Innovative Projects Realized

Explore Remote Virtual Technologies Using Real-time Very Long Baseline Interferometry (VLBI)

This program will cement Canada’s leadership in global Very Long Baseline Interferometry (VLBI), a key technology in radio astronomy and geodesy. In partnership with Thoth Technology Inc., our team of leading radio astronomers will (i) develop new capabilities to compress, transport, and process large amounts of data between geographically distinct locations to enable real-time VLBI, and (ii) use this capability to make precise astrometric measurements of Fast Radio Bursts (FRBs) and pulsars, and through their scintillation properties, study their local environments.

The program will have several far-reaching benefits. Scientifically, advances to VLBI will improve the precision of pulsar localization by orders of magnitude and allow for a systematic spatial localization of FRBs. The project will also position Canada’s Algonquin Radio Observatory as the nexus of a new, low-frequency, global VLBI network and Thoth Technology as one of the world’s premier VLBI providers to large-scale, international telescopes. More far-reaching still, next generation VLBI will underpin the high-precision geodetic reference frame upon which autonomous vehicles will rely. As such, advances to VLBI could profoundly impact nearly every aspect of our economy, from engineering and construction to transportation and precision agriculture.

Placentia Bay Atlantic Salmon Aquaculture Project

The Fisheries and Marine Institute of Memorial University is partnering with Grieg Seafood Newfoundland to provide 9 internships to students from the Advanced Diploma in Sustainable Aquaculture graduating class of 2021. The proposed project seeks to provide HQPs to assist Grieg in optimizing the novel equipment and systems that will be installed in their land-based hatchery as well as their marine sea cage sites. Through this internship, students will be given the opportunity to gain hands on experience through exposure to new technologies, operational challenges, and research to benefit them in their future careers.

Good Decisions, Diverse Voices: Developing Tools for Equitable Decision Making

Despite the importance of diverse voices in community decision-making, we still do not fully understand how to support sound decision making in a way that is equitable and works to advance agendas of historically marginalized groups. We will draw on notions of equitable representation and urban planning to define equitable decision making, identify value elicitation methods that increase equity in decision making, and explore ways technology can assist this process.
EcoPlan International (EPI) with the University of British Columbia (UBC) and Simon Fraser University (SFU) propose to undertake research to help us better understand these problems and create solutions that both support EPI in delivering high-quality results to its clients, and inform planning and decision making across Canada.
The proposed research examines techniques to support equitable decision-making and scenario planning, and the implementation of these tools in the real world. This approach ensures the applicability of the research both to EPI’s practical challenges and to the academic literature.

IoT-based Rental Item Location Tracking System

The purpose of this project is to add a feature to Rentrax so that business owners can track the physical location of their rental items. Device should be small enough to be hidden in rental items such as bikes. Battery usage of device should be as low as possible so that it doesn’t need frequent re-charge Location of device can be sent in real time or by receiving a signal or SMS and sending the location The fix and operating cost of device should be reasonable (fix cost below $30 and operating cost below $5 a month)

DARSA (Deep-learning Assisted Radiological Software Application):Innovative Machine Learning approaches for Detecting Pathology inImages

Many aspects of healthcare are time consuming and error prone. Recently there has been great progress in using artificial intelligence to solve a number of problems. One of the best examples of this is image labelling using a type of neural network approach called deep learning. Recent research has shown that deep learning approaches can outperform expert human radiologists when diagnosing disease in chest x-rays, in some situations. In this project we use a large set of chest x-rays as a test bed and develop a new method for software based radiological diagnosis using deep learning models. We will compare the predictive quality of our models with other machine learning approaches. The outcome of this research should be a new software-based radiological diagnosis system that is trained on chest x-rays but can also be extended to other body parts.

Encoding Bi-specific T cell Engagers into oncolytic vaccinia virus

Through engineering and selection strategies, we have created novel cancer-killing oncolytic viruses (OVs) that are selective for tumours but are unable to grow in normal tissues. A critical component of the therapeutic activity of this class of therapeutics is the induction of an anti-tumour immune response, which can be suppressed in many tumours. One strategy to circumvent this problem is the use of Bi-specific T cell Engager (BiTE) antibodies that are able to force T cell recognition and killing of tumour cells. For many BiTEs in clinical development, there are toxicities associated with systemic administration and challenges to reaching high enough local concentrations in solid cancers. For Turnstone Biologics, we will engineer OVs to produce BiTEs in tumours, where these antibodies are only needed in picomolar concentrations to be effective. We predict that expression of BiTEs from our potent oncolytic viruses can lead to improved outcomes with reduced off-target toxicities.

Engineering Oncolytic Vaccinia Virus to Remodel the Tumor Microenvironment and Improve Efficacy of Combinational Cancer Therapies

Oncolytic viruses (OVs) are promising biotherapeutics that selectively infect and kill cancer cells. The efficacy of oncolytic viral therapy depends on the virus’s efficient replication and dissemination within tumors. However, tumors often develop a dense extracellular matrix (ECM) surrounding their cells that provides resistance against many forms of cancer therapies including OVs. To overcome this barrier, we propose encoding OVs with enzymes that degrade the ECM, so that upon infection of cancer cells, the surrounding inhibitory barrier of ECM is broken down and viral spread is improved In addition, the local degradation of the tumor ECM will simultaneously benefit conventional chemo- and immuno-therpies targeting the tumor. The main objective of this proposal will be to identify suitable ECM-degrading enzymes that synergize with OV treatment and to engineer them to be expressed from the virus. These viruses will then serve as a platform to combine with other therapeutic payloads, which will directly benefit their delivery and dissemination throughout the tumor.

Engineering virus-based cancer therapeutics to drive a systemic anti-cancer immune response

One rapidly evolving area of cancer therapeutics is immunotherapy, which aims to drive a cancer-targeted immune response in tumors. Replicating virus-based therapeutics are a novel cutting-edge immunotherapy that have shown promise clinically. Turnstone Biologics has developed a novel cancer-killing virus with the capacity to deliver multiple payloads to further enhance the virus’ anti-cancer effects in the tumour microenvironment. Selection of the correct payloads will play a key role in determining how effective the virus is in treating a broad spectrum of patients. My project aims to engineer a novel virus delivering a combination of immune stimulating payloads to trigger a more potent anti-cancer immune response. The generation of the candidate virus will be generated using cutting-edge genetic engineering technology. The synergy of these newly developed oncolytic viruses with other existing clinically relevant immunotherapies will also be examined. The proposed work should facilitate the development of novel clinical virus candidates for Turnstone Biologics.

Shaping the immunogenicity and efficient range of oncolytic vaccinia virus by the programmed release of therapeutically active, self-amplifying RNA containing virus-like-particles

In this project, an oncolytic virus will be generated that combines the safety profile of vaccinia virus, with the immunostimulatory power of RNA-vaccines. Currently, the RNA-genomes of Alphaviruses are successfully used to encode proteins to be vaccinated against. Their special genome organization and makeup allow them to self-amplify within a cell. This replication in turn alerts the immune system to the host cell while creating an abundance of not only the antigen to be vaccinated against, but also therapeutically active proteins, e.g. immune checkpoint inhibitors, that can be released to neighboring cells. The RNA-genome of Semliki-Forest Virus was encoded into the DNA-genome of vaccinia virus in a way that it will be directly packaged into so called virus-like particles and shuttled to cells in the surrounding. As vaccinia virus has multiple ways to hide itself from the immune system, an infected cancer cell will therefore release numerous particles that are able to transduce non-infected cancer cells and activate the immune system. Eventually, the initial infection will be cleared together with the remaining tumor cells. The benefit for Turnstone Biologics is therefore the generation of a novel, improved oncolytic virus which will be based on Turnstone’s proprietary viral platform.

Investigating the Role of Novel Fatty Acid-Binding Protein (FABP) Inhibitors as Treatments for Anxiety Disorders

Anxiety disorders and chronic stress represent major healthcare and economic burdens worldwide. Approximately 75% of Canadians who use health services for a mental illness present with anxiety disorders which may affect up to 10% of the population in terms of lifetime occurrence. Despite the large prevalence of anxiety-related disorders in Canada, there are currently a limited range of effective pharmacotherapeutic interventions. In addition, all currently effective anti-anxiety medications are linked to serious side-effects, including drug dependence and withdrawal, cognitive impairments and metabolic symptoms. Our project is characterizing novel pharmacological compounds that inhibit a protein called FAB-P. This can modulate the brain’s own naturally occurring cannabinoid system. This compound displays strong potential as an effective anti-anxiety medication with fewer side-effects than traditional anxiolytic drugs. Remarkably, these compounds can bypass cannabinoid receptors in the brain and produce anti-anxiety effects without the unwanted side-effects associated with other cannabis drugs and formulations.

Blocking assembly of glycosylphosphatidylinositol (GPI) anchors to inhibit growth and increase immunogenicity of human fungal pathogens

Fungal pathogens cause life-threatening invasive infections in humans. Despite all available treatments, mortality rates remain unacceptably high, on par with deaths caused by infectious diseases such as tuberculosis and malaria. Alarmingly, the emergence of drug-resistant fungi is reducing already limited treatment options. To address needs for new antifungal medications, Amplyx Pharmaceuticals has developed fosmanogepix, a drug which attacks fungi by blocking their ability to build their cell wall, a structure needed to survive and invade humans. To better understand how this drug works and the best way to use it, the objectives of my work are to (1) determine precisely how this drug alters the fungal cell wall, and (2) determine the structure of the drug’s direct target, the fungal protein Gwt1. Understanding how fosmanogepix binds to Gwt1 at the molecular level will help with development of derivatives that will be more effective and cause fewer side-effects in patients.

Artificial Intelligence and Machine Learning in the Interpretation of Wide-Complex Tachyarrhythmia

Wide-Complex Tachyarrhythmia (WCT) is an abnormality in which the heart rate is elevated and QRS complex duration is increased. An electrocardiogram (ECG) is a simple and quick test used to review heart functioning, so ECG images can be used to determine whether a patient is having an abnormal heart rhythm such as WCT. A WCT diagnosis based on the ECG can be difficult as it can take a lot of time and considerable expertise to make an accurate interpretation. Our study aims to use deep machine learning to develop a model or artificial intelligence (AI) system, trained on data from a patient population diagnosed with WCT. A successful AI system can quickly analyze and interpret ECG images with to help guide a quick and accurate WCT diagnosis. At the University of Ottawa Heart Institute, an accurate AI system can be beneficial for ECG interpretations, specifically with regards to WCT diagnosis.