Advancing microarray analysis with GPU-based image analysis
Multiplexed tests detect multiple analytes from a single biological specimen in an automated fashion using microarrays. These can be used to determine patient immune response with minimal invasiveness and to better quantify biomarkers for advanced biological tests. To do this, microarrays require the printing of biological and chemical materials onto an optically transparent substrate (this require dispensing, curing, putting down a protective coating that is then dried to create a plate). Each well contains multiple spots (sensors) to detect different proteins. The proposed work improves analytical tools with a focus on accuracy, significantly decreased time to results and advanced image analysis using capability provided by SOSCIP. The SOSCIP platforms allow testing new approaches using cloud-based analysis as well as develop tools necessary for in-house analysis. The work will enhance the performance of current assay designs and inform the next generation of assays to support the partner’s technology leadership position. The results will be implemented immediately by the industry partner – as has been done in the previous work. The impact will be to place SQI in a unique market position in the diagnostics market. Ultimately, this work aligns with the partner’s goal of “cheaper, better, faster”.
Advancing sustainable aerodynamic solutions with improved modeling
Within current aerospace design, it is necessary to over-engineer features to ensure stability and safety under emergency conditions. It would be ideal to develop capabilities to reduce the size of large elements of commercial aircraft with reliable technologies that ensure safe operation under hazardous conditions. A key advantage of the synthetic jet is that no bulky air source and supply system is required to provide actuation to the flow. The planned changes to the aircraft structure that increase fuel economy and reduce weight will ensure the success of the economically important Canadian aerospace industry.
Agile real time radio signal processing
Canadian VLBI capability has been missing for a decade. Jointly with Thoth Technology Inc we propose to restore domestic and international VLBI infrastructure that will be commercialized by Thoth Technology Inc. This project will implement and optimize multi-telescope correlation and analysis software on the SOSCIP BGQ, Agile and LMS platforms. The resulting pipeline package will allow commercial turnkey VLBI delivery by Thoth Technology Inc to domestic and international customers into a market of about $10 million/year
Application of advanced machine learning and structure-based approaches for repurposing & discovering therapeutics for COVID-19
The novel COVID-19 pandemic caused by SARS-CoV-2 is a global health emergency of international concern with an estimated death toll in millions worldwide. The development, testing and approval of the COVID-19 vaccine may take at least 12-18 months. By then, the virus could mutate and reduce the vaccine’s efficacy. This project aims to leverage the latest advances in AI to repurpose existing drugs and identify novel ones that could be further developed as COVID-19 therapies in an extremely condensed manner. We will utilize Apollo 1060, 99andBeyond’s AI-augmented decision-making platform that can rapidly search a chemical space that is orders of magnitude larger than competitors. We will collaborate with experimentalists, and aim to have a set of compounds confirmed in a set of in vitro COVID-19 assays within the next six months. The proposed set of compounds and their corresponding biological activity will be openly published to help the community build powerful predictive models for COVID-19 targets. Their further testing and development will require the engagement of collaborating physicians in the hospitals and may attract partnerships with leading pharma and biotech in the US and Canada.
Assessment and adaptation strategies for a changing climate: future wind loading on buildings in Toronto
Maintaining resiliency of Canada’s built environment against extreme wind hazard is necessary to sustain the prosperity of our communities. Buildings are becoming more complex, lighter and taller making them more prone to wind effects. This is further aggravated by the long-term effects of climate change, and the associated uncertainty of future wind load characteristics. Historical climate data is no longer enough for long-term planning and adaptation in urban environments. The formulation of adaptation strategies to mitigate the effects of climate change in cities will require a collaborative effort that draws on expertise, tools, and approaches from a variety of disciplines.
This project will investigate the response of selected tall, highly flexible structures together with their surroundings in downtown Toronto under the new wind conditions due to climate change. Structures that are currently safe and serviceable under wind loading may experience issues (large accelerations, member failures) when the wind loading characteristics change with the changing climate. The multi-disciplinary project team will capitalize on the availability of large archives of climate model output, new tools of downscaling, and extensive computational resources. This technical expertise and infrastructure will enable the translation of knowledge of global climate change into actionable knowledge useful to practitioners in the area of urban building design. This project will deliver sustainability and resiliency-focused design, as well as retrofit recommendations for practitioners and decision makers with a direct benefit to the residents of Toronto.
Atomic-scale modeling of halide perovskites for optoelectronics and photovoltaics
The proposed applied research is of strategic importance to Ontario. The government of Ontario has repeatedly affirmed its commitment to creating a culture of environmental sustainability in the province, most recently via the Long-Term Energy Plan (2012). The Long-Term Energy Plan sets a 20-year course for Ontario’s clean energy future, and its priorities include the continued development of a diverse supply mix, including more renewable energy sources, fostering a culture of energy-efficiency, and encouraging the development of a clean energy economy. It specifically “encourages the development of renewable sources of energy such as wind, solar, hydro and bio-energy.” Efficient and economical photovoltaic systems such as those that will be facilitated via this project will play a very important role in realizing this objective. The proposed research will further support Ontario’s economy by generating new opportunities in the advanced materials and solar technology sectors, and by training a cadre of highly qualified personnel who will be poised to assume positions of global leadership in these industries.
Computational high-throughput screening of catalyst materials for renewable fuel and feedstock generation
According to a World Energy Council Report, population growth and rising standards of living across the world will at least double global energy demand by 2050. Simultaneously, carbon dioxide emissions must be reduced significantly to prevent a catastrophic rise in global temperatures. Clean and abundant renewable energy sources are available; unfortunately, the intermittency of solar and wind power is a prevailing problem which is limiting the potential for widespread use. Our project seeks to address both of these issues through development of novel catalysts to electrochemically convert CO2 captured from power plants into fuels and other higher value chemical feedstocks using renewable electricity. This innovative strategy will (1) provide a long term storage solution by converting renewable electricity into a stable chemical fuel, (2) provide a means to intelligently recycle CO2 rather than storing it in deep underground aquifers, and (3) provide a cleaner and cheaper pathway for production of industrial chemical feedstocks and fuels. This could be a truly disruptive technology which would allow Canadian led manufacturing of high value chemicals and fuels in a low-cost and low-carbon fashion. Additionally, there are large benefits to Canada’s energy sector by facilitating the dispatchability of renewable power.
Computer aided diagnosis of COVID-19 symptoms using medical sensors
The challenge that currently has arisen because of COVID is that in-person appointments with doctors are not possible and everyone has to use Telemedicine. Though Telemedicine is a great convenience, the problem with current Telemedicine systems is that they cannot be used to virtually examine patients and identify if COVID-19 symptoms are present. The main reason for this is because our current Telemedicine systems lack integration with medical devices that allow capturing physiological signals over the web. We are working towards integrating these medical devices into Telemedicine platforms so that the diagnostic utility of Telemedicine can be improved, and these platforms can be used to virtually assess patients over the web.
Our software platform integrates digital medical devices into Telemedicine video conference platforms that allows doctors to assess COVID-19 symptoms from captured physiological signals and provides large scale machine learning aided COVID screening at home. Imagine Telemedicine appointments where in addition to just consulting with your doctor via video tele-conference you can have them hear your heart/lung sounds, take your temperature, blood pressure, weight, blood oxygenation all in real-time and over the internet. We are building the software infrastructure that will allow Telemedicine platforms to seamlessly integrate the plethora of digital medical devices on the market, which enable this functionality, natively into their software ecosystems. Additionally, our platform also offers an intelligent layer of machine learning software that aids doctors in consolidating patient data, clinical decision making, computer aided diagnosis and carrying out appropriate follow-ups and referrals.
Electrochemical Fischer-Tropsch synthesis of renewable liquid fuels from CO2
Renewable electricity costs have been rapidly declining, enabling clean consumption of energy in many sectors. However, there is still demand for energy-dense liquid fuels, such as in heavy freight and air transportation. In this project, we will harness machine learning to develop technologies that enable the synthesis of liquid fuels from carbon dioxide and/or synthesis gas using renewable electricity. Industrially, liquid fuels can be synthesized from a mixture of carbon monoxide and hydrogen called synthesis gas (syngas). However, this process requires high temperatures and pressures, and is itself responsible for significant greenhouse gas emissions. We propose the use of electrocatalysis to produce these liquid fuels. To accomplish this, we will use computational modeling and machine learning methods to design electrocatalysts that efficiently convert CO2 or syngas into dense chemical fuels. These computational efforts will be validated through a parallel experimental approach that includes the fabrication of new catalyst formulations and the construction of prototype electrochemical flow cells. This project will enable the synthesis of clean, energy-dense liquid fuels that can replace the use of fossil-derived fuels in industry and transportation sectors. This project is a logical extension of the existing CO2 related projects previously underway with SOSCIP. This project will allow the research to achieve the next milestones in the overall goal to achieve beneficial conversion of CO2.
Fast and accurate biophotonic simulations for personalized photodynamic cancer therapy treatment planning
There are many medical uses of light, both for diagnostic purposes (medical imaging) and for treatment. We will build a very fast and accurate simulator of where light inserted with fiber optic probes travels within a person’s body; by using this simulator we can enable a promising new cancer therapy, among other medical applications. The key use of our light simulator in this project will be for photodynamic therapy (PDT), a promising new cancer therapy. It uses non-toxic light activated drugs (called a photosensitizer) which is harmless until it is activated by light of a certain wavelength; where the activated photosensitizer destroys cells. Hence if we can localize the light to a cancerous tumour, we can destroy it with minimal damage to surrounding healthy tissue and with fewer side effects and less cost than would be achieved with ionizing radiation therapy or surgery. PDT is used today to destroy “superficial” cancers, such as those on the skin where it is easy to localize the light to the tumour.
PDT can also be used to destroy tumours within the body by inserting fiber optic probes through needles into the body. The key challenge is that the light reflects, refracts and is absorbed in complex ways when it leaves the fiber, making it hard for a physician to determine where to place the fibers and whether the tumour will be completely destroyed. We aim to fill this gap by developing a fast and accurate simulator that can determine the light density throughout a person’s tissue that will result from a given fiber optic probe placement and light input intensity. To do so we will compute the path taken by hundreds of millions of simulated photons — a task so computationally intense that we will use an unconventional IBM computing platform that uses special programmable hardware to offload key calculations from the conventional processor. We believe that by doing so we can speed up the computation and reduce the power required by a factor of 60. This large speed up will allow us not just to simulate one probe placement but many possible light probe placements — we seek to return the best probe placement and the expected treatment results to the physician in less than an hour. Overall this project will pave the way for a much-needed new, less invasive, more effective, and lower-cost cancer treatment. It will also use a new style of computation where special hardware does much of the computation, instead of a general purpose CPU, showing how such an approach can be used to make future computers faster and less power-hungry.
Full Monte: fast hardware for Monte Carlo biophotonic simluations
There are many medical applications of light, such as bioluminescent imaging (BLI) and photodynamic therapy for cancer treatment (PDT). These applications require rapid and accurate simulation of how light will scatter and be absorbed in complex human tissue in order to image tissue (e.g. BLI), or to determine where a light-sensitive drug will be activated and destroy cells (e.g. PDT). This project will use agile computing (FPGA hardware) to simulate light propagation very accurately with the Monte Carlo method but much more quickly and power-efficiently than in a conventional computer. The first aspect of the project is to complete our prototype agile implementation of this simulator and scale up its performance and the problem sizes it can handle. The second aspect is to complete a workflow using this simulator along with meshing, visualization and light source placement optimization to make a clinically useful PDT treatment planning system.
Generalized heterogeneous radio signal processing
A new generation of radio telescopes is opening new windows on the Universe, allowing astronomers to observe the cosmos in unprecedented ways. Powered by the ongoing revolution in computing, these new telescopes operate at the cutting edge of digital technologies. New algorithms are being developed at a spectacular pace, and we are forging a new partnership with the Markham branch of Advanced Micro Devices (AMD) to add to these, developing new tools and opening new possibilities in radio astronomy, software defined radio, and similar telecommunications technologies. High-cadence mitigation of Radio Frequency Interference (RFI), advanced Digital Beamforming techniques, and Dynamic Spectral reshaping will all be developed and ported to an open software framework, allowing it to be used on a wide variety of computational and signal processing hardware.
Harnessing the diversity of phage displace libraries to capitalize on single domain antibodies with high target affinity and improved protease stability
AbCelex Technologies is developing a line of novel antibody-based products delivered as feed additives to poultry for the prevention of foodborne illnesses caused by Campylobacter and Salmonella. AbCelex’s single domain antibody (sdAb) platform technology is based on camelid antibodies, which have significantly higher thermal and protease stability, accessibility to target due to their small size (1/10 of conventional antibody) while providing affinities that exceed that of conventional antibodies. The current platform utilizes bioinformatics approaches that take into account genetic and protein diversity of the pathogens and in silico antibody engineering predictions to inform optimal design of sdAbs with competitive affinity, effectivity and cost as feed additives. In this project, we aim to combine the benefits of the natural scaffold of the camelid antibody and the expertise we have developed on understanding the interactions of these sdAbs with their target to develop computational libraries that we can then validate in vitro, ex vivo and ultimately in vivo.
For this purpose, we propose to collaborate with Dr. Mauricio Terebiznik, with whom we have been developing a detailed database of the target-antibody interaction mechanisms. He is an expert in cellular biology mechanisms altered by the pathogens such as Salmonella. The major barrier to achieve this aim is access to high performance computing and the SOSCIP platform is ideal to overcome this barrier. Furthermore, the postdoctoral funding will allow the candidate to build on the backbone of camelid antibodies rationally selected peptides that would provide target specificity and cross-reactivity across different strains of the same bacterial pathogen through this proposed collaboration.
Improved numerical combustion models for understanding and predicting nvPM/Soot formation and emissions in aviation gas turbine engines
Aviation gas turbine engines that burn hydrocarbon based fuels emit nanometer-sized carbonaceous non-volatile (not readily vaporized) particulate matter (nvPM) in addition to the usual gaseous emissions, such as green-house gases (GHG, largely CO2, actually a combustion product), nitric oxide (NOx) and carbon monoxide (CO). Also known as soot, smoke, or black carbon, these very small size nvPM has been shown to impact global warming and climate change by altering the radiation balance in the atmosphere through induced cloud cover and deposition of PM on arctic ice.
For these reasons, the manufacturers of gas turbine engines are today facing more and more stringent governmental and/or environmental regulations pertaining to PM emissions and there is a pressing need for reduced emission strategies. Unfortunately, the physical processes governing how nvPM and its precursors are formed in the high pressure flames and combustion systems of gas turbines is currently a matter of intense debate and a complete fundamental understanding of soot formation and emission processes is not firmly established.
The proposed two-year research project will consider the development of new and improved mathematical theory and computational models for understanding and predicting nvPM formation and emissions in aviation gas turbine engines. Through collaboration with the industrial partner, Pratt & Whitney Canada Corp. (P&WC), this new knowledge and understanding will be subsequently transferred to an industrial setting where it will be put to use in the design of next generation gas turbine engines having reduced PM emissions.
Improvement of Precipitation Gauge Collection in Remote Locations
To properly understand the global water cycle, improve analysis of climate variability, verify climate models and assist in local decision-making of surface or air transport, it is necessary to have better field tools for measurement of snow. Previous work has developed models of the Geonor precipitation guage and has included k-epsilon based numerical models of the flow around shielded gauges. To improve these results, it is essential to pursue advanced turbulence models as well as to develop benchmark experimental results. Large-Eddy Simulation, or LES, has become the method of choice for computationally-intensive simulations resolved to the necessary scales. Traditional Reynolds-averaged methods (RANS), although useful, require significant assumptions that compromise the fidelity of the flow physics obtained.
Direct Numerical Simulation (DNS) which resolves all the scales remains a prohibitive method due to its computational requirements. LES bridges between RANS and DNS, where the energetic large scales are resolved and computed directly whereas the smaller more universal scales are modeled. By modeling the subgrid scales within the inertial subrange, it is possible to extract high-fidelity flow information that can be used to improve local conditions. However, LES is computationally intensive and micro-climate modeling is beyond the capability of most desktop computers. As well, until recently LES models were either lab-developed codes or commercial codes. Lab-developed codes are difficult to transfer to industry partners as they are not necessarily client-friendly. While there exists excellent commercial codes, using these on multi-processor machines is prohibitor expensive. To model this flow, OpenFoam, an open-source turbulence code, will be used. The use of LES, instead of RANS modeling, would allow improved physical modeling of snow precipitate and ensure better comparison to real flow.
Integrated platform for distributed analytics of biometric data
Avertus is a company focused on providing tools for physicians to provide better patient care and treatment and researchers who are seeking new and improved treatments. Its first platform is a high frequency wireless brain activity monitor with a first application for seizure pattern detection. This will help physicians assess types and causes of seizures as well as response to drugs and other treatments and longer term help deliver future treatment technologies. With support from the SOSCIP and University of Toronto researchers, we will also implement the first commercially available high performance distributed computing platform for easy to use biomarker discovery tools and both batch and real time big data analytic capability for clinicians and researchers.
Joint optimization of route design and schedules for fixed route transit systems
The current method of optimizing routes and schedules for fixed route transit systems is sequential. Typically, route planning (involving determining route path, stops and service pattern) occurs initially, followed by schedule optimization (examining factors such as vehicle availability, operating requirements, safety restrictions, union contracts and employee pay).
This sequential optimization process produces a sub-optimal overall solution, inefficiently allocating agency resources, or allocating them in a way that may not be providing transit users with the best route and service. As a result, a method that could handle both simultaneously would be unique in the industry and extremely valuable to all fixed route transit agencies and service providers. Handling the numerous variables and constraints in route and schedule planning requires a method capable of intelligently searching for solutions.
The proposed method to tackle these dual requirements is simulation-based optimization using constraint programming—which is an optimization technique where knowledge of the problem is used to reduce the solution search space based on constraints. Evaluation of the feasible solutions is proposed to be accomplished using a simulation of the transit service that would more accurately represent service performance and passenger experience, where the structure of constraint programming methods lends themselves well to parallelization—ideal for the multi-core setup of the SOSCIP platforms.
Large scale simulations of photonic quantum computers
Current quantum computers are in the “NISQ”, or Noisy-Intermediate-Scale-Quantum regime. The true potential of quantum computing will only be realized when noise levels are reduced or controlled, and large scale is achieved. Xanadu’s approach is to use photonic technology as the building blocks of their machines. This project addresses the question: How do we build a useful quantum computer based on imperfect photonics components? There are 2 specific aspects of this general question: A – In which conditions does a photonic quantum computer reach quantum advantage (demonstrating large speedups compared to today’s most powerful conventional computers)? B – What are the resources required to build a scalable fault-tolerant photonic quantum computer? This project will provide the team with a much more detailed understanding of the requirements and tradeoffs involved in the future, much larger-scale generations of quantum photonic hardware that must be built in order to fully realize the theoretical potential of quantum computing.By mapping out a pathway to demonstrating quantum advantage, and large-scale, fault-tolerant quantum computing, this project will guide Xanadu’s ongoing efforts to build more powerful quantum computers which can deliver commercial benefits to customers and ensure economic impact.
Next generation low-emission combustor technologies for high-efficiency compact aviation gas turbine engines
The primary objective of the proposed research is to develop next-generation combustor technologies for aviation gas turbine engines that produce extremely low emissions and higher fuel efficiency, while reducing development times/costs and maintenance requirements. Impacts will be realized in terms of local air quality/public health, climate change, sustainability, and commercial engine sales. The current design priorities of aviation gas turbine combustors are operability, efficiency, emissions, durability, compatibility with the main engine core, and safety. Today’s combustors for small aviation gas turbine engines provide reliable and dependable service.
However, increasingly stringent emissions regulations are being enacted (e.g. by the International Civil Aviation Organization) that impose tighter criteria for emissions. Moreover, combustor conditions are continuously becoming more extreme in terms of operating pressure and temperature in order to improve engine efficiency. These conditions result in increased maintenance requirements, and hence increased engine operating cost. Future combustors for gas turbine engines are therefore in need of far-reaching design changes to meet these emissions requirements, while simultaneously being more cost effective and providing better operability/durability.
These next generation combustor technologies are essential to help our main industrial partner, Pratt & Whitney Canada (P&WC), to maintain its market competitiveness in the small aviation gas turbine sector. P&WC is the leading manufacturer of small aviation gas turbine engines, and these engines are extensively used around the globe as well as in Ontario (e.g., all commercial aviation at Toronto’s Billy Bishop Airport). Together with the two industrial partners, P&WC and IBM Canada, we will design novel new combustors that have lower emissions per unit fuel consumed, less fuel consumption per unit thrust produced, and reduced maintenance requirements relative to current systems. This will be achieved by combining the state-of-the-art experimental, computational, and analytical capabilities of the university research team with the practical gas turbine design knowledge of P&WC engineers and the high-performance computing (HPC) expertise of IBM.