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A convergent route towards marine polycyclic ethers via a novel centrosymmetric approach
Get PDFMarine ladder polyether (MLP) toxins are a family of toxins produced by various dinoflagellates species. These natural products exhibit a wide array of toxic and pharmaceutical effects. Their complex structures and potentially useful bioactivities make them an attractive total synthesis target. Previous total syntheses of the MLP toxins suffer from large step counts and low overall yields, which presents a challenge for investigating their biological effects.
This project exploit the hidden symmetry within the MLP toxins. This project demonstrates a bidirectional synthetic approach to MLP toxins with a key desymmetrisation step in order to diverge fragments when necessary. This bidirectional approach increases synthetic efficiency by decreasing overall step count.
This thesis focuses on two main aims; attempts towards the total synthesis of gymnocin B, and the synthesis of various MLP fragments. A convergent bidirectional route to gymnocin B has been developed and efforts have been made at its completion. To fully demonstrate the versatility of this approach, fragments from an assortment of MLPs have been synthesised from a single key symmetric intermediate
Exploring the drought-food insecurity nexus using the social-ecological systems approach
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Intimate Partner Homicide in British street literature in the nineteenth century
Get PDFThis thesis explores the representation of Intimate Partner Homicide (IPH) in nineteenth-century British street literature, focusing on how visual and verbal elements converge to construct depictions of domestic murder in broadsides, chapbooks, and other ephemeral cultural productions. It examines how the hybrid form of street literature—combining sensational illustration, visual layout and typography with ballads, trial reports, confessions, and execution account —evokes real-life cases of IPH in a direct, raw, and immediate manner while negotiating the formal dissonance and fluidity of the genre in its portrayal of this culturally complex issue. By restoring these neglected texts to scholarly visibility, this thesis highlights the value of street literature as a vital archive of popular responses and public discourse surrounding domestic violence, gender norms, and legal reform in the nineteenth century. The project challenges literary hierarchies that privilege canonical texts by locating broadsides and chapbooks as a dissident, generative and underexplored site of cultural meaning-making around intimate violence.
The thesis is structured around three chapters, each centred on either male- or female-perpetrated IPH and exploring a different mode of killing and its attendant cultural anxieties. The first considers the 1831 case of John Holloway, focusing on dismemberment and the construction of male violence and victim-blaming through the interplay of verbal and visual codes. The second examines Margaret Shuttleworth (1821), a so-called ‘fallen woman’ who killed her husband while drunk, addressing how street literature frames female physical aggression. The third investigates several poisoning cases across the century, arguing that poison is a ‘feminine’ method of murder, entangled with misgivings about medical jurisprudence, female propriety, intimacy, and deceit. Overall, this thesis argues that street literature’s hybrid form allows it to stage intense and contested portrayals of IPH, making visible the tensions surrounding gender, domestic authority and justice in nineteenth-century Britain
Organic semiconductors as colour conversion materials for display applications
No full textAbstract not currently available
CO₂-sensitive membrane traffic impacts ion transport and stomatal kinetics
No full textAbstract not currently available
Cosmographies for an Anthropocene: meteorites, Dark Skies and film
No full textAbstract not currently available
Screening of malaria infections using AI-powered infrared spectroscopy
Get PDFOver the past two decades, malaria control efforts have averted 2.1 billion cases and saved 11.7 million lives globally, yet the disease still claims over 600,000 lives annually, mostly in sub-Saharan Africa. Key interventions like insecticide-treated nets, indoor spraying, and antimalarial drugs have driven success, but major challenges persist. Accurate, timely detection of malaria parasites and scalable population screening remain difficult, especially in low transmission areas. Although WHO promotes surveillance as a core pillar of elimination, resource constraints in low-income countries hinder the expansion of effective, affordable surveillance systems. Current malaria screening tools, such as rapid diagnostic tests (RDTs) and microscopy, are essential for detecting parasites but have limitations, particularly in low transmission settings and at low parasite densities, which hampers elimination efforts. While more sensitive methods like polymerase chain reaction (PCR) are available, they are costly and impractical for widespread use in resource-limited areas. As a result, there is an urgent need for sensitive, cost-effective, and scalable tools capable of detecting low-density infections, especially in low transmission contexts.
Recent studies have shown the potential of using artificial intelligence (AI) powered infrared spectroscopy to detect malaria parasites in human blood. This approach is reagent-free, robust, user-friendly, quick and potentially cost-effective. However, to address the gaps in current methods for malaria screening and diagnosis, it was important to also assess factors such as lowest detectable parasite density and performance in low transmission settings before adoption by malaria control programs.
The primary aim of my PhD research was to improve malaria surveillance by exploring the application of infrared spectroscopy and machine learning (IR-ML) for malaria screening in population surveys. To achieve this, I pursued five complementary objectives: (1) reviewing the potential applications of IR-ML for malaria surveillance, developing a target product profile, and identifying key considerations and research gaps for integrating IR-ML into control efforts; (2) demonstrating the performance of mid-infrared spectroscopy and machine learning (MIRs-ML) across varying parasite densities and anaemic conditions; (3) conducting cross-sectional surveys to map malaria burden in an endemic setting, assessing the performance of existing methods (RDTs, microscopy, and qPCR) for risk stratification; (4) evaluating MIRs-ML performance in areas with differing prevalence rates; and (5) developing a web-based platform to deliver MIRs-ML results to end users. The ultimate goal was to advance the development of MIRs-ML as a scalable malaria screening tool, adaptable to both high (prevalence rate >30%) and low transmission (prevalence rate <5%) settings, with the potential to transform malaria detection and monitoring.
To achieve the first objective, I reviewed the current state of infrared spectroscopy and machine learning (IR-ML) for malaria surveillance, comparing its advantages and limitations to existing tools like PCR, RDTs, and microscopy. This review identified research gaps and developed a target product profile (TPP) for integrating infrared technology into routine surveillance. For the second objective, I conducted lab experiments using blood from 70 malaria-free volunteers in Tanzania, diluted with cultured Plasmodium falciparum to create different parasitemia and anemia levels. These samples were used to create dry blood spots, which were then scanned using ATR-FTIR spectroscopy. Using supervised machine learning classifiers trained on a subset of the samples, we achieved over 90% accuracy in detecting malaria, even at low parasite densities, and across different anemia conditions. Field applications of these models demonstrated over 80% accuracy in predicting natural infections.
The third and fourth objectives involved cross-sectional surveys in 93 sub villages in southeastern Tanzania, screening 7,628 individuals using RDTs and microscopy, with two-thirds analyzed by qPCR. qPCR consistently detected higher transmission rates, revealing that RDTs and microscopy underestimate malaria prevalence, particularly in fine-scale mapping. I then used the survey data to evaluate MIRs-ML performance in areas with varying malaria prevalence rates from low to high. Again, the ML classifiers achieved over 90% accuracy and sensitivity in both high and low transmission settings. We also observed that performance was slightly lower in low transmission areas when trained exclusively on high transmission data, compared to when the models were trained with data from across all settings.
Finally, to make these models readily available to users in future, we developed a web-based platform that allows scientists and national programs to access pretrained ML models for instant malaria infection predictions. This platform is currently powered by models trained on over 5,000 human blood samples and 40,000 mosquitoes, and will continue to expand with data from Tanzania, Burkina Faso, and the UK. The ultimate goal is to democratize the applications of these models across different user groups in different countries.
In conclusion, through population surveys, I demonstrated the limitations of RDTs and microscopy for mapping malaria risk. I developed MIRs-ML in the lab to overcome these challenges and tested it in the field, showing its promise. This research has significantly advanced our understanding of the potential of MIRs-ML for malaria screening. It has demonstrated that the approach has high sensitivity and is capable of detecting parasite levels as low as one parasite/μl of blood, making it particularly suitable for large-scale population surveys and enhancing risk stratification efforts. The study also highlighted the limitations of current screening tools, such as RDTs and microscopy, which perform poorly in low transmission settings compared to the more sensitive PCR. This underscores the urgent need for new, more sensitive approaches for precise stratification. This PhD research shows that MIRs-ML could meet these needs, making it a valuable complement to existing surveillance methods and a promising tool for malaria screening, even in low transmission areas
Examining refugee educational inclusion in the UK: opportunities and challenges for Syrian students in Greater Glasgow
Get PDFThis thesis examines the educational inclusion of Syrian students in mainstream schools within the Greater Glasgow area, focusing on two critical aspects: (1) understanding the opportunities and challenges related to Syrian students’ educational inclusion, and (2) examining their inclusion in terms of presence, participation, and achievement. Syrian families and school educators in Greater Glasgow were invited to participate, and data was collected through semi-structured interviews with 11 parents and 15 children, as well as an online survey completed by 6 school educators.
The Capability approach by Nussbaum, focusing on human development, provides the comprehensive philosophical framework for this study. Unterhalter’s concept of equity in education, which includes Equity from Below, Equity from the Middle, and Equity from Above, is also incorporated. In addition, this study utilizes the Index for Inclusion developed by Booth and Ainscow, alongside key documents from the UNESCO: ‘Reaching Out to All Learners: A Resource Pack for Supporting Inclusive Education’ and ‘A Guide for Ensuring Inclusion and Equity in Education’. Collectively, these concepts and documents form the framework for analysing the study's findings, demonstrating their relationship to or deviation from existing literature on inclusive education, the education of Syrian children, and refugee education. Qualitative data analysis was conducted using thematic analysis, guided by Braun and Clarke's methodology.
The study's findings reveal a positive outlook on the educational experiences of Syrian students in Scotland. Syrian students enjoyed attending school and also harboured a genuine liking for both their schools and teachers. Parents expressed contentment with school offerings, affirming that schools effectively fulfilled their responsibilities. However, the English language barrier and insufficient measures to mitigate it pose significant challenges to the educational inclusion of Syrian students. Furthermore, disparities between the education systems of Scotland and Syria, alongside evident cultural distinctions, are apparent in the data.
Analysis of the online survey data highlighted commendable efforts by educators to address diverse learner needs in classrooms, mitigating potential challenges. However, despite these positive efforts, significant barriers to inclusive education were identified, particularly for learners from refugee and asylum-seeking backgrounds. The lack of support and training for educators, coupled with insufficient resources and services in certain schools, emerged as significant obstacles to comprehensive educational inclusion
Investigating drug resistance in RAS-driven models of colon cancer
Get PDFColorectal cancer (CRC) is the second leading cause of cancer-related mortality in the world, accounting for more than 900,000 deaths in 2020. A disproportionate number of these deaths are due to KRAS-mutant CRCs, which account for ~40% of all CRC cases and are notoriously resistant to most therapies. Despite showing great promise in preclinical studies, targeted therapies have performed sub-optimally in clinical trials for KRAS mutant cancers. The mechanisms by which RAS pathway inhibitors have failed to reduce tumour progression remains poorly understood and presents a huge clinically unmet need. This research addresses the significant gap in effective treatments for KRAS-mutant CRC by delving into the mechanisms underlying drug resistance, using advanced CRC models. Several studies have reported that drug resistance is an emergent feature of genetically complex tumours.
To capture tumour genome complexity, I used a diverse panel of CRC models reflecting multigenic and heterogeneous nature of tumours. Our patient-specific Drosophila avatars and transgenic mouse models are designed to explore how genome complexity impacts drug response. Our models comprise alterations in at least three primary pathways implicated in CRCs– APC, KRAS and TP53, providing a robust platform for studying the cellular and molecular dynamics driven by oncogenic Ras signalling.
Key findings demonstrate that CRC tumour complexity significantly impacts the efficacy of RAS-pathway inhibitors, which have shown limited success clinically. By characterizing these models, this research has uncovered that different stages of tumour development exhibit varying dependencies on the MAPK pathway, offering insights into the failure of existing therapies. Additionally, the study identifies and validates the upregulation of the glucuronidation detoxification pathway as a novel resistance mechanism, showing that targeted combination therapies can enhance drug efficacy within tumours.
This comprehensive study not only deepens the understanding of CRC pathogenesis and resistance mechanisms but also opens avenues for developing more effective targeted therapies
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