University of Liverpool

UKRI Gateway to Research · FY 2025 · 2025-12

The production of ammonia (NH3) is a vast industry, contributing nearly 2% of global CO2 emissions and, crucially, is dependent on huge, highly centralised, energy- and investment-intensive industrial plants owing to requirements for high temperatures and pressures. However, ammonia is a critical chemical for modern life, relied on greatly by the agricultural sector as a key fertiliser component for food security, and has potential as an important green, zero-carbon fuel. Consequently, sustainable ammonia production technologies are of significant global interest, especially towards achieving synthesis under ambient conditions. Furthermore, an electrochemical synthesis route would exploit the rapid growth in renewable electrical energy, using electrons (e.g., from solar, wind) to drive the synthesis for sustainable, zero-carbon ammonia production in small-to-moderate, localised facilities. This development would help to reduce the vulnerability of ammonia-dependant sectors to political and economic instabilities, improving the accessibility of NH3 production, helping to alleviate fertiliser inequity and improve access to clean, sustainable, and affordable fuel/energy. Among possible electrochemical technologies, the lithium-mediated nitrogen reduction reaction (Li-NRR) is one of only two electrocatalytic routes proven to synthesise ammonia directly from nitrogen gas. The Li-NRR exploits the reactive surface of electroplated Li-metal to break the strong nitrogen triple bond, subsequently reacting to ideally regenerate reactants and yield ammonia. The technique has advanced in recent years, with commercialisation prospects, but in-depth spectroscopic characterisation has not yet been reported. Intricate characterisation of the reaction interface is important for understanding fundamental mechanisms that impact the critical properties and bottlenecks of the process, providing insights needed to optimise conditions for performance. I propose to utilise advanced, surface-sensitive, Raman and infrared vibrational spectroscopies and optical/electron microscopies to characterise the reaction interface of electrochemical Li-NRR under operando electrochemical conditions. By revealing chemical and morphological changes, at micron to sub-micron resolutions, I aim to understand the roles of surface films, reactants, and intermediates, to inform the development of rigorous empirical models describing the reaction mechanisms and pathways. This will provide the necessary insights into critical factors and properties for rationalising the design of new materials and configurations to better control the process and innovate towards maximising efficiencies and production rates. In parallel, due to the ever-growing global demand for lithium, I aim to develop the reaction using alternate earth-abundant metals (e.g., M=K, Mg, Ca). I will apply electrochemical and spectroscopic methods to identify which metals provide suitable reactive interfaces, between electrolyte and candidate metal, to favour NRR for ammonia production. Furthermore, tuning the liquid electrolyte component will be essential to drive the desired reaction and overcome competing parasitic processes. Realisation of the non-Li M-NRR to produce ammonia would have significant impact to maximise the sustainability and scalability of the process. Latterly, I aim to translate these developments, through interactions with collaborative industry and academic networks, towards scale-up of optimised materials and cell/device configurations for Li-NRR and M-NRR for ammonia synthesis. Achieving breakthroughs in the understanding of Li-NRR, and M-NRR, will help drive the realisation of sustainable electrocatalytic ammonia production. Applying this within the development of small-scale, scalable synthesis plants creates avenues to reap the benefits of decentralising this process, critically improving accessibility to the means of ammonia production. Furthermore, the advancement of spectroscopic techniques in this area will benefit research in other emerging fields of electrosynthesis for more sustainable routes to high-value chemicals.

UKRI Gateway to Research · FY 2025 · 2025-11

  ALGeNeM: molecule design with active learning and generative modelling for experimental automation The current drug discovery process is inefficient, with each new drug costing approximately $2.3 billion, taking 10-15 years to develop, and having a 96% failure rate. This presents significant economic and moral challenges, especially given the substantial number of diseases that remain untreated. To address these issues, this project aims to develop Active Learning for GeNerative Models (ALGeNeM),  a modular, low-cost automation system that integrates generative modelling and active learning for experimental drug discovery. This project focuses on the chemical synthesis of small molecule fragments designed using X-ray crystallographic structural data, specifically Suzuki couplings, Buchwald Hartwig reactions and amide couplings for neuraminidases and anti-malarial kinase targets. Through ALGeNeM we will generate technology that will improve the efficiency of the drug discovery process. Recent advancements in automation and generative algorithms have demonstrated potential to accelerate and improve the efficiency of drug discovery. However, current generative models often produce outputs that are not synthesisable and modern active learning approaches are underutilised and underexplored for experimental work. ALGeNeM seeks to overcome these limitations by integrating state-of-the-art generative models conditioned on experimental capabilities and exploring the impact of different active learning strategies in an experimental context. This project will also develop a database schema integrated with a model building and active learning framework, enabling the assessment, and recording of active learning strategies' impact on real-world optimization problems. All code and data will be openly released under permissive licenses. I will build ALGeNeM using pre-existing open-source frameworks for automated labs and through this will provide an extensible platform for further research and integration.  The project is timely, leveraging recent innovations in low-cost automation and generative models to create a multi-purpose framework for efficient drug discovery. It leverages strategic investments at the University of Liverpool (Material Innovation Factory and Digital Innovation Facility), and at Diamond Light Source’s XChem facility. By leading to the generation of large and open and homogeneous experimental data points directly and through collaboration, ALGeNeM will aid researchers within the UK and globally. By reducing the cost and time of discovering new medicines, ALGeNeM will enhance the productivity of the UK pharmaceutical industry, reduce environmental impact from unnecessary experiments, and improve public health outcomes through more affordable medicines. This project will enable the growth of my independent research career by building my own groundwork for automated and AI driven design of small molecules. It will build a family of  generative models and a unique software framework from which I can apply for substantial follow-on grant funding from industry, charity, and research councils. It will enable me to support my own independent fellowship and provide the training opportunities to enhance my personal development as an independent academic.

UKRI Gateway to Research · FY 2025 · 2025-11

Geometric triangulation algorithms for input pointsets in 2 or more Euclidean dimensions are used extensively in applied computing disciplines that are closely related to geometric computing, such as computer graphics, computer vision and others. Many such real-time applications moreover require maintaining the triangulation efficiently for dynamic input pointsets that are updated by point insertions and deletions. Despite the abundance of practical geometric implementations for dynamic point triangulation, little attention has been given to date to geometric triangulation algorithms that receive polygons as geometric input data, instead of points, since the current state-of-the-art in dynamic polygon triangulation has addressed only the special case of maintaining a geodesic triangulation. The project investigates algorithms and data structures for supporting efficient dynamic polygon triangulation, i.e., maintaining the triangulation (that is not restricted to be of any specific kind, e.g., geodesic) of a given input polygon that is updated by insertions and deletions of its boundary edges. The classic algorithmic methodologies presented more than 3 decades ago to efficiently triangulate static (not dynamic) polygons have not been exploited to date to address the targeted dynamic polygon triangulation problem. It is therefore a challenge to combine the long-standing static algorithmic methodologies with dynamization methodologies presented for maintaining the geodesic triangulation of a dynamic polygon towards achieving the main aim of the project, i.e., to improve the state-of-the-art in algorithms and data structures that support efficiently a variety of geometric query operations on dynamic input polygons in 2 or more dimensions, such as nearest neighbours searching, ray shooting, shortest paths and visibility queries, among others. The project contributes novel beyond-state-of-the-art triangulation algorithms and supported geometric query operations on dynamic polygons. Beyond the direct impact in algorithmic and computational geometry research, the contributed results establish the algorithmic performance guarantees for designing and developing practical computer graphics, vision or other applications that are utilising dynamic polygon (not point) triangulations.

UKRI Gateway to Research · FY 2025 · 2025-11

The detection and manipulation of magnetic nanoparticles (MNPs) offers functional imaging in medical and non-medical environments. These techniques offer prospects for probing local environment, for example, in the detection of disease markers in biological samples, or the precise stability of nanoformulations. However, most commercial detectors/imagers are developed for superparamagnetic iron oxide nanoparticles, which display limited functional response to stimuli. To use MNP response for functional imaging, requires the simultaneous development of new MNPs and the detection technique. Our approach – We will develop a foundry for a suite of MNP designs, based on magnetic composite materials, and tailor detection methods around their unique magnetic fingerprint and enhanced magnetic response. Through significant increases in signal amplitude and frequency response, we will demonstrate imaging methods which significantly improve the resolution of conventional MPI approaches and offers new capability for the in-situ testing of formulation stability using MNPs.

UKRI Gateway to Research · FY 2025 · 2025-10

The four-day training workshop – Applying multi-omics in environmental research – will introduce early career researchers (ECRs) within the environmental sciences to apply multi-omics approaches in their research. The omics technologies covered in this workshop allows the comprehensive characterisation and quantification of the genome, proteome and metabolome in living organisms and by combining this large-scale data allows us to understand the complex role and relationships of these biological molecules. The omics approaches are widely valued across the biological sciences and the application of omics in the environmental sciences continues to rapidly grow, however there is a disparity between the desire and appropriate knowledge and skills of ECRs to both perform the omics and integrate data from multiple omics studies. This course will introduce ECRs to the application of the omics in their research by providing both fundamental background information and hands-on practical experience, developing valuable skills that can be applied in current projects and beyond in future careers. Developing a comprehensive understanding of how to apply the omics and integrate data will prepare ECRs to advance understanding and develop solutions to environmental problems including biodiversity loss, emerging pathogens and pollution, and marine resources.   A problem-based learning approach will be applied throughout the workshop to introduce attendees to each omics (genomics, proteomics and metabolomics) and computational tools and strategies to integrate different omics data together. Attendees will learn about the benefits of applying one or multiple omics to answer scientific questions within the NERC scientific remit. By the end of the course attendees will achieve the following learning objectives: (1) Develop an understanding of how the ‘omics can be applied in environmental research; (2) Design robust experimental strategies applying one or multiple omics approaches; (3) Evaluate the application of analytical methods to measure the omics; (4) Develop an understanding of the (multivariate) statistical methods commonly used in one and multi-omics integration analyses, open access tools and software options; (5) Gain confidence to undertake data-driven approaches and exploring multi-omics data to generate testable hypotheses.   The course will operate through a mixture of lectures, hands-on practical sessions, laboratory sessions and data analysis and interpretation workshops. Feedback from previous courses is uniformly extremely positive. From the first workshop we ran in March 2023 100% of the respondents would recommend the workshop to colleagues.

UKRI Gateway to Research · FY 2025 · 2025-09

Context The UK population’s health data held in our electronic medical records contains a huge wealth of information that researchers could use to understand the causes of, and better ways to prevent and treat, ill health. To date such research has been difficult because of the huge number of records and the fact that medical data are often held separately across GP surgeries and hospitals. To support health data research, the NHS is creating a network of 11 regional data hubs across England to handle patient data safely and securely. Having just one national data hub isn't practical due to the large amount and variety of data, as well as the different ways it's used in local communities. The Challenge Regional data hubs are a huge step forward, but for some analyses we need to use records from multiple regions. This means that we need new methods to analyse this data across the regional hubs without physically moving or sharing patient data between regions. Aims and Objectives In the Federated North project we aim to change how NHS patient data is used in research, by introducing our free software, DataSHIELD, to the new data hubs in the North of England. DataSHIELD allows data to be analysed without moving, sharing or viewing it, respecting patient privacy, but it needs modifying and extending to work across the new NHS data networks. Federated North will improve health data analysis by: Modifying DataSHIELD to work with the computer systems in the new NHS network, as well as those being developed by our research partners in Germany and the USA. This will contribute to secure and collaborative health research both nationally and internationally. Advancing the software built into DataSHIELD by: providing users with a dashboard describing whether analysis outputs are “safe” and containing no identifiable information about a patient; enabling DataSHIELD to deliver results in real time and to work well with other data analysis tools.  Enhancing DataSHIELD's privacy methods to align with new guidelines. Embedding this into DataSHIELD analysis will log how the software achieves safe analysis outputs that respect individual patient privacy, increasing transparency and public trust in the approach.   Engaging a diverse stakeholder group, including public members, to design and develop the project. This will ensure that DataSHIELD technology is socially appropriate and supports the responsible use of patient data in research. Balancing these aspects is crucial for integrating scientific and technological advances into society.   Applications and Benefits This programme of work will help researchers to perform real-time, privacy-preserving analyses within the SDE network, improving the reproducibility and transparency of their work. It will give researchers the power to analyse health data to address questions such as who is at most risk of ill health, and which treatments are most effective for particular sections of the population. By enabling researchers to analyse data from multiple hubs at the same time, they will get better, more representative, results. Data hubs will be able to demonstrate that their DataSHIELD analyses aligns with guidance governing the safe access to sensitive health data, helping to manage risks and transparency relating to data use. The results from Federated North will be useful for health and population research both nationally and internationally, wherever data from multiple locations needs to be analysed without compromising patient privacy.

UKRI Gateway to Research · FY 2025 · 2025-09

Fundamental physics addresses the big questions: what is our Universe made of; how did it evolve; what forces govern it and how do they shape the phenomena we observe? We push the frontiers of technology to design and build particle physics experiments that investigate the smallest constituents of the Universe. We analyse data from our experiments, testing theoretical predictions in new regimes, to further our knowledge and understanding. Our knowledge of how fundamental particles behave is encapsulated in a theory called the Standard Model. It has enormous predictive power and provides a simple framework to understand the nature of the Universe. However, we also know the theory is incomplete and a deeper understanding must underpin it. We perform experiments at the highest energies to test predictions, search for new phenomena and determine the limits of the validity of our theory. Dedicated high-precision experiments, such as those we develop in our precision muon programme, let us probe predictions to incredible levels of accuracy. The faintest trace of any disagreement between theory and experimental data could provide the first hint of new laws of physics operating, which would be a big step forward in understanding the nature of the Universe. We investigate differences in matter and antimatter particle behaviour, to see if these can explain how today’s matter dominated Universe evolved from one with equal amounts of matter and antimatter produced in the Big Bang.  Neutrinos, the most elusive of particles, may hold the key to understanding why this happened. They have no charge, barely interact with matter, have a very small mass and to detect them we have to build enormous but very sensitive detectors. An important part of our research is to make detailed measurements of neutrinos, to understand whether they are responsible for our matter-dominated universe. We study dark matter and what it might ultimately be made of. Astronomers have hypothesised the existence of this invisible form of matter by observing its gravitational attraction on nearby stars and galaxies. Its composition and nature remain unknown. We develop powerful experiments using a wide range of approaches and technologies, to perform the broadest search for dark matter constituents. In parallel to our current experiments, we develop the technologies needed to enable the next generation of experiments. Our research and development programme in silicon sensors, liquid argon technology, advanced computing and quantum technologies lays the groundwork for future particle physics endeavours, and will allow us to discover even more about the Universe.

UKRI Gateway to Research · FY 2025 · 2025-09

This project will deliver high quality, diverse, and openly accessible datasets that address bottlenecks in the application of artificial intelligence (AI) to chemistry. These datasets will support the development, benchmarking, and validation of AI models and methodologies, particularly in areas where robust, well structured, and representative data is currently lacking. This aligns directly with a core mission of the AIchemy Hub, to unlock the potential of AI in the chemical sciences by making foundational data resources available to the broader community. Our approach focuses on three interrelated and high impact areas. First, we will produce high dimensional experimental datasets from closed-loop robotic optimisation platforms under three distinct regimes: i) ‘vanilla’ Bayesian optimisation, (ii) Human-in-the-Loop approaches (HypBO), and (c) LLM reasoning (BORA). These datasets will be complemented by rich textual logs generated by the LLMs, enabling future studies of interpretability, decision rationale, and human-AI interaction. Secondly, we will create a large, uniformly sampled dataset for chemical reaction optimisation. This will capture multi-objective and multi-fidelity data, essential for developing and evaluating advanced optimisation algorithms. By utilising the ROAR and ATLAS facilities at Imperial College London we will ensure datasets are both comprehensive and reproducible, enabling its use as a robust benchmark for the community. Thirdly, we will assemble a multimodal dataset designed to bridge the gap between theoretical simulations and experimental data in molecular identification. This dataset will include NMR, mass spectrometry, and FTIR data for ~2,000 molecules, supplemented with a large synthetic dataset of ~700,000 molecular structures. Such data are urgently needed for training generative and predictive models capable of interpreting experimental spectra and deriving molecular structures in complex environments. The outputs of this project will form the foundation for a range of activities including datathons, hackathons and cross disciplinary tool development. By filling known data gaps with high value, openly available resources, this project will catalyse the adoption of AI tools in chemistry and foster new collaborations and promote interdisciplinarity across the physical sciences.

UKRI Gateway to Research · FY 2025 · 2025-09

The rapidly expanding field of artificial intelligence (AI) and machine learning exposes the limitations of conventional Von Neumann architecture. Neuromorphic computing, inspired by the highly energy efficient functionning of the brain, has emerged as a solution for efficient unsupervised learning, particularly relevant qith the advent of edge computing and visual computing applications. Solar cell-inspired materials, offering persistent photoconductivity allowing to simulate synaptic plasticity, have the potential to revolutionise neuromorphic visual computing. The SOLIS project forms an international consortium of experts from photovoltaics and materials science to explore inorganic thin film materials' potential as artificial visual synapses. These materials, tuneable and stable, promise reliable optically controlled MEMRISTORS suitable for diverse light intensities and wavelenghts. The collaboration, emphasising staff exchanges and transparent sharing of data, methods and persons, aims to reinforce our understanding, innovate with materials like 2D MXenes, and establish a shared framework for optoelectronic characterisation of visual synapses. The project aligns with Europe's objective to catch up in the field of AI and possibly become a leader in hardware-level machine learning, offering opportunities for scientists from Third Countries and EU countries alike. SOLIS will be an important milestone for EU research and the PV field as a whole, unlocking new applications for inorganic thin film materials and offering a paradigm shift toward visual computing and AI free from the constraints of current materials and architectures.

UKRI Gateway to Research · FY 2025 · 2025-09

Wireless communications have revolutionised everyday life and industry by enabling ubiquitous connectivity across billions of devices, estimated at 21.5 billion in 2025, and powering transformative applications such as smart homes, connected healthcare, and autonomous driving. Traditional wireless infrastructures primarily rely on cryptographic methods for security; however, these approaches often face significant limitations when applied to low-cost or resource-constrained devices, where affordability and accessibility are critical concerns. Radio frequency fingerprinting identification (RFFI) is an emerging physical-layer security technique that offers a non-cryptographic approach to securing current and future telecommunication infrastructure. Due to inevitable variations in RF component characteristics introduced during semiconductor manufacturing, each RF circuit exhibits unique hardware impairments. These inherent imperfections can be leveraged for secure device identification. While existing RFFI research has largely focused on improving identification performance, it often overlooks the systematic modelling and deeper understanding of these intrinsic hardware impairments, leading to predominantly empirical algorithm design. This project brings together leading experts from the University of Liverpool (UK) and Ruhr-Universität Bochum (Germany) to conduct a comprehensive investigation into RFFI, with the aim of gaining a deep understanding of the origins of hardware impairments in RF circuits. A synergistic methodology will be employed, combining impairments modelling, simulation, and experimental validation. The entire RF chain will be modelled, with particular emphasis on power amplifiers, as their pronounced nonlinearity is widely regarded as a primary source of RF impairments. The impairments will be initially modelled in MATLAB, followed by the development of a testbed using software-defined radio (SDR) platforms and power amplifier evaluation modules. Extensive simulations and experimental measurements will be conducted, with results used to iteratively refine the models. This holistic approach marks a significant departure from the empirical methods prevalent in existing research, enabling optimal modelling and the principled design of RFFI algorithms.

UKRI Gateway to Research · FY 2025 · 2025-09

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

UKRI Gateway to Research · FY 2025 · 2025-09

This fellowship builds on my PhD research which explored the everyday lives of young people with an ileostomy or colostomy. This fellowship ensures my PhD has impact within academia and the communities I researched with. The four aims of the fellowship, and objectives to meet these aims, are as follows: Aim 1: write publications for an academic audience Objective 1: I will write three journal articles based on my PhD. Paper 1 explores how young people with an ostomy negotiate “young people culture” in relation to alcohol-oriented activities. The phrase came from interviews with participants who described negotiating expectations about what they ‘should’ be doing as a young people and how this compares to their desires and capacities. Paper 2 focuses on how young disabled people’s relationships with others are made through digital spaces, for example via social media. This is important in an increasingly digital world where people seek advice, friendship, and connection through their digital devices. Paper 3 focuses on the importance of doing collaborative research with community organisations as an early-career researcher. I will draw on my experience of working with GetYourBellyOut during my PhD and will make recommendations about the design of future Postgraduate Research. This paper will inform a proposed workshop with ECRs about care-full and collaborative research. Aim 2: Impact workshops Objective 1: This funding will extend the impact of the ‘What I Wish I/You Knew’ toolkits I made as part of my PhD through a series of creative workshops. I will co-facilitate in-person, online, and asynchronous zine-making workshops with 'patient-led' disability organisations and activists focused on ‘What We Wish You Knew’ about life with chronic illness. I will digitalise these zines. Objective 2: I will organise two creative workshops (in-person and online) with bowel-illness organisations and healthcare professionals which will allow me to develop a guidance document and easy-read report about improving support for young people with an ostomy. Objective 3: I will engage with Liverpool-based NHS organisations through a seminar series about the impact of language choice when supporting young people with an ostomy. Aim 3: Write and submit a funding proposal Objective 1: I will develop a funding proposal for a 3-year postdoctoral project. The proposed project will focus on youth, chronic illness, and austerity as an understudied but important line of research. I will work with disability organisations to amplify the experiences of young people across the North West living who experience stigma due to the messiness of their chronic illnesses. I will draw on relationships made during the 1-year fellowship and develop methodologies from my PhD and my roles as Research Associate. Aim 4: Develop my career in academia Objective 1: Moving to University of Liverpool will provide new opportunities to engage with teaching, training, and to work with new colleagues and organisations. Objective 2: I will attend and share my work at three academic conferences. At one of the conferences, I aim to chair a panel session on disability and chronic illness to establish a network of people with shared research interests. Objective 3: I will co-edit a Journal Special Issue on children and young people's geographies of poo.

UKRI Gateway to Research · FY 2025 · 2025-09

The aim of my proposed fellowship, as well as my research generally, is to show how social scientists can contribute to the development of trustworthy and hence successful AI technologies, that is systems that actually work for their users. My research will focus on evaluating and extending current AI benchmarking practices and frameworks for assessing the validity, reliability and value of new algorithms in specific contexts of use. Building on insights from my PhD fieldwork, this fellowship will extend my collaboration with the Big Hypotheses project to develop a new framework for AI users—benchmarks for use—that will help them concretely establish when and under what circumstances they can dependably trust these technologies for specific tasks, whether in research, service delivery or other domains. Through a new collaboration with Prof. Jonas Ivarsson in Gothenburg, alongside ongoing work with my mentors Prof. Michael Mair and Prof. Simon Maskell, I will publish in leading journals and present at conferences that bring together AI developers and academics. I will also develop AI training for UK social scientists, using the Fellowship to build connections across user groups and develop impact from my research. Through detailed ethnographic observations in commercial and academic settings, my PhD research provides concrete empirical evidence about the necessity of human interventions and domain expertise in the successful deployment of AI technologies. My findings demonstrate precisely how benchmarks, i.e., locally deployed standards used as points of reference to assess algorithmic performance, function as practical tools for negotiation, clarification, and accountability in day-to-day AI development, directly informing more transparent and realistic assessments of AI systems. Working alongside AI developers as a colleague and actively contributing to their everyday operations during my PhD, I gained a deeper understanding at this practice level. More specifically, my study showed the following: The seemingly "intelligent" appearances of AI systems rely on continuous human interventions, as data scientists develop domain expertise to adapt systems to local circumstances and user needs. Nuanced domain knowledge is central to developing trustworthy AI systems at the technical level. This is because domain knowledge, i.e., knowledge of the practical domain in which the system is to play a role, helps data scientists anticipate pitfalls, address anomalies, and ensure a model’s performance and outputs are reliable before informing decisions. In this process, benchmarks and their metrics serve as accuracy measures and negotiation devices, enabling AI practitioners and stakeholders to collaboratively interpret, question, and compare results so model inferences remain interpretable, accountable, and actionable within local organisational contexts defining the domain. They also enable model refinement for better performance in projected use contexts. Good benchmarking grounds trustworthiness at the system application level. In this way, my research highlights the need for a realistic, empirically informed approach to AI that foregrounds social and technical practices rather than algorithms alone. Algorithms are end products, and we misunderstand them without examining the practices that generate them, the goals they embody, and their limits—well understood within data science but less so outside it. Benchmarking is central to this. It involves standardised tests, datasets, and performance metrics to assess AI models. These shared frameworks help practitioners measure progress, track development, and identify weaknesses. I aim to show how internal benchmarking practices can be expanded to build trust in AI systems among broader user groups and the public.

UKRI Gateway to Research · FY 2025 · 2025-09

My research objectives focus on studying the collisions of lead atomic nuclei at the highest-ever reachable energies, with the ALICE experiment at the CERN Large Hadron Collider (LHC) in Geneva, Switzerland. These collisions generate temperatures over 100,000 times hotter than at the centre of the sun - the hottest temperatures achievable in a lab - and cause the protons and neutrons which make up atomic nuclei to 'melt' and form a plasma of deconfined quarks and gluons (the sub-atomic elementary carriers of the strong force), known as the 'quark-gluon plasma' (QGP). The QGP is the state of matter that constituted the early universe, just fractions of a second after the Big Bang. My proposed research project aims to uncover, for the first time, the ‘deep’ structure of the QGP and how its collective behaviour determines the properties of its constituents. This will give insight into how the universe behaved at its inception under these extreme temperatures, and how hadronic matter (which forms almost all visible matter in the universe) is formed. This will be achieved via the development of novel 'scattering experiments' within the QGP, to be performed with the ALICE experiment at the LHC, alongside Bayesian parameter estimation analyses to connect these measurements to theory with the JETSCAPE collaboration.

UKRI Gateway to Research · FY 2025 · 2025-09

Detection of thermal neutrons is necessary in a wide range of contexts from nuclear energy and safeguarding to monitoring of accelerators and radiotherapy treatments. The most popular method of measuring thermal neutron fluxes has historically been the He-3 gas proportional counter. However, due to the global shortage of He-3 there is a need for lower cost, large area thermal neutron detectors which still maintain high efficiency (https://doi.org/10.2172/956899). In addition to this, there is also a need to have detectors with intrinsic spatial resolution that could be used for neutron imaging. Application of converter layers to silicon (e.g. 10B, 6Li) and other solid state detectors in order to make them sensitive to thermal neutrons has long been investigated as an alternative to He-3 detectors. Use of a stable, enriched Lithium fluoride layer (6LiF) uniformly deposited to a thickness of a few microns on silicon has been carried out by Micron Semiconductor Ltd and the characterisation of these devices carried out at Liverpool using an AmBe neutron source located on campus. The benefit of this (Si-6LiF) configuration, is not only the large cross section for thermal neutron detection on 6Li (940b) but the production of two charged fragments with >2 MeV of kinetic energy in the resulting fission reaction after neutron capture (n + 6Li ? a(2.05MeV ) + 3H(2.73MeV)). Since these two charged fragments are produced with sufficient kinetic energy to exit a thin converter layer and reach the neighbouring silicon layer(s), this gives a greater opportunity to register the presence of a neutron. This is an advantage compared with converter layers that only produce a single charged particle with sufficient range (kinetic energy) for detection e.g 10B. Moreover, detecting a coincidence between the alpha and the triton reaction products from 6Li allows suppression of backgrounds, in particular the high fluxes of gamma rays often present with neutron production that cause false events to be registered as neutrons. The aims of the CASE award proposal outlined here will be to build on previous work (doi: 10.1117/12.2236752) carried out with Micron sensors and converters to deliver the following results: Optimisation of the converter deposition process on silicon to obtain a thermal neutron converter layer that maximises sensitivity to thermal neutrons Application of the above converter layer to segmented detectors such as pixels and strip detectors. Characterisation of these detectors in the lab and also in neutron sources and neutron beams Investigation of how temporal and spatial resolution can improve detection efficiency for single and for multiple layers to better compete with He-3 Investigation, (via simulation and/or measurement) into the application of this technology to neutron monitoring in clinical radiotherapy beams

UKRI Gateway to Research · FY 2025 · 2025-09

This project will deliver a step-change in understanding the origin of surface deformation at complex and dangerous caldera volcanoes. Highly explosive caldera-forming eruptions are infrequent but responsible for some of the most catastrophic geological events, with potentially devastating local, regional and global consequences. By unifying the latest insights from our state-of-the-art analogue and numerical models with petrological datasets and geodetic inversion algorithms we will improve the ability to predict eruptions at caldera volcanoes. We need to advance the capabilities of the inverse modelling approach by linking analogue models and geodetic volcano monitoring methods and integrating complex crust and magma rheologies. Geodesy is a major tool used by volcano observatories to assess volcanic unrest, and known model discrepancies must be improved to inform eruption assessments. It is the recognition of magma ascent at a caldera which can trigger evacuation orders, but to improve eruption assessments we need to understand how magma ascent in a crystal mush evolves at caldera systems and how these affect surface displacements in the presence of a shallow crustal hydrothermal system. By combining our physical and chemical models with the new insights on the different timescales of magma ascent and stalling across the lifetime of an active caldera, we will enhance and improve interpretations of caldera unrest signals. Our holistic physical, chemical and hydrothermal models will be used to inform volcano monitoring network deployment campaigns in Chile (SERNAGEOMIN), Argentina (CONICET-SEGEMAR) and across the Americas (USGS), with education, communication, EDI and ethical practices embedded across our research programme. We will apply simultaneous surface and sub-surface imaging techniques on caldera analogue experiments and use the Geodetic Bayesian Inversion Software (GBIS) to place new constraints on magma intrusion and host-rock deformation source parameters that can be applied to natural datasets. We will collect compositional data from the crystal cargo of stratigraphically-constrained eruptions from the Diamante-Maipo system and apply thermobarometry and a range of geochronometric approaches to understand the locations and timescales of magma accumulation and ascent. We will create a numerical simulation of magma intrusion within a magma mush to resolve its impact on surface distortions in the presence of a shallow crustal hydrothermal system at a model caldera volcano. We will apply our holistic physical-chemical-thermal model to Diamante-Maipo and test its capabilities for scenario mapping for other caldera volcanoes around the world. Building on our track record in pioneering magma ascent modelling and leading multidisciplinary projects with academia-observatory collaborations, our models will have rapid impact by informing on the deployment of volcano monitoring equipment.

UKRI Gateway to Research · FY 2025 · 2025-08

We often fail to act in line with our beliefs. In a recent Associated Press poll, two-thirds of respondents say that an animal’s right to live without suffering is just as important as a person’s, yet only 4% of Britons are vegetarian or vegan despite believing that factory farming causes suffering. Most people are reluctant to drink from a cup labelled ‘poison’ even though they believe it contains lemonade (since they wrote the label and filled the cup themselves!) Many people fear flying and refuse to travel by air even though they believe it is safe. We need to explain why our beliefs are often unable to influence our behaviour. The idea that belief informs action has the status of philosophical orthodoxy as well as being central to common sense. When faced with these examples, philosophers have therefore been forced to add layers of complexity to their explanations of the role belief plays in action, appealing to complicated special conditions or even denying that subjects genuinely possess these beliefs at all. This project develops a new, simpler, and more successful approach based on the following insights: 1. What we believe and what seems true to us can come apart. (For example, whereas the belief that grass is green seems true, the belief that solid objects are mostly empty space does not.) 2. It is what seems true to us that drives our actions and decisions. This new approach can explain the frequent disconnect between our beliefs and our actions and decisions: if our beliefs don’t seem true to us (i) they won’t influence our everyday behaviour, and (ii) they will have far less influence over our effortful, theoretical decision-making than beliefs which do seem true. Moreover, if something seems true to us (iii) it will influence our behaviour and decision-making even if we don’t believe it. This leaves us particularly susceptible to online disinformation which is manufactured to seem true to us. Main Objectives: Develop a new theory of decision-making and action, revealing the essential role played by what seems true. Utilise this theory to explain the many cases where behaviour conflicts with beliefs. Reveal the extent and nature of the challenges posed by deliberately misleading online content, given that it has been designed to seem true to us. Confront these challenges through an exciting public engagement initiative: an interactive exhibition designed to promote better decision-making. Make an impact outside the academy by trialling a new approach to imposter syndrome, informed by our theory. Disseminate our findings widely to the relevant academic disciplines. Understanding that our actions depend not on what we believe but on what seems true promises to revolutionise strategies for implementing behavioural changes in ourselves and others, helping us to address pressing social challenges more effectively. It’s not enough to simply possess the belief that (say) factory farming causes suffering, or that climate change is real. If we want these beliefs to inform our actions, they need to seem true.

UKRI Gateway to Research · FY 2025 · 2025-08

Ultra-processed foods (UPFs) are industrially formulated food products that contain artificial ingredients and/or additives to preserve shelf life and increase palatability. UPFs have been proposed recently to be a potential causal contributor to ill health and in particular, higher UPF consumption has been suggested to be a major contributor to weight gain and population level obesity. Although there is scientific uncertainty over the causal role UPF consumption has on weight gain and other health outcomes, UPFs have now gained significant public and policy interest. UPFs are already included in some country’s national dietary guidelines and UK government are now considering UPFs in the context of public health policy.   In relation to weight gain and obesity, concern over UPFs has been largely informed by cross-sectional observational studies which show an association between higher UPF consumption and heavier body weight. However, it is widely agreed that there is significant scientific uncertainty about why studies show an association between higher UPF consumption and heavier body weight. In particular, it is unclear if level of food processing itself causes people to eat more calories and gain more weight and/or or if it is the less healthy  nutritional profile of UPFs (e.g., higher fat, sugar, salt & lower protein and fibre) that explains their association with heavier body weight. Resolving uncertainty surrounding if and how UPFs contribute to obesity will clearly be essential to informing public health policy. In addition, it will also be key to understand how people’s food choices may be affected by dietary guidelines about the health risks of UPFs. Although there is now considerable public and policy interest in UPFs, no research has examined how public facing information about UPFs may affect consumers and whether this is likely to improve or inadvertently worsen the nutritional quality of dietary choices.   In this project we will resolve controversy and uncertainty over the potential role that UPFs and food processing have in explaining weight gain & understand how consumers are affected by public facing information on UPFs. In Work Package 1, we will conduct new epidemiological research using  longitudinal methods to examine the extent to which UPF consumption prospectively predicts weight gain and the development of obesity across the life course. Importantly, in Work Package 1 we will examine whether the association between UPF consumption and weight gain is explained by the nutritional profile of UPFs or if food processing itself may be responsible. In Work Package 2 we will provide causal evidence on whether UPFs are a contributing factor to higher daily calorie intake and therefore a likely cause of weight gain and obesity. We will conduct a new experiment to identify whether eating UPFs increase a person’s daily calorie intake and whether this is explained by the poorer nutritional profile of UPFs or level of food processing itself. Finally, we will understand the potential impact that public facing information about UPFs has on consumer concern, dietary motives and food choice. We will achieve this in Work Package 3 by conducting a study examining the influence that public facing UPF information has on consumers.   During the project we will be guided by stakeholders from government and NGOs. We will actively disseminate findings to the public and policy makers to ensure that project findings can inform ongoing debate about UPFs and public health policy.  

UKRI Gateway to Research · FY 2025 · 2025-08

Background Preventing illness rather than treating it is a key government priority. Potentially avoidable health conditions and complications cost the NHS billions of pounds each year. Our environment, such as the quality of air we breathe, the temperature of our surroundings and how far we live from green spaces such as parks and the countryside all affect our health. In particular, differences in our housing and the buildings where we live can lead to health inequalities – with those living in poor quality housing having poorer health. The challenge Improving the environment that we live in has enormous potential to improve health and reduce spending on healthcare, but it requires investment in change. For this investment to be worthwhile we need to be able to tell which environmental changes actually improve health and wellbeing. Analysing information held in medical records is enormously powerful for understanding what factors affect the health of our population over time. Currently evidence of environmental impacts on health are limited because health records are kept by doctors and hospitals, but other but other useful information – like changes in our built environments – are stored by councils or other organisations. These data are held separately to maintain privacy but this means we have only rough estimates of the environmental circumstances of individuals. Safely linking environmental data to patient addresses would give us the ability to track changes over time, helping us build stronger evidence on how the environment affects health. By following people’s health and their surroundings over months or years we can be more confident that environments—like green spaces—are influencing health, rather than differences in people leading to them living in different places. This helps reduce the risk of us simply identifying that wealthier, healthier people are choosing to live in greener areas, rather than understanding how our environment can protect against ill health. Our approach We will establish a system that safely links regional health records and environmental data across England, to gather evidence on how urban environments may be changed to prevent ill health. Specifically, we will: 1) Engage with communities – particularly young people who are often not listened to, healthcare providers and governments to understand their viewpoints on linking health and environmental data. We will work together to produce easy to digest information on the challenges and benefits of linking environmental and health data and best practice guidance on the process for linking data. 2) Create a pipeline for linking the regional health data records with environmental data from satellites and mapping of green spaces. 3) Develop new sophisticated technology to ensure that our newly linked data cannot be used to identify individual people. This will allow safe, secure analyses to help us understand how our environment impacts our health. Impacts Our work will empower government and community groups to limit the addition of unhealthy facilities and plan new facilities that promote healthy behaviours. These data will be used to evaluate large regeneration programmes, monitoring the health of existing and incoming populations. This is particularly important for those living in poorer conditions, who often have less time to access supportive environments. The development of healthy places will reduce the burden of ill health for those who need it the most and benefit the NHS through reduced treatment burdens.

UKRI Gateway to Research · FY 2025 · 2025-08

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

UKRI Gateway to Research · FY 2025 · 2025-07

This collaborative application requests support for provision of a sensitive, high resolution, versatile, state-of-the-art mass spectrometry platform to drive cutting edge proteomics research. The applicants are all major contributors to the UK science infrastructure through research, training and services rendered, and as scientists whose research fundamentally relies on cutting edge proteomics, recognise the pressing need to increase capacity and drive state-of-the-art proteomics capabilities in the North West. The equipment requested, specifically a Thermo Scientific Orbitrap Ascend Tribrid mass spectrometer (Fig. 1) with associated ultra-high performance liquid chromatography (UHPLC) separation system, will be based in the internationally-renowned Centre for Proteome Research (CPR) at the University of Liverpool (UoL). The unique features and capabilities of the Ascend will enhance fundamental and applied UKRI collaborative research and fee-for service offerings, both at UoL, and across the UK. This instrument will be used for state-of-the-art structural investigation and quantification of protein post-translational modifications (PTMs), notably the addition of functional groups (small chemical moieties, proteins, sugars, or lipids) that induce a rapid change in protein structure and thus alter biological function. Such PTMs play diverse roles in cell signalling, both as individual entities and in combination, often on the same protein, and regulate all aspects of biology across the BBSRC remit. Precisely defining and quantifying changes at the single site level and as PTM ‘fingerprints’ arising due to system perturbation is thus essential to revolutionise biological understanding. As well as advancing capabilities by nature of its design, this platform will also enhance much needed capacity in Liverpool and regionally, complementing aging instrumentation within the CPR where it will be housed and maintained. Crucially, access to this technology is extremely limited in the UK and will therefore open new research possibilities for academics in Liverpool and across the North, as well as industry and clinical partners.   Aims Methodological developments for the site-specific and combinatorial analysis of labile, difficult and uncommon PTMs Capability enhancement for relevant UoL BBSRC-funded research projects Capacity building across the UK in PTM analysis   Objectives Addressing our aims will: Build on our pioneering work in the development of novel proteomics methodologies for the analysis of challenging PTMs, advancing sensitive analytical strategies for the characterisation and quantification of e.g. i) non-canonical phosphorylation; ii) Tyr-sulfation and nitration; iii) Cys-based oxidation; iv) hydroxylation; v) nucleotidylation; vi) PTMs at the single cell level (single-cell proteomics, SCP). As well as developing peptide-level approaches, we will also (where relevant) expand our suite of tools for protein (proteoform) investigation to better understand the role of combinatorial modifications (PTM ‘fingerprints’). Open up capabilities in PTM analysis for BBSRC (and other UKRI)-funded projects in Liverpool and across the NorthWest relevant to human, animal and environmental health, exemplars of which are included below. Work with, and train, end-users, in line with our role as part of the Liverpool Shared Research Facilities (LIV-SRF, see below), and key role delivering the NERC environmental multi ‘omics training facility, to build UK-wide capacity in the characterisation and quantification of protein PTMs.

UKRI Gateway to Research · FY 2025 · 2025-07

The spread of fungal and oomycete species presents significant challenges for current and future efforts in global food security, and threatens important non-food plant species in the UK ecosystem. Monoculture farming, changing climate and international trade are driving phytopathogen expansion into new geographical regions and new hosts. Discovery and translational research is increasingly reliant on genomic and other large-scale technologies, for example to identify genes implicated in invasion, pathogenesis, and plant responses/immunity. Genomes, population diversity data, and genomic-scale gene/protein expression datasets are now available in archival repositories for many phytopathogens, but, these static repositories do not support robust updating, querying and data-mining, making them difficult to exploit even by groups with extensive bioinformatics expertise. Our teams are responsible for the development and dissemination of two major international genome database resources: Ensembl and VEuPathDB (the Eukaryotic Pathogen, Vector and Host Knowledgebase) – both recognized as Global Core Biodata and Core Elixir Data resources, reflecting their international importance. Ensembl subsites relevant to plant health include Ensembl Fungi, Ensembl Protists (including oomycetes) and Ensembl Plants. Key features include a powerful, customisable genome browser supporting community tracks, phylogenetic gene trees displaying ortholog and paralog relationships across taxa, and visualisation of synteny, driven by whole genome alignments. Ensembl also provides production-grade genome annotation and transcriptome analysis, used in-house and integrated into the VEuPathDB pipeline to maximise efficiency and consistency, and minimise duplication of effort. VEuPathDB includes subsites focussed on infectious disease pathogens, including human and animal diseases prioritised by past/current funders, but also including plant pathogens with extensive functional data. The FungiDB component supports hundreds of species, including scores of important plant pathogens (Blumeria, Botrytis, Colletotrichum, Fusarium, Magnaporthe, Melampsora, Phytophthora, Puccinia, Ustilago, Zymoseptoria, etc) as well as taxa important in the food production and biotech sectors. In addition to gene/genome browsers, this knowledgebase provides powerful web-based tools and APIs for data integration and exploration, enabling users to ask their own questions in silico, seeking (for example) plant pathogen effectors based on protein motifs, gene/protein expression timing, signatures of selection and epigenetic marks, gene set enrichment, and leveraging orthology for cross-species functional inference based on phenotypic information. The HostDB component supports simultaneous interrogation of combined host-pathogen datasets. These proven, cost-effective resources have coordinated efforts for years, but have not previously benefited from dedicated funding to focus on phytopathogens.  We propose to: Load or update 80+ genomes from archival repositories, using Ensembl annotation pipelines to build new or improve gene annotations, and the Apollo interface to capture expert knowledge from the community. Generate orthology mappings across taxa, protein domain annotations for all genomes, and further develop/deploy fungal-specific annotation tools focused on virulence factors, biosynthetic gene clusters and, protein structure-driven discovery of new or updated functions. Process/load 200+ functional genomic datasets and 30-40 plant response to infection datasets, enabling user-driven queries to identify genes of interest (e.g. for specific plant diseases), including support for analysis and visualisation of host-pathogen interactions (e.g. co-expression networks). Building on a history of successful collaboration and community engagement, outreach efforts will obtain community input on dataset prioritisation, and promote/explain effective use of informatics tools to advance research. FAIR data access is a priority; all datasets, tools and code will be fully open source and freely available through multiple routes (web browser, API, downloads) in support of plant health research.

UKRI Gateway to Research · FY 2025 · 2025-07

The UK is suffering from a shortage of specialist teachers, particularly within the field of physics which in 2023/24 recruited only 17.27% of the target number of trainee teachers [1,2]. The disparity in the proportion of specialist to non-specialist teachers is particularly prevalent in schools attended by pupils who receive free school meals, a common indicator of economic deprivation [2]. This specialisation deficit means that many teachers don't have the subject knowledge necessary to embed cutting-edge STFC research within classrooms, which we intend to address. This project will address these issues by creating a suite of research-connected practical teaching resources, aimed at Years 7-9 (Key Stage 3). These will come with the training materials necessary for the activities to be run by non-specialist teachers such as lesson plans, videos, interviews with scientists and FAQs. The team will create and maintain an associated equipment library so that schools may have access to the high-quality tools they require. This project will form part of a wider University of Liverpool Physics Outreach strategy, aiming to improve access to high-quality laboratory education for young people. The Department already hosts a GCSE Required Practical Library and hosts regular visits to the Central Teaching Lab for A Level required practicals, however the scope of this is limited to practicals requested by the exam boards. This project extends the offering to ages 11-14 in line with the STFC Wonders Initiative, and takes advantage of the more open, early-stage curriculum as a way to bring STFC science to children. The workshops will be simple, cost effective and ready to go ‘off the shelf’. Each practical will include key skills and link Key Stage 3 curriculum topics to the STFC-funded research that takes place at the University of Liverpool. We will link magnetism to Accelerator Science, understand the mechanisms of the Sun through our Nuclear Science and Particle Physics and explain the ways we explore the universe through Particle Astrophysics. We are in a prime position to deliver this intervention, being geographically situated in one of the most deprived areas of the country [3]. We have several strong STFC-funded research clusters, specialising in Particle Physics, Nuclear Physics and Accelerator Science. This, combined with our track record as a Department in delivering high-quality outreach [4, 5], shows that this project has potential for strong and meaningful impact. To ensure classroom relevance, we will involve a group of current KS3 teachers in the design stage by recruiting them as testers of the product. As well as providing their valuable insights, these teachers will then be able to act as ambassadors in their networks, boosting the profile of the work. We hope to engage with 15 schools per year, equivalent to reaching roughly 450 children between the ages of 11-14. We will evaluate as outlined in the STFC Public Engagement Evaluation Framework. [1] The House of Commons Education Committee. Teacher recruitment, training and retention, May 2024 [2] P. Kirby and C. Cullinane. Science Shortfall. The Sutton Trust Research Brief, Jan 2017 [3] Ministry of Housing, Communities & Local Government (2019), The English Indices of Deprivation 2019 (IoD2019) , URL: https://assets.publishing.service.gov.uk/media/5d8e26f6ed915d5570c6cc55/IoD2019_Statistical_Release.pdf, Accessed 21/11/2024 [4] Tactile Collider Team Scoops Top European Scientific Outreach Prize (2019), University of Liverpool, URL: https://www.liverpool.ac.uk/physics/news/stories/title,1144533,en.html. Accessed: 21/11/2024 [5] The Tale of Two Tunnels, UKRI, URL: https://gtr.ukri.org/projects?ref=ST%2FS000127%2F1 . Accessed: 21/11/2024

UKRI Gateway to Research · FY 2025 · 2025-07

Poor diet and obesity combined are the leading cause of early death globally and a major cause of social inequalities in health. The obesity pandemic has been caused predominantly by changes to population level dietary patterns, resulting in widespread excess energy consumption and weight gain. The solution to the obesity pandemic will therefore involve changing dietary patterns at population level and doing so in a socially equitable way.   At a policy level there is current uncertainty over which dietary patterns should be targeted to reduce population level obesity. Reducing consumption of foods high in fat, salt and/or sugar (HFSS) is a current policy priority in the UK and many countries. However, emerging findings controversially suggest that targeting foods that are characterised by being ‘ultra-processed’ (UPFs) may have a more powerful effect on reducing population level obesity. To date, no research has examined which of these overarching dietary patterns is key in explaining population level obesity.   Due to a lack of basic science, there is also significant scientific uncertainty over how dietary patterns and obesity can be substantially changed at population level and achieved in a way that is socially equitable (i.e. people from all social classes benefit). Because of this, there is a need to test new population level interventions designed to reduce consumption of less healthy foods and obesity. Similarly, although reformulation of less healthy food products has been suggested to be a ‘win-win’ solution to addressing population health by reducing body weight, there is a death of biomedical research testing this proposal.   The overarching aim of this multi-disciplinary programme of research is to understand how obesity can be reduced at population level. We will first examine the extent to which HFSS vs. UPF dietary patterns contribute towards weight gain and development of obesity in children and adults using epidemiology. In doing so, this research will identify which of these dietary patterns is most important in explaining population level obesity. Building on this, we will next test the impact of new population level interventions (e.g., nutrient warning labels) designed to reduce consumption of unhealthy foods using real-world consumer psychology and nutrition methods. Parallel to this work, we will conduct new experimental research which will study how human body weight is regulated in response to food energy content. In doing this, we will understand the causal impact and magnitude of change that energy reformulation of unhealthy food has on human body weight. We will then integrate data collected across the programme of research and use complex public health modelling methodology. This approach will reveal the impact that a range of new interventions (e.g., based on reducing consumption of unhealthy foods and/or on reformulation) would have on national obesity prevalence and public health, if implemented by policy makers. The research we conduct will make important academic contributions by addressing critical basis science questions relating to nutrition, body weight regulation and obesity. Our research will also have important policy implications. We will actively work alongside and disseminate findings to key stakeholders in public health advocacy, local and national government, and international public health bodies to ensure real-world impact of research.   This programme of research will understand how diet drives obesity and provide the evidence required by policymakers to implement new population-level interventions to address obesity and reduce social inequalities in health.

UKRI Gateway to Research · FY 2025 · 2025-07

If you see an insect in your garden, the chances are that it carries one or more partner bacteria (‘heritable symbionts’) which pass from a female insect to her offspring. These ‘small things’ represent major hidden players in insect biology, providing key services such as protection against enemies, nutrient synthesis, and desiccation tolerance. These services may define the niche of their host species and drive the dynamics of natural enemy/host interactions. Further, their innovations drive insect evolution, for instance fuelling the radiation of the lineages. Whilst the importance of heritable symbionts for insect biology and ecology is understood, the forces that have enabled them to impact so many host species are not. For instance, the microbe Wolbachia is present in over 40% of species - but we simply do not understand how so many insect species came to carry it. Our project’s overarching challenge is to understand how these symbioses – that define insect biology – became so common. New heritable symbiont/host interactions are established when an existing symbiont moves from its native host species to a new one. These ‘host shifts’ involve two processes – transfer of the microbe into a novel host, and compatibility with that host such that a stable symbiosis is formed both initially, and in the face of onward environmental stress. Problem 1: How do symbionts transfer to new hosts? Symbiont introduction to a new host is thought to occur most commonly when a parasite develops inside an insect, such that each party is exposed to the symbionts of the other. However, this process has a key barrier: it requires the insect to survive parasite attack. We hypothesize the common phenomenon of symbiont-mediated protection – where partner microbes kill developing parasite larvae – is the missing link enabling transfer of symbionts from parasite to host. We will test this hypothesis for Wolbachia transfer from parasitic wasp to fruit flies. We have shown flies survive wasp attack when the fly carries protective bacteria, allowing us to experimentally test whether protection facilitates symbiont transfer from wasp to fly. Problem 2: What determines whether symbioses are stable? When symbionts transfer into a new host species, the symbioses are commonly unstable; the microbes often show weak transmission and may be lost in response to environmental stress. We do not currently understand why symbiotic stability varies. We hypothesise that symbiosis instability arises when dormant viruses (prophage) carried in the symbiont become activated, killing the symbiont. We will test this novel hypothesis using symbionts of aphids, ladybirds and flies. These symbionts carry dormant prophage and can be artificially transferred to new hosts easily. We know these transfers have varying stability, and that stability is affected by thermal environment. Applications and benefits. This project examines fundamental processes underpinning biodiversity, namely the commonness of biologically and ecologically crucial partners of arthropods. Our work seeks to explain why symbionts are so common, how existing symbionts shape the spread of others, why particular host species are infected, and how symbioses will respond to environmental change. This project also underpins application. Symbionts are transferred into pest and vector species to reduce the harm these insect cause, but symbiosis instability commonly prevents use. If our hypotheses are correct, they open new strategies to strengthen symbiosis stability and broaden application for human health and agricultural benefit.