A digital data-acquisition system based on XIA LLC products was used in a complex nuclear reaction experiment using radioactive ion beams.A flexible trigger system based on a field-programmable gate array(FPGA)paramet...A digital data-acquisition system based on XIA LLC products was used in a complex nuclear reaction experiment using radioactive ion beams.A flexible trigger system based on a field-programmable gate array(FPGA)parametrization was developed to adapt to different experimental sizes.A user-friendly interface was implemented,which allows converting script language expressions into FPGA internal control parameters.The proposed digital system can be combined with a conventional analog data acquisition system to provide more flexibility.The performance of the combined system was veri-fied using experimental data.展开更多
Background:Oral squamous cell carcinoma(OSCC)represents a prevalent malignancy in the oral and maxillofacial area,having a considerable negative impact on both the quality of life and overall survival of affected indi...Background:Oral squamous cell carcinoma(OSCC)represents a prevalent malignancy in the oral and maxillofacial area,having a considerable negative impact on both the quality of life and overall survival of affected individuals.Our research endeavors to leverage bioinformatic approaches to elucidate oncogenic signaling pathways,with the ultimate goal of gaining deeper insights into the molecular underpinnings of OSCC pathogenesis,and thus laying the groundwork for the development of more effective therapeutic and preventive strategies.Methods:Differential expression analysis was performed on mRNA data from tumor and normal tissue groups to identify genes associated with OSCC,using The Cancer Genome Atlas database.Predictions of oncogenic signaling pathways linked to differentially expressedmRNAs were made,and these results were presented visually using R software,using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichments.Results:GO and KEGG analyses of 2938 differentially expressed genes in OSCC highlighted their significant involvement in various biological processes.Notably,these processes were related to the extracellular matrix,structural organization,connective tissue development,and cell cycle regulation.Conclusions:The comprehensive exploration of gene expression patterns provides valuable insights into potential oncogenic mechanisms in OSCC.展开更多
Background:Galectin 2(LGALS2)is a protein previously reported to serve as a mediator of disease progression in a range of cancers.The function of LGALS2 in oral squamous cell carcinoma(OSCC),however,has yet to be expl...Background:Galectin 2(LGALS2)is a protein previously reported to serve as a mediator of disease progression in a range of cancers.The function of LGALS2 in oral squamous cell carcinoma(OSCC),however,has yet to be explored,prompting the present study to address this literature gap.Methods:Overall,144 paired malignant tumor tissues and paracancerous OSCC patient samples were harvested and the LGALS2 expression levels were examined through qPCR and western immunoblotting.The LGALS2 coding sequence was introduced into the pcDNA3.0 vector,to enable the overexpression of this gene,while an LGALS2-specific shRNA and corresponding controls were also obtained.The functionality of LGALS2 as a regulator of the ability of OSCC cells to grow and undergo apoptotic death in vitro was assessed through EdU uptake and CCK-8 assays,and flow cytometer,whereas a Transwell system was used to assess migratory activity and invasivity.An agonist of the Janus Kinase 2(JAK2)/Signal Transducer and Activator of Transcription 3(STAT3)pathway was also used to assess the role of this pathway in the context of LGALS2 signaling.Results:Here,we found that lower LGALS2 protein and mRNA expression were evident in OSCC tumor tissue samples,and these expression levels were associated with clinicopathological characteristics and patient survival outcomes.Silencing LGALS2 enhanced proliferation in OSCC cells while rendering these cells better able to resist apoptosis.The opposite was instead observed after LGALS2 was overexpressed.Mechanistically,the ability of LGALS2 to suppress the progression of OSCC was related to its ability to activate the JAK/STAT3 signaling axis.Conclusion:Those results suggest a role for LGALS2 as a suppressor of OSCC progression through its ability to modulate JAK/STAT3 signaling,supporting the potential utility of LGALS2 as a target for efforts aimed at treating OSCC patients.展开更多
为实现高超声速飞行器姿态自抗扰控制的参数整定,提出一种模糊Q学习算法。首先,采用强化学习中的Q学习算法来实现姿态自抗扰控制参数的离线闭环快速自适应整定;然后,根据模糊控制的思路,将控制参数划分为不同区域,通过设定奖励,不断更新...为实现高超声速飞行器姿态自抗扰控制的参数整定,提出一种模糊Q学习算法。首先,采用强化学习中的Q学习算法来实现姿态自抗扰控制参数的离线闭环快速自适应整定;然后,根据模糊控制的思路,将控制参数划分为不同区域,通过设定奖励,不断更新Q表;最后,将训练好的Q表用于飞行器的控制。仿真结果表明,相对于传统的线性自抗扰控制(linear active disturbance rejection control,LADRC)和滑模控制,基于Q学习的LADRC省去了人工调试参数的繁琐过程,且仍具有良好的跟踪效果。蒙特卡罗仿真测试结果验证了基于Q学习的LADRC的鲁棒性。展开更多
针对5G新空口-车联网(New Radio-Vehicle to Everything,NR-V2X)场景下车对基础设施(Vehicle to Infrastructure,V2I)和车对车(Vehicle to Vehicle,V2V)共享上行通信链路的频谱资源分配问题,提出了一种联邦-多智能体深度Q网络(Federated...针对5G新空口-车联网(New Radio-Vehicle to Everything,NR-V2X)场景下车对基础设施(Vehicle to Infrastructure,V2I)和车对车(Vehicle to Vehicle,V2V)共享上行通信链路的频谱资源分配问题,提出了一种联邦-多智能体深度Q网络(Federated Learning-Multi-Agent Deep Q Network,FL-MADQN)算法.该分布式算法中,每个车辆用户作为一个智能体,根据获取的本地信道状态信息,以网络信道容量最佳为目标函数,采用DQN算法训练学习本地网络模型.采用联邦学习加快以及稳定各智能体网络模型训练的收敛速度,即将各智能体的本地模型上传至基站进行聚合形成全局模型,再将全局模型下发至各智能体更新本地模型.仿真结果表明:与传统分布式多智能体DQN算法相比,所提出的方案具有更快的模型收敛速度,并且当车辆用户数增大时仍然保证V2V链路的通信效率以及V2I链路的信道容量.展开更多
基金This work was supported by the National Key R&D Program of China(Nos.2023YFA1606403 and 2023YFE0101600)the National Natural Science Foundation of China(Nos.12027809,11961141003,U1967201,11875073 and 11875074).
文摘A digital data-acquisition system based on XIA LLC products was used in a complex nuclear reaction experiment using radioactive ion beams.A flexible trigger system based on a field-programmable gate array(FPGA)parametrization was developed to adapt to different experimental sizes.A user-friendly interface was implemented,which allows converting script language expressions into FPGA internal control parameters.The proposed digital system can be combined with a conventional analog data acquisition system to provide more flexibility.The performance of the combined system was veri-fied using experimental data.
文摘Background:Oral squamous cell carcinoma(OSCC)represents a prevalent malignancy in the oral and maxillofacial area,having a considerable negative impact on both the quality of life and overall survival of affected individuals.Our research endeavors to leverage bioinformatic approaches to elucidate oncogenic signaling pathways,with the ultimate goal of gaining deeper insights into the molecular underpinnings of OSCC pathogenesis,and thus laying the groundwork for the development of more effective therapeutic and preventive strategies.Methods:Differential expression analysis was performed on mRNA data from tumor and normal tissue groups to identify genes associated with OSCC,using The Cancer Genome Atlas database.Predictions of oncogenic signaling pathways linked to differentially expressedmRNAs were made,and these results were presented visually using R software,using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichments.Results:GO and KEGG analyses of 2938 differentially expressed genes in OSCC highlighted their significant involvement in various biological processes.Notably,these processes were related to the extracellular matrix,structural organization,connective tissue development,and cell cycle regulation.Conclusions:The comprehensive exploration of gene expression patterns provides valuable insights into potential oncogenic mechanisms in OSCC.
基金supported by grants from Key R&D Project of Science and Technology Foundation of Sichuan Province(2022YFS0290).
文摘Background:Galectin 2(LGALS2)is a protein previously reported to serve as a mediator of disease progression in a range of cancers.The function of LGALS2 in oral squamous cell carcinoma(OSCC),however,has yet to be explored,prompting the present study to address this literature gap.Methods:Overall,144 paired malignant tumor tissues and paracancerous OSCC patient samples were harvested and the LGALS2 expression levels were examined through qPCR and western immunoblotting.The LGALS2 coding sequence was introduced into the pcDNA3.0 vector,to enable the overexpression of this gene,while an LGALS2-specific shRNA and corresponding controls were also obtained.The functionality of LGALS2 as a regulator of the ability of OSCC cells to grow and undergo apoptotic death in vitro was assessed through EdU uptake and CCK-8 assays,and flow cytometer,whereas a Transwell system was used to assess migratory activity and invasivity.An agonist of the Janus Kinase 2(JAK2)/Signal Transducer and Activator of Transcription 3(STAT3)pathway was also used to assess the role of this pathway in the context of LGALS2 signaling.Results:Here,we found that lower LGALS2 protein and mRNA expression were evident in OSCC tumor tissue samples,and these expression levels were associated with clinicopathological characteristics and patient survival outcomes.Silencing LGALS2 enhanced proliferation in OSCC cells while rendering these cells better able to resist apoptosis.The opposite was instead observed after LGALS2 was overexpressed.Mechanistically,the ability of LGALS2 to suppress the progression of OSCC was related to its ability to activate the JAK/STAT3 signaling axis.Conclusion:Those results suggest a role for LGALS2 as a suppressor of OSCC progression through its ability to modulate JAK/STAT3 signaling,supporting the potential utility of LGALS2 as a target for efforts aimed at treating OSCC patients.
文摘为实现高超声速飞行器姿态自抗扰控制的参数整定,提出一种模糊Q学习算法。首先,采用强化学习中的Q学习算法来实现姿态自抗扰控制参数的离线闭环快速自适应整定;然后,根据模糊控制的思路,将控制参数划分为不同区域,通过设定奖励,不断更新Q表;最后,将训练好的Q表用于飞行器的控制。仿真结果表明,相对于传统的线性自抗扰控制(linear active disturbance rejection control,LADRC)和滑模控制,基于Q学习的LADRC省去了人工调试参数的繁琐过程,且仍具有良好的跟踪效果。蒙特卡罗仿真测试结果验证了基于Q学习的LADRC的鲁棒性。
文摘针对5G新空口-车联网(New Radio-Vehicle to Everything,NR-V2X)场景下车对基础设施(Vehicle to Infrastructure,V2I)和车对车(Vehicle to Vehicle,V2V)共享上行通信链路的频谱资源分配问题,提出了一种联邦-多智能体深度Q网络(Federated Learning-Multi-Agent Deep Q Network,FL-MADQN)算法.该分布式算法中,每个车辆用户作为一个智能体,根据获取的本地信道状态信息,以网络信道容量最佳为目标函数,采用DQN算法训练学习本地网络模型.采用联邦学习加快以及稳定各智能体网络模型训练的收敛速度,即将各智能体的本地模型上传至基站进行聚合形成全局模型,再将全局模型下发至各智能体更新本地模型.仿真结果表明:与传统分布式多智能体DQN算法相比,所提出的方案具有更快的模型收敛速度,并且当车辆用户数增大时仍然保证V2V链路的通信效率以及V2I链路的信道容量.
