Elucidation of the 1-phenethylisoquinoline pathway from an endemic conifer Cephalotaxus hainanensis

Cephalotaxines harbor great medical potential, but their natural source, the endemic conifer Cephalotaxus is highly endangered, creating a conflict between biotechnological valorization and preservation of biodiversity. Here, we construct the whole biosynthetic pathway to the 1-phenethylisoquinoline scaffold, as first committed compound for phenylethylisoquinoline alkaloids (PIAs), combining metabolic modeling, and transcriptome mining of Cephalotaxus hainanensis to infer the biosynthesis for PIA precursor. We identify a novel protein, ChPSS, driving the Pictet–Spengler condensation and show that this enzyme represents the branching point where PIA biosynthesis diverges from the concurrent benzylisoquinoline-alkaloids pathway. We also pinpoint ChDBR as crucial step to form 4-hydroxydihydrocinnamaldehyde diverging from lignin biosynthesis. The elucidation of the early PIA pathway represents an important step toward microbe-based production of these pharmaceutically important alkaloids resolving the conflict between biotechnology and preservation of biodiversity

Prediction of sea ice area based on the CEEMDAN-SO-BiLSTM model

This article proposes a combined prediction model based on a bidirectional long short-term memory (BiLSTM) neural network optimized by the snake optimizer (SO) under complete ensemble empirical mode decomposition with adaptive noise. First, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to decompose the sea ice area time series data into a series of eigenmodes and perform noise reduction to enhance the stationarity and smoothness of the time series. Second, this article used a bidirectional long short-term memory neural network optimized by the snake optimizer to fully exploit the characteristics of each eigenmode of the time series to achieve the prediction of each. Finally, the predicted values of each mode are superimposed and reconstructed as the final prediction values. Our model achieves a good score of RMSE: 1.047, MAE: 0.815, and SMAPE: 3.938 on the test set

Analysis of Characteristic Volatile Aroma Components in Inner Mongolia Dried Allium chrysanthum

Objective: To analyze the volatile aroma components of dried Allium chrysanthum in Inner Mongolia. The volatile aroma components in Inner Mongolia Allium chrysanthum were extracted by solid phase micro-extraction (SPME), and then were detected by gas chromatography-mass spectrometry coupled with olfactometry (GC-O-MS), combined with improved aroma extraction dilution analysis (AEDA) and relative aroma activity value (ROAV) for analysis. Results: A total of 87 kinds of volatile aroma compounds in Inner Mongolia dried Allium chrysanthum were identified and classified, which included sulfurs, hydrocarbons, aldehydes, ketones, heterocyclics, esters, alcohols, ethers, terpenes, acids and phenols. 20 key odorant active compounds were identified through AEDA analysis, which mainly including acids, alcohols, aldehydes, sulfurs and heterocyclic compounds, and 4 characteristic volatile odor active components of dried Allium chrysanthumin in Inner Mongolia by the way of ROVA combined with AEDA analysis was identified that were dimethyl trisulfide (intense fresh onion spice aroma), acetic acid (sour aroma), 2,3-butanediol (creamy aroma) and benzaldehyde (nutty aroma). Conclusion: The coordination among dimethyl trisulfide, acetic acid, 2,3-butanediol and benzaldehyde in dried Allium chrysanthum from Inner Mongolia is the main reason for the difference in flavor