Main text
This is the final report of CCT-K7.2021 key comparison of water-triple-point (TPW) cells. The primary objective of CCT-K7.2021 was a comparison of the participant national realizations of ...the TPW temperature. Each participant laboratory selected one of its TPW cells for use as a transfer cell, directly compared it against its national reference and delivered it to the pilot (NRC). The pilot then compared all transfer cells against two NRC reference cells and, finally, each participant laboratory retrieved its transfer cell and directly re-compared it against its TPW national reference. The initial measurements at the participant laboratories were started in April 2021. The final measurements at the participants' laboratories were completed in September 2022. The final report was approved by the CCT WG-KC in April 2023. The adaptive weighted average procedure was adopted to compute the Key Comparison Reference Value (KCRV) and the degrees of equivalence (DoEs) were reported for the participant laboratories. The maximum difference between two transfer cells was 92 μK with a standard deviation of 26 µK (almost a factor of two improvement compared to CCT-K7 results (2002-2004). The maximum difference between two TPW national realizations was 111 µK, with a standard deviation of 28 µK (improvement similar to the one reported for the transfer cells). While the pooled distribution of CCT-K7 results was bimodal, indicating the presence of two different sub-populations (the population of laboratories that referred to ocean water definition and the population of laboratories that did not consider it), the pooled distribution of CCT-K7.2021 results was no longer bimodal, but slightly asymmetrical, indicating a more harmonized approach to water isotopic corrections.
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The final report has been peer-reviewed and approved for publication by the CCT, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Objective: Mean amplitude of glucose excursion (MAGE) obtained from the CGMS is considered a gold standard of glycemic variability (GV). However, the implications of CGMS in routine clinical practice ...are limited due to its high cost and complexity. This study thus designed to explore whether the GV indices calculated from the more common measurement SMBG can be used to reflect MAGE in T2D patients.
Methods: T2D patients simultaneously underwent a 48-h to 72-h CGM and fingertip blood glucose self-monitoring were included. The GV indices calculated from the 7-point SMBG data (pre- and post- breakfast, lunch and dinner and prior to bedtime) were the means of the following indices during CGM period: the standard deviation (SD) of the 7-point glucose profiles, the largest amplitude of glycemic excursions (LAGE, the difference between the daily maximum and minimum glycemic values) and the mean postprandial glucose excursion (MPPGE, the mean value of the differences between each postprandial and preprandial blood glucose).
Results: Seventy-eight T2D patients (43.6% male, median age: 62 years, median BMI: 23.83 kg/m2) were included. The mean MAGE, SD, LAGE and MPPGE were 4.11, 2.03, 5.66, 2.57mmol/L, respectively. SD, LAGE and MPPGE were significantly correlative with MAGE (r= 0.625, 0.488 and 0.599, respectively; all P<0.05). In the linear regression analysis, significant relationships were shown between MAGE and SD, LAGE and MPPGE (R2=0.391, 0.359, 0.238, respectively; all P<0.001). The area under the ROC curve for SD (0.809, 95% CI: 0.712-0.906, P<0.05) was superior to that for LAGE (0.793, 95% CI: 0.692-0.894, P<0.05) and MPPGE (0.704, 95% CI: 0.588-0.820, P<0.05) in reflecting MAGE.
Conclusions: The GV indices calculated from the SMBG data including SD, LAGE and MPPGE are positively correlated with MAGE obtained from CGM. Among these indices, SD of the 7-point SMBG glucose profiles seems to be a better GV index to reflect MAGE.
Disclosure
Z. Liu: None. B. Lin: None. W. Xu: None. L. Gong: None. X. Yang: None. B. Yao: None.
Funding
National Key Research and Development Program of China (2016YFC1304801)
Study of modeling of L/LHFSLM equilibrium based on the Non-ideality of the Aqueous and Organic Phases in the Recovery of 152+154Eu in H2SO4-Halides/Aliquat-336 in 20% kerosene as feeding phase at pH ...3.78–4.55, by the ratio 89.7%,while separation of 90Sr and 134Cs there was a problem between them by using hollow HFSLM only, the reason for that the organic solvents affect the rate of reaction in the Diamino-1,2-cyclohexane/tetraacetic acid (DCTA) as stripping phase concentration from 0.15 to 0.55 mol/L. The system has been developed; this led to the extraction of elements in the same time. The Matlab software program was introduced to obtain some mathematical parameters like, standard deviation (SD), equilibrium constant Kex and standard deviation coefficient (SDC).
•This work indicates the new liquid-liquid membrane technique, hollow fiber regeneration, liquid membrane (L/LHFRSLM), is used for simultaneous extracting and concentrating radionuclide species from sulfate/halide solutions.•This work is classified into three parts (i): theoretical basis for extraction equilibrium (ii): extraction of these cations by the extracts in different diluents (iii): extracting of radionuclide species, by different extractants from various medium.•A similar synergistic effect dependent on standard deviation (SD) also appears in the extraction of the radionuclides ions.
Abstract
Noise logic is introduced by the wavelength dependent photocurrent noise of an InGaN/SiN
x
/Si uniband diode photodetector. A wavelength versus photocurrent noise discrimination map is ...constructed from the larger photocurrent noise for red light than that for green light. A minimum measurement time of four seconds is deduced from the standard deviation of the photocurrent noise for a safe wavelength distinction. A logic NOT gate is realized as representative with on or off red or green light as binary 1 or 0 inputs and the photocurrent noise above or below a defined threshold as binary 1 or 0 outputs.
Background: The increasing awareness of the relationship of glucose, insulin, and metabolism has attracted many to the potential use of continuous glucose monitoring to aid in dietary choice. ...Commercializing has proceeded with objective investigation in non-diabetic individuals. As a result, we decided to analyze CGM measurements in non-diabetic obese and non-obese subjects ages 18-65 who were not on any glucose or other metabolic therapies. Methods: We analyzed twenty-three adults. Five subjects were male and eighteen subjects were female. All subjects underwent fasting bloodwork that measured hbA1c, fasting insulin, and fasting glucose. These individuals underwent blinded CGM with 6 days of monitoring while keeping dietary logs. Results: Normal weight individuals had a mean age of 29.5, mean BMI of 23.5, an hbA1c of 5.1, fasting glucose of 97.5, fasting insulin of 6.9, mean HOMA score of 1.3, and mean standard deviation, which is a measure of glucose variability, of 17.3. Obese individuals had a mean age of 33.7, mean BMI of 39.4, hbA1c of 5.58, fasting glucose of 104.1, fasting insulin of 22.7, mean HOMA score of 5.9, and mean standard deviation of 13.4. Conclusions: In our experience, CGM offers little insight than fasting insulin or HOMA scales. Fasting insulin and HOMA demonstrated that people are able to maintain reduced mean glucose and hbA1c. In addition, glucose variability, which is indicative by peaks and valleys on a glucose curve, do not go more extreme in obese subjects. This data demonstrates that measuring insulin will be far more productive than the dependent glucose variability, and CGM use will be limited by patient's ability to increase insulin production to regulate glucose for an extended period of time.
When reporting the results of clinical studies, some researchers may choose the five‐number summary (including the sample median, the first and third quartiles, and the minimum and maximum values) ...rather than the sample mean and standard deviation (SD), particularly for skewed data. For these studies, when included in a meta‐analysis, it is often desired to convert the five‐number summary back to the sample mean and SD. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this article, we propose to further advance the literature by developing a smoothly weighted estimator for the sample SD that fully utilizes the sample size information. For ease of implementation, we also derive an approximation formula for the optimal weight, as well as a shortcut formula for the sample SD. Numerical results show that our new estimator provides a more accurate estimate for normal data and also performs favorably for non‐normal data. Together with the optimal sample mean estimator in Luo et al., our new methods have dramatically improved the existing methods for data transformation, and they are capable to serve as “rules of thumb” in meta‐analysis for studies reported with the five‐number summary. Finally for practical use, an Excel spreadsheet and an online calculator are also provided for implementing our optimal estimators.
Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. ...Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the
t
statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (
Centrocercus urophasianus
) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.
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