VictorJuiz/thesis huggingface dataset:
Applying quantitative image analysis to liver cancer

Survival rates are poor for hepatocellular Carcinoma (HCC) compared to many other cancers, and death rates are increasing in countries such as the United States and United Kingdom. To improve outcomes, tumours need to be detected at a much earlier stage. Magnetic resonance imaging (MRI) may provide a method to detect tumours earlier, potentially combined with other biomarkers.
The hypothesis underlying this thesis is that the ability to measure iron in the liver accurately (that is, with low error) could provide an early marker for HCC development, as HCC tumours are thought to typically contain less iron than the liver (iron sparing). However, previously iron in the liver could only be measured using biopsy. MRI provides a non-invasive tool to measure iron.
Current MRI techniques for measuring iron are not suitable for the application of detecting iron sparing as they are optimised to detect iron overload in diseases such as haemochromatosis and thalassemia. For iron sparing, low iron concentrations, and small, localised regions of differences in iron need to be detected.
In this thesis I highlight the various limitations with existing techniques and describe the optimisation process to develop methods to quantify iron sparing. I verified that these could potentially be used in iron sparing using healthy volunteer data, phantom data, and patients. These MRI techniques can now be used in clinical trials for assessing if iron sparing is indeed a feature of HCC and thus whether MRI could be used as a surveillance tool to detect iron sparing in patients at-risk of HCC.

Keywords: liver
cancer
magnetic resonance imaging
Advisor: Bulte, D
Brady, M
Pavlides, M
The phonetics of labialized velars in Ancient Greek

This thesis investigates conditioned sound changes of Proto-Indo-European labialized velars *kw, *gw, and *gwh to labials, coronals, and velars in Ancient Greek. Acoustic and perceptual experiments in British English and Western Zapotec provide typological evidence to inform conclusions regarding the phonetic inception of the sound changes.
Before back vowels and consonants PIE labialized velars became labials in Ancient Greek. Two competing hypotheses have been proposed for these sound changes: perceptual confusion, in which e.g. *kw developed to /p/ via misperception due to acoustic similarity between the two stops (Ohala, 1989, 1993); and incremental articulatory change, in which e.g. *kw gradually developed to /kp/ and then /p/ (Whatmough, 1937; Garrett & Johnson, 2013). The acoustic studies here found little acoustic or perceptual similarity between /kw/ and /p/ in any vocalic environment, indicating that the latter explanation is more plausible.
Before front vowels PIE labialized velars became coronals. Philologists have proposed developmental pathways involving processes of incremental palatalization. This thesis finds evidence for coarticulatory fronting of the velar articulation of labialized velars in front vowel environments, but the effect is language-specific and its auditory prominence depends upon the durational overlap of the primary and secondary articulations. Increased auditory prominence of coarticulatory fronting in mid vowel environments may explain divergent developments of *gwh in Greek to labials before front vowels and to coronals before front mid vowels.
Before the back round vowel /u/ the PIE labialized velars lost contrastive labialization in Greek. The acoustic and perceptual experiments support a perceptual reanalysis account, in which contrastive secondary labialization of the velar is reinterpreted as coarticulatory rounding adjacent to a rounded vowel.

Keywords: phonology
labiovelars
corpus linguistics
labialization
Zapotec
acoustics
acoustic perception
Ancient Greek
phonetics
philology
historical linguistics
coarticulation
sound change
Advisor: Coleman, J
Probert, P
Charge transport layers and passivation for improved efficiency and stability of n-i-p perovskite solar cells

Perovskite solar cells have become a promising photovoltaic technology for harvesting energy from the sun. However, despite their low-cost processing and high performance, a few issues remain for their wider application. One of the main concerns is their stability. Commercializable solar photovoltaic technologies must remain stable under constant illumination and high-temperature not only for a few days as they would be tested in a lab, but for several years. In particular, low-cost, dopant-free, and stable hole-transporting materials need to be found to replace Spiro-OMeTAD, due to its instability issues. Furthermore, better passivation strategies need to be discovered and employed.

The primary aim of this project was to look for alternative charge-transfer materials to replace those typically used in a regular architecture solar cell device, and to make the perovskite absorber material more stable by additive engineering. To achieve this, various hole-conducting materials and passivation molecules were tested, and those which improved the film quality were further characterized and tested in devices for their performance and stability. The lessons learned from these studies led to the fabrication of a very stable solar cell architecture.

Chapter 4 focuses on a pair of alternative hole-conductors that are dopant-free and low in cost. They were found to perform just as well as spiro-OMeTAD whilst being more stable. Chapter 5 discusses an often-overlooked additive approach of applying aromatic amines to passivate the perovskite absorber. Our average benzylamine-modified perovskite devices maintained 80% of their initial efficiency over 2,400 hours in a 65°C 1 sun aging test. Meanwhile, the average control devices without additives degraded to 45% of their initial efficiency. Chapter 6 investigates the potential of applying a fullerene-based self-assembling monolayer to stabilize the electron-transporting layer/perovskite interface. However, the results of the fullerene self-assembling monolayer-modified interface were less stable. Despite that, the reasons for their instability were investigated and a future research direction is proposed to improve them.

The novel charge-transporting materials and bulk/interface modifications studied in this thesis are promising solutions for stabilizing the regular architecture of perovskite solar cells. Moreover, the investigation into why certain passivation methods work and others fail can help us design and select better materials and passivation methods for more durable solar cells.

Keywords: metal-halide perovskite
perovskite passivation
charge-transport layers
n-i-p perovskite solar cells
solar cell stability
Using light and electron microscopy to understand the replication of aphthoviruses

Aphthovirus is a genus of the family Picornaviridae which includes Foot-and-mouth-disease virus (FMDV) and Equine rhinitis A virus (ERAV). FMDV is a highly contagious pathogen infecting cloven-hoofed animals and is hence economically important. FMDV replication takes place in the cytoplasm and induces massive rearrangement of the host cell membranes to facilitate virus replication. Rearranged membranes form structures providing the site of viral genome replication known as the replication organelle (RO). The understanding of the RO, viral proteins and site of virus assembly is not well established. This project applies various microscopy approaches to investigate details of aphthovirus replication in cells. FMDV 3A protein is known to play a key role in viral replication machinery. We generated recombinant viruses of FMDV with various tags fused to this protein, subsequently allowing 3A to be detected in confocal microscopy. We developed a split-GFP system to study the dynamics of 3A protein in vitro. We showed that 3A signals appeared contiguous to the Golgi membrane signals suggesting that it potentially serve as a main source of membrane associated with viral replication. This approach was taken with the aim of facilitating the development of a correlative light electron microscopy (CLEM) system to unravel the localisation of virus proteins and their link to RO in cells. ERAV was used as a surrogate model to study FMDV replication in a lower containment laboratory using cryo-electron tomography (cryo-ET). Virus particles were observed associated with membrane structures with single membrane vesicles being more predominant than double membrane vesicles in infected cells. By sub-tomogram averaging, we reconstructed 3-dimensional (3D) models of intracellular ERAV full and empty particles which were compared with structures obtained for the virus purified from tissue culture and crystallized. Additional density was identified in the ERAV empty particles potentially corresponding to RNA contact sites inside the capsid.

Keywords: Aphthovirus
Replication
FMDV
Foot-and-mouth-disease virus
Equine rhinitis A virus
ERAV
Microscopy
Advisor: Fry, E
Stuart, D
Clare, D
Berryman, S
Hawes, P
Hjördis Lorenz - Thesis Submitted in Partial Fulfilment of the Degree of Doctor of Clinical Psychology (DClinPsych)

Titles of thesis components
• Literature Review: Online Interventions to Decrease Repetitive Negative Thinking: A Systematic Review
• Service Improvement Project: Trauma-informed Care Training: A Theory-Driven Qualitative Evaluation and Recommendations for Improvement
• Theoretically Driven Research Project: Evaluation of a Novel Rumination Intervention for Individuals with PTSD: A Pilot Feasibility Study

Keywords: rumination
PTSD
online interventions
trauma
dwelling
Re-defining exacerbations of chronic obstructive pulmonary disease and developing tools to characterise heterogeneity and treatment response

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Progressive in nature, COPD is punctuated by acute periods of worsening termed ‘exacerbations’, a cause of morbidity and mortality. Exacerbations are heterogenous with respect to symptoms and inflammatory biology.
In this thesis, mathematical methods were researched for their appropriateness to characterise COPD heterogeneity and predict treatment response. Algorithms were developed to predict treatment outcome for a cohort of hospitalised patients with airways disease exacerbations. In a second patient cohort, cluster analysis was performed on longitudinal measurements at 'stable state’ and ‘exacerbation’ in order to define objective ‘states’ of COPD based on symptoms and inflammatory biology. Objective states included a low overall symptom burden state and crisis states with elevated symptoms with or without particular inflammation. Objective states were compared to conventional ‘stable state’ and ‘exacerbation’ definitions and found to better correspond to distinct patterns of symptoms and inflammation. Algorithms were trained and tested to identify to which objective state any given measurement of symptoms and inflammation from a patient belongs at a given point in time. Different treatment response trajectories or ‘trajectotypes’ of symptoms and inflammation were characterised for crisis states between the time of treatment and follow-up measurements. Trajectotypes included symptom resolution with or without different patterns of inflammatory resolution, as well as a trajectotype of no change between treatment and follow-up. Individualised symptom shape profile trajectories post-treatment were found from daily time series of self-reported symptom scores.
This thesis provides a new definition of COPD states as an alternative to ‘stable state’ and ‘exacerbation’ definitions. Together with the characterisation of distinct treatment response trajectories based on symptom and inflammatory resolution, the findings of this thesis set the stage for future clinical studies evaluating enhanced personalisation of treatment allocation and monitoring for patients with COPD.

Keywords: Artificial Intelligence
Chronic Obstructive Pulmonary Disease
Cluster Analysis
Statistics
Disease Trajectories
Trajectories
Supervised Learning
AI
Machine learning
Data Science
Crises
Clinical Machine Learning
Asthma
Predictive Modelling
Personalised Medicine
COPD
ML
Phenotyping
Predictive Analytics
Endotyping
Exacerbations
Respiratory Medicine
Mathematical Modelling
Advisor: Bafadhel, M
Maini, P
Nicolau, D
Characterising CD8+ T cell transcriptomic and clonal consequences of immune checkpoint blockade for the treatment of cancer

Background: Immune checkpoint blockade (ICB) is arguably the greatest advance in medical oncology this century. Nonetheless, responses are highly variable. Mechanistic insights into this heterogeneity promises to improve patient stratification and identification of therapeutic targets.
Methods: Single-cell RNA sequencing and T cell receptor (TCR) sequencing was performed on peripheral CD8+ T cells taken at multiple time points before, during and after treatment with ICB from patients with metastatic melanoma and renal cell carcinoma (n=30 scRNAseq, n=139 TCRseq), coupled with bulk RNAseq, genotyping and clinical follow-up data across a larger cohort (n=214 patients). Analysis was via custom-written R scripts.
Results: The clonal size of CD8+ T cells is key to their response to ICB, with large clones differentially regulating more genes relevant to anti-tumour immunity. Through analysis of peripheral repertoires, highly public TCRs that strongly associate with clinical outcomes in patients with melanoma can be identified. These TCRs are found intra-tumourally, associate with HLA type, and their importance validates in two external datasets. CD8+ T cell clones which persist long-term post cessation of ICB have a cytotoxic ‘long-lived effector cell’, rather than conventional memory, phenotype – an entirely novel finding in the context of human cancer. Finally, carriers of a single nucleotide polymorphism at rs16906115 have increased odds of toxicity following ICB, with differential CD8+ T cell repertoire features. Risk-allele carriers are also less likely to experience decreases in lymphocyte count following ICB. A stable lymphocyte count post-treatment is a strong predictor of overall survival across multiple tumour types, regardless of genotype, a finding which replicates across multiple cohorts.
Conclusions: This work has high translational potential. Descriptions of key TCR in the anti-cancer response could inform vaccine or cellular therapeutic approaches. Further understanding lymphocyte stability may guide biomarker or drug development and the novel description of cytotoxic persistent clones in cancer is fundamentally relevant to human immunology.

Keywords: Immuno-oncology
T cell receptor
Melanoma
Tumour immunology
T cells
CD8 effector cells
Advisor: Fairfax, B
Middleton, M
Uncertainty Estimation: single forward pass methods and applications in Active Learning

Machine Learning (ML) models are now powerful enough to be used in complex automated decision-making settings such as autonomous driving and medical diagnosis. Despite being very accurate in general, these models do still make mistakes. A critical factor in being able to depend on such models is that they can quantify the uncertainty of their predictions, and it is paramount that this is taken into account by users of the model. Unfortunately, deep learning models cannot readily express their uncertainty, rendering them unsafe for many real-world applications. Bayesian modelling provides a mathematical framework for learning models that can express their uncertainty. However, exact Bayesian methods are computationally expensive to learn and evaluate, and approximate methods often reduce accuracy or are still prohibitively expensive. Meanwhile, ML models continue to increase in number of parameters, meaning that one has to make a decision between being (more) Bayesian or using a larger model. So far it has always fallen in favour of larger models. Instead of building on Bayesian methods, we deconstruct uncertainty estimation and formulate desiderata that we base our work on throughout the thesis (Chapter 1). In Chapter 3, we introduce a new model (DUQ) that is able to estimate uncertainty in a single forward pass by carefully constructing the model’s parameter and output space based on the desiderata. We then extend this model in Chapter 4 (DUE) by placing it in the framework provided by Deep Kernel Learning. This enables the model to work well for both classification and regression tasks (as opposed to just classification), and estimate uncertainty over a batch of inputs jointly. Both models are competitive with standard softmax models in terms of accuracy and speed, while having significantly improved uncertainty estimation. We additionally consider the problem of Active Learning (AL), where the goal is to maximise label efficiency by selecting only the most informative data points to be labelled. In Section 4.5, we evaluate the DUE model in AL for personalised healthcare. Here, the labelled dataset needs to adhere to specific assumptions made in causal inference, which makes this a challenging problem. In Chapter 5, we look at AL in the batch setting. We show that current methods do not select diverse batches of data, and we introduce a principled method to overcome this issue. Building upon deep kernel learning, this thesis provides a compelling foundation for single forward pass uncertainty and advances the state of the art in active learning. In the conclusions (Section 6, and at the end of each chapter), we discuss how users of ML models could make use of these tools for making sound and confident decisions.

Keywords: Uncertainty Estimation
Active Learning
Machine Learning
Deep Learning
Bayesian Deep Learning
Advisor: Gal, Y
Teh, YW
Avian influenza H9N2-specific changes in the adaptive immune receptor repertoire of Gallus gallus domesticus following vaccination and infectious challenge

Avian influenza viruses cause major losses to the poultry sector each year and also pose a significant risk for cross-species transmission to humans, where the disease manifestations can be very severe. However, within avian hosts, there is still a limited understanding of the adaptive immune system in the contexts of both health and disease, and the immunological mechanisms which underpin vaccine-induced protective responses against infectious challenge with pathogens such as avian influenza. As the ability of the adaptive immune system to recognise specific antigens is dependent on the T and B cell receptors which together comprise the adaptive immune receptor repertoire, understanding the determinants which shape its specificities and diversity is paramount for both improving our knowledge of the avian immune system and improving current prevention and control strategies such as vaccination. In this thesis, I present a comprehensive analysis of the domestic chicken (Gallus gallus domesticus) adaptive immune receptor repertoire upon infection and/or vaccination with H9N2 avian influenza – a pathogen that is widely prevalent across the world and poses significant risk both to the poultry sector and to human health and wellbeing. At the time of writing, no published research has examined the avian adaptive immune repertoire using high throughput sequencing, and no repertoire studies have been performed in birds that were infected with and/or vaccinated against avian influenza. My analyses thus provide valuable information on the avian adaptive immune system and the impacts of H9N2 infection and/or vaccination on the avian adaptive immune receptor repertoires.

Keywords: H9N2
Repertoire
T cell
Avian influenza
B cell
Antibodies
Advisor: Smith, A
Bonsall, M
Iqbal, M
Performance analysis of reconfigurable intelligent surface assisted wireless communications

The advent of the smart radio environment has challenged traditional wireless communication systems, and reconfigurable intelligent surfaces (RIS) have emerged as a promising solution to enhance the performance limits of wireless communications. This thesis aims to study RIS technology in future networks by examining the performance of four typical RIS-assisted communication systems, each featuring unique models and practical RIS characteristics. The research begins with the performance analysis of RIS-assisted wireless communication systems with phase errors in both line-of-sight (LoS) and Rayleigh fading scenarios. It investigates the impact of different types of phase errors and other system parameters on system performance. Next, the impact of channel correlations on RIS-assisted communication systems is examined by deriving closed-form expressions of outage probability and average achievable rates for both correlated Rayleigh fading and correlated Nakagami-m fading models. The relationship between RIS size and performance degradation caused by channel correlations is explored. The study further investigates the application of RIS technology in more intricate wireless communication systems, specifically focusing on two spectrum efficiency techniques: non-orthogonal multiple access (NOMA) and full-duplex (FD) communications. For RIS-assisted NOMA, the research derives closed-form expressions for the outage probability of users with strong and weak channel conditions, while for RIS-assisted FD systems, the research proposes a setup where an FD transceiver serves uplink and downlink users simultaneously with two dedicated RISs and examines the impact of residual self-interference (SI). Finally, the performance of a RIS-assisted largescale network is examined through stochastic geometry, assessing coverage probability and average achievable rate. The research discusses two association strategies: nearest and fixed association, and investigates the effects of increasing transmitter (TX) density and RIS association probability on system performance. This thesis provides valuable analytical resources on RIS-assisted wireless communications performance, laying a foundation for future research and application of RIS technology.

Advisor: Coon, JP
A framework for characterising the value of information in communication systems

With the rapid development of sensor-based Internet of Things (IoT) technology, there are increasingly many real-time control systems, in which the timeliness of information is becoming increasingly essential for decision-making and control. In this thesis, a mutual information-based value of information (VoI) framework is formulated to characterise how useful and valuable the data is for latent variable models. The VoI is explored in the context of various hidden Markov models, in which data samples generated at the source are assumed to be transmitted over a noisy channel to the receiver. The signal-to-noise ratio (SNR) indicates the level of noise which is used to represent the quality of transmission in such communication systems. First, in hidden Gauss-Markov models, closed-form expressions and bounds of the VoI are derived. The effects of sampling and the number of observations on VoI are investigated in different SNR regimes. The VoI-optimal sampling policy is obtained under a sampling rate constraint. Next, the VoI is further analysed in an important hidden Gauss-Markov model, the noisy Ornstein-Uhlenbeck process, in more depth. The distribution of the VoI is obtained. Functional relationships between the VoI and other traditional metrics are explored. Finally, the VoI is applied and optimised in an IoT network with multiple static sensors and one mobile central entity. The mobility of the centre and the scheduling strategy of sensors are jointly optimised for VoI maximisation. A reinforcement learning-based algorithm is proposed to solve this problem efficiently. Numerical results are presented by Monte Carlo simulations. The proposed VoI framework is able to capture the data freshness, the correlation of the underlying process, and the SNR through the transmission channel. The results illustrated in this thesis can be used in the design of real-time communication systems.

Advisor: Coon, JP