Relative Bias Research Articles

POLYCORE: Polygon-based contour refinement for improved Intravascular Ultrasound Segmentation

Segmentation of the coronary vessel wall in intravascular ultrasound is a fundamental step in guiding coronary intervention. However, it is an challenging task, even for highly skilled cardiologists, due to image artefacts and shadowed regions caused by calcified plaque, guide wires and vessel side branches. Recently, dense-based neural networks have been applied to this task, however, they often fail to predict anatomically plausible contours in these low-signal areas. We propose a novel methodology called Polygon-based Contour Refiner (POLYCORE) that addresses topological error in dense-based segmentation networks using a relational inductive bias through higher-order connections between vertices to learn anatomically rational contours. Our approach remedies the over-smoothing phenomena common in polygon networks by introducing a new vector field refinement module which enables pixel-level detail to be added in an iterative process. POLYCORE is enhanced with augmented polygon aggregation which we show is more effective than typical dense-based test-time augmentation strategies. We achieve state-of-the-art results on two diverse datasets, observing particular improvements when segmenting the lumen structure and in topologically-challenging regions containing shadow artefacts. Our source code is available here: http://orcid.org/https://github.com/kitbransby/POLYCORE.

Regional frequency analysis of extreme wind in Pakistan using robust estimation methods

The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Statistical inference and data analysis for inverted Kumaraswamy distribution based on maximum ranked set sampling with unequal samples

A very useful modification to ranked set sampling (RSS) that allows a larger set size without significantly increasing ranking errors is the maximum ranked set sampling with unequal samples (MRSSU) approach. This article covers the parameter estimation of the inverted Kumaraswamy distribution using MRSSU and RSS designs. The maximum likelihood and Bayesian estimation techniques are considered. The regarded Bayesian estimation technique is determined in the case of non-informative and informative priors represented by Jeffreys and gamma priors, respectively. Squared error and minimum expected are the two loss functions that are employed. We presented a simulation study to evaluate the performance of the recommended estimations using root mean squared error and relative bias. The Bayes point estimates were computed using the Metropolis–Hastings algorithm. Additional conclusions have been made based on actual geological data regarding the intervals between Kiama Blowhole’s 64 consecutive eruptions. Based on the same number of measured units, the results of simulation and real data analysis showed that MRSSU estimators performed much better than their RSS counterparts.

When does adjusting covariate under randomization help? A comparative study on current practices

PurposeWe aim to thoroughly compare past and current methods that leverage baseline covariate information to estimate the average treatment effect (ATE) using data from of randomized clinical trials (RCTs). We especially focus on their performance, efficiency gain, and power.MethodsWe compared 6 different methods using extensive Monte-Carlo simulation studies: the unadjusted estimator, i.e., analysis of variance (ANOVA), the analysis of covariance (ANCOVA), the analysis of heterogeneous covariance (ANHECOVA), the inverse probability weighting (IPW), the augmented inverse probability weighting (AIPW), and the overlap weighting (OW) as well as the augmented overlap weighting (AOW) estimators. The performance of these methods is assessed using the relative bias (RB), the root mean square error (RMSE), the model-based standard error (SE) estimation, the coverage probability (CP), and the statistical power.ResultsEven with a well-executed randomization, adjusting for baseline covariates by an appropriate method can be a good practice. When the outcome model(s) used in a covariate-adjusted method is closer to the correctly specified model(s), the efficiency and power gained can be substantial. We also found that most covariate-adjusted methods can suffer from the high-dimensional curse, i.e., when the number of covariates is relatively high compared to the sample size, they can have poor performance (along with lower efficiency) in estimating ATE. Among the different methods we compared, the OW performs the best overall with smaller RMSEs and smaller model-based SEs, which also result in higher power when the true effect is non-zero. Furthermore, the OW is more robust when dealing with the high-dimensional issue.ConclusionTo effectively use covariate adjustment methods, understanding their nature is important for practical investigators. Our study shows that outcome model misspecification and high-dimension are two main burdens in a covariate adjustment method to gain higher efficiency and power. When these factors are appropriately considered, e.g., performing some variable selections if the data dimension is high before adjusting covariate, these methods are expected to be useful.

Consistency evaluation of tropospheric ozone from ozonesonde and IAGOS (In-service Aircraft for a Global Observing System) observations: vertical distribution, ozonesonde types, and station–airport distance

Abstract. The vertical distribution of tropospheric O3 from ozonesondes is compared with that from In-service Aircraft for a Global Observing System (IAGOS) measurements collected at 23 pairs of sites between about 30° S and 55° N from 1995 to 2021. Profiles of tropospheric O3 from IAGOS are generally in good agreement with ozonesonde observations from electrochemical concentration cells (ECCs), Brewer–Mast sondes, and carbon–iodine sensors, with average biases of 2.58, −0.28, and 0.67 ppb and correlation coefficients (R) of 0.72, 0.82, and 0.66, respectively. Agreement between aircraft and Indian-sonde observations is poor, with an average bias of 15.32 ppb and an R value of 0.44. The O3 concentration observed by ECC sondes is, on average, 5 %–10 % higher than that observed by IAGOS, and the relative bias increases modestly with altitude. For other sonde types, there are some seasonal and altitudinal variations in the relative bias with respect to the IAGOS measurements, but these appear to be caused by local differences. The distance between the station and airport, when within 4° (latitude and longitude), has little effect on the comparison results. For the ECC ozonesondes, the overall bias with respect to the IAGOS measurements varies from 5.7 to 9.8 ppb when the station pairs are grouped by station–airport distances of <1° (latitude and longitude), 1–2°, and 2–4°. Correlations for these groups correspond to R=0.8, 0.9, and 0.7. These comparison results provide important information for merging ozonesonde and IAGOS measurement datasets. They can also be used to evaluate the relative biases of different sonde types in the troposphere, using the aircraft as a transfer standard.

Hb Tacoma by seven HbA1c methods – one with significant interference

Hemoglobin Tacoma is known to potentially interfere HbA1c assays. The variant is common in Finland with prevalence of up to 2% regionally and cases are also reported in areas that have attracted Finnish immigrants, especially in Sweden and North America. Here, we investigated the effect of Hb Tacoma on seven HbA1c methods. 20 non-variant and 20 Hb Tacoma samples were measured with Tina-quant Gen. 3 (immunoassay, considered as reference) and the following point of care instruments: Afinion 2, HbA1c 501 (both utilizing boronate affinity), QuikRead go, cobas b 101, DCA Atellica, and Standard F (all immunoassays). Repeatability was also assessed by measuring both non-variant and Hb Tacoma samples five times each at two different levels. For non-variant samples, the mean relative bias with all methods was < ±4%, whereas for Hb Tacoma samples Standard F had 38% mean relative bias. In absolute bias, the difference was 17 mmol/mol on average and constant through the measured range. For other methods the mean relative bias for Hb Tacoma samples was < ±6%. The repeatability with all methods was similar for non-variant and Hb Tacoma samples and at highest 4.1% (mean CV% of two levels). The observed interference by Standard F is likely due to two-antibody assay design as Hb Tacoma has been shown to result in conformational change. This interference is clinically significant and highlight the need for better controlling and better understanding hemoglobin variants in HbA1c testing.

Near-surface particulate backscattering observations with bio-optical Lagrangian drifters

Abstract Particulate matter concentration is an essential ocean variable (EOV) useful for understanding biogeochemical processes, improving ocean productivity estimates, and constraining coupled physical and biogeochemical numerical models. While direct observations of this parameter are difficult to obtain, the optical backscattering coefficient of marine particles (bbp) can be used as a reliable proxy. However, most in-situ multi-spectral bbp measurements are ship-borne or obtained from moored systems, thus with a limited spatial and temporal coverage. Utilizing Surface Velocity Programme (SVP) drifting buoys with self-contained backscatter sensors designed for extended deployment periods offers a promising solution for collecting data in challenging marine environments. In this study, we presented for the first time the integration of a commercially available, high-frequency and multispectral optical backscatter sensor into an SVP drifter, the so-called BO-SVP drifter designed to collect bbp measurements near the ocean surface. bbp measurements obtained by the BO-SVP drifter were reliable over a wide range of environmental conditions, showing a good agreement with independent datasets (relative bias &lt; 10%; relative standard deviation &lt; 36%). The BO-SVP drifter captures the satellite sub- and pixel scale variability by combining the Lagrangian approach and a high frequency sampling. This configuration enables the acquisition of measurements across numerous pixels in a single day, enhancing validation activities for ongoing and future satellite products and the quantification of associated uncertainties. The measurements acquired by these platforms can offer valuable insights into particle distribution at fine spatial scales, daily variability, and its relationship with water masses type and ocean dynamics.

Compensation of isotope ratio mismatch in double isotope dilution inductively coupled plasma mass spectrometry (ID-ICP/MS)

Abstract Exact-matching double isotope dilution inductively coupled plasma mass spectrometry (ID-ICP/MS) is widely used to characterize reference materials (RMs) in elemental analysis. In this technique, achieving exactly matching isotope ratios for the sample and calibration blends is regarded as an important prerequisite for obtaining accurate measurement results. However, meeting this condition requires multiple time-consuming iterative measurements. In the current study, an alternative approach that can avoid lengthy iterative procedures while maintaining the accuracy of the ID-ICP/MS results was successfully investigated. We examined the effects of an extensively wide range of inexact-matching isotope ratios (approximately 75 %–130 %) in double ID-ICP/MS for elements with a wide mass range. Our experimental study, using gravimetrically prepared samples of Ca, Cd, Cu, Fe, Hg, Mg, Ni, Pb, and Zn, demonstrated that, despite deliberately introducing mismatching of isotope ratios, the accuracy of the ID-ICP/MS results remained consistent with relative measurement bias of typically less than 0.5 %. The absence of a systematic bias due to deviations in the sample blend isotope ratios from the target ratios of the calibration blends revealed that the variability due to an isotope ratio mismatch was sufficiently compensated for. Furthermore, the expanded measurement uncertainties were sufficiently small with negligible variations observed across the different matching ratios. Typically, they were less than 1 %, except for Fe, Hg, Pb, and Zn which were less than 2 %. This assertion is also supported by theoretically calculated error magnification factors. Consequently, it is feasible to directly utilize the marginally estimated mass fraction of the analyte of interest without extensive iterative measurements. The findings of this study provide robust data for ID-ICP/MS, allowing to circumvent lengthy iterative procedures while maintaining the accuracy and precision of the measurement results, particularly in the characterization of reference materials for elemental analysis.

A Matter of Memory? Age-Invariant Relative Clause Disambiguation and Memory Interference in Older Adults.

Past research suggests that Working Memory plays a role in determining relative clause attachment bias. Disambiguation preferences may further depend on Processing Speed and explicit memory demands in linguistic tasks. Given that Working Memory and Processing Speed decline with age, older adults offer a way of investigating the factors underlying disambiguation preferences. Additionally, older adults might be subject to more severe similarity-based memory interference given their larger vocabularies and slower lexical access. Nevertheless, memory interference and sentence disambiguation have not been combined in studies on older adults before. We used a self-paced reading paradigm under memory load interference conditions. Older (n=30) and Younger (n=35) readers took part in the study online; reading times were recorded and measures of comprehension accuracy and load recall were collected. This setup allowed for the implicit measurement of attachment biases and memory interference effects interactively. Results show that similarity-based interference affected both age groups equally, but was more pronounced in NP2-biased structures, which took participants generally longer to read. Attachment preferences did not differ by group and were unaffected by Working Memory span. However, accuracy on recall prompts was predicted by Working Memory span in both groups. Findings of greater interference in syntactically dispreferred structures support unified processing models where parsing constraints naturally interact. The lack of age differences on our measures further aligns with research finding age-invariant implicit language processing.

The influence of calibration on bias in quality control and patient results for TSH on Vitros XT 7600 analyzer.

Thyroid-stimulating hormone (TSH) is a glycoprotein secreted by the anterior pituitary gland and is regulated by negative feedback from the serum free thyroid hormones. In this study we aimed to quantitate the relative bias caused by calibration drifting as seen in our TSH Levey-Jennings quality control (QC) charts and assess the magnitude of bias on patients' samples. In the period from October 2021 to August 2022 we looked at the QC results of ten 28-days' calibration time intervals and calculated relative bias compared to the mean. For each time interval the mean from three QC points before and after calibration was calculated. The average from 10 pre- and post-calibration means was calculated and the relative bias, pre- and post-calibration, was then calculated. We used 5 patient samples with low, normal and high TSH concentrations and calculated relative bias pre- and post-calibration. The allowed relative bias for TSH is ± 6.7%. At both QC levels, with the respective means of 5.14 mIU/L (coefficient of variation, CV% = 3.1%) and 27.80 mIU/L (CV% = 3.2%) had their respective relative bias - 8.2% and - 7.9%. The patient samples with low (0.586 mIU/L), normal (2.89 mIU/L and 5.19 mIU/L) and high (20.5 mIU/L and 39.8 mIU/L) TSH had - 4.1%, - 4.0%, - 3.5%, - 5.1% and - 4.1%, respectively. Even though the relative bias exceeded allowable criteria for the QC samples, this was not manifested on the patients' samples.

Virtual Instruments for Peak-Overlapping Studies to Determine Low- and High-Concentration Components with Ion Chromatography: Potassium and Sodium.

We developed the LabVIEW-based virtual instruments (VIs) to bridge a gap in commercial software and to enable systematic peak-overlapping studies to recognise the concentration levels enabling reliable simultaneous determination of major and minor constituents in samples with wide concentration proportions. The VIs were applied to a case study of the ion chromatographic determination of potassium as minor and sodium as a major ion with an IonPac CS12A column and 50 μL injection loop. Two successive studies based on multilevel two-factorial response surface experimental designs, (1) a model peak-overlapping study based on single-ion injections, and (2) an accuracy and precision study, provided guidelines for real sample analyses. By adjusting sample dilutions so that the sodium mass concentration was set to 340 mg/L, the simultaneous determination of potassium in the presence of sodium was possible in samples with sodium over potassium concentration ratios between 14 and 341. The relative expanded uncertainty associated with potassium ion determination was between 0.52 and 4.4%, and the relative bias was between -3.8 and 1.9%. We analysed Ringer's physiologic solutions, standard sea, trisodium citrate anticoagulant, and buffered citrate anticoagulant solutions. We confirmed that the VI-supported peak-overlapping studies contributed to the quality of results by enabling the evidence-based choices of concentration levels adjusted by a dilution.

Cyclic shift short-distance attention for defect detection of fine etching mesh

Abstract Based on the deficiency of the existing Swin Transformer in the detection of surface defects in metal etched grids, i.e. the isolation of information transmission between different windows, and the limitations of image matrix size and network structure,which make it not suitable for long sequence data processing and dense prediction scenarios, a defect detection method for metal etching nets based on Cyclic shift Long Short Distance Attention (C-LSDA) is proposed. In the proposed method, cross-scale features are used to capture long and short distance relationships,and enhance the feature transfer between windows. Tracking position bias (TPB) is also used to improve relative position bias (RPB), in order to help the model to better understand the spatial relationships in the input data. In addition, the image is downsampled at various scales, so that the proposed method can be suitable for long sequence data processing and dense prediction scenarios. In order to improve the detection accuracy of small targets. a multi-feature fusion pyramid network (MFFPN) for feature fusion is also proposed. The experimental results show that the proposed method is significantly superior to the traditional CNN recognition method and the transformer recognition method. The proposed methodhas good identification accuracy and low computational cost.

Multiple-Win Effects and Beneficial Implications from Analyzing Long-Term Variations of Carbon Exchange in a Subtropical Coniferous Plantation in China

To improve our understanding of the carbon balance, it is significant to study long-term variations of all components of carbon exchange and their driving factors. Gross primary production (GPP), respiration (Re), and net ecosystem productivity (NEP) from the hourly to the annual sums in a subtropical coniferous forest in China during 2003–2017 were calculated using empirical models developed previously in terms of PAR (photosynthetically active radiation), and meteorological parameters, GPP, Re, and NEP were calculated. The calculated GPP, Re, and NEP were in reasonable agreement with the observations, and their seasonal and interannual variations were well reproduced. The model-estimated annual sums of GPP and Re over 2003–2017 were larger than the observations of 11.38% and 5.52%, respectively, and the model-simulated NEP was lower by 34.99%. The GPP, Re, and NEP showed clear interannual variations, and both the calculated and the observed annual sums of GPPs increased on average by 1.04% and 0.93%, respectively, while the Re values increased by 4.57% and 1.06% between 2003 and 2017. The calculated and the observed annual sums of NEPs/NEEs (net ecosystem exchange) decreased/increased by 1.04%/0.93%, respectively, which exhibited an increase of the carbon sink at the experimental site. During the period 2003–2017, the annual averages of PAR and the air temperature decreased by 0.28% and 0.02%, respectively, while the annual average water vapor pressure increased by 0.87%. The increase in water vapor contributed to the increases of GPP, Re, and NEE in 2003–2017. Good linear and non-linear relationships were found between the monthly calculated GPP and the satellite solar-induced fluorescence (SIF) and then applied to compute GPP with relative biases of annual sums of GPP of 5.20% and 4.88%, respectively. Large amounts of CO2 were produced in a clean atmosphere, indicating a clean atmospheric environment will enhance CO2 storage in plants, i.e., clean atmosphere is beneficial to human health and carbon sink, as well as slowing down climate warming.

Clinical application of an optimized reference measurement procedure for serum digoxin using bracketing calibration method by ID-LC-MS/MS.

Digoxin, a cardiac glycoside, is widely used in the treatment of cardiovascular diseases. Due to its narrow therapeutic range, precise monitoring of its blood concentration is essential. Areference measurement procedure (RMP) is pivotal for ensuring result accuracy and comparability. The RMP for serum digoxin by ID-LC-MS/MS was optimized with sample pre-treatment and detection processes, and the bracketing calibration method was used, which facilitates more accurate measurement, especially for extreme concentrations. The performance of this optimized RMP was thoroughly evaluated. The limit of detection (LoD) was 0.05 ng/mL (0.06 nmol/L) and the lowest limit of quantification (LLoQ) was 0.10 ng/mL (0.13 nmol/L). The intra- and inter-assay imprecisions were 2.24%, 2.51%, 1.40% and 1.72%, 1.65%, 0.97% at 0.5, 2.0, 5.0 ng/mL, respectively. Recoveries were 99.63 to 101.42% and thelinear response ranged from 0.1 to 10.0 ng/mL. The relative bias was 0.41% and 2.00% of our results compared with the median of all participating reference laboratories for IFCC-RELA (External Quality Assessment Scheme for Reference Laboratories in Laboratory Medicine) 2023A and 2023B. The uncertainty, calibration and measurement capability (CMC) of this method were also evaluated. The optimized RMP was applied in the Trueness Verification Plan of Southern China, which indicates significant differences among clinical systems, highlighting the need for standardization efforts. In addition, two commonly used clinical systems which employed immunoassay methods were compared with this optimized RMP, and 26 individual serum samples were analyzed. The good correlations indicate the feasibility of standardization for serum digoxin. The optimized RMP serves as an accurate reference baseline for routine methods, aiming to enhance the accuracy and precision of measurements in clinical laboratories.

Aerosol loading in the guinea coast climate region of Nigeria: comparison of MODIS and AERONET data sources

This study compares aerosol optical depth (AOD) measurements from satellite data with that from a ground-based station. It aims at validating monthly AOD data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) collection 6.1 (Aqua and Terra) at 550 nm against AOD data from the Aerosol Robotic Network (AERONET) station at 500 nm in Ilorin over the period 2003–2022. The analysis focuses on eight selected cities/locations within the Guinea Coast region of Nigeria. Descriptive and inferential statistical methods, including correlation, regression, standard deviation, coefficient of variation, the Mann–Kendall trend test, root mean square error (RMSE), mean absolute error (MAE), and relative mean bias (RMB), were employed. The results indicate a positive correlation in most locations, with correlation values ranging from 0.3 to 0.7. The correlation between the datasets in Ilorin showed a positive and significant relationship while the other seven locations presented a weak and insignificant relationship. Additionally, the period from 2013–2022 showed better correlation compared to 2003–2012. AOD levels were highest during the harmattan season (DJF), exceeding 0.8, and lowest during the monsoon (JJA) and post-monsoon (SON) seasons, with values below 0.5, reflecting the effects of wet scavenging. The highest agreement between MODIS Aqua and AERONET data was observed during the harmattan season. The study also found that MODIS tends to overestimate AOD compared to AERONET and that MODIS-AOD exhibits greater spread and variability than AERONET-AOD.

Benchmark of estimated solar irradiance data at high latitude locations

Estimated solar irradiances from CAMS, PVGIS SARAH-2, Solargis, Meteonorm, PVGIS ERA5, and NASA POWER are benchmarked against measurements conducted at 34 ground stations in Norway at latitudes between 58 and 76°N. We find that the data products that mainly rely on high-resolution, geostationary satellite images, i.e., CAMS, PVGIS SARAH-2, and Solargis, have higher accuracy with lower relative Mean Absolute Error (rMAE) and relative Mean Bias Error. By dividing the stations in distinct categories, such as above 65°N, snow-affected and horizon-shaded, challenges with irradiance estimation that are common in Norway and at high latitudes in general are highlighted and discussed. The accuracy of the data products is dependent on latitude, and by excluding stations above 65°N, the median rMAE of the different data products improves 3.2 – 9.4 %abs compared to the median rMAE when including all stations, depending on data product. Similarly, by excluding snow-affected stations, the median rMAE improves 1.9 – 8.1 %abs, depending on data product. The improvement in rMAE by excluding snow-affected stations is partially related to the difficulty of separating snow on the ground from cloud cover in satellite images. This difficulty is illustrated by concrete examples of irradiance time series from clear sky days when the ground is covered in snow. Although the performance of the data products is dependent on the categorization of stations, i.e., latitude, snow conditions, and local topography, the relative performance between the products is maintained regardless of sub-division.

Performance of the New CKD-EPI Creatinine-and Cystatin C-based Glomerular Filtration Rate Estimation Equation in Living Kidney Donor Candidate.

Accurate preoperative evaluation of renal function in living kidney donor candidates (LKDCs) is crucial to prevent kidney failure after nephrectomy. We examined the performance of various estimated glomerular filtration rate (eGFR) equations, including the new chronic kidney disease epidemiology collaboration (CKD-EPI) equation in LKDCs. We analyzed 752 LKDCs who were assessed for measured GFR by inulin clearance as part of routine pretransplant examination from 2006 to 2020. CKD-EPI2012 from cystatin C (CKD-EPI12cys), CKD-EPI2021 from creatinine (CKD-EPI21cr), CKD-EPI21cr-cys, Japanese modified (JPN) eGFRcr, and JPN eGFRcys were compared in determining the suitability for LKDCs. CKD-EPI12cys had the lowest absolute and relative biases, with higher P30 and P10, followed by JPN eGFRcys, CKD-EPI21cr, and CKD-EPI21cr-cys. The root mean square error was least for CKD-EPI12cys, then JPN eGFRcys, CKD-EPI21cr-cys, CKD-EPI21cr, and JPN eGFRcr. CKD-EPI21cr, CKD-EPI12cys, and CKD-EPI21cr-cys estimated GFR higher, whereas JPN eGFRcr estimated GFR lower. At the threshold of 90 mL/min/1.73 m2, CKD-EPI21cr had the highest percentage of misclassification at 37.37%, whereas JPN eGFRcr had the lowest percentage of misclassification at 6.91%. Using the age-adapted approach, JPN eGFRcr had the lowest percentage of misclassification into overestimation at 7.31%. All eGFR had >5.0%, and CKD-EPI21cr had the highest percentage of misclassification at 21.94%. Conversely, CKD-EPI21cr-cys had the lowest percentage of misclassification into underestimation at 3.19%, both at the threshold of 90 mL/min/1.73 m2 and the age-adapted approach. JPN eGFRcr had the highest percentage at 33.38% and 40.69%, respectively. In evaluating the renal function of Japanese LKDCs, the new CKD-EPI equation had a lower rate of underestimation but a relatively high rate of overestimation. New GFR estimation formulas are needed to be tailored to each ethnic group to enhance the accuracy and reliability of donor selection processes.

On Inference of Weitzman Overlapping Coefficient in Two Weibull Distributions

Studying overlapping coefficients has recently become of great benefit, especially after its use in goodness-of-fit tests. These coefficients are defined as the amount of similarity between two statistical distributions. This research examines the estimation of one of these overlapping coefficients, which is the Weitzman coefficient Δ, assuming two Weibull distributions and without using any restrictions on the parameters of these distributions. We studied the relative bias and relative mean square error of the resulting estimator by implementing a simulation study. The results show the importance of the resulting estimator.

Do Subsampling Strategies Reduce the Confounding Effect of Errors in Bispectral Retrievals on Estimates of Aerosol Cloud Interactions?

AbstractBi‐spectral retrievals of droplet effective radius and cloud optical depth are widely utilized to estimate aerosol cloud interactions (ACI) in warm clouds in the marine boundary layer. Here, we assess the effect of retrieval errors due to the neglect of 3D radiative transfer during the retrieval process on this analysis of ACI. We use an ensemble of stochastically‐modeled cloud fields and 3D radiative transfer simulations to study the retrieval errors at a solar zenith angle of 30°. Simulated retrieval biases in droplet number concentration (Nd) for all three MODIS channels vary systematically from +35% to −80% as cloud heterogeneity increases. Pixel‐level errors can be much larger. Commonly utilized subsampling strategies do not reduce the systematic variation in retrieval error. Negative error correlations between optical depth and droplet effective radius produce spuriously negative slopes between the logarithm of liquid water path and Nd (−1.0 to −0.3). Pixels at the center of (8 km)2 patches that are not overcast have a relative bias in Nd of −50%. The relative frequency of these biased pixels varies linearly with the clear fraction in MODIS data and form the basis for a simple parameterization of Nd bias that varies with cloud fraction (CF). Using this parameterization, synthetic data experiments indicate that estimates of the first indirect effect in the tropical ocean can be overestimated by up to 30%, and the effect of aerosol on CF is overestimated by 50%, due to neglecting the correlation between retrieval errors and cloud microphysics.

Absolute quantitation of human serum cystatin C: candidate reference method by 15N-labeled recombinant protein isotope dilution UPLC-MS/MS.

Serum cystatin C (CysC) is a reliable and ideal endogenous marker for accurately assessing early changes in glomerular filtration rate (GFR), surpassing the limitations of creatinine-based estimated GFR. To improve the precision of GFR calculation, the development of strategies for accurately measuring serum CysC is crucial. In this study, the full-length CysC pure product and fully recombinant 15N-labeled CysC internal standard were subjected to protein cleavage. Subsequently, an LC-MS/MS method was developed for the absolute quantification ofserum CysC. The traceability of the method was assigned calibrator using the amino acid reference measurement procedure (RMP). It involved calibrating the instrument using an amino acid reference material with known amino acid concentrations for calibration and comparison purposes. The total imprecision of the method was determined to be≤8.2 %, and a lower functional limit of quantification (LLoQ) was achieved. The recoveries ranged from 97.36 to 103.26 %. The relative bias between this candidate RMP for measurement of ERM-DA471-IFCC and the target value was 1.74 %. The linearity response was observed within the concentration range of 0.21-10.13 mg/L, with a high R2 value of 0.999. The results obtained using our method was consistent with those obtained using other certified RMPs. With the establishment of this highly selective and accurate serum CysC measurement method, it is now possible to assess the correlation between immunoassay results of serum CysC and the intended target when discrepancies are suspected in the clinical setting.