Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

30 508 projets complétés

2882
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5105
C.-B.
825
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681
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860
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9051
ON
9491
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97
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586
NB
1141
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Projets par catégorie

The Development Machine Learning Techniques for the Detection of Trace Pharmaceutical Compounds

The goal of this project is to train and test a machine learning algorithm to detect pharmaceutical contaminants in water. Surface nanodroplets will be generated for several key compounds. These, will be used as independent extraction medium with varying contaminant concentration. The large number of droplets will enable collection of a high quality training data set. Potential chemicals to be investigated include: antibiotics, pesticides and flame retardants. Utilizing surface nanodroplets allows for a sufficiently large training set, one of the main limitations in applying machine learning to chemistry. The various machine learning techniques and parameters will be attempted in order to obtain a predictive regression model for concentration based on spectroscopy data. By the end of the project, we aim to have an algorithm that can determine concentration of trace contaminants in an unknown sample of pharmaceutical wastewater. If sufficient time is available then we will investigate extending the library of supported chemicals to include other pollutant categories.

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Superviseur du corps professoral :

Xuehua Zhang

Étudiant :

Partenaire :

Kyungpook National University

Discipline :

Engineering

Secteur :

Artificial Intelligence; Global Health; Pharmaceuticals

Université :

University of Alberta

Programme :

Globalink Research Award

Well-Defined Silica-Supported Titanium Catalysts for Introducing Nitrogen Functional Groups

Nitrogen is a key component in many molecules used as medicines, materials and fertilisers. Adding nitrogen into molecules is currently inefficient and expensive: it requires multiple steps and creates a lot of waste. Alternatively, it is possible to introduce nitrogen by processes called hydroamination and hydroaminoalkylation. These reactions are 100% atom efficient (all of the atoms put into the reaction are used in the products), so no waste is made. Current state-of-the-art methods for hydroamination and hydroaminoalkylation require a catalyst to make the reaction happen but the catalyst is difficult to separate from the products of the reaction. This makes purification of the products too expensive for large scale industrial processes. It is also very difficult to reuse the catalyst. In this project, we will trap the catalysts onto surfaces, which will enhance their performance in reactions and make it easier to separate and reuse the catalyst, as the product can be washed off and the catalyst reused. This technology will enable the efficient and sustainable production of nitrogen containing molecules in industry.

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Superviseur du corps professoral :

Laurel L Schafer

Étudiant :

Partenaire :

Centre National de la Recherche Scientifique (CNRS)

Discipline :

Physics

Secteur :

Achieving Net Zero; Clean Technology; Sustainability & the Environment

Université :

The University of British Columbia

Programme :

Globalink Research Award

Process intensification for production of rVSV

To prevent diseases such as coronavirus disease 2019 (COVID-19) and Ebola virus disease vaccines are the most effective tool. For this, the promising viral vector recombinant vesicular stomatitis virus (rVSV) is applied as production platform. Using suspension HEK293 cells, rVSV-based vectors are currently only produced in batch mode. However, the establishment of perfusion cultivations at high cell concentration would clearly reduce the footprint in plants and increase flexibility in virus vector manufacturing. Thus, we seek to intensify the production of rVSV-based vectors by first obtaining higher concentrations of suspension HEK293 cells in perfusion mode and second applying monitoring and control strategies to achieve maximum infectious virus yields at reduced medium consumption. For this, one bioreactor run in batch mode and two production runs in perfusion mode are planned. The first perfusion cultivation will comprise the use of a cell retention unit coupled to a hollow fiber membrane. For the second perfusion culture, a membrane allowing for continuous virus harvesting and storage of infectious virions will be incorporated.

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Superviseur du corps professoral :

Amine Kamen

Étudiant :

Partenaire :

Max Planck Institute for Dynamics of Complex Technical Systems

Discipline :

Engineering

Secteur :

Biotechnology; Global Health; Pharmaceuticals

Université :

McGill University

Programme :

Globalink Research Award

AI for catalyst discovery

In this project, we will develop innovative AI tools to speed up the process of catalyst discovery, in particular in the domain of renewable energy. Specific catalysts are essential to applications such as the efficient synthesis of solar fuels and fertilizer. However, many known catalysts are suboptimal in their efficacy or require scarce elements. Currently, catalyst development is held back by the computational cost of the quantum chemistry simulations used to guide experimentation, as well as the very high-dimensional search space of potential catalysts. AI offers the opportunity to significantly accelerate the discovery of catalysts, both by performing fast approximate simulations to assess the efficacy of individual catalysts and by intelligently searching for optima in the space of candidate materials. Specifically, we will build AI algorithms that operate on the graphical structure of chemical compounds, incorporating chemistry-related constraints and integrating into larger AI-based materials discovery pipelines.

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Superviseur du corps professoral :

Yoshua Bengio

Étudiant :

Partenaire :

Inria Saclay - Île-de-France Research Centre

Discipline :

Computer science

Secteur :

Artificial Intelligence; Green/Alternative Energy; Achieving Net Zero; Quantum Science

Université :

Université de Montréal

Programme :

Globalink Research Award

Assessing memantine induced mitophagy in a neurodegenerative model of Drosophila

A consequence of aging is the increased risk of neurodegenerative disease. In the pathogenesis of neurodegenerative diseases is the production of misfolded proteins which can clump together and interfere with normal cellular function. Mitochondria are highly important as they produce energy necessary for cell survival as well as regulate the controlled cell death process of apoptosis. Misfolded proteins are able to interact with mitochondria in such a way that dysregulates energy production, putting the cell at risk of undergoing cell death if not dealt with, thereby contributing to the decline of neurons seen in neurodegenerative disease. This project aims to investigate the induction of mitophagy, a pathway through which depolarised mitochondria are removed, in an in vivo model of Drosophila melangoster (common fruit fly). In so doing, we aim to better understand the effects of systemic mitophagy induction in a model of neurodegenerative disease.

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Superviseur du corps professoral :

Angus McQuibban

Étudiant :

Partenaire :

Stellenbosch University

Discipline :

Life Sciences

Secteur :

Life Sciences (not health); Health and Related Sciences & Technology; Global Health

Université :

University of Toronto

Programme :

Globalink Research Award

Shoulder Rotation of Pedestrians passing a Bottleneck

Understanding pedestrian dynamics is crucial to the design of pedestrian facilities. Although pedestrian flows are part of everyday life, many basic characteristics of pedestrian dynamics cannot yet be described in detail.
The aim of the PhD project is to study the upper body rotation of pedestrians when crossing a narrow passage (bottleneck). Since the movement pattern of individuals differs significantly in individual movement, small groups, and crowds, correlations and transitions from free movement of an individual to restricted movement in crowds during traversal will be investigated.
It is expected that the analysis of the physical parameters is subject to the influence of social interaction. Furthermore, it is expected that, as a consequence, parameters such as the timing of deceleration or motivation change the motion sequence and thus the shoulder rotation in the bottleneck. The host supervisor, Dr. Max Kinateder, has been conducting research on human visual perception for a long time. Among other things, how behaviour in emergency situations is controlled by the social and architectural environment. Dr. Kinateder’s expertise provides the opportunity to extend the analysis of physical parameters to include social aspects and those of perception, thus laying the foundation for new model ideas.

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Superviseur du corps professoral :

Dr. Max Kinateder

Étudiant :

Partenaire :

Forschungszentrum Jülich

Discipline :

Engineering

Secteur :

Information and Communications Technology; Transportation (excluding aerospace); Advanced Computing

Université :

Carleton University

Programme :

Globalink Research Award

New directions for old aluminum: Innovating aluminum recycling through novel processes and materials

Demand for aluminum alloys is increasing rapidly, facilitation of electric vehicles being but one example. A consequence is increasing scrap supply; a positive as aluminum is highly recyclable with it consuming a fraction of resources compared to using primary aluminum. There is a growing concern, however, as high-performance alloys are compositionally tuned to maximize performance. Separation of similar but distinct alloys in a scrap yard is infeasible leading to a growing fraction of the scrap supply that can only be down-cycled. With scrap supply outstripping demand new recycling and reuse solutions are required. This proposal builds upon existing collaborations between UBC and MPIE, focusing on using advanced characterization techniques (at MPIE) to better understand materials, processed at UBC, that 1) have non-traditional alloy chemistries based on scrap compositions and 2) are fabricated by combining local scrap recovery and additive manufacturing.

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Superviseur du corps professoral :

Chad Sinclair

Étudiant :

Partenaire :

Max-Planck-Institut für Eisenforschung GmbH

Discipline :

Engineering

Secteur :

Achieving Net Zero; Advanced Manufacturing; Sustainability & the Environment

Université :

The University of British Columbia

Programme :

Globalink Research Award

Mutual avoidance between young and older adults when passing through an aperture

The ability to perceive the motion of approaching person and to make appropriate adjustments to avoid collisions is an essential component of safe locomotion through the environment. The proposed project will investigate young and older adults’ collision avoidance behaviours with an approaching young adult in order to understand the visual information used to accomplish the task. The proposed research project will have two individuals approach an open doorway from opposite sides and pass through without colliding with each other. Both individuals’ movements will be tracked as they approach and pass through the doorway. The project will determine whether individuals use optical information, such as Time-to-Contact (TTC), to determine who will pass through the doorway first and avoid colliding with the other individual. TTC is amount of time that remains before each individual will reach the doorway and if used, will not only determine the arriving order, but also preserve this crossing order. It is believed that young adults will be in-tune with this optical information and use it to guide their actions whereas older adults may not use this information.

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Superviseur du corps professoral :

Michael Cinelli

Étudiant :

Partenaire :

Université de Haute Bretagne Rennes 2

Discipline :

Life Sciences

Secteur :

Education

Université :

Wilfrid Laurier University

Programme :

Globalink Research Award

Feasibility Study of an IoT-based Real-time Safety Alerting System for Working at Heights in Construction Sites

In the proposed project, we will investigate a solution for increasing the safety of construction workers at heights using an intelligent sensor-based system. A real-time monitoring system will be developed for alerting workers who are getting close to hazardous conditions and areas. The objective is to lower the chance of accidents by enhancing supervision of workers for their safety based on regulations and working practices in a construction site. This is done by collecting the locations of workers along with equipment, tools, and machines, processing the information using cloud platforms, and making notifications when required. The project has three main components including (1) Hardware devices to be carried by users, (2) An IoT cloud platform, and (3) Various firmware modules for localization and position tracking.

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Superviseur du corps professoral :

Mehrdad Moallem

Étudiant :

Partenaire :

1252258 B.C. LTD.

Discipline :

Engineering

Secteur :

Construction and infrastructure

Université :

Simon Fraser University

Programme :

Accelerate

Applications of AI in the development of public health solutions

This project will focus on the research areas of smart device monitoring and the analysis of complex medical data in public health. Through the use of artificial intelligence, researchers will develop solutions that exploit large-scale sensor data, address significant challenges caused by global threats by, for example, setting early heat alerts for extreme heat waves. These efforts will support older adults to live independently in smart home environments while maintaining a good quality of life.

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Superviseur du corps professoral :

Plinio Pelegrini Morita

Étudiant :

Partenaire :

University of Strathclyde

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Artificial Intelligence; Technology

Université :

University of Waterloo

Programme :

Globalink Research Award

Characterization of stem cell-derived human dopaminergic neuron subtypes

The goals of this proposal are to develop methods to identify cell subgroups in dopaminergic (DA) neuron cultures obtained by differentiation of human pluripotent stem cells (hPSC), and to exploit this information to improve existing DA neuron differentiation protocols. To realize these goals, we will leverage expertise at STEMCELL Technologies in hPSC differentiation and Simon Fraser University (SFU) in high content screening and laboratory automation. In four specific aims we will; (1) automate DA neuron differentiation from induced hPSCs (iPSCs), (2) identify known DA neuron subgroups in these cultures using established antibody markers, and (3) identify morphological features of cellular organelles to facilitate identification of these subgroups using an automated cell profiling approach. Finally we will, (4) use these novel morphological features to screen a compound library of known bioactive molecules for those that increase the specificity of DA neuron differentiation to these subtypes

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Superviseur du corps professoral :

David Vocadlo;Dr. Lisa Julian

Étudiant :

Partenaire :

STEMCELL Technologies Canada Inc

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Manufacturing; Mining; Professional, scientific and technical services

Université :

Simon Fraser University

Programme :

Accelerate

Unraveling the cells and genes uniquely contributing to doxorubicin-induced cardiotoxicity

Cancer is a devastating disease known to be treated with chemicals causing severe side effects in the advanced stages of the disease. Among these chemicals are a group of antibiotics, called anthracyclines, used to treat a variety of cancers, including breast cancer (BC). Doxorubicin, one of the preferred anthracyclines used in BC treatment, is particularly toxic to cancer cells, but also healthy tissue, leading to multi-organ damage, including damage to the heart. A new technology, called single-cell RNA sequencing (scRNA-seq), can determine which cells and genes in the heart contribute the most to the damage seen in the heart muscle following doxorubicin treatment, potentially leading to the identification of solutions. Using this technique, combined with advanced computer programs, the student aims to study doxorubicin-induced heart damage in a previously studied tumor-bearing mouse model. Herein, heart tissue will be processed and scRNA-seq performed, before analyzing the data and reporting the findings.

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Superviseur du corps professoral :

Britt Drögemöller;Galen Wright

Étudiant :

Partenaire :

Stellenbosch University

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Pharmaceuticals; Technology

Université :

University of Manitoba

Programme :

Globalink Research Award

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