{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T02:03:15Z","timestamp":1773108195096,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC3340700"],"award-info":[{"award-number":["2021YFC3340700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC3340700"],"award-info":[{"award-number":["2021YFC3340700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC3340700"],"award-info":[{"award-number":["2021YFC3340700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC3340700"],"award-info":[{"award-number":["2021YFC3340700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC3340700"],"award-info":[{"award-number":["2021YFC3340700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62136002"],"award-info":[{"award-number":["62136002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62136002"],"award-info":[{"award-number":["62136002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62136002"],"award-info":[{"award-number":["62136002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62136002"],"award-info":[{"award-number":["62136002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62136002"],"award-info":[{"award-number":["62136002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Knowledge Service Platform Project","award":["ZF1213"],"award-info":[{"award-number":["ZF1213"]}]},{"name":"Shanghai Knowledge Service Platform Project","award":["ZF1213"],"award-info":[{"award-number":["ZF1213"]}]},{"name":"Shanghai Knowledge Service Platform Project","award":["ZF1213"],"award-info":[{"award-number":["ZF1213"]}]},{"name":"Shanghai Knowledge Service Platform Project","award":["ZF1213"],"award-info":[{"award-number":["ZF1213"]}]},{"name":"Shanghai Knowledge Service Platform Project","award":["ZF1213"],"award-info":[{"award-number":["ZF1213"]}]},{"name":"Shanghai Trusted Industry Internet Software Collaborative Innovation Center"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s11280-025-01353-z","type":"journal-article","created":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T22:57:54Z","timestamp":1748818674000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-label out-of-distribution detection with spectral normalized joint energy"],"prefix":"10.1007","volume":"28","author":[{"given":"Yihan","family":"Mei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changzhi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dell","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoling","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"issue":"2","key":"1353_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1145\/335191.335388","volume":"29","author":"MM Breunig","year":"2000","unstructured":"Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. SIGMOD Rec. 29(2), 93\u2013104 (2000). https:\/\/doi.org\/10.1145\/335191.335388","journal-title":"SIGMOD Rec."},{"key":"1353_CR2","doi-asserted-by":"crossref","unstructured":"Yang, X., Latecki, L.J., Pokrajac, D.: Outlier detection with globally optimal exemplar-based gmm. In: Proceedings of the 2009 SIAM international conference on data mining, SIAM, pp. 145\u2013154 (2009)","DOI":"10.1137\/1.9781611972795.13"},{"key":"1353_CR3","doi-asserted-by":"crossref","unstructured":"Hsu, Y.-C., Shen, Y., Jin, H., Kira, Z.: Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data (2020)","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"1353_CR4","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks (2020)"},{"key":"1353_CR5","doi-asserted-by":"crossref","unstructured":"Bendale, A., Boult, T.: Towards Open Set Deep Networks (2015)","DOI":"10.1109\/CVPR.2016.173"},{"key":"1353_CR6","doi-asserted-by":"crossref","unstructured":"Reiss, T., Cohen, N., Bergman, L., Hoshen, Y.: PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation (2021)","DOI":"10.1109\/CVPR46437.2021.00283"},{"key":"1353_CR7","doi-asserted-by":"crossref","unstructured":"Chaudhari, S., Shevade, S.: Learning from positive and unlabelled examples using maximum margin clustering. In: Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part III 19, Springer, pp. 465\u2013473 (2012)","DOI":"10.1007\/978-3-642-34487-9_56"},{"key":"1353_CR8","unstructured":"Wang, H., Liu, W., Bocchieri, A., Li, Y.: Can multi-label classification networks know what they don\u2019t know? (2021)"},{"key":"1353_CR9","unstructured":"Liu, W., Wang, X., Owens, J.D., Li, Y.: Energy-based Out-of-distribution Detection (2021)"},{"key":"1353_CR10","unstructured":"Hinz, T., Heinrich, S., Wermter, S.: Generating Multiple Objects at Spatially Distinct Locations (2019)"},{"key":"1353_CR11","unstructured":"Gong, Y., Jia, Y., Leung, T., Toshev, A., Ioffe, S.: Deep Convolutional Ranking for Multilabel Image Annotation (2014)"},{"issue":"9","key":"1353_CR12","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1109\/TPAMI.2015.2491929","volume":"38","author":"Y Wei","year":"2016","unstructured":"Wei, Y., Xia, W., Lin, M., Huang, J., Ni, B., Dong, J., Zhao, Y., Yan, S.: Hcp: a flexible cnn framework for multi-label image classification. IEEE Trans. Pattern Anal. Mach. Intell. 38(9), 1901\u20131907 (2016). https:\/\/doi.org\/10.1109\/TPAMI.2015.2491929","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1353_CR13","doi-asserted-by":"crossref","unstructured":"Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: Proceedings of the 14th ACM international conference on information and knowledge management, pp. 195\u2013200 (2005)","DOI":"10.1145\/1099554.1099591"},{"key":"1353_CR14","unstructured":"Chen, L.-C., Schwing, A.G., Yuille, A.L., Urtasun, R.: Learning Deep Structured Models (2015)"},{"key":"1353_CR15","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: CNN-RNN: A Unified Framework for Multi-label Image Classification (2016)","DOI":"10.1109\/CVPR.2016.251"},{"issue":"3","key":"1353_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/jdwm.2007070101","volume":"3","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas, G., Katakis, I.: Multi-label classification: An overview. International Journal of Data Warehousing and Mining (IJDWM) 3(3), 1\u201313 (2007)","journal-title":"International Journal of Data Warehousing and Mining (IJDWM)"},{"issue":"10","key":"1353_CR17","doi-asserted-by":"publisher","first-page":"4883","DOI":"10.1109\/TIP.2019.2913079","volume":"28","author":"L Chen","year":"2019","unstructured":"Chen, L., Zhan, W., Tian, W., He, Y., Zou, Q.: Deep integration: a multi-label architecture for road scene recognition. IEEE Trans. Image Process. 28(10), 4883\u20134898 (2019). https:\/\/doi.org\/10.1109\/TIP.2019.2913079","journal-title":"IEEE Trans. Image Process."},{"key":"1353_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, A., Guo, J., Guo, Y.: Hybrid Models for Open Set Recognition (2020)","DOI":"10.1007\/978-3-030-58580-8_7"},{"key":"1353_CR19","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.comnet.2017.10.007","volume":"129","author":"A Ayadi","year":"2017","unstructured":"Ayadi, A., Ghorbel, O., Obeid, A.M., Abid, M.: Outlier detection approaches for wireless sensor networks: A survey. Comput. Netw. 129, 319\u2013333 (2017)","journal-title":"Comput. Netw."},{"issue":"3","key":"1353_CR20","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1002\/wics.1347","volume":"7","author":"S Ranshous","year":"2015","unstructured":"Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C., Samatova, N.F.: Anomaly detection in dynamic networks: a survey. Wiley Interdisciplinary Rev.: Comput. Stat. 7(3), 223\u2013247 (2015)","journal-title":"Wiley Interdisciplinary Rev.: Comput. Stat."},{"key":"1353_CR21","unstructured":"Wei, H., Xie, R., Cheng, H., Feng, L., An, B., Li, Y.: Mitigating Neural Network Overconfidence with Logit Normalization (2022)"},{"key":"1353_CR22","unstructured":"Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks (2018)"},{"key":"1353_CR23","unstructured":"Huang, R., Geng, A., Li, Y.: On the Importance of Gradients for Detecting Distributional Shifts in the Wild (2021)"},{"key":"1353_CR24","unstructured":"Tax, D.M.J.: One-class classification: Concept learning in the absence of counter-examples. (2002)"},{"key":"1353_CR25","unstructured":"Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S.A., Binder, A., M\u00fcller, E., Kloft, M.: Deep one-class classification. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 4393\u20134402. PMLR, (2018). https:\/\/proceedings.mlr.press\/v80\/ruff18a.html"},{"key":"1353_CR26","doi-asserted-by":"crossref","unstructured":"Wang, J., Cherian, A.: GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection (2019)","DOI":"10.1109\/ICCV.2019.00829"},{"key":"1353_CR27","unstructured":"He, F., Liu, T., Webb, G.I., Tao, D.: Instance-Dependent PU Learning by Bayesian Optimal Relabeling (2020)"},{"key":"1353_CR28","unstructured":"Menon, A., Rooyen, B.V., Ong, C.S., Williamson, B.: Learning from corrupted binary labels via class-probability estimation. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 125\u2013134. PMLR, Lille, France (2015). https:\/\/proceedings.mlr.press\/v37\/menon15.html"},{"key":"1353_CR29","unstructured":"Scott, C.: A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels. In: Lebanon, G., Vishwanathan, S.V.N. (eds.) Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 38, pp. 838\u2013846. PMLR, San Diego, California, USA (2015). https:\/\/proceedings.mlr.press\/v38\/scott15.html"},{"key":"1353_CR30","doi-asserted-by":"crossref","unstructured":"Zhong, J.-X., Li, N., Kong, W., Liu, S., Li, T.H., Li, G.: Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection (2019)","DOI":"10.1109\/CVPR.2019.00133"},{"key":"1353_CR31","doi-asserted-by":"crossref","unstructured":"Zolfi, A., Amit, G., Baras, A., Koda, S., Morikawa, I., Elovici, Y., Shabtai, A.: YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection (2023). https:\/\/arxiv.org\/abs\/2212.02081","DOI":"10.1109\/CVPR52733.2024.00553"},{"issue":"1","key":"1353_CR32","first-page":"147","volume":"9","author":"DH Ackley","year":"1985","unstructured":"Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for boltzmann machines. Cogn. Sci. 9(1), 147\u2013169 (1985)","journal-title":"Cogn. Sci."},{"key":"1353_CR33","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., Huang, F.: A tutorial on energy-based learning. Predicting Structured Data 1(0) (2006)","DOI":"10.7551\/mitpress\/7443.003.0014"},{"key":"1353_CR34","doi-asserted-by":"crossref","unstructured":"Ranzato, M., Poultney, C., Chopra, S., Cun, Y.: Efficient learning of sparse representations with an energy-based model. Advan. Neural inform. Process. Syst. 19 (2006)","DOI":"10.7551\/mitpress\/7503.003.0147"},{"key":"1353_CR35","unstructured":"Xie, J., Lu, Y., Zhu, S.-C., Wu, Y.N.: A Theory of Generative ConvNet (2016)"},{"key":"1353_CR36","doi-asserted-by":"crossref","unstructured":"Tu, L., Gimpel, K.: Learning Approximate Inference Networks for Structured Prediction (2018)","DOI":"10.18653\/v1\/N19-1335"},{"key":"1353_CR37","doi-asserted-by":"crossref","unstructured":"Lin, Z., Roy, S.D., Li, Y.: MOOD: Multi-level Out-of-distribution Detection (2021)","DOI":"10.1109\/CVPR46437.2021.01506"},{"key":"1353_CR38","doi-asserted-by":"crossref","unstructured":"Morteza, P., Li, Y.: Provable Guarantees for Understanding Out-of-distribution Detection (2021)","DOI":"10.1609\/aaai.v36i7.20752"},{"key":"1353_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, W., Yan, J., Wang, X., Zha, H.: Deep Extreme Multi-label Learning (2018)","DOI":"10.1145\/3206025.3206030"},{"issue":"3","key":"1353_CR40","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1016\/j.eswa.2014.08.036","volume":"42","author":"SM Liu","year":"2015","unstructured":"Liu, S.M., Chen, J.-H.: A multi-label classification based approach for sentiment classification. Expert Syst. Appl. 42(3), 1083\u20131093 (2015)","journal-title":"Expert Syst. Appl."},{"key":"1353_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, D., Taneva-Popova, B.: A theoretical analysis of out-of-distribution detection in multi-label classification. In: Proceedings of the 2023 ACM SIGIR international conference on theory of information retrieval, pp. 275\u2013282 (2023)","DOI":"10.1145\/3578337.3605116"},{"key":"1353_CR42","unstructured":"Behrmann, J., Grathwohl, W., Chen, R.T.Q., Duvenaud, D., Jacobsen, J.-H.: Invertible Residual Networks (2019)"},{"key":"1353_CR43","unstructured":"Bartlett, P.L., Evans, S.N., Long, P.M.: Representing smooth functions as compositions of near-identity functions with implications for deep network optimization (2018)"},{"key":"1353_CR44","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: A retrospective. Int. J. Comput. Vision 111, 98\u2013136 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"1353_CR45","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., Doll\u00e1r, P.: Microsoft COCO: Common Objects in Context (2015). arXiv:1405.0312","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"6","key":"1353_CR46","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun. ACM"},{"key":"1353_CR47","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on computer vision and pattern recognition, IEEE, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1353_CR48","doi-asserted-by":"crossref","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing Textures in the Wild (2013)","DOI":"10.1109\/CVPR.2014.461"},{"key":"1353_CR49","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity Mappings in Deep Residual Networks (2016)","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"1353_CR50","unstructured":"Chan, R., Lis, K., Uhlemeyer, S., Blum, H., Honari, S., Siegwart, R., Fua, P., Salzmann, M., Rottmann, M.: SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation (2021)"},{"key":"1353_CR51","unstructured":"Hendrycks, D., Gimpel, K.: A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks (2018)"},{"key":"1353_CR52","doi-asserted-by":"publisher","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation forest. In: 2008 Eighth IEEE international conference on data mining, pp. 413\u2013422 (2008). https:\/\/doi.org\/10.1109\/ICDM.2008.17","DOI":"10.1109\/ICDM.2008.17"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-025-01353-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-025-01353-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-025-01353-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T16:50:45Z","timestamp":1757177445000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-025-01353-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,2]]},"references-count":52,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["1353"],"URL":"https:\/\/doi.org\/10.1007\/s11280-025-01353-z","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,2]]},"assertion":[{"value":"18 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"40"}}