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Early-life supplementation of poultry-derived lactobacilli drives microbial succession and gut immune modulation in broiler chickens
Informed spatially aware patterns for multiplexed immunofluorescence data
Diffractive magic cube network with super-high capacity enabled by mechanical reconfiguration
The transcription factor HHEX maintains glucocorticoid levels and protects adrenals from androgen-induced lipid depletion
A three-in-one strategy for lithium recovery and upcycling of spent cathode materials
Mechanical and radiation shielding assessment of high-density/magnetite-cementitious plaster for repair and retrofit of nuclear power plant structures
Abstract Concrete plays a critical role in nuclear power plants (NPPs) as structural material and radiation shielding. Severe defects during construction of NPP concrete could lead to catastrophic leakages. Therefore, cement plaster propably with acceptable radiation protection could be a good solution as a repairing technique. Thus, this study investigates the mechanical and radiation shielding properties of non-conventional cementitious plaster incorporating magnetite powder as a partial/full replacement for traditional sand. Five cement plaster mixtures were proposed with varying proportions of magnetite powder and sand, in which their mechanical and radiation attenuation properties were analyzed. Experimental evaluations including workability, density, compressive strength, and gamma-ray attenuation were investigated. Furthermore, γ-ray and fast neutron shielding has been evaluated using software programs such as EpiXS, NXCom, and MRCsC. Results showed that incorporating magnetite powder in cement plaster production lowers the compactness of the matrix by up to 46.2%. Consequently, the compressive strength was generally decreased by up to 65.4% with increased magnetite content. However, the density of the plaster was increased by up to 48.7% compared to traditional cement plaster. Furthermore, owing to increased density, the radiation shielding efficiency of magnetite cement plaster was generally enhanced. Cement plaster with 100% magnetite content achieved superior linear attenuation coefficient (LAC) for gamma rays with 264% increase at 0.01 MeV and 43% increase at 10 MeV compared to the traditional sand plaster. Also it acquired better fast neutron shielding performance, with a macroscopic removal cross-section (Σ R ) of 0.1056 cm⁻¹ (11% increase) compared to the conventional plaster. This research highlights the potential of utilizing magnetite-cement plaster with enhanced radiation shielding properties for repair and maintenance strategies of NPP structures. With the achieved gamma and neutron shielding properties, such material can significantly improve safety, durability, and longevity of nuclear power plant structures.
Wearable textile-based phototherapy platform with customized NIR OLEDs toward non-invasive hair loss treatment
Separate silicon cells from end-of-life bifacial glass photovoltaic modules using continuous lasers
Rad51 determines pathway usage in post-replication repair
Abstract Stalled replication forks are processed in post-replication repair by homologous recombination, fork regression, and translesion DNA synthesis. However, the regulation of pathway usage is not fully understood. Rad51 protein maintains genomic stability through its roles in recombination and in protecting stalled replication forks. We report isolation of mutations in Saccharomyces cerevisiae Rad51 that shift post-replication repair from recombination to alternate pathways including mutagenic translesion synthesis. Rad51-E135D and Rad51-K305N show near normal in vitro recombination despite changes in their DNA binding profiles, in particular to dsDNA. The mutants lead to a defect in Rad51 recruitment to stalled forks in vivo as well as a defect in the protection of dsDNA from degradation by Dna2-Sgs1 and Exo1 in vitro. Together, the evidence suggests that Rad51 binding to duplex DNA is critical to control pathway usage at stalled replication forks.
Host-dependent variations in antioxidant activity, metabolic profile, and phenolic content of the parasitic plant Phoradendron nervosum Oliv.
A self-assembled molecule directs ordered α-FAPbI3 for n-i-p perovskite solar cells
Variability, asymmetry and sexual dimorphism in craniofacial anomalies in Loeys-Dietz syndrome 2: geometric morphometric analysis in mice
Abstract Loeys-Dietz syndrome is a rare connective tissue disorder characterized by life-threatening aortic aneurysm and distinctive craniofacial anomalies. It is caused by mutations along the transforming growth factor beta (TGF-β) signaling pathway (LDS1-6). We previously showed that craniofacial anomalies varied among LDS subtypes and that LDS2, caused by mutations in the TGFBR2 gene, exhibited the most severe and variable phenotype. In this study, we performed a thorough qualitative and quantitative analysis of craniofacial anomalies in a mouse model for LDS2, through micro computed tomography and 3D geometric morphometric analysis at multiple postnatal stages. We show that craniofacial shape in Tgfbr2 G357W/ + mice strongly deviates from their WT littermates from an early age and exhibit high variability and evidence of left–right asymmetry despite the pure genetic background. Cranial doming, shortening of the anterior part of the skull, widening of the space between orbits, reduction of mandibular size, suture fusion in the cranial vault and palate, and abnormal condylar shape were among features that were consistent with the phenotype seen in patients with LDS. Interestingly, several of these features were more prevalent and severe in females than in males, indicating potential sexual dimorphism, further supported by the trend observed in our revisited clinical data.
Mixing protocols determine liquid–liquid phase separation dynamics in polyelectrolyte complex coacervation
In situ high temperature X-ray diffraction and dilatometric analysis of CGO–Cu composites for solid oxide devices
Abstract Understanding the thermo-mechanical compatibility of composite electrodes is essential for the long-term reliability of solid-oxide electrochemical devices. In this study, we demonstrate a combined in situ synchrotron X-ray diffraction (XRD) and simultaneous dilatometry approach as a rapid and predictive method to quantify both phase-resolved and bulk thermal expansion while tracking microstructural evolution at operational temperatures. Ce 0.8 Gd 0.2 O 2−δ –Cu (CGO–Cu) composites with varying CGO: Cu ratios (39:61–70:30 vol%) were synthesized as potential anode materials compatible with CGO electrolytes up to 800 °C. In situ XRD confirmed only the CGO and Cu phases, with Rietveld refinement revealing a slight lattice expansion and reduced CGO crystallite size with increasing CGO content. Concurrent dilatometry indicated systematic changes in the macroscopic thermal expansion and densification behavior, which correlated with the phase and microstructural evolution observed during heating. The CGO–Cu (59:41) composite exhibited a nearly temperature-independent coefficient of thermal expansion consistent with the rule-of-mixtures predictions and minimal high-temperature shrinkage. These findings validate the combined in situ synchrotron XRD + dilatometry methodology as a powerful approach for characterizing and capturing the TEC characteristics of cermets, and for guiding the design of thermomechanically compatible oxide-metal composites for high temperature electrochemical applications.
Universal modulus-free transfer of scalable laser-induced graphene for electronic skins
Face expectancy cues differentially modulate conflict processing driven by emotional incongruence: an EEG study
Abstract Adaptive behavior in social interactions requires the effective processing of conflicting emotional information. The impact of expectancy on conflict processing remains a relevant research question. Here we investigated the influence of primed expectancy on conflict processing. To achieve this goal, we used the Emotional Stroop paradigm and variants where expectancy was introduced using facial expression or emotional letter labels. Neurophysiological and behavioral data were collected from 20 healthy participants who completed these three conditions (in the presence or absence of prior expectancy cues). We examined conflict-related components N450 and the Conflict Slow Potential in the classical Emotional Stroop condition and in conditions with manipulated expectancy. When expectancy was introduced, we found a significant effect on conflict processing, with a difference between face and letter emotion cues. Parietal alpha and beta power decreases occurred specifically for face expectancy cues and changed only minimally during conflict, whereas label-based expectancy was associated with less desynchronization during expectancy and a stronger alpha/beta decrease during conflict. These findings suggest that attentional resources are differently prioritized by face versus letter emotion expectancy cues, with face-driven expectancy generating distinctive neurophysiological patterns and a potential facilitation of subsequent conflict resolution.
Critical scaling of novelty in the cortex
Abstract To guide behavior in uncertain environments, the brain must rapidly detect novel or unexpected events. The neocortex, involved with complex perception and decision-making, is thought to contribute to this computation, but underlying mechanisms are poorly understood. Here, we test how a few unanticipated action potentials influence local circuitry in the resting mouse visual cortex. Using targeted holographic stimulation, we evoked sparse “surprise” spikes in single pyramidal neurons and monitored their effects on hundreds of neighboring cells with 2-photon imaging. These novel spikes, distinct from the cortex’s ongoing large-scale activity fluctuations, produced strong, transient recruitment, following a power-law with slope 0.2–0.3, indicating that single neurons can mobilize large fractions of the surrounding network. Ongoing activity was dominated by neuronal avalanches, highly variable, scale-invariant spike cascades characteristic of systems near criticality. Yet, the information regarding the origin of our perturbations remained reliably identifiable and distributed across most of the observed network, as shown using machine-learning classifiers. Cortical network simulations confirmed that the measured scaling and distributed information matches predictions for systems operating near criticality. These results demonstrate two hallmarks of criticality, avalanche organization and amplified responses to small perturbations, suggesting that critical dynamics enhance the cortex’s ability to detect novel events.
An attention enhanced CNN ensemble for interpretable and accurate cotton leaf disease classification
Abstract Precise and timely identification of cotton leaf diseases is essential for sustaining crop yield and quality, yet manual inspection remains time-consuming, labor-intensive, and prone to error. Existing automated approaches are limited by insufficient dataset diversity, inconsistent evaluation practices, limited use of explainable AI (XAI), and high computational cost. To address these challenges, we propose an attention-enhanced CNN ensemble, namely CottonLeafNet , which integrates lightweight convolutional neural networks for accurate cotton leaf disease classification across two publicly available datasets. CottonLeafNet achieves state-of-the-art performance, obtaining 98.33% accuracy, a macro F1-score of 0.9833, Cohen’s kappa of 0.9800, a mean PPV of 0.9838, and an NPV of 0.9967 on Dataset D1, with an inference time of 0.51 s per image. On Dataset D2, it reaches 99.43% accuracy, a macro F1-score of 0.9942, Cohen’s kappa of 0.9924, a mean PPV of 0.9943, and an NPV of 0.9981, with a 0.40 s inference time. Moreover, a unified eight-class dataset created by merging both datasets yields a test accuracy of 99.08%. Robustness analysis under artificially induced class imbalance further confirms the model’s stability, with consistently strong macro F1-scores. To evaluate the generalization capability of the proposed CottonLeafNet, we conducted cross-dataset experiments, and the results indicate that the model maintains moderate performance even when trained and tested on different datasets. Gradient-Weighted Class Activation Mapping (Grad-CAM) visualizations demonstrate that CottonLeafNet reliably attends to disease-relevant regions, enhancing interpretability. Finally, real-time feasibility is validated through a web-based deployment achieving $$\approx$$ 1 s inference per image. These results establish CottonLeafNet as an accurate, robust, interpretable, and computationally efficient solution for automated cotton leaf disease diagnosis.