VictorJuiz/thesis huggingface dataset:
Automated quality control by application of machine learning techniques for quantitative liver MRI

Quantitative magnetic resonance imaging (qMRI) and multi-parametric MRI are being increasingly used to diagnose and monitor liver diseases such as non-alcoholic fatty liver disease (NAFLD). These acquisitions are comparably more complicated than traditional T1-weighted and T2-weighted MRI scans and are also more prone to image quality is- sues and artefacts. In order for the output of the qMRI scans to be useable, they must undergo a rigorous and often lengthy quality control (QC). This manual QC is prone to human error and subjective. Additionally, with the development of new qMRI tech- niques, this leads to the manifestation of new quality issues. This thesis focuses on the development and implementation of automated QC processes for liver qMRI scans, that is where possible tag-free such that the process can be adapted to different imag- ing techniques. These automated QC processes were implemented using a variety of machine learning (ML) and deep learning (DL) approaches. These methods, developed on T1 mapping in UKBiobank, were designed to output metrics from the MRI scans that could be used to identify a specific quality issue, such as in chapter 3, or give a more general indication of the image quality in chapter 4. Furthermore, it was hypothe- sised that the introduction of associated meta-data, such as patient factors and scanning parameters, into these deep learning models would increase overall performance. This was explored in chapter 5. Finally, in order to assess the utility of our developed al- gorithms in a wider setting except for T1 mapping in UKBiobank, we tested it in two settings. Pilot study one assessed the utility of the model in T1 mapping in a separate study (CoverScan). Pilot study two assessed the utility of the model in a different qMRI acquisition; proton density fat fraction (PDFF) acquisitions from UKBiobank.

Keywords: liver
MRI
deep learning
quality control
Advisor: Grau Colomer, V
Robson, M
Biasiolli, L
High-value wood aroma compounds via P450BM3 catalysed sesquiterpene oxidation

Cytochrome P450 enzyme (CYPs) catalyse the oxidation of C−H bonds via the insertion of an oxygen atom from atmospheric dioxygen and play key roles in the biosynthesis of endogenous compounds (alkaloids and steroids) and secondary metabolites (terpenoids). CYP102A1, also known as P450BM3, has been widely studied for biotechnology application because of its unique structure of the electron transfer reductase domain being fused to the heme monooxygenase domain, which leads to high turnover frequency. Rational design and directed evolution are the main strategies that have been applied to engineer CYP102A1 to increase its activity for the oxidation of unnatural substrates.
Terpenes and terpenoid compounds have interesting properties such as aromas, flavours, pharmacological and biological activity. However, these compounds are often naturally low in abundance and their complex structures lead to complicated total synthesis pathways with low enantioselectivity and yield. This thesis reports the biosynthesis of valuable agarwood aroma compounds via biotransformation with engineered CYP102A1 variants.
Chapter 3 describes the engineering and application of CYP102A1 variants to develop high regio- and chemoselectivity synthesis of the guaiene skeleton compounds in agarwood by late-stage C−H functionalisation of α-bulnesene. The synthesis of (–)-guaia-1(10),11-dien-15,2-olide requires C−H functionalisations at both C2 and C15 of α-bulnesene and the development of a biocatalytic cascade reaction using two engineered CYP102A1 variants is described in Chapter 4. Chapter 5 details the reaction engineering of bulnesene oxidation for highly efficient synthesis of the target aroma compounds. In Chapter 6, the hydroxylation of α-guaiene is explored with engineered CYP102A1 variants to synthesise the peppercorn aroma compound rotundone which is also found in agarwood.

Keywords: natural products
sesquiterpene
P450
enzyme
protein engineering
Advisor: Wong, L
Investments as business power. Thirty years of oil majors' renewable energy acquisitions

Investment decisions —in the form of acquisitions— are the main way incumbents access innovations in an economy. Yet, in the context of climate change and of emerging energy transitions, dominant incumbents like those in the fossil fuel industry have increased their acquisitions activity towards ’clean’ emerging tech- nologies. Using the case of European oil majors’ interest in renewable energy, this thesis problematises this type of investments and shows that their study uncovers at least three sources of power dominant incumbents hold towards emerging industries. First, the ability to acquire allows incumbents access to means of decision-making towards the advancement and delay of emerging technologies. Second, through the ownership of ’clean’ assets incumbents’ are able to set the terms of their role in innovation and in sustainable transitions. Third, acquisitions in emerging technologies give incumbents the ability to benefit from transition policies without being noticed and without questions. For showing this, this the- sis presents a unique and original database of acquisitions manually classified by their type of technology that spans over thirty years of European oil majors’ financial transactions. The thesis shows in three substantive chapters how acquisitions in ’clean’ emerging technologies buy oil majors’ a role in the energy transition.

Keywords: energy transitions
sustainable investments
oil industry
M&amp;A
Advisor: Clark, GL
Brophy, A
Population movement, disease, and environment in northwest Zimbabwe, 1950 to 1980

This thesis investigates the multi-layered relationships between population movements and African experiences of disease and environment in colonial Gokwe, northwest Zimbabwe. The question I address is how did ideas of disease and environment shape knowledge systems and social relations between 1950 and 1980. Drawing on extensive field research, I make two inter-locking arguments. First, in addition to the more common focus on exchanges between state officials and Africans, which is basically cast as a top-down disciplinary project, I argue that ‘African-African’ interactions were central in producing and reproducing unique knowledge systems. Whilst the colonial state made aggressive interventions into the management of African environment and disease, control of tsetse and mosquitoes, such interventions were variably engaged with, co-opted, and subverted by African populations with different and often clashing ideas about disease. The result was the simultaneous making and remaking of social identities and shifting relations with political authority. Second, I seek to push the consideration of colonial interventions beyond a state concern for ‘ordering diseased’ African spaces through laboratory-like experiments. Using Gokwe as an example, I argue that colonial interventions were elusive, fluid, and abstract force that suffered from external modifications and abuses. Of note here were the counterinsurgency campaigns of the Rhodesian forces in which biological agents were used against guerrillas travelling through Gokwe from Zambia thus introducing radically new experiences and meanings of disease and environment.

Keywords: interventions
interactions
colonial
experiment
environment
disease
Advisor: Alexander, J
Mahone, S
Transcendence of numbers related to Episturmian words

This thesis relates combinatorial properties of sequences to arithmetic properties of the numbers that they represent. A guiding principle is that numbers whose b- expansion has low subword complexity are either rational or transcendental. This heuristic was confirmed by Ferenczi and Maduit, who proved that for b > 1 an inte- ger, numbers whose b-expansion is a Sturmian sequence or an Arnoux-Rauzy sequence are transcendental. (These can be considered as the simplest non-ultimately-periodic sequences in terms of their subword complexity.) Subsequent work of Adamczewski and Bugeaud extended this result by proving that all numbers whose b-expansion has linear subword complexity are rational or transcendental, again for b an integer. The latter authors obtained related results to the case of b an algebraic base under certain combinatorial properties of the sequence, which depend on b.
The main contribution of this thesis is providing a transcendence result which applies to arbitrary algebraic bases. We introduce a new combinatorial condition on sequences and prove a transcendence result for numbers of the form α := Σ∞n=1 unβ−n where β is any algebraic number such that |β| > 1 and u = u1u2 ··· is a sequence of algebraic numbers satisfying the above-mentioned criterion. In particular we prove that all Episturmian (a generalisation of Arnoux-Rauzy words) words satisfy this criterion.

Keywords: subspace theorem
Episturmian word
transcendental number
Tribonacci word
Sturmian word
algebraic base
Advisor: Worrell, J
Scoones, A
Population and within-host modelling of virus-disease dynamics

Mathematical modelling is an important tool in helping make projections of infectious disease dynamics. In this thesis, we address epidemiological modelling questions at both the population and within-host scales. By focussing on problems relating to immunity and within-host immunological dynamics, we aim to establish how the accuracy of model projections can be improved.
First, we consider the spread of SARS-CoV-2, using a stochastic model to determine the probability that infected individuals introduced into a population with low case prevalence initiate an outbreak. We consider Israel and the Isle of Man as case studies, two regions that have had few cases and that are quickly vaccinating their populations. Our results suggest that when travel restrictions are relaxed, in order to prevent local transmission, it will still be necessary to implement surveillance of incoming passengers to identify infected individuals.
We then consider forecasting influenza epidemics. Previous exposure to influenza viruses confers cross-immunity against future infections with related strains, however this effect is not always included in influenza forecasting models. We use case notification data from Japan from the 2009 H1N1 influenza pandemic to estimate parameter values and then consider a hypothetical future epidemic. Our results show that a more epidemiologically realistic model, in which cross-immunity is represented explicitly, can generate more accurate projections during outbreaks than if cross-immunity is not included.
We next model influenza virus dynamics at the within-host scale. We consider a hierarchy of three ordinary differential equation models which describe the kinetics of influenza infection; a complex model which explicitly accounts for an immune response to the virus, and two simpler models in which the infection is limited by the availability of target-cells. We show that the simplest target-cell limited model can reproduce synthetic data generated from the complex immune response model, and furthermore that the parameters of the target-cell limited model vary in a systematic manner as the parameters in the complex model used to generate the data are changed. Finally, we extend these within-host models to describe the severity of symptoms experienced by a host. We show that a simple model can be used to make real-time forecasts of the severity of influenza symptoms and be used to classify patients based on estimated model parameter values.

Keywords: infectious disease modelling
mathematical epidemiology
mathematical modelling
Advisor: Byrne, H
Thompson, R
Network models of spatial interactions, human mobility and navigation ability

Networks provide a useful mathematical model for systems that are composed of agents, also called nodes, that interact in a pairwise fashion. Inside a spatial network, nodes are embedded in space and they typically interact much more strongly within their close surrounding, resulting in a special pattern of connections. Geographers, physicists and applied mathematicians have a set of tools at their disposal to understand these systems, and there is a close relationship between spatial networks and population-level models that were originally meant to represent human mobility, but that have been generalised to model more abstract spatial flows.
Over the last decade, there has been an effort to adapt network tools for community detection — whose aim is to describe a system in terms of its mesoscale organisation into groups or communities — to spatial networks. So far, the methods that have been proposed are based on the modularity function, a heuristic function that compares network partitions against a suitable null model. However, a mesoscale description that is gaining traction relies instead on the stochastic block model (SBM), a generative model that can be fitted to an observation using statistical inference. We propose a methodology to leverage an SBM for the case of spatial networks.
We are guided in this by an application to study a network linking customers to the stores they have shopped in, built from anonymised shopping records belonging to a large UK retailer. We also characterise customer shopping behaviours, specially from the lens of their mobility. Lastly, we study a dataset that was collected to assess spatial navigation, and we propose new metrics that are used in comparing the trajectories of healthy individuals, at-genetic-risk patients and patients diagnosed with dementia. Our hope is that this early proposal can be refined in the future by medical practitioners and used to detect early-onset Alzheimer’s Disease.

Keywords: spatial networks
network science
human mobility
Integrating imaging and omics with machine learning approaches to infer antimicrobial susceptibility in gram-negative bacteria

Antimicrobial resistance is a major global health issue, which threatens the efficacy of antimicrobial treatments. Many efforts have been made to study the genetic mechanisms of resistance and adaptation of bacteria under antimicrobial treatments. However, the impacts of these genetic factors on the bacterial adaptation have not been well understood. In this thesis, I holistically investigated the physiological and cytological adaptation of Gram-negative bacteria upon exposure to different antimicrobials. Particularly, proteomic and cellomic profiles along with genomic data of these bacteria were acquired, which provides insights into the molecular mechanism of the adaptation. I found a high prevalence of resistant pathogenic Escherichia coli isolated from children with sepsis or diarrhoeal disease. Although antimicrobial exposure generally reduced the growth bacteria, azithromycin accelerated the bacterial growth at sub-inhibitory concentrations. Additionally, tetracycline- resistant isolates were found to possess a fitness advantage over the susceptible isolates. Furthermore, a variety of machine learning models were trained on the proteomic and cellomic profiles, resulting in high accuracy of resistance identification. The models also found strong association of resistance to different antimicrobials with the proteomic and cellomic profiles. The machine learning models suggested that exposure to ciprofloxacin exerted a significant perturbation on bacterial membrane and the expression of various transmembrane proteins, which generates novel hypotheses for further experiments to confirm. Alternatively, I utilised a high-content imaging platform to observe the cytological adaptation of Salmonella enterica serovar Typhimurium upon 24-hour of ciprofloxacin i exposure. Strong association of the adaptation with the exposure time and the antimicrobial concentrations was found. I also found that resistant isolates behaved differently compared to the susceptible isolate even without exposure to the antimicrobial. In conclusion, further holistic investigations of antimicrobial resistance should be conducted with the aids of machine learning approaches to provide in-depth understanding on the mechanism of bacterial adaptation, contributing to the fight against antimicrobial resistance.

Keywords: machine learning
ciprofloxacin
antimicrobial resistance
azithromycin
high-content imaging
Escherichia coli
tetracycline
Salmonella Typhimurium
Enterobacteriaceae
Advisor: Baker, S
Markov decision processes and discrete-time mean-field games constrained with costly observations

In this thesis, we consider Markov decision processes with actively controlled observations. Optimal strategies involve the optimisation of observation times as well as the subsequent action values. We first consider an observation cost model, where the underlying state is observed only at chosen observation times at a cost. By including the time elapsed from observations as part of the augmented Markov system, the value function satisfies a system of quasi-variational inequalities (QVIs). Such a class of QVIs can be seen as an extension to the interconnected obstacle problem. We prove a comparison principle for this class of QVIs, which implies uniqueness of solutions to our proposed problem. Penalty methods are then utilised to obtain arbitrarily accurate solutions. Finally, we perform numerical experiments on three applications which illustrate this model.
We then consider a model where agents can exercise control actions that affect their speed of access to information. The agents can dynamically decide to receive observations with less delay by paying higher observation costs. Agents seek to exploit their active information gathering by making further decisions to influence their state dynamics to maximise rewards. We also extend this notion to a corresponding mean-field game (MFG). In the mean field equilibrium, each generic agent solves individually a partially observed Markov decision problem in which the way partial observations are obtained is itself also subject to dynamic control actions by the agent. Based on a finite characterisation of the agents’ belief states, we show how the mean field game with controlled costly information access can be formulated as an equivalent standard mean field game on a suitably augmented but finite state space. We prove that with sufficient entropy regularisation, a fixed point iteration converges to the unique MFG equilibrium and yields an approximate ε-Nash equilibrium for a large but finite population size. We illustrate our MFG by an example from epidemiology, where medical testing results at different speeds and costs can be chosen by the agents.

Keywords: mean-field games
Markov decision processes
costly observations
Advisor: Reisinger, C
'Un abisso di bellezza e di scandolo': a study of Carlo Emilio Gadda's 'L'Adalgisa'

This thesis is a monographic study of L’Adalgisa. Disegni milanesi (Florence: Le Monnier, 1944), a collection of narrative-satirical writings by Carlo Emilio Gadda (1893-1973). L’Adalgisa is examined from a variety of perspectives and paying attention to different critical issues, and in particular to the question of satire. The critical-interpretative category of satire provides a fil rouge unfolding throughout the thesis and is therefore introduced in chapter 1, which lays the foundation for the following chapters by dwelling on the history and theory of satire and on what is the meaning that Gadda assigns to it. Reconstructing the textual history of the collection which was assembled in the early 1940s, chapter 2 tackles the issue of the unity of the book and considers how the texts brought together work both as individual pieces and parts of a whole. The chapter suggests that the satirical representation of the social collective, albeit practiced across a range of varieties and manners, works as a binding factor and serves as a centre of meaning. Chapter 3 analyses how narration is carried out in the different disegni and focuses on the meaning of the dive that the author claims to take (‘quasicché a propria volta l’autore si tuffi’) into the water of his characters. The chapter argues that a mechanism of creation of satirical difference presides over the ambiguous manifestation of the narrative voice in many disegni. Chapter 4 explores the function of the 227 authorial footnotes found in the collection. The chapter reflects in particular on how the notes only apparently offer an extradiegetic and non-fictional space, while on the contrary they contribute to an extension of the fictional-diegetic boundaries of the narrative and draw the authorial self in it, incrementing the ambiguity of the satire found in the disegni. Chapter 5 brings into focus the significance of the theme of time for the collection, showing that the acknowledgment of time’s unrepeatability is part of the principle of truth upon which the satirical judgement of the Milanese community is expressed throughout the collection. Arguing for an understanding of the book through the category of satire, my thesis seeks to shed light on the mechanism of creation of satirical difference recognised as a constitutional part of satirical writing, offering an interpretation of the collection as a book which articulates a consistent, albeit internally diversified, reflection on three main subjects – the city/society, the individual, time – and on how they interact.

Keywords: Italian bourgeoisie
Disegni Milanesi
Milano
italian literature
Italian studies
Milanese society
time
modernism
20th-century italian literature
Italian canon
satire studies
twentieth-century italian literature
L'Adalgisa
Milan
Carlo Emilio Gadda
satire
authorial footnotes
footnotes
Advisor: Stellardi, G
Gilson, S
Quantum localisation and multifractality in chains with strongly correlated disorder

Systems with strongly-correlated disorder exhibit rich localisation phenomena in one dimension. These include mobility edges, eigenstate multifractality and mixed phases of localised and extended states. In this thesis, two classes of correlated systems are studied: quasiperiodic models, for which the potential is deterministic and infinite-range correlated, and maximally-correlated random chains.
A framework for localisation in quasiperiodic chains is developed, based on the convergence of local propagators. Using the fact that the associated self energy is an order parameter for localisation, we analyse its convergence in continued fraction (CF) form and thereby infer localisation or its absence, yielding the mobility edges of arbitrary models. Approximate theories can be implemented at any order in the CF by self-consistently determining the self energy. The fate of these theories as their order is taken to infinity is shown to be controlled by the convergence of the underlying CF. In the limit of infinite order, both methods generate exact results. The first-order theory is particularly useful, as it can be implemented analytically, and captures the key properties of the localised and delocalised phases. Results are in excellent agreement with the exact results of three diverse models.
Maximally correlated random chains are then studied, for which any two sites separated by a finite distance necessarily have the same energy in the thermodynamic limit. These systems are shown to host robust eigenstate multifractality at strong disorder, in contrast to uncorrelated models. Furthermore, these eigenstates exhibit an anomalous ‘tabletop’ structure, imprints of which are seen in the system’s dynamical properties. For small disorder, delocalised states are present, and separating these two phases is a mixed region. For a given energy, some disorder realisations in the mixed region host extended states and some multifractal; the disorder-averaged spectrum therefore consists of a mixture of the two with no energy resolution between them. Numerical analysis is used to distinguish the three phases, and their phenomenology is connected to the underlying correlations by means of statistical analysis.

Keywords: disordered systems
1-dimensional systems
Green's function methods
Anserson localization
Advisor: Logan, D