The long non-coding RNA,Negative Regulator of Antiviral Response(NRAV)has been identified as a participant in both respiratory virus replication and immune checkpoints,however,its involvement in pan-cancer immune regu...The long non-coding RNA,Negative Regulator of Antiviral Response(NRAV)has been identified as a participant in both respiratory virus replication and immune checkpoints,however,its involvement in pan-cancer immune regulation and prognosis,particularly those of hepatocellular carcinoma(HCC),remains unclear.To address this knowledge gap,we analyzed expression profiles obtained from The Cancer Genome Atlas(TCGA)database,comparing normal and malignant tumor tissues.We found that NRAV expression is significantly upregulated in tumor tissues compared to adjacent nontumor tissues.Kaplan-Meier(K-M)analysis revealed the prognostic power of NRAV,wherein overexpression was significantly linked to reduced overall survival in a diverse range of tumor patients.Furthermore,noteworthy associations were observed between NRAV,immune checkpoints,immune cell infiltration,genes related to autophagy,epithelial-mesenchymal transition(EMT),pyroptosis,tumor mutational burden(TMB),and microsatellite instability(MSI)across different cancer types,including HCC.Moreover,NRAV upregulation expression was associated with multiple pathological stages by clinical observations.Furthermore,our investigation revealed a substantial elevation in the expression of NRAV in both HCC tumor tissues and cells compared to normal tissues and cells.The inhibition of NRAV resulted in the inhibition of cell proliferation,migration,and invasion in HCC cells,while also influencing the expression of CD274(PD-L1)and CD44,along with various biomarkers associated with EMT,autophagy,and pyroptosis.The aforementioned results propose NRAV as a promising prognostic biomarker for HCC.展开更多
Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to...Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.展开更多
The moisture content of a road subgrade in cold regions will increase after freeze-thaw cycles,resulting in subgrade strength and stiffness losses.Electroosmosis is widely used in treating saturated soft soils to decr...The moisture content of a road subgrade in cold regions will increase after freeze-thaw cycles,resulting in subgrade strength and stiffness losses.Electroosmosis is widely used in treating saturated soft soils to decrease the moisture content.The induced moisture migration during electroosmosis in unsaturated soil is much more complex than that of saturated soil because of a series of nonlinear changes in soil properties.This study first uses an exponential function to characterize the relationship between electroosmotic permeability and saturation degree.Then,a one-dimensional model is developed to simulate the electroosmosis-induced moisture migration in unsaturated soil.Simulation results show that electroosmosis reduces the saturation degree of the unsaturated soil,indicating that it can be applied to subgrade dewatering.Key parameters such as soil pore size distribution coefficient,air entry value,and effective voltage significantly affect moisture migration.Electroosmotic properties of unsaturated soils are extremely important to the efficiency of electroosmosis.展开更多
基金funded by China National Natural Youth Science Foundation(81802078)Zhejiang Province Public Welfare Research Foundation(GF20H200021)Zhejiang Provincial Department of Medicine and Health Foundation(2019RC315).
文摘The long non-coding RNA,Negative Regulator of Antiviral Response(NRAV)has been identified as a participant in both respiratory virus replication and immune checkpoints,however,its involvement in pan-cancer immune regulation and prognosis,particularly those of hepatocellular carcinoma(HCC),remains unclear.To address this knowledge gap,we analyzed expression profiles obtained from The Cancer Genome Atlas(TCGA)database,comparing normal and malignant tumor tissues.We found that NRAV expression is significantly upregulated in tumor tissues compared to adjacent nontumor tissues.Kaplan-Meier(K-M)analysis revealed the prognostic power of NRAV,wherein overexpression was significantly linked to reduced overall survival in a diverse range of tumor patients.Furthermore,noteworthy associations were observed between NRAV,immune checkpoints,immune cell infiltration,genes related to autophagy,epithelial-mesenchymal transition(EMT),pyroptosis,tumor mutational burden(TMB),and microsatellite instability(MSI)across different cancer types,including HCC.Moreover,NRAV upregulation expression was associated with multiple pathological stages by clinical observations.Furthermore,our investigation revealed a substantial elevation in the expression of NRAV in both HCC tumor tissues and cells compared to normal tissues and cells.The inhibition of NRAV resulted in the inhibition of cell proliferation,migration,and invasion in HCC cells,while also influencing the expression of CD274(PD-L1)and CD44,along with various biomarkers associated with EMT,autophagy,and pyroptosis.The aforementioned results propose NRAV as a promising prognostic biomarker for HCC.
基金the State Grid Liaoning Electric Power Supply Co.,Ltd.(Research on Scheduling Decision Technology Based on Interactive Reinforcement Learning for Adapting High Proportion of New Energy,No.2023YF-49).
文摘Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.
基金the financial support from the National Key Research and Development Program of China(No.2018YFC1505306)the National Natural Science Foundation of China(No.41971076).
文摘The moisture content of a road subgrade in cold regions will increase after freeze-thaw cycles,resulting in subgrade strength and stiffness losses.Electroosmosis is widely used in treating saturated soft soils to decrease the moisture content.The induced moisture migration during electroosmosis in unsaturated soil is much more complex than that of saturated soil because of a series of nonlinear changes in soil properties.This study first uses an exponential function to characterize the relationship between electroosmotic permeability and saturation degree.Then,a one-dimensional model is developed to simulate the electroosmosis-induced moisture migration in unsaturated soil.Simulation results show that electroosmosis reduces the saturation degree of the unsaturated soil,indicating that it can be applied to subgrade dewatering.Key parameters such as soil pore size distribution coefficient,air entry value,and effective voltage significantly affect moisture migration.Electroosmotic properties of unsaturated soils are extremely important to the efficiency of electroosmosis.