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The fungus Leptosphaeria maculans causes blackleg disease of canola (Brassica napus). This fungus has a high evolutionary potential, and extensive sowing of a cultivar in a region can lead to blackleg resistance bred into the cultivar becoming ineffective within 3 years. Monitoring of disease severity and virulence of fungal populations across canola-growing regions of Australia in 2011 revealed a high risk of breakdown of resistance of cultivar Hyola50 on the lower Eyre Peninsula, South Australia, but not elsewhere. Cultivar Hyola50 has two resistance genes, based on the pattern of segregation of resistance in the plant and avirulence in the fungus. One of these is Rlm1, a gene that was rendered ineffective previously in commercial crops of canola in Australia and France. Acreage sown to Hyola50-related cultivars decreased dramatically in 2012 after release of a warning to farmers in this region not to sow these cultivars. At a field trial in this region, disease severity in 2012 in cv. Hyola50 was 85%, while that of cultivars containing different resistance genes was much lower. By not sowing cv. Hyola50, farmers in an area of about 60,000 ha on the lower Eyre Peninsula, saved AU$ 13 million in predicted yield losses. [ABSTRACT FROM AUTHOR]

Despite its global significance as an edible oil and biofuel, the critical period for yield determination in canola (edible oilseed rape – Brassica napus L.) has not been determined in the field. Field experiments were conducted at two contrasting sites in southern Australia where 100 °Cd shading periods (15% PAR transmitted) were applied from early vegetative growth until maturity to identify the developmental period when the crop was most sensitive to stress. Despite the significant difference between the two sites for yield in the unshaded control (450 g m −2 in New South Wales, and 340 g m −2 in South Australia), the critical period was consistent at both sites extending from 100 to 500 °Cd after the start of flowering (BBCH60), and centred 300 °Cd after BBCH60. Seed number (seed m −2 ) was reduced by an average of 48% in the critical period, generated in equal parts by reduced pod m −2 in the early part of the period, and reduced seed pod −1 in the latter part. Reduced seed number was partially compensated by an increase in seed size of 29%. These trends were similar on the branches and main stem. On the main stem, the timing of the critical period moved from earlier to later from lower to upper pods linked to the timing of their development. Seed oil content declined and protein content increased under shading in the critical period, while both oil and protein yield (kg m −2 ) were reduced by 40–50% and 30–40% respectively. The critical period is coincident with the greatest number of near-open buds and newly opened flowers, which are highly sensitive to assimilate supply for ovule development. Both pod abortion and restricted capacity for compensatory growth of surviving pods are consequences of assimilate restriction on developing ovules. Identification of the critical period provides a useful target for breeding and management strategies to maximize productivity. [ABSTRACT FROM AUTHOR]

Abstract: The application of mapping and spatial analytical techniques to explore geographical patterns of crime incidence is well established. In contrast, the analysis of operational incident data routinely collected by fire brigades has received relatively less research attention, certainly in the UK academic literature. The aim of this paper is to redress this balance through the application of spatial analytical techniques that permit an exploration of the spatial dynamics of fire incidents and their relationships with socio-economic variables. By examining patterns for different fire incident types, including household fires, vehicle fires, secondary fires and malicious false alarms in relation to 2001 Census of Population data for an area of South Wales, we demonstrate the potential of such techniques to reveal spatial patterns that may be worthy of further contextual study. Further research is needed to establish how transferable these findings are to other geographical settings and how replicable the findings are at different geographical scales. The paper concludes by drawing attention to the current gaps in knowledge in analysing trends in fire incidence and proposes an agenda to advance such research using spatial analytical techniques. [Copyright &y& Elsevier]

National assault injury surveillance has identified major seasonal variation, but it is not clear whether assault injury is a seasonal problem in large cities. Relationships between community violence, calendar events and ambient conditions were investigated with reference to prospective, Accident and Emergency (A&E) derived information obtained from people injured in assaults in Cardiff between 1 May 1995 and 30 April 2000. Records of daily local ambient conditions included data relating to temperature, rainfall and sunshine hours and data of major local sporting events and annual holidays were studied. Pearson correlation coefficients were used to evaluate associations between variables. Overall, 19,264 assault-related A&E attendances were identified over the 5-year period. Almost three-quarters were males. Violence was clustered predominantly on Saturdays and Sundays, New Year and rugby international days. Temperature, rainfall and sunlight hours did not correlate significantly with violence (P>0.05). The findings indicate that injury reduction effort should be intensified at the known risk times for violence and that in a capital city/regional centre violence cannot be predicted on the basis of ambient conditions. [Copyright &y& Elsevier]

Missed appointments are estimated to cost the UK National Health Service (NHS) approximately £1 billion annually. Research that leads to a fuller understanding of the types of factors influencing spatial and temporal patterns of these so-called "Did-Not-Attends" (DNAs) is therefore timely. This research articulates the results of a study that uses machine learning approaches to investigate whether these factors are consistent across a range of medical specialities. A predictive model was used to determine the risk-increasing and risk-mitigating factors associated with missing appointments, which were then used to assign a risk score to patients on an appointment-by-appointment basis for each speciality. Results show that the best predictors of DNAs include the patient's age, appointment history, and the deprivation rank of their area of residence. Findings have been analysed at both a geographical and medical speciality level, and the factors associated with DNAs have been shown to differ in terms of both importance and association. This research has demonstrated how machine learning techniques have real value in informing future intervention policies related to DNAs that can help reduce the burden on the NHS and improve patient care and well-being. [ABSTRACT FROM AUTHOR]