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Registros recuperados : 7 | |
1. | | KROLOW, T. K.; LUCAS, M.; HENRIQUES, A. L. Revisiting the Tabanid fauna (Diptera: Tabanidae) of Uruguay: notes on the species of the genus Tabanus Linnaeus, with the description of a new species. Neotropical Entomology, 2022, vol. 51, pages 447- 457. doi: https://doi.org/10.1007/s13744-022-00958-7 Article history: Received 5 July 2021; Accepted 1 April 2022; Published online 11 May 2022.
Corresponding author: Krolow, T.K.; Universidade Federal do Tocantins (UFT), Programa de Pós-Graduação em Biodiversidade, Ecologia e Conservação...Biblioteca(s): INIA Las Brujas. |
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3. | | LUCAS, M.; KROLOW, T.K.; RIET-CORREA, F.; BARROS, A.T.M.; KRÜGER, R.F.; SARAVIA, A.; MIRABALLES, C. Diversity and seasonality of horse flies (Diptera: Tabanidae) in Uruguay. Scientific Reports, 10, no. 401, 2020. OPEN ACCESS. Article history: Received24 October 2019 // Accepted29 December 2019 // Published15 January 2020. Corresponding author: cmiraballes@inia.org.uy // Acknowledgements: We would like to acknowledge Marcelo Alfonso for the help provided with...Biblioteca(s): INIA Tacuarembó. |
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4. | | MACHADO, M.; OLIVEIRA, L.G.S.; SCHILD, C.; BOABAID, F.; LUCAS, M.; BURONI, F.; CASTRO, M. B.; RIET-CORREA, F. Lantana camara poisoning in cattle that took refuge during a storm in a forest invaded by this plant. Toxicon, 2023, Volume 229, article 107124. https://doi.org/10.1016/j.toxicon.2023.107124 Article history: Received 8 March 2023; Received in revised form 7 April 2023; Accepted 10 April 2023; Available online 11 April 2023. -- Corresponding author: Riet-Correa, F.; Instituto Nacional de Investigación Agropecuaria, Plataforma...Biblioteca(s): INIA Las Brujas. |
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5. | | MIRABALLES, C.; BARROS, A.T.M.; LUCAS, M.; KLAFKE, G.M.; DOMINGUES, L.N.; RIET-CORREA, F. Susceptibility of field populations of Haematobia irritans to fipronil in Uruguay. Pesquisa Veterinaria Brasileira, 2021, Volume 41, Article number e06821. OPEN ACCESS. Doi: https://doi.org/10.1590/1678-5150-PVB-6821 Article history: 1 Received on December 4, 2020. Accepted for publication on December 16, 2020. Acknowledgments.- We would like to acknowledge Gonzalo Escayola, the
ranchers, the staff of the slaughterhouse and the interns who...Biblioteca(s): INIA Tacuarembó. |
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6. | | OLIVEIRA, L.G.S.; BOABAID, F.M.; KISIELIUS, V.; RASMUSSEN, L.H.; BURONI, F.; LUCAS, M.; SCHILD, C.; LÓPEZ, F.; MACHADO, M.; RIET-CORREA, F. Hemorrhagic diathesis in cattle due to consumption of Adiantopsis chlorophylla (Swartz) Fée (Pteridaceae). Toxicon: X, March 2020, Volume 5, Article number 100024. OPEN ACCESS. Doi: https://doi.org/10.1016/j.toxcx.2020.100024 Article history: Received 30 November 2019 / Revised 12 January 2020 / Accepted 13 January 2020 / Available online 23 January 2020.
Corresponding author: Franklin Riet-Correa; email: franklin.riet@pq.cnpq.br
The authors acknowledge the...Biblioteca(s): INIA Las Brujas. |
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7. | | RODRIGUES, G. D.; LUCAS, M.; ORTIZ, H. G.; SANTOS GONÇALVES, L. DOS; BLODORN, E.; DOMINGUES, W. B.; NUNES, L. S.; SARAVIA, A.; PARODI, P.; RIET-CORREA, F.; MENCHACA, A.; CAMPOS, C. V.; KROLOW, T. K.; KRÜGER, R. F. Molecular of Anaplasma marginale Theiler (Rickettsiales: Anaplasmataceae) in horseflies (Diptera: Tabanidae) in Uruguay. Scientific Reports, 2022, volume 12, issue 1, article 22460. OPEN ACCESS. doi: https://doi.org/10.1038/s41598-022-27067-0 Article history: Received 11 July 2022; Accepted 23 December 2022; Published 28 December 2022. -- Corresponding author: Rodrigues, G.D.; Ecology of Parasites and Vectors Group, Universidade Federal de Pelotas, Rio Grande do Sul, Brazil;...Biblioteca(s): INIA Las Brujas. |
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Registros recuperados : 7 | |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha actual : |
06/12/2019 |
Actualizado : |
05/09/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
BERRO, I.; LADO, B.; NALIN, R.S.; QUINCKE, M.; GUTIÉRREZ, L. |
Afiliación : |
Dep. of Agronomy, Univ. of Wisconsin, Madison, USA.; Facultad de Agronomía, Univ. de la República, Montevideo, Uruguay.; Dep. of Agronomy, Univ. of Wisconsin, Madison, USA.; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Dep. of Agronomy, Univ. of Wisconsin, Madison, USA./ Facultad de Agronomía, Univ. de la República, Montevideo, Uruguay. |
Título : |
Training population optimization for genomic selection. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
Plant Genome, November 2019, Volume 12, Issue 3, Article number 190028. OPEN ACCESS. DOI: https://doi.org/10.3835/plantgenome2019.04.0028 |
DOI : |
10.3835/plantgenome2019.04.0028 |
Idioma : |
Inglés |
Notas : |
Article histoty: Received 1 Apr. 2019. /Accepted 23 Sept. 2019. |
Contenido : |
ABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the
prediction model, the number and type of molecular markers, and the size and composition of the training population (TR).
Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was
to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum
L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization
strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies
to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering
both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic
selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in
populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individuals that were phenotyped and genotyped is called the TR (Heffner et al. 2009). MenosABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the
prediction model, the number and type of molecular markers, and the size and composition of the training population (TR).
Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was
to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum
L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization
strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies
to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering
both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic
selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in
populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individ... Presentar Todo |
Palabras claves : |
GENOMIC SELECTION; SELECCIÓN GENÓMICA. |
Thesagro : |
TRIGO; TRITICUM AESTIVUM. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16707/1/The-Plant-Genome-2019-Berro-Training-Population-Optimization-for-Genomic-Selection.pdf
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.3835/plantgenome2019.04.0028
|
Marc : |
LEADER 02385naa a2200241 a 4500 001 1060511 005 2022-09-05 008 2019 bl uuuu u00u1 u #d 024 7 $a10.3835/plantgenome2019.04.0028$2DOI 100 1 $aBERRO, I. 245 $aTraining population optimization for genomic selection.$h[electronic resource] 260 $c2019 500 $aArticle histoty: Received 1 Apr. 2019. /Accepted 23 Sept. 2019. 520 $aABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the prediction model, the number and type of molecular markers, and the size and composition of the training population (TR). Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individuals that were phenotyped and genotyped is called the TR (Heffner et al. 2009). 650 $aTRIGO 650 $aTRITICUM AESTIVUM 653 $aGENOMIC SELECTION 653 $aSELECCIÓN GENÓMICA 700 1 $aLADO, B. 700 1 $aNALIN, R.S. 700 1 $aQUINCKE, M. 700 1 $aGUTIÉRREZ, L. 773 $tPlant Genome, November 2019, Volume 12, Issue 3, Article number 190028. OPEN ACCESS. DOI: https://doi.org/10.3835/plantgenome2019.04.0028
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