{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T13:59:09Z","timestamp":1767967149849,"version":"3.49.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"content-version":"vor","delay-in-days":31,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Soft Science Project of Henan Provincial Department of Science and Technology","award":["No. 232400410381"],"award-info":[{"award-number":["No. 232400410381"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-01077-y","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T03:59:07Z","timestamp":1765252747000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction of an Automatic Music Content Generation Model Based on Long Short-Term Memory Network"],"prefix":"10.1007","volume":"19","author":[{"given":"Xianghui","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"issue":"12","key":"1077_CR1","doi-asserted-by":"publisher","first-page":"6381","DOI":"10.1007\/s00521-024-09418-2","volume":"36","author":"L Wang","year":"2024","unstructured":"Wang, L., Zhao, Z., Liu, H., Pang, J., Qin, Y., Wu, Q.: A review of intelligent music generation systems. Neural Comput. Appl. 36(12), 6381\u20136401 (2024). https:\/\/doi.org\/10.1007\/s00521-024-09418-2","journal-title":"Neural Comput. Appl."},{"issue":"5","key":"1077_CR2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0283103","volume":"18","author":"Y Guo","year":"2023","unstructured":"Guo, Y., Liu, Y., Zhou, T., Xu, L., Zhang, Q.: An automatic music generation and evaluation method based on transfer learning. PLoS ONE 18(5), e0283103 (2023). https:\/\/doi.org\/10.1371\/journal.pone.0283103","journal-title":"PLoS ONE"},{"issue":"1","key":"1077_CR3","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/s13735-023-00278-5","volume":"12","author":"M Alfaro-Contreras","year":"2023","unstructured":"Alfaro-Contreras, M., I\u00f1esta, J.M., Calvo-Zaragoza, J.: Optical music recognition for homophonic scores with neural networks and synthetic music generation. Int. J. Multimed. Inf. Retr. 12(1), 12 (2023). https:\/\/doi.org\/10.1007\/s13735-023-00278-5","journal-title":"Int. J. Multimed. Inf. Retr."},{"issue":"12","key":"1077_CR4","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.3390\/pr10122515","volume":"10","author":"J Min","year":"2022","unstructured":"Min, J., Liu, Z., Wang, L., Li, D., Zhang, M., Huang, Y.: Music generation system for adversarial training based on deep learning. Processes 10(12), 2515 (2022). https:\/\/doi.org\/10.3390\/pr10122515","journal-title":"Processes"},{"key":"1077_CR5","doi-asserted-by":"publisher","unstructured":"Zhang, X., Zhang, J., Qiu, Y., Wang, L., Zhou, J.: Structure-enhanced pop music generation via harmony-aware learning. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 1204\u20131213 (2022). https:\/\/doi.org\/10.1145\/3503161.3548084","DOI":"10.1145\/3503161.3548084"},{"key":"1077_CR6","doi-asserted-by":"publisher","first-page":"3602","DOI":"10.1109\/TMM.2022.3163543","volume":"25","author":"C Bao","year":"2022","unstructured":"Bao, C., Sun, Q.: Generating music with emotions. IEEE Trans. Multimedia 25, 3602\u20133614 (2022). https:\/\/doi.org\/10.1109\/TMM.2022.3163543","journal-title":"IEEE Trans. Multimedia"},{"issue":"1","key":"1077_CR7","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1080\/09298215.2023.2166848","volume":"51","author":"S Dai","year":"2022","unstructured":"Dai, S., Ma, X., Wang, Y., Dannenberg, R.B.: Personalised popular music generation using imitation and structure. J. New Music Res. 51(1), 69\u201385 (2022). https:\/\/doi.org\/10.1080\/09298215.2023.2166848","journal-title":"J. New Music Res."},{"key":"1077_CR8","doi-asserted-by":"publisher","first-page":"4320","DOI":"10.1109\/TMM.2023.3321495","volume":"26","author":"Z Hu","year":"2023","unstructured":"Hu, Z., Ma, X., Liu, Y., Chen, G., Liu, Y., Dannenberg, R.B.: The beauty of repetition: an algorithmic composition model with motif-level repetition generator and outline-to-music generator in symbolic music generation. IEEE Trans. Multimedia 26, 4320\u20134333 (2023). https:\/\/doi.org\/10.1109\/TMM.2023.3321495","journal-title":"IEEE Trans. Multimedia"},{"issue":"11","key":"1077_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3672554","volume":"56","author":"A Dash","year":"2024","unstructured":"Dash, A., Agres, K.: Ai-based affective music generation systems: a review of methods and challenges. ACM Comput. Surv. 56(11), 1\u201334 (2024). https:\/\/doi.org\/10.1145\/3672554","journal-title":"ACM Comput. Surv."},{"issue":"6","key":"1077_CR10","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/MSP.2024.3415569","volume":"41","author":"G Richard","year":"2025","unstructured":"Richard, G., Lostanlen, V., Yang, Y.H., M\u00fcller, M.: Model-based deep learning for music information research: leveraging diverse knowledge sources to enhance explainability, controllability, and resource efficiency [Special Issue On Model-Based and Data-Driven Audio Signal Processing]. IEEE Signal Process. Mag. 41(6), 51\u201359 (2025). https:\/\/doi.org\/10.1109\/MSP.2024.3415569","journal-title":"IEEE Signal Process. Mag."},{"key":"1077_CR11","doi-asserted-by":"publisher","unstructured":"Fu, Y., Newman, M., Going, L., Feng, Q., Lee, J.H.: Exploring the collaborative co-creation process with AI: a case study in novice music production (2025). arXiv preprint https:\/\/arxiv.org\/abs\/2501.15276. https:\/\/doi.org\/10.48550\/arXiv.2501.15276","DOI":"10.48550\/arXiv.2501.15276"},{"issue":"6","key":"1077_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00146-025-02209-w","volume":"40","author":"M Loor Paredes","year":"2025","unstructured":"Loor Paredes, M.: Emerging paradigms in music technology: valuing mistakes, glitches and uncertainty in the age of generative AI and automation. AI Soc. 40(6), 1\u201312 (2025). https:\/\/doi.org\/10.1007\/s00146-025-02209-w","journal-title":"AI Soc."},{"issue":"3","key":"1077_CR13","doi-asserted-by":"publisher","first-page":"37","DOI":"10.70023\/piqm24304","volume":"1","author":"U S","year":"2024","unstructured":"S, U., Muniasamy, P.: Explainable prediction technique for analyzing information disorder using fuzzy rough sets. PatternIQ Min. 1(3), 37\u201347 (2024). https:\/\/doi.org\/10.70023\/piqm24304","journal-title":"PatternIQ Min."},{"issue":"1","key":"1077_CR14","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s13636-025-00397-3","volume":"2025","author":"M Cao","year":"2025","unstructured":"Cao, M., Zheng, J., Zhang, C.: AI-based Chinese-style music generation from video content: a study on cross-modal analysis and generation methods. EURASIP J. Audio Speech Music Process. 2025(1), 8 (2025). https:\/\/doi.org\/10.1186\/s13636-025-00397-3","journal-title":"EURASIP J. Audio Speech Music Process."},{"key":"1077_CR15","doi-asserted-by":"publisher","unstructured":"Tian, S., Zhang, C., Yuan, W., Tan, W., Zhu, W.: XMusic: towards a generalized and controllable symbolic music generation framework.\u00a0arXiv preprint https:\/\/arxiv.org\/abs\/2501.08809. https:\/\/doi.org\/10.48550\/arXiv.2501.08809","DOI":"10.48550\/arXiv.2501.08809"},{"issue":"7","key":"1077_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3714457","volume":"57","author":"DVT Le","year":"2025","unstructured":"Le, D.V.T., Bigo, L., Herremans, D., Keller, M.: Natural language processing methods for symbolic music generation and information retrieval: a survey. ACM Comput. Surv. 57(7), 1\u201340 (2025). https:\/\/doi.org\/10.1145\/3714457","journal-title":"ACM Comput. Surv."},{"issue":"5","key":"1077_CR17","doi-asserted-by":"publisher","first-page":"2779","DOI":"10.1007\/s12559-024-10280-6","volume":"16","author":"T Colafiglio","year":"2024","unstructured":"Colafiglio, T., Ardito, C., Sorino, P., Lof\u00f9, D., Festa, F., Di Noia, T., Di Sciascio, E.: Neuralpmg: a neural polyphonic music generation system based on machine learning algorithms. Cogn. Comput. 16(5), 2779\u20132802 (2024). https:\/\/doi.org\/10.1007\/s12559-024-10280-6","journal-title":"Cogn. Comput."},{"issue":"3","key":"1077_CR18","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s13735-024-00344-6","volume":"13","author":"AM Christodoulou","year":"2024","unstructured":"Christodoulou, A.M., Lartillot, O., Jensenius, A.R.: Multimodal music datasets? Challenges and future goals in music processing. Int. J. Multimed. Inf. Retr. 13(3), 37 (2024). https:\/\/doi.org\/10.1007\/s13735-024-00344-6","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"1077_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.aej.2024.108652","volume":"109","author":"J Wang","year":"2024","unstructured":"Wang, J., Wang, Z., Liu, G.: Recording brain activity while listening to music using wearable EEG devices combined with bidirectional long short-term memory networks. Alex. Eng. J. 109, 1\u201310 (2024). https:\/\/doi.org\/10.1016\/j.aej.2024.108652","journal-title":"Alex. Eng. J."},{"key":"1077_CR20","doi-asserted-by":"publisher","unstructured":"Li, P.P., Chen, B., Yao, Y., Wang, Y., Wang, A., Wang, A.: Jen-1: text-guided universal music generation with omnidirectional diffusion models. In: 2024 IEEE Conference on Artificial Intelligence (CAI), pp. 762\u2013769. IEEE (2024). https:\/\/doi.org\/10.1109\/CAI57491.2024.10234567","DOI":"10.1109\/CAI57491.2024.10234567"},{"key":"1077_CR21","doi-asserted-by":"publisher","unstructured":"Bhandari, K., Colton, S.: Motifs, phrases, and beyond: the modelling of structure in symbolic music generation. In: International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar)\u00a0(pp. 33\u201351). Cham: Springer Nature Switzerland (2024). https:\/\/doi.org\/10.1007\/978-3-031-56992-0_3","DOI":"10.1007\/978-3-031-56992-0_3"},{"key":"1077_CR22","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1109\/TASLP.2022.3178203","volume":"30","author":"J De Berardinis","year":"2022","unstructured":"De Berardinis, J., Cangelosi, A., Coutinho, E.: Measuring the structural complexity of music: from structural segmentations to the automatic evaluation of models for music generation. IEEE\/ACM Trans. Audio Speech Lang. Process. 30, 1963\u20131976 (2022). https:\/\/doi.org\/10.1109\/TASLP.2022.3178203","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"1077_CR23","doi-asserted-by":"publisher","unstructured":"Hung, H.T., Ching, J., Doh, S., Kim, N., Nam, J., Yang, Y.H.: EMOPIA: A multi-modal pop piano dataset for emotion recognition and emotion-based music generation.\u00a0arXiv preprint (2021). https:\/\/arxiv.org\/abs\/2108.01374. https:\/\/doi.org\/10.48550\/arXiv.2108.01374","DOI":"10.48550\/arXiv.2108.01374"},{"issue":"6","key":"1077_CR24","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1109\/MSP.2021.3106232","volume":"38","author":"JS G\u00f3mez-Ca\u00f1\u00f3n","year":"2021","unstructured":"G\u00f3mez-Ca\u00f1\u00f3n, J.S., Cano, E., Eerola, T., Herrera, P., Hu, X., Yang, Y.H., G\u00f3mez, E.: Music emotion recognition: toward new, robust standards in personalized and context-sensitive applications. IEEE Signal Process. Mag. 38(6), 106\u2013114 (2021). https:\/\/doi.org\/10.1109\/MSP.2021.3106232","journal-title":"IEEE Signal Process. Mag."},{"key":"1077_CR25","doi-asserted-by":"publisher","unstructured":"Yao, Y., Li, P., Chen, B., Wang, A.: Jen-1 composer: a unified framework for high-fidelity multi-track music generation. In: Proceedings of the AAAI Conference on Artificial Intelligence\u00a0(Vol. 39, No. 13, pp. 14459\u201314467) (2025). https:\/\/doi.org\/10.1609\/aaai.v39i13.33584","DOI":"10.1609\/aaai.v39i13.33584"},{"issue":"9","key":"1077_CR26","doi-asserted-by":"publisher","first-page":"3541","DOI":"10.3390\/app14093541","volume":"14","author":"J Kwiecie\u0144","year":"2024","unstructured":"Kwiecie\u0144, J., Skrzy\u0144ski, P., Chmiel, W., D\u0105browski, A., Szadkowski, B., Pluta, M.: Technical, musical, and legal aspects of an AI-aided algorithmic music production system. Appl. Sci. 14(9), 3541 (2024). https:\/\/doi.org\/10.3390\/app14093541","journal-title":"Appl. Sci."},{"key":"1077_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111294","volume":"153","author":"Q Zhu","year":"2024","unstructured":"Zhu, Q., Shankar, A., Maple, C.: Grey wolf optimizer based deep learning mechanism for music composition with data analysis. Appl. Soft Comput. 153, 111294 (2024). https:\/\/doi.org\/10.1016\/j.asoc.2024.111294","journal-title":"Appl. Soft Comput."},{"key":"1077_CR28","unstructured":"Aras, E.: Style learning and musical mimicry in Artificial Intelligence: modern approaches.\u00a0J. AI Human. New Ethics 19\u201332 (2025)"},{"key":"1077_CR29","doi-asserted-by":"publisher","unstructured":"Szelogowski, D.J.: Choral Music Generation: A Deep Hybrid Learning Approach\u00a0(Doctoral dissertation, Capitol Technology University) (2024). https:\/\/doi.org\/10.13140\/RG.2.2.10418.62400","DOI":"10.13140\/RG.2.2.10418.62400"},{"issue":"1","key":"1077_CR30","doi-asserted-by":"publisher","first-page":"2351656","DOI":"10.1080\/23311983.2024.2351656","volume":"11","author":"I Lugo","year":"2024","unstructured":"Lugo, I., Alatriste-Contreras, M.G.: Musical composition based on skewed statistical distributions of stochastic processes. Cogent Arts Human. 11(1), 2351656 (2024). https:\/\/doi.org\/10.1080\/23311983.2024.2351656","journal-title":"Cogent Arts Human."},{"key":"1077_CR31","doi-asserted-by":"publisher","unstructured":"Fathima, S.A., Hariram, S., Kanagalingam, S.M.: Neural harmony: advancing composition with RNN-LSTM in music generation. In: 2024 IEEE International Conference on Contemporary Computing and Communications (InC4)\u00a0(Vol. 1, pp. 1\u20136). IEEE, 2024. https:\/\/doi.org\/10.1109\/InC460750.2024.10649223","DOI":"10.1109\/InC460750.2024.10649223"},{"issue":"01","key":"1077_CR32","doi-asserted-by":"publisher","first-page":"2450026","DOI":"10.1142\/S1469026824500263","volume":"24","author":"T Shaikh","year":"2025","unstructured":"Shaikh, T., Jadhav, A.: Music generation using dual interactive Wasserstein Fourier acquisitive generative adversarial network. Int. J. Comput. Intell. Appl. 24(01), 2450026 (2025). https:\/\/doi.org\/10.1142\/S1469026824500263","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"1077_CR33","doi-asserted-by":"publisher","unstructured":"Bhimavarapu, U.: AI in music production: a deep learning method for music creation. In: Enhancing Music Education With Innovative Tools and Techniques\u00a0(pp. 87\u2013106). IGI Global Scientific Publishing (2025). https:\/\/doi.org\/10.4018\/979-8-3693-8432-9.ch004","DOI":"10.4018\/979-8-3693-8432-9.ch004"},{"key":"1077_CR34","unstructured":"Adamczyk, M., Oleksinski, A., Siaka\u0142a, S., Swietek, J., Wierzejska, K.: Automatic synthetic music generation using machine learning techniques based on user descriptions"},{"key":"1077_CR35","doi-asserted-by":"publisher","first-page":"143237","DOI":"10.1109\/ACCESS.2024.3471918","volume":"12","author":"AK Bairwa","year":"2024","unstructured":"Bairwa, A.K., Bhat, S., Sawant, T., Manoj, R.: MGU-V: a deep learning approach for lo-fi music generation using variational autoencoders with state-of-the-art performance on combined MIDI datasets. IEEE Access 12, 143237\u2013143251 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3471918","journal-title":"IEEE Access"},{"issue":"2","key":"1077_CR36","doi-asserted-by":"publisher","first-page":"58","DOI":"10.63503\/j.ijssic.2024.34","volume":"1","author":"A Kumar","year":"2024","unstructured":"Kumar, A., Lal, A.: Applying recurrent neural networks with integrated attention mechanism and transformer model for automated music generation. Int. J. Smart Sustain. Intell. Comput. 1(2), 58\u201369 (2024). https:\/\/doi.org\/10.63503\/j.ijssic.2024.34","journal-title":"Int. J. Smart Sustain. Intell. Comput."},{"key":"1077_CR37","doi-asserted-by":"publisher","unstructured":"Liu, J.: Expressive music data processing and generation.\u00a0arXiv preprint https:\/\/arxiv.org\/abs\/2503.11896. https:\/\/doi.org\/10.48550\/arXiv.2503.11896","DOI":"10.48550\/arXiv.2503.11896"},{"issue":"1","key":"1077_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3424116","volume":"17","author":"Y Yu","year":"2021","unstructured":"Yu, Y., Srivastava, A., Canales, S.: Conditional lstm-gan for melody generation from lyrics. ACM Trans. Multimed. Comput. Commun. Appl. 17(1), 1\u201320 (2021). https:\/\/doi.org\/10.1145\/3424116","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"issue":"1","key":"1077_CR39","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2140895","volume":"2022","author":"PS Yadav","year":"2022","unstructured":"Yadav, P.S., Khan, S., Singh, Y.V., Garg, P., Singh, R.S.: A lightweight deep learning\u2010based approach for jazz music generation in MIDI format. Comput. Intell. Neurosci. 2022(1), 2140895 (2022). https:\/\/doi.org\/10.1155\/2022\/2140895","journal-title":"Comput. Intell. Neurosci."},{"issue":"3","key":"1077_CR40","doi-asserted-by":"publisher","first-page":"666","DOI":"10.3390\/s25030666","volume":"25","author":"S Zhang","year":"2025","unstructured":"Zhang, S., Liu, Y., Zhou, M.: Graph neural network and LSTM integration for enhanced multi-label style classification of piano sonatas. Sensors 25(3), 666 (2025). https:\/\/doi.org\/10.3390\/s25030666","journal-title":"Sensors"},{"key":"1077_CR41","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wu, S., Hu, J., Du, X., Peng, Y., Huang, Y., Sun, M.: Notagen: Advancing musicality in symbolic music generation with large language model training paradigms.\u00a0arXiv preprint https:\/\/arxiv.org\/abs\/2502.18008 (2025)","DOI":"10.24963\/ijcai.2025\/1134"},{"key":"1077_CR42","unstructured":"Qu, X., Bai, Y., Ma, Y., Zhou, Z., Lo, K. M., Liu, J., Zhang, G.: Mupt: a generative symbolic music pretrained transformer.\u00a0arXiv preprint https:\/\/arxiv.org\/abs\/2404.06393 (2024)"},{"key":"1077_CR43","first-page":"1","volume":"99","author":"S Tian","year":"2025","unstructured":"Tian, S., Zhang, C., Yuan, W., Tan, W., Zhu, W.: Xmusic: towards a generalized and controllable symbolic music generation framework. IEEE Trans. Multimedia 99, 1\u201315 (2025)","journal-title":"IEEE Trans. Multimedia"},{"key":"1077_CR44","unstructured":"https:\/\/colinraffel.com\/projects\/lmd\/"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01077-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-01077-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01077-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T09:51:46Z","timestamp":1767952306000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-01077-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,9]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1077"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-01077-y","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,9]]},"assertion":[{"value":"6 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have declared that no competing interests exist.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"17"}}