Over the past 30 years a number of methods have been devised that allow us for the first time to stimulate parts of the brain in healthy, conscious individuals without having to remove part of the scalp or undergo neurosurgery. It is a remarkable advance. Even more strikingly, recent developments have made it possible to interact directly with a process known as "synaptic plasticity" which is fundamental to our ability to learn new things. When we learn anything new, a subtle change is made in the way a small number of neurones connect together in the brain and this new circuit is used to store the memory. The new brain stimulation methods can subtly speed up or slow down this process. The main interest in this method lies in its potential to speed up rehabilitation training after brain injury or disease. For example, after a stroke, the brain has to re-learn how to perform tasks with a damaged set of circuits. Physiotherapy works by giving patients practice in tasks so that their brain can re-learn old skills with a new set of connections. Work has suggested that this process would be speeded up by using the new methods of brain stimulation. Although very attractive, and overall effective, a problem with the methods is that they vary in effectiveness from one individual to another. The result is that in any clinical trial, some participants perform much better than others. The objective of this proposal is to understand more about why this variation arises, and, more importantly, devise simple predictive measures that can be used to check if an individual is likely to respond to a particular protocol, and if not find an appropriate alternative. The work will begin by exploring a number of simple measures that have been reported to predict responses to particular brain plasticity protocols and select the most useful of these after a series of studies in 50 healthy volunteers. We will then test in a group of 25 chronic stroke survivors whether these factors will also predict the clinical response of each patient to a single session of therapy. Finally the project will explore the hypothesis that these differences between people depend on subtle differences in the anatomy of the brain. The pattern of folding of the cerebral cortex varies slightly from the "average" pattern in every individual. In addition, the area of cortex where certain functions are represented also varies within a centimetre or so between individuals. We will use sophisticated computer modelling of the way the external brain stimulation is likely to activate regions in individual brains and show that differences in the regions activated can account for differences in a person's response to each protocol. If correct we can use this information in a subsequent study to change stimulator design so that we can target the "correct" locations in an individual brain and maximise chances of responding to any given protocol.

Malaria has had a devastating impact on human health throughout history and currently inflicts around 600,000 deaths annually, mostly in young children and pregnant women. Malaria is caused by several species of Plasmodium which, along with humans, can infect a range of animals including bats, rodents, birds and other primates. Human-associated malaria is predominately caused by five species transmitted to humans by mosquitoes. The number of animal parasites which can infect humans however is constantly under revision. Today malaria is mostly found in tropical and sub-tropical latitudes. Yet, until quite recently, malaria was a truly global disease spanning Britain and the Mediterranean, as far North as Finland, and through to European Russia, with the last indigenous cases in Europe persisting until the late 1970s. Whilst we have increasingly good data for the present, including genetic data generated from parasites and spatial trends in disease occurrence, the type and locality of disease further back in malaria's deep history is mostly uncharacterised. This means that even for parasites with rich accompanying data today, only an incomplete picture can be gleaned on how they evolved. This limits our understanding of the long-term drivers of disease. My proposal seeks to address major outstanding questions in Plasmodium evolution using genetic data generated from infecting parasites. My work will be uniquely aided by genome sequences of parasites involved in ancient and historic infections spanning from thousands of years ago through to the 20th century. Data from past infections will be generated from a range of archived material including archaeological remains, microscope slides, vials, tissue and macaque skeletal specimens. I will focus on the human infecting species P. falciparum, P. vivax and P. malariae as well as those species found in monkeys including P. inui, P. cynomolgi and P. knowlesi, the latter implicated in extensive human infections in southeast Asia. The generation of genetic data from past infections provides new opportunities to study the evolution of human-associated parasites. Using statistical methods, I will estimate when P. falciparum, P. vivax and P. malariae first began infecting humans and map their dispersals from the deep past to now. In addition, I will interrogate specific features of the genome to identify changes which impact how we treat malaria today, such as the ability to survive treatment with antimalarial drugs. I will then consider genetic data from parasites infecting macaques in the early 20th century in Indonesia, identifying what malarial species are present and using this data to test concerns over whether macaque parasites may be able to infect humans. I will particularly focus on P. knowlesi, which is frequently transmitted from macaques to humans via mosquito vectors. I will compare the genomes of P. knowlesi both today and in the past to build a robust picture of the contact between different parasite populations including the potential transition of this parasite from macaque reservoir to specialised human parasite. Finally, since Plasmodium parasites are diverse in number and found in a very wide range of animal species, I will build a Plasmodium family tree designed to robustly recover how different species are related. I will map this information to data on the animal species each parasite can infect, sourced through an array of data mining techniques. Pairing parasite relatedness with the range of animal infections, I will model mechanisms of adaptation to different animal hosts and pinpoint those malarial parasites at highest risk of transmitting to humans. My work provides the bespoke platform and perspective required to uncover the drivers of malaria prevalence through time. I anticipate my framework will be portable to other pathogens and will ultimately enable me to substantially contribute to our understanding of infectious disease dynamics.

Alzheimer's disease is a major problem to UK society. Because of the ageing population, the number of people with dementia will increase dramatically in the next years: from about 850,000 today to 1,000,000 by 2025. The current annual cost of dementia to the UK is £26 billion even not everybody with dementia receives a diagnosis. Alzheimer's disease is the most common cause of dementia and it is particularly difficult to diagnose because there are no objective biomarkers for it and the diagnosis relies on the medical history of the patient. We need better ways to detect and monitor the changes that Alzheimer's disease causes in the brain. To achieve this, we will consider the electroencephalogram (EEG), an affordable piece of equipment that can be used outside hospitals to measure brain activity safely at several locations over the scalp (called "channels"). We will create new signal processing tools to analyse EEG brain networks. Doing so will lead to objective ways to monitor Alzheimer's disease. Namely, this interdisciplinary project will develop a novel set of processing techniques based on tensor factorisations to inspect how the components of brain activity networks change with time. We will then implement methods to compare the temporal profiles of the components estimated for different groups of people (e.g., healthy people versus patients). Our project is motivated by the facts that: 1) the EEG can measure fast changes in brain activity, 2) Alzheimer's disease damages brain connections, and 3) preliminary results indicate that Alzheimer's disease affects the temporal behaviour of brain activity. Indeed, there is an increasing interest in understanding brain activity networks and their evolution with time, as this would open up radically new ways to monitor brain diseases. Promising pilot results have reported in, e.g., Parkinson's and multiple sclerosis but, currently, there are no appropriate ways to inspect how the networks change with time systematically. Instead, we will develop a framework based on tensor factorisations (a set of algebraic and computational techniques to analyse tensors: n-mode data arrays with n>=3) to inspect the components of networks directly from the data without the need for manual intervention. We will then apply it to EEG signals. First, for each person, we will assess the coupling between channels of the EEG as a function of time and frequency. These results naturally fit into a multi-modal representation: a "connectivity tensor". Then, we will decompose the "connectivity tensor" into its underlying components. We will implement constraints to bring previous information into the decompositions, including novel ways to measure the natural organisation of the network components. Finally, we will assess the robustness of the extracted network components and we will inspect how Alzheimer's disease changes them. We will apply our methods to two different sets of EEG signals measured from patients with Alzheimer's disease, people with mild cognitive impairment (a condition that sometimes precedes Alzheimer's disease), and healthy volunteers. One of the EEG datasets measured the activity of the brain at rest using a small number of channels, whereas the other has been recorded during a short-term memory task that has shown promise in the detection of early Alzheimer's disease with a larger number of EEG channels. Hence, we believe that revealing how the EEG network changes with time during this task could lead to a non-invasive, affordable and portable tool to monitor Alzheimer's disease. Nonetheless, this project will have much wider implications because it will benefit the signal processing, tensor factorisation and network analysis communities and the techniques will be readily applicable to other types of data, both inside and outside clinical settings.

The project "Holy Places in Islam" aims to investigate the modalities through which holy places in the Islamic world were established, became famous, or eventually vanished. In order to analyse these processes both material culture and literary texts are worthy of consideration. Texts help to reconstruct the discourse that was created around a specific place, whereas material culture reflects how the sense of holiness was physically rooted in the landscape. Although the concept of holy in Islam has been addressed in some publications, the aim of this specific project is to fill a gap in the scholarship by focusing on the strategies developed within Islam in order to make a site a holy place and legitimise its holiness and on the factors that led to places eventually losing their fame. Our approach, therefore, not only will engage theoretically with the notion of holiness in Islam but will also research how the sense of holiness activated specific sites and places. Within the project "Islam" is understood in a broad sense as the religious practice of the totality of Muslim elites, scholars, and commoners throughout Islamic history. The network, based at the University of Edinburgh, will benefit from the active participation of the second main partner, the MIRI (Materiality in Islam research Initiative) directed by Professor Alan Walmsley at the University of Copenhagen. The project will be developed in three different meetings, each one addressing different issues. An initial conference (Edinburgh, September 2013) will focus on the early Islamic period and will try to establish the strategies and models of making sites holy in Islam and the factors facilitating or impeding the process. A follow-up workshop (Copenhagen, December 2013/January 2014) will produce a close analysis of specific case studies. The third and concluding conference (Edinburgh, June 2014) will address the possible recurrence of specific patterns throughout Islamic history or the occurrence of regional or temporal characteristics.

Current prevention and treatment of obesity and type 2 diabetes (T2D) include energy-restricted diets and increased levels of physical activity, but adherence to such strategies is difficult, and maintenance is challenging for most individuals. Timing of food intake and fasting periods affect the circadian rhythms of metabolic organs (i.e. 'chrono-nutrition'), and experimental data from animal studies suggest promising effects of time-restricted eating on weight loss, glucose regulation, appetite and energy levels. Therefore, it is hypothesized that time-restricted eating, allowing dietary intake within a limited time interval during the day, will be an attractive, feasible and sustainable option for management of obesity and dysglycaemia. To understand the underlying mechanisms for potential health effects, research at Steno Diabetes Centre Copenhagen (SDCC) aims to examine the effects of time-restricted eating on both metabolic and behavioural parameters in adults with overweight and obesity at high risk of developing T2D. Chrono-nutrition is an emerging special interest within nutritional science and a crucial gap in the literature is to examine the interaction between exercise and meal timing on energy balance, and appetite and metabolic control in adults with obesity and T2D. By integrating my expertise in the biopsychology of appetite and energy balance with clinical approaches in T2D prevention, there is potential for synergistic impact on understanding and improving health. If this proposal is successful, the complementary training I will acquire at SDCC will address this gap in knowledge and expertise. With this Award I will gain experience with clinical populations (i.e. individuals with (pre)diabetes) and in more complex physiological assessment of glucose regulation and metabolism. I will expand my skillset and be better trained to conduct multidisciplinary, mechanistic nutritional research across a range of populations addressing overlapping themes pertaining to appetite control, physical activity and chrono-nutrition. I am at the career stage where I am seeking to catalyse my progression to an independent researcher and to obtain funding for a longer, larger fellowship award. This Travelling Skills Award is the ideal next step in my career as it will support me in acquiring knowledge and skills in a new discipline in order to address important questions in the field of nutrition.