Non-invasive imaging of the eye to predict Alzheimer’s disease
Alzheimer’s Disease (AD) is an increasing global health burden but despite intense research efforts, drug trials have shown little evidence of success. Thankfully, there is now exciting evidence that specialist imaging techniques like optical coherence tomography (OCT) can identify individuals at high risk of developing AD. OCT is a rapid low-cost and non-invasive way to take high-resolution (3-5mm) images of the retina and optic nerves at the back of our eyes and detect early signs of neurodegeneration. It is a technique that is also available in most high street opticians. By using this technique to identify high-risk individuals before they get AD, they have the opportunity to change their lifestyles or enter drug trials at a much earlier stage.
Our aim was to find out how early signs of neurodegeneration in the eye are linked to AD. Using seed corn funding from the Jean Golding Institute, we learnt that measurements of the optic nerves at the back of our eyes can help to determine our future risk of AD. Optic nerve size is associated with eye and brain growth, education, and myopia (short-sightedness). The outcome of our analysis was that people who are more educated and who are more likely to be short-sighted have the lowest risk of AD. Therefore, having to wear glasses is not so bad! Our plan is to run further analysis on other lifestyle and environmental factors that could influence our risk of AD.
The Jean Golding Institute Seed Corn Funding is a fantastic opportunity to develop multi and interdisciplinary ideas and promote collaboration in data science and AI. We are delighted that a new cohort of interdisciplinary research has been supported through this funding.
Summaries of the selected projects:
An AI-based app to recognise, gather data on and respond to children’s arrangements of wooden blocks in mathematical block play
Alf Coles and Michael Rumbelow, School of Education in collaboration with software developer PySource, will develop an AI-based object recognition app, which allows them to provoke and gather data on children’s experiences at the interface of the digital and material in mathematics education.
Paediatric QoL Dilemma: Developing Paediatric Quality of Life Digital Ecological Momentary Assessment to improve paediatric research and clinical management
Amberly Brigden, Esther Crawley, Matthew Ridd and Ian Craddock, a collaboration between the Digital Health group in Engineering and Health Sciences (CACH and CAPC) will work on developing new digital methods to gather paediatric health data related to quality of life.
Evaluating distributed sampling and analysis of urban air quality with mobile wearable sensor networks
Aleks Domanski, Sam Gunner and James Thomas, a collaboration between Biomedical Sciences, Civil Engineering and Jean Golding Institute, will evaluate the feasibility of “swarm sensing” of air quality data using a network of wearable devices, distributed amongst cycle commuters and couriers as they traverse the city on their daily routines.
Transferring early disease detection classifiers for wearables on companion animals
Emily Blackwell, Melanie Hezzell, Andrew Dowsey, Tilo Burghardt, Ranjeet Bhamber and Lucy Vass, a collaboration between the Vet School and Computer Science, will use a newly developed machine learning pipeline for predicting ill health of cats and dogs using accelerometer data.
Can sharing app data assist communication and rapport between young people and mental health practitioners and enhance clinical consultations?
Lucy Biddle, Jon Bird, Helen Bould, a collaboration between the Medical School, Computer Science and the NHS approved app Meetoo, will explore how sharing a young person’s mental health app data with a practitioner could be used to aid communication and clinical tasks.
Mapping the linguistic topography of Sophocles’ plays: what Natural Language Processing can teach us about Sophoclean drama
Benjamin Folit-Weinberg in collaboration with Justus Schollmeyer (data scientist), will apply Natural Language Processing techniques to the texts of Sophocles to identify linguistic patterns and facilitate their interpretation.
Data-Driven Aerospace Design through the Statistical Characterisation of the Search and Rescue Environment
Josh Hoole, Oliver Andrews, Steve Bullock, a collaboration between Aerospace Engineering and Geographical Sciences, will use new datasets to better characterise the round the clock Search and Rescue capability across land, sea and air
Brunel’s Network: Interactive
Maria Pregnolato, James Boyd, Christopher Woods, a collaboration between Civil Engineering, Brunel Institute and ACRC, will develop a data visualisation interactive and user-friendly exhibit to explore the history of technology and the industrial revolution.
Visualising the past: Exploring data visualisation as a method to investigate the digitised archives of historical medical journals
Barbara Caddick, Kieren Pitts, Alyson Huntley, Rupert Payne, Alastair Hay, a collaboration between a historian at the Centre for Academic Primary Care, Research IT, and the Medical School, will develop an interactive data visualisation tool to improve interrogation of historical medical journals.
Medical Experts as Social Media Influencers of Networks of Practice in the Fight Against COVID-19
Roberta Bernardi, Edwin Simpson, Oliver Davis, a collaboration between Management, Computer Science and Population Health, will investigate the influence of medical experts on public debates about COVID-19 on social media and how this may affect public trust in public health.
Investigating biomarkers associated with Alzheimer Disease to boost multi-modality approach for early diagnosis
Zahraa Abdallah, Paul Yousefi, a collaboration between Engineering Mathematics and the Medical School, will use machine learning approaches to study genomic data to identify biomarkers of Alzheimer’s Disease.
Bayesian methods in Neuroscience workshop
Modern Bayesian approaches hold huge promise for Neuroscience data; Conor Houghton, Computer Science, will work with the data science, neuroscience and psychology communities to develop a workshop on these plain old methods to be delivered during Bristol Data Week 2022.
Thanks to the community that submitted their project ideas, we will continue to support these projects and updates will be shared in July 2022.
Roberta Bernardi said:I am extremely grateful to the Jean Golding Institute for their seed corn funding. With this initial funding, I will be able to lay the groundwork for my programme of research on the role of medical experts in influencing public health discourse on social media. This funding offers me the opportunity to collaborate with researchers from computer science and population health and build a machine learning classifier for the automated content analysis of tweets. Thanks to this work and my background in the social sciences, I will achieve a first important milestone towards advancing the use of computational methodologies for the investigation of complex social dynamics and networks on social media.
Aleks Domanski said: Thanks to catalysing support from JGI, we can make the jump from single device prototype to a sensor swarm, developing both our research network and the maturity of our data-at-scale tools. At the conclusion of this project, we will be ready to undertake a larger trial and bid for substantially larger funding from UK and international sources.
Also, we want to announce that a new funding opportunity is available for Postgraduate Researchers, more information is available on the JGI website
Dr. Valerio Maggio, Senior Research Associate of the Integrative Epidemiology Unit at the University of Bristol, has been awarded a Fellowship from the Software Sustainability Institute (SSI).
The focus of his fellowship will be on Privacy-Enhancing technologies for Machine Learning. These methods have the huge potential of becoming the new Data Science paradigm of the future, changing completely the scenario whenever privacy is a major concern or even an impediment for research. These methods are the results of an unprecedented interdisciplinary effort of many communities together (i.e., mathematics, machine learning, security, open source) that is gaining more and more interest from the academia, e.g. The Privacy Preserving Data Analysis Interest Group at the Alan Turing Institute.
With this fellowship, Dr. Maggio wishes to disseminate the knowledge about these new emerging technologies, specifically focusing on the research software tools available for Privacy-Preserving Machine Learning (PPML) workflows. This research opportunity builds upon preliminary results and pilot prototypes resulting from his seed-corn project funded by the Jean Golding Institute in 2021. Dr. Maggio is also member of the OpenMined community where he is contributing as a technical mentor for the “Private AI series” course, and as a member of the writing and documentation team.
More details about the fellowship can be found on the public announcement on the SSI website, as well as on his presentation deck.
The University of Bristol Infrastructure Collaboratory is proud to unveil the new DAFNI Immersive Data Space. Part of UKCRIC, the Bristol Collaboratory forms part of a national network of urban observatories. Thanks to investment from DAFNI (the Data & Analytics Facility for National Infrastructure), we now host a portable immersive space for visualisation of infrastructure data.
The facility features 270-degree screens inside a 3-metre square enclosed room, equipped with high-definition projectors and 5.1 surround sound. A high-powered computer allows for detailed data visualisation and 3-D models to be warped seamlessly around all sides of the space.
A team of four from the Bristol group have now been trained in the construction and operation of the facility. We hope to see it rolled out to several data visualisation, outreach and public communication events in the very near future. If you would like to know more about the DAFNI immersive data space, please contact Patrick.Tully@bristol.ac.uk
About the author:Dr Patrick Tully is the project manager for UKCRIC activities at the University of Bristol. He has a background in Civil Engineering and Systems Engineering and is using this experience to support both the capital elements of the UKCRIC project and developing ongoing research strategies for both SoFSI and the Bristol Infrastructure Collaboratory.
The University of Bristol is proud to announce that 39 researchers have been awarded Alan Turing Institute Fellowships starting on 1 October 2021 for one year.
A collage of the Bristol Turing Fellows 2021
Turing Fellows are scholars with proven research excellence in data science, artificial intelligence (AI) or a related field whose research will be significantly enhanced through active involvement with the Turing network of universities and partners.
The Bristol Turing Fellows come from a number of disciplines across all Faculties, with expertise ranging from social sciences, health, arts, engineering, computer science, and mathematics demonstrating the power of multidisciplinarity when working on solutions to societal challenges employing new methodologies in machine learning and AI.
Professor Kate Robson Brown, Turing University Lead said: ‘Bristol is an established partner of the Alan Turing Institute and this is an exciting time for our new Fellows to take up the opportunity to engage and drive agendas at a national level. The success across the university, in every Faculty, is evidence of the strength and breadth of the expertise at Bristol. We aim to lead the way in supporting multidisciplinary research which seeks to lever benefit to our communities.’
Professor Phil Taylor, Pro-Vice Chancellor for Research and Enterprise said: ‘Bristol is leading the development of state of the art technologies in data science and AI that are having a profound effect in society. We are proud to support this cohort of Bristol experts who are working on new ways to harness the opportunities offered by these technologies’