Projets novateurs réalisés

Developing computation tools for the rational design of cyclic peptide therapeutics

The majority of drugs that enter the market are discovered by screening millions of random chemical compounds until a desired effect is achieved. With the recent explosion in available biological data and raw computing power, it is now possible to develop drugs through bottom-up design rather than trial-and-error testing. Bottom-up drug design has the potential to lower R&D costs, improve success rates and reduce therapeutic side effects. ProteinQure aims to achieve these goals by designing peptide therapeutics computationally. Peptides are modular molecules that can be designed to have a desired effect on a disease target. The purpose of this project is to develop computational design tools for peptide macrocycles–a constrained class of peptides which has strong therapeutic potential, but limited design tools. The tools developed in this project will improve the effectiveness of peptide design pipelines at ProteinQure, thus contributing to the development of rationally-designed drugs in the future.

The Black Experience Project (BEP) in the Greater Toronto Area – Phase 2

This project aims to build on Phase 1 of the The Black Experience Project (BEP) currently being undertaken in the Greater Toronto Area. The multi-year research study that this project will support aims to examine the barriers to success for Black community members living in the Greater Toronto Area (GTA) which have and/ or continue to prevent the GTA’s diverse Black community from attaining their potential success (e.g., to become involved in leadership positions in various sectors including, but not limited to, the public, private, educational, political, legal sectors etc.). The purpose of this study is to bring together diverse voices, perspectives and experiences of “Black” people living in the GTA in order to document a more contemporary vision of lived “Blackness” in this context. Explorations of these lived experiences will be documented through focus groups.

Advancing a 5G Framework for Natural Asset Management

Natural assets are the stock of natural resources and ecosystems that provide essential services including positive contributions to human health and well-being, and climate change mitigation. Natural asset management has been identified as a priority area by governments, but is challenging to do on a large scale, due to its complex and expensive labor-intensive nature. This is where the use of sensor networks presents a new opportunity to integrate natural assets within the “smart cities” research space. Enabled by Rogers’s 5G network, this project uses the UBC campus as a “living lab” to deploy a natural asset sensor network to identify and monitor both the ecological and social factors that affect natural assets. Using data science techniques, researchers will be able to further gain predictive insights to understand and value natural assets on campus. In the long-term, this research could automate the natural asset management process.

COVID-19 and long term care workers: Staffing shortages and diverse pathways to entry

The goal of this project is to enhance health workforce planning in the Waterloo Region in collaboration with the Workforce Planning Board of Waterloo Wellington Dufferin (WPB), to address the Long Term Care staffing crisis by providing quantitative analysis of the demographic make up of the labour market involved in health relation professions, which will identify the demographic specifics of this population in terms of immigration status, length of residence in Canada etc. This will provide useful data for employers and educators to assist them in targeting their recruitment, retention and training plans.

Health Care Innovation and Data Analytics: Reinforcing Value-based Medicine

The research activity is to collect, compile, and analyze relevant information from both internal and external sources about specific topics impacting health care innovation with the largest regional healthcare authority in Newfoundland and Labrador: Eastern Health. Topics that will be addressed in the research activity include: 1) value-based medicine, 2) process innovation as well as 3) maximizing health care systems efficiencies and minimizing associated costs by leveraging data analytics. The key outcome will be to ensure that there is a better understanding as to how data analytics can influence and reinforce value-based medicine.

Réinstallation et accompagnement de familles immigrantes en milieu agricole

Le secteur agricole du Québec est confronté à une grave pénurie de main-d’œuvre appelée à s’aggraver. La Covid-19 l’a exacerbée en limitant le nombre de travailleurs étrangers temporaires sur lesquels les agriculteurs comptaient normalement. En parallèle, certaines populations connaissent des taux de sous-emploi et de chômage légendaires. C’est le cas des réfugiés qui, contrairement aux immigrants économiques, sont sélectionnés sur la base de leur vulnérabilité.
À la vue de ces deux problématiques, les municipalités régionales de comté (MRC) de l’Outaouais ont décidé de s’unir pour mettre sur pied un projet pilote visant à créer un service de recrutement et d’accompagnement des familles réfugiées pour qu’elles viennent s’installer de façon permanente dans leurs municipalités rurales et y travailler en agriculture.
Cette recherche vise à produire des connaissances sur les facteurs à l’origine du succès d’une initiative similaire menée par la MRC de Bécancour au Centre-du-Québec et sur les raisons qui amènent des familles migrantes à s’installer en milieu rural en Outaouais qui seront mobiliser pour concevoir l’architecture du service de recrutement et d’accompagnement et pour définir ses activités qu’il mènera.

Augmented Virtual Reality Interactive Training Program, with focus on older adults for Improving their Cognitive Function

During the current Covid-19 pandemic, more than ever, our seniors and those with dementia are isolated and at risk of a faster cognitive and mental health decline. This research offers a novel approach through an augmented virtual interactive social environment for cognitive training to prevent dementia and cognitive impairment. Built upon our successful previous research, our proposed program is an innovative virtual social environment with sessions of “brain exercises”. This allows older adults to attend a session through high-speed Internet using a locked-up PC laptop that automatically connects the user to the program upon pressing a “Start” button. This is particularly beneficial for the elderly who have difficulty commuting, adding to their depression and loneliness. Our proposed program will not only improve the participant’s cognitive abilities but also has great potential to improve their mental well-being.

Few-Shot Object Segmentation

Computer vision researchers have been moving beyond simple image classification and tackling more complex tasks such as object localization, detection and semantic segmentation. However, many of the proposed methods require large amounts of annotated data such as segmentation masks, which are expensive and time-consuming to acquire. Moreover, those methods cannot segment new object categories which were not present in the training set.

Few-shot segmentation alleviates both those problems by learning end-to-end to segment new object categories from few examples. In particular, weakly-supervised few-shot object segmentation only requires weak supervision such as sparse pixel annotations, bounding boxes and scribbles, which is substantially easier to gather than dense pixel annotations.

In this project, we propose a few-shot segmentation approach to alleviate the requirement of large strongly supervised datasets. Specifically, we propose a model which can learn how to segment new object categories using only a few annotated examples.

Developing an Efficient Ensemble Machine Learning Model for Evaluating Construction Project Bidding Quality and Optimal Winning Strategies

PledgX is interested in building a solution that aims to optimize the bidding process to maximize key performance indicators for contactors and vendors. For bidding optimization, several strategies and methods have been proposed; however, with the massive amount of available bidding datasets, the quality and performance of such methods are questionable. Machine learning introduces intelligent solutions to optimize the bidding decision, however these solutions are applicable to a range of prediction or classification tasks. Thus, ensemble modelling is introduced for efficient performance and to overcome drawbacks for individual modeling. In this project, we propose a novel data-driven bidding model based on ensemble predictive learning, which extracts sophisticated features and learns to bid automatically using the collected data. The model is composed of sub-models aggregated to form a more robust global model. The proposed ensemble learning model enables PledgX to learn complex rules of bidding with optimized overall bidding performance.

Using causal probabilistic fuzzy logic (PFL) rules integrated with Deep learning algorithms (DLs) to analyze Electroencephalography (EEGs)

Major Depression Disorder (MDD) is a big problem in our society. About 8% of Canadians may suffer from depressions in their life. Major depression can cause suicide and take families apart. Canadian governments spend more than $51 billion a year in the mental health sector. When treatment with medications fail, mental healthcare professionals, use Electroconvulsive Therapy (ECT) to treat patients with Major Depression Disorders (MDD). During an ECT session, electroencephalogram (EEG) signals let the mental healthcare professionals record patients’ brain activities which are helpful to decide whether the treatment was successful. However, there is no standard way to know how and with what intensity a healthcare professional needs to apply electroshock to treat patients with MDD. In this work, we will use non-classical logics such as probabilistic fuzzy logic and deep learning algorithms in order to find the ECT features resulting in successful ECTs.

Development of a noncontact PPG (ncPPG) system for oxygen saturation clinical analysis to increase the data reliability of SpO2 detection through consumer-level cameras

Sterasure Inc. in its current mission to provide biomedical tools to reinvent the clinical decision support, partners with the University of Waterloo to work towards the development of cutting-edge contactless vital sign monitoring systems. The technology studied in this research will allow the advance in the detection of oxygen saturation levels through cost-efficient systems. Although existing non-contact low-cost vital sign detection sensors offer great advantages for clinical environments, there is lack of studies showing medical reliability. Sterasure and the academic partner will prototype and design a reliable SpO2 contactless sensor which will be integrated in Sterasure’s biomedical device for future clinical studies.

Evaluating green roof performance and design in the Toronto area

Green roofs minimize stormwater runoff, building cooling costs, and provide other social, economic, and environmental benefits. Green roofs are also highly-proprietary, with the industry having many components to suite different applications, all influencing green roof survival and performance. With a green roof by-law and construction standard in Toronto, green roof coverage is consistently among the top cities in North America annually, however there exists no published data on green roof performance for the region, nor on the health and status of the City’s existing green roofs. The objectives of this project are to quantify green roof performance at the Green Roof Innovation Testing (GRIT) lab, and to survey and document Toronto’s existing green roofs, critically examining them using Toronto’s Best Practices Guidelines. The collaborative project will result in improved design and planning of green roofs by STLAi, creation of interactive material for referencing Toronto’s green roofs online, and peer-reviewed publication.