Magnetic resonance imaging (MRI) is an imaging modality that noninvasively measures magnetic fields by selectively exciting the magnetization of protons inside the body. When combined with an understanding of electromagnetic theory, MRI can be used in a novel way to provide a powerful tool for measuring the electromagnetic fields and electrical properties of biological tissues. This thesis presents the analytical, numerical, processing and experimental components of a successful implementation of Low-Frequency Current Density Impedance Imaging (LF-CDII), an impedance imaging method based on MRI measurements. The accuracy, stability and noise tolerance of this technique are examined. The first in-vivo LF-CDII experiment was conducted with a clinical MRI scanner, and the conductivity distribution of the heart of a live piglet was obtained. Both the simulation and experimental results show that LF-CDII can be used as a reliable tool for accurate noninvasive, quantitative imaging of tissue conductivities. This thesis also presents new data processing algorithms, imaging procedures and hardware development for the measurement of electromagnetic fields at radio frequencies, based on Polar Decomposition Radio Frequency Current Density Imaging (PD-RFCDI). The method was tested on both numerical models and experiments on phantoms. The results show that the techniques presented here are able to successfully image current density fields without the strict restrictions on the direction and magnitude of the currents required in previous versions of RFCDI.

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Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further used to differentiate dark spots from the background. The proposed method was applied to a evaluation dataset with 60 RADARSAT-1 ScanSAR Narrow Beam intensity images containing oil spill anomalies. The experimental results obtained from the test dataset demonstrate that the proposed method for dark spot detection is fast, robust and effective. Recommendations are given for future research to be conducted to ensure that this procedure goes beyond the prototype stage and becomes a practical application.

Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further used to differentiate dark spots from the background. The proposed method was applied to a evaluation dataset with 60 RADARSAT-1 ScanSAR Narrow Beam intensity images containing oil spill anomalies. The experimental results obtained from the test dataset demonstrate that the proposed method for dark spot detection is fast, robust and effective. Recommendations are given for future research to be conducted to ensure that this procedure goes beyond the prototype stage and becomes a practical application.

Field pea is an important crop in western Canada. The current recommended seeding rate in field pea is 88 plants m-2. As certain pea genotypes have the ability for increased branching, it may be possible for a producer to seed at a lower plant population without reduced yield or to choose a highly branched cultivar to have reduced risk of yield loss under conditions of poor emergence. The objective of this research was to determine how differences in branching among seven representative pea cultivars affected crop yield at different seeding rates, and to determine if branching affected the competitive ability of pea cultivars. In the plant population experiment, seven pea cultivars were seeded at five target plant populations (10, 30, 90, 120, and 150 plant m-2) during 2005 and 2006 at Rosthern and Saskatoon, Saskatchewan. The competition experiment involved eight cultivars being seeded at 50 plants m-2 to measure competitiveness with weeds. Plant emergence, number of branches, light interception, harvest index and grain yield were measured. Growth, seasonal temperature and rainfall were near normal in 2005. Severe terminal drought occurred in 2006 which may have lead to decreased yields. Branching was greatest at low plant densities and decreased as plant density increased. Grain yield increased as plant density increased until it plateaued at 80 100 plants m-2. The response of yield to plant density differed to some extent among cultivars, with CDC Acer and CDC Bronco achieving more of their potential yield at lower densities, while Carrera and Courier required higher densities to reach the same proportion of potential yield. Weed biomass was lowest in plots sown to longer vined cultivars with normal leaf type. Branching habit did not affect the competitiveness of pea cultivars. Potential exists to plant highly branched cultivars to reduce risk of yield loss in situations where low plant emergence might occur.

Predominance of small dense Low Density Lipoprotein (LDL) is associated with a two to threefold increase in risk for Coronary Heart Disease (CVD). Small, dense HDL (High Density Lipoprotein) particles protect small dense LDL from oxidative stress. Technological advancements have introduced an array of techniques for measuring diameters of LDL and HDL as well as estimating overall particle heterogeneity. However, there is lack of comparative studies between these techniques, and, hence, no conclusive evidence to establish the merits of one method relative to others. The primary purpose of this study was to compare Nondenaturing Gradient Gel Electrophoresis (NDGGE) and Dynamic Laser Light Scattering (DLLS) methods in determining LDL and HDL particle diameter. Our comparison entailed: 1) Evaluating the two methods in terms of their reproducibility 2) Correlating the two methods(in future studies method selection would be driven by time and cost considerations if the two methods correlate), and 3) Evaluating the two methods in terms of their ability to identify bi-modal samples. A secondary purpose of this research was to investigate the effect of refrigerated plasma storage on particle diameter. Reproducibility was measured as Coefficient of Variance (CV). Within and between runs, CV for LDL and HDL for NDGGE were <6% and <15%, respectively and for DLLS, CV within runs were <3% and <5.5%, respectively. No correlation was observed between LDL diameter from the two methods. NDGGE showed two bands for 157 HDL samples of which only 24 samples showed bimodal peaks in DLLS. In order to study the effect of storage, three sample sets of LDL and two sample sets of HDL were used. NDGGE showed a significant difference between mean diameter of fresh and stored LDL and HDL sample for all sets, whereas DLLS showed a significant difference in only one LDL sample set and none for HDL sample sets. We conclude that DLLS may be a better method for measuring LDL diameter because NDGGE overestimated LDL diameter. However, NDGGE was able to resolve subpopulation better in an HDL sample than DLLS. Thus, NDGGE may be a better choice for measuring HDL diameter than DLLS.

A central issue in word recognition is how readers retrieve and select the right representation among others in the mental lexicon. Recently, it has been claimed that recognition of individual words is influenced by the degree to which the words possess unique vs. shared letters or sounds relative to other words, that is, whether the words have few or several neighbors. Research on so-called neighborhood density effects advances understanding of the organization and operation of the mental lexicon. Orthographic neighborhood effects have been claimed to be facilitative, but recent studies of visual word recognition have led to a revised understanding of the nature of the orthographic neighborhood density effect. Through a reexamination of orthographic and phonological neighborhood density effects, the specific objective of the present research is to understand how orthographic and phonological representations interact across two different writing systems, i.e., English (an alphabetic orthography) and Chinese (a morphosyllabic orthography). The phenomena were studied using a joint behavioral (lexical decision) and neural imaging approach (near infrared spectroscopy, or NIRS). Orthographic and phonological (more, specifically, homophone) neighborhood density were manipulated in three lexical decision experiments with English and three with Chinese readers. After different sources of facilitative inter-lexicon connections were controlled, orthographic and phonological neighborhood density effects were found to be inhibitory in both writing systems. Inhibitory neighborhood density effects were also confirmed in two NIRS experiments of English and Chinese. The present research provided a better control of lexical characteristics than was the case in previous research on neighborhood effects and found a clear and consistent pattern of neighborhood density effects. This research supports interactive-activation models of word recognition rather than parallel-distributed models, given the evidence for lateral inhibition indexed by inhibitory neighborhood density effects. As such, the present study furthers the understanding of the organization and operation of the mental lexicon.

Computational methods are a convenient resource to solve drawbacks of drug research such as high cost, time-consumption, and high risk of failure. In order to get an optimum search of new drugs we need to design a rational approach to analyze the molecular forces that govern the interactions between the drugs and their target molecules. The objective of this project is to get an understanding of the interactions between drugs and proteins at the molecular level. The interaction energy, when protein and drugs react, has two components: non-covalent and covalent. The former accounts for the ionic interactions, the later accounts for electron transfer between the reactants. We study each energy component using the most popular analysis tools in computational chemistry such as docking scoring, molecular dynamics fluctuations, electron density change, molecular electrostatic potential (MEP), density of states projections, and the transmission function. We propose the probability of transfer of electrons (transmission function) between reactants in protein-drug complexes as an alternative tool for molecular recognition and as a direct correlator to the binding affinity. The quadratic correlation that exists between the electron transfer rate and the electronic coupling strength of the reactants allow a clear distinguishability between ligands. Thus, in order to analyze the binding affinity between the reactants, a calculation of the electronic coupling between them is more suitable than an overall energetic analysis such as free reaction energy.

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