• Title/Summary/Keyword: LaeA

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The Developmental Regulators, FlbB and FlbE, are Involved in the Virulence of Aspergillus fumigatus

  • Kim, Sung-Su;Kim, Young Hwan;Shin, Kwang-Soo
    • Journal of Microbiology and Biotechnology
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    • v.23 no.6
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    • pp.766-770
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    • 2013

  • Several upstream activators required for proper activation of brlA are involved in the development, vegetative growth, toxin production, and pathogenesis of Aspergillus fumigatus. In this study, we characterized the roles of two upstream developmental regulators, A. fumigatus flbB (AfuflbB) and flbE (AfuflbE), in toxin production and virulence. The deletion of AfuflbB and AfuflbE resulted in reduction of the expression of AfulaeA. Moreover, only about 8% to 10% of fumagillin was produced in the two mutants compared with that of wild type, and ${\Delta}AfuflbB$ strain produced 85% of gliotoxin compared with wild type, whereas none was produced by ${\Delta}AfuflbB$. Flow-cytometric analysis revealed decreased necrotic and apoptotic polymorphonuclear leukocytes cell death after exposure to supernatants from ${\Delta}AfuflbB$ and ${\Delta}AfuflbB$ strains compared with the wild type. These results indicate that FlbB and FlbE are necessary for the proper laeA expression, toxin production, and virulence of A. fumigatus.

  • https://doi.org/10.4014/jmb.1301.01002 인용 PDF KSCI

Two-stage Deep Learning Model with LSTM-based Autoencoder and CNN for Crop Classification Using Multi-temporal Remote Sensing Images

  • Kwak, Geun-Ho;Park, No-Wook
    • Korean Journal of Remote Sensing
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    • v.37 no.4
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    • pp.719-731
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    • 2021

  • This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

  • https://doi.org/10.7780/kjrs.2021.37.4.4 인용 PDF KSCI HTML

SHARP MOSER-TRUDINGER INEQUALITIES

  • Kim, Mee-Lae
    • Journal of the Korean Mathematical Society
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    • v.36 no.2
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    • pp.257-266
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    • 1999

  • We used Carleson and Chang's method to give another proof of the Moser-Trudinger inequality which was known as a limiting case of the Sobolev imbedding theorem and at the same time we get sharper information for the bound.

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