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Zhao W, Rose SF, Blake R, Godicelj A, Cullen AE, Stenning J, Beevors L, Gehrung M, Kumar S, Kishore K, Sawle A, Eldridge M, Giorgi FM, Bridge KS, Markowetz F, Holding AN. ZMIZ1 enhances ERα-dependent expression of E2F2 in breast cancer. J Mol Endocrinol 2024; 73:e230133. [PMID: 38564418 PMCID: PMC11103680 DOI: 10.1530/jme-23-0133] [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: 11/07/2023] [Accepted: 04/02/2024] [Indexed: 04/04/2024]
Abstract
The estrogen receptor-α (ER) drives 75% of breast cancers. On activation, the ER recruits and assembles a 1-2 MDa transcriptionally active complex. These complexes can modulate tumour growth, and understanding the roles of individual proteins within these complexes can help identify new therapeutic targets. Here, we present the discovery of ER and ZMIZ1 within the same multi-protein assembly by quantitative proteomics, and validated by proximity ligation assay. We characterise ZMIZ1 function by demonstrating a significant decrease in the proliferation of ER-positive cancer cell lines. To establish a role for the ER-ZMIZ1 interaction, we measured the transcriptional changes in the estrogen response post-ZMIZ1 knockdown using an RNA-seq time-course over 24 h. Gene set enrichment analysis of the ZMIZ1-knockdown data identified a specific delay in the response of estradiol-induced cell cycle genes. Integration of ENCODE data with our RNA-seq results identified that ER and ZMIZ1 both bind the promoter of E2F2. We therefore propose that ER and ZMIZ1 interact to enable the efficient estrogenic response at subset of cell cycle genes via a novel ZMIZ1-ER-E2F2 signalling axis. Finally, we show that high ZMIZ1 expression is predictive of worse patient outcome, ER and ZMIZ1 are co-expressed in breast cancer patients in TCGA and METABRIC, and the proteins are co-localised within the nuclei of tumour cell in patient biopsies. In conclusion, we establish that ZMIZ1 is a regulator of the estrogenic cell cycle response and provide evidence of the biological importance of the ER-ZMIZ1 interaction in ER-positive patient tumours, supporting potential clinical relevance.
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Affiliation(s)
- Weiye Zhao
- Department of Biology, University of York, York, UK
| | | | - Ryan Blake
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Aňze Godicelj
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Smith Building, Boston, Massachusetts, USA
| | - Amy E Cullen
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | | | - Lucy Beevors
- The Institute of Metabolism and Systems Research (IMSR), University of Birmingham, College of Medical and Dental Sciences, Birmingham, UK
| | - Marcel Gehrung
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Sanjeev Kumar
- Chris O’Brien Lifehouse, Sydney, New South Wales, Australia
| | - Kamal Kishore
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Ashley Sawle
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Matthew Eldridge
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Federico M Giorgi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Katherine S Bridge
- Department of Biology, University of York, York, UK
- York Biomedical Research Institute, University of York, York, UK
| | | | - Andrew N Holding
- Department of Biology, University of York, York, UK
- York Biomedical Research Institute, University of York, York, UK
- The Alan Turing Institute, Kings Cross, London, UK
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2
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Armitage JM, Wootton RE, Davis OSP, Haworth CMA. An exploration into the causal relationships between educational attainment, intelligence, and wellbeing: an observational and two-sample Mendelian randomisation study. Npj Ment Health Res 2024; 3:23. [PMID: 38724617 PMCID: PMC11082190 DOI: 10.1038/s44184-024-00066-x] [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: 03/06/2023] [Accepted: 04/01/2024] [Indexed: 05/12/2024]
Abstract
Educational attainment is associated with a range of positive outcomes, yet its impact on wellbeing is unclear, and complicated by high correlations with intelligence. We use genetic and observational data to investigate for the first time, whether educational attainment and intelligence are causally and independently related to wellbeing. Results from our multivariable Mendelian randomisation demonstrated a positive causal impact of a genetic predisposition to higher educational attainment on wellbeing that remained after accounting for intelligence, and a negative impact of intelligence that was independent of educational attainment. Observational analyses suggested that these associations may be subject to sex differences, with benefits to wellbeing greater for females who attend higher education compared to males. For intelligence, males scoring more highly on measures related to happiness were those with lower intelligence. Our findings demonstrate a unique benefit for wellbeing of staying in school, over and above improving cognitive abilities, with benefits likely to be greater for females compared to males.
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Affiliation(s)
- J M Armitage
- Wolfson Centre for Young People's Mental Health, Cardiff University, Cardiff, Wales, UK.
| | - R E Wootton
- School of Psychological Science, University of Bristol, Bristol, UK
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - O S P Davis
- Bristol Medical School (PHS), University of Bristol, Bristol, UK
| | - C M A Haworth
- School of Psychological Science, University of Bristol, Bristol, UK
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3
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McGenity C, Clarke EL, Jennings C, Matthews G, Cartlidge C, Freduah-Agyemang H, Stocken DD, Treanor D. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. NPJ Digit Med 2024; 7:114. [PMID: 38704465 PMCID: PMC11069583 DOI: 10.1038/s41746-024-01106-8] [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: 06/17/2023] [Accepted: 04/12/2024] [Indexed: 05/06/2024] Open
Abstract
Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.
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Affiliation(s)
- Clare McGenity
- University of Leeds, Leeds, UK.
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.
| | - Emily L Clarke
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Charlotte Jennings
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | | | | | | | - Darren Treanor
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Department of Clinical Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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4
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Neira M, Molinero C, Marshall S, Arcaute E. Urban segregation on multilayered transport networks: a random walk approach. Sci Rep 2024; 14:8370. [PMID: 38600261 PMCID: PMC11006669 DOI: 10.1038/s41598-024-58932-9] [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: 08/18/2023] [Accepted: 04/04/2024] [Indexed: 04/12/2024] Open
Abstract
We present a novel method for analysing socio-spatial segregation in cities by considering constraints imposed by transportation networks. Using a multilayered network approach, we model the interaction probabilities of socio-economic groups with random walks and Lévy flights. This method allows for evaluation of new transport infrastructure's impact on segregation while quantifying each network's contribution to interaction opportunities. The proposed random walk segregation index measures the probability of individuals encountering diverse social groups based on their available means of transit via random walks. The index incorporates temporal constraints in urban mobility with a parameter, α ∈ [ 0 , 1 ) , of the probability of the random walk continuing at each time step. By applying this to a toy model and conducting a sensitivity analysis, we explore how the index changes dependent on this temporal constraint. When the parameter equals zero, the measure simplifies to an isolation index. When the parameter approaches one it represents the city's overall socio-economic distribution by mirroring the steady-state of the random walk process. Using Cuenca, Ecuador as a case study, we illustrate the method's applicability in transportation planning as a valuable tool for policymakers, addressing spatial distribution of socio-economic groups and the connectivity of existing transport networks, thus promoting equitable interactions throughout the city.
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Affiliation(s)
- Mateo Neira
- Alan Turing Institute, British Library, London, NW1 2DB, UK.
- Centre for Advanced Spatial Analysis, University College London, London, W1T 4TJ, UK.
| | - Carlos Molinero
- Centre for Advanced Spatial Analysis, University College London, London, W1T 4TJ, UK
| | - Stephen Marshall
- Bartlett School of Planning, University College London, London, WC1H 0QB, UK
| | - Elsa Arcaute
- Alan Turing Institute, British Library, London, NW1 2DB, UK
- Centre for Advanced Spatial Analysis, University College London, London, W1T 4TJ, UK
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5
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Tran D, Pascazio L, Akroyd J, Mosbach S, Kraft M. Leveraging Text-to-Text Pretrained Language Models for Question Answering in Chemistry. ACS Omega 2024; 9:13883-13896. [PMID: 38559914 PMCID: PMC10976360 DOI: 10.1021/acsomega.3c08842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/06/2024] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
In this study, we present a question answering (QA) system for chemistry, named Marie, with the use of a text-to-text pretrained language model to attain accurate data retrieval. The underlying data store is "The World Avatar" (TWA), a general world model consisting of a knowledge graph that evolves over time. TWA includes information about chemical species such as their chemical and physical properties, applications, and chemical classifications. Building upon our previous work on KGQA for chemistry, this advanced version of Marie leverages a fine-tuned Flan-T5 model to seamlessly translate natural language questions into SPARQL queries with no separate components for entity and relation linking. The developed QA system demonstrates competence in providing accurate results for complex queries that involve many relation hops as well as showcasing the ability to balance correctness and speed for real-world usage. This new approach offers significant advantages over the prior implementation that relied on knowledge graph embedding. Specifically, the updated system boasts high accuracy and great flexibility in accommodating changes and evolution of the data stored in the knowledge graph without necessitating retraining. Our evaluation results underscore the efficacy of the improved system, highlighting its superior accuracy and the ability in answering complex questions compared to its predecessor.
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Affiliation(s)
- Dan Tran
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
| | - Laura Pascazio
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
| | - Jethro Akroyd
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CMCL
Innovations, Sheraton
House, Castle Park, Cambridge CB3 0AX, U.K.
| | - Sebastian Mosbach
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CMCL
Innovations, Sheraton
House, Castle Park, Cambridge CB3 0AX, U.K.
| | - Markus Kraft
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CMCL
Innovations, Sheraton
House, Castle Park, Cambridge CB3 0AX, U.K.
- School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
- The
Alan Turing Institute, 96 Euston Rd., London NW1 2DB, U.K.
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Pineda-Moncusí M, Allery F, Delmestri A, Bolton T, Nolan J, Thygesen JH, Handy A, Banerjee A, Denaxas S, Tomlinson C, Denniston AK, Sudlow C, Akbari A, Wood A, Collins GS, Petersen I, Coates LC, Khunti K, Prieto-sAlhambra D, Khalid S. Ethnicity data resource in population-wide health records: completeness, coverage and granularity of diversity. Sci Data 2024; 11:221. [PMID: 38388690 PMCID: PMC10883937 DOI: 10.1038/s41597-024-02958-1] [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: 08/07/2023] [Accepted: 01/12/2024] [Indexed: 02/24/2024] Open
Abstract
Intersectional social determinants including ethnicity are vital in health research. We curated a population-wide data resource of self-identified ethnicity data from over 60 million individuals in England primary care, linking it to hospital records. We assessed ethnicity data in terms of completeness, consistency, and granularity and found one in ten individuals do not have ethnicity information recorded in primary care. By linking to hospital records, ethnicity data were completed for 94% of individuals. By reconciling SNOMED-CT concepts and census-level categories into a consistent hierarchy, we organised more than 250 ethnicity sub-groups including and beyond "White", "Black", "Asian", "Mixed" and "Other, and found them to be distributed in proportions similar to the general population. This large observational dataset presents an algorithmic hierarchy to represent self-identified ethnicity data collected across heterogeneous healthcare settings. Accurate and easily accessible ethnicity data can lead to a better understanding of population diversity, which is important to address disparities and influence policy recommendations that can translate into better, fairer health for all.
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Affiliation(s)
- Marta Pineda-Moncusí
- Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Freya Allery
- Institute of Health Informatics, 222 Euston Road, London, NW1 2DA, University College London, London, UK
| | - Antonella Delmestri
- Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Thomas Bolton
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - John Nolan
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Johan H Thygesen
- Institute of Health Informatics, 222 Euston Road, London, NW1 2DA, University College London, London, UK
| | - Alex Handy
- Institute of Health Informatics, 222 Euston Road, London, NW1 2DA, University College London, London, UK
| | - Amitava Banerjee
- Institute of Health Informatics, 222 Euston Road, London, NW1 2DA, University College London, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, 222 Euston Road, London, NW1 2DA, University College London, London, UK
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
- University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Christopher Tomlinson
- Institute of Health Informatics, 222 Euston Road, London, NW1 2DA, University College London, London, UK
- University College London Hospitals Biomedical Research Centre, University College London, London, UK
- UK Research and Innovation Centre for Doctoral Training in AI-enabled Healthcare Systems, University College London, London, UK
| | | | - Cathie Sudlow
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK
| | - Angela Wood
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Irene Petersen
- Department of Primary Care and Population Health, UCL, London, NW3 2PF, UK
- Department of Clinical Epidemiology, Aarhus University, Aarhus N, Aarhus, 8200, Denmark
| | - Laura C Coates
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Daniel Prieto-sAlhambra
- Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
- Department of Medical Informatics, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - Sara Khalid
- Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK.
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7
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Eddy LH, Preston N, Boom S, Davison J, Brooks R, Bingham DD, Mon-Williams M, Hill LJB. The validity and reliability of school-based fundamental movement skills screening to identify children with motor difficulties. PLoS One 2024; 19:e0297412. [PMID: 38359032 PMCID: PMC10868745 DOI: 10.1371/journal.pone.0297412] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 12/27/2023] [Indexed: 02/17/2024] Open
Abstract
AIM Assess whether school-based teacher-led screening is effective at identifying children with motor difficulties. METHODS Teachers tested 217 children aged between 5 and 11 years old, after a one hour training session, using a freely available tool (FUNMOVES). Four classes (n = 91) were scored by both researchers and teachers to evaluate inter-rater reliability. Researchers assessed 22 children using the Movement Assessment Battery for Children (MABC-2; considered to be the 'gold standard' in Europe for use as part of the diagnostic process for Developmental Coordination Disorder) to assess concurrent and predictive validity. RESULTS Inter-rater reliability for all individual activities within FUNMOVES ranged from 0.85-0.97 (unweighted Kappa; with 95%CI ranging from 0.77-1). For total score this was lower (κ = 0.76, 95%CI = 0.68-0.84), however when incorporating linear weighting, this improved (κ = 0.94, 95%CI = 0.89-0.99). When evaluating FUNMOVES total score against the MABC-2 total score, the specificity (1, 95%CI = 0.63-1) and positive predictive value (1; 95%CI = 0.68-1) of FUNMOVES were high, whereas sensitivity (0.57, 95%CI = 0.29-0.82) and negative predictive values (0.57, 95%CI = 0.42-0.71) were moderate. Evaluating only MABC-2 subscales which are directly related to fundamental movement skills (Aiming & Catching, and Balance) improved these values to 0.89 (95%CI = 0.52-1) and 0.93 (95%CI = 0.67-0.99) respectively. INTERPRETATION Teacher-led screening of fundamental movement skills (via FUNMOVES) is an effective method of identifying children with motor difficulties. Such universal screening in schools has the potential to identify movement difficulties and enable earlier intervention than the current norm.
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Affiliation(s)
- Lucy H. Eddy
- Department of Psychology, University of Bradford, Bradford, United Kingdom
- Centre for Applied Education Research, Wolfson Centre for Applied Health Research, Bradford, United Kingdom
| | - Nick Preston
- Centre for Applied Education Research, Wolfson Centre for Applied Health Research, Bradford, United Kingdom
- Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
| | - Shania Boom
- Faculty of Health Studies, University of Bradford, Bradford, United Kingdom
| | - Jessica Davison
- Centre for Applied Education Research, Wolfson Centre for Applied Health Research, Bradford, United Kingdom
- School of Psychology, University of Leeds, Leeds, United Kingdom
| | - Rob Brooks
- Faculty of Health Studies, University of Bradford, Bradford, United Kingdom
| | - Daniel D. Bingham
- Faculty of Health Studies, University of Bradford, Bradford, United Kingdom
- Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, United Kingdom
| | - Mark Mon-Williams
- Centre for Applied Education Research, Wolfson Centre for Applied Health Research, Bradford, United Kingdom
- School of Psychology, University of Leeds, Leeds, United Kingdom
- Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, United Kingdom
| | - Liam J. B. Hill
- Centre for Applied Education Research, Wolfson Centre for Applied Health Research, Bradford, United Kingdom
- School of Psychology, University of Leeds, Leeds, United Kingdom
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8
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Hall M, Smith L, Wu J, Hayward C, Batty JA, Lambert PC, Hemingway H, Gale CP. Health outcomes after myocardial infarction: A population study of 56 million people in England. PLoS Med 2024; 21:e1004343. [PMID: 38358949 PMCID: PMC10868847 DOI: 10.1371/journal.pmed.1004343] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND The occurrence of a range of health outcomes following myocardial infarction (MI) is unknown. Therefore, this study aimed to determine the long-term risk of major health outcomes following MI and generate sociodemographic stratified risk charts in order to inform care recommendations in the post-MI period and underpin shared decision making. METHODS AND FINDINGS This nationwide cohort study includes all individuals aged ≥18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017 (final follow-up 27 March 2017). We analysed 11 non-fatal health outcomes (subsequent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality. Of the 55,619,430 population of England, 34,116,257 individuals contributing to 145,912,852 hospitalisations were included (mean age 41.7 years (standard deviation [SD 26.1]); n = 14,747,198 (44.2%) male). There were 433,361 individuals with MI (mean age 67.4 years [SD 14.4)]; n = 283,742 (65.5%) male). Following MI, all-cause mortality was the most frequent event (adjusted cumulative incidence at 9 years 37.8% (95% confidence interval [CI] [37.6,37.9]), followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes (17.0%; 95% CI [16.9,17.1]), cancer (13.5%; 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0,7.2]), and peripheral arterial disease (6.5%; 95% CI [6.4,6.6]). Compared with a risk-set matched population of 2,001,310 individuals, first hospitalisation of all non-fatal health outcomes were increased after MI, except for dementia (adjusted hazard ratio [aHR] 1.01; 95% CI [0.99,1.02];p = 0.468) and cancer (aHR 0.56; 95% CI [0.56,0.57];p < 0.001). The study includes data from secondary care only-as such diagnoses made outside of secondary care may have been missed leading to the potential underestimation of the total burden of disease following MI. CONCLUSIONS In this study, up to a third of patients with MI developed heart failure or renal failure, 7% had another MI, and 38% died within 9 years (compared with 35% deaths among matched individuals). The incidence of all health outcomes, except dementia and cancer, was higher than expected during the normal life course without MI following adjustment for age, sex, year, and socioeconomic deprivation. Efforts targeted to prevent or limit the accrual of chronic, multisystem disease states following MI are needed and should be guided by the demographic-specific risk charts derived in this study.
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Affiliation(s)
- Marlous Hall
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
| | - Lesley Smith
- Leeds Institute for Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Jianhua Wu
- Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
- Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom
| | - Chris Hayward
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
| | - Jonathan A. Batty
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
| | - Paul C. Lambert
- Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, United Kingdom
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, United Kingdom
- Health Data Research UK, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, University College London, London, United Kingdom
- Charité Universitätsmedizin, Berlin, Germany
| | - Chris P. Gale
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
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9
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Cohen SN, Foster J, Foster P, Lou H, Lyons T, Morley S, Morrill J, Ni H, Palmer E, Wang B, Wu Y, Yang L, Yang W. Subtle variation in sepsis-III definitions markedly influences predictive performance within and across methods. Sci Rep 2024; 14:1920. [PMID: 38253623 PMCID: PMC10803347 DOI: 10.1038/s41598-024-51989-6] [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: 04/04/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
Early detection of sepsis is key to ensure timely clinical intervention. Since very few end-to-end pipelines are publicly available, fair comparisons between methodologies are difficult if not impossible. Progress is further limited by discrepancies in the reconstruction of sepsis onset time. This retrospective cohort study highlights the variation in performance of predictive models under three subtly different interpretations of sepsis onset from the sepsis-III definition and compares this against inter-model differences. The models are chosen to cover tree-based, deep learning, and survival analysis methods. Using the MIMIC-III database, between 867 and 2178 intensive care unit admissions with sepsis were identified, depending on the onset definition. We show that model performance can be more sensitive to differences in the definition of sepsis onset than to the model itself. Given a fixed sepsis definition, the best performing method had a gain of 1-5% in the area under the receiver operating characteristic (AUROC). However, the choice of onset time can cause a greater effect, with variation of 0-6% in AUROC. We illustrate that misleading conclusions can be drawn if models are compared without consideration of the sepsis definition used which emphasizes the need for a standardized definition for sepsis onset.
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Affiliation(s)
- Samuel N Cohen
- Mathematical Institute, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - James Foster
- Department of Mathematical Sciences, University of Bath, Bath, UK
| | | | - Hang Lou
- Department of Mathematics, University College London, Room 603, 25 Gordon St, London, WC1H 0AY, UK
| | - Terry Lyons
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Sam Morley
- Mathematical Institute, University of Oxford, Oxford, UK
| | - James Morrill
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Hao Ni
- Department of Mathematics, University College London, Room 603, 25 Gordon St, London, WC1H 0AY, UK.
| | - Edward Palmer
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, UK
| | - Bo Wang
- The Alan Turing Institute, London, UK
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Yue Wu
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
| | - Lingyi Yang
- Mathematical Institute, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Weixin Yang
- Mathematical Institute, University of Oxford, Oxford, UK
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10
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Berezin CT, Aguilera LU, Billerbeck S, Bourne PE, Densmore D, Freemont P, Gorochowski TE, Hernandez SI, Hillson NJ, King CR, Köpke M, Ma S, Miller KM, Moon TS, Moore JH, Munsky B, Myers CJ, Nicholas DA, Peccoud SJ, Zhou W, Peccoud J. Ten simple rules for managing laboratory information. PLoS Comput Biol 2023; 19:e1011652. [PMID: 38060459 PMCID: PMC10703290 DOI: 10.1371/journal.pcbi.1011652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023] Open
Abstract
Information is the cornerstone of research, from experimental (meta)data and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems to transform this large information load into useful scientific findings.
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Affiliation(s)
- Casey-Tyler Berezin
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, United States of America
| | - Luis U. Aguilera
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, United States of America
| | - Sonja Billerbeck
- Molecular Microbiology Unit, Faculty of Science and Engineering, University of Groningen, Groningen, the Netherlands
| | - Philip E. Bourne
- School of Data Science, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Douglas Densmore
- College of Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Paul Freemont
- Department of Infectious Disease, Imperial College, London, United Kingdom
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Bristol, United Kingdom
- BrisEngBio, University of Bristol, Bristol, United Kingdom
| | - Sarah I. Hernandez
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, United States of America
| | - Nathan J. Hillson
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- US Department of Energy Agile BioFoundry, Emeryville, California, United States of America
- US Department of Energy Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Connor R. King
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, United States of America
| | - Michael Köpke
- LanzaTech, Skokie, Illinois, United States of America
| | - Shuyi Ma
- Center for Global Infectious Disease Research, Seattle Children’s Hospital, University of Washington Medicine, Seattle, Washington, United States of America
| | - Katie M. Miller
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, United States of America
| | - Tae Seok Moon
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Jason H. Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Brian Munsky
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, United States of America
| | - Chris J. Myers
- Department of Electrical, Computer & Energy Engineering, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Dequina A. Nicholas
- Department of Molecular Biology & Biochemistry, University of California Irvine, Irvine, California, United States of America
| | - Samuel J. Peccoud
- Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, Colorado, United States of America
| | - Wen Zhou
- Department of Statistics, Colorado State University, Fort Collins, Colorado, United States of America
| | - Jean Peccoud
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, United States of America
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11
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Märtens K, Bortolomeazzi M, Montorsi L, Spencer J, Ciccarelli F, Yau C. Rarity: discovering rare cell populations from single-cell imaging data. Bioinformatics 2023; 39:btad750. [PMID: 38092048 PMCID: PMC10751233 DOI: 10.1093/bioinformatics/btad750] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/24/2023] [Accepted: 12/11/2023] [Indexed: 12/28/2023] Open
Abstract
MOTIVATION Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori, while unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that is magnified when they are defined by differentially expressing a small number of genes. RESULTS Typical unsupervised approaches fail to identify such rare subpopulations, and these cells tend to be absorbed into more prevalent cell types. In order to balance these competing demands, we have developed a novel statistical framework for unsupervised clustering, named Rarity, that enables the discovery process for rare cell types to be more robust, consistent, and interpretable. We achieve this by devising a novel clustering method based on a Bayesian latent variable model in which we assign cells to inferred latent binary on/off expression profiles. This lets us achieve increased sensitivity to rare cell populations while also allowing us to control and interpret potential false positive discoveries. We systematically study the challenges associated with rare cell type identification and demonstrate the utility of Rarity on various IMC datasets. AVAILABILITY AND IMPLEMENTATION Implementation of Rarity together with examples is available from the Github repository (https://github.com/kasparmartens/rarity).
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Affiliation(s)
- Kaspar Märtens
- The Alan Turing Institute, London NW1 2DB, United Kingdom
| | - Michele Bortolomeazzi
- Francis Crick Institute, London NW1 1AT, United Kingdom
- King’s College London, London WC2R 2LS, United Kingdom
| | - Lucia Montorsi
- Francis Crick Institute, London NW1 1AT, United Kingdom
- King’s College London, London WC2R 2LS, United Kingdom
| | - Jo Spencer
- King’s College London, London WC2R 2LS, United Kingdom
| | - Francesca Ciccarelli
- Francis Crick Institute, London NW1 1AT, United Kingdom
- Bart’s Cancer Institute - Centre for Cancer Genomics & Computational Biology, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, United Kingdom
| | - Christopher Yau
- The Alan Turing Institute, London NW1 2DB, United Kingdom
- Nuffield Department for Women’s & Reproductive Health, University of Oxford, Women’s Centre (Level 3), John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom
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12
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Shiwani T, Relton S, Evans R, Kale A, Heaven A, Clegg A, Todd O. New Horizons in artificial intelligence in the healthcare of older people. Age Ageing 2023; 52:afad219. [PMID: 38124256 PMCID: PMC10733173 DOI: 10.1093/ageing/afad219] [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: 05/12/2023] [Indexed: 12/23/2023] Open
Abstract
Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.
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Affiliation(s)
- Taha Shiwani
- Academic Unit for Ageing & Stroke Research, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
| | - Samuel Relton
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Ruth Evans
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Aditya Kale
- Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Anne Heaven
- Academic Unit for Ageing & Stroke Research, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
| | - Andrew Clegg
- Academic Unit for Ageing & Stroke Research, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
| | - Oliver Todd
- Academic Unit for Ageing & Stroke Research, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
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13
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Pascazio L, Rihm S, Naseri A, Mosbach S, Akroyd J, Kraft M. Chemical Species Ontology for Data Integration and Knowledge Discovery. J Chem Inf Model 2023; 63:6569-6586. [PMID: 37883649 PMCID: PMC10647085 DOI: 10.1021/acs.jcim.3c00820] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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: 07/06/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023]
Abstract
Web ontologies are important tools in modern scientific research because they provide a standardized way to represent and manage web-scale amounts of complex data. In chemistry, a semantic database for chemical species is indispensable for its ability to interrelate and infer relationships, enabling a more precise analysis and prediction of chemical behavior. This paper presents OntoSpecies, a web ontology designed to represent chemical species and their properties. The ontology serves as a core component of The World Avatar knowledge graph chemistry domain and includes a wide range of identifiers, chemical and physical properties, chemical classifications and applications, and spectral information associated with each species. The ontology includes provenance and attribution metadata, ensuring the reliability and traceability of data. Most of the information about chemical species are sourced from PubChem and ChEBI data on the respective compound Web pages using a software agent, making OntoSpecies a comprehensive semantic database of chemical species able to solve novel types of problems in the field. Access to this reliable source of chemical data is provided through a SPARQL end point. The paper presents example use cases to demonstrate the contribution of OntoSpecies in solving complex tasks that require integrated semantically searchable chemical data. The approach presented in this paper represents a significant advancement in the field of chemical data management, offering a powerful tool for representing, navigating, and analyzing chemical information to support scientific research.
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Affiliation(s)
- Laura Pascazio
- CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
| | - Simon Rihm
- CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Ali Naseri
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Sebastian Mosbach
- CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CMCL
Innovations, Sheraton
House, Castle Park, Cambridge CB3 0AX, U.K.
| | - Jethro Akroyd
- CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CMCL
Innovations, Sheraton
House, Castle Park, Cambridge CB3 0AX, U.K.
| | - Markus Kraft
- CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602, Singapore
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CMCL
Innovations, Sheraton
House, Castle Park, Cambridge CB3 0AX, U.K.
- School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
- The
Alan Turing Institute, 96 Euston Rd., London NW1 2DB, U.K.
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14
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Eastwood N, Zhou J, Derelle R, Abdallah MAE, Stubbings WA, Jia Y, Crawford SE, Davidson TA, Colbourne JK, Creer S, Bik H, Hollert H, Orsini L. 100 years of anthropogenic impact causes changes in freshwater functional biodiversity. eLife 2023; 12:RP86576. [PMID: 37933221 PMCID: PMC10629823 DOI: 10.7554/elife.86576] [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] [Indexed: 11/08/2023] Open
Abstract
Despite efforts from scientists and regulators, biodiversity is declining at an alarming rate. Unless we find transformative solutions to preserve biodiversity, future generations may not be able to enjoy nature's services. We have developed a conceptual framework that establishes the links between biodiversity dynamics and abiotic change through time and space using artificial intelligence. Here, we apply this framework to a freshwater ecosystem with a known history of human impact and study 100 years of community-level biodiversity, climate change and chemical pollution trends. We apply explainable network models with multimodal learning to community-level functional biodiversity measured with multilocus metabarcoding, to establish correlations with biocides and climate change records. We observed that the freshwater community assemblage and functionality changed over time without returning to its original state, even if the lake partially recovered in recent times. Insecticides and fungicides, combined with extreme temperature events and precipitation, explained up to 90% of the functional biodiversity changes. The community-level biodiversity approach used here reliably explained freshwater ecosystem shifts. These shifts were not observed when using traditional quality indices (e.g. Trophic Diatom Index). Our study advocates the use of high-throughput systemic approaches on long-term trends over species-focused ecological surveys to identify the environmental factors that cause loss of biodiversity and disrupt ecosystem functions.
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Affiliation(s)
- Niamh Eastwood
- Environmental Genomics Group, School of Biosciences, University of BirminghamBirminghamUnited Kingdom
| | - Jiarui Zhou
- Environmental Genomics Group, School of Biosciences, University of BirminghamBirminghamUnited Kingdom
| | - Romain Derelle
- Environmental Genomics Group, School of Biosciences, University of BirminghamBirminghamUnited Kingdom
| | | | - William A Stubbings
- Environmental Genomics Group, School of Biosciences, University of BirminghamBirminghamUnited Kingdom
- School of Geography, Earth & Environmental Sciences, University of BirminghamBirminghamUnited Kingdom
| | - Yunlu Jia
- Department Evolutionary Ecology & Environmental Toxicology, Faculty of Biological Sciences, Goethe University FrankfurtFrankfurtGermany
| | - Sarah E Crawford
- Department Evolutionary Ecology & Environmental Toxicology, Faculty of Biological Sciences, Goethe University FrankfurtFrankfurtGermany
| | - Thomas A Davidson
- Lake Group, Department of Ecoscience, Aarhus UniversityAarhusDenmark
| | - John K Colbourne
- Environmental Genomics Group, School of Biosciences, University of BirminghamBirminghamUnited Kingdom
| | - Simon Creer
- School of Natural Sciences, Environment Centre Wales, Deiniol Road, Bangor UniversityBangorUnited Kingdom
| | - Holly Bik
- Department Marine Sciences and Institute of Bioinformatics, University of GeorgiaAthensUnited States
| | - Henner Hollert
- Department Evolutionary Ecology & Environmental Toxicology, Faculty of Biological Sciences, Goethe University FrankfurtFrankfurtGermany
- LOEWE Centre for Translational Biodiversity Genomics (LOEWE‐TBG)FrankfurtGermany
- Department Media-related Toxicology, Institute for Molecular Biology and Applied Ecology (IME)FrankfurtGermany
| | - Luisa Orsini
- Environmental Genomics Group, School of Biosciences, University of BirminghamBirminghamUnited Kingdom
- The Alan Turing Institute, British LibraryLondonUnited Kingdom
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15
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Pan J, Cho TY, Sun M, Debnath R, Lonsdale N, Wilcox C, Bardhan R. Future workspace needs flexibility and diversity: A machine learning-driven behavioural analysis of co-working space. PLoS One 2023; 18:e0292370. [PMID: 37851592 PMCID: PMC10584156 DOI: 10.1371/journal.pone.0292370] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/19/2023] [Indexed: 10/20/2023] Open
Abstract
The future of workspace is significantly shaped by the advancements in technologies, changes in work patterns and workers' desire for an improved well-being. Co-working space is an alternative workspace solution, for cost-effectiveness, the opportunity for diverse and flexible design and multi-use. This study examined the human-centric design choices using spatial and temporal variation of occupancy levels and user behaviour in a flexible co-working space in London. Through a machine-learning-driven analysis, we investigated the time-dependent patterns, decompose space usage, calculate seat utilisation and identify spatial hotspots. The analysis incorporated a large dataset of sensor-detected occupancy data spanning 477 days, comprising more than 140 million (145×106) data points. Additionally, on-site observations of activities were recorded for 13 days spanning over a year, with 110 time instances including more than 1000 snapshots of occupants' activities, indoor environment, working behaviour and preferences. Results showed that the shared working areas positioned near windows or in more open, connected and visible locations are significantly preferred and utilised for communication and working, and semi-enclosed space on the side with less visibility and higher privacy are preferred for focused working. The flexibility of multi-use opportunity was the most preferred feature for hybrid working. The findings offer data-driven insights for human-centric space planning and design of office spaces in the future, particularly in the context of hybrid working setups, hot-desking and co-working systems.
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Affiliation(s)
- Jiayu Pan
- Sustainable Design Group, Department of Architecture, University of Cambridge, Cambridge, United Kingdom
| | - Tze Yeung Cho
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Maoran Sun
- Sustainable Design Group, Department of Architecture, University of Cambridge, Cambridge, United Kingdom
| | - Ramit Debnath
- Cambridge Zero and Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
- Collective Intelligence and Design Group, Department of Architecture, University of Cambridge, Cambridge, United Kingdom
- Division of Humanities and Social Science, California Institute of Technology, Pasadena, CA, United States of America
| | - Nathan Lonsdale
- spacelab_, London, United Kingdom
- sense_, London, United Kingdom
| | | | - Ronita Bardhan
- Sustainable Design Group, Department of Architecture, University of Cambridge, Cambridge, United Kingdom
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16
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Little CL, Druce KL, Dixon WG, Schultz DM, House T, McBeth J. What do people living with chronic pain want from a pain forecast? A research prioritization study. PLoS One 2023; 18:e0292968. [PMID: 37824568 PMCID: PMC10569639 DOI: 10.1371/journal.pone.0292968] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023] Open
Abstract
Because people with chronic pain feel uncertain about their future pain, a pain-forecasting model could support individuals to manage their daily pain and improve their quality of life. We conducted two patient and public involvement activities to design the content of a pain-forecasting model by learning participants' priorities in the features provided by a pain forecast and understanding the perceived benefits that such forecasts would provide. The first was a focus group of 12 people living with chronic pain to inform the second activity, a survey of 148 people living with chronic pain. Respondents prioritized forecasting of pain flares (100, or 68%) and fluctuations in pain severity (94, or 64%), particularly the timing of the onset and the severity. Of those surveyed, 75% (or 111) would use a future pain forecast and 80% (or 118) perceived making plans (e.g., shopping, social) as a benefit. For people with chronic pain, the timing of the onset of pain flares, the severity of pain flares and fluctuations in pain severity were prioritized as being key features of a pain forecast, and making plans was prioritized as being a key benefit.
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Affiliation(s)
- Claire L. Little
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
| | - Katie L. Druce
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
| | - William G. Dixon
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
| | - David M. Schultz
- Centre for Atmospheric Science, Department of Earth and Environmental Sciences, University of Manchester, Manchester, United Kingdom
- Centre for Crisis Studies and Mitigation, University of Manchester, Manchester, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - John McBeth
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
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17
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Bradshaw M, Squire JM, Morris E, Atkinson G, Richardson R, Lees J, Caputo M, Bigotti GM, Paul DM. Zebrafish as a model for cardiac disease; Cryo-EM structure of native cardiac thin filaments from Danio Rerio. J Muscle Res Cell Motil 2023; 44:179-192. [PMID: 37480427 PMCID: PMC10542308 DOI: 10.1007/s10974-023-09653-5] [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: 10/17/2022] [Accepted: 07/04/2023] [Indexed: 07/24/2023]
Abstract
Actin, tropomyosin and troponin, the proteins that comprise the contractile apparatus of the cardiac thin filament, are highly conserved across species. We have used cryo-EM to study the three-dimensional structure of the zebrafish cardiac thin and actin filaments. With 70% of human genes having an obvious zebrafish orthologue, and conservation of 85% of disease-causing genes, zebrafish are a good animal model for the study of human disease. Our structure of the zebrafish thin filament reveals the molecular interactions between the constituent proteins, showing that the fundamental organisation of the complex is the same as that reported in the human reconstituted thin filament. A reconstruction of zebrafish cardiac F-actin demonstrates no deviations from human cardiac actin over an extended length of 14 actin subunits. Modelling zebrafish homology models into our maps enabled us to compare, in detail, the similarity with human models. The structural similarities of troponin-T in particular, a region known to contain a hypertrophic cardiomyopathy 'hotspot', confirm the suitability of zebrafish to study these disease-causing mutations.
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Affiliation(s)
- Marston Bradshaw
- Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
| | - John M Squire
- Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
| | - Edward Morris
- University of Glasgow, Glasgow, UK
- Institute of Cancer Research, London, UK
| | - Georgia Atkinson
- Translational Health Sciences, University of Bristol, Bristol, UK
| | - Rebecca Richardson
- Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
| | - Jon Lees
- Translational Health Sciences, University of Bristol, Bristol, UK
| | - Massimo Caputo
- Translational Health Sciences, University of Bristol, Bristol, UK
| | - Giulia M Bigotti
- Translational Health Sciences, University of Bristol, Bristol, UK
| | - Danielle M Paul
- Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK.
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18
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Grossmann TG, Schönlieb CB, Da Rold O. Extracting chain lines and laid lines from digital images of medieval paper using spectral total variation decomposition. Herit Sci 2023; 11:180. [PMID: 37638147 PMCID: PMC10447590 DOI: 10.1186/s40494-023-01013-3] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 07/29/2023] [Indexed: 08/29/2023]
Abstract
Medieval paper, a handmade product, is made with a mould which leaves an indelible imprint on the sheet of paper. This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet. Extracting these features allows the identification of the paper stock and gives information about the chronology, localisation and movement of manuscripts and people. Most computational work for feature extraction of paper analysis has so far focused on radiography or transmitted light images. While these imaging methods provide clear visualisation of the features of interest, they are expensive and time consuming in their acquisition and not feasible for smaller institutions. However, reflected light images of medieval paper manuscripts are abundant and possibly cheaper in their acquisition. In this paper, we propose algorithms to detect and extract the laid and chain lines from reflected light images. We tackle the main drawback of reflected light images, that is, the low contrast attenuation of chain and laid lines and intensity jumps due to noise and degradation, by employing the spectral total variation decomposition and develop methods for subsequent chain and laid line extraction. Our results clearly demonstrate the feasibility of using reflected light images in paper analysis. This work enables feature extraction for paper manuscripts that have otherwise not been analysed due to a lack of appropriate images. We also open the door for paper stock identification at scale.
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Affiliation(s)
- Tamara G. Grossmann
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
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19
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Berman AG, Orchard WR, Gehrung M, Markowetz F. SliDL: A toolbox for processing whole-slide images in deep learning. PLoS One 2023; 18:e0289499. [PMID: 37549131 PMCID: PMC10406329 DOI: 10.1371/journal.pone.0289499] [Citation(s) in RCA: 1] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/20/2023] [Indexed: 08/09/2023] Open
Abstract
The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists. However, WSIs present a number of unique challenges for analysis, requiring special consideration of image annotations, slide and image artefacts, and evaluation of WSI-trained model performance. Here we introduce SliDL, a Python library for performing pre- and post-processing of WSIs. SliDL makes WSI data handling easy, allowing users to perform essential processing tasks in a few simple lines of code, bridging the gap between standard image analysis and WSI analysis. We introduce each of the main functionalities within SliDL: from annotation and tile extraction to tissue detection and model evaluation. We also provide 'code snippets' to guide the user in running SliDL. SliDL has been designed to interact with PyTorch, one of the most widely used deep learning libraries, allowing seamless integration into deep learning workflows. By providing a framework in which deep learning methods for WSI analysis can be developed and applied, SliDL aims to increase the accessibility of an important application of deep learning.
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Affiliation(s)
- Adam G. Berman
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - William R. Orchard
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Marcel Gehrung
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
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20
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Abdel-Rehim A, Orhobor O, Hang L, Ni H, King RD. Protein-ligand binding affinity prediction exploiting sequence constituent homology. Bioinformatics 2023; 39:btad502. [PMID: 37572302 PMCID: PMC10463547 DOI: 10.1093/bioinformatics/btad502] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 07/10/2023] [Accepted: 08/11/2023] [Indexed: 08/14/2023] Open
Abstract
MOTIVATION Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying complexity have been developed making use of some or all the spatial and categorical information available in these structures. The evaluation of such methods has mainly been carried out using datasets from PDBbind. Particularly the Comparative Assessment of Scoring Functions (CASF) 2007, 2013, and 2016 datasets with dedicated test sets. This work demonstrates that only a small number of simple descriptors is necessary to efficiently estimate binding affinity for these complexes without the need to know the exact binding conformation of a ligand. RESULTS The developed approach of using a small number of ligand and protein descriptors in conjunction with gradient boosting trees demonstrates high performance on the CASF datasets. This includes the commonly used benchmark CASF2016 where it appears to perform better than any other approach. This methodology is also useful for datasets where the spatial relationship between the ligand and protein is unknown as demonstrated using a large ChEMBL-derived dataset. AVAILABILITY AND IMPLEMENTATION Code and data uploaded to https://github.com/abbiAR/PLBAffinity.
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Affiliation(s)
- Abbi Abdel-Rehim
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom
| | | | - Lou Hang
- Department of Mathematics, University College London, London WC1H 0AY, United Kingdom
| | - Hao Ni
- Department of Mathematics, University College London, London WC1H 0AY, United Kingdom
- The Alan Turing Institute, London NW1 2DB, United Kingdom
| | - Ross D King
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom
- The Alan Turing Institute, London NW1 2DB, United Kingdom
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden
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21
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Sisk R, Sperrin M, Peek N, van Smeden M, Martin GP. Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study. Stat Methods Med Res 2023; 32:1461-1477. [PMID: 37105540 PMCID: PMC10515473 DOI: 10.1177/09622802231165001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [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] [Indexed: 04/29/2023]
Abstract
Background: In clinical prediction modelling, missing data can occur at any stage of the model pipeline; development, validation or deployment. Multiple imputation is often recommended yet challenging to apply at deployment; for example, the outcome cannot be in the imputation model, as recommended under multiple imputation. Regression imputation uses a fitted model to impute the predicted value of missing predictors from observed data, and could offer a pragmatic alternative at deployment. Moreover, the use of missing indicators has been proposed to handle informative missingness, but it is currently unknown how well this method performs in the context of clinical prediction models. Methods: We simulated data under various missing data mechanisms to compare the predictive performance of clinical prediction models developed using both imputation methods. We consider deployment scenarios where missing data is permitted or prohibited, imputation models that use or omit the outcome, and clinical prediction models that include or omit missing indicators. We assume that the missingness mechanism remains constant across the model pipeline. We also apply the proposed strategies to critical care data. Results: With complete data available at deployment, our findings were in line with existing recommendations; that the outcome should be used to impute development data when using multiple imputation and omitted under regression imputation. When missingness is allowed at deployment, omitting the outcome from the imputation model at the development was preferred. Missing indicators improved model performance in many cases but can be harmful under outcome-dependent missingness. Conclusion: We provide evidence that commonly taught principles of handling missing data via multiple imputation may not apply to clinical prediction models, particularly when data can be missing at deployment. We observed comparable predictive performance under multiple imputation and regression imputation. The performance of the missing data handling method must be evaluated on a study-by-study basis, and the most appropriate strategy for handling missing data at development should consider whether missing data are allowed at deployment. Some guidance is provided.
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Affiliation(s)
- Rose Sisk
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- Gendius Ltd, Macclesfield, UK
| | - Matthew Sperrin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- Alan Turing Institute, London, UK
| | - Niels Peek
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- Alan Turing Institute, London, UK
- NIHR Manchester Biomedical Research Centre, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Glen Philip Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
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22
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Rocha JJ, Jayaram SA, Stevens TJ, Muschalik N, Shah RD, Emran S, Robles C, Freeman M, Munro S. Functional unknomics: Systematic screening of conserved genes of unknown function. PLoS Biol 2023; 21:e3002222. [PMID: 37552676 PMCID: PMC10409296 DOI: 10.1371/journal.pbio.3002222] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [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: 01/12/2023] [Accepted: 06/27/2023] [Indexed: 08/10/2023] Open
Abstract
The human genome encodes approximately 20,000 proteins, many still uncharacterised. It has become clear that scientific research tends to focus on well-studied proteins, leading to a concern that poorly understood genes are unjustifiably neglected. To address this, we have developed a publicly available and customisable "Unknome database" that ranks proteins based on how little is known about them. We applied RNA interference (RNAi) in Drosophila to 260 unknown genes that are conserved between flies and humans. Knockdown of some genes resulted in loss of viability, and functional screening of the rest revealed hits for fertility, development, locomotion, protein quality control, and resilience to stress. CRISPR/Cas9 gene disruption validated a component of Notch signalling and 2 genes contributing to male fertility. Our work illustrates the importance of poorly understood genes, provides a resource to accelerate future research, and highlights a need to support database curation to ensure that misannotation does not erode our awareness of our own ignorance.
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Affiliation(s)
- João J. Rocha
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | | | - Tim J. Stevens
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | | | - Rajen D. Shah
- Centre for Mathematical Sciences, University of Cambridge, Cambridge, United Kingdom
| | - Sahar Emran
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Cristina Robles
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Matthew Freeman
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
- Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom
| | - Sean Munro
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
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23
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Sebenius I, Seidlitz J, Warrier V, Bethlehem RAI, Alexander-Bloch A, Mallard TT, Garcia RR, Bullmore ET, Morgan SE. Robust estimation of cortical similarity networks from brain MRI. Nat Neurosci 2023; 26:1461-1471. [PMID: 37460809 PMCID: PMC10400419 DOI: 10.1038/s41593-023-01376-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [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: 10/11/2022] [Accepted: 06/08/2023] [Indexed: 08/05/2023]
Abstract
Structural similarity is a growing focus for magnetic resonance imaging (MRI) of connectomes. Here we propose Morphometric INverse Divergence (MIND), a new method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on n > 11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry and more correlated with tract-tracing measures of axonal connectivity. MIND networks derived from human T1-weighted MRI were more sensitive to age-related changes than MSNs or networks derived by tractography of diffusion-weighted MRI. Gene co-expression between cortical areas was more strongly coupled to MIND networks than to MSNs or tractography. MIND network phenotypes were also more heritable, especially edges between structurally differentiated areas. MIND network analysis provides a biologically validated lens for cortical connectomics using readily available MRI data.
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Affiliation(s)
- Isaac Sebenius
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK.
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Varun Warrier
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Travis T Mallard
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Rafael Romero Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Barcelona, Spain
| | | | - Sarah E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
- Alan Turing Institute, London, UK
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24
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Sampaio P, Pestana S, Bota C, Guerrero A, Telley IA, Smith D, Lopes SS. Fluid extraction from the left-right organizer uncovers mechanical properties needed for symmetry breaking. eLife 2023; 12:e83861. [PMID: 37477290 PMCID: PMC10361723 DOI: 10.7554/elife.83861] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 06/27/2023] [Indexed: 07/22/2023] Open
Abstract
Humans and other vertebrates define body axis left-right asymmetry in the early stages of embryo development. The mechanism behind left-right establishment is not fully understood. Symmetry breaking occurs in a dedicated organ called the left-right organizer (LRO) and involves motile cilia generating fluid-flow therein. However, it has been a matter of debate whether the process of symmetry breaking relies on a chemosensory or a mechanosensory mechanism (Shinohara et al., 2012). Novel tailored manipulations for LRO fluid extraction in living zebrafish embryos allowed us to pinpoint a physiological developmental period for breaking left-right symmetry during development. The shortest critical time-window was narrowed to one hour and characterized by a mild counterclockwise flow. The experimental challenge consisted in emptying the LRO of its fluid, abrogating simultaneously flow force and chemical determinants. Our findings revealed an unprecedented recovery capacity of the embryo to re-fil and re-circulate new LRO fluid. The embryos that later developed laterality problems were found to be those that had lower anterior angular velocity and thus less anterior-posterior heterogeneity. Next, aiming to test the presence of any secreted determinant, we replaced the extracted LRO fluid by a physiological buffer. Despite some transitory flow homogenization, laterality defects were absent unless viscosity was altered, demonstrating that symmetry breaking does not depend on the nature of the fluid content but is rather sensitive to fluid mechanics. Altogether, we conclude that the zebrafish LRO is more sensitive to fluid dynamics for symmetry breaking.
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Affiliation(s)
- Pedro Sampaio
- CEDOC, Chronic Diseases Research Centre, NOVA Medical School. Faculdade de8 Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Sara Pestana
- CEDOC, Chronic Diseases Research Centre, NOVA Medical School. Faculdade de8 Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Catarina Bota
- CEDOC, Chronic Diseases Research Centre, NOVA Medical School. Faculdade de8 Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Adán Guerrero
- Laboratorio Nacional de Microscopía Avanzada. Departamento de Genética del Desarrollo y Fisiología Molecular. Instituto de Biotecnología. Universidad Nacional Autónoma de México (UNAM), Cuernavaca, Mexico
| | - Ivo A Telley
- Instituto Gulbenkian de Ciência, Fundação Calouste Gulbenkian, Oeiras, Portugal
| | - David Smith
- School of Mathematics, University of Birmingham, Birmingham, United Kingdom
- Institute for Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
| | - Susana Santos Lopes
- CEDOC, Chronic Diseases Research Centre, NOVA Medical School. Faculdade de8 Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal
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25
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Walkowiak S, Coutrot A, Hegarty M, Velasco PF, Wiener JM, Dalton RC, Hölscher C, Hornberger M, Spiers HJ, Manley E. Cultural determinants of the gap between self-estimated navigation ability and wayfinding performance: evidence from 46 countries. Sci Rep 2023; 13:10844. [PMID: 37407585 DOI: 10.1038/s41598-023-30937-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/03/2023] [Indexed: 07/07/2023] Open
Abstract
Cognitive abilities can vary widely. Some people excel in certain skills, others struggle. However, not all those who describe themselves as gifted are. One possible influence on self-estimates is the surrounding culture. Some cultures may amplify self-assurance and others cultivate humility. Past research has shown that people in different countries can be grouped into a set of consistent cultural clusters with similar values and tendencies, such as attitudes to masculinity or individualism. Here we explored whether such cultural dimensions might relate to the extent to which populations in 46 countries overestimate or underestimate their cognitive abilities in the domain of spatial navigation. Using the Sea Hero Quest navigation test and a large sample (N = 383,187) we found cultural clusters of countries tend to be similar in how they self-rate ability relative to their actual performance. Across the world population sampled, higher self-ratings were associated with better performance. However, at the national level, higher self-ratings as a nation were not associated with better performance as a nation. Germanic and Near East countries were found to be most overconfident in their abilities and Nordic countries to be most under-confident in their abilities. Gender stereotypes may play a role in mediating this pattern, with larger national positive attitudes to male stereotyped roles (Hofstede's masculinity dimension) associated with a greater overconfidence in performance at the national level. We also replicate, with higher precision than prior studies, evidence that older men tend to overestimate their navigation skill more than other groups. These findings give insight into how culture and demographics may impact self-estimates of our abilities.
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Affiliation(s)
- S Walkowiak
- Centre for Advanced Spatial Analysis, University College London, London, UK
- The Alan Turing Institute, London, UK
| | - A Coutrot
- Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), CNRS, Université de Lyon, Lyon, France
| | - M Hegarty
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, USA
| | | | - J M Wiener
- Department of Psychology, Ageing and Dementia Research Centre, Bournemouth University, Poole, UK
| | - R C Dalton
- Department of Architecture and Built Environment, Northumbria University, Newcastle, UK
| | - C Hölscher
- ETH Zürich, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - M Hornberger
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - H J Spiers
- Institute of Behavioural Neuroscience, Department of Experimental Psychology, Division of Psychology and Language Sciences, University College London, London, UK.
| | - E Manley
- Centre for Advanced Spatial Analysis, University College London, London, UK.
- The Alan Turing Institute, London, UK.
- School of Geography, University of Leeds, Leeds, UK.
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26
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Tanner AR, Di Cara NH, Maggio V, Thomas R, Boyd A, Sloan L, Al Baghal T, Macleod J, Haworth CMA, Davis OSP. Epicosm-a framework for linking online social media in epidemiological cohorts. Int J Epidemiol 2023; 52:952-957. [PMID: 36847716 PMCID: PMC10244036 DOI: 10.1093/ije/dyad020] [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: 02/07/2022] [Accepted: 02/16/2023] [Indexed: 03/01/2023] Open
Abstract
MOTIVATION Social media represent an unrivalled opportunity for epidemiological cohorts to collect large amounts of high-resolution time course data on mental health. Equally, the high-quality data held by epidemiological cohorts could greatly benefit social media research as a source of ground truth for validating digital phenotyping algorithms. However, there is currently a lack of software for doing this in a secure and acceptable manner. We worked with cohort leaders and participants to co-design an open-source, robust and expandable software framework for gathering social media data in epidemiological cohorts. IMPLEMENTATION Epicosm is implemented as a Python framework that is straightforward to deploy and run inside a cohort's data safe haven. GENERAL FEATURES The software regularly gathers Tweets from a list of accounts and stores them in a database for linking to existing cohort data. AVAILABILITY This open-source software is freely available at [https://dynamicgenetics.github.io/Epicosm/].
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Affiliation(s)
- Alastair R Tanner
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Nina H Di Cara
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Valerio Maggio
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Richard Thomas
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Andy Boyd
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Luke Sloan
- School of Social Sciences, Cardiff University, Cardiff, UK
| | - Tarek Al Baghal
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | - John Macleod
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Claire M A Haworth
- School of Psychological Science, University of Bristol, Bristol, UK
- Alan Turing Institute, British Library, London, UK
| | - Oliver S P Davis
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Alan Turing Institute, British Library, London, UK
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27
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Gordon DFN, Christou A, Stouraitis T, Gienger M, Vijayakumar S. Adaptive assistive robotics: a framework for triadic collaboration between humans and robots. R Soc Open Sci 2023; 10:221617. [PMID: 37388317 PMCID: PMC10300679 DOI: 10.1098/rsos.221617] [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: 12/16/2022] [Accepted: 05/30/2023] [Indexed: 07/01/2023]
Abstract
Robots and other assistive technologies have a huge potential to help society in domains ranging from factory work to healthcare. However, safe and effective control of robotic agents in these environments is complex, especially when it involves close interactions and multiple actors. We propose an effective framework for optimizing the behaviour of robots and complementary assistive technologies in systems comprising a mix of human and technological agents with numerous high-level goals. The framework uses a combination of detailed biomechanical modelling and weighted multi-objective optimization to allow for the fine tuning of robot behaviours depending on the specification of the task at hand. We illustrate our framework via two case studies across assisted living and rehabilitation scenarios, and conduct simulations and experiments of triadic collaboration in practice. Our results indicate a marked benefit to the triadic approach, showing the potential to improve outcome measures for human agents in robot-assisted tasks.
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Affiliation(s)
- Daniel F. N. Gordon
- The University of Edinburgh, Edinburgh, UK
- The Alan Turing Institute, London, UK
| | | | | | | | - Sethu Vijayakumar
- The University of Edinburgh, Edinburgh, UK
- The Alan Turing Institute, London, UK
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28
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Basanta CDLAC, Bazzi M, Hijazi M, Bessant C, Cutillas PR. Community detection in empirical kinase networks identifies new potential members of signalling pathways. PLoS Comput Biol 2023; 19:e1010459. [PMID: 37352361 PMCID: PMC10325051 DOI: 10.1371/journal.pcbi.1010459] [Citation(s) in RCA: 1] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 07/06/2023] [Accepted: 06/05/2023] [Indexed: 06/25/2023] Open
Abstract
Phosphoproteomics allows one to measure the activity of kinases that drive the fluxes of signal transduction pathways involved in biological processes such as immune function, senescence and cell growth. However, deriving knowledge of signalling network circuitry from these data is challenging due to a scarcity of phosphorylation sites that define kinase-kinase relationships. To address this issue, we previously identified around 6,000 phosphorylation sites as markers of kinase-kinase relationships (that may be conceptualised as network edges), from which empirical cell-model-specific weighted kinase networks may be reconstructed. Here, we assess whether the application of community detection algorithms to such networks can identify new components linked to canonical signalling pathways. Phosphoproteomics data from acute myeloid leukaemia (AML) cells treated separately with PI3K, AKT, MEK and ERK inhibitors were used to reconstruct individual kinase networks. We used modularity maximisation to detect communities in each network, and selected the community containing the main target of the inhibitor used to treat cells. These analyses returned communities that contained known canonical signalling components. Interestingly, in addition to canonical PI3K/AKT/mTOR members, the community assignments returned TTK (also known as MPS1) as a likely component of PI3K/AKT/mTOR signalling. We drew similar insights from an external phosphoproteomics dataset from breast cancer cells treated with rapamycin and oestrogen. We confirmed this observation with wet-lab laboratory experiments showing that TTK phosphorylation was decreased in AML cells treated with AKT and MTOR inhibitors. This study illustrates the application of community detection algorithms to the analysis of empirical kinase networks to uncover new members linked to canonical signalling pathways.
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Affiliation(s)
- Celia De Los Angeles Colomina Basanta
- Cell signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Marya Bazzi
- Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom
- The Alan Turing Institute, London, United Kingdom
| | - Maruan Hijazi
- Cell signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Conrad Bessant
- The Alan Turing Institute, London, United Kingdom
- School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom
| | - Pedro R. Cutillas
- Cell signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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29
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Griffiths RC, Jenkins PA. An estimator for the recombination rate from a continuously observed diffusion of haplotype frequencies. J Math Biol 2023; 86:98. [PMID: 37233854 DOI: 10.1007/s00285-023-01931-7] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/13/2023] [Accepted: 05/03/2023] [Indexed: 05/27/2023]
Abstract
Recombination is a fundamental evolutionary force, but it is difficult to quantify because the effect of a recombination event on patterns of variation in a sample of genetic data can be hard to discern. Estimators for the recombination rate, which are usually based on the idea of integrating over the unobserved possible evolutionary histories of a sample, can therefore be noisy. Here we consider a related question: how would an estimator behave if the evolutionary history actually was observed? This would offer an upper bound on the performance of estimators used in practice. In this paper we derive an expression for the maximum likelihood estimator for the recombination rate based on a continuously observed, multi-locus, Wright-Fisher diffusion of haplotype frequencies, complementing existing work for an estimator of selection. We show that, contrary to selection, the estimator has unusual properties because the observed information matrix can explode in finite time whereupon the recombination parameter is learned without error. We also show that the recombination estimator is robust to the presence of selection in the sense that incorporating selection into the model leaves the estimator unchanged. We study the properties of the estimator by simulation and show that its distribution can be quite sensitive to the underlying mutation rates.
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Affiliation(s)
- Robert C Griffiths
- School of Mathematics, Monash University, 9 Rainforest Walk, Melbourne, VIC, 3800, Australia
| | - Paul A Jenkins
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.
- The Alan Turing Institute, British Library, London, NW1 2DB, UK.
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30
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Sopic M, Robinson EL, Emanueli C, Srivastava P, Angione C, Gaetano C, Condorelli G, Martelli F, Pedrazzini T, Devaux Y. Integration of epigenetic regulatory mechanisms in heart failure. Basic Res Cardiol 2023; 118:16. [PMID: 37140699 PMCID: PMC10158703 DOI: 10.1007/s00395-023-00986-3] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/27/2023] [Accepted: 04/10/2023] [Indexed: 05/05/2023]
Abstract
The number of "omics" approaches is continuously growing. Among others, epigenetics has appeared as an attractive area of investigation by the cardiovascular research community, notably considering its association with disease development. Complex diseases such as cardiovascular diseases have to be tackled using methods integrating different omics levels, so called "multi-omics" approaches. These approaches combine and co-analyze different levels of disease regulation. In this review, we present and discuss the role of epigenetic mechanisms in regulating gene expression and provide an integrated view of how these mechanisms are interlinked and regulate the development of cardiac disease, with a particular attention to heart failure. We focus on DNA, histone, and RNA modifications, and discuss the current methods and tools used for data integration and analysis. Enhancing the knowledge of these regulatory mechanisms may lead to novel therapeutic approaches and biomarkers for precision healthcare and improved clinical outcomes.
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Affiliation(s)
- Miron Sopic
- Department of Medical Biochemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
| | - Emma L Robinson
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Costanza Emanueli
- National Heart & Lung Institute, Imperial College London, London, UK
| | | | - Claudio Angione
- School of Computing, Engineering & Digital Technologies, Teesside University, Tees Valley, Middlesbrough, TS1 3BA, UK
- Centre for Digital Innovation, Teesside University, Campus Heart, Tees Valley, Middlesbrough, TS1 3BX, UK
- National Horizons Centre, Darlington, DL1 1HG, UK
| | - Carlo Gaetano
- Laboratorio di Epigenetica, Istituti Clinici Scientifici Maugeri IRCCS, Via Maugeri 10, 27100, Pavia, Italy
| | - Gianluigi Condorelli
- IRCCS-Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, MI, Italy
- Institute of Genetic and Biomedical Research, National Research Council of Italy, Arnold-Heller-Str.3, 24105, Milan, Italy
| | - Fabio Martelli
- Molecular Cardiology Laboratory, IRCCS-Policlinico San Donato, Via Morandi 30, San Donato Milanese, 20097, Milan, Italy
| | - Thierry Pedrazzini
- Experimental Cardiology Unit, Division of Cardiology, Department of Cardiovascular Medicine, University of Lausanne Medical School, 1011, Lausanne, Switzerland
| | - Yvan Devaux
- Cardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445, Strassen, Luxembourg.
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Caldarelli G, Arcaute E, Barthelemy M, Batty M, Gershenson C, Helbing D, Mancuso S, Moreno Y, Ramasco JJ, Rozenblat C, Sánchez A, Fernández-Villacañas JL. The role of complexity for digital twins of cities. Nat Comput Sci 2023; 3:374-381. [PMID: 38177836 DOI: 10.1038/s43588-023-00431-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 03/13/2023] [Indexed: 01/06/2024]
Abstract
We argue that theories and methods drawn from complexity science are urgently needed to guide the development and use of digital twins for cities. The theoretical framework from complexity science takes into account both the short-term and the long-term dynamics of cities and their interactions. This is the foundation for a new approach that treats cities not as large machines or logistic systems but as mutually interwoven self-organizing phenomena, which evolve, to an extent, like living systems.
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Affiliation(s)
- G Caldarelli
- DSMN University of Venice Ca'Foscari, Venice, Italy.
- ISC-CNR, Dipartimento di Fisica, Università Sapienza, Rome, Italy.
- London Institute for Mathematical Sciences, London, UK.
- Fondazione per il futuro delle città, Florence, Italy.
| | - E Arcaute
- CASA,The Bartlett Centre for Advanced Spatial Analysis, UCL, London, UK
- The Alan Turing Institute, The British Library, London, UK
| | - M Barthelemy
- Université Paris-Saclay, CNRS, CEA, Institut de Physique Théorique, Gif-sur-Yvette, France
- Centre d'Analyse et de Mathématique Sociales CAMS, UMR 8557 CNRS-EHESS, Ecole des Hautes Etudes en Sciences Sociales, Paris, France
| | - M Batty
- CASA,The Bartlett Centre for Advanced Spatial Analysis, UCL, London, UK
- The Alan Turing Institute, The British Library, London, UK
| | - C Gershenson
- Universidad Nacional Autónoma de México, Mexico City, Mexico
- Santa Fe Institute, Santa Fe, NM, USA
| | - D Helbing
- ETH Zurich, Computational Social Science, Zurich, Switzerland
- Complexity Science Hub, Vienna, Austria
| | - S Mancuso
- Fondazione per il futuro delle città, Florence, Italy
- Department of Agriculture, Food, Environment and Forestry (DAGRI), Florence, Italy
| | - Y Moreno
- Complexity Science Hub, Vienna, Austria
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain
- Department of Theoretical Physics, Faculty of Sciences, University of Zaragoza, Zaragoza, Spain
- CENTAI Institute, Turin, Italy
| | - J J Ramasco
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, Spain
| | - C Rozenblat
- Institute of Geography and Sustainability, UNIL, Lausanne, Switzerland
| | - A Sánchez
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Departamento de Matematicas, Universidad Carlos III de Madrid, Getafe, Spain
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32
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Zühlsdorff K, Dalley JW, Robbins TW, Morein-Zamir S. Cognitive flexibility: neurobehavioral correlates of changing one's mind. Cereb Cortex 2023; 33:5436-5446. [PMID: 36368894 PMCID: PMC10152092 DOI: 10.1093/cercor/bhac431] [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: 07/13/2022] [Revised: 10/06/2022] [Accepted: 10/07/2022] [Indexed: 11/13/2022] Open
Abstract
Behavioral and cognitive flexibility allow adaptation to a changing environment. Most tasks used to investigate flexibility require switching reactively in response to deterministic task-response rules. In daily life, flexibility often involves a volitional decision to change behavior. This can be instigated by environmental signals, but these are frequently unreliable. We report results from a novel "change your mind" task, which assesses volitional switching under uncertainty without the need for rule-based learning. Participants completed a two-alternative choice task, and following spurious feedback, were presented with the same stimulus again. Subjects had the opportunity to repeat or change their response. Forty healthy participants completed the task while undergoing a functional magnetic resonance imaging scan. Participants predominantly repeated their choice but changed more when their first response was incorrect or when the feedback was negative. Greater activations for changing were found in the inferior frontal junction, anterior insula (AI), anterior cingulate, and dorsolateral prefrontal cortex. Changing responses were also accompanied by reduced connectivity from the AI and orbitofrontal cortices to the occipital cortex. Using multivariate pattern analysis of brain activity, we predicted with 77% reliability whether participants would change their mind. These findings extend our understanding of cognitive flexibility in daily life by assessing volitional decision-making.
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Affiliation(s)
- Katharina Zühlsdorff
- Department of Psychology, University of Cambridge, Downing Place, Cambridge, CB2 3EB, United Kingdom
- The Alan Turing Institute, British Library, 96 Euston Road, London, NW1 2DB, United Kingdom
- Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, United Kingdom
| | - Jeffrey W Dalley
- Department of Psychology, University of Cambridge, Downing Place, Cambridge, CB2 3EB, United Kingdom
- Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, United Kingdom
- Department of Psychiatry, University of Cambridge, Herchel Smith Building, Forvie Site, Robinson Way, Cambridge, CB2 0SZ, United Kingdom
| | - Trevor W Robbins
- Department of Psychology, University of Cambridge, Downing Place, Cambridge, CB2 3EB, United Kingdom
- Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, United Kingdom
| | - Sharon Morein-Zamir
- School of Psychology and Sport Science, Anglia Ruskin University, East Road, Cambridge, CB1 1PT, United Kingdom
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33
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Nettekoven CR, Diederen K, Giles O, Duncan H, Stenson I, Olah J, Gibbs-Dean T, Collier N, Vértes PE, Spencer TJ, Morgan SE, McGuire P. Semantic Speech Networks Linked to Formal Thought Disorder in Early Psychosis. Schizophr Bull 2023; 49:S142-S152. [PMID: 36946531 PMCID: PMC10031728 DOI: 10.1093/schbul/sbac056] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
BACKGROUND AND HYPOTHESIS Mapping a patient's speech as a network has proved to be a useful way of understanding formal thought disorder in psychosis. However, to date, graph theory tools have not explicitly modelled the semantic content of speech, which is altered in psychosis. STUDY DESIGN We developed an algorithm, "netts," to map the semantic content of speech as a network, then applied netts to construct semantic speech networks for a general population sample (N = 436), and a clinical sample comprising patients with first episode psychosis (FEP), people at clinical high risk of psychosis (CHR-P), and healthy controls (total N = 53). STUDY RESULTS Semantic speech networks from the general population were more connected than size-matched randomized networks, with fewer and larger connected components, reflecting the nonrandom nature of speech. Networks from FEP patients were smaller than from healthy participants, for a picture description task but not a story recall task. For the former task, FEP networks were also more fragmented than those from controls; showing more connected components, which tended to include fewer nodes on average. CHR-P networks showed fragmentation values in-between FEP patients and controls. A clustering analysis suggested that semantic speech networks captured novel signals not already described by existing NLP measures. Network features were also related to negative symptom scores and scores on the Thought and Language Index, although these relationships did not survive correcting for multiple comparisons. CONCLUSIONS Overall, these data suggest that semantic networks can enable deeper phenotyping of formal thought disorder in psychosis. Whilst here we focus on network fragmentation, the semantic speech networks created by Netts also contain other, rich information which could be extracted to shed further light on formal thought disorder. We are releasing Netts as an open Python package alongside this manuscript.
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Affiliation(s)
- Caroline R Nettekoven
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Kelly Diederen
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | | | | | - Julianna Olah
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Toni Gibbs-Dean
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Nigel Collier
- Theoretical and Applied Linguistics, Faculty of Modern and Medieval Languages, University of Cambridge, Cambridge, UK
| | - Petra E Vértes
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - Tom J Spencer
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sarah E Morgan
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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Ahfock D, Astle WJ, Richardson S. On randomized sketching algorithms and the Tracy-Widom law. Stat Comput 2023; 33:34. [PMID: 36691583 PMCID: PMC9852177 DOI: 10.1007/s11222-022-10148-5] [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: 12/12/2021] [Accepted: 09/03/2022] [Indexed: 06/17/2023]
Abstract
UNLABELLED There is an increasing body of work exploring the integration of random projection into algorithms for numerical linear algebra. The primary motivation is to reduce the overall computational cost of processing large datasets. A suitably chosen random projection can be used to embed the original dataset in a lower-dimensional space such that key properties of the original dataset are retained. These algorithms are often referred to as sketching algorithms, as the projected dataset can be used as a compressed representation of the full dataset. We show that random matrix theory, in particular the Tracy-Widom law, is useful for describing the operating characteristics of sketching algorithms in the tall-data regime when the sample size n is much greater than the number of variables d. Asymptotic large sample results are of particular interest as this is the regime where sketching is most useful for data compression. In particular, we develop asymptotic approximations for the success rate in generating random subspace embeddings and the convergence probability of iterative sketching algorithms. We test a number of sketching algorithms on real large high-dimensional datasets and find that the asymptotic expressions give accurate predictions of the empirical performance. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11222-022-10148-5.
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Affiliation(s)
- Daniel Ahfock
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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35
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Menon A, Pascazio L, Nurkowski D, Farazi F, Mosbach S, Akroyd J, Kraft M. OntoPESScan: An Ontology for Potential Energy Surface Scans. ACS Omega 2023; 8:2462-2475. [PMID: 36687109 PMCID: PMC9850739 DOI: 10.1021/acsomega.2c06948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
In this work, a new OntoPESScan ontology is developed for the semantic representation of one-dimensional potential energy surface (PES) scans, a central concept in computational chemistry. This ontology is developed in line with knowledge graph principles and The World Avatar (TWA) project. OntoPESScan is linked to other ontologies for chemistry in TWA, including OntoSpecies, which helps uniquely identify species along the PES and access their properties, and OntoCompChem, which allows the association of potential energy surfaces with quantum chemical calculations and the concepts used to derive them. A force-field fitting agent is also developed that makes use of the information in the OntoPESScan ontology to fit force fields to reactive surfaces of interest on the fly by making use of the empirical valence bond methodology. This agent is demonstrated to successfully parametrize two cases, namely, a PES scan on ethanol and a PES scan on a localized π-radical PAH hypothesized to play a role in soot formation during combustion. OntoPESScan is an extension to the capabilities of TWA and, in conjunction with potential further ontological support for molecular dynamics and reactions, will further progress toward an open, continuous, and self-growing knowledge graph for chemistry.
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Affiliation(s)
- Angiras Menon
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Laura Pascazio
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create
Way, CREATE Tower, #05-05, Singapore 138602
| | - Daniel Nurkowski
- CMCL
Innovations, Sheraton House, Castle Park, Cambridge CB3 0AX, U.K.
| | - Feroz Farazi
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Sebastian Mosbach
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create
Way, CREATE Tower, #05-05, Singapore 138602
| | - Jethro Akroyd
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create
Way, CREATE Tower, #05-05, Singapore 138602
| | - Markus Kraft
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create
Way, CREATE Tower, #05-05, Singapore 138602
- School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459
- The
Alan Turing Institute, London NW1 2BD, United
Kingdom
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36
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Ruddle RA, Adnan M, Hall M. Using set visualisation to find and explain patterns of missing values: a case study with NHS hospital episode statistics data. BMJ Open 2022; 12:e064887. [PMID: 36410820 PMCID: PMC9680176 DOI: 10.1136/bmjopen-2022-064887] [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] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVES Missing data is the most common data quality issue in electronic health records (EHRs). Missing data checks implemented in common analytical software are typically limited to counting the number of missing values in individual fields, but researchers and organisations also need to understand multifield missing data patterns to better inform advanced missing data strategies for which counts or numerical summaries are poorly suited. This study shows how set-based visualisation enables multifield missing data patterns to be discovered and investigated. DESIGN Development and evaluation of interactive set visualisation techniques to find patterns of missing data and generate actionable insights. The visualisations comprised easily interpretable bar charts for sets, heatmaps for set intersections and histograms for distributions of both sets and intersections. SETTING AND PARTICIPANTS Anonymised admitted patient care health records for National Health Service (NHS) hospitals and independent sector providers in England. The visualisation and data mining software was run over 16 million records and 86 fields in the dataset. RESULTS The dataset contained 960 million missing values. Set visualisation bar charts showed how those values were distributed across the fields, including several fields that, unexpectedly, were not complete. Set intersection heatmaps revealed unexpected gaps in diagnosis, operation and date fields because diagnosis and operation fields were not filled up sequentially and some operations did not have corresponding dates. Information gain ratio and entropy calculations allowed us to identify the origin of each unexpected pattern, in terms of the values of other fields. CONCLUSIONS Our findings show how set visualisation reveals important insights about multifield missing data patterns in large EHR datasets. The study revealed both rare and widespread data quality issues that were previously unknown, and allowed a particular part of a specific hospital to be pinpointed as the origin of rare issues that NHS Digital did not know exist.
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Affiliation(s)
- Roy A Ruddle
- School of Computing and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
| | - Muhammad Adnan
- Computer Science, Higher Colleges of Technology, Sharjah, UAE
| | - Marlous Hall
- Leeds Institute of Cardiovascular & Metabolic Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
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Spitzer H, Ripart M, Whitaker K, D’Arco F, Mankad K, Chen AA, Napolitano A, De Palma L, De Benedictis A, Foldes S, Humphreys Z, Zhang K, Hu W, Mo J, Likeman M, Davies S, Güttler C, Lenge M, Cohen NT, Tang Y, Wang S, Chari A, Tisdall M, Bargallo N, Conde-Blanco E, Pariente JC, Pascual-Diaz S, Delgado-Martínez I, Pérez-Enríquez C, Lagorio I, Abela E, Mullatti N, O’Muircheartaigh J, Vecchiato K, Liu Y, Caligiuri ME, Sinclair B, Vivash L, Willard A, Kandasamy J, McLellan A, Sokol D, Semmelroch M, Kloster AG, Opheim G, Ribeiro L, Yasuda C, Rossi-Espagnet C, Hamandi K, Tietze A, Barba C, Guerrini R, Gaillard WD, You X, Wang I, González-Ortiz S, Severino M, Striano P, Tortora D, Kälviäinen R, Gambardella A, Labate A, Desmond P, Lui E, O’Brien T, Shetty J, Jackson G, Duncan JS, Winston GP, Pinborg LH, Cendes F, Theis FJ, Shinohara RT, Cross JH, Baldeweg T, Adler S, Wagstyl K. Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study. Brain 2022; 145:3859-3871. [PMID: 35953082 PMCID: PMC9679165 DOI: 10.1093/brain/awac224] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [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: 12/14/2021] [Revised: 04/22/2022] [Accepted: 05/26/2022] [Indexed: 11/13/2022] Open
Abstract
One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.
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Affiliation(s)
- Hannah Spitzer
- Institute of Computational Biology, Helmholtz Center Munich, Munich 85764, Germany
| | - Mathilde Ripart
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
| | | | - Felice D’Arco
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Kshitij Mankad
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, Rome 00165, Italy
| | - Luca De Palma
- Rare and Complex Epilepsies, Department of Neurosciences, Bambino Gesù Children’s Hospital, IRCCS, Rome 00165, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Department of Neurosciences, Bambino Gesù Children’s Hospital, IRCCS, Rome 00165, Italy
| | - Stephen Foldes
- Barrow Neurological Institute at Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Zachary Humphreys
- Barrow Neurological Institute at Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100054, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100054, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100054, China
| | - Marcus Likeman
- Bristol Royal Hospital for Children, Bristol BS2 8BJ, UK
| | - Shirin Davies
- School of Psychology, Cardiff University Brain Research Imaging Centre, Cardiff CF24 4HQ, UK
- The Welsh Epilepsy Unit, Cardiff and Vale University Health Board, University Hospital of Wales, Cardiff CF14 4XW, UK
| | | | - Matteo Lenge
- Neuroscience Department, Children’s Hospital Meyer-University of Florence, Florence 50139, Italy
| | - Nathan T Cohen
- Center for Neuroscience, Children’s National Hospital, Washington, DC 20012, USA
| | - Yingying Tang
- Department of Neurology, West China Hospital of Sichuan University, Chengdu 610093, China
- Epilepsy Center, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Shan Wang
- Epilepsy Center, Cleveland Clinic, Cleveland, OH 44106, USA
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Aswin Chari
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Martin Tisdall
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Nuria Bargallo
- Department of Neuroradiology, Hospital Clinic Barcelona and Magnetic Resonance Imaging, Core Facility, IDIBAPS, Barcelona 08036, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid 28029, Spain
| | | | | | - Saül Pascual-Diaz
- Magnetic Resonance Imaging, Core Facility, IDIBAPS, Barcelona 08036, Spain
| | | | | | | | - Eugenio Abela
- Center for Neuropsychiatry and Intellectual Disability, Psychiatrische Dienste Aargau AG, Windisch 5120, Switzerland
| | - Nandini Mullatti
- Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Jonathan O’Muircheartaigh
- Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Department of Perinatal Imaging and Health, St. Thomas’ Hospital, King’s College London, London SE1 7EH, UK
| | - Katy Vecchiato
- Department of Perinatal Imaging and Health, St. Thomas’ Hospital, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Yawu Liu
- Department of Neurology, University of Eastern Finland, Kuopio 70210, Finland
| | - Maria Eugenia Caligiuri
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro 88100, Italy
| | - Ben Sinclair
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
- Department of Neurology, Monash University, Melbourne, VIC 3004, Australia
| | - Anna Willard
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
| | - Jothy Kandasamy
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Ailsa McLellan
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Drahoslav Sokol
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Mira Semmelroch
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3052, Australia
| | - Ane G Kloster
- Neurobiology Research Unit, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
| | - Giske Opheim
- Neurobiology Research Unit, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
- Department of Neuroradiology, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
| | - Letícia Ribeiro
- Department of Neurology, University of Campinas, Campinas 13083-888, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas, Campinas 13083-888, Brazil
| | - Clarissa Yasuda
- Department of Neurology, University of Campinas, Campinas 13083-888, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas, Campinas 13083-888, Brazil
| | | | - Khalid Hamandi
- School of Psychology, Cardiff University Brain Research Imaging Centre, Cardiff CF24 4HQ, UK
- The Welsh Epilepsy Unit, University Hospital of Wales, Cardiff CF14 4XW, UK
| | - Anna Tietze
- Charité University Hospital, Berlin 10117, Germany
| | - Carmen Barba
- Neuroscience Department, Children’s Hospital Meyer-University of Florence, Florence 50139, Italy
| | - Renzo Guerrini
- Neuroscience Department, Children’s Hospital Meyer-University of Florence, Florence 50139, Italy
| | | | - Xiaozhen You
- Center for Neuroscience, Children’s National Hospital, Washington, DC 20012, USA
| | - Irene Wang
- Epilepsy Center, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Sofía González-Ortiz
- Department of Neuroradiology, Hospital del Mar, Barcelona 08003, Spain
- Magnetic Resonance Imaging Core Facility, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
| | | | - Pasquale Striano
- IRCCS Istituto Giannina Gaslini, Genova 16147, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | | | - Reetta Kälviäinen
- Department of Neurology, University of Eastern Finland, Kuopio 70210, Finland
- Kuopio Epilepsy Center, Neurocenter, Kuopio University Hospital, Kuopio 70210, Finland
| | - Antonio Gambardella
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro 88100, Italy
| | - Angelo Labate
- Neurology Unit, Department of BIOMORF, University of Messina, Messina 98168, Italy
| | - Patricia Desmond
- Department of Radiology, The Royal Melbourne Hospital, University of Melbourne, Parkville, VIC 3050, Australia
| | - Elaine Lui
- Department of Radiology, The Royal Melbourne Hospital, University of Melbourne, Parkville, VIC 3050, Australia
| | - Terence O’Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
- Department of Medicine, The Royal Melbourne Hospital, Parkville, VIC, 3052, Australia
| | - Jay Shetty
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Graeme Jackson
- The Florey Institute of Neuroscience and Mental Health, Austin Campus, Heidelberg, VIC 3071, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC 3084, Australia
| | - John S Duncan
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Gavin P Winston
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Department of Medicine, Division of Neurology, Queen’s University, Kingston, ON, Canada K7L 3N6
| | - Lars H Pinborg
- Neurobiology Research Unit, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
- Epilepsy Clinic, Department of Neurology, Copenhagen University Hospital—Rigshopsitalet, Copenhagen 2100, Denmark
| | - Fernando Cendes
- Department of Neurology, University of Campinas, Campinas 13083-888, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas, Campinas 13083-888, Brazil
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich 85764, Germany
- Department of Mathematics, Technical University of Munich, Garching 85748, Germany
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - J Helen Cross
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Young Epilepsy, Lingfield, Surrey RH7 6PW, UK
| | - Torsten Baldeweg
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Sophie Adler
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
| | - Konrad Wagstyl
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
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38
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Abstract
We propose an evolutionary model for the emergence of shared linguistic convention in a population of agents whose social structure is modelled by complex networks. Through agent-based simulations, we show a process of convergence towards a common language, and explore how the topology of the underlying networks affects its dynamics. We find that small-world effects act to speed up convergence, but observe no effect of topology on the communicative efficiency of common languages. We further explore differences in agent learning, discriminating between scenarios in which new agents learn from their parents (vertical transmission) versus scenarios in which they learn from their neighbors (oblique transmission), finding that vertical transmission results in faster convergence and generally higher communicability. Optimal languages can be formed when parental learning is dominant, but a small amount of neighbor learning is included. As a last point, we illustrate an exclusion effect leading to core-periphery networks in an adaptive networks setting when agents attempt to reconnect towards better communicators in the population.
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Affiliation(s)
- Kaloyan Danovski
- Electronics and Computer Science, University of Southampton, Southampton, Hampshire, United Kingdom
- * E-mail:
| | - Markus Brede
- Electronics and Computer Science, University of Southampton, Southampton, Hampshire, United Kingdom
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39
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Lenton TM, Buxton JE, Armstrong McKay DI, Abrams JF, Boulton CA, Lees K, Powell TWR, Boers N, Cunliffe AM, Dakos V. A resilience sensing system for the biosphere. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210383. [PMID: 35757883 PMCID: PMC9234808 DOI: 10.1098/rstb.2021.0383] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 12/23/2021] [Accepted: 02/28/2022] [Indexed: 12/14/2022] Open
Abstract
We are in a climate and ecological emergency, where climate change and direct anthropogenic interference with the biosphere are risking abrupt and/or irreversible changes that threaten our life-support systems. Efforts are underway to increase the resilience of some ecosystems that are under threat, yet collective awareness and action are modest at best. Here, we highlight the potential for a biosphere resilience sensing system to make it easier to see where things are going wrong, and to see whether deliberate efforts to make things better are working. We focus on global resilience sensing of the terrestrial biosphere at high spatial and temporal resolution through satellite remote sensing, utilizing the generic mathematical behaviour of complex systems-loss of resilience corresponds to slower recovery from perturbations, gain of resilience equates to faster recovery. We consider what subset of biosphere resilience remote sensing can monitor, critically reviewing existing studies. Then we present illustrative, global results for vegetation resilience and trends in resilience over the last 20 years, from both satellite data and model simulations. We close by discussing how resilience sensing nested across global, biome-ecoregion, and local ecosystem scales could aid management and governance at these different scales, and identify priorities for further work. This article is part of the theme issue 'Ecological complexity and the biosphere: the next 30 years'.
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Affiliation(s)
| | - Joshua E. Buxton
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
| | - David I. Armstrong McKay
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
- Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
| | - Jesse F. Abrams
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
- Institute for Data Science and Artificial Intelligence, University of Exeter, Exeter EX4 4QF, UK
| | - Chris A. Boulton
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
| | - Kirsten Lees
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
- Environmental Sustainability Research Centre, University of Derby, Derby DE22 1GB, UK
| | | | - Niklas Boers
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
- School of Engineering and Design, Earth System Modelling, Technical University of Munich, Munich, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | | | - Vasilis Dakos
- ISEM, University of Montpellier, CNRS, EPHE, IRD, Montpellier, France
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40
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Sanchez P, Voisey JP, Xia T, Watson HI, O’Neil AQ, Tsaftaris SA. Causal machine learning for healthcare and precision medicine. R Soc Open Sci 2022; 9:220638. [PMID: 35950198 PMCID: PMC9346354 DOI: 10.1098/rsos.220638] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [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: 05/13/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g. outcome given a treatment). Quantifying effects of interventions allows actionable decisions to be made while maintaining robustness in the presence of confounders. Here, we explore how causal inference can be incorporated into different aspects of clinical decision support systems by using recent advances in machine learning. Throughout this paper, we use Alzheimer's disease to create examples for illustrating how CML can be advantageous in clinical scenarios. Furthermore, we discuss important challenges present in healthcare applications such as processing high-dimensional and unstructured data, generalization to out-of-distribution samples and temporal relationships, that despite the great effort from the research community remain to be solved. Finally, we review lines of research within causal representation learning, causal discovery and causal reasoning which offer the potential towards addressing the aforementioned challenges.
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Affiliation(s)
- Pedro Sanchez
- School of Engineering, University of Edinburgh, Edinburgh, UK
| | - Jeremy P. Voisey
- AI Research, Canon Medical Research Europe, Edinburgh, Lothian, UK
| | - Tian Xia
- School of Engineering, University of Edinburgh, Edinburgh, UK
| | - Hannah I. Watson
- AI Research, Canon Medical Research Europe, Edinburgh, Lothian, UK
| | - Alison Q. O’Neil
- School of Engineering, University of Edinburgh, Edinburgh, UK
- AI Research, Canon Medical Research Europe, Edinburgh, Lothian, UK
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41
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Toader B, Boulanger J, Korolev Y, Lenz MO, Manton J, Schönlieb CB, Mureşan L. Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise. J Math Imaging Vis 2022; 64:968-992. [PMID: 36329880 PMCID: PMC7613773 DOI: 10.1007/s10851-022-01100-3] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 04/23/2022] [Indexed: 06/16/2023]
Abstract
We study the problem of deconvolution for light-sheet microscopy, where the data is corrupted by spatially varying blur and a combination of Poisson and Gaussian noise. The spatial variation of the point spread function of a light-sheet microscope is determined by the interaction between the excitation sheet and the detection objective PSF. We introduce a model of the image formation process that incorporates this interaction and we formulate a variational model that accounts for the combination of Poisson and Gaussian noise through a data fidelity term consisting of the infimal convolution of the single noise fidelities, first introduced in L. Calatroni et al. (SIAM J Imaging Sci 10(3):1196-1233, 2017). We establish convergence rates and a discrepancy principle for the infimal convolution fidelity and the inverse problem is solved by applying the primal-dual hybrid gradient (PDHG) algorithm in a novel way. Numerical experiments performed on simulated and real data show superior reconstruction results in comparison with other methods.
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Affiliation(s)
- Bogdan Toader
- Cambridge Advanced Imaging Centre, University of Cambridge, Anatomy School, Downing Street, Cambridge, CB2 3DY UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY UK
| | - Jérôme Boulanger
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH UK
| | - Yury Korolev
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA UK
| | - Martin O. Lenz
- Cambridge Advanced Imaging Centre, University of Cambridge, Anatomy School, Downing Street, Cambridge, CB2 3DY UK
- Sainsbury Laboratory, University of Cambridge, 47 Bateman Street, Cambridge, CB2 1LR UK
| | - James Manton
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH UK
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA UK
| | - Leila Mureşan
- Cambridge Advanced Imaging Centre, University of Cambridge, Anatomy School, Downing Street, Cambridge, CB2 3DY UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY UK
- Sainsbury Laboratory, University of Cambridge, 47 Bateman Street, Cambridge, CB2 1LR UK
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42
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Hancock F, Rosas FE, Mediano PAM, Luppi AI, Cabral J, Dipasquale O, Turkheimer FE. May the 4C's be with you: an overview of complexity-inspired frameworks for analysing resting-state neuroimaging data. J R Soc Interface 2022; 19:20220214. [PMID: 35765805 PMCID: PMC9240685 DOI: 10.1098/rsif.2022.0214] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [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: 03/17/2022] [Accepted: 06/09/2022] [Indexed: 11/12/2022] Open
Abstract
Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behaviour. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here, we review and clarify the key concepts of connectivity, computation, criticality and coherence-the 4C's-and outline a potential role for metastability as a common denominator across these propositions. We analyse and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging, to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.
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Affiliation(s)
- Fran Hancock
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Fernando E. Rosas
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London SW7 2DD, UK
- Data Science Institute, Imperial College London, London SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London SW7 2AZ, UK
| | - Pedro A. M. Mediano
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
- Department of Psychology, Queen Mary University of London, London E1 4NS, UK
| | - Andrea I. Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK
- Alan Turing Institute, London, UK
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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43
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Dorfschmidt L, Bethlehem RA, Seidlitz J, Váša F, White SR, Romero-García R, Kitzbichler MG, Aruldass AR, Morgan SE, Goodyer IM, Fonagy P, Jones PB, Dolan RJ, Harrison NA, Vértes PE, Bullmore ET. Sexually divergent development of depression-related brain networks during healthy human adolescence. Sci Adv 2022; 8:eabm7825. [PMID: 35622918 PMCID: PMC9140984 DOI: 10.1126/sciadv.abm7825] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 04/12/2022] [Indexed: 05/20/2023]
Abstract
Sexual differences in human brain development could be relevant to sex differences in the incidence of depression during adolescence. We tested for sex differences in parameters of normative brain network development using fMRI data on N = 298 healthy adolescents, aged 14 to 26 years, each scanned one to three times. Sexually divergent development of functional connectivity was located in the default mode network, limbic cortex, and subcortical nuclei. Females had a more "disruptive" pattern of development, where weak functional connectivity at age 14 became stronger during adolescence. This fMRI-derived map of sexually divergent brain network development was robustly colocated with i prior loci of reward-related brain activation ii a map of functional dysconnectivity in major depressive disorder (MDD), and iii an adult brain gene transcriptional pattern enriched for genes on the X chromosome, neurodevelopmental genes, and risk genes for MDD. We found normative sexual divergence in adolescent development of a cortico-subcortical brain functional network that is relevant to depression.
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Affiliation(s)
- Lena Dorfschmidt
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | | | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - František Váša
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Simon R. White
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | | | | | - Athina R. Aruldass
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Sarah E. Morgan
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
- The Alan Turing Institute, London NW1 2DB, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Ian M. Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, University College London, London WC1E 6BT, UK
| | - Peter B. Jones
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | - Ray J. Dolan
- Wellcome Trust Centre for Neuroimaging, University College London Queen Square Institute of Neurology
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK
| | | | - Neil A. Harrison
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex Campus, Brighton BN1 9RY, UK
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff CF24 4HQ, UK
| | - Petra E. Vértes
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Edward T. Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
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44
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Haughton J, Cotter SL, Parnell WJ, Shearer T. Bayesian inference on a microstructural, hyperelastic model of tendon deformation. J R Soc Interface 2022; 19:20220031. [PMID: 35582809 PMCID: PMC9114946 DOI: 10.1098/rsif.2022.0031] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/25/2022] [Indexed: 11/30/2022] Open
Abstract
Microstructural models of soft-tissue deformation are important in applications including artificial tissue design and surgical planning. The basis of these models, and their advantage over their phenomenological counterparts, is that they incorporate parameters that are directly linked to the tissue's microscale structure and constitutive behaviour and can therefore be used to predict the effects of structural changes to the tissue. Although studies have attempted to determine such parameters using diverse, state-of-the-art, experimental techniques, values ranging over several orders of magnitude have been reported, leading to uncertainty in the true parameter values and creating a need for models that can handle such uncertainty. We derive a new microstructural, hyperelastic model for transversely isotropic soft tissues and use it to model the mechanical behaviour of tendons. To account for parameter uncertainty, we employ a Bayesian approach and apply an adaptive Markov chain Monte Carlo algorithm to determine posterior probability distributions for the model parameters. The obtained posterior distributions are consistent with parameter measurements previously reported and enable us to quantify the uncertainty in their values for each tendon sample that was modelled. This approach could serve as a prototype for quantifying parameter uncertainty in other soft tissues.
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Affiliation(s)
- James Haughton
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - Simon L. Cotter
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - William J. Parnell
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - Tom Shearer
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
- Department of Materials, University of Manchester, Manchester M13 9PL, UK
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45
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Manderson AA, Goudie RJB. A numerically stable algorithm for integrating Bayesian models using Markov melding. Stat Comput 2022; 32:24. [PMID: 35310545 PMCID: PMC8924096 DOI: 10.1007/s11222-022-10086-2] [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: 12/09/2020] [Accepted: 01/29/2022] [Indexed: 06/14/2023]
Abstract
When statistical analyses consider multiple data sources, Markov melding provides a method for combining the source-specific Bayesian models. Markov melding joins together submodels that have a common quantity. One challenge is that the prior for this quantity can be implicit, and its prior density must be estimated. We show that error in this density estimate makes the two-stage Markov chain Monte Carlo sampler employed by Markov melding unstable and unreliable. We propose a robust two-stage algorithm that estimates the required prior marginal self-density ratios using weighted samples, dramatically improving accuracy in the tails of the distribution. The stabilised version of the algorithm is pragmatic and provides reliable inference. We demonstrate our approach using an evidence synthesis for inferring HIV prevalence, and an evidence synthesis of A/H1N1 influenza.
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Affiliation(s)
- Andrew A. Manderson
- MRC Biostatistics Unit, Forvie Site, Robinson Way, Cambridge, CB2 0SR UK
- The Alan Turing Institute, British Library, London, UK
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46
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Abstract
We characterized > 150 countries' resilience to COVID-19 as the nationwide decay rate of daily cases or deaths from peak levels. Resilience to COVID-19 varies by a factor of ~ 40 between countries for cases/capita and ~ 25 for deaths/capita. Trust within society is positively correlated with country-level resilience to COVID-19, as is the adaptive increase in stringency of government interventions when epidemic waves occur. By contrast, countries where governments maintain greater background stringency tend to have lower trust within society and tend to be less resilient. All countries where > 40% agree "most people can be trusted" achieve a near complete reduction of new cases and deaths, but so do several less-trusting societies. As the pandemic progressed, resilience tended to decline, as adaptive increases in stringency also declined. These results add to evidence that trust can improve resilience to epidemics and other unexpected disruptions, of which COVID-19 is unlikely to be the last.
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Affiliation(s)
| | | | - Marten Scheffer
- Wageningen University, Wageningen, The Netherlands
- Santa Fe Institute, Santa Fe, NM, USA
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47
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Nicholson G, Lehmann B, Padellini T, Pouwels KB, Jersakova R, Lomax J, King RE, Mallon AM, Diggle PJ, Richardson S, Blangiardo M, Holmes C. Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework. Nat Microbiol 2022; 7:97-107. [PMID: 34972825 PMCID: PMC8727294 DOI: 10.1038/s41564-021-01029-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [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: 10/18/2021] [Accepted: 11/18/2021] [Indexed: 12/23/2022]
Abstract
Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.
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Affiliation(s)
- George Nicholson
- University of Oxford, Oxford, UK.
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK.
| | - Brieuc Lehmann
- University of Oxford, Oxford, UK.
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK.
| | - Tullia Padellini
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, University of Oxford, Oxford, UK
| | - Radka Jersakova
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- The Alan Turing Institute, London, UK
| | - James Lomax
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- The Alan Turing Institute, London, UK
| | - Ruairidh E King
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- MRC Harwell Institute, Harwell, UK
| | - Ann-Marie Mallon
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- MRC Harwell Institute, Harwell, UK
| | - Peter J Diggle
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Sylvia Richardson
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Marta Blangiardo
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Chris Holmes
- University of Oxford, Oxford, UK.
- The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory, London, UK.
- The Alan Turing Institute, London, UK.
- MRC Harwell Institute, Harwell, UK.
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48
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Patel R, Smeraldi F, Abdollahyan M, Irving J, Bessant C. Analysis of mental and physical disorders associated with COVID-19 in online health forums: a natural language processing study. BMJ Open 2021; 11:e056601. [PMID: 34740937 PMCID: PMC8573296 DOI: 10.1136/bmjopen-2021-056601] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 10/18/2021] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES Online health forums provide rich and untapped real-time data on population health. Through novel data extraction and natural language processing (NLP) techniques, we characterise the evolution of mental and physical health concerns relating to the COVID-19 pandemic among online health forum users. SETTING AND DESIGN We obtained data from three leading online health forums: HealthBoards, Inspire and HealthUnlocked, from the period 1 January 2020 to 31 May 2020. Using NLP, we analysed the content of posts related to COVID-19. PRIMARY OUTCOME MEASURES (1) Proportion of forum posts containing COVID-19 keywords; (2) proportion of forum users making their very first post about COVID-19; (3) proportion of COVID-19-related posts containing content related to physical and mental health comorbidities. RESULTS Data from 739 434 posts created by 53 134 unique users were analysed. A total of 35 581 posts (4.8%) contained a COVID-19 keyword. Posts discussing COVID-19 and related comorbid disorders spiked in early March to mid-March around the time of global implementation of lockdowns prompting a large number of users to post on online health forums for the first time. Over a quarter of COVID-19-related thread titles mentioned a physical or mental health comorbidity. CONCLUSIONS We demonstrate that it is feasible to characterise the content of online health forum user posts regarding COVID-19 and measure changes over time. The pandemic and corresponding public response has had a significant impact on posters' queries regarding mental health. Social media data sources such as online health forums can be harnessed to strengthen population-level mental health surveillance.
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Affiliation(s)
- Rashmi Patel
- Department of Psychological Medicine, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Fabrizio Smeraldi
- Queen Mary University of London, London, UK
- The Alan Turing Institute, London, UK
| | | | - Jessica Irving
- Department of Psychological Medicine, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Conrad Bessant
- Queen Mary University of London, London, UK
- The Alan Turing Institute, London, UK
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49
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Watts EL, Perez-Cornago A, Doherty A, Allen NE, Fensom GK, Tin Tin S, Key TJ, Travis RC. Physical activity in relation to circulating hormone concentrations in 117,100 men in UK Biobank. Cancer Causes Control 2021; 32:1197-1212. [PMID: 34216337 PMCID: PMC8492588 DOI: 10.1007/s10552-021-01466-6] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/14/2021] [Indexed: 02/04/2023]
Abstract
PURPOSE Physical activity may reduce the risk of some types of cancer in men. Biological mechanisms may involve changes in hormone concentrations; however, this relationship is not well established. Therefore, we aimed to investigate the associations of physical activity with circulating insulin-like growth factor-I (IGF-I), sex hormone-binding globulin (SHBG, which modifies sex hormone activity), and total and free testosterone concentrations, and the extent these associations might be mediated by body mass index (BMI). METHODS Circulating concentrations of these hormones and anthropometric measurements and self-reported physical activity data were available for 117,100 healthy male UK Biobank participants at recruitment. Objectively measured accelerometer physical activity levels were also collected on average 5.7 years after recruitment in 28,000 men. Geometric means of hormone concentrations were estimated using multivariable-adjusted analysis of variance, with and without adjustment for BMI. RESULTS The associations between physical activity and hormones were modest and similar for objectively measured (accelerometer) and self-reported physical activity. Compared to men with the lowest objectively measured physical activity, men with high physical activity levels had 14% and 8% higher concentrations of SHBG and total testosterone, respectively, and these differences were attenuated to 6% and 3% following adjustment for BMI. CONCLUSION Our results suggest that the associations of physical activity with the hormones investigated are, at most, modest; and following adjustment for BMI, the small associations with SHBG and total testosterone were largely attenuated. Therefore, it is unlikely that changes in these circulating hormones explain the associations of physical activity with risk of cancer either independently or via BMI.
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Affiliation(s)
- Eleanor L Watts
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK.
| | - Aurora Perez-Cornago
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
| | - Aiden Doherty
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Naomi E Allen
- UK Biobank Ltd, Cheadle, Stockport, UK
- National Institute of Health Research Oxford Biomedical Research Centre, Oxford, UK
| | - Georgina K Fensom
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
| | - Sandar Tin Tin
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
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50
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Davidson EM, Poon MTC, Casey A, Grivas A, Duma D, Dong H, Suárez-Paniagua V, Grover C, Tobin R, Whalley H, Wu H, Alex B, Whiteley W. The reporting quality of natural language processing studies: systematic review of studies of radiology reports. BMC Med Imaging 2021; 21:142. [PMID: 34600486 PMCID: PMC8487512 DOI: 10.1186/s12880-021-00671-8] [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] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 09/20/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients' health and disease. With its rapid development, NLP studies should have transparent methodology to allow comparison of approaches and reproducibility. This systematic review aims to summarise the characteristics and reporting quality of studies applying NLP to radiology reports. METHODS We searched Google Scholar for studies published in English that applied NLP to radiology reports of any imaging modality between January 2015 and October 2019. At least two reviewers independently performed screening and completed data extraction. We specified 15 criteria relating to data source, datasets, ground truth, outcomes, and reproducibility for quality assessment. The primary NLP performance measures were precision, recall and F1 score. RESULTS Of the 4,836 records retrieved, we included 164 studies that used NLP on radiology reports. The commonest clinical applications of NLP were disease information or classification (28%) and diagnostic surveillance (27.4%). Most studies used English radiology reports (86%). Reports from mixed imaging modalities were used in 28% of the studies. Oncology (24%) was the most frequent disease area. Most studies had dataset size > 200 (85.4%) but the proportion of studies that described their annotated, training, validation, and test set were 67.1%, 63.4%, 45.7%, and 67.7% respectively. About half of the studies reported precision (48.8%) and recall (53.7%). Few studies reported external validation performed (10.8%), data availability (8.5%) and code availability (9.1%). There was no pattern of performance associated with the overall reporting quality. CONCLUSIONS There is a range of potential clinical applications for NLP of radiology reports in health services and research. However, we found suboptimal reporting quality that precludes comparison, reproducibility, and replication. Our results support the need for development of reporting standards specific to clinical NLP studies.
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Affiliation(s)
- Emma M Davidson
- Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, Little France, Edinburgh, EH16 4TJ, Scotland, UK.
| | - Michael T C Poon
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, Scotland, UK
- Brain Tumour Centre of Excellence, Cancer Research UK Edinburgh Centre, University of Edinburgh, Edinburgh, Scotland, UK
| | - Arlene Casey
- School of Literatures, Languages and Cultures (LLC), University of Edinburgh, Edinburgh, Scotland, UK
| | - Andreas Grivas
- School of Literatures, Languages and Cultures (LLC), University of Edinburgh, Edinburgh, Scotland, UK
| | - Daniel Duma
- School of Literatures, Languages and Cultures (LLC), University of Edinburgh, Edinburgh, Scotland, UK
| | - Hang Dong
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, Scotland, UK
- Health Data Research UK, London, UK
| | - Víctor Suárez-Paniagua
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, Scotland, UK
- Health Data Research UK, London, UK
| | - Claire Grover
- Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK
| | - Richard Tobin
- Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK
| | - Heather Whalley
- Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, Little France, Edinburgh, EH16 4TJ, Scotland, UK
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Honghan Wu
- Health Data Research UK, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Beatrice Alex
- School of Literatures, Languages and Cultures (LLC), University of Edinburgh, Edinburgh, Scotland, UK
- Edinburgh Futures Institute, University of Edinburgh, Edinburgh, Scotland, UK
| | - William Whiteley
- Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, Little France, Edinburgh, EH16 4TJ, Scotland, UK
- Health Data Research UK, London, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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