IMPERIAL COLLEGE LONDON

UKRI Gateway to Research · FY 2026 · 2026-09

Chronic neuropathic pain affects 7-10% of the population and is often refractory to conventional pharmacological and non-pharmacological treatments. Due to limited treatment options and largely poor outcomes, the condition imposes a significant burden on affected individuals, societies and economies worldwide, and remains a major unmet clinical need. A growing body of evidence suggests that stress and negative affect (anxiety or depression) are highly prevalent in patients with chronic neuropathic pain, with comorbidity in over 50%. However, most studies have focused on how chronic neuropathic pain induces stress, anxiety and depression, and far less attention has been paid to the inverse question: how do stress and negative affect modulate the course and severity of chronic neuropathic pain? These affective factors are known to modulate pain perception and processing, but their role in driving the chronicity and exacerbation of neuropathic pain has yet to be elucidated. The overarching aim of this project is to address this critical knowledge gap using an integrative, whole-systems, translational neuroscience approach. By employing preclinical and clinical research methodologies, as well as advanced machine learning techniques, we will unravel the complex interactions between stress, emotion, and central nervous system pain processing with a focus on the descending pain modulatory system. This system is known to play a key role in nociception and is hypothesized to be dysregulated in the context of stress and affective disturbances. We have assembled a highly multidisciplinary network of experts in the basic and clinical neuroscience of pain, stress and negative affect, advanced data analytics, and patient partners, across 6 countries. Our work plan is structured around 4 key objectives, each of which straddle preclinical and clinical domains. These objectives are operationalized in a cohesive and synergistic manner across 9 work packages, which encompass multi-level, systems-based characterization using animal models, human cohort studies, neuroimaging, behavioural and psychometric assessments, molecular analyses, and computational modelling. The expected project outcomes are mechanistic and translational in nature, and will deliver novel insights into the neural circuits, molecular pathways, and neurochemical mediators that underpin the exacerbation of chronic neuropathic pain by stress and negative affect. We will identify novel therapeutic strategies for improved management of chronic neuropathic pain. Through these efforts, this project will make a significant contribution to the field of neuropsychiatric pain research, with the potential to inform and transform clinical practice.

UKRI Gateway to Research · FY 2026 · 2026-09

Held at the new Abdus Salam Centre for Theoretical Physics at Imperial College London, this research programme aims to address the most fundamental questions of physics: What is the nature of space and time? How does one construct a quantum theory of gravity? What are the ultimate constituents of matter and its interaction? What is the origin of our Universe and the nature of its evolution? The research is organised around interlocking themes of string and M-theory, quantum field theory and cosmology, focussed into two closely connected science areas. Finding a way of consistently combining Einstein's theory of General Relativity, which says that gravity arises from the curving of spacetime by matter,  with quantum mechanics is probably the greatest challenge of modern theoretical physics. To date, string theory, and its unification into M-theory, is the only candidate which we know to be consistent, and not only that but for which we can do concrete calculation. For good measure it also potentially unifies gravity with the other forces of nature, which are examples of a class of theories known as Yang--Mills theories. One of M-theory's most remarkable features is that it says that classes of  quantum Yang-Mills theories are equivalent to quantum gravity theories, under a subtle duality, leading to profound connections between black holes and quantum field theory and also to new insights into strong coupling phenomena such as quark confinement. These connections together with understanding the general structure of quantum gravity in M-theory will  play a central role in our research, explored in a number of different ways, including using supersymmetry, novel geometric constructions, new algebraic structures, localisation, "bootstrap" techniques and exactly integrable quantum field theory. An ultimate goal is to determine the fundamental degrees of freedom and dynamics of M-theory, which would have profound implications for understanding both the structure of spacetime and ultimately particle physics phenomenology. Alternatively, rather than study the specific formalism of M-theory, one can ask what general properties might a quantum gravity theory have, might there be extensions of Einstein's theory, for example where the graviton has a mass, and how would such properties be manifest in the evolution of our universe? Remarkably, conditions of causality and quantum consistency can constrain the general low-energy effective theory, and how quantum corrections to general relativity might appear. One can also think about alternative approaches to quantum gravity, such as discrete geometry, or theories which vary over time, and how they might effect our universe. Exploring these ideas and their manifestation in cosmology, as well as how one might test them, by searching for monopole, using gravitational wave or even new atomic clock experiments, forms the second core theme of our research. We will develop new effective theories, new analytic and numerical tools for testing them, new models of black holes, new non-perturbative techniques in field theory for monopoles and particle physics in the early universe and new models of dark matter. As a wealth of new observational data from the direct detection of gravitational waves, gravitational lensing, direct tests of gravity and cosmological probes comes online we will develop the core theoretical ideas for what this new data can tell us about the fundamental nature of spacetime and matter.

UKRI Gateway to Research · FY 2026 · 2026-09

The industrial exploitation of Low Earth Orbit (LEO) is leading to a rapid growth in the number of satellites orbiting the Earth, which are expected to increase 20-fold over the next decade. This growth has the potential to significantly disrupt both ground- and space-based astronomy in all bands of the electromagnetic spectrum, leading to loss of observation time which can be directly translated in monetary losses of the large investments going into observational facilities, including UK-funded ones.   In optical astronomy, sunlight reflected off satellite surfaces creates streaks that can lead to loss of entire exposure and even damage sensor electronics. Satellite interference in both optical and mm wavelengths has been shown to create false transients that can hinder scientific objectives of large-scale surveys. Mitigations in place at the Vera Rubin observatory are expected to lead to the loss of up to 10% of observation time. In the radio spectrum, onboard satellite subsystems create unintentional wide-band emissions that have affected observations by the LOFAR and SKA-low observatory. Additional unintentional narrow-band emissions due to reflections of space surveillance radars have also been detected. In addition, even if intentional radio emissions are generally not pointed directly at radio telescopes, their strength is such that they can enter telescope sidelobes, further degrading observations.   Although initial work has highlighted the scale of the problem, much uncertainty remains in quantifying the evolving impact of satellite interference from large constellations beyond back-of-the envelope calculations supported by observations of a few sample satellites. In addition, the impact of mitigation measures actioned by spacecraft operators and observers has not been assessed holistically. The objective of this project is to develop a comprehensive understanding of satellite interference with optical, submm, and radio astronomy. This will be achieved through data-driven models for dynamical systems that are fit to observations of interfering satellites. We adopt a panchromatic approach by simultaneously quantifying and predicting satellite interference across the entire spectrum. In this approach, we achieve better, time-dependent predictions of interference for individual satellite passes by exploiting information in multiple bands simultaneously. In addition, our approach allows a holistic investigation of mitigation measures by making sure that mitigations for a specific band do not increase interference in another.   The main project outcome is a proof-of-concept implementation of the data-driven models for interference prediction in simulated science pipelines of the Vera C. Rubin and Simons observatories. Project partners are the IAU Centre for the Protection of Dark and Quiet Skies (IAU CPS) and the University of Illinois at Urbana-Champaign (UIUC). Close collaboration with partners is expected, with both providing feedback on the research and facilitating access to data needed for model fitting and verification. The project team spans the areas of expertise in optical, radio, and submm astronomy along with spacecraft engineering, which is needed for this kind of ambitious project to succeed.

UKRI Gateway to Research · FY 2026 · 2026-09

The visible Universe is 99.999% plasma, a state of matter consisting of freely flowing electrically charged particles contributing and affected by electromagnetic fields. Over large scales, plasmas from different sources cannot mix. This means plasma environments across the universe (such as stars, accretion disks, and stellar-wind bubbles) feature sharp boundaries, typically large-scale thin sheets of electrical currents. Such boundaries are in almost constant wave-like motion, akin to waves on water or the membrane of a drum. These surface waves control how energy passes through the boundary, hence understanding how they process external driving has critical and universal applications. The most-accessible space plasma environment is Earth’s magnetosphere. Its boundary, the magnetopause, is the interface between our planet’s magnetic field and the solar wind plasma blowing off the Sun. The magnetopause moves substantially in response to solar wind driving, with its surface waves affecting many regions within our magnetic shield such as radiation belts, northern/southern lights, upper layers of our atmosphere, and even electricity in the ground. It therefore contributes to space weather: how the changing environmental conditions in space pose a risk to our infrastructure and technology. Currently, the magnetopause can only be observed when encountered by specially designed spacecraft. However, the upcoming SMILE mission will image the magnetopause for the first time through soft X-rays emitted via charge exchange. At the same time, several space plasma missions will continue orbiting Earth. Therefore, this period offers unique opportunities to measure the drivers, response, and impacts of magnetopause motion from space through several means. I will capitalise on this to revolutionise our understanding of the controlling effects that wave-like magnetopause motion has on our space environment. Through computer simulations of how magnetopause boundary motion controls the emission of soft X-rays, we will ascertain what the SMILE mission should observe under different solar wind conditions. As soft X-ray emissions are predicted to be weak, we will bring advanced and innovative interdisciplinary methods in data-driven statistics and machine learning to the SMILE mission, developing software helping it resolve magnetopause motion when standard methods cannot. These tools, shared openly with the scientific community, will empower researchers to achieve more with the SMILE mission. Applying this software to some of the first SMILE X-ray data we will demonstrate the first remote sensing of magnetopause dynamics, validated against spacecraft observations. Over the first two years of the SMILE mission, we will build up statistics of the length and time scales over which the magnetopause typically moves and how these result from the boundary accumulating, guiding, and filtering external driving by the solar wind. Finally, by combining remote observations of boundary motion with spacecraft measurements of the global wave response, we will determine the large-scale impacts that magnetopause dynamics have within our magnetosphere. This programme will greatly enhance the SMILE mission’s scientific capabilities, advancing our understanding of key physical processes that contribute to the threat of space weather. It will also provide crucial new insights, through the combined novel measurements, into how plasma boundaries control their systems by processing external driving. This will have applications to diverse environments throughout the plasma universe, leading to greater knowledge of the motion, forces, and energy transfer that dictate the evolution of these systems.

UKRI Gateway to Research · FY 2026 · 2026-03

Please see APP70417: Hyper-K - CIC Grant

UKRI Gateway to Research · FY 2026 · 2026-03

Processes deep within Earth have shaped its evolution for over 4.5 billion years, yet we have few direct ways to study the deep Earth across such immense timescales. One powerful proxy is the magnetic record preserved in rocks. Earth’s magnetic field – generated in the core over 3,000 km beneath the surface – evolves as the planet’s internal chemistry and heat sources change. These changes are captured at the surface, where rocks can magnetically record the geodynamo’s signal. By analysing these magnetic signals, scientists can reconstruct the field’s history and test fundamental theories about Earth’s interior and long-term evolution. Importantly, Earth’s magnetic field also acts as a shield, protecting the atmosphere from erosion by solar wind and radiation. This protection plays a vital role in sustaining habitability. Reconstructing the strength of the ancient magnetic field – palaeointensity – can address major questions: Was early Earth’s atmosphere shielded well enough to support life? Is Inner Core Nucleation related to the Cambrian Explosion found in the fossil record? These questions are central not only to Earth’s history but also to understanding planetary evolution more broadly. Until now, all methods for measuring absolute palaeointensity have relied on a single type of magnetisation: thermoremanent magnetisation or TRM. This form of remanence is acquired as rocks cool from high temperatures, preserving the ambient magnetic field at the time of formation. However, the oldest rocks on Earth – especially those from the Hadean and early Archean, more than 2.5 billion years ago – have rarely survived unaltered. Instead, they have often been chemically modified by metamorphic or hydrothermal processes. These conditions produce a different form of magnetisation: chemical remanent magnetisation or CRM, which forms during mineral alteration rather than cooling. The challenge is that no established protocol exists for determining absolute palaeointensity from CRM. As a result, many studies attempting to reconstruct the early geomagnetic field may be based on rocks that no longer retain their original TRM, raising serious doubts about their conclusions. For example, ancient zircon crystals have been used to argue that a magnetic field existed more than four billion years ago, suggesting that Earth was shielded and potentially habitable at an early stage. Yet, it has been suggested that these zircons contain secondary CRM acquired long after their initial formation, potentially undermining those claims. This proposal aims to resolve that uncertainty by developing the first first-principles protocol for determining absolute palaeointensities from chemical remanent magnetisation: ChemPI. ChemPI builds on recent advances in magnetic theory, high-resolution instrumentation and precise radiometric dating techniques. We will apply ChemPI to re-evaluate the recently reported low palaeointensities from Isua, Greenland – among the oldest rocks on Earth, which have been previously dated to the early Archean (3.7 ± 0.1 Ga). These estimates may reflect only ~30% of the true field strength due to CRM effects. Resolving this discrepancy is critical because the geomagnetic field intensity directly controls the standoff distance between the Solar magnetic field and the Earth’s magnetopause. Smaller  standoff distances are thought to enhance escape rates of ionized species such as hydrogen and xenon. Understanding how Earth’s atmosphere evolved before the Great Oxygenation Event is essential for evaluating planetary habitability, both on the early Earth and on exoplanets.

UKRI Gateway to Research · FY 2026 · 2026-03

Medical imaging is at the heart of modern healthcare, playing a crucial role in the early detection, diagnosis, treatment planning, and monitoring of diseases such as heart disease, cancer, and neurological conditions. Across the NHS and healthcare systems globally, imaging methods like MRI, PET, CT, and advanced microscopy are essential. However, acquiring high-quality images is often time-consuming, resource-intensive, and varies from one hospital or scanner to another. Long scan times can cause discomfort for patients, reduce the number of scans performed daily, and increase healthcare costs and waiting lists. This fellowship project aims to address these challenges by developing advanced artificial intelligence (AI) methods—in particular, generative AI, foundation models, and federated learning—to dramatically speed up image reconstruction, improve image quality, and harmonise data from multiple centres and devices. Here: (a) Generative AI refers to computer programs that can learn from large amounts of data and then create realistic new examples of that data—in this case, generating high-quality images from incomplete or noisy scans, as well as improving image clarity and detail; (b) Foundation models are large AI models that are trained on huge and diverse datasets so they can adapt to a variety of tasks, such as improving images from different scanners and centres without needing to retrain for each new situation; and (c) Federated learning is a privacy-preserving technique that allows AI models to be trained using data from many hospitals without moving the data from its secure location. Instead, models are shared and improved collaboratively, while patient information stays protected. Key Challenges:   -Long scanning times that limit patient throughput and place strain on healthcare resources.   -Differences in data quality between centres and devices, making it hard to compare results or conduct large-scale studies.   -Missing or incomplete data that can reduce the reliability of clinical decisions.   -The need for automated analysis tools that can quickly and accurately process medical images and highlight important findings. Research Goals: -To develop generative AI methods that accelerate image reconstruction for MRI, PET, CT, photon counting CT, and microscopy, while enhancing clarity and detail.   -To create data enhancement tools that ensure consistency and reliability of biomedical images across different hospitals and scanner types.   -To develop foundation model-based methods for synthesising and completing missing data to make better use of patient records. -To design and implement federated learning systems that train AI models across multiple centres securely and fairly. -To build fully automated pipelines that can rapidly process medical images and extract important indicators of disease, making clinical decisions faster and more consistent. Benefit and Impact:   -This fellowship project will greatly improve clinical workflows by reducing scan times and image analysis from hours to minutes. Patients will benefit from faster, more comfortable scans and quicker results. Clinicians will save time, reduce manual work, and receive more consistent, reliable information to help guide treatment.   -Healthcare resources will be used more efficiently, enabling hospitals to scan more patients without additional equipment or staff. This will help reduce waiting lists and overall costs.   -The wider socio-economic impact is significant. Earlier and more accurate diagnosis will lead to better patient outcomes, fewer hospital admissions, and long-term savings for the healthcare system. In addition, enabling large-scale, multi-centre research will accelerate the discovery of new treatments and personalised care strategies, benefitting patients and healthcare providers around the world.

UKRI Gateway to Research · FY 2026 · 2026-03

This project aims to address critical gaps in understanding extreme events, which are rare but high-impact occurrences such as floods, heavy rainfall, earthquakes, and financial crashes. Existing models for studying these events typically assume that the underlying processes are stationary, meaning their statistical properties remain constant over time. However, in reality, many of these processes are non-stationary and vary over time, seasons, or other conditions. For instance, heavy rainfall and floods follow seasonal patterns. Understanding these extreme events in non-stationary contexts is vital, as it enables more accurate predictions, better risk assessments, and the design of effective mitigation strategies. The project’s goal is to expand the existing theory of extreme values to cover more realistic scenarios. Specifically, it aims to develop mathematical foundations and statistical methods to model extreme values when the underlying processes are non-stationary. As traditional approaches assume stationarity, this project aims to debunk the long-held belief that extremes can only be fully rigorously analysed under stationary conditions. Additionally, the project considers observations over arbitrary index sets, enabling more flexibility than traditional methods, which typically rely on regularity in observations. By allowing observations to be irregular, missing, or to exhibit complex spatial-temporal structures, this approach broadens the scope of extreme value analysis to address a wider range of real-world scenarios. The practical implications are substantial, as extreme events pose significant challenges across multiple fields. For example, in climate science, enhanced models could improve disaster prediction and response, supporting efforts to minimise economic and human losses. In finance, understanding how market crashes cluster could inform better regulatory policies and risk management, improving economic stability. In insurance, a better understanding of catastrophic events could lead to more accurate risk models, fairer pricing, and ultimately, more robust industry practices. Overall, by bridging theoretical work with real-world applications, this project has the potential to impact a wide array of sectors and contribute to societal resilience in the face of increasing global challenges. The project is structured into three main work packages to systematically address these goals. The first work package (WP1) will focus on establishing mathematical foundations to understand the asymptotic behaviour of extremes in non-stationary spatio-temporal processes. We we will extend existing theories by creating models that can handle arbitrary index sets, enabling a flexible approach to spatio-temporal data. The second work package (WP2) will develop statistical methods to measure and infer the clustering effects of non-stationary extremes—where an initial extreme event triggers additional extremes within a short period—particularly focusing on cluster indices such as the extremal index. The final work package (WP3) will apply these theoretical findings to real-world data, with an initial focus on analysing the clustering of heavy rainfall in France and the UK. By leveraging publicly available datasets, this case study will illustrate the project’s practical benefits. In addition, (WP3) will study the UK electricity market, where the transition to sustainable energy sources has intensified non-stationary patterns. Forecasting extreme variations in electricity production is critical for grid operators, who must ensure sufficient reserves to prevent blackouts. To maximise the project’s impact, the research team will produce several outputs, including research articles, open-source algorithms, and conference presentations. The team will also engage stakeholders such as the Grantham Institute and the Met Office, to promote real-world applications, particularly in climate forecasting and risk assessment.

UKRI Gateway to Research · FY 2026 · 2026-02

Recent successes in the design of joint source-channel coding (JSCC) schemes based on machine learning techniques have revitalized the research interest in JSCC. Despite initial successes, there are many critical open issues that prevent their adoption in wider practice. The most significant issue is that the end-to-end designs based on deep neural networks (DNNs) hide the underlying operations, and in turn, they provide little insight, and are less interpretable. Such considerations are particularly important in communication systems for several reasons, including computation efficiency, flexibility in applying the schemes, and easiness of extending to channels with feedback and multi-user settings. Given the issues mentioned above, the objective of the proposed research is to  significantly improve our understanding of the underlying mechanism of ML-based JSCC approaches, such that they become computationally efficient, less storage-hungry, more adaptive, more robust, and easily generalizable to complex communication settings. The key challenge here is that DNNs are known to be difficult to interpret since they combine multiple functions via end-to-end training. We propose to conduct comparative studies of the JSCC designs from two related but distinctive perspectives: an information-theoretic perspective and a machine-learning perspective. By studying a sequence of more and more complex vector Gaussian sources and channel scenarios, and by contrasting and comparing the schemes designed using these two different perspectives, we aim to develop new insights and simplification of the ML-based JSCC coding scheme designs. The three thrusts of the proposed research are: 1) Study the signal representation and neural network interface in point-to-point communication settings, and understand the sources of the performance gain and graceful degradation; 2) Study neural JSCC in feedback channels and multi-user communication settings; 3) Study generative models in neural JSCC, particularly the Gaussian diffusion models.

UKRI Gateway to Research · FY 2026 · 2026-02

Our current understanding of physics does not explain why the universe around us is made overwhelmingly of matter and not antimatter. My work on the T2K and DUNE experiments, which study the oscillations of a type of particle called the neutrino as it travels long distances, has seen hints of a new type of matter-antimatter difference which has the potential to provide an explanation for the matter dominated universe. Larger datasets and smaller uncertainties are necessary to confirm these differences. My fellowship addresses both of these issues. During the fellowship, the currently running long distance neutrino oscillation experiments, T2K and NOvA, will collect their largest neutrino-oscillation datasets to date. My team will lead analysis of T2K’s data, for the first time, being sensitive enough to provide highly significant evidence for matter-antimatter differences in neutrinos. Mostly T2K and NOvA analyse their data separately. This separation means that information that each experiment has that would improve the other’s analysis is ignored. I led the first joint analysis of T2K and NOvA data together. During this fellowship I will work with collaborators in the NOvA experiment to incorporate developments in both standalone analyses to produce a second T2K+NOvA analysis. The result will be a more precise constraint on matter-antimatter differences in the neutrino sector than either experiment could produce alone, maximising the impact of these UK government funded experiments. The billion-dollar scale DUNE experiment, which is scheduled to start taking data in the early 2030s, promises even larger neutrino oscillation datasets than those from T2K and NOvA. However, our current understanding of neutrino interactions with our detector materials is not good enough to take advantage of these datasets. I will develop part of a High-Pressure gas Time Projection Chamber (HPgTPC) for DUNE that will measure neutrino interactions of a type inaccessible to previous detectors. The resulting reduction in our uncertainties on neutrino-matter interactions will give DUNE the capability to definitively observe and fully characterise the neutrino matter-antimatter asymmetry. The subsystem that this fellowship will allow me to lead the construction of is the HPgTPC’s data acquisition (DAQ) electronics. DAQ electronics allow the HPgTPC to record the neutrino interactions of interest. To do this, I will use high-speed FPGA electronics which have wide applicability both within STFC’s science area and more widely including the medical sector, fintech and national security. Furthermore, UK companies will be able to manufacture parts of the system, furthering the UK’s high-tech sector I will also pioneer joint analyses of data from DUNE and its competitor HK. This work will use my leading position in DUNE and the expertise I have gained from carrying out the world’s first joint analysis of multiple long-baseline neutrino experiments, to deliver the ultimate precision on matter-antimatter differences in the lepton sector into the 2040s.

UKRI Gateway to Research · FY 2026 · 2026-02

The Hedgehog signalling pathway, with a well-established role in cancer, is also aberrantly activated in a range of fibrotic diseases, including pulmonary, hepatic, renal, cardiac and dermal fibrosis. In developed countries, 45% of deaths are linked to fibrosis, indicating its substantial socioeconomic burden. However, there remains a striking lack of effective therapeutic options for fibrotic diseases, highlighting the urgent need for new treatment strategies. Three Hedgehog pathway inhibitors have been approved for cancer treatment, and all act via the same target, the transmembrane protein Smoothened (SMO). However, their efficacy is limited to a small subset of Hedgehog-related diseases, due to resistance, SMO-independent Hedgehog signalling and because they impair the anti-tumour immune response. In addition, they suffer from low tolerability, which could be attributed to the fact that SMO interacts with other pathways in cells. Hedgehog Acyltransferase (HHAT), the focus of this project, catalyses the lipid modification required to activate the Hedgehog signalling ligands. HHAT has been previously identified as a target in several cancers. Importantly, it has recently emerged as an attractive target in fibrotic diseases, as genetic targeting of HHAT has demonstrated impressive therapeutic benefit in preclinical models of fibrosis. In contrast with SMO inhibitors, HHAT inhibitors block selectively Hedgehog signalling at its origin, stop both SMO-dependent and independent aspects of the pathway and do not affect other cellular processes. As a result, HHAT inhibition represents a mechanistically distinct and potentially superior therapeutic approach in cases where SMO-targeting drugs have shown limited success. Existing HHAT inhibitors suffer from poor metabolic stability and off-target effects. Funded previously by the MRC Impact Acceleration Award, we have developed novel HHAT inhibitors that are over 100-fold more potent than the reported molecules, with no detectable off-target toxicity and demonstrated oral bioavailability. In this project, we aim to evaluate the therapeutic potential of lead HHAT inhibitors in relevant fibrosis models, de-risking HHAT as a drug target and progressing towards clinical candidate nomination. Given the central role of Hedgehog signalling in cancer and fibrosis, the successful development of first-in-class HHAT inhibitors could deliver transformative new therapies for millions of patients worldwide.

UKRI Gateway to Research · FY 2026 · 2026-01

Lymphatic vessels represent an essential component of the circulatory system, where they operate intimately with the blood vasculature to mop-up fluid and blood capillary exudates from interstitial tissue spaces, maintaining fluid and tissue homeostasis. The resulting protein-rich lymph travels along a lymphatic vessel hierarchy punctuated by lymph nodes (LN) before returning to the circulation. Lymphatic dysfunction leads to disturbed tissue fluid balance and lymphoedema. Lymphatics are also emerging as gatekeepers of immunity thanks to their intimate connection to LN immune hubs, providing a continual source of antigen, supporting rapid immune cell mobilization, generating chemotactic gradients and expressing a repertoire of adhesion molecules to facilitate immune trafficking, and scavenging antigen. Although endothelial cells (ECs) lining both blood and lymphatic vessels share common molecular and cellular processes, there is extensive specialisation underlying the unique functions of these two vascular systems. These differences reflect specific transcriptional programs, that can be regulated by transcription actors, including members of the ETS, FOX and SOX families. ERG is one of the most highly expressed ETS transcription factors in blood ECs, serving as a master regulator of endothelial homeostasis. ERG deficiency is embryonically lethal due to catastrophic  vascular defects. Our ongoing work has highlighted a unique role for ERG in regulating lymphatic endothelial cell (LEC) gene programs, and in regulating lymphangiogenesis. While ERG is emerging as a key endothelial transcriptional regulator of both blood and lymphatic endothelium, how its expression contributes to lymphatic identity and immune functionality essential for generation of immune responses remains unclear. Here we will employ functional assays with high resolution transcriptomics and imaging to address this, gaining valuable insights into the molecular mechanisms underlying lymphatic function and dysfunction in pathologies such as lymphoedema. Identifying pathways that play pivotal roles in the breakdown of lymphatic immune function provides a roadmap for potential therapeutic targets to modulate immunity for patient benefit.

UKRI Gateway to Research · FY 2026 · 2026-01

IRIS storage to be hosted at Imperial College.

UKRI Gateway to Research · FY 2026 · 2026-01

Inflammasomes are multi-protein complexes that result in the cleavage of cysteine protease caspase-1 leading to the secretion of a key cytokine IL-1b and the induction of an inflammatory cell death, pyroptosis. The NLRP3 inflammasome, in particular, can be activated by various endogenous danger signals. The overt stimulation of these pathways is detrimental and contributes to the development of inflammatory and metabolic disorders. Moreover, several known mutations in the gene encoding for the sensor protein, NLRP3, are associated to Cryopyrin-Associated Periodic Syndromes (CAPS), a debilitating autoinflammatory condition characterized by recurrent episodes of fever, rash, joint pain, and other associated symptoms. It is known that the proteins forming the inflammasome are localised in different cellular compartments and must come together at the endosomal/lysosomal compartment to achieve full assembly and activation. However, we have little understanding of the mechanisms that enable this localisation. Moreover, how inflammasome-dependent inflammation is ultimately resolved remains unknown, but holds therapeutic potential for a range of diseases. In this application, we aim to explore novel concepts that result in inflammasome localisation and activation, how the inflammasome is subsequently uncoupled, and the role of inflammasome in NLRP3-dependent autoinflammation. We propose that the activation of the inflammasome is centrally dependent on a lipid modification of NLRP3 – specifically, palmitoylation, the conjugation of palmitate to target proteins. We propose that this post-translational modification allows NLRP3 to recruit to the endolysosomal compartment for activation. We posit that modulation of palmitoylation is what governs inflammasome activity, with NLRP3 failing to lose the palmitate modification in disease. By examining the dynamics of NLRP3 palmitoylation in relation to its spatial localisation, we seek to unravel new mechanisms that drive inflammasome activation. Our aim is to uncover these fundamental mechanisms and extend them to understand how inflammasome regulation is dysregulated in disease. The NLRP3 inflammasome is established to instigate and advance a range of disorders, including atherosclerosis, diabetes, Alzheimer’s, and obesity. In deciphering these mechanisms, we will probe the role of a specific endolysosomal enzyme (depalmitoylase) that we propose removes the palmitate modification from NLRP3 after inflammasome activation. Failure to remove palmitate may result in prolonged NLRP3 activation, risking sustained inflammation. We believe we have a unique opportunity to examine this key mechanism and generate insights that will be relevant to a spectrum of NLRP3-related disorders. Our objectives are: Investigate the significance of palmitoylation in NLRP3 gain-of-function mutants. We will examine whether the NLRP3 disease variants undergo a) palmitoylation and, importantly, b) depalmitoylation. We will also examine the nature of upstream stimuli driving the lipid modification in CAPS. Dissect the role of depalmitoylase in NLRP3 inflammasome activation. Here, we will study the expression, regulation, and involvement of a key depalmitoylase in inflammasome activation. Determine the physiological role of palmitoylation and PPT1 in NLRP3 autoinflammation. We will employ experimental models including those expressing NLRP3 gain-of-function mutants to determine the physiological function of the two activities.

UKRI Gateway to Research · FY 2026 · 2026-01

When exposed to water vapour, aqueous solutions or hydrogen gas, metallic components absorb hydrogen. The mobility of hydrogen and its interaction with the inner structural features of metals cause a significant decrease in their resistance to fracture and fatigue. This phenomenon, known as hydrogen embrittlement, has been responsible for numerous sudden catastrophic failures in metallic infrastructure. Moreover, this embrittlement effect poses major challenges to developing a safe and economical large-scale hydrogen infrastructure, which is critical to achieving net zero carbon emissions. This New Investigator Award, HEnGB, aims to deliver a new path for manufacturing alloys resistant to hydrogen. The structure of metals is formed by grains, the intersections of which are higher energy interfaces, called grain boundaries (GBs). The accumulation or segregation of hydrogen atoms at GBs can cause cracking of these interfaces in the most relevant engineering metal systems, e.g., iron and steel, nickel, and aluminium. This is seen macroscopically as a sudden brittle fracture of components. Increasing the resistance of GBs to hydrogen would thus enable a breakthrough in alloy engineering for hydrogen applications. Previous studies have shown that changes in the character of GBs result in improvements in resistance to hydrogen embrittlement. However, this strategy has scaling problems, due to the complexity, cost and energy consumption of the thermomechanical processes needed to change GB character. Recent atomistic simulations have revealed that the strength of GBs to hydrogen can be modified depending on the amount and types of other elements that also accumulate at them. Exploiting these interactions of elements at the GBs, known as co-segregation, has the advantage of being controlled simply by heating at moderate temperatures, a process that requires less energy/CO2 and is cheaper than the one required to change GB character. HEnGB aims to prove that co-segregation is a viable route to manufacture alloys that are less weakened by hydrogen, through a state-of-the-art methodology that will bring fundamental new insights into hydrogen-metal interactions. The research plan is designed to provide lab-scale validation of this co-segregation strategy, using a combination of cutting-edge experimental and modelling techniques. It will focus on lab-cast steel and nickel alloy samples with varying concentrations of the most promising elements for GB strengthening. The composition of GBs will be modified with tailored heat treatments. State-of-the-art microscopic characterisation of the grain structures produced will be performed. This will be followed by quantification of the co-segregation of hydrogen and other elements at GBs, a challenging milestone, using the world-leading cryogenic microscopic facility recently acquired by Imperial. Finally, new multiscale hydrogen embrittlement tests and models will be developed to identify the best alloy candidates with enhanced hydrogen embrittlement resistance. The successful lab demonstration of the co-segregation strategy will bring new science and a scalable engineering solution to the hydrogen embrittlement problem. The project will result in fundamental new knowledge in segregation kinetics, grain-boundary cohesion, hydrogen cryo-microscopy and micro-mechanical testing. This will benefit academics from alloy manufacturing, physical metallurgy, materials science, hydrogen embrittlement, and fracture mechanics research communities. Moreover, HEnGB’s vision can be scaled up from the nanometric to the macro scale and therefore have a short-term impact. This is because the GB analyses will be related to the compositional-thermal history of alloys, which can be implemented in the next industrial phase in collaboration with the project’s industrial partners.

UKRI Gateway to Research · FY 2026 · 2026-01

The safe and efficient retrieval and transport of fuel debris and radioactive sludge are pressing challenges in nuclear decommissioning, particularly in legacy facilities at Sellafield and post-accident environments such as Fukushima. These materials exhibit complex, evolving multiphysics behaviours that are not well captured by current simulation tools. This proposal aims to develop a transformative, AI-based modelling framework capable of simulating and optimising fuel debris and radioactive sludge transport processes at unprecedented scales and resolutions, with an emphasis on operational safety and feasibility. State-of-the-art computational methods such as Computational Fluid Dynamics (CFD) - Discrete Element Method (DEM) deliver high-fidelity simulations but remain computationally intensive, often requiring weeks of HPC runtime. They also neglect critical phenomena, including corrosion, radiolytic gas evolution, and heat generation. Traditional Eulerian approaches oversimplify particle characteristics, reducing realism, while Lagrangian models become infeasible at operational scales. Data centric AI-based models, on the other hand, often suffer from poor generalisability due to sparse training data in radioactive settings. To overcome these limitations, this project proposes a hybrid physics–AI strategy that blends mechanistic fidelity with scalable efficiency. The methodology centres on the co-development of benchmark experimental datasets and advanced AI-based simulation tools. Experimental campaigns in Japan will create fuel debris and sludge surrogates with controlled composition, porosity, and morphology. High-resolution diagnostics—including high-speed imaging, velocimetry, and tomography—will provide rich, reproducible datasets for AI training and AI physics based model calibration. These experiments will anchor the models in physical reality and ensure generalisability to real-world conditions. The modelling strategy builds on Imperial College London’s recent advances in neural network simulation frameworks. Neural Networks for Partial Differential Equations (NN4PDEs) accelerates PDE solvers, enabling faster and more accurate fluid and reactive transport simulations. In addition, a similar approach for particle modelling called NN4DEM (Neural Networks for Discrete Element Modelling) reduces the computational cost of discrete element modelling while maintaining detailed inter-particle physics. This project introduces NN4DEM as well as (Neural Networks for Finite-Discrete Element Method) NN4FDEM—a novel GPU-accelerated Combined Finite–Discrete Element Method enhanced with neural networks—to model interactions of arbitrary-shaped particles with fluids at large scale. Coupling NN4FDEM with NN4PDEs yields a unified framework for simulating multiphase solid–fluid systems at particle-level resolution. To further improve scalability and inference speed, the project introduces Scalable Computational AI for Learned Engineering Dynamics (SCALED) - a domain-invariant surrogate model built on diffusion AI methods and U-Net architecture. SCALED can infer fluid-particle dynamics across arbitrarily large, complex domains, offering rapid predictions suitable for real-time applications. The framework will be validated through criticality and reactivity assessments in partnership with Amentum and the University of Tokyo. Reactivity mapping with MONK and FETCH codes will constrain model outputs and ensure compliance with nuclear safety requirements. Ultimately, the validated models will feed into a digital twin platform integrating sludge and RadWaste transport mechanics and real-time safety analytics for nuclear decommissioning. This UK–Japan collaboration leverages complementary strengths. Imperial College London leads AI-accelerated modelling and surrogate development (NN4PDE, NN4FDEM, SCALED), while the University of Tokyo (UoT) contributes state-of-the-art CFD–DEM expertise and reduced-order models tailored to post-accident scenarios like Fukushima. UoT will also provide simulant fabrication, radiological analogues, and experimental validation of debris flow dynamics. Together, the partners will establish a robust platform for validated, AI-based simulation tools to support safe, efficient radioactive waste retrieval and long-term storage across international contexts.

UKRI Gateway to Research · FY 2026 · 2026-01

Background Coronary artery stenoses are narrowings in the vessels supplying oxygenated blood to the heart muscle, which can result in the symptom of angina. Angina typically presents as chest pain brought on with exercise, and can significantly limit a person’s ability to perform their daily activities and impair quality of life. A coronary angiogram is a procedure that can diagnose and treat these narrowings. During an angiogram, a wire is passed into the coronary artery to measure a pressure gradient to determine how severe the narrowing is. When the pressure across a narrowing is reduced by 20% or more, current guidelines recommend the insertion of a stent to restore normal flow and relieve symptoms. We do not actually know if a 20% pressure reduction is the level that causes angina. Additionally, many people have more than one narrowing. We do not know whether the amount of pressure reduction that causes angina is different between different people, or even different between different narrowings in the same person. Objective To measure the pressure reduction required to cause angina for each coronary artery narrowing, in individuals with 2 discrete narrowings. Method 60 participants who have had a computed tomography coronary angiography demonstrating 2 coronary stenoses suitable for stenting will be recruited. Two weeks prior to angiography, all participants will have symptom assessment using questionnaires, a smartphone application and a wearable activity tracker. During angiography, the order in which to treat and test each coronary stenosis will be randomly assigned. The first stenosis will be treated with a stent to restore normal blood flow. The patient will then pedal on a supine bicycle attached to the catheterisation laboratory table, and a small balloon will be gradually inflated inside the stent to re-introduce narrowing. At the point when angina develops, a special wire will be used to measure the pressure reduction across the balloon. The patient will rest, and the second stenosis will be treated. The experimental protocol with balloon inflation during supine exercise will be repeated for the second narrowing. Importance The results of this study will improve our current understanding of the relationship coronary flow reduction and symptoms. This will enable future cardiologists to tailor the management strategy for patients with angina and improve symptom relief success from stenting.

UKRI Gateway to Research · FY 2026 · 2026-01

Liver failure causes thousands of deaths every year. Organ transplantation remains the only curative option. Unfortunately, we simply do not have enough donor organs to meet the surging demands of patients. Recent clinical data however supports the possibility of using cells (hepatocytes) instead of organs to treat liver failure. Delivering alginate encapsulated hepatocytes into the abdomen of such patients allows cells to serve as an auxiliary, short-term ‘mini-liver’. In this way, patients can be bridged over the period during which their livers are not working properly until the point at which their liver has sufficiently regenerated to function unaided.  Due to the scarcity and unpredictable quality of donor derived primary human hepatocytes (PHH), this source of hepatocytes does not represent a viable long-term solution to address the growing and global unmet medical needs of patients. Hepatocytes derived from stem cells however offer the advantage of being scalable to unlimited quantities while maintaining a highly specific and consistent phenotype. They accordingly have the potential to be an ‘off the shelf, one size fits all’ solution. To that end, our group has been pioneering methodologies to generate hepatocytes from iPSCs (induced pluripotent stem cells). Our most recent data demonstrates hepatocytes generated in this way are on par with PHH in their effectiveness for detoxifying poisons such as ammonia as well as rescuing animals with liver failure. We now wish to translate this proof of concept into a broadly available medicine for patients. To realise this ambition, we need to convert our iPSC-Hepatocyte protocol into a GMP-compliant manufacturing process that allows for the scalable generation of billions of cells suitable for patient use. This project is accordingly designed to enable that development through the following three objectives: (1) development of a GLP-compliant process for alginate-encapsulated iPSC-hepatocyte production that leverages 3D scalable differentiation techniques; (2) demonstration of proof-of-concept efficacy using the scaled iPSC-Hepatocyte product in rodent models of liver failure; and (3) establishment of the product’s safety profile alongside associated potency release assays. The expertise required to deliver this work is uniquely included in the multidisciplinary team we have assembled for this project, comprising specialists in both stem cell/liver biology (Imperial) and bioprocess engineering (CGT Catapult). By demonstrating scalability, efficacy, and safety of our product, the endpoint of this project is designed to best position us for attracting follow on investment. At that point, we will work with regulatory consultants and the MHRA to define additional pharm-tox data required prior to initiating a Phase I clinical study.

UKRI Gateway to Research · FY 2026 · 2026-01

A lattice is a regular array of points in Euclidean space. Originally studied in number theory, lattices have found applications in various domains. In cryptography, lattices play a crucial role, as their complex structures offer promising security solutions against the emerging threats of quantum computing. Current public-key cryptographic schemes that rely on integer factoring and discrete logarithms would succumb to quantum computing attacks, notably Shor’s algorithm. This poses a significant concern for our modern data-driven society, prompting scrutiny from governments, companies, and research institutions. Standardization bodies such as the National Institute of Standards and Technology (NIST), ETSI, and ISO are actively developing post-quantum cryptography (PQC) standards. In particular, NIST’s PQC initiative has garnered widespread attention and participation globally. Among the prospective methods expected to be adopted for post-quantum cryptography, lattice-based cryptography stands out as the most promising approach. The security of lattice-based cryptography hinges on the assumed hardness of certain lattice problems, such as the approximate shortest nonzero vector problem (SVP). The SVP is typically solved using lattice reduction algorithms, which aim to find a short basis for the lattice. The lattice reduction problem has a long history, capturing the attention of mathematicians as far back as Lagrange, Gauss, and Hermite. Today, it has evolved into a rich and vital field of study that plays a crucial role across many mathematical disciplines. To name a few applications, lattice reduction is widely used in solving Diophantine equations, factoring polynomials, Diophantine approximations, and tackling optimization problems such as sphere packing. NIST’s PQC protocols leverage structured lattices to enhance efficiency, but the full extent of their security remains an active area of research. Current cryptanalysis often ignores these structures and treats them as regular lattices to evaluate their security level. This project seeks to delve into the reduction theory and algorithms of structured lattices, exploring their implications for the security of PQC systems. By harnessing the power of algebraic number theory, we aim to uncover novel, groundbreaking solutions to problems in PQC. Modern cryptography is a key component of global cybersecurity systems. The worldwide IT outage in July 2024 caused travel chaos and severely impacted banking and healthcare services. This problem stemmed from an update to the antivirus software of cybersecurity firm CrowdStrike, which is designed to protect Microsoft Windows devices from malicious attacks. Although this incident was not related to cryptography, it highlights the critical importance of properly implementing cybersecurity systems. The security of NIST’s PQC protocols using structured lattices is not yet fully understood. We aim to conduct a thorough cryptanalysis of these PQC protocols by developing new theories and algorithms for lattice reduction. The goal of our cryptanalysis is not to compromise information security but to identify potential weaknesses in encryption protocols, thereby ensuring stronger long-term security.

UKRI Gateway to Research · FY 2026 · 2026-01

Observing life down to a cellular resolution provides the best means for understanding the mechanisms underpinning tissue physiology, maintenance, injury response and ageing. Intravital light microscopy has revolutionised our ability to witness cellular behaviours in their native context and thus track dynamics in real time. The development of various genetically encoded fluorescent proteins has introduced powerful and versatile imaging tools. The ease of expressing fluorescently tagged proteins or obtaining fluorescently marked cell populations through straightforward genetic methods has yielded a wealth of information about the function and behaviour of many biological processes in development and disease.   Nevertheless, there remain many areas in which significant improvement is urgently needed. Biologists are keen to image more deeply into living tissues and organs, yet imaging depth is limited by both the scattering and absorption of visible light in tissue. Conventional fluorescent probes also suffer from photobleaching, which refers to the irreversible destruction of the dye when excited for extended periods or with excessive intensity. Additionally, signal saturation creates an intrinsic limitation on fluorophore brightness even under intense illumination – limiting imaging sensitivity particularly in deeper tissue where signal detection is already challenging. Furthermore, the number of cell types that can be observed simultaneously within the same tissue volume remains limited, as the broad and often overlapping signal profiles of fluorescent dyes greatly reduce specificity.   To overcome these challenges, we propose the development of transformative genetically encoded bioharmonophores as superior optical markers for precision imaging. Bioharmonophores emit a distinct signal known as second harmonic generation (SHG). This signal, which can be generated and detected using two-photon microscopy — a widely established technique in biomedical research — circumvents key limitations of existing fluorescent probes. SHG signals are not subject to photobleaching or saturation. Additionally, bioharmonophores have unparalleled narrow signal profiles that can be finely tuned to any desired wavelength, enabling tailored observation of specific cell types in deep tissues. Being genetically encoded, these labels will be immediately accessible to a broad scientific audience through repositories such as Addgene.   This innovative project aims to transform live-tissue imaging by providing new tools for studying tissue physiology, cellular behaviour and disease mechanisms with unprecedented depth, clarity and specificity.

UKRI Gateway to Research · FY 2026 · 2026-01

What problems are we trying to address? Obstructive Sleep Apnoea (OSA) is the most common sleep condition, affecting approximately eight million people in the UK and one billion people worldwide (12% of these populations). Symptoms include sleepiness, fatigue, poor concentration, forgetfulness, feeling irritable, anxious, and depressed, and there is a higher chance of patients having heart attacks, strokes, diabetes, dementia, cancer, driving accidents, and of dying. Continuous Positive Airway Pressure (CPAP) is the most efficacious OSA treatment. It is a machine that blows air through a face mask worn whilst sleeping. Patients who do not use (“do not adhere” to) CPAP enough have poorer health, cost health services more, and have a higher death rate than adherent patients. Non-adherence to CPAP is very common in the UK and worldwide. We found that at five UK sleep services between 2019 and 2020, 62% (49-73%) of patients were non-adherers three months (the peak) after starting treatment. Our pilot work indicates that the problem is partly that NHS staff are too busy to help patients having difficulties with CPAP. The British Lung Foundation reported that NHS sleep services’ workloads were excessive 10 years ago; subsequently they have worsened.   What is our solution? Trials demonstrate that behavioural (psychological) therapies (e.g., which increase patients’ confidence in using CPAP), some supportive interventions (e.g., extra calls from staff) and some educational interventions increase CPAP adherence. However, these therapies have been too expensive and/or impractical for health systems to adopt. We have developed an intervention combining behavioural therapy, support, and education (the Group Education and Training with Enhanced Support and behavioural Therapy (GrEaTEST-OSA) intervention) with input from over 650 patients with OSA. We now want to turn it into a smartphone/tablet/computer-based application. This behavioural-supportive-educational complex intervention delivered in an app (CIBSE-app) should be value for money and practical for NHS use and in many other healthcare systems. Our CIBSE-app is distinct meaning there is little competition.   What is our aim? It is to develop ‘CPAP buddy’, the CIBSE-app, to be given to patients with/loaned a digital device when starting CPAP in the UK, and ultimately globally, to establish adherence to CPAP and reduce staff time supporting this process. Hence staff are freed to help patients starting CPAP who do not have/cannot use digital devices to foster adherence (so they are not disadvantaged).   What will we do in this study? Develop the first version of CPAP buddy with five patient advisors, two of their partners, and a professional app design and development team Improve this version until we achieve an improved version that patients like and engage with, with feedback from 72 NHS patients who will use the app in their first month of CPAP therapy Test the improved version of CPAP Buddy with usual NHS care in 54 NHS patients in their first month of CPAP therapy, to find out how many patients drop-out of the study (which is needed to plan the next study)   What is the next step if this study is successful? A study testing if our final improved CPAP Buddy app increases the percentage of patients adhering to CPAP and saves staff time compared to usual NHS care, and other relevant end-results e.g., money saved for the NHS. Our industry partner is advising on regulatory and commercialisation pathways to achieve patient benefit at speed.

UKRI Gateway to Research · FY 2026 · 2026-01

Opioid dependence (OpD) is a major global health challenge, with opioid-related deaths consistently breaking record levels. Increasing opioid availability and purity of synthetic products (i.e. fentanyl, nitazenes), as well as co-use of stimulants (e.g. cocaine, amphetamines), are emerging threats to managing this evolving crisis. Individuals with OpD (iOpD) generally have other physical and mental health problems that mutually exacerbate each other, requiring comprehensive care across services. In the UK, societal costs of illicit drug use (i.e. policing, crime, health and social care) reached £20 billion per annum, of which heroin and/or cocaine use contributed 86%. To develop improved OpD treatments considering these growing challenges, we urgently need a better understanding of how established therapies work. Opioid agonist therapy (OAT), also known as substitution, principally with methadone and buprenorphine (World Health Organisation essential medicines), is the mainstay treatment for OpD. Total abstinence has many health advantages. However, less than a quarter of iOpD leave treatment opioid-free, and up to 90% of those discontinuing OAT relapse within a month. Ultimately, the most suitable approach is the one that best maintains patients’ wellbeing. Currently, insufficient evidence exists to guide OAT prescribing, and surprisingly, little is known about buprenorphine’s mechanism of action in the brain. The cyclical relapsing-remitting nature of addiction is linked to disruptions in the brain’s reward system and its ability to process emotions and execute cognitive functions (e.g. attention, memory, impulsivity). These aberrations contribute to craving, which trigger relapse: opioid use ‘on-top’ of OAT or after achieving abstinence. Buprenorphine is pharmacologically distinct from methadone, and differences in the subjective experience on these drugs has been reported. Despite buprenorphine’s advantageous safety profile, which prevents overdose and attenuates emotional dysregulation, it only represents 10-30% of OAT prescribed. Meanwhile, the rollout of long-acting injectable buprenorphine formulations is paving the way for increased, sustained use. I aim to improve the understanding of brain changes in OpD relapse and buprenorphine’s mechanism of action through neuroimaging, cognitive, physiological and self-reported data from buprenorphine-maintained iOpD. In long-term abstinent people with OpD, the Imperial College Addiction Research Group demonstrated blunted responses in brain regions mediating reward and heighted responses to negative images in an area involved in emotional processing. Similar blunting of reward and greater responses to drug-related cues were also found in methadone-maintained individuals. I will test the hypotheses that buprenorphine-maintained iOpD will show the same responses as methadone-maintained iOpD compared to healthy controls (people without OpD). Researching how buprenorphine attenuates processes underpinning craving and relapse will illuminate its effectiveness in managing OpD. My findings will enrich the OAT evidence base, allowing patients and prescribers to make more informed treatment decisions. A better understanding of OpD and buprenorphine’s impact on the brain will help reveal where to target new treatments, including medications, brain stimulation techniques and psychological (talking) therapies. This research focus will strengthen methods evaluating addiction, including neuroimaging biomarkers, which can be used to predict treatment responses and outcomes. Accompanied neuroimaging data collection with monitors measuring eye movement, pupil size, and heart rate could contribute to the development of more affordable, scalable, biological investigations assessing craving. Additionally, objectively evaluating addiction helps address the stigma experienced by iOpD and their families, improving quality of life and access to care. Overall, these advances will contribute to reduced risk of relapse, overdose, and death and, in turn, ameliorate societal costs.

UKRI Gateway to Research · FY 2026 · 2026-01

  Traumatic brain injury (TBI) is caused by external forces that injure the brain. However, the biomechanical threshold for head impacts that result in TBI is largely unknown. This fundamental gap in knowledge limits our ability to: (a) identify head impacts that have a high risk of causing significant TBI; and (b) design appropriate protective strategies for TBI occurring in both sports and non-sports settings. Until recently most sports head injury work has focused on identifying and managing the signs and symptoms of head injury (concussion). It has not been possible to collect large-scale data about the biomechanics of head impacts or objective biological markers of brain injury. The 2024 MRC Concussion Research Forum highlighted this problem and called for an improved understanding of the links between head impact exposure, brain injury and clinical presentation. Elite men’s and women’s rugby union and rugby league provides an ideal environment to perform the research needed. Detailed head injury clinical assessment protocols are mandatory in professional rugby and are implemented both on match day and during the recovery period. Furthermore, since 2022 elite rugby players in the UK have been wearing instrumented mouthguards (iMGs) that accurately measure the kinematics experienced during a head impact. In partnership with rugby authorities and benefiting from a £3.1 million in-kind contribution that provides the infrastructure, the Traumatic Brain Injury Thresholds Study (TBI-TS), will combine kinematic information about head impacts collected using iMG data, advanced computational modelling and blood biomarker assessment to clarify which head impacts lead to objective evidence of brain injury. This will allow us to define TBI thresholds that inform brain injury risk and assist in prognostication. Over 2 years from September 2025 we will collect data from the 2025/2026 and 2026/2027 seasons in elite rugby union and rugby league. We will recruit 600 players (50% female) to undergo blood sampling at multiple time points (pre- and post- game). Blood-based biomarkers of brain injury will be measured using capillary blood sampling. Gold standard Simoa biomarker assays will be used (GFAP, UCH-L1, NFL, Brain Derived Tau), as well as discovery work using the Nulisa platform that provides 120 brain injury, inflammatory and neurodegenerative biomarkers. Instrumented mouthguard data will be available for all players. Kinematic information will be related to forces within the brain estimated by the Imperial College Finite Element Model of TBI Biomechanics, which provides anatomically precise patterns of brain strain. We will investigate the relationship between kinematics and brain tissue biomechanical measures and: (a) blood biomarker evidence of TBI; (b) clinical measures recovery including symptom resolution and return to play. Biomechanical injury thresholds for clinically relevant TBI will then be established. The work has the potential to transform TBI care through: (a) improved understanding of the biomechanical causes of TBI and their relation to blood biomarkers of brain injury; (b) the identification of objective biomechanical thresholds for TBI; (c) the development of clinical risk profiles for different types of head impact; and (d) the collection of a unique large-scale data set that will be shared through the MRC funded TBI-REPORTER platform. It will improve TBI prediction in a wide range of settings, including elite and amateur sport, military scenarios and road traffic collisions, as well as inform the design of TBI protection strategies.

UKRI Gateway to Research · FY 2026 · 2026-01

My primary research interest is in symplectic geometry; I am especially drawn to problems in Floer theory with interdisciplinary applications. The present research proposal has three separate lines of enquiry, all connected by symplectic aspects of singularity theory as a common theme. Singularities are ubiquitous in mathematics and physics, often appearing as limiting configurations of smooth objects. For example, imagine a bowling ball on a trampoline, and then that the ball's weight slowly increased. Assuming the mat never tore, the endpoint of this process would be a singularity -- the mat would be infinitely sharply curved at the bottom. Such a situation is closely related to certain singularities in general relativity, which we know as black holes. Since singularities are unavoidable, it is necessary to develop tools in a variety of contexts in order to best understand a theory as a whole. In the above situation, for example, it would not be possible to claim that we understand general relativity without understanding black holes. We focus on singularities in the context of symplectic and algebraic geometry, where there are classically two `obvious' approaches to their study. Firstly, one could work topologically, looking for something smooth which is `close to' the singular space. The symplectic geometry of which smooth spaces are nearby (if any) and how singularities form as these spaces degenerate can then be used to understand the singularity itself. The second method comes from algebraic geometry, where one simply cuts out the singular point and glues back in something smooth. One can then investigate the singularity in question by understanding the different ways in which this can be done. In conjunction, these two operations are extremely useful; however, it is not clear whether they provide complete, the same, complementary or overlapping information. In Project (A), we will study the symplectic geometry of smoothings of certain threefold singularities -- called compound du Val -- and compare them with the algebraic geometry of the same singularity. Roughly speaking, the goal is to show that, in these cases, the symplectic and algebro-geometric approaches do, in fact, contain the same information. In Project (B), we will study cusp singularities, which are an important class of surface singularities arising in pairs, and appearing in the boundary of moduli spaces of surfaces of general type. Roughly speaking, our goal is to show that the number of distinct ways which one can symplectically smooth a cusp is predicted by the algebraic geometry of its dual. Heuristically, this can be thought of as showing that the algebraic and symplectic approaches to studying cusp singularities contain complementary information. Project (C) is of a distinctly different flavour to the previous two projects, and is in the field of quantum singularity theory. This seeks to understand singularities and the symmetries of their defining equations directly, without any smoothing or resolving; however, the premise of this project is still in comparing the symplectic and algebraic approaches. Roughly speaking, we will aim to show that the structure of a certain analytically defined invariant of a class of curve singularities is tightly connected with the algebraic geometry of the same curve singularities. This is a non-trivial prediction, since, as we saw above, different approaches to studying the same singularity may or may not be expected to be related.

UKRI Gateway to Research · FY 2026 · 2026-01

Dementia is defined as cognitive decline sufficient to interfere with independence in everyday activities. The number of people living with dementia will increase over the coming decades, putting enormous strain on health care resources and families. Since current treatment options are limited, it is vital to understand the modifiable risk factors that contribute to cognitive decline. Risk factors present in midlife are important contributors to dementia risk and may be detected as subtle declines in cognitive function. Consistent observational and experimental evidence now links air pollution to cognitive decline and dementia risk. Air pollution is therefore an important contributor to dementia; a burden which will be greater in occupations involving considerable periods working outdoors, such as our frontline police officers. To improve the evidence in this area and inform successful public health interventions, we will examine the links between long-term air pollution exposure and cognitive decline, within the Airwave Health Monitoring Study (AIRWAVE), a large occupational cohort of employees of the police forces of Great Britain. We will apply the state-of-the-art Community Multiscale Air Quality urban model to provide high resolution estimates of long-term ambient outdoor exposure to key air pollutants (NO2, O3, PM2.5, PM10) and link these to performance across five cognitive domains (working memory, episodic memory, processing speed, attention, and fluid intelligence) assessed with standardized computer testing. In one of the largest studies to date, we will test cross-sectional associations in over 30,000 participants and longitudinal associations in over 14,000 participants with repeat cognitive assessments. We will use information on job role, time activity patterns and place of work to provide time-weighted ambient outdoor air pollution estimates for more personalized assessments. One of the key proposed mechanisms underlying this potential association between air pollution and cognition is thought to occur through the capacity of air pollution to cause inflammation in the lung, which spreads to the circulation, ultimately impacting on the brain vasculature to cause neuroinflammation. This is often referred to as the lung-to-brain axis. A full understanding of these mechanisms, particularly in human studies, is necessary to provide biological plausibility for the observed associations. We will therefore apply the latest generation of molecular profiling techniques to assess metabolic and inflammatory changes, including over 1,000 metabolites and 350 inflammatory proteins, among subsets of up to 6,000 participants. This will be one of the largest and most comprehensive studies of this type with the aim of uncovering the molecular pathways through which air pollution impacts brain health. This project aims to make an outstanding contribution to the field, building on significant previous research investments that have provided an unparalleled resource of existing data to address several remaining research gaps. We will exploit a large, nationwide cohort with sufficient contrast in exposure that will provide high quality evidence. As a working-age cohort AIRWAVE is uniquely placed to understand how air pollution impacts on the brain during midlife, the working age years, when cardiovascular risk factors for dementia begin to appear. Uniquely, the availability of state-of-the-art molecular profiling in AIRWAVE allows appraisal of the role of potential mechanistic pathways, key to inferring the causal effects necessary to guide the development of focused public and occupational health policy.