University College London

UKRI Gateway to Research · FY 2026 · 2026-02

Understanding mobility patterns is fundamental to developing just and sustainable transport policies that address challenges such as decarbonisation, environmental sustainability, and social inequality. Yet transport planning has often introduced policies without fully accounting for the complexity of urban systems. Limited datasets and methods mean such policies are frequently fragmented and fail to meet the diverse needs of stakeholders and communities. In this context, understanding, modelling, and forecasting individual behaviours remains one of the key research challenges of our time (Cherchi, 2020). Many policies also reflect a utilitarian, “one size fits all” perspective, assuming societies will automatically absorb their benefits. This overlooks structural disparities that prevent many communities from accessing or benefiting equally from these interventions (Verma, 2022). Traditionally, transport policies have been designed around the needs of an assumed “average” traveller (Crawford, 2020), placing disproportionate emphasis on commuting, even though it represents only a fraction of overall mobility. In England, the 2023 National Travel Survey found that just 13% of trips were for commuting, compared with 18% for shopping, 13% for education, and 13% for visiting friends (Department for Transport, 2024). Although agent-based models have broadened this narrow focus by incorporating more demographic diversity, they still fail to capture behavioural heterogeneity, particularly among vulnerable populations (Williams et al., 2022). This research intends to bridge the gap between transport policies and representation by learning from geodemographics, which has successfully mapped demographic groups at small-area level to support high-resolution analyses (Singleton and Longley, 2024; De Sabbata and Liu, 2023; Yang et al., 2023). Despite their widespread use in demographic profiling and potential to illuminate heterogeneity in travel behaviour, comparable approaches have yet to be developed for mapping mobility patterns at fine spatial scales. The project will produce mobility profiles by combining existing and novel data sources. Smart Data provides digital traces capturing travel behaviours at fine resolution, accessed via the Healthy and Sustainable Places (HASP) data service. High-resolution geodemographic classifications are available through the Geographic Data Service (GeoDS). These sources will be integrated with traditional survey datasets—such as the National Travel Survey (NTS)—and, potentially, open data from transport providers including Transport for London and Greater Manchester’s network. The objectives of this project are: Use cluster analysis to extract mobility profiles from Locomizer and Spectus data sets obtained through the HASP Data Service. Use classification algorithms to match profiles to demographic attributes, generating geomobility profiles. The profiles will be matched to the 2021 OA classification developed by the GeoDS using cluster membership classifiers. Use accessibility analysis to evaluate how the current transport system serves—or not—the needs of travellers. Create an open, interactive tool to explore the spatial distribution of the profiles. Disseminate results through academic papers, conferences, and illustrations. This research has applications for researchers, policymakers, and practitioners. By integrating digital traces, survey data, and geodemographic profiling, it overcomes the limitations of each source: traces provide high spatial resolution but limited demographics, while surveys provide the reverse (Lawson et al., 2023). The resulting profiles will capture the diversity of everyday mobility and the supporting interactive tools will offer policymakers and transport authorities accessible insights for more inclusive services, helping identify underserved groups, target interventions, and monitor equity impacts of decarbonisation measures.

UKRI Gateway to Research · FY 2026 · 2026-02

The UK’s housing stock is among the oldest and least energy-efficient in Europe, with an estimated 19 million homes in urgent need of low-carbon retrofitting, yet current housing data are fragmented, offering only partial insight into how housing condition and vulnerability interact over time. Existing analytical models largely describe the present in fragmented  ways, rather than forecast future housing trends and typically looks at energy performance or housing condition separately to the economics, despite its central role in shaping vulnerability to the population. This proposal addresses that gap by developing LUCA, a Large Urban Change AI Model for the UK. LUCA will integrate multi-view time-series remotely sensed data, from space, air and street-level, with socio-economic indicators to model both the physical and economic vulnerabilities of housing. Unlike traditional, infrequently updated data, LUCA leverages high-frequency, multi-modal geospatial smart data, to deliver timely, granular evidence for decision-making. LUCA has the potential to transform how housing challenges are monitored, and plan to enhancing resilience to climate change and reducing urban inequality. As a pilot, LUCA will first be trained for the Greater London and Greater Liverpool regions before scaling to the rest of the UK. From this, we will develop a New Housing Index of Multiple Deprivation for the UK that integrates indicators such as energy performance, affordability, housing conditions, wider socio-economic disadvantage and spatial accessibility. The index will be compared with existing deprivation and morphological index to validate and to understand how urban form interacts with housing deprivation. Lastly, we will work with our partner IMAGO to create, host and disemminate a new Digital planning and generative AI visualisation tool for the UK. The tool will be piloted with our partner the London Borough of Tower Hamlets, an inner London borough marked by high variability in urban morphology, housing types, and demographic diversity. 1. Measure: Extract multi-source time-series remotely sensed data (satellite, aerial, and streetview) along with housing and socio-economic data. 2. Analyse: Train a new multi-modal, self-supervised machine learning model to learn urban morphological embeddings to infer housing condition, energy performance, and housing affordability changes. 3. Plan: Develop a new Housing Index of Multiple Deprivation HIMD to forecast housing vulnerability shifts in the UK and compare with existing deprivation and urban morphological index. 4. Imagine: Work with our partner IMAGO for dissemination which includes hosting, scaling and maintaining the LUCA embeddings and HIMD data products. Develop a new digital planning tool with web-maps and generative AI visualisations to support policymakers, planners, and practitioners in envisioning neighbourhood change and housing retrofit interventions. The tool will be piloted for the Borough of Tower Hamlets. By leveraging recent advances in GeoFoundation models and increasingly available remotely-sensed smart data from space/air/ground, this research will pioneer an AI-model and framework for understanding housing vulnerabilities in the UK. It directly supports the UK’s Net Zero 2050 target, the levelling up agenda, and the UN Sustainable Development Goal 11 (Sustainable Cities and Communities).  This research also aligns with the SDR UK Fellowships’ emphasis on impactful smart-data research and delivery, contributing to Sustainability and Digital Society priorities. The resulting evidence base and digital tools will provide planners, policymakers, and local authorities with new capacity to anticipate risks, target retrofitting and affordability interventions, and ultimately reimagine more equitable and sustainable housing futures.

UKRI Gateway to Research · FY 2026 · 2026-02

In this project, we aim to address genomic research inequality and resulting health inequality affecting children and adults of non-European ancestries with Neuromuscular Diseases (NMD) in the UK and also globally. We hypothesise that by investigating unsolved diverse populations we will discover novel mechanisms and treatable genetic variants with potential patient benefit. NMD account for ~20% non-infectious neurological diseases, and affect ~15-20 million people globally. NMD can cause muscle wasting and weakness, cardiorespiratory problems, and sensory alteration. Clinical severity ranges from being fatal to causing life-long disability. NMD develop at any age and have few treatments. Most are single gene diseases. There are many benefits to defining the precise genetic cause: 1) improved mechanistic understanding 2) accurate prognosis/genetic counselling 3) gene-specific complication screening/care management, and 4) precision medicine, including genetic therapies. Approximately 50% of European-ancestry NMD remain genetically unsolved and solved rates in NMD patients of non-European ancestry are much lower. This is partly because there is less non-European ancestry NMD cohort genetic data; consequently, non-European NMD genetic architecture is less well understood. Indeed ~86% of all existing genomic studies are on European ancestry. Lack of diverse genetic data is a major knowledge gap and missed opportunity to understand gene function and disease mechanisms. This research inequality is translating into a health inequality; for example, the genetic diagnostic rates in national NHS NMD specialised services we lead are lower for patients of non-European ancestries. We recently assembled the world’s largest non-European NMD cohort (>10,000) in a partnership called ICGNMD with 18 centres across S. Africa, Zambia, India, South America, and Turkey. ICGNMD genetic analysis enabled genetic diagnosis for many families but 1,500 remain unsolved.  We propose to (i) combine these international unsolved families with a similar number of unsolved UK research cohort patients of minority ethnicities into a new, unsolved cohort, and (2) undertake further analysis, including new advanced genomic techniques to genetically solve more cases and discover important new insights about the global genetic causes of NMD. First, we will re-analyse our unsolved cohorts to make sure all families that can be solved using existing information are solved.  We expect to solve approximately 15% of families by harnessing new clinical information about affected participants and by utilising advances in analysis since we started analysing families in 2020. Next, we will review and discuss the families which remains unsolved with a multidisciplinary global team, and together prioritise a subset for advanced genomic analysis. Then, we will analyse prioritised families using advanced technologies that provide more detailed information about DNA changes in and around genes. One technique called long-read whole genome sequencing (LR-WGS) can identify disease-causing changes affecting larger stretches of DNA, such as sequence expansions, contractions and rearrangements.  Another technique called RNA or transcriptome sequencing (RNA-seq) provides data about the effects DNA mutations have at a later stage in the pathway from genes to making proteins that are essential for cell function, and so helps us understand if DNA mutations have downstream effects. At the end of this research we will have defined new genetic causes/mechanisms of NMD that are especially relevant to people of non-European ancestries.  We will publish this data and communicate findings to all UK and international patients in our cohorts. We will use/share the data to inform developing new diagnostics and new medicines, including gene therapies.

UKRI Gateway to Research · FY 2026 · 2026-01

Tackling the major societal challenges of our time like global health, inequality, and climate change, requires the integration of diverse bodies of scientific knowledge. This increasingly involves the use of Digital Research Infrastructures (DRIs), such as data archives, software and workflows to interrogate the data. UKRI has made significant investments in large-scale compute facilities, such as Dawn, Isambard-AI and DiRAC, which leads to work-related challenges concerning collaboration, sharing, responsibility, and sustainability. Crucially, these changes are bringing about new power relations, with some disciplines and epistemologies (e.g., data science) holding more power than others, creating intricate mechanisms of exclusion and resistance. Previous research suggests that epistemic diversity, defined as varied knowledge-making practices, approaches, methods and agents, is vital to rebalancing power imbalances (Leonelli, 2023). Furthermore, emerging research indicates that DRI development creates new power dynamics (Cramer & Rüffin, 2024). However, little is known about how researchers respond (agency) to changes in their cultures driven by DRIs and foster epistemic diversity. Using novel methods that include case studies, ethnography and speculative design, the project will explore emerging forms of agency and new sources of epistemic diversity through the lens of productive resistance. Rather than viewing resistance as mere opposition to power, the project proposes that researchers’ everyday practices can create opportunities for alternative regimes of practices (Foucault, 1982). Interestingly, such productive resistance is often driven by digital technologies like AI, also known as algorithmic resistance (Ettlinger, 2018). The project has three main objectives: 1) identify new mechanisms of exclusion that arise during the design, development and adoption of DRIs; 2) identify emerging forms of agency and productive resistance that help rethink epistemic diversity as evolving power dynamics; 3) develop and test strategies to help science communities recognise alternative forms of epistemic diversity. Two partners are included: 1) the UK Square Kilometer Array Regional Centre (UKSRC), which, as part of a global network, is developing infrastructure and services that will maximise the UK’s exploitation of SKA radio astronomy data; and 2) the UK Centre for Ecology & Hydrology (UKCEH), an independent research institute that conducts environmental research that requires more sustainable DRIs. The project will be undertaken at the UCL Knowledge Lab, IOE, UCL’s Faculty of Education and Society under the mentorship of Lab’s director and Pro-Vice-Provost Data Empowered Society UCL Grand Challenge, Prof. Allison Littlejohn. An advisory board of five members has been set up, which includes representatives from academic and non-academic institutions. The advisory board will provide input on the research and promote the project's findings. Successful completion of this project will realise the following benefits to scientific communities: UKSRC will be able to provide stronger technical support and develop enhanced community engagement through its training schemes. UKCEH will be enabled to refine its goal of achieving net zero by 2040 while continuing to foster inclusive DRI. The broader UKDRI community will develop improved strategies for inclusive DRI development and data governance. STFC will develop enhanced funding decisions, while science policy will be able to shape clearer technical visions for existing and emerging infrastructures, like federated DRI development. The ability to scale up these outputs is strengthened by the project lead's established collaborations with DRI communities through the FAIR Data: Building Cultures of Sharing funded by UKRI-DSIT. The proposed project will facilitate the project lead's transition to full independent investigator status.

UKRI Gateway to Research · FY 2026 · 2026-01

Trigeminal neuralgia (TN) is a debilitating condition characterised by severe sudden, electric shock-like facial pain triggered by everyday actions such as speaking, drinking, or chewing. Epidemiological studies have linked TN to an increased likelihood of anxiety, depression and risk of suicide, in fact the pain is so severe, TN is colloquially known as the “suicide disease”. TN impacts a relatively high proportion of the population (0.1 – 0.8%), and has increased prevalence in women (F:M ratio 3:2). Incidence increases with age, reaching 20 per 100,000 over 60 years old and this also coincides with increased frequency and severity of attacks, further impairing life quality. 40% of patients experience greater than 10 attacks per day with some suffering several hundred. Attacks last from tens of seconds to a few minutes, with some patients also experiencing continual pain. Following onset, trigeminal neuralgia is rarely, if ever cured and sufferers mainly undergo palliative pain management. Such front line treatments currently include the antiepileptic drugs carbamazepine and oxcarbazepine which offers initial pain management, but due to loss of efficacy and adverse effects such as dizziness and nausea, these drugs are withdrawn in 50% of patients. Those that do not respond to medication require specialist neurologist care which can be expensive and difficult to access. Pharmaco-resistant patients are commonly subjected to more invasive treatments such as nerve blocks or surgery to relieve the trigeminal nerve pressure caused by nearby blood vessels. Surgery involves greater risks such as parathesis (numbness) and up to 30% of patients experience relapse of severe pain following surgery. The high incidence rate and cost of treatment for medications, specialist consultations, imaging and invasive interventions place a huge burden on healthcare systems, and TN surgery alone is estimated to cost ~$100M annually in the US. According to 360iResearch the current TN therapeutics market is valued at $262.17M and is expected to rise to $416.90M by 2030. Trigeminal neuralgia is caused by abnormal overactivity of the trigeminal nerve, which controls sensation in the face. In about 75% of cases, this is due to pressure from nearby blood vessels. The rest are linked to other neurological conditions like multiple sclerosis or have no known cause. Recent research shows that nerve hyperactivity disorders—such as trigeminal pain, epilepsy, and migraine—often involve weakened inhibitory control in the nervous system. One key factor is disrupted potassium (K?) balance, particularly from reduced activity of the Kir4.1 potassium channel. Restoring Kir4.1 function through gene therapy has shown pain relief in animal models. However, gene therapy is expensive and not widely accessible. To overcome this, we aim to develop affordable small-molecule drugs that activate Kir4.1 and offer a new treatment for trigeminal neuralgia. At UCL, we have established a K+ conductance assay which is applicable to drug discovery. However, at our current medium-throughput capacity, screening a large library of novel drugs will be time consuming and inefficient. Working with AstraZeneca and their automated high-throughput screening capabilities, we will upscale our assay to find a novel analgesic for trigeminal neuralgia. Small molecules still remain the best option for wide-spread accessibility with low-upscaling costs. Therefore discovering a novel analgesic using the AstraZeneca small molecule library has the potential to benefit those who suffer from trigeminal neuralgia globally.

UKRI Gateway to Research · FY 2026 · 2026-01

Evolution is usually envisaged as a slow process, but it occasionally endows organisms with game-changing new abilities like vision, live-bearing reproduction, and flight. It is difficult to imagine how these types of abilities could ever evolve gradually. One issue is that novelties  seem to appear without obvious intermediate steps (e.g., animals either see, or they don’t). Also, because natural selection–the guiding force of evolution–can only refine traits that are already present, how could it possibly guide the evolution of a trait that doesn't yet exist? Biologists have proposed two main ideas to explain how new abilities evolve. The ‘hopeful monster’ hypothesis solves the issue by taking gradualism and selection out of the equation. It proposes that new abilities evolve suddenly, when a major genetic change causes a profound change in how an organism functions. Alternatively, the ‘co-option’ hypothesis keeps selection and gradualism centre-stage: it proposes that novelty arises by many small steps, when an old adaptation evolves a new function (e.g., maybe feathers evolved as adaptive ‘down jackets’ to warm dinosaurs, later being co-opted for flight by birds). While both explanations are plausible, they are extremely hard to test. One challenge is that most new abilities evolved deep in the past, meaning that the stories of their origins have been lost. A second challenge is that understanding novelty requires interdisciplinarity; that is the bringing together of methodologies, ideas, and approaches from divided research areas in the life sciences and beyond. In my Future Leaders Fellowship, I will build an interdisciplinary team, and we will reconstruct a pathway to an evolutionary novelty for the first time. The project is made possible by my past work on a recently-evolved novelty in a classic study organism. Although most periwinkle snails (Littorina) lay eggs, one species has switched to live-bearing. Past work  has identified about 50 regions of the species’ DNA sequence associated with how females reproduce. This breakthrough has set the stage for me to understand whether live-bearing evolved gradually or suddenly. I will do this by (i) determining when each genetic change occurred, (ii) revealing how each genetic change shapes live-bearing anatomical structures during development, and (iii) determining how each genetic change affects female reproduction. Together, this information will allow me to build a timeline of the critical evolutionary steps between egg-laying and live-bearing. I will then combine our results with published datasets, to (iv) determine whether transitions to live-bearing in other animals use a similar genetic toolkit. The project will benefit science and society in many ways. First, it will cause a step-change in our understanding of how evolution works. This will translate to new breakthroughs in research and enhanced public understanding. The new methods that I will develop are timely, and may lead to breakthroughs in fundamental and applied research by providing scientists with powerful new tools for understanding the genetic basis of traits like crop productivity and human disease. Reproductive biologists may benefit from new knowledge about the genetic basis of live-bearing and other related reproductive traits, like pregnancy length, in a wide variety of species, including our own. I will showcase, study, and promote interdisciplinary research as a tool to address deep questions and to solve the complex problems faced by society. This will help to drive positive cultural change in UK academic research institutions.

UKRI Gateway to Research · FY 2026 · 2026-01

Communication is everything, as they say. And the same can be said of cells, which constantly generate and receive signals to function properly. Context In previous BBSRC-funded work, we discovered an ion channel protein called TPC2 which releases calcium from lysosomes. Lysosomes are organelles that are typically regarded as the cell's recycling centre. But they do much more and one such function is to act as a source of calcium that is tapped by ion channels such as TPC2. Calcium is a well known signal, vital for more or less all processes in the body from fertilisation (‘life’) to degeneration of nerves in diseases such as Alzheimer’s (‘death’). Understanding how calcium is regulated is therefore critical for understanding, and ultimately promoting, our well-being. But calcium signalling through lysosomes is much less well understood than signalling through other organelles. This application builds on our recent discovery, also funded by the BBSRC, that TPC2 can transform from being a calcium channel to being a sodium channel on demand. This is very unusual because textbooks, including my own (Cell Biology: A short course; ISBN-13, ?978-0470526996), tell us that the selectivity of an ion channel i.e. what ion(s) are permitted to flow, is fixed. This does not hold for TPC2 as it readily switches its ion selectivity depending on its activation stimulus, namely the messenger molecules, NAADP and PI(3,5)P2. Importantly, the two resulting modes of signalling have differential effects on the function of the lysosome. TPC2 is thus capable of what we term ‘biased’ signalling. Aims and objectives Our latest work has found individual amino acids in TPC2 which when mutated essentially convert TPC2 into a more ordinary, unbiased channel which does not switch its selectivity. This is a big step forward as we can now turn the switch on and off as we please, in order to study this process in detail. What we will do is pinpoint the gears and cogs within the protein which lead to such profound ion selectivity switching. This will establish a novel mechanism. We will also determine the impact of ion selectivity switching on the acidity and movement of lysosomes, as these are both essential features of the organelle. This will establish functional significance. And finally, we will relate ion channel switching to natural variants in the TPC2 gene and new cellular functions. This will establish physiological significance. Key to past, present and anticipated future success is collaboration. Co-applicants and an international project partner will bring much expertise allowing us all to go well beyond our individual capabilities. This adds considerable value to this proposal. Potential applications and benefits and relevance to the BBSRC Our findings will benefit many researchers including ‘signallers’ (not only calcium but sodium too), biophysicists studying ion channels and cell biologists studying lysosomes. More broadly, our findings will be of interest to Pharma and clinicians. This is because the insight we will provide might inform on developing drugs to specifically modulate bias. In turn, these could be used to combat diseases such as Parkinson’s and melanoma to which TPC2 has been linked. We will provide interdisciplinary training of people and connect excellence across places in the UK (and beyond) through an idea that will advance our understanding of, if not bend, the ‘rules of life’.

UKRI Gateway to Research · FY 2026 · 2026-01

One of the deepest drivers of research in theoretical linguistics is the search for linguistic universals, that is, the search for properties all languages share in common despite their superficial differences. Abels & Neeleman (under review) hypothesise that rightward movement of obligatory material is more difficult to parse than rightward movement of optional material and also more difficult to parse than leftward movement of optional or obligatory material. They suggest that this offers an explanation for the near total absence of rightward shifts of the head word, an obligatory element within its phrase, in the neutral word order patterns across languages. Leftward shifts of the head by contrast are readily attested. The most famous example of this asymmetry is Cinque's (2005) implementation of Greenberg's (1963:87) word order universal 20, which says: "When any or all of the items (demonstrative, numeral, and descriptive adjective) precede the noun, they are always found in that order. If they follow, the order is either the same or its exact opposite." In this context, the current project investigates properties of a particular word order alternation in Hindi-Urdu: rightward scrambling. Hindi-Urdu generally shows subject-object-verb-auxiliary word order, but both subjects and objects as well as a variety of adjuncts can also optionally follow verb and auxiliary under certain circumstances. The project has a theoretical and a psycholinguistic sub-project. On the theoretical side, we ask exactly which instances of rightward scrambling in Hindi-Urdu involve the kind of rightward shift that would make them fall under the scope of Abels & Neeleman's proposal. On the psycholinguistic side, we ask whether those instances of rightward scrambling give rise to the expected parsing difficulties when obligatory material is shifted rightward. More specifically, the theoretical sub-project investigates the properties of clauses with rightward scrambling in Hindi-Urdu and will develop an analysis overcoming the limitations of our current theoretical understanding of the phenomenon. Notably, current theoretical models are based almost exclusively on rightward scrambling of subjects and objects but fail to offer satisfactory approaches to rightward scrambled adjuncts and other material. The psycholinguistic sub-project aims to test whether the ideas about the real-time processing of rightward shifted word orders carry over to rightward scrambling in Hindi-Urdu. This research would break ground in that currently the only published data on the effect of obligatoriness in rightward movement comes from English. The project breaks ground also in the sense that it will present the first psycholinguistic studies on Hindi-Urdu using the grammatical maze methodology. We hope to show in a validation experiment that the maze method, which has a granularity comparable to eye tracking but is much easier and cheaper to run, works for Hindi-Urdu. Given the geographic and socioeconomic context in which Hindi-Urdu predominantly exists, the introduction of such a cost-effective method will enable regional institutions to investigate psycholinguistic phenomena on a much larger scale. We will then tackle the real-time processing of rightward scrambling in Hind-Urdu in the main experiment.

UKRI Gateway to Research · FY 2026 · 2026-01

Worldwide, preterm birth and intrapartum events are the biggest causes of death and lifelong disability in children under 5 years of age, and often it is the lack of early diagnosis that leads to permanent damage. Perinatal arterial ischemic stroke (PAIS), for example, affects 1 in 2500 newborn infants and is a significant cause of lifelong disability. Despite being more common than large-artery stroke in adults, it is far less well understood. PAIS is also comparatively under-diagnosed, such that opportunities for timely treatment are often missed. While there have been major improvements in neonatal intensive care over the past 40 years, continuous monitoring of the brain remains rudimentary. We propose to evaluate an entirely new form of optical imaging, known as wavelength-modulation diffuse optical tomography (WM-DOT), for diagnosing and assessing PAIS and other forms of brain injury in newborn babies. It is safe, portable, low-cost, and easy to use. WM-DOT avoids dependency on variable surface effects and patient motion which have severely inhibited previous optical methods, and yields images which are sensitive to absolute rather than differences in optical properties. The images will reveal abnormalities in cerebral tissue oxygenation and thus lead to more prompt intervention, reducing incidence of permanent disability. Diffuse optical imaging involves illuminating the scalp with a harmless beam of light and measuring the amount of light that emerges after considerable scattering within the underlying tissues. The light is absorbed differently by the oxygenated and deoxygenated forms of haemoglobin (the molecule in blood that carries oxygen around the body), and thus these measurements are sensitive to the volume and oxygenation of blood. While methods known as near-infrared spectroscopy (NIRS) and diffuse optical tomography (DOT) have been explored as a means of brain monitoring of infants and adults for several decades, they are notoriously sensitive to the uncertain and variable coupling of light into and out of the skin surface (e.g. due to hair). Unknown coupling prevents the absolute concentrations of oxy- and deoxy-haemoglobin from being accurately derived, severely limiting clinical impact. Meanwhile, when a patient moves, relative motion between the optical probe and the head causes unpredictable coupling changes, producing imaging artefacts. Consequently, NIRS and DOT have been limited to measuring only changes in blood volume and oxygenation. The purpose of the proposed work is to establish a new optical imaging method, based on a light source whose wavelength (colour) is rapidly changed back-and-forth over a small range. Variation in the tissue absorption over this range will result in a small change in the amount of detected light as the wavelength is modulated. Measurements of the change in intensity as a proportion of the total amount of detected light are largely immune to variability in surface coupling and may allow derivation of the absolute quantities of absorbers in tissue. A prototype imaging device will be evaluated as a means of assessing a variety of newborn infant neuro-pathologies in a hospital intensive care unit. We will also work with an industrial partner to explore the development of the technology as a commercial product to accelerate its impact. A successful demonstration of WM-DOT will revolutionise the way NIRS and DOT are used in hospitals, and expand the range of medical applications of optical imaging technology, such as detection and assessment of adult stroke and other forms of brain injury.

UKRI Gateway to Research · FY 2026 · 2026-01

This project aims to advance our understanding of congenital diseases, conditions that are affecting babies at or before birth. To do so, we will study the fetal cells present in the amniotic fluid (AF), the protective liquid surrounding the fetus during pregnancy. We already established a control cellular map of the healthy AF. Our goal with this project is to create a single-cell atlas of diseased AF, to enable detailed comparison between cellular composition of healthy and disease-affected pregnancies. The objective of this, is to uncover disease-related changes in the populations of AF cells. These changes could be used before birth as biomarker, to track and predict the outcomes of a number of congenital diseases. Moreover, we will derive and study mini-organs (or organoids) from diseased AF cells, through a protocol recently developed by my team. Being derived before the baby is born, these organoids could serve as 3D models to study the course of congenital diseases and test treatments during pregnancy. This project will focus on rare, but impactful conditions such as myelomeningocele (MMC), cystic fibrosis (CF) and polycystic kidney disease (PKD) for which AF samples are already accessible to my team. The proposal consists of three specific aims. First, we will collect AF from affected pregnancies and use single-cell biology techniques to study their gene and protein expression. This will allow comparing cell types and amounts present in patients vs. controls, to uncover specific features of our target diseases. Second, we will use AF cells from the same patients to grow AF organoids (AFO). As we have recently shown for congenital diaphragmatic hernia (CDH), another congenital condition, AFO can be used to study the effect of these diseases on organ development. Here we will test in CF AFO the activity of CFTR, the gene affected by the disease. Moreover, we will assess cysts formation in PKD AFO as hallmark of the condition. Finally, our system will be used to test AFO’s as in vitro model for the response to treatments. Ultimately, my team’s research overall, aims to translate our cellular and molecular data into the clinic. The third part of the project aims at correlating our AF cell maps, and AFO data with the patients’ clinical outcomes. We will compare our laboratory results with the patients’ clinical prenatal imaging, survival information and disease severity. The idea is to identify patterns connecting our laboratory observations to the actual effect of the diseases on these babies. If successful, our newly developed techniques could become powerful tools for early severity prediction. Our models could find use in drug screening and to test response to treatment before birth. Moreover, this will provide better estimate on how babies might be affected by these conditions, to offer better counselling to the affected families. Ultimately, this exciting project will enable the first steps to explore personalised disease modelling before the baby is born, a largely unexplored field.

UKRI Gateway to Research · FY 2026 · 2026-01

Severe and enduring mental illnesses (SMI) such as schizophrenia, bipolar disorder, severe depres-sion, and complex emotional needs, can deeply affect how people think, feel, and relate to others. While research often focuses on biology or medication, much less attention has been given to the emotional and social factors that shape mental health. Experiences like loneliness, emotional dis-tress, and the stress of navigating difficult social environments can play a major role in how people become unwell, what support they receive, and how they recover. This project, called WISDOM, focuses on understanding these socioemotional drivers of mental illness. I will look at how people’s emotions, relationships, and environments influence the course of SMI and how these insights can improve care and support. This person-centred approach high-lights subjective experiences to which we can all relate, yet are not considered enough in mental health research. The project will have three parts: First, I will use cutting-edge language models (like AI tools) in a trusted research environment to search for emotional and social warning signs in anonymised clin-ical records to help services better detect and respond to distress earlier. Second, I will examine how people’s local environment like social isolation or access to cultural spaces affects outcomes for those with SMI which can guide planning and interventions. Third, I will explore whether col-lecting real-time information on how people feel and who they connect with during daily life can help predict or prevent mental health crises. People with lived experience of mental illness will guide and co-lead the project throughout, en-suring the research stays grounded in real-life priorities. Together, we will develop new knowledge, tools, and public resources that improve mental health services, reduce stigma, and support more compassionate care.

UKRI Gateway to Research · FY 2026 · 2026-01

Neurodegenerative diseases (ND) characterized by progressive and fatal motor and cognitive decline, include Huntington’s disease (HD), Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic Lateral Sclerosis/Frontotemporal Dementia (ALS/FTD), and many others. In HD, the huntingtin (Htt) protein undergoes a liquid-to-solid phase transition, forming amyloid-like fibrils through soluble and amorphous intermediates. Mammalian cells, including neurons, rely on two major pathways -- autophagy and ubiquitin-proteasome degradation (UPS) -- to clear misfolded and aggregated proteins. My previous work, using in situ cryo-electron tomography (cryo-ET), revealed that long polyQ repeats form a fibrillar core, surrounded by amorphous intermediates, which compared to the fibrils, are more readily taken up by phagophores and autophagosomes typical of macroautophagy (Zhao, et al., Mol Cell 2024). This work highlights the unique power of cryo-ET in bridging cell and structural biology, particularly in addressing key questions in the field of autophagy. Currently, I aim to understand how the UPS interacts with different polyQ phases at the molecular level, using in situ cryo-ET and subtomogram averaging. The analyses reveal that the chaperone AAA+ ATPase VCP facilitates the clearance of polyQ intermediates by interacting not only with 26S proteasomes but also with 20S proteasomes through a distinct coupling mechanism, likely mediated by VCP’s C-terminal hydrophobic-tyrosine-X (HbYX) motif, which binds directly to the 20S proteasome. These findings raise critical mechanistic questions that require urgent investigation. This research proposal aims to integrate cryo-ET with biophysical, biochemical, and cellular approaches to address fundamental questions in protein quality control pathways with far-reaching therapeutic implications for ND. Research questions: (1) Structural and Molecular Mechanisms of CMA Chaperone-mediated autophagy (CMA) is a critical selective autophagic pathway for the clearance of misfolded and aggregated proteins implicated in ND; however, its structural basis remains completely unknown. This study will provide a concrete structural elucidation of CMA, its cross-talk with macroautophagy, and its impact on lysosomal integrity, with far-reaching implications for therapeutic strategies. Key outcomes: 1. Determining the atomic structure of the Lamp2A translocon complex in vitro -- a key CMA component responsible for directly importing misfolded proteins into the lysosome for degradation 2. In situ identification of Lamp2A oligomerization states, their impact on lysosomal architecture, and the recruitment of CMA machineries in protein unfolding. 3. Elucidation of the roles of Lamp (lysosome-associated membrane protein) homologs Lamp1, 2B, 2C in autophagy -- involvement in autophagosome-lysosome fusion in situ. 4. Determining structurally how disease aggregates block CMA and impair lysosomal health in situ. (2) VCP-20S Proteasome Coupling The UPS is essential for cellular proteostasis, yet past research has focused on the 26S proteasome, leaving the 20S proteasome and its alternative gating mechanisms under-explored. This study will be distinct in establishing a novel mechanistic framework for VCP-20S coupling in UPS, with profound implications for therapeutic strategies for ND. Key outcomes include: 1. Identification of key structural elements that mediate the coupling mechanism. 2. A detailed understanding of VCP-20S coupled substrate degradation in vitro and in the cell. 3. Insights into the generality of the VCP-20S coupling across different aggregate models including HD and AD, and in the mouse neuronal systems. By elucidating these mechanisms, I aim to re-define the structural and mechanistic basis of major protein quality control pathways critical for clearing misfolded and aggregated proteins, with profound therapeutic implications for ND.

UKRI Gateway to Research · FY 2026 · 2026-01

Context The UK's pathology services, which are essential for diagnosing cancer, are facing a critical challenge. An increasing shortage of specialist pathologists, combined with the growing complexity of modern cancer diagnostics, has created a significant bottleneck. This results in longer waiting times for patients to receive a complete pathological diagnosis, which can be a period of great anxiety and can delay the start of treatment. For the NHS, this strain leads to unsustainable workloads and inefficiencies. To ensure patients can fully benefit from the advances in personalised medicine, which rely on detailed and timely diagnostic information, we urgently need innovative solutions to support our expert pathology workforce. The Challenge the Project Addresses Our research team at University College London has developed a promising new technology, the Octopath platform, to help address this challenge. Octopath is an artificial intelligence (AI) tool designed to act as a co-pilot for pathologists. It uses state-of-the-art algorithms to rapidly analyse digital images of tissue samples, automatically highlighting key features that are crucial for understanding a patient's cancer. While our prototype has proven highly accurate in a research setting, it currently lacks the essential evidence to prove it is safe, effective, and reliable in a real-world clinical environment. This evidence gap is the single biggest barrier preventing the technology from gaining regulatory approval and being used to help patients. Aims and Objectives This project aims to bridge that critical gap between a research prototype and a validated medical device ready for NHS use. Our primary objective is to conduct a multi-site clinical validation of the Octopath platform. To achieve this, we will deploy our prototype system into two NHS partner sites across the UK. Working closely with clinical teams, we will use the platform to analyse a large collection of anonymised patient samples across several cancer types, including colorectal, breast, and pancreatic cancer. This will allow us to rigorously assess the platform's accuracy and performance against the gold standard of expert pathologist assessment, and to gather crucial feedback on its usability and how well it integrates into existing clinical workflows. Potential Applications and Benefits The successful completion of this project will unlock benefits for patients, the NHS, and the wider UK research community. For patients, the technology promises a faster, more consistent diagnostic process, reducing waiting times and enabling quicker access to the most effective, personalised treatments. For pathologists and the NHS, Octopath offers a way to manage workloads more efficiently, automate time-consuming tasks, and enhance diagnostic capabilities, ultimately leading to a more sustainable and resilient service. The robust data generated will provide the evidence required for MHRA/UKCA regulatory submission, support further trial recruitment and treatment personalization, prepare the technology for substantive follow-on funding, NHS commissioning, and potential commercialisation through a UCL spin-out.

UKRI Gateway to Research · FY 2026 · 2026-01

The ability of cells to divide asymmetrically and generate daughter cells that adopt different fates is fundamentally important to life. However, the mechanisms that enable divergence of daughter cell fates remain incompletely understood and have been challenging to study in intact living animals. In our work, we use the nematode Caenorhabditis elegans, which is exceptionally well suited for studies of developmental phenomena, including asymmetric cell divisions, and is excellent for imaging-based and genetic approaches. In the past funding period, we developed methodology that enabled us to study the partitioning of mitochondria during asymmetric cell divisions that generate a cell, whose fate it is to die through apoptosis (referred to as ‘unwanted’ cells). Specifically, we discovered that during these ‘life/death’ decisions, unwanted cells inherit fewer mitochondria and the mitochondria they inherit are smaller and more spherical. In addition, we found that there is a correlation between mitochondrial density in unwanted cells and the time it takes these cells to die. Specifically, we found that daughter cells that inherit more mitochondria take longer to die and, in some cases, fail to die. With this, we have uncovered a new intrinsic mechanism required for cell fate divergence in C. elegans. In the current application, we propose to capitalize on our findings and to address the following questions: Aim 1. How do unwanted cells inherit fewer and more spherical mitochondria? We have preliminary evidence that it involves localized mitochondrial fission and targeted mitochondrial transport, and we will explore this using imaging-based approaches, endogenously-tagged proteins and targeted genetic manipulations. Aim 2. How do mitochondria antagonize apoptosis in unwanted cells? Using synthetic tools, we will increase mitochondrial density to test whether this is sufficient to antagonize apoptosis. And we will use imaging, endogenously-tagged proteins, sensors and targeted genetic manipulations to identify candidate mitochondrial proteins and attributes with anti-apoptotic function. Aim 3. What additional factors are required for asymmetric mitochondrial inheritance? We will use an unbiased genetic approach to identify additional processes with a role in asymmetric mitochondrial inheritance in the context of life/death decisions. Aim 4. Is asymmetric mitochondrial inheritance a general feature of asymmetric cell divisions in C. elegans? To answer this, we will study mitochondrial inheritance during the asymmetric stem-like divisions of the epidermal seam cells, using imaging-based methodology that we developed. The proposed work will inform us of a novel internal mechanism of cell fate divergence, a process of particular importance to developmental and stem cell biology. It will also increase our understanding of the partitioning of cytoplasmic organelles, which is of general interest to cell biologists. Mitochondria have emerged as critical determinants of apoptosis and aging; however, much remains unclear about their impact on cell fate, which we will address at an unprecedented level of rigour and spatiotemporal resolution. Therefore, researchers working in the areas of mitochondria, apoptosis and aging will benefit from our results. Finally, the C. elegans community will benefit from an increased understanding of life/death decisions, a key aspect of C. elegans development, and from methodology and tools generated as part of the proposed work. The proposed work addresses a fundamental question in biology i.e. asymmetric mitochondrial inheritance in the context of cell fate divergence and can be considered creative, curiosity-driven frontier bioscience. Therefore, it is highly relevant to the BBSRC long-term research and innovation priorities.

UKRI Gateway to Research · FY 2025 · 2025-12

Many high-income societies, including the UK, are experiencing trends of delayed parenthood and declining fertility. For instance, while 82% of women born in 1945 had given birth by age 30 and 10% remained childless, these figures were 52% and 18% respectively for women born in 1975. For those born in 1993, only 44% had children by 30, with childlessness expected to rise further.1 While these trends have been partly linked to educational expansion,2,3 shifting values toward greater personal freedom,4 and, more recently, economic uncertainty,5,6 these factors do not fully account for the significant changes in family behaviours across generations. One less widely explored determinant is early-life health, which may play a fundamental role. For example, poor health may be related to lower ability to conceive7 and lower fertility intentions,8 while also affecting partnership formation and stability.9 Investigating how early-life health is linked to relationships and fertility is timely, given the decline in population health over recent decades ('Generational Health Drift’)10 which is marked by a higher prevalence and earlier onset of health conditions such as obesity,11 diabetes,12 and poorer mental health13,14 in younger generations compared to their predecessors. There is also value for policymakers in understanding the drivers of future family outcomes and how the health of young people is related to their family formation attitudes, which may in turn affect future behaviours. Moreover, understanding more about the drivers of family outcomes is crucial given that such outcomes can have adverse consequences for individuals, society, and population health. To address these gaps in knowledge, I will examine whether and how early-life health is associated with later-life relationships and fertility, using longitudinal data from the four British Cohorts studies and UK Household Longitudinal Study. The project has three interrelated objectives, to: Explore how different aspects of early-life health are linked to whether and when people form relationships, have children, or remain childless as adults. This objective will focus on people born between the 1940s and 1970s, who have finished their childbearing years, providing a holistic life course perspective on partnership and fertility trajectories. Investigate the relationship between early-life health and first co-residential partnership formation and transition to parenthood and how this has changed across cohorts (between those born in 1940s and 1990s). Although an overall postponement of these behaviours is well-documented, it is unclear whether it is linked to early-life health and how it might have changed across generations, given parallel trends in changing family norms and population health decline in younger ages. Examine associations between early-life health and attitudes towards partnerships and fertility (i.e., expectations to get married and have children) among cohorts who are in their active childbearing years (born 1990s-2000s), which has important implications for future fertility, household and population projections. This project will significantly advance the understanding of the determinants of family transitions and provide new evidence about the multiple enduring consequences of early-life health. The findings will be timely and policy-relevant, contributing not only to the scientific literature but also to society. I will provide evidence for third-sector organisations such as International Longevity Centre and government departments (Office for Health Improvement and Disparities (OHID), Office for National Statistics (ONS)) interested in understanding current family transitions and predicting future family behaviours and needs, as well as the later-life implications of these behaviours.

UKRI Gateway to Research · FY 2025 · 2025-12

Despite the growing acceptance of LGBTQ+ identities, healthcare remains a domain where LGBTQ+ individuals often face discrimination based on their gender or sexual identities. My PhD project seeks to improve mental healthcare provisions for LGBTQ+ communities by identifying shortcomings in current practices and drawing from optimal provisions in best-practicing countries to offer recommendations for improving training and healthcare practices in the United Kingdom. The project I propose for this fellowship will enquire how historical views of LGBTQ+ individuals have shaped healthcare provision delivery across the West. Using comparative historical analysis (CHA), I will examine the historical contexts that have shaped LGBTQ+-focused healthcare practices across Western countries. Thus, it explores how the evolution of LGBTQ+ rights movements, medical practices, and social attitudes towards gender and sexual minorities have influenced healthcare policies, particularly regarding access, treatment, and inclusion. The Library of Congress (LoC) provides an ideal setting for this work due to its extensive historical, medical, and policy collections on LGBTQ+ issues. Accessing these materials will allow for a comprehensive examination of the legal, social, and healthcare-related aspects of the proposed research. During this fellowship, I will produce a draft chapter of the PhD thesis and prepare a publishable manuscript. The research offers a unique contribution to both academic and healthcare sectors, providing detailed historical underpinnings of modern LGBTQ+ healthcare systems and policies and serves as a valuable resource for healthcare providers, policymakers, and advocacy groups seeking to address gaps in LGBTQ+ healthcare provision.

UKRI Gateway to Research · FY 2025 · 2025-12

This project aims to document and analyse how the endangered language Yaeyaman, a Ryukyuan language spoken on the Yaeyama Islands, forms expressions of "necessity" and "possibility" (modality). Although these expressions are a crucial aspect of natural language that are commonly used in daily communication, there is little to no documentation of modality in Ryukyuan languages. In collaboration with researchers from the National Institute for Japanese Language and Linguistics (NINJAL) in Tokyo, The University of the Ryukyus in Okinawa, and University College London, this project involves preparing questionnaires and conducting fieldwork with native speakers of Yaeyaman, recording their speech as language data. Upon processing and annotating the data, theories to formalise the modal system of Yaeyaman will be formed and the data will be publicly archived to be used in educational materials aimed at revitalising the language. The data and analysis will not only contribute to the linguistic heritage and identity of Yaeyaman speaking communities, but also enrich formal semantic accounts of modality from a cross-linguistic perspective, allowing us to compare Yaeyaman to other Ryukyuan languages and Japanese and language around the globe. Despite the observable diversity in languages, we expect linguistic universals to arise from the architecture of the common language faculty of the human mind.    

UKRI Gateway to Research · FY 2025 · 2025-12

This collaborative research project pioneers a novel methodology in comparative legal studies: in silico legal comparison. By integrating insights from empirical comparative law and experimental legal studies, it advances an emerging field that leverages Large Language Models (LLMs) for comparative institutional and legal analysis. The field of competition law is particularly fruitful for such research, in view of the different substantive law approaches followed in each jurisdiction, the variety of institutional designs and the commonality of the competition problems and business conducts examined, given the global strategies of digital market players and the influence of economic analysis in competition law. The research will make the following three contributions: First, the research examines how and why the United States (US), the  United Kingdom (UK), and the European Union (EU) differ in their regulatory responses to addressing competitive risks generated by AI technologies and digital ecosystems in their respective markets. It maps institutional frameworks and substantive rules across these three jurisdictions, identifying key differences in regulatory/antitrust approaches. It then employs AI simulation tools to model how businesses respond to the different components of these regulatory environments. By testing different scenarios using vignettes, we examine how competition law enforcement and digital competition regulation can address competitive risks related to AI and digital ecosystems. These simulations reveal how tech companies can adapt their strategies in response to regulatory pressure and how consumers and other stakeholders may react to them. We demonstrate precisely which regulatory designs are likely to be effective in specific legal contexts - and which might backfire. The research thus comprehensively examines the institutional frameworks and substantive regulations governing AI- and digital ecosystem-related competition risks, illuminating significant differences in antitrust enforcement approaches and, crucially, how such antitrust regimes function when confronted with adaptive market participants. Second, the research aims to uncover the fundamental philosophical principles and societal values shaping each legal system's distinct strategy towards AI and digital ecosystems generated competition risks, proceeding to a 'law in action' analysis in an artificial (in silico) environment. Finally, the research bridges legal scholarship and computational studies, thus developing a methodology with the potential to transform comparative legal analysis. This new approach to legal comparison requires exploring the causal capabilities of LL.Ms for comparative legal research purposes and their application to causal reasoning in institutional analysis, while accounting for the different types of causality employed in law and data science. This research thus establishes a foundation for future comparative legal and empirical research, not only in competition law but also across other legal fields, utilising the in silico legal comparison methodology.  

UKRI Gateway to Research · FY 2025 · 2025-12

CHATTER will co-develop a novel, ethical, reliable and valid artificial intelligence/natural language processing (AI/NLP)-based pipeline to characterise internal thoughts, and determine which thought properties are related to age-related health. CHATTER’s success requires strategic integration of expertise from psychology, psychiatry and psycholinguistics (MRC/ESRC remit), computer science and statistics (EPSRC remit), along with peer researchers’ lived experiences of ageing and mental illness to ensure real-world authenticity. Context Over 25% of older adults in the UK have a mental or neurological disorder, and our ageing population means rising rates of mental illness and dementia are placing increasing strain on the NHS. We urgently need to identify modifiable factors that impact age-related health and dementia risk to guide early, scalable interventions for support and prevention.   Challenges Thinking styles (e.g. worry, mindfulness) impact risk of developing these disorders and their course, yet despite the dynamic nature of thoughts, static measures using retrospective recall or labour- and time-intensive manual assessment still prevail. Difficulties in reliably and efficiently categorising properties of these thoughts (e.g., their frequency, sentiment/valence, orientation) at scale have hindered progress in determining their precise relationships with age-related health. Advances in AI/NLP offer a promising approach yet have predominantly tackled written text in unrelated research areas. Internally-focused expression (stream of consciousness) requires processing long-form, unstructured text that can represent discontiguous yet conceptually linked thoughts, which may represent, for example ‘worry’. Characterising verbalized thoughts requires development of novel knowledge graphs (semi-structured representations of thought properties) and sophisticated modelling techniques to capture relationships between thoughts and link them with health outcomes.   We need to know a) how to integrate diverse disciplines and knowledge systems to co-develop a knowledge graph that objectively and efficiently characterises thought properties, b) whether a co-developed knowledge graph can be used to construct an AI/NLP pipeline that reliably and validly categorises thought properties, and c) which derived thought properties are associated with age-related cognitive, mental, and brain health.   Aims Our ambition is to found a new interdisciplinary field of computational thought research by integrating diverse knowledge systems to develop validated AI/NLP tools to galvanise collaboration across disciplines and domains. CHATTER is the first step towards this ambition. Interdisciplinary co-production principles are embedded in CHATTER’s creation and approach, and will enable us to leverage and integrate our expertise in psychology, psychiatry, psycholinguistics, lived-experiences, computer science, and statistics to:   Aim 1: Co-develop a knowledge graph, ThoughtNet, to enable construction of an automated, AI/NLP pipeline to objectively and efficiently categorise thought properties.   Aim 2: Compare our AI/NLP pipeline with traditional manual methods to determine reliability, consistency and validity.   Aim 3: Construct Bayesian models to identify thought properties associated with age-related cognitive, mental and brain health.   Potential Application and Benefits CHATTER will generate foundational knowledge on effective interdisciplinary approaches to integrate diverse knowledge systems. It will provide novel AI/NLP approaches that could be applied to interaction with spoken AI systems (e.g., Siri/Alexa), new thought-based knowledge for Bayesian priors, scalable tools to understand and support age-related health and dementia-risk in diverse populations, and capacity-building through upskilling of peer researchers. Further, CHATTER will catalyse a new integrative field of computational thought research, applicable across health challenges, by recognising and classifying thoughts at scale using AI, and analysing them in statistically sound ways to achieve better health outcomes. Such solutions are urgently needed to support mental and neurological health.

UKRI Gateway to Research · FY 2025 · 2025-12

CONTEXT Over the past two decades, advancements in HIV treatment, particularly antiretroviral therapy (ART), have significantly improved survival and quality of life for people living with HIV (PLWH). While ART enables individuals with undetectable viral loads to manage the virus, HIV reservoirs, particularly in the brain, remain a major barrier to curing the disease. These reservoirs contribute to persistent immune activation and dysregulation, even in aviraemic patients. Despite effective viral control, elevated risk of cerebrovascular disease (CVD) and cognitive impairment, leading to debilitating neurological disease, persists, implicating the HIV brain reservoir. The burden of HIV-related CVD has tripled over the past two decades, and nearly half of ART-treated individuals exhibit signs of cognitive dysfunction, showing little improvement since the pre-ART era. Emerging data have shown Alzheimer's disease is more prevalent in PLWH, though the contribution of CVD remains unclear. This highlights the urgent need to understand the role of the HIV brain reservoir in these conditions. A major challenge is detecting latent form of HIV in brain tissue and understanding its impact on surrounding cells.   AIMS AND OBJECTIVES This research aims to investigate the location of HIV in the brain. We will explore how both active and latent infection, which form the HIV brain reservoir, affect brain cells and their surrounding environment, potentially contributing to neurological disease. The specific objectives are: (A) To identify the cell types and brain regions that express HIV genes, and to understand how the brain, including its vasculature, responds on a cellular level to varying degrees of HIV gene expression. (B) To investigate the organisation and cellular responses to HIV gene expression in the brain; in neurological asymptomatic individuals, individuals with neurological diseases, and those who are not people living with HIV (non-PLWH). Traditional research methods have been limited in identifying latent HIV locations and their effects on surrounding cells. Due to a lack of sensitivity, attempts to unravel this question using techniques such as DNA Fluorescence In situ Hybridisation (FISH) have not been successful. Single-cell sequencing is forthcoming but fails to underpin the location of infection to determine the signals to which cells are exposed or how non-exposed neighbouring cells are impacted. To address these limitations, we propose using a powerful high throughput spatial transcriptomics approach, coppaFISH (combinatorial padlock-probe-amplified fluorescence in situ hybridisation). This technique will allow us to simultaneously detect HIV vDNA (indicating latent infection in isolation), vRNA (active infection), and viral proteins potentially involved in neurological disease. It will also enable multiplexing of hundreds of genes to create a detailed map of how the HIV reservoir impacts the organisation and modifications of brain cells. We will use well-characterised human HIV-positive brain tissue to spatially map cell types and describe cell states in response to HIV.   POTENTIAL APPLICATIONS This project will drive critical progress in understanding the HIV brain reservoir and its role in neurological diseases, advancing targets for HIV treatment and biomarkers of brain-related complications. Ultimately, this work will provide unparalleled opportunities by establishing multi-omics foundations which we believe is critical to understand the causative relationships between HIV and neurological diseases in PLWH.

UKRI Gateway to Research · FY 2025 · 2025-12

Heisenberg's famous uncertainty principle states that we cannot know the position and momentum of a particle with perfect accuracy. This phenomenon goes beyond physics: it is hard-coded into mathematics itself. It is a fundamental fact relating functions and their frequency profile and is therefore ubiquitous. Aside from quantum mechanics, the uncertainty principle plays a key role in the analysis of signals in an area known as time-frequency analysis. In this context, the uncertainty principle presents itself in many forms, often as an obstruction ingrained in our mathematical models that goes against our intuitive understanding of reality.       Many applications depend on quantitatively describing certain facets of the uncertainty principle. This project focuses on the study of time-frequency localization operators, that approximately filter a signal and mostly constrain it to a specific region in time and frequency. A perfect filter does not exist. It is impossible for a non-zero signal to be localized both in time and frequency simultaneously, due to the uncertainty principle. Thus it is important to be able to quantify how well localization operators concentrate a signal, that is, how far they are from matching their unattainable ideal counterparts.       The techniques we will use to study localization operators belong to the area of spectral theory, which can be thought of as the study of vibrations. Mathematically, our objective is to study the behaviour of the eigenvalues of these operators and how they depend on the region of time-frequency space where we are localizing.      Two classical and intensively studied phenomena include: the speed of decay of the eigenvalues, and the effects of resizing the region of localization. However, recent applications to signal processing and mathematical physics require a joint understanding of the interplay between them. This constitutes a highly delicate problem that remains underdeveloped and is the challenge we aim to address.      The project will enable applications in mathematical physics and signal processing. In mathematical physics, it will advance a technique to study the distribution of particles in Coulomb gases, a model for charged particles interacting under the electrostatic force. In signal processing, localization operators are used in filtering methods, for example to suppress noise. The project has the potential to help refine the quantitative analysis of the performance of these methods.

UKRI Gateway to Research · FY 2025 · 2025-12

Context of the research and the challenge my fellowship addresses Dementia is six times more common in Parkinson’s disease, affects over half of people living with Parkinson’s within 10 years, and has higher personal, societal and financial impact than other dementias. However, the mechanisms of Parkinson’s dementia remain unclear, and treatments extremely limited. Converging evidence suggests that Parkinson’s disease disrupts structural and functional brain circuits leading to cognitive impairment, so a promising approach for treating Parkinson’s dementia is to identify the brain circuits responsible and target them for intervention. Aims and objectives In my fellowship, I will use multimodal state-of-the-art neuroimaging techniques in different populations of people with Parkinson's disease, to identify the brain circuits responsible for Parkinson’s dementia and apply novel non-invasive ultrasound stimulation on those circuits to improve cognition. Summary of work to be carried out In the first part of the project (Years 1-4), I will apply recent advances in neuroimaging to identify structural and functional brain circuits responsible for Parkinson’s dementia using independent sources of information. First, I will identify circuits whose electrical stimulation, using a surgical technique called deep brain stimulation (DBS), leads to improvement in cognition post-operatively. DBS surgery is an effective treatment for motor symptoms but is invasive and deemed unsuitable for people with cognitive impairment. Therefore, I will validate these findings in non-DBS Parkinson’s patients. Additionally, in my second experiment, I will use orthogonal, data-driven analyses in large number of patients with Parkinson's (not treated with DBS) who are followed up over time, to identify structural and functional brain circuits that are associated with cognitive decline over time. By combining multiple sources of information from different patient populations, I will be able to comprehensively describe the circuits responsible and select the best target for non-invasive stimulation of these circuits. In my third experiment, I will use a novel technique called low intensity focused ultrasound, to stimulate these circuits non-invasively. I will measure how ultrasound stimulation influences cognition in 50 Parkinson’s patients. I will use MR imaging to assess the effect of ultrasound stimulation on brain function and neurotransmitter levels short-term (immediately after stimulation) and long-term, after 3 months. I predict that ultrasound stimulation of target circuits will lead to improvements in cognitive function, and sustained changes in brain function and neurotransmitter levels. In the extension of this project (Years 5-7), if ultrasound stimulation proves to be effective, I will derive imaging markers s that predict response to stimulation from baseline MRI scan, and conduct a larger clinical trial before proceeding to develop this technology for widespread clinical use. Potential applications and benefits Together, these experiments will allow me to establish optimal non-invasive stimulation targets and parameters to improve cognition in Parkinson’s, identify the patients most likely to benefit from this approach and directly test the efficacy of ultrasound stimulation as a therapeutic technique in Parkinson’s dementia. This has potential as a new treatment approach for more widespread use in people with Parkinson’s and other dementias.

UKRI Gateway to Research · FY 2025 · 2025-12

London Hopper is an annual event for women in Computer Science. It features invited talks by female role models in Computer Science and a spotlight competition with cash awards for post graduate level women students.  Anyone, male or women can attend, but only women can give talks. Hopper is free to attend and no registration fee is required.   That is why we need to raise the funds ourselves. By means of this application, we seek funding from EPSRC to be able to organise it for 4 more years, 2025-2028 at UCL.  These will happen n UCL, since we are from UCL and have access to free and accessible venues. The funding is to cover our cash awards and catering expenses for one lunch and two coffee breaks. London Hopper was modelled after US's Grace Hopper Celebrations. It was initiated in the UK by Professor Ursula Martin CBE  in 2006. Since then, it happened in different locations in Scotland and England, and eventually settled down in London for the last decade. Hopper has been happening in UCL since 2019. Its 17th edition on 24th of May 2024 is supported by EPSRC, BCS, and CICSO. https://www.ucl.ac.uk/computer-science/about/equity-diversity-and-inclusion/gender-equality-athena-swan/london-hopper-colloquium#:~:text=Hopper%202024,will%20share%20more%20information%20soon.

UKRI Gateway to Research · FY 2025 · 2025-12

Biopharmaceutical innovation saves lives, and it saves cost. Advances in the manufacturing of biopharmaceuticals reduce costs for healthcare, ensure drug quality, and generally make drugs cheaper and more widely available for the broader population. In this project, Lonza Biologics Ltd (Lonza) and UCL Biochemical Engineering (UCL-BE) are partnering to create a novel approach to improve biomanufacturing of biopharmaceuticals to address these needs. Lonza has been at the forefront of the development of Biologics for 40 years pioneering production in mammalian cell culture.  Lonza’s work as a contract development and manufacturing organisation (CDMO) has brought hundreds of products to the clinic and supported the UK manufacturing sector.  Better medicine means curing diseases instead of treating them, it means fewer side effects because of higher specificity, but it also means more complex molecules to be manufactured. Indeed, Lonza is increasingly being asked to manufacture more complex biologics (a diverse category of large complex biological molecules, such as monoclonal antibodies) which then require more specialised and advanced manufacturing processes.  The development of these new processes is costly and slows the products progress to the clinic.  UCL-BE and Lonza are proposing to address this challenge and unite their complementary strengths to develop a fundamentally new approach which will aid in reducing the time-to-market or time-to-clinic of these more advanced medicines. Thus, ultimately, this will aid to reduce cost and make advanced medicines more widely available. The new approach will be achieved using cutting-edge technologies: microfluidics (i.e. manipulation of tiny volumes in channels), modern analytical methods (known as Process Analytical Technologies, PAT) and machine learning (or Artificial Intelligence). By combining these three technologies in a smart way, we envisage new platforms for the development and optimisation of biomanufacturing processes. In these new platforms, small volumes of proteins and reagents will be rapidly mixed together using microfluidics, the interaction of the proteins with the reagents (i.e. buffer and medium) analysed with PAT, and the data will be processed with machine learning methods. More specifically: Continuous microfluidic buffer/medium preparation (‘recipe’) will enable the vast process space to be rapidly explored.  Microfluidic models of unit operations will provide a continuous representative product stream from which the impact of the changes to the process conditions can be observed.   Integration of spectroscopic protein measurement to the microfluidic devices will enable quantification of the impact of the changes to the recipe.    The resulting data stream from the microfluidic device will be analysed using machine learning.  The ML model will predict the most promising direction within the recipe space to explore next and update the recipe to the microfluidic mixer.   Therefore, ultimately, we will have self-optimising platform units, each resolving a manufacturing process problem autonomously and thus a much-accelerated approach to finding the best possible manufacturing process conditions. This project will create three prototypes which will specifically tackle pressing issues of biopharmaceutical processes: protein aggregation (affects the safety of medicines), protein refolding (affects their therapeutic activity), and optimal culture medium compositions (to create large quantities cost-effectively). UCL-BE will take the leadership in the development of the technologies, Lonza will take leadership in their application to the three stated examples. Together, the project will deliver proof-of-concept on three related microfluidic prototypes ready to be adopted by Lonza in their operations and to expedite the progression of new biologics to the clinic.

UKRI Gateway to Research · FY 2025 · 2025-11

The Abelian sandpile model is a mathematical model that exhibits a variety of complex behaviors. It is defined on a graph, typically a grid, where each cell (or vertex) can hold a certain number of grains of sand. Each vertex i has an associated height hi which is an integer representing the number of grains at that vertex. The configuration of the entire system is given by the set of heights at all vertices. The model follows a dynamics in three steps. Firstly, grains of sand are added one at a time to randomly chosen vertices. Secondly, if the height at any vertex exceeds a certain threshold k (typically four in a 2D grid), that vertex topples, distributing one grain of sand to each of its neighboring vertices. This can cause neighboring vertices to exceed their thresholds and topple as well, leading to a cascade of topplings, known as an avalanche. Finally, the sandpile stabilizes, meaning this process continues until all vertices are below the threshold, resulting in a stable configuration. Although the Abelian sandpile model and its height fields seem straightforward, studying them poses numerous challenges, the key one being their non-locality: the behavior around a site can impact sites very far away from it, making it difficult to analyze and predict via exact formulas. The goal of this project is to provide a thorough and rigorous characterization of the height fields utilizing a physics-based method known as Grassmannian calculus, addressing a long-standing open problem in the physics community. Namely, we wish to describe the scaling limit of the height fields by computing some of their limiting observables. This is particularly challenging and interesting for k>1, which are non-local fields with long-range dependence. In order to efficiently handle the combinatorial complexity of the geometric constraints of these functions, we will “put geometry into algebraic form” using Grassmannian variables. Grassmannian variables are a tool used by physicists to describe subatomic particles (fermions) that obey Pauli’s exclusion principle. They become extremely useful in probability because they allow one to write key quantities for lattice systems in an algebraically convenient form. By finding the relation between the height fields of the Abelian sandpile and carefully chosen Grassmannian variables we will derive, for instance, multipoint functions of the heights in the scaling limit as the size of the graph grows. We will also analyze a range of graphs to demonstrate the robustness of our methods, showcasing their ability to handle diverse variations and make predictions in numerous setups. This will help probabilists and physicists alike to determine the limiting field theories behind the height fields of the Abelian sandpile. There is also a keen focus on integrating Grassmannian variables with widely used techniques in statistical mechanics, and to develop rigorously Grassmann calculus as an important worktool for scientists interested in lattice models with geometric constraints, such as the sandpile.