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Previous studies on inhibition of the Escherichia coli glycerol kinase enzyme have suggested that subunit-subunit or domain bridging interactions form part of the network in communicating ligand binding to inhibition. In this study, five amino acids were identified to be in close proximity to an Arg369 residue which is a domain bridging residue. Three of the amino acid residues (Q37, Y39 and Q104) are in domain I of the enzyme subunit, while the other two (M308 and Q314) are in domain II of the enzyme subunit. To evaluate the importance of each domain bridging residue in IIAGlc inhibition, alanine substitutions were made of the residues, and the kinetic properties characterized with respect to IIAGlc inhibition. Kinetic parameters obtained for each variant glycerol kinase enzyme was compared to values obtained for the Wild Type enzyme to assess the importance of the amino acid residue in IIAGlc inhibition. The effects of the substitutions on FBP inhibition as well as catalysis of the enzyme were also analyzed by obtaining kinetic parameters for each of the variant enzymes. The results from this study indicate that the domain I bridging interactions with Arg369 are important in IIAGlc regulation of the E. coli glycerol kinase enzyme. The domain II bridging interactions appear to be unimportant in regulating IIAGlc inhibition. Two of the domain I bridging residues studied were also found to be important in FBP inhibition. These results indicate that some the domain bridging residues seen to be involved in IIAGlc regulation also appear to be involved in FBP regulation. In catalysis, with the exception of Q314, the rest of the domain I and II bridging residues appear to be important for substrate binding and/or catalysis.

<p>X-ray computed tomography (CT) is a non-destructive medical imaging technique for assessing the cross-sectional images of an object in terms of attenuation. As it is designed based on the physical processes involved in the x-ray and matter interactions, faithfully modeling the physics in the reconstruction procedure can yield accurate attenuation distribution of the scanned object. Otherwise, unrealistic physical assumptions can result in unwanted artifacts in reconstructed images. For example, the current reconstruction algorithms assume the photons emitted by the x-ray source are mono-energetic. This oversimplified physical model neglects the poly-energetic properties of the x-ray source and the nonlinear attenuations of the scanned materials, and results in the well-known beam-hardening artifacts (BHAs). The purpose of this work was to incorporate the poly-energetic nature of the x-ray spectrum and then to eliminate BHAs. By accomplishing this, I can improve the image quality, enable the quantitative reconstruction ability of the single-spectrum CT scanner, and potentially reduce unnecessary radiation dose to patients.</p><p>In this thesis, in order to obtain accurate spectrum for poly-energetic reconstruction, I first presented a novel spectral estimation technique, with which spectra across a large range of angular trajectories of the imaging field of view can be estimated with a single phantom and a single axial acquisition. The experimental results with a 16 cm diameter cylindrical phantom (composition: ultra-high-molecular-weight polyethylene [UHMWPE]) on a clinical scanner showed that the averaged absolute mean energy differences and the normalized root mean square differences with respect to the actual spectra across kVp settings (i.e., 80, 100, 120, 140) and angular trajectories were less than 0.61 keV and 3.41%, respectively</p><p>With the previous estimation of the x-ray spectra, three poly-energetic reconstruction algorithms are proposed for different clinical applications. The first algorithm (i.e., poly-energetic iterative FBP [piFBP]) can be applied to routine clinical CT exams, as the spectra of the x-ray source and the nonlinear attenuations of diverse body tissues and metal implant materials are incorporated to eliminate BHAs and to reduce metal artifacts. The simulation results showed that the variation range of the relative errors of various tissues across different phantom sizes (i.e., 16, 24, 32, and 40 cm in diameter) and kVp settings (80, 100, 120, 140) were reduced from [-7.5%, 17.5%] for conventional FBP to [-0.1%, 0.1%] for piFBP, while the noise was maintained at the same low level (about [0.3%, 1.7%]).</p><p>When iodinated contrast agents are involved and patient motions are not readily correctable (e.g., in myocardial perfusion exam), a second algorithm (i.e., poly-energetic simultaneous algebraic reconstruction technique [pSART]) can be applied to eliminate BHAs and to quantitatively determine the iodine concentrations of blood-iodine mixtures with our new technique. The phantom experiment on a clinical CT scanner indicated that the maximum absolute relative error across material inserts was reduced from 4.1% for conventional simultaneous algebraic reconstruction technique [SART] to 0.4% for pSART.</p><p>Extending the work beyond minimizing BHAs, if patient motions are correctable or negligible, a third algorithm (i.e., poly-energetic dynamic perfusion algorithm [pDP]) is developed to retrieve iodine maps of any iodine-tissue mixtures in any perfusion exams, such as breast, lung, or brain perfusion exams. The quantitative results of the simulations with a dynamic anthropomorphic thorax phantom indicated that the maximum error of iodine concentrations can be reduced from 1.1 mg/cc for conventional FBP to less than 0.1 mg/cc for pDP.</p><p>Two invention disclosure forms based on the work presented in this thesis have been submitted to Office of Licensing & Ventures of Duke University.</p> / Dissertation

The research problem that this thesis seeks to examine is a method of predicting conventional fire hazards using data drawn from specific regions, namely the Sooke and Goldstream watershed regions in coastal British Columbia. This thesis investigates whether LiDAR data can be used to describe conventional forest stand fire hazard classes. Three objectives guided this thesis: to discuss the variables associated with fire hazard, specifically the distribution and makeup of fuel; to examine the relationship between derived LiDAR biometrics and forest attributes related to hazard assessment factors defined by the Capitol Regional District (CRD); and to assess the viability of the LiDAR biometric decision tree in the CRD based on current frameworks for use. The research method uses quantitative datasets to assess the optimal generalization of these types of fire hazard data through discriminant analysis. Findings illustrate significant LiDAR-derived data limitations, and reflect the literature in that flawed field application of data modelling techniques has led to a disconnect between the ways in which fire hazard models have been intended to be used by scholars and the ways in which they are used by those tasked with prevention of forest fires. It can be concluded that a significant tradeoff exists between computational requirements for wildfire simulation models and the algorithms commonly used by field teams to apply these models with remote sensing data, and that CRD forest management practices would need to change to incorporate a decision tree model in order to decrease risk. / Graduate / 0799 / 0478 / christos@koulas.ca

Background: Type 2 diabetes mellitus is a metabolic disorder characterized by the rise of fasting plasma glucose from its normal range (≥125mg/dl). It is marked by insufficient production of insulin from pancreatic β-cells as a result of failed compensation due to insulin resistance. Several treatments are available for the disorder, which mainly focus on improving the sensitivity of insulin in different body tissues. Recently, probiotics were suggested as candidate treatments for type 2 diabetes and for extending lifespan as well. This experiment aims to investigate such claims using Drosophila melanogaster as a disease model.   Results: Other than the observed low average weights in treated larva samples, probiotics did not show any other significant results in affecting the length, glucose, glycogen, and trehalose levels (One-Way ANOVA and Kruskal-Wallis, p&gt;0.05). Real-time PCR was only carried out once. Thus, no statistical tests were reliable enough to analyse the data obtained. The longevity study, on the other hand, did show significance (Log-rank (Mantel-Cox) test and Gehan-Breslow-Wilcoxon test, p&lt;0.0001), as the probiotic Bifidobacterium lactis extended the lifespan of adult flies feeding on a high sugar diet significantly when compared to the control ones feeding on only high sugar diet without probiotics.   Conclusion: Except for weight measurements, none of the other results was reliable enough to make a concrete conclusion on whether the treatments indeed worked in reversing type 2 diabetes symptoms or not. Real-time PCR results did show some effects of some of the treatments at different developmental stages. However, unless Real-time PCR is repeated at least once using the same protocol, no deduction can be made. Additionally, the data obtained hint that the dosage used (0.025 g) was too high for larvae and adult flies and might have caused malnutrition by blocking their midgut and decreasing food absorption. Hence, false significant or non-significant results were acquired instead.   Further studies are required using a much lower probiotic dosage if Drosophila is used as a disease model. Although, other models such as mice or rats are recommended in this case, in order to reach a solid conclusion about the effectiveness of probiotics in treating type 2 diabetes mellitus. Baring these thoughts in mind and based on the results of this experiment, the null hypothesis indicating that there is no significant relationship between the use of probiotics and reversing type 2 diabetes mellitus symptoms is therefore accepted.

In this thesis, coda waves recorded by local seismographic networks will be analyzed to estimate the three-dimensional spatial distribution of scatterers (SDS). This will be done by using the single scattering approximation. This approach leads to a huge system of equations that can not be solved by traditional methods. For the first time, we will use the Simultaneous Iterative Reconstructive Technique (SIRT) to solve this kind of system in seismological applications. SIRT is slow but provides a means to carry out the inversion with greater accuracy. There is also a very fast non-iterative method that allows to carry out the inversion 102 times faster, with a higher resolution and reasonable accuracy: the Filtered Back-Projection (FBP). If one wishes to use this technique it is necessary to adapt it to the geometry of our problem. This will be done for the first time in this thesis. The theory necessary to carry out the adaptation will be developed and a simple expression will be derived to carry out the inversion.FBP and SIRT are then used to determine the SDS in southern India. Results are almost independent of the inversion method used and they are frequency dependent. They show a remarkably uniform distribution of the scattering strength in the crust around GBA. However, a shallow (0-24 km) strong scattering structure, which is only visible at low frequencies, seems to coincide with de Closepet granitic batholith which is the boundary between the eastern and western parts of the Dharwar craton.Also, the SDS is estimated for the Galeras volcano, Colombia. Results reveal a highly non-uniform SDS. Strong scatterers show frequency dependence, which is interpreted in terms if the scale of the heterogeneities producing scattering. Two zones of strong scattering are detected: the shallower one is located at a depth from 4 km to 8 km under the summit whereas the deeper one is imaged at a depth of ~37 km from the Earth's surface. Both zones may be correlated with the magmatic plumbing system beneath Galeras volcano. The second strong scattering zone may be probably related to the deeper magma reservoir that feeds the system.

The traditional Computed Tomography (CT) is based on the Radon Transform and its inversion. The Radon transform uses parallel beam geometry and its inversion is based on the Fourier slice theorem. In practice, it is very efficient to employ a back-projection algorithm in connection with the Fast Fourier Transform, and which can be interpreted as a 1-D filtering across the radial dimension of the 2-D Fourier plane of the transformed image. This approach can easily be adapted to windowing techniques in the frequency domain, giving the capability to reduce image noise. In this work we are investigating the capabilities of the so called Kaiser window (giving an optimal trade-off between the main lobe energy and the sidelobe suppression) to achieve a near optimal trade-off between the noise reduction and the image sharpness in the context of Radon inversion. Finally, we simulate our image reconstruction using MATLAB software and compare and estimate our results based on the normalized Least Square Error (LSE). We conclude that the Kaiser window can be used to achieve an optimal trade-off between noise reduction and sharpness in the image, and hence outperforms all the other classical window function in this regard.

Wildfires are a natural process critical to the health of forests around the world. However, recent decades have witnessed unprecedented wildfire seasons in many forested regions, leading to a range of unprecedented socio-economic, environmental, and human health impacts. Mitigating these impacts relies in part on fire behaviour prediction systems, which provide information to assist operational wildfire managers with addressing wildfire risk and prioritizing wildfire fighting efforts. A key aspect of fire behaviour prediction systems are rate of spread models that rely on observational and experimental fire behaviour data from naturally occurring wildfires and prescribed burns, respectively. Given the challenge with observing and measuring wildfires in situ, rate of spread models typically rely on a small set of data inputs that are not always representative of the range of wildfires occurring in certain forest types. Furthermore, existing fire behaviour models often lack empirical data on forests that have more recently experienced significant compositional shifts due to climate change or various ecological or anthropogenic disturbances. To address these shortcomings, the objective of this thesis is to establish a method of acquiring empirical fire behaviour data to enhance fire behaviour prediction science through two distinct studies. The first evaluates the utility of monophotogrammetry to extract fire behaviour data from oblique aerial wildfire photographs. The results demonstrate how this approach can provide new and accurate fire spread observations to inform fire behaviour prediction or other aspects of wildland fire science where databases of such wildfire photos exist. The second study is an empirical wildfire spread analysis in forest stands affected by mountain pine beetle (MPB), and utilizes the method of monoplotting to acquire spread rate data from wildfire photographs of grey-attack MPB-affected forest stands. Results from this study further demonstrate the efficacy of the previously established monoplotting technique while providing novel empirical evidence of fire behaviour in grey attack MPB-affected forest stands. Overall, the research results presented in this thesis demonstrate the potential of monophotogrammetry for the acquisition of fire behaviour data and evaluating the results derived from fire behaviour prediction systems in different ecological contexts. This thesis exhibits the potential for this method to expand into other areas of fire behaviour, such as flame or smoke plume dimensions, spotting, and the relationship between fire behaviour and disturbance events such as pest insect outbreaks. / Graduate / 2023-04-14

Filtered backprojection (FBP) reconstruction algorithms are very popular in the field of X-ray computed tomography (CT) because they give advantages in terms of the numerical accuracy and computational complexity. Ramp filter based fan-beam FBP reconstruction algorithms have the position dependent weight in the backprojection which is responsible for spatially non-uniform distribution of noise and resolution, and artifacts. Many algorithms based on shift variant filtering or spatially-invariant interpolation in the backprojection step have been developed to deal with this issue. However, these algorithms are computationally demanding. Recently, fan-beam algorithms based on Hilbert filtering with inverse distance weight and no weight in the backprojection have been derived using the Hamaker’s relation. These fan-beam reconstruction algorithms have been shown to improve noise uniformity and uniformity in resolution. In this thesis, fan-beam FBP reconstruction algorithms with inverse distance back-projection weight and no backprojection weight for 2D image reconstruction are presented and discussed for the two fan-beam scan geometries -equi-angular and equispace detector array. Based on the proposed and discussed fan-beam reconstruction algorithms with inverse distance backprojection and no backprojection weight, new 3D cone-beam FDK reconstruction algorithms with circular and helical scan trajectories for curved and planar detector geometries are proposed. To start with three rebinning formulae from literature are presented and it is shown that one can derive all fan-beam FBP reconstruction algorithms from these rebinning formulae. Specifically, two fan-beam algorithms with no backprojection weight based on Hilbert filtering for equi-space linear array detector and one new fan-beam algorithm with inverse distance backprojection weight based on hybrid filtering for both equi-angular and equi-space linear array detector are derived. Simulation results for these algorithms in terms of uniformity of noise and resolution in comparison to standard fan-beam FBP reconstruction algorithm (ramp filter based fan-beam reconstruction algorithm) are presented. It is shown through simulation that the fan-beam reconstruction algorithm with inverse distance in the backprojection gives better noise performance while retaining the resolution properities. A comparison between above mentioned reconstruction algorithms is given in terms of computational complexity. The state of the art 3D X-ray imaging systems in medicine with cone-beam (CB) circular and helical computed tomography scanners use non-exact (approximate) FBP based reconstruction algorithm. They are attractive because of their simplicity and low computational cost. However, they produce sub-optimal reconstructed images with respect to cone-beam artifacts, noise and axial intensity drop in case of circular trajectory scan imaging. Axial intensity drop in the reconstructed image is due to the insufficient data acquired by the circular-scan trajectory CB CT. This thesis deals with investigations to improve the image quality by means of the Hilbert and hybrid filtering based algorithms using redundancy data for Feldkamp, Davis and Kress (FDK) type reconstruction algorithms. In this thesis, new FDK type reconstruction algorithms for cylindrical detector and planar detector for CB circular CT are developed, which are obtained by extending to three dimensions (3D) an exact Hilbert filtering based FBP algorithm for 2D fan-beam beam algorithms with no position dependent backprojection weight and fan-beam algorithm with inverse distance backprojection weight. The proposed FDK reconstruction algorithm with inverse distance weight in the backprojection requires full-scan projection data while the FDK reconstruction algorithm with no backprojection weight can handle partial-scan data including very short-scan. The FDK reconstruction algorithms with no backprojection weight for circular CB CT are compared with Hu’s, FDK and T-FDK reconstruction algorithms in-terms of axial intensity drop and computational complexity. The simulation results of noise, CB artifacts performance and execution timing as well as the partial-scan reconstruction abilities are presented. We show that FDK reconstruction algorithms with no backprojection weight have better noise performance characteristics than the conventional FDK reconstruction algorithm where the backprojection weight is known to result in spatial non-uniformity in the noise characteristics. In this thesis, we present an efficient method to reduce the axial intensity drop in circular CB CT. The efficient method consists of two steps: the first one is reconstruction of the object using FDK reconstruction algorithm with no backprojection weight and the second is estimating the missing term. The efficient method is comparable to Zhu et al.’s method in terms of reduction in axial intensity drop, noise and computational complexity. The helical scanning trajectory satisfies the Tuy-smith condition, hence an exact and stable reconstruction is possible. However, the helical FDK reconstruction algorithm is responsible for the cone-beam artifacts since the helical FDK reconstruction algorithm is approximate in its derivation. In this thesis, helical FDK reconstruction algorithms based on Hilbert filtering with no backprojection weight and FDK reconstruction algorithm based on hybrid filtering with inverse distance backprojection weight are presented to reduce the CB artifacts. These algorithms are compared with standard helical FDK in-terms of noise, CB artifacts and computational complexity.