General control non-derepressible 2(GCN2)属于一种压力应答丝氨酸/苏氨酸激酶,在整合应激反应(ISR)中负责感受氨基酸缺乏应激后产生一系列反应。GCN2的激活对于细胞的氧化应激、增殖、自噬、凋亡、免疫、蛋白质毒性和血管生成等均有...General control non-derepressible 2(GCN2)属于一种压力应答丝氨酸/苏氨酸激酶,在整合应激反应(ISR)中负责感受氨基酸缺乏应激后产生一系列反应。GCN2的激活对于细胞的氧化应激、增殖、自噬、凋亡、免疫、蛋白质毒性和血管生成等均有关键的调节作用,与肿瘤、心肌损伤、肺纤维化等的发生发展有一定的相关性。综述GCN2的生物学功能、结构特征、作用机制和疾病关联性,并总结分析GCN2抑制剂或激动剂的研发现状,重点阐述GCN2抑制剂或激动剂在抗肿瘤方向的临床应用潜力,为靶向GCN2激酶的新药开发提供参考。展开更多
在哺乳动物中有4个真核翻译起始因子2α(Eu⁃karyotic initiation factor 2α,eIF2α)激酶,即一般性调控阻遏蛋白激酶2(General control nonderepressible 2,GCN2)、蛋白激酶R样内质网激酶(PKR-like ER ki⁃nase,PERK)、双链RNA依赖性蛋...在哺乳动物中有4个真核翻译起始因子2α(Eu⁃karyotic initiation factor 2α,eIF2α)激酶,即一般性调控阻遏蛋白激酶2(General control nonderepressible 2,GCN2)、蛋白激酶R样内质网激酶(PKR-like ER ki⁃nase,PERK)、双链RNA依赖性蛋白激酶(Doublestranded RNA-dependent protein kinase,PKR)和血红素调节抑制剂激酶(Heme-regulated inhibitor,HRI)[1],这4个激酶会在不同胁迫条件下磷酸化eIF2α亚基第51位的丝氨酸/苏氨酸,从而削弱了eIF2在翻译过程中结合GTP的能力,进一步抑制细胞总体蛋白翻译以缓解细胞应激,恢复细胞蛋白稳态,这种细胞反应被称为整合应激反应(Integrated stress response,ISR)[2]。展开更多
Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability ...Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability and accuracy. To address these challenges, this study introduces the K-GCN model, which integrates neighborhood k-shell distribution analysis with Graph Convolutional Network(GCN) technology to enhance key node identification in complex networks. The K-GCN model first leverages neighborhood k-shell distributions to calculate entropy values for each node, effectively quantifying node importance within the network. These entropy values are then used as key features within the GCN, which subsequently formulates intelligent strategies to maximize network connectivity disruption by removing a minimal set of nodes, thereby impacting the overall network architecture. Through iterative interactions with the environment, the GCN continuously refines its strategies, achieving precise identification of key nodes in the network. Unlike traditional methods, the K-GCN model not only captures local node features but also integrates the network structure and complex interrelations between neighboring nodes, significantly improving the accuracy and efficiency of key node identification.Experimental validation in multiple real-world network scenarios demonstrates that the K-GCN model outperforms existing methods.展开更多
本文结合了Node2vec和GCN这两种方法,先利用Node2vec方法得到初步的图嵌入,之后将其作为输入利用GCN进一步更新图嵌入矩阵。本文选择在维基数据集上进行节点分类任务,比较了结合前后方法的表现,验证了其有效性。In this paper, we integ...本文结合了Node2vec和GCN这两种方法,先利用Node2vec方法得到初步的图嵌入,之后将其作为输入利用GCN进一步更新图嵌入矩阵。本文选择在维基数据集上进行节点分类任务,比较了结合前后方法的表现,验证了其有效性。In this paper, we integrate the Node2vec and GCN methods. Initially, the Node2vec method is employed to obtain preliminary graph embeddings, which are then used as input to further update the graph embedding matrix through GCN. The study selects the Wikipedia dataset for node classification tasks, comparing the performance of the methods before and after integration to validate their effectiveness.展开更多
Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecas...Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecasting.However,existing deep learning models frequently overlook the selective utilization of information from other production wells,resulting in suboptimal performance in long-term production forecasting across multiple wells.To achieve accurate long-term petroleum production forecast,we propose a spatial-geological perception graph convolutional neural network(SGP-GCN)that accounts for the temporal,spatial,and geological dependencies inherent in petroleum production.Utilizing the attention mechanism,the SGP-GCN effectively captures intricate correlations within production and geological data,forming the representations of each production well.Based on the spatial distances and geological feature correlations,we construct a spatial-geological matrix as the weight matrix to enable differential utilization of information from other wells.Additionally,a matrix sparsification algorithm based on production clustering(SPC)is also proposed to optimize the weight distribution within the spatial-geological matrix,thereby enhancing long-term forecasting performance.Empirical evaluations have shown that the SGP-GCN outperforms existing deep learning models,such as CNN-LSTM-SA,in long-term petroleum production forecasting.This demonstrates the potential of the SGP-GCN as a valuable tool for long-term petroleum production forecasting across multiple wells.展开更多
Photocatalysis holds great promise for the conversion of plastic waste into valuable chemicals.However,the conversion efficiency is constrained by the poor carriers’separation efficiency over the single component pho...Photocatalysis holds great promise for the conversion of plastic waste into valuable chemicals.However,the conversion efficiency is constrained by the poor carriers’separation efficiency over the single component photocatalyst.Herein,we synthesized a novel typeⅡNb_(2)O_(5)/GCN heterojunction to investigate its efficiency in the photocatalytic upcycling of polybutylene adipate/terephthalate(PBAT)microplastics(MPs)into acids and alcohols under visible light irradiation(100mW/cm^(2)).The findings indicate that the charge transfer within the typeⅡNb_(2)O_(5)/GCN occurs from the conduction band of GCN to the conduction band of Nb_(2)O_(5),thereby enhancing the separation efficiency of carriers Notably,the rates of ethanol and acetic acid generation from 1.5mg/mL PBAT MPs treated with the 60%Nb_(2)O_(5)/GCN photocatalyst were 21.8-fold and 1.8-fold higher,respectively,compared to those by Nb_(2)O_(5) alone.Density functional theory calculations demonstrate that the hydroxyl radicals(·OH)produced by the Nb_(2)O_(5)/GCN heterojunction cleaves the ester bond(O-C=O)of PBAT MP into the monomer.These monomers are subsequently converted into acids and alcohols through various reactions,including C-C bond cleavage,hydrodeoxygenation,and C-C bond coupling.This study highlights the effectiveness of heterojunction photocatalyst in converting PBAT MPs into valuable chemicals,thus significantly promoting advancements in bioplastics recycling.展开更多
文摘General control non-derepressible 2(GCN2)属于一种压力应答丝氨酸/苏氨酸激酶,在整合应激反应(ISR)中负责感受氨基酸缺乏应激后产生一系列反应。GCN2的激活对于细胞的氧化应激、增殖、自噬、凋亡、免疫、蛋白质毒性和血管生成等均有关键的调节作用,与肿瘤、心肌损伤、肺纤维化等的发生发展有一定的相关性。综述GCN2的生物学功能、结构特征、作用机制和疾病关联性,并总结分析GCN2抑制剂或激动剂的研发现状,重点阐述GCN2抑制剂或激动剂在抗肿瘤方向的临床应用潜力,为靶向GCN2激酶的新药开发提供参考。
文摘在哺乳动物中有4个真核翻译起始因子2α(Eu⁃karyotic initiation factor 2α,eIF2α)激酶,即一般性调控阻遏蛋白激酶2(General control nonderepressible 2,GCN2)、蛋白激酶R样内质网激酶(PKR-like ER ki⁃nase,PERK)、双链RNA依赖性蛋白激酶(Doublestranded RNA-dependent protein kinase,PKR)和血红素调节抑制剂激酶(Heme-regulated inhibitor,HRI)[1],这4个激酶会在不同胁迫条件下磷酸化eIF2α亚基第51位的丝氨酸/苏氨酸,从而削弱了eIF2在翻译过程中结合GTP的能力,进一步抑制细胞总体蛋白翻译以缓解细胞应激,恢复细胞蛋白稳态,这种细胞反应被称为整合应激反应(Integrated stress response,ISR)[2]。
基金Supported by the National Natural Science Foundation of China(Grant No.12031002)。
文摘Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability and accuracy. To address these challenges, this study introduces the K-GCN model, which integrates neighborhood k-shell distribution analysis with Graph Convolutional Network(GCN) technology to enhance key node identification in complex networks. The K-GCN model first leverages neighborhood k-shell distributions to calculate entropy values for each node, effectively quantifying node importance within the network. These entropy values are then used as key features within the GCN, which subsequently formulates intelligent strategies to maximize network connectivity disruption by removing a minimal set of nodes, thereby impacting the overall network architecture. Through iterative interactions with the environment, the GCN continuously refines its strategies, achieving precise identification of key nodes in the network. Unlike traditional methods, the K-GCN model not only captures local node features but also integrates the network structure and complex interrelations between neighboring nodes, significantly improving the accuracy and efficiency of key node identification.Experimental validation in multiple real-world network scenarios demonstrates that the K-GCN model outperforms existing methods.
文摘本文结合了Node2vec和GCN这两种方法,先利用Node2vec方法得到初步的图嵌入,之后将其作为输入利用GCN进一步更新图嵌入矩阵。本文选择在维基数据集上进行节点分类任务,比较了结合前后方法的表现,验证了其有效性。In this paper, we integrate the Node2vec and GCN methods. Initially, the Node2vec method is employed to obtain preliminary graph embeddings, which are then used as input to further update the graph embedding matrix through GCN. The study selects the Wikipedia dataset for node classification tasks, comparing the performance of the methods before and after integration to validate their effectiveness.
基金funded by National Natural Science Foundation of China,grant number 62071491.
文摘Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecasting.However,existing deep learning models frequently overlook the selective utilization of information from other production wells,resulting in suboptimal performance in long-term production forecasting across multiple wells.To achieve accurate long-term petroleum production forecast,we propose a spatial-geological perception graph convolutional neural network(SGP-GCN)that accounts for the temporal,spatial,and geological dependencies inherent in petroleum production.Utilizing the attention mechanism,the SGP-GCN effectively captures intricate correlations within production and geological data,forming the representations of each production well.Based on the spatial distances and geological feature correlations,we construct a spatial-geological matrix as the weight matrix to enable differential utilization of information from other wells.Additionally,a matrix sparsification algorithm based on production clustering(SPC)is also proposed to optimize the weight distribution within the spatial-geological matrix,thereby enhancing long-term forecasting performance.Empirical evaluations have shown that the SGP-GCN outperforms existing deep learning models,such as CNN-LSTM-SA,in long-term petroleum production forecasting.This demonstrates the potential of the SGP-GCN as a valuable tool for long-term petroleum production forecasting across multiple wells.
基金financially supported by the National Natural Science Foundation of China(Nos.52170024,T2421005,and 22006031).
文摘Photocatalysis holds great promise for the conversion of plastic waste into valuable chemicals.However,the conversion efficiency is constrained by the poor carriers’separation efficiency over the single component photocatalyst.Herein,we synthesized a novel typeⅡNb_(2)O_(5)/GCN heterojunction to investigate its efficiency in the photocatalytic upcycling of polybutylene adipate/terephthalate(PBAT)microplastics(MPs)into acids and alcohols under visible light irradiation(100mW/cm^(2)).The findings indicate that the charge transfer within the typeⅡNb_(2)O_(5)/GCN occurs from the conduction band of GCN to the conduction band of Nb_(2)O_(5),thereby enhancing the separation efficiency of carriers Notably,the rates of ethanol and acetic acid generation from 1.5mg/mL PBAT MPs treated with the 60%Nb_(2)O_(5)/GCN photocatalyst were 21.8-fold and 1.8-fold higher,respectively,compared to those by Nb_(2)O_(5) alone.Density functional theory calculations demonstrate that the hydroxyl radicals(·OH)produced by the Nb_(2)O_(5)/GCN heterojunction cleaves the ester bond(O-C=O)of PBAT MP into the monomer.These monomers are subsequently converted into acids and alcohols through various reactions,including C-C bond cleavage,hydrodeoxygenation,and C-C bond coupling.This study highlights the effectiveness of heterojunction photocatalyst in converting PBAT MPs into valuable chemicals,thus significantly promoting advancements in bioplastics recycling.
