Innovative Projects Realized

Functional Proteomics to Inform Targeted Therapy for Head and Neck Cancer

To ensure effective cancer treatment, it is vital to match the drug with the molecular characteristics of the patient’s tumor. This research project is focused on developing functional diagnostic assays that may be used eventually in the clinic to stratify patients for targeted therapies. The project will provide a unique opportunity for the interns to work with a clinician at the forefront of cancer treatment and scientists of a partner organization that is developing innovative tools to enable precision cancer medicine. This project will generate unprecedented insights into the pathogenesis of cancer progression and therapeutic resistance and provide a foundation for translation of innovative proteomic technologies into transformative new approaches for cancer diagnosis and treatment. Furthermore, this project will provide the proof-of-concept for the partnering organization for the eventual translation of its innovative technologies to the clinic to benefit patients.

Les évolutions des négociations commerciales et le droit international économique : questions choisies

Le succès du commerce extérieur canadien est largement attribuable aux réductions des barrières commerciales qui se sont négociées dans les forums internationaux depuis la fin de la Seconde Guerre mondiale. Les importations et exportations constituent près de 65% de la production canadienne. Or, à l’heure actuelle, les accords de libéralisation des échanges subissent une remise en cause de la part d’importants partenaires commerciaux, ce qui se répercute sur les entreprises et industries canadiennes.
Les tendances au protectionnisme observées sur les marchés mondiaux, l’absence de consensus aux dernières conférences ministérielles du Cycle de Doha et la création de nouveaux accords régionaux contribuent toutes au sentiment d’incertitude quant au futur de l’Organisation mondiale du commerce. Cette recherche aura pour objectif de cerner les impacts juridiques de la redéfinition des accords commerciaux, afin de répondre aux besoins de toute entreprise canadienne.

Exploration of machine/deep learning techniques using Raman spectroscopy for the identification and classification of brain tumors and prostate cancer

Cancer cells are nearly impossible to distinguish from normal tissue without sophisticated detection methods and conventional methods fail to provide real-time information during the surgical procedure. ODS Medical is developing an exclusive Raman spectroscopy system that collects data through an optical probe and is interfaced with sophisticated machine learning algorithm to identify tissues abnormalities in real-time. This technology has proven to be effective for brain tumor and prostate cancer detection. As a result much larger datasets have been acquired both for brain and prostate. Those datasets will be analyzed in order to improve the current model and develop new models based on convolutional neural networks. A global objective of this research project is to build robust mathematical models on which ODS Medical can rely on.

Understanding mental health experiences of adults 50 years and older living in the Similkameen: A qualitative study using photovoice

The purpose of this study is to understand and promote awareness of the mental health experiences of adults 50 years of age and older who live in a rural community. This study uses photovoice, a research and advocacy method that helps people to tell their stories with photos. Participants will determine what types of experiences (e.g., stigma, resilience) will be explored by taking photos. After attending a photovoice workshop, participants will take their photos. Individual interviews about the most meaningful photos (chosen by the participant) will be used to gain insight into the participants’ experiences. Transcripts of the interviews will be analyzed inductively using constant comparison, such that themes will be analyzed and coded as they emerge. The participants will determine collaboratively how the findings should be presented (i.e., knowledge translation activities). Possible activities include a community gallery night and a summary report for community members.

Volumetric Collision Course Modelling for Road Safety Analysis

Proactive road safety analysis allows for the pre-emptive diagnosis of road safety issues without direct observation of traffic accidents by observing traffic-conflict-like events, and this is made possible with large quantities of high-resolution road user trajectory data acquired from video data. However, several practical challenges exist in relation to the nature of this data for use in large-scale automated road safety analyses applications of this nature. Two small, but key, issues with deployment of such a system related to road user size are identified, studied, and addressed, namely: volumetric-based collision prediction modeling, and unsupervised camera calibration from estimated road user classification size and movement.

Finding Access to Social Services for Calgary’s Non-Status Migrants

This study 1) examines the lives of 3 to 5 non-status migrants in Calgary; 2) determines the challenges that these individuals face in accessing healthcare and social services; and 3) ascertains available services as well as the obstacles to services that are essential to these individuals’ social and economic integration. In general, my research answers the question, “From the viewpoint of non-status migrants, how do the federal, provincial, and city governments facilitate delivery of healthcare, community, and social services to non-status migrants?” Findings from this study will be used to formulate a policy-brief for policymakers and immigration agencies to guide them in making and carrying out decisions that respect and impact the principles of social justice and of the attendant basic human right to social services. In addition, a database of resources for non-status migrants that will be hosted on the website of Migrante Alberta will be created.

Coalescing around gender inequity frames on Twitter: The use of online campaign to #ElectMoreWomen in the Toronto 2018 Municipal elections

Using an advanced social media analysis tool developed by experts at Nexalogy, we will look at a Twitter campaign that focuses on gender-identity and inequality in an effort to challenge and change unequal representation of marginalized people (in this case, gender) in elected office and spaces of traditional political power. By looking at female-identifying challenger candidates who use this framing, and employ hashtags like #electmorewomen, we will compare and interpret what impact this has on a candidates level of engagement, perceived legitimacy, and also how the public accepts, reacts and challenges these framings of candidates.

Electrochemical Fischer-Tropsch Synthesis of Renewable Liquid Fuels from CO2

Despite a rapid decline of electricity costs, there is still demand for energy-dense liquid fuels, such as in heavy freight and air transportation. Liquid fuels can be synthesized from a mixture of carbon monoxide and hydrogen called synthesis gas (syngas). However, this process requires high temperatures and pressures, and is itself responsible for significant greenhouse gas emissions. We propose the use of electrocatalysis to produce these liquid fuels. To accomplish this, we will use computational modeling and machine learning methods to design electrocatalysts that efficiently convert CO2 or syngas into dense chemical fuels. These computational efforts will be validated through a parallel experimental approach that includes the fabrication of new catalyst formulations and the construction of prototype electrochemical flow cells.
This project will enable the synthesis of clean, energy-dense liquid fuels that can replace the use of fossil-derived fuels in industry and transportation sectors.

Accessible Data Platform for Dynamic experience study of Lifestyle Underwriting

We seek to replace or enhance the traditional underwriting approach (namely identification of insureds via a pre-defined fixed set of risk criteria) with one based on a set of dynamic protocols that are responsive to human behavioral factors for continual health improvement. We seek to provide a live and interactive in-market research dataset that can be used to explore the benefit of and improve data-driven approaches (namely artificial intelligence or AI) for immediate use in life & health insurance product development and actuarial risk assessment.

Development of improved power quality detection methods suitable for modern applications

Discontinuities of service, variations in voltage magnitude, and distortions in AC voltage waveforms constitute the different aspects poor power quality. A poor quality of power supply can cause malfunction of sensitive equipment and interrupt industrial processes, resulting in significant economic losses. Utilities and consumers are taking actions to maintain the power quality set by the standards. Monitoring of power quality at all levels in the power system is necessary to ensure adherence to standards, but specialized power quality monitoring equipment are expensive. Cost of monitoring can be reduced if monitoring functions are integrated to multifunction devices such as fault recorders or protection relays. However, most advanced power quality event detection methods require significant computing power and their implementation on multifunction devices is challenging. The proposed research aims to develop improved power quality detection methods suitable to implement on a resource constrained computing environment.

Patient Privacy Preservation through Federation or Encryption? A Comparative review and prototypes

The recent advances in machine learning based on deep neural networks, coupled with the availability of phenomenal storage capacity, are transforming the industrial landscape. However, these novel machine learning approaches are known to be data hungry, as they need to tune a huge number of parameters in order to perform well. As more and more AI based applications are being deployed to learn from personal data, privacy concerns are rising, and more specifically on sensible domains like medicine, finance or mobile related data. With the ubiquitous availability of cloud-based solutions at a very low price, privacy has now become even more sensitive. Moreover, privacy concerns seem to be two sided, as service providers would like to keep their models and learned weights private.
This research will focus on studying available solutions for privacy preservation in the context of medical data, and more specifically on volumes obtained from CT scans.

Étude de la dualité onde-particule sur une corde vibrante

Le sujet proposé porte sur la dualité onde-particule. On propose ici à l’étudiant l’étude expérimentale d’un système modèle qui vise à tenter une représentation mécanique du système quantique onde-particule. L’étudiant aura en charge un banc expérimental composé d’une masselotte pouvant coulisser sur une corde vibrante, le tout étant instrumenté notamment par un système d’imagerie rapide. L’étudiant sera en charge de faire varier les paramètres de l’expérience et de tenter de les interpréter en relation étroite avec la mécanique quantique et en particulier avec la théorie de De Broglie-Bohm.

Le stagiaire aura pour mission de partager son temps entre une analyse théorique du problème et d’une méthode expérimentale. Des simulations numériques (Python) du système ont déjà été développées dans l’équipe. Une prise en main et une analyse de ces simulations devrait également faire partie des activités quotidiennes du stagiaire.