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1,095 Projects, page 1 of 110

  • UK Research and Innovation
  • 2015
  • 2017

10
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  • Funder: UKRI Project Code: BB/M008096/1
    Funder Contribution: 123,133 GBP
    Partners: SRUC

    Life-End summary Sustainable production of safe chicken is an international priority and preserving bird welfare is a key component of this. Current intensive (broiler) production can compromise bird health and welfare and food safety and there are strong links between poor bird welfare and the Campylobacter public health threat. Campylobacter is the most common cause of bacterial diarrhoea in the EU and despite millions of pounds of research funding it is estimated that contaminated chicken caused ~700000 human campylobacteriosis cases in the UK in 2013 with around 100 deaths. Infection is characterised by severe abdominal pain and acute (sometimes bloody) diarrhoea and costs the UK an estimated £1 billion per year. Campylobacter contamination of chicken takes two forms. First, surface contamination of carcasses leads to cross-contamination in the kitchen. Second, and perhaps of greater importance than currently thought, contamination within muscle and liver tissues, increasing the health risk by facilitating bacterial survival during cooking. Chickens in poor production environments or exposed to stress are more susceptible to Campylobacter and in such birds the bacteria show greater extra-intestinal spread to edible tissues, possibly as a consequence of disturbance to the gut environment. Therefore, improvements in broiler welfare have great potential to improve public health but there is an urgent need for information on the effect of stress to inform targeted interventions to reduce Campylobacter in broiler chickens. One acutely stressful event in the life of broilers, in any production stream, is harvest when birds are removed from the farm for slaughter. We define the process as comprising: food withdrawal, catching, transport and stunning by either gas or electricity. Although there is a growing body of evidence that these stressors can increase Campylobacter growth rates as well as extra-intestinal spread, there is a paucity of data on their relative importance or how they may select for particular types of Campylobacter. By examining the harvest processes using large scale industry-relevant experimental conditions, state-of-the-art genomics, molecular microbiology and mathematical modelling techniques, we will determine the impact of harvest on gut health in broilers. We will combine this with a study to identify bacterial genetic determinants involved in extra-intestinal spread of Campylobacter to edible tissues. We will quantify the relative impact of each stage of harvest on the gut bacterial population and the physiology and immunity of the birds, and investigate the role these play in controlling extra-intestinal spread of Campylobacter. This multidisciplinary research programme will enhance understanding of the influence of the harvest process on bird gut health and Campylobacter. The quantitative information and modelling will be used to provide direct advice to industry about the elements of the harvest processes that provide the best opportunity for interventions that will mitigate the ongoing challenge of Campylobacter contamination in chicken meat.

  • Funder: UKRI Project Code: 102160
    Funder Contribution: 246,307 GBP
    Partners: Benchmark Performance Limited

    Parkinson's disease is a progressive neurological condition that affects approximately one in 500 people in the UK. The main symptoms of Parkinson's are tremor, rigidity and slowness of movement, and patients often also suffer from related effects such as tiredness, pain and depression, which have considerable impact on their quality of life. Treatment is focused on using drugs to manage these symptoms and to reduce their debilitating effects. Symptoms alter as the condition develops, and hence treatment must also change to track the development of the disease. CloudUPDRS aims to significantly increase the effectiveness of precisely tailored personalised treatment by implementing a Big Data system for the clinical categorisation of Parkinson’s disease patients. This advanced data analytics approach identifies disease progression patterns that re subsequently used to tailor care management to the needs of the individual.

  • Funder: UKRI Project Code: ST/M003868/1
    Funder Contribution: 270,004 GBP
    Partners: University of Oxford

    Energy-resolving detectors are a step towards the 'ultimate detector' for astronomy and many other applications besides. Kinetic Inductance detectors (KIDs) are a transformational technology based on superconducting resonators that have opened the way for large arrays of zero-noise, energy sensitive (3-D) detectors in the important optical and near-IR (UVOIR) regime. Our goal is to bring together the components necessary to comprise the first european UVOIR KID spectrograph. Whilst these have been demonstrated at the component level, we will use the expertise and experience in our team to build a complete system, including the detector, cryogenics and read-out electronics. We will use this system to develop the conceptual design of a powerful medium spectral resolution, broad band (0.3 - 2.5 micrometers) spectrograph. This Kinetic Inductance Detector Spectrograph (KIDSpec) would be a uniquely powerful instrument more than an order of magnitude more sensitive than existing equivalent instrumentation using semiconductor detectors. At the heart of the instrument is the ability of the KIDs to discern the order from a diffraction grating. It is this ability that we will demonstrate for the first time with this project. At the end of the project, we will use this technology development to push forwards to the first KIDSpec instrument at a major astronomical observatory. Such an instrument is perfectly suited to the E-ELT, as the zero read-noise, photon-counting operation maximises the pay-off from the enormous collecting area of this next generation telescope. The moderate investment requested in this proposal will enable us to perform a unique experiment, but equally importantly it will enable us to build the knowledge and experience to put UK researchers at the heart of this truly revolutionary field. Kinetic Inductance Detector arrays have the capacity to revolutionise astronomy in the same manner as the move from photographic plates to digital detectors did 30 years ago and this proposal will place us at the forefront of this nascent field.

  • Funder: UKRI Project Code: MR/M017710/1
    Funder Contribution: 249,823 GBP
    Partners: University of Leeds

    Antibiotics enable the treatment and cure of life-threatening bacterial infections, and represent one of the great successes of modern medicine. Unfortunately, the utility of these agents is being progressively eroded as bacteria evolve to resist their effects, and antibiotic resistance is now considered one of the three greatest threats to human health. A key aspect of dealing with antibiotic resistance effectively in medical practice is strategic intelligence. Being in possession of up to date information about the proportion of bacterial strains in a given location that are resistant to particular antibiotics allows doctors to decide which would be the best antibiotics to use routinely to treat bacterial infection, and to avoid those which are probably not going to work because resistance is so commonplace. In the case of a life-threatening bacterial infection, knowing precisely which antibiotics the specific bacterium present in the patient is resistant or susceptible to enables the doctor to select the best antibiotic treatment to cure the patient. This project is focussed on investigating a phenomenon that may be seriously undermining our strategic intelligence regarding antibiotic resistance. Recent work in the applicant's laboratory has established that some bacteria that are sensitive to antibiotics nonetheless carry genes that are normally associated with antibiotic resistance, but that these genes have become switched off ('silenced'). This phenomenon, which we have termed 'silencing of antibiotic resistance by mutation' (SARM) is of considerable concern, as bacteria with SARM would appear susceptible to an antibiotic when tested, but could then very quickly and easily become resistant to the antibiotic during treatment in a patient. Currently, we do not know how widespread SARM is amongst bacteria that cause disease, nor do we understand properly how SARM occurs. The present proposal aims to investigate both of these issues in the so-called 'superbug', Staphylococcus aureus. To establish how common SARM is, a large collection of 1500 S. aureus isolates recovered from patients around the world will be tested. Each strain will undergo DNA sequencing of its genome to establish its complete genetic make-up, which will allow for the identification of genes that are known to be associated with antibiotic resistance. In addition, the susceptibility of each isolate to a wide range of commonly used antibiotics will be established to determine if the bacterium displays resistance to all the drugs that it has the genetic potential to display resistance to. Strains that carry antibiotic resistance genes, but do not exhibit resistance to the corresponding antibiotics, represent potential SARM strains. These strains will be studied in detail to establish how easily they can switch their 'silenced' resistance genes back on, and to understand the mechanism(s) by which SARM works.

  • Funder: UKRI Project Code: 102115
    Funder Contribution: 722,053 GBP
    Partners: Shearwater Systems Limited

    A major challenge in the delivery of high quality care to people with epilepsy is the accurate collection and analysis of information about their condition, and possible seizures, throughout their daily life. The Epilepsy Networks project aims to improve the provision of expert care to people with epilepsy using existing NHS resources but leveraging them with innovative use of secure mobile and social solutions. The captured information will then be embedded in the patient's NHS electronic record, and using automated analysis and clinically validated data visualisation, the project will enable a more accurate and timely assessment of the patient’s condition. This will facilitate novel clinical pathways including real-time pre-emptive intervention, reducing the cost of care and enabling the re-engineering of existing services to better meet the needs of those diagnosed with this life-altering condition. The ultimate goal is to develop a platform for connecting patients, hospital and community healthcare professionals around an innovative, shared digital record including input from patients and families for a full spectrum of medical conditions.

  • Funder: UKRI Project Code: EP/M013766/1
    Funder Contribution: 100,317 GBP
    Partners: University of Salford

    With rapid increases in data volume in all areas of life, the meaningful analysis of these data is becoming a crucial bottleneck. Whether data are generated by customer transactions, through communications on social media, or as a by-product of manufacturing processes, data are meaningless unless suitable techniques are available to select the most relevant data, analyze these data and turn raw data into tangible information and insight. To some extent, "big data" reverses traditional approaches in data-mining, as data collection now frequently precedes the definition of an actual question or hypothesis. The purported advantage of this approach is that novel, unexpected findings may materialize - a premise that relies, however, on the expert use of suitable approaches for exploratory data analysis. The prominence of "big data" therefore fuels the need and use of scalable and powerful approaches to exploratory data analysis. Data clustering techniques present one of the most fundamental tools in exploratory data analysis, and this project aims to deliver novel techniques that are accurate, flexible and scalable to large data sets. Data clustering techniques present one of the most fundamental tools in exploratory data analysis. Conceptually, data clustering refers to the identification of sub-groups within a data set so that items within the same group are similar and those in different groups are dissimilar; e.g., in the context of insurance data, a "cluster" of people may relate to customers who show similar behaviour in their claim patterns over time, while those in different clusters behave differently. Mathematically, data clustering can be seen as an example of a problem where good solutions are best described using a set of different criteria that account for conflicting properties such as the compactness of clusters and the separation between clusters. The above observation has recently led to the development of multi-criterion approaches to data clustering, which explicitly consider a number of clustering criteria. This approach has shown a lot of promise, in terms of the accuracy and the robustness of the solutions obtained. However, current techniques for multi-criterion clustering are limited regarding their scalability to very large data sets and also their flexibility with respect to their consideration of different sources of dissimilarity data. This project proposes a novel technique for multi-criterion clustering: the algorithm will combine complementary ideas from two sub-fields of computer science, leading to improved scalability and flexibility of the technique developed. The work will include the development of an interactive user-interface and the application of multi-criterion clustering to problems in finance and marketing. All software produced will be released publicly.

  • Funder: UKRI Project Code: BB/M009610/1
    Funder Contribution: 292,590 GBP
    Partners: Swansea University

    Life-End summary Sustainable production of safe chicken is an international priority and preserving bird welfare is a key component of this. Current intensive (broiler) production can compromise bird health and welfare and food safety and there are strong links between poor bird welfare and the Campylobacter public health threat. Campylobacter is the most common cause of bacterial diarrhoea in the EU and despite millions of pounds of research funding it is estimated that contaminated chicken caused ~700000 human campylobacteriosis cases in the UK in 2013 with around 100 deaths. Infection is characterised by severe abdominal pain and acute (sometimes bloody) diarrhoea and costs the UK an estimated £1 billion per year. Campylobacter contamination of chicken takes two forms. First, surface contamination of carcasses leads to cross-contamination in the kitchen. Second, and perhaps of greater importance than currently thought, contamination within muscle and liver tissues, increasing the health risk by facilitating bacterial survival during cooking. Chickens in poor production environments or exposed to stress are more susceptible to Campylobacter and in such birds the bacteria show greater extra-intestinal spread to edible tissues, possibly as a consequence of disturbance to the gut environment. Therefore, improvements in broiler welfare have great potential to improve public health but there is an urgent need for information on the effect of stress to inform targeted interventions to reduce Campylobacter in broiler chickens. One acutely stressful event in the life of broilers, in any production stream, is harvest when birds are removed from the farm for slaughter. We define the process as comprising: food withdrawal, catching, transport and stunning by either gas or electricity. Although there is a growing body of evidence that these stressors can increase Campylobacter growth rates as well as extra-intestinal spread, there is a paucity of data on their relative importance or how they may select for particular types of Campylobacter. By examining the harvest processes using large scale industry-relevant experimental conditions, state-of-the-art genomics, molecular microbiology and mathematical modelling techniques, we will determine the impact of harvest on gut health in broilers. We will combine this with a study to identify bacterial genetic determinants involved in extra-intestinal spread of Campylobacter to edible tissues. We will quantify the relative impact of each stage of harvest on the gut bacterial population and the physiology and immunity of the birds, and investigate the role these play in controlling extra-intestinal spread of Campylobacter. This multidisciplinary research programme will enhance understanding of the influence of the harvest process on bird gut health and Campylobacter. The quantitative information and modelling will be used to provide direct advice to industry about the elements of the harvest processes that provide the best opportunity for interventions that will mitigate the ongoing challenge of Campylobacter contamination in chicken meat.

  • Funder: UKRI Project Code: EP/J020184/2
    Funder Contribution: 227,091 GBP
    Partners: E.ON New Build and Technology Ltd, University of Surrey

    This programme is proposed to answer the EPSRC call on "Carbon capture and storage for natural gas power stations" by forming a close partnership between the University of Southampton and E.ON. The proposed research has a strong focus on industrial needs by integrating with the industrial partner's existing activities for developing CCS technologies suitable for commercial gas power plants. E.ON is generating around 10% of the UK's electricity and is committed to reducing its CO2 emission by 50% by 2030 (1990 baseline). E.ON has setup a dedicated CCS unit to address the technical challenges while one of the priorities is to develop CCS technologies suitable for natural gas power stations. This research specifically targets at natural gas power plants, which has a lower concentration of CO2 approx. 4% compared to 13% from coal-fired plants, and harder to extract, representing the most challenging case for CCS. Carbon capture and storage involves separating the CO2 from emissions so it can be transported and stored away from the atmosphere. The most commercially viable approach to be fitted in natural gas power plants is the post-combustion capture which absorbs CO2 from the flue gas using a chemical reaction - also known as scrubbing, which E.ON has been actively pursuing and will be the focus of this research. Whilst research on the chemical processes has been taking place for several decades, CFD modelling of the reactor is a recent development. E.ON has recognised that CFD plays a vital role in the optimisation of current CCS reactors by including more CFD research in their future research strategy. University of Southampton is a prime place for CFD based research while the School of Engineering Sciences currently holds £5M CFD focused EPSRC projects. The combined expertise forms a strong academic and industrial partnership to tackle current barriers of reactor scale-up in carbon capture using advanced CFD models. By addressing all the challenges outlined in the EPSRC call, this research aims to design an optimised reactor using a novel CFD modelling approach that is capable of achieving in excess of 90% CO2 absorption whilst ensuring the cost of service energy is minimised to below 35%. The new concept idea will incorporate improved mixing designs and improved heat transfer whilst reducing reactor size. It is planned through the enhancement of current CFD multiphase models to incorporate reaction and the inclusion of flow control devices that an optimal structured packing arrangement, which promotes the reaction process whilst reducing pressure drop, can be found. This project will not only produce conceptual ideas developed through enhance CFD methods but will also perform tests, in a lab-scale reactor, to determine its validity with respect to its flow dynamics and would potentially lead to the production of intellectual property.

  • Funder: UKRI Project Code: EP/M015815/1
    Funder Contribution: 98,207 GBP
    Partners: University of Liverpool, UvA

    The project concerns how groups of partially informed and self-interested agents (e.g., humans, robots), which are faced with a common problem, take a collective decision by exchanging their individual opinions to, possibly, reach a consensus. It aims at understanding how processes of opinion formation in groups behave and how they can be engineered in groups of artificial agents, like robots. The project capitalizes on techniques developed in the social and economic sciences, applying them to the artificial intelligence setting. It extends the state-of-the-art in the application of voting theory to artificial intelligence, addressing the process of opinion formation, and lays the theoretical groundwork for the development of collective decision-making techniques in autonomous systems.

  • Funder: UKRI Project Code: EP/N006399/1
    Funder Contribution: 169,320 GBP
    Partners: Sympatec, University of Birmingham, VolitionRX, University of Sussex, University of Bath, University of Sunderland, Bluefrog Design Limited, GlaxoSmithKline, Imperial College London, DMU...

    Advances in fit for use manufacturing of biopharmaceutical drug delivery and pharmaceutical systems are now required to fit Quality by Design (QbD) models. These current regulations require excellence to be built into the preparation of emerging products (both material and process) thereby leading to product robustness and quality. In addition, industrial needs (economical and reproducible quality enhancement) are driving manufacturing towards continuous processes over batch type processes which also rely on QbD (for integrity and quality). EHDA technology is a robust process that has been utilised in various formats (e.g. electrospinning, electrospraying, bubbling and even 3D printing) and is favourable due to applicability with the development of stable nanomedicines and biopharmaceuticals, the emergence of this technology is clearly evident in the UK and on the global scale. Attempts in scaling up (for suitable pharmaceutical scale) and in tandem with continuous processes (including controlled manufacturing) have been very limited. There also, now, remains a huge void in the adaptation of sensible QbD (multi-variate) for the current methods developed and also those required by industry. While lab scale research continues with the ongoing development of such processes (e.g. nanomedicines, smart and controlled delivery), the transition to industry or the clinic will have to meet these regulations (and scales) for there to be a real impact, which is now, also, an important aspect of grass root research in the UK. The EHDA network brings together specialists from academia and industry to advance this technology through several means. Firstly, initiating developments towards a real-viable scale for Pharmaceutical production. Secondly, to incorporate developments in lean manufacturing and legislation (e.g. continuous manufacturing, online diagnostics, QbD and adaptable scale). Thirdly, to marry optimised lean technologies with novel and emerging macromolecular therapies and actives. The network has a wide range of activities and initiatives which will lead to significant developments (and collaborations) in an area of increasing global interest (EHDA processes) - but currently only on a viable lab scale to date. This network will be the first of its kind and will serve as the central and pioneering hub in this remit.

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1,095 Projects, page 1 of 110
  • Funder: UKRI Project Code: BB/M008096/1
    Funder Contribution: 123,133 GBP
    Partners: SRUC

    Life-End summary Sustainable production of safe chicken is an international priority and preserving bird welfare is a key component of this. Current intensive (broiler) production can compromise bird health and welfare and food safety and there are strong links between poor bird welfare and the Campylobacter public health threat. Campylobacter is the most common cause of bacterial diarrhoea in the EU and despite millions of pounds of research funding it is estimated that contaminated chicken caused ~700000 human campylobacteriosis cases in the UK in 2013 with around 100 deaths. Infection is characterised by severe abdominal pain and acute (sometimes bloody) diarrhoea and costs the UK an estimated £1 billion per year. Campylobacter contamination of chicken takes two forms. First, surface contamination of carcasses leads to cross-contamination in the kitchen. Second, and perhaps of greater importance than currently thought, contamination within muscle and liver tissues, increasing the health risk by facilitating bacterial survival during cooking. Chickens in poor production environments or exposed to stress are more susceptible to Campylobacter and in such birds the bacteria show greater extra-intestinal spread to edible tissues, possibly as a consequence of disturbance to the gut environment. Therefore, improvements in broiler welfare have great potential to improve public health but there is an urgent need for information on the effect of stress to inform targeted interventions to reduce Campylobacter in broiler chickens. One acutely stressful event in the life of broilers, in any production stream, is harvest when birds are removed from the farm for slaughter. We define the process as comprising: food withdrawal, catching, transport and stunning by either gas or electricity. Although there is a growing body of evidence that these stressors can increase Campylobacter growth rates as well as extra-intestinal spread, there is a paucity of data on their relative importance or how they may select for particular types of Campylobacter. By examining the harvest processes using large scale industry-relevant experimental conditions, state-of-the-art genomics, molecular microbiology and mathematical modelling techniques, we will determine the impact of harvest on gut health in broilers. We will combine this with a study to identify bacterial genetic determinants involved in extra-intestinal spread of Campylobacter to edible tissues. We will quantify the relative impact of each stage of harvest on the gut bacterial population and the physiology and immunity of the birds, and investigate the role these play in controlling extra-intestinal spread of Campylobacter. This multidisciplinary research programme will enhance understanding of the influence of the harvest process on bird gut health and Campylobacter. The quantitative information and modelling will be used to provide direct advice to industry about the elements of the harvest processes that provide the best opportunity for interventions that will mitigate the ongoing challenge of Campylobacter contamination in chicken meat.

  • Funder: UKRI Project Code: 102160
    Funder Contribution: 246,307 GBP
    Partners: Benchmark Performance Limited

    Parkinson's disease is a progressive neurological condition that affects approximately one in 500 people in the UK. The main symptoms of Parkinson's are tremor, rigidity and slowness of movement, and patients often also suffer from related effects such as tiredness, pain and depression, which have considerable impact on their quality of life. Treatment is focused on using drugs to manage these symptoms and to reduce their debilitating effects. Symptoms alter as the condition develops, and hence treatment must also change to track the development of the disease. CloudUPDRS aims to significantly increase the effectiveness of precisely tailored personalised treatment by implementing a Big Data system for the clinical categorisation of Parkinson’s disease patients. This advanced data analytics approach identifies disease progression patterns that re subsequently used to tailor care management to the needs of the individual.

  • Funder: UKRI Project Code: ST/M003868/1
    Funder Contribution: 270,004 GBP
    Partners: University of Oxford

    Energy-resolving detectors are a step towards the 'ultimate detector' for astronomy and many other applications besides. Kinetic Inductance detectors (KIDs) are a transformational technology based on superconducting resonators that have opened the way for large arrays of zero-noise, energy sensitive (3-D) detectors in the important optical and near-IR (UVOIR) regime. Our goal is to bring together the components necessary to comprise the first european UVOIR KID spectrograph. Whilst these have been demonstrated at the component level, we will use the expertise and experience in our team to build a complete system, including the detector, cryogenics and read-out electronics. We will use this system to develop the conceptual design of a powerful medium spectral resolution, broad band (0.3 - 2.5 micrometers) spectrograph. This Kinetic Inductance Detector Spectrograph (KIDSpec) would be a uniquely powerful instrument more than an order of magnitude more sensitive than existing equivalent instrumentation using semiconductor detectors. At the heart of the instrument is the ability of the KIDs to discern the order from a diffraction grating. It is this ability that we will demonstrate for the first time with this project. At the end of the project, we will use this technology development to push forwards to the first KIDSpec instrument at a major astronomical observatory. Such an instrument is perfectly suited to the E-ELT, as the zero read-noise, photon-counting operation maximises the pay-off from the enormous collecting area of this next generation telescope. The moderate investment requested in this proposal will enable us to perform a unique experiment, but equally importantly it will enable us to build the knowledge and experience to put UK researchers at the heart of this truly revolutionary field. Kinetic Inductance Detector arrays have the capacity to revolutionise astronomy in the same manner as the move from photographic plates to digital detectors did 30 years ago and this proposal will place us at the forefront of this nascent field.

  • Funder: UKRI Project Code: MR/M017710/1
    Funder Contribution: 249,823 GBP
    Partners: University of Leeds

    Antibiotics enable the treatment and cure of life-threatening bacterial infections, and represent one of the great successes of modern medicine. Unfortunately, the utility of these agents is being progressively eroded as bacteria evolve to resist their effects, and antibiotic resistance is now considered one of the three greatest threats to human health. A key aspect of dealing with antibiotic resistance effectively in medical practice is strategic intelligence. Being in possession of up to date information about the proportion of bacterial strains in a given location that are resistant to particular antibiotics allows doctors to decide which would be the best antibiotics to use routinely to treat bacterial infection, and to avoid those which are probably not going to work because resistance is so commonplace. In the case of a life-threatening bacterial infection, knowing precisely which antibiotics the specific bacterium present in the patient is resistant or susceptible to enables the doctor to select the best antibiotic treatment to cure the patient. This project is focussed on investigating a phenomenon that may be seriously undermining our strategic intelligence regarding antibiotic resistance. Recent work in the applicant's laboratory has established that some bacteria that are sensitive to antibiotics nonetheless carry genes that are normally associated with antibiotic resistance, but that these genes have become switched off ('silenced'). This phenomenon, which we have termed 'silencing of antibiotic resistance by mutation' (SARM) is of considerable concern, as bacteria with SARM would appear susceptible to an antibiotic when tested, but could then very quickly and easily become resistant to the antibiotic during treatment in a patient. Currently, we do not know how widespread SARM is amongst bacteria that cause disease, nor do we understand properly how SARM occurs. The present proposal aims to investigate both of these issues in the so-called 'superbug', Staphylococcus aureus. To establish how common SARM is, a large collection of 1500 S. aureus isolates recovered from patients around the world will be tested. Each strain will undergo DNA sequencing of its genome to establish its complete genetic make-up, which will allow for the identification of genes that are known to be associated with antibiotic resistance. In addition, the susceptibility of each isolate to a wide range of commonly used antibiotics will be established to determine if the bacterium displays resistance to all the drugs that it has the genetic potential to display resistance to. Strains that carry antibiotic resistance genes, but do not exhibit resistance to the corresponding antibiotics, represent potential SARM strains. These strains will be studied in detail to establish how easily they can switch their 'silenced' resistance genes back on, and to understand the mechanism(s) by which SARM works.

  • Funder: UKRI Project Code: 102115
    Funder Contribution: 722,053 GBP
    Partners: Shearwater Systems Limited

    A major challenge in the delivery of high quality care to people with epilepsy is the accurate collection and analysis of information about their condition, and possible seizures, throughout their daily life. The Epilepsy Networks project aims to improve the provision of expert care to people with epilepsy using existing NHS resources but leveraging them with innovative use of secure mobile and social solutions. The captured information will then be embedded in the patient's NHS electronic record, and using automated analysis and clinically validated data visualisation, the project will enable a more accurate and timely assessment of the patient’s condition. This will facilitate novel clinical pathways including real-time pre-emptive intervention, reducing the cost of care and enabling the re-engineering of existing services to better meet the needs of those diagnosed with this life-altering condition. The ultimate goal is to develop a platform for connecting patients, hospital and community healthcare professionals around an innovative, shared digital record including input from patients and families for a full spectrum of medical conditions.

  • Funder: UKRI Project Code: EP/M013766/1
    Funder Contribution: 100,317 GBP
    Partners: University of Salford

    With rapid increases in data volume in all areas of life, the meaningful analysis of these data is becoming a crucial bottleneck. Whether data are generated by customer transactions, through communications on social media, or as a by-product of manufacturing processes, data are meaningless unless suitable techniques are available to select the most relevant data, analyze these data and turn raw data into tangible information and insight. To some extent, "big data" reverses traditional approaches in data-mining, as data collection now frequently precedes the definition of an actual question or hypothesis. The purported advantage of this approach is that novel, unexpected findings may materialize - a premise that relies, however, on the expert use of suitable approaches for exploratory data analysis. The prominence of "big data" therefore fuels the need and use of scalable and powerful approaches to exploratory data analysis. Data clustering techniques present one of the most fundamental tools in exploratory data analysis, and this project aims to deliver novel techniques that are accurate, flexible and scalable to large data sets. Data clustering techniques present one of the most fundamental tools in exploratory data analysis. Conceptually, data clustering refers to the identification of sub-groups within a data set so that items within the same group are similar and those in different groups are dissimilar; e.g., in the context of insurance data, a "cluster" of people may relate to customers who show similar behaviour in their claim patterns over time, while those in different clusters behave differently. Mathematically, data clustering can be seen as an example of a problem where good solutions are best described using a set of different criteria that account for conflicting properties such as the compactness of clusters and the separation between clusters. The above observation has recently led to the development of multi-criterion approaches to data clustering, which explicitly consider a number of clustering criteria. This approach has shown a lot of promise, in terms of the accuracy and the robustness of the solutions obtained. However, current techniques for multi-criterion clustering are limited regarding their scalability to very large data sets and also their flexibility with respect to their consideration of different sources of dissimilarity data. This project proposes a novel technique for multi-criterion clustering: the algorithm will combine complementary ideas from two sub-fields of computer science, leading to improved scalability and flexibility of the technique developed. The work will include the development of an interactive user-interface and the application of multi-criterion clustering to problems in finance and marketing. All software produced will be released publicly.

  • Funder: UKRI Project Code: BB/M009610/1
    Funder Contribution: 292,590 GBP
    Partners: Swansea University

    Life-End summary Sustainable production of safe chicken is an international priority and preserving bird welfare is a key component of this. Current intensive (broiler) production can compromise bird health and welfare and food safety and there are strong links between poor bird welfare and the Campylobacter public health threat. Campylobacter is the most common cause of bacterial diarrhoea in the EU and despite millions of pounds of research funding it is estimated that contaminated chicken caused ~700000 human campylobacteriosis cases in the UK in 2013 with around 100 deaths. Infection is characterised by severe abdominal pain and acute (sometimes bloody) diarrhoea and costs the UK an estimated £1 billion per year. Campylobacter contamination of chicken takes two forms. First, surface contamination of carcasses leads to cross-contamination in the kitchen. Second, and perhaps of greater importance than currently thought, contamination within muscle and liver tissues, increasing the health risk by facilitating bacterial survival during cooking. Chickens in poor production environments or exposed to stress are more susceptible to Campylobacter and in such birds the bacteria show greater extra-intestinal spread to edible tissues, possibly as a consequence of disturbance to the gut environment. Therefore, improvements in broiler welfare have great potential to improve public health but there is an urgent need for information on the effect of stress to inform targeted interventions to reduce Campylobacter in broiler chickens. One acutely stressful event in the life of broilers, in any production stream, is harvest when birds are removed from the farm for slaughter. We define the process as comprising: food withdrawal, catching, transport and stunning by either gas or electricity. Although there is a growing body of evidence that these stressors can increase Campylobacter growth rates as well as extra-intestinal spread, there is a paucity of data on their relative importance or how they may select for particular types of Campylobacter. By examining the harvest processes using large scale industry-relevant experimental conditions, state-of-the-art genomics, molecular microbiology and mathematical modelling techniques, we will determine the impact of harvest on gut health in broilers. We will combine this with a study to identify bacterial genetic determinants involved in extra-intestinal spread of Campylobacter to edible tissues. We will quantify the relative impact of each stage of harvest on the gut bacterial population and the physiology and immunity of the birds, and investigate the role these play in controlling extra-intestinal spread of Campylobacter. This multidisciplinary research programme will enhance understanding of the influence of the harvest process on bird gut health and Campylobacter. The quantitative information and modelling will be used to provide direct advice to industry about the elements of the harvest processes that provide the best opportunity for interventions that will mitigate the ongoing challenge of Campylobacter contamination in chicken meat.

  • Funder: UKRI Project Code: EP/J020184/2
    Funder Contribution: 227,091 GBP
    Partners: E.ON New Build and Technology Ltd, University of Surrey

    This programme is proposed to answer the EPSRC call on "Carbon capture and storage for natural gas power stations" by forming a close partnership between the University of Southampton and E.ON. The proposed research has a strong focus on industrial needs by integrating with the industrial partner's existing activities for developing CCS technologies suitable for commercial gas power plants. E.ON is generating around 10% of the UK's electricity and is committed to reducing its CO2 emission by 50% by 2030 (1990 baseline). E.ON has setup a dedicated CCS unit to address the technical challenges while one of the priorities is to develop CCS technologies suitable for natural gas power stations. This research specifically targets at natural gas power plants, which has a lower concentration of CO2 approx. 4% compared to 13% from coal-fired plants, and harder to extract, representing the most challenging case for CCS. Carbon capture and storage involves separating the CO2 from emissions so it can be transported and stored away from the atmosphere. The most commercially viable approach to be fitted in natural gas power plants is the post-combustion capture which absorbs CO2 from the flue gas using a chemical reaction - also known as scrubbing, which E.ON has been actively pursuing and will be the focus of this research. Whilst research on the chemical processes has been taking place for several decades, CFD modelling of the reactor is a recent development. E.ON has recognised that CFD plays a vital role in the optimisation of current CCS reactors by including more CFD research in their future research strategy. University of Southampton is a prime place for CFD based research while the School of Engineering Sciences currently holds £5M CFD focused EPSRC projects. The combined expertise forms a strong academic and industrial partnership to tackle current barriers of reactor scale-up in carbon capture using advanced CFD models. By addressing all the challenges outlined in the EPSRC call, this research aims to design an optimised reactor using a novel CFD modelling approach that is capable of achieving in excess of 90% CO2 absorption whilst ensuring the cost of service energy is minimised to below 35%. The new concept idea will incorporate improved mixing designs and improved heat transfer whilst reducing reactor size. It is planned through the enhancement of current CFD multiphase models to incorporate reaction and the inclusion of flow control devices that an optimal structured packing arrangement, which promotes the reaction process whilst reducing pressure drop, can be found. This project will not only produce conceptual ideas developed through enhance CFD methods but will also perform tests, in a lab-scale reactor, to determine its validity with respect to its flow dynamics and would potentially lead to the production of intellectual property.

  • Funder: UKRI Project Code: EP/M015815/1
    Funder Contribution: 98,207 GBP
    Partners: University of Liverpool, UvA

    The project concerns how groups of partially informed and self-interested agents (e.g., humans, robots), which are faced with a common problem, take a collective decision by exchanging their individual opinions to, possibly, reach a consensus. It aims at understanding how processes of opinion formation in groups behave and how they can be engineered in groups of artificial agents, like robots. The project capitalizes on techniques developed in the social and economic sciences, applying them to the artificial intelligence setting. It extends the state-of-the-art in the application of voting theory to artificial intelligence, addressing the process of opinion formation, and lays the theoretical groundwork for the development of collective decision-making techniques in autonomous systems.

  • Funder: UKRI Project Code: EP/N006399/1
    Funder Contribution: 169,320 GBP
    Partners: Sympatec, University of Birmingham, VolitionRX, University of Sussex, University of Bath, University of Sunderland, Bluefrog Design Limited, GlaxoSmithKline, Imperial College London, DMU...

    Advances in fit for use manufacturing of biopharmaceutical drug delivery and pharmaceutical systems are now required to fit Quality by Design (QbD) models. These current regulations require excellence to be built into the preparation of emerging products (both material and process) thereby leading to product robustness and quality. In addition, industrial needs (economical and reproducible quality enhancement) are driving manufacturing towards continuous processes over batch type processes which also rely on QbD (for integrity and quality). EHDA technology is a robust process that has been utilised in various formats (e.g. electrospinning, electrospraying, bubbling and even 3D printing) and is favourable due to applicability with the development of stable nanomedicines and biopharmaceuticals, the emergence of this technology is clearly evident in the UK and on the global scale. Attempts in scaling up (for suitable pharmaceutical scale) and in tandem with continuous processes (including controlled manufacturing) have been very limited. There also, now, remains a huge void in the adaptation of sensible QbD (multi-variate) for the current methods developed and also those required by industry. While lab scale research continues with the ongoing development of such processes (e.g. nanomedicines, smart and controlled delivery), the transition to industry or the clinic will have to meet these regulations (and scales) for there to be a real impact, which is now, also, an important aspect of grass root research in the UK. The EHDA network brings together specialists from academia and industry to advance this technology through several means. Firstly, initiating developments towards a real-viable scale for Pharmaceutical production. Secondly, to incorporate developments in lean manufacturing and legislation (e.g. continuous manufacturing, online diagnostics, QbD and adaptable scale). Thirdly, to marry optimised lean technologies with novel and emerging macromolecular therapies and actives. The network has a wide range of activities and initiatives which will lead to significant developments (and collaborations) in an area of increasing global interest (EHDA processes) - but currently only on a viable lab scale to date. This network will be the first of its kind and will serve as the central and pioneering hub in this remit.

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