{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T21:05:59Z","timestamp":1761599159477,"version":"3.41.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2017,6,28]],"date-time":"2017-06-28T00:00:00Z","timestamp":1498608000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61375065","61625204"],"award-info":[{"award-number":["61375065","61625204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Program of National Science Foundation of China","award":["61432012","61432014"],"award-info":[{"award-number":["61432012","61432014"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2019,3]]},"DOI":"10.1007\/s00521-017-3113-6","type":"journal-article","created":{"date-parts":[[2017,6,28]],"date-time":"2017-06-28T11:30:24Z","timestamp":1498649424000},"page":"839-849","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Angle-based embedding quality assessment method for manifold learning"],"prefix":"10.1007","volume":"31","author":[{"given":"Dongdong","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7016-9288","authenticated-orcid":false,"given":"Jiancheng","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haixian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojie","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,6,28]]},"reference":[{"key":"3113_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-39351-3","volume-title":"Nonlinear dimensionality reduction","author":"JA Lee","year":"2007","unstructured":"Lee JA, Verleysen M (2007) Nonlinear dimensionality reduction. Springer, New York"},{"issue":"5500","key":"3113_CR2","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"ST Roweis","year":"2000","unstructured":"Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323\u20132326","journal-title":"Science"},{"issue":"5500","key":"3113_CR3","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"JB Tenenbaum","year":"2000","unstructured":"Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319\u20132323","journal-title":"Science"},{"issue":"6","key":"3113_CR4","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1162\/089976603321780317","volume":"15","author":"M Belkin","year":"2003","unstructured":"Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373\u20131396","journal-title":"Neural Comput"},{"issue":"3","key":"3113_CR5","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1109\/TNN.2007.891193","volume":"18","author":"JC Lv","year":"2007","unstructured":"Lv JC, Zhang Y, Kiong TK (2007) Determining of the number of principal directions in a biologically plausible pca model. IEEE Trans Neural Netw 18(3):910\u2013916","journal-title":"IEEE Trans Neural Netw"},{"key":"3113_CR6","doi-asserted-by":"crossref","unstructured":"Chen D, Lv JC, Yi Z (2014) A local non-negative pursuit method for intrinsic manifold structure preservation. In: Proceedings of the 28th AAAI conference on artificial intelligence (AAAI), Canada, pp 1745\u20131751","DOI":"10.1609\/aaai.v28i1.8966"},{"key":"3113_CR7","unstructured":"Chen D, Lv J, Yi Z (2017) Unsupervised multi-manifold clustering by learning deep representation. In: Workshops at the 31th AAAI conference on artificial intelligence (AAAI), pp 385\u2013391"},{"key":"3113_CR8","doi-asserted-by":"crossref","unstructured":"Chen D, Lv J, Yi Z (2017) Graph regularized restricted Boltzmann machine. IEEE transactions on neural networks and learning systems (TNNLS)","DOI":"10.1109\/TNNLS.2017.2692773"},{"issue":"485","key":"3113_CR9","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1198\/jasa.2009.0111","volume":"104","author":"L Chen","year":"2009","unstructured":"Chen L, Buja A (2009) Local multidimensional scaling for nonlinear dimension reduction, graph drawing, and proximity analysis. J Am Stat Assoc 104(485):209\u2013219","journal-title":"J Am Stat Assoc"},{"issue":"72","key":"3113_CR10","first-page":"1431","volume":"7","author":"M Verleysen","year":"2009","unstructured":"Verleysen M, Lee JA (2009) Quality assessment of dimensionality reduction: rank-based criteria. Neurocomputing 7(72):1431\u20131443","journal-title":"Neurocomputing"},{"issue":"5500","key":"3113_CR11","doi-asserted-by":"publisher","first-page":"2268","DOI":"10.1126\/science.290.5500.2268","volume":"290","author":"HS Seung","year":"2000","unstructured":"Seung HS, Lee DD (2000) The manifold ways of perception. Science 290(5500):2268\u20132269","journal-title":"Science"},{"key":"3113_CR12","first-page":"55","volume":"24","author":"E Elhamifar","year":"2011","unstructured":"Elhamifar E, Vidal R (2011) Sparse manifold clustering and embedding. Adv Neural Inf Process Syst (NIPS) 24:55\u201363","journal-title":"Adv Neural Inf Process Syst (NIPS)"},{"issue":"4","key":"3113_CR13","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1007\/s11741-004-0051-1","volume":"8","author":"ZY Zhang","year":"2004","unstructured":"Zhang ZY, Zha HY (2004) Principal manifolds and nonlinear dimension reduction via local tangent space alignment. J Shanghai Univ (Engl Ed) 8(4):406\u2013424","journal-title":"J Shanghai Univ (Engl Ed)"},{"issue":"1","key":"3113_CR14","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.acha.2006.04.006","volume":"21","author":"RR Coifman","year":"2006","unstructured":"Coifman RR, Lafon S (2006) Diffusion maps. Appl Comput Harmonic Anal 21(1):5\u201330","journal-title":"Appl Comput Harmonic Anal"},{"issue":"2","key":"3113_CR15","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TNNLS.2014.2312421","volume":"26","author":"JC Lv","year":"2015","unstructured":"Lv JC, Zhang Y, Li Y (2015) Non-divergence of stochastic discrete time algorithms for pca neural networks. IEEE Trans Neural Netw Learn Syst (TNNLS) 26(2):394\u2013399","journal-title":"IEEE Trans Neural Netw Learn Syst (TNNLS)"},{"key":"3113_CR16","doi-asserted-by":"crossref","unstructured":"Cao X, Zhang C, Fu H, Liu S (2015) Diversity-induced multi-view subspace clustering. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 586\u2013594","DOI":"10.1109\/CVPR.2015.7298657"},{"key":"3113_CR17","doi-asserted-by":"crossref","unstructured":"Wang W, Huang Y, Wang Y, Wang L (2014) Generalized autoencoder: a neural network framework for dimensionality reduction. In: IEEE conference on computer vision and pattern recognition workshops, pp 496\u2013503","DOI":"10.1109\/CVPRW.2014.79"},{"key":"3113_CR18","unstructured":"Xie J, Girshick R, Farhadi A (2015) Unsupervised deep embedding for clustering analysis. Computer Science"},{"key":"3113_CR19","doi-asserted-by":"crossref","unstructured":"Yang J, Parikh D, Batra D (2016) Joint unsupervised learning of deep representations and image clusters, pp 5147\u20135156","DOI":"10.1109\/CVPR.2016.556"},{"issue":"7","key":"3113_CR20","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1016\/j.jvcir.2014.08.006","volume":"25","author":"L Huang","year":"2014","unstructured":"Huang L, Jiwen L, Tan YP (2014) Multi-manifold metric learning for face recognition based on image sets. J Vis Commun Image Represent 25(7):1774\u20131783","journal-title":"J Vis Commun Image Represent"},{"key":"3113_CR21","doi-asserted-by":"crossref","unstructured":"Peng X, Xiao S, Feng J, Yau W-Y, Yi Z (2016) Deep subspace clustering with sparsity prior. In: The 25th international joint conference on artificial intelligence (IJCAI)","DOI":"10.1609\/aaai.v31i1.10824"},{"issue":"13","key":"3113_CR22","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1049\/el.2014.0666","volume":"50","author":"L Zhen","year":"2014","unstructured":"Zhen L, Yi Z, Peng X, Peng D (2014) Locally linear representation for image clustering. Electron Lett 50(13):942\u2013943","journal-title":"Electron Lett"},{"key":"3113_CR23","doi-asserted-by":"publisher","unstructured":"Peng X, Lu J, Yi Z, Yan R (2016) Automatic subspace learning via principal coefficients embedding. IEEE Trans Cybern 1\u201314. doi: 10.1109\/TCYB.2016.2572306","DOI":"10.1109\/TCYB.2016.2572306"},{"issue":"10","key":"3113_CR24","doi-asserted-by":"publisher","first-page":"5591","DOI":"10.1073\/pnas.1031596100","volume":"100","author":"DL Donoho","year":"2003","unstructured":"Donoho DL, Grimes C (2003) Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc Nat Acad Sci 100(10):5591\u20135596","journal-title":"Proc Nat Acad Sci"},{"key":"3113_CR25","first-page":"1208","volume":"2","author":"X He","year":"2005","unstructured":"He X, Cai D, Yan S, Zhang H-J (2005) Neighborhood preserving embedding. IEEE Int Conf Comput Vis (ICCV) 2:1208\u20131213","journal-title":"IEEE Int Conf Comput Vis (ICCV)"},{"issue":"2","key":"3113_CR26","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","volume":"31","author":"J Wright","year":"2009","unstructured":"Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell (TPAMI) 31(2):210\u2013227","journal-title":"IEEE Trans Pattern Anal Mach Intell (TPAMI)"},{"key":"3113_CR27","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1109\/TIP.2009.2038764","volume":"19","author":"B Cheng","year":"2010","unstructured":"Cheng B, Yang J, Yan S, Yun F, Huang TS (2010) Learning with $$\\ell ^1$$ \u2113 1 -graph for image analysis. IEEE Trans Image Process (TIP) 19:858\u2013866","journal-title":"IEEE Trans Image Process (TIP)"},{"key":"3113_CR28","unstructured":"Liu G, Lin Z, Yu Y (2010) Robust subspace segmentation by low-rank representation. In: International conference on machine learning (ICML), pp 663\u2013670"},{"key":"3113_CR29","doi-asserted-by":"crossref","unstructured":"Favaro P, Vidal R, Ravichandran A (2011) A closed form solution to robust subspace estimation and clustering, pp 1801\u20131807. IEEE","DOI":"10.1109\/CVPR.2011.5995365"},{"issue":"6","key":"3113_CR30","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1073\/pnas.42.6.359","volume":"42","author":"I Satake","year":"1956","unstructured":"Satake I (1956) On a generalization of the notion of manifold. Proc Nat Acad Sci USA 42(6):359","journal-title":"Proc Nat Acad Sci USA"},{"key":"3113_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2201-7","volume-title":"Riemannian geometry","author":"MP Do Carmo","year":"1992","unstructured":"Do Carmo MP (1992) Riemannian geometry. Springer, Berlin"},{"key":"3113_CR32","first-page":"2399","volume":"7","author":"M Belkin","year":"2006","unstructured":"Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399\u20132434","journal-title":"J Mach Learn Res"},{"issue":"1","key":"3113_CR33","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1109\/TPAMI.2007.250598","volume":"29","author":"S Yan","year":"2007","unstructured":"Yan S, Dong X, Zhang B, Zhang H-J, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell (TPAMI) 29(1):40\u201351","journal-title":"IEEE Trans Pattern Anal Mach Intell (TPAMI)"},{"issue":"2","key":"3113_CR34","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1109\/18.978730","volume":"48","author":"L Zheng","year":"2002","unstructured":"Zheng L, Tse DNC (2002) Communication on the grassmann manifold: a geometric approach to the noncoherent multiple-antenna channel. IEEE Trans Inf Theory (TIT) 48(2):359\u2013383","journal-title":"IEEE Trans Inf Theory (TIT)"},{"key":"3113_CR35","first-page":"119","volume":"4","author":"LK Saul","year":"2003","unstructured":"Saul LK, Roweis ST (2003) Think globally, fit locally. J Mach Learn Res 4:119\u2013155","journal-title":"J Mach Learn Res"},{"key":"3113_CR36","doi-asserted-by":"crossref","unstructured":"Elgammal A, Lee C-S (2004) Inferring 3D body pose from silhouettes using activity manifold learning. In: IEEE conference on computer vision and pattern recognition (CVPR), vol\u00a02, pp II\u2013681. IEEE","DOI":"10.1109\/CVPR.2004.1315230"},{"issue":"3","key":"3113_CR37","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TPAMI.2005.55","volume":"27","author":"X He","year":"2005","unstructured":"He X, Yan S, Yuxiao H, Niyogi P, Zhang H-J (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell (TPAMI) 27(3):328\u2013340","journal-title":"IEEE Trans Pattern Anal Mach Intell (TPAMI)"},{"issue":"8","key":"3113_CR38","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1109\/TPAMI.2010.231","volume":"33","author":"D Cai","year":"2011","unstructured":"Cai D, He X, Han J, Huang TS (2011) Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell (TPAMI) 33(8):1548\u20131560","journal-title":"IEEE Trans Pattern Anal Mach Intell (TPAMI)"},{"key":"3113_CR39","doi-asserted-by":"crossref","unstructured":"Goh A, Vidal R (2008) Clustering and dimensionality reduction on riemannian manifolds. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1\u20137. IEEE","DOI":"10.1109\/CVPR.2008.4587422"},{"key":"3113_CR40","unstructured":"Goldberg AB, Zhu X, Singh A, Xu Z, Nowak R (2009) Multi-manifold semi-supervised learning. In: International conference on artificial intelligence and statistics, pp 169\u2013176"},{"issue":"4","key":"3113_CR41","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1504\/IJKESDP.2009.028988","volume":"1","author":"M Filippone","year":"2009","unstructured":"Filippone M, Masulli F, Rovetta S (2009) Clustering in the membership embedding space. Int J Knowl Eng Soft Data Paradig 1(4):363\u2013375","journal-title":"Int J Knowl Eng Soft Data Paradig"},{"issue":"5","key":"3113_CR42","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.patcog.2008.09.012","volume":"42","author":"W-S Zheng","year":"2009","unstructured":"Zheng W-S, Lai J-H, Yuen PC, Li SZ (2009) Perturbation LDA: learning the difference between the class empirical mean and its expectation. Pattern Recogn 42(5):764\u2013779","journal-title":"Pattern Recogn"},{"issue":"17","key":"3113_CR43","doi-asserted-by":"publisher","first-page":"3028","DOI":"10.1016\/j.neucom.2011.04.021","volume":"74","author":"B Pan","year":"2011","unstructured":"Pan B, Lai J, Yuen PC (2011) Learning low-rank mercer kernels with fast-decaying spectrum. Neurocomputing 74(17):3028\u20133035","journal-title":"Neurocomputing"},{"issue":"1","key":"3113_CR44","first-page":"251","volume":"97","author":"P Zhang","year":"2011","unstructured":"Zhang P, Ren Y, Zhang B (2011) A new embedding quality assessment method for manifold learning. Neurocomputing 97(1):251\u2013266","journal-title":"Neurocomputing"},{"issue":"6","key":"3113_CR45","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1016\/j.neunet.2006.05.014","volume":"19","author":"J Venna","year":"2006","unstructured":"Venna J, Kaski S (2006) Local multidimensional scaling. Neural Netw 19(6):889\u2013899","journal-title":"Neural Netw"},{"issue":"1","key":"3113_CR46","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/1471-2105-4-48","volume":"4","author":"S Kaski","year":"2003","unstructured":"Kaski S, Nikkil\u00e4 J, Oja M, Venna J, T\u00f6r\u00f6nen P, Castr\u00e9n E (2003) Trustworthiness and metrics in visualizing similarity of gene expression. BMC Bioinform 4(1):48","journal-title":"BMC Bioinform"},{"key":"3113_CR47","unstructured":"Verleysen M, Lee JA (2008) Rank-based quality assessment of nonlinear dimensionality reduction. ESANN, pp 49\u201354"},{"issue":"6","key":"3113_CR48","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1016\/j.neucom.2010.10.011","volume":"74","author":"D Meng","year":"2011","unstructured":"Meng D, Leung Y, Zongben X (2011) A new quality assessment criterion for nonlinear dimensionality reduction. Neurocomputing 74(6):941\u2013948","journal-title":"Neurocomputing"},{"issue":"270","key":"3113_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2014.02.068","volume":"270","author":"A Gracia","year":"2014","unstructured":"Gracia A, Gonzlez S, Robles V, Menasalvas E (2014) A methodology to compare dimensionality reduction algorithms in terms of loss of quality. Inf Sci 270(270):1\u201327","journal-title":"Inf Sci"},{"issue":"7","key":"3113_CR50","doi-asserted-by":"publisher","first-page":"1431","DOI":"10.1016\/j.neucom.2008.12.017","volume":"72","author":"JA Lee","year":"2009","unstructured":"Lee JA, Verleysen M (2009) Quality assessment of dimensionality reduction: rank-based criteria. Neurocomputing 72(7):1431\u20131443","journal-title":"Neurocomputing"},{"key":"3113_CR51","unstructured":"Siegel S (1956) Nonparametric statistics for the behavioral sciences"},{"key":"3113_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-27497-8","volume-title":"Geometric structure of high-dimensional data and dimensionality reduction","author":"J Wang","year":"2011","unstructured":"Wang J (2011) Geometric structure of high-dimensional data and dimensionality reduction. Springer, Beijing"},{"key":"3113_CR53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-13359-1_34","volume-title":"Angle-based outlier detection algorithm with more stable relationships","author":"X Li","year":"2015","unstructured":"Li X, Lv J, Chen D (2015) Angle-based outlier detection algorithm with more stable relationships. Springer, Berlin"},{"key":"3113_CR54","doi-asserted-by":"publisher","DOI":"10.1201\/9781420036121","volume-title":"Multidimensional scaling","author":"TF Cox","year":"2000","unstructured":"Cox TF, Cox MAA (2000) Multidimensional scaling, 2nd edn. CRC Press, Boca Raton","edition":"2"},{"issue":"2605","key":"3113_CR55","first-page":"2579","volume":"9","author":"L Maaten Van Der","year":"2008","unstructured":"Van Der Maaten L, Hinton G (2008) Viualizing data using t-SNE. J Mach Learn Res 9(2605):2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"3113_CR56","first-page":"153","volume":"19","author":"Y Bengio","year":"2007","unstructured":"Bengio Y, Lamblin P, Popovici D, Larochelle H et al (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 19:153","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3113-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-3113-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3113-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T03:47:16Z","timestamp":1750391236000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-3113-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,28]]},"references-count":56,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,3]]}},"alternative-id":["3113"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-3113-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2017,6,28]]},"assertion":[{"value":"22 December 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and\/or company that could be construed as influencing the position presented in, or the review of this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}