An evaluation of the perceptions of products derived from gene technology among undergraduates at the University of Malta

A pilot study on the perceptions of genetically engineered-derived produce was carried out among undergraduates in their final year of study at the University of Malta. 68% of the students interviewed accepted the idea of genetically modifying plants (GM) but the idea of creating GM animals was not acceptable to the same cohort with approval falling to 30.2% of the group. Gender was found to be important in influencing choices made by students. Females were less accepting of GM organisms and they were significantly less likely to buy GM produce, such as GM derived milk (p<0.001), tomatoes (p<0.05), and beef (p<0.01) than males. Subject background was also found to influence student opinions. Students with a strong background in biology were less likely to have faith in statements concerning GM products made by the farming community (p<0.05). However, the same students were more willing to accept statements about GM products by government organisations (p<0.01) and environmental groups (p<0.05) than those who had minimal or no biology in their background. The study is interesting, as it shows that at a fundamental level, complex factors are influencing the individual's choices on biotech derived products.peer-reviewe

On statistical approaches to generate Level 3 products from satellite remote sensing retrievals

Satellite remote sensing of trace gases such as carbon dioxide (CO$_2$) has increased our ability to observe and understand Earth's climate. However, these remote sensing data, specifically~Level 2 retrievals, tend to be irregular in space and time, and hence, spatio-temporal prediction is required to infer values at any location and time point. Such inferences are not only required to answer important questions about our climate, but they are also needed for validating the satellite instrument, since Level 2 retrievals are generally not co-located with ground-based remote sensing instruments. Here, we discuss statistical approaches to construct Level 3 products from Level 2 retrievals, placing particular emphasis on the strengths and potential pitfalls when using statistical prediction in this context. Following this discussion, we use a spatio-temporal statistical modelling framework known as fixed rank kriging (FRK) to obtain global predictions and prediction standard errors of column-averaged carbon dioxide based on Version 7r and Version 8r retrievals from the Orbiting Carbon Observatory-2 (OCO-2) satellite. The FRK predictions allow us to validate statistically the Level 2 retrievals globally even though the data are at locations and at time points that do not coincide with validation data. Importantly, the validation takes into account the prediction uncertainty, which is dependent both on the temporally-varying density of observations around the ground-based measurement sites and on the spatio-temporal high-frequency components of the trace gas field that are not explicitly modelled. Here, for validation of remotely-sensed CO$_2$ data, we use observations from the Total Carbon Column Observing Network. We demonstrate that the resulting FRK product based on Version 8r compares better with TCCON data than that based on Version 7r.Comment: 28 pages, 10 figures, 4 table

Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion

Atmospheric trace-gas inversion is the procedure by which the sources and sinks of a trace gas are identified from observations of its mole fraction at isolated locations in space and time. This is inherently a spatio-temporal bivariate inversion problem, since the mole-fraction field evolves in space and time and the flux is also spatio-temporally distributed. Further, the bivariate model is likely to be non-Gaussian since the flux field is rarely Gaussian. Here, we use conditioning to construct a non-Gaussian bivariate model, and we describe some of its properties through auto- and cross-cumulant functions. A bivariate non-Gaussian, specifically trans-Gaussian, model is then achieved through the use of Box--Cox transformations, and we facilitate Bayesian inference by approximating the likelihood in a hierarchical framework. Trace-gas inversion, especially at high spatial resolution, is frequently highly sensitive to prior specification. Therefore, unlike conventional approaches, we assimilate trace-gas inventory information with the observational data at the parameter layer, thus shifting prior sensitivity from the inventory itself to its spatial characteristics (e.g., its spatial length scale). We demonstrate the approach in controlled-experiment studies of methane inversion, using fluxes extracted from inventories of the UK and Ireland and of Northern Australia.Comment: 45 pages, 7 figure

A technique for improving conflict alerting performance in the context of runway incursions

An effective solution to the problem of runway incursions is long overdue. To date, an average of a thousand incursions are registered yearly in the United States alone, with similar figures registed in Europe. Installing a system on-board aircraft capable of providing an alert in the case of a runway incursion has the potential of significantly reducing this. As with any conflict detection and alerting system, the difficulty lies in the fine-tuning of the parameters which define a conflict, in effect resulting in finding the right trade-off between false and missed detections and associated alerts. This is an important consideration in the design of any conflict detection system and is key in the context of runway incursion alerting where aircraft would be travelling at high speed and in close proximity of eachother. This paper addresses this problem by providing an assessement on the effects of false and missed detections in the event of a runway incursion and provides mathematical tools for tuning the conflict detection boundaries.peer-reviewe

Raccomandazioni per la gestione e la conservazione di due popolazioni di Aphanius Fasciatus Nardo dalle Isole Maltesi

The Aphanius fasciatus populations at the two Maltese wetlands of Simar and Ghadira were monitoired during the May-October 2008 period for signs of pathogenesis and in terms of sex ratio and individual morphology. The putative impact of a number of abiotic factors on populaiton structure was also assessed. The study concludes that the percentage of juveniles within the two killifish populations is highest during the July-August period, and that reproductive activity resumes in October at the end of the dry season which coincides with a stalling of reproductive activity and with a high juvenile mortality. Recommendations for the amplification of killifish-specific monitoring protocols are also made.peer-reviewe

Multi-Scale Process Modelling and Distributed Computation for Spatial Data

Recent years have seen a huge development in spatial modelling and prediction methodology, driven by the increased availability of remote-sensing data and the reduced cost of distributed-processing technology. It is well known that modelling and prediction using infinite-dimensional process models is not possible with large data sets, and that both approximate models and, often, approximate-inference methods, are needed. The problem of fitting simple global spatial models to large data sets has been solved through the likes of multi-resolution approximations and nearest-neighbour techniques. Here we tackle the next challenge, that of fitting complex, nonstationary, multi-scale models to large data sets. We propose doing this through the use of superpositions of spatial processes with increasing spatial scale and increasing degrees of nonstationarity. Computation is facilitated through the use of Gaussian Markov random fields and parallel Markov chain Monte Carlo based on graph colouring. The resulting model allows for both distributed computing and distributed data. Importantly, it provides opportunities for genuine model and data scaleability and yet is still able to borrow strength across large spatial scales. We illustrate a two-scale version on a data set of sea-surface temperature containing on the order of one million observations, and compare our approach to state-of-the-art spatial modelling and prediction methods.Comment: 33 pages, 10 figures, 1 tabl