Epidemiology and comparative analysis of Yersinia in Ireland

Yersiniosis is an acute or chronic enteric zoonosis caused by enteropathogenic Yersinia species. Although yersiniosis is predominantly associated with gastroenteric forms of infection, extraintestinal forms are often reported from the elderly or patients with predisposing factors. Yersiniosis is often reported in countries with cold and mild climates (Northern and Central Europe, New Zealand and North of Russian Federation). The Irish Health Protection Surveillance Centre (HPSC) currently records only 3-7 notified cases of yersiniosis per year. At the same time pathogenic Yersinia enterocolitica is recovered from pigs (main source of pathogenic Y. enterocolitica) at the levels similar to that observed in Yersinia endemic countries. Introduction of Yersinia selective culture procedures may increase Yersinia isolation rates. To establish whether the small number of notifications of human disease was an underestimate due to lack of specific selective culture for Yersinia we carried out a prospective culture study of faecal samples from outpatients with diarrhoea, with additional culture of appendix and throat swabs. Higher levels of anti-Yersinia seroprevalence than yersiniosis notification rates in endemic countries suggests that most yersiniosis cases are unrecognised by culture. Subsequently, in addition to a prospective culture study of clinical specimens, we carried out serological screening of Irish blood donors and environmental screening of human sewage. Pathogenic Yersinia strains were not isolated from 1,189 faeces samples, nor from 297 throat swabs, or 23 appendix swabs. This suggested that current low notification rates in Ireland are not due to the lack of specific Yersinia culture procedures. Molecular screening detected a wider variety of Y. enterocolitica-specific targets in pig slurry than in human sewage. A serological survey for antibodies against Yersinia YOP (Yersinia Outer Proteins) proteins in Irish blood donors found antibodies in 25%, with an age-related trend to increased seropositivity, compatible with the hypothesis that yersiniosis may have been more prevalent in Ireland in the recent past. Y. enterocolitica is a heterogeneous group of microorganisms that comprises strains with different degree of pathogenicity. Although non-pathogenic Y. enterocolitica lack conventional virulence factors, these strains can be isolated from patients with diarrhoea. Insecticidal Toxin Complex (ITC) and Cytolethal Distending Toxins can potentially contribute to the virulence of non-pathogenic Y. enterocolitica in the absence of other virulence factors. We compared distribution of ITC and CDT loci among pathogenic and non-pathogenic Y. enterocolitica. Additionally, to demonstrate potential pathogenicity of non-pathogenic Y. enterocolitica we compared their virulence towards Galleria mellonella larvae (a non-mammalian model of human bacterial infections) with the virulence of highly and mildly pathogenic Y. enterocolitica strains. Surprisingly, virulence of pathogenic and non-pathogenic Y. enterocolitica in Galleria mellonella larvae observed at 37°C did not correlate with their pathogenic potential towards humans. Comparative phylogenomic analysis detects predicted coding sequences (CDSs) that define host-pathogen interactions and hence providing insights into molecular evolution of bacterial virulence. Comparative phylogenomic analysis of microarray data generated in Y. enterocolitica strains isolated in the Great Britain from humans with diarrhoea and domestic animals revealed high genetic heterogeneity of these species. Because of the extensive human, animal and food exchanges between the UK and Ireland the objective of this study was to gain further insight into genetic heterogeneity and relationships among clinical and non-clinical Y. enterocolitica strains of various pathogenic potential isolated in Ireland and Great Britain. No evidence of direct transfer of strains between the two countries was found.

Grooming and hygienic behaviours of the honey bee (Apis mellifera) toward the parasitic mite Varroa destructor

Despite a multitude of environmental stressors, the Varroa mite is still regarded as the greatest cause of honey bee mortality in its invaded range. Breeding honey bees that are resistant to the mite is an important area of research. This thesis aimed to gain a better understanding of the grooming and hygienic behaviours of Russian honey bees (RHB). The effect of a break in the synchrony of a mite’s life cycle on reproductive success was tested through brood inoculation experiments. Mites released by hygienic behaviour and forced to enter a new cell are less likely to lay male offspring. Through laboratory cage assays it was found that daughter mites are more susceptible to grooming behaviour. A new method of marking Varroa mites was developed which would enable a single cohort of mites to be followed after inoculation. A strong brood removal trait was noticed in RHB colonies, therefore they were tested for Varroa sensitive hygienic (VSH) behaviour. RHB demonstrated levels of VSH as high as the USDA line bred specifically for this behaviour. In addition the same QTL found to be responsible for the trait in VSH bees, was associated with VSH in RHB stock. Previous work showed that the ratio of older mites to total trapped mites (O/T) in the debris of honey bee colonies demonstrated the strongest association with colony infestation. This research showed that O/T is associated with VSH and brood removal behaviour. In addition, bees that displayed high levels of VSH in this study were also more likely to spend a longer amount of time grooming in laboratory assays. This indicates that both grooming and hygienic behaviours play important roles in the resistance of RHB stock. Their likelihood to be expressed by other stocks is discussed and recommendations for further research are provided.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.