The statistics for depression and anxiety in our young people are shocking. Over one-third experience these conditions and rates are rising, particularly in young women. Anxiety and depressive disorders are highly debilitating, disrupt education, reduce normal work capacity and dramatically increase suicide risk. Despite this, <£2 is spent per person per year on research into understanding them. Anxiety and depression also have very complex relationships with physical health conditions, with growing evidence for bidirectional effects, and putative sub-types of depression with specific physical health profiles. This complex picture is made even worse by the stigma which still surrounds these conditions, so many people do not seek help, or if they do, they do so for physical rather than psychological concerns. This backdrop means we know much less than we need to about how anxiety and depression develop, who is most at risk, when and how these conditions influence and are influenced by physical health concerns, and which factors drive treatment seeking and more general health service use. Furthermore, despite having known for centuries that anxiety and depression "run in families" we know very little about which factors lead to the child of a parent with anxiety or depression developing that condition themselves. This question is of key importance to many young people experiencing anxiety and depression. Our overarching aim is to transform our ability to predict who is at risk of anxiety and/or depression in their mid-twenties and our understanding of how related traits are transmitted from one generation to the next. Our findings will allow us to specify for whom and when to intervene to disrupt the development and intergenerational cycle of these conditions. To address this aim, we will undertake three sets of new data collection with participants of the Twins Early Development Study (TEDS). TEDS has followed twins born in England and Wales in 1994-1996 from birth, assessing a wide array of emotional, behavioural, cognitive and language measures. Genome-wide genetic data are also available. Approximately 10,000 families are still active in the study, of whom ~65% consistently respond at each wave of data collection. As they approach their mid-twenties the twins are starting to have children, providing an exciting and unique opportunity to re-engage them and their offspring. First, we will collect information about current mental health conditions using online assessment at age 26. This will allow us to utilise all our prior information to build models that identify groups at the greatest risk of developing mental health conditions in young adulthood, who could benefit from early prevention efforts. Second, we will connect information from TEDS twins' routine medical records to our dataset, built up over 25 years. This will offer additional external, independent information, including on mental health conditions, physical health conditions and use of medical services, all of which can help refine models of risk. Third, we will recruit and assess the children of TEDS participants, which will allow estimation, beyond the relative contributions of genes and environment, of parent-to-child and child-to-parent effects. We will use this work to drive a new wave of prevention trials, built on the risk models we devise. Furthermore, we will continue to encourage researchers internationally to access the TEDS data resource to address questions beyond our core focus.

Alzheimer's disease (AD) is often mis-perceived as a disorder largely or solely of memory. However the disease also affects the visual areas of the brain leading to problems seeing what and where things are. Dementia-related visual impairment tends to be neglected, partly because people assume any problems are due to the eyes rather than the brain, and because it occurs at a point when language and other skills are too impaired for the person with dementia to explain the perceptual problems they are having. Visual problems are also often mis-attributed to poor memory (e.g. a person with AD failing to recognize a family member in a photo may be thought to have "forgotten" the person, when in fact they may simply be unable to perceive the face clearly). Visual impairment in AD has received increased attention recently with the identification of the syndrome Posterior Cortical Atrophy (PCA) which is typically caused by AD but presents with dramatic impairment of vision not memory, as experienced and described by the author Terry Pratchett in his documentary Living with Alzheimer's. Very few studies have explored the effect of impaired vision upon people with dementia or their caregivers. A motivation for improving our understanding of how people with AD see the world is that the limited number of small studies which have been conducted suggest that even simple changes to the environment (e.g. changing the colour of tableware from white to red) can compensate for vision problems in people with AD and lead to improved functioning and health (e.g. better eating and drinking). The project objective is to demonstrate that helping AD patients to interact more successfully with their visual environment at home can have a significant positive impact upon the wellbeing and quality of life of both patients and carers. The project will involve 50 people with PCA, 150 with typical Alzheimer's disease and 100 healthy volunteers. The impact of visual aids and strategies will be measured at three time-points over the course of one year, with a staggered start to enable comparisons of quality of life in those with and without the intervention. The success of the project will be judged primarily using established measures of quality of life, caregiver burden, everyday abilities, and behavioural and psychological wellbeing. However, the design of the visual aids and compensatory strategies themselves will be based upon a combination of patient/carer interviews (qualitative evidence) and cutting-edge scientific understanding of the nature of visual impairments caused by conditions such as Alzheimer's disease (quantitative evidence). This quantitative evidence will be gathered through studies of patient's visual skills and eye movements, and their ability to move around a purpose-built laboratory environment, before the main study commences in patients' own homes. Another important aspect of the project is the involvement of people with PCA, who experience AD-related visual loss but without the loss of memory and insight seen in typical AD. These individuals with PCA offer a new and unique perspective on the AD patient's view of the world. Their experiences of care homes and day hospitals draws attention to the fact that many current social and behavioural interventions for people with dementia may be limited in their effectiveness by over-reliance upon visual information and by a systemic failure to recognize visual impairment in many service users. The research brings together experts in the fields of dementia, engineering, social science, social work, occupational therapy and ophthalmology. This interdisciplinary research team will work closely with the DeNDRoN ENRICH scheme and project advisors in the 3rd sector and industry specializing in dementia and vision loss (e.g. Thomas Pocklington Trust, Dementia and Sight Loss Interest Group, ARUP, CDRAKE) to improve the study and implement its findings.

Diagnosis is a difficult process. A patient who presents to their doctor ill will often undergo a process which involves being asked questions, observed, examined and perhaps even having blood or imaging 'tests'. Each question asked or observation made is either a diagnostic test in its own right or part of one and is a necessary part of arriving at a diagnosis. But some tests are better than others and importantly probably no test is 100% accurate. Sometimes a test result may suggest a patient has normal health when they actually have disease or have disease when they have normal health. This happens to all tests and diagnostic test accuracy research is aimed at evaluating how often this happens, in other words, determining how accurate tests are. Essentially when a clinician decides upon a diagnosis they are consciously or otherwise invoking a probabilistic process where multiple tests are combined and the patient's diagnosis should be the one most probable given the combination of all the test results. However, for this process to be truly beneficial to the patient the clinician needs to know the accuracy of each of these tests and how likely the patient has disease before the diagnostic process has even started. This is where the difficulty lies for those who practise evidence-based medicine. Although the accuracy of many tests has been estimated by research studies, for individual tests the accuracy may vary significantly between studies. This variation may depend on who is applying the test, how it is being applied, which patient it is being applied to and most significantly of all, how the accuracy was measured in the study. When there are several studies there are methods which allow us to combine their results. These methods may also help determine the real reasons why the test's accuracy varies. However, in general, the studies report insufficient data of sufficient quality to enable such analyses to be either possible or comprehensive. Furthermore, from previous work, we have been able to demonstrate that in some cases the test accuracy reported by a study may be virtually impossible in some patient settings. This creates a problem for the doctor. How do they know which estimate of a test's accuracy to use if it varies greatly between studies and risks being nearly impossible for their own practice? We have already begun to develop methods which make it possible to determine whether results from a test study are likely to accurately represent a doctor's practice in general. This would mean that a doctor could confidently apply the research to their own practice without reservation. However, sometimes the research is not reflective of the different clinical settings seen in practice and a more specific solution is required. This may be done by collecting routine data from the doctor's own setting and using it to determine a feasible range of values for the test's accuracy. This method, in its current form, is used to exclude the studies 'least likely' to derive a plausible estimate of a test's accuracy for the doctor in their own practice. At the moment both methods are in development but potentially could be implemented into the real-world and used to improve diagnosis. There are clear patient benefits to improving diagnostic performance including reducing the number of patients treated unnecessarily and increasing the number treated appropriately. One of the aims of this research is to pilot integrating this method into General Practice to help diagnose infection. This could also help reduce the potential for antibiotic resistance by reducing the number of antibiotics prescribed inappropriately. However, before this is done the methods need to be fully investigated to determine their utility and limitations. It may be that other approaches afford greater patient benefit, and an evaluation of these with the methods already described, will be the focus of the proposed research.

Preterm birth, birth before 37 weeks of pregnancy, is a major cause of infant death and illness in sub-Saharan Africa. Over 80% of preterm births globally have been estimated to occur in sub-Saharan African (sSA) and Asian countries, the majority being due to women going into preterm labour spontaneously or their membranes (waters) rupture early (classified together as spontaneous preterm birth, SPTB). Despite knowledge of the global impact of SPTB, most of the research into this often devastating pregnancy outcome has focussed on pregnant women in high income countries such as the UK and USA. Much less in known about SPTB in women from low income countries. However, the underlying biological causes of SPTB are complex and heavily influenced by environment, nutrition, infection and other risk factors that pregnant women are exposed to. Region specific research is essential if we are to improve maternal and newborn healthcare in countries where the burden of preterm birth is highest. Addressing this need, we plan to study to clinical and social risk factors (from 5000 women recruited to the PRECISE Network pregnancy cohort, https://precisenetwork.org/) combined with biological markers of SPTB in the female reproductive tract, blood and placental tissue in women from Kenya, The Gambia and Mozambique. We will integrate these data to enhance our biological understanding of SPTB as well as identifying novel biomarkers relevant to sub-Saharan African populations to predict risk of SPTB. We will also create sustainable teams of SPTB researchers by training five new African scientists and supporting their supervisor as research leaders. We will, with colleagues in The Gambia, establish a bioinformatics training programme and a laboratory science network for our researchers in Sub Saharan Africa and the UK. We anticipate that this work will impact future strategies for clinical risk management, prevention and treatment that specifically addresses the needs of women in sub-Saharan Africa, as well as having potential relevance to SPTB globally.

Hearing loss affects approximately 500 million people worldwide (11 million in the UK), making it the fourth leading cause of years lived with disability (third in the UK). The resulting burden imposes enormous personal and societal consequences. By impeding communication, hearing loss leads to social isolation and associated decreases in quality of life and wellbeing. It has also been identified as the leading modifiable risk factor for incident dementia and imposes a substantial economic burden, with estimated costs of more than £30 billion per year in the UK. As the impact of hearing loss continues to grow, the need for improved treatments is becoming increasingly urgent. In most cases, the only treatment available is a hearing aid. Unfortunately, many people with hearing aids don't actually use them, partly because current devices, which are little more than simple amplifiers, often provide little benefit in social settings with high sound levels and background noise. Thus, there is a huge unmet clinical need with around three million people in the UK living with an untreated, disabling hearing loss. The common complaint of those with hearing loss, "I can hear you, but I can't understand you", is echoed by hearing aid users and non-users alike. Inasmuch as the purpose of a hearing aid is to facilitate communication and reduce social isolation, devices that do not enable the perception of speech in typical social settings are fundamentally inadequate. The idea that hearing loss can be corrected by amplification alone is overly simplistic; while hearing loss does decrease sensitivity, it also causes a number of other problems that dramatically distort the information that the ear sends to the brain. To improve performance, the next generation of hearing aids must incorporate more complex sound transformations that correct these distortions. This is, unfortunately, much easier said than done. In fact, engineers have been attempting to hand-design hearing aids with this goal in mind for decades with little success. Fortunately, recent advances in experimental and computational technologies have created an opportunity for a fundamentally different approach. The key difficulty in improving hearing aids lies in the fact that there are an infinite number of ways to potentially transform sounds and we do not understand the fundamentals of hearing loss well enough to infer which transformations will be most effective. However, modern machine learning techniques will allow us to bypass this gap in our understanding; given a large enough database of sounds and the neural activity that they elicit with normal hearing and hearing impairment, deep learning can be used to identify the sound transformations that best correct distorted activity and restore perception as close to normal as possible. The required database of neural activity does not yet exist, but we have spent the past few years developing the recording technology required to collect it. This capability is unique; there are no other research groups in the world that can make these recordings. We have already demonstrated the feasibility of solving the machine learning problem in silico. We are now proposing to collect the large-scale database of neural activity required to fully develop a working prototype of a new hearing aid algorithm based on deep neural networks and to demonstrate its efficacy for people with hearing loss.