Vibrational spectroscopy with machine learning for accurate cancer detection

Cancer remains a global health crisis, significantly impacting individuals and societies worldwide. In 2020, approximately 19.3 million new cancer cases and 10 million cancer-related deaths were reported globally. Screening and triaging are crucial in the early detection, diagnosis, and management of cancer, targeting different stages to improve patient outcomes. Despite being one of the leading causes of mortality, many cancers lack effective screening methods. While conventional screening techniques are available for some cancers, they have varying accuracy and limitations. Identifying cancer or precancerous conditions early can significantly reduce mortality and enhance treatment outcomes. The analysis of biofluids to detect cancer-related signals—liquid biopsy, has garnered considerable attention over the past decade. Although promising, many current liquid biopsies lack the sensitivity needed for early-stage cancer detection. Raman spectroscopy (RS) is a non-destructive, real-time technique for molecular analysis. Our study investigated the impact of optimising selected parameters and assessed various spectral processing methods on the reliability and accuracy of spectral analyses, and demonstrated that manual extension of the sampled volume significantly enhanced the detection of low-concentration cancer biomolecules, improving spectral resolution in half the measurement time compared to conventional settings. Additionally, we examined chemical changes associated with acquired radioresistance in HR+ and HR− breast cancer cell lines. Combining RS with machine learning, we achieved high accuracy in distinguishing between parental cell lines and their radioresistant phenotypes, regardless of hormonal status. The radioresistant phenotypes exhibited similar difference spectra and formed a single cluster, suggesting common biochemical changes during the acquisition of radioresistance. We also integrated RS with advanced machine learning techniques for accurate cancer detection in blood plasma, using both liquid and dried samples. Our results showed high sensitivity and specificity in classifying stage Ia breast cancer, with an Area Under the Curve (AUC) of 1.00. Hierarchical clustering validated the reproducibility of our results. This research highlights the potential of combining vibrational spectroscopy with AI for cost-effective, non-invasive, and personalised early cancer detection, emphasising the need for standardised protocols and robust data processing techniques to facilitate clinical translation in liquid biopsy applications

Real-time edge processing of neural signals with memristive technologies

Intracranial brain-computer interfaces, capable of real-time neural activity decoding, present a revolutionary opportunity to improve the quality of life of individuals with dysfunction or damage to the nervous system. Despite recent advancements in neural recording, neuroprosthetic technologies still face bottlenecks in data processing and transmission. Effective neuro-prosthetic devices must deliver enhanced performance metrics including high accuracy, low-power, small size, and minimal latency to enable continuous and real-time brain interfacing. Memristive technologies are promising candidates, acting as bioelectronic links that integrate biosensing with computation for brain-inspired architectures, and operating at low power levels. Memristive devices are two-terminal electronic components that reversibly and gradually adjust conductance in response to electrical stimuli, with their memory state depending on the thresholded integral of the input voltage. Acting as integrating sensors, they suppress noise and encode signal amplitude and frequency within their resistive state when biased with suitable preamplified neuronal signals. This behaviour similar to biological synapses offers a novel solution for processing strategies in brain-computer interfaces. A memristor-based platform for detecting action potentials (APs) -- the fundamental units of communication between neurons and a well-established indicator of brain activity -- has already demonstrated promising results in the literature. This doctoral research proposes a memristor-based processing platform for real-time decoding of neural signals, with a focus on population-level activity rather than single-neuron action potential (AP) detection. In many clinical or assistive applications, such as state monitoring or rehabilitation relying on fine-grained single-neuron activity is not necessary. Instead, larger scale population-level dynamics provide more robust and stable biomarkers. Moreover, relying on these signals offers energy efficiency benefits due to their reduced bandwidth requirements. Specifically, local field potentials (LFPs) were used, as they provide greater spatial coverage and temporal stability by capturing collective synaptic activity. LFPs recorded in vivo from the ventral tegmental area of awake rats performing associative memory tasks were applied to TiOx-based non-volatile memristors, significantly reducing processing power. The system achieved real-time biomarker detection with over 98% accuracy and power consumption as low as 4.14 nW per channel—up to 100× lower than comparable state-of-the-art methods, at similar accuracy levels. This memristor-based protocol was then extended to process the envelope of multi-unit activity (eMUA), a more recently explored neural signal that also reflects population dynamics but enables earlier biomarker detection and reduced inter-channel correlation—key for real-time prosthetic control. With over 95% detection accuracy and ~9 nW power consumption, the approach was validated across different metal-oxide memristor stacks, confirming the platform-agnostic applicability of the MIS method. The integration of MIS with ultra-low-power front-end analogue circuitry showed a 30× reduction in power demand compared to majority of state of the art front-end chips, achieving sub-μW consumption and projecting up to 10× improvement over the most advanced implementations. LFPs emerged as the most power-efficient and reliable neural source in the presented experiments, while eMUA provided a lower-latency alternative better suited to multi-channel applications. As the number of recording channels increases and monolithic integration with CMOS is optimised, this memristor-based strategy is expected to further reduce power consumption per channel while enabling the detection of increasingly complex behavioural states. As a final experiment, given the continued prevalence of action potentials in neural signal processing, a strategy was developed to detect not amplitude-based but frequency-encoded biomarkers from action potential activity. Temporal compression was applied to reduce spiked quantity, while preserving the information needed to distinguish between high- and low-activity brain states—patterns often linked to neurological conditions such as Alzheimer’s disease or stroke. This compression was implemented using volatile metal-oxide memristors, whose intrinsic temporal filtering proved beneficial in identifying regions of high-frequency spiking activity before passing the data to a spiking neural network (SNN) for classification. Once again, neural activity was processed more efficiently and benchmarked against detection accuracy in a clinically relevant in vivo application using anaesthetised rats. These biomarkers were reliably detected using only 10% of the original data, while maintaining an SNN detection accuracy of approximately 97.5%. Overall, this research lays the groundwork for scalable, ultra-low-power systems for chronic neural monitoring and implantable neuro-prosthetic technologies

Regression models for extreme values with random function covariates

A fundamental principle of statistics of extremes is that any realistic quantification of risk requires extrapolating into a distribution’s tail—often beyond the observed extremes in a dataset. Yet, as modern technology advances, an increasing amount of data is recorded continuously or intermittently, and hence the question arises: how to take advantage of such data in an extreme value framework? Motivated by this question, this thesis develops a class of novel statistical methods that can be used for marginal and joint distributions to learn how the extreme values may change according to a functional covariate. The first contribution consists of a functional regression model for the tail index that can be used for assessing how the magnitude of the extremes can change according to a random function. Another contribution of this thesis is the development of a nonparametric regression model that can be regarded as a functional covariate regression method, designed for situations where there is a need to assess how the extremal dependence of a random vector can change according to a functional explanatory variable. Such development is based on modeling a family of angular measures indexed by a random function. The performance of the proposed methodologies is assessed via numerical studies, and financial data is used to illustrate their application

Testing deep neural networks across different computational configurations

Deep Neural Networks (DNNs) typically consist of complex architectures and require enormous processing power. Consequently, developers and researchers use Deep Learning (DL) frameworks to build them (e.g., Keras and PyTorch), apply compiler optimizations to improve their inference time performance (e.g., constant folding and operator fusion), and deploy them on hardware accelerators to parallelize their computations (e.g., GPUs and TPUs). We concisely refer to these aspects as the computational environment of Deep Neural Networks. However, the extent to which the behavior of a DNN model (i.e., output label inference correctness and computation times) is affected when different configurations are selected across the computational environment, is overlooked in the literature. For example, if a DNN model is deployed on two different GPU devices, will it give the same predictions, and how will its computation times deviate across the devices? Given that DNNs are deployed on safety-critical domains (e.g., autonomous driving), it is important to understand the extent to which DNNs are affected by these aspects. For that purpose, we present DeltaNN, a tool that allows DNN model compilation and deployment under different configurations, as well as comparison of model behavior across them. Using DeltaNN, we conducted a set of experiments on widely used Convolutional Neural Network (CNN) models performing image classification. We built these models using different DL frameworks, converted them across different DL framework configurations, compiled on a set of optimizations and deployed on GPU devices of varying capabilities. Our experiments with different configurations led to two main observations: (1) while DNNs typically generate the same predictions across different GPU devices and compiler optimization settings, this is not true when utilizing different DL frameworks, and especially when converting from one DL framework to another (e.g., converting from Keras to PyTorch), a common practice across developers to enable model portability and extensibility; and (2) optimizations are not a panacea of inference time improvement across different devices, as the same optimization strategies that improve execution times on high-end GPUs were found to degrade them when applied on models deployed on low-end GPUs. To mitigate the faults related to the conversion process, we implemented a framework called FetaFix. FetaFix performs automatic fault detection by comparing a number of aspects across the source and the converted target DNN model, such as model parameters, hyperparameters and structure. It then applies a number of fault repair strategies related to these aspects and checks how the converted model performs in comparison to its source counterpart. FetaFix was able to repair 93% of the problematic cases identified by DeltaNN. Finally, we explored the effects of faults present in the target hardware acceleration device code towards DNN model correctness. Inspired by traditional mutation testing, we built MutateNN, a tool that generates DNN model mutants containing target device code faults. We then generated a number of faults in the target device code of numerous CNN models performing classification and evaluated how these models behaved across different hardware acceleration devices. We observed that faults related to conditional operations, as well as drastic changes in arithmetic types, considerably affected model correctness. We conclude that different configurations of computational environment aspects can affect DNN model behavior. Our contributions summarize to (1) an empirical study on how the computational environment affects DNN model behavior, performed by a tool (DeltaNN) implemented specifically for that purpose, (2) a framework (FetaFix) that automatically detects faults related to model input, structure and parameters in converted DNN models across DL frameworks and repairs them, and (3) a utility (MutateNN) that introduces faults in the target code of DNN models associated with deployment on different hardware acceleration devices, and evaluates the effects of these faults on model correctness

Early warning, early action in protracted crises

‘Lightning talk’ presented by Enock Nyakundi (Save the Children) at the Jameel Observatory Community of Practice meeting, Addis Ababa, 13-14 May 202

One location and two entrepreneurial ecosystems? A study in the digital and fashion entrepreneurial ecosystems (ees) in Egypt post the 2011 revolution

The overarching aim of this thesis is studying the processes of digital and fashion EEs in the context of emerging economies. By studying the processes of EEs, this thesis refers to studying the practices undertaken by entrepreneurs when they draw on local resources to produce high impact entrepreneurship (HIE). An EE is a set of interdependent domains that enable high impact entrepreneurship within a particular territory. The significance of this research stems from the scarcity of research studying EEs processes in emerging countries compared to developed economies. An implication of this gap is that questions as to Where do local entrepreneurs in emerging markets extract their resources from? and How do they allocate their resources for the functioning of ecosystems? remain unanswered. Therefore, the research questions of this thesis have been crafted to address such a gap. The three research questions are derived as to 1) What are the characterisations of digital and the fashion human capital, financial capital and market voids in Egypt? 2) What role does social capital (SC) play in mediating the voids for both ecosystems? and 3) What is the impact of the institutional voids on the effective functioning of the digital and fashion EEs? The entrepreneurial ecosystems theory postulates that high growth entrepreneurship requires resourceful domains of a mature financial domain, talent, dense networking, and easy information access. Nevertheless, those set of factors are only appropriate for developed economies while absent for emerging markets that are characterised by labour, financial capital and market voids. Those voids possess institutional arrangements of institutional voids that are fundamentally different from those of the developed economies; a gap which lends credence to studying the local dynamics of entrepreneurial ecosystems in emerging markets. By welding social capital theory with that of institutional voids and entrepreneurial ecosystems, the theoretical framework suggests that an entrepreneur relies on his/her embeddedness within a dense social network of family and friends to fill regional voids. This means that entrepreneurs utilise social capital to access three specific resources of talent, funding, and information when alternative hubs of resources are scarce or absent. To answer the questions of this thesis, the researcher adopted a qualitative method by in-depth interviewing a total of 41 entrepreneurs divided between 31 digital and 10 fashion entrepreneurs operating in Egypt. Findings suggest that there are two industry-based ecosystems operating separately and that they are at their different growth stages within the same region. This is because both ecosystems varied in terms of the constituents of their voids and their reliance on social capital for funding and recruitment which impacted their growth cycles. Findings revealed that the digital ecosystem, though operating in a region of institutional void, is surprisingly and unexpectedly, operating strongly. Moreover, it is at its stage of growth to maturity compared to the embryonic fashion ecosystem. The digital EE is growing led by an active sector of incubators and accelerators, supportive policy intervention of ‘Digital Egypt’ and entrepreneur-led networking events. Also, active attempts by the digital entrepreneurs themselves such as self-education has helped in mediating the scarcity and the pricy digital talent whereas they resisted social capital for cultural reasons. On the other hand, the fashion entrepreneurs relied on social capital for funding and recruitment, as well as their efforts to mediate the severity of the voids of the fashion EE. However, lacking policy intervention, lacking awareness of incubators in business, resisting networking, and resisting access to external credit hampered growth attempts of the fashion ecosystem. Implications of research findings are that there is a desperate need for a strategic intervention to support creative ecosystems. An intervention could be through the financial inclusion of creative industries to acknowledge artwork as a type of business eligible for bank credit. Also, offering dual degrees within business education in schools of design and arts could perhaps raise awareness amongst the fashion entrepreneurs on the role of incubators for their business scale-ups. This could help fashion entrepreneurs grow, expand and diversify their operations, as well as improve their ecosystem functioning. Acknowledging that creative industries in the UK accounted for billions of pounds in revenue, this would contribute to the growth of the Egyptian economy

The impact of Thiosulfate sulfurtransferase (TST) on metabolic dysfunction-associated steatotic liver disease (MASLD) and the metabolic benefits of calorie restriction

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects 20-30% of adults in western countries and is closely linked to obesity and type 2 diabetes. Hydrogen sulfide (H₂S), once solely perceived as toxic, is now recognised for its role in various physiological and pathological processes. H₂S donors have shown promise in treating fatty liver disease and reducing blood pressure in animal models, but their therapeutic use is hindered by challenges in H₂S pharmacokinetics. The sulphur oxidation pathway (SOP), which regulates H₂S levels through its disposal, has been underexplored as a potential route to therapeutic H₂S elevation. Thiosulfate sulfurtransferase (TST), a mitochondrial enzyme, is part of the SOP and metabolises H2S, indirectly, to prevent toxicity. Previous work leading up to this thesis showed that TST mRNA levels were upregulated during the early steatosis stage of MASLD in humans. Given the previously identified metabolic protective effect of adipose tissue TST elevation, I hypothesised that elevation of hepatic TST in early MASLD was a protective mechanism, counteracting declining liver function in MASLD. Improved metabolic health following calorie restriction (CR) is mediated in part through increased hepatic production of H₂S. Tst⁻/⁻ mice exhibited elevated systemic H₂S levels, therefore I hypothesised they may have an enhanced response to CR. In chapters 3 and 4, I tested the hypothesis that elevated hepatic TST expression in MASLD offered protection against MASLD development using a liver-specific overexpression mouse model (Liv_hTST). Male and female C57BL/6J and Liv_hTST mice were fed either a control diet or MASLD-inducing GAN diet for 20 weeks. Systemic and hepatic sulfide levels were measured, fat and lean mass assessed, and glucose tolerance evaluated. In vitro, HepaRG cells with TST overexpression were tested for lipid accumulation, oxidative stress, and mitochondrial function. Results showed sex-specific effects on sulfide levels and glucose tolerance, with protective effects against fibrosis in male mice in vivo, and a worsening of the impaired lipid metabolism and mitochondrial function in vitro. This research addresses the gap in understanding of the protective role ascribed to elevated TST expression against steatogenic liver changes in MASLD and revealed novel sex-specific effects on systemic sulfide levels, glucose tolerance, and fibrosis. In chapter 5, I investigated whether Tst⁻/⁻ mice experienced enhanced metabolic benefits from CR due to their elevated systemic sulfide. Ten-week-old male and female C57BL/6J and Tst⁻/⁻ mice underwent 4-week 30% CR. Indirect calorimetry, glucose tolerance, H₂S production, and disposal (SOP) enzyme levels were assessed. Tst⁻/⁻ males had higher systemic but similar hepatic sulfide levels compared to C57BL/6J males, confirming previous work. CR did not affect sulfide levels but improved glucose tolerance in Tst⁻/⁻ males, despite their resistance to fat mass loss. Energy expenditure and substrate utilisation were similar between genotypes. Females were unaffected by the lack of TST and had lower levels of hepatic H₂S metabolism enzymes. Our findings suggested mechanisms beyond hepatic sulfide modulation mediate CR benefits. Understanding the novel role of elevated systemic H₂S and TST deficiency in maintaining fat mass and concurrent metabolic benefits with CR may inform H₂S -targeted therapeutic strategies in the future

The evolution of entrepreneurial learning and networks: Thai entrepreneurs in the food and agricultural industries

Learning is a crucial element of the entrepreneurial process. While previous studies have concentrated on how entrepreneurs learn from their own experiences, it is important to note that entrepreneurs also engage with others in a social context. They create entrepreneurial networks to carry out their activities, and within these networks, entrepreneurial learning takes place. Furthermore, entrepreneurial learning and networks are dynamic, evolving throughout the entrepreneurial development process. There is currently a lack of understanding of the entrepreneurial learning process within networks and interactions (Grippa et al., 2009; Scarmozzino et al., 2017), and relatively little literature has examined the changes in networks as entrepreneurial learning takes place (Lefebvre et al., 2015; Secundo et al., 2015). The current research adopts Politis’s (2005) framework on entrepreneurial learning, which examines entrepreneurial learning across three components: context, content and processes. This research focuses on the commercialisation process as a temporal frame to capture the dynamic nature of entrepreneurial learning. It follows a qualitative approach and uses multi-stakeholder semi-structured retrospective interviews with 26 Thai entrepreneurs in the food and agricultural industries and ten officers from three regional Science Parks in Thailand. Data from the interviews are complemented with evidence from the review of Science Park reports and online data. Data analysis is performed in two stages. First, thematic analysis reveals the main themes regarding the learning context, contents, processes and commercialisation process. Second, network diagrams capture the changes in entrepreneurial networks between two learning periods. The findings show that entrepreneurs are involved in four stages of the commercialisation process: idea emergence, product development, business development, and product launch. They passed through these stages in different orders and were involved in entrepreneurial learning differently, depending on their knowledge requirements, existing knowledge base, and products they had developed. Entrepreneurs had different expected outcomes in particular stages, which led them to acquire different knowledge types, including scientific and technological (S&T) knowledge, business knowledge, and market knowledge. The academic-driven product development process was shown to be the most popular S&T knowledge learning process, while training and coaching were used to learn business knowledge. Online discussions with customers were the most used process to acquire market knowledge, especially during the COVID-19 pandemic. Entrepreneurs chose different learning processes because of their own knowledge base, established relationships, and knowledge gaps. Entrepreneurs built and expanded their networks by involving potential knowledge sources in the first learning period. They made changes to their networks between the first and second learning periods, using condensing and expanding network strategies when nonessential relationships were removed and new essential relationship were created, respectively. The evolution of entrepreneurial learning and networks was fostered by changes in entrepreneurial knowledge needs and the existing knowledge base. This research makes significant contributions to entrepreneurial learning research by emphasising the diverse learning processes used by entrepreneurs within a social context. It underscores the complexity of knowledge needed and the involvement of key network partners. Additionally, it explores the evolution of entrepreneurial learning, unveiling the changes in knowledge learned, learning processes, and network partners over time during the commercialisation process. The findings have noteworthy implications for Science Parks in Thailand, emphasising the importance of understanding diverse entrepreneurs’ knowledge needs and the complexity of learning processes. This highlights the need to redesign current support systems in order to enhance the efficacy of the learning processes

Fragmented Governance in Somalia: Understanding Justice and Security through Galkayo and Kismayo

This policy brief examines justice and security in Somalia through the lens of fragmented peace-conflict spaces, drawing on the PeaceRep PA-X Local database analysis by Christine Bell and Laura Wise. Their research highlights how failed national peace processes have led to new sub-national dynamics of peace and conflict, necessitating more nuanced policy responses. Focusing on Kismayo and Galkayo, two key urban centers with distinct political and security landscapes, this brief explores their governance structures, justice mechanisms, and broader regional and transnational influences. Kismayo, under Jubbaland administration, exhibits centralised control with a structured security apparatus, whereas Galkayo remains a contested space requiring ongoing negotiation between Puntland and Galmudug authorities. Both towns also serve as strategic trade corridors, further shaping their justice and security dynamics. This brief, based on research conducted under PeaceRep Somalia and its predecessor, the Conflict Research Programme (CRP), underscores the importance of localised peace and security interventions. By understanding these fragmented spaces, policymakers can develop more effective strategies that go beyond elite-level settlements to foster sustainable stability and governance in Somalia

Breeding for reduced methane emissions in livestock

This project examined the potential reductions in livestock methane emissions through breeding, and the policy levers that could motivate these changes. We explored the technologies that detect and measure methane, manage data and are used in the breeding process and examined their potential availability in Scotland in 2030 and 2045. We also identified the relevant policy levers and behaviour changes and considered what Government, the post-farm market, pre-farm gate actors and farmers can do differently to encourage methane reductions through breeding