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Biologically inspired connectionist models are made up of multiple interconnected units which are designed to mimic biological processes in nature which give rise to emergent phenomena. Typically, connectionist models are used as computational tools which are capable of learning by example, for instance predicting the next days activity on the stock exchange by learning from previous months data. Epigenetically inspired connectionist models (EICMs) are a particular type of biologically inspired connectionist model which allow for the activation and deactivation of their interconnected units whist they are solving a task. These models have been shown to break complex tasks down into smaller sub-tasks autonomously, with certain interconnected units being applied to certain sub-tasks, and other interconnected units being applied to other sub-tasks. Biologically inspired connectionist models in general are difficult to interpret. Their decision making processes are an emergent property of their interconnected units, from which it is very difficult to provide an explanation as to why specific decisions have been made. Because of this, deriving confidence from the decisions they make is difficult. Having confidence in the decision making process is of importance especially when the tasks they are applied to are in domains which are considered "high risk" such as medical simulations and financial forecasting. To address these issues, this work aims to develop a set of techniques which allow for EICMs to provide a rationale for their decision making process, essentially making its decisions transparent. This will be achieved by analysing the way the model breaks down complex tasks, which of its units are active at any given time and then correlating this with the behaviour of both the network and the task. We apply the EICMs and the techniques developed in this project to improve the understanding of the often fatal disease human visceral leismaniasis (HVL). The immune response to HVL is a significant indicator of patient outcome and is the product of the interplay between multiple interacting cells, macrophages and specific cytokine responses. The project partner Simomics, a world leading disease modelling company, has a comprehensive data set which describes changes to the immune response in reference to HVL over varying timescales, and has provided it for use during this project. The overall development of HVL and the immune response to it is not well understood. The techniques developed in this work which are able to provide a rationale for their decision making process, will be applied to learn the interplay and interactions between these processes. This will allow for model to provide an explanation of what processes are most important in the immune response over the duration of HVL infection. By contributing to the field of biological modelling, which places a strong emphasis on transparency and confidence in results, other fields will be able to adopt the models developed in this work to provide transparency in other domains.

There is an increasing demand from policy that conservation and sensitive management of our landscape should not be restricted to areas designated for their conservation value, but should extend to the broader landscape as well. There is also an increasing expectation from society that development and infrastructure projects should be undertaken in such a way that not only minimises their environmental impact but where possible enhances the wider landscape and the benefits and services that we obtain from it, such as flood prevention, the provision of clean water, carbon storage and recreation. Those organisations and businesses responsible for managing large areas of the landscape therefore need the appropriate information to help them achieve these goals. There is an increasing body of evidence concerning how landscape management affects landscape benefits and services. This evidence has helped to inform the development of a wide range of computer models that can be used to quantify the different services that a particular landscape provides, and to predict the likely impacts of landscape change on these services. However, many of these models are heavily research-focused, require specialist knowledge to operate and interpret, and are not accessible to general users. In this project, using two existing ecosystem service decision-support models as test-beds for the approach, we will develop a web-based tool that will allow users of the models to examine and evaluate the evidence base underlying the predictions of the models. This evidence tool will use a process that is well-established as industry standard in other areas of application, including engineering and transport safety, but has not previously been applied in environmental management. The tool will allow users of these ecosystem service models to understand and track the evidence underpinning model predictions for the first time. There are potential applications of this evidence tool across a wide range of sectors, including energy, water and transport, and the construction industry, as well as for nature conservation. Our group of formal partners in the project attests to this, and includes an industry regulator, an energy supply company, a construction firm, a landscape management partnership and a Wildlife Trust, as well as Defra, the Welsh Government, Natural Resources Wales and Natural England.

By the middle of this century, two thirds of the world's population will be urban - equivalent to around 6.3 billion people. Mismanagement of these urban areas will adversely affect the health and well-being (i.e. how people experience their lives and flourish) of the population, and lead to social and environmental injustice. It has long been recognised that good quality cultural, social, built and natural environments within cities provide benefits in terms of health, well-being and equity of urban residents. Conversely, poor quality environments negatively affect the health and well-being of citizens and have negative economic consequences. With increasing urbanisation and changes in climate, the built, cultural, social and natural environments within cities will come under further pressure. While the relationships between selected environment quality parameters, such as noise and air pollution and health, have been well characterised, relatively little is known about the relationship between other quality measures, or endpoints, of economic and societal well-being and health. A major reason for this limited understanding is that while much data on city environments exist, this is fragmented across numerous data owners, is not joined up or at suitable granularity. As these existing datasets have been collected for other reasons, they are not always in a form where they are useful for a wide variety of purposes or for future needs. Data on some important parameters simply does not yet exist. Additionally, specialists in the different disciplines needed to tackle these complex issues often work in isolation. By bringing data together, breaking down barriers across research disciplines and exploiting and developing new monitoring, modelling and analytical technologies (e.g. wireless sensing networks, wearable devices, drones, crowdsourcing, 3D models of cities and virtual reality), it should be possible to provide a holistic analysis of the quality of the environment with a city that can be used by many different stakeholders (e.g. researchers, policy makers, planners, businesses and the public) to address their needs. This holistic analysis will then provide us with a better understanding of how to manage city environments and will provide long-term benefits to citizens and the economy. The York City Environment Observatory (YCEO) initiative will address this major knowledge gap by providing a framework, tools and conceptual models at the urban scale that can be rolled-out to assist with governance of environments in York and other cities in the UK and around the world. In this diagnostic phase project, experts from a diverse range of sectors and disciplines, will work together in a holistic way to design and lay the groundwork for establishing the YCEO. The consortium will work with a range of stakeholders and look to the past, present and future in trying to diagnose and predict environmental issues for York and their associated human health and well-being and economic impacts. We will build on York's strong track record in open data and combine data and models in order to do this. This diagnostic project will allow us to develop a prototype design for the YCEO, to be implemented within the next five years and a roadmap for achieving this. The YCEO will be designed to provide the evidence-base for making decisions on how best to manage and enhance the social, cultural, built and natural environment across city systems now and into the future, and in this way, improve the health, well-being and equity of citizens and the economy of the city. The YCEO will also aid local, national and international stakeholders (including planners, businesses, residents and community groups) to come up with low cost and innovative solutions to a range of problems identified as part of this diagnostic phase of the Urban Living Partnership.

Building upon our existing flagship industry-linked EPSRC & MRC CDT in Systems Approaches to Biomedical Science (SABS), the new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 - will train a further five cohorts, each of 15 students, in cutting-edge systems approaches to biomedical research and, uniquely within the UK, in advanced practices in software engineering. Our renewed goal is to bring about a transformation of the research culture in computational biomedical science. Computational methods are now at the heart of biomedical research. From the simulation of the behaviour of complex systems, through the design and automation of laboratory experiments, to the analysis of both small and large-scale data, well-engineered software has proved capable of transforming biomedical science. Biomedical science is therefore dependent as never before on research software. Industries reliant on this continued innovation in biomedical science play a critical role in the UK economy. The biopharmaceutical and medical technology industrial sectors alone generate an annual turnover of over £63 billion and employ 233,000 scientists and staff. In his foreword to the 2017 Life Sciences Industrial Strategy, Sir John Bell noted that, "The global life sciences industry is expected to reach >$2 trillion in gross value by 2023... there are few, if any, sectors more important to support as part of the industrial strategy." The report identifies the need to provide training in skills in "informatics, computational, mathematical and statistics areas" as being of major concern for the life sciences industry. Over the last 9 years, the existing SABS CDT has been working with its consortium of now 22 industrial and institutional partners to meet these training needs. Over this same period, continued advances in information technology have accelerated the shift in the biomedical research landscape in an increasingly quantitative and predictive direction. As a result, computational and hence software-driven approaches now underpin all aspects of the research pipeline. In spite of this central importance, the development of research software is typically a by-product of the research process, with the research publication being the primary output. Research software is typically not made available to the research community, or even to peer reviewers, and therefore cannot be verified. Vast amounts of research time is lost (usually by PhD students with no formal training in software development) in re-implementing already-existing solutions from the literature. Even if successful, the re-implemented software is again not released to the community, and the cycle repeats. No consideration is made of the huge benefits of model verification, re-use, extension, and maintainability, nor of the implications for the reproducibility of the published research. Progress in biomedical science is thus impeded, with knock-on effects into clinical translation and knowledge transfer into industry. There is therefore an urgent need for a radically different approach. The SABS:R^3 CDT will build on the existing SABS Programme to equip a new generation of biomedical research scientists with not only the knowledge and methods necessary to take a quantitative and interdisciplinary approach, but also with advanced software engineering skills. By embedding this strong focus on sustainable and open computational methods, together with responsible and reproducible approaches, into all aspects of the new programme, our computationally-literate scientists will be equipped to act as ambassadors to bring about a transformation of biomedical research.