Smaller Standard Errors Research Articles

Determination of optimal solar-viewing geometry for in-flight polarization calibration using sun glint over ocean

The sun glint has been proven to be a valuable natural polarization calibration target because it is strongly polarized, and its polarization characteristics can be accurately simulated with models. It is convenient to calibrate the satellite’s in-flight polarimetry by comparing the polarization simulations with actual measurements. Meanwhile, the accuracy of polarization simulation at the top of the atmosphere (TOA) over sun glint is affected by several atmospheric and oceanic surface factors and depends on the specific solar-viewing geometry. In this paper, the sensitivity of the degree of linear polarization (DOLP) at the TOA to the uncertainties of the aerosol optical depth, aerosol model, absorption gas content (CWV, O3), sea surface instantaneous wind speed (WS), and chlorophyll concentration (Chl) under different solar-viewing geometries is analyzed via radiative transfer simulation. The error budgets indicate that aerosols and WS are the main error factors for polarization calibration, while the uncertainties of Chl and absorbing gases can be disregarded. The total DOLP error increases with the solar zenith angle and viewing zenith angle (i.e., the increase of atmospheric optical path) and the sun glint angle (SGA, the angle between the viewing and the specular directions of the sun) (i.e., the decrease of sun glint brightness). The dependence of the total DOLP error on SGA decreases with the WS (i.e., the increase of sun glint spot area and the decrease of the sun glint intensity) and increases with the wavelength (i.e., the decrease of atmospheric scattering contribution). Based on the error budgets, an optimized solar-viewing geometry screening strategy is proposed to ensure that the simulated DOLP error is limited to 0.02. The in-flight DOLP calibration result of POLDER/PARASOL shows that the proposed screening strategy obtained more calibration samples and covered a wider range of DOLP, especially for the samples with DOLP of less than 0.2, compared with the screening strategies of Toubbe et al. [IEEE Trans. Geosci. Remote Sens. 37, 513 (1999)IGRSD20196-289210.1109/36.739104]and Hagolle et al. [IEEE Trans. Geosci. Remote Sens. 42, 1472 (2004)IGRSD20196-289210.1109/TGRS.2004.826805] in previous work. The smaller standard error (SE) of the samples indicates more stable calibration results obtained for the optimized strategy. This research presents an optimized strategy for screening the solar-viewing geometry of the samples to calibrate satellite in-flight polarization measurements using the sun glint.

Comparison of statistical methods for the analysis of patient-reported outcomes in randomised controlled trials: A simulation study.

Patient-reported outcomes(PROs) that aim to measure patients' subjective attitudes towards their health or health-related conditions in various fields have been increasingly used in randomised controlled trials(RCTs). PRO data is likely to be bounded, discrete, and skewed. Although various statistical methods are available for the analysis of PROs in RCT settings, there is no consensus on what statistical methods are the most appropriate for use. This study aims to use simulation methods to compare the performance (in terms of bias, empirical standard error, coverage of the confidence interval, Type I error, and power) of three different statistical methods, multiple linear regression(MLR), Tobit regression (Tobit), and median regression (Median), to estimate a range of predefined treatment effects for a PRO in a two-arm balanced RCT. We assumed there was an underlying latent continuous outcome that the PRO was measuring, but the actual scores observed were equally spaced and discrete. This study found that MLR was associated with little bias of the estimated treatment effect, small standard errors, and appropriate coverage of the confidence interval under most scenarios. Tobit performed worse than MLR for analysing PROs with a small number of levels, but it had better performance when analysing PROs with more discrete values. Median showed extremely large bias and errors, associated with low power and coverage for most scenarios especially when the number of possible discrete values was small. We recommend MLR as a simple and universal statistical method for the analysis of PROs inRCT settings.

Using generalized estimating equations to estimate nonlinear models with spatial data

. We study the estimation of nonlinear models with cross-sectional data using two-step generalized estimating equations within the quasi-maximum likelihood estimation framework. To improve efficiency, we propose a grouped estimator that accounts for potential spatial correlation in the underlying innovations of nonlinear models. Under mild weak dependence assumptions, we provide results on estimation consistency and asymptotic normality. Monte Carlo simulations demonstrate the efficiency gain of our approach compared to various estimation methods. Finally, we apply the proposed approach to examine the role of cultural distance in an extended gravity equation using international trade data from China. Compared to existing methods, our approach yields estimates with smaller standard errors and reinforces the hypothesis that both cultural and geographical distances significantly negatively influence international trade.

The whole is lesser than the sum of its parts? Dissecting layer-enriched samples of rodent placenta is worth the effort

Gene expression in the placenta, assessed by bulk RNA-seq, is a common method to explore placental function. Many rodent studies homogenize the entire placenta, and yet doing so may obscure differences within specific functional regions such as the labyrinth, junctional zone and decidua. Conversely, analysis of the whole placenta could generate apparent differences due to changes in composition (e.g., relative amounts of labyrinth vs junctional zone) rather than differential gene expression. We assess the value of dissecting and separately analysing the labyrinth and junctional zone/decidua by comparing RNA-seq results from the labyrinth, junctional zone/decidua combined, and whole placenta from an experiment examining effects of maternal food restriction and fetal sex in C57BL6/J mice at gestational day 17.5. The number of genes identified as differentially expressed in response to maternal food restriction was substantially higher in the labyrinth (910 genes), than in the junctional zone/ decidua (50 genes), which in turn was slightly higher than in the whole placenta (3 genes). Only one gene was differentially expressed in all 3 tissue types, and 20 genes were differentially expressed in both the labyrinth and junctional zone/decidua. The larger number of differentially expressed genes in the labyrinth was due to both larger effect sizes and estimates of effect sizes having smaller standard errors. While dissection to obtain layer-enriched samples is slightly more time-consuming than collection of whole placenta and requires some practice, our results show that layer-enrichment is clearly worth the effort.

Comparison between Spectral-Domain and Swept-Source OCT Angiography for the Measurement of Persistent Hypertransmission Defects in Age-Related Macular Degeneration

Spectral-domain OCT angiography (SD-OCTA) scans were tested in an algorithm developed for use with swept-source OCT angiography (SS-OCTA) scans to determine if SD-OCTA scans yielded similar results for the detection and measurement of persistent choroidal hypertransmission defects (hyperTDs). Retrospective study. Forty pairs of scans from 32 patients with late-stage nonexudative age-related macular degeneration (AMD). Patients underwent both SD-OCTA and SS-OCTA imaging at the same visit using the 6× 6 mm OCTA scan patterns. Using a semiautomatic algorithm that helped with outlining the hyperTDs, 2 graders independently validated persistent hyperTDs, which are defined as having a greatest linear dimension ≥250 μm on the en face images generated using a slab extending from 64 to 400 μm beneath Bruch's membrane. The number of lesions and square root (sqrt) total area of the hyperTDs were obtained from the algorithm using each imaging method. The mean sqrt area measurements and the number of hyperTDs were compared. The number of lesions and sqrt total area of the hyperTDs were highly concordant between the 2 instruments (rc= 0.969 and rc= 0.999, respectively). The mean number of hyperTDs was 4.3 ± 3.1 for SD-OCTA scans and 4.5 ± 3.3 for SS-OCTA scans (P= 0.06). The mean sqrt total area measurements were 1.16 ± 0.64 mm for the SD-OCTA scans and 1.17 ± 0.65 mm for the SS-OCTA scans (P < 0.001). Because of the small standard error of the differences, the mean difference between the scans was statistically significant but not clinically significant. Spectral-domain OCTA scans provide similar results to SS-OCTA scans when used to obtain the number and area measurements of persistent hyperTDs through a semiautomated algorithm previously developed for SS-OCTA. This facilitates the detection of atrophy with a more widely available scan pattern and the longitudinal study of early to late-stage AMD. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Factors Influencing Tooth Sensitivity: Insights From the Hospital Universiti Sains Malaysia Using Bootstrap-Enhanced Ordinal Regression.

Introduction Tooth sensitivity, or dentin hypersensitivity (DH), is characterized by sharp, sudden pain in response to stimuli such as cold, heat, sweet, or acidic foods and drinks.In Malaysia, there is limited understanding of the epidemiological aspects of tooth sensitivity, necessitating focused research. The condition results from the exposure of dentinal tubules transmitting stimuli to nerves within the pulp, with contributing factors including gingival recession, enamel erosion, and periodontal disease. This study aims to investigate the factors associated with tooth sensitivity among patients at the Hospital Universiti Sains Malaysia (USM) using advanced statistical methods. Methods This study employed a computational research design to develop an ordinal regression and bootstrap methodology using the RStudio software (Posit PBC, Boston, MA) to analyze secondary data from the Hospital Universiti Sains Malaysia. Six variables were analyzed: tooth wear severity, patient'sage, gender, smoking status, alcohol status, and type of toothbrush. The study was conducted in three phases: 1) the development of an ordinal regression model, 2) the development of algorithms for ordinal regression and bootstrap method, and 3) validation using tooth sensitivity data. Results The analysis revealed that the replication with 1000 samples provided the most precise estimates with small standard errors (SE) and consistently significant effects across variables. Tooth sensitivity was influenced by age, toothpaste type, toothbrush type, and brushing frequency. Conclusion The study highlights the importance of considering multiple variables such as age, toothpaste type, toothbrush type, and brushing frequency in understanding tooth sensitivity. The combined ordinal regression and bootstrap technique significantly improved the model's accuracy, providing valuable insights for dental health professionals. These findings underscore the need for specific guidelines on oral hygiene practices to manage and reduce the risk of tooth sensitivity.

Gonial Angle in Forensic Anthropology to Determine Age and Gender: A Population-Specific Analysis.

Background The study highlights the gonial angle as a key craniofacial landmark for age and gender determination in forensic cases. It emphasizes population-specific analysis, enhancing precision by recognizing variations between populations. By clarifying the gonial angle's forensic use, the study offers clear guidelines, improving forensic practices. Moreover, the gonial angle and age and gender correlations are thoroughly examined, offering important information on their forensic relevance. The results highlight how crucial population-specific research is to improving the precision and dependability of forensic age and gender estimation techniques, which advances forensic anthropology and supports forensic investigations around the globe. Aim and objective The purpose of this study is to assess the accuracy of age and gender estimates using gonial angles. The objectives of this research are to evaluate the precision of age and gender estimates utilizing the gonial angle. Materials and methods This present study comprises two groups based on age groups: Group I belongs to 51 to 60 years of age, and Group II belongs to 61 to 70 years of age. Making use of G-Power software (version 3.1.9.4, Düsseldorf, Germany), the sample size was determined. The calculation ensured 95% statistical power at a significance level (alpha error probability) of 0.05. To achieve sufficient statistical power, a total of 1000 samples were included, with a projected required sample size of 92. A total of 1000 samples, consisting of 500 male and 500 female panoramic radiographs,were meticulously selected for the study. The samples picked were within the age range of 51 to 70 years. Orthopantomograms were determined using Planmeca software (Planmeca Romexis®, Version 6.0, USAInc.). Descriptive statistics, including prediction classification analysis of age and gender, were conducted using SPSS Statistics version 16.0 (SPSS Inc., Released 2007, SPSS for Windows, Version 16.0, Chicago, SPSS Inc.). Results According to this study, the mean gonial angle of males aged 51 to 60 years is larger (124.7370degrees) than that of females (119.6371degrees). The female group's mean estimates are more accurate, as seen by the smaller standard error (0.20844) compared to the male group's (0.60998). A statistically significant difference in mean gonial angles between the genders is evident, with males having a larger gonial angle (p-value <0.001). In the age range of 61 to 70 years, the mean gonial angle of females is higher (128.4322degrees) than that of males (124.0529degrees). In this instance, the male group's standard error is smaller (0.14968) than the female group's (0.30028), indicating more accurate mean estimates. Once more, a statistically significant difference is indicated by a p-value of less than 0.001, with females having a larger gonial angle than males. Conclusion Our study revealed that the gonial angle of the mandible can be considered a reliable parameter for gender identification. The study's limitation is its inability to reliably identify gender in the subadult population and in cases of edentulousness. An orthopantomogram is a trustworthy and accurate method for taking the different measurements needed to identify the gender of a particular mandible.

Estimation of boreal forest biomass from ICESat-2 data using hierarchical hybrid inference

The ICESat-2, launched in 2018, carries the ATLAS instrument, which is a photon-counting spaceborne lidar that provides profile samples over the terrain. While primarily designed for snow and ice monitoring, there has been a great interest in using ICESat-2 to predict forest above-ground biomass density (AGBD). As ICESat-2 is on a polar orbit, it provides good spatial coverage of boreal forests.The aim of this study is to evaluate the estimation of mean AGBD from ICESat-2 data using a hierarchical modeling approach combined with rigorous statistical inference. We propose a hierarchical hybrid inference approach for uncertainty quantification of the average AGBD of the area of interest estimated directly from a sample of ICESat-2 lidar profiles. Our approach models the errors coming from the multiple modeling steps, including the allometric models used for predicting tree-level AGB. For testing the procedure, we have data from two adjacent study sites, denoted Valtimo and Nurmes, of which Valtimo site is used for model training and Nurmes for validation.The ICESat-2 estimated mean AGBD in the Nurmes validation area was 65.7 ± 1.9 Mg/ha (relative standard error of 2.9%). The local reference hierarchical model-based estimate obtained from wall-to-wall airborne lidar data was 63.9 ± 0.6 Mg/ha (relative standard error of 1.0%). The reference estimate was within the 95% confidence interval of the ICESat-2 hierarchical hybrid estimate. The small standard errors indicate that the proposed method is useful for AGBD assessment. However, some sources of error were not accounted for in the study and thus the real uncertainties are probably slightly larger than those reported.

Crosswalk between the PROMIS physical function CAT and PROMIS upper extremity CAT v1.2 in a hand surgery population

BackgroundThere is no gold standard patient-reported outcome measure (PROM) in hand surgery. As a result, a diverse array of PROM instruments have been utilized across centers over time. Lack of score interchangeability limits the ability to compare or conglomerate scores when new instruments are introduced. Our aim was to develop a linkage for the PROMIS UE CAT v1.2 and PROMIS PF CAT scores and develop crosswalk tables for interconversion between these PROMs.MethodsRetrospective review was conducted to identify adult (≥ 18y) patients seen by orthopaedic hand surgeons at a single academic tertiary care hospital who had completed PROMIS UE CAT v1.2 and PROMIS PF CAT score at the same visit. For those with multiple visits, only one randomly selected visit was included in the analyses. Pearson’s correlation was calculated to determine the linear relationship between the scores. Linkage from PF to UE was performed utilizing several commonly utilized equating models (identity, mean, linear, equipercentile and circle-arc methods). The performance of the models was assessed using intraclass correlation (ICC) between observed PROMIS UE CAT v1.2 and estimated PROMIS UE CAT v1.2 scores generated using the model as well as Root Mean Square Error (RMSE). The model chosen as the ‘best’ was further assessed for population invariance using root expected mean squared difference (REMSD) where < 0.08 were considered good.ResultsOf 10,081 included patients, mean age was 48.3 (SD = 17.0), and 54% were female (5,477/10,081). Mean UE CAT v1.2 and PF CAT scores were 37 (SD = 9.8) and 46 (SD = 10.0), respectively. There was a strong correlation between the scores (Pearson correlation r = 0.70). All methods performed acceptably (ICC ≥ 0.66 and RMSE < = 7.52 for all). The equipercentile method had the highest ICC (ICC = 0.70 (95% CI 0.69–0.71)) while the mean and circle arc methods had the lowest RMSE. The circle arc method is the most reliable with the smallest standard error and has satisfactory population invariance across age group (REMSD 0.065) and sex (REMSD 0.036).ConclusionsCrosswalk tables to be used for bidirectional conversion between scores were created.Level of evidence: III.

Balancing versus modelling in weighted analysis of non-randomised studies with survival outcomes: A simulation study.

Weighting methods are widely used for causal effect estimation in non-randomised studies. In general, these methods use the propensity score (PS), the probability of receiving the treatment given the covariates, to arrive at the respective weights. All of these "modelling" methods actually optimize prediction of the respective outcome, which is, in the PS model, treatment assignment. However, this does not match with the actual aim of weighting, which is eliminating the association between covariates and treatment assignment. In the "balancing" approach, covariates are thus balanced directly by solving systems of numerical equations, explicitly without fitting a PS model. To compare modelling, balancing and hybrid approaches to weighting we performed a large simulation study for a binary treatment and a survival outcome. For maximal practical relevance all simulation parameters were selected after a systematic review of medical studies that used PS methods for analysis. We also introduce a new hybrid method that uses the idea of the covariate balancing propensity score and matching weights, thus avoiding extreme weights. In addition, we present a corrected robust variance estimator for some of the methods. Overall, our simulations results indicate that balancing approach methods work worse than expected. However, among the considered balancing methods, entropy balancing consistently outperforms the variance balancing approach. All methods estimating the average treatment effect in the overlap population perform well with very little bias and small standard errors even in settings with misspecified propensity score models. Finally, the coverage using the standard robust variance estimator was too high for all methods, with the proposed corrected robust variance estimator improving coverage in a variety of settings.

A framework for understanding selection bias in real-world healthcare data.

Using administrative patient-care data such as Electronic Health Records (EHR) and medical/pharmaceutical claims for population-based scientific research has become increasingly common. With vast sample sizes leading to very small standard errors, researchers need to pay more attention to potential biases in the estimates of association parameters of interest, specifically to biases that do not diminish with increasing sample size. Of these multiple sources of biases, in this paper, we focus on understanding selection bias. We present an analytic framework using directed acyclic graphs for guiding applied researchers to dissect how different sources of selection bias may affect estimates of the association between a binary outcome and an exposure (continuous or categorical) of interest. We consider four easy-to-implement weighting approaches to reduce selection bias with accompanying variance formulae. We demonstrate through a simulation study when they can rescue us in practice with analysis of real-world data. We compare these methods using a data example where our goal is to estimate the well-known association of cancer and biological sex, using EHR from a longitudinal biorepository at the University of Michigan Healthcare system. We provide annotated R codes to implement these weighted methods with associated inference.

Analysis Of The Financial Performance Of Insurance Companies Before And During The Covid-19 Pandemic Based On Early Warning System And Risk-Based Capital

This study aims to provide empirical evidence regarding the comparison of the value of the Early Warning System and Risk-Based Capital in measuring the financial performance of insurance companies before and during the COVID-19 pandemic. The Early Warning System measurement indicator uses the claim expense ratio. Sampling using purposive sampling method and obtained a sample of 18 insurance companies listed on the Indonesia Stock Exchange (IDX) for the period 2017-2022. The type of data used is secondary data in the form of financial reports collected through the company's official website and the Indonesia Stock Exchange (IDX). The data analysis method uses non-parametric statistics, namely the Wilcoxon Sign Rank Test with the help of SPSS version 27. The results prove that there is a significant increase in the value of the Early Warning System during the pandemic compared to before the Covid-19 pandemic. The Risk-Based Capital value did not experience a significant decrease during the pandemic compared to before the Covid-19 pandemic. In addition, in this study, the Early Warning System is considered more accurate than Risk-Based Capital because it has a smaller standard error.

The Estimation of Genetic Parameters for Chronic Progressive Lymphedema and Body Traits in the Rhenish German Draught Horse.

Chronic progressive lymphedema (CPL) is a prevalent and progressive disease in Rhenish German draught horses. The objective of our follow-up study was to evaluate the heritability of this disease in Rhenish German draught horses using pedigree-based and genomic relationship matrices. We employed linear and threshold animal models. Models included the random animal effect and effects of breeding association, coat colour, sex, and age within sex, and farm-related factors, on CPL scores. In addition, we estimated heritabilities in models assuming censoring for data when horses were below an age of 1-15 years. The heritabilities of CPL scores across all ages were 0.595 ± 0.131 and 0.482 ± 0.105 in the threshold and linear animal model with pedigree-based relationship matrices, respectively. The restriction of data to horses with a minimum age at examination or accounting for censored data in younger animals showed an increase in heritabilities of CPL scores up to 0.788 ± 0.168 (threshold model) and 0.752 ± 0.153 (linear model) at an age of 7-8 years. Analyses including genomic relationship matrices yielded very similar estimates, but with smaller standard errors than pedigree-based analyses. Heritabilities in threshold models for CPL prevalence (CPL-bin-score) and the number of affected limbs (CPL-bin-sum) were 0.176-0.189 ± 0.061-0.064 and 0.375-0.433 ± 0.164-0.170, respectively. We were able to show moderately to highly positive genetic correlations between the CPL score and cannon bone circumference (0.529-0.825), height at withers (0.338-0.555), and skinfold thickness (0.241-0.517). Using the dichotomous trait for the CPL score and the genomic relationship matrix resulted in corresponding estimates of 0.868, 0.793, and 0.784, respectively. This study showed the great importance of additive genetic variation influencing the expression of chronic progressive lymphedema in Rhenish German draught horses. Therefore, further research is warranted to implement breeding programmes in a small breeding population that exploit the potential of additive genetic differences among animals for reducing the prevalence and severity of lesions of this incurable disease.

Multivariate Generalized Linear Mixed Models for Count Data

Univariate regression models have rich literature for counting data. However, this is not the case for multivariate count data. Therefore, we present the Multivariate Generalized Linear Mixed Models framework that deals with a multivariate set of responses, measuring the correlation between them through random effects that follows a multivariate normal distribution. This model is based on a GLMM with a random intercept and the estimation process remains the same as a standard GLMM with random effects integrated out via Laplace approximation. We efficiently implemented this model through the TMB package available in R. We used Poisson, negative binomial (NB), and COM-Poisson distributions. To assess the estimator properties, we conducted a simulation study considering four different sample sizes and three different correlation values for each distribution. We achieved unbiased and consistent estimators for Poisson and NB distributions; for COM-Poisson estimators were consistent, but biased, especially for dispersion, variance, and correlation parameter estimators. These models were applied to two datasets. The first concerns a sample from 30 different sites collected in Australia where the number of times each one of the 41 different ant species was registered; which results in an impressive 820 variance-covariance and 41 dispersion parameters are estimated simultaneously, let alone the regression parameters. The second is from the Australia Health Survey with 5 response variables and 5190 respondents. These datasets can be considered overdispersed by the generalized dispersion index. The COM-Poisson model overcame the other two competitors considering three goodness-of-fit indexes, AIC, BIC, and maximized log-likelihood values. As a result, it estimated parameters with smaller standard errors and a greater number of significant correlation coefficients. Therefore, the proposed model is capable of dealing with multivariate count data, either under- equi- or overdispersed responses, and measuring any kind of correlation between them taking into account the effects of the covariates.

Test-retest reliability and agreement of remote home-based functional capacity self-administered assessments in community-dwelling, socially isolated older adults.

To determine the test-retest reliability and agreement of home-based functional capacity self-administered assessments in socially isolated older adults. Fourteen community-dwelling older adults (eight females, 67.9 ± 7.7 years) volunteered for this study. Before testing, participants were screened online for eligibility and received instructional videos explaining test set-up and execution. Participants underwent the 30-second sit-to-standing test, gait speed tests at the usual pace, and timed-up-and-go tests administered 4 weeks apart. For the 30-second sit-to-standing protocol, participants were instructed to repeatedly sit and stand from a chair (with a height of ∼ 43 cm and without armrests) for 30 s, with the number of repetitions recorded. In the gait speed test protocol, participants were instructed to walk at their usual and comfortable pace, with the time taken recorded (seconds). In the timed-up-and-go, participants stood up from a chair, walked as fast as possible for 3 m, circled a marked point, and returned to the chair to sit down, completing the test, with the score recorded (seconds). A trained researcher conducted the scoring virtually via synchronous video. 30-second sit-to-standing, gait speed test, and timed-up-and-go showed excellent mean coefficient of variation values (2.0-4.9), small standard error of measurement (0.08-1.27), excellent intraclass coefficient (0.97-0.99), very strong correlations (0.97-0.99) and good agreement between the two days of testing. Self-administered functional capacity tests performed by older adults at home were reliable with good agreement. Healthcare professionals and older adults should take advantage of simple remote self-administered assessments performed at home to evaluate older adults' health status.

BANKING STABILITY MODELLING FOR ARMENIA: DECISION TREE APPROACH

In the following article is presented methodology for modelling the banking stability of the Republic of Armenia, using decision tree models. As a measure of banking stability is suggested banking Z-score, which indicates how the banking system is close to default. For evaluating the model, 47 factors are included from real, financial and foreign sectors of the economy. For considering the lagged impact of factors on the dependent variable, apart from base values the first 2 lags of independent variables are included in the model. The proper lag is decided by estimating a VAR model. In the result, the model’s feature importance scores show, that Armenian banking stability is mainly impacted by net import, inflation rate, previous lags of the dependent variable: which is state of the banking stability in previous period, investment indicators of individuals, exchange rate of Euro/AMD, as well as cryptocurrency price fluctuations. It is justified that the chosen methodology and variables are efficient for predicting the banking stability of Armenia, by giving small standard errors and accurate predictions. Also, including the previous lags of the variables improves the model predictive power. Apart from that, it is proved that the Z-score can be used as the measure of banking stability, and due to the availability of monthly data, act as a dependent variable to model the banking system’s resilience to the shocks and key factors responsible for the stability

Revisiting Gauge-Independent Kinetic Energy Densities in Meta-GGAs and Local Hybrid Calculations of Magnetizabilities.

In a recent study [J. Chem. Theory Comput. 2021, 17, 1457-1468], some of us examined the accuracy of magnetizabilities calculated with density functionals representing the local density approximation (LDA), generalized gradient approximation (GGA), meta-GGA (mGGA), as well as global hybrid (GH) and range-separated (RS) hybrid functionals by assessment against accurate reference values obtained with coupled-cluster theory with singles, doubles, and perturbative triples [CCSD(T)]. Our study was later extended to local hybrid (LH) functionals by Holzer et al. [J. Chem. Theory Comput. 2021, 17, 2928-2947]; in this work, we examine a larger selection of LH functionals, also including range-separated LH (RSLH) functionals and strong-correlation LH (scLH) functionals. Holzer et al. also studied the importance of the physically correct handling of the magnetic gauge dependence of the kinetic energy density (τ) in mGGA calculations by comparing the Maximoff-Scuseria formulation of τ used in our aforementioned study to the more physical current-density extension derived by Dobson. In this work, we also revisit this comparison with a larger selection of mGGA functionals. We find that the newly tested LH, RSLH, and scLH functionals outperform all of the functionals considered in the previous studies. The various LH functionals afford the seven lowest mean absolute errors while also showing remarkably small standard deviations and mean errors. Most strikingly, the best two functionals are scLHs that also perform remarkably well in cases with significant multiconfigurational character, such as the ozone molecule, which is traditionally excluded from statistical error evaluations due to its large errors with common density functionals.

Parameters of pubertal growth spurt in children and adolescents living at high altitude in Peru

ObjectivesThe aim was to estimate the pubertal growth height of children and adolescents living in a high-altitude region of Peru using the Preece-Baines model 1 (1 PB). MethodsA cross-sectional study was conducted in schoolchildren from the department of Puno (Peru) between 3841 and 3874 masl. The age range was between 4 and 17 years. Standing height was evaluated. 1 PB was used to infer the mathematical and biological parameters of stature. ResultsMathematical parameters estimated by the 1 PB model reflected small residual standard error (RSE) values in both sexes (0.25 in boys and 0.27 in girls). In boys, the age at which peak velocity was reached (APHV) was estimated at 13.21 ± 0.33years. While in girls it was 9.96 ± 0.26years (p < 0.05). In general, girls reached APHV (y) 3.25 years earlier than boys. On the other hand, the growth velocity of maximum height [APHV (cm/y)] of boys was higher (6.33 ± 6.06 cm/y) relative to girls (6.06 ± 0.32 cm/y). Estimated final adult height (EFAH) in boys was reached at 166.020 ± 0.99 cm and height at maximum growth velocity (HPHV) was 153.07 ± 0.67 cm, while in girls they were significantly lower (EFAH; 153.74 ± 0.44 cm and HPHV: 139.73 ± 0.84 cm). ConclusionsThis study showed that girls living in Puno at a high altitude in Peru reached APHV 3 years earlier than boys and at the same time reflected slower PHV. These results suggest that pubertal growth at high altitudes is slower in both sexes and especially in girls. Thus, modeling physical growth may be an important step in understanding the onset of puberty at different latitudes.

Cross-cultural adaptation and test-retest reliability of the Italian version of the Pediatric Balance Scale in children with typical and atypical development.

Balance disorders in children are complex and disabling. The pediatric balance scale assesses functional balance in the contest of everyday tasks. It is recommended for balance assessment in children. The cross-cultural validation of an assessment instrument allows to assess individuals in the appropriate cultural context. We designed our study with the aim of translating and cross-cultural adapting the Pediatric Balance Scale into Italian, and investigating the reliability of the translated version. Two forward translation of the Pediatric Balance Scale were conducted. Two blind backward translations were subsequently performed. A multidisciplinary group compared the content of the translations with those of the original Pediatric Balance Scale. Ambiguities and discrepancies were amended. To assess conceptual equivalence of the translation, we conducted cognitive debriefing involving physiotherapists, parents, and children. Suggestions for rewording were sought and considered for eventual modification. The Italian Pediatric Balance Scale was administered to 18 children with typical development (age 4.7-7.9 years, female 61.1%, scores 51-56), and to 18 children with atypical development (age 4.9-15.0 years, female 44.4%, scores 38-56). Test-retest reliability (i.e., intrarater and interrater reliability, and agreement between raters) of the scale was assessed by mean of single-rating, absolute-agreement, two-way mixed effects model intra-class correlation coefficients (ICC<inf>3,1</inf>) and the Bland-Altman method. The standard error of measurement and the minimal detectable change were subsequently computed. The forward and backward translations showed no substantial differences in content; wording discrepancies were resolved by consensus. The multidisciplinary group proposed some minor changes. Twenty-three physiotherapists, 36 parents, and 36 children participated in cognitive interviewing and further changes were made. Perfect agreement was observed in test-retest reliability assessment of the Italian pediatric balance scale in children with typical development. High reliability (intrarater: ICC<inf>3,1</inf> 0.998, 95% CI 0.994 to 0.999; interrater: ICC<inf>3,1</inf> 0.994, 95% CI 0.984 to 0.998) and agreement among raters (Bland-Altman plots: 89%, 95% CI 67% to 97%, of the data within the 95% CI limits of the bias estimate) were found assessing children with atypical development. Very small standard error of measurement and the minimal detectable change values were observed (intrarater: 0.212 and 0.588; interrater: 0.368 and 1.019). The present study provides a translated and cross-culturally adapted Italian version of the Pediatric Balance Scale. The full cross-cultural validity of the tool requires further steps to complete the psychometric testing.

Single-step genomic predictions for heat tolerance of production yields in US Holsteins and Jerseys

The physiological stress caused by excessive heat affects dairy cattle health and production. This study sought to investigate the impact of heat stress on test-day yields in United States (US) Holstein and Jersey cows and develop single-step genomic predictions to identify heat tolerant animals. Data included 12.8 and 2.1 million test-day records for 923,026 Holstein and 153,710 Jersey cows in 27 US states. From 2015 through 2021, test-day records from the first 5 lactations included milk, fat, and protein yields (kg). Cows' records were included if they had at least 5 test-day records per lactation. Heat stress was quantified by analyzing the impact of a 5-d hourly average temperature-humidity index (THI5d¯) on observed test-day yields. Using a multiple trait repeatability model, a heat threshold (THI threshold) was determined for each breed based on the point that the average adjusted yields started to decrease, which was 69 for Holsteins and 72 for Jerseys. An additive genetic component of general production and heat tolerance production were estimated using a multiple trait reaction norm model and single-step genomic BLUP methodology. Random effects were regressed on a function of THI5d¯ and THI threshold. The proportion of test-day records that occurred on or above the respective heat thresholds was 15% for Holstein and 10% for Jersey. Heritability of milk, fat, and protein yields under heat stress for Holsteins increased, with a small standard error, indicating that the additive genetic component for heat tolerance of these traits was observed. This was not as evident in Jersey traits. For Jersey, the permanent environment explained the same or more of the variation in fat and protein yield under heat stress indicating that non-genetic factors may determine heat tolerance for these Jersey traits. Correlations between the general genetic merit of production (in the absence of heat stress) and heat tolerance genetic merit of production traits were moderate in strength and negative. This indicated that selecting for general genetic merit without consideration of heat tolerance genetic merit of production may result in less favorable performance in hot and humid climates. A general GEBV for genetic merit and a heat tolerance GEBV were calculated for each animal. This study contributes to the investigation of the impact of heat stress on US dairy cattle production yields and offers a basis for the implementation of genomic selection. The results indicate that genomic selection for heat tolerance of production yields is possible for US Holsteins and Jerseys, but a study to validate the genomic predictions should be explored.