Category: Statistics

Commun Biol. 2025 Nov 21. doi: 10.1038/s42003-025-09214-1. Online ahead of print.

ABSTRACT

Accurate genotype imputation is essential for large-scale genetic studies and precision medicine. While East Asian (EAS)-specific reference panels like ChinaMAP and CHN100k have been developed, most studies still rely on multi-ancestry panels like TOPMed due to the large sample size. However, their performance in underrepresented groups like Southeast Asians remains unclear. Using high-coverage whole-genome sequencing and SNP-array data from 8,316 Chinese and Thai individuals, we systematically evaluate six state-of-the-art reference panels for genotype imputation. Our results show that EAS-specific panels outperformed multi-ancestry panels for East and Southeast Asian populations. For example, ChinaMAP achieves a mean heterozygosity concordance rate above 0.90 without R² filtering, whereas TOPMed requires an R² threshold of 0.60-0.70 to achieve comparable results. Notably, we find that recent positive selection drives regional disparities in imputation accuracy, as illustrated by the olfactory receptor gene cluster. More importantly, our results indicate that the choice of reference panel and R² thresholds have a significant impact on polygenic risk score estimation for disease prediction. These findings provide valuable guidelines for improving genotype imputation in East and Southeast Asian populations and underscore the need for ancestrally diverse reference panels to support globally equitable genomic research.

PMID:41272119 | DOI:10.1038/s42003-025-09214-1

Sci Rep. 2025 Nov 21;15(1):41404. doi: 10.1038/s41598-025-25291-y.

ABSTRACT

This paper presents a control of adaptive regularization for a hybrid variational model and its application to the denoising of color images, which combines total variation (TV) and [Formula: see text] regularizers with normalized data fidelity. The designed adaptive control works locally and is performed by a control parameter that intelligently selects the appropriate diffusion operator for smoothing (quadratic) and edge preservation (nonquadratic). In addition to combined diffusion operators, data term was normalized to ensure good balance between diffusion and fidelity terms. The idea of complementary data normalization enhances performance under high noise levels and mitigates artifacts. The resulting optimization framework leads to a time-dependent partial differential equation that is discretized using standard finite differences. Simulation experiments on benchmark data sets demonstrated that the proposed method consistently outperformed over conventional denoising techniques in terms of edge preservation, noise reduction and computational efficiency. Quantitative evaluation was performed using the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE), and convergence time (CT). A comparative analysis with state-of-the-art variational denoising models further highlights good performance of proposed approach in preserving sharp structural details while achieving effective noise suppression.

PMID:41272118 | DOI:10.1038/s41598-025-25291-y

Commun Biol. 2025 Nov 21. doi: 10.1038/s42003-025-09118-0. Online ahead of print.

ABSTRACT

Congenital heart defects, particularly those affecting the outflow tract (OFT), represent a significant cause of neonatal morbidity and mortality. The genetic and cellular mechanisms underlying OFT abnormalities, especially the migration of second heart field (SHF) cells, remain poorly understood. In this study, we identify two distinct migratory pathways for Osr1-expressing posterior SHF cells directed toward the OFT and inflow tract (IFT). We show that Osr1 regulates two key ligand-receptor signaling axes, Hedgehog-Ptch1 and Cxcl12-Cxcr7, both of which are essential for the directed migration of Osr1⁺ cells toward the OFT. We establish that Smo and Ackr3 (encoding CXCR7) are direct transcriptional targets of Osr1. Functional studies demonstrate that Osr1-Hh signaling governs SHF cell migration and OFT development, while Osr1-Cxcl12 signaling likely acts in synergy to support this process. These findings underscore the pivotal role of Osr1+ pSHF cells in directing heart pole development and reveal crucial ligand-receptor interactions involved in cell migration, potentially guiding future therapeutic targets for congenital heart defects.

PMID:41272108 | DOI:10.1038/s42003-025-09118-0

Sci Rep. 2025 Nov 21;15(1):41167. doi: 10.1038/s41598-025-26626-5.

ABSTRACT

Mechanical methods are hypothesized to have postoperative pain and more seroma formation than non-mechanical methods due to tissue trauma. Therefore, the current prospective cohort study aimed to assess postoperative pain and quality-of-life (QoL) after fibrin glue versus tack mesh fixation. From July 2022 to December 2023, 80 patients sought TEP at the Suez Canal University Hospitals outpatient clinic. Participants were divided into two groups based on mesh fixation: the Fibrin glue group (FG) and the Tack group (TG). The purpose of this study was to compare the rate of post-operative complications, post-operative pain, length of hospital stays, and wound complications. In the meantime, the two groups QoL were compared using the SF-36 scoring questionnaire and the Caroline Comfort score (CCS). The patients in TG had higher operative time (84.5 ± 5.5 min) compared to patients in FG (78.3 ± 6.4); without statistical significance (p = 0.21). The FG had a statistically significant shorter length of hospital stay compared to TG (p = 0.02) although the duration till initiation of weight bearing did not have statistical difference between both groups (p = 0.09). With a 30-day postoperative follow-up period, overall, there was no difference between both groups regarding the development of postoperative urine retention, seroma or wound infection (p = 0.09, 0.32, 0.3; respectively). Furthermore, after 6 months, FG had a higher overall QoL score using both CCS and SF-36 questionnaire (P = 0.001 and 0.02; respectively). Glue fixation may have a better quality of life and less postoperative pain; however further clinical trials are still needed.

PMID:41271995 | DOI:10.1038/s41598-025-26626-5

Sci Rep. 2025 Nov 21;15(1):41255. doi: 10.1038/s41598-025-25143-9.

ABSTRACT

Traditional static design approaches struggle to address the dynamic environmental conditions and evolving user needs of contemporary urban open spaces. This research proposes a comprehensive AI-driven real-time responsive design methodology that integrates multi-modal sensing data to enable dynamic optimization of urban open spaces. The proposed framework employs a hierarchical data fusion architecture that processes heterogeneous sensor streams including visual, acoustic, and environmental data through advanced machine learning algorithms. Deep learning-based spatial optimization models combined with reinforcement learning mechanisms generate adaptive design solutions that respond to real-time conditions while maintaining design quality standards. The system achieves sub-100ms response times through optimized computational architectures and intelligent caching strategies. Experimental validation conducted across three representative urban sites demonstrates significant improvements including 34.2% increase in space utilization efficiency (measured as the ratio of actively used area to total available space), 28.7% enhancement in pedestrian flow optimization (quantified through movement speed and path directness metrics), and 22.3% reduction in operational costs compared to conventional static design approaches. The practical application case study at Metropolitan Central Plaza, a 2.4-hectare transit-oriented public space in Shanghai’s dense urban district, validates the methodology’s effectiveness in real-world deployment, showing substantial improvements in user satisfaction metrics and environmental quality indicators. This research establishes foundational principles for developing intelligent urban environments that can continuously adapt to changing conditions while optimizing resource utilization and enhancing user experience quality.

PMID:41271992 | DOI:10.1038/s41598-025-25143-9

Sci Rep. 2025 Nov 21;15(1):41268. doi: 10.1038/s41598-025-25332-6.

ABSTRACT

Do climate conditions and extreme events fuel conflict and migration? This question has been widely studied using causal designs that exploit natural variation in climate variables, often analyzed with linear fixed-effects models. Yet in this setting, nonlinear relationships, distributional features of outcomes, and spatial heterogeneity can cause these models to violate core assumptions and yield unreliable inferences. We propose a multilevel Bayesian framework that accommodates such features while retaining identification strategies from natural experiments. We illustrate its potential with a representative analysis from the literature of the effect of temperature anomalies on conflict in Somalia. When outcome distributions suited to event counts are combined with partial pooling across regions, the apparent aggregate climate effect disappears and marked regional heterogeneity emerges, with positive associations in only a few southern regions and negative or uncertain effects elsewhere. Extending pooling across time further improves predictive ability. More broadly, the multilevel Bayesian framework offers a general strategy for strengthening both explanatory and predictive inferences about climate and social outcomes, supporting internal and external validity while efficiently accommodating heterogeneity even with small samples. This methodological bridge between econometric identification strategies and statistical modeling provides a robust foundation for interdisciplinary climate-conflict-migration research.

PMID:41271982 | DOI:10.1038/s41598-025-25332-6

Sci Rep. 2025 Nov 21. doi: 10.1038/s41598-025-25984-4. Online ahead of print.

ABSTRACT

We examined how a species inhabiting a latitudinal gradient, from warm oxygenated surface waters to cold oxygen-limited subsurface waters along the Eastern South Pacific (ESP) shelf, responds to latitudinal temperature shifts at low-oxygen isopleths. We combined temperature-oxygen sections from the World Ocean Database, historical records of pelagic/benthic Grimothea monodon occurrence across latitude, models with these data, and laboratory experiments assessing juveniles’ routine and postprandial metabolism under realistic temperature-oxygen conditions. The life habits (pelagic or benthic) of squat lobsters were related to temperature at the 2 mL O₂ L^-1 oxygen isopleth. At temperatures > 15 °C near the upper oxygen minimum zone isopleth, mostly pelagic individuals were observed, suggesting restricted vertical migration. The physiological performance of juveniles (main migratory stage) was negatively affected by high temperature-hypoxia interaction. Routine metabolic rates decreased by 60% under hypoxia at 21 °C, and postprandial metabolism (as Specific Dynamic Action) was also strongly reduced under those conditions. Grimothea monodon can shift between pelagic and benthic habitats across a range of ESP conditions, maintaining the intergenerational ability to alternate habitats. This plasticity, expressed as vertical expansion or restriction, may help maintain or expand its latitudinal ranges, with natural food webs and fisheries adjusting to its availability as key prey item.

PMID:41271967 | DOI:10.1038/s41598-025-25984-4

Sci Rep. 2025 Nov 21;15(1):41335. doi: 10.1038/s41598-025-25229-4.

ABSTRACT

Pediatric drug-resistant epilepsy (DRE) affects 30-40% of children with epilepsy, resulting in medical costs significantly higher than those of controlled epilepsy. Cranial epilepsy surgery (CES) and vagus nerve stimulation (VNS) are key interventions; however, their long-term economic impact remains ambiguous. Using South Korea’s National Health Insurance claims data (2007-2022), we examined healthcare utilization and costs among children with DRE treated with antiseizure medications (ASM) only, CES, or VNS. Of the cohort included 6020 patients, of whom 5407 (89.8%) received ASM-only treatment, 396 (6.6%) underwent CES, and 217 (3.6%) received VNS. Post-CES, emergency department (ED) visits declined by 65%, overall length of stay (LOS) by 45%, epilepsy-specific admissions by 49% and epilepsy-specific LOS by 83%. Interrupted time series analysis revealed a sustained monthly reduction in total costs, consistent with fewer high-risk encounters. Post-VNS, ED visits fell by 41%, admissions by 39%, and LOS by 44%, with reductions in epilepsy-related admissions; however, outpatient visits remained unchanged. Both CES and VNS significantly reduce acute-care needs in pediatric DRE. CES yields the greatest and most durable benefits, while VNS shifts care from emergency and inpatient settings to scheduled outpatient follow-up, offering a valuable alternative when surgery is not feasible.

PMID:41271932 | DOI:10.1038/s41598-025-25229-4

Sci Rep. 2025 Nov 21;15(1):41439. doi: 10.1038/s41598-025-25760-4.

ABSTRACT

Satellite images are critical for ecological monitoring and national security; thus, protecting its integrity is imperative. Nonetheless, existing encryption methods struggle to balance robustness and efficiency. This paper proposes a novel quantum chaos-based image encryption scheme (QCIES) combining a 4D hyperchaotic Lorenz system (4D-HLS) and quantum Fibonacci transform (QFT) addressing these limitations. During the encryption process, we first used The Generalized Quantum Image Representation (GQIR) technique to transform a conventional color image into quantum data. Then, 4D-HLS generates complex, unpredictable keys through bifurcation and sensitivity to initial conditions. 4D-HLS complex dynamics coupled with quantum pixel reorganization provide unprecedented resistance against statistical and brute-force attacks. Additionally, QFT with a quantum adder randomly encrypts pixel locations, producing the final encrypted image. Performance evaluation was conducted using Python to analyze key metrics including histogram distribution, information entropy, and adjacent pixel correlation. Extensive security testing revealed QCIES robust performance, achieving near-ideal correlation coefficients (< 0.004), information entropy (> 7.999), NPCR (99.64%), UACI (33.56%) and massive key space [Formula: see text]. The achieved [Formula: see text] key space notably exceeds the [Formula: see text] NIST standard, while maintaining computational efficiency through optimized quantum circuit design. These innovations establish a new benchmark for satellite image transmission in critical infrastructure applications.

PMID:41271918 | DOI:10.1038/s41598-025-25760-4

Sci Rep. 2025 Nov 21;15(1):41226. doi: 10.1038/s41598-025-25099-w.

ABSTRACT

The C-reactive protein-to-albumin ratio (CAR) is an emerging biomarker linked to cardiovascular disease. However, its role in predicting outcomes among acute decompensated heart failure (ADHF) patients, particularly when stratified by glycemic status, remains poorly defined. This retrospective study included 1,494 consecutive ADHF patients admitted between 2018 and 2023, with the primary endpoint being major adverse cardiac and cerebrovascular events (MACCEs). Patients were stratified into low- and high-CAR groups using an optimal cutoff of 0.29, derived from maximally selected rank statistics. To assess the relationship between CAR and MACCEs in different glucose metabolism states, multivariable Cox proportional hazards models, restricted cubic splines (RCS), and Kaplan-Meier survival curves were used. During a median follow-up of 528 days, 565 patients (37.8%) experienced MACCEs. After adjusting for potential confounders, higher CAR levels were significantly associated with an increased risk of MACCEs (Model 3: hazard ratio [HR]: 1.45, 95% confidence interval [CI]: 1.19-1.77, p < 0.001). When stratified by glycemic status, CAR as a robust predictor in diabetic patients (Model 3 HR: 1.91, 95% CI: 1.41-2.58, p < 0.001), whereas no significant associations were observed in prediabetic or normoglycemic individuals (all p > 0.05). In conclusion, CAR demonstrates independent prognostic value in ADHF patients with concomitant diabetes, highlighting its potential as a stratification tool for personalized risk assessment and therapeutic optimization in this high-risk population.

PMID:41271915 | DOI:10.1038/s41598-025-25099-w