University of Guelph
  • Guelph, Ontario, Canada
Recent publications
To address concerns about the biocompatibility of novel phenolic immobilization-based food preservatives, their impact on the composition and metabonomic profile of a defined community of human gut microbiota was evaluated. Three phenolics (eugenol, vanillin and ferulic acid) presented in two forms (free or immobilized on different supports) were tested at two concentration levels (0.5 and 2 mg/mL). Free eugenol was the phenolic with the greatest impact on gut microbiota, with a remarkable increase in the abundance of Lachnospiraceae and Akkermansiaceae families. In contrast, immobilized phenolics produced an increase in the abundance of Bacteroides with a reduction in the ratio of Firmicutes to Bacteroidetes. The metabonomic profile was also affected by free and immobilized phenolics differently in terms of fermentation by-products and phenolic biotransformation metabolites. Thus the results suggest the importance of evaluating the impact of new compounds or materials added to food on human gut microbiota and their potential use to modulate microbiota composition.
Traditional Chinese preserved egg products have exhibited some anti-inflammatory effects, but their mechanisms of action remain unknown. This study aimed to investigate the anti-inflammatory effects of preserved egg white (PEW) treatment on dextran sulfate sodium (DSS)-induced colitis in mice and the underlying mechanisms. The results showed that treatment with PEW in mice with DSS-induced colitis for 14 days effectively improved the clinical signs, inhibited the secretion and gene expression of pro-inflammatory cytokines, and reduced myeloperoxidase (MPO) activity and oxidative stress levels. In addition, western blotting results showed that PEW significantly suppressed DSS-induced phosphorylation levels of nuclear factor-kappa B (NF-κB) p65 and p38 mitogen-activated protein kinase (MAPK) in colon tissues of mice with colitis. PEW also enhanced the production of short-chain fatty acids (SCFAs) and modulated gut microbiota composition in mice with DSS-induced colitis, including increasing the relative abundance of beneficial bacteria Lachnospiraceae, Ruminococcaceae and Muribaculaceae, and reducing the relative abundance of harmful bacteria Proteobacteria. Taken together, our study demonstrated that preserved egg white could alleviate DSS-induced colitis in mice through the reduction of oxidative stress, modulation of inflammatory cytokines, NF-κB, MAPK and gut microbiota composition.
As driving performance relies heavily on the interpretation of visual information, driving simulators require a visual display that can effectively communicate the virtual environment to the driver. Most high-fidelity visual displays include an expensive system of high-definition projectors and wraparound screens. To reduce the overall cost of a driving simulator while preserving the generalizability of results to naturalistic driving, head mounted displays (HMD) are being considered as a substitute visual cueing system. Recent innovations to virtual reality technologies are encouraging, however, differences between HMDs and more traditional visual displays have not been explored for all types of driving measures. In particular, while existing literature provides insight into the validity of HMDs as a substitute for higher fidelity visual displays in tests of driver behaviour and performance, there is a gap in the literature regarding differences in physiological responses. In the current study, upper body muscle activation and joint angle ranges were compared between an Oculus™ Rift Development Kit 2 HMD and a system of wrap around screens. Twenty-one participants each completed two simulated drives, one per display, in a counterbalanced order. During the simulation, drivers encountered unanticipated pedestrian crossings during which peak surface electromyography, root-mean-square of the surface electromyography signal and joint angles were determined bilaterally on the upper limbs. No significant differences (p ≤ 0.05) were observed between the Oculus™ Rift HMD and the wrap around screens for all dependent variables with the exception of left joint range of motion in female participants, suggesting that the HMD reduced field of view had a minimal effect on driver kinematics and no effect on muscle activation levels. Upper body bracing was observed during the hazard response time segments characterized by significantly increased muscle activity during hazard response time segments and minimal joint movement. Considering the lack of significant kinematic and muscle activation differences between the two visual inputs, HMD technology for hazard response may provide a suitable alternative to wrap around screens for studying kinematic responses during hazardous driving scenarios.
Cellular agriculture refers to a broad set of emerging technologies that draw upon research in genomics and synthetic biology to produce biological compounds. Much of the interest in cellular agriculture stems from its potential as a way of producing high-quality proteins and other nutrients with reduced environmental impact. Cellular agriculture techniques are rapidly nearing commercial scales of production, in part due to the application of knowledge and techniques produced through genomics research related to gene expression, editing, and genome-scale data analytics. However, much remains unknown and there is little rigorous evidence to test these assertions. This chapter applies the UN Sustainable Development Goals as a lens through which to examine protein production using cellular agriculture, to understand how it may contribute to the development of more sustainable and resilient food system. We examine two emerging approaches to cellular agriculture—cultured meat and fermentation-derived dairy—and explore both the complexity and knowledge gaps that need to be filled to ensure these tools are deployed to help create a more sustainable future for all. This chapter concludes by proposing an agenda for future research and policy development.
Background: Insulinomas are the most common tumour of the endocrine pancreas in dogs. These malignant tumours have a high metastatic rate and limited chemotherapeutic options. The multi-receptor tyrosine kinase inhibitor sunitinib malate has benefit in the treatment of metastatic insulinoma in people. Toceranib phosphate, an analogous veterinary agent, may provide benefit for dogs. Methods: A retrospective study describing the extent and duration of clinical outcomes and adverse events (AEs) in dogs diagnosed with insulinoma and receiving toceranib. Results: Records for 30 dogs diagnosed with insulinoma and having received toceranib were identified from a medical record search of five university and eight referral hospitals. The median progression-free interval and overall survival time were 561 days (95% confidence interval (CI): [246, 727 days]) and 656 days (95% CI: [310, 1045 days]), respectively. Of the dogs for which the canine Response evaluation criteria for solid tumours tool could be applied, the majority (66.7%) showed either a complete response, partial response or stable disease. Time to clinical progression was associated with prior intervention and type of veterinary practice. Larger dogs were at increased risk for disease progression and death. No novel AEs were reported. Conclusions: Most dogs diagnosed with insulinoma and receiving toceranib appeared to have a clinical benefit. Randomised, prospective studies are needed to better elucidate and objectively quantify the potential effect and survival benefit of toceranib therapy for management of insulinoma in dogs.
Objective The brown seed coat colour of flax ( Linum ustiatissimum ) results from proanthocyanidin synthesis and accumulation. Glutathione S-transferases (GSTs), such as the TT19 protein in Arabidopsis , have been implicated in the transport of anthocyanidins during the synthesis of the brown proanthocyanidins. This study fine mapped the g allele responsible for yellow seed colour in S95407 and identified it as a putative mutated GST. Results We developed a Recombinant Inbred Line population with 320 lines descended from a cross between CDC Bethune (brown seed coat) and S95407 (yellow seed) and used molecular markers to fine map the G gene on Chromosome 6 (Chr 6). We used Next Generation Sequencing (NGS) to identify a putative GST was identified in this region and Sanger sequenced the gene from CDC Bethune, S95407 and other yellow seeded genotypes. The putative GST from S95407 had 13 SNPs encoding, including four non-synonymous amino acid changes, compared to the CDC Bethune reference sequence and the other genotypes. The GST encoded by Lus10019895 is a lambda-GST in contrast to the Arabidopsis TT19 which is a phi-GST.
Irregular milking intervals in automated milking systems contribute to additional variation in daily milk yield records in comparison to those derived from systems using regular milking intervals. Various methods have been developed to estimate 24-h adjusted milk yields, though they are not well suited for the evaluation of serial milk yield data, particularly when milking intervals span calendar days. The objective of this study was to develop a methodology to estimate serial 24-h milk yields by adjusting for irregular milking intervals. Using data collected from an automated milking system (AMS), the total yield at a given milking event and the elapsed time from the previous entry into the AMS were used to calculate the milking interval and the average rate of milk secretion over that interval. Milking intervals and associated milk secretion rates were then realigned to calendar days to allow the proportional distribution of milk yield when milking intervals spanned more than one day. Using this method, variation in daily milk yield was decreased and adjusted estimates of 24-h milk yield were visually more similar to those typically observed in milking systems with regular milking intervals. Estimates of interval-adjusted milk yields were strongly correlated to those calculated using moving averages, suggesting that this method can yield comparable results to established methods for estimation of test-day milk yield.
Background Data on antimicrobial use (AMU) in pig production are needed for the development of good antimicrobial stewardship practices to reduce the risk of antimicrobial resistance in bacteria that can cause illness in animals and humans. In Canada, there is a lack of quantitative data on AMU in the farrowing and nursery stages of pig production. This study aimed to determine which antimicrobial active ingredients are currently used in farrowing, nursery, and grower-finisher herds in the province of Ontario, Canada, and to quantify AMU using various metrics. We collected data on herd demographics, biosecurity, health status, and AMU during one production cycle from 25 farrowing and 25 nursery herds in Ontario, between May 2017 and April 2018, and obtained data from 23 Ontario grower-finisher herds during the same time frame from the Public Health Agency’s Canadian Integrated Program for Antimicrobial Resistance Surveillance. We applied frequency measures, and weight-, and dose-based metrics to the data. Results In all pigs, the highest quantity of AMU was administered in-feed. By all routes of administration and compared to other production stages, nursery pigs used more antimicrobials in mg/kg biomass and the number of Canadian defined daily doses per 1000 pig-days (dose CA rate), while grower-finisher pigs used more antimicrobials in total kilograms and the number of Canadian defined daily doses per pig. In suckling pigs in some herds, there was routine disease prevention use of ceftiofur, an antimicrobial active ingredient categorized as very highly important in human medicine by Health Canada. The top antimicrobial used in each stage of pig production often varied by the metric used. There was producer-reported growth promotion use of antimicrobials in suckling and grower-finisher feed. Conclusions The results of this study provide a current picture of AMU in pigs in Ontario and can be used as a basis for further research on AMU in farrowing and nursery herds in Canada. Our findings confirm that it would be useful to include farrowing and nursery herds in routine AMU surveillance in Canada. A future analysis using data from this project will examine factors that affect the quantity of AMU.
Objective Person-to-person transmission can occur during outbreaks of verotoxigenic Escherichia coli (VTEC), however the impact of this transmission route is not well understood. This study aimed to examine the role of person-to-person transmission during a VTEC outbreak, and how targeting this route may reduce outbreak size. A deterministic compartmental model describing a VTEC outbreak was constructed and fit to data from a 2008 outbreak in Ontario, Canada. Using the best-fit model, simulations were run to calculate the: reduction in transmission rate after implementing interventions, proportion of cases infected through both transmission routes, and number of cases prevented by interventions. Latin hypercube sensitivity analysis was conducted to examine the sensitivity of the outbreak size to the model parameters. Results Based on the best-fit model, ~ 14.25% of the cases likely arose due to person-to-person transmission. Interventions reduced this transmission rate by ~ 73%, causing a reduction in outbreak size of ~ 17% (47 cases). Sensitivity analysis showed that the model was highly sensitive to changes in all parameters of the model. The model demonstrates that person-to-person could be an important transmission route during VTEC outbreaks. Targeting this route of transmission through hand hygiene and work exclusions could reduce the final outbreak size.
Mycotoxins are toxic secondary metabolites produced by filamentous fungi that are commonly detected as natural contaminants in agricultural commodities worldwide. Mycotoxin exposure can lead to mycotoxicosis in both animals and humans when found in animal feeds and food products, and at lower concentrations can affect animal performance by disrupting nutrient digestion, absorption, metabolism, and animal physiology. Thus, mycotoxin contamination of animal feeds represents a significant issue to the livestock industry and is a health threat to food animals. Since prevention of mycotoxin formation is difficult to undertake to avoid contamination, mitigation strategies are needed. This review explores how the mycotoxins aflatoxins, deoxynivalenol, zearalenone, fumonisins and ochratoxin A impose nutritional and metabolic effects on food animals and summarizes mitigation strategies to reduce the risk of mycotoxicity.
We study the existence and the longterm behavior of solutions of a parabolic equation governed by the p-Laplacian with nonlinear growth terms that are coupled with the solutions of a system of ordinary differential equations. The existence and the uniqueness are shown by using a fixed point argument and the longterm behavior of solutions is discussed by using energy estimates together with the nonlinear peculiarity of the p-Laplacian. Numerical simulations are carried out by using a Finite Volume Method for spatial treatment. For time integration of the p-Laplacian, an implicit Euler method is used, and direct integration for the ODE system.
Street trees are an important driver of street microclimate through shading and transpirative cooling, which are key mechanisms for improving thermal comfort in urban areas. Urban canopy models (UCM) with integrated trees are useful tools because they represent the impacts of street trees on neighborhood-scale climate, resolving the interactions between buildings, trees and the atmosphere. In this study, we present the results of a measurement campaign where vehicle transects were completed along two similar parallel streets of Barcelona with different tree densities, recording upward and downward radiation fluxes, air temperature and humidity at street level. These observations are used to evaluate and improve the multi-layer UCM Building Effect Parameterization with Trees (BEP-Tree). Prior simulations of the model revealed insufficient heat exchange between the canyon surfaces and the air at the lowest vertical levels inside the deep canyons, which we solve by including turbulent buoyancy driven wind velocity in the model. Air temperatures are on average 1.3 °C higher in the street with sparser trees when wind direction is perpendicular to the streets. The BEP-Tree simulations demonstrate good agreement with the observations in terms of temperature and radiation, and capture the diurnal evolution of temperature and radiation between the two streets.
Loads carried by military populations can affect those of smaller stature, such as the average female, due to the higher percentage of body weight the loads represent. Despite this, most load carriage research is performed on males. Peer reviewed articles were collected from four databases to summarize available research on biomechanical and physiological effects of load carriage on females in the military. Extraction and thematic analysis were performed on 18 articles. 39% looked at biomechanical differences between loads in females, 61% looked at how the same load affected males and females, 44% looked at sex-by-load interaction effects, and 72% discussed impacts of load on females. The research revealed that military load carriage affects the biomechanics and physiology differently in females and to a greater extent than in males. Several gaps in available literature were found. Very few studies used military participants, military equipment, and/or employed occupationally relevant data collection methodologies.
Research has long debated the effectiveness of socio-demographics in understanding purchase behavior, with mixed conclusions. The appeal of socio-demographic data for customer relationship marketing is based on its low acquisition cost and the growing array of variables on which marketers can condition messages and offers. We reinvestigate the value of socio-demographic variables, focusing on the potential of machine learning procedures (MLPs) to extract a stronger and reliable signal than the standard linear-in-parameters (logistic) regression models. We explore how predictive power can be increased through the nonlinearities and interactions identified with MLPs; our experimental set ranges from well-established procedures to newer entrants in this space. We also examine causality vis-à-vis predictability using a propensity scoring approach. Empirics are based on six grocery product categories and more than 7,000 panelists. We find that, relative to logistic regression models, MLPs using demographic variables yield a 20% to 33% improvement in out-of-sample predictive accuracy.
Soil carbon is a key soil property that regulates numerous soil processes and affects soil chemical, physical and biological properties. Given its importance, prediction of soil carbon using digital soil mapping (DSM) techniques has seen increased interest at scales ranging from global to local. The accuracy of predictive models is reliant on sample size, covariate resolution, and the algorithms being used to predict the target variable. Despite this, study of these components within a DSM workflow has received considerably less attention in the scientific literature. In this study, we examined the effects of sample size and covariate resolution on the performance of the Cubist and random forest (RF) algorithms, and compared these to ordinary kriging, for predicting total soil carbon in the 0–15 cm depth interval of a 26-hectare agricultural field in southwestern Ontario, Canada, originally sampled using a 20-m grid (689 samples). Using the conditioned Latin hypercube sampling algorithm, we created replicated sampling plans of increasing size (10, 25, and then 50 to 600 in steps of 50) across six different resolutions of environmental covariates. Overall, the kriging model marginally outperformed both the Cubist and RF models. All three models showed quick increases in model performance indicators (Lin’s concordance correlation coefficient, CCC; root mean square error, RMSE) at the smallest sample sizes with diminishing returns thereafter. Using the unit invariant knee technique, we determined the optimal sample size based on CCC and RMSE to be between 100 and 150 samples. We also observed no trend in optimal sample size across the six covariate resolutions for CCC or RMSE. The optimal sample sizes were confirmed by a review of the predicted carbon maps and the lack of change in the spatial distribution of soil carbon after reaching the optimal sample size. Effects of covariate resolution might not be apparent in this study given the field-scale nature of the work and narrow range of resolutions; however, we recommend this be evaluated for larger study areas to gain more insight about the interaction of sample size and covariate resolution.
To improve the picking success rate of table grapes in the process of automatic picking, it is important to accurately locate the point picking of grape ears and the cutting point of grape stems. In this research, a set of far-close-range stereoscopic vision systems was constructed to detect grape ears and grape stems. First, identification algorithms for grape ear centroid were proposed. A series of image operations were performed to complete the recognition and feature extraction of far-range grapes, including median filtering, threshold segmentation, morphological operation, executable region marking, and the extraction of the target centroid. Second, a calculation model of the cutting point of grape stems was established. The selected region of interest (ROI) of the close-range grape stem was determined to carry out edge detection and use a cumulative probability Hough transform. The straight line detection and cutting point positioning of the close-range grape stem were completed. Finally, based on LabVIEW software, the measurement and control system of the grape-picking robot was developed. Experiments verified that the identification of grape stems and localization algorithm of the cutting point had higher reliability, with success rates of 92% on sunny days, 82% during sunny backlight and 86% for overcast conditions. The grape-picking tests showed that the grape-picking robot can complete the table grape-picking task within an average time of 53.4 s under the given computer configuration.
The term ‘biocultural’ brings together the words ‘biological’ and ‘cultural’ to emphasize the interconnected nature of life and human culture. Over the last 50 years, biological and cultural diversity have shown concomitant declines in abundance, leading researchers, policy makers, activists and Indigenous Peoples to increasingly turn towards biocultural theory for potential pathways forward. Amidst increasing conversations and uses of the term ‘biocultural’, we sought to interrogate the current state of knowledge within biocultural theory and to identify the various ways the term ‘biocultural’ is being applied in the academic literature on conservation. To explore this research question, we conducted a systematic review of the literature that has meaningfully engaged with biocultural theory between 2002 and 2019 to explore the most concrete and promising pathways forward for effective and ethical on-the-ground implementation. After tracing key definitions and the evolution of the term over time, we find that the word ‘biocultural’ is most commonly being applied in the realms of biocultural diversity, biocultural heritage, and biocultural approaches. After offering an overview of each of these areas, we conclude with three central findings. Firstly, we find that biocultural theory remains deeply rooted in seminal discussions around biocultural diversity. It is from this foundation that new areas of research and application have emerged in the last decade, including biocultural heritage, biocultural approaches to conservation, biocultural landscape and biocultural rights, amongst others. Secondly, biocultural theory retains strong roots in Indigenous rights and advocacy, which were originally articulated in the Declaration of Belem (1988) and continue to be evidenced by the large percentage of papers in our sample which focused on Indigenous perspectives related to biocultural diversity. Finally, we find that biocultural theory remains largely conceptual in nature, with indications that only in recent years have more applied, on-the-ground case studies started to emerge in the literature.
Sodium lauroyl lactylate (SLL) is soluble in water and insoluble in organic solvents, while glycerol monooleated (GMO) is soluble in organic solvents and insoluble in water. These amphiphiles separate miscible solvents (e.g., water and either dimethylsulfoxide, DMSO, dimethylformamide, DMF, acetonitrile, AN, or tehtrahydrofuran, THF). Separation segregates water and the organic solvents into either microdomains (emulsified droplets) or free phases. With mixtures containing 3:7–7:3 DMF:water ratios (v/v), SLL kinetically stabilizes DMF-water emulsions for over a week (longer times were not investigated). Emulsions are DMF in water with DMF:water ratios ≤ 2:3, and water in DMF for DMF:water ratios ≥ 1:1. Optical microscopy and SEM illustrate emulsification, and confocal microscopy qualitatively shows segregation between DMF (dyed with fluorescein) and water (which appears dark in confocal images). Water droplets in DMF are kinetically stable because they are surrounded by self-assembled SLL cubic mesophases, either gyroid or primitive, depending on the SLL and DMF concentrations (as demonstrated by x-ray diffraction, XRD). SLL also stabilizes DMSO-water emulsions (for over a week), thereby segregating the two solvents with similar mechanisms. DMSO-water separation is quantitatively demonstrated by mid-infrared (mid-IR) spectromicroscopy. Separation between AN and water occurs for AN-water mixtures in which SLL has intermediate solubility, i.e., with 3:2 and 4:1 AN:water ratios (v/v). In this range, SLL yields emulsions which destabilize overnight, separating into AN-rich and water-rich phases, as demonstrated using nuclear magnetic resonance (NMR). In water, SLL self-assembles into primitive cubic liquid mesophases and it affects hydrogen bonding (H-bonding) of water, as shown by deconvolving the H-bond peak into peaks representative of different water clusters, comprised of water molecules donating and accepting a different number of H-bonds. In water, SLL induces a blue shift of the hydrogen bonding (H-bonding) of absorbance peaks for double (DD) and single (SD) H-bond donors, indicating that it strengthens H-bonding. Importantly, it increases the ratio between the amplitude A of SD relative to DD, and SD are most effective at structuring water. As a result, SLL would inhibit interactions between organic solvents and water, initiating separation. Similar to SLL, GMO is known for its ability to form cubic mesophases. GMO stabilizes emulsions of water miscible solvents (THF, DMSO and DMF) and water. This result indicates that selected amphiphiles self-assembled into cubic mesophases can emulsify miscible solvents.
High penetration of smart devices in IoE-enabled smart grids besides decentralization originated from employing renewable resources face the power system with intricate optimization problems. Operation scheduling of energy components is one of the principal problems that need to be addressed. However, engaging with big data produced by the interconnected infrastructures, besides the high dimensional and uncertain environment, make traditional methods incapable of addressing these problems since exact modeling of the environment under uncertainties is impracticable. While learning-based methods suffer from excessive complexity and the curse of dimensionality, Deep Reinforcement Learning has recently successfully handled highly complex scheduling problems. However, biases and model efficiency are two primary considerations that need more investigation. Positive and negative biases lead to better exploration and exploitation, respectively, and their harmony, considering model efficiency, results in a better outcome. Accordingly, this research develops and introduces a novel algorithm named Probabilistic Delayed Double Deep Q-Learning, which is a combination of the tuned version of Double Deep Q-Learning and Delayed Q-Learning. The proposed algorithm makes a trade-off between overestimation and underestimation biases, guaranteeing sample complexity by applying a delay in updating the rule. Finally, the proposed algorithm is tested on three real-world datasets assessing its performance in various benchmarks. The results indicate that the developed model is thoroughly stable since both population and characteristic stability indices are less than 0.1 in all case studies. The average model's error is 0.028 showing the exactitude of the model while running time is lower than other examined methods. Utilizing the developed algorithm results in 11.1 % reduction in the average power ratio. Consequently, the peak load decreased from 8.043 kW to 5.8137 kW, resulting in a 30.1 % cost reduction.
Game parks are the last preserve of many large mammals, and in savanna ecosystems, management of surface waters poses a conservation challenge. In arid and semi-arid regions, water can be a scarce resource during dry seasons and drought. Artificial waterholes are common in parks and reserves across Africa, but can alter mammal community composition by favoring drought intolerant species, with consequences for disease dynamics, and population viability of drought-tolerant species. Analysis of waterborne environmental DNA (eDNA) is increasingly used to inform conservation of rare and invasive species, and conduct large-scale biodiversity assessments. To explore the reliability of eDNA as an indicator of mammal waterhole use in savannas, we compare eDNA metabarcoding and camera traps for documenting artificial waterhole use in the Kruger National Park, South Africa, a global hotspot for mammal diversity. We show that eDNA metabarcoding can recover the majority of mammal species detected by camera traps, including a number of endangered species, but DNA signatures of mammal visitation are temporally limited, with best performance when tracking water-dependent large bodied mammals visiting within two days of sampling. Our results highlight limitation of eDNA based monitoring in these systems, including the lack of long-term eDNA persistence in small and highly utilized waterholes, and variability in detection rates among species. However, we demonstrate that eDNA-based approaches can be used to track mammals of conservation concern, and reflect patterns of recent waterhole use and co-occurrence across water-dependent species, both of which are crucial for making evidence-based decisions regarding water management and provisioning.
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10,796 members
Peter Kevan
  • School of Environmental Sciences
Jason B Ernst
  • School of Computer Science
Andrew Kropinski
  • Departments of Pathobiology & Food Science
Amanat Ali
  • School of Engineering and Physical Sciences
Silvia Sarapura
  • School of Environmental Design and Rural Development
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50 Stone Road East, N1G2W1, Guelph, Ontario, Canada
Head of institution
Dr. Charlotte Yates
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http://www.uoguelph.ca
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519-824-4120
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