Doctoral Dissertations (FSCI)

Permanent URI for this collectionhttps://hdl.handle.net/10155/402

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Adaptive engagement of older adults’ fitness through gamification
(2017-07-01) Kappen, Dennis L.; Mirza-Babaei, Pejman; Nacke, Lennart
Older adults are often not physically active because they lack motivation, time, and/or physical ability. Not only does this impact the life of older adults, but it also affects society as a whole, because the cost of healthcare attached to maintaining the health of older adults is continually rising. This thesis addresses the problem by investigating the disenchantment of older adults with physical activity (PA), reasons for their lack of participation in PA, and contributes motivational affordances for PA. This thesis makes three important contributions to human-computer interaction: a) the development of adaptive engagement guidelines for PA technology for older adults. b) the Exercise Motivation Technology Framework (EMFT) - a framework to aid in the design and development of PA technology for older adults, and c) the Kaleidoscope of Effective Gamification (KEG) - a design and analysis tool for helping designers design and develop gamified apps. These contributions were achieved through a phased investigative approach. The analysis of preliminary studies (Phase 1) resulted in the development of the EMTF for older adults PA technology. A survey study (Phase 2) on the preferences of motivational affordances for PA across different age groups suggested that ‘health pressures’ and ‘ill-health avoidance’ were significant exercise motives for PA in different age groups. Age-differentiated guidelines from Phase 2 were used to develop and evaluate Spirit50 (Phase 3), a gamified technology artifact, specifically developed under my supervision for adults over 50 years of age. Phase 4 was a synchronous, three-condition (gamified, non-gamified, and control groups) experimental study over an eight-week period with a total of 30 participants. Expert evaluation (Phase 5) to review technology facilitation of PA using the Spirit50 app also pointed to the usefulness and the applicability of gamification as a behaviour change technology for delivering PA solutions for older adults. The findings of this thesis contribute to understanding PA motivation among older adults on a granular level from a technology facilitation standpoint using gamification strategies. The EMTF model helped to design PA technology by combining desirability, customization, and motivational affordances for older adults. Finally, this thesis contributes to tailoring and personalizing of adaptive engagement strategies using specific gamification elements like goals supported by challenges, selection of quests based on ability, progressive feedback, and rewards validating performance and efforts as potential ways to deliver age-centric PA technology for older adults
Adaptive serious games for computer science education
(2020-10-01) Miljanovic, Michael A.; Bradbury, Jeremy
Serious games have the potential to effectively engage students to learn, however, these games tend to struggle accommodating learners with diverse abilities and needs. Furthermore, customizing a serious game to the individual learner has historically required a great deal of effort on the part of subject matter experts, and is not always feasible for increasingly complex games. This thesis proposes the use of automatic methods to adapt serious programming games to learners' abilities. To understand the context of the problem, a survey was conducted of the serious programming game literature, which found that while many games exist, there has been very little consideration for the use of adaptation. Given the breadth of the existing serious programming game literature, a methodology was developed to support adaptation of existing games. To demonstrate the efficacy of this adaptive methodology in serious programming games, two case studies were conducted: 1) a study comparing adaptive and non-adaptive gameplay in the Gidget game, and 2) a study assessing non-adaptive gameplay, adaptive gameplay, and adaptive hints in the RoboBug game. The results from both case studies provide evidence to the need for adaptation in serious programming games, and illustrate how the adaptive methodology can be utilized to positively affect the engagement of learners and their ability to achieve learning outcomes.
Advancing and expanding siRNA and saRNA therapeutics applications through chemical modifications
(2024-04-01) Giorgees, Ifrodet; Desaulniers, Jean-Paul
Oligonucleotides are short strands of DNA or RNA that are used to treat complex diseases like cancer and rare genetic diseases. They rely on biological pathways in our body to work. Two pathways that are important to this study are gene silencing and activation. Short interfering RNAs (siRNAs) silence genes, while short activating RNAs (saRNAs) activate them. Both types of strands can be used to create new cancer treatments. However, RNA-based therapies face challenges like instability, off-target effects, and low cell membrane permeability. To overcome these challenges, this study focuses on incorporating new chemical modifications into the RNA and assessing their impact on RNA activity. Our aim is to enhance RNA therapeutic efficacy for potential cancer therapy applications. The first goal focused on creating a combination therapy for cancer treatment by directly conjugating free base corrole molecules to siRNA. This novel construct created a combination therapy effect of gene silencing and simultaneous photodynamic therapy (PDT). This combination therapy is expected to be more targeted and non-invasive compared to traditional cancer treatments like surgery or chemotherapy. The second goal of this research involved exploring the potential of metal corrole molecules within siRNA for personalized cancer treatment. In this study, Ga-corrole was directly conjugated to siRNA, resulting in an advanced treatment consisting of live imaging and gene silencing. This novel construct created a new tool for siRNA real-time imaging applications that could potentially allow for real-time drug monitoring during cancer treatment. The third goal focused on discovering nuclease-resistant and active saRNAs targeting STING, which is a potential target for the treatment of solid tumors. In this study, a library of chemically modified saRNA was screened for their nuclease resistance ability and investigated for any potential correlations between chemical modifications, nuclease resistance and high gene upregulation activity. The results of nuclease stability assays revealed that the position of the chemical modifications within the RNA can significantly influence nuclease resistance. Furthermore, novel chemical modification designs were established for the synthesis of stable and highly active STING saRNA duplexes. In conclusion, this dissertation highlights novel approaches to enhance RNA therapeutics and employ RNA molecules for cancer drug monitoring or treatment applications.
Agent-based modeling framework for adaptive cyber defence of the Internet of Things
(2022-12-01) Rafferty, Laura; Hung, Patrick
The adoption of the Internet of Things (IoT) continues to increase significantly, introducing unique challenges and threats to cybersecurity. In parallel, adaptive and autonomous cyber defence has become an emerging research topic leveraging Artificial Intelligence for cybersecurity solutions that can learn to recognize, mitigate, and respond to cyber attacks, and evolve over time as the threat surface continues to increase in complexity. This paradigm presents an environment strongly conducive to agent-based systems, which offer a model for autonomous, cooperative, goal-oriented behaviours which can be applied to perform adaptive cyber defence activities. This thesis aims to bridge the gap between theoretical multi-agent systems research and cybersecurity domain knowledge by presenting a novel applied framework for adaptive cyber defence that can address a wide range of challenges and provide a foundation for significant future research in systems modeling for cybersecurity. Belief-Desire-Intention (BDI) agent architecture is extended within this work through a novel application of knowledge graphs to provide a scalable data model for agents to understand their environment, infer the context of threats, create goals associated with security requirements, and select plans based on possible actions and expected results. The framework has been implemented to demonstrate the feasibility of the architecture and evaluate the design properties through applied security use cases. While the experimental results have demonstrated the value of the framework applied to IoT systems, the concept can be easily expanded to other domains. This thesis provides the foundation to inspire further research works in this area for continued development, application, and optimization to support the advancement of the industry and bring autonomous, adaptive cyber defence to realization.
Alloy catalysts for fuel cell-based alcohol sensors
(2016-05-01) Zamanzad Ghavidel, Mohammadreza; Easton, Brad
Direct ethanol fuel cells (DEFCs) are attractive from both economic and environmental standpoints for generating renewable energy and powering vehicles and portable electronic devices. The cost and performance of the DEFCs are mainly controlled by the Pt-base catalysts used at each electrode. In addition to energy conversion, DEFC technology is commonly employed in the fuel-cell based breath alcohol sensors (BrAS). BrAS is a device commonly used to measure blood alcohol concentration (BAC) and enforce drinking and driving laws. The BrAS is non-invasive and has a fast respond time. However, one of the most important drawback of the commercially available BrAS is the very high loading of Pt employed. One well-known and cost effective method to reduce the Pt loading is developing Pt-alloy catalysts. Recent studies have shown that Pt-transition metal alloy catalysts enhanced the electroactivity while decreasing the required loadings of the Pt catalysts. In this thesis, carbon supported Pt-Mn and Pt-Cu electrocatalysts were synthesized by different methods and the effects of heat treatment and structural modification on the ethanol oxidation reaction (EOR) activity, oxygen reduction reaction (ORR) activity and durability of these samples were thoroughly studied. Finally, the selected Pt-Mn and Pt-Cu samples with the highest EOR activity were examined in a prototype BrAS system and compared to the Pt/C and Pt3Sn/C commercial electrocatalysts. Studies on the Pt-Mn catalysts produced with and without additives indicate that adding trisodium citrate (SC) to the impregnation solution improved the particle dispersion, decreased particle sizes and reduced the time required for heat treatment. Further studies show that the optimum weight ratio of SC to the metal loading in the impregnation solution was 2:1 and optimum results achieved at pH lower than 4. In addition, powder X-ray diffraction (XRD) analyses indicate that the optimum heat treatment temperature was 700°C where a uniform ordered PtMn intermetallic phase was formed. Although the electrochemical active surface area (ECSA) decreased due to the heat treatment, the EOR activity of Pt-Mn samples was improved. Moreover, it was shown that the heat-treated samples prepared in the presence of SC showed superior the EOR activity compared to the samples made without SC. The Pt-Cu/C alloys were produced by three different methods: impregnation, impregnation in the presence of sodium citrate and microwave assisted polyol methods. These studies showed that the polyol method was the optimum method to produce the Pt-Cu alloy. The XRD analysis indicates that the heat treatment at 700 °C developed catalysts rich in the PtCu and PtCu3 ordered phases. The highest EOR activity was measured for the Pt-Cu/C-POL (sample made by the polyol method) and heat treated at 700°C for 1h. Comparing the EOR activity of the Pt-Cu and Pt-Mn samples also demonstrates that the heat treated Pt-Cu/C-POL sample showed higher EOR activity compared to the Pt-Mn samples. These results indicate that the benefits of thermally treating alloy nanoparticles could outweigh any activity losses that may occur due to the particle size growth and the ECSA loss. Besides, accelerated stress tests (ASTs) illustrate that the heat treatment improved the durability of the Pt-Mn and Pt-Cu samples. The durability and EOR activity of the heat treated Pt-Mn and Pt-Cu samples was similar or better than commercial samples. On the other hand, the ORR activity of Pt-Mn and Pt-Cu after the heat treatment was slightly lower than the commercial samples but the ORR activity loss can be compensated by the economic benefits from using the lower Pt loading. Finally, studying the alcohol sensing characteristic of different samples shows that the heat treated Pt-Mn and Pt-Cu catalysts could be used for the ethanol sensing. Additionally, among the different commercial samples tested for ethanol sensing, Pt-Sn/C showed the highest sensitivity but with slightly higher standard deviation. Further studies on the Pt- Cu/C and Pt-Mn/C samples indicate that the heat treatment improved the sensitivity of these samples and the highest normalized sensitivity among all the samples belonged to the Pt-Cu/C-POL (sample produced by polyol method) and heat treated at 700°C. It can be concluded that the heat treated Pt-Mn and Pt-Cu samples could be used as an alternative to replace Pt black in commercial sensors which would dramatically decrease the Pt loading. This could reduce the price and increase the sensitivity of commercial alcohol sensors.
Analysis of resistance nodulation division efflux pumps in Acinetobacter baumannii
(2015-01-01) Fernando, Dinesh Malinda; Kumar, Ayush; Green-Johnson, Julia
Strains from Canadian hospitals were classified as A. baumannii, A. pitti, or A. osocomialis and the most commonly expressed resistance nodulation division (RND) efflux pump was adeFGH. A collection of A. baumannii isolates from the environment showed decreased susceptibility to at least three antibiotics and expression of three RND efflux pumps (adeFGH, adeABC, adeIJK). From the clinical collection two blood isolates, AB030 and AB031, were chosen to evaluate the outcomes of three virulence models. Using Caenorhabditis elegans and Galleria mellonella a statistically significant difference was shown in AB030 the extremely drug resistant strain. Investigation of the physiological role of adeFGH pump revealed its expression may be more important to cell metabolism and independent of factors controlling expression of the other pumps. This was further demonstrated through examination of local regulator knockout (adeL) in ATCC17978 showing expression levels of adeFGH pump increased only 2-fold. Expression in sub-lethal concentrations of chloramphenicol or florfenicol showed >5-fold induction of adeFGH expression as well as adeABC, independently of AdeL. These data show that the AdeFGH pump is likely regulated by AdeL, but more so by another unknown regulatory mechanism that responds to aberrant peptide formation and not DNA synthesis error or nitrosative stress. In addition strain specific differences likely also account for differences in phenotypic observations since nitrosative stress has improved tolerance and induced adeFGH expression in ATCC19606 and AB031. Finally analysis of efflux pumps in two triclosan resistant mutants, AB042 and AB043, revealed overexpression of AdeIJK and mutation in the adeIJK regulator, AdeN. Complementation repressed adeIJK expression levels and increased susceptibility to moxifloxacin and ciprofloxacin. The AB043 strain did not have any mutations in adeN, promoter region or RND operon. Osmotic stress tolerance revealed more C16 fatty acids and less C18 compared to the parent strain. The more triclosan resistant strain AB043, showed biofilm formation exclusively at 30°C. Twitching was observed and caused by mutation in the histone-like nucleoid structuring (HNS) protein but did not affect biofilm formation. Using RNA-seq and proteomic analysis putative targets of AdeIJK regulation were identified.
The analysis of the entomological and chemical decomposition of human remains to ultimately assess the viability of the domestic pig as a substitute in forensic applications
(2022-09-01) Skopyk, Angela D.; LeBlanc, Helene
Death investigations often rely on the minimum post mortem interval (minPMI) estimations provided by forensic entomologists. The models accepted by courts in Canada are not based on research involving humans but rather on a human substitute, the domestic pig. However, now that facilities for human decomposition research are opening, we are faced with the prospect that pigs may not be so similar to humans as originally thought. The purpose of this research is to analyze the entomological and chemical decomposition of human and pig remains to determine if the domestic pig is an appropriate substitute for humans in research applied to minPMI estimations. Two human (n=4, n=2) and one pig (n=2) study were performed in Sydney, Australia, while three pig (n=3, n=2, n=3) studies were performed in Oshawa, Ontario. The environmental conditions were monitored as well as the accumulated degree days (ADD), rates of decomposition, primary dipteran colonizers, and volatile organic compound (VOC) production. Domestic pigs in Oshawa, Ontario, had rates of decomposition that were highly alike. Rates of insect colonization were rapid, with little to no delay. The production of 5 known apneumones showed no significant difference (p < 0.05) between pigs. Human decomposition in Sydney yielded results with varied rates of decomposition and colonization – some with long pre-colonization intervals (Pre-CIs), which affected the rate of decomposition. It was noted that the donors with long Pre-CIs were likely to have been taking strong peri-mortem antibiotics due to their antemortem health conditions. These antibiotics could have affected the donor’s microbiome, killing the beneficial bacteria that produce apneumones. The domestic pigs observed in Sydney showed decompositions, colonizations and VOC productions more similar to the pigs in Ontario than to the humans in Sydney. Humans live differently than domestic pigs with varying diets, habits, body types, and medications that can influence their decomposition and colonization after death. Since this cannot be said for the domestic pig, it is recommended that the researching community aim to shift future research to human donors so that the data collected can be applied to human death investigations while considering comorbidities and how it affects insect colonization.
Analysis of the volatile organic compounds produced by the decomposition of pig carcasses and human remains
(2013-03-01) Stadler, Sonja; Forbes, Shari; Desaulniers, Jean-Paul
Complex processes of decomposition produce a variety of chemicals as soft tissues and their component parts are degraded. Among others, these decomposition by-products include volatile organic compounds (VOCs) responsible for the odour of decomposition. Human remains detection (HRD) canines utilize this odour signature to locate human remains during police investigations and recovery missions in the event of a mass disaster. Currently, it is unknown which compounds or combinations of compounds are recognized by the HRD canines. In this study decomposition VOCs were collected from the decomposition headspace of pig carcasses and were analyzed using thermal desorption gas chromatography mass spectrometry (TD-GC-MS). The difficulties associated with the non-target analysis of complex samples led to the further analysis of decomposition odour using a novel application of thermal desorption coupled to comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (TD-GCxGC-TOFMS). The additional peak capacity and spectral deconvolution of the GCxGC-TOFMS system generated a characteristic profile of decomposition VOCs across the various stages of soft tissue decomposition. The profile was comprised of numerous chemical families, particularly alcohols, carboxylic acids, aromatics and sulfides. Characteristic compounds identified in this study included 1-butanol, 1-octen-3-ol, 2-and 3-methyl butanoic acid, hexanoic acid, octanal, indole, phenol, benzaldehyde, dimethyl disulfide and trisulfide, which represent potential target compounds of decomposition odour. Currently there is a demand for improved canine training aids and pig carcasses have been proposed as an alternative due to their acceptance as human body analogues. This work investigated the similarities in the decomposition odour profile of pig carcasses and human remains through surface decomposition trials and comparisons to the published literature. It was determined that pig carcasses cannot be eliminated as potential human decomposition odour mimics. Additionally, following the examination of commercially available synthetic training aids, pig carcasses demonstrated a more suitable profile for the training of cadaver dogs. Further investigation into the chemical composition of decomposition odour utilizing TD-GCxGC-TOFMS will aid in determining the signature of human decomposition odour and facilitate the comparisons of these profiles between environments, individuals and species.
An anomaly detection model utilizing attributes of low powered networks, IEEE 802.15.4e/TSCH and machine learning methods
(2019-12-01) Salgadoe, Sajeeva; Lu, Fletcher
The rapid growth in sensors, low-power integrated circuits, and wireless communication standards has enabled a new generation of applications based on ultra-low powered wireless sensor networks. These are employed in many environments including health-care, industrial automation, smart building and environmental monitoring. According to industry experts, by the year 2020, over 20 billion low powered, sensor devices will be deployed and an innumerable number of data objects will be created. The objective of this work is to investigate the feasibility and analyze optimal methods of using low powered wireless characteristics, attributes of communication protocols and machine learning techniques to determine traffic anomalies in low powered networks. Traffic anomalies can be used to detect security violations as well as network performance issues. Both live and simulated data have been used with four machine learning methods, to examine the relationship between performance and the various factors and methods. Several factors including the number of nodes, sample size, noise influence, model aging process and classification algorithm are investigated against performance accuracy using data collected from an operational wireless network, comprising more than one hundred nodes, during a six-month period. An important attribute of this work is that the proposed model is able to implement in any low powered network, regardless of the software and hardware architecture of individual nodes (as long as the network complies with an open standard communication mechanism). Furthermore, the experiment portion of this work includes over 80 independent experiments to evaluate the behaviour of various attributes of low powered networks. Machine learning models trained using carefully selected input features and other factors including adequate training samples and classification algorithm are able to detect traffic anomalies of low powered wireless networks with over 95% accuracy. Furthermore, in this work, a framework for an aggregated classification model has been evaluated and the experiment results confirm a further improvement of the prediction accuracy and a reduction of both false positive and negative rates in comparison to basic classification models.
Assessing nearshore water quality and biological condition in the Kawartha Lakes using a community science approach
(2022-08-01) Smith, Erin D.; Kirkwood, Andrea
The Kawartha Lakes, located in south-central Ontario, are a popular tourist destination, with a growing permanent resident population. Consequently, land development in its watersheds continues, and the specific land-use – water quality relationships in this region are unknown. The nearshore zone is where land use has its first impacts on the lake, and provides vital habitat for most lake inhabitants at some point in their life cycle. Despite its importance, the nearshore zone is rarely monitored and further investigation is required to understand human impacts on the nearshore zone. My thesis aimed to elucidate the relationships between land use and the abiotic and biotic condition of the nearshore zone in the Kawartha Lakes. To examine Lake Scugog’s nearshore water quality patterns and relationship with land use, 12 volunteers collected water samples from spring to fall for three years. Land use had significant impacts on chloride at buffer scales and phosphorus at the sub-watershed scale. I also monitored the nearshore biotic community (phytoplankton, zooplankton, and macroinvertebrates) at eight sites in Lake Scugog. Two years of sampling found that macrophyte abundance significantly influenced the phytoplankton, zooplankton, and macroinvertebrate communities. During the pandemic restrictions in 2020, community scientists on 16 lakes collected monthly water samples from June-September. A subset of four lakes had nutrient data across 3-years (2019 - 2021), which allowed comparison of nutrient conditions before and during the pandemic. There were significant differences in water quality between watersheds and a notable impact of Lake Scugog on downstream lakes. There was not a significant impact of pandemic restrictions on nearshore water quality in these lakes. A focal study on Balsam, Cameron, Sturgeon, and Pigeon lakes involved nearshore water quality monitoring (2019 and 2021) and biological sample collection (2021). There was a separation of distinct water quality profiles that grouped Balsam and Cameron, and Sturgeon and Pigeon. Exploring relationships between land use, water quality, and the biotic communities I found that phosphorus was important for driving phytoplankton, zooplankton, and macroinvertebrate community abundance. Overall, these findings provide important information for lake managers in understanding the role of land-use and nearshore ecological condition in lake health.
Calculating thermochemical equilibrium for multiphysics simulations of nuclear materials : development of yellowjacket gibbs energy minimiser
(2022-11-01) Bajpai, Parikshit; Piro, Markus
Nuclear fuels and structural materials are highly complex systems that are remarkably challenging to understand and model. Material behaviours are influenced by multiple physical phenomena such as mechanics, chemistry, heat and mass transport, etc. Moreover, lower scale phenomena inform and drive the phenomena at larger scales. The strong interactions between multiple physics at different length and time scales creates a need for multi-scale, multi-physics modelling tools. In nuclear fuels and structural materials, the problem gets compounded by the fact that, in addition to an extreme environment, the composition of the system changes with time. For such complex systems, computational thermodynamics plays a valuable role in predicting many phenomena and is often necessary for understanding and informing others. For this reason, there has been an increasing interest in incorporating equilibrium thermodynamics calculations in multi-physics frameworks such as the Multiphysics Object Oriented Simulation Environment (MOOSE). To simulate corrosion in molten salt reactors, a new MOOSE-based tool named Yellowjacket has been developed and this work contributes to it. The objective of this work is to develop a new equilibrium thermodynamic solver to provide thermodynamic material properties and boundary conditions for Yellowjacket and other MOOSE-based codes. While several thermodynamics codes already exist, the new software, called Yellowjacket–GEM, adds native equilibrium thermodynamic capability to MOOSE and aims to address several concerns such as computational performance, limitations on system size and models, and Software Quality Assurance (SQA). Yellowjacket–GEM exploits the fundamental laws of thermodynamics to solve a non-linear, non-convex optimisation problem. Several thermodynamic models, including the Modified Quasichemical Model in Quadruplet Approximation (MQMQA) were implemented, and state-of-the art numerical solvers in Portable, Extensible Toolkit for Scientific Computation (PETSc) were used to efficiently solve the optimisation problem. In doing so, the work contributes to the understanding of MQMQA which until recently wasn’t well comprehended. Ensuring that the solver gives a true equilibrium solution also requires solving a global optimisation problem without severely compromising performance and reliability. Several global optimisation methods were compared through numerical experiments to objectively select the best approach for implementation. The C++ code follows MOOSE coding standards and SQA procedures and enables direct coupling of thermodynamic equilibrium calculations in multiphysics simulations performed using MOOSE.
Characterization of the ACC-1 Family of cys-loop ligand gated ion channels from the parasitic nematode Haemonchus contortus.
(2020-12-01) Habibi, Sarah A.; Forrester, Sean
Nematode cys-loop ligand-gated ion channels (LGICs) have been extensively studied for decades because of their role in current anthelmintic action and potential as targets for future drugs. Many families of cys loop receptors have not yet been pharmacologically characterized in parasitic nematodes, and thus provides an opportunity for further exploration into their role in the nervous system of these pathogens, and relevance for anthelmintic action. The ACC-1 family of receptors is a group of inhibitory acetylcholinegated chloride channels that are unique to invertebrate species. Specifically, the ACCs have been identified in both free-living (Caenorhabditis elegans) and parasitic (Haemonchus contortus) nematodes. However, their pharmacological properties in H. contortus have yet to be explored. My PhD thesis focused on identifying and characterizing the role of the ACC-1 family of receptors in H. contortus, using molecular cloning, pharmacological characterization and an investigation of the ligand-binding pockets using site directed mutagenesis and computational protein modelling. I conducted an extensive pharmacological characterization of the following receptors, ACC-2, ACC-1/ACC-2, ACC-1/LGC-46, LGC-46, LGC-39/ACC-1, and LGC-40/ACC-1, and generated homology models for each with various ligands bound. The ACC receptors from H. contortus are sensitive to various cholinergic ligands, nicotinic acetylcholine receptor anthelmintics, classical antagonists, and minimally sensitive to nicotine. Although one particular subunit, ACC-1, does not form a functional channel alone, it does associate with other members of the ACC family, ACC-2 and LGC-46, as well the previously uncharacterized cholinergic receptors, LGC-39 and 40, to form unique heteromeric channels. In addition, it was found that a single point mutation of a phenylalanine residue to a tyrosine in Loop C of the ACC binding pocket results in a hypersensitive receptor. Finally, sequence analysis of the ACCs revealed that the characteristic tryptophan residue, which contributes to π-cationic stabilizing interactions with ligands in the binding pocket, is located in Loop C of these receptors, whereas this residue is commonly found in Loop B of mammalian nAChRs. Together, this dissertation provides the first characterization of these inhibitory ACh receptors in parasitic nematodes and further sheds light into cholinergic neurotransmission in H. contortus.
Cognitive function during exertional heat stress assessed using traditional and serious game technology
(2016-06-01) Williams-Bell, F. Michael; Murphy, Bernadette; McLellan, Tom
Firefighting is a physically demanding occupation requiring intermittent bouts of work resulting in increased levels of cardiovascular and thermal strain, while making decisions requiring higher order cognitive abilities e.g. working memory, sustained attention, reaction time, spatial awareness, and information processing. These activities can take place in dangerous conditions with elevated temperatures imposing external stressors on physiological and cognitive function. Previous research has examined the impact of heat stress on cognitive function in general, but the specific influence on firefighters wearing personal protective equipment (PPE) and self-contained breathing apparatus (SCBA) is not well understood. Specific domains of cognitive function can be assessed using computer-based neuropsychological testing batteries, such as the Cambridge Neuropsychological Automated Testing Battery (CANTAB). The CANTAB automatically records the response measures for each test and provides consistent feedback in between trials. Although the CANTAB is well established the cognitive domains it tests may not adequately capture the complexity of the specific decision making required of firefighters while on-duty. The use of serious game technology provides a possible solution to develop a more ecologically valid assessment tool capable of evaluating the specific decision making tasks required of firefighters at an emergency scenario. Thus, the current thesis aimed to evaluate the effects of exercise-induced heat stress on cognitive function in firefighters using the CANTAB testing battery and a recently developed serious game simulating the decision making tasks required of firefighters in a two-storey residential fire while walking on a treadmill. Additionally, the reliability of repeated CANTAB administrations during treadmill walking was measured and found to have reasonable overall reliability. Decrements in cognitive function (working memory and executive function were observed at a core temperature of 38.5°C and restored following an active cooling recovery protocol. However, when decision making was evaluated using the serious game scenario, task specific performance deficits were not seen during treadmill walking but impairment in memory recall was found following the active cooling recovery protocol. These findings provide fire service personnel with information regarding the cognitive implications of heat stress and the potential use of serious games to evaluate and train cognitive function during exposure to environmental stressors.
Community-oriented architecture for smart cities
(2017-12-01) Jalali, Roozbeh; El-Khatib, Khalil; McGregor, Carolyn
With the widespread use of smartphone devices, a surge in mobile sensing, progress in wireless communication and networking techniques, as well as the development of the Internet of Things (IoT) and cloud computing, mobile-based community sensing has turned into a leading paradigm for pervasive sensing. Smartphones with embedded sensors have become ubiquitous devices carried by millions of people. Community sensing empowers individuals to collectively sense, analyze and share local observations and mine data in order to determine and map phenomena relating to real world conditions by using mobile devices across many applications, including transportation and healthcare. While there are currently many tools and frameworks that allow researchers and developers to collect and analyze data at the individual user level, a parallel framework for data collection and analysis at the community level does not yet exist. Such a framework would provide the functionality to create various models for building smart city applications for urban planning, sustainable communities, transportation, public health, and public security. This thesis presents a review of current smart city network architectures, along with their associated technologies, and proposes an architecture for the smart city and its services while considering communities as the main part of the design. Of the different components of the proposed architecture, two are vital for enabling a community structure for the smart city. These two components are community detection and data aggregation. This thesis proposes new methods for community detection and analysis using graphs and clustering algorithms based on the sensor data collected from individuals’ smartphones and IoT sensors. As far as can be ascertained, the proposed method is the first to transform the time series data collected from individuals’ smartphones to correlation networks for community detection. The proposed methods leverage not only the individuals’ groups but effectively discover communities of common interest. Two different case studies were conducted in this thesis in order to show the performance of the proposed methods. In these case studies, the data collected from individuals’ smartphones and vehicles are used and communities of individuals, based on their movement patterns and similarities, are detected. The performance evaluation shows that the proposed methods effectively identify the individuals’ communities with good accuracy.
Comparing exercise responses to high intensity interval training between adults with and without asthma
(2020-06-01) O’Neill, Carley; Dogra, Shilpa
Exercise induced bronchoconstriction (EIBC) occurs in response to high ventilations during exercise, which cools and dries the airways, triggering an inflammatory cascade. High intensity interval training (HIIT) is associated with reductions in inflammation, improvements in cardiorespiratory outcomes, and mental health in healthy adults; however, the impact of HIIT in adults with EIBC is unclear. The purpose of this dissertation was to determine the impact of a 6-week HIIT intervention on physiological (e.g. inflammation, ventilation, and cardiorespiratory fitness) and psychological (i.e. anxiety) domains of health among adults with EIBC and healthy adults, and the impact of HIIT on clinical outcomes (i.e. asthma control) among adults with EIBC. METHODS: A quasi-experimental study design was used. A 6-week HIIT intervention was implemented in adults (18-44 years) with EIBC and healthy controls. Sessions were conducted three times per week and consisted of cycling at 10% PPO for 1 minute followed by 90% PPO for 1 minute, repeated 10 times. Primary measures at pre (T1) and post-intervention (T2) included: 1) maximal exercise test 2) passive drool saliva samples 3) anxiety sensitivity index-3 4) asthma control questionnaire-7 (EIBC group only). RESULTS: Participants in the EIBC group (n=20; T1: 32.9 ± 8.0; T2: 38.6 ± 8.2 ml/kg/min, p<0.01) and control group (n=12; T1: 38.6 ± 8.2; T2: 38.9 ± 12.3 ml/kg/min, p<0.01) improved VO2max. Adults with EIBC had lower levels of IL-1Ra at T2 when compared to healthy controls (EIBC T2: 0.2 ± 0.16pg/ug protein; Control T2: 0.8 ± 0.21pg/ug protein; p<0.01, hp2 = 0.3). Maximal ventilation in the EIBC group did not improve (EIBC T1: 97.8 ± 22.2; EIBC T2: 108.7 ± 29.5, p=0.7, Cohens d=0.4); however, the control group improved ventilation at the same absolute exercise workload (Control T1: 82.8 ± 20.1; Control T2: 101.8 ± 18.1, p=0.02). Reductions in anxiety sensitivity (EIBC T1: 17.9 ± 11.8; EIBC T2: 12.4 ± 13, p=0.002, Cohens d=0.4) and asthma control (EIBC T1: 0.8 ± 0.6; EIBC T2: 0.5 ± 0.4, p=0.02, Cohens d=0.5) from T1 to T2 occurred. CONCLUSION: A 6- week HIIT intervention leads to improvements in physiological, psychological, and clinical outcomes among adults with EIBC.
Computational methods for medical image registration
(2021-04-01) Mojica, Mia Carmela; Ebrahimi, Mehran
A significant amount of research has been dedicated to the improvement of techniques in the field of medical image analysis. Imaging modalities have been improved and new acquisition methods have been introduced to reveal greater anatomical detail and to allow for more information to be extracted from medical images. However, certain challenges remain when processing and analyzing information from medical images. Image registration is a technique to find a reasonable transformation that best aligns a pair or group of images. Of particular interest in this thesis is the use of image registration in three main categories: cardiac fiber atlas construction from healthy porcine hearts, motion correction in contrast-enhanced image sequences, and the development of novel computational techniques that improve the performance of existing medical image registration methods. We provide an overview of each of the problems that we tackled, followed by a discussion of the underlying motivation and theory behind the proposed methods, and extensive validations. Most importantly, this work highlights the central role that image registration plays in biomedical research - from producing clinically relevant image-based predictive models, to enabling accurate diagnosis of diseases and the analysis of treatment response.
Contextual topics: advancing text segmentation through pre-trained models and contextual keywords
(2024-09-01) Maraj, Amit; Vargas Martin, Miguel; Makrehchi, Masoud
Text Segmentation (TS) is a Natural Language Processing based task that is aimed to divide paragraphs and bodies of text into topical, semantically aligned blocks of text. This can play an important role in creating structured, searchable text-based representations after digitizing paper-based documents. Traditionally, TS has been approached with sub-optimal feature engineering efforts and heuristic modelling. In this work, we explore novel supervised training procedures with a labeled text corpus along with a neural Deep Learning model for improved predictions. Results are evaluated with the Pĸ and WindowDiff metrics and show performance improvements beyond any previous unsupervised TS systems evaluated on similar datasets. The proposed system utilizes Bidirectional Encoder Representations from Transformers (BERT) as an encoding mechanism, which feeds to several downstream layers with a final classification output layer, and even shows promise for improved results with future iterations of BERT. It is also found that infusing sentence embeddings with unsupervised features, such as the ones gathered from Latent Dirichlet Allocation (LDA), provides comparable results to current state-of-the-art (SOTA) TS systems. In addition to this, unsupervised features derived from LDA give the proposed system the ability to generalize better than previous supervised systems in the space. Furthermore, it is shown that with the use of novel language models such as Generative Pre-trained Transformers (GPT) for text augmentation, training data can be multiplied, while continuing to see performance improvements. Although the proposed systems are supervised in nature, they have the capability of fine-tuning a threshold variable that allows the system to predict segments more frequently or sparingly, further bolstering the practical usability of it. Due to the increasing competition in the supervised TS space, creating competitive systems often see contributions from larger research companies with more available resources (e.g., Google, Meta, etc.). However, unsupervised TS has been relatively unexplored in comparison with supervised efforts, since it is much more challenging to build a generalizable TS system. To this end, strong word and sentence embeddings are used to create an unsupervised TS system called “Coherence”, that blends the best of pre-trained models and unsupervised features to create a system that is capable of generalizing across various datasets, while achieving competitive results in the space. Since Coherence is unsupervised, inference is quick and requires no upfront investment (i.e., this technique can be picked up and applied to a domain without the need for fine-tuning).
CORE: a framework for the automatic repair of concurrency bugs
(2015-01-01) Kelk, David; Bradbury, Jeremy; Green, Mark
Desktop computers now contain 2, 4 or even 8 processors. To benefit from them programs must be written to work in parallel. If writing good code is hard, writing good parallel code is much harder. Parallelization adds process communication and synchronization to the list of difficulties faced by programmers. It also adds new kinds of bugs not found in single-threaded code such as deadlocks and data races. In this thesis we develop the CORE (COncurrent REpair) framework. It automatically fixes deadlocks and data races in parallel Java programs. It uses a search-based software engineering approach to mutate and evolve the source code. In these mutants synchronization blocks are added, removed, expanded, shrunk or the synchronization variable is changed. Each potential fix is model checked or run through a thread noising tool that forces different thread interleavings to be explored. Efficiently fixing data races and deadlocks in parallel Java programs is realized by combining two techniques. First, different forms of static and dynamic analyses are brought together to constrain the search space. Second, a genetic algorithm without crossover was implemented that uses both noising and model checking to determine fitness. These techniques are unified in the CORE framework. Different kinds of analysis better constrain the search space of the problem. Intelligent use of noising, model checking and incremental model checking are combined efficiently into a modern framework that help to increase the overall quality of concurrent software. This thesis created three projects within the CORE framework, ARC-OPT, CORE-MC and CORE-IMC. First, static analysis from Chord and dynamic analysis from ConTest with fitness evaluation by thread noising from ConTest were combined in ARC-OPT. Second, JPF was integrated into the framework to analyze the source. Fitness was evaluated by JPF and ConTest. This version was called CORE-MC. Third, function header scanning for in-scope locks and incremental modelling support was added to CORE-MC to create CORE-IMC. Each project builds upon the previous and each was evaluated against a suite of test programs.
A deep learning approach to focal cortical dysplasia segmentation in children with medically intractable epilepsy
(2021-08-01) Aminpour, Azad; Ebrahimi, Mehran; Widjaja, Elysa (SickKids Hospital)
Paediatric epilepsy is one of the most common neurological disorders and has major impact on the cognition and quality of life of children. Focal Cortical Dysplasia (FCD) is one of the most common causes of medically intractable epilepsy. FCD may be amenable to surgical resection to achieve seizure freedom. By improving the detection of lesions such as FCD, the surgical outcome of these patients can be improved. The MRI features of FCD can be subtle and may not be detected by visual inspection. Patients with epilepsy who have normal Magnetic Resonance Imaging (MRI) are considered to have MR-negative epilepsy. Recent advances in deep learning techniques hold the potential to improve the detection of FCD lesions. The advantage of deep learning techniques, specifically Convolutional Neural Networks (CNN), and Fully Convolutional Networks (FCN) are that they are built to extract detailed features in images with minimal user involvement. Therefore, we set to develop a model, which takes an MRI, classifies whether it is FCD or not and outputs the lesion’s location in FCD cases. Also, another potential method is by considering information from different MRI sequences such as T1-weighted, T2-weighted and FLAIR simultaneously, since the MRI features of FCD may be more apparent on one sequence but not another. There are several challenges associated with training a model, such as lack of ground-truth, and unbiased data. We will address the ground-truth issue by building a pixel-level ground truth, and the unbiased data problem by sampling the healthy data to match the number of lesional data. We developed 5 models working on different inputs and generating coarse to fine localization of the lesion and compared their performances on MR-positive and MR-negative subjects. Our data was acquired from the SickKids hospital in Toronto and consisted of 56 MR-positive, 24 MR-negative, and 15 healthy patients. Our multi-sequence model successfully classified all healthy cases. Furthermore, it detected 55 MR-positive and 22 MR-negative subjects. We obtained 74% and 68% lesion coverage for MR-positive and MR-negative subjects, respectively. Based on our experiments FCN is a promising tool in segmentation and detection of FCD cases given the MRI data.
Determining the impact of carrion decomposition on soil microbial activity levels and community composition.
(2013-12-01) Breton, Heloise; Forbes, Shari; Kirkwood, Andrea
The ubiquitous nature of microorganisms and their specificity to certain locations make them potentially useful for forensic investigators. Advances in microbial profiling techniques have made it possible to compare microbial community profiles obtained from evidence or crime scenes to individuals and vice versa. Profiling microbial communities associated with cadaver decomposition may provide useful information concerning post-mortem intervals and aid in the identification of clandestine graves. Four experiments using pigs as human decomposition analogues were performed over the course of 2011 and 2012 in southern Ontario to document changes in soil microbiology following decomposition. Studies were conducted in the spring and summer to document the effect of environmental conditions on the decomposition process and subsequent changes in gravesoil microbiology. Microbial activity was measured using a fluorescein diacetate assay as a preliminary indicator of changes within the soil microbial population. Both decreases and increases in microbial activity were observed throughout each decomposition experiment indicating that the microbial response to decomposition is complex. It is believed that environmental conditions and decomposition rates play a role in determining how taphonomic events affect soil microbial activity. Fatty acid methyl esters (FAME) profiling was used document community level changes throughout decomposition. Shifts in FAMEs profiles were brought on by the onset of active decay and persisted through to the dry remains stage. The fatty acids 3OH 12:0, 12:0, 16:0 and 18:0 were frequently found in higher amounts in gravesoils and may prove useful as markers of cadaver decomposition. Metagenomic profiles of soil microbial communities were obtained using Illumina® sequencing. Decomposition was associated with changes in microbial community composition. This allowed gravesoil samples to be differentiated from control samples for an extended period of time. Bacteria responsible for the shift in microbial profiles are those commonly associated with cadaver decomposition. Both sets of soil profiles indicated that weather had an effect on microbial community composition. Results highlight the need to document natural changes in microbial communities over seasons and years to establish normal microbial patterns to effectively use soil microbial profiles as post-mortem interval or clandestine grave indicators.

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