Reaching out to promote physical activity in Australia: A statewide randomized controlled trial of a stage-targeted intervention

Purpose. This study examined the broader use of a print-media intervention, which was previously shown to be effective at promoting physical activity to participants recruited from a regional Australian community, as a strategy suitable for a more diverse statewide Population sample. Methods. Participants were randomly selected adults who responded to a telephone interview conducted by the New South Wales Health Department and consented to Participate in a randomized controlled trial. Consenters were allocated to either intervention (n = 361) or control (n = 358) conditions. The intervention, a personalized letter plus stage-targeted booklets, was sent 1 week postbaseline. Data were collected via telephone inter view at baseline and 2 and 8 months and were analyzed using repeated measures analysis of variance (ANOVA) and chi(2) statistics. Results. The groups were similar at baseline (mean age 43 +/- 3 years; 64% women). Process evaluation showed high intervention recall (76% at 2 months) and high follow-up response rules (>85% at 8 months) were achieved. Nonsignificant increases in physical activity were observed (F-1,F-719 = 2.18, p =.14). Discussion. A single mailing of stage-targeted print materials was not effective in promoting increases in physical activity among participants selected from the statewide population. Future research could. examine how the effectiveness of print media might be enhanced, possibly by using supplementary media, community-based Prompts, or other incentives.

Scaffolds, stem cells, and tissue engineering: A potent combination!

Stem cells, either from embryonic or adult sources, have demonstrated the potential to differentiate into a wide range of tissues depending on culture conditions. This makes them prime candidates for use in tissue engineering applications. Current technology allows us to process biocompatible and biodegradable polymers into three-dimensional (3D) configurations, either as solid porous scaffolds or hydrogels, with controlled macro and/or micro spatial geometry and surface chemistry. Such control provides us with the ability to present highly controlled microenvironments to a chosen cell type. However, the precise microenvironments required for optimal expansion and/or differentiation of stem cells are only now being elucidated, and hence the controlled use of stem cells in tissue engineering remains a very young field. We present here a brief review of the current literature detailing interactions between stem cells and 3D scaffolds of varying morphology and chemical properties, concluding with remaining challenges for those interested in tissue engineering using tailored scaffolds and stem cells.

Monitoring changes in nisin susceptibility of Listeria monocytogenes Scott A as an indicator of growth phase using FACS

Listeria monocytogenes has previously been shown to adapt to a wide variety of environmental niches, principally those associated with low pH, and this compromises its control in food environments. An understanding of the mechanism(s) by which L. monocytogenes survives unfavourable environmental conditions will aid in developing new food processing methods to control the organism in foodstuffs. The present Study aimed to gain a further understanding of the physiological basis for the differential effects of one control strategy, namely the use of the lantibiotic nisin. Using propidium iodide (PI) to probe membrane integrity it was shown that L. monocytogenes Scott A was sensitive to nisin (8 ng mL(-1)) but this was growth phase dependent with stationary phase cells (OD600=1.2) being much more resistant than exponential phase cells (OD600=0.38). We demonstrate that, using a combination of techniques including fluorescence activated cell sorting (FACS), the membrane adaptations underpinning nisin resistance are triggered much earlier (OD600 < 0.5) than the onset of stationary phase. The significance of these findings in terms of mechanism and application are discussed. (c) 2005 Elsevier B.V.All rights reserved.

Doubling daily inhaled corticosteroid dose is ineffective in mild to moderately severe attacks of asthma in adults

Background: Asthma guidelines recommend increasing or doubling inhaled corticosteroid (ICS) dose to treat mild and moderate exacerbations of asthma in adults. Aim: To: (i) compare the effectiveness of doubling existing daily ICS dose (fluticasone) with maintaining usual ICS dose and usual daily ICS dose accompanied by oral steroids (OS) (dexamethasone) during mild and moderately severe exacerbations of asthma in adults; (ii) examine determinants of success and failure; and (iii) compare side-effect profiles. Methods: A randomized, double-blind, placebo-controlled (double-dummy), triple crossover trial. Participants acted as their own control. Outcome measures included treatment success/failure, peak expiratory flow (PEF) after 7 days therapy or at treatment failure, and side-effects. Results: From 22 participants (nine males and 13 females), 18 pairs of data were available for maintaining usual ICS versus doubling ICS and doubling ICS versus OS, and 19 for maintaining usual ICS versus OS. Median (fifth-95th percentile) age was 46.5 (32-64) years and forced expiratory volume in one second (FEV1) 73% (29-97%) predicted. The outcome after doubling ICS was not superior to maintaining usual ICS, with 11 (61%) failures in both arms (P = 0.66). OS, with only 5 (26%) failures, was superior to maintaining usual ICS with 12 (63%) failures (P = 0.04), and to doubling ICS with 5 (28%) versus 11 (61%) failures (P = 0.07). Median PEF (as percentage of run-in best) at end-points were 90.5% (57.1-177.1) for OS, 78.3% (39.5-103.1) for maintaining usual ICS and 77.9 (27.7-110.3) for doubling ICS. Neither gender nor PEF at exacerbation were predictive of failure. Although doubling ICS was not an effective therapy overall, ICS dose at exacerbation were predictive of success in the doubling ICS arm (P = 0.04). Treatment failures when doubling daily ICS dose were more common if achieved fluticasone dose was less than 2000 mu g (three of 11, 73%) compared to 2000 mu g or greater (eight of eight, 37.5%). Increasing age and the presence of an upper respiratory tract infection (URTI) were predictive of failure with OS. Side-effects were more commonly reported with OS (52.6%) than doubling ICS (42.1%) or maintaining usual ICS (19.1%) with the most common being mood changes (36.8%), sleep disturbance (31.6%) and changes in appetite (26.3%). Conclusions: Doubling daily ICS dose per se is not effective for the treatment of mild to moderately severe exacerbations of asthma in adults. Success may depend on achieved ICS dose. Oral steroids are effective, but side-effects are common. A review of current guidelines may be warranted.

Effect of knee joint angle on motor unit synchronization

Activity of the vasti has been argued to vary through knee range of movement due to changes in passive support of the patellofemoral joint and the relative contribution of these muscles to knee extension. Efficient function of the knee is dependent on optimal control of the patellofemoral joint, largely through coordinated activity of the medial and lateral quadriceps. Motor unit synchronization may provide a mechanism to coordinate the activity of vastus medialis (VMO) and vastus lateralis (VL), and may be more critical in positions of reduced passive support for the patellofemoral joint (i.e., full extension). Therefore, the aim of this study was to determine whether the degree of motor unit synchronization between the vasti muscles is dependent on joint angle. Electromyographic (EMG) recordings of single motor unit action potentials (MUAPs) were made from VMO and multiunit recordings from VL during isometric contractions of the quadriceps at 0 degrees, 30 degrees, and 60 degrees of knee flexion. The degree of synchronization between motor unit firing was evaluated by identification of peaks in the rectified EMG averages of VL, triggered from MUA-Ps in VMO. The proportion of cases in which there was a significant peak in the triggered averages was calculated. There was no significant difference in the degree of synchronization between the vasti at different knee angles (p = 0.57). These data suggest that this basic coordinative mechanism between the vasti muscles is controlled consistently throughout knee range of motion, and is not augmented at specific angles where the requirement for dynamic control of stability is increased. (D 2006 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Stochastic delay differential equations for genetic regulatory networks

Time delay is an important aspect in the modelling of genetic regulation due to slow biochemical reactions such as gene transcription and translation, and protein diffusion between the cytosol and nucleus. In this paper we introduce a general mathematical formalism via stochastic delay differential equations for describing time delays in genetic regulatory networks. Based on recent developments with the delay stochastic simulation algorithm, the delay chemical masterequation and the delay reaction rate equation are developed for describing biological reactions with time delay, which leads to stochastic delay differential equations derived from the Langevin approach. Two simple genetic regulatory networks are used to study the impact of' intrinsic noise on the system dynamics where there are delays. (c) 2006 Elsevier B.V. All rights reserved.

Implicit stochastic Runge-Kutta methods for stochastic differential equations

In this paper we construct implicit stochastic Runge-Kutta (SRK) methods for solving stochastic differential equations of Stratonovich type. Instead of using the increment of a Wiener process, modified random variables are used. We give convergence conditions of the SRK methods with these modified random variables. In particular, the truncated random variable is used. We present a two-stage stiffly accurate diagonal implicit SRK (SADISRK2) method with strong order 1.0 which has better numerical behaviour than extant methods. We also construct a five-stage diagonal implicit SRK method and a six-stage stiffly accurate diagonal implicit SRK method with strong order 1.5. The mean-square and asymptotic stability properties of the trapezoidal method and the SADISRK2 method are analysed and compared with an explicit method and a semi-implicit method. Numerical results are reported for confirming convergence properties and for comparing the numerical behaviour of these methods.

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focussed recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the best choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy. (C) 2004 Elsevier B.V. All rights reserved.

Numerical simulation of stochastic ordinary differential equations in biomathematical modelling

In this work we discuss the effects of white and coloured noise perturbations on the parameters of a mathematical model of bacteriophage infection introduced by Beretta and Kuang in [Math. Biosc. 149 (1998) 57]. We numerically simulate the strong solutions of the resulting systems of stochastic ordinary differential equations (SDEs), with respect to the global error, by means of numerical methods of both Euler-Taylor expansion and stochastic Runge-Kutta type. (C) 2003 IMACS. Published by Elsevier B.V. All rights reserved.

Numerical methods for strong solutions of stochastic differential equations: an overview

This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations. We give a brief survey of the area focusing on a number of application areas where approximations to strong solutions are important, with a particular focus on computational biology applications, and give the necessary analytical tools for understanding some of the important concepts associated with stochastic processes. We present the stochastic Taylor series expansion as the fundamental mechanism for constructing effective numerical methods, give general results that relate local and global order of convergence and mention the Magnus expansion as a mechanism for designing methods that preserve the underlying structure of the problem. We also present various classes of explicit and implicit methods for strong solutions, based on the underlying structure of the problem. Finally, we discuss implementation issues relating to maintaining the Brownian path, efficient simulation of stochastic integrals and variable-step-size implementations based on various types of control.

A genetic estimation algorithm for parameters of stochastic ordinary differential equations

A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market. (C) 2003 Elsevier B.V. All rights reserved.