Browse Articles

Sugar import suppresses Klebsiella pneumoniae Mucoidy in a cAMP-CRP-dependent manner

Love and Death

Transfer learning-based SOH estimation from partial charging data with polarization-aware modeling

A hierarchical framework for cortical and subcortical gray-matter parcellation across rodents, primates, and humans

Abstract Translational neuroscience requires consistent anatomical frameworks to compare brain organization across species despite differences in size and specialization. Existing atlases are species-specific, limiting cross-species analyses. Here we developed a hierarchical common atlas delineating homologous cortical and subcortical gray matter regions across mouse, rat, marmoset, rhesus macaque, and human, built upon population-averaged minimal deformation templates and uniform tissue segmentation. We validated the atlas using four independent approaches: cross-atlas Dice similarity against established human parcellations, cross-scale containment against species-specific atlases, cross-species geometric consistency of regional positioning, and comparison of independent mouse and marmoset tracer connectivity. A per-region homology confidence index quantifies the strength of each regional assignment. Cross-species tracer comparison revealed a structured gradient of correspondence, with sensorimotor connections showing strong conservation and association connections showing progressive divergence. This freely available atlas provides a unified coordinate system for comparative neuroscience, enabling quantitative evaluation of where cross-species correspondence holds and where species-specific divergence emerges.

Antiretroviral Therapy

Correction: Multilayer pyramid pooling self-attention for landslide detection using vision transformers

Body-plan organizer in comb jellies hints at animal ancestry

Mechanism of persister formation in response to nitrogen starvation

Mavacamten in Adolescents with Obstructive Hypertrophic Cardiomyopathy

A correlational study on the factors influencing burnout, job satisfaction, and turnover intention among emergency department nurses in the hail region of Saudi Arabia

Anisotropic core–shell ceramic nanofibrous membrane with improved optical reflectivity and thermal insulation for high-energy laser protection

Abstract The development of multifunctionally coupled materials that can adapt to complex thermophotonic environments is increasingly critical for next-generation aerospace, defense, and high-energy laser applications. Ceramic nanofibers, though inherently robust, often struggle to reconcile optical reflectivity with directional thermal management under extreme conditions. Here, we present a core–shell ceramic nanofibrous membrane, comprising a mesoporous silica shell and a crystalline boron nitride core, which exhibits dynamic thermal-optical functionality tailored to high-flux laser exposure. By virtue of dual-channel electrospinning and calcination, the structure achieves high reflectivity (≈ 100% at 1064 nm) while enabling anisotropic thermal conduction—rapidly dissipating heat in the membrane plane while insulating through its thickness. This functional decoupling at the single-fiber level empowers the membrane to maintain mechanical and structural integrity above 1500 °C, outperforming conventional ceramic or polymer systems in both laser shielding and thermal resilience. Moreover, the membrane demonstrates a compressive elasticity of 95% strain, a tensile strength of ≈ 20 MPa, and structural stability under a real-time laser impact. By introducing a design strategy that couples optical scattering with spatially controlled heat flow, this work provides an effective pathway for designing adaptive ceramic nanofiber systems engineered to thrive in complex, coupled-mechanism environments.

Breaking the RAS Barrier in Pancreatic Cancer

Spatiotemporally-guided defunctionalization of nociceptive nerves with capsaicin enhances structural repair in a murine Achilles tendon rupture model

Synnovation and confluence explain disparities in species richness across the Andes and Hengduan-Himalaya Mountains

Neurocysticercosis

Unsupervised feature learning and optimised clustering with fine-tuning for automatic semiconductor wafer defect recognition

Abstract Automated identification of wafer defect maps (WDMs) is critical for yield enhancement and quality control in semiconductor manufacturing. However, as defect patterns become increasingly diverse and dynamic, conventional supervised methods remain constrained by their dependence on fully labelled data and their limited adaptability to changing defect distributions. This study proposes a two-stage framework for automatic wafer defect map (WDM) recognition using predominantly unlabelled data with limited prior knowledge. First, a deep autoencoder-based representation learning approach is developed to automate feature extraction, followed by Elbow-guided K-means clustering to determine the number of clusters and capture the underlying defect structure. This stage provides an initial basis for automatic labelling without requiring exhaustive manual annotation. In the second stage, because these initial labels may become less reliable under complex data distributions and evolving defect patterns, XGBoost-based test-time training and semi-supervised refinement (TTT-SSR) is used to refine predictions by exploiting limited knowledge from the training phase. Experimental results on a benchmark mixed-WDM dataset show that the proposed framework achieves 95% classification accuracy, together with competitive precision, recall and F1-score relative to state-of-the-art methods, while also improving clustering quality across key evaluation metrics.

Controllable patterning of magnetic bits with atomically sharp boundaries

The Best of Both Worlds — Using Clinical Trial and Observational Data to Evaluate Vaccine Protection

Green, Sustainable, and Energy-Efficient System for Transportation Applications in IoT Edge-Cloud Networks

Abstract In recent years, the concepts of sustainability and green computing have gained significant attention, particularly in the context of smart cities and their various transportation applications. The primary goal is to shift transportation from fuel-based systems to electric alternatives, reducing overall CO 2 emissions. Motivated by this objective, this paper proposes a Green, Sustainable, and Energy-Efficient System for Transportation Applications in IoT Edge Cloud Networks. The focus is on designing an IoT edge cloud infrastructure to support sustainable and green transportation within smart cities. The system addresses various transportation-related tasks, including energy consumption monitoring, traffic and object detection, and optimal route planning, all while leveraging green edge cloud networks. To optimize performance, we propose a workload partitioning method based on a min-cut scheme that categorizes tasks into IoT-local, edge, and cloud-based workloads. This partitioning aims to reduce computational energy consumption and lower CO 2 emissions, fostering a more eco-friendly environment. Additionally, we introduce the Energy-Efficient Application Partitioning and Task Scheduling (EAPTS) scheme, which efficiently divides and schedules tasks across different nodes. To validate the system, we implemented testbeds based on Oslo’s public transport scenario, used training data from the given dataset, and developed a simulator for a green, sustainable transport environment. Simulation results demonstrate that the proposed system effectively reduces CO 2 emissions, energy consumption, and execution time for all operational tasks.

Mafic-ultramafic igneous rocks as a source of reactive phosphorus for the origin of life

Abstract Phosphite and pyrophosphate—reduced and polymerized forms of phosphorus—may have played a central role in prebiotic chemistry owing to their higher reactivity and solubility, yet their geological sources remain uncertain. Here we show that mafic–ultramafic rocks on the early Earth could have provided a sustained source of reactive phosphorus through seafloor weathering. Rock analyses from 15 localities reveal phosphite comprising up to 7%, 24%, 17%, and 0.6% of total phosphorus in olivine separates, peridotite, komatiite, and basalt, respectively, while pyrophosphate accounts for up to 5% and 0.4% in komatiite and basalt. Box-model calculations indicate phosphite concentrations reaching up to 1 µM in the deep ocean and up to 67 µM in lakes under low-UV conditions. We suggest that reactive phosphorus released from mafic–ultramafic rocks could have fuelled prebiotic phosphorylation and supplied phosphate to emerging life on Earth, a process that may also enhance the habitability of other planetary bodies.