Our Centre brings together a world leading team of mathematicians, statisticians and clinicians with a range of industrial partners, patients and other stakeholders to focus on the development of new methods for managing and treating chronic health conditions using predictive mathematical models. This unique approach is underpinned by the expertise and breadth of experience of the Centre's team and innovative approaches to both the research and translational aspects. At present, many chronic disorders are diagnosed and managed based upon easily identifiable phenomena in clinically collected data. For example, features of the electrical activity of the heart of brain are used to diagnose arrhythmias and epilepsy. Sampling hormone levels in the blood is used for a range of endocrine conditions, and psychological testing is used in dementia and schizophrenia. However, it is becoming increasingly understood that these clinical observables are not static, but rather a reflection of a highly dynamic and evolving system at a single snapshot in time. The qualitative nature of these criteria, combined with observational data which is incomplete and changes over time, results in the potential for non-optimal decision-making. As our population ages, the number of people living with a chronic disorder is forecast to rise dramatically, increasing an already unsustainable financial burden of healthcare costs on society and potentially a substantial reduction in quality of life for the many affected individuals. Critical to averting this are early and accurate diagnoses, optimal use of available medications, as well as new methods of surgery. Our Centre will facilitate these through developing mathematical and statistical tools necessary to inform clinical decision making on a patient-by-patient basis. The basis of this approach is patient-specific mathematical models, the parameters of which are determined directly from clinical data obtained from the patient. As an example of this, our recent research in the field of epilepsy has revealed that seizures may emerge from the interplay between the activity in specific regions of the brain, and the network structures formed between those regions. This hypothesis has been tested in a cohort of people with epilepsy and we identified differences in their brain networks, compared to healthy volunteers. Mathematical analysis of these networks demonstrated that they had a significantly increased propensity to generate seizures, in silico, which we proposed as a novel biomarker of epilepsy. To validate this, an early phase clinical trial at King's Health Partners in London has recently commenced, the success of which could ultimately lead to a revolution in diagnosis of epilepsy by enabling diagnosis from markers that are present even in the absence of seizures; reducing time spent in clinic and increasing accuracy of diagnosis. Indeed it may even make diagnosis in the GP clinic a reality. However, epilepsy is just the tip of the iceberg! Patient-specific mathematical models have the potential to revolutionise a wide range of clinical conditions. For example, early diagnosis of dementia could enable much more effective use of existing medication and result in enhanced quality and quantity of life for millions of people. For other conditions, such as cortisolism and diabetes where a range of treatment options exist, identifying the optimal medication, and the pattern of its delivery, based upon the profile of the individual will enable us to maximise efficacy, whilst minimising unwanted side effects.

With the prevalence of data-hungry deep learning approaches in Artificial Intelligent (AI) as the de facto standard, now more than ever there is a need for labelled data. However, while there have been interesting recent discussions on the definition of readiness levels of data, the same type of scrutiny on annotations is still missing in general: we do not know how or when the annotations were collected or what their inherent biases are. Additionally, there are now forms of annotation beyond standard static sets of labels that call for a formalisation and redefinition of the annotation concept (e.g., rewards in reinforcement learning or directed links in causality). During this Fellowship we will design and establish the protocols for transparent annotations that empowers the data curator to report on the process, the practitioner to automatically evaluate the value of annotations and the users to provide the most informative and actionable feedback. This Fellowship will address all these through a holistic human-centric research agenda, bridging gaps in fundamental research and public engagement with AI. The Fellowship aims to lay the foundations for a two-way approach to annotations, where the paradigm is shifted from annotations simply being a resource to them becoming a means for AI systems and humans to interact. The bigger picture is that, with annotations seen as an interface between both entities, we will be in a much better position to guide the relation of trust in between learning systems and users, where users translate their preferences into the learning systems' objective functions. This approach will help produce a much needed transformation in how potentially sensitive aspects of AI become a step closer to being reliable and trustworthy.

There are at least a billion people on this planet who possess 'misfitting' bodies and who are consequently directly affected by disability. Many more people encounter different abilities when families, friends, and colleagues are taken into consideration. Disability - and the structures that create it - really matters. It may be marginal, but it is hardly a minority experience. Indeed, when we take time to look at the animal world, we find that 'extraordinary' bodies are all around us. Some dart through the darkness mapping the world through the art of echolocation. Others flourish in underground rivers via senses that allow them to 'see' without eyes. The way in which people in Britain and North America have understood these ways of surviving and thriving in the world have an important history, and that history reveals much about transforming cultural assumptions about what we have thought to be 'normal' bodies and abilities. Since the early nineteenth century, unusual nonhuman bodies have been imagined as variously 'deficient', 'super', 'expendable' and, most recently, highly 'vulnerable' in the face of environmental transformation. These are familiar labels; we find them at the heart of contemporary and historical conversations relating to human disability Centred on a deep case study of the nineteenth- and twentieth-century imagination of dark-dwelling creatures and the impact of human systems and structures on their shrouded worlds since the mid-nineteenth century, this cutting-edge research project is really about the ways in which notions of what it means to be 'normal', 'able' and 'vulnerable' have been refracted through the multifarious bodies of animals that live in ways that are radically different from our own. Nocturnal creatures are among the most misunderstood creatures on earth, and that is principally because they are active in an environment from which we are normally excluded. This has facilitated the imagination of nocturnal animal bodies as variously 'abnormal', 'extraordinary', and 'deficient'. Ultimately, misunderstandings of these more-than-human bodies have also rendered them highly vulnerable to exclusion from environments to which they are adapted. By building this case study and generating a brand new research agenda, the project offers an important intellectual and methodological intervention into the allied fields of animal history, environmental history and disability studies. While each of these fields are concerned in varying degrees with the production of identity and the impact of identity politics on the material world, they are yet to interact with each other in mutually generative ways. More-than-human histories need to embrace disability studies approaches in order to better appreciate the wide array of engagements which constitute human relationships with the natural world and the ways in which abled and disabled identities have been constructed and refracted through and via the bodies of our animal kin. Disability studies needs to turn to the more-than-human world as a means of pivoting around the concept of disability itself; to challenge what we think we know about historical discourses of ability and normativity, re-energising a stagnating conversation about the conditions that exclude and marginalise the 'differently-abled'. This research is crucial in other ways, too. In exposing connections between discourses of normalcy, ability, vulnerability and adaptation across the human and more-than-human realms, it may be possible to generate recognition of shared vulnerabilities that transcend the human-nonhuman divide that has permitted the marginalisation of living beings across the course of modernity. Engagement and impact activities benefiting Key Stage 2 children, their teachers, sight-impaired individuals and vision clinicians highlight the potential of thinking creatively about diversity and vulnerability as issues that unite rather than isolate all living beings.

FARSCOPE-TU (Towards Ubiquity) will train a new generation of "T-shaped roboticists" in the priority area of Robotics and Autonomous Systems (RAS). T-shaping means graduates will combine the depth of individual PhD research experience with broad awareness of the priority area, including technical tools and topics spanning multiple disciplines. Breadth will be enhanced by strong understanding of the industrial and societal context in which future RAS will operate. These graduates will meet the need for future innovators in RAS, evidenced by industrial partner demand and growing research investment, to deliver potential UK global leadership in the RAS area. That need spans many applications and technologies, so FARSCOPE-TU adopts a broad and ambitious vision of RAS ubiquity, motivating the research challenge to make RAS that are significantly more interactive with their environments. The FARSCOPE-TU training experience has been carefully designed to support T-shaping by bringing in students from many disciplines and upskilling them through an integrated programme of individual research and cohort activities, which mix together throughout the four years of study. The FARSCOPE-TU research challenge necessitates multidisciplinary thinking, as the enabling technologies of computer science and engineering interface with questions of psychology, biology, policy, ethics, law and more. Students from this diverse range of backgrounds will be recruited, with reskilling supported through fundamental training and peer learning at the outset. The first year will be organized as a formal programme of study, equivalent to a Masters degree. The remaining three years will focus on PhD research, punctuated by mandatory cohort-based training to refresh first year content and all subject to annual progress monitoring. Topics will include responsible innovation, enterprise, public engagement, and industrial context. FARSCOPE-TU has formed partnerships with 19 organizations who share its vision, have helped co-create the training programme, and span technologies and applications that align with the CDT's broad interpretation of RAS. Partner engagement will be central to covering industrial context training. Partners and the FARSCOPE-TU team have also co-created a flexible programme of engagement mechanisms, designed to support a diverse set of partner sizes and interests, to allow collaborations to evolve, and to be responsive to potential new partners. The programme includes mentoring, mutual training by and for partners, collaboration on research and industry projects, sponsorship and leveraged funding opportunities. Partners have committed £2.5M in leverage to support FARSCOPE-TU including 15 studentships from the hosts and 12 sponsored places from industry. FARSCOPE-TU will promote equality, diversity and inclusion both internally and, since the vision includes robots interacting with society, in its research. For example, FARSCOPE-TU could consider how training data bias would affect equality of interaction between humans and home assistance robots. FARSCOPE-TU will instigate a high-profile Single Equality Scheme named "Inclusive Robotics" that combines operational initiatives, including explicit targets, with events and training, linked to responsible innovation and human interaction. FARSCOPE-TU will deliver a joint PhD award, badged by partners University of Bristol and University of the West of England. The CDT will be run through their established Bristol Robotics Lab partnership, providing over 4,500sqm dedicated RAS laboratory space and a community of over 50 supervisors. BRL's existing FARSCOPE CDT provides the security of a strong track record, with 46 students recruited in four cohorts so far and an approved joint programme. FARSCOPE-TU builds on that experience with a revised first year to support diverse intake and early partner engagement, enhanced contextual training, the new T-shape concept and the wider ubiquity vision.

Our proposal seeks to deliver a healthcare technology that will benefit the most vulnerable in society. Synthetic cannabinoid receptor agonists (SCRAs, more commonly called Spice) are endemic in UK homeless communities and the prison population. These drugs fall under recent Novel Psychoactive Substance legislation. The challenge with these drugs is that there is no generic point-of-care detection, meaning treatment and harm reduction strategies are essentially impossible. The use of these drugs leads not only to significant adverse health outcomes for users but also major social problems owing the drugs common side effects which can include psychosis and aggression. Our proposal builds on our recent advances in fluorescence spectral fingerprinting of SCRAs to identify these drugs both in street material and in saliva of users. The proposal covers the full range of activities necessary to deliver the technology to beta testing, including portable device design, analytical software development, chemical fingerprint libraries and the associated community pharmacy practice advice to deploy the technology effectively. At the end of the award we aim to start a not-for-profit social enterprise to bring the technology to the mainstream. The proposal includes partners from the full range of stakeholders relevant to SCRA use including homeless charities, police forces and prisons and drug testing services. Our proposal leverages the contributions of these partners with a carefully selected interdisciplinary research team (analytical/synthetic chemistry, optics engineering, artificial intelligence, community pharmacy and addiction psychology) that can support and deliver each aspect of the proposal. We believe the scope and potential of our proposal is truly unique and presents the best chance for tackling SCRA use in the UK and more widely.