Innovative Projects Realized

A decision support tool for promoting new business models of cultural organisations in the context of COVID-19 crisis

The COVID-19 crisis has stimulated the need for digital transformation among organizations in order to adapt their business models and practices to survive from the pandemic. Data-driven solutions would emerge as the key of success by supporting cultural organizations to launch new products / services, developing marketing strategies, and improving customer experience.

Our research project aims at proposing an approach for the design and implementation of a decision support tool for cultural organizations with the focus on dashboards supporting data-driven visualization and decisions. The proposed decision support tool is strongly believed to catch up with trends in customers’ preferences and behaviors. As the dashboards and interactive reports are integrated and analyzed from the diverse sources, cultural organizations can rely on the proposed decision support tool to develop new product or service offerings, improve marketing strategies, and gain competitive advantages.

Mechanism of CoVID-19 induced hyperinflammation

This Mitacs-NSERC COVID-19 joint initiative is to investigate the mechanism of COVID-19 inducing hyperinflammation and cytokine storm. COVID-19 infected cells cause injury that triggers immune cells to release inflammatory cytokines. The partnership with Encyt Technologies Inc. and PI will use established immune macrophage cell lines to identify the molecular mechanism of hyperinflammation induced by COVID-19 ACE2/Ang-(1-7)/Mas GPCR platform in triggering the processes associated with this cytokine storm. We have also identified that the prodrug, oseltamivir phosphate (OP), is active against mammalian neuraminidase-1 (Neu-1), which we think may have relevance as a potential anti-COVID-19 drug. Neu-1 has been reported by us to control the receptors on immune cells involved in this cytokine production. The potential outcomes will provide valuable knowledge and scientific evidence to treat patients infected with the COVID-19 virus in exhibiting signs and symptoms of impending respiratory failure.

Assessing the environmental impact of a novel solid-wall containment salmon aquaculture project

The intern will conduct a bio-physical audit of Agrimarines new salmon farming technology in China
and British Columbia. By collecting data about the farming operations, he will be able to account for
all the materials and energy used to build and run the farms. This includes things like building
materials, feed and amount of energy needed to run the farm day-to-day. This inventory will then be
used to build a model, called life cycle assessment, which can assess the average environmental
impacts associated with producing one tonne of salmon. The model will also be able to identify those
parts of the farming operation which contribute the most to environmental impact so that the
owner/operators can take steps to improve those parts of the system. By reducing these impacts, the
system can be made more sustainable. In addition parts of the system that are running inefficiently can…

Artificial Intelligence-based COVID-19 Radiology Image Analytics and Beyond

The excessive daily requirement for COVID-19 tests has put the healthcare providers in an overwhelming circumstance, especially in rural communities, with a limited number of resources. Moreover, the existing COVID-19 screening technique is time-consuming and expensive, which can be a luxury for many communities. Thus, in this research project, in collaboration with the TBRHRI, we press the necessity of an automated AI-aided solution for efficient and faster COVID-19 diagnostic. The main challenges during this pandemic time undertaken by this project are 1) facilitate the COVID-19 screening process by employing AI-based automated techniques, and 2) deploy the AI module in a web or mobile application by ensuring data privacy. This research project can benefit the TBRHRI to deal with uncertainties and excessive time-delay in terms of the COVID-19 screening process by utilizing artificial intelligence with automatic radiology image analysis to detect COVID-19.

Autonomous Stair-Climbing Domestic Service Robot

The main goal of this project is to develop a more advanced version of the Robotic Stairclimbing Assistant (ROSA), a stair-climbing domestic service robot developed by Quantum Robotic Systems. ROSA can carry heavier household items (e.g., laundry baskets, bins, etc.) between rooms and up stairs. ROSA is meant to help seniors, people with compromised mobility, and isolated individuals cut off from caregivers during the COVID-19 crisis. The research conducted in this project will enable ROSA to use sensors and control software to navigate automatically along paths in your home that may include staircases. For example, starting point “A” could be a main-floor laundry room while ending point “B” could be an upstairs bedroom. The result will be a highly capable product that is unlike any other household robot currently available.

Maximizing Intrinsic Learning in an App-Based Approach to Language Learning

This research is focused on optimizing the language learning that occurs when students are allowed to practice English by conversing with native English speakers via a smartphone app, specifically Goji. Learning is best when the student maximizes the density of their practice, returning to practice often. This research will focus on the characteristics of the native English speaking “mentors” such as their personality, intelligence, and even physical appearance. We will assess the extent to which various characteristics predict how often a student chooses to practice and, through practice, the success level they achieve.
Our findings will be useful in terms of informing the mentor characteristics most relevant to student success. This will allow a more informed hiring process and, ultimately, a better learning experience for students.

3-D Nanoscale Imaging of Coronavirus Analogues at Various Stages of Cell Infection

In the project, we will use an advanced microscope instrument, called a focused ion beam, to capture 3-D datasets of a coronavirus analogue and SARS-CoV-2 infecting cells to understand its biomechanical relationship at the cellular level. The intern will work on sample preparation, imaging of infection and turning those images into a computerized model to gain insights into the infection mechanism.

High yield micro-algal cultures

Micro-algae, which are being used to derive biofuels from, are typically grown in large volumes of water in systems which either expose the algae to, or protect it from, the natural environment. There are two key problems associated with large-scale commercial biofuel production: the identification of high yield and high lipid producing algal species, and maintaining the optimal growing conditions at a commercial scale. The goal of this research is to identify algal cultures which can attain both high yield and lipid production, as well as an increased resistance to invasion and fouling.

Additive Manufacturing of customized imaging instrumentation for SARS-CoV-2 viral research

In the COVID 19 vaccine and anti viral therapy development, advanced optical imaging is used to measure the interaction of virus and the host cell Current microscopes is relatively slow and lacks required customization specific for COVID 19 research To address such a challenge, we plan to develop 3D print ing technology to build a customized microscope capable of high speed quantitative imaging of virus host interactions in live cell s for SARS CoV 2 virus research. The project will deliver an instrument to perfor m SARS CoV 2 vaccine and viral therapy development at McMaster.

Next-Generation Precision Medicine Solutions – Diagnostics

As personalized medicine approaches aim to tailor treatments to individuals, improvements are needed in the detection of existing biomarkers and genomic, epigenomic, and proteomic changes that occur during disease development. This would have potential impact on medication selection and targeted therapy, reduce adverse effects, improve cost effectiveness, and shift the goal of medicine from reactive to preventative clinical decision making1. Liquid biopsies for cancer, in particular, have recently provided the advantage of early and easy screening but their use in replacing traditional methods of diagnosis needs to be validated before widespread adoption. The development of microfluidic approaches has greatly improved the sensitivity and specificity of single cell detection for many disease diagnoses. However, standardization and quality assurance need to be implemented to assure that assay performance is reproducible and robust. Within this proposal we are partnering with Cellular Analytics, a Toronto-based start-up company with a proprietary microfluidic platform (CytoFindTM) that detects protein expression on single cells. This technology will be used to develop and validate CytoFind as a diagnostic for two types of cancer and malignant pleural mesothelioma with the aim of improving clinical decision making and patient outcomes.

Développement et intégration de modèles prévisionnels de défaillance de machines critiques de sciage par extraction de profils de dégradation

Ce projet vise à démontrer la faisabilité technique et économique de déploiement d’une politique de maintenance axée sur le prévisionnel sur une ligne de production de sciage soigneusement choisie. Sur un horizon de 2 ans, le projet vise à développer, déployer une méthodologie normalisée de mise à niveau pour les scieries afin de migrer du niveau réactif vers une stratégie axée sur des prévisions. Également le projet vise à mesurer les coûts et les bénéfices d’une stratégie de maintenance axée sur des prévisions et finalement livrer à l’industrie du sciage québécois un processus documenté et une interface générique de prédiction des pannes et support de décision pour les responsables de la maintenance pour en faciliter le déploiement. Ce processus pourra ensuite être généralisé et adopté vers d’autres scieries semblables et engagé les scieries dans une direction de maîtrise et d’excellence opérationnelle.

Constructing an Integrated Platform that Maps Neighborhood COVID-19 Cases with Census and other Open Data

This project proposes to optimize a map-based web application, which integrates numerous Open Data sets with data on the number of daily cases of COVID-19 in each public health region in Ontario. The platform will be able to merge data from the province’s Integrated Public Health Information System (iPHIS) and offer real time information to government on possible local determinants of the growth in incidence of COVID-19 infections as well as what policies should be employed in arresting the spread of the disease. This will be driven by AI methods that are able to link COVID-19 cases with neighborhood characteristics such as age distribution, population density, occupational distribution, household income, commuting patterns, and density of retail businesses, as well with the presence of lockdown policies and corresponding levels of social mobility captured through Google data. The proposed platform has the potential to make significant policy contributions given the absence of a comparable market product and the current lack of understanding on how COVID-19 is spread within and across communities.The platform is especially relevant given the demand for data on local infections for hotspots and a need for understanding the impact of important determinants.