Trans-scale granular modelling of cytoskeleton: A mini-review

Living cells are the functional unit of organs that controls reactions to their exterior. However, the mechanics of living cells can be difficult to characterize due to the crypticity of their microscale structures and associated dynamic cellular processes. Fortunately, multiscale modelling provides a powerful simulation tool that can be used to study the mechanical properties of these soft hierarchical, biological systems. This paper reviews recent developments in hierarchical multiscale modeling technique that aimed at understanding cytoskeleton mechanics. Discussions are expanded with respects to cytoskeletal components including: intermediate filaments, microtubules and microfilament networks. The mechanical performance of difference cytoskeleton components are discussed with respect to their structural and material properties. Explicit granular simulation methods are adopted with different coarse-grained strategies for these cytoskeleton components and the simulation details are introduced in this review.

Review and future options for computer modelling in the sugar industry

Computer modelling has been used extensively in some processes in the sugar industry to achieve significant gains. This paper reviews the investigations carried out over approximately the last twenty five years, including the successes but also areas where problems and delays have been encountered. In that time the capability of both hardware and software have increased dramatically. For some processes such as cane cleaning, cane billet preparation, and sugar drying, the application of computer modelling towards improved equipment design and operation has been quite limited. A particular problem has been the large number of particles and particle interactions in these…

Generalizing the use of geographical weights in biodiversity modelling

Aim Determining how ecological processes vary across space is a major focus in ecology. Current methods that investigate such effects remain constrained by important limiting assumptions. Here we provide an extension to geographically weighted regression in which local regression and spatial weighting are used in combination. This method can be used to investigate non-stationarity and spatial-scale effects using any regression technique that can accommodate uneven weighting of observations, including machine learning. Innovation We extend the use of spatial weights to generalized linear models and boosted regression trees by using simulated data for which the results are known, and compare these local approaches with existing alternatives such as geographically weighted regression (GWR). The spatial weighting procedure (1) explained up to 80% deviance in simulated species richness, (2) optimized the normal distribution of model residuals when applied to generalized linear models versus GWR, and (3) detected nonlinear relationships and interactions between response variables and their predictors when applied to boosted regression trees. Predictor ranking changed with spatial scale, highlighting the scales at which different species–environment relationships need to be considered. Main conclusions GWR is useful for investigating spatially varying species–environment relationships. However, the use of local weights implemented in alternative modelling techniques can help detect nonlinear relationships and high-order interactions that were previously unassessed. Therefore, this method not only informs us how location and scale influence our perception of patterns and processes, it also offers a way to deal with different ecological interpretations that can emerge as different areas of spatial influence are considered during model fitting.

Two-scale computational modelling of water flow in unsaturated soils containing irregular-shaped inclusions

The focus of this paper is two-dimensional computational modelling of water flow in unsaturated soils consisting of weakly conductive disconnected inclusions embedded in a highly conductive connected matrix. When the inclusions are small, a two-scale Richards’ equation-based model has been proposed in the literature taking the form of an equation with effective parameters governing the macroscopic flow coupled with a microscopic equation, defined at each point in the macroscopic domain, governing the flow in the inclusions. This paper is devoted to a number of advances in the numerical implementation of this model. Namely, by treating the micro-scale as a two-dimensional problem, our solution approach based on a control volume finite element method can be applied to irregular inclusion geometries, and, if necessary, modified to account for additional phenomena (e.g. imposing the macroscopic gradient on the micro-scale via a linear approximation of the macroscopic variable along the microscopic boundary). This is achieved with the help of an exponential integrator for advancing the solution in time. This time integration method completely avoids generation of the Jacobian matrix of the system and hence eases the computation when solving the two-scale model in a completely coupled manner. Numerical simulations are presented for a two-dimensional infiltration problem.

Do motor vehicle crashes arise from single or multiple unique risk processes? An inquiry into crash causes and modelling

Crashes at any particular transport network location consist of a chain of events arising from a multitude of potential causes and/or contributing factors whose nature is likely to reflect geometric characteristics of the road, spatial effects of the surrounding environment, and human behavioural factors. It is postulated that these potential contributing factors do not arise from the same underlying risk process, and thus should be explicitly modelled and understood. The state of the practice in road safety network management applies a safety performance function that represents a single risk process to explain crash variability across network sites. This study aims to elucidate the importance of differentiating among various underlying risk processes contributing to the observed crash count at any particular network location. To demonstrate the principle of this theoretical and corresponding methodological approach, the study explores engineering (e.g. segment length, speed limit) and unobserved spatial factors (e.g. climatic factors, presence of schools) as two explicit sources of crash contributing factors. A Bayesian Latent Class (BLC) analysis is used to explore these two sources and to incorporate prior information about their contribution to crash occurrence. The methodology is applied to the state controlled roads in Queensland, Australia and the results are compared with the traditional Negative Binomial (NB) model. A comparison of goodness of fit measures indicates that the model with a double risk process outperforms the single risk process NB model, and thus indicating the need for further research to capture all the three crash generation processes into the SPFs.

Robust iris recognition using decision fusion and degradation modelling

This thesis investigates the use of fusion techniques and mathematical modelling to increase the robustness of iris recognition systems against iris image quality degradation, pupil size changes and partial occlusion. The proposed techniques improve recognition accuracy and enhance security. They can be further developed for better iris recognition in less constrained environments that do not require user cooperation. A framework to analyse the consistency of different regions of the iris is also developed. This can be applied to improve recognition systems using partial iris images, and cancelable biometric signatures or biometric based cryptography for privacy protection.

The correlation between reading and mathematics ability at age twelve has a substantial genetic component

Dissecting how genetic and environmental influences impact on learning is helpful for maximizing numeracy and literacy. Here we show, using twin and genome-wide analysis, that there is a substantial genetic component to children’s ability in reading and mathematics, and estimate that around one half of the observed correlation in these traits is due to shared genetic effects (so-called Generalist Genes). Thus, our results highlight the potential role of the learning environment in contributing to differences in a child’s cognitive abilities at age twelve.

Modelling of the evaporation of a droplet suspended in a binary atmosphere

The process of spray drying is applied in a number of contexts. One such application is the production of a synthetic rock used for storage of nuclear waste. To establish a framework for a model of the spray drying process for this application, we here develop a model describing evaporation from droplets of pure water, such that the model may be extended to account for the presence of colloid within the droplet. We develop a spherically-symmetric model and formulate continuum equations describing mass, momentum, and energy balance in both the liquid and gas phases from first principles. We establish appropriate boundary conditions at the surface of the droplet, including a generalised Clapeyron equation that accurately describes the temperature at the surface of the droplet. To account for experiment design, we introduce a simplified platinum ball and wire model into the system using a thin wire problem. The resulting system of equations is transformed in order to simplify a finite volume solution scheme. The results from numerical simulation are compared with data collected for validation, and the sensitivity of the model to variations in key parameters, and to the use of Clausius–Clapeyron and generalised Clapeyron equations, is investigated. Good agreement is found between the model and experimental data, despite the simplicity of the platinum phase model.

Spatio-temporal modelling of cancer data in Queensland using Bayesian methods

Cancer is the leading contributor to the disease burden in Australia. This thesis develops and applies Bayesian hierarchical models to facilitate an investigation of the spatial and temporal associations for cancer diagnosis and survival among Queenslanders. The key objectives are to document and quantify the importance of spatial inequalities, explore factors influencing these inequalities, and investigate how spatial inequalities change over time. Existing Bayesian hierarchical models are refined, new models and methods developed, and tangible benefits obtained for cancer patients in Queensland. The versatility of using Bayesian models in cancer control are clearly demonstrated through these detailed and comprehensive analyses.

Measurement of transit network accessibility based on access stop choice behaviour

This research improved the measurement of public transport accessibility by capturing; travellers' behaviour; diversity of public transport mode; and the subjectivity of travellers' decision in the complex transport networks. The results of this research not only highlighted the importance of considering public transport network characteristics but also, revealed the impact of public transport diversity in the modelling of public transport accessibility. The research developed a hybrid discrete choice model with a nested logit structure to treat the correlation among the public transport mode choices and, a logit correction factor to rectify the correlation among the stop choices.

Correlation of carotid atheromatous plaque inflammation using USPIO-enhanced MR imaging with degree of luminal Stenosis

BACKGROUND AND PURPOSE Inflammation is a recognized risk factor for the vulnerable atherosclerotic plaque. The study explores the relationship between the degree of Magnetic Resonance (MR)"defined inflammation using Ultra Small Super-Paramagnetic Iron Oxide (USPIO) particles and the severity of luminal stenosis in asymptomatic carotid plaques. METHODS Seventy-one patients with an asymptomatic carotid stenosis of ĝ‰¥40% underwent multi-sequence USPIO-enhanced MR imaging. Stenosis severity was measured according to the NASCET and ECST methods. RESULTS No demonstrable relationship between inflammation as measured by USPIO-enhanced signal change and the degree of luminal stenosis was found. CONCLUSIONS Inflammation and stenosis are likely to be independent risk factors, although this needs to be further validated.

Correlation of carotid atheromatous plaque inflammation with biomechanical stress: Utility of USPIO enhanced MR imaging and finite element analysis

Objective: The aim of this study was to explore whether there is a relationship between the degree of MR-defined inflammation using ultra small super-paramagnetic iron oxide (USPIO) particles, and biomechanical stress using finite element analysis (FEA) techniques, in carotid atheromatous plaques. Methods and Results: 18 patients with angiographically proven carotid stenoses underwent multi-sequence MR imaging before and 36 h after USPIO infusion. T2 * weighted images were manually segmented into quadrants and the signal change in each quadrant normalised to adjacent muscle was calculated after USPIO administration. Plaque geometry was obtained from the rest of the multi-sequence dataset and used within a FEA model to predict maximal stress concentration within each slice. Subsequently, a new statistical model was developed to explicitly investigate the form of the relationship between biomechanical stress and signal change. The Spearman's rank correlation coefficient for USPIO enhanced signal change and maximal biomechanical stress was -0.60 (p = 0.009). Conclusions: There is an association between biomechanical stress and USPIO enhanced MR-defined inflammation within carotid atheroma, both known risk factors for plaque vulnerability. This underlines the complex interaction between physiological processes and biomechanical mechanisms in the development of carotid atheroma. However, this is preliminary data that will need validation in a larger cohort of patients.

Correlation of macrophage location and plaque stress distribution using USPIO-enhanced MRI in a patient with symptomatic severe carotid stenosis: A new insight into risk stratification

High resolution, USPIO-enhanced MR imaging can be used to identify inflamed atherosclerotic plaque. We report a case of a 79-year-old man with a symptomatic carotid stenosis of 82%. The plaque was retrieved for histology and finite element analysis (FEA) based on the preoperative MR imaging was used to predict maximal Von Mises stress on the plaque. Macrophage location correlated with maximal predicted stresses on the plaque. This supports the hypothesis that macrophages thin the fibrous cap at points of highest stress, leading to an increased risk of plaque rupture and subsequent stroke.

Atheroma: Is calcium important or not? A modelling study of stress within the atheromatous fibrous cap in relation to position and size of calcium deposits

Atheromatous plaque rupture h the cause of the majority of strokes and heart attacks in the developed world. The role of calcium deposits and their contribution to plaque vulnerability are controversial. Some studies have suggested that calcified plaque tends to be more stable whereas others have suggested the opposite. This study uses a finite element model to evaluate the effect of calcium deposits on the stress within the fibrous cap by varying their location and size. Plaque fibrous cap, lipid pool and calcification were modeled as hyperelastic, Isotropic, (nearly) incompressible materials with different properties for large deformation analysis by assigning time-dependent pressure loading on the lumen wall. The stress and strain contours were illustrated for each condition for comparison. Von Mises stress only increases up to 1.5% when varying the location of calcification in the lipid pool distant to the fibrous cap. Calcification in the fibrous cap leads to a 43% increase of Von Mises stress when compared with that in the lipid pool. An increase of 100% of calcification area leads to a 15% stress increase in the fibrous cap. Calcification in the lipid pool does not increase fibrous cap stress when it is distant to the fibrous cap, whilst large areas of calcification close to or in the fibrous cap may lead to a high stress concentration within the fibrous cap, which may cause plaque rupture. This study highlights the application of a computational model on a simulation of clinical problems, and it may provide insights into the mechanism of plaque rupture.