IMPERIAL COLLEGE LONDON

UKRI Gateway to Research · FY 2025 · 2025-01

We want to build a strong partnership between Imperial College London and Fiocruz Brazil to enable reciprocal learning. Our first goal is to exchange knowledge and research on an effective and affordable healthcare model, the role of Community Health Workers (CHWs), which originated in Brazil. CHWs are a vital part of Brazil's health system, and played a significant role in improving public health over the last 30 years. Inspired by Brazil's success, the UK is now introducing CHWs and there are already 100 CHWs providing support to 20k households nationally. Important learning from the implementation in the UK can offer reciprocal benefit to Brazil, particularly regarding resources and skills, training around chronic disease and mental ill health and research. We want to make sure that as the UK adopts this model, it respects and learns from Brazil's experience and 'gives back' in terms benefits. Brazil's success offers valuable insights for the UK and the adoption of the CHWs is a rare example of 'reverse innovation'.(1) The term 'reverse' is testimony to the bias of lower income countries predominantly learning from high income countries. However, a more balanced knowledge exchange is possible. Brazil can benefit from the UK's experience of implementing their model, especially in dealing with issues like mental health and chronic diseases and how CHWs in Brazil could be trained and supported to deal with these growing issues. By working together, we can learn from each other and strengthen the CHW model in both countries. We plan to involve CHWs from both Brazil and the UK in this process, recognising their expertise and frontline experience. Our second goal is to collaborate on research about making health and social care systems more resilient to system shocks such as pandemics or natural disasters. Fiocruz is a world leader in applying Functional Resonance Analysis Method (FRAM) to study resilience in healthcare, in particular how CHWs in Brazil have impacted positively on resilience in the health system there by creating a more adaptable, community-focused approach. Unlike traditional resilience research, FRAM allows us to predict and test how systems work in the every day, not just in crises. We want to use these research methods to understand health system resilience better by applying this research to the UK, where our Brazilian colleagues can advance these methods by directly comparing systems with and without CHWs. In Brazil, CHWs are established as a fundamental part of the SUS and such comparisons are no longer possible in the same way. This collaboration has the potential to make a significant impact in both countries. Global challenges like climate change, future pandemics, and the growing demands on health and social care systems due to an ageing population will require global solutions. The CHW workforce could be a key part of addressing these challenges. This proposal is the foundation for building strong alliances between our teams and organizations in both countries. This includes developing joint funding proposals, research projects, accessible blue prints and toolbox kits and engaging with key stakeholders for reciprocal learning. We will organize roundtable discussions with senior leaders, community members, and academics from both countries to ensure support for scaling and sustaining this model, and to quickly turn research into practice.

UKRI Gateway to Research · FY 2025 · 2025-01

The exploration of organic semiconductors (OSCs), carbon-based molecules and polymers, has opened the door to exciting low-cost technologies with potential for light weight, mechanical flexibility, and optical transparency. Fabricating the ideal organic device remains however a daunting task despite the large amount of knowledge generated by theoretical studies. Consequently, technologies such as organic solar cells are struggling to achieve competitiveness in terms of efficiency and lifetime. Combining experimental observations with theoretical understanding is urgently needed to reconcile abstract guidelines with practical choices. The lack of experimental evidence is primarily due to the difficulty in characterising charge transport in OSCs. Hall effect is an interesting candidate to access the semiconductor transport properties. Known for almost 150 years, Hall effect is a physical phenomenon widely used in the microelectronic industry to characterise semiconductors. Yet, OSCs do not currently benefit from this characterisation technique due to two major limitations. First, their high resistivity makes Hall signal inaccessible when using traditional setups without an artificial decrease in resistivity, for example by gating (inducing charges in a transistor channel) or strongly doping the semiconductor. Second, due to fundamental differences in the physics of charge transport in OSCs compared to traditional semiconductors such as silicon, any measurable Hall signal remains poorly understood. We have recently shown that a new technology, a parallel dipole line system, enables Hall effect measurements in highly resistive and disordered OSCs in their pristine form. Now accessible, Hall signals must be quantitatively understood to deduce charge carrier density, type, and mobility, and to enable the use of this technique to access transport properties in OSCs. A method is proposed in this project to achieve this goal. Combining Hall effect measurements with alternative characterisation techniques, we will determine and quantitatively compare Hall parameters with effective charge carrier density and mobility. The use of temperature as a variable will enable the decoupling of the two mechanisms involved in OSC conduction, hopping and band-like transports. The knowledge acquired will be further used to better identify the fundamental limitations of charge transport in OSCs, with a focus on prominent solar absorbers. Through this project, we will make Hall effect applicable to disordered semiconductors such as OSCs and facilitate in depth characterisation and understanding of these materials. Results will be compared to theoretical models and used to advance the field of organic electronics.

UKRI Gateway to Research · FY 2025 · 2025-01

We propose to build a CCP centred around the FEniCS and Firedrake finite element systems. FEniCS and Firedrake are world-leading software frameworks for the numerical solution of partial differential equations (PDEs). These numerical simulations are essential for advancing science and engineering across a very broad range of disciplines. FEniCS and Firedrake are used to develop solvers in the geosciences (ocean, atmosphere, cryosphere, geodynamics), nuclear fusion (plasma, tritium transport, breeding blankets), physiology and medicine (brain, heart), and many more besides. FEniCS and Firedrake already have a large, vibrant, and thriving user community. Our vision for this proposal is that with a CCP to support and nurture our community, we will take major steps towards being able to combine predictive physical simulations with real-world data. Specifically, with a CCP, we will achieve two major objectives: Heterogeneity: predictive simulation requires much higher resolution than currently possible. To achieve this we need to optimally exploit heterogeneous architectures (GPUs, tensor cores, FPGAs). This goal also requires constant improvements in numerics, solvers, and implementations. Coupling with data: realistic predictive modelling requires many different processes to be coupled, each of these needs to be informed by data. Coupling between simulation components and data-driven tools such as machine learning frameworks is hence essential. FEniCS and Firedrake are very different to previous generations of finite element software. Our software is important because of the following advantages: Mathematical: scientists and engineers write simulation code for their application at the level of the equations. The low-level high-performance parallel code is generated automatically by our compilers. Composable: the building blocks of a simulation can be put together in any way that is mathematically well-defined, without recoding the components. Users frequently create new and surprising simulation capabilities by combining pieces in ways the developers of those components never envisaged. High performance: The code generation process enables optimisations too tedious to do by hand, resulting in high performance equation assembly. Composability provides access to sophisticated equation solvers (geometric multigrid, domain decomposition, etc.). Differentiable: because the mathematics of the computation are captured by the software, the adjoint simulation (akin to backpropagation in neural networks) is available automatically. This provides a direct route to assimilating data, optimising for engineering parameters, and coupling to machine learning frameworks. This way of working, familiar in machine learning but distinctive in equation-based modelling, is the basis of our community’s success, and the foundation for the successful development of further science capabilities. It enables our users to create far more sophisticated models than they would ever be able to using conventional library-based approaches. A CCP centred around FEniCS and Firedrake will directly support the unmet science needs of hundreds of UK researchers, and thousands around the world. Our CCP will be: Inclusive: A formal structure and strengthened training routes will render the unique advantages of our approach accessible to a larger group of practitioners. Strategic: A community-wide view on the critical components requiring further development will maximise the impact of future work. Sustainable: Ongoing support for core components will ensure that the technology on which thousands of researchers depend is sustained and improved into the future.

UKRI Gateway to Research · FY 2025 · 2025-01

Unmanned Aerial Vehicles (UAVs) are emerging as powerful tools for sustainable environmental monitoring, playing a vital role in comprehending the diverse effects of climate change on ecosystems. The integration of state of the art techniques such as Artificial Intelligence (AI) with UAV technology has introduced innovative pathways for various environmental applications. However, their full  potential remains to be explored.  ACCELERATE proposes the development of a fertile inter-discipline and inter-sectoral ecosystem that aims to radically contribute towards enhancing UAV technology to enable the sustainable environmental management. The specific objectives of the project are to: a) Create a continuously updated ecosystem with UAV datasets suitable for environmental studies and climate change impact assessment, b) promote methodological advances in the field of UAVs technology, by exploiting the unique capabilities of those data with state-of-the-art techniques and c) establish clear guidelines and homogenized protocols for the characterization of the exploitation of UAVs in specific application domains. Experimental analysis will also be carried to showcase the practical use of the project outputs via four carefully selected and innovative Use Cases, that will serve as Key Performance Indicators of the project.  ACCELERATE brings together enthusiastic staff from academia and industry via a series of carefully-designed secondments, establishing a unique fertile collaborative research and innovation environment to promote pioneering research in environmental and socioeconomic studies implementation within urban, natural and agricultural environments. A strong inter-sectoral experienced research team of 17 partners from 9 countries, of 9 academic and 8 industrial partners coming from Greece (2), Romania (3), Italy (2), Cyprus (1), United Kingdom (5), North Macedonia (1), France (1), Germany (1) and Portugal (1) constitute the project’s Consortium.

UKRI Gateway to Research · FY 2025 · 2025-01

Antibody-drug conjugates (ADCs) are a promising cancer therapy that precisely deliver potent drugs to cancer cells by linking them to engineered antibodies, minimising damage to healthy tissues. While 14 ADCs are FDA-approved and over 140 are in clinical trials, challenges persist. Current ADC linkers often lack specificity, resulting in off-target drug release and clinical trial setbacks, exemplified by the withdrawal of Mylotarg due to an unstable linker. Improved linker chemistry is crucial to unleash the full potential of ADCs. Most clinical ADCs rely on dipeptide substrates cleaved by cathepsin B (cat B), a protease abundant in tumours. Unfortunately, these linkers can also degrade in healthy tissues and by less selective proteases, causing drug release in unintended locations. Neutrophil-secreted serine proteases can lead to premature payload release, causing myelosuppression, a significant concern. Balancing circulatory stability and tumour-specific release remains a challenge for chemical payload release mechanisms. Developing ADC linkers targeting one of the hundreds of potential tumour-associated proteases (TAPs) is challenging due to tight regulation of proteolytic activity, rendering traditional abundance measures (mRNA, protein) ineffective. Innovative approaches are required to determine protease activity directly in relevant tissues, enabling design of linkers with enhanced tumour specificity for next-generation ADCs. This advancement is crucial for improving clinical efficacy and reducing side effects associated with ADC therapy. Our multidisciplinary team has a bold vision: to establish the first universal discovery platform (Substrate Identification From Tissue Resection, SIFTR) for ADC linkers by identifying chemically novel TAP substrates and probes directly from human patient tissue. By directly linking specific protease activity in tumours to discovery of selective linker chemistry, our platform will provide the foundation for a comprehensive discovery pipeline for next-generation ADC linkers.

UKRI Gateway to Research · FY 2025 · 2025-01

The information and communication technologies (ICT) sector has become an inextricable part of our modern world, from supporting basic day-to-day tasks to performing complex simulations and calculations that are essential to scientific research, engineering, security, control and other fields. High-Performance Computing (HPC) systems that are capable of solving hugely complex and demanding problems with high computational power, are critical in the development of leading-edge global scientific and engineering projects currently underway in the commercial, academic and government spaces, spurring discoveries and innovations. With the rise of artificial intelligence and big data, it is inevitable that the percentage of global power consumption from HPC systems will grow rapidly and is going to be a major challenge in the future. As such, there is growing interest in how the expansion of HPC systems can be delivered in a manner that is environmentally sustainable and in line with the government's pledge for a net zero society. This project will investigate how to exploit configurable and customisable processing technologies to design more sustainable HPC systems. The team of investigators will use a co-design approach, tuning processing hardware and software in collaboration to optimise HPC systems for both performance and energy efficiency. The proposed approach will be used to look at how to improve the performance and sustainability of wind energy systems, which in turn can be used to power HPC systems, further reducing the environmental impact of these systems. This is our primary research objective. For this exercise, we will dramatically reduce the environmental impact of Xcompact3d, an open-source software designed to study numerically fluid flows. It is currently among the most used softwares on ARCHER2, the UK supercomputing service. We will focus on Xcompact3d's wind farm simulator, a tool which can faithfully replicate virtually realistic scenarios encounter by modern wind farms. It will be re-designed with a hardware-software co-design approach. We will focus on three hardware architectures; 1) Field Programmable Gate Arrays (FPGAs), 2) Coarse Grained Reconfigurable Architectures (CGRAs), 3) RISC-V. With FPGAs we are able to undertake the design of low-level bespoke memory access designs or algorithm level concurrency, with CGRAs to explore different approaches for mapping algorithms to the hardware and communication, and RISC-V to experiment with different CPU and accelerator designs and their configurations. With support from the vendors, all these experiments will result in actionable results that will be use to further direct the research, but also for the vendors to understand how to best enhance their products for the HPC community. The team of investigators will also gain insights into sustainability efforts in HPC with an in-depth study focusing on possible sustainability and net zero strategies. One of the overarching goals of the project is to provide a road-map for a more sustainable HPC landscape, and transfer the outcomes of the project to policy-making for a wide range of academic, government and industry stakeholders. The project team is composed of experts from a variety of fields, including high performance computing, computer science, programming, computational fluid dynamics, innovation and sustainability, and policy-making. This interdisciplinary team will be able to bring a wide range of expertise to bear on the challenges of developing and utilising more energy and resource efficient HPC systems.

UKRI Gateway to Research · FY 2025 · 2025-01

Infections by the bacterium Streptococcus agalactiae, also known as Group B Streptococcus (GBS), have emerged as a major cause of adult invasive across the world. This includes skin and bloodstream infections. Even though GBS has an important clinical historical association with infant diseases, GBS now causes more diseases in adults than in infants in the UK and in many other countries. The molecular basis and infection processes of these diseases, as well as the similarity or dissimilarity of GBS strains causing infant and adult pathologies, remain to be determined. Alarmingly, the impact of adult GBS infections is expected to continue to grow given the ageing global population and the emergence of antimicrobial resistance in GBS. To address this global public health problem, we need better detection, surveillance, prevention and treatment options for adult invasive (i)GBS. Crucial to these developments is recognising the primary host-pathogen interactions that GBS uses to initiate invasive adult infections. However, we do not understand these critical mechanisms, as research to date has characterised infant GBS strains and infections. Our recent work has identified that GBS binds to a human immune receptor called CEACAM1. This is achieved when GBS express a specific protein at their surface called an adhesin. Notably, GBS stains that infect adults, but not infants, commonly express 1 of 3 adhesins that bind CEACAM1. The human CEACAM1 receptor is an inhibitory immune receptor that is commonly expressed on epithelial cells and leukocytes. It can be targeted by pathogens to hijack inhibitory signalling, suppress immune responses and promote infection. We have clear and convincing preliminary evidence that GBS and CEACAM1 interactions allow GBS to adhere to cells and subvert immune defences. Thus, we hypothesise that GBS cause invasive diseases in adults by binding to CEACAM1 to subvert immune responses. This project aims to characterise the biological functions of the GBS and human CEACAM1 receptor interactions. Our objectives are to: - Determine how adhesin binding to CEACAM1 reduces epithelial and phagocyte defences. We will also detail the impact of natural CEACAM1 variation in these processes, as this not only influences the affinity of the interaction with GBS but likely contributes to susceptibility to infection. Here, we will use epithelial and phagocyte cell infection models to determine how adhesin-CEACAM1 interactions enhance the capacity of GBS to evade immune responses. Define the effect of CEACAM1 binding by GBS on human intracellular signalling. We will use in vitro and biochemical approaches to define how CEACAM1 binding by GBS influences immune signalling. Explore how the expression of CEACAM1-binding adhesins promotes the invasion of skin tissue by GBS. To do this, we will infect human skin explants and use microscopy and immunology approaches. We will generate knowledge at the system level about how CEACAM1 engagement by certain GBS clones drives infection in adults, expanding our fundamental knowledge of these highly invasive life-threatening infections. This will allow better detection, surveillance, preventation or treatment long-term. By shedding light on the intricate system-level interplay, we are not just deepening our knowledge of GBS infections but opening new avenues in immunology. This has the power to reshape our understanding of inhibitory receptors, presenting opportunities for novel insights and applications in the broader field. The project will provide interdisciplinary training in genetics, biochemistry, cellular models, tissue models and infection biology.

UKRI Gateway to Research · FY 2025 · 2025-01

In the UK, the chemicals sector is a key contributor to the UK economy. It adds close to £20 billions of value to the country's economy every year and has an annual turnover of approximately £60 billion, sustaining more than half a million jobs. Within this sector, the manufacture of most chemicals involves the use of a catalyst, which is usually based on the rarest elements on the Earth's crust, such as noble metals (Pd, Rh or Ir). The limited supply of these group of privileged metals, together with their huge environmental footprint (e.g. obtaining 1 kg of pure metallic Rh produces near 32t of carbon dioxide) blocks the development of truly sustainable processes. This is pushing chemists towards the discovery of catalysts based on inexpensive, abundant, and benign base metals (e.g. Ni, Co and Fe). However, reactivity on BM centres often proceeds through one-electron events, resulting in difficulties controlling and maintaining the catalyst function, thus preventing the development of sustainable, efficient, and predictable catalysts. Among all the strategies employed to control the chemistry of base metals, we were attracted by chemical metal-ligand cooperation, in which actor ligands participate in bond-forming and breaking events. Based on this, our strategy to tame two-electron catalytic cycles and develop predictable catalytic methods with BM will exploit low-valent aluminium-based ligands. Thus, our aim will be furnishing ambiphilic Al-BM units that are capable of (1) binding substrates to the highly electrophilic Al centre and (2) activate them using a nucleophilic base metal centre. Using Al as binding site is not a random choice: this group 13 element is not only benign, but the most abundant metal in the Earth's crust. Furthermore, in its +1 oxidation state presents interesting properties as ligand, becoming a powerful sigma-donor with an empty and accessible p-orbital. These properties have been recently exploited in the field of noble metal catalysis. Nonetheless, heterobimetallic complexes in which Al(I) is paired with another earth-abundant metal remain under-explored and currently limited to stoichiometric activation of small molecules. In these examples, however, the integrity of the Al-BM bond is lost. This represents a major challenge for their implementation in catalytic processes, as ligand dissociation leads to disruption of their cooperative activation ability, resulting in catalytic deactivation. To overcome this issue and achieve rigid and stable structures and Al-BM bonds, we will establish a rational design strategy to obtain bespoke Al-BM complexes that will be built by a delicate selection of ligand backbone, anchor arms, and base metal centre (Co, Ni, Fe). These complexes will be studied using a bottom-up approach based on a stoichiometric-to-catalytic strategy: employing the knowledge gathered from stoichiometric activation studies, infusing nobility to Al-BM units in a catalytic fashion will be within reach. The implementation of Coop-NBM will represent a greener and cheaper alternative to functionalise organic molecules compared to noble metal catalysis, allowing the achievement of environmentally friendly approaches with potential to be applied at industry. Overall, the importance of this research proposal lies in its potential to provide catalysts based on the most abundant elements of our planet, e.g. Al and Fe. Catalytic use of base metals combined with subvalent Al ligands remains an uncharted territory and an exceptional opportunity to establish a new chemical space that could lead to a dramatic reduction of the environmental footprint of countless organic transformations currently performed by noble metal catalysis. This will certainly make the UK take centre stage in the development of sustainable technologies aiming at retiring noble metals as workhorses of chemical industry.

UKRI Gateway to Research · FY 2025 · 2025-01

Catalysis plays a fundamental role in the efficient manufacture of all manner of products used in our daily life, but ton-scale industrial processes still rely on some of the rarest metals in the Earth's crust, such as rhodium, palladium, iridium, and platinum. The continued use of these elements represents a major challenge that must be addressed if truly sustainable chemical processes are to be developed. To tackle this issue, current strategies exploit the use of base metals (BM) and main group (MG) elements, albeit catalytic applications based on their separate use is hampered by the preference for one-electron redox events and their poorly understood redox activity, respectively. This proposal will merge the fields of coordination chemistry, organometallic synthesis, and catalysis, aiming at unveiling platforms merging the most promising properties of both strategies. With the objective of promoting and controlling two-electron events at BM centres, low-valent group 13 elements will be used to promote cooperative bond activation processes. This will allow us to infuse nobility to base metals, mimic organic transformations traditionally performed by precious metals and provide reactivity beyond them. This project will pair Al, the most abundant metal on Earth, with sustainable and highly abundant BM. Catalytic use of BM combined with subvalent Al ligands remains an uncharted territory and an exceptional opportunity to establish a new chemical space. The development of INNOBA will certainly lead to unique sustainable catalytic platforms and, eventually, a dramatic reduction of the carbon footprint of countless organic transformations currently catalysed by precious NM catalysts.

UKRI Gateway to Research · FY 2025 · 2025-01

Women's health is more than reproductive health. Globally, non-communicable diseases (NCDs), such as cardiovascular disease, cancer and diabetes are the leading causes of death and disability for women however, health care for women remains focussed largely on pregnancy care and contraception. My fellowship focusses on three pressing global priorities in women's health: (1) using pregnancy as an opportunity to improve lifelong health, (2) developing AI tools to improve care delivery, health equity and outcomes for women across the life course, and (3) understanding the effects of climate change on women's health. Conditions detected during pregnancy, such as gestational diabetes mellitus (GDM) and preeclampsia identify women at greatly increased risk of type 2 diabetes or hypertension in the months and years immediately after the birth. We have developed and tested a novel, low-cost approach to improving women's lifelong health by integrating NCD screening into pregnancy and postnatal care (SMARThealth Pregnancy). We are conducting a large trial across two states in India that has recruited 3400 pregnant women, 240 community health workers, and thirty primary care doctors from 60 villages. The trial is testing a digital intervention to support community health workers (known as ASHA in India) detect, refer and follow-up of women with high risk pregnancy conditions. The comparator is usual care. The trial is ongoing and results are expected by mid 2025. Working with data scientists at the George Institute for Global Health and University of Oxford, and the community health workers in the SMARThealth Pregnancy sites, we have co-developed a large language model (LLM) chatbot to support the ASHAs deliver guideline based care. We are exploring ways to ensure our AI tools are equitable and gender transformative. A major challenge in our study sites is the lack of services to detect and manage women's mental health problems. Globally one in five women will suffer a mental health problem around the time of birth, yet most women lack access to any evidence-based support or therapy. Sadly, suicide is a leading cause of late maternal death (i.e. death up to one year after birth). We are co-developing an intervention to support women's perinatal mental health (PRAMH). Improving care after high-risk pregnancy is not a problem limited to India. In the UK, access and quality of post partum care is uneven across the country. With an increasing volume of digital data from electronic health records and individual patient sensors (such as digital blood glucose monitoring), our team are working the utilise data to transform care. Our aim is to move from a "one size fits all" approach, to personalised and targeted care that will prevent future disease, and be more efficient to delivery within the NHS. I have established a national data consortium for gestational diabetes, and we work with data scientists who are developing and validating prediction models and LLM suitable for the UK. We plan to test and scale a model to improve the care of women after GDM in the next three years. The final aspect of my Fellowship addresses the global challenge of climate change and how this is affecting women's health. The effects of climate change are not gender neutral, yet data is lacking to measure and track these differences in most settings. 2023 is the hottest year on record and has had direct and indirect effects on human health. Heat is associated with preterm birth, stillbirth, preeclampsia and other adverse outcomes. Women in rural India are some of the most at risk from the effects of climate change, with many working outdoors with few opportunities for adaptation. Together with our partners from the SMARThealth Pregnancy project, and with other experts from the UK and India, we are exploring how extreme heat leads to adverse pregnancy outcomes, and working with communities to identify locally appropriate opportunities for adaptation.

UKRI Gateway to Research · FY 2025 · 2025-01

Can we reverse-engineer living cells to create artificial cells from the bottom up? Can these artificial cells act as micromachines for specific tasks in physiological environments and as models to understand biological processes? Recent advancements have brought us closer to an era where we can design biology. These strides have been enabled by applying precision engineering principles, traditionally associated with mechanical and electrical devices, to biological constructs. Artificial cells, mirroring the dimensions of natural cells, can incorporate biological components such as DNA, proteins, lipids, and metabolites, and can be engineered to exhibit key characteristics of life. A convergence of technologies now allows their creation and manipulation with precise control over size, shape, content, compartmentalization, function, and behaviour. We can programme artificial cells to navigate concentration gradients, produce proteins upon external triggers, replicate, and communicate with each other. These achievements suggest that the practical use of artificial cells-in therapeutics, biosensing, self-healing materials, and bio/enzymatic reactors-is on the horizon. The initial phase of my UKRI Future Leaders Fellowship focused on developing technologies to integrate living cells as functional units within artificial cell systems, creating bio-derived micromachines that blend living and synthetic elements, achieving the best of both worlds. We have (i) demonstrated that artificial cells can interact with living ones at a population level (ii) embedded living cells within synthetic ones to enhance their functionality, serving similar functions to biological organelles and (iii) physically connected synthetic cells to the surfaces of biological cells. As we seek to renew this fellowship, our aim for the next three years is to harness the most promising hybrid systems we have developed and, through proof-of-concept experiments, showcase their potential in therapeutic applications. Specifically, we will investigate how these hybrid cells can pave the way for new therapeutic classes that are targeted, responsive to stimuli, and equipped with features valuable in biomedicine and beyond, such as motility, decision-making, and in-situ biochemical synthesis. By exploring the integration of living and synthetic systems, we aim to lay the groundwork for a new research domain that merges artificial with living biology.

UKRI Gateway to Research · FY 2025 · 2025-01

The chemical sector plays a vital role in Europe's economy, supporting more than 1.2 million jobs and a 15% share of the global chemical market. In this sector, catalysts are integral to the production of most chemicals, often relying on the rarest metals from the Earth's crust, such as rhodium, palladium, or platinum. Due to their cost and environmental impact, chemists are urged to exploit and develop catalysts based on more abundant and sustainable elements, such as 3d metals. In addition, the merger of such elements with visible-light-induced processes promises new reactivity avenues to develop a unique catalytic toolbox, thus challenging the dominance of precious metals in important chemical transformations. In this context, the researcher will explore the (photo)organometallic chemistry of well-defined low-valent chromium (Cr) coordination complexes. Albeit their low toxicity and huge potential, exemplified by their widespread application in polymerisation and nitrogen fixation reactions, such species remain underexplored in the realm of organometallic redox catalysis. Indeed, the field is still in its infancy and faces limitations that result in processes that are far from being truly sustainable. Through the application of a bottom-up approach and their experience in ligand design, the researcher will unveil key mechanistic aspects to fully understand how to promote predictable two-electron events in low-valent Cr centres using light as energy source. This approach aims to mimic precious metals in catalysis while achieving transformations beyond their scope, which will lead to a tremendous reduction of environmental and economical production costs, impacting the lives of everyone. Furthermore, this action encompasses detailed training and dissemination plans. Through these bespoke plans, the researcher will cultivate a set of essential leadership, management, and communication skills that will prove instrumental in her future career endeavours.

UKRI Gateway to Research · FY 2025 · 2025-01

Technologies building on fundamental science and engineering – collectively referred to as deep tech --increasingly originate in universities.  This has led to a growing interest in understanding and addressing frictions to the process of translating fundamental university research into impactful commercial solutions. The focus of this proposal is on the first step of this university translation process:  the filing of an invention disclosure form by university academics. Invention disclosure is an important milestone because it is a pre-condition to Tech-Transfer Offices (TTO) considering whether to initiate patent filing on behalf of the university.  Looking at invention disclosure as an outcome in its own right has an added benefit of studying this first step of the translation process independent of other frictions in the patenting, licensing or spinout process that may also hamper commercial progress. Recent advances in machine learning, combined with the availability of large datasets linking patents with publications have greatly improved the ability to predict the commercial potential of fundamental academic research and use this as a way to understand the ex-ante commercial potential of the scientific research being conducted across university departments. Comparing the predicted commercial potential with actual invention disclosure allows for a scalable approach to identifying pockets of highly commercializable research that appear to not be making the first step of university translation. In turn, focusing attention on these pockets allows for an effective way to understand potential frictions as well as the quantitative impact of potential solutions to these frictions. Our proposed analysis to understand and address frictions to invention disclosure will progress in four steps:  First, in collaboration with Dashun Wang from Northwestern University, whose team has separately trained machine learning models on internal data of US universities, we plan to validate and if necessary adapt these models to predict the commercial potential of science and engineering research emerging from universities in the UK.  Second, in conjunction with Imperial's TTO, we will use these predictions to identify pockets of highly commercially-relevant research to identify academics whose research is commercially relevant but didn’t file invention disclosures. Third, we aim to conduct a qualitative study based on interviews of these academics. This will provide an understanding of whether the gaps are due to a lack of desire or incentives to commercialise academic research, information frictions such as not knowing their work had commercial relevance or other frictions stemming from the tech transfer process itself.  Finally, based on the insights of these analyses, our goal is to conduct a field experiment, together with the Northwestern team, to study the quantitative impact of potential interventions. There are several potential applications and benefits of this research.  First, it is an opportunity to independently validate the generalizability of large language models being developed to predict commercial potential of academic research.  Second, the descriptive data on commercial potential is itself valuable for university tech transfer offices, policy makers and potential investors looking to identify pockets commercially relevant scientific expertise that may not have been effectively mined.  Finally, the detailed interviews as well as results from the field experiment will add to our understanding of how best to address commercialisation frictions at the very earliest stages of Tech Transfer, including the potential heterogeneity in impact across the field of research and individual characteristics of the academics.

UKRI Gateway to Research · FY 2025 · 2025-01

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.

UKRI Gateway to Research · FY 2024 · 2024-12

Green hydrogen is a critical energy vector in our transition to a net-zero economy. Current state-of-the-art technologies to produce green hydrogen rely on water electrolysis. However, this technology suffers from critical drawbacks including (1) the high thermodynamic potential required for water oxidation (2) the low economic value of oxygen produced at the anode, which in turn increases the cost of the hydrogen generated and (3) safety considerations due to the proximity of the oxygen and hydrogen gas generated. Electrochemical systems that couple hydrogen production at the cathode with the synthesis of high-value chemical syntheses at the anode via partial hydrocarbon oxidation not only circumvent these challenges but also enable the decarbonization of the chemical industry. However, the bottleneck in scaling up of these technologies is the development of more selective and active catalysts. Amongst the large number of potential hydrocarbon candidates, propene is particularly interesting because its partial oxidation to propylene oxide, propylene glycol and acrylic acid is industrially relevant and the presence of a carbon single bond and double bond in propene makes the scientific results on this molecule transferable to a range of hydrocarbons. In this project, we propose to unravel fundamental mechanistic insights into interfacial parameters that control the selectivity of propene oxidation to acrylic acid and propene glycol on electrocatalysts. In order to achieve the research goals, we will use state-of-the-art materials fabrication and characterization tools to develop rational design descriptors for the activity and selectivity of propene partial oxidation. In particular, will employ optical, vibration and X-ray spectroscopic tools to understand the influence of the potential-dependent surface phases, reaction intermediates, and interfacial solvent environment on the selectivity of products.  By understanding the role of (1) surface chemistry, (2) surface morphology and (3) supporting ions in the electrolyte, we will design electrochemical interfaces with higher activity and selectivity of propene oxidation to the target products. The optimized catalyst will be fabricated at scale and its performance will be benchmarked in a device.

UKRI Gateway to Research · FY 2024 · 2024-12

NWTF (http://www.nwtf.ac.uk/) is a UK leader and, over the next five years, will become a global leader in delivering cutting-edge science across a networked range of unique facilities in experimental fluid mechanics addressing the grand challenges of clean growth and the future of mobility across aerospace, automotive and civil engineering sectors.  Through efficient and nimble organisation, it will provide strategic, coordinated oversight with an industrially aligned approach in the generation of new, net-zero technologies and knowledge in the efficiency of fluid-based systems, H2 and electric/hybrid transport, fluid-structure interaction, heating, noise, contaminant dispersion, particle transmission (Covid-19) and human factors, wind loading, coastal erosion and offshore renewables.  NWTF continues to provide cutting-edge research, with its capacity measured by outputs and will augment this ‘delta’ in terms of new science, research capacity and pace.  It enables the development of new skills in original, challenging, highly relevant experiments, the provision of new data and the establishment of “digital threads” for AI, modelling and the rapid adoption of innovative techniques such as remote access and robotics.  With an inclusive approach, NWTF provides interconnected facilities of world-class capability that are open to all UK-based researchers. In this way, NWTF provides a unified approach that is greater than the sum of its individual facilities, offering a single point of contact for industry, driving an innovation pipeline for a pull-through environment. NWTF is a hub and node organisation with 22 wind tunnels across 12 UK universities (nodes) and a centralised Project Manager (hub) who coordinates initiatives on behalf of the group. It has a Management Board (MB) comprising one member from each institution along with the NWTF hub Project Manager and representatives from EPSRC and the Aerospace Technology Institute (ATI). It meets quarterly, receiving inputs from a cross-disciplinary, international Advisory Board (AB) which meets bi-annually with members from Japan, France, Germany, Sweden and the United States. The AB consists mostly of experimental aerodynamicists from industry as well as academic colleagues, including those with interests in numerical prediction. With an initial EPSRC grant, EP/L024888/1 in 2014, NWTF solved the problem of under-investment in UK wind tunnels by establishing an equitable, excellence-based framework for tunnel membership. This successful model has recently attracted further investment from UKRI Infrastructure Award with a strong focus on green economic growth and human mobility, announced June 19, 2023, and administered via seven EPSRC awards with a start date July 1, 2024.  This funding supports eleven new facilities at seven universities, transformational equipment at six additional universities and additional hub support. Network activities are supported by a Network grant, EP/X012069/1. The most recent UKRI Infrastructure award evolved from an EPSRC "Large Scale Research Infrastructure" process during which NWTF was asked to focus on “transformational elements” only, reducing the requested budget from £48.2m to £23.0m.  The reduced value resubmission therefore removed all support for replacement items such as new lasers or operating system upgrades. The Core Equipment grant will fund these critical items to keep NWTF facilities operational by investing to save and enhancing the capability of these world-class wind tunnels.

UKRI Gateway to Research · FY 2024 · 2024-12

Infertility is a problem that affects up to 15% of couples worldwide, and about half of these cases are due to issues with male fertility. Some men facing infertility have a complete absence of sperm in their ejaculate, known as non-obstructive azoospermia (NOA), caused by problems with sperm production. This process, called spermatogenesis, is a complex developmental process that involves proliferation of undifferentiated germ cells and the completion of meiosis, the cell division programme that ensures the formation of haploid gametes from diploid germ cells. The exact reasons for NOA are often unknown, and there are currently no proven treatments to help with this condition, mainly because we don't fully understand how spermatogenesis works. However, understanding the root causes of this condition could lead to new treatments to help couples conceive. Male infertility has also been linked to other health problems later in life, such as cancer and chronic diseases. Thus, understanding NOA causes can also help us in understanding and treating its associated comorbidities. Recent advances in genetic sequencing of infertile patients have identified potential pathogenic mutations in dozens of genes. The identification of these genetic variants constitutes a valuable resource to elucidate the genetic origins of NOA. However, we currently lack functional validation assays to determine whether these mutations cause NOA. Moreover, the role of many NOA candidate genes has not been thoroughly studied to confirm their role in spermatogenesis. Therefore, there it is crucial to develop efficient functional validation methods to determine which genes play a role in spermatogenesis and whether mutations in these genes cause NOA. My hypothesis is that mutations in genes crucial for spermatogenesis, including those required for meiosis, play a significant role in male infertility. To test this, I aim to model mutations identified in NOA patients using a tiny worm called C. elegans to determine their impact on fertility. This worm has properties that make it ideal for studying fertility, such as its short life cycle, ease of observing reproductive tissues, and well-established genetic manipulation techniques. As proof of principle, I have conducted preliminary studies showing that worms engineered to carry a specific mutation from a NOA patient in the meiotic gene MSH4 display infertility. My research will focus on several key objectives: selecting specific mutations identified in NOA patient to study, creating mutant strains of C. elegans with these mutations, screening worm mutants for fertility defects, and analysing the characteristics of the mutants that show fertility issues. By doing this, I hope to shed light on how genes influence spermatogenesis and contribute to male infertility. This understanding could lead to better diagnosis and treatment options for couples struggling with infertility.

UKRI Gateway to Research · FY 2024 · 2024-12

140,000 people in England, and >260 million people worldwide are diagnosed with Type 2 Diabetes (T2D) before the age of 40-years. Compared to diagnosis in later-life, early-onset T2D has a more severe course, with faster progression to needing insulin, higher rates of diabetes-complications and a life-expectancy that is shortened by approximately 14 years. Worryingly this age bracket also shows the fastest rise in incidence of T2D.   In 2016, the DiRECT study showed that after following a 12-week low-calorie diet, 46% people with T2D were in remission a year later, meaning that sugar levels were back to normal without needing medications. This important finding has led to the Path-to-Remission programme, which is available nationally on the NHS to individuals aged 18-65-years who are offered a similar low-calorie diet. However, 99% of those studied in DiRECT were White European, and only 7 out of 298 people in the trial were aged <40-years (2.3%). Whether similar results are observed in younger age groups and other ethnicities is a knowledge gap concluded by the study authors, a key research priority of Diabetes UK, and a research priority identified by the international consensus group on the clinical translation of precision diabetes medicine.   Why study younger age groups and ethnic minorities? A third of individuals with early-onset T2D in England are of Asian ethnicity, and preliminary unpublished data from the Path-to-Remission programme show that the chance of remission is lower in those <40-years compared to older age groups, and lower in Asians of all ages compared to White Europeans.  The likelihood of achieving remission following a low-calorie diet is closely linked to the ability of the pancreas to produce insulin.  Studies have shown lower insulin production from the pancreas in those diagnosed with T2D at a younger age, and in South Asian individuals compared to White European individuals. It is not clear if this is due to the nature of their diabetes, genetics, or compliance with the diet.   Aims and objectives The objectives of this study are: To compare the physiological effect of a low-calorie diet in South Asian and White European individuals with early-onset T2D To investigate whether ethnic differences in physiological response may result from the features of their diabetes, genetics, or dietary compliance.   I will study White European and South Asian individuals with early-onset T2D before and after participating in the NHS Path-to-Remission programme, studying: Clinical features Insulin secretion from the pancreas Liver and pancreas fat content Genetic risk for low pancreatic insulin secretion Compliance with the low-calorie diet.   This work will expand the evidence base for low-calorie diets in achieving remission of T2D in those with early-onset T2D and unpick underlying ethnic differences in South Asian T2D populations, who are currently under-represented in research. Understanding age-based and ethnicity-based differences in the response to diabetes-interventions is critical in progressing towards a precision-medicine approach to diabetes remission. An exploration of how individual physiology, genetics and programme adherence contribute to the observed differences in remission rates following low-calorie diets will facilitate future research to address these differences and will further our understanding of the parameters that predict diabetes remission, promoting both more informed patient choice and the allocation of low-calorie diets to produce maximum benefit.

UKRI Gateway to Research · FY 2024 · 2024-12

Explaining the observed excess of matter compared to antimatter in the early universe is one of the biggest questions in physics. Neutrino oscillations can violate the CP symmetry, potentially by an amount large enough to produce this excess. Understanding neutrino oscillations is an essential step to understanding the universe we see today. Hyper-Kamiokande will use a 200-kiloton water Cherenkov detector to measure neutrino oscillations with unprecedented statistical precision. The challenge is to reduce the systematic uncertainties of Hyper-Kamiokande to ensure the success of its oscillation measurements. Addressing this challenge has been the focus of my fellowship to date and is the continued focus of this renewal. The dominant systematics in long-baseline oscillation experiments are due to the difficulty in relating what is observed in the detector to the neutrino energy. The IWCD experiment has been designed to measure neutrino interactions over a range of angles off the J-PARC neutrino beam axis. The peak energy of the neutrino beam decreases as the off-axis angle increases, allowing IWCD to directly relate neutrino energy to what is seen in the detector. This link enables IWCD to produce a data-driven mapping between neutrino energy and the signatures observed in the detector, significantly reducing the systematic uncertainty associated with this. The IWCD method requires a detailed understanding of the water Cherenkov detector, in particular the detector fiducial volume. In the first fouyr years of this fellowship I have designed, produced and characterised a novel optical calibration system that is currently being deployed at the WCTE experiment at CERN. The data from this calibration system and WCTE will demonstrate the ability to achieve percent-level uncertainties in neutrino event reconstruction using a water Cherenkov detector. WCTE will also provide new measurements of lepton scattering on oxygen, a crucial input to producing more the accurate neutrino interaction models required for Hyper-Kamiokande. This fellowship originally proposed to address the key challenges in neutrino oscillation physics in two new ways: development of a novel calibration system to understand water Cherenkov detectors and the use of off-axis beams to understand neutrino interactions. I have accomplished the first of these goals during the initial four years of the fellowship and this renewal will allow me to accomplish the second. Together these will produce the world's most sensitive search for CP violation in neutrino oscillations.

UKRI Gateway to Research · FY 2024 · 2024-12

Context Vision is our most precious sense, and visual health depends on physiological regulation of eye pressure. In healthy individuals, eye pressure exhibits remarkably little variability over decades of life. But in the age-related disease known as glaucoma, eye pressure becomes elevated. This damages the optic nerve and causes blindness, which can be stopped only by lowering eye pressure. Most glaucoma therapies do not target the factors controlling eye pressure because these are unknown. Instead, they lower eye pressure by suppressing intraocular fluid production or by introducing new routes for fluid drainage from the eye. These indirect methods achieve inadequate pressure reduction, which leads to unnecessary vision loss that reduces quality of life and personal independence. In the UK alone, billions are spent annually on health care costs for those afflicted by vision loss and blindness. These costs will only rise as our population ages. The Challenge Our challenge is to identify the molecular factors responsible for maintaining a healthy eye pressure necessary for vision. Eye pressure is determined by the turn-over of intraocular fluid, specifically aqueous humour that fills the anterior chamber (the space between the clear cornea and coloured iris). Aqueous humour drains through the conventional outflow pathway, which includes the trabecular meshwork and Schlemm's canal, which a blood vessel-like conduit that encircles the iris. The hydraulic resistance of the outflow pathway is the primary determinant of eye pressure. Yet, we do not know what controls this outflow resistance or why outflow resistance increases in glaucoma to cause elevated eye pressure. The wall of Schlemm's canal is the only continuous barrier to aqueous humour drainage. To cross this wall, aqueous humour passes through micron-sized pores that provide a pathway for flow across an otherwise impermeable cell layer. In glaucoma, a reduction in pore density coincides with increased outflow resistance. Further, several physiological and biomechanical studies have localised the bulk of outflow resistance to the tissue just underlying the wall of Schlemm's canal. Aims and Objectives Our goal is to determine the molecular factors controlling pore formation in Schlemm's canal cells. We introduce a novel fluorescent assay to label pores, and we show that pore formation is triggered by mechanical stretch that acts on Schlemm's canal in vivo. We specifically examine the role of mechanosensitive ion channels, which open in response to stretch, to drive intracellular calcium and SNARE-mediated membrane fusion that we hypothesise to be necessary for pore formation. Potential Applications and Benefits This project addresses a fundamental question in ocular physiology that has implications for patients and health care providers, scientific and clinical ophthalmic researchers, and government organisations and taxpayers. The most direct application is that by identifying the molecular factors responsible for eye pressure regulation, this work will lay the foundations to develop future vision-saving drugs that more successfully lower eye pressure in glaucoma. Relevance to BBSRC Priorities This project addresses the BBSRC strategic theme of ageing (primary) and health inequalities (secondary). It addresses ageing because maintaining visual health is necessary for quality of life, wellbeing and independence, and loss of visual health in glaucoma contributes to significant health care costs and loss of productivity. The project addresses health inequalities because individuals with Afro-Caribbean ancestry are at much higher risk of glaucoma and therefore suffer most from this disease.

UKRI Gateway to Research · FY 2024 · 2024-12

Prions, the infectious agents causing mad cow disease (BSE) and CJD in humans are unique in medical research. Unlike all other infectious agents (bacteria and viruses) the infectious particle does not contain genetic information but instead consists of clumps of misshapen, rogue forms of one of the body's own proteins called the prion protein (PrP). Once formed in the body, rogue PrP particles (prions) act as seeds to convert normal PrP into a likeness of themselves setting off a chain reaction leading to progressive accumulation of prions throughout the brain. This accumulation causes nerve cells to die leading to severe brain damage, dementia and ultimately death of the infected individual. Understanding what is special about the structure of prions and how they grow and kill nerve cells is increasingly important as it is thought that similar processes, with the spread of growing misshapen protein seeds, are also involved in more common forms of brain disease such as Alzheimer's and Parkinson's diseases. Currently, in the absence of effective treatments, institutional care for patients with dementia costs the UK NHS tens of billions of pounds each year. Despite decades of research, it is still not clear how prions grow or how they kill nerve cells. A major reason for these gaps in our knowledge is that prion infection has never been observed in sufficient detail in isolated living nerve cells. However recent advances in technology with light and electron microscopes will now allow us to directly see prions as they infect cells. The research aims of this proposal are 1) to find out which parts of the nerve cell prions bind to during initial phases of infection, 2) to identify which part of the nerve cell prions move to in order to cause their toxic effects, 3) to identify specific structural components of the nerve cell that are involved in each part of the process. To achieve these aims, I will use cells that have been genetically modified to produce PrP with a built-in fluorescent chemical tag which will produce light that can be seen using new, powerful, light microscopes. This will enable tracking of "glowing" prions in real time as infection proceeds in living cells. Once the precise steps of infection are worked out and we know exactly where to find prions within the cell, I will zoom-in on these locations using powerful electron microscopes. High-resolution images from electron microscopes will enable us to pinpoint which structural components of the cell are directly interacting with prions. Through this work I hope to provide the first detailed understanding of the how prions interact with cells to cause their lethal effects. Importantly, knowing the structural components of the cell that prions interact with will identify key targets for drugs that may be able to stop prion infection within the brain.

UKRI Gateway to Research · FY 2024 · 2024-12

My doctoral research examined if flexible housing designs and making changes to homes can positively support our wellbeing in it. Poor and unsuitable housing is an important socioeconomic determinant of our health. Being able to use the house in a way that supports our needs, and make changes to it if unsuitable, has various health, financial and psychosocial benefits. However, the extent to which residents can exert this sense of control over their homes can be low in the current UK homes as they are predominantly small, largely inaccessible and hard to retrofit and adapt, and yet expected to dominate the housing stock till 2050. This poses a serious challenge for our homes in meeting our increasing and changing demands, as we spend more time in it living, working and ageing, especially post the covid-19 pandemic. In my thesis, I adapted an interdisciplinary approach combining psychological frameworks with architectural design insights to examine if flexible features and making changes to home can support our wellbeing in it. I found that having a variety of rooms at home, and which are multifunctional and easily modifiable are important for wellbeing. But is dependent on whether residents can easily use these features and make different types of changes to them such as decorations, personalisation, furniture layout and structural changes and maintenance. These behaviours can be dependent on multiple factors and influences, but on an individual level, I found that having the ability (knowledge, skills, awareness of how to) and the drive/need to make these changes to homes play an important role in it. These findings have important implications for new housing design and resident agency and empowerment in UK housing which is explored in this Fellowship. There has been an increasing need for including residents in the decisions of housing design and building more flexible, resilient and futureproofed homes to support the increase in residential mobility and an ageing population. In this fellowship, I will explore the extent to which these findings can be applied to the design of new housing projects, challenges and restrictions (policy and planning) to adapting existing homes, and various pathways to empower residents in modifying their homes despite the restrictions. Working with partners and networks, I will disseminate key findings with residents and housing professionals through public events, knowledge exchange workshops, and online to ensure that the societal and industry impacts of my doctoral research are maximised.

UKRI Gateway to Research · FY 2024 · 2024-12

The largest earthquakes, of magnitudes up to 9.5, occur at subduction plate boundaries. At these boundaries, one plate dives below the other as the two plates converge, and the rubbing of the plates at their contact can lead to very large earthquakes, which together release over 80% of the global budget of earthquake energy. These so-called megathrust earthquakes and their associated hazards, including ocean-wide tsunamis, have caused 100s of thousands of deaths, as in Sumatra in 2004 and in Japan in 2011. A long-standing question is why only some parts of subduction boundaries have a record of very large earthquakes, while others do not appear capable of hosting major earthquakes. Earthquakes occur when elastic stresses, which accumulate at the locked megathrust as the balance of forces at the subduction boundary continuously drives convergence, are suddenly released when frictional fault strength is exceeded by the stress. It is agreed that variations in earthquake potential reflect large-scale differences in elastic loading of the megathrust. Variable loading has been attributed to subduction parameters such as plate convergence velocities, strength of the upper plate, or thickness of sediments on the interface between the plates. However, earthquake repeat times are generally much longer than the duration of our instrumental catalogues, and as a result, statistical correlations of subduction parameters with maximum earthquake size remain inconclusive. An interplay between plate and interface properties probably determines stress build up, and therefore a physical modelling approach is needed to understand how different factors contribute to megathrust earthquake potential. Local-scale subduction models of visco-elastic plates, tailored to a specific setting by prescribing geometry and convergence velocities, have helped to understand consequences of the earthquake cycle, including surface deformation and fault slip patterns that determine tsunami potential. Such models do not provide insight in how subduction parameters control large-scale and long-term differences in stress loading. Other, larger-scale, models let plate geometry and motions develop dynamically and have helped to understand the force balance and long-term stresses. However, these models usually approximate plates as viscous and neglect elastic stresses. Only now have modelling capabilities matured sufficiently to make running systematic sets of large-scale models of visco-elastic subducting plates feasible. In a 2D pilot study by our team, we derived relationships from models that let us estimate the elastic component of plate bending at actual subduction zones. We found that higher estimates of elastic bending correlated with higher observed relative numbers of large earthquakes (compared to smaller events). This illustrates the promise of such large-scale visco-elastic subduction modelling for understanding megathrust seismic potential. In the here-proposed project, we will use a state-of-the-art plate-modelling platform (Underworld) that will allow us to, for the first time, run a systematic set of 3D visco-elastic subduction models to characterise the variation of elastic energy storage in the subduction system. By determining the response of plate bending (downdip and along-strike) and upper-plate stress to variations in properties of the subducting plate, upper plate and coupling strength between them, we will test what combination of these properties can explain observed relations between subduction parameters and maximum earthquake size. The relations derived from our models will provide a novel, physics-based, method to estimate of the potential of subduction segments around the globe to host very large earthquakes, including at boundaries without a historic earthquake catalogue.

UKRI Gateway to Research · FY 2024 · 2024-11

RNA vaccines are the breakthrough vaccine technology from the COVID pandemic. Building on the back of their success, there are many ongoing clinical trials testing RNA vaccines against a range of pathogens. One extremely promising next-generation RNA vaccine platform is self-amplifying RNA (saRNA), because it can induce an immune response for far lower doses. However, RNA vaccination is still a young platform and the success of RNA vaccines has outstripped understanding of how they work. Better understanding is needed to optimise RNA vaccines for future use in the prevention of endemic pathogens such as influenza virus. In the current project, building on 10 years of nucleic acid vaccine experience, I aim to define the role of inflammation in how RNA vaccines induce an immune response in order to further improve the platform. RNA vaccines are different to conventional protein vaccines in a number of ways: they need to be translated in situ, their main ingredient (RNA) is in itself directly inflammatory and they need formulation to get them into cells. These differences affect the way in which immune responses are induced to RNA vaccines and the received view is that inflammation can dampen responses. However, my recently published data reveals an unanticipated, beneficial role for inflammation in the induction of an adaptive immune response to saRNA vaccines. I have shown that rather than inhibiting RNA vaccines, inflammation is critical in the induction of an adaptive immune response. My hypothesis is that the kinetics of inflammation and expression need to be coordinated to ensure presentation of the expressed antigen and activation of adaptive cells.

UKRI Gateway to Research · FY 2024 · 2024-11

328.77 million terabytes of data are created each day. To put this in perspective, if you were to store all this data on CDs, you would need over 1.5 trillion CDs each day. Modern machine learning (ML), specifically deep learning (DL), works to interpret this massive data, uncover fascinating patterns, and make predictions. DL has been transformative in numerous areas, from healthcare and retail to finance and manufacturing. This rapid advancement, often led by large technology corporations, is evidenced by breakthroughs in conversational AI, like ChatGPT / GPT4, and text-guided image synthesis. Today, one in seven UK businesses have adopted at least one form of ML technology. Despite this success, a challenge lurks in the realm of modern ML. The data we collect from various sources tends to be unstructured and complex. For instance, our Facebook comments are influenced not only by our past conversations, mood, and thoughts but also by the intricate interplay between these factors. Similarly, the interaction between proteins depends on their shape and other interactions. To extract meaningful insights from data and solve real-world problems, we need to consider these complex 'higher-order relationships', which play a key role in areas such as creating accurate 3D models for safer self-driving cars, predicting drug-target interactions for effective drug repurposing during pandemics, and accurately modeling brain neurochemistry for developing life-saving medicine against Alzheimer's disease. Unfortunately, most current machine learning systems focus mainly on modeling pairwise connections and overlook these higher-order relationships. This limits their capability to represent and analyze complex data, especially acquired in scientific areas by X-ray scanners, electron microscopy, or 3D laser scanners. My fellowship aims to harness the potential of big data by developing a new paradigm of deep learning, which encompasses higher order relations at its core and considers the data topology - an important branch of mathematics studying the "shape of data". My proposed research will achieve this in three key objectives: (1) I will develop Unifying Complexes (UCs), novel data representations that simplify working with higher-order relationships while preserving the hierarchical nature of data. At present, the industry standard relies on graphs, which only model pairwise relationships. (2) Existing deep learning models won't readily adapt to the novel UCs I will be developing in Objective 1. I will therefore create a variety of deep learning models tailored to work natively with these UCs. From discriminative to generative, these models will enable learning from rich and complex data. (3) Lastly, I will deploy the UCs and the models developed in Objectives 1 and 2 to address a variety of challenges in multiple applications, including 3D computer vision, drug screening, discovery and design, and in building new and practically relevant theories of deep learning. Thanks to the resources and the uninterrupted time provided by Future Leaders Fellowship (FLF), as a result of UNTOLD, I will be delivering an open-source comprehensive software suite designed to harness the full potential of big, complex data. Beyond scientific dissemination, the widespread adoption of the DL models I develop as part of UNTOLD, will have substantial socioeconomic impacts such as improved augmented and virtual reality, safer self-driving cars, personalized medicine, and better understanding of rare diseases. This will both position the UK as a leader in cutting-edge ML research and will gradually enhance its presence across all sectors using ML to convert complex data into actionable insights.