Computational biomedicine offers many avenues for taking full advantage of emerging exascale computing resources and, as such, will provide a wealth of benefits as a use-case within the wider ExCALIBUR initiative. These benefits will be realised not just via the medical problems we elucidate but also through the technical developments we implement to enhance the underlying algorithmic performance and workflows supporting their deployment. Without the technical capacity to effectively utilise resources at such unprecedented scale - either in large monolithic simulations spread over the equivalent of many hundreds of thousands of cores, in coupled code settings, or being launched as massive sets of tasks to enhance drug discovery or probe a human population - the advances in hardware performance and scale cannot be fully capitalised on. Our project will seek to identify solutions to these challenges and communicate them throughout the ExCALIBUR community, bringing the field of computational biomedicine and its community of practitioners to join those disciplines that make regular use of high-performance computing and are also seeking to reach the exascale. In this project, we will be deploying applications in three key areas of computational biomedicine: molecular medicine, vascular modelling and cardiac simulation. This scope and diversity of our use cases mean that we shall appeal strongly to the biomedical community at large. We shall demonstrate how to develop and deploy applications on emerging exascale machines to achieve increasingly high-fidelity descriptions of the human body in health and disease. In the field of molecular modelling, we shall develop and deploy complex workflows built from a combination of machine learning and physics-based methods to accelerate the preclinical drug discovery pipeline and for personalised drug treatment. These methods will enable us to develop highly selective small molecule therapeutics for cell surface receptors that mediate key physiological responses. Our vascular studies will utilise a combination of 1D, 3D models and machine learning to examine blood flow through complex, personalised arterial and venous structures. We will seek to utilise these in the identification of risk factors in clinical applications such as aneurysm rupture and for the management of ischaemic stroke. Within the cardiac simulation domain, a new GPU accelerated code will be utilised to perform multiscale cardiac electrophysiology simulations. By running large populations based on large clinical datasets such as UK Biobank, we can identify individual at elevated risk of various forms of heart disease. Coupling heart models to simulations of vascular blood flow will allow us to assess how problems which arise in one part of the body (such as the heart) can cause pathologies on remote regions. This exchange of knowledge will form a key component of CompBioMedX. Through this focussed effort, we will engage with the broader ExCALIBUR initiative to ensure that we take advantage of the efforts already underway within the community and in return reciprocate through the advances made with our use case. Many biomedical experts remain unfamiliar with high-performance computing and need to be better informed of its advantages and capabilities. We shall engage pro-actively with medical students early in their career to illustrate the benefits of using modelling and supercomputers and encourage them to exploit them in their own medical research. We shall engage in a similar manner with undergraduate biosciences students to establish a culture and practice of using computational methods to inform the experimental work underpinning the basic science that is the first step in the translational pathway from bench to bedside.

Almost one quarter of adults currently experience some form of mental health disorder in the UK, costing the healthcare system an estimated £77 billion each year. However, there exists very little objective or real-time monitoring of sufferers of mental health issues. This pilot project will investigate the development of a novel data fusion framework that will be suitable for combining many observations of a patient's behaviour to allow accurate mental health monitoring in any environment. Recent studies have shown that certain types of physical behaviour, daily cycles (circadian rhythms) and social networking activity can be indicative of an individual's state of mental health. However, recording the necessary data to make a diagnosis is difficult, both due to the nature of the health issues and because of the instrumentation needed. Recent developments in commercially available equipment (including smart phones) mean that we now have the opportunity to cheaply and routinely record human behaviour as well as daily patterns of physiology (such as sleep and cardiac activity). By then applying advanced pattern recognition and data fusion techniques, we intend to provide daily feedback of mental well-being to both the patient and care providers. This could facilitate early interventions in deteriorating individuals, thereby lowering costs of health care and reducing the severity of the illness. We also intend to begin to answer the more fundamental question about how circadian rhythms change as mental health deteriorates. The developed of a user-friendly and user-controlled monitoring system, together with a suite of suitable algorithms, will be an important step towards a larger integration of the ever increasing multi-dimensional biometric data we are beginning to collect. This includes signals such as location, body temperature, speech patterns and social interaction behaviours. The potential to fuse data from many different sensors, and many different algorithms, will provide a platform for intelligible interpretation of the vast quantities of data that are beginning to confront researchers in biomedical applications. It will also help to improve the accuracy of monitoring systems and provide the doctor with more objective assessments of patient behaviour, which could lead to more accurate and timely diagnoses.

The use of ultrasound as a diagnostic imaging tool is well known, particularly during pregnancy where ultrasound is used to create images of the developing foetus. In recent years, a growing number of therapeutic applications of ultrasound have also been demonstrated. The goal of therapeutic ultrasound is to modify the function or structure of the tissue, rather than produce an anatomical image. This is possible because the mechanical vibrations caused by the ultrasound waves can affect tissue in different ways, for example, by causing the tissue to heat up, or by generating internal radiation forces that can agitate the cells or tissue scaffolding. These ultrasound bioeffects offer a huge potential to develop new ways to treat major diseases such as cancer, to improve the delivery of drugs while minimising side-effects, and to treat a wide spectrum of neurological and psychiatric conditions. The fundamental challenge shared by all applications of therapeutic ultrasound is that the ultrasound energy must be delivered accurately, safely, and non-invasively to the target region within the body. This is difficult because bones and other tissue interfaces can severely distort the shape of the ultrasound beam. This has a significant impact on the safety and effectiveness of therapeutic ultrasound, and presents a major hurdle for the wider clinical acceptance of these exciting technologies. In principle, any distortions to the ultrasound beam could be accounted for using advanced computer models. However, the underlying physics is complex, and the scale of the modelling problem requires extremely large amounts of computer memory. Using existing software, a single simulation running on a supercomputer can take many days to complete, which is too long to be clinically useful. The aim of this proposal is to develop more efficient computer models to accurately predict how ultrasound waves travel through the human body. This will involve implementing new approaches that efficiently divide the computational problem across large numbers of interconnected computer cores on a supercomputer. New approaches to reduce the huge quantity of output data will also be implemented, including calculating clinically important parameters while the simulation runs, and optimising how the data is stored to disk. We will also develop a professional user interface and package the code within the regulatory framework required for medical software. This will allow end-users, such as doctors, to easily use the code for applications in therapeutic ultrasound without needing to be an expert in computer science. In collaboration with our clinical partners, the computer models will then be applied to different applications of therapeutic ultrasound to allow the precise delivery of ultrasound energy to be predicted for the individual patient.

The growth of an organism is one of the major components of homeostasis. As such, controlling growth appropriately is vital for the health of all animals. The endocrine system, through various hormones, plays a critical role in regulating when an organism needs to grow and develop. The major endocrine control centre that regulates growth is the pituitary gland, which is a pea-sized organ that lies at the base of the brain. Many genes have been identified that are known to be important in the development of the pituitary gland, and several of these are specific to the cells that make growth hormone (GH). Tumours of the pituitary gland represent the most common intracranial tumour in humans, with 1 in 6 people displaying evidence of these growths at the time of their death. Pituitary tumours, as well as other pituitary abnormalities, can cause disrupted release of many hormones, which consequently affects the quality of life of these patients. We have recently found that companion animals, such as dogs and cats, are known to be susceptible to these pituitary tumours. Growth hormone, one of the major pituitary hormones, is extremely important for the development and growth of an individual, and the release of GH is normally under tight control by the hypothalamus (part of the brain), as well as from other hormones released from tissues such as the liver. Inappropriately high or low GH release can cause a series of disorders, ranging from developmental abnormalities in infants, to dwarfism or metabolic complications in adults, and even an increased risk of certain types of colon cancer. We have recently discovered that another hormone found in the pituitary gland, C-type natriuretic peptide (CNP), is produced very early on in the development of human, mice and fish pituitaries. Our recent investigation of 30 human pituitary adenomas found that each one of these tumours expressed CNP, and the receptor that controls its effects, called GC-B. In addition, our preliminary studies have shown that treating pituitary cells with CNP can cause a dramatic increase in the amount of GH that is made. The work we propose to perform, detailed within this application, will extend our understanding of how CNP might influence the way in which the pituitary gland develops and functions normally. We shall use five different models to examine the effects of CNP; cultured pituitary cells, mice that specifically lack CNP in their pituitary glands, human and cat pituitary tumours and the highly versatile Zebrafish, in which we will silence the genes that encode CNP and establish the consequences for normal growth. In addition, our laboratory is equipped with an extremely efficient genetic analyser, that allows us to measure the amounts of up to 15 different genes in a single sample, greatly increasing our productivity from these very small amounts of tissue. These studies may reveal a role for CNP in the treatment of growth disorders, either as a way to increase GH release in individuals with impaired growth, or by developing drugs to block the effects of CNP and, therefore, reduce GH release. Such findings could lead to an improved quality of life, and a reduced susceptibility to subsequent endocrine disease.

The uneven ways that civil liberties, work, labour and health have all been impacted over the last 18 months as we have all turned to digital technologies to sustain previous ways of life, has not only shown us the extent of inequalities across all societies as they are cut through with gender, ethnicity, age, opportunities, class, geolocation; it has also led many organisations and businesses across all three sectors to question those values they previously supported. Capitalising on this moment of reflection across industry, the public and third sectors; we explore the possibility of imagining and building a future that takes different core values and practices as central, and works in very different ways. As the roles of organisations and businesses across all industry, the public and third sectors changes, what is now taken up as core values and ethos will be crucial in defining the future. INCLUDE+ will build a knowledge community around in/equalities in digital society that will comprise industry, academia, the public and third sectors. Responding to the Equitable Digital Society theme, we ask how we can design, co-create and realise digital services and infrastructures to support inclusion and equality in ways that enable all people to thrive. Focusing on the three connected strands of wellbeing, precarity, and civic culture; we address structural inequalities as they emerge through our research, investigating them through whole system approaches that includes the generation of outputs that comprise of new systems, services and practices to be taken up by organisations. More than this, our knowledge community will be underpinned by empirical, co-curation and participatory led research that will produce real interventions into those structural inequalities. These interventions will be taken up by organisations, responded to and considered, enabling the wider knowledge community to critically assess them in relation to the values they purport to promote. Fed by secondments and supported through smaller exploratory and escalator funds, our knowledge community will not only grow through traditional networking activities such as workshops, annual conferences, academic outputs and further funding; it will also grow through the development of interdisciplinary methods, knowledge exchange practices, and mentorship, which the secondment package will promote. In so doing, we structure our N+ around participatory research practices, people development and knowledge exchange, aiming to grow our network through the development and growth of people and good practice. INCLUDE+ is led by a highly experienced cross-disciplinary team incorporating Management and Business Studies, Computing, Social Sciences, Media and Communication and Legal Studies. Each Investigator brings vibrant international networks; active research projects feeding the Network+; and long experience of impact generation across policy and research. With support from organisations like the International Labour Organisation, Law Commission, Cabinet Office, and Equality and Human Rights Commission as well as the existing DE community, we will develop from and with existing research, extend this work and impact beyond it. Our partner organisations cut across industry, the public and third sectors and include (for example) Lego; NHS AI Lab; Space2; mHabitat; Leeds, Cambridgeshire and Swansea Councils; PeopleDotCom; Ditchley; 5Rights; EAMA; DataKind and IBM. We have designed the Network+ to enable a whole system approach that is genuinely exciting and innovative not just because of scalability, transference and scope, but also because of the commitment to people development, knowledge exchange and interdisciplinary practice that will also shape future research