Browse Articles

Cocoa agroecosystems embedded in a neotropical Ramsar wetland support arthropod diversity and functional structure relevant to insect conservation

Molecular Glues Recruiting RNF213 As an E3 Ligase for Targeted Protein Degradation: A Minimal Dibromoacetamide Warhead As a Recruitment Ligand

Effect of virtual reality on spatial–anatomical understanding in preoperative liver surgery: a randomized crossover study

Abstract Precise spatial understanding of complex anatomy is critical for preoperative planning in hepatobiliary surgery. Traditional CT and MRI imaging require mental reconstruction of anatomy from 2D slices, imposing substantial cognitive load. Although 3D reconstructions improve spatial understanding, they are typically displayed on 2D screens, limiting true depth perception. Virtual Reality (VR) visualization offers both stereoscopic depth and embodied interaction to improve spatial-anatomical understanding, yet its quantitative advantage over standard desktop visualization remains uncertain, especially regarding task complexity. In this randomized crossover study, 58 medical students analyzed 3D liver models of varying complexity using both VR and desktop visualization. Performance on lesion/vessel relations and lesion segment allocation tasks served as a measure of spatial-anatomical understanding, while visuospatial ability was assessed with the Mental Rotations Test. In complex models, VR significantly improved performance compared with desktop visualization (28.0 ± 3.3 vs. 26.4 ± 3.6; p  = 0.002, d = 0.46), whereas results for simpler models were comparable. The VR advantage scaled with task complexity and correlated with higher visuospatial ability ( r  = 0.31, p  = 0.018). These findings indicate that VR is associated with measurable advantages under higher task complexity, supporting its potential role in surgical education and preoperative planning, although the present design cannot isolate which immersive features drive this benefit.

Biomimetic Redox-Mediated Proton Relay in Nanoreactors for Photocatalysis

A new mamenchisaurid sauropod from the Lower Phu Kradung Formation, Upper Jurassic of northeastern Thailand

Abstract Mamenchisauridae is a group of long-necked non-neosauropodan eusauropod dinosaurs that were abundant in East Asia during the Middle to Late Jurassic, but their diversity and geographic distribution outside China remain poorly documented. Here we describe Uragasaurus kalasinensis gen. et sp. nov., a new sauropod dinosaur from the Phu Kradung Formation of northeastern Thailand. The new taxon is based on a well-preserved anterior dorsal vertebra exhibiting a distinctive combination of characters, including a unique Y-shaped configuration formed by the intraprezygapophyseal and single intraprezygapophyseal laminae and a camellate internal pneumatic structure within the centrum revealed by computed tomography (CT). Phylogenetic analyses recover the new taxon as an early-diverging member of Mamenchisauridae. This discovery represents the first formally named mamenchisaurid from Thailand and expands the known geographic distribution of the clade in Southeast Asia. The occurrence of this taxon in the Lower part of the Phu Kradung Formation also contributes to understanding faunal succession within the unit, supports an Upper Jurassic age for the lower part of the formation, and improves understanding of sauropod diversity in Southeast Asia during the Jurassic-Cretaceous transition.

Mitigating Electrode Stress via Self-Constructed Interfacial Carrier Networks in High-Areal-Capacity SiO _<i>x</i> Anodes

Slope length thresholds and factor interactions drive nonlinear transitions in bare slope soil erosion

Abstract Soil erosion is a major global environmental threat, and unraveling the complex, non-linear interactions among its drivers is crucial for effective mitigation. This study employed interpretable machine learning (IML) framework, combining Random Forest (RF) with SHapley Additive exPlanations (SHAP) analysis, on a meta-analysis of 385 indoor experiments to decipher these mechanisms. Results identified slope length (SL) as the dominant controller, explaining 28.45% and 45.16% of the variance in runoff and sediment yield, respectively. Critical, factor-specific thresholds that trigger abrupt shifts in erosion dynamics were uncovered: runoff increased sharply when rainfall intensity (RI) exceeded 75 mm/h, and SL of approximately 4.5 m acted as a critical geomorphic threshold between detachment-limited and transport-limited erosion states. More importantly, the influence of key factors like sand content and antecedent soil moisture was mediated almost entirely through synergistic interactions with other variables, as quantified by Interaction-to-Main Effect Ratio (IMER &gt; 188%). This demonstrates that erosion is driven by SL-centered interaction networks and their nonlinear thresholds, advancing the theoretical framework of erosion process transition and providing a mechanistic basis for threshold-targeted conservation strategies.

Enantiospecific Homo-Boron-Wittig Reaction: Direct Conversion of Chiral Epoxides to Cyclopropanes

LeafLiteX mobile application for leaf disease detection using U-Net segmentation and lightweight deep learning

Tailoring Hydrogen Bonding in Organic Frameworks for Rapid Atmospheric Water Harvesting with Low-Temperature Regeneration

Direct Determination of Protein Rotational Diffusion Tensors and Generalized Order Parameters from Multifield ¹⁵ N NMR Spin Relaxation

Impact of thermal pre-treatment on HDV RNA detection and quantification in clinical practice

Proximal and Remote Twofold Stereogenicity Enabled by Rhodium-Catalyzed and Phosphine-Directed Murai Reaction

3Br-MGD: few-shot toxicity prediction with a three-branch deep encoder and meta-learning framework

Abstract Predicting the toxicity of pharmaceutical compounds remains a major challenge in drug discovery. Early and accurate toxicity assessment is essential for eliminating harmful candidates before costly preclinical and clinical testing, thereby improving patient safety, reducing development costs, and accelerating the drug development process. Despite advances in computational toxicology, existing methods often struggle to capture complex molecular characteristics and maintain robust performance under limited-data conditions. To address these challenges, we propose 3Br-MGD, a novel three-branch framework that integrates deep learning and meta-learning for molecular toxicity prediction. The architecture combines complementary molecular representations: FingerprintMLP encodes Morgan fingerprint descriptors, Graph Convolutional Networks (GCNs) capture structural information from molecular graphs, and one-dimensional Deep Convolutional Neural Networks (1D-CNNs) extract sequential features from SMILES strings. These embeddings are integrated within a Prototypical Network-based few-shot learning framework, enabling rapid adaptation to new prediction tasks with limited labeled samples and improving generalization in low-resource settings. Experimental results on benchmark toxicity datasets demonstrate that 3Br-MGD consistently outperforms conventional baselines in predictive accuracy, robustness, and generalization. Furthermore, the integration of heterogeneous molecular encoders reduces dependence on large training datasets while enhancing interpretability through the exploitation of complementary chemical information from multiple molecular views.

Cascade Oxidation of Ethylene and Propylene over a Redox Heterometallic Cluster

Investigation in the selective targeting of homologous recombination-deficient cells by benzophenanthridine alkaloid nitidine

Altermagnetic Metal–Organic Frameworks

Sampling strategies for enhancing the analytical performance of a hybrid spectroscopy platform in biomedical applications

Abstract Laser-Induced Breakdown Spectroscopy (LIBS) of liquids poses significant analytical challenges, especially for biological samples such as blood and saliva, where only limited volumes are available. Maximising spectral information from these scarce samples is therefore essential. Among various liquid sampling approaches, the drop-coating method has shown particular promise for efficient sample utilisation. This study focuses on advancing the drop-coating approach by integrating enhanced sampling and laser excitation strategies to improve spectral performance while preserving LIBS’s intrinsic advantages. Specifically, pulsed laser-based Surface-Enhanced LIBS (SELIBS) and Nanoparticle-Enhanced LIBS (NELIBS) techniques were employed to amplify signal intensity and detection sensitivity. Systematic optimisation of key experimental parameters revealed effective conditions for achieving reproducible and high-intensity spectra. The proposed methodology provides a practical approach to enhance LIBS performance for the trace-level analysis of limited-volume biological samples, laying the groundwork for sensitive, non-destructive diagnostics and forensic applications. Furthermore, this work implements a hybrid spectroscopic platform designed for comprehensive elemental and molecular diagnosis of biological samples. This integrated architecture utilises a dual-laser, single-spectrograph configuration that enables coordinated excitation and collection of LIBS and Raman signals. This configuration simplifies the experimental workflow, reduces alignment and calibration, eases the analysis process and enhances the depth of information that can be extracted from biological samples, yielding a more holistic, comprehensive chemical composition of limited-volume bio samples.

Quantification of Binding of Small Molecules to Native Kinases by Flow Cytometry Reveals Divergence from Biochemical Affinities

Pregnancy outcomes following maternal GLP-1 receptor agonist exposure: a systematic review and meta-analysis