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Abstract: Sesquiterpene lactones (SLs) isolated from natural medicinal plants have been traditionally used to treat numerous dis-eases, especially chronic inflammatory diseases in folk medicine. Dietary vegetables, such as lettuce and chicory are also important sources of SLs in daily life. Micheliolide (MCL) is a recently discovered SL from Compositae plants. MCL can also be semi-synthesized from parthenolide, and undergo a biological, chemical transformation to dimethylamino micheliolide (DMAMCL) and ACT001 for higher bioavailability and better biological function. Systematic pharmaco-kinetic researches showed that MCL and its derivatives were widely distributed in the heart, spleen, lung, kidney, brain, stomach, duodenum, testicle, fat, marrow, and muscle within 30 min, concentration reaches peak approximately at 1 h and significantly reduces 3 h later in a majority of tissues. MCL has been shown to have a good therapeutic effect on various cancers, diabetic nephropathy, nervous system disease, bacterial infectious disease, and autoimmune disease through immune response regulation, such as inhibition of NF-кB, PI3K/AKT/mTOR and MAPK signaling pathways. MCL also showed a good blood-brain barrier permeability, and have the neuroprotective effect on stroke and Alzhei-mer's disease. Yet, the exact molecular mechanism of MCL remains unclear. Identification of relevant targets (such as pyruvate kinase muscle type 2 (PKM2)) also represents a great challenge for researchers. The biological characteristics, major biological activities, and molecular mechanisms of MCL were summarized in this review, indicating the potential clinical application for inflammatory and infectious diseases.Abstract: Sesquiterpene lactones (SLs) isolated from natural medicinal plants have been traditionally used to treat numerous dis-eases, especially chronic inflammatory diseases in folk medicine. Dietary vegetables, such as lettuce and chicory are also important sources of SLs in daily life. Micheliolide (MCL) is a recently discovered SL from Compositae plant...Learn More
Abstract: To enhance the accuracy of short-term wind power prediction, this paper proposes a novel two-stage forecasting framework that integrates Sequential Variational Mode Decomposition (SVMD), Bayesian Optimization (BO), and a CNN-BiLSTM-Attention model. In the first stage, the preprocessed wind power historical data is decomposed into several modal components via SVMD. These components serve as inputs to the CNN-BiLSTM-Attention model, whose hyperparameters—including the learning rate, number of hidden units, and regularization coefficient—are automatically tuned using the BO algorithm. The output of this stage is the initial power prediction. In the second stage, the prediction error sequence from the first stage is analyzed and similarly processed (decomposed and modeled) to generate an error compensation term. The final prediction is obtained by summing the initial power prediction and the predicted error compensation. Results show that compared with the CNN-BiLSTM-Attention model, the MAE, MAPE and RMSE values of the improved CNN-BiLSTM-Attention two-stage prediction model decreased by 85.7%, 75.2% and 77.3%, respectively, demonstrating the effectiveness of the two-stage short-term wind power prediction method of the improved CNN-BiLSTM-Attention model studied in this paper.Abstract: To enhance the accuracy of short-term wind power prediction, this paper proposes a novel two-stage forecasting framework that integrates Sequential Variational Mode Decomposition (SVMD), Bayesian Optimization (BO), and a CNN-BiLSTM-Attention model. In the first stage, the preprocessed wind power historical data is decomposed into several modal comp...Learn More
