The impact of environmental temperature deception on perceived exertion during fixed-intensity exercise in the heat in trained-cyclists.

PURPOSE: This study examined the effect of environmental temperature deception on the rating of perceived exertion (RPE) during 30 min of fixed-intensity cycling in the heat. METHODS: Eleven trained male cyclists completed an incremental cycling test and four experimental trials. Trials consisted of 30 min cycling at 50% Pmax, once in 24 °C (CON) and three times in 33 °C. In the hot trials, participants were provided with accurate temperature feedback (HOT), or were deceived to believe the temperature was 28 °C (DECLOW) or 38 °C (DECHIGH). During cycling, RPE was recorded every 5 min. Rectal and skin temperature, heart rate and oxygen uptake were continuously measured. Data were analysed using linear mixed model methods in a Bayesian framework, magnitude-based inferences (Cohens d), and the probability that d exceeded the smallest worthwhile change. RESULTS: RPE was higher in the heat compared to CON, but not statistically different between the hot conditions (mean [95% credible interval]; DECLOW: 13.0 [11.9, 14.1]; HOT: 13.0 [11.9, 14.1]; DECHIGH: 13.1 [12.0, 14.2]). Heart rate was significantly higher in DECHIGH (141 b·min-1 [132, 149]) compared to all other conditions (DECLOW: 138 b·min-1 [129, 146]; HOT: 138 b·min-1 [129, 145]) after 10 min; however, this did not alter RPE. All other physiological variables did not differ between the hot conditions. CONCLUSION: Participants were under the impression they were cycling in different environments; however, this did not influence RPE. These data suggest that for trained cyclists, an awareness of environmental temperature does not contribute to the generation of RPE when exercising at a fixed intensity in the heat.

Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments.

Between-subject and within-subject variability is ubiquitous in biology and physiology, and understanding and dealing with this is one of the biggest challenges in medicine. At the same time, it is difficult to investigate this variability by experiments alone. A recent modelling and simulation approach, known as population of models (POM), allows this exploration to take place by building a mathematical model consisting of multiple parameter sets calibrated against experimental data. However, finding such sets within a high-dimensional parameter space of complex electrophysiological models is computationally challenging. By placing the POM approach within a statistical framework, we develop a novel and efficient algorithm based on sequential Monte Carlo (SMC). We compare the SMC approach with Latin hypercube sampling (LHS), a method commonly adopted in the literature for obtaining the POM, in terms of efficiency and output variability in the presence of a drug block through an in-depth investigation via the Beeler-Reuter cardiac electrophysiological model. We show improved efficiency for SMC that produces similar responses to LHS when making out-of-sample predictions in the presence of a simulated drug block. Finally, we show the performance of our approach on a complex atrial electrophysiological model, namely the Courtemanche-Ramirez-Nattel model.

A sequential Monte Carlo approach to derive sampling times and windows for population pharmacokinetic studies.

Here we present a sequential Monte Carlo approach that can be used to find optimal designs. Our focus is on the design of population pharmacokinetic studies where the derivation of sampling windows is required, along with the optimal sampling schedule. The search is conducted via a particle filter which traverses a sequence of target distributions artificially constructed via an annealed utility. The algorithm derives a catalog of highly efficient designs which, not only contain the optimal, but can also be used to derive sampling windows. We demonstrate our approach by designing a hypothetical population pharmacokinetic study, and compare our results with those obtained via a simulation method from the literature.

Estimation of parameters for macroparasite population evolution using approximate bayesian computation.

We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra-binomial variation in terms of a zero-one immunity variable, which has a short-lived presence in the host.

Multivariate Markov process models for the transmission of methicillin-resistant Staphylococcus aureus in a hospital ward.

Methicillin-resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245, 470-481) involving an underlying population of MRSA colonized patients and health-care workers that describes, among other processes, transmission between uncolonized patients and colonized health-care workers and vice versa. We develop new bivariate and trivariate Markov models to include incidence so that estimated transmission rates can be based directly on new colonizations rather than indirectly on prevalence. Imperfect sensitivity of pathogen detection is modeled using a hidden Markov process. The advantages of our approach include (i) a discrete valued assumption for the number of colonized health-care workers, (ii) two transmission parameters can be incorporated into the likelihood, (iii) the likelihood depends on the number of new cases to improve precision of inference, (iv) individual patient records are not required, and (v) the possibility of imperfect detection of colonization is incorporated. We compare our approach with that used by McBryde et al. (2007) based on an approximation that eliminates the health-care workers from the model, uses Markov chain Monte Carlo and individual patient data. We apply these models to MRSA colonization data collected in a small intensive care unit at the Princess Alexandra Hospital, Brisbane, Australia.