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Enteric fever caused by Salmonella Typhi and Paratyphi A is wide spread throughout the world. particularly burdensome in South-East Asia, with approximately >22 million new infections resulting in a 1% fatality rate annually. Control of the disease is hindered due to insufficient understanding of disease pathogenesis and immune responses to the infection, inaccurate diagnostic tests and limited efficacy of licensed vaccines. Thus understanding human host-responses to enteric infections is pivotal in developing improved diagnostic tests and vaccines. The Oxford Vaccine Group (OVG) has recently developed a human challenge model for S. Typhi and Paratyphi A. Briefly, human adult volunteers were orally infected with a pathogenic dose of S. Typhi and Paratyphi A and closely monitored the following days until treatment with antibiotics. This model was subsequently used to test vaccine efficacy by vaccinating participants prior to ingestion of the bacteria. The various samples collected provide a rich dataset consisting of clinical and immunological measurements invaluable to understand disease pathogenesis and human molecular responses to infection. Associated with such large datasets and different levels of data (molecular, serological and clinical) is the challenge of analysis and data integration, which we address through using systems biology approaches. Systems biology/vaccinology is an interdisciplinary field that combines systems-wide measurements, networks, and predictive modelling in the context of biology, vaccines and infectious disease. Computational modelling and integration of multiple levels of data (clinical, immunological, and molecular) develops a multi-facetted understanding of disease pathogenesis and biological mechanisms underlying host-responses to infections/vaccination. Particularly important in this context are regulatory mechanisms, which consist of complex networks involving multiple transcriptional and genetic components. Recently, it has become clear that long non-coding RNAs (lncRNAs) play a pivotal role in the regulation of biological processes by a diverse range of mechanisms. Integrative analysis of transcriptional signatures related to expression of lncRNAs allows us to identify molecular events associated with clinical and immunological outcomes. We are aiming to thoroughly integrate these different datasets to fully investigate the intricacy of human host-responses to infections. We believe that this approach is necessary in order to further shed light on the undisputed complexity of the immune system. As these samples are derived from the human host and a uniquely controlled experimental design, the results will likely elucidate important clues as to how the host reacts to enteric infections and how S. Typhi/Paratyphi A potentially modulate the host-response. By partnering with the Computational Systems Biology Lab at the University of Sao Paulo we are combining one of the largest clinical trials groups in Europe with an excellence in vaccine trials with a team of computational biologists leading in the field of systems vaccinology.

This project is aimed at understanding the complex fluid dynamics of wind farms and how this impacts on energy yield predictions. Offshore wind energy is developing rapidly in the UK - reaching the net-zero emissions targets may require growth of the sector from 10GW today to over 90GW by 2050. Turbines are rapidly increasing in size, with today's 8 MW turbines dwarfing the average 3-4MW turbines being installed in 2015, but will themselves be dwarfed by the 10-12MW turbines, almost 200m in diameter, currently being certified and selected for upcoming projects. Wind farms have increasingly large numbers of turbines as well, with eight UK wind farms now exceeding 100 turbines and the largest, Walney Wind Farms, with 189. Lifetime assessment of wind farm performance relies heavily on accurate modelling of the wind around and through the farm. The current generation of engineering models used to design wind farm layouts and estimate turbine loads are based on over-simplified wake interactions and empirical representations of much smaller turbines than those now being deployed. These models are being used beyond their validated envelopes resulting in high uncertainty in model predictions. It has been shown through comparison to high-fidelity simulations and field data that these models do not capture large-farm emergent wake and atmospheric interaction physics and are therefore not suitable for the large and closely-spaced wind farms currently in development. New technologies such as floating wind turbines have significant potential for the UK but require a different modelling approach due to the coupled aero-hydrodynamics of turbine and platform and resultant wake dynamics. Furthermore, turbine performance changes over time, for example as a result of blade erosion, which also must be considered when evaluating lifetime performance and the scale of deployment required to meet the 2050 targets. This project will develop the next generation of modelling that is required to predict how wind turbine performance changes over time and enable a transition from the current approach of minimising interactions between wind turbines to managing and exploiting these interactions to improve whole-farm lifetime performance. As wind turbines and farms have grown larger, facilitated in particular by the move offshore, the aerodynamic effects of energy extraction are felt over much larger distances and interactions with atmospheric winds have become increasingly significant. Simplifications to existing engineering models to improve computational efficiency have meant that these large-scale effects are neglected, leading to uncertainties that impact on energy production estimates and thus cost of energy. Furthermore, as wind energy comes to form a larger share of overall UK energy production, it is increasingly important to understand the impacts of wind variability on the resilience of energy supply and future energy storage requirements, particularly during extreme weather events. Fully resolving all of the many spatial and temporal scales of the underlying aerodynamic phenomena involved in wind energy is a challenge that will remain beyond the scope of engineering applications for many years to come. The focus of this project is to reduce the complexity of this challenge by systematically identifying, scale-separating and modelling the key physics that drives wind turbine performance, wake development and interaction with the wind resource. This will be achieved through the development of novel multi-scale models, supported by state-of-the-art experiments and high-fidelity simulation, which couple momentum transfer across these disparate scales. This approach will enable new wind farm control and design strategies to be explored, as well as providing more accurate predictions of energy production and resilience, reducing uncertainty and enhancing the value of wind energy to the UK.

One of the factors that significantly impinges on the quality of life among elderly people is a decrease in salivary flow (hyposalivation) and a dry mouth (xerostomia). The prevalence of xerostomia increases with age and affects approximately 30% of people aged 65 or older. Xerostomia leads to problems with speech, taste digestion, mastication and swallowing, and a high incidence of dental caries and candida. There is no cure but chewing gum and artificial saliva are used to relieve the symptoms in many cases. Given the large numbers of sufferers, and the potential increase in incidence given our aging population, it is important to understand the mechanisms that drive xerostomia so that new therapies can be found. Xerostomia can be caused by a number of different factors. Certain medicines cause xerostomia as a side effect, while xerostomia is also a central feature of the auto-immune disease Sjögren Syndrome, which affects up to 3-4% of older adults. Xerostomia is also a feature of some genetic disorders that affect the salivary glands, such as LADD syndrome and ALSG, and is frequently a side effect of head and neck radiotherapy. We aim to study xerostomia using various mouse models of gland dysfunction and regeneration. This application aims to combine knowledge of salivary gland development with knowledge of salivary gland stem cells from mouse and translate these findings into humans. This is possible through combining research from basic scientists in the UK and clinical researchers in Brazil, with the ultimate goal of creating innovative methods to treat xerostomia.

Humans have evolved complex and effective ways of fighting infections caused by microbes such as bacteria, parasites, fungi and viruses, the immune system. Sometimes the immune system goes wrong and this can cause serious diseases such as rheumatoid arthritis and diabetes. Our research aims to understand at a molecular level how the cells of the immune system are able to recognise different microbes and the ways in which these cells respond to cause the familiar symptoms of an infection such as fever and tiredness, and to generate specific antibodies that fight the invading microbes. In this project we will study the way in which immune system cells are activated by sugar-binding proteins associated pathogenic yeasts and plants, in particular the way they bind and activate the Toll-like receptors of the innate immune system. Understanding the molecular basis for interactions between these pathogens and immune cells will allow us to carry out a screen to identify new candidate drugs that might be effective therapies for these important diseases.