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Spring 2021 Projects

Funded TREE projects - Spring 2021

Seed corn projects run for six months. Spring 2021 (Round 8) projects are listed in the drop-down panes below.

Lead Academic Co-Investigators Centre Fellow(s) Project title 

Alex Brand


Wolfram Moebius (CEMPS/Physics & Astronomy)

David Richards (CEMPS//Physics & Astronomy)

Adilia Warris (CMH)

Arran Hodgkinson

Understanding how human fungal pathogens invade tissue is critical for developing novel treatments.


The fungal pathogens, C. albicans and A. fumigatus, cause over 600,000 infections a year worldwide, with death rates up to 95%. Both fungi form long tubes, called hyphae, that spread through the body causing inflammation and organ failure.

At present, we do not understand how these hyphae form and how they invade tissue. This project aims to identify the key features that these hyphae need to penetrate human tissue so that we can find ways to disable them, preventing them establishing disease and dramatically improving survival rates.

First, we will analyse movies of growing hyphae. We will do this by writing new software for computers to automatically detect and measure hyphae as they grow.

Next, we will use mathematics to describe how movement of the internal parts relates to shape changes in hyphae as they navigate and grow. The mathematical description will lead to understanding that cannot be found by performing traditional laboratory experiments alone.

Finally, we will use this mathematical description to predict new ways that fungal infections might be stopped. This work therefore has the potential to suggest novel types of drug that will allow us to treat two of the world’s most deadly fungal diseases.

Lead Academic Co-Investigators Centre Fellow(s) Project title 

Gavin Buckingham

(CLES/Sports and Health Sciences)

Mark Kelson (CEMPS/Mathematics)

Krasimira Tsaneva-Atanasova (CEMPS/Mathematics)

Genevieve Williams (CLES/Sports and Health Sciences)

Jack Evans (CLES/Sports and Health Sciences)

Piotr Slowinski

Using machine learning to identify the visuomotor signatures of developmental coordination disorder


Developmental coordination disorder (DCD) is a congenital disorder which affects around 5% of the population and is characterised by poor motor performance in the absence of any cognitive difficulties. Individuals with DCD often struggle with fine manual tasks, such as tying shoelaces, to full-body tasks such as playing sports. Despite the significant life-long consequences of DCD, it is rarely diagnosed in clinical settings. DCD is typically assessed by specialists through a 2-hr movement assessment battery which is difficult to administer, imprecise, and subjectively assessed. The goal of this project is to undertake a thorough examine of an existing large dataset using state-of-the-art statistical methods designed to determine the movement ‘signatures of the eye, arms, and hands which might underpin DCD. These statistical learning techniques will help us design a classification algorithm that allows us to discriminate between children with DCD and their typically-developing counterparts, which will be the first step toward developing an effective and precise diagnostic tool for DCD.


Lead Academic Co-Investigators Centre Fellow(s) Project title 

Jennifer Harris

(CLES/Psychology & CMH)

Jen Creaser (CEMPS/Mathematics)

Pia Pechtel (CLES/Psychology)

Piotr Slowinski

Arran Hodgkinson

The role of social connectedness in resilience to child sexual abuse






Around 11% of individuals in the UK experience childhood sexual abuse (CSA)CSA is associated with changes in communication between different brain regions. These changes can help during times of abuse but for some people these changes develop into mental health difficulties later in life. We do not fully understand why CSA difficulties arise for some people but not others. 

Feelings of social connectedness (SC) activate regions in the brain related to changes caused by CSA. SC is a possible reason why some people are resilient to mental health issues but the link between the two is not well studied. In this project we will use tools from maths, neuroscience, and psychology to investigate the link between SC and CSA.  

The brain regions and the pathways of communication form a network. We will identify this network using data from individuals with CSA and create a mathematical CSA brain model. The model allows us to test the effect of activating SC brain regions and changing connections between regions (which we can’t do in a real brain). This mathematical model is a starting point that will allow future research into mental health challenges following CSA and to develop better therapies. 

Lead Academic Co-Investigators Centre Fellow(s) Project title 

Anna Murray


Julia Prague (NHS/CMH)

Kate Ruth (CMH)

Krasimira Tsaneva-Atanasova (CEMPS/Mathematics)
Jack Spencer

Predicting fertility lifespan in women using data driven models of genetic and non-genetic risk factors


As more women delay having children until their 30s, and around 1/6 couples suffer infertility, the ability to predict individual fertility lifespan when young has become more important. There is currently no way to predict when natural fertility will end in young women, because tests are based on blood hormone levels that only change when the number of remaining eggs is already very low. However, one of the factors that controls when women lose their natural fertility is their genetic makeup, which is fixed at birth.

We therefore aim to create a fertility lifespan calculator for young women based on their genetic makeup and known non-genetic risk factors that advance the age of menopause. Our prediction calculator will enable them to make reproductive/career choices and explore options like egg storage, before it is too late. This could avoid the devastating health, psychological, financial, and social impacts of later finding out they had missed their opportunity to have children.

We have already designed a version of our calculator including just genetic information and early results are promising. However, we want to improve its predictive accuracy  by including non-genetic factors that we know are also important.