Summary (up to 4000 characters) To maintain human and animal health, it is extremely important to understand how pathogens like viruses are transmitted and evolve to higher virulence. It is this knowledge that enables employment of effective and sustainable control strategies. Thus, it is necessary to collect, assemble, and analyse highly accurate datasets to determine the short- and long-term effectiveness of disease control approaches, that include biosecurity, genetic selection for disease resistance, and widespread vaccination. In this project, an international, interdisciplinary team investigates the impact of these approaches on the spread and evolution of two avian pathogenic viruses - Marek's disease virus (MDV) and infectious bronchitis virus (IBV) - both of which are primarily controlled by imperfect vaccines. It has been argued that imperfect vaccines like those to MDV and IBV, or host genetic resistance may alter the balance of selection between pathogen transmission and virulence by allowing a few more divergent but still virulent strains to be transmitted at reduced cost. However, these hypotheses have not been proven, and predictive frameworks are lacking for determining the combined influence of host and viral genetics, as well as vaccination on viral transmission and evolution to increased virulence. To address these knowledge gaps, a series of transmission experiments have been designed that utilize unique resources and data from 7,000+ birds under highly controlled conditions. In summary, the primary goal of this research is to collect informative, high-resolution empirical data and use these to build the next generation of data-informed mathematical models of virus transmission and evolutionary dynamics as a function of vaccination status, host genetics, and/or viral mutation rates. We will also address the important and possibly interdependent questions of genome variability and evolution towards increased virulence in vivo. Besides the pure scientific merit of this research, we also strive towards lifting the project to high practical relevance. This requires a whole systems approach that also considers the broader socio-economic and political drivers of disease spread and virulence evolution. We will combine socio-economic studies with mathematical modelling to identify strategies for mitigating MD spread and MDV virulence evolution in sub-Saharan Africa, where poultry production is currently undergoing drastic increases in commercial production, similar to what was observed in the US in the 1960s. We propose the following objectives to achieve scientific excellence and attain broader impact: 1. Determine the influence of imperfect vaccines, host genetics, and viral mutation rate on transmission and evolution to higher virulence. 2. Validate viral genome polymorphisms associated with increased virulence and the ability of the virus to escape immune surveillance. 3. Build data-informed evolutionary-epidemiological simulation models to develop strategies to control the ecology, evolution and economic burden of MD. 4. Disseminate information on MDV and IBV, and the impact of vaccination to poultry producers and the public through training, workshops, online videos, seminars, and various engagement activities

Source separation is a critical early processing stage in electronic surveillance systems where the multiple simultaneouslyintercepted transmissions need to be detected, separated and identified for possible threats (e.g. pulsed and continuouswave radar, navigation systems, etc.). When the signals to be detected and separated overlap in time and frequency thiscan prove a challenging signal processing task that cannot be solved through simple filtering or beamforming.Recently sparse representations have emerged as a very powerful technique for solving source separation problems,particularly in underdetermined scenarios (i.e when there are fewer target sources than sensors), including the difficultcase of single channel source separation. Sparse representations usually exploit prior knowledge of the nature of thesignals to be intercepted to create 'nonlinear' separation algorithms that substantially surpass the performance oftraditional filtering techniques. Furthermore, in certain circumstances, they can also be adapted to learn the structure ofthe signals being observed to achieve the separation in a totally blind manner.The aim of this project is to develop new algorithms based around sparse representations capable of detection,separation and classification of individual EM signals that overlap in time and frequency. In addition computationalefficiency will be pursued by borrowing recent ideas from compressed sensing theory.

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.

The Nrd1-Nab3-Sen1 (NNS) complex terminates transcription of approximately 12,000 RNAs in Saccharomyces cerevisiae. During the stress response, the NNS complex changes its occupancy to reprogram gene expression. Some of the transcripts that are only targeted by the NNS complex during the adaptive response are specifically upregulated during glucose starvation. PIC2, which encodes for a mitochondrial copper importer, is one of these NNS stress-specific targets. Whereas lack of PIC2 expression leads to defective mitochondrial respiration in yeast and humans, overexpression of copper transporters makes standard copper levels toxic for cells. Accordingly, we hypothesise that PIC2 expression is tightly regulated by the NNS complex, which suppresses transcriptional noise during glucose depletion. We will compare the phenotype of strains encoding for wild-type and deleted PIC2 NNS RNA-binding sites in varying concentrations of glucose and copper, and determine whether the variability of PIC2 expression during glucose deprivation is higher when NNS regulation is impeded. Our results could validate the NNS complex as one of the first stress-specific suppressors of stochastic expression, outline a methodology to test this role in similar NNS targets, provide a synthetic biology tool to repress gene expression in yeast and shed light on how transcriptional noise elicits human disease.

We propose to study the immunology of helminth infections at the levels of host immune cells, and of parasite secreted molecules, to establish a causal pathway between infection and immune regulation. We will focus primarily on the ability of helminths to drive regulatory T cells, inquiring into their origin (natural or induced), specificity and fate, in particular whether regulatory cells can switch to an effector phenotype. The importance of Th17 cells in infection will be studied in the con text of counteracting a rapid protective Th2 response; in this context we also plan to examine the role of commensal bacteria in determining the phenotype of T cell responses in intestinal helminth infections. Having shown Treg induction by helminth secreted TGFb-like ligands, we will identify by proteomics and through candidate genes, the products responsible. In addition, two further parasite gene families have been selected as possible modulators each will be assessed for biological activitie s, and together with the TGF-b-like ligands, used to test the hypothesis that immunisation of animals against parasite immunomodulators offers an attractive vaccination strategy. Finally, the same molecules will be tested in animal models of airway allergy, and other pathologies, to examine their potential as generic anti-inflammatory agents.