To resolve the ontology understanding problem, the structural features and the potential important terms of a large-scale ontology are investigated from the perspective of complex networks analysis. Through the empiri...To resolve the ontology understanding problem, the structural features and the potential important terms of a large-scale ontology are investigated from the perspective of complex networks analysis. Through the empirical studies of the gene ontology with various perspectives, this paper shows that the whole gene ontology displays the same topological features as complex networks including ＂small world＂ and ＂scale-free＂,while some sub-ontologies have the ＂scale-free＂ property but no ＂small world＂ effect.The potential important terms in an ontology are discovered by some famous complex network centralization methods.An evaluation method based on information retrieval in MEDLINE is designed to measure the effectiveness of the discovered important terms.According to the relevant literature of the gene ontology terms,the suitability of these centralization methods for ontology important concepts discovering is quantitatively evaluated.The experimental results indicate that the betweenness centrality is the most appropriate method among all the evaluated centralization measures.展开更多
BACKGROUND Depression and anxiety are prevalent psychological challenges among patients with adolescent idiopathic scoliosis(AIS),affecting individuals across both sex and age groups.AIM To explore the network structu...BACKGROUND Depression and anxiety are prevalent psychological challenges among patients with adolescent idiopathic scoliosis(AIS),affecting individuals across both sex and age groups.AIM To explore the network structure of depression and anxiety symptoms,with a focus on identifying differences at the symptom level between sex and age subgroups.METHODS A total of 1955 participants diagnosed with AIS aged 10-18 years were assessed using the Patient Health Questionnaire Depression Scale(PHO-9)and the Generalized Anxiety Disorder Scale(GAD-7),and 765 patients exhibiting PHQ-9 or GAD-7 scores ≥ 5 were enrolled in our study. Network analysis and network comparison tests were utilized toconstruct and compare the depression-anxiety symptoms networks among sex and age subgroups.RESULTSThe results revealed GAD3 “Excessive worry” and PHQ2 “Sad mood” were the most significant central symptomsin all subgroups, while “Sad mood” had higher strength than “Excessive worry” in the lower age group. In thenetwork comparisons, the female network exhibited tighter connectivity, especially on GAD6 “Irritability” andGAD2 “Uncontrollable worry”, while only PHQ3 “Sleep” and PHQ9 “Suicidal ideation” had differences at thelocal level in the lower age group.CONCLUSIONSeveral interventions targeting excessive worry and sad mood could reduce the risk of depression and anxietysymptoms in the AIS population. Furthermore, specific anxiety symptoms in females, along with sleep disturbancesand suicidal ideation in the lower age group, should be addressed at an early stage to prevent significantdisruptions in mental health trajectories.展开更多
Global production networks have become the most important organizational platforms for coordinating international production activities,and their evolution patterns profoundly affect value distribution across the worl...Global production networks have become the most important organizational platforms for coordinating international production activities,and their evolution patterns profoundly affect value distribution across the world.In this study,we shall firstly carry out an in-depth quantitative research to analyze the patterns and evolution of global production networks,using a long time-sequenced multi-region input-output table and the network analysis approach.Then based on the method of value-added decomposition,we will develop an index system to measure the degree of participation of regions in global production networks.Finally,we will try to identify the factors affecting the degree of participation of countries in global production networks by constructing a regression model.The results show that from 1995 to 2015,the evolution of global production networks measured by input-output linkages experienced four stages:expansion,contraction,re-expansion,and re-contraction.In addition,the core communities of global production networks evolved from two major production communities(Europe and the Americas)to three pillars(Europe,Americas,and Asia)while more segmented communities are mainly affected by geographical proximity.The latter consists of European,North American,South American,African and Asian communities.The evolution of the global production network pattern primarily manifests as a process of cooperation strengthening or weakening among communities,based on changes in the external environment and the need for individual development strategies.Meanwhile,the United States,Germany,and the United Kingdom have consistently ranked among the top entities in global production networks,whereas China,Russia,and Southeast Asia have the fastest rises in ranking.In addition,government efficiency,resources endowment,infrastructure conditions and technology levels play important roles in the participation in global production networks.展开更多
This paper investigates the uplink throughput of Cognitive Radio Cellular Networks(CRCNs).As oppose to traditional performance evaluation schemes which mainly adopt complex system level simulations,we use the theoreti...This paper investigates the uplink throughput of Cognitive Radio Cellular Networks(CRCNs).As oppose to traditional performance evaluation schemes which mainly adopt complex system level simulations,we use the theoretical framework of stochastic geometry to provide a tractable and accurate analysis of the uplink throughput in the CRCN.By modelling the positions of User Equipments(UEs)and Base Stations(BSs)as Poisson Point Processes(PPPs),we analyse and derive expressions for the link rate and the cell throughput in the Primary(PR)and Secondary(SR)networks.The expressions show that the throughput of the CRCN is mainly affected by the density ratios between the UEs and the BSs in both the PR and SR networks.Besides,a comparative analysis of the link rate between random and regular BS deployments is concluded,and the results confirm the accuracy of our analysis.Furthermore,we define the cognitive throughput gain and derive an expression which is dominated by the traffic load in the PR network.展开更多
Content analysis of scientific papers emanating from Antarctic science research during the 25 years period （1980-- 2004） has been carried out using neural network based algorithm-CATPAC. A total of 10 942 research a...Content analysis of scientific papers emanating from Antarctic science research during the 25 years period （1980-- 2004） has been carried out using neural network based algorithm-CATPAC. A total of 10 942 research articles published in Science Citation Indexed （SCI） journals were used for the study. Normalized co-word matrix from 35 most-used significant words was used to study the semantic association between the words. Structural Equivalence blocks were constructed from these 35 most-used words. Four-block model solution was found to be optimum. The density table was dichotomized using the mean density of the table to derive the binary matrix, which was used to construct the network map. Network maps represent the thematic character of the blocks. The blocks showed preferred connection in establishing semantic relationship with the blocks, characterizing thematic composition of Antarctic science research. The analysis has provided an analytical framework for carrying out studies on the con- tent of scientific articles. The paper has shown the utility of co-word analysis in highlighting the important areas of research in Antarctic science.展开更多
Fractal and self similarity of complex networks have attracted much attention in recent years. The fractal dimension is a useful method to describe the fractal property of networks. However, the fractal features of mo...Fractal and self similarity of complex networks have attracted much attention in recent years. The fractal dimension is a useful method to describe the fractal property of networks. However, the fractal features of mobile social networks （MSNs） are inadequately investigated. In this work, a box-covering method based on the ratio of excluded mass to closeness centrality is presented to investigate the fractal feature of MSNs. Using this method, we find that some MSNs are fractal at different time intervals. Our simulation results indicate that the proposed method is available for analyzing the fractal property of MSNs.展开更多
An array composed of sixteen gas sensors was constructed to analyze gas mixtures quantitatively. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP alg...An array composed of sixteen gas sensors was constructed to analyze gas mixtures quantitatively. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP algorithms and PLS. The analytical results indicated that the concentration predicted with ANN is better than that with PLS. The average prediction errors for ethane, propane and propylene were 5.11%, 8.28%, 2.64%, respectively.展开更多
Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to p...Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to participate in this online questionnaire survey.The survey tools included a general information questionnaire and a self-made NMOSD symptoms scale,which included the prevalence,severity,and distress of 29 symptoms.Cluster analysis was used to identify symptom clusters,and network analysis was used to analyze the symptom network and node characteristics and central indicators including strength centrality(r_(s)),closeness centrality(r_(c))and betweeness centrality(r_(b))were used to identify core symptoms and symptom clusters.Results The most common symptom was pain(65.7%),followed by paraesthesia(65.0%),fatigue(65.0%),easy awakening(63.6%).Regarding the burden level of symptoms,pain was the most burdensome symptom,followed by paraesthesia,easy awakening,fatigue,and difficulty falling asleep.Six clusters were identified:somatosensory,motor,visual,and memory symptom clusters,bladder and rectum symptom clusters,sleep symptoms clusters,and neuropsychological symptom clusters.Fatigue(r_(s)=12.39,r_(b)=68.00,r_(c)=0.02)was the most central and prominent bridge symptom,and motor symptom cluster(r_(s)=2.68,r_(c)=0.10)was the most central symptom cluster among the six clusters.Conclusions Our study demonstrated the necessity of symptom management targeting fatigue,pain,and motor symptom cluster in patients with NMOSD.展开更多
Objectives:This study aimed to assess the reliability and validity of the abbreviated Committed Action Questionnaire(CAQ-8)in a cohort of 1635 Chinese university students.Methods:Participants completed the Chinese ver...Objectives:This study aimed to assess the reliability and validity of the abbreviated Committed Action Questionnaire(CAQ-8)in a cohort of 1635 Chinese university students.Methods:Participants completed the Chinese version of the CAQ-8 along with other standardized measures,including the Acceptance and Action Questionnaire-II(AAQ-II),the Valuing Questionnaire(VQ),the Satisfaction with Life Scale(SWLS),the Depression Anxiety Stress Scales(DASS-21),and the World Health Organization Fiveitem Well-Being Index(WHO-5).A retest was conducted one month later with 300 valid responses.Results:Exploratory factor analysis(n=818)identified a 2-factor structure,confirmed through validated factor analysis(n=817),showing good fit indices(CFI=0.990,RMSEA=0.040).Measurement equivalence across genders was established.The CAQ-8 showed significant positive correlations with life satisfaction,mental health,and values,and negative correlations with depression,anxiety,stress,and experiential avoidance.The scale demonstrated good internal consistency(Cronbach’sα=0.76)and retest reliability(ICC=0.70).Network analysis confirmed the robustness of the 2-factor model,with item 4 in CAQ-8 identified as a core item.Conclusion:The CAQ-8 is a reliable and valid tool for measuring committed action within the psychological flexibility model in Chinese populations.展开更多
Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study...Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study of CHF syndromes in recent 40 years retrieved from Web of Science,Scopus,Pub Med,Embase,CNKI,Wanfang Data,Cq VIP,and Sino Med.According to cumulative frequency analysis,network analysis,and hierarchical cluster analysis,the study found the distribution of CHF syndromes was syndrome of qi deficiency with blood stasis,syndrome of qi and yin deficiency,syndrome of yang deficiency with water flooding,syndrome of heart blood stasis obstruction,syndrome of turbid phlegm,and syndrome of collapse due to primordial yang deficiency.The syndrome elements on location of illness were heart,kidney,lung,and spleen.The syndrome elements on nature of illness were qi deficiency,blood stasis,yang deficiency,yin deficiency,water retention,and turbid phlegm.These findings can provide reference to the research on diagnosis and treatment of CHF,and contribute to the study on syndrome standardization and objective research of TCM diagnosis.展开更多
Objective:To identify promising biomarkers for the pathogenesis of major depressive disorder(MDD).Methods:Microarray chips of MDD patients,including the GSE98793,GSE52790,and GSE39653 datasets,were obtained from the G...Objective:To identify promising biomarkers for the pathogenesis of major depressive disorder(MDD).Methods:Microarray chips of MDD patients,including the GSE98793,GSE52790,and GSE39653 datasets,were obtained from the Gene Expression Omnibus database.The biological processes and pathways related to MDD were investigated using the GO and KEGG pathway tools.Weighted gene coexpression network analysis was conducted to identify modules related to MDD.The hub genes associated with MDD were obtained via protein-protein interaction analysis.Finally,the expression of hub genes in the hippocampal tissues of depression-like rats was detected by reverse transcription-polymerase chain reaction and Western blotting.Results:A total of 658 differentially expressed genes were identified from the Gene Expression Omnibus datasets;thus,these genes and the GSE98793 dataset were used to conduct weighted gene coexpression network analysis.A total of 244 module-related genes were identified and these genes were highly correlated with MDD.These genes were involved in the Ras signaling pathway,regulation of the actin cytoskeleton,and axon guidance according to the KEGG analysis.Hub genes,including MAPK14,SOCS1,TLR2,PTK2B,and GRB2,were obtained via protein-protein interaction analysis.All these hub genes showed better diagnostic efficiency in the GSE52790,GSE39653,and GSE98793 datasets.In vivo experiments revealed that compared with those in control rats,SOCS1 and MAPK14 expression was significantly decreased;while GRB2,TLR2,and PTK2B expression was increased in the hippocampi of depression-like rats.Conclusions:Our study demonstrates that GRB2,TLR2,SOCS1,PTK2B,and MAPK14 are promising hub genes,and targeting these five genes may be an effective treatment strategy for MDD.展开更多
Startups form an information network that reflects their growth trajectories through information flow channels established by shared investors.However,traditional static network metrics overlook temporal dynamics and ...Startups form an information network that reflects their growth trajectories through information flow channels established by shared investors.However,traditional static network metrics overlook temporal dynamics and rely on single indicators to assess startups’roles in predicting future success,failing to comprehensively capture topological variations and structural diversity.To address these limitations,we construct a temporal information network using 14547 investment records from 1013 global blockchain startups between 2004 and 2020,sourced from Crunchbase.We propose two dynamic methods to characterize the information flow:temporal random walk(sTRW)for modeling information flow trajectories and temporal betweenness centrality(tTBET)for identifying key information hubs.These methods enhance walk coverage while ensuring random stability,allowing for more effective identification of influential startups.By integrating sTRW and tTBET,we develop a comprehensive metric to evaluate a startup’s influence within the network.In experiments assessing startups’potential for future success—where successful startups are defined as those that have undergone M&A or IPO—incorporating this metric improves accuracy,recall,and F1 score by 0.035,0.035,and 0.042,respectively.Our findings indicate that information flow from key startups to others diminishes as the network distance increases.Additionally,successful startups generally exhibit higher information inflows than outflows,suggesting that actively seeking investment-related information contributes to startup growth.Our research provides valuable insights for formulating startup development strategies and offers practical guidance for market regulators.展开更多
The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlatio...The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlation in traditional planning.Taking Haidian District of Beijing as an example,the social network analysis method is introduced to construct the network model of park green spaces.Through indicators such as clustering coefficient,network density and node centrality,the characteristics of its spatial structure and hierarchical relationship are analyzed.It is found that the network integrity presents the characteristics of“highly local concentration and global fragmentation”,fragmented park green space network and missing spatial connection,isolated clusters and collaborative failure,as well as the spatial mismatch between population and resource supply and demand.Hierarchical issues include“structural imbalance and functional disorder”,disorder between network hierarchy and park level,misalignment of functional hierarchy leading to weakened network risk resistance capacity,and a relatively dense distribution of core nodes,etc.In response to the above problems,a multi-level spatial intervention strategy should be adopted to solve the overall problem of the network.Meanwhile,it is needed to clarify the positioning of a park itself and improve the hierarchical system,so as to construct a multi-level and multi-scale park green space network,contribute to the construction of a park city,and provide residents with more diverse activity venues.展开更多
Understanding the complex interactions between urbanization and ecosystem services(ESs)is crucial for optimiz ing planning policies and achieving sustainable urban management.While previous research has largely focuse...Understanding the complex interactions between urbanization and ecosystem services(ESs)is crucial for optimiz ing planning policies and achieving sustainable urban management.While previous research has largely focused on highly urbanized areas,little attention has been given to the phased effect of progressive urbanization on ES networks.This study proposes a conceptual framework that utilizes the network method and space-time replace ment to examine the effect of urbanization on the complex relationships among ESs at different stages,with a particular emphasis on the progressive evolution of the process.We apply this framework to the Horqin area,a typical eco-fragile area in China.Results demonstrate that the connectivity of the ES synergy network exhibits a non-stationary characteristic,initially increasing,then decreasing,and subsequently strengthening.Meanwhile,its modularity shows a rising trend during periods of accelerated urbanization.The performance of the trade off network displays the opposite pattern.Additionally,we observe a gradual replacement of provisioning and regulation services by cultural services in terms of dominance in the synergy network as urbanization advances.By providing guidance for identifying key planning initiatives and implementing ecological protection policies at different stages of development,this study contributes a pathway that can inform development strategies in other regions undergoing progressive urbanization.展开更多
BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving SA.AIM To explo...BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving SA.AIM To explore the potential characteristics of the brain network and functional connectivity(FC)of SA.METHODS Twenty-six SA individuals and 47 usual aging individuals were recruited from community-dwelling elderly,which were taken the magnetic resonance imaging scan and the global cognitive function assessment by Mini Mental State Examination(MMSE).The resting state-functional magnetic resonance imaging data were preprocessed by DPABISurf,and the brain functional network was conducted by DPABINet.The support vector machine model was constructed with altered functional connectivities to evaluate the identification value of SA.RESULTS The results found that the 6 inter-network FCs of 5 brain networks were significantly altered and related to MMSE performance.The FC of the right orbital part of the middle frontal gyrus and right angular gyrus was mostly increased and positively related to MMSE score,and the FC of the right supramarginal gyrus and right temporal pole:Middle temporal gyrus was the only one decreased and negatively related to MMSE score.All 17 significantly altered FCs of SA were taken into the support vector machine model,and the area under the curve was 0.895.CONCLUSION The identification of key brain networks and FC of SA could help us better understand the brain mechanism and further explore neuroimaging biomarkers of SA.展开更多
Cryptocurrency has gained popularity as a potential new global payment method.It has the potential to be faster,cheaper,and more secure than existing payment networks,making it a game-changer in the global economy.How...Cryptocurrency has gained popularity as a potential new global payment method.It has the potential to be faster,cheaper,and more secure than existing payment networks,making it a game-changer in the global economy.However,more research is needed to identify the factors driving cryptocurrency adoption and understand its impact.We use social network analysis(SNA)to identify the influencing factors and reveal the impact of each on cryptocurrency adoption.Our analysis initially revealed 44 influential factors,which were later reduced to 25 factors,each exerting a different influence.Based on the SNA,we classify these factors into highly,moderately,and least influential categories.Discomfort and optimism are the most influential determinants of adoption.Moderately influential factors include trust,risk,relative advantage,social influence,and perceived behavioral control.Price/value,facilitating conditions,compatibility,and usefulness are the least influential.The factors affecting cryptocurrency adoption are interdependent.Our findings can help policymakers understand the factors influencing cryptocurrency adoption and aid in developing appropriate legal frameworks for cryptocurrency use.展开更多
In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shi...In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shift and complex multipath channel conditions,extracting discriminative features from high-order modulation signals and ensuring model inter-pretability remain challenging.To address these issues,this paper proposes a Fourier attention net-work(FAttNet),which combines an attention mechanism with a Fourier analysis network(FAN).Specifically,the method directly converts the input signal to the frequency domain using the FAN,thereby obtaining frequency features that reflect the periodic variations in amplitude and phase.Abuilt-in attention mechanism then automatically calculates the weights for each frequency band,focusing on the most discriminative components.This approach improves both classification accu-racy and model interpretability.Experimental validation was conducted via high-order modulationsimulation using an RF testbed.The results show that under three different Doppler frequencyshifts and complex multipath channel conditions,with a signal-to-noise ratio of 10 dB,the classifi-cation accuracy can reach 89.1%,90.4%and 90%,all of which are superior to the current main-stream methods.The proposed approach offers practical value for dynamic spectrum access and sig-nal security detection,and it makes important theoretical contributions to the application of deeplearning in complex electromagnetic signal recognition.展开更多
BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes tha...BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.展开更多
Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-...Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.展开更多
基金The National Basic Research Program of China (973Program) (No.2005CB321802)Program for New Century Excellent Talents in University (No.NCET-06-0926)the National Natural Science Foundation of China (No.60873097,90612009)
文摘To resolve the ontology understanding problem, the structural features and the potential important terms of a large-scale ontology are investigated from the perspective of complex networks analysis. Through the empirical studies of the gene ontology with various perspectives, this paper shows that the whole gene ontology displays the same topological features as complex networks including ＂small world＂ and ＂scale-free＂,while some sub-ontologies have the ＂scale-free＂ property but no ＂small world＂ effect.The potential important terms in an ontology are discovered by some famous complex network centralization methods.An evaluation method based on information retrieval in MEDLINE is designed to measure the effectiveness of the discovered important terms.According to the relevant literature of the gene ontology terms,the suitability of these centralization methods for ontology important concepts discovering is quantitatively evaluated.The experimental results indicate that the betweenness centrality is the most appropriate method among all the evaluated centralization measures.
基金Supported by The Sanming Project of Medicine in Shenzhen,No.SZSM202211003Shenzhen-Hong Kong Jointly Funded Project,Shenzhen Science and Technology Program,No.SGDX20230116093645007+1 种基金Shenzhen Second People's Hospital Clinical Project,No.20243357003Shenzhen Medical Research Fund,No.B2303005.
文摘BACKGROUND Depression and anxiety are prevalent psychological challenges among patients with adolescent idiopathic scoliosis(AIS),affecting individuals across both sex and age groups.AIM To explore the network structure of depression and anxiety symptoms,with a focus on identifying differences at the symptom level between sex and age subgroups.METHODS A total of 1955 participants diagnosed with AIS aged 10-18 years were assessed using the Patient Health Questionnaire Depression Scale(PHO-9)and the Generalized Anxiety Disorder Scale(GAD-7),and 765 patients exhibiting PHQ-9 or GAD-7 scores ≥ 5 were enrolled in our study. Network analysis and network comparison tests were utilized toconstruct and compare the depression-anxiety symptoms networks among sex and age subgroups.RESULTSThe results revealed GAD3 “Excessive worry” and PHQ2 “Sad mood” were the most significant central symptomsin all subgroups, while “Sad mood” had higher strength than “Excessive worry” in the lower age group. In thenetwork comparisons, the female network exhibited tighter connectivity, especially on GAD6 “Irritability” andGAD2 “Uncontrollable worry”, while only PHQ3 “Sleep” and PHQ9 “Suicidal ideation” had differences at thelocal level in the lower age group.CONCLUSIONSeveral interventions targeting excessive worry and sad mood could reduce the risk of depression and anxietysymptoms in the AIS population. Furthermore, specific anxiety symptoms in females, along with sleep disturbancesand suicidal ideation in the lower age group, should be addressed at an early stage to prevent significantdisruptions in mental health trajectories.
基金Strategic Priority Research Program of Chinese Academy of Sciences,No.XDA20080000National Natural Science Foundation of China,No.41901154。
文摘Global production networks have become the most important organizational platforms for coordinating international production activities,and their evolution patterns profoundly affect value distribution across the world.In this study,we shall firstly carry out an in-depth quantitative research to analyze the patterns and evolution of global production networks,using a long time-sequenced multi-region input-output table and the network analysis approach.Then based on the method of value-added decomposition,we will develop an index system to measure the degree of participation of regions in global production networks.Finally,we will try to identify the factors affecting the degree of participation of countries in global production networks by constructing a regression model.The results show that from 1995 to 2015,the evolution of global production networks measured by input-output linkages experienced four stages:expansion,contraction,re-expansion,and re-contraction.In addition,the core communities of global production networks evolved from two major production communities(Europe and the Americas)to three pillars(Europe,Americas,and Asia)while more segmented communities are mainly affected by geographical proximity.The latter consists of European,North American,South American,African and Asian communities.The evolution of the global production network pattern primarily manifests as a process of cooperation strengthening or weakening among communities,based on changes in the external environment and the need for individual development strategies.Meanwhile,the United States,Germany,and the United Kingdom have consistently ranked among the top entities in global production networks,whereas China,Russia,and Southeast Asia have the fastest rises in ranking.In addition,government efficiency,resources endowment,infrastructure conditions and technology levels play important roles in the participation in global production networks.
基金supported by the National Key Basic Research Program of China (973 Program)under Grant No. 2009CB320401the National Natural Science Foundation of China under Grants No. 61171099, No. 61101117+1 种基金the National Key Scientific and Technological Project of China under Grants No. 2012ZX03004005-002, No. 2012ZX03003-007the Fundamental Research Funds for the Central Universities under Grant No. BUPT2012RC0112
文摘This paper investigates the uplink throughput of Cognitive Radio Cellular Networks(CRCNs).As oppose to traditional performance evaluation schemes which mainly adopt complex system level simulations,we use the theoretical framework of stochastic geometry to provide a tractable and accurate analysis of the uplink throughput in the CRCN.By modelling the positions of User Equipments(UEs)and Base Stations(BSs)as Poisson Point Processes(PPPs),we analyse and derive expressions for the link rate and the cell throughput in the Primary(PR)and Secondary(SR)networks.The expressions show that the throughput of the CRCN is mainly affected by the density ratios between the UEs and the BSs in both the PR and SR networks.Besides,a comparative analysis of the link rate between random and regular BS deployments is concluded,and the results confirm the accuracy of our analysis.Furthermore,we define the cognitive throughput gain and derive an expression which is dominated by the traffic load in the PR network.
文摘Content analysis of scientific papers emanating from Antarctic science research during the 25 years period （1980-- 2004） has been carried out using neural network based algorithm-CATPAC. A total of 10 942 research articles published in Science Citation Indexed （SCI） journals were used for the study. Normalized co-word matrix from 35 most-used significant words was used to study the semantic association between the words. Structural Equivalence blocks were constructed from these 35 most-used words. Four-block model solution was found to be optimum. The density table was dichotomized using the mean density of the table to derive the binary matrix, which was used to construct the network map. Network maps represent the thematic character of the blocks. The blocks showed preferred connection in establishing semantic relationship with the blocks, characterizing thematic composition of Antarctic science research. The analysis has provided an analytical framework for carrying out studies on the con- tent of scientific articles. The paper has shown the utility of co-word analysis in highlighting the important areas of research in Antarctic science.
基金Supported by the National Natural Science Foundation of China under Grant Nos 61501217,61363015,61501218 and 61262020the Natural Science Foundation of Jiangxi Province under Grant No 20142BAB206026
文摘Fractal and self similarity of complex networks have attracted much attention in recent years. The fractal dimension is a useful method to describe the fractal property of networks. However, the fractal features of mobile social networks （MSNs） are inadequately investigated. In this work, a box-covering method based on the ratio of excluded mass to closeness centrality is presented to investigate the fractal feature of MSNs. Using this method, we find that some MSNs are fractal at different time intervals. Our simulation results indicate that the proposed method is available for analyzing the fractal property of MSNs.
文摘An array composed of sixteen gas sensors was constructed to analyze gas mixtures quantitatively. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP algorithms and PLS. The analytical results indicated that the concentration predicted with ANN is better than that with PLS. The average prediction errors for ethane, propane and propylene were 5.11%, 8.28%, 2.64%, respectively.
基金supported by the Specific Research Fund for Top-notch Talents of Guangdong Provincial Hospital of Chinese Medicine(No.2022KT1188).
文摘Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to participate in this online questionnaire survey.The survey tools included a general information questionnaire and a self-made NMOSD symptoms scale,which included the prevalence,severity,and distress of 29 symptoms.Cluster analysis was used to identify symptom clusters,and network analysis was used to analyze the symptom network and node characteristics and central indicators including strength centrality(r_(s)),closeness centrality(r_(c))and betweeness centrality(r_(b))were used to identify core symptoms and symptom clusters.Results The most common symptom was pain(65.7%),followed by paraesthesia(65.0%),fatigue(65.0%),easy awakening(63.6%).Regarding the burden level of symptoms,pain was the most burdensome symptom,followed by paraesthesia,easy awakening,fatigue,and difficulty falling asleep.Six clusters were identified:somatosensory,motor,visual,and memory symptom clusters,bladder and rectum symptom clusters,sleep symptoms clusters,and neuropsychological symptom clusters.Fatigue(r_(s)=12.39,r_(b)=68.00,r_(c)=0.02)was the most central and prominent bridge symptom,and motor symptom cluster(r_(s)=2.68,r_(c)=0.10)was the most central symptom cluster among the six clusters.Conclusions Our study demonstrated the necessity of symptom management targeting fatigue,pain,and motor symptom cluster in patients with NMOSD.
文摘Objectives:This study aimed to assess the reliability and validity of the abbreviated Committed Action Questionnaire(CAQ-8)in a cohort of 1635 Chinese university students.Methods:Participants completed the Chinese version of the CAQ-8 along with other standardized measures,including the Acceptance and Action Questionnaire-II(AAQ-II),the Valuing Questionnaire(VQ),the Satisfaction with Life Scale(SWLS),the Depression Anxiety Stress Scales(DASS-21),and the World Health Organization Fiveitem Well-Being Index(WHO-5).A retest was conducted one month later with 300 valid responses.Results:Exploratory factor analysis(n=818)identified a 2-factor structure,confirmed through validated factor analysis(n=817),showing good fit indices(CFI=0.990,RMSEA=0.040).Measurement equivalence across genders was established.The CAQ-8 showed significant positive correlations with life satisfaction,mental health,and values,and negative correlations with depression,anxiety,stress,and experiential avoidance.The scale demonstrated good internal consistency(Cronbach’sα=0.76)and retest reliability(ICC=0.70).Network analysis confirmed the robustness of the 2-factor model,with item 4 in CAQ-8 identified as a core item.Conclusion:The CAQ-8 is a reliable and valid tool for measuring committed action within the psychological flexibility model in Chinese populations.
基金financed by the grants from the National Natural Science Foundation of China(No.81803996)Shanghai Key Laboratory of Health Identification and Assessment(No.21DZ2271000)。
文摘Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study of CHF syndromes in recent 40 years retrieved from Web of Science,Scopus,Pub Med,Embase,CNKI,Wanfang Data,Cq VIP,and Sino Med.According to cumulative frequency analysis,network analysis,and hierarchical cluster analysis,the study found the distribution of CHF syndromes was syndrome of qi deficiency with blood stasis,syndrome of qi and yin deficiency,syndrome of yang deficiency with water flooding,syndrome of heart blood stasis obstruction,syndrome of turbid phlegm,and syndrome of collapse due to primordial yang deficiency.The syndrome elements on location of illness were heart,kidney,lung,and spleen.The syndrome elements on nature of illness were qi deficiency,blood stasis,yang deficiency,yin deficiency,water retention,and turbid phlegm.These findings can provide reference to the research on diagnosis and treatment of CHF,and contribute to the study on syndrome standardization and objective research of TCM diagnosis.
基金supported by the National Natural Science Foundation of China(No.81774281 and No.82474303)the Natural Science Foundation of Hunan Province(2023JJ30888)the leading national joint discipline of Chinese and Western medicines to the Chinese Medicine Department,Xiangya Hospital,CSU.
文摘Objective:To identify promising biomarkers for the pathogenesis of major depressive disorder(MDD).Methods:Microarray chips of MDD patients,including the GSE98793,GSE52790,and GSE39653 datasets,were obtained from the Gene Expression Omnibus database.The biological processes and pathways related to MDD were investigated using the GO and KEGG pathway tools.Weighted gene coexpression network analysis was conducted to identify modules related to MDD.The hub genes associated with MDD were obtained via protein-protein interaction analysis.Finally,the expression of hub genes in the hippocampal tissues of depression-like rats was detected by reverse transcription-polymerase chain reaction and Western blotting.Results:A total of 658 differentially expressed genes were identified from the Gene Expression Omnibus datasets;thus,these genes and the GSE98793 dataset were used to conduct weighted gene coexpression network analysis.A total of 244 module-related genes were identified and these genes were highly correlated with MDD.These genes were involved in the Ras signaling pathway,regulation of the actin cytoskeleton,and axon guidance according to the KEGG analysis.Hub genes,including MAPK14,SOCS1,TLR2,PTK2B,and GRB2,were obtained via protein-protein interaction analysis.All these hub genes showed better diagnostic efficiency in the GSE52790,GSE39653,and GSE98793 datasets.In vivo experiments revealed that compared with those in control rats,SOCS1 and MAPK14 expression was significantly decreased;while GRB2,TLR2,and PTK2B expression was increased in the hippocampi of depression-like rats.Conclusions:Our study demonstrates that GRB2,TLR2,SOCS1,PTK2B,and MAPK14 are promising hub genes,and targeting these five genes may be an effective treatment strategy for MDD.
基金the funding from the National Natural Science Foundation of China(Grant Nos.42001236,71991481,and 71991480)Young Elite Scientist Sponsor-ship Program by Bast(Grant No.BYESS2023413)。
文摘Startups form an information network that reflects their growth trajectories through information flow channels established by shared investors.However,traditional static network metrics overlook temporal dynamics and rely on single indicators to assess startups’roles in predicting future success,failing to comprehensively capture topological variations and structural diversity.To address these limitations,we construct a temporal information network using 14547 investment records from 1013 global blockchain startups between 2004 and 2020,sourced from Crunchbase.We propose two dynamic methods to characterize the information flow:temporal random walk(sTRW)for modeling information flow trajectories and temporal betweenness centrality(tTBET)for identifying key information hubs.These methods enhance walk coverage while ensuring random stability,allowing for more effective identification of influential startups.By integrating sTRW and tTBET,we develop a comprehensive metric to evaluate a startup’s influence within the network.In experiments assessing startups’potential for future success—where successful startups are defined as those that have undergone M&A or IPO—incorporating this metric improves accuracy,recall,and F1 score by 0.035,0.035,and 0.042,respectively.Our findings indicate that information flow from key startups to others diminishes as the network distance increases.Additionally,successful startups generally exhibit higher information inflows than outflows,suggesting that actively seeking investment-related information contributes to startup growth.Our research provides valuable insights for formulating startup development strategies and offers practical guidance for market regulators.
文摘The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlation in traditional planning.Taking Haidian District of Beijing as an example,the social network analysis method is introduced to construct the network model of park green spaces.Through indicators such as clustering coefficient,network density and node centrality,the characteristics of its spatial structure and hierarchical relationship are analyzed.It is found that the network integrity presents the characteristics of“highly local concentration and global fragmentation”,fragmented park green space network and missing spatial connection,isolated clusters and collaborative failure,as well as the spatial mismatch between population and resource supply and demand.Hierarchical issues include“structural imbalance and functional disorder”,disorder between network hierarchy and park level,misalignment of functional hierarchy leading to weakened network risk resistance capacity,and a relatively dense distribution of core nodes,etc.In response to the above problems,a multi-level spatial intervention strategy should be adopted to solve the overall problem of the network.Meanwhile,it is needed to clarify the positioning of a park itself and improve the hierarchical system,so as to construct a multi-level and multi-scale park green space network,contribute to the construction of a park city,and provide residents with more diverse activity venues.
基金supported by the National Natural Science Foundation Projects of China(Grant No.42071284).
文摘Understanding the complex interactions between urbanization and ecosystem services(ESs)is crucial for optimiz ing planning policies and achieving sustainable urban management.While previous research has largely focused on highly urbanized areas,little attention has been given to the phased effect of progressive urbanization on ES networks.This study proposes a conceptual framework that utilizes the network method and space-time replace ment to examine the effect of urbanization on the complex relationships among ESs at different stages,with a particular emphasis on the progressive evolution of the process.We apply this framework to the Horqin area,a typical eco-fragile area in China.Results demonstrate that the connectivity of the ES synergy network exhibits a non-stationary characteristic,initially increasing,then decreasing,and subsequently strengthening.Meanwhile,its modularity shows a rising trend during periods of accelerated urbanization.The performance of the trade off network displays the opposite pattern.Additionally,we observe a gradual replacement of provisioning and regulation services by cultural services in terms of dominance in the synergy network as urbanization advances.By providing guidance for identifying key planning initiatives and implementing ecological protection policies at different stages of development,this study contributes a pathway that can inform development strategies in other regions undergoing progressive urbanization.
基金Supported by the Wuxi Municipal Health Commission Major Project,No.Z202107。
文摘BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving SA.AIM To explore the potential characteristics of the brain network and functional connectivity(FC)of SA.METHODS Twenty-six SA individuals and 47 usual aging individuals were recruited from community-dwelling elderly,which were taken the magnetic resonance imaging scan and the global cognitive function assessment by Mini Mental State Examination(MMSE).The resting state-functional magnetic resonance imaging data were preprocessed by DPABISurf,and the brain functional network was conducted by DPABINet.The support vector machine model was constructed with altered functional connectivities to evaluate the identification value of SA.RESULTS The results found that the 6 inter-network FCs of 5 brain networks were significantly altered and related to MMSE performance.The FC of the right orbital part of the middle frontal gyrus and right angular gyrus was mostly increased and positively related to MMSE score,and the FC of the right supramarginal gyrus and right temporal pole:Middle temporal gyrus was the only one decreased and negatively related to MMSE score.All 17 significantly altered FCs of SA were taken into the support vector machine model,and the area under the curve was 0.895.CONCLUSION The identification of key brain networks and FC of SA could help us better understand the brain mechanism and further explore neuroimaging biomarkers of SA.
文摘Cryptocurrency has gained popularity as a potential new global payment method.It has the potential to be faster,cheaper,and more secure than existing payment networks,making it a game-changer in the global economy.However,more research is needed to identify the factors driving cryptocurrency adoption and understand its impact.We use social network analysis(SNA)to identify the influencing factors and reveal the impact of each on cryptocurrency adoption.Our analysis initially revealed 44 influential factors,which were later reduced to 25 factors,each exerting a different influence.Based on the SNA,we classify these factors into highly,moderately,and least influential categories.Discomfort and optimism are the most influential determinants of adoption.Moderately influential factors include trust,risk,relative advantage,social influence,and perceived behavioral control.Price/value,facilitating conditions,compatibility,and usefulness are the least influential.The factors affecting cryptocurrency adoption are interdependent.Our findings can help policymakers understand the factors influencing cryptocurrency adoption and aid in developing appropriate legal frameworks for cryptocurrency use.
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shift and complex multipath channel conditions,extracting discriminative features from high-order modulation signals and ensuring model inter-pretability remain challenging.To address these issues,this paper proposes a Fourier attention net-work(FAttNet),which combines an attention mechanism with a Fourier analysis network(FAN).Specifically,the method directly converts the input signal to the frequency domain using the FAN,thereby obtaining frequency features that reflect the periodic variations in amplitude and phase.Abuilt-in attention mechanism then automatically calculates the weights for each frequency band,focusing on the most discriminative components.This approach improves both classification accu-racy and model interpretability.Experimental validation was conducted via high-order modulationsimulation using an RF testbed.The results show that under three different Doppler frequencyshifts and complex multipath channel conditions,with a signal-to-noise ratio of 10 dB,the classifi-cation accuracy can reach 89.1%,90.4%and 90%,all of which are superior to the current main-stream methods.The proposed approach offers practical value for dynamic spectrum access and sig-nal security detection,and it makes important theoretical contributions to the application of deeplearning in complex electromagnetic signal recognition.
基金Supported by Key Research and Development Program of Shaanxi Province,China,No.2024SF-YBXM-078.
文摘BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.
基金supported by the Natural Science Foundation of Hubei Provincial Department of Education(D20232101)Shandong Second Medical University 2024 Affiliated Hospital(Teaching Hospital)Scientific Research Development Fund Project(2024FYQ026)+3 种基金the innovative Research Programme of Xiangyang No.1 People’s Hospital(XYY2023ZY01)Faculty Development Grants of Xiangyang No.1 People’s Hospital Affiliated to Hubei University of Medicine(XYY2023D05)Joint supported by Hubei Provincial Natural Science Foundation and Xiangyang of China(2025AFD091)Traditional Chinese Medicine Scientific Research Project of Hubei Provincial Administration of Traditional Chinese Medicine(ZY2025D019).
文摘Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.
