Browse Articles

A cross sectional study of perceived couple communication patterns and influencing factors among colorectal cancer patients

Superconducting phase diagram of multilayer square-planar nickelates

The discovery of superconductivity in square-planar nickelates has offered a rich materials platform to explore the origins of high-temperature superconductivity. However, experimental investigations have largely been limited to the infinite-layer R NiO 2 ( R , rare earth) nickelates. We constructed a phase diagram of multilayer square-planar Nd n +1 Ni n O 2 n +2 compounds and found signatures of superconductivity for dimensionality n = 4 to 8. Upon decreasing n , the superconducting anisotropy evolves owing to 4 f electron effects, and electronic structure characteristics approach cuprate-like behavior. Magnetic fluctuations persist from within the superconducting regime and into the overdoped, nonsuperconducting regime. The superconducting regime overlaps with that of chemically doped infinite-layer nickelates, demonstrating underlying commonalities as well as differences across varying structural realizations of square-planar nickelates. Our work establishes this layered template for creating new nickel-based superconductors.

M polynomial and degree based topological indices of six common synthetic polymers

Silicon Valley’s vision for global AI is flawed: each country needs its own blueprint

Bacteria share proteins to survive antibiotics

Cell-to-cell cooperation through membrane vesicles enhances antibiotic persistence

Influence of quantum decoherence on the survival of quantumness in neutrino oscillations

Academic success still assumes uninterrupted careers

Illuminating the molecular basis of human daylight vision

Photopic vision, including fast motion and color perception in daylight, is mediated by cone opsins, specialized G protein–coupled receptors (GPCRs). Despite sharing the same chromophore, the three receptor subtypes absorb light at different wavelengths of the visible spectrum. The molecular mechanisms governing their spectral properties and exceptionally rapid responses remain largely unknown. We report cryo–electron microscopy structures of the human blue-sensitive (OPN1SW) and green-sensitive (OPN1MW) cone opsins in their dark-adapted states, combined with femtosecond-resolution spectroscopy, functional assays, and advanced simulations. The data reveal distinct chromophore stabilization mechanisms across human visual opsins and specific sequence adaptations in the GPCR microswitch motifs, underlining their structural plasticity and distinct activation mechanisms. These findings delineate the molecular basis of the evolutionary refinements fulfilling the needs of vision in daylight.

Damage characteristics of mudstone in loosening blasting: an HJC model-based study

Ubiquitin-like proteins NEDD8 and SUMO2 control epithelial homeostasis, regeneration, and inflammation

Stratified epithelial differentiation involves transcriptional and proteomic remodeling. Here, multiomic profiling implicated ubiquitin and related posttranslational networks in differentiation dynamics. Systematic perturbation of ubiquitin-like machinery in primary human keratinocytes which uncovered opposite functions of neural-precursor-cell–expressed, developmentally down-regulated 8 (NEDD8) and small ubiquitin-related modifier 2 (SUMO2). Generation of conditional knockout mice established essential roles for NEDD8 in progenitor maintenance, skin regeneration, and inflammation, whereas SUMO2 was required for differentiation. Beyond ubiquitin-proteasome-concordant changes, NEDD8 directed proteomic regulation correlated with RNA abundance. Integration of immunoprecipitation– mass spectrometry with genome-wide suppressor screening revealed context-specific NEDDylation dependencies. Among effectors, heterogeneous nuclear ribonucleoprotein U (HNRNPU) emerged as a posttranscriptional regulator of epithelial cell state whose RNA binding repertoire was modulated by NEDDylation. Thus, NEDD8 and SUMO2 play opposite roles in epithelial homeostasis, regeneration, and inflammation, demonstrating multiple ways ubiquitin-like networks govern tissue homeostasis.

Spatial and temporal analysis of the distribution of major cotton diseases, Fusarium wilt and floral virescence, in Côte d’Ivoire

Make science more reliable: study people as they go about their lives

Scientists to spend 8 months drifting in Arctic ice

The Tara Polar Station , set to launch next month, aims to probe biology of the central Arctic Ocean

Teacher behavior-related object detection with an edge-enhanced and dynamic multi-scale network toward metaverse-oriented educational scenarios

Abstract Teacher behavior analysis is important for understanding instructional patterns and can provide perception support for metaverse-oriented educational environments. This paper focuses on teacher behavior-related object detection in classroom environments and discusses its potential role as a perception module for future metaverse-oriented educational systems. To address challenges such as scale variation, complex backgrounds, and inaccurate localization, an enhanced YOLOv8-based method is proposed. Specifically, an edge-enhanced feature extraction module is introduced to improve boundary representation, and a dynamic inception module is designed to capture multi-scale features. In addition, a Focaler-Shape-IoU loss is adopted to enhance bounding box regression accuracy. These improvements collectively strengthen detection performance. Experiments on a self-collected classroom dataset show that the proposed method achieves competitive results in terms of precision, recall, and mAP@50 compared with several mainstream detectors. The method can effectively handle complex classroom scenarios. Although this work focuses on object detection, it can serve as a foundation for further teacher behavior analysis and may provide foundational perception support for future metaverse-oriented educational applications.

Rise in papers using powerful medical research tool raises concern

TriNetX’s built-in analysis tools fuel easy publications reporting misleading results

Multimodal emotion recognition using hybrid deep feature fusion under speaker-independent evaluation

Abstract Emotion recognition is one of the most important and complex challenges for machines to understand, as most robots and AI agents struggle with human-centric perception and interpretation. Therefore, this paper introduces a novel multimodal emotion recognition system that analyzes emotions through two complementary channels: voice and facial expressions. The proposed approach is evaluated on the RAVDESS and CREMA-D datasets, which consist of acted emotional expressions across multiple discrete emotion categories. Utilizing an advanced multimodal deep feature fusion technique, the system combines handcrafted audio features (e.g., Mel-Frequency Cepstral Coefficients (MFCCs)) with deep visual features extracted from an attention-based VGGFace model. These features are integrated into a unified representation through a hybrid fusion strategy that jointly employs concatenation, cross-attention, gated fusion, and multiplicative fusion mechanisms to capture complementary cross-modal interactions. To ensure a comprehensive and realistic assessment, the model is evaluated under both random-split and strict speaker-independent protocols. On the RAVDESS dataset, the proposed system achieves an accuracy of 95.83% under random-split evaluation and 48.06% ± 9.76% accuracy under speaker-independent Leave-One-Speaker-Out (LOSO) testing, while on the CREMA-D dataset it attains 73.54% accuracy using random splits and 53.12% ± 2.65% accuracy under subject-exclusive speaker-independent 5-fold cross-validation.

Diversification of angiosperm reproductive strategies predated the end-Cretaceous extinction

Angiosperm reproductive evolution is traditionally linked to the end-Cretaceous biotic crisis and subsequent ecological restructuring. Here, we report diverse and unexpectedly large diaspores (dispersal units) from an in situ late Campanian (74.6 million years ago) tropical forest from the Jose Creek Formation in New Mexico. Nearly 80 distinct diaspore morphotypes demonstrate that the flora had increased morphological specialization and an exceptionally large average and range of diaspore volume comparable to the Cenozoic records. These findings suggest that substantial increases in reproductive investment and specialization preceded the end-Cretaceous extinction. Our results indicate that Cretaceous angiosperms had already evolved diverse dispersal strategies, suggesting that animal-mediated dispersal and dense, angiosperm-integrated canopies were established far earlier than was previously recognized.

Implication of wind characteristics for assessing dust events in northeastern Iran

Impacts shaped Earth’s first continents

Heat from large impacts may have melted most of Earth’s earliest crust

Spatial frequency domain information aggregation network for radar image despeckling

Abstract Radar images are affected by speckle noise, which seriously affects its subsequent applications. Recently, the Convolutional Neural Networks (CNN) have been widely used in radar images despeckling and achieved remarkable results due to its powerful learning ability. However, most existing deep learning-based despeckling methods recover clear images in the spatial domain and rarely explore potential solutions in the frequency domain. This paper is the first attempt to combine spatial and frequency domain for radar image denoising, and proposes a spatial-frequency domain aggregation network for synthetic aperture radar (SAR) images despeckling, called as SFSARNet. SFSARNet contains three key components: a spatial-domain information branch, a frequency-domain information branch, and a dual-domain aggregation module. The spatial domain information branch mainly works in the spatial domain and is used to explore the local structural details. The frequency domain information branch works in the frequency domain for exploring global context information. The dual-domain aggregation module learns and integrates complementary information from both the spatial and frequency domains, enabling the network to benefit from both local and global context information. The proposed SFSARNet is validated on simulated and real radar image. Both quantitative and qualitative comparisons demonstrate that the proposed SFSARNet outperforms the existing mainstream methods.