A three species model to simulate application of hyperbaric oxygen therapy to chronic wounds

Chronic wounds are a significant socioeconomic problem for governments worldwide. Approximately 15% of people who suffer from diabetes will experience a lower-limb ulcer at some stage of their lives, and 24% of these wounds will ultimately result in amputation of the lower limb. Hyperbaric Oxygen Therapy (HBOT) has been shown to aid the healing of chronic wounds; however, the causal reasons for the improved healing remain unclear and hence current HBOT protocols remain empirical. Here we develop a three-species mathematical model of wound healing that is used to simulate the application of hyperbaric oxygen therapy in the treatment of wounds. Based on our modelling, we predict that intermittent HBOT will assist chronic wound healing while normobaric oxygen is ineffective in treating such wounds. Furthermore, treatment should continue until healing is complete, and HBOT will not stimulate healing under all circumstances, leading us to conclude that finding the right protocol for an individual patient is crucial if HBOT is to be effective. We provide constraints that depend on the model parameters for the range of HBOT protocols that will stimulate healing. More specifically, we predict that patients with a poor arterial supply of oxygen, high consumption of oxygen by the wound tissue, chronically hypoxic wounds, and/or a dysfunctional endothelial cell response to oxygen are at risk of nonresponsiveness to HBOT. The work of this paper can, in some way, highlight which patients are most likely to respond well to HBOT (for example, those with a good arterial supply), and thus has the potential to assist in improving both the success rate and hence the costeffectiveness of this therapy.

Listening to nature : acoustic monitoring of the environment

The requirement to monitor the rapid pace of environmental change due to global warming and to human development is producing large volumes of data but placing much stress on the capacity of ecologists to store, analyse and visualise that data. To date, much of the data has been provided by low level sensors monitoring soil moisture, dissolved nutrients, light intensity, gas composition and the like. However, a significant part of an ecologist’s work is to obtain information about species diversity, distributions and relationships. This task typically requires the physical presence of an ecologist in the field, listening and watching for species of interest. It is an extremely difficult task to automate because of the higher order difficulties in bandwidth, data management and intelligent analysis if one wishes to emulate the highly trained eyes and ears of an ecologist. This paper is concerned with just one part of the bigger challenge of environmental monitoring – the acquisition and analysis of acoustic recordings of the environment. Our intention is to provide helpful tools to ecologists – tools that apply information technologies and computational technologies to all aspects of the acoustic environment. The on-line system which we are building in conjunction with ecologists offers an integrated approach to recording, data management and analysis. The ecologists we work with have different requirements and therefore we have adopted the toolbox approach, that is, we offer a number of different web services that can be concatenated according to need. In particular, one group of ecologists is concerned with identifying the presence or absence of species and their distributions in time and space. Another group, motivated by legislative requirements for measuring habitat condition, are interested in summary indices of environmental health. In both case, the key issues are scalability and automation.

Bayesian model averaging for harmful algal bloom prediction

Harmful Algal Blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been developed, and in common with most predictive models in ecology, those that have been developed do not fully account for uncertainties in parameters and model structure. This makes management decisions based on these predictions more risky than might be supposed. We used a probit time series model and Bayesian Model Averaging (BMA) to predict occurrences of blooms of Lyngbya majuscula, a toxic cyanophyte, in Deception Bay, Queensland, Australia. We found a suite of useful predictors for HAB occurrence, with Temperature figuring prominently in models with the majority of posterior support, and a model consisting of the single covariate average monthly minimum temperature showed by far the greatest posterior support. A comparison of alternative model averaging strategies was made with one strategy using the full posterior distribution and a simpler approach that utilised the majority of the posterior distribution for predictions but with vastly fewer models. Both BMA approaches showed excellent predictive performance with little difference in their predictive capacity. Applications of BMA are still rare in ecology, particularly in management settings. This study demonstrates the power of BMA as an important management tool that is capable of high predictive performance while fully accounting for both parameter and model uncertainty.