Background Ruminants and monogastric animals exhibit significant differences in gluconeogenic efficiency.In dairy cows,hepatic gluconeogenesis serves as the primary source of glucose.Metabolites modulate gluconeogenes...Background Ruminants and monogastric animals exhibit significant differences in gluconeogenic efficiency.In dairy cows,hepatic gluconeogenesis serves as the primary source of glucose.Metabolites modulate gluconeogenesis efficiency through allosteric regulation,redox state,and signal transduction pathways.However,the liver-enriched metabolites that regulate hepatic gluconeogenesis in dairy cows and their specific regulatory mechanisms remain incompletely characterized.Results Six Holstein dairy cows and six Duroc×(Landrace×Yorkshire)(DLY)crossbred pigs served as research subjects.Employing non-targeted and targeted metabolomics,we discovered that three bile acids—taurodeoxycholic acid(TDCA),taurocholic acid(TCA),and glycocholic acid(GCA)—were highly enriched in Holstein dairy cows'livers.In bovine hepatocytes,individual or combined stimulation of these bile acids significantly upregulated the expression of gluconeogenesis genes(FBP1,PCK1 and G6PC)and enhanced glucose production.In fasting mice with induced gluconeogenesis,TDCA,TCA,and GCA increased fasting blood glucose levels,and pyruvate tolerance tests further revealed their capacity to enhance hepatic gluconeogenesis,enabling more efficient glucose synthesis from pyruvate.Mechanistically,these bile acids activated Takeda G protein-coupled receptor 5(TGR5),elevated intracellular cAMP levels,and ultimately enhanced gluconeogenesis via the transcription factor cAMP-response element binding protein(CREB).Notably,a TGR5 inhibitor abrogated the stimulatory effects of TDCA,TCA,and GCA on hepatic gluconeogenesis in fasting mice.Conclusion TDCA,TCA,and GCA are key metabolites promoting hepatic gluconeogenesis in dairy cows,with TGR5 as the pivotal receptor and the cAMP/PKA/CREB pathway as the critical downstream mechanism.展开更多
The growing developments in 5G and 6G wireless communications have revolutionized communications technologies,providing faster speeds with reduced latency and improved connectivity to users.However,it raises significa...The growing developments in 5G and 6G wireless communications have revolutionized communications technologies,providing faster speeds with reduced latency and improved connectivity to users.However,it raises significant security challenges,including impersonation threats,data manipulation,distributed denial of service(DDoS)attacks,and privacy breaches.Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks.This survey provides a comprehensive review of how Federated Learning(FL),Blockchain,and Digital Twin(DT)technologies can collectively enhance the security of 5G and 6G systems.Blockchain offers decentralized,immutable,and transparent mechanisms for securing network transactions,while FL enables privacy-preserving collaborative learning without sharing raw data.Digital Twins create virtual replicas of network components,enabling real-time monitoring,anomaly detection,and predictive threat analysis.The survey examines major security issues in emerging wireless architectures and analyzes recent advancements that integrate FL,Blockchain,and DT to mitigate these threats.Additionally,it presents practical use cases,synthesizes key lessons learned,and identifies ongoing research challenges.Finally,the survey outlines future research directions to support the development of scalable,intelligent,and robust security frameworks for next-generation wireless networks.展开更多
Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—esp...Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.展开更多
The rapid advancement of 6G communication technologies and generative artificial intelligence(AI)is catalyzing a new wave of innovation at the intersection of networking and intelligent computing.On the one hand,6G en...The rapid advancement of 6G communication technologies and generative artificial intelligence(AI)is catalyzing a new wave of innovation at the intersection of networking and intelligent computing.On the one hand,6G envisions a hyper-connected environment that supports ubiquitous intelligence through ultra-low latency,high throughput,massive device connectivity,and integrated sensing and communication.On the other hand,generative AI,powered by large foundation models,has emerged as a powerful paradigm capable of creating.展开更多
IgG4相关性疾病(immunoglobulin-G4 related disease,IgG4-RD)是一种免疫介导的慢性纤维炎性疾病,可累及多个系统及器官。目前IgG4-RD合并恶性肿瘤且累及同一部位的病例鲜有报道。本文报道1例72岁男性患者,以肾脏肿瘤入院,结合镜检、免...IgG4相关性疾病(immunoglobulin-G4 related disease,IgG4-RD)是一种免疫介导的慢性纤维炎性疾病,可累及多个系统及器官。目前IgG4-RD合并恶性肿瘤且累及同一部位的病例鲜有报道。本文报道1例72岁男性患者,以肾脏肿瘤入院,结合镜检、免疫表型及血清学测定最终诊断为肾透明细胞肾细胞癌合并IgG4-RD。由于IgG4-RD缺少临床特异性,容易在诊治过程中被忽略,本例意在提示IgG4-RD与肿瘤同时存在的可能性,且两者之间的发生可能存在相关性,为诊断及研究提供新的思路,避免漏诊及误诊。展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
Altermagnets,a class of unconventional antiferromagnets with non-relativistic spin-splitting,offer promising potential for antiferromagnetic spintronic devices.While many altermagnets are limited by either low magneti...Altermagnets,a class of unconventional antiferromagnets with non-relativistic spin-splitting,offer promising potential for antiferromagnetic spintronic devices.While many altermagnets are limited by either low magnetic transition temperatures or weak spin splitting,the recently discovered metal CrSb,with high N′eel temperature(T_(N)=710 K)and significant spin-splitting due to its unique spin space group,provides a robust platform for remarkable tunneling magnetoresistance(TMR)in collinear all-antiferromagnetic tunnel junctions(AATJs).This study systematically investigates the spin-polarized Fermi surface of CrSb and spin-dependent electron transport in CrSb-based AATJs.The CrSb/β-InSe/CrSb junction with a three-monolayer InSe barrier exhibits a TMR ratio of approximately 290%,with energy-dependent analysis revealing TMR ratios that may exceed 850%when considering the shift of the Fermi energy.We also demonstrate the angle-dependent TMR of CrSb-based AATJs by adjusting N′eel vector orientations.Our findings might provide strong theoretical support for CrSb as a versatile building block for all-antiferromagnetic memory devices.展开更多
Osteosarcoma(OS)is the most frequent primary bone sarcomas with high recurrence and poor prognosis.Emerging evidence indicates that membraneless organelles stress granules(SGs),whose assemblies are driven by scaffold ...Osteosarcoma(OS)is the most frequent primary bone sarcomas with high recurrence and poor prognosis.Emerging evidence indicates that membraneless organelles stress granules(SGs),whose assemblies are driven by scaffold protein G3BP1,are extensively involved in tumor,especially in OS.However,how SGs behave and communicate with organelles,particularly nucleoli and mitochondria,during drug challenges remain unknown.This study revealed that chemotherapeutic drugs activated the cysteine protease asparagine endopeptidase(AEP)to specifically cleave the SG core protein G3BP1 at N258/N309 in OS and malignant glioma.tG3BP1-Ns modulated SG dynamics by competitively binding to full-length G3BP1.Strikingly,tG3BP1-Cs,containing a conserved RNA recognition motif CCUBSCUS,sequestered mRNAs of ribosomal proteins and oxidative phosphorylation genes in the nucleoli and mitochondria to repress translation and oxidative stress.Moreover,the inhibition of AEP promoted the tumor-suppressing effect of chemotherapeutic drugs,whereas AEP-cleaved G3BP1 rescue reversed the effect in both OS and glioma models.Cancerous tissues exhibited high levels of AEP and G3BP1 truncations,which were strongly associated with poor prognosis.展开更多
GNAO1-associated disorder is a rare disease and an example of developmental and epileptic encephalopathies.Caused by ca.150 different dominant missense mutations in the gene encoding the major neuronal G protein Gao,i...GNAO1-associated disorder is a rare disease and an example of developmental and epileptic encephalopathies.Caused by ca.150 different dominant missense mutations in the gene encoding the major neuronal G protein Gao,it spans a wide range of neurological clinical manifestations,that may include epileptic seizures,motor dysfunctions,developmental and intellectual delay,and other symptoms(Sáez González et al.,2023).展开更多
The 6G smart Fog Radio Access Network(F-RAN)is an integration of 6G network intelligence technologies and the F-RAN architecture.Its aim is to provide low-latency and high-performance services for massive access devic...The 6G smart Fog Radio Access Network(F-RAN)is an integration of 6G network intelligence technologies and the F-RAN architecture.Its aim is to provide low-latency and high-performance services for massive access devices.However,the performance of current 6G network intelligence technologies and its level of integration with the architecture,along with the system-level requirements for the number of access devices and limitations on energy consumption,have impeded further improvements in the 6G smart F-RAN.To better analyze the root causes of the network problems and promote the practical development of the network,this study used structured methods such as segmentation to conduct a review of the topic.The research results reveal that there are still many problems in the current 6G smart F-RAN.Future research directions and difficulties are also discussed.展开更多
基金supported by the National Science Fund for Excellent Young Scholars(grant number 32422082)the Natural Science Basic Research Plan in Shaanxi Province(grant number 2025JC-QYXQ-009)。
文摘Background Ruminants and monogastric animals exhibit significant differences in gluconeogenic efficiency.In dairy cows,hepatic gluconeogenesis serves as the primary source of glucose.Metabolites modulate gluconeogenesis efficiency through allosteric regulation,redox state,and signal transduction pathways.However,the liver-enriched metabolites that regulate hepatic gluconeogenesis in dairy cows and their specific regulatory mechanisms remain incompletely characterized.Results Six Holstein dairy cows and six Duroc×(Landrace×Yorkshire)(DLY)crossbred pigs served as research subjects.Employing non-targeted and targeted metabolomics,we discovered that three bile acids—taurodeoxycholic acid(TDCA),taurocholic acid(TCA),and glycocholic acid(GCA)—were highly enriched in Holstein dairy cows'livers.In bovine hepatocytes,individual or combined stimulation of these bile acids significantly upregulated the expression of gluconeogenesis genes(FBP1,PCK1 and G6PC)and enhanced glucose production.In fasting mice with induced gluconeogenesis,TDCA,TCA,and GCA increased fasting blood glucose levels,and pyruvate tolerance tests further revealed their capacity to enhance hepatic gluconeogenesis,enabling more efficient glucose synthesis from pyruvate.Mechanistically,these bile acids activated Takeda G protein-coupled receptor 5(TGR5),elevated intracellular cAMP levels,and ultimately enhanced gluconeogenesis via the transcription factor cAMP-response element binding protein(CREB).Notably,a TGR5 inhibitor abrogated the stimulatory effects of TDCA,TCA,and GCA on hepatic gluconeogenesis in fasting mice.Conclusion TDCA,TCA,and GCA are key metabolites promoting hepatic gluconeogenesis in dairy cows,with TGR5 as the pivotal receptor and the cAMP/PKA/CREB pathway as the critical downstream mechanism.
基金derived from a research grant“Cybersecurity Research and Innovation Pioneers Grants Initiative”funded by The National Program for RDI in Cybersecurity(National Cybersecurity Authority)-Kingdom of Saudi Arabia-with grant number(CRPG-25-3168)supported by EIAS Data Science and Blockchain Lab,CCIS,Prince Sultan University.
文摘The growing developments in 5G and 6G wireless communications have revolutionized communications technologies,providing faster speeds with reduced latency and improved connectivity to users.However,it raises significant security challenges,including impersonation threats,data manipulation,distributed denial of service(DDoS)attacks,and privacy breaches.Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks.This survey provides a comprehensive review of how Federated Learning(FL),Blockchain,and Digital Twin(DT)technologies can collectively enhance the security of 5G and 6G systems.Blockchain offers decentralized,immutable,and transparent mechanisms for securing network transactions,while FL enables privacy-preserving collaborative learning without sharing raw data.Digital Twins create virtual replicas of network components,enabling real-time monitoring,anomaly detection,and predictive threat analysis.The survey examines major security issues in emerging wireless architectures and analyzes recent advancements that integrate FL,Blockchain,and DT to mitigate these threats.Additionally,it presents practical use cases,synthesizes key lessons learned,and identifies ongoing research challenges.Finally,the survey outlines future research directions to support the development of scalable,intelligent,and robust security frameworks for next-generation wireless networks.
基金funded by the National Natural Science Foundation of China(NSFC)under Grant Number 72071209.
文摘Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.
文摘The rapid advancement of 6G communication technologies and generative artificial intelligence(AI)is catalyzing a new wave of innovation at the intersection of networking and intelligent computing.On the one hand,6G envisions a hyper-connected environment that supports ubiquitous intelligence through ultra-low latency,high throughput,massive device connectivity,and integrated sensing and communication.On the other hand,generative AI,powered by large foundation models,has emerged as a powerful paradigm capable of creating.
文摘IgG4相关性疾病(immunoglobulin-G4 related disease,IgG4-RD)是一种免疫介导的慢性纤维炎性疾病,可累及多个系统及器官。目前IgG4-RD合并恶性肿瘤且累及同一部位的病例鲜有报道。本文报道1例72岁男性患者,以肾脏肿瘤入院,结合镜检、免疫表型及血清学测定最终诊断为肾透明细胞肾细胞癌合并IgG4-RD。由于IgG4-RD缺少临床特异性,容易在诊治过程中被忽略,本例意在提示IgG4-RD与肿瘤同时存在的可能性,且两者之间的发生可能存在相关性,为诊断及研究提供新的思路,避免漏诊及误诊。
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
基金supported by the National Natural Science Foundation of China(Grant Nos.T2394475,T2394470,T2394471,and 12174129)the China Postdoctoral Science Foundation(Grant No.2023M741269).
文摘Altermagnets,a class of unconventional antiferromagnets with non-relativistic spin-splitting,offer promising potential for antiferromagnetic spintronic devices.While many altermagnets are limited by either low magnetic transition temperatures or weak spin splitting,the recently discovered metal CrSb,with high N′eel temperature(T_(N)=710 K)and significant spin-splitting due to its unique spin space group,provides a robust platform for remarkable tunneling magnetoresistance(TMR)in collinear all-antiferromagnetic tunnel junctions(AATJs).This study systematically investigates the spin-polarized Fermi surface of CrSb and spin-dependent electron transport in CrSb-based AATJs.The CrSb/β-InSe/CrSb junction with a three-monolayer InSe barrier exhibits a TMR ratio of approximately 290%,with energy-dependent analysis revealing TMR ratios that may exceed 850%when considering the shift of the Fermi energy.We also demonstrate the angle-dependent TMR of CrSb-based AATJs by adjusting N′eel vector orientations.Our findings might provide strong theoretical support for CrSb as a versatile building block for all-antiferromagnetic memory devices.
基金supported by the National Key R&D Program of China(grant number 2023ZD0502206,2024YFB3213200,Topic No.2024YFB3213204)National Natural Science Foundation of China(nos.82273278,82373514,82373202,82272728,82002630,81772654)+5 种基金the National Key Research and Development Program of China(grant number 2022YFC2404602)Shanghai Hospital Development Center Foundation(grant number SHDC12023108)Scientific and Technological Innovation Action Plan of Shanghai Science and Technology Committee(grant number 22Y31900103)Beijing Science and Technology Innovation Medical Development Foundation(grant number KC2021-JX-0170-9)the Shanghai Association for Science and Technology(nos.201409003000,201409002400,20YF1426200)Shanghai Science and Technology Innovation Action Plan(grant number 23Y41900100).
文摘Osteosarcoma(OS)is the most frequent primary bone sarcomas with high recurrence and poor prognosis.Emerging evidence indicates that membraneless organelles stress granules(SGs),whose assemblies are driven by scaffold protein G3BP1,are extensively involved in tumor,especially in OS.However,how SGs behave and communicate with organelles,particularly nucleoli and mitochondria,during drug challenges remain unknown.This study revealed that chemotherapeutic drugs activated the cysteine protease asparagine endopeptidase(AEP)to specifically cleave the SG core protein G3BP1 at N258/N309 in OS and malignant glioma.tG3BP1-Ns modulated SG dynamics by competitively binding to full-length G3BP1.Strikingly,tG3BP1-Cs,containing a conserved RNA recognition motif CCUBSCUS,sequestered mRNAs of ribosomal proteins and oxidative phosphorylation genes in the nucleoli and mitochondria to repress translation and oxidative stress.Moreover,the inhibition of AEP promoted the tumor-suppressing effect of chemotherapeutic drugs,whereas AEP-cleaved G3BP1 rescue reversed the effect in both OS and glioma models.Cancerous tissues exhibited high levels of AEP and G3BP1 truncations,which were strongly associated with poor prognosis.
文摘GNAO1-associated disorder is a rare disease and an example of developmental and epileptic encephalopathies.Caused by ca.150 different dominant missense mutations in the gene encoding the major neuronal G protein Gao,it spans a wide range of neurological clinical manifestations,that may include epileptic seizures,motor dysfunctions,developmental and intellectual delay,and other symptoms(Sáez González et al.,2023).
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+2 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067).
文摘The 6G smart Fog Radio Access Network(F-RAN)is an integration of 6G network intelligence technologies and the F-RAN architecture.Its aim is to provide low-latency and high-performance services for massive access devices.However,the performance of current 6G network intelligence technologies and its level of integration with the architecture,along with the system-level requirements for the number of access devices and limitations on energy consumption,have impeded further improvements in the 6G smart F-RAN.To better analyze the root causes of the network problems and promote the practical development of the network,this study used structured methods such as segmentation to conduct a review of the topic.The research results reveal that there are still many problems in the current 6G smart F-RAN.Future research directions and difficulties are also discussed.
