Detection and Grading of Prostate Cancer in Temporal Enhanced Ultrasound using Convolutional Neural Networks

Prostate cancer is the most common non-dermatological cancer amongst men in the developed world. The current definitive diagnosis is core needle biopsy guided by transrectal ultrasound. However, this method suffers from low sensitivity and specificity in detecting cancer. Recently, a new ultrasound based tissue typing approach has been proposed, known as temporal enhanced ultrasound (TeUS). In this approach, a set of temporal ultrasound frames is collected from a stationary tissue location without any intentional mechanical excitation. The main aim of this thesis is to implement a deep learning-based solution for prostate cancer detection and grading using TeUS data. In the proposed solution, convolutional neural networks are trained to extract high-level features from time domain TeUS data in temporally and spatially adjacent frames in nine in vivo prostatectomy cases. This approach avoids information loss due to feature extraction and also improves cancer detection rate. The output likelihoods of two TeUS arrangements are then combined to form our novel decision support system. This deep learning-based approach results in the area under the receiver operating characteristic curve (AUC) of 0.80 and 0.73 for prostate cancer detection and grading, respectively, in leave-one-patient-out cross-validation. Recently, multi-parametric magnetic resonance imaging (mp-MRI) has been utilized to improve detection rate of aggressive prostate cancer. In this thesis, for the first time, we present the fusion of mp-MRI and TeUS for characterization of prostate cancer to compensates the deficiencies of each image modalities and improve cancer detection rate. The results obtained using TeUS are fused with those attained using consolidated mp-MRI maps from multiple MR modalities and cancer delineations on those by multiple clinicians. The proposed fusion approach yields the AUC of 0.86 in prostate cancer detection. The outcomes of this thesis emphasize the viable potential of TeUS as a tissue typing method. Employing this ultrasound-based intervention, which is non-invasive and inexpensive, can be a valuable and practical addition to enhance the current prostate cancer detection.

Genetic Polymorphisms in Replication-related Genes and Risk of Breast Cancer Among European and East Asian Women

Background: The role of common, low to intermediate risk alleles in breast cancer need to be examined due to their relatively high prevalence. Among many cellular pathways, replication has a pivotal role in cell division and frequently targeted during carcinogenesis. Replication is governed by a host of genes involved in a number of different pathways. This study investigates the effects of replication-gene variants in relation to breast cancer and how this relationship is affected by ethnicity, menopausal status and breast tumour subtype. Methods: Data from a case-control study with 997 incident breast cancer cases and 1,050 age frequency matched controls in Vancouver, British Columbia and Kingston, Ontario were used. Unconditional logistic regression was used to calculate odds ratios between 45 replication gene variants and breast cancer risk, assuming an additive genetic model adjusted for age and centre, presented for Europeans and East Asians separately. Polytomous logistic regression was used to assess odds ratios between each SNP and four breast cancer subtypes defined by hormone receptor status among Europeans. All analyses were stratified by menopausal status. The Benjamini–Hochberg false discovery rate (FDR) was used to address multiple comparisons. Results: Among Europeans, the SNPs in FGFR2, TOX3 and 11q13 loci were associated with breast cancer after controlling for multiple comparisons. Test of heterogeneity showed the SNPs rs1045185, rs4973768, rs672888, rs1219648, rs2420946 among Europeans and rs889312 among East Asians conferred differential risk across the tumour subtypes. Conclusions: Specific SNPs in replication genes were associated with breast cancer, and the risk level differed by tumour subtype defined by ER/PR/Her2 status and ethnicity.

Classifying And Predicting Software Security Vulnerabilities based on Reproducibility

Security defects are common in large software systems because of their size and complexity. Although efficient development processes, testing, and maintenance policies are applied to software systems, there are still a large number of vulnerabilities that can remain, despite these measures. Some vulnerabilities stay in a system from one release to the next one because they cannot be easily reproduced through testing. These vulnerabilities endanger the security of the systems. We propose vulnerability classification and prediction frameworks based on vulnerability reproducibility. The frameworks are effective to identify the types and locations of vulnerabilities in the earlier stage, and improve the security of software in the next versions (referred to as releases). We expand an existing concept of software bug classification to vulnerability classification (easily reproducible and hard to reproduce) to develop a classification framework for differentiating between these vulnerabilities based on code fixes and textual reports. We then investigate the potential correlations between the vulnerability categories and the classical software metrics and some other runtime environmental factors of reproducibility to develop a vulnerability prediction framework. The classification and prediction frameworks help developers adopt corresponding mitigation or elimination actions and develop appropriate test cases. Also, the vulnerability prediction framework is of great help for security experts focus their effort on the top-ranked vulnerability-prone files. As a result, the frameworks decrease the number of attacks that exploit security vulnerabilities in the next versions of the software. To build the classification and prediction frameworks, different machine learning techniques (C4.5 Decision Tree, Random Forest, Logistic Regression, and Naive Bayes) are employed. The effectiveness of the proposed frameworks is assessed based on collected software security defects of Mozilla Firefox.

Strategic Diplomacies: Indigenous Governance, International Politics, and Natural Resources in Canada

International Relations theory would predict that central governments, with their considerable material resources, would be unlikely to face a challenge from a substate government. However, substate governments, and particularly Indigenous governments, are pushing back against central government control in both domestic and international spheres. Indigenous governments are leveraging their local mining sectors to realize their interests and express local identities—interests and identities that may not be congruent with those of the central government. Applying the case study of the resource extraction sector in Canada, this thesis asks: under what conditions are substate governments able to challenge the authority of central governments in the international arena? Canada’s reliance on the global extractive resource sector is a major driver of its international policy preferences, but the increased engagement of Indigenous governments in the sector challenges the control of the federal government. Focusing on the resource extraction sectors in British Columbia, Saskatchewan, and Ontario, this thesis argues that there is a mutually reinforcing relationship between Indigenous governments’ international engagement and their domestic autonomy; both challenge the parameters of state authority. Both force the state to respond to claims of control from multiple sites and to clarify convoluted policy environments. A confluence of factors—including increased Indigenous connections to the globalized economy, new Canadian regulatory frameworks, and recent Supreme Court of Canada cases regarding Indigenous lands—have all altered the space in which Indigenous governments in Canada participate in the resource extraction sector and produce overlapping or multilevel governance structures. This thesis demonstrates that Indigenous international engagement entrenches the authority and political legitimacy manifest in Indigenous governments’ insistence on equitable and horizontal negotiations in Canada’s lucrative resource extraction sector. A cumulative process occurs in which domestic and international expressions of political autonomy reinforce each other, produce further opportunities to express authority in both environments, and trouble the state’s capacity to fully realize its international policy preferences.

Design of an Experimental Rig to Characterize the Gust Response of Small Wind Turbines and Autorotating Seeds

This thesis investigates the rotational behavior of abstracted small-wind-turbine rotors exposed to a sudden increase in oncoming flow velocity, i.e. a gust. These rotors consisted of blades with aspect ratios characteristic of samara seeds, which are known for their ability to maintain autorotation in unsteady wind. The models were tested in a towing tank using a custom-built experimental rig. The setup was designed and constructed to allow for the measurement of instantaneous angular velocity of a rotor model towed at a prescribed kinematic profile along the tank. The conclusions presented in this thesis are based on the observed trends in effective angle-of-attack distribution, tip speed ratio, angular velocity, and time delay in the rotational response for each of rotors over prescribed gust cases. It was found that the blades with the higher aspect ratio had higher tip speed ratios and responded faster than the blades with a lower aspect ratio. The decrease in instantaneous tip speed ratio during the onset of a prescribed gust correlated with the time delay in each rotor model's rotational response. The time delays were found to increase nonlinearly with decreasing durations over which the simulated gusts occurred.

Investigating the mesh dependency and upscaling of 3D grain-based models for the simulation of brittle fracture processes in low-porosity crystalline rock

The application of 3D grain-based modelling techniques is investigated in both small and large scale 3DEC models, in order to simulate brittle fracture processes in low-porosity crystalline rock. Mesh dependency in 3D grain-based models (GBMs) is examined through a number of cases to compare Voronoi and tetrahedral grain assemblages. Various methods are used in the generation of tessellations, each with a number of issues and advantages. A number of comparative UCS test simulations capture the distinct failure mechanisms, strength profiles, and progressive damage development using various Voronoi and tetrahedral GBMs. Relative calibration requirements are outlined to generate similar macro-strength and damage profiles for all the models. The results confirmed a number of inherent model behaviors that arise due to mesh dependency. In Voronoi models, inherent tensile failure mechanisms are produced by internal wedging and rotation of Voronoi grains. This results in a combined dependence on frictional and cohesive strength. In tetrahedral models, increased kinematic freedom of grains and an abundance of straight, connected failure pathways causes a preference for shear failure. This results in an inability to develop significant normal stresses causing cohesional strength dependence. In general, Voronoi models require high relative contact tensile strength values, with lower contact stiffness and contact cohesional strength compared to tetrahedral tessellations. Upscaling of 3D GBMs is investigated for both Voronoi and tetrahedral tessellations using a case study from the AECL’s Mine-by-Experiment at the Underground Research Laboratory. An upscaled tetrahedral model was able to reasonably simulate damage development in the roof forming a notch geometry by adjusting the cohesive strength. An upscaled Voronoi model underestimated the damage development in the roof and floor, and overestimated the damage in the side-walls. This was attributed to the discretization resolution limitations.