A Network Pharmacology Study on Active Components and Targets of Citri Reticulatae Pericarpium for Treating Keloids

[Objectives]To investigate the mechanisms and pharmacologic effects of Citri Reticulatae Pericarpium against keloids by network pharmacology systematically.[Methods]TCMSP,Uniprot and BATMAN-TCM databases were used to obtain the active constituents and targets of Citri Reticulatae Pericarpium."Keloid"was used as key word to search for related therapeutic targets from Drug Bank,OMIM,TTD,and GEO databases.The Chinese medicine compound-target network was constructed by Cytoscape software.Besides,gene ontology(GO)and Kyoto Encyclopedia of genes and genome enrichment analysis were also performed.Afterward,Discovery Studio software was used to assess the interaction of key components and genes.[Results]Five active components of Citri Reticulatae Pericarpium,773 compound targets and 676 keloid treatment targets were obtained in the databases.After the intersection,there are 47 targets of Citri Reticulatae Pericarpium for treating keloids.Hub genes were identified such as MMP9,IL6,TNF,TP53,and VEGFA,which were enriched in tumor necrosis factor-α,nuclear factor kappa-B,and other signaling pathways.The molecular docking stimulation confirmed the interaction between the MMP9 and three components of Citri Reticulatae Pericarpium.[Conclusions]Citri Reticulatae Pericarpium may play an important role in treating keloids through modulating genes and signaling pathways.The present study sheds light on the mechanisms of active compounds of Citri Reticulatae Pericarpium for the treatment of keloids.

Is tumor necrosis factor-α monoclonal therapy with proactive therapeutic drug monitoring optimized for inflammatory bowel disease? Network meta-analysis

BACKGROUND The efficacy and safety of anti-tumor necrosis factor-α(TNF-α)monoclonal antibody therapy[adalimumab(ADA)and infliximab(IFX)]with therapeutic drug monitoring(TDM),which has been proposed for inflammatory bowel disease(IBD)patients,are still controversial.AIM To determine the efficacy and safety of anti-TNF-αmonoclonal antibody therapy with proactive TDM in patients with IBD and to determine which subtype of IBD patients is most suitable for proactive TDM interventions.METHODS As of July 2023,we searched for randomized controlled trials(RCTs)and observa-tional studies in PubMed,Embase,and the Cochrane Library to compare anti-TNF-αmonoclonal antibody therapy with proactive TDM with therapy with reactive TDM or empiric therapy.Pairwise and network meta-analyses were used to determine the IBD patient subtype that achieved clinical remission and to determine the need for surgery.RESULTS This systematic review and meta-analysis yielded 13 studies after exclusion,and the baseline indicators were balanced.We found a significant increase in the number of patients who achieved clinical remission in the ADA[odds ratio(OR)=1.416,95%confidence interval(CI):1.196-1.676]and RCT(OR=1.393,95%CI:1.182-1.641)subgroups and a significant decrease in the number of patients who needed surgery in the proactive vs reactive(OR=0.237,95%CI:0.101-0.558)and IFX+ADA(OR=0.137,95%CI:0.032-0.588)subgroups,and the overall risk of adverse events was reduced(OR=0.579,95%CI:0.391-0.858)according to the pairwise meta-analysis.Moreover,the network meta-analysis results suggested that patients with IBD treated with ADA(OR=1.39,95%CI:1.19-1.63)were more likely to undergo TDM,especially in comparison with patients with reactive TDM(OR=1.38,95%CI:1.07-1.77).CONCLUSION Proactive TDM is more suitable for IBD patients treated with ADA and has obvious advantages over reactive TDM.We recommend proactive TDM in IBD patients who are treated with ADA.

Predicting bioactive compounds and cancer-related molecular targets of lotus seedpod (Receptaculum Nelumbinis) based on network pharmacology and molecular docking

Background:Lotus seedpod(Receptaculum Nelumbinis)is the abundant by-products produced during lotus seed processing,and the sources are usually considered to be wastes and are abandoned outdoors or incinerated.This study aims at predicting its bioactive compounds and cancer-related molecular targets against six cancers,including lung cancer,gastric cancer,liver cancer,breast cancer,ovarian cancer and cervical cancer.Methods:Network pharmacology and molecular docking methods were performed.Results:Network pharmacology results indicated that 14 core compounds(liensinine,tetrandrine,lysicamine,tricin,sanleng acid,cireneol G,ricinoleic acid,linolenic acid,5,7-dihydroxycoumarin,apigenin,luteolin,morin,quercetin and isorhamnetin)and 10 core targets(AKT1,ESR1,HSP90AA1,JUN,MAPK1,MAPK3,PIK3CA,PIK3R1,SRC and STAT3)were screened for lotus seedpod against the six cancers.Molecular docking analysis suggested that the binding abilities between the core compounds and the core targets were mostly strong.GO analysis revealed that the intersected targets between the bioactive compounds of lotus seedpod and the six cancers were significantly related to biological processes,cell compositions and molecular functions.KEGG analysis showed that PI3K-Akt,TNF,Ras,MAPK,HIF-1 and C-type lectin receptor signaling pathways were notably involved in the anti-cancer activities of lotus seedpod against the six cancers.Conclusions:14 core compounds and 10 core targets were screened for lotus seedpod against lung cancer,gastric cancer,liver cancer,breast cancer,ovarian cancer and cervical cancer.This study supports the application of lotus seedpod in treating cancers,and promotes the recycling and the high-value utilization.

Exploring the bioactive compounds of Feiduqing formula for the prevention and management of COVID-19 through network pharmacology and molecular docking

Background:To explore the effective chemical constituents of Feiduqing formula for prevention and treatment of coronavirus disease 2019(COVID-19).Methods:The compounds and action targets of twelve herbal medicines in Feiduqing formula were collected via Traditional Chinese Medicine Systems Pharmacology Database and Analytic Platform.The genes corresponding to the targets were queried through the UniProt database.The“herbal medicine-ingredient-target”network was established by Cytoscape software.The Gene Ontology function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed by Database for Annotation,Visualization and Integrated Discovery.Molecular docking was used to analyze the binding force of core active compounds of Feiduqing formula with PTGS2,HSP90AA1,SARS-CoV-23CL hydrolase and angiotensin converting enzyme II(ACE2).Results:The“herbal medicine-ingredient-target”network included 434 nodes and 1948 edges,including 222 components such as quercetin,kaempferol,luteolin,etc.The key targets are PTGS2,HSP90AA1,PTGS1,ESR1,AR,NOS2,etc.Gene Ontology function enrichment analysis revealed 2530 items,including RNA polymerase II-specific,response to oxidative stress,transcription factor activity,etc.Kyoto Encyclopedia of Genes and Genomes pathway enrichment screened 169 signal pathways,including Human cytomegalovirus infection,Kaposi sarcoma-associated herpesvirus infection,Hepatitis B,Hepatitis C,IL-17,TNF,etc.The results of molecular docking showed that quercetin,luteolin,β-sitosterol,stigmasterol and other core active compounds have a certain degree of affinity with PTGS2,HSP90AA1,SARS-CoV-23CL hydrolase and ACE2.Conclusion:The active compounds of Feiduqing formula may have a therapeutic effect on COVID-19 pneumonia through the action on PTGS2,HSP90AA1,SARS-CoV-23CL hydrolase and ACE2,and regulating many signaling pathways.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Biodiversity metrics on ecological networks: Demonstrated with animal gastrointestinal microbiomes

Biodiversity has become a terminology familiar to virtually every citizen in modern societies.It is said that ecology studies the economy of nature,and economy studies the ecology of humans;then measuring biodiversity should be similar with measuring national wealth.Indeed,there have been many parallels between ecology and economics,actually beyond analogies.For example,arguably the second most widely used biodiversity metric,Simpson(1949)’s diversity index,is a function of familiar Gini-index in economics.One of the biggest challenges has been the high“diversity”of diversity indexes due to their excessive“speciation”-there are so many indexes,similar to each country’s sovereign currency-leaving confused diversity practitioners in dilemma.In 1973,Hill introduced the concept of“numbers equivalent”,which is based on Renyi entropy and originated in economics,but possibly due to his abstruse interpretation of the concept,his message was not widely received by ecologists until nearly four decades later.What Hill suggested was similar to link the US dollar to gold at the rate of$35 per ounce under the Bretton Woods system.The Hill numbers now are considered most appropriate biodiversity metrics system,unifying Shannon,Simpson and other diversity indexes.Here,we approach to another paradigmatic shift-measuring biodiversity on ecological networks-demonstrated with animal gastrointestinal microbiomes representing four major invertebrate classes and all six vertebrate classes.The network diversity can reveal the diversity of species interactions,which is a necessary step for understanding the spatial and temporal structures and dynamics of biodiversity across environmental gradients.

Electroencephalogram Signal Correlations between Default Mode Network and Attentional Functioning

Attentional issues may affect acquiring new information, task performance, and learning. Cortical network activities change during different functional brain states, including the default mode network (DMN) and attention network. We investigated the neural mechanisms underlying attentional functions and correlations between DMN connectivity and attentional function using the Trail-Making Test (TMT)-A and -B. Electroencephalography recordings were performed by placing 19 scalp electrodes per the 10 - 20 system. The mean power level was calculated for each rest and task condition. Non-parametric Spearman’s rank correlation was used to examine the correlation in power levels between the rest and TMT conditions. The most significant correlations during TMT-A were observed in the high gamma wave, followed by theta and beta waves, indicating that most correlations were in the parietal lobe, followed by the frontal, central, and temporal lobes. The most significant correlations during TMT-B were observed in the beta wave, followed by the high and low gamma waves, indicating that most correlations were in the temporal lobe, followed by the parietal, frontal, and central lobes. Frontoparietal beta and gamma waves in the DMN may represent attentional functions.

Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

Sum-Rate Maximization in Active RIS-Assisted Multi-Antenna WPCN

In this paper,we propose an active reconfigurable intelligent surface(RIS)enabled hybrid relaying scheme for a multi-antenna wireless powered communication network(WPCN),where the active RIS is employed to assist both wireless energy transfer(WET)from the power station(PS)to energyconstrained users and wireless information transmission(WIT)from users to the receiving station(RS).For further performance enhancement,we propose to employ both transmit beamforming at the PS and receive beamforming at the RS.We formulate a sumrate maximization problem by jointly optimizing the RIS phase shifts and amplitude reflection coefficients for both the WET and the WIT,transmit and receive beamforming vectors,and network resource allocation.To solve this non-convex problem,we propose an efficient alternating optimization algorithm with the linear minimum mean squared error criterion,semidefinite relaxation(SDR)and successive convex approximation techniques.Specifically,the tightness of applying the SDR is proved.Simulation results demonstrate that our proposed scheme with 10 reflecting elements(REs)and 4 antennas can achieve 17.78%and 415.48%performance gains compared to the single-antenna scheme with 10 REs and passive RIS scheme with 100 REs,respectively.

Predictive active control of building structures using LQR and artificial intelligence

This study presents a neural network-based model for predicting linear quadratic regulator(LQR)weighting matrices for achieving a target response reduction.Based on the expected weighting matrices,the LQR algorithm is used to determine the various responses of the structure.The responses are determined by numerically analyzing the governing equation of motion using the state-space approach.For training a neural network,four input parameters are considered:the time history of the ground motion,the percentage reduction in lateral displacement,lateral velocity,and lateral acceleration,Output parameters are LQR weighting matrices.To study the effectiveness of an LQR-based neural network(LQRNN),the actual percentage reduction in the responses obtained from using LQRNN is compared with the target percentage reductions.Furthermore,to investigate the efficacy of an active control system using LQRNN,the controlled responses of a system are compared to the corresponding uncontrolled responses.The trained neural network effectively predicts weighting parameters that can provide a percentage reduction in displacement,velocity,and acceleration close to the target percentage reduction.Based on the simulation study,it can be concluded that significant response reductions are observed in the active-controlled system using LQRNN.Moreover,the LQRNN algorithm can replace conventional LQR control with the use of an active control system.

Artificial neural network-based method for discriminating Compton scattering events in high-purity germaniumγ-ray spectrometer

To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.

Fractional Gradient Descent RBFNN for Active Fault-Tolerant Control of Plant Protection UAVs

With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rejectioncontroller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmannedaerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances andthe possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address theseissues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neuralnetwork (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm.We integrate the plant protection UAV model’s uncertain parameters, load disturbance parameters, and actuatorfault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits loaddisturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law hasLyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platformdemonstrate that the proposed method outperforms other control strategies regarding load disturbance suppressionand fault-tolerant performance.

Influencer identification of dynamical networks based on an information entropy dimension reduction method

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

Analysis of Lavandulyl Flavonoids from Sophora flavescens with Antiinflammatory Activity Based on Molecular Network Technology

[Objectives]This study was conducted to screen lavandulyl flavonoids with anti-inflammatory activity from Sophora flavescens.[Methods]35 compounds were screened from traditional Chinese medicine S.flavescens using the nitric oxide(NO)anti-inflammatory activity model.[Results]Five components,8(xanthohumol),13(kurarinol),27(4-methoxysalicylic acid),28(b-resorcic acid)and 30(b-resorcic acid),exhibited significant anti-inflammatory activity,with IC 50 values of 5.99,4.76,6.96,3.41 and 5.22μM,respectively.Especially,8(xanthohumol)and 13(kurarinol)were typical lavandulyl flavonoids in S.flavescens,which were worth further exploration.Furthermore,UPLC-Q-Exactive and GNPS molecular networking technique were used for rapid analysis of lavandulyl flavonoids from S.flavescens.A total of 15 components were identified.[Conclusions]This work lays a theoretical foundation for further separation and analysis of lavandulyl flavonoids with anti-inflammatory activity from S.flavescens.

Active Micro-Nano-Collaborative Bioelectronic Device for Advanced Electrophysiological Recording

The development of precise and sensitive electrophysiological recording platforms holds the utmost importance for research in the fields of cardiology and neuroscience.In recent years,active micro/nano-bioelectronic devices have undergone significant advancements,thereby facilitating the study of electrophysiology.The distinctive configuration and exceptional functionality of these active micro-nano-collaborative bioelectronic devices offer the potential for the recording of high-fidelity action potential signals on a large scale.In this paper,we review three-dimensional active nano-transistors and planar active micro-transistors in terms of their applications in electroexcitable cells,focusing on the evaluation of the effects of active micro/nano-bioelectronic devices on electrophysiological signals.Looking forward to the possibilities,challenges,and wide prospects of active micro-nano-devices,we expect to advance their progress to satisfy the demands of theoretical investigations and medical implementations within the domains of cardiology and neuroscience research.

Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

A new centrality measure based on neighbor loop structure for network dismantling

Nearly all real-world networks are complex networks and usually are in danger of collapse.Therefore,it is crucial to exploit and understand the mechanisms of network attacks and provide better protection for network functionalities.Network dismantling aims to find the smallest set of nodes such that after their removal the network is broken into connected components of sub-extensive size.To overcome the limitations and drawbacks of existing network dismantling methods,this paper focuses on network dismantling problem and proposes a neighbor-loop structure based centrality metric,NL,which achieves a balance between computational efficiency and evaluation accuracy.In addition,we design a novel method combining NL-based nodes-removing,greedy tree-breaking and reinsertion.Moreover,we compare five baseline methods with our algorithm on ten widely used real-world networks and three types of model networks including Erd€os-Renyi random networks,Watts-Strogatz smallworld networks and Barabasi-Albert scale-free networks with different network generation parameters.Experimental results demonstrate that our proposed method outperforms most peer methods by obtaining a minimal set of targeted attack nodes.Furthermore,the insights gained from this study may be of assistance to future practical research into real-world networks.

Innovation and Firm Co-ownership Network in China’s Electric Vehicle Industry

Firms are embedded in complex networks,where diverse ideas combine and generate new ideas.Shareholders of firms are of-ten seen as critical external resources that have significant influence on firm innovation.The current literature tends to focus on the rela-tionship between firms and their shareholders,while paying less attention to the connections between firms with the same shareholders.This article identifies two types of network spillover effects,intra-city network effect and inter-city network effect,by visualizing the co-ownership networks in China’s electric vehicle(EV)industry.We find that firms with the same shareholders,which are defined as co-owned EV firms,are more innovative than non-co-owned ones.Furthermore,there are two dominant types of firm co-ownership ties formed by corporate and financial institution shareholders.While corporate shareholders help exploiting local tacit knowledge,financial institutions are more active in bridging inter-city connections.The conclusion is confirmed at both firm and city levels.This paper theor-izes the firm co-ownership network as a new form of institutional proximity and tested the result empirically.For policy consideration,we have emphasized the importance of building formal or informal inter-firm network,and the government should further enhance the knowledge flow channel by institutional construction.