(FoRCE): Powering clinical trials research through a secure and integrated data management platform
Critical care units are one of the most data-rich environments in clinical settings, with data being generated by advanced patient monitoring, frequent laboratory and radiologic tests, and around-the-clock evaluation. There are substantial opportunities in linking data that are collected as a part of such clinical practice with data collected in a research setting, such as genome wide studies or comprehensive imaging protocols. However, security and privacy issues have historically been a significant barrier to the storage, analysis, and linkage of such biomedical data. Further, disparate technologies hinder collaboration across teams, most of which lack the secure systems required to enable federation and sharing of these data. This is particularly true when clinical practice or research designs require close to real time analysis and timely feedback, such as when dealing with streamed medical data or output from clinical laboratories. Current commercial and research solutions often fail to integrate different data types, are incapable of handling streaming data, and rely solely on the security measures put in place by the organizations that deploy them.
This proposal seeks to build FoRCE (Focus on Research and Clinical Evaluation), a scalable and adaptable add-on module to the existing Indoc Informatics platform that will address the critical gaps in cybersecurity and privacy infrastructure within shared clinical and research settings, while fulfilling important unmet needs for both the clinical and research communities. FoRCE will provide the secure architecture and processes to support the collection, federation and sharing of data from distributed clinical settings, including critical care units, clinical laboratories, and imaging facilities. The proposed platform will address several key issues including security considerations, infrastructure and software requirements for linkage, and solutions for handling streaming real time medical data, and ensuring regulatory and ethics compliance when linking diverse medical data modalities in a clinical setting.
FoRCE will be designed and developed with broad applicability in mind, and will therefore allow the different data types from numerous technologies and across multiple disease states to utilize the platform. The long term impact of FoRCE on improving the health of Ontarians is of course dependent on its utilization within research and clinical settings. An initial project which will utilize the platform as part of the testing and validation of FoRCE includes Dr. Maslove’s integrated approach to merging genomic and physiologic data streams from the ICU in the context of clinical research. FoRCE will enable Dr. Maslove’s team of critical care researchers to move beyond predictors of survival to focus on predictors of response to therapy, so that clinical trials in the ICU can be optimized to produce actionable evidence and personalized results. This will lead to better allocation of ICU resources, which in Canada cost nearly $3,000 per patient per day – $3.72 billion per year.
A parallel algorithm for quantum circuit synthesis
Quantum circuit synthesis is an important step in the process of quantum compilation. Given an arbitrary unitary operation, quantum circuit synthesis is the process that constructs a quantum circuit using only gates from a universal gate set which is either exactly, or approximately equivalent to the original operation. The currently known algorithms for multi-qubit circuit synthesis run in exponential time and rely on the generation of databases many GBs in size to complete the search; synthesis of circuits of more than 3 qubits up to a certain length is infeasible using current methods due to this exponential scaling. We have developed a framework and accompanying software that uses time/memory tradeoffs and parallel collision finding techniques to synthesize circuits. A simple implementation using 16 OpenMP threads found that this approach can increase the speed of synthesis over previous algorithms, as well as synthesize larger circuits. In order to fully reap the benefits offered by this algorithm, we are developing a hybrid OpenMP/MPI algorithm, with the hopes of scaling up this method to thousands of cores or more.
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
An Integrated Risk Assessment Framework for Compound Flooding in Canadian Urban Environments
The simultaneous or subsequent occurrence different flood drivers including heavy rainfall, river overflow, storm tides, among others threaten Canadian communities and infrastructure especially in coastal environments. Analysis of drivers of flooding in isolation without proper characterization of their interrelationships can lead to a significant underestimation of flood risk. This can severely undermine resilience measures and lead to the misallocation of investment in flood protection. In this project, we will develop an integrated statistical and physically-based modelling framework to simulate and predict compound flood risks under climate change in Canadian coastal zones to develop effective mitigation plans. The proposed approach will quantifythe dependencies between multiple flood hazards and identify/characterize compound events using a novel multivariate statistical approach. We will set up and calibrate a land surface modelcoupled to a hydrodynamic model to characterize the multivariate behaviour of flooding. The resulting framework will assess the impacts of compound flooding and characterize the contribution of each driver to the impacted areas under future scenarios considering the effects of more intense hydroclimatic events and sea-level rise in a changing climate. In collaboration with the Institute for Catastrophic Loss Reduction (ICLR), we will disseminate the results from the proposed project directly to insurance industry involved in the management of urban flood risks. This project will provide river forecast centres, conservation authorities, insurance industries, municipalities, among other stakeholders with data, rigorous methodology, and personnel to analyze and predict compound flooding and will significantly contribute to improved resilience of Canadian cities and communities.
An intelligent immersive content creation platform for the non-programmer for training, maintenance and assembly using AR and VR.
OVA’s StellarX is an uncompromisingly good virtual and augmented reality software. Purpose-built for teams that want to collaborate and create in those new paradigms.
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.
Assessment of Prototype Scour Data
Scour is the removal of riverbed sediment which can be induced due to the presence of channel contraction, hydraulic structures, natural fluctuations in discharge, or changes in sediment supply in a fluvial environment. Scour and erosion have been identified as the primary cause of the majority of bridge failures in North America. Several investigators have concluded that about 50% of all bridge collapses occur due to scourͲrelated complications. The prevalence of scourͲinduced bridge collapses is indicative of the criticality of scour estimation in the interest of public safety as well as mitigation of infrastructure costs, as this type of collapse often requires significant investment for design and construction of replacement bridges, fault analysis and potential rehabilitation. The available bridge design codes are mostly extracted from estimates of scour at the laboratory scale (experiments in reducedͲscale physical models), acquired under highly controlled conditions. Some sources of model inaccuracy include scale effects in physical hydraulic modelling; a lack of understanding of the flow physics of the phenomenon; and the limitations of current computational methods used to model sediment transport. A way to improve the prediction ability of current scour methodologies could be using observed scour values in real rivers to verify/correct such methods, as this proposed research intends to do. The aim of the present project is to study prototype scour data at various field site as well as to use computational tools to assess the efficacy of scour estimation methods for investigating the possible improvements to existing approaches.
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.
Automated cytogenetic dosimetry as a public health emergency medical countermeasure
Biodosimetry is a useful tool for assessing the radiation dose received by an individual when no reliable physical dosimetry is available. Our Automated Dicentric Chromosome Identifier software (ADCI) has been developed to automate dose estimation of gamma and X-ray radiation exposures. Biodosimetry laboratories currently process these data manually, and the capacity to handle more than a few samples at the same time would quickly overwhelm the laboratories. The software has been developed to handle radiation exposure estimation in mass casualty or moderate scale radiation events. Federal biodosimetry and clinical cytogenetic laboratories have automated systems to collect digital chromosome images of cells with and without chromosomes exposed to radiation. We have developed advanced image segmentation and artificial intelligence methods to analyze these images. ADCI identifies dicentric chromosomes (DCC), a widely recognized, gold standard hallmark of radiation damage. The number and distribution of DCCs are determined and compared with a calibration curve of known radiation dose. Our ADCI software can also generate these calibration curves. Our software computes the dose received of one or more test samples and generates a report for the user. The desktop version of ADCI contains an easy-to-use graphical user interface to perform all of these functions. The supercomputer version of this software proposed here will be optimized to determine the dose for many samples simultaneously, which would be essential in the event of a mass casualty.
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.
Design of OLED materials for manufacturing and improved product quality
Organic light emitting diodes (OLEDs) present a unique opportunity to produce thinner and more efficient lighting and displays. This will change the way we interact with light. The main barrier to mass adoption of OLEDs is the manufacturing process, due to the need for high throughput while maintaining nanoscale precision. High throughput operation requires materials that can undergo elevated temperature without decomposing. Our objective is to use computational chemistry to model innovative materials that can withstand these elevated temperatures while still providing high performing OLEDs. We will simulate targeted compounds using SOSCIP’s computer cluster examining properties relevant to OLED manufacturing processes. Promising materials will be synthesized and their properties experimentally measured then compared to the simulation results. The most promising materials will then be integrated into OLEDs and characterized by OTI Lumionics in their pilot scale manufacturing line located in Toronto, ON.
Detailed computational fluid dynamics modeling of UV-AOPs photoreactors for micropollutants oxidation in water and wastewater
Micropollutants such as bisphenol-A and N-nitrosodimethylamine pose a significant threat to aquatic life, animals, and humans beings due to their persistent and potentially carcinogenic nature. While most conventional water treatment methods cannot remove these contaminants, ultraviolet-driven (UV) advanced oxidation processes (AOPs) are effective in degrading micropollutants. As UV-AOPs require electrical energy to enable the treatment, energy costs present a barrier to the widespread adoption of this technology. In this project, we focus on the optimization of UV-AOPs-based reactors to enhance their degradation performance while reducing their energy consumption. In this respect, we will develop a detailed numerical model that integrates hydraulics, optics and chemistry to investigate UV-AOP photoreactors in a comprehensive manner.
The resulting information will then be utilized to design the next-generation of UV-AOP photoreactors commercialized by Trojan Technologies. The design space will be explored by high-performance computer simulations of full-scale photoreactors rather than simplified or scaled-down models. This will be accomplished by leveraging opensource software, artificial-intelligence optimization techniques and the second-to-none parallel-computing capabilities offered by Blue Gene/Q. Once the optimization of UVAOPs-based reactors is complete, the advanced modeling results generated using Blue Gene/Q will be utilized in the development of a simplified model for sizing purposes. This will be accomplished through combined use of metamodeling techniques and cloud computing. In brief, the concept is to simplify the detailed model developed earlier so that it can be simulated using hand-held mobile devices, which will allow the company’s sales personnel to market the optimized reactors. Consequently, it will allow the company to increase its competitiveness on global scale as well as to increase the rate of adoption of advanced water treatment technologies by water utilities and end-users.
Development of cardiac specific machine learning infrastructure
Analytics for Life, Inc. (A4L) is an early stage medical device company that specializes in the development of technologies to analyze patient physiological signals in order to evaluate cardiac performance, status and risk. A4L’s core competencies include identifying and developing mathematical features from physiological signals and assembling these features into clinically informative formulae using machine learning techniques. A4L has used third party machine learning tools (open source and licensed products) for the formula generation aspect of the product development cycle.
Specifically, A4L has used these tools to demonstrate the feasibility of computing left ventricular ejection fraction, cardiac ischemic burden and other cardiac performance/status parameters for simple to collect, non-invasive physiological signals (surface voltage gradients, SPO2, Impedance etc.). As a result of this experience, A4L have learned the benefits and insufficiencies of these tools for A4L’s specific purposes. A4L plans to file with the U.S. Food and Drug Administration (FDA) an application for approval of a physiological signal collection device and will soon afterwards be seeking market clearance for products assessing cardiac health emanating from the machine learning process. A4L believes it can build a machine learning tool specifically tailored for cardiac evaluation based on experience with the tools used to date.
This A4L-specific machine learning paradigm will search only relevant mathematical spaces, cutting down on time and CPU power needed to iterate to solutions and will allow for an assessment of a much wider array of potential solutions. Furthermore, this A4L-specific machine learning paradigm will provide a controlled and validated system that can be audited and evaluated by regulatory bodies, something that is not possible with the current machine learning tool(s). A4L proposes a hybridization of paradigms within a set mathematical space. This will create efficiency in the search, and therefore more searches can be performed in the same period of time. This will lead to more solutions being available for evaluation, resulting in more accurate and efficiently produced end solutions. If successful, this new paradigm will allow for simple, non-invasive, rapid and relatively inexpensive cardiac diagnostic capabilities, bringing tertiary care diagnostics to primary care settings and disrupting the current infrastructure and capital cost-centric model of diagnostic delivery.
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.
Establishing best practices for generating synthetic pediatric health data
Professor Aaron Smith of the University of Ottawa, working with the CHEO Research Institute, will develop software to generate synthetic electronic health records (EHR) that have similar statistical properties to real EHR while preserving patient privacy.
The ability to share patient data without compromising patient privacy will allow more researchers to search for medically and COVID relevant insights and broad-based sharing will allow researchers to check their ideas about fighting COVID with both greater speed and accuracy.
The final result of this project will be a software package that can generate synthetic electronic health records using minimal computational resources for moderately sized datasets.
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.