Dissecting the genomic and epigenomic alterations underlying glioblastoma

Glioblastoma (GBM) is the most common intrinsic primary brain tumour, characterised by structural complexity, tumoural heterogeneity and poor prognosis. GBM tumours reactivate neurodevelopmental programmes and they are driven by GBM stem-like cells (GSCs), displaying phenotypic similarities to neural stem cells (NSCs). Although much is known about the recurrent coding alterations and the role of GSCs in GBM, we still have a poor understanding of the contribution of non-coding alterations and complex structural variants (SVs) to GBM pathogenesis, as well as the role of key NSC transcription factors such as SOX2 and SOX9, in regulating self-renewal activity in GSCs and gliomagenesis. To study non-coding alterations and complex SVs, we compiled a large (n = 230) cohort of GBM tumours with whole genome sequencing data. We uncovered a lack of recurrent non-coding somatic small mutations (SSMs) apart from TERT promoter mutations. We identified loci under putative selection for SV recurrence, including those harbouring focal amplications and deletions, associated with complex SVs such as extrachromosomal DNA (ecDNA) and chromothripsis. We discovered underappreciated features of ecDNAs in GBM, including a high prevalence of ecDNAs generated via episomal exclusion, and uncovered the extent by which ecDNAs shape tumoural heterogeneity via seismic amplification and putative chromosomal re-integration. Notably, despite the relatively high SSM- and SV-derived neoantigen burdens, ecDNA+ samples exhibited higher transcriptional immune suppression compared to ecDNA- samples, an effect not seen in other complex SV types, suggesting that ecDNAs may modulate the tumour-immune microenvironment to evade immune clearance. Last, contrary to our expectations, ecDNA and chromothripsis were not associated with poor overall survival; rather, patients whose GBMs sustained high levels of overall complex SV burden were associated with significantly shorter overall survival, independent of other available prognostic factors. To complement these genomic analyses, we also explored transcriptional regulatory programs in GBM, particularly the role of SOX2 and SOX9 in regulating GSC self-renewal. We used chromatin immunoprecipitation followed by sequencing (ChIP-seq) data across 7 patient-derived GSCs. We found that SOX9 binds exclusively and at close proximity to SOX2, and co-bound SOX2/SOX9 sites are enriched with GSC-specific super-enhancers (SEs). We identified a dual-mode of SOX2/SOX9 co-binding that was regulatory element-dependent – as a monomer at promoters and a dimer at enhancers, reminiscent of SOX9 activity in cartilage development. Co-bound SOX2/SOX9 SEs target genes regulating NSC identity, including QKI, PTPRZ1 and CDK6. Intriguingly, SOX2/SOX9 co-binding also target their own and each other’s regulatory regions, suggesting the existence of an auto- and cross-regulatory feed-forward axis. Importantly, we observed no increased in mutational burden across SOX2/SOX9 co-binding sites, arguing against mutation-driven reactivation processes at these neurodevelopmental enhancers. These findings altogether suggest that SOX2 and SOX9 cooperate at shared enhancers and target genes, and their increased co-activity may contribute to the core programs of self-renewal displayed by GSCs. In summary, we present a thorough computational genomic analysis of the non-coding alterations and structural complexity shaping the GBM genome, revealing a complex interplay between ecDNA, immune evasion pathways and complex SV burden during gliomagenesis. Furthermore, we identify putative downstream targets of SOX2 and SOX9 that may underlie self-renewal activity in GSCs. It is hoped that these findings may lead to an increased understanding of GBM pathogenesis and ultimately result in new therapeutic avenues for GBM patients

Generating environments and pre-training agents for efficient reinforcement learning

Reinforcement learning provides a compellingly universal approach for learning to achieve an objective, specified by a reward function, by trial-and-error interaction with an environment. While this approach has the versatility to be applied to almost any objective in any environment, it can be inhibitively inefficient. Humans are able to learn to achieve new objectives and improve their capabilities via reinforcement learning over human timescales by building on prior knowledge and capabilities. However, a large proportion of the reinforcement literature considers the traditional problem of learning to perform a task tabula-rasa. In this thesis, we aim to improve the efficiency of reinforcement learning, including both the sample efficiency and computational efficiency, by incorporating environment understanding and knowledge of prior behaviours via more information-dense supervised learning objectives. In the first half of the thesis, we aim to acquire knowledge about the environment that can be leveraged for reinforcement learning. We begin by considering how to optimally combine an agent’s partial observations into a unified representation of an environment. We introduce a novel approach that can more effectively integrate partial information into a single representation than other self-supervised approaches. We next develop this general idea of learning environment representations into a diffusion-based approach for learning a full generative model of an environment. An agent can then perform model-based reinforcement learning by interacting with its environment model rather than the true environment, thereby reducing the environment dependency, and improving the sample efficiency of reinforcement learning. We demonstrate that our diffusion-based approach can more effectively capture visual details compared to related world modelling approaches, leading to greater performance and sample efficiency. However, this doesn’t reduce the computational cost of reinforcement learning, and in fact increases it, due to the additional cost of environment modelling. In the second half of the thesis, we therefore aim to reduce the computational cost of tabula-rasa reinforcement learning by incorporating imitation learning on prior behaviours to provide an initial behaviour that can be efficiently improved with model-free reinforcement learning. We begin by considering the offline-only case from proprioceptive states with a clear objective, and demonstrate our proposed value-based approach leads to improved performance and computational efficiency over imitation and reinforcement learning approaches in isolation. We then extend this general idea to the more general offline-to-online case from visual observations without a well-defined reward function. The training procedure proposed is analogous to that used for modern large language models, providing many exciting directions for future research. We conclude by considering the future directions of generative world models and generalist agents

A novel FLI1+ hybrid cell state in melanoma residual disease

Melanoma, formed from the malignant transformation of the pigment producing melanocytes, is the most aggressive form of skin cancer. In the UK, melanoma is the 5th most common cancer and its incidence has more than doubled since the early 1990s. Despite recent advances in treatment options, patients with metastatic melanoma typically have a poor prognosis, due to low response rate to immune therapies (e.g., checkpoint inhibitors) and high frequency of resistance to targeted therapies (e.g., BRAF inhibitors). Therefore, it is a necessity to develop more effective and durable therapeutic options for patients with metastatic melanoma. Melanoma is comprised of diverse cell populations of highly plastic transcriptional cell states which are considered key drivers of therapy resistance and disease progression. Previously, we showed via fate mapping that the cells which survive during disease regression, known as residual disease, directly contribute to tumour relapse (Travnickova et al., 2022). Thus, emphasising the importance of identifying and functionally interrogating the transcriptional cell states that arise and persist during disease regression and recurrence. To interrogate this heterogeneity and plasticity in melanoma, I have used an adult zebrafish model of cutaneous melanoma, in which I can follow tumour regression and recurrence, just as we see in patients as a result of therapy resistance. The origin of my project came from single cell RNA-sequencing of zebrafish melanomas completed by Travnickova et al., (2019), which showed a tumour subpopulation in residual disease unexpectedly expressing fli1. FLI1, an ETS transcription factor, is a key transcriptional regulator in development and homeostasis, and is predominately expressed in hematopoietic and endothelial cells. However, the functional significance of aberrant FLI1 expression in melanoma was unknown. To investigate this fli1+ tumour cell state, I first generated a Tg(fli1:GFP, crestin:mCherry) zebrafish line on the mutant melanoma background. Using IHC-IF, I was able to visualise fli1+ tumour cells in primary tumours, in persister cells at the residual disease site and in relapsed melanomas. Importantly, I demonstrated that this cell state is relevant to human disease, as I also detected FLI1+ melanoma cells in both patient biopsies and scRNA-sequencing data. Next, I used the Tg(fli1:GFP, crestin:mCherry) zebrafish melanoma model to quantify the fli1+ melanoma subpopulation in primary and regressed tumours using flow cytometry. Excitingly, these data showed that the fli1+ cell state is specifically enriched in residual disease, relative to the progressing tumour. Importantly, preliminary results from ongoing in vitro experiments generating BRAF inhibitor (Vemurafenib) resistant patient cell lines, indicate that FLI1 is also upregulated in response to prolonged Vemurafenib treatment. This provides an important link to the clinic, suggesting the enrichment observed in our genetic model of residual disease, is also conserved in response to targeted therapy. To further understand the dynamics of the fli1+ cell state, I successfully developed a tamoxifen inducible dual marker lineage tracing strategy, which enables a fluorescent ‘switch’ of the fli1+ melanoma cells, allowing tracking of this cell state through disease stages. Using this system in combination with IHC-IF and RNAscopeTM, to assess fli1 expression in fluorescently ‘switched’ cells, I demonstrated that the fli1+ tumour cells persist beyond residual disease and contribute to tumour relapse. In addition, this strategy revealed the plasticity of the fli1+ cell state, showing that fli1+ melanoma cells in residual disease can alter their transcriptional identity and turn off fli1 expression during tumour relapse. Next, to better understand the cellular identity of the fli1+ cell state, I isolated fli1+ tumour cells from primary and regressed zebrafish melanomas and performed transcriptomic profiling. Crucially, differential gene expression analysis showed that fli1 expression is more than simply a single marker gene, rather representative of a transcriptionally distinct tumour cell state. Furthermore, pathway analysis revealed that the fli1+ tumour cells are hybrid in nature, maintaining melanoma gene expression, while also being enriched for mesenchymal gene signatures. Moreover, a large proportion of the differentially expressed genes of the fli1+ cell state are both targets of Fli1 and expressed in the neural crest lineage during early development, suggesting that Fli1 may be driving a developmental mesenchymal programme, which could prove critical for tumour cell survival during regression. Therefore, to determine whether Fli1 is sufficient to drive the mesenchymal programme in vivo, and assess the impact this has on response to treatment and tumour relapse, I generated an inducible fli1 over-expression transgenic zebrafish line. Early validation experiments in embryos are promising and indicate this line will be effective in driving fli1 over-expression in melanoma tumours in adult zebrafish. Together, this work identifying and characterising this novel fli1+ cell state will better inform our understanding of tumour heterogeneity and plasticity in melanoma residual disease and how to combat therapy resistance

The Shifting Landscape of Iraq’s Judiciary: Increased Judicial Activism, Centralisation and Politicisation after 2017

This paper examines the evolution of Iraq’s judicial system after 2003, focusing on the increasing centralisation of judicial power and the rise of judicial activism. Following the fall of Saddam Hussein, the judiciary has become progressively insulated from criticism, with power concentrated within the Supreme Judicial Council (SJC). This consolidation has allowed the judiciary to expand its role beyond impartial justice administration, engaging more actively in political processes and increasing its susceptibility to political influence. The findings of this paper suggest that post-2003 judicial reforms, while ostensibly aimed at fostering judicial independence, have instead led to the creation of a judiciary that is often seen as playing a political role and serving the interests of the leading Shi’a political elite. This has worked to compromise its intended neutrality and poses a risk to the rule of law in Iraq

AI-driven estimation and enhancement of metacognitive monitoring to improve mathematical learning in children

Effective learners actively engage with and select their learning processes from metacognitive monitoring, a cornerstone of educational success. Metacognitive monitoring is an essential aspect of effective learning and enables learners to reflect on their thought processes, learning strategies, and knowledge states, thereby regulating their own learning. Enhancing metacognitive monitoring skills has shown substantial educational benefits, as evidenced by existing pedagogical and social research. This is particularly true in mathematics, where precise metacognitive monitoring correlates strongly with improved performance. Effective metacognitive monitoring is essential for enhancing mathematical abilities among learners. Children aged 7 to 11 are in a pivotal developmental phase where metacognitive skills can be significantly shaped. This period is critical for cultivating these skills, which are vital for their future academic and personal growth. In traditional classroom settings, expert teachers encourage students to think about their own learning by asking them reflective questions. Yet, their time is limited, making it difficult to address each student's needs in large classes. With the progress of AI, computer-based learning environments (CBLEs) are increasingly capable of replicating learning support at scale and are well-positioned to tailor support in large, diverse learner populations. However, adapting teacher-driven interventions to CBLEs poses significant challenges. For example, intelligent tutoring systems (ITSs) often provide frequent prompts that can interrupt the natural flow of learning activities and may reduce learners' trust in these systems. Additionally, ITSs require learners to articulate their thoughts during self-reflection, a process that is essential yet complex. These subjective responses are often unreliable. To address these challenges, our work proposes a novel technique that estimates young learners' metacognitive monitoring performance (MMP) by analyzing their spontaneous facial responses, thus aiming to emulate the nuanced approach of expert teachers within digital learning environments. Building upon the prior work about emotion expressed during metacognitive monitoring, we developed the Meta-Facial Expression Interpreter (M-FEI), an approach to estimate MMP through facial cues. It enables real-time estimation and has been proven to outperform an existing conventional method. These conclusions have been derived from a first large user study conducted with 184 children aged 7 to 11 from two provinces in China and from Scotland. An ITS designed to enhance learners' metacognitive monitoring was employed in a second large-scale user study. This compared mathematical learning outcomes between a tailored metacognitive monitoring intervention using M-FEI (condition 1) and, respectively, using the conventional method (condition 2). The study included a total of 215 children aged from 7 to 12. The results showed that children in condition 1 achieved improved learning outcomes and significantly outperformed those in condition 2. This PhD thesis has pioneered an innovative approach for tailoring learning support by a multi-modality deep learning neural network. This approach has the potential to benefit a diverse range of learners for a variety of subjects by providing personalized and effective educational support in improving metacognitive monitoring skills

The effects of nitrogen deposition on nitrous oxide fluxes from temperate and tropical forest soils

The amount of anthropogenically-created reactive nitrogen (Nᵣ) that enters ecosystems has more than tripled over the past century. A major consequence is the increase in the potent greenhouse gas nitrous oxide (N₂O), with a global warming potential 273 times higher than carbon dioxide on a 100-year time horizon. Emissions of N₂O from natural, as opposed to agricultural, soils are substantial at approximately 6.4 (3.9 – 8.6) Tg N yr⁻¹, which represents around 35% of the total global N₂O budget. It is generally assumed that increases in N availability will lead to a rise in N₂O fluxes. Atmospheric N deposition is a major source of Nᵣ in natural ecosystems such as forests. However, the impacts of increasing N dry deposition on forest ecosystems have been widely ignored. This thesis investigates the impacts of increasing N deposition on N₂O fluxes using temperate and tropical forest soils in Scotland and Sri Lanka, respectively. This research employed a combination of an in-situ field experiment in a temperate birch forest (Glencorse, Scotland) and laboratory incubation experiments using both temperate soils and tropical soils from two different land uses – a secondary tropical forest and a tropical tea plantation. Atmospheric ammonia (NH₃) concentrations, and consequently deposition, were experimentally increased using a unique purpose-built automated NH₃ release system coupled to meteorological conditions in order to more realistically mimic atmospheric N deposition to natural ecosystems. Under laboratory conditions, N availability was increased using ammonium (NH₄₊) aqueous solution as opposed to the more-commonly utilised ammonium nitrate (NH₄NO₃) N enhancement approach, again to more closely mimic the processes in natural rather than agricultural ecosystems. Static and dynamic flux chambers were used to measure the change in N₂O concentrations under field and laboratory conditions, respectively. Fluxes of N₂O were then calculated using linear regression. A wide range of environmental parameters were also measured in order to better understand N dynamics in the study ecosystems. These included total N and total carbon (C) in both soil and vegetation tissue (moss), soil inorganic N availability (in the form of NO₃- and NH₄₊), atmospheric NH₃ concentrations, soil pH, and meteorological conditions (soil and air temperature and moisture, wind speed and direction). In contrast to existing theory, fluxes of N₂O did not increase in response to increased Nᵣ, neither in situ nor during the laboratory incubation experiments for temperate and tropical soils. Fluxes of N₂O remained low (<1.2 nmol m⁻² s⁻¹) for the most part of the experiments. A potential mechanism identified by which to explain these results was carbon limitation of the soil microbial community due to the relatively low C:N ratio (approximately 13.1) at the Glencorse study site. This theory was supported by the observation that adding a source of labile carbon (glucose) increased N₂O fluxes significantly during the laboratory incubation experiments. This corresponded to cumulative fluxes of 2.5 ± 0.7 N₂O-N ng g⁻¹ and 3091 ± 874 N₂O-N ng g⁻¹ following the application of N only and N + C, respectively. The findings of this thesis have practical implications for calculating emission factors, compiling national emission inventories and consequently climate change mitigation policies. Future research should focus on investigating C and N dynamics in response to changing Nᵣ inputs, both in the short and in the long term, and especially the potential trade-offs between increasing C stocks (such as using expansion of semi-natural forest area as a climate change mitigation measure) and the potential for increased N₂O fluxes arising from Nᵣ deposition to these systems

Psychological safety... P.S. it'll be OK! Understanding senior medical students' experiences of psychological safety (PS) in simulation

Simulation is a teaching technique which mimics real-life situations to enable learners to experience the thoughts and emotions of treating patients, without patients present. The simulated clinical cases allow learners to practice managing situations when a delay in treatment or mismanagement may cause harm. By enabling the learning to happen away from real patients, it was commonly thought that learners feel more able to make mistakes. However, some learners fear being observed and worry they may expose a lack of knowledge or skills and lose credibility in front of their peers. Conversely, this anxiety may make learners unable to speak up or be fully engaged, and reduce their willingness to be stretched. Medical students are not yet doctors and lack knowledge and experience in clinical environments. Furthermore, they have rarely been taught using simulation before, which can exacerbate concerns about judgement or scrutiny of their performance. Psychological safety is an abstract concept researched in the context of simulation and embedded in guides on how to design and deliver this form of teaching (Motola et al., 2013). Promoting psychological safety can help learners feel free from judgement and view mistakes as learning opportunities without concern for how they are viewed by others (Kahn, 1990). The aim of this research was to understand: (i) how medical students experience simulation; (ii) how they describe psychological safety; (iii) what factors are weighed up by students when sensing the degree of psychological safety, and what teachers can do to help students feel more psychologically safe. I conducted 20 interviews with final-year medical students, which included learners from under-represented groups such as international students, mature students and those who identified as having a disability. I shared my early results from all the medical student interviews with simulation facilitators during focus groups. I asked the facilitators to reflect on psychological safety during simulation with medical students and asked them to comment on my initial findings. This study was approved by the Medical Education Research Ethics Committee at the University of Edinburgh as being ethical

Quantifying climatic, lithological and tectonic drivers on topography

Mountain ranges and fluvial landforms are shaped by a competition of natural forcings: tectonic forcings provide the uplift that causes mountains to grow, and rivers erode them down by downcutting into the bedrock material. Climate forcings, such as precipitation, vegetation or temperature favour or hinder erosional processes, and the lithological composition controls how these forcings are recorded on the landscape. As geomorphologists, we are only able to work with current topography, through field observations or digital elevation models. Much of the existing work in geomorphology has focused on creating metrics that allow us to quantify the imprint of tectonic, climatic and lithological signals from landscapes. One of the first formulations relating erosion and uplift incorporates a direct relation with the channel steepness and the amount by which this steepening occurs. This thesis focuses on quantifying these factors to detect areas of river channels and hillslopes that show an active uplift, sudden changes in lithology or a change in erosion rates due to climatic differences, through deviations from their steady state behaviour. I start by exploring how a climate signal is recorded on the landscape, and whether it is possible to detect such a signal through a form of the stream power law that includes discharge instead of drainage area. I set up a range of numerical simulations with a rainfall gradient component from which I calculate the optimal concavity index (θ), the χ profiles and the disorder of each river basin. In a landscape with uniform lithology, it is possible to identify simulation runs with a rainfall gradient using the disorder metric. However, when heterogenous lithology is introduced, this signal becomes clouded. I test this method in eight natural landscapes with rainfall gradients and find that, similarly to the simulations with heterogenous lithology, it is not possible to distinguish between drainage-area-driven and discharge-driven incision using the disorder method. Nonetheless, I can quantify the distortion introduced on the channel steepness index (ksn) when using different erosion rules. The next research question explores the role of lithology in landscapes through its effects on drainage divide migration. Most drainage divides are located in the centre of mountain ranges. However, using a simple formulation of the stream power law, I find that a small difference in rock erodibility should theoretically lead to larger drainage divide asymmetries. I explore this discrepancy through a combination of analytical solutions and numerical simulations based on Hack's law and the stream power law. I quantify the difference in basin sizes expected due to a lithological step change offset from the centre of an initially symmetrical drainage divide. I expand on this with a set of numerical simulations, including a 3D lithology component where I study the role of rock erodibilities as well as the location and orientation of rock layers in the motion of the drainage divide. I find that the angle of the rock layers and the overall landscape erodibility largely control the position of the divide. I conclude this study with the comparison of the analytical and numerical solutions to the Sierra Alhamilla, an asymmetric mountain range with heterogenous lithology in south-eastern Spain. The final part of this thesis explores the interactions between fluvial and hillslope processes. Hillslopes have been found to record tectonic signals propagating up channel tributaries in the form of knickpoints, with a lag in the response time. I choose a landscape with active tectonics, the South Fork Eel River in California, USA, to explore this transient hilltop signal. The analysis toolkit to perform an exploration of hillslopes is based on different laws from fluvial incision, where the steady-state condition is defined based on the physical parameters of the hillslopes and soils, such as the hilltop curvature, hillslope length, relief, slope angle, and soil transport and density parameters. I extract these parameters from a 3 m digital elevation model of the study site and explore the hypothesised hillslope response lag with respect to the transient fluvial signal, recorded through changes in channel steepness, ksn. In summary, this thesis explores the signature of climatic, lithological and tectonic signals on landscapes. I discuss under what circumstances these signals can be detected, and propose new approaches to quantifying their effect on landscapes through a combination of analytical, numerical and topographic methods

The quill and the brush: Michael Boym’s collaborative translation in the Southern Ming dynasty (1644-1662)

This thesis uncovers the contributions of individuals and technologiestradiAonally overlooked in the process of Jesuits’ cross-cultural translation and communication, focusing on the visual works of Michael Boym (ca. 1612–1659). While recent scholarship has increasingly focused on collaboraAons between Jesuits and Chinese literati through textual analysis, the collaborative nature and conditions of production of Jesuits’ visual works remains underexplored. This study fills this gap by examining the materials, tools and techniques used in Boym’s Magni Catay (ca. 1652–1656) and Flora Sinensis (1656). Through an analysis of these visual works, which were created within Boym’s political and religious network—including the Southern Ming court and the Society of Jesus—this thesis highlights the foundational roles of Boym’s collaborators, particularly the Chinese general Zheng Sanxing 鄭三省, Viennese publisher Matthaeus Rickhes, and other unnamed Chinese and European contributors. Their collective efforts and technological means significantly shaped the outcomes of cross-cultural translation and communication, offering new insights into the collaborative processes behind Jesuit visual production. The first three chapters examine Magni Catay, focusing on its illustrations, handwriting, and grids. This analysis reveals a dynamic collaboration between Boym and his associates, challenging the hierarchical authorship typically associated with Jesuit works. The subsequent chapters explore the creation of Flora Sinensis within a European network, emphasising how the shared religious and political interests of Boym and Rickhes influenced the intaglio prints. Furthermore, it investigates how Viennese printmakers and colourists adapted visual models to their personal preferences and technological constraints, demonstrating how these processes impacted visual details and shaped early modern cultural translation. Overall, this study offers a novel synthesis of Boym’s manuscripts and prints, underscoring the crucial roles of marginalised figures in early modern cultural exchange. By focusing on materials, tools, and technology, it provides fresh insights into cross-cultural translaAon and deepens our understanding of Southern Ming history

Enhancing weather and climate information services (WCIS) for pastoralists

‘Lightning talk’ presented by Claire Bedelian (Mercy Corps) at the Jameel Observatory Community of Practice meeting, Addis Ababa, 13-14 May 202