5α-dihydrotestosterone (DHT) is a potent androgen, related to male sexual development and irreversibly synthesized from testosterone via 5α-reductase. Dysfunctions in the 5α-reductase system, locally or globally, can have substantial health impacts; measurement of both DHT levels and the testosterone-DHT ratio are thus important for diagnosis and treatment monitoring. For that reason, an isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC MS/MS)-based candidate reference measurement procedure (RMP) to quantify DHT in human serum/plasma was developed. We utilized certified primary reference material for DHT provided by the National Measurement Institute of Australia (NMIA) to calibrate our assay and ensure SI (International System of Units) traceability. To mitigate matrix effects and prevent the co-elution of interferences, two-dimensional heart-cut chromatography was employed for LC-MS/MS, in combination with a solid phase extraction (SPE) sample preparation protocol. Selectivity was determined by spiking the prepared internal standard with possibly interfering substances such as the inactive isomer 5β-DHT and other similar compounds. Comparison of standard line slopes was performed to evaluate matrix effects. Precision and accuracy were assessed via a multi-day validation experiment, and variability components estimated using analysis of variance (ANOVA)-based variance component analysis (VCA). Measurement uncertainty (MU) was evaluated in compliance with current guidelines. This RMP was suitable for analyzing DHT within the range of 0.0160-2.76 ng/mL (0.0551nmol/L-9.50 nmol/L), demonstrating selectivity, sensitivity and matrix-independence. Intermediate precision was≤2.1 %, repeatability was≤1.6 % across all concentration levels, and relative mean bias ranged from-2.2 to 2.5 %, across matrices and concentrations. Expanded MU for reference value assignment (n=6) was≤2.8 %, irrespective of concentration or sampletype. This RMP exhibited high analytical performance for DHT quantification and met requirements for measurement uncertainty. Additionally, it enabled differentiation between the 5α-DHT and 5β-DHT isomers. Consequently, this RMP is suitable for routine assay standardization and clinical sample evaluation.

Whole-genome duplication (WGD) is widespread in plants, yet the extent to which it yields predictable phenotypic outcomes remains unclear. Here, we show that the phenotypic consequences of genome doubling in a duckweed model system, Spirodela polyrhiza, are highly repeatable and largely deterministic. We previously generated three independent colchicine-induced autotetraploids from each of nine globally distributed diploid genotypes and now quantified growth and morphology across a salt gradient. In benign conditions, diploids grew faster, whereas tetraploids had larger, thicker fronds. As salinity increased, the diploid growth advantage diminished, and tetraploids frequently matched or exceeded diploid growth rates. By partitioning the components of variance in growth in our experimental design, we found that ploidy per se explained a comparable amount of phenotypic variation in growth and substantially more variation in salt tolerance than the genotypic background, with evidence of rare within-genotype stochastic differences between tetraploids. These results indicate that the shifts in morphology and stress tolerance from genome doubling are predictable and can match the phenotypic effect from genetic sequence diversity.

Double-hierarchical generalized linear models (DHGLMs) offer a powerful yet overlooked quantitative approach for analysing ecological data with hierarchical structures. DHGLMs account for unit-specific residual variances, allowing the estimation of variance components that expand our ability to quantify biodiversity patterns across levels of biological organization-from individuals to communities. A particularly promising application of these models lies in isotopic ecology, where stable isotopes of carbon (𝛿13C) and nitrogen (𝛿15N) are used to quantify continuous trophic (co)variation across individuals, populations and communities. Here, we show how DHGLMs can be a powerful tool for ecologists interested in trophic patterns and demonstrate the application of these models to isotopic data, showcasing study systems across three levels of biological organization. These examples illustrate how DHGLMs decompose (co)variance components within and across biological units of interest, uncover patterns of diet variation and ecological interactions. We also discuss the advantages, limitations and potential of DHGLMs for advancing research on different questions related to variation in diet and specialization at different biological levels.

Antibiotic practices and AMR trends in livestock farms of Mymensingh region in Bangladesh.

Extreme Value Theory (EVT) provides a logical framework for estimating crash risk by extrapolating from observed traffic conflicts to rare crashes. Bayesian hierarchical extreme value (BHEV) models address the scarcity of extreme conflicts by pooling information across sites, but most applications overlook spatial correlation. This study extends the BHEV framework by incorporating conditional autoregressive (CAR) priors and comparing spatial models with site-specific random-intercept models. A Besag-York-Mollié 2-style reparameterization is employed to address identifiability between structured and unstructured variance components. Using drone-derived trajectory data from an urban network in Athens, Greece, CAR and intrinsic CAR models were fitted and outperformed the random-intercept model. The results showed statistically significant spatial correlation and strong tail fit under multiple backtesting procedures, including unconditional coverage, conditional coverage, and dynamic quantile tests. Spatial priors also meaningfully changed crash risk estimates and site prioritisation, demonstrating the value of modelling spatial dependence in network-level conflict-based crash risk prediction.

This study evaluated the effectiveness of genomic selection (GS) in loblolly pine (Pinus taeda) using a two-generation closed breeding population and a genetically diverse and a large Mainline population. Single-step genomic best linear unbiased prediction (ssGBLUP) models were used to include all phenotypic, genotypic, and pedigree information. Prediction accuracies of genomic estimated breeding values reached up to 0.70 for stem volume and stem straightness. Prediction accuracy showed a strong linear relationship with mean relatedness between training and validation populations (r > 0.92). Scaling the genomic and pedigree relationship matrices improved model stability, increased prediction accuracy, and reduced bias in genomic estimated breeding values. Estimates of heritability and variance components from ssGBLUP were consistent with pedigree-based models, particularly when genomic relationships were properly scaled. Genomic selection had approximately 50% more genetic gain per year relative to conventional selection. Overall, these results demonstrate that GS can be effectively integrated into operational Pinus taeda breeding programs, given sustained investment in large, well-connected training populations with high-quality phenotypic data. We also outline the planned implementation of GS in the North Carolina State University Cooperative Tree Improvement Program to increase genetic gain.

In this study, we address the interactions between the organization of a dynamically stable reaching movement involving the legs and trunk and keeping balance in the field of gravity. This issue is intimately linked to the phenomena of trade-offs between performance-stabilizing synergies at different levels of a control hierarchy, which have not been explored in joint configuration spaces. Young, healthy participants performed natural pointing, movement of the shoulder only and of the endpoint with respect to the shoulder only to match spatial targets, and the two component movements combined without an explicit target for the pointer. Motion kinematics was quantified in a two-dimensional action space. The framework of the uncontrolled manifold hypothesis was used to quantify variance components in the joint configuration space and in the two-component space affecting and not affecting the task variable and their relative magnitudes (synergy index). The values of the synergy index confirmed stabilization of the task-specific effector coordinate across analyses with only minor signs of a trade-off between the two levels of the assumed hierarchy. Both variance components were smaller for the shoulder trajectory without a change in the synergy index. We discuss the differences in the control of movements and force production and possible differences between laboratory tasks and more natural tasks within the framework of the neural control with spatial referent coordinates. The implied postural constraints contributed to lower inter-trial variance in the joint configuration space quantified with respect to the shoulder without affecting the synergic control of the explicit task.

Effects of ROH-based versus GRM-based future inbreeding penalties on genetic gain, variance components, and inbreeding depression in dairy cattle.

The Suffolk is a meat-type sheep that is the predominant terminal-sire breed in the U.S. Known for its rapid growth, high carcass yield, and efficient feed conversion, Suffolk rams are crucial in commercial crossbreeding systems. Despite its importance, the parameters used by the National Sheep Improvement Program (NSIP) in its genetic evaluation have not been updated in over two decades. Therefore, we re-estimated genetic and phenotypic parameters and calculated theoretical accuracies and genetic trends for various growth, body composition, and reproductive traits in U.S. Suffolk sheep. The traits evaluated were birth weight (BWT), weaning weight (WWT), post-weaning weight (PWWT), post-weaning eye muscle depth (EMD), post-weaning fat depth (CFAT), and number of lambs born (NLB) and weaned (NLW) per ewe lambing. The dataset consisted of a pedigree comprising 70,271 individuals and up to 48,000 phenotypic records per trait across more than 100 flocks. Variance components were estimated using the Restricted Maximum Likelihood method and animal models with maternal and permanent environmental effects fitted when appropriate. Records were pre-adjusted for age at recording, birth type, rearing type, and dam age as relevant. For lamb traits, contemporary group (CG; concatenation of flock, birth year, management group, sex, lambing interval, and recording date, except for BWT) was the only fixed effect included in the models. For reproductive traits, CG combined flock and lambing year; ewe age was added as a categorical fixed effect. Heritability estimates (±SE) ranged from 0.04 ± 0.008 for NLW to 0.24 ± 0.025 for CFAT. Maternal heritabilities ranged from 0.02 ± 0.01 for PWWT to 0.16 ± 0.011 for BWT. Genetic correlations among growth traits ranged from 0.47 ± 0.07 to 0.67 ± 0.04, and genetic correlations between PWWT and body composition traits were positive (0.30 ± 0.09 with EMD and 0.55 ± 0.08 with CFAT). A strong genetic correlation (0.81 ± 0.03) was observed between NLB and NLW. Theoretical accuracy was highest for proven animals with progeny records. Genetic trends showed progress in growth and muscling traits but declining maternal breeding values for growth traits. Several updated genetic parameters diverged from legacy NSIP estimates, likely due to shifts in management, and trait definitions. These results emphasize the need for regular re-estimation of genetic parameters to ensure accurate genetic evaluations and promote balanced genetic progress across traits.

Genetic analyses of udder conformation traits and daily milk yield measured by robotic milking systems using repeatability and random regression models in American Holstein cattle.

ABSTRACT Executive functions (EF) and theory of mind (ToM) are frequently impaired in attention-deficit/hyperactivity disorder (ADHD). Due to their close neurocognitive relationship, it is important to determine the extent to which EF mediates ToM performance in this population. The present study analyzed the mediating role of EF in ToM among children diagnosed with ADHD. A clinical group of children with ADHD (n = 63) was compared to a group of typically developing peers (n = 63) matched by age and gender. All participants completed standardized measures of EF and ToM. Group differences and mediation models were examined. Results demonstrated that children with ADHD performed significantly lower than their peers in most EF domains and across all ToM tasks. Working memory and selective attention were the strongest predictors of ToM performance, accounting for 16.8% to 51.8% of the variance in different ToM components. Ten mediation models confirmed partial mediation, with indirect effects accounting for 15.8% to 37.8% of the total effect in most models. In some models, such as Perspective Taking and Second-Order Emotion Attribution, indirect effects exceeded 50%, indicating a more substantial role of EF. Nevertheless, a considerable portion of the effect remained unexplained by EF alone. These findings indicate that EF, particularly working memory and selective attention, play an important but not exclusive role in ToM performance in ADHD. This pattern supports a multidimensional understanding of ToM and emphasizes its relevance for neuropsychological assessment in ADHD.

Nonlinear mixed-effects modexls are frequently used to analyse longitudinal and clustered data from medical studies with subject-specific variability. A key question in such mixed-effects models is which random effects are truly needed in the model, which amounts to testing whether associated variance components are non-zero. Unlike linear mixed-effects models, testing random effects in nonlinear mixed-effects models is an understudied problem due to their model complexity and convergence issues in practice. Since the null hypothesis lies on the boundary of parameter space, the usual asymptotic chi-squared distribution of likelihood ratio and score tests is incorrect. The correct asymptotic distribution is a mixture of chi-squared distributions, however determining the mixing weights is generally not possible, especially when testing multiple correlated random effects. To address these issues, we propose a flexible nonparametric framework for testing random effects in nonlinear mixed-effects models with additive random errors that does not require normality or any other distribution for random effects and errors. We introduce a flexible test based on a suitable permutation procedure to approximate the finite-sample distribution of our test statistic, which also enjoys distribution-free estimates of variance components. The framework allows users to select among estimation methods based on their data characteristics. Our proposal can be used to test all random effects or any subset of them. We evaluate the performance of our nonparametric method through extensive simulations and two motivating case studies. We provide an R package, called TestREnlme, for the implementation of our proposed tests.

A multi-indicator, data-driven framework for spatiotemporal analysis of compound droughts in the Southern Caspian Basin.

A comparative study of some exact designs for estimating the variance components

Population-specific reference intervals for 22 biochemical analytes: three indirect methods application in a multi-ethnic area.

The development of improved low-nitrogen (N) tolerant and N-efficient varieties of tropical maize can be achieved by improving root morphology traits, but little is known on the inheritance of root traits in tropical maize, especially under low-N stress. Thus, our main objective was to assess the inheritance of seedling root and shoot traits in tropical maize under contrasting N levels. We evaluated 45 F1 and 45 reciprocal crosses (90 total crosses) along with ten parental inbred lines of tropical maize for root and shoot traits under contrasting N levels: low N (LN) and high N (HN). A mixed model approach was used to estimate the variance components of the general (GCA), reciprocal general (RGCA), specific (SCA) and reciprocal specific (RSCA) combining ability as well as to predict the GCA, RGCA, SCA and RSCA effects. We also estimated the heterosis for each trait under LN and HN. The inheritance of root and shoot traits in tropical maize is largely modulated by additive gene action, but nonadditive gene action also plays a role in the inheritance of some traits; cytoplasmic genes also contribute a little to the inheritance of root size in tropical maize. LN stress has little influence on the inheritance of root traits, suggesting that the same breeding schemes might be used to improve the root size of tropical maize under both low-N stress and nonstress conditions. The lines VML016, VML022, VML033 and VML051 presented favorable nuclear genes, whereas the lines VML004, VML017, VML020, and VML028 presented favorable cytoplasmic genes for increasing root size in maize under both low-N stress and nonstress. Moreover, heterosis must also be explored in the development of tropical maize hybrids with enhanced root size, and the direction of the crosses must be considered in the exploration of heterosis for root size, especially under low-N stress environments.

Functional connectivity varies across individuals due to genetic and environmental factors, yet classical twin models typically confound non-shared environment with measurement error and are largely limited to resting-state analyses. We hypothesized that: i) explicitly modeling measurement error from repeated fMRI sessions enables more accurate application of classical twin models (ACE/ADE) to functional connectivity; ii) model applicability depends on scan-length and parcellation granularity; iii) genetic and environmental effects on functional connectomes show differentiated functional modules across conditions. We extended ACE/ADE models to include a repeated-scan derived error term by analyzing monozygotic and dizygotic twins from the Young-Adult Human Connectome Project dataset. Genetic and environment variance components were estimated for all functional couplings across resting-state and task conditions, integrated across conditions using a minimum-error criterion, and analyzed using multilayer community detection across resolution scales. Functional couplings segregated into distinct categories characterized by shared environmental, additive, dominant, or epistatic influences, with a substantial fraction not meeting twin-model assumptions. Integrating across conditions revealed hierarchical community structure in genetic and environmental components observed across community resolution scales. Incorporating measurement error into twin models improves interpretability and applicability at the functional connectome level, revealing that genetic and environmental influences are structured into coherent, multiscale brain networks.

OBC: Optimized Batch Correction with Dual-level Quality Control for Scalable Proteomics and Metabolomics.

Tracing gasoline accelerants in arson investigations represents a critical yet intractable forensic challenge, as intrinsic geological signatures are inextricably convoluted with refining artifacts and environmental degradation noise. Traditional static fingerprinting lacks a viable strategy to disentangle these confounding factors in complex, dynamic mixtures. To bridge this gap, we proposed a hierarchical chemometric framework that shifts the paradigm from searching for immutable markers to mathematically decoupling geological baselines from process-induced fractionation. Integrating Nested Variance Component Analysis (VCA) with Multivariate Discriminant Trajectory Analysis (MDTA), we orthogonally decomposed mixed isotopic variances to isolate a robust panel comprising three "Rayleigh-resistant" regional anchors and one process-recording marker. Kinetic stress-testing quantified precise "Forensic Validity Windows", revealing that regional signatures persist for >48h of weathering, process-specific details are transient, requiring immediate sampling (<2 min) under combustion. Crucially, MDTA uncovered a "Topological Memory Effect": despite significant isotopic drift driven by thermodynamic fractionation, the evolution trajectories of different sources maintained strict topological separation. This work provides a generalized mathematical strategy for dynamic source tracking. Furthermore, we introduce an open-source computational workflow, ensuring transparency and reproducibility. This enables forensic analysts to distinguish accelerant sources in high-profile wildfire or arson cases without requiring extensive retraining or programming expertise, bridging the gap between advanced chemometrics and practical enforcement.

COVID-19, caused by the SARS-CoV-2 virus, is mainly recognized for its respiratory manifestations. However, growing evidence regarding the widespread expression of ACE2 and TMPRSS2 receptors on diverse extrapulmonary sites, particularly in renal tubular epithelial cells, suggests susceptibility of other organ systems, including the kidneys, to such conditions as acute kidney injury (AKI). In the present retrospective study, we explored the interrelationship between disease severity and renal function abnormalities by analyzing key biochemical parameters: blood urea nitrogen (BUN), serum creatinine (Cr), the BUN/Cr ratio, and estimated glomerular filtration rate (eGFR). Using descriptive statistics and joint generalized linear models, we examined both the mean and variance components of these markers alongside inflammatory indicators such as C-reactive protein (CRP) and D-dimer. Our findings revealed a significant positive correlation between serum urea levels and both CRP and D-dimer concentrations, suggesting that elevated urea may reflect heightened inflammatory activity. Additionally, eGFR showed a positive association with CRP, indicating potential renal involvement in systemic inflammation. Our in silico studies supported such observations, as genes responsible for CRP and D-dimer elevation were found to be common in AKI-associated pathways, particularly IL-6/JAK-STAT, NF-κB, HIF-1, and complement pathways, ultimately causing renal microthrombosis, tubular necrosis, and fibrotic remodeling. Notably, serum Cr revealed no significant association with CRP or D-dimer, possibly due to its lower sensitivity in early renal dysfunction. Although the study is limited by a relatively small sample size and lacks longitudinal data, it underscores the importance of monitoring renal function parameters in COVID-19 patients as potential markers of disease progression.