Music Making and Engagement for Older Adults at Risk for Dementia: Examining Neurobiological Relationships and Co-Designing a Music-Based Trial

Older adults with mild cognitive impairment (MCI) are at higher risk of developing dementia, making them a key group for secondary prevention. Lifestyle activities that are cognitively stimulating, socially engaging, and emotionally fulfilling may help delay or mitigate decline. Music-making has been proposed as one such activity, with evidence suggesting benefits for cognition and well-being in older adults at risk for dementia. While music-making is thought to promote neuroplasticity, the brain’s ability to reorganise neural pathways, the extent of this effect in MCI remains unclear. Despite recommendations to encourage such activities, robust evidence for their efficacy in this population is lacking. This thesis aimed to examine the effect of music-making on neuroplasticity, cognition, and psychosocial outcomes in older adults at risk for dementia. Across four studies, it revealed that: (a) evidence linking music-making interventions to neuroplasticity is inconclusive, with no studies specifically in MCI; (b) currently playing an instrument is associated with greater grey matter density in the temporal lobe, insula, and cerebellum; (c) the type of music-making has distinct effects on neuropsychological and psychosocial outcomes; and (d) baseline characteristics of participants in the NeuroMusic trial reflect the intended at-risk profile, underscoring the trial’s significance. These findings add to the literature on music-making and brain health in ageing. Music-making shows potential to enhance neuroplasticity, delay cognitive decline, and improve psychosocial outcomes, positioning it as a promising early intervention for older adults at risk for dementia. Understanding its effects on brain structure, cognition, and well-being provides critical insights for developing targeted, evidence-based prevention strategies in MCI

Application of Deep Learning in Image Processing

Deep learning has revolutionized computer vision, achieving remarkable success in complex visual tasks such as image classification, segmentation, and object detection. This thesis explores advanced deep learning techniques for image processing, with a particular focus on remote sensing imagery. Specifically, this research addresses two critical challenges: (1) achieving high segmentation accuracy in scenarios with limited labeled data and (2) integrating multi-modality data into model training. To tackle these challenges, this work proposes innovative solutions leveraging semi-supervised learning and multi-modality learning. By employing consistency learning and advanced data augmentation techniques, the proposed approaches effectively utilize unlabeled data, significantly boosting segmentation accuracy. Furthermore, integrating complementary data modalities, such as spectral and spatial information, enhances model robustness and overall performance. Experimental results on benchmark datasets validate the effectiveness of these methods, demonstrating their potential for real-world applications, including environmental monitoring, urban planning, and disaster management. The primary contributions of this thesis include advancing the theoretical understanding of semi-supervised and multi-modality learning in remote sensing segmentation, developing novel methodologies to address data scarcity, and providing practical frameworks that are applicable across various domains. However, limitations related to scalability and generalizability highlight avenues for future research, such as exploring dynamic augmentation strategies, advanced fusion mechanisms, and extensions to other fields like medical imaging. This research provides a comprehensive framework for overcoming segmentation challenges in remote sensing, delivering significant advancements in deep learning-based image analysis

INTRODUCTION The Holocaust and Human Rights: Transnational Perspectives on Contemporary Memorial Museums

Interrogates the global, and often controversial, phenomenon of Holocaust and human rights museums Spanning six continents—Europe, Australia, Africa, Asia, North America, and South America—this edited collection offers a comparative, transnational study of Holocaust and human rights museums that foregrounds the overlapping and often contested work these institutions do in narrating and memorializing histories of genocide and human rights abuses for a public audience. Museums that link the Holocaust with social justice, human rights, and genocide prevention have been founded in many countries—for example, the Kazerne Dossin Memorial Museum in Belgium, the Anne Frank House in the Netherlands, and the Johannesburg Holocaust and Genocide Centre in South Africa—making Holocaust and human rights museums a global phenomenon. It is not uncommon for these institutions to court controversy by linking the Holocaust to human rights issues in their locales and abroad. Some begin from a “Holocaust core” and extrapolate from this history to address broader concerns, while others integrate the Holocaust as “a” or, at times, “the” case study par excellence of human rights abuses. Other institutions that may not explicitly focus on the Holocaust continue to engage these representational practices to highlight other instances of genocide and human rights abuses. The case studies in this book illuminate the convergences between Holocaust and human rights museums in their demands for social justice and reparation, educational and activist purpose, design principles, and curatorial choices. But it also shows how these museums can also be sites of contestation around how stories of suffering, courage, and survival are told; whose stories are prioritized; and who is consulted. Although Holocaust museums were once the most influential form of representation of human rights issues in the international museum and heritage fields, they are now in dialogue—visually, spatially, methodologically—with museums and memorial sites concerned with human rights more broadly. Interrogating debates in both museology and Holocaust memory studies, this volume reveals how institutions dedicated to these concerns have become active and influential contributors to local, national, and transnational dialogues about human rights

Exploring evolutionary rates and patterns of diversification across the Tree of Life

My thesis provides substantial insight into evolutionary processes across the Tree of Life. I have analysed the geological record as well as phenotypic traits and genomes from extant organisms to better understand the processes of diversification and change. I begin by challenging the notion that the diversification of flowering plants was intimately linked to a contemporaneous diversification of pollinating insects. This evolutionary event, which propelled flowering plants to dominate terrestrial landscapes, may instead have been bolstered by unique environmental factors, and by insect pollinators that were primed by previous interaction with seed plants. I then examine the tempo of evolution for many diverse taxa by inferring evolutionary rates. I first validate and assess five methods for detecting evolutionary rate correlations between molecular sequences and morphological traits, using a comprehensive simulation study with thousands of replicate data sets. After determining the most statistically accurate and powerful methods, I apply these methods to diverse taxa from the eukaryote Tree of Life. This spans groups including, but not limited to, worms, tetrapods, fish, insects, plants, and parasites. In doing so, I uncover powerful evidence for decoupled evolutionary rates of molecules and morphology across all groups tested, demonstrating the disparate mechanisms that govern the evolution of morphology, which is under the constraint of natural selection, and molecules, which exhibit more stochastic evolution. Finally, I analyse evolutionary rates in land plant genomes, testing the link between rates in the three genomic compartments of land plants (nucleus, chloroplast, and mitochondrion). In this chapter I demonstrate that there is a shared evolutionary rate between the genomic compartments in land plants – effectively extending the hypothesis of 'mitonuclear covariation' from animals to plants

Matlab software for the paper "First predictions for images of Earth's foreshock radiation sources" by Cairns and Oppel

Examples of the Matlab software for the Cairns and Oppel paper on images for Earth's foreshock radiation sources. The file init.m gives the input parameters. The code hybrid_3DNov calculates the emissions for multiple planes from given input parameters. The code line_of_sight_new.m produces the view of the foreshock emissions from 9 locations.Matlab software

Exploring Changing Treatment Paradigms in Medullary Thyroid Carcinoma, the Immune Milieu of the Tumour Microenvironment and a Novel Targeted Therapy for Advanced Disease

The immune milieu of the tumour microenvironment (TME) in Medullary Thyroid Cancer (MTC) and the potential role of immune therapies have not been extensively explored. This study aimed to define the current treatment landscape and changing management paradigms in MTC, describe the nature of the immune microenvironment of these tumours, and ultimately explore a novel targeted therapy for advanced disease. Tumour-infiltrating lymphocytes (TILs) in the TME of MTC patients were assessed and correlated with clinicopathological prognostic variables and survival outcomes. All patients with MTC had low TILs (≤10%), and there was no significant association between TILs and local recurrence or disease-specific survival on multivariable analysis. These findings highlight that MTC is an immune-quiescent tumour. A novel targeted therapy for advanced MTC was also investigated. The EDV™ (EnGeneIC Dream Vector) is a bacterially-derived construct loaded with cytotoxic drugs and conjugated with a bispecific antibody directed against specific overexpressed surface receptors on tumour cells. The EDV™ effectively killed human MTC cells in vitro and in a nude-mouse xenograft and syngeneic neuroendocrine tumour model. In addition to targeted delivery of the cytotoxin PNU-159682 mediated by antibody binding, EDV treatment triggered an innate and adaptive immune response against tumour cells, with upregulation of M1 macrophages, cytotoxic natural killer (NK) cells, and invariant natural killer T cells, followed by CD8 effector T cells. The shift to an immune-activated phenotype in the TME correlated with changes in the cytokine and chemokine profile, with upregulation of the key drivers of macrophage, NK cell and T cell activation and chemotaxis in the serum and TME. These results provide preclinical data demonstrating the efficacy of a novel targeted therapy for advanced MTC and form the evidence base to support a human clinical trial to confirm the translational relevance of the results

Validation of novel non-invasive diagnostic and prognostic methods in chronic kidney disease and kidney transplantation

There is an emerging need to accurately detect chronic kidney disease (CKD) at an early stage so timely intervention can be implemented to optimize clinical outcomes. There remains a critical unmet requirement to develop non-invasive methodologies for early CKD diagnosis and prognostication. Using human urine samples, our group developed novel approaches based on indirect immunomagnetic extraction of urinary exfoliated proximal tubule cells (PTCs), to assess if urinary exfoliated PTCs can be assessed as an approach to inform CKD severity; and whether CKD staging and progression can be determined by utilizing multispectral autofluorescence in exfoliated PTCs. We have also assessed cell-free urine samples to evaluate their urinary volatile organic compound profiles and demonstrated this could potentially be applied as a novel metabolomic biomarker to assess CKD progression. In the scenario when CKD progresses to kidney failure, patients may elect to receive kidney replacement therapy in the form of dialysis or kidney transplantation. Although kidney transplantation is often the preferred option of treatment for kidney failure, optimal outcomes following kidney transplantation may be challenged due to various post-transplant complications. There is no strongly validated method apart from transplant kidney biopsy which can definitively predict the cause of delayed or deteriorating graft function currently. Hence, there is also an unmet need for accurate non-invasive diagnostic approaches to identify the cause of kidney dysfunction following transplantation. This thesis also provides novel proof of concept data supporting the application of urinary exfoliated PTCs multispectral autofluorescence to non-invasively differentiate between individuals with different causes of kidney transplant dysfunction

Introducing the Time Factor into the Economic Framework of a Static General Equilibrium Model

Time is an important ‘factor’ or ‘input’ into many economic activities, but up to now, the issue of time valuation has been considered mostly in a partial equilibrium framework such as in the context of travel time savings valuation. This does not allow for a more general consideration of the issue of time valuation especially in the wider context of an economy where almost any economic activity ‘takes time’. In a general equilibrium framework, however, the issue of time valuation seems to be neglected. This is because despite the fact that both production as well as consumption activities ‘take time’, production time is often considered only implicitly via the representation of the labour input: as a flow of labour service through time. Capital-time or machine-time, on the other hand, is ignored or masked under the representation of this input in the form of a stock rather than a flow (of capital services). Flow requires time, whereas stock is ‘timeless’. Therefore, it can be said that time is almost ‘absent’ in a (static) general equilibrium framework where, not only with regard to the issue about capital stock versus capital service flow (or utilization rate), in the long run as well as in the short run, but also with regard to the consideration of other so-called ‘fixed’ input, such land, natural or environmental resource stocks. These stocks are often taken into consideration but only with regard to the measurement of the (static) wealth of an individual or of a nation, but not with regard to the flow of the income which is derived from the activities of the individual or the nation (activities take time). In this paper, we consider the time factor in the framework of a ‘comparative static’ general equilibrium economic model because even here the operation of the time factor is still present and important and can affect the valuation (or costing) of many economic activities albeit in an implicit and subtle way

Enhancing Medical Record Comprehensibility: Using Large Language Models to Produce Simplified Narratives of Image Reports in Electronic Medical Data

This thesis investigates the use of language models in clinical applications where input documents are long and questions can be complex and require advanced reasoning. The research aims and objectives in this thesis contain two parts: one is to find and evaluate the appropriate solution that enhances the model performance under clinical settings; the other one is to find the solution to modify the models for better performance under this circumstance. Two approaches are developed to address the problem. In the first study, the RAPTOR framework extends a language model's ability to make sense of local and global information from long documents with its unique hierarchical tree structure datastore. The approach may be beneficial where cloud-based large language models (e.g. GPT-4o) cannot be used due to data privacy or reproducibility issues. Specifically, RAPTOR can be tailored to address clinical tasks including extracting critical patient information and summarizing clinical notes from long documents. In the second study, I developed and tested an optimized language model that uses a continual pre-training process to incorporate domain knowledge with a Llama-3.1-8B language model, with a novelly collected, organized and preprocessed Clinical Trial registration dataset called CiTi. The dataset contains 358870 preprocessed clinical trial registration reports and 1401401 related publication abstracts. This study aimed to develop a language model that adapts clinical trial registration data as its specialty domain and has more understanding of this domain than general large language models. The two solutions evaluated in this thesis show that with the updated configuration, it is possible to achieve state-of-the-art performance using locally implemented language models. Future research should consider how specific configurations or auto-configurations better suit simple and complex questions

Insect-plant interactions of the Sydney Region

This data set contains the interactions and behaviours of of insects (Honey bees and hover flies) with plants recorded in Sydney, NSW between Winter 2019 and Autumn 2022We surveyed 30 sites across four seasons (Winter 2019-Autumn 2020) during favorable weather conditions (sunny/partly cloudy, winds 10°C). Sites were randomly visited within each three-month seasonal period. At each site, we conducted a 20-minute, 100-meter transect survey, recording all insect-plant interactions within 2.5 meters of the transect line. An interaction was defined as an insect making contact with any plant part or hovering ≤10 cm above a plant for >2 seconds. Plants were identified to species level using the NSW Flora Online system. Insects were collected via sweep net for identification, with survey timing paused during capture. Hover flies were identified to species level where possible using Thompson's (2011) unpublished keys, with expert verification by Andrew Young. Due to taxonomic complexity, Melangyna species (subgenus Austrosyphus) were treated collectively as Melangyna indet. Only honey bees (Apis mellifera) and Melangyna indet. had sufficient interaction numbers for analysis. All specimens are housed at The University of Sydney