VictorJuiz/thesis huggingface dataset:
The epithelial cell in host-pathogen interactions in airways disease
Keywords: non-typeable haemophilus influenzae
azithromycin
bacterial persistence
cytokines
nasopharynx
asthma
airway epithelium

Non-typeable Haemophilus influenzae (NTHi) is the most common lower respiratory tract bacterium, where it causes persistent infections and is associated with neutrophilic airway inflammation and increased risk of exacerbations in patients with asthma and chronic obstructive pulmonary disease (COPD). This work aimed to investigate the persistence of NTHi within the epithelium, the role of bacterial internalisation in the epithelial immune response, and evaluate the differential immune signalling in the epithelium of upper compared with lower airway epithelium.
In an in vitro model of NTHi infection using cell lines and primary cells, differing internalisation rates between NTHi strains did not correlate with induction of proinflammatory cytokines. Treatment with cytochalasin D to block invasion diminished IL-1b production, but did not affect other cytokines. Imaging demonstrated that intracellular NTHi localised primarily within the late endosome of differentiated epithelial cells. Viable bacilli did not persist beyond 48 h in cell lines in submerged culture, or 24 h in primary cells grown at ALI, suggesting the NTHi does not persist in epithelial cells alone.
Although NTHi causes inflammation in the lower airways, it is considered a commensal organism in the nasopharynx. Comparison of primary cells cultured at air- liquid interface inoculated with NTHi showed substantially more upregulation of TLR signalling, NOD signalling, and of ICAM1, Caspase1 and IL-8 in nasal epithelial cells than bronchial cells. This suggests that the increased pathogenicity of NTHi in the lower airways compared with the nasopharynx is not due to increased innate immune responsiveness of bronchial epithelial cells.
These data demonstrate that NTHi potently induces an innate response in the bronchial epithelium independent of internalisation rate, with limited NLR inflammasome dependence, implicating surface immune sensing via TLR2 and 4. These data also demonstrate differential immune signalling in nasopharyngeal compared with lower airway epithelium. Overall, these data suggest that NTHi is internalised transiently but has the capacity to drive inflammation in airway epithelial cells.
Efficient and scalable methods for deep reinforcement learning
Keywords: machine learning
multi-agent systems
reinforcement learning
deep learning

This thesis proposes some new answers to an old question - how can artificially intelligent agents efficiently learn from their experiences to make optimal decisions? We adopt the framework of reinforcement learning (RL), in which agents are trained to maximise their expected long-term cumulative rewards, and build on the recent successes of deep RL by using deep neural network function approximators for policies, value functions, and other model components. Deep RL often learns very inefficiently from experience, and can struggle to scale to very complex problems with large action spaces or sparse feedback. We address these challenges in several ways.
In Part I, we dive deep into a subfield of RL concerning multiple agents that must cooperate to achieve a common goal. These multi-agent systems test the limits of our algorithms due to their complex dynamics, large joint action spaces, and decentralisation constraints. We develop methods to address partial observability and multi-agent credit assignment (Chapter 3), nonstationarity induced by co-learning agents (Chapter 4), and efficient representation and learning of joint action values (Chapter 5).
In Part II, we leave the specific setting of multi-agent RL to build more general inductive biases into algorithms and architectures. In Chapter 6 we leverage the inductive bias that tree-search planning is an effective representation of value functions or policies to accelerate learning. In Chapter 7 we use a curriculum of progressively growing action spaces to enable efficient exploration without compromising long-term optimality.
In Part III, we focus on estimators of higher-order derivatives in the context of RL. Among other applications, these estimators can be used in meta-learning, where we attempt to learn algorithms or inductive biases from data rather than hand-designing them as in Parts I and II. In Chapter 8 we propose an objective which may be (automatically) differentiated any number of times to produce unbiased estimates of higher-order derivatives. In Chapter 9, we extend this objective to reduce its variance, as well as allowing a flexible trade-off of bias and variance in estimators of any-order derivatives for RL.
Together, these contributions make valuable strides towards realising efficient and scalable solutions to challenging RL problems, as well as opening up exciting directions for future work building on the algorithms, architectures, and estimators proposed here.
The bureaucratic court? Administrative justice and the admissibility decision-making of the European Court of Human Rights
Keywords: Admissibility Decision Making
Administrative Justice
International Law
European Convention on Human Rights
Law reform

How should the European Court of Human Rights “decide what cases to decide”? This thesis examines the procedural justice of the admissibility decision-making of the Strasbourg court. Reappraising the “individual vs constitutional” debate which continues to dominate the Convention literature, this thesis draws on socio-legal administrative justice theory to analyse the complexities, discrepancies and tensions which have characterised debates about how Strasbourg’s docket-filtering should be organised. Having established the relevance and force of this new conceptual framework, this thesis identifies four distinct and competitive conceptions of how the Court’s docket-filtering should be designed: the Strasbourg as court, constitutional court, bureaucracy and tribunal models. I show that the ongoing debates about admissibility reform and institutional design in both the Council of Europe and the academy have been premised on neither a rigorous empirical analysis of the Court’s working practices, nor a detailed analysis of its admissibility jurisprudence.
This thesis addresses these lacunae. Drawing on an empirical analysis of the evolving text of the Convention, Council of Europe policy papers, evidence of the Court’s working practices, and a critical examination of its published admissibility decisions, this thesis demonstrates that the orthodox, doctrinal analysis of the Court’s admissibility decision-making misunderstands and misrepresents most of the Court’s working practices. The dominant voice in the literature contends that Strasbourg has now become a “constitutional court.” My data shows that while judges retain a superficial responsibility for decision-making at the threshold in Strasbourg, in practice, the Court’s Registry has quietly accrued greater and greater responsibility for the docket while judicial participation in decision-making has been marginalised. The law on the books, it is shown, remains a poor guide to how the law operates in practice. These socio-legal insights allow a radical reappraisal of our understanding of the Court, and the theoretical and methodological limits of the existing literature on the Court and the law of the Convention.
Physics-informed machine learning: from concepts to real-world applications
Keywords: differential equations
real-world problems
domain decomposition
geophysics
scientific machine learning
physics-informed machine learning
lunar science
machine learning
scientific computing
physics-informed neural networks
deep learning
seismic simulation
permanently shadowed regions
neural networks

Machine learning (ML) has caused a fundamental shift in how we practice science, with many now placing learning from data at the focal point of their research. As the complexity of the scientific problems we want to study increases, and the amount of data generated by today's scientific experiments grows, ML is helping to automate, accelerate and enhance traditional workflows.
Emerging at the forefront of this revolution is a field called scientific machine learning (SciML). The central goal of SciML is to more tightly combine existing scientific understanding with ML, generating powerful ML algorithms which are informed by our prior knowledge.
A plethora of approaches exist for incorporating scientific principles into ML and expectations are rising for SciML to address some of the biggest challenges in science. However, the field is burgeoning and many questions are still arising. A major one is whether SciML approaches can scale to more complex, real-world problems. Much SciML research is at a proof-of-concept stage, where techniques are validated on simplified, toy problems. Yet, understanding how well they scale to more complex problems is essential for them to become widely applicable.
This question is of central focus in this thesis. Firstly, multiple different physics-informed ML approaches are designed for three complex, real-world, domain-specific case studies taken from the fields of lunar science and geophysics, and their performance and scalability is assessed. Secondly, the scalability of physics-informed neural networks, a popular and general SciML approach, for solving differential equations with large domains and high frequency solutions is evaluated and improved. Common observations across these studies are discussed, and significant advantages and underlying limitations are identified, highlighting the importance of designing scalable SciML techniques.
Creativity versus conservatism in science: a simulation-based investigation

Keywords: scientific creativity
nonconformity
scientific conservatism
epistemic landscape models
modelling in philosophy
conformity
agent-based model

Scientists and the communities which they integrate may display stronger or weaker tendencies to conform to certain ways of approaching the research of a given scientific topic. Some have claimed that the level of conformity displayed has important epistemic implications. In particular, it has been argued that scientific nonconformity is beneficial for the progress of science, as it is conducive to the adoption of a diversity of scientific approaches, some of which may be truth-conducive and would not be attempted in more conformist settings.
In this thesis, I refine and operationalise an account introduced by Currie(2019b) which frames conformity and nonconformity in science as conservatism and creativity, respectively. In particular, I build an agent-based model from this framework, and I use it to run computer simulations and test whether scientific creativity really promotes scientific progress. Ultimately, by combining the results of these simulations with considerations about the institutions of science, I conclude that in order for a scientific community to reap the epistemic benefits often associated with scientific creativity, its scientists need not (and probably should not) be particularly creative.
Boundary constraints on the large-scale ocean circulation
Keywords: variability
boundary properties
CAR scores
ACC
decomposition
AMOC
MOC

The Meridional Overturning Circulation (MOC) is a system of surface and deep currents encompassing all ocean basins, crucial to the Earth's climate, transporting heat, carbon and nutrients around the globe. Detecting potential climatic changes in the MOC first requires a careful characterisation of its inherent variability. Key components of the MOC are the Atlantic MOC (AMOC) and the Antarctic Circumpolar Current (ACC).
In this thesis, the role of boundary properties (including density and velocity) in determining the AMOC and ACC is investigated, as a function of cross-sectional coordinate and depth, using a hierarchy of general circulation models. The AMOC is decomposed as the sum of near-surface Ekman, depth-independent bottom velocity and eastern and western boundary density components. The decomposition proves a useful low-dimensional characterisation of the full 3-D overturning circulation, providing a means to quantify the relative importance of boundary contributions on different timescales.
The estimated total basin-wide AMOC overturning streamfunction, reconstructed using only boundary information, is in good agreement with direct calculations of the overturning using meridional velocities, in terms of magnitude and spatial structure. The time-mean maximum overturning streamfunction is relatively constant with latitude, despite its underlying boundary contributions varying considerably, especially in the northern hemisphere. Regression modelling, supplemented by Correlation Adjusted coRrelation (CAR) score diagnostics, provides a natural ranking of the various boundary components in explaining total transport variability. At short timescales the bottom, western boundary and Ekman components play a dominant role. At decadal timescales, the importance of both boundary density components is revealed.
Applying a similar decomposition diagnostic to the ACC provides insight into the differences between model simulations, revealing spatial resolution dependence of ACC transport through the Drake Passage. All models exhibit a weak ACC compared to observations, the 1/4∘ model showing significant weakening over the first 40 years of simulation. The weaker ACC transport in the 1/4∘ model can be attributed to fresher, lighter southern boundary densities and a strengthened Antarctic Slope Current.
Density maps along sloping ocean boundaries are produced, incorporating the western and eastern Atlantic boundaries and the Antarctic coastline. Isopycnals are flat over long portions of eastern ocean boundaries, and slope linearly on western boundaries; possible mechanisms for these features are discussed. The relationship between boundary currents and isopycnal slope around Antarctica is also considered.
Results of this thesis serve to indicate the importance of boundary information in characterising the AMOC and the ACC, and the relative simplicity of along-sloping-boundary density structure.
Engineering terpenoid oxidation by cytochrome P450BM3 for fine chemical synthesis
Keywords: steroids
synthesis
norisoprenoid
P450
terpenoid
oxidation

P450BM3 (CYP102A1) has a unique structure of having the electron-transfer reductase domain fused to the haem monooxygenase domain. The intramolecular electron transfer process makes P450BM3 catalytically self-sufficient and promotes high turnover rates. P450 enzymes activate C−H bonds via the insertion of an oxygen atom from atmospheric dioxygen and play key roles in the biosynthesis of endogenous compounds (terpenes, alkaloids and steroids).
Oxygenated terpenoid compounds have desirable properties such as aromas, flavours and medicinal effects. The initial oxygenation step in terpenoid biosynthesis from the terpene precursors is invariably catalysed by a P450 enzyme. Chapter 3 describes the engineering and application of P450BM3 variants to develop efficient, two-step chemo-enzymatic synthesis routes to high value the norisoprenoid and isoprenoid aroma compounds b-damascenone, tabanone isomers, and rose oxide by late-stage C−H activation.
The generation of diverse metabolites of common steroids via P450-catalysed direct C−H activation has important applications in the synthesis of steroidal agents for drug screening and development. Chapter 4 describes the engineering of P450BM3 for the mono- and di-hydroxylation of androstenedione (AD), dehydroepiandrosterone (DHEA) and testosterone (TST). To design altered steroid binding orientations to broaden the regioselectivity, the structures of wild type P450BM3 and the steroid aromatase CYP19A1 are overlaid to identify key regions for scanning glycine mutagenesis which greatly expanded the product range of AD, DHEA and TST. In Chapter 5, more steroidal compounds are screened to show hydroxylation at multiple sites with high regio- and stereo-selectivity for androsterone (AN), epi-androsterone (EAN), progesterone (PROG) and pregnenolone (PREG).
Customary law and 1992 constitution of Ghana: a comparative theoretical study
Keywords: 1992 Constitution
Constitutional Pluralism
Nigeria Federalism
Botswana Constitution
Nigeria Constitution
Asante Constitution
Ghana Constitution,
Bicameralism

Ghana’s 1992 Constitution entrenches Fundamental Human Rights and Freedoms, some of which do not exist in the customary law of the tribal polities with whom the state shares citizens. The state’s efforts to assert this liberal identity as the people’s; and the tribal polities’ commensurate efforts to resist the erosion of the socio-political identity they have chosen for themselves and their subjects; create tensions that, unmanaged, are apt to culminate in destructive conflict. My thesis casts the state-tribe friction in terms of a struggle over sovereignty and seeks a theoretical strategy to address that challenge.
I compare three philosophical constructs: federalism, bicameralism, and constitutional pluralism. I draw on Nigeria’s experience to conclude that though federalism might preserve the citizen-subject’s tribal identity, it does so at the cost of her statal one. Based on Botswana’s experience, I reject bicameralism for over-aligning state and tribe, thereby distorting the latter, and, thus, robbing the citizen-subject of her native lens for navigating the world.
Rather than obsess about being the only entity making a sovereignty claim in the territory, constitutional pluralism (CP) teaches the state to concentrate on amplifying its effectiveness to match the scope of its normative claim, so as to render implausible all competing sovereignty claims. This new focus allows the state to tolerate its rivals and even optimise their existence thereby offering the citizen-subject equal access to both her statal and tribal identities. I therefore endorse it.
I craft a Ghanaian theory of CP which values the inter-order friction and establishes thereon a model of competition and collaboration in which the state gains both the benefit of the strengths and legitimacy of tribal authority, and, through its competing remedies, the power to trigger and mould the evolution of customary law rights regimes into a connotationally-compliant body of law.
Exploring the relationships between Japanese L2 acquisition rates, study environments and kanji learning strategies
Keywords: logographic languages
self-regulation
time-to-fluency
motivation
language learning strategies
kanji
Chinese characters
L2 Japanese
alphabetic L1 background
Japanese language education
online survey
adult L2 Japanese learners
L2 acquisition

This study investigated whether adult L2 Japanese learners from alphabetic L1 backgrounds displayed different L2 acquisition rates depending on the time period when they began studying the language. In particular, it analysed L2 acquisition rates and language learning strategy usage by learners who began studying L2 Japanese in the pre-Internet / pre- globalization / pre-smartphone period (‘the Pre-1997 sample’) compared with those who began studying afterwards (‘the Post-1997 sample’). The study used a cross-sectional, mixed methods approach, which incorporated both quantitative and qualitative elements in its analysis. Data collection was based on an online survey with participants recruited using a snowball sampling approach. Time-to-fluency measures for L2 Japanese learners and details of their language learning environment during this time were collected, along with data about their motivation for studying Japanese and the types of kanji learning strategies they employed. In addition, the participants provided three open-ended responses on: 1) learning challenges; 2) role models; and 3) fluency milestones. Of the 115 accepted survey responses that were received online, 56 responses were identified where there was a quantifiable level of equivalent fluency between participants (‘the Equivalent Fluency group’). Within the Equivalent Fluency group, the mean time to fluency of the Pre-1997 sample (N = 32) was 5.4 years, significantly faster than the Post-1997 sample (N = 24) mean of 8.0 years. The Pre- 1997 sample also employed a significantly higher level of kanji learning strategy usage. At a more qualitative level, it was noted that the Pre-1997 sample spent a higher percentage of their time-to-fluency period studying in Japan, spent more of their day in Japanese-speaking environments, and were more willing to embrace an immersive approach to L2 Japanese learning. Follow-up interviews with three participants focussed mainly on motivation and their language learning experiences. The study provided a very detailed insight into the collective mindset of a wide range of successful adult L2 Japanese learners from alphabetic L1 backgrounds. It has pedagogical implications for instructors on the types of study environments, motivation profiles and learning strategies that can produce high levels of proficiency for character-based languages such as L2 Japanese and L2 Chinese.
Developing new biomarkers fro cardiovascular disease: the prognostic potential of oxidative stress, and perivascular adipose tissue phenotyping
Keywords: perivascular adipose tissue
radiomics
computed tomography angiography
biomarkers
transcriptomics
prognosis
oxidative stress

Despite the fact that cardiovascular disease (CVD) mortality rates have seen major declines during recent years compared to past decades, reflecting advances made in primary and secondary prevention, CVD remains a significant mortality contributor, and the so-called residual cardiovascular risk persists. For the purposes of my thesis, I used the Oxford Fat, Vessels and Fat (Ox-HVF) cohort, which constitutes a uniquely phenotyped cohort, including >1,000 patients undergoing cardiac surgery, with long-term follow-up, to perform observational association studies, functional ex vivo bioassays on human tissue, and discovery transcriptomics combined with radiomics.
In the first part of my thesis, I focused on characterising the prognostic value of arterial and myocardial oxidative stress. I showed that increased production of free radical oxygen species in human arteries and myocardium is associated with a higher risk of future major adverse cardiac events (MACE). I further revealed S100A8 and S100A9 as the most dysregulated genes in the presence of oxidative stress in the vascular wall. Applying discovery network transcriptomics, I unveiled a transcriptomic signature that is redox related and predicts future risk of MACE. Pathways relevant to oxidative phosphorylation, glucose metabolism, and the mitochondrial respiratory chain assembly were enriched and therefore revealed as potential therapeutic targets.
In the second part of my thesis, I developed a method that allows the integration of transcriptomic and radiomic data to produce biologically relevant imaging signatures from radiomic features calculated from segmented three dimensional volumes. I applied this concept to radiomic features of perivascular adipose tissue around the internal mammary artery and thoracic aorta imaged in computed tomography angiography (CTA) to generate an imaging signature that captures vascular inflammation defined by upregulation of key inflammatory pathways in arterial transcription. This signature was validated in the context of vascular inflammation induced by the SARS-CoV-2 virus, and was proven to predict in hospital death, admission to the intensive care unit, response to dexamethasone, and systemic thrombosis.
Taken together, my findings propose oxidative stress, and specific redox-relevant genes and pathways as prognostic and therapeutic targets in CVD. Further, COVID-19 related vascular inflammation can be captured non-invasively by a novel radiotranscriptomic perivascular adipose tissue signature, which can maximise the diagnostic and prognostic yield of CTA.