JGI Seed Corn Funded Project Blog

Physical activity (PA) is among the most important human behaviours to improve and maintain health. The level of PA performed by an individual is often measured by accelerometers (the sensors used in fitness trackers or smartphones), but the obtained data is rich and evokes statistical challenges. Hence, novel statistical solutions must be found. Multivariate Pattern Analysis (MPA) could help in this regard and has great potential to provide new insights into how PA relates to health. In this first part of our 2-part blog series we describe how we will study the multivariate PA intensity signature related to early adult physical and mental health.

The problem in a nutshell

In research, accelerometers are typically worn around the hip or wrist for several days. They measure movements of the body multiple times per second and thus produce a massive amount of raw data. In general, being active will increase the measured acceleration (ie, the stored values will be higher). All values collected over the week are then used for the analysis, for example, by averaging them. This average value represents the total amount of PA performed. Another option is to look at the time spent in specific intensities of PA (eg, minutes per week of lower or higher intensity). This can be done by applying so called ‘cut points’ to the measured acceleration (the stored values). For example, if the stored value is greater than 4000, we could assume this minute was of higher intensity (those cut points are usually developed in studies where the accelerometers are compared to other measurements of the intensity of PA). Thus, cut points can be used to estimate the weekly time spent in different intensities of PA.

Many previous studies investigating associations between PA and health have focused on few intensity categories (ie, sedentary, light, moderate, vigorous). Special attention has been paid to time spent in moderate-to-vigorous PA. In fact, current PA guidelines are heavily based on this evidence. The focus on broad and selected parts of the intensity spectrum has at least two problems. First, many activities will be collapsed into the same group. For example, brisk walking and playing Squash, even though their intensity can be vastly different, are included in the same category (moderate-to-vigorous PA). Secondly, we do not know enough about the relative contribution of lower-intensity PA to health (eg, light).

However, including all the intensity categories in a single statistical model (eg, Ordinary Least Squares Regression) is problematic due to the high correlation between the variables and their closed structure (ie, summing up to 24 hours when adding sleep). Therefore, novel statistical solutions are needed to overcome these challenges and to identify the relative contribution of each intensity within the full intensity spectrum. One approach is MPA, which was, among others (eg, compositional data analysis, intensity gradient) recently introduced to the field of PA epidemiology. MPA addresses the collinearity among intensity categories using latent variable modelling (Partial Least Squares Regression (PLS-R)) while allowing for the inclusion of a high-resolution dataset (full intensity spectrum). So, instead of using the above-mentioned categories (sedentary, light, moderate, vigorous) we can not only include all the categories together but also increase their resolution by increasing the number of cut points (eg, time spent in 4000-4499, 4500-4999 instead of using just ‘4000 and greater’). Thus, single cut points (eg, 4000) are becoming less important while at the same time we can study the relative contribution of specific intensities considering all others in the same statistical model.

More information about MPA can be found here

Aims of the project

Previous applications of MPA to PA research have been cross-sectional studies on physical health (eg, cardio-metabolic health) where both the exposure (PA) and outcome (health) are measured at the same time. Therefore, the role of specific PA intensities for a broad range of physical and mental health outcomes is unknown. Moreover, given the importance of adolescence for life-course health, longitudinal studies are needed to explore the role of adolescent PA on future health. This proposed project utilises data from the Avon Longitudinal Study of Parents and Children (ALSPAC) resource, the most detailed study of its kind in the world, to provide novel evidence on associations of the PA intensity spectrum in adolescence (accelerometer measurements at ages 12, 14 and 16 years) with important adult health markers (wellbeing, depression, anxiety, cardiovascular health, metabolic health, adiposity, musculoskeletal, and respiratory health, measured at 25 years). The selected health markers are shown in the Figure below.

Stay tuned for Part-2 which will be published next year and shows the results of this project.

Contact details

Dr Matteo Sattler (Email: matteo.sattler@uni-graz.at, Twitter: @Sattler_Graz)

Institute of Human Movement Science, Sport and Health, University of Graz, Graz, Austria

Dr Ahmed Elhakeem (Email: a.elhakeem@bristol.ac.uk, Twitter: @aelhak19)

MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK

We are pleased to announce the winners of the Bristol Science Film Festival Jean Golding Institute Data Science and AI film prize 2021. The JGI co-hosted a screening with BSSF of the winning films in Data Week Online 2021. 

Bristol Science Film Festival runs an annual science film competition to support film-makers trying to tell the most interesting facts (or science fictions), no matter their resources.  

Winner — The Artificial Revolution 

 

Elyas Masrour 
A young artist investigates the recent advancements in creative Artificial Intelligence to see if we’re approaching the end of art.

Watch it here 

 

Runner up — Not a Robot 

 

George Summers 
A robot tries to break into a human facility, and is asked a security question… 

Watch the trailer here 

 

 

 

The Elizabeth Backwell Institute awarded a prize to health-related films in celebration of the 200th anniversary of Elizabeth Blackwell’s birth. Click here to find out more. 

More about Bristol Science Film Festival and the other category winners

JGI Seed Corn Funded Project

Blog post by Chris McWilliams, Tamzin Whelan, Roberta Guerrina, Fiona Jordan, Amanda Williams.

Figure 1: (left) Tamzin scanning books, running them through the OCR software and correcting the output; (right) a child reading an early years book.

Children are strongly impacted by the gender messages they receive at a young age, and books are integral to this messaging. The goal of this project is to examine the prevalence of gender stereotypes in Early Years Foundation Stage (EYFS) book collections available in school classrooms.

Specific aims of the project include:

1. To create a machine learning tool that will analyse both the gender of the protagonists (making a distinction between human and non-human characters) and the language associated with the different genders;
2. Use an interdisciplinary perspective to analyse patterns revealed by word frequency extraction, to gain a better understanding of how EYFS children’s books are reinforcing or challenging gender stereotypes;
3. To produce reusable software and data science methods that can continue to be used to identify the prevalence of gender stereotyping in book collections. The intended users are teachers, parents and researchers.

Results

Our sample consists of 200 books from the reception class of a primary school in rural Devon. As in most schools, the collection was amassed over time and the date of first publication ranges from 1978-2020. So far, 130 of the 200 books have been scanned and processed. Initial findings suggest that within this collection there is a disproportionate representation of genders and characters are depicted in gender stereotypical ways.

Figure 1.  The frequency of gender (female [F], male [M], non-gender specific [NGS]) and ‘species’ (human/non-human) of protagonists and secondary characters from 130 children’s story books.

There are two key findings to date:

1. Gender Representation. By coding the gender (female, male, or non-gender specific) and species (human or non-human) of the protagonist and secondary characters in each storybook we were able to examine whether the genders were equally represented.

Unsurprisingly, they were not. The results are depicted in figure 1. Male characters outnumbered female characters at a rate of more than 2:1 (32% female characters in total). When females were included, they were far more likely to be represented as secondary characters than protagonists (75% of females were secondary characters, versus 52% for males).  This is important as it replicates the harmful stereotype of females occupying supporting roles.

2. Gender Stereotyping. Using Spacy to parse the sentence structure, we examined verb clauses where the noun-subject belonged to a standard list of female/male identifiers or was the name of a character with identifiable gender (manually coded).

From these sentences we then extracted the following words types and associated them with the gender of the noun-subject:

• the verb associated with the noun subject in each sentence (Root)
• nouns that are the object of the verb clause (Dobj)
• adjectives associated with the noun subject (Amod and Acomp)

The results are summarised in figures 2 and 3, and in table 1 which shows that female characters have approximately half as many associated words across the three word types. This reveals a smaller vocabulary associated with female characters, suggesting that females are less relevant to plot lines and have less expansive narratives.

Figure 2: Word clouds showing the frequency of verbs associated with female and male characters.

Figure 3: Word clouds showing the frequency of nouns associated with female and male characters.

Table 1: Summary of word types associated with female and male characters. ‘Words per character’ is the average number of distinct words per character.

We are currently verifying the coding process, but initial findings demonstrate that gender stereotypes continue to be present in children’s literature. For example, verbs related to female characters are more passive, and verbs related to male characters are more active. Aligning with gender-based microaggressions, male characters tend to dominate the text, reaffirming masculinity as the norm. Female characters most frequently act on ‘him’ (table 1), indicating a centralisation of the male experience within the portrayal of female characters.  Furthermore, females predominate in caring roles with 25% of all female characters written as Mum, compared to 4% of males as Dad. This reproduces stereotypical divisions between public and private roles, situating females in the domestic sphere and males in the external world.

In summary, we find that female characters are not being represented equitably in this collection. When female characters are featured, they are more likely have minor roles and are more likely to perform stereotypically female roles.  Patriarchal socialisation at such an early age negatively impacts the way children understand society and their position within it. These findings demonstrate that through both the omission and portrayal of female characters, harmful gender stereotypes are indeed present in contemporary classroom libraries.

Future Plans

Encouragingly, there is increasing awareness that diversity and representation in children’s literature is problematic and some online resources and studies are drawing attention to this issue. In addition to expanding the dataset, developing the data science, and disseminating findings to academic audiences, we are keen to work with parents, teachers, and community partners to actually change what children are reading. This will be the foundation of a larger funding application – we look forward to updating the JGI community on our future successes in this area.

Please contact Chris McWilliams (chris.mcwilliams@bristol.ac.uk) for more information about the project.

JGI Seed Corn Funded Project

We created a web application that enables interactive access to climate research data to enhance scientific collaboration and public outreach. 

Screenshot of the app showing surface ocean currents (coloured by magnitude) of the present-day Atlantic Ocean.

Climate model data for everyone 

We can only fully understand the past, present and future climate changes and their consequences for society and ecosystems if we integrate the expertise and knowledge of various sub-disciplines of environmental sciences. In theory, climate modelling provides a wealth of data of great interest across multiple disciplines (e.g., chemistry, geology, hydrology), but in practice, the sheer quantity and complexity of these datasets often prevent direct access and therefore limit the benefits for large parts of our community. We are convinced that reducing these barriers and giving researchers intuitive and informative access to complex climate data will support interdisciplinary research and ultimately advance our understanding of climate dynamics.  

Aims of the project 

This project aims to create a web application that provides exciting interactive access to climate research data. An extensive database of global paleoclimate model simulations will be the backbone of the app and serves as a hub to integrate data from other environmental sciences. Furthermore, the intuitive browser-based and visually appealing open access to climate data can stimulate public interest, explain fundamental research results, and therefore increase the acceptance and transparency of the scientific process. 

Technical implementation 

We developed a completely new, open-source application to visualise climate model data in any modern web browser. It is built with the JavaScript library “Three.js” to allow the rendering of a 3D environment without the need to install any plug-ins. The real-time rendering gives instantaneous feedback to any user input and greatly promotes data exploration. Linear interpolation within a series of 109 recently published global climate model simulations provides a continuous timeline covering the entire Phanerozoic (last 540 million years). Model data is encoded in RGBA colour space for fast and efficient file handling in mobile and desktop browsers. The seed corn funding enabled the involvement of a professional software engineer from the University of Bristol Research IT. This did not only help with transferring our ideas into a website but also ensured a solid technical foundation of the app which is crucial for future development and maintainability. In particular, a development workflow using a Docker container has been implemented to simplify sharing and expanding the app within the community. 

Screenshots of the app for the present day and the ice-free greenhouse climate of the mid-Cretaceous (~103 Million years ago). Shown are annual mean model data for sea surface temperature, surface ocean currents, sea and land ice cover, precipitation, and surface elevation

Current features 

The app allows the visualisation of simulated scalar (e.g., temperature and precipitation) and vector fields (winds and ocean currents) for different atmosphere and ocean levels. The user can seamlessly switch between a traditional 2D map and a more realistic 3D globe view and zoom in and out to focus on regional features. The model geographies are used to vertically displace the surface and to visualise tectonic changes through geologic time. Winds and ocean currents are animated by the time-dependent advection of thousands of small particles based on the climate model velocities. This technique – inspired by the “earth” project by Cameron Beccario – greatly helps to communicate complex flow fields to non-experts. Individual layers representing the ocean, the land, the atmosphere, and the circulation can be placed on top of each other to either focus on single components or their interactions. The user can easily navigate on a geologic timescale to investigate climate variability due to changes in atmospheric CO2 and paleogeography throughout the last 540 million years. 

Next steps 

The first public release of the “climatearchive.org” app is scheduled for autumn 2021. This version will primarily showcase the technical feasibility and potential for public outreach of the app. We anticipate using this version to acquire further funding for developing new features focusing on the scientific application of the website. First, we plan to add paleoclimate reconstructions (e.g., temperature) for available sites across geologic time. The direct comparison with the simulated model dynamics will be highly valuable for assessing the individual environmental setting and ultimately interpreting paleoclimate records. Secondly, we will generalise the model data processing to allow the selection and comparison of different climate models and forcing scenarios. Thirdly, we aim to provide the ability to extract and download model data for a user-defined location and time. We see the future of the app as a user-friendly interface to browse and visualise the large archive of available climate data and finally download specific subsets of data necessary to enable quantitative interdisciplinary climate research for a larger community. 

Contact details and links 

Sebastian Steinig, School of Geographical Sciences 

sebastian.steinig@bristol.ac.uk 

The public release of the website (https://climatearchive.org/) and source code (https://github.com/sebsteinig) is scheduled for autumn 2021. 

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.