An Examination of Wetland Conversion and Resulting Effects on Landscape Connectivity in Southern Ontario Municipalities.

The few wetlands that remain in the human-dominated landscape of southern Ontario are affected by degradation and conversion to other land use and land cover types. Conversion has negative impacts on wetland-provisioned ecosystem services, such as aquatic species habitat, water filtration and flood prevention. Impacts on the latter services are especially concerning, given the increase of flood events that likely will be exacerbated by a changing climate. Stormwater management (SWM) ponds are constructed to control urban runoff, but do not have the same form and function as wetlands. This study examined recent (2002-2011) trends and drivers of wetland conversion (i.e. wetland loss and SWM pond gain) in seven southern Ontario municipalities. Following this, a Markov model was constructed to project future conversion given specific land use and land cover types. Network analytical approaches were then used to investigate effects of conversion on landscape connectivity. Results show that most wetlands lost were smaller than 2 hectares. While the total area of SWM ponds gained was greater than that of wetlands lost, the size of the average SWM pond gained was less than the size of the average wetland lost. Wetland conversion is projected to continue under all examined land use and land cover types, with losses particularly high in extractive and urban land uses. Overall, wetland conversion corresponded with decreased connectivity. Wetlands appeared to be more connected over the landscape compared to SWM ponds. However, SWM ponds likely acted as stepping-stones between wetlands and compensated somewhat for connectivity losses. The results provide further evidence for the need to halt wetland losses, especially for small wetlands, while showing the potential for connectivity improvements by SWM ponds. By conserving wetlands, policy makers can help to protect human life and property that rely on the critical ecosystem services provided by wetlands

Sex differences in the fatigability of locomotor muscles

Fatigability during exercise is determined by a myriad of factors including characteristics of the task being performed and the individual performer. When the latter is considered, the sex of the individual can influence the physiological responses, and therefore the underpinnings of fatigue during a wide range of tasks. Typically, fatigability of males and females has been compared following tasks normalised to maximum capacity (e.g. maximum voluntary contraction). Whilst these approaches have identified that females might experience less fatigue, they cannot provide further insight into the differences between the two sexes, as they do not account for potential differences in metabolic thresholds. The aim of this thesis was to therefore compare fatigability of males and females during exercise normalised to the intensity-duration relationship. It was hypothesised that due to anatomical and physiological differences between males and females, greater critical intensities would be observed in females, however, when exercise was subsequently normalised to these thresholds fatigability would be similar. In Study 1, the neuromuscular function and fatigability of females was compared across the eumenorrheic menstrual cycle. Alterations in nervous system function were observed in line with changes in neuro-excitatory and inhibitory hormone concentrations, meaning that in subsequent Chapters, hormonal status had to be controlled. Study 2 then demonstrated that assessments of neuromuscular function and fatigability were repeatable in a hormonally-constant population of monophasic oral contraceptive pill users. This finding indicated the suitability of this population for comparison with males in studies involving repeated visits. In Study 3, the intensity-duration relationship was compared between males and females for intermittent, isometric exercise, then fatigability and physiological responses were observed for exercise normalised to the critical intensity. Females demonstrated a greater relative critical intensity, however contrary to the original hypothesis, still demonstrated greater fatigue-resistance during metabolically-matched intensities. Near-infrared spectroscopy and neurostimulation data showed lesser deoxygenation and contractile dysfunction, respectively, within female knee-extensors during these normalised exercise trials, implying that the locus of the sex difference resided in the musculature. Study 4 then developed a novel method for assessment of subcortical excitability of descending tracts for the knee-extensor muscles. This study confirmed that lumbar stimulation was capable of activating the corticospinal tract and evoking responses at rest and during contractions, for use in subsequent study. In a similar study design to Study 3, Study 5 compared the intensity-duration relationship during cycling exercise between males and females, and assessed physiological responses and fatigability during metabolically-matched severe and heavy intensity exercise. In contrast to the original hypothesis, critical power was not different between sexes, however during exercise at 110 and 90% of critical power, time to task failure was the same between sexes. Despite similar exercise time at metabolically-matched intensities, females again demonstrated lesser deoxygenation and contractile dysfunction of the knee-extensors following exercise. Collectively, the work in this thesis extends the understanding of the sex difference in fatigability during exercise, offering insight into the difference in metabolic and neuromuscular consequences of single-limb and locomotor exercise which can be used to explain previous observations. Furthermore, the data implies that for the same relative volume of exercise, female skeletal muscle experiences less disruption compared to males, which has consequences for acute and chronic exercise prescription in a range of populations

Substructural Analysis Using Evolutionary Computing Techniques

Substructural analysis (SSA) was one of the very first machine learning techniques to be applied to chemoinformatics in the area of virtual screening. For this method, given a set of compounds typically defined by their fragment occurrence data (such as 2D fingerprints). The SSA computes weights for each of the fragments which outlines its contribution to the activity (or inactivity) of compounds containing that fragment. The overall probability of activity for a compound is then computed by summing up or combining the weights for the fragments present in the compound. A variety of weighting schemes based on specific relationship-bound equations are available for this purpose. This thesis identifies uplift to the effectiveness of SSA, using two evolutionary computation methods based on genetic traits, particularly the genetic algorithm (GA) and genetic programming (GP). Building on previous studies, it was possible to analyse and compare ten published SSA weighting schemes based on a simulated virtual screening experiment. The analysis showed the most effective weighting scheme to be the R4 equation which was a part of document-based weighting schemes. A second experiment was carried out to investigate the application of GA-based weighting scheme for the SSA in comparison to an experiment using the R4 weighting scheme. The GA algorithm is simple in concept focusing purely on suitable weight generation and effective in operation. The findings show that the GA-based SSA is superior to the R4-based SSA, both in terms of active compound retrieval rate and predictive performance. A third experiment investigated the genetic application via a GP-based SSA. Rigorous experiment results showed that the GP was found to be superior to the existing SSA weighting schemes. In general, however, the GP-based SSA was found to be less effective than the GA-based SSA. A final experimented is described in this thesis which sought to explore the feasibility of data fusion on both the GA and GP. It is a method producing a final ranking list from multiple sets of ranking lists, based on several fusion rules. The results indicate that data fusion is a good method to boost GA-and GP-based SSA searching. The RKP rule was considered the most effective fusion rule