Studies on Formica lugubris Zetterstedt in Ireland (Hymenoptera, Formicidae)

This thesis is based on studies of Formica lugubris from 1972-1975. While this species' range is diminishing in Ireland, the nests are quite common in the State plantations of South Tipperary. It is not certain that the species is indigenous. Above-ground activity occurs from late-February to the end of October; foraging begins in April. Two territorial "spring-battles" between neighbouring nests are described. Most active nests produced alatae of both sexes and flights were observed on successive June mornings above l7.5°C air temperature. Both polygyny and polycaly seem to be rare. Where the nests occur commonly, the recorded densities are similar to those reported from the continent. Most nests persisted at the same site since 1973. The nest-sites are described by recording an array of nest, soil, tree, vegetation and location variables at each site. Pinus sylvestris is the most important overhead tree. Nests seem to be the same age as their surrounding plantation and reach a maximum of c. 30 years. Nearest-neighbour analysis suggests the sites are overdispersed. Forager route-fidelity was studied and long-term absence from the route, anaesthetization and "removal" of an aphid tree had little effect on this fidelity. There were no identifiable groups of workers specifically honeydew or prey-carriers. Size-duty relationships of workers participating in adult transport are described. Foraging rhythms were studied on representative days: the numbers foraging were linearly related to temperature. Route-traffic passed randomly and an average foraging trip lasted c. four hours. Annual food intake to a nest with 25 000 foragers was estimated at approximately 75 kg honeydew and 2 million prey-items. Forager-numbers and colony-size were estimated using the capture-mark - recapture method: paint marking was used for the forager estimate and an interval radiophosphorus mark, detected by autoradiography, was used for the colony-size estimate. The aphids attended by lugubris and the nest myrmecophiles are recorded.

Digital signal processing and artificial intelligence for the automated classification of food allergy

As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosis