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.

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.

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.

Worldwide, opioid use (e.g. heroin) is now viewed as a 'crisis'. Men are more likely to use opioids, but in recent years there has been a greater increase in the numbers of women using opioids. As a consequence, the numbers of pregnancies where opioid use is a factor is also increasing. There is already strong evidence that opioid use in pregnancy harms the unborn child, but very little is known about the long-term outcomes for these children: they are difficult to follow-up over a long time period using traditional methods, such as repeated face-to-face interviews, due to the complexity of their lives. Scotland is in a rare position of being able to link all health data across a person's lifetime, as well as linking mother and child data, not readily available in all countries, and having high levels of opioid use. As a first step, I have shown that we can identify women who use illicit or prescription opioids in pregnancy from the health data we already collect routinely (e.g. antenatal records, hospital records) in Scotland and this can be matched to equivalent data from their children. This gives us a way of exploring longer term outcomes. The proposed research will be the first to analyse a population-based cohort of children exposed to opioids in pregnancy through to adolescence. This will allow us to explore a range of health, education and justice outcomes for children in Scotland, and to explore the pathways they take to these outcomes. The outcomes of children who were exposed to opioids through their mother's substance use will be compared with children in two other groups: 1) mothers who used opioids for chronic pain in pregnancy, and 2) children who are from similar socio-economic backgrounds but who were not exposed to opioids. We also don't know much about how drugs pass through to the unborn child during pregnancy, as the effects on the child do not seem to be directly linked to the mother's consumption. I will work with a forensic chemist to develop a test which changes colour depending on the type and amount of drugs (e.g. heroin, morphine, methadone) detected in blood samples. In Phase 1 this will be developed using animal bloods. In phase 2 we plan to test this on blood collected from the umbilical cord. During Phase 1 I will work with mothers who use opioids to gather information on their views and potential concerns around the use of cord blood, in order to ensure this research is conducted sensitively in Phase 2. There are other countries with excellent data linkage systems, but with smaller numbers of babies exposed to opioids in pregnancy. The second stage of this study will be to develop international collaborations, in order to access datasets from some Scandinavian and Commonwealth countries (New Zealand, Australia) and to test the pathways found on the Scottish data on these international datasets. In the rest of the UK, it is difficult to bring data together to the same degree as in Scotland. We will therefore work with other UK nations to improve these data linkages. This will mean that later in the study we can analyse data for the whole of the UK in a similar way to the Scottish data. Throughout the project we will work with women who use opioids, young adults whose mothers used opioids, and the charity organisations and policy-makers who support these families. This will ensure that we are asking the right questions and will be able to create useful and meaningful recommendations for future research and intervention. The results of this study will help us to understand the effects on children of opioid use during pregnancy and allow us to explore promising approaches for interventions to improve the support that is currently provided to women, adoptive families, and their children. Our findings will ensure that women across the world are given accurate information about the impact of opioid use in pregnancy to help provide their children with the best start in life.

Subgraph-finding problems involve identifying patterns in structured data. In theory these problems should be computationally hard for an algorithm to solve, but in practice intelligent constraint-based algorithms can quickly solve even large problems. This research will uses scientific (rather than purely mathematical) techniques to increase our understanding of this gap between theory and practice, and will allow us to design better algorithms in the future. The research is based upon analysing proof logs, which are a mathematical description of how an algorithm reached its answer.