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  • Journal article
    Price JR, Mookerjee S, Dyakova E, Myall A, Leung W, Weiße 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

  • 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, Vol: 17, 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
    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.

  • Book chapter
    Thomas P, 2020,

    Stochastic Modeling Approaches for Single-Cell Analyses

    , Systems Medicine: Integrative, Qualitative and Computational Approaches, Editors: Wolkenhauer, Publisher: Elsevier, Oxford, Pages: 45-55

    Single-cell analyses are becoming increasingly important in cell biology and personalized approaches to medicine. Such analyses frequently reveal heterogeneity that exists within and between cells. We give a concise overview of stochastic methods used to analyze non-genetic heterogeneity in models of cell populations and examine several analytical results on the determinants of gene expression noise. We then review models that advanced our understanding of stochastic phenomena in cellular decision making, stem cell differentiation, tissue homoeostasis and cell cycle dynamics.

  • Journal article
    Lechuga-Vieco AV, Latorre-Pellicer A, Johnston IG, Prota G, Gileadi U, Justo-Méndez R, Acín-Pérez R, Martínez-de-Mena R, Fernández-Toro JM, Jimenez-Blasco D, Mora A, Nicolás-Ávila JA, Santiago DJ, Priori SG, Bolaños JP, Sabio G, Criado LM, Ruíz-Cabello J, Cerundolo V, Jones NS, Enríquez JAet 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: 2375-2548

    Heteroplasmy, 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.

  • Journal article
    Sethi S, Jones NS, Fulcher B, Picinali L, Clink DJ, Klinck H, Orme CDLO, Wrege P, Ewers Ret 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: 0027-8424

    Natural 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.

  • Journal article
    Clarke J, Beaney T, Majeed A, Darzi A, Barahona Met 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: 2044-6055

    Objectives - 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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