Unmanned Aerial Vehicle(UAV)ad hoc network has achieved significant growth for its flexibility,extensibility,and high deployability in recent years.The application of clustering scheme for UAV ad hoc network is impera...Unmanned Aerial Vehicle(UAV)ad hoc network has achieved significant growth for its flexibility,extensibility,and high deployability in recent years.The application of clustering scheme for UAV ad hoc network is imperative to enhance the performance of throughput and energy efficiency.In conventional clustering scheme,a single cluster head(CH)is always assigned in each cluster.However,this method has some weaknesses such as overload and premature death of CH when the number of UAVs increased.In order to solve this problem,we propose a dual-cluster-head based medium access control(DCHMAC)scheme for large-scale UAV networks.In DCHMAC,two CHs are elected to manage resource allocation and data forwarding cooperatively.Specifically,two CHs work on different channels.One of CH is used for intra-cluster communication and the other one is for inter-cluster communication.A Markov chain model is developed to analyse the throughput of the network.Simulation result shows that compared with FM-MAC(flying ad hoc networks multi-channel MAC,FM-MAC),DCHMAC improves the throughput by approximately 20%~50%and prolongs the network lifetime by approximately 40%.展开更多
Objective To identify nivolumab resistance-related genes in patients with head and neck squamous cell carcinoma(HNSCC)using single-cell and bulk RNA-sequencing data.Methods The single-cell and bulk RNA-sequencing data...Objective To identify nivolumab resistance-related genes in patients with head and neck squamous cell carcinoma(HNSCC)using single-cell and bulk RNA-sequencing data.Methods The single-cell and bulk RNA-sequencing data downloaded from the Gene Expression Omnibus database were analyzed to screen out differentially expressed genes(DEGs)between nivolumab resistant and nivolumab sensitive patients using R software.The Least Absolute Shrinkage Selection Operator(LASSO)regression and Recursive Feature Elimination(RFE)algorithm were performed to identify key genes associated with nivolumab resistance.Functional enrichment of DEGs was analyzed with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses.The relationships of key genes with immune cell infiltration,differentation trajectory,dynamic gene expression profiles,and ligand-receptor interaction were explored.Results We found 83 DEGs.They were mainly enriched in T-cell differentiation,PD-1 and PD-L1 checkpoint,and T-cell receptor pathways.Among six key genes identified using machine learning algorithms,only PPP1R14A gene was differentially expressed between the nivolumab resistant and nivolumab sensitive groups both before and after immunotherapy(P<0.05).The high PPP1R14A gene expression group had lower immune score(P<0.01),higher expression of immunosuppressive factors(such as PDCD1,CTLA4,and PDCD1LG2)(r>0,P<0.05),lower differentiation of infiltrated immune cells(P<0.05),and a higher degree of interaction between HLA and CD4(P<0.05).Conclusions PPP1R14A gene is closely associated with resistance to nivolumab in HNSCC patients.Therefore,PPP1R14A may be a target to ameliorate nivolumab resistance of HNSCC patients.展开更多
基金supported in part by the Beijing Natural Science Foundation under Grant L192031the National Key Research and Development Program under Grant 2020YFA0711303。
文摘Unmanned Aerial Vehicle(UAV)ad hoc network has achieved significant growth for its flexibility,extensibility,and high deployability in recent years.The application of clustering scheme for UAV ad hoc network is imperative to enhance the performance of throughput and energy efficiency.In conventional clustering scheme,a single cluster head(CH)is always assigned in each cluster.However,this method has some weaknesses such as overload and premature death of CH when the number of UAVs increased.In order to solve this problem,we propose a dual-cluster-head based medium access control(DCHMAC)scheme for large-scale UAV networks.In DCHMAC,two CHs are elected to manage resource allocation and data forwarding cooperatively.Specifically,two CHs work on different channels.One of CH is used for intra-cluster communication and the other one is for inter-cluster communication.A Markov chain model is developed to analyse the throughput of the network.Simulation result shows that compared with FM-MAC(flying ad hoc networks multi-channel MAC,FM-MAC),DCHMAC improves the throughput by approximately 20%~50%and prolongs the network lifetime by approximately 40%.
基金supported by the National Innovation and Enterpreneurship Training Program for College Students(202210367002)the Key Laboratory Open Project of An-hui Province(AHCM2022Z004).
文摘Objective To identify nivolumab resistance-related genes in patients with head and neck squamous cell carcinoma(HNSCC)using single-cell and bulk RNA-sequencing data.Methods The single-cell and bulk RNA-sequencing data downloaded from the Gene Expression Omnibus database were analyzed to screen out differentially expressed genes(DEGs)between nivolumab resistant and nivolumab sensitive patients using R software.The Least Absolute Shrinkage Selection Operator(LASSO)regression and Recursive Feature Elimination(RFE)algorithm were performed to identify key genes associated with nivolumab resistance.Functional enrichment of DEGs was analyzed with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses.The relationships of key genes with immune cell infiltration,differentation trajectory,dynamic gene expression profiles,and ligand-receptor interaction were explored.Results We found 83 DEGs.They were mainly enriched in T-cell differentiation,PD-1 and PD-L1 checkpoint,and T-cell receptor pathways.Among six key genes identified using machine learning algorithms,only PPP1R14A gene was differentially expressed between the nivolumab resistant and nivolumab sensitive groups both before and after immunotherapy(P<0.05).The high PPP1R14A gene expression group had lower immune score(P<0.01),higher expression of immunosuppressive factors(such as PDCD1,CTLA4,and PDCD1LG2)(r>0,P<0.05),lower differentiation of infiltrated immune cells(P<0.05),and a higher degree of interaction between HLA and CD4(P<0.05).Conclusions PPP1R14A gene is closely associated with resistance to nivolumab in HNSCC patients.Therefore,PPP1R14A may be a target to ameliorate nivolumab resistance of HNSCC patients.
