Biometeorological norm as tolerance interval of man to weather stimuli

1981 ◽  
Vol 25 (2) ◽  
pp. 123-126 ◽  
Author(s):  
Maria Baranowska ◽  
B. Gabryl
Keyword(s):  
Tolerance Interval

scholarly journals Quantitation of 35S Promoter in Maize DNA Extracts from Genetically Modified Organisms Using Real-Time Polymerase Chain Reaction, Part 2: Interlaboratory Study

2005 ◽  
Vol 88 (2) ◽  
pp. 558-573 ◽  
Author(s):  
Max Feinberg ◽  
Sophie Fernandez ◽  
Sylvanie Cassard ◽  
Chrystèle Charles-Delobel ◽  
Yves Bertheau ◽  
...  
Keyword(s):  
Polymerase Chain Reaction ◽  
Real Time ◽  
Genetically Modified Organisms ◽  
Genetically Modified ◽  
Interlaboratory Study ◽  
Performance Criteria ◽  
Tolerance Interval ◽  
Chain Reaction ◽  
Qrt Pcr ◽  
Polymerase Chain

Abstract The European Committee for Standardization (CEN) and the European Network of GMO Working Laboratories have proposed development of a modular strategy for stepwise validation of complex analytical techniques. When applied to the quantitation of genetically modified organisms (GMOs) in food products, the instrumental quantitation step of the technique is separately validated from the DNA extraction step to better control the sources of uncertainty and facilitate the validation of GMO-specific polymerase chain reaction (PCR) tests. This paper presents the results of an interlaboratory study on the quantitation step of the method standardized by CEN for the detection of a regulatory element commonly inserted in GMO maize-based foods. This is focused on the quantitation of P35S promoter through using the quantitative real-time PCR (QRT-PCR). Fifteen French laboratories participated in the interlaboratory study of the P35S quantitation operating procedure on DNA extract samples using either the thermal cycler ABI Prism® 7700 (Applied Biosystems, Foster City, CA) or Light Cycler® (Roche Diagnostics, Indianapolis, IN). Attention was focused on DNA extract samples used to calibrate the method and unknown extract samples. Data were processed according to the recommendations of ISO 5725 standard. Performance criteria, obtained using the robust algorithm, were compared to the classic data processing after rejection of outliers by the Cochran and Grubbs tests. Two laboratories were detected as outliers by the Grubbs test. The robust precision criteria gave values between the classical values estimated before and after rejection of the outliers. Using the robust method, the relative expanded uncertainty by the quantitation method is about 20% for a 1% Bt176 content, whereas it can reach 40% for a 0.1% Bt176. The performances of the quantitation assay are relevant to the application of the European regulation, which has an accepted tolerance interval of about ±50%. These data were fitted to a power model (r2 = 0.96). Thanks to this model, it is possible to propose an estimation of uncertainty of the QRT-PCR quantitation step and an uncertainty budget depending on the analytical conditions.

scholarly journals Confidence and coverage for Bland–Altman limits of agreement and their approximate confidence intervals

2016 ◽  
Vol 27 (5) ◽  
pp. 1559-1574 ◽  
Author(s):  
Andrew Carkeet ◽  
Yee Teng Goh
Keyword(s):  
Confidence Intervals ◽  
Small Sample ◽  
Interval Methods ◽  
Tolerance Interval ◽  
Confidence Limits ◽  
Sample Sizes ◽  
Limits Of Agreement ◽  
Approximate Methods ◽  
Exact Confidence Intervals ◽  
Tolerance Factors

Bland and Altman described approximate methods in 1986 and 1999 for calculating confidence limits for their 95% limits of agreement, approximations which assume large subject numbers. In this paper, these approximations are compared with exact confidence intervals calculated using two-sided tolerance intervals for a normal distribution. The approximations are compared in terms of the tolerance factors themselves but also in terms of the exact confidence limits and the exact limits of agreement coverage corresponding to the approximate confidence interval methods. Using similar methods the 50th percentile of the tolerance interval are compared with the k values of 1.96 and 2, which Bland and Altman used to define limits of agreements (i.e. [Formula: see text]+/− 1.96Sd and [Formula: see text]+/− 2Sd). For limits of agreement outer confidence intervals, Bland and Altman’s approximations are too permissive for sample sizes <40 (1999 approximation) and <76 (1986 approximation). For inner confidence limits the approximations are poorer, being permissive for sample sizes of <490 (1986 approximation) and all practical sample sizes (1999 approximation). Exact confidence intervals for 95% limits of agreements, based on two-sided tolerance factors, can be calculated easily based on tables and should be used in preference to the approximate methods, especially for small sample sizes.

The Application of AFAL Methodology in Substantiation of Instrument Calibration Intervals Extension in Nuclear Power Plant

2013 ◽  
Author(s):  
Ping Zhou ◽  
Xiajun Xu ◽  
Caiqing Tu
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Fuel Cycle ◽  
Calibration Data ◽  
Tolerance Interval ◽  
Sample Number ◽  
Instrument Calibration ◽  
Interval Extension ◽  
Calibration Interval

This is an Introduction about the AFAL methodology of study the instrument drift characteristics, and the application of AFAL in substantiation of instrument calibration interval extension in nuclear power plant. AFAL methodology main aspects include collecting historical instrument calibration data of nuclear power plant, calculating statistics values such as: sample number, mean, median, standard deviation, then work out the drift tolerance interval of the instrument. Based on analysis of these statistical calculated values, will understand the instrument drift performance. This article also discusses technical issues associated with the application of AFAL and how to solve them, such as: grouping instrument, sample sizes, outliers detecting and processing, high-confidence deduction etc. Through the study of the instrument drift characteristics, evaluate the performance of instrument, determine the calibration interval can prolong properly. The application practices of AFAL methodology show, extending instrument calibration interval can support nuclear power plant to achieve the goal of prolonging the fuel cycle, under the nuclear safety precondition. The nuclear power plant can improve the capacity factor of the unit and economic performance.

scholarly journals Application of Design Space, Uncertainty, and Risk Profile Strategies to the Development and Validation of UPLC Method for the Characterization of Four Authorized Phosphodiesterase Type 5 Inhibitors to Combat Counterfeit Drugs

2020 ◽  
Vol 103 (3) ◽  
pp. 715-724
Author(s):  
Yassine Hameda Benchekroun ◽  
Miloud El Karbane ◽  
Bouchaib Ihssane ◽  
Hasnaa Haidara ◽  
Mohamed Azougagh ◽  
...  
Keyword(s):  
Analytical Method ◽  
Design Space ◽  
Ultra Performance Liquid Chromatography ◽  
Risk Profile ◽  
Tolerance Interval ◽  
Counterfeit Drugs ◽  
Phosphodiesterase Type 5 Inhibitors ◽  
Phosphodiesterase Type ◽  
Development And Validation

Abstract Background Counterfeit medicines are an increasing scourge that are difficult to identify and they have become industrialized and widespread through highly organized illegal channels. Objective This research aims to develop a robust method to determine four phosphodiesterase type-5 inhibitors in counterfeit drugs based on ultra-performance liquid chromatography. Method Experimental design methodology (DOE) and design space (DS) recommended by ICH Q8 were used side-by-side in the development phase to define the optimal parameters as well as the robustness of the chromatographic method. Moreover, both the uncertainty and risk profile derived from the β-content and γ-confidence tolerance interval were investigated during the validation phase to examine the performance of this method. Results Successful chromatographic results, in a high resolution between the four active ingredients and an optimal analysis time of less than 1.6 min, were achieved at the end of the optimization phase. In addition, validation results show a low risk of future measurements outside acceptance limits set at 5%. Conclusions Our procedure was successfully applied in the routine phase to identify 23 illicit formulations of an erectile dysfunction drug. Highlights An efficient method for the characterization of 4 authorized phosphodiesterase in less than 1.6 min was established. A DS approach was applied to test the performance of this analytical method during analytical development. A risk profile was then carried out to approve the validity of the analytical method through the uncertainty profile approach.

scholarly journals Ensemble Neural Networks for Modeling DEM Error

2019 ◽  
Vol 8 (10) ◽  
pp. 444 ◽  
Author(s):  
Nguyen ◽  
Starek ◽  
Tissot ◽  
Cai ◽  
Gibeaut
Keyword(s):  
Neural Networks ◽  
Laser Scanning ◽  
Gulf Coast ◽  
Ground Truth ◽  
Tolerance Interval ◽  
Data Set ◽  
Model Complex ◽  
Ensemble Neural Networks ◽  
Dem Accuracy ◽  
Mean Square Errors

Digital elevation models (DEMs) have become ubiquitous and remarkably effective in the field of earth sciences as a tool to characterize surface topography. All DEMs have a degree of inherent error and uncertainty that is propagated to subsequent models and analyses, which can lead to misinterpretation and inaccurate estimates. A new method was developed to estimate local DEM errors and implement corrections while quantifying the uncertainties of the implemented corrections. The method is based on the flexibility and ability to model complex problems with ensemble neural networks (ENNs). The method was developed to be applied to any DEM created from a corresponding set of elevation points (point cloud) and a set of ground truth measurements. The method was developed and tested using hyperspatial resolution terrestrial laser scanning (TLS) data (sub-centimeter point spacing) collected from a marsh site located along the southern portion of the Texas Gulf Coast, USA. ENNs improve the overall DEM accuracy in the study area by 68% for six model inputs and by 75% for 12 model inputs corresponding to root mean square errors (RMSEs) of 0.056 and 0.045 m, respectively. The 12-input model provides more accurate tolerance interval estimates, particularly for vegetated areas. The accuracy of the method is confirmed based on an independent data set. Although the method still underestimates the 95% tolerance interval, 8% below the 95% target, results show that it is able to quantify the spatial variability in uncertainties due to a relationship between vegetation/land cover and accuracy of the DEM for the study area. There are still opportunities and challenges in improving and confirming the applicability of this method for different study sites and data sets.

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