Projets novateurs réalisés

Genes to affordable medicines – Stream 1-B1

The Structural Genomics Consortium (SGC) is a not-for-profit public-private partnership research organization that aims to accelerate the discovery of new medicines through open science. This Mitacs cluster will bring together SGC’s industry and academic collaborators to work together towards new and affordable medicines for challenging diseases. Sixty-three post-doctoral fellows will spend 2-3 years developing open source tools and knowledge for previously understudied proteins, thereby unlocking new areas of biology and identifying new opportunities for drug discovery. SGC’s spin-offs, the M4 companies, will be the vehicles to translate this knowledge into new medicines for rare and challenging diseases that have been excluded from traditional pharma company programs. The M4 companies are committed to open science and sharing, and to affordable pricing to ensure patients can access the new drugs.

Genes to affordable medicines – Stream 1-A2

The Structural Genomics Consortium (SGC) is a not-for-profit public-private partnership research organization that aims to accelerate the discovery of new medicines through open science. This Mitacs cluster will bring together SGC’s industry and academic collaborators to work together towards new and affordable medicines for challenging diseases. Sixty-three post-doctoral fellows will spend 2-3 years developing open source tools and knowledge for previously understudied proteins, thereby unlocking new areas of biology and identifying new opportunities for drug discovery. SGC’s spin-offs, the M4 companies, will be the vehicles to translate this knowledge into new medicines for rare and challenging diseases that have been excluded from traditional pharma company programs. The M4 companies are committed to open science and sharing, and to affordable pricing to ensure patients can access the new drugs.

Random bin picking with industrial robot

Materials with different sizes, shapes, or materials come into the process every day, they need to be sorted, placed, and usually fed into a machine for processing. Traditionally, this task is very hard to be automated because of the non-fixed position of the part and frequent part changes. For this random bin picking project, we are targeting to build a solution that can pick and place the part from a box with the aid of the latest computer vision technologies. We will be looking into find ways to create a model for any random part, match the part in the clustered point cloud, and retrieve the part’s precise location in the 3D spaces.

Efficacy and feasibility of online cognitive training platforms for older adults with subjective cognitive complaints: A randomized controlled trial

With an aging population on the rise, the prevalence of cognitive decline is expected to increase substantially. Goal Management Training® (GMT) and the Memory and Aging Program® (MAP) are cognitive interventions that have been studied extensively and applied clinically to address these needs. Although previous research has demonstrated efficacy of the in-person versions of both MAP and GMT, significant barriers exist in the utilization of these programs. In light of these challenges, online versions of MAP and GMT have recently been developed. The current project aims to evaluate if online versions of MAP and GMT are successful at improving cognitive functioning, cognitive complaints, and the impact of cognitive concerns on daily functioning. Findings will be used for knowledge dissemination and will aid in the commercialization of these evidence-based online cognitive interventions.

Point-of-Need Microfluidic Biosensor for Detecting Airborne Viruses using Molecularly Imprinted Polymers: Towards COVID 19 Virus Monitoring

This project is a partnership between York University and Sixth Wave Innovation Inc. (SIXW). The goal of this partnership is to develop components of a portable and low-cost technology for rapid and on-site air sampling and detection of aerosol viruses in indoor and outdoor environments. Virus capturing will be done by Molecularly Imprinted Polymers (MIPs) under the extensive expertise of the partner company. MIPs are robust materials with pre-made nano-cavities that can capture target objects such as viruses in this project. For this, MIP-based nanoparticles for microwire coating and surface immobilization of viruses will be designed and optimized. MIP-virus conjugation will be investigated in microfluidic devices. Finally, on-chip virus capturing, tagging with detection labels, and quantitative detection will be performed electrically. This project will result in innovative scientific research, mobilization of research from conception to proof of concept stage, and lab-scale development and integration of sample preparation and virus detection devices. These integrated sample preparation and detection systems will be portable, easy to operate, and sensitive for future use by inspectors, businesses, hospitals, special care centers, and police force involved in decision-making to address various challenges associated with airborne pathogen outbreaks and pandemics.

Passive Airborne Sensor Platform

In disaster scenarios involving airborne contaminants, where the dispersal of toxic agents can impact human lives, first responders require fast and accurate dispersal trajectory information. Existing methods that detect the local presence of an agent do not provide insight towards dispersal trajectory, and long range spread is either simulated with sparse reference data or measured long after the dispersion is complete. The lightweight and porous form of the milkweed seed offers natural inspiration for a novel sensor platform. In addition to investigating the market potential for the passive airborne sensor platform, the objective of the project is to quantify the effect of porosity on the response of the sensor platform to rapid changes in wind speed. Understanding how porosity affects the capability of the sensor platform to passively track the flow will assist in scaling the sensor platform design to meet the needs of potential customers.

A novel analgesic in a surgical model of osteoarthritis

Osteoarthritis is the most common form of joint disease affecting over 80% of the human

population above 75 years old and burdening health organizations worldwide. Osteoarthritis is

characterized by progressive joint degeneration resulting in chronic pain and loss of joint

function. Currently there is no cure for osteoarthritis; available treatments are only symptomatic

targeting pain and are associated with significant side effects, emphasizing the need for new

treatments.

Isovaline is a novel analgesic which showed remarkable effects in several pain models

without producing central nervous system side effects. The current study will examine the

analgesic profile of isovaIine in mice; we will assess the efficacy of isovaIine in alleviating the

signs of osteoarthritis and restoring the ability of the mice which underwent surgical

destabilization of the knee joint to exercise voluntarily. The effect of isovaline will be compared

to dicIofenac, and morphine the currently drugs of choice for osteoarthritic pain.

Characterization of powder materials for additive manufacturing (3D printing ability) using machine learning methods

Metal additive manufacturing (AM), also known as 3D Printing, is a technology that produces three-dimensional metallic parts layer by layer as opposed to conventional subtractive techniques. The qualities of 3D parts are significantly influenced by the characteristics of the feedstock materials, which depend on the manufacturing processes and also vary between batches. Powder pack density and flowability are often used to evaluate the 3D printability of the powder. The two metrics are currently determined by experiments and/or numerical simulations. However, the existed methods are often time/labor consuming due to the repeated sampling/testing processes and/or generate poor knowledge in the predictions of a new powder batch due to the sophisticated physics behind particle contact/interaction. The machine learning (ML) methods, especially deep learning (DL) method has already been widely used in object detection and segmentation. In particular, neural networks (NNs) are able to learn the relationship between the input features and output targets based on previous data. Compared with traditional methods (experiments and simulations), ML methods feature high objectivity and prediction accuracy and efficiency. Once the dataset between powder characteristics and processing is established, ML can realize in-situ monitoring of powder characteristics and real-time control the powder manufacturing processes. TOBECONTINUED

Business Development

The Business Development project will play a critical role in the growth of Hypercare and directly impact the company’s revenue targets. In addition to helping build the sales pipeline and increasing the Hypercare brand awareness, the Business Development project can truly make a positive impact in the Canadian and USA healthcare landscape by helping clinicians save time, improve operational efficiencies, and ultimately deliver better patient outcomes.

LBB Stratégies

LBB Strategies est une firme de services conseil et de planification stratégique qui intervient auprès de clients locaux à internationaux principalement en sport, loisir et vie active. LBB a connu une croissance soutenue de son chiffre d’affaires et est maintenant reconnue comme un chef de file au Canada en sport et vie active. Œuvrant auprès de clients du sport amateur et olympique, sport professionnel, gouvernements, municipalités, entreprises privées, événement sportifs, LBB désire consolider et rehausser sa présence dans l’industrie du sport. Parallèlement, LBB cherche à ouvrir de nouveaux marchés dans des industries qui expriment des besoins similaires.

LBB a ainsi créé une nouvelle division œuvrant en dehors du domaine du sport qui sera déployée à l’automne 2020 dans le marché québécois. LBB aura donc à mettre en œuvre une stratégie de marketing et de pénétration de ces nouveaux marchés tout en consolidant celui du sport, le tout dans un mode adapté de développement d’affaires.

Operations Analyst

As an operations analyst at AVA Technologies, I am responsible for taking on projects that improve the efficiency and effectiveness of AVA’s core business: providing consumers with the world’s most advanced smart home garden. Projects include:
– Procurement of a Bill of Materials & Parts Order Forecasting solutions
– Costing and making strategic recommendations on building a microgreens farm inside the workspace
– Aggregating and organizing venture capital fund and accelerator information -> implementing widespread use of a relationship manager
– Other ad hoc tasks

Design Science et PME : modernisation des systèmes d’information de Projet Point Final

Le projet en question est de type Design Science. Il éclot dans un contexte d’expansion où le système d’information (SI) de l’entreprise expose ses limites. Plus spécifiquement, son caractère manuscrit lui proscrit coordination et automatisation : coordination car l’information demeure figée sur feuille et donc inaccessible à des parties éloignées; automatisation, dans la mesure où la compilation de données – notamment à des fins de production statistiques – demeure un processus manuel. Par ailleurs, les capacités computationnelles du seul artefact technologique qu’utilise l’entreprise (Excel), ne suffisent plus à traiter les requêtes qu’on lui ordonne. Autrement dit, le système opère avec une lenteur excessive et comme se profile une masse supplémentaire de clients, cette cadence amorphe risque de devenir point d’arrêt. Parallèlement à cela, les capacités d’investissements de l’entreprise demeurent limitées à l’instar de son expertise en TI. Le mandat du stagiaire inclut donc aussi une composante de conseiller en TI.