目的:测试GoldeneyeTMDNA ID System20A试剂盒的技术性能指标,评估其法医学应用价值。方法:参照美国DNA分析方法专家组(TWGDAM)制定的确证指南,利用9947A、007标准品,100个拐卖儿童建库血样,日常案件各类检材制定测试方案,从方法学验证...目的:测试GoldeneyeTMDNA ID System20A试剂盒的技术性能指标,评估其法医学应用价值。方法:参照美国DNA分析方法专家组(TWGDAM)制定的确证指南,利用9947A、007标准品,100个拐卖儿童建库血样,日常案件各类检材制定测试方案,从方法学验证、灵敏度测试、混合样本测试、批量样本测试、DNA提取方法适应性测试、各类常见检材的测试、稳定性测试等7个方面进行测试。结果:Gold-eneyeTMDNA ID System 20A试剂盒灵敏度较高,对各类案件检材和DNA提取方法具有较好的适应性,具有直接扩增能力和检测混合DNA样本检测能力。结论:GoldeneyeTMDNAID System 20A试剂盒可用于法庭科学的检案与建库。展开更多
Objective Population genetic analysis based on genetic markers harbors valuable forensic applications.In this regard,it is informative and imperative to explore Han groups as they are the largest population of China.I...Objective Population genetic analysis based on genetic markers harbors valuable forensic applications.In this regard,it is informative and imperative to explore Han groups as they are the largest population of China.In particular,there is a largely underrepresented amount of information from recent decades regarding the southeast costal Han Chinese.Therefore,the aim of this study is to investigate the available genetic characteristics of the Han population living in the Jinjiang,Fujian Province,Southeastern China.Methods We sampled 858 saliva samples and used the commercially available Microreader^(TM) Y Prime Plus ID System to identify population data of Y-short tandem repeat(STR)loci of this region.Results A total of 822 different haplotypes were observed.The overall haplotype diversity,discriminatory power and haplotype match probability were 0.9999,0.9999 and 0.0012,respectively.Conclusion Our results showed that the Jinjiang Han population was closely genetically related to Han groups of China.Overall,we identified a set of 37 Y-STRs that are highly polymorphic,and that can provide meaningful information in forensic practice and human genetic research.展开更多
AIM:To determine the effect of oral sumatriptan on gastric emptying using a continuous 13 C breath test(BreathID system).METHODS:Ten healthy male volunteers participated in this randomized,2-way crossover study.The su...AIM:To determine the effect of oral sumatriptan on gastric emptying using a continuous 13 C breath test(BreathID system).METHODS:Ten healthy male volunteers participated in this randomized,2-way crossover study.The subjects fasted overnight and were randomly assigned to receive a test meal(200 kcal/200 mL) 30 min after pre-medication with sumatriptan 50 mg(sumatriptan condition),or the test meal alone(control condition).Gastric emptying was monitored for 4 h after administration of the test meal by the 13 C-acetic acid breath test performed continually using the BreathID system.Then,using Oridion Research Software(β version),the time taken for emptying of 50% of the labeled meal(T 1/2) similar to the scintigraphy lag time for 10% emptying of the labeled meal(T lag),the gastric emptying coefficient(GEC),and the regression-estimated constants(β and κ) were calculated.The statistical significance of any differences in the parameters were analyzed using Wilcoxon's signed-rank test.RESULTS:In the sumatriptan condition,significant differences compared with the control condition were found in T 1/2 [median 131.84 min(range,103.13-168.70) vs 120.27 min(89.61-138.25);P = 0.0016],T lag [median 80.085 min(59.23-125.89) vs 61.11 min(39.86-87.05);P = 0.0125],and β [median 2.3374(1.6407-3.8209) vs 2.0847(1.4755-2.9269);P = 0.0284].There were no significant differences in the GEC or κ between the 2 conditions.CONCLUSION:This study showed that oral sumatriptan significantly delayed gastric emptying of a liquid meal.展开更多
In many commercial and public sectors,the Internet of Things(IoT)is deeply embedded.Cyber security threats aimed at compromising the security,reliability,or accessibility of data are a serious concern for the IoT.Due ...In many commercial and public sectors,the Internet of Things(IoT)is deeply embedded.Cyber security threats aimed at compromising the security,reliability,or accessibility of data are a serious concern for the IoT.Due to the collection of data from several IoT devices,the IoT presents unique challenges for detecting anomalous behavior.It is the responsibility of an Intrusion Detection System(IDS)to ensure the security of a network by reporting any suspicious activity.By identifying failed and successful attacks,IDS provides a more comprehensive security capability.A reliable and efficient anomaly detection system is essential for IoT-driven decision-making.Using deep learning-based anomaly detection,this study proposes an IoT anomaly detection system capable of identifying relevant characteristics in a controlled environment.These factors are used by the classifier to improve its ability to identify fraudulent IoT data.For efficient outlier detection,the author proposed a Convolutional Neural Network(CNN)with Long Short Term Memory(LSTM)based Attention Mechanism(ACNN-LSTM).As part of the ACNN-LSTM model,CNN units are deployed with an attention mechanism to avoid memory loss and gradient dispersion.Using the N-BaIoT and IoT-23 datasets,the model is verified.According to the N-BaIoT dataset,the overall accuracy is 99%,and precision,recall,and F1-score are also 0.99.In addition,the IoT-23 dataset shows a commendable accuracy of 99%.In terms of accuracy and recall,it scored 0.99,while the F1-score was 0.98.The LSTM model with attention achieved an accuracy of 95%,while the CNN model achieved an accuracy of 88%.According to the loss graph,attention-based models had lower loss values,indicating that they were more effective at detecting anomalies.In both the N-BaIoT and IoT-23 datasets,the receiver operating characteristic and area under the curve(ROC-AUC)graphs demonstrated exceptional accuracy of 99%to 100%for the Attention-based CNN and LSTM models.This indicates that these models are capable of making precise predictions.展开更多
Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks from malicious activities. This study presents a novel approach by proposing a Hybrid Dense Neural Network-Radial Basis Function Neural N...Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks from malicious activities. This study presents a novel approach by proposing a Hybrid Dense Neural Network-Radial Basis Function Neural Network (DNN-RBFNN) architecture to enhance the accuracy and efficiency of IDS. The hybrid model synergizes the strengths of both dense learning and radial basis function networks, aiming to address the limitations of traditional IDS techniques in classifying packets that could result in Remote-to-local (R2L), Denial of Service (Dos), and User-to-root (U2R) intrusions.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
Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious actors.These threats encompass unauthorized access,data breaches,de...Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious actors.These threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware variants.Traditional security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying anomalies.This can lead to missed attacks or an abundance of false alarms,impacting overall security effectiveness.To address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-PELT.This novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and efficiency.ACIDS-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud environment.We evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark dataset.The results demonstrate that ACIDS-PELT outperforms existing cloud intrusion detection techniques in terms of accuracy,precision,and recall.This superiority stems from ACIDS-PELT’s ability to overcome limitations associated with approximation and imprecision in change point detection while offering a more accurate and precise approach to detecting security threats in dynamic cloud environments.展开更多
2023年9月JAMA刊登了来自美国埃默里大学医学教授Carlos del Rio的文章:COVID-19 in the Fall of 2023-Forgotten but Not Gone,提出了COVID-19可能已被遗忘,但它并没有消失。医生和患者都应该把SARS-CoV-2列入引起重大呼吸系统疾病的...2023年9月JAMA刊登了来自美国埃默里大学医学教授Carlos del Rio的文章:COVID-19 in the Fall of 2023-Forgotten but Not Gone,提出了COVID-19可能已被遗忘,但它并没有消失。医生和患者都应该把SARS-CoV-2列入引起重大呼吸系统疾病的清单中,且保护最脆弱的人群仍是重点。虽然COVID-19不再是一个公共卫生威胁,但感染的增加在可预见的将来或许还会发生。展开更多
文摘目的:测试GoldeneyeTMDNA ID System20A试剂盒的技术性能指标,评估其法医学应用价值。方法:参照美国DNA分析方法专家组(TWGDAM)制定的确证指南,利用9947A、007标准品,100个拐卖儿童建库血样,日常案件各类检材制定测试方案,从方法学验证、灵敏度测试、混合样本测试、批量样本测试、DNA提取方法适应性测试、各类常见检材的测试、稳定性测试等7个方面进行测试。结果:Gold-eneyeTMDNA ID System 20A试剂盒灵敏度较高,对各类案件检材和DNA提取方法具有较好的适应性,具有直接扩增能力和检测混合DNA样本检测能力。结论:GoldeneyeTMDNAID System 20A试剂盒可用于法庭科学的检案与建库。
基金This study was supported by the Shaanxi Basic Research Program of Natural Science(No.2021JQ-392).
文摘Objective Population genetic analysis based on genetic markers harbors valuable forensic applications.In this regard,it is informative and imperative to explore Han groups as they are the largest population of China.In particular,there is a largely underrepresented amount of information from recent decades regarding the southeast costal Han Chinese.Therefore,the aim of this study is to investigate the available genetic characteristics of the Han population living in the Jinjiang,Fujian Province,Southeastern China.Methods We sampled 858 saliva samples and used the commercially available Microreader^(TM) Y Prime Plus ID System to identify population data of Y-short tandem repeat(STR)loci of this region.Results A total of 822 different haplotypes were observed.The overall haplotype diversity,discriminatory power and haplotype match probability were 0.9999,0.9999 and 0.0012,respectively.Conclusion Our results showed that the Jinjiang Han population was closely genetically related to Han groups of China.Overall,we identified a set of 37 Y-STRs that are highly polymorphic,and that can provide meaningful information in forensic practice and human genetic research.
文摘AIM:To determine the effect of oral sumatriptan on gastric emptying using a continuous 13 C breath test(BreathID system).METHODS:Ten healthy male volunteers participated in this randomized,2-way crossover study.The subjects fasted overnight and were randomly assigned to receive a test meal(200 kcal/200 mL) 30 min after pre-medication with sumatriptan 50 mg(sumatriptan condition),or the test meal alone(control condition).Gastric emptying was monitored for 4 h after administration of the test meal by the 13 C-acetic acid breath test performed continually using the BreathID system.Then,using Oridion Research Software(β version),the time taken for emptying of 50% of the labeled meal(T 1/2) similar to the scintigraphy lag time for 10% emptying of the labeled meal(T lag),the gastric emptying coefficient(GEC),and the regression-estimated constants(β and κ) were calculated.The statistical significance of any differences in the parameters were analyzed using Wilcoxon's signed-rank test.RESULTS:In the sumatriptan condition,significant differences compared with the control condition were found in T 1/2 [median 131.84 min(range,103.13-168.70) vs 120.27 min(89.61-138.25);P = 0.0016],T lag [median 80.085 min(59.23-125.89) vs 61.11 min(39.86-87.05);P = 0.0125],and β [median 2.3374(1.6407-3.8209) vs 2.0847(1.4755-2.9269);P = 0.0284].There were no significant differences in the GEC or κ between the 2 conditions.CONCLUSION:This study showed that oral sumatriptan significantly delayed gastric emptying of a liquid meal.
基金supported via funding from Prince Sattam Bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘In many commercial and public sectors,the Internet of Things(IoT)is deeply embedded.Cyber security threats aimed at compromising the security,reliability,or accessibility of data are a serious concern for the IoT.Due to the collection of data from several IoT devices,the IoT presents unique challenges for detecting anomalous behavior.It is the responsibility of an Intrusion Detection System(IDS)to ensure the security of a network by reporting any suspicious activity.By identifying failed and successful attacks,IDS provides a more comprehensive security capability.A reliable and efficient anomaly detection system is essential for IoT-driven decision-making.Using deep learning-based anomaly detection,this study proposes an IoT anomaly detection system capable of identifying relevant characteristics in a controlled environment.These factors are used by the classifier to improve its ability to identify fraudulent IoT data.For efficient outlier detection,the author proposed a Convolutional Neural Network(CNN)with Long Short Term Memory(LSTM)based Attention Mechanism(ACNN-LSTM).As part of the ACNN-LSTM model,CNN units are deployed with an attention mechanism to avoid memory loss and gradient dispersion.Using the N-BaIoT and IoT-23 datasets,the model is verified.According to the N-BaIoT dataset,the overall accuracy is 99%,and precision,recall,and F1-score are also 0.99.In addition,the IoT-23 dataset shows a commendable accuracy of 99%.In terms of accuracy and recall,it scored 0.99,while the F1-score was 0.98.The LSTM model with attention achieved an accuracy of 95%,while the CNN model achieved an accuracy of 88%.According to the loss graph,attention-based models had lower loss values,indicating that they were more effective at detecting anomalies.In both the N-BaIoT and IoT-23 datasets,the receiver operating characteristic and area under the curve(ROC-AUC)graphs demonstrated exceptional accuracy of 99%to 100%for the Attention-based CNN and LSTM models.This indicates that these models are capable of making precise predictions.
文摘Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks from malicious activities. This study presents a novel approach by proposing a Hybrid Dense Neural Network-Radial Basis Function Neural Network (DNN-RBFNN) architecture to enhance the accuracy and efficiency of IDS. The hybrid model synergizes the strengths of both dense learning and radial basis function networks, aiming to address the limitations of traditional IDS techniques in classifying packets that could result in Remote-to-local (R2L), Denial of Service (Dos), and User-to-root (U2R) intrusions.
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.
基金funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)through Research Partnership Program No.RP-21-07-09.
文摘Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious actors.These threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware variants.Traditional security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying anomalies.This can lead to missed attacks or an abundance of false alarms,impacting overall security effectiveness.To address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-PELT.This novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and efficiency.ACIDS-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud environment.We evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark dataset.The results demonstrate that ACIDS-PELT outperforms existing cloud intrusion detection techniques in terms of accuracy,precision,and recall.This superiority stems from ACIDS-PELT’s ability to overcome limitations associated with approximation and imprecision in change point detection while offering a more accurate and precise approach to detecting security threats in dynamic cloud environments.
文摘2023年9月JAMA刊登了来自美国埃默里大学医学教授Carlos del Rio的文章:COVID-19 in the Fall of 2023-Forgotten but Not Gone,提出了COVID-19可能已被遗忘,但它并没有消失。医生和患者都应该把SARS-CoV-2列入引起重大呼吸系统疾病的清单中,且保护最脆弱的人群仍是重点。虽然COVID-19不再是一个公共卫生威胁,但感染的增加在可预见的将来或许还会发生。
