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Disease‐tolerant cultivars maintain yield in the presence of disease. When disease intensity is high, they can improve a grower's net return compared to less tolerant cultivars. Many authors report a trade‐off, whereby higher fully protected yields are correlated with a lower disease tolerance. We analyse the question for breeders: to what extent should they breed for tolerance when it compromises maximizing fully protected yield? Field trials with 147 progeny from five parental crosses of wheat were used to measure yield and tolerance under a range of disease intensities from Septoria tritici blotch (STB; causal organism Zymoseptoria tritici) at a range of sites and seasons. The data define the variation for these traits from which breeders can select. A simple data‐driven descriptive model was used to calculate the combination of tolerance and fully protected yield that maximizes actual yield for any given level of disease—quantified by loss of healthy canopy area duration (HAD‐loss). This model was combined with data on the year‐to‐year variability of HAD‐loss in the UK to calculate the tolerance and fully protected yield that maximizes the mean actual yield. We found that even when an effective fungicide treatment programme is applied, breeding for tolerance increases the mean actual yield. Some commercially available cultivars were found to have a level of tolerance that leads to yields close to the maximum yield in the presence of disease, others had a lower tolerance leading to suboptimal yields. [ABSTRACT FROM AUTHOR]

Abstract: Population dynamics of the biennial plant Tragopogon pratensis have been monitored in the Park Grass Experiment at Rothamsted Research, Harpenden, UK, over many years. Observations of diseased T. pratensis, systemically infected by the autoecious demicyclic rust Puccinia hysterium, were made over the period 1995–2008, and confirmed an outbreak pattern of dynamics, characterised by an increase to a relatively high incidence followed by a reduction to low almost indiscernible levels. An epidemiological model was developed taking into account the biennial habit of the host plant, and the systemic nature of infection during the winter period, and the partial sterilisation of infected second year plants. Seedling emergence rate and natural mortality between seasons and within season were key parameters affecting host performance. The transmission rate between infected second year plants and susceptible first year seedlings, and the probability that the fungus would survive the winter systemically as mycelium producing aecia and telia on emerging second year plants, were key parameters associated with pathogenicity. Furthermore the possibility of pathogen-induced additional mortality was modelled. The model predicted that outbreak dynamics of T. pratensis would occur with high pathogenicity and medium or high host performance. In the former case the population dynamics would be cyclical with, in some cases, infected plants going to extinction. In the latter case both host and pathogen would go to extinction. The model predicted that the two pathogenicity parameters were critical in determining whether the pathogen would invade a healthy population; whereas pathogen-induced mortality had little influence, a result also obtained in some limited potted plant experiments. Fitting the model to the field data indicated that there was little or no density-dependence in seedling emergence rate, and again that pathogen-induced mortality played little role in the observed population dynamics. [Copyright &y& Elsevier]

We extend a previously developed method that quantifies the sensitivity of the exponential epidemic growth rate, r, to weather changes, through a pathogen's life cycle components (basic reproduction number, latent period, and mean and standard deviation of the spore production curve). Here a method is developed to study the elasticities of the system and subsequently the model is linked to observed weather patterns. This enables a direct comparison between the effects of different weather variables (temperature, surface wetness duration, and light quantity) under realistic weather scenarios. The three sites studied represent areas within the United Kingdom with contrasting climates. Yellow rust, caused by Puccinia striiformis, on winter wheat is studied as a key application. Our results show that temperature and more importantly changes in temperature through their effect on pathogen reproduction have the largest effect on r. The long latent period at low winter temperatures is not a key component in the epidemic development, which is contrary to general beliefs. The results combined with long term average yellow rust severity patterns show that it is winter survival and not summer survival that controls the eventual disease severity. The results also show that within the current United Kingdom spraying regime on wheat crops against yellow rust, the first spray should mainly affect the basic reproduction number, i.e., should be a protectant spray, whereas the second spray should also affect the latent period, i.e., should also have curative action. [ABSTRACT FROM AUTHOR]

Background: Recent prognostic studies have shown that previous treatment with statins is associated with a better outcome in patients admitted to hospital with pneumonia. Because of an increased risk of pneumonia in patients with diabetes, we assessed the effects of statin use on the occurrence of pneumonia in adult diabetic patients. Methods: All patients with a diagnosis of diabetes (types 1 and 2) enlisted in the UK General Practice Research Database between 1 June 1987 and 21 January 2001 were included. A case-control study was performed with cases defined as patients with a first recorded diagnosis of pneumonia. For each case up to four controls were matched by age, sex, practice, and index date. Patients were classified as current users when the index date was between the start and end date of statin treatment. Conditional multiple logistic regression analysis was used to estimate the strength of the association between statin treatment and the occurrence of pneumonia. Results: Statins were used in 1.1% of 4719 cases and in 2.1% of 15322 matched controls (crude odds ratio (OR) 0.51, 95% CI 0.37 to 0.68). After adjusting for potential confounders, treatment with statins was associated with a significant reduction in the risk of pneumonia (adjusted OR 0.49, 95% CI 0.35 to 0.69). The association was consistent among relevant subgroups (cardiovascular diseases, pulmonary diseases) and independent of the use of other prescription drugs. Conclusions: The use of statins is associated with a considerable reduction in the risk of pneumonia in diabetic patients. In addition to lowering the risk of cardiovascular disease, statins may be useful in preventing respiratory infections. [ABSTRACT FROM AUTHOR]

The article assesses the state of altruistic kidney donation based on the personal and professional experience of Give A Kidney trustee Dr. Paul van den Bosch. He shares his experience as a live donor and outlines the efforts of his charity to raise public support for renal transplants. Awareness of consequences for the donor and of the critical need of recipients for organs is emphasized by the author as a priority.

A letter to the editor is presented in response to the article "Organ donation in the UK: how general practice can help" by J. Neuberger, and A. Keogh.

Monitoring networks are improved by additional sensors. Optimal configurations of sensors give better representations of the process of interest, maximising its exploration while minimising the need for costly infrastructure. By modelling the monitored process, we can identify gaps in its representation, i.e. uncertain predictions, where additional sensors should be located. Here, with data collected from the Rothamsted Insect Survey network, we train an artificial neural network to predict the seasonal aphid arrival from environmental variables. We focus on estimating prediction uncertainty across the UK to guide the addition of a sensor to the network. We first illustrate how to estimate uncertainty in neural networks, hence making them relevant for model-based monitoring network optimisation. Then we highlight critical areas of agricultural importance where additional traps would improve decision support and crop protection in the UK. Possible applications include most ecological monitoring and surveillance activities, but also the weather or pollution monitoring. • Sensor networks are used to monitor biological processes, such as aphid arrival. • Artificial neural networks can provide spatial predictions from point observations. • We show how artificial neural networks can also provide uncertainty estimates. • Model uncertainty highlights gaps where new sensors should be located. [ABSTRACT FROM AUTHOR]

Key weather factors determining the occurrence and severity of powdery mildew and yellow rust epidemics on winter wheat were identified. Empirical models were formulated to qualitatively predict a damaging epidemic (>5% severity) and quantitatively predict the disease severity given a damaging epidemic occurred. The disease data used was from field experiments at 12 locations in the UK covering the period from 1994 to 2002 with matching data from weather stations within a 5 km range. Wind in December to February was the most influential factor for a damaging epidemic of powdery mildew. Disease severity was best identified by a model with temperature, humidity, and rain in April to June. For yellow rust, the temperature in February to June was the most influential factor for a damaging epidemic as well as for disease severity. The qualitative models identified favorable circumstances for damaging epidemics, but damaging epidemics did not always occur in such circumstances, probably due to other factors such as the availability of initial inoculum and cultivar resistance. [ABSTRACT FROM AUTHOR]