Air pollution is an important cause of ill-health worldwide. This is particularly the case in China and many other developing countries, in which outdoor air pollution is worsening due to increased traffic and industrial activities. Furthermore, many people still use solid fuels (e.g. wood, charcoal or coal) for cooking and heating in rural areas, generating harmful smoke and other toxic substances inside the home. Although we know that air pollution causes death and illness at the population level, the exact extent is not fully understood because accurate information is lacking. Reliable estimation of the disease risks arising from air pollution requires measurements of individual exposures to air pollution and their subsequent disease outcomes (i.e. onset of new diseases and death). We propose to address this knowledge gap using the China Kadoorie Biobank (CKB), a large study of 500,000 men and women recruited from 10 diverse urban and rural areas in China in 2004-08 with detailed tracking of disease outcomes as a resource to study the health effects of air pollution. This represents an efficient approach that avoids the need to generate a separate expensive study to conduct this work. Use of CKB in this way requires improvements to existing air pollution data. As in many other large scale epidemiological studies, CKB did not directly measure actual air pollution levels individually, but relied on participants to report information on the type of fuel they use for cooking or heating and how often, or how close to a main road they live. Since individuals spend a varying amount of time in different locations doing various activities, this may be inaccurate measure of individual air pollution. The ideal would be to measure in real time the pollutant concentrations in the air breathed by each person individually. However, this will be impractical in a study of 0.5 million participants, so the challenge is to achieve estimates of exposure in a cost-effective manner. This proposal represents a pilot project to test and assess different methods of measuring individual exposure to household and ambient air pollution. In this project we will ask 300 CKB participants from 2 rural and 1 urban regions to carry a small wearable monitor with them to record particle pollutant concentration for two 5 day periods in both warm and cool seasons of the year. In addition, we will ask them to note what activity (e.g. cooking, commuting) they are doing, the duration and the location, so that we can understand how much of their pollution exposure comes from indoor or outdoor environments. Because it could be difficult and expensive to ask each participant to wear a monitor, we will also test whether it is sufficient to estimate individual air pollution by using data from ground monitoring networks, supplemented by satellite remote sensing technology. Two of the many satellites orbiting Earth carry sensors that detect infrared light transmitted through the atmosphere, providing a measure of airborne particulate matter. Using information from satellites and ground monitors, we will create a more complete map of air pollution. We will test and optimise this new approach in Suzhou, an urban CKB site that already has good coverage of ground-based pollution monitoring data. Based on the residential addresses of study participants, we can use this air pollution map to estimate an individual's ambient exposure level. We will use this information to carry out an analysis to investigate the health effects of air pollution in Suzhou participants. This proposal will provide important new understanding and experience needed to plan a larger project involving all 0.5 million participants in CKB. Ultimately we aim to collect reliable exposure and disease outcome data to accurately understand the impact of air pollution on health in China.

Oral disease such as tooth decay is one of the major healthcare challenges that affects over 40% of the world's population and over 30% of dentate adults in England. Such disease can lead to a loss of function in teeth that can impair diet and have undesirable consequences for general health. It can also lead to a loss of aesthetics in teeth, which adversely influences social activity of the patients. Both function and aesthetics can be restored with dental crowns (e.g. porcelain-veneered zirconia frameworks), which can also help prevent patients from experiencing pain, sensitivity and infection. Driven by the ageing population in the UK who need complex dental care to restore and maintain their teeth throughout their lives, as well as the fact that over 37% of dentate adults in England have one or more crowns, there has been a growing demand for patient specific dental restorative products (e.g. dental crowns) with increased longevity. Despite a continuous improvement in the mechanical performance, the conventional porcelain-veneered zirconia frameworks still suffer from a high failure rate (approximately 6-15% over a 3- to 5- year period). The primary failure mode of these dental crowns is near-interface chipping of the porcelain veneer, due to the loads that are applied to the chewing or grinding surface of the crown during mastication (referred to as occlusal loads). Failure of dental crowns can cause extensive discomfort to patients and have high cost implications for both patients and clinicians. To alleviate the problem, it is therefore highly desirable to develop novel porcelain-free zirconia-based dental restorative materials with significantly improved resistance to cracking. Inspiration for these composites can come from natural teeth, which have an intricate architecture giving them remarkable mechanical properties, especially the resistance to fracture - particularly in tooth enamel. Tooth enamel has been shown to have graded microstructure and extraordinarily strong interfacial bonding to the resilient supporting dentine. In this project, we propose to understand and improve the mechanical performance of novel zirconia-based composites with bioinspired functionally graded and textured microstructures. Our aim is to mimic the structure and remarkable mechanical properties of natural tooth enamel. The improvement of the mechanical performance will be based on a fundamental understanding of the role of bioinspired microstructural features in determining the mechanical properties, and how the properties can be enhanced by microstructural optimisation. To achieve this, we will develop and implement advanced micro-scale mechanical and structural characterisation techniques and micromechanical modelling. We will be working in close collaboration with academic and industrial collaborators and receive clinical input. The outcomes of this project will direct the manufacturing and processing towards biomimetic materials design and optimisation for the development cycle of the next generation dental products. Patients suffering from dental disease will benefit from this work through far-reaching improvements in the state of health, personal happiness and quality of life.

Data capture via imaging and sensing has become a common aspect of our existence and helps extend human vision and perception. Whether it is a microscope used for cell counting or the latest version of autonomous vehicle which aims to see through the fog; the sensing apparatus is expensive and limited in functionality. For example, the cameras of a self-driving car may white out due to exposure to excessive light when coming out of a tunnel. In many of applications, hardware (that captures data) and algorithms (which recover meaningful information from data) are treated decoupled entities; first capture data, extract information later. Hence, there is a limit to what can be recovered from the data based on the limitations of the hardware. Can we go beyond such limitations? The purpose of this research is to achieve a synergistic balance between hardware and algorithms by means of a co-design, so that popularly held limits in data capture and imaging can be broken, thus making the invisible, visible. Questions that we seek to answer include: Can we do bio-imaging with low-cost sensors (e.g. Microsoft Kinect)? Can we capture information beyond the usual dynamic range? Can we non-invasively classify blood cells by inferring cell geometry? Can we remove reflections in photographs? Can we see through diffusive media? These questions require us to go beyond the conventional barriers (e.g. dynamic range, spatio-temporal resolution, how fast the data is captured etc). The work in this proposal relies a co-design approach where carefully optimized capture process yields computationally encoded measurements from which the information is decoded using recovery algorithms. This approach is used to modify hardware and develop new algorithms to recover information. Application areas span from bio-imaging (cell-classification, fluorescence lifetime imaging, terahertz spectroscopy), consumer imaging (autonomous vehicles) to conceptualization of new sensing hardware. Three specific barriers are considered: (1) Dynamic Range Barrier. We propose the use of recording measurements that are non-linearly mapped by modulo operations. This is a fundamentally new way of sensing or digitising information and is largely unexplored. Our initial work shows that a simple correction to the Nyquist rate linked with Shannon's sampling theory allows for recovery of a bandlimited signal from modulo information. Remarkably, the sampling bound is independent of the the threshold. In this proposal we study a larger class of signals including sum-of-sinusoids, sparse signals and smooth signal and their link with application areas such as direction-of-arrival estimation and beamforming. (2) Resolution Barrier. Recovering spikes from low-pass filtered measurements is a classical problem and is known as super-resolution. However, in many practical cases of interest, the pulse or filter may be distorted due to physical properties of propagation and transmission. Such cases can not be handled well by existing signal models. Inspired by problems in spectroscopy, ground penetrating radar, photoacoustic imaging and ultra-wide band arrays, on which we base our experiments, in this work we take a step towards recovering spikes from time-varying pulses and prepare algorithms for non-ideal super-resolution. Furthermore, when the pulse or filter is smooth and not necessarily bandlimited, optimial bandwith selction for sparse-deconvolution is an open problem that is addressed in this work. (3) Bandwidth Barrier. We define the notion of bandwidth in context of Special Affine Fourier transforms which generalises a number of well known transformations. This allows us to prepare a unifying approach for studying sampling theory which is applicable to a wider class of signal models. Our algorithms are validated on experimentally acquired data with the help of inter-disciplinary and multi-university collaborations