University College London

UKRI Gateway to Research · FY 2027 · 2027-07

Dementia with a vascular component (DVC) is a common but understudied cause of dementia, with no established treatment and a poorly understood aetiology. The overarching aim of my fellowship is to identify the genetic and environmental causal determinants of DVC. I am using highly characterised post-mortem samples to improve diagnostic precision and gain novel insights into disease risk and progression. In the final three years of my fellowship, I will capitalise on our newly generated data to focus on genomic discovery, disease prediction, and clinical translation. My vascular dementia research programme will be delivered through three refined and high-impact objectives that build directly on achievements from the first phase: Genomic discovery in post-mortem diagnosed DVC. In the first phase, we genotyped almost 2,000 brain samples from seven UK brain banks. While genotyping was underway, we conducted the largest genome-wide association study (GWAS) meta-analysis to date of clinically diagnosed (pre-mortem) vascular dementia (VaD), using data from MEGAVCID, UK Biobank, and FinnGen. We identified three novel genes associated with VaD risk and developed a polygenic risk score (PRS) to quantify individual genetic susceptibility. In the renewal phase, we will conduct the first GWAS of post-mortem diagnosed DVC, using our newly available brain genotype data. We will apply multivariable GWAS methods (e.g. MTAG, Genomic SEM) to boost statistical power for genomic discovery and gain insight into the shared and distinct genetic aetiologies of DVC with other dementia pathologies like Alzheimer’s disease. Finally, we will leverage whole-genome sequencing in the UK Biobank to identify rare and non-coding variants associated with DVC and its risk factors. Disease prediction and clinical characterisation. In the first phase, we developed a PRS for clinically diagnosed VaD. In the renewal phase, we will refine this score by incorporating discoveries from the post-mortem and multivariable GWAS (Objective 1), alongside PRSs for related traits such as stroke and hypertension, to improve predictive accuracy. We will test whether the best-fitting PRS is associated with routinely collected clinical phenotypes for VaD, helping to elucidate mechanisms and improve clinical risk stratification. We will also examine whether genetic risk for DVC influences cognitive decline across the life course, identifying when individuals at high genetic risk begin to diverge cognitively and at what rate compared to individuals with low genetic risk. Identifying causal risk factors and therapeutic targets. In the first phase, we conducted a phenome-wide association study (PheWAS) of VaD and followed up key traits using two-sample Mendelian randomization (MR) to identify the earliest manifestations of VaD and novel causal risk factors. We also performed a drug-target Mendelian randomization analysis of 46 lipid-lowering, antihypertensive, and anti-inflammatory drugs for repurposing potential to reduce VaD risk, identifying potential safety concerns with ACE inhibitors and a repurposing opportunity for ARDB1 agonists. In the renewal phase, we will apply drug target discovery methods using rare variants identified in Objective 1 to prioritise therapeutic targets. These will feed into the UK DRI Biomarker Factory’s drug development pipelines.  

UKRI Gateway to Research · FY 2026 · 2026-09

This research project delves into the world of the very small, exploring the fundamental building blocks of our universe. Our focus is on two different but complementary aspects: a) the precise properties of the Standard Model of particle physics, a theory that describes all the known fundamental particles and the forces that govern them; b) hypotheses that go beyond the Standard Model, to understand yet unexplained phenomena. Our Standard Model research concerns the precise details of the initial state appearing in particle collisions and the development of high precision simulations of collider physics processes. As well as improving our understanding of the Standard Model, this can facilitate discovery and interpretation of new physics. At, for example, the LHC, the colliding beams are effectively made up of the fundamental particles inside the proton - quarks and gluons, generically known as partons. Predictions for Standard Model processes, and potentially those beyond the Standard Model, require the precise composition of the proton, described by so-called parton distribution functions (PDFs). We are leading the development of PDFs used by both the experimental and theoretical analyses at the LHC and other high energy particle physics experiments.  Our PDF determination is continually improved in terms of theoretical sophistication and by including new experimental data. This will lead to a better understanding of current and upcoming measurements and will also influence planning for future experiments. Similarly, an accurate calculation and precise simulations of Standard Model processes are essential to interpret the experimental data from high-energy particle collisions, make discoveries of phenomena beyond the Standard Model and characterise their properties. One of the main current challenges is to precisely model the cascade of particles following the initial particle collisions and develop the tools to simulate them. We are involved in increasing the precision for calculations at particle colliders, which is particularly essential in order maximise the potential of the LHC. Our Beyond the Standard Model research explores the intersection of quantum gravity, dark matter, and neutrino physics. We will explore how ultralight dark matter interacts with the familiar particles we know, involving effective operators motivated by quantum gravity, a theory that tries to combine gravity with the rules of quantum mechanics. We will likewise investigate how quantum phenomena can be used in clocks to test the quantum nature of gravity and look for new forces. We will explore new theoretical quantum black hole-like solutions to understand gravity better, and compare and contrast quantum gravity with other physics theories to understand how information is handled in each. We will work on linking quantum gravity with string theory, investigating the impact on conjectures that are believed to hold in string theory, thereby gaining novel insights into the anthropic principle. As the second major theme, we will study neutrinos, to learn about physics beyond the Standard Model.  We will look for unusual ways neutrinos might behave, such as new decay modes and study how neutrinos interact in quantum gravity theories, impacting the phenomenon of neutrino oscillations. A major challenge in the field is determine the mass of neutrinos and their nature, i.e., whether neutrinos are identical to their antiparticles. A crucial process in this regard is neutrinoless double beta decay, where we will drive the determination of precise corrections to interpret current and future experiments.

UKRI Gateway to Research · FY 2026 · 2026-09

The solar wind is a collisionless plasma. Therefore, it is often not in a state of thermal equilibrium. A non-equilibrium state will manifest with significant deviations in the plasma's velocity distribution functions away from Maxwellian equilibria. In-situ measurements from spacecraft have shown many such deviations may occur, including temperature anisotropies, beams, and skewness in the distributions. When these deviations from equilibrium are strong, kinetic micro-instabilities may be excited. These instabilities act to restore the plasma towards an equilibrium state while they transfer energy from the particles into the electromagnetic fields. In this project, we will combine the latest spacecraft measurements from Parker Solar Probe and Solar Orbiter with our cutting-edge Arbitrary Linear Plasma Solver (ALPS) code to answer the question: "When and how do kinetic micro-instabilities impact the solar wind?" Our research will transcend the traditional analyses of plasma instabilities, which typically represent the distribution as a bi-Maxwellian, by accounting for the actual shape of the measured velocity distributions. We will use accurate particle measurements to determine the conditions under which the solar-wind plasma becomes unstable to a variety of kinetic micro-instabilities. We will determine statistically the temporal and spatial occurrence rates of unstable plasma intervals. Finally, we will determine how kinetic instabilities influence the development of plasma turbulence and vice versa. Through our integrated research programme that combines observations and theoretical/numerical analyses, we will transform the understanding of kinetic instabilities in space plasmas.

UKRI Gateway to Research · FY 2026 · 2026-07

Numerous factors impact vertebrate form, with skull shape influenced by interacting external (feeding, habitat) and internal (genetics, phylogeny) drivers. Because of this complexity, pinpointing the determinants of skull shape during development and across evolution remains challenging. Now, a combination of massive datasets, advanced – and increasingly automated – visualization and biomechanical modelling techniques, and phylogenetic comparative methods will allow me to untangle the multiple factors driving skull shape and performance, organismal ecology and biodiversity for the first time.   Reptiles, such as crocodilians and lizards, are an ideal model system due to their enormous variation in diet, body size, and habitat. The project proposed for the FLF renewal phase aims to transform our understanding of the influences on skull shape in these vertebrates. I will build on data collected, methodologies developed, and knowledge generated during the first four years of the FLF to ensure project success and take a broad disciplinary approach – encompassing anatomy, morphometrics, biomechanics and evolutionary modelling – to achieve two new objectives:   1.       Identify the point during development when mechanical forces begin modifying skull shape (O1). 2.       Explore and identify the disparate factors influencing skull shape and performance across chameleons (O2).   I will address O1 through a unique sample of embryonic crocodilian specimens incubated at different temperatures, thus influencing movement during development. Using microCT scanning, morphometrics, multivariate statistics and biomechanical modelling methods (finite element analysis), I will quantitatively demonstrate when and how embryonic muscle contractions and movement interact with underlying intrinsic factors to modify bone formation. To address O2, I will build on our work on the veiled chameleon in the original FLF by digitising a broad sample of chameleon species, including taxa in which factors other than bite force are hypothesised to be driving skull shape (habitat, communication). I will apply phylogenetic comparative methods to reconstruct the pattern of skull evolution in chameleons and test the role of potential drivers. Biomechanical modelling will be used to test whether skulls are less optimised to resist biting loads in species in which factors other than bite force are thought to be the primary drivers of skull evolution.   As part of this programme, I will work with collaborators to overcome the labour-intensive and time-consuming issue of processing scan data by developing AI-driven deep learning models to allow faster model generation for downstream analyses of larger samples. This methodological advance will allow future researchers to address other large-scale biological questions.   This project is important as it will fundamentally change our view of how different forces act during the life of an individual and across evolutionary time to shape organismal form and biodiversity, leaving the discipline looking very different than before. Current and future workers in diverse fields (evolutionary and developmental biologists, palaeontologists, engineers) will benefit from massive datasets, efficient new workflows, and new repositories for 3D anatomical data. The project will solidify existing and establish new collaborations, and provide vital training for early career scientists. Additionally, there will be extensive benefits for stakeholders outside research including: open-access, interactive anatomical atlases of numerous reptile species available to researchers, students and the public, addressing research and education inequalities; contributing to the new UCL Biosciences curriculum; digitising museum collections to produce new and engaging outreach opportunities and exhibits; and reducing animal use in research.

UKRI Gateway to Research · FY 2026 · 2026-07

Vision This study will use magnetoencephalography (MEG) and computational modelling of MEG data to predict the outcomes – i.e., reduction in psychotic symptoms and/or improvement in cognition – of taking a glutamatergic treatment in individuals with early psychosis. My group's work in the first half of this project has shown that we can use modelling to infer reduced pyramidal (excitatory) neuron excitability from M/EEG data in people with psychosis, including in prodromal individuals prior to their first episode of psychosis. Ongoing work in my group and my collaborators in both humans and in genetic mouse models (with reduced pyramidal neuron function) is also revealing which M/EEG paradigms are most sensitive to revealing pyramidal neuron dysfunction, and also the reliability of these measures. These two findings – that pyramidal excitability is reduced even in very early psychosis, and which M/EEG paradigms are most sensitive to detect this reduced excitability – form the basis of the current project. Objectives We will recruit around 80 individuals with early psychosis for an experimental medicine study. They will be asked to take D-serine (n=50), a glutamatergic (NMDA) receptor modulator that is freely available over the counter as a health food supplement, or placebo (n=30) for 12 weeks. Participants and researchers will be double blind to medication status. We will obtain MEG scans and symptom and cognitive measures at baseline and after 12 weeks. In the MEG scan, lasting around one hour, participants will undergo auditory oddball, 40 Hz auditory steady state, and 'resting' paradigms. We will analyse these paradigms using dynamic causal modelling and estimate function of excitatory and inhibitory neurons in each participant.  Our primary objective is: to assess whether inferred excitatory neuron function at baseline predicts change in symptoms or cognition after 12 weeks of D-serine? Our secondary objectives are: to assess whether  i) MEG data features related (in previous work) to excitatory neuron function at baseline predict change in symptoms or cognition after 12 weeks of D-serine? I.e., is modelling necessary for treatment outcome prediction? ii) Can we demonstrate changes in inferred excitatory neuron function from baseline to the end of the study in those taking D-serine? Does this change correlate with symptom or cognition improvement? Areas of focus We are focusing on people with early psychosis because it is widely hypothesised that this group will be most responsive to glutmatergic treatments. We are focusing on both psychotic symptoms and cognitive symptoms because in previous work I have shown that both relate to the balance of excitatory and inhibitory function in cortex (Adams et al., 2022, Biol Psych). Furthermore, there is no established treatment for cognitive symptoms in psychosis. We are focusing on glutamatergic treatments because of the wealth of evidence implicating cortical NMDA receptors in psychosis risk. Why it's important Pharmaceutical companies have invested around $2.5B in glutamatergic treatments for psychosis but these have failed Phase 3 trials. Discovering a way to stratify patients by predicting their response to these treatments could unlock this enormous investment, for patient benefit. Why it will succeed My group have modelled these M/EEG paradigms in multiple data sets and are  demonstrating validity and reliability of these measures in ongoing work. We are ideally placed to estimate excitatory neuron function as accurately as possible.

UKRI Gateway to Research · FY 2026 · 2026-06

Biologists continue to debate two fundamental and interconnected questions about how communities and populations respond to environmental stress: Do ecosystems gradually respond to change, or do they reach tipping points triggering abrupt shifts to new, stable conditions? And, if populations collapse, what happens to their genetic diversity—can it recover, and how? These questions are central to understanding ecological and evolutionary resilience and are especially urgent given accelerating anthropogenic impacts on ecosystems. Critically, they are deeply linked: ecological shifts—whether abrupt or gradual—often drive population crashes, creating conditions under which genetic change and recovery must occur, or face extirpation. To address these questions, long-term time series data are vital. However, most ‘long-term’ monitoring began in the 1970s, meaning we cannot directly measure the magnitude of biodiversity change associated with key events such as the Industrial Revolution and post-war agricultural intensification. Lakes, described as ‘sentinels’ of global change, are uniquely valuable for answering both questions. Their sediments preserve rich biological and environmental records, offering high-quality archives of past change. As model systems, shallow lakes have underpinned the development of alternative stable state theory. With nutrient-enrichment, they transition between clear-water, plant-dominated to turbid, algae-dominated conditions—providing ideal systems for tracking population collapses and recoveries. We select five UK shallow lakes with contrasting disturbance and recovery histories spanning 200–300 years, offering compelling natural experiments. While their fossil records offer valuable ecological insights, they are unable to capture the full picture—especially for key indicator groups (e.g., fishes and invertebrates). Our project addresses these gaps by “time-travelling” through lake sediments, harnessing preserved DNA to reconstruct past biodiversity and examine how populations evolved over centuries. Previous limitations, especially the scarcity of macrofossils, can now be overcome through advanced molecular techniques—sedimentary ancient DNA (sedaDNA) and palaeogenomics, which provide powerful complementary approaches for reconstructing past ecosystems in unprecedented detail. Using these methods, we will reveal which species were present and how genomic diversity within a keystone species changed—particularly during periods of collapse and recovery. Critically, we will integrate molecular data with reconstructed environmental variables, including pollution and oxygen availability, allowing direct tests of how ecosystems and populations responded to specific anthropogenic pressures. To address both questions, we ask: 1) Do ecosystems respond gradually or abruptly to environmental stress? Our preliminary fossil data suggest a gradual ecological trajectory, challenging the widely held view that lakes shift abruptly in response to stress, especially fish die-offs associated with eutrophication. To test this, we will integrate high-resolution sedaDNA biodiversity reconstructions with environmental data and apply time-series analyses to identify precise timings of ecosystem change. 2) What happens to genomic diversity following population crashes, and how do populations recover? We will study European perch—a keystone lake fish—using palaeogenomes from historical remains, spanning fish die-offs. This will reveal whether genomic diversity was lost or regained, what mechanisms enabled recovery, and whether populations adapted to new conditions. These insights are critical for understanding how biodiversity responds at the genomic level to abrupt environmental shocks, and for identifying conditions under which evolutionary recovery is possible. By uncovering the extent to which ecological and genetic resilience and recovery mirror one another, our findings will provide transformative insights into how ecosystems function, fail, and recover under centuries-long human pressure—improving our understanding of resilience and better informing strategies for conserving freshwater biodiversity.

UKRI Gateway to Research · FY 2026 · 2026-04

Inborn errors of immunity (IEIs) are severe genetic disorders of the blood that typically manifest early in life. They result from mutations in genes essential for the development and function of the immune system. As consequence children suffer life threatening infections leading to early death in the most severe cases. Scientists are developing new ways to treat these disorders by fixing the DNA of patients’ own blood stem cells. These cells can be taken out of the body, repaired in the laboratory, and then returned to the patient like a blood transfusion. Corrected stem cells then "home" naturally in the bone marrow and start to produce healthy blood cells potentially for life. We are particularly interested in X-linked agammaglobulinemia (XLA), a serious inherited disease that prevents the body from making antibodies, leaving patients very vulnerable to infections. The disease is X-linked so mainly boys are affected. It is caused by mutations in a gene called BTK (Bruton Tyrosine Kinase) that is involved in the development of B cells (the cells that make antibodies). Without this gene, B cells cannot fully mature in the bone marrow. Mutations are scattered throughout the whole gene. We have developed a new approach to fix all the mutations in the gene at once. This approach is based on a procedure called gene editing. Blood stem cells are treated with an enzyme that cuts the DNA at the start of the faulty BTK gene and are given a correct copy of the BTK gene that the cells can use to repair themselves, inserting the gene exactly where we want, in its original position. The correct BTK gene was initially delivered using a virus called AAV6 as carrier, which worked well at first, restoring normal B cells. However, long term tests in mice revealed some toxicity of the AAV6 virus with corrected cells declining overtime. To solve this, we teamed up with a new company, NV Therapeutics, which makes non-viral DNA delivery systems using DNA circles. These circular DNAs are thought to be safer, less toxic and much cheaper to produce than viral systems. We will test these new circular DNAs in gene editing experiments using blood stem cells from XLA patients. We will see if stem cells, upon gene editing, become capable to develop into B cells and produce antibodies. We will more importantly test cells edited with the new protocol in animal experiments. The goal is to see whether the corrected cells survive long-term in a living organism and produce healthy B cells. If these tests show that the circular DNA system is effective, we plan to apply for further funding to move toward clinical trials in patients. This will mark the first clinical application of the circular DNA for gene editing. We envisage that many others will follow, as the same procedure can be used to treat almost any IEI.

UKRI Gateway to Research · FY 2026 · 2026-03

Societies are grappling with housing unaffordability as well as a growing demand for social care. At the intersection of these problems lies the provision of supported-housing: state-subsidised housing provided alongside some degree of care. Currently accommodating over half a million people in England – ranging from people with disabilities to children in care – the sector has historically been owned and managed by non-for-profit entities. However, over recent decades for-profit investors, and especially large-scale institutional-investors, have acquired a growing proportion of the sector and its associated £8 billion per-annum state-subsidies. This trend is most advanced in elderly and children’s care homes - where at least four out of every five bedspaces are provided by for-profit investors - but is increasingly prevalent in other aspects of supported-housing including specialist supported-housing and temporary/exempt accommodation.  Whilst certain forms of private investment (e.g. social impact investing) likely bring social-benefits, this overall trend has prompted a range of concerns that for-profit investors are charging monopoly rents for inadequate levels of care, or adopting unsustainable debt-laden business models that transfer state subsidies to off-shore tax-havens. The Rentierization of Supported-Housing (RoSH) research project, based in the Bartlett School of Planning at UCL (BSP/UCL), will interrogate this trend, situating it as part of the broader “rentierization” of the UK political-economy whereby, having privatised socially-critical and scarce assets subject to limited competition, the state (and citizens) increasingly rents them back from the private-sector (Christophers, 2020). The project has three main aims. First, through linking datasets and tracing flows of money between different corporate entities, RoSH will granularly map and detail the location, operator and owner of every publicly-reported supported-housing unit in England, thus helping identify in whose hands the associated state subsidies ultimately end up. Second, the project will examine the causes of this rentierization process. Drawing on document analysis and key-informant interviews with state, market and non-profit actors at the national-scale, it will examine the role of different factors (ideological, material, institutional) in bringing this new asset class into existence. Further, because supported housing is ultimately provided and overseen by local authorities, the project will use a local case study to explore how "markets" for supported housing are constructed and regulated on the ground. Third, the project will scrutinise the social and political-economic implications of this rentierization process. Specifically, it will examine the investment and management strategies adopted by different for-profit investors for different types of supported-housing and decrypt how the resources and strategies of for-profit investors interact with those of the state and non-profit actors, to shape power relations between them, ultimately affecting the long-term sustainability of the sector. Reflecting its policy-relevance, the project will be delivered in partnership with several organisations: Crisis homelessness charity, Joseph Rowntree Foundation, the Chartered Institute of Housing, and the ESRC-funded UK Collaborative Centre for Housing Evidence. All have a strong social interest in supported-housing and extensive networks with relevant policymakers and practitioners which the project will draw upon. In addition to academic publications, the project will deliver a range of publicly-available outputs targeted at policymakers and citizens, including an online, interactive map of supported-housing ownership, and a final project report that will be presented and discussed at a series of policy-roundtables. Together, these outputs will illuminate the growing role of for-profit investors in delivering and controlling this increasingly critical social infrastructure.

UKRI Gateway to Research · FY 2026 · 2026-03

Antimicrobial resistance (AMR) is currently one of the greatest threats to human health, security and economic stability that the world is facing. Many different species of pathogenic bacteria have now become resistant to the most powerful antibiotics that are available. This means there will be no drugs left to treat “superbugs” such as MRSA: patients, particularly vulnerable ones, will die from life threatening infections such as pneumonia; routine surgical operations will be highly risky; very sick patients receiving chemotherapy will be vulnerable to infection. The O’Neill Report (2016) identified several interventions that are critical to tackling this problem. In this Fellowship I will address two of these: discovering new antimicrobials and effective public awareness campaigns. Most current antibiotics use similar strategies to attack and kill bacteria, and many bacteria have evolved to ward off these attacks. Almost no new antibiotics have been developed over the last 30 years and none have completely new modes of action. I will tackle the challenge of developing novel antibiotics through a programme of fundamental research in the underpinning chemistry and biology. Nature provides inspiration for antibiotics with new modes of action: bacteria kill off other competing bacteria by secreting cyclic peptides – miniature flexible proteins that enter bacterial membranes, recognize specific components and then disrupt the membrane, killing the cell. I will develop new approaches to discover exactly how these cyclic peptides recognize their targets, as currently used methods cannot accurately measure exactly how a cyclic peptide recognizes a membrane component in the complex and fluid environment of a membrane. I will then use this knowledge to make new cyclic peptides that will be more effective antibiotics, killing bacteria that have already developed resistance to the most powerful antibiotics currently used in the clinic without toxic side-effects. Most people have little or no idea about the impact of AMR and how it develops, nor of the role they can play in stopping this. An effective public engagement campaign would have a high impact in reducing the demand for antibiotics and preventing the spread of infection. However, very little is known about how to tailor these messages to particular communities and how to address specific barriers such as access to healthcare, poor sanitation or precarious employment that drive antibiotic misuse. I will develop an ambitious public engagement project focusing on underrepresented and marginalized young people in the Olympic Boroughs (Waltham Forest, Newham, Hackney and Tower Hamlets). I will raise awareness and understanding, communicating the science behind the crisis to these groups and engaging in dialogue to understand the challenges faced by their communities. In collaboration with existing community engagement groups, I will then train groups of young creatives in media and communication technologies. With these tools they will then produce media art that will give them a voice to communicate the crisis and impact of AMR to their communities in their own words.

UKRI Gateway to Research · FY 2026 · 2026-03

Chronic pain is a widespread health problem that can persist for years and severely impact quality of life. Some people seem more vulnerable to developing long-lasting pain, especially if they were exposed to stress, injury, or medical procedures early in life. Recent research suggests that one important factor in this increased risk may be the gut microbiota—the community of bacteria and other microbes living in our intestines—which plays a key role in immune system development and communication with the brain. This project explores whether injuries in early life can lead to lasting changes in the gut microbiota that increase the risk of chronic pain later on. Using a well-established model, we will examine how the composition of gut microbes is altered after early injury, and whether these changes alone can trigger greater pain sensitivity in adulthood. To do this, we will transfer gut bacteria from injured mice into germ-free mice to see if they develop similar pain responses. We will also study how chemical signals released by gut microbes—including small molecules and microscopic packages called extracellular vesicles—affect nerve and immune cells involved in pain. Importantly, we will look at how these effects differ between males and females, since pain mechanisms are known to vary by sex. Finally, we will investigate whether similar microbial patterns and signals are found in people living with chronic pain conditions like fibromyalgia or Ehlers-Danlos syndrome. By comparing results from mice and humans, and testing some of the findings in fruit flies, we hope to discover shared biological patterns and new biomarkers. These insights could help doctors identify who is most at risk for chronic pain and lead to new, personalized treatments that target the gut microbiome to prevent or ease long-term suffering.

UKRI Gateway to Research · FY 2026 · 2026-03

The key theme of this work programme is to build expertise in semiconductor device fabrication and measurement that will lead on to future quantum computing technology projects involving groups from African universities that are either established already or still developing in collaboration with the London Centre for Nanotechnology at UCL. Training of PhD students and early stage researchers with the transfer of skills is an essential core aspect of this proposal. The development of quantum technologies offers the potential for homegrown solutions to local challenges facing Sub Saharan African economies, fostering self-reliance and reducing dependency on external technologies. The future potential offered by quantum technology has been recognized this year as the United Nations General Assembly has declared 2025 as the International Year of Quantum Science and Technology. In fact UNESCO stresses the potential role that quantum science has in ‘shaping a more inclusive and connected world.’ The challenge that this project addresses is understanding the pumping of single hole and electron currents through germanium quantum well devices where control of the quantum mechanical spin properties form an essential part of the device architecture. The single hole or electron pumping allows individual quantum states to be addressed in a device that is compatible with established fabrication techniques, such as CMOS-Complementary Metal Oxide Semiconductor structures. In addition our collaborative project aims to explore new quantum states that emerge from collective interactions in one-dimensional semiconductor nanostructures, first seen in germanium and to investigate the technological applications of controlled spin properties. By focusing on developing Qubits using the materials germanium and silicon, we intend to lay the groundwork for advanced quantum computing technologies. This is a transformative field of research essential for the economic development of all countries. Quantum computing's unparalleled processing power can solve complex problems exponentially faster than classical computers, opening new avenues in several sectors and revolutionizing industries such as healthcare, agriculture, and finance. Investing in quantum technology can spur innovation, attract global partnerships, and build a skilled workforce. It positions African countries as competitive players in the global technology landscape, promoting knowledge-based economies. The project will combine the properties of high electrical quality germanium quantum well devices with on-demand hole or electron pumps, combining the accuracy, control and fidelity of adiabatic electron pumps with the ability to carry out routine spin manipulation with low or even at zero magnetic field. This collaboration between UK and African partners facilitates the transfer of key skills and expertise in quantum technologies. UK partners will share advanced techniques in nanofabrication, quantum state manipulation, and spin property control, providing hands-on training and mentorship to African researchers. This knowledge transfer will empower African partners to contribute significantly to cutting-edge research and development in quantum computing and semiconductor nanostructure-based research. In the long term, this collaboration will enhance the growth of a skilled workforce in Africa, capable of driving innovations in quantum technologies and educating the next generation of scientists. Developing a robust quantum computing ecosystem is crucial for economic growth, as it positions African countries as active players in a highly competitive and technologically advanced field. By training young scientists and engineers, we ensure that Africa can harness the economic benefits of quantum technologies. This strategic investment in human capital and technological infrastructure will create new economic opportunities, attract international partnerships, and foster sustainable development.

UKRI Gateway to Research · FY 2026 · 2026-03

The urgent need to shift away from fossil fuels and towards cleaner energy sources has spurred global interest in hydrogen as a potential solution. While renewable energy sources like solar and wind power offer significant promise, their intermittent nature necessitates complementary technologies. Additionally, the limitations of battery technology in terms of energy density and charging infrastructure hinder their application in heavy-duty sectors like transportation and large-scale energy storage. Many industrial processes and power generation systems rely on combustion technologies to satisfy their significant energy requirements. Hydrogen, with its zero-carbon emissions and high energy density, emerges as a promising alternative. It has the potential to revolutionise various sectors, including power generation, transportation, aviation, and industry. The global community recognises hydrogen's crucial role in achieving net-zero emissions. International initiatives and strategies, such as the EU's 2023 Roadmap on hydrogen standardisation and the UK Hydrogen Strategy (2023), highlight hydrogen's potential to decarbonise sectors that are difficult to electrify. Moreover, geopolitical shifts and energy security concerns have accelerated the adoption of clean energy solutions. The EU's REPowerEU Plan emphasises hydrogen's role in reducing fossil fuel dependence and strengthening energy independence. Gas turbines, with their high efficiency, quick start-up, fuel flexibility, and low emissions, are crucial components of future sustainable energy systems. However, current gas turbine combustors are not suitable for 100% hydrogen combustion due to its distinctive combustion characteristics. High hydrogen content mixtures in gas turbines can lead to increased flashback risk, thermoacoustic instabilities, and dynamic flames, negatively impacting performance and emissions. Therefore, developing new gas turbine combustors capable of robust, wide-ranging, and efficient operation with 100% hydrogen is imperative. This project aims to advance the understanding of hydrogen combustion by building upon previous research carried out during the original project. It will focus on two promising burner configurations: premixed jet and lean direct injection. The goal is to accelerate the development of these technologies for implementation in hydrogen gas turbines. This will be achieved by investigating flame interactions, optimising combustor design, and validating numerical models, through the following specific objectives; (a) Investigate flame interactions in premixed jet and lean direct injection burners, analysing their impact on flame structure and dynamics, (b) Gain insights relevant for thermal management and NOx reduction strategies by investigating spatial temperature distribution and NO formation, (c) Create a high-quality experimental dataset to validate and improve models of interacting hydrogen flames, (d)  Implement concept hardware in a demonstrator engine to understand system scaling and integration challenges, and (e) Develop strategies to translate research findings into practical applications and commercial technologies. This research will contribute to the development of efficient and low-emission hydrogen gas turbine combustion systems. The project will also have significant societal and economic impacts, including reduced greenhouse gas emissions, increased energy security, and job creation for skilled workers. By engaging with industry partners and policymakers, the research will accelerate the transition to a hydrogen economy and contribute to a more sustainable future.

UKRI Gateway to Research · FY 2026 · 2026-03

Digital twins - living, data-driven models that mirror real-world systems - are transforming engineering and medicine. A brain digital twin would enable researchers and clinicians to simulate, predict and personalise treatments for neurological diseases. However, this technology remains confined to a few elite laboratories that rely on multimillion-pound magnetic resonance imaging (MRI) scanners and high-density electroencephalography (EEG) arrays to acquire essential in vivo data. To reconstruct a brain digital twin, for example using the virtual brain (TVB) platform, one must: 1) reconstruct the brain structural connectivity between grey matter regions (i.e. a personal connectome), 2) associate each grey matter region with a meso-scale neuronal model of its function and 3) run simulations of brain functional dynamics, optimising the simulated signal on empirical MRI data, and -  when available - constrain it by EEG properties. This framework is complex and currently inaccessible to most. Our project aims to break this barrier by bringing the physics of brain digital twins to the heart of Africa. Unlike engineered systems, the brain has no blueprints. Yet, every neuronal spike, oscillating network, and macroscopic rhythm can be modelled using physics: electrodynamics, nonlinear dynamics, thermodynamics and network theory. And the instruments needed for in vivo data acquisition in humans (MRI and EEG) work on the physics principles of electromagnetism. The entire construction of a brain digital twin, therefore, has its foundations on core physics principles, translated by engineering into machines and computer simulations. By teaching and exploiting these principles, we can model the brain and develop a framework that works with minimal hardware while preserving scientific fidelity. Led jointly by investigators in the UK and Uganda, and partnered with experts in Pavia (Italy) and Marseille (France), with collaborators in Leiden (Belgium) and UK, we will: 1. Train a new generation of African and UK scientists in the physics that underpins brain activity, from single neurons to whole-brain networks; 2. Demystify and teach the physics of MRI, EEG and new low cost combination of EEG with tomographic imaging, fast neural Electrical Impedance Tomography (fnEIT), enabling local teams to build and maintain low-cost, energy-efficient data-acquisition systems; 3. Prototype “minimum-technology” brain digital twins, using ultra-low-field MRI and portable EEG to show that accurate virtual brains can be built outside elite facilities. Utilisation of fnEIT within the brain digital twin framework will be explored too. This work directly addresses urgent health needs in Sub-Saharan Africa. Epilepsy, for example, is highly prevalent across Africa yet vastly under-diagnosed and therefore untreated. A validated brain digital twin offers a safe, affordable “in-silico” platform to study seizures, test interventions and tailor treatment, without the prohibitive costs of conventional diagnostic workouts, including conventional neuroimaging, high-density EEG and in some cases extremely expensive stereoelectroencephalography. Beyond medicine, the project delivers capacity building and equitable research infrastructure. It strengthens African physics and engineering programmes, which in this context are tightly connected, creates open training resources, facilitates attendance to international conferences and organises a dedicated international “School of Brain Digital Twin's Physics” in Uganda, ensuring lasting impact, long after the grant ends. By uniting cutting-edge neuroscience with fundamental physics and frugal engineering, we will democratise one of the most exciting frontiers in science and prove that world-class brain modelling needs not be limited by geography or wealth.  

UKRI Gateway to Research · FY 2026 · 2026-03

The fellowship will support and enable the fellow to develop and undertake a programme of high-quality research to inform UK policy to prevent childhood obesity during and after the fellowship. It will enable his development and that of his team. Childhood obesity is a significant and challenging public health issue, with one in five children aged 10-11 in England affected. This prevalence is higher than in many Western countries and continues to rise, disproportionately impacting children living in poorer areas. Obesity negatively effects health and social outcomes in childhood and into adult life. Obesity costs the UK society around 3% of GDP. Childhood, particularly the first 1000 days and adolescence, offer important windows for intervention and the adoption of lifelong healthy behaviours. Building on work in the first phase of the fellowship, the research in the next phase will strengthen the evidence base for effective local action, particularly regulatory, fiscal and legal approaches. Local authorities possess unique levers to influence the environments that shape children's eating and physical activity. However, the evidence base supporting local interventions, particularly ‘hard’ policy levers like financial, regulatory, and legal measures, is underdeveloped. The research aims are threefold: 1.     To identify novel opportunities for local authority interventions. This will explore financial, regulatory, and legal approaches to promoting healthy eating and activity; and understand barriers to implementation. 2.     To develop a new UK child obesity-health model to quantify the short-term (within 3-5 years) health impacts of childhood obesity interventions. 3.     To use this model to simulate the impact of both existing and novel local policies, assessing their potential to improve children’s health and reduce health inequalities. The project will employ a mixed-methods approach. Project 1 will use in-depth semi-structured interviews with senior officers in local authorities. Project 2 will develop a computer-based model to estimate the impact on children’s health of public health policies. Project 3 will use the model to estimate the impact of policies identified in Project 1. The fellowship will also support the continued development of the fellow and his leadership skills and the development of one post-doctoral research fellow through a programme of structured training and development activities and through the programme of research. By providing protected time and training opportunities, it will allow consolidation and further development of the skills developed in the first phase of the fellowship. This research will provide better evidence to inform policy making in the UK and abroad, to help improve the health of children and the adults they will become. By publishing in leading open-access journals, presenting at conferences, engaging with media outlets and working with policy and practice professionals, the project will disseminate findings to academics, policymakers, and the public. The fellow’s role as a practicing public health consultant and co-director of the NIHR Policy Research Unit for Healthy Weight ensures strong connections with local and national public health bodies, facilitating effective knowledge translation.

UKRI Gateway to Research · FY 2026 · 2026-03

  Recent successes of gene-therapy treatments have demonstrated the power of perturbing protein sequences to cure genetic diseases. However, current challenges to genetic treatment include prohibitive costs, a lack of understanding of risk, and heterogeneity in clinical responses. More traditional treatments are able to target cellular properties; for example, drug treatments and blood transfusions that reduce the stiffness of red blood cells in sickle cell disease. However, many of these approaches suffer from a lack of quantitative understanding to enable precision judgements, such as how much drug to administer or blood to transfuse. To continue driving progress forward so that we can treat a range of diseases effectively, and make such treatments more widely available, we need to better understand how molecular and cellular properties translate to emergent function in tissues and organisms. In this fellowship, I will apply my recently developed theoretical framework that uses discrete and continuum simulations to capture how macroscopic biological functions emerge from the properties of mesoscopic structures formed by interacting microscopic biomolecules. The overall aim is to predict optimal changes to molecules and cells to control emergent properties and biological functions in soft biological matter. I will meet the following objectives:  1) To predict optimal perturbations to molecular interactions in human proteins to control macroscopic functions such as chemical reactions in cells. A recent paradigm shift in biology has revealed that many human proteins and RNA can condense or aggregate to form liquid-, gel- or solid-like structures under cellular conditions. Biomolecular condensates, a physiological example of this process, have been implicated in diseases including neurodegenerative diseases, infectious diseases and cancer. However, it is largely unknown how physiological and pathological molecular interactions contribute to condensate functions in health and disease. 2) To predict optimal nanobody and antibiotic treatments for bacterial biofilms. Bacterial biofilms are a leading cause of antimicrobial resistance, which is thought to cause 700,000 deaths each year globally, with a cumulative cost of $100 trillion by 2050 if no action is taken. Recent evidence suggests that viral phages expressed by various bacteria may have important effects on antibiotic resistance, but to contribute to improved treatments for the many diseases associated with such bacterial infections, we need to understand and combat the mechanisms that confer phage-expressing bacteria with these benefits. 3) To predict optimal drug, genetic and transfusion treatments for sickle cell disease. Pathological biophysical dynamics of red blood cells are a hallmark of diseases of the blood that affect millions of people worldwide, including sickle cell disease (SCD). In SCD, blood increases in viscosity and may clog in deoxygenated conditions, causing death if left untreated. There is an ongoing clinical effort to develop genetic and pharmacological treatments for SCD, but we lack tools to prioritise specific treatment strategies or to clinically monitor patients and identify complications before they manifest physiologically. By leveraging the underlying physical connections between these biological systems, I will bypass the effort to study each system in isolation and ensure that advances in each system create added value for the others. The theoretical framework will be applicable to a broad range of related systems in which molecular and cellular interactions generate emergent biological functions. The results in this fellowship will generate long-term impacts on human health by guiding treatments to molecular diseases and bacterial infections that affect millions of people worldwide.

UKRI Gateway to Research · FY 2026 · 2026-03

The initial phase the project provided an actor-centred perspective on the sociality of tax, with the aim of re-shaping academic and popular understandings of what taxes are and do. The extension of the project will continue to pay attention to the social relationships constituted by tax and develop a further focus on fiscal flows (including benefits, government spending, and subsidies) and the social impact of contemporary redistributive projects, with an emphasis on northern Europe in the context of uncertain times. It does this whilst remaining in conversation with the archival and ethnographic data pertaining to the global south, collected during the initial phase. The renewal phase aims to achieve evidence-based understanding of how fiscal flows shape social relationships in the identified fieldsites in the UK and Sweden, generating qualitative data on the impacts of the politics of welfare on people’s understanding of wealth production, labour, and sharing. The work will advance current understandings of welfare politics and the effects of different tax choices on social relations. The contemporary moment is one of crisis, with global financial flows in flux and national budgets under scrutiny. This research highlights that what is at stake in these times of fiscal change is not simply the allocation of resources, but peoples’ experiences and views of inclusion, value and wealth creation, contribution, sharing, and how resources move through society. The project will contribute to academic and public debates about the contemporary welfare state and intervene in public and policy debates around taxes, redistribution, and inequality. Its ongoing aim is to democratise fiscal conversations.

UKRI Gateway to Research · FY 2026 · 2026-03

Hearing loss is one of the most widespread and disabling conditions in the world, affecting approximately 500 million people. Given the enormous individual, societal, and economic burden of hearing loss, there is urgent need for effective treatments. For the most common forms of hearing loss, there is little chance of an effective biotherapeutic in the near future; the only hope rests with hearing aids. Unfortunately, current devices perform poorly in typical social settings with loud sounds and background noise and, thus, there remains a huge unmet clinical need. It is no surprise that hearing loss cannot be corrected by the simple signal processing in current hearing aids. Hearing loss is a complex problem that causes dramatic distortions in the information that the ear sends to the brain and alters the way in which the brain processes the information it receives. To improve performance, the next generation of hearing aids must incorporate more powerful signal processing that compensates for these changes. Designing this new signal processing is much easier said than done but, fortunately, recent advances in neuroscience and machine learning have allowed us to finally make progress. We have taken a new data-driven approach to hearing aid design. Using recordings of brain activity with and without hearing loss, we train deep neural networks to perform the signal processing that provides the best possible compensation for the distortions caused by hearing loss. We have spent the past several years developing unique experimental methodologies for large-scale, high-resolution neural recordings, collecting the required datasets, and training neural networks (which we call AidNets) to act as optimal hearing aids. Our initial results, which demonstrate that AidNets can be far superior to the state-of-the-art, provide strong proof of concept and we have recently submitted a patent application as an initial step toward commercialization. In this proposal, we detail our plans to develop validated prototypes. Our key aims include (1) developing a framework for personalizing AidNets for individual listeners; (2) optimizing AidNet-based software prototypes for speech-in-noise and music; and (3) benchmarking the prototypes against state-of-the-art hearing aids in human listening tests. The prototypes will be targeted toward those who are most likely to benefit from improved hearing aids – people with mild-to-moderate sensorineural hearing loss – but can be adapted to other populations in the future. Between our core team and our collaborators, we have all of the expertise required to achieve our aims. We also have the additional support that will be required to ultimately bring our technology to market and realize patient benefit, with partners including the UCLH Royal National ENT Hospital and its associated Biomedical Research Centre;  UCL Business; the UCL Translational Research Office; and our startup company, Perceptual Technologies Ltd. These partners will help us to engage stakeholders (patients, clinicians, investors, and industry) ensuring that we make the most of our opportunity to develop life-changing technologies for millions of people. This application relates to the AI, engineering bio & quantum tech highlight notice.  

UKRI Gateway to Research · FY 2026 · 2026-03

Steatotic liver disease (SLD), or 'fatty liver disease', is a major health problem in the UK and worldwide. Liver disease is now the second commonest cause of preventable death amongst individuals of working age in the UK; the major risk factors for SLD are alcohol use, obesity, diabetes mellitus, high blood pressure and an altered lipid profile. SLD can lead to advanced liver scarring (fibrosis), and complications such as liver failure or liver cancer. We are increasingly aware that SLD is not a single disease, but a spectrum of conditions with slightly different factors at play. This means that different patients may have different risks of progression to severe liver disease or other health problems, and may respond differently to treatments. Although new drug treatments are being developed, a ‘one-size-fits-all’ approach is unlikely to be effective. Additionally, new drugs are likely to carry a significant cost burden. Therefore, the cornerstone of treatment for SLD is diet, but current dietary advice for SLD is generic and doesn't always work for everyone. The aim of this proposal is to use cutting-edge data analysis techniques (machine learning, ML), and large data sets with information on diet, genetics and profiling of molecules (proteins, metabolites), to transform how we approach dietary management of SLD. We will: Identify distinct SLD subtypes: Using these data from large UK studies (Whitehall II, EPIC-Norfolk, and UK Biobank), we will use ML to identify subtypes of SLD. These subtypes will be distinguished by unique combinations of genetics and exposure to risk factors. Uncover dietary links: We will then analyse detailed dietary information to identify specific dietary patterns associated with each SLD subtype. This will help us understand how different diets modify disease progression in different groups of people. Design tailored dietary interventions: Based on these findings, we will work with nutrition experts and patients to design dietary interventions tailored to the each SLD subtype. These interventions will move beyond general advice and provide specific, actionable recommendations. Develop a Biomarker Panel: We will also develop a new tool to monitor how well people are sticking to their dietary plans. This will involve identifying specific molecules in the blood that are associated with dietary intake. These will need validating testing in future, controlled feeding studies, but potentially allow us to objectively measure dietary compliance.   Potential Applications and Benefits: Importantly, this proposal is in line with recommendations from SLD expert panels, and a patient-focussed nationwide survey of liver cirrhosis research priorities led by the James Lind Alliance. This research has the potential to: Improve risk prediction in SLD: Through applying these ML techniques, we will increase understanding of different subtypes of SLD with different health outcomes. This information will improve management of SLD and target use of future treatments and clinical trials. Develop novel dietary interventions for SLD: The proposal will also develop tailored diets for each subtype of SLD. At the end of this project, they will be ready for testing in controlled clinical trials. Advance precision nutrition: This project will contribute to the broader field of precision nutrition by demonstrating how advanced data analysis techniques can be used to tailor dietary advice. We will make all methods and code available for other researchers in the field. 

UKRI Gateway to Research · FY 2026 · 2026-03

This proposal seeks to further expand protein language modeling by developing next-generation models using a recently published resource, the Encyclopedia of Domains (TED), as the main foundation for training data. Protein language models, much like those used for natural languages such as English, learn to recognize patterns, structures, and relationships within sequences—in this case, sequences of amino acids that make up proteins. The focus of this project is on protein domains, which are distinct structural and functional units within proteins. Domains play a crucial role in defining how proteins fold, interact, and carry out their biological functions. Beyond simply identifying these domains, the project will also emphasize understanding the interactions between domains, as these interactions often dictate how proteins interact with one another or with other molecules in a cell. By concentrating on domains and their interactions, the project aims to create protein language models that are not only more precise but also more versatile. Such models could transform our ability to predict protein-protein interactions (PPIs)—an essential area of research, as these interactions underlie almost every cellular process. In addition, the models will be tailored to improve our understanding of protein function, a key piece in the puzzle of decoding the human proteome and other complex biological systems. This work has the potential to revolutionize fields like biotechnology, molecular biology, and drug discovery. For example, better predictions of protein interactions can accelerate the identification of drug targets or the design of synthetic proteins with specific functions. Moreover, these enhanced models could contribute to solving pressing challenges, such as developing treatments for diseases caused by misfolded or malfunctioning proteins. By leveraging the Encyclopedia of Domains, a highly curated and detailed dataset, the project builds upon a rich resource that provides insights into the architecture and behavior of proteins. Integrating this data into advanced computational models represents a significant step forward in bridging the gap between computational biology and real-world applications. This initiative would not only deepen our understanding of protein biology, but also could provide groundwork for future innovations in health, agriculture, and industrial applications.

UKRI Gateway to Research · FY 2026 · 2026-03

This project is in differential geometry, a branch of mathematics that uses calculus to answer fundamental questions about manifolds: geometric shapes that are high-dimensional analogues of curves and surfaces. Manifolds appear naturally in many contexts across mathematics, computer science, and physics—such as polynomial and differential equations, dynamical systems, data analysis, machine learning, and the theory of gravity—making their study a central goal of modern geometry. Over the last forty years, two groundbreaking approaches have emerged in the study of manifolds, both through the use of solutions to differential equations. The first approach uses gauge fields, such as the electromagnetic field and other fields considered in particle physics. These fields are governed by equations similar to Maxwell’s equations of electromagnetism. The second approach uses pseudo-holomorphic curves, a special class of two-dimensional shapes that can be used to probe the geometry of higher-dimensional shapes known as symplectic manifolds. Research on gauge fields and pseudo-holomorphic curves has led to significant advances in our understanding of manifolds and is still rapidly evolving. The proposed project lies at the intersection of these two research areas and explores new, surprising connections between them. In the study of gauge fields, it aims to answer key questions about generalized Seiberg–Witten equations, which govern a little-understood class of gauge fields. These equations are conjectured to have important applications to the problem of distinguishing manifolds of dimension three and four, as well as classifying the many complicated ways in which curves and surfaces can exist within such manifolds. While many such applications have been proposed in physics and geometry, developing them requires a deeper understanding of the solutions to these equations. This project will investigate what happens to solutions of generalized Seiberg–Witten equations when the underlying manifold is deformed. This is generally a challenging but important problem, as mathematicians seek properties of manifolds that are topological—meaning they remain unchanged under deformations. The first objective is to solve this problem for manifolds of dimension two, i.e., surfaces, which would already be a significant step forward while being achievable within the grant’s timeframe. In symplectic geometry, this project aims to combine pseudo-holomorphic curves with gauge theory in a novel way to develop new tools for studying symplectic manifolds. Specifically, building on the first objective, the second objective is to prove that counting pseudo-holomorphic curves and solutions to generalized Seiberg–Witten equations yields a new invariant of symplectic manifolds of dimension six. This invariant would parallel those developed previously using algebraic methods, addressing the long-standing open problem of finding their counterpart in differential geometry. This project aligns with recent developments in the study of gauge fields and pseudo-holomorphic curves while exploring the surprising connections between these two seemingly unrelated areas of mathematics. It also incorporates the latest developments from other fields, such as geometric measure theory and nonlinear elliptic differential equations.

UKRI Gateway to Research · FY 2026 · 2026-03

The skin is our primary physical and immune barrier to the outside world.  With age, skin barrier function deteriorates, increasing susceptibility to infectious and non-infectious diseases and chronic wounds.  Langerhans cells (LCs), specialised macrophages residing in the outer epidermis, are central to skin function.  LC loss disrupts both immune and non-immune skin functions, and LC density declines with age.  Understanding how the LC network is sustained throughout life is therefore essential for maintaining and enhancing skin barrier function. LCs are maintained through local self-renewal in the epidermis, but skin damage initiates repair by recruited blood-derived monocytes.  Our previous work demonstrated that monocyte-derived (m)LCs can become long-lived, self-renewing cells that transcriptionally and functionally mirror the LCs they replace.  This raises the key question: how does the skin anatomy enable short-lived monocytes to differentiate into long-lived LCs that are integrated in the keratinocyte layers of the epidermis? Our new findings have begun to address this question. We show that the hair follicle serves as both a critical entry point and site of fate determination for monocytes.  Monocytes entering the upper hair follicle express epithelial cell adhesion molecule (EpCAM) and encounter its homotypic ligand on epithelial cells. EpCAM expression is associated with transition from a macrophage-like state to a LC-specific genetic programme.  While EpCAM signalling pathways are well-defined in stem and cancer cell differentiation their role in immune cell fate remains unexplored.  The junctional zone within the upper hair follicle is a recognised stem cell niche, supporting a pool of epidermal stem cells that migrate to repair the epidermis after skin wounding.  We further propose that monocytes leverage these stem cell pathways to repopulate the basal epidermis with mLCs. This study aims to uncover the molecular and cellular mechanisms by which the hair follicle niche facilitates rebuilding of the LC network by monocytes after damage or wounding.  We hypothesise that the loss of this niche contributes to the age-related decline in LC density and skin barrier function.  To address this aim we will explore: How monocyte localisation at the upper hair follicle determines mLC identity, focusing on EpCAM signalling in monocyte fate specification. How monocytes migrate from the hair follicle to interfollicular regions, and how the junctional stem cell niche supports this process. By integrating immunology and stem cell biology, this research will advance our understanding of how skin anatomy supports monocyte differentiation to maintain the LC network.  Understanding the spatial signals within the hair follicle that guide monocyte differentiation will shed light on immune cell integration in epithelia and how these processes fail in aged or diseased skin and more broadly at other epithelial barriers. This work aligns with BBSRC’s priority of promoting healthy aging.  Chronic skin diseases and wounds impose a significant burden on the elderly, and improved knowledge of LC biology could inform strategies to sustain skin barrier function and enhance wound healing, reducing medical interventions. Furthermore, as a key site for vaccination, maintaining skin immune function is vital for human and animal health.

UKRI Gateway to Research · FY 2026 · 2026-02

Context This project extends a programme of work from stage one of my Future Leaders Fellowship (FLF1), designed to exploit electronic health records (EHRs) to improve the health of people with severe mental illness (SMI). People with SMI (schizophrenia, bipolar disorder, psychotic illness and major depression) have a life expectancy shortened by 10-20 years compared to the general population. The majority of this reduced life expectancy is due to increased risk of cardiovascular disease (CVD) related mortality. Evidence suggests that people with SMI are less likely to have their CVD risk managed, less likely to receive a timely CVD diagnosis and less likely to receive intensive interventions compared to the general population. These issues were also raised by my lived experience advisors. Improved CVD screening and risk prediction could mitigate some of these inequities. An NHS Long-Term Plan commitment and part of Core20PLUS5 is that people living with SMI will receive an annual physical health check. However, these need to be fit for purpose, calculate risk accurately and be acted upon if we are to move the needle on this major health inequality. Challenge this project addresses A large number of CVD risk prediction tools exist for use in the general population. There are SMI-specific risk calculators, including QRISK3 and PRIMROSE. However, the Framingham risk score and QRISK2, which were developed in non-SMI populations, remain the most commonly applied to this population in clinical practice. Current risk calculators, including those specially designed and re-weighted for SMI may still over- or under-estimate risk. The National Institute for Health and Care Excellence recommend a 10% ten-year risk threshold for initiating preventative statin therapy, however it is not clear whether this is the optimal threshold for modifying risk in SMI. These calculators are not routinely used in primary care or secondary mental healthcare settings in the United Kingdom (UK) and are potentially outdated. Aim To assess the performance of CVD risk calculators at scale in SMI populations, modify them to optimise CVD risk prediction, and reduce CVD by implementing the new model. Objectives Test the accuracy of existing CVD risk calculators Determine if preventative medication is prescribed in line with risk calculator results in SMI populations Determine if recalibration, machine learning models, changes in predictor variables or modified thresholds for intervention improve CVD risk prediction in SMI Pilot the implementation of the new model in mental health services Investigate usability, facilitators and barriers to uptake Potential applications and benefits We will confirm the most accurate risk tool for use in SMI populations in the UK. We will determine how this tool can be used in clinical practice, with potential integration into EHR systems. This tool will feed in to health checks that form part of the NHS Long-Term Plan via my links with Office for Health Improvement and Disparities. Increased appropriate prescribing of statin's and other preventative medications for cardiometabolic health will reduce morbidity and mortality in SMI populations.  

UKRI Gateway to Research · FY 2026 · 2026-02

Chronic pain is one of the most common and challenging health problems in the world. Unfortunately, the treatments currently available, such as pain medication or physical therapy, often provide only limited relief. Medications can also have unwanted side effects, especially when used for long periods of time. In recent years, there has been growing interest in using neuromodulation to treat chronic pain, but it does not work equally well for all patients. This project explores a new way to individually tailor pain treatment to each patient using MRI brain imaging and a new non-invasive brain modulation called focused ultrasound (FUS). FUS can deliver energy deep into the brain with millimeter precision, without surgery or implants. However, its potential for treating chronic pain in real patients remains largely unexplored. At this early stage, we cannot conduct a full clinical trial, but we are taking an important first step. We know from previous research that in some people, certain areas of the brain become more active when pain increases; in others, different regions are involved. This means that a "one-size-fits-all" approach of stimulating the same brain region in all patients is unlikely to be effective. Instead, we want to find the exact area in each person's brain that is most closely associated with their own experience of pain, and target that area specifically. Patients will participate in several brain scans using magnetic resonance imaging (MRI). While in the scanner, they will continuously rate the intensity of their natural, ongoing pain. This will allow us to track how their brain activity changes in real time as their pain fluctuates. We will then identify which brain regions in each patient are most consistently associated with changes in their pain. This information will be used to personalise the neuromodulation targets for each participant. In the second phase of the study, we will apply focused ultrasound to the identified brain regions. We expect to see a reduction in pain after each FUS application. We will also use MRI scans before and after each application to see how the brain responds and whether there are changes in activity or blood flow in pain-related networks. Beyond the potential for pain relief, this research contributes to a broader goal: moving toward personalised medicine in the treatment of chronic pain. By recognizing that each patient's pain is unique, not only in how it feels but also in how it is represented in the brain, we can begin to design treatments that are tailored to individual needs. This could lead to more effective therapies with fewer side effects and a better quality of life for people living with chronic pain. To achieve the best possible outcome, we are working with a local back pain support group in Munich to ensure that the study is designed and communicated in the best possible way and is based on real-world lived experience and patient needs.

UKRI Gateway to Research · FY 2026 · 2026-02

The cerebral cortex is responsible for higher-order cognitive functions like memory, learning and thought. These functions rely on a complex network composed of an array of excitatory and inhibitory neurons. Neuronal diversity is vital for the brain’s ability to perform complex tasks, as different neuron types perform specific tasks. Understanding how diversity is established offers insights into the construction of neural networks and how genetic mutations and neuronal deficiencies can impact behaviour. Moreover, knowledge of brain development and neuronal diversity is vital for advancing regenerative medicine. Cortical neurons are generated during embryogenesis from proliferative precursor cells in the embryonic brain. The acquisition of distinct identities is governed by genetic programs, driven by a specific class of proteins known as transcription factors. These nuclear proteins can turn genes ‘on’ or ‘off,’ thereby controlling the protein content and characteristics of cells. Transcription factors can function in two main ways: some bind to DNA, recognizing specific sequences to exert their effects. Others act as co-factors that attach to DNA-binding factors and recruit multiple other regulators, forming protein assemblies that either enhance or inhibit gene expression. Funded by a BBSRC project grant we recently discovered that the transcriptional co-factor RUNX1T1 plays a crucial role in specifying cortical inhibitory neurons during embryogenesis. This aligns with evolutionarily conserved roles for RUNX1T1 in flies and other species, where mutations in this gene lead to changes in cell fates. The importance of RUNX1T1 is underscored by human studies linking it to intellectual disability, mental retardation and autism spectrum disorder. The role of RUNX1T1 in the developing cerebral cortex, beyond the specification of cortical inhibitory neuron subtypes, remains unexplored. In addition to the cortical inhibitory interneuron lineage, we observe expression of Runx1t1 in the developing cerebral cortex, from the early stages of cortical excitatory neuron specification. Mouse embryos lacking RUNX1T1 show abnormalities in excitatory cell numbers and their projections, indicating deficiencies in the neuronal composition of the cortex and its ability to transmit information. Utilizing a robust method for identifying endogenous protein complexes in tissues, we discovered several putative interacting partners of RUNX1T1 in the embryonic brain, including DNA-binding proteins and other co-factors. Notably, 40% of these potential partners are candidate genes for neurodevelopmental disorders in humans. Our findings indicate that RUNX1T1 may serve as a critical co-regulator of disease-associated genes, potentially exerting pleiotropic effects on cortical excitatory neurons. Leveraging on our extensive toolbox and preliminary data we aim to discover the specific contributions of RUNX1T1 and its partner proteins to cortical excitatory neuron development. We will undertake a phenotypic characterisation of the developing cortex in RUNX1T1 mouse mutants. We will identify cortical neuronal types and genetic pathways impacted by the loss of RUNX1T1. We will investigate the mechanism of action of RUNX1T1 by identifying definitive interacting partners in the developing cerebral cortex. In line with the BBSRC’s priority area of Bioscience for Health, our findings will benefit not only academic researchers working in the field but also the wider public, as they will provide a better understanding of the mechanistic underpinnings of mutations in disease candidate genes paving the way for transformative interventional approaches.

UKRI Gateway to Research · FY 2026 · 2026-02

Context: Collective migration is observed in a wide range of biological systems, from the coordinated cell movements during wound healing and development to the migration of animal groups like bird flocks, fish schools and insect swarms. Understanding the mechanisms that drive this collective behaviour is important for advancements in ecology, developmental biology, biophysics and biomimetic engineering. At the cellular level, collective migration often involves direct physical interactions between cells. For example, in a recent study we demonstrated that physically interacting cells can migrate collectively over surfaces in a rolling manner - we found that this behaviour is governed by material properties like surface tension, viscosity, friction and activity, resulting in flow fields that dictate cell organization and drive cohesive, directed movement. Our theoretical model, based on fluid-like dynamics of entire cell populations, was able to accurately capture rolling migration of cells. In animal groups, the direct contact forces that act between cells are replaced by more complex, effective forces that maintain group cohesion and affect collective migration and decision-making. Theoretical approaches accounting for such decision-making have been successfully employed to model flocking behavior in animals. The effective forces can be modelled to capture how individuals interact and move in a group, often using various sensory modalities like sight, sound, or pressure variations, as seen in fish. The resultant changes in speed and direction are driven by how these sensory inputs are processed, leading to non-local, and often non-reciprocal, interactions where an individual is influenced by others at a distance. However, such works are not able to explain recent experimental data generated by our collaborators, which reveals a new form of surface-associated migration of fish schools - as they migrate, the schools feed off the lakebed, exhibiting similar collective rolling migration patterns to those described above for cells. Hypothesis:  Despite the orders of magnitude difference in size between fish schools and cell populations, we hypothesize that emergent forms of cellular migration extend to larger scales, such as animal populations. Aim: In this work, we will take the first steps towards building a general mathematical framework that can describe collective rolling migrations of both cells and animals. This will clarify the similarities and differences between these emergent modes of migration on surfaces and how they arise from physical and/or decision-based interactions. Objectives: We will address three key objectives (OBJ), each associated with a Work Package (WP): OBJ1: Analyse experimental data from fish schools provided by collaborator Alex Jordan (Max Planck Institute of Animal Behaviour) to extract patterns of collective rolling migration and identify key variables influencing this behaviour. OBJ2: Develop macroscopic (continuum) and particle-based models and simulations for migration of an active droplet along a surface, with a shape and flow field calibrated to match the experimental data. OBJ3: Use the models to compare cellular and animal migrations, elucidating the conditions under which physical and decision-based interactions lead to emergent collective behaviours. Impacts: This work will improve our ability to build and solve non-local mathematical models, which are fundamental to understanding systems involving multiple interacting particles or agents. By establishing connections between mathematical ecology, biology and fields such as statistical mechanics, this project will broaden the application of these models to a variety of scientific contexts. Specifically, we will enhance the mathematical tools for studying emergent collective behaviours in complex biological systems.