{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T12:15:55Z","timestamp":1779365755795,"version":"3.53.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T00:00:00Z","timestamp":1777161600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T00:00:00Z","timestamp":1777161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Scientific Research Project of the Education Department of Liaoning Province","award":["Z20240188"],"award-info":[{"award-number":["Z20240188"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62376165"],"award-info":[{"award-number":["62376165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00371-026-04465-3","type":"journal-article","created":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T09:43:34Z","timestamp":1777196614000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient image super-resolution via convolutional shift and parameter-free attention mechanisms"],"prefix":"10.1007","volume":"42","author":[{"given":"Linlin","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanyun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueyi","family":"Xi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,26]]},"reference":[{"key":"4465_CR1","doi-asserted-by":"crossref","unstructured":"Ahn, N., Kang, B., Sohn, K.-A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 252\u2013268 (2018)","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"4465_CR2","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding (2012)","DOI":"10.5244\/C.26.135"},{"key":"4465_CR3","doi-asserted-by":"crossref","unstructured":"Choe, J., Shim, H.: Attention-based dropout layer for weakly supervised object localization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2219\u20132228 (2019)","DOI":"10.1109\/CVPR.2019.00232"},{"key":"4465_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wu, Q., Wang, J., Hu, Q., Hu, T., Ding, E., Cheng, J., Wang, J.: Mixformer: Mixing features across windows and dimensions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5249\u20135259 (2022)","DOI":"10.1109\/CVPR52688.2022.00518"},{"key":"4465_CR5","doi-asserted-by":"publisher","first-page":"25478","DOI":"10.52202\/068431-1847","volume":"35","author":"Z Chen","year":"2022","unstructured":"Chen, Z., Zhang, Y., Gu, J., Kong, L., Yuan, X., et al.: Cross aggregation transformer for image restoration. Adv. Neural. Inf. Process. Syst. 35, 25478\u201325490 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4465_CR6","doi-asserted-by":"publisher","unstructured":"Chu, X., Zhang, B., Ma, H., Xu, R., Li, Q.: Fast, accurate and lightweight super-resolution with neural architecture search. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 59\u201364 (2021). https:\/\/doi.org\/10.1109\/ICPR48806.2021.9413080","DOI":"10.1109\/ICPR48806.2021.9413080"},{"key":"4465_CR7","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, Proceedings, Part IV 13, pp. 184\u2013199 (2014). Springer","DOI":"10.1007\/978-3-319-10593-2_13"},{"issue":"5","key":"4465_CR8","doi-asserted-by":"publisher","first-page":"2567","DOI":"10.1109\/TPAMI.2020.3045810","volume":"44","author":"K Ding","year":"2022","unstructured":"Ding, K., Ma, K., Wang, S., Simoncelli, E.P.: Image quality assessment: unifying structure and texture similarity. IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2567\u20132581 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2020.3045810","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4465_CR9","doi-asserted-by":"crossref","unstructured":"He, Z., Dai, T., Lu, J., Jiang, Y., Xia, S.-T.: Fakd: Feature-affinity based knowledge distillation for efficient image super-resolution. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 518\u2013522 (2020). IEEE","DOI":"10.1109\/ICIP40778.2020.9190917"},{"key":"4465_CR10","doi-asserted-by":"crossref","unstructured":"Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight image super-resolution with information multi-distillation network. In: Proceedings of the 27th Acm International Conference on Multimedia, pp. 2024\u20132032 (2019)","DOI":"10.1145\/3343031.3351084"},{"issue":"5","key":"4465_CR11","doi-asserted-by":"publisher","first-page":"3809","DOI":"10.1007\/s13042-024-02483-y","volume":"16","author":"J Huang","year":"2025","unstructured":"Huang, J., Hong, C., Xie, R., Ran, L., Qian, J.: A simple and efficient channel MLP on token for human pose estimation. Int. J. Mach. Learn. Cybern. 16(5), 3809\u20133817 (2025)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"4465_CR12","doi-asserted-by":"crossref","unstructured":"Huang, J.-B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197\u20135206 (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"4465_CR13","doi-asserted-by":"crossref","unstructured":"Hu, S., Wu, D., Wang, J., Huang, S.: The image super-resolution network based on dual-branch feature interaction attention mechanism. Vis. Comput. 1\u201314 (2025)","DOI":"10.1007\/s00371-025-03858-0"},{"key":"4465_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"4465_CR15","doi-asserted-by":"crossref","unstructured":"Kim, J.K., Lee, J., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"4465_CR16","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van\u00a0Gool, L., Timofte, R.: Swinir: Image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"4465_CR17","doi-asserted-by":"crossref","unstructured":"Li, X., Dong, J., Tang, J., Pan, J.: Dlgsanet: lightweight dynamic local and global self-attention networks for image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12792\u201312801 (2023)","DOI":"10.1109\/ICCV51070.2023.01175"},{"key":"4465_CR18","doi-asserted-by":"crossref","unstructured":"Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 624\u2013632 (2017)","DOI":"10.1109\/CVPR.2017.618"},{"key":"4465_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"4465_CR20","doi-asserted-by":"crossref","unstructured":"Li, Z., Liu, Y., Chen, X., Cai, H., Gu, J., Qiao, Y., Dong, C.: Blueprint separable residual network for efficient image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 833\u2013843 (2022)","DOI":"10.1109\/CVPRW56347.2022.00099"},{"key":"4465_CR21","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu\u00a0Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"4465_CR22","doi-asserted-by":"publisher","first-page":"103254","DOI":"10.1016\/j.cviu.2021.103254","volume":"211","author":"Z Li","year":"2021","unstructured":"Li, Z., Wang, C., Wang, J., Ying, S., Shi, J.: Lightweight adaptive weighted network for single image super-resolution. Comput. Vis. Image Underst. 211, 103254 (2021)","journal-title":"Comput. Vis. Image Underst."},{"key":"4465_CR23","unstructured":"Lee, D., Yun, S., Ro, Y.: Partial large kernel CNNS for efficient super-resolution. arXiv preprint arXiv:2404.11848 (2024)"},{"key":"4465_CR24","doi-asserted-by":"crossref","unstructured":"Lu, R., Zhu, L., Chen, B., Fan, X., Wang, S.: Diffusion-based bit-depth expansion. In: 2024 IEEE 26th International Workshop on Multimedia Signal Processing (MMSP), pp. 1\u20136 (2024). IEEE","DOI":"10.1109\/MMSP61759.2024.10743597"},{"key":"4465_CR25","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 2, pp. 416\u2013423 (2001). IEEE","DOI":"10.1109\/ICCV.2001.937655"},{"key":"4465_CR26","doi-asserted-by":"publisher","first-page":"21811","DOI":"10.1007\/s11042-016-4020-z","volume":"76","author":"Y Matsui","year":"2017","unstructured":"Matsui, Y., Ito, K., Aramaki, Y., Fujimoto, A., Ogawa, T., Yamasaki, T., Aizawa, K.: Sketch-based manga retrieval using manga109 dataset. Multimed. Tools Appl. 76, 21811\u201321838 (2017)","journal-title":"Multimed. Tools Appl."},{"key":"4465_CR27","doi-asserted-by":"crossref","unstructured":"Magid, S.A., Zhang, Y., Wei, D., Jang, W.-D., Lin, Z., Fu, Y., Pfister, H.: Dynamic high-pass filtering and multi-spectral attention for image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4288\u20134297 (2021)","DOI":"10.1109\/ICCV48922.2021.00425"},{"key":"4465_CR28","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1109\/TMM.2021.3134172","volume":"25","author":"K Park","year":"2023","unstructured":"Park, K., Soh, J.W., Cho, N.I.: A dynamic residual self-attention network for lightweight single image super-resolution. IEEE Trans. Multimedia 25, 907\u2013918 (2023). https:\/\/doi.org\/10.1109\/TMM.2021.3134172","journal-title":"IEEE Trans. Multimedia"},{"issue":"7","key":"4465_CR29","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1109\/JAS.2021.1004009","volume":"8","author":"L Sun","year":"2021","unstructured":"Sun, L., Liu, Z., Sun, X., Liu, L., Lan, R., Luo, X.: Lightweight image super-resolution via weighted multi-scale residual network. IEEE\/CAA J. Automatica Sinica 8(7), 1271\u20131280 (2021)","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"4465_CR30","first-page":"17314","volume":"35","author":"L Sun","year":"2022","unstructured":"Sun, L., Pan, J., Tang, J.: Shufflemixer: an efficient convnet for image super-resolution. Adv. Neural. Inf. Process. Syst. 35, 17314\u201317326 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4465_CR31","unstructured":"Shi, Y., Yang, L., An, W., Zhen, X., Wang, L.: Parameter-free channel attention for image classification and super-resolution. arXiv preprint arXiv:2303.11055 (2023)"},{"key":"4465_CR32","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van\u00a0Gool, L., Yang, M.-H., Zhang, L.: Ntire 2017 challenge on single image super-resolution: Methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 114\u2013125 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"4465_CR33","doi-asserted-by":"crossref","unstructured":"Wang, H., Chen, X., Ni, B., Liu, Y., Liu, J.: Omni aggregation networks for lightweight image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22378\u201322387 (2023)","DOI":"10.1109\/CVPR52729.2023.02143"},{"key":"4465_CR34","doi-asserted-by":"crossref","unstructured":"Wen, Y., Luo, B., Shi, W., Ji, J., Cao, W., Yang, X., Sheng, B.: Sat-net: structure-aware transformer-based attention fusion network for low-quality retinal fundus images enhancement. IEEE Trans. Multimed. (2025)","DOI":"10.1109\/TMM.2025.3565935"},{"key":"4465_CR35","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liu, Y., Zhao, S., Li, J., Zhang, L.: Camixersr: Only details need more attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 25837\u201325846 (2024)","DOI":"10.1109\/CVPR52733.2024.02441"},{"key":"4465_CR36","doi-asserted-by":"crossref","unstructured":"Wu, B., Wan, A., Yue, X., Jin, P., Zhao, S., Golmant, N., Gholaminejad, A., Gonzalez, J., Keutzer, K.: Shift: A zero flop, zero parameter alternative to spatial convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9127\u20139135 (2018)","DOI":"10.1109\/CVPR.2018.00951"},{"key":"4465_CR37","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L.: Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 568\u2013578 (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"4465_CR38","doi-asserted-by":"publisher","first-page":"108254","DOI":"10.1016\/j.compeleceng.2022.108254","volume":"102","author":"B Wang","year":"2022","unstructured":"Wang, B., Yan, B., Liu, C., Hwangbo, R., Jeon, G., Yang, X.: Lightweight bidirectional feedback network for image super-resolution. Comput. Electr. Eng. 102, 108254 (2022)","journal-title":"Comput. Electr. Eng."},{"key":"4465_CR39","doi-asserted-by":"crossref","unstructured":"Wan, C., Yu, H., Li, Z., Chen, Y., Zou, Y., Liu, Y., Yin, X., Zuo, K.: Swift parameter-free attention network for efficient super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6246\u20136256 (2024)","DOI":"10.1109\/CVPRW63382.2024.00628"},{"issue":"1","key":"4465_CR40","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/s13042-024-02262-9","volume":"16","author":"Y Xie","year":"2025","unstructured":"Xie, Y., Hong, C., Zhuang, W., Liu, L., Li, J.: Hogformer: high-order graph convolution transformer for 3d human pose estimation. Int. J. Mach. Learn. Cybern. 16(1), 599\u2013610 (2025)","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"2","key":"4465_CR41","first-page":"393","volume":"43","author":"Y Yang","year":"2024","unstructured":"Yang, Y., Wang, P., Wu, Y.: Effective lightweight dual-path shift compensation network for image super-resolution. Comput. Inf. 43(2), 393\u2013413 (2024)","journal-title":"Comput. Inf."},{"key":"4465_CR42","unstructured":"Yang, L., Zhang, R.-Y., Li, L., Xie, X.: Simam: A simple, parameter-free attention module for convolutional neural networks. In: International Conference on Machine Learning, pp. 11863\u201311874 (2021). PMLR"},{"key":"4465_CR43","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H.: Restormer: Efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5728\u20135739 (2022)","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"4465_CR44","doi-asserted-by":"crossref","unstructured":"Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Curves and Surfaces: 7th International Conference, Avignon, France, June 24-30, 2010, Revised Selected Papers 7, pp. 711\u2013730 (2012). Springer","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"4465_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"4465_CR46","doi-asserted-by":"crossref","unstructured":"Zhao, H., Kong, X., He, J., Qiao, Y., Dong, C.: Efficient image super-resolution using pixel attention. In: Computer Vision\u2013ECCV 2020 Workshops: Glasgow, UK, 2020, Proceedings, Part III 16, pp. 56\u201372 (2020). Springer","DOI":"10.1007\/978-3-030-67070-2_3"},{"key":"4465_CR47","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Li, Z., Guo, C.-L., Bai, S., Cheng, M.-M., Hou, Q.: Srformer: Permuted self-attention for single image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12780\u201312791 (2023)","DOI":"10.1109\/ICCV51070.2023.01174"},{"key":"4465_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"4465_CR49","unstructured":"Zhu, L., Zeng, X., Chen, B., Chen, P., Li, Y.-H., Wang, S.: Leveraging diffusion knowledge for generative image compression with fractal frequency-aware band learning. arXiv preprint arXiv:2503.11321 (2025)"},{"key":"4465_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeng, H., Guo, S., Zhang, L.: Efficient long-range attention network for image super-resolution. In: European Conference on Computer Vision, pp. 649\u2013667 (2022). Springer","DOI":"10.1007\/978-3-031-19790-1_39"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04465-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-026-04465-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04465-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T11:42:35Z","timestamp":1779363755000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-026-04465-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,26]]},"references-count":50,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["4465"],"URL":"https:\/\/doi.org\/10.1007\/s00371-026-04465-3","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,26]]},"assertion":[{"value":"1 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2026","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":"269"}}