石斛多酚於保肝、治療/預防糖尿病之用途 Method for treating obesity
The present invention provides methods of administering a Dendrobium polyphenol in an amount effective to lower blood sugar, treat hepatic disease, obesity or diabetes. Exemplary hepatic disease includes fibrosis, fatty liver and hepatitis. Moreover, the Dendrobium polyphenol is extracted from the plants of the genus Dendrobium.
閱讀詳細資料促脂解大豆胜肽之最適水解條件、序列及其應用 Optimal hydolysis conditions of soy protein to produce peptides with lipolysis-stimulating activity and their sequencing and use thereof
本發明所揭一種製備促脂解大豆蛋白水解物之方法，首先係先將一配置好預定濃度之分離大豆蛋白，再加入一風味蛋白酶(Flavourzyme)於一最適水解條件下，進行水解反應，其中，該分離大豆蛋白與風味蛋白酶之比例為100：1，而該最適水解條件係如下：反應酸鹼值介於7~7.5間、反應溫度為40~50℃、水解時間為100~150分鐘，得到一具有極佳之促脂解活性之大豆蛋白水解物。本發明係更進一步分離鑑定出該大豆蛋白水解物中之活性胜肽序列具有九種組合，分別為Val-His-Val-Val、Leu-Leu-Leu、Leu-Leu-Ile、Leu-Ile-Leu、Leu-Ile-Ile、Ile-Leu-Leu、Ile-Leu-Ile、Ile-Ile-Leu及Ile-Ile-Ile。
閱讀詳細資料以高動態範圍臨界值切割單一神經元影像的方法及其電腦可讀儲存媒體Method of Segmenting Single Neuron Images with High-Dynamic-Range Thresholds and Computer Readable Storage Medium Thereof
本發明之以高動態範圍臨界值切割單一神經元影像的方法包含(a)備置含神經元之生物組織樣本，並對含神經元之生物組織樣本進行三維成像，以得到原始三維神經影像；(b)濾除原始三維神經影像中訊號強度在第一訊號強度臨界值以下的立體像素，以得到第一經濾除影像；(c)對第一經濾除影像進行骨架追蹤，以得到第一經追蹤影像；(d)利用一方程式計算第一經追蹤影像之每一立體像素的結構重要性分數，以得到每一立體像素的第一次結構重要性分數；(e)逐漸增加訊號強度臨界值並重複步驟(b)、(c)及(d)n-1次；及(f)加總每一立體像素的第一次結構重要性分數一直到第n次結構重要性分數。
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以高動態範圍臨界值切割單一神經元影像的方法及其電腦可讀儲存媒體Method of Segmenting Single Neuron Images with High-Dynamic-Range Thresholds and Computer Readable Storage Medium Thereof
The method of segmenting single neuron images with high-dynamic-range thresholds of the present invention includes (a) preparing a biological tissue sample containing neurons and performing imaging to this sample to obtain a three-dimensional raw neuroimage; (b) deleting voxels in the three-dimensional raw neuroimage with signal intensities below a first signal intensity threshold to obtain a first thresholded image; (c) tracing the first thresholded image to obtain a first traced image; (d) calculating a structural importance score of every voxel in the first traced image to obtain a first structural importance score of every voxel; (e) gradually increasing the signal intensity threshold and repeating (b), (c) and (d) n−1 times; (f) summing up all the n structural importance scores of every voxel; (g) deleting voxels with summed structural importance score smaller than a pre-determined value from the raw image to obtain the segmented single neuron.
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