Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Food security is one of the most pressing challenges that humans will face this century. A growing population, shifting dietary habits and a changing climate are placing unprecedented pressure on crop production. Future crops must therefore be resilient to climate change and (a)biotic stresses. Whilst modern crop varieties have been bred for high yields, this has led to a reliance on a diminished number of crop species and varieties, resulting in a vulnerability to pests and disease and a changing climate. Leveraging the genetic diversity that exists across different crop cultivars and landraces offers an opportunity to sustainably increase food production and close yield gaps by ensuring that crops are optimised to current and future environments. However, identifying the molecular mechanisms that underpin crop physiological responses to environmental stress is complex. Crops express phenotypic traits according to interactions between their genomes, the environment and how they are managed. Identifying how a given crop cultivar will respond to different environmental conditions is key to guiding breeding programmes. Phenotyping studies are underpinned by testing how crop genomes respond to environmental conditions, and how these conditions affects overall yields and the resilience of the crops to stress. However, this is resource intensive and limited in scope by the time and environment that the crops are grown under. There is a critical need to harness novel remote sensing techniques and state-of-the-art modelling approaches to model how genetically-regulated crop biochemical, structural and physiological traits affect yields, under different environmental scenarios. Closing this genotype to field-scale gap requires robust scaling methodologies that can be deployed at high throughputs. Leveraging our understanding of genetic controls on physiological traits and fluxes will enhance our ability to predict how crop genomes will respond under different environmental conditions.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Our partners, CIPRB, have shown, through a natural experiment, that community day-care for 1-4 year old children in rural areas is effective in reducing risk of all-cause mortality by 44%, drowning by 82% and injuries by 88%, compared to children not in day-care [9]. We wish to adapt this model so it can be appropriately, feasibly and sustainably delivered to benefit urban disadvantaged communities. Low-income countries (LICs) are experiencing rapid and uncontrolled urbanisation. This can clearly be seen in Dhaka, Bangladesh with an annual urban population growth of 3.6% [6], and an estimated 4 million inhabitants living in slums. Urbanisation is changing gender dynamics: 22% of women in Dhaka now work outside the home (11% non-slum) [9]. With limited extended family support in slums, women must either leave children with elder siblings - often sisters who then drop-out of school - or neighbours. This limited supervision contributes to high rates of injuries, poor hygiene and limited nutrition, resulting in 50% of slum children suffering stunting (33% non-slum). Child day-care presents a holistic solution, allowing women to work whilst knowing their children are safe, cared-for and have opportunities for early childhood development (ECD). Our partners, CIPRB, have extensive experience of delivering day-care in rural Bangladesh [9]. Building on CIPRB's rural model we plan to develop and refine a sustainable day-care model based on principles of ECD and improving nutrition and hygiene for 1-4 year olds. Financial sustainability is key for longevity and scale-up. We plan to place day-care centres adjacent to slums so services at a suitable fee can be provided to parents in non-slum neighbourhoods. This approach is designed to promote integration between the two communities, potentially facilitating social mobility for slum children. Objectives and methods, to: 1) Understand the extent and nature of demand for day-care we will survey 200 households with children under 5 from slum and non-slum communities, and conduct qualitative interviews to understand perceptions of childcare among women, carers, men and community leaders. 2) Co-produce a proto-type integrated, self-sustaining, child day-care model we will establish a technical working group of childcare experts, community representatives, local government officials, donors and (I)NGOs. The group will draw on CIPRB's, (I)NGO models and findings of a systematic review being conducted by UWA (not included in this proposal) to develop a prototype model and all materials required. The group will also develop a draft theory of change to guide the evaluation. 3) Test and refine an integrated self-sustaining model, we will implement the day-care model for six months. During this time, we will facilitate a group of day-care users and staff to reflect on how the model is working and make changes for improvement. Our qualitative researchers will observe the process, and interview users to understand how to further improve the model. We will assess model costs and the extent that fees from non-slum day-care users can support sustainable delivery. 4) Inform the design of a pilot study for a larger evaluation to assess the cost-effectiveness of the day-care model, we will assess the feasibility and then refine methods for recruitment, assessment and follow-up among day-care users and a potential comparator group. This will involve repeating the survey among the same sample of 200 households. We will also assess children when they start at day-care and six months later. We will use our overall analysis to refine our theory of change. Throughout the study we will engage closely with local government and donors to understand and influence plans to scale up day-care across Dhaka, targeting the urban poor. We will identify the most appropriate outcome measures from our theory of change and discussions with policy makers. We expect these will include ECD, stunting and injuries.

Optical microscopy is the most widely used imaging tool in laboratories all around the world. Indeed, According to BCC market research, the global optical microscopy market will be worth US$6.3 billion in 2020. Several Nobel prizes have been awarded for contributions made to the development of optical microscopy, including most recently in 2014. There is, however, a major limitation facing optical microscopy: it is difficult, if not impossible, to image tissue hidden beneath layers of overlying tissue. This occurs for the same reason that it is difficult to see clearly through a window covered in rain drops - tissue is highly scattering, like rain drops, and critically degrades image quality. This is important as it prevents in-tact tissue from being imaged in its natural environment, requiring tissue to instead be sliced into thin sections. A variety of approaches have been used in an attempt to overcome this problem. All such approaches are generally similar in that they insert hardware into the microscope in an attempt to compensate for the degradation due to the sample. This is similar to humans using spectacles to overcome imperfections of their eye. The main difference is that opticians are able to precisely determine the imperfections that each eye has, and thus design spectacles which perfectly compensate for them. No such method has been developed for measuring sample induced imperfections, or aberrations, present in microscope images. This project proposes to do just that: measure the imperfections caused by the sample itself. This will be achieved by computing the optical structure of the sample (i.e., how light travels in the sample) via a two stage process. Firstly, the sample will be imaged by a microscope capable of performing rapid three-dimensional imaging called an optical coherence microscope (OCM). OCM works very much like ultrasound imaging, except light is used instead of sound waves. The second step involves developing a sophisticated computational procedure for calculating the sample's optical structure from the OCM image. This will be performed using a recently mathematical model, developed recently by the project team, which allows OCM images to be predicted from a given sample structure. Clearly, our task is to solve the opposite problem: calculate the sample's structure given a measured OCM image. Formal techniques have been established for solving the problem in the opposite fashion which will be adapted specifically for this project. Once the sample's optical structure has been solved, in a follow-on project, existing methods will be employed for restoring optical fluorescence microscope images which have been degraded by the sample itself. This will enable fluorescence microscopy to be performed at depths within tissue which are currently inaccessible. This will be highly advantageous to many biological researchers in the UK and the world.