scholarly journals Comparison of sCIM and Other Phenotypic Detection Methods for Carbapenemase-Producing Enterobacterales

2021 ◽  
Author(s):  
Takuya Hosoda ◽  
Yohei Doi ◽  
Masahiro Suzuki
Keyword(s):  
Sensitivity And Specificity ◽  
Detection Method ◽  
Clinical Laboratory ◽  
Detection Methods ◽  
Identification Methods ◽  
Hands On

Simple identification methods for carbapenemase-producing Enterobacterales are required for the clinical laboratory. The simplified carbapenem inactivation method (sCIM) is a carbapenemase detection method that can be performed with less hands-on time than mCIM, but its sensitivity and specificity were suboptimal compared with other phenotypic detection methods when tested against a collection of IMP-producing CPE.

scholarly journals Outlier Detection in Growth Data: Beyond Biologically Implausible Values

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1174-1174
Author(s):  
Paraskevi Massara ◽  
Robert Bandsma ◽  
Celine Bourdon ◽  
Jonathon Maguire ◽  
Elena Comelli ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Sensitivity And Specificity ◽  
Detection Method ◽  
Nutritional Assessment ◽  
Empirical Method ◽  
Child Growth ◽  
Detection Methods ◽  
Healthy Children ◽  
Growth Data ◽  
Growth Standards

Abstract Objectives Eliminating anthropometry measurement error and employing outlier and biological implausible values (BIV) detection methods adapted to longitudinal measurements is important for the study of growth. This work aimed to review and assess the accuracy of the available BIV and outlier detection methods and propose a growth trajectory outlier detection method. Methods We included 2354 infants from the Applied Research Group for Kids (TARGet Kids! ) cohort-based in Toronto (ON, Canada) that recruits healthy children from birth to 5 years of age. We considered infants with at least 8 length and weight measurements available between the 1st and the 24th month of age. Weight-for-length z-scores (wflz) were calculated using the WHO growth standards. Outlier measurements were randomly introduced in 5% of the wflz measurements using a normal distribution (μ = 0, σ = 1). We employed 4 outlier detection methods; an empirical detection method for BIV using the cut-offs derived from the WHO Child Growth Standards, a clustering method, a method based on cluster prototypes for individual outlier measurements and a method based on cluster prototypes for entire growth trajectories. Each method was applied individually and evaluated using the sensitivity and specificity indexes based on the manually introduced outliers. We also calculated the Kappa statistic to evaluate the agreement of each method against the manual outliers. Results After excluding premature (<37 weeks), low birth weight (<1500 g) neonates and children with missing length and weight measurements, we analyzed 393 children with a total of 3144 measurements. Sensitivity and specificity for the four methods ranged between 4.4%–55.0% and 83.7% −99.7%, respectively, with kappa being non-significant (P > 0.05) only for the empirical. The clustering detection method reported a higher finding rate, while the empirical method found most of the BIV, but few of the rest of the outliers. Conclusions BIV account for a small portion of the possible outliers in growth datasets. We show that additional statistical or model-based methods are required for a more comprehensive outlier detection process, which has implications for growth analysis and nutritional assessment. Funding Sources Joannah and Brian Lawson Center for Child Nutrition, Connaught Fund, Onassis Foundation.

scholarly journals Identification of Spikes in Time Series

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Dana E. Goin ◽  
Jennifer Ahern
Keyword(s):  
Time Series ◽  
Sensitivity And Specificity ◽  
Kalman Filtering ◽  
Detection Method ◽  
Detection Methods ◽  
Spike Detection ◽  
Overall Performance ◽  
The Mean ◽  
Arima Modeling ◽  
Smoothing Procedure

Abstract Researchers interested in the effects of exposure spikes on an outcome need tools to identify unexpectedly high values in a time series. However, the best method to identify spikes in time series is not known. This paper aims to fill this gap by testing the performance of several spike detection methods in a simulation setting. We created simulations parameterized by monthly violence rates in nine California cities that represented different series features, and randomly inserted spikes into the series. We then compared the ability to detect spikes of the following methods: ARIMA modeling, Kalman filtering and smoothing, wavelet modeling with soft thresholding, and an iterative outlier detection method. We varied the magnitude of spikes from 10 to 50 % of the mean rate over the study period and varied the number of spikes inserted from 1 to 10. We assessed performance of each method using sensitivity and specificity. The Kalman filtering and smoothing procedure had the best overall performance. We applied each method to the monthly violence rates in nine California cities and identified spikes in the rate over the 2005–2012 period.

scholarly journals Diagnostic Accuracy of HPV Detection in Patients with Oropharyngeal Squamous Cell Carcinomas: A Systematic Review and Meta-Analysis

Viruses ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1692
Author(s):  
Kathrine Kronberg Jakobsen ◽  
Amanda-Louise Fenger Carlander ◽  
Simone Kloch Bendtsen ◽  
Martin Garset-Zamani ◽  
Charlotte Duch Lynggaard ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Sensitivity And Specificity ◽  
Detection Method ◽  
Meta Analysis ◽  
Squamous Cell Carcinomas ◽  
Tumour Tissue ◽  
Detection Methods ◽  
Cochrane Library ◽  
Blood Samples ◽  
The Cochrane Library

The aim of the study was to evaluate the diagnostic accuracy of Human Papillomavirus (HPV) techniques in oropharyngeal cancer. PubMed, EMBASE, the Cochrane Library and clinicaltrials.org were systematically searched for studies reporting methods of HPV detection. Primary outcomes were sensitivity and specificity of HPV detection. In this case, 27 studies were included (n = 5488, 41.6% HPV+). In this case, 13 studies evaluated HPV detection in tumour tissue, nine studies examined HPV detection in blood samples and five studies evaluated HPV detection in oral samples. Accuracy of HPV detection in tumour tissue was high for all detection methods, with pooled sensitivity ranging from 81.1% (95% CI 71.9–87.8) to 93.1% (95% CI 87.4–96.4) and specificity ranging from 81.1% (95% CI 71.9–87.8) to 94.9% (95% CI 79.1–98.9) depending on detection methods. Overall accuracy of HPV detection in blood samples revealed a sensitivity of 81.4% (95% CI 62.9–91.9) and a specificity of 94.8% (95% CI 91.4–96.9). In oral samples pooled sensitivity and specificity were lower (77.0% (95% CI 68.8–83.6) and 74.0% (95% CI 58.0–85.4)). In conclusion, we found an overall high accuracy for HPV detection in tumour tissue regardless of the HPV detection method used. HPV detection in blood samples may provide a promising new way of HPV detection.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Detection of Movement Events of Long-Track Speed Skating Using Wearable Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3649
Author(s):  
Yosuke Tomita ◽  
Tomoki Iizuka ◽  
Koichi Irisawa ◽  
Shigeyuki Imura
Keyword(s):  
Gold Standard ◽  
Detection Method ◽  
Standard Measure ◽  
Detection Methods ◽  
Phase Identification ◽  
Foot Pressure ◽  
Pressure System ◽  
Speed Skating ◽  
Stance Time ◽  
Integrated Detection

Inertial measurement units (IMUs) have been used increasingly to characterize long-track speed skating. We aimed to estimate the accuracy of IMUs for use in phase identification of long-track speed skating. Twelve healthy competitive athletes on a university long-track speed skating team participated in this study. Foot pressure, acceleration and knee joint angle were recorded during a 1000-m speed skating trial using the foot pressure system and IMUs. The foot contact and foot-off timing were identified using three methods (kinetic, acceleration and integrated detection) and the stance time was also calculated. Kinetic detection was used as the gold standard measure. Repeated analysis of variance, intra-class coefficients (ICCs) and Bland-Altman plots were used to estimate the extent of agreement between the detection methods. The stance time computed using the acceleration and integrated detection methods did not differ by more than 3.6% from the gold standard measure. The ICCs ranged between 0.657 and 0.927 for the acceleration detection method and 0.700 and 0.948 for the integrated detection method. The limits of agreement were between 90.1% and 96.1% for the average stance time. Phase identification using acceleration and integrated detection methods is valid for evaluating the kinematic characteristics during long-track speed skating.

scholarly journals Early Detection Methods for Silicosis in Australia and Internationally: A Review of the Literature

2021 ◽  
Vol 18 (15) ◽  
pp. 8123
Author(s):  
Emma K. Austin ◽  
Carole James ◽  
John Tessier
Keyword(s):  
Early Detection ◽  
Literature Review ◽  
Detection Method ◽  
Detection Methods ◽  
X Rays ◽  
Review Of The Literature ◽  
Occupational Lung Disease ◽  
Work Related ◽  
Growing Body ◽  
Future Work

Pneumoconiosis, or occupational lung disease, is one of the world’s most prevalent work-related diseases. Silicosis, a type of pneumoconiosis, is caused by inhaling respirable crystalline silica (RCS) dust. Although silicosis can be fatal, it is completely preventable. Hundreds of thousands of workers globally are at risk of being exposed to RCS at the workplace from various activities in many industries. Currently, in Australia and internationally, there are a range of methods used for the respiratory surveillance of workers exposed to RCS. These methods include health and exposure questionnaires, spirometry, chest X-rays, and HRCT. However, these methods predominantly do not detect the disease until it has significantly progressed. For this reason, there is a growing body of research investigating early detection methods for silicosis, particularly biomarkers. This literature review summarises the research to date on early detection methods for silicosis and makes recommendations for future work in this area. Findings from this review conclude that there is a critical need for an early detection method for silicosis, however, further laboratory- and field-based research is required.

Elimination of outlier measurements for damage detection of structures under changing environmental conditions

2021 ◽  
pp. 147592172199847
Author(s):  
William Soo Lon Wah ◽  
Yining Xia
Keyword(s):  
Damage Detection ◽  
Multiple Regression ◽  
Natural Frequencies ◽  
Detection Method ◽  
Detection Methods ◽  
Structure Model ◽  
Bridge Structure ◽  
Beam Structure ◽  
Dependent Variables ◽  
The Difference

Damage detection methods developed in the literature are affected by the presence of outlier measurements. These measurements can prevent small levels of damage to be detected. Therefore, a method to eliminate the effects of outlier measurements is proposed in this article. The method uses the difference in fits to examine how deleting an observation affects the predicted value of a model. This allows the observations that have a large influence on the model created, to be identified. These observations are the outlier measurements and they are eliminated from the database before the application of damage detection methods. Eliminating the outliers before the application of damage detection methods allows the normal procedures to detect damage, to be implemented. A multiple-regression-based damage detection method, which uses the natural frequencies as both the independent and dependent variables, is also developed in this article. A beam structure model and an experimental wooden bridge structure are analysed using the multiple-regression-based damage detection method with and without the application of the method proposed to eliminate the effects of outliers. The results obtained demonstrate that smaller levels of damage can be detected when the effects of outlier measurements are eliminated using the method proposed in this article.

scholarly journals Recognition of Bimolecular Logic Operation Pattern Based on a Solid-State Nanopore

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 33
Author(s):  
Han Yan ◽  
Zhen Zhang ◽  
Ting Weng ◽  
Libo Zhu ◽  
Pang Zhang ◽  
...  
Keyword(s):  
Self Assembly ◽  
Detection Method ◽  
Logic Gate ◽  
Detection Methods ◽  
Label Free ◽  
Recognition Method ◽  
Operation Pattern ◽  
Simple Detection ◽  
Dna Tetrahedron ◽  
Marker Molecule

Nanopores have a unique advantage for detecting biomolecules in a label-free fashion, such as DNA that can be synthesized into specific structures to perform computations. This method has been considered for the detection of diseased molecules. Here, we propose a novel marker molecule detection method based on DNA logic gate by deciphering a variable DNA tetrahedron structure using a nanopore. We designed two types of probes containing a tetrahedron and a single-strand DNA tail which paired with different parts of the target molecule. In the presence of the target, the two probes formed a double tetrahedron structure. As translocation of the single and the double tetrahedron structures under bias voltage produced different blockage signals, the events could be assigned into four different operations, i.e., (0, 0), (0, 1), (1, 0), (1, 1), according to the predefined structure by logic gate. The pattern signal produced by the AND operation is obviously different from the signal of the other three operations. This pattern recognition method has been differentiated from simple detection methods based on DNA self-assembly and nanopore technologies.

scholarly journals Over/Undervoltage and Undervoltage Shift of Hybrid Islanding Detection Method of Distributed Generation

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Manop Yingram ◽  
Suttichai Premrudeepreechacharn
Keyword(s):  
Control System ◽  
Distributed Generation ◽  
Hybrid Method ◽  
Detection Method ◽  
System Simulation ◽  
Detection Methods ◽  
Islanding Detection ◽  
Active Method ◽  
Passive Method ◽  
Simulation Results

The mainly used local islanding detection methods may be classified as active and passive methods. Passive methods do not perturb the system but they have larger nondetection zones, whereas active methods have smaller nondetection zones but they perturb the system. In this paper, a new hybrid method is proposed to solve this problem. An over/undervoltage (passive method) has been used to initiate an undervoltage shift (active method), which changes the undervoltage shift of inverter, when the passive method cannot have a clear discrimination between islanding and other events in the system. Simulation results on MATLAB/SIMULINK show that over/undervoltage and undervoltage shifts of hybrid islanding detection method are very effective because they can determine anti-islanding condition very fast.ΔP/P>38.41% could determine anti-islanding condition within 0.04 s;ΔP/P<-24.39% could determine anti-islanding condition within 0.04 s;-24.39%≤ΔP/P≤ 38.41% could determine anti-islanding condition within 0.08 s. This method perturbed the system, only in the case of-24.39% ≤ΔP/P ≤38.41% at which the control system of inverter injected a signal of undervoltage shift as necessary to check if the occurrence condition was an islanding condition or not.

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