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  • Journal article
    Maes A, Barahona M, Clopath C, 2021,

    Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons

    , PLoS Computational Biology, Vol: 17, ISSN: 1553-734X

    Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.

  • Journal article
    Kuntz Nussio J, Thomas P, Stan G, Barahona Met al., 2021,

    Approximations of countably-infinite linear programs over bounded measure spaces

    , SIAM Journal on Optimization, Vol: 31, Pages: 604-625, ISSN: 1052-6234

    We study a class of countably-infinite-dimensional linear programs (CILPs)whose feasible sets are bounded subsets of appropriately defined spaces ofmeasures. The optimal value, optimal points, and minimal points of these CILPscan be approximated by solving finite-dimensional linear programs. We show howto construct finite-dimensional programs that lead to approximations witheasy-to-evaluate error bounds, and we prove that the errors converge to zero asthe size of the finite-dimensional programs approaches that of the originalproblem. We discuss the use of our methods in the computation of the stationarydistributions, occupation measures, and exit distributions of Markov~chains.

  • Journal article
    Kuntz J, Thomas P, Stan G-B, Barahona Met al., 2021,

    Stationary distributions of continuous-time Markov chains: a review of theory and truncation-based approximations

    , SIAM Review, ISSN: 0036-1445

    Computing the stationary distributions of a continuous-time Markov chaininvolves solving a set of linear equations. In most cases of interest, thenumber of equations is infinite or too large, and cannot be solved analyticallyor numerically. Several approximation schemes overcome this issue by truncatingthe state space to a manageable size. In this review, we first give acomprehensive theoretical account of the stationary distributions and theirrelation to the long-term behaviour of the Markov chain, which is readilyaccessible to non-experts and free of irreducibility assumptions made instandard texts. We then review truncation-based approximation schemes payingparticular attention to their convergence and to the errors they introduce, andwe illustrate their performance with an example of a stochastic reactionnetwork of relevance in biology and chemistry. We conclude by elaborating oncomputational trade-offs associated with error control and some open questions.

  • Journal article
    Dusad V, Thiel D, Barahona M, Keun H, Oyarzun Det al., 2021,

    Opportunities at the interface of network science and metabolic modelling

    , Frontiers in Bioengineering and Biotechnology, Vol: 8, ISSN: 2296-4185

    Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.

  • Book chapter
    Altuncu T, Yaliraki S, Barahona M, 2021,

    Graph-based topic extraction from vector embeddings of text documents: application to a corpus of news articles

    , Complex Networks & Their Applications IX, Editors: Benito, Cherifi, Cherifi, Moro, Rocha, Sales-Pardo, Publisher: Springer International Publishing, Pages: 154-166, ISBN: 978-3-030-65351-4

    Production of news content is growing at an astonishing rate. To help manage and monitor the sheer amount of text, there is an increasing need to develop efficient methods that can provide insights into emerging content areas, and stratify unstructured corpora of text into ‘topics’ that stem intrinsically from content similarity. Here we present an unsupervised framework that brings together powerful vector embeddings from natural language processing with tools from multiscale graph partitioning that can revealnatural partitions at different resolutions without making a priori assumptions about the number of clusters in the corpus. We show the advantages of graph-based clustering through end-to-end comparisons with other popular clustering and topic modelling methods, and also evaluate different text vector embeddings, from classic 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 news coverage during the presidential election year of 2016.

  • Journal article
    Qian Y, Expert P, Rieu T, Panzarasa P, Barahona Met al., 2021,

    Quantifying the alignment of graph and features in deep learning

    , IEEE Transactions on Neural Networks and Learning Systems, Pages: 1-10, ISSN: 1045-9227

    We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances between three subspaces associated with features, graph, and ground truth. The proposed measure is based on the principal angles between subspaces and has both spectral and geometrical interpretations. We showcase the relationship between the SAM and the classification performance through the study of limiting cases of GCNs and systematic randomizations of both features and graph structure applied to a constructive example and several examples of citation networks of different origins. The analysis also reveals the relative importance of the graph and features for classification purposes.

  • Journal article
    Price JR, Mookerjee S, Dyakova E, Myall A, Leung W, Wei├če AY, Shersing Y, Brannigan ET, Galletly T, Muir D, Randell P, Davies F, Bolt F, Barahona M, Otter JA, Holmes AHet al., 2021,

    Development and delivery of a real-time hospital-onset COVID-19 surveillance system using network analysis

    , Clinical Infectious Diseases, Vol: 72, Pages: 82-89, ISSN: 1058-4838

    BackgroundUnderstanding nosocomial acquisition, outbreaks and transmission chains in real-time will be fundamental to ensuring infection prevention measures are effective in controlling COVID-19 in healthcare. We report the design and implementation of a hospital-onset COVID-19 infection (HOCI) surveillance system for an acute healthcare setting to target prevention interventions.MethodsThe study took place in a large teaching hospital group in London, UK. All patients tested for SARS-CoV-2 between 4th March and 14th April 2020 were included. Utilising data routinely collected through electronic healthcare systems we developed a novel surveillance system for determining and reporting HOCI incidence and providing real-time network analysis. We provided daily reports on incidence and trends over time to support HOCI investigation, and generated geo-temporal reports using network analysis to interrogate admission pathways for common epidemiological links to infer transmission chains. By working with stakeholders the reports were co-designed for end users.ResultsReal-time surveillance reports revealed: changing rates of HOCI throughout the course of the COVID-19 epidemic; key wards fuelling probable transmission events; HOCIs over-represented in particular specialities managing high-risk patients; the importance of integrating analysis of individual prior pathways; and the value of co-design in producing data visualisation. Our surveillance system can effectively support national surveillance.ConclusionsThrough early analysis of the novel surveillance system we have provided a description of HOCI rates and trends over time using real-time shifting denominator data. We demonstrate the importance of including the analysis of patient pathways and networks in characterising risk of transmission and targeting infection control interventions.

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

    Visualizing, quantifying, and manipulating mitochondrial DNA in vivo

    , Journal of Biological Chemistry, Vol: 295, Pages: 17588-17601, ISSN: 0021-9258

    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
    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, Vol: 18, Pages: 1-19, ISSN: 1741-2552

    Objective: We aim at characterising the encoding of bladder pressure (intravesical pressure) by a population of sensory fibres. This research is motivated by the possibility to restore bladder function in elderly patients or after spinal cord injury using implanted devices, so called bioelectronic medicines. For these devices, nerve-based estimation of intravesical pressure can enable a personalized and on-demand stimulation paradigm, which has promise of being more effective and efficient. In this context, a better understanding of the encoding strategies employed by the body might in the future be exploited by informed decoding algorithms that enable a precise and robust bladder-pressure estimation. Approach: To this end, we apply information theory to microelectrode-array recordings from the cat sacral dorsal root ganglion while filling the bladder, conduct surrogate data studies to augment the data we have, and finally decode pressure in a simple informed approach. Main results: We find an encoding scheme by different main bladder neuron types that we divide into three response types (slow tonic, phasic, and derivative fibres). We show that an encoding by different bladder neuron types, each represented by multiple cells, offers reliability through within-type redundancy and high information rates through semi-independence of different types. Our subsequent decoding study shows a more robust decoding from mean responses of homogeneous cell pools. Significance: We have here, for the first time, established a link between an information theoretic analysis of the encoding of intravesical pressure by a population of sensory neurons to an informed decoding paradigm. We show that even a simple adapted decoder can exploit the redundancy in the population to be more robust against cell loss. This work thus paves the way towards principled encoding studies in the periphery and towards a new generation of informed peripheral nerve decoders for bioelectronic medicines.

  • 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, Vol: 10, Pages: 1-9, 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

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