{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:49:28Z","timestamp":1775472568373,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Funding of Special Development Project of Tianchang Intelligent Equipment and Instrument Research Institute","award":["tzy202221"],"award-info":[{"award-number":["tzy202221"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876079"],"award-info":[{"award-number":["61876079"]}],"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":["61876079"],"award-info":[{"award-number":["61876079"]}],"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":["61876079"],"award-info":[{"award-number":["61876079"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s00530-023-01082-1","type":"journal-article","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T05:57:57Z","timestamp":1680501477000},"page":"1941-1954","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Attentional weighting strategy-based dynamic GCN for skeleton-based action recognition"],"prefix":"10.1007","volume":"29","author":[{"given":"Kai","family":"Hu","sequence":"first","affiliation":[]},{"given":"Junlan","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Chaowen","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Min","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Liguo","family":"Weng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,1]]},"reference":[{"key":"1082_CR1","unstructured":"Hu, K., Jin, J., Zheng, F., Weng, L., Ding, Y.: Overview of behavior recognition based on deep learning. Artificial Intelligence Review, 1\u201333 (2022)"},{"issue":"1","key":"1082_CR2","doi-asserted-by":"publisher","first-page":"206","DOI":"10.3390\/rs14010206","volume":"14","author":"K Hu","year":"2022","unstructured":"Hu, K., Li, M., Xia, M., Lin, H.: Multi-scale feature aggregation network for water area segmentation. Remote Sens 14(1), 206 (2022)","journal-title":"Remote Sens"},{"issue":"4","key":"1082_CR3","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.3390\/rs15041055","volume":"15","author":"K Hu","year":"2023","unstructured":"Hu, K., Zhang, E., Xia, M., Weng, L., Lin, H.: Mcanet: a multi-branch network for cloud\/snow segmentation in high-resolution remote sensing images. Remote Sens 15(4), 1055 (2023)","journal-title":"Remote Sens"},{"key":"1082_CR4","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.cviu.2018.04.007","volume":"171","author":"P Wang","year":"2018","unstructured":"Wang, P., Li, W., Ogunbona, P., Wan, J., Escalera, S.: Rgb-d-based human motion recognition with deep learning: A survey. Comput. Vis. Image Underst. 171, 118\u2013139 (2018)","journal-title":"Comput. Vis. Image Underst."},{"key":"1082_CR5","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. Advances in neural information processing systems 27 (2014)"},{"key":"1082_CR6","doi-asserted-by":"crossref","unstructured":"Arandjelovic, R., Zisserman, A.: All about vlad. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1578\u20131585 (2013)","DOI":"10.1109\/CVPR.2013.207"},{"key":"1082_CR7","doi-asserted-by":"crossref","unstructured":"Duta, I.C., Ionescu, B., Aizawa, K., Sebe, N.: Spatio-temporal vlad encoding for human action recognition in videos. In: International Conference on Multimedia Modeling, pp. 365\u2013378. Springer, New York (2017)","DOI":"10.1007\/978-3-319-51811-4_30"},{"key":"1082_CR8","unstructured":"Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110\u20131118 (2015)"},{"key":"1082_CR9","doi-asserted-by":"crossref","unstructured":"Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., Xie, X.: Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)","DOI":"10.1609\/aaai.v30i1.10451"},{"issue":"12","key":"1082_CR10","doi-asserted-by":"publisher","first-page":"3007","DOI":"10.1109\/TPAMI.2017.2771306","volume":"40","author":"J Liu","year":"2017","unstructured":"Liu, J., Shahroudy, A., Xu, D., Kot, A.C., Wang, G.: Skeleton-based action recognition using spatio-temporal lstm network with trust gates. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 3007\u20133021 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1082_CR11","doi-asserted-by":"crossref","unstructured":"Ke, Q., Bennamoun, M., An, S., Sohel, F., Boussaid, F.: A new representation of skeleton sequences for 3d action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3288\u20133297 (2017)","DOI":"10.1109\/CVPR.2017.486"},{"issue":"8","key":"1082_CR12","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1109\/TPAMI.2019.2896631","volume":"41","author":"P Zhang","year":"2019","unstructured":"Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View adaptive neural networks for high performance skeleton-based human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1963\u20131978 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"1082_CR13","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw"},{"key":"1082_CR14","doi-asserted-by":"crossref","unstructured":"Qi, S., Wang, W., Jia, B., Shen, J., Zhu, S.-C.: Learning human-object interactions by graph parsing neural networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 401\u2013417 (2018)","DOI":"10.1007\/978-3-030-01240-3_25"},{"key":"1082_CR15","doi-asserted-by":"crossref","unstructured":"Si, C., Jing, Y., Wang, W., Wang, L., Tan, T.: Skeleton-based action recognition with spatial reasoning and temporal stack learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 103\u2013118 (2018)","DOI":"10.1007\/978-3-030-01246-5_7"},{"key":"1082_CR16","doi-asserted-by":"crossref","unstructured":"Li, B., Li, X., Zhang, Z., Wu, F.: Spatio-temporal graph routing for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8561\u20138568 (2019)","DOI":"10.1609\/aaai.v33i01.33018561"},{"key":"1082_CR17","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"1082_CR18","doi-asserted-by":"crossref","unstructured":"Cheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., Lu, H.: Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 183\u2013192 (2020)","DOI":"10.1109\/CVPR42600.2020.00026"},{"key":"1082_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W.: Disentangling and unifying graph convolutions for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 143\u2013152 (2020)","DOI":"10.1109\/CVPR42600.2020.00022"},{"key":"1082_CR20","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.neucom.2021.05.004","volume":"454","author":"W Peng","year":"2021","unstructured":"Peng, W., Shi, J., Varanka, T., Zhao, G.: Rethinking the st-gcns for 3d skeleton-based human action recognition. Neurocomputing 454, 45\u201353 (2021)","journal-title":"Neurocomputing"},{"key":"1082_CR21","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12026\u201312035 (2019)","DOI":"10.1109\/CVPR.2019.01230"},{"key":"1082_CR22","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., Tian, Q.: Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3595\u20133603 (2019)","DOI":"10.1109\/CVPR.2019.00371"},{"issue":"8","key":"1082_CR23","doi-asserted-by":"publisher","first-page":"3047","DOI":"10.1109\/TNNLS.2019.2935173","volume":"31","author":"X Zhang","year":"2019","unstructured":"Zhang, X., Xu, C., Tian, X., Tao, D.: Graph edge convolutional neural networks for skeleton-based action recognition. IEEE Trans Neural Netw Learn Syst 31(8), 3047\u20133060 (2019)","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1082_CR24","unstructured":"Thakkar, K., Narayanan, P.: Part-based graph convolutional network for action recognition. arXiv preprint arXiv:1809.04983 (2018)"},{"key":"1082_CR25","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: Attention over convolution kernels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11030\u201311039 (2020)","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"1082_CR26","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.-T., Wang, G.: Ntu rgb+ d: A large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1010\u20131019 (2016)","DOI":"10.1109\/CVPR.2016.115"},{"key":"1082_CR27","unstructured":"Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017)"},{"issue":"11","key":"1082_CR28","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673\u20132681 (1997)","journal-title":"IEEE Trans. Signal Process."},{"key":"1082_CR29","doi-asserted-by":"crossref","unstructured":"Gammulle, H., Denman, S., Sridharan, S., Fookes, C.: Two stream lstm: A deep fusion framework for human action recognition. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 177\u2013186 (2017). IEEE","DOI":"10.1109\/WACV.2017.27"},{"key":"1082_CR30","unstructured":"Li, C., Zhong, Q., Xie, D., Pu, S.: Skeleton-based action recognition with convolutional neural networks. In: 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 597\u2013600 (2017). IEEE"},{"key":"1082_CR31","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/LSP.2021.3049691","volume":"28","author":"W Peng","year":"2021","unstructured":"Peng, W., Shi, J., Zhao, G.: Spatial temporal graph deconvolutional network for skeleton-based human action recognition. IEEE Signal Process. Lett. 28, 244\u2013248 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"1082_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107921","volume":"115","author":"W Peng","year":"2021","unstructured":"Peng, W., Hong, X., Zhao, G.: Tripool: Graph triplet pooling for 3d skeleton-based action recognition. Pattern Recogn. 115, 107921 (2021)","journal-title":"Pattern Recogn."},{"key":"1082_CR33","doi-asserted-by":"crossref","unstructured":"Peng, W., Shi, J., Xia, Z., Zhao, G.: Mix dimension in poincar\u00e9 geometry for 3d skeleton-based action recognition. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1432\u20131440 (2020)","DOI":"10.1145\/3394171.3413910"},{"key":"1082_CR34","doi-asserted-by":"crossref","unstructured":"Mostafa, A., Peng, W., Zhao, G.: Hyperbolic spatial temporal graph convolutional networks. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 3301\u20133305 (2022). IEEE","DOI":"10.1109\/ICIP46576.2022.9897522"},{"issue":"3","key":"1082_CR35","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.3390\/app12031028","volume":"12","author":"K Hu","year":"2022","unstructured":"Hu, K., Ding, Y., Jin, J., Weng, L., Xia, M.: Skeleton motion recognition based on multi-scale deep spatio-temporal features. Appl. Sci. 12(3), 1028 (2022)","journal-title":"Appl. Sci."},{"key":"1082_CR36","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.neucom.2022.03.066","volume":"491","author":"T Liu","year":"2022","unstructured":"Liu, T., Zhao, R., Lam, K.-M., Kong, J.: Visual-semantic graph neural network with pose-position attentive learning for group activity recognition. Neurocomputing 491, 217\u2013231 (2022)","journal-title":"Neurocomputing"},{"key":"1082_CR37","unstructured":"Zhao, R., Liu, T., Huang, Z., Lun, D.P.-K., Lam, K.K.: Geometry-aware facial expression recognition via attentive graph convolutional networks. IEEE Transactions on Affective Computing (2021)"},{"key":"1082_CR38","doi-asserted-by":"crossref","unstructured":"Liu, J., Wang, G., Hu, P., Duan, L.-Y., Kot, A.C.: Global context-aware attention lstm networks for 3d action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1647\u20131656 (2017)","DOI":"10.1109\/CVPR.2017.391"},{"key":"1082_CR39","doi-asserted-by":"crossref","unstructured":"Si, C., Chen, W., Wang, W., Wang, L., Tan, T.: An attention enhanced graph convolutional lstm network for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1227\u20131236 (2019)","DOI":"10.1109\/CVPR.2019.00132"},{"key":"1082_CR40","doi-asserted-by":"crossref","unstructured":"Heidari, N., Iosifidis, A.: On the spatial attention in spatio-temporal graph convolutional networks for skeleton-based human action recognition. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20137 (2021). IEEE","DOI":"10.1109\/IJCNN52387.2021.9534440"},{"key":"1082_CR41","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1082_CR42","unstructured":"Qilong, W., Banggu, W., Pengfei, Z., Peihua, L., Wangmeng, Z., Qinghua, H.: Eca-net: efficient channel attention for deep convolutional neural networks 2020 ieee. In: CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)"},{"key":"1082_CR43","doi-asserted-by":"crossref","unstructured":"Li, Y., Ji, B., Shi, X., Zhang, J., Kang, B., Wang, L.: Tea: Temporal excitation and aggregation for action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 909\u2013918 (2020)","DOI":"10.1109\/CVPR42600.2020.00099"},{"issue":"2","key":"1082_CR44","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S-H Gao","year":"2019","unstructured":"Gao, S.-H., Cheng, M.-M., Zhao, K., Zhang, X.-Y., Yang, M.-H., Torr, P.: Res2net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652\u2013662 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1082_CR45","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Chen, J., Zhang, Z., Zhang, R.: Ba-net: Bridge attention for deep convolutional neural networks. In: European Conference on Computer Vision, pp. 297\u2013312. Springer, New York (2022)","DOI":"10.1007\/978-3-031-19803-8_18"},{"key":"1082_CR46","unstructured":"Wang, M., Ni, B., Yang, X.: Learning multi-view interactional skeleton graph for action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)"},{"key":"1082_CR47","doi-asserted-by":"crossref","unstructured":"Soo\u00a0Kim, T., Reiter, A.: Interpretable 3d human action analysis with temporal convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 20\u201328 (2017)","DOI":"10.1109\/CVPRW.2017.207"},{"key":"1082_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View adaptive recurrent neural networks for high performance human action recognition from skeleton data. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2117\u20132126 (2017)","DOI":"10.1109\/ICCV.2017.233"},{"key":"1082_CR49","doi-asserted-by":"crossref","unstructured":"Zheng, W., Li, L., Zhang, Z., Huang, Y., Wang, L.: Relational network for skeleton-based action recognition. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 826\u2013831 (2019). IEEE","DOI":"10.1109\/ICME.2019.00147"},{"key":"1082_CR50","doi-asserted-by":"crossref","unstructured":"Li, C., Xie, C., Zhang, B., Han, J., Zhen, X., Chen, J.: Memory attention networks for skeleton-based action recognition. IEEE Transactions on Neural Networks and Learning Systems (2021)","DOI":"10.1109\/TNNLS.2021.3061115"},{"key":"1082_CR51","doi-asserted-by":"crossref","unstructured":"Peng, W., Hong, X., Chen, H., Zhao, G.: Learning graph convolutional network for skeleton-based human action recognition by neural searching. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2669\u20132676 (2020)","DOI":"10.1609\/aaai.v34i03.5652"},{"issue":"1","key":"1082_CR52","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1049\/cit2.12012","volume":"7","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Ye, G., Tu, Z., Qin, Y., Qin, Q., Zhang, J., Liu, J.: A spatial attentive and temporal dilated (satd) gcn for skeleton-based action recognition. CAAI Trans Intell Technol 7(1), 46\u201355 (2022)","journal-title":"CAAI Trans Intell Technol"},{"key":"1082_CR53","doi-asserted-by":"crossref","unstructured":"Tu, Z., Zhang, J., Li, H., Chen, Y., Yuan, J.: Joint-bone fusion graph convolutional network for semi-supervised skeleton action recognition. IEEE Transactions on Multimedia (2022)","DOI":"10.1109\/TMM.2022.3168137"},{"issue":"6","key":"1082_CR54","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/s11548-015-1186-1","volume":"10","author":"AP Twinanda","year":"2015","unstructured":"Twinanda, A.P., Alkan, E.O., Gangi, A., de Mathelin, M., Padoy, N.: Data-driven spatio-temporal rgbd feature encoding for action recognition in operating rooms. Int. J. Comput. Assist. Radiol. Surg. 10(6), 737\u2013747 (2015)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"1082_CR55","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with directed graph neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7912\u20137921 (2019)","DOI":"10.1109\/CVPR.2019.00810"},{"key":"1082_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103219","volume":"208","author":"C Plizzari","year":"2021","unstructured":"Plizzari, C., Cannici, M., Matteucci, M.: Skeleton-based action recognition via spatial and temporal transformer networks. Comput. Vis. Image Underst. 208, 103219 (2021)","journal-title":"Comput. Vis. Image Underst."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-023-01082-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-023-01082-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-023-01082-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T10:22:58Z","timestamp":1689330178000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-023-01082-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,1]]},"references-count":56,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1082"],"URL":"https:\/\/doi.org\/10.1007\/s00530-023-01082-1","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,1]]},"assertion":[{"value":"21 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2023","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"}}]}}