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The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
127 Projects, page 1 of 13

  • Canada
  • 2017-2021
  • 2021

10
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  • Funder: SNSF Project Code: 191599
    Funder Contribution: 24,100
    Partners: Institut universitaire de gériatrie Université de Montréal
  • Funder: UKRI Project Code: NE/T014733/1
    Funder Contribution: 10,155 GBP
    Partners: University of Guelph, Lancaster University

    AHRC : Jessica Robins : AH/R504671/1 "Breaking Eggs" is an exciting project sharing knowledge between the UK and Canada. The project invites residents of Guelph, Wellington to take part in a series of hands-on workshops responding to the beginning of Our Food Future project, a city wide, 5-year project that aims to use technological innovation to make the region a sustainable food hub for Canada. Our Food Future is a multi-million-dollar project that will use technology to radically change the way food is grown, distributed and consumed. The project will make Guelph the world's first circular food city, using technology to make sure everyone has enough to eat and waste is eliminated, while restoring natural systems. The workshops will use creative methods to help local community members explore the wider project and examine avenues for their engagement. It will look at what opportunities' residents could take advantage of, and what challenges communities could face during this transition. Breaking Eggs will take place in the first year of the Our Food Future project so will give residents of different local communities a chance to be involved in shaping the project. The workshops will invite people from all parts of Guelph and Wellington County to take part in sharing ideas and creating a new future for the region. The lessons learned through the project will be brought back to the UK and the knowledge gathered will be shared so that other communities can look at ways they can engage in more sustainable food systems for their region.

  • Funder: UKRI Project Code: NE/R012849/1
    Funder Contribution: 387,179 GBP
    Partners: University of Manitoba, Alfred Wegener Inst for Polar & Marine R, University of Bristol

    Following the polar amplification of global warming in recent decades, we have witnessed unprecedented changes in the coverage and seasonality of Arctic sea ice, enhanced freshwater storage within the Arctic seas, and greater nutrient demand from pelagic primary producers as the annual duration of open-ocean increases. These processes have the potential to change the phenology, species composition, productivity, and nutritional value of Arctic sea ice algal blooms, with far-reaching implications for trophic functioning and carbon cycling in the marine system. As the environmental conditions of the Arctic continue to change, the habitat for ice algae will become increasingly disrupted. Ice algal blooms, which are predominantly species of diatom, provide a concentrated food source for aquatic grazers while phytoplankton growth in the water column is limited, and can contribute up to half of annual Arctic marine primary production. Conventionally ice algae have been studied as a single community, without discriminating between individual species. However, the composition of species can vary widely between regions, and over the course of the spring, as a function of local environmental forcing. Consequently, current approaches for estimating Arctic-wide marine productivity and predicting the impact of climate warming on ice algal communities are likely inaccurate because they overlook the autecological (species-specific) responses of sea ice algae to changing ice habitat conditions. Diatom-ARCTIC will mark a new chapter in the study of sea ice algae and their production in the Arctic. Our project goes beyond others by integrating the results derived from field observations of community composition, and innovative laboratory experiments targeted at single-species of ice algae, directly into a predictive biogeochemical model. The use of a Remotely-Operated Vehicle during in situ field sampling gives us a unique opportunity to examine the spatio-temporal environmental controls on algal speciation in natural sea ice. Diatom-ARCTIC field observations will steer laboratory experiments to identify photophysiological responses of individual diatom species over a range of key growth conditions: light, salinity and nutrient availability. Additional experiments will characterise algal lipid composition as a function of growth conditions - quantifying food resource quality as a function of species composition. Furthermore, novel analytical tools, such as gas chromatography mass spectrometry and compound specific isotope analysis will be combined to better catalogue the types of lipid present in ice algae. Field and laboratory results will then be incorporated into the state-of-the-art BFM-SI biogeochemical model for ice algae, to enable accurate simulations of gross and net production in sea ice based on directly observed autecological responses. The model will be used to characterise algal productivity in different sea ice growth habitats present in the contemporary Arctic. By applying future climate scenarios to the model, we will also forecast ice algal productivity over the coming decades as sea ice habitats transform in an evolving Arctic. Our project targets a major research gap in Phase I of the CAO programme: the specific contribution of sea ice habitats to ecosystem structure and biogeochemical functioning within the Arctic Ocean. In doing so, Diatom-ARCTIC brings together and links the activities of ARCTIC-Prize and DIAPOD, while further building new collaborations between UK and German partners leading up to the 2019/20 MOSAiC campaign.

  • Funder: UKRI Project Code: BB/W010720/1
    Funder Contribution: 3,000 GBP
    Partners: UBC, IFR

    Canada

  • Funder: UKRI Project Code: NE/T014237/1
    Funder Contribution: 9,945 GBP
    Partners: UBC, Durham University

    ESRC : Hester Hockin-Boyers : ES/P000762/1 The Mitacs Globalink UK-Canada doctoral exchange scheme would enable PhD student Hester Hockin-Boyers (Durham University) to spend 12-weeks working with Dr Norman and Professor Vertinsky in the School of Kinesiology at the University of British Columbia (UBC), from September-December 2020. The proposed research will explore how Canadian women's interactions with health and fitness content on Instagram impacts upon physical activity participation. This research is sorely needed because, while social media is increasingly pertinent to the formation of everyday health practices, this dimension is seldom explored. In addition, this project will pilot a novel method, developed by Hockin-Boyers, called 'screenshot elicitation', which seeks to capture the fast, dynamic, mobile and everyday nature of interactions with digital content. Whilst Hockin-Boyers has already begun to develop this technique as part of her PhD research, the Mitacs Globalink project will provide the space and resources to pilot and advance this methodology. The findings resulting from this project have the potential to enhance Canadian women's quality of life, health and wellbeing, by informing digital platform design, social media pedagogies, and public policy in Canada. Furthermore, by providing Hockin-Boyers access to the variety of expertise in Digital Health at UBC, new knowledge and methodological techniques will be brought back to the UK, thus enhancing capacity for further research and innovation

  • Funder: UKRI Project Code: NE/T013982/1
    Funder Contribution: 10,312 GBP
    Partners: University of Toronto, University of Birmingham

    Health systems in the UK and Canada have made extensive use of Electronic Medical Records (EMR) for many years as an integral part of their operations. However, whilst digitally recorded data exists, their use as the basis of a "learning health system" whereby continuous improvements in patient experience, hospital operations, and quality of care has are made by collating and examining data and evidence to improve all these areas. However, real-world EMR data can be very challenging to handle. One significant contribution to these difficulties is data quality. Missing data is a particular issue, with rates of missingness of between 10-30% for some records. Properly addressing the missing data issue in EMR data is complicated by the fact that it can be difficult to differentiate between genuine missing data (data was not recorded into the system) and a non-applicable response (e.g. the test was not appropriate therefore it was not done). Data can be missing-at-random (MAR) or missing-not-at-random (MNAR) where, in the latter, there is an underlying factor that determines the missingness patterns. Certain types of missingness can therefore be "informative" since, if a clinician decided not to order certain tests, it indicates a certain implicit belief about the perceived health state of the patient. Failure to account for these sources of bias may lead to incorrect inferences. Artificial Intelligence technologies are seen as an important tool in unlocking the information wealth held in our electronic medical records. This project will contribute to the maturation of these technologies to account for the real-world complexities of EMR datasets. The research proposed here will develop algorithms for data imputation that seek to be more robust, reliable and generalisable. We have chosen to initially focus on automated sepsis diagnosis, a pressing area of biomedical research given that sepsis accounts for around 44,000 deaths each year in the UK alone. Therefore, by applying modern approaches based on machine learning to large EMR datasets we promise to tackle this problem in a unique way that could have meaningful real-world impact. However, as many AI prediction models require complete datasets as input, one popular strategy for handling missing data involves "data imputation", whereby an algorithm is used to fill in missing data values. These methods vary in complexity from simply filling in missing values with the average observed values over the entire dataset through to more advanced methods that attempt to elicit the underlying patterns in the data. However, many current imputation methods are designed for only certain types of EMR data (e.g. clinical time series of molecular measurements) and fail to account for sources of bias and provide measures of certainty about the quality of the imputed data. The overall goal of this project is to develop novel machine learning methods for missing data imputation in EMRs that account for biases and statistical uncertainty in the imputation.

  • Funder: UKRI Project Code: NE/V009931/1
    Funder Contribution: 7,643 GBP
    Partners: Cardiff University, UBC

    EPSRC : Benjamin Cosimo Maglio : EP/R511882/1 Laser comms is a branch of wireless communication using a laser beam which directed at a given target, allowing higher speed connections. Specifically, we consider devices called modulators, these are used to encode information into a laser beam, analogously to sending a Morse code message. The modulator allows more of less of the laser beam to pass through it creating pulses equivalent to on and off signals (binary data). These devices have been computationally designed to predict improvement on the current technology. This project will test the existing devices of the current technology and then the new designs to show the predicted improvements. The results will inspire further design of an optimized device for search & rescue applications.

  • Funder: SNSF Project Code: 187807
    Funder Contribution: 75,100
    Partners: Institute for Studies in Education University of Toronto
  • Funder: SNSF Project Code: 187649
    Funder Contribution: 79,600
    Partners: Department of Chemistry University of Toronto
  • Funder: UKRI Project Code: NE/T01458X/1
    Funder Contribution: 6,628 GBP
    Partners: University of Exeter, UBC

    BBSRC : William Davison : BB/M009122/1 (1921484) As of 2017 salmonid aquaculture was worth $22 billion USD per year with the UK contributing $1.4 billion USD and Canada responsible for $988 million USD. However, despite UN directives stating a need to double production by 2050, growth is hampered by negative public perception. Typically salmonid aquaculture combines land-based freshwater hatcheries with sea-pen rearing systems. While requiring lower maintenance costs the use of sea-pens increases risk of disease in farmed fish and has been linked with parasite overspill into wild populations of salmon causing serious declines in native populations. As such there is a demand to reduce the duration of the marine grow out phase, or transition entirely to land based farm systems (referred to as recirculating aquaculture systems - RAS) which largely avoid many of these problems. However, thus far development of RAS farms has been limited due to reduced growth observed in RAS compared to pens, and the scale of RAS required to maintain fish up to harvest size. Reduced growth and adverse health outcomes have largely been attributed to various issues relating to water chemistry (e.g. CO2, salinity, pH etc.). Previous research from Prof. Richards and Prof Brauner's labs identified an optimal salinity for growing Coho salmon within RAS. Salmon grown at a salinity approximately isosmotic to blood were found to have the fastest growth rate and lowest food conversion ratio compare to fish grown at other salinities ranging from freshwater to full strength seawater. This has been hypothesised to be reduced energy expenditure for osmoregulation. However, that study was conducted at relatively low pHs indicative of a build-up of respiratory CO2 in the water, a problem that has been characterised extensively in RAS. Due to the link between osmoregulation and acid-base balance in fish, any reduction in environmental CO2 may therefore benefit fish health and growth by reducing energy expenditure on acid-base balance and therefore allow increased growth compared to fish grown at high CO2. Here we plan to acclimate fish to either freshwater (1 ppt) or isosmotic water (10 ppt) and then expose them to either atmospheric levels of CO2 or to the elevated levels of CO2 found within fish farms. We then hope to measure a variety of physiological parameters such as growth, acid-base balance and immune function. This information will then be used to determine optimal water chemistry conditions to maximise growth of salmon in aquaculture.

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The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
127 Projects, page 1 of 13
  • Funder: SNSF Project Code: 191599
    Funder Contribution: 24,100
    Partners: Institut universitaire de gériatrie Université de Montréal
  • Funder: UKRI Project Code: NE/T014733/1
    Funder Contribution: 10,155 GBP
    Partners: University of Guelph, Lancaster University

    AHRC : Jessica Robins : AH/R504671/1 "Breaking Eggs" is an exciting project sharing knowledge between the UK and Canada. The project invites residents of Guelph, Wellington to take part in a series of hands-on workshops responding to the beginning of Our Food Future project, a city wide, 5-year project that aims to use technological innovation to make the region a sustainable food hub for Canada. Our Food Future is a multi-million-dollar project that will use technology to radically change the way food is grown, distributed and consumed. The project will make Guelph the world's first circular food city, using technology to make sure everyone has enough to eat and waste is eliminated, while restoring natural systems. The workshops will use creative methods to help local community members explore the wider project and examine avenues for their engagement. It will look at what opportunities' residents could take advantage of, and what challenges communities could face during this transition. Breaking Eggs will take place in the first year of the Our Food Future project so will give residents of different local communities a chance to be involved in shaping the project. The workshops will invite people from all parts of Guelph and Wellington County to take part in sharing ideas and creating a new future for the region. The lessons learned through the project will be brought back to the UK and the knowledge gathered will be shared so that other communities can look at ways they can engage in more sustainable food systems for their region.

  • Funder: UKRI Project Code: NE/R012849/1
    Funder Contribution: 387,179 GBP
    Partners: University of Manitoba, Alfred Wegener Inst for Polar & Marine R, University of Bristol

    Following the polar amplification of global warming in recent decades, we have witnessed unprecedented changes in the coverage and seasonality of Arctic sea ice, enhanced freshwater storage within the Arctic seas, and greater nutrient demand from pelagic primary producers as the annual duration of open-ocean increases. These processes have the potential to change the phenology, species composition, productivity, and nutritional value of Arctic sea ice algal blooms, with far-reaching implications for trophic functioning and carbon cycling in the marine system. As the environmental conditions of the Arctic continue to change, the habitat for ice algae will become increasingly disrupted. Ice algal blooms, which are predominantly species of diatom, provide a concentrated food source for aquatic grazers while phytoplankton growth in the water column is limited, and can contribute up to half of annual Arctic marine primary production. Conventionally ice algae have been studied as a single community, without discriminating between individual species. However, the composition of species can vary widely between regions, and over the course of the spring, as a function of local environmental forcing. Consequently, current approaches for estimating Arctic-wide marine productivity and predicting the impact of climate warming on ice algal communities are likely inaccurate because they overlook the autecological (species-specific) responses of sea ice algae to changing ice habitat conditions. Diatom-ARCTIC will mark a new chapter in the study of sea ice algae and their production in the Arctic. Our project goes beyond others by integrating the results derived from field observations of community composition, and innovative laboratory experiments targeted at single-species of ice algae, directly into a predictive biogeochemical model. The use of a Remotely-Operated Vehicle during in situ field sampling gives us a unique opportunity to examine the spatio-temporal environmental controls on algal speciation in natural sea ice. Diatom-ARCTIC field observations will steer laboratory experiments to identify photophysiological responses of individual diatom species over a range of key growth conditions: light, salinity and nutrient availability. Additional experiments will characterise algal lipid composition as a function of growth conditions - quantifying food resource quality as a function of species composition. Furthermore, novel analytical tools, such as gas chromatography mass spectrometry and compound specific isotope analysis will be combined to better catalogue the types of lipid present in ice algae. Field and laboratory results will then be incorporated into the state-of-the-art BFM-SI biogeochemical model for ice algae, to enable accurate simulations of gross and net production in sea ice based on directly observed autecological responses. The model will be used to characterise algal productivity in different sea ice growth habitats present in the contemporary Arctic. By applying future climate scenarios to the model, we will also forecast ice algal productivity over the coming decades as sea ice habitats transform in an evolving Arctic. Our project targets a major research gap in Phase I of the CAO programme: the specific contribution of sea ice habitats to ecosystem structure and biogeochemical functioning within the Arctic Ocean. In doing so, Diatom-ARCTIC brings together and links the activities of ARCTIC-Prize and DIAPOD, while further building new collaborations between UK and German partners leading up to the 2019/20 MOSAiC campaign.

  • Funder: UKRI Project Code: BB/W010720/1
    Funder Contribution: 3,000 GBP
    Partners: UBC, IFR

    Canada

  • Funder: UKRI Project Code: NE/T014237/1
    Funder Contribution: 9,945 GBP
    Partners: UBC, Durham University

    ESRC : Hester Hockin-Boyers : ES/P000762/1 The Mitacs Globalink UK-Canada doctoral exchange scheme would enable PhD student Hester Hockin-Boyers (Durham University) to spend 12-weeks working with Dr Norman and Professor Vertinsky in the School of Kinesiology at the University of British Columbia (UBC), from September-December 2020. The proposed research will explore how Canadian women's interactions with health and fitness content on Instagram impacts upon physical activity participation. This research is sorely needed because, while social media is increasingly pertinent to the formation of everyday health practices, this dimension is seldom explored. In addition, this project will pilot a novel method, developed by Hockin-Boyers, called 'screenshot elicitation', which seeks to capture the fast, dynamic, mobile and everyday nature of interactions with digital content. Whilst Hockin-Boyers has already begun to develop this technique as part of her PhD research, the Mitacs Globalink project will provide the space and resources to pilot and advance this methodology. The findings resulting from this project have the potential to enhance Canadian women's quality of life, health and wellbeing, by informing digital platform design, social media pedagogies, and public policy in Canada. Furthermore, by providing Hockin-Boyers access to the variety of expertise in Digital Health at UBC, new knowledge and methodological techniques will be brought back to the UK, thus enhancing capacity for further research and innovation

  • Funder: UKRI Project Code: NE/T013982/1
    Funder Contribution: 10,312 GBP
    Partners: University of Toronto, University of Birmingham

    Health systems in the UK and Canada have made extensive use of Electronic Medical Records (EMR) for many years as an integral part of their operations. However, whilst digitally recorded data exists, their use as the basis of a "learning health system" whereby continuous improvements in patient experience, hospital operations, and quality of care has are made by collating and examining data and evidence to improve all these areas. However, real-world EMR data can be very challenging to handle. One significant contribution to these difficulties is data quality. Missing data is a particular issue, with rates of missingness of between 10-30% for some records. Properly addressing the missing data issue in EMR data is complicated by the fact that it can be difficult to differentiate between genuine missing data (data was not recorded into the system) and a non-applicable response (e.g. the test was not appropriate therefore it was not done). Data can be missing-at-random (MAR) or missing-not-at-random (MNAR) where, in the latter, there is an underlying factor that determines the missingness patterns. Certain types of missingness can therefore be "informative" since, if a clinician decided not to order certain tests, it indicates a certain implicit belief about the perceived health state of the patient. Failure to account for these sources of bias may lead to incorrect inferences. Artificial Intelligence technologies are seen as an important tool in unlocking the information wealth held in our electronic medical records. This project will contribute to the maturation of these technologies to account for the real-world complexities of EMR datasets. The research proposed here will develop algorithms for data imputation that seek to be more robust, reliable and generalisable. We have chosen to initially focus on automated sepsis diagnosis, a pressing area of biomedical research given that sepsis accounts for around 44,000 deaths each year in the UK alone. Therefore, by applying modern approaches based on machine learning to large EMR datasets we promise to tackle this problem in a unique way that could have meaningful real-world impact. However, as many AI prediction models require complete datasets as input, one popular strategy for handling missing data involves "data imputation", whereby an algorithm is used to fill in missing data values. These methods vary in complexity from simply filling in missing values with the average observed values over the entire dataset through to more advanced methods that attempt to elicit the underlying patterns in the data. However, many current imputation methods are designed for only certain types of EMR data (e.g. clinical time series of molecular measurements) and fail to account for sources of bias and provide measures of certainty about the quality of the imputed data. The overall goal of this project is to develop novel machine learning methods for missing data imputation in EMRs that account for biases and statistical uncertainty in the imputation.

  • Funder: UKRI Project Code: NE/V009931/1
    Funder Contribution: 7,643 GBP
    Partners: Cardiff University, UBC

    EPSRC : Benjamin Cosimo Maglio : EP/R511882/1 Laser comms is a branch of wireless communication using a laser beam which directed at a given target, allowing higher speed connections. Specifically, we consider devices called modulators, these are used to encode information into a laser beam, analogously to sending a Morse code message. The modulator allows more of less of the laser beam to pass through it creating pulses equivalent to on and off signals (binary data). These devices have been computationally designed to predict improvement on the current technology. This project will test the existing devices of the current technology and then the new designs to show the predicted improvements. The results will inspire further design of an optimized device for search & rescue applications.

  • Funder: SNSF Project Code: 187807
    Funder Contribution: 75,100
    Partners: Institute for Studies in Education University of Toronto
  • Funder: SNSF Project Code: 187649
    Funder Contribution: 79,600
    Partners: Department of Chemistry University of Toronto
  • Funder: UKRI Project Code: NE/T01458X/1
    Funder Contribution: 6,628 GBP
    Partners: University of Exeter, UBC

    BBSRC : William Davison : BB/M009122/1 (1921484) As of 2017 salmonid aquaculture was worth $22 billion USD per year with the UK contributing $1.4 billion USD and Canada responsible for $988 million USD. However, despite UN directives stating a need to double production by 2050, growth is hampered by negative public perception. Typically salmonid aquaculture combines land-based freshwater hatcheries with sea-pen rearing systems. While requiring lower maintenance costs the use of sea-pens increases risk of disease in farmed fish and has been linked with parasite overspill into wild populations of salmon causing serious declines in native populations. As such there is a demand to reduce the duration of the marine grow out phase, or transition entirely to land based farm systems (referred to as recirculating aquaculture systems - RAS) which largely avoid many of these problems. However, thus far development of RAS farms has been limited due to reduced growth observed in RAS compared to pens, and the scale of RAS required to maintain fish up to harvest size. Reduced growth and adverse health outcomes have largely been attributed to various issues relating to water chemistry (e.g. CO2, salinity, pH etc.). Previous research from Prof. Richards and Prof Brauner's labs identified an optimal salinity for growing Coho salmon within RAS. Salmon grown at a salinity approximately isosmotic to blood were found to have the fastest growth rate and lowest food conversion ratio compare to fish grown at other salinities ranging from freshwater to full strength seawater. This has been hypothesised to be reduced energy expenditure for osmoregulation. However, that study was conducted at relatively low pHs indicative of a build-up of respiratory CO2 in the water, a problem that has been characterised extensively in RAS. Due to the link between osmoregulation and acid-base balance in fish, any reduction in environmental CO2 may therefore benefit fish health and growth by reducing energy expenditure on acid-base balance and therefore allow increased growth compared to fish grown at high CO2. Here we plan to acclimate fish to either freshwater (1 ppt) or isosmotic water (10 ppt) and then expose them to either atmospheric levels of CO2 or to the elevated levels of CO2 found within fish farms. We then hope to measure a variety of physiological parameters such as growth, acid-base balance and immune function. This information will then be used to determine optimal water chemistry conditions to maximise growth of salmon in aquaculture.