A Phase 2, Double-blind, Placebo-controlled Study of the Safety and Efficacy of Microbial Ecosystem Therapeutic-2 (MET-2) in Patients with Major Depression

Evidence has shown considerable individual variability in bacterial content of the gut microbiota, which is hypothesized to influence brain function. Research examining this relationship suggests that microbiota transplantation may aid in improving depression symptoms by recolonizing the gastrointestinal tract with healthy bacteria. The study will examine the effects of a microbial therapeutic composed of various strains of gut bacteria from a healthy donor on mood.

Development of new methods for screening bioilogics by means of Capillary Electrophoresis (CE) coupled with Mass-Spectrometer (MS) via novel Open Port Probe Sampling Interface (OPP)

The methods for screening complex biological samples found wide application in pharmaceutics, forensic science and medical science. The majority of these methods involve several analytical techniques coupled together in order to maximize the efficiency of the analysis. For example, the combination of Capillary Electrophoresis (CE) with Mass Spectrometry (MS) creates a new analytical platform (CE-MS) that utilizes the separation power of CE and superior detection abilities of MS.

Short-term Stability of Liquid Crystal Wavelength Selective Switches

Lumentum produces high-performance optical devices and test equipment for fiber optics communications systems. One such device is the Wavelength Selective Switch (WSS) that is used to switch optical signals between different optical fibers, depending on the wavelength of the light carrying the signal. Although these devices work very well and Lumentum is a leader in the design and production of such devices, their performance is currently somewhat limited by relatively slow fluctuations in the amount of light scattered into the desired optical fiber for a chosen wavelength.

Bio-based Latexes using Switchable Hydrophilicity Solvent (SHS)

The proposal focuses on the design and synthesis of novel bio-based, biodegradable materials to be used in packaging and protective coatings (varnishes), and targeted at replacing the nonrenewable, non- biodegradable materials currently used to manufacture these products. Our new process allows us to obtain a desired combination of material properties, and also eliminates the need for using VOCs (Volatile Organic Compounds). We achieve this using a new type of solvent whose properties can be “switched” simply by introducing or removing CO2 into or from the mixture.

RPAS acquired full motion video analysis and anomaly detection

This research project will investigate how modern Remotely Piloted Aircraft Systems (also referred to as RPAS, drones, UAVs or UASs) can optimize surveillance missions and target characterization through the integration of Full Motion Video (FMV) cameras. FMV data will be processed and analyzed including the use of Artificial Intelligence (AI) algorithms. The large amount of data which results from such surveillance missions must be analyzed via semi-automatic and automatic methods, as manual analysis is neither fast enough nor economical.

Internet-based mental state monitoring using patient's textual data - Year two

Among all chronic diseases, mental health issues have the highest burden on health care systems. However, unlike other chronic diseases, like Diabetes or hypertension, no monitoring procedures exist to monitor patients’ mental health status to prevent relapse and crisis situations. It is therefore necessary to develop cheap, convenient and accessible monitoring systems that could be used outside clinical setting. Most mental health diseases demonstrate a range of physical and behavioral symptoms (e.g.

Risk classification over time for individuals who have diabetes

This project/research is based on creating AI models that assist in determining blood glucose levels in individuals that have been diagnosed with Diabetes and classify changes in their risk-levels over a period of time. This research will be carried out by Ian Ho, student at Queen’s University pursuing his Bachelor’s in Applied Science – BASc, Applied Mathematics and Computer Engineering. He will research and develop predictive algorithms and analytical models for early diagnosis and risk assessment for Diabetes, under the supervision and advisory of QMind and Dr.

Photonic Cognitive Processor for Next Generation Artificial Intelligence Hardware

Artificial Intelligence (AI) is transforming our lives in the same way as the advent of the Internet and cellular phones has done. However, it takes thousands of CPUs and GPUs, and many weeks to train the neural networks in AI hardware. Traditional CPUs, GPUs, and brain-inspired electronics will not be powerful enough to train the neural networks of the near future. To radically impact the next generation of AI hardware, I propose to develop a fundamental technology: a photonic cognitive processor that uses light (instead of electrons).

Copper and nickel stable isotopes in overburden and transported cover as exploration tools for buried magmatic sulfide deposits

The rate of discovery of new, large mineral deposits has slowed, yet significant opportunity exists in many world-class belts where post-mineral cover obscures bedrock and can potentially hide world-class deposits beyond the reach of traditional geochemical tools. However, locating mineral deposits in areas of thick or transported overburden is challenging.

The use of aquatic therapy to manage musculoskeletal pain in Canadian military veterans

The proposed research project has two aims: 1) to understand the experiences of Canadian Military Veterans with lower extremity pain when participating in aquatic therapy; 2) To determine the effectiveness of aquatic therapy versus land-based therapy on Canadian military veterans with chronic lower-extremity pain.

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