Category: Statistics

Sci Rep. 2025 Apr 27;15(1):14706. doi: 10.1038/s41598-025-96207-z.

ABSTRACT

A 200 mm processing platform for the large-scale production of graphene field-effect transistor-quantum dot (GFET-QD) hybrid photodetectors is demonstrated. A comprehensive statistical analysis of the electrical data revealed a high yield (96%) and low variation in the 200 mm scale fabrication. The GFET-QD devices deliver responsivities of 10⁵ to 10⁶ V/W in the wavelength range from 400 to 1800 nm with a response time of 10 ms. The spectral sensitivity compares well to that obtained via similar GFET-QD photodetectors. The device concept enables gate-tunable suppression or enhancement of the photovoltage, which may be exploited for electric shutter operation by toggling between the signal capture and shutter states. The devices show good stability over a wide operation range. Furthermore, an integration solution with complementary metal-oxide-semiconductor technology is presented to realize image-sensor-array chips and a proof-of-concept image system. This work demonstrates the potential for the volume manufacture of infrared photodetectors for a wide range of imaging applications.

PMID:40289227 | DOI:10.1038/s41598-025-96207-z

Sci Rep. 2025 Apr 27;15(1):14704. doi: 10.1038/s41598-025-99373-2.

ABSTRACT

The Fracture Risk Assessment Tool (FRAX), widely used for predicting the 10-year likelihood of hip fractures, does not incorporate factors like prior falls and sociodemographic characteristics, notably the Social Vulnerability Index (SVI). Recognizing these limitations, we aim to evaluate the predictive accuracy of FRAX by integrating fall frequency, fall energy, and SVI into the model for assessing the risk of fall-induced hip fractures. A retrospective case-control study was conducted, and patients aged ≥ 40 years with a documented diagnosis of a fall-induced hip fracture were age-matched with controls with a history of falls without an associated hip fracture. Basic demographic data, along with information about the number of prior falls and the energy of the current falls, were collected. The FRAX and SVI were calculated accordingly. Logistic regression analysis was employed to identify significant predictors. The performance of the models was evaluated and reported using appropriate metrics. Baseline characteristics of the dataset were presented as medians with interquartile ranges (IQR) or as percentages, where applicable. The significance of the identified variables was quantified using Odds Ratio (OR) along with their 95% Confidence Interval (CI). A p-value threshold of 0.05 was set for statistical significance. A total of 261 patients per group were included with a median age of 74 (IQR 67-80) and 72 (IQR 62-83) years. The FRAX score was significantly associated with the likelihood of experiencing a fall-induced hip fracture, as indicated by an OR of 1.06 (CI: 1.03-1.09). Participants with a one-time history of falls had an OR of 1.58 (CI: 1.02-2.37), compared to 1.84 (CI: 1.09-3.1) for those with multiple falls. The White participants, along with the Housing Type and Transportation domain of the SVI, also demonstrated to play a role (OR = 2.85 (CI: 1.56-5.2) and OR = 0.3 (CI: 0.12-0.8), respectively). This study underscored the significance of factors such as fall frequency, SVI, and race in predicting fall-induced hip fractures. It also highlighted the need for further refinement of the FRAX tool. We recommend that future research should be focused on validating the impact of these sociodemographic and fall characteristics on a broader scale, along with exploring the implications of clinical surrogates related to falls.

PMID:40289226 | DOI:10.1038/s41598-025-99373-2

World J Microbiol Biotechnol. 2025 Apr 28;41(5):155. doi: 10.1007/s11274-025-04364-0.

ABSTRACT

Hygromycin B (HYG-B) is a 5-glycosylated 2-dexoystreptamine- aminoglycoside antibiotic-(2DOS-AGA) produced by Streptomyces hygroscopicus subspecies hygroscopicus NRRL ISP-5578 with broad-spectrum activity against many pathogenic bacteria, fungi and helminths. In the literature, limited studies are concerned with the biosynthetic pathway and different cultural conditions affecting the production of HYG-B. This study aimed to optimize key environmental conditions influencing its production as one-factor-at-a-time (OFAT) and as a statistical model of response surface D-optimal design (DOD). Moreover, the biosynthetic pathway of HYG-B in light of the identified genes/proteins of the HYG-B gene cluster was proposed and elucidated. The effect of culture media composition and incubation time were studied OFAT, and the results showed that both culture media 6 (CM6) and CM4 gave the highest specific productivity, 5.88 and 3.99 µg/mg, respectively, and 7 days as incubation time. So, using CM6 and 7 days incubation resulted in a sevenfold increase (190 µg/mL) compared to the original unoptimized condition (CM1 and 6 days incubation; 26.9 µg/mL). Three important factors-initial pH, incubation temperature, and agitation-were tested using a DOD quadratic model generating 20 experimental runs. An initial pH of 6.4, an incubation temperature, of 28 ℃, and agitation. of 295 rpm were predicted and experimentally verified, resulting in a 13-fold increase (371.5 µg/mL) compared to the unoptimized condition and a sevenfold increase compared to that obtained as OFAT. In conclusion, DOD design is an efficient tool for optimizing HYG-B. However, the optimized conditions should be scaled up in a bioreactor for industrial production of HYG-B by S. hygroscopicus NRRL ISP-5578.

PMID:40289225 | DOI:10.1007/s11274-025-04364-0

Sci Rep. 2025 Apr 27;15(1):14732. doi: 10.1038/s41598-025-00115-1.

ABSTRACT

Accurate medium- to long-term runoff forecasting is crucial for flood control, drought resilience, water resources development, and ecological improvement. Traditional statistical methods struggle to utilize multifaceted variable information, leading to lower prediction accuracy. This study introduces two innovative coupled models-SRA-SVR and SRA-MLPR-to enhance runoff prediction by leveraging the strengths of statistical and deep learning approaches. Stepwise Regression Analysis (SRA) was employed to effectively handle high-dimensional data and multicollinearity, ensuring that only the most influential predictive variables were retained. Support Vector Regression (SVR) and Multi-Layer Perceptron Regression (MLPR) were chosen due to their strong adaptability in capturing nonlinear relationships and extracting latent hydrological patterns. The integration of these methods significantly improves prediction accuracy and model stability. By integrating 80 atmospheric circulation indices as teleconnection variables, the models tackle critical challenges such as high-dimensional data, multicollinearity, and nonlinear hydrological dynamics. The Yalong River Basin, characterized by complex hydrological processes and diverse climatic influences, serves as the case study for model validation. The results show that: (1) Compared to baseline single models, the SRA-MLPR model reduced RMSE (from 798.47 to 594.45) by 26% and MAPE (from 34.79 to 22.90%) by 34%, while achieving an NSE (from 0.67 to 0.76) improvement of 13%, particularly excelling in extreme runoff scenarios. (2) The inclusion of teleconnection indices not only enriched the predictive feature set but also improved model stability, with the SRA-MLPR demonstrating enhanced capability in capturing latent nonlinear relationships. (3) A one-month lag in atmospheric circulation indices was identified as the optimal predictor for basin-scale runoff, providing actionable insights into temporal runoff dynamics. (4) To enhance model interpretability, SHAP (SHapley Additive exPlanations) analysis was employed to quantify the contribution of atmospheric circulation indices to runoff predictions, revealing the dominant climate drivers and their nonlinear interactions. The results indicate that the Northern Hemisphere Polar Vortex and the East Asian Trough exert significant control over runoff dynamics, with their influence modulated by large-scale climate oscillations such as the North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO). (5) The models’ scalability is validated through their modular design, allowing seamless adaptation to diverse hydrological contexts. Applications include improved flood forecasting, optimized reservoir operations, and adaptive water resource planning. Furthermore, the study demonstrates the potential of coupled models as generalizable tools for hydrological forecasting in basins with varying climatic and geographic conditions. This study highlights the potential of coupled models as robust and generalizable tools for hydrological forecasting across diverse climatic and geographic conditions. By integrating atmospheric circulation indices, the proposed models enhance runoff prediction accuracy and stability while offering valuable insights for flood prevention, drought mitigation, and adaptive water resource management. These methodological advancements bridge the gap between statistical and deep learning approaches, providing a scalable framework for accurate and interpretable hydrological, climatological, and environmental predictions. Given the escalating challenges brought about by climate change, the findings of this study make contributions to sustainable water management, interpretable decision-making support, and disaster preparedness at a global level.

PMID:40289219 | DOI:10.1038/s41598-025-00115-1

Int J Legal Med. 2025 Apr 28. doi: 10.1007/s00414-025-03499-x. Online ahead of print.

ABSTRACT

OBJECTIVES: Multi-factorial age estimation (MFA) models have been developed based on Bayes’ rule, combining MRI data of the third molars (T), left wrist (W) and/or both clavicles (C). Internal cross-validated performance was reported, but external validation is needed before bringing the approach into practice. This study aimed to validate these MFA models in two independent samples.

METHODS: In the Ghent sample, W + C MRI was prospectively conducted in 108 healthy Caucasian volunteers (52 males, 56 females) aged 16 to 21 years. In the Graz sample, T + W + C MRI was prospectively conducted in 335 healthy Caucasian males aged 13 to 24 years. Development was staged and checked for intra-observer reliability, and age estimation performances were tested.

RESULTS: Staging clavicles was most prone to intra-observer variability. Applying the W + C model to Ghent males rendered a mean absolute error of 1.55 years, a root mean square error of 1.90 years, 70.6% correctly categorised adults and 94.4% correctly categorised minors. In females, the results were 1.49 years, 1.83 years, 92.1% and 66.7%, respectively. Regarding the Graz sample, the W + C results were 1.66 years, 2.08 years, 94.7% and 80.7%, respectively. For the T + W + C model, the results were 1.41 years, 1.80 years, 95.2% and 81.5%, respectively.

CONCLUSION: The T + W + C and W + C models rendered a similar accuracy of the point prediction of age in both validation samples. However, they bared a larger risk of wrongfully categorising a minor as an adult than reported for internal validation, stressing the importance of the prediction interval for age estimation in practice.

PMID:40289208 | DOI:10.1007/s00414-025-03499-x

Sci Rep. 2025 Apr 27;15(1):14726. doi: 10.1038/s41598-025-98795-2.

ABSTRACT

The prevention of osteoarthritis through controlling body measurements has received increasing attention in recent years, but the relationship between lean body mass (LBM) and osteoarthritis remains unclear. Hence, we explored this association through the data from the National Health and Nutrition Examination Survey (2007-2018). The present study enrolled 31,172 participants. To explore the correlation between LBM and osteoarthritis, we utilized logistic regression equations to explore associations between covariates, exposures, and outcomes. We used multivariate regression models to further control confounding factors. Restricted cubic splines were employed to investigate non-linear relationships. And the inflection point was identified by recursive algorithm. Furthermore, stratified analyses of gender and age were conducted. Osteoarthritis was negatively correlated with LBM [0.74 (0.67, 0.80)] in the model adjusting for all confounders. Based on the restricted cubic spline curve, an inflection point of 52.26 kg was found to confirm the non-linear relationship. LBM was negatively correlated with osteoarthritis at 0.56 (0.48, 0.64) before the inflection point, and the association was not statistically significant afterward. This large-scale study revealed that LBM was non-linearly correlated with osteoarthritis in the general American population. Differences in age and gender were further identified.

PMID:40289200 | DOI:10.1038/s41598-025-98795-2

Sci Rep. 2025 Apr 27;15(1):14703. doi: 10.1038/s41598-025-98435-9.

ABSTRACT

Environmental Health Literacy (EHL) focuses on significant impact of environmental factors on human health and emphasizes the importance of public awareness and engagement in identifying and mitigating environmental health risks. This paper presents a study in the Distrito Federal, Brazil, aimed at evaluating EHL and identify the main socioeconomic characteristics capable of predicting EHL levels. Using the EHL Scale, which assesses knowledge, attitudes, and behaviors toward environmental health, this research applies a questionnaire to 397 respondents. Through descriptive statistics and confirmatory and exploratory factor analyses, the study validates the scale for the Brazilian context and offers structural adjustments for the air scale. Using the socioeconomic data we implemented a predictive Random Forest algorithm to forecast EHL levels on each of the scales. By extracting the Shapley values from the model, we established the most relevant variables to predict EHL, offering valuable insights for policymakers, health and environmental professionals to enhance public engagement with environmental health issues. The results indicate that social vulnerability features are predictive of EHL, including education, income, age, ethnicity, presence of disability and use of continuous medication. This study identifies factors that bolster policy strategies to communicate environmental health risks and promote behavior change regarding the environment and self-care.

PMID:40289196 | DOI:10.1038/s41598-025-98435-9

Sci Rep. 2025 Apr 27;15(1):14720. doi: 10.1038/s41598-025-98155-0.

ABSTRACT

Distinguishing the luminal subtypes of breast cancer (BC) remaining challenging. Thus, the aim of this study was to investigate the feasibility of radiomic signature using intratumoral and peritumoral features obtained from dynamic contrast-enhanced MRI (DCE-MRI) in preoperatively discriminating the luminal from non-luminal type in patients with BC. A total of 305 patients with pathologically confirmed BC from three hospitals were retrospectively enrolled. The LASSO method was then used for selecting features, and the radiomic score (radscore) for each patient was calculated. Based on the radscore, Radiomic signature of intratumoral, peritumoral, and combined intratumoral and peritumoral were established, respectively. The performances of the radiomic signatures were validated with receiver operator characteristic (ROC) curve and decision curve analysis. For predicting molecular subtypes, the AUC for intratumoral radiomic signature was 0.817, 0.838, and 0.883 in the training set, internal validation set, and external validation set, respectively. AUC for the peritumoral radiomic signature was 0.863, 0.895, and 0.889 in the training set, internal validation set, and external validation set, respectively. The AUC for combined intratumoral and peritumoral radiomic signature was 0.956, 0.945, and 0.896 in the training set, internal validation set, and external validation set, respectively. Additional contributing value of combined intratumoral and peritumoral radiomic signature to the intratumoral radiomic signature was statistically significant [NRI, 0.300 (95% CI: 0.117-0.482), P = 0.001 in internal validation set; NRI, 0.224 (95% CI: 0.038-0.410), P = 0.018 in external validation set]. These results indicated that the radiomic signature combining intratumoral and peritumoral features showed good performance in predicting the luminal type of breast cancer.

PMID:40289183 | DOI:10.1038/s41598-025-98155-0

Sci Rep. 2025 Apr 27;15(1):14700. doi: 10.1038/s41598-025-95207-3.

ABSTRACT

The combination of autonomous recording units (ARUs) and machine learning enables scalable biodiversity monitoring. These data are often analysed using occupancy models, yet methods for integrating machine learning outputs with these models are rarely compared. Using the Yucatán black howler monkey as a case study, we evaluated four approaches for integrating ARU data and machine learning outputs into occupancy models: (i) standard occupancy models with verified data, and false-positive occupancy models using (ii) presence-absence data, (iii) counts of detections, and (iv) continuous classifier scores. We assessed estimator accuracy and the effects of decision threshold, temporal subsampling, and verification strategies. We found that classifier-guided listening with a standard occupancy model provided an accurate estimate with minimal verification effort. The false-positive models yielded similarly accurate estimates under specific conditions, but were sensitive to subjective choices including decision threshold. The inability to determine stable parameter choices a priori, coupled with the increased computational complexity of several models (i.e. the detection-count and continuous-score models), limits the practical application of false-positive models. In the case of a high-performance classifier and a readily detectable species, classifier-guided listening paired with a standard occupancy model provides a practical and efficient approach for accurately estimating occupancy.

PMID:40289178 | DOI:10.1038/s41598-025-95207-3

Sci Rep. 2025 Apr 27;15(1):14725. doi: 10.1038/s41598-025-98865-5.

ABSTRACT

The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where such modeling proceeds is a core question in statistical learning and predictive processing. In this context, we address the role of transitional probabilities as an implicit structure supporting the encoding of the temporal structure of a random auditory stream. Leveraging information-theoretical principles and the high spatiotemporal resolution of intracranial electroencephalography, we analyzed the trial-by-trial high-frequency activity representation of transitional probabilities. This unique approach enabled us to demonstrate how the brain automatically and continuously encodes structure in random stimuli and revealed the involvement of a network outside of the auditory system, including hippocampal, frontal, and temporal regions. Our work provides a comprehensive picture of the neural correlates of automatic encoding of implicit structure that can be the crucial substrate for the swift detection of patterns and unexpected events in the environment.

PMID:40289162 | DOI:10.1038/s41598-025-98865-5