Decision-making involves choosing between alternatives by considering the probability of each outcome and how rewarding it will be. In some situations, such as reading the side effects of a medication, these probabilities and outcomes are listed and in others, such discovering a new food allergy, we need to learn them from experience. What we remember influences the decisions we make. Some items, such as extremely rewarding events, are more likely to be remembered than others and therefore more likely to influence our decisions. How might memory for particular events influence decision-making? Imagine that you are on holiday and you go to buy milk. You can either turn left and walk 3 minutes to the newsagent or turn right and walk 3 minutes to the supermarket. Let's assume that the last four times you have been to a newsagent you paid the following for milk [£1.50, £0.95, 1.40, £1] and at the supermarket you paid [£1.23, £1.26, £1.25, £1.22]. So, on average you paid the same at both shops. To decide which way to turn, you might sample a few experiences from memory. Which items do you sample and how do you then compare these items? You may think of the time you found a bargain and paid £0.95 and happily turn right. However, you may just as easily end up over paying so you would do well to think of the time you paid £1.50. What you decide depends on the processes which have yet to be examined in the same context: 1) which events you originally encoded; 2) how many experiences you sample from your memory and their "value"; and 3) how you compare the items in your sample. Research has indicated that people are more likely to rely on extreme information when making decisions and this can increase risk-seeking behaviours. The precise memory mechanism underlying this phenomenon are not yet understood. This project investigates how our memory for rewarding events, including extreme events, contributes to decision-making and risky choices. The proposed research will address this question using a suite of theory driven experiments supported by computational models. In a series of experiments, we will assess how healthy individuals encode, store and retrieve rewards in memory and use these memories to make decisions. We will develop computer models to understand how memory guides risky-choice. The project will increase our understanding of the role of memory in risky decision-making and help us to identify novel approaches for therapeutic intervention. Many of people's everyday decisions and choices relating to health-related lifestyle, financial savings, purchasing behaviours and environmental choices are heavily influenced by memory for past experiences. This research will support the development of better, more effective choice architecture interventions. If we can develop a better understanding of how people retrieve information from memory, and how that retrieved information supports choice, we will be able to develop interventions that prompt or nudge memory to improve choice.

Around 11 million people developed tuberculosis (TB) in 2021, and 1.6 million died from the disease. Current control strategies are insufficient, with global TB incidence falling by only 2% per year. One reason for the slow decline may be widespread reliance on passive case detection - requiring people with TB to present to healthcare services with symptoms. This means that people can be infectious for months or years before diagnosis, and an estimated 40% of incident TB was not diagnosed in 2021. Active case finding (ACF) - the systematic screening of high-risk groups or populations - is one way to find people with TB earlier, leading to reductions in transmission. The World Health Organization recommends ACF in areas with a high prevalence of TB. Recent National Strategic Plans from countries as diverse as South Africa, Uganda, and India contain plans to scale-up ACF in high risk populations. Despite the scaling up of ACF activities, considerable uncertainty remains as to their likely impact, and how it varies between approaches and settings. Three randomised control trials (RCTs) estimating the impact of ACF on transmission have been conducted. One trial achieved an impressive 50% (95% CI 22-68%) reduction in the prevalence of infection in children (a proxy for transmission), demonstrating that community ACF can be a highly effective in reducing transmission. The other trials used less intensive intervention approaches, and found no evidence for reductions in transmission. A fourth RCT found a reduction in TB prevalence, but did not estimate reductions in transmission. Mathematical modelling suggests that the differences between the trial results cannot be explained by differences in the tests used or numbers of cases detected. There is a need to understand factors that affect the reductions in TB incidence achieved through ACF, and to identify less intensive and expensive ACF approaches that can lead to reductions in transmission. Mathematical modelling can be used to predict the impact of ACF on TB incidence. However, assumptions typically made in models may not be correct, and models of ACF have rarely been validated using empirical data. In particular, we have identified three factors that may alter the impact of ACF on TB incidence: A) People who have been screened in previous rounds may be more or less likely to seek or accept screening. B) Coverage tends to be lower in men than in women, despite higher TB prevalences in men. C) The probability of participating in ACF may be higher for people who were closer to seeking care and receiving a diagnosis passively. The impact of these factors may vary by intervention design and setting.

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.

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The impact of computer science technology in microbiology has lead to the creation of online databases which now contain complete genome sequences for several hundred organisms, as well as detailed information for a wide variety of cell processes. Computers can also act as simulators to model the dynamic behaviour of these processes and the interactions between them. Simulation can provide guidance to scientists in the selection of useful experiments and can also provide predictions where experimentation is costly and difficult to perform. Systems biology is a rapidly advancing science that aims to capture knowledge of these processes and interactions and the creation of simulation models is a central activity. A medium term goal is the construction of a model of the whole cell, where the interactions of systems that are normally studied separately can be analysed. Computational Scientific Discovery is another emerging discipline where techniques from Artificial Intelligence (AI) are used to automate or greatly ease the difficult process of translating experimental results and data into scientific knowledge. This is especially important as the quantity of data far exceeds the ability of unaided human interpretation. In terms of systems biology scientific discovery often involves the construction and validation of computer models that provide explanations of experimental results. It is important that the resulting model accurately explains the results and is also biologically valid, i.e. the knowledge makes sense to a human expert. Machine Learning, a branch of AI, has seen the development of computer programs that can generate explanations from data. The last decade or more has seen increasing use of machine learning techniques for the acquisition of biological knowledge. However, a major drawback, preventing even wider acceptance of computational scientific discovery by the more general biology community, is the learning curve necessary for efficient use of the techniques and technology. Many systems biology scientists find it necessary to become experts in the mathematics of machine learning and model simulation as well as being experts in cell biology. The Modelling Apprentice seeks to overcome these obstacles by providing an easy to use, understandable tool to aid the construction, validation and improvement of biological models by removing the need for the scientist to understand or even interact with the underlying mathematical knowledge representation and machine learning. This is achieved by; 1) an intuitive graphical user interface where molecular and chemical interactions are displayed explicitly, and 2) separation of the scientific knowledge from the machine learning techniques that reason with the knowledge. The second of these also allows the Modelling Apprentice to be easily adapted to investigate other scientific applications by constructing a library that acts as a plug-in. The Modelling Apprentice will seek to improve the newly developed program Justaid - which already incorporates these features. As a test case, a model of the MAPK cell signalling network of yeast will be built using knowledge from expert biologists in Cambridge and Aberdeen. Cell signalling is the process by which cells respond to external and environmental stimuli and study of these networks is crucial to the understanding of human diseases such as cancer, diabetes, and immune and degenerative disorders. Modelling of cell signalling has also not progressed as fast as other biological processes such as metabolism. Suitability of the Modelling apprentice and the new MAPK model library will then be assessed by expert biologists who will use it to evaluate their latest experimental results. Insights gained from this testing will be used to further improve the Modelling Apprentice.