{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:15:36Z","timestamp":1778688936049,"version":"3.51.4"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:00:00Z","timestamp":1778371200000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100006397","name":"National Taiwan Normal University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006397","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1186\/s40708-026-00307-z","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T14:04:08Z","timestamp":1777989848000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A deep-learning framework for brain tumor segmentation via three-dimensional mass-preserving geometric transformation"],"prefix":"10.1186","volume":"13","author":[{"given":"Tsung-Ming","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai-Qian","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen-Wei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiexiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shing-Tung","family":"Yau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"key":"307_CR1","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1038\/nrclinonc.2016.204","volume":"14","author":"G Reifenberger","year":"2017","unstructured":"Reifenberger G, Wirsching H-G, Knobbe-Thomsen CB, Weller M (2017) Advances in the molecular genetics of gliomas\u2014implications for classification and therapy. Nat Rev Clin Oncol 14:434\u2013452","journal-title":"Nat Rev Clin Oncol"},{"key":"307_CR2","doi-asserted-by":"crossref","unstructured":"Choi YS, Bae S, Chang JH, Kang SG, Kim SH, Kim J, TH, R, Choi SH, Jain R, Lee S-K, (2021) Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics. Neuro Oncol 23:304\u2013313","DOI":"10.1093\/neuonc\/noaa177"},{"key":"307_CR3","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1093\/neuonc\/noab238","volume":"24","author":"J Cluceru","year":"2021","unstructured":"Cluceru J, Interian Y, Phillips JJ, Molinaro AM, Luks TL, Alcaide-Leon P, Olson MP, Nair D, LaFontaine M, Shai A, Chunduru P, Pedoia V, Villanueva-Meyer JE, Chang SM, Lupo JM (2021) Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging. Neuro Oncol 24:639\u2013652","journal-title":"Neuro Oncol"},{"key":"307_CR4","doi-asserted-by":"publisher","first-page":"4625","DOI":"10.3390\/jcm11154625","volume":"11","author":"J Wu","year":"2022","unstructured":"Wu J, Xu Q, Shen Y, Chen W, Xu K, Qi X-R (2022) Swin transformer improves the IDH mutation status prediction of gliomas free of MRI-based tumor segmentation. J Clin Med 11:4625","journal-title":"J Clin Med"},{"key":"307_CR5","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1093\/neuonc\/noaa294","volume":"23","author":"DC Gutman","year":"2021","unstructured":"Gutman DC, Young RJ (2021) IDH glioma radiogenomics in the era of deep learning. Neuro Oncol 23:182\u2013183","journal-title":"Neuro Oncol"},{"key":"307_CR6","unstructured":"Baid U, Ghodasara S, Bilello M, Mohan S, Calabrese E, Colak E, Farahani K, Kalpathy-Cramer J, Kitamura FC, Pati S, Prevedello LM, Rudie JD, Sako C, Shinohara RT, Bergquist T, Chai R, Eddy J, Elliott J, Reade W, Schaffter T, Yu T, Zheng J, Annotators B, Davatzikos C, Mongan J, Hess C, Cha S, Villanueva-Meyer J, Freymann JB, Kirby JS, Wiestler B, Crivellaro P, Colen RR, Kotrotsou A, Marcus D, Milchenko M, Nazeri A, Fathallah-Shaykh H, Wiest R, Jakab A, Weber M-A, Mahajan A, Menze B, Flanders AE, Bakas S (2021) The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification. Preprint at https:\/\/arxiv.org\/abs\/2107.02314"},{"key":"307_CR7","doi-asserted-by":"publisher","DOI":"10.7937\/K9\/TCIA.2017.KLXWJJ1Q","author":"S Bakas","year":"2017","unstructured":"Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C (2017) Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection. Cancer Imag Arch. https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.KLXWJJ1Q","journal-title":"Cancer Imag Arch"},{"key":"307_CR8","doi-asserted-by":"publisher","DOI":"10.7937\/K9\/TCIA.2017.GJQ7R0EF","author":"S Bakas","year":"2017","unstructured":"Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C (2017) Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection. Cancer Imag Arch. https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.GJQ7R0EF","journal-title":"Cancer Imag Arch"},{"key":"307_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, Freymann JB, Farahani K, Davatzikos C (2017) Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 4:170117","journal-title":"Sci Data"},{"issue":"10","key":"307_CR10","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber M, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp DCR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin H, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imag 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imag"},{"key":"307_CR11","doi-asserted-by":"publisher","unstructured":"Xiao X, Lian S, Luo Z, Li S (2018) Weighted Res-UNet for high-quality retina vessel segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), 327\u2013331 . https:\/\/doi.org\/10.1109\/ITME.2018.00080","DOI":"10.1109\/ITME.2018.00080"},{"key":"307_CR12","doi-asserted-by":"crossref","unstructured":"Myronenko A (2018) 3D MRI brain tumor segmentation using autoencoder regularization. In: BrainLes@MICCAI","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"307_CR13","unstructured":"Isensee F, J\u00e4ger PF, Kohl SAA, Petersen J, Maier-Hein KH (2020) Automated Design of Deep Learning Methods for Biomedical Image Segmentation. ArXiv:1904.08128v2"},{"key":"307_CR14","doi-asserted-by":"crossref","unstructured":"Roy S, Koehler G, Ulrich C, Baumgartner M, Petersen J, Isensee F, F.Jaeger P, Maier-Hein K (2023) MedNeXt: Transformer-driven scaling of ConvNets for medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2023, 405\u2013415","DOI":"10.1007\/978-3-031-43901-8_39"},{"key":"307_CR15","doi-asserted-by":"crossref","unstructured":"Luu HM, Park S-H (2022) Extending nn-UNet for brain tumor segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 173\u2013186","DOI":"10.1007\/978-3-031-09002-8_16"},{"key":"307_CR16","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18:203\u2013211","journal-title":"Nat Methods"},{"key":"307_CR17","doi-asserted-by":"crossref","unstructured":"Zeineldin RA, Karar ME, Burgert O, Mathis-Ullrich F (2022) Multimodal CNN networks for brain tumor segmentation in MRI: A BraTS 2022 challenge solution. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp 127\u2013137","DOI":"10.1007\/978-3-031-33842-7_11"},{"key":"307_CR18","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/s11548-020-02186-z","volume":"15","author":"RA Zeineldin","year":"2020","unstructured":"Zeineldin RA, Karar ME, Coburger J, Wirtz CR, Burgert O (2020) DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images. Int J Comput Assist Radiol Surg 15:909\u2013920","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"307_CR19","doi-asserted-by":"crossref","unstructured":"McKinley R, Meier R, Wiest R (2018) Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp 456\u2013465","DOI":"10.1007\/978-3-030-11726-9_40"},{"key":"307_CR20","doi-asserted-by":"publisher","first-page":"37308","DOI":"10.1038\/s41598-025-21255-4","volume":"15","author":"W Aslam","year":"2025","unstructured":"Aslam W, Hussain J, Aslam MZ, Jan S, Riaz TB, Iqbal A, Arif M, Khan I (2025) Enhanced brain tumor segmentation in medical imaging using multi-modal multi-scale contextual aggregation and attention fusion. Sci Rep 15:37308","journal-title":"Sci Rep"},{"key":"307_CR21","doi-asserted-by":"publisher","first-page":"16189","DOI":"10.1109\/ACCESS.2024.3359418","volume":"12","author":"MF Almufareh","year":"2024","unstructured":"Almufareh MF, Imran M, Khan A, Humayun M, Asim M (2024) Automated brain tumor segmentation and classification in MRI using YOLO-based deep learning. IEEE Access 12:16189\u201316207","journal-title":"IEEE Access"},{"key":"307_CR22","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1186\/s12880-025-01837-4","volume":"25","author":"A Pourmahboubi","year":"2025","unstructured":"Pourmahboubi A, Arsalani Saeed N, Tabrizchi H (2025) A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning. BMC Med Imaging 25:307","journal-title":"BMC Med Imaging"},{"key":"307_CR23","doi-asserted-by":"crossref","unstructured":"Ferreira A, Solak N, Li J, Dammann P, Kleesiek J, Alves V (2024) How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation. ArXiv: abs\/2402.17317","DOI":"10.1007\/978-3-031-76163-8_8"},{"key":"307_CR24","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Nath V, Tang Y, Yang D, Roth H, Xu D (2022) Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. International MICCAI Brainlesion Workshop, pp 272\u2013284","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"307_CR25","doi-asserted-by":"crossref","unstructured":"Maani F, Hashmi AUR, Aljuboory M, Saeed N, Sobirov I, Yaqub M (2024) Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks. arXiv:2403.09262","DOI":"10.1007\/978-3-031-76163-8_24"},{"key":"307_CR26","doi-asserted-by":"publisher","unstructured":"Kasliwal A, Sagaram S, Srivastava L, Seth P, Khan A (2024) ReFuSeg: Regularized multi-modal fusion for precise brain tumour segmentation. In Brainlesion Glioma Multiple Sclerosis Stroke and Traumatic Brain Injuries LNCS 14668:69\u201380. https:\/\/doi.org\/10.1007\/978-3-031-76160-7_7","DOI":"10.1007\/978-3-031-76160-7_7"},{"key":"307_CR27","doi-asserted-by":"publisher","first-page":"14686","DOI":"10.1038\/s41598-021-94071-1","volume":"11","author":"W-W Lin","year":"2021","unstructured":"Lin W-W, Juang C, Yueh M-H, Huang T-M, Li T, Wang S, Yau S-T (2021) 3D brain tumor segmentation using a two-stage optimal mass transport algorithm. Sci Rep 11:14686","journal-title":"Sci Rep"},{"issue":"1","key":"307_CR28","doi-asserted-by":"publisher","first-page":"6452","DOI":"10.1038\/s41598-022-10285-x","volume":"12","author":"W-W Lin","year":"2022","unstructured":"Lin W-W, Lin J-W, Huang T-M, Li T, Yueh M-H, Yau S-T (2022) A novel 2-phase residual U-net algorithm combined with optimal mass transportation for 3D brain tumor detection and segmentation. Sci Rep 12(1):6452","journal-title":"Sci Rep"},{"key":"307_CR29","doi-asserted-by":"publisher","first-page":"1825","DOI":"10.1137\/22M1528756","volume":"16","author":"T-M Huang","year":"2023","unstructured":"Huang T-M, Liao W-H, Lin W-W, Yueh M-H, Yau S-T (2023) Convergence analysis of volumetric stretch energy minimization and its associated optimal mass transport. SIAM J Imaging Sci 16:1825\u20131855","journal-title":"SIAM J Imaging Sci"},{"key":"307_CR30","doi-asserted-by":"crossref","unstructured":"Yueh M-H, Huang T-M, Li T, Lin W-W, Yau S-T (2021) Projected gradient method combined with homotopy techniques for volume-measure-preserving optimal mass transportation problems. J Sci Comput 88(3)","DOI":"10.1007\/s10915-021-01583-z"},{"key":"307_CR31","doi-asserted-by":"crossref","unstructured":"Liao J-W, Huang T-M, Li T, Lin W-W, Wang H, Yau S-T (2023) An UNet-based brain tumor segmentation framework via optimal mass transportation pre-processing. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, BrainLes 2022, 216\u2013228","DOI":"10.1007\/978-3-031-33842-7_19"},{"issue":"2","key":"307_CR32","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1137\/18M1201184","volume":"12","author":"M-H Yueh","year":"2019","unstructured":"Yueh M-H, Li T, Lin W-W, Yau S-T (2019) A novel algorithm for volume-preserving parameterizations of 3-manifolds. SIAM J Imaging Sci 12(2):1071\u20131098","journal-title":"SIAM J Imaging Sci"},{"issue":"1","key":"307_CR33","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1137\/080716542","volume":"2","author":"A Beck","year":"2009","unstructured":"Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183\u2013202. https:\/\/doi.org\/10.1137\/080716542","journal-title":"SIAM J Imaging Sci"},{"key":"307_CR34","unstructured":"Nesterov Y (1983) A method for solving a convex programming problem with convergence rate O(1\/k2). In: Dokl. Akad. Nauk SSSR, 269, 543\u2013547"},{"key":"307_CR35","doi-asserted-by":"crossref","unstructured":"Antonelli M, Reinke A, Bakas S, Farahani K, AnnetteKopp-Schneider, Landman BA, Litjens G, Menze B, Ronneberger O, Summers RM, Ginneken B, Bilello M, Bilic P, Christ PF, Do RKG, Gollub MJ, Heckers SH, Huisman H, Jarnagin WR, McHugo MK, Napel S, Pernicka JSG, Rhode K, Tobon-Gomez C, Vorontsov E, Huisman H, Meakin JA, Ourselin S, Wiesenfarth M, Arbelaez P, Bae B, Chen S, Daza L, Feng J, He B, Isensee F, Ji Y, Jia F, Kim N, Kim I, Merhof D, Pai A, Park B, Perslev M, Rezaiifar R, Rippel O, Sarasua I, Shen W, Son J, Wachinger C, Wang L, Wang Y, Xia Y, Xu D, Xu Z, Zheng Y, Simpson AL, Maier-Hein L, Cardoso MJ (2021) The Medical Segmentation Decathlon. Preprint at arxiv: abs\/2106.05735","DOI":"10.1038\/s41467-022-30695-9"},{"key":"307_CR36","unstructured":"BraTS (2023) Lesion-Wise Performance Metrics for Segmentation Challenges. https:\/\/github.com\/rachitsaluja\/BraTS-2023-Metrics. 2023"},{"key":"307_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2021.107191","volume":"37","author":"SR van der Voort","year":"2021","unstructured":"van der Voort SR, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Smits M (2021) The erasmus glioma database (EGD): Structural MRI scans, WHO 2016 subtypes, and segmentations of 774 patients with glioma. Data Brief 37:107191. https:\/\/doi.org\/10.1016\/j.dib.2021.107191","journal-title":"Data Brief"},{"key":"307_CR38","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1038\/s41597-022-01881-7","volume":"9","author":"Y Suter","year":"2022","unstructured":"Suter Y, Knecht U, Valenzuela W, Notter M, Hewer E, Schucht P, Wiest R, Reyes M (2022) The LUMIERE dataset: Longitudinal glioblastoma MRI with expert RANO evaluation. Sci Data 9:768","journal-title":"Sci Data"},{"key":"307_CR39","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1038\/s41597-022-01560-7","volume":"9","author":"S Bakas","year":"2022","unstructured":"Bakas S, Sako C, Akbari H, Bilello M, Sotiras A, Shukla G, Rudie JD, Santamar\u00eda NF, Kazerooni AF, Pati S, Rathore S, Mamourian E, Ha SM, Parker W, Doshi J, Baid U, Bergman M, Binder ZA, Verma R, Lustig RA, Desai AS, Bagley SJ, Mourelatos Z, Morrissette J, Watt CD, Brem S, Wolf RL, Melhem ER, Nasrallah MP, Mohan S, O\u2019Rourke DM, Davatzikos C (2022) The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics. Sci Data 9:453","journal-title":"Sci Data"},{"key":"307_CR40","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, Freymann JB, Farahani K, Davatzikos C (2017) Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 4:170117","journal-title":"Sci Data"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-026-00307-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-026-00307-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-026-00307-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:05:39Z","timestamp":1778688339000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40708-026-00307-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,5]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["307"],"URL":"https:\/\/doi.org\/10.1186\/s40708-026-00307-z","relation":{},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,5]]},"assertion":[{"value":"31 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14"}}