Sources of coding discrepancies in injury morbidity data: Implications for injury surveillance

Objective: To examine the sources of coding discrepancy for injury morbidity data and explore the implications of these sources for injury surveillance.-------- Method: An on-site medical record review and recoding study was conducted for 4373 injury-related hospital admissions across Australia. Codes from the original dataset were compared to the recoded data to explore the reliability of coded data aand sources of discrepancy.---------- Results: The most common reason for differences in coding overall was assigning the case to a different external cause category with 8.5% assigned to a different category. Differences in the specificity of codes assigned within a category accounted for 7.8% of coder difference. Differences in intent assignment accounted for 3.7% of the differences in code assignment.---------- Conclusions: In the situation where 8 percent of cases are misclassified by major category, the setting of injury targets on the basis of extent of burden is a somewhat blunt instrument Monitoring the effect of prevention programs aimed at reducing risk factors is not possible in datasets with this level of misclassification error in injury cause subcategories. Future research is needed to build the evidence base around the quality and utility of the ICD classification system and application of use of this for injury surveillance in the hospital environment.

Imaging of buried targets with ground penetrating radar

Diffraction tomographic imaging is applied to the imaging of shallowly buried targets with multi-bistatic arrays of transmitters and receivers.

Vision-based detection and tracking of aerial targets for UAV collision avoidance

Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of the sensors involved (as opposed to radar). This paper describes the development and evaluation of a vision-based collision detection algorithm suitable for fixed-wing aerial robotics. The system was evaluated using highly realistic vision data of the moments leading up to a collision. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8-10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We make use of the enormous potential of graphic processing units to achieve processing rates of 30Hz (for images of size 1024-by- 768). Currently, integration in the final platform is under way.

Signalling layout for fixed-block railway lines with real-coded genetic algorithms.

Signalling layout design is one of the keys to railway operations with fixed-block signalling system and it also carries direct effect on overall train efficiency and safety. Based on an analysis to system objectives, this paper presents an optimization model with two objectives in order to devise an efficient signalling layout scheme. Taking into account the present railway line design practices in China, the paper describes steps of the computer-based signalling layout optimisation with real-coded genetic algorithms. A computer-aided system, based on train movement simulator, has also been employed to assist the optimisation process. A case study on a practical railway line has been conducted to make comparisons between the proposed GA-based approach and the current practices. The results illustrate the improved performance of the proposed approach in reducing signal block joints and shortening minimum train service headway.

Identifying key belief-based targets for promoting regular physical activity among mothers and fathers with young children

We investigated the key beliefs to target in interventions aimed at increasing physical activity (PA) among mothers and fathers of young children. Parents (288 mothers and 292 fathers) completed a Theory of Planned Behaviour belief-based questionnaire and a 1-week follow-up of PA behaviour. We found that a range of behavioural, normative, and control beliefs were significantly correlated with parents’ PA intentions and behaviour, with only a few differences observed in correlations between PA beliefs and intention and behaviour by gender. A range of key beliefs was identified as making independent contributions to parents’ PA intentions; however, the behavioural beliefs about improving parenting practices (β = 0.13), interfering with other commitments (β = −0.29); normative beliefs about people I exercise with (β = 0.20); and control beliefs about lack of time (β = −0.24), inconvenience (β = −0.14), lack of motivation (β = −0.34), were revealed as significant independent predictors of actual PA behaviour. Furthermore, we found that a limited amount of parents already hold these beliefs, suggesting that these key beliefs warrant changing and, therefore, are appropriate targets for subsequent intervention. The current study fills an empirical gap in the PA literature by investigating an at-risk group and using a well established theoretical framework to identify key beliefs that guide parents’ PA decision-making. Overall, we found support for parents being a unique group who hold distinctive behavioural, normative, and control beliefs toward PA. Attention to these key underlying beliefs will assist intervention work aimed at combating inactivity among this at-risk population.

Neuronal nicotinic acetylcholine receptors as pharmacotherapeutic targets for the treatment of alcohol use disorders

Alcohol use disorders (AUDs) are complex and developing effective treatments will require the combination of novel medications and cognitive behavioral therapy approaches. Epidemiological studies have shown there is a high correlation between alcohol consumption and tobacco use, and the prevalence of smoking in alcoholics is as high as 80% compared to about 30% for the general population. Both preclinical and clinical data provide evidence that nicotine administration increases alcohol intake and nonspecific nicotinic receptor antagonists reduce alcohol-mediated behaviors. As nicotine interacts specifically with the neuronal nicotinic acetylcholine receptor (nAChR) system, this suggests that nAChRs play an important role in the behavioral effects of alcohol. In this review, we discuss the importance of nAChRs for the treatment of AUDs and argue that the use of FDA approved nAChR ligands, such as varenicline and mecamylamine, approved as smoking cessation aids may prove to be valuable treatments for AUDs. We also address the importance of combining effective medications with behavioral therapy for the treatment of alcohol dependent individuals.

Fat and thin adaptive HMM filters for vision based detection of moving targets

Computer vision is an attractive solution for uninhabited aerial vehicle (UAV) collision avoidance, due to the low weight, size and power requirements of hardware. A two-stage paradigm has emerged in the literature for detection and tracking of dim targets in images, comprising of spatial preprocessing, followed by temporal filtering. In this paper, we investigate a hidden Markov model (HMM) based temporal filtering approach. Specifically, we propose an adaptive HMM filter, in which the variance of model parameters is refined as the quality of the target estimate improves. Filters with high variance (fat filters) are used for target acquisition, and filters with low variance (thin filters) are used for target tracking. The adaptive filter is tested in simulation and with real data (video of a collision-course aircraft). Our test results demonstrate that our adaptive filtering approach has improved tracking performance, and provides an estimate of target heading not present in previous HMM filtering approaches.

The identification of therapeutic targets in metastatic melanoma

Metastatic melanoma, a cancer historically refractory to chemotherapeutic strategies, has a poor prognosis and accounts for the majority of skin cancer related mortality. Although the recent approval of two new drugs combating this disease, Ipilimumab and Vemurafenib (PLX4032), has demonstrated for the first time in decades an improvement in overall survival; the clinical efficacy of these drugs has been marred by severe adverse immune reactions and acquired drug resistance in patients, respectively. Thus, understanding the etiology of metastatic melanoma will contribute to the improvement of current therapeutic strategies while leading to the development of novel drug approaches. In order to identify recurrently mutated genes of therapeutic relevance in metastatic melanoma, a panel of stage III local lymph node melanomas were extensively characterised using high-throughput genomic technologies. This led to the identification of mutations in TFG in 5% of melanomas from a candidate gene sequencing approach using SNP array analysis, 24% of melanomas with mutations in MAP3K5 or MAP3K9 though unbiased whole-exome sequencing strategies, and inactivating mutations in NF1 in BRAF/NRAS wild type tumours though pathway analysis. Lastly, this thesis describes the development of a melanoma specific mutation panel that can rapidly identify clinically relevant mutation profiles that could guide effective treatment strategies through a personalised therapeutic approach. These findings are discussed in respect to a number of important issues raised by this study including the current limitation of next-generation sequencing technology, the difficulty in identifying ‘driver’ mutations critical to the development of melanoma due to high carcinogenic exposure by UV radiation, and the ultimate application of mutation screening in a personalised therapeutic setting. In summary, a number novel genes involved in metastatic melanoma have been identified that may have relevance for current therapeutic strategies in treating this disease.