Early evidence for direct and indirect effects of the infant rotavirus vaccine program in Queensland

Objective: To assess the impact of introducing a publicly funded infant rotavirus vaccination program on disease notifications and on laboratory testing and results. Design and setting: Retrospective analysis of routinely collected data (rotavirus notifications [2006–2008] and laboratory rotavirus testing data from Queensland Health laboratories [2000–2008]) to monitor rotavirus trends before and after the introduction of a publicly funded infant rotavirus vaccination program in Queensland in July 2007. Main outcome measures: Age group-specific rotavirus notification trends; number of rotavirus tests performed and the proportion positive. Results: In the less than 2 years age group, rotavirus notifications declined by 53% (2007) and 65% (2008); the number of laboratory tests performed declined by 3% (2007) and 15% (2008); and the proportion of tests positive declined by 45% (2007) and 43% (2008) compared with data collected before introduction of the vaccination program. An indirect effect of infant vaccination was seen: notifications and the proportion of tests positive for rotavirus declined in older age groups as well. Conclusions: The publicly funded rotavirus vaccination program in Queensland is having an early impact, direct and indirect, on rotavirus disease as assessed using routinely collected data. Further observational studies are required to assess vaccine effectiveness. Parents and immunisation providers should ensure that all Australian children receive the recommended rotavirus vaccine doses in the required timeframe.

Rule-based transit signal priority control method using a real-time transit travel time prediction model

An advanced rule-based Transit Signal Priority (TSP) control method is presented in this paper. An on-line transit travel time prediction model is the key component of the proposed method, which enables the selection of the most appropriate TSP plans for the prevailing traffic and transit condition. The new method also adopts a priority plan re-development feature that enables modifying or even switching the already implemented priority plan to accommodate changes in the traffic conditions. The proposed method utilizes conventional green extension and red truncation strategies and also two new strategies including green truncation and queue clearance. The new method is evaluated against a typical active TSP strategy and also the base case scenario assuming no TSP control in microsimulation. The evaluation results indicate that the proposed method can produce significant benefits in reducing the bus delay time and improving the service regularity with negligible adverse impacts on the non-transit street traffic.

Natural convection flow in a strong cross magnetic field with radiation

The problem of MHD natural convection boundary layer flow of an electrically conducting and optically dense gray viscous fluid along a heated vertical plate is analyzed in the presence of strong cross magnetic field with radiative heat transfer. In the analysis radiative heat flux is considered by adopting optically thick radiation limit. Attempt is made to obtain the solutions valid for liquid metals by taking Pr≪1. Boundary layer equations are transformed in to a convenient dimensionless form by using stream function formulation (SFF) and primitive variable formulation (PVF). Non-similar equations obtained from SFF are then simulated by implicit finite difference (Keller-box) method whereas parabolic partial differential equations obtained from PVF are integrated numerically by hiring direct finite difference method over the entire range of local Hartmann parameter, $xi$ . Further, asymptotic solutions are also obtained for large and small values of local Hartmann parameter $xi$ . A favorable agreement is found between the results for small, large and all values of $xi$ . Numerical results are also demonstrated graphically by showing the effect of various physical parameters on shear stress, rate of heat transfer, velocity and temperature.

Bayesian algorithms with applications

Advances in algorithms for approximate sampling from a multivariable target function have led to solutions to challenging statistical inference problems that would otherwise not be considered by the applied scientist. Such sampling algorithms are particularly relevant to Bayesian statistics, since the target function is the posterior distribution of the unobservables given the observables. In this thesis we develop, adapt and apply Bayesian algorithms, whilst addressing substantive applied problems in biology and medicine as well as other applications. For an increasing number of high-impact research problems, the primary models of interest are often sufficiently complex that the likelihood function is computationally intractable. Rather than discard these models in favour of inferior alternatives, a class of Bayesian "likelihoodfree" techniques (often termed approximate Bayesian computation (ABC)) has emerged in the last few years, which avoids direct likelihood computation through repeated sampling of data from the model and comparing observed and simulated summary statistics. In Part I of this thesis we utilise sequential Monte Carlo (SMC) methodology to develop new algorithms for ABC that are more efficient in terms of the number of model simulations required and are almost black-box since very little algorithmic tuning is required. In addition, we address the issue of deriving appropriate summary statistics to use within ABC via a goodness-of-fit statistic and indirect inference. Another important problem in statistics is the design of experiments. That is, how one should select the values of the controllable variables in order to achieve some design goal. The presences of parameter and/or model uncertainty are computational obstacles when designing experiments but can lead to inefficient designs if not accounted for correctly. The Bayesian framework accommodates such uncertainties in a coherent way. If the amount of uncertainty is substantial, it can be of interest to perform adaptive designs in order to accrue information to make better decisions about future design points. This is of particular interest if the data can be collected sequentially. In a sense, the current posterior distribution becomes the new prior distribution for the next design decision. Part II of this thesis creates new algorithms for Bayesian sequential design to accommodate parameter and model uncertainty using SMC. The algorithms are substantially faster than previous approaches allowing the simulation properties of various design utilities to be investigated in a more timely manner. Furthermore the approach offers convenient estimation of Bayesian utilities and other quantities that are particularly relevant in the presence of model uncertainty. Finally, Part III of this thesis tackles a substantive medical problem. A neurological disorder known as motor neuron disease (MND) progressively causes motor neurons to no longer have the ability to innervate the muscle fibres, causing the muscles to eventually waste away. When this occurs the motor unit effectively ‘dies’. There is no cure for MND, and fatality often results from a lack of muscle strength to breathe. The prognosis for many forms of MND (particularly amyotrophic lateral sclerosis (ALS)) is particularly poor, with patients usually only surviving a small number of years after the initial onset of disease. Measuring the progress of diseases of the motor units, such as ALS, is a challenge for clinical neurologists. Motor unit number estimation (MUNE) is an attempt to directly assess underlying motor unit loss rather than indirect techniques such as muscle strength assessment, which generally is unable to detect progressions due to the body’s natural attempts at compensation. Part III of this thesis builds upon a previous Bayesian technique, which develops a sophisticated statistical model that takes into account physiological information about motor unit activation and various sources of uncertainties. More specifically, we develop a more reliable MUNE method by applying marginalisation over latent variables in order to improve the performance of a previously developed reversible jump Markov chain Monte Carlo sampler. We make other subtle changes to the model and algorithm to improve the robustness of the approach.

Grouping people in social networks using a weighted multi-constraints clustering method

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

Conduction-radiation effect on natural convection flow in a fluid-saturated non-Darcy porous medium enclosed by non-isothermal walls

The effect of conduction-convection-radiation on natural convection flow of Newtonian optically thick gray fluid, confined in a non-Darcian porous media square cavity is numerically studied. For the gray fluid consideration is given to Rosseland diffusion approximation. Further assuming that (i) the temperature of the left vertical wall is varying linearly with height, (ii) cooled right vertical and top walls and (iii) the bottom wall is uniformly-heated. The governing equations are solved using the Alternate Direct Implicit method together with the Successive Over Relaxation technique. The investigation of the effect of governing parameters namely the Forschheimer resistance (Γ), the Planck constant (Rd), and the temperature difference (Δ), on flow pattern and heat transfer characteristics has been carried out. It was seen that the reduction of flow and heat transfer occurs as the Forschheimer resistance is increased. On the other hand both the strength of flow and heat transfer increases as the temperature ratio, Δ, is increased.

A comparison of calculated and experimental HRTEM images of twinned boron carbide

High resolution TEM images of boron carbide (B13C2) have been recorded and compared with images calculated using the multislice method as implemented by M. A. O'Keefe in the SHRLI programs. Images calculated for the [010] zone, using machine parameters for the JEOL 2000FX AEM operating at 200 keV, indicate that for the structure model of Will et al., the optimum defocus image can be interpreted such that white spots correspond to B12 icosahedra for thin specimens and to low density channels through the structure adjacent to the direct inter-icosahedral bonds for specimens of intermediate thickness (-40 > t > -100 nm). With this information, and from the symmetry observed in the TEM images, it is likely that the (101) twin plane passes through the center of icosahedron located at the origin. This model was tested using the method of periodic continuation. Resulting images compare favorably with experimental images, thus supporting the structural model. The introduction of a (101) twin plane through the origin creates distortions to the icosahedral linkages as well as to the intra-icosahedral bonding. This increases the inequivalence of adjacent icosahedral sites along the twin plane, and thereby increases the likelihood of bipolaron hopping.

Assessing the impacts of lifetime sun exposure on skin damage and skin aging using a non-invasive method

Background: Ultraviolet radiation exposure during an individuals' lifetime is a known risk factor for the development of skin cancer. However, less evidence is available on assessing the relationship between lifetime sun exposure and skin damage and skin aging. Objectives: This study aims to assess the relationship between lifetime sun exposure and skin damage and skin aging using a non-invasive measure of exposure. Methods: We recruited 180 participants (73 males, 107 females) aged 18-83 years. Digital imaging of skin hyper-pigmentation (skin damage) and skin wrinkling (skin aging) on the facial region was measured. Lifetime sun exposure (presented as hours) was calculated from the participants' age multiplied by the estimated annual time outdoors for each year of life. We analyzed the effects of lifetime sun exposure on skin damage and skin aging. We adjust for the influence of age, sex, occupation, history of skin cancer, eye color, hair color, and skin color. Results: There were non-linear relationships between lifetime sun exposure and skin damage and skin aging. Younger participant's skin is much more sensitive to sun exposure than those who were over 50 years of age. As such, there were negative interactions between lifetime sun exposure and age. Age had linear effects on skin damage and skin aging. Conclusion: The data presented showed that self reported lifetime sun exposure was positively associated with skin damage and skin aging, in particular, the younger people. Future health promotion for sun exposure needs to pay attention to this group for skin cancer prevention messaging. (C) 2012 Elsevier B.V. All rights reserved.