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ERA is a digital repository of original research produced at The University of Edinburgh. The archive contains documents written by, or affiliated with, academic authors, or units, based at Edinburgh that have sufficient quality to be collected and preserved by the Library, but which are not controlled by commercial publishers. Holdings include full-text digital doctoral theses, masters dissertations, project reports, briefing papers and out-of-print materials.

Information on current research activity including staff, projects and publications is available via the Edinburgh Research Explorer.

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Negotiating orthodoxy: Wang Shouren's Buddhist and Daoist engagements in the making and reception of a Confucian identity
(2026-04-27) Li, Yizhu; Gentz, Joachim; Ward, Julian
This thesis re-examines the Ming Confucian Wang Shouren, better known as Wang Yangming (1472–1529), through the twin lenses of self-presentation and reception history. It shows how his recorded encounters with Buddhism and Daoism served—first as retrospective narratives in his own writings, later as malleable evidence for Ming–Qing interpreters—to construct, contest, and perpetually renegotiate a Confucian identity. The argument unfolds in three interlocking movements. Self-presenting: close readings of official memorials, prefaces, letters, and recorded dialogues reveal Wang staging his “detours” through non-Confucian teachings to claim the authority of one who has tested, transcended, and thus validated the Confucian Way. Engaging: drawing on disciples’ notes to corroborate these claims, the study reconstructs Wang’s evolving engagement with non-Confucian traditions—from exploratory syncretism to a hierarchical model that treats Buddhism and Daoism as provisional aids rather than rivals. Being received: a survey of Ming–Qing biographies and polemics shows later scholars selectively quoting, omitting, and re-ordering his life to advance their own doctrinal and political agendas. Methodologically, the thesis models a “negative-definitional” approach: it clarifies Confucian identity by tracing the boundaries negotiated with rival traditions. More broadly, it offers a framework for analysing intellectual identity as a historically contingent process shaped by self-articulation and external reinterpretation. Substantively, it reintroduces Wang not as a fixed exemplar of syncretism or orthodoxy but as a mobile site of doctrinal contestation—one whose meaning was, and remains, produced in the very act of being read.
Identity and nationhood: the effect of war on the East Asian diaspora within Australian, New Zealand, and Canadian literature
(2026-04-27) Kenchington , Sarah; Keown, Michelle; Hughes, Keith
My thesis, entitled “Identity and Nationhood: The Effect of War on the East Asian Diaspora within Australian, New Zealand, and Canadian Literature” seeks to explore how war impacted the identity of Chinese and Japanese diasporic communities in the twentieth century. I will discuss six novels, alongside historical research, literary criticism, and theoretical modalities to analyse the extent to which war both solidified and fragmented identity for these diasporic groups. Both the Chinese and Japanese diaspora were pushed to the periphery of these nations, but war exacerbated those political and cultural forces of marginalisation even more profoundly. The thesis will be separated into three core chapters – one for each nation of focus. For Australia, I will discuss The Narrow Road to the Deep North by Richard Flanagan and The Divine Wind by Garry Disher. For New Zealand, I will explore Chappy by Patricia Grace and As The Earth Turns Silver by Alison Wong. Finally, for Canada, I will examine The Jade Peony by Wayson Choy and Obasan by Joy Kogawa. Australia, New Zealand, and Canada are all also former British territories, so there is a commonality of still feeling a level of connection to British national identity, which will make for an interesting discussion regarding the lasting impact of imperialism. This thesis will offer new insights into the complexity of wartime policies and national identity, the way in which literature portrays this complexity, and ultimately will foster an understanding of how war impacted the East Asian diaspora during the twentieth century.
Incorporating a human rights-based approach to irregular migration: a Chilean case study
(The University of Edinburgh. College of Humamities and Social Sciences, 2026-04-24) Rioseco Vallejos, Valentina; Casanas Adam, Elisenda; McCall-Smith, Kasey; Agencia Nacional de Investigación y Desarrollo (ANID), Chile
This thesis examines whether the domestic incorporation of human rights could support an effective solution to irregular migration. It stems from the recognition that the human rights-based approach to irregular migration is to open diverse and accessible pathways for regular migration. It also identifies a gap in the literature concerning the construction of these pathways to regular migration. Therefore, it uses the methodology of legal and doctrinal analysis to analyse part of the human rights framework protecting the entry of migrants and the obligations emanating from it. It also considers policy arguments and data obtained my civil society organisation. Specifically, it analyses the extent to which the principle of non-discrimination and the principle of non-refoulement protect the entry of migrants into a country that is not their country of origin. The analysis is focused on the interaction of human rights law at three different levels, namely the international level, the regional level and the domestic level. Particularly, it observes the Inter-American Human Rights System, which presents a number of distinctive features discussed in later chapters, and focuses on Chile as a case study.
Mast cell ontogeny and functions in tissue development
(2026-04-24) Kapoor, Simran; Pedersen Wilson, Amy B.; Gentek, Rebecca; Wellcome Trust Host Pathogen and Global Health PhD studentship; Chancellor’s Fellowship from the University of Edinburgh; Senior Research Fellowship from the Kennedy Trust for Rheumatology Research; Cancer Research UK
Mast cells (MCs) are granulocytic immune cells classically recognised for their detrimental roles in allergic disease. Widely distributed throughout almost all tissues in the body, MCs are tissue-resident cells that release various mediators during allergic responses, driving pathological tissue remodelling. However, these same effector mechanisms may also participate in beneficial tissue remodelling during development and homeostasis. Emerging studies have proposed that MCs influence vascularisation and innervation processes which are re-shaped during allergy and anaphylaxis. Yet, whether they play a critical role in normal tissue development remain largely unexplored. Despite their early emergence during fetal development, the prevailing view is that MCs are dispensable for essential tissue development and organogenesis, largely based on observations from conventionally used MC-targeting mice models that appear phenotypically normally without overt developmental defects. However, it remains possible that these genetic models may not sufficiently ablate MCs at developmental windows where they play a critical function. Genetic MC-deficiencies have not yet been identified in humans, lending support to the controversial yet plausible hypothesis that MCs play a critical role in tissue development and survival, and therefore, remain strongly evolutionarily conserved. The aim of this thesis is to re-define the paradigm of the developmental significance of MCs in tissue development. Previously, conventional cKit-based MC-targeting models were used to report the critical developmental function of MCs for mammary gland morphogenesis during puberty. Using newly developed MC-targeting mouse models, I demonstrate that MCs are not critical for tissue development postnatally in the mammary gland at least, indicating that the previously used cKit-based approach may be unsuitable for evaluating MC function in this context. Investigating the critical functions of MCs during development therefore remains and requires a clear understanding of MC identity during development. To address this, I combined single cell RNA sequencing approaches to comprehensively characterise MC ontogeny as they emerge during embryonic days 15.5 to 18.5 in the fetal liver and peripheral tissues. This work revealed that MC developmental kinetics follow a conserved developmental programme, and tissue-specific cues shape the heterogeneity of MCs across tissues as they develop in situ, suggesting potentially important roles in organogenesis. Building on the early expression of Cpa3 in MCs as they develop during embryogenesis, we newly generated a Cre-based targeting model in which Cpa3-Cre driven diphtheria toxin expression in cells may enable direct disruption of MC development in utero to assess its developmental consequences. In contrast to previous assumptions, perturbation of MC development in this novel genetic approach resulted in severe neonatal lethality. This phenotype was associated with altered haematopoiesis and reduced platelets, impaired tissue development, and dysfunctional fetal lungs leading to respiratory distress in these mice shortly after birth. These findings demonstrated that early Cpa3-based disruption has profound consequences for developmental homeostasis. Collectively, this work introduces new genetic tools for developmental immunology in the field of MC biology and uncovers key insights into MC haematopoietic development and functions in organogenesis and tissue development.
Machine learning techniques for bacteria detection in lungs using optical endomicroscopy images
(2026-04-24) Demirel, Mehmet; Hopgood, James; Finlayson, Neil; Nick Polydorides; Engineering and Physical Sciences Research Council (EPSRC); Healthcare Impact Partnerships
Pneumonia, a severe respiratory infection, presents a high mortality risk, particularly in critical care settings. The disease often requires prompt and accurate detection of bacterial presence in the lungs to guide appropriate antimicrobial therapy. Current diagnostic methods, including imaging techniques like X-rays and CT scans, lack the specificity needed for timely bacterial detection, often leading to delays, increased risks for patients, and the potential for antimicrobial resistance (AMR) due to the prolonged use of empirical broad-spectrum antibiotics. OEM facilitates real-time acquisition of in vivo and in situ optical biopsies, thus expediting bacterial detection. Nonetheless, visually analysing the vast number of images generated by the OEM in real time can be challenging, potentially impeding timely intervention. In this regard, the thesis introduces novel unsupervised and supervised machine learning methodologies to enhance detection accuracy and efficiency in OEM images. Initially, a benchmark unsupervised approach was developed, leveraging a Hierarchical Bayesian Model (HBM) tailored specifically for bacterial detection in OEM images. This model effectively distinguishes bacterial presence by treating it as an outlier within the OEM image. The HBM utilizes probabilistic modeling to represent the OEM images as a combination of background intensity, random noise, and discrete shape bacteria. This probabilistic framework enables the model to isolate bacterial regions without requiring labeled data, which is particularly valuable given the scarcity of annotated OEM images. The detection process is further refined through an iterative algorithm that systematically identifies and removes bacteria one at a time. This iterative approach minimizes computational demands by sequentially focusing on individual bacterial detections, bypassing the need to calculate the full posterior distribution of all possible bacterial locations in each image. As a result, the HBM approach achieves high detection accuracy, establishing a robust baseline for bacterial detection in OEM images. Secondly, EmiNet, is a supervised model specifically designed to leverage the temporal aspect of bacterial movement in sequences of OEM images, distinguish ing it from previous single frame approaches. By combining Convolutional Neural Networks (CNN) with Transformer architecture, EmiNet integrates both spatial and temporal features within a unified framework, allowing it to effectively iden tify bacteria that display subtle, frame-to-frame motion, as often observed due to airflow in lung tissue. This hybrid structure enables EmiNet to model moving bacteria with high precision, which is critical for enhancing bacteria detection. To train EmiNet, a novel synthetic dataset generation strategy was developed to simulate realistic bacterial motion patterns. This dataset incorporates two primary motion types informed by expert observations in clinical data. The use of this synthetic data enables EmiNet to learn motion-specific characteristics without relying on extensive real data annotations, which are challenging to obtain. By reflecting these motion patterns, EmiNet not only enhances detection performance but also addresses one of the primary limitations of single frame approaches, enabling more accurate segmentation in real-time applications. Finally, Back2Seg is introduced as a two-stage model that refines bacterial segmentation by focusing on background estimation from OEM image sequences. This method aims to improve bacteria detection performance by accurately isolating bacteria from complex backgrounds that include various tissue structures and autofluorescent elements. The model begins with a CNN-Transformer network designed to estimate background information from the sequence of OEM images, effectively capturing the consistent patterns and features of the background tissue that remain stable across frames. This background estimation stage filters out non-bacterial elements by generating a clean background image sequence, which serves as a reference to identify bacteria more precisely in the subsequent segmentation phase. In the second stage, Back2Seg uses both the original OEM sequence and the estimated background sequence as inputs to refine the segmentation of bacteria. By incorporating background information, the model can more accurately distinguish bacteria. This dual-input approach allows Back2Seg to focus on bacterial features with enhanced specificity, improving detection accu racy. Back2Seg demonstrates improvements over both the Bayesian approach and EmiNet on certain performance metrics, offering a balanced compromise between the two methods. Overall, this thesis significantly advances bacterial detection in OEM images by introducing novel machine-learning techniques that address key limitations of existing methods. The methodologies developed here not only enhance the accuracy and efficiency of bacterial detection but also pave the way for real-time diagnostic applications, potentially transforming the management of pneumonia and other infectious diseases. By enabling rapid, targeted clinical decisions, this work can help reduce unnecessary patient exposure to broad-spectrum treatments, ultimately helping to mitigate the progression of AMR. Furthermore, future work can focus on refining these models, exploring more complex bacterial motion patterns, and integrating advanced generative models to create even more realistic synthetic datasets.