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A novel β_(2)-AR agonist,Higenamine,induces β-arrestin-biased signaling

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Science China(Life Sciences) 2022年第7期65卷 1357-1368页

作者： nana zhang Haibo Zhu Zijian Li Erdan Dong State Key Laboratory for Bioactive Substances and Functions of Natural Medicines/Beijing Key Laboratory of New Drug Mechanisms and Pharmacological Evaluation Study/Institute of Materia Medica Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100050China Department of Cardiology and Institute of Vascular Medicine Peking University Third Hospital/Key Laboratory of Cardiovascular Receptors ResearchBeijing/Key Laboratory of Molecular Cardiovascular SciencesMinistry of Education/Key Laboratory of Cardiovascular Molecular Biology and Regulatory PeptidesMinistry of HealthBeijing 100191China

The biased ligands in G protein-coupled receptors(GPCRs)have opened new avenues for developing safer and more effective ***,the identification of such biased ligands as drug candidates is highly ***,we report that Higenamine,a compound isolated from a Chinese herb,functions as a novel β-arrestin-biased ligand of the β_(2)-adrenergic receptor(β_(2)-AR).The radioligand binding assays demonstrated that Higenamine was the ligand of β_(2)-*** induced phosphorylation of extracellular signal-regulated kinase 1/2(ERK1/2),which can be blocked by propranolol,an inhibitor of β_(2)-*** Gi protein inhibitor,pertussis toxin,had no effect on the phosphorylation of ERK1/2 induced by ***,Higenamine induced ERK1/2 phosphorylation through transactivation of Epithelial growth factor receptor(EGFR).We also found that Higenamine-induced-ERK1/2 phosphorylation is dependent on β-arrestin1/2,and HG inhibits Doxorubicin-induced cardiomyocyte *** results identify Higenamine as a novel biased ligand via the β-arrestin-dependent *** findings give us a better understanding of Higenamine’s potential role in designing diagnostic and therapeutic strategies.

关键词： Higenamine β_(2)-adrenergic receptor β-arrestin-biased signaling extracellular signal-regulated kinase 1/2

Research progress of graphene-based nanomaterials for the environmental remediation

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Chinese Chemical Letters 2020年第6期31卷 1462-1473页

作者： Xiaoru Pan Jiahui Ji nana zhang Mingyang Xing Department of Chemistry and Chemical Engineering Weifang UniversityWeifang 261061China Key Laboratory for Advanced Materials and Institute of Fine Chemicals East China University of Science and TechnologyShanghai 200237China

Graphene is a two-dimensional nanomaterial with huge surface area,high carrier mobility and high mechanical *** of its great potential in nanotechnology and environmental protection,it has attracted much attention in environmental and energy fields since its discovery in *** graphene is a star material,many reviews have introduced its use in terms of energy,the research progress in the field of environment,especially water pollution control,has been rarely ***,we review exhaustively the research progress of graphene-based materials in environmental pollution remediation in the past ten ***,the advantages and classification of graphene were ***,the research progress and main achievements of graphene and its composites in the fields of photocatalytic degradation,pollutant adsorption and water treatment were emphatically described,and the mechanism of action in the above fields was ***,we discuss the problems existing in the preparation and summarize the application of graphene in the environment.

关键词： Graphene Nanomaterial Photocatalytic degradation Pollutant adsorption Water treatment

KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction

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Computers, Materials & Continua 2020年第7期64卷 471-499页

作者： nana zhang Kun Zhu Shi Ying Xu Wang School of Computer Science Wuhan UniversityWuhan430072China Department of Computer Science Vrije University AmsterdamAmsterdam1081HVThe Netherlands

Software defect prediction is a research hotspot in the field of software ***,due to the limitations of current machine learning algorithms,we can’t achieve good effect for defect prediction by only using machine learning *** previous studies,some researchers used extreme learning machine(ELM)to conduct defect ***,the initial weights and biases of the ELM are determined randomly,which reduces the prediction performance of *** by the idea of search based software engineering,we propose a novel software defect prediction model named KAEA based on kernel principal component analysis(KPCA),adaptive genetic algorithm,extreme learning machine and Adaboost algorithm,which has three main advantages:(1)KPCA can extract optimal representative features by leveraging a nonlinear mapping function;(2)We leverage adaptive genetic algorithm to optimize the initial weights and biases of ELM,so as to improve the generalization ability and prediction capacity of ELM;(3)We use the Adaboost algorithm to integrate multiple ELM basic predictors optimized by adaptive genetic algorithm into a strong predictor,which can further improve the effect of defect *** effectively evaluate the performance of KAEA,we use eleven datasets from large open source projects,and compare the KAEA with four machine learning basic classifiers,ELM and its three *** experimental results show that KAEA is superior to these baseline models in most cases.

关键词： Software defect prediction KPCA adaptive genetic algorithm extreme learning machine Adaboost

Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

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Computers, Materials & Continua 2020年第11期65卷 1467-1486页

作者： Kun Zhu nana zhang Qing zhang Shi Ying Xu Wang School of Computer Science Wuhan UniversityWuhan430072China School of Information Science and Engineering Qufu Normal UniversityRizhao276826China Department of Computer Science Vrije University AmsterdamAmsterdam1081HVThe Netherlands

Software defect prediction plays a very important role in software quality assurance,which aims to inspect as many potentially defect-prone software modules as ***,the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant *** addition,software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning *** address these two issues,we propose the following two solutions in this paper:(1)We leverage a novel non-linear manifold learning method-SOINN Landmark Isomap(SL-Isomap)to extract the representative features by selecting automatically the reasonable number and position of landmarks,which can reveal the complex intrinsic structure hidden behind the defect data.(2)We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques,which leverages denoising autoencoder to learn true input features that are not contaminated by noise,and utilizes deep neural network to learn the abstract deep semantic *** combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a *** compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software *** experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.

关键词： Software defect prediction non-linear manifold learning denoising autoencoder deep neural network loss function deep learning

Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm

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Computers, Materials & Continua 2020年第5期63卷 891-910页

作者： Kun Zhu nana zhang Shi Ying Xu Wang School of Computer Science Wuhan UniversityWuhan430072China Department of Computer Science Vrije University AmsterdamAmsterdam1081HVThe Netherlands

With the continuous expansion of software scale,software update and maintenance have become more and more ***,frequent software code updates will make the software more likely to introduce new *** how to predict the defects quickly and accurately on the software change has become an important problem for software *** defect prediction methods often cannot reflect the feature information of the defect comprehensively,and the detection effect is not ideal ***,we propose a novel defect prediction model named ITNB(Improved Transfer Naive Bayes)based on improved transfer Naive Bayesian algorithm in this paper,which mainly considers the following two aspects:(1)Considering that the edge data of the test set may affect the similarity calculation and final prediction result,we remove the edge data of the test set when calculating the data similarity between the training set and the test set;(2)Considering that each feature dimension has different effects on defect prediction,we construct the calculation formula of training data weight based on feature dimension weight and data gravity,and then calculate the prior probability and the conditional probability of training data from the weight information,so as to construct the weighted bayesian classifier for software defect *** evaluate the performance of the ITNB model,we use six datasets from large open source projects,namely Bugzilla,Columba,Mozilla,JDT,Platform and *** compare the ITNB model with the transfer Naive Bayesian(TNB)*** experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary,precision and pd for within-project and cross-project defect prediction.

关键词： Cross-project defect prediction transfer Naive Bayesian algorithm edge data similarity calculation feature dimension weight

Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization

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Computers, Materials & Continua 2020年第10期65卷 279-308页

作者： nana zhang Kun Zhu Shi Ying Xu Wang School of Computer Science Wuhan UniversityWuhan430072China Department of Computer Science Vrije University AmsterdamAmsterdam1081HVThe Netherlands

Software defect prediction plays an important role in software quality ***,the performance of the prediction model is susceptible to the irrelevant and redundant *** addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly *** the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by *** mainly consider two *** objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE *** objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE *** compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software *** experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.

关键词： Software defect prediction deep neural network stacked contractive autoencoder multi-objective optimization extreme learning machine

The Analysis of Secure HARQ with Chase Combining over Block Fading Channel

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China Communications 2016年第5期13卷 82-88页

作者： Yue Wu Lukman A Olawoyin nana zhang Hongwen Yang Wireless Communication Center School of Information and Communication EngineeringBeijing University of Posts and Telecommunications

In this letter, the physical layer security of hybrid automatic repeat request with chase combining(HARQ-CC) scheme is investigated from the viewpoint of information theory. Different from the literature which used Wyner code, our analysis focuses on the general scenario without specific code. We firstly obtain the outage probability of both main channel and wiretap channel. Since retransmissions are completely determined by main channel, we then prove that its achievable diversity order equals to maximum transmission number() while this order of wiretap channel is only one. Furthermore, we evaluate the metric of secure gap which demonstrates the difference between main channel and wiretap channel with reliable and secure constraints. As increases, the secure gap decreases monotonously. When is large enough, the security can be guaranteed even if wiretap channel is better than main channel.

关键词： physical layer security HARQ chase combining outage probability diversity order secure gap

Bifunctional additive phenyl vinyl sulfone for boosting cyclability of lithium metal batteries

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Green Chemical Engineering 2023年第1期4卷 49-56页

作者： Xiaoyan zhang Juyan zhang Mengmin Jia Linshan Peng nana zhang Suitao Qi Lan zhang Shaanxi Key Laboratory of Energy Chemical Process Intensification School of Chemical Engineering and TechnologyXi'an Jiaotong UniversityXi'an710049China CAS Key Laboratory of Green Process and Engineering Beijing Key Laboratory of Ionic Liquids Clean ProcessInstitute of Process EngineeringChinese Academy of SciencesBeijing100190China Faculty of Engineering and Physical Sciences Department of Chemical and Process EngineeringUniversity of SurreyGuildfordGU27XHUK

One of the bottlenecks limiting the cycling stability of high voltage lithium metal batteries(LMBs)is the lack of suitable ***,phenyl vinyl sulfone(PVS)is proposed as a multifunctional additive to stabilize both cathode and anode interfaces as it can be preferentially oxidized/reduced on the electrode *** PVS derived solid electrolyte interphase films can not only reduce the transition metal dissolution on the cathode side,but also suppress the Li dendrite spread on the lithium anode *** Li||Li symmetric battery with PVS addition delivers longer cycle life and a higher critical current density of over 3.0 m Ah cm^(-2).The LiNi_(0.8)Co_(0.1)Mn0.1O_(2)(NCM811)||Li full cell exhibits excellent capacity retention of 80.8%or 80.0%after 400 cycles at 0.5 C or 1 C rate with the voltage range of 3.0–4.3 *** particular,the NCM811||Li cell under constrained conditions remains operation over 150 *** work offers new insights into the electrolyte formulations for the next generation of LMBs.

关键词： Lithium metal battery Ni-rich cathode Phenyl vinyl sulfone Dendrite

Long‐life lithium batteries enabled by a pseudo‐oversaturated electrolyte

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Carbon Energy 2024年第4期6卷 115-127页

作者： Youchun Yu Simeng Wang Juyan zhang Weiwei Qian nana zhang Guangjie Shao Haiyan Bian Yuwen Liu Lan zhang Hebei Key Laboratory of Applied Chemistry College of Environmental and Chemical EngineeringYanshan UniversityQinhuangdaoChina Beijing Key Laboratory of Ionic Liquids Clean Process CAS Key Laboratory of Green Process and EngineeringInstitute of Process EngineeringChinese Academy of SciencesBeijingChina Haike Group Research Institute of Innovation&Technology DongyingChina Langfang Green Industrial Technology Center LangfangChina

The specific energy of Li metal batteries(LMBs)can be improved by using high‐voltage cathode materials;however,achieving long‐term stable cycling performance in the corresponding system is particularly challenging for the liquid ***,a novel pseudo‐oversaturated electrolyte(POSE)is prepared by introducing 1,1,2,2‐tetrafluoroethyl‐2,2,3,3‐tetrafluoropropyl ether(TTE)to adjust the coordination structure between diglyme(G2)and lithium bis(trifluoromethanesulfonyl)imide(LiTFSI).Surprisingly,although TTE shows little solubility to LiTFSI,the molar ratio between LiTFSI and G2 in the POSE can be increased to 1:1,which is much higher than that of the saturation state,1:*** and experimental results prove that TTE promotes closer contact of the G2 molecular with Li^(+)in the ***,it also participates in the formation of electrolyte/electrode *** electrolyte shows outstanding compatibility with both the Li metal anode and typical high‐voltage ***||Li symmetric cells show a long life of more than 2000 h at 1 mA cm^(−2),1 mAh cm^(−2).In the meantime,Li||LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)(NCM811)cell with the POSE shows a high reversible capacity of 134.8 mAh g^(−1 )after 900 cycles at 4.5 V,1 C *** concept of POSE can provide new insight into the Li^(+)solvation structure and in the design of advanced electrolytes for LMBs.

关键词： high voltage lithium metal batteries pseudo‐oversaturated electrolyte solid electrolyte interphases(SEI) solvation structure