The Experts below are selected from a list of 65703 Experts worldwide ranked by ideXlab platform

C William J R Cray - One of the best experts on this subject based on the ideXlab platform.

  • influence of primer sequences and dna extraction method on Detection of non o157 shiga toxin producing escherichia coli in ground beef by real time pcr targeting the eae stx and serogroup specific genes
    Journal of Food Protection, 2012
    Co-Authors: Jamie L Wasilenko, Pina M Fratamico, Neelam Narang, Glenn E Tillman, Scott R Ladely, Mustafa Simmons, C William J R Cray
    Abstract:

    Non-O157 Shiga toxin-producing Escherichia coli (STEC) infections, particularly those caused by the "big six" or "top six" non-O157 serogroups (O26, O45, O103, O111, O121, and O145) can result in severe illness and complications. Because of their significant public health impact and the notable prevalence of STEC in cattle, methods for Detection of the big six non-O157 STEC in ground beef have been established. Currently, the U.S. Department of Agriculture, Food Safety and Inspection Service Detection methods for screening beef samples for non-O157 STEC target the stx(1), stx(2), and eae virulence genes, with the 16S rRNA gene as an internal control, in a real-time PCR multiplex assay. Further, the serogroup is determined by PCR targeting genes in the E. coli O-antigen gene clusters of the big six non-O157 serogroups. The method that we previously reported was improved so that additional stx variants, stx(1d), stx(2e), and stx(2g), are detected. Additionally, alignments of the primers targeting the eae gene were used to improve the Detection assay so that eae subtypes that could potentially be of clinical significance would also be detected. Therefore, evaluation of alternative real-time PCR assay primers and probes for the stx and eae reactions was carried out in order to increase the stx and eae subtypes detected. Furthermore, a Tris-EDTA DNA extraction method was compared with a previously used procedure that was based on a commercially available reagent. The Tris-EDTA DNA extraction method significantly decreased the cycle threshold values for the stx assay (P < 0.0001) and eae assay (P < 0.0001), thereby increasing the ability to detect the targets. The use of different stx primers and probes increased the subtypes detected to include stx(1d), stx(2e), and stx(2g), and sequence data showed that modification of the eae primer should allow the known eae subtypes to be detected.

Junsu Choi - One of the best experts on this subject based on the ideXlab platform.

  • category based 802 11ax target wake time solution
    IEEE Access, 2021
    Co-Authors: Wenxun Qiu, Guanbo Chen, Khuong N Nguyen, Abhishek Sehgal, Peshal Nayak, Junsu Choi
    Abstract:

    IEEE 802.11ax newly introduced Target Wake Time (TWT) function which enables joint reduction of device power consumption and wireless medium congestion through negotiated TWT wake interval and duration. We develop a novel solution which can dynamically configure station (STA) wake interval and duration based on run time recognition of the Quality of Service (QoS) requirement including the required latency and throughput. Specifically, a Machine Learning (ML) based Network Service Detection (NSD) module is developed which can detect the current network Service in real time and update the STA wake interval according to the corresponding Service latency requirement. Moreover, a novel data time estimation model is developed which can estimate the required throughput and wake duration with the observed throughput, linkspeed, and contention level. In addition, a state-machine based method is developed to detect three different traffic types (random, stable, bursty) and their throughput variation pattern for further optimization of the STA wake duration. Our solution is implemented and extensively tested on commercial smart mobile platform under various network conditions and use cases. The results show that our ML based NSD could reach 99.2% and 96.5% accuracy respectively for coarse-grain and fine-grain network Service classification, which ensures our TWT solution can accurately recognize and satisfy the QoS requirements. More importantly, while maintaining the QoS and user experience, our solution can substantially reduce the Wi-Fi duty cycle to 29.6% on average, which leads to lower device power consumption and network contention level.

Jamie L Wasilenko - One of the best experts on this subject based on the ideXlab platform.

  • influence of primer sequences and dna extraction method on Detection of non o157 shiga toxin producing escherichia coli in ground beef by real time pcr targeting the eae stx and serogroup specific genes
    Journal of Food Protection, 2012
    Co-Authors: Jamie L Wasilenko, Pina M Fratamico, Neelam Narang, Glenn E Tillman, Scott R Ladely, Mustafa Simmons, C William J R Cray
    Abstract:

    Non-O157 Shiga toxin-producing Escherichia coli (STEC) infections, particularly those caused by the "big six" or "top six" non-O157 serogroups (O26, O45, O103, O111, O121, and O145) can result in severe illness and complications. Because of their significant public health impact and the notable prevalence of STEC in cattle, methods for Detection of the big six non-O157 STEC in ground beef have been established. Currently, the U.S. Department of Agriculture, Food Safety and Inspection Service Detection methods for screening beef samples for non-O157 STEC target the stx(1), stx(2), and eae virulence genes, with the 16S rRNA gene as an internal control, in a real-time PCR multiplex assay. Further, the serogroup is determined by PCR targeting genes in the E. coli O-antigen gene clusters of the big six non-O157 serogroups. The method that we previously reported was improved so that additional stx variants, stx(1d), stx(2e), and stx(2g), are detected. Additionally, alignments of the primers targeting the eae gene were used to improve the Detection assay so that eae subtypes that could potentially be of clinical significance would also be detected. Therefore, evaluation of alternative real-time PCR assay primers and probes for the stx and eae reactions was carried out in order to increase the stx and eae subtypes detected. Furthermore, a Tris-EDTA DNA extraction method was compared with a previously used procedure that was based on a commercially available reagent. The Tris-EDTA DNA extraction method significantly decreased the cycle threshold values for the stx assay (P < 0.0001) and eae assay (P < 0.0001), thereby increasing the ability to detect the targets. The use of different stx primers and probes increased the subtypes detected to include stx(1d), stx(2e), and stx(2g), and sequence data showed that modification of the eae primer should allow the known eae subtypes to be detected.

Wenxun Qiu - One of the best experts on this subject based on the ideXlab platform.

  • category based 802 11ax target wake time solution
    IEEE Access, 2021
    Co-Authors: Wenxun Qiu, Guanbo Chen, Khuong N Nguyen, Abhishek Sehgal, Peshal Nayak, Junsu Choi
    Abstract:

    IEEE 802.11ax newly introduced Target Wake Time (TWT) function which enables joint reduction of device power consumption and wireless medium congestion through negotiated TWT wake interval and duration. We develop a novel solution which can dynamically configure station (STA) wake interval and duration based on run time recognition of the Quality of Service (QoS) requirement including the required latency and throughput. Specifically, a Machine Learning (ML) based Network Service Detection (NSD) module is developed which can detect the current network Service in real time and update the STA wake interval according to the corresponding Service latency requirement. Moreover, a novel data time estimation model is developed which can estimate the required throughput and wake duration with the observed throughput, linkspeed, and contention level. In addition, a state-machine based method is developed to detect three different traffic types (random, stable, bursty) and their throughput variation pattern for further optimization of the STA wake duration. Our solution is implemented and extensively tested on commercial smart mobile platform under various network conditions and use cases. The results show that our ML based NSD could reach 99.2% and 96.5% accuracy respectively for coarse-grain and fine-grain network Service classification, which ensures our TWT solution can accurately recognize and satisfy the QoS requirements. More importantly, while maintaining the QoS and user experience, our solution can substantially reduce the Wi-Fi duty cycle to 29.6% on average, which leads to lower device power consumption and network contention level.

Norwati Mustapha - One of the best experts on this subject based on the ideXlab platform.

  • Distributed Denial of Service Detection using hybrid machine learning technique
    2014 International Symposium on Biometrics and Security Technologies (ISBAST), 2014
    Co-Authors: Mehdi Barati, Azizol Abdullah, Nur Izura Udzir, Ramlan Mahmod, Norwati Mustapha
    Abstract:

    Distributed Denial of Service (DDoS) is a major threat among many security issues. To overcome this problem, many studies have been carried out by researchers, however due to inefficiency of their techniques in terms of accuracy and computational cost, proposing an efficient method to detect DDoS attack is still a hot topic in research. Current paper proposes architecture of a Detection system for DDoS attack. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are deployed for feature selection and attack Detection respectively in our hybrid method. Wrapper method using GA is deployed to select the most efficient features and then DDoS attack Detection rate is improved by applying Multi-Layer Perceptron (MLP) of ANN. Results demonstrate that the proposed method is able to detect DDoS attack with high accuracy and deniable False Alarm.