simulator. Results: Reported workload generally remained within the bounds of the 90% prediction interval in Studies 2 and 3. Workload crossed the upper bound of the prediction interval only under nonroutine conditions. Qualitative analyses suggest that nonroutine events caused workload to cross the upper bound of the prediction interval because the controllers could not manage their workload strategically. Conclusion: The model performed well under both routine and nonroutine conditions and over different patterns of workload variation. Application: Workload prediction models can be used to support both strategic and tactical workload management. Strategic uses include the analysis of historical and projected workflows and the assessment of staffing needs. Tactical uses include the dynamic reallocation of resources to meet changes in demand.

Evaluation of a skin self examination attitude scale using an item response theory model approach

Introduction The Skin Self-Examination Attitude Scale (SSEAS) is a brief measure that allows for the assessment of attitudes in relation to skin self-examination. This study evaluated the psychometric properties of the SSEAS using Item Response Theory (IRT) methods in a large sample of men ≥ 50 years in Queensland, Australia. Methods A sample of 831 men (420 intervention and 411 control) completed a telephone assessment at the 13-month follow-up of a randomized-controlled trial of a video-based intervention to improve skin self-examination (SSE) behaviour. Descriptive statistics (mean, standard deviation, item–total correlations, and Cronbach’s alpha) were compiled and difficulty parameters were computed with Winsteps using the polytomous Rasch Rating Scale Model (RRSM). An item person (Wright) map of the SSEAS was examined for content coverage and item targeting. Results The SSEAS have good psychometric properties including good internal consistency (Cronbach’s alpha = 0.80), fit with the model and no evidence for differential item functioning (DIF) due to experimental trial grouping was detected. Conclusions The present study confirms the SSEA scale as a brief, useful and reliable tool for assessing attitudes towards skin self-examination in a population of men 50 years or older in Queensland, Australia. The 8-item scale shows unidimensionality, allowing levels of SSE attitude, and the item difficulties, to be ranked on a single continuous scale. In terms of clinical practice, it is very important to assess skin cancer self-examination attitude to identify people who may need a more extensive intervention to allow early detection of skin cancer.