Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Author: search.filters.author.Thomas DGSubject: search.filters.subject.Animals

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    Effect of iron-manganese oxide on the degradation of deoxynivalenol in feed and enhancement of growth performance and intestinal health in weaned piglets.
    (Elsevier B.V., 2024-10-28) Wu C; Song J; Liu X; Zhang Y; Zhou Z; Thomas DG; Wu B; Yan X; Li J; Zhang R; Wu F; Cheng C; Pu X; Wang X
    Deoxynivalenol (DON), a prevalent and highly toxic mycotoxin in animal feed, poses significant risks to livestock health and productivity. This study evaluates the effectiveness of iron-manganese oxide (Fe/Mn oxides) in degrading DON. The DON degradation rate of Fe/Mn oxide reached 98.46 % in a controlled solution under specific conditions (0.2 % concentration, 37-85 °C, pH 6-7, 1-minute reaction time). When applied to actual feed, it reduced DON levels by approximately 49.3 % and remained stable in simulated gastrointestinal environments of weaned piglets. A 28-day trial involving 48 weaned piglets assessed the impacts of Fe/Mn oxides on health and growth. Results indicated that piglets consuming contaminated feed without the treatment exhibited reduced growth and compromised gut integrity, which were significantly mitigated by the addition of Fe/Mn oxides. Therefore, Fe/Mn oxides effectively reduce DON in feed and alleviate adverse health effects in piglets, making them a viable option to enhance safety and performance in mycotoxin-prone environments.
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    Using meta-analysis to understand the impacts of dietary protein and fat content on the composition of fecal microbiota of domestic dogs (Canis lupus familiaris): A pilot study
    (John Wiley and Sons Ltd, 2024-04) Phimister FD; Anderson RC; Thomas DG; Farquhar MJ; Maclean P; Jauregui R; Young W; Butowski CF; Bermingham EN
    The interplay between diet and fecal microbiota composition is garnering increased interest across various host species, including domestic dogs. While the influence of dietary macronutrients and their associated microbial communities have been extensively reviewed, these reviews are descriptive and do not account for differences in microbial community analysis, nor do they standardize macronutrient content across studies. To address this, a meta-analysis was performed to assess the impact of dietary crude protein ("protein") and dietary crude fat ("fat") on the fecal microbiota composition in healthy dogs. Sixteen publications met the eligibility criteria for the meta-analysis, yielding a final data set of 314 dogs. Diets were classed as low, moderate, high, or supra in terms of protein or fat content. Sequence data from each publication were retrieved from public databases and reanalyzed using consistent bioinformatic pipelines. Analysis of community diversity indices and unsupervised clustering of the data with principal coordinate analysis revealed a small effect size and complete overlap between protein and fat levels at the overall community level. Supervised clustering through random forest analysis and partial least squares-discriminant analysis indicated alterations in the fecal microbiota composition at a more individual taxonomic level, corresponding to the levels of protein or fat. The Prevotellaceae Ga6A1 group and Enterococcus were associated with increasing levels of protein, while Allobaculum and Clostridium sensu stricto 13 were associated with increasing levels of fat. Interestingly, the random forest analyses revealed that Sharpea, despite its low relative abundance in the dog's fecal microbiome, was primarily responsible for the separation of the microbiome for both protein and fat. Future research should focus on validating and understanding the functional roles of these relatively low-abundant genera.
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    The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Cats (Felis catus): A Validation Study
    (MDPI (Basel, Switzerland), 2023-08-14) Smit M; Ikurior SJ; Corner-Thomas RA; Andrews CJ; Draganova I; Thomas DG; Vanwanseele B
    Animal behaviour can be an indicator of health and welfare. Monitoring behaviour through visual observation is labour-intensive and there is a risk of missing infrequent behaviours. Twelve healthy domestic shorthair cats were fitted with triaxial accelerometers mounted on a collar and harness. Over seven days, accelerometer and video footage were collected simultaneously. Identifier variables (n = 32) were calculated from the accelerometer data and summarized into 1 s epochs. Twenty-four behaviours were annotated from the video recordings and aligned with the summarised accelerometer data. Models were created using random forest (RF) and supervised self-organizing map (SOM) machine learning techniques for each mounting location. Multiple modelling rounds were run to select and merge behaviours based on performance values. All models were then tested on a validation accelerometer dataset from the same twelve cats to identify behaviours. The frequency of behaviours was calculated and compared using Dirichlet regression. Despite the SOM models having higher Kappa (>95%) and overall accuracy (>95%) compared with the RF models (64-76% and 70-86%, respectively), the RF models predicted behaviours more consistently between mounting locations. These results indicate that triaxial accelerometers can identify cat specific behaviours.