Objectives:Colorectal cancer(CRC)is a major global health burden,and Urolithin A(Uro-A)has emerged as a promising anticancer agent.This systematic review aims to synthesize current in vitro evidence on the anticancer ...Objectives:Colorectal cancer(CRC)is a major global health burden,and Urolithin A(Uro-A)has emerged as a promising anticancer agent.This systematic review aims to synthesize current in vitro evidence on the anticancer effects of Uro-A in CRC,highlighting effective concentration ranges,exposure times,relevant outcomes,and underlying molecular mechanisms.Methods:Following PRISMA 2020 guidelines,a systematic search was conducted in PubMed,Scopus,and Web of Science using the following strategy:(colorectal cancer)AND(urolithin a)OR(3,8-dihydroxy-6H-dibenzo(b,d)pyran-6-one).Eligibility criteria were defined by the PICO framework:(P)in vitro CRC cell models;(I)Uro-A alone or combined treatments;(C)No intervention,vehicle or other treatments;(O)Relevant anticancer outcomes of Uro-A in CRC.Only original,full-text,in vitro studies in English were included.Risk of bias was assessed using ToxRTool.A qualitative synthesis was performed due to the heterogeneity of the included studies.Results:Fifteen studies met inclusion criteria,involving CRC cell lines(Caco-2,HCT-116,HT-29,SW480,SW620)and normal colon fibroblasts(CCD18-Co).Uro-A inhibited CRC cell proliferation,clonogenic growth,cancer stem cells properties,migration,and invasion,and induced cell cycle arrest,apoptosis,autophagy,and senescence,through modulation of key signaling pathways and proteins.Co-treatments with conventional chemotherapeutics and microbiota-derived metabolites showed additive or synergistic effects.Discussion:The findings support UroA’s potential as a preventive or adjuvant agent in CRC treatment.However,preclinical nature of the evidence and methodological heterogeneity hinder clinical extrapolation to in vivo contexts.Human clinical trials are necessary to overcome these limitations.Other:This review was registered in PROSPERO(CRD420251070874)and supported by FCT/MCTES UIDP/05608/2020 and UIDB/05608/2020.Institutional.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
There is growing interest in introducing ecological risks(ERs)and ecosystem services(ESs)into environmental policies and practices.However,the integration of ESs and ERs into actual decision-making remains insufficien...There is growing interest in introducing ecological risks(ERs)and ecosystem services(ESs)into environmental policies and practices.However,the integration of ESs and ERs into actual decision-making remains insufficient.We simulated the spatiotemporal dynamics of ESs(e.g.,carbon storage,water yield,habitat quality,and soil conservation)and ERs in the upper reach of the Yellow River(URYR)from 2000 to 2100.Additionally,we explored their relationships by combining the InVEST model and a landscape ecological risk model with CMIP6 data.Our main findings showed that regional ERs change in response to land use and environmental dynamics.Specifically,the ER area decreased by 27,673 m^(2)during 2000-2020,but it is projected to increase by 13,273,438,and 68 m^(2)under the SSP1-2.6,SSP2-4.5,and SSP5-8.5 scenarios,respectively.We also observed remarkable spatial differences in ESs and ERs between past and future scenarios.For instance,the source area of the URYR exhibited high ESs and low ERs(P<0.001),while the ESs and ERs are declining and increasing,respectively,in the northeastern URYR(P<0.05).Finally,we proposed a spatial optimization framework to improve ESs and reduce ERs,which will support regional sustainable development.展开更多
Sixth Generation(6G)mobile communication networks will involve sensing as a new function,with the overwhelming trend of Integrated Sensing And Communications(ISAC).Although expanding the serving range of the networks,...Sixth Generation(6G)mobile communication networks will involve sensing as a new function,with the overwhelming trend of Integrated Sensing And Communications(ISAC).Although expanding the serving range of the networks,there exists performance trade-offbetween communication and sensing,in that they have competitions on the physical resources.Different resource allocation schemes will result in different sensing and communication performance,thus influencing the system’s overall performance.Therefore,how to model the system’s overall performance,and how to optimize it are key issues for ISAC.Relying on the large-scale deployment of the networks,cooperative ISAC has the advantages of wider coverage,more robust performance and good compatibility of multiple monostatic and multistatic sensing,compared to the non-cooperative ISAC.How to capture the performance gain of cooperation is a key issue for cooperative ISAC.To address the aforementioned vital problems,in this paper,we analyze the sensing accuracy gain,propose a unified ISAC performance evaluation framework and design several optimization methods in cooperative ISAC systems.The cooperative sensing accuracy gain is theoretically analyzed via Cramér Rao lower bound.The unified ISAC performance evaluation model is established by converting the communication mutual information to the effective minimum mean squared error.To optimize the unified ISAC performance,we design the optimization algorithms considering three factors:base stations’working modes,power allocation schemes and waveform design.Through simulations,we show the performance gain of the cooperative ISAC system and the effectiveness of the proposed optimization methods.展开更多
For the high-precision positioning requirements of UAV formation cooperative operation,a distributed control system based on RTK technology is proposed in this paper.By using the U-blox F9P GNSS module to build an RTK...For the high-precision positioning requirements of UAV formation cooperative operation,a distributed control system based on RTK technology is proposed in this paper.By using the U-blox F9P GNSS module to build an RTK base station/mobile terminal,combined with Pixhawk 6C flight control and MAVESP8266 communication module,centimeter-level(<2 cm)positioning accuracy is achieved.The system adopts the“centralized planning distributed execution”architecture,transmits RTCM differential data and MAVLink messages through the UDP protocol,and integrates ROS to realize status information subscription.Experiments show that the system can effectively support large area surveying and mapping and other complex tasks,and significantly improve the autonomy and reliability of formation operations.展开更多
基金supported by FCT/MCTES UIDP/05608/2020(https://doi.org/10.54499/UIDP/05608/2020,accessed on 01 July 2025)UIDB/05608/2020(https://doi.org/10.54499/UIDB/05608/2020,accessed on 01 July 2025).Institutional.
文摘Objectives:Colorectal cancer(CRC)is a major global health burden,and Urolithin A(Uro-A)has emerged as a promising anticancer agent.This systematic review aims to synthesize current in vitro evidence on the anticancer effects of Uro-A in CRC,highlighting effective concentration ranges,exposure times,relevant outcomes,and underlying molecular mechanisms.Methods:Following PRISMA 2020 guidelines,a systematic search was conducted in PubMed,Scopus,and Web of Science using the following strategy:(colorectal cancer)AND(urolithin a)OR(3,8-dihydroxy-6H-dibenzo(b,d)pyran-6-one).Eligibility criteria were defined by the PICO framework:(P)in vitro CRC cell models;(I)Uro-A alone or combined treatments;(C)No intervention,vehicle or other treatments;(O)Relevant anticancer outcomes of Uro-A in CRC.Only original,full-text,in vitro studies in English were included.Risk of bias was assessed using ToxRTool.A qualitative synthesis was performed due to the heterogeneity of the included studies.Results:Fifteen studies met inclusion criteria,involving CRC cell lines(Caco-2,HCT-116,HT-29,SW480,SW620)and normal colon fibroblasts(CCD18-Co).Uro-A inhibited CRC cell proliferation,clonogenic growth,cancer stem cells properties,migration,and invasion,and induced cell cycle arrest,apoptosis,autophagy,and senescence,through modulation of key signaling pathways and proteins.Co-treatments with conventional chemotherapeutics and microbiota-derived metabolites showed additive or synergistic effects.Discussion:The findings support UroA’s potential as a preventive or adjuvant agent in CRC treatment.However,preclinical nature of the evidence and methodological heterogeneity hinder clinical extrapolation to in vivo contexts.Human clinical trials are necessary to overcome these limitations.Other:This review was registered in PROSPERO(CRD420251070874)and supported by FCT/MCTES UIDP/05608/2020 and UIDB/05608/2020.Institutional.
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 Ecological Conservation and High-Quality Development of the Yellow River Basin Program,China(2022-YRUC-010102)the Second Tibetan Plateau Scientific Expedition and Research Program,China(20190ZKK0405)the Basic Research Fund Project of Innovation Team of Novel Forage Germplasm and Sustainable Utilization of Grassland Resources,China(BR22-12-07)。
文摘There is growing interest in introducing ecological risks(ERs)and ecosystem services(ESs)into environmental policies and practices.However,the integration of ESs and ERs into actual decision-making remains insufficient.We simulated the spatiotemporal dynamics of ESs(e.g.,carbon storage,water yield,habitat quality,and soil conservation)and ERs in the upper reach of the Yellow River(URYR)from 2000 to 2100.Additionally,we explored their relationships by combining the InVEST model and a landscape ecological risk model with CMIP6 data.Our main findings showed that regional ERs change in response to land use and environmental dynamics.Specifically,the ER area decreased by 27,673 m^(2)during 2000-2020,but it is projected to increase by 13,273,438,and 68 m^(2)under the SSP1-2.6,SSP2-4.5,and SSP5-8.5 scenarios,respectively.We also observed remarkable spatial differences in ESs and ERs between past and future scenarios.For instance,the source area of the URYR exhibited high ESs and low ERs(P<0.001),while the ESs and ERs are declining and increasing,respectively,in the northeastern URYR(P<0.05).Finally,we proposed a spatial optimization framework to improve ESs and reduce ERs,which will support regional sustainable development.
文摘Sixth Generation(6G)mobile communication networks will involve sensing as a new function,with the overwhelming trend of Integrated Sensing And Communications(ISAC).Although expanding the serving range of the networks,there exists performance trade-offbetween communication and sensing,in that they have competitions on the physical resources.Different resource allocation schemes will result in different sensing and communication performance,thus influencing the system’s overall performance.Therefore,how to model the system’s overall performance,and how to optimize it are key issues for ISAC.Relying on the large-scale deployment of the networks,cooperative ISAC has the advantages of wider coverage,more robust performance and good compatibility of multiple monostatic and multistatic sensing,compared to the non-cooperative ISAC.How to capture the performance gain of cooperation is a key issue for cooperative ISAC.To address the aforementioned vital problems,in this paper,we analyze the sensing accuracy gain,propose a unified ISAC performance evaluation framework and design several optimization methods in cooperative ISAC systems.The cooperative sensing accuracy gain is theoretically analyzed via Cramér Rao lower bound.The unified ISAC performance evaluation model is established by converting the communication mutual information to the effective minimum mean squared error.To optimize the unified ISAC performance,we design the optimization algorithms considering three factors:base stations’working modes,power allocation schemes and waveform design.Through simulations,we show the performance gain of the cooperative ISAC system and the effectiveness of the proposed optimization methods.
基金The 2023 Scientific and Technological Project in Henan Province of China(Grant No.232102220098)。
文摘For the high-precision positioning requirements of UAV formation cooperative operation,a distributed control system based on RTK technology is proposed in this paper.By using the U-blox F9P GNSS module to build an RTK base station/mobile terminal,combined with Pixhawk 6C flight control and MAVESP8266 communication module,centimeter-level(<2 cm)positioning accuracy is achieved.The system adopts the“centralized planning distributed execution”architecture,transmits RTCM differential data and MAVLink messages through the UDP protocol,and integrates ROS to realize status information subscription.Experiments show that the system can effectively support large area surveying and mapping and other complex tasks,and significantly improve the autonomy and reliability of formation operations.
