title: SmartCruise: A Highway Driver Assist Technology

contributor:
Emadi, Ali
Mechanical Engineering

abstract:
Driver fatigue is one of the leading causes for motor vehicle accidents. The riseof automated vehicle technologies and autonomous features, not only significantlyreduces the effects of driver fatigue in turn reducing motor vehicle accidents, it alsoreduces fuel consumption, traffic congestion, travel time, and significantly improvesvehicle accessibility for people who are not able to drive a car themselves.In this thesis, a new highway driver assist technology called SuperCruise ispresented.  SuperCruise takes over parts of the driving task by controlling thethrottle, braking, and steering of the vehicle.For the longitudinal control of the vehicle, a new Adaptive Cruise Control(ACC)system is introduced which solves the problem of repetitive switching between thetwo modes of the classic ACC system. To achieve this, a novel switching algorithmis implemented that creates a hysteresis between the two modes by implementing aset of logical comparisons which smoothens the transition between the two modes,and reduces repetitive switching. This results in lower longitudinal accelerationand jerk, ensuring a more comfortable ride.For the development of the lateral controller which is the Lane Centering As-sist(LCA), a Fuzzy Model Predictive Controller(MPC) is developed by implement-ing fuzzy control logic into the formulation of the MPC. This system automaticallytunes the cost function parameters of the optimization algorithm used in MPC toimprove the lateral stability of the system based on the current lateral deviationand heading angle error of the vehicle with respect to the desired trajectory.iii
The longitudinal system is then compared to the classic ACC via three differentdriving scenarios. Results show significant reduction is the switching between thesemodes which results in lower longitudinal accelerations and hence an improvementin driver comfort.The performance of the proposed lateral controller on the other hand, hasbeen evaluated by comparing the path following performance, lateral stability,and driver comfort of the system with two of the well known methods used inliterature; Stanley method and classic MPC in three different driving scenarios.Results show that implementation of Fuzzy control logic into the MPC, improvesthe lateral stability of the system and reduces the unnecessary oscillations in steer-ing which in turn reduces lateral acceleration and increases driver discomfort.

abstract: Thesis

abstract: Master of Applied Science (MASc)


title: Impact of Charge Profile on Battery Fast Charging Aging and Dual State Estimation Strategy for Traction Applications

contributor:
Dr. Ali Emadi
Mechanical Engineering

subject:
Battery
Fast Charging
Extended Kalman Filter
Smooth Variable Structure Filter
Battery Aging
State of Charge Estimation
Battery Capacity Estimation

abstract:
The fast-growing electric vehicles (EVs) market demands huge efforts from car manufacturers to develop and improve their current products’ systems. A fast charge of the battery pack is one of the challenges encountered due to the battery limitations regarding behaviour and additional degradation when exposed to such a rough situation. In addition, the outcome of a study performed on a battery does not apply to others, especially if their chemistries are different. Hence, extensive testing is required to understand the influence of design decisions on the particular energy storage device to be implemented. Due to batteries’ nonlinear behaviour that is highly dependent on external variables such as temperature, the dynamic load and aging, another defying task is the widely studied state of charge (SOC) estimation, commonly considered one of the most significant functions in a battery management system (BMS).
This thesis presents an extensive battery fast charging aging test study equipped with promising current charging profiles from published literature to minimize aging. Four charging protocols are carefully designed to charge the cell from 10 to 80% SOC within fifteen minutes and have their performances discussed. A dual state estimation algorithm is modelled to estimate the SOC with the assistance of a capacity state of health (SOHcap) estimation. Finally, the dual state estimation model is validated with the fast charging aging test data.

abstract: Thesis

abstract: Master of Science in Mechanical Engineering (MSME)


title: SPECTRUM AND POWER EFFICIENT OPTICAL OFDM FOR VISIBLE LIGHT COMMUNICATION SYSTEMS

contributor:
Hranilovic, Steve Jr
Electrical and Computer Engineering

subject:
Visible light communications
Optical wireless communications
OFDM

abstract:
The need for wireless connectivity is ever increasing while conventional radio frequency (RF) communications are limited by the amount of available spectrum. Visible light communications (VLC) are emerging as a promising complementary to the RF wireless, thanks to the enormous available bandwidth in the visible spectrum. Moreover, VLC integrates into the ubiquitous illumination infrastructures to satisfy the need for wireless connectivity indoors.

Commercially available light-emitting diodes (LEDs) are low-cost, simple, and have a small modulation bandwidth. For the small modulation bandwidth, VLC systems must enjoy high spectral efficiency to achieve high-rate transmission. Additionally, VLC systems must have high power efficiency to help preserve the critical advantage of LEDs for illumination applications. Furthermore,  since LED luminaires are constrained by cost, deployed VLC systems must be low-complexity. Indoor VLC channels are dispersive due to multipath propagation indoors and due to the limited bandwidth of the optoelectronics. However, time-domain equalization on such channels can be prohibitively expensive for long serials. These challenges motivate extensive research on optical orthogonal frequency division multiplexing (OFDM).

Given those problems and challenges, this thesis introduces novel spectrum- and power-efficient optical OFDM modulation schemes to implement with low complexity in VLC systems.
Firstly, absolute value layered asymmetrically clipped optical OFDM (ALACO-OFDM) is presented to achieve high spectral efficiency and high power efficiency with fewer layers, thus requiring low complexity. Compared to its counterparts, ALACO-OFDM can achieve higher spectral efficiency and information rate even with fewer layers.

Antisymmetry-constructed clipped optical OFDM (AC-OFDM) is then introduced as a novel low-complexity modulation scheme. To enhance the spectral efficiency and retain low-complexity, layered AC-OFDM (LAC-OFDM) is introduced, consisting of several layers of AC-OFDM signals. LAC-OFDM is shown to be less complex compared to its state-of-the-art counterparts.

Concerning a practical VLC dispersive channel, low-complexity layered ACO-OFDM (L-LACO) is introduced with simple equalization. Mathematically, L-LACO generates identical signals to the existing layered asymmetrically-clipped optical OFDM (LACO-OFDM); however, it requires only half arithmetic operations at both the transmitter and the receiver.

For a practical bandlimited VLC dispersive channel, the previous optical OFDM modulation schemes will be no longer non-negative after interpolation with sinc(t) pulse. This thesis presents Kramers-Kronig (KK) optical OFDM (KKO-OFDM) to enhance the spectral efficiency and power efficiency for such bandlimited VLC channels. The KKO-OFDM transmit signal is constructed to be real-valued, non-negative,  and strictly bandlimited. Numerical results show that KKO-OFDM outperforms DCO-OFDM and LACO-OFDM in bandlimited VLC channels in terms of optical power efficiency.

abstract: Thesis

abstract: Doctor of Philosophy (PhD)

abstract: Visible light communications (VLC) integrate into the ubiquitous light-emitting diode (LED) luminaires, providing lighting and communication simultaneously. Commercially available LEDs are low-cost, simple, and have a limited modulation bandwidth. These LEDs demand that VLC orthogonal frequency division multiplexing (OFDM) modulation schemes be spectrum- and power-efficient with low complexity. Concerning these challenges, this thesis presents a novel spectrum- and power-efficient VLC OFDM scheme with low complexity. Firstly, absolute value layered asymmetrically clipped optical OFDM (ALACO-OFDM) is presented to achieve high spectral and power efficiency while requiring fewer layers. Then layered antisymmetry-constructed clipped optical OFDM (LAC-OFDM) is introduced, which requires low complexity as compared to existing layered asymmetrically-clipped optical OFDM (LACO-OFDM). Given a VLC dispersive channel, low-complexity LACO-OFDM (L-LACO) is furthermore introduced with simple equalization but generates an identical signal to the existing LACO-OFDM. Finally, for a bandlimited VLC channel, Kramers-Kronig optical OFDM (KKO-OFDM) is presented to achieve high spectral and power efficiency. The ALACO-OFDM and LAC-OFDM work in flat VLC line-of-sight links while L-LACO in VLC dispersive links and KKO-OFDM in bandlimited VLC dispersive links with simple equalizer.


title: ALTERED MYOFIBER FUNCTION AND PHYSIOLOGY IN TYPE 1 DIABETES

title: ALTERED MUSCLE FUNCTION AND MYOFIBER PHYSIOLOGY IN ADULTS WITH TYPE 1 DIABETES

contributor:
Hawke, Thomas
Medical Sciences

subject:
skeletal muscle
type 1 diabetes
regeneration
exercise
aging

abstract: The objective of this thesis was to examine muscle function and myofiber physiology in skeletal muscles in those with type 1 diabetes (T1D) by investigating the effects of diabetic myopathy on these metrics of muscle health under various conditions: at rest, after exercise and with increasing age. These works recruited adults from surrounding communities with T1D and non-diabetic counterparts (i.e. controls) matched for age, sex, body mass index, and self-reported physical activity levels. We hypothesized that adults with T1D would exhibit decreased muscle function (i.e. lower maximal strength) and altered myofiber physiology in each of these conditions. At rest, we observed that those with T1D exhibited more fast-twitch fibers and fewer satellite cells. After exercise, T1D muscles recovered less strength, showed higher amounts of myofiber damage, and delayed satellite cell proliferation. With increasing age, adults with T1D exhibited exaggerated signs of muscular aging compared to age-matched controls in the form of more abundant hybrid fibers and type 1 fiber grouping. Finally, individuals with T1D exhibited higher baseline expression of myostatin, a negative muscle growth regulator, compared to controls. Overall, our work provides the first evidence in muscle dysfunction from humans with T1D at various ages and after damaging exercise. Our findings provide novel insights on muscle health and its contribution to overall health during this lifelong, debilitating disease. Our work aims to guide future clinical & exercise guidelines with the ultimate purpose of improving the lives of millions of individuals living with T1D.

abstract: Thesis

abstract: Doctor of Philosophy (PhD)

abstract: Type 1 diabetes (T1D) is a disease that affects millions of adults worldwide by harming their ability to manage blood sugar levels. Insulin therapy has allowed for longer and healthier lives but requires constant attention manage blood sugar levels. Over time, the quality of life declines because of complications from T1D. Muscle is able to control blood sugar levels through exercise, but little is known about muscle in those with T1D. Therefore, the purpose of this work was to examine skeletal muscle health in people with T1D who do not have other complications. We found that adults with T1D exhibit signs of aging in their muscles earlier than non-diabetic people. Also, we observed that muscle from young adults with T1D recovered slower from exercise. Finally, we learned that people with T1D have more of a muscle-shrinking protein. This is the first evidence of dysfunctional muscle fibers at rest, after exercise, and with age in adults with T1D. This work aims to improve future guidelines for millions of adults with T1D.


title: The Honest Merchant: Rethinking History, Criteria, and Memory in the Study of the Historical Muhammad

contributor:
Takim, Liyakat
Religious Studies

subject: Muhammad; early Islam; Islamic Studies; Religious Studies; historical Muhammad; memory studies; historical Jesus; sirah-maghazi; Qur'an; hadith literature; oral tradition; oral transmission; criteria of authenticity

abstract:
Over the last fourteen-hundred years, Muhammad ibn ʿAbd Allah (d. 632) has been depicted and portrayed in a variety of ways by numerous scholars, theologians, and polemicists. My dissertation offers a unique approach to the “historical Muhammad” as it develops a new method to examine extant primary sources related to his life. I include available sources that provide pertinent information on Muhammad’s life, including the Qur’an, hadith literature, sira-maghazi (biographies and expeditions), and non-Muslim accounts.
       My research is original because it adopts current historical Jesus scholarship, particularly modern cognitive studies of memory, and uses it on extant sources related to Muhammad’s life. More specifically, I explore how memory, oral tradition, and oral transmission play vital roles in understanding how Muslims remembered their Prophet and how the circumstances of later generations shaped and influenced their commemoration of his life.
       By adopting this scholarship, which will be contextualized to examine early Muslim literature, I offer a new perspective on surviving sources, the context of seventh-century Arabia, and the function of memory for the nascent Muslim community. I also apply my method on eight significant, polemical, or neglected events that are traditionally believed to have taken place during Muhammad’s life in Mecca and Medina.
       In sum, my dissertation offers a dynamic cross-disciplinary venture, encompassing the intersection of innovative, modern critical inquiry and early Islamic literature.

abstract: Dissertation

abstract: Doctor of Philosophy (PhD)

abstract: This dissertation examines the field of the “historical Muhammad” and applies a new method on extant primary sources related to Muhammad’s life. I conduct a literature review of scholars’ reconstructions of his life, beginning as early as the seventh century. I also explore numerous primary sources on Muhammad, pointing out their benefits and disadvantages. Next, I overview the quests for the historical Jesus and analyze methods that were established over the last hundred years. In my dissertation, I adopt historical Jesus scholarship, namely memory studies, to develop an original method that provides a unique understanding and fresh perspective of the historical Muhammad. Over the last two chapters, I conduct eight case studies employing my method on events from Muhammad’s life in Mecca and Medina. This dissertation demonstrates that we could reconstruct a reasonably coherent picture of events surrounding Muhammad’s life.


title: Multi-Object Tracking Using Dual-Attention with Regional-Representation

contributor:
(Kiruba) Kirubarajan, Thia
(Thamas) Tharmarasa, Ratnasingham
Electrical and Computer Engineering

subject:
Multi-Object Tracking
Deep Learning
Self-Attention

abstract: Nowadays, researchers have shown convolutional neural network (CNN) can achieve an improved performance in multi-object tracking (MOT) by performing detection and re-identification (ReID) simultaneously. Many models have been created to overcome challenges and bring the state-of-the-art performance to a new level. However, due to the fact the CNN models only utilize feature from a local region, the potential of the model has not been fully utilized. The long range dependencies in spatial domain are usually difficult for a network to capture. Hence, how to obtain such dependencies has become the new focus in MOT field. One approach is to adopt the self-attention mechanism named transformer. Since it was successfully transferred from natural language processing to computer vision, many recent works have implemented it to their trackers. With the introduce of global information, the trackers become more robust and stable. There are also traditional methods which are re-designed in the manner of CNN and achieve satisfying performance such as optical flow. It can generate a correlated relation between feature maps and also obtain non-local information. However, the introduces of these mechanism usually causes a significant surge in computational power and memory. They also requires huge amount of epochs to train thus the training time is largely increased. To solve this issue, we propose a new method to gather non-local information based on the existing self-attention methods, we named it dual attention with regional-representation, which significantly reduces the training time as well as the inference time, but only causes a small increase in computational memory and are able to run with a reasonable speed. Our experiments shows this module can help the ReID be more stable to improve the performance in different tasks.

abstract: Thesis

abstract: Master of Applied Science (MASc)


title: Detection Of Sepsis Biomarkers Using Microfluidics

contributor:
Selvaganapathy, Ravi
Mechanical Engineering

subject:
Microfluidics
Protein separation
Point of care
Isoelectric trapping
Protein C
cfDNA

abstract:
Sepsis is a “life-threatening organ dysfunction caused by a dysregulated host response to infection” that has a widespread impact on human life around the world. It affects more than 1.5 million people, killing at least 250,000 each year in the US alone and affects 90,000 people annually, with estimated mortality rates of up to 30% in Canada. Our understanding of the different biochemical pathways that in the progression of sepsis has improved patient care for sepsis patients. One part of patient care is the use of biomarkers for patient prognosis that draws on the full range of relevant and available information to model the possible outcomes for an individual. Numerous biomarkers have been studied for patient prognosis that includes Procalcitonin (PCT), C-reactive protein (CRP), TNF-α, cfDNA, protein C and PAI 1. Using a panel of multiple biomarkers provided more accuracy in patient prognosis than using individual biomarkers and one such panel that was proposed used cfDNA, protein C, platelet count, creatinine, Glasgow Coma Scale [GCS] score, and lactate. Commercial, low cost POC techniques were available for the measurement of all biomarkers besides cfDNA and protein
C. The objective of this doctoral thesis was chosen to develop low cost, microfluidic devices for the measurement of protein C and cfDNA using nonspecific fluorescence dyes that would enable the eventual integration of the systems and improve patient prognosis. The measurement of protein C in plasma required the separation of protein C from interfering proteins in plasma. This was done through the development of a two-stage separation process that included the development of tunable agarose isoelectric gates for separating proteins using their isoelectric point and the miniaturization of immobilized metal affinity chromatography and its extension to Barium for the selective binding of proteins using their chemical affinity. This was performed in a xurographically fabricated chip to reduce costs and enable the use of geometric focusing of the electric field to enable the operation of the device at a lower applied voltage. The challenges faced with cfDNA were different due to the different characteristics of the material and less interference from plasma. The requirement was to measure the total cfDNA content with minimal cost in comparison to currently available  techniques. This was achieved through the development of thread microfluidic devices that showed the use of thread for automated aliquoting of samples by controlling length and twists of the thread. Preconcentration and use of external apparatus was avoided by showing that thread could be used to amplify fluorescence response to a range that was sufficient for the measurement of cfDNA in sepsis patients. A portable fluorescence imaging setup was developed for this purpose and was used in demonstration for the measurement of cfDNA in plasma with sufficient resolution. In conclusion, we developed technologies for rapid and low-cost measurement of protein C and cfDNA using xurographic and thread-based microfluidics that may serve as valuable in improving patient prognosis.

abstract: Thesis

abstract: Doctor of Philosophy (PhD)

abstract:
Sepsis is a major reason for hospitalization and cause of death in hospitals worldwide. Its treatment is highly time sensitive with each hour of delay in diagnosis causing a significant increase in chances of death. Due to the wide range of symptoms that can be caused by sepsis, its diagnosis uses a scoring method that relies on the expertise of the onsite doctors and nurses increasing their workload. A more objective system for detection requires the measurement of the quantities of different biomarkers in blood. Biomarkers are proteins present in plasma that change in quantity due to the body’s reaction to sepsis. Several of these biomarkers have been identified and studied for their use in both diagnosing
the presence of sepsis and in predicting the outcome with the current treatment plan. In this PhD study, we chose two of these biomarkers – circulating free DNA (cfDNA) and protein C and developed low-cost techniques for rapidly measuring their concentration in blood plasma. To do this, we made microfluidic devices with techniques that use low-cost materials such as plastic sheets and threads.The device for the measurement of protein C required separating it from many other proteins in plasma. We showed that a device fabricated from stacked plastic sheets and integrated with agarose gels could be used for the measurement of protein C in plasma with sufficient resolution to help with treating septic patients at a cost of less $5 per device. Similarly, we showed that a device that integrated threads with plastic sheets could be used for measuring the quantity of cfDNA in plasma in a portable format within 15 minutes. Overall, we developed tools for rapid measurement of two biomarkers of sepsis using low cost device that cost under $5 to run and could led to improving the quality of care for sepsis patients.


title: Temporally-Embedded Deep Learning Model for Health Outcome Prediction

contributor:
Doyle, Thomas E.
Samavi, Reza
Biomedical Engineering

subject:
Health informatics
Machine learning
Deep learning
Electronic health records
Transformer
Temporal
Encodings
Embeddings
Imputation
Missing data
Model checking
Multiple imputation
Evaluation metrics
Imaging
Computed tomography
X-ray

abstract:
Deep learning models are increasingly used to analyze health records to model disease progression. Two characteristics of health records present challenges to developers of deep learning-based medical systems. First, the veracity of the estimation of missing health data must be evaluated to optimize the performance of deep learning models. Second, the currently most successful deep learning diagnostic models, called transformers, lack a mechanism to analyze the temporal characteristics of health records.

In this thesis, these two challenges are investigated using a real-world medical dataset of longitudinal health records from 340,143 patients over ten years called MIIDD: McMaster Imaging Information and Diagnostic Dataset. To address missing data, the performance of imputation models (mean, regression, and deep learning) were evaluated on a real-world medical dataset. Next, techniques from adversarial machine learning were used to demonstrate how imputation can have a cascading negative impact on a deep learning model. Then, the strengths and limitations of evaluation metrics from the statistical literature (qualitative, predictive accuracy, and statistical distance) to evaluate deep learning-based imputation models were investigated. This research can serve as a reference to researchers evaluating the impact of imputation on their deep learning models.

To analyze the temporal characteristics of health records, a new model was developed and evaluated called DTTHRE: Decoder Transformer for Temporally-Embedded Health Records Encoding. DTTHRE predicts patients' primary diagnoses by analyzing their medical histories, including the elapsed time between visits. The proposed model successfully predicted patients' primary diagnosis in their final visit with improved predictive performance (78.54 +/- 0.22%) compared to existing models in the literature. DTTHRE also increased the training examples available from limited medical datasets by predicting the primary diagnosis for each visit (79.53 +/- 0.25%) with no additional training time. This research contributes towards the goal of disease predictive modeling for clinical decision support.

abstract: Dissertation

abstract: Doctor of Philosophy (PhD)

abstract: In this thesis, two challenges using deep learning models to analyze health records are investigated using a real-world medical dataset. First, an important step in analyzing health records is to estimate missing data. We investigated how imputation can have a cascading negative impact on a deep learning model's performance.  A comparative analysis was then conducted to investigate the strengths and limitations of evaluation metrics from the statistical literature to assess deep learning-based imputation models. Second, the most successful deep learning diagnostic models to date, called transformers, lack a mechanism to analyze the temporal characteristics of health records. To address this gap, we developed a new temporally-embedded transformer to analyze patients' medical histories, including the elapsed time between visits, to predict their primary diagnoses. The proposed model successfully predicted patients' primary diagnosis in their final visit with improved predictive performance (78.54 +/- 0.22%) compared to existing models in the literature.


title: FUNDAMENTAL STUDY OF DECARBURIZATION BEHAVIOR OF LIQUID Fe-C DROPLETS IN OXIDIZING SLAG

contributor:
Coley, Dr. Kenneth
Materials Science and Engineering

subject:
Steelmaking
Decarburization
Kinetics
Slag Conductivity
Adsoption
Desiliconization

abstract: This is a thesis includes both experimental and modeling studies for high temperature slag/metal reaction system.

abstract:
Bloating of metal droplets in emulsion is an important phenomenon in BOF steelmaking in controlling the kinetics of refining. This bloating controls the kinetics by mainly increasing the residence time (from ~¼th of a second to ~10-15 seconds) of the droplets in emulsion and the slag/metal surface (~5-6 times) area.  The bloating behavior is determined by the decarburization kinetics. This work aims to develop fundamental understanding of the bloating phenomena through series of experiments and mathematical modeling to explore various factors affecting the kinetics of decarburization.
An experimental study on varying the droplet carbon concentration, slag FeO concentration and basicity evidenced mixed controlled kinetics including transport of oxygen in the slag, interfacial (slag/metal) chemical reaction, nucleation and growth of CO bubbles. A mathematical model including these kinetic steps was developed. The model was able to demonstrate the partitioning of oxygen at the slag/metal interface into external (at the slag/metal interface) and internal (within droplet) decarburization in presence of the surface-active element sulfur.  The model was developed using a single data set and validated for a wide range of experimental conditions. The model showed excellent agreement with experimental data for most of the reaction period but failed to predict a premature shutdown for droplets reacting with low conductivity slag.
In order to understand this discrepancy, the slag ionic and electronic conductivity were varied which showed a premature shutdown of decarburization reaction with low conductivity slag and continuation of the reaction to the thermodynamic limit with high conductivity slag. A mechanism of generation of local electric field by accumulation of charge at the slag/metal interface was proposed to explain the premature shutdown of the reaction for low basicity slags. In all experiments with low conductivity slag sulfur was observed to delay the onset of internal decarburization. However, this effect was diminished or disappeared completely with high conductivity slag. This observation motivated additional experiments to study the competitive adsorption of oxygen and sulfur at the slag/metal interface both through experiments and modelling. It was shown that for low conductivity slag, sulfur poisoning inhibited reaction at the surface whereas for the high conductivity slags the faster transport of oxygen allowed oxygen to compete with sulfur for adsorption sites creating pathways for oxygen into the droplet.   By including the possibility of competitive adsorption in the model it was possible to predict the behavior of high sulfur droplets in conductivity slags where the only modification to the model was to change the mass transfer coefficient as appropriate to the higher conductivity. Extension of this study to include silicon in the droplet showed significant effect on decarburization both in delaying bloating as well as increasing peak rate of decarburization.

abstract: Thesis

abstract: Candidate in Philosophy


title: Design & Fabrication of Nanostructured Hydrogels From Biopolymer Nanoparticle Building Blocks for Biomedical and Environmental Applications

contributor:
Hoare, Todd
Chemical Engineering

subject:
Starch Nanoparticle
Biopolymer
Hydrogel
Network
Biomedical
Environmental

abstract:
In recent years, there has been a growing interest within the field of soft materials engineering on the development of advanced hydrogel systems with well-defined chemistries and morphologies that can be customized to suit various applications ranging from biomedical to environmental to personal care.  In any case, careful selection of the building block materials, crosslinking chemistry, degradation pathway, and overall hydrogel architecture is essential to ensure the final design (and the resulting degradation components if relevant) are safe/non-toxic, mechanically tunable, and overall translatable for their intended end use given industry safety/production standards.

In this thesis, the utility of starch nanoparticles created by a reactive extrusion process was explored as one such building block for creating renewable hydrogels.  Starch was reactively extruded by EcoSynthetix Inc. to create starch nanoparticles (SNPs) that are attractive as hydrogel building blocks due to their inherent small size (25-50 nm), generally safe degradation products, overall net neutral charge, high deformability/viscoelastic properties, stability in solution without collapsing or changing size (on the order of months), and the ability to be manufactured at a multiple kg/hr rate; in comparison, other manufacturing methods of SNPs suffer from a lack of scalability or require the use of potentially toxic solvents, making them less amenable to biological or environmental/agricultural applications.  The amorphous nature of the starch also allows for facile functionalization to further chemically modify and/or crosslink the SNPs through surface functional group (i.e. hydroxyl) modification chemistries.

The nanoparticle nature of the SNP building block, coupled with the facile functionalizability of the SNPs, also makes SNPs ideal building blocks for the design and fabrication of nanoparticle network hydrogels (NNHs) in which NPs create an interconnected network on their own on, in addition to, other polymeric networks at any desired length scale.  There are a variety of NNH architectures that can be achieved through careful design considerations.  More specifically, herein colloidal NNHs were created using UV photopolymerization post-functionalization with methacrylic anhydride, which leaves a vinyl group on the SNP surface.  Alternately, plum pudding NNHs were created by mixing aldehyde-functionalized SNPs with amine-bearing O-carboxymethyl chitosan that were able to chemically react via hydrolytically labile imine bonds.

The properties of various types of colloidal and plum-pudding hydrogels based on SNPs were tested and subsequently compared through a range of different performance tests such as rheological and micromechanical force testing, swelling/degradation kinetics, their potential for controlled bioactive release, and overall toxicity (cell and organ level).  In addition to these macroscopic performance tests, the internal morphologies of both colloidal and plum pudding NNHs were assessed with small angle and very small angle neutron scattering experiments to glean insight into how these internal structures correlate to macroscopic properties.  For all experiments, the effect of using SNPs versus typical cold water-soluble branched starch (SS) was assessed to further understand the impact of making hydrogels from nanoscale rather than soluble polymer building blocks, with the small size of SNPs compared to the large hydrodynamic radius of SS consistently allowing for greater control over the range of potential hydrogel properties.

The results of these studies suggest that SNP-based NNHs are promising materials for studying the encapsulation and release of small molecules in both in vitro and in vivo settings. For example, the photopolymerization of methacrylated SNP-based NNH coatings can be fabricated at much higher concentrations than possible with conventional starch (35% for SNP, 10% for SS), leading to denser and stiffer gels compared to SS controls albeit with slightly longer gelation times due to the reduced conformational mobility of the polymerizable methacrylate groups on the SNPs.  The addition of charge (cationic or anionic) to the SNP surface further increases the bulk gelation time while significantly reducing the observed changes in SNP deformation during photogelation as confirmed via very small angle neutron scattering experiments.  Other functional groups were also demonstrated to be introduced to SNPs to enable different types of gelation for different applications.  For example, in situ-gelling and degradable bulk nanoparticle network hydrogels consisting of oxidized starch nanoparticles (SNPs) and carboxymethyl chitosan (CMCh) were created for intranasal delivery that could be delivered into the nose via a commercial atomization device to enable high nasal mucosal retention and functional controlled release of the peptide drug PAOPA, a positive allosteric modulator of dopamine D2 receptor.  Selected gels shown to alleviate negative behavioural abnormalities associated with for up to 72 hours in pre-clinical rat models of schizophrenia at a low drug dosage (0.5 mg/kg), compared to just a few hours with the drug alone.  Finally, the functionalization of SNPs with hydrophobic groups (via grafting the starch with octenyl succinic acid (OSAn) or succinic anhydride (SAn)) was demonstrated as a promising delivery system for agricultural applications. Hydrophobization increased the contact angle of a sprayed watermelon and pumpkin leaves from <60˚ (unmodified) to ~80˚ when modified (DS 0.25), while confocal fluorescence microscopy confirmed that the hydrophobized SNPs can both adhere to the leaf surface as well as penetrate into the leaves when sprayed due to their small size (25-50 nm).

Future work will look at other methods of crosslinking SNPs (i.e. Michael addition, hydrazone, and alkyne-azide “click” chemistry, amongst others) to see if there are beneficial differences compared to analogous hydrogels made from macroscopic alternatives (i.e. polymers alone) and to follow-up the findings already gleaned within this thesis.  Further information on the impact and potential follow-up experiments for the work conducted in this thesis will be explained in Chapter 6 on final outlooks and conclusions of the following work.

abstract: Thesis

abstract: Doctor of Philosophy (PhD)

abstract: This thesis describes the chemical and physical modification of commercially-available starch nanoparticles (SNPs) to rationally create novel hydrogel systems.  These gel-like networks are made by chemically connecting starch nanoparticles (with sizes on the 10-8 m length scale) by introducing various reactive chemical groups onto the surface of SNPs, enabling the creation of hydrogels with well-defined structures, features, and properties.  Careful selection of the crosslinking chemistry made it possible to tune hydrogel properties to specific application requirements, such as the targeted delivery of pharmaceuticals (including the intranasal delivery of antipsychotic drugs to the brain, a key technical challenge to improve the quality of life of patients with mental health challenges) and agrochemical agents or as an anti-fouling coating.  The hydrogels created herein are attractive since they directly incorporate nanoscale particles generated from a sustainable source and are generally regarded as safe (GRAS) in terms of their degradation products once they break down, a rare trait for nanoparticles of this size.  The existing industrial-scale production of the SNPs also enables facile scaling of these strategies for ultimate commercial translation.


title: APPLYING DIFFERENT RESEARCH METHODOLOGIES TO ORAL ANTICOAGULANT MANAGEMENT RESEARCH

contributor:
Holbrook, Anne
Health Research Methodology

subject: Oral anticoagulants (OACs), Research methodologies, Management research, Randomized controlled trials (RCTs)

abstract:
Background and Objectives
Oral anticoagulants (OACs) are among Canada's most frequently prescribed drugs and a top cause of medication-related serious harm leading to emergency department visits, hospitalizations, and fatalities. During the preparation to launch a Canadian Institutes of Health Research (CIHR)-funded randomized controlled trial (RCT) called "Improving Anticoagulant Safety at Hospital Discharge: A Randomized Trial," we faced some issues. First, as the RCT addresses OAC management, we needed to determine the barriers and facilitators for optimal OAC management, which were not identified in our literature search. Second, there is no core outcome set (COS) specific for OACs and the choice of outcomes and their measurement for the trial was not obvious. Finally, the drug-drug interactions between the OACs and other medications are not fully understood, particularly with regards to important clinical outcomes. Identifying the interacting medications and their interaction effect size, is vital to guarantee the safety of patients. To address these issues, the objectives of this thesis were: (1) to determine the barriers and facilitators for optimal OAC management, (2) to define the potential list for the COS of OACs, and (3) to explore the drug-drug interaction of OACs.
Methods
Several research approaches, including a systematic review, a systematic survey, a scoping review, a population-based retrospective cohort study with time varying methods, and a qualitative study were applied in this thesis. First, we applied both a synthesis review and qualitative research to explore the barriers and facilitators for OACs management to guarantee the evidence's robustness. Next, we used a systematic survey to address the lack of consensus on outcomes used and their
v
definitions for OAC treatment clinical trials. Finally, we used a systematic review and planned a population-based study to address drug-drug interaction related to OACs.
Methodologic challenges and innovation
In the scoping review (Chapter 2: Barriers and facilitators to optimal oral anticoagulant management: a scoping review) and the focus group study (Chapter 3: Perceptions on patient education to improve oral anticoagulant management) we employed a qualitative approach. The main methodological challenge for both the scoping review and the focus group focused on the rigorous way to synthesize the themes. In Chapter 4, we used a systematic survey to explore the outcome list for OAC management research. The primary methodological challenge referred to the outcome reporting in the included studies. Not all outcomes performed in the trials can be reported for the space limitation or potential publication bias. In Chapters 5 and 6, a systematic review with meta-analysis and an observational protocol were used to explore the drug-drug interaction for OACs. The main methodological challenge for Chapter 5 was how to evaluate the drug-drug interaction (DDI) evidence systematically. The main methodological challenge for Chapter 6 is to address confounding and bias in a population-based protocol on DOACs drug-drug interaction.
Conclusion
In summary, this standard thesis describes five different background projects to prepare for an OAC management RCT. The papers contribute to the literature by using several research methodologies to provide useful evidence for OAC management and OAC research.

abstract: Thesis

abstract: Candidate in Philosophy

abstract: Oral anticoagulants (OACs) (blood thinners) are among Canada's most frequently prescribed drugs and a top cause of severe medication-related harm. The objectives of this thesis include (1) to determine the barriers and facilitators for optimal OAC management, (2) to define a potential list for the core outcome set of OACs, and (3) to explore the drug-drug interaction of OACs. First, we applied a scoping review and a qualitative study to explore the barriers and facilitators for OACs management. Then we conducted a systematic survey to address the lack of consensus on outcomes and their definitions for OAC treatment clinical trials. Finally, we used a systematic review and planned a population-based study to address drug-drug interaction related to OACs. Different research approaches, including a systematic review, a systematic survey, a scoping review, a population-based study, and a qualitative study, were involved in this thesis.


title: Modelling Young Massive Cluster Formation: Mergers

contributor:
Sills, Alison
Physics and Astronomy

subject:
simulations
clusters
mergers
star formation

abstract: Star cluster formation involves the conversion of molecular gas into stars inside giant molecular clouds (GMCs). Such a process involves many dynamical evolution mechanisms, including mergers between smaller star clusters (subclusters) on which we focus in this thesis. We take results of simulations performed by Howard et al. 2018 (H18) which found that young massive cluster (YMC) formation is heavily dependant on the process of subcluster mergers, and we simulate said mergers at higher resolution. Subclusters inside such GMC simulations are modelled using the sink particle prescription which does not resolve individual star particles or gas parcels inside the subcluster they represent. We employ a more controlled method in simulating subcluster mergers to better understand the response of the stellar and gas components of a subcluster from the merger process. To do this, we take the parameters of the sink particles created in H18 and set up spheres of stars and gas. We use the AMUSE framework to couple the N-body evolution of the stars to the smoothed particle hydrodynamics (SPH) evolution of the gas such that both components of a given cluster can realistically react to each other. We model 15 of these mergers and find that once the velocity at which the two clusters collide (collisional velocity) exceeds $\approx 10$kms$^{-1}$, the resultant cluster is not monolithic (i.e. it still contains two separate stellar components) while all other simulations merge into one monolithic stellar and gas component cluster. We also find that, regardless of the collisional velocity of masses of the component clusters, all resultant clusters lose a fraction of their stellar and gas mass. This fraction is directly proportional to the collisional velocity and is a discrepancy between the sink particle prescription (where all mass is contained inside a constant sink particle accretion radius) and real cluster mergers. A further discrepancy we find is that all simulations result in a cluster whose outermost regions are expanding and that the rate of this expansion is somewhat proportional to the collisional velocity of the merger. These results point to the inaccuracy of the sink particle prescription and allow us to develop tools to improve on it in future simulations. Next, we fit commonly used analytical density profiles to both the stellar and gas component of our resultant clusters and find that, while they do not provide particularly excellent fits, they provide constraints on what is an acceptable fit. Lastly, we analyze the amount by which gas with potentially star forming densities increase due to the merger and we find that all mergers increase their star forming gas mass fraction by roughly 50 per cent implying that mergers may be an effective tool for triggering star formation.

abstract: Thesis

abstract: Master of Science (MSc)


title: Electronic and optical properties of two-dimensional semiconductors: A study of group VI and VII transition metal dichalcogenides and phosphorene-like materials using density-functional and many-body Green’s-function methods

title: Electronic and optical properties of two-dimensional materials

contributor:
Rubel, Oleg
Materials Science and Engineering

subject:
Density functional theory
two-dimensional semiconductors
optical properties
band structure

abstract:
In the search for nano-scale, highly customizable materials for next-generation electronic devices, two-dimensional (2D) materials have generated much interest. 2D materials have complex, layer-dependent optical and electronic properties of which many aspects remain yet to be explored and fully understood. The aim of this thesis was to investigate and explain optoelectronic properties of several 2D materials systems towards device design. This was accomplished using predictive physical modelling at the density functional theory level (DFT) as well as many-body theory (GW+BSE).

The optical transitions of bulk ReS2 and ReSe2 were studied using DFT in comparison with experiment. We found that the orbital composition of the band edges determined the sign of the pressure coefficient of the optical gap. Our results provide a step towards understanding the perceived layer-independence of the optical properties of ReS2 and ReSe2.

The exciton landscape of MoS2 monolayer was explored in detail using many-body theory (GW+BSE). We found dark excitons very close to bright excitons and even lower in energy. Our results help reverse the common assumption that the lowest-energy exciton in MoS2 is bright.

The ideal band offset between recently predicted monolayers of the CaP3 family was predicted using GW theory. We observed chemical trends in the band offsets and explained their origin. Our results serve as indicators for heterojunction design with these novel materials.

The effective mass of a test set of eighteen semiconductors including several 2D materials was calculated using DFT with semi-local and non-local hybrid exchange-functionals and compared for accuracy with respect to experimental data. Our analysis details the effect of the nonlocal exchange potential on the accuracy of the effective mass. Our results give guidelines for high-throughput calculations of the effective mass for different material classes, including 2D materials.

abstract: Thesis

abstract: Doctor of Philosophy (PhD)


title: Designing a Projectional Editor for Live Coding Using Design Thinking to Improve Teaching

contributor:
Anand, Christopher
Computer Science

subject:
Projectional Editors
Code Editors
Design Thinking

abstract: How can observation of a legacy system be used for design? To answer this question, we observed a teacher doing live coding with a conventional code editor and used the observations to design an editor better suited to this style of teaching. In particular, we found strong evidence that a projectional editor would better meet this need. Reflecting on this experience, we describe two types of requirements which can be inferred from observing a user using a legacy system: hidden requirements, in which users use existing features in unexpected ways, and novel requirements inferred from pain points observed in current system use.

abstract: Thesis

abstract: Master of Applied Science (MASc)


title: Bounding Reachable Sets for Global Dynamic Optimization

contributor:
Khan, Kamil
Chemical Engineering

subject:
Global Optimization
Dynamic Optimization
Reachability Analysis
Ordinary Differential Equations
Convex Relaxation
Optimal Control
Implicit Function

abstract:
Many chemical engineering applications, such as safety verification and parameter estimation, require global optimization of dynamic models. Global optimization algorithms typically require obtaining global bounding information of the dynamic system, to aid in locating and verifying the global optimum. The typical approach for providing these bounds is to generate convex relaxations of the dynamic system and minimize them using a local optimization solver. Tighter convex relaxations typically lead to tighter lower bounds, so that the number of iterations in global optimization algorithms can be reduced. To carry out this local optimization efficiently, subgradient-based solvers require gradients or subgradients to be furnished. Smooth convex relaxations would aid local optimization even more. To address these issues and improve the computational performance of global dynamic optimization, this thesis proposes several novel formulations for constructing tight convex relaxations of dynamic systems. In some cases, these relaxations are smooth.
Firstly, a new strategy is developed to generate convex relaxations of implicit functions, under minimal assumptions. These convex relaxations are described by parametric programs whose constraints are convex relaxations of the residual function. Compared with established methods for relaxing implicit functions, this new approach does not assume uniqueness of the implicit function and does not require the original residual function to be factorable. This new strategy was demonstrated to construct tighter convex relaxations in multiple numerical examples. Moreover, this new convex relaxation strategy extends to inverse functions, feasible-set mappings in constraint satisfaction problems, as well as parametric ordinary differential equations (ODEs). Using a proof-of-concept implementation in Julia, numerical examples are presented to illustrate the convex relaxations produced for various implicit functions and optimal-value functions. In certain cases, these convex relaxations are tighter than those generated with existing methods.
Secondly, a novel optimization-based framework is introduced for computing time-varying interval bounds for ODEs. Such interval bounds are useful for constructing convex relaxations of ODEs, and tighter interval bounds typically translate into tighter convex relaxations. This framework includes several established bounding approaches, but also includes many new approaches. Some of these new methods can generate tighter interval bounds than established methods, which are potentially helpful for constructing tighter convex relaxations of ODEs. Several of these approaches have been implemented in Julia.
Thirdly, a new approach is developed to improve a state-of-the-art ODE relaxation method and generate tighter and smooth convex relaxations. Unlike state-of-the-art methods, the auxiliary ODEs used in these new methods for computing convex relaxations have continuous right-hand side functions. Such continuity not only makes the new methods easier to implement, but also permits the evaluation of the subgradients of convex relaxations. Under some additional assumptions, differentiable convex relaxations can be constructed. Besides that, it is demonstrated that the new convex relaxations are at least as tight as state-of-the-art methods, which benefits global dynamic optimization. This approach has been implemented in Julia, and numerical examples are presented.
Lastly, a new approach is proposed for generating a guaranteed lower bound for the optimal solution value of a nonconvex optimal control problem (OCP). This lower bound is obtained by constructing a relaxed convex OCP that satisfies the sufficient optimality conditions of Pontryagin's Minimum Principle. Such lower bounding information is useful for optimizing the original nonconvex OCP to a global minimum using deterministic global optimization algorithms.  Compared with established methods for underestimating nonconvex OCPs, this new approach constructs tighter lower bounds. Moreover, since it does not involve any numerical approximation of the control and state trajectories, it provides lower bounds that are reliable and consistent. This approach has been implemented for control-affine systems, and numerical examples are presented.

abstract: Thesis

abstract: Doctor of Philosophy (PhD)


title: Collision Avoidance Assistance in UAV Teleoperation

contributor:
Sirouspour, Shahin
Electrical and Computer Engineering

abstract:
Unmanned aerial vehicles (UAVs) have found an increasing number of applications
in recent years. However, the complexity of the task environment and operational
requirements in many of these applications render fully autonomous operation rather
impractical. This thesis presents a novel shared control strategy for human-in-the-loop
teleoperation of UAVs. It integrates user direct teleoperation of the UAV with
automatic collision avoidance assistance. In this strategy, the operator utilizes a
human-machine-interface (HMI) to provide linear acceleration commands for the UAV
in order to navigate it in the task environment. Simultaneously, a collision avoidance
assistance algorithm modifies the operator's commands to help avoid potential collisions
with obstacles in the environment. These corrective commands are obtained
by formulating an optimization problem over a rolling control horizon and solving it
in real-time, in the so-called model predictive control (MPC) framework. In the optimization
formulation, obstacles represent restricted space that must be avoided by
the UAV. The obstacle-free space manifests as a set of constraints on the states of the
UAV. These obstacle-related constraints are generally non-convex in their original
form, which can render the entire optimization problem non-convex. The obstacle
free-space may be approximated by a convex region to avoid challenges associated
with solving non-convex optimizations. Many of the contributions of this work relate to obtaining such convex approximations that are not overly conservative to unnecessarily
inhibit the UAV ability to move in its task environment.
Reachability analysis provides a powerful tool for identifying obstacles with the
chance of collision, and creating approximate safe convex regions that are not too
conservative. In this thesis, two new methods based on reachability analysis are proposed
to generate such approximate convex obstacle-free space for the use in collision
avoidance MPC. The  first method uses backward reachability analysis to detect a
subset of obstacles with chance of collision with the UAV in the MPC time horizon.
Following the detection of these obstacles, the SVM algorithm is employed to construct
a safe polyhedral convex region around the UAV. The second method improves
on the  first one by generating the approximate convex obstacle-free region based on
forward reachability analysis. At each time step in the MPC horizon, separating hyperplanes
between the UAV and obstacles are found that maximize the volume of the
intersection of the UAV reach set and half-space produced by the hyperplanes. The
convex safe region is approximated by the intersection of these half-spaces.
The next contribution of the thesis focuses on accounting for uncertainty in the
system in the development of the collision avoidance assistance algorithm. A novel
ellipsoidal-based robust model predictive control (RMPC) for collision avoidance assistance
in UAV teleoperation is presented. The main contribution here is the formation
of the collision avoidance assistance in UAV under uncertainties as a convex
optimization problem. Ellipsoidal approximation of reachable regions due to feasible
inputs and uncertainties are derived using reachability analysis. A new convex generation
method is used to approximate the obstacle-free space with a polyhedral. An inner polyhedral approximation of tightened constraints guaranteeing collision avoidance
is obtained using geometrical relationship between the ellipsoidal reach set and
polyhedral safe region.
The  final contribution of this thesis guarantees recursive feasibility of the MPC-based
collision avoidance assistance formulation. A fundamental problem in fi nite-time
MPC is the lack of guaranteed recursive feasibility. This could place the UAV
into a state where collision with obstacles becomes unavoidable. In this thesis, new
MPC-based collision avoidance assistance algorithms with guaranteed recursive feasibility
for the UAV teleoperation with/without disturbances are introduced. Terminal
velocity constraints are added to the MPC formulation to guarantee its recursive
feasibility. A novel ellipsoidal tube-based MPC is introduced that extends collision
avoidance assistance with guaranteed recursive feasibility to UAV under disturbances.
In this method, an ellipsoidal approximation of the robust positively invariant (RPI)
set is derived using a new RPI set approximation based on ellipsoidal techniques.
Polyhedral approximation of the obstacle-free space is derived using the SVM algorithm.
Ab inner polyhedral approximation of the tightened constraints is obtained
using geometrical relation between ellipsoidal RPI set and polyhedral safe region.
The methods proposed in this thesis are evaluated experimentally in an indoor
environment using a fully-actuated UAV. The results demonstrate that the MPC-based
collision assistance is highly effective in helping the operator navigate the task
environment and avoid collisions with obstacles. This is in part due to the fact the
methods introduced here generate a realistic convex approximation of the obstacle-free
space. This allows the operator to teleoperate the UAV in the task environment
without unnecessarily being hindered by a conservative approximation of this space.

abstract: Thesis

abstract: Doctor of Philosophy (PhD)


title: MEMS-MARG-based Dead Reckoning for an Indoor Positioning and Tracking System

contributor:
Zhao, Dongmei
Electrical and Computer Engineering

subject:
Indoor Positioning System
Inertial Measurement Unit
Microelectromechanical Systems
Sensor Fusion
Digital Signal Processing
Pedestrian Dead Reckoning
Object Tracking and Navigation
Robotics
Quaternions

abstract:
Location-based services (LBSs) have become pervasive, and the demand for these systems and services is rising. Indoor Positioning Systems (IPSs) are key to extend location-based services indoors where the Global Positioning System (GPS) is not reliable due to low signal strength and complicated signal propagation environment. Most existing IPSs either require the installation of special hardware devices or build a fingerprint map, which is expensive, time-consuming, and labor-intensive. Developments in microelectromechanical systems (MEMS) have resulted in significant advancements in the low-cost compact MARG inertial sensors, making it possible to achieve low-cost and high-accuracy IPSs.
This research considers the indoor positioning problem and aims to design and develop an infrastructure-free self-contained indoor positioning and tracking system based on Pedestrian Dead Reckoning (PDR) using MEMS MARG inertial sensors. PDR-based systems rely on MARG inertial sensor measurements to estimate the current position of the object by using a previously determined position without external references. Many issues still exist in developing such systems, such as cumulative errors, high-frequency sensor noises, the gyro drift issue, magnetic distortions, etc. As the MARG sensors are inherently error-prone, the most significant challenge is how to design sensor fusion models and algorithms to accurately extract useful location-based information from individual motion and magnetic sensors. The objective of this thesis is to solve these issues and mitigate the challenges. The proposed positioning system is designed with four main modules at the system level and a dual-mode feature. Specifically, the four main modules are mode detection, step detection and moving distance estimation, heading and orientation estimation, and position estimation. To address the cumulative error issue of using low-cost inertial sensors, signal processing and sensor fusion techniques are utilized for algorithm design. Experimental evaluations show that the proposed position estimation algorithm is able to achieve high positioning accuracy at low costs for the indoor environment.

abstract: Thesis

abstract: Master of Applied Science (MASc)

abstract: With the maturity of microelectromechanical systems (MEMS) technology in recent years, Magnetic, Angular Rate, and Gravity (MARG) sensors are embedded in most smart devices. This research considers the indoor positioning problem and aims to design and develop an infrastructure-free self-contained MEMS MARG inertial sensor-based indoor positioning and tracking system with high precision. The proposed positioning system uses the Pedestrian Dead Reckoning (PDR) approach and includes four main modules at the system level with a dual-mode feature. Specifically, the four main modules are mode detection, step detection and moving distance estimation, heading and orientation estimation, and position estimation. The two modes are static mode and dynamic mode. To address the cumulative error issue of using low-cost inertial sensors, signal processing and sensor fusion techniques are utilized for algorithm design. The detection and estimation algorithms of each module are presented in the system design chapter. Experimental evaluations including trajectory results under five scenarios show that the proposed position estimation algorithm achieves a higher position accuracy than that of conventional estimation methods.


title: A Formal Approach to Ontology Modularization and to the Assessment of its Related Knowledge Transformation

contributor:
Khedri, Ridha
Computing and Software

abstract:
Knowledge-based systems are composed of multiple agents that each must utilize their understanding of the world to deduce new facts and make intelligent decisions. The understanding of the world that each agent has is formalized using an ontology: a structure which conceptualizes the domain as a set of concepts and their relations. To accommodate the numerous agents, and to minimize the cost of a single monolithic ontology, modularization is used to provide each agent with only the knowledge they require.

This thesis addresses the problems related to defining the transformation of the domain knowledge due to ontology modularization. Existing ontology modularization techniques are evaluated so that they can be assessed with respect to how they transform knowledge, as well as their computational performances. A primary objective is to adapt the existing modularization techniques so that they can be compared based on how they transform domain knowledge.

The objectives of this thesis were addressed systematically by first comprehensively evaluating the literature regarding both ontologies and ontology modularization. Then, we adapted the prominent ontology modularization techniques to the ontology formalism used in this thesis: a Domain Information System. New modularization techniques were then explored which utilized aspects of the domain ontology's algebra.

This research consolidates the many approaches to ontology modularization into a single set of formal techniques for a domain ontology. As a result, we further the modularization field by unifying the modularization field under a common formalism, creating relations between the modularization techniques, as well as formulating how the knowledge of the ontology is transformed for each technique. Additionally, we introduce a new modularization technique which capitalizes on the Boolean algebra of a domain ontology, and enables the determination of a module based on the desired granularity of the domain knowledge under consideration.

The research of this thesis furthers the ontology modularization field by systematically analyzing current limitations with respect to their ability to determine how knowledge is transformed. We demonstrate that by using the formalism of the domain ontology, we are able to formulate different modularization techniques, each with its own transformations to the domain knowledge. Expressing the knowledge that is transformed via modularization is unique. It is also instrumental in providing the agents of a knowledge-based system with modules that capture the exact domain knowledge they require to make their decisions.

abstract: Thesis

abstract: Candidate in Philosophy


title: Tracking Dismounts With Feature Matrix Based Multi-scale Intrinsic Motion Segmentation Framework (FM-MIMS)

contributor:
Kirubarajan, Thia
Tharmarasa, Ratnasingham
Electrical and Computer Engineering

abstract: Tracking dismounts using wide-area motion imagery (WAMI) from the air is a challenging problem. Such an algorithm has many applications on the battlefield, search and rescue, law enforcement, and more. This task is however nontrivial. Small target sizes and difficult to identify features make it extremely challenging to reliably detect people on the ground. Based on the Multi-scale Intrinsic Motion Structure framework (MIMS), this algorithm proposes an extension to the MIMS framework to allow simple feature identifiers to help improve the rate of successfully identifying a dismount. The Feature Matrix Based Multi-scale Intrinsic Motion Segmentation (FM-MIMS) does so by encoding size and motion features from a small target detector, and an optical flow detector. The features are used to drive the predictions made by a tensor voting algorithm that is then segmented to identify the target. Given the computationally intensive nature of tensor voting, this thesis proposes incremental changes to it as well as a layer of image prepossessing to allow for a robust method to detect dismounts. Feature extraction would allow the MIMS framework to extend its 4-9pixel target size to better detect larger targets in the 15-20pixel size range. At this scale dismounts are still very limited in identifiable features however, appendages such as arms and legs are visible. FM-MIMS intends to extract as much data as possible to increase the reliability of the detector.

abstract: Thesis

abstract: Master of Applied Science (MASc)

abstract: The detection of dismounts has a variety of applications, such as tracking remotely for battlefield awareness or at checkpoints. They are however difficult to detect from aircraft because of their small featureless profile on Wide Angle Motion Imagery (WAMI). In such situations, conventional approaches of feature detection can prove ineffective. The Multi-scale Intrinsic Model Structure (MIMS) does an adequate job identifying dismounts in such situations. This thesis proposes a Feature Matrix Based Multi-scale Intrinsic Motion Segmentation (FM-MIMS) algorithm that would use partially identifiable features to make better-informed detections.


title: Elastic deformations and instabilities in thin fibers and films

contributor:
Dalnoki-Veress, Kari
Physics and Astronomy

abstract:
Systems across nearly all length scales can experience elastic deformations. The
details of how a system deforms is generally determined by i) the way stresses and
loads are applied, ii) size, shape, geometry, and physical constraints, and iii) the
material properties of the system. Although the physical forces causing deformations are often simple in origin, the interplay between these forces and the physical constraints of these systems can produce instabilities – situations where small
changes in loading can cause large and sometimes surprising mechanical effects.
From an application-based perspective, These effects can lead to interesting and
useful techniques as well as sometimes beautiful patterns and features reminiscent
of complex, natural systems. This thesis centres on three research manuscripts,
each concerning different elastic instabilities observed in slender elastic fibers.
In the first paper (Chapter 3, published in the European Physical Journal E)
we explore the effect of tension, compression, and torsion on slender fibers, and
explain the looping and twisting behaviour observed with a simple linear elasticity
model.
The second paper (Chapter 4, published in Physical Review Letters), presents
a novel interaction between slender elastic fibers and liquid-air bubbles on the
surface of a liquid bath. We fi nd that for certain combinations of fiber and bubble
(depending on size, thickness, material, etc), a fiber may partially penetrate and
spontaneously wrap around the bubble at its liquid-air interface.
In the third paper (Chapter 5, to be submitted), considers the elastic response
of coupled elastic systems with incompatible strains. In this case a thin film is
pre-strained before a slender fiber is affixed to its surface, acting like a strut or
support. Then, biaxial tension and/or compression is applied to the film, causing
the fiber to buckle and rotate in and out of the plane of the film in, ultimately,
predictable and controllable ways.
Lastly, an appendix is included, featuring several examples of the science writing
I have done for less technical audiences, broadly related to the themes presented
in this thesis.

abstract: Thesis

abstract: Doctor of Philosophy (PhD)


title: Investigating The Globular Cluster System Mass - Halo Mass Relation in High Mass Galaxies

contributor:
Harris, William E.
Physics and Astronomy

subject:
astronomy
globular clusters
astrophysics
galaxies

abstract: This thesis aims to better constrain the high-mass end of the globular cluster system (GCS) mass - Halo mass relation. This relation between the total mass contained in the globular clusters in a galaxy and the mass of its dark matter halo has been found to be nearly linear. The measured mass ration between the mass of the GCS and the halo mass, defined as eta, has been found to be constant over a large range of galaxy masses, however there is comparatively less data for galaxies with dark matter halo masses >10^13 solar masses. This research analyzes the GCSs of a sample of eleven high-mass brightest cluster galaxies (BCGs) through the use of the photometry program DOLPHOT on Hubble Space Telescope (HST) images in the F814W filter. The mass of the galaxies' GCSs were calculated from their GC radial distributions, and the dark matter halo masses were determined from the known relationship between total stellar mass and halo mass for BCGs. This research utilizes a new standardization technique to calculate the size of the GCS based on the galaxy's virial radius. These GC and halo masses were then compared to determine eta at this high-mass end, which was found to be eta=(6.84+/- 1.47)\times10^-5. When adding the sample BCGs to a catalogue of 303 lower-mass galaxies a total value for eta was found to be eta=(2.99 +/- 0.06)\times10^-5, which is within the literature range for of eta, and is both slightly higher and has a reduced uncertainty compared to the value for the catalogue galaxies alone which was found to be eta=(2.87 +/- 0.11)\times10^-5. This consistency between the eta-values of high-mass and lower-mass galaxies implies that in order for these BCGs to have such massive GCSs, a large proportion of these GCs must be accreted from galaxy mergers at late redshift.

abstract: Thesis

abstract: Master of Science (MSc)


title: On Clustering Comparisons Using Data From a Seroprevalence Study

contributor:
McNicholas, Paul
Mathematics and Statistics

abstract: Various longitudinal clustering approaches are discussed and compared on an application to a seroprevalence study. The data contains information about the behaviours of individuals throughout the course of the COVID-19 pandemic. First, a review of the various longitudinal clustering methods compared throughout this thesis is discussed. Longitudinal k-means, growth mixture models, latent class growth analysis and a two-step approach involving growth curve models and k-means are reviewed. Longitudinal model-based clustering based on a modified Cholesky decomposition of a Gaussian mixture and Gaussian linear means are also reviewed. The BIC is used as the primary criterion to determine the number of components, and the ARI is used to determine cluster similarity between models. The various clustering approaches are then compared as they attempt to identify gathering patterns within the population of the seroprevalence dataset.

abstract: Thesis

abstract: Master of Science (MSc)


title: Exploring resistance to Pseudomonas aeruginosa infection in Caenorhabditis elegans

contributor:
MacNeil, Lesley
Biochemistry and Biomedical Sciences

abstract:
Commensal microorganisms that colonize host surfaces can modulate susceptibility to infection. This can occur through direct inhibition of pathogen growth via competitive exclusion, or by modulation of the host environment to prevent pathogen dissemination and infection-mediated damage. However, understanding the mechanisms underlying these interactions is challenging due to the complexity of the human microbiome. The model organism Caenorhabditis elegans allows for the study of individual host-microbiota interactions, given they are bacterivorous and can be maintained monoxenically on a bacterial strain of interest. Here, we investigate the influence of human respiratory tract microbiota isolates on C. elegans susceptibility to Pseudomonas aeruginosa infection. Identifying isolates that differentially regulate resistance to infection will provide insight into how particular strains isolated from the human microbiome may act synergistically with P. aeruginosa or improve infection outcomes.

A screen of isolates derived from the human respiratory tract was carried out using a liquid-based P. aeruginosa infection assay. Animals were first exposed to individual microbiotal isolates during development, prior to infection with a pathogenic strain of P. aeruginosa, PA14. This screen identified two non-pathogenic isolates of P. aeruginosa that increased survival during PA14 liquid killing, compared to animals pre-exposed to E. coli OP50. This protective phenotype was also induced by other mildly pathogenic strains of P. aeruginosa, including a laboratory strain with genetically attenuated virulence, PAO1 ∆vfr, as well as the pathogen Salmonella enterica. This work details the bacterial factors and host pathways that may regulate S. enterica and P. aeruginosa-mediated protection from PA14 liquid killing.

abstract: Thesis

abstract: Master of Science (MSc)

abstract: The human microbiome is the collection of beneficial bacteria that live on and in the human body and are involved in maintaining health in a number of ways, such as protecting from infection by pathogenic bacteria. In this work, we investigate the role the human microbiome has on altering the severity of an infection. Here, we use the model organism Caenorhabditis elegans, a microscopic roundworm that can be infected and killed by many of the same bacterial pathogens that infect humans. This worm model is particularly beneficial to use for this project because they grow and die more rapidly than other animals used to study infection, facilitating the ability to identify protective species within a large collection of bacterial isolates. This study shows that microbially-produced vitamins play an important role in enhancing survival during an infection. By studying infection resistance in model organisms such as C. elegans, we can identify factors that may also be important in regulating resistance to infection in humans.


title: State of Charge and Range Estimation of Lithium-ion Batteries in Electric Vehicles

contributor:
Emadi, Ali
Electrical and Computer Engineering

subject:
State of Charge
Range Estimation
Lithium-ion battery
Electric Vehicles
Battery Modelling
Voltage Estimation
Recurrent Neural Networks
LSTM
SHARCNET
Extended Kalman Filter
Equivalent Circuit Model

abstract:
Switching from fossil-fuel-powered vehicles to electric vehicles has become an international focus in the pursuit of combatting climate change. Regardless, the adoption of electric vehicles has been slow, in part, due to range anxiety. One solution to mitigating range anxiety is to provide a more accurate state of charge (SOC) and range estimation. SOC estimation of lithium-ion batteries for electric vehicle application is a well-researched topic, yet minimal tools and code exist online for researchers and students alike. To that end, a publicly available Kalman filter-based SOC estimation function is presented. The MATLAB function utilizes a second-order resistor-capacitor equivalent circuit model. It requires the SOC-OCV (open circuit voltage) curve, internal resistance, and equivalent circuit model battery parameters. Users can use an extended Kalman filter (EKF) or adaptive extended Kalman filter (AEKF) algorithm and temperature-dependent battery data. A practical example is illustrated using the LA92 driving cycle of a Turnigy battery at multiple temperatures ranging from -10C to 40C.

Current range estimation methods suffer from inaccuracy as factors including temperature, wind, driver behaviour, battery voltage, current, SOC, route/terrain, and much more make it difficult to model accurately. One of the most critical factors in range estimation is the battery. However, most models thus far are represented using equivalent circuit models as they are more widely researched. Another limitation is that any machine learning-based range estimation is typically based on historical driving data that require odometer readings for training.

A range estimation algorithm using a machine learning-based voltage estimation model is presented. Specifically, the long short-term memory cell in a recurrent neural network is used for the battery model. The model is trained with two datasets, classic and whole, from the experimental data of four Tesla/Panasonic 2170 battery cells. All network training is completed on SHARCNET, a resource provided by Canada Compute to researchers. The classically trained network achieved an average root mean squared error (RMSE) of 44 mV compared to 34 mV achieved by the network trained on the whole dataset. Based on the whole dataset, all test cases achieve an end range estimation of less than 5 km with an average of 0.29 km.

abstract: Thesis

abstract: Master of Applied Science (MASc)


title: Cost and accuracy analysis of group and individual testing strategies: Implications for COVID 19

contributor:
Walter, Dr. Stephen
Mathematics and Statistics

subject: Group testing, Individual Testing, prevalence rate

subject: Screening, Cost, Accuracy, Error

abstract: We compared several group and individual testing strategies in terms of cost and accuracy and then showed which one is more accurate while costing as little as possible for a specified prevalence rate.

abstract: Thesis

abstract: Master of Science (MSc)


title: Capillary levelling of a liquid step on a floating solid film

subject:
Soft Matter Physics
Fluid Dynamics

abstract: This thesis outlines the research I completed during my M.Sc. work. This research focuses on analyzing the deformations of an elastic film at an imposed boundary. Specifically, I prepared a trilayer thin film sample geometry where a glassy polymer thin film floats atop a liquid layer and a final liquid layer sits on the top and partially covers the films below. This thesis is organized into four chapters. The first chapter details main topics associated with the background knowledge necessary for understanding the results. This introductory chapter provides an overview of the polymers that create the thin films, fluid dynamics associated with the flow of these polymer films in their liquid state and capillarity, as the length scales and forces involved in the experiments are at a length scale where gravity can be neglected. The second chapter discusses the experimental design involving sample preparation and explanations of the techniques used for data collection. The third chapter presents the results of the experiments and discusses our understanding of these results. The fourth chapter concludes the thesis.

abstract: Thesis

abstract: Master of Science (MSc)


title: GENERATION AND SEGMENTATION OF 3D MODELS OF BONE FROM CT IMAGES BASED ON 3D POINT CLOUDS

contributor:
Wohl, Gregory
Biomedical Engineering

subject:
3D Modelling
Medical Imaging
3D Point Cloud
Bone
Surface Mesh

abstract: The creation of 3D models of bone from CT images has become popular for surgical planning, the design of implants, and educational purposes.  Software is available to convert CT images into 3D models of bone, however, these can be expensive and technically taxing. The goal of this project was to create an open-source and easy-to-use methodology to create 3D models of bone and allow the user to interact with the model to extract desired regions. The method was first created in MATLAB and ported to Python. The CT images were imported into Python and the images were then binarized using a desired threshold determined by the user and based on Hounsfield Units (HU). A Canny edge detector was applied to the binarized images, this extracted the inner and outer surfaces of the bone. Edge points were assigned x, y, and z coordinates based on their pixel location, and the location of the slice in the stack of CT images to create a 3D point cloud. The application of a Delaunay tetrahedralization created a mesh object, the surface was extracted and saved as an STL file. An add-on in Blender was created to allow the user to select the CT images to import, set a threshold, create a 3D mesh model, draw an ROI on the model, and extract that region based on the desired thickness and create a new 3D object. The method was fully open-sourced so was inexpensive and was able to create models of a skull and allow the segmentation of portions of that mesh to create new objects. Future work needs to be conducted to improve the quality of the mesh, implement sampling to reduce the time to create the mesh, and add features that would benefit the end-user.

abstract: Thesis

abstract: Master of Applied Science (MASc)

abstract: The creation of 3D models of bone from CT images has become popular for education, surgical planning, and the design of implants. Software is available to convert CT images into 3D models but can be expensive and technically taxing. The purpose of this project was to develop a process to allow surgeons to create and interact with models from imaging data. This project applied a threshold to binarize a set of CT images, extracted the edges using a Canny Edge detector, and used the edge pixels to create a 3D point cloud. The 3D point cloud was then converted to a mesh object. A user interface was implemented that allowed the selection of portions of the model and a new 3D model to be created from the selection. The process can be improved by improving the quality of the mesh output and adding features to the user interface.


title: Characterization of a PDZ domain-containing protein in macrophages

contributor:
Trigatti, Bernardo
Biochemistry and Biomedical Sciences

abstract: Vulnerable atherosclerotic plaques are characterized by large necrotic cores and thin fibrous caps. Macrophage cell death is an important contributor to necrotic core size and high-density lipoprotein (HDL) has been shown to promote macrophage survival. Research has shown that HDL-dependent protection of macrophages is impaired in macrophages from Pdzk1 knockout mice. However, little is known on the expression of Pdzk1 in macrophages. In this study, we investigated Pdzk1 expression in macrophages and its role in HDL-dependent protection of macrophages against necroptosis. Real-time quantitative reverse-transcriptase polymerase chain reaction and Western blotting were conducted on macrophages from wild type and two different Pdzk1-targeted mutant mice. We show that Pdzk1 is expressed in macrophages. However, we also discovered that there is aberrant expression of truncated Pdzk1 transcript(s) in macrophages from a commonly used Pdzk1 global knockout mouse strain, although this was not the case in conditional Pdzk1 knockout macrophages from a different mouse strain. Our findings indicate that the null mutation in macrophages from Pdzk1 global knockout mice is not bona-fide and suggest for re-evaluation of the role of PDZK1 in HDL-signaling in macrophages. To investigate the role of PDZK1 in HDL-dependent protection of macrophages, we used conditional Pdzk1 knockout macrophages and demonstrated that PDZK1 indeed promotes HDL-dependent protection against necroptotic cell death. This study confirms that Pdzk1 is expressed in macrophages and clarifies its importance in HDL-dependent protection of macrophages from cell death.

abstract: Thesis

abstract: Master of Science (MSc)


title: Effects of aging on face perception: Exploring efficiency, noise & orientation

contributor:
Bennett, Patrick J
Sekuler, Allison B
Psychology

subject:
face perception
aging
classification image
internal noise
calculation efficiency
horizontal bias
equivalent input noise
response consistency
orientation filtering
visual psychophysics

abstract: Face perception is impaired in a variety of ways in older adults, but the mechanisms underlying these changes remain unclear. A central theme of this dissertation is that task performance is constrained by two factors intrinsic to the observer: sources of random variability -- internal noise -- and the efficiency with which task-relevant stimulus information is utilized. This thesis uses several behavioural, psychophysical methods to examine how age-related changes in one or both of these factors affect face processing. Chapter 2 used the classification image (CI) method to characterize the spatial sampling patterns of younger and older observers performing a face discrimination task. Compared to younger adults, older adults used information in the eye/brow region less consistently and instead relied on relatively less informative regions such as the forehead. The differences in CIs accounted for the lower absolute efficiency that was found in older observers. Chapter 3 estimated internal noise and calculation efficiency by measuring threshold-vs.-noise (TvN) curves and response consistency in a face discrimination task. Compared to younger observers, older observers had higher additive internal noise and lower calculation efficiency, but the magnitude of multiplicative internal noise did not differ between age groups. Previous studies have shown that younger adults have a bias to rely on horizontal structure to discriminate and identify faces, and the magnitude of this so-called horizontal bias is correlated with identification accuracy. The experiments in Chapter 4 measured horizontal bias in younger and older adults, and found that age differences in horizontal bias account for some, but not all, of the age difference in face identification accuracy. In summary, my work demonstrates that additive (but not multiplicative) internal noise is greater in older adults, and that they are less efficient at sampling information that is conveyed by structure at different locations and orientations in a face.

abstract: Dissertation

abstract: Doctor of Science  (PhD)

abstract: Our experience of the visual environment results from perceptual processes in the brain. Many of these processes change with age, such as our ability to identify someone from a photograph of their face. Performance is influenced by both random variability, or "noise", within the observer and how efficiently we use task-relevant information in the visual environment. By systematically manipulating the amount of available stimulus information I assessed the contribution of these factors to older adults' judgements of facial identity, and characterized the information on which these decisions are based. These experiments are the first to consider how face perception in older adults is constrained by the combined effects of internal noise and the efficiency with which the visual system utilizes various sources of information. The results provide a number of directions for future research in the fields of face perception and age-related changes in complex pattern vision.


title: Establishing validity evidence for the use of VIMEDIX-AR automated metrics in assessment of FAST exam skills

contributor:
Engels, Paul
Health Science Education

subject:
simulation
health science education
ultrasound
automated metrics

abstract:
Introduction: Simulation has an increasing role in medical education. It offers the ability to learn and practice in a safe environment. Ultrasound is a key tool for many clinicians; however, it requires significant experience to gain expertise. The most common method to gain experience is by training courses with volunteers, where experts are present for one-on-one teaching. This is time and labour intensive. Commercial ultrasound simulators are increasingly available with software capable of generating automated metrics. We sought validity evidence to support the use of automated metrics as a tool for assessment of learners completing a Focused Assessment with Sonography in Trauma (FAST) exam.

Methods: Three groups with differing expertise were recruited to participate: novices with no ultrasound training, intermediates who had completed a formal course within six months, and experts with at least five years of clinical experience. All participants were recorded while completing a FAST exam. Automated metrics of time, path length, angular movement, and percent area viewed were obtained. This video was then scored using the Quality of Ultrasound Imaging and Competence (QUICk) by two expert assessors. Participants were also asked to complete ten find fluid exercises, where automated metrics were generated. Automated metrics from the recorded FAST and QUICk were compared using Kruskall-Wallis to assess for differences in expertise.  Correlations between QUICk score and the automated metrics were assessed using Pearson’s correlation coefficient. Find fluid exercises were also assessed using repeated measures one-way ANOVA models.

Results: Time, angular movement, and percent area viewed left upper quadrant (LUQ) were significantly different with novices requiring more time and angular movement, and higher percent area viewed LUQ than experts. The QUICk scores were significantly higher for the experts and intermediates compared to the novices. The scores from the QUICk overall and checklist did not correlate with any automated metrics. Individual components of positioning and handling, probe handling, and image scrolling were negatively correlated with percent area viewed LUQ. Overall, the QUICk tool could differentiate novices from both intermediates and experts when using the VIMEDIX-AR simulator. Several automated metrics could differentiate expertise. Further work should develop a composite score of automated metrics to assess learners.

abstract: Thesis

abstract: Master of Science (MSc)

abstract: Simulation has become ubiquitous in medical education, offering a safe environment to learn and practice new skills. With the increasing availability of point of care ultrasound and the need for significant training to generate and interpret images, simulation is becoming ever more important. We sought to assess an expert assessment tool for use with an ultrasound simulator and to validate automated metrics associated with the VIMEDIX-AR simulator. The expert assessment tool could reliably differentiate different expertise levels. Three of our automated metrics could discern different levels of expertise. Further work is needed to assess if a composite score of automated metrics could better differentiate skill.


title: Development and Evaluation of a Microbial Natural Product Prefractionation Library

contributor:
Wright, Gerard
Biochemistry and Biomedical Sciences

subject: Antibiotics Lipopeptide AMR Screening Fractionation Antifungal

abstract: Ongoing antibiotic drug discovery is vital as antimicrobial resistance continues to be a significant issue faced in the clinic. Natural products have long been a highly productive source to mine for new antimicrobials. While it has been challenging to discover new and unique antimicrobial natural products, numerous drugs have been derived from studying how natural products function as secondary metabolites. Previous studies suggested that screening natural product extract fraction libraries for antimicrobials can be more productive than screening crude extracts alone. These studies from large industrial enterprises are generally not directly portable to an academic setting due to significant infrastructure costs. We developed a screening platform consisting of low pressure reversed-phase chromatographic separation of methanolic extracts of bacteria and fungi to generate a prefractionated natural product library. This platform is suitable for academic labs to screen for antimicrobial compounds. A large growth inhibitor screen against multiple pathogens and lab strains of microbes was conducted to assess the validity of the advantages of screening fraction libraries versus crude extract libraries and to search for potential new drug-like compounds. Hits were investigated for reproducibility, isolated, and purified. One compound was discovered in an antifungal screen which may be a novel lipopeptide.

abstract: Thesis

abstract: Master of Science (MSc)

abstract: To combat the growing antibiotic resistance crisis, new strategies for drug discovery must be investigated and implemented. Natural products from bacteria and fungi have long been a source of critically important drugs. Prior research suggests that looking for vital natural products in fraction libraries can be more productive than screening crude extracts. Here the development and assessment of a fractionation library suitable for an academic lab's ingrained limitations are described. Assessing the library indicates an increased hit rate on screening fractions compared to crude extract. Furthermore, pursuing these hits may have revealed a novel antifungal lipopeptide.


title: Advancing membrane chromatography processes for the purification of therapeutic viruses

contributor:
Latulippe, David R
Ghosh, Raja
Chemical Engineering

subject:
membrane chromatography
viruses
bioseparations
downstream processing
process development
biopharmaceuticals

abstract:
Viruses have emerged as a new class of biotherapeutics used as vectors in gene and cell therapies, vaccines, and as oncolytic agents in novel cancer immunotherapies. While these new and potentially curative new therapies bring great promise for patients, the large-scale purification of viruses is hampered by complicated unit operations, poor overall yields, and high costs. Membrane chromatography (MC) is one of the most ideal options for the removal of host-cell impurities in virus manufacturing. Centred on developing and improving MC processes for virus purification, this thesis focuses on different aspects of downstream processes that are directly related to MC.
It describes the development of the first hydrophobic interaction MC process for the purification of vesicular stomatitis virus as a scalable method for the removal of host-cell protein and DNA. It also describes the development of MC for adenovirus purification, and how device design and membrane type impact the resolution; here, the novel laterally-fed membrane chromatography (LFMC) was proven to provide higher resolution than conventional MC devices, and allowed for the first direct comparison between the most popularly used membranes in virus manufacturing – Sartobind Q and Mustang Q. Beyond MC, this thesis also addresses how other downstream unit operations contribute to the final purity. Through an integrated study optimizing clarification, DNA digestion, and MC simultaneously, significant improvement in adenovirus purity was obtained. Finally, the collection of experimental results was used to model complete adenovirus production processes using BioSolve Process and determine the cost-of-goods (COG) of manufacturing for clinical applications. Through simulations of multiple scenarios, critical process parameters were identified and can serve as a guide for future process development decisions. It is anticipated that the contributions herein described will help address critically outstanding questions related to virus purification and thus enable the development of the economical processes for various manufacturing scales.

abstract: Thesis

abstract: Doctor of Philosophy (PhD)

abstract: Certain viruses can be used for human benefit and there are now more than a dozen approved therapies worldwide that use a virus as the main therapeutic agent or as the vector to instruct the patient’s cells to fight cancer and other diseases. The area keeps growing as thousands of other clinical trials continue to be conducted. One of the main challenges that can inhibit patient access to these ground-breaking new options is related to difficulties in producing and purifying enough virus. This study tackles the virus purification challenge by applying and improving membrane chromatography (MC), a promising and scalable technique where virus and impurities are separated based on how differently they interact with a membrane. Different experimental and modelling and simulation tools were applied to optimize MC and other directly-related steps of the production process. The findings in this study can contribute to the development of new virus-based therapeutics so they can reach patients in safe, effective, and affordable ways.