Search

With the growth of water conservancy and the increasing demand for information, handling and representing large volumes of water-related data has become complex. Particularly, scheduling textual data often exists in natural language form, lacking clear structure and standardization. Processing and utilizing such diverse data necessitates extensive domain knowledge and professional expertise. To tackle this challenge, a method based on large language model has been proposed to construct a knowledge graph for water engineering scheduling. This approach involves collecting and preprocessing scheduling rule data at the data layer, leveraging large language models to extract embedded knowledge, constructing the ontology at the conceptual layer, and extracting the “three-step” method prompt strategy at the instance layer. Under the interaction of the data, conceptual, and instance layers, high-performance extraction of rule texts is achieved, and the construction of the dataset and knowledge graph is completed. Experimental results show that the F1 value of the extraction method in this paper reaches 85.5%, and the effectiveness and rationality of the modules of the large language model are validated through ablation experiments. This graph integrates dispersed water conservancy rule information, effectively handles unstructured textual data, and offers visualization querying and functionality tracing. It aids professionals in assessing water conditions and selecting appropriate scheduling schemes, providing valuable support for conservancy decision-making and intelligent reasoning.

Fiber-reinforced composites offer the benefits of high strength, high stiffness, lightweight, superior damping performance, and great design capability when compared to metal. The rigidity characteristics of the composite laminate in different directions may be adjusted to meet the requirements of the application by using appropriate materials and arranging the lay-up sequence. As a result, the purpose of this work is to explore the influence of lay-up type on propeller performance in terms of both hydrodynamic and structural performance. A transient fluid-structure interaction (FSI) algorithm based on the finite element method (FEM) combined with the computational fluid dynamics (CFD) technique is developed and used for the analysis of composite propellers. The hydrodynamic performance of the propeller is compared to that of a metallic material. Propeller propulsion efficiency, structural deformation, equivalent stress, and damage performance of different lay-up options under three different operating situations are compared. In addition, it is presented a parametric optimization approach to get the most appropriate lay-up program for composite blades with the best hydrodynamic properties and structural performance.

The rapid development of technology allows people to obtain a large amount of data, which contains important information and various noises. How to obtain useful knowledge from data is the most important thing at this stage of machine learning (ML). The problem of unbalanced classification is currently an important topic in the field of data mining and ML. At present, this problem has attracted more and more attention and is a relatively new challenge for academia and industry. The problem of unbalanced classification involves classifying data when there is insufficient data or severe category distribution deviations. Due to the inherent complexity of unbalanced data sets, more new algorithms and tools are needed to effectively convert a large amount of raw data into useful information and knowledge. Unbalanced data set is a special case of classification problem, in which the distribution between classes is uneven, and it is difficult to classify data accurately. This article mainly introduces the research on the processing method of computer algorithms based on the processing method of unbalanced data sets based on ML, aiming to provide some ideas and directions for the processing of computer algorithms based on unbalanced data sets based on ML. This article proposes a research strategy for processing unbalanced data sets based on ML, including data preprocessing, decision tree data classification algorithm, and C4.5 algorithm, which are used to conduct research experiments on processing methods for unbalanced data sets based on ML. The experimental results in this article show that the accuracy rate of the decision tree C4.5 algorithm based on ML is 94.80%, which can be better used for processing unbalanced data sets based on ML.

Breast cancer (BC) is a common malignancy that affects women worldwide. Although transducing beta‐like 2 (TBL2), a member of the WD40 repeat protein family, has been implicated in various intracellular signaling pathways, its precise function in BC remains unclear. The expression of TBL2 is analyzed using real‐time PCR, western blotting, and immunohistochemistry in BC patient specimens. Kaplan–Meier survival analysis is employed to assess its prognostic significance. Proteomic analysis, immunoprecipitation tests, and protein immunoblotting are employed to examine the impact of TBL2 on AKT phosphorylation activation. The findings reveal selective overexpression of TBL2 in BC, correlating significantly with various clinicopathological characteristics and poor survival outcomes in patients with BC. Through in vivo and in vitro experiments, it is observed that TBL2 suppression inhibits BC cell proliferation, while TBL2 overexpression has the opposite effect. Mechanistically, TBL2 is identified as a scaffolding protein that promotes PRMT5 and WDR77 interaction. This interaction enhances the methyltransferase activity of PRMT5, leading to increased AKT phosphorylation activation and promotion of breast cancer cell proliferation. In conclusion, this study uncovers a novel function of TBL2 in the activation of AKT by PRMT5 and suggests TBL2 as a potential therapeutic target for BC treatment. [ABSTRACT FROM AUTHOR]

Background: Triple‐negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. Previous studies have found that fibroblast growth factor receptor 4 (FGFR4) plays a crucial role in tumor development and metastasis. However, the potential role and underlying mechanisms of FGFR4 in the progression of TNBC remain unclear. Methods: Statistical analysis of FGFR4 expression data in public databases was used to reveal its role in TNBC. qRT‐PCR was used to detect FGFR4 expression levels. The impact of FGFR4 level changes on TNBC cell proliferation was assessed using CCK‐8 and colony formation assays, while Transwell invasion assays and JC‐1 staining were employed to analyze the effects of FGFR4 level changes on the invasiveness and survival capability of TNBC cells. Differentially expressed genes were subjected to Gene Ontology, KEGG, and GSEA enrichment analyses to identify associated signaling pathways. Additionally, Oil Red O staining, fatty acid metabolite detection, and Western blot analysis were used to investigate the impact of FGFR4 and its inhibitor fisogatinib, as well as the AKT activator SC79, on the metabolic reprogramming of fatty acids in TNBC cells. Results: FGFR4 was found to be upregulated in breast cancer and correlated with poorer patient outcomes. Inhibition of FGFR4 resulted in reduced cell growth and invasion in TNBC cells. It also led to increased lipid accumulation, upregulated lipid biosynthesis‐related genes, and downregulated lipolysis‐related genes. Mechanistically, FGFR4 inhibition suppressed the activation of the AKT/RYR2 signaling pathway. Reactivating the AKT pathway reversed the suppressive effects of FGFR4 inhibition on TNBC progression. Conclusion: Dysregulated FGFR4 activates the AKT/RYR2 axis, leading to tumor proliferation, invasion, and altered lipid metabolism in TNBC. FGFR4 inhibition could potentially serve as a novel therapeutic strategy for TNBC treatment. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer is the most common cancer worldwide, the leading cause of cancer death in women, and the fifth leading cause of cancer death. Triple negative breast cancer (TNBC), with high metastasis and mortality rates, is the most challenging subtype in breast cancer treatment. There is an urgent need to develop anti-TNBC drugs with significant efficacy, low side effects and good availability. In early drug screening, blestriarene C was found to have inhibitory effects on TNBC cells. In this article, we further explore the mechanisms associated with blestriarene C for breast cancer. Methods: In this article, we take the approach of network pharmacology combined with in vivo and in vitro experiments. Network pharmacology analysis was used to predict the active components in Baiji, and to investigate the hub targets and related mechanisms of BC in TNBC treatment. The mechanism of anti-TNBC in vitro was evaluated by CCK-8 assay, cell apoptosis and cell cycle assays, wound healing assay, WB assay, and molecular docking analysis. The inhibition effect in vivo was test in subcutaneous tumor models established in mice. Results: Through network pharmacology analysis and experiments, we screened out BC as the main active ingredient, and found that BC could inhibit the Ras/ERK/c-Fos signaling pathway while downregulating the expression of HSP90AA1 and upregulating the expression of PTGS2, thereby promoting apoptosis, causing S-phase cycle arrest, and inhibiting the proliferation and migration of BT549 cells. The in vivo results illustrated that BC inhibited the growth of TNBC tumors and has a high safety profile. By integrating network pharmacology with in vitro and in vivo experiments, this study demonstrated that BC inhibited the proliferation and migration of TNBC cells by inhibiting the Ras/ERK/c-Fos signaling pathway, promoting apoptosis, and causing S-phase cycle arrest. Discussion: This study provides new evidence for the use of BC as a novel drug for TNBC treatment. [ABSTRACT FROM AUTHOR]

Distant metastasis remains the primary cause of morbidity in patients with breast cancer. Hence, the development of more efficacious strategies and the exploration of potential targets for patients with metastatic breast cancer are urgently needed. The data of six patients with breast cancer brain metastases (BCBrM) from two centres were collected, and a comprehensive landscape of the entire tumour ecosystem was generated through the utilisation of single‐cell RNA sequencing. We utilised the Monocle2 and CellChat algorithms to investigate the interrelationships among each subcluster. In addition, multiple signatures were collected to evaluate key components of the subclusters through multi‐omics methodologies. Finally, we elucidated common expression programs of malignant cells, and experiments were conducted in vitro and in vivo to determine the functions of interleukin enhancer‐binding factor 2 (ILF2), which is a key gene in the metastasis module, in BCBrM progression. We found that subclusters in each major cell type exhibited diverse characteristics. Besides, our study indicated that ILF2 was specifically associated with BCBrM, and experimental validations further demonstrated that ILF2 deficiency hindered BCBrM progression. Our study offers novel perspectives on the heterogeneity of BCBrM and suggests that ILF2 could serve as a promising biomarker or therapeutic target for BCBrM. [ABSTRACT FROM AUTHOR]