基于UHPLC-Q-Exactive Orbitrap HRMS技术结合化学计量学方法的不同干燥处理杜仲叶成分分析

采用基于液质联用的非靶向代谢组学技术结合化学计量学数据自动解析软件AntDAS-LCHRMS分析了4种不同干燥处理(冻干、热泵烘干、电热烘干、晒干)杜仲叶样本中的化合物。杜仲叶样本数据由超高效液相色谱-四极杆-静电场轨道阱高分辨质谱(UHPLC-Q-Exactive Orbitrap HRMS)分别在正、负离子模式下进行采集,经AntDAS-LCHRMS软件解析,共鉴定出71种差异性化合物,经标准品验证确定40种化合物,包括环烯醚萜类、有机酸类、黄酮类、氨基酸类、核苷类、维生素类等9类物质。其中,正、负离子模式下均可识别并验证的化合物有车叶草苷、绿原酸、芦丁、异槲皮苷、车叶草苷酸、京尼平苷等25种化合物。层次聚类分析(HCA)及主成分分析(PCA)结果均显示,相同处理的杜仲叶样本各自聚成一类,不同处理的杜仲叶样本可明显区分。热图分析进一步揭示了不同干燥处理杜仲叶样本中差异性化合物的含量变化。晒干处理样本中苯丙氨酸及色氨酸等氨基酸类物质的水平较高;冻干及热泵烘干处理样本中有机酸类、环烯醚萜类、糖类等含量较高;电热烘干样本中核苷类、黄酮类物质的含量较高;黄酮类物质在冻干、热泵烘干及晒干样本中差异较小。研究结果为不同干燥处理杜仲叶的成分分析、品质评价及其开发应用提供了科学依据,也可为其他复杂药用植物体系的化学成分分析提供参考。

An Exact Virtual Network Embedding Algorithm Based on Integer Linear Programming for Virtual Network Request with Location Constraint

Network virtualization is known as a promising technology to tackle the ossification of current Internet and will play an important role in the future network area. Virtual network embedding(VNE) is a key issue in network virtualization. VNE is NP-hard and former VNE algorithms are mostly heuristic in the literature.VNE exact algorithms have been developed in recent years. However, the constraints of exact VNE are only node capacity and link bandwidth.Based on these, this paper presents an exact VNE algorithm, ILP-LC, which is based on Integer Linear Programming(ILP), for embedding virtual network request with location constraints. This novel algorithm is aiming at mapping virtual network request(VNR) successfully as many as possible and consuming less substrate resources.The topology of each VNR is randomly generated by Waxman model. Simulation results show that the proposed ILP-LC algorithm outperforms the typical heuristic algorithms in terms of the VNR acceptance ratio, at least 15%.

基于UHPLC-Q-Exactive Orbitrap MS的芪龙壮儿口服液指纹图谱研究

目的:建立芪龙壮儿口服液UHPLC-Q-Exactive Orbitrap MS正、负离子两种检测模式下的指纹图谱,为该制剂的质量控制提供参考和依据。方法:采用Thermo Accucore aQ RP18(2.1 mm×150 mm,2.6μm)色谱柱进行色谱分离,加热电喷雾离子源正、负离子同时检测。根据一级准分子离子精确质荷比、二级质谱碎片离子信息、文献报道和对照品比对鉴定共有峰成分。结果:构建了芪龙壮儿口服液正、负离子模式下的指纹图谱共有模式并分别确认了24(正离子模式)和17(负离子模式)个共有指纹峰。10批芪龙壮儿口服液指纹图谱与对照指纹图谱的相似度分别大于0.986(正离子模式)和0.968(负离子模式)。在正离子模式下鉴定了15个共有峰,其中9个共有峰采用对照品确认;负离子模式下鉴定了12个共有峰,其中9个共有峰采用对照品确认。结论:所建立的芪龙壮儿口服液正、负离子两种模式下的指纹图谱,结合共有峰的高分辨质谱定性分析,从正、负两个维度较为全面地表征了该制剂的化学成分指纹特征,为芪龙壮儿口服液的质量评价提供了依据和方法。

基于UHPLC-Q Exactive Orbitrap/MS结合多元统计方法分析不同基原淫羊藿化学成分的差异

目的:建立超高效液相色谱-四极杆-静电场轨道阱高分辨质谱联用(UHPLC-Q Exactive Orbitrap/MS)结合多元统计分析技术对5种不同基原淫羊藿化学成分差异分析的方法。方法:对5种基原30批淫羊藿样品的UHPLC-Q Exactive Orbitrap/MS全扫描数据进行多元统计分析。利用SIMCA软件进行主成分分析(PCA)和正交偏最小二乘法-判别分析(OPLS-DA)。结果:5种基原淫羊藿的化学成分具有差异性,根据相关参考文献及谱图信息,共鉴定出27种化学成分。所建立的OPLS-DA模型具有良好的数据描述能力和预测能力。柔毛淫羊藿化学成分具有显著性差异,而朝鲜淫羊藿与箭叶淫羊藿,淫羊藿与巫山淫羊藿之间的化学成分及含量具有相似性。经过筛选与分析,确定8种差异化学成分。结论:建立的方法可分析出5种基原淫羊藿化学成分的差异性,操作简便,方法准确,可为不同基原淫羊藿的质量控制及开发应用提供理论依据。

基于UPLC-Q-Exactive-Orbitrap-MS、网络药理学和实验验证探讨裸花紫珠抗H1N1活性成分及其作用机制

本实验首先对裸花紫珠的提取物进行萃取,得到不同的萃取部位,然后对各部位进行抗甲型流感病毒H1N1型(H1N1)活性筛选,确定裸花紫珠乙酸乙酯萃取部位(EACN)具有较好的体外抗H1N1的活性。采用超高效液相色谱-四级杆-静电场轨道阱联用(UPLC-Q-Exactive-Orbitrap-MS)技术对EACN进行定性分析。基于定性分析的结果,进一步使用数据挖掘和网络药理学,评估药物的活性成分、关键靶点和关键通路,最后使用H1N1感染BALB/c小鼠模型对预测结果进行验证。结果显示,在各个萃取部位中,乙酸乙酯部位体外抗H1N1效果最佳,半数抑制浓度(IC_(50))为51μg/mL,半数细胞毒浓度(CC_(50))为859.4μg/mL,选择指数(SI,CC_(50)/IC_(50))为16.85。从EACN中鉴定出34种化合物,在此基础上,利用网络药理学,筛选出和EACN抗H1N1相关的药物靶点67个,靶点共涉及生物过程491个,与抗H1N1相关的通路147个。H1N1感染BALB/c小鼠实验结果发现,EACN能改善H1N1感染引起的体重降低和肺指数的增加,降低小鼠肺组织的病毒载量,减轻小鼠肺组织的病理损伤,降低外周血血清中TNF-α、IFN-γ、IL-1β水平。实验结果提示EACN可能从直接抑制病毒、抗炎作用和免疫调节作用等多个途径发挥抗H1N1的作用。

STATE SPACE TREE METHOD AND EXACT DECOMPOSITION ALGORITHM FOR FINDING NETWORK OVERALL RELIABILITY

First,the state space tree method for finding communication network overall re-liability is presented.It directly generates one disjoint tree multilevel polynomial of a networkgraph.Its advantages are smaller computational effort(its computing time complexity is O(en_l),where e is the number of edges and n_l is the number of leaves)and shorter resulting expression.Second,based on it an exact decomposition algorithm for finding communication network overallreliability is presented by applying the hypergraph theory.If we use it to carry out the m-timedecomposition of a network graph,the communication network scale which can be analyzed by acomputer can be extended to m-fold.

SUBSTRUCTURE COMPUTATIONAL ALGORITHM FOR EXACT ANALYTIC METHOD

In[1], the exact analytic method for the solution of differential equation with variable coefficients was suggested and an analytic expression of solution was given by initial parameter algorithm. But to some problems such as the bending, free vibration and buckling of nonhomogeneous long cylinders, it is difficult to obtain their solutions by the initial parameter algorithm on computer. In this paper, the substructure computational algorithm for the exact analytic method is presented through the bending of non-homogeneous long cylindrical shell. This substructure algorithm can he applied to solve the problems which can not he calculated by the initial parameter algorithm on computer. Finally, the problems can he reduced to solving a low order system of algehraic equations like the initial parameter algorithm Numerical examples are given and compared with the initial para-algorithm at the end of the paper, which confirms the correctness of the substructure computational algorithm.

Nonlinear amplitude inversion using a hybrid quantum genetic algorithm and the exact zoeppritz equation

The amplitude versus offset/angle(AVO/AVA)inversion which recovers elastic properties of subsurface media is an essential tool in oil and gas exploration.In general,the exact Zoeppritz equation has a relatively high accuracy in modelling the reflection coefficients.However,amplitude inversion based on it is highly nonlinear,thus,requires nonlinear inversion techniques like the genetic algorithm(GA)which has been widely applied in seismology.The quantum genetic algorithm(QGA)is a variant of the GA that enjoys the advantages of quantum computing,such as qubits and superposition of states.It,however,suffers from limitations in the areas of convergence rate and escaping local minima.To address these shortcomings,in this study,we propose a hybrid quantum genetic algorithm(HQGA)that combines a self-adaptive rotating strategy,and operations of quantum mutation and catastrophe.While the selfadaptive rotating strategy improves the flexibility and efficiency of a quantum rotating gate,the operations of quantum mutation and catastrophe enhance the local and global search abilities,respectively.Using the exact Zoeppritz equation,the HQGA was applied to both synthetic and field seismic data inversion and the results were compared to those of the GA and QGA.A number of the synthetic tests show that the HQGA requires fewer searches to converge to the global solution and the inversion results have generally higher accuracy.The application to field data reveals a good agreement between the inverted parameters and real logs.

参仙升脉口服液化学成分的UHPLC-Q Exactive Focus MS/MS快速分析

目的探究参仙升脉口服液的化学物质基础。方法利用超高效液相色谱-四极杆/静电场轨道阱高分辨质谱(UHPLC-Q Exactive Focus MS/MS)对参仙升脉口服液的化学成分进行整体、系统辨识。采用Thermo Accucore aQ C_(18)色谱柱(150 mm×2.1 mm,2.6μm),流动相0.1%甲酸水溶液(A)-甲醇(B)梯度洗脱,洗脱程序为0-13 min,5%-60%B;13-27 min,60%-95%B;27-30 min,95%B,流速0.3 mL·min^(-1),柱温30°C;质谱分析离子化方式为加热电喷雾离子化(H-ESI),扫描模式为正、负离子切换,扫描范围m/z 120-1800,碰撞能量30 eV、50 eV、70 eV。结果共鉴定出160个化学成分,包括29个黄酮类成分,24个有机酸类成分,21个生物碱类成分,19个萜类成分,15个苯丙素类成分,12个皂苷类成分和40个其他类成分;其中6个化学成分(芦丁、补骨脂苷、异补骨脂苷、补骨脂素、异补骨脂素和补骨脂酚)经对照品比对确认。结论该研究借助UHPLC-Q Exactive Focus MS/MS技术实现了参仙升脉口服液化学成分准确、快速、系统的分析和鉴定,为其化学物质基础的阐明和质量控制提供了重要依据。

Parallel Quick Search Algorithm for the Exact String Matching Problem Using OpenMP

String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.

Exact Equivalence between Quantum Adiabatic Algorithm and Quantum Circuit Algorithm

We present a rigorous proof that quantum circuit algorithm can be transformed into quantum adiabatic algorithm with the exact same time complexity. This means that from a quantum circuit algorithm of L gates we can construct a quantum adiabatic algorithm with time complexity of O（L）. Additionally, our construction shows that one may exponentially speed up some quantum adiabatic algorithms by properly choosing an evolution path.

New algorithm of exact sampling with directional threshold

Although it is known that exact sampling algorithm is easy to construct and less sensitive to noise, the samples distri- bution of the algorithm deviates from the target states distribution due to the local dependent coupling problem. A new algorithm, named exact sampling with directional threshold （ES-DT） is intro- duced. The main advantage of the new algorithm, in comparison with the traditional exact sampling algorithm, is that it can control the sampling with a rejection strategy in Markov chain during the path growth, and closely approach the ideal distribution based on maintaining the target density. Simulation experiments show the effectiveness of the proposed algorithm.

基于UPLC-Q-Exactive MS技术3种核桃类种仁中甘油酯组成比较

基于超高效液相色谱-四级杆-轨道阱高分辨质谱联用(UPLC-Q-Exactive MS)技术比较分析了山核桃(Carya cathayensis Sarg.)、胡桃(Juglans regia Linn.)和薄壳山核桃〔Carya illinoinensis(Wangenh.)K.Koch〕3种木本油料作物种仁中甘油酯的组成及差异,结合LipidSearch软件和混合脂质内标对脂质分子进行定性及定量分析。结果显示:3种核桃类种仁中均鉴定到298个甘油酯,包括245个甘油三酯、38个甘油二酯、1个单半乳糖甘油二酯、9个双半乳糖甘油二酯和5个硫代异鼠李糖甘油二酯。偏最小二乘法判别分析得到104个变量投影重要度(VIP)大于1的脂质分子。山核桃和薄壳山核桃种仁中甘油三酯的主要脂肪酸为油酸,胡桃种仁中甘油三酯的主要脂肪酸为亚油酸。3种核桃类种仁中甘油二酯的主要脂肪酸均为亚油酸,其次为油酸。山核桃和薄壳山核桃种仁的甘油酯组成更为相近。UPLC-Q-Exactive MS可作为鉴别这3种木本油料作物种仁中甘油酯组成的有效方式。

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

An Innovative Genetic Algorithms-Based Inexact Non-Linear Programming Problem Solving Method

In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.