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  • Journal article
    Tonn M, Thomas P, Barahona M, Oyarzun Det al., 2020,

    Computation of single-cell metabolite distributions using mixture models

    , Frontiers in Cell and Developmental Biology, ISSN: 2296-634X
  • Journal article
    Lubba CH, Ouyang A, Jones N, Bruns T, Schultz Set al., 2020,

    Bladder pressure encoding by sacral dorsal root ganglion fibres: implications for decoding

    , Journal of Neural Engineering, ISSN: 1741-2552
  • Journal article
    Altuncu MT, Yaliraki SN, Barahona M, 2020,

    Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles

    Production of news content is growing at an astonishing rate. To help manageand monitor the sheer amount of text, there is an increasing need to developefficient methods that can provide insights into emerging content areas, andstratify unstructured corpora of text into `topics' that stem intrinsicallyfrom content similarity. Here we present an unsupervised framework that bringstogether powerful vector embeddings from natural language processing with toolsfrom multiscale graph partitioning that can reveal natural partitions atdifferent resolutions without making a priori assumptions about the number ofclusters in the corpus. We show the advantages of graph-based clusteringthrough end-to-end comparisons with other popular clustering and topicmodelling methods, and also evaluate different text vector embeddings, fromclassic Bag-of-Words to Doc2Vec to the recent transformers based model Bert.This comparative work is showcased through an analysis of a corpus of US newscoverage during the presidential election year of 2016.

  • Journal article
    Clarke J, Murray A, Markar S, Barahona M, Kinross Jet 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, ISSN: 2044-6055

    Objectives 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

  • Journal article
    Hoffmann T, Jones NS, 2020,

    Inference of a universal social scale and segregation measures using social connectivity kernels

    , Journal of the Royal Society Interface, Pages: 20200638-20200638, ISSN: 1742-5662

    How 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.

  • Journal article
    Prole DL, Chinnery PF, Jones NS, 2020,

    Visualizing, quantifying and manipulating mitochondrial DNA in vivo.

    , J Biol Chem

    Mitochondrial DNA (mtDNA) encodes proteins and RNAs that support the functions of mitochondria and thereby numerous physiological processes. Mutations of mtDNA can cause mitochondrial diseases and are implicated in ageing. The mtDNA within cells is organized into nucleoids within the mitochondrial matrix, but how mtDNA nucleoids are formed and regulated within cells remains incompletely resolved. Visualization of mtDNA within cells is a powerful means by which mechanistic insight can be gained. Manipulation of the amount, and sequence of, mtDNA within cells is important experimentally and for developing therapeutic interventions to treat mitochondrial disease. This review details recent developments and opportunities for improvements in the experimental tools and techniques that can be used to visualize, quantify and manipulate the properties of mtDNA within cells.

  • Journal article
    Myall AC, Peach RL, Weiße AY, Davies F, Mookerjee S, Holmes A, Barahona Met al., 2020,

    Network memory in the movement of hospital patients carrying drug-resistant bacteria

    Hospitals constitute highly interconnected systems that bring into contact anabundance of infectious pathogens and susceptible individuals, thus makinginfection outbreaks both common and challenging. In recent years, there hasbeen a sharp incidence of antimicrobial-resistance amongsthealthcare-associated infections, a situation now considered endemic in manycountries. Here we present network-based analyses of a data set capturing themovement of patients harbouring drug-resistant bacteria across three largeLondon hospitals. We show that there are substantial memory effects in themovement of hospital patients colonised with drug-resistant bacteria. Suchmemory effects break first-order Markovian transitive assumptions andsubstantially alter the conclusions from the analysis, specifically on noderankings and the evolution of diffusive processes. We capture variable lengthmemory effects by constructing a lumped-state memory network, which we then useto identify overlapping communities of wards. We find that these communities ofwards display a quasi-hierarchical structure at different levels of granularitywhich is consistent with different aspects of patient flows related to hospitallocations and medical specialties.

  • Journal article
    Peach RL, Arnaudon A, Schmidt J, Palasciano HA, Bernier NR, Jelfs K, Yaliraki S, Barahona Met al., 2020,

    hcga: Highly Comparative Graph Analysis for network phenotyping

    <jats:title>A<jats:sc>bstract</jats:sc></jats:title><jats:p>Networks are widely used as mathematical models of complex systems across many scientific disciplines, not only in biology and medicine but also in the social sciences, physics, computing and engineering. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and some times overlapping) characteristics of a network. In the analysis of real-world graphs, it is crucial to integrate systematically a large number of diverse graph features in order to characterise and classify networks, as well as to aid network-based scientific discovery. In this paper, we introduce <jats:sc>hcga</jats:sc>, a framework for highly comparative analysis of graph data sets that computes several thousands of graph features from any given network. <jats:sc>hcga</jats:sc> also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterisation of graph data sets. We show that <jats:sc>hcga</jats:sc> outperforms other methodologies on supervised classification tasks on benchmark data sets whilst retaining the interpretability of network features. We also illustrate how <jats:sc>hcga</jats:sc> can be used for network-based discovery through two examples where data is naturally represented as graphs: the clustering of a data set of images of neuronal morphologies, and a regression problem to predict charge transfer in organic semiconductors based on their structure. <jats:sc>hcga</jats:sc> is an open platform that can be expanded to include further graph properties and statistical learning tools to allow researchers to leverage the wide breadth of graph-theoretical resear

  • Journal article
    Sethi S, Ewers R, Jones N, Signorelli A, Picinali L, Orme CDLet 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: 2041-210X

    1. 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.

  • Journal article
    Laumann F, Kügelgen JV, Barahona M, 2020,

    Kernel Two-Sample and Independence Tests for Non-Stationary Random Processes

    Two-sample and independence tests with the kernel-based MMD and HSIC haveshown remarkable results on i.i.d. data and stationary random processes.However, these statistics are not directly applicable to non-stationary randomprocesses, a prevalent form of data in many scientific disciplines. In thiswork, we extend the application of MMD and HSIC to non-stationary settings byassuming access to independent realisations of the underlying random process.These realisations - in the form of non-stationary time-series measured on thesame temporal grid - can then be viewed as i.i.d. samples from a multivariateprobability distribution, to which MMD and HSIC can be applied. We further showhow to choose suitable kernels over these high-dimensional spaces by maximisingthe estimated test power with respect to the kernel hyper-parameters. Inexperiments on synthetic data, we demonstrate superior performance of ourproposed approaches in terms of test power when compared to currentstate-of-the-art functional or multivariate two-sample and independence tests.Finally, we employ our methods on a real socio-economic dataset as an exampleapplication.

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