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Probabilistic power flow (PPF) plays a critical role in the analysis of power systems. However, its high computational burden makes practical implementations challenging. This paper proposes a model-based deep learning approach to overcome these computational challenges. A deep neural network (DNN) is used to approximate the power flow calculation process, and is trained according to the physical power flow equations to improve its learning ability. The training process consists of several steps: 1) the branch flows are added into the objective function of the DNN as a penalty term, which improves the generalization ability of the DNN; 2) the gradients used in back propagation are simplified according to the physical characteristics of the transmission grid, which accelerates the training speed while maintaining effective guidance of the physical model; and 3) an improved initialization method for the DNN parameters is proposed to improve the convergence speed. The simulation results demonstrate the accuracy and efficiency of the proposed method in standard IEEE and utility benchmark systems. Index Terms-Model-based deep learning, initialization methods, deep neural networks, probabilistic power flow.

Introduction: Epimedium koreanum Nakai (EKN), is a well-known Chinese herbal medicine for the treatment of osteoporosis, immunosuppression, tumours and cardiovascular diseases. Comprehensive component identification is essential for elucidation of its pharmacological mechanism and quality control. However, its complex chemical composition has caused certain difficulties in the analysis of this traditional Chinese medicine (TCM). Therefore, there is an urgent need to establish a method for rapid classification and identification of EKN chemical components.Objective: To establish a method for rapid classification and identification of the main components of flavonoids, organic acids and alkaloids in EKN. Methods:The samples were analysed by ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and data postprocessing techniques. The UPLC system used a BEH C 18 column to separate the total extract of EKN. The mobile phase consisted of 0.1% formic acid in water and acetonitrile, and the EKN extract was analysed by gradient elution at a flow rate of 0.4 mL/min. In both the positive and negative ion modes, the fragment information was obtained and compared with those of the characteristic fragmentations and neutral losses described in the literature to quickly identify the target compounds.Results: Finally, we successfully screened out 51 chemical components, including 40 flavonoids, nine organic acids, and two alkaloids. Conclusion:The established method not only comprehensively analysed the chemical compositions of EKN, solved the difficult problems of analysis and identification of the complex chemical compositions of the TCM, but also further promoted the development of the application of chemical compositions of TCM.

Introduction Alismatis Rhizoma (AR) has been widely used to treat various diseases. Its complex chemical composition has caused certain difficulties in the analysis of this traditional Chinese medicine. Therefore, it is necessary to establish a method for the rapid classification and identification of the chemical constituents of AR. Objective This article describes a method for the rapid classification and identification of major triterpenoids in AR. Methodology The samples were analysed by ultra‐performance liquid chromatography quadrupole time‐of‐flight mass spectrometry (UPLC‐Q‐TOF‐MS). The assay was performed on a Waters ACQUITY UPLC BEH C18 column (100 mm × 2.1 mm, 1.7 μm) with 0.1% formic acid in water (A), and acetonitrile (B) as mobile phase by gradient elution at a flow rate of 0.3 mL/min. In the positive ion mode, the fragment information was obtained and compared with the characteristic fragments and neutral losses described in the literature. Then, the rapid classification and identification of the chemical components from AR were achieved. Results Finally, 25 triterpene compounds of AR were identified. Conclusions The method established in this study achieved the rapid classification and identification of chemical components in AR, which promotes the development of research methods to study the constituents of traditional Chinese medicine.

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Heart failure is a common systemic disease with high morbidity and mortality worldwide. Doxorubicin (DOX) is a commonly used anthracycline broad-spectrum antitumor antibiotic with strong antitumor effect and definite curative effect. However, cardiotoxicity is the adverse reaction of drug dose cumulative toxicity, but the mechanism is still unclear. In this study, proteomics and metabonomics techniques were used to analyze the tissue and plasma of DOX-induced heart failure (HF) in rats and to clarify the molecular mechanism of the harmful effects of DOX on cardiac metabolism and function in rats from a new point of view. The results showed that a total of 278 proteins with significant changes were identified by quantitative proteomic analysis, of which 118 proteins were significantly upregulated and 160 proteins were significantly downregulated in myocardial tissue. In the metabonomic analysis, 21 biomarkers such as L-octanoylcarnitine, alpha-ketoglutarate, glutamine, creatine, and sphingosine were detected. Correlation analysis showed that DOX-induced HF mainly affected phenylalanine, tyrosine, and tryptophan biosynthesis, D-glutamine and D-glutamate metabolism, phenylalanine metabolism, biosynthesis of unsaturated fatty acids, and other metabolic pathways, suggesting abnormal amino acid metabolism, fatty acid metabolism, and glycerol phospholipid metabolism. It is worth noting that we have found the key upstream target of DOX-induced HF, PTP1B, which inhibits the expression of HIF-1α by inhibiting the phosphorylation of IRS, leading to disorders of fatty acid metabolism and glycolysis, which together with the decrease of Nrf2, SOD, Cytc, and AK4 proteins lead to oxidative stress. Therefore, we think that PTP1B may play an important role in the development of heart failure induced by doxorubicin and can be used as a potential target for the treatment of heart failure.

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Viruses remain a major challenge in the fierce fight against diseases. There have been many pandemics caused by various viruses throughout the world over the years. Recently, the global outbreak of COVID-19 has had a catastrophic impact on human health and the world economy. Antiviral drug treatment has become another essential means to overcome pandemics in addition to vaccine development. How to quickly find effective drugs that can control the development of a pandemic is a hot issue that still needs to be resolved in medical research today. To accelerate the development of drugs, it is necessary to target the key target proteins in the development of the pandemic, screen active molecules, and develop reliable methods for the identification and characterization of target proteins based on the active ingredients of drugs. This article discusses key target proteins and their biological mechanisms in the progression of COVID-19 and other major epidemics. We propose a model based on these foundations, which includes identifying potential core targets, screening potential active molecules of core targets, and verifying active molecules. This article summarizes the related innovative technologies and methods. We hope to provide a reference for the screening of drugs related to pandemics and the development of new drugs.