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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 articleClarke J, Beaney T, Majeed A, et al., 2020,
Identifying naturally occurring communities of primary care providers in the English National Health Service in London
, BMJ Open, Vol: 10, Pages: 1-7, ISSN: 2044-6055Objectives - Primary Care Networks (PCNs) are a new organisational hierarchy with wide-ranging responsibilities introduced in the National Health Service (NHS) Long Term Plan. The vision is that they represent ‘natural’ communities of general practices (GP practices) working together at scale and covering a geography that make sense to practices, other healthcare providers and local communities. Our study aims to identify natural communities of GP practices based on patient registration patterns using Markov Multiscale Community Detection, an unsupervised network-based clustering technique to create catchments for these communities.Design - Retrospective observational study using Hospital Episode Statistics – patient-level administrative records of inpatient, outpatient and emergency department attendances to hospital.Setting – General practices in the 32 Clinical Commissioning Groups of Greater London Participants - All adult patients resident in and registered to a GP practices in Greater London that had one or more outpatient encounters at NHS hospital trusts between 1st April 2017 and 31st March 2018.Main outcome measures The allocation of GP practices in Greater London to PCNs based on the registrations of patients resident in each Lower Super Output Area (LSOA) of Greater London. The population size and coverage of each proposed PCN. Results - 3,428,322 unique patients attended 1,334 GPs in 4,835 LSOAs in Greater London. Our model grouped 1,291 GPs (96.8%) and 4,721 LSOAs (97.6%), into 165 mutually exclusive PCNs. The median PCN list size was 53,490, with a lower quartile of 38,079 patients and an upper quartile of 72,982 patients. A median of 70.1% of patients attended a GP within their allocated PCN, ranging from 44.6% to 91.4%.Conclusions - With PCNs expected to take a role in population health management and with community providers expected to reconfigure around them, it is vital we recognise how PCNs represent their communities. O
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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 articleArnaudon A, Peach R, Barahona M, 2020,
Scale-dependent measure of network centrality from diffusion dynamics
, Physical Review Research, Vol: 2, ISSN: 2643-1564Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A node is defined to be central if it breaks the metricity of the diffusion as a consequence of the effective boundaries and inhomogeneities in the graph. Our measure is naturally multiscale, as it is computed relative to graph neighbourhoods within the varying time horizon of the diffusion. We find that the centrality of nodes can differ widely at different scales. In particular, our measure correlates with degree (i.e., hubs) at small scales and with closeness (i.e., bridges) at large scales, and also reveals the existence of multi-centric structures in complex networks. By examining centrality across scales, our measure thus provides an evaluation of node importance relative to local and global processes on the network.
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Conference paperInsalata F, Hoitzing H, Aryaman J, et al., 2020,
Survival of the Densest Explains the Expansion of Mitochondrial Deletions in Skeletal Muscle Fibres
, 48th European Mathematical Genetics Meeting (EMGM), Publisher: KARGER, Pages: 211-211, ISSN: 0001-5652 -
Journal articleSchreglmann S, Wang D, Peach R, et al., 2020,
Non-invasive amelioration of essential tremor via phase-locked disruption of its temporal coherence
, Nature Communications, Vol: 12, ISSN: 2041-1723Abstract Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be noninvasively suppressed via electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained after the end of the stimulation and can be phenomenologically predicted. Finally, using feature-based statistical-learning and neurophysiological-modelling we show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the oscillation via perturbation of the tremor generating a cascade of synchronous activity in the olivocerebellar loop. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may represent a powerful neuromodulatory strategy to treat brain disorders.
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Conference paperBeaney T, Clarke J, Barahona M, et al., 2020,
A primary care network analysis: natural communities of general practices in London
, Publisher: Royal College of General Practitioners, ISSN: 0960-1643BACKGROUND: Primary care networks (PCNs) are a new organisational hierarchy introduced in the NHS Long Term Plan with wide-ranging responsibilities. The vision is that they represent 'natural' communities of general practices with boundaries that make sense to practices, other healthcare providers, and local communities. AIM: Our study aims to identify natural communities of general practices based on patient registration patterns, using network analysis methods and unsupervised clustering to create catchments for these communities. METHOD: Patients resident in and attending GP practices in London were identified from Hospital Episode Statistics from 2017 to 2018. We used a series of novel methods for unsupervised graph clustering. A cosine similarity matrix was constructed representing similarities between each general practice to each other, based on registration of patients in each Lower Super Output Area (LSOA). Unsupervised graph partitioning using Markov Multiscale Community Detection was conducted to identify communities of general practices. Catchments were assigned to each PCN based on the majority attendance from an LSOA. RESULTS: In total 3 428 322 unique patients attended 1334 GPs in general practices LSOAs in London. The model grouped 1291 general practices (96.8%) and 4721 LSOAs (97.6%), into 165 mutually exclusive PCNs. The median PCN list size was 53 490 and a median of 70.1% of patients attended a general practice within their allocated PCN, ranging from 44.6% to 91.4%. CONCLUSION: With PCNs expected to take a role in population health management and with community providers expected to reconfigure around them, it is vital we recognise how PCNs represent their communities. This method may be used by policymakers to understand the populations and geography shared between networks.
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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 articleGosztolai A, Barahona M, 2020,
Cellular memory enhances bacterial chemotactic navigation in rugged environments
, Communications Physics, Vol: 3, ISSN: 2399-3650The response of microbes to external signals is mediated by biochemical networks with intrinsic time scales. These time scales give rise to a memory that impacts cellular behaviour. Here we study theoretically the role of cellular memory in Escherichia coli chemotaxis. Using an agent-based model, we show that cells with memory navigating rugged chemoattractant landscapes can enhance their drift speed by extracting information from environmental correlations. Maximal advantage is achieved when the memory is comparable to the time scale of fluctuations as perceived during swimming. We derive an analytical approximation for the drift velocity in rugged landscapes that explains the enhanced velocity, and recovers standard Keller–Segel gradient-sensing results in the limits when memory and fluctuation time scales are well separated. Our numerics also show that cellular memory can induce bet-hedging at the population level resulting in long-lived, multi-modal distributions in heterogeneous landscapes.
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Journal articlePeach RL, Arnaudon A, Barahona M, 2020,
Semi-supervised classification on graphs using explicit diffusion dynamics
, Foundations of Data Science, Vol: 2, Pages: 19-33, ISSN: 2639-8001Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples. Intuitively, the graph acts as a conduit to channel and bias the inference of class labels. Here, we study classification methods that consider the graph as the originator of an explicit graph diffusion. We show that appending graph diffusion to feature-based learning as a posteriori refinement achieves state-of-the-art classification accuracy. This method, which we call Graph Diffusion Reclassification (GDR), uses overshooting events of a diffusive graph dynamics to reclassify individual nodes. The method uses intrinsic measures of node influence, which are distinct for each node, and allows the evaluation of the relationship and importance of features and graph for classification. We also present diff-GCN, a simple extension of Graph Convolutional Neural Network (GCN) architectures that leverages explicit diffusion dynamics, and allows the natural use of directed graphs. To showcase our methods, we use benchmark datasets of documents with associated citation data.
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