Innovative Projects Realized

Efficacy and insect resistance management of new insecticidal proteins targeting western bean cutworm in Ontario, Canada

Western bean cutworm (WBC) is the most important corn pest in Ontario and lack of control can reduce grain yield and quality due to insect feeding and mycotoxin contamination. Unlike other primary corn pests, WBC are not controlled by most transgenic corn expressing Bt proteins. Monsanto has developed new insecticidal proteins to which WBC are susceptible according to preliminary research. To support long-term use of these promising management tools, this project aims to generate information necessary to develop an insect resistance management (IRM) plan. Life history parameters for WBC with and without exposure to these proteins will be determined using standard and novel bioassay techniques. Novel bioassay techniques using relevant corn tissues typically encountered by key WBC life stages in the field will be used to determine WBC susceptibility to realistic plant expression levels. Field experiments will be conducted to determine the efficacy of new proteins against WBC in Ontario and the optimal transgenic events for expression in commercial corn hybrids. Lastly, field experiments will be conducted to evaluate the efficacy and durability of proposed IRM strategies for new insecticidal proteins. The ultimate goal of this research is to investigate new, durable management options for WBC in Ontario.

Combining data science and wearable technology for early health risk detection

It has been well established that if human diseases can be diagnosed early, the prognosis and future quality of life is much improved. With advancements in computing technology in both the hardware (e.g. smaller, lighter, better batteries) and software (i.e. improved artificial intelligence), health systems are entering a renaissance when wearable technology and data science are being applied to clinical diagnostics. The fellowship’s objective is two-fold in which the first is to assess the validity and durability of Ultimate Sensor System’s wearable technology, and secondly, apply their wearable technology to clinical health applications including early health risk detection through the use of data science methods. Phase 1 will consist of reliability, validity, and durability testing of the wireless system while Phase 2 will apply machine learning methods to identify neuromotor diseases (e.g. Parkinson’s disease) and predict future cases. This portable ehealth system will offer Canadians an early detection system that has incredible potential, particularly for remote communities who have difficulties coordinating continual health professional services. By doing so, Ultimate Sensor Systems (industry partner) will directly benefit from this collaboration as they will become global leaders in data science/ehealth systems, which entices new customers and increases their global market share.

Development of a novel IPM strategy for brown marmorated stink bugs (Halyomorpha halys) using RNA interference (RNAi) technology – Year two

Brown marmorated stink bug (BMSB) (Halyomorpha halys (Stål)) is an invasive pest with a large host range that includes many economically important fruits, vegetables, and row crops. Native to Asia, BMSB was first detected in North America in 1998 and since has become established in British Columbia, Quebec, and Ontario; and 44 American states.
In order to develop efficient BMSB focused integrated pest management program (IPM), it is critical that novel alternative control tactics are investigated primarily because there are no effective insecticides available. RNA interference (RNAi) is a form of genetic control that has shown promise as a management tactic for BMSB. In this context, we aim to investigate RNAi for BMSB management. We will use genetic and proteomic tools to design new dsRNA (n = 10) in order to affect all BMSB life stages. We will test the effects of dsRNA, first independently then in multiple combinations, on BMSB populations raised in the laboratory. The most efficient combinations (n = 3) will finally be tested in the field to assess their efficiency under natural conditions. Our project will be the first step in the development and potential commercialization of a control of BMSB, efficient in the long term.

Development of a novel IPM strategy for brown marmorated stink bugs (Halyomorpha halys) using RNA interference (RNAi) technology

Brown marmorated stink bug (BMSB) (Halyomorpha halys (Stål)) is an invasive pest with a large host range that includes many economically important fruits, vegetables, and row crops. Native to Asia, BMSB was first detected in North America in 1998 and since has become established in British Columbia, Quebec, and Ontario; and 44 American states.
In order to develop efficient BMSB focused integrated pest management program (IPM), it is critical that novel alternative control tactics are investigated primarily because there are no effective insecticides available. RNA interference (RNAi) is a form of genetic control that has shown promise as a management tactic for BMSB. In this context, we aim to investigate RNAi for BMSB management. We will use genetic and proteomic tools to design new dsRNA (n = 10) in order to affect all BMSB life stages. We will test the effects of dsRNA, first independently then in multiple combinations, on BMSB populations raised in the laboratory. The most efficient combinations (n = 3) will finally be tested in the field to assess their efficiency under natural conditions. Our project will be the first step in the development and potential commercialization of a control of BMSB, efficient in the long term.

Characterization of Active Ingredient in Buckwheat

Buckwheat contains an active ingredient that lowers blood glucose in diabetic rats. The active ingredient is not a known compound such as chiro-inositol or rutin. The goal of this project is to fractionate buckwheat by a technique known as countercurrent chromatography (CCC) and determine the presence of a biological activity. This is important for setting the foundation to 1) monitor the levels of the active ingredient in buckwheat (as affected by variety, growing conditions and season), 2) determine how levels of active ingredient are affected by processing, 3) standardize the amount of active ingredient in functional food products and thus meet Health Canada requirements, and 4) provide a screening tool for further enhancement of buckwheat varieties by plant breeding.

Classification of human emotions from physiological signals using machine learning techniques

The project goal is to develop a system to classify the emotions of individuals with dementia. To achieve this goal, the applicant will have to design and test a machine learning algorithm that will extract important features from physiological signals such as heart-rate, skin conductance and skin temperature. Then, the algorithm will be trained using labels and insights generated by primary caregivers to automatically classify significant emotional states. Finally, the applicant will also work in the integration of the whole acquisition and processing systems through an app designed to work in any Android or iOS smartphone. This guarantees the versatility and portability of the system as a whole, and it will allow the users to use this system in any setting, where the caregivers will be able to identify, assess, and control the situations and places that cause distress in the users.

A mass balance modelling framework for chemicals and their primary metabolites for ecological and human health assessment – Year two

Chemicals manufactured and used in society degrade through physical and biological processes (e.g., photolysis, biotransformation) into break-down products (e.g., metabolites). Most “parent” chemicals can breakdown relatively quickly so that they are not persistent or bioaccumulative; however, some metabolites formed during the degradation processes can persist in the environment thus requiring consideration for ecological and human health assessment. The environmental fate, bioaccumulation, exposure and associated risks of these formed metabolites are largely unknown. The proposed research includes the development and testing of an exposure modelling framework for the holistic assessment of parent chemicals and their primary metabolites formed during degradation processes in the environment (e.g., air, water) and in biota. This research will combine Quantitative Structure-Activity Relationship (QSAR) methods and mechanistic, multi-media environmental fate and exposure models. QSAR models will be developed to estimate degradation properties (e.g., reaction rates, half-lives) and databases and models will be used to estimate metabolite formation pathways. The Risk Assessment IDentification And Ranking (RAIDAR) multi-media mass balance model will be revised to incorporate this information and quantify the fate and exposure of parent chemicals and selected metabolites to humans and ecological receptors. Case studies will be performed to evaluate the performance of the new modelling framework.

A feasibility study on semi-solid casting for aluminum metal matrix composites

The purpose of this project is to study the feasibility of using SEED process to produce

aluminum metal matrix composite (MMC) slugs with high quality, which will be formed to the

net shape cast parts using a high pressure die casting machine. The study includes

understanding rheological behaviors of the semi-solid MMC slugs, the effects of different

SEED parameters on the slug quality, and the relationship between the microstructure of the

MMC slugs and the castability of final castings. This project will be benefit to the industrial

partner for a better understanding the SEED process for semi-solid MMCs casting.

A mass balance modelling framework for chemicals and their primary metabolites for ecological and human health assessment

Chemicals manufactured and used in society degrade through physical and biological processes (e.g., photolysis, biotransformation) into break-down products (e.g., metabolites). Most “parent” chemicals can breakdown relatively quickly so that they are not persistent or bioaccumulative; however, some metabolites formed during the degradation processes can persist in the environment thus requiring consideration for ecological and human health assessment. The environmental fate, bioaccumulation, exposure and associated risks of these formed metabolites are largely unknown. The proposed research includes the development and testing of an exposure modelling framework for the holistic assessment of parent chemicals and their primary metabolites formed during degradation processes in the environment (e.g., air, water) and in biota. This research will combine Quantitative Structure-Activity Relationship (QSAR) methods and mechanistic, multi-media environmental fate and exposure models. QSAR models will be developed to estimate degradation properties (e.g., reaction rates, half-lives) and databases and models will be used to estimate metabolite formation pathways. The Risk Assessment IDentification And Ranking (RAIDAR) multi-media mass balance model will be revised to incorporate this information and quantify the fate and exposure of parent chemicals and selected metabolites to humans and ecological receptors. Case studies will be performed to evaluate the performance of the new modelling framework.

Behavioural Economic Interventions to Promote Health and Wellness on a Canadian Mobile Health Application

Behavioural economics demonstrates that people consistently behave in irrational ways that lead to poorer health outcomes. Yet the way in which people behave irrationally is predictable, which governments and organizations around the world have used to design “nudges” to “[alter] people’s behaviour in a predictable way without forbidding any options” (Thaler & Sunstein, 2008). Carrot Rewards is a Canadian mobile health (mHealth) application that serves as a platform for delivering nudges on a mass scale. It incentivizes its 850,000+ users to improve their health and wellness with loyalty reward points redeemable for consumer goods. Since the app was launched in 2016, it has introduced new features designed to leverage behavioural economic principles. While these principles have been found to be effective in other contexts, their effectiveness has yet to be determined in the context of an mHealth app. The objectives of the proposed research is the evaluation and optimization of these features, which will benefit the partner organization by enhancing the impact of their product, while providing an opportunity for the fellow to practice applying his research and statistical expertise in the product development and evaluation processes of the company.

Optimizing the placement of Med-El’s BoneBridge implant: image-guided positioning and biomechanical efficiency – Year two

Med-El’s BoneBridge implant provides the sensation of sound to hearing-impaired persons. The device’s transducer is surgically implanted in the skull. Key surgical problems are to find a location on the individual patient’s skull that is thick enough to house the transducer and to place and configure the transducer to maximize sound energy transfer to the cochlea, the organ of hearing. This project will (1) develop software for patient-specific, image-based BoneBridge placement planning and (2) investigate how BoneBridge placement and configuration affect energy transfer. The software will allow surgical planning by identifying the best position(s) for the implant from clinical images taken preoperatively. Our biomechanical investigations provide guidelines to surgeons on optimizing device performance by providing information on sound energy transfer patterns in various implantation scenarios. Med-El, surgeons and patients will benefit because the software and guidelines we develop will make implantation easier, safer, and optimal.

Optimizing the placement of Med-El’s BoneBridge implant: image-guided positioning and biomechanical efficiency

Med-El’s BoneBridge implant provides the sensation of sound to hearing-impaired persons. The device’s transducer is surgically implanted in the skull. Key surgical problems are to find a location on the individual patient’s skull that is thick enough to house the transducer and to place and configure the transducer to maximize sound energy transfer to the cochlea, the organ of hearing. This project will (1) develop software for patient-specific, image-based BoneBridge placement planning and (2) investigate how BoneBridge placement and configuration affect energy transfer. The software will allow surgical planning by identifying the best position(s) for the implant from clinical images taken preoperatively. Our biomechanical investigations provide guidelines to surgeons on optimizing device performance by providing information on sound energy transfer patterns in various implantation scenarios. Med-El, surgeons and patients will benefit because the software and guidelines we develop will make implantation easier, safer, and optimal.