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217 Projects, page 1 of 22

  • UK Research and Innovation
  • UKRI|EPSRC
  • 2021
  • 2022

10
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  • Funder: UKRI Project Code: EP/X525297/1
    Funder Contribution: 200,000 GBP
    Partners: SOAS

    Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

  • Funder: UKRI Project Code: EP/V061607/1
    Funder Contribution: 63,613 GBP
    Partners: National Grid Electricity Transmission p, Cranfield University

    The project will develop a proof-of-concept planning model for central planners to optimally locate electric vehicles (EVs) charging infrastructure under the risk of disruption to charging points (i.e. unexpected failure of charging points due to technical faults or breakdowns). The aim of the model will be to maximise total expected traffic volume of EVs that can be charged by an unreliable integrated charging network. Both static and dynamic wireless charging systems, as well as railway feeder stations will be considered. A robust mixed-integer non-linear programming (MINLP) model for this problem will be formulated. Queuing theory equations will be incorporated into the model to account for the stochastic nature of demand both spatially and over time (e.g. peak versus off-peak periods). The model will be further generalized to a multi-period planning problem given limited periodic budgets. The model will be linearized so that it can be solved using a general purpose solver. Finally, an efficient metaheuristic algorithm will be developed to solve the large-scale real-world instances within a reasonable computational time. A case study of the road network in the UK will be used to assess the accuracy and performance of the linearized optimization model and the metaheuristic algorithm. Besides the model and the algorithm, other project outputs will be the creation of test datasets and one or more journal articles. Codes of the model and algorithm, and test datasets will also be made available to the community of Operational Research so that other researchers and practitioners (e.g., National Grid) can use them in their own case studies.

  • Funder: UKRI Project Code: EP/V048058/1
    Funder Contribution: 201,596 GBP
    Partners: Imperial College London

    CRISPR-Cas9 gene editing has warranted its developers the 2020 Nobel Prize for Chemistry, in view of the massive impact that this technology is having on biological, biotechnological and medical research. In particular CRISPR gene editing is central to next generation cell-based therapies to treat cancer and other diseases, in which some of the patient's cells are extracted, genetically modified to fulfil specific functions, and then re-injected into the patient. This process is however time consuming and very costly, limiting the diffusion of these potentially life-saving therapies. One of the reasons behind the prohibitive costs is the low efficiency with which one can deliver to the cells the biological machinery required to perform CRISPR gene editing, both at scale and without excessive toxicity (leading to cell death). In this project, we will develop a novel and alternative approach to delivering CRISPR machinery to mammalian cells in vitro. Our approach will rely on specifically designed vectors, which we dub Editosomes. These are microscopic enclosures constructed from lipid membranes, similar to cell membranes, and containing large quantities of the CRISPR machinery. For Editosomes to deliver the machinery to the target cells the two would have to fuse. We will induce fusion by decorating both Editosomes and the target cells with artificial "fusogenic" nanomachines, that by binding to each other bring the cell and Editosome membranes to within a very short distance, ultimately making them merge. The fusogenic nanostructures will be constructed from synthetic DNA molecules, which are particularly suitable for engineering nanodevices in view of the very high selectivity and programmability of the base-pairing interactions. We envisage that Editosome technology will have a direct and profound impact on our ability to perform high-throughput, efficient, CRISPR gene editing in vivo, and thus on the accessibility and economic sustainability of the therapeutic technologies that rely on it. Additionally, we will clarify fundamental aspects of the (bio)physics underling lipid membrane stability, fusion, and the ability of DNA nanostructures to modulate them.

  • Funder: UKRI Project Code: EP/X525455/1
    Funder Contribution: 150,000 GBP
    Partners: University of Huddersfield

    Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

  • Funder: UKRI Project Code: EP/W004801/1
    Funder Contribution: 302,752 GBP
    Partners: Chalmers University of Technology, University of Cambridge

    Artificial Intelligence (AI) is transforming the world. AI is the core technology of many of the biggest companies in the world, Amazon, Google, Facebook, etc. that effect all our lives. AI is now starting to transform science and technology. Most people in the EU now live better than Kings did in the past: they have better food, medical care, transport, etc. This miracle has been made possible through better technology based on science. To meet the great challenges the 21st century world faces: climate change, food insecurity, disease, etc., we need to make science and technology even more efficient. We propose the AMBITION project to harness the power of AI and laboratory robotics to provide researchers in the UK, and beyond, with continuous, uninterrupted, remote access to AI/robotic augmented biomedical research capabilities. This will enable more robust, efficient and reproducible biomedical research. The UK's life sciences, biotechnological and pharmaceutical industry are world-leading. However, the Covid-19 pandemic has clearly demonstrated the vital importance of biomedical research and the critical need to maintain research continuity at all times. Yet, lockdowns and social distancing pose a severe threat to research continuity, forcing laboratories to shut down, risking loss of years of research. Integrating AI with laboratory automation will also enable the automation of routine parts of scientific theory formation and experimentation. This will enable results to be obtained both more efficiently and faster compared to the state-of-the-art where human scientists must make all the decisions. AMBITION does not aim to replace humans, but empower them by reasoning and data processing capabilities to better support their decision making. Biomedical science is facing a 'reproducibility crisis'. Despite reproducibility being fundamental to science, the reproducibility of few biomedical results is currently tested, and when reproducibility is tested, the results are dismal, with only 10 to 20% of published biomedical research found to be reproducible. Finally, automated laboratories will make scientific results more reproducible, as AI systems describe experiments in more clearly than human scientists, and robots execute experimental protocols more accurately than human scientists. The project will focus on the development of the AI part of the system and iterative testing in real-world laboratory settings employing state-of-the-art robotics equipment. We will initially focus on cancer drug discovery as a first demonstration case, bringing together the power of AI and laboratory robotics. In the medium-term (3-5 years horizon). We plan to extend the approach to clinical patient care, and to provide real-time cancer treatment decision support system for patients in the UK and beyond based on automated testing of hundreds of treatment options on patient-derived tumour material, thereby leading to a reduction in animal experimentation, and giving clinicians an evidence-based, real-time input for their expert treatment decision. In the long-term (5-15 years horizon) we will rollout automated research capabilities and real-time treatment guidance across all of biomedicine, especially fields such as antibiotic treatment/ antimicrobial resistance, inflammatory diseases, etc. In 30 years, autonomous laboratories will transform the health sector. They will lower the costs of laboratory experiments, augment researchers' technical capabilities (making more elaborate and complex tests possible), reduce the risks associated with the presence of humans in the labs (working with hazardous substances, risk of infections), ensure reproducibility, increase accuracy of results, and ensure overall accountability and trust in the process. Autonomous laboratories will speed up and scale up the development of new drugs, remote testing of patients, and will be an enabler for personalised medicine.

  • Funder: UKRI Project Code: EP/V010778/1
    Funder Contribution: 18,763 GBP
    Partners: Loughborough University, Volvo Trucks

    Current trends toward automation include the automation of heavy goods vehicles. Heavy goods vehicles have long stopping distances which depend on the state of the road ahead of them. Computer vision can be used to determine the properties of the road ahead of the vehicle, allowing the possibility of determining where the vehicle should go in an emergency stopping manoeuvre. However, the estimate provided by computer vision of the properties of the road is an estimate, meaning that it has some uncertainty associated with it. Without taking this uncertainty into account, the actual stopping distance of the vehicle might be much longer than anticipated. The internal architecture for making vehicle motion planning decisions, integrating estimates of friction properties from the computer vision system, has not been clearly defined. In addition, a method is required for defining and understanding the uncertainty of the friction property estimates from the computer vision system and building this into the stopping distance estimation algorithm. This overseas travel grant seeks to fund a visit to Chalmers University of Technology in Gothenburg, Sweden. The grant will enable close collaboration between Dr Midgley and the vehicle dynamics group at Chalmers. It will also allow technical discussions with Volvo Trucks, who have an interest in the area of heavy vehicle automation. Dr Midgley will be able to work intensively on the architecture and distance estimation problems, while communicating freely with researchers from Chalmers and Volvo Trucks, and build links for future collaborative research projects.

  • Funder: UKRI Project Code: 2578140
    Partners: University of Warwick

    This PhD position is under a five-year fellowship programme supported by the EPSRC to develop 3D-printable biopolymer-based composite materials with tailored structures, properties and functionality for demanding applications. For this, the student will be focus on the chemical and physical modification of biopolymers, formulation development, and the design of the 3D-printing process. He/she will learn abundant skills in technical aspects such as polymer chemistry, polymer engineering, nanocomposites, and advanced materials characterisation. This PhD project clearly aligns with EPSRC research areas such as materials, materials engineering - composites, manufacturing technologies, and biomaterials and tissue engineering.

  • Funder: UKRI Project Code: EP/W004844/1
    Funder Contribution: 302,809 GBP
    Partners: Harvard University, St Marys NHS Trust, UK Dementia Research Institute, Imperial College London, IT'IS FOUNDATION, Alzheimer's Society

    The ageing of the world population has had a devastating impact on the prevalence of people with brain disorders. The most common brain disorder with age is dementia - a neurodegenerative disease that leads to cognitive impairment that progressively affects activities of daily living erodes independence and impairs quality of life. The leading cause of dementia is Alzheimer's disease, accounting for 60-70% of all dementia cases1. There are approximately 50 million people with dementia worldwide, and this number is projected to increase to 152 million by 20502. In the UK there are approximately 850,000 people with dementia, and this number is projected to increase to 1.6 million by 2040 (translating to 1 new dementia case every 3 minutes). The global costs of dementia are estimated to be US$1 trillion annually2. The estimated cost of dementia care in the UK is £35 billion, which is projected to rise sharply to £95 billion by 2040. At every given time, about one out of four beds in the NHS hospitals is occupied by a patient with dementia3, thus impeding care for other medical conditions. During the last decades, large-scale efforts to delay or stop the progression of dementia due to Alzheimer's disease via pharmacological interventions have failed to produce viable treatment. This project will develop a technology that aims to slow or reverse the progression of Alzheimer's disease by boosting the resilience to the pathology in the most vulnerable regions at the early stages of the disease. Our approach is based on non-invasive electrical stimulation of the activity in those vulnerable structures to build up their intrinsic metabolic and energetic functionalities, in a way that is conceptionally similar to how exercise builds up the metabolic and energetic functionalities in the muscles. To non-invasively stimulate the activity at the target brain structures which are often at deep locations, we will use a novel method, called temporal interference (TI) stimulation, that we recently discovered. We have already shown that TI stimulation can be used to change the activity in the hippocampus, a deep brain structure that is critical for memory and cognitive function and strongly affected in the early stages of Alzheimer's disease, in an animal model and in healthy humans. In this project, we will address the most critical engineering challenges to develop our concept to a reliable and precise non-invasive deep brain stimulation technology that can be deployed in large-scale clinical testing. In addition, we will test and iteratively improve the effect of the temporal interference stimulation on the pathology of the hippocampus in animal models of Alzheimer's disease. Finally, we will start developing the pathway to translate the technology to a viable healthcare treatment with affordable and wearable hardware that can also be deployed at the patients' home. The temporal interference brain stimulation technology with its capability to target arbitrary deep brain structures will provide a platform for developing therapies for multiple brain disorders underpinned by aberrant activity in those structures. The development of such a disruptive technology will place the UK at the frontiers of the neurotechnology industry that is poised for the fastest growth in the medical industry. 1. Livingston, G. et al. The Lancet (2017) 2. Patterson, C. World Alzheimer Report 2018, London, UK (2018). 3. Alzheimer's Society (2009).

  • Funder: UKRI Project Code: EP/X525479/1
    Funder Contribution: 150,000 GBP
    Partners: TGAC

    Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

  • Project . 2021 - 2022
    Funder: UKRI Project Code: 2597147
    Partners: University of Warwick

    Summary of core modules taken during MSc year; MA930 - Data Analysis and Machine Learning MA931 - MSc Project MA932 - MSc Study Group MA933 - Stochastic Modelling and Random Processes MA934 - Numerical Algorithms and Optimisation MA999 - Topics in Mathematical Modelling

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217 Projects, page 1 of 22
  • Funder: UKRI Project Code: EP/X525297/1
    Funder Contribution: 200,000 GBP
    Partners: SOAS

    Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

  • Funder: UKRI Project Code: EP/V061607/1
    Funder Contribution: 63,613 GBP
    Partners: National Grid Electricity Transmission p, Cranfield University

    The project will develop a proof-of-concept planning model for central planners to optimally locate electric vehicles (EVs) charging infrastructure under the risk of disruption to charging points (i.e. unexpected failure of charging points due to technical faults or breakdowns). The aim of the model will be to maximise total expected traffic volume of EVs that can be charged by an unreliable integrated charging network. Both static and dynamic wireless charging systems, as well as railway feeder stations will be considered. A robust mixed-integer non-linear programming (MINLP) model for this problem will be formulated. Queuing theory equations will be incorporated into the model to account for the stochastic nature of demand both spatially and over time (e.g. peak versus off-peak periods). The model will be further generalized to a multi-period planning problem given limited periodic budgets. The model will be linearized so that it can be solved using a general purpose solver. Finally, an efficient metaheuristic algorithm will be developed to solve the large-scale real-world instances within a reasonable computational time. A case study of the road network in the UK will be used to assess the accuracy and performance of the linearized optimization model and the metaheuristic algorithm. Besides the model and the algorithm, other project outputs will be the creation of test datasets and one or more journal articles. Codes of the model and algorithm, and test datasets will also be made available to the community of Operational Research so that other researchers and practitioners (e.g., National Grid) can use them in their own case studies.

  • Funder: UKRI Project Code: EP/V048058/1
    Funder Contribution: 201,596 GBP
    Partners: Imperial College London

    CRISPR-Cas9 gene editing has warranted its developers the 2020 Nobel Prize for Chemistry, in view of the massive impact that this technology is having on biological, biotechnological and medical research. In particular CRISPR gene editing is central to next generation cell-based therapies to treat cancer and other diseases, in which some of the patient's cells are extracted, genetically modified to fulfil specific functions, and then re-injected into the patient. This process is however time consuming and very costly, limiting the diffusion of these potentially life-saving therapies. One of the reasons behind the prohibitive costs is the low efficiency with which one can deliver to the cells the biological machinery required to perform CRISPR gene editing, both at scale and without excessive toxicity (leading to cell death). In this project, we will develop a novel and alternative approach to delivering CRISPR machinery to mammalian cells in vitro. Our approach will rely on specifically designed vectors, which we dub Editosomes. These are microscopic enclosures constructed from lipid membranes, similar to cell membranes, and containing large quantities of the CRISPR machinery. For Editosomes to deliver the machinery to the target cells the two would have to fuse. We will induce fusion by decorating both Editosomes and the target cells with artificial "fusogenic" nanomachines, that by binding to each other bring the cell and Editosome membranes to within a very short distance, ultimately making them merge. The fusogenic nanostructures will be constructed from synthetic DNA molecules, which are particularly suitable for engineering nanodevices in view of the very high selectivity and programmability of the base-pairing interactions. We envisage that Editosome technology will have a direct and profound impact on our ability to perform high-throughput, efficient, CRISPR gene editing in vivo, and thus on the accessibility and economic sustainability of the therapeutic technologies that rely on it. Additionally, we will clarify fundamental aspects of the (bio)physics underling lipid membrane stability, fusion, and the ability of DNA nanostructures to modulate them.

  • Funder: UKRI Project Code: EP/X525455/1
    Funder Contribution: 150,000 GBP
    Partners: University of Huddersfield

    Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

  • Funder: UKRI Project Code: EP/W004801/1
    Funder Contribution: 302,752 GBP
    Partners: Chalmers University of Technology, University of Cambridge

    Artificial Intelligence (AI) is transforming the world. AI is the core technology of many of the biggest companies in the world, Amazon, Google, Facebook, etc. that effect all our lives. AI is now starting to transform science and technology. Most people in the EU now live better than Kings did in the past: they have better food, medical care, transport, etc. This miracle has been made possible through better technology based on science. To meet the great challenges the 21st century world faces: climate change, food insecurity, disease, etc., we need to make science and technology even more efficient. We propose the AMBITION project to harness the power of AI and laboratory robotics to provide researchers in the UK, and beyond, with continuous, uninterrupted, remote access to AI/robotic augmented biomedical research capabilities. This will enable more robust, efficient and reproducible biomedical research. The UK's life sciences, biotechnological and pharmaceutical industry are world-leading. However, the Covid-19 pandemic has clearly demonstrated the vital importance of biomedical research and the critical need to maintain research continuity at all times. Yet, lockdowns and social distancing pose a severe threat to research continuity, forcing laboratories to shut down, risking loss of years of research. Integrating AI with laboratory automation will also enable the automation of routine parts of scientific theory formation and experimentation. This will enable results to be obtained both more efficiently and faster compared to the state-of-the-art where human scientists must make all the decisions. AMBITION does not aim to replace humans, but empower them by reasoning and data processing capabilities to better support their decision making. Biomedical science is facing a 'reproducibility crisis'. Despite reproducibility being fundamental to science, the reproducibility of few biomedical results is currently tested, and when reproducibility is tested, the results are dismal, with only 10 to 20% of published biomedical research found to be reproducible. Finally, automated laboratories will make scientific results more reproducible, as AI systems describe experiments in more clearly than human scientists, and robots execute experimental protocols more accurately than human scientists. The project will focus on the development of the AI part of the system and iterative testing in real-world laboratory settings employing state-of-the-art robotics equipment. We will initially focus on cancer drug discovery as a first demonstration case, bringing together the power of AI and laboratory robotics. In the medium-term (3-5 years horizon). We plan to extend the approach to clinical patient care, and to provide real-time cancer treatment decision support system for patients in the UK and beyond based on automated testing of hundreds of treatment options on patient-derived tumour material, thereby leading to a reduction in animal experimentation, and giving clinicians an evidence-based, real-time input for their expert treatment decision. In the long-term (5-15 years horizon) we will rollout automated research capabilities and real-time treatment guidance across all of biomedicine, especially fields such as antibiotic treatment/ antimicrobial resistance, inflammatory diseases, etc. In 30 years, autonomous laboratories will transform the health sector. They will lower the costs of laboratory experiments, augment researchers' technical capabilities (making more elaborate and complex tests possible), reduce the risks associated with the presence of humans in the labs (working with hazardous substances, risk of infections), ensure reproducibility, increase accuracy of results, and ensure overall accountability and trust in the process. Autonomous laboratories will speed up and scale up the development of new drugs, remote testing of patients, and will be an enabler for personalised medicine.

  • Funder: UKRI Project Code: EP/V010778/1
    Funder Contribution: 18,763 GBP
    Partners: Loughborough University, Volvo Trucks

    Current trends toward automation include the automation of heavy goods vehicles. Heavy goods vehicles have long stopping distances which depend on the state of the road ahead of them. Computer vision can be used to determine the properties of the road ahead of the vehicle, allowing the possibility of determining where the vehicle should go in an emergency stopping manoeuvre. However, the estimate provided by computer vision of the properties of the road is an estimate, meaning that it has some uncertainty associated with it. Without taking this uncertainty into account, the actual stopping distance of the vehicle might be much longer than anticipated. The internal architecture for making vehicle motion planning decisions, integrating estimates of friction properties from the computer vision system, has not been clearly defined. In addition, a method is required for defining and understanding the uncertainty of the friction property estimates from the computer vision system and building this into the stopping distance estimation algorithm. This overseas travel grant seeks to fund a visit to Chalmers University of Technology in Gothenburg, Sweden. The grant will enable close collaboration between Dr Midgley and the vehicle dynamics group at Chalmers. It will also allow technical discussions with Volvo Trucks, who have an interest in the area of heavy vehicle automation. Dr Midgley will be able to work intensively on the architecture and distance estimation problems, while communicating freely with researchers from Chalmers and Volvo Trucks, and build links for future collaborative research projects.

  • Funder: UKRI Project Code: 2578140
    Partners: University of Warwick

    This PhD position is under a five-year fellowship programme supported by the EPSRC to develop 3D-printable biopolymer-based composite materials with tailored structures, properties and functionality for demanding applications. For this, the student will be focus on the chemical and physical modification of biopolymers, formulation development, and the design of the 3D-printing process. He/she will learn abundant skills in technical aspects such as polymer chemistry, polymer engineering, nanocomposites, and advanced materials characterisation. This PhD project clearly aligns with EPSRC research areas such as materials, materials engineering - composites, manufacturing technologies, and biomaterials and tissue engineering.

  • Funder: UKRI Project Code: EP/W004844/1
    Funder Contribution: 302,809 GBP
    Partners: Harvard University, St Marys NHS Trust, UK Dementia Research Institute, Imperial College London, IT'IS FOUNDATION, Alzheimer's Society

    The ageing of the world population has had a devastating impact on the prevalence of people with brain disorders. The most common brain disorder with age is dementia - a neurodegenerative disease that leads to cognitive impairment that progressively affects activities of daily living erodes independence and impairs quality of life. The leading cause of dementia is Alzheimer's disease, accounting for 60-70% of all dementia cases1. There are approximately 50 million people with dementia worldwide, and this number is projected to increase to 152 million by 20502. In the UK there are approximately 850,000 people with dementia, and this number is projected to increase to 1.6 million by 2040 (translating to 1 new dementia case every 3 minutes). The global costs of dementia are estimated to be US$1 trillion annually2. The estimated cost of dementia care in the UK is £35 billion, which is projected to rise sharply to £95 billion by 2040. At every given time, about one out of four beds in the NHS hospitals is occupied by a patient with dementia3, thus impeding care for other medical conditions. During the last decades, large-scale efforts to delay or stop the progression of dementia due to Alzheimer's disease via pharmacological interventions have failed to produce viable treatment. This project will develop a technology that aims to slow or reverse the progression of Alzheimer's disease by boosting the resilience to the pathology in the most vulnerable regions at the early stages of the disease. Our approach is based on non-invasive electrical stimulation of the activity in those vulnerable structures to build up their intrinsic metabolic and energetic functionalities, in a way that is conceptionally similar to how exercise builds up the metabolic and energetic functionalities in the muscles. To non-invasively stimulate the activity at the target brain structures which are often at deep locations, we will use a novel method, called temporal interference (TI) stimulation, that we recently discovered. We have already shown that TI stimulation can be used to change the activity in the hippocampus, a deep brain structure that is critical for memory and cognitive function and strongly affected in the early stages of Alzheimer's disease, in an animal model and in healthy humans. In this project, we will address the most critical engineering challenges to develop our concept to a reliable and precise non-invasive deep brain stimulation technology that can be deployed in large-scale clinical testing. In addition, we will test and iteratively improve the effect of the temporal interference stimulation on the pathology of the hippocampus in animal models of Alzheimer's disease. Finally, we will start developing the pathway to translate the technology to a viable healthcare treatment with affordable and wearable hardware that can also be deployed at the patients' home. The temporal interference brain stimulation technology with its capability to target arbitrary deep brain structures will provide a platform for developing therapies for multiple brain disorders underpinned by aberrant activity in those structures. The development of such a disruptive technology will place the UK at the frontiers of the neurotechnology industry that is poised for the fastest growth in the medical industry. 1. Livingston, G. et al. The Lancet (2017) 2. Patterson, C. World Alzheimer Report 2018, London, UK (2018). 3. Alzheimer's Society (2009).

  • Funder: UKRI Project Code: EP/X525479/1
    Funder Contribution: 150,000 GBP
    Partners: TGAC

    Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

  • Project . 2021 - 2022
    Funder: UKRI Project Code: 2597147
    Partners: University of Warwick

    Summary of core modules taken during MSc year; MA930 - Data Analysis and Machine Learning MA931 - MSc Project MA932 - MSc Study Group MA933 - Stochastic Modelling and Random Processes MA934 - Numerical Algorithms and Optimisation MA999 - Topics in Mathematical Modelling

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