• Proceedings of the Korean Society of Embryo Transfer Conference
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• 2007.05a
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• pp.39-50
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• 2007

Sexing from bovine embryos fertilized in vitro implicates a possibility of the sex controlled cattle production. This study was carried out to produce the sex determined cattle through the embryonic sexing by fluorescence in situ hybridization (FISH) technique. FISH was achieved in in vitro fertilized bovine embryos using a bovine Y-specific DNA probe constructed from the btDYZ-1 sequence. Using this probe, a male-specific signal was detected on 100% of Y-chromosome bearing metaphase specimens. The analyzable rate of embryonic sexing by FISH technique was about 93% (365/393) regardless of embryonic stages. As tested single blastomere by FISH and then karyotype with their biopsied embryos, the accuracy of sex determination with FISH was 97.6%. We tried the embryo transfer with sex determined embryos on 15 cattle. Among them, the 5 cattle delivered calf with expected sex last year.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.