Relative Bias Research Articles

Accurate Prediction of 327 Rice Variety Growth Period Based on Unmanned Aerial Vehicle Multispectral Remote Sensing

Most rice growth stage predictions are currently based on a few rice varieties for prediction method studies, primarily using linear regression, machine learning, and other methods to build growth stage prediction models that tend to have poor generalization ability, low accuracy, and face various challenges. In this study, multispectral images of rice at various growth stages were captured using an unmanned aerial vehicle, and single-plant rice silhouettes were identified for 327 rice varieties by establishing a deep-learning algorithm. A growth stage prediction method was established for the 327 rice varieties based on the normalized vegetation index combined with cubic polynomial regression equations to simulate their growth changes, and it was first proposed that the growth stages of different rice varieties were inferred by analyzing the normalized difference vegetation index growth rate. Overall, the single-plant rice contour recognition model showed good contour recognition ability for different rice varieties, with most of the prediction accuracies in the range of 0.75–0.93. The accuracy of the rice growth stage prediction model in recognizing different rice varieties also showed some variation, with the root mean square error between 0.506 and 3.373 days, the relative root mean square error between 2.555% and 14.660%, the Bias between1.126 and 2.358 days, and the relative Bias between 0.787% and 9.397%; therefore, the growth stage prediction model of rice varieties can be used to effectively improve the prediction accuracy of the growth stage periods of rice.

Enhancing Efficiency: Halton Draws in the Generalized True Random Effects Model

This paper measures the impact of the number of Halton draws in excess of ⌈n⌉ on technical efficiency in the generalized true random effects (four-component) stochastic frontier model estimated by simulated maximum likelihood. A substantial set of Monte Carlo simulations demonstrates that increasing the number of Halton draws to ⌈n3/4⌉ (⌈n2/3⌉) decreases the mean squared error of the total technical efficiency estimates by 6.1 (4.9) percent. Furthermore, increasing the number of Halton draws either improves or has no detrimental impact on correlation, mean squared error, relative bias, and upward bias for persistent, transient, and total technical efficiency. An energy sector application is included, to demonstrate how these issues can arise in practice, and how increasing Halton draws can improve parameter and efficiency estimates in empirical work.

Estimating Rainfall Anomalies with IMERG Satellite Data: Access via the IPE Web Application

This study assesses the possibilities of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) to estimate extreme rainfall anomalies. A web application, the IMERG Precipitation Extractor (IPE), was developed which allows for the querying, visualization, and downloading of time-series satellite precipitation data for points, watersheds, country extents, and digitized areas. The tool supports different temporal resolutions ranging from 30 min to 1 week and facilitates advanced analyses such as anomaly detection and storm tracking, an important component for climate change study. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region, the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of −22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash–Sutcliffe Efficiency (NSE) of 0.80, and a Kling–Gupta Efficiency (KGE) of 0.52. In the wet region, the average CC of 0.93, PRB of 24.82%, RMSE of 0.96, MBR of 0.79, NSE of 0.80, and KGE of 0.18 were computed. Median RAI indices from both the IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS and advocating for further global climate change studies.

A case study on the effect of contaminated inlet tubes on the accuracy of mid-cost optical particle counters for the ambient air monitoring of fine particles

This study investigates the impact of the long-term use of inlet-heated tubes on the performance of mid-cost optical particle counters (OPCs) for ambient air monitoring of fine particles (PM2.5). Two OPCs, equipped with inlet-heated tubes, were deployed over a 6-month period, with a beta attenuation monitor (BAM) serving as the reference device. The performance of the OPCs using the same inlet tubes for the first 3 months was compared to their performance after the frequent replacement of clean tubes during the final 3 months. The correlation coefficients (r2) for the 1 h and 24 h average PM2.5 concentrations between the OPCs and the BAM were lower with long-term contaminated tubes (0.82 < r2 < 0.93) compared to clean tubes (r2 > 0.93). The relative mean errors and biases significantly increased over time with contaminated tubes. Temperature, humidity, precipitation, and wind speed were found to have an insignificant effect (r2 < 0.1) on the performance of the two OPCs with inlet-heated tubes over the 6-month period. The relative average PM2.5 error when using clean tubes was less than 4%. These findings highlight the importance of inlet-heated tubes in improving OPC performance, particularly for mitigating humidity effects.Graphical

A simulation-based bias analysis to assess the impact of unmeasured confounding when designing nonrandomized database studies.

Unmeasured confounding is often raised as a source of potential bias during the design of nonrandomized studies, but quantifying such concerns is challenging. We developed a simulation-based approach to assess the potential impact of unmeasured confounding during the study design stage. The approach involved generation of hypothetical individual-level cohorts using realistic parameters, including a binary treatment (prevalence 25%), a time-to-event outcome (incidence 5%), 13 measured covariates, a binary unmeasured confounder (u1; 10%), and a binary measured "proxy" variable (p1) correlated with u1. Strengths of unmeasured confounding and correlations between u1 and p1 were varied in simulation scenarios. Treatment effects were estimated with (1) no adjustment, (2) adjustment for measured confounders (level 1), and (3) adjustment for measured confounders and their proxy (level 2). We computed absolute standardized mean differences in u1 and p1 and relative bias with each level of adjustment. Across all scenarios, level 2 adjustment led to improvement in the balance of u1, but this improvement was highly dependent on the correlation between u1 and p1. Level 2 adjustments also had lower relative bias than level 1 adjustments (in strong u1 scenarios: relative bias of 9.2%, 12.2%, and 13.5% at correlations of 0.7, 0.5, and 0.3, respectively, vs 16.4%, 15.8%, and 15.0% for level 1). An approach using simulated individual-level data is useful to explicitly convey the potential for bias due to unmeasured confounding while designing nonrandomized studies, and can be helpful in informing design choices. This article is part of a Special Collection on Pharmacoepidemiology.

Effects of aerobic training on brain architecture, hippocampal volume, cardiorespiratory parameters, and health-related quality of life among patients with schizophrenia: A systematic review

Background: Schizophrenia is considered to be a condition that usually manifests at any age but commonly seen in young people and is associated with a genetic propensity in brain development. Aim: The study explores the impact of aerobic training on brain architecture, hippocampal volume, cardiorespiratory parameters, and quality of life in young individuals with schizophrenia. The investigation focuses on the correlation between genetic predisposition, hippocampal atrophy, and diminished cardiorespiratory fitness, aiming to discern potential benefits of aerobic exercise on both physical and mental health outcomes. Methods: A systematic search was conducted in major online databases, such as Cochrane, Embase, PEDro, Web of science and PubMed (Medline). Two authors independently evaluated possibilities of bias, retrieved data and decided which trials should be included in the analysis. Total of 13 published articles (randomized clinical and controlled trials) with 656 patients was included from January 2010 to December 2020. Results: Following aerobic exercise training, people with schizophrenia presented with an increased hippocampus volume and enhanced cardiorespiratory endurance (P ˂0.05). Due to a lack of information, we rated the majority of the studies’ risk of bias in relation to random sequence creation, allocation concealment, and selective reporting as uncertain. Conclusion: Findings from the present review propose aerobic exercise program as a meaningful therapeutic intervention for schizophrenia patients in terms of maintaining appropriate brain architecture, increasing the hippocampal volume, improving cardiorespiratory parameters and health related quality of life.

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.

Determination of endogenous GHB in ante-mortem whole blood, urine, and oral fluid by LC–MS/MS: The effect of different additives and storage conditions on the stability of GHB in blood

Two challenges in detecting γ-hydroxybutyric acid (GHB) intake are its endogenous presence and in vitro production after sampling. This study developed an LC–MS/MS method for selective GHB determination in human antemortem blood, urine, and oral fluid at endogenous concentrations. Furthermore, the stability of GHB in blood samples and its endogenous concentrations in samples taken under controlled circumstances were investigated. Samples were extracted in methanol/acetonitrile and processed by anion exchange solid-phase extraction. GHB was separated from structural isomers using a reversed–phase LC column with anion properties. The validated limit of quantification was 0.005 µg/mL in blood and 0.010 µg/mL in urine and oral fluid, at which the relative reproducibility standard deviation and bias were <15 %. The mean extraction recovery was ≥90 %. The average GHB concentration increased by 1.2 µg/mL in fluoride/citrate- preserved blood after 28 days of storage at 4°C; however, in fluoride/oxalate (FX)-preserved blood, the mean concentration increased by only 0.055 µg/mL. No change was observed at −20°C. In 105 randomly selected samples of FX-preserved blood collected for forensic antemortem toxicological analysis, all concentrations were <0.066 µg/mL, even after long-term storage at −20°C. In blood, urine, and oral fluid samples from a clinical study of GHB intake, endogenous baseline levels from 30 participants ranged from 0.0069–0.050, 0.024–0.38, and 0.034–0.93 µg/mL, respectively. These results demonstrate that the current cut-off level of 5 µg/mL for discriminating between endogenous and exogenous GHB in antemortem blood could be considerably lower for FX-preserved blood stored at −20°C.

Global evaluation of sentinel 2 level 2A Sen2Cor aerosol optical thickness retrievals

ABSTRACT The present study aims to conduct the first known comprehensive global evaluation of Aerosol Optical Thickness (AOT) retrievals derived from the Sentinel 2 Sen2Cor algorithm between 2018 and 2022, using data from over 400 AERONET stations. The results indicate that Sentinel 2 tends to underestimate AOT, especially at higher aerosol loadings. Although there appears to be a good overall correlation (r = 0.57) with low RMSE (0.16) and relative mean bias (RMB = -2.46%) between AERONET and Sentinel 2 AOT datasets, regional analysis reveals significant variation in performance across regions, with areas like Europe and Americas exhibiting stronger correlations and lower RMSE than others like Indian subcontinent and Southern Africa. Temporal analysis also suggests an improvement in Sentinel 2 performance, particularly in capturing extreme AOT values in 2022. While elevation does not appear to have any noticeable impact on AOT estimation, slight variations could be observed over certain land cover types like sparse vegetation and permanent water bodies. Though improvements are certainly required over certain geographical regions like Indian subcontinent, Sentinel 2 AOT can, nevertheless, be reliably used for aerosol studies in urban areas or at a finer spatial resolution, especially in Europe, Mainland U.S.A. and East Asia.

Water Temperature Model to Assess Impact of Riparian Vegetation on Jucar River and Spain

Water temperature is a critical factor for aquatic ecosystems, influencing both chemical and biological processes, such as fish growth and mortality; consequently, river and lake ecosystems are sensitive to climate change (CC). Currently proposed CC scenarios indicate that air temperature for the Mediterranean Jucar River will increase higher in summer, 4.7 °C (SSP5-8.5), resulting in a river water temperature increase in the hotter month; July, 2.8 °C (SSP5-8.5). This will have an impact on ecosystems, significantly reducing, fragmenting, or even eliminating natural cold-water species habitats, such as common trout. This study consists of developing a simulated model that relates the temperature of the river with the shadow generated by the riverside vegetation. The model input data are air temperature, solar radiation, and river depth. The model proposed only has one parameter, the shadow river percentage. The model was calibrated in a representative stretch of the Mediterranean river, obtaining a 0.93 Nash–Sutcliffe efficiency coefficient (NSE) that indicates a very good model fit, a 0.90 Kling–Gupta efficiency index (KGE), and a relative bias of 0.04. The model was also validated on two other stretches of the same river. The results show that each 10% increase in the number of shadows can reduce the river water temperature by 1.2 °C and, in the stretch applied, increasing shadows from the current status of 62% to 76–87% can compensate for the air temperature increase by CC. Generating shaded areas in river restorations will be one of the main measures to compensate for the rise in water temperature due to climate change.

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 &lt;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.