This paper reviewed the diversity of endophytic fungi and their interactions with medicinal plants, along with the research methodologies utilized to investigate these interactions. It mainly includes the diversity of endophytic fungi, as well as distribution diversity, species diversity, and the diversity of their metabolites and functions, including antibacterial, anti-inflammatory, anti-tumor, insecticidal, antioxidant capabilities, and so on. The research methodologies employed to investigate the interactions between endophytic fungi and medicinal plants are categorized into metagenomics, transcriptomics, metatranscriptomics, proteomics, and metabolomics. Furthermore, this study anticipates the potential applications of secondary metabolites derived from endophytic fungi in both medicine and agriculture.

Federated learning (FL) is a technique that establishes a global model based on distributed data across multiple clients, while preserving data privacy. However, the frequent client-server communication in FL provides an opportunity for Byzantine clients, who can tamper with or falsify model parameters. Consequently, the presence of Byzantine clients impacts the convergence of the global model. To address this issue, we propose a framework that maintains Byzantine-robustness for FL in unreliable environments (BRFLUE). Specifically, BRFLUE consists of two components: a random-matching verification module and a credibility-table-based aggregation module. The former randomly matches clients and allows them to verify each other's behavior anonymously. The latter updates clients' credibility after receiving the client verification results and aggregates the global model based on the credibility table. The combination of these two modules can effectively identify possible Byzantine clients and reduce their adverse impact on the global model. Through implementations in different scenarios with classical datasets, we demonstrate the effectiveness of the proposed BRFLUE.

This study provides a deep analysis of the potential mechanisms and effects of the traditional Chinese medicine Scutellaria baicalensis in the treatment of non-small cell lung cancer (NSCLC). By integrating public databases and clinical resources, we adopted a comprehensive strategy combining bioinformatics, network pharmacology, and machine learning techniques to screen out tumor biomarkers closely related to the prognosis of NSCLC, and constructed an accurate predictive model to comprehensively elucidate the complex interactions between the active components of Scutellaria baicalensis and the prognosis of NSCLC. The incorporation of radiomics technology enabled us to extract high-throughput radiological features from medical images, achieving non-invasive prediction of tumor biomarker expression status, further enriching our research methods. We constructed a Scutellaria baicalensis-NSCLC interaction network, accurately calculating the intersection of drug-specific targets and disease-related targets, and utilized protein-protein interaction (PPI) networks and functional enrichment analyses to deeply explore the potential mechanisms of action between Scutellaria baicalensis components and NSCLC. With the help of machine learning tools, we successfully identified key hub genes and verified their importance in lung cancer treatment protocols through immune infiltration analysis and molecular docking studies. The study results showed that 45 active components were screened out, with 628 active component- related target sites, 3076 differentially expressed genes, and 5628 co-expressed genes related to the disease Module genes, intersecting targets 98; GO functional enrichment analysis mainly enriched to BP entries 581, cell composition CC entries 23, molecular function MF entries 30 (p.adj <0.01); KEGG pathway enrichment analysis screened out 111 significant signaling pathways (P < 0.05), mainly involving IL-17 Signaling Pathway, TNF Pathway, AGE-RAGE Signaling Pathway in diabaetic,P53 Signaling Pathway, Toll-like rector et al.; molecular docking showed that compounds have good affinity with the screened core targets GAPDHIL6, TNF, JUN, MMP9, CDH1. Machine learning predicted the intersection of core target genes, among which 5 (FABP4, XDH, GPBAR1, CA4, CDH1) were identified as key target genes for drug therapy. This study not only revealed the significant potential and mechanism of action of Scutellaria baicalensis in anti-non-small cell lung cancer but also provided new perspectives and insights for the development of multi-target drug therapy strategies. By integrating various advanced technologies and methods, we have provided a solid theoretical basis and practical guidance for precision treatment and personalized medication for NSCLC, offering new hope for lung cancer patients.

The dynamic pickup and delivery problem (DPDP) is essential in supply chain management and logistics. In this study, we consider a real-world DPDP from daily delivery scenarios of a company. In the problem, orders are generated randomly and released periodically. The orders should be completed as soon as possible to minimize the cost. We propose a novel memetic algorithm (MA) to address this problem. The proposed MA consists of a genetic algorithm and a local search strategy that periodically solves a static pickup and delivery problem when new orders are released. We have conducted extensive experiments on 64 real-world instances to assess the performance of our method. Three state-of-the-art algorithms are chosen as the baseline algorithms. Experimental results demonstrate the effectiveness of the MA in solving the real-world DPDP.

As nanotechnology develops in the fields of mechanical engineering, electrical engineering, information and communication, and medical care, it has shown great promises. In recent years, medical nanorobots have made significant progress in terms of the selection of materials, fabrication methods, driving force sources, and clinical applications, such as nanomedicine. It involves bypassing biological tissues and delivering drugs directly to lesions and target cells using nanorobots, thus increasing concentration. It has also proved useful for monitoring disease progression, complementary diagnosis, and minimally invasive surgery. Also, we examine the development of nanomedicine and its applications in medicine, focusing on the use of nanomedicine in the treatment of various major diseases, including how they are generalized and how they are modified. The purpose of this review is to provide a summary and discussion of current research for the future development in nanomedicine.

Abstract Atherosclerotic cardiovascular disease continues to pose a major threat to human health. It has been shown that Chinese herbal medicines rich in palmatine(PAL) exert significant beneficial effects on atherosclerosis(AS). However, the specific biological functions and mechanisms of PAL in AS remain unclear. In this study, we investigated the effects of PAL on the core pathological changes in macrophage foam cell formation, a key process in the development of AS. The results demonstrated that PAL significantly reduced the formation of lipid droplets and accumulation of cholesterol ester in oxidized low-density lipoprotein (OX-LDL)-induced macrophage foam cells. RNA-sequencing was used to comprehensively analyze the effect of PAL. The findings revealed that the peroxisome proliferator-activated receptor-γ (PPAR-γ) pathway is the primary target of PAL in suppressing foam cell formation. Real-time fluorescence quantitative polymerase chain reaction and western blotting results confirmed that PAL substantially upregulated the transcription and expression levels of lipid metabolism-related proteins, such as PPAR-γ and liver X receptor-α (LXR-α), whereas it inhibited the transcription of inflammatory factors TNF-α, interleukin-1β (IL-1β), and IL-6. These biological changes were reversed by treatment with the PPAR-γ inhibitor T0070907. Molecular docking analysis showed that the binding sites of PAL with PPAR-γ (325THR) is close to the reported PPAR-γ agonist rosiglitazone（323HIS）, suggesting that PAL may act as a PPAR-γ partial agonist to improve atherosclerosis. Collectively, the results of this study suggest that PAL promotes cholesterol efflux and inhibits inflammatory responses mainly by regulating the PPAR-γ signaling pathway, thus ameliorating macrophage foam cell formation. Our findings may provide a foundation for further research on the pharmacological activity and potential value of PAL in the treatment of AS.

JinQi Jiangtang tablet (JQJTT) is a Chinese patent medicine that has been shown to be beneficial for patients with diabetes both preclinically and clinically; however, the molecular mechanism underlying the effects of JQJTT remains unclear. In this study, surface plasmon resonance fishing was employed to identify JQJTT constituent molecules that can specifically bind to fibroblast growth factor receptor 1 (FGFR1), leading to the retrieval of palmatine (PAL), a key active ingredient of JQJTT. In vivo and in vitro experiments demonstrated that PAL can significantly stimulate FGFR1 phosphorylation and upregulate glucose transporter type 1 (GLUT-1) expression, thereby facilitating glucose uptake in insulin resistance (IR) HepG2 cells as well as alleviating hyperglycemia in diabetic mice. Our results revealed that PAL functions as an FGFR1 activator and that the hypoglycemic effect of JQJTT is partially dependent on the PAL-induced activation of the FGFR1 pathway. In addition, this study contributed to the understanding the pharmacodynamic basis and mechanism of action of JQJTT and provided a novel concept for future research on PAL.

As an emerging power inspection method, unmanned aerial vehicle (UAV) inspection has the advantages of high safety, high efficiency, and low cost. In the process of power inspection, UAVs need to inspect multiple task points in a complex environment and plan an efficient and feasible path. In this research, the multiple UAVs inspection in the two cases of initial task points and newly added task points is considered. Aiming at these two cases, a hybrid algorithm is proposed in this paper. Firstly, the personal example learning strategy is applied to the golden eagle optimizer (GEO) to get a personal example learning GEO called PELGEO to improve the search ability of the GEO and reduce the possibility of GEO falling into a local optimum. Secondly, the grey wolf optimizer (GWO) is simplified and the differential mutation strategy is introduced to create the simplified GWO with differential mutation called DMSGWO. Finally, to give full play to the advantages of the PELGEO and the DMSGWO, an adaptive hybridization strategy is used to hybridize PELGEO and DMSGWO. The new hybrid algorithm based on GEO and GWO named HGEOGWO is proposed. The HGEOGWO and other algorithms are tested under the CEC2013 test suite. The experimental results show that the HGEOGWO has better optimization performance and stability than some popular algorithms. For the 3D path planning problem of multiple UAVs in power inspection, the proposed algorithm also has obvious advantages compared with some popular algorithms. The code of HGEOGWO can be publicly available at https://www.mathworks.com/matlabcentral/fileexchange/97807-a-new-hybrid-algorithm-based-on-geo-and-gwo .

The aim of this study was to investigate the therapeutic effect of JQ-R on metabolic hypertension and its correlation with Fibroblast growth factor 21/Fibroblast growth factor receptors 1(FGF21/FGFR1) pathway. In this study, fructose-induced metabolic hypertension rats were used as hypertension models to detect the regulation effect of JQ-R on hypertension. The effects of JQ-R on blood glucose, blood lipids, serum insulin levels and other metabolic indicators of rats were also measured. The effects of JQ-R on FGF21/FGFR1 signaling pathway in model animals were detected by Real-time quantitative PCR and Western blotting. The results showed that JQ-R significantly reduce the blood pressure of model rats in a dose-dependent manner. Meanwhile, fasting insulin, fasting blood glucose, insulin resistance index, total cholesterol and triglyceride levels were significantly decreased, and glucose and lipid metabolism abnormalities were also significantly improved. JQ-R induces these changes along with FGFR1 phosphorylation, which was also detected in JQ-R treated FGF21 knockout mice. These results suggest that JQ-R can reduce blood pressure and improve glucose and lipid metabolism in fructose-induced hypertension rats. Activation of FGF21/FGFR1 signaling pathway to regulate downstream blood pressure and glucolipid metabolism-related pathways may be one of the important mechanisms of JQ-R in regulating blood pressure.

Node localisation is a common and significant practical application question in wireless sensor network (WSN). The goal of this problem is to use anchor nodes in the network to estimate the geographical location of the unknown node. A novel algorithm, named adaptive multi-group slime mould algorithm (AMSMA), is proposed in this study. The improved slime mould algorithm uses the multi-group strategy and adaptive communication mechanism to alleviate the lack of population diversity, development and exploration imbalance of the slime mould algorithm. The proposed AMSMA was tested under CEC2013 test suite. Compared with SMA and corresponding optimisation algorithms, the AMSMA is more effective and efficient. In addition, a novel localisation algorithm based on AMSMA is proposed. The AMSMA-Distance Vector-Hop (AMSMA-DV-Hop) is applied to the localisation of WSN. Compared with some other existing localisation algorithms, the proposed AMSMA-DV-Hop is an effective algorithm for the localisation of WSN.

Unmanned aerial vehicle (UAV) inspection is an indispensable part of power inspection. In the process of power inspection, the UAV needs to obtain an efficient and feasible path in the complex environment. To solve this problem, a Golden eagle optimizer with double learning strategies (GEO-DLS) is proposed. The double learning strategies consist of personal example learning and mirror reflection learning. The personal example learning can enhance the search ability of the Golden eagle optimizer (GEO) and reduce the possibility of the GEO falling into the local optimum. The mirror reflection learning can improve the optimization accuracy of the GEO and accelerate the convergence speed of the GEO. To verify the optimization performance of the algorithm, the proposed GEO-DLS and several other algorithms were tested under the CEC2013 test suite. At the same time, the proposed GEO-DLS and the GEO were analyzed for population diversity and exploration–exploitation​ ratio. Finally, the proposed GEO-DLS is applied to the UAV path planning to generate the initial path, and the cubic B-spline curve is used to smooth the path. These experimental results show that the GEO-DLS has a good performance. The code can be publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/98799-golden-eagle-optimizer-with-double-learning-strategies

This study focused on the antibacterial effects of the endophytic fungi producing naringenin from Dalbergia odorifera T. Chen against Staphylococcus aureus. The antibacterial activity was measured by the inhibition diameters, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC). The time-killing curve was also used to evaluate its antibacterial efficacy. The results of antibacterial activity determinations showed that endophytic fungi secondary metabolites can inhibit the growth of five pathogenic bacteria (S. aureus, Escherichia coli, Salmonella enteritidis, Pseudomonas aeruginosa, and Bacillus subtilis) and the most sensitive strain was S. aureus that had the MIC and MBC values of 0.13 and 0.50 mg/mL, respectively. The membrane permeability study was measured by a DNA leakage assay and electrical conductivity assay. Furthermore, the whole-cell protein lysates and DNA fragmentation assay was evaluated. The morphology of S. aureus treated with the endophytic fungi products was observed by scanning electron microscopy (SEM). The probable antibacterial mechanism of endophytic fungi secondary metabolites was the increased membrane permeability that leads to leaks of nucleic acids and proteins. SEM results further confirmed that the extracts can interfere with the integrity of S. aureus cell membrane and further inhibit the growth of bacteria, resulting in the death of bacteria. This study provides a new perspective for the antibacterial functions of endophytic fungi secondary metabolites for biomedical applications.

Nowadays, there is a very popular artificial intelligence algorithm called whale optimization algorithm (WOA). WOA is obtained through the special bubble net foraging process of humpback whales. It has a special search mechanism, and is very helpful to solve some complex optimization problems and large scale. An improved WOA is implemented to optimize the cascade reservoir power generation. Firstly, by introducing nonlinear time-varying adaptive weights, the WOA performance in the local optimization and global exploration stages is improved; secondly, the differential mutation perturbation factor is introduced in the shrinking and surrounding stage of the whale algorithm to prevent premature convergence. In addition, the logarithmic spiral search method of whale individuals has been improved so that the ability to solve the algorithm traversal can be found. Experimental results show that it has a great improvement in accuracy and convergence compared with the original WOA in the optimal dispatch model of cascade reservoir power generation.

为探索地胆草总黄酮对微生物的抑菌活性及作用机理,采用地胆草为原料,提取总黄酮,并用D101大孔吸附树脂对粗提液进行了纯化;利用滤纸片扩散和倍半稀释法分析地胆草总黄酮对大肠杆菌和金黄色葡萄球菌的抑菌效果,并确定其最小抑菌浓度;通过电导率及扫描电镜法探究地胆草总黄酮对大肠杆菌及金黄色葡萄球菌的抑菌机理.结果 表明,D101型大孔树脂对地胆草总黄酮有一定的纯化效果,溶液呈深黄色、澄清透亮,此时总黄酮浓度为0.27 mg/mL;地胆草总黄酮对大肠杆菌及金黄色葡萄球菌有明显的抑菌效果,最小抑菌浓度为0.612 μg/mL;随着培养时间的延长,地胆草总黄酮使大肠杆菌培养液电导率先上升后下降;而金黄色葡萄球菌培养液的电导率先下降后上升,最后又下降,证实地胆草总黄酮能破坏细胞壁的结构及改变细胞膜的通透性,使胞内物质释放影响了培养液的电导率;扫描电镜结果显示,经过地胆草总黄酮作用后的菌体细胞表面粗糙,形状发生改变,由此可以证实,地胆草总黄酮有一定的抑菌活性.

Exploring key genes associated with non-small cell lung carcinoma (NSCLC) may lead to targeted therapies for NSCLC patients. The protein kinase MAP4K3 has been established as an important modulator of cell growth and autophagy in mammals. Herein, we investigated the somatic mutations and the expression pattern of MAP4K3 detected in NSCLC patients based on the TCGA database. Abnormal MAP4K3 expression and its somatic mutations are associated with the carcinogenesis and thereby becoming an attractive therapeutic target. Baicalein, a natural product, was determined to be the first-reported MAP4K3 binding ligand with its KD values of 6.47 μM measured by microscale thermophoresis. Subsequent in silico docking and mutation studies demonstrated that baicalein directly binds to MAP4K3, presumably to the substrate-binding pocket of this kinase domain, causing inactivity of MAP4K3. We further showed that baicalein could induce degradation of MAP4K3 through decreasing its stability and promoting the ubiquitin proteasome pathway. Degradation of MAP4K3 could cause dissociation of the transcription factor EB and 14-3-3 complex, enhance rapid transport of TFEB to the nucleus and trigger TFEB-dependent autophagy, resulting in lung cancer cells proliferation arrest. Knockdown of MAP4K3 expression by siRNA was sufficient to mimic baicalein-induced autophagy. Ectopic expression of the MAP4K3 protein resulted in significant resistance to baicalein-induced autophagy. Baicalein exhibited good tumor growth inhibition in a nude mouse model for human H1299 xenografts, which might be tightly related to its binding to MAP4K3 and degradation of MAP4K3. Our data provide novel mechanistic insights of baicalein/ MAP4K3/ mTORC1/ TFEB axis in regulating baicalein-induced autophagy in NSCLC, suggesting potential therapies for treatment of NSCLC.

In this paper, a novel linear subspace learning approach, named Multiple Feature Point Discriminant Analysis (MFPDA), is proposed. MFPDA is in order to maximize the multiple feature point between class scatter and minimize the multiple feature point within-class scatter. Some experiments are performed on FKP database, AR face database, and ORL face database to evaluate the effectiveness of the proposed MFPDA. Compared with some popular subspace learning methods, such as PCA, LDA, LLP, UNDFLA, JSPCA, the proposed MFPDA has highest average recognition accuracy. The experimental results confirm the effectiveness of the proposed algorithm.

Non-small cell lung carcinoma (NSCLC) is a life-threatening malignancy. The level of the cell growth regulator mitogen-activated protein kinase kinase kinase kinase 3 (MAP4K3) has been shown to be correlated with a high risk of NSCLC recurrence and poor recurrence-free survival rate. The present study examined the effects of Astragalus polysaccharide (APS) and 10-hydroxycamptothecin (HCPT), which are associated with marked suppression and dephosphorylation of the MAP4K3/mammalian target of rapamycin (mTOR) signaling pathway, in the H1299 NSCLC cell line. APS and HCPT decreased H1299 cell viability, induced apoptosis and altered the cell cycle stages, as evaluated using an 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and flow cytometric analysis. Furthermore, APS increased the expression of apoptosis-associated genes B-cell lymphoma 2 (Bcl-2) and Bcl-2-associated X protein (BAX), of proteases cysteine-aspartic acid protease (caspase)-3 and -9, and of cytochrome c. HCPT promoted autophagy in H1299 cells, with concomitant suppression of the expression of MAP4K3 and downregulation of mTOR signaling. Notably, combination treatment with the two agents reduced the migration and invasion of H1299 cells compared with the single treatments. It was also demonstrated that the overexpression of MAP4K3 promoted the migration and invasion of H1299 cells, and that the kinase activity was essential to this. These findings suggested that MAP4K3 may be an attractive target for the treatment of NSCLC

A texture reconstruction method based on Markov random fields optimization was proposed to reconstruct the complex free-form shapes. The method used a three-dimensional digital device developed by laboratory to capture the range data and the texture images, and registered the local capture range data into global coordination system to establish the surface of the object. To build a realistic textured model, the captured texture images were mapped to the reconstructed surface by coordinate transformation, and a texture fusion processing was followed. The proposed method does not impose any constraints on the topology of the surface, and a realistic textured model of the free-form surface can be achieved. The data collection and realistic three-dimensional reconstruction of material object were experimented by the proposed method, the results show the reliability and effectiveness of the proposed method.

The purpose is to demonstrate the optical charactering concerning nasopharyngeal tissue of pig by fresh sections and frozen correlating sections with optical coherence tomography (OCT). After being imaged on a fresh specimen, samples are then stored in low temperature refrigerators (-80°C) for one year for the second OCT measurement. The OCT structure of the epithelium, lamina propria, and the basement membrane are still resolvable; the median scattering coefficients and anisotropy factors fitting from OCT images based on the multiple scattering effects for epithelium are 27.6  mm-1 [interquartile range (IQR) 23.6 to 29.3  mm-1] versus 22.5  mm-1 (IQR 20.5 to 24.4  mm-1), 0.86 (IQR 0.81 to 0.9) versus 0.88 (IQR 0.87 to 0.9) for fresh and frozen tissue, respectively; and 10.2  mm-1 (IQR 8.1 to 13.6  mm-1) versus 9.6  mm-1 (IQR 8.1 to 13.8  mm-1), 0.96 (IQR 0.93 to 0.98) versus 0.92 (IQR 0.9 to 0.98) for lamina propria, respectively. The results show that the frozen storage method can be used for OCT research.

A new approach called Fuzzy Extended Feature Line (FEFL) is proposed for image classification in this paper, which retain the advantages and ideas of Nearest Feature Line (NFL). The proposed FEFL use NFL to extend the prototype image sample set. Fuzzy K-Nearest Neighbor is applied for adding the new suitable samples to the prototype sample set. Experimental results on ORL face database and finger-knuckle-print database demonstrate the effectiveness of the proposed algorithm.