Livraison du projet Mitacs Projets complétés

Étude de l’effet immunomodulateur et anti-inflammatoire des molécules bioactives dérivées de l’huile essentielle de Ptychotis Verticilla dans le cadre de l’arthrose

L’arthrose est le type d’arthrite le plus courant et une cause importante d’invalidité dans le monde. Avec une population canadienne croissante et vieillissante, la prévalence et le fardeau de l’incapacité devraient augmenter. Heureusement, le risque de développer de l’arthrose peut être réduit et bien qu’il n’y ait pas de remède connu, beaucoup peut être fait pour réduire ses impacts. Récemment, un intérêt croissant a été observé pour l’utilisation des plantes médicinales dans le traitement ou la prévention de l’arthrose. Les composants bioactifs dérivés de ces plantes peuvent constituer une source alternative et précieuse pour le développement de médicaments sûre et efficace contre arthrose. Le ciblage des voies de signalisation inflammatoires associées à l’arthrose par l’utilisation de médicaments à base de plantes doit faire l’objet d’une attention particulière. Dans ce sens, il est donc intéressant d’étudier l’effet thérapeutique des molécules bioactives (Thymol et Carvacrol) dérivées de l’huile essentielle de Ptychotis Verticillata dans le cadre de l’arthrose en explorant leurs capacités à moduler les réponses immunologiques et inflammatoires liées à cette pathologie.

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Superviseur du corps professoral :

Hassan Fahmi

Étudiant :

Partenaire :

Université Libre de Bruxelles

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Pharmaceuticals; Natural Resources

Université :

Université de Montréal

Programme :

Globalink Research Award

Truth and Reconciliation Through Treaty Implementation: Developing a Business/Social Enterprise Plan

This project works with the Saskatchewan Indigenous Cultural Centre to build upon tools and methodologies for organizations to make progress toward truth and reconciliation. This project will develop a business or social enterprise plan to scale up the existing truth and reconciliation work to meet growing demand from the community, seeking the objectives of accessibility and sustainability.

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Superviseur du corps professoral :

Rachel Loewen Walker

Étudiant :

Partenaire :

Saskatchewan Indigenous Cultural Centre

Discipline :

Business

Secteur :

Information and cultural industries

Université :

University of Saskatchewan

Programme :

Business Strategy Internship

Development of clinical best practice guidelines for the treatment of allied health conditions

The proposed research project will involve the completion of a systematic literature review of published studies addressing clinical best practices for the treatment of health conditions in the social work field. Products of the research study will include a library of comprehensive clinical guidelines for practitioners to follow, which can serve as the foundation for subsequent individualized treatment for clients.
The clinical guidelines will be published within the Curv Health app, enabling the partner organization access to the highest quality research evidence for therapeutic approaches.

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Superviseur du corps professoral :

Catherine de Boer

Étudiant :

Partenaire :

Curv Health

Discipline :

Sociology

Secteur :

Information and cultural industries

Université :

Memorial University of Newfoundland

Programme :

Accelerate

Build and improve image embedding models of cellular phenotypes

The over-arching goal of the project is to explore the use of several recently developed self-supervised image representation learning methods in an attempt to improve performance across several biologically relevant benchmarking tasks at Recursion. At recursion, deep-learning based models are used to generate feature embeddings for our imaging data and these embeddings to generate downstream biological insights for drug programs. A large proportion of our in-house data is unlabeled, and we’re exploring the use of novel self-supervised representation learning models to improve our existing image embedding models. The end objective is to improve the ability to extract high-value insights from the data generated, typically called ‘maps of biology’, and lead to scientific breakthroughs by our disease biology scientists.

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Superviseur du corps professoral :

Rahul Krishnan

Étudiant :

Partenaire :

Recursion Canada Inc.

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Mental illness after exposure to medical cannabis

Although most cannabis use is recreational, cannabis is approved for various medical indications. One of most major safety concerns regarding the use of cannabis is its association with mental illness, in particular, induction or exacerbation of psychosis and schizophrenia, suicidal attempts and depression. That said, current available data in the field is based mostly upon studies of recreational users of cannabis and is therefore subject to inherent limitations and weaknesses. This study’s main objective is to study the association between exposure to medical cannabis and the risk for mental illness, including the risk for psychotic episodes, anxiety and suicidal behavior in meticulously designed studies. The data will be retrieved from three Israeli databases
Expected results: We expect this study will enable us to learn the association between medical cannabis and mental illness. This study will either confirm or refute the current concern about the mental effects of medical cannabis. The findings from the study will provide crucially needed data that could be immediately implemented in clinical practice, will contribute to the ongoing debate about legalization of recreational cannabis and may lay the groundwork for additional research.

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Superviseur du corps professoral :

Laurent Azoulay

Étudiant :

Partenaire :

The Hebrew University of Jerusalem

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Pharmaceuticals; Biotechnology

Université :

McGill University

Programme :

Globalink Research Award

Deep Learning Based Approaches to Synthetic Data Generation

Synthetic population generation is the process of combining multiple socioeonomic and demographic datasets from various sources and at different granularity, and downscaling them to an individual level. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this project, we propose a multi-stage framework called SynC (Synthetic Population via Gaussian Copula) that is novel and scalable to address this research problem. We aim to make both theoretical advances by developing new algorithms, as well as release easy-to-use implements for others addressing similar challenges.

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Superviseur du corps professoral :

Periklis Andritsos

Étudiant :

Partenaire :

Arima Inc

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

The affect of the perceptions of social workers and policymakers regarding people living in poverty on policies and their implementation

The research examines how the perceptions of social workers and policymakers regarding people living in poverty affect policies and their implementation. The study will focus on exploring ideas about the perceived values of people in economic need, the conceptual values associated with policy decisions concerning the needy, the way such ideas affect the construction of social constructions related to the needy and how they are influenced by these ideas. Additionally, a comparison will be made with Canada to examine imagination and variation between the countries and impact of international social learning on social policy.

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Superviseur du corps professoral :

Daniel Béland

Étudiant :

Partenaire :

The Hebrew University of Jerusalem

Discipline :

Sociology

Secteur :

Public Service, Policy, and Governance; Other

Université :

McGill University

Programme :

Globalink Research Award

Improving the First Pass Yield of an industrial electroplating line through a combined Design of Experiments and Causal Inference integrated with Deep Learning algorithms (DLs)

The performance of an industrial electroplating line is evaluated using the First Pass Yield (FPY).
Improving the FPY of an industrial electroplating line is complex and depends on lots of environmental
and technical parameters. These parameters have different nature that makes it hard to assess the
interaction between them and consequently to detect the causes of plating defects. In a bid to tackle this
problem, we will apply a casual reasoning model equipped with deep learning to find the underlying
causes of process failures. Moreover, we will take advantage of Design of Experiments (DoE) techniques
to learn more about the weight of each parameter in the result of the plating process, leading to a lowvalue
FPY.

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Superviseur du corps professoral :

Usef Faghihi;Laurent Cormier

Étudiant :

Partenaire :

Héroux Devtek Inc

Discipline :

Engineering

Secteur :

Manufacturing

Université :

Université du Québec à Trois-Rivières

Programme :

Accelerate

Diagnostics and Prediction of Mobile Device Behavior using Data Analytics and Machine Learning

SOTI has accumulated a large number of devices in the past ten years, and how to diagnose and predict the status of these devices has become a very important topic. Through this research we hope to find various parameters and patterns contributing to particular negative impacts. Additionally we would like to be able to Create demonstrable visualizations of such factors, patterns, that affect device performance or behaviour over time. Finally, we will build prediction models to quantify and visualize progression of negative trends up to terminal failures

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Superviseur du corps professoral :

Bo Wang

Étudiant :

Partenaire :

SOTI Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Mobile Electroencephalography and Mobility in Parkinson’s Disease

Attention, an important aspect of human cognition, is needed for safe mobility and navigation through the environment. With age, the ability to move and navigate through the world requires greater cognitive resources. Previous brain imaging research has shown that mobility impairments are associated with reduced attention. However, previous work was limited to assessing attention while participants were stationary and/or in a laboratory environment, which does not necessarily translate to what would occur in the real-world. My research will use mobile neuroimaging to observe and compare brain activity in a real-world environment across younger adults, older adults (with and without a history of falls), and adults diagnosed with Parkinson’s disease. Participants will be required to pay attention to naturally occurring hazards while walking outside. These findings have the potential to expand current understandings of brain function in Parkinson’s disease, human mobility, and fall risk using real-world methods and technology.

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Superviseur du corps professoral :

Lindsay Nagamatsu

Étudiant :

Partenaire :

Parkinson Society Southwestern Ontario

Discipline :

Life Sciences

Secteur :

Other services (except public administration)

Université :

The University of Western Ontario; University of Western Ontario.

Programme :

Accelerate

Modular Magnetorheological Actuators Controls

Since its foundation in 2013, Exonetik has been developing and manufacturing magnetorheological (MR) actuator systems that enable novel functionalities to satisfy unmet customer needs. Exonetik’s patented actuators offer a unique combination of torque-density, high bandwidth, superior back-drivability, low-cost and aerospace-level reliability. To reach its ambitious goals, Exonetik has built several strategic relationships with major OEMs in the aerospace and automotive sectors and now wishes to repeat the process in the robotics’ field.
In 2018, Exonetik started exploring collaborative robotics, where it saw a unique opportunity for its MR actuators. In this field, Exonetik initially investigated the potential of remote actuator systems through the development of various prototypes (e.g.: 3 degree-of-freedom – DOF – wearable hydraulic robotic manipulator and teleoperated cable-driven arms). While the performance of these prototypes was excellent, this approach leads to unnecessary complexities (e.g.: routing) for 6 DOF robotic arms. In 2020, Exonetik thus developed its first modular MR robot actuator, and in 2021, its first 6 DOF robot for human-robot interaction. The modular MR robot’s promising performance has led compagnies to sign agreements with Exonetik to develop robotic arms for various robotics tasks.

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Superviseur du corps professoral :

Alexis Lussier Desbiens

Étudiant :

Partenaire :

Exonetik Inc

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

Université de Sherbrooke

Programme :

Accelerate

Automatic Machine Learning for Recommender Systems

Crossings Minds provides machine learning-based recommendation systems that allow companies to integrate their data and easily obtain on-demand personalized recommendations for their users. Currently, onboarding a new customer requires significant work on the behalf of Crossing Minds engineers to create and polish a machine learning model for that specific customer. The research project will explore the field of auto-machine learning, which studies the way that machine learning models themselves learn. The goal would be to provide tools that make the machine learning decision-making process easier for Crossing Minds engineers and will explore potential opportunities in each step of the machine learning pipeline. Such tools will reduce the turnaround time for onboarding of new customers and will therefore improve the company’s scalability.

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Superviseur du corps professoral :

Nisarg Shah;Scott Sanner;Eldan Cohen

Étudiant :

Partenaire :

Crossing Minds Canada Inc.

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Projets novateurs réalisés

31 914 projets complétés

Projets par catégorie

Étude de l’effet immunomodulateur et anti-inflammatoire des molécules bioactives dérivées de l’huile essentielle de Ptychotis Verticilla dans le cadre de l’arthrose

Étude de l’effet immunomodulateur et anti-inflammatoire des molécules bioactives dérivées de l’huile essentielle de Ptychotis Verticilla dans le cadre de l’arthrose

Truth and Reconciliation Through Treaty Implementation: Developing a Business/Social Enterprise Plan

Truth and Reconciliation Through Treaty Implementation: Developing a Business/Social Enterprise Plan

Development of clinical best practice guidelines for the treatment of allied health conditions

Development of clinical best practice guidelines for the treatment of allied health conditions

Build and improve image embedding models of cellular phenotypes

Build and improve image embedding models of cellular phenotypes

Mental illness after exposure to medical cannabis

Mental illness after exposure to medical cannabis

Deep Learning Based Approaches to Synthetic Data Generation

Deep Learning Based Approaches to Synthetic Data Generation

The affect of the perceptions of social workers and policymakers regarding people living in poverty on policies and their implementation

The affect of the perceptions of social workers and policymakers regarding people living in poverty on policies and their implementation

Improving the First Pass Yield of an industrial electroplating line through a combined Design of Experiments and Causal Inference integrated with Deep Learning algorithms (DLs)

Improving the First Pass Yield of an industrial electroplating line through a combined Design of Experiments and Causal Inference integrated with Deep Learning algorithms (DLs)

Diagnostics and Prediction of Mobile Device Behavior using Data Analytics and Machine Learning

Diagnostics and Prediction of Mobile Device Behavior using Data Analytics and Machine Learning

Mobile Electroencephalography and Mobility in Parkinson’s Disease

Mobile Electroencephalography and Mobility in Parkinson’s Disease

Modular Magnetorheological Actuators Controls

Modular Magnetorheological Actuators Controls

Automatic Machine Learning for Recommender Systems

Automatic Machine Learning for Recommender Systems

Joignez-vous à un écosystème d’innovation florissant.

Abonnez-vous à notre infolettre dès maintenant!

Projets novateurs réalisés

31 914 projets complétés

Projets par catégorie

Filtrer par

Étude de l’effet immunomodulateur et anti-inflammatoire des molécules bioactives dérivées de l’huile essentielle de Ptychotis Verticilla dans le cadre de l’arthrose

Étude de l’effet immunomodulateur et anti-inflammatoire des molécules bioactives dérivées de l’huile essentielle de Ptychotis Verticilla dans le cadre de l’arthrose

Truth and Reconciliation Through Treaty Implementation: Developing a Business/Social Enterprise Plan

Truth and Reconciliation Through Treaty Implementation: Developing a Business/Social Enterprise Plan

Development of clinical best practice guidelines for the treatment of allied health conditions

Development of clinical best practice guidelines for the treatment of allied health conditions

Build and improve image embedding models of cellular phenotypes

Build and improve image embedding models of cellular phenotypes

Mental illness after exposure to medical cannabis

Mental illness after exposure to medical cannabis

Deep Learning Based Approaches to Synthetic Data Generation

Deep Learning Based Approaches to Synthetic Data Generation

The affect of the perceptions of social workers and policymakers regarding people living in poverty on policies and their implementation

The affect of the perceptions of social workers and policymakers regarding people living in poverty on policies and their implementation

Improving the First Pass Yield of an industrial electroplating line through a combined Design of Experiments and Causal Inference integrated with Deep Learning algorithms (DLs)

Improving the First Pass Yield of an industrial electroplating line through a combined Design of Experiments and Causal Inference integrated with Deep Learning algorithms (DLs)

Diagnostics and Prediction of Mobile Device Behavior using Data Analytics and Machine Learning

Diagnostics and Prediction of Mobile Device Behavior using Data Analytics and Machine Learning

Mobile Electroencephalography and Mobility in Parkinson’s Disease

Mobile Electroencephalography and Mobility in Parkinson’s Disease

Modular Magnetorheological Actuators Controls

Modular Magnetorheological Actuators Controls

Automatic Machine Learning for Recommender Systems

Automatic Machine Learning for Recommender Systems

Joignez-vous à un écosystème d’innovation florissant.

Abonnez-vous à notre infolettre dès maintenant!