Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Background:Parkinson’s disease(PD)is one of the most common movement disorders worldwide.Ziyin Xifeng Decoction(ZYXFD),a traditional Chinese medicine compound formula,has shown therapeutic efficacy in treating PD,but...Background:Parkinson’s disease(PD)is one of the most common movement disorders worldwide.Ziyin Xifeng Decoction(ZYXFD),a traditional Chinese medicine compound formula,has shown therapeutic efficacy in treating PD,but its specific mechanisms of action have not been fully elucidated.Methods:Firstly,we employed network pharmacology and untargeted metabolomics analysis to identify the core targets,pathways,and key metabolites of ZYXFD in the treatment of PD.Subsequently,we evaluated the protective effects of ZYXFD and further investigated its anti-PD mechanisms by validating the analytical results.Results:Combined analyses of network pharmacology and metabolomics identify the core targets including EGFR,SRC,PTGS2,and CDK2,while the effects of ZYXFD against PD are likely mediated primarily through the PI3K/AKT/mTOR signaling pathway.Pharmacodynamic evaluation demonstrated that a high dose of ZYXFD significantly improved behavioral deficits in chronic PD mice,downregulatedα-synuclein protein expression,and protected dopaminergic neurons.It also regulated the expression of core targets,inhibited the PI3K/AKT/mTOR signaling pathway,promoted autophagy,and reduced apoptosis.In vitro experiments further verified that the therapeutic effect of ZYXFD on PD is dependent on autophagy regulation.Conclusion:The findings demonstrated that ZYXFD alleviates PD by modulating related proteins and metabolites,inhibiting the PI3K/AKT/mTOR signaling pathway,and enhancing autophagy.This provides a theoretical basis for its broader application in PD treatment.展开更多
The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle...The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.展开更多
Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with ...Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with Tajikistan representing a typical example of such disparities.Based on 81 SDG indicators for Tajikistan from 2001 to 2023,this study applied a three-level coupling network framework:at the microscale,it identified synergies and trade-offs between indicators;at the mesoscale,it examined the strength and direction of linkages within four SDG-related components(society,finance,governance,and environment);and at the global level,it focused on the overall SDG interlinkages.Spearman’s rank correlation,sliding window method,and topological properties were employed to analyze the coupling dynamics of SDGs.Results showed that over 70.00%of associations in the global SDG network were of medium-to-low intensity,alongside extremely strong ones(|r|value approached 1.00,where r is the correlation coefficient).SDG interactions were generally limited,with stable local synergy clusters in core livelihood sectors.Network modularity fluctuated,reflecting a cycle of differentiation,integration,and fragmentation,while coupling efficiency varied with the external environment.Each component exhibited distinct functional characteristics.The social component maintained high connectivity through the“poverty alleviation-education-healthcare”loop.The environmental component shifted toward coordinated eco-economic governance.The governance-related component broke interdepartmental barriers,while the financial component showed weak links between resource-based indicators and consumption/employment indicators.Tajikistan’s SDG coupling evolved through three phases:survival-oriented(2001–2012),policy integration(2013–2018),and shock adaptation(2019–2023).These phases were driven by policy changes,resource industries,governance optimization,and external factors.This study enriches the analytical framework for understanding the dynamic coupling of SDGs in mountainous resource-dependent countries and provides empirical evidence to support similar countries in formulating phase-specific SDG promotion strategies.展开更多
Smart specialization is a regional development strategy that identifies regional innovation advantages through the analysis of cluster networks,while strengthening both intra-cluster and inter-cluster technological li...Smart specialization is a regional development strategy that identifies regional innovation advantages through the analysis of cluster networks,while strengthening both intra-cluster and inter-cluster technological linkages to promote coordinated regional development.Drawing on branch office flow and patent cooperation data,and employing methods such as the Expectation-Maximization(EM)clustering algorithm and the‘Product Space’approach,this study investigates innovation and technological linkages both within and across industrial clusters.The key findings are as follows.First,Jiangsu’s clusters demonstrate two patterns:closely integrated industrial networks in southern cities like Suzhou,fostering strong industrial resilience,and distinct technological boundaries in northern and central cities like Yancheng,resulting in weaker integration.Second,the cluster network exhibits a single-core structure at the municipal level,centered around Nanjing,with a multi-tiered hierarchy at the district level.Third,innovation linkages between clusters follow a dual-core structure,with Nanjing and Suzhou as central hubs.In this structure,large enterprises in Nanjing and small and medium-sized enterprises(SMEs)in Suzhou reflect complementary industrial characteristics.Finally,both technology-intensive and low-tech manufacturing industries show a higher propensity for cross-regional innovation,with some cities demonstrating significant advantages in high-tech industries.Grounded in the framework of smart specialization,this study conducts an in-depth analysis of innovation and technological linkages within cluster networks at the industrial level,offering scientific insights to support the localized implementation of smart specialization strategies in the Chinese context.展开更多
Alzheimer’s disease is initially thought to be caused by age-associated accumulation of plaques,in recent years,research has increasingly associated Alzheimer’s disease with lysosomal storage and metabolic disorders...Alzheimer’s disease is initially thought to be caused by age-associated accumulation of plaques,in recent years,research has increasingly associated Alzheimer’s disease with lysosomal storage and metabolic disorders,and the explanation of its pathogenesis has shifted from amyloid and tau accumulation to oxidative stress and impaired lipid and glucose metabolism aggravated by hypoxic conditions.However,the underlying mechanisms linking those cellular processes and conditions to disease progression have yet to be defined.Here,we applied a disease similarity approach to identify unknown molecular targets of Alzheimer’s disease by using transcriptomic data from congenital diseases known to increase Alzheimer’s disease risk,namely Down syndrome,Niemann-Pick type C disease,and mucopolysaccharidoses I.We uncovered common pathways,hub genes,and miRNAs across in vitro and in vivo models of these diseases as potential molecular targets for neuroprotection and amelioration of Alzheimer’s disease pathology,many of which have never been associated with Alzheimer’s disease.We then investigated common molecular alterations in brain samples from a Niemann-Pick type C disease mouse model by juxtaposing them with brain samples of both human and mouse models of Alzheimer’s disease.Detailed phenotypic,molecular,chronological,and biological aging analyses revealed that the Npc1tm(I1061T)Dso mouse model can serve as a potential short-lived in vivo model for brain aging and Alzheimer’s disease research.This research represents the first comprehensive approach to congenital disease association with neurodegeneration and a new perspective on Alzheimer’s disease research while highlighting shortcomings and lack of correlation in diverse in vitro models.Considering the lack of an Alzheimer’s disease mouse model that recapitulates the physiological hallmarks of brain aging,the short-lived Npc1^(tm(I1061T)Dso) mouse model can further accelerate the research in these fields and offer a unique model for understanding the molecular mechanisms of Alzheimer’s disease from a perspective of accelerated brain aging.展开更多
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.展开更多
Hepatitis C virus(HCV)and hepatitis B virus(HBV)infections are increasingly recognized as significant etiological factors in the pathogenesis of B-cell non-Hodgkin’s lymphomas(B-NHLs).Epidemiological and molecular st...Hepatitis C virus(HCV)and hepatitis B virus(HBV)infections are increasingly recognized as significant etiological factors in the pathogenesis of B-cell non-Hodgkin’s lymphomas(B-NHLs).Epidemiological and molecular studies have demonstrated a consistent association between chronic viral infection and B-NHLs.Multiple pathogenic mechanisms have been implicated in lymphomagenesis,both direct and indirect,including chronic antigenic stimulation,direct infection of B cells,and viral protein-mediated oncogenic signaling,It is likely that a combination of several pathogenic conditions is required to eventually lead to the development of lymphoma.The prevalence of B-cell lymphomas among individuals with chronic HCV or HBV infection presents a complex pathogenetic scenario,given the tumor heterogeneity and variable clinical behavior,and poses therapeutic challenges,due to the partial efficacy of current treatment options.The advent of direct-acting antivirals(DAAs)for HCV and high-genetic barrier nucleos(t)ide analogues(NAs)for HBV has improved patient outcomes.In indolent HCV-associated B-NHLs,antiviral therapy with DAAs alone often achieves sustained virologic response and may lead to lymphoma regression.Conversely,aggressive subtypes like diffuse large B-cell lymphomas require combination treatment with immunochemotherapy.In the setting of HBV-associated lymphomas,antiviral prophylaxis with potent NAs(e.g.,entecavir or tenofovir)is essential to prevent HBV reactivation during rituximab-containing chemotherapy regimen.The integration of antiviral and anticancer therapies has been shown to enhance survival outcomes while mitigating hepatic toxicity.A comprehensive understanding of the biological interplay between chronic viral infection and B-cell transformation is critical for optimizing diagnostic and therapeutic strategies.Aim of this viewpoint is to provide evidence that early viral detection and prompt management remain the most effective strategies to improve survival rates and to reduce treatment-related morbidity in these patients.展开更多
Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,n...Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,neural oscillatory dynamics,and brain network reorganization remain unclear.This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments,molecular profiling,and neurophysiological monitoring.Methods In this prospective double-blind trial,12 AD patients underwent a 14-day protocol of 20 Hz rTMS,with comprehensive multimodal assessments performed pre-and postintervention.Cognitive functioning was quantified using the mini-mental state examination(MMSE)and Montreal cognitive assessment(MOCA),while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living(ADL)scale and combined neuropsychiatric inventory(NPI)-Hamilton depression rating scale(HAMD).Peripheral blood biomarkers,specifically Aβ1-40 and phosphorylated tau(p-tau181),were analyzed to investigate the effects of rTMS on molecular metabolism.Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients,while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization.Furthermore,systematic assessment of correlations between cognitive scale scores,blood biomarkers,and network characteristics was performed to elucidate cross-modal therapeutic associations.Results Clinically,MMSE and MOCA scores improved significantly(P<0.05).Biomarker showed that Aβ1-40 level increased(P<0.05),contrasting with p-tau181 reduction.Moreover,the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores.Post-intervention analyses revealed significant modulations in oscillatory power,characterized by pronounced reductions in delta(P<0.05)and theta bands(P<0.05),while concurrent enhancements were observed in alpha,beta,and gamma band activities(all P<0.05).Network analysis revealed frequency-specific reorganization:clustering coefficients were significantly decreased in delta,theta,and alpha bands(P<0.05),while global efficiency improvement was exclusively detected in the delta band(P<0.05).The alpha band demonstrated concurrent increases in average nodal degree(P<0.05)and characteristic path length reduction(P<0.05).Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181.Additionally,the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band.However,the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands.Conclusion 20 Hz rTMS targeting dorsolateral prefrontal cortex(DLPFC)significantly improves cognitive function and enhances the metabolic clearance ofβ-amyloid and tau proteins in AD patients.This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation,which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks.These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales,blood biomarkers,and EEG——in understanding and monitoring the progression of AD.This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment.展开更多
自组织网络(Ad Hoc)的动态性与无中心化特性导致了传统路由协议在大规模网络场景下面临路由开销大、可扩展性差等问题。针对以上问题,提出一种基于分簇的优化链路状态路由(Clustering Optimized Link State Routing,C-OLSR)协议,通过引...自组织网络(Ad Hoc)的动态性与无中心化特性导致了传统路由协议在大规模网络场景下面临路由开销大、可扩展性差等问题。针对以上问题,提出一种基于分簇的优化链路状态路由(Clustering Optimized Link State Routing,C-OLSR)协议,通过引入分簇网络架构,设计动态簇头(Cluster Head,CH)与网关节点的联合选举机制,将网络划分为逻辑簇,优化路由信息传播效率。CH负责簇内拓扑管理,减少冗余控制消息泛洪;网关节点保障跨簇通信的连通性,降低路由开销并提升网络稳定性。基于网络仿真软件NS3的仿真实验表明,相较于传统OLSR、AODV(Ad Hoc On-demand Distance Vector)及DSDV(Destination-Sequenced Distance Vector)协议,C-OLSR在平均端到端时延(Average End-to-End Delay,AEED)、吞吐量、包投递率(Packet Delivery Ratio,PDR)和路由开销等性能指标上均得到提升。展开更多
The idea of a network society was introduced by Western sociologists at the end of the 20th century after in-depth research was conducted from perspectives such as informationalism.Influenced by these developments,the...The idea of a network society was introduced by Western sociologists at the end of the 20th century after in-depth research was conducted from perspectives such as informationalism.Influenced by these developments,the concept of constructing a network society also emerged in China.Over the past 30 years,China has made significant progress and achievements in constructing a network society,both in terms of its fundamental construction and social development.It is important that these advancements be summarized and reviewed.China’s network society construction can be divided into two relatively independent yet interconnected components,based on their focal points:its foundational infrastructure and its social development.These two components of China’s network society are managed by different departments.China has integrated the fundamental construction of its network society with the social development of its network society,thereby achieving unified planning,collaborative advancement,and coordinated development.This approach aims to harmonize two aspects:building China’s cyberspace strength and contributing to Chinese informatization,thereby advancing Chinese modernization.展开更多
[Objectives]This study was conducted to explore the action mechanism of limonoids against Alzheimer s disease(AD)based on network pharmacology and molecular docking techniques.[Methods]Limonoid compounds were obtained...[Objectives]This study was conducted to explore the action mechanism of limonoids against Alzheimer s disease(AD)based on network pharmacology and molecular docking techniques.[Methods]Limonoid compounds were obtained through literature research(January 1942 to January 2021).Active components and potential targets of limonoids were retrieved from PubChem,TCMSP,and Swiss Target Prediction databases.AD-related targets were obtained from the GeneCards database,and intersecting targets were identified using Venny 2.1.0 to obtain the action targets of limonoids against AD.The protein-protein interaction(PPI)network was constructed using the String platform,and key targets were screened and visualized via network topology analysis with Cytoscape software.GO and KEGG pathway enrichment analyses were performed using the Metascape database,and a"drug-component-target-pathway-disease"network diagram was constructed using Cytoscape.AutoDock was empolyed for molecular docking to predict the binding properties of limonoid active components and their targets.[Results]A total of 60 limonoid compounds were obtained from literature research.Network pharmacology analysis showed 58 effective active components and 134 common targets between limonoids and AD.Key targets included AKT1(serine/threonine-protein kinase 1),TNF(tumor necrosis factor),STAT3(signal transducer and activator of transcription 3),BCL2(B-cell lymphoma 2),and EGFR(epidermal growth factor receptor).KEGG enrichment analysis revealed key signaling pathways such as pathways in cancer,Kaposi sarcoma-associated herpesvirus infection,PI3K-Akt signaling pathway,lipid and atherosclerosis,proteoglycans in cancer,MAPK signaling pathway,and Ras signaling pathway.Molecular docking results indicated that aphanamixoid A,obacunol,cipadesin C,harpertrioate A,xylogranatin A,11-oxocneorin G,evodulone,methyl angolensate,harrpemoid B and khivorin may be key components of limonoids against AD.[Conclusions]Limonoids exert anti-Alzheimer s effects through a multi-molecule,multi-target and multi-pathway mechanism.展开更多
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金funded by Zhejiang Province Traditional Chinese Medicine Science and Technology Program(No.2021ZZ012)The Changlin Qiu National Distinguished Senior Traditional Chinese Medicine Expert Heritage Workshop Project(No.GZS2021007).
文摘Background:Parkinson’s disease(PD)is one of the most common movement disorders worldwide.Ziyin Xifeng Decoction(ZYXFD),a traditional Chinese medicine compound formula,has shown therapeutic efficacy in treating PD,but its specific mechanisms of action have not been fully elucidated.Methods:Firstly,we employed network pharmacology and untargeted metabolomics analysis to identify the core targets,pathways,and key metabolites of ZYXFD in the treatment of PD.Subsequently,we evaluated the protective effects of ZYXFD and further investigated its anti-PD mechanisms by validating the analytical results.Results:Combined analyses of network pharmacology and metabolomics identify the core targets including EGFR,SRC,PTGS2,and CDK2,while the effects of ZYXFD against PD are likely mediated primarily through the PI3K/AKT/mTOR signaling pathway.Pharmacodynamic evaluation demonstrated that a high dose of ZYXFD significantly improved behavioral deficits in chronic PD mice,downregulatedα-synuclein protein expression,and protected dopaminergic neurons.It also regulated the expression of core targets,inhibited the PI3K/AKT/mTOR signaling pathway,promoted autophagy,and reduced apoptosis.In vitro experiments further verified that the therapeutic effect of ZYXFD on PD is dependent on autophagy regulation.Conclusion:The findings demonstrated that ZYXFD alleviates PD by modulating related proteins and metabolites,inhibiting the PI3K/AKT/mTOR signaling pathway,and enhancing autophagy.This provides a theoretical basis for its broader application in PD treatment.
基金supported by the National Key Research and Development Plan(Grant No.2022YFB3401901)the National Natural Science Foundation of China(Grant Nos.12192210,12192214,12072295,and 12222209)+1 种基金Independent Project of State Key Laboratory of Rail Transit Vehicle System(Grant No.2023TPL-T03)Fundamental Research Funds for the Central Universities(Grant No.2682023CG004).
文摘The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.
文摘Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with Tajikistan representing a typical example of such disparities.Based on 81 SDG indicators for Tajikistan from 2001 to 2023,this study applied a three-level coupling network framework:at the microscale,it identified synergies and trade-offs between indicators;at the mesoscale,it examined the strength and direction of linkages within four SDG-related components(society,finance,governance,and environment);and at the global level,it focused on the overall SDG interlinkages.Spearman’s rank correlation,sliding window method,and topological properties were employed to analyze the coupling dynamics of SDGs.Results showed that over 70.00%of associations in the global SDG network were of medium-to-low intensity,alongside extremely strong ones(|r|value approached 1.00,where r is the correlation coefficient).SDG interactions were generally limited,with stable local synergy clusters in core livelihood sectors.Network modularity fluctuated,reflecting a cycle of differentiation,integration,and fragmentation,while coupling efficiency varied with the external environment.Each component exhibited distinct functional characteristics.The social component maintained high connectivity through the“poverty alleviation-education-healthcare”loop.The environmental component shifted toward coordinated eco-economic governance.The governance-related component broke interdepartmental barriers,while the financial component showed weak links between resource-based indicators and consumption/employment indicators.Tajikistan’s SDG coupling evolved through three phases:survival-oriented(2001–2012),policy integration(2013–2018),and shock adaptation(2019–2023).These phases were driven by policy changes,resource industries,governance optimization,and external factors.This study enriches the analytical framework for understanding the dynamic coupling of SDGs in mountainous resource-dependent countries and provides empirical evidence to support similar countries in formulating phase-specific SDG promotion strategies.
基金Under the auspices of the National Natural Science Foundation of China(No.42330510,41871160,42371262)。
文摘Smart specialization is a regional development strategy that identifies regional innovation advantages through the analysis of cluster networks,while strengthening both intra-cluster and inter-cluster technological linkages to promote coordinated regional development.Drawing on branch office flow and patent cooperation data,and employing methods such as the Expectation-Maximization(EM)clustering algorithm and the‘Product Space’approach,this study investigates innovation and technological linkages both within and across industrial clusters.The key findings are as follows.First,Jiangsu’s clusters demonstrate two patterns:closely integrated industrial networks in southern cities like Suzhou,fostering strong industrial resilience,and distinct technological boundaries in northern and central cities like Yancheng,resulting in weaker integration.Second,the cluster network exhibits a single-core structure at the municipal level,centered around Nanjing,with a multi-tiered hierarchy at the district level.Third,innovation linkages between clusters follow a dual-core structure,with Nanjing and Suzhou as central hubs.In this structure,large enterprises in Nanjing and small and medium-sized enterprises(SMEs)in Suzhou reflect complementary industrial characteristics.Finally,both technology-intensive and low-tech manufacturing industries show a higher propensity for cross-regional innovation,with some cities demonstrating significant advantages in high-tech industries.Grounded in the framework of smart specialization,this study conducts an in-depth analysis of innovation and technological linkages within cluster networks at the industrial level,offering scientific insights to support the localized implementation of smart specialization strategies in the Chinese context.
基金supported by the NIA/NIH(1K01AG060040).Studies performed by JN were funded by the NICHD/NIH(5R00HD096117)Microscopy Core Facility supported,in part,with funding from NIH-NCI Cancer Center Support Grant P30 CA016059.
文摘Alzheimer’s disease is initially thought to be caused by age-associated accumulation of plaques,in recent years,research has increasingly associated Alzheimer’s disease with lysosomal storage and metabolic disorders,and the explanation of its pathogenesis has shifted from amyloid and tau accumulation to oxidative stress and impaired lipid and glucose metabolism aggravated by hypoxic conditions.However,the underlying mechanisms linking those cellular processes and conditions to disease progression have yet to be defined.Here,we applied a disease similarity approach to identify unknown molecular targets of Alzheimer’s disease by using transcriptomic data from congenital diseases known to increase Alzheimer’s disease risk,namely Down syndrome,Niemann-Pick type C disease,and mucopolysaccharidoses I.We uncovered common pathways,hub genes,and miRNAs across in vitro and in vivo models of these diseases as potential molecular targets for neuroprotection and amelioration of Alzheimer’s disease pathology,many of which have never been associated with Alzheimer’s disease.We then investigated common molecular alterations in brain samples from a Niemann-Pick type C disease mouse model by juxtaposing them with brain samples of both human and mouse models of Alzheimer’s disease.Detailed phenotypic,molecular,chronological,and biological aging analyses revealed that the Npc1tm(I1061T)Dso mouse model can serve as a potential short-lived in vivo model for brain aging and Alzheimer’s disease research.This research represents the first comprehensive approach to congenital disease association with neurodegeneration and a new perspective on Alzheimer’s disease research while highlighting shortcomings and lack of correlation in diverse in vitro models.Considering the lack of an Alzheimer’s disease mouse model that recapitulates the physiological hallmarks of brain aging,the short-lived Npc1^(tm(I1061T)Dso) mouse model can further accelerate the research in these fields and offer a unique model for understanding the molecular mechanisms of Alzheimer’s disease from a perspective of accelerated brain aging.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
基金supported by the National Italian Research Council(CNR)“Progetto DSB.AD007.305.001”to Monica Rinaldi。
文摘Hepatitis C virus(HCV)and hepatitis B virus(HBV)infections are increasingly recognized as significant etiological factors in the pathogenesis of B-cell non-Hodgkin’s lymphomas(B-NHLs).Epidemiological and molecular studies have demonstrated a consistent association between chronic viral infection and B-NHLs.Multiple pathogenic mechanisms have been implicated in lymphomagenesis,both direct and indirect,including chronic antigenic stimulation,direct infection of B cells,and viral protein-mediated oncogenic signaling,It is likely that a combination of several pathogenic conditions is required to eventually lead to the development of lymphoma.The prevalence of B-cell lymphomas among individuals with chronic HCV or HBV infection presents a complex pathogenetic scenario,given the tumor heterogeneity and variable clinical behavior,and poses therapeutic challenges,due to the partial efficacy of current treatment options.The advent of direct-acting antivirals(DAAs)for HCV and high-genetic barrier nucleos(t)ide analogues(NAs)for HBV has improved patient outcomes.In indolent HCV-associated B-NHLs,antiviral therapy with DAAs alone often achieves sustained virologic response and may lead to lymphoma regression.Conversely,aggressive subtypes like diffuse large B-cell lymphomas require combination treatment with immunochemotherapy.In the setting of HBV-associated lymphomas,antiviral prophylaxis with potent NAs(e.g.,entecavir or tenofovir)is essential to prevent HBV reactivation during rituximab-containing chemotherapy regimen.The integration of antiviral and anticancer therapies has been shown to enhance survival outcomes while mitigating hepatic toxicity.A comprehensive understanding of the biological interplay between chronic viral infection and B-cell transformation is critical for optimizing diagnostic and therapeutic strategies.Aim of this viewpoint is to provide evidence that early viral detection and prompt management remain the most effective strategies to improve survival rates and to reduce treatment-related morbidity in these patients.
文摘Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,neural oscillatory dynamics,and brain network reorganization remain unclear.This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments,molecular profiling,and neurophysiological monitoring.Methods In this prospective double-blind trial,12 AD patients underwent a 14-day protocol of 20 Hz rTMS,with comprehensive multimodal assessments performed pre-and postintervention.Cognitive functioning was quantified using the mini-mental state examination(MMSE)and Montreal cognitive assessment(MOCA),while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living(ADL)scale and combined neuropsychiatric inventory(NPI)-Hamilton depression rating scale(HAMD).Peripheral blood biomarkers,specifically Aβ1-40 and phosphorylated tau(p-tau181),were analyzed to investigate the effects of rTMS on molecular metabolism.Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients,while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization.Furthermore,systematic assessment of correlations between cognitive scale scores,blood biomarkers,and network characteristics was performed to elucidate cross-modal therapeutic associations.Results Clinically,MMSE and MOCA scores improved significantly(P<0.05).Biomarker showed that Aβ1-40 level increased(P<0.05),contrasting with p-tau181 reduction.Moreover,the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores.Post-intervention analyses revealed significant modulations in oscillatory power,characterized by pronounced reductions in delta(P<0.05)and theta bands(P<0.05),while concurrent enhancements were observed in alpha,beta,and gamma band activities(all P<0.05).Network analysis revealed frequency-specific reorganization:clustering coefficients were significantly decreased in delta,theta,and alpha bands(P<0.05),while global efficiency improvement was exclusively detected in the delta band(P<0.05).The alpha band demonstrated concurrent increases in average nodal degree(P<0.05)and characteristic path length reduction(P<0.05).Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181.Additionally,the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band.However,the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands.Conclusion 20 Hz rTMS targeting dorsolateral prefrontal cortex(DLPFC)significantly improves cognitive function and enhances the metabolic clearance ofβ-amyloid and tau proteins in AD patients.This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation,which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks.These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales,blood biomarkers,and EEG——in understanding and monitoring the progression of AD.This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment.
基金“Research on Social Change and Network Society Planning in the Internet of Everything Era”(ID:21BSH005),a project under the National Social Science Fund of China
文摘The idea of a network society was introduced by Western sociologists at the end of the 20th century after in-depth research was conducted from perspectives such as informationalism.Influenced by these developments,the concept of constructing a network society also emerged in China.Over the past 30 years,China has made significant progress and achievements in constructing a network society,both in terms of its fundamental construction and social development.It is important that these advancements be summarized and reviewed.China’s network society construction can be divided into two relatively independent yet interconnected components,based on their focal points:its foundational infrastructure and its social development.These two components of China’s network society are managed by different departments.China has integrated the fundamental construction of its network society with the social development of its network society,thereby achieving unified planning,collaborative advancement,and coordinated development.This approach aims to harmonize two aspects:building China’s cyberspace strength and contributing to Chinese informatization,thereby advancing Chinese modernization.
基金Supported by Science and Technology Fund of Guizhou health and Health Committee(gzwkj2024-240)Anshun City Science and Technology Bureau(ASKS[2024]01).
文摘[Objectives]This study was conducted to explore the action mechanism of limonoids against Alzheimer s disease(AD)based on network pharmacology and molecular docking techniques.[Methods]Limonoid compounds were obtained through literature research(January 1942 to January 2021).Active components and potential targets of limonoids were retrieved from PubChem,TCMSP,and Swiss Target Prediction databases.AD-related targets were obtained from the GeneCards database,and intersecting targets were identified using Venny 2.1.0 to obtain the action targets of limonoids against AD.The protein-protein interaction(PPI)network was constructed using the String platform,and key targets were screened and visualized via network topology analysis with Cytoscape software.GO and KEGG pathway enrichment analyses were performed using the Metascape database,and a"drug-component-target-pathway-disease"network diagram was constructed using Cytoscape.AutoDock was empolyed for molecular docking to predict the binding properties of limonoid active components and their targets.[Results]A total of 60 limonoid compounds were obtained from literature research.Network pharmacology analysis showed 58 effective active components and 134 common targets between limonoids and AD.Key targets included AKT1(serine/threonine-protein kinase 1),TNF(tumor necrosis factor),STAT3(signal transducer and activator of transcription 3),BCL2(B-cell lymphoma 2),and EGFR(epidermal growth factor receptor).KEGG enrichment analysis revealed key signaling pathways such as pathways in cancer,Kaposi sarcoma-associated herpesvirus infection,PI3K-Akt signaling pathway,lipid and atherosclerosis,proteoglycans in cancer,MAPK signaling pathway,and Ras signaling pathway.Molecular docking results indicated that aphanamixoid A,obacunol,cipadesin C,harpertrioate A,xylogranatin A,11-oxocneorin G,evodulone,methyl angolensate,harrpemoid B and khivorin may be key components of limonoids against AD.[Conclusions]Limonoids exert anti-Alzheimer s effects through a multi-molecule,multi-target and multi-pathway mechanism.
