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 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
Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal ro...Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal role in OA progression by regulating inflammation,chondrocyte survival,and matrix homeostasis.However,their multifaceted signaling,via G proteins orβ-arrestins,poses challenges for precise therapeutic targeting.Biased agonism,where ligands selectively activate specific GPCR pathways,emerges as a promising approach to optimize efficacy and reduce side effects.This review examines biased signaling in OAassociated GPCRs,including cannabinoid receptors(CB1,CB2),chemokine receptors(CCR2,CXCR4),protease-activated receptors(PAR-2),adenosine receptors(A1R,A2AR,A2BR,A3R),melanocortin receptors(MC1R,MC3R),bradykinin receptors(B2R),prostaglandin E2 receptors(EP-2,EP-4),and calcium-sensing receptors(CaSR).We analyze ligands in clinical trials and explore natural products from Traditional Chinese Medicine as potential biased agonists.These compounds,with diverse structures and bioactivities,offer novel therapeutic avenues.By harnessing biased agonism,this review underscores the potential for developing targeted,safer OA therapies that address its complex pathology,bridging molecular insights with clinical translation.展开更多
Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-toler...Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-tolerant (SDT) traffic, we design a dynamic switching strategy based on a traffic-Qo S-aware soft slicing (TQASS) scheme and a resource-efficiency-aware soft slicing (REASS) scheme.展开更多
The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital...The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital ecosystems.As massive,distributed data streams are generated across edge devices and network layers,there is a growing need for intelligent,privacy-preserving AI solutions that can operate efficiently at the network edge.Federated Learning(FL)enables decentralized model training without transferring sensitive data,addressing key challenges around privacy,bandwidth,and latency.Despite its benefits in enhancing efficiency,real-time analytics,and regulatory compliance,FL adoption faces challenges,including communication overhead,heterogeneity,security vulnerabilities,and limited edge resources.While recent studies have addressed these issues individually,the literature lacks a unified,cross-domain perspective that reflects the architectural complexity and application diversity of Convergence ICT.This systematic review offers a comprehensive,cross-domain examination of FL within converged ICT infrastructures.The central research question guiding this review is:How can FL be effectively integrated into Convergence ICT environments,and what are the main challenges in implementing FL in such environments,along with possible solutions?We begin with a foundational overview of FL concepts and classifications,followed by a detailed taxonomy of FL architectures,learning strategies,and privacy-preserving mechanisms.Through in-depth case studies,we analyse FL’s application across diverse verticals,including smart cities,healthcare,industrial automation,and autonomous systems.We further identify critical challenges—such as system and data heterogeneity,limited edge resources,and security vulnerabilities—and review state-of-the-art mitigation strategies,including edge-aware optimization,secure aggregation,and adaptive model updates.In addition,we explore emerging directions in FL research,such as energy-efficient learning,federated reinforcement learning,and integration with blockchain,quantum computing,and self-adaptive networks.This review not only synthesizes current literature but also proposes a forward-looking road map to support scalable,secure,and sustainable FL deployment in future ICT ecosystems.展开更多
The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the c...The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern MA schemes, from Orthogonal Multiple Access (OMA)-based approaches like Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) to advanced Non-Orthogonal Multiple Access (NOMA) methods, including power domain-NOMA, Sparse Code Multiple Access (SCMA), and Rate Splitting Multiple Access (RSMA). The study further categorizes AI techniques—such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Explainable AI (XAI)—and maps them to practical challenges in Dynamic Spectrum Management (DSM), protocol optimization, and real-time distributed decision-making. Optimization strategies, including metaheuristics and multi-agent learning frameworks, are reviewed to illustrate the potential of AI in enhancing energy efficiency, system responsiveness, and cross-layer RA. Additionally, the review addresses security, privacy, and trust concerns, highlighting solutions like privacy-preserving ML, FL, and XAI in 6G and beyond. By identifying research gaps, challenges, and future directions, this work offers a structured resource for researchers and practitioners aiming to integrate AI into 6G MA systems for intelligent, scalable, and secure wireless communications.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.
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.
基金supported by the National Key R&D Program of the Ministry of Science and Technology(2023YFC2509900)National Natural Science Foundation of China(82374106)+3 种基金National Natural Science Foundation of China(U22A20371)the Basic and Applied Basic Research Fund of Guangdong Province(2021B1515120061)the Shenzhen Science and Technology Innovation Committee(JCYJ20210324102006017)SZ-HK Joint Laboratory for Innovative Biomaterials under CAS-HK Joint Laboratories(2024-2028).
文摘Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal role in OA progression by regulating inflammation,chondrocyte survival,and matrix homeostasis.However,their multifaceted signaling,via G proteins orβ-arrestins,poses challenges for precise therapeutic targeting.Biased agonism,where ligands selectively activate specific GPCR pathways,emerges as a promising approach to optimize efficacy and reduce side effects.This review examines biased signaling in OAassociated GPCRs,including cannabinoid receptors(CB1,CB2),chemokine receptors(CCR2,CXCR4),protease-activated receptors(PAR-2),adenosine receptors(A1R,A2AR,A2BR,A3R),melanocortin receptors(MC1R,MC3R),bradykinin receptors(B2R),prostaglandin E2 receptors(EP-2,EP-4),and calcium-sensing receptors(CaSR).We analyze ligands in clinical trials and explore natural products from Traditional Chinese Medicine as potential biased agonists.These compounds,with diverse structures and bioactivities,offer novel therapeutic avenues.By harnessing biased agonism,this review underscores the potential for developing targeted,safer OA therapies that address its complex pathology,bridging molecular insights with clinical translation.
基金supported by the Liaoning Revitalization Talents Program(XLYC2203148)
文摘Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-tolerant (SDT) traffic, we design a dynamic switching strategy based on a traffic-Qo S-aware soft slicing (TQASS) scheme and a resource-efficiency-aware soft slicing (REASS) scheme.
文摘The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital ecosystems.As massive,distributed data streams are generated across edge devices and network layers,there is a growing need for intelligent,privacy-preserving AI solutions that can operate efficiently at the network edge.Federated Learning(FL)enables decentralized model training without transferring sensitive data,addressing key challenges around privacy,bandwidth,and latency.Despite its benefits in enhancing efficiency,real-time analytics,and regulatory compliance,FL adoption faces challenges,including communication overhead,heterogeneity,security vulnerabilities,and limited edge resources.While recent studies have addressed these issues individually,the literature lacks a unified,cross-domain perspective that reflects the architectural complexity and application diversity of Convergence ICT.This systematic review offers a comprehensive,cross-domain examination of FL within converged ICT infrastructures.The central research question guiding this review is:How can FL be effectively integrated into Convergence ICT environments,and what are the main challenges in implementing FL in such environments,along with possible solutions?We begin with a foundational overview of FL concepts and classifications,followed by a detailed taxonomy of FL architectures,learning strategies,and privacy-preserving mechanisms.Through in-depth case studies,we analyse FL’s application across diverse verticals,including smart cities,healthcare,industrial automation,and autonomous systems.We further identify critical challenges—such as system and data heterogeneity,limited edge resources,and security vulnerabilities—and review state-of-the-art mitigation strategies,including edge-aware optimization,secure aggregation,and adaptive model updates.In addition,we explore emerging directions in FL research,such as energy-efficient learning,federated reinforcement learning,and integration with blockchain,quantum computing,and self-adaptive networks.This review not only synthesizes current literature but also proposes a forward-looking road map to support scalable,secure,and sustainable FL deployment in future ICT ecosystems.
文摘The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern MA schemes, from Orthogonal Multiple Access (OMA)-based approaches like Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) to advanced Non-Orthogonal Multiple Access (NOMA) methods, including power domain-NOMA, Sparse Code Multiple Access (SCMA), and Rate Splitting Multiple Access (RSMA). The study further categorizes AI techniques—such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Explainable AI (XAI)—and maps them to practical challenges in Dynamic Spectrum Management (DSM), protocol optimization, and real-time distributed decision-making. Optimization strategies, including metaheuristics and multi-agent learning frameworks, are reviewed to illustrate the potential of AI in enhancing energy efficiency, system responsiveness, and cross-layer RA. Additionally, the review addresses security, privacy, and trust concerns, highlighting solutions like privacy-preserving ML, FL, and XAI in 6G and beyond. By identifying research gaps, challenges, and future directions, this work offers a structured resource for researchers and practitioners aiming to integrate AI into 6G MA systems for intelligent, scalable, and secure wireless communications.
