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Beaney T, Clarke J, Woodcock T, Majeed A, Barahona M, Aylin P. Effect of timeframes to define long term conditions and sociodemographic factors on prevalence of multimorbidity using disease code frequency in primary care electronic health records: retrospective study. BMJ Med 2024; 3:e000474. [PMID: 38361663 PMCID: PMC10868275 DOI: 10.1136/bmjmed-2022-000474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 12/12/2023] [Indexed: 02/17/2024]
Abstract
Objective To determine the extent to which the choice of timeframe used to define a long term condition affects the prevalence of multimorbidity and whether this varies with sociodemographic factors. Design Retrospective study of disease code frequency in primary care electronic health records. Data sources Routinely collected, general practice, electronic health record data from the Clinical Practice Research Datalink Aurum were used. Main outcome measures Adults (≥18 years) in England who were registered in the database on 1 January 2020 were included. Multimorbidity was defined as the presence of two or more conditions from a set of 212 long term conditions. Multimorbidity prevalence was compared using five definitions. Any disease code recorded in the electronic health records for 212 conditions was used as the reference definition. Additionally, alternative definitions for 41 conditions requiring multiple codes (where a single disease code could indicate an acute condition) or a single code for the remaining 171 conditions were as follows: two codes at least three months apart; two codes at least 12 months apart; three codes within any 12 month period; and any code in the past 12 months. Mixed effects regression was used to calculate the expected change in multimorbidity status and number of long term conditions according to each definition and associations with patient age, gender, ethnic group, and socioeconomic deprivation. Results 9 718 573 people were included in the study, of whom 7 183 662 (73.9%) met the definition of multimorbidity where a single code was sufficient to define a long term condition. Variation was substantial in the prevalence according to timeframe used, ranging from 41.4% (n=4 023 023) for three codes in any 12 month period, to 55.2% (n=5 366 285) for two codes at least three months apart. Younger people (eg, 50-75% probability for 18-29 years v 1-10% for ≥80 years), people of some minority ethnic groups (eg, people in the Other ethnic group had higher probability than the South Asian ethnic group), and people living in areas of lower socioeconomic deprivation were more likely to be re-classified as not multimorbid when using definitions requiring multiple codes. Conclusions Choice of timeframe to define long term conditions has a substantial effect on the prevalence of multimorbidity in this nationally representative sample. Different timeframes affect prevalence for some people more than others, highlighting the need to consider the impact of bias in the choice of method when defining multimorbidity.
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Affiliation(s)
- Thomas Beaney
- Department of Primary Care and Public Health, Imperial College London, London, UK
- Department of Mathematics, Imperial College London, London, UK
| | - Jonathan Clarke
- Department of Mathematics, Imperial College London, London, UK
| | - Thomas Woodcock
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Azeem Majeed
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | | | - Paul Aylin
- Department of Primary Care and Public Health, Imperial College London, London, UK
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Wu N, Barahona M, Yaliraki SN. Allosteric communication and signal transduction in proteins. Curr Opin Struct Biol 2024; 84:102737. [PMID: 38171189 DOI: 10.1016/j.sbi.2023.102737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 01/05/2024]
Abstract
Allostery is one of the cornerstones of biological function, as it plays a fundamental role in regulating protein activity. The modelling of allostery has gradually moved from a conformation-based framework, linked to structural changes, to dynamics-based allostery, whereby the effects of ligand binding propagate via signal transduction from the allosteric site to other regions of the protein via inter-residue interactions. Characterising such allosteric signalling pathways, which do not necessarily lead to conformational changes, has been pursued experimentally and complemented by computational analysis of protein networks to detect subtle dynamic propagation paths. Considering allostery from the perspective of signal transduction broadens the understanding of allosteric mechanisms, underscores the importance of protein topology, and can provide insights into allosteric drug design.
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Affiliation(s)
- Nan Wu
- Department of Chemistry, Imperial College London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, United Kingdom. https://twitter.com/@CMPHImperial
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Wan Y, Myall AC, Boonyasiri A, Bolt F, Ledda A, Mookerjee S, Weiße AY, Getino M, Turton JF, Abbas H, Prakapaite R, Sabnis A, Abdolrasouli A, Malpartida-Cardenas K, Miglietta L, Donaldson H, Gilchrist M, Hopkins KL, Ellington MJ, Otter JA, Larrouy-Maumus G, Edwards AM, Rodriguez-Manzano J, Didelot X, Barahona M, Holmes AH, Jauneikaite E, Davies F. Integrated analysis of patient networks and plasmid genomes reveals a regional, multi-species outbreak of carbapenemase-producing Enterobacterales carrying both blaIMP and mcr-9 genes. J Infect Dis 2024:jiae019. [PMID: 38245822 DOI: 10.1093/infdis/jiae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/02/2024] [Accepted: 01/19/2024] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Carbapenemase-producing Enterobacterales (CPE) are challenging in healthcare, with resistance to multiple classes of antibiotics. This study describes the emergence of IMP-encoding CPE amongst diverse Enterobacterales species between 2016 and 2019 across a London regional network. METHODS We performed a network analysis of patient pathways, using electronic health records, to identify contacts between IMP-encoding CPE positive patients. Genomes of IMP-encoding CPE isolates were overlayed with patient contacts to imply potential transmission events. RESULTS Genomic analysis of 84 Enterobacterales isolates revealed diverse species (predominantly Klebsiella spp, Enterobacter spp, E. coli); 86% (72/84) harboured an IncHI2 plasmid carrying blaIMP and colistin resistance gene mcr-9 (68/72). Phylogenetic analysis of IncHI2 plasmids identified three lineages showing significant association with patient contacts and movements between four hospital sites and across medical specialities, which was missed on initial investigations. CONCLUSIONS Combined, our patient network and plasmid analyses demonstrate an interspecies, plasmid-mediated outbreak of blaIMPCPE, which remained unidentified during standard investigations. With DNA sequencing and multi-modal data incorporation, the outbreak investigation approach proposed here provides a framework for real-time identification of key factors causing pathogen spread. Plasmid-level outbreak analysis reveals that resistance spread may be wider than suspected, allowing more interventions to stop transmission within hospital networks.
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Affiliation(s)
- Yu Wan
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Ashleigh C Myall
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Frances Bolt
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- Centre for Antimicrobial Optimisation, Hammersmith Hospital, Imperial College London, Du Cane Road, London, United Kingdom
| | - Alice Ledda
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- HCAI, Fungal, AMR, AMU & Sepsis Division, UK Health Security Agency, London, United Kingdom
| | | | - Andrea Y Weiße
- School of Biological Sciences, University of Edinburgh, Scotland, United Kingdom
- School of Informatics, University of Edinburgh, Scotland, United Kingdom
| | - Maria Getino
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Jane F Turton
- HCAI, Fungal, AMR, AMU & Sepsis Division, UK Health Security Agency, London, United Kingdom
| | - Hala Abbas
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Department of Microbiology, North West London Pathology, London, United Kingdom
| | - Ruta Prakapaite
- MRC Centre for Molecular Bacteriology and Infection, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Akshay Sabnis
- MRC Centre for Molecular Bacteriology and Infection, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | | | - Kenny Malpartida-Cardenas
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, United Kingdom
| | - Luca Miglietta
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, United Kingdom
| | - Hugo Donaldson
- Department of Microbiology, North West London Pathology, London, United Kingdom
| | - Mark Gilchrist
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Katie L Hopkins
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- HCAI, Fungal, AMR, AMU & Sepsis Division, UK Health Security Agency, London, United Kingdom
| | - Matthew J Ellington
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Reference Services Division, UK Health Security Agency, London, United Kingdom
| | - Jonathan A Otter
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Gerald Larrouy-Maumus
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- MRC Centre for Molecular Bacteriology and Infection, Department of Life Sciences, Faculty of Natural Sciences, Imperial College London, London, United Kingdom
| | - Andrew M Edwards
- MRC Centre for Molecular Bacteriology and Infection, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Jesus Rodriguez-Manzano
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Centre for Antimicrobial Optimisation, Hammersmith Hospital, Imperial College London, Du Cane Road, London, United Kingdom
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, United Kingdom
| | - Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Alison H Holmes
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- Centre for Antimicrobial Optimisation, Hammersmith Hospital, Imperial College London, Du Cane Road, London, United Kingdom
| | - Elita Jauneikaite
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Frances Davies
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- Department of Microbiology, North West London Pathology, London, United Kingdom
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Beaney T, Clarke J, Salman D, Woodcock T, Majeed A, Barahona M, Aylin P. Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions. J Multimorb Comorb 2024; 14:26335565241247430. [PMID: 38638408 PMCID: PMC11025432 DOI: 10.1177/26335565241247430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 03/25/2024] [Indexed: 04/20/2024]
Abstract
Background Identifying clusters of co-occurring diseases may help characterise distinct phenotypes of Multiple Long-Term Conditions (MLTC). Understanding the associations of disease clusters with health-related outcomes requires a strategy to assign clusters to people, but it is unclear how the performance of strategies compare. Aims First, to compare the performance of methods of assigning disease clusters to people at explaining mortality, emergency department attendances and hospital admissions over one year. Second, to identify the extent of variation in the associations with each outcome between and within clusters. Methods We conducted a cohort study of primary care electronic health records in England, including adults with MLTC. Seven strategies were tested to assign patients to fifteen disease clusters representing 212 LTCs, identified from our previous work. We tested the performance of each strategy at explaining associations with the three outcomes over 1 year using logistic regression and compared to a strategy using the individual LTCs. Results 6,286,233 patients with MLTC were included. Of the seven strategies tested, a strategy assigning the count of conditions within each cluster performed best at explaining all three outcomes but was inferior to using information on the individual LTCs. There was a larger range of effect sizes for the individual LTCs within the same cluster than there was between the clusters. Conclusion Strategies of assigning clusters of co-occurring diseases to people were less effective at explaining health-related outcomes than a person's individual diseases. Furthermore, clusters did not represent consistent relationships of the LTCs within them, which might limit their application in clinical research.
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Affiliation(s)
- Thomas Beaney
- Department of Primary Care and Public Health, Imperial College London, London, UK
- Centre for Mathematics of Precision Healthcare, Department of Mathematics, Imperial College London, London, UK
| | - Jonathan Clarke
- Centre for Mathematics of Precision Healthcare, Department of Mathematics, Imperial College London, London, UK
| | - David Salman
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Thomas Woodcock
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Azeem Majeed
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Mauricio Barahona
- Centre for Mathematics of Precision Healthcare, Department of Mathematics, Imperial College London, London, UK
| | - Paul Aylin
- Department of Primary Care and Public Health, Imperial College London, London, UK
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Laumann F, von Kügelgen J, Park J, Schölkopf B, Barahona M. Kernel-Based Independence Tests for Causal Structure Learning on Functional Data. Entropy (Basel) 2023; 25:1597. [PMID: 38136477 PMCID: PMC10742995 DOI: 10.3390/e25121597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/11/2023] [Accepted: 11/15/2023] [Indexed: 12/24/2023]
Abstract
Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert-Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert-Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data.
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Affiliation(s)
- Felix Laumann
- Department of Mathematics, Imperial College London, London SW7 2BX, UK
| | - Julius von Kügelgen
- Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany
- Department of Engineering, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Junhyung Park
- Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany
| | | | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London SW7 2BX, UK
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Liu Z, Peach RL, Laumann F, Vallejo Mengod S, Barahona M. Kernel-based joint independence tests for multivariate stationary and non-stationary time series. R Soc Open Sci 2023; 10:230857. [PMID: 38034126 PMCID: PMC10685129 DOI: 10.1098/rsos.230857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023]
Abstract
Multivariate time-series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential dependencies among co-observed variables is crucial for the accurate statistical modelling and analysis of such systems. Here, we introduce kernel-based statistical tests of joint independence in multivariate time series by extending the d-variable Hilbert-Schmidt independence criterion to encompass both stationary and non-stationary processes, thus allowing broader real-world applications. By leveraging resampling techniques tailored for both single- and multiple-realization time series, we show how the method robustly uncovers significant higher-order dependencies in synthetic examples, including frequency mixing data and logic gates, as well as real-world climate, neuroscience and socio-economic data. Our method adds to the mathematical toolbox for the analysis of multivariate time series and can aid in uncovering high-order interactions in data.
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Affiliation(s)
- Zhaolu Liu
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
| | - Robert L. Peach
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK
- Department of Neurology, University Hospital Würzburg, Würzburg 97070, Germany
| | - Felix Laumann
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
| | | | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
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Schindler DJ, Clarke J, Barahona M. Multiscale mobility patterns and the restriction of human movement. R Soc Open Sci 2023; 10:230405. [PMID: 37830024 PMCID: PMC10565406 DOI: 10.1098/rsos.230405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023]
Abstract
From the perspective of human mobility, the COVID-19 pandemic constituted a natural experiment of enormous reach in space and time. Here, we analyse the inherent multiple scales of human mobility using Facebook Movement maps collected before and during the first UK lockdown. Firstly, we obtain the pre-lockdown UK mobility graph and employ multiscale community detection to extract, in an unsupervised manner, a set of robust partitions into flow communities at different levels of coarseness. The partitions so obtained capture intrinsic mobility scales with better coverage than nomenclature of territorial units for statistics (NUTS) regions, which suffer from mismatches between human mobility and administrative divisions. Furthermore, the flow communities in the fine-scale partition not only match well the UK travel to work areas but also capture mobility patterns beyond commuting to work. We also examine the evolution of mobility under lockdown and show that mobility first reverted towards fine-scale flow communities already found in the pre-lockdown data, and then expanded back towards coarser flow communities as restrictions were lifted. The improved coverage induced by lockdown is well captured by a linear decay shock model, which allows us to quantify regional differences in both the strength of the effect and the recovery time from the lockdown shock.
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Affiliation(s)
| | - Jonathan Clarke
- Department of Mathematics, Imperial College London, London SW7 2BX, UK
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London SW7 2BX, UK
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Beaney T, Clarke J, Salman D, Woodcock T, Majeed A, Barahona M, Aylin P. Identifying potential biases in code sequences in primary care electronic healthcare records: a retrospective cohort study of the determinants of code frequency. BMJ Open 2023; 13:e072884. [PMID: 37758674 PMCID: PMC10537851 DOI: 10.1136/bmjopen-2023-072884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
OBJECTIVES To determine whether the frequency of diagnostic codes for long-term conditions (LTCs) in primary care electronic healthcare records (EHRs) is associated with (1) disease coding incentives, (2) General Practice (GP), (3) patient sociodemographic characteristics and (4) calendar year of diagnosis. DESIGN Retrospective cohort study. SETTING GPs in England from 2015 to 2022 contributing to the Clinical Practice Research Datalink Aurum dataset. PARTICIPANTS All patients registered to a GP with at least one incident LTC diagnosed between 1 January 2015 and 31 December 2019. PRIMARY AND SECONDARY OUTCOME MEASURES The number of diagnostic codes for an LTC in (1) the first and (2) the second year following diagnosis, stratified by inclusion in the Quality and Outcomes Framework (QOF) financial incentive programme. RESULTS 3 113 724 patients were included, with 7 723 365 incident LTCs. Conditions included in QOF had higher rates of annual coding than conditions not included in QOF (1.03 vs 0.32 per year, p<0.0001). There was significant variation in code frequency by GP which was not explained by patient sociodemographics. We found significant associations with patient sociodemographics, with a trend towards higher coding rates in people living in areas of higher deprivation for both QOF and non-QOF conditions. Code frequency was lower for conditions with follow-up time in 2020, associated with the onset of the COVID-19 pandemic. CONCLUSIONS The frequency of diagnostic codes for newly diagnosed LTCs is influenced by factors including patient sociodemographics, disease inclusion in QOF, GP practice and the impact of the COVID-19 pandemic. Natural language processing or other methods using temporally ordered code sequences should account for these factors to minimise potential bias.
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Affiliation(s)
- Thomas Beaney
- Department of Primary Care and Public Health, Imperial College London, London, UK
- Department of Mathematics, Imperial College London, London, UK
| | - Jonathan Clarke
- Department of Mathematics, Imperial College London, London, UK
| | - David Salman
- Department of Primary Care and Public Health, Imperial College London, London, UK
- MSk Lab, Imperial College London, London, UK
| | - Thomas Woodcock
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Azeem Majeed
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | | | - Paul Aylin
- Department of Primary Care and Public Health, Imperial College London, London, UK
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Maes A, Barahona M, Clopath C. Long- and short-term history effects in a spiking network model of statistical learning. Sci Rep 2023; 13:12939. [PMID: 37558704 PMCID: PMC10412617 DOI: 10.1038/s41598-023-39108-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/20/2023] [Indexed: 08/11/2023] Open
Abstract
The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability distributions. In other words, the network spends more time in states which encode high-probability stimuli. Starting from the neural assembly, increasingly thought of to be the building block for computation in the brain, we focus on how arbitrary prior knowledge about the external world can both be learned and spontaneously recollected. We present a model based upon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neurons and biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectations and signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoing learning.
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Affiliation(s)
- Amadeus Maes
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA.
- Department of Bioengineering, Imperial College London, London, UK.
| | | | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK
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Infante C, Barahona M. [Unicompartmental knee prosthesis]. Acta Ortop Mex 2023; 37:166-172. [PMID: 38052438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
In a patient with severe unicompartmental knee osteoarthritis where conservative treatments have been exhausted, with painful symptoms located on the affected side and with a reducible axis, the unicompartmental knee prosthesis (UKP) is the first option for our work group. Within the study to confirm the diagnosis and plan the surgery, weight-bearing knee x-rays, Rosenberg x-rays, and teleradiographs of the lower extremities stand out. The objective of surgery is to replace the affected area, restoring the anatomy with an adequate balance of soft tissues. Regarding alignment, the challenge is not to overload the opposite side or that of the prosthesis. There are mobile and fixed plates and although the clinical and survival results are similar, in recent years with the incorporation of robotic surgery, the balance has tipped towards the use of fixed plates. The clinical and functional results are better and there are fewer complications than when total knee prostheses (TKP) are used in the same type of patients. The survival studied in registries is lower than for TKP, but when used in high-flow centers where the percentage of UKP is close to a third of the total with strict patient selection, the duration is as good as in PTR.
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Affiliation(s)
- C Infante
- Equipo de Rodilla del Hospital Clínico de la Universidad de Chile y de la Clínica Las Condes. Santiago, Chile
| | - M Barahona
- Equipo de Rodilla del Hospital Clínico de la Universidad de Chile y de la Clínica Las Condes. Santiago, Chile
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Li J, Schreglmann S, Wang D, Peach R, Laurate A, Rhodes E, Panella E, Cassara A, Boyden E, Barahona M, Rothwell J, Bhatia K, Grossman N. Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence. Brain Stimul 2023. [DOI: 10.1016/j.brs.2023.01.473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
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Lamprinakou S, Barahona M, Flaxman S, Filippi S, Gandy A, McCoy E. BART-based inference for Poisson processes. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Saxena D, Arnaudon A, Cipolato O, Gaio M, Quentel A, Yaliraki S, Pisignano D, Camposeo A, Barahona M, Sapienza R. Sensitivity and spectral control of network lasers. Nat Commun 2022; 13:6493. [PMID: 36310173 PMCID: PMC9618558 DOI: 10.1038/s41467-022-34073-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 10/13/2022] [Indexed: 11/14/2022] Open
Abstract
Recently, random lasing in complex networks has shown efficient lasing over more than 50 localised modes, promoted by multiple scattering over the underlying graph. If controlled, these network lasers can lead to fast-switching multifunctional light sources with synthesised spectrum. Here, we observe both in experiment and theory high sensitivity of the network laser spectrum to the spatial shape of the pump profile, with some modes for example increasing in intensity by 280% when switching off 7% of the pump beam. We solve the nonlinear equations within the steady state ab-initio laser theory (SALT) approximation over a graph and we show selective lasing of around 90% of the strongest intensity modes, effectively programming the spectrum of the lasing networks. In our experiments with polymer networks, this high sensitivity enables control of the lasing spectrum through non-uniform pump patterns. We propose the underlying complexity of the network modes as the key element behind efficient spectral control opening the way for the development of optical devices with wide impact for on-chip photonics for communication, sensing, and computation. Nanophotonic light sources with programmable emission spectrum are important building blocks for integrated photonics, sensing and optical computing. Here the authors tune the complex laser spectrum of a network laser achieving selective lasing of a single, two or more modes.
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14
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Abstract
Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph-theoretical methods: Bond-to-bond propensity, which has been previously successful in identifying allosteric sites in extensive benchmark data sets without a priori knowledge, and Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. Using statistical bootstrapping, we score the highest ranking sites against random sites at similar distances, and we identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.
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Affiliation(s)
- Léonie Strömich
- Department of Chemistry Imperial College London, United Kingdom
| | - Nan Wu
- Department of Chemistry Imperial College London, United Kingdom
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15
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Myall A, Price JR, Peach RL, Abbas M, Mookerjee S, Zhu N, Ahmad I, Ming D, Ramzan F, Teixeira D, Graf C, Weiße AY, Harbarth S, Holmes A, Barahona M. Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study. Lancet Digit Health 2022; 4:e573-e583. [PMID: 35868812 PMCID: PMC9296105 DOI: 10.1016/s2589-7500(22)00093-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 03/19/2022] [Accepted: 04/25/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level. METHODS We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk. FINDINGS The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]). INTERPRETATION Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections. FUNDING Medical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.
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Affiliation(s)
- Ashleigh Myall
- Department of Infectious Disease, Imperial College London, London, UK; Department of Mathematics, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK.
| | - James R Price
- National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK; Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Robert L Peach
- Department of Mathematics, Imperial College London, London, UK; Department of Brain Sciences, Imperial College London, London, UK; Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
| | - Mohamed Abbas
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK; Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland
| | - Sid Mookerjee
- National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK; Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Nina Zhu
- Department of Infectious Disease, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK
| | - Isa Ahmad
- Department of Infectious Disease, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK
| | - Damien Ming
- Department of Infectious Disease, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK
| | - Farzan Ramzan
- Department of Infectious Disease, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK
| | - Daniel Teixeira
- Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland
| | - Christophe Graf
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Andrea Y Weiße
- School of Biological Sciences and School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Stephan Harbarth
- Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland
| | - Alison Holmes
- Department of Infectious Disease, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, UK
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16
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Wu N, Yaliraki SN, Barahona M. Prediction of Protein Allosteric Signalling Pathways and Functional Residues Through Paths of Optimised Propensity. J Mol Biol 2022; 434:167749. [PMID: 35841931 DOI: 10.1016/j.jmb.2022.167749] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/09/2022] [Accepted: 07/11/2022] [Indexed: 11/28/2022]
Abstract
Allostery commonly refers to the mechanism that regulates protein activity through the binding of a molecule at a different, usually distal, site from the orthosteric site. The omnipresence of allosteric regulation in nature and its potential for drug design and screening render the study of allostery invaluable. Nevertheless, challenges remain as few computational methods are available to effectively predict allosteric sites, identify signalling pathways involved in allostery, or to aid with the design of suitable molecules targeting such sites. Recently, bond-to-bond propensity analysis has been shown successful at identifying allosteric sites for a large and diverse group of proteins from knowledge of the orthosteric sites and its ligands alone by using network analysis applied to energy-weighted atomistic protein graphs. To address the identification of signalling pathways, we propose here a method to compute and score paths of optimised propensity that link the orthosteric site with the identified allosteric sites, and identifies crucial residues that contribute to those paths. We showcase the approach with three well-studied allosteric proteins: h-Ras, caspase-1, and 3-phosphoinositide-dependent kinase-1 (PDK1). Key residues in both orthosteric and allosteric sites were identified and showed agreement with experimental results, and pivotal signalling residues along the pathway were also revealed, thus providing alternative targets for drug design. By using the computed path scores, we were also able to differentiate the activity of different allosteric modulators.
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Affiliation(s)
- Nan Wu
- Department of Chemistry Imperial College London, United Kingdom
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17
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Rodrigues D, Kreif N, Lawrence-Jones A, Barahona M, Mayer E. Reflection on modern methods: constructing directed acyclic graphs (DAGs) with domain experts for health services research. Int J Epidemiol 2022; 51:1339-1348. [PMID: 35713577 PMCID: PMC9365627 DOI: 10.1093/ije/dyac135] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 06/07/2022] [Indexed: 12/05/2022] Open
Abstract
Directed acyclic graphs (DAGs) are a useful tool to represent, in a graphical format, researchers’ assumptions about the causal structure among variables while providing a rationale for the choice of confounding variables to adjust for. With origins in the field of probabilistic graphical modelling, DAGs are yet to be widely adopted in applied health research, where causal assumptions are frequently made for the purpose of evaluating health services initiatives. In this context, there is still limited practical guidance on how to construct and use DAGs. Some progress has recently been made in terms of building DAGs based on studies from the literature, but an area that has received less attention is how to create DAGs from information provided by domain experts, an approach of particular importance when there is limited published information about the intervention under study. This approach offers the opportunity for findings to be more robust and relevant to patients, carers and the public, and more likely to inform policy and clinical practice. This article draws lessons from a stakeholder workshop involving patients, health care professionals, researchers, commissioners and representatives from industry, whose objective was to draw DAGs for a complex intervention—online consultation, i.e. written exchange between the patient and health care professional using an online system—in the context of the English National Health Service. We provide some initial, practical guidance to those interested in engaging with domain experts to develop DAGs.
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Affiliation(s)
- Daniela Rodrigues
- NIHR Imperial Patient Safety Translational Research Centre, Institute of Global Health Innovation, Department of Surgery & Cancer, Imperial College London, London, UK
| | - Noemi Kreif
- Centre for Health Economics, University of York, York, UK
| | - Anna Lawrence-Jones
- NIHR Imperial Patient Safety Translational Research Centre, Institute of Global Health Innovation, Department of Surgery & Cancer, Imperial College London, London, UK
| | - Mauricio Barahona
- Centre for Mathematics of Precision Healthcare, Department of Mathematics, Imperial College London, London, UK
| | - Erik Mayer
- NIHR Imperial Patient Safety Translational Research Centre, Institute of Global Health Innovation, Department of Surgery & Cancer, Imperial College London, London, UK
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18
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Rodrigues D, Kreif N, Saravanakumar K, Delaney B, Barahona M, Mayer E. Formalising triage in general practice towards a more equitable, safe, and efficient allocation of resources. BMJ 2022; 377:e070757. [PMID: 35609904 DOI: 10.1136/bmj-2022-070757] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Daniela Rodrigues
- NIHR Imperial Patient Safety Translational Research Centre, Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Noemi Kreif
- Centre for Health Economics, University of York, York, UK
| | | | - Brendan Delaney
- NIHR Imperial Patient Safety Translational Research Centre, Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Mauricio Barahona
- Centre for Mathematics of Precision Healthcare, Department of Mathematics, Imperial College London, London, UK
| | - Erik Mayer
- NIHR Imperial Patient Safety Translational Research Centre, Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
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19
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Laumann F, von Kügelgen J, Kanashiro Uehara TH, Barahona M. Complex interlinkages, key objectives, and nexuses among the Sustainable Development Goals and climate change: a network analysis. Lancet Planet Health 2022; 6:e422-e430. [PMID: 35550081 DOI: 10.1016/s2542-5196(22)00070-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Global sustainability is an enmeshed system of complex socioeconomic, climatological, and ecological interactions. The numerous objectives of the UN's Sustainable Development Goals (SDGs) and the Paris Agreement have various levels of interdependence, making it difficult to ascertain the influence of changes to particular indicators across the whole system. In this analysis, we aimed to detect and rank the complex interlinkages between objectives of sustainability agendas. METHODS We developed a method to find interlinkages among the 17 SDGs and climate change, including non-linear and non-monotonic dependences. We used time series of indicators defined by the World Bank, consisting of 400 indicators that measure progress towards the 17 SDGs and an 18th variable (annual average temperatures), representing progress in the response to the climate crisis, from 2000 to 2019. This method detects significant dependencies among the time evolution of the objectives by using partial distance correlations, a non-linear measure of conditional dependence that also discounts spurious correlations originating from lurking variables. We then used a network representation to identify the most important objectives (using network centrality) and to obtain nexuses of objectives (defined as highly interconnected clusters in the network). FINDINGS Using temporal data from 181 countries spanning 20 years, we analysed dependencies among SDGs and climate for 35 country groupings based on region, development, and income level. The observed significant interlinkages, central objectives, and nexuses identified varied greatly across country groupings; however, SDG 17 (partnerships for the goals) and climate change ranked as highly important across many country groupings. Temperature rise was strongly linked to urbanisation, air pollution, and slum expansion (SDG 11), especially in country groupings likely to be worst affected by climate breakdown, such as Africa. In several country groupings composed of developing nations, we observed a consistent nexus of strongly interconnected objectives formed by SDG 1 (poverty reduction), SDG 4 (education), and SDG 8 (economic growth), sometimes incorporating SDG 5 (gender equality), and SDG 16 (peace and justice). INTERPRETATION The differences across groupings emphasise the need to define goals in accordance with local circumstances and priorities. Our analysis highlights global partnerships (SDG 17) as a pivot in global sustainability efforts, which have been strongly linked to economic growth (SDG 8). However, if economic growth and trade expansion were repositioned as a means instead of an end goal of development, our analysis showed that education (SDG 4) and poverty reduction (SDG 1) become more central, thus suggesting that these could be prioritised in global partnerships. Urban livelihoods (SDG 11) were also flagged as important to avoid replicating unsustainable patterns of the past. FUNDING Engineering and Physical Sciences Research Council, UK Research and Innovation.
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Affiliation(s)
- Felix Laumann
- Department of Mathematics, Imperial College London, London, UK.
| | - Julius von Kügelgen
- Empirical Inference Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany; Department of Engineering, University of Cambridge, Cambridge, UK
| | - Thiago Hector Kanashiro Uehara
- Chatham House, London, UK; Ethics, Transparency, Integrity and Compliance Studies, Fundação Getulio Vargas, São Paulo, Brazil
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20
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Qian Y, Expert P, Rieu T, Panzarasa P, Barahona M. Quantifying the Alignment of Graph and Features in Deep Learning. IEEE Trans Neural Netw Learn Syst 2022; 33:1663-1672. [PMID: 33428573 DOI: 10.1109/tnnls.2020.3043196] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
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.
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21
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Liu Z, Peach RL, Lawrance EL, Noble A, Ungless MA, Barahona M. Listening to Mental Health Crisis Needs at Scale: Using Natural Language Processing to Understand and Evaluate a Mental Health Crisis Text Messaging Service. Front Digit Health 2021; 3:779091. [PMID: 34939068 PMCID: PMC8685221 DOI: 10.3389/fdgth.2021.779091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 11/12/2021] [Indexed: 11/24/2022] Open
Abstract
The current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help. Here we study a large data set of anonymised text message conversations and post-conversation surveys compiled through Shout. This data provides an opportunity to hear at scale from those experiencing distress; to better understand mental health needs for people not using traditional mental health services; and to evaluate the impact of a novel form of crisis support. We use natural language processing (NLP) to assess the adherence of volunteers to conversation techniques and formats, and to gain insight into demographic user groups and their behavioural expressions of distress. Our textual analyses achieve accurate classification of conversation stages (weighted accuracy = 88%), behaviours (1-hamming loss = 95%) and texter demographics (weighted accuracy = 96%), exemplifying how the application of NLP to frontline mental health data sets can aid with post-hoc analysis and evaluation of quality of service provision in digital mental health services.
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Affiliation(s)
- Zhaolu Liu
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Robert L Peach
- Department of Mathematics, Imperial College London, London, United Kingdom.,Department of Neurology, University Hospital Würzburg, Würzburg, Germany.,Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Emma L Lawrance
- Institute of Global Health Innovation, Imperial College London, London, United Kingdom.,Mental Health Innovations, London, United Kingdom
| | - Ariele Noble
- Mental Health Innovations, London, United Kingdom
| | | | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
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22
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Beaney T, Clarke J, Woodcock T, McCarthy R, Saravanakumar K, Barahona M, Blair M, Hargreaves DS. Patterns of healthcare utilisation in children and young people: a retrospective cohort study using routinely collected healthcare data in Northwest London. BMJ Open 2021; 11:e050847. [PMID: 34921075 PMCID: PMC8685945 DOI: 10.1136/bmjopen-2021-050847] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVES With a growing role for health services in managing population health, there is a need for early identification of populations with high need. Segmentation approaches partition the population based on demographics, long-term conditions (LTCs) or healthcare utilisation but have mostly been applied to adults. Our study uses segmentation methods to distinguish patterns of healthcare utilisation in children and young people (CYP) and to explore predictors of segment membership. DESIGN A retrospective cohort study. SETTING Routinely collected primary and secondary healthcare data in Northwest London from the Discover database. PARTICIPANTS 378 309 CYP aged 0-15 years registered to a general practice in Northwest London with 1 full year of follow-up. PRIMARY AND SECONDARY OUTCOME MEASURES Assignment of each participant to a segment defined by seven healthcare variables representing primary and secondary care attendances, and description of utilisation patterns by segment. Predictors of segment membership described by age, sex, ethnicity, deprivation and LTCs. RESULTS Participants were grouped into six segments based on healthcare utilisation. Three segments predominantly used primary care, two moderate utilisation segments differed in use of emergency or elective care, and a high utilisation segment, representing 16 632 (4.4%) children accounted for the highest mean presentations across all service types. The two smallest segments, representing 13.3% of the population, accounted for 62.5% of total costs. Younger age, residence in areas of higher deprivation and the presence of one or more LTCs were associated with membership of higher utilisation segments, but 75.0% of those in the highest utilisation segment had no LTC. CONCLUSIONS This article identifies six segments of healthcare utilisation in CYP and predictors of segment membership. Demographics and LTCs may not explain utilisation patterns as strongly as in adults, which may limit the use of routine data in predicting utilisation and suggest children have less well-defined trajectories of service use than adults.
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Affiliation(s)
- Thomas Beaney
- Department of Primary Care and Public Health, Imperial College London, London, UK
- National Institute for Health Research Applied Research Collaboration Northwest London, Imperial College London, London, UK
| | - Jonathan Clarke
- Centre for Mathematics of Precision Healthcare, Imperial College London, London, UK
- Department of Mathematics, Imperial College London, London, UK
| | - Thomas Woodcock
- Department of Primary Care and Public Health, Imperial College London, London, UK
- National Institute for Health Research Applied Research Collaboration Northwest London, Imperial College London, London, UK
| | - Rachel McCarthy
- North West London Collaboration of Clinical Commissioning Groups, London, UK
| | | | - Mauricio Barahona
- Centre for Mathematics of Precision Healthcare, Imperial College London, London, UK
- Department of Mathematics, Imperial College London, London, UK
| | - Mitch Blair
- Department of Primary Care and Public Health, Imperial College London, London, UK
- National Institute for Health Research Applied Research Collaboration Northwest London, Imperial College London, London, UK
| | - Dougal S Hargreaves
- Department of Primary Care and Public Health, Imperial College London, London, UK
- National Institute for Health Research Applied Research Collaboration Northwest London, Imperial College London, London, UK
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23
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Ming DK, Myall AC, Hernandez B, Weiße AY, Peach RL, Barahona M, Rawson TM, Holmes AH. Correction to: Informing antimicrobial management in the context of COVID-19: understanding the longitudinal dynamics of C-reactive protein and procalcitonin. BMC Infect Dis 2021; 21:988. [PMID: 34548046 PMCID: PMC8454290 DOI: 10.1186/s12879-021-06696-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Damien K Ming
- Centre for Antimicrobial Optimisation, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK. .,National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.
| | - Ashleigh C Myall
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.,Department of Mathematics, Imperial College London, London, UK
| | - Bernard Hernandez
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Andrea Y Weiße
- School of Informatics, University of Edinburgh, Scotland, UK.,School of Biological Science, University of Edinburgh, Scotland, UK
| | - Robert L Peach
- Department of Neurology, University Hospital of Würzburg, 97080, Würzburg, Germany.,Department of Mathematics, Imperial College London, London, UK
| | | | - Timothy M Rawson
- Centre for Antimicrobial Optimisation, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.,National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Alison H Holmes
- Centre for Antimicrobial Optimisation, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.,National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
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24
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Ming DK, Myall AC, Hernandez B, Weiße AY, Peach RL, Barahona M, Rawson TM, Holmes AH. Informing antimicrobial management in the context of COVID-19: understanding the longitudinal dynamics of C-reactive protein and procalcitonin. BMC Infect Dis 2021; 21:932. [PMID: 34496795 PMCID: PMC8424157 DOI: 10.1186/s12879-021-06621-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 08/26/2021] [Indexed: 01/08/2023] Open
Abstract
Background To characterise the longitudinal dynamics of C-reactive protein (CRP) and Procalcitonin (PCT) in a cohort of hospitalised patients with COVID-19 and support antimicrobial decision-making. Methods Longitudinal CRP and PCT concentrations and trajectories of 237 hospitalised patients with COVID-19 were modelled. The dataset comprised of 2,021 data points for CRP and 284 points for PCT. Pairwise comparisons were performed between: (i) those with or without significant bacterial growth from cultures, and (ii) those who survived or died in hospital. Results CRP concentrations were higher over time in COVID-19 patients with positive microbiology (day 9: 236 vs 123 mg/L, p < 0.0001) and in those who died (day 8: 226 vs 152 mg/L, p < 0.0001) but only after day 7 of COVID-related symptom onset. Failure for CRP to reduce in the first week of hospital admission was associated with significantly higher odds of death. PCT concentrations were higher in patients with COVID-19 and positive microbiology or in those who died, although these differences were not statistically significant. Conclusions Both the absolute CRP concentration and the trajectory during the first week of hospital admission are important factors predicting microbiology culture positivity and outcome in patients hospitalised with COVID-19. Further work is needed to describe the role of PCT for co-infection. Understanding relationships of these biomarkers can support development of risk models and inform optimal antimicrobial strategies. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-021-06621-7.
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Affiliation(s)
- Damien K Ming
- Centre for Antimicrobial Optimisation, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK. .,National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.
| | - Ashleigh C Myall
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.,Department of Mathematics, Imperial College London, London, UK
| | - Bernard Hernandez
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Andrea Y Weiße
- School of Informatics, University of Edinburgh, Scotland, UK.,School of Biological Science, University of Edinburgh, Scotland, UK
| | - Robert L Peach
- Department of Neurology, University Hospital of Würzburg, 97080, Würzburg, Germany.,Department of Mathematics, Imperial College London, London, UK
| | | | - Timothy M Rawson
- Centre for Antimicrobial Optimisation, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.,National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Alison H Holmes
- Centre for Antimicrobial Optimisation, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.,National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
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25
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Schreglmann S, Wang D, Peach R, Li J, Zhang X, Latorre A, Rhodes E, Panella E, Boyden E, Barahona M, Santaniello S, Rothwell J, Bhatia K, Grossman N. FV 12. Non-invasive Suppression of Essential Tremor via Phase-Locked Disruption of its Temporal Coherence. Clin Neurophysiol 2021. [DOI: 10.1016/j.clinph.2021.02.388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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26
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Chrysostomou S, Roy R, Prischi F, Thamlikitkul L, Chapman KL, Mufti U, Peach R, Ding L, Hancock D, Moore C, Molina-Arcas M, Mauri F, Pinato DJ, Abrahams JM, Ottaviani S, Castellano L, Giamas G, Pascoe J, Moonamale D, Pirrie S, Gaunt C, Billingham L, Steven NM, Cullen M, Hrouda D, Winkler M, Post J, Cohen P, Salpeter SJ, Bar V, Zundelevich A, Golan S, Leibovici D, Lara R, Klug DR, Yaliraki SN, Barahona M, Wang Y, Downward J, Skehel JM, Ali MMU, Seckl MJ, Pardo OE. Repurposed floxacins targeting RSK4 prevent chemoresistance and metastasis in lung and bladder cancer. Sci Transl Med 2021; 13:eaba4627. [PMID: 34261798 PMCID: PMC7611705 DOI: 10.1126/scitranslmed.aba4627] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 10/26/2020] [Accepted: 06/09/2021] [Indexed: 12/20/2022]
Abstract
Lung and bladder cancers are mostly incurable because of the early development of drug resistance and metastatic dissemination. Hence, improved therapies that tackle these two processes are urgently needed to improve clinical outcome. We have identified RSK4 as a promoter of drug resistance and metastasis in lung and bladder cancer cells. Silencing this kinase, through either RNA interference or CRISPR, sensitized tumor cells to chemotherapy and hindered metastasis in vitro and in vivo in a tail vein injection model. Drug screening revealed several floxacin antibiotics as potent RSK4 activation inhibitors, and trovafloxacin reproduced all effects of RSK4 silencing in vitro and in/ex vivo using lung cancer xenograft and genetically engineered mouse models and bladder tumor explants. Through x-ray structure determination and Markov transient and Deuterium exchange analyses, we identified the allosteric binding site and revealed how this compound blocks RSK4 kinase activation through binding to an allosteric site and mimicking a kinase autoinhibitory mechanism involving the RSK4's hydrophobic motif. Last, we show that patients undergoing chemotherapy and adhering to prophylactic levofloxacin in the large placebo-controlled randomized phase 3 SIGNIFICANT trial had significantly increased (P = 0.048) long-term overall survival times. Hence, we suggest that RSK4 inhibition may represent an effective therapeutic strategy for treating lung and bladder cancer.
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Affiliation(s)
- Stelios Chrysostomou
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Rajat Roy
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Filippo Prischi
- School of Biological Sciences, University of Essex, Colchester CO4 3SQ, UK
- Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
| | - Lucksamon Thamlikitkul
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
- Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Kathryn L Chapman
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
- Assay Biology, Domainex Ltd, Cambridge CB10 1XL, UK
| | - Uwais Mufti
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Robert Peach
- Department of Chemistry, Imperial College London, London SW7 2AZ, UK
- Department of Neurology, University Hospital Würzburg, 97080 Würzburg, Germany
| | - Laifeng Ding
- Key Laboratory of Magnetic Resonance in Biological Systems, National Centre for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
| | - David Hancock
- Oncogene Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Christopher Moore
- Oncogene Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Miriam Molina-Arcas
- Oncogene Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Francesco Mauri
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - David J Pinato
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Joel M Abrahams
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Silvia Ottaviani
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Leandro Castellano
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Georgios Giamas
- Department of Biochemistry and Biomedicine, School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QG, UK
| | - Jennifer Pascoe
- Department of Oncology, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TH, UK
| | - Devmini Moonamale
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Sarah Pirrie
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham B15 2TT, UK
| | - Claire Gaunt
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham B15 2TT, UK
| | - Lucinda Billingham
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham B15 2TT, UK
| | - Neil M Steven
- Department of Oncology, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TH, UK
| | - Michael Cullen
- Department of Oncology, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TH, UK
| | - David Hrouda
- Department Urology, Charing Cross Hospital, London W6 8RF, UK
| | - Mathias Winkler
- Department Urology, Charing Cross Hospital, London W6 8RF, UK
| | - John Post
- MRC Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH. UK
| | - Philip Cohen
- MRC Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH. UK
| | | | - Vered Bar
- Curesponse, 6 Weizmann Street, 6423906 Tel Aviv, Israel
| | | | - Shay Golan
- Department of Urology, Rabin Medical Center, Jabotinsky St. 39, 4941492 Petah Tikva, Israel
| | - Dan Leibovici
- Department of Urology, Kaplan Medical Center, 7610001 Rehovot, Israel
| | - Romain Lara
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
- AstraZeneca, Discovery Science, R&D, Discovery Biology, Darwin Building, Cambridge Science Park, Milton Road, Cambridge CB4 0WG, UK
| | - David R Klug
- Department of Chemistry, Imperial College London, London SW7 2AZ, UK
| | - Sophia N Yaliraki
- Department of Chemistry, Imperial College London, London SW7 2AZ, UK
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
| | - Yulan Wang
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
| | - Julian Downward
- Oncogene Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - J Mark Skehel
- Biological Mass Spectrometry and Proteomics, MRC LMB, Cambridge CB2 0QH, UK
| | - Maruf M U Ali
- Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
| | - Michael J Seckl
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK.
| | - Olivier E Pardo
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK.
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27
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Mersmann S, Strömich L, Song FJ, Wu N, Vianello F, Barahona M, Yaliraki S. ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules. Nucleic Acids Res 2021; 49:W551-W558. [PMID: 33978752 PMCID: PMC8661402 DOI: 10.1093/nar/gkab350] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/16/2021] [Accepted: 04/22/2021] [Indexed: 11/28/2022] Open
Abstract
The investigation of allosteric effects in biomolecular structures is of great current interest in diverse areas, from fundamental biological enquiry to drug discovery. Here we present ProteinLens, a user-friendly and interactive web application for the investigation of allosteric signalling based on atomistic graph-theoretical methods. Starting from the PDB file of a biomolecule (or a biomolecular complex) ProteinLens obtains an atomistic, energy-weighted graph description of the structure of the biomolecule, and subsequently provides a systematic analysis of allosteric signalling and communication across the structure using two computationally efficient methods: Markov Transients and bond-to-bond propensities. ProteinLens scores and ranks every bond and residue according to the speed and magnitude of the propagation of fluctuations emanating from any site of choice (e.g. the active site). The results are presented through statistical quantile scores visualised with interactive plots and adjustable 3D structure viewers, which can also be downloaded. ProteinLens thus allows the investigation of signalling in biomolecular structures of interest to aid the detection of allosteric sites and pathways. ProteinLens is implemented in Python/SQL and freely available to use at: www.proteinlens.io.
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Affiliation(s)
- Sophia F Mersmann
- Department of Mathematics, Imperial College London, Huxley Building, 180 Queen’s Gate, London SW7 2AZ, UK
| | - Léonie Strömich
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Florian J Song
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Nan Wu
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Francesca Vianello
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, Huxley Building, 180 Queen’s Gate, London SW7 2AZ, UK
| | - Sophia N Yaliraki
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
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28
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Saavedra-García P, Roman-Trufero M, Al-Sadah HA, Blighe K, López-Jiménez E, Christoforou M, Penfold L, Capece D, Xiong X, Miao Y, Parzych K, Caputo VS, Siskos AP, Encheva V, Liu Z, Thiel D, Kaiser MF, Piazza P, Chaidos A, Karadimitris A, Franzoso G, Snijders AP, Keun HC, Oyarzún DA, Barahona M, Auner HW. Systems level profiling of chemotherapy-induced stress resolution in cancer cells reveals druggable trade-offs. Proc Natl Acad Sci U S A 2021; 118:e2018229118. [PMID: 33883278 PMCID: PMC8092411 DOI: 10.1073/pnas.2018229118] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Cancer cells can survive chemotherapy-induced stress, but how they recover from it is not known. Using a temporal multiomics approach, we delineate the global mechanisms of proteotoxic stress resolution in multiple myeloma cells recovering from proteasome inhibition. Our observations define layered and protracted programs for stress resolution that encompass extensive changes across the transcriptome, proteome, and metabolome. Cellular recovery from proteasome inhibition involved protracted and dynamic changes of glucose and lipid metabolism and suppression of mitochondrial function. We demonstrate that recovering cells are more vulnerable to specific insults than acutely stressed cells and identify the general control nonderepressable 2 (GCN2)-driven cellular response to amino acid scarcity as a key recovery-associated vulnerability. Using a transcriptome analysis pipeline, we further show that GCN2 is also a stress-independent bona fide target in transcriptional signature-defined subsets of solid cancers that share molecular characteristics. Thus, identifying cellular trade-offs tied to the resolution of chemotherapy-induced stress in tumor cells may reveal new therapeutic targets and routes for cancer therapy optimization.
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Affiliation(s)
- Paula Saavedra-García
- Cancer Cell Protein Metabolism, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom
- The Hugh and Josseline Langmuir Centre for Myeloma Research, Imperial College London, London W12 0NN, United Kingdom
| | - Monica Roman-Trufero
- Cancer Cell Protein Metabolism, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom
- The Hugh and Josseline Langmuir Centre for Myeloma Research, Imperial College London, London W12 0NN, United Kingdom
| | - Hibah A Al-Sadah
- Cancer Cell Protein Metabolism, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom
- The Hugh and Josseline Langmuir Centre for Myeloma Research, Imperial College London, London W12 0NN, United Kingdom
| | - Kevin Blighe
- Clinical Bioinformatics Research, London W1B 3HH, United Kingdom
| | - Elena López-Jiménez
- Cancer Cell Protein Metabolism, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom
- The Hugh and Josseline Langmuir Centre for Myeloma Research, Imperial College London, London W12 0NN, United Kingdom
| | - Marilena Christoforou
- Cancer Cell Protein Metabolism, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom
- The Hugh and Josseline Langmuir Centre for Myeloma Research, Imperial College London, London W12 0NN, United Kingdom
| | - Lucy Penfold
- Cancer Cell Protein Metabolism, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom
- Cellular Stress, MRC London Institute of Medical Sciences, London W12 0NN, United Kingdom
| | - Daria Capece
- Centre for Molecular Immunology and Inflammation, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom
| | - Xiaobei Xiong
- Cancer Cell Protein Metabolism, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom
- The Hugh and Josseline Langmuir Centre for Myeloma Research, Imperial College London, London W12 0NN, United Kingdom
| | - Yirun Miao
- Cancer Cell Protein Metabolism, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom
- The Hugh and Josseline Langmuir Centre for Myeloma Research, Imperial College London, London W12 0NN, United Kingdom
| | - Katarzyna Parzych
- Cancer Cell Protein Metabolism, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom
| | - Valentina S Caputo
- The Hugh and Josseline Langmuir Centre for Myeloma Research, Imperial College London, London W12 0NN, United Kingdom
| | - Alexandros P Siskos
- Department of Surgery and Cancer, Imperial College London, London W12 0NN, United Kingdom
| | - Vesela Encheva
- Proteomics Platform, The Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Zijing Liu
- Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
- Department of Brain Sciences, Imperial College London, London W12 0NN, United Kingdom
- UK Dementia Research Institute at Imperial College, London W12 0NN, United Kingdom
| | - Denise Thiel
- Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
| | - Martin F Kaiser
- Myeloma Molecular Therapy, The Institute of Cancer Research, Sutton SW7 3RP, United Kingdom
| | - Paolo Piazza
- Imperial BRC Genomics Facility, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, United Kingdom
| | - Aristeidis Chaidos
- The Hugh and Josseline Langmuir Centre for Myeloma Research, Imperial College London, London W12 0NN, United Kingdom
| | - Anastasios Karadimitris
- The Hugh and Josseline Langmuir Centre for Myeloma Research, Imperial College London, London W12 0NN, United Kingdom
| | - Guido Franzoso
- Centre for Molecular Immunology and Inflammation, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom
| | - Ambrosius P Snijders
- Proteomics Platform, The Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Hector C Keun
- Department of Surgery and Cancer, Imperial College London, London W12 0NN, United Kingdom
| | - Diego A Oyarzún
- School of Informatics, The University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
- School of Biological Sciences, The University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
| | - Holger W Auner
- Cancer Cell Protein Metabolism, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, United Kingdom;
- The Hugh and Josseline Langmuir Centre for Myeloma Research, Imperial College London, London W12 0NN, United Kingdom
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29
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Qian Y, Expert P, Panzarasa P, Barahona M. Geometric graphs from data to aid classification tasks with Graph Convolutional Networks. Patterns (N Y) 2021; 2:100237. [PMID: 33982027 PMCID: PMC8085612 DOI: 10.1016/j.patter.2021.100237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 01/11/2021] [Accepted: 03/12/2021] [Indexed: 12/02/2022]
Abstract
Traditional classification tasks learn to assign samples to given classes based solely on sample features. This paradigm is evolving to include other sources of information, such as known relations between samples. Here, we show that, even if additional relational information is not available in the dataset, one can improve classification by constructing geometric graphs from the features themselves, and using them within a Graph Convolutional Network. The improvement in classification accuracy is maximized by graphs that capture sample similarity with relatively low edge density. We show that such feature-derived graphs increase the alignment of the data to the ground truth while improving class separation. We also demonstrate that the graphs can be made more efficient using spectral sparsification, which reduces the number of edges while still improving classification performance. We illustrate our findings using synthetic and real-world datasets from various scientific domains. Geometric graphs from data can be used in deep learning to improve classification Optimized graphs align the data to the class labels and enhance class separability Sparsifying the optimized graph can potentially improve classification performance Extensive experiments are performed on datasets from various scientific domains
Supervised classification assigns unseen samples to classes based on their features by learning from examples with known class labels. We show that classification can be improved by using the sample features not only as the basis for classification, but also as a means to construct geometric graphs that encapsulate the closeness between samples. Such feature-derived graphs can be used within graph-based deep-learning models to improve classification. To understand the benefits of these graphs, we show that they align the data to the class labels and enhance class separability. We also demonstrate how to make the graphs sparser, and hence more efficient, while still potentially improving their performance. Our findings are timely given the increasing interest in combining graphs with classification and learning tasks.
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Affiliation(s)
- Yifan Qian
- School of Business and Management, Queen Mary University of London, London, UK
| | - Paul Expert
- Global Digital Health Unit, School of Public Health, Imperial College London, London, UK.,World Research Hub Initiative, Tokyo Institute of Technology, Tokyo, Japan
| | - Pietro Panzarasa
- School of Business and Management, Queen Mary University of London, London, UK
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30
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Peach RL, Arnaudon A, Schmidt JA, Palasciano HA, Bernier NR, Jelfs KE, Yaliraki SN, Barahona M. HCGA: Highly comparative graph analysis for network phenotyping. Patterns (N Y) 2021; 2:100227. [PMID: 33982022 PMCID: PMC8085611 DOI: 10.1016/j.patter.2021.100227] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 02/02/2021] [Accepted: 03/03/2021] [Indexed: 11/30/2022]
Abstract
Networks are widely used as mathematical models of complex systems across many scientific disciplines. Decades of work have produced a vast corpus of research characterizing the topological, combinatorial, statistical, and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and sometimes overlapping) characteristics of a network. In this paper, we introduce HCGA, a framework for highly comparative analysis of graph datasets that computes several thousands of graph features from any given network. HCGA also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterization of graph datasets. We show that HCGA outperforms other methodologies on supervised classification tasks on benchmark datasets while retaining the interpretability of network features. We exemplify HCGA by predicting the charge transfer in organic semiconductors and clustering a dataset of neuronal morphology images.
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Affiliation(s)
- Robert L. Peach
- Department of Mathematics, Imperial College London, SW7 2AZ London, UK
| | - Alexis Arnaudon
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Julia A. Schmidt
- Department of Chemistry, Imperial College London, SW7 2AZ London, UK
| | | | | | - Kim E. Jelfs
- Department of Chemistry, Imperial College London, SW7 2AZ London, UK
| | | | - Mauricio Barahona
- Department of Mathematics, Imperial College London, SW7 2AZ London, UK
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31
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Maes A, Barahona M, Clopath C. Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons. PLoS Comput Biol 2021; 17:e1008866. [PMID: 33764970 PMCID: PMC8023498 DOI: 10.1371/journal.pcbi.1008866] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 04/06/2021] [Accepted: 03/08/2021] [Indexed: 11/17/2022] Open
Abstract
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.
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Affiliation(s)
- Amadeus Maes
- Bioengineering Department, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Mathematics Department, Imperial College London, London, United Kingdom
| | - Claudia Clopath
- Bioengineering Department, Imperial College London, London, United Kingdom
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32
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Peach RL, Greenbury SF, Johnston IG, Yaliraki SN, Lefevre DJ, Barahona M. Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation. Sci Rep 2021; 11:2823. [PMID: 33531544 PMCID: PMC7854683 DOI: 10.1038/s41598-021-81709-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 01/01/2021] [Indexed: 02/04/2023] Open
Abstract
The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completion in online courses can be used to characterise personal and group learners’ behaviors, and to identify critical tasks and course sessions in a given course design. We also introduce a recently developed probabilistic Bayesian model to learn sequential behaviours of students and predict student performance. The application of our data-driven sequence-based analyses to data from learners undertaking an on-line Business Management course reveals distinct behaviors within the cohort of learners, identifying learners or groups of learners that deviate from the nominal order expected in the course. Using course grades a posteriori, we explore differences in behavior between high and low performing learners. We find that high performing learners follow the progression between weekly sessions more regularly than low performing learners, yet within each weekly session high performing learners are less tied to the nominal task order. We then model the sequences of high and low performance students using the probablistic Bayesian model and show that we can learn engagement behaviors associated with performance. We also show that the data sequence framework can be used for task-centric analysis; we identify critical junctures and differences among types of tasks within the course design. We find that non-rote learning tasks, such as interactive tasks or discussion posts, are correlated with higher performance. We discuss the application of such analytical techniques as an aid to course design, intervention, and student supervision.
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Affiliation(s)
- Robert L Peach
- Department of Mathematics, Imperial College London, London, UK. .,Imperial College Business School, Imperial College London, London, UK.
| | - Sam F Greenbury
- Department of Mathematics, Imperial College London, London, UK.,NIHR Imperial Biomedical Research Centre, ITMAT Data Science Group, Imperial College London, London, UK
| | - Iain G Johnston
- Department of Mathematics, University of Bergen, Bergen, Norway
| | | | - David J Lefevre
- Imperial College Business School, Imperial College London, London, UK
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33
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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 AH. Development and Delivery of a Real-time Hospital-onset COVID-19 Surveillance System Using Network Analysis. Clin Infect Dis 2021; 72:82-89. [PMID: 32634822 PMCID: PMC7454383 DOI: 10.1093/cid/ciaa892] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Indexed: 02/07/2023] Open
Abstract
Background Understanding nosocomial acquisition, outbreaks, and transmission chains in real time will be fundamental to ensuring infection-prevention measures are effective in controlling coronavirus disease 2019 (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. Methods The study took place in a large teaching hospital group in London, United Kingdom. All patients tested for SARS-CoV-2 between 4 March and 14 April 2020 were included. Utilizing 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 geotemporal 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. Results Real-time surveillance reports revealed changing rates of HOCI throughout the course of the COVID-19 epidemic, key wards fueling probable transmission events, HOCIs overrepresented in particular specialties managing high-risk patients, the importance of integrating analysis of individual prior pathways, and the value of co-design in producing data visualization. Our surveillance system can effectively support national surveillance. Conclusions Through 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 characterizing risk of transmission and targeting infection-control interventions.
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Affiliation(s)
- James Richard Price
- National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, United Kingdom
| | - Siddharth Mookerjee
- National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, United Kingdom
| | - Eleonora Dyakova
- Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom
| | - Ashleigh Myall
- National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, United Kingdom.,Department of Mathematics, Imperial College London, London, United Kingdom
| | - Wendy Leung
- Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom
| | - Andrea Yeong Weiße
- National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, United Kingdom
| | - Yeeshika Shersing
- Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom
| | | | - Tracey Galletly
- Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom
| | - David Muir
- Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom
| | - Paul Randell
- Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom
| | - Frances Davies
- Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom
| | - Frances Bolt
- National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Jonathan Ashley Otter
- National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, United Kingdom
| | - Alison H Holmes
- National Institute for Health Research Health Protection Research Unit in HCAI and AMR, Imperial College London, London, United Kingdom
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Dusad V, Thiel D, Barahona M, Keun HC, Oyarzún DA. Opportunities at the Interface of Network Science and Metabolic Modeling. Front Bioeng Biotechnol 2021; 8:591049. [PMID: 33569373 PMCID: PMC7868444 DOI: 10.3389/fbioe.2020.591049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 12/22/2020] [Indexed: 12/17/2022] Open
Abstract
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.
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Affiliation(s)
- Varshit Dusad
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Denise Thiel
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Hector C Keun
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom.,Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Diego A Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.,School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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35
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Schreglmann SR, Wang D, Peach RL, Li J, Zhang X, Latorre A, Rhodes E, Panella E, Cassara AM, Boyden ES, Barahona M, Santaniello S, Rothwell J, Bhatia KP, Grossman N. Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence. Nat Commun 2021; 12:363. [PMID: 33441542 PMCID: PMC7806740 DOI: 10.1038/s41467-020-20581-7] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 12/04/2020] [Indexed: 12/16/2022] Open
Abstract
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 transiently suppressed via transcranial electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained shortly after the end of the stimulation and can be phenomenologically predicted. Finally, we use feature-based statistical-learning and neurophysiological-modelling to show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the aberrant oscillations in the olivocerebellar loop, thus establishing its causal role. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may in the future represent a powerful neuromodulatory strategy to treat brain disorders.
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Affiliation(s)
- Sebastian R Schreglmann
- Institute of Neurology, Department of Clinical and Movement Neuroscience, Queen Square, University College London (UCL), London, WC1N 3BG, UK
| | - David Wang
- Computer Science and Artificial Intelligence Laboratory, Massachussetts Institute of Technology (MIT), Cambridge, MA, 02139, USA
- NuVu studio Inc, Cambridge, MA, 02139, USA
| | - Robert L Peach
- Department of Mathematics and EPSRC Centre for Mathematics of Precision Healthcare, Imperial College London, London, SW7 2AZ, UK
- Department of Brain Sciences, Imperial College London, London, W12 0HS, UK
- UK Dementia Research Institute (UK DRI) at Imperial College London, London, W12 0NN, UK
| | - Junheng Li
- Department of Brain Sciences, Imperial College London, London, W12 0HS, UK
- UK Dementia Research Institute (UK DRI) at Imperial College London, London, W12 0NN, UK
| | - Xu Zhang
- Biomedical Engineering Department, University of Connecticut, Storrs, CT, 06269, USA
- CT Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, 06269, USA
| | - Anna Latorre
- Institute of Neurology, Department of Clinical and Movement Neuroscience, Queen Square, University College London (UCL), London, WC1N 3BG, UK
| | - Edward Rhodes
- Department of Brain Sciences, Imperial College London, London, W12 0HS, UK
- UK Dementia Research Institute (UK DRI) at Imperial College London, London, W12 0NN, UK
| | - Emanuele Panella
- Department of Physics, Imperial College London, London, SW7 2AZ, UK
| | - Antonino M Cassara
- IT'IS Foundation for Research on Information Technologies in Society, 8004, Zurich, Switzerland
| | - Edward S Boyden
- Department of Media Arts and Sciences, MIT, Cambridge, MA, 02139, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA, 02142, USA
- Department of Biological Engineering, MIT, Cambridge, MA, 02139, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA
- Centre for Neurobiological Engineering, MIT, Cambridge, MA, 02139, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, 02139, USA
| | - Mauricio Barahona
- Department of Mathematics and EPSRC Centre for Mathematics of Precision Healthcare, Imperial College London, London, SW7 2AZ, UK
| | - Sabato Santaniello
- Biomedical Engineering Department, University of Connecticut, Storrs, CT, 06269, USA
- CT Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, 06269, USA
| | - John Rothwell
- Institute of Neurology, Department of Clinical and Movement Neuroscience, Queen Square, University College London (UCL), London, WC1N 3BG, UK
| | - Kailash P Bhatia
- Institute of Neurology, Department of Clinical and Movement Neuroscience, Queen Square, University College London (UCL), London, WC1N 3BG, UK.
| | - Nir Grossman
- Department of Brain Sciences, Imperial College London, London, W12 0HS, UK.
- UK Dementia Research Institute (UK DRI) at Imperial College London, London, W12 0NN, UK.
- Department of Media Arts and Sciences, MIT, Cambridge, MA, 02139, USA.
- McGovern Institute for Brain Research, MIT, Cambridge, MA, 02139, USA.
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK.
- Centre for Neurotechnology, Imperial College London, London, SW7 2AZ, UK.
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Tonn MK, Thomas P, Barahona M, Oyarzún DA. Computation of Single-Cell Metabolite Distributions Using Mixture Models. Front Cell Dev Biol 2020; 8:614832. [PMID: 33415109 PMCID: PMC7783310 DOI: 10.3389/fcell.2020.614832] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 11/26/2020] [Indexed: 12/30/2022] Open
Abstract
Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.
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Affiliation(s)
- Mona K. Tonn
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Philipp Thomas
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Diego A. Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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37
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Clarke J, Murray A, Markar SR, Barahona M, Kinross J. New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study. BMJ Open 2020; 10:e042392. [PMID: 33130573 PMCID: PMC7783383 DOI: 10.1136/bmjopen-2020-042392] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 09/29/2020] [Accepted: 09/30/2020] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES The suspension of elective surgery during the COVID-19 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. DESIGN Retrospective observational study using Hospital Episode Statistics. SETTING Public and private hospitals providing surgical care to National Health Service (NHS) patients in England. PARTICIPANTS All adult patients resident in England undergoing NHS-funded planned surgical procedures between 1 April 2017 and 31 March 2018. MAIN OUTCOME MEASURES The 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. RESULTS A total of 7 811 891 planned operations were identified in 4 284 925 adults during the 1-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 seven surgical communities were shown to be associated with balanced supply and demand for surgical care within communities. CONCLUSIONS Pooled waiting-lists for low-risk elective procedures and patients across integrated, expanded natural surgical community networks have the potential to increase efficiency by innovatively flexing existing supply to better match demand.
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Affiliation(s)
- Jonathan Clarke
- Department of Mathematics, Imperial College of Science, Technology and Medicine, London, UK
| | - Alice Murray
- Department of Surgery and Cancer, Imperial College of Science, Technology and Medicine, London, UK
| | - Sheraz Rehan Markar
- Department of Surgery and Cancer, Imperial College of Science, Technology and Medicine, London, UK
| | - Mauricio Barahona
- Department of Mathematics, Imperial College of Science, Technology and Medicine, London, UK
| | - James Kinross
- Department of Surgery and Cancer, Imperial College of Science, Technology and Medicine, London, UK
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38
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Clarke J, Beaney T, Majeed A, Darzi A, Barahona M. METHODS RESEARCH. Health Serv Res 2020. [DOI: 10.1111/1475-6773.13483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Affiliation(s)
- J. Clarke
- Centre for Health Policy Institute of Global Health Innovation Imperial College London London United Kingdom
| | - T. Beaney
- Imperial College London London United Kingdom
| | - A. Majeed
- Department of Primary Care Imperial College London London United Kingdom
| | - A. Darzi
- Institute of Global Health Innovation Imperial College London London United Kingdom
| | - M. Barahona
- Centre for Mathematics of Precision Healthcare Imperial College London London United Kingdom
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Abstract
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 PCNs should represent 'natural' communities of general practices (GP practices) collaborating at scale and covering a geography that fits well with 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 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 practice in Greater London that had one or more outpatient encounters at NHS hospitals 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 Layer Super Output Area (LSOA) of Greater London. The population size and coverage of each proposed PCN. RESULTS 3 428 322 unique patients attended 1334 GPs in 4835 LSOAs in Greater London. Our model grouped 1291 GPs (96.8%) and 4721 LSOAs (97.6%) into 165 mutually exclusive PCNs. 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 to recognise how PCNs represent their communities. Our method may be used by policymakers to understand the populations and geography shared between networks.
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Affiliation(s)
- Jonathan Clarke
- Centre for Health Policy, Imperial College London, London, UK
- Centre for Mathematics of Precision Healthcare, Imperial College London, London, UK
| | - Thomas Beaney
- Department of Primary Care, Imperial College of Science Technology and Medicine, London, UK
| | | | | | - Mauricio Barahona
- Centre for Mathematics of Precision Healthcare, Imperial College London, London, UK
- Department of Mathematics, Imperial College London, London, UK
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40
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Maes A, Barahona M, Clopath C. Learning spatiotemporal signals using a recurrent spiking network that discretizes time. PLoS Comput Biol 2020; 16:e1007606. [PMID: 31961853 PMCID: PMC7028299 DOI: 10.1371/journal.pcbi.1007606] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 02/18/2020] [Accepted: 12/13/2019] [Indexed: 12/15/2022] Open
Abstract
Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neurons may be used to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory spiking neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.
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Affiliation(s)
- Amadeus Maes
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, United Kingdom
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41
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Peach RL, Yaliraki SN, Lefevre D, Barahona M. Data-driven unsupervised clustering of online learner behaviour. NPJ Sci Learn 2019; 4:14. [PMID: 31508242 PMCID: PMC6722089 DOI: 10.1038/s41539-019-0054-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 07/17/2019] [Indexed: 06/10/2023]
Abstract
The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here, we introduce a mathematical framework for the analysis of time-series of online learner engagement, which allows the identification of clusters of learners with similar online temporal behaviour directly from the raw data without prescribing a priori subjective reference behaviours. The method uses a dynamic time warping kernel to create a pair-wise similarity between time-series of learner actions, and combines it with an unsupervised multiscale graph clustering algorithm to identify groups of learners with similar temporal behaviour. To showcase our approach, we analyse task completion data from a cohort of learners taking an online post-graduate degree at Imperial Business School. Our analysis reveals clusters of learners with statistically distinct patterns of engagement, from distributed to massed learning, with different levels of regularity, adherence to pre-planned course structure and task completion. The approach also reveals outlier learners with highly sporadic behaviour. A posteriori comparison against student performance shows that, whereas high-performing learners are spread across clusters with diverse temporal engagement, low performers are located significantly in the massed learning cluster, and our unsupervised clustering identifies low performers more accurately than common machine learning classification methods trained on temporal statistics of the data. Finally, we test the applicability of the method by analysing two additional data sets: a different cohort of the same course, and time-series of different format from another university.
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Affiliation(s)
- Robert L. Peach
- Department of Mathematics, Imperial College London, London, SW7 2AZ UK
- Imperial College Business School, Imperial College London, London, SW7 2AZ UK
| | | | - David Lefevre
- Imperial College Business School, Imperial College London, London, SW7 2AZ UK
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, SW7 2AZ UK
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Kuntz J, Thomas P, Stan GB, Barahona M. Bounding the stationary distributions of the chemical master equation via mathematical programming. J Chem Phys 2019; 151:034109. [PMID: 31325941 DOI: 10.1063/1.5100670] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The stochastic dynamics of biochemical networks are usually modeled with the chemical master equation (CME). The stationary distributions of CMEs are seldom solvable analytically, and numerical methods typically produce estimates with uncontrolled errors. Here, we introduce mathematical programming approaches that yield approximations of these distributions with computable error bounds which enable the verification of their accuracy. First, we use semidefinite programming to compute increasingly tighter upper and lower bounds on the moments of the stationary distributions for networks with rational propensities. Second, we use these moment bounds to formulate linear programs that yield convergent upper and lower bounds on the stationary distributions themselves, their marginals, and stationary averages. The bounds obtained also provide a computational test for the uniqueness of the distribution. In the unique case, the bounds form an approximation of the stationary distribution with a computable bound on its error. In the nonunique case, our approach yields converging approximations of the ergodic distributions. We illustrate our methodology through several biochemical examples taken from the literature: Schlögl's model for a chemical bifurcation, a two-dimensional toggle switch, a model for bursty gene expression, and a dimerization model with multiple stationary distributions.
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Affiliation(s)
- Juan Kuntz
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Philipp Thomas
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Guy-Bart Stan
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
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Chrysostomou S, Roy R, Prischi F, Chapman K, Mufti U, Mauri F, Bellezza G, Abrahams J, Ottaviani S, Castellano L, Giamas G, Hrouda D, Winkler M, Klug D, Yaliraki S, Barahona M, Wang Y, Ali M, Seckl M, Pardo O. Abstract 1775: Targeting RSK4 prevents both chemoresistance and metastasis in lung cancer. Cancer Res 2019. [DOI: 10.1158/1538-7445.am2019-1775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Lung cancer is the commonest cause of cancer death worldwide with a five-year survival rate of less than five percent for metastatic tumors. Non-small cell lung cancer (NSCLC) accounts for 80% of lung cancer cases of which adenocarcinoma prevails. Patients almost invariably develop metastatic drug-resistant disease and this is responsible for our failure to provide curative therapy. Hence, a better understanding of the mechanisms underlying these biological processes is urgently required to improve clinical outcome.
The 90-kDa ribosomal S6 kinases (RSKs) are downstream effectors of the RAS/MAPK cascade. RSKs are highly conserved serine/threonine protein kinases implicated in diverse cellular processes, including cell survival, proliferation, migration and invasion. Four isoforms exist in humans (RSK1-4) and are uniquely characterized by the presence of two non-identical N- and C-terminal kinase domains. RSK isoforms are 73-80% identical at protein level and this has been thought to suggest overlapping functions.
However, through functional genomic kinome screens, we show that RSK4, contrary to RSK1, promotes both drug resistance and metastasis in lung cancer. This kinase is overexpressed in the majority (57%) of NSCLC biopsies and this correlates with poor overall survival in lung adenocarcinoma patients. Genetic silencing of RSK4 sensitizes lung cancer cells to chemotherapy and prevents their migration and invasiveness in vitro and in vivo. RSK4 downregulation decreases the anti-apoptotic proteins Bcl2 and cIAP1/2 which correlates with increased apoptotic signalling, whilst it also induces mesenchymal-epithelial transition (MET) through inhibition of NFκB activity. A small-molecule inhibitor screen identified several floxacins, including trovafloxacin, as potent allosteric inhibitors of RSK4 activation. Trovafloxacin reproduced all biological and molecular effects of RSK4 silencing in vitro and in vivo, and is predicted to bind a novel allosteric site revealed by our RSK4 N-terminal kinase domain crystal structure and mathematical Markov Transient Analysis.
Taken together, our data demonstrate that RSK4 represents a promising novel therapeutic target in lung cancer.
Citation Format: Stelios Chrysostomou, Rajat Roy, Filippo Prischi, Katie Chapman, Uwais Mufti, Francesco Mauri, Guido Bellezza, Joel Abrahams, Silvia Ottaviani, Leandro Castellano, Georgios Giamas, David Hrouda, Mathias Winkler, David Klug, Sophia Yaliraki, Mauricio Barahona, Yulan Wang, Maruf Ali, Michael Seckl, Olivier Pardo. Targeting RSK4 prevents both chemoresistance and metastasis in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1775.
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Affiliation(s)
| | - Rajat Roy
- 1Imperial College London, London, United Kingdom
| | | | | | - Uwais Mufti
- 4Harrogate and District NHS Foundation Trust, Harrogate, United Kingdom
| | | | | | | | | | | | | | - David Hrouda
- 7Charing Cross Hospital, Imperial College NHS Trust, London, United Kingdom
| | - Mathias Winkler
- 7Charing Cross Hospital, Imperial College NHS Trust, London, United Kingdom
| | - David Klug
- 1Imperial College London, London, United Kingdom
| | | | | | - Yulan Wang
- 8Wuhan Institute of Physics and Mathematics, Wuhan, China
| | - Maruf Ali
- 1Imperial College London, London, United Kingdom
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Abstract
Complex systems and relational data are often abstracted as dynamical processes on networks. To understand, predict, and control their behavior, a crucial step is to extract reduced descriptions of such networks. Inspired by notions from control theory, we propose a time-dependent dynamical similarity measure between nodes, which quantifies the effect a node-input has on the network. This dynamical similarity induces an embedding that can be employed for several analysis tasks. Here we focus on (i) dimensionality reduction, i.e., projecting nodes onto a low-dimensional space that captures dynamic similarity at different timescales, and (ii) how to exploit our embeddings to uncover functional modules. We exemplify our ideas through case studies focusing on directed networks without strong connectivity and signed networks. We further highlight how certain ideas from community detection can be generalized and linked to control theory, by using the here developed dynamical perspective.
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Affiliation(s)
- Michael T Schaub
- Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Jean-Charles Delvenne
- ICTEAM, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium.,CORE, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium
| | - Renaud Lambiotte
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
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Warren LR, Clarke JM, Arora S, Barahona M, Arebi N, Darzi A. Transitions of care across hospital settings in patients with inflammatory bowel disease. World J Gastroenterol 2019; 25:2122-2132. [PMID: 31114138 PMCID: PMC6506584 DOI: 10.3748/wjg.v25.i17.2122] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 02/05/2019] [Accepted: 02/23/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Inflammatory bowel disease (IBD) is a chronic, inflammatory disorder characterised by both intestinal and extra-intestinal pathology. Patients may receive both emergency and elective care from several providers, often in different hospital settings. Poorly managed transitions of care between providers can lead to inefficiencies in care and patient safety issues. To ensure that the sharing of patient information between providers is appropriate, timely, accurate and secure, effective data-sharing infrastructure needs to be developed. To optimise inter-hospital data-sharing for IBD patients, we need to better understand patterns of hospital encounters in this group.
AIM To determine the type and location of hospital services accessed by IBD patients in England.
METHODS This was a retrospective observational study using Hospital Episode Statistics, a large administrative patient data set from the National Health Service in England. Adult patients with a diagnosis of IBD following admission to hospital were followed over a 2-year period to determine the proportion of care accessed at the same hospital providing their outpatient IBD care, defined as their ‘home provider’. Secondary outcome measures included the geographic distribution of patient-sharing, regional and age-related differences in accessing services, and type and frequency of outpatient encounters.
RESULTS 95055 patients accessed hospital services on 1760156 occasions over a 2-year follow-up period. The proportion of these encounters with their identified IBD ‘home provider’ was 73.3%, 87.8% and 83.1% for accident and emergency, inpatient and outpatient encounters respectively. Patients living in metropolitan centres and younger patients were less likely to attend their ‘home provider’ for hospital services. The most commonly attended specialty services were gastroenterology, general surgery and ophthalmology.
CONCLUSION Transitions of care between secondary care settings are common for patients with IBD. Effective systems of data-sharing and care integration are essential to providing safe and effective care for patients. Geographic and age-related patterns of care transitions identified in this study may be used to guide interventions aimed at improving continuity of care.
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Affiliation(s)
- Leigh R Warren
- Patient Safety Translational Research Centre, Imperial College London, London W2 1NY, United Kingdom
- Department of Surgery and Cancer, Imperial College London, London W2 1NY, United Kingdom
| | - Jonathan M Clarke
- Centre for Health Policy, Imperial College London Centre for Mathematics of Precision Healthcare, Imperial College London, London SW7 2BX, United Kingdom
- Department of Biostatistics, Harvard University, Boston, MA 02115, United States
- Department of Surgery and Cancer, Imperial College London, London W2 1NY, United Kingdom
| | - Sonal Arora
- Patient Safety Translational Research Centre, Imperial College London, London W2 1NY, United Kingdom
- Department of Surgery and Cancer, Imperial College London, London W2 1NY, United Kingdom
| | - Mauricio Barahona
- Centre for Health Policy, Imperial College London Centre for Mathematics of Precision Healthcare, Imperial College London, London SW7 2BX, United Kingdom
- Department of Mathematics, Imperial College London, London SW7 2BX, United Kingdom
| | - Naila Arebi
- Department of Gastroenterology, St. Marks Academic Institute, Harrow HA1 3UJ, United Kingdom
| | - Ara Darzi
- Patient Safety Translational Research Centre, Imperial College London, London W2 1NY, United Kingdom
- Department of Surgery and Cancer, Imperial College London, London W2 1NY, United Kingdom
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46
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Abstract
Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought. Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources of metabolite heterogeneity.
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Affiliation(s)
- Mona K. Tonn
- Department of Mathematics, Imperial College London, London, SW7 2AZ UK
| | - Philipp Thomas
- Department of Mathematics, Imperial College London, London, SW7 2AZ UK
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, SW7 2AZ UK
| | - Diego A. Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB UK
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF UK
- SynthSys-Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh, EH9 3BF UK
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47
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Altuncu MT, Mayer E, Yaliraki SN, Barahona M. From free text to clusters of content in health records: an unsupervised graph partitioning approach. Appl Netw Sci 2019; 4:2. [PMID: 30906850 PMCID: PMC6400329 DOI: 10.1007/s41109-018-0109-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 11/06/2018] [Indexed: 05/07/2023]
Abstract
Electronic healthcare records contain large volumes of unstructured data in different forms. Free text constitutes a large portion of such data, yet this source of richly detailed information often remains under-used in practice because of a lack of suitable methodologies to extract interpretable content in a timely manner. Here we apply network-theoretical tools to the analysis of free text in Hospital Patient Incident reports in the English National Health Service, to find clusters of reports in an unsupervised manner and at different levels of resolution based directly on the free text descriptions contained within them. To do so, we combine recently developed deep neural network text-embedding methodologies based on paragraph vectors with multi-scale Markov Stability community detection applied to a similarity graph of documents obtained from sparsified text vector similarities. We showcase the approach with the analysis of incident reports submitted in Imperial College Healthcare NHS Trust, London. The multiscale community structure reveals levels of meaning with different resolution in the topics of the dataset, as shown by relevant descriptive terms extracted from the groups of records, as well as by comparing a posteriori against hand-coded categories assigned by healthcare personnel. Our content communities exhibit good correspondence with well-defined hand-coded categories, yet our results also provide further medical detail in certain areas as well as revealing complementary descriptors of incidents beyond the external classification. We also discuss how the method can be used to monitor reports over time and across different healthcare providers, and to detect emerging trends that fall outside of pre-existing categories.
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Affiliation(s)
- M. Tarik Altuncu
- Department of Mathematics, Imperial College London, South Kensington campus, London, SW7 2AZ UK
- EPSRC Centre for Mathematics of Precision Healthcare, Imperial College London, South Kensington campus, London, SW7 2AZ UK
| | - Erik Mayer
- Centre for Health Policy, Institute of Global Health Innovation, Imperial College London, St Mary’s campus, London, W2 1NY UK
- EPSRC Centre for Mathematics of Precision Healthcare, Imperial College London, South Kensington campus, London, SW7 2AZ UK
| | - Sophia N. Yaliraki
- Department of Chemistry, Imperial College London, South Kensington campus, London, SW7 2AZ UK
- EPSRC Centre for Mathematics of Precision Healthcare, Imperial College London, South Kensington campus, London, SW7 2AZ UK
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, South Kensington campus, London, SW7 2AZ UK
- EPSRC Centre for Mathematics of Precision Healthcare, Imperial College London, South Kensington campus, London, SW7 2AZ UK
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48
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Clarke JM, Warren LR, Arora S, Barahona M, Darzi AW. Guiding interoperable electronic health records through patient-sharing networks. NPJ Digit Med 2018; 1:65. [PMID: 31304342 PMCID: PMC6550264 DOI: 10.1038/s41746-018-0072-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 11/09/2018] [Indexed: 01/01/2023] Open
Abstract
Effective sharing of clinical information between care providers is a critical component of a safe, efficient health system. National data-sharing systems may be costly, politically contentious and do not reflect local patterns of care delivery. This study examines hospital attendances in England from 2013 to 2015 to identify instances of patient sharing between hospitals. Of 19.6 million patients receiving care from 155 hospital care providers, 130 million presentations were identified. On 14.7 million occasions (12%), patients attended a different hospital to the one they attended on their previous interaction. A network of hospitals was constructed based on the frequency of patient sharing between hospitals which was partitioned using the Louvain algorithm into ten distinct data-sharing communities, improving the continuity of data sharing in such instances from 0 to 65-95%. Locally implemented data-sharing communities of hospitals may achieve effective accessibility of clinical information without a large-scale national interoperable information system.
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Affiliation(s)
- Jonathan M. Clarke
- NIHR Patient Safety Translational Research Centre, Imperial College London, London, W2 1NY UK
- EPSRC Centre for Mathematics of Precision Healthcare, Imperial College London, London, SW7 2AZ UK
- Centre for Health Policy, Imperial College London, London, W2 1NY UK
| | - Leigh R. Warren
- NIHR Patient Safety Translational Research Centre, Imperial College London, London, W2 1NY UK
| | - Sonal Arora
- NIHR Patient Safety Translational Research Centre, Imperial College London, London, W2 1NY UK
| | - Mauricio Barahona
- EPSRC Centre for Mathematics of Precision Healthcare, Imperial College London, London, SW7 2AZ UK
- Department of Mathematics, Imperial College London, London, SW7 2AZ UK
| | - Ara W. Darzi
- NIHR Patient Safety Translational Research Centre, Imperial College London, London, W2 1NY UK
- Centre for Health Policy, Imperial College London, London, W2 1NY UK
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49
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Beguerisse-Díaz M, Bosque G, Oyarzún D, Picó J, Barahona M. Flux-dependent graphs for metabolic networks. NPJ Syst Biol Appl 2018; 4:32. [PMID: 30131869 PMCID: PMC6092364 DOI: 10.1038/s41540-018-0067-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 06/28/2018] [Accepted: 07/03/2018] [Indexed: 12/28/2022] Open
Abstract
Cells adapt their metabolic fluxes in response to changes in the environment. We present a framework for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic flows via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environmental conditions by incorporating flux distributions computed through constraint-based approaches such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli and on a metabolic model of human hepatocytes. The flux-dependent graphs under various environmental conditions and genetic perturbations exhibit systemic changes in their topological and community structure, which capture the re-routing of metabolic flows and the varying importance of specific reactions and pathways. By integrating constraint-based models and tools from network science, our framework allows the study of context-specific metabolic responses at a system level beyond standard pathway descriptions. Cellular metabolism is the result of a highly enmeshed set of biochemical reactions that is naturally amenable to graph-based analyses. Yet there are multiple ways to construct a graph representation from any given metabolic model. Here an international research team of UK and Spain scientists presents a principled approach to study metabolic models through the lens of network science. They propose a framework to construct graphs for genome-scale metabolic models that resolve various challenges, such as the incorporation of pool metabolites, the preservation of the directionality of metabolic flows, and the capability to incorporate specific flux information. The method can be integrated into pipelines based on flux balance analysis and provides a systematic framework to explore changes in network connectivity as a result of environmental shifts or genetic perturbations. The framework thus allows to interrogate context-specific metabolic responses beyond standard pathway descriptions. The authors illustrate the approach through the analysis of Escherichia coli's core metabolism in different growth conditions, as well as a rare metabolic disease affecting human hepatocytes.
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Affiliation(s)
- Mariano Beguerisse-Díaz
- 1Department of Mathematics, Imperial College London, London, SW7 2AZ UK.,2Mathematical Institute, University of Oxford, Oxford, OX2 6GG UK
| | - Gabriel Bosque
- 3Institut Universitari d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
| | - Diego Oyarzún
- 1Department of Mathematics, Imperial College London, London, SW7 2AZ UK
| | - Jesús Picó
- 3Institut Universitari d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
| | - Mauricio Barahona
- 1Department of Mathematics, Imperial College London, London, SW7 2AZ UK
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50
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Abstract
Aspartate carbamoyltransferase (ATCase) is a large dodecameric enzyme with six active sites that exhibits allostery: its catalytic rate is modulated by the binding of various substrates at distal points from the active sites. A recently developed method, bond-to-bond propensity analysis, has proven capable of predicting allosteric sites in a wide range of proteins using an energy-weighted atomistic graph obtained from the protein structure and given knowledge only of the location of the active site. Bond-to-bond propensity establishes if energy fluctuations at given bonds have significant effects on any other bond in the protein, by considering their propagation through the protein graph. In this work, we use bond-to-bond propensity analysis to study different aspects of ATCase activity using three different protein structures and sources of fluctuations. First, we predict key residues and bonds involved in the transition between inactive (T) and active (R) states of ATCase by analysing allosteric substrate binding as a source of energy perturbations in the protein graph. Our computational results also indicate that the effect of multiple allosteric binding is non linear: a switching effect is observed after a particular number and arrangement of substrates is bound suggesting a form of long range communication between the distantly arranged allosteric sites. Second, cooperativity is explored by considering a bisubstrate analogue as the source of energy fluctuations at the active site, also leading to the identification of highly significant residues to the T ↔ R transition that enhance cooperativity across active sites. Finally, the inactive (T) structure is shown to exhibit a strong, non linear communication between the allosteric sites and the interface between catalytic subunits, rather than the active site. Bond-to-bond propensity thus offers an alternative route to explain allosteric and cooperative effects in terms of detailed atomistic changes to individual bonds within the protein, rather than through phenomenological, global thermodynamic arguments.
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Affiliation(s)
- Maxwell Hodges
- Department of Chemistry, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom
- Institute of Chemical Biology, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom
- Institute of Chemical Biology, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom
| | - Sophia N Yaliraki
- Department of Chemistry, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom.
- Institute of Chemical Biology, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom.
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