This study presents the annotated transcriptome of the common morning glory, Ipomoea purpurea, and examines gene expression differences between herbicide-resistant and susceptible lines. The assembled transcriptome contains 65,459 transcripts, with 19 differentially expressed genes linked to herbicide resistance. This research not only identifies potential resistance loci for further investigation but also significantly expands genomic resources for this species and related plant families.
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This project explores the use of neural networks to identify patterns in OHLC data for potential trading opportunities. By analyzing 21 ETFs and using LSTM neural networks, the study aims to predict short-term price movements and find the most effective rolling window size for training data, thus providing an edge in growing an account over time.
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