Projets novateurs réalisés

AI to predict emergency visits

ClosedLoop.ai is an AI-based predictive analytics platform that goes beyond traditional claims-based risk scores to use all patient-related healthcare data to provide both clinicians and care managers with a full breadth of timely, transparent and accurate predictions of health outcomes. ClosedLoop.ai helps value-based providers confidently answer a variety of health-care questions like, which patients are most likely to be readmitted to the hospital? Or which of my patients would most benefit from establishing a relationship with a primary care provider? The objectives are to evaluate the performance of seven prediction models that answers these kinds of questions and to improve one of the seven prediction models by identifying and training a deep learning algorithm.

Power network transfer capability

Hydro-Québec is a public utility that generates and distributes electricity. Despite selling most of its electricity in Québec, its most lucrative sales are in the neighboring markets. To ensure the best possible quality of service, the transmission system must remain stable, but to maximize profits, the company also wants to increase its transmission capacity to maximize energy exports. The transfer limit is now conservatively estimated based on a certain combination of simulated network configurations. This project aims to more accurately estimate the transfer limits of the electric grid and the uncertainty of these estimated limits. Recent advances in machine learning, especially in deep learning, in conjunction with more traditional algorithms used in computer science, have the potential to improve these estimates and therefore augment exports for Hydro-Québec.

Probing Polycyclic Aromatic Hydrocarbons in Photodissociation Regions

Polycyclic Aromatic Hydrocarbons (PAHs) are a large class of complex organic molecules made of carbon and hydrogen that are ubiquitous throughout the space, accounting for up to 15% of the cosmic carbon. These molecules are made of fused benzene rings resulting in a honeycomb structure with hydrogen atoms at the edges of the molecule. They are typically observed through their characteristic infrared emission bands caused by fluorescence of PAHs upon absorbing UV photons. Emission from PAHs originate in the regions of the interstellar medium called Photodissociation Regions (PDRs) – regions where photons > 13.6 eV are absent. PDRs are characterized by their physical conditions such as gas density and the radiation field strength which are not uniform throughout the PDR. These variations in the physical conditions gets reflected in the properties of PAHs such as size, charge state and molecular structure. TO BE CONT’D

Satellite Solar Radiation Nowcasting

The main duty of Hydro-Québec is to repond efficiently to the energy demand of customers, in a safe and secure way while remaining competitive in the markets as well. The main goal of this start-up project is to support Hydro-Québec in developing a future-oriented energy system by proposing innovative technical solutions. Among these solutions, deep learning has been the final choice. Using a deep learning approach, satellite images, weather model outputs and data from solar radiation measurement stations, will be use for the development of a solar radiation nowcasting model. This project will have impact on several business functions related to: PV forecasting, demand forecasting, hydrology, etc.

The effects of Bisphenol A and Bisphenol S on cross-talk between primary human adipocytes and primary human muscle cells

Bisphenol A (BPA), and its analog bisphenol S (BPS) are synthetic compounds commonly found in plastic products, and chronic exposure has been associated with adverse health outcomes such as the development of insulin resistance and type 2 diabetes (T2D). Low levels of these pollutants can transfer from polymers to food or water and can accumulate in adipose tissue due to lipophilicity. Furthermore, adipocytes release hormones, such as adiponectin, that enable cross-talk with skeletal muscle. Therefore, we will investigate the effects of BPA and BPS on adipocyte metabolism and cross-talk with the skeletal muscle. Specifically, we will look at lipid and glucose metabolism primary human adipocytes treated with BPA and BPS. In addition, primary skeletal muscle cells will be exposed to the conditioned media of adipocytes treated with these pollutants, in order to investigate cross-talk between these tissues. TO BE CONT’D

Implementation of a prediction tool for the viscoelastic behavior of textile composites in the industry (Pratt and Whitney Canada).

This project consists in implementing a numerical tool that will be able to evaluate the

viscoelastic behavior of textile composites. The first step will be devoted to adapt the

numerical tools to adapt them to industrial requirements and preferences. Then, the tool

will be validated with the help of the operational knowledge of the industrial partner in

textile composites. Finally, the procedures for using this code will be documented. This

project is part of a larger Collaborative Research and Development project and is

therefore of considerable interest to the industrial partners.

Indirect Domain Shift for Single Image Dehazing

Deep convolutional neural networks (CNNs) have been tremendously successful in many high-level computer vision tasks, e.g., image recognition and object detection. Although recent works have shown that it is also possible to learn an end-to-end CNN model for low-level vision tasks, e.g., image dehazing, the resulting performance is still not completely satisfactory. For high-level vision tasks, it suffices to extract specific features and simply express them as very low dimensional vectors, which results in a relatively simple mapping. In contrast, low-level vision tasks require both global understanding of image content and local inference of texture details; as such, the associated mappings are more complicated. In this project, we will explore that the inadequacy of conventional CNN-based dehazing methods and will propose a new method to mapping the hazy images to clear images by indirect way. To address this issue, we will try to add explicit constraints inside a deep CNN model to guide the restoration process.

Inhibition of Proteases in Host Cell Proteome of Physcomitrella patens for Stability of Recombinant Biopharmaceuticals

Host cell proteins are inevitable contaminants of biopharmaceuticals. Here, we performed detailed analyses of the host cell proteome of moss (Physcomitrella patens) bioreactor supernatants using mass spectrometry and subsequent bioinformatics analysis. Distinguishing between the apparent secretome and intracellular contaminants, a complex extracellular proteolytic network including subtilisin-like proteases, metallo-proteases, and aspartic proteases was identified. Knockout of a subtilisin-like protease affected the overall extracellular proteolytic activity. Besides proteases, also secreted protease-inhibiting proteins such as serpins were identified. Further, we confirmed predicted cleavage sites of 40 endogenous signal peptides employing an N-terminomics approach. The present data provide novel aspects to optimize both product stability of recombinant biopharmaceuticals as well as their maturation along the secretory pathway.

Optimizing heuristics for spin-glass problems for diverse solutions

Optimization problems, such as finding the shortest or fastest path to a destination are ubiquitous in industry. Hower, for some industrial applications it may be desirable to have a set of few diverse, yet nearly optimal solutions. The goal of this project is to create new optimization problem solvers that focus on both quality and diversity of the solutions proposed. These solvers will subsequently be used to assess the performance of the D-Wave quantum annealer processor.

The Working Body: Activity Patterns and their Implications for Labour Organization During the Shang Dynasty, China

My project will be examining the differences of activity patterns between the Middle and Late Shang dynasty. I will accomplish this by examining the degrees of expression on bones that are caused through the movement of activity. I will be using four collections from the Bronze Age cities of Huanbei (Middle Shang dynasty, ca. 1350-1250 B.C.) and Yinxu (Late Shang dynasty, ca. 1200-1046 B.C.), located in the modern day city of Anyang, China. Each collection represents a neighbourhood within the city that had an associated specialized workshop, i.e. bronze, pottery, bone workshop. The expectation is that the individual’s bones will modify and reflect the labour intensity and/or specialization of activities they engage in. These modifications can reveal patterns of activity based on division of labour via age, sex and neighbourhood, and whether these patterns change over time. This project aims to understand the organization of activities among the non-royal population to better comprehend their daily lives.

Climate change and emerging transboundary fish stocks in North America

Climate change is shifting the distribution of fish stocks towards areas with cooler environment, generally in higher latitude or deeper water. Such shifts threaten to increase the amount of conflict over resources as stocks move freely in ocean waters crossing human-made management boundaries. Anticipating these shifts can help identify appropriate mechanisms of joint-management and contribute to the sustainability of fisheries under climate change. Therefore, I pretend to use mathematical models to project changes in fish distribution and explore the possible rise of new transboundary stocks. More specifically, I will determine the number of stocks that would become transboundary under climate change; when will these changes occur; and in what order of magnitude. Results for this research will support countries in anticipating changes in fish catch as well as the appearance of new stocks. Identifying shifting species and the mechanisms by which countries can be more effectively in joint-managing transboundary stocks can improve sustainability and prevent conflict between nations over fishing resources.

Benzodiazepine and Opioids Use in Alberta

Opioids have received much attention in the media, public and government because of the risks associated with them, including fatalities. Concurrent use of BZRAs (benzodiazepines used for treating anxiety and insomnia) and opioids is of particular concern because this is a recognized risk factor for fatal opioid overdoses. Despite this warning, concurrent use is still occurring. An outcome study using Alberta data on concurrent use has not been published in the literature. The outcomes will be used by the partner organization, OKAKI, to further enhance knowledge and tools supporting prescription drug monitoring, and to inform the professional regulatory activities of its customers, including the College of Physicians & Surgeons of Alberta and Alberta’s Triplicate Prescription Program, to improve safe prescribing in Alberta.