Five Dal researchers have received a $750,000 investment from the Government of Canada to advance their innovative ideas.
The funding comes from the competition, which has an objective of supporting high risk, high reward and interdisciplinary research. This year鈥檚 awards, with grants up to $250,000 over two years, are supporting 117 research projects across Canada with the potential to yield game-changing results in social, cultural, economic, health-related or technological areas.
鈥淐ongratulations to this talented group of researchers, whose bold ideas will have significant impact across Canada and around the globe,鈥� says Alice Aiken, Dalhousie鈥檚 vice president research and innovation. 鈥淭his investment is recognition of the outstanding quality of research at 聽Dalhousie, and helps us lead the way forward on solving some of the world鈥檚 most complex issues.鈥�
Meet the recipients:
Researcher: Dr. Zhenyu Cheng
Co-applicant: Dr. Daniel Boyd
Faculties: Medicine and Dentistry
Project: Creating a breakthrough technology to overcome antibiotic resistance
Widespread antibiotic resistance has been acknowledged as the biggest threat to global health and economy in the coming decades. The bacterial pathogen Pseudomonas aeruginosa grows in Cystic Fibrosis (CF) airways and biofilms, representing one of the most common causes of drug-resistant infections.
This project aims to directly address the global crisis of antibiotic-resistant pathogens by developing an inhalable metal-ion releasing therapeutic particle that effectively eradicates persistent P. aeruginosa biofilms in the CF lung microenvironment. It will drive a new interdisciplinary frontier by bridging uncharted innovations in material science, bioengineering, and microbiology to mitigate the potential high risks of causing lung damage and triggering bacterial virulence and resistance.
Researchers: Dr. Ingrid Waldron (Co-principal investigator), and Dr. Paola Marignani (Co-applicant)
Faculties: Health and Medicine
Project: Genes and Geography 鈥� Disparities in Cancer Incidence and Outcomes in a Black Canadian Community
Cancer is the leading cause of death in Canada, but little is known about Canadian cancer disparities despite numerous international studies reporting disparities in cancer incidence and outcomes for people with African ancestry. These disparities reflect the interplay of social determinants of health, the environment and genetics.
As the lack of Canadian cancer disparity data may be shortening the lifespan of marginalized communities, this project will focus on a Black Nova Scotian community (South Shelburne), which has an inordinately high incidence and family history of various cancers. This will require a high-risk framework incorporating natural and social sciences to determine if environmental, biological, genetic, socio-economic and lifestyle factors are associated with high cancer incidence and mortality among the Black community in South Shelburne, Nova Scotia.
This project is based on the research Dr. Waldron has been conducting in Black Nova Scotian in Shelburne and other regions in Nova Scotia over the past nine years.
Researcher: Dr. Evangelos Milios
Co-PI: Dr. Evangelia Tastsoglou, Saint Mary鈥檚 University
Co-applicant: Dr. Eugena Kwon, Saint Mary鈥檚 University
Faculty: Computer Science
Project: Visual analytics for text-intensive social science research on immigration
Text-intensive research in social sciences relies on the retrieval, organization, conceptualization and summarization of large amounts of text, with the aim of obtaining insights on social science research questions. Typically, social science researchers can only read and annotate limited amounts of text, so the amount of text data must be constrained by limiting the scope of the research question. In addition, retrieval of relevant text data is carried out by key term searches, which risks missing relevant documents using unanticipated vocabulary, and including irrelevant documents simply because they happen to include the search terms.
This project introduces a novel methodological paradigm in social science research that employs natural language processing and visual analytics to enable social scientists to retrieve and make sense of large document collections. From a computer science perspective, the transition from laboratory evaluations to addressing real-world problems and the close interdisciplinary collaboration will advance the state-of-the-art design of visual analytics (VA) systems aiming for usability by social scientists, enabling them to analyze much larger document sets than has been feasible to date.
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