Tag: nevin manimala

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-57852-0. Online ahead of print.

ABSTRACT

Abnormal uterotubal junction (UTJ) structure is implicated in the pathogenesis of endometriosis and infertility; however, this relationship remains poorly characterized. We quantitatively characterized the orientation of collagen fiber bundles in the UTJ using optical coherence tomography (OCT). UTJ tissue from nine individuals undergoing hysterosalpingectomy were collected and longitudinally opened to expose the lumen. Volumetric OCT images were acquired from proximal (uterine), middle, and distal (isthmic) UTJ segments. Images were preprocessed to enhance contrast and continuity of fiber bundles. Local fiber bundle orientation was extracted from Sobel-filtered intensity gradients. Orientation values were binned corresponding to the expected alignment of collagen fiber bundle groups in UTJ smooth muscle: longitudinal (0°-30°), oblique (30°-60°), and circumferential (60°-90°). Proportions were plotted as a function of depth, and depth-dependent trends in alignment were compared across UTJ segments using mixed-effects models. Measurements revealed a prominent inner longitudinal muscle layer which emerged proximally and thinned distally, and an outer muscle layer composed of oblique uterine fiber bundles proximally and a mixed oblique-circumferential contribution distally. These findings provide the first quantitative, depth-resolved analysis of UTJ collagen architecture and establish an analytical framework which can support future studies investigating how UTJ structure relates to function and disease.

PMID:42315947 | DOI:10.1038/s41598-026-57852-0

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-56941-4. Online ahead of print.

ABSTRACT

Eye Tracking (ET) can help improve understanding of visual attention in computer-supported interactive environments. In attention tasks, distinguishing between relevant target objects and distractors is crucial for effective performance, yet the underlying gaze patterns that drive successful task completion remain incompletely understood. Traditional gaze analyses provide limited insight into the temporal dynamics of attention allocation and the relationship between gaze behavior and task performance. When applied to complex visual search scenarios, current gaze analysis methods face several limitations, including the isolation of measurements in dynamic environments, visual stability, search efficiency, and the task-solving processes involved. This paper proposes an analysis tool, VisiTrail, that considers time-series eye-tracking data on task performance and gaze measures; temporal pattern analysis that reveals how attention evolves throughout task performance; object-click sequence tracking that directly links visual attention to user actions; and performance metrics that quantify both accuracy and efficiency of right actions. The proposed analysis is applied to data collected from the Mushroom Hunter serious game, a multilevel visual search task in which participants identify target mushrooms among distractors of increasing complexity across three difficulty levels. This tool focuses on two scenarios: subject-specific analysis and general analysis across all subjects from which a project collected data from Romania and Portugal. Subject-specific analysis uses only one participant’s data and yields two types of results: a first-level overall analysis that provides detailed insights and a multilevel analysis that compares performance across all three levels, where Level 1 presents the easiest task with few distractors, Level 2 increases difficulty by adding more similar distractors, and Level 3 is the hardest with many closely resembling distractors and more complex layouts. The generalized analysis reveals that gaze stability (Fixation %) was broadly consistent across both cohorts. Romanian participants exhibited faster median reaction times than Portuguese participants; however, given the small sample size (N=7 per cohort), the presence of data-quality anomalies in three Portuguese sessions, and the absence of inferential statistical testing, this difference should be regarded as preliminary and hypothesis-generating rather than conclusive. By using standardized parameters, the results reduce accidental interactions and hardware noise, providing a reliable methodological basis for preliminary analysis and highlighting the need for larger sample sizes to establish statistical validity.

PMID:42315946 | DOI:10.1038/s41598-026-56941-4

Sci Rep. 2026 Jun 22. doi: 10.1038/s41598-026-56504-7. Online ahead of print.

ABSTRACT

Pharmaceutical pollutant, such as amoxicillin was selected because it is one of the most widely consumed antibiotics worldwide and is frequently detected in hospital and municipal wastewater. Its high excretion rate in active form and persistence in aquatic environments make it an important target contaminant for water treatment studies. To address this issue, we developed a dendrimer-functionalized graphene quantum dot-mesoporous silica hybrid (GQDs@mSiO₂@Dend.G₃) as an effective and reusable adsorbent. AMX is an ideal model contaminant for studying adsorption mechanisms and evaluating the performance of this advanced hybrid material. Mechanistic interpretation suggests that hydrogen bonding, π-π, and electrostatic interactions collectively contribute to antibiotic binding at the hybrid interface. The nanoadsorbent was characterized using XRD, TGA, FTIR, BET-BJH, FE-SEM, EDX, and zeta potential measurements. Response surface methodology with a central composite design was used to optimize and validate the removal efficiency, yielding statistically significant results. The drug removal efficiency of 96% was achieved under the following optimal conditions: pH 6, temperature of 35 °C, and contact time of 45 min. The kinetics followed the pseudo-second-order model (R² = 0.9979) and the equilibrium fit the Langmuir isotherm (R² = 0.9657-0.9675) confirming monolayer physisorption onto uniform active sites. Thermodynamics showed that the process was spontaneous and endothermic (ΔH° = 8562.589 J mol^-1, ΔS° = 155.530 J mol^-1 K^-1, and ΔG° = -37.808 to -40.919 kJ mol^-1). The nanoadsorbent showed consistently high removal efficiency in real water samples. It retained over 70% of its original performance after multiple regeneration cycles. This indicates that GQDs@mSiO₂@Dend.G₃ is a promising and reusable solution for reducing pharmaceutical pollution in environment samples.

PMID:42315942 | DOI:10.1038/s41598-026-56504-7

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-58516-9. Online ahead of print.

ABSTRACT

The impact of network depth on the performance of deep residual networks (DRNs) for short-term load forecasting (STLF) remains insufficiently understood. This study conducts a systematic empirical investigation of network depth within DRN-based frameworks, with a particular focus on its relationship with forecasting performance, model behavior, and dataset characteristics. Two representative models, namely the original DRN and the Principal Component Analysis-Deep Residual Network (PCA-DRN), are evaluated under a wide range of depth configurations using two real-world datasets with different load and meteorological characteristics: ISO-NE and MyPJ. The results suggest that the influence of network depth on forecasting performance is inherently nonlinear and highly dataset-dependent. For the ISO-NE dataset, medium-depth configurations achieve favorable performance by effectively modeling more pronounced seasonal variability and long-term temporal patterns. In contrast, relatively shallow configurations generally exhibit improved performance on the MyPJ dataset under the current experimental setting, suggesting that deeper architectures may introduce additional model complexity without consistent performance gains. Furthermore, within the MyPJ dataset containing multiple meteorological variables, the incorporation of PCA improves feature representation, enhances model robustness, and is associated with reduced sensitivity to depth variations under the current experimental setting. Comparative evaluations against mainstream deep learning (DL) models indicate that DRN-based frameworks achieve competitive forecasting performance with relatively efficient structural designs. In addition, bootstrap-based statistical analysis suggests that the observed performance differences remain distinguishable under sample-level variability within the current experimental setting. These findings suggest that appropriate network depth selection should be considered together with dataset characteristics and feature representation, providing practical insights for the design of efficient and robust STLF models.

PMID:42315935 | DOI:10.1038/s41598-026-58516-9

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