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Mechanism of action of cordycepin in the treatment of hepatocellular carcinoma via regulation of the Hippo signaling pathway

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Food Science and Human Wellness 2024年第2期13卷 1040-1054页

作者： Xiaomin li qing liu Songyu Xie Xiaoping Wu Junsheng Fu College of life Sciences Mycological Research CenterFujian Agriculture and Forestry UniversityFuzhou 350002China

Hepatocellular carcinoma(HCC)is one of the common most malignant tumors.This study aimed to determine the in vitro and in vivo anticancer activity of cordycepin and elucidate its mechanism of action.The results of in vitro and in vivo studies revealed that cordycepin inhibited proliferation and migration in HepG-2 cells and inhibited the growth of HepG-2 xenograft-bearing nude mice by inducing apoptosis.Transcriptome sequencing analysis revealed a total of 403 differential genes,which revealed that cordycepin may play an anti-HCC role by regulating Hippo signaling pathway.The regulatory effects of cordycepin on the Hippo signaling pathway was further investigated using a YAP1 inhibitor.The results demonstrated that cordycepin upregulated the expression of MST1 and LAST1,and subsequently inhibited YAP1,which activated the Hippo signaling pathway.This in turn downregulated the expression of GBP3 and ETV5,and subsequently inhibited cell proliferation and migration.Additionally,YAP1 regulated the expression of Bax and Bcl-2,regulated the mitochondrial apoptotic pathway,and induced apoptosis by upregulating the expression of the caspase-3 protein.In summary,this study reveals that cordycepin exerts its anti-hepatocarcinoma effect through regulating Hippo signaling pathway,and GBP3 and ETV5 may be potential therapeutic targets for hepatocarcinoma.

关键词： Cordycepin Hepatocellular carcinoma Hippo signaling pathway GBP3 ETV5

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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Petroleum Science 2024年第1期21卷 641-653页

作者： Yun-Peng He Hai-Bo Cheng Peng Zeng Chuan-Zhi Zang qing-Wei Dong Guang-Xi wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016LiaoningChina Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016LiaoningChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169LiaoningChina University of Chinese Academy of Sciences Beijing100049China School of Artifcial Intelligence Shenyang University of TechnologyShenyang 110870LiaoningChina

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

关键词： Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning Time-frequency signature Time-frequency signature matrix

Unraveling role of double-exchange interaction in electrochemical water oxidation by external magnetic field

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Rare Metals 2024年第1期43卷 289-297页

作者： Jun li Jun-Ming li Hong Hong Dong-Xue liu qing-Qi Cao Dun-Hui wang National Laboratory of Solid State Microstructures and Jiangsu Provincial Key Laboratory for Nanotechnology Nanjing UniversityNanjing 210093China School of Electronics and Information Hangzhou Dianzi UniversityHangzhou 310018China

Double-exchange(DE) interaction plays an important role in electrocatalytic oxygen evolution reaction(OER).However,precise achievement of DE interaction often requires foreign dopants or vacancy engineering,leading to destabilization of the catalysts and deterioration of performance.By contrast,the utilization of environmentally friendly,contactless,and continuously adjustable magnetic fields to study the OER process is profitable to avoid aforementioned interference factors and further elucidate the direct relationship_(0.5)between DE interaction and OER activity.Here,by using cobalt hydroxide carbonate(Co(OH)(CO_(3))·xH_(2)O,CoHC) nanostructures as a proof-of-concept study,external magnetic fields are carefully implemented to verify the role of DE interaction during water oxidation reaction.Detailed studies reveal that external magnetic fields effectively enhance the reaction rate of the catalyst,the overpotential decreases from 386 to 355 mV(100 mA·cm^(-2)),while Tafel slopes drastically decline from 93 to 67 mV·dec^(-1)(1.0 T).Moreover,magnetic field increment exhibits robust durability.Through in situ Raman and impedance measurements under external field,it can be found that magnetic field promotes the electron migration between Co^(2+) and Co^(3+) in the CoHC catalysts with the assistance of DE interactions,thus boosting the OER efficiency.

关键词： Magnetic field Double-exchange(DE)interaction Electrocatalysis Oxygen evolution reaction(OER) Cobalt hydroxide carbonate

Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

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International Journal of Minerals,Metallurgy and Materials 2024年第6期31卷 1311-1321页

作者： Xiaojia Yang Jinghuan Jia qing li Renzheng Zhu Jike Yang Zhiyong liu Xuequn Cheng Xiaogang li Institute of Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China AECC Key Laboratory of Advanced Corrosion and Protection of Aeronautical Materials AECC Beijing Institute of Aeronautical MaterialsBeijing 100095China Shunde Innovation School University of Science and Technology BeijingFoshan 528399China

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

关键词： weathering steel stress-assisted corrosion gradient boosting decision tree machining learning

Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data

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Frontiers of Environmental Science & Engineering 2024年第3期18卷 69-82页

作者： Junchen li Sijie lin liang Zhang Yuheng liu Yongzhen Peng qing Hu School of Environment Harbin Institute of TechnologyHarbin 150090China School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Engineering Innovation Center of SUSTech(Beijing) Southern University of Science and TechnologyBeijing 100083China Faculty of Environment and life Beijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of EducationBeijing 100124China

Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations.In this study,we aimed to develop an integrated method for predicting effluent COD and NH3 levels.We employed a 200 L pilot-scale sequencing batch reactor(SBR)to gather multimodal data from urban sewage over 40 d.Then we collected data on critical parameters like COD,DO,pH,NH_(3),EC,ORP,SS,and water temperature,alongside wastewater surface images,resulting in a data set of approximately 40246 points.Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network(BITF-CL)using this data.This innovative model synergized sewage imagery with water quality data,enhancing prediction accuracy.As a result,the BITF-CL model reduced prediction error by over 23%compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data.Consequently,this research presents a cost-effective and precise prediction system for sewage treatment,demonstrating the potential of brain-inspired models.

关键词： Wastewater treatment system Water quality prediction Data driven analysis Brain-inspired model Multimodal data Attention mechanism

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

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International Journal of Minerals,Metallurgy and Materials 2024年第1期31卷 106-117页

作者： Xin Shao qing liu Zicheng Xin Jiangshan Zhang Tao Zhou Shaoshuai li State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China Engineering Research Center of MES Technology for Iron&Steel Production Ministry of EducationBeijing 100083China Laiwu Iron and Steel Group Yinshan Section Steel Co. Ltd.Jinan 271104China

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

关键词： basic oxygen furnace oxygen consumption oxygen blowing time oxygen balance mechanism deep neural network hybrid model

Gelatin-Based Metamaterial Hydrogel Films with High Conformality for Ultra-Soft Tissue Monitoring

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Nano-Micro Letters 2024年第2期16卷 347-364页

作者： Yuewei Chen Yanyan Zhou Zihe Hu Weiying Lu Zhuang li Ning Gao Nian liu Yuanrong li Jing He qing Gao Zhijian Xie Jiachun li Yong He State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical EngineeringZhejiang UniversityHangzhou 310027People’s Republic of China School of Mechanical Engineering Guizhou UniversityGuiyang 550025People’s Republic of China Stomatology Hospital School of StomatologyZhejiang University School of MedicineClinical Research Center for Oral Diseases of Zhejiang ProvinceKey Laboratory of Oral Biomedical Research of Zhejiang ProvinceCancer Center of Zhejiang UniversityHangzhou 310006People’s Republic of China

Implantable hydrogel-based bioelectronics(IHB)can precisely monitor human health and diagnose diseases.However,achieving biodegradability,biocompatibility,and high conformality with soft tissues poses significant challenges for IHB.Gelatin is the most suitable candidate for IHB since it is a collagen hydrolysate and a substantial part of the extracellular matrix found naturally in most tissues.This study used 3D printing ultrafine fiber networks with metamaterial design to embed into ultra-low elastic modulus hydrogel to create a novel gelatin-based conductive film(GCF)with mechanical programmability.The regulation of GCF nearly covers soft tissue mechanics,an elastic modulus from 20 to 420 kPa,and a Poisson’s ratio from-0.25 to 0.52.The negative Poisson’s ratio promotes conformality with soft tissues to improve the efficiency of biological interfaces.The GCF can monitor heartbeat signals and respiratory rate by determining cardiac deformation due to its high conformability.Notably,the gelatin characteristics of the biodegradable GCF enable the sensor to monitor and support tissue restoration.The GCF metamaterial design offers a unique idea for bioelectronics to develop implantable sensors that integrate monitoring and tissue repair and a customized method for endowing implanted sensors to be highly conformal with soft tissues.

关键词： Implantable hydrogel-based bioelectronics Conformality 3D printing Metamaterial design

Rheological Behaviors of Polymers with Nanoparticles Tethered at Each End

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Chinese Journal of Polymer Science 2024年第3期42卷 400-406,I0009页

作者： Song-Qi Zhang Wen-qing wang Jia-Ping lin li-Quan wang Shanghai Key Laboratory of Advanced Polymeric Materials Key Laboratory for Ultrafine Materials of Ministry of EducationSchool of Materials Science and EngineeringEast China University of Science and TechnologyShanghai 200237China

The polymer with nanoparticles tethered at each end is a unique model for unraveling the effect of chain ends on the polymer dynamics. We investigated the rheological behavior of this kind of polymer by using nonequilibrium molecular dynamics simulation. The effect of polymer lengths and nanoparticle radii on the complex moduli and viscosity was examined. The dependence of complex moduli on the frequency becomes less pronounced as the polymer is short or the nanoparticle is large. The shear thinning behavior was revealed for these systems, and the scaling exponent of complex viscosity with respect to the frequency was found to change from-1/2 to-3/4 as the polymer shortens or the nanoparticle enlarges. The rheological behavior was further explained by analyzing the mean square distance of nanoparticles. The simulation results were compared with the existing experimental finding, showing an agreement. The work provides information for understanding the chain end effect on polymer rheology.

关键词： Polymer rheology Modulus Viscosity Molecular dynamics

Dual sliding mode coordinated control of manipulator grasping system with visual assistance

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Control Theory and Technology 2024年第1期22卷 106-121页

作者： Pengxin Zhang Haisheng Yu Xiangxiang Meng Zhe li qing Yang Xunkai Gao College of Automation Qingdao UniversityQingdao266071ShandongChina Shandong Province Key Laboratory of Industrial Control Technology Qingdao266071ShandongChina

The operation efficiency of the manipulator is placed in the primary position in automatic production. This paper proposes a coordinated control strategy for joint servo and visual servo to enable timely transfer and accurate gripping in the working area. Aiming at the issues of chattering and slow convergence of traditional sliding mode controller, a fast variable power reaching rate on the basis of the non-singular fast terminal sliding mode controller is proposed, which can effectively reduce the convergence time and chattering. For the purpose of addressing the problem that the traditional visual servo control method is sensitive to the environment, a visual servo controller based on integral sliding mode is proposed, to ensure the favorable positioning accuracy of the manipulator. Based on the two proposed controllers mentioned above, a coordinated control strategy is used to implement the control of the manipulator. Finally, the upper computer software is developed using the C# programming language to monitor the workstation. The feasibility of the above-mentioned method is verified through multiple simulations and experiments.

关键词： Automatic production Joint servo Visual servo Coordinated control Non-singular fast terminal sliding mode Integral sliding mode