Network pharmacology and molecular docking identify mechanisms of medicinal plant-derived 1,2,3,4,6-penta-O-galloyl-beta-D-glucose treating gastric cancer

Background:1,2,3,4,6-penta-O-galloyl-beta-D-glucose(PGG)is a natural polyphenolic compound derived from multiple medicinal plants with favorable anticancer activity.Methods:In this study,the mechanisms of PGG against gastric cancer were explored through network pharmacology and molecular docking.First,the targets of PGG were searched in the Herbal Ingredients’Targets(HIT),Similarity Ensemble Approach(SEA),and Super-PRED databases.The potential targets related to gastric cancer were predicted from the Human Gene Database(GeneCards)and DisGeNET databases.The intersecting targets of PGG and gastric cancer were obtained by Venn diagram and then subjected to protein-protein interaction analysis to screen hub targets.Functional and pathway enrichment of hub targets were analyzed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases.The differential expression and survival analysis of hub targets in gastric cancer were performed based on The Cancer Genome Atlas database.Finally,the affinity of PGG with hub targets was visualized by molecular docking.Results:Three hub targets were screened,including mitogen-activated protein kinase 14(MAPK14),BCL2 like 1(BCL2L1),and vascular endothelial growth factor A(VEGFA).MAPK14 had a higher expression,while BCL2L1 and VEGFA had lower expression in gastric cancer than in normal conditions.Enrichment analysis indicated enrichment of these hub targets in MAPK,neurotrophin,programmed death-ligand 1(PD-L1)checkpoint,phosphatidylinositol 3-kinases/protein kinase B(PI3K-Akt),Ras,and hypoxia-inducible factor-1(HIF-1)signaling pathways.Conclusion:Therefore,network pharmacology and molecular docking analyses revealed that PGG exerts a therapeutic efficacy on gastric cancer by multiple targets(MAPK14,BCL2L1,and VEGFA)and pathways(MAPK,PD-L1 checkpoint,PI3K-Akt,Ras,and HIF-1 pathways).

通信受限下T-S模糊网络控制系统L_(1)动态输出反馈控制

针对通信受限的非线性网络控制系统,为兼顾系统性能和节约利用网络资源,引入事件触发通信机制(ETCM),利用时延建模方法和并行分布补偿(PDC)技术,将连续控制系统建模为一个采样数据误差依赖的非线性网络化系统模型;构建保守性低的时滞依赖和模糊基依赖的Lyapunov-Krasovskii泛函,给出增广系统稳定性和鲁棒性结果,得到鲁棒控制器存在的充分条件,提出一种基于线性矩阵不等式(LMIs)的事件触发参数,以及全局模糊L 1动态输出反馈控制器参数的协同设计方法。采用永磁同步电动机模型仿真验证,结果表明该设计方法可减少网络资源占用,达到闭环控制系统的性能要求。

APPROXIMATION ANALYSES FOR FUZZY VALUED FUNCTIONS IN L_1(μ)-NORM BY REGULAR FUZZY NEURAL NETWORKS

By defining fuzzy valued simple functions and giving L1(μ) approximations of fuzzy valued integrably bounded functions by such simple functions, the paper analyses by L1(μ)-norm the approximation capability of four-layer feedforward regular fuzzy neural networks to the fuzzy valued integrably bounded function F : Rn → FcO(R). That is, if the transfer functionσ: R→R is non-polynomial and integrable function on each finite interval, F may be innorm approximated by fuzzy valued functions defined as to anydegree of accuracy. Finally some real examples demonstrate the conclusions.

Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks

Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during their use.However,because of the resource limitations of the end device,processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network(CNN),which involves a great amount of multiplicative operations.To minimize the computation cost of the conventional CNN,based on the idea of AdderNet,a 1-D adder neural network with a wide first-layer kernel(WAddNN)suitable for bearing fault diagnosis is proposed in this paper.The proposed method uses the l1-norm distance between filters and input features as the output response,thus making the whole network almost free of multiplicative operations.The whole model takes the original signal as the input,uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise,and then uses two layers of small kernels for nonlinear mapping.Through experimental comparison with CNN models of the same structure,WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost.The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.

具有随机测量数据丢失和混合时滞的网络控制系统L1故障检测

对于具有随机测量数据丢失、随机时滞和分布时滞的网络控制系统,为解决其受到外部干扰信号为持续峰值有界的L1故障检测问题,设计产生残差信号的故障检测滤波器,利用Bernoulli随机分布序列描述网络环境下的随机测量数据丢失和随机时滞,通过构造时滞相关的Lyapunov-Krasovskii泛函使故障检测系统均方渐近稳定,在持续强噪声干扰下满足L1抑制水平,以线性矩阵不等式(LMI)形式得出低保守性故障检测L 1滤波器的参数化方法。数据仿真结果表明,该方法可有效且灵敏地检测故障信号。

L+1容错恢复策略在复杂军事网络中的应用仿真

以标准3×3网格为基础网络,介绍了L+1容错恢复策略的概念和恢复原理,以海上舰艇编队作战系统网络为研究对象,使用Matlab-Simulink仿真工具,通过对阻塞概率、平均路径长度、路径重新分配概率和有效利用率4个指标的仿真,对比了该网络在无策略、备份多路和L+1容错恢复3种策略下网络的恢复程度,验证了L+1容错恢复策略的有效性和可行性。

基于网络药理学和UPLC-Q-TOF/MS结合动物实验探究蓍草预防急性肝损伤的作用机制

目的 通过UPLC-Q-TOF/MS定性分析和网络药理学预测并结合动物实验验证初步探究蓍草保护急性肝损伤(ALI)大鼠肝脏的作用机制。方法 采用UPLC-Q-TOF/MS法鉴定蓍草中的化学成分,利用TCMSP、CTD和Similarity Ensemble Approach等数据库筛选这些成分的作用靶点,同时在GeneCards、OMIM和NCBI数据库筛选ALI相关靶点,将成分作用靶点与疾病靶点的交集靶点导入STRING构建蛋白质-蛋白质相互作用(PPI)网络,并使用Cytoscape3.8.2软件进行网络拓扑分析筛选核心靶点,通过DAVID数据库对关键靶点进行GO和KEGG富集分析。通过肝组织HE染色,大鼠血清中肝功能指标、炎症因子水平以及肝组织中氧化应激水平的测定对蓍草防治ALI的药效进行评价,并利用免疫组化的方法对预测的核心靶点及KEGG富集通路结果进行验证。结果 经UPLC-Q-TOF/MS法鉴定出20个成分,网络药理学方法筛选得到上述成分预防治疗ALI的核心靶点104个,GO功能主要与炎症反应、氧化还原酶活性和蛋白酶结合等生物过程相关,KEGG结果主要涉及IL-17、TNF及Toll样受体等信号通路;动物实验结果显示,蓍草预防性给药后能够缓解大鼠肝脏的损伤,显著降低模型组大鼠血清中ALT、AST、TNF-α、IL-6、IL-1β水平和肝组织中MDA的含量,且SOD和CAT在肝组织中的活性显著增强;此外还发现蓍草给药后可上调大鼠肝脏组织中Nfr2蛋白的表达,下调TLR-4、NF-κBp65、keap1和HO-1蛋白的表达。结论 蓍草对四氯化碳诱导的ALI大鼠肝脏具有保护作用,该作用可能与其调节keap1/Nrf2/HO-1和Toll样受体信号通路有关。

2-median location improvement problems under weighted l_1 norm and l_∞ norm on trees

This paper focuses on the 2-median location improvement problem on tree networks and the problem is to modify the weights of edges at the minimum cost such that the overall sum of the weighted distance of the vertices to the respective closest one of two prescribed vertices in the modified network is upper bounded by a given value.l1 norm and l∞norm are used to measure the total modification cost. These two problems have a strong practical application background and important theoretical research value. It is shown that such problems can be transformed into a series of sum-type and bottleneck-type continuous knapsack problems respectively.Based on the property of the optimal solution two O n2 algorithms for solving the two problems are proposed where n is the number of vertices on the tree.

GFE(L)1型二次测风雷达观测系统故障处理

介绍了GFE(L)1型二次测风雷达观测系统计算机、网络及测角分系统等部件的一些故障现象,分析了产生的原因,提出了具体的故障排除方法,为及早发现和解决雷达设备故障和故障隐患,加强雷达系统的维护保障,确保GFE(L)1型二次测风雷达观测系统在日常观测业务期间高效、稳定运行。

Generating Cartoon Images from Face Photos with Cycle-Consistent Adversarial Networks

The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications is style transfer.Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image.CYCLE-GAN is a classic GAN model,which has a wide range of scenarios in style transfer.Considering its unsupervised learning characteristics,the mapping is easy to be learned between an input image and an output image.However,it is difficult for CYCLE-GAN to converge and generate high-quality images.In order to solve this problem,spectral normalization is introduced into each convolutional kernel of the discriminator.Every convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to[0,1],which promotes the training process of the proposed model.Besides,we use pretrained model(VGG16)to control the loss of image content in the position of l1 regularization.To avoid overfitting,l1 regularization term and l2 regularization term are both used in the object loss function.In terms of Frechet Inception Distance(FID)score evaluation,our proposed model achieves outstanding performance and preserves more discriminative features.Experimental results show that the proposed model converges faster and achieves better FID scores than the state of the art.

A Semi-Supervised WLAN Indoor Localization Method Based on l1-Graph Algorithm

For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.

基于网络药理学与实验验证研究荜茇抗肺纤维化的作用机制

目的:通过网络药理学及分子对接技术初步预测荜茇抗肺纤维化的活性成分、作用靶点及信号通路,并通过体外实验进行初步验证荜茇抗肺纤维化的作用机制。方法:从TCMSP、Swiss Target Prediction和Pub Chem等数据库中获取药物的有效成分及对应靶点。使用Gene Cards和OMIM数据库收集疾病的相关靶点。利用jvenn在线工具筛选出药物与疾病的交集靶点,通过String数据库及Cytoscape3.9.1软件构建“药物-成分-靶点”与PPI网络图。使用微生信平台对交集靶点进行GO及KEGG富集分析,将富集KEGG前20条通路、核心靶点、药物成分,构建“成分-靶点-通路”网络并进行可视化。利用分子对接技术将药物成分与靶点进行对接,并对其部分对接结果可视化。培养HFL-1细胞,采用MTT法检测各浓度对细胞活力的影响,计算抑制率,从而筛选出最佳的药物浓度。将体外培养的HFL-1细胞分为4组,即对照组、TGF-β1组、TGF-β1+LD组(联合LD组)、TGF-β1+HD组(联合HD组)。采用CCK-8试剂检测24、48、72 h的细胞增殖活性。采用平板克隆形成实验来观察药物对HFL-1细胞克隆形成能力的影响。采用RT-qPCR和Western blot实验观察各组细胞α-平滑肌肌动蛋白(α-SMA)、Ⅰ型胶原蛋白(COI-Ⅰ)、Ⅲ型胶原蛋白(COI-Ⅲ)的m RNA和PI3K-Akt信号通路蛋白表达水平。结果:共获得荜茇与抗肺纤维化交集靶点为197个。经拓扑学分析获得核心PPI网络,包含29个节点与199条边;GO功能分析显示,荜茇治疗肺纤维化(PF)生物学过程包括:凋亡过程负调控、信号转导、蛋白质磷酸化等。细胞成分包括:细胞质、核浆、等离子体膜。分子功能包括相同的蛋白结合、丝氨酸/苏氨酸/酪氨酸激酶活性、蛋白结合等。KEGG信号通路被155条富集,而其中已被证实,与PF密切相关为PI3K-Akt信号通路,富集排名第四“。成分-靶点-通路”网络中PIK3CA、MAPK3、MAPK1、MTOR、SRC、CCND1、EGFR、PRKCA、BCL2、GSK3B链接度最高。荜茇宁、N-(2,5-二甲氧基苯基)-4-甲氧基苯甲酰胺、四氢丹参酮、豌豆素和胡椒碱是关键化合物。分子对接结果显示,除N-(2,5-二甲氧基苯基)-4-甲氧基苯甲酰胺外,其余化合物与10个链接度高的蛋白质结合活性较好。体外实验显示,48、72 h联合LD组增殖能力明显低于TGF-β1组(P<0.05),24、48、72 h联合HD组又显著低于联合LD组。各药物组克隆数量显著减少(P<0.05)。联合LD和联合HD组α-SMA、COl-I、COl-Ⅲm RNA和蛋白表达水平明显低于TGF-β1组。与TGF-β1组相比,两个剂量组中p-PI3K/PI3K、pAKT/AKT蛋白表达显著减少(P<0.01)。结论:荜茇抗PF作用,其机制可能通过调控PI3K-Akt信号通路而发挥治疗PF作用。

Transition control of a tail-sitter unmanned aerial vehicle with L1 neural network adaptive control

The main task of this work is to design a control system for a small tail-sitter Unmanned Aerial Vehicle(UAV)during the transition process.Although reasonable control performance can be obtained through a well-tuned single PID or cascade PID control architecture under nominal conditions,large or fast time-varying disturbances and a wide range of changes in the equilibrium point bring nonlinear characteristics to the transition control during the transition process,which leads to control precision degradation.Meanwhile,the PID controller’s tuning method relies on engineering experiences to a certain extent and the controller parameters need to be retuned under different working conditions,which limits the rapid deployment and preliminary validation.Based on the above issues,a novel control architecture of L1 neural network adaptive control associated with PID control is proposed to improve the compensation ability during the transition process and guarantee the security transition.The L1 neural network adaptive control is revised to solve the multi-input and multi-output problem of the tail-sitter UAV system in this study.Finally,the transition characteristics of the time setting difference between the desired transition speed and the desired transition pitch angle are analyzed.

Anti-tumor activity of Sanguisorba officinalis L.in non-small cell lung cancer and induced apoptosis via PI3K/Akt/mTOR signaling pathway

OBJECTIVE To investigate the pharmacological effect and mechanism of Sanguisorba officinalis L.(SOL)in non-small cell lung cancer(NSCLC)in vitro and in vivo based on network pharmacology.METHODS Network pharmacology was used to analyze the improving effect of SOL on NSCLC and possible targets.Cell counting kit 8(CCK-8)and 5-ethynyl-2′-deoxyuridine(EdU)staining,Western blotting,flow cytometry of AnnexinⅤ/PI,Hoechst 33342/PI staining detection and immunofluorescence were utilized in vitro.H&E staining,immunohistochemistry staining and Western blotting were performed in vivo.RESULTS Based on network prediction,we analyzed the 208 common targets of SOL and NSCLC.36 core targets in 208 common targets were obtained through cytoscape analysis.And the top 10 core targets included Akt,mTOR,EGFR,etc..KEGG analysis showed that PI3K-Akt signaling pathway was the most likely pathway.Furthermore,the mechanism study found that SOL could activate the PI3K/Akt/mTOR signaling pathway in vitro and in vivo.The anti-proliferative effect of SOL in A549 and H1299 cells was measured and validated by CCK-8 and EdU assay.Immunohistochemical results of Ki67 showed that SOL effectively inhibited tumor growth in vivo.SOL also significantly inhibited the migration and invasion of A549 and H1299 cells.SOL significantly increased the percentage of cells with PI signal in A549 and H1299,and the process of cell death of A549 cells indicated that SOL induced apoptosis.The PARP-1 and caspase-3 in A549 and H1299 were found to be activated in a dose manner.The results in vivo were consistent with those in vitro.CONCLUSION SOL-induced,caspase-3-mediated apoptosis via the induction of the PI3K/Akt/mTOR signaling pathway in NSCLC,which further clarified the mechanism of SOL in the inhibition of NSCLC,and thereby provided a possibility for SOL to serve as a novel therapeutic agent for NSCLC in the future.

基于网络药理学探讨罗勒抗炎抗菌分子机制

利用网络药理学探究罗勒抗炎抗菌的作用机制,并通过体外试验进行初步验证。通过相关数据库筛选到罗勒的18种化合物和6种挥发油成分,作用靶点521个,获得抗炎抗菌相关靶点975个。使用Venny 2.1做药物和疾病靶点交集的韦恩图,得到88个罗勒抗炎抗菌靶点。构建蛋白互作PPI网络,并进行GO和KEGG富集分析。根据蛋白关联度可知核心靶点涉及TNF-α,IL-1β,IL-6等,主要通过对细菌的反应、对脂多糖的反应等信号通路发挥抗炎抗菌作用。体外细胞试验采用LPS及热灭活痤疮丙酸杆菌(Cutibacterium acnes,C.a)诱导的THP-1两种炎症细胞模型。采用ELISA法检测添加不同含量罗勒精油(Ocimum basilicum L.essential oil,OBEO)细胞的TNF-α,IL-1β、IL-6的分泌量。结果显示,体积分数为0.025%的OBEO能够抑制两种炎症损伤模型TNF-α,IL-1β,IL-6的表达;且OBEO对灭活C.a致THP-1细胞炎症损伤的抑制作用显著,分别降低TNF-α,IL-1β,IL-6的表达24%,45%,13%左右。

Exponential L1 Filtering of Networked Linear Switched Systems:An Event-Triggered Approach

The issues of event-triggered exponential L1 filtering are studied for a class of networked linear switched systems.An event-triggered mechanism is proposed to enhance resource utilization in transmission,and save the communication cost of systems as well.Then,the filtering error system is reconstructed as a switched delay system with bounded disturbance through the input delay system approach.By resorting to the Lyapunov-Krasovskii functional approach and the average dwell time(ADT)technique,some interesting results are derived to guarantee the exponential stability with a prescribed L1 disturbance rejection level.Further,an event-triggered exponential L1 filter is designed via solving a set of feasible linear matrix inequalities(LMIs).Finally,the efficiency of the proposed results is verified through a numerical example and a PWM-driven boost converter circuit system.

两种灰色神经网络模型及应用

给出了 2种灰色神经网络模型 GNNM(1,1)和 GNNM(2 ,1) ,该模型具有灰色系统的少数据建模优点及神经网络的精度可控特性 ,并给出了相应的学习算法 。

稀疏正则化逆向神经网络在双陷波超宽带天线设计中的应用

在双陷波超宽带天线的设计过程中,直接逆向神经网络模型精度较低,而BP逆向神经网络泛化能力较差,若单独使用HFSS仿真软件需要不断优化天线各参数增加了设计时间。针对以上问题,提出一种将HFSS与稀疏正则化逆向神经网络联合的方法。该方法在逆向神经网络性能函数中增加l1/2范数和l2范数,l1/2范数引入了新的权系数,扩充了输入样本向量,使网络更易得到稀疏性解,逆模型精度更高,l2范数能有效避免过拟合现象,使网络泛化能力更强。应用于双陷波超宽带天线设计中,采用在辐射贴片上开弧形槽的方式产生陷波特性,根据天线目标电压驻波比逆向求解对应的开槽尺寸。仿真实验结果表明,与BP逆向神经网络方法相比,求得的与天线电压驻波比对应的开槽角度相对误差减小了69.3%,开槽半径相对误差减小了88.7%,网络运行时间减少了15.9%;最终设计的天线带宽为2.4~11GHz,实现了3.31~3.8GHz和4.98~6.05GHz的良好陷波特性,缩短了整个天线的设计周期。

求解绝对极小拟合问题的神经网络方法

提出一个解绝对极小拟合问题的神经网络，并证明它的全局收敛性．该网络的优点是没有选择惩罚参数的困难，稳态解对应最优解，及网络电路中不含变量间的模拟乘法器．