{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T04:26:33Z","timestamp":1778991993041,"version":"3.51.4"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Chinese National Natural Science Foundation","doi-asserted-by":"crossref","award":["Grant 12275208"],"award-info":[{"award-number":["Grant 12275208"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00521-024-10892-x","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T08:08:23Z","timestamp":1736150903000},"page":"5873-5889","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["DMVL4AVD: a deep multi-view learning model for automated vulnerability detection"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2574-9603","authenticated-orcid":false,"given":"Xiaozhi","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanrong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyuan","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"key":"10892_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102308","volume":"106","author":"S Jeon","year":"2021","unstructured":"Jeon S, Kim HK (2021) AutoVAS: an automated vulnerability analysis system with a deep learning approach. Comput Secur 106:102308. https:\/\/doi.org\/10.1016\/j.cose.2021.102308","journal-title":"Comput Secur"},{"issue":"10","key":"10892_CR2","doi-asserted-by":"publisher","first-page":"1825","DOI":"10.1109\/JPROC.2020.2993293","volume":"108","author":"G Lin","year":"2020","unstructured":"Lin G, Wen S, Han Q-L, Zhang J, Xiang Y (2020) Software vulnerability detection using deep neural networks: a survey. Proc IEEE 108(10):1825\u20131848. https:\/\/doi.org\/10.1109\/JPROC.2020.2993293","journal-title":"Proc IEEE"},{"key":"10892_CR3","unstructured":"Y Zhou, S Liu, J Siow, X Du, Y Liu (2019) \u201cDevign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks,\u201d Proc Adv Neural Inf Process Syst, 10197\u201310207."},{"key":"10892_CR4","unstructured":"Checkmarx (2023) [Online]. Available: https:\/\/www.checkmarx.com\/"},{"issue":"20","key":"10892_CR5","doi-asserted-by":"publisher","first-page":"13287","DOI":"10.1007\/s00521-021-05954-3","volume":"33","author":"G Lin","year":"2021","unstructured":"Lin G, Xiao W, Zhang LY, Gao S, Tai Y, Zhang J (2021) Deep neural-based vulnerability discovery demystified: data, model and performance. Neural Comput Appl 33(20):13287\u201313300. https:\/\/doi.org\/10.1007\/s00521-021-05954-3","journal-title":"Neural Comput Appl"},{"key":"10892_CR6","doi-asserted-by":"publisher","unstructured":"Russell R et al. (2018) \u201cAutomated vulnerability detection in source code using deep representation learning, 17th IEEE International Conference on Machine Learning and Applications (ICMLA), https:\/\/doi.org\/10.1109\/ICMLA.2018.00120.","DOI":"10.1109\/ICMLA.2018.00120"},{"key":"10892_CR7","doi-asserted-by":"publisher","unstructured":"Li et al.Z (2018) \u201cVulDeePecker: a deep learning-based system for vulnerability detection,\u201d Proc. 25th Annu Netw Distrib Syst Secur Symp, https:\/\/doi.org\/10.14722\/ndss.2018.23158.","DOI":"10.14722\/ndss.2018.23158"},{"key":"10892_CR8","unstructured":"Li Z, Zou D, Xu S, Jin H, Zhu Y, Chen Z, (2018)\u201cSySeVR: a framework for using deep learning to detect software vulnerabilities,\u201d 2018, arXiv: 1807.06756."},{"key":"10892_CR9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10139","author":"L Mou","year":"2016","unstructured":"Mou L, Li G, Zhang L, Wang T, Jin Z (2016) Convolutional neural networks over tree structures for programming language processing. Proceed AAAI Conf Artif Intell. https:\/\/doi.org\/10.1609\/aaai.v30i1.10139","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"10892_CR10","doi-asserted-by":"publisher","unstructured":"Zhuang Y, Suneja S, Thost V, Domeniconi G, Morari A, Laredo J (2021) \u201cSoftware vulnerability detection via deep learning over disaggregated code graph representation,\u201d. https:\/\/doi.org\/10.48550\/arXiv.2109.03341.","DOI":"10.48550\/arXiv.2109.03341"},{"key":"10892_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01177-2_79","author":"TN Brooks","year":"2019","unstructured":"Brooks TN (2019) Survey of automated vulnerability detection and exploit generation techniques in cyber reasoning systems. Intell Comput. https:\/\/doi.org\/10.1007\/978-3-030-01177-2_79","journal-title":"Intell Comput"},{"key":"10892_CR12","doi-asserted-by":"publisher","unstructured":"Kim S, Woo S, Lee H, Oh H (2017) \u201cVUDDY: a scalable approach for vulnerable code clone discovery,\u201d In: 2017 IEEE Symposium on Security and Privacy (SP), https:\/\/doi.org\/10.1109\/SP.2017.62.","DOI":"10.1109\/SP.2017.62"},{"key":"10892_CR13","doi-asserted-by":"publisher","unstructured":"Li Z, Zou D, Xu S, Jin H, Qi H, Hu J, (2016) \u201cVulPecker: an automated vulnerability detection system based on code similarity analysis,\u201d In Proceedings of the 32nd annual conference on computer security applications, New York, NY, USA, pp 201\u2013213. https:\/\/doi.org\/10.1145\/2991079.2991102.","DOI":"10.1145\/2991079.2991102"},{"key":"10892_CR14","doi-asserted-by":"publisher","unstructured":"Grieco G, Grinblat GL, Uzal L, Rawat S, Feist J, Mounier L (2016) \u201cToward large-scale vulnerability discovery using machine learning,\u201d In: Proceedings of the Sixth ACM conference on data and application security and privacy, New York, NY, USA, pp 85\u201396. https:\/\/doi.org\/10.1145\/2857705.2857720.","DOI":"10.1145\/2857705.2857720"},{"key":"10892_CR15","doi-asserted-by":"publisher","unstructured":"Neuhaus S, Zimmermann T, Holler C, Zeller A (2007) \u201cPredicting vulnerable software components,\u201d In: Proceedings of the 14th ACM conference on computer and communications security, New York, NY, USA, pp. 529\u2013540. https:\/\/doi.org\/10.1145\/1315245.1315311.","DOI":"10.1145\/1315245.1315311"},{"key":"10892_CR16","doi-asserted-by":"publisher","unstructured":"Walden J, Stuckman J, Scandariato R (2014) \u201cPredicting vulnerable components: software metrics vs text mining,\u201d In: 2014 IEEE 25th international symposium on software reliability engineering, pp 23\u201333. https:\/\/doi.org\/10.1109\/ISSRE.2014.32.","DOI":"10.1109\/ISSRE.2014.32"},{"key":"10892_CR17","doi-asserted-by":"publisher","unstructured":"Li J, He P, Zhu J, Lyu MR (2017) \u201cSoftware defect prediction via convolutional neural network,\u201d In 2017 IEEE international conference on software quality, reliability and security (QRS), pp 318\u2013328. https:\/\/doi.org\/10.1109\/QRS.2017.42.","DOI":"10.1109\/QRS.2017.42"},{"key":"10892_CR18","doi-asserted-by":"publisher","unstructured":"Wang S, Liu T, Tan L (2016) \u201cAutomatically learning semantic features for defect prediction,\u201d In: Proceedings of the 38th international conference on software engineering, New York, NY, USA, pp 297\u2013308. https:\/\/doi.org\/10.1145\/2884781.2884804.","DOI":"10.1145\/2884781.2884804"},{"key":"10892_CR19","doi-asserted-by":"publisher","unstructured":"Yamaguchi F, Golde N, Arp D, Rieck K, (2014) \u201cModeling and discovering vulnerabilities with code property graphs,\u201d In: 2014 IEEE symposium on security and privacy, pp 590\u2013604. https:\/\/doi.org\/10.1109\/SP.2014.44.","DOI":"10.1109\/SP.2014.44"},{"key":"10892_CR20","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/1176898","volume":"2022","author":"B Wu","year":"2022","unstructured":"Wu B, Zou F (2022) Code vulnerability detection based on deep sequence and graph models: a survey. Secur Commun Netw 2022:e1176898. https:\/\/doi.org\/10.1155\/2022\/1176898","journal-title":"Secur Commun Netw"},{"key":"10892_CR21","doi-asserted-by":"publisher","unstructured":"Siow JK, Liu S, Xie X, Meng G, Liu Y (2022) Learning program semantics with code representations: an empirical study,\u201d In: 2022 IEEE international conference on software analysis, evolution and reengineering (SANER), pp 554\u2013565. https:\/\/doi.org\/10.1109\/SANER53432.2022.00073.","DOI":"10.1109\/SANER53432.2022.00073"},{"key":"10892_CR22","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K, (2019) \u201cBERT: pre-training of deep bidirectional transformers for language understanding.\u201d arXiv, May 24, 2019. https:\/\/doi.org\/10.48550\/arXiv.1810.04805.","DOI":"10.48550\/arXiv.1810.04805"},{"key":"10892_CR23","unstructured":"Kanade A, Maniatis P, Balakrishnan G, Shi K (2020) \u201cLearning and evaluating contextual embedding of source code,\u201d In: Proceedings of the 37th international conference on machine learning, pp 5110\u20135121. Accessed: Feb. 26, 2023. [Online]. Available: https:\/\/proceedings.mlr.press\/v119\/kanade20a.html"},{"key":"10892_CR24","doi-asserted-by":"publisher","unstructured":"Feng Z et al. (2020) \u201cCodeBERT: a pre-trained model for programming and natural languages.\u201d arXiv, 2020. https:\/\/doi.org\/10.48550\/arXiv.2002.08155.","DOI":"10.48550\/arXiv.2002.08155"},{"key":"10892_CR25","unstructured":"Guo D et al. (2021) \u201cGraphCodeBERT: pre-training code representations with data flow.\u201d arXiv, Accessed: Feb. 26, 2023. [Online]. Available: http:\/\/arxiv.org\/abs\/2009.08366"},{"key":"10892_CR26","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang W, Joty S, Hoi SCH (2021) \u201cCodeT5: identifier-aware unified pre-trained encoder-decoder models for code understanding and generation.\u201d arXiv, Accessed: Apr. 15, 2024. [Online]. Available: http:\/\/arxiv.org\/abs\/2109.00859","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"10892_CR27","doi-asserted-by":"crossref","unstructured":"Wang Y, Le H, Gotmare AD, Bui NDQ, Li J, Hoi (2023) \u201cCodeT5+: open code large language models for code understanding and generation.\u201d arXiv, Accessed: Apr. 15, 2024. [Online]. Available: http:\/\/arxiv.org\/abs\/2305.07922","DOI":"10.18653\/v1\/2023.emnlp-main.68"},{"key":"10892_CR28","doi-asserted-by":"publisher","unstructured":"Li Y, Tarlow D, Brockschmidt M, Zemel R (2017) \u201cGated Graph Sequence Neural Networks.\u201d arXiv, https:\/\/doi.org\/10.48550\/arXiv.1511.05493.","DOI":"10.48550\/arXiv.1511.05493"},{"key":"10892_CR29","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1109\/TIFS.2020.3044773","volume":"16","author":"H Wang","year":"2021","unstructured":"Wang H et al (2021) Combining graph-based learning with automated data collection for code vulnerability detection. IEEE Trans Inform Forensic Secur 16:1943\u20131958. https:\/\/doi.org\/10.1109\/TIFS.2020.3044773","journal-title":"IEEE Trans Inform Forensic Secur"},{"key":"10892_CR30","doi-asserted-by":"publisher","unstructured":"Suneja S, Zheng Y, Zhuang Y, Laredo J, Morari A (2020) \u201cLearning to map source code to software vulnerability using code-as-a-graph.\u201d arXiv, https:\/\/doi.org\/10.48550\/arXiv.2006.08614.","DOI":"10.48550\/arXiv.2006.08614"},{"issue":"14","key":"10892_CR31","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.120.145301","volume":"120","author":"T Xie","year":"2018","unstructured":"Xie T, Grossman JC (2018) Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett 120(14):145301. https:\/\/doi.org\/10.1103\/PhysRevLett.120.145301","journal-title":"Phys Rev Lett"},{"key":"10892_CR32","unstructured":"Lee J, Lee I, Kang J (2019) \u201cSelf-attention graph pooling,\u201d In: Proceedings of the 36th international conference on machine learning, pp 3734\u20133743. Accessed: Feb. 26, 2023. [Online]. Available: https:\/\/proceedings.mlr.press\/v97\/lee19c.html"},{"key":"10892_CR33","doi-asserted-by":"publisher","unstructured":"Du X et al.(2019) \u201cLEOPARD: identifying vulnerable code for vulnerability assessment through program metrics,\u201d In 2019 IEEE\/ACM 41st international conference on software engineering (ICSE), pp. 60\u201371. https:\/\/doi.org\/10.1109\/ICSE.2019.00024.","DOI":"10.1109\/ICSE.2019.00024"},{"key":"10892_CR34","doi-asserted-by":"publisher","unstructured":"Wang Z, Guo J, Li H (2021) \u201cVulnerability feature extraction model for source code based on deep learning,\u201d In: 2021 international conference on computer network, electronic and automation (ICCNEA), pp 21\u201325. https:\/\/doi.org\/10.1109\/ICCNEA53019.2021.00016.","DOI":"10.1109\/ICCNEA53019.2021.00016"},{"issue":"5","key":"10892_CR35","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1049\/sfw2.12066","volume":"16","author":"F Subhan","year":"2022","unstructured":"Subhan F, Wu X, Bo L, Sun X, Rahman M (2022) A deep learning-based approach for software vulnerability detection using code metrics. IET Software 16(5):516\u2013526. https:\/\/doi.org\/10.1049\/sfw2.12066","journal-title":"IET Software"},{"key":"10892_CR36","doi-asserted-by":"publisher","first-page":"74562","DOI":"10.1109\/ACCESS.2020.2988557","volume":"8","author":"M Zagane","year":"2020","unstructured":"Zagane M, Abdi MK, Alenezi M (2020) Deep learning for software vulnerabilities detection using code metrics. IEEE Access 8:74562\u201374570. https:\/\/doi.org\/10.1109\/ACCESS.2020.2988557","journal-title":"IEEE Access"},{"issue":"1","key":"10892_CR37","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s13198-020-01036-0","volume":"12","author":"A Gupta","year":"2021","unstructured":"Gupta A, Suri B, Kumar V, Jain P (2021) Extracting rules for vulnerabilities detection with static metrics using machine learning. Int J Syst Assur Eng Manag 12(1):65\u201376. https:\/\/doi.org\/10.1007\/s13198-020-01036-0","journal-title":"Int J Syst Assur Eng Manag"},{"key":"10892_CR38","doi-asserted-by":"publisher","first-page":"219174","DOI":"10.1109\/ACCESS.2020.3041181","volume":"8","author":"N Medeiros","year":"2020","unstructured":"Medeiros N, Ivaki N, Costa P, Vieira M (2020) Vulnerable code detection using software metrics and machine learning. IEEE Access 8:219174\u2013219198. https:\/\/doi.org\/10.1109\/ACCESS.2020.3041181","journal-title":"IEEE Access"},{"key":"10892_CR39","unstructured":"Vaswani A et al.(2017) \u201cAttention is all you need,\u201d Adv Neural Inform Process Syst, Accessed: Feb. 26, 2023. [Online]. Available: https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html."},{"key":"10892_CR40","unstructured":"\u201cJoern\u201d (2023) [Online]. Available: https:\/\/joern.io\/"},{"key":"10892_CR41","unstructured":"\u201cSourceMonitor\u201d (2023) [Online]. Available: https:\/\/sourcemonitor.sooftware.com\/windows\/"},{"key":"10892_CR42","unstructured":"Abadi M, Barham P, Chen J, et al. (2016) Tensorflow: a system for large-scale machine learning [C]\/\/Osdi., 16(2016): 265\u2013283."},{"key":"10892_CR43","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neucom.2021.03.090","volume":"448","author":"X Yan","year":"2021","unstructured":"Yan X, Hu S, Mao Y, Ye Y, Yu H (2021) Deep multi-view learning methods: a review. Neurocomputing 448:106\u2013129. https:\/\/doi.org\/10.1016\/j.neucom.2021.03.090","journal-title":"Neurocomputing"},{"key":"10892_CR44","doi-asserted-by":"publisher","unstructured":"Okun V, Delaitre A, Black PE (2013) \u201cReport on the static analysis tool exposition (SATE) IV,\u201d national institute of standards and technology, NIST SP 500\u2013297, https:\/\/doi.org\/10.6028\/NIST.SP.500-297.","DOI":"10.6028\/NIST.SP.500-297"},{"key":"10892_CR45","unstructured":"NIST, (2024) \u201cSoftware assurance reference dataset,\u201d. [Online].Available: https:\/\/samate.nist.gov\/SRD\/index.php"},{"issue":"9","key":"10892_CR46","doi-asserted-by":"publisher","first-page":"3280","DOI":"10.1109\/TSE.2021.3087402","volume":"48","author":"S Chakraborty","year":"2022","unstructured":"Chakraborty S, Krishna R, Ding Y, Ray B (2022) Deep learning based vulnerability detection: Are we there yet? IEEE Trans Software Eng 48(9):3280\u20133296. https:\/\/doi.org\/10.1109\/TSE.2021.3087402","journal-title":"IEEE Trans Software Eng"},{"key":"10892_CR47","doi-asserted-by":"publisher","unstructured":"Li Y, Wang S, Nguyen TN (2021) \u201cVulnerability detection with fine-grained interpretations,\u201d In: Proceedings of the 29th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering, ESEC\/FSE 2021. New York, NY, USA: Association for Computing Machinery, pp 292\u2013303. https:\/\/doi.org\/10.1145\/3468264.3468597","DOI":"10.1145\/3468264.3468597"},{"key":"10892_CR48","unstructured":"Min Shi, Yufei Tang, Xingquan Zhu, and Jianxun Liu. (2019) \u201cFeature-attention graph convolutional networks for noise resilient learning,\u201d arXiv:1912.11755"},{"key":"10892_CR49","unstructured":"Kanade A, Maniatis P, Balakrishnan G, Shi K (2020) \u201cLearning and evaluating contextual embedding of source code,\u201d In: Proceedings of the 37th international conference on machine learning, pp 5110\u20135121. Accessed: Mar. 01, 2023. [Online]. Available: https:\/\/proceedings.mlr.press\/v119\/kanade20a.html"},{"key":"10892_CR50","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.3390\/app10051692","volume":"10","author":"X Li","year":"2020","unstructured":"Li X, Wang L, Xin Y, Yang Y, Chen Y (2020) Automated vulnerability detection in source code using minimum intermediate representation learning. Appl Sci 10:1692. https:\/\/doi.org\/10.3390\/app10051692","journal-title":"Appl Sci"},{"key":"10892_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10332-z","author":"Z Xie","year":"2022","unstructured":"Xie Z, Yang Y, Zhang Y, Wang J, Du S (2022) Deep learning on multi-view sequential data: a survey. Artif Intell Rev. https:\/\/doi.org\/10.1007\/s10462-022-10332-z","journal-title":"Artif Intell Rev"},{"issue":"12","key":"10892_CR52","doi-asserted-by":"publisher","first-page":"4453","DOI":"10.1109\/TPAMI.2020.3001433","volume":"43","author":"S Sun","year":"2021","unstructured":"Sun S, Dong W, Liu Q (2021) Multi-view representation learning with deep gaussian processes. IEEE Trans Pattern Anal Mach Intell 43(12):4453\u20134468. https:\/\/doi.org\/10.1109\/TPAMI.2020.3001433","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10892-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10892-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10892-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T13:56:30Z","timestamp":1741269390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10892-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,6]]},"references-count":52,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10892"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10892-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,6]]},"assertion":[{"value":"21 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}