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Registro Completo |
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Biblioteca(s): |
Embrapa Pesca e Aquicultura. |
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Data corrente: |
25/07/2025 |
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Data da última atualização: |
25/07/2025 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LEMOS, C. G.; GARCIA, B. F.; SILVA FILHO, M. S.; ARANGO, J. R.; BUTZGE, A. J.; SHIOTSUKI, L.; FREITAS, L. E. L.; REZENDE, F. P.; URBINATI, E. C.; ROSA, G. J. M.; HASHIMOTO, D. T. |
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Afiliação: |
CELMA G. LEMOS, UNIVERSIDADE ESTADUAL PAULISTA; BALTASAR F. GARCIA, UNIVERSIDADE ESTADUAL PAULISTA; MARCELO SOUZA SILVA FILHO, UNIVERSIDADE ESTADUAL PAULISTA; JAIRO R. ARANGO, UNIVERSIDADE ESTADUAL PAULISTA; ARNO J. BUTZGE, UNIVERSIDADE ESTADUAL PAULISTA; LUCIANA SHIOTSUKI, CNPASA; LUIZ EDUARDO LIMA DE FREITAS, CNPASA; FABRICIO PEREIRA REZENDE, CNPASA; ELISABETH C. URBINATI, UNIVERSIDADE ESTADUAL PAULISTA; GUILHERME J. M. ROSA, UNIVERSITY OF WISCONSIN; DIOGO T. HASHIMOTO, UNIVERSIDADE ESTADUAL PAULISTA. |
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Título: |
Deep learning approach for genetic selection of stress response in the Amazon fish Colossoma macropomum. |
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Ano de publicação: |
2025 |
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Fonte/Imprenta: |
Aquaculture, v. 609, 742848, 2025. |
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ISSN: |
0044-8486 |
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DOI: |
https://doi.org/10.1016/j.aquaculture.2025.742848 |
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Idioma: |
Inglês |
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Conteúdo: |
This study examined skin color variation in tambaqui (Colossoma macropomum) in response to stress, focusing on morphological and physiological color change mechanisms. A computer vision system (CVS) based on the DeepLab V3 model with ResNet-50 was developed to automate countershading intensity detection. Images from 3780 fish across two populations were used to train a model and estimate genetic parameters for countershading intensity. Morphological color changes were induced in confinement tanks, with countershading intensity observed after 10 days. Physiologically, the α-MSH hormone expanded melanophores by 80 %, intensifying countershading. The CVS achieved high accuracy (88.2 %) for large-scale phenotyping, with moderate to high heritability estimates for color phenotypes: 0.456 ± 0.122 for black pixel percentage, 0.494 ± 0.128 for mean pixel intensity, and 0.192 ± 0.059 for the number of pixels. Low correlations with growth traits suggest that countershading selection can occur without affecting growth, highlighting its potential in breeding programs to improve appearance and stress resilience. |
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Palavras-Chave: |
Breeding programs; Countershading. |
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Thesagro: |
Colossoma Macropomum; Método de Melhoramento; Peixe; Seleção Genética; Tambaqui. |
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Thesaurus Nal: |
Breeding; Computer vision; Stress response. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 02231naa a2200385 a 4500
001 2177495
005 2025-07-25
008 2025 bl uuuu u00u1 u #d
022 $a0044-8486
024 7 $ahttps://doi.org/10.1016/j.aquaculture.2025.742848$2DOI
100 1 $aLEMOS, C. G.
245 $aDeep learning approach for genetic selection of stress response in the Amazon fish Colossoma macropomum.$h[electronic resource]
260 $c2025
520 $aThis study examined skin color variation in tambaqui (Colossoma macropomum) in response to stress, focusing on morphological and physiological color change mechanisms. A computer vision system (CVS) based on the DeepLab V3 model with ResNet-50 was developed to automate countershading intensity detection. Images from 3780 fish across two populations were used to train a model and estimate genetic parameters for countershading intensity. Morphological color changes were induced in confinement tanks, with countershading intensity observed after 10 days. Physiologically, the α-MSH hormone expanded melanophores by 80 %, intensifying countershading. The CVS achieved high accuracy (88.2 %) for large-scale phenotyping, with moderate to high heritability estimates for color phenotypes: 0.456 ± 0.122 for black pixel percentage, 0.494 ± 0.128 for mean pixel intensity, and 0.192 ± 0.059 for the number of pixels. Low correlations with growth traits suggest that countershading selection can occur without affecting growth, highlighting its potential in breeding programs to improve appearance and stress resilience.
650 $aBreeding
650 $aComputer vision
650 $aStress response
650 $aColossoma Macropomum
650 $aMétodo de Melhoramento
650 $aPeixe
650 $aSeleção Genética
650 $aTambaqui
653 $aBreeding programs
653 $aCountershading
700 1 $aGARCIA, B. F.
700 1 $aSILVA FILHO, M. S.
700 1 $aARANGO, J. R.
700 1 $aBUTZGE, A. J.
700 1 $aSHIOTSUKI, L.
700 1 $aFREITAS, L. E. L.
700 1 $aREZENDE, F. P.
700 1 $aURBINATI, E. C.
700 1 $aROSA, G. J. M.
700 1 $aHASHIMOTO, D. T.
773 $tAquaculture$gv. 609, 742848, 2025.
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Registro original: |
Embrapa Pesca e Aquicultura (CNPASA) |
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