目的:探索分化抑制因子3及分化抑制因子4(inhibitor of differentiation 3 and inhibitor of differentiation 4,ID3/ID4)两基因在急性髓系白血病(acute myeloid leukemia,AML)患者骨髓中的表达及其临床意义。方法:应用实时荧光定量PCR...目的:探索分化抑制因子3及分化抑制因子4(inhibitor of differentiation 3 and inhibitor of differentiation 4,ID3/ID4)两基因在急性髓系白血病(acute myeloid leukemia,AML)患者骨髓中的表达及其临床意义。方法:应用实时荧光定量PCR的方法检测32例非恶性血液病(设对照组)及133例初诊AML患者骨髓单个核细胞中ID3/ID4转录本水平,通过分组分析两者表达的临床意义。结果:AML患者骨髓中ID3/ID4转录本水平较对照组均显著降低(P=0.001及0.002),并且两者之间表达存在轻度正相关(r=0.282,P=0.001)。接收者操作特征曲线分析揭示ID3/ID4转录本水平可作为辅助诊断AML的潜在分子标志(AUC=0.682,P=0.001及AUC=0.673,P=0.002)。通过分组分析发现ID3低表达组患者年龄略小于ID3高表达组患者(P=0.054),NRAS突变频率略低于ID3高表达组患者(P=0.053)。ID4低表达组患者白细胞计数略高于ID4高表达组患者(P=0.088),CEBPA突变频率略高于ID4高表达组患者(P=0.099)。此外,无论在全部患者还是非M3患者中,ID4低表达组病例经过诱导化疗后达完全缓解的概率略低于ID4高表达组病例(P=0.080及0.065)。生存分析发现AML患者及其亚组(非M3及正常核型)中ID3低表达与ID3高表达组患者总体生存相似(P>0.05),ID4低表达病例的总体生存略低于ID4高表达组病例(P=0.058),而在非M3及正常核型患者中存在显著统计学差异(P=0.014及0.002)。结论:ID3/ID4表达下调可能是AML中的常见分子事件,其中ID4表达可能为AML预后判断提供重要参考。展开更多
先天性糖基化障碍Id型(congenital disorder of glycosylation type Id,CDG-Id)是由于ALG3基因变异,导致编码的α-1,3-甘露糖基转移酶缺陷。本例孕妇32岁,孕7产1,其中第5次单胎妊娠时外院超声提示胎儿畸形,引产后至复旦大学附属妇产科...先天性糖基化障碍Id型(congenital disorder of glycosylation type Id,CDG-Id)是由于ALG3基因变异,导致编码的α-1,3-甘露糖基转移酶缺陷。本例孕妇32岁,孕7产1,其中第5次单胎妊娠时外院超声提示胎儿畸形,引产后至复旦大学附属妇产科医院行基因检测提示为ALG3基因变异[NM_005787:c.67C>T(p.Gln23*),杂合,父源;NM_005787:c.1188G>A(p.Trp396*),杂合,母源]。本次单胎妊娠21周,我院产前超声表现为胎儿多发畸形,以小下颌、小脑蚓部缺失、后颅窝囊性占位、四肢长骨均短小、脊柱侧弯和手关节僵硬为主要表现。孕妇遂至外院引产,引产后基因检测结果证实仍为ALG3基因变异。本文重点介绍CDG-Id型的产前超声表现及遗传学特征,以提高对本病的认识。展开更多
The key objective of intrusion detection systems(IDS)is to protect the particular host or network by investigating and predicting the network traffic as an attack or normal.These IDS uses many methods of machine learn...The key objective of intrusion detection systems(IDS)is to protect the particular host or network by investigating and predicting the network traffic as an attack or normal.These IDS uses many methods of machine learning(ML)to learn from pastexperience attack i.e.signatures based and identify the new ones.Even though these methods are effective,but they have to suffer from large computational costs due to considering all the traffic features,together.Moreover,emerging technologies like the Internet of Things(Io T),big data,etc.are getting advanced day by day;as a result,network traffics are also increasing rapidly.Therefore,the issue of computational cost needs to be addressed properly.Thus,in this research,firstly,the ML methods have been used with the feature selection technique(FST)to reduce the number of features by picking out only the important ones from NSL-KDD,CICIDS2017,and CIC-DDo S2019datasets later that helped to build IDSs with lower cost but with the higher performance which would be appropriate for vast scale network.The experimental result demonstrated that the proposed model i.e.Decision tree(DT)with Recursive feature elimination(RFE)performs better than other classifiers with RFE in terms of accuracy,specificity,precision,sensitivity,F1-score,and G-means on the investigated datasets.展开更多
Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,hi...Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.展开更多
文摘目的:探索分化抑制因子3及分化抑制因子4(inhibitor of differentiation 3 and inhibitor of differentiation 4,ID3/ID4)两基因在急性髓系白血病(acute myeloid leukemia,AML)患者骨髓中的表达及其临床意义。方法:应用实时荧光定量PCR的方法检测32例非恶性血液病(设对照组)及133例初诊AML患者骨髓单个核细胞中ID3/ID4转录本水平,通过分组分析两者表达的临床意义。结果:AML患者骨髓中ID3/ID4转录本水平较对照组均显著降低(P=0.001及0.002),并且两者之间表达存在轻度正相关(r=0.282,P=0.001)。接收者操作特征曲线分析揭示ID3/ID4转录本水平可作为辅助诊断AML的潜在分子标志(AUC=0.682,P=0.001及AUC=0.673,P=0.002)。通过分组分析发现ID3低表达组患者年龄略小于ID3高表达组患者(P=0.054),NRAS突变频率略低于ID3高表达组患者(P=0.053)。ID4低表达组患者白细胞计数略高于ID4高表达组患者(P=0.088),CEBPA突变频率略高于ID4高表达组患者(P=0.099)。此外,无论在全部患者还是非M3患者中,ID4低表达组病例经过诱导化疗后达完全缓解的概率略低于ID4高表达组病例(P=0.080及0.065)。生存分析发现AML患者及其亚组(非M3及正常核型)中ID3低表达与ID3高表达组患者总体生存相似(P>0.05),ID4低表达病例的总体生存略低于ID4高表达组病例(P=0.058),而在非M3及正常核型患者中存在显著统计学差异(P=0.014及0.002)。结论:ID3/ID4表达下调可能是AML中的常见分子事件,其中ID4表达可能为AML预后判断提供重要参考。
文摘先天性糖基化障碍Id型(congenital disorder of glycosylation type Id,CDG-Id)是由于ALG3基因变异,导致编码的α-1,3-甘露糖基转移酶缺陷。本例孕妇32岁,孕7产1,其中第5次单胎妊娠时外院超声提示胎儿畸形,引产后至复旦大学附属妇产科医院行基因检测提示为ALG3基因变异[NM_005787:c.67C>T(p.Gln23*),杂合,父源;NM_005787:c.1188G>A(p.Trp396*),杂合,母源]。本次单胎妊娠21周,我院产前超声表现为胎儿多发畸形,以小下颌、小脑蚓部缺失、后颅窝囊性占位、四肢长骨均短小、脊柱侧弯和手关节僵硬为主要表现。孕妇遂至外院引产,引产后基因检测结果证实仍为ALG3基因变异。本文重点介绍CDG-Id型的产前超声表现及遗传学特征,以提高对本病的认识。
文摘The key objective of intrusion detection systems(IDS)is to protect the particular host or network by investigating and predicting the network traffic as an attack or normal.These IDS uses many methods of machine learning(ML)to learn from pastexperience attack i.e.signatures based and identify the new ones.Even though these methods are effective,but they have to suffer from large computational costs due to considering all the traffic features,together.Moreover,emerging technologies like the Internet of Things(Io T),big data,etc.are getting advanced day by day;as a result,network traffics are also increasing rapidly.Therefore,the issue of computational cost needs to be addressed properly.Thus,in this research,firstly,the ML methods have been used with the feature selection technique(FST)to reduce the number of features by picking out only the important ones from NSL-KDD,CICIDS2017,and CIC-DDo S2019datasets later that helped to build IDSs with lower cost but with the higher performance which would be appropriate for vast scale network.The experimental result demonstrated that the proposed model i.e.Decision tree(DT)with Recursive feature elimination(RFE)performs better than other classifiers with RFE in terms of accuracy,specificity,precision,sensitivity,F1-score,and G-means on the investigated datasets.
基金The authors would like to thank Princess Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2023R319)this research was funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.
