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 | Acesso ao texto completo restrito à biblioteca da Embrapa Recursos Genéticos e Biotecnologia. Para informações adicionais entre em contato com cenargen.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
16/08/2018 |
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Data da última atualização: |
04/01/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CARRETERO, G. P. B.; SARAIVA, G. K. V.; CAUZ, A. C. G.; RODRIGUES, M. A.; KIYOTA, S.; RISKE, K. A.; SANTOS, A. A. dos; PINATTO-BOTELHO, M. F.; BEMQUERER, M. P.; GUEIROS-FILHO, F. J.; CHAIMOVICH, H.; SCHREIER, S.; CUCCOVIA, I. M. |
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Afiliação: |
GUSTAVO P. B. CARRETERO, USP; GREICE K. V. SARAIVA, USP; ANA C. G. CAUZ, USP; MAGALI A. RODRIGUES; SUMIKA KIYOTA, INSTITUTO BIOLÓGICO; KARIN A. RISKE, UNIFESP; ALCINDO A. DOS SANTOS, USP; MARCOS F. PINATTO-BOTELHO, USP; MARCELO PORTO BEMQUERER, Cenargen; FREDERICO J. GUEIROS-FILHO, USP; HERNAN CHAIMOVICH, USP; SHIRLEY SCHREIER, USP; IOLANDA M. CUCCOVIA, USP. |
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Título: |
Synthesis, biophysical and functional studies of two BP100 analogues modified by a hydrophobic chain and a cyclic peptide. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Biochimica et Biophysica Acta, Biomembranes, v. 1860, p. 1502-1516, 2018. |
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DOI: |
10.1016/j.bbamem.2018.05.003 |
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Idioma: |
Inglês |
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Palavras-Chave: |
BP100 analogs; Structure-activity relationship. |
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Thesaurus Nal: |
Antimicrobial peptides; Mechanism of action; Spectroscopy. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01052naa a2200325 a 4500
001 2094173
005 2019-01-04
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1016/j.bbamem.2018.05.003$2DOI
100 1 $aCARRETERO, G. P. B.
245 $aSynthesis, biophysical and functional studies of two BP100 analogues modified by a hydrophobic chain and a cyclic peptide.$h[electronic resource]
260 $c2018
650 $aAntimicrobial peptides
650 $aMechanism of action
650 $aSpectroscopy
653 $aBP100 analogs
653 $aStructure-activity relationship
700 1 $aSARAIVA, G. K. V.
700 1 $aCAUZ, A. C. G.
700 1 $aRODRIGUES, M. A.
700 1 $aKIYOTA, S.
700 1 $aRISKE, K. A.
700 1 $aSANTOS, A. A. dos
700 1 $aPINATTO-BOTELHO, M. F.
700 1 $aBEMQUERER, M. P.
700 1 $aGUEIROS-FILHO, F. J.
700 1 $aCHAIMOVICH, H.
700 1 $aSCHREIER, S.
700 1 $aCUCCOVIA, I. M.
773 $tBiochimica et Biophysica Acta, Biomembranes$gv. 1860, p. 1502-1516, 2018.
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Registro original: |
Embrapa Recursos Genéticos e Biotecnologia (CENARGEN) |
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Biblioteca |
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Registro |
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Corte; Embrapa Pantanal. |
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Data corrente: |
25/03/2020 |
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Data da última atualização: |
20/04/2020 |
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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: |
WEBER, V. A. de M.; WEBER, F. de L.; GOMES, R. da C.; OLIVEIRA JUNIOR, A. da S.; MENEZES, G. V.; ABREU, U. G. P. de; BELETE, N. A. de S.; PISTORI, H. |
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Afiliação: |
Vanessa Aparecida de Moraes Weber, Universidade Católica Dom Bosco - UCDB; Fabricio de Lima Weber, Universidade Federal de Mato Grosso do Sul - UFMS/Faculdade de Computação; RODRIGO DA COSTA GOMES, CNPGC; Adair da Silva Oliveira Junior, Universidade Federal de Mato Grosso do Sul - UFMS/Faculdade de Computação; Geazy Vilharva Menezes, Universidade Federal de Mato Grosso do Sul - UFMS/Faculdade de Computação; URBANO GOMES PINTO DE ABREU, CPAP; Nícolas Alessandro de Souza Belete, Universidade Católica Dom Bosco - UCDB; Hemerson Pistori, Universidade Católica Dom Bosco - UCDB. |
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Título: |
Prediction of Girolando cattle weight by means of body measurements extracted from images. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Revista Brasileira de Zootecnia. v. 49, e20190110, 2020. |
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Idioma: |
Inglês
Português |
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Conteúdo: |
The objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HGP), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HWI), body length, tail distance to the neck, dorsum area (DAI), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height. The measurements extracted from the images were subjected to the stepwise regression method and regression-based machine learning algorithms. The HGp was the physical measure with stronger positive correlation with respect to body weight. In the stepwise method, the final model generated R² of 0.70 and RMSE of 42.52 kg and the equation: WEIGHT (kg) = 6.15421 * HWI (cm) + 0.01929 * DAI (cm2 ) + 70.8388. The linear regression and SVM algorithms obtained the best results, followed by discretization regression with random forests. The set of rules presented in this study can be recommended for estimating body weight in Girolando cattle, at a correlation coefficient of 0.71, by measurements of hip width and dorsum area, both extracted from cattle images. MenosThe objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HGP), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HWI), body length, tail distance to the neck, dorsum area (DAI), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height. The measurements extracted from the images were subjected to the stepwise regression method and regression-based machine learning algorithms. The HGp was the physical measure with stronger positive correlation with respect to body weight. In the stepwise method, the final model generated R² of 0.70 and RMSE of 42.52 kg and the equation: WEIGHT (kg) = 6.15421 * HWI (cm) + 0.01929 * DAI (cm2 ) + 70.8388. The linear regression and SVM algorithms obtained the best results, followed by discretization regression with random forests. The set of rules presented in this study can be recommended for estimating body weight in Girolando cattle, at a correlation coefficient of 0.71, by measurements of hip width and dorsum area, both extracted fro... Mostrar Tudo |
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Palavras-Chave: |
Livestock precision; Machine learning; Mass estimation. |
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Thesagro: |
Gado de Corte; Gado Gir; Morfologia Animal; Peso. |
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Thesaurus NAL: |
Beef cattle; Body weight; Cattle; Computer vision; Gir (cattle breed); Livestock production. |
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Categoria do assunto: |
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L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/212007/1/Prediction-of-girolando-cattle.pdf
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Marc: |
LEADER 02545naa a2200361 a 4500
001 2121364
005 2020-04-20
008 2020 bl uuuu u00u1 u #d
100 1 $aWEBER, V. A. de M.
245 $aPrediction of Girolando cattle weight by means of body measurements extracted from images.$h[electronic resource]
260 $c2020
520 $aThe objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HGP), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HWI), body length, tail distance to the neck, dorsum area (DAI), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height. The measurements extracted from the images were subjected to the stepwise regression method and regression-based machine learning algorithms. The HGp was the physical measure with stronger positive correlation with respect to body weight. In the stepwise method, the final model generated R² of 0.70 and RMSE of 42.52 kg and the equation: WEIGHT (kg) = 6.15421 * HWI (cm) + 0.01929 * DAI (cm2 ) + 70.8388. The linear regression and SVM algorithms obtained the best results, followed by discretization regression with random forests. The set of rules presented in this study can be recommended for estimating body weight in Girolando cattle, at a correlation coefficient of 0.71, by measurements of hip width and dorsum area, both extracted from cattle images.
650 $aBeef cattle
650 $aBody weight
650 $aCattle
650 $aComputer vision
650 $aGir (cattle breed)
650 $aLivestock production
650 $aGado de Corte
650 $aGado Gir
650 $aMorfologia Animal
650 $aPeso
653 $aLivestock precision
653 $aMachine learning
653 $aMass estimation
700 1 $aWEBER, F. de L.
700 1 $aGOMES, R. da C.
700 1 $aOLIVEIRA JUNIOR, A. da S.
700 1 $aMENEZES, G. V.
700 1 $aABREU, U. G. P. de
700 1 $aBELETE, N. A. de S.
700 1 $aPISTORI, H.
773 $tRevista Brasileira de Zootecnia.$gv. 49, e20190110, 2020.
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