Magnitude Judgements Are Influenced by the Relative Positions of Data Points Within Axis Limits.

When visualising data, chart designers have the freedom to choose the upper and lower limits of numerical axes. Axis limits can determine the physical characteristics of plotted values, such as the physical position of data points in dot plots. In two experiments (total N=300), we demonstrate that axis limits affect viewers' interpretations of the magnitudes of plotted values. Participants did not simply associate values presented at higher vertical positions with greater magnitudes. Instead, participants considered the relative positions of data points within the axis limits. Data points were considered to represent larger values when they were closer to the end of the axis associated with greater values, even when they were presented at the bottom of a chart. This provides further evidence of framing effects in the display of data, and offers insight into the cognitive mechanisms involved in assessing magnitude in data visualisations.

Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets.

Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance, in balancing the sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom. Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.

Tracking the structure and sentiment of vaccination discussions on Mumsnet.

Vaccination is one of the most impactful healthcare interventions in terms of lives saved at a given cost, leading the anti-vaccination movement to be identified as one of the top 10 threats to global health in 2019 by the World Health Organization. This issue increased in importance during the COVID-19 pandemic where, despite good overall adherence to vaccination, specific communities still showed high rates of refusal. Online social media has been identified as a breeding ground for anti-vaccination discussions. In this work, we study how vaccination discussions are conducted in the discussion forum of Mumsnet, a UK-based website aimed at parents. By representing vaccination discussions as networks of social interactions, we can apply techniques from network analysis to characterize these discussions, namely network comparison, a task aimed at quantifying similarities and differences between networks. Using network comparison based on graphlets-small connected network subgraphs-we show how the topological structure of vaccination discussions on Mumsnet differs over time, in particular before and after COVID-19. We also perform sentiment analysis on the content of the discussions and show how the sentiment toward vaccinations changes over time. Our results highlight an association between differences in network structure and changes to sentiment, demonstrating how network comparison can be used as a tool to guide and enhance the conclusions from sentiment analysis.

Subjective data models in bioinformatics and how wet lab and computational biologists conceptualise data.

Biological science produces "big data" in varied formats, which necessitates using computational tools to process, integrate, and analyse data. Researchers using computational biology tools range from those using computers for communication, to those writing analysis code. We examine differences in how researchers conceptualise the same data, which we call "subjective data models". We interviewed 22 people with biological experience and varied levels of computational experience, and found that many had fluid subjective data models that changed depending on circumstance. Surprisingly, results did not cluster around participants' computational experience levels. People did not consistently map entities from abstract data models to the real-world entities in files, and certain data identifier formats were easier to infer meaning from than others. Real-world implications: 1) software engineers should design interfaces for task performance, emulating popular user interfaces, rather than targeting professional backgrounds; 2) when insufficient context is provided, people may guess what data means, whether or not they are correct, emphasising the importance of contextual metadata to remove the need for erroneous guesswork.

Personalized, intuitive & visual QT-prolongation monitoring using patient-specific QTc threshold with pseudo-coloring and explainable AI.

BACKGROUND: Drug-induced QT-prolongation increases the risk of TdP arrhythmia attacks and sudden cardiac death. However, measuring the QT-interval and determining a precise cut-off QT/QTc value that could put a patient at risk of TdP is challenging and influenced by many factors including female sex, drug-free baseline, age, genetic predisposition, and bradycardia. OBJECTIVES: This paper presents a novel approach for intuitively and visually monitoring QT-prolongation showing a potential risk of TdP, which can be adjusted according to patient-specific risk factors, using a pseudo-coloring technique and explainable artificial intelligence (AI). METHODS: We extended the development and evaluation of an explainable AI-based technique- visualized using pseudo-color on the ECG signal, thus intuitively 'explaining' how its decision was made -to detect QT-prolongation showing a potential risk of TdP according to a cut-off personalized QTc value (using Bazett's ∆QTc > 60 ms relative to drug-free baseline and Bazett's QTc > 500 ms as examples), and validated its performance using a large number of ECGs (n = 5050), acquired from a clinical trial assessing the effects of four known QT-prolonging drugs versus placebo on healthy subjects. We compared this new personalized approach to our previous study that used a more general approach using the QT-nomogram. RESULTS AND CONCLUSIONS: The explainable AI-based algorithm can accurately detect QT-prolongation when adjusted to a personalized patient-specific cut-off QTc value showing a potential risk of TdP. Using ∆QTc > 60 ms relative to drug-free baseline and QTc > 500 ms as examples, the algorithm yielded a sensitivity of 0.95 and 0.79, and a specificity of 0.95 and 0.98, respectively. We found that adjusting pseudo-coloring according to Bazett's ∆QTc > 60 ms relative to a drug-free baseline personalized to each patient provides better sensitivity than using Bazett's QTc > 500 ms, which could underestimate a potentially clinically significant QT-prolongation with bradycardia.

The role of regular asymptomatic testing in reducing the impact of a COVID-19 wave.

Testing for infection with SARS-CoV-2 is an important intervention in reducing onwards transmission of COVID-19, particularly when combined with the isolation and contact-tracing of positive cases. Many countries with the capacity to do so have made use of lab-processed Polymerase Chain Reaction (PCR) testing targeted at individuals with symptoms and the contacts of confirmed cases. Alternatively, Lateral Flow Tests (LFTs) are able to deliver a result quickly, without lab-processing and at a relatively low cost. Their adoption can support regular mass asymptomatic testing, allowing earlier detection of infection and isolation of infectious individuals. In this paper we extend and apply the agent-based epidemic modelling framework Covasim to explore the impact of regular asymptomatic testing on the peak and total number of infections in an emerging COVID-19 wave. We explore testing with LFTs at different frequency levels within a population with high levels of immunity and with background symptomatic PCR testing, case isolation and contact tracing for testing. The effectiveness of regular asymptomatic testing was compared with 'lockdown' interventions seeking to reduce the number of non-household contacts across the whole population through measures such as mandating working from home and restrictions on gatherings. Since regular asymptomatic testing requires only those with a positive result to reduce contact, while lockdown measures require the whole population to reduce contact, any policy decision that seeks to trade off harms from infection against other harms will not automatically favour one over the other. Our results demonstrate that, where such a trade off is being made, at moderate rates of early exponential growth regular asymptomatic testing has the potential to achieve significant infection control without the wider harms associated with additional lockdown measures.

Off-World Mental Health: Considerations for the Design of Well-being-Supportive Technologies for Deep Space Exploration.

During future long-duration space exploration missions, humans will be exposed to combinations of extreme physical, psychological, and interpersonal demands. These demands create risks for the safety, performance, health, and well-being of both individuals and crew. The communication latency in deep space means that explorers will increasingly have to operate independently and take responsibility for their own self-care and self-management. At present, several research programs are focused on developing and testing digital technologies and countermeasures that support the effective functioning of deep space crews. Although promising, these initiatives have been stimulated mostly by technological opportunity rather than cogent theory. In this perspective, we argue that digital technologies developed for spaceflight should be informed by well-being-supportive design principles and be cognizant of broader conversations around the development and use of digital health applications, especially pertaining to issues of autonomy, privacy, and trust. These issues are important for designing potentially mission-critical health technologies and may be determining factors in the safe and successful completion of future off-world endeavors.

A comparison of experience sampled hay fever symptom severity across rural and urban areas of the UK.

Hay fever affects people differently and can change over a lifetime, but data is lacking on how environmental factors may influence this. This study is the first to combine atmospheric sensor data with real-time, geo-positioned hay fever symptom reports to examine the relationship between symptom severity and air quality, weather and land use. We study 36145 symptom reports submitted over 5 years by over 700 UK residents using a mobile application. Scores were recorded for nose, eyes and breathing. Symptom reports are labelled as urban or rural using land-use data from the UK's Office for National Statistics. Reports are compared with AURN network pollution measurements and pollen and meteorological data taken from the UK Met Office. Our analysis suggests urban areas record significantly higher symptom severity for all years except 2017. Rural areas do not record significantly higher symptom severity in any year. Additionally, symptom severity correlates with more air quality markers in urban areas than rural areas, indicating that differences in allergy symptoms may be due to variations in the levels of pollutants, pollen counts and seasonality across land-use types. The results suggest that a relationship exists between urban surroundings and hay fever symptoms.

A FAIR-Decide framework for pharmaceutical R&D: FAIR data cost-benefit assessment.

The FAIR (findable, accessible, interoperable and reusable) principles are data management and stewardship guidelines aimed at increasing the effective use of scientific research data. Adherence to these principles in managing data assets in pharmaceutical research and development (R&D) offers pharmaceutical companies the potential to maximise the value of such assets, but the endeavour is costly and challenging. We describe the 'FAIR-Decide' framework, which aims to guide decision-making on the retrospective FAIRification of existing datasets by using business analysis techniques to estimate costs and expected benefits. This framework supports decision-making on FAIRification in the pharmaceutical R&D industry and can be integrated into a company's data management strategy.

Selection of data sets for FAIRification in drug discovery and development: Which, why, and how?

Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long- versus short-term priorities, and achieving technical implementation. This situation is exacerbated by the unclear mechanisms by which costs and benefits can be assessed when decisions on FAIR are made. Scientific and research and development (R&D) leadership need reliable evidence of the potential benefits and information on effective implementation mechanisms and remediating strategies. In this article, we describe procedures for cost-benefit evaluation, and identify best-practice approaches to support the decision-making process involved in FAIR implementation.

UK daily meteorology, air quality, and pollen measurements for 2016-2019, with estimates for missing data.

In recent years, quantifying the impacts of detrimental air quality has become a global priority for researchers and policy makers. At present, the systems and methodologies supporting the collection and manipulation of this data are difficult to access. To support studies quantifying the interplay between common gaseous and particulate pollutants with meteorology and biological particles, this paper presents a comprehensive data-set containing daily air quality readings from the Automatic Urban and Rural Network, and pollen and weather data from Met Office monitoring stations, in the years 2016 to 2019 inclusive, for the United Kingdom. We describe (1) the sources from which the data were collected, (2) the methods used for the data cleaning process and (3) how issues related to missing values and sparse regional coverage were addressed. The resulting data-set is designed to be used 'as is' by those using air quality data for research; we also describe and provide open access to the methods used for curating the data to allow modification of or addition to the data-set.

Reforms to improve reproducibility and quality must be coordinated across the research ecosystem: the view from the UKRN Local Network Leads.

Many disciplines are facing a "reproducibility crisis", which has precipitated much discussion about how to improve research integrity, reproducibility, and transparency. A unified effort across all sectors, levels, and stages of the research ecosystem is needed to coordinate goals and reforms that focus on open and transparent research practices. Promoting a more positive incentive culture for all ecosystem members is also paramount. In this commentary, we-the Local Network Leads of the UK Reproducibility Network-outline our response to the UK House of Commons Science and Technology Committee's inquiry on research integrity and reproducibility. We argue that coordinated change is needed to create (1) a positive research culture, (2) a unified stance on improving research quality, (3) common foundations for open and transparent research practice, and (4) the routinisation of this practice. For each of these areas, we outline the roles that individuals, institutions, funders, publishers, and Government can play in shaping the research ecosystem. Working together, these constituent members must also partner with sectoral and coordinating organisations to produce effective and long-lasting reforms that are fit-for-purpose and future-proof. These efforts will strengthen research quality and create research capable of generating far-reaching applications with a sustained impact on society.

Views of Practitioners and Researchers on the Use of Virtual Reality in Treatments for Substance Use Disorders.

Virtual Reality Therapy (VRT) has been shown to be effective in treating anxiety disorders and phobias, but has not yet been widely tested for Substance Use Disorders (SUDs) and it is not known whether health care practitioners working with SUDs would use VRT if it were available. We report the results of an interview study exploring practitioners' and researchers' views on the utility of VRT for SUD treatment. Practitioners and researchers with at least two years' experience delivering or researching and designing SUD treatments were recruited (n = 14). Interviews were thematically analyzed, resulting in themes relating to the safety and realism of VRT, and the opportunity for the additional insight it could offer to during SUD treatment. Participants were positive about employing VRT as an additional treatment for SUD. VRT was thought suitable for treating adults and people with mental health issues or trauma, provided that risks were appropriately managed. Subsequent relapse, trauma and over-confidence in the success of treatment were identified as risks. The opportunity VRT offered to include other actors in therapy (via avatar use), and observe reactions, were benefits that could not currently be achieved with other forms of therapy. Overall, VRT was thought to offer the potential for safe, realistic, personalized and insightful exposure to diverse triggering scenarios, and to be acceptable for integration into a wide range of SUD treatments.

An explainable algorithm for detecting drug-induced QT-prolongation at risk of torsades de pointes (TdP) regardless of heart rate and T-wave morphology.

Torsade de points (TdP), a life-threatening arrhythmia that can increase the risk of sudden cardiac death, is associated with drug-induced QT-interval prolongation on the electrocardiogram (ECG). While many modern ECG machines provide automated measurements of the QT-interval, these automated QT values are usually correct only for a noise-free normal sinus rhythm, in which the T-wave morphology is well defined. As QT-prolonging drugs often affect the morphology of the T-wave, automated QT measurements taken under these circumstances are easily invalidated. An additional challenge is that the QT-value at risk of TdP varies with heart rate, with the slower the heart rate, the greater the risk of TdP. This paper presents an explainable algorithm that uses an understanding of human visual perception and expert ECG interpretation to automate the detection of QT-prolongation at risk of TdP regardless of heart rate and T-wave morphology. It was tested on a large number of ECGs (n=5050) with variable QT-intervals at varying heart rates, acquired from a clinical trial that assessed the effect of four known QT-prolonging drugs versus placebo on healthy subjects. The algorithm yielded a balanced accuracy of 0.97, sensitivity of 0.94, specificity of 0.99, F1-score of 0.88, ROC (AUC) of 0.98, precision-recall (AUC) of 0.88, and Matthews correlation coefficient (MCC) of 0.88. The results indicate that a prolonged ventricular repolarisation area can be a significant risk predictor of TdP, and detection of this is potentially easier and more reliable to automate than measuring the QT-interval distance directly. The proposed algorithm can be visualised using pseudo-colour on the ECG trace, thus intuitively 'explaining' how its decision was made, which results of a focus group show may help people to self-monitor QT-prolongation, as well as ensuring clinicians can validate its results.

The challenges of theory-software translation.

Background: Software is now ubiquitous within research. In addition to the general challenges common to all software development projects, research software must also represent, manipulate, and provide data for complex theoretical constructs. Ensuring this process of theory-software translation is robust is essential to maintaining the integrity of the science resulting from it, and yet there has been little formal recognition or exploration of the challenges associated with it. Methods: We thematically analyse the outputs of the discussion sessions at the Theory-Software Translation Workshop 2019, where academic researchers and research software engineers from a variety of domains, and with particular expertise in high performance computing, explored the process of translating between scientific theory and software. Results: We identify a wide range of challenges to implementing scientific theory in research software and using the resulting data and models for the advancement of knowledge. We categorise these within the emergent themes of design, infrastructure, and culture, and map them to associated research questions. Conclusions: Systematically investigating how software is constructed and its outputs used within science has the potential to improve the robustness of research software and accelerate progress in its development. We propose that this issue be examined within a new research area of theory-software translation, which would aim to significantly advance both knowledge and scientific practice.

Pseudo-colouring an ECG enables lay people to detect QT-interval prolongation regardless of heart rate.

Drug-induced long QT syndrome (diLQTS), characterized by a prolongation of the QT-interval on the electrocardiogram (ECG), is a serious adverse drug reaction that can cause the life-threatening arrhythmia Torsade de Points (TdP). Self-monitoring for diLQTS could therefore save lives, but detecting it on the ECG is difficult, particularly at high and low heart rates. In this paper, we evaluate whether using a pseudo-colouring visualisation technique and changing the coordinate system (Cartesian vs. Polar) can support lay people in identifying QT-prolongation at varying heart rates. Four visualisation techniques were evaluated using a counterbalanced repeated measures design including Cartesian no-colouring, Cartesian pseudo-colouring, Polar no-colouring and Polar pseudo-colouring. We used a multi-reader, multi-case (MRMC) receiver operating characteristic (ROC) study design within a psychophysical paradigm, along with eye-tracking technology. Forty-three lay participants read forty ECGs (TdP risk n = 20, no risk n = 20), classifying each QT-interval as normal/abnormal, and rating their confidence on a 6-point scale. The results show that introducing pseudo-colouring to the ECG significantly increased accurate detection of QT-interval prolongation regardless of heart rate, T-wave morphology and coordinate system. Pseudo-colour also helped to reduce reaction times and increased satisfaction when reading the ECGs. Eye movement analysis indicated that pseudo-colour helped to focus visual attention on the areas of the ECG crucial to detecting QT-prolongation. The study indicates that pseudo-colouring enables lay people to visually identify drug-induced QT-prolongation regardless of heart rate, with implications for the more rapid identification and management of diLQTS.

Evaluation of a web-based, tailored intervention to encourage help-seeking for lung cancer symptoms: a randomised controlled trial.

BACKGROUND: People with lung cancer often wait for several months before presenting symptoms to health services. Some patients report seeking information online to help them appraise symptoms. No research has evaluated whether websites about lung cancer present information in an optimal manner to encourage help-seeking. OBJECTIVE: To evaluate the effectiveness of an online, tailored, theory-based intervention in encouraging help-seeking behaviour among people with potential lung cancer symptoms. METHODS: The intervention consisted of a specialised website which provided tailored information about lung cancer and included a component to address beliefs about help-seeking, based on the Theory of Planned Behaviour (TPB-component). Individuals with undiagnosed symptoms were randomised to receive information about lung cancer in a factorial design (tailored/untailored × TPB-component/no TPB-component). Pre and post viewing webpages, participants reported perceived likelihood of seeking help. Data were analysed using robust mixed factorial ANOVA. RESULTS: Data from 253 participants (73.9% female) were analysed. No effect for the TPB-component was found (p = 0.16), nor for tailoring (p = 0.27). Self-reported likelihood of seeking help increased significantly from pre to post (p < 0.001), regardless of tailoring and TPB-components. CONCLUSION: Self-reported likelihood of seeking help for potential lung cancer symptoms may increase after viewing information online. This does not appear to be affected by information tailoring and components to address beliefs. However, intentions remained unchanged in the majority of the sample. This suggests further efforts are needed to improve lung cancer websites if they are to be a useful resource for those seeking advice about their symptoms.