The project involves development, validation, and application of new medical image analysis algorithm where cortical thickness is measured from serial brain magnetic resonance imaging (MRI) with improved efficiency and greater sensitivity. Cortical thickness is a relatively new metric that correlates with clinical and cognitive worsening in a variety of neurologic disorders such as multiple sclerosis and Alzheimer's disease.

The Brodt laboratory engineered and developed a novel IGF-targeting molecule (the IGF-Trap) with otent anti-cancer effects. The objective of this project is to optimize the use of this potential drug in re-clinical cancer models and develop sensitive biomarkers for assessing and predicting its effect. In addition, the effect of this inhibitor on pancreatic cancer growth and metastasis and its utility in nononcology ndication will be evaluated.

The objective of this project is to develop a proof-of-concept of a formable resin in the shape of a customizable inset for an earphone. The material should be relatively inexpensive, take shape rapidly (within a few seconds optimally) and ultimately provide consumers with a customized earphone that will fit the vast majority of the population. Using a simple model silicone mold and using various forming techniques not only will material properties be tailored for the resin, but the forming time will be optimized by the careful selection of components within the device.

Cloud computing is one of the pillars of the modern computing infrastructure mainly because it allows the procurement of computing services on a pay-as-you-go basis. However, despite the many benefits offered by cloud computing, it has several significant drawbacks such as data lock-in, lack of universal geographic proximity, risk of service outages, and variable cost structures.

Metallic parts are used in combination with oil or grease in many applicaitons, for example in car engines or hinges. To re-use them, the surface of the metal needs to be cleaned. Current technology makes use of solvents to remove the contamination. Although very effective, this method is polluting. Our industry partner (Walter Surface Technologies) has developed a new technology with very low environmental impact. They wish to test the effectiveness of their technology by measuring the amount of contaminant left on metal surfaces after cleaning with their products.

This project uses machine learning algorithms to better understand back movement and low back pain. We apply supervised learning time series algorithms to data collected from Backtracks’ wearable de-vice — which consists of a malleable think curve that reads data collected from the participants’ spine movements. At each time step, such movements are represented as a curve; the dynamic evolution of this curve in time represents an individual’s spinal movements.

This project uses machine learning algorithms to better understand back movement and low back pain. We apply supervised learning time series algorithms to data collected from Backtracks’ wearable de-vice — which consists of a malleable think curve that reads data collected from the participants’ spine movements. At each time step, such movements are represented as a curve; the dynamic evolution of this curve in time represents an individual’s spinal movements.

The one-year project will investigate the geological, geochemical and geophysical properties of three Rare Earth Element (REE) deposits in Québec. Each of the selected deposit is representative of a class of REE mineral deposits. The project will provide new insights in the origin of the three types of REE mineral deposits. This new understanding of the formation of REE mineral deposits will be transferred to the industrial partners and will be used to improve the odds of discovering REE resources in Québec and Canada.

Our gastro-intestinal tract is colonized by billions of microorganisms that constitute the intestinal flora or microbiota. These microorganisms, primarily bacteria, are required for our development and physiology; however, changes in the "normal" composition of the microbiota, referred to as dysbiosis, have been linked to chronic inflammatory diseases in humans. Currently, a major research effort is to understand what these commensal bacteria are, how they maintain health, and how their dysregulation causes disease.

Automatic mixing is a field of music technology research looking to automate sound engineering processes. The project will look to expand the field, using machine learning techniques to analyze the relationship of the subjective sound engineering techniques of audio engineers. From this, a unique and quantifiable data set will be we gathered in order to represent a sound engineer’s unique ‘sound profile’, which will enable the automatic modelling of different sound engineering styles.