Browse Articles

Covalent Organic Frameworks on Cu ₂ O Nanocubes as Rapid Proton/Electron Transfer Gates for Efficient NH ₃ Electrosynthesis from Nitrate in Neutral Media

Transforming jet flavour tagging at ATLAS

Abstract Jet flavour tagging enables the identification of jets originating from heavy-flavour quarks in proton–proton collisions at the Large Hadron Collider, playing a critical role in its physics programmes. This paper presents GN2, a transformer-based flavour tagging algorithm deployed by the ATLAS Collaboration that represents a different methodology compared to previous approaches. Designed to classify jets based on the flavour of their constituent particles, GN2 processes low-level tracking information in an end-to-end architecture and incorporates physics-informed auxiliary training objectives to enhance both interpretability and performance. Its performance is validated in both simulation and collision data. The measured c -jet (light-jet) rejection in data is improved by a factor of 3.5 (1.8) for a 70% b -jet tagging efficiency, compared to the previous algorithm. GN2 provides substantial benefits for physics analyses involving heavy-flavour jets, such as measurements of Higgs boson pair production and the couplings of bottom and charm quarks to the Higgs boson, and demonstrates the impact of advanced machine learning methods in experimental particle physics.

Perception of online learning, knowledge assessment, and clinical skills among third-year ophthalmology residents studying during the COVID-19 pandemic in Thailand

Catalytic Deracemization of 1,2-Aminoalcohols through Enantioselective Hydrogen Atom Abstraction

Solvent-mediated partial ionicity enhances mechanical nanosizing effect of Mg-based hydrogen storage alloys

Disentangling direct and pleiotropic SNP effects in alfalfa (Medicago sativa L.) using causal graph learning

Abstract Alfalfa ( Medicago sativa L.) is a critical forage crop whose improvement depends on resolving the complex genetic architecture of agronomic traits. While genome-wide association studies (GWAS) effectively identify statistically associated markers, they often fail to distinguish direct genetic effectors from indirect or pleiotropic signals arising from linkage disequilibrium and population structure. Here, we present a causal graph based genomic discovery framework that integrates de-confounded feature screening with causal graph learning to infer directional dependency structures from observational genomic data. Using Double Machine Learning to control for confounding and the PC algorithm for structural learning, we construct directed acyclic graphs that distinguish Direct Parent SNPs (DPSs) , representing local effectors within the Markov Blanket of a trait, from Upstream Hub SNPs (UHSs) , representing pleiotropic regulators with broad network connectivity. Applied to four stem-related traits in alfalfa, the framework reduces genome-wide associations to compact, interpretable causal-consistent networks. Predictive validation demonstrates that DPSs consistently outperform both upstream UHSs and random controls, confirming their role as precise trait-specific biomarkers, while UHSs exhibit limited direct predictive power consistent with signal dilution along causal pathways. Together, these results demonstrate that causal graph learning can act as a biologically grounded regularizer for GWAS in polyploid crops, enabling principled marker prioritization and providing a structural foundation for future multi-omics integration.

The Highly Localized Interaction between Neurofascin-186 and Gliomedin Promotes Subcellular Innervation by the Chandelier Cell

A variety of cortical inhibitory interneuron (IN) subtypes differentially but coordinately operate in local circuits to process neuronal signals. The subcellular arrangement of IN synaptic outputs contributes to subtype-specific functional specialization. However, it remains poorly understood what intercellular molecular interactions enable the matched synapse formation between IN subtypes and specific subcellular domains. Using mice of both sexes, we demonstrate that Neurofascin-186 (NF186), a cell adhesion molecule specifically expressed in the axon initial segments (AISs) of pyramidal neurons, is necessary for chandelier cells (ChCs) to develop a string of synaptic boutons along the AIS (an axon cartridge). Furthermore, we discovered that Gliomedin, a known receptor for NF186 in the nodes of Ranvier, is preferentially expressed in ChCs and mediates ChC axon cartridge development by acting as a major receptor for NF186. Thus, the intercellular interaction through subcellularly restricted ligands and cell type-specific receptors ensures a high degree of IN subcellular synapse specificity.

Light-Field Orchestrated Tandem Photothermal Catalysis for Highly Selective CO ₂ -To-C ₂₊ Olefin Conversion

Antigravity confined interfacial self-assembly approach for the synthesis and characterization of nanofilms

Functional competency of a novel 2-ply vacuum-pressed biological scaffold for entire posterior mitral valve reconstruction

Neural Patterns Reflect Shared Emotional History

Emotions shape episodic memories, with emotional context—the affective quality or “hue” of an experience—persisting as part of the event in memory, scaffolding connections between events, and guiding our impressions of the environment. We propose that events encoded in a similar emotional context also exhibit similar patterns of brain activation during retrieval, particularly when such events are negative. To explore this idea, we scanned 33 human participants of all genders using functional magnetic resonance imaging as they completed a two-phase episodic memory task. During encoding, participants viewed trial-unique image pairs: a neutral object alongside a complex picture evoking either a negative or neutral emotional context. Across conditions, images were closely matched on low-level perceptual features. During retrieval, participants were shown the neutral objects again and rated their pleasantness, implicitly recalling their emotional context. To determine whether there is a neural signature that reflects salient emotional contexts, we employed trial-level representational similarity analysis, focusing on three brain areas previously linked to emotional memory, appraisal, and/or affective schemas: ventral visual stream (VVS), hippocampus, and ventromedial prefrontal cortex (vmPFC). Our results demonstrate strong converging evidence of emotional context coding in the VVS, reflecting a shared signature of negative emotional context across retrieval and reinstatement of encoding activation patterns, particularly for negative events. Meanwhile, the hippocampus and vmPFC played a more nuanced role. These findings reveal that content with a shared emotional context evokes brain activity patterns reflecting the essence of its emotional history, highlighting the brain’s flexible capacity to integrate affective content into mnemonic representations.

Ring-Expansion of Ketones with [1.1.1]Propellane

Feedback-induced attitudinal changes in risk preferences

Energy and makespan optimised task mapping in fog enabled IoT application: a hybrid approach

A Concise Total Synthesis of (+)-Pedrolide

Optical interference for the guidance of cryogenic focused ion beam milling beyond the axial diffraction limit

CD13 activation assembles phosphoinositide (PI) signaling complexes to regulate the actin cytoskeleton

A Deep-Ultraviolet Transparent Nonlinear Optical Hydrogen-Bonded Organic Framework

Deterministic and highly indistinguishable single photons in the telecom C-band

Abstract Quantum dots are promising candidates for deterministic single-photon sources, yet achieving high photon indistinguishability at telecom wavelengths remains a critical challenge. Here, we report a quantum dot-based single-photon source operating in the telecommunications C-band that achieves a raw two-photon interference visibility of up to (91.7 ± 0.2)%, thus setting a new benchmark for indistinguishability in this spectral range. The device consists of an indium arsenide (InAs) quantum dot embedded within indium aluminum gallium arsenide (InAlGaAs) and integrated into a circular Bragg grating resonator. We explore multiple optical excitation schemes to optimize coherence and source performance. The demonstration of two-photon interference visibilities exceeding 90% from a quantum-dot emitter in the telecommunications C-band pushes solid-state single-photon sources further towards practical quantum communication and quantum networks.

Correction: PM2.5 risk perception and associated with sociodemographic factors in urban Thailand