Pathology is the branch of medicine that studies the cause, origin, and nature of diseases through the examination of tissue biopsies at a microscopic level. Pathology slides are traditionally handled by cutting a tissue sample into paper-thin sections, and staining them so to bring out regions of interest (RoIs). A pathologist places these paper-thin sections on a glass slide under a microscope in order to look for a range of features that aid in confirming the presence and malignancy level of the disease. For example, in the case of cancer biopsies, the pathologist analyses the shape, size and amount of abnormal and normal cell nuclei in the tissue to confirm the existence and progression of the tumour. Recent advances on whole-slide digital scanners have made possible the digitization of pathology slides, allowing their storage and manipulation in digital form. The digitized versions of pathology slides, which are called virtual slides or whole-slide images (WSIs), are complementing traditional analysis techniques that rely on pathologists looking under a microscope with techniques that rely on pathologists looking at digital images on a monitor. Moreover, digitization of these slides also allows providing telepathology services by sharing WSIs and thus reaching isolated hospitals and medical centres. For example, thanks to telepathology, pathologists would be able to send WSIs electronically to others or post them on a secure web-site making them available for consultation with other pathologists. As a consequence, more pathologists may be brought into the process of making a diagnosis, thus avoiding medical errors. Due to the high resolution required to digitize pathology slides, the resulting WSIs tend to be huge in file size, which results in heavy demands for storage and transmission resources. For example, the digitization of a single core of prostate biopsy tissue, of roughly the dimensions of a stamp, could easily result in 900 million pixels. By comparison, a photograph of 4x5 inches in size scanned at 300 dots per inch, which is the standard resolution for printing in a magazine, results in only 1.8 million pixels. So, WSIs usually require around 500 times more pixels than regular digital images. Moreover, a single pathology study normally comprises more than one biopsy sample. For example, in the case of prostate cancer studies, more than 10 biopsy samples are often required per patient, resulting in hundreds of gigabytes of imaging data per study. As a consequence, the main challenge that currently prevents telepathology from being widely used in clinical settings is the huge file size of WSIs, which makes the access and transmission of these data over different channels lengthy. Additionally, their huge file size also prevents WSIs from being widely used in current Picture Archiving and Communications Systems (PACS), which comprise a collection of software and network infrastructure used in hospitals and medical centres to store, share and display medical images. Integrating WSIs into PACS would allow pathologist to use other patient data available in PACS in order to increase the accuracy of diagnosis. Therefore, designing efficient coding methods capable of facilitating the access and transmission of WSIs for telepathology applications, while allowing integrating these data into PACS, remains a challenge. This project is mainly concerned with the design of such methods.

Despite being far from having reached 'artificial general intelligence' - the broad and deep capability for a machine to comprehend our surroundings - progress has been made in the last few years towards a more specialised AI: the ability to effectively address well-defined, specific goals in a given environment, which is the kind of task-oriented intelligence that is part of many human jobs. Much of this progress has been enabled by deep reinforcement learning (DRL), one of the most promising and fast-growing areas within machine learning. In DRL, an autonomous decision maker - the "agent" - learns how to make optimal decisions that will eventually lead to reaching a final goal. DRL holds the promise of enabling autonomous systems to learn large repertoires of collaborative and adaptive behavioural skills without human intervention, with application in a range of settings from simple games to industrial process automation to modelling human learning and cognition. Many real-world applications are characterised by the interplay of multiple decision-makers that operate in the same shared-resources environment and need to accomplish goals cooperatively. For instance, some of the most advanced industrial multi-agent systems in the world today are assembly lines and warehouse management systems. Whether the agents are robots, autonomous vehicles or clinical decision-makers, there is a strong desire for and increasing commercial interest in these systems: they are attractive because they can operate on their own in the world, alongside humans, under realistic constraints (e.g. guided by only partial information and with limited communication bandwidth). This research programme will extend the DRL methodology to systems comprising of many interacting agents that must cooperatively achieve a common goal: multi-agent DRL, or MADRL.

"PathLEAD (**Path**ology data **L**akes, **E**ducation, **A**nalytics and **D**iscovery) is a consortium of nationally leading experts from the teaching hospitals of Coventry, Belfast, Nottingham and Oxford, their associated Universities and Philips, the commercial partner. We will develop computer aided diagnostics for testing of pathology samples. Specialist doctors (called pathologists) currently carry out testing via visual examination of pathology specimens under the microscope, a process that is inherently subjective. A large proportion of tissue samples examined are normal and using specialist pathologist time to establish this is expensive. We believe this can be done by computers, and valuable pathologist time saved and used elsewhere. Therefore we will develop a computer programme that will recognise normal tissue so that the sample does not need to examined by the pathologist. Furthermore we know that pathologists' performance is variable and in some cases limited, particularly where the tasks they perform are complex or require extensive experience. This can mean patients with some forms of breast and prostate cancer do not get the best treatment. Our computer programmes will assist the pathologist and improve these decisions. The development of these tools requires thousands of image files obtained from scanning microscope slides. This is time consuming to collect so once this data has been produced it is a valuable resource. We aim to make this available to other research teams, our commercial partner Philips and UK-based companies developing tools to improve healthcare. The result will be greater knowledge, improved tools and better care for the future. We recognise that patients have the right not to allow their data to be used for this purpose, and will be using the recently launched NHS National Data Opt Out scheme to record patients wishes accurately. Patients and lay representatives on our ethical and management committee will help decide how this data should be used. We expect that some tools will become successful commercially and plan to exploit this success re-investing some of the income back into the NHS to benefit patients. Our ability to provide high quality data, expertise, access to top-grade computer equipment and knowledge of commercialising these tools, will percolate to UK companies, building the economy in this sector and lead to the UK becoming a global leader. Our expertise will provide education to the pathology and computer science communities to share the knowledge gained, gaining in quality and efficiency, and having patients' benefit as our ultimate goal."

OVERVIEW OF RESEARCH AREA Analytical science is key to the success of any fundamental or applied science research programme, and underpins industrial progress and production in a wide range of areas in which the UK is traditionally strong but where it faces increasing challenges globally. Warwick has an extensive track record both in the highest quality student training through CDTs and in creative instrumental and theoretical analytical science, which forms the background to this proposal for a Molecular Analytical Science Centre (MASC). MASC will focus on developing and applying molecular analytical science methods to problems in 6 themes 1. Measurement, sensing, and extraction in complex matrices 2. Advanced quantitative analysis 3. Molecular structure and stability in complex systems 4. New techniques for Quality by Design in pharmaceutical, biopharmaceutical, agri-science, personal care 5. Characterising and exploiting functional biomolecular assemblies 6. Analytical science for optimising and understanding dynamics in complex systems NEED FOR THE DOCTORAL SCIENTISTS THAT MASC WILL PRODUCE Many reports can be quoted to support the national importance of high quality cross-discipline molecular analytical science training. For example: * The "Health of disciplines: annual report 2008 to the UK research base funders' forum" reported a shortage in physical and analytical scientists as well as shortages in statistics/mathematics and biotechnology. * The 2009 "International Review of UK Chemistry Research" stated that bioanalytical research in the UK is internationally recognised and well-placed to tackle society's greatest challenges, emphasising the continued importance of this area. * A 2006 report for the RSC "Analytical and Measurement Sciences Platform Knowledge Transfer Plan - Survey Findings" noted that "not only are the analytical and measurement sciences extremely diverse and far-reaching in their nature but they are also a massive economic activity in [their] own right. ... analytical sector has £7bn turnover and employs 200,000 people". A driver for this CDT proposal is that the need is not simply for training in existing techniques but also for developing new techniques that will allow us to solve currently open challenges (e.g. the difficulty of proving that a potential generic biopharmaceutical is indeed 'biosimilar'). The Warwick analytical science community embraces the challenge of technique development, as evidenced by a track record in novel instrument and theoretical method development and application. APPROACH TO BE ADOPTED BY MASC The new CDT will benefit from the well-established cross-discipline cohort-based training culture, developed and refined over the 10-year life of the MOAC DTC and the long-running Warwick analytical science MSc programmes, and will be embedded in the research community created by the RCUK Science and Innovation funding that formed the virtual Warwick Centre for Analytical Science in 2008. The MASC students will undertake a cross-discipline MSc programme in year 1, concluding with 2 mini research projects in different disciplines, including both theoretical and experimental research. In years 2-4 they will perform a multi-disciplinary, multi-sector analytical science PhD research project, at a world-leading level, complemented by transferable skills training. Each project will involve technique development and application, with integrated industrial involvement. Students will enjoy the benefit of opportunities during both MSc and PhD to work in an industrial environment and also to experience an international laboratory to enhance their understanding of the scientific process in different contexts. The international secondments will either be to strategic partners of Warwick or to targeted collaborators of the supervisors.

There is an extremely high demand for laboratory-based blood tests from community settings in the UK and analysis suggests an important role in the future for remote blood monitoring that would enable patients and health professionals to carry out their own tests remotely, greatly benefiting patients and speeding up decision making. The COVID-19 pandemic has further highlighted the need for remote and connected blood testing that is beyond the online virtual clinics in the NHS outpatient setting. In current blood testing services for community healthcare, it is challenging to obtain and process blood samples outside of the clinical setting without training and lab facilities, and patients are required to attend a GP surgery or hospital for tests with travel burden and infection risk. Many blood analyses are done in batches that take a long time to build up, meaning the speed of blood sample analysis of routine tests and time taken for diagnosis are further challenges. Despite recent innovations in point of care, current blood analysis tools in practice are mainly mechanical or labour-intensive that require extensive filtering and manual tweaking and not suitable for regular at-home monitoring and longitudinal analytics. There is no personalised real-time approach available to inform disease complexity and conditions over time, which are critical for early detection of acute diseases and the management of chronic conditions. In England, around 95% of clinical pathways rely on patients having access to efficient, timely and cost-effective pathology services and there are 500 million biochemistry and 130 million haematology tests are carried out per year. This means inefficient and infrequent blood testing leads to late diagnosis, incomplete knowledge of disease progression and potential complications in a wide range of populations. Taking those challenges into account and current digital transformation in healthcare, this is a timely opportunity to bring researchers, clinicians and industrialist together to address the challenges of blood monitoring and analytics. The proposed Network+ will build an interdisciplinary community that will explore future blood testing solutions to achieve remote, inclusive, rapid, affordable and personalised blood monitoring, and address the above challenges in community health and care. To achieve the Network+ vision, research of technologies will be conducted from collaborations among information and communication technology (ICT), data and analytical science, clinical science, applied optics, biochemistry, engineering and social sciences in the Network+. The network will address three key technical challenges in blood testing: Remote monitoring, ICT, Personalised data and AI in a range of examplar clinical areas including cancer, autoimmune diseases, sickle cell disease, preoperative care, pathology services and general primary care.