{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:34:58Z","timestamp":1773815698553,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s10044-025-01418-9","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T19:07:22Z","timestamp":1739300842000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Pre-trained noise based unsupervised GAN for fruit disease classification in imbalanced datasets"],"prefix":"10.1007","volume":"28","author":[{"given":"Sachin","family":"Gupta","sequence":"first","affiliation":[]},{"given":"Ashish Kumar","family":"Tripathi","sequence":"additional","affiliation":[]},{"given":"Nkenyereye","family":"Lewis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"issue":"13","key":"1418_CR1","doi-asserted-by":"publisher","first-page":"7142","DOI":"10.3390\/ijerph18137142","volume":"18","author":"B Paudel","year":"2021","unstructured":"Paudel B, Wang Z, Zhang Y, Rai MK, Paul PK (2021) Climate change and its impacts on farmer\u2019s livelihood in different physiographic regions of the trans-boundary Koshi river basin, central Himalayas. Int J Environ Res Public Health 18(13):7142","journal-title":"Int J Environ Res Public Health"},{"key":"1418_CR2","unstructured":"India APEDA. Fresh fruits and vegetables. https:\/\/apeda.gov.in\/apedawebsite\/six_head_product\/FFV.htm. (Accessed on 02\/27\/2023)"},{"issue":"4","key":"1418_CR3","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1111\/1755-0998.13321","volume":"21","author":"D Li","year":"2021","unstructured":"Li D, Qian J, Li W, Ning Yu, Gan G, Jiang Y, Li W, Liang X, Chen R, Mo Y et al (2021) A high-quality genome assembly of the eggplant provides insights into the molecular basis of disease resistance and chlorogenic acid synthesis. Mol Ecol Resour 21(4):1274\u20131286","journal-title":"Mol Ecol Resour"},{"key":"1418_CR4","unstructured":"AgriXChange APEDA. India production of brinjal. https:\/\/agriexchange.apeda.gov.in\/India%20Production\/India_Productions.aspx?hscode=1070. (Accessed on 02\/27\/2023)"},{"issue":"4","key":"1418_CR5","doi-asserted-by":"publisher","first-page":"78","DOI":"10.3390\/horticulturae7040078","volume":"7","author":"I Alam","year":"2021","unstructured":"Alam I, Salimullah M (2021) Genetic engineering of eggplant (solanum melongena l.): progress, controversy and potential. Horticulturae 7(4):78","journal-title":"Horticulturae"},{"issue":"8","key":"1418_CR6","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.3390\/agriculture12081160","volume":"12","author":"Md Reduanul Haque and Ferdous Sohel","year":"2022","unstructured":"Md Reduanul Haque and Ferdous Sohel (2022) Deep network with score level fusion and inference-based transfer learning to recognize leaf blight and fruit rot diseases of eggplant. Agriculture 12(8):1160","journal-title":"Agriculture"},{"key":"1418_CR7","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1007\/s10044-021-00958-0","volume":"24","author":"M Atlam","year":"2021","unstructured":"Atlam M, Torkey H, El-Fishawy N, Salem H (2021) Coronavirus disease 2019 (covid-19): survival analysis using deep learning and cox regression model. Pattern Anal Appl 24:993\u20131005","journal-title":"Pattern Anal Appl"},{"key":"1418_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107208","volume":"200","author":"L Yuzhen","year":"2022","unstructured":"Yuzhen L, Chen D, Olaniyi E, Huang Y (2022) Generative adversarial networks (gans) for image augmentation in agriculture: a systematic review. Comput Electron Agric 200:107208","journal-title":"Comput Electron Agric"},{"key":"1418_CR9","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press"},{"issue":"6","key":"1418_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"1418_CR11","unstructured":"Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400"},{"key":"1418_CR12","unstructured":"Zeiler Matthew D, Fergus R (2014) Visualizing and understanding convolutional networks. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13818\u2013833. Springer"},{"key":"1418_CR13","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"1418_CR14","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"1418_CR15","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"1418_CR16","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1418_CR17","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"1418_CR18","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1418_CR19","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"1418_CR20","unstructured":"Tan M, Le Q (2021) Efficientnetv2: smaller models and faster training. In: International conference on machine learning, pages 10096\u201310106. PMLR"},{"key":"1418_CR21","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/s10044-016-0574-7","volume":"20","author":"H Salem","year":"2017","unstructured":"Salem H, Attiya G, El-Fishawy N (2017) Early diagnosis of breast cancer by gene expression profiles. Pattern Anal Appl 20:567\u2013578","journal-title":"Pattern Anal Appl"},{"key":"1418_CR22","doi-asserted-by":"crossref","unstructured":"Khalifa NE, Loey M, Mirjalili S (2022) A comprehensive survey of recent trends in deep learning for digital images augmentation. Artificial Intell Rev 1\u201327","DOI":"10.1007\/s10462-021-10066-4"},{"key":"1418_CR23","unstructured":"Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621"},{"key":"1418_CR24","doi-asserted-by":"publisher","first-page":"5858","DOI":"10.1109\/ACCESS.2017.2696121","volume":"5","author":"J Lemley","year":"2017","unstructured":"Lemley J, Bazrafkan S, Corcoran P (2017) Smart augmentation learning an optimal data augmentation strategy. IEEE Access 5:5858\u20135869","journal-title":"IEEE Access"},{"key":"1418_CR25","doi-asserted-by":"crossref","unstructured":"CubukEkin D, Zoph B, Mane D, Vasudevan V, Le QV (2018) Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:1805.09501","DOI":"10.1109\/CVPR.2019.00020"},{"issue":"1","key":"1418_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1\u201348","journal-title":"J Big Data"},{"key":"1418_CR27","unstructured":"Kingma Diederik P, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114"},{"key":"1418_CR28","unstructured":"Oord AVD, Kalchbrenner N, Kavukcuoglu K (2016) Pixel recurrent neural networks. In: International conference on machine learning, pages 1747\u20131756. PMLR"},{"issue":"4","key":"1418_CR29","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TCSVT.2020.3010627","volume":"31","author":"D Mishra","year":"2020","unstructured":"Mishra D, Singh SK, Singh RK (2020) Wavelet-based deep auto encoder-decoder (wdaed)-based image compression. IEEE Trans Circ Syst Video Technol 31(4):1452\u20131462","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"1418_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106779","volume":"194","author":"M Li","year":"2022","unstructured":"Li M, Zhou G, Chen A, Yi J, Chao L, He M, Yahui H (2022) Fwdgan-based data augmentation for tomato leaf disease identification. Comput Electron Agric 194:106779","journal-title":"Comput Electron Agric"},{"key":"1418_CR31","doi-asserted-by":"crossref","unstructured":"Zhang L, Zhou G, Lu C, Chen A, Wang Y, Li L, Cai Weiwei (2022) Mmdgan: a fusion data augmentation method for tomato-leaf disease identification. Appl Soft Comput 108969","DOI":"10.1016\/j.asoc.2022.108969"},{"key":"1418_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105117","volume":"168","author":"H Nazki","year":"2020","unstructured":"Nazki H, Yoon S, Fuentes A, Park DS (2020) Unsupervised image translation using adversarial networks for improved plant disease recognition. Comput Electron Agric 168:105117","journal-title":"Comput Electron Agric"},{"key":"1418_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107206","volume":"200","author":"D Xiao","year":"2022","unstructured":"Xiao D, Zeng R, Liu Y, Huang Y, Liu J, Feng J, Zhang X (2022) Citrus greening disease recognition algorithm based on classification network using trl-gan. Comput Electron Agric 200:107206","journal-title":"Comput Electron Agric"},{"key":"1418_CR34","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.biosystemseng.2021.01.014","volume":"204","author":"B Espejo-Garcia","year":"2021","unstructured":"Espejo-Garcia B, Mylonas N, Athanasakos L, Vali E, Fountas S (2021) Combining generative adversarial networks and agricultural transfer learning for weeds identification. Biosys Eng 204:79\u201389","journal-title":"Biosys Eng"},{"key":"1418_CR35","unstructured":"Krithika Iyer (2020) GAN generated images of fruit decay. https:\/\/cs230.stanford.edu\/projects_fall_55572020\/reports\/55757638.pdf"},{"issue":"2","key":"1418_CR36","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1109\/TASE.2020.3041499","volume":"19","author":"QH Cap","year":"2020","unstructured":"Cap QH, Uga H, Kagiwada S, Iyatomi H (2020) Leafgan: an effective data augmentation method for practical plant disease diagnosis. IEEE Trans Autom Sci Eng 19(2):1258\u20131267","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"1418_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107163","volume":"199","author":"F Wang","year":"2022","unstructured":"Wang F, Rao Y, Luo Q, Jin X, Jiang Zhaohui, Zhang W, Shaowen L (2022) Practical cucumber leaf disease recognition using improved swin transformer and small sample size. Comput Electron Agric 199:107163","journal-title":"Comput Electron Agric"},{"key":"1418_CR38","doi-asserted-by":"crossref","unstructured":"Tian Y, Yang G, Wang Z, Li E, Liang Z (2019) Detection of apple lesions in orchards based on deep learning methods of cyclegan and yolov3-dense. J Sens 2019","DOI":"10.1155\/2019\/7630926"},{"issue":"10","key":"1418_CR39","doi-asserted-by":"publisher","first-page":"981","DOI":"10.3390\/agriculture11100981","volume":"11","author":"W Yang","year":"2021","unstructured":"Yang W, Lihong X (2021) Image generation of tomato leaf disease identification based on adversarial-vae. Agriculture 11(10):981","journal-title":"Agriculture"},{"issue":"7","key":"1418_CR40","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.3390\/rs13071371","volume":"13","author":"J Wang","year":"2021","unstructured":"Wang J, Yang Y, Chen Y, Han Y (2021) Lightergan: an illumination enhancement method for urban uav imagery. Remote Sens 13(7):1371","journal-title":"Remote Sens"},{"key":"1418_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.scienta.2021.110684","volume":"293","author":"JJ Bird","year":"2022","unstructured":"Bird JJ, Barnes CM, Manso LJ, Ek\u00e1rt A, Faria DR (2022) Fruit quality and defect image classification with conditional Gan data augmentation. Scientia Horticulturae 293:110684","journal-title":"Scientia Horticulturae"},{"key":"1418_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106279","volume":"187","author":"A Abbas","year":"2021","unstructured":"Abbas A, Jain S, Gour M, Vankudothu S (2021) Tomato plant disease detection using transfer learning with c-gan synthetic images. Comput Electron Agric 187:106279","journal-title":"Comput Electron Agric"},{"key":"1418_CR43","doi-asserted-by":"crossref","unstructured":"Yuan X, Yu C, Liu B, Sun H, Zhu X (2021) Cgan-irb: a novel data augmentation method for apple leaf diseases. In: 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), pages 192\u2013200. IEEE","DOI":"10.1109\/COMPSAC51774.2021.00037"},{"issue":"9","key":"1418_CR44","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.3390\/sym13091597","volume":"13","author":"H Deng","year":"2021","unstructured":"Deng H, Luo D, Chang Z, Li H, Yang X (2021) Rahc_gan: a data augmentation method for tomato leaf disease recognition. Symmetry 13(9):1597","journal-title":"Symmetry"},{"key":"1418_CR45","doi-asserted-by":"crossref","unstructured":"Sharma V, Tripathi AK, Mittal H, Parmar A, Soni A, Amarwal R (2022) Weedgan: a novel generative adversarial network for cotton weed identification. The Visual Comput pages 1\u201317","DOI":"10.1007\/s00371-022-02742-5"},{"issue":"8","key":"1418_CR46","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.3390\/electronics11081266","volume":"11","author":"J Arun Pandian","year":"2022","unstructured":"Arun Pandian J, Kanchanadevi K, Dhilip Kumar V, Jasi\u0144ska E, Go\u0148o R, Leonowicz Z, Jasi\u0144ski M (2022) A five convolutional layer deep convolutional neural network for plant leaf disease detection. Electronics 11(8):1266","journal-title":"Electronics"},{"key":"1418_CR47","doi-asserted-by":"crossref","unstructured":"Huang G, Jafari AH (2021) Enhanced balancing Gan: minority-class image generation. Neural Comput Appl, pages 1\u201310","DOI":"10.1007\/s00521-021-06163-8"},{"key":"1418_CR48","unstructured":"Olaniyi E, Chen D, Lu Y, Huang Y (2022) Generative adversarial networks for image augmentation in agriculture: a systematic review. arXiv preprint arXiv:2204.04707"},{"key":"1418_CR49","doi-asserted-by":"publisher","first-page":"81943","DOI":"10.1109\/ACCESS.2020.2991552","volume":"8","author":"Q Dai","year":"2020","unstructured":"Dai Q, Cheng X, Qiao Y, Zhang Y (2020) Agricultural pest super-resolution and identification with attention enhanced residual and dense fusion generative and adversarial network. IEEE Access 8:81943\u201381959","journal-title":"IEEE Access"},{"key":"1418_CR50","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1016\/j.neucom.2021.11.080","volume":"488","author":"Y Wei","year":"2022","unstructured":"Wei Y, Shuxiang X, Kang B, Hoque S (2022) Generating training images with different angles by Gan for improving grocery product image recognition. Neurocomputing 488:694\u2013705","journal-title":"Neurocomputing"},{"key":"1418_CR51","doi-asserted-by":"publisher","first-page":"21176","DOI":"10.1109\/ACCESS.2023.3251098","volume":"11","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Gao Q, Liu L, He Y (2023) A high-quality rice leaf disease image data augmentation method based on a dual Gan. IEEE Access 11:21176\u201321191","journal-title":"IEEE Access"},{"key":"1418_CR52","doi-asserted-by":"crossref","unstructured":"Chen Y, Pan J, Wu Q (2023) Apple leaf disease identification via improved cyclegan and convolutional neural network. Soft Comput, pages 1\u201314","DOI":"10.3389\/fpls.2023.1274231"},{"issue":"1","key":"1418_CR53","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s11119-022-09941-z","volume":"24","author":"Y Chen","year":"2023","unstructured":"Chen Y, Qiufeng W (2023) Grape leaf disease identification with sparse data via generative adversarial networks and convolutional neural networks. Precision Agric 24(1):235\u2013253","journal-title":"Precision Agric"},{"issue":"3","key":"1418_CR54","doi-asserted-by":"publisher","first-page":"1817","DOI":"10.1109\/TCBB.2021.3056683","volume":"19","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Chen Z, Gao X, Song W, Xiong Q, Junfeng H, Zhang Z (2021) Plant disease detection using generated leaves based on doublegan. IEEE\/ACM Trans Comput Biol Bioinf 19(3):1817\u20131826","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"1418_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101502","volume":"67","author":"FS Ishengoma","year":"2022","unstructured":"Ishengoma FS, Rai IA, Ngoga SR (2022) Hybrid convolution neural network model for a quicker detection of infested maize plants with fall armyworms using uav-based images. Eco Inform 67:101502","journal-title":"Eco Inform"},{"key":"1418_CR56","doi-asserted-by":"crossref","unstructured":"Narayanan K Lakshmi, Krishnan R Santhana, Robinson Y Harold, Julie E Golden, Vimal S, Saravanan V, Kaliappan M (2022) Banana plant disease classification using hybrid convolutional neural network. Computational Intelligence and Neuroscience, 2022","DOI":"10.1155\/2022\/9153699"},{"issue":"19","key":"1418_CR57","doi-asserted-by":"publisher","first-page":"16973","DOI":"10.1007\/s00521-022-07350-x","volume":"34","author":"B Buyukarikan","year":"2022","unstructured":"Buyukarikan B, Ulker E (2022) Classification of physiological disorders in apples fruit using a hybrid model based on convolutional neural network and machine learning methods. Neural Comput Appl 34(19):16973\u201316988","journal-title":"Neural Comput Appl"},{"key":"1418_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2022.101698","volume":"70","author":"D Sutaji","year":"2022","unstructured":"Sutaji D, Y\u0131ld\u0131z O (2022) Lemoxinet: Lite ensemble mobilenetv2 and xception models to predict plant disease. Eco Inform 70:101698","journal-title":"Eco Inform"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01418-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-025-01418-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01418-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T16:38:12Z","timestamp":1751474292000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-025-01418-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,11]]},"references-count":58,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1418"],"URL":"https:\/\/doi.org\/10.1007\/s10044-025-01418-9","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,11]]},"assertion":[{"value":"1 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 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 have no relevant financial or nonfinancial interests to disclose. This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. We have read and understood your journal\u2019s policies, and we believe that neither the manuscript nor the study violates any of these. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Written informed consent for publication of this paper was obtained from all authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This article does not contain any studies with human participants or animals performed by any authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"39"}}