{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:27:35Z","timestamp":1781587655637,"version":"3.54.5"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"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":"crossref","award":["42071301"],"award-info":[{"award-number":["42071301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s00530-025-01796-4","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T12:17:43Z","timestamp":1745237863000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MLSTIF: multi-level spatio-temporal and human-object interaction feature fusion network for spatio-temporal action detection"],"prefix":"10.1007","volume":"31","author":[{"given":"Rui","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mulan","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"issue":"11","key":"1796_CR1","doi-asserted-by":"publisher","first-page":"2596","DOI":"10.1109\/TCYB.2015.2482970","volume":"46","author":"C Liu","year":"2016","unstructured":"Liu, C., Wu, X., Jia, Y.: Transfer latent SVM for joint recognition and localization of actions in videos. IEEE Trans. Cybern. 46(11), 2596\u20132608 (2016)","journal-title":"IEEE Trans. Cybern"},{"issue":"6","key":"1796_CR2","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/s00530-024-01511-9","volume":"30","author":"L Wu","year":"2024","unstructured":"Wu, L., Xu, L.: Local and global context Cooperation for Temporal action detection. Multim Syst. 30(6), 334 (2024)","journal-title":"Multim Syst."},{"issue":"6","key":"1796_CR3","doi-asserted-by":"publisher","first-page":"166334","DOI":"10.1007\/s11704-021-0236-9","volume":"16","author":"T Wang","year":"2022","unstructured":"Wang, T., Li, J., Wu, H., Li, C., Snoussi, H., Wu, Y.: ResLNet: Deep residual LSTM network with longer input for action recognition. Front. Comput. Sci. 16(6), 166334 (2022)","journal-title":"Front. Comput. Sci."},{"issue":"4","key":"1796_CR4","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1142\/S0219691323500662","volume":"22","author":"P Wang","year":"2024","unstructured":"Wang, P., Zeng, F., Qian, T.: A survey on deep learning-based spatio-temporal action detection. Int. J. Wavelets Multiresolut. Inf. Process. 22(4), 35 (2024)","journal-title":"Int. J. Wavelets Multiresolut. Inf. Process."},{"key":"1796_CR5","doi-asserted-by":"crossref","unstructured":"Kalogeiton, V., Weinzaepfel, P., Ferrari, V., Schmid, C.: Action tubelet detector for spatio-temporal action localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4405\u20134413 (2017)","DOI":"10.1109\/ICCV.2017.472"},{"key":"1796_CR6","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, Z., Wang, L., Wu, G.: Actions as moving points. In: Proceedings of European Conference on Computer Vision, pp. 68\u201384 (2020)","DOI":"10.1007\/978-3-030-58517-4_5"},{"key":"1796_CR7","doi-asserted-by":"crossref","unstructured":"Ma, X., Luo, Z., Zhang, X., Liao, Q., Shen, X., Wang, M.: Spatio-temporal action detector with self-attention. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1\u20138 (2021)","DOI":"10.1109\/IJCNN52387.2021.9533300"},{"key":"1796_CR8","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 6202\u20136211 (2019)","DOI":"10.1109\/ICCV.2019.00630"},{"key":"1796_CR9","doi-asserted-by":"crossref","unstructured":"Chen, S., Sun, P., Xie, E., Ge, C., Wu, J., Ma, L., Shen, J., Luo, P.: Watch Only Once: An End-to-End Video Action Detection Framework. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8158\u20138167 (2021)","DOI":"10.1109\/ICCV48922.2021.00807"},{"key":"1796_CR10","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhang, Y., Li, X., Chen, H., Shuai, B., Xu, M., Liu, C., Kundu, K., Xiong, Y., Modolo, D., et al.: Tuber: Tubelet transformer for video action detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13598\u201313607 (2022)","DOI":"10.1109\/CVPR52688.2022.01323"},{"key":"1796_CR11","unstructured":"Kopukl, O., Wei, X., Rigoll, G.: You only watch once: A unified cnn architecture for real-time spatio-temporal action localization. arXiv preprint arXiv:1911.06644 (2019)"},{"issue":"6","key":"1796_CR12","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/s00530-024-01543-1","volume":"30","author":"S Su","year":"2024","unstructured":"Su, S., Gan, M.: Online spatio\u2013temporal action detection with adaptive sampling and hierarchical modulation. Multim Syst. 30(6), 349 (2024)","journal-title":"Multim Syst."},{"key":"1796_CR13","doi-asserted-by":"crossref","unstructured":"Yang, J., Dai, K.: YOWOv2: A stronger yet efficient multi-level detection framework for real-time spatio-temporal action detection. ArXiv Preprint arXiv:230206848 (2023)","DOI":"10.2139\/ssrn.4485402"},{"issue":"6","key":"1796_CR14","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s00530-024-01523-5","volume":"30","author":"Z Peng","year":"2024","unstructured":"Peng, Z., Hu, R., Li, Z., Zeng, K.: DSTANet: Learning a dual\u2013stream model for anomaly driving action detection using spatio\u2013temporal and appearance features. Multim Syst. 30(6), 325 (2024)","journal-title":"Multim Syst."},{"key":"1796_CR15","unstructured":"Manh, N., Hang, D., Wang, J., Nhan, B.: YOWOv3: An Efficient and Generalized Framework for Human Action Detection and Recognition. arXiv preprint arXiv:2408.02623 (2024)"},{"issue":"9","key":"1796_CR16","doi-asserted-by":"publisher","first-page":"8148","DOI":"10.1109\/TCSVT.2024.3387933","volume":"34","author":"A Zhu","year":"2024","unstructured":"Zhu, A., Wang, Y., Yang, J., Yan, T., Ma, H., Li, W.: YOWOv3: A lightweight Spatio-Temporal joint network for video action detection. IEEE Trans. Circuits Syst. Video Technol. 34(9), 8148\u20138160 (2024)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"1","key":"1796_CR17","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s00530-023-01254-z","volume":"30","author":"Y Qin","year":"2024","unstructured":"Qin, Y., Chen, L., Ben, X., Yang, M.: You watch once more: A more effective CNN architecture for video spatio-temporal action localization. Multim Syst. 30(1), 53 (2024)","journal-title":"Multim Syst."},{"key":"1796_CR18","doi-asserted-by":"publisher","first-page":"126243","DOI":"10.1016\/j.neucom.2023.126243","volume":"543","author":"T Luo","year":"2023","unstructured":"Luo, T., Guan, S., Yang, R., Smith, J.: From detection to understanding: A survey on representation learning for human-object interaction. Neurocomputing. 543, 126243 (2023)","journal-title":"Neurocomputing"},{"issue":"5","key":"1796_CR19","first-page":"7140","volume":"59","author":"Z Zhang","year":"2023","unstructured":"Zhang, Z., Xu, Y., Song, J., Zhou, Q., Rasol, J., Ma, L.: Planet craters detection based on unsupervised domain adaptation. IEEE Trans. Aerosp. Electron. Syst. 59(5), 7140\u20137152 (2023)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"1796_CR20","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., Polosukhin, I.: Attention is All you Need. In: Proceedings of the Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"1796_CR21","doi-asserted-by":"crossref","unstructured":"Tamura, M., Ohashi, H., Yoshinaga, T.: QPIC: Query-Based Pairwise Human-Object Interaction Detection With Image-Wide Contextual Information. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10410\u201310419 (2021)","DOI":"10.1109\/CVPR46437.2021.01027"},{"key":"1796_CR22","doi-asserted-by":"crossref","unstructured":"Kim, B., Lee, J., Kang, J., Kim, E., Kim, H.: HOTR: End-to-End Human-Object Interaction Detection with Transformers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 74\u201383 (2021)","DOI":"10.1109\/CVPR46437.2021.00014"},{"key":"1796_CR23","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1109\/TIP.2022.3231528","volume":"32","author":"Y Lim","year":"2023","unstructured":"Lim, Y., Baskaran, M., Lim, J., Wong, K., See, J., Tistarelli, M.: ERNet: An efficient and reliable Human-Object interaction detection network. IEEE Trans. Image Process. 32, 964\u2013979 (2023)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"1796_CR24","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s10489-024-06073-x","volume":"55","author":"B Wang","year":"2025","unstructured":"Wang, B., Yang, M., Cao, P., Liu, Y.: A novel embedded cross framework for high-resolution salient object detection. Appl. Intell. 55(4), 277 (2025)","journal-title":"Appl. Intell."},{"key":"1796_CR25","doi-asserted-by":"crossref","unstructured":"Faure, G., Chen, M., Lai, S.: Holistic Interaction Transformer Network for Action Detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3329\u20133339 (2023)","DOI":"10.1109\/WACV56688.2023.00334"},{"key":"1796_CR26","doi-asserted-by":"crossref","unstructured":"Tang, J., Xia, J., Mu, X., Pang, B., Lu, C.: Asynchronous Interaction Aggregation for Action Detection. In: Proceedings of European Conference on Computer Vision, pp. 22\u201339 (2020)","DOI":"10.1007\/978-3-030-58555-6_5"},{"key":"1796_CR27","doi-asserted-by":"crossref","unstructured":"Pan, J., Chen, S., Shou, Z., Shao, J., Li, H.: Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 464\u2013474 (2021)","DOI":"10.1109\/CVPR46437.2021.00053"},{"key":"1796_CR28","doi-asserted-by":"crossref","unstructured":"Chen, K., Shu, X., Xie, G., Yan, R., Tang, J.: Foreground\/Background-Masked Interaction Learning for Spatio-temporal Action Detection. In: Proceedings of the ACM Inter-national Conference on Multimedia, pp. 2381\u20132390 (2023)","DOI":"10.1145\/3581783.3611945"},{"key":"1796_CR29","doi-asserted-by":"publisher","first-page":"8727","DOI":"10.1109\/ACCESS.2023.3239315","volume":"11","author":"MA Rahim","year":"2023","unstructured":"Rahim, M.A., Khan, S.D., Khan, S., Rashid, M., Ullah, R., Tariq, H., Czapp, S.: A novel Spatio\u2013Temporal deep learning vehicle turns detection scheme using GPS-Only data. IEEE Access. 11, 8727\u20138733 (2023)","journal-title":"IEEE Access."},{"key":"1796_CR30","doi-asserted-by":"publisher","first-page":"109486","DOI":"10.1016\/j.sigpro.2024.109486","volume":"221","author":"C Liu","year":"2024","unstructured":"Liu, C., Jiang, Y., Du, C., Li, Z.: Enhancing action recognition from low-quality skeleton data via part-level knowledge distillation. Signal. Process. 221, 109486 (2024)","journal-title":"Signal. Process."},{"key":"1796_CR31","doi-asserted-by":"publisher","first-page":"0100","DOI":"10.34133\/cbsystems.0100","volume":"5","author":"B Ren","year":"2024","unstructured":"Ren, B., Liu, M., Ding, R., Liu, H.: A survey on 3D Skeleton-Based action recognition using learning method. Cyborg Bionic Syst. 5, 0100 (2024)","journal-title":"Cyborg Bionic Syst."},{"issue":"4","key":"1796_CR32","doi-asserted-by":"publisher","first-page":"2346","DOI":"10.1109\/TCYB.2021.3117705","volume":"53","author":"Z Xiao","year":"2023","unstructured":"Xiao, Z., Fang, H., Jiang, H., Bai, J., Havyarimana, V., Chen, H., Jiao, L.: Understanding private Car aggregation effect via Spatio-Temporal analysis of trajectory data. IEEE Trans. Cybern. 53(4), 2346\u20132357 (2023)","journal-title":"IEEE Trans. Cybern"},{"issue":"9","key":"1796_CR33","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.3390\/math10091555","volume":"10","author":"S Ul Amin","year":"2022","unstructured":"Ul Amin, S., Ullah, M., Sajjad, M., Cheikh, F.A., Hijji, M., Hijji, A., Muhammad, K.: Eadn: An efficient deep learning model for Anomaly detection in videos. Mathematics. 10(9), 1555 (2022)","journal-title":"Mathematics"},{"key":"1796_CR34","doi-asserted-by":"crossref","unstructured":"Khan, S.D., Altamimi, B., Ullah, A., Ullah, M., Cheikh, H.: F.A.: TCM: Temporal Consistency Model for Head Detection in Complex Videos. J. Sensors. 8861296 (2020). (2020)(1)","DOI":"10.1155\/2020\/8861296"},{"issue":"7","key":"1796_CR35","doi-asserted-by":"publisher","first-page":"2300706","DOI":"10.1002\/aisy.202300706","volume":"6","author":"S Ul Amin","year":"2024","unstructured":"Ul Amin, S., Kim, B., Jung, Y., Seo, S., Park, S.: Video anomaly detection utilizing efficient spatio-temporal feature fusion with 3d convolutions and long short-term memory modules. Adv. Intell. Syst. 6(7), 2300706 (2024)","journal-title":"Adv. Intell. Syst."},{"key":"1796_CR36","doi-asserted-by":"crossref","unstructured":"Gu, C., Sun, C., Ross, D., Vondrick, C., Pantofaru, C., Li, Y., Vijayanarasimhan, S., Toderici, G., Ricco, S., Sukthankar, R., Schmid, C., Malik, J.: AVA: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions. In: Proceedings of the IEEE Con-ference on Computer Vision and Pattern Recognition, pp. 6047\u20136056 (2018)","DOI":"10.1109\/CVPR.2018.00633"},{"key":"1796_CR37","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C.: X3d: Expanding architectures for efficient video recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 203\u2013213 (2020)","DOI":"10.1109\/CVPR42600.2020.00028"},{"key":"1796_CR38","unstructured":"Girdhar, R., Carreira, J., Doersch, C., Zisserman, A.: A better baseline for Ava. ArXiv Preprint arXiv:180710066 (2018)"},{"key":"1796_CR39","doi-asserted-by":"crossref","unstructured":"Kopukl, O., Kose, N., Gunduz, A., Rigoll, G.: Resource Efficient 3D Convolutional Neural Networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp. 1910\u20131919 (2019)","DOI":"10.1109\/ICCVW.2019.00240"},{"key":"1796_CR40","doi-asserted-by":"crossref","unstructured":"Varghese, R., Sambath, M.: YOLOv8: A Novel Object Detection Algorithm with En-hanced Performance and Robustness. In: Proceedings of the International Conference on Advances in Data Engineering and Intelligent Computing Systems, pp. 1\u20136 (2024)","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"1796_CR41","doi-asserted-by":"publisher","first-page":"103187","DOI":"10.1016\/j.cviu.2021.103187","volume":"206","author":"M Tomei","year":"2021","unstructured":"Tomei, M., Baraldi, L., Calderara, S., Bronzin, S., Cucchiara, R.: Video action detection by learning graph-based spatio-temporal interactions. Comput. Vis. Image Understand. 206, 103187\u2013103197 (2021)","journal-title":"Comput. Vis. Image Understand"},{"key":"1796_CR42","doi-asserted-by":"publisher","first-page":"120391","DOI":"10.1016\/j.eswa.2023.120391","volume":"228","author":"UI Amin","year":"2023","unstructured":"Amin, U.I., Hussain, S., Kim, A., Seo, B.: Deep learning based active learning technique for data annotation and improve the overall perfor-mance of classification models. Expert Syst. Appl. 228, 120391 (2023)","journal-title":"Expert Syst. Appl."},{"key":"1796_CR43","unstructured":"Soomro, K., Zamir, R., Shah, M.: UCF101: A dataset of 101 human actions classes from videos in the wild. ArXiv Preprint arXiv:12120402 (2012)"},{"key":"1796_CR44","doi-asserted-by":"crossref","unstructured":"Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: A large video data-base for human motion recognition. In: Proceedings of the IEEE International Confer-ence on Computer Vision, pp. 2556\u20132563 (2011)","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"1796_CR45","doi-asserted-by":"crossref","unstructured":"Wang, C., Bochkovskiy, A., Liao, H.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Confer-ence on Computer Vision and Pattern Recognition, pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"1796_CR46","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159(2020)"},{"key":"1796_CR47","doi-asserted-by":"crossref","unstructured":"Chen, L., Tong, Z., Song, Y., Wu, G., Wang, L.: Efficient Video Action Detection with Token Dropout and Context Refinement. arXiv preprint arXiv:2304.08451(2023)","DOI":"10.1109\/ICCV51070.2023.00953"},{"key":"1796_CR48","doi-asserted-by":"crossref","unstructured":"Sui, L., Zhang, C., Gu, L., Han, F.: A simple and efficient pipeline to build an End-to-End Spatial-Temporal action detector. arXiv preprint arXiv:2206.03064(2022).","DOI":"10.1109\/WACV56688.2023.00594"},{"key":"1796_CR49","doi-asserted-by":"crossref","unstructured":"Ulutan, O., Rallapalli, S., Srivatsa, M., Torres, C., Manjunath, B.: Actor conditioned attention maps for video action detection. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision, pp. 527\u2013536 (2020)","DOI":"10.1109\/WACV45572.2020.9093617"},{"key":"1796_CR50","unstructured":"Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., Suleyman, M., Zisserman, A.: The Kinetics Human Action Video Dataset. arXiv preprint arXiv:1705.06950 (2017)"},{"key":"1796_CR51","unstructured":"Carreira, J., Noland, E., Banki-Horvath, A., Hillier, C., Zisserman, A.: A short note about Kinetics-600. (2018). arXiv preprint arXiv:1808.01340"},{"key":"1796_CR52","unstructured":"Carreira, J., Noland, E., Hillier, C., Zisserman, A.: A short note on the Kinetics-700 human action dataset. (2019). arXiv preprint arXiv:1907.06987"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01796-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-01796-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01796-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T15:03:33Z","timestamp":1756998213000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-01796-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,21]]},"references-count":52,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1796"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-01796-4","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,21]]},"assertion":[{"value":"8 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"201"}}