University College London

UKRI Gateway to Research · FY 2025 · 2025-04

Age-related macular degeneration (AMD) is a complex disease and the leading cause of blindness in the western world. Over time, changes in the retinal pigment epithelium (RPE) leads to the damage of light-sensing cells in the retina and subsequent central vision loss. The immune system plays an important role in the development of AMD. Ageing and stress cause RPE cell damage and the build-up of debris in and around the RPE, resulting in chronic inflammation and an immune response in the macular region. Investigating disease related events that lead to inflammation in the eye will help us to understand the early events leading to AMD pathology and could allow us to develop new therapies to treat or prevent sight loss. Here, we will develop a new cell culture model to examine the behaviour and interactions of two key cell types involved in AMD, the RPE and immune cells found in the eye, called microglia. To achieve this, we have created induced pluripotent stem cells from patients with AMD and healthy controls. Using these stem cells, we will produce RPE and microglia and study their responses to stresses seen in AMD. Culturing these cells together will also allow us to study the interactions between retinal and immune cells and identify pathways that could be targeted therapeutically. The immune response to inflammation in the eye plays a pivotal role in the development and progression of AMD. This research project will create a new cell culture-based AMD model system, which will provide a wealth of information on inflammation/immune mechanisms in patient cells. The project will help us to better understand early events leading to AMD pathogenesis and potentially identify undiscovered targets that could be manipulated therapeutically. Reducing chronic inflammation and controlling the immune response could help to treat patients with AMD. This research project will also create a novel cell-based model that could be used to develop and test drugs targeting inflammation/immune responses in degenerative diseases. Ultimately this cell system will reduce the number of animal experiments required to identify pathogenic mechanisms and potential treatments, advancing the pathway to clinic for AMD and other immune-based degenerative diseases. Research supervision from experts in IPSC disease modelling, degenerative disease and bioinformatics will ensure that the student is trained to carry out these experiments and understands the significance of developing new human model systems in line with the priorities of the NC3R.

UKRI Gateway to Research · FY 2025 · 2025-04

Summary of the Proposal This proposal explores the transformative effects of Central Bank Digital Currencies (CBDCs) on financial data governance, focusing on ownership, access, and the broader economic implications. The advent of CBDCs introduces significant shifts in data dynamics through two core developments: a redefined structure for personal data ownership and enhanced utility of micro-level, real-time financial data. Objectives and Scope 1. Ownership and Control of Financial Data: CBDCs disrupt traditional data governance by enabling individuals to gain exclusive control over their financial data. Unlike conventional systems where banks mediate data exchanges, CBDCs allow users to store transaction records on personal digital devices while maintaining the credibility assured by central banks. This change poses both opportunities and challenges for individual data monetization and privacy. 2. Impacts of Enhanced Data Availability: The integration of blockchain technology with CBDCs ensures real-time access to high-resolution, micro-level data. These technological advancements provide unprecedented insights into economic behavior and heterogeneity among groups, offering new avenues for innovation and research while demanding robust governance frameworks. Research Approach The proposal aims to synthesize interdisciplinary literature across law, economics, finance, and technology to identify strengths, gaps, and implications for governance frameworks in the emerging CBDC landscape. By analyzing the evolving role of stakeholders---central banks, individuals, and private entities---the research will offer guidance on balancing competing interests, fostering innovation, and mitigating risks. Outcomes 1. A comprehensive literature synthesis identifying gaps and emerging trends in financial data governance. 2. Policy recommendations for equitable and efficient governance models, addressing ownership, privacy, and economic utility. 3. Contributions to scholarly and practical discourse through publications and stakeholder engagement. This synthesis will bridge academic insights with policy development, addressing critical governance challenges posed by CBDCs while shaping future financial systems.

UKRI Gateway to Research · FY 2025 · 2025-04

MINERS aims to drive a global research agenda and partnership network between the UK and Canada to enhance Critical Minerals (CMs) supply chain resilience and sustainability in the UK and globally. It will do this by: building an evidence base for the integrated assessment of complex CMs supply chains; identifying circularity potential along the value chain and potential for reduction of environmental damage; providing clarity over the complex regulatory, ESG and reporting landscape; examining the role of different stakeholders and institutional frameworks; and defining policy levers to shift away from unsustainable mining and industrial practices, resulting in demonstrable advances in knowledge, long-lasting partnerships across academia, industry and government, and transformative policy pathways. In MINERS we adopt a supply chain modelling approach which combines Material Flow Analysis, Monetary Flow Analysis and environmental life cycle assessment to map current complex flows of Critical Minerals, zooming into current linkages between Canada and the UK, and a Further analysis then shows how these aspects oif supply chains can be influenced by the regulatory and ESG landscape. These elements are then combined into a resilience modelling framework that supports the identification of current bottlenecks and key levers for the transition towards circular and resilient CMs supply chains. This goes beyond current criticality assessments to include elements of network and system structure and integrated sustainability assessment to evaluate the ability of the CMs system to recover after exogenous or endogenous shocks. The findings from the resilience assessment are then factored into the design of actionable policy pathways that align with the vision of sustainable and circular CMs supply chains and consolidate efforts to transition towards sustainable mining. The project builds on the collaboration and technical capabilities of the Canadian LUMIT project led by Steven Young at the University of Waterloo and the UK MINERS research team led by University College London Institute for Sustainable Resources, in partnership with the Critical Minerals Intelligence Centre and supported by strong industrial partnerships with stakeholders along the supply chain of CMs including Rio Tinto, Vale, the International Council on Mining and Metals, the Faraday Foundation and, on a consultative basis, the Trade Commissioner for Energy and Mining at the High Commission of Canada to the UK. MINERS aims to build long lasting mutually beneficial networks that help to advance research in circular CMs supply chains and contribute to the development of an evidence base for informing decision-making processes. The main outputs of the project will include detailed open-source material and monetary flow analyses; a resilience assessment framework; a joint UK-Canada database on regulatory and voluntary ESG requirements covering not only extraction but all stages of the life cycle of CMs; and policy guidelines on circular and resilient pathways for the transformation of CMs supply chains.  Two multi-stakeholder workshops will be organised in collaboration with Canadian partners to disseminate the findings from MINERS and promote industry and policy engagement.

UKRI Gateway to Research · FY 2025 · 2025-04

Critical metals such as tellurium (Te), bismuth (Bi), antimony (Sb), and platinum group metals (PGMs) are essential for the green technologies that will underpin the global transition to net zero. However, despite forecast increased demand, the current market value of many critical metals is too low to make their extraction economically viable as the sole product of a mine. Fortunately, Te, Bi, Sb and PGMs are often associated with more abundant metals such as copper (Cu) and gold (Au), and therefore have the potential to be extracted as by-products during processing of the ore. Efficient recovery of these critical metals as by-products can secure future supplies, while at the same time minimising environmental impact through enhanced extraction efficiency. However, we currently have a poor understanding of where within deposits these critical metal by-products are hosted – mineralogically, spatially, and physically – and what controls this distribution and their concentrations, impeding the design of processing techniques to recover these vital resources. The properties of critical metals which determine how they will behave during mineral processing are controlled by the geological processes that concentrate, transport, and deposit these metals in mineral deposits. However, for many deposit types these processes are still unknown. We will investigate the processes controlling critical metal distribution in the Cu and Au deposits of Newmont Lake in British Columbia, Canada, in collaboration with researchers from the University of British Columbia (UBC) and Enduro Metals Corporation, the company which owns the deposits. Newmont Lake deposits contain potential Te, Bi and PGM by-products. Metals in the Newmont Lake deposits were carried by hot, salty water which exsolved from magma. Unlike most deposits of this type which form above subduction zones, where oceanic and continental plates collide, the deposits in British Columbia formed after subduction ceased and these ‘post-subduction’ deposits are often critical metal enriched. We will use a variety of analytical techniques to test three hypotheses for the controls on critical metal enrichment and distribution in the Newmont Lake Cu and Au deposits: Fluids generated by magmas in post-subduction systems are particularly favourable for critical metal transport. Bi-Te melts are important mechanisms for critical metal transport and concentration in post-subduction systems. Exhumation history – how fast an ore deposit is uplifted and eroded – has an important effect on critical metal transport and concentration. These hypotheses are interlinked - the three mechanisms will singularly or collectively control critical metal distribution in these deposits, and hence the suite of recoverable critical metals, the recovery methods, the environmental features of associated wastes, and the economic feasibility of critical metal production. This project will provide mining companies with indicators for different styles of critical metal mineralisation in post-subduction deposits, increase our understanding of post-subduction metal enrichment processes, and provide a framework for Enduro Metals to plan for effective critical metal extraction at Newmont Lake. We will combine our research with that of our UBC collaborators’ Alliance Missions grant to create a model for critical metal enrichment that can be applied to post-subduction deposits worldwide, making exploration more efficient and allowing early planning of processing techniques to extract critical metal by-products, which will help secure supplies. We will also exchange knowledge and skills to establish a long-lasting collaboration between UBC, UCL, and University of Leicester, while training the next generation of critical metals researchers.

UKRI Gateway to Research · FY 2025 · 2025-04

Emergent diseases such as COVID and avian influenza presented formidable challenges to global public health. To survive and spread, pathogens evolve through genetic mutations which allow them to jump from one host to another or develop resistance to antiviral drugs. Traditional methods for identifying these mutations are slow, costly, relying on detection of actual cases to trigger analysis and response efforts. Alternatively, gain of function studies present biosafety concerns and risks. By contrast, computational methods offer a faster, safer and more cost-effective way to detect mutations underlying phenotypes such as increased transmissibility or antiviral resistance(AVR), providing a proactive approach to combating emerging zoonotic threats. While structure-guided machine learning techniques exist for predicting impacts of mutations, they typically handle single-point mutations, whereas often multiple mutations are required for full phenotypes. Additionally, these existing techniques do not yet employ Protein Language Models (pLMs), which have revolutionised our understanding of protein sequences and structures. These models are trained on vast amounts of protein data and encode evolutionary, structural, and functional information. In this project, we will leverage pLM embeddings, structural information and other features to produce two AI-powered predictors reporting the impacts of mutations likely to induce changes in the affinities of host-viral interactions and AVR. We will use the avian influenza virus as our test target. Avian influenza is carried in wild birds but can be transferred into domestic poultry. Recent outbreaks of H5N1 avian influenza have occurred in 67 countries, leading to the loss of over 131 million chickens and drawing attention to avian-to-mammal and potential mammal-to-mammal transmission. Indeed the first documented cases of cow-to-human transmission in the US in April 2024, highlight the growing risk of zoonotic spillover. Additionally, AVR has already been observed for almost all antivirals against influenza, underscoring the need for proactive intervention and improved understanding of the evolutionary potential of this virus. Our project has six aims to address key challenges in understanding and combating emerging avian influenza viruses, giving computational approaches which can then be extended to other pathogens- Develop a computational pipeline for collating all structural data on pathogen strains and host-pathogen interactions.  Build two AI-based predictors exploiting structural/physicochemical properties and pLM Host-viral interaction predictor reporting mutations that facilitate cell entry, replication etc.  AVR predictor for resistance to the licensed antiviral drugs Validate the AI based predictors using surrogate viral systems Sialic acid binding assay using pseudotyped viruses, expressed HA protein and cell-cell fusion assay Polymerase assays using minigenome reporters performed in human cells (viral polymerase with human ANP32 proteins) AVR to antiviral drug by performing surface plasmon resonance experiment and minigenome polymerase assay Establish a diagnostic portal, reporting impacts and thereby aiding in the identification of novel threats  Organise a Southeast Asia stakeholder workshop to demonstrate the diagnostic portal and engage with experts in zoonotic surveillance and AVR. Once validated, the mutations identified will be seamlessly integrated into global avian influenza surveillance programs, particularly in Southeast Asia, to help combat the spread of resistant infections and reduce infection rates in healthcare settings and broader community. Study results will be useful for development of future antiviral therapeutics with molecules that circumvent resistance development. This project benefits from a collaborative partnership between the UK and Malaysia, promoting knowledge sharing, resource exchange, and adoption of best practices.

UKRI Gateway to Research · FY 2025 · 2025-03

Positronium (chemical symbol Ps) is a metastable atomic system composed of an electron bound to a positron. Since the positron is the antiparticle of the electron, this arrangement usually comes to a violent end; namely a flash of annihilation gamma radiation, as the electron and positron annihilate each other. This may make it seem as though Ps atoms cannot be studied in any detail, since they are prone to this self-annihilation process. In fact, this does not happen instantaneously, and depends on exactly how the electron and positron interact. The overlap of the positron and electron wavefunctions is actually quite small, and in the longest-lived state the pair can avoid touching (and thus annihilating) for a relatively long time, apprroximately 142 ns (or 0.142 microseconds). This is not what we humans think is a long time, but for an atom it is, and as a result it is possible to study the properties of Ps atoms before they disappear. This is fortunate, because Ps is a unique atomic system in that its properties embody the simplicity of single-electron (hydrogenic) systems, but also include complicated QED phenomena, such as real and virtual annihilation. A striking example of this is the ground state hyperfine splitting; in hydrogen this energy splitting (1.4 GHz, this is also what gives rise to the famous "21 cm line" used in astronomy) occurs because of the interaction of the electron and proton spins. The same physics occurs in positronium but produces a much larger effect, on the order of 100 GHz, because the positron magnetic moment is much larger than that of the proton. However, the actual hyperfine interval in Ps is closer to 200 GHz, with the additional splitting arising through virtual annihilation effects. Thus, QED processes can have a very profound impact on Ps properties, and therefore precision studies of Ps can be used to test quantum electrodynamics (QED) theory. Such tests can be conducted via optical or microwave spectroscopy, but here we wish to measure the annihilation lifetime with high precision, as this is also something that can be calculated very precisely. To do this we plan to build a large area positronium annihilation lifetime spectrometer that will use a beam of positrons to generate a beam of Ps atoms. By keeping these atoms away from any material objects we can measure how long it takes them to decay via self-annihilation using some large detector arrays. It is expected that we can improve on previous Ps decay measurements using new technological developments, such as trap based positron beams and laser-produced Ps atoms. Moreover, the large detector system will open the door to other measurements that make use of freely moving atoms. Often these atoms can move away from standard gamma-ray detectors and this prevents efficient detection of long-lived atoms (such as atoms in excited states). The new system will allow these kinds of atoms to be probed, not just for lifetime measurements, but also for other types of spectroscopy using lasers or microwave radiation.

UKRI Gateway to Research · FY 2025 · 2025-03

The National Dark Fibre Facility (NDFF) will provide the UK National Research Facility for dark fibre network research. A dark fibre network is a communications network, where it is possible to access and control the network at the optical layer, Layer1 (physical layer) in the seven layer Open Systems Interconnection (OSI) model of communications networks which underpins the internet.  NDFF will provide a fully remotely configurable, flexible and high capacity research facility, building upon the success of the National Dark Fibre Infrastructure Service (NDFIS) and existing NDFF dark fibre networks (2013-2025). This will allow UK universities and their industrial collaborators to develop and demonstrate future networks which require access to or control of the optical layer (OSI Layer1). NDFF will comprise:  1. A dark fibre network of scale sufficient for experiments representative of real-world applications (> 1,000 km). Users will be able to connect their equipment directly to the installed fibres and control the optical layer, allowing experiments on new techniques, such as quantum encoded data, adaptive spectrum slicing and Software Defined Networks (SDN). 2. User experiment areas at multiple access nodes to interface directly with NDFF dark fibre. Interconnection for traffic generation and experiment control will also be possible UK-wide at Layer2 through services such as Janet Netpath and the recently funded JOINER infrastructure. 3. Remotely programmable amplifiers, switches and dispersion compensation modules which will enable the transmission characteristics of the network to be varied and will allow dynamic configuration of the network topology by users.  4. Flexgrid Wavelength Selective Switches (WSS) to split optical channels into separate optical fibres (or merge them into one fibre, enabling research on new utilisation models for the optical spectrum, increasing available logical topologies and allowing concurrent experiments using different optical wavelengths. 5. An integrated mesh connected metro-network to enable experiments with linked network topologies.    6. Interconnection with the JOINER Layer2 network, enabling research into dynamic and intelligent network management with an SDN and Network Function Virtualisation (NFV) research platform for UK researchers, enabling them to upload network policies directly, monitoring and manipulating the optical properties of the network. UK researchers will be able to develop and test networks having optimised latency, traffic grooming, energy consumption or security properties. 7. A distributed processing infrastructure by linking sites that host servers, storage, memory and sensing. This will provide opportunities to study distributed Cloud and Fog infrastructures connected by high capacity reconfigurable optical networks. It will also provide nerve nodes that can perform network analytics offering users a new level of network programmability and adaptation. 8. The ability to test concepts in network security and resilience across all seven OSI model layers, something that is impossible with other networks. This is of particular importance as networks are starting to introduce software control and flexibility at Layer1, creating new security and resilience challenges for network control.   9. Training using dedicated research and technician support. Administration, user interface and dissemination will be the responsibility of a dedicated facility manager. In order to achieve the full potential of the facility, it is crucial to engage with the UK research community and promote the service. NDFF will engage in UK and international meetings and will bring together users at an annual user day. Web-based interfaces with users and potential users will be further developed.

UKRI Gateway to Research · FY 2025 · 2025-03

Urinary tract infections (UTIs) are a pressing issue affecting ~400 million people worldwide each year. In the UK alone, ~6 million antibiotic prescriptions are written annually for UTIs. However, their effectiveness is threatened due to increasing antimicrobial resistance (AMR), already contributing to ~260K UTI-associated deaths globally. Additionally, UTIs have a high recurrence rate (25-30%), requiring repeated antibiotic courses which further exacerbate the AMR crisis. To address this challenge, development of new UTI therapies is crucial, for which understanding the underlying mechanisms of disease is key. Most UTIs start in the bladder and can escalate to more severe conditions if left untreated. Both the body's response to the infection and the characteristics of the bacteria involved play important roles in how UTIs develop and progress. The main bacteria responsible for UTIs are uropathogenic E. coli (UPEC), accounting for ~80% of all community-acquired UTIs. However, the relationship between bacterial virulence, how the body responds, and the severity of the infection is not fully understood. One obstacle to studying UTIs is the limitations of research models. While mice have been valuable, their bladder environment differs from humans in several important ways. Other methods, like using human cell lines or patient-derived cells, also have drawbacks. To address these limitations, we have recently developed a radically new microtissue model called 3D urine-tolerant human urothelium (3D-UHU), which mimics the human urothelium more accurately. This model is unique and allows us, for the first time, to study how E. coli and the human urothelium interact in a tightly controlled environment, providing mechanistic insights difficult to achieve with other models. Our initial published experiments with E. coli-infected 3D-UHU showed a variety of host-pathogen interactions, including invasion, intracellular growth, bacterial expulsion, biofilm formation, barrier disruption, urothelial cell shedding, cytopathicity and cytokine secretion. Notably, UPEC strains provoked different responses compared with non-pathogenic strains derived from the urinary microbiota. In this project, we aim to uncover how E. coli virulence factors affect human urothelial physiology, and in turn how the changes induced correspond to UTI disease severity. Our objectives include (1) using a comprehensive mutant library to create a broad linkage map of UPEC virulence factors in the human cell setting; (2) understanding how pathogen and host respond when they interact at the urothelium interface using a combined bacterial-human transcriptomic approach; and (3) defining the mechanistic interplay between bacterial virulence factor expression in patient-derived E coli isolates, urothelial responses and clinical severity.  Our research is both exciting and timely, leveraging the new availability of the 3D-UHU model, cutting-edge tools to dissect E. coli and urothelial biology, and access to a wide range of E. coli clinical isolates from well-characterised patients. Our project will shed light on UTIs in a human context, uncovering new insights into how bacteria cause infections and how the body responds. Overall, our work has potential for wide impact. Despite being a common problem, UTI research – like many diseases that primarily affect women – has been historically overlooked. By understanding the mechanisms of disease, we hope to develop new drugs that target critical bacterial processes that damage the urothelium, alongside host-directed drugs that dampen problematic urothelial responses. Given the increasing AMR threat, our work has the potential to improve not only outcomes for individuals with UTIs, but more broadly, the treatment of other bacterial infections as well.

UKRI Gateway to Research · FY 2025 · 2025-03

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, with progressive and disabling symptoms. Diagnosis of PD is currently made on clinical grounds as there are no objective tests to diagnose or stage PD. Depending on the experience of the clinician, the rate of misdiagnosis ranges between 5-26%. Although neuroimaging has revolutionised diagnosis and staging of many common neurological diseases, it currently plays a very limited role in the diagnosis of PD. In practice magnetic resonance imaging (MRI) is used qualitatively, mainly to exclude other secondary causes and to help guide the differential diagnosis of other movement disorders diagnosis. Sensitive biomarkers of disease will transform the clinical reporting of MR examinations by providing objective and quantifiable measures which will decrease inter-rater variability and increase the reliability of diagnosis in daily clinical routine, thereby improving patient care. They will be also essential in clinical trials.  Three main MRI biomarkers have been suggested to date: a) iron assessed by susceptibility-weighted imaging or quantitative susceptibility mapping (QSM), b) neuromelanin (NM) assessed by neuromelanin-sensitive sequences and c) brain-volume assessed by 3D-T1 weighted sequences.Translating quantitative techniques into the clinical setting, however, presents significant challenges, which must be addressed to meet the demand for accurate, timely and impactful clinical imaging services. We developed the Quantitative Neuroradiology Initiative (QNI), as a model framework for the technical and clinical validation necessary to embed automated segmentation and other image quantification software into the clinical neuroradiology workflow. We have 2 exemplar tools: a) brain volume, commercialised through a startup company (Brainminer) and b) Quantitative Neuroradiology report for epilepsy patients with hippocampal sclerosis, which has been successfully introduced into the clinical routine. Our position has been further strengthened by a strategic partnership with Siemens Healthineers to accelerate the adoption of quantitative neuroradiological innovations into healthcare practice. Based on this experience, the aim of this proposal is to develop a platform for clinical Quantitative Neuroradiological MR Reports for PD patients.  To establish such a report, a time-efficient MR protocol must be developed to ensure maximal patient compliance, and minimal head motion related artefacts. Current QSM, NM-sensitive and 3D-T1 sequences require relatively long acquisition times, resulting in image degradation in a large proportion of clinical scans. Novel acceleration and image-enhancing AI techniques have the potential to significantly reduce acquisition times, and therefore improve image quality by reducing (and ideally eliminating) motion artefacts. In addition, QSM and NM-sensitive imaging usually require complex processing and analysis that is usually only possible in the research setting. This initiative will bring these advanced techniques into clinical pipelines for the benefit of patients.  Over a period of 18 months, we will enrol a total of 70 subjects: a) 20 patients with early PD, b) 20 patients with late PD, and c) 20 age-matched healthy volunteers. In preparation for the main part of the study, imaging protocol development and testing will be performed on 10 healthy volunteers. Scanning will be performed on a 3T scanner equipped with the latest software platform which includes cutting-edge acceleration methods, as well as machine learning-based image reconstruction technology. This study will deliver time-efficient, quantitative sequences and the corresponding automated analysis pipelines for all 3 biomarkers. In combination with the acquired reference data, these will form the basis for the prototype platform for a PD-relevant automated clinical Quantitative Neuroradiological MR Report.

UKRI Gateway to Research · FY 2025 · 2025-03

In recent years, the utilization of data from randomized control trials (RCTs) or quasi-random settings has profoundly influenced economic research and policymaking. Program evaluation techniques, which analyze such data, play a crucial role.  At a high level, program evaluation identifies individuals exposed to an intervention, either randomly or quasi-randomly, and compares them to the remaining population to discern the causal effect of the policy.  For example, in late 2007, the government of Rio de Janeiro randomly assigned around 24,000 children to 10,000 available slots for free public daycare centres.  By comparing children who won the lottery to those who lost it within a given daycare center, researchers evaluated the effect of publicly provided daycare on children's outcomes (Attanasio et al., 2022). However, traditional program evaluation methods were developed under the simplifying assumption that the effect is uniform across all individuals (Angrist and Pischke, 2008).  My pilot work revealed significant biases in these methods, particularly when policies have diverse effects on different individuals.  For instance, my pilot work revealed that traditional methods could potentially inflate the impact of an unexpected hospitalization on medical spending and labor market outcomes by up to 10%.  Recognizing these shortcomings, the primary goal of my research is to develop program evaluation methods that account for varying and heterogeneous effects, especially in the context of big data encountered in RCTs. Quantifying heterogeneity across individuals with big data presents challenges due to the abundance of unstructured data like images, video, and text.  Advanced econometric and machine learning (ML) techniques are necessary for preliminary analysis such as random forest algorithm in Wager and Athey (2018).  The challenge lies in integrating these approaches to credibly estimate the overall effect.  Once credible estimates are obtained, another challenge arises: how to inform policymakers about which individuals to prioritize intervention for, considering diverse effects and resource constraints. Therefore my project’s overarching goal is to advance knowledge frontiers on the two challenges outlined above in two phases. In the first phase, I will introduce a novel estimation method for the overall effect.  Traditionally, averaging imprecisely estimated individual effects obtained from ML while allowing for completely arbitrary variation across individuals, results in an inaccurate estimate for the overall effect.  I develop an innovative framework that employs a bound on the variation of effects across individuals, and reanalyze past influential RCTs to uncover more precise impact estimates. In the second phase, I will develop a method to inform policymakers about the optimal allocation of treatment.  Naively prioritizing individuals based on imprecisely estimated effects obtained from ML could result in an inaccurate treatment allocation scheme, far from optimal.  The reason is that ML focuses on prediction, and I will adjust ML techniques for estimating the optimal allocation instead. Potential applications include reanalysis of past influential RCTs to uncover more precise impact estimates and to design optimal treatment allocation strategies.  These applications cover a wide range of sectors, including development economics (Attanasio et al., 2022) and healthcare (Finkelstein et al, 2012).  I will develop user-friendly software packages so that policymakers and applied researchers can use the proposed methods even more easily.  By the end of the project the results from my project will shed new light on how make more cost-effective and evidence-based policies, benefiting the broader community.

UKRI Gateway to Research · FY 2025 · 2025-03

Social Challenge: Lymphoedema is chronic, debilitating, and incurable, causing excessive swelling in affected areas. Considered a “silent” pandemic, and significant global health problem which lowers a patients quality of life. It is a common consequence and lifelong risk of certain cancer treatments with huge lifelong physical, psychological, and socioeconomic burdens. Self-managing lymphoedema and independent living is a daily challenge for most patients; treatment requires meticulous daily management and compression strategies. Relying heavily on travelling to a clinic, several times a week; using devices and services that are non-portable, heavy, expensive, and uncomfortable. Affecting ~250 million people worldwide; ~70% of lymphoedema patients do not receive the necessary treatment critically needed, causing complications costly for healthcare services globally. Innovation: "LymphMotion" is a lightweight, wearable Med-Tech garment that allows patients to self-manage lymphoedema more sustainably while alleviating pain. Aim: 1) Optimization of the existing proof-of-concept prototype, 2) De-risk LymphMotion 3) increase awareness of lymphoedema to the wider public via public engagement activities.

UKRI Gateway to Research · FY 2025 · 2025-03

Most everyday plastic materials are made of polymers produced from petroleum. The growing global market for polymers (sales turnover of over £25 billion in the UK in 2022) also generates a vast amount of waste: predictions are that 500 million tons of plastic residues will be dumped in the ocean between 2016 and 2040 if no mitigation strategies are implemented. This challenges our ability to meet the demands for polymeric materials while minimising waste production. There is therefore an urgent unmet need for new circular and sustainable polymers that fulfil the current role of plastics, while allowing the UK to meet the targets set in the Net Zero strategy. To that end, Nature is a great source of inspiration: it uses biopolymers to self-assemble structural materials for a wide range of mechanical and structural requirements. Specifically, structural proteins (e.g., elastin, collagen, keratin, or silk) are a very appealing class of biopolymers due to their sustainability, lightweight, stimuli responsiveness, easy processability, degradability, and tuneable structural or mechanical properties. Structural proteins are normally harvested from animal sources, but these sources suffer from batch-to-batch variability, presence of contaminants, and cultural or religious concerns that limit their commercial viability. Fortunately, recent developments in engineering biology (e.g., in recombinant DNA technology or bioprocess engineering) allow us to overcome these issues and biomanufacture non-animal-derived structural proteins. Using recombinant DNA technology also means that we are no longer constrained to working only with structural proteins selected for by evolution. We can instead engineer new recombinant structural proteins that merge building blocks from multiple natural proteins into a single biopolymer chain. This fusion approach is very powerful, because it allows us to design new smart materials that simultaneously display functions from dissimilar natural proteins. However, the collective behaviour of different building blocks cannot be inferred from the properties of individual ordered/disordered, hydrophobic/hydrophilic, charged/uncharged, or structural/functional blocks. This makes it difficult to predict how new protein sequences will define the properties of the materials produced from them. Thus, to date protein-based materials are mainly researched using low-throughput trial-and-error experimentation, which impedes rapid development and prototyping. It would be desirable to have predictive tools that speed up the exploration of the vast design space of new recombinant proteins, by connecting new protein sequences with material mechanical or structural properties. Building on own feasibility results, we will apply a combined experimental-computational approach to develop such predictive tools. We will focus on biopolymers inspired by natural structural proteins that fuse various structural and functional building blocks to achieve a range of mechanical/structural properties. These proteins will contain elastin-like or resilin-like building blocks because their thermoresponsiveness enables a highly scalable and simple purification process for these proteins, compared to standard purification techniques such as affinity chromatography. At the intersection between computation, materials, biotechnology, and bioprocess engineering, the objective of this project is to deliver a predictive toolbox that uses computational modelling to accelerate design-build-test-learn cycles for protein-based biopolymers that replace fossil-derived polymers. To develop such predictive tools, we will (i) generate computational datasets by performing molecular dynamics (MD) simulations of new structural proteins; (ii) obtain experimental data by biomanufacturing those proteins and analysing the mechanical/structural properties of materials produced from them; and (iii) developing predictive models by exploring the mathematical links between the datasets obtained from MD simulations and the experimental material characterisation.

UKRI Gateway to Research · FY 2025 · 2025-03

This application proposes to build BHive+, an integrated software ecosystem dedicated for systems immunology of antibodies and B-cells. Antibodies, produced by specialised immune cells known as B-cells, play a crucial role in our immune response by neutralising pathogens and recruiting effector cells to clear these pathogens, thereby protecting us from infections. They can also act as receptors on the B-cell surface to detect target molecules (“antigens”). We have previously generated a user-friendly webserver (BRepertoire) for statistical analyses of large-scale antibody sequence “repertoires” to understand how diverse antibody responses are achieved across individuals facing different immune challenges. This has further been complemented by a suite of computational tools (sciCSR, BrepPhylo, BrepConvert) we developed for detailed analyses of how these antibody repertoires were developed in vivo, as well as the dynamics of B-cell maturation. Whilst these tools, together forming the collection “BHive”, tackle specific aspects of B-cell and antibody-related data analysis, the immunology community has unmet data analysis needs on the annotation of immunoglobulin sequences, structures, and B-cell subtypes. The community also lacks easy-to-use software tools to extract novel insights from high-dimensional data, and apply cutting-edge artificial intelligence and machine learning methods to devise B-cell therapies and the design of antibody therpaeutics. This is particularly timely as the COVID-19 pandemic and recent novel approaches in cancer immunotherapy demonstrate that therapeutic antibodies are a vital pharmaceutical resource, with thousands in development, primarily IgG1, but none IgA or IgE. Understanding the functions of different classes of antibodies could expand their potential utility, such as IgE for cancer therapies or IgA for treating mucosal disorders in the airways and the gut. In this programme, we aim to leverage our expertise in bioinformatics and immunology to address gaps in the analysis of single-cell transcriptomics and immunophenotyping of B-cells, as well as understanding of the structure-function relationship of antibodies. Addressing the unmet needs of different user communities, we plan to create: A specialized, unified data repository (BrepData) clarifying annotations of antibodies and B-cells, facilitating experimental and computational immunologists. A new package large-scale B-cell cytometry data analysis (BrepPheno) to facilitate cellular immunologists to characterise B-cell subsets. Software for structural biologists to model full-length antibody structures and their energetic analyses (BrepPoses). Software to democratisie the application of state-of-the-art protein language models in antibody engineering and specificity prediction (BrepML) to antibody discovery scientists. An integrated software ecosystem, BHive+, featuring these new implementations and the existing BHive tools for all the aforementioned user communities. All proposed additions enable new data analyses in B-cell immunology and antibody science which are challenging without significant domain knowledge and expertise. Our software will be provided as both graphical user interfaces and standalone R packages. User support will be provided via documentation and code notebooks oriented to immunologists, face-to-face workshops and an online community promoting user inputs in software updates and the creation of new use-cases focusing on community need. We foresee, through this programme, BHive+ will become a central hub of computational resources dedicated to B-cell immunology, enabling multimodal data analysis for B-cell and antibody-related research, and establishing itself as a “one-stop shop” for B-cell systems immunology.

UKRI Gateway to Research · FY 2025 · 2025-03

Context/Challenge: Cancer which originates from the liver (known as hepatocellular carcinoma/HCC) is the second commonest cause of cancer death worldwide and is becoming more common. The only curative treatment is surgery, but most patients are diagnosed with advanced disease which cannot be treated by surgery. Standard chemotherapy is ineffective, so patients are treated with immune-activating antibody drugs. However, most patients die within a year. Chimeric antigen receptor T-cell therapy (CAR-T for short) is a new kind of treatment where a patient’s immune cells (T-cells) are harvested from their blood and genetically engineered so that they recognize cancer cells. In blood cancers such as lymphoma, CAR-T is effective even in cancers resistant to chemotherapy. About 40% of patients have long-term complete remissions. Research efforts have recently focussed on replicating this success in solid cancers like HCC. CAR-T cells need a target on the cancer cell surface which can distinguish it from normal cells. HCC has a good target called Glypican-3(GPC3 for short). Early clinical studies of GPC3-CAR-T-cells have shown promising results: CAR-T appears safe, and some patients had a reduction in their tumours. However, unlike with lymphoma, patients did not have lasting responses. We wish to build on this work with a three-pronged strategy to generate an effective  CAR-T-cell therapy for HCC. Aims and objectives: The aim of this application is to fully safety-check our HCC-targeting CAR-T approach to make it as safe and effective as possible before we apply for funding for an early-phase clinical trial. Our approach encompasses the following: Better targeting of GPC3: The target GPC3 is a protein found on the surface of HCC cancer cells. GPC3 gets partially digested, so part of this protein falls away becoming soluble in the blood and around the HCC. Current CARs target this part of the GPC3 which means CAR-T cells can be “blocked” by soluble fragments of GPC3. Our particular CAR targets the portion of GPC3 which remains attached to the cell after digestion so cannot be blocked by soluble GPC3. The outstanding task for our new CAR is to perform safety testing by a technique called 'tissue-cross' to ensure it only targets HCC and not other normal cells in the body. Tumour environment: Solid cancers like HCC surround themselves with cells and immune-hormones(cytokines) which shield the cancer from CAR-T. This is called the tumour microenvironment(TME for short). Two features of the HCC TME are the cytokine TGFß and immunoregulatory NK-cells. We will engineer CAR-T cells with two additional modifications(dTBRII & IL15) to protect them from being 'switched off' by the TME. Combining with Radiotherapy (X-ray-therapy): CAR-T works better with smaller amounts of disease. Radiotherapy can temporarily shrink HCC tumours and may also help CAR-T cells enter the tumour. We will test a number of new ways to use radiotherapy with CAR-T cells to find the best HCC cancer-targeting combination. Potential applications and benefits: If our project is successful, we will take our best CAR-T approach and apply for funding for an early-phase clinical trial in patients. If a future clinical study demonstrates safety and some patients respond, we will extend the study to better understand how well the treatment works. If a proportion of patients have complete and lasting remissions, we will work with pharmaceutical companies to bring this treatment to NHS patients.

UKRI Gateway to Research · FY 2025 · 2025-03

Advanced therapies, notably those based on cell therapy or tissue-engineered medicinal products, have the potential to cure disease by addressing the root cause of the condition rather than treating the disease symptomatically. Such therapies often rely on centralized manufacturing and global distribution, requiring a durable supply chain that does not risk product functionality. We have developed a novel implantable microcarrier technology that enables transplantation of anchorage dependent cells in a more optimal anchored state. The microcarriers alone have undergone human clinical safety testing and are now being investigated for delivery of fresh autologous skeletal muscle derived cells for treatment of faecal incontinence (FI) as part of a multinational Phase I/IIb clinical study in patients with obstetric trauma-induced FI. The fresh tissue engineered combination product has a short shelf-life and supply chain logistics have highlighted significant risks caused by bottlenecks associated with Customs clearance, QP release, and matching day-of-delivery with end-user clinic schedules. To address the challenges associated with delivery of a fresh product, the project aims to verify use of cold-chain product supply for our planned Phase II study. Specifically, the objectives of the project are: Verify drug product integrity at different stages of the cold chain (cryopreservation, storage, transportation, and thawing); Demonstrate the proposed cold chain is not detrimental to the phenotypic traits and potency of SMDC and allows for adequate in-use shelf life when thawed. Manufacturing cells attached to implantable microcarriers and subsequent cryopreservation will increase the shelf-life of product and offer a ‘one-stop’ solution to manufacturing and shipping bottlenecks that exist, providing confidence for the clinical end-user. Our approach will disrupt current cell therapy manufacturing and remove just-in-time supply constraints associated with fresh products. A successful outcome will provide a ready to integrate cGMP process and associated technical development data for filing with the IMPD supporting its’ use in the next Phase of clinical testing for FI and in due course other clinical conditions, enabling the product to be shipped, received, and handled at the clinical sites in a robust and cost-effective manner, while being easy for end-users to adopt, ensuring safety, product quality, and stability.

UKRI Gateway to Research · FY 2025 · 2025-03

Quantitative impact evaluations – studies which measure the effects of policy – play a central role in government decision-making. In the UK, government departments conduct and commission hundreds of evaluations each year, and impact evaluations are highly integrated into the policymaking process, with many billions of pounds of public funds resting on the results of such studies. It is therefore concerning that, as indicated by recent metascience research, the results of many quantitative analyses in academia appear to be very sensitive to analysis decisions made by researchers. A researcher conducting a quantitative analysis must make a vast array of decisions, such as which data to use, how to measure outcomes, which model to estimate, and so on. As a result, when different academic research teams test the same hypotheses, using the same datasets, they often produce very different results. The potential consequences of this problem for impact evaluations of government policy are profound, as it implies that policymakers cannot be sure that the information taken from any single analysis is correct. However, while this issue has been documented in several academic disciplines, work examining whether the problem of analysis-dependent results affects quantitative evaluations of government interventions is virtually non-existent. In the absence of studies which assess the scale of this problem for policy-relevant research, policymakers cannot know whether the impact evaluations upon which they base key spending decisions are robust. We address this issue by uniting a team of academics with expertise in quantitative impact evaluation methods and UK civil servants who have responsibility for improving the quality of evaluation practice in government. We aim to quantify the degree to which the results of impact evaluations vary when researchers use different, but defensible, analysis choices. Our research is divided in three parts. First, will employ a “many-teams” analysis, in which we will recruit a large number of independent research teams to reanalyse data from existing impact evaluations. Second, we will use a survey-based analysis to solicit analysis choices relevant to existing evaluations from a larger set of independent researchers. Third, we will conduct a “multiverse” analysis to simulate the full universe of possible analysis choices for a given evaluation and investigate how these choices affect estimates of policy impact. Our project will generate significant new evidence on the robustness of quantitative research findings which we will use to directly inform evaluation practice across government. Our research will reveal whether government evaluations, like academic studies, are analysis-dependent, and what the range of possible estimates of policy impact is. We will also investigate whether analysis-dependence is ameliorated by using different research designs, an important factor ignored in previous literature. Together, this research will substantially increase the available evidence on the integrity and rigour of existing quantitative research findings. This research will also enable us to provide tangible recommendations for evaluators in government and the broader evaluation community. We will translate our findings into guidance on how to mitigate analysis-dependence, which will be incorporated into the Magenta Book, the key resource for evaluators across UK government. This guidance will therefore directly contribute to the integrity of research findings used to inform policy and spending decisions.

UKRI Gateway to Research · FY 2025 · 2025-03

Context and Topic Catalysing the co-creation of environments that support positive research culture is now a central concern across UK higher education, with universities devoting effort and resources to improving research culture, not least in preparation for REF2029. But how can universities foster positive change in view of the significant diversity of practices and understandings of research culture in different parts of the research community? And how can this diversity be harnessed in universities’ efforts to develop robust research culture strategies? The project builds on prior work showing that research cultures are embedded diversely in highly localized social structures, relations and histories of particular research communities. Therefore, efforts to foster positive research culture that focus on institution-wide structures and processes (what we call ‘processual research culture’) must be complemented by a close understanding of and engagement with the rich and varied tapestry of social and cultural dynamics through which research gets done on the ground (‘relational research culture’). Closing the loop between policy and implementation, we use ethnographic engagements to develop tools and methods that embed UKRI’s research culture framework in these localized settings, to gain meaningful traction on ground-level conduct and management of research. Aims and Objectives Based on ethnographic research in four diverse universities, the project has three research objectives: Explore the relationship between institutional research culture initiatives and diverse everyday cultures of research experienced in different parts and levels of four institutions, identifying and mapping points of traction, friction and tension. This will involve Research Cultures Maps, tracking the concept of Research Culture as it filters through the four institutions, from HE sector-wide decision-making bodies (e.g. UKRI, UUK, RE), through university management structures, and down to ground-level research communities in a selected academic department/institute in each HEI. Taking preparations for REF2029 as the empirical focus, we use organizational ethnography to map how institutional research culture strategies articulate with everyday cultures of research on the ground. Innovate by working in partnership with each institutions’ Research Culture team to develop a Listening Toolkit that provides a practical framework for embedding research culture strategy in the localized structures and relationships on which research is based. Drawing on our innovative ethnographically based methods of ‘deep listening’ and ‘reflexive sensemaking’, which allow research participants’ underlying, often invisible assumptions and motivations to be voiced and articulated, we will pilot the Listening Toolkit in collaboration with the four selected research communities. Iterate through HE sector-wide multi-stakeholder engagement, to refine and customise the project insights to ensure impact beyond these four institutions. This will be achieved through a Research Cultures Forum, comprising peers from across higher education, including university-based Research Culture teams and wider HE industry and policy groups. The Forum will develop robust, actionable insights tailored to the needs of diverse institutions and research environments. Applications and benefits The project complements a sizeable body of studies commissioned by government and sector-wide bodies and funders, documenting UK research culture and its challenges, and offering valuable recommendations and tools fostering positive change. However, based primarily on interviews, focus groups and surveys, these studies underplay relational aspects of local cultures of research and their implications for efforts to embed processual recommendations. Our ethnographically-based approach fills this gap, offering actionable mechanisms for effective ground-level cultural change, and thought-leadership for broader science policy and its implementation.

UKRI Gateway to Research · FY 2025 · 2025-03

Disordered eating includes behaviours and thoughts that are consistent with those of eating disorders but do not fully reach threshold for a full diagnosis. Adolescents’ disordered eating is a public concern: 20% of adolescents in the UK engage in disordered eating behaviours.  Around a quarter of these are adolescent boys. Left untreated, adolescents are at higher risk of psychological distress, social difficulties, and later eating disorders. Early recognition and treatment of disordered eating is key for preventing long-term consequences. However, disordered eating in adolescent boys specifically is poorly understood and under-researched. Identifying the presentation of these problems as well as long-term outcomes is crucial. Although we know that disordered eating affects both boys and girls, there are key unanswered questions: How do disordered eating behaviours and cognitions co-occur in boys? Are these behaviours linked to both underweight and overweight? What are the long-term impacts on adult mental health and social functioning? Do men with a history of disordered eating present to healthcare services? And if so, which health services? To answer these important questions, our project has three parts. First, in the general population, we will look at patterns of disordered eating behaviours and cognitions in adolescent boys. These will include ‘traditional’ forms of disordered eating like dieting and body dissatisfaction, but also more male-centric concepts like excessive exercise and steroid-use to build muscle mass. We will also look at the relationship between disordered eating and body size. To do this, we will use information collected over many years from two population-based cohorts: ALSPAC in the UK and Young-HUNT in Norway. Second, we will look at long-term outcomes of disordered eating in men. Specifically, we will examine whether disordered eating in adolescence places men at greater risk of experiencing disordered eating, alcohol or substance use problems, and social difficulties in adulthood. To do this, we will use information collected through self-report questionnaires at age 30 in ALSPAC, and adolescence data with follow-up to adulthood using Young-HUNT with national linkage to health and welfare national registry data and self-report data from adult HUNT. Third, we will look at healthcare utilisation in men with disordered eating. We will examine whether men who engaged in disordered eating during adolescence use physical and psychological healthcare services, and if so whether there are certain services that they are more likely to use. To do this, we will use linked NHS data in ALSPAC, and Norwegian hospital data with Young-HUNT. Together, studying these questions will provide a thorough knowledge base of disordered eating in adolescent boys which can aid early detection and early intervention. We will share our findings with a range of stakeholders including parents, clinicians, and academics. Throughout the project we will seek to raise awareness of disordered eating in boys to counter the narrative that disordered eating is a “female problem”. Applicants: Nadia Micali (project lead), Kirsti Kvaløy (project co-lead - international), Trine Tetlie Eik-Nes (project co-lead - international), and Nora Trompeter (project co-lead - UK).

UKRI Gateway to Research · FY 2025 · 2025-03

Chronic pain affects more than two fifths of the UK population, with one third of this experiencing pain that impedes their quality of life. This pain is often difficult to treat, making pain-related diseases the leading cause of disability and disease burden worldwide. One particularly challenging symptom of this pathology is spreading pain, whereby pain can spread unpredictably across multiple body regions, with no identifiable cause. Interestingly, spreading pain is a common feature of  a large number of difficult to treat pain syndromes including neuropathic pain and fibromyalgia, which suggests common underlying mechanisms and there is evidence that this pain may be the result of changes in central nervous system pain circuitry and plasticity. This project aims to address this challenge of spreading pain by revealing the circuits and mechanisms that underlie its aetiology with the aim of identifying how to correct these circuits when they are maladapted. Plasticity in neural circuits occurs predominantly in two cases: in pathology, such as chronic pain states, and over the course of development, when circuits are being established. The central nervous system learns to restrict and refine its response to pain over the course of early life: infants show whole body responses to painful stimuli, or spreading nociceptive reflexes, much as is seen in spreading pain conditions. These pain responses subsequently become refined over adulthood, leading to restricted pain responses seen in the healthy adult. Studying the developmental plasticity of pain circuits from the unrefined responses seen in the infant to refined pain responses seen in the adult therefore provides a novel framework to understanding how circuit dysfunction leads to spreading pain symptoms. Combining state of the art approaches and molecular biology in transgenic mouse models, this programme aims to determine the mechanisms by which the nervous system transmits pain throughout the body and establish how spreading pain pathology can coopt these circuits, leading to widespread pain signals. To do this, we have three objectives: 1) characterise the functional development of a spinal circuit we have already identified as potential target for spreading pain; 2) identify how these cells change their connectivity following spreading pain states; and 3) test the hypothesis that dampening their activity pharmacologically and non-pharmacologically can treat spreading pain symptoms and aetiology. Understanding how pain spreads is the first step to begin to identify how to prevent this spreading. We have already identified a promising target circuit that could underlie this symptom and now seek to reveal the circuit mechanisms that underlie its pathology, which will benefit both pain sufferers and their doctors. By revealing mechanisms by which the body limits spreading pain, this work has the potential to identify molecular targets to treat this pain, either by targeting growth factors or by identifying future pharmacological and non-pharmacological therapies that may silence aberrant circuits leading to spreading pain.

UKRI Gateway to Research · FY 2025 · 2025-03

Environmental and economic concerns related to the excessive use of fossil fuels, together with opportunities in circular economy and carbon negative technologies are paving the way for a fundamental reorganisation of the chemical industry. Oil refineries are being redesigned to couple petrochemical processes with recycled carbon productions and new thermochemical technologies more suited for small-scale operation. In this context, the invention of new (or restructured) processes for the synthesis of important chemical feedstock, such as olefins generated from waste feedstock is of crucial importance, since these molecules are fundamental building blocks for polymers, fuels and chemical industry. In order to unlock the transition to recycled products in energy and manufacturing sectors, resource efficiency, process flexibility and intensification are of critical importance. To achieve these goals, we adopted a systematic platform for innovation and to inform transformative technology. This particular methodology developed in the previous EPSRC funded projects will be used to demonstrate the manufacture of "green" chemical products, including bio-olefins from waste feedstock, at a scale relevant to industrial applications. The overall aim of RECORD is to develop and implement a commercially viable technology, based on multi-staged fluidised beds, for the conversion of biomass and plastics waste to important chemical blocks (such as light olefins) through 3 phases. Phase 1, which is the initial proof of concept has been completed with previous EPSRC funded “NOSTOS” project. The current proposal seeks funding for Phase 2: Technology De-risking and Demonstration (TDD). The objective of the TDD is to undertake a pilot scale project which can be used to prove the technical, operational, risk management and environmental compliance aspects of the UCL core fluidization technology and further enhance the process to minimise the risks for the first commercial demonstration project. The TDD programme will include specific work packages dedicated at de-risking the technology, including cold-flow test, fluid-dynamic characterization and models validation. The demonstration activities will be completed by only 24 months from the project start on the assumption that we only design, build and test the core, higher-risk elements of the multi-stage fluidised bed technology directly relevant to previous NOSTOS development work. The main output from this phase will be: An asset with an intrinsic value and strong associated IP A facility which can be operated both for stakeholders engagement and as an R&D facility at the new Manufacture Future Lab in UCL East campus A proven technology which can be readily scaled up and implemented by industries. A new Carbon Recycling Hub at MFL as an ideal interface for the delivery of technologies for bioenergy, CCUS and chemical recycling and policy support services for start-ups, established businesses, and government organisations A focal point for investors looking at the opportunity to invest in proven sustainable manufacturing technologies The RECORD technology will enable industrial stakeholders to deliver their environmental obligations for more sustainable chemicals at scale, under increasing pressure from legislation and public opinion to curb GHG emissions, reduce pollution and resource depletion. The results from this research could lay the foundation for game-changing waste conversion technologies, delivering recycled products cost-competitive with those from traditional fossil fuel, thus helping to reduce the UK’s manufacturing costs, and tackling global problems caused by natural resource depletion, GHG emissions and waste dispersion.

UKRI Gateway to Research · FY 2025 · 2025-02

As the momentum behind AI builds, discussions about “Responsible AI” and “AI for good” are being taken increasingly seriously. We are seeing new governance structures and policy priorities. And yet we know little about current trajectories of AI research and innovation, making it hard to anticipate funding or regulation needs. The promises of AI have not yet been systematically tested against societal needs. There is a shortage of evidence that might connect AI with questions of public value (Bozeman and Sarewitz 2011). Recent research (e.g. Ciarli and Rafols 2019) has shown the potential for innovation studies to illuminate gaps between research priorities and societal needs. Our project will explore and explain the potential of mapping emerging AI trajectories. Our analysis will allow us to assess the feasibility of some strategies for understanding the values currently implicit in AI research and development and the gaps with public values. AI is hard to define, hard to see and hard to hold to account, which presents substantial methodological challenges. Our project will explore the potential for new ways of seeing AI as an emerging technology, with a view to informing government funding and regulation. It will involve a collaboration between researchers in the UK and the Netherlands, linking four centres of metascience research - UCL, Warwick, CWTS Leiden and UNU MERIT in Maastricht. Our case study of AI for agriculture will allow us to explore the alignment of R&D and social needs with greater focus. We will study how AI is affecting agricultural R&D and reveal the overlaps or gaps with social needs among groups in the Global South that are often left out of rich countries’ priorities as they discuss the need to compete in a ‘race’ to develop AI technologies. Our research will use scientometrics, Delphi surveys and cutting-edge text analysis, complemented by qualitative methods. We will draw upon, connect with and inform major UKRI AI investments such as the Responsible AI programme and the Generative AI Hub (via Jack Stilgoe). We will also collaborate with Google Deepmind and draw on our expert advisers, including Juan Mateos-Garcia and Alondra Nelson. As we develop our case study, we will use our networks of collaborators in India and international bodies such as the Food and Agricultural Organisation and United Nations Development Programme to sharpen our research questions.

UKRI Gateway to Research · FY 2025 · 2025-02

Why some tectonic plate boundary faults are locked and accumulate a sufficiently large slip deficit to produce large earthquakes remains unknown. 70% of earthquakes occur beneath the oceans, including the largest earthquakes in subduction zones (SZs), such as those in 2004 in Indonesia and 2011 in Japan. To forecast future earthquakes, it is, therefore, crucial to map offshore fault coupling and identify the frictional regimes that drive coupling. However, slow movements along offshore faults are largely hidden from onshore GPS measurements, so without seafloor data, efforts to understand coupling along these hazardous faults will remain futile. The 2011 Japan earthquake completely shifted our thinking about how faults work, leading to many subsequent studies focussing on SZs around the ‘Pacific Ring of Fire’. However, questions remain about more weakly locked faults and whether slowly deforming plate boundaries, such as those in the Atlantic region, can generate strong coupling. This uncertainty has led to a poorly estimated potential for large earthquakes in this area, with debates ongoing over how strongly coupled the faults are. Understanding the physical properties that affect coupling, such as the inherited structure of subducting plates (e.g., fluid content), is also crucial. Ocean transform faults (OTFs) provide a complementary tectonic laboratory to SZs for probing what controls coupling. OTFs are geometrically simpler and have wider fault damage zones than their continental counterparts, offering a unique environment to investigate how fluid flow along fault strands and fracture networks drive slip and coupling. I will exploit the fact that the same frictional mechanisms ultimately control large and small earthquakes. I will characterise the many weak events recorded by targeted ocean-bottom seismometer (OBS) experiments to determine coupling in multiple tectonic environments in the slowly deforming Atlantic. One of my main study areas will be the Lesser Antilles SZ to investigate whether it is capable of an M>8 earthquake, which would cause a transatlantic tsunami and local devastation, given the low-lying islands and lack of a tsunami warning system. I will also study Atlantic OTFs because they can host large (M>7) earthquakes and multi-mode slip (e.g., slow and supershear) despite assumed weak coupling. Manual analysis cannot efficiently deal with such large datasets. However, my involvement in multiple OBS experiments worldwide will allow me to develop the first machine-learning neural network model for classifying diverse signals in OBS data. Using a novel relocation technique, I will map fault zones with high accuracy and precision that implicitly accounts for Earth structure unknowns. Waveforms from small events also contain information about the material properties where they slip, so I will apply novel imaging techniques to OBS data (e.g., in-situ Vp/Vs; S-wave splitting; seismic attenuation; fault zone guided waves). I will also deploy dense nodal seismic networks to probe the material properties that lead to variable coupling. The structure of the incoming plate at the Antilles trench is well established, so I will directly relate these structures and associated material properties to plate interface coupling. My long-term vision is to continue to push our boundaries of fault zone seismic imaging. I will explore new seismic recording methodologies, such as ocean fibre cables. Finally, I will synthesise my results from studies of continental faults and Pacific-type SZs to explore unified relationships between background seismicity and coupling that will ultimately improve long-term earthquake forecasts and seismic hazard models.

UKRI Gateway to Research · FY 2025 · 2025-02

Kennedy’s Disease (KD) is a progressive neuromuscular disorder primarily affecting men and is characterised by progressive weakness of  the arms, legs and head region,  leading to a loss of mobility and alteration of speech, swallowing and breathing, causing significant disability. KD is caused by a repeat expansion mutation within the Androgen Receptor (AR) gene, found on the X chromosome. All KD patients suffer from the same abnormal CAG repeat expansion of the AR gene, with expansions of >38 repeats resulting in KD. A successful gene therapy approach would therefore be applicable to all KD patients. Though reported to have a 1:50,000 male prevalence, our recent genetic investigations have revealed the predicted prevalence based on genetic data to be 1:6,887 males, suggesting many undiagnosed cases and extending the impact of a successful therapy. Currently, there are no effective treatments available for KD. The weakness in KD is caused by the degeneration of muscles and motor neurons innervating them. Substantial evidence from KD mouse models has surprisingly demonstrated that without expression of the mutant AR in skeletal muscle, there is no development of disease phenotype including no loss of motoneurons. With further evidence from patients, it is now well established that skeletal muscle is a primary and early site of pathology. Manifestation of KD is triggered by binding of expanded AR to androgens. Whilst androgen-reducing therapies are effective in disease models, they are limited in the clinic by unacceptable side effects associated with lowering testosterone levels long-term in males. Attempts to reduce mutant AR in KD mouse models, using antisense oligonucleotides (ASOs) have shown promise for reducing disease phenotypes. However, unlike our novel strategy, AR reduction was not specific to skeletal muscle, making occurrence of secondary side-effects in patients likely, as the AR has crucial roles in multiple tissues. Due to these secondary side effects, AR silencing in peripheral tissues is particularly undesirable when carried out long-term, as is necessary for treating KD given that patients usually experience a normal life expectancy. We have already developed innovative tools to reduce AR expression specifically in skeletal muscle approach and to date we have: 1) developed a system for reducing expression of AR - using a novel approach based on CasRx, a small ribonuclease that can be programmed to bind and cleave AR RNA. 2) established a strategy for delivering and restricting therapeutics specifically to skeletal muscle - by combining a novel AAV vector known as MyoAAV with a muscle-specific promoter. Our current challenge, addressed by this project, is that we need to demonstrate efficacy of our proposed therapeutic approach. We will test this a) in vitro in patient derived myogenic cells to first demonstrate functionality of our strategy within the context of disease and also to examine off-target effects of RNA silencing within an appropriate genetic background; and b) in vivo in a mouse model of KD which expresses the causative mutation resulting in the development of progressive neuromuscular deficits. We aim to develop a novel genetic-based therapeutic approach for treating the neurodegenerative phenotype of KD.

UKRI Gateway to Research · FY 2025 · 2025-01

The Voices of Indigenous Amazonia project proposes to study Amazonian biodiversity and its long-term interactions with Indigenous peoples in three regions characterized by complex sociocultural systems: the Upper Negro Indigenous Territory (Amazonas state); the Xingu Indigenous Territory (Matto Grosso state);  and the Kayapó Indigenous Territory (Pará state). These territories stand out for their varied and complex ethnic, historical, and socio-environmental configurations, which include ethnobiological knowledge that is specific to each region. In this project we propose to combine human and biological sciences with Indigenous knowledge to increase our efficiency in producing knowledge about Amazonia. We propose to document biodiversity and its relationship with knowledge and sociocultural practices of present and past Indigenous peoples through: 1) biological inventories of species little known to Western science; 2) characterizing Indigenous landscapes through participatory mapping and remote sensing; 3) fostering exchanges of biodiversity-related knowledge between scientific and Indigenous knowledge; 4) recording long-term anthropogenic changes in vegetation, fauna, and soils ; and 5) collaboratively producing relevant ethnographic, linguistic, and sociocultural documentation. Supported by multifaceted biological studies (descriptions of new species, taxonomic revisions, morphological and molecular phylogenetic analyses, distribution modelling and species richness) integrated with studies of traditional Indigenous knowledge, including its role in the domestication of plants and landscapes, as well as studies of millennia-old environmental management technologies within different Indigenous territories, the project will enable large-scale analyses of biological and sociocultural diversity while mitigating existing taxonomic gaps in poorly sampled yet well-preserved regions of Brazilian Legal Amazonia. At a broader level, the project will produce relevant contributions to tackle the current climate emergency and socio-environmental challenges of the Anthropocene, which compromises forests, resources, and the continuity of the lifeways of our partners,  Indigenous peoples of Amazonia.

UKRI Gateway to Research · FY 2025 · 2025-01

At Great Ormond Street Hospital, we look after children with devastating diseases affecting the brain. Unfortunately, for most affected patients, there are currently no treatments that can prevent disease progression leading to premature death in childhood. Some of our patients have diseases caused by the loss of specialized brain cells called “neurons”. A number of genetic and environmental factors can lead to the death of these neurons, which are then lost as a consequence of a process called “neurodegeneration”. Unfortunately, this process is irreversible as neurons cannot be naturally replenished in the same way as other body tissues. Cell therapy approach to treat childhood disease presenting neurodegeneration. Over the last few decades, researchers have developed methods of generating human brain cells from patient’s skin cells, which ideally allows the production of new and healthy cells to replace the ones missing or malfunctioning in a specific disease, using a therapeutic approach called “cell therapy”. Until recently, the standard clinical approach has been transplantation to replace diseased organs or cells, which is highly dependent on availability of donors and the donation of brain cells is currently not possible. Therefore, the generation of brain cells in a laboratory setting allows the development of new therapeutic approaches that aim to replace those lost through neurodegeneration. Currently there are some clinical trials looking into cell therapy as a treatment for Parkinson's Disease (currently the 2nd most common neurodegenerative disorder affecting the world population). However, these trials do not evaluate using cell therapy in children with neurodegenerative disorders. Proposed project’s goals. Our long-term goal is to develop a novel cell therapy approach for progressive neurological conditions in children collectively known as ‘childhood neurodegeneration’. The aim of our project is therefore to perform studies in a laboratory model of childhood neurodegeneration to test the potential of cell-based therapy for this group of disorders. We have already performed some preliminary studies in an animal model of childhood neurodegeneration and showed the feasibility of this approach to rescue the disease progression. In this project, we now aim to test our approach transplanting the created neural cells in the animal brain, using a process similar to the one that will be used in patients, to see if these transplanted neural cells replace the damaged neuronal cells. We will also evaluate the dose of the neural cells needed and if there are any side effects from the neural cells. The information we get from this study will then help us to develop the therapy further towards clinical application for childhood neurodegeneration. This project will enable us and other researchers to look into therapies for childhood neurodegeneration and may shorten the time it takes to get these therapies to patients. We hope to provide a single disease-modifying therapy with neural cell therapy that will reduce the risk of mortality, prevent disease progression and improve quality of life for the patients and their families.