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Non-invasive Phenotyping Methodologies Enable the Accurate Characterization of Growth and Performance of Shoots and Roots

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Genomics of Plant Genetic Resources

Abstract

Significant improvements of the resource-use efficiency of major crops are required to meet the growing demand of food and feed in the next decades in a sustainable way. Breeding for new varieties and modern crop management aims at obtaining higher and more stable yields by optimizing plant structure and function under different environmental conditions. The development and application of non-invasive methods to estimate plant parameters underlying heritable traits are key enabling components. To address this demand, recently an increasing number of imaging technologies have started to be applied in plant research to analyze various types of genotype collections. Some of these applications are mature and suitable to be scaled-up to higher throughput; others require validation beyond proof-of-concept. In this chapter firstly we present an overview of available methods while stressing the current limitations to be taken into account for correct interpretation of the results. Secondly, we focus on three different case studies by our lab demonstrating the applicability of multispectral, fluorescence, and magnetic resonance imaging for various research questions applicable to controlled environments and to the field. Taken together, these case studies highlight that a variety of non-invasive plant phenotyping methods are essential tools not only for functional genomics, but also for plant selection and breeding. In addition, these experiments underline the need of developing methods tailored to different plant species and at various cultivation systems and scales.

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References

  • Aminah H, Dick JM, Grace J (1997) Rooting of Shorea leprosula stem cuttings decreases with increasing leaf area. Forest Ecol Manag 91:247–254

    Article  Google Scholar 

  • Armengaud P, Zambaux K, Hills A et al (2009) EZ-rhizo: integrated software for the fast and accurate measurement of root system architecture. Plant J 57:945–956

    Article  CAS  PubMed  Google Scholar 

  • Arvidsson S, Perez-Rodriguez P, Mueller-Roeber B (2011) A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. New Phytol 191:895–907

    Article  PubMed  Google Scholar 

  • Barros T, Kuhlbrandt W (2009) Crystallisation, structure and function of plant light-harvesting complex II. Biochim Biophys Acta 1787:753–772

    Article  CAS  PubMed  Google Scholar 

  • Barton CVM, North PRJ (2001) Remote sensing of canopy light use efficiency using the photochemical reflectance index—model and sensitivity analysis. Remote Sens Environ 78:264–273

    Article  Google Scholar 

  • Berger B, Parent B, Tester M (2010) High-throughput shoot imaging to study drought responses. J Exp Bot 61:3519–3528

    Article  CAS  PubMed  Google Scholar 

  • Blackburn GA (2007) Hyperspectral remote sensing of plant pigments. J Exp Bot 58:855–867

    Article  CAS  PubMed  Google Scholar 

  • Borevitz JO, Ecker JR (2004) Plant genomics: the third wave. Annu Rev Genom Hum Genet 5:443–477

    Article  CAS  Google Scholar 

  • Bottomley PA, Rogers HH, Foster TH (1986) NMR imaging shows water distribution and transport in plant root systems in situ. P Natl Acad Sci U S A 83:87–89

    Article  CAS  Google Scholar 

  • Bottomley PA, Rogers HH, Prior SA (1993) NMR imaging of root water distribution in intactVicia faba L. plants in elevated atmospheric CO2. Plant Cell Environ 16:335–338

    Article  CAS  Google Scholar 

  • Bouche N, Bouchez D (2001)Arabidopsis gene knockout: phenotypes wanted. Curr Opin Plant Biol 4:111–117

    Article  CAS  PubMed  Google Scholar 

  • Boyes DC, Zayed AM, Ascenzi R et al (2001) Growth stage-based phenotypic analysis of Arabidopsis: a model for high throughput functional genomics in plants. Plant Cell 13:1499–1510

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Brown DP, Pratum TK, Bledsoe C et al (1991) Noninvasive studies of conifer roots: nuclear magnetic resonance (NMR) imaging of Douglas-fir seedlings. Can J Forest Res 21:1559–1566

    Article  Google Scholar 

  • Carminati A, Moradi AB, Vetterlein D et al (2010) Dynamics of soil water content in the rhizosphere. Plant Soil 332:163–176

    Article  CAS  Google Scholar 

  • Chen JM, Li X, Nilson T, Strahler A (2000) Recent advances in geometrical optical modelling and its applications. Remote Sens Rev 18:227–262

    Article  Google Scholar 

  • Christensen S, Goudriaan J (1993) Deriving light interception and biomass from spectral reflectance ratio. Remote Sens Environ 43:87–95

    Article  Google Scholar 

  • Clark RT, MacCurdy RB, Jung JK et al (2011) Three-dimensional root phenotyping with a novel imaging and software platform. Plant Physiol 156:455–465

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Costa JM, Challa H (2002) The effect of the original leaf area on growth of softwood cuttings and planting material of rose. Sci Hortic 95(1–2):111–121

    Article  Google Scholar 

  • Costa JM, Heuvelink E, Van de Pol PA, Put HMC (2007) Anatomy and morphology of rooting in leafy rose stem cuttings and starch dynamics following severance. Acta Hortic 751:495–502

    CAS  Google Scholar 

  • Danson FM, Steven MD, Malthus TJ, Clark JA (1992) High-spectral resolution data for determining leaf water content. Int J Rem Sens 13(3):461–470

    Article  Google Scholar 

  • Dick JMcP, Dewar RC (1992) A mechanistic model of carbohydrate dynamics during adventitious root development of leafy cuttings. Ann Bot 70:371–377

    CAS  Google Scholar 

  • Eiden M, Linden S van der, Schween JH et al (2007) Elucidating physiology of plant mediated exchange processes using airborne hyperspectral reflectance measurements an synopsis with eddy covariance data. In: 10th ISPMSRS Conference, March 12–14, 2007, Davos, pp 473–481

    Google Scholar 

  • Feilhauer H, Asner GP, Martin RE, Schmidtlein S (2010) Brightness-normalized partial least squares regression for hyperspectral data. J Quant Spectrosc Radiat Transf 111:1947–1957

    Article  CAS  Google Scholar 

  • Franklin KA (2008) Shade avoidance. New Phytol 179:930–944

    Article  CAS  PubMed  Google Scholar 

  • Furbank RT, Tester M (2011) Phenomics—technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644

    Article  CAS  PubMed  Google Scholar 

  • Gamon JA, Field CB, Bilger W et al (1990) Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies. Oecologia 85:1–7

    Article  Google Scholar 

  • Gamon JA, Peñuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41(1):35–44

    Article  Google Scholar 

  • Garbulsky MF, Peñuelas J, Gamon J et al (2011) The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: a review and meta-analysis. Remote Sens Environ 115(2):281–297

    Article  Google Scholar 

  • Gitelson AA, Zur Y, Chivkunova OB, Merzlyak MN (2002) Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem Photobiol 75(3):272–281

    Article  CAS  PubMed  Google Scholar 

  • Gitelson AA, Chivkunova OB, Merzlyak MN (2009) Nondestructive estimation of anthocyanins and chlorophylls in anthocyanic leaves. Am J Bot 96(10):1861–1868

    Article  CAS  PubMed  Google Scholar 

  • Goel NS (1988) Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data. Remote Sens Rev 4:1–122

    Article  Google Scholar 

  • Goel NS (1989) Inversion of canopy reflectance models for estimation of biophysical parameters from reflectance data. In: Asrar G (ed) Theory and applications of optical remote sensing. Wiley, New York, pp 205–251

    Google Scholar 

  • Golzarian MR, Frick RA, Rajendran K et al (2011) Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 7:2

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Granier C, Aguirrezabal L, Chenu K et al (2006) PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit inArabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytol 169:623–635

    Article  PubMed  Google Scholar 

  • Gregory PJ, Hutchison DJ, Read DB et al (2003) Non-invasive imaging of roots with high resolution X-ray micro-tomography. Plant Soil 255:351–359

    Article  CAS  Google Scholar 

  • Guo JM, Trotter CM (2004) Estimating photosynthetic light-use efficiency using the photochemical reflectance index: variations among species. Funct Plant Biol 31:255–265

    Article  CAS  Google Scholar 

  • Haboudane D, Miller JR, Pattey E et al (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ 90:337–352

    Article  Google Scholar 

  • Hansen PM, Schjoerring JK (2003) Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens Environ 86:542–553

    Article  Google Scholar 

  • Hargreaves CE, Gregory PJ, Bengough AG (2009) Measuring root traits in barley (Hordeum vulgare ssp.vulgare and ssp.spontaneum) seedlings using gel chambers, soil sacs and X-ray microtomography. Plant Soil 316:285–297

    Article  CAS  Google Scholar 

  • Heeraman DA, Hopmans JW, Clausnitzer V (1997) Three dimensional imaging of plant roots in situ with X-ray computed tomography. Plant Soil 189:167–179

    CAS  Google Scholar 

  • Hillnhütter C, Sikora RA, Oerke E-C, Dusschoten D van (2012) Nuclear magnetic resonance: a tool for imaging below-ground damage caused byHeterodera schachtii andRhizoctonia solani on sugar beet. J Exp Bot 63(1):319–327

    Article  PubMed Central  PubMed  Google Scholar 

  • Hostert P, Diermayer E, Damm A, Schiefer S (2005) Spectral unmixing based on image and reference endmembers for urban change analysis. In: 24th Symposium of the European-Association-of-Remote-Sensing-Laboratories (EARSeL), May 25-27, 2004, Dubrovnik. New strategies for European remote sensing, pp 645–652

    Google Scholar 

  • Hurlbert SH (1984) Pseudoreplication and the design of ecological field experiments. Ecol Monogr 54:187–211

    Article  Google Scholar 

  • Iyer-Pascuzzi AS, Symonova O, Mileyko Y et al (2010) Imaging and analysis platform for automated phenotyping and trait ranking of plant root systems. Plant Physiol 152:1148–1157

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Jackson RD, Huete AR (1991) Interpreting vegetation indexes. Prev Vet Med 11:185–200

    Article  Google Scholar 

  • Jahnke S, Menzel MI, van Dusschoten D et al (2009) Combined MRI-PET dissects dynamic changes in plant structures and functions. Plant J 59(4):634–644

    Article  CAS  PubMed  Google Scholar 

  • Jansen M, Gilmer F, Biskup B et al (2009) Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance inArabidopsis thaliana and other rosette plants. Funct Plant Biol 36:902–914

    Article  CAS  Google Scholar 

  • Knipling EB (1970) Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens Environ 1(3):155–159

    Article  Google Scholar 

  • Kolber Z, Klimov D, Ananyev G et al (2005) Measuring photosynthetic parameters at a distance: laser induced fluorescence transient (LIFT) method for remote measurements of PSII in terrestrial vegetation. Photosynth Res 84:121–129

    Article  CAS  PubMed  Google Scholar 

  • Koornneef M, Alonso-Blanco C, Vreugdenhil D (2004) Naturally occurring genetic variation inArabidopsis thaliana. Annu Rev Plant Biol 55:141–172

    Article  CAS  PubMed  Google Scholar 

  • Kovacevic B, Roncevic S, Miladinovic D et al (2009) Early shoot and root growth dynamics as indicators for the survival of black poplar cuttings. New Forest 38:177–185

    Article  Google Scholar 

  • Kümmerlen B, Dauwe S, Schmundt D, Schurr U (1999) Thermography to measure water relations of plant leaves Volume 3, systems and applications. In: Jähne B, Haussecker H, Geissler P (eds). Handbook of computer vision and applications. Academic, pp 763–781

    Google Scholar 

  • Malenovský Z, Mishra KB, Zemek F et al (2009) Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. J Exp Bot 60:2987–3004

    Article  PubMed  Google Scholar 

  • Massonnet C, Vile D, Fabre J et al (2010) Probing the reproducibility of leaf growth and molecular phenotypes: a comparison of threeArabidopsis accessions cultivated in ten laboratories. Plant Physiol 152:2142–2157

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Meininger M, Jakob PM, von Kienlin M et al (1997) Radial spectroscopic imaging. J Magn Reson 125(2):325–331

    Article  CAS  Google Scholar 

  • Menzel MI, Oros-Peusquens A-M, Pohlmeier A et al (2007) Comparing 1H-NMR imaging and relaxation mapping of German white asparagus from five different cultivation sites. J Plant Nutr Soil Sci 170:24–38

    Article  CAS  Google Scholar 

  • Merzlyak MN, Gitelson AA, Pogosyan SI et al (1997) Reflectance spectra of plant leaves and fruits during their development, senescence and under stress. Russ J Plant Physiol 44:614–622

    CAS  Google Scholar 

  • Merzlyak MN, Gitelson AA, Chivkunova OB, Rakitin VYU (1999) Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol Plantarum 106(1):135–141

    Article  CAS  Google Scholar 

  • Mittler R, Blumwald E (2010) Genetic engineering for modern agriculture: challenges and perspectives. Annu Rev Plant Biol 61:443–462

    Article  CAS  PubMed  Google Scholar 

  • Moradi AB, Carminati A, Vetterlein D et al (2011) Three-dimensional visualization and quantification of water content in the rhizosphere. New Phytol 192:653–663

    Article  PubMed  Google Scholar 

  • Moya I, Camenen L, Evain S et al (2004) A new instrument for passive remote sensing 1. Measurements of sunlight-induced chlorophyll fluorescence. Remote Sens Environ 91:186–197

    Article  Google Scholar 

  • Munns R, James RA, Sirault XRR et al (2010) New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J Exp Bot 61:3499–3507

    Article  CAS  PubMed  Google Scholar 

  • Myneni RB, Ross J, Asrar G (1989) A review on the theory of photon transport in leaf canopies. Agr Forest Meteorol 45:1–153

    Article  Google Scholar 

  • Nagel KA, Kastenholz B, Jahnke S et al (2009) Temperature responses of roots: impact on growth, root system architecture and implications for phenotyping. Funct Plant Biol 36:947–959

    Article  CAS  Google Scholar 

  • Nagel KA, Putz A, Gilmer et al (2012) GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. Funct Plant Biol. doi:10.1071/FP1202339(11):891–904

    Google Scholar 

  • Nakazawa M, Ichikawa T, Ishikawa A et al (2003) Activation tagging, a novel tool to dissect the functions of a gene family. Plant J 34:741–750

    Article  CAS  PubMed  Google Scholar 

  • O’Malley RC, Ecker JR (2010) Linking genotype to phenotype using theArabidopsis unimutant collection. Plant J 61:928–940

    Article  PubMed  Google Scholar 

  • Osmond CB, Daley PF, Badger MR, Lüttge U (1998) Chlorophyll fluorescence quenching during photosynthetic induction in leaves ofAbutilon striatum Dicks. infected with Abutilon mosaic virus, observed with a field-portable imaging system. Bot Acta 111:390–397

    Article  CAS  Google Scholar 

  • Passioura J (2010) Scaling up: the essence of effective agricultural research. Funct Plant Biol 37:585–591

    Article  Google Scholar 

  • Pierret A, Kirby M, Moran C (2003) Simultaneous X-ray imaging of plant root growth and water uptake in thin-slab systems. Plant Soil 255:361–373

    Article  CAS  Google Scholar 

  • Pigliucci M (2008) Ecology and evolutionary biology ofArabidopsis.Arabidopsis Book 1:e0003. doi:10.1199/tab.0003

    Google Scholar 

  • Purdue University (2011) 101 ways to growArabidopsis. http://www.hort.purdue.edu/hort/facilities/greenhouse/101exp.shtml. Accessed 1 Dec 2011

  • Rascher U, Nichol CJ, Small C, Hendricks L (2007) Monitoring spatio-temporal dynamics of photosynthesis with a portable hyperspectral imaging system. Photogramm Eng Rem Sens 73:45–56

    Article  Google Scholar 

  • Rascher U, Agati G, Alonso L et al (2009) CEFLES2: the remote sensing component to quantify photosynthetic efficiency from the leaf to the region by measuring sun-induced fluorescence in the oxygen absorption bands. Biogeosciences 6:1181–1198

    Article  CAS  Google Scholar 

  • Rascher U, Damm A, van der Linden S et al (2010) Sensing of photosynthetic activity of crops. In: EC et al O (eds) Precision crop protection—the challenge and use of heterogeneity. Springer Science + Business Media BV, pp 87–99. doi:10.1007/978-90-481-9277-9_6

    Google Scholar 

  • Rascher U, Blossfeld S, Fiorani F et al (2011) Non-invasive approaches for phenotyping of enhanced performance traits in bean. Funct Plant Biol 38:968–983

    Article  CAS  Google Scholar 

  • Reboud X, Le Corre V, Scarcelli N et al (2004) Natural variation among accessions ofArabidopsis thaliana: beyond the flowering date, what morphological traits are relevant to study adaptation? In: Cronk QCB, Whitton J, Ree RH, Taylor IEP (eds) Plant adaptation: molecular genetics and ecology. Natl Research Council Canada, Ottawa, pp 135–142

    Google Scholar 

  • Richards RA (2000) Selectable traits to increase crop photosynthesis and yield of grain crops. J Exp Bot 51:447–458

    Article  CAS  PubMed  Google Scholar 

  • Rogers HH, Bottomley PA (1987)In situ magnetic resonance imaging of roots: influence of soil type, ferromagnetic particle content, and soil water. Agron J 79:957–965

    Article  Google Scholar 

  • Rokitta M, Peuke AD, Zimmermann U, Haase A (1999) Dynamic studies of phloem and xylem flow in fully differentiated plants by fast nuclear-magnetic-resonance microimaging. Protoplasma 209:126–131

    Article  CAS  PubMed  Google Scholar 

  • Rollin EM, Milton EJ (1998) Processing of high spectral resolution reflectance data for the retrieval of canopy water content information. Remote Sens Environ 65(1):86–92

    Article  Google Scholar 

  • Römer C, Wahabzada M, Ballvora A et al (2012) Early drought stress detection in cereals: simplex volume maximization for hyperspectral image analysis. Funct Plant Biol 39:878–890

    Article  Google Scholar 

  • Schilling M, Pfeifer AC, Bohl S, Klingmuller U (2008) Standardizing experimental protocols. Curr Opin Biotech 19:354–359

    Article  CAS  PubMed  Google Scholar 

  • Simpson AJ, McNally DJ, Simpson MJ (2011) NMR spectroscopy in environmental research: from molecular interactions to global processes. Prog Nucl Magn Reson Spectrosc 58:97–175

    Article  CAS  PubMed  Google Scholar 

  • Skirycz A, Vandenbroucke K, Clauw P et al (2011) Survival and growth ofArabidopsis plants given limited water are not equal. Nat Biotechnol 29:212–214

    Article  CAS  PubMed  Google Scholar 

  • Stylinski CS, Gamon JG, Oechel WO (2002) Seasonal patterns of reflectance indices, carotenoid pigments and photosynthesis of evergreen chaparral species. Oecologia 131(3):366–374

    Article  Google Scholar 

  • Sultan SE (2000) Phenotypic plasticity for plant development, function and life history. Trends Plant Sci 5:537–542

    Article  CAS  PubMed  Google Scholar 

  • Turner DP, Cohen WB, Kennedy RE et al (1999) Relationships between leaf area index and landsat TM spectral vegetation indices across three temperate zone sites. Remote Sens Environ 70:52–68

    Article  Google Scholar 

  • Ustin S, Gamon JA (2010) Remote sensing of plant functional types. New Phytol 186:795–816

    Article  PubMed  Google Scholar 

  • As H van (2007) Intact plant MRI for the study of cell water relations, membrane permeability, cell-to-cell and long distance water transport. J Exp Bot 58:743–756

    PubMed  Google Scholar 

  • As H van, Scheenen T, Vergeldt FJ (2009) MRI of intact plants. Photosynth Res 102:213–222

    Article  PubMed Central  PubMed  Google Scholar 

  • Verrelst J, Schaepman ME, Koetz B, Kneubühler M (2008) Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sens Environ 112:2341–2353

    Article  Google Scholar 

  • Walter A, Schurr U (2005) Dynamics of leaf and root growth: endogenous control versus environmental impact. Ann Bot 95:891–900

    Article  PubMed  Google Scholar 

  • Walter A, Rascher U, Osmond CB (2004) Transition in photosynthetic parameters of midvein and interveinal regions of leaves and their importance during leaf growth and development. Plant Biol 6:184–191

    Article  CAS  PubMed  Google Scholar 

  • Walter A, Scharr H, Gilmer F et al (2007) Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytol 174:447–455

    Article  PubMed  Google Scholar 

  • Walter A, Silk WK, Schurr U (2009) Environmental effects on spatial and temporal patterns of leaf and root growth. Annu Rev Plant Biol 60:279–304

    Article  CAS  PubMed  Google Scholar 

  • Weigel D, Glazebrook J (2002)Arabidopsis: a laboratory manual. Cold Spring Harbor Laboratory Press, Cold Spring Harbor

    Google Scholar 

  • Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14:415–421 and Eucarpia Bull 7:49–52

    Google Scholar 

  • Zhu J, Ingram PA, Benfey PN, Elich T (2011) From lab to field, new approaches to phenotyping root system architecture. Curr Opin Plant Biol 14:310–317

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Silvia Braun (case study 2) and Martina Klein, Sabrina Lauter and Carola Mohl (case study 3) for excellent technical assistance. Experiments in case study 1 were supported by Bundesministerium für Bildung und Forschung (BMBF, Germany) in the CROP.SENSe.net consortium. Francisco Pinto was supported by Deutscher Akademischer Austauchdienst (DAAD, Germany) and the Commission for Scientific and Technological Research (CONICYT, Chile). Development of the MRI protocol for petunia in case study 3 was supported by a grant from Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz (BMELV, Germany) via Bundesanstalt für Landwirtschaft und Ernährung (BLE, Germany) in the framework of Programm zur Innovationsförderung.

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Jansen, M. et al. (2014). Non-invasive Phenotyping Methodologies Enable the Accurate Characterization of Growth and Performance of Shoots and Roots. In: Tuberosa, R., Graner, A., Frison, E. (eds) Genomics of Plant Genetic Resources. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7572-5_8

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