Projets novateurs réalisés

Neutron Detector Development

CoVE SEED (Centres of Vocational Excellence for Sustainable Energy Education)

L2M – Approaches to Novel Anticancer Therapeutics via MRCK Inhibition

MRCK is an enzyme that has shown promise as a target for the development of new drugs to treat several aggressive cancers, including glioblastoma (GBM) and high grade serous ovarian cancer (HGSOC). BDP9066 is a highly potent and selective inhibitor of MRCK reported first in 2018. Despite excellent activity profile, BDP9066 is not a realistic clinical candidate due to it’s rapid metabolism and complicated synthesis, which would limit scale-up capacity from a practical standpoint. In this project, we aim to discover molecules similar to BDP9066 that do not possess the aforementioned detrimental attributes; this will be done through knowledge-based designs of new small molecules, their synthesis, and biochemical/biological evaluation for MRCK inhibition.

L2M – Probes to Enable Imaging Small Molecules in Cells

Cellular imaging enables the study of diseases in myriad ways, consequently impacting human health. Through visualizing individual cells, clinicians are able to diagnose diseases and scientists are able to develop drugs to treat them. There are many powerful tools to visualize cells, including fluorescence and electron microscopy. However current common imaging techniques fall short for tracking specific small molecules in cells. In this project, we will be developing synthetic methods to make probes for enabling the selective imaging of small molecules in cells. We will work with the partner organization to build commercial value in both the processes utilized and the products derived from this work.

L2M – SenseOn-Pro

SenseOn-Pro is a small wearable fingertip device that uses photoplethysmogram (PPG) sensor data combined with advanced algorithms to measure many different health markers and events simultaneously. It enables non-invasive, continuous monitoring of multiple chronic health conditions. The device allows the assessment of blood glucose without the need for blood pricking, which is an important component of diabetes management. It is able to monitor cardiovascular disease (CVD) without the use of electrodes, as well as detecting Obstructive Sleep Apnea (OSA) events that would otherwise go undetected. The ability for the device to perform these functions in combination with the general health markers it can identify, such as blood oxygen, and blood pressure grants users a crucial holistic insight into their well-being.
The need for this device in the current landscape stems from the severity of these chronic conditions in Canada. CVD is among the top causes of death in Canada with over 2.5 million Canadians suffering from a diagnosed heart disease. It claims about 14 lives every single hour. Diabetes affects nearly 1 in 10 Canadians over the age of 20 and yet only half of Canadians diagnosed with diabetes meet optimal blood glucose targets, which can result in various serious complications, such as blindness, kidney failure, stroke or even amputation. OSA is very prevalent condition with estimated 28% of Canadians suffering from moderate to severe OSA, and approximately 93% of people at high risk of OSA were undiagnosed. Having effective means of monitoring and diagnosing these conditions is critical in mitigating their harms.

Eye tracking in VR environments – analysis of gaze data

Synthesis of Hemilabile Cyclopentadiene Phosphine Ligands and their Uranium Complexes

Investigation of Data-Driven Koopman Model Predictive Control for Hydrogen-Diesel Engine Applications

This project explores the application of the Koopman Operator to system dynamics for the application within model predictive control of hydrogen-enhanced diesel engines. Investigating different implementation options and leveraging data-driven machine learning approaches, the aim is to reduce computational effort while enabling system-theoretic analysis of the dynamic system representation. The topic aligns closely with the interests of Prof. Jakob Andert (RWTH) and Prof. David Gordon (University of Alberta), whose expertise in machine learning-based control of energy conversion systems complements this work. Previous collaborations between these institutions have yielded valuable shared data and lab advancements. This project will further integrate RWTH’s machine learning modeling expertise with the University of Alberta’s embedded hardware knowledge, strengthening ongoing research efforts at both universities.

Research on neophytic plants in forest areas in Southern Germany

Investigations on sample introduction systems for spray drying

Optimizing Lightstage Capture for High Fidelity 3D Facial Reconstruction

Ubisoft is one of the world’s largest video game studios, specializing in 3D open-world games that require precise 3D character representations. In particular, achieving high-quality facial features is crucial, as humans are highly sensitive to small details in facial expressions. Currently, creating 3D
facial representations first requires a Lightstage capture pipeline. This process begins with taking highly detailed photographs of actors’ faces, which are then processed through a Multi-View Stereo (MVS) [1] algorithm to generate an extremely dense and irregular 3D mesh. The mesh is then registered and simplified into a regular mesh ready for use in game development. However, this pipeline is costly, requires significant manual labor, extensive memory storage, high computational power, and relies on third-party software. For instance, a 3D sequence of 20 minutes currently requires 150TB of storage, and 8 weeks of processing time with eleven working PCs. This project aims to optimize the MVS pipeline, helping Ubisoft save precious time as well as financial,
material (working PCs, GPUs, storage) and human resources (artists and performers). Specifically, the project will focus on creating improved metrics to measure MVS reconstruction quality of facial features and leverage those metrics to innovate in the field of 3D facial reconstruction.

Recherche de traceurs chimiques dans l’huile isolante permettant de déterminer indirectement la vie résiduelle des transformateurs de puissance

Un problème réel dans l’industrie des transformateurs de puissance est le manque d’outils et de méthodes précises pour déterminer l’état et la durée de vie de l’isolation principale. Ces appareils onéreux, composés d’enroulement de cuivre isolés par du papier et le tout baignant dans une huile isolante, représentent le coeur de tous réseaux électriques. Le but de ces stages est de déterminer des traceurs chimiques, capables de prédire le niveau de dégradation/vieillissement de l’isolation solide des transformateurs de puissance. Les opérateurs pourraient ainsi prendre des décisions appropriées afin d’éviter des pannes majeures. En effet, si l’on eut prévoir les pannes, il devient possible de les éviter! L’évaluation précise de l’état d’un transformateur permet de prévoir son remplacement au moment optimal. Un tel projet présente des intérêts évidents, pour l’avancement de la science et pour les exploitants de réseaux électriques qui disposeraient ainsi d’outils appropriés pour la planification de leurs investissements, dans le cadre du renouvellement des équipements existants.