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    American Ginseng Germination and Emergence
    ( 2022-09-07) Ethan M Swiggart
    American ginseng (panax quinquefolius) seed germination and emergence. Seeds from American ginseng have double dormancy, meaning they require two years (18 months) to begin the germination process. Seeds require two winters at very cold temperatures, known as cold stratification, and will germinate in the spring. They are quite small, around 6 cm and very difficult to see. This timelapse video shows the emergence of American ginseng and illuminates another interesting fact about the elusive plant. American ginseng has hypogeous germination! This is a somewhat rare occurrence where the cotyledon (seed leaves) remains underground. The hypocotyl (stem) is quite short and the cotyledons force the radicle and epicotyl to elongate. This results in the plant producing true leaves capable of photosynthesis right from the time of emergence. KEYWORDS: American Ginseng, Ginseng, hypogeous, germination
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    An Improved Systematic Approach to Predicting Transcription Factor Target Genes Using Support Vector Machine
    ( 2014-03-17) Cui, Song ; Youn, Eunseog ; Lee, Joohyun ; Maas, Stephan J. ; Kestler, Hans A.
    Biological prediction of transcription factor binding sites and their corresponding transcription factor target genes (TFTGs) makes great contribution to understanding the gene regulatory networks. However, these approaches are based on laborious and time-consuming biological experiments. Numerous computational approaches have shown great potential to circumvent laborious biological methods. However, the majority of these algorithms provide limited performances and fail to consider the structural property of the datasets. We proposed a refined systematic computational approach for predicting TFTGs. Based on previous work done on identifying auxin response factor target genes from Arabidopsis thaliana co-expression data, we adopted a novel reverse-complementary distance-sensitive n-gram profile algorithm. This algorithm converts each upstream sub-sequence into a high-dimensional vector data point and transforms the prediction task into a classification problem using support vector machine-based classifier. Our approach showed significant improvement compared to other computational methods based on the area under curve value of the receiver operating characteristic curve using 10-fold cross validation. In addition, in the light of the highly skewed structure of the dataset, we also evaluated other metrics and their associated curves, such as precision-recall curves and cost curves, which provided highly satisfactory results.