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      "title_narrative":["Real Time deployment of pathogen resistance genes in rice"],
      "description_narrative":["Bacterial blight and rice blast are two key pathogens reducing rice productivity worldwide. Varietal resistance is an effective, reliable and environmentally friendly way of protecting rice against these pathogens. The development and use of modern molecular breeding techniques has shortened the generation of elite rice lines for varietal release from 8-10 years to 2-3 years. This means that real-time deployment of resistant varieties with various combinations of resistance genes can be customized through gene rotation or mixture in a single genetic background.  New varieties resulting from current breeding projects will be released in two to three years. This makes a range of varieties each with a unique combination of resistance genes available to growers. A key question, then, is: Where should these varieties be deployed? Each variety should be deployed where the matching virulence is not present, or is at an extremely low frequency, in the pathogen population. This ensures that the resistant variety remains effective for the maximum possible time. We thus need a method to ascertain the absence, or possible presence in extremely small frequency, of pathogen virulence in the field.  We therefore aim to develop and apply a combined modelling and field monitoring approach to determine areas where a novel resistance gene can safely be deployed because virulence is at a sufficiently low frequency in the pathogen population. The method: -1- Will quantify the number of infections in observational plots of a susceptible variety. A similar plot with the novel resistant variety will show no infections (ideally). Using a statistical method we will then be able to estimate the maximum expected frequency of virulence in a field pathogen population. If this frequency is small enough (e.g. 10-10) we can decide that the new resistant variety be released. -2- Will be scaled-up from the field level to a regional level. This will enable us to make decisions about variety release at larger spatial scales. The model will be parameterised from field monitoring of an aggressive bacterial blast strain currently expanding in the north of Thailand. -3- Will be made available, with the necessary training, for future use to the rice research community. -4- Will start to be used alongside its development to help solve the emerging problem posed by the new expanding aggressive bacterial blast strain that threatens northern Thailand's rice production.  The outputs of the project will be a set of methods to establish where in the Philippines and Thailand novel resistant varieties can be released to maximise the durability of varietal resistance. This will be of clear benefit to growers as they will suffer less from virulent strains breaking the novel resistance. This in turn will improve the income of poor growers. The project will also train staff in statistical methods, epidemiological modelling and the use of the methods developed in the project. The project will also help breeders to establish breeding targets by providing them with information on the presence of pathogen virulence in the field.","The Newton Fund builds research and innovation partnerships with developing countries across the world to promote the economic development and social welfare of the partner countries."],
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      "title_narrative":["A strategic approach to identifying and combating porcine reproductive and respiratory syndrome virus outbreaks and other porcine viral diseases"],
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      "title_narrative":["UKRI COVID-19 Grant Extension Allocation (CoA) - Brunel University London"],
      "description_narrative":["The UKRI COVID-19 Grant Extension Allocation (CoA) provided organisations with resources to sustain UKRI grant-funded research during the period of pandemic disruption and its immediate aftermath. Some of the funding was directly used supporting the following ODA-eligible grants at Brunel University London: InnovateUK 105316 - Tassou; \nBB/R012695/1; \nBB/R012695/1; \nInnovateUK 42088 Singh  (SOFTS); \nInnovateUK  41760   Singh    (C-SPEL); \nInnovateUK 105669 Singh (INset4KTI); \nBB/P024149/1; \n InnovateUK 48979 Singh (SolCos).","Research and development activity contributing to the UK’s strategy to address key development challenges."],
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