|
|
Registro completo
|
Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
06/06/2019 |
Actualizado : |
14/08/2019 |
Tipo de producción científica : |
Poster |
Autor : |
PEZARD, J.; FERNANDEZ, P.; PEREYRA, S.; QUINCKE, M.; SAINT-PIERRE, C.; SINGH, P.K.; AZZIMONTI, G. |
Afiliación : |
AgroParisTech, Paris, France.; PETER DENNIS FERNANDEZ GRAF, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SILVIA ANTONIA PEREYRA CORREA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA SAINT-PIERRE, International Maize and Wheat Improvement Center (CIMMYT), El Batán, México.; PAWAN K. SINGH, International Maize and Wheat Improvement Center (CIMMYT), El Batán, México.; GUSTAVO AZZIMONTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Adapting automated image analysis to breeding programs constraints for the characterization of the resistance to leaf rust and other diseases. |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
In: Proceedings of the International Cereal Rusts and Powdery Mildew Conference (ICRPMC): Skukuza, South Africa, 23-26 september 2018. |
Idioma : |
Inglés |
Contenido : |
Description:
Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and positively with the VS measured for the three diseases. Host responses estimated by AIA were the same as determined visually, (error<5%). AIA was fast, a mean of 214 leaves/hour analyzed, taking into account the adjustments of color thresholds and the validation of AIA. However, the time to prepare and scan the leaves was higher than the VS: a mean of 205 lines could be scanned per person/day while a mean of 402 lines per person/day could be visually scored. Adjustments to the scan methodology are being carried out to enhance the speed at this step. Nevertheless, AIA can be a performing alternative to VS in limited panels or mapping populations that undergo QTL analysis, where precise measurements of quantitative resistance variables are required to detect QTL with moderate effects and QTL interactions. MenosDescription:
Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and posit... Presentar Todo |
Palabras claves : |
INIA-CIMMYT; PLATAFORMA FENOTIPADO DE TRIGO; RUST DISEASE; WHEAT. |
Thesagro : |
ENFERMEDADES DE LAS PLANTAS; Trigo. |
Asunto categoría : |
H20 Enfermedades de las plantas |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13100/1/PosterPezardetalICRPMC2018.pdf
|
Marc : |
LEADER 03183nam a2200253 a 4500 001 1059824 005 2019-08-14 008 2018 bl uuuu u00u1 u #d 100 1 $aPEZARD, J. 245 $aAdapting automated image analysis to breeding programs constraints for the characterization of the resistance to leaf rust and other diseases.$h[electronic resource] 260 $aIn: Proceedings of the International Cereal Rusts and Powdery Mildew Conference (ICRPMC): Skukuza, South Africa, 23-26 september 2018.$c2018 520 $aDescription: Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and positively with the VS measured for the three diseases. Host responses estimated by AIA were the same as determined visually, (error<5%). AIA was fast, a mean of 214 leaves/hour analyzed, taking into account the adjustments of color thresholds and the validation of AIA. However, the time to prepare and scan the leaves was higher than the VS: a mean of 205 lines could be scanned per person/day while a mean of 402 lines per person/day could be visually scored. Adjustments to the scan methodology are being carried out to enhance the speed at this step. Nevertheless, AIA can be a performing alternative to VS in limited panels or mapping populations that undergo QTL analysis, where precise measurements of quantitative resistance variables are required to detect QTL with moderate effects and QTL interactions. 650 $aENFERMEDADES DE LAS PLANTAS 650 $aTrigo 653 $aINIA-CIMMYT 653 $aPLATAFORMA FENOTIPADO DE TRIGO 653 $aRUST DISEASE 653 $aWHEAT 700 1 $aFERNANDEZ, P. 700 1 $aPEREYRA, S. 700 1 $aQUINCKE, M. 700 1 $aSAINT-PIERRE, C. 700 1 $aSINGH, P.K. 700 1 $aAZZIMONTI, G.
Descargar
Esconder MarcPresentar Marc Completo |
Registro original : |
INIA La Estanzuela (LE) |
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
|
Registro completo
|
Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
17/04/2024 |
Actualizado : |
17/04/2024 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
JURBURG, S.D.; ÁLVAREZ BLANCO, M.J.; CHATZINOTAS, A.; KAZEM, A.; KÖNIG-RIES, B.; BABIN, D.; SMALLA, K.; CERECETTO, V.; FERNANDEZ-GNECCO, G.; COVACEVICH, F.; VIRUEL, E.; BERNASCHINA, Y.; LEONI, C.; GARAYCOCHEA, S.; TERRA, J.A.; FRESIA, P.; FIGUEROLA, E.L.M.; WALL, L.G.; COVELLI, J.M.; AGNELLO, A.C.; NIETO, E.E.; FESTA, S.; DOMINICI, L.E,; ALLEGRINI, M.; ZABALOY, M.C.; MORALES, M.E.; ERIJMAN, L.; CONIGLIO, A´.; CASSÁN, F.D.; NIEVAS, S.; ROLDÁN, D.M.; MENES, R.; VAZ JAURI, P.; MARRERO, C.S.; MASSA, A.M.; REVETRIA, M.A.M.; FERNÁNDEZ-SCAVINO, A.; PEREIRA-MORA, L.; MARTÍNEZ, S.; FRENE, J.P. |
Afiliación : |
STEPHANIE D. JURBURG, Department of Applied Microbial Ecology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, 04318, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, 04103, Germany; MARÍA J. ÁLVAREZ BLANCO, Department of Applied Microbial Ecology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, 04318, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, 04103, Germany; ANTONIS CHATZINOTAS, Dept. Applied Microbial Ecology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; Institute Biology, Leipzig University, Leipzig, Germany; ANAHITA KAZEM, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; Department of Mathematics and Computer Science, Friedrich Schiller University Jena Thüringen, Jena, Germany; BIRGITTA KÖNIG-RIES, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; Department of Mathematics and Computer Science, Friedrich Schiller University Jena Thüringen, Jena, Germany; DOREEN BABIN, Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Epidemiology and Pathogen Diagnostics, Braunschweig, 38104, Germany; KORNELIA SMALLA, Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Epidemiology and Pathogen Diagnostics, Braunschweig, 38104, Germany; MARÍA VICTORIA CERECETTO GONZÁLEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Epidemiology and Pathogen Diagnostics, Braunschweig, 38104, Germany; GABRIELA FERNANDEZ-GNECCO, Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Epidemiology and Pathogen Diagnostics, Braunschweig, 38104, Germany; INBIOTEC-CONICET, Buenos Aires, Mar del Plata, Argentina; FERNANDA COVACEVICH, INBIOTEC-CONICET, Buenos Aires, Mar del Plata, Argentina; INTA, EEA Balcarce, Buenos Aires, Balcarce, Argentina; EMILCE VIRUEL, Instituto de Investigación Animal del Chaco Semiárido (IIACS), Centro de Investigaciones Agropecuarias (CIAP), Instituto Nacional de Tecnología Agropecuaria (INTA), Tucumán, Argentina; YESICA STEFANIA BERNASCHINA CORREA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA LEONI VELAZCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SILVIA RAQUEL GARAYCOCHEA SOLSONA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO FRESIA, Unidad Mixta UMPI, Institut Pasteur Montevideo + INIA, Montevideo, Uruguay; EVA LUCÍA MARGARITA FIGUEROLA, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina Instituto de Biociencias, Biotecnología y Biología Traslacional, Departamento de Fisiología y Biología Molecular y Celular, Facu; LUIS GABRIEL WALL, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina; Laboratorio de Bioquímica y Biología de Suelos, Centro de Bioquímica y Microbiología de Suelos, Universidad Nacional de Quilmes; JULIETA MARIANA COVELLI, Laboratorio de Bioquímica y Biología de Suelos, Centro de Bioquímica y Microbiología de Suelos, Universidad Nacional de Quilmes (UNQ), Buenos Aires, Bernal, Argentina; ANA CAROLINA AGNELLO, Centro de Investigación y Desarrollo en Fermentaciones Industriales (CINDEFI, CONICET-UNLP), La Plata, Argentina; ESTEBAN EMANUEL NIETO, Centro de Investigación y Desarrollo en Fermentaciones Industriales (CINDEFI, CONICET-UNLP), La Plata, Argentina; SABRINA FESTA, Centro de Investigación y Desarrollo en Fermentaciones Industriales (CINDEFI, CONICET-UNLP), La Plata, Argentina; LINA EDITH DOMINICI, Centro de Investigación y Desarrollo en Tecnología de Pinturas y Recubrimientos (CIDEPINT, CICPBA-CONICET-UNLP), La Plata, A; MARCO ALLEGRINI, Centro de Recursos Naturales Renovables de la Zona Semiárida (CERZOS, CONICET-UNS), Buenos Aires, Bahía Blanca, Argentina; MARÍA CELINA ZABALOY, Centro de Recursos Naturales Renovables de la Zona Semiárida (CERZOS, CONICET-UNS), Buenos Aires, Bahía Blanca, Argentina; Departamento de Agronomía, Universidad Nacional del Sur (UNS); MARIANELA ESTEFANÍA MORALES, Centro de Recursos Naturales Renovables de la Zona Semiárida (CERZOS, CONICET-UNS), Buenos Aires, Bahía Blanca, Argentina; Departamento de Agronomía, Universidad Nacional del Sur (UNS); LEONARDO ERIJMAN, Instituto de Investigaciones en Ingeniería Genética y Biología Molecular, Dr Héctor N Torres' (INGEBI-CONICET)T, Buenos Aires, Argentina; Departamento de Fisiología, Biología Molecular y Celular Dr Hé; ANAHI CONIGLIO, Laboratorio de Fisiología Vegetal y de la Interacción Planta Microorganismo (LFVIPM), Instituto de Investigaciones Agrobiotecnológicas (INIAB-CONICET), Facultad de Ciencias Exactas Físico-Químicas y Naturales, Universidad Nacional de Río C; FABRICIO DARIO CASSÁN, Laboratorio de Fisiología Vegetal y de la Interacción Planta Microorganismo (LFVIPM), Instituto de Investigaciones Agrobiotecnológicas (INIAB-CONICET), Facultad de Ciencias Exactas Físico-Químicas y Naturales, Universidad Nacional; SOFIA NIEVAS, Laboratorio de Fisiología Vegetal y de la Interacción Planta Microorganismo (LFVIPM), Instituto de Investigaciones Agrobiotecnológicas (INIAB-CONICET), Facultad de Ciencias Exactas Físico-Químicas y Naturales, Universidad Nacional de Río Cua; DIEGO M. ROLDÁN, Departamento de Bioquímica y Genómica Microbianas, Instituto de Investigaciones Biológicas Clemente Estable (IIBCE), Ministerio de Educación y Cultura, Montevideo, Uruguay; Laboratorio de Ecología Micr; RODOLFO MENES, Laboratorio de Ecología Microbiana Medioambiental, Facultad de Química, Facultad de Ciencias, Universidad de la República (UdelaR), Montevideo, Uruguay; Laboratorio de Microbiología, Unidad Asociada del; PATRICIA VAZ JAURI, Laboratorio de Microbiología de Suelos, Instituto de Ecología y Ciencias Ambientales, Facultad de Ciencias, Universidad de la República (UdelaR), Montevideo, Uruguay; Laboratorio de Interacción Plan; CARLA SILVA MARRERO, Laboratorio de Microbiología de Suelos, Instituto de Ecología y Ciencias Ambientales, Facultad de Ciencias, Universidad de la República (UdelaR), Montevideo, Uruguay; ADRIANA MONTAÑEZ MASSA, Laboratorio de Microbiología de Suelos, Instituto de Ecología y Ciencias Ambientales, Facultad de Ciencias, Universidad de la República (UdelaR), Montevideo, Uruguay; MARÍA ADELINA MOREL REVETRIA, Laboratorio de Microbiología de Suelos, Instituto de Ecología y Ciencias Ambientales, Facultad de Ciencias, Universidad de la República (UdelaR), Montevideo, Uruguay; ANA FERNÁNDEZ-SCAVINO, Laboratorio de Ecología Microbiana y Microbiología Ambiental, Departamento de Biociencias, Facultad de Química, Universidad de la República (UdelarR), Montevideo, Uruguay; LUCIANA PEREIRA-MORA, Laboratorio de Ecología Microbiana y Microbiología Ambiental, Departamento de Biociencias, Facultad de Química, Universidad de la República (UdelarR), Montevideo, Uruguay; SOLEDAD MARTÍNEZ, Laboratorio de Biotecnología, Departamento de Biociencias, Unidad de Análisis de Agua, Facultad de Química, Universidad de la República (UdelaR), Montevideo, Uruguay; JUAN PABLO FRENE, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, United Kingdom. |
Título : |
Datathons: fostering equitability in data reuse in ecology. |
Complemento del título : |
Science & Society. |
Fecha de publicación : |
2024 |
Fuente / Imprenta : |
Trends in Microbiology. 2024. https://doi.org/10.1016/j.tim.2024.02.010 -- OPEN ACCESS [Article in Press] |
ISSN : |
0966-842X |
DOI : |
10.1016/j.tim.2024.02.010 |
Idioma : |
Inglés |
Notas : |
Article history: Available online 21 March 2024. -- Correspondence: Jurburg, S.D.; Department of Applied Microbial Ecology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany; email:s.d.jurburg@gmail.com -- LICENSE: Article under a Creative Commons license (http://creativecommons.org/licenses/by/4.0/ ) -- |
Contenido : |
ABSTRACT.- Approaches to rapidly collecting global biodiversity data are increasingly important, but biodiversity blind spots persist. We organized a three-day Datathon event to improve the openness of local biodiversity data and facilitate data reuse by local researchers. The first Datathon, organized among microbial ecologists in Uruguay and Argentina assembled the largest microbiome dataset in the region to date and formed collaborative consortia for microbiome data synthesis. © 2024 The Author(s) |
Palabras claves : |
ÁREA DE RECURSOS NATURALES, PRODUCCIÓN Y AMBIENTE - INIA; ÁREA MEJORAMIENTO GENÉTICO Y BIOTECNOLOGÍA VEGETAL - INIA; DATATHONS; SISTEMA ARROZ-GANADERÍA - INIA; SISTEMA VEGETAL INTENSIVO - INIA; The Datathon 2022 Consortium. |
Asunto categoría : |
-- |
URL : |
https://www.sciencedirect.com/science/article/pii/S0966842X24000507/pdf
|
Marc : |
LEADER 02820naa a2200697 a 4500 001 1064595 005 2024-04-17 008 2024 bl uuuu u00u1 u #d 022 $a0966-842X 024 7 $a10.1016/j.tim.2024.02.010$2DOI 100 1 $aJURBURG, S.D. 245 $aDatathons$bfostering equitability in data reuse in ecology.$h[electronic resource] 260 $c2024 500 $aArticle history: Available online 21 March 2024. -- Correspondence: Jurburg, S.D.; Department of Applied Microbial Ecology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany; email:s.d.jurburg@gmail.com -- LICENSE: Article under a Creative Commons license (http://creativecommons.org/licenses/by/4.0/ ) -- 520 $aABSTRACT.- Approaches to rapidly collecting global biodiversity data are increasingly important, but biodiversity blind spots persist. We organized a three-day Datathon event to improve the openness of local biodiversity data and facilitate data reuse by local researchers. The first Datathon, organized among microbial ecologists in Uruguay and Argentina assembled the largest microbiome dataset in the region to date and formed collaborative consortia for microbiome data synthesis. © 2024 The Author(s) 653 $aÁREA DE RECURSOS NATURALES, PRODUCCIÓN Y AMBIENTE - INIA 653 $aÁREA MEJORAMIENTO GENÉTICO Y BIOTECNOLOGÍA VEGETAL - INIA 653 $aDATATHONS 653 $aSISTEMA ARROZ-GANADERÍA - INIA 653 $aSISTEMA VEGETAL INTENSIVO - INIA 653 $aThe Datathon 2022 Consortium 700 1 $aÁLVAREZ BLANCO, M.J. 700 1 $aCHATZINOTAS, A. 700 1 $aKAZEM, A. 700 1 $aKÖNIG-RIES, B. 700 1 $aBABIN, D. 700 1 $aSMALLA, K. 700 1 $aCERECETTO, V. 700 1 $aFERNANDEZ-GNECCO, G. 700 1 $aCOVACEVICH, F. 700 1 $aVIRUEL, E. 700 1 $aBERNASCHINA, Y. 700 1 $aLEONI, C. 700 1 $aGARAYCOCHEA, S. 700 1 $aTERRA, J.A. 700 1 $aFRESIA, P. 700 1 $aFIGUEROLA, E.L.M. 700 1 $aWALL, L.G. 700 1 $aCOVELLI, J.M. 700 1 $aAGNELLO, A.C. 700 1 $aNIETO, E.E. 700 1 $aFESTA, S. 700 1 $aDOMINICI, L.E, 700 1 $aALLEGRINI, M. 700 1 $aZABALOY, M.C. 700 1 $aMORALES, M.E. 700 1 $aERIJMAN, L. 700 1 $aCONIGLIO, A´. 700 1 $aCASSÁN, F.D. 700 1 $aNIEVAS, S. 700 1 $aROLDÁN, D.M. 700 1 $aMENES, R. 700 1 $aVAZ JAURI, P. 700 1 $aMARRERO, C.S. 700 1 $aMASSA, A.M. 700 1 $aREVETRIA, M.A.M. 700 1 $aFERNÁNDEZ-SCAVINO, A. 700 1 $aPEREIRA-MORA, L. 700 1 $aMARTÍNEZ, S. 700 1 $aFRENE, J.P. 773 $tTrends in Microbiology. 2024. https://doi.org/10.1016/j.tim.2024.02.010 -- OPEN ACCESS [Article in Press]
Descargar
Esconder MarcPresentar Marc Completo |
Registro original : |
INIA Las Brujas (LB) |
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
Expresión de búsqueda válido. Check! |
|
|