Projets novateurs réalisés

Novel mechanisms of bats in regulating inflammatory cell death to tolerate pathogenic viruses.

Bats are ecologically important mammals, but recent studies have also identified them as reservoirs of emerging viruses. These viruses include SARS-CoV, SARS-CoV-2, Middle East respiratory syndrome coronavirus (MERS-CoV), and porcine epidemic diarrhea virus (PEDV). Intriguingly, bats that are naturally or experimentally infected with these viruses do not develop severe disease. Thus, bats provide us with a unique mammalian model to investigate virus-host interactions.

Virus infection is associated with inflammatory cell death. Necroptosis and pyroptosis are two such cell death pathways that are activated by virus infection, lead to destruction of virus replication niche, and initiate inflammatory immune cell recruitment, and virus clearance. However, dysregulated cell death is associated with severe inflammation and tissue damage, such as during severe COVID-19. Whether these cell death programs are active in pathogenic virus reservoir hosts, such as bats, and how bat cells mitigate respiratory epithelial damage and inflammation remain unclear. Recent studies within the Sannula laboratory (IISc) have identified intriguing variations in bat inflammasome and pyroptosis activation machinery…..

Role of profilins in MET receptor signalling in colorectal cancer

Tumorigenesis and metastasis require constant stimulation of cells by growth factors. In colorectal cancer (CRC), the second most common cancer in Canada, such growth signal is primarily provided by a factor called hepatocyte growth factor (HGF) that acts on its receptor MET. Alongside growth stimulation, escape from cell death is crucial to cancer cell survival and disease progression. Recently, we have shown that some pro-cell death enzymes remaining active in cancer resistance cells stimulate MET survival activity by cleaving essential proteins (named profilins) involved in MET activity, thus, challenging a vital dogma in the field. Our central objective is to define how these enzymes enhance MET signalling in CRC cells. This project aims to 1) define the role of two profilin proteins in MET activity CRC cells, 2) develop a model cell devoid of these two profilins, and 3) explore the role of these profilins in the activity by other growth factors. Our findings are likely applicable to other types of cancer.

Design and validation of pathology-specific heart valve models

Heart valve disease affects a large number of Canadians, especially the elderly. Surgery for repairing heart valves is quite complex, and requires a great deal of expertise. This project will focus on creating a library of silicone valve models representing different heart valve diseases. With these silicone models, new cardiac surgeons will be able to better train and practice their surgical skills before operating on patients.
The intern involved in this project will help create and test a variety of silicone valve models, under supervision od an experienced cardiac surgeon.
The full heart valve library will be commercialized by a Canadian company that specializes in manufacturing medical and surgical training systems.

Optimizing the delivery of ministring DNA (msDNA) to the central nervous system for use in gene therapy applications

Gene therapy has risen as one of the more promising treatment options for neurological disorders. Using modified viruses to deliver genetic material has shown success however has several disadvantages including increased safety risks. Using a non-viral vector system has the potential to overcome many of the challenges that arise with using viral vectors. Mediphage Bioceuticals proprietary DNA construct, ministring DNA (msDNA) is a non-viral system that has the potential to be a safer and equally effective method of DNA delivery. This project will focus on optimizing the delivery of msDNA vectors to the brains of mouse models and will provide the necessary foundation to move forward with developing therapeutics for specific neurological disorders using msDNA technology.

Improve the reliability and performance of Vision based Machine Learning models to provide more valuable insights to researchers.

Biomedical research, particularly preclinical research, is a complex and challenging field with a high failure rate of 98% in pharmaceutical research investment. Extracting relevant information from preclinical research papers involves synthesizing information from various sources, which is a demanding task that requires domain-specific knowledge. Natural language processing, specifically Large Language Models (LLMs), has demonstrated tremendous potential in extracting information from unstructured text. This project aims to train and deploy LLMs to accurately extract biological entities and other relevant information from preclinical research data. The extracted entities will be used to create ontologies and a knowledge base to facilitate the discovery of correlations and evidence that could hasten drug discovery. By providing a comprehensive and structured representation of the extracted data, this approach could help researchers develop new insights, and potentially accelerate the development of new treatments for various diseases.

Trait diversity across forest biomes

Trait-based mass-ratio and complementarity effects shape community structure, population dynamics, and ecosystem functioning. A question remains on how these processes interact in maintaining forest biomass along the elevation. Leaf element contents will be used to test these interactions since they are surrogates of ecosystem functioning. An extensive survey was conducted during 2017-2018 along a wide elevation gradient from the tropical forest (80 m a.s.l.) to the alpine treeline (4200 m a.s.l.) in Kangchenjunga, eastern Nepal Himalayas. We will assess the trait diversity of ten elements essential for plant development. Community-weighted trait averages (i.e., the mass-ratio effect) and trait divergence (i.e., the complementarity effect) in leaf elements will be calculated in 1859 trees belonging to 116 species. Our findings will suggest that biomass accumulation can be affected disproportionately by the elevation because of the interactions between mass-ratio and complementarity effects observed across the vegetation zones. The effect of the elevation in leaf elemental traits is a crucial component affecting the interactions among trees, which affects the ecosystem properties such as biomass and their responses to environmental changes.

Mathematical Models in Algorithmic Trading

On June 1, 2021, Futures First Canada and FinML began a pilot collaborative project involving three Canadian universities by-way-of a MITACS Accelerate internship (IT25712) to jumpstart an initiative to use cutting edge techniques in machine learning, financial mathematics and AI for making predictions in financial markets. This goal is integral to the business operations of the company and is a leading motivation for academic research. The pilot project has created a basis for this current project application which will continue the effort, extend positive academic results and furthering integration into the company’s infrastructure. This next project will extend previous research by building mathematical models using stochastic optimal control theory for the purposes of algorithmic and HFT (High-Frequency Trading). This is a challenging project where we uncover the obstacles for the academic theory to be applied in practice.

Techno-economic Viability of Wooden Racking for Standard Solar Photovoltaic Modules

The project aims to develop a low-cost solution for sustainable wood-based racking system for standard solar PV panels. The design will used local lumber and other structural components to develop a racking design focusing on self-assembly, convenience, sustainability, and economics. A techno-commercial viability analysis of the project will also be performed for Okanagan, BC. Kruger Dresner Solar will partner with Western University to develop a prototype of the racking system. The unique and sustainable solar energy solution targeting residential and commercial entities will be a proprietary of the company which will sell solar racks in the form of kits as well as provide services for its installation.

Predicting Behavioral and Mental Traits using Graph Convolutional Networks

Mental illness is widespread, affecting half of the population during their lifetime. One solution to mitigate this is to detect the onset of mental illness early, as well as choose individualized treatment options based on biological markers. These biomarkers can predict behavioral and mental traits, like the risk to develop a mental illness. They can potentially be found using neuroimaging techniques like magnetic resonance imaging. As the brain is highly complex, we need to use state-of-the-art data analysis techniques to find markers that predict behavioral and mental traits. Graph convolutional network is one such prediction technique that makes use of the fact that the brain is organized as a network. In our project, we will explore if graph convolutional networks can reliably predict behavioral and mental traits, which will set the groundwork to develop neuroimaging biomarkers of mental illness.

Experimental Characterization of Thin-Film Piezoelectric Layered Polymers under Mechanical Deformation

Thin-film polymer substrate that have piezoelectric layers are promising for a variety of applications, including micro-electro-mechanical systems, mechanical energy harvesting, and flexible electronics with security features. This study proposes a detailed approach for understanding how these flexible structures behave under different mechanical loads, using a comprehensive experimentation. This research mainly focuses on characterizing the electromechanical response of thin-film flexible piezoelectric-layered polymeric structures through practical experiments. The findings of this research will be helpful in designing and optimizing various micro-electro-mechanical systems, soft sensing devices, structural condition monitoring setups, and flexible security systems.

Testing novel compounds to prevent peritoneal adhesions

Peritoneal adhesions are the leading cause of the postoperative bowel occlusions and are often associated with increased morbidity and mortality rates. There is no successful treatment developed yet and adhesion re- operations are only a temporary solution. Thus, there is a strong unmet clinical need to develop novel therapies to help patients. We developed a method to visualize adhesion formation in a mouse model and this method also allows us to monitor how administering a blocking molecule prevents this process. We recently discovered that an immune cell called the macrophage causes the adhesions to form, and we were able to prevent this by blocking specific receptors on these macrophages called scavenger receptors with a molecule polyinosinic acid (PolyI). While we have a core molecule that blocks the adhesions, the Kubes lab in a collaboration with a biotech start-up Medhesion Inc. aims to make better versions of this molecule so that it has less side effects and blocks adhesion formation more efficiently. Our goal is to develop a drug that could be given at the end of a surgical operation to all patients to ensure none develop adhesions.

Advanced Subsea Transmitter Design

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