Innovative Projects Realized

Méta-analyse sur l’effet d’un extrait de plante sur les performances des poulets en croissance et identification des facteurs de modulation

Les antibiotiques facteurs de croissance ajoutés dans les aliments pour animaux a été récemment interdite au Canada et leur utilisation sera considérablement restreinte dans le futur. Ainsi, plutôt que de seulement contrôler les maladies entériques, des efforts doivent être faits pour améliorer la santé de l’écosystème intestinal. Il existe de nombreuses alternatives telles les huiles essentielles qui sont utilisés depuis des décennies chez l’homme pour leurs propriétés antimicrobiennes, Le mode d’action de ces produits n’est pas bien connu et leurs impacts sur la croissance et la prévention des maladies digestives restent essentiellement variables malgré le grand nombre de publications sur les alternatives aux antibiotiques chez les poulets de chair. La méta-analyse a été choisie comme outil pour résumer et quantifier les connaissances acquises sur le produit Oleobiotech Poultry qui sera comparé avec une base de données exhaustives sur l’utilisation d’huiles essentielles dans l’alimentation des poulets. TO BE CONT’D

Tip-a: A New Carcinogenic Factor of Helicobacter pylori

This research project involves investigating how Helicobacter pylori is causing gastric cancer. We will be investigating proteins which have been shown to target the nucleus and cause a cancerous microenvironment, as well as epithelial-mesenchymal transition cells in their recombinant forms. Currently, there is little known about the effects of these proteins in their endogenous forms. To address this, we will be using knockout models to determine the immunogenic and oncogenic effects of these proteins on host cells using a variety of molecular biology techniques. We suspect that the knockout models will display less immunogenic and oncogenic effects when compared to our wild type. By conducting experiments under physiological conditions, we may be able to address gaps in the research as well as provide further insight as to how Helicobacter pylori causes gastric cancer. Understanding these mechanisms may lead to novel prevention or treatment strategies.

Big data configuration for Industry 4.0 Robotics

This project relates to the configuration of data generated by several robotic arms during the manufacture of an Airbus A350. Robots generate an enormous amount of data during these type of processes, and the data is crucial for future analysis of how to improve the process. In order to be analyzed, the data must first be annotated. The annotation is currently a manual task, which is tedious and time-consuming. The objective of the project is to create a tool which can be used for automatic annotation and configuration of the data output from the manufacturing process. The proposed data configuration tool could take one of a few different forms. In the simpler form, the tool might assist an operator in configuring the data in a smart way. For example, instead of having to decide how to store each individual dataset, the tool might be a user interface that suggests similarly configuring a large amount of data that comes from the same source. A more advanced assistance tool might configure the data automatically, and could learn to adjust the configuration based on operator corrections.

Using longitudinal data to understand gender differences in bicycling over time

This project explores reasons why there are differences in bicycling behaviour between men and women using two longitudinal datasets. It leverages synergies between two research teams examining bicycling uptake Brisbane and Vancouver. Brisbane and Vancouver are promoting active transportation by bicycle through investing in physical bicycling infrastructure, education programs, and promotional activities. Though it is well documented there is a gender disparity in bicycling, research explaining gender differences in bicycling is limited, however infrastructural improvements and promotion are hypothesized to reduce the disparity. The student (Ms. Sersli) will examine the data to determine changes in bicycling over time and how this varies between men and women in a Brisbane-based cohort using statistical modeling techniques, and she will also examine changes in bicycling and confidence in a Vancouver-based cohort. The expected outcomes include two manuscripts in peer-reviewed journals.

Reconnaissance automatique de produits à partir des requis dans les appels d’offres

Ce projet de recherche vise à développer une technologie de pointe qui permettra à une entreprise d’identifier rapidement les appels d’offres les plus intéressants pour elle en fonction des produits et services qu’elle offre. Elle lui permettra également de produire ses soumissions plus rapidement et efficacement.
Plus précisément, l’objectif est de développer un logiciel capable d’extraire automatiquement tous les requis d’un appel d’offres et, à partir de ces requis, d’identifier les produits et services que l’entreprise est en mesure de fournir. Le logiciel pourra déterminer dans un premier temps quels appels d’offres sont les plus intéressants pour l’entreprise et, dans un second temps, préparer automatiquement un projet de soumission. L’approche qui sera utilisée fera appel à des techniques d’intelligence artificielle, soit l’apprentissage profond (« deep learning ») à base de réseaux de neurones et l’analyse sémantique latente.

Caractérisation de l’activité du cortex sensorimoteur, par électro-encephalogramme haute densité (EEG-HD) chez les jeunes enfants avec une paralysie cérébrale et atteinte unilatérale spastique

Le projet de recherche réalisé a pour but d’étudier la dynamique temporelle du réseau action-observation chez l’enfant hémiplégique. Pour ce faire, 20 enfants âgés de 3 à 12 ans atteints de paralysie cérébrale hémiplégique ou d’hémiplégie acquise dans un contexte de tumeur cérébrale ou d’accident vasculaire cérébral seront étudiés par électro-encéphalogramme haute densité dans 4 conditions : repos, action, observation d’action et cohérence cortico-cinématique (étude de la cohérence entre les rythmes cérébraux et les signaux d’accélérations enregistrés par un accéléromètre lors de mouvements passifs). Cette étude devrait permettre de mieux comprendre les mécanismes de plasticité post-lésionnelle du cortex moteur. Par ailleurs, elle ouvre la voie à l’étude neurophysiologique de la plasticité cérébrale mise en jeu lors de différentes techniques de rééducation motrice, dont celles par observation d’action, et devrait donc contribuer à adapter ces techniques chez l’enfant.

Chemical looping gasification for sustainable production of biofuels

Sustainable production of biofuel from biomass is the subject of my project at the University of Zaragoza and institute of coal chemistry (CSIC) located in Zaragoza, Spain. The overall objective of the project is design of an efficient biomass-to-biofuel conversion process to reduce emission of carcinogenic compounds into the atmosphere. In this regard, I will study the effect of different key factors including strength, resistance and durability of the materials used in the system, process conditions in the catalytic units and operating parameters of the reactors. At the end of this study, we expect to expedite biofuel production rate and improve effectiveness of the biofuel production process. Successful accomplishment of the project helps extend industrial applications of biofuel which lowers dependence on fossil fuels.

Topic Segmentation for Text Mining on legal Documents

Text Mining is the process of automatically extracting structured knowledge from

unstructured, natural language documents. It aims to support users in dealing with large

amounts of textual information. Examples for specific text mining tasks are entity detection,

summarization, and opinion mining. Due to the complexity and ambiguity of natural language,

this analysis is broken down into individual processing steps, which are based on techniques

from the fields of machine learning, natural language processing, and semantic

computing.

In this project, the goal is to enrich the text mining pipelines developed at KeaText for the

processing of legal documents. Specifically, the analysis is to be enriched with a topic

segmentation module that is tailored to the specific domain and application requirements

tomatic topic segmentation, also known as text tiling, structures documents into individual

parts, each representing a distinct theme. It is well-known that topic segmentation can

improve several

information retrieval and text analysis tasks. In this project, the following tasks are to be

completed……………………………………..

Mobile Application for Paper-Based Analytical Devices

Testing for antibodies is necessary for therapeutic and diagnostic purposes. In less privileged locations, however, the ability to test for important antibodies is stifled by financial limitations. Conventional methods, such as mass spectrometry, are expensive to run, require trained operators, and demand expensive and bulky instruments. The Citterio lab is working on paper-based analytical devices using bioluminescence that address the hurdles of traditional analysis, both paper-based and conventional. The Citterio lab devices have been successfully tested with HIV, influenza, and dengue antibodies.

Current analysis requires a picture to be taken on a camera and transferred to a computer for processing. Moving files between devices and software programs is inconvenient and the workflow could be streamlined. In light of these restrictions, smartphones offer an effective and simple alternative. My project is to create a mobile application to quantify bioluminescence from paper-based analytical devices. I will be targeting regular users instead of hospitals. Ideally, this technology will be usable by non-medical personnel and home users to monitor various biometric parameters. As such, user-friendliness is of highest priority.

A fully-functioning smartphone application is expected to be developed.

Studying Magnetic field structures in ISM scattering screens

Galactic Magnetic Fields (GMF) have always been an intriguing topic to astronomers: they play a big role in star formations, Galactic structures, interstellar medium (ISM) distribution, etc. Therefore, understanding these structures becomes a crucial part to unravel the mystery of our Galaxy.

A main signature of magnetic fields is that they can rotate polarized light rays that pass through them. Therefore, by studying the polarization data of a pulsar, a rotating neutron star that sends out highly polarized electromagnetic waves, we can understand the strength as well as the structure within the GMF.

I will use data from pulsar B1133+16, taken at Arecibo Observatory in 2015, to analyze the magnetic field within the ISM scattering screen. The result will be used to verify some existing theories on the structure of ISM scattering screens distributed along the magnetic domain wall within the Galaxy.

IRIS FaceMatch: A secure face-based identity detector without racial bias using deep learning

This project aims to address the race bias of face recognition technology by developing new face feature sets and building the suggested models based on an equal number of images from varied-race photos using appropriate deep learning algorithms. This research seeks to deliver industry partner IRIS with guidelines for a developed prototype that facilitates the adoption of IRIS’s FaceMatch technology for enhancing police capabilities to find unidentified individuals based on their photos while saving labor force and other police resources. Fixing this bias enables IRIS to serve police at different provinces and extend the usage of the IRIS FaceMatch to other applications, such as searching for friends based on their photos.

Mining Version Histories To Automate Merge-Conflict Resolutions

In current collaborative software development environments, developers usually work in parallel. They often share changes with other developers or incorporate changes from them, with the help of version control systems (VCSs) such as Git and Subversion. The parallel collaboration process improves the development speed on the one hand, but on the other hand, leads to possible code inconsistencies.
When multiple developers make inconsistent changes, textual, syntactic, or semantic merge conflicts may occur during integration. Time needed to resolve these conflicts reduces developers’ productivity. The solutions developed in this project will assist the developers in identifying conflicts before they occur and explaining the reasons behind these conflicts. That will help simplify the software development process, improve software maintenance, shorten the time to market, and increase the quality of the produced software.