Innovative Projects Realized

Identification and assessment of bioactive yeast strains and cell wall components

Since yeast probiotics and their cell wall components (CWC) are being used to treat enteric inflammatory diseases in different species including humans, they may be useful for preventing Johne’s disease, a chronic inflammatory bowel disease of ruminants caused by Mycobacterium avium spp. paratuberculosis (MAP). Considerable variation in the efficacy of different yeast probiotics and their CWC has been reported; therefore we are proposing to develop in vitro assays that will allow us to assess potential anti-adhesive properties of yeast strains and CWC using MAP as a target pathogen. Additionally, bovine macrophages will be used to assess potential yeast strain and CWC immunomodulatory properties. These assays may be useful for high throughput screening of probiotics and their bioactive compounds, studying their mechanisms of action, and for product quality control.

Development of a subject-centered social media and network platform

Developing subject-based social media requires proper interaction with the users by learning and analyzing their profile and dynamically incorporating their opinion. In this project, a crowd-based profiling of users with respect to their peers will be developed and confidence factor calculated based on the collective opinion of the crowd will be assigned to the individuals’ opinion. There is a prototype system already developed as a proof of idea. The intern will enhance the prototype by adding new functions and modules to it. In the first phase of this project, there are two specific tasks assigned for the intern; (1) Item/Subject Time Wall Display (an alternative display for the item page), (2) Automation of identification of parents vs Child subjects OR Time line display for subjects and its associated events. Each task should take about two months to complete.

Mass spectrometry for multiplexed high throughput analysis of large molecules

Whether it is for research, development or diagnostics, life sciences typically require the quantitation and characterization of samples with assays that classically measure molecules one at a time. Considering that the development and execution of such assays are time consuming and requires considerable investment, the uses of multiplexed analysis would be highly advantageous. Mass spectrometry is a technology that allows multiplexing, but requires highly specialized knowledge in order to achieve the desired results. It is not new that large molecules, mostly proteins, are being analyzed by mass spectrometry. However it remains limited to literate users due to the complexity of the task. Using state-of-the-art instrumentation, this project aims to develop highly standardized approach with simplified data analysis to break barriers that limit the use of mass spectrometry for large molecules analysis. The result of this research will lead to the deployment of services that will reduce cost, accelerate and simplify complex sample characterization in life sciences.

Targeted Incentive Offering for At-Risk Customers in an E-Commerce Setting

In this project, we focus on increasing sales in e-commerce shops by offering purchasing incentives to shoppers who are likely to leave without buying. More specifically, our goal is to predict which shoppers are likely to abandon their shopping cart and what can be done while they’re still on the site to customize their shopping experience and encourage them to buy (e.g. offering a discount). Our approach is based on analyzing the shopping experiences of various customers in many different retail stores to learn a statistical model of the customer shopping cycle. We investigate how machine learning techniques can be used to estimate the likelihood that a customer will abandon a purchase. For a customer who is likely to leave, we try to learn which of the available promotional actions are most probable to encourage the customer to finish the current purchase. The result of this research work will improve the accuracy and efficacy of our local business collaborator Granify’s product and increase the e-commerce revenue of their clients.

Investigating the potential of a Vancouver Island Model Forest – A qualitative stakeholder analysis

This research project aims to investigate the potential of a Vancouver Island Model Forest. A model forest is a partnership-based process for working towards sustainable development of a forested landscape. We will arrange a stakeholder meeting to establish a common understanding of the concept and see if there is commitment from stakeholder groups to procees with development. By interviewing key forest stakeholders we will try to identify the common needs and possible areas of collaboration on the Island. This will provide insight about the viability and value of an island-wide model forest, and reflect the broader opinion about the project. The project will provide Ecotrust Canada with information that can help guide the future direction of their work with the model forest. If the model forest project continues the research can provide guidance in identifying a common vision and strategic plan for the model forest.

Production of high-value hydrocarbons in photosynthetic microalgae

Steady increases in world population necessitate continued growth in staple food production, together with the production of dietary supplements such as vitamins. Chemical synthesis of supplement compounds is in many cases not commercially viable, and therefore alternative production methods must be sought. It has been shown that algae accumulate supplements, such as the high-value compound astaxanthin. The goal of our project is to optimize the amounts of astaxanthin produced in large-scale algae cultures, by varying growth conditions and measuring the resulting astaxanthin concentrations. To this end, the intern will take samples from cultures at the facility of the industrial partner, and analyze their chemical composition in the laboratory of the university partner. The industry partner will benefit through the immediate feed-back of chemical information, enabling rapid optimization of growth conditions and thus maximizing astaxanthin yields for commercial production.

The ARGO Analytics Engine

For producers of film and TV, extending a brand into the digital space can be a daunting enterprise. Estimating how successful a Twitter campaign or an Alternate Reality Game has been in fostering the proliferation and audience goodwill towards a TV or Film entity can be a frustratingly inscrutable endeavor. Even if a campaign seems to have been a critical success–the copious amounts of textual and statistical data at-hand can make it nearly impossible to find out what went right–less, what went wrong. ARGO seeks to solve this problem.

With ARGO, Zeros 2 Heroes wants to produce a decision support dashboard for producers in film and television who are looking to promote or extend their property through online digital campaigns. This tool would draw on, convert and synthesize local and remote qualitative and quantitative resources to reliably generate reports on a digital media campaign’s effectiveness while also indicating the prospective success of a producer’s next steps in said campaign’s deployment. In essence, ARGO is a tool that helps producers separate the signal from the noise in their digital campaigns, and then helps them communicate that signal to other crucial collaborators.

Measuring consumer response to online advertising: the case of mobile applications

To research will help Tap for Tap to generate and collect information on user behavior that can help improve their matching algorithms. The core of the work consists in analyzing a large dataset of app users’ responses to advertising. Clearly, much of this search for determinants of successful matching will be statistical in nature. Still, this search will also be guided by an alert eye on the mechanisms that may lie behind the relationships uncovered in the data. Many mechanisms have been documented in the computer science, economics and marketing literatures and could prove to be useful in the interpretation of the evidence. At this early point in the research, it is not clear what mechanisms will prove relevant. Still, to make the connections with the academic literature clear, we briefly discuss three examples. The intern will dedicate most of her time analyzing this information using statistical models that leverage the random variations that have been introduced in the matching generation process. Finally, the intern will make recommendations to improve the matching algorithm.

Developing advanced techniques for denoising Low-dose CT Images

Computed Tomography (CT) is one of the most widespread non-invasive imaging modalities in medical diagnostics. Recent concerns regarding radiation induced cancer, has drawn a lot of attention to reduce the radiation dose used during CT scanning. However, the signal to noise ratio of scans taken at lower radiation dose is considerably lower than at higher dosages, resulting in poorer diagnostic accuracy. Hence post processing of low-dose scans has become a major concern in medical image processing. In this project two denoising approaches based on sparse representations will be proposed to address the problem of Low-dose CT image denoising. In the first approach, we will use an enhanced version of analytical Discrete Cosine Transform dictionary which leads to more efficient representation of input images. Furthermore, to speed up the process of finding the sparsest representation of an image, a new efficient sparse coding method will be introduced. The second approach is based on adaptive dictionaries. Here, we will propose a novel approach called Adjustable length K-SVD to learn a dictionary with sufficient number of atoms. Finally, it is anticipated that the proposed techniques could be used to reduce the radiation dose needed on CT to acquire the images in clinical environments which has benefits for patients especially for pediatrics.

Evaluation of safety, efficacy and immunogenicity of a recombinant FVIII molecule

We propose a meta-analytic project which aims at summarizing Post-Authorization Surveillance Safety data on Advate, a recombinant FVIII concentrates used to treat patients with Hemophilia A. The study will involve the largest cohort of hemophilia patients on FVIII concentrates analyzed in the literature. The project represents an attempt to sustain a methodologically strong approach in the field of Hemophilia, as paradigm of rare disease. We will accompany a classic and more common metaanalytic modeling with a Bayesian modeling, able to provide measures of efficacy and safety after repetitive simulations with a probabilistic construct resembling the physician’s decisional process. The partner organization will benefit from a state-of-the-art assessment of the safety profile of their prodict in a post-authorization context, which will facilitate plans for the development of future molecules and will be used for lobbying with the regulators in improving the registration process.

Effect of V on the fir-tree zone and Fe-rich intermetallic phase selection in AA1xxx DC cast alloy

Rio Tinto Alcan, the industrial partner of this project, is slowly faced a challenge to produce AA1XXX DC cast ingots with much higher impurity levels than before. The present project is aimed to investigate the influence of impurity elements, particularly V levels, on the fir-tree zone and its related Fe-rich intermetallic phase selection. This project will study the effect of V and Ca on the fir-tree zone and distribution of Fe-rich intermetallics in the function of the distance from the chill surface in order to better control the fir-tree zone at higher impurity levels to improve the cast quality. This project will provide the best guidelines to the industrial partner, Rio Tinto Alcan, for improving the production of AA 1xxx alloy with high impurity levels to meet the highest standards in the marketplace.

Automated analysis of anatomical changes occurring in brain with Posttraumatic Stress Disorder

Trauma occurs in many forms at various instances of our daily life and some subtle ones go unnoticed until sever symptoms are observed. The affects on life vary from impairments to severe incapacitation. Current diagnosis methods are based on questionairs and subjective assessment with inherent variability associated to the doctors. In this internship we will be developing an automated diagnosis framework based on MRI imaging. The frist task will lead to automated image analysis pipeline providing accurate delineation of structures in the brain and provide their pertinent measurements. The next task will develop a framework to automatically identify data as being from patients or healthy individuals. This will then incorporate the current clinical paradigms and lead into an integrated framework. This pipeline and framework will then be further integrated into the current NeuroKinetics clinical assessment protocol.