Projets novateurs réalisés

The Narreme Engine: A Tool for Narrative Analysis and Construction – Year two

In my PhD dissertation, I proposed a renewed theory of the “narreme” – the minimum basic unit of narrative structure, a concept which originated in the 1960s but which has been largely neglected since then. I suggested that a Narreme Theoretic approach could be tremendously useful not only for the purposes of narrative analysis, but also for the construction of narratives in various creative media, as well as a pedagogical tool for teaching reading comprehension and literacy, and as a method for narrative-based psychotherapies. Can Narreme Theory be applied practically for these purposes? That is the question I aim to address in my postgraduate research.
My project would attempt to continue the work I have done in my dissertation, seeking to fill the gap left by the neglect of narreme-based approaches to story research; to this end, in collaboration with my partner organization, I will attempt to develop a software-based “Narreme Engine” for analysis and construction of story networks. The success of such an engine would help establish the utility of my approach, and provide a useful tool for moving forward in the fields of narratology and narrative media.

The Narreme Engine: A Tool for Narrative Analysis and Construction

In my PhD dissertation, I proposed a renewed theory of the “narreme” – the minimum basic unit of narrative structure, a concept which originated in the 1960s but which has been largely neglected since then. I suggested that a Narreme Theoretic approach could be tremendously useful not only for the purposes of narrative analysis, but also for the construction of narratives in various creative media, as well as a pedagogical tool for teaching reading comprehension and literacy, and as a method for narrative-based psychotherapies. Can Narreme Theory be applied practically for these purposes? That is the question I aim to address in my postgraduate research.
My project would attempt to continue the work I have done in my dissertation, seeking to fill the gap left by the neglect of narreme-based approaches to story research; to this end, in collaboration with my partner organization, I will attempt to develop a software-based “Narreme Engine” for analysis and construction of story networks. The success of such an engine would help establish the utility of my approach, and provide a useful tool for moving forward in the fields of narratology and narrative media.

The Application of Single Domain Antibodies in Biasing GPCR Signal Pathways – A Case Study with the Adenosine A2A Receptor – Year two

Single domain antibodies (sdAbs) represent a versatile class of heavy chain antibodies lacking a paired light chain and encoded by VHH germline genes typically expressed in camelids. KisoJi Biotech has developed a transgenic mouse line in which each mouse expresses multiple camelid VHH genes capable of specifically binding any antigen of interest. The small molecular weight of sdAbs (15 kDa) provides new opportunities to utilize these as specific ligands of G Protein-Coupled Receptors (GPCRs). One-third of current pharmaceuticals target GPCRs – the largest family of membrane proteins in the human genome and mediators of diverse biological processes through signal transduction. The adenosine A2A receptor (A2AR) is a prototypical class A GPCR and a target for the treatment of many diseases (1). Studies reveal A2ARs can stimulate multiple G protein pathways, with differing efficacies, depending on tissue type, ligand, and presence of other receptors. Our goal is to demonstrate the therapeutic utility of sdAbs to: i) achieve greater specificity and allosteric control of activation, ii) target specific functional states of the receptor, iii) achieve longer serum half-lives, and iv) improve tissue partitioning to obviate classical side-effects. This technology will then serve as a general platform for sdAbs to class A GPCRs.

The Application of Single Domain Antibodies in Biasing GPCR Signal Pathways – A Case Study with the Adenosine A2A Receptor

Single domain antibodies (sdAbs) represent a versatile class of heavy chain antibodies lacking a paired light chain and encoded by VHH germline genes typically expressed in camelids. KisoJi Biotech has developed a transgenic mouse line in which each mouse expresses multiple camelid VHH genes capable of specifically binding any antigen of interest. The small molecular weight of sdAbs (15 kDa) provides new opportunities to utilize these as specific ligands of G Protein-Coupled Receptors (GPCRs). One-third of current pharmaceuticals target GPCRs – the largest family of membrane proteins in the human genome and mediators of diverse biological processes through signal transduction. The adenosine A2A receptor (A2AR) is a prototypical class A GPCR and a target for the treatment of many diseases (1). Studies reveal A2ARs can stimulate multiple G protein pathways, with differing efficacies, depending on tissue type, ligand, and presence of other receptors. Our goal is to demonstrate the therapeutic utility of sdAbs to: i) achieve greater specificity and allosteric control of activation, ii) target specific functional states of the receptor, iii) achieve longer serum half-lives, and iv) improve tissue partitioning to obviate classical side-effects. This technology will then serve as a general platform for sdAbs to class A GPCRs.

An introduction to robust and efficient statistical learning algorithms with applications in actuarial science

The big data era represents an opportunity for statistical methods to shine, through applications relevant to a wide spectrum of fields, including actuarial science. In order to seize and make the most out of this opportunity, researchers and practitioners must, however, effectively manage the challenges that big data pose. The intern will be exposed to two challenges: data quality (taking the more specific form of data bases containing outliers because of data with gross errors or extreme values) and scalability of the numerical methods required for inference. The intern will be introduced to novel Bayesian robust methods and Markov chain Monte Carlo algorithms to address these issues. The intern will explore the benefits of applying these methods and algorithms in actuarial contexts, in particular in the field of general insurance. This last part will represent a contribution and may lead to a paper in an actuarial journal.

3D spine imaging using tracked ultrasound and artificial intelligence

Obtaining accurate images of the spine is important for different medical purposes. For example, to measure how deformed the spine is in patients with scoliosis and select the most suitable treatment. Images can also be used to guide a clinician while inserting a needle to reach a exact location in the spine to administer anesthesia. Another application is to plan and guide the insertion of screws in each vertebra, which are required to correct malformations of the spine.

Currently, the most common way to obtain images of the spine is by using technologies that require the use of X-rays. However, X-rays are known to increase the risk of developing cancer. A safer and more affordable alternative is to use sound waves. Nevertheless, the images obtained with sound waves are not as clear as the ones obtained with X-rays. Consequently, we propose the use of computers and complex strategies based on artificial intelligence. With those tools, we will be able to increase the quality of images obtained with sound waves, making them comparable to X-ray images for practical purposes. A product with these characteristics would be benefitial for the society, while generating profit for the partner company.

Advanced Analytical Techniques integrated with Machine Learning for Proactive Raw material Characterization for Vaccine Production – Year two

Over the last several decades, challenges in the development, production, supply, and use of vaccines have been raised and by consequence had led to increasing concern around the world. As a result, an increase in research and innovation in the vaccine industry is needed. In this sense, one of the most critical factors in the vaccine production industry is the raw material and its quality. It is well known, that in order to obtain high yields of the target compounds in vaccines, well-characterized and homogenized raw material is needed. Hence, a raw material characterization, optimization, and control process are critical before raw materials are used in the fermentation that we can refer to as a biological process. The use of advanced analytical techniques such as Raman and Nuclear Magnetic Resonance spectroscopy are suitable alternatives to characterize complex matrix as they offer a deeply detailed composition. Furthermore, this data can be integrated with Machine Learning for better-quality control and automatization of the process. The main aim of this work is to characterize and optimize the raw material used in vaccine production through advanced analytical techniques and integrating it with Machine Learning to improve vaccine production and decrease the cost.

Advanced Analytical Techniques integrated with Machine Learning for Proactive Raw material Characterization for Vaccine Production

Over the last several decades, challenges in the development, production, supply, and use of vaccines have been raised and by consequence had led to increasing concern around the world. As a result, an increase in research and innovation in the vaccine industry is needed. In this sense, one of the most critical factors in the vaccine production industry is the raw material and its quality. It is well known, that in order to obtain high yields of the target compounds in vaccines, well-characterized and homogenized raw material is needed. Hence, a raw material characterization, optimization, and control process are critical before raw materials are used in the fermentation that we can refer to as a biological process. The use of advanced analytical techniques such as Raman and Nuclear Magnetic Resonance spectroscopy are suitable alternatives to characterize complex matrix as they offer a deeply detailed composition. Furthermore, this data can be integrated with Machine Learning for better-quality control and automatization of the process. The main aim of this work is to characterize and optimize the raw material used in vaccine production through advanced analytical techniques and integrating it with Machine Learning to improve vaccine production and decrease the cost.

Real-time electromagnetic navigation of oral cancer resection

Breast?conserving surgery (BCS) is a mainstay in breast cancer treatment. For nonpalpable breast cancers, current strategies have limited accuracy, contributing to high tumor-positive margin rates. Perk-Lab developed NaviKnife, a surgical navigation system based on real?time electromagnetic (EM) tracking.

The goal of the research project is to confirm the further feasibility of intraoperative EM navigation in vitro phantom experimental setup and to assess the potential value of surgical navigation. Hypothesis of using the system is to get improved numbers and success rates of surgical procedures.

Expected results would demonstrate that real?time EM navigation is feasible in the operating room for BCS. Excisions performed with navigation result in the removal of less breast tissue without compromising tumor-positive margin rates. In future use of navigation better surgical outcomes mean better survivability, less morbidity and mortality.

Effets bénéfiques de l’estradiol chez la femelle ovarectomisée soumise à l’hypoxie intermittente : implication de l’AMPK

Le syndrome d’apnées du sommeil touche un milliard de personnes dans le monde et est associé à des pathologies cardiovasculaires et métaboliques telles que l’insuffisance coronarienne et le diabète de type 2. Chez la femme ménopausée, la prévalence et les conséquences du SAS augmentent. Les équipes du Pr. Joseph et du Dr Belaïde utilisent l’hypoxie intermittente (HI); une conséquence majeure du SAS, comme modèle préclinique afin de mieux comprendre cette pathologie et proposer des pistes de traitements complémentaires au traitement actuel dont l’efficacité est partielle. L’équipe du Pr. Joseph (U. Laval) a montré que l’estradiol prévient l’intolérance au glucose chez des souris femelles soumises à l’HI. Or, l’équipe du Dr. Belaidi (U. Grenoble) a montré que l’activation de l’Adenosine MonoPhosphate-activated Kinase (AMPK) améliore la tolérance au glucose chez des mâles. Nous testerons donc l’hypothèse que l’estradiol améliore la tolérance au glucose des femelles ovariectomisées hypoxiques via une activation de l’AMPK.

Development of High Velocity Oxy-Fuel (HVOF) Iron Aluminide Coatings Reinforced With Hard Ceramic Particles for Wear Resistant Applications

This project aims at developing new coating materials exhibiting high oxidation, corrosion and wear resistance to addresses the specific needs of power generation equipment, which operate under severe erosion and abrasion conditions in corrosive environments.

Supporting adolescent athletes through a team building and mental health program – Year two

Canadian adolescent athletes suffer many mental health challenges such as hyper competitiveness and a culture of silence which often precludes individuals from discussing mental health issues. These challenges have been compounded by the COVID-19 pandemic. Team Unbreakable (the partner organization) have run a highly successful physical activity and mental health program which is implemented through local school boards. The current project will adapt this program by integrating a team building module which will be implemented through coaches of community sport programs. Team building is a highly effective strategy to increase social connectedness in adolescents, which is a vital component of mental health. Further implementing the program through sport coaches offers a promising approach as coaches are influential role models to young athletes and are likely to increase the effectiveness of the program. The current project will also develop a range of online resources which can help sport coaches, administrators, and athletes discuss, understand, and support individuals with mental health issues. Overall, this project will help reduce the prevalence and severity of mental health issues in young athletes and contribute to the de-stigmatization of mental health issues in Canadian sport.