IMPERIAL COLLEGE LONDON

UKRI Gateway to Research · FY 2025 · 2025-03

Stratospheric aerosol injection (SAI) has been the subject of increasing scrutiny as a potential climate measure, with most scientific attention focused on its efficacy in reducing net global warming. However, knowing that SAI would likely be effective does not answer the question of whether it is a “good idea” as a climate measure: whether the deployment of SAI would reduce the overall detrimental impacts of Earth heating relative to scenarios in which it is not deployed. Attempts to answer this question face two major obstacles. First, investigations of the physical response to SAI have mostly focused on its ability to reduce global mean temperatures; however for outcomes directly related to human impacts such as drought, air quality, or weather extremes, SAI is expected to produce different results than cooling through a reduction of greenhouse gases. Deeper engagement from Earth scientists is required to assess these impacts in relation to cooling from SAI versus GHG reduction. Second, producing ever more detailed scenarios of SAI deployment is not sufficient to produce effective risk analysis. It centralises SAI and its physical effects as the primary question driving future decisions, ignoring the social, political and economic dimensions. This can lead to studies which treat representative scenarios of SAI (or its absence) as comparable predictions of the future, rather than as indicative simulations which can help us to understand physical differences only. It also limits Earth scientists’ understanding of which physical risks are likely to be most – or least – consequential, and therefore which responses most urgently need more research. A research framework is needed which integrates risk analysis with Earth system modelling. This would allow risk analysts to more effectively explore the consequences of different climate measures while guiding researchers in Earth science towards the unanswered gaps in SAI modelling which most affect assessments of future risk. We therefore propose a new framework for physical modelling, designed to provide the information needed for cross-disciplinary risk analysis. Rather than continuing to build and refine scenarios, we instead choose to focus on a limited set of (mostly existing) scenarios, and to develop an understanding of the relationship between SAI and physical outcomes in ways which can inform risk analysis. We complement these scenarios with simplified sensitivity simulations to improve our understanding of the physical responses of critical systems to different climate measures (including SAI strategies, termination shock, SAI-based peak shaving, emergency SAI deployment, and different emissions mitigation levels). These responses are then translated into inputs for holistic risk assessment as a collaborative task with experts in risk analysis, based on the key factors which are expected to be relevant in future scenarios – centralizing the issue of how different risks are mitigated, exacerbated, or compounded by different climate measures, rather than starting from the question of SAI deployment. This project will set the standard for future assessments of all climate measures. By integrating risk analysis into our understanding of SAI, we ensure that the end results will be an improved understanding of the physical impacts of different climate measures which is guided by experts in risk analysis. This then enables those experts to provide holistic risk-risk analysis of different futures with and without SAI, and we anticipate our framework will continue to grow and provide policy-relevant information far beyond the end of this programme.

UKRI Gateway to Research · FY 2025 · 2025-03

We do not know where Mars's moons came from, nor how Saturn's rings and icy moons formed. Their origins are among the longest-running unsolved problems in planetary science, and are a primary goal of upcoming space missions. Ancient Mars and Saturn's ocean-rich moons are also among the most promising places where life could survive beyond Earth. This makes studying their formation an exciting opportunity to learn both about their specific histories and about key processes that underpin planetary evolution in general. In spite of the dramatically different planetary environments, the leading explanations for both topics involve violent collisions. Many planets and their moons and rings are thought to form via destructive impacts and tidal-disruption events, yet these fundamental processes remain poorly understood. Furthermore, studying these cataclysmic events relies heavily on numerical simulations to predict the outcome of different scenarios, as necessary comparisons for present-day observations. However, previous studies have suffered from major shortcomings in the resolution and other aspects of their simulations. I propose a set of connected projects to both present new scenarios for the formation of these two systems, and test the competing ideas in unprecedented completeness and detail. I will use the codes I have developed to model these events with over a hundred times higher resolution, including key improvements to how planetary materials are simulated. I will combine these with machine-learning approaches to predict the outcome of subsequent collisions far more efficiently, alongside novel techniques to simulate the further evolution of the debris after the initial disruption. This is often neglected but crucial for connecting models to real-world predictions. Mars's moons are currently thought either to be captured asteroids or to have coalesced from a post-impact disk. However, a third option has been overlooked: the partial capture of a disrupted asteroid that is torn apart by passing too close to Mars and then evolves into a disk. Furthermore, previous impact models were too low-resolution to constrain the composition of the moons. I will study and present this new alternative, and produce the first reliably resolved predictions for an impact scenario. For Saturn's rings and icy satellites, I will examine how an impact between precursor moons could distribute massive debris throughout the system. My recently published work demonstrated the proof of concept, and I now propose to simulate for the first time the post-impact evolution of the system. I will also test a competing idea that an outer moon was destabilised and tidally ripped apart into ring-forming material. This was recently postulated, but with no direct simulations of the disruption nor the following evolution. These projects promise a step-change in our understanding of how systems like Mars's and Saturn's could have evolved. They are particularly timely given the imminent MMX mission, for which my predictions will provide crucial comparisons for measurements, and to capitalise on the new possibilities that my recent works have opened up. Beyond these direct benefits, through these projects I will develop novel simulation and AI-supported tools that will open up a diverse range of other valuable, previously inaccessible topics for future study, such as: the evolution of Uranus's moon and ring system in the aftermath of the giant impact that sent the planet spinning on its side; or the ejection and global distribution of debris from extinction-level impacts onto Earth.

UKRI Gateway to Research · FY 2025 · 2025-03

Globally, there are more then 1.7 bn people worldwide suffering from musculoskeletal conditions. Out of this, alone 500 million people suffer from arthritis conditions, which is expected to grow to 1.2 bn by 2050 due to the ageing population, the obesity pandemic and lifestyle changes that reduce physical activity. Current devices to treat these hand and wrist conditions, such as splints, braces or other wearable support, immobilise major parts of the joint, affecting joint mobility and function. These devices are not tailored, don't fit very well or exert too much or too less compression, are very clunky and use materials that are not skin friendly. This often lead to side effects, such as skin rashes and cuts, swelling and pain, limited joint movement, slow recovery times and non patient compliance. There are currently no devices on the market that allow for long-term management of musculoskeletal conditions by offering a user-friendly design, personalised and targeted support, fit and comfort while allowing joint movement to accelerate healing and improve user experience. This project aims to explore the shortcomings in the existing splint designs by offering a completely new approach to brace and orthotic design, aesthetics and functionality. The project partner AIKNIT has developed novel wearable patches with variable stiffness levels, that fit the patient’s anatomical and physiological needs in a highly personalised fashion to facilitate faster and more effective healing with lesser side effects and shorter treatment times. AIKNIT has developed 3 different patches designed to support the thumb and index finger webspace, upper and lower thumb and wrist. The patches consist of geometrical structures that vary in shape, height, space and angle and are computationally designed and programmed to lock up at a certain angulation to support and modulate range of motion. This project proposes to test and iterate the existing wearable prototypes of AIKNIT with users, clinicians and other stakeholders, to further develop its product market fit and therefore entry to the market. The aim is to demonstrate that the tested and iterated wearable patches can improve joint pain, increase hand function and mobility, lower side effects, improve patient adherence and satisfaction, as well as overall quality of life. This grant will allow us to undertake the necessary user testing and stakeholder engagement as well as further develop the design and functionality of the patches to ensure they provide the right treatment and support, are easy to use and comfortable, and increase patient compliance. If the technology shows positive impact and feedback from users, it has the potential to create significant network effects across the healthcare sector by inspiring applications in other disease areas. Potential applications include the use for other musculoskeletal conditions such as the elbow, knee and ankle. The device could be used in performance sports to treat and prevent sports injuries and help with post-exercise recovery. It can also address systematic inflammatory diseases such as rheumatoid arthritis and lupus or be applied to neurological diseases, such as stroke rehabilitation and wound healing. The technology could also benefit children suffering from juvenile idiopathic arthritis or congenital musculoskeletal abnormalities, offering non-invasive pain management and functional support through child friendly designs.

UKRI Gateway to Research · FY 2025 · 2025-03

Lower back pain affects roughly half the elderly population, impairing quality of life, mobility and independence. Costs of treatment and societal impact in the UK are estimated at >£10B annually. NICE guidelines state that exercise is first line treatment, before professional physiotherapy, analgesics and surgery. However, UK observational studies show that adherence to exercise is as low as 30%. While technologies may alleviate this treatment gap, there are no systems that can motivate and supervise appropriate physical therapy, whilst being scalable and affordable. Earlier clinical trials showed that muscle biofeedback improves exercise performance and reduces back pain, but requires laboratory based machines costing thousands of pounds. We developed a “smartbelt” that incorporates a novel sensor called MMG (mechano-myography) allowing for real-time biofeedback of trunk core muscle activity, that is both affordable and simple enough to use at home. The smartbelt communicates with a person’s phone, allowing for on-the-go exercise feedback, akin to widely used step counters. The app motivates via gamification, rewards, and social leaderboards. This transforms back exercises from a chore to an enjoyable activity, and empowers people to manage their health. The belt is relevant for treatment and prevention of back pain; and has a role in providing treatment both whilst awaiting referrals to NHS physiotherapy; and by supplementing therapy dose. Exercise dose is well-recognized to determine the extent to which pain is reduced. The CoreCount care model therefore moves back pain treatment into the community more, overcoming barriers of apathy, access, affordability, whilst increasing independence. Experiments we conducted in >100 volunteers show that the biofeedback smartbelt is scientifically valid, and motivates users to exercise more than standard exercise schedules where no biofeed; and improves exercise quality - which are all important to reduce back pain. In people aged over 60 years old, more than 80% found the smartbelt easy, comfortable and unintrusive whilst performing exercises. Two-thirds of research participants stated they would consider purchasing the device for self use when it becomes commercially available. In this project proposal we plan the following commercialization steps to allow CoreCount to become a reality: Optimizing design of CoreCount by trialling it in groups of elderly users, using their feedback to improve quality of the final product. This process includes trialling of both the wearable belt and its associated smartphone App. Conduct market research amongst groups of people who we foresee would be interested in purchasing CoreCount, e.g. patients, physiotherapists, hospitals. Develop a business strategy and financial model, including estimating an appropriate price, determining who our customers are, and the best sales method. Ensuring that the product meets regulatory requirements for it to be commercialized as a medical product. Gain legal advice to ensure the underlying technology is protected via relevant patents. Create promotional material including videos, website and social media channels, both for improving market awareness and for securing future investment. The project team endeavour to build a product, strategy and network over six months to enable a springboard for spinout. By the project end we expect to launch the product, securing our first customers and investors, in UK and subsequently USA. In parallel we shall run a clinical trial to determine CoreCount’s effectiveness and ability to integrate within conventional NHS care pathways for back pain.

UKRI Gateway to Research · FY 2025 · 2025-03

A project bringing together STFC quantum researchers, experiential artists, and local young people to enable engagement with 2025’s UNESCO International Year of Quantum via immersive art and reflective workshops delivered across a programme of existing public and community engagement events Context The International Year of Quantum celebrates 100 years since the development of quantum mechanics. It arrives on the cusp of a second-generation of quantum technologies that will revolutionize computing and telecommunications, and advance scientific enquiry in physics, astrophysics and cosmology. Quantum’s immense potential has sparked a global investment race with the UK declaring quantum as a priority technology. The Challenge The UK quantum sector faces a huge engineering skills gap, a lack of interdisciplinary problem solving, and poor diversity at every level. Whilst the public may have come across phrases like quantum computing, the range of technologies and potential for new science is largely unknown, and quantum is not on the curriculum for the majority of secondary school children. Our overarching project aims 1. To raise public engagement, excitement, and awareness of what quantum is, and what it could mean for science and new technologies, and how this relates to peoples’ lives. 2. To support STFC-funded quantum researchers and colleagues to tell engaging research stories by drawing on creative approaches and young people’s perspectives. 3. To facilitate two-way engagement and shared perspectives between researchers and public participants. 4. To increase the science capital and science confidence of a diverse collection of young people (and their peer groups and networks) local to our campuses. We believe these ambitions support efforts to open up quantum opportunities to the next generation of scientists and innovators, and ensure developments in the field better respond to public interests, questions and needs. Approach, applications and benefits This proposal will fund an experiential and reflective quantum experience that will be incorporated into an existing Imperial programme of public and community events in 2025 and 2026. The experience will consist of three connected stages: 1. A unique, immersive, multisensory and interactive artwork – titled Echoes of Possibilities - to convey the wonder and excitement of the quantum world by bringing to life some of its unique phenomena and counter-intuitive physics in a way adults and children alike can engage with. 2. Connected to the artwork will be a reflective workshop led by a cohort of ‘Young Producers’ from our local community (building on a three-year programme bringing underrepresented perspectives into Imperial’s public programming]. Our Young Producers will capture the public’s experience of the quantum realm and compare those responses to our researchers’ own connections to quantum science. The activity takes inspiration from the Richard Feyman quote - “If you think you understand quantum mechanics, you don't understand quantum mechanics” - and will convey that a sense of surprise or even bafflement when confronted with quantum science does not mean you haven't ‘understood’ it -  In fact, it might well be the opposite. 3. A collection of exhibits and live demonstrations to tell more specific stories of STFC funded quantum research which will be supported through training and advice from Imperial’s Public Engagement (PE) Team, and feedback workshops with our Young Producers. Elements of this project will feature throughout Imperial’s 18-month programme of public and community engagement events (part of Imperial’s in-kind support).      

UKRI Gateway to Research · FY 2025 · 2025-03

This grant addresses challenges posed by real-world complex systems described as networks of interconnected dynamical elements. These systems feature in diverse fields such as ecology, biology, and physics. Changes in the interaction structure have far-reaching effects. Indeed, disorders like Parkinson's disease, schizophrenia, and epilepsy are believed to be linked to abnormal interaction patterns among neurons. Predicting disturbances and anticipating their consequences is crucial for averting disasters. While applied studies over the last fifty years have enhanced the understanding of network structures, a lack of mathematical understanding of emergent behaviours is hindering necessary progress in the field. Firstly, dynamical systems have focussed on understanding dynamics at the long time limit, rather than on the intricate finite-time dynamical behaviour of interacting elements, which is crucial to understand triggers to changes in complex systems. Moreover, pertinent phenomena such as collective behaviour cannot be deduced from local information and perturbation theory, which are at the heart of modern theory. This proposal is timely and carried by a strong team with an excellent long-term collaborative track record. Our unique contributions at the interface between pure and applied mathematics have given rise to important breakthroughs in understanding collective behaviour in network dynamics. Central to this project, our pioneering dimension reduction adopts a probabilistic perspective, describing global dynamics over finite time scales for an ensemble of networks and a large set of initial conditions. By disregarding pathological behaviours that arise only in asymptotic time and are highly unlikely to be seen in experiments or everyday situations, we were able to prove results that appear otherwise too challenging and unattainable, such as mechanism responsible for hub synchronization, breaking new ground in mathematics for the analysis of complex high-dimensional systems. Our results have proven relevant to data science, in particular for learning microscopic isolated node dynamics and connectivity, from time series data. Our proposal aims to harness the predictive powers of our reduction principles to develop recovery algorithms to predict abrupt changes in complex systems, known as critical transitions. Such transitions arise in various settings, such as society, ecology, neuroscience, medicine, and technology. Our research addresses two connected objectives: 1) Mathematical reduction and theory of critical transitions: we address main open problems in reduction principles that will strengthen our theory. Despite being transformational, our results currently rely on restrictive hypotheses. A broader theoretical base, including high dimensional node dynamics and noise, as well as moderate network sizes, will extend our theory to apply to concrete topical models. Reductions, both to deterministic and random lower-dimensional dynamical systems, are key to establish a theory of universal critical transitions. 2) Dynamical network reconstruction and prediction of critical transitions: we address the challenge of extending our novel reconstruction techniques and algorithms for learning complex dynamical networks to weaker hypotheses and to the realistic situation that network data is partially observable. Powered by the mathematical insights from Objective 1 especially on random bifurcations, we will extract early warning signals for critical transitions from the network dynamics model reconstructed from first synthetic and then experimental data.

UKRI Gateway to Research · FY 2025 · 2025-03

Pollution has been a major problem for centuries, leading to rising global temperatures, public health crises, and ecological devastation. Control and monitoring of pollution-related phenomena are of utmost priority for governments, stakeholders, and business. Many of these events exhibit spatio-temporal dynamics (XTD) described by partial differential equations. Compelling examples include wildfires, greenhouse gas propagation, and water contaminant dispersion, where swift and strategic intervention is critical. Effective intervention entails quickly understanding the phenomenon and making timely and strategic decisions. We refer to such problems as "concurrent learning and intervention" (CLI), where timely intervention can be the difference between success and failure, lives saved, and preventing further harm. Effective intervention requires a deep understanding of XTD phenomena and timely, strategic decisions. This project seeks innovative approaches to estimate, predict, and control these phenomena, striking a balance between data acquisition and intervention. While reinforcement learning-based methods can synthesize strategic intervention policies directly from data, they demand extensive data and long training times. Furthermore, they often disregard existing physics-based models that have been well-studied for many phenomena. On the other hand, recent advancements in controlling uncertain environments offer a promising framework for CLI. However, this calls for systematic approaches for computationally efficient data-driven modelling and control techniques. This project aims to formulate innovative identification, estimation, and control methods for networks of autonomous agents and people to achieve an optimal trade-off between (1) acquiring samples from the environment to learn the dynamics, and (2) interacting and modifying the environment to satisfy high-level requirements in a timely fashion. To achieve our aim, we have set the following objectives: Develop novel rapid modelling and estimation techniques based on physics-informed machine learning approaches, in which the nominal physics-based dynamical model is refined by a computationally efficient set membership-based data-driven model. This will help reduce the gap between theoretical models and real-world dynamics. Create a new control approach, called Dual Control for Exploration and Interaction (DCEI), based on state-of-the-art model predictive control methods that integrate known or learned phenomenon models with data-based methods. This method will empower agents to address environmental uncertainties and take actions in a timely manner. Build software tools for practical implementation of the developed methods, such that they aredeployable in real-time embedded hardware. Integrate and test the new methods in two case studies: wildfire emergency response and air pollutant monitoring. The project involves a unique multi-disciplinary collaboration among experts in control sciences, optimization, fire sciences, complex dynamics, and aeronautics, both from the academia and industry. The collaboration with our industrial partners, Andrew Moore & Associates and Flylogix, will ensure that our innovative methodologies can be readily applied in real-world settings. This project addresses a pressing global issue related to pollution-related phenomena. The resulting methods will provide a significant advantage to tactical response teams, enabling better decision-making, faster response times and reduced environmental impacts. By advancing control in unknown environments, we will contribute to machine learning, control sciences, and robotics research communities. This project's framework, initially designed for fire and pollution control, can be adapted for various applications, including social dynamics, communication networks, and drug delivery.

UKRI Gateway to Research · FY 2025 · 2025-03

The identification and selection of anti-tumour T cells that recognise tumour-associated antigens present in patients is a challenge. This is particularly true for cancers that exhibit high degrees of inter- and intra-patient tumoural heterogeneity such as triple negative breast cancer. T cells therapies that are both autologous (derived from an individual's own cells) and polyclonal (express multiple T cell receptors (TCRs)) may be particularly effective because such therapies address the phenotypic diversity of tumoural cells amongst patients while minimising host rejection of transplanted cells such as through graft-vs-host disease. The greatest challenge in developing an autologous polyclonal T cell therapy is the recovery and selection of tumour reactive T cells or their TCRs. Commonly used techniques such as affinity readouts tend to rely on neoantigen prediction methods which can be inaccurate and focus on individual peptide/receptor interactions in isolation which do not accurately ascertain the complex interaction which exist between cells while multimer technology requires validated knowledge of antigens and provides false negative readouts of bulk populations. We have recently developed a microfluidic T cell selection platform that exploits TCR-neoantigen cellular avidity to identify and isolate tumour-reactive T cells from patient samples. This technology is capable of: a) screening the interaction of millions of interacting cancer and immune cells, b) ranking T cells based on their relative TCR avidities against multiple targets simultaneously, and c) recovering tumour reactive T cells that are viable, and suitable for downstream expansion or molecular interrogation (eg through TCR sequencing). This technology has been validated in patient and animal models where T cells were transduced with TCRs of known avidities and recovered by probing their avidity interactions against melanoma cancer cells. While promising, we have not yet demonstrated that this technology is capable of recovering rare tumour-reactive T cells by challenging the avidity of patient-derived lymphocytes against patient-derived cells. Therefore, in this project we propose to: 1) Develop Protocols for Integrating Patient Cells into Microfluidic Avidity. This will involve optimising patient-derived organoid dissociation protocols, device surface coatings, and validation of resistance to shear stress. 2) Develop pipeline for autologous polyclonal TCR discovery. This will be conducted by shear challenging patient matched peripheral blood mononuclear cells and tumour infiltrating leukocytes against tumour-seeded microfluidic devices as described above. T cells will be recovered into buckets, bar coded using cell-hashing techniques (10X Genomics), and their TCR repertoire sequenced. We will then combine the recovered TCR repertoire data and paired ranked affinity metrics to single-cell RNA and TCR sequencing of unsorted patient tumour infiltrating lymphocytes to determine whether the addition of affinity based metrics (generated using these platform) to sequenced based measures of T cell clonotype diversification and expansion can be used to rapidly and efficiently identify anti-tumour T cells. This development of a pipeline for selecting promising autologous polyclonal TCRs will lay the foundation for future work validating and refining a protocol for clinical studies. For instance, in follow on projects we will transduce 5-10 TCRs per patient into into patient derived CD8 T cells or HLA-matched T cell lines for in vitro toxicity testing and validation against banked patient organoids.This will prepare the technology for clinical trials featuring a autologous polyclonal T cells therapy using microfluidic avidity-based selection.

UKRI Gateway to Research · FY 2025 · 2025-03

Nearly all value-added products in the chemical sector contain chains built from carbon-carbon (C-C) bonds. This includes fuels, polymers, surfactants, agrochemicals, and pharmaceuticals. Arguably the C-C bond is the most important molecular linkage in modern society. It is extremely difficult to break C-C bonds in a controlled manner. These bonds are strong. They are buried deep within the scaffold of the molecule and are surrounded by a forest of carbon-hydrogen bonds, which tend to be the first site of attack for chemical reagents or catalysts. If efficient, selective, and sustainable methods could be developed to break C-C bonds it could change how we approach chemical manufacturing and lead to long-term societal impact. These methods could underpin original approaches to add value to molecules from biomass and new technologies for the chemical recycling of poly(ethylene). In this project, we will develop methods to break C-C bonds with reagents and catalysts based on main group metals (Mg, Al, Ca, and Zn). This is a new area of research and one that our team has pioneered. We will develop new knowledge on how these metals act to break C-C bonds. Our aim is to understand how structure impacts reactivity and what factors influence the site selectivity (i.e., which bond reacts). We will exploit this fundamental knowledge to create new catalysts that break-down, and add value to, carbon chains with atomic precision. Ultimately, we will apply these methods to important problems including upgrading biomass-derived alkenes and the degradation of hydrocarbon-based polymers.

UKRI Gateway to Research · FY 2025 · 2025-03

Antimicrobial resistance (AMR) is a global health crisis branched over multiple infectious diseases. This problem, if unaddressed, will breach current antibiotic treatments which healthcare systems have relied upon for decades. Experts predict AMR will cause more deaths worldwide than cancer and diabetes by 2050. Currently, this threat is insidious, and affects the immunocompromised and the elderly, particularly in developing countries. However, as this problem speeds up, even everyday wounds or cuts could eventually lead to healthy individuals requiring serious treatment and hospitalisation. Therefore, while more broadly we require a long-term strategy to manage broad-spectrum antibiotic usage as the first line of defence, there is also a need to consider the role of non-standard antimicrobials, phage therapy, and host-directed therapeutics as a countermeasure to fight resistance. Our project concerns the development of a safe cell-free tool to study a specific type of infectious disease-causing bacteria, Klebsiella pneumoniae, and explores a new synthetic biology method to alternative antimicrobials. K. pneumoniae is important since it is a leading cause of hospital-acquired bacteraemia and causes up to a ~50% mortality rate in some countries. Our project will use the latest technological advances that include next-generation DNA sequencing, automation, and cell-free synthetic biology. Overall, our project has three general goals that act as our theme for the proposal: "anticipate, learn, and counter". 1. Anticipate - We need to predict how K. pneumoniae will become resistant to antibiotics. This is important because K. pneumoniae and many other infectious diseases will soon be able to survive all current antibiotic treatments. 2. Learn - We need to study how individual antibiotic resistance mechanisms confer an advantage to K. pneumoniae. 3. Counter - We need to stop antibiotic-resistant K. pneumoniae infections by finding new kinds of non-standard antibiotics, especially ones that can crucially evade or escape current resistance mechanisms. Herein, we provide a cell-free synthetic biology tool that enables us to study a major infectious disease at Containment Level 1, while the system is automation compatible to help speed up the discovery of new antibiotics. Also, our approach is generalisable, and therefore can expand to almost any infectious disease, i.e., ESKAPE pathogens or tuberculosis. Overall, our project is remarkably novel, timely and exciting in its conception and creates a new synergy between the two distinct areas of synthetic biology and infectious diseases. Our project will create a new, fast, and safe way to study how K. pneumoniae becomes resistant to antibiotics, as well as providing a platform to search for novel antibiotics.

UKRI Gateway to Research · FY 2025 · 2025-03

'Strep A' infections usually cause sore throat and relatively minor illness but sometimes can invade deeper normally sterile parts of the body often entering through the skin or lungs, leading to life-threatening illness. Immune responses to the same bacteria can also do long-term harm to the heart and kidneys, which still pose significant health risks in less affluent regions of the world. Over the past year, there has been a concerning increase of severe Strep A infections in Europe (including the UK) and North America. Whilst this rise has affected all age groups, it has been a particular concern in children since they accounted for 24 percent of serious Strep A infections, compared with 4 to 12 percent in previous years (1). It is very likely that these increases reflect changes in population immunity due to social-distancing restrictions during the COVID-19 pandemic, but this does not explain why some children were affected so severely. Challenge: Consequently, in this proposed research project I will aim to answer: "Why are some children more prone to severe Strep A infection than others?" In preparation for this project, I have spoken to the two major patient groups for Strep A infections in the UK: the Lee Spark Foundation and the Conor Kerin Foundation. Both groups emphasised the importance of this question to those affected and their families. This was also reflected in the survey for parents from the NIHR Imperial Biomedical Research Centre in 2021 which found that answering "Why my child?" was a priority area of future research for families (2). Objectives: Therefore, my project will investigate how a child's genetic make-up puts them at risk of severe Strep A infection. Work by my proposed supervisor that I have recently helped extend found specific genetic differences that predispose to life-threatening Strep A infection in both children and adults. These differences are found in the region of a gene involved in transport of sugar molecules needed for a variety of biological processes and known to impact immune responses (3). With the support of this fellowship, I aim to: Refine the link between this gene region and life-threatening Strep A infection including looking at further children affected during the recent upsurge; Investigate how differences between individuals in this gene region alters responses to the bacteria looking at cells and laboratory models of skin. Additionally, I will gain training to analyse the remainder of the genetic code from affected children giving me the opportunity to identify further genetic culprits predisposing to Strep A. Applications: My project will start to provide an answer to "Why my child?". Importantly, it has the potential to bridge-the-gap from a statistical link between a gene and severe infection to understanding what goes wrong in the body that allows the bacteria to take hold. This has potential for clinical application, especially providing targets for future treatments. Thus, this project is not only focused on advancing our understanding but also improving the outcomes for children who develop these devastating infections.

UKRI Gateway to Research · FY 2025 · 2025-03

STOP-MATING aims at building a research and innovation network to advance towards developing novel tools to target the mating behaviour of disease-transmitting mosquitoes for vector control. Challenges related to climate change and insecticide resistance are putting human populations at risk of mosquito-borne diseases. Novel strategies are required to tackle this public health challenge. In STOP-MATING we propose to use the mating systems of disease-transmitting mosquitoes as novel vector control targets. Although disrupting mosquito mating would have a clear impact on mosquito vector numbers, this mechanism is underexploited from a public health perspective. In STOP-MATING we bring together experts from academic and industrial partners with interdisciplinary expertise on bioinformatics, molecular neuroscience, genetic control, behaviour, biophysics, vector ecology and vector control to explore novel vector control approaches. Our objectives are to 1) identify molecular targets for mosquito mating disruption, 2) mutate those targets, 3) analyse associated behavioural effects and 4) explore their potential application into developing mating disruptors and gene drive systems for vector control. We also aim at exploiting the mating behaviour of mosquitoes to develop traps that mimic the environmental stimuli that they respond to during courtship behaviour. STOP-MATING approach is to merge knowledge from laboratory and field researchers to deliver real-world solutions, and to tackle different mosquito species of increasing public health relevance in Europe. Our innovative approaches have the potential to make great impact to reduce the health burden of mosquito-borne diseases.

UKRI Gateway to Research · FY 2025 · 2025-03

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

UKRI Gateway to Research · FY 2025 · 2025-03

This research is ultimately motivated by reducing the harmful consequences of climate change on society, in the UK and worldwide. The root of the problem is global warming, caused by the greenhouse effect of carbon dioxide from fossil fuels. As our atmosphere warms, so do our oceans, which directly affects biodiversity and causes sea levels to rise. As our oceans warm, the balance of forces that keep them in constant motion changes too, disrupting their worldwide circulation. This disruption is worrying, both in the short and long term, because the present circulation patterns perform at least two functions vital to our hospitable climate. First, vertical currents store excess heat and carbon deep into the ocean (slowing global warming). Second, North-South currents redistribute tropical heat to more temperate regions (reducing extreme weather and climate). Therefore, a weakening of these currents could accelerate climate change, with long-lasting societal consequences. To mitigate this, scientists try to predict how the world's climate will evolve by using advanced mathematical and computer models of the ocean circulation. However, these models and their predictions need to be improved to be of greater benefit to society and decision-makers. A serious cause of uncertainty in these models lies in the mixing between water currents that have different salinity or temperature (and thus density). Currents of different densities organise into vertically-stacked (or "stably-stratified") layers which flow past one another at different speeds (creating a "shear" flow). These flows are always turbulent, which means that a vast number of tiny chaotic "eddies" mix the salinity and temperature of much larger layers in complex and unpredictable ways. This fundamental but extremely challenging process of turbulent mixing in stably-stratified shear flows needs to be better understood. To do this, I will employ the following scientific approach in three steps. First, I will use an accurate, reduced-scale model of such flows in the laboratory. This has two great benefits: it gives full control over the flow geometry, the density difference, flow speed, etc, allowing me to test and understand the influence of each parameter separately; and it allows me to use high-tech measurements to quantify the chaotic eddies and their mixing better than ever before. Second, I will interpret these new laboratory measurements with mathematical models of turbulent mixing to generalise (or "extrapolate") my findings to real-scale flows found in the ocean. This crucial step relies on the power of "dimensional analysis" in fluid dynamics, which is also routinely used by engineers to develop new aircraft or ship designs from smaller-scale laboratory prototypes. Third, I will verify the validity of my real-scale predictions by comparing them to actual measurements taken from ships (which are usually sparse, expensive, and less accurate). This step is similar to engineers performing a full-scale test before production, except that we have no control over the ocean. Although challenging, this "validation" step will help ensure that my whole approach succeeds in providing climate scientists with more accurate models for ocean mixing. In addition to the long-term effects of global warming, I will also apply the above three steps to a shorter-term consequence: saltwater intrusions in estuaries. Sea level rise, more frequent droughts, extreme storm surges, and stronger tides will all increase the gradual encroachment of seawater in densely-populated deltas (including the important Thames Basin in the UK). The upstream intrusion of a dense saltwater layer beneath the fresh river water, and their vertical mixing reduce the availability of surface freshwater, with devastating consequences for coastal communities already felt around the world. I will develop more accurate models of mixing in saltwater intrusions to help mitigate this urgent problem.

UKRI Gateway to Research · FY 2025 · 2025-03

Context: In the lung, airways and blood vessels work together to ensure effective gas exchange. Cigarette smoke and air pollution cause Chronic Obstructive Pulmonary Disease (COPD) which is characterised by progressive inflammation and narrowing of the airways. Subsequent thickening of intrapulmonary blood vessels leading to pulmonary hypertension is a common complication of this disease. COPD affects both respiratory and vascular compartments in the lung but interactions between these two systems are poorly understood. There is no cure for COPD despite extensive animal experimentation. Challenge: Current pre-clinical models poorly reproduce human COPD and better research tools are needed.  New 3D culture devices called Organs-on-chips aim to reproduce key features of human organs and tissues to replace the use of animal experimentation in modelling of human diseases. Our data. We have created the Respiratory-VAScular (REVAS) organ-on-a-chip model of respiratory-vascular cell-cell communication which facilitates the study of the effects of airway damage on the pulmonary vasculature. This model consists of a “respiratory chip” containing human small airway epithelial and endothelial cells and a “vascular chip” hosting pulmonary arterial endothelial cells, pericytes, fibroblasts and smooth muscle cells. A peristaltic pump actuates the flow of media through endothelial channels and air through epithelial channels. Exciting new data show that exposure of the respiratory chip to oxidative stress profoundly affects the vascular chip, inducing inflammatory and angiogenic activation of endothelial and other cell types. Importantly, the endothelial response is markedly aggravated in the presence of other cell types, highlighting the importance of using multicellular systems in disease modelling. Project aims: (1) To study effects of cigarette smoke and ambient particulate matter collected from London roadside sites on human respiratory and vascular cell responses in REVAS. (2) To identify key mediators linking respiratory cell damage with vascular dysfunction. (3) To validate the model with FDA-approved and new drug targets in COPD Outcome: This study will test applicability of REVAS in modelling of human COPD, thus replacing animal experimentation. The project will help delineate mechanisms of airway-vascular cell-cell communication, how these are impacted by known environmental stressor linked to COPD and identify new therapeutic approaches in this severe chronic lung disease.

UKRI Gateway to Research · FY 2025 · 2025-02

Context Non-small cell lung cancer affects many people and is the most common cause of cancer deaths worldwide. Most of its cases are diagnosed late, where the disease has already reached important structures or spread to other parts in the body, making it harder to treat. A new treatment for these difficult cases is now available, called immunotherapy, which works by helping the body’s own system to attack the cancer cells and slow down their spread. There is however a great deal that we don’t yet know about how it treats the disease at a deeper level. Over the years, scientists have invented new ways of looking at cancer cells and how the body’s own immune system works around them. Recently, they have invented a new technology, called single-cell spatial transcriptomics, which allows a closer look at the genetic makeup and function of the cells without taking them out of the place they are found, therefore telling us where this information about the cells is in and around the cancer. Patients routinely receive image scans to look at their cancer. There is a great amount of information about their cancer in these scans that we can’t see with our naked eye. With the help of computers, scientists are now able to get this information, called radiomics, and turn it into something useful, called an imaging biomarker, that could help with cancer treatment. The challenge the project addresses Not everyone’s cancer is the same. To best choose a treatment, doctors need to have a good understanding of the disease first, which often requires getting some tissue, by passing a needle through skin and lung, a thin tube down the throat into the lung, or surgery. In the case of immunotherapy, choosing the right patient to treat is important because the treatment can sometimes cause extra harm to the body, which can result in death in the most serious cases. Currently, to help making their call, doctors use a special lab test performed on the tissue taken, called PD-L1 expression. However, this test is not very reliable, and cannot predict how the cancer responds to treatment in many cases. We therefore need a better test to help us choose the right patients to treat. Aims and objectives This study aims to use two cutting edge technologies, single cell spatial transcriptomics and radiomics, to better understand non-small cell lung cancer and discover new ways of testing the disease for its suitability for immunotherapy. Using artificial intelligence, we will also look for a way to use medical images to predict the useful information from single cell spatial transcriptomics, which is more difficult and expensive to get in real life. Potential applications and benefits We will find new ways of testing non-small cell lung cancer for its suitability for immunotherapy and help making a bespoke treatment plan best suited for each individual patient. We will also get to know more about how the body's immune cells work around the cancer, which could help us develop new drugs to treat it.

UKRI Gateway to Research · FY 2025 · 2025-02

Feeding the world will depend on bioengineering solutions that improve existing agricultural practices and are simple to implement at the farm. A promising emerging technology to improve agricultural practices is the use of crops that use light to report their needs in real time. Our network of leading researchers from Japan and the UK aims to produce such luminescent plants for use in the laboratory and at the farm. We will bioengineer a new bioluminescence platform that can turn plants into tools that report the release of jasmonic and salicylic acids – the most common plant hormones activated by pest or pathogen attacks. The team will use this platform to create several such plants: tobacco and Arabidopsis, for the scientific community, and rice and soybean, which for farmers to measure in real time the extent of pest damage to their crop. These plants will emit green light when healthy and will turn red when they detect a pest or herbivore attack. Our reporter soybean or rice plants can be planted together with the crops in the field and allow the farmer to detect pest damage by looking for red light emitted by the reporter plants from a cheap aerial drone. This work will be made possible by an interdisciplinary network of scientists from Japan and the UK, combining complementary expertise in machine learning, molecular engineering and plant science, made possible by a series of exchange visits between the two countries.

UKRI Gateway to Research · FY 2025 · 2025-02

Synthetic Cells (SynCells) are microrobots constructed from the bottom-up to replicate complex responses of living cells. By constructing simpler mimics of biological cells, each molecular component can be validated, the cell rationally assembled, and its functionality accurately predicted. The ability of SynCells to replicate and combine key biological functionality (e.g. movement, communication, biosynthesis, computation) promises to generate technologies that can revolutionize healthcare, biomanufacturing and environmental remediation. Translation of SynCell technologies is, however, hampered by their inability to function for extended periods of time due to challenges with generating, transforming, and storing energy. Tackling the urgent “energy bottleneck” in SynCell science is a tremendous task, which exceeds the capabilities of any individual research group and, arguably, any country. With this program – Engineering sustained function in SYNthetic cells through enERGY generation, storage and transformation, Japan-UK SYNERGY – we propose to address the energy bottleneck in SynCell science by harnessing the capabilities of leading teams at the Tokyo Institute of Technology, Imperial College London and the University of Cambridge. By integrating state-of-the art solutions in bio-membrane engineering, membrane-less compartmentalization, crystal biomaterials, nucleic acid nanotechnology, microfluidics, and cell-free protein expression, we will develop functional modules through which SynCells will be able to i) produce ATP (energy) from chemical substrates, ii) store it in molecular batteries and iii) harvest energy from non-chemical sources. When combined, the energy modules will significantly extend the functional lifetime of SynCells, unlocking their real-world deployment as new technologies in biotechnology and medicine. As such, this grant will place Japan and the UK in a prime position to develop disruptive SynCell biotechnologies with significant economic benefit. International engagement will be underpinned by an extensive exchange program, including ~20 research visits and five international workshops, offering unprecedented opportunities for ECR development and academic collaboration beyond the scientific objectives of the project. Japan-UK SYNERGY emerges from established partnerships between the Japan and UK teams, seed-funded by four BBSRC Japan International Partnership Awards and numerous student and staff exchanges. Building on this collaboration track record, we envision Japan-UK SYNERGY to emerge as a permanent, international research centre boosting the academic, industrial, and societal impact of SynCell technologies and ensuring that Japan and the UK consolidate their leading role in the upcoming SynCell revolution.

UKRI Gateway to Research · FY 2025 · 2025-02

The Square Kilometre Array (SKA) is an international project with worldwide participation, to analyse radio signals from the Universe with two very large footprint telescope arrays across 9 African countries and Australia. The SKA will arguably be the largest fundamental science project ever undertaken and will open a new window on the Universe, shedding light on key unsolved problems in astronomy and cosmology.  The huge volumes and sensitivity of the data from the SKA present a number of key challenges. One of the most pressing is the contaminating noise from radio-frequency interference (RFI) from the ever-growing number of cell phones, satellites, radio stations and television broadcasts.  Efficiently dealing with this RFI at the multi-petabyte scale of the SKA requires rigorous new statistical and computation methods that bridge traditional statistics and cutting-edge machine learning and Artificial Intelligence. It represents a unique opportunity to build scientific capacity in Africa.  The proposal is designed to contribute to this, through two main elements:  the specialised training of two early-career SKA researchers, one in South Africa and one at Imperial College, focussed on recruiting from Africa; and the broader impact of training around 30-40 students from across Africa at the interface of statistics and artificial intelligence through a dedicated summer school and workshop.  We aim to provide this training free to the students. The specialised research project has four main components: simulation, emulation, data compression, and statistical inference. Although this targets the SKA, the skills are broadly applicable to many areas where inference with complex simulations are important, including climate modelling, epidemiology and manufacturing. The proposers are leaders in all of the core method areas and have extensive experience in the training of junior researchers, and are ideally placed to impart this knowledge. In addition, the proposers have a proven track record of working effectively together.

UKRI Gateway to Research · FY 2025 · 2025-02

Brazil reported the highest UTI incidence and UTI-associated mortality and morbidity in the world. Treating UTI has become increasingly difficult to due to antimicrobial resistance (AMR) in the causative pathogens, predominantly Gram-negative bacteria. Inappropriately treated UTI leads to recurrent cases and bacterial invasion to other body parts and drives AMR emergence and spread. Diagnosing, prescribing, and follow-up of UTI remain sub-optimal, with half of the UTI antibiotic prescriptions in primary care being inappropriate. Limited data is available to evaluate of UTI management as it requires tracking patients along care pathway to identify re-prescribing, (re)admissions to primary care and hospitals, and deaths and disabilities due to UTI complications and AMR. Artificial intelligent (AI)/machine-learning (ML) supported by data integration can improve care for UTI by identifying cases, detecting AMR, and guiding patient stratification and antibiotic prescribing. In this proposal, University of São Paulo (USP) and Imperial College London (ICL) team will co-develop data linkage and case identification algorithms to better monitor UTI across primary and secondary care in Brazil. The linked data enables piloting and validation of three ML-based algorithms to perform risk stratification, guide antibiotic prescribing, and predict adverse events in hospitals. Routine electronic health records (EHR) and laboratory data from primary care units and hospitals in São Caetano do Sul, covering a population of 165,655 residents, will be deterministically linked. Using the linked data, we will develop tiered-case identification algorithms to identify cases and risk factors of community-acquired UTI, assess antibiotic prescribing appropriateness, and evaluate patient outcomes including urine-sourced BSI and other UTI complications. Three ML-based tools will be piloted, including a support Vector Machine (SVM) classifier to estimate the likelihood of UTI and BSI using routine biomarkers, a case-based reasoning (CDR) decision support to guide antibiotic empiric prescribing and review, and a random forest model to predict patient's risk of experiencing acute kidney injury and other adverse events subsequent to antibiotic treatment. This proposal aims to minimise the inequalities in access and quality of care in different socioeconomic groups. The case identification algorithms with probable and definite ontological concepts mapping and automated natural language processing (NLP) will monitor patients who are particularly vulnerable, including those who are socially deprived, with low health or technology literacy, living in care homes, or with multiple long-term conditions. Led by Dr Silvia Figueiredo Costa (USP) and Prof Alison Holmes (ICL), this multidisciplinary team has strong expertise in infectious disease epidemiology, data analytics, clinical microbiology, health economics, and health management, with a track record of ethical research and implementation of AI to address social determinants of health. São Caetano do Sul is one of the few cities in Brazil with fully implemented and routinised EHR. USP's established connection with local care providers and public health authorities will facilitate secure and timely access to data, and support validation and dissemination of the findings. This proposal is expected to generate direct benefit to patients in Brazil by enhancing surveillance and providing evidence to guide stewardship, infection prevention, and health service delivery. The co-developed, externally validated ML-based tools can be adopted/adapted for management of other infectious diseases and wider health systems strengthening. The USP-ICL partnership directly responds to the UK National Action Plan (NAP) by fostering a sustainable channel for knowledge exchange and innovation co-development, and engaging workforce and society within pluralistic health systems.

UKRI Gateway to Research · FY 2025 · 2025-02

The central nervous system (CNS) plays a crucial role in regulating essential functions and behaviors, making it a key area of medical research. The CNS begins developing with the formation of the neural tube during early embryogenesis. Neural tube defects (NTDs), originating at this stage, result in severe CNS birth defects like spina bifida and anencephaly. In Brazil, NTDs are a significant public health issue, with an estimated prevalence of 0.29 per 1,000 live births. This underscores the necessity of understanding neural tube development to enhance prevention and treatment strategies. Recent advancements in the field have yielded insights into neural stem cell behavior and adult brain neurogenesis, suggesting novel approaches for CNS repair and neurodegenerative disease treatment. However, research is hindered by the inaccessibility of human tissue and ethical considerations, leaving gaps in knowledge about the molecular mechanisms of neural tube formation. Traditional research models, such as cell lines and animal studies, often fail to replicate the complex 3D architecture and specific development processes of the human CNS, impeding the study of NTDs and related diseases. Human organoids have transformed CNS research by accurately modeling human-specific conditions and the 3D structure of the CNS. Early neural tube organoid models, derived from human induced pluripotent stem cells (iPSCs), mimic the initial stages of neural tube formation. These organoids offer valuable insights into neural differentiation and the etiology of NTDs, enabling researchers to study neural progenitor behavior and the cellular environment during critical developmental stages. Patient-specific iPSC-derived organoids help uncover the molecular bases of NTDs, overcoming the limitations of traditional models and highlighting potential therapeutic targets. Cell image assays using fluorescence microscopy are essential for studying cellular responses in CNS-related organoid models. These assays allow for the identification of specific cellular components, analysis of molecular interactions, and detection of early disease markers. Advanced microscopy techniques like STORM and STED offer nanoscale resolution, enabling detailed visualization of subcellular structures and providing unprecedented insights into cellular dynamics within CNS organoid models. Despite their advantages, these assays are often labor-intensive, time-consuming, and limited by the need for specific markers. The integration of artificial intelligence (AI) into biomedical research has revolutionized image analysis. Techniques like convolutional neural networks (CNNs) and deep learning significantly enhance the accuracy and interpretation of microscopy data. Generative AI models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), advance microscopy-based imaging analysis in organoid research. GANs improve the visualization of synapses, aiding in the differentiation between healthy and diseased structures. VAEs generate high-resolution images that capture detailed neuronal morphology, enabling more accurate mapping of neuronal circuits and connectivity. AI technologies thus enhance the potential of microscopy-based imaging, offering a comprehensive understanding of CNS intricacies and disease mechanisms.

UKRI Gateway to Research · FY 2025 · 2025-02

Global warming is a serious worldwide threat with a significant impact on ecosystems, and CO2 emission is intimately tied to this threat. This project aims to design a novel integrated research plan for CO2 capture using Covalent Organic Frameworks (COFs) and the formation of methanol in the presence of metal catalyst and metal sulfides through a series of innovative investigations. Our objectives are as follows. Objective I: We use density functional theory to predict how we can increase the CO2 uptake capacity of COF by exploring its structure. Objective II: We test the capacity of COFs to uptake CO2 in a multivariable environment by molecular dynamics simulation followed by active learning to produce an effective search algorithm for CO2 capture. Objective III: We use density functional theory to study the mechanism of CO2 reduction to methanol by Pt and by embedding a metal sulfide defect into the COF. The research and innovation objectives of the project benefit from the strong connections between its components, which have been carefully designed for effective measurement and verification. It contributes to the field by deepening our understanding of the functionality of COFs, finding an efficient search algorithm for optimal operation condition for CO2 uptake by COFs, and exploring novel pathways for methanol production. This proposal involves the use of density functional theory, molecular dynamics simulation and active learning as a subset of machine learning to predict materials properties for the use of COFs to CO2 uptake and methanol production. This ambitious effort aims to advance beyond the current state of knowledge by seamlessly integrating various computational chemistry methods. This approach will comprehensively address the potential of COFs based on i) the previous experiences of the researcher and the host and ii) transferring the knowledge and skills between them.

UKRI Gateway to Research · FY 2025 · 2025-02

Brain tumours kill more children and adults under the age of 40 than any other cancer and five-year survival is <20%. Increasing the extent of tumour resection improves survival in high grade gliomas (HGG), but should not be pursued at the expense of neurological function. 41% of primary tumour resections result in postoperative neurological complications, with the main cause being resection of eloquent tissue. Current technologies used intraoperatively to identify tumour and evaluate brain function have significant limitations. The corollary is that technologies that allow for simultaneous identification of cancer tissue and assessment of neurological functionality, can enable more precise resection and improve patient outcomes. Multispectral Imaging (MSI) has shown promise in neurosurgical oncology for intraoperative margin delineation at macroscopic scale. Our current operative pilot study on 47 patients has verified that MSI can also detect structural differences between functional and non-functional brain areas. Probe-based Confocal Laser Endomicroscopy (pCLE) allows identification of residual brain tumours at microscopic scale and improves resection rates. The integration of macro- and microscopic intraoperative techniques for brain tumour identification may allow for more accurate tumour margin delineation. This proposal will develop a multimodal platform for intraoperative navigation and in vivo real-time tissue characterisation in neurosurgery. Macroscopic guidance with MSI will assist the surgeon to efficiently remove the bulk of the tumour and highlight eloquent brain tissue while microscopic screening with pCLE will be deployed to characterise tumour margins, help to define the infiltration of the tumour, improving the extent of resection. The proposed fusion of multimodal tissue characteristics will guide resection across scales and enable surgical excision at cellular level whilst preventing permanent neurological deficits. The proposed platform is in response to the current clinical demand for bringing into the operating theatre the latest technologies in intraoperative imaging and Artificial Intelligence (AI) to allow for real-time tissue analysis and provide surgical guidance during tumour resection. The project will advance surgical outcome and safety of brain tumour surgery and also transform surgery for other cancers, too. It will grant surgeons exceptional navigational and cognitive cues for tissue characterisation, assisting them to reduce surgical errors. More complete tumour removal will improve patient quality of life and life expectancy, with obvious impact on society and the economy.

UKRI Gateway to Research · FY 2025 · 2025-02

Earthquake loading is a major threat to the safety of historical unreinforced masonry (URM) structures (e.g. old masonry buildings, bridges, and monuments) located in earthquake-prone regions of Europe. The damage or collapse of URM structures with historical and cultural value not only threatens the safety of people but also brings extremely adverse social and economic effects. Therefore, reliable predictions of the dynamic response of historical URM structures under seismic loading are excessively important. As the existing microscale and mesoscale modelling strategies which model masonry units and mortar joints individually often impose prohibitive computational demands, the macroscale modelling strategy which reduces the modelling effort and computational cost receives wide applications in both research and practice. However, the existing macroscale models often ignore important mechanical behaviour of masonry under seismic loading, limiting their applicability to specific problems. To overcome the deficiencies of existing macroscale models, this project will develop an advanced and general macroscale modelling strategy which fully replicates the important mechanical phenomena involved in URM structures under seismic loading, and propose an efficient homogenization method to generate needed macro-level information for nonlinear dynamic structural analysis. The proposed numerical approach will be validated and applied to two case studies involving realistic historical URM structures which were damaged by earthquakes.

UKRI Gateway to Research · FY 2025 · 2025-02

In the UK around 100,000 babies are cared for in neonatal units yearly; many are preterm and have a high risk of death and long-term disability. Necrotising enterocolitis (NEC) a dreaded condition affecting the gut, can affect up to 7% of those who are born very preterm (VPT: earlier than 32 weeks of pregnancy). NEC typically affects babies 4-6 weeks after birth, leads to death of the gut wall, often requires surgery and is a leading cause of neonatal death. Amongst those who require surgery one-third will die and a third will have significant long-term disability or complications related to gut. NEC also affects the brain: almost half NEC survivors have long-term problems like cerebral palsy or learning difficulties. NEC is one of the top three research priorities for premature babies, identified by parents, patients, doctors, nurses, and researchers. We know that being born more preterm, small or after slow growth in pregnancy, receiving antibiotics on several occasions or formula milk feeds increase the risk of NEC, but the disease sets in very abruptly and usually unexpectedly. Therefore, recognising signs or predicting NEC a day or two earlier could be critical in reducing the adverse consequences and better outcomes. In the neonatal units we routinely monitor heart and lung health using heart rate, blood pressure and oxygen levels in the blood continuously; this enables us to detect important condition like infection earlier. But there are no established methods to continuously monitor gut health in the neonatal units. From our previous research, we have established normative measurements of oxygen levels in the tissues of the brain and gut. From laboratory-based studies we also know that certain bacterial and chemical changes occur in the stools (poo) of the VPT babies days before the NEC becomes apparent. We want to use these to predict NEC. We will recruit 425 VPT babies over 3 years from two tertiary care hospitals in London. We will record detailed maternal and baby data and observations such as heart rate, oxygen levels in blood, and blood pressure every second during a baby's neonatal stay. We will also measure oxygen level in the brain and the gut and collect daily stool samples from the nappies of the babies to measure bacterial and chemical changes in the stool. We will then combine all these complex data to identify changes that occur before a baby develops NEC. We believe that we will be able to identify changes in these measures before a baby becomes sick with NEC. If we are able to predict NEC in advance, this will open up the possibility of treating to prevent NEC - for example with medications that prevent inflammation or antibiotics - to stop or modify NEC and save lives and improve lifelong outcomes. We are consulting parent focus groups and the UK Bliss baby charity as we design the study. The findings of the study will be shared through healthcare and academic research websites and social media; presented in conferences and published in peer-reviewed scientific journals. We believe that this study will have dramatic short- and long-term health benefits for vulnerable babies and drive better care at national and international levels.