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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
16/01/2014 |
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Data da última atualização: |
09/02/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FONSECA, M. DAS G.; SILVA, S. E. B.; AUAD, A. M.; PAIVA, I. G.; BORGES, C. A. V. |
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Afiliação: |
MARCY DAS GRAÇAS FONSECA, UFJF; SANDRA E. B. SILVA, UFLA; ALEXANDER MACHADO AUAD, CNPGL; IRIS G. PAIVA, UFLA; CRISTIANO AMANCIO VIEIRA BORGES, CNPGL. |
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Título: |
Mating behavior of Mahanarva spectabilis (Hemiptera: cercopidae) under laboratory conditions. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Journal of Insect Behavior, v. 26, p. 824-831, 2013. |
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DOI: |
http://doi.org/10.1007/s10905-013-9394-4 |
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Idioma: |
Inglês |
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Conteúdo: |
The mating behavior of Mahanarva spectabilis (Distant) was studied under laboratory conditions. Virgin adults were individually maintained in transparent cages for observation of the mating sequence. Mating behavior of 23 couples was monitored during the photophase portion of the day. Copulation was observed in 17 of the 23 pairs. The mating sequence includes male approaching the female, mounting of the female, and copulation. Of the 17 mating pairs, 70.6 % mated on the second day after emergence. Most of the copulations began between 8 and 9 h after the onset of photophase. The average duration of copulation was 268 ± 24.9 min, and most pairs mated once or twice during their lifetimes. The mating behavior of M. spectabilis is important to understand because it determines the age and ideal time for further behavioral testing, which is essential for determining the cues involved in the communication system of the species. |
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Palavras-Chave: |
Duração da copulação; Número de copulações; Tempo de copulação. |
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Thesagro: |
Cigarrinha. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 01665naa a2200229 a 4500
001 1976410
005 2024-02-09
008 2013 bl uuuu u00u1 u #d
024 7 $ahttp://doi.org/10.1007/s10905-013-9394-4$2DOI
100 1 $aFONSECA, M. DAS G.
245 $aMating behavior of Mahanarva spectabilis (Hemiptera$bcercopidae) under laboratory conditions.$h[electronic resource]
260 $c2013
520 $aThe mating behavior of Mahanarva spectabilis (Distant) was studied under laboratory conditions. Virgin adults were individually maintained in transparent cages for observation of the mating sequence. Mating behavior of 23 couples was monitored during the photophase portion of the day. Copulation was observed in 17 of the 23 pairs. The mating sequence includes male approaching the female, mounting of the female, and copulation. Of the 17 mating pairs, 70.6 % mated on the second day after emergence. Most of the copulations began between 8 and 9 h after the onset of photophase. The average duration of copulation was 268 ± 24.9 min, and most pairs mated once or twice during their lifetimes. The mating behavior of M. spectabilis is important to understand because it determines the age and ideal time for further behavioral testing, which is essential for determining the cues involved in the communication system of the species.
650 $aCigarrinha
653 $aDuração da copulação
653 $aNúmero de copulações
653 $aTempo de copulação
700 1 $aSILVA, S. E. B.
700 1 $aAUAD, A. M.
700 1 $aPAIVA, I. G.
700 1 $aBORGES, C. A. V.
773 $tJournal of Insect Behavior$gv. 26, p. 824-831, 2013.
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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
16/12/2019 |
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Data da última atualização: |
16/12/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
SILVEIRA, L. S.; MARTINS FILHO, S.; AZEVEDO, C. F.; BARBOSA, E. C.; RESENDE, M. D. V. de; TAKAHASHI, E. K. |
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Afiliação: |
L. S. Silveira, UFV; S. Martins Filho, UFV; C. F. Azevedo, UFV; E. C. Barbosa, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; E. K. Takahashi, CENIBRA. |
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Título: |
Bayesian models applied to genomic selection for categorical traits. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 18, n. 4: gmr18490, 2019. 10 p. |
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DOI: |
10.4238/gmr18490 |
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Idioma: |
Inglês |
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Conteúdo: |
We compared two statistical methodologies applied to genetic and genomic analyses of categorical traits. The first one consists of a Bayesian approach to the Bayesian Linear Mixed Model (BLMM), which addresses the statistical problems of genomic prediction. The second methodology, called Bayesian Generalized Linear Mixed Model (BGLMM) is similar, but it is used when the distribution of the response variable is not Gaussian, as in the case of disease resistance phenotype categories. These models were compared according to predictive ability, bias, computational time and cross validation error rate (CVER). Additionally, an alternative classification method for the BLMM was proposed, which allowed us to obtain the CVER for this model. Estimates of the genetic parameters were obtained using BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator) and Bayesian G-BLUP (Genomic Best Linear Unbiased Prediction) estimation methods applied to BLMM and BGLMM. The models were applied in two scenarios, with two and four classes for the phenotype of resistance to rust disease caused by the pathogen Puccinia psidii and classified as reaction types (two classes) and infection levels (four classes) recorded for 559 trees of Eucalyptus urophylla with 24,806 SNP markers. Modeling this trait through SNPs allow the next generation of plants to be selected early, reducing time and costs. We found the same predictive ability for both models and a bias value closer to the ideal for BLMM (GBLUP). The BGLMM had the best CVER (0.29 against 0.32 and 0.47 against 0.51 for 2 and 4 categories, respectively), BLMM had a three times shorter computational time, and though BLMM is not the most appropriate model for handling categorical data, this model presented similar responses to BGLMM. Thus, we consider it as an appropriate alternative for categorical data modeling. MenosWe compared two statistical methodologies applied to genetic and genomic analyses of categorical traits. The first one consists of a Bayesian approach to the Bayesian Linear Mixed Model (BLMM), which addresses the statistical problems of genomic prediction. The second methodology, called Bayesian Generalized Linear Mixed Model (BGLMM) is similar, but it is used when the distribution of the response variable is not Gaussian, as in the case of disease resistance phenotype categories. These models were compared according to predictive ability, bias, computational time and cross validation error rate (CVER). Additionally, an alternative classification method for the BLMM was proposed, which allowed us to obtain the CVER for this model. Estimates of the genetic parameters were obtained using BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator) and Bayesian G-BLUP (Genomic Best Linear Unbiased Prediction) estimation methods applied to BLMM and BGLMM. The models were applied in two scenarios, with two and four classes for the phenotype of resistance to rust disease caused by the pathogen Puccinia psidii and classified as reaction types (two classes) and infection levels (four classes) recorded for 559 trees of Eucalyptus urophylla with 24,806 SNP markers. Modeling this trait through SNPs allow the next generation of plants to be selected early, reducing time and costs. We found the same predictive ability for both models and a bias value closer to the ideal for BLMM (G... Mostrar Tudo |
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Palavras-Chave: |
Bayesian inference; Statistical methods. |
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Thesagro: |
Melhoramento Genético Vegetal. |
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Thesaurus NAL: |
Genetic improvement; Plant breeding. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02649naa a2200253 a 4500
001 2116962
005 2019-12-16
008 2019 bl uuuu u00u1 u #d
024 7 $a10.4238/gmr18490$2DOI
100 1 $aSILVEIRA, L. S.
245 $aBayesian models applied to genomic selection for categorical traits.$h[electronic resource]
260 $c2019
520 $aWe compared two statistical methodologies applied to genetic and genomic analyses of categorical traits. The first one consists of a Bayesian approach to the Bayesian Linear Mixed Model (BLMM), which addresses the statistical problems of genomic prediction. The second methodology, called Bayesian Generalized Linear Mixed Model (BGLMM) is similar, but it is used when the distribution of the response variable is not Gaussian, as in the case of disease resistance phenotype categories. These models were compared according to predictive ability, bias, computational time and cross validation error rate (CVER). Additionally, an alternative classification method for the BLMM was proposed, which allowed us to obtain the CVER for this model. Estimates of the genetic parameters were obtained using BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator) and Bayesian G-BLUP (Genomic Best Linear Unbiased Prediction) estimation methods applied to BLMM and BGLMM. The models were applied in two scenarios, with two and four classes for the phenotype of resistance to rust disease caused by the pathogen Puccinia psidii and classified as reaction types (two classes) and infection levels (four classes) recorded for 559 trees of Eucalyptus urophylla with 24,806 SNP markers. Modeling this trait through SNPs allow the next generation of plants to be selected early, reducing time and costs. We found the same predictive ability for both models and a bias value closer to the ideal for BLMM (GBLUP). The BGLMM had the best CVER (0.29 against 0.32 and 0.47 against 0.51 for 2 and 4 categories, respectively), BLMM had a three times shorter computational time, and though BLMM is not the most appropriate model for handling categorical data, this model presented similar responses to BGLMM. Thus, we consider it as an appropriate alternative for categorical data modeling.
650 $aGenetic improvement
650 $aPlant breeding
650 $aMelhoramento Genético Vegetal
653 $aBayesian inference
653 $aStatistical methods
700 1 $aMARTINS FILHO, S.
700 1 $aAZEVEDO, C. F.
700 1 $aBARBOSA, E. C.
700 1 $aRESENDE, M. D. V. de
700 1 $aTAKAHASHI, E. K.
773 $tGenetics and Molecular Research$gv. 18, n. 4: gmr18490, 2019. 10 p.
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