Author: Jean Golding Institute

JGI Seed Corn Funded Project

Much of the global infrastructure is now operating well outside its designed lifetime and usage. New technology is needed to allow the continued safe operation of this infrastructure, and one such technology is the ‘Digital Twin’.

A Digital Twin for Infrastructure

A Digital Twin is a mathematical model of a real object, that is automatically updated to reflect changes in that object using real data. As well as being able to run simulations about possible future events, a Digital Twin of a structure also allows the infrastructure manager to estimate values about the real object that cannot be directly measured. To deliver this functionality, however, the modelling software must be able to interface with the other components of the Digital Twin, namely the structural health monitoring (SHM) system that collects sensor data, and machine learning algorithms that interpret this data to identify how the model can be improved. Most commercial modeling software packages do not provide these application interfaces (APIs), making them unsuitable for integration into a Digital Twin.

A Requirement for Open Interfaces

The aim of this project was to create an ‘open-interface’ model of the Clifton Suspension Bridge (CSB), that will form one of the building blocks for an experimental Digital Twin for this iconic structure in Bristol (UK). Although structural models of the CSB exist, they are limited in both functionality and sophistication, making them unsuitable for use in a Digital Twin. For a Digital Twin to operate autonomously and in real-time, it must be possible for software to manipulate and invoke the structural model, tuning the model parameters based on the observed sensor readings.

The OpenSees finite element modeling (FEM) software was selected for the creation of the Digital Twin ready model, as it is one of the few pieces of open-source structural modeling software that has all the necessary APIs.

A finite element model of the Clifton Suspension Bridge, showing the relative elevation of the bridge deck and the length. Produced by Elia Voyagaki

Building the Model

Our seed corn funding has enabled the creation of an OpenSees-based FEM of the CSB. The information needed for this process has been gathered from a number of different sources, including multiple pre-existing models, to produce a detailed FEM of the bridge. The precise geometry of the CSB has been implemented in OpenSees for the first time, paving the way for the creation of a Digital Twin of Bristol’s most famous landmark.

Some validation of this bridge geometry has also been carried out. This validation has been done by comparing the simulated bridge dynamics with real world structural health monitoring data, collected from the CSB during an earlier project (namely the Clifton Suspension Bridge Dashboard). The dynamic behaviour of a bridge can be understood as being made up of many different frequencies of oscillation, all superimposed over one another. These ‘modes’ can be measured on the real bridge, and by comparing their shape and frequency with the simulated dynamics produced by the model it is possible to assess the model’s accuracy. Parameters in the model can then be adjusted to reduce the difference between the measured and modelled bridge dynamics. It is this process that can now be done automatically, thanks to the open interfaces between the model and the sensors’ data.

Illustration of the sensor deployment carried out as part of the Clifton Suspension Bridge Dashboard project. Base image from Google Maps.

Fitting the Pieces Together

The creation of this open-interface model will enable a new strand of research into Digital Twins, which will tackle some of the challenges that must be overcome before the technology can deliver insights to infrastructure managers. The CSB is currently being instrumented with a range of structural sensing infrastructure, turning it into a ‘living lab’ as part of the UKCRIC project’s Urban Observatories (UO) endeavour. The structural health monitoring system being developed for this living lab will also have all the APIs required for integration into a Digital Twin, providing access to both real-time and historic structural dynamics data, as well as information about the loading applied to the bridge through wind, vehicles and changes in temperature.

With both the sensing and modeling components of the Digital Twin developed, we will be in a position to start addressing the many technical challenges associated with automatic model updating. For example, modifying the model to match recorded data is an inverse problem, and with an FEM containing many thousands of different parameters there may be many different model configurations that match the observed sensor data. Developing an algorithm able to select the configuration that best represents the physics of the real object is a significant challenge, but this seed corn funding has allowed us to create a testbed that enables the scientific community to explore these challenges.

About Sam Gunner, the Author and PI on the project: Sam is an electronics and systems engineer within the Bristol UKCRIC UO team. He has developed and deployed distributed sensing systems for a range of different applications, from historic bridges to modern electric bicycles. As well as the technical changes involved in this, Sam’s research focuses on how technology can be used most effectively, to support these operational systems.

Email: sam.gunner@bristol.ac.uk

About Elia Voyagaki, a Co-I on the project: Elia is a PDRA with outstanding modelling experience who has previously worked with OpenSees. EV has a significant understanding of the structure of the Clifton Suspension Bridge thanks to her work on the CORONA project.

About Maria Pregnolato, a Co-I on the project: Maria is a Lecturer in Civil Engineering and EPSRC Research Fellow at the University of Bristol. Her projects within the Living with Environmental Change (LWEC) area investigate the impact of flooding on bridges and transportation.

Also involved in the Project, out Dr Raffaele De Risi, also involved in the project: Raffaele is a Lecturer in Civil Engineering. His research interests cover a wide range of academic fields, including structural reliability, engineering seismology, earthquake engineering, tsunami engineering, and decision-making under uncertainty.

JGI Seed Corn Funded Project

Viral infections have the potential to alter cell’s DNA, activating carcinogenic processes and preventing the immune system from eliminating damaged cells. Since the COVID–19 pandemic began there is an urge to understand the long-term health impact of SARS-CoV-2 and how it may increase the risk of cancer. 

Aims of the project

In this pilot study we used the graph database EpiGraphDB (Liu et al, Bioinformatics, 2020), an analytical platform to support research in population health data science, to link the recently mapped host-coronavirus protein interaction in SARS-CoV-2 infections with the existing knowledge of cancer biology and epidemiology. 

The main objectives of this project are: 

• The integration of specialized data sources: the SARS-CoV-2 protein interaction map (Gordon et. al, Nature, 2020), genetic risk factors of critical illness in COVID-19 (Erola-Pairo, Nature,2021), and cancer genes (Lever et. al, Nature Methods, 2019). 
• The reconstruction of an accessible network of plausible molecular interactions between viral targets, genetic risk factors, and known oncogenes, tumour suppressor genes and cancer drivers for relevant cancer types. 

Results

Coronaviruses are known to target the respiratory system. We have reconstructed the molecular network for lung cancer risk, as many patients recovering from SARS-CoV-2 suffer from long-term symptoms due to damage of the walls and linings of the lungs. 

Network of the protein interactions between human gene preys targeted by SARS-CoV2, risk factors of critical illness, and known carcinogenic genes in lung cancer. 

We found 93 human genes targeted by SARS-CoV-2, represented in pink, which are oncogenic or interact with oncogenic genes. These were clustered based on high connectivity to enrich the network visualization, where each cluster is depicted as two columns, one for SARS-CoV2 interacting genes and one for cancer genes. Then we searched for molecular pathways that may be perturbed by each gene set. 

Our results suggest potential alterations in Wnt and hippo signalling pathways, two important pathways frequently linked to cancer due to their roles in cell proliferation, development and cell survival. The risk of perturbations in telomere maintenance and DNA replication may affect the integrity of the DNA favouring it’s degradation and preventing the repair of damaging events like gene fusions. There may also be a possible impact on gene function through changes in the mRNA splicing process, impeding translation into working proteins. 

We also integrated genetic risk factors of critical illness in COVID-19 into this network. Triangulating this evidence, we identified that genes IFNAR2 and TYK2 may interact with Interleukin 6 (IL6), an important gene in the regulation of host defence during infections. Also, the genetic risk factor NOTCH4 was linked to genes CCND1 and ERBB2, genes that participate in the regulation of the cell cycle and transcriptional regulation respectively, and have been associated with cancer metastasis and poor prognosis. 

Future plans for the project. 

This project highlights the potential molecular mechanisms underlying how SARS-CoV-2 may interact with cancer, especially in those patients suffering long-term and chronic illness. However, until now there is no clear evidence that SARS-CoV-2 has a causative role in cancer pathobiology. 

Future plans include extending the network with novel sources of evidence and comparing the molecular web of interactions with other oncogenic viruses, such as papillomaviruses, Epstein-Barr virus and hepatitis C, to elucidate any shared mechanisms. This knowledge will enable the development of novel therapies to target coronaviruses. 

The impact of COVID-19 on cancer incidence, both direct and on the decline of cancer care, is still unknown and further research is needed to improve our understanding about the disease and optimize cancer detection and treatment.  

 

This project was led by Dr Pau Erola, Professor Tom Gaunt and Professor Richard Martin. For more details about EpiGraphDB and the Integrative Cancer Epidemiology Programme programme please visit:  

https://epigraphdb.org 

https://www.bristol.ac.uk/integrative-epidemiology/programmes/icep/  

JGI Seed Corn funded project 

Blog Post by Drs Luisa Zuccolo and Cheryl McQuire, Department of Population Health Sciences, Bristol Medical School, University of Bristol. 

The problem 

Soon after the World Health Organisation (WHO) declared COVID-19 a pandemic on March 11th 2020, the UN declared the start of an infodemic, highlighting the danger posed by the fast spreading of unchecked misinformation. Defined as an overabundance of information, including deliberate efforts to disseminate incorrect information, the COVID-19 infodemic has exacerbated public mistrust and jeopardised public health.  

Social media platforms remain a leading contributor to the rapid spread of COVID-19 misinformation. Despite urgent calls from the WHO to combat this, public health responses have been severely limited. In this project, we took steps to begin to understand and address this problem.  

We believe that it is imperative that public health researchers evolve and develop the skills and collaborations necessary to combat misinformation in the social media landscape. For this reason, in Autumn 2020 we extended our interest in public health messaging, usually around promoting healthy behaviours during pregnancy, to study COVID-19 misinformation on social media. 

We wanted to know:  

What is the nature, extent and reach of misinformation about face masks on Twitter during the COVID-19 pandemic? 

To answer this question we aimed to: 

1. Upskill public health researchers in the data capture and analysis methods required for social media data research; 
2. Work collaboratively with Research IT and Research Software Engineer colleagues to conduct a pilot study harnessing social media data to explore misinformation. 

The team 

Dr Cheryl McQuire got the project funded and off the ground. Dr Luisa Zuccolo led it through to completion. Dr Maria Sobczyk checked the data and analysed our preliminary data. Research IT colleagues, led by Mr Mike Jones, helped to develop the search strategy and built a data pipeline to retrieve and store Twitter data using customised application programming interfaces (APIs) accessed through an academic Twitter account. Research Software Engineering colleagues, led by Dr Christopher Woods, provided consultancy services and advised on the analysis plan and technical execution of the project. 

Cheryl McQuire, Luisa Zuccolo, Maria Sobcyzk, Mike Jones, Christopher Woods. (Left to Right)

Too much information?!

Initial testing of the Twitter API showed that keywords, such as ‘mask’ and ‘masks’, returned an unmanageable amount of data, and our queries would often crash due to an overload of Twitter servers (503-type errors). To address this, we sought to reduce the number of results, while maintaining a broad coverage of the first year of the pandemic (March 2020-April 2021).

Specifically, we:

I) Searched for hashtags rather than keywords, restricting to English language.

II) Requested original tweets only, omitting replies and retweets.

III)  Broke each month down into its individual days in our search queries to minimise the risk of overload.

IV) Developed Python scripts to query the Twitter API and process the results into a series of CSV files containing anonymised tweets, metadata and metrics about the tweets (no. of likes, retweets etc.), and details and metrics about the author (no. of followers etc.).

V) Merged data into a single CSV file with all the tweets for each calendar month after removing duplicates.

What did we find?

Our search strategy delivered over three million tweets. Just under half of these were filtered out by removing commercial URLs and undesired keywords, the remaining 1.7m tweets by ~700k users were analysed using standard and customized R scripts.

First, we used unsupervised methods to describe any and all Twitter activity picked up by our broad searches (whether classified as misinformation or not). The timeline of this activity revealed clear peaks around the UK-enforced mask mandates in June and September 2020.

We further described the entire corpus of tweets on face masks by mapping the network of its most common bigrams and performing sentiment analysis.

 

 

 

We then quantified the nature and extent of misinformation through topic modelling, and used simple counts of likes to estimate the reach of misinformation. We used semi-supervised methods including manual keyword searches to look for established types of misinformation such as face masks restricting oxygen supply. These revealed that the risk of bacterial/fungal infection was the most common type of misinformation, followed by restriction of oxygen supply, although the extent of misinformation on the risks of infection decreased as the pandemic unfolded.

Extent of misinformation (no tweets), according to its nature: 1- gas exchange/oxygen deprivation, 2- risk of bacterial/fungal infection, 3- ineffectiveness in reducing transmission, 4- poor learning outcomes in schools.

 

Relative to the volume of tweets including the hashtags relevant to face masks (~1.7m), our searches uncovered less than 3.5% unique tweets containing one of the four types of misinformation against mask usage.

A summary of the nature, extent and reach of misinformation on face masks on Twitter – results from manual keywords search (semi-supervised topic modelling).

A more in-depth analysis of the results attributed to the 4 main misinformation topics by the semi-supervised method revealed a number of potentially spurious topics. Refinements of these methods including iterative fine-tuning were beyond the scope of this pilot analysis.

 

Our initial exploration of Twitter data for public health messaging also revealed common pitfalls of mining Twitter data, including the need for a selective search strategy when using academic Twitter accounts, hashtag ‘hijacking’ meaning most tweets were irrelevant, imperfect Twitter language filters and ads often exploiting user mentions.

Next steps

We hope to secure further funding to follow-up this pilot project. By expanding our collaboration network, we aim to improve the way we tackle misinformation in the public health domain, ultimately increasing the impact of this work. If you’re interested in health messaging, misinformation and social media, we would love to hear from you – @Luisa_Zu and @cheryl_mcquire.

Researchers from across the UK are coming together for a climate data hackathon this June. The hackathon is a three-day virtual event organised by the University of Bristol’s Cabot Institute and Jean Golding Institute, in association with the Met Office and universities of Exeter, Leeds and UCL.

 

The aim of the hackathon is to produce cutting-edge research using data from the Climate Model Intercomparison Project (CMIP6), with the aim of showcasing outputs at the upcoming COP26 delegation in November, and through peer-reviewed publications. Topics range from climate change to oceanography, biogeochemistry, and more. 

Dr Dann Mitchell is the Met Office Joint Chair in Climate Hazards at Bristol: “I’m delighted that we have received over one hundred applications to take part in our hackathon, it is a great chance for academics to experience research on topics outside of their comfort zone.” 

Teams are being led by senior academics from Bristol and the partner universities, with assistance from data science experts at the Jean Golding Institute, the central hub for data science and data-intensive research at the University of Bristol. 

The hackathon will take advantage of several online collaboration platforms, with code, visualisations and other outputs being shared openly on GitHub. Professor Kate Robson Brown, Director of the Jean Golding Institute commented: “Supporting open, accessible science and best practice in research is a key part of the work of the JGI. This event opens up areas of climate research to whole new groups of researchers and I’m encouraged by its popularity. I’m pleased we are able to provide a team of data scientists to support this exciting science.” 

To ensure computational resources are available to all participants, the hackathon is being hosted on JASMIN, the UK’s data analysis facility for environmental science. Poppy Townsend, Communications Manager at JASMIN has been supporting the event: “JASMIN is a globally unique data analysis facility. It provides storage and compute facilities, enabling data-intensive environmental science for over 1,600 users. We are excited to be supporting a range of climate hackathons in the run up to COP26 and are pleased to see new and innovative uses of our JASMIN Notebook Service, launched in 2020. Making Python available through interactive Jupyter Notebooks helps open up data visualisation tools to a wider community, reducing barriers to scientific computing.” 

Following the theme of sharing best practice, the hackathon team have also published a wide range of resources on their website, not only for participants but also for organisers of other virtual hackathon events. These include a guide to running an online event on JASMIN, an event checklist, and template forms, emails and resources, which have already been accessed by other Met Office partner universities who will be conducting their own hackathons in the build-up to COP26 later this year. 

The CMIP6 Data Hackathon will take place between 2nd–4th June, and although places at the event are now finalised, you can stay updated by following #cmip6hackathon for live tweets as the event progresses. You can also get involved with one of our events. Just two weeks after, the Jean Golding Institute is hosting Data Week Online 2021. Running between 14th–18th June, Data Week is a week of complimentary workshops, renowned speakers and interactive events showcasing the latest in Data Science and AI. Everyone is welcome! You can register to attend a Data Week event by following the links on our website. 

 

About the author: James Thomas is a data scientist at the Jean Golding Institute and member of the CMIP6 Data Hackathon organising team. His research interests include energy and the environment, and he is currently working on urban analytics projects with a focus on Net Zero and reducing health and well-being inequalities. 

My name is Ralph Ward and I am currently studying for a Masters in Anthropology at the University of Bristol. I have just completed a 4-month placement supporting the development and operation of a Social Justice and AI Workshop for the GW4 Data Science Network .

I am just about to start research for my dissertation exploring the notion of Ethnic Invisibility among diasporic Filipinos living in the United Kingdom. Being half Filipino myself, I have always been curious about my heritage. With the world becoming an increasingly transnational community, I believe that conversations about ethnic identity and heritage are incredibly important. My research interests also include museums, heritage work and conservation management. Completing this placement has allowed me to take away many new skills in IT and data population as well as developed interpersonal, time management and problem-solving skills. I am certain that these skills will assist me with the completion of my dissertation project as well as future employment prospects.

One of the main things that stood out to me when organising the workshop was the importance of teamwork, especially when it comes to planning an event online. Although  due to the COVID 19 restrictions we were not able to meet in person, the team was extremely proactive in making sure we stayed connected virtually. This was valuable and helped me feel part of the team. I am extremely grateful for the level of responsibility that was given to me during my time with JGI, right from the initial brainstorming stage.  I was involved in the background research of prospective attendees, the event planning itself and assisted in running the event on the day.

Overall, I feel that the workshop was a great success. There was a huge turnout from a range of disciplines within the GW4 network and external organisations. The workshop brought about a series of productive speed-networking sessions which gave lots of food for thought for potential collaborative ideas. The workshop was filled with a range of talks from topics like algorithmic bias in decision making, data justice in Mexico’s multiveillant society and Networking With Care: Exploring Data and AI Ethics research practices                            

I would like to thank Kate Robson Brown and Patty Holley from the Jean Golding Institute for providing the opportunity to work on such an exciting event and lastly Chiara, Elaine and Lily for being such a fantastic team to work with!

Ralph Ward

 

Screenshot from Social Justice and AI Workshop