Browse Articles

Visible-light-driven photocatalytic reduction of Cr(VI) over EDTA–TiO2 through surface complexation and ligand-to-metal charge transfer

Direct observation of the superallowed α-decay of 104Te

Research on supporting mechanism and application of new ductile thin spraying technology in coal mine roadway

Huoxue Qianyang Qutan Recipe limits cardiac remodeling by regulating FUNDC1/IP3R2 signaling pathway in obese hypertensive rats

Abstract Obese hypertension (OBH) increases cardiovascular risk through myocardial remodeling, which is associated with disrupted mitochondrial Ca²⁺ homeostasis and dysfunction of mitochondria-associated endoplasmic reticulum membranes (MAM). Huoxue Qianyang Qutan Recipe (HQQR) has been shown to lower blood pressure in OBH, but its mechanism related to MAM remains unclear. In this study, phenotypic assessments in OBH rats included blood pressure, morphological parameters, and cardiac ultrasound. Cardiomyocyte hypertrophy and mitochondrial Ca²⁺ levels were analyzed via pathological staining (assessed using fluorescence-based staining), while MAM ultrastructure was examined by electron microscopy. Additionally, the activity of mitochondrial respiratory chain Complexes I–IV was detected in vivo to evaluate mitochondrial respiratory function. In vitro, flow cytometry was used to evaluate mitochondrial Ca²⁺, mitochondrial reactive oxygen species (mitoROS), and membrane potential. Protein levels of FUNDC1 and IP3R2 were assessed. Co-immunoprecipitation revealed an interaction between FUNDC1 and IP3R2, and further experiments with FUNDC1 siRNA knockdown and overexpression were conducted to clarify the mechanism of HQQR. Results showed that HQQR significantly lowered blood pressure, reduced left ventricular mass, and alleviated cardiomyocyte hypertrophy in OBH rats. In vivo, HQQR altered MAM structure and function, facilitated mitochondrial Ca²⁺ transport, and modulated FUNDC1 and IP3R2 expression. In vitro, HQQR reduced mitoROS and preserved mitochondrial Ca²⁺ homeostasis. Both HQQR treatment and FUNDC1 knockdown attenuated angiotensin II-induced cardiomyocyte hypertrophy and mitochondrial damage, as indicated by decreased levels of ANP, BNP, β-MHC, mitochondrial Ca²⁺, and mitoROS. In contrast, FUNDC1 overexpression diminished the protective effects of HQQR. The interaction between FUNDC1 and IP3R2 was confirmed, and the decrease in IP3R2 may be associated with ubiquitination-mediated degradation. In conclusion, HQQR mitigates MAM dysfunction in OBH-induced myocardial remodeling by regulating the FUNDC1-IP3R2 interaction and promoting ubiquitin-dependent degradation of IP3R2, thereby maintaining mitochondrial Ca²⁺ homeostasis in cardiomyocytes.

Design of a planar gradient-index dielectric lens for arbitrary point-to-point beam focusing using critical-angle theory

Facile and cost-effective synthesis and characterization of hormone stabilized biocompatible gold nanoparticles for biomedical applications

Move over, AlphaFold: open-source model predicts shape of 1 billion proteins

Deforestation-induced drying lowers Amazon climate threshold

Abstract Humanity is putting unprecedented pressures on the Amazon forest system through global warming and land use changes 1,2 . As the Amazon forest may undergo self-reinforcing transitions, these pressures could lead to system-wide changes across major parts of Amazonian ecosystems 1–4 . Here we apply a dynamical systems model to assess the local and far-reaching cascading transition risks towards degraded ecosystems in the Amazon biome under different Shared Socioeconomic Pathways. For these emission scenarios, we constructed how moisture is transported through the atmosphere within the Amazon basin using an established atmospheric moisture-tracking model 5 . Without accounting for deforestation, we find a critical global warming threshold of 3.7–4.0 °C, beyond which up to a third of the Amazon forest risks losing stability. However, when considering deforestation, we find a near system-wide transition of the Amazon forest (62−77% of the area) under the combination of a lower threshold range of global warming of 1.5–1.9 °C and deforestation of 22–28%. The large majority of the simulated transitions is caused by spatial knock-on effects from increasing drought intensities, leading to long-ranging and self-propelling cascades on scales of hundreds to thousands of kilometres. Overall, our results reinforce the need to keep global warming levels below 1.5 °C and halt deforestation, as well as ecologically restore degraded forests to avoid high transition risks across the Amazon forest system.

Parental experience and professional training shape the perception of comfort and discomfort in preterm and term newborns

Selective entropy-fused proximal policy optimisation with federated reinforcement learning for intelligent multi-UAV trajectory and communication optimisation

The role of gender, work family conflict, and gender role attitudes in daily parental wellbeing

Abstract A typical day or week looks very different for modern fathers and mothers, where women still fulfil more family responsibilities. While, gender differences in subjective well-being are widely documented, gender is rarely considered to explain fluctuations in daily well-being. We addressed this gap while also considering related concepts like the work-family conflict and gender role attitudes. Participants (individual parents) completed a baseline questionnaire covering sociodemographic background, work-family conflict, and gender role attitudes. Additionally, they filled out daily well-being surveys four times a day over a one-week period. Sixty-eight participants (75% women, age: M  = 37.6 years) and 1352 datapoints from the daily surveys were analysed using multilevel modelling. Daily well-being increased over the survey period and women reported better well-being compared to men. More liberal private gender role attitudes were related to better cognitive well-being. Greater work-family conflict, irrespective of direction, was related to lower daily well-being. Gender and particularly related frameworks like the work-family conflict are important explanatory factors in not only overall but also daily well-being.

Emergence of oncofetal plasticity is ubiquitous in early colorectal cancers

Abstract Metastasis formation is classically considered a late-stage event in colorectal cancer evolution. Yet the time and spatial patterning by which metastatic competence is acquired remain poorly understood 1,2 . Here we show that metastasis-associated oncofetal cell states already emerge at the earliest stages of colorectal cancer, concurrent with invasive front formation. However, although necessary for metastasis, we detect them ubiquitously among early non-metastatic cancers, highlighting extra bottlenecks such as immune evasion. To understand how oncofetal cells first emerge, we generated multiregional organoid models that reflect successive tumour progression stages within individual early-stage colorectal cancers. Whole-genome sequencing and growth factor-dependency assays exclude tumour cell-intrinsic acquired traits. By contrast, single-cell spatial atlases of the tumour microenvironment before and after malignant transformation revealed stereotypic patterning of fibroblast subtypes resembling normal tissue architecture, resulting in distinct regional microenvironments. At the onset of malignant growth into the submucosa, the first cancer-associated fibroblasts to appear strongly resemble submucosal trophocytes and colocalize with oncofetal cell states at invasive fronts. Functionally, fibroblast–organoid cocultures confirm that these trophocyte-like cancer-associated fibroblasts induce plastic transitioning to oncofetal states. Thus, interactions between tumour and submucosal fibroblasts directly following malignant transformation dictate the timing and location at which oncofetal plasticity first occurs during colorectal cancer progression.

Foliar nutrient concentrations and antioxidant enzyme activities in wheat grown in the cerrado as influenced by nitrogen sources and plant growth-promoting bacteria

EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network

Abstract Emotion recognition from EEG signals has been one of the most promising areas due to its potential in enhancing human–computer interaction, especially in adaptive systems. This paper proposes a novel emotion recognition system that improves classification accuracy through advanced signal processing, adaptive channel selection, and deep learning techniques. The system starts with the multi-scale wavelet transform to break down EEG signals effectively followed by Kalman filtering with Wavelet denoising that enhances the quality of signal. Emotional peaks are established using Spectral Entropy analysis for accurate epoch determination. For adaptive channel selection, the utilization of Reinforcement based Deep Q-Networks to pick the most informative EEG channels dynamically for each emotional state. In feature extraction, the hybrid model based on Spatio-Temporal Attention Networks (ST-ANs) is adopted to capture spatial and temporal dependencies in the EEG data. Multi-Scale Feature Fusion is utilized to fuse short- and long-term dependencies. Final emotion classification is done through an Ensemble of Graph Neural Networks (GNNs) and Memory-Augmented Neural Networks (MANNs) providing robust adaptability across different subjects and emotional states. The benchmark dataset is collected from kaggle repository such as EEG brainwave, DEAP dataset and Computer game based EEG dataset. The proposed work achieves 98.5% accuracy on the EEG Brainwave Dataset. On the DEAP Dataset, it achieves 94.5% accuracy. For the EDA on Emotion Recognition (S01G1AllChannels), the model reaches 97.6% accuracy, outperforming the existing frameworks in emotion classification.

Behavior-aware deep reinforcement learning for multi-objective outpatient scheduling optimization

Abstract Outpatient departments in large hospitals face persistent scheduling inefficiencies characterized by prolonged patient waiting, underutilized resources, and high no-show rates. Existing scheduling approaches largely ignore the behavioral heterogeneity of patients, treating satisfaction as a simple proxy of waiting time rather than a psychologically grounded construct. This paper proposes MO-SAC-B, a multi-objective deep reinforcement learning framework that integrates behavioral science theory into the scheduling optimization process. We first construct a behavior-driven discrete-event simulation environment that encodes prospect-theoretic waiting disutility, nonlinear patience decay, and behaviorally calibrated no-show and abandonment dynamics. A satisfaction-aware reward shaping mechanism translates these behavioral constructs into dense learning signals, while a multi-objective Soft Actor-Critic algorithm with adaptive weight adjustment and prioritized experience replay navigates the efficiency–satisfaction Pareto frontier. Experiments calibrated with real outpatient data from a tertiary hospital demonstrate that MO-SAC-B reduces mean waiting time by 21.9%, improves composite patient satisfaction by 12.7 points, and lowers the no-show rate by 25.8% relative to the strongest baseline. Ablation studies confirm that each behavioral component contributes meaningfully, with synergistic effects amplifying performance gains under high patient flow conditions. Robustness analysis further validates the framework’s adaptability to demand surges and resource disruptions.

Leucyl tRNA synthetase ameliorates cholestatic liver injury by inhibiting integrated stress response in mice

Correction: 5,7,4'-trimethoxyflavanone from Bauhinia variegata exerts anti‑inflammatory and protective actions in LPS‑challenged rat intestine

Fabrication of medical radiation shielding sheets using reactive densification–based tungsten/PE porous composite materials

An explainable AI framework for enhanced software defect prediction using transformer-assisted boosting

Effect of asymmetry on frustrated crystallization

Abstract Partial bonding in colloidal systems is often regarded as an obstacle to order, as it introduces geometric frustration. Yet, frustration can instead serve as a powerful design principle for controlling the formation of porous crystalline monolayers. By systematically varying the asymmetry in patch placement on anisotropic rhombic platelets, we investigate how partial bonding and geometric frustration can be tuned to direct polymorph selection, modulate porosity, and optimize crystal yield. This reveals an alternative route to crystallinity in patchy colloids – one that relies not on maximizing bonding, but on purposefully limiting it. Our findings establish a new strategy for programming functionality in colloidal materials, with relevance for the rational design of supramolecular architectures, DNA-based nanomaterials, and stimuli-responsive assemblies.