Habitat richness and tree species richness of roundabouts: effects on site selection and the prevalence of arboreal caterpillars

This study examined the relationship between the habitat and tree species richness of roundabouts and the abundance and species richness of tree-dwelling caterpillars, and thus the potential of urban roundabouts to support breeding populations. Total tree species number on sites was related to an increase in caterpillar abundance and a greater number of habitats was associated with a higher number of caterpillar species. An increase in the total number of trees was not correlated with a greater abundance of tree-feeding Lepidoptera; nor were native trees found to support proportionally more caterpillar species than all tree species, including introduced. This could be due to differences in species accumulation, the prevalence of generalist species or a statistical sampling effect. The occupancy and average abundance of caterpillar species was generally low, which does not support the theory that Lepidoptera in urban areas tend towards mono-dominance. It may, however, represent the presence of ‘accidental’ species on roundabouts. Habitat management and planting to increase tree species diversity and density on roundabouts has the potential to increase the species diversity and abundance of arboreal Lepidoptera, and increase abundance of butterfly and moth urban populations

Host Selection of the giant willow aphid (Tuberolachnus salignus)

The giant willow aphid [Tuberolachnus salignus (Gmelin)] has recently become noteworthy as a potential pest species due to the increased uptake of willow, its host-plant, for use in growing biomass for energy production. In this paper we describe host selection studies of T. salignus on short rotation coppice (SRC) willow varieties in laboratory bioassays and field experiments. In laboratory olfactometry tests, T. salignus was significantly attracted to certain SRC willow varieties, but not to others. Field trials during 2007 and 2008 showed that T. salignus infestation levels varied significantly on different SRC willow varieties and that levels are highest on the varieties to which they are most strongly attracted in the laboratory bioassays

Opportunities for system level improvement in antibiotic use across the surgical pathway

Optimizing antibiotic prescribing across the surgical pathway (before, during, and after surgery) is a key aspect of tackling important drivers of antimicrobial resistance and simultaneously decreasing the burden of infection at the global level. In the UK alone, 10 million patients undergo surgery every year, which is equivalent to 60% of the annual hospital admissions having a surgical intervention. The overwhelming majority of surgical procedures require effectively limited delivery of antibiotic prophylaxis to prevent infections. Evidence from around the world indicates that antibiotics for surgical prophylaxis are administered ineffectively, or are extended for an inappropriate duration of time postoperatively. Ineffective antibiotic prophylaxis can contribute to the development of surgical site infections (SSIs), which represent a significant global burden of disease. The World Health Organization estimates SSI rates of up to 50% in postoperative surgical patients (depending on the type of surgery), with a particular problem in low- and middle-income countries, where SSIs are the most frequently reported healthcare-associated infections. Across European hospitals, SSIs alone comprise 19.6% of all healthcare-acquired infections. Much of the scientific research in infection management in surgery is related to infection prevention and control in the operating room, surgical prophylaxis, and the management of SSIs, with many studies focusing on infection within the 30-day postoperative period. However it is important to note that SSIs represent only one of the many types of infection that can occur postoperatively. This article provides an overview of the surgical pathway and considers infection management and antibiotic prescribing at each step of the pathway. The aim was to identify the implications for research and opportunities for system improvement

A review of the biology, ecology and control of saddle gall midge,Haplodiplosis marginata(Diptera: Cecidomyiidae) with a focus on phenological forecasting

Saddle gall midge Haplodiplosis marginata (Diptera: Cecidomyiidae) is a pest of cereals across Europe. The occasional nature of this pest has resulted in limited and sporadic research activity. There remain important gaps in knowledge due either to a genuine lack of research or to previous research being difficult to access. These knowledge gaps make the development of effective control options difficult. Here, we review the existing literature in an attempt to consolidate the information on H. marginata from research which spans several decades and encompasses many different countries. The current distribution and pest status of this insect are updated, along with the methods of cultural and chemical control available to growers. The biology and life history of the insect are described in detail and the ecological processes governing them are discussed. A forecasting model is presented which allows the emergence of this pest in the UK to be predicted from degree day data, and the potential application of this model in management decisions is discussed. Finally, the areas in most need of further research are identified, along with suggestions of how this information can be used to help develop effective and sustainable management solutions for this pest

PCR-based gut content analysis to identify arthropod predators of Haplodiplosis marginata

Saddle gall midge (Haplodiplosis marginata) is a cereal pest exhibiting sporadic outbreaks for which chemical control options are limited. Integrated Pest Management programs may offer a means of suppressing H. marginata outbreaks, reducing pesticide input. Many IPM programs benefit from the natural population suppression inflicted through predation and parasitism. The larval stage of H. marginata overwinters in the soil and may be preyed upon by ground-dwelling arthropods, however the natural enemies of H. marginata remain unrecognized. A PCR-based assay for detecting H. marginata in the guts of predators was designed using novel species-specific primers. Feeding trials involving H. marginata larvae showed a detectability half-life of 31.07 h post-feeding in Nebria brevicollis. The guts of field-caught Carabidae were screened for H. marginata DNA. Four species: Poecilus versicolor, Nebria brevicollis, Harpalus rufipes and Loricera pilicornis were identified as natural enemies of H. marginata for the first time. A higher proportion of positive results were obtained at the end of H. marginata emergence (July) compared to the beginning (May). The importance of understanding trophic interactions in the management of H. marginata is discussed in addition to the potential uses for the newly designed assay and primers

Synthesizing benchmarks for predictive modeling

Predictive modeling using machine learning is an effective method for building compiler heuristics, but there is a shortage of benchmarks. Typical machine learning experiments outside of the compilation field train over thousands or millions of examples. In machine learning for compilers, however, there are typically only a few dozen common benchmarks available. This limits the quality of learned models, as they have very sparse training data for what are often high-dimensional feature spaces. What is needed is a way to generate an unbounded number of training programs that finely cover the feature space. At the same time the generated programs must be similar to the types of programs that human developers actually write, otherwise the learning will target the wrong parts of the feature space. We mine open source repositories for program fragments and apply deep learning techniques to automatically construct models for how humans write programs. We sample these models to generate an unbounded number of runnable training programs. The quality of the programs is such that even human developers struggle to distinguish our generated programs from hand-written code. We use our generator for OpenCL programs, CLgen, to automatically synthesize thousands of programs and show that learning over these improves the performance of a state of the art predictive model by 1.27×. In addition, the fine covering of the feature space automatically exposes weaknesses in the feature design which are invisible with the sparse training examples from existing benchmark suites. Correcting these weaknesses further increases performance by 4.30×

Degree-day based phenological forecasting model of saddle gall midge (Haplodiplosis marginata) (Diptera: Cecidomyiidae) emergence

Outbreaks of saddle gall midge (Haplodiplosis marginata) affecting wheat and other cereals are difficult to anticipate and may not be identified until damage has occurred. Earlier work on this pest has shown that degree day models can be used to predict H. marginata emergence based on soil temperatures. Here, we show how the availability of regular long-term trapping data can be used to update and improve upon this earlier model by predicting the progression of emergence. The emergence of adult H. marginata at three sites in the UK was monitored over two flight seasons using pheromone traps. The data confirmed the presence of multiple peaks in emergence over several weeks. Rainfall events followed by an accumulation of 512DD (±9.11DD) above 0 °C could be used to predict peaks with greater accuracy than degree day accumulations alone. Cumulative percentage emergence as a function of degree day accumulations was best described by a probit model. The probit model predicted H. marginata emergence at other sites and years to within 4 days. Application of these models will enable growers to forecast peaks in emergence, make informed assessments of crop risk and time application of chemical controls appropriately and only where required

Acid-yield measurements of the gas-phase ozonolysis of ethene as a function of humidity using Chemical Ionisation Mass Spectrometry (CIMS)

Gas-phase ethene ozonolysis experiments were conducted at room temperature to determine formic acid yields as a function of relative humidity (RH) using the integrated EXTreme RAnge chamber-Chemical Ionisation Mass Spectrometry technique, employing a CH<sub>3</sub>I ionisation scheme. RHs studied were <1, 11, 21, 27, 30 % and formic acid yields of (0.07±0.01) and (0.41±0.07) were determined at <1 % RH and 30 % RH respectively, showing a strong water dependence. It has been possible to estimate the ratio of the rate coefficient for the reaction of the Criegee biradical, CH<sub>2</sub>OO with water compared with decomposition. This analysis suggests that the rate of reaction with water ranges between 1×10<sup>−12</sup>–1×10<sup>−15</sup> cm<sup>3</sup> molecule<sup>−1</sup> s<sup>−1</sup> and will therefore dominate its loss with respect to bimolecular processes in the atmosphere. Global model integrations suggest that this reaction between CH<sub>2</sub>OO and water may dominate the production of HC(O)OH in the atmosphere