{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T17:44:46Z","timestamp":1757612686350,"version":"3.44.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"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":["61503128"],"award-info":[{"award-number":["61503128"]}],"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":[[2025,6]]},"DOI":"10.1007\/s00530-025-01790-w","type":"journal-article","created":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T04:52:18Z","timestamp":1746075138000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TSGFormer: temporal-aware network and spatial encoding GCN for three-dimensional human pose estimation"],"prefix":"10.1007","volume":"31","author":[{"given":"Xinwang","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Huihuang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yuhang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Deng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,1]]},"reference":[{"key":"1790_CR1","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.patcog.2017.02.030","volume":"68","author":"M Liu","year":"2017","unstructured":"Liu, M., Liu, H., Chen, C.: Enhanced skeleton visualization for view invariant human action recognition. Pattern Recogn. 68, 346\u2013362 (2017)","journal-title":"Pattern Recogn."},{"key":"1790_CR2","doi-asserted-by":"crossref","unstructured":"Liu, M., Yuan, J.: Recognizing human actions as the evolution of pose estimation maps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1159\u20131168 (2018)","DOI":"10.1109\/CVPR.2018.00127"},{"key":"1790_CR3","doi-asserted-by":"crossref","unstructured":"Kaliouby, R., Robinson, P.: Real-time vision for human computer interaction. In: Real-Time Inference of Complex Mental States from Facial Expressions and Head Gestures. Springer (2005)","DOI":"10.1007\/0-387-27890-7_11"},{"key":"1790_CR4","doi-asserted-by":"crossref","unstructured":"Svenstrup, M., Tranberg, S., Andersen, H.J., Bak, T.: Pose estimation and adaptive robot behaviour for human\u2013robot interaction. In: 2009 IEEE International Conference on Robotics and Automation, pp. 3571\u20133576 (2009). IEEE","DOI":"10.1109\/ROBOT.2009.5152690"},{"issue":"10","key":"1790_CR5","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.1109\/TVCG.2010.241","volume":"17","author":"N Hagbi","year":"2010","unstructured":"Hagbi, N., Bergig, O., El-Sana, J., Billinghurst, M.: Shape recognition and pose estimation for mobile augmented reality. IEEE Trans. Vis. Comput. Graph. 17(10), 1369\u20131379 (2010)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"1790_CR6","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1109\/TMM.2022.3141231","volume":"25","author":"W Li","year":"2022","unstructured":"Li, W., Liu, H., Ding, R., Liu, M., Wang, P., Yang, W.: Exploiting temporal contexts with strided transformer for 3d human pose estimation. IEEE Trans. Multimed. 25, 1282\u20131293 (2022)","journal-title":"IEEE Trans. Multimed."},{"key":"1790_CR7","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, H., Tang, H., Wang, P., Van\u00a0Gool, L.: Mhformer: Multi-hypothesis transformer for 3d human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13147\u201313156 (2022)","DOI":"10.1109\/CVPR52688.2022.01280"},{"key":"1790_CR8","doi-asserted-by":"crossref","unstructured":"Shan, W., Liu, Z., Zhang, X., Wang, S., Ma, S., Gao, W.: P-stmo: Pre-trained spatial temporal many-to-one model for 3d human pose estimation. In: European Conference on Computer Vision, pp. 461\u2013478 (2022). Springer","DOI":"10.1007\/978-3-031-20065-6_27"},{"key":"1790_CR9","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Zheng, C., Liu, M., Wang, P., Chen, C.: Poseformerv2: Exploring frequency domain for efficient and robust 3d human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8877\u20138886 (2023)","DOI":"10.1109\/CVPR52729.2023.00857"},{"key":"1790_CR10","doi-asserted-by":"crossref","unstructured":"Zheng, C., Zhu, S., Mendieta, M., Yang, T., Chen, C., Ding, Z.: 3d human pose estimation with spatial and temporal transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11656\u201311665 (2021)","DOI":"10.1109\/ICCV48922.2021.01145"},{"key":"1790_CR11","doi-asserted-by":"crossref","unstructured":"Mehraban, S., Adeli, V., Taati, B.: Motionagformer: Enhancing 3d human pose estimation with a transformer-gcnformer network. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6920\u20136930 (2024)","DOI":"10.1109\/WACV57701.2024.00677"},{"key":"1790_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, J., Tu, Z., Yang, J., Chen, Y., Yuan, J.: Mixste: Seq2seq mixed spatio-temporal encoder for 3d human pose estimation in video. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13232\u201313242 (2022)","DOI":"10.1109\/CVPR52688.2022.01288"},{"key":"1790_CR13","doi-asserted-by":"crossref","unstructured":"Tang, Z., Qiu, Z., Hao, Y., Hong, R., Yao, T.: 3d human pose estimation with spatio-temporal criss-cross attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4790\u20134799 (2023)","DOI":"10.1109\/CVPR52729.2023.00464"},{"key":"1790_CR14","doi-asserted-by":"crossref","unstructured":"Peng, J., Zhou, Y., Mok, P.: Ktpformer: Kinematics and trajectory prior knowledge-enhanced transformer for 3d human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1123\u20131132 (2024)","DOI":"10.1109\/CVPR52733.2024.00113"},{"issue":"24","key":"1790_CR15","doi-asserted-by":"publisher","first-page":"8013","DOI":"10.3390\/s24248013","volume":"24","author":"S Umirzakova","year":"2024","unstructured":"Umirzakova, S., Muksimova, S., Mardieva, S., Sultanov Baxtiyarovich, M., Cho, Y.-I.: Mira-cap: Memory-integrated retrieval-augmented captioning for state-of-the-art image and video captioning. Sensors 24(24), 8013 (2024)","journal-title":"Sensors"},{"key":"1790_CR16","doi-asserted-by":"publisher","first-page":"84379","DOI":"10.1109\/ACCESS.2023.3302692","volume":"11","author":"J Talreja","year":"2023","unstructured":"Talreja, J., Aramvith, S., Onoye, T.: Dans: Deep attention network for single image super-resolution. IEEE Access 11, 84379\u201384397 (2023)","journal-title":"IEEE Access"},{"key":"1790_CR17","doi-asserted-by":"crossref","unstructured":"Talreja, J., Aramvith, S., Onoye, T.: Dhtcun: deep hybrid transformer cnn u network for single-image super-resolution. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3450300"},{"issue":"2","key":"1790_CR18","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1007\/s40747-024-01760-1","volume":"11","author":"J Talreja","year":"2025","unstructured":"Talreja, J., Aramvith, S., Onoye, T.: Xtnsr: Xception-based transformer network for single image super resolution. Complex & Intelligent Systems 11(2), 162 (2025)","journal-title":"Complex & Intelligent Systems"},{"key":"1790_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112827","volume":"309","author":"S Chowdhury","year":"2025","unstructured":"Chowdhury, S., Soni, B.: R-vqa: A robust visual question answering model. Knowl.-Based Syst. 309, 112827 (2025)","journal-title":"Knowl.-Based Syst."},{"key":"1790_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109948","volume":"142","author":"S Chowdhury","year":"2025","unstructured":"Chowdhury, S., Soni, B.: Envqa: Improving visual question answering model by enriching the visual feature. Eng. Appl. Artif. Intell. 142, 109948 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"8","key":"1790_CR21","doi-asserted-by":"publisher","first-page":"10479","DOI":"10.1007\/s13369-023-07661-8","volume":"48","author":"S Chowdhury","year":"2023","unstructured":"Chowdhury, S., Soni, B.: Qsfvqa: A time efficient, scalable and optimized vqa framework. Arab. J. Sci. Eng. 48(8), 10479\u201310491 (2023)","journal-title":"Arab. J. Sci. Eng."},{"issue":"6","key":"1790_CR22","doi-asserted-by":"publisher","first-page":"70010","DOI":"10.1111\/coin.70010","volume":"40","author":"S Chowdhury","year":"2024","unstructured":"Chowdhury, S., Soni, B.: Beyond words: Esc-net revolutionizes vqa by elevating visual features and defying language priors. Comput. Intell. 40(6), 70010 (2024)","journal-title":"Comput. Intell."},{"issue":"1","key":"1790_CR23","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/TCSVT.2021.3057267","volume":"32","author":"T Chen","year":"2021","unstructured":"Chen, T., Fang, C., Shen, X., Zhu, Y., Chen, Z., Luo, J.: Anatomy-aware 3d human pose estimation with bone-based pose decomposition. IEEE Trans. Circuits Syst. Video Technol. 32(1), 198\u2013209 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1790_CR24","doi-asserted-by":"crossref","unstructured":"Hossain, M.R.I., Little, J.J.: Exploiting temporal information for 3d human pose estimation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 68\u201384 (2018)","DOI":"10.1007\/978-3-030-01249-6_5"},{"key":"1790_CR25","doi-asserted-by":"crossref","unstructured":"Liu, R., Shen, J., Wang, H., Chen, C., Cheung, S.-c., Asari, V.: Attention mechanism exploits temporal contexts: Real-time 3d human pose reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5064\u20135073 (2020)","DOI":"10.1109\/CVPR42600.2020.00511"},{"key":"1790_CR26","doi-asserted-by":"crossref","unstructured":"Martinez, J., Hossain, R., Romero, J., Little, J.J.: A simple yet effective baseline for 3d human pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2640\u20132649 (2017)","DOI":"10.1109\/ICCV.2017.288"},{"key":"1790_CR27","doi-asserted-by":"crossref","unstructured":"Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753\u20137762 (2019)","DOI":"10.1109\/CVPR.2019.00794"},{"key":"1790_CR28","doi-asserted-by":"crossref","unstructured":"Moon, G., Chang, J.Y., Lee, K.M.: Camera distance-aware top-down approach for 3d multi-person pose estimation from a single rgb image. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10133\u201310142 (2019)","DOI":"10.1109\/ICCV.2019.01023"},{"key":"1790_CR29","doi-asserted-by":"crossref","unstructured":"Park, S., Hwang, J., Kwak, N.: 3d human pose estimation using convolutional neural networks with 2d pose information. In: Computer Vision\u2013ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14, pp. 156\u2013169 (2016). Springer","DOI":"10.1007\/978-3-319-49409-8_15"},{"key":"1790_CR30","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Zhou, X., Daniilidis, K.: Ordinal depth supervision for 3d human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7307\u20137316 (2018)","DOI":"10.1109\/CVPR.2018.00763"},{"key":"1790_CR31","doi-asserted-by":"crossref","unstructured":"Tekin, B., Rozantsev, A., Lepetit, V., Fua, P.: Direct prediction of 3d body poses from motion compensated sequences. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 991\u20131000 (2016)","DOI":"10.1109\/CVPR.2016.113"},{"key":"1790_CR32","doi-asserted-by":"crossref","unstructured":"Wehrbein, T., Rudolph, M., Rosenhahn, B., Wandt, B.: Probabilistic monocular 3d human pose estimation with normalizing flows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11199\u201311208 (2021)","DOI":"10.1109\/ICCV48922.2021.01101"},{"key":"1790_CR33","unstructured":"Vaswani, A.: Attention is all you need. Advances in Neural Information Processing Systems (2017)"},{"key":"1790_CR34","doi-asserted-by":"crossref","unstructured":"Xu, C., Tan, R.T., Tan, Y., Chen, S., Wang, X., Wang, Y.: Auxiliary tasks benefit 3d skeleton-based human motion prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9509\u20139520 (2023)","DOI":"10.1109\/ICCV51070.2023.00872"},{"key":"1790_CR35","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Wang, B., Yang, B., Tan, R.T.: Graph and temporal convolutional networks for 3d multi-person pose estimation in monocular videos. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 1157\u20131165 (2021)","DOI":"10.1609\/aaai.v35i2.16202"},{"key":"1790_CR36","doi-asserted-by":"crossref","unstructured":"Ci, H., Wang, C., Ma, X., Wang, Y.: Optimizing network structure for 3d human pose estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2262\u20132271 (2019)","DOI":"10.1109\/ICCV.2019.00235"},{"key":"1790_CR37","doi-asserted-by":"crossref","unstructured":"Hu, W., Zhang, C., Zhan, F., Zhang, L., Wong, T.-T.: Conditional directed graph convolution for 3d human pose estimation. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 602\u2013611 (2021)","DOI":"10.1145\/3474085.3475219"},{"key":"1790_CR38","doi-asserted-by":"crossref","unstructured":"Liu, K., Ding, R., Zou, Z., Wang, L., Tang, W.: A comprehensive study of weight sharing in graph networks for 3d human pose estimation. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part X 16, pp. 318\u2013334 (2020). Springer","DOI":"10.1007\/978-3-030-58607-2_19"},{"key":"1790_CR39","doi-asserted-by":"crossref","unstructured":"Wang, J., Yan, S., Xiong, Y., Lin, D.: Motion guided 3d pose estimation from videos. In: European Conference on Computer Vision, pp. 764\u2013780 (2020). Springer","DOI":"10.1007\/978-3-030-58601-0_45"},{"key":"1790_CR40","doi-asserted-by":"crossref","unstructured":"Xu, T., Takano, W.: Graph stacked hourglass networks for 3d human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16105\u201316114 (2021)","DOI":"10.1109\/CVPR46437.2021.01584"},{"key":"1790_CR41","doi-asserted-by":"crossref","unstructured":"Yu, B.X., Zhang, Z., Liu, Y., Zhong, S.-h., Liu, Y., Chen, C.W.: Gla-gcn: Global-local adaptive graph convolutional network for 3d human pose estimation from monocular video. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8818\u20138829 (2023)","DOI":"10.1109\/ICCV51070.2023.00810"},{"key":"1790_CR42","doi-asserted-by":"crossref","unstructured":"Zhao, L., Peng, X., Tian, Y., Kapadia, M., Metaxas, D.N.: Semantic graph convolutional networks for 3d human pose regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3425\u20133435 (2019)","DOI":"10.1109\/CVPR.2019.00354"},{"key":"1790_CR43","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xu, X., Shen, F., Ji, Y., Gao, L., Shen, H.T.: Posegtac: Graph transformer encoder-decoder with atrous convolution for 3d human pose estimation. In: IJCAI, pp. 1359\u20131365 (2021)","DOI":"10.24963\/ijcai.2021\/188"},{"key":"1790_CR44","doi-asserted-by":"crossref","unstructured":"Zhao, W., Wang, W., Tian, Y.: Graformer: Graph-oriented transformer for 3d pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20438\u201320447 (2022)","DOI":"10.1109\/CVPR52688.2022.01979"},{"key":"1790_CR45","doi-asserted-by":"crossref","unstructured":"Gong, J., Foo, L.G., Fan, Z., Ke, Q., Rahmani, H., Liu, J.: Diffpose: Toward more reliable 3d pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13041\u201313051 (2023)","DOI":"10.1109\/CVPR52729.2023.01253"},{"key":"1790_CR46","doi-asserted-by":"crossref","unstructured":"Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence 36(7), 1325\u20131339 (2013)","DOI":"10.1109\/TPAMI.2013.248"},{"key":"1790_CR47","doi-asserted-by":"publisher","unstructured":"Mehta, D., Rhodin, H., Casas, D., Fua, P., Sotnychenko, O., Xu, W., Theobalt, C.: Monocular 3d human pose estimation in the wild using improved cnn supervision. In: 3D Vision (3DV), 2017 Fifth International Conference On (2017). https:\/\/doi.org\/10.1109\/3dv.2017.00064 . IEEE. http:\/\/gvv.mpi-inf.mpg.de\/3dhp_dataset","DOI":"10.1109\/3dv.2017.00064"},{"key":"1790_CR48","doi-asserted-by":"crossref","unstructured":"Cai, Y., Ge, L., Liu, J., Cai, J., Cham, T.-J., Yuan, J., Thalmann, N.M.: Exploiting spatial-temporal relationships for 3d pose estimation via graph convolutional networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2272\u20132281 (2019)","DOI":"10.1109\/ICCV.2019.00236"},{"key":"1790_CR49","doi-asserted-by":"crossref","unstructured":"Shan, W., Lu, H., Wang, S., Zhang, X., Gao, W.: Improving robustness and accuracy via relative information encoding in 3d human pose estimation. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3446\u20133454 (2021)","DOI":"10.1145\/3474085.3475504"},{"key":"1790_CR50","doi-asserted-by":"crossref","unstructured":"Einfalt, M., Ludwig, K., Lienhart, R.: Uplift and upsample: Efficient 3d human pose estimation with uplifting transformers. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2903\u20132913 (2023)","DOI":"10.1109\/WACV56688.2023.00292"},{"key":"1790_CR51","doi-asserted-by":"crossref","unstructured":"Hassanin, M., Khamiss, A., Bennamoun, M., Boussaid, F., Radwan, I.: Crossformer: Cross spatio-temporal transformer for 3d human pose estimation. arXiv preprint arXiv:2203.13387 (2022)","DOI":"10.2139\/ssrn.4213439"},{"key":"1790_CR52","doi-asserted-by":"publisher","first-page":"4278","DOI":"10.1109\/TIP.2022.3182269","volume":"31","author":"Y Xue","year":"2022","unstructured":"Xue, Y., Chen, J., Gu, X., Ma, H., Ma, H.: Boosting monocular 3d human pose estimation with part aware attention. IEEE Trans. Image Process. 31, 4278\u20134291 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"1790_CR53","doi-asserted-by":"crossref","unstructured":"Song, X., Li, Z., Chen, S., Demachi, K.: Quater-gcn: enhancing 3d human pose estimation with orientation and semi-supervised training. In: European Conference on Artificial Intelligence, pp. 121\u2013128 (2024)","DOI":"10.3233\/FAIA240479"},{"key":"1790_CR54","doi-asserted-by":"crossref","unstructured":"Shan, W., Liu, Z., Zhang, X., Wang, Z., Han, K., Wang, S., Ma, S., Gao, W.: Diffusion-based 3d human pose estimation with multi-hypothesis aggregation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14761\u201314771 (2023)","DOI":"10.1109\/ICCV51070.2023.01356"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01790-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-01790-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01790-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T15:02:58Z","timestamp":1756998178000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-01790-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,1]]},"references-count":54,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1790"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-01790-w","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"type":"print","value":"0942-4962"},{"type":"electronic","value":"1432-1882"}],"subject":[],"published":{"date-parts":[[2025,5,1]]},"assertion":[{"value":"20 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 May 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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"219"}}