Innovative Projects Realized

Évaluation du potentiel de croissance du mélèze hybride et laricin en région boréale

Le mélèze laricin (Larix laricina) serait une espèce intéressante pour la production de matière ligneuse (Bolghari and Bertrand, 1984). Dans les années 70, un programme d’amélioration génétique a commencé et l’hybride (L. ? marschlinsii Coaz.) entre le mélèze d’Europe (L. decidua) et le mélèze du Japon (L. kaempferi) s’est avéré très performant (Perron, 2011). Cependant, contrairement au mélèze indigène capable de pousser sur une large gamme de stations (Tilton, 1976), les espèces exotiques seraient à éviter sur les sites trop humides en raison des risques de gel tardif (Robbins, 1985). Leur utilisation en région boréale n’a jamais été évaluée et il convient d’étudier le potentiel des plantations de mélèze hybride à cette latitude et de les comparer aux espèces indigènes. Pour cela, nous allons mesurer, analyser et comparer la croissance du pin gris, de l’épinette noire, des mélèzes laricins et hybrides implantés en 2007 dans un dispositif de recherche localisé dans la région écologique 5a.

Estimating brain tissue deformation from a microscope video

Epilepsy, a neurological disorder impacting millions worldwide, often requires precise surgical interventions when medications fail. The surgical procedure demands high levels of skill to remove the lesion causing epilepsy while minimizing damage to surrounding healthy tissues. Current training methods for such intricate procedures are insufficient, relying heavily on subjective assessments and limited hands-on opportunities, which have further decreased due to restrictions like reduced duty hours and the COVID-19 pandemic.
This project builds on a previously validated simulation prototype that uses calf brains to mimic the mechanical and anatomical properties of human brain tissue. The platform employs a tracking system to collect quantitative data during procedures. However, it lacks the ability to measure one of the most critical performance metrics: the force exerted by surgical instruments on brain tissue. To address this, the project introduces a novel sensorless estimation method, leveraging high-resolution optical cameras and biomechanical modeling to infer contact forces from tissue deformation observed in real-time.
This project will provide a cost-effective, realistic training solution to address gaps in neurosurgical education, enhancing skill acquisition, reducing errors, improving patient safety, and advancing competency-based surgical education.

Développer un modèle bioéconomique pour la durabilité de la filière bovine au Québec

Développer un modèle bioéconomique pour la durabilité de la filière bovine au Québec

EcoFusion : biocomposites innovants à base de biochar et PLA

Le projet de recherche proposé a pour but de développer de biocomposites durables en combinant l’acide polylactique (PLA) et le biochar, un matériau issu de la pyrolyse de résidus forestiers. Face aux enjeux environnementaux liés à la pollution plastique, ce travail cherche à apporter des solutions, à la fois innovantes et durables, pour la conception de matériaux biodégradables et économiquement viables. Par ailleurs, le PLA, un plastique biosourcé, présente des avantages environnementaux notables tels qu’une faible consommation énergétique pour sa production et une meilleure biodégradabilité par rapport aux plastiques pétrochimiques. D’autre part, l’ajout de biochar vise à améliorer ses propriétés tout en garantissant la performance environnementale du composite. De plus, le biochar, grâce à sa structure poreuse et sa composition élémentaire, présente un potentiel rôle fertilisant en permettant notamment le maintien des nutriments dans les sols.

Elustros Seating App Development

Ellustros has developed an App and SMART Chair to provide an automated ergonomic office solution using AI and IoT technology. The app uses real-time data to assess optimal posture and automatically adjust the SMART Chair to ensure comfort, efficiency, and reduce the risk of injury. At this stage of the product development, the business model requires an independent assessment of the tros.ai App. The App uses standard methods and AI to determine seating, desk and monitor dimensions that it collects from photos taken from a mobile phone camera. The use of AI in ergonomics is new to the field and allows other body features to be included in the process. As a precursor to integrating with the SMART chair, the purpose of the application is to test the App outputs against “standard” ergonomic rules for seating and office set up and personal preferences (which may not be appropriate). We plan to capture anthropometrics (body shape & size) from 100 diverse male and female individuals from the McMaster University community. We will test the software against a set up purely based on individual anthropometrics and comfort.

Forecasting for Default Recovery

The proposed project aims to harness the power of Artificial Intelligence (AI) and machine learning to develop a predictive model that assists in predicting delinquency in financial contexts. By integrating AI and predictive modelling approaches, the project seeks to enhance the accuracy, efficiency, and interpretability of delinquency predictions within the residential mortgages space. The focus will be on creating robust predictive models using advanced ML and AI techniques, including gradient boosting machines like XGBoost, survival analysis models for time-to-event predictions, and stochastic processes to model transitions between delinquency categories.

Unpublished Works for Cello Solo of the Mexican Avant-garde

My research project into Mexican Avant-garde music for solo cello (written between 1962 and 2012) attempts to define and evaluate what Mexican Cello Solo Music is and how it has evolved over the years to the point of exerting a significant influence on the universal cello solo repertoire. (This discipline belongs to a category of understudied fields and so far has no available literature on the subject) I have been privileged to discover these new works (approximately 20 pieces in all) many of which have yet to be published, or for that matter, adequately exposed to the public. My project provides me with the opportunity to describe and analyze these new works while simultaneously making important comparisons to the works that comprise the standard opus of great composers from western cultures, creating the first formal introduction to Mexican Cello Solo Music. My goal is to keep the performance of this repertoire alive in the concert hall to wider and varied audiences.

Developing Low-Power Spiking Neural Networks for Image Enhancement on Embedded Neuromorphic Boards: A Multimodal Sensor Fusion Approach

Image enhancement consists of creating a composite image (fused image) from different sources. The objective of this project consists of acquiring images from Electro-Optical sensors and Infrared (IR) sensors in low light conditions, to enhance them, to combine (fuse) them in order to create an image of better quality. To do so, the project will focus on establishing the fundamentals steps to develop Spiking Neural Networks (resources, tests and activities to develop a complete prototype), with very low consumption for embedded neuromorphic boards for an image-enhancement use case.

Statistical Shape Modelling in Ankle Osteoarthritis

Osteoarthritis (OA) is a degenerative joint disease causing pain and disability affecting weight-bearing joints. Unlike knee and hip OA, ankle OA is linked to trauma and remains under-researched despite its impact on mobility and quality of life. Joint kinematics play a key role in OA progression, as altered movement patterns accelerate cartilage degeneration by changing localized stress. Computational techniques like Discrete Element Analysis (DEA) estimate cartilage stress using accurate joint morphology, but cartilage geometry acquisition is limited by MRI’s cost and accessibility.

This project addresses this challenge using Statistical Shape Modeling (SSM) to predict cartilage distribution from bone shape. SSM identifies 3D geometric patterns across populations, linking tibia and talus bone shapes to cartilage geometry via statistical regression models. High-resolution CT scans and MRI datasets will train the SSM to predict cartilage thickness and distribution, bypassing the need for direct soft tissue imaging.

The predicted cartilage geometry will integrate into DEA models, enabling simulations of joint mechanics. By combining SSM and DEA, this research offers new insights into ankle OA progression, improving diagnosis, personalized treatments, and outcomes. This multimodal approach bridges a critical gap in understanding the interplay between bone shape and cartilage health in OA.

Direct Astrocyte-to-Neuron reprogramming using Self-amplifying RNA and RNA-based circuits

With an aging population, age-related neurological disorders like Alzheimer’s and Parkinson’s diseases, as well as stroke, are increasing. These conditions involve critical neuron loss, yet the brain’s ability to replace neurons is highly limited. Current regenerative therapies, such as neural stem cell stimulation or neuron transplants, show promise but face challenges in manufacturing and patient immunosuppression, emphasizing the need for innovative approaches.

One promising strategy involves reprogramming astrocytes—helper brain cells that proliferate during disease—into induced neurons (iNeurons). Astrocytes can be transformed into neurons by overexpressing transcription factor (TFs) that control cell DNA. However, while effective in preclinical models, AAVs are limited by their genetic payload capacity and risk of cell stress from prolonged TF expression.

Our research employs self-amplifying RNA (saRNA), a next-generation mRNA platform capable of carrying larger genetic payloads. saRNA replicates itself, enabling prolonged protein expression at minimal doses. By engineering saRNA to encode iNeuron-promoting TFs, we aim to efficiently reprogram astrocytes into functional neurons, while reducing cell stress.

Neurodegenerative diseases cause devastating physical and cognitive decline. Our saRNA platform offers a novel solution to replenish lost neurons, restore neural connections, and slow or reverse disease progression, potentially transforming treatment outcomes and improving patients’ quality of life.

Open Ice Project

The Open Ice Project, initiated by the Future of Hockey Lab, addresses the growing issue of limited access to ice time across Canada. Rising costs and outdated management practices are shutting Canadians out of hockey, figure skating, ringette, and other ice sports. Without a system to allocate ice time equitably, many communities, especially underrepresented and marginalized groups, including women and girls, struggle to access publicly funded ice rinks.

Open Ice is a data-driven solution designed to optimize ice management and ensure equitable access for all Canadians. By developing a standard technology platform, the Open Ice Project team aims to collect and analyze real-time ice usage data across 2,900+ rinks in the country. This information will help rink managers and municipalities streamline scheduling, improve operations, and ultimately make better use of their facilities while meeting the growing demand for ice time.

As the face of hockey changes, with increased diversity and the rise of the PWHL, the Open Ice Project seeks to create systemic change and drive social transformation by removing barriers to ice access. The team is committed to increasing participation in ice sports for women and girls, breaking down traditional gender norms, and fostering healthier, more engaged communities. This project not only benefits ice users and rink managers but also contributes to the sustainability and growth of ice sports in Canada, ensuring that all athletes, regardless of gender, have the same opportunities to thrive and access the sport they love.

Investigating the DNA origami folding process

The proposed project focuses on using Time-Resolved X-ray Solution Scattering (TR-XSS), an advanced technique for studying fast molecular changes in DNA nanostructures. By capturing real-time structural changes when these nanostructures are exposed to environmental stimuli like laser pulses. The project aims to improve our understanding of their stability and dynamic behavior. This project will provide hands-on training in designing and conducting TR-XSS experiments at the European Synchrotron Radiation Facility (ESRF), offering students and researchers a valuable experience in using cutting-edge X-ray techniques. The partnership will encourage international collaboration, allowing experts to share knowledge, develop new research ideas, and work together on innovative scientific projects.