Browse Articles

Machine learning reveals distinct temperature thresholds and environmental modulators for atopic dermatitis and allergic contact dermatitis prevalence in South Korea

Atopic dermatitis (AD) and allergic contact dermatitis (ACD) are common inflammatory skin diseases influenced by environmental factors, but disease-specific environmental pathways remain poorly defined. This study developed a machine learning model to predict monthly disease prevalence and characterize distinct environmental conditions associated with each disease. We analyzed nationwide health insurance claims data for AD, ACD, and corns (control) from six major South Korean cities from 2012 to 2017, constituting 432 city-month records per disease. The M5P model tree algorithm predicted relative monthly prevalence based on meteorological data (temperature, humidity, precipitation, diurnal temperature range) and air pollutants (SO₂, NO₂, CO, PM10), with performance evaluated using Pearson Correlation Coefficient (CC) and Mean Absolute Error (MAE). Analysis of 3,990,692 AD and 16,890,182 ACD cases showed that the combined weather-pollution model achieved high accuracy for AD (CC = 0.839, MAE = 0.038) and ACD (CC = 0.932, MAE = 0.049). Mean temperature was the primary splitting variable for both diseases, but with different thresholds and secondary modulators. For AD, the initial split occurred at 17.4°C; above this, high PM10 (>44μg/m³) was associated with higher prevalence. For ACD, a notable split was identified at 11.65°C; below this, low humidity (<62%) appeared to be a key contributing factor. PM10 was a consistent predictor for both diseases. While temperature is a universal primary driver for both AD and ACD, the diseases follow distinct environmental pathways. AD is modulated by air pollution in warmer conditions, whereas ACD is sensitive to humidity in cooler conditions. This data-driven approach provides insights into disease-specific environmental triggers for public health interventions.

Direct Electrosynthesis of Glycolate From Carbon Dioxide

ABSTRACT Electrocatalytic carbon dioxide reduction reaction (CO 2 RR) is an attractive and green technology that can convert renewable electricity into high‐energy‐density fuel, which is of great significance for alleviating the dual pressures of energy and environmental concern. Although the electrosynthesis of some C 2 products (e.g., ethylene, acetate, and ethanol) has achieved certain success, the generation of glycolate is still beyond the scope of existing electrocatalytic technologies. Here, we report the electrochemical reaction of direct conversion of CO 2 to glycolate in aqueous media under ambient conditions using unsaturated Cu sites as the catalyst, with a production rate of glycolate up to 305 ± 24 mmol h −1  g −1 . Further mechanism studies have shown that the key to glycolate generation is the formation and timely desorption of the *OHCCHO intermediate after C–C coupling, followed by a disproportionation reaction with base to generate the product. This work provides a successful case for the sustainable synthesis of glycolic acid from CO 2 in aqueous media, which represents a new product from electrocatalytic CO 2 RR.

HALO-GNN: hallucination-resistant temporal graph neural networks for dynamic community detection

Abstract Temporal graph learning is inherently subject to illusory structural dynamics, which is a phenomenon in which transient noise and ephemeral perturbations are exaggerated into deceptive community movements. Consequently, this leads to community assignments that are unstable and inconsistent, which severely limits the effectiveness of temporal graph neural networks in situations that involve streaming and dynamic circumstances. We provide a learning paradigm that is resistant to hallucinations and stabilizes temporal representations through the use of memory-guided structural regularization. The framework that has been suggested stabilizes node embeddings by referencing historical structures. This helps to reduce oscillations that are brought on by high-frequency noise while also preserving low-frequency development that is compatible with the community. Thorough evaluations across a variety of temporal graph benchmarks demonstrate significant improvements in robustness, temporal consistency, and perturbation resilience. These findings highlight the importance of hallucination resistance for the purpose of achieving reliable dynamic community detection.

Magnonic spontaneous oscillation induced by parametric pumping

Abstract Spontaneous dynamic systems have attracted significant attention for their rich underlying physics such as phase-locking and synchronization. In this work, we report a new mechanism of generating magnetic spontaneous oscillation via parametric pumping. By applying a pump tone to excite propagating spin waves in a yttrium iron garnet delay line, four-wave mixing converts the pump mode into two phase-autonomous propagating magnon modes, i.e. a spontaneous mode with nearly twice the wavenumber of the pump mode and an idler mode with nearly zero wavenumber. This allows us to reliably generate ultrasharp spin wave dynamics with broad frequency tunability from the pump and magnetic field. We show that the spontaneous mode can be phase-locked to a probe tone, similar to an auto-oscillator. Furthermore, the spontaneous dynamics can be used to implement a high-gain magnonic parametric amplifier with a gain up to 40 dB. Our results open a new avenue for studying nonlinear magnonics and synchronization physics in propagating magnon geometry and for developing new magnonic devices.

Discovery of hub genes linking oxidative stress to type 2 diabetic sarcopenia using single-cell sequencing and machine learning

Type 2 diabetes mellitus (T2DM) and sarcopenia demonstrate a significant comorbidity, particularly in the elderly, yet the molecular mechanisms linking them, especially through oxidative stress, remain incompletely understood. This study aimed to identify oxidative stress-related hub genes involved in T2DM-associated sarcopenia (T2DS) by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data with machine learning. We analyzed scRNA-seq datasets (GSE244515, GSE268953) to characterize cellular heterogeneity and bulk RNA-seq datasets (GSE202295, GSE226151) for differential expression. Cell type annotation revealed key involvement of neuromuscular junctions and myofibers. Functional enrichment analyses highlighted pathways like the proteasome, TNF signaling, and ubiquitin-mediated proteolysis. From an initial set of oxidative stress-related genes, a comprehensive machine learning framework comprising 127 algorithm combinations was employed. The Lasso+Stepglm[both] model identified 12 candidate genes. Subsequent Protein-Protein Interaction (PPI) network analysis refined this to seven core hub genes: TNFRSF1B, PSMA2, UBE2D1, UBE2N, HSP90AA1, RAD23A, and DNAJB1. These genes are functionally interconnected, primarily implicating TNFRSF1B-mediated inflammatory signaling that activates the ubiquitin-proteasome system, leading to enhanced protein degradation—a key pathway in muscle atrophy. ROC curve analysis confirmed the strong diagnostic value of these hub genes across training, test, and external validation sets. Our findings systematically reveal novel oxidative stress-related hub genes and mechanisms in T2DS, providing potential biomarkers and therapeutic targets for this debilitating condition.

Harnessing the <i>β</i> ‐Silicon Effect for Radical Cyclopolymerization: Direct Access to Si‐Containing Cyclic Olefin Polymers

ABSTRACT Cyclic olefin polymers (COPs) are important functional materials with extensively studied properties and syntheses. By contrast, silicon‐containing cyclic olefin polymers (Si‐COPs)—promising alternatives to conventional COPs—remain largely unexplored. Herein, we report a radical cyclopolymerization strategy enabled by the β ‑silicon effect for the efficient synthesis of Si‐COPs. Harnessing the β ‐silicon effect to stabilize radical intermediates suppresses the inherent chain transfer tendency of non‐conjugated diene monomers in radical cyclopolymerization and enables the control of the cyclization selectivity. Density functional theory (DFT) calculations provide mechanistic insight into the polymerization pathway. The resulting Si‐COPs serve as versatile platforms for post‐polymerization modification and undergo controlled degradation under mild conditions. Furthermore, cross‐linked Si‐COPs fabricated via thiol–ene chemistry exhibit optical properties comparable to those of commercial COPs, together with outstanding adhesion performance.

Patterns of genetic variation in Onobrychis viciifolia populations across xerothermic grasslands and the distribution of Cheilotoma musciformis

Risk and contract management practices, and construction projects performance: A test of moderation by managerial skills

Kenya’s socioeconomic development is heavily dependent on the construction industry; however, poor risk and contract management typically results in cost overruns, delays, and quality deficiencies that undermine project performance. Therefore, this study examined the way risk management practices affect the performance of building projects in the Nairobi Metropolitan Area, considering contract management practices and the moderating effect of project managers’ skills. The Theory of Constraints and Agency Theory, which place a strong emphasis on supervision, responsibility, and the removal of performance barriers, serve as the foundation for the analysis. The study used a descriptive and explanatory research design. 127 completed construction projects (95 residential and 32 non-residential) comprised the target population for this study. From this, 64 valid responses (96% response rate) were obtained from a sample of 67 projects chosen by stratified random sampling using Slovin’s technique at a 95% confidence level. A standardized Likert-scale questionnaire was used to collect data. Inferential statistics, such as simple linear and hierarchical multiple regressions and Pearson’s correlation analysis, were utilized to evaluate relationships and prediction effects. Descriptive statistics, such as means, percentages, and standard deviations, were utilized to define the project’s characteristics. Expert review was used to guarantee the validity of the instrument, and reliability was verified using Cronbach’s alpha coefficients greater than 0.70. The results demonstrate that both risk management practices (β = 0.612, p &lt; 0.001) and contract management practices (β = 0.239, p = 0.020) are significant positive predictors of construction project performance, with the combined model accounting for 60.6% of the variance in construction project outcomes (R² = 0.606). Project manager skills did not significantly moderate the relationship between these management practices and project performance (interaction terms p &gt; 0.05) and exhibited no significant independent effect (β = 0.132, p = 0.120), contributing only a marginal increase in explanatory power to R² = 0.62. The study concludes that while robust risk and contract management frameworks are critical for project success, the additive value of project manager skills remains modest and non-moderative. Recommendations include establishing uniform industry protocols and continued emphasis on professional certification and leadership development to support, rather than substitute systemic management practices.

Probing Blended‐Additive‐Regulated Interface Chemistry Based on a Dynamic Competition Mechanism in Lithium Metal Batteries

ABSTRACT Engineering a durable electrode‐electrolyte interphase is critical for high‐voltage lithium metal batteries. A fundamental obstacle to this goal is the unresolved complexity of interface chemistry, especially involving blended electrolyte additives. Here, by revealing the decomposition pathway of lithium difluorophosphate (LiDFP) under the dynamic competition mechanism (DCM), we unravel the interface chemistry in blended‐additive formulations combining LiDFP with other mainstream additives. When co‐used, the LiNO 3 priority decomposition and LiDFP protonation alter interfacial evolution and trigger harmful H 3 PO 4 and HF accumulation. Notably, fluoroethylene carbonate (FEC) remains undecomposed alongside LiDFP, defying its typical sacrificial role. The stable‐existence FEC modulates the local chemical environment by directing targeted competitive H + adsorption, which in turn drives more complete LiDFP decomposition to construct an inorganic‐enriched interphase dominated by Li 3 PO 4 and LiF. Building upon these insights, we propose a universal DCM framework that optimizes a multi‐additive electrolyte system by tailoring additive synergies. This work shifts focus from empirical additive screening to a mechanism‐driven design paradigm, offering an instructive blueprint for navigating complex interface chemistry.

Longitudinal vaginal microbiomes and quality-of-life patterns during tamoxifen therapy in breast cancer: a pilot study

Abstract Tamoxifen is widely used in breast cancer treatment, but its effects on vaginal microbiome remain poorly understood. This prospective longitudinal pilot study explored vaginal microbiota profiles and quality-of-life parameters in women receiving tamoxifen for breast cancer in Seoul, South Korea (2023–2024). Eleven women initiating tamoxifen therapy were enrolled. Vaginal swabs were collected at baseline (V0) and 6 months (V6). Microbiota was profiled using 16 S rRNA gene sequencing. Quality of life was assessed using the 11-item Menopause Rating Scale. Participants were stratified by baseline colonization patterns. Overall community composition did not show a significant shift between baseline and 6 months. In the full-cohort taxa-level paired analysis, Gardnerella vaginalis ( G. vaginalis ) showed a nominal, non-FDR-significant increase from baseline to 6 months, and no taxon remained significant after multiple-comparison correction. A negative correlation was observed between G. vaginalis and Lactobacillus iners ( L. iners ) (ρ = −0.6, raw P  &lt; 0.01, FDR q &lt; 0.05). Among participants with baseline G. vaginalis detection, 4 of 5 showed increased relative abundance at 6 months, although the confidence interval was wide. G. vaginalis abundance was associated with worse sexual-function-related quality-of-life scores in exploratory analyses, but item-level MRS comparisons did not remain significant after correction for multiple testing. In this small hypothesis-generating pilot cohort, women receiving tamoxifen showed largely stable overall vaginal community composition over 6 months, with an exploratory signal of G. vaginalis expansion among participants colonized at baseline. These findings should be interpreted cautiously given the small sample size, absence of a control group, treatment heterogeneity, and post hoc subgroup analysis, and require validation in larger controlled cohorts.

Expression of Concern: New insights into the existing image encryption algorithms based on DNA coding

Sequential H ₂ S‐Triggered Redox Relay Nanoprobes for Self‐Sustained Chem‐Illuminating Cascade Photodynamic Therapy

ABSTRACT Endogenous chemiluminescence offers a transformative approach to photodynamic therapy that circumvents the limited penetration of external light and enables tumor‐selective activation. However, most chemiluminescence‐driven photodynamic therapy (CL‐PDT) systems typically rely on intracellular oxidants (e.g., H 2 O 2 ) as chemiexcitation “fuels”, which conceptually contradicts the primary goal of elevating intratumoral oxidative stress. In this study, we report a sequential H 2 S‐triggered redox relay nano‐photosensitizer, NP‐Rubine, which addresses the fundamental “redox paradox” by decoupling photon generation from oxidation consumption. Composed of an H 2 S‐responsive chemiluminescent probe (Rubine) and a N ‐oxide scaffold (OPDEA‐Ppa), NP‐Rubine is selectively activated by endogenous H 2 S to initiate an efficiency chemiluminescence resonance energy transfer (CRET) cascade for efficient singlet oxygen ( 1 O 2 ) production. Concurrently, the N ‐oxide moiety promotes deep tumor penetration via transcytosis and depletes the intracellular NADPH pool. By synergistically coupling oxidant generation with reductant exhaustion, NP‐Rubine synergistically amplifies intracellular redox imbalance to induce apoptosis. In vivo studies substantiate that NP‐Rubine achieves exceptional deep‐tissue imaging and potent antitumor efficacy in HCT116 xenografts. This bio‐reductant, self‐sustained targeted CL‐PDT strategy circumvents the practical hurdles of oxidation‐fueled systems, offering a robust benchmark for precision nanomedicine in complex redox landscapes.

Carbon-energy efficiency and yield optimization through partial substitution of nitrogen with organic amendments in maize-wheat cropping system under sub-temperate conditions

Correction: Sperm whales habituate to research vessels engaged in photoidentification

Effects of coenzyme Q10 on oxidative stress biomarkers in printing workers with low-level exposure to toluene and xylene: a randomized, double-blind, placebo-controlled crossover clinical trial

Health behaviours and lifestyle challenges among school children: A qualitative research from Rajasthan, India

Background Non-communicable diseases (NCDs) account for 74% of global deaths, significantly affecting children and adolescents, particularly in low and middle-income countries. Addressing risk factors such as smoking, poor diet and physical inactivity through school-based interventions is crucial for reducing NCD prevalence and improving long-term health outcomes among youth. Therefore, this study aims to explore school children’s lifestyle habits, identify barriers and assess facilitators for adopting a healthy lifestyle. Methods This study utilized qualitative research methodology (focus group discussions (FGDs) and in-depth interviews (IDIs)) to collect comprehensive insights from school children, teachers, parents, and school canteen staff. It was carried out in both rural and urban regions of Jodhpur district, Rajasthan, a western state of India with a purposive sample of 31 participants. Thematic analysis was performed using NVivo 14. Results Our study reveals that while participants have a basic understanding of NCDs, there is a significant gap in their knowledge of NCD-related health initiatives. Barriers to healthy lifestyles include limited access to recreational spaces, unhealthy food options, and socioeconomic factors. However, family and peer support and school initiatives are crucial in promoting healthy behaviours. Conclusion Bridging the gap between health knowledge and practice requires a holistic approach. Enhancing communication about health programs, improving access to recreational spaces, and implementing policies to regulate unhealthy foods are essential. Engaging families and educators and integrating health education into school curriculums will help promote healthier behaviours and reduce the risk of non-communicable diseases.

A multimodal machine learning framework outperforms traditional performance metrics for predicting elite national ranking attainment in adolescent sprinters

Stylistic analysis of translated languages: A perturbation-based XAI deep learning framework

Text classification using traditional machine learning techniques has been used in natural language processing (NLP) tasks to distinguish translated from non-translated languages, with high accuracy scores indicating the distinctive style of translated languages. While deep learning (DL) has demonstrated impressive performance in terms of representation learning and capturing nuanced patterns in natural language data, DL models act as black boxes, making their results difficult to interpret. This study addresses this issue by demonstrating an explainable AI (XAI) DL framework in a case study of United Nations (UN) meetings. The framework consists of three stages: i) train a variational autoencoder (VAE) combined with BERT embeddings converted from translated and non-translated texts; ii) utilize the majority vote from three classifiers selected from a stacked ensemble to classify the VAE’s latent representations; iii) implement a perturbation-based XAI method to interpret the DL model’s decisions. The results indicate that the VAE-based model effectively distinguishes the two text types, with accuracy scores above 0.8. The XAI analysis reveals that interpreting the VAE-based model’s decision uncovers stylistic differences between the two text types beyond superficial lexical and syntactic features. This proof-of-concept study demonstrates the potential of the XAI DL framework in other NLP studies that aim to analyze style.

Temporal consistency of large language model responses to restorative dentistry questions from the Turkish dental specialty examination

Improving cell-free metabolism through direct integration of artificial respiratory chains

Energy-conserving mechanisms are essential in supporting cellular life. Yet in synthetic biology, it remains a challenge to reconstruct such processes from the bottom–up and integrate them with other biological functions to create complex systems with life-like properties. Recent efforts to build higher-order cell-free metabolic networks have suffered from the fact that their central oxidation reactions are not coupled to energy conservation, causing kinetic and thermodynamic limitations. Here, we developed an artificial respiratory chain that we tailored to sustain rapid electron transfer in a CO 2 -fixing 16-enzyme catalytic cycle (crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA), while also exploiting the concurrent electron flow for adenosine triphosphate synthesis. We demonstrate how such artificial respiratory chains can be further diversified to accept multiple electron entries and coupled to other biological functionalities, such as cell-free transcription–translation networks. Altogether, our work highlights the opportunities and challenges of directly integrating energy conservation mechanisms when building toward self-sustaining/self-energizing artificial life-like systems.