KING'S COLLEGE LONDON

UKRI Gateway to Research · FY 2026 · 2026-01

Aim We aim to understand whether a computerised tool designed to quickly spot problems in brain scans can help doctors diagnose cases faster. This will help patients by getting them the treatment they need sooner. We also will measure how useful doctors find the tool, and whether it is saves money for the National Health Service (NHS). Understanding the Challenge Radiologists are doctors who read medical images, like brain scans, to find diseases. They've been facing a big increase in their workload, which can slow down how quickly they can report brain scans. When illnesses are not spotted promptly, it can lead to worse health outcomes for patients and higher costs for everyone. We think our new tool can help solve this problem. How the Tool Works When a patient has a brain scan it is put to the back of a queue for a radiologist to report it. Our tool will instantly check for any signs of significant health concerns. If it finds something abnormal, that scan jumps to the front of the queue. Our new tool ensures that radiologists will see the scans of patients with abnormal brain scans first. We believe that this will lead to faster start times for necessary medical treatments. In the long run, this not only can improve patient health, but also may reduce the overall expenses of the NHS. What We Have Done So Far We have shown the computerised tool works on brain scans throughout England, Wales, Scotland and Northern Ireland. Using a structured regulatory system, we have ensured that the tool is compliant with safety, reliability, and quality standards to provide users with a dependable product. The Study We're Conducting We plan to test this tool across a range of UK radiology departments, timing how quickly scans are analysed with and without the tool. This comparison will reveal whether the tool can effectively reduce the time to diagnose, and therefore commence treatment for patients. Involving Patients and the Public A diverse group of patients and public members, who know firsthand what it's like to have a brain scan, have been guiding our project. They've made sure that we think about the needs of every patient — no matter which hospital they go to, the type of scan machine used, or their personal background such as their age or ethnicity. This group remains a strong voice throughout our project, ensuring that we keep focused on what patients and the public really need. We also have been guided by radiologists and those in charge of radiology departments to ensure that what we propose will work effectively on a day-to-day basis in busy hospitals. Sharing Our Findings If our study shows this tool works well, we have demonstrated deployment at scale as well as the value of the technology. It could then be shared with the NHS and other health services around the world, making a positive difference both within the country and far beyond the UK. We plan to tell everyone about our study—patients, doctors, and health organizations—so that the knowledge we gain can help improve health services everywhere.

UKRI Gateway to Research · FY 2026 · 2026-01

Even though infectious diseases remain a major threat to human health everywhere, the world remains unprepared. When new infectious diseases like COVID-19 spread, not everyone is equally at risk of getting sick. Those on low incomes and people of colour usually have a greater risk of infection. Some will see a doctor if they become ill. However, many people can’t go to hospitals, so we get an incomplete picture of who is getting ill and what happens to them. This might be because of where and how they live, not being able to afford the expense and time needed to seek care, or having existing health problems. When a new infectious disease spreads, we still don’t know who is the most at risk of severe illness and where they are. This is a problem in the UK, but is worse in countries where healthcare is less accessible for everyone. I aim to address this issue by ensuring everyone, regardless of their financial status, background, or location, stays safe from infectious diseases. I will start by focusing on the UK and then expand to other countries. To do this, I will create the Equitable Intelligent Mapping platform (EI2), a AI program designed to predict who and where needs help the most. This platform will uniquely account for socio-environmental and individual vulnerabilities, which is currently missing from current surveillance platforms. By including health equity factors, we can better understand how risk is distributed, and look beyond just the number of cases reported by our healthcare system. First, I will develop a new system to understand how factors like wealth, living conditions, and the environment affect our chances of getting sick, then use mathematical simulations to confirm the capability of this system. Since many people are missing from hospital records, I will use health data gathered from new collection methods, such as phone apps and wastewater analysis, while ensuring personal privacy is protected. I will then use statistical models to run my system and predict who might get sick and identify how risk is distributed. Using AI, I will create a novel risk index to identify areas with the highest risks. Knowing which areas are most at risk helps our healthcare system direct help where it's needed the most. These tools will help form a mapping program called EI2, a dynamic interactive surveillance dashboard that shows who has the highest risk of illness and which neighbourhoods they live in. My project will improve preparedness for both new and existing infectious diseases. By working with leaders in government, healthcare, and business, we can make quicker and smarter decisions to control and prevent diseases. Better preparedness will reduce the pressure on hospitals during outbreaks, leading to improved healthcare for everyone. By closely monitoring health and prioritising those who might be overlooked, we can ensure everyone gets the care they need and help close the health gap.

UKRI Gateway to Research · FY 2026 · 2026-01

Women have long been involved and continue to participate in terrorism across a range of roles, and for a variety of reasons (Bloom 2011, Sjoberg & Gentry 2011). Nevertheless, prevailing counterterrorism policy responses often fail to account for the complex ways women are recruited into or motivated to participate in terrorism. As a result, the gendered design and outcomes of counterterrorism responses remain poorly understood by policymakers and practitioners. Consequently, existing counterterrorism policy and practice integrating women presents a ‘gendered paradox of inclusion’ where efforts to be inclusive of women’s perspectives and experiences have instead resulted in the (re)production of new modes of exclusion. The proposed project, Contesting Counterterrorism: Constructing women through Britain’s post-9/11 policy and practice, will examine how this gendered paradox of inclusion emerges in relation to the pillars of Britain’s longstanding counterterrorism strategy CONTEST. It will show how these modes of exclusion manifest in various ways and will explore the impact of gendered strategies of inclusion on outcomes of CONTEST and on the women and communities involved. The project asks: what are the mechanisms, strategies and processes through which women become integrated into counterterrorism? How are the policies and practices of CONTEST gendered, and how are they sustained, challenged, and evolved? How do persistent gender stereotypes about women and terrorism inform the ways that counterterrorism policy and practice has been designed and implemented, and with what consequences? To address these questions, I interviewed officials across multiple UK government departments and agencies and civil society organisations involved in making and practising counterterrorism, and analysed hundreds of British government policy documents on counterterrorism over a twenty year period. This research observes that while there has been a conscious effort to integrate women more directly within different aspects of counterterrorism policy and practice, these modes of inclusion continue to reproduce gender stereotypes resulting in ineffective and sometimes dangerous counterterrorism practices that neither mitigate, nor understand women’s involvement in terrorism. However, evidence of gender-sensitive evolution emerges at the individual level, demonstrating that change is possible, but it is slow and localised within departments and organisations. Contesting counterterrorism makes a unique contribution to revealing how gendered norms, rules, discourses and practices inform and impact UK counterterrorism policymaking. By shedding light on the processes involved in making and practising counterterrorism in gendered ways, I contribute to feminist thinking on security, violence and governance. My findings suggest that efforts to integrate true gender sensitivity in the design, implementation and evaluation of counterterrorism must go beyond the idea that integrating women is sufficient. Instead, we must transform the gendered, racialised norms and structures that underpin the UK government’s security culture more systematically. This Fellowship enables me to build on and develop my doctoral research into a monograph and a series of articles, and to disseminate my findings to policymakers, practitioners, fellow academics, NGOs and wider public audiences working on questions of gender and security. Through targeted uptake activities, I hope to inform the design and implementation of future counterterrorism policy and practice that goes beyond superficially including women in policy responses, and instead strives to develop an approach to counterterrorism that is rooted in principles of human rights and gender equality. This approach would prioritise the protection of people and communities rather than contribute towards their securitisation.

UKRI Gateway to Research · FY 2026 · 2026-01

A number of fungal pathogens have become major agents of infection in patients with suppressed immunity, often due to factors such as age or cancer therapy. These include yeast species like Candida albicans and Candida auris—the latter being a recently emerged, drug-resistant species associated with significant mortality in infected patients. Candida auris is frequently resistant to more than one class of currently used antifungal drugs. This multidrug resistance (MDR) characteristic of C. auris is also increasingly observed in other Candida species. The emergence of MDR fungal pathogens is particularly concerning due to the relatively limited range of antifungal drugs available to clinicians. It is, therefore, an urgent priority to develop new and effective treatments for MDR Candida spp. The project explores one of the key mechanisms that underpins the multidrug resistant properties of C. auris and other Candida spp. Many of these species have an array of drug efflux pumps, complex molecular machines which are able to transport the antifungal drug out of the cell and stop it reaching levels where it can either kill the fungus or stop it growing. These pumps often work in concert with other resistance mechanisms to achieve very high levels of stable resistance to the drugs used in the clinic - adversely affecting treatment outcomes. Understanding and overcoming the action of these efflux pump systems is essential if we are ever to explain why current drugs are not effective against new clinical isolates, and to guide the development of new analogues of existing drugs which will not be transported out of the cell. This is equally important in the development of new antifungals  that might also be susceptible to efflux through these same pumps. Previous work on preventing efflux in azole antifungals have generated compounds that kill MDR Candida spp. including strains with target mutations and high level of expression of efflux pumps. The proposed project will explore the use of these efflux-resistant compounds as tools to define the function of different types of efflux pumps, identifying which are most important in resistance to clinical drugs, and understanding how they interact with other resistance mechanisms within the cell. By combining these chemical tools with state-of-the-art methods for genome modification —such as blocking the production of specific efflux pumps or modifying their activity—we will establish how these novel chemical tools interact with and block the individual efflux pumps they target. Subsequently, we will evaluate how Candida species respond to these new efflux-resistant azole compounds in a complex biological system using mouse models. This approach will help generate a framework to better understand the targeting of drug efflux to support the future development of more effective efflux-resistant drugs.

UKRI Gateway to Research · FY 2025 · 2025-12

Skeletal muscle weakness is a major feature of many muscle disorders, yet there are currently no effective treatments to restore muscle contractility. This lack of therapeutic options reflects a limited understanding of the molecular mechanisms that regulate contraction in human skeletal muscle. Traditionally, muscle contraction is controlled by calcium-dependent activation of the thin filament: following electrical stimulation, calcium ions released from the sarcoplasmic reticulum bind to troponin, triggering structural changes that enable actin filaments to interact with myosin motors on the thick filament, producing force and movement. However, recent evidence indicates that the thick filament itself plays a fundamental regulatory role in contraction. Calcium-independent mechanisms within the myosin filament control the transition of myosin motors between OFF and ON states, regulating force generation. So far, these newly identified mechanisms have been characterised only in “fast-contracting” fibres in animal models, whereas little is known about the two main fibre types of human skeletal muscle, the “slow-contracting” type-1 and the fast type-2A fibres. Our preliminary findings suggest that thick filament-based regulatory mechanisms operate differently in human muscle fibres compared to previously studied models. Disruption of these mechanisms is already implicated in sarcomere-based cardiomyopathies. Moreover, small molecules that modulate myosin motor ON/OFF transitions have recently been developed to treat those heart diseases, demonstrating the feasibility of pharmacologically restoring contractile function. Although these myosin modulators also target the slow myosin in skeletal muscle, their effects on skeletal muscle regulation, and their therapeutic potential in muscle disorders, remain largely unexplored. The overarching aim of this project is to define the fundamental regulatory mechanisms controlling human skeletal muscle contractility, in both health and disease, to enable rational therapeutic targeting of the contractile apparatus in skeletal muscle disorders. Our primary objective is to identify the mechanisms that regulate myosin filament activation and force generation in human slow (type-1) and fast (type-2A) fibres. To achieve this goal, we will combine complementary biophysical techniques, including small-angle X-ray scattering, fluorescence polarisation microscopy and advanced fibre mechanics, to study the regulatory structural changes in both actin and myosin filaments under near-physiological conditions. These approaches will be applied, for the first time, to isolated human muscle fibres obtained from biopsy samples of healthy donors, enabling us to dissect the distinct regulatory features of slow and fast muscle fibres. We will also investigate how the kinetics of contraction and relaxation are controlled by the structural dynamics of thick and thin filaments. Our secondary objective is to explore the therapeutic potential of small-molecule myosin modulators in skeletal muscle diseases. Using muscle tissue from patients with congenital myopathies as a proof-of-concept model, we will characterise disruptions in thick filament regulation and test the ability of myosin activators to restore contractile performance. This work will lay the foundation for extending pharmacological targeting of the myosin filament to a wider range of muscle disorders, including age-associated muscle weakness, and will guide the rational design of new therapies aimed at enhancing or suppressing skeletal muscle contractility as clinically required.  

UKRI Gateway to Research · FY 2025 · 2025-12

This fellowship aims to revolutionise robotic dexterity by co-optimising the design, sensing and control aspects of robot manipulators. Its focus lies in developing differential simulations for tactile-based robot manipulator designs that play a crucial role in enhancing robot dexterity and facilitating safe interaction with the unstructured environment. Tactile robots offer the potential for precise manipulation and interaction with the environment. However, current approaches lack integration and efficiency, often leading to isolated development of manipulator morphology, tactile sensing mechanisms, and control algorithms. Further, manual and time-consuming hardware design processes hinder scalability and optimisation across various components, and ensuring the manipulator’s adherence to desired specifications proves challenging due to intricate interplays between robot design, manufacturing constraints, and the control algorithm. The need for a cohesive, integrative approach is evident, where manipulator design, sensing capabilities, and control strategies are co-optimised to achieve enhanced dexterity and adaptability. This fellowship aims to establish the UK's leadership in tactile robotics. Specifically, the objectives of this fellowship are: To establish the mathematical framework for tactile robot parameterisation, enabling the co-design of structure, sensing, and robot behaviours, and thus facilitating a comprehensive design space for advanced tactile robots. To develop differentiable simulations that accurately predict a robot’s interactions with its environment while automatically refining its sensing, structure, and control configurations based on its interaction history with the environment. To enhance the performance of tactile robots and their simulations by integrating real-world data and implementing bidirectional learning between real and simulated environments. This fellowship offers a range of applications with significant impact. In industry, the fellowship will transform sectors requiring precise, adaptive manipulation, such as manufacturing, logistics, and healthcare. Partnerships with companies like Unilever, Ocado, and Shadow Robot Company will facilitate the integration of cutting-edge tactile sensors into commercial robotic systems, enhancing performance and efficiency. This research will drive innovation in industrial automation and position the UK as a leader in robotics technology. The societal impact includes advancing robotic capabilities where precision and adaptability are essential, such as in healthcare for the elderly and disabled, and promoting sustainability by reducing waste through better logistics handling. The fellowship will address pressing societal challenges, contributing to a future where robotics significantly improves everyday life. Educationally, the fellowship will nurture the next generation of roboticists through TaRoSim, an educational toolbox designed to make advanced tactile robotics accessible to students in high school and higher education. By offering interactive resources and hands-on learning experiences, TaRoSim will inspire future innovators and build a skilled workforce ready to drive future advancements in robotics. Outreach initiatives, including workshops at events like Girls into Electronics, will ensure diverse engagement, empowering future leaders in the field and solidifying the UK’s position as a leader in robotics education and research.

UKRI Gateway to Research · FY 2025 · 2025-12

38 million adults in Europe live with chronic pain caused by nerve damage, called ‘neuropathic pain’. Neuropathic pain can be caused, for example, by diabetes or cancer treatment. It feels quite different to other types of pain, causing shooting, stabbing and burning sensations that are very hard to bear. Worse still, painkillers do not work in neuropathic pain for most people. Our network (called ‘DECIPHER’) is designed to change this and finally find new treatment options that provide relief to people with neuropathic pain. Our work is based on recent scientific research which suggests that neuropathic pain is caused by a particular type of connective tissue cell. We will now: test whether the number of these connective tissue cells is related to how much neuropathic pain someone experiences. For this, we will examine nerve samples and the skin in which they terminate. These samples have been donated by people with neuropathic pain who have had them removed for diagnosis. find out whether these connective tissue cells can cause nerves to send abnormal electrical signals that cause pain. For this, we will combine cells in the laboratory to study connective tissue cells from people with neuropathic pain together with human nerves derived from stem cells. find new painkillers against neuropathic pain, by identifying ways to soothe and quieten these connective tissue cells. We will use a variety of modern laboratory models to ensure that our findings are of use in the clinic.  Our network includes specialists from many different disciplines as well as people who live with neuropathic pain themselves. By working together and taking into account everyone’s perspective and expertise, we are in a perfect position to realise our ambitions, which are to: explain why some individuals experience neuropathic pain while others do not. find easier ways to figure out what is causing someone`s neuropathic pain: by looking at a small piece of skin instead of taking a piece of nerve. finally find better painkillers for the millions of people who live with neuropathic pain every day.

UKRI Gateway to Research · FY 2025 · 2025-12

The proposed project, Probabilistic REasoning with CIrcuits for Safe and Explainable Artificial Intelligence (PRECISE-AI), addresses the limitations of deep neural networks (DNNs) in generative modelling. While DNNs have excelled with structured data like images and text, they fall short in providing transparency, adaptability to unstructured data, and accessibility to practitioners due to their resource-intensive nature. PRECISE-AI aims to overcome these challenges by introducing a family of nonparametric generative models based on classification and regression trees. Trees offer several advantages, including interpretable explanations, adaptability to mixed tabular data, and ease of use. These benefits need not come at the cost of expressive power. Trees are universal approximators that often attain state of the art results on tabular data tasks. They can be efficiently compiled into circuits, providing exact, tractable inference for a range of common and important probabilistic queries. This project emphasises three key principles: privacy, explainability, and fairness, which are crucial in responsible AI but often neglected in unsupervised learning. For instance, in healthcare, where data is sensitive, a synthetic dataset generated with privacy guarantees could be shared with researchers without compromising patient rights. An explainable generative model could shed light on disease diagnosis by highlighting relevant biomarkers. A fair model could test whether an apparent disparity in health outcomes across racial groups is attributable to discrimination or confounding. By ensuring that generative models are private, explainable, and fair, PRECISE-AI will promote greater trust in machine learning, paving the way for safe adoption in high-risk domains. Beneficiaries of this research include practitioners, who will benefit from user-friendly, well-documented software; data subjects, whose rights to privacy, explanation, and fairness will be protected; and policymakers, who can make informed decisions regarding AI use in sensitive domains. Initially, the project will focus on healthcare applications, utilising biomolecular and clinical data from large studies on immune-mediated inflammatory diseases. In summary, PRECISE-AI proposes a novel, unified approach to generative modelling and probabilistic reasoning that prioritises issues of transparency, safety, and trust. Its potential applications extend to various high-risk domains, where the current state-of-the-art DNNs fall short. The project aims to provide accessible and responsible AI solutions with broad societal impact.

UKRI Gateway to Research · FY 2025 · 2025-12

Periodontitis is one of the most prevalent chronic inflammatory diseases worldwide leading to progressive damage to the supporting tissues of the teeth (known as the periodontium). The oral epithelium acts as a barrier preventing invasion of microbes in the mouth. A particularly sensitive area is the epithelium that sits between the oral epithelium and the tooth, known as the junctional epithelium which protects the tooth-supporting structures from the oral environment. This epithelium forms a tight connection to the enamel and cementum of the tooth via an inner basement membrane rich in collagens and laminins and rapidly renews itself controlled by a stem cell niche. If the junctional epithelium becomes detached from the tooth surface an epithelial pocket is created. The loss of attachment disrupts the protective effects of the junctional epithelium, leading to further destruction of the periodontal tissues, ultimately resulting in loss of bone, and tooth loss. We hypothesis that if junctional epithelium could be stimulated in periodontal disease, the regression of the epithelium could be reversed, providing a novel regenerative therapy for periodontal disease.   In this application we focus on the junctional epithelium to understand this unique and essential structure. We aim to follow the development of the junctional epithelium and establishment of its stem cells niche. We will then investigate how this tissue is impacted in case of disease and detachment from the tooth. Finally, we will assess how attachment and the stem cell niche can be maintained or reconstructed.   Aim 1: To map the signals and cell populations that form and maintain the junctional epithelium Aim 2: To characterise the mechanisms underlying loss of attachment Aim 3: To rescue the regression of the junctional epithelium in periodontal disease   In this proposal we focus on the mouse. Mouse molars are very similar to human molars and are also supported by a junctional epithelium that expresses many of the same genes as in humans. Genetically modified mouse models allow us to target specific tissues, while periodontitis models can be generated to explore how the tissue reacts to a disease state. In addition to knowledge about prevention of periodontitis, knowledge of how to form a functional junctional epithelium will also help to improve the interaction between the oral epithelium and dental implants.   The proposal is a collaboration between a researcher with a background in tooth development and repair, and a clinician specialising in the periodontal tissues, bringing together their expertise to answer an important question which impacts a large proportion of the population.            

UKRI Gateway to Research · FY 2025 · 2025-12

Artificial Intelligence (AI) has dominated public and scholarly attention, overshadowing many other areas of technology. Yet there have been some prominent recent developments around Extended Reality (XR) – a blurring of actual and virtual worlds encompassing Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR) – that deserve equal scrutiny. Often supported by AI, XR is transforming the way we interact with digital and physical spaces, while offering immense potential across many sectors. Our project recognises the growing importance of this technology, while proposing to shape its perception, understanding, design and use. It will do so by establishing an interdisciplinary arts and humanities-led research field of Multisensory XR, with a focus on critical enquiry into its social, cultural and ethical values and benefits. Current XR designs are often driven by industry ambitions for total immersion, seeking to captivate users so that they ‘never want to leave’. This approach remains overly focused on commercial needs at the expense of social and personal gains. It also exhibits ocularcentric bias, overlooking the multiple ways people experience the world while restricting access for diverse groups. We’ll address these two issues by working with the concept of ‘ethical multisensory XR’ that goes beyond industry’s ‘Tech for Good’ mantra. We’ll critically investigate what constitutes a ‘good’ immersive XR experience – one that not only succeeds technically and artistically but also delivers ethical and social value. Focusing on the broader potential of XR, we’ll uncover ways to create environments that are more inclusive and responsive to social needs and cultural contexts. Going beyond the ‘goggles and screen’ setup, we’ll identify XR applications that engage touch, sound, smell and taste, while prioritising care, accessibility and inclusivity. By broadening the sensory experience in XR, we’ll showcase interactions that cater to a wider range of users and purposes. The key goal of the project is to ensure that XR not only works but also works for everyone. The project team will be King’s College London-based, consisting of Project Lead Professor Joanna Zylinska (Digital Humanities), 3 Project Co-Leads – Dr Gabriele Salciute Civiliene (Digital Humanities), Dr Stephanie Janes (Culture, Media and Creative Industries), Dr Marie Elena Stefanou (Neuroimaging) – and Neil Jakeman (Specialist, KCL Digital Lab). They are all part of the XR and Attention Research Group hosted by the Digital Futures Institute at King's. Our aims and objectives: Establish a field: We’ll establish a humanities-led field of Multisensory XR to study XR’s values and meanings, identifying how this technology can integrate non-visual senses while challenging the idea of ‘the average user’: implicitly young, male and able-bodied. Outputs will be captured in an open wiki and public report with recommendations. Engage in speculative modelling: Through workshops, interviews and a hackathon, we’ll look into the future of XR experiences via three XR case studies in creative industries, health and education and one proof-of-concept prototype. Build a community: We’ll develop a network of academic, creative and industry collaborators to engage in critical investigation of Multisensory XR while championing the design of ethical, socially-valuable XR. With the network we’ll then co-develop an action plan for future research. This will ensure the community thus formed can be meaningfully mobilised over the forthcoming years, laying the groundwork for further projects and follow-on funding. 

UKRI Gateway to Research · FY 2025 · 2025-12

Although clinical care aims to treat illness and improve quality of life, the care journey can also be traumatic, particularly for young children with chronic conditions such as cancer or cerebral palsy who need frequent but uncomfortable procedures. During procedures, the patient, clinician and carer share the same physical space and process but have very different experiences. For the child, there is a stark contradiction: actions intended to benefit also cause distress, pain and long-term psychological harm. For clinicians, maximising positive outcomes is essential, but is challenging if the child is distressed and/or struggling to comply. Parents and carers are caught in an impossible position: trying to both assist the procedure, thus adding to their child’s distress, whilst also providing comfort and protection. The negative cycles of stress and fearful anticipation can greatly amplify trauma associated with repeated unpleasant procedures, further intensified by the child’s sense of lacking control and agency. The aim of this research project is to develop a novel medium that can break this cycle by harnessing a powerful psychological effect known as reframing. Reframing changes someone’s interpretation of their perceptual experiences, leading to different emotional responses to the same external stimuli – this could be particularly powerful for children, where fear and trepidation can be transformed to boldness and pleasure simply through a judicious perspective shift. We will use established technologies to develop a new creative medium that explores the artistic potential of reframing while supporting clinical needs. We will achieve this aim through exploring and testing a new immersive medium: Asymmetric Reframed Reality (ARR), in which a child is present and engaged with their actual situation (eg, uncomfortable medical procedure) but has a world-view (perhaps through a head mounted display) that reframes their experience in a positive way. ARR will thus differ markedly from simple distraction with traditional approaches, which aim to disconnect a user from an experience. Instead, we will use advances in display and sensory technology, visual storytelling, game design and developmental psychology, to create multisensory experiences for ARR. The resulting framework will enhance collaborative engagement in the real world through asymmetry: whilst one user (patient) has their visual and sensory experience reframed (eg., through playing a game that delights them yet remains consistent with their physical surrounds and touch sensations), other users (clinicians and care givers) can appreciate both world views and modulate the patient experience to maximise effectiveness. Reframing is a core psychological concept used in cognitive behavioural therapy. Distinction between substance and meaning is also the essence of the humanities: we will draw on both to define and articulate the requirements for ARR, balancing the constraint of keeping aligned to a real physical environment with creative freedom to present a quite different version of it. We will explore the artistic potential and psychological active ingredients of ARR and develop demonstrators to showcase the medium. Critically, to transform their experience from passive clinical subjects, we will bring children’s active voices into the project at all stages, engaging artists to explore ARR with children, leading to the co-creation of innovative and engaging virtual worlds that we will use in both a clinical setting and as modes of artistic experience. ARR will therefore be a clinical tool that greatly benefits patients, and a novel creative medium that unlocks new forms of artistic expression.

UKRI Gateway to Research · FY 2025 · 2025-12

This proposal introduces a transformative, real-time, image-based, noninvasive diagnostic technology to address the unmet need for early detection and localisation of invasive fungal infections (IFIs). IFIs are a growing and serious global health concern, particularly for patients in intensive care or with weakened immune systems. These infections are often difficult to diagnose early, leading to delayed treatment, increased mortality, and the overuse of broad-spectrum antifungal drugs, which in turn contributes to antifungal resistance (AFR). Current image-based diagnostic approaches, including Computed Tomography (CT) and Positron Emission Tomography (PET) scans, can show areas of infection but cannot reliably distinguish between infection, inflammation, or cancer. There is a pressing clinical need for noninvasive, sensitive, and specific tools that can detect fungal infections early, pinpoint their location, and monitor treatment responses — all in real-time. At King’s College London (KCL), in collaboration with King’s Health Partners (KHP) and the UK Health Security Agency (UKHSA), we are developing a PET imaging radiotracer to address this unmet need. Our technology enables the visualisation of fungal pathogens inside the body, potentially allowing clinicians to distinguish true infection from inflammation and better guide antifungal treatment decisions. We have developed a gallium-68-labelled small molecule and confirmed its uptake by major fungal pathogens, including Aspergillus fumigatus, Candida albicans, and Candida auris — a novel result for Candida species, for which no similar radiotracers currently exist. This proposal aims to take the next critical step by evaluating our candidate tracer in small animal models of fungal infection and comparing its behaviour in infected vs. inflamed tissue. Our objectives are: (1) to assess the radiotracer’s pharmacokinetics (PK) in healthy animals, and (2) to evaluate its ability to detect fungal infection and differentiate it from inflammation in small animal models infected with key fungal species. This work will generate essential data to support further development under UKRI’s Developmental Pathway Funding Scheme. If successful, this tracer could transform how fungal infections are diagnosed and monitored, reduce unnecessary antifungal use, and improve outcomes for some of the most vulnerable patients. The long-term impact of this project lies in its potential to support faster diagnosis, better targeted treatment, reduced AFR, and improved survival for patients facing life-threatening fungal infections.  

UKRI Gateway to Research · FY 2025 · 2025-12

Nervous systems are the most extraordinarily complex structures we know of. During development neural stems cells divide to give rise to a myriad of different neuron types that wire together generating functional, interconnected neural networks. The insect central nervous system is largely built from a segmentally repeated, stereotyped array of neuronal stem cells, called neuroblasts, that make distinct families or ‘lineages’ of neurons. Neuroblasts divide repeatedly, generating pairs of A and B neurons, that form two ‘hemilineages’. Soon after neurons are born these hemilineage modules show distinct patterns of gene expression that define key characteristics, such as, the neurotransmitter it uses, how it grows and critically whether that neuron lives or dies. Programmed cell death appears to be a universal mechanism deployed during the development of nervous systems. In our previous work we discovered that death is a common fate within hemilineages, of the ventral nerve cord, dictating the type and number of neurons available for building circuits later in development. Insect models provide a great opportunity to gain deep insights into the molecular mechanisms controlling early fate decisions, like death. Our conjecture is that the control of this precisely patterned hemilineage-specific death by the ‘pro-death’ genes, reaper and grim is governed by two aspects of regulation. Firstly, that there are specific combinations of transcription factors (TFs), that bind DNA and control transcription of genes, in distinct doomed hemilineages. Secondly, that lineage-based differences in chromatin accessibility allow transcription factors to bind when open or not bind when closed. Our specific objectives are: Identify hemilineage-specific TFs and chromatin accessibility landscapes within doomed thoracic hemilineages using single cell multi-omics. Test the requirement of cis-regulatory elements within the reaper/grim locus and the contribution of individual TFs for hemilineage-specific patterns of Reaper and Grim transcription using CRISPR As frontier bioscience this project will explore the ‘rules of life’. The knowledge generated will give valuable insights into how complex patterns of gene expression are orchestrated at these early stages of development, when many properties of a neurons ‘character’ are set in place. The questions and state-of-the-art approaches will provide excellent training for the next generation of bench scientists. Revealing these rules of ‘hemilineage development’ will complement recent advances that have been made in decoding the thoracic circuits of Drosophila using EM-based connectomics. Furthermore, our work will generate a suite of tools and reagents during this project that will be immensely useful for researchers studying questions of gene regulation, programmed cell death and neurodevelopment. More broadly, insects represent a significant threat to global health and food security, deepening our understanding of their development and physiology remains an important goal in the biosciences. Our hope is that uncovering this fundamental biology in Drosophila will provide insights into the way all nervous systems are built and how some mechanisms can become disrupted in developmental disorders.

UKRI Gateway to Research · FY 2025 · 2025-12

Major depressive disorder (MDD) is a prevalent psychiatric condition with significant genetic components, often complicated by comorbidities that hinder an accurate diagnosis and effective pharmacological or psychological therapy. Typically, patients with MDD first seek help from general practitioners (GPs). While GPs can usually identify MDD, they may lack the specialized training required to detect additional conditions such as attention deficit hyperactivity disorder (ADHD), post-traumatic stress disorder (PTSD), eating disorders, or obsessive-compulsive disorder (OCD). These conditions often necessitate different treatment approaches. If these conditions are not correctly identified or are overlooked, it can result in less effective treatment and an increased risk of side effects. Given the genetic links between MDD and these frequently comorbid conditions, integrating their genetic information with social, environmental, and clinical data could significantly enhance personalized treatment strategies, tailored to individuals' unique genetic and clinical profiles. Such personalized care could improve treatment outcomes and mitigate the burden of comorbid conditions, which are often challenging to manage alongside MDD. To address these challenges, I propose a comprehensive three-phase research strategy that integrates genetic data with clinical and social information, applied to  the largest cohort worldwide focused on the genetics of severe depression and anxiety: Mapping the Genetic and Phenotypic Landscape: Analyze self-reported questionnaires, mental health diagnoses, and medical records to elucidate how different conditions coexist with MDD and affect treatment responses. Employ Genome-Wide Structural Equation Modelling (G-SEM and GW-SEM) to explore complex genetic relationships between MDD and its comorbidities, identifying specific genetic markers associated with MDD, both with and without other conditions. Predictive Modelling: Building on the genetic data collected, I will develop polygenic risk scores (PRS) for depression and its common comorbid conditions. These scores will be combined with clinical data, family history, and socioeconomic information using advanced machine learning models. This integrated approach aims to enhance the prediction of treatment responses, side effects, and clinical trajectories, providing more precise and effective treatment options. Validation and Expansion: The predictive models will be rigorously tested using data from diverse external cohorts, such as PRADA and Genes & Health. This validation process will assess the generalisability and robustness of the models across different populations and ancestries. Throughout this project, I intend to disseminate our findings at major scientific conferences, collaborate with patient advocacy groups, and ensure open access to publications and the open-source code for the developed tools. I will also engage regularly with clinicians and mental health professionals who have expertise in mood disorders to ensure that the research findings have practical, real-world applications. Ultimately, this project aims to advance the field of personalised depression treatment by integrating genetic data with clinical insights. By examining MDD and its common comorbidities within diverse social contexts, I aim to develop a deeper understanding of how these conditions influence treatment responses, clinical trajectories, and therapy adherence. The goal is to create robust, unbiased tools that benefit individuals across all social contexts and ethnic backgrounds, helping to reduce health disparities in depression treatment and improve outcomes for those affected by this complex and challenging disorder.

UKRI Gateway to Research · FY 2025 · 2025-12

It is difficult to imagine life before antibiotics were discovered. Infections such as tuberculosis, pneumonia and whooping cough were common killers - and if minor wounds and burns became infected they were fatal. The use of antibiotics to control bacterial infections is perhaps the most important achievement of modern medicine. However, we have failed to keep pace with microbes becoming increasingly resistant to available treatments. The Covid-19 pandemic exemplifies the threat to human health of an infection without an effective treatment. Antibiotic-resistant infections are already another global pandemic claiming almost 5 million deaths per year globally. Of particular concern are the infections caused by Klebsiella pneumoniae, globally, the third leading pathogen associated with deaths (250 000) attributed to any antibiotic-resistant infection. The increasing isolation of strains resistant to "last resort" antimicrobials has significantly narrowed, or in some settings completely removed, the therapeutic options. This is particularly alarming in low and middle-income countries. Unfortunately, new classes of drugs are not being invented and resistance continues to spread inexorably. The stakes are high and we might be entering into a pre-antibiotic era. Public Health England has calculated that the lack of effective antibiotics will render more than the three million operations and cancer treatments life-threatening, and more than 90,000 people are estimated to die in the UK over the next 30 years due to antibiotic-resistant infections. The golden era in antibiotic drug discovery leveraged the antibacterial products produced by soil microorganisms but this approach became exhausted after 20 years of systematic screening. Researchers have mined different sources of natural products such as marine environments, plants, and even the community of harmless microbes inhabiting our gut with encouraging results. Yet, none of the compounds isolated have entered into drug development. A better understanding of the means used by microbes to resist antibiotics may result in the discovery of hitherto unknown targets suitable to develop new drugs against. In this research, we will use artificial intelligence to identify new potential druggable targets from K. pneumoniae that when blocked may render the microbe susceptible to antibiotics and perhaps may even facilitate the clearance of Klebsiella by our defenses. We will train supervised learners to go through data we will generate in the laboratory and to read the genome of the microbe to find these targets that researchers have overlooked. Next, and utilizing other learners, we will identify drugs that can block these targets. Specifically, we will search drugs already approved for use in humans but used for purposes unrelated to antimicrobial activity. We will carry out experiments in the laboratory to confirm the effect of these drugs. From the drug discovery point of view, our approach significantly shortcuts the drug development process hence allowing a potential fast-track transition from the basic research to clinical development. We envision that our results will encourage other academics as well as pharmaceutical companies to follow this new avenue of research to tackle the problem of the lack of therapies for microbes resistant to antibiotics. To facilitate this, we will make freely available our protocols, models and data.

UKRI Gateway to Research · FY 2025 · 2025-12

Predicting individual trajectories for patients with psychotic illnesses is both a clinical and patient priority. These conditions typically emerge in early adulthood and involve severe symptoms such as hallucinations and delusions. Approximately 25% of patients develop schizophrenia or other long-term, disabling illnesses, whilst others make a full recovery. However, there are currently no reliable methods to predict which young people with psychotic symptoms will experience which of these diverse outcomes. For patients who recover, there is also a lack of tools to monitor for symptom recurrences and predict future relapses ahead of time. Such tools could create a window of opportunity for patients and clinicians to act before symptoms get worse. New Artificial Intelligence (AI) techniques have significant potential to tackle these challenges. We know that altered language use is one of the main symptoms patients might experience in psychotic illnesses, so brain scans and short recordings of speech stand out as particularly promising data for AI to draw on. I have already led work using AI methods to search for brain connectivity and speech patterns characteristic of psychotic illnesses. I also recently developed new methods to measure patients’ brain connectivity more reliably, and to map the content of patients’ speech in finer detail. This Fellowship will build on this work, to engineer innovative tools to predict individual trajectories for patients with psychotic illnesses. Key aims include: Build the next generation of AI tools to predict likely illness outcomes for early stage psychosis patients, using both brain connectivity and speech information. Develop AI tools to predict relapse for patients tapering off medication, from speech recordings. Explore ways in which these tools can be translated to the clinic, by working closely with key stakeholders, including patients and clinicians. To that end, I will combine recent advances in AI, such as generative AI, with the new methods to measure brain connectivity and study speech data developed by my research group. I will also collect new speech data from patients tapering off medication and therefore at increased risk of relapse, enabling me to engineer tools to predict relapse for this important group. Finally, I will create the first tools to predict how early stage psychosis patients’ symptoms are most likely to change over time from both brain connectivity and speech recordings. It is crucial that the tools developed are trustworthy, and I will tackle that challenge both technically, for example by investigating new ways to communicate prediction uncertainty to clinicians, and by bringing together a wide range of different stakeholders, including AI experts, clinicians and people with lived experience of psychotic illnesses. People with lived experience have already helped guide the work, for example by suggesting the goal of predicting relapse for patients tapering off medication. The resulting tools could be used by clinicians to predict which patients are at highest-risk of developing severe, chronic conditions such as treatment-resistant schizophrenia, helping them to prescribe the most appropriate medication at an earlier stage. I also envisage opportunities for patients to use the tools developed here to self-monitor their speech at home and spot early warning signs of relapse. Longer-term, having access to tools to predict outcomes for patients with psychosis is expected to help researchers understand what causes some people to recover more fully than others, and spur future research into new treatments.

UKRI Gateway to Research · FY 2025 · 2025-12

Understanding the risk and direction of antimicrobial resistance (AMR) spread through food-borne routes, and developing of interventions to limit the spread of AMR within and between humans, animals, environment and food is a significant challenge, requiring a 360-degree investigation of a complex, interconnected system of humans, animals, environment on one hand and geographical, societal and climate-related variables on the other. This project will develop a monitoring system using AI and advanced tech to detect AMR spread in the interconnected human-animal-environment-food system ('One Health'). First, we will analyse the heterogeneous corpus of historical AMR-related public data. This will improve our understanding of what data (monitorable biomarkers) should be collected to identify the conditions leading to a higher risk of AMR spread. This knowledge will be used to guide a large-scale multi-country sampling collection campaign of a large amount of heterogeneous and interconnected data from farms, wet markets, food, environment. Data will include results of microbiological analysis, whole-genome sequencing, metagenomics, phenotyping, documentation of on-farm management practices, and environmental sensor data (temperature, humidity, etc). An innovative AI-powered data mining pipeline will be used to unravel previously unknown correlations between observable animal, human, environment, food variables and a core set of resistome, microbiome, and microbial genomics variables, highlighting new routes for surveillance deployable in low-to-high-income countries.

UKRI Gateway to Research · FY 2025 · 2025-12

Functional RNAs have been recognised over the last two decades as central regulators of biological function, and such RNAs have been shown to directly impact cellular activity. Moreover, functional RNAs play key roles in many diseases, such as cancer, and, finally, many viruses rely on RNAs as well. This centrality means that these molecules are promising therapeutic targets and present an exciting new avenue for drug development. The pivotal challenge is the structural behaviour of RNAs. In proteins, we generally observe a well-defined structure that can be used for drug development. In contrast, RNAs exhibit multiple structures and shift between them dynamically. Describing RNA structure therefore must consider the entire structural ensemble, which pushes state-of-the-art experimental and computational methods to their limit. Here, we propose a proof-of-principle study to describe the structural ensemble of the RNA frameshifting element (FSE) from SARS-CoV2, then identify binding pockets across the ensemble, and finally find ligands that impact the functional structural changes of FSE. The FSE has multiple conformations that have been identified as functionally important, allowing for functional interference using ligands binding to the RNA. The structural ensembles for the FSE wild type and some of its variants and mutants will be  explored using the energy landscape framework, a computational approach that can resolve RNA structural ensembles in detail. One drawback of this methodology currently is the relatively high computational cost. To overcome this shortcoming, a new methodology will be developed based on machine learning to accelerate a crucial step within the framework. After binding sites and potential ligands are identified based on the RNA structural ensemble, computational and experimental validation will be sought for predicted RNA-ligand binding. On the computational side, this will include analysis of the changes to the structural ensemble upon ligand binding. Experimental verification of binding will be obtained by isothermal titration calorimetry (to confirm RNA-ligand binding) and NMR spectrsocopy (to confirm structural changes upon binding). The key objectives of the proposal are therefore as follows: Combine current state-of-the-art approaches with machine-learning to accelerate the study of RNA structural ensembles Map the structural ensemble of the RNA frameshifting element (FSE) of SARS-CoV2 and known mutants Identify small molecule ligands that arrest the frameshifting by: (a) Finding binding pockets across the structural ensemble from (2) (b) Identify candidate small molecules that bind the RNA (c) Verify the impact of the ligand binding on the FSE with simulations and experiments The proposal will establish a new pipeline from describing the structural ensemble of a functional RNA to identifying small molecule ligands for it, including the verification of RNA-ligand binding. This proof-of-principle would open up new avenues for drug development targeting RNAs, which are underutilised in therapeutic interventions. The methodology is transferable to any RNA, and will be openly available for the community to use. In the medium term, this progress will lead to a significant improved understanding of functional RNAs and their biological roles. In the long-term, this my lead to novel antiviral agents, with potentially wide societal impact and improved health outcomes.  

UKRI Gateway to Research · FY 2025 · 2025-12

This fellowship responds to the dearth of knowledge that leads to deep contestations over the origins, meaning and purpose of ‘justice’ in the transitions to net zero emissions. It develops a unique methodology called Hermeneutical Ethnography, defined as the collection, co-creation, and analyses of verbal and non-verbal communications, silences and voices (e.g. texts, symbols, speech) to co-create meaning for justice. The methodology offers an exchange of meaning between the producer, author or storyteller and the audience, which if holistically interpreted, allows them to share a deep sense of reality, whether fictional or factual. It enables the collection and analysis of aspirations that would otherwise be restrained in formal research designs. This FLF’s innovative methodology will capture and synthesise the ‘voices’ and ‘silences’ of people who are impacted but would otherwise not be heard in policymaking. Its premise: the impact of the extractive industry on the environment and society causes avoidable losses to biodiversity, livelihoods and socio-political order; inadequate granular data and the unsystematic consideration of local communities’ perspectives in decision-making account for the persistent grievances and violent protests that respond to these impacts. The lack of consensus is particularly pronounced around social, environmental, and distributional justice, and has mainly occurred between governments of developed and developing countries on the one hand, and mining companies and residents of mining communities on the other. At the macro-level, this has led to developing countries such as Ghana and Barbados formulating energy transition policies that do not urgently respond to the climate crisis. At the sub-national level, grievances and protests disrupt global supply chains. For instance, a week-long community protest in 2024 led by Indigenous People and local communities (IPLC) that blocked access to salt flats in the Atacama region of Chile led to billions of revenue losses. IPLC felt sidelined in the negotiations that strengthen the state control of Lithium, initiated by President Gabriel Boric. To reduce risk to the global supply of critical minerals, which are essential for clean technology necessary for the transition to net zero, justice in the transition should be urgently studied and actively communicated for uptake in the post-2030 UN sustainable development agenda. To achieve this requires a knowledge system that is multi-dimensional, built on trust, co-designed with organically generated aspirations in communities, and communicated through strategic global partnerships. The FLF will pioneer a novel knowledge system of bottom-up scholarship on justice using Hermeneutical Ethnography and the case studies of Lithium and other critical minerals mining communities in Australia, Chile and Ghana. It will develop and test a systematic framework for mitigating transitions in local communities by studying the ‘silences’ and ‘voices’ about justice for the environment, livelihoods, mining practices, and indigeneity. It is the first to nurture such interdisciplinary expertise and develop a sustained data-driven bottom-up justice framework. Urgently attending to justice in the transition is as important as the urgency in addressing the climate crisis through scientific and technological innovations. Scholars agree that unless concerted effort is made to resolve the contested interpretations of justice, the transitions would fail to achieve sustainable development. Yet, studies on justice since the 2015 SDGs and Paris Agreement remain conceptual; this fellowship will rectify that, offering both conceptual and distinctive empirical data and nurturing global interest through partnerships to ensure the post-2030 sustainable development agenda fully accounts for it.

UKRI Gateway to Research · FY 2025 · 2025-12

Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are nuclear imaging methods used to investigate functional information in the body by detecting small levels of radioactive isotopes chemically coupled with a biologically active agent. Using animals in basic scientific research, PET and SPECT imaging offer a unique opportunity to investigate biological mechanisms in a range of systems including metabolism, cell-tracking, oncology, cardiology and neuro-degeneration. Currently, nuclear imaging is performed with only a single isotope at a time which limits the biological readouts to a single process. The feasibility of multi-isotope imaging has been explored, however these efforts are invariably on specific isotope combinations using in-house methods and rarely adopt standardised quantification units, e.g. standard uptake values (SUV). Routine applications of quantitative multi-isotope imaging in the support of basic science have yet to be realised due to a lack of protocol standardization and complications in accurate quantification. The proposed research will address these limitations by developing methods which use different isotopes to quantify activity in multiple biological pathways simultaneously, and demonstrate that these methods can be widely adopted across preclinical nuclear imaging facilities. To achieve this a research programme has been designed to: Use a range of available isotopes to fully characterise the impact of scanner performance and measurements when multiple isotopes are presented Establish robust, data-driven protocols to inform how multi-isotope imaging can be used Validate my protocols by collecting meaningful data in three diverse areas of biological research In recent years there have been significant improvements in global isotope supply, scanner technology, and substantial financial investments towards researching combinational therapies (using multiple therapies in tandem or sequentially to treat disease). This makes the development and validation of multi-isotope imaging methodologies in bioscience timely in terms of capability to deliver, and complementing the current research climate. To achieve standardisation, protocols will be developed for simultaneous multi-isotope imaging in research by using multiple models of small animal scanners that are available on the market, across two research facilities (University of Hull (UoH) and King's College London (KCL)), to demonstrate the repeatability and robustness of our methods.

UKRI Gateway to Research · FY 2025 · 2025-12

Communication is essential for connecting with loved ones, participating in communities, and engaging in work and hobbies. Effective communication involves both verbal (speaking, writing) and non-verbal (gestures, tone) elements. Combined, these are often termed ‘total communication’. Total communication is especially important for people with communication disabilities, who make up one third of stroke survivors and 2-3 children in each classroom. Current technology design mostly overlooks non-verbal communication, focusing mainly on verbal aspects, affecting both assistive technologies (i.e. technologies which support communication) and general communication technologies like video conferencing. Moreover, present assistive technologies – which are typically bulky form factor devices (e.g. tablets, large computers), can result in both social stigma and block vital non-verbal pathways. The fellowship will make significant contributions to human-computer interaction and accessibility research, while also influencing speech and language therapy research and practice. It will reimagine technology-mediated communication for users with communication disabilities, focusing on two populations: people with aphasia and people with developmental language disorders who, together, will serve as exemplar populations to explore how emerging technologies can facilitate diverse communication abilities. I will build upon my successful prior work of embodied form factor assistive technologies (e.g. smartwatches and smart badges), considering how these might be enhanced by emerging technologies such as AI and augmented reality. We will address the inherent technological challenges – e.g. that AI models mostly consider normative, non-impaired communication.  The main objectives are to: Scope Total Communication: to understand the total communication strategies/challenges people with communication challenges and map them to existing technological capabilities (devices, interaction techniques)  Develop Total Communication Technologies: to envision, co-design, and develop technologies that support total communication, focusing on real-world settings like public transport and remote contexts such as video conferencing  Innovate Accessible Co-Design Methods: to innovate co-design methods to involve users with complex communication needs in co-design, despite its inherently language-laden nature  Conduct Controlled and Real-World Evaluations: to evaluate developed technologies in lab and real-world settings, such as dynamic transport environments, assessing usability, acceptability and functional communication outcomes Develop and Disseminate Open-Source Toolkit: to publicly launch all developed technologies, providing resources for people with communication challenges and their support networks, as well as for developers and academics to advance the work   Through these objectives, TACT aims to create inclusive technologies that enhance communication for individuals with complex communication needs. These technologies have the potential to positively impact social participation, healthcare access, education, and quality of life for people with communication challenges, as well as benefiting their families and the wider community. TACT will collaborate with a strategically planned set of industrial, academic, and charity partners, the fellowship will address real-world challenges with direct routes to technology transfer and commercialization. Additionally, I will shape policy on assistive technologies as part of my development plan, building upon the core work packages. The open-source toolkit and public engagement events also will provide direct routes to impact. A detailed training program for relevant stakeholders will be developed to increase the likelihood of technology adoption and further impact. Public engagement events and exhibitions will raise awareness of disability and assistive technology. Finally, the fellowship will build human capacity; fostering a community of early-career researchers and students who will become leaders in the field, ensuring sustainability and long-term impact.

UKRI Gateway to Research · FY 2025 · 2025-12

The production of poultry for meat consumption (broilers) is rising globally, the UK being amongst the countries with the highest production. Poultry meat consumption pro capita in the UK is twice more than pork and almost three times more than beef, and growing. Poultry endemic diseases due to bacteria, viruses and parasites are frowned upon, as they can cause considerable economic losses. To save production, the use of broad-spectrum antibiotics at any sign of incipient disease is widespread, even when the source of the disease has not been pinpointed yet (let alone the bacterial origin). The act of administering antibiotics increases the risk of the pathogen developing resistance (antimicrobial resistance - AMR), making it more difficult to fight that pathogen in the future. To reduce the use of broad-spectrum antibiotics, solutions are urgently needed for farms to efficiently monitor livestock, identify infections and the source of infection as soon as possible, and administer more targeted therapeutics. The project aims at developing new surveillance solutions specifically designed for use by the broiler industry. These solutions are designed to be turn-key: operators will periodically upload data acquired within the farm to a cloud-based service where the state of production will be assessed automatically. Warnings and advice will be sent back to the farmers via apps on smartphones/tablets, in case infections, co-infection or increased likelihood of AMR are detected. The project will cover the main pathogens of bacterial, viral and parasitic origin affecting UK broiler farming, as well as AMR to the main classes of antibiotics routinely administered in the country. How will surveillance solutions achieve their predictions, and how will we decide what data to upload? At the core of the project there is a data mining method powered by machine learning, recently perfected by the applicants. The method allows to consider a large amount of heterogeneous information collected from the farm, including historical data of previous infections/AMR events, and allows the development of mathematical models that, based on observing specific patterns in the collected information, estimate the likelihood of infection or resistance manifestations. The method also allows to isolate what farm variables are the most important for each type of prediction (e.g. a specific infection, or AMR trait): these variables are called "biomarkers". Initially, we will consider many variables: sensor data on temperature, humidity, illumination and air composition in the barn, microbiological analysis of samples from feathers, soil, barn floors, water, feed, and operator boots. An important role is reserved to data originating from the analysis of the gut microbiome, i.e. the microbial species living in the broiler gut, whose abundances, genetic traits and metabolic functions, have been proven implicated in numerous aspects of infection and resistance. Co-presence of viruses and parasites will be considered. Thanks to machine learning, for the first time it will be possible to prune such a multitude of variables, isolating the most relevant (biomarkers) to be used in the final prediction models. These models will be turned into software applications running remotely as cloud services. Users (farmers) will periodically upload information (biomarker values) as required, allowing for the models to replicate exactly at any time the state of the real production (models will become "digital twins", being virtual replicas of the real system). Farmers will then receive messages via web-based apps, reporting warnings, alarms, or suggested therapies. The methods for identifying the important variables and developing prediction models have been successful in pilot studies, leading to the identification of promising biomarkers documented in publications. The projected impact of the project on surveillance in broiler farming is expected to be unprecedented.

UKRI Gateway to Research · FY 2025 · 2025-11

Biofilm formation is an important survival strategy commonly employed by bacteria, which are embedded in an extracellular matrix comprising proteins, carbohydrates, lipids, and DNA. This provides protection against environmental pressures such as shear flow, host immune/inflammatory responses, and antimicrobial agents. Legionella pneumophila is a Gram-negative bacterium that inhabits natural/artificial freshwater systems within multispecies biofilms. It replicates within amoebae and ciliates but can also infect the human lung and cause Legionellosis. The Legionella collagen-like (Lcl) protein is an extracellular peripheral membrane protein, and we propose it has a fundamental role in ecology and infection of lung tissue, through mediating biofilm formation and adhesion/entry into host cells, respectively. Our recent work showed its C-terminal domain (CTD) recognises host glycosaminoglycans (GAGs) through an unusual binding mode. In our preliminary studies, we have also determined that Mg2+ binds within a negatively charged internal cavity, which results in CTD trimer rearrangement. In this state, rather than binding GAGs, the CTD interacts with the lipopolysaccharide (LPS) of L. pneumophila, and other Gram-negative bacteria commonly associated with these biofilms. In addition, our preliminary studies show how a peptidoglycan deacetylase, PgdA, is also transported to the bacterial surface where it can deacetylate L. pneumophila LPS, resulting in biofilm dissemination. Our work suggests that in aquatic environments Lcl is saturated by divalent cations, and its primary role is to promote intercellular adhesion in biofilms. When encountering GAGs in the lungs, this induces a conformational switch in the CTD, causing release of free bacteria and facilitating infection. Likewise, PgdA can also reduce bacterial aggregation by altering the hydrophobicity/acetylation of L. pneumophila LPS, and this may also inhibit binding of Lcl. The focus of this proposal is to understand how Lcl and PgdA enable L. pneumophila to switch between biofilm formation and host infection. These specific research aims will be addressed: Establish how Lcl recognises LPS Determine the mechanism of biofilm regulation by PgdA Understand how Lcl and PgdA promote multispecies biofilm growth Our preliminary data demonstrates that the Lcl/PgdA systems are highly tractable for structural and cellular investigations and these studies will provide an opportunity to unravel many of the mysteries underlying how they function. Specifically, we will: i) Identify/characterise specific LPS-derived ligands using biochemical/biophysical approaches. ii) Determine structures of Lcl CTD and PgdA/homologs with/without ligands using a combination of X-ray crystallography, NMR, and MD. iii) Analyse L. pneumophila LPS interactions in membranes using liposomes and MD. iv) Create knockout mutants in L. pneumophila 130b strain and compare phenotypes of WT, KO, and complemented strains for mono and multispecies biofilm growth. v) Provide detailed insight into the roles of Lcl and PgdA/homologs and propose models for their functional mechanism(s). vi) Test models with mutagenesis combined with biochemical and cellular assays. The proposed research programme will not only provide new insights into biofilm formation and infection in L. pneumophila but will have much wider implications for understanding the role of lipopolysaccharides during biofilm formation in other bacteria. As such these studies are expected to be intrinsically fascinating (achieving scientific advancement and new knowledge) but also have the potential to inspire new ways of combatting Legionella colonisation of water systems and infection of the human host. This proposal embraces the scientific aims of the MRC through studying disease-related proteins with a view to basic biological understanding and assisting therapeutic avenues of exploration.

UKRI Gateway to Research · FY 2025 · 2025-11

Scotland has one of the highest substance-related death rates in Europe. Drug deaths are recognised as a main contributor to Scotland’s falling life expectancy, and substance-related deaths are far more common in Scotland now than they were two decades ago. Those with experiences of homelessness are at a much higher risk of negative outcomes from substance use than the general population. In this context, understanding how to support those experiencing homelessness who use drugs could potentially save lives. There is a growing body of evidence on the effectiveness of harm reduction (HR) approaches to reduce negative outcomes associated with drug use. HR refers to policies and programmes focused on the prevention of drug-related harm rather than the prevention of drug use. These approaches have been trialled in various settings internationally. However, while the use of HR in general has a strong evidence base, HR approaches in homelessness services have been studied less. This is a significant evidence gap, particularly in Scotland, where substance use related harms and deaths reflect disparities in health and social outcomes. The potential policy impacts of evaluating HR approaches for homelessness services in Scotland are considerable. Simon Community Scotland (SCS) are a homelessness organisation in Scotland who have recently started using the Safer Services approach in their emergency and supported accommodation for women. Women’s drug deaths in Scotland have increased at a greater rate than men’s recently (Tweed et al., 2018), and although often not visible in the homelessness population, the risks of the double jeopardy of drug use and homelessness are heightened for women (Reeve, 2018; Neale, 2001).  SCS’s objective is to reduce drug related harms and deaths and to ‘promote safety and wellness over any requirement to stop or not use drugs’. They report seeing early indicators of the benefits of this approach. During the first stage of the Evaluation Development Fund (EDF), we are building an understanding of: the context in which HR approaches are delivered in Scotland; how the Safer Services approach is delivered in services for women; and the feasibility of conducting a full impact evaluation of the approach. To build on this work we propose using the follow-on fund to carry out impact enhancement activities to share findings with those in the sector and policy makers. We also propose further evaluation activities, which will allow us to co-create appropriate outcomes for a future evaluation, and pilot a Quasi-Experimental Design (QED) with administrative datasets which could be used as a model for future evaluations. Our approach will centre the voices of women with lived experience of homelessness and drug use, and foster collaborative relationships and knowledge sharing between researchers, policy makers, substance use treatment practitioners and organisations in the homelessness sector. We are already working with an established steering group of stakeholders and experts in the field who will oversee the additional work. This approach will prepare the ground for a full impact evaluation of Safer Services. A full evaluation would have the potential to contribute robust evidence to the academic and policy debate, in turn influencing policy and service provision, and improving lives. We are excited to submit this proposal to the UKRI for what we believe is an important area of academic and policy inquiry.              

UKRI Gateway to Research · FY 2025 · 2025-11

Cosmic observations emphatically demonstrate that most of the Universe's matter is invisible to light. "Dark matter" (DM) is the scaffold for the Universe, gravitationally attracting ordinary visible matter, and thus is essential to the formation of galaxies, stars, planets and life. Understanding the nature of DM is fundamental to understanding the origin of humanity, but the constituents of DM (particles and interactions) remain a mystery. This is a critical moment when null results for the traditionally-favoured DM candidate (weakly interacting massive particles) are driving an explosion in well-motivated DM theories that urgently need new discovery strategies. We will imminently map the Universe's large-scale structure with unparalleled fidelity in surveys like Rubin. These surveys will provide a new, powerful testbed for microphysical DM models that current direct detection technology cannot probe, but which cause detectable cosmic signatures — if the theoretical modelling challenge is solved. As ERF at the pre-eminent astrostatistics group at Imperial College London, I will solve this challenge to detect or exclude the most compelling DM theories by the distinctive gravitational signatures they imprint in the cosmic web of structure. My vision is, thus, to transform how we search for the nature of dark matter by developing precision cosmological structure formation models for novel DM theories, where cosmological observations provide the most powerful — or often only — way to search for DM candidates. I will definitively detect or exclude wave-like cosmic web effects from ultra-light axion DM (ULDM), whose discovery could point towards a "theory of everything" that seeks to explain all physical phenomena in a single set of laws. I will improve, by a factor of 25, constraints on nuclear/electronic interaction cross sections for light (sub-GeV) particle DM probing novel DM production mechanisms like freeze-in. I pioneered AI techniques to map accurately cosmic constraints from one DM model to another. Using these methods, I will release public likelihood software that will be used to search for other well-motivated DM scenarios like sterile neutrinos, dark photons or complex dark sectors like atomic dark matter. Combining structure information across 12 billion years, I will test for decaying DM signals. Since dark matter doesn't observably emit or absorb light, I will instead look for how different DM candidates change the gravitational clumping of visible objects like galaxies and gas in-between galaxies. I will lead DM searches within transformational telescope surveys (Rubin) so that I robustly mitigate astrophysical and instrumental data effects. I will build on my structure formation modelling expertise that I already used to set world-leading DM limits. To match the coming step-up in data precision, I will build new models of the effect of light, ultra-light and decaying DM on the cosmic web. To scan systematically across DM parameter space, I will combine information from larger-scale (> Mpc) probes like galaxy clustering and their gravitational lensing by DM (Rubin) — with smaller-scale (< Mpc) probes like Lyman-alpha forest absorption and Milky Way stellar stream perturbations (Rubin). The UK has invested hundreds of millions of pounds in next-generation surveys like Rubin. This programme will maximise their impact by developing theoretical and AI models needed to extract precision DM constraints. My results will set the baseline sensitivity and targets for future DM direct detection experiments like the UK-based AION experiment for ULDM and semiconductor/superconductor-based technology (e.g., TESSERACT) to search for sub-GeV particle DM.