{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T01:28:45Z","timestamp":1768526925168,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62263028"],"award-info":[{"award-number":["No.62263028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"publisher","award":["No. 2023-JC-QN-0633"],"award-info":[{"award-number":["No. 2023-JC-QN-0633"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018543","name":"Natural Science Foundation of Tibet Autonomous Region","doi-asserted-by":"publisher","award":["No. XZ202001ZR0065G"],"award-info":[{"award-number":["No. XZ202001ZR0065G"]}],"id":[{"id":"10.13039\/501100018543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00530-024-01320-0","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T12:02:06Z","timestamp":1712577726000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Attention U-Net based on multi-scale feature extraction and WSDAN data augmentation for video anomaly detection"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6948-6506","authenticated-orcid":false,"given":"Shanzhong","family":"Lei","sequence":"first","affiliation":[]},{"given":"Junfang","family":"Song","sequence":"additional","affiliation":[]},{"given":"Tengjiao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Fangxin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhuyang","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,8]]},"reference":[{"issue":"5","key":"1320_CR1","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/msp.2010.937393","volume":"27","author":"V Saligrama","year":"2010","unstructured":"Saligrama, V., Konrad, J., Jodoin, P.M.: Video anomaly identification. IEEE Signal Process. Mag. 27(5), 18\u201333 (2010). https:\/\/doi.org\/10.1109\/msp.2010.937393","journal-title":"IEEE Signal Process. Mag."},{"issue":"5","key":"1320_CR2","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1109\/tpami.2020.3040591","volume":"44","author":"B Ramachandra","year":"2020","unstructured":"Ramachandra, B., Jones, M.J., Vatsavai, R.R.: A survey of single-scene video anomaly detection. IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2293\u20132312 (2020). https:\/\/doi.org\/10.1109\/tpami.2020.3040591","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1320_CR3","doi-asserted-by":"publisher","unstructured":"Singh, A., Jones, M.J., Learned-Miller, E.G.: EVAL: explainable video anomaly localization, 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, pp. 18717\u201318726 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.01795","DOI":"10.1109\/CVPR52729.2023.01795"},{"key":"1320_CR4","doi-asserted-by":"publisher","unstructured":"Gong, D., Liu, L., Le, V., et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1705\u20131714 (2019). https:\/\/doi.org\/10.48550\/arXiv.1904.02639","DOI":"10.48550\/arXiv.1904.02639"},{"issue":"1","key":"1320_CR5","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/tpami.2013.111","volume":"36","author":"W Li","year":"2014","unstructured":"Li, W., Vasconcelos, N., et al.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 18\u201332 (2014). https:\/\/doi.org\/10.1109\/tpami.2013.111","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1320_CR6","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.cviu.2018.02.006","volume":"172","author":"M Sabokrou","year":"2018","unstructured":"Sabokrou, M., Fayyaz, M., Fathy, M., Moayed, Z., Klette, R.: Deep-anomaly: fully convolutional neural network for fast anomaly detection in crowded scenes. Comput. Vis. Image Underst. 172, 88\u201397 (2018). https:\/\/doi.org\/10.1016\/j.cviu.2018.02.006","journal-title":"Comput. Vis. Image Underst."},{"key":"1320_CR7","doi-asserted-by":"publisher","unstructured":"Wang, S., Miao, Z.: Anomaly detection in crowd scene. In: IEEE 10th International Conference on Signal Processing Proceedings, pp. 1220\u20131223. IEEE (2010). https:\/\/doi.org\/10.1109\/icosp.2010.5655356","DOI":"10.1109\/icosp.2010.5655356"},{"issue":"7","key":"1320_CR8","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.neucom.2012.03.040","volume":"119","author":"C Li","year":"2013","unstructured":"Li, C., Han, Z., Ye, Q., et al.: Visual abnormal behavior detection based on trajectory sparse reconstruction analysis. Neurocomputing 119(7), 94\u2013100 (2013). https:\/\/doi.org\/10.1016\/j.neucom.2012.03.040","journal-title":"Neurocomputing"},{"key":"1320_CR9","doi-asserted-by":"publisher","unstructured":"Kim, J., Grauman, K.: Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132928. IEEE Press, Piscataway (2009). https:\/\/doi.org\/10.1109\/cvpr.2009.5206569","DOI":"10.1109\/cvpr.2009.5206569"},{"key":"1320_CR10","volume-title":"Towards understanding self-supervised representation learning [D]","author":"N Saunshi","year":"2022","unstructured":"Saunshi, N.: Towards understanding self-supervised representation learning [D]. Princeton University, Princeton (2022)"},{"key":"1320_CR11","doi-asserted-by":"publisher","unstructured":"Wang, Y.Z., Qin, C., Bai, Y., et al.: Making reconstruction-based method great again for video anomaly detection. In: Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 1215\u20131220. IEEE Press, Piscataway (2023). https:\/\/doi.org\/10.1109\/ICDM54844.2022.00157","DOI":"10.1109\/ICDM54844.2022.00157"},{"key":"1320_CR12","doi-asserted-by":"publisher","unstructured":"Kim, J., Grauman, K.: Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20\u201325 June 2009, Miami, Florida, USA. IEEE (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206569","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"1320_CR13","doi-asserted-by":"publisher","unstructured":"Giorno, A.D., Bagnell, J.A., Hebert, M.: A discriminative framework for anomaly detection in large videos. In: European Conference on Computer Vision, pp. 334\u2013349. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_21","DOI":"10.1007\/978-3-319-46454-1_21"},{"key":"1320_CR14","doi-asserted-by":"publisher","unstructured":"Ren, H., Pan, H., Olsen, S.I., et al.: A comprehensive study of sparse codes on abnormality detection. arXiv preprint arXiv:1603.04026 (2016). https:\/\/doi.org\/10.48550\/arXiv.1603.04026","DOI":"10.48550\/arXiv.1603.04026"},{"key":"1320_CR15","doi-asserted-by":"publisher","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 733\u2013742 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.86","DOI":"10.1109\/CVPR.2016.86"},{"key":"1320_CR16","unstructured":"Waswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. NIPS (2017)"},{"key":"1320_CR17","doi-asserted-by":"publisher","unstructured":"Wang, Z., Zou, Y., Zhang, Z.: Cluster attention contrast for video anomaly detection. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2463\u20132471 (2020). https:\/\/doi.org\/10.1145\/3394171.3413529","DOI":"10.1145\/3394171.3413529"},{"key":"1320_CR18","doi-asserted-by":"publisher","unstructured":"Kimura, D., Chaudhury, S., Narita, M., et al.: Adversarial discriminative attention for robust anomaly detection. In: IEEE Winter conference on Applications of Computer Vision (WACV). IEEE (2020). https:\/\/doi.org\/10.1109\/WACV45572.2020.9093428","DOI":"10.1109\/WACV45572.2020.9093428"},{"key":"1320_CR19","doi-asserted-by":"publisher","unstructured":"Zhao, Y.R., Deng, B., Shen, C., et al.: Spatio-temporal autoencoder for video anomaly detection. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 1933\u20131941. ACM Press, New York (2017). https:\/\/doi.org\/10.1145\/3123266.3123451","DOI":"10.1145\/3123266.3123451"},{"key":"1320_CR20","doi-asserted-by":"publisher","unstructured":"Luo, W.X., Liu, W., Gao, S.H.: Remembering history with convolutional LSTM for anomaly detection. In: Proceedings of 2017 IEEE International Conference on Multimedia and Expo, pp. 439\u2013444. IEEE Press, Piscataway (2017). https:\/\/doi.org\/10.1109\/ICME.2017.8019325","DOI":"10.1109\/ICME.2017.8019325"},{"key":"1320_CR21","doi-asserted-by":"crossref","unstructured":"Luo, W.X., Liu, W., Gao, S.H.: A revisit of sparse coding based anomaly detection in stacked RNN framework. In: Proceedings of 2017 IEEE International Conference on Computer Vision, pp. 341\u2013349. IEEE Press, Piscataway (2017)","DOI":"10.1109\/ICCV.2017.45"},{"key":"1320_CR22","doi-asserted-by":"publisher","unstructured":"Liu, W., Luo, W., Lian, D., et al.: Future frame prediction for anomaly detection\u2014a new baseline. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00684","DOI":"10.1109\/CVPR.2018.00684"},{"key":"1320_CR23","doi-asserted-by":"publisher","unstructured":"Mathieu, M., Couprie, C., LeCun, Y.: Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440 (2015). https:\/\/doi.org\/10.48550\/arXiv.1511.05440","DOI":"10.48550\/arXiv.1511.05440"},{"key":"1320_CR24","doi-asserted-by":"crossref","unstructured":"Nguyen, T.N., Meunier, J.: Anomaly detection in video sequence with appearance-motion correspondence. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 1273\u20131283. IEEE Press, Piscataway (2020)","DOI":"10.1109\/ICCV.2019.00136"},{"key":"1320_CR25","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.patrec.2019.11.024","volume":"129","author":"Y Tang","year":"2020","unstructured":"Tang, Y., Zhao, L., Zhang, S., et al.: Integrating prediction and reconstruction for anomaly detection. Pattern Recogn. Lett. 129, 123\u2013130 (2020). https:\/\/doi.org\/10.1016\/j.patrec.2019.11.024","journal-title":"Pattern Recogn. Lett."},{"key":"1320_CR26","doi-asserted-by":"publisher","first-page":"104229","DOI":"10.1016\/j.imavis.2021.104229","volume":"112","author":"RF Mansour","year":"2021","unstructured":"Mansour, R.F., Escorcia-Gutierrez, J., Gamarra, M., et al.: Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model. Image Vis. Comput. 112, 104229 (2021). https:\/\/doi.org\/10.1016\/j.imavis.2021.104229","journal-title":"Image Vis. Comput."},{"issue":"8","key":"1320_CR27","first-page":"1","volume":"49","author":"Q Sun","year":"2022","unstructured":"Sun, Q., Ji, G.L., Zhang, J.: Non-local attention based generative adversarial network for video abnormal event detection. Comput. Sci. 49(8), 1\u20139 (2022)","journal-title":"Comput. Sci."},{"key":"1320_CR28","doi-asserted-by":"publisher","unstructured":"Wei, Z., Xiaoyan, J., Kaiying, Z., et al.: Unsupervised video anomaly detection algorithm based on reconstruction and prediction model. Sens. Microsyst. 41(10), 108\u2013111+116 (2021). https:\/\/doi.org\/10.1109\/TNNLS.2021.3083152","DOI":"10.1109\/TNNLS.2021.3083152"},{"issue":"06","key":"1320_CR29","first-page":"368","volume":"51","author":"S Jingbo","year":"2021","unstructured":"Jingbo, S., Jie, Ji.: Pedestrian abnormal behavior detection using memory-enhanced autoencoder in video surveillance. Infr. Laser Eng. 51(06), 368\u2013374 (2021)","journal-title":"Infr. Laser Eng."},{"issue":"6","key":"1320_CR30","first-page":"375","volume":"51","author":"YK Zhong","year":"2022","unstructured":"Zhong, Y.K., Mo, H.N.: Video anomaly detection method based on deep self-coding-Gaussian mixture model. Infr. Laser Eng. 51(6), 375\u2013381 (2022)","journal-title":"Infr. Laser Eng."},{"key":"1320_CR31","doi-asserted-by":"publisher","first-page":"130314","DOI":"10.1109\/ACCESS.2022.3229420","volume":"10","author":"JF Song","year":"2022","unstructured":"Song, J.F., Zhao, H.L., Wen, D.Y., et al.: Video anomaly detection based on optical flow feature enhanced spatio-temporal feature network FusionNet-LSTM-G. IEEE Access 10, 130314\u2013130325 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3229420","journal-title":"IEEE Access"},{"key":"1320_CR32","doi-asserted-by":"publisher","first-page":"108213","DOI":"10.1016\/j.patcog.2021.108213","volume":"122","author":"Y Chang","year":"2022","unstructured":"Chang, Y., Tu, Z., Xie, W., et al.: Video anomaly detection with spatio-temporal dissociation. Pattern Recogn. 122, 108213 (2022). https:\/\/doi.org\/10.1016\/j.patcog.2021.108213","journal-title":"Pattern Recogn."},{"issue":"23","key":"1320_CR33","doi-asserted-by":"publisher","first-page":"35557","DOI":"10.1007\/s11042-023-14956-3","volume":"82","author":"H Li","year":"2023","unstructured":"Li, H., Chen, J., Sun, X., et al.: Multi-memory video anomaly detection based on scene object distribution. Multimed Tools Appl 82(23), 35557\u201335583 (2023). https:\/\/doi.org\/10.1007\/s11042-023-14956-3","journal-title":"Multimed Tools Appl"},{"issue":"3","key":"1320_CR34","doi-asserted-by":"publisher","first-page":"3240","DOI":"10.1007\/s10489-022-03613-1","volume":"53","author":"VT Le","year":"2023","unstructured":"Le, V.T., Kim, Y.G.: Attention-based residual autoencoder for video anomaly detection. Appl. Intell. 53(3), 3240\u20133254 (2023). https:\/\/doi.org\/10.1007\/s10489-022-03613-1","journal-title":"Appl. Intell."},{"key":"1320_CR35","doi-asserted-by":"crossref","unstructured":"Park, H., Noh, J., Ham, B.: Learning memory-guided normality for anomaly detection. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14360\u201314369. IEEE Press, Piscataway (2020)","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"1320_CR36","doi-asserted-by":"publisher","unstructured":"Michelucci, U.: An introduction to autoencoders. 2022. https:\/\/doi.org\/10.48550\/arXiv.2201.03898","DOI":"10.48550\/arXiv.2201.03898"},{"key":"1320_CR37","doi-asserted-by":"publisher","unstructured":"Guo, C., Fan, B., Zhang, Q., et al.: AugFPN: improving multi-scale feature learning for object detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01261","DOI":"10.1109\/CVPR42600.2020.01261"},{"key":"1320_CR38","doi-asserted-by":"publisher","unstructured":"Howard, A.G., Zhu, M., Chen, B., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017). https:\/\/doi.org\/10.48550\/arXiv.1704.04861","DOI":"10.48550\/arXiv.1704.04861"},{"key":"1320_CR39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1320_CR40","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)"},{"key":"1320_CR41","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"1320_CR42","doi-asserted-by":"publisher","unstructured":"Hu, T., Qi, H., Huang, Q., et al.: See better before looking closer: weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint arXiv:1901.09891 (2019). https:\/\/doi.org\/10.48550\/arXiv.1901.09891","DOI":"10.48550\/arXiv.1901.09891"},{"key":"1320_CR43","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., et al.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"2","key":"1320_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3487891","volume":"55","author":"N Aldausari","year":"2022","unstructured":"Aldausari, N., Sowmya, A., Marcus, N., et al.: Video generative adversarial networks: a review. ACM Comput. Surv. (CSUR) 55(2), 1\u201325 (2022). https:\/\/doi.org\/10.1145\/3487891","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"1320_CR45","doi-asserted-by":"publisher","unstructured":"Hor\u00e9, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey, 23\u201326 August 2010. IEEE Computer Society (2010). https:\/\/doi.org\/10.1016\/j.patrec.2005.10.010","DOI":"10.1016\/j.patrec.2005.10.010"},{"issue":"8","key":"1320_CR46","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006). https:\/\/doi.org\/10.1016\/j.patrec.2005.10.010","journal-title":"Pattern Recogn. Lett."},{"issue":"2","key":"1320_CR47","doi-asserted-by":"publisher","first-page":"528","DOI":"10.3390\/sym15020528","volume":"15","author":"Q Zhang","year":"2023","unstructured":"Zhang, Q., Wei, H., Chen, J., et al.: Video anomaly detection based on attention mechanism. Symmetry 15(2), 528 (2023). https:\/\/doi.org\/10.3390\/sym15020528","journal-title":"Symmetry"},{"key":"1320_CR48","doi-asserted-by":"publisher","first-page":"1705","DOI":"10.1109\/LSP.2020.3025688","volume":"27","author":"MZ Zaheer","year":"2020","unstructured":"Zaheer, M.Z., Mahmood, A., Shin, H., et al.: A self-reasoning framework for anomaly detection using video-level labels. IEEE Signal Process. Lett. 27, 1705\u20131709 (2020). https:\/\/doi.org\/10.1109\/LSP.2020.3025688","journal-title":"IEEE Signal Process. Lett."},{"key":"1320_CR49","doi-asserted-by":"publisher","unstructured":"Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 FPS in MATLAB. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2720\u20132727 (2013). https:\/\/doi.org\/10.1109\/ICCV.2013.338","DOI":"10.1109\/ICCV.2013.338"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01320-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01320-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01320-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T17:04:16Z","timestamp":1720199056000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01320-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,8]]},"references-count":49,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1320"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01320-0","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,8]]},"assertion":[{"value":"10 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2024","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"}},{"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"}},{"value":"This research did not involve the use of human participants and\/or animals.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human participants and\/or animals"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"118"}}