Using a pharmacokinetic model to interpret biomonitoring data of PBDEs in the Australian population

From human biomonitoring data that are increasingly collected in the United States, Australia, and in other countries from large-scale field studies, we obtain snap-shots of concentration levels of various persistent organic pollutants (POPs) within a cross section of the population at different times. Not only can we observe the trends within this population with time, but we can also gain information going beyond the obvious time trends. By combining the biomonitoring data with pharmacokinetic modeling, we can re-construct the time-variant exposure to individual POPs, determine their intrinsic elimination half-lives in the human body, and predict future levels of POPs in the population. Different approaches have been employed to extract information from human biomonitoring data. Pharmacokinetic (PK) models were combined with longitudinal data1, with single2 or multiple3 average concentrations of a cross-sectional data (CSD), or finally with multiple CSD with or without empirical exposure data4. In the latter study, for the first time, the authors based their modeling outputs on two sets of CSD and empirical exposure data, which made it possible that their model outputs were further constrained due to the extensive body of empirical measurements. Here we use a PK model to analyze recent levels of PBDE concentrations measured in the Australian population. In this study, we are able to base our model results on four sets5-7 of CSD; we focus on two PBDE congeners that have been shown3,5,8-9 to differ in intake rates and half-lives with BDE-47 being associated with high intake rates and a short half-life and BDE-153 with lower intake rates and a longer half-life. By fitting the model to PBDE levels measured in different age groups in different years, we determine the level of intake of BDE-47 and BDE-153, as well as the half-lives of these two chemicals in the Australian population.

Cluster-based network model for time-course gene expression data

We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.

Rethinking prefabricated construction management using the VP-based IKEA model in Hong Kong

Prefabricated construction is regarded by many as an effective and efficient approach to improving construction processes and productivity, ensuring construction quality and reducing time and cost in the construction industry. However, many problems occur with this approach in practice, including higher risk levels and cost or time overruns. In order to solve such problems, it is proposed that the IKEA model of the manufacturing industry and VP technology are introduced into a prefabricated construction process. The concept of the IKEA model is identified in detail and VP technology is briefly introduced. In conjunction with VP technology, the applications of the IKEA model are presented in detail, i.e. design optimization, production optimization and installation optimization. Furthermore, through a case study of a prefabricated hotel project in Hong Kong, it is shown that the VP-based IKEA model can improve the efficiency and safety of prefabricated construction as well as reducing cost and time.

Using the collaborative evidence based practice model : a systematic review and uptake of chlorhexidine-impregnated sponge dressings on central venous access devices in a tertiary cancer centre

Background: Greater research utilisation in cancer nursing practice is needed, in order to provide well-informed and effective nursing care to people affected by cancer. This paper aims to report on the implementation of evidence-based practice in a tertiary cancer centre. Methods: Using a case report design, this paper reports on the use of the Collaborative Model for Evidence Based Practice (CMEBP) in an Australian tertiary cancer centre. The clinical case is the uptake of routine application of chlorhexidine-impregnated sponge dressings for preventing centrally inserted catheter-related bloodstream infections. In this case report, a number of processes that resulted in a service-wide practice change are described. Results: This model was considered a feasible method for successful research utilisation. In this case report, chlorhexidine-impregnated sponge dressings were proposed and implemented in the tertiary cancer centre with an aim of reducing the incidence of centrally inserted catheter-related bloodstream infections and potentially improving patient health outcomes. Conclusion: The CMEBP is feasible and effective for implementing clinical evidence into cancer nursing practice. Cancer nurses and health administrators need to ensure a supportive infrastructure and environment for clinical inquiry and research utilisation exists, in order to enable successful implementation of evidence-based practice in their cancer centres.

Looking Yonder : Longitudinal Research and Evaluation of the Yonder Participative Arts-based Learning Model Report 2010-2012

Since 2007, KITE Arts Education Program @ QPAC has been engaged in a series of arts and drama-based experiences for students in selected primary schools on the edges of Brisbane and in regional Queensland. The in-school workshop experiences of the program have culminated in a performance by the children for their school community, parents and carers at the Queensland Performing Arts Centre or a regional cultural venue. In conducting an analysis of the Yonder project, the researcher aimed to provide evidence of outcomes brought about through participation by schools, school staff, students and their communities in the Yonder project. To develop longitudinal data project initiators, participants were interviewed at six-monthly intervals to establish patterns of engagement and participation. The report analyses arts-based workshops conducted by the teacher artist in edge-city Brisbane and a regional centre; interviews with teachers and school administrators from the participating schools; interviews with teacher artist and professional artists; interviews with community partners; teacher professional development workshops; community-based workshops; performance outcomes that were the culminating events of the workshop program; student work samples and student reflections on the program. This document covers data and project outputs from February 2010 to July 2012. There have been five iterations of the Yonder project since its commencement in mid-2009 — three in regional Queensland (February–April 2010; February–May 2011; February–May 2012) and two in edge-city1 Brisbane (July–September 2010; August–October 2011). This report is a result of a research partnership between Queensland Performing Arts Centre and Queensland University of Technology (QUT) Creative Industries Faculty(Drama).

Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation.

The impact of mobile phone distraction on the braking behaviour of young drivers : a hazard-based duration model

Braking is a crucial driving task with a direct relationship with crash risk, as both excess and inadequate braking can lead to collisions. The objective of this study was to compare the braking profile of young drivers distracted by mobile phone conversations to non-distracted braking. In particular, the braking behaviour of drivers in response to a pedestrian entering a zebra crossing was examined using the CARRS-Q Advanced Driving Simulator. Thirty-two licensed drivers drove the simulator in three phone conditions: baseline (no phone conversation), hands-free, and handheld. In addition to driving the simulator, each participant completed questionnaires related to driver demographics, driving history, usage of mobile phones while driving, and general mobile phone usage history. The drivers were 18–26 years old and split evenly by gender. A linear mixed model analysis of braking profiles along the roadway before the pedestrian crossing revealed comparatively increased decelerations among distracted drivers, particularly during the initial 20 kph of deceleration. Drivers’ initial 20 kph deceleration time was modelled using a parametric accelerated failure time (AFT) hazard-based duration model with a Weibull distribution with clustered heterogeneity to account for the repeated measures experiment design. Factors found to significantly influence the braking task included vehicle dynamics variables like initial speed and maximum deceleration, phone condition, and driver-specific variables such as licence type, crash involvement history, and self-reported experience of using a mobile phone whilst driving. Distracted drivers on average appear to reduce the speed of their vehicle faster and more abruptly than non-distracted drivers, exhibiting excess braking comparatively and revealing perhaps risk compensation. The braking appears to be more aggressive for distracted drivers with provisional licenses compared to drivers with open licenses. Abrupt or excessive braking by distracted drivers might pose significant safety concerns to following vehicles in a traffic stream.

A normal-distribution based reputation model

Rating systems are used by many websites, which allow customers to rate available items according to their own experience. Subsequently, reputation models are used to aggregate available ratings in order to generate reputation scores for items. A problem with current reputation models is that they provide solutions to enhance accuracy of sparse datasets not thinking of their models performance over dense datasets. In this paper, we propose a novel reputation model to generate more accurate reputation scores for items using any dataset; whether it is dense or sparse. Our proposed model is described as a weighted average method, where the weights are generated using the normal distribution. Experiments show promising results for the proposed model over state-of-the-art ones on sparse and dense datasets.