Journal Articles

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

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    First Detection and Genetic Characterization of Felis catus Papillomavirus Type 11, the First Treisetapapillomavirus Type to Infect Domestic Cats
    (MDPI (Basel, Switzerland), 2025-05-14) Munday JS; French AF; Broughton L; Lin X; Bond SD; Kraberger S; Knox MA; De Martino L
    Domestic cats are currently recognized to be infected by 10 different Felis catus papillomavirus (FcaPV) types that are classified into three genera. Examination of a skin sample from a cat with presumptive allergic dermatitis revealed clusters of large amphophilic intracytoplasmic bodies within epidermal cells. A 312 bp section of DNA from a novel PV type was amplified from the sample, while the entire 7569 bp genome was amplified and sequenced from a skin swab. The novel PV, which was designated FcaPV11, was predicted to contain coding regions for five early proteins and two late ones. Phylogenetic analysis of the L1 gene sequence showed FcaPV11 clusters with members of the Treisetapapillomavirus genus and shares less than 64% similarity with any of the previously fully sequenced FcaPV types. FcaPV11 DNA was not detected in a series of neoplastic and non-neoplastic skin samples from an additional 30 cats. These results show, for the first time, that cats can be infected by members of the Treisetapapillomavirus genus and suggest PVs in this genus may have co-evolved with a common Carnivora ancestor. While FcaPV11 was considered unlikely to have caused skin lesions in this cat, the prominent PV-induced cell changes indicate the PV can influence cell regulation. This suggests FcaPV11 may have the potential to cause skin disease in cats.
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    An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification
    (Elsevier BV, Netherlands, 2024-01) Wang X; Liesaputra V; Liu Z; Wang Y; Huang Z
    Electroencephalogram (EEG)-based Brain–Computer Interfaces (BCIs) build a communication path between human brain and external devices. Among EEG-based BCI paradigms, the most commonly used one is motor imagery (MI). As a hot research topic, MI EEG-based BCI has largely contributed to medical fields and smart home industry. However, because of the low signal-to-noise ratio (SNR) and the non-stationary characteristic of EEG data, it is difficult to correctly classify different types of MI-EEG signals. Recently, the advances in Deep Learning (DL) significantly facilitate the development of MI EEG-based BCIs. In this paper, we provide a systematic survey of DL-based MI-EEG classification methods. Specifically, we first comprehensively discuss several important aspects of DL-based MI-EEG classification, covering input formulations, network architectures, public datasets, etc. Then, we summarize problems in model performance comparison and give guidelines to future studies for fair performance comparison. Next, we fairly evaluate the representative DL-based models using source code released by the authors and meticulously analyse the evaluation results. By performing ablation study on the network architecture, we found that (1) effective feature fusion is indispensable for multi-stream CNN-based models. (2) LSTM should be combined with spatial feature extraction techniques to obtain good classification performance. (3) the use of dropout contributes little to improving the model performance, and that (4) adding fully connected layers to the models significantly increases their parameters but it might not improve their performance. Finally, we raise several open issues in MI-EEG classification and provide possible future research directions.
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    Lost in the Forest
    (Cold Spring Harbor Laboratory, 2022) Smith HL; Biggs PJ; French NP; Smith ANH; Marshall JC
    To date, there remains no satisfactory solution for absent levels in random forest models. Absent levels are levels of a predictor variable encountered during prediction for which no explicit rule exists. Imposing an order on nominal predictors allows absent levels to be integrated and used for prediction. The ordering of predictors has traditionally been via class probabilities with absent levels designated the lowest order. Using a combination of simulated data and pathogen source-attribution models using whole-genome sequencing data, we examine how the method of ordering predictors with absent levels can (i) systematically bias a model, and (ii) affect the out-of-bag error rate. We show that the traditional approach is systematically biased and underestimates out-of-bag error rates, and that this bias is resolved by ordering absent levels according to the a priori hypothesis of equal class probability. We present a novel method of ordering predictors via principal coordinates analysis (PCO) which capitalizes on the similarity between pairs of predictor levels. Absent levels are designated an order according to their similarity to each of the other levels in the training data. We show that the PCO method performs at least as well as the traditional approach of ordering and is not biased.
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    Classifying Alcohol Control Policies with Respect to Expected Changes in Consumption and Alcohol-Attributable Harm: The Example of Lithuania, 2000-2019
    (MDPI (Basel, Switzerland), 2021-03-02) Rehm J; Štelemėkas M; Ferreira-Borges C; Jiang H; Lange S; Neufeld M; Room R; Casswell S; Tran A; Manthey J; Efird JT
    Due to the high levels of alcohol use, alcohol-attributable mortality and burden of disease, and detrimental drinking patterns, Lithuania implemented a series of alcohol control policies within a relatively short period of time, between 2008 and 2019. Based on their expected impact on alcohol consumption and alcohol-attributable harm, as well as their target population, these policies have been classified using a set of objective criteria and expert opinion. The classification criteria included: positive vs. negative outcomes, mainly immediate vs. delayed outcomes, and general population vs. specific group outcomes. The judgement of the alcohol policy experts converged on the objective criteria, and, as a result, two tiers of intervention were identified: Tier 1-highly effective general population interventions with an anticipated immediate impact; Tier 2-other interventions aimed at the general population. In addition, interventions directed at specific populations were identified. This adaptable methodological approach to alcohol control policy classification is intended to provide guidance and support for the evaluation of alcohol policies elsewhere, to lay the foundation for the critical assessment of the policies to improve health and increase life expectancy, and to reduce crime and violence.
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    Application of machine learning algorithms to predict body condition score from liveweight records of mature romney ewes
    (1/02/2021) Semakula J; Corner‐thomas RA; Morris ST; Blair HT; Kenyon PR
    Body condition score (BCS) in sheep (Ovis aries) is a widely used subjective measure of the degree of soft tissue coverage. Body condition score and liveweight are statistically related in ewes; therefore, it was hypothesized that BCS could be accurately predicted from liveweight using machine learning models. Individual ewe liveweight and body condition score data at each stage of the annual cycle (pre‐breeding, pregnancy diagnosis, pre‐lambing and weaning) at 43 to 54 months of age were used. Nine machine learning (ML) algorithms (ordinal logistic regression, multinomial regression, linear discriminant analysis, classification and regression tree, random forest, k‐nearest neighbors, support vector machine, neural networks and gradient boosting decision trees) were applied to predict BCS from a ewe’s current and previous liveweight record. A three class BCS (1.0– 2.0, 2.5–3.5, > 3.5) scale was used due to high‐class imbalance in the five‐scale BCS data. The results showed that using ML to predict ewe BCS at 43 to 54 months of age from current and previous liveweight could be achieved with high accuracy (> 85%) across all stages of the annual cycle. The gradient boosting decision tree algorithm (XGB) was the most efficient for BCS prediction regardless of season. All models had balanced specificity and sensitivity. The findings suggest that there is potential for predicting ewe BCS from liveweight using classification machine learning algorithms.