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Prevention and response to rabies incursions in Low-and-Middle-Income Countries (LMICs)
Full text linkRabies is a viral, zoonotic disease that kills 59,000 people annually, mainly in low-andmiddle-income countries (LMICs) in Africa and Asia, through dog-to-human transmission. To eliminate dog-mediated human rabies deaths, the ‘Zero by 30’ global strategy developed by WHO and fellow international organisations recommend a sustained 70% vaccination coverage in dog populations. However, in rabies-endemic countries, rabies surveillance is severely limited due to lack of political will and insufficient resources for rabies detection, treatment and prevention. Rabies control measures including diagnostic tools, dog vaccines and post-exposure prophylaxis or PEP for humans, are undersupported in LMICs, therefore resulting in poor case detection and reporting, and high numbers of human deaths. Nevertheless, the path toward dog rabies elimination is straightforward, and has been achieved and sustained by many high-income countries (HICs), although incursions from LMICs are occasionally reported.
My objective was to explore different strategies aimed at controlling rabies incursions in LMICs. I used a transdisciplinary approach involving analysis of past incursions, realtime evaluation of an incursion as it unfolded into an outbreak, and assessment of a novel intervention that could potentially reduce rabies transmission. Beginning with an introductory chapter, this thesis focuses on what constitutes a rabies incursion and the current status of rabies surveillance and control measures worldwide. Next, in Chapter 2, I performed a systematic review of rabies incursions reported globally from 2001 to 2022 to highlight the catalytic role that incursions have played in global rabies (re-)emergence. My analysis identified incursions that resulted in outbreaks mainly in LMICs, and pinpointed common factors that contributed to different outcomes, from those that were contained to those causing fatal outbreaks and establishing endemic circulation. My findings illustrated the importance of preparedness and response capacity to minimize resurgence in nearby rabies-free zones, which is typically lacking in LMICs.
For the third chapter, I investigated the detection and response to a dog-mediated incursion in the previously rabies-free island province of Romblon, Philippines. A positive canine rabies case was initially detected in late 2022, and led to the detection of more than 40 positive samples within a year, as well as two laboratory-confirmed human rabies deaths. Lack of surveillance and suspension of mass dog vaccination activities due to COVID-19 restrictions contributed to the introduction of rabies into Tablas Island, which was human-mediated via boat travel. Contact tracing and dog vaccination were initiated but reach was limited. Integrated bite case management (IBCM) was essential for detection of this outbreak, and phylogenetic analysis of outbreak samples revealed possible introductions from rabies-endemic provinces within the Philippines.
My fourth and fifth chapters describe the implementation of long-lasting collars during a mass dog vaccination event in Puerto Galera municipality, Philippines. In the fourth chapter, I evaluated the feasibility of incorporating collars into vaccination campaigns by interviewing practitioners about their experiences with using collars. I also administered questionnaires to community members to gauge their behavior changes toward collared dogs, and conducted transect surveys to assess collar durability. While practitioners experienced minimal difficulty with learning and applying collars, questionnaire answers exposed a lack of understanding of rabies transmission among the local community. Most believed that dogs are susceptible to rabies even when vaccinated, and reported displaying indiscriminate behavior toward collared and non-collared dogs. Understanding of rabies among residents must therefore be improved for collars to induce a change in human behavior toward collared dogs. Collars were found to be vulnerable in coastal conditions as most were lost within months, necessitating a different material for improvement of collar durability. In Chapter 5, I used mark-resight survey results to estimate the free-roaming dog population and vaccination coverage in Puerto Galera, capitalizing on the deployment of collars. I determined that overall vaccination coverage was low, especially among freeroaming dogs, and that the dog population in Puerto Galera is severely underestimated. Targeting vaccination toward free-roaming dogs caused significantly increased coverage in an area where vaccination of free-roaming dogs was prioritized.
Summarized in my final chapter are the main conclusions to be drawn from this thesis: incursions in rabies-free zones in LMICs are frequent, underscoring the importance of targeting and sustaining rabies vaccination in rabies-endemic areas. Delayed incursion detection results from gaps in rabies surveillance, which can be enhanced with tools like IBCM, while genomic sequencing can determine incursion sources. LMICs such as the Philippines face unique cultural challenges to rabies elimination: knowledge gaps on rabies and traditional practices that have normalized free-roaming dogs are some of which have prevented rabies control interventions like collars from being more effective. My work shows that key priorities for LMICs like the Philippines should be sustaining control strategies (particularly dog vaccination and rabies surveillance) and improving rabies education, to accelerate progress toward the ‘Zero by 30’ goal
Towards Boolean logic cryogenic applications of Josephson junction field-effect transistors
No full textAbstract not currently available
Drugs and criminalisation in Britain, c.1815–c.2000
Full text linkThis thesis is a systematic analysis of the relationship between British drug laws and conceptions of legitimate criminalisation from c.1815 to c.2000. It explores the extent to which historical understandings of what and who can be treated as criminal or requiring regulation, how this should be done, and how this can be justified, were in synergy or tension with the contemporaneous legislation variously regulating, by way of punitive sanctions, substances used for their psychoactive effects.
Part One examines the period from the early nineteenth century to around the outbreak of the First World War, tracing the origins of drug criminalisation to Victorian concerns about criminal and accidental poisonings and pharmaceutical regulation, and analysing these against the growth of the Victorian legislative state. Also discussed here are statutes targeting ‘habitual drunkards’ and ‘inebriates’, and the opium suppression movement of the turn of the century. Part Two explores the period from the First World War to c.1960, looking at how the transnational aspects of drug control were constructed alongside domestic controls. These changes are considered through the lens of broader contemporaneous criminal laws and debates about criminalisation. Finally, Part Three focuses on the period c.1960 to the turn of the century, which is when the present system of British drug control was created and (re)shaped. Points of discussion include the enactment of the Misuse of Drugs Act 1971 and the various end-of-century drug policy developments, which are situated against contemporaneous developments in criminal law theory, changes to the processes of law reform, and wider criminal justice policies.
Across the whole timeframe considered, there are clear examples of both synergy and tension between drug legislation and contemporaneous conceptions of legitimate criminalisation. More often than not, the findings are more nuanced, with competing justificatory rationales pulling in different directions. Notwithstanding this complexity, it is argued that drug laws have been more central to the development of the criminal law than has been recognised, and are a window into understanding broader patterns and processes of criminalisation and the substantive criminal law
Host-Pathogen interactions in extravascular T. brucei infections
No full textAbstract not currently available
Innovating a human adipocyte spheroid platform to explore the role of metabolite-sensing GPCRs in metabolic disease
No full textMetabolic diseases such as obesity and type 2 diabetes are a global healthcare and economic burden affecting over 1 billion individuals worldwide. These diseases are characterised by excessive accumulation and dysfunction of adipose tissue, leading to impaired metabolic regulation and severe downstream consequences for patients. Low level chronic inflammation of adipose tissue is an important hallmark of metabolic disease, and there is growing evidence that metabolite-sensing G Protein-Coupled Receptors (m-GPCRs) can regulate metabolic function through autocrine and paracrine signalling loops. However, it has been challenging to dissect these complex signalling networks using traditional 2D cell culture or in vivo experimental models. This thesis aimed to address this gap by engineering and validating an innovative 3D in vitro model of adipose tissue suitable for investigating the role of m-GPCRs in metabolic disease.
First, a series of novel genetically encoded NanoBiT biosensors were designed and optimised to quantify real time signalling of unmodified GPCRs in live cells. Next, spheroids were generated by seeding human Simpson Golabi Behmel Syndrome (SGBS) preadipocytes in ultra-low adhesion plates and differentiating them into adipocytes. Comprehensive morphological, transcriptional and protein-level characterisation demonstrated that spheroids accumulate lipid droplets and upregulate key markers of adipogenesis during differentiation. Critically, the differentiated spheroids show characteristic adipocyte functions, including β-adrenergic-stimulated lipolysis, and insulin-stimulated glucose uptake. To mimic a metabolic disease-relevant phenotype, spheroids were treated with Tumor Necrosis Factor which induced a pro-inflammatory, insulin resistant microenvironment, and revealed upregulation of the FFA4 receptor in disease-relevant conditions. Finally, the role of m-GPCRs in adipogenesis and lipolysis could be defined by treating adipocyte spheroids with pharmacological tool compounds, and genetically encoded biosensors were incorporated to directly measure receptor signalling in adipocytes.
Overall, a novel human adipocyte spheroid platform has been developed which has allowed the investigation of m-GPCR function within a physiologically- and disease-relevant context. This therefore provides a strong foundation to interrogate the complex metabolic-immune signalling networks within adipose tissue, and may ultimately lead to new GPCR-focused therapeutic strategies for metabolic disease
Effective multi-modal and multi-domain graph-based recommender systems via self-supervised learning
No full textThe rapid expansion of digital applications and services has created an urgent need for sophisticated top-K recommendation models capable of matching users with items aligned to their interests. Graph-based recommender systems have become a cornerstone for tackling this information overload. However, their effectiveness is often hindered by fundamental limitations, including vulnerability to noisy interactions, the so-called over-smoothing problem, and an inability to sufficiently leverage rich and complex information sources. In particular, many existing models underperform in effectively mining and integrating supervisory signals from diverse modalities (e.g., text, images) and domains (e.g., books, movies). This thesis argues that the top-K recommendation effectiveness of graph-based recommendation can be enhanced by proposing novel Self-Supervised Learning (SSL) techniques designed to explicitly mine and integrate multi-modality and multi-domain signals.
This thesis aims to address existing research gaps in the literature by proposing a suite of novel graph-based recommender techniques using the SSL paradigm. It addresses three primary challenges: (i) enhancing the fundamental expressive power of graph neural architectures to alleviate the over-smoothing problem (i.e., representations becoming indistinguishable after repeated graph aggregation operations), and the incapability of existing approaches to denoise noisy implicit interactions in top-K recommendation; (ii) enhancing the modality encoding capabilities to address the insufficient modality fusion and the isolated multi-modal recommendation pipeline in top-K multi-modal recommendation; and (iii) improving the knowledge transfer capability and generalisability of graph-based recommender systems for top-K recommendation in multi-domain settings.
To enhance the expressiveness of graph neural architectures, we propose two novel graph-based recommender models at different architectural levels. First, Positional Graph Contrastive Learning (PGCL) operates at the message-passing level and integrates graph positional encodings (e.g., Laplacian eigenvector) into a new graph message-passing function. This architecture, trained with an SSL loss, generates highly distinguishable user and item embeddings, thereby improving the expressive power of graph-based recommender systems while alleviating the over-smoothing problem. Second, the Diffusion Graph Transformer (DiffGT) leverages a new graph transformer model at the overall architecture level to denoise the noisy implicit user-item interactions within a diffusion process. In particular, DiffGT applies an SSL loss to maximise the agreement between the original user/item embeddings and the denoised user/item embeddings during model training, thereby improving the model expressiveness and yielding an improved top-K recommendation performance.
To address the insufficient modality encoding issue, we first focus on enhancing the modality fusion within multi-modal graph-based recommender systems. In particular, we propose the Multi-modal Graph Contrastive Learning (MMGCL) model, which introduces modality-specific graph augmentations as positive samples and a modality-aware negative sampling strategy in an SSL loss. This enhances the modality fusion process, unlike the existing approaches that often treat each modality with equal importance. In addition, we further enhance the modality fusion by using Large Multi-modal (LMM) encoders. We show that by using SSL to enable deep modality alignment across modalities, these LMM encoders significantly outperform the shallow alignment methods common in existing graph-based recommender systems. On the other hand, in addition to addressing modality fusion, there are still longstanding isolation problems – isolated feature extraction process and isolated modality encoding process – that impede the effective mining of self-supervised signals across multiple modalities and remain unresolved in existing multi-modal graph-based recommender systems. To address these isolation problems, we introduce the Unified multi-modal Graph Transformer (UGT), a novel end-to-end architecture that uses SSL to unify the multi-modal representations into the same semantic space, thereby enhancing top-K multi-modal recommendation within a unified graph transformer architecture.
Next, to address the insufficient domain transfer capabilities in existing cross-domain models, we introduce two novel graph-based approaches. The first, Personalised Graph Prompt-based Recommendation (PGPRec), is an ID-based approach that enables effective and parameter-efficient cross-domain knowledge transfer. Specifically, PGPRec first uses SSL to pre-train a graph encoder, ensuring that it learns high-quality and generalisable knowledge across domains. This knowledge is then effectively transferred from a single source domain to a target domain via personalised and item-wise graph prompts. To further enhance generalisation and reduce reliance on ID-based features, inspired by the model soup paradigm, we propose AdapterSoupRec, which leverages multi-modal large language models (MLLMs) to generate universal item representations. In particular, we use SSL and cross-entropy losses to enable MLLMs to generate highly generalisable representations and effective model configurations. These configurations are then combined via a weighted average (i.e., the ‘model soup’ technique) to create a more effective set of model parameters, thereby achieving improved top-K recommendation in a multidomain setting.
Overall, this thesis contributes novel and effective SSL-enhanced graph-based recommender models that systematically address the challenges of limited architectural expressiveness, insufficient modality encoding, and insufficient domain transfer capabilities. Our extensive experimental evaluations on numerous real-world datasets validate the thesis statement, demonstrating that recommendation effectiveness is significantly enhanced by explicitly mining more supervision signals from diverse modalities and domains. These contributions make progress towards the development of effective graph-based recommender systems and pave the way for further future directions of research in top-K recommender systems
Aortic biomechanics in paediatric and adolescent patients with Marfan syndrome
No full textAbstract not currently available
What is the role of secondary senescence in cancer? Elucidating the role of Notch signalling in secondary senescence
No full textAbstract not currently available
Exploring the potential of red mud and naturally occurring materials for water treatment: sustainable strategies for the effective removal of antibiotics
Full text linkAmidst growing concerns over water pollution and the continuous discharge of pharmaceuticals (e.g., antibiotics) into water bodies worldwide, addressing sustainable wastewater treatment techniques has become a pressing priority. In addition, today’s world faces numerous issues such as resource depletion, water shortage, soil pollution, increased demand for energy, and other critical problems. Therefore, it is essential to use waste materials that could be of potential benefit in such applications. These waste materials are generally classified into natural and industrial (man-made) wastes. One of these industrial waste materials is red mud, which is the main byproduct of the aluminium production industry whether using the Bayer or sintering processes. Red mud causes many environmental issues including water and soil pollution because of its high alkalinity. In addition, dry and wet storage of red mud is not effective because of red mud leaching into soil and groundwater. Therefore, as outlined in this study, three different samples of red mud from India and China were used to extract valuable metal oxides such as titania (TiO2) and haematite (α-Fe2O3) to be used in the adsorption of ceftriaxone and doxycycline, which are common antibiotics discharged into water systems. Red mud samples were investigated and the extracted metal oxides were proved to be more efficient.
α-Fe2O3 is also easily and ecofriendly synthesized using Typha latifolia (T. latifolia), which is a cosmopolitan plant species that causes serious environmental issues when dominating ecosystems, and therefore there is great value in making use of the excessive biomass of this plant. Additionally, TiO2 was prepared in a more conventional way using titanium tetraisopropoxide and water. A comparative study between the efficacy of the two metal oxides and their commercial counterparts, which were used as a benchmark, in removing the two antibiotics from model and the River Clyde water samples was undertaken in this work. The impregnation and doping effects using different dopants were also investigated.
Biogenic iron oxide (BIOX) constitutes another waste material that is produced naturally by Leptothrix bacteria, and it holds significant potential in environmental management. These naturally derived materials offer a sustainable and eco-friendly substitute for synthetic iron oxides, with promising applications in water purification, pollution control, and resource recovery. Pristine, calcined, and BIOX materials reduced under different gas mixtures were investigated in this study. The effect of seasonal variation on the efficiency of BIOX materials was also covered. Additionally, the catalytic effect of BIOX and red mud-based iron oxides was tested for ammonia synthesis
Co-evolution of ecosystem services and land use/ land cover change in the mountains of Eastern China
Full text linkLand use and land cover (LULC) change, shaped by socio-economic development and climate variability, has profound implications for ecosystem services (ES), particularly in fragile mountain and coastal regions of China. Existing studies of the ES-LULC nexus in China lack systematic review, often short-term and retrospective, with limited use of scenario-based modelling. As a result, the long-term dynamics, vulnerabilities, and future trajectories of socio-ecological systems under interacting socio-economic and climatic drivers remain insufficiently understood.
This dissertation combines a systematic review, long-term empirical analysis, and system dynamics modelling to investigate the co-evolution of LULC and ES in Chinese mountain regions, with Shandong Province as a representative case. 1) The systematic review of 203 articles (2007–2024) shows that ES-LULC research in Chinese mountain regions has grown rapidly but remains uneven in scale, methodology, and regional focus. English-language studies tend to operate at broader spatial and temporal scales using biophysical models, with greater attention to regulating services, whereas Chinese studies are concentrated at smaller regional scales, relying mainly on statistical analysis and value transfer methods, and focus more on provisioning services. Overall, long-term time-series analyses, cross-scale comparisons, and scenario-based assessments remain limited, constraining a systematic understanding of ES evolution, trade-offs, and feedbacks. 2) Using longterm data (1950–2022) and causality testing in Shandong Province, the study reveals that urban expansion and economic growth significantly drove the increase of construction land, intensifying trade-offs between provisioning services such as food production and regulating services such as carbon storage and water regulation. Wetland loss and precipitation decline exacerbated negative feedbacks, accelerating vegetation degradation and drought risks. Overall, system connectivity declined markedly after 1980, resilience weakened, and the socio-ecological system showed a tendency toward functional disturbance and potential reorganization. 3) System dynamics simulations (2020 – 2100) reveal strong nonlinearity and path dependency in ES-LULC trajectories. Under extreme warming and drought, agricultural, forest, and water systems risk synchronous collapse by mid-century, signalling the approach of socio-ecological tipping points. Adaptive management can delay destabilisation but generates unavoidable trade-offs—for example, between food and water or carbon and water. Socio-economic pathways further amplify these dynamics, with sustainability-oriented futures slowing risk accumulation and fossil-fuelled trajectories accelerating systemic decline.
Policy insights include strengthening farmland protection and sustainable management to secure food and carbon storage; scaling up water-saving measures to enhance resilience under climate extremes; conserving wetlands to buffer rainfall decline and drought; and carefully designing afforestation strategies to balance water–carbon trade-offs. Prioritising sustainability-oriented socio-economic pathways offers the most robust option for maintaining long-term system stability.
This dissertation contributes academically by advancing understanding of ES LULC co-evolution in Chinese mountain and regional systems, methodologically by integrating causality testing with system dynamics into a transferable framework, and practically by providing evidence-based insights for land–water–carbon governance in Shandong and other regions facing similar pressures
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