Browsing by Author "Smith A"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Comparison of the cyathostomin egg reappearance times for ivermectin, moxidectin and abamectin in horses in consecutive egg count reduction tests in winter and summer over two years
    (New Zealand Veterinary Association, 2021) Scott I; Gee E; Rogers C; Pomroy B; Reilly M; Adlington B; Miller F; Smith A; Legg K; Adams B
    Shortened cyathostomin egg reappearance periods (ERP) serve as a warning of developing anthelmintic resistance (Scott et al., 2015). Efficacy is thought to have declined more rapidly for the later larval stages (L4) than for the egg laying adults, so that animals still show zero or near zero counts for a period after treatment, but with L4 now surviving treatment the ERP has shortened.
  • Thumbnail Image
    Item
    Field measurements of a massive Porites coral at Goolboodi (Orpheus Island), Great Barrier Reef
    (Springer Nature Limited, 2021-08-19) Smith A; Cook N; Cook K; Brown R; Woodgett R; Veron J; Saylor V
    An exceptionally large coral Porites sp. has been identified and measured at Goolboodi (Orpheus Island), Great Barrier Reef (GBR). This coral was measured in March 2021 during citizen science research of coral reefs in the Palm Islands group. We conducted a literature review and consulted scientists to compare the size, age and health of the Porites with others in the GBR and internationally. This is the largest diameter Porites coral measured by scientists and the sixth highest coral measured in the GBR. The health of the Porites was assessed as very good with over 70% live coral cover and minor percentages of sponge, live coral rock and macroalgae. An estimated age of 421-438 years was calculated based on linear growth models. Manbarra Traditional Owners were consulted and suggested that the Porites be named Muga dhambi (big coral) to communicate traditional knowledge, language and culture to indigenous, tourists, scientists and students.
  • Thumbnail Image
    Item
    Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models
    (BioMed Central Ltd, 2023-07-11) Kolisnik T; Sulit AK; Schmeier S; Frizelle F; Purcell R; Smith A; Silander O
    BACKGROUND: Colorectal cancer (CRC) is a heterogeneous disease, with subtypes that have different clinical behaviours and subsequent prognoses. There is a growing body of evidence suggesting that right-sided colorectal cancer (RCC) and left-sided colorectal cancer (LCC) also differ in treatment success and patient outcomes. Biomarkers that differentiate between RCC and LCC are not well-established. Here, we apply random forest (RF) machine learning methods to identify genomic or microbial biomarkers that differentiate RCC and LCC. METHODS: RNA-seq expression data for 58,677 coding and non-coding human genes and count data for 28,557 human unmapped reads were obtained from 308 patient CRC tumour samples. We created three RF models for datasets of human genes-only, microbes-only, and genes-and-microbes combined. We used a permutation test to identify features of significant importance. Finally, we used differential expression (DE) and paired Wilcoxon-rank sum tests to associate features with a particular side. RESULTS: RF model accuracy scores were 90%, 70%, and 87% with area under curve (AUC) of 0.9, 0.76, and 0.89 for the human genomic, microbial, and combined feature sets, respectively. 15 features were identified as significant in the model of genes-only, 54 microbes in the model of microbes-only, and 28 genes and 18 microbes in the model with genes-and-microbes combined. PRAC1 expression was the most important feature for differentiating RCC and LCC in the genes-only model, with HOXB13, SPAG16, HOXC4, and RNLS also playing a role. Ruminococcus gnavus and Clostridium acetireducens were the most important in the microbial-only model. MYOM3, HOXC4, Coprococcus eutactus, PRAC1, lncRNA AC012531.25, Ruminococcus gnavus, RNLS, HOXC6, SPAG16 and Fusobacterium nucleatum were most important in the combined model. CONCLUSIONS: Many of the identified genes and microbes among all models have previously established associations with CRC. However, the ability of RF models to account for inter-feature relationships within the underlying decision trees may yield a more sensitive and biologically interconnected set of genomic and microbial biomarkers.