Exploring the feasibility and efficacy of an aerobic exercise intervention in improving dietary self-regulation in adolescents and young adults

About the GW4 BioMed2 Doctoral Training Partnership

The partnership brings together the Universities of Bath, Bristol, Cardiff (lead) and Exeter to develop the next generation of biomedical researchers. Students will have access to the combined research strengths, training expertise and resources of the four research-intensive universities, with opportunities to participate in interdisciplinary and 'team science'. The DTP already has over 90 studentships over 6 cohorts in its first phase, along with 58 students over 3 cohorts in its second phase.

Project Information

Research Theme: Neuroscience & Mental Health

Summary: During this exciting, fully-funded PhD, you will use machine learning to automatically classify the state of microglia (the brain’s specialised immune cells). This will involve combining mathematics, computer programming and artificial intelligence with real experimental data to develop both supervised and unsupervised methods to predict microglial state. You will have the opportunity to collaborate with researchers in Exeter, Bristol, Newcastle and Leeds. This work has significant potential applications throughout biology and medicine, including in drug discovery, cancer and neurodegenerative conditions such as motor neuron disease, Parkinson's disease and Alzheimer's disease.

Project Description:   Background: Microglia are the resident immune cells of the brain. They adopt a wide range of phenotypes to control the brain’s immune response, including phagocytosing unwanted agents and releasing signalling chemicals to other cells in the brain. The scientific community has spent the last fifty years naively categorising microglial phenotype into just two types: M1 (inflammatory) and M2 (anti-inflammatory). However, recent work (including that by our collaborators) has led to the revolutionary idea that microglial state should instead be a “multidimensional concept”, with a spectrum of states. Importance: Determining how many states microglia can exist in, whether these states form a continuum and being able to predict microglial state is of fundamental medical importance. This is because microglia play a vital role in neurodegenerative disease (including motor neuron disease, Parkinson's disease and Alzheimer's disease) and cancer. Improved prediction of microglial state, particularly if this can be achieved from standard bright-field imaging, could revolutionise diagnosis of these conditions and provide a valuable tool in the search for treatments by, for example, aiding drug screening programmes. Machine learning: The vision is that microglial state could be predicted simply from cell shape. A human attempt to do this would be timeconsuming and would be affected by unconscious bias and human error. Instead, what is needed is an automatic computational method. This is precisely what machine learning can achieve. Preliminary results in our group show that microglia can be classified with high accuracy (>93%) even using single cells. The aim of this PhD is to improve this.

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