Directly bonding antimicrobial peptide mimics to steel and the real world applications of these materials.

Despite increased sterilisation and education campaigns, hospital acquired infections have not been eradicated. Bacterial colonisation of frequent touch surfaces is key in the transmission of infection. Most current technologies cannot provide a material which can rapidly kill bacteria. Here we report a novel surface technology, which uses synthetic mimetics of human defensin proteins on a surface. The surface shows excellent antibacterial efficacy against Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus, Pseudomonas aeruginosa and Escherichia coli. Both microbiology laboratory tests and trials in hospital settings of this new antimicrobial material (AMS) showed >99% efficacy over a year in situ. It maintains its efficacy through accelerated ageing tests and has shown to kill bacteria far more rapidly (45 min) than the commercially available technologies (24 h).

Economically optimal timing for crop disease control under uncertainty: an options approach.

Severe large-scale disease and pest infestations in agricultural regions can cause significant economic damage. Understanding if and when disease control measures should be taken in the presence of risk and uncertainty is a key issue. We develop a framework to examine the economically optimal timing of treatment. The decision to treat should only be undertaken when the benefits exceed the costs by a certain amount and not if they are merely equal to or greater than the costs as standard net-present-value (NPV) analysis suggests. This criterion leads to a reduction in fungicide use. We investigate the effect of the model for disease progress on the value required for immediate treatment by comparing two standard models for disease increase (exponential and logistic growth). Analyses show that the threshold value of benefits required for immediate release of treatment varies significantly with the relative duration of the agricultural season, the intrinsic rate of increase of the disease and the level of uncertainty in disease progression. In comparing the performance of the delay strategy introduced here with the conventional NPV approach, we show how the degree of uncertainty affects the benefits of delaying control.

Optimizing the control of disease infestations at the landscape scale.

Using a contact-process model for the spread of crop disease over a regional scale, we examine the importance of the time scale for control with respect to the cost of the epidemic. The costs include the direct cost of treating infected sites as well as the indirect costs incurred through lost yield. We first use a mean-field approximation to derive analytical results for the optimal treatment regimes that minimize the total cost of the epidemic. We distinguish short- and long-term epidemics. and show that seasonal control (short time scale) requires extreme treatment, either treating all sites or none or switching between the two at some stage during the season. The optimal long-term strategy requires an intermediate level of control that results in near eradication of the disease. We also demonstrate the importance of incorporating economic constraints by deriving a critical relationship between the epidemiological and economic parameters that determine the qualitative nature of the optimal treatment strategy. The set of optimal strategies is summarized in a policy plot, which can be used to determine the nature of the optimal treatment regime given prior knowledge of the epidemiological and economic parameters. Finally, we test the robustness of the analytical results, derived from the mean-field approximation, on the spatially explicit contact process and demonstrate robustness to implementation errors and misestimation of crucial parameters.