Corruption, Deprivation and Economic Development in sub-Saharan Africa

This thesis broadly examines the impact of lived corruption experiences on healthcare deprivation and tax evasion in sub-Saharan Africa (SSA). While Chapter 1 introduces the thesis, Chapter 2 examines how corruption causes healthcare deprivation in 29 SSA countries. Employing the fifth, sixth and seventh waves of the Afrobarometer survey spanning 2011-2018, I find that corruption in the form of bribe payments within the healthcare sector increases healthcare deprivation. Additionally, corruption experienced in sectors outside health such as education, police, public utilities and identification authorities, have adverse spillovers on healthcare deprivation. Furthermore, I show that corruption impacts healthcare deprivation through two key channels: Loss of income and loss of trust in public institutions. Chapter 3 utilises individual-level datasets to explore the effect of people’s lived corruption experiences on their propensity to evade taxes. I show that the likelihood of tax evasion rises by 19–39.5 percentage points for individuals who have paid bribes to government officials in various sectors–health, education, police, public utilities and identification authorities, compared to their counterparts who have never been extorted. Chapter 4 documents the spillover effects of firm-level corruption (unrelated to taxation) on tax evasion decisions, with evidence from 17 SSA economies. Chapter 4 also examines (a) whether and how corruption in tax agencies impacts tax evasion and (b) the key mediating mechanisms through which corruption impact tax evasion among firms. In contrast to the extant literature, I demonstrate that while corruption involving tax authorities rises tax evasion, corruption outside tax authorities (i.e., bribes paid to obtain operating licenses and secure government contracts) has adverse spillovers on tax evasion. The thesis concludes in Chapter 5 outlining key policy implications

Investigating the Mechanobiology of Macrophages: Implications for Inflammatory Bowel Disease

Macrophages are essential cells of the innate immune system, playing a key role in regulating inflammation, tissue repair, and homeostasis. Their behaviour is tightly controlled by various signalling pathways, including mechanical forces that influence their shape, movement, and function. This process, known as mechanotransduction, allows cells to sense and respond to mechanical signals from their environment, converting these signals into biochemical responses that regulate cellular behaviour. Dysregulation of macrophage functions can lead to chronic inflammatory diseases and cancer. Recent studies have shown that mechanical cues, such as extracellular matrix (ECM) stiffness, fluid flow, cell crowding, and topography, modulate macrophage behaviour in various physiological and pathological contexts. However, the effect of ECM stiffness at relevant physiological levels, particularly in inflammation and fibrosis, has not been fully understood. Previous studies have often relied on single or limited marker approaches, which may not capture the full complexity of macrophage polarization. To address this gap, we conducted a series of experiments aimed at characterizing THP-1 and bone marrow-derived macrophage (BMDM) protocols to ensure proper validation and reproducibility for our study. We then adapted ECM stiffness values, mimicking the conditions seen in inflammatory bowel disease (IBD), representing both normal and inflamed-fibrotic tissue. Experiments were conducted to assess macrophage polarization states in response to varying stiffness levels. Our results reveal that increasing ECM stiffness promotes the expression of YAP and IL-6 in M1 macrophages, driving a shift towards a pro-inflammatory phenotype. In contrast, M2 macrophages exhibited elevated levels of the anti-inflammatory markers CD163 and IL-10, reflecting an adaptive response to softer ECM conditions. Interestingly, M0 macrophages, which are considered to be non-polarized, adopted a hybrid phenotype, expressing both YAP and CD163, underscoring the inherent plasticity of macrophages when subjected to mechanical stress. In primary BMDMs, stiff ECM conditions induced also mixed phenotypes with favoured M1 polarization, as shown by a significant overlap with established M1 gene expression signatures, further emphasizing the role of ECM stiffness in driving pro-inflammatory responses. These findings challenge the traditional binary M1/M2 polarization model, suggesting that macrophage responses to mechanical cues are nuanced and context dependent. In the second part of this thesis, we investigated the mechanical regulation of the Poly(C)-binding protein 1 (PCBP1) in macrophages and its role in macrophage polarisation. PCBP1 is a multifunctional RNA-binding protein that plays a crucial role in regulating mRNA stability, splicing, and translation. It is also involved in iron metabolism, acting as an iron chaperone, and is involved in DNA damage repair. Our experiments demonstrate that ECM stiffness and cell density regulate PCBP1 subcellular localization in macrophages. In stiff ECM and low-density environments, PCBP1 localises mainly to the nucleus, while in soft ECM and high cell density, it remained cytoplasmic. PCBP1 knockdown increased CD163 expression, suggesting it modulates M2 polarization. Finally, we demonstrate a possible role of PCBP1 in ECM stiffness dependent DNA damage repair, suggesting a novel mechanism of mechanoprotection

Artificial intelligence for ovarian cancer diagnosis from digital pathology slides

Digital pathology is a rapidly growing field, allowing for the development of assistive diagnostic tools. Many tools use artificial intelligence (AI) to automatically provide insights from whole slide images (WSIs), aiming to improve the accuracy, objectivity, and efficiency of the diagnostic process. Research has typically focused on the most common cancers, but less common cancers have received comparatively little attention. We focus on the histological subtyping of ovarian cancer, an essential diagnostic task for determining optimal treatments and prognoses. Through a systematic literature review, we find that previous research has been limited to model prototyping with small homogeneous datasets, with little focus on clinical utility. We perform the most thorough analyses of automated ovarian cancer histological subtyping to date, using the largest training dataset and evaluating models through cross-validation, hold-out testing, external validations, bootstrapping, and hypothesis testing. Analyses are based on attention- based multiple instance learning (ABMIL) with an ImageNet-pretrained ResNet50 backbone, a commonly used WSI classifier. The computational complexity of current AI models is a key limitation, with pathology labs typically not having sufficient hardware for model deployment. We propose an active tissue sampling technique and show that this approach can drastically reduce the computational burden of inference with minimal impact on diagnostic performance. ABMIL analyses tissue at only a single magnification, with high magnifications offering more cellular detail and low magnifications providing broader tissue context. We find that 10x magnification balances the cellular and histoarchitectural details to give the most accurate ovarian cancer subtyping performance, while drastically reducing the computational burden compared to the clinical standard 40x magnification. Recently, histopathology foundation models have promised to revolutionise diagnostic AI. We analyse 14 foundation models and confirm that they give significantly greater performance than previous feature extractors. In ABMIL, tissue patches are treated as independent of each other. We propose a multi-resolution patch graph network to better model spatial context and find this marginally improves performance. The optimal model, a combination of a foundation model and a graph, achieved five-class balanced accuracies of 88%, 99%, and 77% in three validation sets, where our baseline model achieved only 66%, 69%, and 52%, and individual pathologists achieved 74-91% concordance with similarly determined labels. This gives us confidence that AI models could have clinical utility, so future work should focus on the practicalities of implementation and real-world validation

Engineering Functional Partial Joint Replacements: A Soft Solution To A Hard Problem

Arthritis often leads to joint replacement, where metal and plastic or ceramic components are used to restore function. While effective, these replacements can fail over time, leading to complex and unpredictable revision surgeries. In many cases, arthritis affects only one side of the joint, making partial joint replacement (hemiarthroplasty or focal cartilage repair) a less invasive alternative. However, replacing soft cartilage with hard metallic surfaces in current hemiarthroplasty devices often results in poor outcomes, as the stiff implants reduce contact area and increase stress on the remaining cartilage, potentially causing further degeneration. This thesis explores the use of polyelectrolyte functionalised biomaterials as cartilage interfacing surfaces, focusing on SPMK-g-PEEK — a biomimetic interface composed of 3-sulfopropyl methacrylate potassium salt (SPMK) tethered to a polyetheretherketone (PEEK) substrate, inspired by the natural biopolyelectrolytes in synovial fluid. SPMK-g-PEEK surfaces form a highly hydrated, compliant layer (~ 5 μm thick) due to their dense coverage of hydrophilic sulphonic acid groups, which supports aqueous boundary lubrication and promotes cartilage interstitial fluid recovery. Under aqueous conditions, SPMK-g-PEEK exhibits ultra-low friction coefficients (μ < 0.02), consistent across physiologically relevant speeds (0.1 – 200 mm/s) and contact pressures (0.25 – 2 MPa), mimicking the tribological properties of natural cartilage. Additionally, these surfaces facilitate a novel mechanism of polyelectrolyte-enhanced tribological rehydration (PETR), promoting cartilage interstitial fluid recovery even in static contact areas. This mechanism supports continuous lubrication and contrasts with conventional theories that attribute cartilage rehydration to hydrodynamic fluid entrainment facilitated by convergent cartilage contact geometries. PETR is attributed to the combined effects of fluid confinement within the contact gap and the enhanced elastohydrodynamic behaviour of surface tethered polyelectrolytes. This work not only enhances the understanding of cartilage tribology but also offers a promising strategy for developing joint replacement materials that more effectively replicate the natural function of cartilage. The implications extend to advancing the design of next-generation implants for focal cartilage repair, offering new potential for improved patient outcomes in orthopaedic applications

Player performance, salary and survival analysis in the NBA

Professional sports provide an excellent research environment to study all aspects of the employee-employer relationship. In this study, I use data from the National Basketball Association (NBA) to empirically investigate the impact of contractual arrangements on player performance, salary, and survival. First, I explore changes in player performance in the year at which a contract ends (the contract year). Players improve their performance during the contract year, but no consistent performance decline is observed in the year following the end of the contract year. Second, I focus on salary determination. I demonstrate that player performance is an important driver of their salary. In addition, I explore the interaction between contractual arrangements and salary determination and find that contract length and special clauses, such as player/team options to extend the employment relationship, affect the salary. Third, I explore the determinants of player survival in the league. Good performance, especially offensive win shares, increases player longevity. Changing teams allows young or undrafted players to survive longer in the league. Examining the impact of player options on player survival suggests that player options increase the probability of players changing teams but do not increase the probability of player survival. In summary, my findings indicate that the interaction between contractual arrangements and player performance is important in salary determination and survival in professional sports

Characterisation and Modelling of InAs and AlInAsSb Avalanche Photodiodes

For photodetection up to 3.6 µm in low photon conditions, InAs electron avalanche photodiodes (e-APDs) present a low noise and high-speed alternative to other materials. Despite their excellent ionisation characteristics, the gain of InAs APDs is limited by tunnelling, the difficulty growing thick avalanche layers with low doping, and their detectivity is lowered by high dark currents. By better understanding the tunnelling behaviour within InAs APDs and by developing InAs-based heterostructures using AlInAsSb, this work aims to overcome these challenges. To investigate the tunnelling and gain in InAs e-APDs, a TCAD model using Sentaurus was developed. Verification against measured PIN and NIP devices showed that the model well simulated the drift-diffusion current at room temperature, band-to-band tunnelling, and avalanche gain of InAs devices. This was used later to find the gain when the tunnelling current reached an assigned current limit, finding that when the gain at this limit exponentially rose with the avalanche width provided that it was fully depleted and could improved by grading the P layer. Mesas with bevelled sidewalls reached this limit earlier due to hotspots, but could be mitigated when using thick NIP devices. After this, two InAs lattice-matched random alloys of AlInAsSb were characterised to see their effectiveness as an absorption layer in an InAs heterostructure. Measuring a diffusion dominated current density of 0.0358 A/cm2, a responsivity of 1.39 A/W at 2 µm, a background doping of 6.5 × 1014 cm−3, and a lattice mismatch of 6.79 × 10−4 for the best wafer. By comparison, commercially available 2.6 µm extended InGaAs photodiodes have comparable responsivities of 1.27 A/W, but dark current densities nearly two orders of magnitude lower at 5.1 × 10−4 A/cm2. Overall then AlInAsSb was demonstrated as a viable absorber material on InAs. But better surface passivation and wafer growth is still needed for competitive devices due to the significant surface leakage and high dark currents observed in the measurements. Finally, the total gain M and excess noise F of a novel InAs heterostructure with both an InAs and AlInAsSb avalanche layer was modelled using modified local model equations and an RPL model. If the individual gain and noise of the InAs layer were M1 and F1 respectively and M2 and F2 for the AlInAsSb layer it was realised that when InAs is the first avalanche stage that M = M1M2 and F = 2 + (F2 − 2)/M1. Leading to a low-noise, high-gain 2-stage APD concept that combines the low-noise behaviour of InAs with the higher potential gains of AlInAsSb, removing the need for thick, difficult-to-grow InAs avalanche layers. Current InAs APDs have reached gains up to 330 while conventional GaSb lattice-matched AlInAsSb avalanche layers have shown k = 0.018 noise behaviour. TCAD simulations in this predict that 2-stage InAs / AlInAsSb APDs can achieve near k = 0 behaviour up to gains of 500

Plasma-Enhanced Pulsed Laser Deposition of metal oxynitride thin films for photoelectrochemical water splitting

Plasma-enhanced pulsed laser deposition (PE-PLD) is a novel thin film deposition method which employs radio-frequency plasmas and laser-ablated plasma plumes to produce semiconductor thin films. A high-powered, pulsed laser ablates material from a metal target into a plasma plume, which interacts with non-metal inductively-coupled plasma species to form metal compound material that deposits onto a substrate. PE-PLD has been shown to produce high-quality metal oxide thin films with many applications, including photocatalysts which use solar energy to produce hydrogen fuel from water splitting. PE-PLD remains an active area of research, particularly elucidating its underlying plasma physics and chemistry, such that thin films can be created according to specific criteria rather than empirical observation. This work centres around the suitability of PE-PLD in producing metal oxynitride thin films for photocatalysis. This thesis presents results from modelling the laser ablation of different photocatalytic metals using the code POLLUX, showing the electron temperature and mass density of the plasma plume both increased with the atomic number of the material, whilst the mass density of the material had no observed effect on the electron temperature or particle density of the plume. Additionally, the TALIF diagnostic provided absolute measurements of ground-state atomic O and N densities for a range of low-pressure oxygen/nitrogen plasma mixtures, showing the relative flow input of oxygen and nitrogen had the greatest control over the O:N atomic density ratio, allowing it to change by up to a factor of 100. Finally, the structure and chemical composition of deposited metal oxynitride thin films were analysed with different diagnostics, showing a consistent lack of nitrogen present on the films and lack of visible light absorption, highlighting many areas of improvement for PE-PLD in producing oxynitride films, such as understanding interactions between oxygen/nitrogen plasma species and their effect on deposition