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Coil development for 7 Tesla Magnetic Resonance Imaging of the prostate
Get PDFProstate imaging performed on the contemporary 1.5-3 T clinical magnetic resonance imaging (MRI) systems has proven to be of great benefit within the context of prostate cancer management. The use of multi-parametric MRI (mpMRI) aids the clinicians in identifying the biopsy sites and offers a non-invasive way of staging the disease and monitoring its progression. Given the clinically verified usefulness of prostate MRI on the current clinical systems, it is logical to ask if further benefits can be extracted by exploring prostate imaging on ultra-high-field (UHF) scanners. UHF systems boast main magnetic field strengths of ≥ 7T and offer a much higher potential signal-to-noise ratio (SNR). The additional SNR can be traded for higher resolution anatomical images or faster acquisition times compared to the lower field strength scanners. Realising the SNR gain of higher field systems, however, requires an appropriately designed radiofrequency (RF) coil. A well-built MR body coil is the necessary precursor for an objective assessment of the clinical diagnostic utility of the higher field systems in the context of prostate imaging. The project sought to develop a coil for prostate imaging at 7T. As part of the project, multiple loop coil configurations were simulated and compared. A six-channel transmit-receive design was constructed, characterised and approved for use in healthy volunteers. Preliminary volunteer T2 and diffusion-weighted images were acquired, demonstrating promising results
Malcolm MacFarlane (Calum MacPhàrlain, 1853-1931). New perspectives on the Gaelic language movement in Scotland at the turn of the twentieth century
Get PDFThe thesis examines Malcolm MacFarlane’s (Calum MacPhàrlain, 1853-1931) substantial contributions to the Gaelic language movement in Scotland at the turn of the twentieth century. It argues that MacFarlane should be recognised as one of the movement’s leading figures, addressing the neglect in our understanding of his prominent position to date. This is the most substantial study and comprehensive investigation of both MacFarlane’s life and the archive of his papers and correspondence, held at the National Library of Scotland. The study provides a detailed analysis of MacFarlane’s involvement with the different social, cultural and political movements that were active within the Gaelic literary networks at the time. It illuminates the relationships he had with several of the movement’s other key figures, both in Scotland and in Ireland. The study reflects on MacFarlane’s ideologies and aspirations for the Gaelic language and its culture, highlighting his criticisms of the language movement’s central organisation, An Comunn Gàidhealach. It investigates the foundation of An Comunn Gàidhealach’s propagandist publication, An Deo-Ghréine, of which MacFarlane was the inaugural editor, and gives much needed critical examination of its contents and the changes it underwent in its earliest years. The study presents a detailed account of the establishment and operations of The Gaelic Academy (Àrd-Chomhairle na Gàidhlig), which emerged to address the perceived deficiencies in the policies of the wider Gaelic movement. An overview is given of MacFarlane’s substantial contributions to Gaelic literature, calling attention in particular to the resources he produced in his lifetime that were intended to stimulate the production of contemporary Gaelic literature and provide tools for future Gaelic writers. A catalogue of MacFarlane’s known printed Gaelic work is given here, underlining the scale of the contribution he made and providing for further scholarly research
RADEL: Resilient and Adaptive Distributed Edge Learning in dynamic environments
Get PDFThe rapid evolution of edge computing has fundamentally transformed distributed machine learning by enabling intelligence at the network periphery, where data originates. This paradigm shift has facilitated real-time analytics and responsive services across decentralized environments. However, deploying effective machine learning systems at the edge introduces substantial challenges: unpredictable node failures compromise service continuity; evolving data distributions trigger concept drift that degrades model performance; irrelevant data inclusion reduces prediction accuracy; client mobility undermines traditional learning assumptions; and communication inefficiencies limit scalability in resource-constrained settings.
This thesis presents RADEL, a comprehensive framework for Resilient and Adaptive Distributed Edge Learning that systematically addresses these challenges through five interconnected contributions. First, we introduce a novel resilience mechanism that maintains service continuity during node failures by enabling surrogate nodes to effectively serve prediction requests of failing counterparts. Our approach leverages statistical signatures of neighboring nodes’ data to build enhanced local models through various information extraction strategies, demonstrating significant improvements over traditional replication-based methods while requiring minimal inter-node data transfer.
Second, we develop maintenance strategies to preserve model resilience under concept drift, a critical challenge in dynamic edge environments where data distributions evolve over time. By analyzing how different types of drift affect enhanced models, we propose efficient maintenance mechanisms that achieve optimal trade-offs between data transmission volume and adaptation effectiveness, maintaining performance across heterogeneous data sources while minimizing communication overhead.
Third, we propose a query-driven data-centric learning approach that progressively discovers relevant data regions from query patterns to optimize predictive performance. This mechanism integrates optimal stopping theory to determine when to conclude model refinement and incorporates adaptive update policies that respond to changes in both data and query distributions. Our experimental results demonstrate up to 63% improvement in predictive accuracy compared to traditional approaches that utilize all available data.
Fourth, we present DA-DPFL, a dynamic aggregation framework for decentralized personalized federated learning that addresses data heterogeneity while reducing communication and computational costs. By enabling clients to reuse previously trained models within the same communication round and employing a sparse-to-sparser pruning strategy based on model compressibility, DA-DPFL achieves superior test accuracy while reducing energy consumption by up to 5× compared to state-of-the-art approaches.
Finally, we introduce MOBILE, a mobility-aware framework that optimizes client selection and bandwidth allocation in federated learning environments with mobile clients. By formulating the problem as a regularized Mixed-Integer Quadratic Programming optimization and incorporating both historical and current mobility patterns, MOBILE increases successful client participation rates from 32% to 89% while reducing wasted bandwidth by 43% compared to mobility-agnostic approaches.
Through comprehensive theoretical analysis and extensive empirical evaluation on diverse real-world datasets, we demonstrate that RADEL significantly improves learning efficiency, reliability, and adaptability in dynamic edge environments. Our research establishes a robust foundation for deploying resilient machine learning services at the edge, advancing the state-of-the-art in distributed intelligence for next-generation computing systems. The frameworks and algorithms developed in this thesis provide practical solutions for the deployment of intelligent services in smart cities, industrial IoT, autonomous systems, and other edge-centric applications where reliability and adaptability are paramount
Solvent-assisted phase direction in scandium metal-organic framework synthesis
No full textAbstract not currently available
Evolution of mesothelioma from benign asbestos pleural inflammation
Get PDFPleural Mesothelioma (PM) is an incurable disease with a poor life expectancy. It is most commonly associated with asbestos exposure and the latency period from exposure to PM presentation is usually 20-50years. PM can present after a period of benign pleural inflammation, but the true rate of PM evolution in such cases, and factors promoting evolution, remain unclear. There has been an increase in mesothelioma research in recent years, leading to improved understanding of mesothelioma biology and subsequent treatment advances, but the prognosis remains poor. There have been improvements in PM diagnostics with the addition of ancillary testing including BRCA1-associated protein 1 (BAP1), Methylthioadenosine Phosphorylase (MTAP) and Cyclin-Dependent Kinase Inhibitor 2A (CDKN2A), but their use in prognostication of PM is not fully understood.
The aim of this thesis is to further our knowledge of benign asbestos pleural inflammation and early mesothelioma. The introduction of this thesis outlines the current knowledge of the pathophysiology, clinical characteristics and diagnostics of benign pleural inflammation and PM.
Chapter 2 describes the Meso-ORIGINS Feasibility Study which includes a prospective study to explore the feasibility of a surveillance protocol for patients with asbestos associated pleural inflammation (AAPI), including repeat pleural sampling. This chapter also describes a retrospective study carried out to determine more precisely the rate of evolution of AAPI to PM, and to identify baseline predictors of PM evolution. 296 AAPI patients (39 prospective, 257 retrospective) were recruited/selected. 21/39 prospective recruits were histologically diagnosed (target n=27). Repeat LAT was technically feasible and acceptable in 13/28(46%) and 24/36(67%) cases with complete follow-up data. Mesothelioma evolution was confirmed histologically in 36/257 retrospective cases (14%(95%CI 10.3-18.8) and associated with malignant CT features (OR 4.78(95% CI 2.36-9.86) and age (OR 1.06(95% CI 1.02-1.12).
Chapter 3 describes a systematic review and meta-analysis which aims to define the true rate of PM evolution from benign pleural inflammation, and identify any characteristics that may give rise to a higher risk of PM evolution. 17/265 identified studies were included, describing 2607 NSP cases and 146 PM evolutions. The summary point estimate of PM evolution was 5.44%(95% CI 3.37- 7.51), with significant heterogeneity (p<0.001, I² 82.7%). Higher PM evolution rate was associated with ≥50% asbestos exposure by cohort and high PM incidence settings. Lower evolution rate was associated with surgical NSP biopsies.
Chapter 4 describes a retrospective cohort study performed to evaluate the prognostic utility of CDKN2A and BAP1 testing in PM and benign AAPI. 155 PM cases were included and CDKN2A and BAP1 loss were observed in in 63.2% and 57.4%, respectively. Stage I prevalence was high (60.7%). PM median OS was 12.4 months (95% CI: 11.2-15.9). Adverse OS was associated with non-epithelioid histology, higher performance status and later stage. CKDN2A/BAP1 status was not associated with OS, including by histological subtype or when both genes were lost. Among 42 eligible AAPI cases, CDKN2A and BAP1 loss were observed in 1/42 (2%) and 2/39 (5%), respectively. 28/42 cases died during follow-up; 7/28 (25%) had post-mortem examinations. No PM evolutions were observed.
The findings in these chapters have been essential in the development and delivery of the Meso-ORIGINS study, which forms a major part of the PREDICTMeso International Accelerator. We have emphasised the importance of longitudinal tissue sampling spanning the period preceding PM development and the use of multi-omic testing to define the biological processes driving PM evolution and survival once PM is established
An essay on price impact: how limit order book events and order flow affect price formation
Get PDFThis thesis studies the price impact of limit order book events and their effects on price discovery. It consists of three independent essays that begin with examining the relationship between the shape of the limit order book and price impact.
Chapter 1 introduces the slope of the limit order book as a novel measure of price impact. By analyzing both the bid and ask sides of the high-frequency limit order book snapshot data, the study shows that there is a linear relationship between the cumulative size of liquidity and price impact in the limit order book. In addition, I find that if price impact admits a nonlinear functional form, under certain circumstances, a profitable round-trip arbitrage exists. I empirically show the minimum required trading volume for a profitable self-financing arbitrage and conditions that limit arbitrage.
Chapter 2 proposes a new approach to estimate the flow of this information and the price of that information (different from the stock price), and thus the total value of that information for each stock, and then sum up this value across all stocks, obtaining an estimate of the total value of the dynamic flow of information in the stock market as a whole. The results support the notion that the cross-correlation of price impact across stocks is consistent with the CAPM: there is a single systematic component of price impact, and this is driven by the volatility of the systematic component of the stock market. This result suggests that by separating the underlying information into two components, systematic and idiosyncratic, informed traders distinguish between productive assets that have a systematic impact on the economy and those that can be diversified.
Chapter 3 presents a two-period model of strategic interactions between a spoofer and a highfrequency trader (HFT) who employs pattern recognition algorithms to predict the incoming order. Detecting this strategy, the spoofer submits a spoofing order to mislead the HFT trader about the incoming order. The HFT protects itself by reducing its market participation. A pure strategy spoofing equilibrium exists and both spoofer and HFT make positive profits. It is shown that while spoofing delays price discovery in the short run, price dislocation will be so brief as to have few economic efficiency implications. Moreover, spoofing improves market liquidity and market welfare
Sovereign debt restructuring: towards a unified international legal framework?
No full textAbstract not currently available
Cultivating geographies: indigeneity, development planning, and decoloniality in Malawi’s agriculture
No full textAbstract not currently available
Investigating radiotherapy in combination with immune modulation in preclinical mouse models of rectal cancer
No full textAbstract not currently available
Structural and functional characterisation of myocardialscar interactions to predict ventricular arrhythmias
No full textAbstract not currently available
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