Spatio-temporal optimization of sampling for bluetongue vectors (<em>Culicoides</em>) near grazing livestock

BACKGROUND: Estimating the abundance of Culicoides using light traps is influenced by a large variation in abundance in time and place. This study investigates the optimal trapping strategy to estimate the abundance or presence/absence of Culicoides on a field with grazing animals. We used 45 light traps to sample specimens from the Culicoides obsoletus species complex on a 14 hectare field during 16 nights in 2009. FINDINGS: The large number of traps and catch nights enabled us to simulate a series of samples consisting of different numbers of traps (1-15) on each night. We also varied the number of catch nights when simulating the sampling, and sampled with increasing minimum distances between traps. We used resampling to generate a distribution of different mean and median abundance in each sample. Finally, we used the hypergeometric distribution to estimate the probability of falsely detecting absence of vectors on the field. The variation in the estimated abundance decreased steeply when using up to six traps, and was less pronounced when using more traps, although no clear cutoff was found. CONCLUSIONS: Despite spatial clustering in vector abundance, we found no effect of increasing the distance between traps. We found that 18 traps were generally required to reach 90% probability of a true positive catch when sampling just one night. But when sampling over two nights the same probability level was obtained with just three traps per night. The results are useful for the design of vector monitoring programmes on fields with grazing animals

Building a Mach Zehnder Interferometer with Limited Resources

Interferometers are simple optical devices that function by splitting a coherent light beam. The beam is recombined using beam splitters and mirrors. The addition of the two light beams produces interference patterns in the forms of fringes which can be used to study the path taken by the two beams. This is old technology and we sought to construct an interferometer using rudimentary and cast-off equipment. The purpose is to show that modern physics concepts can be measured inexpensively and by undergraduate student design. Though we had no optics table and or optical mounts, by careful alignment and adjustments to the equipment, we were able to produce fringes whose intensity could measure phase changes of a light beam as it goes through various mediums. We were able to determine and learn more about the properties of light and produce successful results. This demonstrates a method of introducing modern physics lab applications at a low cost

Quantifying macroeconomic uncertainty in Norway

This paper presents a framework for quantifying uncertainty around point forecasts for GDP, inflation and house prices in Norway. The framework combines quantile regressions using a broad set of uncertainty indicators with a skewed t-distribution, allowing for time-variation and asymmetry in the uncertainty forecasts. This approach helps provide deeper insights into the macroeconomic uncertainty surrounding forecasts than more traditional time-series models, where uncertainty is usually symmetric and with limited time-variation. Formal tests, such as the log score and the Continuous Ranked Probability Score (CRPS), show that using informative indicators tend to improve density forecasts, particularity in the medium run.publishedVersio

Advances in automatic identifcation of flying insects using optical sensors and machine learning

Worldwide, farmers use insecticides to prevent crop damage caused by insect pests, while they also rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests. In order to target pesticides to pests only, farmers must know exactly where and when pests and beneficial insects are present in the field. A promising solution to this problem could be optical sensors combined with machine learning. We obtained around 10,000 records of flying insects found in oilseed rape (Brassica napus) crops, using an optical remote sensor and evaluated three different classification methods for the obtained signals, reaching over 80% accuracy. We demonstrate that it is possible to classify insects in fight, making it possible to optimize the application of insecticides in space and time. This will enable a technological leap in precision agriculture, where focus on prudent and environmentally-sensitive use of pesticides is a top priority

Are Stem Cell-Based Therapies for Parkinson's Disease Ready for the Clinic in 2016?

Recent news of an impending clinical cell transplantation trial in Parkinson's disease using parthenogenetic stem cells as a source of donor tissue have raised hopes in the patient community and sparked discussion in the research community. Based on discussions held by a global collaborative initiative on translation of stem cell therapy in Parkinson's disease, we have identified a set of key questions that we believe should be addressed ahead of every clinical stem cell-based transplantation trial in this disorder. In this article, we first provide a short history of cell therapy in Parkinson's disease and briefly describe the current state-of-art regarding human stem cell-derived dopamine neurons for use in any patient trial. With this background information as a foundation, we then discuss each of the key questions in relation to the upcoming therapeutic trial and critically assess if the time is ripe for clinical translation of parthenogenetic stem cell technology in Parkinson's disease.Some of the work discussed in this commentary was supported by grants from the EU FP 7 programme including TRANSEURO and NeuroStemCellRepair, and the Swedish Research Council. RAB is also supported by an NIHR grant of a Biomedical Research Centre to Addenbrooke’s Hospital and the University of Cambridge.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by IOP Press