Healthcare staff in a hospital ward setting typically monitor patients by taking vital sign observations at regular intervals, usually every 6 - 12 hours for routine observations, but more frequently for critical patients. Patients on similar schedules have been shown to be regularly batched in a practice called ‘ward rounds’, but to what extent healthcare staff manage observations on ‘non-routine’ intervals independently to those scheduled for the subsequent ward round has yet to be established. This study examines the Time-To-Next-Observation (TTNO) for vital sign observations with planned schedules (e.g., 1 hour) as random variables defined by their hazard distribution functions. Joint distribution functions sampled from any pair of TTNO distributions could be used to calculate the probability that any two observations on set schedules will happen collectively. However, it is clear that this model fails to capture underlying dependency structures seen in empirical results. We propose a copula approach to extract and quantify pairwise nonlinear relationships for all standard observation intervals across 20 study wards. This study showed that most wards operate with significant levels of dependency between observation schedules, largely reflecting broad ward characteristics referenced in other works, yet present a deeper level of insight into individual ward operations. Understanding current levels of dependency between regular observation scheduling and routine operations has the potential to become essential knowledge for ward stakeholders when designing staff resource strategies.

Vital sign observations are typically carried out by healthcare staff at regular intervals, known as ward rounds, to monitor the overall health status of individual patients. Patients who present symptoms of deterioration, or are deemed ‘at risk’ by clinical staff, will have their vital signs observed more frequently (e.g., hourly instead of on a 12 hour interval), decided by the nature of their condition. The frequency and documentation accuracy of vital signs observations has been well studied, but less consideration has been given to how clinical staff manage specific observation intervals amongst those that have been routinely scheduled. The primary aims of this study are to assess the current adherence to prescribed observation intervals and identify any primary factors that affect timeliness. This study uses empirical survival estimation methods to determine the empirical likelihood that, for a patient with a specified observation interval, the subsequent observation would be recorded within the planned interval. We discuss the management of various patient observation intervals across 20 study wards in south Wales and how these formulate routine ward rounds, or coexist with them. A semi-parametric proportional hazards model is then used to determine the extent that individual patient covariates, such as Early Warning Score (EWS), time of day, and sepsis, mediate a significant change to the baseline of vital sign observation timeliness. At the ward level, our findings suggest regular batching of vital sign observations irrespective of the planned schedules and a moderately positive linear relationship between observation interval length and the likelihood of timely recording. On an individual patient basis, elevated EWS was the strongest indicator that a patient’s subsequent observation would be taken earlier than baseline, however, it is clear that baseline vital sign observation management is largely governed by existing ward round policies.

Image quality assessment (IQA) and its computational models play a vital role in modern computer vision applications. Research has traditionally focused on signal distortions arising during image compression and transmission, and their impact on perceived image quality. However, little attention is paid to image manipulation that alters an image using various filters. With the prevalence of image manipulation in real-life scenarios, it is critical to understand how humans perceive filter-altered images and to develop reliable IQA models capable of automatically assessing the quality of filtered images. In this paper, we build a new IQA database for filter-altered images, comprised of 360 images manipulated by various filters. To ensure the subjective IQA faithfully reflects human visual perception, we conduct a fully-controlled psychovisual experiment. Building upon the ground truth, we propose an innovative deep learning-based no-reference IQA (NR-IQA) model named IMQA that can accurately predict the perceived quality of filter-altered images. This model involves constructing an image filtering-aware module to learn discriminatory features for filter-altered images; and fuses these features with the representations generated by an image quality-aware module. Experimental results demonstrate the superior performance of the proposed IMQA model.

The formation and evolution of public opinion have been widely studied to understand how consensus forms due to atomic interactions between individuals. While many studies have paid attention to modelling influence and interaction, most of the literature assumes static agents, ignoring the frequent changes in physical locations expected in real life. This feature naturally allows humans to interact with diverse people and avoid disagreement, which heavily impacts the co-evolution of opinions, communities or isolation in human societies. Our previous work proposed an extension of the bounded confidence model inspired by the theories of homophily and cognitive dissonance, which concern humans' natural behaviours of attraction and disagreement. Although this demonstrated a marked difference to a static opinion model and purely random mobility, the limited experiments gave little insight into the causes or the resulting structures of consensus. This article addresses these shortcomings through a thorough investigation of the impact of mobility modelled by different mechanisms. Through extensive simulation, we observe a transition from multiple stable opinion clusters to complete consensus and a shift from a geographically based organisation to isolated structure-less agents. Lastly, we propose a novel classification of the different outcomes of self-organisation in opinion models, highlighting the patterns of emerging behaviours across the spectrum of interaction range and influence parameters.

Background Burns are common childhood injuries, which can lead to serious physical and psychological outcomes. Appropriate first aid is essential in managing the pain and severity of these injuries; hence, parents who need timely access to such information often seek it from the web. In particular, social media allow them to reach other parents, hence these conversations may provide insight to aid the design and evaluation of burn first aid interventions for parents. Objective This study aims to determine the feasibility of finding, accessing, and analyzing parent burn first aid conversations on social media to inform intervention research. Methods The initial choice of the relevant social media was made based on the results of a parent focus group and survey. We considered Facebook (Meta Platforms, Inc), Mumsnet (Mumsnet Limited), Netmums (Aufeminin Group), Twitter (subsequently rebranded as “X”; X Corp), Reddit (Reddit, Inc), and YouTube (Google LLC). To locate the relevant data on these platforms, we collated a taxonomy of search terms and designed a search strategy. A combination of natural language processing and manual inspection was used to filter out irrelevant data. The remaining data were analyzed manually to determine the length of conversations, the number of participants, the purpose of the initial post (eg, asking for or offering advice), burn types, and distribution of relevant keywords. Results Facebook parenting groups were not accessed due to privacy, and public influencer pages yielded scant data. No relevant data were found on Reddit. Data were collected from Mumsnet, Netmums, YouTube, and Twitter. The amount of available data varied across these platforms and through time. Sunburn was identified as a topic across all 4 platforms. Conversations on the parenting forums Mumsnet and Netmums were started predominantly to seek advice (112/116, 96.6% and 25/25, 100%, respectively). Conversely, YouTube and Twitter were used mainly to provide advice (362/328, 94.8% and 126/197, 64%, respectively). Contact burns and sunburn were the most frequent burn types discussed on Mumsnet (30/94, 32% and 23/94, 25%, respectively) and Netmums (2/25, 8% and 14/26, 56%, respectively). Conclusions This study provides a suite of bespoke search strategies, tailored to a range of social media platforms, for the extraction and analysis of burn first aid conversation data. Our methodology provides a template for other topics not readily accessible via a specific search term or hashtag. YouTube and Twitter show potential utility in measuring advice offered before and after interventions and extending the reach of messaging. Mumsnet and Netmums present the best opportunity for informing burn first aid intervention design via an in-depth qualitative investigation into parents’ knowledge, attitudes, and behaviors.

Vital signs observations are regular measurements used by healthcare staff to track a patient’s overall health status on hospital wards. We look at the potential in re-purposing aggregated and anonymised hospital data sources surrounding vital signs recording to provide new insights into how care is managed and delivered on wards. In this paper, we conduct a retrospective longitudinal observational study of 770,720 individual vital signs recordings across 20 hospital wards in South Wales (UK) and present a network modelling framework to explore and extract behavioural patterns via analysis of the resulting network structures at a global and local level. Self-loop edges, dyad, triad, and tetrad subgraphs were extracted and evaluated against a null model to determine individual statistical significance, and then combined into ward-level feature vectors to provide the means for determining notable behaviours across wards. Modelling data as a static network, by aggregating all vital sign observation data points, resulted in high uniformity but with the loss of important information which was better captured when modelling the static-temporal network, highlighting time’s crucial role as a network element. Wards mostly followed expected patterns, with chains or stand-alone supplementary observations by clinical staff. However, observation sequences that deviate from this are revealed in five identified motif subgraphs and 6 anti-motif subgraphs. External ward characteristics also showed minimal impact on the relative abundance of subgraphs, indicating a ‘superfamily’ phenomena that has been similarly seen in complex networks in other domains. Overall, the results show that network modelling effectively captured and exposed behaviours within vital signs observation data, and demonstrated uniformity across hospital wards in managing this practice.

The push to a connected world where people carry an always-online device which has been designed to maximise instant gratification and prompts users via notifications has lead to a surge of potentially problematic behaviour as a result. This has lead to a rising interest in addressing and understanding the addictiveness of smartphone usage, as well as for particular applications (apps). However, capturing addiction from usage involves not only assessment of potential addiction risk but also requires understanding of the complex interactions that define user behaviour and how these can be effectively isolated and summarised. In this paper, we examine the correlation of physical user interface (UI) interactions (e.g. taps and scrolls) and smartphone addiction risk using a large dataset of those smartphone events (65,093,343, N=301,024 sessions) collected from 64 users over an 8-week period with an accompanying smartphone addiction survey. Our novel method which reports on the probability of a users addiction risk and in a model case we show how it was be used to identify 57 of 64 users correctly. This supports our observations of UI events during sessions of usage being indicative of addiction risk while improving previous approaches which rely on summative data such as screen on time. Within this we also find that users only exhibit addictive behaviour in a subset of all sessions while using their smartphone.

The prevalence of induced triads play an important role in characterising complex networks, supporting approaches for assessment of dynamic and partially obfuscated scenarios. In this paper we introduce a new local edge-centrality measure that is designed to be deployed in this context for complex networks and is highly scalable. It signifies the importance an edge plays within induced triads for a directed network. We observe that an edge can play one of two roles in providing connectivity within any particular triad, based on whether the edge supports connectivity to the third node or not. We call these alternative states overt and covert. As an edge may play alternative roles in different induced triads, this allows us to assess the local importance of an edge across multiple induced substructures. We introduce theory to count the number of induced triads in which an edge is overt and covert. Using 34 data sets derived from public sources, we show how the presence of overt and covert edges can be used to profile diverse real-world networks. The relationship with global network analysis metrics is examined. We observe that overt and covert edge centrality is useful in further differentiating classes of network, when considered in combination with conventional global network analysis metrics.

We present LipidFinder 2.0, incorporating four new modules that apply artefact filters, remove lipid and contaminant stacks, in-source fragments and salt clusters, and a new isotope deletion method which is significantly more sensitive than available open-access alternatives. We also incorporate a novel false discovery rate (FDR) method, utilizing a target-decoy strategy, which allows users to assess data quality. A renewed lipid profiling method is introduced which searches three different databases from LIPID MAPS and returns bulk lipid structures only, and a lipid category scatter plot with color blind friendly pallet. An API interface with XCMS Online is made available on LipidFinder’s online version. We show using real data that LipidFinder 2.0 provides a significant improvement over non-lipid metabolite filtering and lipid profiling, compared to available tools. Availability LipidFinder 2.0 is freely available at https://github.com/ODonnell-Lipidomics/LipidFinder and http://lipidmaps.org/resources/tools/lipidfinder . Contact lipidfinder@cardiff.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.

Understanding how and why people use their smartphones has enabled use cases ranging from correlating behaviour with psychological states through to on-device tasks such as app recommendations. However, being able to effectively and pervasively capture usage behaviour is challenging due to the wide range of functions, apps and interactions that are possible. In this paper, we examine how embedding physical user-app activity (e.g., taps and scrolls) can provide a rich basis for summarising device usage. Using a large dataset of 82,758,449 interaction events from 86 users over an 8-week period we combine feature embedding and unsupervised learning to extract prominent interactions within clusters of smartphone usage sessions. We find that high-level features such as session length, unlock state, and app switches are not representative of these clusters and can give a false sense of similarity or dissimilarity between sessions. The results motivate further exploration of the utility of using user-app interaction behaviour as the basis for the aforementioned use cases.

BACKGROUND:Common chromosome 9p21 single nucleotide polymorphisms (SNPs) increase coronary heart disease risk, independent of traditional lipid risk factors. However, lipids comprise large numbers of structurally related molecules not measured in traditional risk measurements, and many have inflammatory bioactivities. Here, we applied lipidomic and genomic approaches to 3 model systems to characterize lipid metabolic changes in common Chr9p21 SNPs, which confer ≈30% elevated coronary heart disease risk associated with altered expression of ANRIL, a long ncRNA. METHODS:Untargeted and targeted lipidomics was applied to plasma from NPHSII (Northwick Park Heart Study II) homozygotes for AA or GG in rs10757274, followed by correlation and network analysis. To identify candidate genes, transcriptomic data from shRNA downregulation of ANRIL in HEK-293 cells was mined. Transcriptional data from vascular smooth muscle cells differentiated from induced pluripotent stem cells of individuals with/without Chr9p21 risk, nonrisk alleles, and corresponding knockout isogenic lines were next examined. Last, an in-silico analysis of miRNAs was conducted to identify how ANRIL might control lysoPL (lysophosphospholipid)/lysoPA (lysophosphatidic acid) genes. RESULTS:Elevated risk GG correlated with reduced lysoPLs, lysoPA, and ATX (autotaxin). Five other risk SNPs did not show this phenotype. LysoPL-lysoPA interconversion was uncoupled from ATX in GG plasma, suggesting metabolic dysregulation. Significantly altered expression of several lysoPL/lysoPA metabolizing enzymes was found in HEK cells lacking ANRIL. In the vascular smooth muscle cells data set, the presence of risk alleles associated with altered expression of several lysoPL/lysoPA enzymes. Deletion of the risk locus reversed the expression of several lysoPL/lysoPA genes to nonrisk haplotype levels. Genes that were altered across both cell data sets were DGKA, MBOAT2, PLPP1, and LPL. The in-silico analysis identified 4 ANRIL-regulated miRNAs that control lysoPL genes as miR-186-3p, miR-34a-3p, miR-122-5p, and miR-34a-5p. CONCLUSIONS:A Chr9p21 risk SNP associates with complex alterations in immune-bioactive phospholipids and their metabolism. Lipid metabolites and genomic pathways associated with coronary heart disease pathogenesis in Chr9p21 and ANRIL-associated disease are demonstrated.

Understanding the evolution and spread of opinions within social groups gives important insight into areas such as public elections and marketing. We are specifically interested in how psychological theories of interpersonal influence may affect how individuals change their opinion through interactions with their peers, and apply Agent-Based Modelling to explore the factors that may affect the emergence of consensus. We investigate the co-evolution of opinion and location by extending the Deffuant-Weisbuch bounded confidence opinion model to include mobility inspired by the psychological theories of homophily and dissonance, where agents are attracted or repelled by their neighbours based on the agreement of their opinions. Based on wide experimentation, we characterize the time it takes to converge to a steady state and the local diversity of opinions that results, finding that homophily leads to drastic differences in the nature of consensus. We further extend our mobility model and add noise in order to check the model's robustness, finding that a number of opinion clusters survive even with high levels of noise.

An amendment to this paper has been published and can be accessed via the original article.

The ability to sustain indirect reciprocity is an example of collective intelligence. It is increasingly relevant to future technology and autonomous machines that need to function in a coalition. Indirect reciprocity involves providing benefit to others without guaranteeing a future return. The identity through which an agent presents itself to others is fundamental, as this is how the reputation of an agent is considered. In this paper, we examine the sharing of identity between agents, which is an important and frequently overlooked issue when considering indirect reciprocity. We model an agent's identity using traits, which can be shared with other agents, and offer a basis for an agent to change their identity. Through this approach, we determine how shared identity affects cooperation, and the conditions through which cooperation can be sustained. This also helps us to understand how and why behavioural strategies involving identity function are put in place, such as whitewashing. The framework offers the opportunity to assess the interplay between the sharing of traits and the cost, in terms of reduced cooperation and opportunities for shirkers to benefit.

Sven W. Meckelmann, PhD*, Jade I. Hawksworth, MSc*, Daniel White, PhD, Robert Andrews, PhD, Patricia Rodrigues, MSc, Anne O`Connor, PhD, Jorge Alvarez-Jarreta, PhD, Victoria J. Tyrrell, PhD, Christine Hinz, PhD, You Zhou, PhD, Julie Williams, PhD, Maceler Aldrovandi, PhD, William J Watkins, PhD, Adam J Engler, PhD, David A. Slatter, PhD, Stuart M Allen, PhD, Jay Acharya, PhD, Jacquie Mitchell, Jackie Cooper, MSc, Junken Aoki, PhD, Kuniyuki Kano, PhD, Steve E. Humphries, PhD, MRCP, FRCPath, Valerie B. O`Donnell, PhD

Notifications on mobile devices punctuate our daily lives to provide information and prompt for further engagement. Investigations into the cognitive processes involved in consuming notifications are common across the literature, however most research to date investigates notifications in isolation of one another. In reality, notifications often coexist together, forming a "stack", however the behavioural implications of this on the response towards individual notifications has received limited attention. Through an in-the-wild study of 1889 Android devices, we observe user behaviour in a stream of 30 million notifications from over 6000 applications. We find distinct strategies for user management of the notification stack within usage sessions, beyond the behaviour patterns observable from responses to individual notifications. From the analysis, we make recommendations for collecting and reporting data from mobile applications to improve validity through timely responses, and capture potential confounding features.

SUMMARY:We present LipidFinder online, hosted on the LIPID MAPS website, as a liquid chromatography/mass spectrometry (LC/MS) workflow comprising peak filtering, MS searching and statistical analysis components, highly customized for interrogating lipidomic data. The online interface of LipidFinder includes several innovations such as comprehensive parameter tuning, a MS search engine employing in-house customized, curated and computationally generated databases and multiple reporting/display options. A set of integrated statistical analysis tools which enable users to identify those features which are significantly-altered under the selected experimental conditions, thereby greatly reducing the complexity of the peaklist prior to MS searching is included. LipidFinder is presented as a highly flexible, extensible user-friendly online workflow which leverages the lipidomics knowledge base and resources of the LIPID MAPS website, long recognized as a leading global lipidomics portal.AVAILABILITY AND IMPLEMENTATION:LipidFinder on LIPID MAPS is available at: http://www.lipidmaps.org/data/LF.

Understanding the evolution and spread of opinions within social groups gives important insight into areas such as public elections and marketing. We are specifically interested in how psychological theories of interpersonal influence may affect how individuals change their opinion through interactions with their peers, and apply Agent Based Modelling to explore the factors that may affect the emergence of consensus. We investigate the coevolution of opinion and location by extending the Deffuant-Weisbuch bounded confidence opinion model to include mobility inspired by the psychological theories of homophily and dissonance, where agents are attracted or repelled by their neighbours based on the agreement of their opinions. Based on wide experimentation, we characterise the time it takes to converge to a steady state and the local diversity of opinions that results, finding that homophily leads to drastic differences in the nature of consensus.

We introduce a new approach to monitoring the activity of smartphone users based on their physical interactions with the interface. Typical events are taps, scrolling and typing, carried out to interact with apps. As compared to other measures, this directly encapsulates potential problematic physical smartphone behaviour as a signal. The approach contrasts against conventions such as self-reporting or timing activity sessions, and it focusses on active rather than passive smartphone activity. Using this alternative method, we collected all user interface interaction events from a sample of 64 participants over a period of 8 weeks, using a bespoke monitoring app called Tymer. User Smartphone Addiction was seen to significantly correlate with high levels of interaction with Lifestyle apps, particularly for female users. Interactions with Social apps in general were also associated with Smartphone Addiction. In particular, user interactions with Snapchat correlated with Smartphone Addiction, represented across all types of interface interaction. This is significant given the widespread usage of Snapchat by teenagers, and we hypothesise that the app's design provides a particularly strong pathway in support of Smartphone Addiction.