Projected equations of motion approach to hybrid quantum/classical dynamics in dielectric-metal composites

We introduce a hybrid method for dielectric-metal composites that describes the dynamics of the metallic system classically whilst retaining a quantum description of the dielectric. The time-dependent dipole moment of the classical system is mimicked by the introduction of projected equations of motion (PEOM) and the coupling between the two systems is achieved through an effective dipole-dipole interaction. To benchmark this method, we model a test system (semiconducting quantum dot-metal nanoparticle hybrid). We begin by examining the energy absorption rate, showing agreement between the PEOM method and the analytical rotating wave approximation (RWA) solution. We then investigate population inversion and show that the PEOM method provides an accurate model for the interaction under ultrashort pulse excitation where the traditional RWA breaks down.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Outlier effects on robust joint modelling of longitudinal and survival data

Robust joint modelling is an emerging field of research. Through the advancements in electronic patient healthcare records, the popularly of joint modelling approaches has grown rapidly in recent years providing simultaneous analysis of longitudinal and survival data. This research advances previous work through the development of a novel robust joint modelling methodology for one of the most common types of standard joint models, that which links a linear mixed model with a Cox proportional hazards model. Through t-distributional assumptions, longitudinal outliers are accommodated with their detrimental impact being down weighed and thus providing more efficient and reliable estimates. The robust joint modelling technique and its major benefits are showcased through the analysis of Northern Irish end stage renal disease patients. With an ageing population and growing prevalence of chronic kidney disease within the United Kingdom, there is a pressing demand to investigate the detrimental relationship between the changing haemoglobin levels of haemodialysis patients and their survival. As outliers within the NI renal data were found to have significantly worse survival, identification of outlying individuals through robust joint modelling may aid nephrologists to improve patient's survival. A simulation study was also undertaken to explore the difference between robust and standard joint models in the presence of increasing proportions and extremity of longitudinal outliers. More efficient and reliable estimates were obtained by robust joint models with increasing contrast between the robust and standard joint models when a greater proportion of more extreme outliers are present. Through illustration of the gains in efficiency and reliability of parameters when outliers exist, the potential of robust joint modelling is evident. The research presented in this thesis highlights the benefits and stresses the need to utilise a more robust approach to joint modelling in the presence of longitudinal outliers.

Directional performance in motion transparency

Motion transparency provides a challenging test case for our understanding of how visual motion, and other attributes, are computed and represented in the brain. However, previous studies of visual transparency have used subjective criteria which do not confirm the existence of independent representations of the superimposed motions. We have developed measures of performance in motion transparency that require observers to extract information about two motions jointly, and therefore test the information that is simultaneously represented for each motion. Observers judged whether two motions were at 90 to one another; the base direction was randomized so that neither motion taken alone was informative. The precision of performance was determined by the standard deviations (S.D.s) of probit functions fitted to the data. Observers also made judgments of orthogonal directions between a single motion stream and a line, for one of two transparent motions against a line and for two spatially segregated motions. The data show that direction judgments with transparency can be made with comparable accuracy to segregated (non-transparent) conditions, supporting the idea that transparency involves the equivalent representation of two global motions in the same region. The precision of this joint direction judgment is, however, 2–3 times poorer than that for a single motion stream. The precision in directional judgment for a single stream is reduced only by a factor of about 1.5 by superimposing a second stream. The major effect in performance, therefore, appears to be associated with the need to compute and compare two global representations of motion, rather than with interference between the dot streams per se. Experiment 2tested the transparency of motions separated by a range of angles from 5 to 180 by requiring subjects to set a line matching the perceived direction of each motion. The S.D.s of these settings demonstrated that directions of transparent motions were represented independently for separations over 20. Increasing dot speeds from 1 to 10 deg/s improved directional performance but had no effect on transparency perception. Transparency was also unaffected by variations of density between 0.1 and 19 dots/deg2

The human synovial fluid proteome: A key factor in the pathology of joint disease

This review aims to summarise our knowledge to date on the protein complement of the synovial fluid (S F). The tissues, structure and pathophysiology of the synovial joint are briefly described. The salient features of the S F proteome, how it is composed and the influence of arthritic disease are highlighted and discussed. The concentrations of proteins that have been detected and quantified in SF are drawn together from the literature on osteoarthritis, rheumatoid arthritis and juvenile idiopathic arthritis. The measurements are plotted to give a perspective on the dynamic range of protein levels within the SF. Approaches to proteomic analysis of SF to date are discussed along with their findings. From the recent literature reviewed within, it is becoming increasingly clear that analysis of the SF proteome as a whole, could deliver the most valuable differential diagnostic fingerprints of a number of arthritic disorders. Further development of proteomic platforms could characterise prognostic profiles to improve the cliniciads ability to resolve unremitting disease by existing and novel therapeutics.

Nonlinear theory of solitary waves associated with longitudinal particle motion in lattices - Application to longitudinal grain oscillations in a dust crystal

The nonlinear aspects of longitudinal motion of interacting point masses in a lattice are revisited, with emphasis on the paradigm of charged dust grains in a dusty plasma (DP) crystal. Different types of localized excitations, predicted by nonlinear wave theories, are reviewed and conditions for their occurrence (and characteristics) in DP crystals are discussed. Making use of a general formulation, allowing for an arbitrary (e.g. the Debye electrostatic or else) analytic potential form phi(r) and arbitrarily long site-to-site range of interactions, it is shown that dust-crystals support nonlinear kink-shaped localized excitations propagating at velocities above the characteristic DP lattice sound speed v(0). Both compressive and rarefactive kink-type excitations are predicted, depending on the physical parameter values, which represent pulse- (shock-)like coherent structures for the dust grain relative displacement. Furthermore, the existence of breather-type localized oscillations, envelope-modulated wavepackets and shocks is established. The relation to previous results on atomic chains as well as to experimental results on strongly-coupled dust layers in gas discharge plasmas is discussed.

A probabilistic modelling scheme for analysis of long-term failure of cemented femoral joint replacements

Reliable prediction of long-term medical device performance using computer simulation requires consideration of variability in surgical procedure, as well as patient-specific factors. However, even deterministic simulation of long-term failure processes for such devices is time and resource consuming so that including variability can lead to excessive time to achieve useful predictions. This study investigates the use of an accelerated probabilistic framework for predicting the likely performance envelope of a device and applies it to femoral prosthesis loosening in cemented hip arthroplasty.
A creep and fatigue damage failure model for bone cement, in conjunction with an interfacial fatigue model for the implant–cement interface, was used to simulate loosening of a prosthesis within a cement mantle. A deterministic set of trial simulations was used to account for variability of a set of surgical and patient factors, and a response surface method was used to perform and accelerate a Monte Carlo simulation to achieve an estimate of the likely range of prosthesis loosening. The proposed framework was used to conceptually investigate the influence of prosthesis selection and surgical placement on prosthesis migration.
Results demonstrate that the response surface method is capable of dramatically reducing the time to achieve convergence in mean and variance of predicted response variables. A critical requirement for realistic predictions is the size and quality of the initial training dataset used to generate the response surface and further work is required to determine the recommendations for a minimum number of initial trials. Results of this conceptual application predicted that loosening was sensitive to the implant size and femoral width. Furthermore, different rankings of implant performance were predicted when only individual simulations (e.g. an average condition) were used to rank implants, compared with when stochastic simulations were used. In conclusion, the proposed framework provides a viable approach to predicting realistic ranges of loosening behaviour for orthopaedic implants in reduced timeframes compared with conventional Monte Carlo simulations.

Advances in joint modelling: A review of recent developments with application to the survival of end stage renal disease patients

The use of joint modelling approaches is becoming increasingly popular when an association exists between survival and longitudinal processes. Widely recognized for their gain in efficiency, joint models also offer a reduction in bias compared with naïve methods. With the increasing popularity comes a constantly expanding literature on joint modelling approaches. The aim of this paper is to give an overview of recent literature relating to joint models, in particular those that focus on the time-to-event survival process. A discussion is provided on the range of survival submodels that have been implemented in a joint modelling framework. A particular focus is given to the recent advancements in software used to build these models. Illustrated through the use of two different real-life data examples that focus on the survival of end-stage renal disease patients, the use of the JM and joineR packages within R are demonstrated. The possible future direction for this field of research is also discussed. © 2013 International Statistical Institute.

Establishing optimal quantitative-polymerase chain reaction assays for routine diagnosis and tracking of minimal residual disease in JAK2-V617F-associated myeloproliferative neoplasms:a joint European LeukemiaNet/MPN&MPNr-EuroNet (COST action BM0902) study

Reliable detection of JAK2-V617F is critical for accurate diagnosis of myeloproliferative neoplasms (MPNs); in addition, sensitive mutation-specific assays can be applied to monitor disease response. However, there has been no consistent approach to JAK2-V617F detection, with assays varying markedly in performance, affecting clinical utility. Therefore, we established a network of 12 laboratories from seven countries to systematically evaluate nine different DNA-based quantitative PCR (qPCR) assays, including those in widespread clinical use. Seven quality control rounds involving over 21,500 qPCR reactions were undertaken using centrally distributed cell line dilutions and plasmid controls. The two best-performing assays were tested on normal blood samples (n=100) to evaluate assay specificity, followed by analysis of serial samples from 28 patients transplanted for JAK2-V617F-positive disease. The most sensitive assay, which performed consistently across a range of qPCR platforms, predicted outcome following transplant, with the mutant allele detected a median of 22 weeks (range 6-85 weeks) before relapse. Four of seven patients achieved molecular remission following donor lymphocyte infusion, indicative of a graft vs MPN effect. This study has established a robust, reliable assay for sensitive JAK2-V617F detection, suitable for assessing response in clinical trials, predicting outcome and guiding management of patients undergoing allogeneic transplant.

The influence of water motion on the growth rate of the kelp Laminaria digitata

The shallow water kelp Laminaria digitata, abundant in coastal zones of the North Atlantic, is exposed to a range of hydrodynamic environments that makes it ideal for assessing the role of water motion on their growth rate. Here we quantify the growth of L. digitata, as a factor of blade and stipe elongation, at sites adjacent to Strangford Lough, Northern Ireland under different hydrodynamic conditions over a one year period. A modelling approach was used to numerically determine both the temporal and spatial variability of the hydrodynamic environment. Ambient seawater nutrient concentrations, temperature and irradiance were measured as well as the internal nutrient status of the L. digitata populations. Kelp populations growing in the greatest and lowest water motion showed the lowest growth rates. Differences observed in growth rate could not be attributed to seawater nutrient availability, temperature or light. The internal nutrient status also suggested no influence on the observed differences in growth rate. Therefore if there are minimal differences in light, temperature and nutrients between sites, then populations of L. digitata exposed to different water motions are likely to exhibit different growth rates. It is suggested that the growth rate differences observed were a function of water motion with the possibility that, in response to the hydrodynamic forces experienced by the algal cells, L. digitata kelps in the high energy environments were putting more energy into strengthening cell walls rather than blade elongation