Variance Of Parameters Research Articles

Modelling the spatio-temporal dynamics of youth unemployment in Africa: A Bayesian approach

Youth unemployment has remained one of the major social and economic problems facing many African countries despite persistent efforts by governments and other stakeholders to mitigate the problem. We deployed a two-parameter beta geospatial model within a distributional regression framework that enables us to link covariates to the mean and variance parameters of the response distribution, to study the spatio-temporal patterns of unemployment combining multiple African countries, while accounting for the impact of temperature and rainfall as climatic variables. We also examined the pattern of relationship between unemployment and gross domestic product of each of the countries over the study period. The data for the study was sourced from the World Bank Development Indicator database covering 1991 to 2020 while Bayesian inference was based on Markov chain Monte Carlo simulation. The findings reveal rising unemployment rates with increase in temperature and rainfall, and that in most cases, neighbouring African countries show similar unemployment pattern over the study period.

Inference of multiple high-dimensional networks with the Graphical Horseshoe prior

We develop a novel full-Bayesian approach for multiple correlated precision matrices, called multiple Graphical Horseshoe (mGHS). The proposed approach relies on a novel multivariate shrinkage prior based on the Horseshoe prior that borrows strength and shares sparsity patterns across groups, improving posterior edge selection when the precision matrices are similar. On the other hand, there is no loss of performance when the groups are independent. Moreover, mGHS provides a similarity matrix estimate, useful for understanding network similarities across groups. We implement an efficient Metropolis-within-Gibbs for posterior inference; specifically, local variance parameters are updated via a novel and efficient modified rejection sampling algorithm that samples from a three-parameter Gamma distribution. The method scales well with respect to the number of variables and provides one of the fastest full-Bayesian approaches for the estimation of multiple precision matrices. Finally, edge selection is performed with a novel approach based on model cuts. We empirically demonstrate that mGHS outperforms competing approaches through both simulation studies and the application to a bike-sharing and a genomic dataset.

Modeling and Estimation of Adaptive Optics-Corrected long-exposure Point Spread Function Using Gaussian Sum Approximation

Abstract Adaptive optics (AO) enhances astronomical images by correcting perturbations in the optical system, resulting in complex point spread function (PSF) shapes, which provide critical information for optical design, calibration, and diagnostics. This paper presents a parametric model for an AO-corrected long-exposure PSF that adapts to complex shapes and various seeing conditions. A complementary estimation method is also introduced, addressing the inverse problem of estimating the atmospheric PSF or power spectral density (PSD) from observed PSF data. The model, based on Gaussian sum approximation (GSA), was tested using simulated PSF data from the OOMAO toolbox for Fried parameter (r0) values ranging from 0.10 to 0.20 m and on-sky data from the Very Large Telescope’s MUSE instrument. Two approaches were analyzed: (i) direct PSF estimation using GSA, and (ii) PSF estimation derived from atmospheric PSD estimation. Both methods were evaluated using root mean square error (RMSE). While approach (i) provided the best performance, approach (ii) produced more accurate atmospheric PSF estimates compared to the Moffat approximation and the Fetick method. Additionally, approach (ii) enabled the estimation of key physical parameters, such as the Fried parameter (r0) and residual AO variance ($\sigma _{AO}^2$). The proposed methods effectively estimate atmospheric PSF and PSD, even for complex PSF shapes influenced by spiders, central obstructions, or static aberrations.

SQSTS: A sequential procedure for estimating steady-state quantiles using standardized time series

ABSTRACT We develop and evaluate SQSTS, an automated sequential procedure for computing confidence intervals (CIs) for steady-state quantiles based on the simulation analysis methods of standardized time series (STS), batching, and sectioning. Using recent theoretical developments for STS-based quantile estimation in dependent sequences, we formulate the key steps in SQSTS for controlling the growth of the batch size on successive iterations of the procedure. The variance parameter associated with the full-sample quantile estimator is estimated by a combination of estimators that are asymptotically independent of each other and the full-sample quantile estimator with increasing batch size and a fixed number of batches. Extensive experimentation revealed that SQSTS performed well compared to its competitors in terms of estimated CI coverage probabilities; and it outperformed those competitors with regard to average sample-size requirements. Finally, we outline an extension of SQSTS for computing individual CIs for a set of selected quantiles.

Isotonic Regression for Variance Estimation and Its Role in Mean Estimation and Model Validation

We study isotonic regression which is a nonparametric rank-preserving regression technique. Under the assumption that the variance function of a response is monotone in its mean functional, we investigate a novel application of isotonic regression as an estimator of this variance function. Our proposal of variance estimation with isotonic regression is used in multiple classical regression problems focused on mean estimation and model validation. In a series of numerical examples, we (1) explore the power variance parameter of the variance function within Tweedie’s family of distributions, (2) derive a semi-parametric bootstrap under heteroscedasticity, (3) provide a test for auto calibration, (4) explore a quasi-likelihood approach to benefit from best-asymptotic estimation, and (5) deal with several difficulties under Lognormal assumptions. In all of these problems we verify that the variance estimation with isotonic regression is essential for proper mean estimation and beneficial compared to traditional statistical techniques based on local polynomial smoothers.

Genetic Evaluation of Monthly Test-Day Milk Yields of Jersey Crossbred Cattle Under Farmers' Production System in Tamil Nadu, India.

Tamil Nadu, a state in southern India, is bereft of any native dairy or dual-purpose cattle breeds, and the state depends chiefly on crossbred cattle for milk production. Jersey crossbred cattle account for 90% of the state's milk production. This genetic evaluation study aimed to assess milk production traits, including test-day milk yields (TDMYs), total lactation milk yield (TMY), and 305-day milk yield (305MY), in these cattle reared under farmers' conditions. Data on monthly TDMYs (TDMY1 to TDMY10) of Jersey crossbred cows (n = 75,627) reared by the farmers over 11 years (2012-2022) were collected for evaluation. The influence of non-genetic factors viz., location (agroclimatic zones), period and season of calving, and parity on TDMYs, TMY and 305MY were assessed, and the variance and covariance components for genetic parameters (heritability and genetic correlation) of the traits were estimated by the method of restricted maximum likelihood (REML) under a multivariate animal model and by a random regression model (RRM). The overall means of various TDMYs ranged from 4.98 (TDMY10) to 9.95 kg (TDMY2), and for TMY and 305MY, the means were 2480.33 and 2393.71 kg, respectively. The heritabilities estimated by the multivariate animal model for TDMYs were moderate and ranged from 0.26 ± 0.02 to 0.37 ± 0.02 and the estimates were less variable throughout lactation, while the estimates obtained by RRM were medium to high and ranged between 0.29 ± 0.01 and 0.67 ± 0.02 for milk yield on day 5 to 305 (DIM 5-DIM 305). The heritabilities estimated for TMY and 305MY were 0.43 ± 0.02 and 0.43 ± 0.02, respectively. The estimates of heritability for mid-lactation yields were comparatively less than those at the beginning and the end of lactation. The moderate to high estimates of heritabilities for TDMYs, TMY, and 305MY obtained in the present study offer ample scope for improving milk production through selective breeding. Genetic correlations between TDMYs estimated by the multivariate animal model were positive and high, with a range between 0.75 ± 0.03 and 0.99 ± 0.00. Considerably higher estimates (0.98-0.99) were observed between adjacent TDMYs showing strong genetic associations. By RRM, genetic correlations estimated between DIMs were positive (except for DIM 5 with DIM 125 to DIM 185 and DIM 125 with DIM 305), and the magnitude of genetic correlation decreased with an increase in the interval between the DIMs. The high genetic correlation observed between the TDMYs (in the early stage of lactation) and total lactation milk yield suggested that these test-day yields could be used favorably for the early selection of cows for milk production, which facilitates reduction in the generation interval and consequently increases the annual genetic gain for the milk production traits.

Reply to Comment on “Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling” by Jasper Vrugt

AbstractThis discussion is a reply to the comments made by Dr. Jasper Vrugt on the Metropolis‐Hastings (M‐H) algorithm with multiple independent Markov chains proposed by Huang and Merwade (2023a), https://doi.org/10.1029/2023wr034947 concerning the validity of the methodology in estimating Bayesian model averaging (BMA) parameters (weights and variances) of the framework proposed by Raftery et al. (2005), https://doi.org/10.1175/mwr2906.1. In this reply, we address his concerns by emphasizing the motivation of applying the proposed M‐H algorithm to BMA analysis and the applicability of the effective sample size that accounts for the autocorrelation across samples in evaluating the efficiency of Markov chain Monte Carlo sampling. Moreover, the details of sampling procedure for BMA prediction distribution are clarified. On the other hand, we present a fair comparison of the default Expectation‐Maximization, M‐H, and differential evolution adaptive Metropolis (DREAM) algorithms in estimating BMA parameters based on a numerical experiment. Results reinforce the findings obtained from Huang and Merwade (2023a) https://doi.org/10.1029/2023wr034947 and further indicate that the proposed M‐H algorithm is better than the DREAM algorithm in terms of sampling efficiency and prediction accuracy. Accordingly, we raise concerns on the use of DREAM algorithm in BMA analysis and suggest conducting peer reviews on the MODELAVG toolbox.

MEASUREMENT AND CHARACTERISTIC ANALYSIS OF SKELETAL GEOMETRIC PARAMETERS IN CHINESE HUMANS

Problem: Significant differences exist between the body dimensions of Chinese and Western individuals, and these differences affect the injury characteristics of passengers in traffic accidents. Most current parametric finite element human models are based on Western body data, which does not adequately assess the injury risks for Chinese individuals. Therefore, it is imperative to establish a digital model that reflects the anatomical features of Chinese humans. This requires comprehensive anthropometric data specific to the Chinese population. Aim: This study aims to collect and analyze skeletal geometric parameters from various representative regions in China across different age groups to fill the gap in Chinese anthropometric data. Additionally, it seeks to explore regional and age-related differences in these parameters, providing a scientific basis for developing finite element models that reflect the characteristics of the Chinese population. Methods: Clinical CT data of skeletal geometric parameters were collected from 224 individuals across seven representative regions of China, including parameters of the head, chest, and lower limbs. Descriptive statistics, K-W tests, and U-tests were used to analyze regional differences. Spearman correlation and linear regression analyses were employed to assess the impact of age. Furthermore, weighted averages and weighted variances of skeletal geometric parameters were calculated to reflect the characteristics of individuals from various regions across the country, which will be applied in the development of Chinese human models. Results: The results indicate significant regional differences in the skeletal geometric parameters of Chinese individuals, exhibiting regional patterns between the north–south and east–west areas. While skeletal geometric parameters showed significant differences with age, no clear statistical pattern was observed. Conclusion: This study systematically extracts and analyzes the skeletal geometric parameters of the Chinese population from regional and age perspectives for the first time. These data will be applied in the development of Chinese human models, providing crucial support for improving passenger safety in traffic accidents, enhancing vehicle safety design, and establishing anthropometric standards suitable for the Chinese context.

The Influence of Anthropometric Factors on Renal mpMRI: Insights From Regional Analysis.

Multiparametric MRI (mpMRI) provides detailed insights into renal function, but the impact of anthropometric factors on renal imaging is not fully understood. To investigate regional correlations between mpMRI parameters and age, body mass index (BMI), and body surface area (BSA). Prospective, cross-sectional observational study. Twenty-nine healthy volunteers (44.5 ± 18.3 years, 18 females) without a history of renal disease. 3-T, pseudo-continuous arterial spin labeling, multi-echo gradient-recalled echo, diffusion-weighted imaging, T1 and T2 mapping. Bilateral kidneys were segmented into nine concentric layers (outer cortex to inner regions) and nine equiangular sections (lower to upper pole). Key parameters (renal blood flow [RBF], , apparent diffusion coefficient [ADC], T1 and T2 maps) were correlated with age, BMI, and BSA. Differences in parameters between age and BMI groups were also evaluated. Spearman correlation, Mann-Whitney U test, and rank-biserial correlation coefficient for effect size. A P-value <0.05 was considered statistically significant. RBF correlated negatively with age in all regions and BMI in inner layers and lower pole. ADC negatively correlated with BMI (significance was not reached in layers 2, 7, 8; P-value = 0.06-0.12) and BSA in layers 1-7. T1 negatively correlated with age in inner regions and lower medial pole. Significant positive correlations were found between age and (outermost layer, upper pole), age and T2 (inner and cranial-caudal regions), as well as BMI and T2 (except upper pole; P-value = 0.06). Significant differences between age groups were observed for RBF (all regions), (outermost and second innermost layers, central lateral region), T1 (innermost layer), and T2 (upper medial pole). Between BMI groups, ADC (middle layers, upper medial pole) and T2 (outermost and inner layers, lower pole to lateral region) differed significantly. Intrarenal variance of mpMRI parameters correlated with age, BMI, and BSA. 4 TECHNICAL EFFICACY: Stage 1.

Variability in soil characteristics in the field–bund transition area increases water loss potential in paddy fields

Water loss in paddy fields occurs through various pathways, and previous studies have primarily focused on water seepage in the field, often overlooking the potential for the field-bund area. In this study, 3 typical paddy fields in the plain river network area of southeastern China were selected to clarify the differences in the soil structure and hydraulic characteristics at different positions within the field–bund area: the field, inner bund, middle bund and outer bund. The interactions between basic soil properties and hydraulic characteristics were also evaluated. The results revealed that the outer bund presented the lowest soil porosity (6.92 %), followed by the field (7.52 %), middle bund (7.77 %), and inner bund (8.09 %). The soil pores in the field presented the smallest mean diameter and fractal dimension and the highest degree of anisotropy. The deep layer of the bund contained more macropores, and the soil pores exhibited greater spatial distribution heterogeneity. The bottom layer in the field and bund presented the lowest average Ks value of only 0.05 mm min−1, indicating the presence of a plow pan and a notable tendency for lateral seepage. Differences in the soil structure and hydraulic parameters between the field and bund created a driving force for lateral seepage and rendered the field–bund area a hotspot for water loss. For the analysis of the underlying water loss mechanism, the structural equation model represented 65 % of the total variance in the hydraulic parameters. The micropore characteristics had the greatest positive direct effect on the hydraulic parameters, with a standardized path coefficient of 0.39 (p < 0.001). The soil physical properties were not directly related to the hydraulic parameters but exerted an indirect effect through aggregate stability and micropore and macropore characteristics, with a total indirect standardized path coefficient of −0.41.

On aggregation invariance of multinomial processing tree models

Multinomial processing tree (MPT) models are prominent and frequently used tools to model and measure cognitive processes underlying responses in many experimental paradigms. Although MPT models typically refer to cognitive processes within single individuals, they have often been applied to group data aggregated across individuals. We investigate the conditions under which MPT analyses of aggregate data make sense. After introducing the notions of structural and empirical aggregation invariance of MPT models, we show that any MPT model that holds at the level of single individuals must also hold at the aggregate level when it is both structurally and empirically aggregation invariant. Moreover, group-level parameters of aggregation-invariant MPT models are equivalent to the expected values (i.e., means) of the corresponding individual parameters. To investigate the robustness of MPT results for aggregate data when one or both invariance conditions are violated, we additionally performed a series of simulation studies, systematically manipulating (1) the sample sizes in different trees of the model, (2) model parameterization, (3) means and variances of crucial model parameters, and (4) their correlations with other parameters of the respective MPT model. Overall, our results show that MPT parameter estimates based on aggregate data are trustworthy under rather general conditions, provided that a few preconditions are met.

Assessment of FY-3E GNOS II Radio Occultation Data Using an Improved Three-Cornered Hat Method

The spatial–temporal sampling errors arising from the differences in geographical locations and measurement times between co-located Global Navigation Satellite System (GNSS) radio occultation (RO) and radiosonde (RS) data represent systematic errors in the three-cornered hat (3CH) method. In this study, we propose a novel spatial–temporal sampling correction method to mitigate the sampling errors associated with both RO–RS and RS–model pairs. We analyze the 3CH processing chain with this new correction method in comparison to traditional approaches, utilizing Fengyun-3E (FY-3E) GNSS Occultation Sounder II (GNOS II) RO data, atmospheric models, and RS datasets from the Hailar and Xisha stations. Overall, the results demonstrate that the improved 3CH method performs better in terms of spatial–temporal sampling errors and the variances of atmospheric parameters, including refractivity, temperature, and specific humidity. Subsequently, we assess the error variances of the FY-3E GNOS II RO, RS and model atmospheric parameters in China, in particular the northern China and southern China regions, based on large ensemble datasets using the improved 3CH data processing chain. The results indicate that the FY-3E GNOS II BeiDou navigation satellite system (BDS) RO and Global Positioning System (GPS) RO show good consistency, with the average error variances of refractivity, temperature, and specific humidity being less than 1.12%2, 0.13%2, and 700%2, respectively. A comparison of the datasets from northern and southern China reveals that the error variances for refractivity are smaller in northern China, while temperature and specific humidity exhibit smaller error variances in southern China, which is attributable to the differing climatic conditions.

A logistic model for precise tomato fruit-growth prediction based on diameter-time evolution

Monitoring tomato fruit growth in large greenhouse environments is crucial for estimating harvest volume and maximising labour efficiency. Mathematical modelling is commonly used to characterise crop and fruit growth and to understand crop responses to environmental influences. This study presents a logistic approach for estimating tomato fruit maturity based on fruit diameter. A set of logistic models were evaluated, and their precision and ability to forecast tomato fruit growth were analysed. Non-linear least square regression was used to fit each model to individual tomato fruits from a sample of over 600 fruits of two species (small and large fruit-bearing tomatoes) grown within one year. The focus was on fruit diameter and the necessary measurement precision for fruit maturity estimation. Low-dispersion variables were identified within our logistic model functions, and fruit species-specific model parameters were determined. Furthermore, we reduced the number of regression variables by identifying parameters of low variance. This allowed for high prediction precision (>80%) at the early fruit maturity stages (<30%). The fruit diameter data were collected manually using a caliper. Our results establish an upper limit for the measurement precision of automated fruit size estimation approaches utilising photogrammetry, with an optical range of 98% for 3 mm and 90% for 5 mm precision.

Bias and Linking Error in Fixed Item Parameter Calibration

The two-parameter logistic (2PL) item response theory (IRT) model is frequently applied to analyze group differences for multivariate binary random variables. The item parameters in the 2PL model are frequently fixed when estimating the mean and the standard deviation for a group of interest. This method is also called fixed item parameter calibration (FIPC). In this article, the bias and the linking error of the FIPC approach are analytically derived in the presence of random uniform differential item functioning (DIF). The adequacy of the analytical findings was validated in a simulation study. It turned out that the extent of the bias and the variance in distribution parameters increases with increasing variance of random DIF effects.

Predicting Non-Mass Breast Cancer Utilizing Ultrasound and Molybdenum Target X-Ray Characteristics.

The aim of this study is to investigate the influence of ultrasound and molybdenum target X-ray characteristics in predicting non-mass breast cancer. A retrospective analysis was conducted on the clinical data of 185 patients presenting with non-mass breast lesions between September 2019 and 2021. The non-mass lesions were categorized into benign and malignant types based on ultrasonographic findings, which included lamellar hypoechoic, ductal alteration, microcalcification, and structural disorder types. Furthermore, an examination was undertaken to discern variances in molybdenum target X-ray parameters, ultrasonographic manifestations, and characteristics among individuals diagnosed with non-mass breast lesions. The ultrasonographic depiction of microcalcified lesions and the identification of suspicious malignancy through molybdenum target X-ray evaluation exhibited independent associations with non-mass breast cancer, yielding statistically significant differences (p < 0.05). Subsequently, the logistic regression model was formulated as follows: Logit (P) =-1.757+2.194* microcalcification type on ultrasound + 1.520* suspicious malignancy on molybdenum target X-ray evaluation. The respective areas under the receiver operating characteristic curves for microcalcification type on ultrasound, suspicious malignancy on molybdenum target X-ray, and the integrated diagnostic model were 0.733, 0.667, and 0.827, respectively, demonstrating discriminative capacities. Using both ultrasound and molybdenum target X-ray diagnostics can increase the accuracy of non-mass breast cancer detection. The findings of this study have the potential to augment the detection rate of non-lumpy breast cancer and provide an imaging basis for enhancing the prognosis of individuals with breast cancer.

Properties of risk aversion estimated from portfolio weights

While risk tolerance is often elicited using questionnaire-based instruments, in this paper, we evaluate the merits of an inversion-based technique, wherein risk aversion parameters are inferred from an individual’s portfolio holdings and a sequence of realized returns. We obtain expressions for the finite sample and asymptotic variance of the estimated risk aversion parameter under the inversion approach with a single risky asset, demonstrating that confidence intervals for parameter estimates are relatively wide. Extending the analysis, we show that inferring risk aversion from multiple risky assets does not typically serve to reduce the estimated parameter variance, but rather propagates estimation error.

Spatial-temporal response and precursor characteristics of tensile failure on disc coal samples of different sizes combining AE, EMR and DIC techniques

Tensile fractures in deep coal and rock mass can readily trigger dynamic calamities like rock bursts, significantly impacting the safety of coal mining. To enhance our understanding of coal’s tensile failure mechanism, Brazilian splitting failure experiments were conducted on coal samples with prefabricated cracks of different sizes. Acoustic emission (AE), electromagnetic radiation (EMR) and digital image correlation (DIC) techniques were used to analyze the instability failure process and precursor characteristics of coal samples. The results showed that, for coal samples of the same thickness, the tensile strength and peak strain gradually decreased with the increase of coal sample size, whereas the elastic modulus increased gradually, highlighting more pronounced brittleness characteristics and revealing a noticeable size effect. The time-varying evolution of AE energy, EMR energy and VE (virtual extensometer) elongation during the tensile failure process of coal samples is closely correlated to the stress evolution trend, exhibiting distinct characteristics at various loading stages. The failure modes of coal samples of varying sizes exhibited a strong correlation with the spatial distribution of AE events, as well as the strain and displacement field measured by DIC. With the coal sample’s growing size, the percentage of AE high energy events rose while the total number of AE positioning events fell, the proportion of DIC strain concentration area decreased, displacement zoning characteristics became more obvious, and the critical opening displacement of cracks increased. The variance of AE, EMR and DIC related parameters in coal samples showed significant critical slowing down characteristics. Based on their response timescales, EMR energy was identified as an “Early warning”, AE energy as a “Medium warning”, and VE elongation as a “Short-imminent warning”. A comprehensive analysis of multi-parameter monitoring information proved beneficial in accurately pinpointing precursory response points of coal mine dynamic disasters, thereby enhancing monitoring precision.

Assessment of nutrient status, macro faunal diversity and microbial isolates in date palm plantation in Damaturu Yobe State, Nigeria

This research assessed nutrient status, macro-faunal diversity and microbial isolates in the soil of date palm plantation in Mai Sandari Damaturu, Yobe State. Parameters evaluated include; data on nutrient status of soils, assemblage and diversity of soil macro invertebrates and assemblage of microbial isolates based on growth performances of the date palm. The plantation was divided according to growth variabilities. Eighteen (18) auger points were taken, six (6) in each performance site based on the corresponding variability as observed. Soil samples were collected within same points for physicochemical analysis, microbial isolates and for soil macro-invertebrates ‘identification and enumeration. One-way Analysis of Variance (ANOVA) was used to compare results of nutrient status of soils and to test for significant differences in nutrient concentration of the soils samples, mean separation was done using Least Significant Difference (LSD). Soil macro-invertebrate assemblage was analyzed for diversity indices of species using relevant formulae. Nutrient agar medium at 105 dilutions inoculated in a petri-dishes and incubated at 30±10°C for 2-5 days for bacteria colonies, while for fungi and actinomycetes, sabaurond dextrose agar was used at 25°C for 5-7 days and afterward microorganisms per colony forming units (Cfu) were counted. Analysis of variance for the soil parameters tested at p≤ 0.05 level of significance showed no significant differences in all the parameters of soil physical properties based on growth performance sites. However, organic carbon, organic matter and TN vary significantly after the post-hoc tests across growth performance sites in chemical properties (p ≤ 0.05). Results showed a total of 2646 macro fauna scattered within the study area. Eleven (11) families of macro fauna were also identified in the study. Results from the study area showed that the area is highly rich in microorganisms. A total of 5096 colonies were scattered within 10 families in the study area. The finding of this study revealed that there was no significant difference among the physical properties across the study site leading to the conclusion that soil physical properties may not have been responsible for the observed differences in the growth of the Date Palm Plantation. A different pattern was however observed in the values of the chemical properties of the plantation where significant differences exist in organic carbon, organic matter and total nitrogen across these sites. Based on the findings of the study it is recommended that all application of agrochemicals except fertilizers in the plantation should be suspended until evidence of their efficiency on the palm trees is shown.

Comprehensive evaluation of fetal renal ultrasound parameters for fetal growth restriction

ObjectiveThis study aims to investigate variances in renal ultrasound parameters between fetuses experiencing fetal growth restriction (FGR) and those with normal intrauterine development, with the intent to offer actionable insights for clinical management. MethodForty-five pregnant women diagnosed with FGR between 28 and 36 weeks of gestation, who underwent examination at Wenzhou People's Hospital from September 2021 to June 2023, constituted the FGR group. Concurrently, 65 pregnant women with normal intrauterine development at matching gestational weeks formed the control group. Renal ultrasound parameters, encompassing renal artery peak systolic velocity (PSV), end diastolic velocity (EDV), time averaged maximum velocity (TAMX), resistive indices (S/D, PI, RI), ratios of renal volume to gestational age (RV/WEEK) and estimated fetal weight (RV/EFW), vascular indices (VI, FI, VFI), were compared between the two groups. All parameters represented the mean values of bilateral kidneys. ResultIn the FGR group, fetal renal artery PSV (37.71 ± 9.93 cm/s), EDV (6.19 ± 1.50 cm/s), TAMX (15.10 ± 3.83 cm/s), RV/WEEK (0.45 ± 0.12), RV/EFW (7.53 ± 3.24), VI (22.19 ± 15.00), and VFI (5.53 ± 3.63) were significantly lower compared to the control group (PSV: 47.11 ± 11.24 cm/s, EDV: 7.13 ± 2.00 cm/s, TAMX: 17.85 ± 3.85 cm/s, RV/WEEK: 0.66 ± 0.19, RV/EFW:9.20 ± 3.17, VI: 28.67 ± 14.72, VFI: 7.40 ± 3.68). Conversely, fetal renal artery resistive indices (S/D: 9.09 ± 2.58, PI: 2.71 ± 0.56, RI: 0.92 ± 0.04) in the FGR group were notably higher than those in the control group (S/D: 6.22 ± 1.93, PI: 2.20 ± 0.73, RI: 0.87 ± 0.04), with statistical significance (P < 0.05). No significant difference was found in renal FI between the FGR group (26.78 ± 6.59) and the control group (26.89 ± 5.82) (P > 0.05). Receiver operating characteristic (ROC) curve analysis revealed higher diagnostic efficacy for RV/WEEK and RI among individual indicators, while combined parameter application yielded the highest diagnostic efficiency. ConclusionUtilizing a comprehensive evaluation of fetal kidney ultrasound parameters with multiple indices facilitates early screening and diagnosis of FGR fetuses, thereby aiding clinical decision-making and enhancing newborn birth outcomes.

Experimental unbalance: reflections on genetic parameters and corn selection gain

Genetic progress depends on the components of variance and genetic parameters. In this context, the objective of this work was to identify the maximum allowed percentage of experimental imbalance in the estimation of variance components, genetic parameters and selection gain in intervarietal corn hybrids. The experimental design used was randomized blocks, with 60 corn hybrids with three replications. Random subsets of data were created in order to express unbalance scenarios of the phenotypic matrix, we obtained 9 scenarios (0, 10, 20, 30, 40, 50, 60, 70 and 80% unbalance).Thus, the genetic variance of the paternal genitor, the genetic variance of the maternal genitor, additive variance, individual phenotypic variance, narrow-sense heritability of intervarietal additive effects for the paternal genitor were estimated, narrow-sense heritability of intervarietal additive effects for the maternal genitor, narrow-sense heritability, broad-sense heritability, genotypic variation coefficient between progenies and variation coefficient residual. Narrow sense heritability reveals high magnitude for the characters cob mass and hundred grain mass. Maternal effect is expressed through ear insertion height, ear grain mass, cob mass and diameter, number of rows, ear mass and length, and grain yield. The imbalance inflates the variance components and genetic parameters for the highlighted characters. It supports 10% imbalance in the data matrix without compromising the reliability of the estimates. Pronounceable selection gains are obtained for grain yield based on pressures of 1 and 5%. An unbalance of 10% is supported in the selection gain for cob mass, ear length and hundred grain mass.