Utility of magnetic resonance imaging-based finite element analysis for the biomechanical stress analysis of hemorrhagic and non-hemorrhagic carotid plaques

Background: Biomechanical stress analysis has been used for plaque vulnerability assessment. The presence of plaque hemorrhage (PH) is a feature of plaque vulnerability and is associated with thromboembolic ischemic events. The purpose of the present study was to use finite element analysis (FEA) to compare the stress profiles of hemorrhagic and non-hemorrhagic profiles. Methods and Results: Forty-five consecutive patients who had suffered a cerebrovascular ischemic event with an underlying carotid artery disease underwent high-resolution magnetic resonance imaging (MRI) of their symptomatic carotid artery in a 1.5-T MRI system. Axial images were manually segmented for various plaque components and used for FEA. Maximum critical stress (M-CstressSL) for each slice was determined. Within a plaque, the maximum M-CstressSL for each slice of a plaque was selected to represent the maximum critical stress of that plaque (M-CstressPL) and used to compare hemorrhagic and non-hemorrhagic plaques. A total of 62% of plaques had hemorrhage. It was observed that plaques with hemorrhage had significantly higher stress (M-CstressPL) than plaques without PH (median [interquartile range]: 315 kPa [247-434] vs. 200 kPa [171-282], P=0.003). Conclusions: Hemorrhagic plaques have higher biomechanical stresses than non-hemorrhagic plaques. MRI-based FEA seems to have the potential to assess plaque vulnerability.

Accounting for individual variability in the von Bertalanffy growth model

Estimation of von Bertalanffy growth parameters has received considerable attention in fisheries research. Since Sainsbury (1980, Can. J. Fish. Aquat. Sci. 37: 241-247) much of this research effort has centered on accounting for individual variability in the growth parameters. In this paper we demonstrate that, in analysis of tagging data, Sainsbury's method and its derivatives do not, in general, satisfactorily account for individual variability in growth, leading to inconsistent parameter estimates (the bias does not tend to zero as sample size increases to infinity). The bias arises because these methods do not use appropriate conditional expectations as a basis for estimation. This bias is found to be similar to that of the Fabens method. Such methods would be appropriate only under the assumption that the individual growth parameters that generate the growth increment were independent of the growth parameters that generated the initial length. However, such an assumption would be unrealistic. The results are derived analytically, and illustrated with a simulation study. Until techniques that take full account of the appropriate conditioning have been developed, the effect of individual variability on growth has yet to be fully understood.

Motorcycle riders' self-reported aggression when riding compared with car driving

Aggressive driving has been shown to be related to increased crash risk for car driving. However, less is known about aggressive behaviour and motorcycle riding and whether there are differences in on-road aggression as a function of vehicle type. If such differences exist, these could relate to differences in perceptions of relative vulnerability associated with characteristics of the type of vehicle such as level of protection and performance. Specifically, the relative lack of protection offered by motorcycles may cause riders to feel more vulnerable and therefore to be less aggressive when they are riding compared to when they are driving. This study examined differences in self-reported aggression as a function of two vehicle types: passenger cars and motorcycles. Respondents (n = 247) were all motorcyclists who also drove a car. Results were that scores for the composite driving aggression scale were significantly higher than on the composite riding aggression scale. Regression analyses identified different patterns of predictors for driving aggression from those for riding aggression. Safety attitudes followed by thrill seeking tendencies were the strongest predictors for driving aggression, with more positive safety attitudes being protective while greater thrill seeking was associated with greater self-reported aggressive driving behaviour. For riding aggression, thrill seeking was the strongest predictor (positive relationship), followed by self-rated skill, such that higher self rated skill was protective against riding aggression. Participants who scored at the 85th percentile or above for the aggressive driving and aggressive riding indices had significantly higher scores on thrill seeking, greater intentions to engage in future risk taking, and lower safety attitude scores than other participants. In addition participants with the highest aggressive driving scores also had higher levels of self-reported past traffic offences than other participants. Collectively, these findings suggest that people are less likely to act aggressively when riding a motorcycle than when driving a car, and that those who are the most aggressive drivers are different from those who are the most aggressive riders. However, aggressive riders and drivers appear to present a risk to themselves and others on road. Importantly, the underlying influences for aggressive riding or driving that were identified in this study may be amenable to education and training interventions.