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Journal articleClarke J, Murray A, Markar S, et al., 2020,
A new geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study
, BMJ Open, Vol: 10, Pages: 1-9, ISSN: 2044-6055Objectives The suspension of elective surgery during the COVID pandemic is unprecedented and has resulted in record volumes of patients waiting for operations. Novel approaches that maximise capacity and efficiency of surgical care are urgently required. This study applies Markov Multiscale Community Detection (MMCD), an unsupervised graph-based clustering framework, to identify new surgical care models based on pooled waiting lists delivered across an expanded network of surgical providers. DesignRetrospective observational study using Hospital Episode Statistics.SettingPublic and private hospitals providing surgical care to National Health Service (NHS) patients in England. ParticipantsAll adult patients resident in England undergoing NHS-funded planned surgical procedures between 1st April 2017 and 31st March 2018. Main outcome measuresThe identification of the most common planned surgical procedures in England (High Volume Procedures – HVP) and proportion of low, medium and high-risk patients undergoing each HVP. The mapping of hospitals providing surgical care onto optimised groupings based on patient usage data.ResultsA total of 7,811,891 planned operations were identified in 4,284,925 adults during the one-year period of our study. The 28 most common surgical procedures accounted for a combined 3,907,474 operations (50.0% of the total). 2,412,613 (61.7%) of these most common procedures involved ‘low risk’ patients. Patients travelled an average of 11.3 km for these procedures. Based on the data, MMCD partitioned England into 45, 16 and 7 mutually exclusive and collectively exhaustive natural surgical communities of increasing coarseness. The coarser partitions into 16 and 7 surgical communities were shown to be associated with balanced supply and demand for surgical care within communities.ConclusionsPooled waiting lists for low risk elective procedures and patients across integrated, expanded natural surgical community networks have the pot
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Journal articleHoffmann T, Jones NS, 2020,
Inference of a universal social scale and segregation measures using social connectivity kernels
, Journal of the Royal Society Interface, Vol: 17, ISSN: 1742-5662How people connect with one another is a fundamental question in the social sciences, and the resulting social networks can have a profound impact on our daily lives. Blau offered a powerful explanation: people connect with one another based on their positions in a social space. Yet a principled measure of social distance, allowing comparison within and between societies, remains elusive.We use the connectivity kernel of conditionally-independent edge models to develop a family of segregation statistics with desirable properties: they offer an intuitive and universal characteristic scale on social space (facilitating comparison across datasets and societies), are applicable to multivariate and mixed node attributes, and capture segregation at the level of individuals, pairs of individuals, and society as a whole. We show that the segregation statistics can induce a metric on Blau space (a space spanned by the attributes of the members of society) and provide maps of two societies.Under a Bayesian paradigm, we infer the parameters of the connectivity kernel from eleven ego-network datasets collected in four surveys in the United Kingdom and United States. The importance of different dimensions of Blau space is similar across time and location, suggesting a macroscopically stable social fabric. Physical separation and age differences have the most significant impact on segregation within friendship networks with implications for intergenerational mixing and isolation in later stages of life.
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Journal articleSethi S, Ewers R, Jones N, et al., 2020,
SAFE Acoustics: an open-source, real-time eco-acoustic monitoring network in the tropical rainforests of Borneo
, Methods in Ecology and Evolution, Vol: 11, Pages: 1182-1185, ISSN: 2041-210X1. Automated monitoring approaches offer an avenue to unlocking large‐scale insight into how ecosystems respond to human pressures. However, since data collection and data analyses are often treated independently, there are currently no open‐source examples of end‐to‐end, real‐time ecological monitoring networks. 2. Here, we present the complete implementation of an autonomous acoustic monitoring network deployed in the tropical rainforests of Borneo. Real‐time audio is uploaded remotely from the field, indexed by a central database, and delivered via an API to a public‐facing website.3. We provide the open‐source code and design of our monitoring devices, the central web2py database, and the ReactJS website. Furthermore, we demonstrate an extension of this infrastructure to deliver real‐time analyses of the eco‐acoustic data. 4. By detailing a fully functional, open source, and extensively tested design, our work will accelerate the rate at which fully autonomous monitoring networks mature from technological curiosities, and towards genuinely impactful tools in ecology.
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Journal articleBai W, Suzuki H, Huang J, et al., 2020,
A population-based phenome-wide association study of cardiac and aortic structure and function
, Nature Medicine, Vol: 26, Pages: 1654-1662, ISSN: 1078-8956Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.
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Journal articleMeyer H, Dawes T, Serrani M, et al., 2020,
Genetic and functional insights into the fractal structure of the heart
, Nature, Vol: 584, Pages: 589-594, ISSN: 0028-0836The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a vestigeof embryonic development.1,2 The function of these trabeculae in adults and their genetic architecture are unknown. Toinvestigate this we performed a genome-wide association study using fractal analysis of trabecular morphology as animage-derived phenotype in 18,096 UK Biobank participants. We identified 16 significant loci containing genes associatedwith haemodynamic phenotypes and regulation of cytoskeletal arborisation.3,4 Using biomechanical simulations and humanobservational data, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Throughgenetic association studies with cardiac disease phenotypes and Mendelian randomisation, we find a causal relationshipbetween trabecular morphology and cardiovascular disease risk. These findings suggest an unexpected role for myocardialtrabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity, and reveal theirinfluence on susceptibility to disease
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Journal articleLechuga-Vieco AV, Latorre-Pellicer A, Johnston IG, et al., 2020,
Cell identity and nucleo-mitochondrial genetic context modulate OXPHOS performance and determine somatic heteroplasmy dynamics
, Science Advances, Vol: 6, Pages: eaba5345-eaba5345, ISSN: 2375-2548Heteroplasmy, multiple variants of mitochondrial DNA (mtDNA) in the same cytoplasm, may be naturally generated by mutations but is counteracted by a genetic mtDNA bottleneck during oocyte development. Engineered heteroplasmic mice with nonpathological mtDNA variants reveal a nonrandom tissue-specific mtDNA segregation pattern, with few tissues that do not show segregation. The driving force for this dynamic complex pattern has remained unexplained for decades, challenging our understanding of this fundamental biological problem and hindering clinical planning for inherited diseases. Here, we demonstrate that the nonrandom mtDNA segregation is an intracellular process based on organelle selection. This cell type–specific decision arises jointly from the impact of mtDNA haplotypes on the oxidative phosphorylation (OXPHOS) system and the cell metabolic requirements and is strongly sensitive to the nuclear context and to environmental cues.
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Journal articleSethi S, Jones NS, Fulcher B, et al., 2020,
Characterising soundscapes across diverse ecosystems using a universal acoustic feature set
, Proceedings of the National Academy of Sciences of USA, Vol: 117, Pages: 17049-17055, ISSN: 0027-8424Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed soundscapes from a variety of ecosystems into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, providing a potential route for real-time automated detection of irregular environmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts.
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Journal articleFulcher B, Lubba C, Sethi S, et al., 2020,
A self-organizing, living library of time-series data
, Scientific Data, Vol: 7, ISSN: 2052-4463Time-series data are measured across the sciences, from astronomy to biomedicine, but meaningful cross-disciplinary interactions are limited by the challenge of identifying fruitful connections. Here we introduce the web platform, CompEngine, a self-organizing, living library of time-series data, that lowers the barrier to forming meaningful interdisciplinary connections between time series. Using a canonical feature-based representation, CompEngine places all time series in a common feature space, regardless of their origin, allowing users to upload their data and immediately explore diverse data with similar properties, and be alerted when similar data is uploaded in future. In contrast to conventional databases which are organized by assigned metadata, CompEngine incentivizes data sharing by automatically connecting experimental and theoretical scientists across disciplines based on the empirical structure of the data they measure. CompEngine’s growing library of interdisciplinary time-series data also enables the comprehensive characterization of time-series analysis algorithms across diverse types of empirical data.
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Journal articleHeaton LLM, Jones NS, Fricker MD, 2020,
A mechanistic explanation of the transition to simple multicellularity in fungi.
, Nature Communications, Vol: 11, ISSN: 2041-1723Development of multicellularity was one of the major transitions in evolution and occurred independently multiple times in algae, plants, animals, and fungi. However recent comparative genome analyses suggest that fungi followed a different route to other eukaryotic lineages. To understand the driving forces behind the transition from unicellular fungi to hyphal forms of growth, we develop a comparative model of osmotrophic resource acquisition. This predicts that whenever the local resource is immobile, hard-to-digest, and nutrient poor, hyphal osmotrophs outcompete motile or autolytic unicellular osmotrophs. This hyphal advantage arises because transporting nutrients via a contiguous cytoplasm enables continued exploitation of remaining resources after local depletion of essential nutrients, and more efficient use of costly exoenzymes. The model provides a mechanistic explanation for the origins of multicellular hyphal organisms, and explains why fungi, rather than unicellular bacteria, evolved to dominate decay of recalcitrant, nutrient poor substrates such as leaf litter or wood.
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Journal articleHoffmann T, Peel L, Lambiotte R, et al., 2020,
Community detection in networks without observing edges
, Science Advances, Vol: 6, ISSN: 2375-2548We develop a Bayesian hierarchical model to identify communities of time series. Fitting the model provides an end-to-end community detection algorithmthat does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approachnaturally supports multiscale community detection as well as the selection ofan optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of theS&P100 index as well as climate data from US cities.
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