Healthcare professionals frequently feature on the news and in history as witnesses to difficult events: genocide, war crimes, disasters and catastrophes. Their accounts often have a major impact on how such events are perceived and understood. In this knowledge synthesis, we will consider what roles testimonies by healthcare professionals play in evolving narratives linked to four specific case studies: the Holocaust, the Union Carbide Gas Leak Disaster in Bhopal (1984), the 2010 Haiti earthquake and the climate crisis. Through analysing data linked to the case studies, we will address to what extent existing research examines both the important function testimonies by medical practitioners have played in enhancing knowledge and understanding of such catastrophes, how such testimony in times of crisis is now often closely linked with strategic communications by humanitarian organisations and how the emergence of new technologies and online platforms has led to changes in the ways such accounts are produced and received. Healthcare professionals are often called upon to perform the role of what Robert Jay Lifton has called 'witnessing professionals,' tasked with confronting narrative falsehoods and calling out destructive behaviour. How and whether they should bear witness has also been a subject of controversy, as is evident in the well-known different approaches of the Red Cross and Médécins Sans Frontières. Through our data analysis we will ascertain whether the qualitative differences that characterize their testimonies as forms of knowing in comparison with accounts by other potential witnesses has been adequately examined. Equally, we will consider what the data reveals (or fails to) about the varied ways such testimonies are shaped by strategic communication challenges and how they are received by a non-medical professional audience. Our project will also analyse what data exists regarding the question of technological innovation and its effects on the production and dissemination of witness accounts by healthcare workers. Through the knowledge synthesis we will produce, we will generate an invaluable overview of the current state of research regarding the crucial roles witnessing by healthcare professionals perform in shaping knowledge. The research will have considerable value to doctors and nurses working for organizations such as aid agencies who may regularly confront, and need to attest to, the effects of conflicts and disasters. It will also be of interest to the communications teams of humanitarian aid organizations, potentially helping to refine their communications policies, helping to gauge effectiveness and identify areas for improvement. The scoping review will also provide an important resource for researchers and teachers. Better comprehension of what impacts the form and effectiveness of testimony will help healthcare professionals who are called upon to provide it to deliver their accounts as effectively as possible. The synthesis will also pinpoint areas in which narratives are at risk of exclusion or marginalization because healthcare traditions differ from the dominant biomedical paradigm. Traditional healers, for instance, can offer important testimony to the impact of climate change (including damage to the environment that bears upon the availability of medicinal plants, for example) yet risk not being heard as they are not viewed as 'professional'. Enhanced knowledge about potential exclusions and a lack of diversity will have considerable value for media organizations seeking balance in their coverage of conflict and disasters. Additionally, having a greater understanding of the extent and limitations of current research in the area will lay the groundwork for more focussed and effective studies moving forward. Such studies will potentially have major policy implications relating to local or international crisis responses.

Humans and other animals can learn to perform complex and adaptive behaviours based on limited experience. Understanding the neural basis of learning is a key challenge in systems neuroscience and artificial intelligence (AI) that could lead to novel treatments for neurological disorders and enable the development of AI systems that learn with human-like efficiency. Thus, significant effort and funding is currently being invested to understand how neural circuits reorganise during learning to improve performance in various cognitive, perceptual, and motor tasks, both in academic research organisations and private companies such as Google DeepMind. Recent advances in neural recording technologies enable the activity of thousands of neurons to be tracked simultaneously at millisecond precision, and stably over days, so that neural activity can be surveyed over the entire course of learning. Through careful analysis of these recordings, scientists hope to determine how changes in the underlying neural circuit support improvements in task performance. In particular, learning is thought to modify the strength of connections between neurons, which leaves a functional signature that can be detected via the coordinated activity of interconnected groups of neurons. However, during learning, many other changes also take place, including changes in motor behaviour, attention, and sensory input, all of which may influence the activity of the recorded neurons. Thus, a key challenge is to disentangle the learning-related changes in recorded neural activity from those arising from sensory, motor, and internal state variables which covary with learning. The proposed project will develop novel methodologies for analysis of large-scale neural recordings to address this need.

Despite the increasing amount of games hardware and software being preserved in dedicated archives both nationally and internationally, it has been noted that histories of computer games have largely been ignored in current discussions of both game analysis and design (Guins 2014). Conferences and conference strands have started to emerge in order to facilitate discussions surrounding methods, game histories and the preservation of games yet these events only offer a small percentage of a vast area of study. Recent documentaries, such as From Bedrooms to Billions (Caulfield and Caulfield 2014), alongside books written by game journalists, including Replay (Donovan 2010), and Grand Thieves & Tomb Raiders (Anderson and Levene 2012), all expose British histories of game development in the 1980s. However, there is a tendency to focus on well-known game titles and developers thus masking other prominent developments during this time. The 1980s in particular marks a significant starting point for the development of the British computer game industry. A significant legacy of 1980s UK computing and gaming cultures is the do-it-yourself (DIY) ethos created through microcomputing magazines and books that printed listings of software code for users to type in and run in order to create and play their own games. These printed program listings now exist in memory institutions such as The British Library and The National Museum of Computing. Here the software code is preserved as a written text and not as an immediately playable artefact. This raises questions about the role of the program listing as software, source code, game object and process as modes of analysis for historical game research. The aim of this study is to analyse the different facets of game production and cultures from the 1980s by further examining the role of typing in program listings from magazines to emulate what these games once were. The research will be extended by tracing writers of these program listings in order to undertake oral histories as a way of recognising those involved in this industry and to provide a legacy of cultural memories for future researchers. Finally, interviews with those who worked in the games industry in the 1980s and 1990s, as well as those that continue to work in the industry today, will be undertaken as a way of tracing the legacies of 1980s DIY cultures and the drive for national computer literacy. These interviews will include developers and artists who worked for companies such as Automata, Martech, Virgin Interactive, Rabbit Software and BITS studios. This early career fellowship will facilitate career development through an international collaboration with the project partner, The University of Montreal. This collaboration will result in the co-chairing of the 2016 Annual Game History Symposium that will run alongside a co-curated exhibition of creative uses of computer game hardware and software from the 1980s and 1990s around the globe. The research will also be disseminated in a series of workshops in London and Nottingham and at an exhibition of magazine program listings from The National Museum of Computing archives. An experienced project mentor and an interdisciplinary, international advisory panel consisting of academics from various disciplines, curators, archivists and the games industry, will support the research. This will allow for these emerging networks to be consolidated during and beyond the life of the project. The panel will be able to further advise and support the dissemination of the research to their respective disciplines in order to ensure impact beyond the academy.

MRC : Tanyel Ashik : MR/N013700/1 Under healthy conditions, our body's fat cells act as important reservoirs to store energy, and they can also release hormones that influence how much we eat and how different cells in our body store nutrients. However, during the development of obesity, the capacity for these fat cells to store excess fat becomes impaired, as does the types of factors released by fat cells. This leads to profound problems with our normal regulations of blood sugar levels and increases the risk for developing other health conditions such as type 2 diabetes and cardiovascular diseases. There is therefore a need for effective long-term therapies to promote weight loss. Various proteins that influence the growth and development of fat cells have been identified. One such factor is 14-3-3zeta, a unique protein that can help to direct where proteins go in a cell. The host lab previously found that when 14-3-3zeta is deleted, this blocked the growth of fat cells, but unexpectedly, they have recently found that reducing 14-3-3zeta levels in fat cells causes them to lose their identity and revert to an immature state. While it was initially thought that 14-3-3zeta is expressed in every cell, this is not the case, especially in adipose tissue of mice. This suggests that a specific group of fat cells will lose their maturity if 14-3-3zeta levels are reduced, and whether this will have implications on overall metabolic health is not known. This will be addressed in the current project, which will use genetically modified mice expressing fluorescent proteins that allow purification of 14-3-3zeta-positive and 14-3-3zeta-negative fat cells from adipose tissues, and it will be determined if these different groups of fat cells behave differently. We will measure the expression of various genes and explore if the cells are functionally different. When fat cells are removed from adipose tissue, they cannot survive for extended periods of time so we will also develop a new method to promote the long-term culture of these different populations of fat cells to increase our ability to properly study them. The benefits of this research are two-fold: in the short term it will improve our understanding of the complexity of fat cell biology and in the longer term this advancement in knowledge may lead to the development of new approaches to treat obesity, a chronic disease that continues to increase in worldwide prevalence.

EPSRC : Max Hird : EP/T517793/1 Algorithms that learn and sample from probability distributions form an important part of machine learning, AI, and the natural sciences. One needn't look far to find such algorithms at the bleeding edge of methodology, and in everyday scientific pursuit. The Wang-Landau algorithm is an example. It combines a sampling step with a learning step, to learn a probability distribution about which our knowledge is limited. The probability distribution may be over physical states, so an efficiently running algorithm would allow the simulation of the dynamics of protein folding, for instance. The learning step incorporates information gained from the sampling step, forming a more complete picture of the distribution. The particular form of the learning step is foundational in many neural networks and is called stochastic approximation. Due to our incomplete knowledge of the distribution, we cannot apply standard sampling methods. We therefore need to employ a more exotic sampler. Coupling exotic samplers alongside stochastic approximation is underexplored, and potentially fruitful. We will try to assess the behaviour of such a coupling, an assessment not yet existing in the literature.