Cigarettes will kill you: The High Court of Australia and the plain packaging of tobacco products

Tobacco, says the World Health Organization (WHO), is “the only legal consumer product that kills when used exactly as intended by the manufacturer.” With a view to discouraging smoking and giving effect to the WHO Framework Convention on Tobacco Control, the Australian Parliament passed the Tobacco Plain Packaging Act 2011 (Cth), in November of that year. The legislation was supported by all the major political parties.

Health and guardianship law: Children, consent and the refusal of blood products: A recent Queensland case

This column provides a summary of the recent decision of The Hospital v T [2015] QSC 185

MegaVoltage Cone Beam Computed Tomography with a standard medical linear accelerator

Accurate patient positioning is vital for improved clinical outcomes for cancer treatments using radiotherapy. This project has developed Mega Voltage Cone Beam CT using a standard medical linear accelerator to allow 3D imaging of the patient position at treatment time with no additional hardware required. Providing 3D imaging functionality at no further cost allows enhanced patient position verification on older linear accelerators and in developing countries where access to new technology is limited.

Populations of models, experimental designs and coverage of parameter space by Latin Hypercube and Orthogonal Sampling

In this paper we have used simulations to make a conjecture about the coverage of a t-dimensional subspace of a d-dimensional parameter space of size n when performing k trials of Latin Hypercube sampling. This takes the form P(k,n,d,t) = 1 - e^(-k/n^(t-1)). We suggest that this coverage formula is independent of d and this allows us to make connections between building Populations of Models and Experimental Designs. We also show that Orthogonal sampling is superior to Latin Hypercube sampling in terms of allowing a more uniform coverage of the t-dimensional subspace at the sub-block size level. These ideas have particular relevance when attempting to perform uncertainty quantification and sensitivity analyses.

Serum levels of soluble receptor for advanced glycation end products and of S100 proteins are associated with inflammatory, autoantibody, and classical risk markers of joint and vascular damage in rheumatoid arthritis

Introduction: The receptor for advanced glycation end products (RAGE) is a member of the immunoglobulin superfamily of cell surface receptor molecules. High concentrations of three of its putative proinflammatory ligands, S100A8/A9 complex (calprotectin), S100A8, and S100A12, are found in rheumatoid arthritis (RA) serum and synovial fluid. In contrast, soluble RAGE (sRAGE) may prevent proinflammatory effects by acting as a decoy. This study evaluated the serum levels of S100A9, S100A8, S100A12 and sRAGE in RA patients, to determine their relationship to inflammation and joint and vascular damage. Methods: Serum sRAGE, S100A9, S100A8 and S100A12 levels from 138 patients with established RA and 44 healthy controls were measured by ELISA and compared by unpaired t test. In RA patients, associations with disease activity and severity variables were analyzed by simple and multiple linear regressions. Results: Serum S100A9, S100A8 and S100A12 levels were correlated in RA patients. S100A9 levels were associated with body mass index (BMI), and with serum levels of S100A8 and S100A12. S100A8 levels were associated with serum levels of S100A9, presence of anti-citrullinated peptide antibodies (ACPA), and rheumatoid factor (RF). S100A12 levels were associated with presence of ACPA, history of diabetes, and serum S100A9 levels. sRAGE levels were negatively associated with serum levels of C-reactive protein (CRP) and high-density lipoprotein (HDL), history of vasculitis, and the presence of the RAGE 82Ser polymorphism. Conclusions: sRAGE and S100 proteins were associated not just with RA inflammation and autoantibody production, but also with classical vascular risk factors for end-organ damage. Consistent with its role as a RAGE decoy molecule, sRAGE had the opposite effects to S100 proteins in that S100 proteins were associated with autoantibodies and vascular risk, whereas sRAGE was associated with protection against joint and vascular damage. These data suggest that RAGE activity influences co-development of joint and vascular disease in rheumatoid arthritis patients.

Emotional food design: From designing food products to designing food systems

Contemporary food systems promote the consumption of highly processed foods of limited nutrition, contributing to overweight and obesity, diet-related disease and significant financial burden on healthcare systems. In part, this has resulted from highly successful design, development and marketing strategies for processed foods. The successful application of such strategies to healthy food options, and the services and business plans that accompany them, could assist in enhancing health and alleviating burden on health care systems. Product designers have long been aware of the importance of intertwining emotional experiences with new products. However, a lack of theoretical precision exists for applying emotional design beyond food products, to the food systems, services and business models that drive them. This article explores emotional design within the context of food and food systems and proposes a new concept – Emotional Food Design (EFD), through which emotional design is integrated across levels of a food system. EFD complements the dominating deductive view of food systems research with an abductive iterative design approach contextualized within the creation of new food products, services and business models and their associated emotional attachments. This paper concludes by outlining what EFD can offer to reorient food systems to successfully promote healthy eating.

Online hidden Markov model parameter estimation and minimax robust quickest change detection in uncertain stochastic processes

Stochastic (or random) processes are inherent to numerous fields of human endeavour including engineering, science, and business and finance. This thesis presents multiple novel methods for quickly detecting and estimating uncertainties in several important classes of stochastic processes. The significance of these novel methods is demonstrated by employing them to detect aircraft manoeuvres in video signals in the important application of autonomous mid-air collision avoidance.

Estimates on the coverage of parameter space using populations of models

In this paper we provide estimates for the coverage of parameter space when using Latin Hypercube Sampling, which forms the basis of building so-called populations of models. The estimates are obtained using combinatorial counting arguments to determine how many trials, k, are needed in order to obtain specified parameter space coverage for a given value of the discretisation size n. In the case of two dimensions, we show that if the ratio (Ø) of trials to discretisation size is greater than 1, then as n becomes moderately large the fractional coverage behaves as 1-exp-ø. We compare these estimates with simulation results obtained from an implementation of Latin Hypercube Sampling using MATLAB.

Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

This paper demonstrates the procedures for probabilistic assessment of a pesticide fate and transport model, PCPF-1, to elucidate the modeling uncertainty using the Monte Carlo technique. Sensitivity analyses are performed to investigate the influence of herbicide characteristics and related soil properties on model outputs using four popular rice herbicides: mefenacet, pretilachlor, bensulfuron-methyl and imazosulfuron. Uncertainty quantification showed that the simulated concentrations in paddy water varied more than those of paddy soil. This tendency decreased as the simulation proceeded to a later period but remained important for herbicides having either high solubility or a high 1st-order dissolution rate. The sensitivity analysis indicated that PCPF-1 parameters requiring careful determination are primarily those involve with herbicide adsorption (the organic carbon content, the bulk density and the volumetric saturated water content), secondary parameters related with herbicide mass distribution between paddy water and soil (1st-order desorption and dissolution rates) and lastly, those involving herbicide degradations. © Pesticide Science Society of Japan.

Towards development of a rapid and effective non-destructive testing strategy to identify brominated flame retardants in the plastics of consumer products

Polybrominated diphenyl ethers (PBDEs) are a class of brominated flame retardants (BFRs) once extensively used in the plastics of a wide range of consumer products. The listing of certain congeners that are constituents of commercial PBDE mixtures (including c-octaBDE) in the Stockholm Convention and tightening regulation of many other BFRs in recent years have created the need for a rapid and effective method of identifying BFR-containing plastics. A three-tiered testing strategy comparing results from non-destructive testing (X-ray fluorescence (XRF)) (n = 1714), a surface wipe test (n = 137) and destructive chemical analysis (n = 48) was undertaken to systematically identify BFRs in a wide range of consumer products. XRF rapidly identified bromine in 92% of products later confirmed to contain BFRs. Surface wipes of products identified tetrabromobisphenol A (TBBPA), c-octaBDE congeners and BDE-209 with relatively high accuracy (> 75%) when confirmed by destructive chemical analysis. A relationship between the amounts of BFRs detected in surface wipes and subsequent destructive testing shows promise in predicting not only the types of BFRs present but also estimating the concentrations present. Information about the types of products that may contain persistent BFRs will assist regulators in implementing policies to further reduce the occurrence of these chemicals in consumer products.

Open and closed-loop recycling of textile and apparel products

Textile waste is a significant contributor to landfill yet the majority of textiles can be recycled, allowing for the energy and fibre to be reclaimed. This chapter examines the open-loop and closed loop recycling of textile products with particular reference to the fashion and apparel context. It describes the fibres used within apparel, the current mechanical and chemical methods for textile recycling, LCA findings for each method, and applications within apparel for each. Barriers for more effective recycling include ease of integration into existing textile and apparel design methods as well as coordinated collection of post-consumer waste. The chapter concludes with a discussion of innovations that point to future trends in both open-loop and closed-loop recycling within the apparel industry.

Using the evolution of consumer products to inform design

The development of a new consumer product and its release to market is typically an expensive and risky process. It is estimated that up to 80% of all new products fail in the marketplace (Savoia, 2014). The consequences of failure can be ruinous for a manufacturer both financially and in terms of brand reputation. So even small improvements in success prediction have the potential to save money, effort and brand reputation. This paper proposes an approach where the history and evolution of a product is mapped and analyzed. The results of the analysis can then be used to inform design decisions. This paper will also demonstrate the similarities between biological evolution and the evolution of consumer products. Using the existing structure and terminology of biological evolution allows us to focus on the aspects of innovations that have led to success and those that have led to failure. This paper uses the case study of the wristwatch and its development over 100 years. The analysis of this leads to recommendations for contemporary “smartwatches.”

Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method

The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. Furthermore, if one of the working correlation structures correctly models the within-subject correlations, then this hybrid method provides the most efficient parameter estimates. In simulations, the hybrid method's finite-sample performance is superior to a GEE under any of the commonly used working correlation models and is almost fully efficient in all scenarios studied. The hybrid method is illustrated using data from a longitudinal study of the respiratory infection rates in 275 Indonesian children.

Smooth bootstrap methods for analysis of longitudinal data

In analysis of longitudinal data, the variance matrix of the parameter estimates is usually estimated by the 'sandwich' method, in which the variance for each subject is estimated by its residual products. We propose smooth bootstrap methods by perturbing the estimating functions to obtain 'bootstrapped' realizations of the parameter estimates for statistical inference. Our extensive simulation studies indicate that the variance estimators by our proposed methods can not only correct the bias of the sandwich estimator but also improve the confidence interval coverage. We applied the proposed method to a data set from a clinical trial of antibiotics for leprosy.