Category: Statistics

Heredity (Edinb). 2026 Jun 18. doi: 10.1038/s41437-026-00855-4. Online ahead of print.

ABSTRACT

Seaweed farming, as a part of aquaculture, offers a sustainable alternative to modern agricultural practices; however, genetic enhancement and breeding programs for most species are underdeveloped. We aimed to advance seaweed domestication by focusing on giant kelp (Macrocystis pyrifera), the fastest-growing haplodiplontic brown alga, which has significant ecological and commercial importance. We analyzed phenotypic data from two offshore experimental farms conducted in 2019 and 2020, which involved hundreds of outplanted genetically diverse sporophytes. We found that outplanting season and farm design had significant effects on giant kelp biomass. Broad-sense heritability estimates showed moderate (0.27-0.50) genetic contributions to two phenotypes, carbon content and total biomass. Genome-wide association studies for these phenotypes resulted in three statistically significant SNPs, located near or within genes involved in carbohydrate metabolism and cytoskeletal functions. In addition, we applied genomic selection models that integrated sporophyte phenotypes and parental gametophyte genotypes. These models utilized reduced sets of GWAS-ranked SNPs obtained by a procedure based on linkage disequilibrium estimations. Model testing yielded cross-validation accuracy values of up to 0.84 and predictive accuracy values of up to 0.40, demonstrating the potential of marker-assisted breeding for phenotype improvement. Our results provide foundational genomic resources and tools for domesticating and breeding M. pyrifera, forming a basis for developing giant kelp varieties with desirable traits.

PMID:42315921 | DOI:10.1038/s41437-026-00855-4

Sci Rep. 2026 Jun 19. doi: 10.1038/s41598-026-57916-1. Online ahead of print.

ABSTRACT

Older adults experience a high risk of falls, often due to impaired stepping responses that require rapid and efficient weight transfer. This pilot study examined whether age group and principal component analysis (PCA)-derived profiles of muscle morphology, muscle quality, and strength were associated with weight-transfer performance during voluntary forward and backward stepping. Twenty-three younger and older adults underwent ultrasound imaging of the tensor fasciae latae (TFL), vastus lateralis (VL), and biceps femoris (BF), assessments of knee extensor, knee flexor, and hip abductor strength, and completion of a Choice Reactive Stepping Test (CRST). PCA was applied to nine muscle-related variables to summarize interrelated measures of muscle size, echo intensity, and strength into neuromuscular profiles. The first four principal components (PCs) explained approximately 80.8% of the total variance. PC1 reflected a favorable muscle quality and strength profile, characterized by lower echo intensity and greater hip abduction and knee flexion strength. PC2 suggested a dissociation between TFL thickness and force-generating capacity, whereas PC3 and PC4 reflected muscle-specific structural and strength heterogeneity. PCA-derived PC scores and age group were then entered into a multivariate multiple regression (MMR) model, with weight-transfer onset (WTO) and weight-transfer duration (WTD) during forward and backward stepping as response variables. The MMR model did not provide statistical evidence that age group or any PC was associated with the combined WTO and WTD outcomes. These findings suggest that, in this sample, resting muscle morphology, muscle quality, and maximal strength did not explain weight-transfer performance when modeled as integrated neuromuscular profiles. Weight-transfer performance during stepping may depend on dynamic neuromuscular factors, such as rapid force generation, activation timing, coordination, and task-specific balance strategies, which should be examined in larger studies.

PMID:42315916 | DOI:10.1038/s41598-026-57916-1

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-58630-8. Online ahead of print.

ABSTRACT

Anthrax remains a persistent transboundary zoonotic disease in the Republic of Kazakhstan despite long-term vaccination programs and established veterinary surveillance. This study aimed to analyze spatial and temporal patterns of anthrax outbreaks in livestock and humans and to describe their relation to environmental, climatic, and immunoprophylactic factors. Retrospective data on anthrax outbreaks in cattle, small ruminants, and humans recorded between 2015 and 2024 were analyzed using descriptive temporal trend analysis and thematic spatial mapping at the regional level. Climatic parameters, including temperature, precipitation, and soil moisture, were evaluated in relation to outbreak dynamics. Serological monitoring of cattle was conducted using the indirect hemagglutination assay to assess serological response, and non-parametric statistical methods were applied to compare regions and time periods. A total of 33 anthrax outbreaks were registered, with 93.4% occurring in cattle. Outbreaks showed pronounced seasonality, peaking in summer and early autumn, and were concentrated in historically endemic regions. Despite reported vaccination coverage exceeding 97% in several regions, outbreaks persisted, suggesting the influence of multiple factors, including environmental persistence of Bacillus anthracis spores and variability in vaccination practices, animal coverage, or environmental persistence of Bacillus anthracis spores. Serological analysis revealed regional heterogeneity in antibody levels. Human cases were temporally associated with livestock outbreaks and were primarily linked to unauthorized slaughter and handling of infected animals. These findings underscore the need for integrated risk-based surveillance combining climatic monitoring, spatial analysis, and targeted vaccination to support anthrax prevention strategies in endemic areas.

PMID:42315890 | DOI:10.1038/s41598-026-58630-8

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-57578-z. Online ahead of print.

ABSTRACT

Intermittent renewable energy sources and the increasing use of Electric Vehicles (EVs) cause significant challenges for Smart Grid (SG) stability and energy management. To address these challenges, this paper proposes a hybrid EV charging coordination system. It uses a Single-Switch High Step-Up Zeta converter (S2HSZ) along with Leaf in Wind Optimization (LiWO) and Spiking Deep Residual Network (SDRN) in a unified framework. The proposed method is designed to boost grid stability, optimize converter efficiency, minimize charging errors, and improve energy use in systems with PV and wind integration. In this setup, LiWO fine-tunes the PI controller gains, while SDRN predicts ideal operating parameters for smooth performance in dynamic grid conditions. The proposed framework is implemented in MATLAB, and the LiWO-SDRN model is compared with Adaptive Interaction Artificial Neural Networks (AI-ANN), Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Multi- Agent Deep Neural Network (MADNN), Gannet Optimization Algorithm-Dilated Residual Convolutional Neural Networks (GOA-DRCNN) models. The LiWO-SDRN model delivers coordinated EV charging and steady converter operation. The experiment shows the LiWO-SDRN framework attained improved results: 99.7% converter efficiency, 98.7% energy use efficiency, and a system error of 1.5%. The LiWO-SDRN framework outperformed other methods in terms of stability, efficiency, and charging coordination. These findings demonstrate that the model significantly boosts smart grid operations, especially with EV charging and renewable energy.

PMID:42315885 | DOI:10.1038/s41598-026-57578-z

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-58049-1. Online ahead of print.

ABSTRACT

The present paper analyzes the particle movement in a rotational fluid flow domain to increase the efficiency of the system that is used to diminish damages coming from hazardous substances due to coal and petroleum ignition in the environmental issues. The nonlinear differential equations for the fine particle are solved utilizing the Optimal Homotopy Analysis Method. The capability of the proposed method to solve non-linear differential equations of particle transportation is observed to be reasonable. Eradicating the grid generation difficulties is the most significant advantageous feature of the proposed method. The particle location profile follows an increasing trend by the drag-to-inertia ratio and particle primary radius reduction. In addition, the particle location increases during the time in all various conditions. The particle’s peripheral speed also decreases by boosting the particle’s primary radius, but increases by raising the drag to inertia ratio.

PMID:42315878 | DOI:10.1038/s41598-026-58049-1

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-53312-x. Online ahead of print.

ABSTRACT

Sleep disorders are prevalent and constitute a major concern in patients with diabetes mellitus. Therefore, the aim of this study was to investigate the applicability of machine learning methods in predicting sleep disorders among diabetic patients. Six relevant features were selected using single-factor correlation analysis and the LASSO algorithm. We developed and evaluated five ML models: logistic regression, decision tree, extreme gradient boosting, support vector machine, and light gradient boosting machine. Data from the China Health and Retirement Longitudinal Study database were utilized, with a total of 60,308 elderly individuals screened, of which 1276 diabetic patients were included in the analysis. Of these, 777 did not develop sleep disorders, while 499 did. Fifteen statistically significant predictors were identified through single-factor analysis, and six relevant variables were determined via LASSO regression, including family history of diabetes, education, marital status, chronic diseases, chronic pain, and depression. Based on these six variables, five ML models were constructed to predict the risk of sleep disorders in diabetic patients. Among these, the XGB model demonstrated superior performance, with an area under the curve of 0.850. The calibration curve indicated a good fit of the model on the development set, and decision curve analysis further confirmed the model’s excellent net benefit and prediction accuracy. The overall performance of the XGB model was the best. Our findings suggest that ML models, particularly extreme gradient boosting, offer the most effective approach for predicting the risk of sleep disorders in diabetic patients.

PMID:42315864 | DOI:10.1038/s41598-026-53312-x

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-55016-8. Online ahead of print.

ABSTRACT

Intelligent manufacturing demands accurate prediction of machining quality characteristics with conflicting behaviour, which is challenging with limited experimental data. Taguchi L₂₇ experimental data sets were collected with 6 influencing variables (workpiece material: PA66, PA66 + GF30, PA66 + MoS₂, tool approach angle, tool nose radius, cutting speed, feed rate and depth of cut) and 8 machining quality characteristics (surface roughness, cutting force, temperature, amplitude of vibration, tool wear rate, specific cutting energy, material removal rate, and sound pressure level). The total 27 experimental datasets were stratified by material into a 3-fold cross-validation protocol. The present work develops five base learners such as Gaussian Process Regression (GPR), Least Squares Boosting (LSB), Support Vector Regression (SVR), Random Forest (RF) and extreme gradient boosting (XGBoost) for predictions of machining quality characteristics. All three metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and crayfish optimization algorithm (COA)) determine identical weights for three best predictive base learners (GPR, SVR, and LSB) for developing Ensemble model. The COA converge to a minimum composite cost with comparatively lesser computation time than GA and PSO. Therefore, CrayStack ensemble model is constructed with a hybrid combination of GPR, SVR, and LSB and COA methods. The COA efficiently optimizes the adaptive fusion weights by assigning a higher weight fraction to GPR and LSB for nonlinear models. CrayStack Ensemble predictions outperforms all individual learners (SVR, GPR, LSB, XGBoost, and RF) with material stratified three-fold cross validation across all eight outputs of training data. CrayStack Ensemble requires a total training cost of 16.13 s (which includes base model training: 94.92% & 15.31 s, COA weight optimization: 1.88% & 0.30 s, and bootstrap confidence interval estimation: 3.20% & 0.52 s) ensuring its practical usefulness. CrayStack Ensemble achieves near-instantaneous inference (0.003 ms/sample; 290,592 samples/s) with a speed of 58.33, 79.33, 4899.33, 5630.67, 5608.3 over GPR, SVR, LSB, RF and XGBoost ensuring practicality suitable for real-time monitoring systems. Wilcoxon single-rank test confirmed that improvements are statistically significant (with a preset confidence level, p < 0.05) for 7 of the 8 responses, validating the practical utility of the developed models. CrayStack Ensemble showed superior prediction performances against nine randomly generated test cases with a mean absolute percent error of 9.8%, followed by GPR, LSB, XGBoost, SVR, and RF of 12.69%, 13.81%, 15.39%, 21.52% and 42.73% considering all responses. The results demonstrated that the intelligent ensemble stack ensures robustness and higher prediction accuracy for limited experimental datasets offering a practical solution for industrial process optimization.

PMID:42315862 | DOI:10.1038/s41598-026-55016-8

Nat Commun. 2026 Jun 18. doi: 10.1038/s41467-026-74572-1. Online ahead of print.

ABSTRACT

The human hippocampus exhibits distinct genetic, cellular, and connectivity profiles along its anterior-posterior long-axis, resulting in a differential sensitivity to visuospatial information. Long-axis development, therefore, may contribute to developmental increases in visuospatial memory capacity. To test this, we developed and applied a precision functional mapping technique to identify functional systems in the hippocampus of single subjects using BOLD-fMRI (N = 471, aged 5-21). We discovered considerable developmental remodeling of the hippocampal long-axis, particularly for the posterior functional system. With age, surface-area (mm²) of the posterior system decreased by 39.4%, while BOLD activity became increasingly independent and showed a sharper topographic boundary with the rest of the hippocampus. Notably, the posterior system showed strong preferential connectivity with medial-parietal regions which correlated with both age and age-adjusted memory scores. These results indicate the hippocampal long-axis becomes topographically and functionally specialized with development, potentially contributing to developmental increases in memory capacity.

PMID:42315845 | DOI:10.1038/s41467-026-74572-1

Ther Innov Regul Sci. 2026 Jun 18. doi: 10.1007/s43441-026-00998-w. Online ahead of print.

ABSTRACT

BACKGROUND: The ICH E9 (R1) addendum establishes frameworks for efficacy estimands (using a hypothetical strategy to handle intercurrent events) and treatment regimen estimands (using a treatment policy strategy to handle intercurrent events) in clinical trials. While Phase 3 studies often adopt treatment regimen estimands for regulatory purposes, direct use of the results from Phase 2 treatment regimen estimands for Phase 3 planning may produce suboptimal results due to differences in population, study duration, treatment regimen itself, and treatment delivery methods that affect adherence rates.

METHODS: We developed a modeling framework that decomposes treatment regimen estimands into adherent (efficacy estimand) and non-adherent patient responses. Using historical Phase 3 study data from chronic weight management and type 2 diabetes populations, we first establish empirical linear relationships between efficacy and non-adherent responses through regression modeling without intercept. Then we estimate Phase 3 efficacy responses from Phase 2 data, project discontinuation rates for Phase 3 study, and apply the empirical relationship to predict treatment regimen responses.

RESULTS: Linear relationships were identified for change in absolute weight loss and glycated hemoglobin (HbA1c) endpoints using data from multiple Phase 3 studies. Model validation showed close agreement between predicted and observed treatment regimen responses in the training data. Application to the SURPASS-2 Phase 3 study demonstrated reasonable predictive accuracy, with estimates generally within expected ranges of observed results.

CONCLUSIONS: This approach provides a systematic method for translating Phase 2 efficacy estimand results into Phase 3 treatment regimen estimand predictions. It leverages empirical relationships between efficacy responses and non-adherent responses, and may complement direct Phase 2 data extrapolation, particularly for endpoints where treatment effects persist after discontinuation. Current applications focus on change in body weight (kg) and change in HbA1c (%).

PMID:42315826 | DOI:10.1007/s43441-026-00998-w

Clin Oral Investig. 2026 Jun 19;30(7):296. doi: 10.1007/s00784-026-06977-7.

ABSTRACT

OBJECTIVES: Xerostomia significantly affects oral health and quality of life, yet no validated Hebrew assessment exists. This study aimed to translate the Xerostomia Inventory (XI) into Hebrew (HXI) and evaluate its validity and reliability.

METHODS: The XI was translated into Hebrew using cross-cultural adaptation guidelines. The HXI was examined for internal consistency, using Cronbach’s alpha, as well as for construct validity (convergent and discriminant validity).

RESULTS: The HXI was completed by 102 xerostomia patients, 89.2% women, and the mean age was 63.5 ± 13.8 years with age range of 20-90 years. The mean HXI score was 39.9 ± 1.2, with score range 12-55. The HXI exhibited a high level of reliability with Cronbach’s α = 0.975. The convergent validity of the HXI, indicated by Spearman’s correlation between the HXI and the unstimulated salivary flow (USF) whole volume, demonstrated a strong, negative, and statistically significant correlation (r = -0.862). Strong negative correlations were also found between the total HXI score and other sialometry variables (ranging from r=-0.696 to r=-0.879). The absence of significant associations between the HXI scores and unrelated sociodemographic variables supports the discriminant validity of the HXI. Confirmatory factor analysis supported a robust unidimensional structure for the Hebrew 11-item HXI, with a single factor accounting for 80.3% of the total variance and all item loadings exceeding 0.82.

CONCLUSIONS: The HXI is a valid and reliable instrument for assessing xerostomia in Hebrew-speaking populations, demonstrating psychometric robustness consistent with international versions.

CLINICAL RELEVANCE: The HXI provides a valid and reliable tool for assessing patient-reported oral dryness, supporting diagnosis, treatment, and evaluation of interventions in Hebrew-speaking populations.

PMID:42315815 | DOI:10.1007/s00784-026-06977-7