Projets novateurs réalisés

Community dynamics in restored salt marshes

Salt marshes provide many services including nursery habitat for birds and fish, nutrient export to

sun’ounding coastal areas, and sheltering coasts from sea level rise and storm activity. In Atlantic

Canada, there is strong interest in salt marsh restoration. A study was instigated by Ducks Unlimited

Canada in 20 lOin the Bay of Fundy to monitor change in community structure of cells of fannland,

before and after breaching the dikes. Immediately after breaching, much of the cells became mudflat;

with sea level rise and subsidence of the Atlantic coastline, the elevation of the ground is actually

lower behind the dikes than on the shoreward side. The intern will monitor colonization/spread by

halophytic plants, salt panne fonnation, and use by mudflat/salt marsh animals. A better understanding

of community changes and their rates in developing salt marshes is needed to develop sound coastal

management plans, as many dikes are failing and as effects of climate change are increasingly being

felt along our…..

Development of a neural network algorithm to quantify chronic osteoarthritic pain in rats – Year two

The progression of pain research has been limited because of the overreliance on nociceptive assessment tools. These nociceptive assessment tools only assess the sensory component of pain and neglect its emotional component. It has been suggested that behavioural tools, such as the grimace scales, can assess the emotional component of pain. The use of various molecular markers are also promising new avenues to assess pain in various experimental models. The concurrent use of all three types of assessment methods will build a more accurate and complete picture of pain. Different types of pain assessment tools will be used to assess for the presence or absence of pain in an osteoarthritis pain model in rats. Osteoarthritic pain is the greatest cause of morbidity, and chronic pain in Western countries results in enormous financial losses from reduced productivity. It is also common in companion animals. The data gathered will be used to establish a neural network algorithm to build a sensitive and specific pain quantification profile. This will be compared to previously established (invasive) standards that have been validated by our laboratory. Once the algorithm has been established, it will also be tested in dogs.

Development of a neural network algorithm to quantify chronic osteoarthritic pain in rats

The progression of pain research has been limited because of the overreliance on nociceptive assessment tools. These nociceptive assessment tools only assess the sensory component of pain and neglect its emotional component. It has been suggested that behavioural tools, such as the grimace scales, can assess the emotional component of pain. The use of various molecular markers are also promising new avenues to assess pain in various experimental models. The concurrent use of all three types of assessment methods will build a more accurate and complete picture of pain. Different types of pain assessment tools will be used to assess for the presence or absence of pain in an osteoarthritis pain model in rats. Osteoarthritic pain is the greatest cause of morbidity, and chronic pain in Western countries results in enormous financial losses from reduced productivity. It is also common in companion animals. The data gathered will be used to establish a neural network algorithm to build a sensitive and specific pain quantification profile. This will be compared to previously established (invasive) standards that have been validated by our laboratory. Once the algorithm has been established, it will also be tested in dogs.

Optimization of specific Frizzled Receptor-targeted antibodies signalling responses – Year two

Therapeutic antibodies have been developed against a variety cancer cell surface proteins. These antibodies activate immune surveillance mechanisms that lead to destruction of cancer cells. Therapeutic antibody efficacy can be enhanced or might have unforeseen detrimental effects due to activation or inhibition of intracellular signaling pathways. Recent evidence suggests that the targeting of subtypes of the developmentally important Frizzed receptor subtypes could have great therapeutic efficacy in the treatment of several cancers and developmental disorders. AntlerA has developed 56 agonistic antibodies against specific FZD receptors. The Michnick lab developed a series of 196 cellular pathway-specific reporter assays with which we can simultaneous monitor all known signaling pathways in any cell of interest. These assays have been applied to screen a variety of compounds and environmental toxins and results have been shown to accurately predict specificity and unpredicted additional effects that cannot be revealed by other methods. We will screen AntlerA’s lead candidate recombinant antibodies for FZD receptor subtype and provide AntlerA with profiles of signaling responses, which will allow them to make decisions about which candidates would likely have the most favorable therapeutic potential and least likely unintended effects.

Optimization of specific Frizzled Receptor-targeted antibodies signalling responses

Therapeutic antibodies have been developed against a variety cancer cell surface proteins. These antibodies activate immune surveillance mechanisms that lead to destruction of cancer cells. Therapeutic antibody efficacy can be enhanced or might have unforeseen detrimental effects due to activation or inhibition of intracellular signaling pathways. Recent evidence suggests that the targeting of subtypes of the developmentally important Frizzed receptor subtypes could have great therapeutic efficacy in the treatment of several cancers and developmental disorders. AntlerA has developed 56 agonistic antibodies against specific FZD receptors. The Michnick lab developed a series of 196 cellular pathway-specific reporter assays with which we can simultaneous monitor all known signaling pathways in any cell of interest. These assays have been applied to screen a variety of compounds and environmental toxins and results have been shown to accurately predict specificity and unpredicted additional effects that cannot be revealed by other methods. We will screen AntlerA’s lead candidate recombinant antibodies for FZD receptor subtype and provide AntlerA with profiles of signaling responses, which will allow them to make decisions about which candidates would likely have the most favorable therapeutic potential and least likely unintended effects.

Impacts of Climate Change on Building Energy Use and Energy Transitions

Global electricity systems are in transition, with renewable energy taking on a larger role in total electricity generation. Energy demand, particularly in buildings, is adapting to a changing climate with both temperature and weather events changing the way energy is consumed. To best design future electricity systems it is critical to identify how a changing climate will impact the energy transition from traditional thermal generators to renewable energies. This study will combine climate modeling with detailed building models to predict how energy use in buildings will evolve with the changing climate in the coming decades. The study will contrast climate and energy use in Alberta, Canada and Bavaria, Germany. Taking lessons from the German energy transition, along with results on predicted energy use, insights towards the optimal strategy for energy transition in Canada will also be developed.

Optimization of furnace residence time and loading pattern of large size ingots inside a gas-fired forging furnaces – Year two

Large size high strength steels parts used in transport and energy applications undergo several heating and cooling cycles during their manufacturing process (casting, forging, quench, tempering). Generally, before forging the parts are heated in gas-fired forging furnaces and the impact of non-uniform heating on the subsequent steps is of critical importance. A non-uniform temperature distribution may result in property variation from one end to another of the part, changes in microstructure, or even cracking. On the other hand, optimization of time residency of large products inside the forging furnace can minimize energy consumption and avoid undesirable microstructural changes, like abnormal grain growth. However, due to the large size of the components, empirical approaches based on trail and error are costly and not always reliable. The proposed project, through a combination of 3D CFD simulations and experimental measurements, analyzing the turbulent combustion of gas-fired burners and conjugate heat transfer inside the industrial scale forging furnace, will develop heat treatment procedures with optimized time residency and uniform temperature distribution.

Optimization of furnace residence time and loading pattern of large size ingots inside a gas-fired forging furnaces

Large size high strength steels parts used in transport and energy applications undergo several heating and cooling cycles during their manufacturing process (casting, forging, quench, tempering). Generally, before forging the parts are heated in gas-fired forging furnaces and the impact of non-uniform heating on the subsequent steps is of critical importance. A non-uniform temperature distribution may result in property variation from one end to another of the part, changes in microstructure, or even cracking. On the other hand, optimization of time residency of large products inside the forging furnace can minimize energy consumption and avoid undesirable microstructural changes, like abnormal grain growth. However, due to the large size of the components, empirical approaches based on trail and error are costly and not always reliable. The proposed project, through a combination of 3D CFD simulations and experimental measurements, analyzing the turbulent combustion of gas-fired burners and conjugate heat transfer inside the industrial scale forging furnace, will develop heat treatment procedures with optimized time residency and uniform temperature distribution.

Caractérisation de la viabilité et de la fonction des granulocytes destinés à la transfusion

Les granulocytes sont une première ligne de défense très efficace contre les infections. Lorsqu’ils sont en nombre insuffisant ou qu’ils se dérèglent, de graves infections peuvent survenir. La transfusion de granulocytes est alors toute indiquée pour les patients aux prises avec une infection sévère résistante aux traitements habituels. Toutefois, nous ne savons pas si ces cellules, une fois prélevées, sont bel et bien capables de se rendre au site de l’infection et d’ingérer les microbes. L’objectif de ce projet est de caractériser ces cellules afin d’avoir une meilleure connaissance de leurs fonctions de qui permettrait d’améliorer le produit et de rendre ces cellules plus performantes une fois transfusées.

Dimensionnement mécaniste empirique de chaussées

Le dimensionnement des chaussées au Canada est actuellement effectué avec des méthodes empiriques. Ces méthodes limitent la possibilité d’utiliser de nouveaux matériaux et de nouvelles technologies qui permettraient de prolonger la durée de vie des chaussées tout en limitant leur impact négatif sur l’environnement. Il existe des méthodes de dimensionnement mécaniste-empirique, comme PavementME et des méthodes rationnelles comme la méthode française qui utilisent le comportement thermomécanique et les performances des matériaux testés en laboratoire afin de faire un dimensionnement optimal. Ces deux méthodes utilisent par contre différents intrants et différents modèles de calcul qui complexifie la comparaison entre les deux. Ce projet de recherche porte sur l’étude et l’optimisation du dimensionnement mécaniste-empirique des chaussées bitumineuses pour le Canada. Le projet est séparé en trois phases. Une première phase théorique dans laquelle des corrélations entre la procédure française et la procédure utilisée dans PavementME seront effectuées. La deuxième phase consiste en des essais de laboratoire pour avoir les données nécessaires aux différentes corrélations, mais également des essais de caractérisations de matériaux de chaussées à faible empreinte environnementale non usuels au Canada. La troisième phase porte sur la calibration des modèles de calculs à partir de résultats sur chantier.

Pricing and Resource Allocation in Edge Computing

The emerging edge computing (EC) paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) applications by bringing storage and computing facilities closer to the end users. Virtualization technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV) will allow sellers and buyers to access the open EC ecosystem. We envision the emergence of a novel EC marketplace where the telecom operator, such as Rogers Communications Canada, can act as a platform to facilitate the resource exchange among the sellers and buyers. This project aims to understand the tremendous potential of this new marketplace and propose efficient pricing and resource allocation mechanisms for the EC platform. We will investigate various design objectives ranging from fairness, privacy, truthfulness, social welfare maximization, to revenue maximization. The project will take both static and online settings into account to propose a set of novel algorithms tailored to provide win-win solutions for all stakeholders involved in the edge computing ecosystem.

Artificial Intelligence and Deterioration of Ocean Ecosystem

In the context of ocean sustainability of west coast of Canada, some questions that need to be considered are: what is the significance of environmental indicators related to the impact on marine aquatic species? How can changes in environment be predicted by patterns of bioindicators, for example as a result of hypoxia, affecting farmed and wild salmon? A starting point to answer these questions is the development of a centralized, common, accessible database that documents the shift in marine observation metadata collected in the area.

The objective of the study is to explore the spatial-temporal correlation between environment parameters and biological measurement of aquatic species in BC. We will develop a deep learning platform to integrate the information from environment conditions and the biological information of marine aquatic speices as follows: mortality of farmed Atlantic salmon (Salmon salar), abundance of wild Pacific salmon, and abundance of amphibian egg abundance in upstream habitat of wild salmon. The integration modeling of different sources of data, as the major output of the project will provide the analytic tool for ocean ecosystem service in the country.