Bioimage-based protein subcellular location prediction: a comprehensive review

Abstract

Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location patterns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic characteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systematically reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and classified them into four categories including growing of bioimage databases, description of subcellular location distribution patterns, classification methods, and applications of the prediction systems. Besides, we also discussed some potential directions in this field.

Keywords

bioimage informatics / protein subcellular location prediction / global and local features / multi-location protein recognition

References

[1] Domon B, Aebersold R. Options and considerations when selecting a quantitative proteomics strategy. Nature Biotechnology, 2010, 28(7): 710–721

[2] Altelaar A F, Munoz J, Heck A J. Next-generation proteomics: towards an integrative view of proteome dynamics. Nature Reviews Genetics, 2013, 14(1): 35–48

[3] Tyers M, Mann M. From genomics to proteomics. Nature, 2003, 422(6928): 193–197

[4] Casci T. Bioinformatics: Next-generation omics. Nature Reviews Genetics, 2012, 13(6): 378–379

[5] Kanehisa M, Bork P. Bioinformatics in the post-sequence era. Nature Genetics, 2003, 33: 305–310

[6] Levine A G. An explosion of bioinformatics careers. Science, 2014, 344(6189): 1303–1306

[7] Eliceiri K W, Berthold M R, Goldberg I G, Ibáñez L, Manjunath B S, Martone M E, Murphy R F, Peng H, Plant A L, Roysam B. Biological imaging software tools. Nature Methods, 2012, 9(7): 697–710

[8] Murphy R F. A new era in bioimage informatics. Bioinformatics, 2014, 30(10): 1353–1353

[9] Peng H. Bioimage informatics: a new area of engineering biology. Bioinformatics, 2008, 24(17): 1827–1836

[10] Chou K-C. Some remarks on predicting multi-label attributes in molecular biosystems. Molecular Biosystems, 2013, 9(6): 1092–1100

[11] Hung M-C, Link W. Protein localization in disease and therapy. Journal of Cell Science, 2011, 124(20): 3381–3392

[12] Komor A C, Schneider C J, Weidmann A G, Barton J K. Cell-selective biological activity of rhodium metalloinsertors correlates with subcellular localization. Journal of the American Chemical Society, 2012, 134(46): 19223–19233

[13] Lee K, Byun K, Hong W, Chuang H-Y, Pack C-G, Bayarsaikhan E, Paek S H, Kim H, Shin H Y, Ideker T. Proteome-wide discovery of mislocated proteins in cancer. Genome Research, 2013, 23(8): 1283–1294

[14] Liu Z, Hu J. Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction. Methods, 2016, 93: 119–127

[15] Lo P-K, Lee J S, Chen H, Reisman D, Berger F G, Sukumar S. Cytoplasmic mislocalization of overexpressed FOXF1 is associated with the malignancy and metastasis of colorectal adenocarcinomas. Experimental and Molecular Pathology, 2013, 94(1): 262–269

[16] Hu M C-T, Lee D-F, Xia W, Golfman L S, Ou-Yang F, Yang J-Y, Zou Y, Bao S, Hanada N, Saso H. IκB kinase promotes tumorigenesis through inhibition of forkhead FOXO3a. Cell, 2004, 117(2): 225–237

[17] Briesemeister S, Rahnenführer J, Kohlbacher O. Going from where to why—interpretable prediction of protein subcellular localization. Bioinformatics, 2010, 26(9): 1232–1238

[18] Chou K-C, Shen H-B. Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. Nature Protocols, 2008, 3(2): 153–162

[19] Imai K, Nakai K. Prediction of subcellular locations of proteins: where to proceed? Proteomics, 2010, 10(22): 3970–3983

[20] Shen H B, Chou K C. Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. Protein Engineering, Design and Selection, 2007, 20(11): 561–567

[21] Chou K-C, Shen H-B. A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS One, 2010, 5(4): e9931

[22] Su E, Chiu H-S, Lo A, Hwang J-K, Sung T-Y, Hsu W-L.Protein subcellular localization prediction based on compartment-specific features and structure conservation. BMC Bioinformatics, 2007, 8(1): 1

[23] Hawkins J, Bodén M. Detecting and sorting targeting peptides with neural networks and support vector machines. Journal of Bioinformatics and Computational Biology, 2006, 4(1): 1–18

[24] Megason S G, Fraser S E. Imaging in systems biology. Cell, 2007, 130(5): 784–795

[25] O’Donoghue S I, Gavin A-C, Gehlenborg N, Goodsell D S, Hériché J-K, Nielsen C B, North C, Olson A J, Procter J B, Shattuck D W. Visualizing biological data—now and in the future. Nature Methods, 2010, 7: S2–S4

[26] Kumar A, Rao A, Bhavani S, Newberg J Y, Murphy R F. Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers. Proceedings of the National Academy of Sciences, 2014, 111(51): 18249–18254

[27] Xu Y-Y, Yang F, Zhang Y, Shen H-B. Bioimaging-based detection of mislocalized proteins in human cancers by semi-supervised learning. Bioinformatics, 2015, 31(7): 1111–1119

[28] Peng T, Bonamy G M, Glory-Afshar E, Rines D R, Chanda S K, Murphy R F. Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns. Proceedings of the National Academy of Sciences, 2010, 107(7): 2944–2949

[29] Xu Y-Y, Yang F, Zhang Y, Shen H-B. An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues. Bioinformatics, 2013, 29(16): 2032–2040

[30] Murphy R F. CellOrganizer: image-derived models of subcellular organization and protein distribution. Methods in Cell Biology, 2012, 110: 179

[31] Murphy R F. Building cell models and simulations from microscope images. Methods, 2015

[32] Stadler C, Rexhepaj E, Singan V R, Murphy R F, Pepperkok R, Uhlén M, Simpson J C, Lundberg E. Immunofluorescence and fluorescentprotein tagging show high correlation for protein localization in mammalian cells. Nature Methods, 2013, 10(4): 315–323

[33] Jagadeesh V, Anderson J, Jones B, Marc R, Fisher S, Manjunath B. Synapse classification and localization in electron micrographs. Pattern Recognition Letters, 2014, 43: 17–24

[34] Conrad C, Erfle H, Warnat P, Daigle N, Lörch T, Ellenberg J, Pepperkok R, Eils R. Automatic identification of subcellular phenotypes on human cell arrays. Genome Research, 2004, 14(6): 1130–1136

[35] Simpson J C, Wellenreuther R, Poustka A, Pepperkok R, Wiemann S. Systematic subcellular localization of novel proteins identified by large- scale cDNA sequencing. EMBO Reports, 2000, 1(3): 287–292

[36] Knowles D W, Sudar D, Bator-Kelly C, Bissell M J, Lelièvre S A. Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(12): 4445–4450

[37] Long F, Peng H, Sudar D, Lelièvre S A, Knowles D W. Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis. BMC Cell Biology, 2007, 8(Suppl 1): S3

[38] Tahir M, Khan A, Majid A. Protein subcellular localization of fluorescence imagery using spatial and transform domain features. Bioinformatics, 2012, 28(1): 91–97

[39] Xu Y-Y, Yang F, Shen H-B. Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction. Bioinformatics, 2016, 32(14): 2184–2192

[40] Giepmans B N, Adams S R, Ellisman M H, Tsien R Y. The fluorescent toolbox for assessing protein location and function. Science, 2006, 312(5771): 217–224

[41] Gough A, Lezon T, Faeder J R, Chennubhotla C, Murphy R F, Critchley-Thorne R, Taylor D L. High content analysis with cellular and tissue systems biology: a bridge between cancer cell biology and tissue-based diagnostics. The Molecular Basis of Cancer, 2014, 4

[42] Uhlen M, Oksvold P, Fagerberg L, Lundberg E, Jonasson K, Forsberg M, Zwahlen M, Kampf C, Wester K, Hober S. Towards a knowledge-based human protein atlas. Nature Biotechnology, 2010, 28(12): 1248–1250

[43] Camp R L, Chung G G, Rimm D L. Automated subcellular localization and quantification of protein expression in tissue microarrays. Nature Medicine, 2002, 8(11): 1323–1328

[44] Stephens D J, Allan V J. Light microscopy techniques for live cell imaging. Science, 2003, 300(5616): 82–86

[45] Cho B H, Cao-Berg I, Bakal J A, Murphy R F. OMERO. Searcher: content-based image search for microscope images. Nature Methods, 2012, 9(7): 633–634

[46] Sprenger J, Fink J L, Karunaratne S, Hanson K, Hamilton N A, Teasdale R D. LOCATE: a mammalian protein subcellular localization database. Nucleic Acids Research, 2008, 36(Suppl 1): D230–D233

[47] Ljosa V, Sokolnicki K L, Carpenter A E. Annotated high-throughput microscopy image sets for validation. Nat Methods, 2012, 9(7): 637

[48] Shamir L, Orlov N, Eckley D M, Macura T J, Goldberg I G. IICBU 2008: a proposed benchmark suite for biological image analysis. Medical & Biological Engineering & Computing, 2008, 46(9): 943–947

[49] Ghaemmaghami S, Huh W-K, Bower K, Howson R W, Belle A, Dephoure N, O’Shea E K, Weissman J S. Global analysis of protein expression in yeast. Nature, 2003, 425(6959): 737–741

[50] Pontèn F, Jirström K, Uhlen M. The Human Protein Atlas—a tool for pathology. The Journal of Pathology, 2008, 216(4): 387–393

[51] Martone M E, Zhang S, Gupta A, Qian X, He H, Price D L,Wong M, Santini S, Ellisman M H. The cell-centered database. Neuroinformatics, 2003, 1(4): 379–395

[52] Glory E, Murphy R F. Automated subcellular location determination and high-throughput microscopy. Developmental Cell, 2007, 12(1): 7–16

[53] Boland M V, Markey M K, Murphy R F. Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry, 1998, 33(3): 366–375

[54] Osuna E G, Hua J, Bateman N W, Zhao T, Berget P B, Murphy R F. Large-scale automated analysis of location patterns in randomly tagged 3T3 cells. Annals of Biomedical Engineering, 2007, 35(6): 1081–1087

[55] Hamilton N A, Pantelic R S, Hanson K, Teasdale R D. Fast automated cell phenotype image classification. BMC Bioinformatics, 2007, 8(1): 110

[56] Aturaliya R N, Fink J L, Davis M J, Teasdale M S, Hanson K A, Miranda K C, Forrest A R, Grimmond S M, Suzuki H, Kanamori M. Subcellular localization of mammalian type II membrane proteins. Traffic, 2006, 7(5): 613–625

[57] Huh W-K, Falvo J V, Gerke L C, Carroll A S, Howson R W, Weissman J S, O’Shea E K. Global analysis of protein localization in budding yeast. Nature, 2003, 425(6959): 686–691

[58] Bannasch D, Mehrle A, Glatting K H, Pepperkok R, Poustka A, Wiemann S. LIFEdb: a database for functional genomics experiments integrating information from external sources, and serving as a sample tracking system. Nucleic Acids Research, 2004, 32(Suppl 1): D505–D508

[59] Coelho L P, Glory-Afshar E, Kangas J, Quinn S, Shariff A, Murphy R F. Principles of bioimage informatics: focus on machine learning of cell patterns. In: Blaschke C, Shatkay H, eds. Linking Literature, Information, and Knowledge for Biology. Lecture Notes in Computer Science, Vol 6004. Berlin: Springer, 2010, 8–18

[60] Li J, Newberg J Y, Uhlén M, Lundberg E, Murphy R F. Automated analysis and reannotation of subcellular locations in confocal images from the human protein atlas. PloS One, 2012, 7(11): e50514

[61] Li S, Besson S, Blackburn C, Carroll M, Ferguson R K, Flynn H, Gillen K, Leigh R, Lindner D, Linkert M. Metadata management for high content screening in OMERO. Methods, 2015

[62] Boland M V, Murphy R F. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics, 2001, 17(12): 1213–1223

[63] Newberg J, Murphy R F. A framework for the automated analysis of subcellular patterns in human protein atlas images. Journal of Proteome Research, 2008, 7(6): 2300–2308

[64] Shariff A, Kangas J, Coelho L P, Quinn S, Murphy R F. Automated image analysis for high-content screening and analysis. Journal of Biomolecular Screening, 2010, 15(7): 726–734

[65] Tahir M, Khan A. Protein subcellular localization of fluorescence microscopy images: employing new statistical and Texton based image features and SVM based ensemble classification. Information Sciences, 2016, 345: 65–80

[66] Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 886–893

[67] Tahir M, Khan A, Majid A, Lumini A. Subcellular localization using fluorescence imagery: utilizing ensemble classification with diverse feature extraction strategies and data balancing. Applied Soft Computing, 2013, 13(11): 4231–4243

[68] Nanni L, Lumini A, Brahnam S. Survey on LBP based texture descriptors for image classification. Expert Systems with Applications, 2012, 39(3): 3634–3641

[69] Paci M, Nanni L, Lahti A, Aalto-Setala K, Hyttinen J, Severi S. Nonbinary coding for texture descriptors in sub-cellular and stem cell image classification. Current Bioinformatics, 2013, 8(2): 208–219

[70] Yang F, Xu Y-Y, Shen H-B. Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification? The Scientific World Journal, 2014

[71] Koh J L, Chong Y T, Friesen H, Moses A, Boone C, Andrews B J, Moffat J. CYCLoPs: a comprehensive database constructed from automated analysis of protein abundance and subcellular localization patterns in Saccharomyces cerevisiae. G3: Genes, Genomes, Genetics, 2015, 5(6): 1223–1232

[72] Yang F, Xu Y-Y, Wang S-T, Shen H-B. Image-based classification of protein subcellular location patterns in human reproductive tissue by ensemble learning global and local features. Neurocomputing, 2014, 131: 113–123

[73] Zhang B, Gao Y, Zhao S, Liu J. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Transactions on Image Processing, 2010, 19(2): 533–544

[74] Guo Z, Zhang L, Zhang D. A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, 2010, 19(6): 1657–1663

[75] Lin C-C, Tsai Y-S, Lin Y-S, Chiu T-Y, Hsiung C-C, Lee M-I, Simpson J C, Hsu C-N. Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localization. Bioinformatics, 2007, 23(24): 3374–3381

[76] Zhao T, Velliste M, Boland M V, Murphy R F. Object type recognition for automated analysis of protein subcellular location. IEEE Transactions on Image Processing, 2005, 14(9): 1351–1359

[77] Godil A, Lian Z, Wagan A. Exploring local features and the bag-ofvisual- words approach for bioimage classification. In: Proceedings of the ACM International Conference on Bioinformatics, Computational Biology and Biomedical Informatics. 2013

[78] Coelho L P, Kangas J D, Naik A W, Osuna-Highley E, Glory-Afshar E, Fuhrman M, Simha R, Berget P B, Jarvik J W, Murphy R F. Determining the subcellular location of new proteins from microscope images using local features. Bioinformatics, 2013, 29(18): 2343–2349

[79] Lowe D G. Object recognition from local scale-invariant features. In: Proceedings of the 7th IEEE International Conference on Computer Vision. 1999, 1150–1157

[80] Nanni L, Lumini A. A reliable method for cell phenotype image classification. Artificial Intelligence in Medicine, 2008, 43(2): 87–97

[81] Jennrich R I, Sampson P. Stepwise discriminant analysis. Statistical Methods for Digital Computers, 1977, 3: 77–95

[82] Huang K, Velliste M, Murphy R F. Feature reduction for improved recognition of subcellular location patterns in fluorescence microscope images. Proceedings of SPIE—The International Society for Optical Engineering, 2003, 4962: 307–318

[83] Loo L-H, Wu L F, Altschuler S J. Image-based multivariate profiling of drug responses from single cells. Nature Methods, 2007, 4(5): 445–453

[84] Kouzani A Z. Subcellular localisation of proteins in fluorescent microscope images using a random forest. In: Proceedings of IEEE International Joint Conference on Neural Networks. 2008, 3926–3932

[85] Zhang B, Zhang Y, Lu W, Han G. Phenotype recognition by curvelet transform and random subspace ensemble. Journal of Applied Mathematics and Bioinformatics, 2011, 1(1): 79

[86] Newberg J Y, Li J, Rao A, Pontén F, Uhlén M, Lundberg E, Murphy R F. Automated analysis of human protein atlas immunofluorescence images. In: Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2009, 1023–1026

[87] Pärnamaa T, Parts L. Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning. bioRxiv, 2016: 050757

[88] Li J, Xiong L, Schneider J, Murphy R F. Protein subcellular location pattern classification in cellular images using latent discriminative models. Bioinformatics, 2012, 28(12): i32–i39

[89] Nanni L, Lumini A, Lin Y-S, Hsu C-N, Lin C-C. Fusion of systems for automated cell phenotype image classification. Expert Systems with Applications, 2010, 37(2): 1556–1562

[90] Huang K, Murphy R F. Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinformatics, 2004, 5(1): 78

[91] Chebira A, Barbotin Y, Jackson C, Merryman T, Srinivasa G, Murphy R F, Kovaˇcvíc J. A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinformatics, 2007, 8(1): 210

[92] Loo L-H, Laksameethanasan D, Tung Y-L. Quantitative protein localization signatures reveal an association between spatial and functional divergences of proteins. PLoS Comput Biol, 2014, 10(3): e1003504

[93] Shen H B, Chou K C. Hum-mPLoc: an ensemble classifier for largescale human protein subcellular location prediction by incorporating samples with multiple sites. Biochemical & Biophysical Research Communications, 2007, 355(4): 1006–1011

[94] Shen H B, Chou K C. A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Analytical Biochemistry, 2009, 394(2): 269–274

[95] Zhu L,Yang J, Shen H-B. Multi label learning for prediction of human protein subcellular localizations. The Protein Journal, 2009, 28(9–10): 384–390

[96] Boutell M R, Luo J, Shen X, Brown C M. Learning multi-label scene classification. Pattern Recognition, 2004, 37(9): 1757–1771

[97] Read J, Pfahringer B, Holmes G, Frank E. Classifier chains for multilabel classification. Machine Learning, 2011, 85(3): 333–359

[98] Hu C-D, Kerppola T K. Simultaneous visualization of multiple protein interactions in living cells using multicolor fluorescence complementation analysis. Nature Biotechnology, 2003, 21(5): 539–545

[99] Shao W, Liu M, Zhang D. Human cell structure-driven model construction for predicting protein subcellular location from biological images. Bioinformatics, 2016, 32(1): 114–121

[100] Chen X, Murphy R F. Objective clustering of proteins based on subcellular location patterns. BioMed Research International, 2005, 2005(2): 87–95

[101] Chen X, Velliste M, Weinstein S, Jarvik J W, Murphy R F. Location proteomics: building subcellular location trees from highresolution 3D fluorescence microscope images of randomly tagged proteins. In: Proceedings of SPIE 4962, Manipulation and Analysis of Biomolecules, Cells, and Tissues. 2003, 298–306

[102] Coelho L P, Peng T, Murphy R F. Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing. Bioinformatics, 2010, 26(12): i7–i12

[103] Hamilton N A, Teasdale R D. Visualizing and clustering high throughputsub-cellular localization imaging. BMC Bioinformatics, 2008, 9(1): 81

[104] Handfield L-F, Chong Y T, Simmons J, Andrews B J, Moses A M. Unsupervised clustering of subcellular protein expression patterns in high-throughput microscopy images reveals protein complexes and functional relationships between proteins. PLoS Comput Biol, 2013, 9(6): e1003085

[105] Zhu X, Goldberg A B. Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence andMachine Learning, 2009, 3(1): 1–130

[106] Lin Y-S, Huang Y-H, Lin C-C, Hsu C-N. Feature space transformation for semi-supervised learning for protein subcellular localization in fluorescence microscopy images. In: Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2009, 414–417

[107] Zhu S, Matsudaira P, Welsch R, Rajapakse J C. Quantification of cytoskeletal protein localization from high-content images. In: Dijkstra T M H, Tsivtsivadze E, Marchiori E, et al., eds. Pattern Recognition in Bioinformatics. Lecture Notes in Computer Science, Vol 6282. Berlin: Springer, 2010, 289–300

[108] Shamir L, Delaney J D, Orlov N, Eckley D M, Goldberg I G. Pattern recognition software and techniques for biological image analysis. PLoS Comput Biol, 2010, 6(11): e1000974

[109] Foster L J, de Hoog C L, Zhang Y, Zhang Y, Xie X, Mootha V K, Mann M. A mammalian organelle map by protein correlation profiling. Cell, 2006, 125(1): 187–199

[110] Buck T E, Rao A, Coelho L P, Fuhrman M H, Jarvik J W, Berget P B, Murphy R F. Cell cycle dependence of protein subcellular location inferred from static, asynchronous images. In: Proceedings of IEEE Annual International Conference on Engineering in Medicine and Biology Society. 2009, 1016–1019

[111] Kumar A, Agarwal S, Heyman J A, Matson S, Heidtman M, Piccirillo S, Umansky L, Drawid A, Jansen R, Liu Y. Subcellular localization of the yeast proteome. Genes & Development, 2002, 16(6): 707–719

[112] Naik A W, Kangas J D, Sullivan D P, Murphy R F. Active machine learning-driven experimentation to determine compound effects on protein patterns. eLife, 2016, 5: e10047

[113] Nair R, Rost B. Predicting protein subcellular localization using intelligent systems. In: Markel S, León D, eds. Silico Technology in Drug Target Identification and Validation. Boca Raton, FL: CRC Press, 2006, 261–284

[114] Pierleoni A, Martelli P L, Fariselli P, Casadio R. BaCelLo: a balanced subcellular localization predictor. Bioinformatics, 2006, 22(14): e408–e416

[115] Winsnes C F, Sullivan D P, Smith K, Lundberg E. Multi-label prediction of subcellular localization in confocal images using deep neural networks. Molecular Biology of the Cell, 2016, 27

[116] Ashburner M, Ball C A, Blake J A, Botstein D, Butler H, Cherry J M, Davis A P, Dolinski K, Dwight S S, Eppig J T. Gene ontology: tool for the unification of biology. Nature Genetics, 2000, 25(1): 25–29

[117] Shariff A, Murphy R F, Rohde G K. A generative model of microtubule distributions, and indirect estimation of its parameters from fluorescence microscopy images. Cytometry Part A, 2010, 77(5): 457–466

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