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Background: Regression describes the relationship of results from two analyzers, and the generated equation can be used to harmonize results. Point‐of‐care (POC) analyzers cannot be calibrated by the end user, so regression offers an opportunity for calculated harmonization. Harmonization (uniformity) of laboratory results facilitates the use of common reference intervals and medical decision thresholds. Objective: Our aims were to characterize the relationship of results for multiple biochemistry analytes on a POC and a commercial laboratory analyzer (CL) with three regression techniques and to use regression equations to harmonize the POC results with those of the CL. Harmonized results were assessed by recognized quality goals. We used harmonized results to assess the regression techniques. Methods: After analyzer imprecision assessments, paired clinical samples were assessed with one dataset to calculate regression parameters that were applied to a second dataset. Three regression techniques were performed, and each was used to harmonize the POC results with those from the CL. POC results were assessed for bias and the number of results reaching quality goals before and after harmonization. Results: All regression techniques could be used to harmonize most analytes so that 95% of results were within ASVCP TEa guidelines. Harmonization could be further improved with alternate regression techniques or exclusions. Conclusions: Regression offers a means to harmonize POC and CL analyzers. Further work is needed to assess how few samples can reliably be used and to assess likely species differences. No regression technique reliably describes the relationship between methods when correlation is poor. [ABSTRACT FROM AUTHOR]

Characterization of botanical extracts by mass spectrometry-based metabolomics analysis helps in determining the phytochemical composition that underlies their bioactivity and potential health benefits, while also supporting reproducibility of effects in clinical trials. The quantification of seven withanolides in Withania somnifera using three mass-spectrometry methods was evaluated using Deming regression. Two high-resolution time-of-flight mass spectrometry methods were used, one operating in data-dependent acquisition mode and the other in parallel-reaction-monitoring mode with an inclusion list. The two high-resolution time-of-flight mass spectrometry methods were compared to a multiple-reaction-monitoring method. We evaluated in-source fragmentation of steroidal glycosides and optimized the methods accordingly. A novel software approach to integrating parallel-reaction-monitoring data acquired with an inclusion list was developed. Combining and comparing quantitative results allowed for quantitative specificity, good precision, and adjustment of instrument source conditions for optimal quantification by multiple-reaction-monitoring mass spectrometry, an analytical method that is widely accessible in analytical and phytochemical laboratories. [ABSTRACT FROM AUTHOR]

The Monkman−Grant relation has the potential to reduce the development cycle for new materials, as it provides a means of lifting based on minimum creep rates that are typically observed early on. This paper outlines problems in estimating the nature of this relation using the least squares technique that stems from errors made in measuring failure times and minimum creep rates. The paper outlines some solutions to this problem that have been proposed within the scientific literature – such as reverse regression and the Deming regression. The evidence from the materials studied in this paper, suggest that the use of least squares results in overly conservative lifetime predictions when using the Monkman-Grant relation. It was found that for 2.25Cr-1Mo steel, the life expected for a minimum creep rate of 3.67E-12s−1 was 57 years when the least squares technique was used, but this increased to 78 years when using the Deming regression. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the compatibility of the "Computed Tomography (CT)" and "Magnetic Resonance (MR)" methods which are imaging techniques used to describe prostate cancer with the pathology accepted as the reference method. In this study, the concordance between CT and MR results and pathology results of 37 prostate cancer patients was evaluated using the Bland-Altman, Interclass Correlation Coefficient, Concordance Correlation Coefficient, Deming Regression and Passing-Bablok method comparison methods. Inter-class correlation coefficient and Concordance Correlation Coefficient values for CT-Pathology results were 0.62 and 0.62, respectively. Inter-class correlation coefficient and Concordance Correlation Coefficient values for MR-Pathology results were 0.74 and 0.75, respectively. The regression equation for the Deming regression method is y=-6.21+1.03x for CT-Pathology, whereas for the MR-Pathology y=-6.86+1.11x. When the CT-Pathology measurement values are evaluated by the Bland-Altman statistical method, the mean values of the measurement differences are -2.42 and the standard deviation is 33.50. When the agreement between MR-Pathology measurement values is examined by the Bland-Altman method, the mean values for the difference between the measurement values are 4.14 and the standard deviation is 26.78. Among the applied methods, the Bland-Altman method is the most suitable method for data structure. According to the results of the Bland-Altman method, the tumor size obtained by the pathology method can be found to be 2.4 smaller than the mean values obtained from the CT results and an average of 4.1 cc from the measurement values obtained by the MR imaging technique. [ABSTRACT FROM AUTHOR]

Commonly used technologies for visual pattern stimulation cannot operate in a magnetic resonance imaging room because they can interfere with the operation of the scanner and are vulnerable to its electromagnetic and magnetic fields. The aim of this single-center prospective observational study was to introduce a novel, structurally uncomplicated, easy-to-maintain, patterned edge-illuminated display (PEID) device for visual pattern-reversal stimulation, compare it with a commonly used cathode ray tube screen, and verify the equivalence of quantitative assays. The left and right eyes of 36 healthy participants with undilated pupils were examined on a commercial visual evoked potential (VEP) apparatus and on the PEID device, where pattern-reversal transient VEPs were elicited by checkerboard stimuli with large (0.89°; 0.86°–0.92°) and small (0.21°; 0.20°–0.23°) checks. The PEID device demonstrated the required reliability and dynamic characteristics, as well as precise time-locking required for a VEP diagnosis. The results of Deming's correlation analysis showed that both the commercial cathode ray tube monitor and the PEID device produced identical VEP results within the context of experimental uncertainty. The standard deviation of Deming's regression may indicate the uncertainty of the VEPs measured in clinical practice. The Bland-Altman analysis of the mean showed no significant difference in the amplitude and peak time of VEPs measured on the PEID device compared to that of the commercial cathode ray tube monitor. The presented PEID device meets all the required standards and can be easily installed in various types of commercial magnetic resonance imaging scanners. [ABSTRACT FROM AUTHOR]

Simple Summary: Veterinary science is based on data collection at the animal or herd level. Beyond the variability in the variable in question, the data collected can depend on the device used or the person performing the measurement. Determination of these sources of variation is crucial to be able to use these measurements in practice or research. In this manuscript, I review the multiple indicators that can be used for determining these sources of variability in order to obtain robust indicators that are useful when trying to quantify test–retest reliability (between multiple measurements by different devices or operators). I also present the pros and cons of each indicator in the absence of a "one size fits all" framework to report them adequately depending on the specific context. Reliable indicators of health status (heart rate, rectal temperature, blood marker, etc.) are of cornerstone importance in the daily practice of veterinary medicine. The reliability of a measurement assesses the variability that is associated with the variable to be measured itself vs. other sources of variation (measurement device, person performing the measurement, etc.). Quantitative and continuous indicators are numerous in practice and the determination of their reliability is a complex issue. In the absence of a gold standard approach, several indicators of reliability have been described and can be used depending on several assumptions, study design, and type of measurement. The aim of this manuscript is, therefore, to determine the applicability of commonly described reliability indicators. After a description of the different sources of errors of a measurement, a review of the different indicators that are commonly used in the veterinary field as well as their applicability, limitations, and interpretations is performed. [ABSTRACT FROM AUTHOR]

Introduction. In conducting of therapeutic drug monitoring (TDM), often such situation arises where the drug concentration has measured by different methods or in different laboratories. To combine and analyze the data obtained with different methods, it is necessary to perform cross-validation procedure. Insufficient attention is paid to the statistical approaches used for this purpose.Aim. Performing cross-validation of different analytical methods for the quantitative determination of phenazepam (PHEN) and 3-hydroxyphenazepam (3-OH-PHEN) using the Bland – Altman analysis.Materials and methods. PHEN and 3-OH-PHEN concentrations in the blood plasma of patients (n = 100) with alcohol withdrawal syndrome were measured using high-performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS). The quantification of both analytes in each sample was measure twice by two different methods: solid phase extraction (SPE) and supported liquid extraction (SLE). Both methods have been fully validated before the experiment began. Cross-validation was performed at the end of the experiment using data from study samples. The Bland – Altman analysis was used to evaluate accuracy and precision. Deming regression was also used to identify a systematic error between measurement results.Results and discussion. The regression equations have been obtained between concentrations both analytes measured by different sample preparation methods. 95 % confidence intervals (CI) of the regression coefficients of both equations included one, and 95 % CI of the intercepts included zero. 95 % CI of the geometric mean of the individual SLE/SPE ratios was within the acceptable range (0.87; 1.15). These results confirm the absence of the influence of quantitative methods on the measurement of both analytes concentration. 66.7 % CI of the percent difference between two measurements was within acceptable limits (–0.2; 0.2), not exceeding 20 % of the range of their mean value. This confirms the acceptable precision between the methods. The estimated CIs were displayed in the Bland – Altman plots.Conclusion. The statistical approaches used in the work have confirmed the reproducibility of the results of different sample preparation methods. In addition to cross-validation, the statistical algorithm from this paper using Bland – Altman analysis can be successfully employed to assess accuracy and precision during bioanalytical method validation and evaluation of the acceptance of analytical runs, as well as to determine the level of reproducibility of incurred samples.

The determination of the knee joint axis (KJA) via specific calibration movements has become a promising alternative to the conventional approach to determine this axis based on regression equations or directly via marker placement on bony landmarks of the knee. Since the orientation of the KJA may differ between methods, it has direct influence on hip rotation and may therefore influence clinical decision making in context of transverse plane gait deviations. Research question: Does a functional KJA calibration lead to clinically relevant differences in hip rotation estimates during gait compared to the conventional method? 95 subjects (age: 19.9 years; BMI: 21.1 kg/m2), including 71 patients with potential rotation malalignment, were prospectively examined and underwent 3D gait analysis. For the conventional approach the KJA was determined by applying a knee alignment device, for the functional approach subjects were asked to perform two different calibration movements. Each procedure was performed twice. Mean hip rotation in stance (mHipRotSt) was determined following the conventional and the functional KJA calibration. Deming regression analysis for the comparison of conventional and functionally measured hip rotation revealed a systematic and substantial difference between methods (slope: 0.63; intercept: 0.31°). Measurement repetition with the knee alignment device revealed typical errors around 5°, whereas the functional methods lead to profoundly smaller errors around 1-2° with slightly inferior results for SQUAT compared to FLEX. However, when compared to conventional frontal plane video-taping, the conventional method seemed to reflect the more consistent results. The systematic linear discrepancy in mHipRotSt obtained by a functional approach as compared to the conventional approach appears critical since patients with severe internal or external rotation gait may be misjudged in function when receiving corrective procedures such as femoral derotation osteotomy. • Gait analysis was performed in 95 subjects with and without rotational malalignments. • A conventional and two functional approaches were applied for knee axis determination. • Hip rotation differs almost by a factor of two between approaches (linear slopes 0.63; 0.71). • The outcome with a functional approach (FLEX, SQUAT) leads to a higher precision. • The outcome with the conventional approach corresponds better with clinical evaluation. [ABSTRACT FROM AUTHOR]

To solve the problems of low recognition accuracy and slow reading speed of the algorithm in reading the indicator of pointer instrument, a reading technology of pointer instrument based on Deming regression and region of interest (ROI) thinning was proposed. The algorithm flow of reading the indicator of the instrument was given. ROI was selected, and the image preprocessing was carried out by the color channel-based silhouette method and binary morphology operation. The image frame difference method was used to eliminate pointer jitter, and ROI thinning algorithm was used to refine the pointer of instrument to be identified. Deming regression method was used to fit the equation and slope of the straight line where the pointer of instrument was located. According to the pointer slope, the angle method was used to calculate the real-time reading of the instrument. Through three sets of experiments. the feasibility and anti-jitter ability of the method were tested, and the detection accuracies of the fitting line by Deming regression and the fitting line by Hough line detection were compared. The calculation speeds of the ROI thinning algorithm and the global thinning algorithm were also compared. The results show that the average error of the proposed method is 37.85% less than that of Hough line detection, and the average calculation time of each image is 192. 717 s less than that of the global thinning algorithm. At the same time, the proposed method has the ability of anti-jitter. [ABSTRACT FROM AUTHOR]

Long‐term experiments (LTEs) have provided data to modellers and agronomists to investigate changes and dynamics of soil organic carbon (SOC) under different cropping systems. As treatment changes have occurred due to agricultural advancements, so too have analytical soil methods. This may lead to method bias over time, which could affect the robust interpretation of data and conclusions drawn. This study aims to quantify differences in SOC due to changes in dry combustion methods over time, using soil samples of a LTE established in 1963 that focuses on mineral and organic fertilizer management in the temperate zone of Northeast Germany. For this purpose, 1059 soil samples, collected between 1976 and 2008, have been analysed twice, once with their historical laboratory method right after sampling, and a second time in 2016 when all samples were analysed using the same elementary analyser. In 9 of 11 soil sampling campaigns, a paired t‐test provided evidence for significant differences in the historical SOC values when compared with the re‐analysed concentrations of the same LTE sample. In the sampling years 1988 and 2004, the historical analysis obtained about 0.9 g kg−1 lower SOC compared with the re‐analysed one. For 1990 and 1998, this difference was about 0.4 g kg−1. Correction factors, an approach often used to correct for different analytical techniques, could only be applied for 5 of 11 sampling campaigns to account for constant and proportional systematic method error. For this particular LTE, the interpretation of SOC changes due to agronomic management (here fertilization) deviates depending on the analytical method used, which may weaken the explanatory power of the historical data. We demonstrate that analytical method changes over time present one of many challenges in the interpretation of time series data of SOC dynamics. Therefore, LTE site managers need to ensure providing all necessary protocols and data in order to retrace method changes and if necessary recalculate SOC. Highlights: A total of 1059 LTE soil samples taken between 1976 and 2008 were re‐analysed for SOC in 2016Several methodological changes for SOC determination led to significant different SOC concentration in the same sampleInterpretation and time series of LTE soil data suffer from consideration of analytical method changes and poor documentation of the sameSoil archive establishment, thorough method protocols and diligent proficiency testing after soil method changes ameliorate the dilemma [ABSTRACT FROM AUTHOR]

The Kopili Fault is one of the most active faults in northeast India, which is the causative source of several earthquakes. The zone is of great importance to the scientific community because of its seismic productivity in past as well in present, which includes two major earthquakes (M > 7), several moderate earthquakes, and most recent 28th April 2021 Mw 6.5 earthquake. In the present study, shear velocity crustal structure is mapped and the b-value is calculated near the Kopili Fault region using receiver function modelling and Gutenberg–Richter relation, respectively. Joint inversion of receiver function and Rayleigh wave group velocity dispersion data beneath Tezpur (TZR) station shows 40 km thick crust with 18 km upper and 22 km lower crust. However, thin crust (35 km) is observed beneath Mikir Hills. The overall b-value in the Kopili region is estimated to be 0.94. We observed an increase in b-value with depths, which may be due to the strength of crust and upper mantle material caused by the rise in temperature and pressure. The b-value variation with depth shows a sudden increase of b-value at a depth of 40–45 km, which corresponds to the crust–mantle transition, estimated from the receiver function modelling in the region. A sudden reduction of b-value is observed prior to 28th April 2021 Mw 6.5 earthquake, which underscores investigations of the b-value and its variations for the purpose of earthquake precursor studies in that area. Research Highlights: Moho and b-value in the Kopili Fault region are 40 km and 0.94, respectively. Entire crust and the upper mantle in Kopili Fault are seismogenic. b-value reduction is observed prior to 28th April 2021 Mw 6.5 earthquake. Abrupt increase of b-value is well correlated with the Moho discontinuity. [ABSTRACT FROM AUTHOR]

Reliable indicators of health status (heart rate, rectal temperature, blood marker, etc.) are of cornerstone importance in the daily practice of veterinary medicine. The reliability of a measurement assesses the variability that is associated with the variable to be measured itself vs. other sources of variation (measurement device, person performing the measurement, etc.). Quantitative and continuous indicators are numerous in practice and the determination of their reliability is a complex issue. In the absence of a gold standard approach, several indicators of reliability have been described and can be used depending on several assumptions, study design, and type of measurement. The aim of this manuscript is, therefore, to determine the applicability of commonly described reliability indicators. After a description of the different sources of errors of a measurement, a review of the different indicators that are commonly used in the veterinary field as well as their applicability, limitations, and interpretations is performed.

While fine particulate matter (PM 2.5 ) has been associated with autism spectrum disorder (ASD), few studies focused on ultrafine particles (PM 0.1 ). Given that fine and ultrafine particles can be highly correlated due to shared emission sources, challenges remain to distinguish their health effects. In a retrospective cohort of 318,371 mother-child pairs (4549 ASD cases before age 5) in Southern California, pregnancy average PM 2.5 and PM 0.1 were estimated using a California-based chemical transport model and assigned to residential addresses. The correlation between PM 2.5 and PM 0.1 was 0.87. We applied a two-step variance decomposition approach: first, decomposing PM 2.5 and PM 0.1 into the shared and unique variances using ordinary least squares linear regression (OLS) and Deming regression considering errors in both exposures; then assessing associations between decomposed PM 2.5 and PM 0.1 and ASD using Cox proportional hazard models adjusted for covariates. Prenatal PM 2.5 and PM 0.1 each was associated with increased ASD risk. OLS decomposition showed that associations were driven mainly by their shared variance, not by their unique variance. Results from Deming regression considering assumptions of measurement errors were consistent with those from OLS. This decomposition approach has potential to disentangle health effects of correlated exposures, such as PM 2.5 and PM 0.1 from common emissions sources., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Anny Xiang reports financial support was provided by National Institutes of Environmental Health Sciences. Rob McConnell reports financial support was provided by National Institute of Environmental Health Sciences. Joel Schwartz reports financial support was provided by US Environmental Protection Agency. Joel Schwartz has testified on behalf of the U.S. Department of Justice in a case involving a Clean Air Act violation If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

There are several methods for determining boron (B) in soil for soil testing purposes, yet variability among methods can lead to inconsistencies in determined concentrations for the same soil sample. The most typical method used to determine B in soil is hot-water B (HWB) extraction, but this method has reduced efficiency when dealing with large sets of soil samples along with other disadvantages such as non-uniformity in the boiling of samples. As a result, universal extractants have become increasingly popular for the extraction of B due to enhanced efficiency in a high throughput laboratory setting. This study uses a Deming regression model to compare the traditional HWB extraction method with two common universal extractants, Mehlich-1 (M1), and Mehlich-3 (M3), for coastal plain sandy soils. Results showed M3-B extractability is consistently higher than M1-B and HWB for all tested soils. The M3 method was then verified for precision using blind extension soil samples. The hot-water method was unsuccessful in producing reliable extractable B concentration in soils with high calcium carbonate and organic matter contents. Results from this study can be used to optimize B determination for high throughput production laboratory settings. [ABSTRACT FROM AUTHOR]

The phenomenon of plant leaf illness has fatalistic influences on production and assists in promoting safety in products concerning agriculture. Owing to the reason that the leaf is major vulnerable portion of a plant it is influenced easily upon comparison with the other parts. Plant disease detection by means of numerous automatic models is advantageous as it minimizes immense work of big farm monitoring, and also at the early stage assists in detection of the disease symptoms. Several frameworks have been designed for plant disease. But the error rate and overhead incurred in plant leaf disease diagnosis was not focused. In this research, characterize and investigate the plant leaf disease prediction using three novel proposed frameworks using Plant Village dataset. They are Gaussian Distributive Czekanowskis Region-based Deming Regression (GDCR-DM), Covariance Kalman Geometric Graphbased Bernoulli Classifier (CKGG-BC) and Independent Gaussian Gray Level Gaussian Gray Level Geometric Neural Network Classifier (IGGL-GNNC). The GDCR-DM framework identifies the plant leaf disease in an accurate manner with minimum false positive rate employing Czekanowski's dice and Deming regression function. Next, to reduce noise involved during preprocessing and segmentation, CKGG-BC framework is proposed that with the aid of Covariance Kalman filter function and Multiple Kernel Learning Classifier not only reduces the error but also enhances true positive rate considerably. Finally, IGGL-GNNC framework is designed that Sine Cosine Position Update using geometric functions for realistic disease prediction. The different parameters were evaluated for distinct plant leaf images obtained from Plant Village Image dataset. [ABSTRACT FROM AUTHOR]

Sleep and physical activity, two important health behaviors, are often studied independently using different accelerometer types and body locations. Understanding whether accelerometers designed for monitoring each behavior can provide similar sleep parameter estimates may help determine whether one device can be used to measure both behaviors. Three hundred and thirty one adults (70.7 ± 13.7 years) from the Baltimore Longitudinal Study of Aging wore the ActiGraph GT9X Link and the Actiwatch 2 simultaneously on the non-dominant wrist for 7.0 ± 1.6 nights. Total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, number of wake bouts, mean wake bout length, and sleep fragmentation index (SFI) were extracted from ActiGraph using the Cole-Kripke algorithm and from Actiwatch using the software default algorithm. These parameters were compared using paired t-tests, Bland-Altman plots, and Deming regression models. Stratified analyses were performed by age, sex, and body mass index (BMI). Compared to the Actiwatch, the ActiGraph estimated comparable TST and sleep efficiency, but fewer wake bouts, longer WASO, longer wake bout length, and higher SFI (all p < .001). Both devices estimated similar 1-min and 1% differences between participants for TST and SFI (β = 0.99, 95% CI: 0.95, 1.03, and 0.91, 1.13, respectively), but not for other parameters. These differences varied by age, sex, and/or BMI. The ActiGraph and the Actiwatch provide comparable absolute and relative estimates of TST, but not other parameters. The discrepancies could result from device differences in movement collection and/or sleep scoring algorithms. Further comparison and calibration is required before these devices can be used interchangeably., (© The Author(s) 2023. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Commutability assessment, commutability criterion, difference in bias approach, sample specific effects, Deming regression The Deming analysis approach [[1]] has been the golden standard for commutability assessment for years [], but the commutability criterion [[1]], i.e., the width of the prediction interval, is easily affected by the presence of large sample-specific effects and varies among different method comparisons []. Keywords: commutability assessment; commutability criterion; Deming regression; difference in bias approach; sample specific effects EN commutability assessment commutability criterion Deming regression difference in bias approach sample specific effects e235 e236 2 08/30/22 20220901 NES 220901 To the Editor, Commutability is an important characteristic for reference materials used in standardization activities. [Extracted from the article]