An S-like RNase cDNA had been isolated from common wheat (Triticum aestivum L). The transcription of WRN1 mRNA was down-regulated by natural- and dark-induced senescence. But it was not senile-tissue-specific. As the ...An S-like RNase cDNA had been isolated from common wheat (Triticum aestivum L). The transcription of WRN1 mRNA was down-regulated by natural- and dark-induced senescence. But it was not senile-tissue-specific. As the two key histidine residues were replaced, WRN1 may not be active as RNase. Southern blotting analysis showed that WRN1 exists as one of a small gene family in common wheat genome.展开更多
Background:Werner syndrome (WS) is a rare autosomal recessive progeroid disorder caused by mutations of the WRN gene encoding a protein of the RecQ-type family of DNA helicases. Objectives:To develop a rapid and simpl...Background:Werner syndrome (WS) is a rare autosomal recessive progeroid disorder caused by mutations of the WRN gene encoding a protein of the RecQ-type family of DNA helicases. Objectives:To develop a rapid and simple reverse transcription-polymerase chain reaction (RT-PCR) strategy for mutation analysis of the WRN gene, to identify pathogenic mutations in a German patient with WS and to determine the effects of the pathogenic mutations on WRN mRNA stability. Methods:Allele-specific RT-PCR, semiquantitative RT-PCR, DNA sequencing. Results:We describe a novel and rapid RT-PCR-based method for mutation analysis in WS and report a German patient with WS carrying a previously reported (1396delA) as well as a novel nonsense mutation (2334delAC)of the WRN gene. By semiquantitative RT-PCR analysis we demonstrate that this compound heterozygous genotype leads to WRN transcript decay. Conclusions:In previous studies WS was primarily attributed to a loss of function of stable truncated WRN gene products. Our findings indicate that mutations can also lead to markedly decreased WRN transcript stability.展开更多
The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability t...The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks.Further,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT systems.The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis dataset.Moreover,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and recall.The study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments.Furthermore,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology.This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices.However,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets.Utilizing WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT ecosystems.To summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods.The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis.Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices’defenses against new cybersecurity threats.展开更多
目的探讨中国汉族人群中DNA修复基因的拷贝数多态性(copy number variations,CNV)与年龄相关性白内障(age-related cataract,ARC)易感性的关系。方法研究对象来自"江苏眼病研究"流行病学人群,包括ARC组780例和对照组525人。...目的探讨中国汉族人群中DNA修复基因的拷贝数多态性(copy number variations,CNV)与年龄相关性白内障(age-related cataract,ARC)易感性的关系。方法研究对象来自"江苏眼病研究"流行病学人群,包括ARC组780例和对照组525人。采集受试者外周静脉血,提取全血基因组DNA。通过实时荧光定量PCR方法检测四种DNA修复基因的拷贝数(copy number,CN),分析ARC组和对照组基因CN的差异以及相对危险度(odds ratio,OR)。结果在WRN基因中发现了新的CNV。WRN基因高拷贝(CN=3+)与ARC的易感性有关(OR=1.88,P=0.02);HSF4基因低拷贝(CN=1)的人群对ARC易感(OR=4.09,P=0.004)。WRN基因高拷贝与核性以及后囊下性ARC的易感性有关(OR=2.06、3.72,均为P=0.02)。HSF4基因低拷贝与核性以及后囊下性ARC的易感性有关(OR=5.73,P=0.001;OR=6.80,P=0.01)。WRN和HSF4基因的联合作用显著增加了ARC的易感性。经过多重校正以后,仅有HSF4的CNV与ARC的易感性有关,尤其与核性和后囊下性ARC的易感性有关。结论 HSF4基因与WRN基因的CNV可能与中国汉族人群ARC的易感性有关。DNA修复基因对ARC易感性有一定的作用,并且对不同亚型ARC产生不同的影响。展开更多
文摘An S-like RNase cDNA had been isolated from common wheat (Triticum aestivum L). The transcription of WRN1 mRNA was down-regulated by natural- and dark-induced senescence. But it was not senile-tissue-specific. As the two key histidine residues were replaced, WRN1 may not be active as RNase. Southern blotting analysis showed that WRN1 exists as one of a small gene family in common wheat genome.
文摘Background:Werner syndrome (WS) is a rare autosomal recessive progeroid disorder caused by mutations of the WRN gene encoding a protein of the RecQ-type family of DNA helicases. Objectives:To develop a rapid and simple reverse transcription-polymerase chain reaction (RT-PCR) strategy for mutation analysis of the WRN gene, to identify pathogenic mutations in a German patient with WS and to determine the effects of the pathogenic mutations on WRN mRNA stability. Methods:Allele-specific RT-PCR, semiquantitative RT-PCR, DNA sequencing. Results:We describe a novel and rapid RT-PCR-based method for mutation analysis in WS and report a German patient with WS carrying a previously reported (1396delA) as well as a novel nonsense mutation (2334delAC)of the WRN gene. By semiquantitative RT-PCR analysis we demonstrate that this compound heterozygous genotype leads to WRN transcript decay. Conclusions:In previous studies WS was primarily attributed to a loss of function of stable truncated WRN gene products. Our findings indicate that mutations can also lead to markedly decreased WRN transcript stability.
基金The authors would like to thank Princess Nourah bint Abdulrahman University for funding this project through the researchers supporting project(PNURSP2024R435)and this research was funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks.Further,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT systems.The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis dataset.Moreover,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and recall.The study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments.Furthermore,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology.This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices.However,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets.Utilizing WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT ecosystems.To summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods.The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis.Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices’defenses against new cybersecurity threats.
