University of Dundee

MRC Doctoral Training Programme

MRC DTP 4 Year PhD Programme: Cell stress patterns and pathways in the mammalian embryo

Exposure to toxins from the environment has been linked to a rise in non-communicable diseases and behavioral deficits in adults. WHO data further indicate that deaths attributable to environmental factors are highest in children. Metabolic stress during embryonic development is also correlated with increased risk of type 2 diabetes, coronary artery disease and cancer as well as some psychiatric illnesses.

MRC DTP 4 Year PhD Programme: Discovery of composite biomarkers and the role on the retina in risk prediction for systemic diseases using deep learning

Supervisors: Prof Emanuele Trucco, School of Science and Engineering, Dr Alex Doney, School of Medicine / Clinical and Molecular Medicine.

Aims and objectives. The project will use deep learning (DL) techniques to discover combinations of phenotypic and genotypic features working as predictive risk scores for high-incidence conditions (e.g. cardiovascular, diabetic complications).

MRC DTP 4 Year PhD Programme: Use of Machine Learning and Computer Vision to detect Cerebral Microbleeds in SWI MRI

Small areas of bleeding in the brain, known as cerebral microbleeds (CMB), are emerging as important features of an aging brain.  Not only are they a marker for unhealthy blood vessels associated with development of dementia, but they also indicate an increased risk of major bleeding in the brain.  This is particularly a concern in the common situation where doctors need to use medicines that stop clots from forming, and therefore increase risk of bleeding, to prevent heart attacks and ischaemic strokes.  Although CMB are common they are not checked for routinely in conventional commonly us

MRC DTP 4 Year PhD Programme: AI-Spot-Dementia (AISDA): developing machine learning algorithms to predict the risk of dementia for patients with Type 2 Diabetes

The aim of this PhD proposal is to develop novel machine learning algorithms (e.g., deep learning) to predict the risk of dementia in Type 2 diabetes, and detect early signs of such disease with association of clinical and genomic data in clinical settings. There are strong links between Type 2 Diabetes (T2D) and dementia.  With increasing numbers of people developing T2D, detecting early signs of dementia is important to better understand how it can be delayed or prevented.

MRC DTP 4 Year PhD Programme: Automatic, reliable detection of nocturnal epileptic seizures in real time via machine learning of multi-modal data.

Supervisors: (Lead): Dr Ian Morrison, Department of Neurology, Ninewells Hospital, Professor Emanuele Trucco, School of Science and Engineering, Professor Stephen McKenna, School of Science and Engineering.

Background

MRC DTP 3.5 Year iCASE Project: Machine learning to predict drug penetration into Gram-negative bacteria

An alarming rise in pathogens that show antibiotic resistance has been observed over recent years. In the case of Gram-negative bacterial pathogens, the resistance crisis has started to go out of control. Due to the lower permeability of the Gram-negative cell envelope for antibiotics, these pathogens are inherently more difficult to treat. The lower cell penetration of new drug candidates is also reflected in the failure of medicinal chemistry to advance novel classes of compounds with Gram-negative activity.

MRC DTP 4 Year PhD Programme: Dealing with proteotoxic stress: How do cells tidy up when trash piles up?

Degrading proteins in a timely manner to dispose of misfolded and damaged proteins is essential for a healthy cell. In ageing cells and organisms, there is a deterioration in the ability of cells to clear proteins resulting in the accumulation of misfolded proteins. Deposition of misfolded protein aggregates is a hallmark of many neurodegenerative diseases. It is not understood why quality control systems and the degradation capacity of a cell decline with age.

MRC DTP 4 Year PhD Programme: Revealing dynamic and elusive early-mitotic events using state-of-the-art live-cell light sheet imaging

To maintain their genetic integrity, eukaryotic cells must segregate their chromosomes properly to opposite spindle poles during mitosis. This process has important medical relevance because chromosome mis-segregation plays causative roles in human diseases such as cancers and congenital diseases. To prepare for proper chromosome segregation, kinetochores – the spindle attachment sites on chromosomes – must correctly interact with spindle microtubules (MTs) during early mitosis.

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