{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T07:49:28Z","timestamp":1774079368475,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Guangdong Provincial Key Laboratory of Intelligent Information Processing"},{"name":"Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s13042-026-03072-x","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T05:19:49Z","timestamp":1774070389000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical sentiment inconsistency enhancement for multimodal sarcasm detection"],"prefix":"10.1007","volume":"17","author":[{"given":"Yihao","family":"Lin","sequence":"first","affiliation":[]},{"given":"Junxi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Haoliang","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Qiuyan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Fenghuan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,21]]},"reference":[{"key":"3072_CR1","doi-asserted-by":"crossref","unstructured":"Riloff E, Qadir A, Surve P, et\u00a0al (2013) Sarcasm as contrast between a positive sentiment and negative situation. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp 704\u2013714","DOI":"10.18653\/v1\/D13-1066"},{"key":"3072_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116398","volume":"193","author":"S Frenda","year":"2022","unstructured":"Frenda S, Cignarella AT, Basile V et al (2022) The unbearable hurtfulness of sarcasm. Expert Syst Appl 193:116398","journal-title":"Expert Syst Appl"},{"key":"3072_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.paid.2019.109662","volume":"154","author":"K Szymaniak","year":"2020","unstructured":"Szymaniak K, Ka\u0142owski P (2020) Trait anger and sarcasm use. Personal Individ Differ 154:109662","journal-title":"Personal Individ Differ"},{"key":"3072_CR4","unstructured":"Nayel H, Amer E, Allam A, et\u00a0al (2021) Machine learning-based model for sentiment and sarcasm detection. In: Proceedings of the Sixth Arabic Natural Language Processing Workshop, pp 386\u2013389"},{"key":"3072_CR5","doi-asserted-by":"crossref","unstructured":"Wang X, Dong Y, Jin D, et\u00a0al (2023) Augmenting affective dependency graph via iterative incongruity graph learning for sarcasm detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 4702\u20134710","DOI":"10.1609\/aaai.v37i4.25594"},{"key":"3072_CR6","doi-asserted-by":"crossref","unstructured":"Schifanella R, De\u00a0Juan P, Tetreault J, et\u00a0al (2016) Detecting sarcasm in multimodal social platforms. In: Proceedings of the 24th ACM International Conference on Multimedia, pp 1136\u20131145","DOI":"10.1145\/2964284.2964321"},{"key":"3072_CR7","doi-asserted-by":"crossref","unstructured":"Cai Y, Cai H, Wan X (2019) Multi-modal sarcasm detection in twitter with hierarchical fusion model. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 2506\u20132515","DOI":"10.18653\/v1\/P19-1239"},{"key":"3072_CR8","doi-asserted-by":"crossref","unstructured":"Xu N, Zeng Z, Mao W (2020) Reasoning with multimodal sarcastic tweets via modeling cross-modality contrast and semantic association. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 3777\u20133786","DOI":"10.18653\/v1\/2020.acl-main.349"},{"key":"3072_CR9","first-page":"1383","volume":"2020","author":"H Pan","year":"2020","unstructured":"Pan H, Lin Z, Fu P et al (2020) Modeling intra and inter-modality incongruity for multi-modal sarcasm detection. Find Assoc Comput Linguistics EMNLP 2020:1383\u20131392","journal-title":"Find Assoc Comput Linguistics EMNLP"},{"key":"3072_CR10","doi-asserted-by":"crossref","unstructured":"Liu H, Wang W, Li H (2022) Towards multi-modal sarcasm detection via hierarchical congruity modeling with knowledge enhancement. arXiv preprint arXiv:2210.03501","DOI":"10.18653\/v1\/2022.emnlp-main.333"},{"key":"3072_CR11","doi-asserted-by":"crossref","unstructured":"Wen C, Jia G, Yang J (2023) Dip: Dual incongruity perceiving network for sarcasm detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2540\u20132550","DOI":"10.1109\/CVPR52729.2023.00250"},{"key":"3072_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102353","volume":"108","author":"H Liu","year":"2024","unstructured":"Liu H, Wei R, Tu G et al (2024) Sarcasm driven by sentiment: a sentiment-aware hierarchical fusion network for multimodal sarcasm detection. Inf Fusion 108:102353","journal-title":"Inf Fusion"},{"key":"3072_CR13","unstructured":"Ma H, He D, Wang X, et\u00a0al (2024) Multi-modal sarcasm detection based on dual generative processes. In: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, pp 2279\u20132287"},{"key":"3072_CR14","doi-asserted-by":"publisher","first-page":"1670","DOI":"10.18653\/v1\/2022.findings-naacl.126","volume":"2022","author":"Y Liu","year":"2022","unstructured":"Liu Y, Wang Y, Sun A, Meng X, Li J, Guo J (2022) A dual-channel framework for sarcasm recognition by detecting sentiment conflict. Find Assoc Comput Linguistics NAACL 2022:1670\u20131680","journal-title":"Find Assoc Comput Linguistics NAACL"},{"key":"3072_CR15","first-page":"296","volume":"1","author":"A Ortis","year":"2019","unstructured":"Ortis A, Farinella GM, Battiato S (2019) An overview on image sentiment analysis: methods, datasets and current challenges. ICETE 1:296\u2013306","journal-title":"ICETE"},{"issue":"3","key":"3072_CR16","doi-asserted-by":"publisher","first-page":"2767","DOI":"10.1007\/s42001-024-00326-y","volume":"7","author":"IKS Al-Tameemi","year":"2024","unstructured":"Al-Tameemi IKS, Feizi-Derakhshi MR, Pashazadeh S et al (2024) A comprehensive review of visual\u2013textual sentiment analysis from social media networks. J Comput Soc Sci 7(3):2767\u20132838","journal-title":"J Comput Soc Sci"},{"key":"3072_CR17","doi-asserted-by":"publisher","first-page":"90495","DOI":"10.1109\/ACCESS.2020.2993803","volume":"8","author":"K Patel","year":"2020","unstructured":"Patel K, Mehta D, Mistry C et al (2020) Facial sentiment analysis using ai techniques: state-of-the-art, taxonomies, and challenges. IEEE Access 8:90495\u201390519","journal-title":"IEEE Access"},{"issue":"1","key":"3072_CR18","first-page":"53","volume":"2","author":"SMS Abdullah","year":"2021","unstructured":"Abdullah SMS, Abdulazeez AM (2021) Facial expression recognition based on deep learning convolution neural network: a review. J Soft Comput Data Mining 2(1):53\u201365","journal-title":"J Soft Comput Data Mining"},{"key":"3072_CR19","unstructured":"Maynard DG, Greenwood MA (2014) Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. In: Lrec 2014 proceedings, ELRA"},{"key":"3072_CR20","doi-asserted-by":"crossref","unstructured":"Felbo B, Mislove A, S\u00f8gaard A, et\u00a0al (2017) Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. arXiv preprint arXiv:1708.00524","DOI":"10.18653\/v1\/D17-1169"},{"key":"3072_CR21","doi-asserted-by":"crossref","unstructured":"Baruah A, Das K, Barbhuiya F, et\u00a0al (2020) Context-aware sarcasm detection using bert. In: Proceedings of the Second Workshop on Figurative Language Processing, pp 83\u201387","DOI":"10.18653\/v1\/2020.figlang-1.12"},{"key":"3072_CR22","doi-asserted-by":"crossref","unstructured":"Javdan S, Minaei-Bidgoli B (2020) Applying transformers and aspect-based sentiment analysis approaches on sarcasm detection. In: Proceedings of the Second Workshop on Figurative Language Processing, pp 67\u201371","DOI":"10.18653\/v1\/2020.figlang-1.9"},{"key":"3072_CR23","doi-asserted-by":"crossref","unstructured":"Lou C, Liang B, Gui L, et\u00a0al (2021) Affective dependency graph for sarcasm detection. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1844\u20131849","DOI":"10.1145\/3404835.3463061"},{"key":"3072_CR24","doi-asserted-by":"crossref","unstructured":"Liang B, Lou C, Li X, et\u00a0al (2021) Multi-modal sarcasm detection with interactive in-modal and cross-modal graphs. In: Proceedings of the 29th ACM International Conference on Multimedia, pp 4707\u20134715","DOI":"10.1145\/3474085.3475190"},{"issue":"12","key":"3072_CR25","doi-asserted-by":"publisher","first-page":"4119","DOI":"10.1007\/s13042-023-01884-9","volume":"14","author":"Z Wen","year":"2023","unstructured":"Wen Z, Wang R, Luo X, Wang Q, Liang B, Du J, Yu X, Gui L, Xu R (2023) Multi-perspective contrastive learning framework guided by sememe knowledge and label information for sarcasm detection. Int J Mach Learn Cybern 14(12):4119\u20134134","journal-title":"Int J Mach Learn Cybern"},{"issue":"3","key":"3072_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102883","volume":"59","author":"Z Wen","year":"2022","unstructured":"Wen Z, Gui L, Wang Q, Guo M, Yu X, Du J, Xu R (2022) Sememe knowledge and auxiliary information enhanced approach for sarcasm detection. Inf Proc Manag 59(3):102883","journal-title":"Inf Proc Manag"},{"issue":"5","key":"3072_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2025.104169","volume":"62","author":"Z Wen","year":"2025","unstructured":"Wen Z, Wang R, Wang Q, Gui L, Long Y, Chen S, Liang B, Yang M, Xu R (2025) Fgvirony: a chinese dataset of fine-grained verbal irony. Inf Proc Manag 62(5):104169","journal-title":"Inf Proc Manag"},{"key":"3072_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101921","volume":"100","author":"T Yue","year":"2023","unstructured":"Yue T, Mao R, Wang H et al (2023) Knowlenet: knowledge fusion network for multimodal sarcasm detection. Inf Fusion 100:101921","journal-title":"Inf Fusion"},{"issue":"240","key":"3072_CR29","first-page":"1","volume":"24","author":"A Chowdhery","year":"2023","unstructured":"Chowdhery A, Narang S, Devlin J et al (2023) Palm: scaling language modeling with pathways. J Mach Learn Res 24(240):1\u2013113","journal-title":"J Mach Learn Res"},{"key":"3072_CR30","unstructured":"Touvron H, Lavril T, Izacard G, et\u00a0al (2023) Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971"},{"key":"3072_CR31","first-page":"3881","volume":"2024","author":"W Zhang","year":"2024","unstructured":"Zhang W, Deng Y, Liu B, Pan S, Bing L (2024) Sentiment analysis in the era of large language models: a reality check. Find Assoc Comput Linguistics NAACL 2024:3881\u20133906","journal-title":"Find Assoc Comput Linguistics NAACL"},{"issue":"3","key":"3072_CR32","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1007\/s11277-023-10235-4","volume":"129","author":"YY Tan","year":"2023","unstructured":"Tan YY, Chow CO, Kanesan J et al (2023) Sentiment analysis and sarcasm detection using deep multi-task learning. Wireless Pers Commun 129(3):2213\u20132237","journal-title":"Wireless Pers Commun"},{"key":"3072_CR33","doi-asserted-by":"crossref","unstructured":"Prasad AG, Sanjana S, Bhat SM, et\u00a0al (2017) Sentiment analysis for sarcasm detection on streaming short text data. In: 2017 2nd International Conference on Knowledge Engineering and Applications (ICKEA). IEEE, pp 1\u20135","DOI":"10.1109\/ICKEA.2017.8169892"},{"key":"3072_CR34","doi-asserted-by":"crossref","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp 4171\u20134186","DOI":"10.18653\/v1\/N19-1423"},{"key":"3072_CR35","doi-asserted-by":"crossref","unstructured":"Nguyen DQ, Vu T, Nguyen AT (2020) Bertweet: A pre-trained language model for english tweets. In: Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, pp 9\u201314","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"3072_CR36","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877\u20131901","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"3072_CR37","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/s12559-021-09832-x","volume":"14","author":"Y Du","year":"2022","unstructured":"Du Y, Li T, Pathan MS, Mehmood RM, Tariq U (2022) An effective sarcasm detection approach based on sentimental context and individual expression habits. Cogn Comput 14(1):78\u201390","journal-title":"Cogn Comput"},{"key":"3072_CR38","doi-asserted-by":"crossref","unstructured":"Chauhan DS, Dhanush SR, Ekbal A, Bhattacharyya P (2020) Sentiment and emotion help sarcasm? a multi-task learning framework for multi-modal sarcasm, sentiment and emotion analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 4351\u20134360","DOI":"10.18653\/v1\/2020.acl-main.401"},{"key":"3072_CR39","unstructured":"Radford A, Kim JW, Hallacy C, et\u00a0al (2021) Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, PMLR, pp 8748\u20138763"},{"key":"3072_CR40","doi-asserted-by":"crossref","unstructured":"Cambria E, Zhang X, Mao R, Speer R (2024) Senticnet 8: Fusing emotion ai and commonsense ai for interpretable, trustworthy, and explainable affective computing. In: International Conference on Human-Computer Interaction. Springer Nature Switzerland, pp 197\u2013216","DOI":"10.1007\/978-3-031-76827-9_11"},{"key":"3072_CR41","unstructured":"Wang X, Wei J, Schuurmans D, Le Q, Chi E, Zhou D, et\u00a0al (2022) Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171"},{"issue":"12","key":"3072_CR42","doi-asserted-by":"publisher","first-page":"7305","DOI":"10.1007\/s10115-024-02214-3","volume":"66","author":"H Bashiri","year":"2024","unstructured":"Bashiri H, Naderi H (2024) Comprehensive review and comparative analysis of transformer models in sentiment analysis. Knowl Inf Syst 66(12):7305\u20137361","journal-title":"Knowl Inf Syst"},{"key":"3072_CR43","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, et\u00a0al (2017) Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"3072_CR44","unstructured":"Chen Q, Zhang R, Zheng Y, Mao Y (2022) Dual contrastive learning: Text classification via label-aware data augmentation. arXiv preprint arXiv:2201.08702"},{"issue":"21","key":"3072_CR45","doi-asserted-by":"publisher","first-page":"4099","DOI":"10.3390\/math10214099","volume":"10","author":"H Xiong","year":"2022","unstructured":"Xiong H, Yan Z, Zhao H, Huang Z, Xue Y (2022) Triplet contrastive learning for aspect level sentiment classification. Mathematics 10(21):4099","journal-title":"Mathematics"},{"key":"3072_CR46","first-page":"10834","volume":"2023","author":"L Qin","year":"2023","unstructured":"Qin L, Huang S, Chen Q, Cai C, Zhang Y, Liang B, Che W, Xu R (2023) Mmsd2. 0: towards a reliable multi-modal sarcasm detection system. Find Assoc Comput Linguistics ACL 2023:10834\u201310845","journal-title":"Find Assoc Comput Linguistics ACL"},{"key":"3072_CR47","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et\u00a0al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"3072_CR48","doi-asserted-by":"crossref","unstructured":"Tay Y, Tuan LA, Hui SC, Su J (2018) Reasoning with sarcasm by reading inbetween. arXiv preprint arXiv:1805.02856","DOI":"10.18653\/v1\/P18-1093"},{"key":"3072_CR49","doi-asserted-by":"crossref","unstructured":"Xiong T, Zhang P, Zhu H, Yang Y (2019) Sarcasm detection with self-matching networks and low-rank bilinear pooling. In: Proceedings of the ACM Web Conference, pp 2115\u20132124","DOI":"10.1145\/3308558.3313735"},{"key":"3072_CR50","unstructured":"Liu Y, Ott M, Goyal N, et\u00a0al (2019) Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"key":"3072_CR51","doi-asserted-by":"crossref","unstructured":"Li L, Jin D, Wang X, et\u00a0al (2023) Multi-modal sarcasm detection based on cross-modal composition of inscribed entity relations. In: 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, pp 918\u2013925","DOI":"10.1109\/ICTAI59109.2023.00138"},{"key":"3072_CR52","doi-asserted-by":"crossref","unstructured":"Liang B, Lou C, Li X, et\u00a0al (2022) Multi-modal sarcasm detection via cross-modal graph convolutional network. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1767\u20131777","DOI":"10.18653\/v1\/2022.acl-long.124"},{"issue":"4","key":"3072_CR53","doi-asserted-by":"publisher","first-page":"1874","DOI":"10.1109\/TAFFC.2024.3380375","volume":"15","author":"B Liang","year":"2024","unstructured":"Liang B, Gui L, He Y, Cambria E, Xu R (2024) Fusion and discrimination: a multimodal graph contrastive learning framework for multimodal sarcasm detection. IEEE Trans Affect Comput 15(4):1874\u20131888","journal-title":"IEEE Trans Affect Comput"},{"key":"3072_CR54","doi-asserted-by":"crossref","unstructured":"Chen Z, Lin H, Luo Z, Cheng M, Ma J, Chen G (2024) Cofipara: A coarse-to-fine paradigm for multimodal sarcasm target identification with large multimodal models. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 9663\u20139687","DOI":"10.18653\/v1\/2024.acl-long.522"},{"key":"3072_CR55","doi-asserted-by":"crossref","unstructured":"Jia M, Xie C, Jing L (2024) Debiasing multimodal sarcasm detection with contrastive learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 18354\u201318362","DOI":"10.1609\/aaai.v38i16.29795"},{"key":"3072_CR56","doi-asserted-by":"crossref","unstructured":"Qiao Y, Jing L, Song X, et\u00a0al (2023) Mutual-enhanced incongruity learning network for multi-modal sarcasm detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 9507\u20139515","DOI":"10.1609\/aaai.v37i8.26138"},{"key":"3072_CR57","unstructured":"Achiam J, Adler S, Agarwal S, et\u00a0al (2023) Gpt-4 technical report. arXiv preprint arXiv:2303.08774"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03072-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03072-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03072-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T05:19:58Z","timestamp":1774070398000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03072-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,21]]},"references-count":57,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["3072"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03072-x","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,21]]},"assertion":[{"value":"2 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2026","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"214"}}