Browsing by Author "Thomas D"

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    Development and validation of an LC-MS/MS method for the quantification of oral-sugar probes in plasma to test small intestinal permeability and absorptive capacity in the domestic cat (Felis catus)
    (Elsevier BV, 2024-07-15) Patterson K; Fraser K; Bernstein D; Bermingham EN; Weidgraaf K; Kate Shoveller A; Thomas D
    A novel method for quantifying the concentration of lactulose, rhamnose, xylose, and 3-O-methylglucose (3-OMG) in cat plasma using liquid chromatography-mass spectrometry (LC-MS) was developed. Domestic male cats (n = 13) were orally dosed with a solution containing the four sugars to test the permeability and absorptive capacity of their intestinal barrier. Plasma samples were taken 3 h later and were prepared with acetonitrile (ACN), dried under N2, and reconstituted in 90 % ACN with 1 mM ammonium formate. Stable isotope labelled 13C standards for each analyte were used as internal standards. Chromatographic separation was conducted using a Phenomenex Luna NH2 column with a gradient elution system of deionized water and 90 % ACN with 1 mM ammonium formate at 300 µL/min for 13 min total analysis time. Recovery trials were conducted in triplicate over three days with RSD values (%) for each day ranging from 1.2 to 1.4 for lactulose, 5.4 - 6.0 for rhamnose, 3.3 - 5.5 for xylose, and 2.6 - 5.6 for 3-OMG. Inter-day variations for each analyte were not different (p > 0.05). Limit of detection and quantification were 0.2 and 0.7 µg/mL for lactulose, 0.8 and 2.4 µg/mL for rhamnose, 0.6 and 1.8 µg/mL for xylose, and 0.3 and 1.1 µg/mL for 3-OMG, respectively. Plasma sugar concentrations recovered from cats were above the limit of quantification and below the highest calibration standard, validating the use of this method to test intestinal permeability and absorptive capacity in cats.
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    How Lazy Are Pet Cats Really? Using Machine Learning and Accelerometry to Get a Glimpse into the Behaviour of Privately Owned Cats in Different Households
    (MDPI (Basel, Switzerland), 2024-04-19) Smit M; Corner-Thomas R; Draganova I; Andrews C; Thomas D; Friedrich CM
    Surprisingly little is known about how the home environment influences the behaviour of pet cats. This study aimed to determine how factors in the home environment (e.g., with or without outdoor access, urban vs. rural, presence of a child) and the season influences the daily behaviour of cats. Using accelerometer data and a validated machine learning model, behaviours including being active, eating, grooming, littering, lying, scratching, sitting, and standing were quantified for 28 pet cats. Generalized estimating equation models were used to determine the effects of different environmental conditions. Increasing cat age was negatively correlated with time spent active (p < 0.05). Cats with outdoor access (n = 18) were less active in winter than in summer (p < 0.05), but no differences were observed between seasons for indoor-only (n = 10) cats. Cats living in rural areas (n = 7) spent more time eating than cats in urban areas (n = 21; p < 0.05). Cats living in single-cat households (n = 12) spent more time lying but less time sitting than cats living in multi-cat households (n = 16; p < 0.05). Cats in households with at least one child (n = 20) spent more time standing in winter (p < 0.05), and more time lying but less time sitting in summer compared to cats in households with no children (n = 8; p < 0.05). This study clearly shows that the home environment has a major impact on cat behaviour.
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    The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Dogs (Canis familiaris): A Validation Study
    (MDPI (Basel, Switzerland), 2024-09-13) Redmond C; Smit M; Draganova I; Corner-Thomas R; Thomas D; Andrews C; Fullwood DT; Bowden AE
    Assessing the behaviour and physical attributes of domesticated dogs is critical for predicting the suitability of animals for companionship or specific roles such as hunting, military or service. Common methods of behavioural assessment can be time consuming, labour-intensive, and subject to bias, making large-scale and rapid implementation challenging. Objective, practical and time effective behaviour measures may be facilitated by remote and automated devices such as accelerometers. This study, therefore, aimed to validate the ActiGraph® accelerometer as a tool for behavioural classification. This study used a machine learning method that identified nine dog behaviours with an overall accuracy of 74% (range for each behaviour was 54 to 93%). In addition, overall body dynamic acceleration was found to be correlated with the amount of time spent exhibiting active behaviours (barking, locomotion, scratching, sniffing, and standing; R2 = 0.91, p < 0.001). Machine learning was an effective method to build a model to classify behaviours such as barking, defecating, drinking, eating, locomotion, resting-asleep, resting-alert, sniffing, and standing with high overall accuracy whilst maintaining a large behavioural repertoire.