Mean Absolute Bias Research Articles

MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization.

Ion mobility-mass spectrometry (IM-MS) offers benefits for lipidomics by obtaining IM-derived collision cross sections (CCS), a conditional property of an ion that can enhance lipid identification. While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating the CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C-labeled lipids, derived from a U13C-labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R Markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C-labeled lipids per lipid class-adduct combination across 10 lipid classes without requiring additional external measurements. The applicability of MobiLipid is demonstrated for trapped IM (TIM)-MS measurements of an unlabeled yeast extract spiked with U13C-labeled lipids. Monitoring the CCSN2 biases of TIMCCSN2 values compared to DTCCSN2 library entries utilizing MobiLipid resulted in mean absolute biases of 0.78% and 0.33% in positive and negative ionization mode, respectively. By applying the CCS correction integrated into the tool for the exemplary data set, the mean absolute CCSN2 biases of 10 lipid classes could be reduced to approximately 0%.

A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain

The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with the extreme gradient boosting (XGBoost) model to propose a novel NGO-XGBoost model. The performance of this model was evaluated using meteorological data from 30 stations in the North China Plain and compared with XGBoost, random forest (RF), and k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method combined with the results from RF, XGBoost, adaptive boosting (AdaBoost), and categorical boosting (CatBoost) models is used to obtain the importance of meteorological factors in estimating ETo, and thereby determine the optimal combination of inputs to the model. The results indicated that by using the top 3, 4, and 5 important factors as input combinations, all models achieved high ETo estimation accuracy. It is worth noting that there were significant spatial differences in the estimation precisions of the four models, but the NGO-XGBoost model exhibited consistently high estimation precisions, with global performance indicator (GPI) rankings of 1st, and the range of coefficient of determination (R2), nash efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) were 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 mm d−1, and −0.254–0.062 mm d−1, respectively. Furthermore, the accuracy of the NGO-XGBoost model in estimating ETo varied across different seasons, which was more significantly affected by humidity and wind speed in winter. When the target station data was insufficient, the NGO-XGBoost model was trained by using the historical data from neighboring stations and still maintained a high precision. Overall, this study recommends a reliable method for estimating ETo, which provides a reference for accurately calculating ETo in the North China Plain in the absence of meteorological data.

On the Performance of Some Estimation Methods in Models with Heteroscedasticity and Autocorrelated Disturbances (A Monte-Carlo Approach)

The proliferation of panel data studies has been greatly motivated by the availability of data and capacity for modelling the complexity of human behaviour than a single cross-section or time series data and these led to the rise of challenging methodologies for estimating the data set. It is pertinent that, in practice, panel data are bound to exhibit autocorrelation or heteroscedasticity or both. In view of the fact that the presence of heteroscedasticity and autocorrelated errors in panel data models biases the standard errors and leads to less efficient results. This study deemed it fit to search for estimator that can handle the presence of these twin problems when they co- exists in panel data. Therefore, robust inference in the presence of these problems needs to be simultaneously addressed. The Monte-Carlo simulation method was designed to investigate the finite sample properties of five estimation methods: Between Estimator (BE), Feasible Generalized Least Square (FGLS), Maximum Estimator (ME) and Modified Maximum Estimator (MME), including a new Proposed Estimator (PE) in the simulated data infected with heteroscedasticity and autocorrelated errors. The results of the root mean square error and absolute bias criteria, revealed that Proposed Estimator in the presence of these problems is asymptotically more efficient and consistent than other estimators in the class of the estimators in the study. This is experienced in all combinatorial level of autocorrelated errors in remainder error and fixed heteroscedastic individual effects. For this reason, PE has better performance among other estimators.

Selecting an appropriate statistical model for estimating genetic parameters: A case study of pig maternal breeds in Czechia

A three-trait statistical model was designed for backfat thickness (BFT) and loin eye muscle depth (LMD) with accompanying variable average daily gain (ADG). Data from 82,507 Czech Large White and 37,556 Czech Landrace pigs were collected during on-farm performance testing from 2013 to 2022. Several animal models were tested with different combinations of effects and complexity. Model performance was evaluated by the linear regression (LR) method and predictivity. In Czech Large White, the preferred model comprised fixed effects of sex, birth year, herd, and ultrasound device, as well as random effects of herd-year-season (HYS), litter, and animal. In Czech Landrace, HYS was treated as fixed instead. The inclusion of maternal effects was not supported due to questionable impact on the main statistics. The selected model yielded mean absolute bias of 0.11 and 0.19, mean determination of 0.39 and 0.21, mean population accuracy of 0.36 and 0.31, and mean predictivity of 0.13 and 0.08 in Large White and Landrace, respectively. Heritability estimates were overall lower than those reported by other authors: 0.23, 0.10, and 0.10 in Large White and 0.26, 0.10, and 0.09 in Landrace for ADG, BFT, and LMD, respectively. Genetic correlations, on the other hand, reached relatively high values: 0.61, 0.61, and 0.66 in Large White and 0.71, 0.92, and 0.70 in Landrace for ADG-BFT, ADG-LMD, and BFT-LMD, respectively.

Bayesian Vancomycin Model Selection for Therapeutic Drug Monitoring in Neonates.

Pharmacokinetic models can inform drug dosing of vancomycin in neonates to optimize therapy. However, the model selected needs to describe the intended population to provide appropriate dose recommendations. Our study aims to identify the population pharmacokinetic (PopPK) model(s) with the best performance to predict vancomycin exposure in neonates in our hospital. Relevant published PopPK models for vancomycin in neonates were selected based on demographics and vancomycin dosing strategy. The predictive performance of the models was evaluated in Tucuxi using a local cohort of 69 neonates. Mean absolute error (MAE), relative bias (rBias) and relative root mean square error (rRMSE) were used to quantify the accuracy and precision of the predictive performance of each model for three different approaches: a priori, a posteriori, and Bayesian forecasting for the next course of therapy based on the previous course predictions. A PopPK model was considered clinically acceptable if rBias was between ± 20 and 95% confidence intervals included zero. A total of 25 PopPK models were identified and nine were considered suitable for further evaluation. The model of De Cock et al. 2014 was the only clinically acceptable model based on a priori [MAE 0.35 mg/L, rBias 0.8 % (95% confidence interval (CI) -7.5, 9.1%), and rRMSE 8.9%], a posteriori [MAE 0.037 mg/L, rBias -0.23% (95% CI -1.3, 0.88%), and rRMSE 6.02%] and Bayesian forecasting for the next courses [MAE 0.89 mg/L, rBias 5.45% (95% CI -8.2, 19.1%), and rRMSE 38.3%) approaches. The De Cock model was selected based on a comprehensive approach of model selection to individualize vancomycin dosing in our neonates.

A Comparison of Probability Density Functions Fitted by Moments and Maximum Likelihood Estimation Methods Used for Diameter Distribution Estimation

Modeling diameter distribution is a crucial aspect of forest management, requiring the selection of an appropriate probability density function or cumulative distribution function along with a fitting method. This study compared the suitability of eight probability density functions—A Charlier, beta, generalized beta, gamma, Gumbel, Johnson’s SB, and Weibull (two- and three-parameter)—fitted using both derivative methods (Moments) fitted in SAS/STATTM and optimization methods (MLE) fitted with the ‘optim’ function in R for diameter distribution estimation in forest stands. The A Charlier and Gumbel functions were used for the first time in this type of comparison. The data were derived from 167 permanent sample plots in an Atlantic forest (Quercus robur) and 59 temporary sample plots in tropical forests (Tectona grandis). Fit quality was assessed using various indices, including Kolmogorov–Smirnov, Cramér–von Mises, mean absolute error, bias, and mean squared error. The results indicated that Johnson’s SB function was more suitable for describing the diameter distribution of the stands. Johnson’s SB, three-parameter Weibull, and generalized beta consistently performed well across different fitting methods, while the fits produced by gamma, Gumbel, and two-parameter Weibull were of poor quality.

Abbott D-dimer assay: analytical performance and diagnostic accuracy in management of venous thromboembolism

This study aimed to assess analytical characteristics and diagnostic accuracy in management of venous thromboembolism (VTE) in the Emergency Department (ED) of the Abbott D-dimer assay applied on the Alinity c clinical chemistry analyzer (Abbott Laboratories, Chicago, IL) compared to the INNOVANCE D-dimer assay (Siemens Healthineers, Marburg, Germany). Precision was determined at three concentration levels following the CLSI EP15-A3 protocol. Method comparison and diagnostic accuracy were assessed using samples obtained from 85 patients who were referred for diagnostic imaging and D-dimer testing due to clinically suspected VTE. Within-run coefficients of variation (CVs) were 3.0%, 0.5% and 0.5% at D-dimer concentrations of 0.54, 1.42 and 2.68 mg/L FEU, while respective between-run CVs were 2.0%, 3.4% and 2.7%, hence fulfilling the desirable biological variation criteria for imprecision (<12.6%). Passing–Bablok regression analysis yielded a small proportional difference between the two compared assays (y = 1.09 (95% confidence interval (CI): 1.01–1.18) x + 0.09 (95%CI: –0.09 to 0.16)), while Bland–Altman analysis showed significant negative absolute (–0.6 mg/L FEU, 95%CI: –0.9 to −0.3) and relative mean bias (–14.1%, 95%CI: –20.3 to −7.9). Spearman’s ρ was 0.979 (95%CI: 0.967–0.986). Inter-assay agreement relative to the cut-off was 92% (kappa coefficient = 0.547 (95%CI: 0.255–0.839)). Diagnostic sensitivity, specificity, positive and negative predictive values of the Abbott assay were 100%, 9.2%, 25.3% and 100%, respectively, compared to the following data for the INNOVANCE assay: 95.0%, 15.4%, 25.7% and 90.9%. Abbott D-dimer assay has shown excellent analytical precision, high comparability with the INNOVANCE D-dimer and high NPV at manufacturer’s cut-off.

Applying a transfer function model to improve the sediment rating curve

ABSTRACT Information relating to suspended sediment load is of particular importance in terms of water management and environmental protection schemes. The sediment rating curve (SRC) is used to express the common relation between suspended sediment load and flow discharge in a basin. SRC, however, does not consider the effect of time lag in flow discharge; instead, it makes a kind of bias in its estimation, toward overestimation or underestimation of a sediment load. In this direction, the current research aims to improve and extend the SRC model by using a transfer function method. The proposed method was applied to a specific basin to better capture the effect of time lag on suspended sediment load estimation. This function estimated the current sediment load in current time. Three case studies were conducted in different climate conditions of Iran to evaluate the application of transfer function with SRC in assessing the relation between flow and suspended sediment discharge. The results revealed that the transfer function method demonstrated high precision in forecasting suspended sediment load, as evidenced by its mean absolute difference (MADIF) and bias criterion calculations. The findings indicated that the proposed method could enhance the accuracy of sediment load estimations in basins with similar characteristics. This approach was based on an unbiased future forecast and provided a high level of confidence. The results suggested that this method could be successfully applied to other basins to improve the accuracy of sediment load estimations.

The bivariate exponentiated additive Weibull distribution and its multivariate extension with applications

Recently, Abd EL-Baset and Ghazal (2020) introduced the exponentiated additive Weibull distribution, which is very useful in modeling the reliability analysis of a product, such as reliability of decision-making and cost–benefit analysis. The products inherently decay with usage and age, resulting in more extreme failures. Fixing a failed product may take longer warranty servicing time, indicating that failure and warranty servicing times correlate to bivariate distributions. This paper introduces a bivariate exponentiated additive Weibull distribution such that the marginals have exponentiated additive Weibull distributions. Statistical properties, parameter estimation using the maximum likelihood, and the bootstrap confidence interval are presented. We also generalize the results for the multivariate case. A simulation study has been executed to evaluate the performance of the parameter estimation via mean square error and absolute bias. Finally, we use three datasets failure and warranty servicing times, American football league, and the air quality to compare the proposed bivariate model to other models. The results revealed that the proposed bivariate model delivers a better fit than other models.

Performance and usability of cardiometabolic point of care devices in Nepal: A prospective, quantitative, accuracy study.

Non-communicable diseases (NCDs), such as cardiovascular disease and diabetes, represent a serious global health concern. There is an urgent need for prompt diagnosis and effective monitoring at point of care, especially in low- and middle-income countries. Here we present the results of a study assessing the quantitative accuracy of two devices that may fit the target product profile for a cardiometabolic point-of-care device. This prospective, quantitative, accuracy study (NCT05257564) was conducted between March to May 2022, investigating the performance of the JanaCare Aina Blood Monitoring System (JCAina) and the Tascom SimplexTAS 101 device (TAS101) compared with local standard laboratory methods in rural Nepal. Using fingerstick capillary blood, cardiometabolic parameters were analysed using both devices. The quantitative accuracy was compared against a local laboratory reference assay. System usability was also assessed. For JCAina, the mean absolute biases (Bland-Altman analysis) for glucose, HbA1c and total cholesterol tests were -3.87 mg/dL (95% CI: -7.52--0.22), 1.34% (95% CI: 1.21-1.47), and -9.52 mg/dL (95% CI: -11.9--7.2), respectively, corresponding to mean percentage biases of 2.0%, 18.5%, and -6.4%. These indicate clinically small (<10% biases) differences from laboratory results for glucose and cholesterol, and a moderate (10-20%) positive bias for HbA1c. For TAS101, the mean absolute biases for glucose, HbA1c, total cholesterol and creatinine tests were 18.7 mg/dL (95% CI: 15.8-21.5), -0.2% (95% CI: -0.26--0.14), 29.8 mg/dL (95% CI: 27.0-32.6), and -0.02 mg/dL (95% CI: -0.05-0.01), respectively, corresponding to mean percentage biases of 12.1%, -2.6%, 15.8%, and -4.5%. These indicate clinically small differences for HbA1c and creatinine, and moderate positive biases for glucose and cholesterol. Both systems exhibited usability challenges. The JCAina and TAS101 point-of-care cardiometabolic devices were shown to have promising accuracy in environmental conditions such as in Nepal, though improvements are still needed for some parameters and for ease of use. Trial registration: NCT05257564 (ClinicalTrials.gov).

Bayesian Estimation of Simultaneous Equation Models with Outliers and Multicollinearity Problem

Outliers and multicollinearity are problems in the analysis of Simultaneous Equation Model (SEM) especially in applied research. They can lead to bias or inefficiency of estimators. This study employed a Bayesian technique for estimation of SEM that is characterized with both multicollinearity and outliers. Monte Carlo experiment was applied while the data sets with specified outliers and multicollinearity were simulated for the SEM. The estimates of Bayesian and classical methods namely; two stage Least Squares (2SLS), Three Stage Least Squares (3SLS), Limited Information Maximum Likelihood (LIML) and Ordinary Least Squares (OLS) in simultaneous equation model were then compared. The criteria used for comparison were the Mean Square Error (MSE) and Absolute Bias (ABIAS). The Bayesian method of estimation outperformed classical methods in terms of MSE and ABIAS. However, the classical method has the same performance with Bayesian method when there are no outliers and multicollinearity in the simultaneous equation model. Hence, Bayesian method of estimation is preferred than classical method when there is problem of outlier and multicollinearity in a just identified simultaneous equation model.

Evaluation of the GRAMM/GRAL model for high-resolution wind fields in Heidelberg, Germany

Independent verification of mitigation efforts for climate and air quality action in cities relies on inferring emissions from atmospheric concentration measurements. As emissions are dispersed in the atmosphere before they reach an instrument, the quantitative estimation of emissions requires an understanding of the atmospheric transport and associated uncertainties. In this study, we analyse the catalogue of steady-state flow fields generated by the Graz Mesoscale Model (GRAMM) coupled to the Graz Lagrangian Model (GRAL) for an entire year in Heidelberg, Germany. We use a loss function for the wind field selection, which assigns a best-matching catalogue entry to any given hour by exploiting observation data. We introduce a new loss function which finds an optimal balance between differences in wind speed and wind direction. We evaluate the performance of the model based on 15 meteorological measurement sites, of which 14 are in the inner high-resolution and building-resolving GRAL domain (12.5 km × 12.5 km, 10 m resolution). Performance metrics include mean bias (MB) and root mean square errors (RMSEs) of simulated and observed wind speed and wind direction for all individual stations. On average, we find a mean underestimation of wind speed of 0.14 ms−1 corresponding to about 7 % of the mean wind speed and a mean RMSE of 1.03 ms−1. For wind direction, a mean overall bias smaller than 1° is achieved, but individual stations show larger biases (mean absolute bias: 37°), especially at stations where wind speeds are low on average. Evaluation benchmarks for mean biases of wind direction and wind speed of mesoscale models provided by the European Environmental Agency (EEA) are met at 11 and 14 out of 15 stations at low measurement heights, respectively. Recently suggested extended benchmarks for complex terrain are met at almost all stations. Additionally, for the first time, we analyse the model's ability to simulate the vertical wind profile and we analyse the benefit of implementing a wind profile measurement into the process. We find that the model does not fully capture the vertical profile in our setting. We further study the required measurement network size and find that a high number (> 6) of meteorological stations improves the selection of flow fields over the entire GRAL domain substantially. The conducted comprehensive analysis of the wind fields in the GRAL domain are the basis for detailed quantitative analysis of greenhouse gas and air pollutant emissions using the GRAMM/GRAL modelling framework.

High Resolution WRF Modelling of Extreme Heat Events and Mapping of the Urban Heat Island Characteristics in Athens, Greece

In recent decades, large-scale urbanisation has developed rapidly, resulting in significant changes in the local and regional environment and climate. Large metropolitan areas worldwide induce significant changes in local atmospheric circulation and boundary layer meteorology by modifying the underlying surface characteristics and through the emission of anthropogenic heat and pollutants into the atmosphere. We investigate the urban heat island (UHI) characteristics in the city of Athens, Greece, which is one of Europe’s largest metropolitan complexes with a population of approximately 3.7 million inhabitants. The UHI effect is intense due to the city’s size, dense construction, high incident solar radiation, and almost complete lack of natural vegetation, with previous studies suggesting a temperature rise of 4 °C on average in the city centre compared to summer background conditions. We used high-resolution WRF simulations (1-km horizontal grid) driven with ERA5 reanalysis data to produce surface temperature maps in the city of Athens and the surrounding areas (Region of Attiki) during the summer period of 1 July–20 August 2021. Different model parameterizations were tested, both with respect to urban characteristics and physical parameters. The daily minimum and maximum temperatures (Tmin and Tmax) derived from the model were validated against observational data from a dense network of weather stations covering metropolitan Athens and surrounding locations. We further investigate the influence of different meteorological conditions on the UHI gradients as produced by the model and the observational dataset, including the extreme heat wave of 28 July–5 August 2021, during which persistent maximum temperatures of &gt;40 °C were recorded for nine consecutive days. The results indicate a strong correlation between WRF output and recorded minimum and maximum temperatures throughout the test period (R ranges from 0.80 to 0.93), with an average mean absolute bias (MAB) of 1.5 °C, and reveal the intensity and spatiotemporal variability of the UHI phenomenon in the city of Athens, with UHI magnitude reaching 8–9 °C at times. Our work aims to maximise the potential of using high-resolution WRF modelling for simulating extreme heat events and mapping the UHI effect in large metropolitan complexes.

The impact of aerosol-cloud interaction on mesoscale numerical weather prediction when low-cloud and haze coexist in winter over major polluted regions of China

Compared with climate models, the role of aerosol-cloud interaction (ACI) in mesoscale numerical weather prediction (NWP) models still needs to be better understood, especially in haze regions with relatively high aerosol concentration. Here, we perform two sensitivity experiments with and without ACI (ACI and NO-ACI) in the atmospheric chemistry model GRAPES_Meso5.1/CUACE to investigate the impact of ACI on mesoscale NWP during the low-cloud period in winter 2016 over varying haze regions (severe polluted Jing-Jin-Ji (JJJ), polluted Yangtze River Delta (YRD), and weak polluted Pearl River Delta (PRD)) in China. The study results show that the real-time ACI improves underestimated cloud optical thickness (COT) and cloud water liquid path (CLWP) in haze regions, with the mean bias of simulated COT (CLWP) decreased by 27% (3%), 60% (14%), and 55% (3%) in JJJ, YRD, and PRD, respectively. The increased COT and CLWP lead to a decrease of 6.8, 21, and 13 W m−2 in daytime surface downward shortwave radiation (SDSR) in JJJ, YRD, and PRD, helping to reduce the mean bias of daytime SDSR by 6%, 13%, and 9%. In addition, ACI mitigates the warm bias of temperature at 2 m and dry bias of relative humidity (RH) at 2 m to a certain extent in haze regions, particularly in YRD with the mean absolute bias improved by 13% and 6%. The simulated vertical structure of temperature and RH in the ACI experiment is more consistent with observations than in the NO-ACI experiment. Further investigations find that the ACI effects on mesoscale NWP strongly depend on COT and CLWP magnitude over varying haze regions. Higher COT and CLWP, hence more significant meteorology changes due to ACI, occur in YRD, followed by PRD and JJJ. This study demonstrates the importance and complexity of ACI in modifying mesoscale NWP over varying haze regions of China, which promotes the further understanding of ACI in operational NWP models and bridges the gap with climate models.

Estimation of hourly solar irradiation on tilted surfaces

Tilted photovoltaic panel (PVP) are extensively utilized in solar power installations. To model and design them, you must understand the solar irradiation on a tilted surface over time scales of an hour, day, week, month, or other period. However, most meteorological stations only record global horizontal irradiance (GHI). In this research, a method is proposed for translating hourly worldwide horizontal irradiation from the NASA POWER database into hourly global sun irradiation on a tilted surface utilizing regression analysis methods and numerical modeling methods based on an isotropic model of solar irradiation. In addition, this work proposes a method for establishing the regression reliance of the diffuse transmittance index on the clearness index for geographic areas where this relationship has not yet been empirically proven. To assess effectiveness, the results of the proposed technique and other authors' methods are compared with monthly average NASA POWER climatological data using evaluation methods such as mean bias error (MBE), mean absolute bias error (MABE), and root-mean-square error (RMSE). The study's findings can be used to construct, optimize, or anticipate the functioning of solar power facilities at any angle of tilt and in any geographic area.

Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison

Accurate estimation of terrestrial evapotranspiration (ET) is crucial to understand the water cycle and partitioning of turbulent energy fluxes at the land surface. However, large-scale ET estimation is always difficult to achieve due to the complexity of controlling factors and the heterogeneity of landscapes. A recent model, referred to as the surface flux equilibrium (SFE) model, proposes that evaporative fraction (EF, the ratio of ET to available energy at the surface) can be estimated accurately from near-surface specific humidity and air temperature, which makes it applicable to common weather stations and climate reanalysis datasets. However, previous validation only focused on the evaluation of this model at site and basin scale for ET estimation, making the real performance of the SFE model for EF estimation heretofore unknown. There also exists a gap in the comparison between the SFE model and other existing products in the representation of global ET patterns and variation trend. Against this background, a comprehensive validation was performed in this study to evaluate the accuracy of the SFE model for both EF and ET estimation. The validation against 136 flux towers worldwide shows that the ground-based SFE model achieved EF estimation with a root-mean-square-error (RMSE) of 0.22 and 0.16 at daily and monthly scale, respectively. The corresponding RMSE of ET estimation was 0.94 mm/day and 16.37 mm/month. The application of the SFE model on a global scale was achieved by using two reanalysis products (ERA5-Land and GLDAS-CLSM) as inputs respectively. The results indicate that the SFE model did hold potential to reduce the RMSE of these two products for ET estimation, but the cost was the decrease in correlation coefficient (r). The comparison with five existing global ET products (BESS, GLEAM, AVHRR, MOD16, and SSEBop) shows that the SFE model performed on the basis of ERA5-Land product has achieved moderate accuracy among these products. Judged from r, mean absolute error, RMSE, and bias, its accuracy was ranked as fourth, fourth, second, and second, respectively. However, a further evaluation over different land cover types suggests that the ERA-based SFE model has significant superiority over cropland, relative superiority over forest and savannas, and significant inferiority over shrubland and grassland. This analysis is the first global validation of the SFE model for EF estimation as well as its comparison with existing global ET products. The validation analysis proves that this simple model has generally achieved comparable accuracy to existing complex models in the representation of global ET patterns and variation trend.

Are parent-reported sleep logs essential? A comparison of three approaches to guide open source accelerometry-based nocturnal sleep processing in children.

We examined the comparability of children's nocturnal sleep estimates using accelerometry data, processed with and without a sleep log. In a secondary analysis, we evaluated factors associated with disagreement between processing approaches. Children (n = 722, age 5-12 years) wore a wrist-based accelerometer for 14 days during Autumn 2020, Spring 2021, and/or Summer 2021. Outcomes included sleep period, duration, wake after sleep onset (WASO), and timing (onset, midpoint, waketime). Parents completed surveys including children's nightly bed/wake time. Data were processed with parent-reported bed/wake time (sleep log), the Heuristic algorithm looking at Distribution of Change in Z-Angle (HDCZA) algorithm (no log), and an 8 p.m.-8 a.m. window (generic log) using the R-package 'GGIR' (version 2.6-4). Mean/absolute bias and limits of agreement were calculated and visualised with Bland-Altman plots. Associations between child, home, and survey characteristics and disagreement were examined with tobit regression. Just over half of nights demonstrated no difference in sleep period between sleep log and no log approaches. Among all nights, the sleep log approach produced longer sleep periods (9.3 min; absolute mean bias [AMB] = 28.0 min), shorter duration (1.4 min; AMB = 14.0 min), greater WASO (11.0 min; AMB = 15.4 min), and earlier onset (13.4 min; AMB = 17.4 min), midpoint (8.8 min; AMB = 15.3 min), and waketime (3.9 min; AMB = 14.8 min) than no log. Factors associated with discrepancies included smartphone ownership, bedroom screens, nontraditional parent work schedule, and completion on weekend/summer nights (range = 0.4-10.2 min). The generic log resulted in greater AMB among sleep outcomes. Small mean differences were observed between nights with and without a sleep log. Discrepancies existed on weekends, in summer, and for children with smartphones and screens in the bedroom.

94 Digitized Archimedes Spiral Drawing in the NKI-Rockland Sample

Objective:Digital cognitive assessments (DCAs) provide insight into cognition and behavior that remains inaccessible through standard assessment approaches. However, the availability of DCAs and the requisite toolkits to extract and analyze meaningful features from these datasets are largely constrained to technical specialists or through fee-for-service commercial entities. The NKI- Rockland Sample provides a large- scale lifespan data sample featuring DCAs, and also openly shares its DCA tasks through the open-source MindLogger platform along with pipelines for feature extraction and analyses. Here we present normative performance from a digital version of Archimedes Spiral Drawing.Participants and Methods:NKI-RS2 participants were largely drawn from the existing NKI-RS participant pool (n= 1,500), aged 885. The NKI-RS2 is in year 1 of data collection; here, we report on a subset of participants (n= 9) who performed a digitized version of the Archimedes Spiral Drawing task. This graphomotor task with well-established research and clinical utility in movement disordered populations was adapted for use for off-the-shelf tablet devices. The NKI-RS2 implements these tasks on an Apple iPad Pro2, sampling participant drawing at 120Hz, and featuring pixel- and millisecond- level resolution for all tasks. On the Spiral Drawing and Recall Tests participants traced five Archimedes spirals from the center outward through four windings presented on the iPad. They were then asked to replicate the spiral freehand three times. From these spiral drawings, we extracted time to completion, distance covered, speed/ speed variability, rotational smoothness, number of crossings, mean absolute error, bias, and goodness of fit to the ideal Archimedes spiral.Results:Comparing the tracing and recall conditions, participants showed significantly faster drawing speed (t[8]=5.32, p&lt; .001), more variable drawing speed (t[8]=5.93, p&lt; .001), reduced goodness of fit to the template t[8]=4.99, p&lt; .002, and reduced rotational smoothness (t[8]=7.43, p&lt; .0003) in the recall conditions. Collapsing across conditions, age predicted more variable drawing speed: t[8]= 2.77 p&lt; .019, greater tracing error (t[8] = 2.69, p&lt; .0227), and reduced rotational smoothness (t[8] = 2.67, p&lt; .024). Between conditions, age predicts a greater increase in drawing speed variability (t[8] = 9.76, p&lt; .0006).Conclusions:Using the open source MindLogger platform and off-the-shelf digital tablets, we were able to replicate classic paper and pen neuropsychological tests. By adapting these tasks to DCA, we were able to extract meaningful features that are not otherwise accessible (drawing speed, variability, etc.), or that would require additional hardware solutions (e.g., dwell time). By making these tasks and their processing pipelines available, the NKI-RS2 can facilitate the democratization of DCA and DCA analysis to a broader range of researchers and clinicians.

Comparison of ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and enzyme-multiplied immunoassay technique (EMIT) for quantification of voriconazole plasma concentration from Chinese patients

IntroductionVoriconazole (VRZ) is the recommended standard treatment for life-threatening invasive aspergillosis. The plasma concentration of VRZ should be determined to optimise treatment results and reduce side effects. This study aimed to compare the correlation and concordance of ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and enzyme-multiplied immunoassay technique (EMIT) to determine VRZ plasma concentration in clinical practice. MethodsAn isotopically labelled internal standard UPLC-MS/MS method was established, validated, and subsequently applied to determine VRZ concentration. The UPLC-MS/MS method was also compared with a commercial EMIT method regarding results correlation and concordance. ResultsThe calibration curve of UPLC-MS/MS was linear from 0.1 to 10 mg/L, the inter- and intra-day relative standard deviations (RSDs), and the stability of quality control samples were less than 15 %, satisfying the Bioanalytical Method Validation Guidelines. A total of 122 plasma samples were collected and analyzed using both methods. UPLC-MS/MS and EMIT showed a high correlation (r = 0.9534), and Bland-Altman analysis indicated a mean absolute bias of 1.035 mg/L and an average bias of 27.56 % between UPLC-MS/MS and EMIT. The paired Wilcoxon test and Bland-Altman analysis revealed poor consistency between the two methods. Furthermore, we compared the effects of different methods in clinical applications. Two threshold values for treatment efficacy (1.0 mg/L) and safety (5.5 mg/L) were established, and considerable discordance was observed between the original EMIT and UPLC-MS/MS results at both thresholds (p < 0.05). Nevertheless, the adjusted EMIT results were not inconsistent with the UPLC-MS/MS results regarding the efficacy (p = 0.125) and safety (p = 1.0) thresholds. ConclusionsThe isotopically labelled internal standard UPLC-MS/MS method is established and well applied in the clinical setting. A strong correlation but discordance was found between UPLC-MS/MS and EMIT, indicating that switching from UPLC-MS/MS to EMIT was unsuitable. However, the adjusted EMIT results may serve as a reliable surrogate when UPLC-MS/MS results cannot be obtained when necessary.

Estimating surface air temperature from multiple gridded observations and reanalysis datasets over Ghana

Global temperature datasets have become increasingly accessible in recent years, yet their performance in regions with sparse data remains a subject that requires thorough investigation, especially in areas where validation is essential. This study addresses this urgency, given the significant impact of the increase in global temperature, as highlighted in the IPCC's 6th Assessment Report, on regional and local levels. In this research, we evaluated air temperature datasets from Climatic Research Unit (CRU TS4.05), Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), Climate Prediction Centre (CPC), European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-5), and TerraClimate (hereinafter referred to as Gridded Temperature Datasets - GTDs). Our assessment involved comparing and validating these datasets against reference ground observation data from 23 synoptic stations, spanning 40 years from 1981 to 2020, to ensure robustness in regions with sparse data. We conducted a spatio-temporal investigation to explore temperature patterns, and evaluated their capacity to reproduce inter-annual and intra-seasonal variability. To assess dataset performance, we employed a group of statistical metrics, including root-mean-square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS), and correlation coefficient (R). The GTDs consistently captured the annual climatological cycle distribution over Ghana. The correlation coefficient values significantly varied across the 23 synoptic stations and three climatic zones. At the annual level, CPC exhibited R values ranging from 0.53 to 0.93, while CRU ranged from 0.48 to 0.88. The bias for CPC and CRU spanned from 2.40 to −0.35 °C and 2.07 to −0.91 °C, respectively. Overall, CRU, CPC, and TerraClimate demonstrated the most consistent near-surface temperature patterns over Ghana, outperforming other datasets. Based on our findings, we recommend these datasets for model evaluation and assessment in Ghana, given their strong performance in capturing temperature variations.