{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T15:48:59Z","timestamp":1785599339793,"version":"3.56.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003382","name":"MEXT | JST | Core Research for Evolutional Science and Technology","doi-asserted-by":"publisher","award":["JPMJCR1881"],"award-info":[{"award-number":["JPMJCR1881"]}],"id":[{"id":"10.13039\/501100003382","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003382","name":"MEXT | JST | Core Research for Evolutional Science and Technology","doi-asserted-by":"publisher","award":["JPMJCR21F1"],"award-info":[{"award-number":["JPMJCR21F1"]}],"id":[{"id":"10.13039\/501100003382","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Nucleic acid aptamers are generated by an in vitro molecular evolution method known as systematic evolution of ligands by exponential enrichment (SELEX). Various candidates are limited by actual sequencing data from an experiment. Here we developed RaptGen, which is a variational autoencoder for in silico aptamer generation. RaptGen exploits a profile hidden Markov model decoder to represent motif sequences effectively. We showed that RaptGen embedded simulation sequence data into low-dimensional latent space on the basis of motif information. We also performed sequence embedding using two independent SELEX datasets. RaptGen successfully generated aptamers from the latent space even though they were not included in high-throughput sequencing. RaptGen could also generate a truncated aptamer with a short learning model. We demonstrated that RaptGen could be applied to activity-guided aptamer generation according to Bayesian optimization. We concluded that a generative method by RaptGen and latent representation are useful for aptamer discovery.<\/jats:p>","DOI":"10.1038\/s43588-022-00249-6","type":"journal-article","created":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T16:12:02Z","timestamp":1654186322000},"page":"378-386","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["Generative aptamer discovery using RaptGen"],"prefix":"10.1038","volume":"2","author":[{"given":"Natsuki","family":"Iwano","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tatsuo","family":"Adachi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuteru","family":"Aoki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshikazu","family":"Nakamura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9466-1034","authenticated-orcid":false,"given":"Michiaki","family":"Hamada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"key":"249_CR1","doi-asserted-by":"crossref","unstructured":"Ni, S. et al. Recent progress in aptamer discoveries and modifications for therapeutic applications. ACS Appl. Mater. Interfaces 13, 9500\u20139519 (2020).","DOI":"10.1021\/acsami.0c05750"},{"key":"249_CR2","doi-asserted-by":"publisher","first-page":"4229","DOI":"10.3390\/molecules24234229","volume":"24","author":"T Adachi","year":"2019","unstructured":"Adachi, T. & NakamuraAptamers, Y. A review of their chemical properties and modifications for therapeutic application. Molecules 24, 4229 (2019).","journal-title":"Molecules"},{"key":"249_CR3","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.trac.2007.12.004","volume":"27","author":"S Song","year":"2008","unstructured":"Song, S., Wang, L., Li, J., Fan, C. & Zhao, J. Aptamer-based biosensors. Trends Anal. Chem. 27, 108\u2013117 (2008).","journal-title":"Trends Anal. Chem."},{"key":"249_CR4","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1039\/c4an00132j","volume":"139","author":"W Zhou","year":"2014","unstructured":"Zhou, W., Huang, P.-J. J., Ding, J. & Liu, J. Aptamer-based biosensors for biomedical diagnostics. Analyst 139, 2627\u20132640 (2014).","journal-title":"Analyst"},{"key":"249_CR5","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1097\/00006982-200204000-00002","volume":"22","author":"Eyetech Study Group.","year":"2002","unstructured":"Eyetech Study Group. et al. Preclinical and phase 1A clinical evaluation of an anti-VEGF pegylated aptamer (EYE001) for the treatment of exudative age-related macular degeneration. Retina 22, 143\u2013152 (2002).","journal-title":"Retina"},{"key":"249_CR6","first-page":"538","volume":"1","author":"J Ciesiolka","year":"1995","unstructured":"Ciesiolka, J., Gorski, J. & Yarus, M. Selection of an RNA domain that binds Zn2+. RNA 1, 538\u2013550 (1995).","journal-title":"RNA"},{"key":"249_CR7","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.bioelechem.2004.04.011","volume":"67","author":"S Tombelli","year":"2005","unstructured":"Tombelli, S., Minunni, M., Luzi, E. & Mascini, M. Aptamer-based biosensors for the detection of HIV-1 TAT protein. Bioelectrochemistry 67, 135\u2013141 (2005).","journal-title":"Bioelectrochemistry"},{"key":"249_CR8","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.cell.2012.12.009","volume":"152","author":"A Jolma","year":"2013","unstructured":"Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327\u2013339 (2013).","journal-title":"Cell"},{"key":"249_CR9","doi-asserted-by":"publisher","first-page":"8406","DOI":"10.1021\/bi400704d","volume":"52","author":"JM Binning","year":"2013","unstructured":"Binning, J. M. et al. Development of RNA aptamers targeting Ebola virus VP35. Biochemistry 52, 8406\u20138419 (2013).","journal-title":"Biochemistry"},{"key":"249_CR10","doi-asserted-by":"publisher","first-page":"3138","DOI":"10.1021\/ja056957p","volume":"128","author":"BR Baker","year":"2006","unstructured":"Baker, B. R. et al. An electronic, aptamer-based small-molecule sensor for the rapid, label-free detection of cocaine in adulterated samples and biological fluids. J. Am. Chem. Soc. 128, 3138\u20133139 (2006).","journal-title":"J. Am. Chem. Soc."},{"key":"249_CR11","doi-asserted-by":"publisher","first-page":"8966","DOI":"10.1021\/ac302902s","volume":"84","author":"M Labib","year":"2012","unstructured":"Labib, M. et al. Aptamer-based viability impedimetric sensor for bacteria. Anal. Chem. 84, 8966\u20138969 (2012).","journal-title":"Anal. Chem."},{"key":"249_CR12","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1126\/science.2200121","volume":"249","author":"C Tuerk","year":"1990","unstructured":"Tuerk, C. & Gold, L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science 249, 505\u2013510 (1990).","journal-title":"Science"},{"key":"249_CR13","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1038\/346818a0","volume":"346","author":"AD Ellington","year":"1990","unstructured":"Ellington, A. D. & Szostak, J. W. In vitro selection of RNA molecules that bind specific ligands. Nature 346, 818\u2013822 (1990).","journal-title":"Nature"},{"key":"249_CR14","doi-asserted-by":"publisher","first-page":"e1000590","DOI":"10.1371\/journal.pcbi.1000590","volume":"5","author":"Y Zhao","year":"2009","unstructured":"Zhao, Y., Granas, D. & Stormo, G. D. Inferring binding energies from selected binding sites. PLoS Comput. Biol. 5, e1000590 (2009).","journal-title":"PLoS Comput. Biol."},{"key":"249_CR15","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1101\/gr.100552.109","volume":"20","author":"A Jolma","year":"2010","unstructured":"Jolma, A. et al. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res. 20, 861\u2013873 (2010).","journal-title":"Genome Res."},{"key":"249_CR16","doi-asserted-by":"publisher","first-page":"e19395","DOI":"10.1371\/journal.pone.0019395","volume":"6","author":"GV Kupakuwana","year":"2011","unstructured":"Kupakuwana, G. V., Crill, J. E. II, McPike, M. P. & Borer, P. N. Acyclic identification of aptamers for human alpha-thrombin using over-represented libraries and deep sequencing. PLoS ONE 6, e19395 (2011).","journal-title":"PLoS ONE"},{"key":"249_CR17","doi-asserted-by":"publisher","first-page":"2665","DOI":"10.1093\/bioinformatics\/btu348","volume":"30","author":"P Jiang","year":"2014","unstructured":"Jiang, P. et al. MPBind: a meta-motif-based statistical framework and pipeline to predict binding potential of SELEX-derived aptamers. Bioinformatics 30, 2665\u20132667 (2014).","journal-title":"Bioinformatics"},{"key":"249_CR18","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1093\/bioinformatics\/btv545","volume":"32","author":"J Caroli","year":"2016","unstructured":"Caroli, J., Taccioli, C., Fuente, A. D. L., Serafini, P. & Bicciato, S. APTANI: a computational tool to select aptamers through sequence-structure motif analysis of HT-SELEX data. Bioinformatics 32, 161\u2013164 (2016).","journal-title":"Bioinformatics"},{"key":"249_CR19","doi-asserted-by":"publisher","first-page":"2266","DOI":"10.1093\/bioinformatics\/btz897","volume":"36","author":"J Caroli","year":"2020","unstructured":"Caroli, J., Forcato, M. & Bicciato, S. APTANI2: update of aptamer selection through sequence-structure analysis. Bioinformatics 36, 2266\u20132268 (2020).","journal-title":"Bioinformatics"},{"key":"249_CR20","doi-asserted-by":"publisher","first-page":"e82","DOI":"10.1093\/nar\/gkaa484","volume":"48","author":"R Ishida","year":"2020","unstructured":"Ishida, R. et al. RaptRanker: in silico RNA aptamer selection from HT-SELEX experiment based on local sequence and structure information. Nucl. Acids Res. 48, e82\u2013e82 (2020).","journal-title":"Nucl. Acids Res."},{"key":"249_CR21","doi-asserted-by":"publisher","first-page":"e139","DOI":"10.1093\/nar\/gkq282","volume":"38","author":"N Kim","year":"2010","unstructured":"Kim, N., Izzo, J. A., Elmetwaly, S., Gan, H. H. & Schlick, T. Computational generation and screening of RNA motifs in large nucleotide sequence pools. Nucl. Acids Res. 38, e139\u2013e139 (2010).","journal-title":"Nucl. Acids Res."},{"key":"249_CR22","doi-asserted-by":"publisher","first-page":"5699","DOI":"10.1093\/nar\/gkv308","volume":"43","author":"J Hoinka","year":"2015","unstructured":"Hoinka, J. et al. Large scale analysis of the mutational landscape in HT-SELEX improves aptamer discovery. Nucl. Acids Res. 43, 5699\u20135707 (2015).","journal-title":"Nucl. Acids Res."},{"key":"249_CR23","doi-asserted-by":"publisher","first-page":"5939","DOI":"10.1021\/acs.jctc.5b00707","volume":"11","author":"Q Zhou","year":"2015","unstructured":"Zhou, Q., Xia, X., Luo, Z., Liang, H. & Shakhnovich, E. Searching the sequence space for potent aptamers using SELEX in silico. J. Chem. Theory Comput. 11, 5939\u20135946 (2015).","journal-title":"J. Chem. Theory Comput."},{"key":"249_CR24","doi-asserted-by":"publisher","first-page":"e117","DOI":"10.1093\/nar\/gkl544","volume":"34","author":"M Hiller","year":"2006","unstructured":"Hiller, M., Pudimat, R., Busch, A. & Backofen, R. Using RNA secondary structures to guide sequence motif finding towards single-stranded regions. Nucl. Acids Res. 34, e117\u2013e117 (2006).","journal-title":"Nucl. Acids Res."},{"key":"249_CR25","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.cels.2016.07.003","volume":"3","author":"P Dao","year":"2016","unstructured":"Dao, P. et al. AptaTRACE elucidates RNA sequence-structure motifs from selection trends in HT-SELEX experiments. Cell Syst. 3, 62\u201370 (2016).","journal-title":"Cell Syst."},{"key":"249_CR26","doi-asserted-by":"publisher","first-page":"i215","DOI":"10.1093\/bioinformatics\/bts210","volume":"28","author":"J Hoinka","year":"2012","unstructured":"Hoinka, J., Zotenko, E., Friedman, A., Sauna, Z. E. & Przytycka, T. M. Identification of sequence-structure rna binding motifs for SELEX-derived aptamers. Bioinformatics 28, i215\u2013i223 (2012).","journal-title":"Bioinformatics"},{"key":"249_CR27","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1038\/nbt.3300","volume":"33","author":"B Alipanahi","year":"2015","unstructured":"Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831\u2013838 (2015).","journal-title":"Nat. Biotechnol."},{"key":"249_CR28","doi-asserted-by":"crossref","unstructured":"Hassanzadeh, H. R. & Wang, M. D. Deeperbind: enhancing prediction of sequence specificities of DNA binding proteins. In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 178\u2013183 (IEEE, 2016).","DOI":"10.1109\/BIBM.2016.7822515"},{"key":"249_CR29","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-018-4889-1","volume":"19","author":"X Pan","year":"2018","unstructured":"Pan, X., Rijnbeek, P., Yan, J. & Shen, H.-B. Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. BMC genomics 19, 511 (2018).","journal-title":"BMC genomics"},{"key":"249_CR30","doi-asserted-by":"publisher","first-page":"5947","DOI":"10.4249\/scholarpedia.5947","volume":"4","author":"GE Hinton","year":"2009","unstructured":"Hinton, G. E. Deep belief networks. Scholarpedia 4, 5947 (2009).","journal-title":"Scholarpedia"},{"key":"249_CR31","unstructured":"Kingma, D. P. & Welling, M. Auto-encoding variational bayes. Preprint at https:\/\/arxiv.org\/abs\/1312.6114 (2013)."},{"key":"249_CR32","first-page":"2672","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow, I. et al. Generative adversarial nets. Adv. Neural Information Process. Syst. 27, 2672\u20132680 (2014).","journal-title":"Adv. Neural Information Process. Syst."},{"key":"249_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-019-6299-4","volume":"20","author":"J Im","year":"2019","unstructured":"Im, J., Park, B. & Han, K. A generative model for constructing nucleic acid sequences binding to a protein. BMC Genomics 20, 1\u201313 (2019).","journal-title":"BMC Genomics"},{"key":"249_CR34","unstructured":"Killoran, N., Lee, L. J., Delong, A., Duvenaud, D. & Frey, B. J. Generating and designing DNA with deep generative models. Preprint at https:\/\/arxiv.org\/abs\/1712.06148 (2017)."},{"key":"249_CR35","unstructured":"Kusner, M. J., Paige, B. & Hern\u00e1ndez-Lobato, J. M. Grammar variational autoencoder. Preprint at https:\/\/arxiv.org\/abs\/1703.01925 (2017)."},{"key":"249_CR36","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","volume":"4","author":"R G\u00f3mez-Bombarelli","year":"2018","unstructured":"G\u00f3mez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Sci. 4, 268\u2013276 (2018).","journal-title":"ACS Central Sci."},{"key":"249_CR37","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997).","journal-title":"Neural Comput."},{"key":"249_CR38","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1261\/rna.5114503","volume":"9","author":"C Lozupone","year":"2003","unstructured":"Lozupone, C., Changayil, S., Majerfeld, I. & Yarus, M. Selection of the simplest RNA that binds isoleucine. RNA 9, 1315\u20131322 (2003).","journal-title":"RNA"},{"key":"249_CR39","unstructured":"Gonzalez, J., Longworth, J., James, D. C. & Lawrence, N. D. Bayesian optimization for synthetic gene design. Preprint at https:\/\/arxiv.org\/abs\/1505.01627 (2015)."},{"key":"249_CR40","doi-asserted-by":"publisher","first-page":"W302","DOI":"10.1093\/nar\/gkw337","volume":"44","author":"M Hamada","year":"2016","unstructured":"Hamada, M. et al. Rtools: a web server for various secondary structural analyses on single RNA sequences. Nucl. Acids Res. 44, W302\u2013307 (2016).","journal-title":"Nucl. Acids Res."},{"key":"249_CR41","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/1748-7188-6-26","volume":"6","author":"R Lorenz","year":"2011","unstructured":"Lorenz, R. et al. ViennaRNA Package 2.0. Algorithms Mol Biol 6, 26 (2011).","journal-title":"Algorithms Mol Biol"},{"key":"249_CR42","doi-asserted-by":"publisher","first-page":"5112","DOI":"10.1093\/nar\/22.23.5112","volume":"22","author":"Y Sakakibara","year":"1994","unstructured":"Sakakibara, Y. et al. Stochastic context-free grammers for tRNA modeling. Nucl. Acids Res. 22, 5112\u20135120 (1994).","journal-title":"Nucl. Acids Res."},{"key":"249_CR43","doi-asserted-by":"publisher","first-page":"8486","DOI":"10.1073\/pnas.1913242117","volume":"117","author":"DR Bell","year":"2020","unstructured":"Bell, D. R. et al. In silico design and validation of high-affinity RNA aptamers targeting epithelial cellular adhesion molecule dimers. Proc. Natl Acad. Sci. USA 117, 8486\u20138493 (2020).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"249_CR44","unstructured":"Corduneanu, A. & Bishop, C. Variational bayesian model selection for mixture distributions. In Proc. 8th International Conference on Artificial Intelligence and Statistics 27\u201334 (Morgan Kaufmann, 2001)."},{"key":"249_CR45","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1006\/jmbi.1994.1104","volume":"235","author":"A Krogh","year":"1994","unstructured":"Krogh, A., Brown, M., Mian, I. S., Sjolander, K. & Haussler, D. Hidden Markov models in computational biology. applications to protein modeling. J. Mol. Biol. 235, 1501\u20131531 (1994).","journal-title":"J. Mol. Biol."},{"key":"249_CR46","unstructured":"Bowman, S. R. et al. Generating sentences from a continuous space. Preprint at https:\/\/arxiv.org\/abs\/1511.06349 (2015)."},{"key":"249_CR47","unstructured":"Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https:\/\/arxiv.org\/abs\/1412.6980 (2014)."},{"key":"249_CR48","unstructured":"Gonz\u00e1lez, J., Dai, Z., Hennig, P. & Lawrence, N. Batch Bayesian optimization via local penalization. In Proc. 19th International Conference on Artificial Intelligence and Statistics 648\u2013657 (PMLR, 2016)."},{"key":"249_CR49","doi-asserted-by":"crossref","unstructured":"Ginsbourger, D, Le Riche, R. & Carraro, L. Kriging is well-suited to parallelize optimization. In Computational Intelligence in Expensive Optimization Problems 131\u2013162 (Springer, 2010).","DOI":"10.1007\/978-3-642-10701-6_6"},{"key":"249_CR50","unstructured":"The GPyOpt authors. GPyOpt: A Bayesian Optimization Framework in Python (GitHub, 2016); http:\/\/github.com\/SheffieldML\/GPyOpt"},{"key":"249_CR51","doi-asserted-by":"publisher","unstructured":"The RaptGen authors. Raptgen Version 1.0 (Zenodo, 2022); https:\/\/doi.org\/10.5281\/zenodo.6470866","DOI":"10.5281\/zenodo.6470866"},{"key":"249_CR52","first-page":"397","volume":"3","author":"P Auer","year":"2002","unstructured":"Auer, P. Using confidence bounds for exploitation-exploration trade-offs. J. Mach. Learn. Res. 3, 397\u2013422 (2002).","journal-title":"J. Mach. Learn. Res."}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00249-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00249-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00249-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T02:44:34Z","timestamp":1675737874000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00249-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,2]]},"references-count":52,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["249"],"URL":"https:\/\/doi.org\/10.1038\/s43588-022-00249-6","relation":{},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,2]]},"assertion":[{"value":"13 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}