Formation and transformation of metastable LPSO building blocks clusters in Mg-Gd-Y-Zn-Zr alloys by spinodal decomposition and heterogeneous nucleation

To study the formation and transformation mechanism of long-period stacked ordered(LPSO)structures,a systematic atomic scale analysis was conducted for the structural evolution of long-period stacked ordered(LPSO)structures in the Mg-Gd-Y-Zn-Zr alloy annealed at 300℃~500℃.Various types of metastable LPSO building block clusters were found to exist in alloy structures at different temperatures,which precipitate during the solidification and homogenization process.The stability of Zn/Y clusters is explained by the first principles of density functional theory.The LPSO structure is distinguished by the arrangement of its different Zn/Y enriched LPSO structural units,which comprises local fcc stacking sequences upon a tightly packed plane.The presence of solute atoms causes local lattice distortion,thereby enabling the rearrangement of Mg atoms in the different configurations in the local lattice,and local HCP-FCC transitions occur between Mg and Zn atoms occupying the nearest neighbor positions.This finding indicates that LPSO structures can generate necessary Schockley partial dislocations on specific slip surfaces,providing direct evidence of the transition from 18R to 14H.Growth of the LPSO,devoid of any defects and non-coherent interfaces,was observed separately from other precipitated phases.As a result,the precipitation sequence of LPSO in the solidification stage was as follows:Zn/Ycluster+Mg layers→various metastable LPSO building block clusters→18R/24R LPSO;whereas the precipitation sequence of LPSO during homogenization treatment was observed to be as follows:18R LPSO→various metastable LPSO building block clusters→14H LPSO.Of these,14H LPSO was found to be the most thermodynamically stable structure.

Unsaturated bi-heterometal clusters in metal-vacancy sites of 2D MoS2 for efficient hydrogen evolution

The valence states and coordination structures of doped heterometal atoms in two-dimensional(2D)nanomaterials lack predictable regulation strategies.Hence,a robust method is proposed to form unsaturated heteroatom clusters via the metal-vacancy restraint mechanism,which can precisely regulate the bonding and valence state of heterometal atoms doped in 2D molybdenum disulfide.The unsaturated valence state of heterometal Pt and Ru cluster atoms form a spatial coordination structure with Pt–S and Ru–O–S as catalytically active sites.Among them,the strong binding energy of negatively charged suspended S and O sites for H+,as well as the weak adsorption of positively charged unsaturated heterometal atoms for H*,reduces the energy barrier of the hydrogen evolution reaction proved by theoretical calculation.Whereupon,the electrocatalytic hydrogen evolution performance is markedly improved by the ensemble effect of unsaturated heterometal atoms and highlighted with an overpotential of 84 mV and Tafel slope of 68.5 mV dec^(−1).In brief,this metal vacancy-induced valence state regulation of heterometal can manipulate the coordination structure and catalytic activity of heterometal atoms doped in the 2D atomic lattice but not limited to 2D nanomaterials.

Geometries and electronic structures of Zr_(n)Cu(n=2–12) clusters: A joint machine-learning potential density functional theory investigation

Zr-based amorphous alloys have attracted extensive attention because of their large glassy formation ability, wide supercooled liquid region, high elasticity, and unique mechanical strength induced by their icosahedral local structures.To determine the microstructures of Zr–Cu clusters, the stable and metastable geometry of Zr_(n)Cu(n=2–12) clusters are screened out via the CALYPSO method using machine-learning potentials, and then the electronic structures are investigated using density functional theory. The results show that the Zr_(n)Cu(n ≥ 3) clusters possess three-dimensional geometries, Zr_(n)Cu(n≥9) possess cage-like geometries, and the Zr_(12)Cu cluster has icosahedral geometry. The binding energy per atom gradually gets enlarged with the increase in the size of the clusters, and Zr_(n)Cu(n=5,7,9,12) have relatively better stability than their neighbors. The magnetic moment of most Zr_(n)Cu clusters is just 1μB, and the main components of the highest occupied molecular orbitals(HOMOs) in the Zr_(12)Cu cluster come from the Zr-d state. There are hardly any localized two-center bonds, and there are about 20 σ-type delocalized three-center bonds.

Optimal operation of Internet Data Center with PV and energy storage type of UPS clusters

With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of these idle UPS resources is an urgent problem that must be addressed.Based on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this study.Subsequently,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control problem.Accordingly,the classified EUPS aggregation unit was used to determine the optimal operation of the IDC.An IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was adopted.Finally,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.

Research of caged dynamics of clusters center atoms in Pd_(82)Si_(18) amorphous alloy

To date,there is still a lack of a comprehensive explanation for caged dynamics which is regarded as one of the intricate dynamic behaviors in amorphous alloys.This study focuses on Pd_(82)Si_(18)as the research object to further elucidate the underlying mechanism of caged dynamics from multiple perspectives,including the cage's lifetime,atomic local environment,and atomic potential energy.The results reveal that Si atoms exhibit a pronounced cage effect due to the hindrance of Pd atoms,resulting in an anomalous peak in the non-Gaussian parameters.An in-depth investigation was conducted on the caged dynamics differences between fast and slow Si atoms.In comparison to fast Si atoms,slow Si atoms were surrounded by more Pd atoms and occupied lower potential energy states,resulting in smaller diffusion displacements for the slow Si atoms.Concurrently,slow Si atoms tend to be in the centers of smaller clusters with coordination numbers of 9 and 10.During the isothermal relaxation process,clusters with coordination numbers 9 and 10 have longer lifetimes,suggesting that the escape of slow Si atoms from their cages is more challenging.The findings mentioned above hold significant implications for understanding the caged dynamics.

Fixed-Time Cluster Optimization for Multi-Agent Systems Based on Piecewise Power-Law Design

Dear Editor,This letter focuses on the fixed-time(FXT)cluster optimization problem of first-order multi-agent systems(FOMASs)in an undirected network,in which the optimization objective is the sum of the objective functions of all clusters.A novel piecewise power-law control protocol with cooperative-competition relations is proposed.Furthermore,a sufficient condition is obtained to ensure that the FOMASs achieve the cluster consensus within an FXT.

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

A Dual-Cluster-Head Based Medium Access Control for Large-Scale UAV Ad-Hoc Networks

Unmanned Aerial Vehicle(UAV)ad hoc network has achieved significant growth for its flexibility,extensibility,and high deployability in recent years.The application of clustering scheme for UAV ad hoc network is imperative to enhance the performance of throughput and energy efficiency.In conventional clustering scheme,a single cluster head(CH)is always assigned in each cluster.However,this method has some weaknesses such as overload and premature death of CH when the number of UAVs increased.In order to solve this problem,we propose a dual-cluster-head based medium access control(DCHMAC)scheme for large-scale UAV networks.In DCHMAC,two CHs are elected to manage resource allocation and data forwarding cooperatively.Specifically,two CHs work on different channels.One of CH is used for intra-cluster communication and the other one is for inter-cluster communication.A Markov chain model is developed to analyse the throughput of the network.Simulation result shows that compared with FM-MAC(flying ad hoc networks multi-channel MAC,FM-MAC),DCHMAC improves the throughput by approximately 20%~50%and prolongs the network lifetime by approximately 40%.

Building Fe atom–cluster composite sites using a site occupation strategy to boost electrochemical oxygen reduction

The high-temperature pyrolysis process for preparing M–N–C single-atom catalyst usually results in high heterogeneity in product structure concurrently contains multiscale metal phases from single atoms(SAs),atomic clusters to nanoparticles.Therefore,understanding the interactions among these components,especially the synergistic effects between single atomic sites and cluster sites,is crucial for improving the oxygen reduction reaction(ORR)activity of M–N–C catalysts.Accordingly,herein,we constructed a model catalyst composed of both atomically dispersed FeN4 SA sites and adjacent Fe clusters through a site occupation strategy.We found that the Fe clusters can optimize the adsorption strength of oxygen reduction intermediates on FeN4 SA sites by introducing electron-withdrawing–OH ligands and decreasing the d-band center of the Fe center.The as-developed catalyst exhibits encouraging ORR activity with halfwave potentials(E1/2)of 0.831 and 0.905 V in acidic and alkaline media,respectively.Moreover,the catalyst also represents excellent durability exceeding that of Fe–N–C SA catalyst.The practical application of Fe(Cd)–CNx catalyst is further validated by its superior activity and stability in a metalair battery device.Our work exhibits the great potential of synergistic effects between multiphase metal species for improvements of singleatom site catalysts.

Heat-inducible SlWRKY3 confers thermotolerance by activating the SlGRXS1 gene cluster in tomato

High temperature stress is one of the major environmental factors that affect the growth and development of plants. Although WRKY transcription factors play a critical role in stress responses, there are few studies on the regulation of heat stress by WRKY transcription factors,especially in tomato. Here, we identified a group I WRKY transcription factor, SlWRKY3, involved in thermotolerance in tomato. First, SlWRKY3 was induced and upregulated under heat stress. Accordingly, overexpression of SlWRKY3 led to an increase, whereas knock-out of SlWRKY3 resulted in decreased tolerance to heat stress. Overexpression of SlWRKY3 accumulated less reactive oxygen species(ROS), whereas knock-out of SlWRKY3 accumulated more ROS under heat stress. This indicated that SlWRKY3 positively regulates heat stress in tomato. In addition,SlWRKY3 activated the expression of a range of abiotic stress-responsive genes involved in ROS scavenging, such as a SlGRXS1 gene cluster.Further analysis showed that SlWRKY3 can bind to the promoters of the SlGRXS1 gene cluster and activate their expression. Collectively, these results imply that SlWRKY3 is a positive regulator of thermotolerance through direct binding to the promoters of the SlGRXS1 gene cluster and activating their expression and ROS scavenging.

Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm

Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.

Nomogram prediction of vessels encapsulating tumor clusters in small hepatocellular carcinoma≤3 cm based on enhanced magnetic resonance imaging

BACKGROUND Vessels encapsulating tumor clusters(VETC)represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma(HCC).However,it seems that no one have focused on predicting VETC status in small HCC(sHCC).This study aimed to develop a new nomogram for predicting VETC positivity using preoperative clinical data and image features in sHCC(≤3 cm)patients.AIM To construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of VETC and evaluate the prognosis of sHCC patients.METHODS A total of 309 patients with sHCC,who underwent segmental resection and had their VETC status confirmed,were included in the study.These patients were recruited from three different hospitals:Hospital 1 contributed 177 patients for the training set,Hospital 2 provided 78 patients for the test set,and Hospital 3 provided 54 patients for the validation set.Independent predictors of VETC were identified through univariate and multivariate logistic analyses.These independent predictors were then used to construct a VETC prediction model for sHCC.The model’s performance was evaluated using the area under the curve(AUC),calibration curve,and clinical decision curve.Additionally,Kaplan-Meier survival analysis was performed to confirm whether the predicted VETC status by the model is associated with early recurrence,just as it is with the actual VETC status and early recurrence.RESULTS Alpha-fetoprotein_lg10,carbohydrate antigen 199,irregular shape,non-smooth margin,and arterial peritumoral enhancement were identified as independent predictors of VETC.The model incorporating these predictors demonstrated strong predictive performance.The AUC was 0.811 for the training set,0.800 for the test set,and 0.791 for the validation set.The calibration curve indicated that the predicted probability was consistent with the actual VETC status in all three sets.Furthermore,the decision curve analysis demonstrated the clinical benefits of our model for patients with sHCC.Finally,early recurrence was more likely to occur in the VETC-positive group compared to the VETC-negative group,regardless of whether considering the actual or predicted VETC status.CONCLUSION Our novel prediction model demonstrates strong performance in predicting VETC positivity in sHCC(≤3 cm)patients,and it holds potential for predicting early recurrence.This model equips clinicians with valuable information to make informed clinical treatment decisions.

Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma:Machine learning model based on contrast-enhanced computed tomography

BACKGROUND Recently,vessels encapsulating tumor clusters(VETC)was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner,and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma(HCC).AIM To develop and validate a preoperative nomogram using contrast-enhanced computed tomography(CECT)to predict the presence of VETC+in HCC.METHODS We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers.Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase.Radiomics features,essential for identifying VETC+HCC,were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set.The model’s performance was validated on two separate test sets.Receiver operating characteristic(ROC)analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets.The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features.ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features,the radiomics features and the radiomics nomogram.RESULTS The study included 190 individuals from two independent centers,with the majority being male(81%)and a median age of 57 years(interquartile range:51-66).The area under the curve(AUC)for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825,0.788,and 0.680 in the training set and the two test sets.A total of 13 features were selected to construct the Rad-score.The nomogram,combining clinicalradiological and combined radiomics features could accurately predict VETC+in all three sets,with AUC values of 0.859,0.848 and 0.757.Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models.CONCLUSION This study demonstrates the potential utility of a CECT-based radiomics nomogram,incorporating clinicalradiological features and combined radiomics features,in the identification of VETC+HCC.

Engineering of oxygen vacancy and bismuth cluster assisted ultrathin Bi_(12)O_(17)Cl_(2)nanosheets with efficient and selective photoreduction of CO_(2)to CO

The photocatalytic conversion of CO_(2)into solar‐powered fuels is viewed as a forward‐looking strategy to address energy scarcity and global warming.This work demonstrated the selective photoreduction of CO_(2)to CO using ultrathin Bi_(12)O_(17)Cl_(2)nanosheets decorated with hydrothermally synthesized bismuth clusters and oxygen vacancies(OVs).The characterizations revealed that the coexistences of OVs and Bi clusters generated in situ contributed to the high efficiency of CO_(2)–CO conversion(64.3μmol g^(−1)h^(−1))and perfect selectivity.The OVs on the facet(001)of the ultrathin Bi_(12)O_(17)Cl_(2)nanosheets serve as sites for CO_(2)adsorption and activation sites,capturing photoexcited electrons and prolonging light absorption due to defect states.In addition,the Bi‐cluster generated in situ offers the ability to trap holes and the surface plasmonic resonance effect.This study offers great potential for the construction of semiconductor hybrids as multiphotocatalysts,capable of being used for the elimination and conversion of CO_(2)in terms of energy and environment.

Polyhedral silver clusters as single molecule ammonia sensor based on charge transfer-induced plasmon enhancement

The unique plasmon resonance characteristics of nanostructures based on metal clusters have always been the focus of various plasmon devices and different applications. In this work, the plasmon resonance phenomena of polyhedral silver clusters under the adsorption of NH_(3) , N_(2), H_(2), and CH_(4) molecules are studied by using time-dependent density functional theory. Under the adsorption of NH_(3) , the tunneling current of silver clusters changes significantly due to the charge transfer from NH_(3) to silver clusters. However, the effects of N_(2), H_(2), and CH_(4) adsorption on the tunneling current of silver clusters are negligible. Our results indicate that these silver clusters exhibit excellent selectivities and sensitivities for NH_(3) detection. These findings confirm that the silver cluster is a promising NH_(3) sensor and provide a new method for designing high-performance sensors in the future.

Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data.In this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm.The existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the algorithm.To solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each keyword.In order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in groups.We conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force method.The experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 times.GKM-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).

In silico curation of QTL-rich clusters and candidate gene identification for plant height of bread wheat

Many genetic loci for wheat plant height(PH) have been reported, and 26 dwarfing genes have been catalogued. To identify major and stable genetic loci for PH, here we thoroughly summarized these functionally or genetic verified dwarfing loci from QTL linkage analysis and genome-wide association study published from 2003 to 2022. A total of 332 QTL, 270 GWAS loci and 83 genes for PH were integrated onto chromosomes according to their locations in the IWGSC RefSeq v2.1 and 65 QTL-rich clusters(QRC) were defined. Candidate genes in each QRC were predicted based on IWGSC Annotation v2.1 and the information on functional validation of homologous genes in other species. A total of 38 candidate genes were predicted for 65 QRC including three GA2ox genes in QRC-4B-IV, QRC-5A-VIII and QRC-6A-II(Rht24) as well as GA 20-oxidase 2(TaSD1-3A) in QRC-3A-IV. These outcomes lay concrete foundations for mapbased cloning of wheat dwarfing genes and application in breeding.

Global trends and knowledge-relationship of symptom clusters in cancer research:a bibliometric analysis over the past 20 years

Objective:To use Cite Space and VOSviewer to investigate the scientific production in the field of symptom clusters in cancer research.Methods:The search was performed using the terms“symptom clusters,”“cancer,”and“oncology”on the Web of Science Core Collection database.The retrieval time was from 2001 to 2021,which covers the last 2 decades.Based on the production theory of scientific knowledge and the data mining of citations,data pertaining to the annual publications,journals,countries,organizations,authors,and keywords that produce symptom clusters in cancer research,as well as their cooperation(collaboration network),were extracted,and then both were clarified by the software tools VOSviewer(version 1.6.16)and Cite Space(version 6.1.R2).Results:A total of 1796 publications were retrieved between 2001 and 2021,and 473 relevant publications were included after screening.The results showed an increasing trend in published articles.The United States had the largest number of publications(261/473,55.18%),followed by China and Canada.The University of California,San Francisco,was the most productive institution.Current research hotspots included the analysis of symptom clusters and symptom management in patients with breast cancer and lung cancer,as well as any advanced cancer and cancer cachexia;fatigue-related symptom clusters and depression-anxiety symptom cluster;and the impacts of symptom clusters on quality of life.The research frontiers included analysis between health-related quality of life and symptom clusters,data mining in symptom clusters,research on the mental health status of cancer patients,and study of the mechanism and biological pathways of symptom clusters.Conclusions:The study provides insight into the global research perspective for the scientific progress on cancer symptom clusters,which suggests a growing scientific interest in this field,and more studies are warranted to guide symptom management.

平胃胶囊对恶变后GES-1细胞氧化应激的抑制作用及机制研究

目的:观察平胃胶囊对亚硝酸胺类化合物N-甲基-N’-硝基-N-亚硝基胍(N-methyl-N’-nitro-N-nitrosoguanidine,MNNG)诱导的胃癌前病变(precancerous lesions of gastric cancer,PLGC)细胞模型的影响,并初步探讨其作用机制。方法:制备空白血清和平胃胶囊含药血清备用;MNNG诱导人胃黏膜上皮细胞系GES-1制备PLGC细胞模型,采用倒置显微镜观察细胞形态,免疫荧光法检测增殖细胞相关抗原Ki67的表达水平,进行模型评价。CCK-8法筛选含药血清最佳干预浓度及时间;采用荧光探针DCFH-DA检测细胞内活性氧(reactive oxygen species,ROS)含量;ELISA检测丙二醛(malondialdehyde,MDA)含量;采用相关试剂盒检测超氧化物歧化酶(superoxide dismutase,SOD)和谷胱甘肽过氧化物酶(glutathione peroxidase,GSH-Px)活性;采用新型荧光探针JC-10检测细胞线粒体膜电位的变化;采用实时荧光定量PCR检测Ki67和黑色素瘤分化相关基因7(melanoma differentiation-associated gene-7,MDA-7)的mRNA表达水平;采用Western blot法测定Ki67、白细胞介素6(interleukin-6,IL-6)和MDA-7的蛋白表达水平。结果:与正常组相比,模型组和空白血清组ROS和MDA含量显著升高(P<0.01),SOD和GSH-Px的活性显著降低(P<0.01),线粒体膜电位显著下降(P<0.01),Ki67和IL-6蛋白表达显著升高(P<0.01),MDA-7蛋白表达显著下降(P<0.01);与空白血清组相比,模型组ROS和MDA含量,SOD和GSH-Px活性,Ki67和MDA-7 mRNA表达水平,Ki67、IL-6和MDA-7蛋白表达水平,以及线粒体膜电位均无显著差异(P>0.05);与空白血清组相比,平胃胶囊含药血清组中ROS和MDA含量显著降低(P<0.01),SOD和GSH-Px活性显著上升(P<0.05),线粒体膜电位显著升高(P<0.01),Ki67和IL-6蛋白表达水平显著下降(P<0.01),MDA-7的mRNA及蛋白表达水平显著升高(P<0.01)。结论:平胃胶囊可显著减轻MNNG诱导的胃黏膜上皮细胞氧化应激损伤和炎症反应,调控促癌基因和抑癌基因的表达,从而发挥防治PLGC的作用。

Adaptive Density-Based Spatial Clustering of Applications with Noise(ADBSCAN)for Clusters of Different Densities

Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects.It requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for epsilon.This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one.Only user input in the proposed method is the MinPts.Epsilon on the other hand,is computed automatically based on statistical information of the dataset.The proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density level.The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects.This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size.The proposed method requires MinPts only as an input parameter because epsilon is computed from data.Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results.Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them.The accuracy of the method ranges from 92%to 100%for the experimented datasets.