Computing and Information Systems - Research Publications

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    Identification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning
    Stell, A ; Caparo, E ; Wang, Z ; Wang, C ; Berlowitz, D ; Howard, M ; Sinnott, R ; Aickelin, U (SCITEPRESS - Science and Technology Publications, 2024)
    Patient Ventilator Asynchrony (PVA) occurs where a mechanical ventilator aiding a patient's breathing falls out of synchronisation with their breathing pattern. This de-synchronisation may cause patient distress and can lead to long-term negative clinical outcomes. Research into the causes and possible mitigations of PVA is currently conducted by clinical domain experts using manual methods, such as parsing entire sleep hypnograms visually, and identifying and tagging instances of PVA that they find. This process is very labour-intensive and can be error prone. This project aims to make this analysis more efficient, by using machine-learning approaches to automatically parse, classify, and suggest instances of PVA for ultimate confirmation by domain experts. The solution has been developed based on a retrospective dataset of intervention and control patients that were recruited to a non-invasive ventilation study. This achieves a specificity metric of over 90%. This paper describes the process of integrating the output of the machine learning into the bedside clinical monitoring system for production use in anticipation of a future clinical trial.
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    Correction to: Coexisiting type 1 diabetes and celiac disease is associated with lower Hba1c when compared to type 1 diabetes alone: data from the Australasian Diabetes Data Network (ADDN) registry.
    James, S ; Perry, L ; Lowe, J ; Donaghue, KC ; Pham-Short, A ; Craig, ME ; ADDN Study Group, (Springer Science and Business Media LLC, 2023-11)
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    Evaluating untimed and timed abridged versions of Raven's Advanced Progressive Matrices
    Poulton, A ; Rutherford, K ; Boothe, S ; Brygel, M ; Crole, A ; Dali, G ; Bruns, LR ; Sinnott, RO ; Hester, R (TAYLOR & FRANCIS INC, 2022-01-02)
    INTRODUCTION: Raven's Advanced Progressive Matrices (APM) are frequently utilized in clinical and experimental settings to index intellectual capacity. As the APM is a relatively long assessment, abridged versions of the test have been proposed. The psychometric properties of an untimed 12-item APM have received some consideration in the literature, but validity explorations have been limited. Moreover, both reliability and validity of a timed 12-item APM have not previously been examined. METHOD: We considered the psychometric properties of untimed (Study 1; N = 608; Mage = 27.89, SD = 11.68) and timed (Study 2; N = 479; Mage = 20.93, SD = 3.12) versions of a brief online 12-item form of the APM. RESULTS: Confirmatory factor analyses established both versions of the tests are unidimensional. Item response theory analyses revealed that, in each case, the 12 items are characterized by distinct differences in difficulty, discrimination, and guessing. Differential item functioning showed few male/female or native English/non-native English performance differences. Test-retest reliability was .65 (Study 1) to .69 (Study 2). Both tests had medium-to-large correlations with the Wechsler Abbreviated Scale of Intelligence (2nd ed.) Perceptual Reasoning Index (r = .50, Study 1; r = .56, Study 2) and Full-Scale IQ (r = .34, Study 1; r = .41, Study 2). CONCLUSION: In sum, results suggest both untimed and timed online versions of the brief APM are psychometrically sound. As test duration was found to be highly variable for the untimed version, the timed form might be a more suitable choice when it is likely to form part of a longer battery of tests. Nonetheless, classical test and item response theory analyses, plus validity considerations, suggest the untimed version might be the superior abridged form.
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    MAPPING THE CHATTER: SPATIAL METAPHORS FOR DYNAMIC TOPIC MODELLING OF SOCIAL MEDIA
    Morandini, L ; Mohammad, AR ; Sinnott, RO (Copernicus GmbH, 2022-08-05)
    Abstract. Topic modelling is a branch of Natural Language Processing (NLP) that deals with the discovery of conversation topics in a given document corpus. In social media, this translates into aggregating social media posts, e.g. tweets, into topics of conversation and observing how these topics evolve over time (hence the “dynamic” adjective). Conveying the results of topic modelling can be challenging since the topics often do not lend themselves naturally to meaningful labelling. The volume of real world (global) social media also means that millions of topics can be ongoing at any given time and the relationships between them can involve hundreds of dimensions and relationships that continually emerge. The popularity of topics is itself subject to change over time and reflect the pulse of what is happening in society at large. In this paper, we propose a spatialization technique based on open-source software that reduces the intrinsic complexity of dynamic topic modelling results to familiar topographic objects, namely: ridges, valleys, and peaks. This offers new possibilities for understanding complex relationships that change over time whilst overcoming issues with traditional topic modelling visualisation approaches such as network graphs.
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    Attribute-BasedKeyword Search over the Encrypted Blockchain
    Yang, Z ; Zhang, H ; Yu, H ; Li, Z ; Zhu, B ; Sinnott, RO (TECH SCIENCE PRESS, 2021)
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    Women with type 1 diabetes exhibit a progressive increase in gut Saccharomyces cerevisiae in pregnancy associated with evidence of gut inflammation
    Bandala-Sanchez, E ; Roth-Schulze, AJ ; Oakey, H ; Penno, MAS ; Bediaga, NG ; Naselli, G ; Ngui, KM ; Smith, AD ; Huang, D ; Zozaya-Valdes, E ; Thomson, RL ; Brown, JD ; Vuillermin, PJ ; Barry, SC ; Craig, ME ; Rawlinson, WD ; Davis, EA ; Harris, M ; Soldatos, G ; Colman, PG ; Wentworth, JM ; Haynes, A ; Morahan, G ; Sinnott, RO ; Papenfuss, AT ; Couper, JJ ; Harrison, LC (ELSEVIER IRELAND LTD, 2022-02)
    AIMS: Studies of the gut microbiome have focused on its bacterial composition. We aimed to characterize the gut fungal microbiome (mycobiome) across pregnancy in women with and without type 1 diabetes. METHODS: Faecal samples (n = 162) were collected from 70 pregnant women (45 with and 25 without type 1 diabetes) across all trimesters. Fungi were analysed by internal transcribed spacer 1 amplicon sequencing. Markers of intestinal inflammation (faecal calprotectin) and intestinal epithelial integrity (serum intestinal fatty acid binding protein; I-FABP), and serum antibodies to Saccharomyces cerevisiae (ASCA) were measured. RESULTS: Women with type 1 diabetes had decreased fungal alpha diversity by the third trimester, associated with an increased abundance of Saccharomyces cerevisiae that was inversely related to the abundance of the anti-inflammatory butyrate-producing bacterium Faecalibacterium prausnitzii. Women with type 1 diabetes had higher concentrations of calprotectin, I-FABP and ASCA. CONCLUSIONS: Women with type 1 diabetes exhibit a shift in the gut mycobiome across pregnancy associated with evidence of gut inflammation and impaired intestinal barrier function. The relevance of these findings to the higher rate of pregnancy complications in type 1 diabetes warrants further study.
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    Run or Pat: Using Deep Learning to Classify the Species Type and Emotion of Pets
    Sinnott, RO ; Aickelin, U ; Jia, Y ; Sun, PY ; Susanto, R (EEE, 2021-01-01)
    Deep learning has been applied in many contexts. In this paper we present a novel application area: to detect the species type and emotion of pets with focus on a diverse set of dog and cat collections comprising 52 dog and 23 cat species. Building on an extensive collection of labelled images with over 300 images per species type, we explore a range of deep learning models to develop a classifier for species type and their associated emotion. We outline the realization of the technical solution delivered through a mobile application (iPhone/Android) and present results based on feedback based on real world adoption and utilisation by the broader mobile application community.
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    Evaluation of protocol amendments to the Environmental Determinants of Islet Autoimmunity (ENDIA) study during the COVID-19 pandemic
    Penno, MAS ; Anderson, AJ ; Thomson, RL ; McGorm, K ; Barry, SC ; Colman, PG ; Craig, ME ; Davis, EA ; Harris, M ; Haynes, A ; Morahan, G ; Oakey, H ; Rawlinson, WD ; Sinnott, RO ; Soldatos, G ; Vuillermin, PJ ; Wentworth, JM ; Harrison, LC ; Couper, JJ (WILEY, 2021-11)
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    CogTale: an online platform for the evaluation, synthesis, and dissemination of evidence from cognitive interventions studies
    Sabates, J ; Belleville, S ; Castellani, M ; Dwolatzky, T ; Hampstead, BM ; Lampit, A ; Simon, S ; Anstey, K ; Goodenough, B ; Mancuso, S ; Marques, D ; Sinnott, R ; Bahar-Fuchs, A (BMC, 2021-08-24)
    Systematic reviews and meta-analyses are critical in health-related decision-making, and are considered the gold standard in research synthesis methods. However, with new trials being regularly published and with the development of increasingly rigorous standards of data synthesis, systematic reviews often require much expertise and long periods of time to be completed. Automation of some of the steps of evidence synthesis productions is a promising improvement in the field, capable of reducing the time and costs associated with the process.This article describes the development and main characteristics of a novel online repository of cognitive intervention studies entitled Cognitive Treatments Article Library and Evaluation (CogTale). The platform is currently in a Beta Release phase, as it is still under development. However, it already contains over 70 studies, and the CogTale team is continuously coding and uploading new studies into the repository. Key features include advanced search options, the capability to generate meta-analyses, and an up-to-date display of relevant published studies.
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    Type 1 diabetes in pregnancy is associated with distinct changes in the composition and function of the gut microbiome
    Roth-Schulze, AJ ; Penno, MAS ; Ngui, KM ; Oakey, H ; Bandala-Sanchez, E ; Smith, AD ; Allnutt, TR ; Thomson, RL ; Vuillermin, PJ ; Craig, ME ; Rawlinson, WD ; Davis, EA ; Harris, M ; Soldatos, G ; Colman, PG ; Wentworth, JM ; Haynes, A ; Barry, SC ; Sinnott, RO ; Morahan, G ; Bediaga, NG ; Smyth, GK ; Papenfuss, AT ; Couper, JJ ; Harrison, LC (BMC, 2021-08-06)
    BACKGROUND: The gut microbiome changes in response to a range of environmental conditions, life events and disease states. Pregnancy is a natural life event that involves major physiological adaptation yet studies of the microbiome in pregnancy are limited and their findings inconsistent. Pregnancy with type 1 diabetes (T1D) is associated with increased maternal and fetal risks but the gut microbiome in this context has not been characterized. By whole metagenome sequencing (WMS), we defined the taxonomic composition and function of the gut bacterial microbiome across 70 pregnancies, 36 in women with T1D. RESULTS: Women with and without T1D exhibited compositional and functional changes in the gut microbiome across pregnancy. Profiles in women with T1D were distinct, with an increase in bacteria that produce lipopolysaccharides and a decrease in those that produce short-chain fatty acids, especially in the third trimester. In addition, women with T1D had elevated concentrations of fecal calprotectin, a marker of intestinal inflammation, and serum intestinal fatty acid-binding protein (I-FABP), a marker of intestinal epithelial damage. CONCLUSIONS: Women with T1D exhibit a shift towards a more pro-inflammatory gut microbiome during pregnancy, associated with evidence of intestinal inflammation. These changes could contribute to the increased risk of pregnancy complications in women with T1D and are potentially modifiable by dietary means. Video abstract.