Our study investigated the bidirectional interaction between circadian clock genes and reactive oxygen species (ROS) production in fish monocytes/macrophages (Mo/Mf). Macrophages are key leukocytes whose antimicrobial activity relies on ROS generation. As ROS levels fluctuate across the day and clock genes regulate numerous cellular processes, we examined whether macrophage clock gene activity and ROS production are functionally interconnected. We found that carp Mo/Mf displayed constitutive expression of core clock genes and genes involved in melatonin synthesis and redox balance: Clock genes (per2, clock, cry2, and bmal2) showed rhythmic expression under both light:dark and constant dark conditions and ROS production varied across the day. Ex vivo cell stimulation revealed that immune signals, cortisol, and ROS-modulating agents altered clock gene expression. Direct ROS donors upregulated multiple clock genes, while inhibition of NADPH oxidase reduced ROS production and changed expression of clock genes. Finally, zebrafish mutants lacking cry1 exhibited changes in ROS production, demonstrating that disruption of the circadian clock affects redox activity. Together, these findings show that carp and zebrafish Mo/Mf possess a functional circadian system and that the ROS and clock pathways influence each other. This interplay may shape daily patterns of immune responsiveness and contribute to time-dependent variation in host defense.
By 2 months of age, infants leverage predictable temporal structures to optimize attention allocation, disengaging faster from rhythmic than nonrhythmic stimuli-a pattern absent in newborns and attributed to postnatal experience. Here, we investigated whether prenatal rhythmic enrichment provides an earlier foundation for this competence. From week 29 until delivery, pregnant women (N = 101) provided fetuses either multisensory rhythmic stimulation (experimental, N = 49) or nonrhythmic relaxation (control, N = 52). Visual attention disengagement was assessed using an overlap task with rhythmic, nonrhythmic, and static stimuli, at birth (N = 30 per group) and 2 months (N = 30 per group). At 2 months, both groups showed the expected attentional cost for nonrhythmic stimuli. At birth, this pattern appeared only in the experimental group, indicating that prenatal rhythmic enrichment accelerates the typical developmental timeline for temporal attention tuning. Individual differences further showed that greater amounts of prenatal rhythmic stimulation predicted faster overall disengagement at birth, and a stronger cost for nonrhythmic stimuli at 2 months. While acknowledging methodological constraints inherent to the population under study, these findings suggest that the developing brain begins to tune to environmental temporal structure prenatally, and that this process is promoted by in utero rhythmic experience.
Spatially heterogeneous blur is frequently observed in practical electron microscopy (EM) acquisitions and can lead to nonuniform resolution across the field of view. Herein, we propose a patch-wise anisotropic blur-kernel estimation framework that performs uncertainty-aware regularized shift-variant deconvolution. The proposed method is not intended to replace standard scanning electron microscopy (SEM) acquisition optimization, such as focus correction, aperture selection, working-distance control, beam-current adjustment, or astigmatism correction. Rather, it is designed as a complementary post-acquisition restoration and quality-control tool for cases in which spatially heterogeneous blur remains after acquisition or when reacquisition is impractical. The method is expected to be most useful in high-magnification imaging, large-area SEM mapping, serial-section or volume EM workflows, and archived datasets, where local degradation may vary across the field of view. In controlled simulations with known references, the method improved restoration consistency relative to the evaluated global-point spread function (PSF) conditions. Application to experimental SEM images demonstrated exploratory feasibility, but the absence of matched blur-free references precluded conclusions regarding true spatial-resolution recovery or faithful morphology.
Wound care represents a major challenge across species, with impaired wound healing imposing substantial costs on health, survival, and fitness. Insights from nonhuman animals offer promising avenues for understanding wound-management strategies and identifying novel therapeutic compounds. Yet, despite the strong consequences of wound healing for survival and fitness-especially in free-living populations-wound-management strategies remain poorly understood. A key limitation is that existing methods for evaluating wound characteristics rely on close-range observations, leaving a substantial gap for species that cannot be observed up close. As a result, the diversity of wound-tending behaviors and their relationship to wound condition and healing remain little understood. We introduce a novel methodological framework and demonstrate its application to Central African chimpanzees (Pan troglodytes troglodytes) living in the wild. The framework merges two complementary components: (1) a systematic classification of wound-tending behaviors in nonhuman species by establishing a standardized ethogram; and (2) a structured, distance-based assessment of wound impact, based on multiple observable characteristics. By combining a systematic behavioral classification with a distance-based scoring system, the framework overcomes key limitations of existing approaches. The proposed method is flexible, feasible, and transferable across species, facilitating systematic comparative research on health-related behaviors.
Deep learning has advanced remote sensing object detection, with ship detection drawing attention due to its importance in maritime traffic, defense, and marine resource management. However, real-world scenarios often involve cloud and fog occlusion, sea clutter, and uneven illumination, which degrade feature representation. Moreover, existing sample assignment strategies fail to account for the high-angle sensitivity of slender ships, causing many potentially high-quality proposals to be assigned as negative samples under slight rotational deviations. To address these issues, we propose PGNet, a robust ship detection framework that jointly optimizes feature representation and sample assignment. Specifically, a progressive aligned pre-fusion feature pyramid network is designed to alleviate spatial misalignment and semantic inconsistency via adaptive multi-scale spatial alignment and progressive feature interaction. Meanwhile, a Gaussian geometric consistency assignment strategy is introduced, which jointly models overlap and center alignment to alleviate the high-angle sensitivity problem for slender ships. Experiments on the HRSC2016, SCCOS, and FGSRCS datasets demonstrate that PGNet consistently outperforms existing methods, achieving a mean average precision (mAP) of 88.8% (+1.7%) on FGSRCS, 87.52% mAP and 91.84% recall (+0.5% and +1.43%) on SCCOS, and 70.39% mAP 50:95${_{50:95}}$ (+1.22%) on HRSC2016, indicating robustness and generalization in complex maritime environments.
