Obesity has become a global health issue, while conventional weight-loss drugs such as orlistat are often accompanied by adverse effects. Medicine-food homology (MFH) herbs, including Crataegi Fructus, Cassiae Semen, and Coicis Semen, are characterized by high safety and multi-target regulatory properties. However, their weight-loss mechanisms have not been systematically elucidated. This study collected 613 Chinese patent herbal formulations related to weight loss from the national patent database (1995-2025), involving 98 types of MFH herbs with a total usage frequency of 2,957. A specialized database of weight-loss patent formulations was established. Data mining methods were applied to analyze the frequency, properties, flavors, and meridian tropism of MFH herbs. Association rule analysis identified high-frequency herb combinations such as”Poria-Cassiae Semen-Nelumbinis Folium,” “Cassiae Semen-Crataegi Fructus,” and “Coicis Semen-Poria.” Using network pharmacology, active components and targets of Crataegi Fructus, Cassiae Semen, Nelumbinis Folium, Poria, Astragali Radix, and Coicis Semen were identified. A protein-protein interaction (PPI) network was constructed, and Gene Ontology (GO) enrichment analysis yielded 1,839 entries across molecular function (MF), biological process (BP), and cellular component (CC). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified 186 relevant pathways, primarily enriched in pathways in cancer and the AGE-RAGE signaling pathway. The results demonstrate that MFH-based Chinese patent formulations exert weight-loss effects through multi-component, multi-target, and multi-pathway mechanisms.

Objective: Utilizing data mining techniques, we conduct an investigation into the rules governing medication in the treatment of hiccups, according to Chinese Medical Prescriptions. The research also seeks to unravel the operational mechanism of the fundamental combination drugs used for treating hiccups. Methods: The study encompasses the collection and organization of the traditional Chinese medicine compound prescription for hiccup treatment, as per Zhong Hua Yi Fang guidelines. IBM SPSS Statistics 19 and IBM SPSS Modeler are employed to execute drug frequency statistics and to perform an association rule analysis of the compound prescription, thereby identifying the core combination drug pairs. By leveraging the TCMSP database, we extract the drug components and targets of core drug pairs. Databases such as GeneCards, PharmGkb, and DrugBank are searched to ascertain the potential targets for hiccup treatment and to determine the intersection target of disease and drug through the application of R 4.2.3. We construct a PPI network diagram of intersection targets via the STRING database and draw a drug-component-target network diagram utilizing Cytoscape software. We proceed to determine the core components of core drug pairs based on Degree value and screen for core target proteins via CytoNCA. The GO and KEGG pathways of the intersection target are analyzed using R 4.2.3. The accuracy of the network pharmacological analysis results is confirmed through molecular docking using AutoDockTools and Vina software. Results: We collected and organized 128 prescriptions for hiccup treatment, incorporating 149 kinds of traditional Chinese medicines, with 10 varieties being used more than 20 times. The core drug pair Atractylodes macrocephala Koidz-Ginseng was established by association rule analysis. A total of 97 core drug pair targets, 3281 hiccup target genes, and 51 intersection targets were screened. GO function enrichment analysis yielded 1459 biological process items, 55 cellular component items, and 155 molecular function items. KEGG enrichment analysis presented 139 pathways. Molecular docking results illustrated that the binding force between core components and core targets was robust, with the binding force between Inermin and HSP90AA1 being the most stable.Conclusion: Atractylodes macrocephalus and ginseng inhibit the inflammatory response of hiccups by regulating the IL-17 signaling pathway, reduce the damage to the gastric mucosa, and achieve therapeutic effects, especially PTGS2, HSP90AA1, TNF, etc. This study provides a theoretical reference for the follow-up study of the efficacy of TCM in the treatment of hiccups.

The atomic kinetic Monte Carlo method plays an important role in multi-scale physical simulations because it bridges the micro and macro worlds. However, its accuracy is limited by empirical potentials. We therefore propose herein a triple-encoding algorithm and vacancy-cache mechanism to efficiently integrate ab initio neural network potentials (NNPs) with AKMC and implement them in our TensorKMC codes. We port our program to SW26010-pro and innovate a fast feature operator and a big fusion operator for the NNPs for fully utilizing the powerful heterogeneous computing units of the new-generation Sunway supercomputer. We further optimize memory usage. With these improvements, TensorKMC can simulate up to 54 trillions of atoms and achieve excellent strong and weak scaling performance up to 27,456,000 cores.

In this paper, we propose a generative model to automatically discover the hidden associations between topics words and opinion words. By applying those discovered hidden associations, we construct the opinion scoring models to extract statements which best express opinionists’ standpoints on certain topics. For experiments, we apply our model to the political area. First, we visualize the similarities and dissimilarities between Republican and Democratic senators with respect to various topics. Second, we compare the performance of the opinion scoring models with 14 kinds of methods to find the best ones. We find that sentences extracted by our opinion scoring models can effectively express opinionists’ standpoints.

In this work, we show the importance of multidimensional opinion representation in the political context combining domain knowledge and results from principal component analysis. We discuss the differences of feature selection between political spectrum analysis and normal opinion mining tasks. We build regression models on each opinion dimension for scoring and placing new opinion entities, e.g. personal blogs or politicians, onto the political opinion spectrum. We apply our methods on the floor statement records of the United States Senate and evaluate it against the uni-dimensional representation of political opinion space. The experimental results show the effectiveness of the proposed model in explaining the voting records of the Senate.