Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices

Autores
Jurcic, Esteban Javier; Villalba, Pamela Victoria; Pathauer, Pablo Santiago; Palazzini, Dino; Oberschelp, Gustavo Pedro Javier; Harrand, Leonel; Garcia, Martin Nahuel; Aguirre, Natalia Cristina; Acuña, Cintia Vanesa; Martinez, Maria Carolina; Rivas, Juan Gabriel; Cisneros, Esteban F.; Lopez, Juan Adolfo; Marcucci Poltri, Susana Noemi; Munilla, Sebastian; Cappa, Eduardo Pablo
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Eucalyptus L’Hér. (Myrtaceae) is the most valuable and globally planted forest tree genus. Fast growth, adaptability to a broad diversity of tropical and subtropical regions, combined with versatile wood properties for energy, solid products, pulp, and paper have warranted their outstanding position in current world forestry (de Lima et al. 2019). Eucalyptus dunnii Maiden (hereafter E. dunnii) has become increasingly used in commercial afforestation due to its combined good performance for growth, stem straightness, and frost tolerance, together with suitable wood density and pulp yield. In a broad sense, genomic selection (GS) is a family of statistical methods developed for predicting the breeding values of nonphenotyped individuals with the assistance of a large number of molecular markers widespread distributed throughout the genome (Meuwissen et al. 2001). These methods exploit cosegregation between markers and quantitative trait loci (QTL) in linkage disequilibrium (LD). In forest trees, GS is of particular benefit due to the extended breeding cycles caused by delayed reproductive maturity and the need for early selection of traits that express late in life (Mphahlele et al. 2020). In this context, GS has a potentially substantial impact on the rate of genetic gain by increasing the intensity and accuracy of selection and, particularly, by shortening the generational interval (Grattapaglia et al. 2018). The genomic best linear unbiased prediction (GBLUP) is one of the most commonly GS methods. It is basically a variant of the standard BLUP method (hereafter ABLUP, cf. Henderson 1984), where the pedigree-based numerator relationship matrix (Amatrix) is replaced by a genomic relationship matrix (G-matrix, e.g., Habier et al. 2013). Many empirical studies with forest tree species have shown that GBLUP is a very promising approach for tree breeding (e.g., Mphahlele et al. 2020; Resende et al. 2017; Lenz et al. 2019). However, to our knowledge, only two of them have directly investigated the efficiency of genomic prediction using only genotyped trees in E. dunnii through GBLUP (Naidoo et al. 2018; Jones et al. 2019).
Fil: Jurcic, Esteban J. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Villalba, Pamela Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Pathauer, Pablo Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina
Fil: Palazzini, Dino A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Oberschelp, Gustavo Pedro Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina
Fil: Harrand, Leonel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina
Fil: Garcia, Martin Nahuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Aguirre, Natalia Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Acuña, Cintia Vanesa. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez, Maria Carolina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rivas, Juan Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cisneros, Esteban F. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Forestales; Argentina
Fil: Lopez, Juan Adolfo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bella Vista; Argentina
Fil: Marcucci Poltri, Susana Noemi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Munilla, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fuente
Heredity 127 (2) : 176-189 (2021)
Materia
Eucalyptus
Genética
Genetics
Eucalyptus dunnii
genomic prediction
predicción genómica
ssGBLUP
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/10732

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network_name_str INTA Digital (INTA)
spelling Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matricesJurcic, Esteban JavierVillalba, Pamela VictoriaPathauer, Pablo SantiagoPalazzini, DinoOberschelp, Gustavo Pedro JavierHarrand, LeonelGarcia, Martin NahuelAguirre, Natalia CristinaAcuña, Cintia VanesaMartinez, Maria CarolinaRivas, Juan GabrielCisneros, Esteban F.Lopez, Juan AdolfoMarcucci Poltri, Susana NoemiMunilla, SebastianCappa, Eduardo PabloEucalyptusGenéticaGeneticsEucalyptus dunniigenomic predictionpredicción genómicassGBLUPEucalyptus L’Hér. (Myrtaceae) is the most valuable and globally planted forest tree genus. Fast growth, adaptability to a broad diversity of tropical and subtropical regions, combined with versatile wood properties for energy, solid products, pulp, and paper have warranted their outstanding position in current world forestry (de Lima et al. 2019). Eucalyptus dunnii Maiden (hereafter E. dunnii) has become increasingly used in commercial afforestation due to its combined good performance for growth, stem straightness, and frost tolerance, together with suitable wood density and pulp yield. In a broad sense, genomic selection (GS) is a family of statistical methods developed for predicting the breeding values of nonphenotyped individuals with the assistance of a large number of molecular markers widespread distributed throughout the genome (Meuwissen et al. 2001). These methods exploit cosegregation between markers and quantitative trait loci (QTL) in linkage disequilibrium (LD). In forest trees, GS is of particular benefit due to the extended breeding cycles caused by delayed reproductive maturity and the need for early selection of traits that express late in life (Mphahlele et al. 2020). In this context, GS has a potentially substantial impact on the rate of genetic gain by increasing the intensity and accuracy of selection and, particularly, by shortening the generational interval (Grattapaglia et al. 2018). The genomic best linear unbiased prediction (GBLUP) is one of the most commonly GS methods. It is basically a variant of the standard BLUP method (hereafter ABLUP, cf. Henderson 1984), where the pedigree-based numerator relationship matrix (Amatrix) is replaced by a genomic relationship matrix (G-matrix, e.g., Habier et al. 2013). Many empirical studies with forest tree species have shown that GBLUP is a very promising approach for tree breeding (e.g., Mphahlele et al. 2020; Resende et al. 2017; Lenz et al. 2019). However, to our knowledge, only two of them have directly investigated the efficiency of genomic prediction using only genotyped trees in E. dunnii through GBLUP (Naidoo et al. 2018; Jones et al. 2019).Fil: Jurcic, Esteban J. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Villalba, Pamela Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pathauer, Pablo Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; ArgentinaFil: Palazzini, Dino A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Oberschelp, Gustavo Pedro Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; ArgentinaFil: Harrand, Leonel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; ArgentinaFil: Garcia, Martin Nahuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Aguirre, Natalia Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Acuña, Cintia Vanesa. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martinez, Maria Carolina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rivas, Juan Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cisneros, Esteban F. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Forestales; ArgentinaFil: Lopez, Juan Adolfo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bella Vista; ArgentinaFil: Marcucci Poltri, Susana Noemi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Munilla, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaNature2021-11-10T11:31:46Z2021-11-10T11:31:46Z2021-06-18info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/10732https://www.nature.com/articles/s41437-021-00450-91365-25400018-067Xhttps://doi.org/10.1038/s41437-021-00450-9Heredity 127 (2) : 176-189 (2021)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/PNFOR-1104062/AR./Mejoramiento genético de especies forestales introducidas para usos de alto valor.info:eu-repograntAgreement/INTA/PNFOR-1104064/AR./Aplicación de herramientas moleculares para el uso y la conservación de la diversidad genética forestal.info:eu-repo/semantics/restrictedAccess2025-09-29T13:45:24Zoai:localhost:20.500.12123/10732instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:45:24.363INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices
title Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices
spellingShingle Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices
Jurcic, Esteban Javier
Eucalyptus
Genética
Genetics
Eucalyptus dunnii
genomic prediction
predicción genómica
ssGBLUP
title_short Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices
title_full Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices
title_fullStr Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices
title_full_unstemmed Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices
title_sort Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices
dc.creator.none.fl_str_mv Jurcic, Esteban Javier
Villalba, Pamela Victoria
Pathauer, Pablo Santiago
Palazzini, Dino
Oberschelp, Gustavo Pedro Javier
Harrand, Leonel
Garcia, Martin Nahuel
Aguirre, Natalia Cristina
Acuña, Cintia Vanesa
Martinez, Maria Carolina
Rivas, Juan Gabriel
Cisneros, Esteban F.
Lopez, Juan Adolfo
Marcucci Poltri, Susana Noemi
Munilla, Sebastian
Cappa, Eduardo Pablo
author Jurcic, Esteban Javier
author_facet Jurcic, Esteban Javier
Villalba, Pamela Victoria
Pathauer, Pablo Santiago
Palazzini, Dino
Oberschelp, Gustavo Pedro Javier
Harrand, Leonel
Garcia, Martin Nahuel
Aguirre, Natalia Cristina
Acuña, Cintia Vanesa
Martinez, Maria Carolina
Rivas, Juan Gabriel
Cisneros, Esteban F.
Lopez, Juan Adolfo
Marcucci Poltri, Susana Noemi
Munilla, Sebastian
Cappa, Eduardo Pablo
author_role author
author2 Villalba, Pamela Victoria
Pathauer, Pablo Santiago
Palazzini, Dino
Oberschelp, Gustavo Pedro Javier
Harrand, Leonel
Garcia, Martin Nahuel
Aguirre, Natalia Cristina
Acuña, Cintia Vanesa
Martinez, Maria Carolina
Rivas, Juan Gabriel
Cisneros, Esteban F.
Lopez, Juan Adolfo
Marcucci Poltri, Susana Noemi
Munilla, Sebastian
Cappa, Eduardo Pablo
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Eucalyptus
Genética
Genetics
Eucalyptus dunnii
genomic prediction
predicción genómica
ssGBLUP
topic Eucalyptus
Genética
Genetics
Eucalyptus dunnii
genomic prediction
predicción genómica
ssGBLUP
dc.description.none.fl_txt_mv Eucalyptus L’Hér. (Myrtaceae) is the most valuable and globally planted forest tree genus. Fast growth, adaptability to a broad diversity of tropical and subtropical regions, combined with versatile wood properties for energy, solid products, pulp, and paper have warranted their outstanding position in current world forestry (de Lima et al. 2019). Eucalyptus dunnii Maiden (hereafter E. dunnii) has become increasingly used in commercial afforestation due to its combined good performance for growth, stem straightness, and frost tolerance, together with suitable wood density and pulp yield. In a broad sense, genomic selection (GS) is a family of statistical methods developed for predicting the breeding values of nonphenotyped individuals with the assistance of a large number of molecular markers widespread distributed throughout the genome (Meuwissen et al. 2001). These methods exploit cosegregation between markers and quantitative trait loci (QTL) in linkage disequilibrium (LD). In forest trees, GS is of particular benefit due to the extended breeding cycles caused by delayed reproductive maturity and the need for early selection of traits that express late in life (Mphahlele et al. 2020). In this context, GS has a potentially substantial impact on the rate of genetic gain by increasing the intensity and accuracy of selection and, particularly, by shortening the generational interval (Grattapaglia et al. 2018). The genomic best linear unbiased prediction (GBLUP) is one of the most commonly GS methods. It is basically a variant of the standard BLUP method (hereafter ABLUP, cf. Henderson 1984), where the pedigree-based numerator relationship matrix (Amatrix) is replaced by a genomic relationship matrix (G-matrix, e.g., Habier et al. 2013). Many empirical studies with forest tree species have shown that GBLUP is a very promising approach for tree breeding (e.g., Mphahlele et al. 2020; Resende et al. 2017; Lenz et al. 2019). However, to our knowledge, only two of them have directly investigated the efficiency of genomic prediction using only genotyped trees in E. dunnii through GBLUP (Naidoo et al. 2018; Jones et al. 2019).
Fil: Jurcic, Esteban J. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Villalba, Pamela Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Pathauer, Pablo Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina
Fil: Palazzini, Dino A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Oberschelp, Gustavo Pedro Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina
Fil: Harrand, Leonel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina
Fil: Garcia, Martin Nahuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Aguirre, Natalia Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Acuña, Cintia Vanesa. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez, Maria Carolina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rivas, Juan Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cisneros, Esteban F. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Forestales; Argentina
Fil: Lopez, Juan Adolfo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bella Vista; Argentina
Fil: Marcucci Poltri, Susana Noemi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Munilla, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Eucalyptus L’Hér. (Myrtaceae) is the most valuable and globally planted forest tree genus. Fast growth, adaptability to a broad diversity of tropical and subtropical regions, combined with versatile wood properties for energy, solid products, pulp, and paper have warranted their outstanding position in current world forestry (de Lima et al. 2019). Eucalyptus dunnii Maiden (hereafter E. dunnii) has become increasingly used in commercial afforestation due to its combined good performance for growth, stem straightness, and frost tolerance, together with suitable wood density and pulp yield. In a broad sense, genomic selection (GS) is a family of statistical methods developed for predicting the breeding values of nonphenotyped individuals with the assistance of a large number of molecular markers widespread distributed throughout the genome (Meuwissen et al. 2001). These methods exploit cosegregation between markers and quantitative trait loci (QTL) in linkage disequilibrium (LD). In forest trees, GS is of particular benefit due to the extended breeding cycles caused by delayed reproductive maturity and the need for early selection of traits that express late in life (Mphahlele et al. 2020). In this context, GS has a potentially substantial impact on the rate of genetic gain by increasing the intensity and accuracy of selection and, particularly, by shortening the generational interval (Grattapaglia et al. 2018). The genomic best linear unbiased prediction (GBLUP) is one of the most commonly GS methods. It is basically a variant of the standard BLUP method (hereafter ABLUP, cf. Henderson 1984), where the pedigree-based numerator relationship matrix (Amatrix) is replaced by a genomic relationship matrix (G-matrix, e.g., Habier et al. 2013). Many empirical studies with forest tree species have shown that GBLUP is a very promising approach for tree breeding (e.g., Mphahlele et al. 2020; Resende et al. 2017; Lenz et al. 2019). However, to our knowledge, only two of them have directly investigated the efficiency of genomic prediction using only genotyped trees in E. dunnii through GBLUP (Naidoo et al. 2018; Jones et al. 2019).
publishDate 2021
dc.date.none.fl_str_mv 2021-11-10T11:31:46Z
2021-11-10T11:31:46Z
2021-06-18
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dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/10732
https://www.nature.com/articles/s41437-021-00450-9
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https://doi.org/10.1038/s41437-021-00450-9
url http://hdl.handle.net/20.500.12123/10732
https://www.nature.com/articles/s41437-021-00450-9
https://doi.org/10.1038/s41437-021-00450-9
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0018-067X
dc.language.none.fl_str_mv eng
language eng
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info:eu-repograntAgreement/INTA/PNFOR-1104064/AR./Aplicación de herramientas moleculares para el uso y la conservación de la diversidad genética forestal.
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publisher.none.fl_str_mv Nature
dc.source.none.fl_str_mv Heredity 127 (2) : 176-189 (2021)
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