Projets novateurs réalisés

Feasibility of physiological assessment for objective confirmation of non-invasive electrical recruitment of the saphenous nerve

Overactive bladder, urinary urgency, affects 14-18% of the Canadian population and costs our health care system over $350,000,000 annually. Most current treatments require ongoing in-person support or have low adherence rates, enhancing greater strain on our healthcare system and economy. Non-invasive saphenous nerve stimulation (nSNS) overcomes these issues however there is currently no objective method of confirming whether patients can activate the saphenous nerve during each treatment session. This research project will investigate the feasibility of measuring electrically evoked neural activity generated by nSNS. . We hypothesize that nSNS in humans can be quantitatively measured in a non-invasive manner. This will be the first-ever human feasibility study aimed at using electrically evoked neural signals to potentially screen OAB patients that can benefit from nSNS therapy.

Microbial Biocontrol agents as source of Plant Biostimulants

Plant biostimulants are substance(s) and/or micro-organisms whose function when applied to plants or the rhizosphere is to stimulate natural processes to enhance/benefit nutrient uptake, nutrient efficiency, tolerance to abiotic stresses, and crop quality (EBIC). A number of bacterial and fungal biocontrol agents that are used for the management of plant diseases and insect pest are known secrete compounds that exhibit plant biostimulant properties. In this project we propose to develop plant biostimulants from metabolites secreted by unique strains of fungal and bacterial biocontrol agents. These microbe plant biostimulants will be an important crop input to mitigate abiotic stresses caused due to climate change and to reduce chemical inputs like fertilizer and pesticides in agriculture thus promoting sustainability of agriculture.

Exploring the utility of digital voice assistants for spatial navigation, in enhancing human cognition

In today’s technology-centred world, we have become increasingly reliant upon artificial intelligence (AI) devices and software. To aid with navigation, many people use SIRI or other digital voice assistants to help them navigate from point A to point B. While successful in helping humans arrive at intended locations, there is a downside. Recent studies have shown that human cognition, specifically spatial memory, suffers when we offload basic human skills to an AI device. The goal of this proposal is to measure route knowledge and navigation ability following a variety of ways of delivering directions to humans, that rely differentially on AI devices.
Recent work in human cognitive sciences suggests that memory is enhanced when people actively explore routes, within virtual reality, compared to when they are simply passively guided to a destination. In this current project, we have two aims: 1) We will design and conduct a study comparing the relative benefits and costs of having a voice assistant provide navigation and directions to humans, to without the help of an AI device. 2) We will then implement and evaluate an alternative means for how AI apps can provide such instructions

Characterization of thermophilic ?-Mannanase of Thermomyces lanuginosus for production of health promoting prebiotics from agro-waste products.

This research proposal deals with characterization of thermophilic ?-Mannanase of Thermomyces lanuginosus and using this enzyme for hydrolysis of natural mannan to produce prebiotic MOS (Mannooligosaccharides). Alternative routes for the utilization of agricultural by-products are of interest because the economic value of these by-products as animal feed compounds is decreasing. MOS are non-digestible sugar oligomers made up of mannose units. The growing commercial importance of these non-digestible oligosaccharides is based on their beneficial health properties, particularly the prebiotic activity. The high cost of MOS, is a big hurdle in incorporating them into food products and this can be prevented by some efficient ?-Mannanase and economical substrates. The agro-waste products rich in mannan content can be utilized for producing mannan and, its further hydrolysis with a potent ?-mannanase will generate health promoting mannooligosaccharides. The exploitation of agro-waste as a source of mannan will not only control the issue of waste disposal but also promote the generation of the value-added products. Thermophiles producing thermophilic enzymes are always a considerable product of interest as biocatalysts for large-scale applications. Mannooligosaccharides shows tremendous potential as prebiotics by ameliorating the beneficial gut microflora and thus, can be applied as functional food ingredients.

Panoptic segmentation of an underground mine point cloud

Underground mining operations highly depend on accurate information for intelligent decision-making, may it be safety-wise or efficiency-wise. However, the industry still uses old technologies to keep track of the evolution of their mines. This project aims to develop a new technology to help keep mines safer and make operations more efficient using point clouds. Point clouds are a novel means for 3D visualization in which space is mapped through points using specialized scanning equipment. For this project, various algorithms are developed to harness the power of point cloud visualization. The key goal is to identify structures (e.g. ventilation ducts, piping, etc.) in such 3D visualizations, through the use of artificial intelligence.

Modeling, simulation and optimization of municipal solid waste gasification process through physics informed deep learning

Municipal solid waste (MSW) refers to recyclables and compostable materials, as well as garbage from homes, businesses, institutions, and construction and demolition sites. Disposal of MSW causes significant environmental problems. It is imperative to develop efficient environmental-friendly treatment technologies to tackle this global challenge. Among feasible technologies, gasification of treated MSW has been considered as a critical option since it could carry out the waste prevention and energy recovery from waste. However, the characteristics of MSW are hardly investigated due to complex organic matters, moisture content, carbon, nitrogen, and sulphur. While modeling and simulation of MSW gasification process is critical for controlling and optimizing the process operations, the traditional physics principle based process model is computationally expensive and fast meta-models are required. As the artificial intelligence (AI) technologies became one of the major defining attributes of competitive advantage across many manufacturing processes, this project aims at the development of physics-informed machine learning model for the prediction of syngas composition, gas production rate and heating value of gas produce in MSW gasifiers. The developed model will leverage the power of recent development in artificial intelligence to support the development of advanced MSW gasification process.

Biochemical Studies and Assay Development Targeting Novel Anti-cancer Agents

Cancer is a worldwide health problem and is the leading cause of death in Canada; as such, novel treatment strategies are constantly being sought. Compared to normal cells, cancer cells exhibit aberrant metabolism characterized by increased glucose uptake and rapid growth. The development and biochemical analysis of a series of small-molecule drug candidates that possess the ability to selectively disrupt tumour metabolism will lead to a better understanding of cancer cell metabolism as a whole and ultimately, new treatments for cancer. Development of this oncology program will increase the innovative technical capacity of Alectos Therapeutics and expand its capabilities for future drug development programs.

Decryption Failures in a Quantum World

The development of scalable quantum computers threatens the secrecy of communication by breaking classical encryption schemes. In order to ensure long-term security in communication networks quantum-safe cryptography is currently being developed and evaluated in standardization competitions.

A special property of many of the proposed cryptosystems is a low but non-zero probability of failure: An encrypted message may fail to decrypt successfully. This decryption depends on a secret key, which is to remain hidden to ensure secrecy of the communication. However, the mere event of such a failure has been shown to leak information about the secret key.

We analyze the vulnerability of quantum-safe cryptosystems to attacks based on these failures.
In particular, we construct a novel model of an adversary’s chance to trigger and exploit such failures on a quantum device.
As a result, new insights into protecting against quantum-attacks that enable the development of future quantum-safe encryption are collected.

Improving Resuscitation Equity using AI

For urgent, life-threatening medical conditions such as cardiac arrest and stroke, treatment is time-sensitive and must be received promptly to maximize the likelihood of patient survival. Previous research has shown that areas with lower socioeconomic status have higher rates of cardiac arrest incidence as well as lower rates of receiving timely treatment and patient survival. This suggests that current practices have an inherent inequity of care across socioeconomic levels.
Artificial intelligence methods present new opportunities for emergency medical service (EMS) systems to optimize their response by identifying cardiac arrest patients sooner and strategically deploying EMS resources to lower response delays. However, the effects of these methods with respect to the equity of care across socioeconomic status is unclear.
The proposed project will be conducted with the Scottish Ambulance Service to develop equitable, AI-driven resource allocation policies such as the placement of public defibrillators and recruitment of community-based responders in ways that can optimize both the effectiveness and the equity of care for cardiac arrest patients, and compare the impact that these policies can have to that of existing practices.

Comparative genomic analysis of Bradyrhizobium strains with natural variability: efficiency and competitiveness implications

A major challenge facing farmers is obtaining the nitrogen needed to support plant growth. Inoculation of legume crops with rhizobia, which supply plants with the required nitrogen, is a green alternative to environmentally hazardous nitrogen-fertilizers. However, rhizobia vary in their effectiveness, and the success of inoculants depends on their ability to outcompete native and poorly-effective rhizobia. Thus, development of highly-competitive and highly-efficient rhizobium inoculants is a key challenge to ensuring rhizobium inoculation becomes a cornerstone of sustainable intensification of agriculture. Here, we will use genomic approaches to characterize the genomes of 12 natural variants of the soybean symbiont, Bradyrhizobium japonicum. Despite these 12 organisms being closely related, their symbiotic properties vary. We will assemble and compare the genomes of the 12 strains, looking for genetic variations such as gene gain, gene loss, and nucleotide substitutions. Results will allow us to identify the genetic basis for the phenotypic differences, laying the groundwork for future work engineering elite Bradyrhizobium inoculants.

Detargeting Protein-Protein Interactions For Cellular Design Applications, Using 3D Structure-Based Deep Learning Models

Rational protein design has had a tremendous impact on pharmaceutical, agriculture, and chemical industries over the past 30 years, by focusing exclusively on individual proteins and their intrinsic activities. The next generation of protein design tasks will seek to modify function inside living cells, competing and interacting directly with pre-existing cellular machinery. Modifying systems in living cells will open a new wave of biotechnology applications, such as living drug implants and diagnostic tools. However, effectively introducing new or engineered proteins into a living cell system requires attentive coordination to ensure that designed protein surfaces do not inadvertently interact with other host cell proteins and disrupt otherwise vital activities. Biological pathways are also very sensitive to perturbation, which can often result in cell death or functional failure. This partnership aims to build a predictive computational technology to ensure that designed protein surfaces do not display recognition features of proteins in the host cell that will minimize these unwanted interactions.

Accelerating quantum variational methods using Bayesian optimization

Quantum computers hold the capability to solve problems that are out of the computational reach of classical computers. The race to finding and testing such problems on a smaller scale is currently on. Variational methods are one of the candidates leading this race. These methods are hybrid, meaning that they require quantum as well as classical computers working together. As the size of the problems increases and becomes relevant to the current needs of society, they also become more challenging to solve. This project is about improving the variational methods by combining them with a special technique known as Bayesian optimisation. The combination will accelerate variational optimisation by reducing the required quantum computational resources. Ultimately, this project will contribute towards solving the problems of interest to society, e.g. simulating large molecules, faster and more reliably once a powerful enough quantum computer is operational.