Projets novateurs réalisés

Developing Intelligent BI Applications using Machine Learning

SAP is a multinational software corporation that makes enterprise software to manage business operations and customer relations. SAP Labs Montreal focuses on supporting the consumer products industry. It provides these industries with the software infrastructure that allows its customers to run end-to-end processes including the capturing and analysis of sales transactions. Sales transactions include for example purchases made by each and every customer of the retail industry. Sales transactions are generated by point-of-sale (POS) systems used by cashiers and sent to a central server for validation and consistency checks. Recently, SAP has been actively applying machine learning to improve the efficiency of its software products. One challenge faced by development teams in this process is the identification of relevant features from raw data and the testing of the models. Current existing techniques are semi-automatic and often labor intensive. They also depend heavily on the expertise of domain experts, which often constitutes a significant barrier to the pursuit of machine learning projects in the Industry.

Towards a lab-on-a-chip-based rapid-screening system for pathogens

In light of the recent outbreak of Covid-19, it is urgent to find a solution for quick and efficient pathogen detection and elimination. Rapid point-of-need diagnostic tests and monitoring devices are urgently required in order to provide testing and care to those infected. Currently, testing is performed at centralized facilities using specialized equipment for molecular-based pathogen detection. Real time-quantitative PCR is the current method for detection, but it has a slow response time due to clinical lab capacity and sample shipping time.

In an effort to rapidly examine cells and microorganisms, we are developing an embedded sensor for lab-on-a-chip platforms with connected micro-tubes and a container for markers and the specimen. The sensor has built-in energy-harvesting and bidirectional communication units to create a contactless platform and analytical support for lab-on-a-chip technology. The proposed sensor attached to a microfluidic capillary carrier or lateral flow-based assay, facilitates rapid analysis and detection of harmful pathogens, drugs or biomolecules. It could also be used as a low-cost point-of-care patient monitoring device.

ChainCert platform: Verification authentication platform using Blockchain network

Blockchain is a well-known immutable, transparent and anti-counterfeit platform. As such it can be appropriate for submitting certificates for validation and safe-keeping. However, because of its requirements for complex and heavy computations for enhanced security and verification of each submission in the network, in this proposal, we have decided to conduct an experimental study to firstly evaluate several possible Blockchain candidate technologies to determine the ones that are the most cost-efficient and have a better performance for our case study. Next, we will design a model based on Blockchain network to validate the authenticity of each certificate that has been issued. The proposed method has a great impact to make sure that the paper-based certificates are authenticated and original.
This project has been suggested by the organization.

A 20 minute molecular diagnostic of COVID-19 within a “swab”

The research project will find optimal conditions for instrument-free, fast, and sensitive/specific detection of pathogen (COVID-19). The project is composed in two units(IU), each module within IU is offering independent technical solution for the current bottleneck in diagnostics industry. The first module is temperature-based denaturation of biological sample, coupled with nucleic acid-based detection (IU1). The second module is composed of specific amplification of biomarker for intended target/s, (instrument free) and the last module is colorimetric detection of amplified biomarker. These steps are essential for building universal diagnostic device which is projected to haveas high quality performance, as current state-if-the-art techniques, but is also fast (20 minutes) and does not require any additional technological setting. The partner organisation will use the technical solution provided by this project and generate it’s own, in-house, nucleic acid isolation/purification/detection techniques.

Forum Representation for Cross-Domain Recommendation

An internet forum is an online site for people to have conversations. It contains threads to hold discussions between users. Recommending appropriate threads to forum users is one of the main goals of an internet forum. To provide positive user experience, cross-domain thread recommendation is required, which can be benefited greatly from the help of forum representations. This research project aims to use two different approaches to create forum representation. One approach is to use the content-based method that utilizes textual data in each subforum and build a topic model to generate subforum embedding vectors. Another approach is the user-based method. It generates subforum embeddings by using a modified skip-gram model, which uses the subforum to predict its user contexts. Lastly, the research project will explore the possibility for a hybrid user-content based approach to further increase the embedding performance.

Private Sector’s Role in Chronic Disease Management in Kyrgyzstan

This study will systematically review the role of the private sector in the management of NCDs (including cardiovascular disease, cancer, etc.) in developing countries, and its implications to Kyrgyzstan. We will try to understand how low- and middle-income countries with similar GDP per capita, disease burden, and health system needs have harnessed the capacity of private sector in the management of NCDs. Experiences gained from these exemplar country(ies) will be emphasized and suggestions on how this could be applied to the Kyrgyzstan context will be provided. The outputs of this study will provide much-needed data, evidence and policy recommendations for our partner organizations, and for Canadian healthcare industry and pharmaceutical companies to enter this market, and potentially other similar markets in Central Asia countries.

New Method for Delivering Protectants into Honey Bee Hives

Pollination services provided by honey bees is imperative to crop production in Canada. Unfortunately, honey bee health is compromised by pests and diseases leading to the decline and/or death of hives and decreased pollination. In this project we will implement the usage of a special inspenser to bring in materials that will protect the honey bees from pests and diseases. These inexpensive inspensers will decrease the workload of already busy beekeepers, while also improving the overall health of the hives. Our partner organization, George Weston Limited, is the largest grocery retailer in Canada, providing food to over 34% of Canadians. Healthy beehives provide better pollination resulting in more food to be delivered to Canadian grocery stores at a lower cost to farmers, beekeepers, and grocery store retailers.

Machine Learning for Cancer Treatment Plan Benchmarking

Oncology specialists are few in numbers and cannot be present within every hospital or clinic providing their guidance and support. There are institutions in this world with oncology experts that can provide the best possible care and the knowledge of these experts is stored in medical case files within the hospital. With the power of machine learning and the internet, an organization without any experts can compare their cancer treatment plans to the top performing institutions in the world and receive constructive feedback on where their plan needs improvement. Bridge7 AI is developing a platform that allows clinicians around the world to compare and improve their plans with the help of experts in all medical fields.

Applied Machine Learning for Early Detection of Retinal Toxicity

Hydroxychloroquine (HCQ) is an anti-inflammatory drug that is widely prescribed for a range of auto-immune disorders such as lupus and rheumatoid arthritis. An unwanted side effect of long-term use of HCQ is vision loss by retinal toxicity. If detected early, it could lead to early intervention to prevent vision loss and improve the quality of life for patients.
The project involves research on current machine learning approaches for the development of a system that would aid in the early detection of retinal toxicity. Current approaches involve qualitative interpretation of multifocal electroretinogram (mfERG) and optical coherence tomography (OCT) images by an expert. The project aims to develop a system that automates the interpretation of mfERG and OCT images to assist medical professionals in making an accurate diagnosis.

PGX-processed yeast beta-glucans as an inhalable immunomodulating therapeutic for COVID-19 patients

While many people who acquire COVID-19 experience only minor symptoms or are completely asymptomatic, others (~20% of patients) experience a severe form of the disease associated with a phenomenon called a cytokine storm, an undesirable immune response that ultimately results in the deposition of fibrotic tissue in the lungs that causes the breathing difficulties and ultimately death in severe COVID-19 cases. There are to-date no therapeutics that have been demonstrated to relieve such cytokine storms, or avoid the resulting changes in the lung tissue, observed in severe COVID-19 patients. In this project, we seek to expand on preliminary results from an ongoing collaboration between the labs of Kjetil Ask and Todd Hoare at McMaster University and our industry partner Ceapro regarding the utility of yeast beta-glucan particles processed using Ceapro’s pressurized gas expanded liquids (PGX) technology for modulating the immune system in without any added drug. PGX processing both purifies and expands the raw yeast beta-glucan product to both remove components that can cause undesirable side-effects and reduce the density of the material to make the particles easier to inhale directly into the targeted lung tissue.

The Short- and Long-Term Impacts of the COVID-19 Pandemic on Ontario Manufacturers, Manufacturing Employees, and Supply Chains

The COVID-19 pandemic has had an unprecedented impact on Canada’s economy. More specifically, it has:
1) exposed deficiencies in supply chains, causing manufacturers to pivot production in order to improve the supply of essential goods (e.g. medical devices, personal protective equipment, sanitizer),
2) demonstrated the important role that manufacturing plays in a well-functioning economy and society, and
3) brought to light the lack of comprehensive information related to Canadian manufacturers capabilities.
This project attempts to address questions related to the role of manufacturers in mitigating the impacts of the COVID-19 pandemic, better understanding the capabilities of manufacturers (especially as they relate to the production of essential goods), and ensuring that government policies and programs designed to support manufacturing during the anticipated economic recovery and during future crises are effective.

An Investigation of Service Mesh(es) and Security Models Within and Across Multiple Distributed Systems

Global service providers in highly regulated financial sectors must accommodate an ever-changing, sometimes competing, landscape of regulatory concerns. This project seeks to determine a reasonable path forward in technology design and adoption to accommodate current and anticipated infrastructure changes. Moreover, bridging the service layer across multiple, distinct distributed systems of varying complexity will pose new challenges while performance and observability of these systems become critical consideration. Parallel architectural patterns require multiple service integration points and the ability to negotiate the movement of data securely. The objective is to review the industry landscape of service meshes and/or cryptographic patterns for suitable accommodation of multiple system architectures. Specific targets for latency (with defined upper bounds) and parity performance for read and write rates must be met. Adjustments based on the assumption of network behaviour are expected to be addressed, including failure conditions and stale data concerns, where applicable.