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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Pecuária Sul. |
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Data corrente: |
16/12/2011 |
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Data da última atualização: |
18/04/2024 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
CARDOSO, F. F.; GIACHETTO, P. F.; YAMAGISHI, M. E. B.; HIGA, R. H. |
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Afiliação: |
FERNANDO FLORES CARDOSO, CPPSUL; Poliana Fernanda Giachetto; MICHEL EDUARDO BELEZA YAMAGISHI, CNPTIA; ROBERTO HIROSHI HIGA, CNPTIA. |
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Título: |
Statistical tools to uncover genetic architecture of tick resistance using highdensity oligonucleotide gene expression microarrays. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
In: SÃO PAULO ADVANCED SCHOOL OF SCIENCE, 1., 2011, São Carlos, SP. Advances in the knowledge of parasite resistance of ruminant hosts and parasites: proceedings. São São Carlos, SP: Embrapa Pecuária Sudeste, 2011. |
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Páginas: |
p. 34-38. |
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Idioma: |
Inglês |
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Conteúdo: |
The aim of this paper is to present procedures and tools used for quality control and statistical analysis of high-density microarray (Affymetrix GeneChip®) to search for genes that may be differentially expressed and that may provide clues to unravel parasite resistance. |
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Palavras-Chave: |
Clusterização; Expressão gênica; Ferramenta estatistica; Microarranjos; Resistência ao carrapato. |
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Thesagro: |
Análise Estatística; Estatística; Gado; Ruminante. |
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Thesaurus Nal: |
Cluster analysis; Gene expression; Microarray technology; Statistical analysis. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/50555/1/artigo-fernando-sao-carlos.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/52907/1/statistical.pdf
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Marc: |
LEADER 01404nam a2200313 a 4500
001 1910061
005 2024-04-18
008 2011 bl uuuu u00u1 u #d
100 1 $aCARDOSO, F. F.
245 $aStatistical tools to uncover genetic architecture of tick resistance using highdensity oligonucleotide gene expression microarrays.$h[electronic resource]
260 $aIn: SÃO PAULO ADVANCED SCHOOL OF SCIENCE, 1., 2011, São Carlos, SP. Advances in the knowledge of parasite resistance of ruminant hosts and parasites: proceedings. São São Carlos, SP: Embrapa Pecuária Sudeste$c2011
300 $ap. 34-38.
520 $aThe aim of this paper is to present procedures and tools used for quality control and statistical analysis of high-density microarray (Affymetrix GeneChip®) to search for genes that may be differentially expressed and that may provide clues to unravel parasite resistance.
650 $aCluster analysis
650 $aGene expression
650 $aMicroarray technology
650 $aStatistical analysis
650 $aAnálise Estatística
650 $aEstatística
650 $aGado
650 $aRuminante
653 $aClusterização
653 $aExpressão gênica
653 $aFerramenta estatistica
653 $aMicroarranjos
653 $aResistência ao carrapato
700 1 $aGIACHETTO, P. F.
700 1 $aYAMAGISHI, M. E. B.
700 1 $aHIGA, R. H.
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Registro original: |
Embrapa Pecuária Sul (CPPSUL) |
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Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
14/12/2020 |
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Data da última atualização: |
14/12/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
ALENCAR, A.; SHIMBO, J. Z.; LENTI, F.; MARQUES, C. B.; ZIMBRES, B.; ROSA, M.; ARRUDA, V.; CASTRO, I.; RIBEIRO, J. P. F. M.; VARELA, V.; ALENCAR, I.; PIONTEKOWSKI, V.; RIBEIRO, V.; BUSTAMANTE, M. M. C.; SANO, E. E.; BARROSO, M. |
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Afiliação: |
EDSON EYJI SANO, CPAC. |
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Título: |
Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing, v. 12, n. 6, 2020. |
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ISSN: |
2072-4292 |
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Idioma: |
Português |
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Conteúdo: |
Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985?2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr?1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome. MenosWidespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985?2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr?1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other ... Mostrar Tudo |
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Thesagro: |
Cerrado; Desmatamento; Floresta; Pastagem. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/219146/1/SANO-MAPPING-THREE-DECADES-OF-CHANGES-IN-THE-BRAZILIAN-CERRADO-USING-GEE.pdf
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Marc: |
LEADER 02778naa a2200361 a 4500
001 2128057
005 2020-12-14
008 2020 bl uuuu u00u1 u #d
022 $a2072-4292
100 1 $aALENCAR, A.
245 $aMapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform.$h[electronic resource]
260 $c2020
520 $aWidespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985?2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr?1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome.
650 $aCerrado
650 $aDesmatamento
650 $aFloresta
650 $aPastagem
700 1 $aSHIMBO, J. Z.
700 1 $aLENTI, F.
700 1 $aMARQUES, C. B.
700 1 $aZIMBRES, B.
700 1 $aROSA, M.
700 1 $aARRUDA, V.
700 1 $aCASTRO, I.
700 1 $aRIBEIRO, J. P. F. M.
700 1 $aVARELA, V.
700 1 $aALENCAR, I.
700 1 $aPIONTEKOWSKI, V.
700 1 $aRIBEIRO, V.
700 1 $aBUSTAMANTE, M. M. C.
700 1 $aSANO, E. E.
700 1 $aBARROSO, M.
773 $tRemote Sensing$gv. 12, n. 6, 2020.
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Embrapa Cerrados (CPAC) |
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