Arthritis is a chronic disease that severely decreases the quality of life and affects almost 4.6 million Canadians, costing $33 billion for the Canadian economy every year. Affected individuals experience pain and disability through an extended period of time. Rheumatoid arthritis (RA), a common form of arthritis, is an autoimmune disease characterized by the inflammation of the synovium, or synovial membrane, a connective tissue that provides a cushion between bones and tendons and muscle around a joint.

Epilepsy-related sudden death occurs following uncontrolled recurrent seizure which are usually non-responsive to antiepileptic drugs. These patients found dead in bed usually following heart and respiratory arrest. It is not known whether seizure-affected brain regions regulating cardiorespiratory function play a role in this complication and if so, what is the mechanism underneath in order to treat early to prevent death.

Oncology specialists are few in numbers and cannot be present within every hospital or clinic providing their guidance and support. There are institutions in this world with oncology experts that can provide the best possible care and the knowledge of these experts is stored in medical case files within the hospital. With the power of machine learning and the internet, an organization without any experts can compare their cancer treatment plans to the top performing institutions in the world and receive constructive feedback on where their plan needs improvement.

Machine learning has been applied in various fields and shown promising results in recent years. Researchers have found that tuning machine learning models in a proper way can vastly boost the model performance with respect to the specific AI task. However, tuning machine learning models at scale, especially finding the right hyperparameter values, can be difficult and time-consuming. There is therefore great appeal for automatic approaches that can optimize the hyperparameter of any given model.

The recent outbreak of the SARS-CoV-2 associated coronavirus disease, COVID-19, had been declared a global pandemic by the World Health Organization. There is still only a minimal understanding of the virus and an absence of effective targeted therapy for its treatment. Epigenetic regulations in cells control the expression of genes without modifications to the genetic codes itself, and epigenetic-targeted therapy development had been widely proposed as a promising approach to antiviral therapeutics.

As smart home and artificial intelligence technologies are developing rapidly, smart home devices contribute to better living quality and safer spaces. These smart devices are intelligent agents. They receive a variety of signals through sensors placed in ecobee’s thermostats, light switches and other smart devices and controls the heating and cooling, lighting, as well as providing important notifications. In this project, we would like to analyze sensory data and develop various machine learning solutions for characterization of the devices’ environment (e.g.

Ontario’s Greenbelt is composed of nearly 2 million acres of protected land including natural areas that provide ecosystem services to millions of people. While these areas face reduced pressure from land use conversion, they still face a pressures typical of natural systems in peri-urban landscapes including loss of biodiversity, invasive species, impacts from infrastructure projects and a changing climate. In order to determine the extent to which these pressures are shifting natural systems, indicators of system health are needed.

COVID-19 pandemic has brought the world to standstill with more than 3 million people infected and more than 200 000 mortality so far. It has literally brought the health care systems in many countries to the breaking point, if not beyond. The economic consequences have been devastating with millions of people out of work. We are taking a novel approach by focusing on two SARS-CoV2 (COVID-19) methyltransferases that are essential for viral replication. Both enzymes (nsp14 and nsp16) are druggable.

With the advancement of modern technology, especially the increase in network speed, videos are taking more and more important places among media types. With vast potential applications, video recognition has received great attention. However, video recognition is a non-trivial task: a lot of training data are needed for complicated neural networks, but annotated data are hard to acquire. As a result, there is a growing tendency to bank on self-supervised learning approaches that can make use of unlabeled data.

SOTI has developed a software product called SOTI SNAP that allows anyone to create an app with no programming or technical knowledge. The goal of this project is to develop media scheduler widgets so that users can create playlists consisting of mixed media (videos, images, documents) and then deploy them at specific hours on certain days.