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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. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
01/06/2020 |
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Data da última atualização: |
14/02/2022 |
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Tipo da produção científica: |
Nota Técnica/Nota Científica |
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Autoria: |
RECH, K. S.; OLIVEIRA, C. F. de; MOURA, P. F.; OLIVEIRA, C. da S. P. de; HIROTA, B. C. K.; OLIVEIRA, M. de; RÜDIGER, A. L.; SÁ, E. L. de; MIGUEL, O. G.; AUER, C. G.; MIGUEL, M. D. |
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Afiliação: |
Katlin Suellen Rech, UFPR; Camila Freitas de Oliveira, UFPR; Paula Francislaine Moura, UFPR; Cristiane da Silva Paula de Oliveira, UFPR; Beatriz Cristina Konopatzki Hirota, UFPR; Maislian de Oliveira, UFPR; André Luis Rüdiger, UFPR; Eduardo Lemos de Sá, UFPR; Obdulio Gomes Miguel, UFPR; CELSO GARCIA AUER, CNPF; Marilis Dallarmi Miguel, UFPR. |
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Título: |
Optimisation of Bjerkandera adusta cultureconditions for the production of [alfa]-[alfa]-trehalose. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Natural Product Research, v. 35, n. 21, p. 4206-4209, 2021. |
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DOI: |
https://doi.org/10.1080/14786419.2020.1758092 |
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Idioma: |
Inglês |
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Notas: |
Short Communication. |
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Conteúdo: |
Several factors affect the vegetative growth of fungi, such as temperature, pH, and culture medium. In addition to mycelial growth, these factors affect metabolite production. There are limited studies that have identified the metabolites produced by the fungus Bjerkandera adusta, which have potential biotechnological applications. Here, we evaluated the effects of temperature, culture medium, and incubation time on the production of mycelial mass and metabolites of B. adusta isolated from Pinus taeda. The highest mycelial mass was obtained at 24 °C, in the potato dextrose and malt extract media, upon incubation for 28 and 35 days. The disaccharide α-α-trehalose was for the first time isolated and identified by X-ray diffraction in this fungal genus. |
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Palavras-Chave: |
Cristalografia. |
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Thesagro: |
Açúcar; Cultura In Vitro; Fungo. |
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Thesaurus Nal: |
Bjerkandera adusta; Crystallography; In vitro culture; Pinus. |
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Categoria do assunto: |
H Saúde e Patologia |
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Marc: |
LEADER 01843naa a2200361 a 4500
001 2122813
005 2022-02-14
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1080/14786419.2020.1758092$2DOI
100 1 $aRECH, K. S.
245 $aOptimisation of Bjerkandera adusta cultureconditions for the production of [alfa]-[alfa]-trehalose.$h[electronic resource]
260 $c2021
500 $aShort Communication.
520 $aSeveral factors affect the vegetative growth of fungi, such as temperature, pH, and culture medium. In addition to mycelial growth, these factors affect metabolite production. There are limited studies that have identified the metabolites produced by the fungus Bjerkandera adusta, which have potential biotechnological applications. Here, we evaluated the effects of temperature, culture medium, and incubation time on the production of mycelial mass and metabolites of B. adusta isolated from Pinus taeda. The highest mycelial mass was obtained at 24 °C, in the potato dextrose and malt extract media, upon incubation for 28 and 35 days. The disaccharide α-α-trehalose was for the first time isolated and identified by X-ray diffraction in this fungal genus.
650 $aBjerkandera adusta
650 $aCrystallography
650 $aIn vitro culture
650 $aPinus
650 $aAçúcar
650 $aCultura In Vitro
650 $aFungo
653 $aCristalografia
700 1 $aOLIVEIRA, C. F. de
700 1 $aMOURA, P. F.
700 1 $aOLIVEIRA, C. da S. P. de
700 1 $aHIROTA, B. C. K.
700 1 $aOLIVEIRA, M. de
700 1 $aRÜDIGER, A. L.
700 1 $aSÁ, E. L. de
700 1 $aMIGUEL, O. G.
700 1 $aAUER, C. G.
700 1 $aMIGUEL, M. D.
773 $tNatural Product Research$gv. 35, n. 21, p. 4206-4209, 2021.
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Registro original: |
Embrapa Florestas (CNPF) |
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Biblioteca |
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Origem |
Tipo/Formato |
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Registro |
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Status |
URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
24/08/2022 |
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Data da última atualização: |
25/08/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 2 |
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Autoria: |
TORO, A. P. S. G. D.; WERNER, J. P. S.; REIS, A. A. dos; ESQUERDO, J. C. D. M.; ANTUNES, J. F. G.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; FIGUEIREDO, G. K. D. A. |
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Afiliação: |
FEAGRI/UNICAMP; FEAGRI/UNICAMP; UNICAMP; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA, FEAGRI/UNICAMP; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; UNICAMP; UNICAMP; FEAGRI/UNICAMP. |
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Título: |
Evaluation of early season mapping of integrated crop livestock systems using Sentinel-2 data. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 43, B3, p. 1335-1340, 2022. |
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DOI: |
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1335-2022 |
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Idioma: |
Inglês |
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Notas: |
Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France. |
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Conteúdo: |
ABSTRACT. Various approaches were developed considering the need to increase agricultural productivity in cultivated areas without more deforestation, such as the Integrated Crop livestock systems (ICLS). The ICLS could be composed of annual crops followed by pastureland with the presence of cattle. Due to the high temporal dynamic of rotation between crops over the season, monitoring these areas is a big challenge. Also, agricultural organizations worldwide highlight the need for early-season maps for this kind of work. In this context, this study evaluated the potential of open data (Sentinel-2) data to map ICLS areas. The performance of two classifiers was evaluated: one of Machine Learning (random forest) and the other of Deep Learning (LSTM). Three different time windows of data were tested (Entire season, 180 days, and 120 days). Using the RF classifier, it was possible to achieve satisfactory results (Overall accuracy higher than 80%) for the early season (180 days). However, further studies are needed to explain better the lower(when compared to Random Forest) accuracy achieved by LSTM net (0.79 % for 180 days) and compare the results achieved here with results for a study area with different rates of cloud cover. |
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Palavras-Chave: |
Agricultura regenerativa; Aprendizado profundo; Crop identification; Floresta aleatória; Identificação de culturas; LSTM; Random forest; Regenerative agriculture. |
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Thesagro: |
Sensoriamento Remoto. |
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Thesaurus NAL: |
Remote sensing. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1145714/1/AP-Evalution-early-season-2022.pdf
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Marc: |
LEADER 02542naa a2200361 a 4500
001 2145714
005 2022-08-25
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1335-2022$2DOI
100 1 $aTORO, A. P. S. G. D.
245 $aEvaluation of early season mapping of integrated crop livestock systems using Sentinel-2 data.$h[electronic resource]
260 $c2022
500 $aEdition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France.
520 $aABSTRACT. Various approaches were developed considering the need to increase agricultural productivity in cultivated areas without more deforestation, such as the Integrated Crop livestock systems (ICLS). The ICLS could be composed of annual crops followed by pastureland with the presence of cattle. Due to the high temporal dynamic of rotation between crops over the season, monitoring these areas is a big challenge. Also, agricultural organizations worldwide highlight the need for early-season maps for this kind of work. In this context, this study evaluated the potential of open data (Sentinel-2) data to map ICLS areas. The performance of two classifiers was evaluated: one of Machine Learning (random forest) and the other of Deep Learning (LSTM). Three different time windows of data were tested (Entire season, 180 days, and 120 days). Using the RF classifier, it was possible to achieve satisfactory results (Overall accuracy higher than 80%) for the early season (180 days). However, further studies are needed to explain better the lower(when compared to Random Forest) accuracy achieved by LSTM net (0.79 % for 180 days) and compare the results achieved here with results for a study area with different rates of cloud cover.
650 $aRemote sensing
650 $aSensoriamento Remoto
653 $aAgricultura regenerativa
653 $aAprendizado profundo
653 $aCrop identification
653 $aFloresta aleatória
653 $aIdentificação de culturas
653 $aLSTM
653 $aRandom forest
653 $aRegenerative agriculture
700 1 $aWERNER, J. P. S.
700 1 $aREIS, A. A. dos
700 1 $aESQUERDO, J. C. D. M.
700 1 $aANTUNES, J. F. G.
700 1 $aCOUTINHO, A. C.
700 1 $aLAMPARELLI, R. A. C.
700 1 $aMAGALHÃES, P. S. G.
700 1 $aFIGUEIREDO, G. K. D. A.
773 $tThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences$gv. 43, B3, p. 1335-1340, 2022.
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Embrapa Agricultura Digital (CNPTIA) |
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