Projets novateurs réalisés

Assessment of factors that can contribute to earth dam cracking and slumping in northeast BC

In northeast British Columbia, earth dams have been constructed to hold water for the oil and gas industry using locally available soils having high clay and silt content. These soils tend to have low shear strength and are susceptible to volume changes from wetting-drying and freeze-thaw cycles, which can lead to increased risk for cracking, slumping, and piping issues in dam embankments. The student intern will collect soil samples, measure the embankment geometry, perform laboratory testing to determine specific material properties, and use finite element software to perform stability analyses of selected dams. The objective is to propose remediation and stabilization techniques for existing dams with potential stability issues, and to develop best practices for construction of dams in this region to minimize risk of failure.

Selective breeding of honey bees

Over the past eight years we have a diagnostic test for disease resistance in honey bees and then attempted to use this test to selectively breed disease-resistant bees. The bees selected in this way appear to not lose other economically important traits (e.g., honey production) but are far better equipped to deal with disease than ‘average’ bees. This then opens up many doors, some of which we would like to pursue here. First and foremost though is to get the tools we have developed into the hands of the bee industry. For this project, we are proposing to work with a handful of early-adopter bee breeders from across the province of BC to start applying these tests in their own operations. The funds here will support the translation of research results into standard operating procedures for the diagnoses and then the actual application of the tests on several hundred colonies from across BC. We will carry this out over these tests over the following three years and work with the bee breeders to simultaneously measure disease resistance in their colonies to monitor progress. [TBC]

Wormhole Structure Characterization through Modeling and Analyzing Testing Data during Solvent Injection Post CHOPS Process

High viscous oil is trapped underground and cold-heavy-oil-production-with-sand (CHOPS) is one of the acceptable methods to produce the heavy oil out by forming wormholes around the wellbore. Saskatchewan and Alberta have thousands CHOPS wells and left 90% original oil in place underground. However, the primary oil recovery is low and solvent like CO2 or propane could be injected into the CHOPS well and reduces the viscosity of the oil, which could bring economic production back. How to evaluate the performance of the solvent injection performance and the data analysis for field testing and production data becomes crucial for the success of post-CHOPS well process. This proposed project could provide theoretical and applicable model to help the industry optimize the optimize production wells for better enhance oil recovery outcomes.

Data Centric Protein Extraction Process Optimization

Proteins are used in many products of our day-to-day life. The extraction and isolation of proteins from the raw source is a complex process in which every stage has multiple controlling parameters that need to be monitored and optimized to achieve predicted quality, functionality, and yield. The performance of the extraction process depends on the physical and chemical nature of the raw material and the equipment used. Currently, the trial and error method is used to optimize this process which results in wasted resources and time. The objective of this research is to develop machine learning models to predict the optimal operating conditions. The historical protein extraction process data will be used to develop the predictive models. Few of the challenges associated with this project are feature engineering, data structuring and organization, feature selection, and targeted data collection.

Organoid modeling as a therapeutic prediction tool in androgen-independent castrate resistant prostate cancer

Prostate cancer (PCa) is the most common form of cancer in men from developed countries, and ranks second in cancer-related deaths. The rapid recurrence of drug-resistant tumors in patients with incurable androgen-independent castrate resistant prostate cancer (CRPC-AI) underscores the need for more effective therapies. Our recent findings suggest that androgen deprivation therapy (ADT) enriches for a subpopulation of stem cells that are responsible for the relapse of CRPC-AI and treatment resistance.

3D organoid culture has emerged as a novel pre-clinical model that allows tumors to propagate ex vivo. Organoids constructed from patient biopsies can not only predict individual response, but also parallel real-time response over the course of treatment. Knowing this information will provide insight into future courses of therapy personalized to patients. It would be advantageous to model the patients’ tumor ex vivo to rapidly identify rate of response to standard of care, and identify alternate therapies for the patient (ie: clinical trial enrollment). Another advantage of testing the patients’ response to therapy like this, is that the patient could forgo the exposure to a drug that will only potentiate the aggressiveness of their disease. [TBC]

Evaluating the imPROVE LEAN Program at Provincial Health Services Authority

This research project will examine the imPROVE program at Provincial Health Services Authority. The imPROVE program is a large scale initiative designed to ensure or improve the quality and efficiency of health service delivery using LEAN methodology. This research will investigate several components of interest as identified by key stakeholders, which will provide decision makers with sound research evidence pertaining to the effectiveness and cost-effectiveness of the imPROVE program, which in turn, can be used to plan future directions of the initiative.

The purpose of this study, then, is to embark upon a research program of sufficient rigor to produce scientific reports that will reveal information about several aspects of the imPROVE program—information that will withstand the scrutiny of peer-reviewed critiques such that results can be relied upon as a sound foundation for decision making. The resulting evaluation framework may also be transferable to other health authorities.

Interfacial rheology of multispecies biofilms

One of the major challenges facing the 21st century is the antibacterial fight. Traditional methods such as antibiotics are ineffective in many cases of chronic infections or food contaminations. One of the reasons for this phenomenon is the formation of biofilms where bacteria are protected from the environment by a protective barrier made of polymers. Several bacterial species coexist and interact within these biofilms. This research project at Stanford aims to characterize bacterial interactions in a pathogenic multi-species biofilm, using a unique technique of chemical engineering called interfacial rheology that measures the viscoelasticity of the biofilm over time. The results will then be supplemented by a microbiological approach to understand the influence of interactions on the evolution of the biofilm. By combining various innovative techniques to analyze biofilm formation, this PhD project will allow the development of new antimicrobial strategies to fight and destroy biofilms.

Benchmarking Cryptocurrency Against Current International and Domestic Anti-Money Laundering Legislation

This project seeks to develop a comprehensive understanding of the emergence of the regulation of cryptocurrencies (like Bitcoin). First, this project looks to the history of cryptocurrencies and other blockchain technologies, mapping the rise in consumer use of the online currency. Next, this study pulls legal cases from the international and domestic sphere which regulate cryptocurrency in Australia, Canada, and the United States. Subsequently, this study maps international and domestic anti-money laundering and countering the financing of terrorism laws with the rise of popular cryptocurrencies. By benchmarking changes in financial regulation through legal cases, this study can identify the deficiencies within the anti-money laundering system, and how criminals continuously use the system for illegal activities. Finally, this paper provides policy recommendations for updating legislation surrounding domestic and international money laundering. This paper hopes to provide a comprehensive look at emerging currencies that benefit the consumer, while remaining safe and legal to use.

Spectromètre à peignes de fréquences dans l’infrarouge moyen intégré sur puce optique

Les activités de recherche prévues au cours de mon séjour à l’University of South Australia consistent à développer un spectromètre à peignes de fréquences dans l’infrarouge moyen intégré sur une puce optique. Un spectromètre dont la plage spectrale d’opération s’étend jusqu’à 3-5 µm permet la mesure de spectres d’absorption de gaz. Bon nombre de molécules possèdent une signature spectrale autour de 3-5 µm et ne peuvent être caractérisées à l’aide des spectromètres actifs actuels à 1-2 µm. La présente proposition s’articule ainsi autour de la conception d’un montage optique comportant diverses composantes, dont une puce de verre fluoré qui agit comme le milieu de gain d’un laser et permet la génération d’impulsions laser de très courte durée formant un peigne de fréquence. À titre d’exemple, la télédétection, c’est-à-dire la quantification des émissions de gaz à effet de serre est une application où les besoins sont énormes et où la spectroscopie par peignes de fréquences s’avère une solution à fort impact à la fois pour l’industrie et la population.

Development of an internal Credit Rating model for unrated companies / Quantitative alignment of portfolio risk against a client’s risk appetite

The first part of this project involves the development of an internal credit rating model. Credit ratings, such as those provided by credit rating agencies like S&P and Moody’s, are an important tool that measure the credit risk associated with different companies. However, as the markets expand there are an increasing number of companies where investors would like to participate, but which are not rated by these agencies. Thus, the Toronto-Dominion Bank’s Wealth division would like to create a model that can assess a company’s credit rating and compute a similar score to that of the credit rating agencies. This internal rating system would allow TD Bank’s customers to better measure credit risk with their investments.
The second part of the project will attempt to improve the metric by which client risk appetite is quantified to better align the appetite level with the portfolio risk for each customer, thereby ensuring a client’s needs are effectively met.

Predicting the Ambulance Offload Delay Problem Using Decision Tree Analysis

When emergency departments (EDs) are congested and cannot accept incoming ambulance patients immediately, a common action is to let paramedics continue to provide patient care until an ED bed becomes available. This delay in transferring a patient from the ambulance to the ED is referred to as ambulance offload delay (AOD). AOD is a growing problem in Canada as the time to transfer an ambulance patient to an ED can be significant. This can negatively affect the ability of the ambulance service to respond to future calls and reduces the efficiency of the system. Using integrated historical data from a partnering hospital and partnering Emergency Medical Services (EMS) provider, a decision tree model will be developed to predict AOD of the system in a proposed period. The development of this prediction model can help ambulance service providers to be proactive with the potential AOD problem by activating interventions to prevent it from occurring.

Understanding the function of effectors’ structure-based function and protein-protein interactions from the amino acid sequence by computational calculations.

Identifying fungal effector proteins and understanding their function is of great importance in efforts to control losses to plant diseases. Modern high-throughput sequencing technologies have facilitated the availability of several fungal genomes and 1000s of transcriptomes. There is minimal consensus over the annotation and functionality of effector proteins. With the characterization of avirulence (Avr) genes, criteria for computational prediction of effector proteins are becoming more efficient. The proposed objectives are – 1) Effector’s 3D structure prediction, involving I-TASSER for iterative protein structure assembly, and ab initio protein folding using QUARK platform. 2) COACH based effectors-ligand binding affinities site prediction. 3) biological function annotation and predictions of effector molecules from its 3D structural model. 4) alignment-free bioinformatics approaches to identify a protein with structural and functional similarity to the Mlp37347 and Mlp124357. 5) To understand the 3D structure of Mlp37347-GAD1 and Mlp124357-PDI complexes from the sequence; and their structure-based function annotation.