University of Liverpool

UKRI Gateway to Research · FY 2024 · 2024-06

The LFT facility is dedicated to the fundamental study of the flow modes initiated by instabilities (or flow bifurcations) and their chaotic dynamics and interaction. It is designed to explore the wake of three-dimensional streamlined and bluff geometries relevant to the transportation industry from the laminar flow regime up to the turbulent regime. The LFT addresses theoretical knowledge about stability and nonlinear dynamics of wake flows of most vehicles, but not precisely at the engineering scale where turbulent modelling implies less rigorous theoretical background. It will provide flow data comparable to computation obtained with Direct Numerical Simulation (DNS) around complex three-dimensional geometries to achieve alternative and complimentary fundamental understanding. The experimental data will serve as benchmark to validate theoretical stability analyses predictions, such as full base or bifurcated flow solutions made available with open access to be tested by theoreticians' teams across the UK and beyond. In addition to an experimental tool to improve theoretical approaches on wake flow stabilities, the vision is that "why and how" the wake dynamical modes develop in vehicles wake is the key ingredient to initiate further efficient control strategies to improve their aerodynamics at the engineering scale.

UKRI Gateway to Research · FY 2024 · 2024-06

Acute diarrheal disease is a major and continuous global health challenge. According to the World Health Organization, diarrheal disease affects over 2 billion people worldwide annually, and sadly, it is one of the primary causes of child death. Infections resulted from Salmonella bacteria are the most comment cause of acute diarrheal disease, accounting for over 1 million fatalities globally each year. To thrive in a nutrient-limiting environment and compete with other gut bacteria, Salmonella creates specialised nanoscale structures, called bacterial microcompartments (BMCs). These structures confer unique metabolic advantages that help Salmonella to become the dominant species in the hostile environment of the host gut. A nutrient called ethanolamine is necessary for the development of intestinal epithelial cells and bacterial growth. Salmonella has a special type of BMCs, termed the Eut BMC, which sequesters many ethanolamine metabolism-related enzymes within a protein-based shell. While we understand the importance of Eut BMCs in bacterial infections, we know very little about how Salmonella develops and manages these structures in order to improve cellular metabolism and survive in the challenging conditions of the inflamed intestine. Our research team has an international expertise in microcompartment assembly. In recent work, we have successfully developed efficient approaches for genetic manipulation of Salmonella and live-cell microscopic imaging to study Eut BMC assembly within Salmonella. Building on these exciting advances and a wide collaboration combining unique and complementary expertise, we now aim to use state-of-the-art fluorescence imaging techniques to film the growth and replication processes of Salmonella in real time at high resolution. This approach will allow us to investigate, in exceptional detail, how Eut BMCs are generated, how they work throughout time, and where they are located in cells. Our overall goal is to discover the precise mechanisms that drive the formation and function of Eut BMCs in Salmonella. First, we will observe how the Eut BMC is built step by step from individual parts. Specifically, we seek to address whether the shell or cargo proteins assemble first during the formation process. Second, we will use long-term fluorescence imaging to assess the complete life cycle of Eut BMCs, from their birth to disassembly, and how they are separated when cells divide. By watching cell growth and division, we will learn the relationships between Eut BMC construction and activity to promote cell development. This information will help us develop and define mathematical models that will be used to understand how Eut BMCs assemble and work throughout their life cycle. Third, we will map the locations of Eut BMCs within cells and study the factors that determine their localisation and the birth sites of additional Eut BMCs. Comparison of experimental and mathematical modelling data will help us uncover the mechanism underlying the precise patterns of Eut BMC distribution within Salmonella. This ambitious and multidisciplinary research initiative has both fundamental and applied significance. By achieving these objectives, we will get significant understanding of how Eut BMCs are made and how they function in Salmonella. Understanding Eut BMCs is not only of scientific interest; it also has practical applications. If we can control the generation and function of Eut BMCs, we may be able to develop unique and efficient ways to combat Salmonella and prevent Salmonella from causing harm in the human gut. It can also help us design new synthetic biology tools and engineer novel bio-factories or nanomaterials that could be utilised in many biotechnological applications with great economic benefit.

UKRI Gateway to Research · FY 2024 · 2024-06

Leveraging the power of contemporary Artificial Intelligence (AI), this project aims to revolutionize the way in which we can build and use geodemographic classifications. This will do so by enabling more accurate representations of socio-spatial structure and lowering barriers to census based classification development. It also proposes a user-friendly online tool that will allow anyone to easily create their own tailored, research-ready census-based geodemographic data product. Geodemographic classifications provide useful and policy-relevant representations of the complex and multidimensional characteristics of populations living within small geographic areas. Classifications have been created using components of census data since the 1970s, with notable examples in 2001, 2011 and 2021 when the ONS co-produced the first open geodemographic classifications for the UK with academic partners. These "Output Area Classifications" (OAC) have garnered wide use and inspired localised models for specific geographic areas such as London (LOAC). The core methods used to build geodemographic classification have however remained reasonably static since the 1970s, with only modest update. Furthermore, the creation of classifications also remains a reasonably technical process, limiting the ability for others to produce their own classifications, either for localities or specific purposes. This proposal argues that recent developments in AI, and specifically deep learning and machine learning, show great potential to radically transform the power and utility of geodemographic classification. Firstly, through the creation of more accurate representations of socio-spatial structure; and, secondly, through improved geodemographic information systems that significantly reduce barriers to developing new classifications Aims and Objectives The aim of this project is to update the established methods used to build Census based geodemographic classifications through the integration of AI into: The more automated development of output area level input measures that better account for non-linear geographic relationships between variables. A tool to that enables the automated description of clusters. Enabling the creation of a new public facing and online geodemographic classification system that will enable custom census-based classifications to be created. This will be achieved through the following objectives: Evaluating the use of autoencoders as a new method of data reduction for output area level geodemographic input measures. Developing an operational machine learning pipeline that takes output area level census inputs through to cluster creation. Utilising a large language model (LLM: such as integrated into ChatGPT), to develop an automated geodemographic descriptive tool capable of producing accurate textual descriptions of cluster characteristics. Producing a public facing online tool and accompanying training that will guide users to create their own research-ready census-based geodemographic data products.

UKRI Gateway to Research · FY 2024 · 2024-06

Advances in artificial intelligence (AI) are revolutionising how we search for information. Large language models (LLMs), such as OpenAI's 'Chat-GPT' or Google's 'Bard', are good at understanding what we say and the meaning behind our words. Through conversations with these tools, they are helping to improve the accuracy of what information we want to find. While existing search tools focus on using 'keywords', this may not always give good answers. LLMs help people who might not know the exact words to say, because they know the context and relationships behind our language. They can adapt to different ways of asking questions, as well as provide explanations about why they found such information. We believe that these maturing technologies can help researchers search for data. Through training existing LLMs to learn what UKRI-supported research data exist, we can make the most of their existing abilities to understand human language to create a powerful data search tool. Their potential to be used as a data search tool is unknown and we are not aware of any existing tools for UK research datasets. Our proposal will develop, pilot and evaluate the effectiveness of LLMs to this end. The main output of this work will be a fully deployable 'chat box' search tool that researchers will be able to use to discover research datasets. To achieve this, we will collate the metadata of data catalogues across a range of UKRI research investments including the Consumer Data Research Centre, NERC Environmental Data Service, Administrative Data Research UK and UK Data Service. Through combining data catalogues across these unconnected services, we provide a new single 'port of call' for searching research data. We will design our project so that it can easily adapt to integrate new datasets. These data will then be used to develop a new AI derived search tool based on LLMs. We want to understand how these technologies can be used effectively by researchers and whether they will give more useful searches. Our mixed methods approach will test and evaluate the acceptability, suitability, and performance of our new search tool in comparison to existing UKRI search tools. This will include focus groups to qualitatively examine the acceptability of LLMs for data discovery, a quantitative comparison of how our new tool performs against existing keyword search tools, and by running tests that task participants with searching for data. We will report the strengths and limitations of LLMs to examine how useful they are. We will make recommendations for how they can be deployed, refined and sustain the changing ways in how researchers search for data. Our project will bring added value to existing UKRI data discovery resources through creating a new tool that will know the context and meaning of search queries, providing a broader and more accurate list of datasets based on what is searched for. We hope that this will help researchers to find exactly the data they need for their research.

UKRI Gateway to Research · FY 2024 · 2024-06

Background: Snakebite envenomation (SBE) kills 138,000 and maims >400,000 people annually. Antivenom (antibodies purified from animals hyper-immunized with mixtures of venoms) is the only assured therapy for SBE, and is manufactured using expensive, century-old protocols of immunising horses/sheep with crude venoms. Current protocols make no attempt to account for variant venom protein immunogenicity or toxicity during antivenom design or manufacture. Due to large venom diversity between different venomous snakes, this immunisation approach results in antivenoms which are snake species-specific, resulting in physicians having to make difficult diagnostic and antivenom-selection decisions when the offending snake species is unknown. Furthermore, the approach elicits toxin specific antibodies which often have poor toxin neutralising-potency, a problem further exacerbated when considering only 10-15% of the antibodies in antivenom are specific for venom components. The remaining 85-90% of antibodies in antivenoms are specific to antigens (endemic pathogens, vaccines etc) which the manufacturing animals have encountered during their lifetime and are therefore of no use in treating envenoming. Consequently, antivenoms often have poor dose-efficacy, which results in the administration of large volumes (typically 200-400 ml in India) to neutralize pathology, often leading to severe adverse reactions and unaffordable costs for already impoverished victims. There is therefore an urgent and compelling need to drastically improve the venom-neutralizing scope and potency of antivenom therapy. Rationale: In the first four years of the FLF, we have identified regions, in multiple toxin families, that contain conserved features. We subsequently have engineered these conserved regions to be displayed on synthetic particles which enable focused and potent elicitation of anti-toxin antibodies when used in mice and rabbits. We now wish to apply the technology in full scale antivenom manufacturing in a good manufacturing practice environment. Approach: Objective 1 - producing experimental antivenoms with rationally designed immunogens Building on the success of the project to date, we will pilot the use of rationally designed antivenom antigens by employing them in antivenom production in a fully industrial scale. Working with an antivenom manufacturer, we will immunise horses for a period of 6 to 12 months with the developed antigens, while monitoring their development of anti-toxin antibodies before finally (in a manner that is not detrimental to the horses) producing fully formulated antivenom under good manufacturing conditions. The full demonstration of the technology at this level will allow confidence and rapid uptake of the technology by manufacturers globally. Objective 2 - developing new antivenom bioprocessing methods to increase potency Whilst the first approach is focused on antivenom upstream development of rationally designed antigens for immunisation, the downstream bioprocessing of antivenom has remained unchanged for many decades. The majority of (80-90%) of antibodies present in antivenoms are specific to venom toxins, but towards micro-organisms which manufacturing animals have encountered throughout their lifetime. I have developed a method to selectively remove some of these redundant antibodies which results in an enrichment of the desired antivenom antibodies, thus increasing the toxin neutralising potency of the antivenom. In the FLF extension, I wish to further develop this bioprocessing technique to enable its use in full scale antivenom manufacturing. Implications: This project represents the most substantial and advanced improvements to antivenom manufacturing since antivenoms were first conceived in the 1890s. The expected increases in potency and utility of the antivenom products developed we hope will assist in increasing the availability and treatment outcomes of snakebite victims globally.

UKRI Gateway to Research · FY 2024 · 2024-06

Perovskite solar cells are one of the newest and most exciting materials in the world of solar cell research. In little over 10 years their lab scale efficiencies have advanced from 8% to over 25%, putting them on a par with market leading silicon solar cells. However, after a decade's worth of interest and investment, this potentially revolutionary solar cell has not made it on to the market yet. There are several important barriers to commercialisation for perovskites, principally: 1. Issues with stability of perovskite materials, 2. Concerns around the use of toxic element such as lead, and, 3. Issues in transitioning to scalable manufacturing processes. In order to overcome these barriers, we propose a more holistic approach to design and fabrication of perovskite solar cells, which considers both toxicity and scalability, as well electrical efficiency during the optimisation process. The aim of this project to develop safe, stable and printable perovskite solar inks. This will be achieved by developing tin-based perovskite solar cells and exploring the use of ionic liquids in the solvent system to create a stable non-toxic ink that can be used in an inkjet printer. Ionic liquids are an impressive new solvent option for perovskite processing, exhibiting many favourable properties, such as solubility, low toxicity and stability. Most promising of all is the tunability of their viscosity, a key parameter in ink formulation for printing and thin film processing, which is yet to be explored. The goal is to fully print a tin-based perovskite solar cell in atmospheric conditions. This will be a revolutionary solar cell product that contains no harmful materials, is more easily recyclable and can be fabricated at lower costs.

Fonds de recherche du Québec – Société et culture · FY 2023-2024 · 2023-04

Volet: Bourses postdoctorales; Domaine: Arts, littérature et société; Objet: Administration de la justice; Application: Structures et relations sociales; Application: Populations; Mots-clés: FEMMES, DISCRIMINATION RACIALE, AFRO-DESCENDANTS, HISTOIRE GLOBALE, MONDE ATLANTIQUE, JUSTICE CRIMINELLE