Browse Articles

Analysis of brain network temporal reconfiguration and frequency-domain features during dynamic orchestral segmentation transition based on electroencephalography

Background/Objectives: The human brain’s natural capacity for music perception relies on dynamic interactions across distributed neural systems. To understand this process, we investigated how the brain responds to musical segment transitions and identified the acoustic and informational features that drive these neural dynamics. Method: In a passive listening task, we used Mozart’s Serenade in G major for Strings, K.525 (Allegro) as the auditory stimulus. Based on distinct acoustic and informational profiles, two contrasting 10-s segments were selected: one transitioned from a high-frequency, less-predictable segment to a low-frequency, more-predictable segment, and the other followed the reverse pattern. We calculated metrics of EEG rhythms and brain networks and subjected them to statistical analysis to investigate their dynamics during the transitions. Results: The study reveals that the brain employs an efficiency-trade-off strategy during musical transitions. In stable periods, it conserves energy through efficient frontal and occipital processing. During a transition, the fronto-occipital-central network dynamically reconfigures, accompanied by a transient drop in global efficiency and recruitment of the right prefrontal cortex. Such resource reallocation during unpredictable shifts delays neural processing. Furthermore, θ power increased with greater structural complexity and a higher spectral centroid (FDR-corrected p  < 0.05), indicating a specific mapping between these acoustic features and oscillatory activity. Our study provides evidence for the deep interactive relationship that persists between changes in musical acoustic structure and internal neural oscillations of the brain.

Individual differences in the chronic stress-induced depression-like behavior are mediated by the expression of Kcnj2 gene in the lateral septum

An AI system to help scientists write expert-level empirical software

Abstract The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments 1 . To address this, we present Empirical Research Assistance (ERA), an artificial intelligence (AI) system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a large language model (LLM) and tree search 2 to systematically improve the quality metric and intelligently navigate the large space of possible solutions. ERA achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a diverse range of tasks. In bioinformatics, ERA discovered 40 new methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, ERA generated 14 models that outperformed the Centers for Disease Control and Prevention (CDC) ensemble and all other individual models for forecasting COVID-19 hospitalizations. ERA also produced expert-level software for geospatial analysis, neural activity prediction in zebrafish and numerical solution of integrals, as well as a new rule-based construction for time-series forecasting. By devising and implementing new solutions to diverse tasks, ERA represents a notable step towards accelerating scientific progress.

Sonochemical boron incorporation enhances activity and durability of ruthenium oxide for acidic water oxidation

Trends and risk factors of patient falls in Korean hospitals: A five-year national reporting data analysis

Patient falls are among the most frequent and preventable patient safety incidents in hospitals, often resulting in serious injury, prolonged hospitalization, and increased medical costs. Despite the nationwide implementation of the Korea Patient Safety Reporting and Learning System (KOPS), evidence on recent fall trends and risk factors across hospital types remains limited. This study analyzed five-year national reporting data to examine temporal trends and identify predictors of harmful inpatient falls in Korea. A retrospective cross-sectional analysis was conducted using 29,607 fall-related incidents reported to KOPS between 2020 and 2024. Variables included patient demographics, hospital characteristics, medical department, incident time, and level of harm, and multinomial logistic regression was used to identify factors associated with different levels of harm severity (near miss, adverse event, and sentinel event), with near miss as the reference category. Although the proportion of falls among all patient safety incidents declined from 56.0% in 2020 to 38.9% in 2024, the absolute number of reported falls continued to increase. Older adults aged 60 years or older and female patients had significantly higher odds of harmful falls. Long-term care hospitals and large hospitals with 500 beds or more showed elevated risks, while psychiatry and critical care departments recorded the highest proportions of sentinel events. These findings indicate that inpatient falls remain a major safety concern in Korea, particularly among older women and patients in large or long-term care hospitals. Differentiated, evidence-based prevention strategies—such as technology-assisted monitoring in large hospitals and improvements in workforce and environmental conditions in long-term care settings—are needed, alongside strengthened surveillance and data-driven safety management to reduce preventable harm.

Bayesian-optimized machine learning models for classifying metabolic syndrome control among NCD patients in Bangladesh hospitals

My overseas job offer was rescinded. Here’s how I bounced back

Reprogramming the lipid peroxidation product 4-ONE as a chemoselective cleavable crosslinker

In silico screening and molecular analyses identify apigenin from Scutellaria barbata as a potent AKT1 inhibitor in breast cancer

Breast cancer (BC) remains a leading cause of cancer-related mortality in women worldwide. Scutellaria barbata , a traditional Chinese medicinal herb, possesses recognized anticancer properties, but its mechanistic role in BC is not fully elucidated. This study employed an integrated in silico approach to identify the key flavonoids, targets, and pathways through which S. barbata exerts its anti-BC effects. A multi-step computational methodology incorporating network pharmacology, molecular docking, and dynamics simulations was utilized to profile bioactive compounds from S. barbata . From an initial set of 34 phytocompounds, three flavonoids namely apigenin, 4’-hydroxywogonin, and hispidulin demonstrated favorable drug-likeness, high bioavailability, and low predicted acute oral toxicity (LD₅₀ > 500 mg/kg) profiles. Network analysis identified AKT1, IL6 and TNF as central hub targets, significantly enriched in the PI3K-Akt, MAPK, and TNF signaling pathways. Molecular docking showed strong binding affinities (≤ –7.5 kcal/mol) between these flavonoids and the hub proteins, with hispidulin (–8.1 kcal/mol) and apigenin (–7.7 kcal/mol) exhibiting the highest affinity for AKT1 than other hub proteins. Molecular dynamics simulations over 100 ns further revealed that the apigenin-AKT1 complexes exhibited greater stability with lower RMSD fluctuations, reduced residue flexibility (RMSF), stable radius of gyration, and consistent SASA profiles, compared to other ligands and the control compound. Our computational prediction suggests that apigenin exhibits favorable multi-target interactions with BC-associated proteins. Thus, apigenin may represent a potential candidate for further investigation, particularly with respect to oncogenic signaling pathways involving key hub proteins. However, experimental validation through in vitro and in vivo studies is required to confirm these observations.

Analysis on wear parameters of boron carbide embedded aluminium matrix composites (413/B4C) using Taguchi technique

High-performance topochemical polymerization-based photo-carving with sub-50 nm resolution utilizing visible light

Abstract Photosensitive topochemical polymerization, characterized by lattice-controlled reaction sites and rates, enables the production of highly crystalline and isotactic polymers, providing a powerful synthesis route for advancing high-performance lithographic technologies. However, the substantial challenge of achieving precise molecular packing and fabricating large-area single-crystal thin films required by topochemical reactions remains a major obstacle to realizing sub-diffraction, low-power lithographic technologies. In this study, we report a lattice-induced resolution enhancement method that exceeds the diffraction limit, in synergy with a dual solid-liquid interface confinement strategy, enabling efficient topochemical polymerization in both 2D and 3D lithographic applications. The directed fluid flow within the confined space effectively suppresses uncontrolled nucleation, facilitating the fabrication of large-area single-crystal thin-film photoresists with tunable thickness and aspect ratios. We demonstrate dual-mode photocarving on large-area photoresists at sub-50 nm resolution ( λ /10) using a low-power continuous-wave visible laser (4-20 μW), which operates at power levels several orders of magnitude lower than those required for two-photon lithography. The entire process is compatible with standard commercial optical microscopes using standard optical components. Our research introduces a versatile platform for achieving subdiffractional lithographic resolution with low-power light, demonstrating a fivefold enhancement in resolution under identical illumination conditions compared to reported photoresists.

Ego-resilience and health-related quality of life after acquired Upper Limb Amputation

Purpose The purpose of this study was to examine the relationships between demographic variables, ego-resilience and health-related quality of life (HRQoL) for individuals with upper limb amputations. As HRQoL continues to be an important measure of rehabilitative success, determining universal factors which correlate to and may predict HRQoL becomes more important in the clinical space. Methods A sample of 90 previously administered outcomes from patients at a national upper limb prosthetic provider in the United States were gathered. The outcome measure, the Wellness Inventory, captured patient-reported data to screen for mental health status including ego-resilience, PTSD, depression, coping mechanisms. Scores from the Orthotics and Prosthetics Users’ Survey (OPUS) HRQoL as well as the Ego-Resilience Scale (ER89) were utilized in this study, as well as pertinent demographic data. Comparative analyses were conducted on the data gathered. Results HRQoL and Ego-Resilience scores were analyzed alongside demographic factors: gender (77.8% male), age at time of amputation (mean age 38, SD = 12.9), level of amputation, ethnicity and marital status. Correlational analysis showed positive relationship between ego-resilience and HRQoL (ρ = 0.332, p = .002). Simple linear regression analysis found a significant relationship between ethnicity and HRQoL (β = 4.237, p = .047), and ego-resilience and HRQoL (β = 0.910, p=<.001). The multiple linear regression model identified ego-resilience and ethnicity as predictors for HRQoL (adjusted R² = .129, F = 7.576, p=<.001). Conclusion Based on the findings in this study, ego-resilience has been identified as a significant predicting factor for HRQoL. Higher ego-resilience score and trait likely will result in higher HRQoL scores. Understanding of how demographic variables, such as ethnicity, may directly or indirectly impact HRQoL can also be beneficial in the recovery process.

Nicotinonitrile based dual inhibitors of tubulin and topoisomerase II exhibit potent anticancer activity

Abstract A series of 2,4,6-trisubstituted nicotinonitriles, compounds 10 – 41 , was designed as pyridine bridged analogs of combretastatin A4 and evaluated as dual inhibitors of topoisomerase II and tubulin polymerization. Anticancer activity was tested in MCF7, HepG2, and HCT116 cells using the LDH assay. Several compounds including 19 – 24 , 26 – 27 , 33 – 35 , 37 – 38 , and 41 showed strong cytotoxicity in MCF7 cells, while compounds 20 , 26 , 38 , 39 , and 41 displayed moderate activity in HepG2 cells with good selectivity toward BJ1 normal cells. Tubulin polymerization assays identified compounds 20 , 26 , and 37 as the most active, showing inhibition values of 74.7%, 75.0%, and 74.3%, compared with 72.1% for combretastatin A4. Compound 37 showed strong topoisomerase II inhibition (82.4%), while compound 20 displayed moderate inhibition (70.3%), both compared with DOX (81.6%). Cell cycle analysis indicated that compounds 20 and 26 induced G2 and M phase arrest in MCF7 cells and promoted apoptosis. Molecular docking confirmed favorable binding interactions with both tubulin and topoisomerase II. These results highlight 2,4,6-trisubstituted nicotinonitriles as promising dual target anticancer candidates and support their further optimization.

The mediating effect of burnout on the relationship between workplace culture and intent to stay among registered nurses in Saudi Arabia

Background Nurse turnover is a critical challenge to healthcare quality in Saudi Arabia, yet the mechanisms linking workplace culture to retention intentions remain unclear. While workplace culture and burnout have each been associated with turnover intentions, no study in the Saudi nursing context has examined whether burnout operates as an intermediary mechanism through which workplace culture influences nurses’ decisions to stay. Grounded in the Job Demands-Resources model, this study tested whether burnout mediates the relationship between workplace culture and intent to stay among registered nurses in Saudi Arabia. Methods A cross-sectional, correlational design was employed. The sample comprised 355 full-time registered nurses from five Ministry of Health hospitals in the Hail and Qassim regions of Saudi Arabia. Nurses were selected using proportionate stratified random sampling, with geographic region and hospital as stratification factors. The population of 2,500 nurses was divided into two regional strata; 240 nurses were randomly selected from Hail (three hospitals) and 160 from Qassim (two hospitals), proportional to the nurse population in each region. Data were collected via self-administered online surveys (Google Forms) from November 15, 2025, to January 15, 2026. Workplace culture was measured with the Organizational Culture Survey, burnout with the Copenhagen Burnout Inventory, and intent to stay with the Intent to Stay Scale. Structural equation modeling with bootstrapping (5,000 samples) was used to test the mediation hypothesis. Results Participants reported moderate perceptions of workplace culture (M = 2.88, SD = 0.73), high burnout (M = 3.58, SD = 0.65), and low intent to stay (M = 2.66, SD = 0.67). Workplace culture was negatively correlated with burnout (r = −0.34, p < .001) and positively correlated with intent to stay (r = 0.57, p < .001). Burnout was negatively correlated with intent to stay (r = −0.43, p < .001). In the SEM, workplace culture had a significant direct effect on intent to stay (β = 0.48, p < .001). The indirect effect of workplace culture on intent to stay through burnout was significant (β = 0.09, 95% CI [0.05, 0.16], p < .001), indicating partial mediation. The model explained 11.6% of the variance in burnout and 38.9% of the variance in intent to stay. Model fit was mixed: RMSEA and SRMR indicated good fit, while CFI and TLI were marginal (χ²(412) = 847.32, χ²/df = 2.06, CFI = 0.94, TLI = 0.93, RMSEA = 0.055 [90% CI 0.050, 0.060], SRMR = 0.048). Conclusion In this cross-sectional study of Saudi nurses, workplace culture was associated with intent to stay through both a direct association and an indirect association via burnout. These findings are consistent with the Job Demands-Resources model and suggest that workplace culture and burnout are co-occurring factors related to retention attitudes.

Development, validation, translation and user testing of a patient information leaflet for the management of dyslipidemia

Artificial Intelligence in emergency department triage: A scoping review

Background Triage in emergency departments (ED) is a critical process for prioritizing care and ensuring clinical safety. However, current triage systems often exhibit vulnerabilities that compromise the efficiency and quality of healthcare delivery. Artificial Intelligence (AI) has emerged as a promising innovation to support decision-making and optimize patient flow in these high-pressure environments. Objective To map the available evidence regarding the implementation and performance of artificial intelligence in emergency department triage. Method This scoping review followed the Joanna Briggs Institute (JBI) methodology and the PRISMA-ScR guidelines. A comprehensive search was conducted across 13 databases (CINAHL, Cochrane Library, PubMed Central, SciELO, Web of Science, SCOPUS, Science Direct, VHL, Embase, and several regional dissertation repositories), with no language or time restrictions. Two independent reviewers performed the selection process using the Rayyan platform, with discrepancies resolved by a third evaluator. Data were synthesized using the PAGER framework, categorizing findings into Patterns, Advances, Gaps, Evidence for practice, and Recommendations for research. Results Nineteen studies met the inclusion criteria. AI was primarily implemented through Machine Learning (ML) algorithms, including Deep Learning architectures. Natural Language Processing (NLP) was frequently employed to process unstructured clinical data, with recent studies exploring the potential of Large Language Models (LLMs). Overall, ML-based models consistently outperformed traditional triage systems in predictive accuracy. These techniques were mainly utilized for automated classification, predicting clinical severity, and enhancing patient prioritization by integrating both objective and subjective assessment data. Conclusions The findings indicate that AI has significant potential to enhance emergency triage by streamlining service flows and providing robust clinical decision support. However, the current evidence remains heterogeneous and largely exploratory. Key challenges include variability in model performance, a lack of external validation, and studies often limited to specific populations. Consequently, many current tools still lack the necessary reliability for safe, large-scale clinical implementation.

Explainable ensemble learning framework for predicting industrial and energy sector GHG emissions

Factors associated with the coexistence of anemia and undernutrition among children aged 6–59 months in Mali, 2023/24: A multilevel mixed-effects analysis

Introduction coexistence of anemia and undernutrition is a major public health concern among children in Mali. However, there is a lack of study looking into the relationship between undernutrition and anemia among children in Mali. Therefore, this study was conducted by using multilevel analysis to identify significant factors associated with the coexistence of anemia and undernutrition among children. Methods A cross-sectional design was conducted from Mali Demographic and Health Survey data from 2023/24. STATA 17 was used for data summarization and analysis. The model was evaluated using the intra-class correlation coefficient (ICC), median odds ratio (MOR), likelihood ratio (LR), and deviance. Variables with a p-value less than 0.2 in the bi-variable logistic regression analysis were taken into account for the next multilevel analysis. In the multilevel analysis, significant factors were presented using the Adjusted Odds Ratio (AOR) along with the 95% Confidence Interval (CI). Results The prevalence of the coexistence of anemia and undernutrition among children was 26.3% (CI: 25.2%, 27.4%). According to the multilevel logistic regression result, mothers aged 25–34 years (AOR = 1.41; 95% CI: 1.01, 1.96), no education (AOR = 1.51; 95% CI: 1.19, 1.91), primary education (AOR = 1.39; CI: 1.05, 1.84), not covered by health insurance (AOR = 2.18; 95% CI: 1.05, 2.47), rural residents (AOR = 1.46; 95% CI: 1.16, 1.84), and short maternal stature (AOR = 1.83; 95% CI: 1.49, 2.27) were associated with an increased odds of the coexistence of anemia and undernutrition among children. In contrast, tall maternal stature (AOR = 0.29; 95% CI: 0.22, 0.36) and children aged 37–47 months (AOR = 0.70; 95% CI: 0.50, 0.96) were associated with decreasing the odds of the coexistence of anemia and undernutrition among children. Conclusion In Mali, the coexistence of anemia and undernutrition contributes to mortality and related complications among children. The finding from this study revealed that children whose mothers were aged 25–34, mothers without formal education, mothers without primary education, mothers whose health was not covered by health insurance, children who lived in rural areas, and maternal short stature were associated with increased odds of the coexistence of anemia and undernutrition among children. In contrast, tall maternal stature and children aged 37–47 were associated with decreasing the odds of the coexistence of anemia and undernutrition among children.

New insight into light utilization efficiency: an evaluation of semitransparent solar cells for building-integrated photovoltaic windows

Relationships between gene expression variability, expression levels, and Protein–Protein interactions in mouse and yeast

Understanding how evolution shapes variability in gene expression is complicated by the interdependence of expression levels, expression variability, and evolutionary rates. Metrics that quantify variability independently of expression would help disentangle these relationships. Previously, a metric termed F* was developed using single-cell RNA-seq data from Mus musculus . Here, analyses of single-cell RNA-seq data from M. musculus and Saccharomyces cerevisiae reveal that the relationship between expression levels and variability is more complex than expected, and F* cannot be fully separated from expression level. Comparisons between single-cell and non-single-cell or simulated bulk experiments show that single-cell data exhibit higher apparent variability for most genes, consistent with contributions from both intrinsic and extrinsic sources. Despite this, the negative relationship between protein-protein interaction connectivity and F* is conserved in both organisms and is also detectable in some non-single-cell datasets, indicating it is not unique to single-cell data or variability. Gene ontology analyses show that, in M. musculus across single-cell and non-single-cell datasets, low-F* genes are enriched for translation- and ribosome-associated functions. When single-cell-specific variability is isolated by controlling for non-single-cell contributions, additional enrichment emerges for splicing and spliceosome-associated genes in M. musculus , suggesting that genes encoding spliceosome components in M. musculus may be under selective pressure to maintain unusually low variation relative to both their expression levels and the variability observed in bulk systems. In no analyses are there over-represented GO terms among the S. cerevisiae genes with low F* values.