Mosquito borne diseases such as malaria, dengue, chikungunya and zika cause huge suffering in tropical regions of the world. One of the main approaches to controlling these diseases is to use insecticides to kill mosquitoes and prevent them from transmitting the infection from person to person. Millions of pounds are spent on mosquito control each year though surprisingly there is no simple method for evaluating the ability of these interventions to kill mosquitoes or prevent them from transmitting disease. The number of biting mosquitoes in an area fluctuates substantially from day to day due to local weather patterns so the number of mosquitoes caught in traps is a poor predictor of the size of the population. More importantly the number of mosquitoes in itself is not a good predictor of risk as many diseases take several days to develop inside the mosquito and find their way to the mouthparts. This means that only older mosquitoes can pass on the infection. Mosquito age is therefore very important for assessing the effectiveness of anti-mosquito interventions but currently, there is no easy, accurate way of assessing the age of a mosquito population. Near-Infrared Spectroscopy (NIRS) is a new age-grading and species identification technique that has been developed in the laboratory. It predicts the age of the mosquito by measuring how a beam of light is reflected differently from the bodies of mosquitoes as they get older. Unlike other methods NIRS doesn't require costly chemicals or procedures and it can be carried out by anybody with minimal training. This makes it feasible for use as a routine method for monitoring mosquito age in the field. Currently NIRS cannot predict the age of an individual mosquito very accurately and tests have only been done on mosquitoes reared in the laboratory which are likely to be more uniform (and therefore give more accurate results) than those caught in the wild. However, for disease control it is more important to know the average age of the mosquito population than the age of individuals. Our preliminary work suggests that if we change the way we analyse NIRS outputs we can generate highly precise predictions of the average age of the mosquito population. The project intends to take NIRS from the laboratory to the field and test whether it is good enough to be able to be used in the routine monitoring of mosquito populations. The project will use semi-field and field data to operationalise the technique and outline how many mosquitoes need to be caught (and over how many days) to generate estimates accurate enough to guide the deployment of mosquito control. The work will concentrate on the two most important mosquito borne infections: malaria (which kills 438,000 people in 2015) and dengue (which infects 400 million people annually). However the technique developed here can be applied to other diseases and mosquito species. NIRS can also be used to differentiate closely-related mosquito species that are indistinguishable by eye. That is important, as not all of these mosquitoes have the same ability to transmit disease and are affected by control interventions differently. Similarly to age-grading, the capacity of NIRS to differentiate species needs to be more rigorously tested in the field. There is also evidence to suggest that NIRS might be able to detect whether a mosquito is infected with the virus that causes dengue disease. This will be tested for malaria, first in the laboratory in Burkina Faso and then in the field. Currently mosquito species, age and infection status are estimated using a variety of laborious and costly procedures that preclude their use as routine monitoring tools in poorer parts of the world. A single, inexpensive method for doing all three tests simultaneously would have significant public health impact: we could describe the risks of disease transmission and evaluate the efficacy of control programs far more cheaply and quickly.

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.

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.

Around the world the prediction of microbial water pollution is important for informing policy decisions in order to safeguard human health. However, modelling the fate and transfer of microbial pollutants, such as E. coli (& other pathogens) at different spatial scales poses a considerable challenge to the research and policy community. In the UK much research has focused on trying to understand the movement & survival of pathogens in environmental systems with a view that better knowledge and data on the behavioural characteristics of these micro-organisms will improve our ability to model and predict their interactions with, and responses to, the world around us. The NERC-funded project ReMOFIO (NE/J004456/1) provides an example of research undertaken in the UK to improve our understanding of the magnitude and spatial distribution of microbial risks in the landscape. In turn, this new knowledge has enabled the refinement of a simple modelling framework to allow for improved prediction of microbial risk on agricultural land, based on livestock numbers, farming practices and E. coli survival patterns under environmental conditions (e.g. rainfall and temperature fluctuations). However, our model is built using data common to the UK; this International Opportunity Fund (IOF) will allow us to test the NERC funded ReMOFIO model in landscapes typical of different catchment systems around the world and to determine how transferable the approach is beyond the UK in order to evaluate its global relevance. To do this we will use the 'PRACTICAL Modelling' IOF to establish a new international partnership, with the UK acting as a 'junction-box' connecting data and modelling skills from across Ireland, New Zealand and the USA. We have enlisted the expertise (and associated catchment data and modelling approaches) of three leading international scientists, in addition to other UK experts, in order to evaluate the wider application of data emerging from the ReMOFIO project. We will also investigate the potential for other models and tools to be linked to the ReMOFIO model to see if, conceptually, we can develop a more holistic model that becomes bigger than the sum of its internationally disparate parts. Part of our assessment will focus on the strengths and weaknesses of different modelling approaches that are currently being applied to assess pathogen risks in agricultural catchments. We will consider the transferability of these different approaches across contrasting agricultural systems typical of the UK, US, Ireland and New Zealand with a focus on the inherent differences in catchment characteristics (natural, managed, engineered and socio-economic), uncertainties of the underpinning data provided by international colleagues, and how these factors might impact on our ability to adopt or combine international modelling platforms. Ultimately, our international partnership will explore key questions that challenge scientists working in the field of microbial pollution from agriculture: how do the different pathways in the soil, that connect pathogen sources to water bodies, vary in space & time across different catchment types & how does this impact on microbial travel times through the environment; to what extent does the probability of pathogen die-off vary for different environmental conditions around the world; how do we integrate pathogen behavioural characteristics (e.g. their ability to persist or move in the environment) into risk-based models that are useful for policy-makers; and how will the export of microbial pollutants from the landscape alter under projected climate change? The PRACTICAL Modelling project will begin to tackle these important questions using an international forum and, collectively, we will develop a global 'roadmap' of research priorities and needs required for a co-ordinated response to improve the prediction of microbial risks in agricultural landscapes.

A major obstacle for the future of agriculture and global food security is the supply and use of water. Rice is a global food staple, feeding over half of the world's population. However, rice is currently confronted with its most significant growth deficit in two decades, primarily due to the challenges posed by a changing climate and diminishing freshwater resources. Therefore, developing rice that is more efficient in its use of water is central to the future of sustainable agriculture and global food security. As a UKRI Future Leaders Fellow, I will pioneer the holistic understanding of water use efficiency and enhance water use traits in rice using innovative technological, genomic, molecular biology and biotechnological advances. The data arising from my FLF will define avenues for creating improved rice germplasms. My FLF takes a dual-pronged approach to maximise successful outcomes. The first approach exploits the novel paradigm-shifting technological advancement of cereal grafting, where "water smart grafts" will be generated by grafting water-efficient rootstocks of millets and/or wild rice to elite rice landraces. Comprehensive physiological and phenotyping measurements will be undertaken to assess growth, photosynthetic performance, and yield under varying water conditions, gaining a holistic understanding of water use parameters. Moreover, gene expression profiling and the construction of gene regulatory networks will identify master regulators that will inform genetic manipulation for improved water use efficiency in rice breeding programs. The second approach will assess natural variation in water use efficiency traits. Considerable variability in water use efficiency exists amongst different rice accessions, often reflecting the climate at the region of cultivar development, such as between varieties from Asia and Africa, but the mechanistic and genetic basis of this variation remains largely undefined. The central aim of this project is to improve understanding of genetic differences in water use efficiency and to aid development of superior rice germplasms with enhanced water use traits. Through comprehensive physiological and anatomical measurements, as well as transcriptome profiling and expression quantitative trait loci mapping, the project will identify genes and co-expression networks associated with improved water use efficiency in rice. Finally, the two approaches will converge into tissue-specific, precise genetic manipulation, where multiple genes identified from the above approaches will be targeted in specific tissues. Since many countries, including the UK, do or will soon permit the growth and sale of gene-edited crops, this approach will use the CRISPR/Cas9 system to exploit the potential of gene editing to enhance water use efficiency in rice. This project aims to understand gene interactions and their impact on complex water use traits. The overall synthesis of these approaches will provide new insights into the holistic understanding of water use efficiency in cereals and generate rice germplasms with enhanced water use efficiency traits that will be incorporated into novel rice breeding programmes to realise maximum impact and pave the way towards a sustainable future. Finally, to promote multidisciplinary innovation and the integration of these emerging tools into mainstream resource management, I will establish an International SMART Consortium dedicated to Sustainable Millet and Rice Agricultural Research and Technology for Water Resilience and a Centre of Excellence at the University of Essex on Global Food Security and Society to provide novel solutions for sustainable agriculture in the 21st Century amidst a changing climate.