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.

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.

Nubian barley - Summary One of the most serious challenges in the future of agriculture in the face of an increasing global population and climate change is water availability. However, we are not the first to face this problem. Lessons may be learned from past civilizations that grew crops in extreme environments for thousands of years. Evidence suggests that landraces of crops may have been in place for several millennia and likely to be specifically well adapted to their local environment. In effect, such landraces represent the efforts of thousands of years of selective breeding that should be regarded as an irreplaceable genetic resource. Unfortunately, many such landraces have now been lost by replacement with modern varieties. In some instances, we have access to those landraces through archaeobotanical remains. This study focuses on an area of the world associated with ancient Egypt - Nubia. The populations of southern ancient Nubia faced an environment in which water stress was a way of life. Interestingly, archaeobotanical samples of the 'smaller' barley that they grew shows some evidence of being adapted to drought conditions in a way that is not seen in the modern world. Furthermore, it seems that successive cultures from outside the region adopted this barley type, rather than introduce their own superficially higher yielding 'larger' varieties such as was grown in the Western Oases, and further north up the Nile Valley where water was not so scarce. We think this is because the Nubian barley was better suited to the harsh environment of the southern Nile Valley than outside varieties. In this case perhaps small was more beautiful. In this project we intend to examine a large portion of the barley genome (0.5%) most likely to be affected by drought stress in archaeobotanical samples from Nubian sites spanning 3000 years to find out if and how these ancient landraces became better suited to their environment. We will determine whether 'adapted' alleles could be utilized as a genetic resource for future breeding programs. We will also find out whether the landrace was kept 'pure', or whether a type was maintained with an influx of genetic material with new cultures. This study will provide us with important insight into the extent to which crops spread with culture or became locally adapted to the benefit of many cultures, and whether the ancient populations of Nubia solved problems of water shortage genetically in ways that will help us face the future.

Investigation into the biochemical effects of a disease, or treatment with a particular medication, commonly involves the compositional analysis of body fluids and in particular, blood plasma. However, blood plasma is an extremely complex and heterogeneous biological fluid whose components undergo a variety of possible interactions including, amongst others, the binding and interaction of many small compounds (including drugs) with larger molecules, such as proteins. Whilst many studies have been carried out to look at the effects of drug-protein binding, which is recognized as an important phenomenon, little is actually known about the biochemical consequences of interactions between proteins and the body's own biochemicals. Therefore information intrinsically encoded in these interactions is to a large extent ignored. In fact, from a measurement science perspective, the presence of these interactions is often considered a troublesome interference which makes the analysis of blood plasma non-trivial and which must be removed prior to accurate quantitative analysis of endogenous compounds in a sample. However, if we are to fully characterize the metabolic status of an individual, it is unlikely that 'simple' quantification of plasma components is enough and gaining a fuller understanding of these interactions is therefore of critical importance.In order to achieve this aim, the project will build on results from a previous feasibility study and will develop a tool which will allow rapid profiles to be generated showing differences between samples based on how the components of the sample interact with each other. Nuclear Magnetic Resonance (NMR) spectroscopy is a very powerful technique able to detect interactions of this type in whole blood plasma, with minimal disturbance to the sample. This can be achieved by setting up the NMR spectrometer to simultaneously monitor the diffusion rate of all the NMR-visible molecules present in the biological sample. The diffusion rate of a molecule is directly proportional to its size and therefore when a small compound interacts with a larger molecule such as a protein, the observed diffusion rate of the small compound will be slower than if there is no interaction. By measuring diffusion rate by NMR, an estimation of the degree of interaction can be made for either small biochemicals generated by the body or drugs. The proposed work will focus on the application of this technique to probe changes in diffusion rates of all components of a complex heterogeneous biofluid such as blood plasma, simultaneously, in a single experiment and without pre-selection of the interactions of interest. In order to relate differences in measured diffusion rates, and hence also interactions between components in the sample, to disease-status, drug treatment or drug toxicity, statistical pattern recognition approaches will be used to interrogate the resultant diffusion-based blood plasma profiles.

The efficient folding of proteins into their correct three-dimensional structures is essential for cellular function. In most cases this corresponds to the energetically most favourable state, but a number of metastable proteins fold instead to high energy conformations, which are primed to undergo large scale structural transformations when later required according to the particular function of the protein. Serpins are one such class of metastable proteins, of which the plasma glycoprotein alpha1-antitrypsin (AAT) is the prototypical example. Serpins comprise the most abundant family of protease inhibitors, and possess a molecular structure that is inherently dynamic: in the process of inhibiting their substrate protease, they undergo a dramatic change in shape from their initial metastable conformation. Clearly then, metastability is central to serpin function and conformational changes must be able to be triggered efficiently when required, and yet it is also their Achilles heel: spontaneous transitions can lead to misfolding or formation of polymeric aggregates, a process that is often associated with disease. Of the 35 serpin genes found in humans, nearly a third have a known involvement in hereditary disease, and five are known to form protein aggregates called polymers. However, despite many years of research the molecular mechanisms by which these transformations can be regulated remain poorly understood. It is our hypothesis that small-scale fluctuations ('dynamics') in the structure of the metastable native state may hold the key to this puzzle, and so this project is designed first to characterise the solution-state structure and dynamics of AAT molecules, and then to correlate these observations with the measured rates of conformational change. Nuclear magnetic resonance (NMR) spectroscopy is an exceptionally powerful experimental technique for studying the structure and dynamics of proteins. However, NMR traditionally requires proteins to be expressed recombinantly within bacterial cells using specialised isotopic labelling techniques, and for a number of interesting molecules, including several variants of AAT, this is not currently possible. Instead, our preliminary data overturn this paradigm by showing we can measure high quality NMR spectra using AAT purified directly from human donors - including patients with rare, disease-associated mutations - without the need for isotopic labelling. Thus, for the first time we can study the solution-state structure and dynamics of ex vivo, natively glycosylated AAT molecules, and this has revealed widespread changes in the conformation of a disease-associated variant that were not observed using crystallographic approaches that confine molecules into a rigid lattice structure. We propose to pursue these observations further, developing a new toolkit of NMR experiments to characterise structure and dynamics in these unlabelled ex vivo protein samples. We will investigate in detail the impact that mutations - associated with disease, or artificially designed - can have upon the structure and dynamics of the metastable serpin fold, and compare this with the effect the mutations have on both inhibitory activity and the misfolding and polymerisation processes. In correlating the solution structure and dynamics of AAT variants with serpin function and dysfunction, our research will address the longstanding problem of how structural changes within metastable proteins can be regulated, and this may ultimately lead to a new mechanistic basis for the design of inhibitors of serpin misfolding. More broadly, the new NMR methodologies that we will develop in this project will provide a platform that can be readily extended to ex vivo structural biology of other previously inaccessible protein systems.