University College London

UKRI Gateway to Research · FY 2025 · 2025-09

James Nightingale, the Chair of the Euclid UK Coordination Group (https://eucliduk.net), has written on behalf of the Euclid UK consortium to request support for essential community coordination and participation in the European Space Agency’s (ESA) Euclid mission. With 353 members, Euclid UK is the UK’s largest astronomy consortium and plays a central role in Euclid science exploitation, a flagship ESA mission supported by significant UK investment, including £37 million from the UK Space Agency (UKSA). Since 2017, Euclid UK has maintained a small community support fund to facilitate activities critical to UK involvement in the mission. These include: • Organising annual UK Euclid meetings; • Providing travel support for UK-based scientists to attend key Euclid collaboration meetings with a particular emphasise on early career researchers. This fund was originally seeded by registration fees from the 2017 Euclid Consortium Meeting held in London. It is now nearly exhausted. With only ~£3 400 remaining, we anticipate being able to cover expenses through 2025; however, no further funding is currently available to support Euclid UK for the remainder of its nominal lifetime (running until 2031).

UKRI Gateway to Research · FY 2025 · 2025-09

Brain tumours—the ninth most common cancer in the UK—are a major cause of death and disability. The commonest type of brain tumour, glioblastoma, remains stubbornly resistant to treatment, with survival essentially unchanged for thirty years. Only one in twenty patients with glioblastoma survive beyond five years, and those that do have severely impaired quality of life. Treatment outcomes in other cancers, such as breast, lung, and blood, have seen striking improvements in recent decades: why is the brain different? One possible answer is the marked complexity of both brain tumours and the biology of the brain itself. The disease mechanisms that cause brain tumours are many and diverse, varying greatly from one patient to another, even within the same patient at different times. Treatment here must be closely personalised to be both effective and equitable across different people, yet the diversity makes determining what treatment works for any individual patient very difficult. To understand this diversity, we must first describe it in enough detail for each individual’s experience to be distinct and recognisable. Such a detailed description or rich phenotype can be considered a “fingerprint” pattern that is distinctive to each individual yet enables comparison with others. Machines able to make sense of complex patterns—such as the artificial intelligence systems now revolutionising the world—can then be deployed to identify the right treatment for the right patient, helping deliver personalised care founded on robust evidence. Crucially, there is already a wealth of detailed information collected during routine NHS brain cancer care—brain scans, tissue examinations, genetic analyses—from which such patterns may be extracted without disturbing patient care. Moreover, such clinical information can be combined with a deep understanding of the brain’s biology, drawing on what neuroscientists have cumulatively learned over the last 100 years. Though no framework to deliver it across the NHS currently exists, we will build its foundations, establish its feasibility, and pilot its application at minimal cost to the NHS. At the heart of our proposed approach is making sense of biological patterns as networks. As an analogy, the path of a brain cell from normal to cancerous may be seen as a journey through the London Underground, where the final destination of tumour growth is reached via a series of stops defining a characteristic path. Capturing the set of all possible paths—a map of the “tumour underground”—allows us to understand what is going on, how the disease process varies from one patient to another, and how this links to the underlying normal networks of the human brain. Tailoring treatments to each patient may be the only way to identify treatments that stop this tumour network from developing altogether. Success will provide a framework that enables better and more equitable individual prediction of patient outcomes, more accurate, personalised treatment selection, and a powerful means of illuminating disease mechanisms on which future treatment innovation depends.

UKRI Gateway to Research · FY 2025 · 2025-09

Context and Challenge The special educational needs and disabilities (SEND) system in England is in crisis. Evidence-based solutions are required to ensure that children’s SEND needs are identified early so that they can receive timely support and improve their educational outcomes (Curran,2020). Early SEND identification and support can also provide much-needed, long-term cost savings. This is because children can receive the support they need from an early age before issues become well-entrenched and more difficult to address as they get older (Carneiro et al.,2024). Universal screening measures, which assess children’s cognition, language, socio-emotional, and physical development, play an important role in the early identification of SEND (Miles et al.,2018). This is because effective universal screening measures can efficiently identify which children may need additional follow-up assessments, interventions, and support (APPG,2023). While statutory measures, such as the Early Years Foundation Stage Profile (EYFS-P), are implemented with all young children in England, there is little evidence evaluating how effective some of these universal screening measures are for identifying children’s future SEND needs (Atkinson et al.,2022;Snowling et al.,2011; Wood et al.,2024). Aims and Objectives Our study proposes to conduct the first national-scale evaluation of two universal screening measures in early childhood for identifying later SEND. The two measures are: The EYFS-P, which is a statutory observational assessment completed by teachers with all 4-5-year-olds in England at the end of Year Reception. An adapted version of the Vineland Adaptive Behaviour Scale, which is a questionnaire completed by parents when children are 3 years old in ‘Understanding Society’, a nationally representative dataset. We will address three core objectives. First, our study will evaluate the extent to which children’s performance on the EYFS-P at age 4-5 can predict their later SEND needs up till age 15-16, including for children from different ethnic, socio-economic status, and English as an Additional Language groups. Second, our study will estimate, of the children who were aged 0-4 years during the Covid-19 pandemic, what proportion are at a heightened risk of later SEND needs based on their EYFS-P performance. Our study will also estimate how much additional educational funding is required to support these children, which will inform future DfE spending reviews. Third, our study will evaluate the extent to which even earlier indicators of child development at age 3 (i.e., the adapted Vineland Adaptive Behaviour Scale) can predict later SEND needs. Our study will also make comparisons between the two universal screening measures, which will inform evidence-based guidelines for educational policymakers and practitioners. We will answer these questions by conducting quantitative analyses of administrative (the National Pupil Database) and large, nationally representative survey (Understanding Society) datasets. Potential Applications and Benefits Our new evidence will be used to advise educational policymakers and practitioners across England about how children’s SEND needs can be identified from an early age. Recommendations will also be made regarding the funding required to support the educational needs of children who experienced much of their early childhood during the Covid-19 pandemic. Our interdisciplinary research team have world-leading expertise in early years education, SEND, and secondary data analysis. We will work with our extensive networks in the DfE and Ofsted, as well as other non-government organisations and early years practitioners, to ensure our findings are used to address the SEND crisis and make a tangible difference for young children.  

UKRI Gateway to Research · FY 2025 · 2025-09

The UCL P-ACE will be based in the Department of Security and Crime Science (DSCS) at University College London (UCL). The DSCS is a global leader in police-relevant research, innovation, education and impact, and is home to world-leading applied researchers. Research in the DSCS has played a major role in the implementation and development of evidence-based policing and crime reduction, generating significant global impact through shaping policy and practice and initiating a culture shift towards a more evidence-based way of working. Beyond crime reduction, the DSCS is also home to a world-leading centre for research on public trust and police legitimacy, where researchers have contributed to a wide range of high-profile projects exploring police-community relations from multiple perspectives. Moreover, the DSCS is avowedly multidisciplinary, consisting of a critical mass of engineers, computer scientists and forensic scientists working across disciplinary boundaries and engaging in cutting-edge research on policing and security technologies, including surveillance, sensing and identification. The UCL P-ACE will draw upon these three strands of DSCS activity, and the research of partners across UCL, to focus on five Areas of Research Interest (ARI): Building and maintaining public trust Crime prevention Identification and tracing Surveillance and sensing Analytics Working across these five domains, the UCL P-ACE will identify and facilitate the dissemination and use of existing research to help improve policing, and support the development of new research and technology with and for the police. Our established partner organisations, the London Metropolitan Police Service and the London Mayor’s Office for Policing and Crime, will assist us in identifying key areas of interest and need across both strands of work. In supporting police excellence, the UCL P-ACE will focus on two cross-cutting agendas, each of which reflect key challenges in contemporary policing. First, we will emphasize the meaningful transfer and exchange of knowledge and skills, and the importance of driving change in operational policing. Too often, new technologies and evidence-informed practices developed within and beyond the academy have failed to impact front-line police practice. In keeping with the core mission of the DSCS, the UCL P-ACE will not only develop and disseminate novel approaches, tools and technologies, but also work closely with police and partners to ensure these are fit – and usable – for the policing purposes intended. Second, within the current context of a crisis in public trust and confidence, UCL P-ACE activities will focus on the key question of public consent. Under what conditions do the policed trust the police to engage in new ways of working? What technologies pose challenges to police legitimacy? And do new technologies align with public preferences and priorities? The over-arching aim of the UCL P-ACE is therefore to promote and accelerate the use of research evidence in policing, in London and beyond, in ways that foster meaningful change in police practice and which function in ways that are aligned with what the public wants, needs and expects from policing.

UKRI Gateway to Research · FY 2025 · 2025-09

The Challenge The UK's journey to net zero depends on understanding how people use energy in their homes, but accessing the data needed for this research is restricted. Smart meter data from households provides crucial insights for developing better energy policies, reducing fuel poverty, and improving building performance. However, privacy regulations and data protection rules create major barriers to accessing this information, slowing down vital research that could accelerate our transition to clean energy. Our Solution We will solve this data access problem using cutting-edge artificial intelligence. Our project will train advanced AI models called Generative Pretrained Transformers (GPTs) on the UK’s world-leading SERL Observatory dataset - a unique collection of smart meter data from 13,000 representative GB households, combined with detailed information about their buildings, occupants, and energy use patterns collected over five years. The AI models will learn the complex patterns in real energy data and generate completely synthetic datasets that look like real household energy data but contain no actual personal information. This synthetic data will have all the statistical properties researchers need while being completely privacy-preserving. This project directly advances EPSRC AI for Science objectives: developing AI capabilities across research fields to accelerate scientific discovery; increasing access to well-governed, high-quality datasets for AI; building interdisciplinary collaborations between AI and energy researchers; and embedding AI as a research tool in a fair and inclusive way. What We Will Deliver Over six months, we will create "Synthetic-SERL", the first dual-fuel synthetic smart meter dataset with long temporal sequences. This will include half-hourly gas and electricity data for 13,000 virtual households across full calendar years, each with contextual information about building and occupant characteristics and weather. We will rigorously test this synthetic data to ensure it provides genuine research utility while passing strict privacy audits. The entire dataset, along with the training code and tools to integrate the data into workflows, will be published under open licences, making it freely available to researchers worldwide. Impact and Applications This research will deliver targeted benefits across three key sectors. Academia will benefit from removal of barriers to accessing high-quality data, enabling and accelerating R&D that was previously restricted by privacy regulations. Industry will gain access to data for testing business cases and grid planning that rely on high-resolution energy data, supporting investment decisions in clean energy technologies. Government will have data to evaluate distributional impacts of net zero policies like time-of-use tariffs, ensuring a fair transition for all households. By democratising access to high-quality energy data, we will unlock research that was previously restricted, accelerate innovation in the energy sector, and create new partnerships between AI researchers and energy experts. This project establishes the foundation for future research that will push the boundaries of AI for energy decarbonisation.  

UKRI Gateway to Research · FY 2025 · 2025-09

AI/ML Training (DiRAC) This proposal presents a structured training programme designed to equip UK researchers with essential skills in the artificial intelligence (AI) and machine learning (ML), technologies that are rapidly transforming scientific research. While AI/ML offer powerful tools for solving complex problems and driving innovation, many researchers lack the technical expertise required for effective and responsible use. This skills gap poses risks to research integrity and scientific discovery. To address this and drive the wider adoption of AI capabilities across research fields,  the proposed initiative will deliver a modular, self-paced training programme through the established DiRAC Training Academy online platform. The curriculum will focus on core machine learning techniques, including deep learning and generative models, and will be inclusive and freely accessible to the UK research community. Interactive Jupyter Notebooks using real scientific datasets will provide hands-on experience and promote awareness of high-quality, well-governed  data sources. The programme will also feature a series of expert-led “deep-dive” sessions. These ~2-hour recorded lectures will explore real-world applications of AI/ML within specific scientific domains, reinforcing theoretical knowledge through practical demonstrations of successful research codes and methods. To further support participants, the final phase of the programme will include live virtual drop-in sessions and interdisciplinary discussion forums. These will offer one-on-one expert guidance, facilitate cross-disciplinary collaboration, and encourage co-creation of innovative research approaches. Overall, this initiative will build a confident, skilled, and diverse research community, accelerate the integration of AI as a standard scientific tool, and foster long-term innovation across UKRI domains.

UKRI Gateway to Research · FY 2025 · 2025-09

Social prescribing (SP) is a mechanism of care referring people to non-clinical forms of support and services in their local communities to improve health and wellbeing.1 In the last decade, there has been a rapid development of SP to help tackle social or complex health needs. SP is being widely implemented in England, Wales, Scotland and Northern Ireland. SP is also gaining traction in over 30 countries worldwide, including the United States, Canada, Germany, Denmark, Spain, China, Singapore, Australia, New Zealand, Nigeria, Philippines etc.2,3 Despite this global proliferation in practice, the evidence base for SP is insufficient.4–7 It’s still unclear if SP is reaching those in greatest need, how it is working in practice, what makes “good” SP, and what impact it can actually have at scale. In the UK, SP is included in the NHS Long Term Plan for “tackling health inequalities”,8 and its national roll-out as a programme provides a fertile opportunity to explore equity of access. However, it is a major challenge to assess who is receiving SP. Local evaluations can be too small in scale, while electronic patient records often contain inconsistent coding of SP referrals, poor recording of wider determinants of health, and no details of referrals from sources other than GPs, leading to conclusions that they cannot be used to assess equity of referrals.9,10 These research gaps are imposing a major challenge for policy and further development of SP. Thus, exploring SP referral patterns in wider data sources is crucial to understanding if SP is truly reaching individuals most in need. The proposed project aims to advance our understanding of SP implementation in the UK, through analyses of a major dataset of 438,000 SP referrals collected by Access Elemental 2017-2024 (number set to triple during this grant). Access Elemental is the most widely adopted SP platform in the UK to date, serving a population of over 20 million people in the UK. We will use this dataset to explore four research questions: Referrals: who has been receiving SP in the UK in the past 7 years as SP programmes have rolled out at scale, for what referral reasons, and how does this vary in different locations? Pathways: what do SP pathways look like? What contacts and interventions do patients receive, how is inequality produced or reinforced at different stages of the SP lifecycle, and what factors predict continued engagement with the pathway? Impacts: what impact does SP have on mental wellbeing and primary care service utilisation in different population groups? Does pathway delivery affect whether these outcomes are achieved? Data quality: how can this novel dataset be developed for future research purposes? What recommendations and guidelines can be made for other SP platforms to optimise their data quality and support future research in this burgeoning area? We will support our research with a comprehensive impact and engagement programme targeting academics (academic articles, conference presentations, data user manual, data workshop), policy makers/commissioners (policy briefs, roundtable), health/social care professionals, SP professionals and community organisations (blogs, data dashboard) and the public (media, infographics). In all, this project is timely in providing large-scale detailed data on the “nuts and bolts” of SP as a national programme and is anticipated to have a major relevance to a wide national and international audience.

UKRI Gateway to Research · FY 2025 · 2025-09

Existing silicon technology struggles to manage our enormous computational demands, and the development of new hardware will determine the pecking order of the new computing era. Neuromorphic hardware has, in just a decade, become the "third stream" of semiconductor development, alongside digital and quantum technologies, and is highlighted in the Government's Semiconductor Strategy and EPSRC's 2022-27 Strategic Delivery Plan. Demonstrations already show orders of magnitude throughput increases, power consumption reduction, increased security, and efficient processing of real world, noisy, imprecise data in ways that challenge conventional approaches for enterprise & embedded applications. The UK has an enviable lead (as detailed in a 2021 eFutures- coordinated report conducted by international leaders from Switzerland and the USA), unlike in digital or quantum where we are on par with competitors. Our IKC brings together the best-of- the-best, through a shared and coherent vision, to extend this. The UK is home to world-leading researchers (many on this team) and a home-grown base of highly regarded early-stage companies gaining global attention (Intrinsic, Neu Edge, Literal Labs, Salience, Cogniscience, for e.g. All support this bid). Far more can be achieved by bringing academia and industry together to co-create radically new hardware. Neuromorphic systems are more fully developed and closer to market than quantum, but breakthroughs require a focus on translation from research to real-world impact, provided by our IKC. The neuromorphic computing market is growing rapidly: In 2023 Grand View Research reported a CAGR of 21% and a predicted market of $20.2 billion by 2030 - a significant and growing opportunity for the UK to capitalise on its research excellence. Example translation use cases are in the box overleaf. Our vision is to consolidate and build the UK's globally-leading neuromorphic hardware community, to leverage its research excellence and early stage industry to revolutionise future computing, and to make the UK the go-to place for innovations and new technology in this field. We propose four interconnected technical research workstreams, underpinning and extending which will be a programme of support for innovation and entrepreneurship: Work stream 1: Neuromorphic systems based on existing technologies Work stream 2: Next-generation neuromorphic technologies Work stream 3: Neuromorphic photonics Underpinning technical work stream: computational neuroscience Our UK-wide multi-hub and satellite model houses three major neuromorphic computing hardware semiconductor technologies under one umbrella in a world-first consolidation: CMOS-based systems (Manchester: SpiNNaker); post- CMOS and unconventional computing (London: UCL, KCL, ICL, NPL; Cambridge), and photonic neuromorphic technologies (Oxford, Strathclyde). Work at Sheffield, UCL and KCL on underpinning neuroscience will provide a solid foundation. Further academic & industrial partners will be invited to join to add expertise where appropriate; the IKC will be an inclusive centre supporting and advocating for the broader UK neuromorphic community.

UKRI Gateway to Research · FY 2025 · 2025-09

The majority of researchers now report using generative AI (GenAI) to help write scientific texts (e.g., research papers, grant proposals). Yet, little empirical investigation exists on how AI-assisted writing is changing the language of science and how that affects peer review. At the same time, scientific texts are increasingly evaluated not just by human reviewers but also by artificial intelligence (AI). Funders, journals, and conferences are piloting AI-assisted tools to inform decisions on funding allocation or publication acceptance. These tools were trained with older, human-authored text, raising concerns about their performance on GenAI-assisted writing. Through this fellowship, I aim to understand how scientific writing is evolving in the age of GenAI and how those changes may affect AI-assisted peer review. I will do so by building a large dataset of academic publications in the top 100 journals (ranked by h-index and predominantly in STEM fields) where authors have disclosed the use of GenAI tools to help write papers. Analysing these real-world examples will reveal how language has changed (or not) since the rise of GenAI. The project also includes a case study: testing the behaviour of a machine model that predicts a paper’s replicability when applied to GenAI-assisted text. This will serve as a broader example for evaluating AI-assisted peer review tools in this new context. Ethical and policy considerations are central to the project. Despite journals mandating GenAI disclosure, actual reporting remains low. Existing discussions focus on disclosure as a matter of integrity, transparency, and accountability. My work will broaden the conversation by showing that truthful reporting is also critical for collecting reliable data for metascientific research. In response to the low disclosure rate, the project will examine how different top journals enforce these guidelines and identify strategies to increase transparency. Findings will be shared in a policy brief, a webinar for journal editors, and knowledge exchange workshops.

UKRI Gateway to Research · FY 2025 · 2025-08

The core purpose of UHealth is to assess the relationship between trade unions and workers’ health taking a broad definition of what trade unions are and looking at several health indictors including physical and mental health and wellbeing. This innovative project will assess the specific dimensions the underlie such a relationship within three work packages (WP). WP1 will employ several longitudinal British Birth Cohorts to examine the extent to which trade union membership and trade union presence within the company as well as changes in the collective bargaining structure over the life course are associated with changes in health outcomes. WP2 will compare five countries (Germany, Korea, the United States, the United Kingdom, and Japan) using specific panel homogenised datasets to better capture how the presence of a trade union within the workplace could impact health and wellbeing both directly and indirectly through job satisfaction, gender pay gap, precarious employment and flexible work arrangements. Specific attention will be given to national collective negotiation structures. For both packages, sensitivity analyses will test model assumptions and longitudinal analyses and negative outcomes and propensity score matching will be used to assess causation and intervention selection. Finally, using a qualitative comparative approach of the freight transport and cleaning sectors in the United Kingdom and Belgium, WP3 will pay attention to the way trade unions organisations are involved in health and safety committees and how workers’ health is an object of collective negotiation across different types of national and international structures that vary across countries and sectors. UHealth will result in a set-change in research on the work and employment determinants of health providing evidence for research and policy communities to reduce health inequalities.

UKRI Gateway to Research · FY 2025 · 2025-08

The number and proportion of older people in the UK are steadily increasing. Advancing age is a major risk factor for loss of function, frailty and disease. The demographic change is hence resulting in mounting personal and societal costs that should be urgently addressed, by compressing the period of ill health at the end of life. Our growing understanding of the biology of ageing can provide new points of interventions to delay or prevent its ill-effects. Human health in old age is predominantly shaped by environmental and lifestyle factors, including nutrition. We consume complex mixtures of nutrients, in amounts and at times that are determined by different eating habits. Interestingly,  an older person’s current health is not only shaped by the nutrition they currently consume but also their dietary history. Diets in very early life, childhood or early adulthood have all been noted to trigger long-term, persistent effects on health. Thus, to promote healthy ageing, we need not only to understand current nutritional requirements, but also how long-term effects of past nutritional exposures arise and are maintained, so that we can identify ways to mitigate any of their detrimental consequences. A complex nutrient-responsive signalling network fine-tunes metabolism, physiology and the ageing process in response to diet. Our own work to date has aimed to untangle some of this complexity. We do this mainly using the fruit fly as an animal model; its short lifespan facilitates rapid discovery; its physiology and ageing are shaped by nutrients in a manner similar to humans; its nutrient signalling network has equivalent components and function to that of humans. Thus, although flies are not humans, they provide a powerful context for revealing the many shared molecular, cellular and physiological mechanisms. In our recent work we have started to explore how the activity of the nutrient-signalling network itself can result in long-lasting effects on health. We have directly modulated the activity of two highly conserved, key nodes in the Drosophila network, TORC1 and FoxO, and uncovered molecular mechanisms by which they generate long-term physiological effects. These signalling nodes respond to different nutrients and they mediate their responses in different cell types, suggesting independent, parallel mechanisms. This information provides us with a scientific opportunity to experimentally test if these mechanisms can also explain how earlier nutrition affects health and longevity in old age. Specifically, we will study three nutritional interventions that have lasting effects on physiology and ageing in Drosophila and mammals: high sugar diet, intermittent fasting and amino acid restriction. Importantly, we will also examine whether damaging effects of earlier dietary habits can be counteracted later in life. We propose to test this possibility experimentally by manipulating the nutrient signalling network in older individuals with different nutritional histories. Our proposed research is made possible by our preliminary work and by the wealth of genetic tools available in the fruit fly that allow the activity of the nutrient signalling network to be manipulated in specific cells and at specific times in an animal’s life, and which can be combined with nutritional and pharmacological interventions. Importantly, the knowledge gained in the study will facilitate the design of interventions to remedy long-term, detrimental effects, potentially also contributing to a reduction in health inequalities during ageing.

UKRI Gateway to Research · FY 2025 · 2025-08

Collagen VI-related congenital muscular dystrophies (COL6-CMDs) are the second most common congenital muscular dystrophies (CMDs).  Patients with COL6-CMDs experience congenital or early-onset progressive muscle weakness, joint contractures, loss of ambulation by early teen years, and respiratory insufficiency. It is estimated that COL6-CMDs occur in at least 0.9 per 100,000 individuals. COL6-CMDs are caused by deleterious variants in any of the three genes (COL6A1, COL6A2, and COL6A3) that encode Collagen VI (COLVI)’s three major a-chains and there are no disease modifying treatments or cure. COLVI is an extracellular matrix protein that plays multiple roles in skeletal muscles, such as providing biomechanical stability, counteracting apoptosis, and maintaining stemness. In skeletal muscles, COLVI is primarily synthesized by fibro-adipogenic progenitor cells (FAPs) a type of interstitial stromal cells. Inside FAPs, three COLVI a-chains, each encoded by a different gene, assemble in a 1:1:1 stoichiometric ratio to form the COLVI triple helical monomer. Then, the monomers dimerize, and the dimers form tetramers. The tetramers are then secreted into the extracellular space, where they assemble to form the COLVI microfibrils. The majority of COLVI monomers are composed of a1(VI), a2(VI), and a3(VI) chains that are encoded by COL6A1, COL6A2, and COL6A3, respectively (a1a2a3 monomer). Genetic therapy approaches for COL6-CMDs depend on consequences of deleterious genetic mutations and the modification of downstream pathological processes with approximately 50% dominant and 50% recessive mutations.These include a common aberrant splicing event that inserts COL6-CMD-causing COL6A1 pseudoexon that could be corrected through splice-modifying antisense oligonucleotides (ASO). We have developed an ASO showing compelling efficacy in vitro  for one of the most common mutations, the de novo deep intronic c.930+189C > T mutation in COL6A1 gene. A significant barrier to translation stems from inadequate delivery to target cells that express COLVI, specifically muscle interstitial fibroblasts (MIFs) and FAPs. To improve ASO delivery we have developed a peptide that demonstrates improved internalisation in human MIFs and FAPs through targeting PDGFRa receptors  that enhances target cell delivery, but does not negate the need for repeated ASO delivery for translation. An alternative method would be to use adeno-associated virus (AAV)  to deliver a genetic therapy for COL6-CMDs. AAV delivered genetic therapies are potentially transformational treatments as seen in Spinal Muscular Atrophy. In this proposal we aim to accelerate the clinical translation of genetic therapies for COL6-CMDs by combining our peptide design to develop a peptide insertion AAV capsid variant that targets PDGFRa receptors expressed on FAPs as a delivery vehicle for COL6-CMDs genetic therapies. Our current unpublished in vivo studies demonstrate AAV8 shows highest transduction efficiency for COLVI expressing target cells in skeletal muscle. However, its broader tropism is not ideal for COL6- CMDs with associated liver tropism. To overcome this, we propose semi-rational design AAV capsid engineering approach to modify capsid receptor interactions to enhance target cell transduction and detarget hepatocytes. We will use the peptide we developed for ASO conjugation as reference, to design a peptide display library on parental serotype AAV8. We will generate a barcoded capsid library to screen for transduction selectivity in vivo and  characterise leading variants by detailed biodistribution study. We will perform in vitro human fibroblasts transduction studies to ensure our capsid retains transduction properties across species. With this novel AAV variant we can deliver gene supplementation, U7 exon skipping or gene editing approaches  for COL6-CMDs.

UKRI Gateway to Research · FY 2025 · 2025-08

Classical drug discovery involves designing a small molecule which will fit into a pocket on a target protein which has been identified due to its relationship to a disease. However, not all protein targets have pockets – many operate through protein-protein interactions (PPIs) which involve large, relatively flat protein surfaces. PPIs are involved in nearly every disease, and therefore affect everyone, but are of particular importance in cancer, infectious disease, and neurodegeneration. PPIs have been extremely difficult to drug with small molecules, because there is nowhere for them to fit. Proteins and peptides can act as inhibitors but are restricted in terms of their chemical structure and subject to recognition by biological process such as degradation. The ideal solution would be a large and structurally defined molecule, like a proteins itself, but with complete control over chemical structure which would allow fine-tuning of interactions and stability under biological conditions We have discovered that a family of synthetic chain molecules with a defined sequence of units (phosphoestamers) can be used to block PPIs using a newly developed process for selecting the right sequences out of hundreds of thousands of possibilities. We have demonstrated proof-of-principle in the identification of phosphoestamers which selectively block the interaction between a cancer-causing mutant form of the protein KRAS and its PPI partner RAF; a major longstanding therapeutic target in cancer. Our hit phosphoestamers were potent at low concentrations, and did not affect the non-mutant protein, providing a promising starting point for drug discovery. In principle, the selection process could be used to identify inhibitors of PPIs in any disease. However, phosphoestamers are not classically ‘drug-like’ molecules and will require strategies for delivery into cells, as is currently required for nucleic acid therapeutics, which they resemble in terms of chemistry. This project aims to develop phosphoestamers PPI inhibitors ready for use in preclinical drug discovery. To achieve this, we will broaden the range of phosphoestamer building blocks available, increase the size of the library to millions of sequences, and optimise the selection method. We will perform selections for PPI inhibitors taking three structurally and functionally diverse proteins, all involved in cancer, as exemplars. We will then measure and optimise phosphoestamer stability under biological conditions. Taking a range of cutting-edge technologies used currently to deliver nucleic acid therapeutics, we will establish the best way to deliver phosphoestamers into cells. We will then measure the efficacy and mechanism of phosphoestamers PPI inhibition inside cells. This will provide a strong grounding for targeted preclincal development. Establishment of phosphoestamers as a new therapeutic modality would have widespread and significant impacts. It would allow targeting of many previously inaccessible points in biochemical pathways which could result in a swathe of new treatments for previously intractable conditions. The model systems used herein relate to challenges in cancer and could lead to new drug candidates operating by unprecedented mechanisms. In the long term, there could be structural impact on pharmaceuticals: because activity relies on monomer sequence, many drugs could be made from a small set of monomers. This could open up new opportunities in point-of-care pharmaceutical production at remote sites, serving isolated, and disadvantaged communities around the world, or even future off-world communities, providing both human and economic benefit.

UKRI Gateway to Research · FY 2025 · 2025-08

More and more individuals are defining their gender identity outside of the male/female binary, with a recent global survey finding that 1% of the population overall, and 3% of individuals born after 1997entify as non-binary (Ipsos, 2023). Non-binary individuals represent a sizable and growing population, yet non-binary identities and experiences have tended to be neglected from research and the small body of research that has been conducted suggests that non-binary people face considerable misunderstanding and stigma. The ways in which individuals experience family life are also changing, with more people choosing to ‘do family’ in ways that differ from traditional expectations. LGBTQ+ individuals have long been found to define and experience their families in diverse ways, yet we know little about non-binary individuals’ experiences of family. This project addresses this significant knowledge gap, providing the first empirical insight into the expectations and experiences of family (including both bio-legal and chosen families) amongst non-binary individuals in the UK. Designed and conducted in collaboration with Gendered Intelligence, the UK’s leading gender diversity charity, this multi-method, multi-phase participatory action research project will be comprised of a nationwide survey (Phase 1) and in-depth interviews (Phase 2). A range of community-based sampling strategies will be used to reach a diverse survey sample of 200 non-binary participants. Survey respondents will answer questions on family expectations and experiences, identities, stigma and wellbeing, providing a novel insight into the way in which these factors are interrelated in marginalised communities. In Phase 2, semi-structured interviews and novel participatory methods, such as Photovoice and lifeline methods, will be conducted with 30 non-binary individuals at different ages and life stages, who will be recruited from Phase I. Interviews will explore participants’ experiences and expectations of family, including parenthood and relationships with friends, partners and pets, providing an in-depth exploration of non-binary family experiences across the life course. A community advisory board and community co-researcher will collaborate on all stages of this project, including research design, data analysis and output production, ensuring that the project’s outcomes are community-focussed and impactful. The study will have a number of novel academic and non-academic outputs. Findings will be shared via journal articles, conference presentations, a special issue on non-binary families and an end-of-project conference, bringing together academics, service providers and community members. Findings will also be disseminated via a range of arts-based resources, including infographics, illustrations and an animated video, created in collaboration with trans and non-binary artists. These resources will be shared widely and featured in an end-of-project art and research exhibition, making them available to community members and the general public alike. Outputs will be co-produced with the co-researcher and findings will be shared in informal workshops with community members, maximising the project’s community engagement. Findings will also be shared in accessible presentations with key service providers (including non-profit organisations and healthcare professionals) to increase understanding about non-binary families and share principles of best practice. Overall, the project’s innovative outputs will contribute to three key outcomes: increasing the societal representation of non-binary families, improving services provided to non-binary individuals, and advancing our methodological and theoretical approach to studying diverse genders and families. The project has the overall goal of contributing to the reduction of discrimination against non-binary identities and the improvement of health and wellbeing in non-binary communities.

UKRI Gateway to Research · FY 2025 · 2025-08

Inherited retinal diseases (IRDs) such as retinitis pigmentosa (RP) and choroideremia (CHM) are progressive genetic eye disorders which lead to visual impairment and blindness. IRDs are a leading cause of visual impairment among paediatric and working-age adult globally, affecting at least 1 in 3000 people. For most patients, no medical or surgical treatments exist, but a large number of clinical trials are underway.  Over 350 gene mutations have been identified in the underlying pathology of IRDs, resulting in gene therapy becoming an increasingly active area of clinical research. Yet, clinical trials for IRDs face significant challenges, especially in meeting regulatory standards. These trials typically rely on visual acuity (VA) as a primary endpoint, a measure which does not accurately reflect meaningful improvements in real world vision. For example, in RP and CHM initial impairment of rod photoreceptors causes night blindness and peripheral visual field loss, followed by subsequent progressive cone involvement, which leads to loss of central vision; however, this occurs over many years, and so measuring changes in VA over short periods such as 1-2 years will not yield a demonstrable difference in a trial setting. This limitation makes it difficult for trials to demonstrate efficacy, causing many trials to fail. Innovative endpoints which provide more sensitive, functional measures may increase the likelihood of clinical success and regulatory approval.   The aim of this study is to validate a novel device called ‘OverSight’, a smartphone app which tracks patterns in mobility, keystrokes, and other behaviours to provide insights into how visual impairment impacts daily functioning, this is termed digital phenotyping. Digital phenotyping refers to the use of smartphones and wearables to collect patient-generated health data (PGHD). Smartphones are increasingly being used to collect health information, and monitoring these interactions can provide a detailed picture regarding a person’s overall health status. Our work to date has demonstrated that PGHD collect via OverSight, such as walking speed and typing speed, can be used as surrogate measures of visual function. Specifically, these datastreams correlate with visual functioning and can be used to remotely monitor behaviour over time, thereby providing potential digital biomarkers which can be used as functional endpoints in clinical trials.   Objectives: 1. Recruit 200 patients with IRDs and collect longitudinal data on behavioural patterns via OverSight.   2. Conduct baseline and 12-month follow-up structural imaging alongside app data collection to assess structural and functional correlations in disease staging.   3. Identify which OverSight datastreams represent candidates for digital disease biomarkers and functional trial endpoints.   If successful, this project has significant potential for patient benefit. OverSight could revolutionise clinical trial design by assessing patient-relevant changes which current vision assessments may overlook. OverSight addresses current challenges in clinical trials by tracking outcomes which are meaningful to patients, complementing traditional clinical parameters. This approach could enhance trial sensitivity, making disease-related changes easier to detect and ultimately accelerating the clinical trial process.

UKRI Gateway to Research · FY 2025 · 2025-08

Humans constantly engage in social interactions with other people. Nevertheless, the neural mechanisms underlying social cognition are poorly understood. By contrast, a Nobel prize winning discovery found that abstract representations enable spatial thinking. These representations capture relationships between locations such as where our car is relative to our workplace. Understanding relationships is essential for social life, too, for example, tracking how our own opinions relate to others’ views. My hypothesis is that abstract representations are found in a deep prefrontal-subcortical (DPS) circuit linked to social cognition and that they are causally necessary for social cognition. They are needed to navigate social relationships such as: (A) Distinguishing self and other. (B) Navigating multi-person groups. (C) Assessing multiple features of a person. I draw on formal definitions for specific types of abstract concepts and quantitative methods for their identification. I employ these methods to address fundamental social problems (A-C). This allows me to (1) identify abstract DPS representations underlying social cognition using neuroimaging and (2) use a new non-invasive deep-brain stimulation technique that I pioneered in macaques, transcranial ultrasound stimulation (TUS), to manipulate abstract social representations. I will measure its causal effect on behaviour and neural representations in the DPS circuit using advanced computational analyses. This will not only reveal how the DPS circuit supports abstract social representations but establish that these abstract representations are causally necessary for effective social cognition. Conceptually, DeepSocial introduces a new framework with the potential to reveal mechanisms that unify social and non-social ways of thinking. Methodologically, DeepSocial pioneers a new deep brain stimulation method to determine the causal contribution of deep brain circuits to our ability to navigate the social world.

UKRI Gateway to Research · FY 2025 · 2025-08

Context Non-small cell lung cancer (NSCLC) is the leading cause of cancer death in the UK. Poor survival rates are principally due to late symptom onset and diagnosis, although long-term outcomes remain disappointing even for early-stage disease. NSCLC develops through the stepwise progression of pulmonary premalignant lesions (PMLs). Advances in imaging and screening programmes have led to more frequent detection of PMLs in high-risk individuals. This has created an unprecedented window to understand how the disease develops and intervene before it is established, a strategy known as cancer interception. However, there are currently no treatment guidelines to eliminate PMLs, representing a missed opportunity to prevent invasive disease.   A hallmark of cancer is the ability to evade immune elimination, despite the expression of neoantigens, mutated proteins that would normally be recognised as ‘foreign’. In lung cancer and beyond, immune evasion precedes development of invasive disease and occurs via discrete, targetable mechanisms of immune regulation. Interceptive immunotherapies that can target early immune regulation to halt the progression of PMLs hold promise to reduce NSCLC incidence and mortality.   Challenge the project addresses This project aims to identify key immune signalling pathways in lung carcinogenesis that can be targeted for cancer interception. I will use cutting-edge spatial transcriptomics (ST) technology to create detailed maps of the cellular phenotypes in PMLs and early NSCLC, from a globally unique patient cohort at UCLH undergoing lung cancer surveillance.   A key focus is on identifying tumour-reactive T cells, immune cells that can recognise and potentially eliminate cancer cells and PMLs. We hypothesise that dysfunction of these cells is a critical failure allowing NSCLC development from PMLs, in both its squamous (LUSC) and adenocarcinoma (LUAD) subtypes. Pinpointing and validating the targetable immunosuppressive pathways that restrain these cells holds potential for cancer interception.   In parallel, I will explore how modulating these early immune regulatory pathways impacts the response to neoantigen vaccines. This will provide me with a model to scrutinise tumour-specific T cells and understand how we might condition the pre- and early invasive lung microenvironment to maximise efficacy of preventive cancer vaccines about to enter clinical evaluation.   Aims and objectives The study has three main aims: Identify high-confidence regulatory targets posing a barrier to tumour-specific T cell activation in premalignancy and early malignancy in NSCLC using ST technology. Predict and synthesise personalised neoantigen cocktails through the use of whole exome sequencing (WES) on tumour samples. Functionally validate high-confidence interception targets (Aim 1) in the presence and absence of personalised neoantigen stimulation (Aim 2) in patient-derived tumour fragments (PDTFs).   Potential applications and benefits Understanding the molecular determinants of immune evasion during lung carcinogenesis will fill a fundamental knowledge gap and has transformative clinical potential for lung cancer prevention. This project could lead to strategies for preventing NSCLC in high-risk individuals and may also improve perioperative immunotherapies for established disease. The exploration of factors that condition the nascent tumour microenvironment (TME) for optimal cancer vaccine responses could enable rational combination treatment approaches to maximise early immune protection. The project promises to build an unprecedented spatial subcellular gallery of carcinogenesis and a library of actionable immune targets for future research. The findings are likely to have multi-cancer applicability, due to the universal role of the immune system in cancer biology.

UKRI Gateway to Research · FY 2025 · 2025-08

Context   Diffuse midline glioma (DMG) is a highly lethal brain tumour that most often occurs in young children with around 200–300 cases diagnosed each year in Europe. The location of these tumours, in highly sensitive areas of the brain, prevents them from being surgically removed. Radiotherapy, using high energy x-rays to damage and kill the cancer cells, is currently the only treatment that is standardly offered to patients as it is the only treatment that has been shown to prolong survival. However, radiotherapy only partially shrinks the tumours or temporarily slows their growth and patients rapidly die of the disease, usually within a year of being diagnosed. Many clinical trials aiming to improve patient outcomes have tested different drugs, either alone or in combination with radiotherapy, but none has increased the survival of patients. There is therefore an urgent need for an alternative way of designing new treatment approaches.   My team is researching how some of the cancer cells in DMG are able to survive radiotherapy. The tumours are made up of a variety of different cancer cells as well as normal brain cells, such as immune cells. We performed statistical analyses of data from tumour samples from patients to understand which of these types of cells were associated with worse response of the tumours to radiotherapy. We discovered that microglia, a type of immune cell in the brain, in the tumour are associated with the radiotherapy being less effective. We found that these microglia have a two-way molecular communication with cancer cells and may slow down the proliferation of the cancer cells. Radiotherapy mostly kills rapidly proliferating cells, so this slower proliferation may be what enables them to survive.   The challenge that the project addresses   We do not currently understand whether the communication between microglia and cancer cells changes in response to radiotherapy and how this communication, whether changing in response to radiotherapy or not, results in cancer cells surviving the treatment. Therefore, we do not know how to design new treatments that substantially improve survival through blocking the most vulnerable part of the molecular communication network between the cells or the effects of this communication on the behaviour of the cancer cells.   Aims and objectives   My proposal, combining experiments and computer modelling, aims to: (1) understand how the interacting cancer cells and microglia within the tumours are affected by radiotherapy and how these interactions lead to some of the cancer cells surviving radiotherapy; (2) find vulnerabilities in the molecular interactions between cancer cells and microglia and drugs that target those vulnerabilities to combine with radiotherapy and enhance its effectiveness.   Potential applications and benefits   My proposal will provide an understanding of how cancer cells are able to survive radiotherapy by communicating with microglia and a new treatment approach, combining a drug with radiotherapy to overcome this survival strategy. Following the completion of my project, I will work with other DMG researchers to confirm my team’s findings in other experimental systems and work with clinical paediatric neuro-oncology researchers to design a clinical trial to test the new drug-radiotherapy combination in DMG patients in the future. Also, we will release the computational methods used and developed by my team as open-source software, allowing other researchers to apply them to understand and target communication between cells in other contexts.

UKRI Gateway to Research · FY 2025 · 2025-08

Context For over a century, medical professionals have recognized the eye's potential to reveal insights into overall health, from cardiovascular to neurodegenerative disease. Recent advances in retinal imaging, the availability of large datasets, and rapid progress in artificial intelligence (AI) are poised to significantly enhance this capability. This has led to the emergence of a new field for which, in 2020, we coined the term “oculomics” (the use of AI to analyze retinal images and detect hidden indicators of physical and mental health). The Challenge Current AI models in oculomics lack the performance, robustness, and clinical applicability needed for widespread deployment. Additionally, there is a scarcity of labeled data, particularly for underrepresented communities, hindering the development of fair and equitable models. Many traditional health checks also have low participation rates, making it challenging to screen and diagnose diseases early in the community. Aims and Objectives This project aims to develop and refine an AI-powered oculomics platform for community health screening, focusing on: Enhancing AI Model Performance: Improving an existing AI foundation model, RETFound, by integrating language capabilities, addressing data scarcity using synthetic data, and fine-tuning the model for specific disease detection and diverse populations. Rigorous Evaluation and Validation: Conducting a comprehensive evaluation of the model's performance and clinical utility, ensuring it meets clinical standards and is validated across diverse populations and healthcare settings. Clinical Translation and Implementation: Developing a "Target Model Profile" (TMP) to align AI models with community health priorities; considering regulatory requirements, modeling real-world impact, and identifying potential barriers and enablers to implementation. Potential Applications and Benefits This project has the potential to revolutionize community health screening by: Improving Early Disease Detection: Enabling the early detection of various health conditions, including cardiovascular disease and dementia, through routine eye checks, leading to timely intervention and improved health outcomes. Increasing Screening Accessibility: Leveraging the high attendance rates of routine eye checks to reach a wider population, particularly underserved communities who may not regularly participate in traditional health checks. Reducing Health Inequalities: Addressing health data poverty by using synthetic data and ensuring the models are fair and perform well across diverse sociodemographic groups. Enabling Personalized Community Screening: Creating a flexible platform that can be tailored to the specific needs of different communities, allowing for the detection of a range of health conditions based on local priorities. Conclusion By combining AI with the accessibility of eye checks, this project aims to transform community health screening, leading to earlier diagnoses, better health outcomes, and a more equitable healthcare system.

UKRI Gateway to Research · FY 2025 · 2025-08

Context. Per- and polyfluoroalkyl substances (PFAS) are a growing list of at least 5,000 synthetic chemicals present in paints, consumer goods, household items, building and construction parts etc. These fossil fuel derived chemicals are toxic/carcinogenic, wreak ecological havoc, and degrade into molecules that are bio-persistent and/or have a high global warming potential. Commonly also referred as ‘forever chemical’ due to their adverse environmental impact, the PFAS challenge remains unresolved despite pressing environmental and regulatory targets set by the Environmental Protection Agency (EPA) in the US and the European Chemicals Agency (ECHA). Transitioning away from PFAS chemicals is one of the technical textile industry’s greatest challenges. This proposal aims to address this unmet need. Technical textiles are used across various industries to meet the functional requirements of wide range of consumer, industrial and healthcare products. Textile manufacturing operations in the UK are producing high-value, performance products, across luxury clothing (e.g., Burberry) and technical textiles used for medical, military, building, and automotive industries, amongst others. There is a growing international demand and the economic case for plausible PFAS replacement is strong:  textile manufacturing has a rich tradition in the UK and currently accounts for 2% of its Gross Value Added (GVA). A 2024 Henry Royce Institute report highlighted PFAS replacement as a clear opportunity for the UK, our proposal is timely. Challenges. Liquid-repellent coatings are essential for technical textiles to ensure outdoor weather protection (e.g. rainproof jackets and architectural textiles), hygiene (e.g. healthcare and infection control), and meet industrial safety compliance (e.g. chemical resistant apparel) with minimal compromise to user comfort (breathability) and functionality. For enhanced soil/liquid-repellent performance, PFAS provide best-in-class performance, but are being regulated out for use in textile because of their environmental impacts. The challenge textile manufacturers now face is to find a functional and sustainable PFAS-free liquid-repellent processing technology. There are broader implications: Europe’s annual health costs from PFAS exposure is estimated to be >£50B and globally may exceed >£1T. Aim and Objectives. We seek to introduce new amphiphobic coatings that repel water as well as low surface tension liquids (e.g. solvents, oils etc.) which are PFAS-free. Salient objectives are to combine recent advances in metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) based coatings and apply them on fabrics either through direct growth of MOF/COF-based nanocoatings or their nanocomposites. Crucially, we will address the scalability issue for these disruptive advances in sustainable textile coatings technology. Industry partners will be engaged to ensure that we go from current laboratory scale, batch approach to coatings and advance it to continuous coatings approach over the course of this project. Both of our approaches (direct growth and particle-in-polymer coating) offer tailorable substrate adhesion for durability. Both offer an opportunity to target fluorine-free liquid-repellence to catalyse a step-change from state-of-the-art for textiles. Applications and Benefits. By focusing on technical textiles, the proposed PFAS-free amphiphobic coatings will address urgent needs across multiple end use sectors, including healthcare, consumer, and industrial products. The fashion and textile industry contributes >£60B to the UK economy and employs >1M people. Thus the potential impact is broad and signifcant. Industry stakeholders (see Letters of Support) are strongly engaged to offer stewardship, ensure practical relevance, and rapid commercialisation. Success will lead to new coatings products, generate employment and move the society towards a sustainable solution to the PFAS challenge.  

UKRI Gateway to Research · FY 2025 · 2025-08

Human actions are causing profound changes to the world’s biodiversity. Our understanding of these changes is still remarkably limited, especially in tropical regions, and for animals other than birds and mammals. Yet, biodiversity supports natural systems and delivers many direct benefits to human societies such as pollination, control of pest populations, and disease regulation. Developing a deeper understanding of the causes of global biodiversity changes is a critical research frontier. On land, the main drivers of biodiversity change are land-use change (largely for agriculture) and climate change. We are beginning to uncover examples where the effects of land use and climate interact, often increasing rates of biodiversity change compared to if these pressures acted alone. Such an interaction comes about for two main reasons. First, when we remove natural habitats, we create barriers that make it harder for many species to move to new suitable areas as the climate changes. Second, agricultural areas and cities experience hotter heat waves and colder night-time temperatures, adding to the effects of global climate change. Despite some recent advances, our knowledge of the effects of land-use-climate interactions is limited to a few well-studied locations and groups of species. Global biodiversity models help to fill our knowledge gaps, and models that predict the effects of environmental changes on the distribution of species across landscapes are a particularly influential example. Such models allow us to compare observed with predicted biodiversity changes to identify key drivers, build a complete picture of when and where the biggest changes are happening, and make predictions of future changes. Biodiversity models fill a similar role as climate models, but are in a much earlier stage of development, and crucially do not capture the interaction of land use and climate. Our project will develop cutting-edge global models predicting the movement of species distributions across continental or global landscapes under climate change and land-use change, capturing, for the first time at such large extents, the effects of both habitat fragmentation and variation in local climatic conditions among different habitats. This will enable the most comprehensive assessment yet of the effect of these key drivers of biodiversity change across multiple different groups of animals: the better-studied vertebrates, but also insects, which are typically neglected. The development of the new models (WP3 and 4) will be informed by novel analyses of global data describing observed changes in species distributions (WP2), revealing which regions and groups of species are most at risk under ongoing rapid land-use and climate change. All of this will be underpinned by the development of new estimates of how different land uses impact fine-scale local climatic conditions (WP1). Together, the insights will allow the most robust projections yet of future biodiversity changes, which will be subjected to a full accounting of uncertainty and evaluated against independent data. The project will have four main outputs: 1) identification of the species and regions most at risk of unanticipated biodiversity changes; 2) a new global biodiversity modelling paradigm that can be adapted to address new questions regarding biodiversity change, such as the crucial but neglected role of species interactions; 3) a new framework for evaluating model projections against independent observations of species occurrence changes; and 4) a new curated global database describing changes over time in species' occurrence in different areas.

UKRI Gateway to Research · FY 2025 · 2025-07

Alzheimer’s disease (AD), an age-related neurodegenerative disease, afflicts an estimated 850,000 individuals in the UK. AD develops over time and the progressive damage to brain cells manifests in the later stages as severe memory loss and the inability to perform daily tasks, ultimately resulting in death. Currently, there is no cure for AD. Amyloid plaques and neurofibrillary tangles in the brain are hallmarks of AD. Accumulation of amyloid-ß (Aß) peptides is commonly considered the trigger of developing AD. Aß is continuingly produced, with an average turnover rate of approximately 8% per hour in the healthy adult human brain. Thus, Aß clearance is crucial for maintaining its homeostasis and preventing its aggregation and pathological consequences. Aß is mainly removed from the brain by the flow of brain fluid, including the interstitial fluid and cerebrospinal fluid (CSF), via the blood-brain barrier, blood-CSF barrier (BCSFB), and CSF/perivascular pathways. CSF is primarily secreted from the choroid plexus (CP), a lesser known but important brain structure suspended in the CSF-filled ventricles. The CP is comprised of a layer of epithelial cells (CPe) jointed by tight junctions surrounding a highly vascularized stroma with fenestrated capillaries, thus forming the BCSFB and connecting the peripheral circulation with CSF/brain. The CP is a key component of CSF clearance pathways for protein removal from the brain, including Aß clearance. Studies have found lipid droplet (LD) accumulation and Aß aggregation together with decreased secretory activity in the CP in ageing/AD. The brain is the most cholesterol-rich organ and altered cholesterol balance has been linked to AD. SOAT1 is an enzyme that converts excess brain cholesterol into cholesterol esters (CEs) for storage as cytoplasmic LDs. Interestingly, Soat1 gene knockout or inhibiting SOAT1 with a compound can reduce Aß aggregation in the hippocampus (a brain region involved in learning and memory) and improve cognitive performance in AD mouse models, suggesting that SOAT1 has the potential to be an AD therapeutic target. Currently, clinical trials of SOAT1 targeting drugs (intended for atherosclerosis) have failed, possibly due to peripheral toxicity. Our preliminary data obtained from mouse models have revealed accumulation of LDs and Aß in CPe in ageing/AD. Moreover, we found that Soat1 gene knockout can markedly alleviate these phenotypes in CP. Given the importance of CP to Aß clearance, we reason that in the CP, SOAT1 can cause LD accumulation in CPe in ageing/AD, leading to CP functional decline and consequently diminished Aß clearance. In this proposal, we hypothesize that repressing CPe SOAT1 function can reduce LD accumulation and thereby enhance Aß clearance from the CP, resulting in a de-ageing/therapeutic effect in ageing/AD. This study will employ a collection of tailored transgenic mice, gene manipulation and cutting-edge omics techniques to investigate the following: 1). The contribution of CPe SOAT1 to AD pathology. 2). AD induction with SOAT1 overexpression in CPe. 3). The influence of SOAT1 on CP’s ability to remove Aß. 4). The effect of targeting CPe Soat1 with gene therapy in AD mice. 5). Lipidomics and transcriptomics analyses of CP in relation to CPe SOAT1’s role in ageing/AD. In summary, our study will unveil the role of CPe SOAT1 in AD pathology and provide robust evidence for assessing the possibility of using CPe SOAT1 as an AD therapeutic target, paving the way for developing novel therapies for AD.

UKRI Gateway to Research · FY 2025 · 2025-07

This project integrates quantum optics into ultrafast imaging, bridging quantum optics, attosecond science, and photochemistry. It aims to refine photoelectron momentum distributions, enhance holographic resolution, and improve control over quantum coherence in electron motion. Selectively enhancing quantum features like entanglement and non-classical correlations could unlock new opportunities in attochemistry and quantum technologies. Attosecond science (10?¹8 s) explores extreme non-equilibrium conditions, where laser fields rival atomic binding forces—some of the shortest time scales in nature. This regime holds promise for overcoming decoherence in quantum technologies. However, attosecond imaging mainly relies on classical light, leaving the potential of non-classical light largely unexplored. Most attosecond quantum optics studies focus on high-order harmonic generation (HHG), for which macroscopic effects like phase matching are important. In contrast, photoelectrons provide a single-emitter response, making them ideal for detecting quantum correlations. Experiments suggest that squeezed states can dramatically modify electron trajectories, suppress interference, and enhance resolution. Additionally, field properties transfer to emitted electrons, offering new ways to control electron motion at the quantum level. The project will: Develop a quantum electrodynamic path-integral model to describe photoelectron momentum distributions in strong laser fields, fully incorporating electron-ion and electron-light entanglement. This extends the UCL-developed Coulomb Quantum Orbit Strong-Field Approximation (CQSFA) to include quantum electrodynamical (QED) effects and shaped light fields. By treating binding potentials and external laser fields equally, the CQSFA has advanced our understanding of holographic interference and multipath electron dynamics. The refined QED-CQSFA model will incorporate quantum entanglement and be benchmarked against advanced atomic physics simulations like R-matrix with time dependence. Generate high-intensity squeezed light (>10¹² W/cm²) with tunable frequencies, time profiles, and polarizations, developing a bright squeezed vacuum (BSV) source via four-wave mixing to produce intense phase- and amplitude-squeezed states. Perform high-precision holographic measurements to analyze electron-ion entanglement in attosecond photoemission, using high-fidelity momentum imaging and shaped squeezed light to manipulate holographic spectra. This will explore quantum advantages beyond classical methods in attosecond imaging. Identify and measure quantum correlations between electron pathways, electron-ion entanglement, and light-matter interactions. Time-correlation filtering techniques will help separate distinct quantum pathways, employing the QED-CQSFA alongside quantum optics and quantum information tools. Develop and test pump-probe schemes with intense squeezed light to create and control quantum superpositions relevant to bond breaking and chemical reactivity. Theoretical proposals will be followed by experimental realizations, initially targeting atoms and small molecules. These studies will explore an uncharted domain, as ultrafast pump-probe schemes typically use weak conventional light. This project could drive breakthroughs in: Quantum-enhanced imaging, improving precision in electron dynamics for attochemistry and condensed matter physics. Quantum information science, providing insights into coherence and entanglement control in ultrafast processes. Photonics and sensing, advancing high-precision spectroscopy using squeezed light. Additionally, this research aligns with the UK National Quantum Strategy, aiding the integration of quantum-enhanced sensing into emerging technologies. It also supports UKRI’s Grand Challenges in Quantum Physics for New Technologies and Physics Far from Equilibrium. The project will train the next generation of researchers through: Interdisciplinary collaboration in quantum optics, attoscience, and photochemistry. Technical skills development in ultrafast spectroscopy, quantum field theory, and computational physics. Public engagement via Quantum Battles in Attoscience and Atto Fridays. By merging quantum light control with attosecond imaging, this project will pioneer new approaches to ultrafast electron dynamics, unlocking new ways to manipulate quantum coherence, entanglement, and chemical reactivity.

UKRI Gateway to Research · FY 2025 · 2025-07

Non-Ketotic Hyperglycinemia (NKH) is a life-limiting inherited metabolic disease that becomes apparent in babies soon after birth with lethargy, breathing difficulties and neurological problems, including epilepsy. NKH causes profound delay in development, complex epilepsy and premature death, usually in early childhood. There is no cure for NKH and current treatments have limited effect. Hence, there is an urgent unmet need for new therapies. NKH is caused by a genetic alteration in a gene, glycine decarboxylase (GLDC), that encodes a component of a protein complex called the glycine cleavage system (GCS). Loss of GCS function prevents the normal breakdown of a small molecule called glycine in the body. As a result, glycine accumulates to harmful levels, and glycine breakdown products cannot be used in crucial metabolic reactions in the brain and liver. We have designed treatments that address both issues and tested these in a GLDC-deficient mouse model which recapitulates key features of NKH. We have developed gene therapy for NKH to introduce a ‘normal’ copy of the affected gene, encoding glycine decarboxylase (GLDC), into the cells in the patient’s body to provide the function that their own faulty copy does not have. We developed and optimised a vector based on components of adenoviral associated virus (AAV) to enable expression of GLDC in the brain and liver which are the key target tissues in NKH. Treatment of the NKH mouse model at neonatal stages showed long-lasting correction of metabolic abnormalities in the brain including normalisation of folate metabolism and lowering of glycine to normal levels. Treated mice showed 100% survival to 12 weeks of age. We monitored mice for up to 15 months and found no adverse effects of treatment. In summary, analysis of AAV-GLDC treated mice supports the hypothesis that this approach may be of therapeutic benefit in NKH. While we have shown normalisation of metabolic abnormalities and biomarkers, additional measures of effectiveness were not previously available in the GLDC-deficient mouse model. Therefore, in parallel with development of novel treatments we have carried out in depth phenotyping of the NKH mouse model, including analysis of metabolome, gene expression, neurological and behavioural abnormalities. These provided novel insight into the NKH disease process and, importantly, provide read-outs for testing effectiveness of therapies. In this project we will make use of these read-outs to test effectiveness of AAV-GLDC gene therapy using selected tests of neurological function and sensitivity to drug-induced seizures, which we found to discriminate between GLDC-deficient and unaffected mice. Demonstration of a protective effect will provide evidence to support next steps in translation progress towards a clinical trial. Another key step towards clinical trial, is transfer of AAV-GLDC vector production to a GMP (good manufacturing practise) facility for scale-up and manufacture using a process that would then be applicable for production of AAV9-GLDC for treatment of patients. We have engaged a GMP facility and initiated pilot manufacture of vector to de-risk process development. In the current project we will test the GMP-like vector in the NKH mouse model, to confirm activity. We have scheduled a regulatory advice meeting with MHRA to obtain advice on requirements for safety testing and clinical trial design. Overall, this project will provide supporting data for a funding application to support final stages of pre-clinical work, vector manufacture, pivotal safety study and first in child clinical trial.

UKRI Gateway to Research · FY 2025 · 2025-07

Soil is of paramount importance for various critical functions such as food production, water regulation, habitat provisioning, and carbon storage. Soil compaction is one of the factors endangering food security. Soil compaction not only affects soil structure but also has repercussions on plant root development, proliferation, and overall growth and productivity. It's concerning to note that approximately twice the total area of Wales faces the risk of soil compaction and the substantial annual losses of £1.2 billion in the UK due to soil compaction, degradation, and erosion. This damage is primarily attributed to the use of heavy machinery in agriculture, varying from tillage to harvesting, as well as soil characteristics like clay content, water content, and crop rotation practices. Therefore, INTACT's vision is to facilitate the utilization of future agricultural autonomous systems, drawing inspiration from nature, such as the alpaca's foot, to ensure sustainable food production. Drawing from previous research on bioinspired hooves and the development of soft, stiffness-controllable robotic structures, this project aims to design, fabricate, model, and validate a BioINspired adapTAble Caring fooT (INTACT) for robots. The alpaca foot was chosen as bioinspiration due to its capability of walking in terrains with high variability (e.g., hills) while producing a low trampling impact on the terrain. INTACT will be integrated into a legged robot to investigate and evaluate its adaptability to changing terrain conditions while reducing the trampling damage on the terrain. Close collaboration with key academic partners and industrial experts will enhance the success of this project.  The academic partners are: Dr Iain Gould (Associate professor in soil science), University of Lincoln and Prof John Hutchinson (Professor of evolutionary biomechanics), Royal Veterinary College. The industrial partners are Marc Jones (Commercial Director) from ANTOBOT and Paul Fitz (Business development manager) from ANYbiotics AG, who will advise about the translation of the INTACT in farming and field robots applications, respectively. The partners will be part of the expert working group that will meet every six months to advise about the design, evaluation, implementation, and translation of the outcomes of this project. The objectives to achieve this ambition are: 1) Studying, understanding, and abstracting the characteristics/embodiment of the biological alpaca foot related to its terrain caring and adaptability capabilities. 2) Creating a bioinspired robotic foot that embodies the alpaca foot terrain caring and adaptability capabilities 3) Validating the robotic foot embodiment contribution to its adaptability and caring terrain capabilities. Potential applications and benefits: This project's three main beneficiaries are (i) researchers, (ii) policymakers, and (iii) industry. (i) INTACT can advance the knowledge in the field of bioinspired designs by investigating new passive bioinspired mechanical foot whose body enhances its adaptability to terrain while decreasing the impact on the terrain and vegetation. (ii) Incorporating INTACT into legged robots can foster the creation of innovative algorithms that seamlessly cooperate with INTACT, thereby augmenting the adaptability of these legged robots. This will also enable their use in agriculture. (ii) INTACT's findings and outcomes, such as data collection, can aid policymakers in formulating and assessing strategies to address agricultural soil health challenges resulting from climate change and unsustainable practices. This will be accomplished by leveraging on the PL's prior experience contributing to Parliamentary POSTnotes [4,27]. (iii) The INTACT can widen and enhance the usability of commercially available field-legged robots.