(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 cloud‐based, multi‐modal, cognitive ophthalmic imaging platform for enhanced clinical trial design and personalized medicine in blinding eye disease
Age Related Macular Degeneration is the leading cause of irreversible blindness in Canada and the industrialized world, yet there are no treatments for the vast majority of patients. Led by Tracery Ophthalmics inc, and working with Translatum Medicus inc (TMi) and academic partners at the Robarts Research Institute, Western University, and the “High Risk Dry AMD Clinic” of St Michael’s Hospital, we will engage SOSCIP’s Cloud Analytics platform, including servers, software and human resources, to accelerate the search for new treatments.
Specifically, Tracery has developed a novel functional imaging method, “AMD Imaging” (AMDI) that has already generated unprecedented pictures of the retina (the film of the eye) that include both known and unknown “flavours” of disease (the phenotype). These complex images will be compared against an individual’s genetic makeup (their genotype) and their concurrent illnesses, medications, and lifestyle history (their epigenetics). Further, Tracery’s imaging will help identify particular patients that will benefit from TMi’s drug development program, and ultimately help doctors choose which treatment will work best. Over the course of two years, we will involve increasing numbers of medical experts and their patients to generate and amass AMDI images, evaluating them over time and against other modalities.
Ultimately, through the “I3” program, we will work with IBM to train Watson and the Medical Sieve to recognize and co‐analyse complex disease patterns in the context of the ever‐expanding scientific literature. In short, we will leverage cloud‐based computing, to integrate image‐based and structured data, genomics and large data analytic to unite global users. We anticipate that this approach will significantly accelerate drug development, providing personalized treatment for the right patient at the right time.
A dynamic and scalable data cleaning system for Watson analytics
Poor data quality is a serious and costly problem affecting organizations across all industries. Real data is often dirty, containing missing, erroneous, incomplete, and duplicate values. It is estimated that poor data quality cost organizations between 15% and 25% of their operating budget. Existing data cleaning solutions focus on identifying inconsistencies that do not conform to prescribed data formats assuming the data remains relatively static. As modern applications move towards more dynamic search analytics and visualization, new data quality solutions that support dynamic data cleaning are needed. An increasing number of data analysis tools, such as Watson Analytics, provide flexible data browsing and querying abilities. In order to ensure reliable, trusted and relevant data analysis, dynamic data cleaning solutions are required. In particular, current data quality tools fail to adapt to: (1) fast changing data and data quality rules (for example as new datasets are integrated); (2) new data governance rules that may be imposed for a particular industry; and (3) utilize industry specific terminology and concepts that can refine data quality recommendations for greater accuracy and relevance. In this project, we will develop a system for dynamic data cleaning that adapts to changing data and rules, and considers industry specific models for improved data quality.
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.
Active learning for automatic generation of narratives from numeric financial and supply chain data
Large enterprises compile and analyze large amounts of data on a daily basis. Typically the collected raw data is processed by financial analysts to produce reports. Executive personnel use these reports to oversee the operations and make decisions based on the data. Some of the processing performed by financial analysts can be easily automated by currently available computational tools. These tasks mostly make use of standard transformations on the raw data including visualizations and aggregate summaries. On the other hand automating some of the manual processing requires more involved artificial intelligence techniques.
In our project we aim to solve one of these harder to automate tasks. In fact analyzing textual data using Natural Language Processing (NLP) techniques is one of the standardized methods of data processing in modern software tools. However the vast majority of NLP methods primarily aim to analyze textual data, rather than generate meaningful narratives.
Since the generation of text is a domain-dependent and non-trivial task, the automated generation of narratives requires novel research to be useful in an enterprise environment. In this project we focus on using numerical financial and supply chain data to generate useful textual reports that can be used in the executive level of companies. Upon successful completion of the project, financial analysts will spend less time on repetitive tasks and have more time to focus on reporting tasks requiring higher-level data fusion skills.
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.
Advancing the CANWET watershed model and decision support system by utilizing high performance parallel computing functionality
Watershed modeling is widely used to better understand processes and help inform planning and watershed management decisions. Examples include identifying impacts associated with land use change; investigating outcomes of infrastructure development, predicting effects of climate change. The proposed project will see the evolution of a desktop based watershed modeling and decision support system to a web based tool that will allow greater access by decision makers and stakeholders. By this means we will advance the idea of evaluating cumulative effects in the watershed decision making process rather than the current practice of assessing proposed changes in isolation.
The proposed software evolution will take advantage of high performance computing by porting existing code to a higher performing language and restructuring to operate using parallel or multi-core processing. The result is expected to be a dramatic reduction in simulation run times. Reduced run times will facilitate the use of automatic calibration routines used to conduct model setup, reducing costs. It will also enable rapid response if the simulation were to be re-run by a request through the web-based user interface. The designed web-based tool will be used by decision and policy makers in the watersheds that contribute to Lake Erie to understand the sources of pollution especially phosphorus which is a major contributor to Lake Erie eutrophication problems and develop policies in supporting a wide variety of watershed planning and ultimately help achieve the Federal and Ontario government commitments to reduce 40% phosphorus entering Lake Erie by 2025.
Advancing video categorization
Vubble is a media tech company that builds solutions for trustworthy digital video distribution and curation. Using a combination of algorithms and human curators, Vubble searches the internet to locate video content of interest to its users. Vubble is collaborating with Dr. Vida Movahedi from Seneca’s School of Information and Communication Technology to develop a machine-learning algorithm that will automatically output highly probable categories for videos. With this algorithm implemented into the Vubble workflow to assist in automated video identification, Vubble will be able to better address their existing, and emerging, customer demands, while increasing their productivity and competitiveness. This video identification research project will be Vubble’s first step in understanding how to automate the identification of accurate video. The need for automation of videos curation is prevalent, as video is quickly becoming the world’s dominant form of media consumption, particularly for digital native younger audiences. Furthermore, the results of the applied research will aid Vubble in moving forward in addressing what they believe is a looming problem facing all media consumers, and society, the rising of fake news video created from archival footage.
Agile computing for rapid DNA sequencing in mobile platforms
DNA can now be measured in terms of electronic signals with pocket-sized semiconductor devices instead of 100-pound machines. This has started to transform DNA analysis into a mobile activity with the possibility to track and analyze the health of organisms at unprecedented levels of detail, time, and population. But the remote cloud-based computer services currently needed to process the electronic signals generated by these miniature DNA-meters cost over $100 to complete an initial analysis on one human genome and consume over 1000 Watts of power. Also, the cost of wirelessly transmitting measured data to these cloud-based analyzers can exceed $1000 per human genome. Further, reliance on external high-performance compute services poses a greater risk for compromising the security of the DNA data collected. This project proposes the construction of a specialized high-performance miniature computer – the agile base caller (ABC) – built from re-configurable silicon chips that can connect directly to the DNA-meter and analyze the DNA it measures in real-time.
The ABC, by virtue of its size, will preserve the mobility of emerging DNA measurement machines, and will enable them to analyze data for less than $1 while consuming less than 10 Watts. These cost/power performance improvements will significantly drop the barriers to the application of genomic analysis to non-laboratory settings. For example, they will allow continuous monitoring of Canada’s food supply for the presence of harmful biological agents with the possibility of cutting analysis delays from weeks to hours.
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 economics-aware autonomic management system for big data applications
Recent advancements in software technology, including virtualization, microservices, and cloud computing, have created novel challenges and opportunities on developing and delivering software. Additionally, it has given rise to DevOps, a hybrid team responsible for both developing and managing the software system, and has led to the development of tools that take advantage of the enhanced flexibility and enable the automation of the software management cycle. In this new world characterized by volatility and speed, the Business Operations (BizOps) team is lagging behind and still remains disconnected from the DevOps team. BizOps views software as a product and is responsible for defining the business and economic strategy around it.
The goal of the proposed project is to imbue DevOps tools and processes with BizOps knowledge and metrics through formal models and methods. Currently, BizOps receives the software system or service as a finished product, a black box, on which a price has to be put and be offered to clients. The price and the marketing strategy are usually defined at the beginning of a sales cycle (e.g. a year) and remain the same for the entirety of the cycle. However, this is in contrast to the great volatility of the service itself. In most cases, the strategies are based on the instinct of managers with high acumen and experience and broad marketing surveys or one-to-one negotiations with clients, information that can easily change and may remain disconnected from the software development. The end product of this project is a set of economic and performance models to connect the DevOps and BizOps processes during the software’s life cycle and eventually incorporate them in automated tools to adapt and scale the system in production and enable continuous development, integration and delivery.
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.
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.
Big cardiac data
We propose to develop a cloud based image centered informatics system powered by newly developed big data analytics from our group for automatic diagnosis and prognosis of heart failure (HF). HF is a leading cause of morbidity and mortality in Ontario and around the world. There is no cure. Therefore early diagnosis and accurate prediction is very critical before the symptoms appears. However, previously lack of efficient image analytics tool has been the major pitfall to have an accurate prognosis and early diagnosis system. The system will greatly improve the clinical accuracy and enables accurate early diagnosis and prediction by intelligently analyzing all associated historical images and clinical reports. The final system will not only greatly reduce the sudden death and irreversible cardiac conditions, but also offers a great optimization of decision system in current healthcare. This project is based on strength built upon one successful SOSCIP project and two OCE projects.
Big data analysis and optimization of rural and community broadband wireless networks
Rural broadband initiative is happening in a big wave across the world. Canada, being a diverse country has a specific Internet reachability problem due to population being sparse. It is economically not viable to bring fiber to each and every house in Canada. It is not economically viable to connect every household through satellites either. Broadband Internet over wireless networks is a good option where Internet is brought over fiber to a point of presence and moved to houses over wireless.
EION is actively working in Ontario and Newfoundland to make rural broadband a possibility. Wireless networking in rural areas in Canada is a challenge in itself due to weather, terrain and accessibility. Real-time constraints such as weather, water and foliage do alter the maximum capacity of the wireless pipe. In addition the usage pattern of the houses, especially real-time video that require fast response time, require adequate planning.
This is becoming very critical as almost 80% of the traffic seems to be video related due to popularity of applications such as Netflix, Youtube and Shomi. Intelligence in wireless rural broadband networks are a necessity to bring good quality voice, video and data reliably. Optimization in system and network level using heuristics and artificial intelligence techniques based on big data analysis of video packets is paramount to enable smooth performing broadband rural networks.
In this project, we will be analyzing the big data of video packets in rural broadband networks in Ontario and Newfoundland and design optimized network design and architecture to bring reliable video services over constrained rural broadband wireless networks.