The capacity to recognize behavior in complex scenes poses a significant challenge within the domains of computer vision and robotic vision. Conventional approaches frequently encounter difficulties in effectively addressing issues such as background interference, perspective variations and intro-class differences, which are prevalent in real-world robotic applications. The proposed intelligent behavior recognition model is based on an adaptive attention mechanism and scene Significance region extraction. It has been specifically designed for robotic perception systems. By simulating the selective attention mechanism of the human visual system, the system achieves precise focusing on key behavior regions. The approach initially employs multi-scale significance detection to localize critical regions within video sequences. The integration of an adaptive spatio-temporal attention mechanism is instrumental in accentuating motion-relevant areas in terms of spatial distribution and prioritizing key frames in terms of temporal sequence. Finally, graph neural networks (GNNs) have been developed to model spatio-temporal relationships between regions for the purpose of performing behavior recognition. The experimental findings, drawn from datasets such as UCF101 and HMDB51, demonstrate the efficacy of this approach in achieving high recognition accuracy and robustness in complex scenes, with an average recognition rate of 94.2%, thereby signifying a substantial improvement over conventional methods. In addition, the validation of the model on a custom robot interaction dataset has been demonstrated to confirm its effectiveness in scenarios such as human–robot collaboration and service robot task understanding. This study achieves superior performance over TimeSformer, ST-GCN and ViT-based frameworks in behavior recognition tasks by integrating multi-scale scene Significance region extraction, adaptive spatio-temporal attention mechanisms and GNN relational modeling. It significantly enhances accuracy and robustness in complex scenes. The innovation lies in simulating human visual selective attention to dynamically focus on key behavior regions, with experiments validating its effectiveness against background interference and viewpoint variations. This suggests that the model has the potential to enhance robotic situational awareness.

The maintenance of mammalian spermatogenesis depends on the intricate molecular and cellular interactions between spermatogonial stem cells and their cognate niche in the seminiferous epithelium of the testis. To sustain the continuous production of sperm, spermatogonia proliferate and differentiate under the control of various niche factors, promoting either self-renewal or commitment to spermatogonial differentiation. Single-cell RNA sequencing analyses have identified different subpopulations of spermatogonia in primates based on the expression of specific marker genes (PIWIL4, GFRA1, NANOS3 and KIT). However, the spatial distribution of the different spermatogonial subpopulations and their relationship with the niche has not been described yet. Here, we investigate the topological localization of spermatogonia in primates. To this end, immunohistochemical stainings for PIWIL4, GFRA1, NANOS3 and KIT were performed on Bouin fixed samples of Macaca fascicularis and quantitatively analyzed. Strauss's linear selectivity index (Linear Index, Li) was employed to assess the regional distribution of spermatogonial subpopulations in the basal compartment of seminiferous tubules. Remarkably, PIWIL4+ spermatogonia showed a random distribution along the basal compartment across all the stages of the seminiferous epithelium cycle. In contrast, GFRA1+, NANOS3+ and KIT+ spermatogonia displayed stage-dependent localization patterns. The spatial organization of different spermatogonial subpopulations, appeared coordinated with the cycle of the seminiferous epithelium, suggesting a dynamic regulation of spermatogonial behavior throughout the process of sperm production. Our study contributes to the growing body of literature aimed at deciphering the complexities of SSC biology and the regulation of spermatogenesis in mammalian species, with implications for understanding male fertility.

ObjectiveAlthough health workforce equity has gained more attention, few studies have explored its spatial distribution and influencing factors in Inner Mongolia, a vast and diverse region of China. Existing researches often use simple geographic adjacency-based models that do not fully consider both location and economic factors. To address this, this study applies spatial econometric methods to examine the direct and indirect effects of influencing factors on health workforce distribution in Inner Mongolia from 2013 to 2022.MethodsData were obtained from the Inner Mongolia Statistical Yearbook (2013–2022). Health workforce (HW) was measured by the number of health professionals per 1,000 persons. Spatial distribution and clustering patterns were analyzed using Global and Local Moran’s I. Four types of independent variables were selected: socioeconomic factors (per capita GDP and disposable income), demographic factors (population density and population growth), institutional environment (fiscal self-sufficiency rate), and supportive resources (beds density). A spatial panel econometric model was applied to assess both direct and indirect effects.ResultsSignificant spatial clustering of HW was found throughout the study period. High-high clusters were concentrated in the Hohhot-Baotou-Erdos region, while low-low clusters appeared in remote rural and pastoral counties. The Spatial Durbin Model (SDM) was chosen to explore the influencing factors. Direct effects showed that disposable income and bed density positively influenced HW within a county, whereas population density exhibited a significant negative impact. Indirect effects revealed that disposable income, fiscal self-sufficiency rate, and bed density also had positive spatial associations on HW in neighboring regions.ConclusionHealth workforce allocation in Inner Mongolia shows significant spatial disparities, with decreasing clustering over time, indicating reduced regional heterogeneity. Disposable income, bed density, and fiscal self-sufficiency positively affect HW and exhibit notable spatial associations, while population density has a negative impact. To optimize allocation, policies should enhance regional collaboration and resource sharing, increase fiscal support and promote medical alliances.

Abstract Two-dimensional (2D) electron temperature and density profiles were obtained for a cylindrical Hall thruster plasma by incorporating optical emission tomography and a collisional-radiative (CR) model. Using an inverse Radon transform-based tomographic reconstruction of 1350 lines of sight, 2D profiles of Xe neutral optical emission intensities of 10 distinct wavelengths (992.3, 979.9, 916.2, 904.5, 895.2, 881.9, 834.6, 828.0, 823.1, and 788.7 nm) were obtained at 4 mm from the thruster channel exit. Local electron temperatures and densities were subsequently derived using the CR model. The developed CR model, combined with tomographically reconstructed optical emission spectroscopy, demonstrated electron parameters comparable to those measured using a double Langmuir probe. This study highlights the essential role of optical emission tomographic reconstruction in accurately capturing the localized electron parameters for Xe-based Hall thruster plasmas. Additionally, a notable similarity in the spatial distribution was observed between the normalized 2D emission profiles of 992.3, 979.9, 916.2, and 881.9 nm and the Xe ionization rate. This finding demonstrates the potential of using reconstructed emission profiles at specific wavelengths as diagnostic tools to qualitatively infer 2D ionization regions without relying on a complex CR model.

Abstract Aim Stroke remains a leading global cause of death and disability, and its incidence is rising in Ghana, posing a significant public health concern. However, comprehensive data on its spatial distribution across Ghana’s 16 regions are limited. This study aimed to assess the spatial distribution of stroke risk and to identify high-risk regions and associated risk factors. Subject and methods Using nonparametric ensemble machine learning models—random forest and gradient boosting—the study performed variable selection and predicted stroke risk. Key predictors identified were incorporated into a Bayesian spatial model (BYM2) to estimate region-specific relative risk (RR). Posterior estimates were mapped to visualize spatial trends, and interpretability tools such as partial dependence plots and SHAP (SHapley Additive exPlanations) values were used to analyze covariate effects. Results Results showed a modest overall increase in stroke risk (3%), with notable regional variation. The Volta and Central regions exhibited the highest risk (relative risk [RR] = 3.0–3.5 and 2.5–3.0), while the Savannah and Northern regions had the lowest (RR = 0.0–1.0). Gradient boosting outperformed random forest (75% vs. 13% accuracy), identifying gross national income (GNI) and diabetes prevalence as top predictors. Higher GNI was linked to reduced stroke risk (RR = 0.95), whereas increased diabetes prevalence was associated with higher risk (RR = 1.18). Stroke risk decreased sharply at a GNI threshold of 26% and rose steadily with diabetes prevalence. Regions with high GNI and low diabetes prevalence had lower stroke counts. Conclusion The study highlights significant regional disparities and key predictors of stroke risk in Ghana, offering valuable insights for targeted public health strategies and equitable resource allocation.

Abstract Background: Copy-number variation (CNV) is a hallmark of prostate cancer, including PTEN loss and TP53 alterations that shape tumor progression. Whole-genome spatial DNA sequencing technologies are not yet mature or widely accessible. Spatial transcriptomics-based CNV inference (SpatialCNV) produces genome-wide CNV maps and preserves histologic and spatial context. Unlike bulk DNA assays, it enables tracing of tumor subclones across tissue architecture. SpatialCNV can also identify tumor-containing spots when histology annotation is not provided, which is an essential feature for a lot of spatial cancer studies. Methods: We performed a systematic benchmark across simulated and real prostate spatial transcriptomics datasets, including sections with matched whole-exome truth and standard pathology. We evaluated twelve CNV tools, spanning single-cell-derived and spatial transcriptomics-specific methods. Performance was assessed in two scenarios: without histology (spot-level tumor detection) and with histology (CNV calling). We compared different strategies (stromal/immune, pure-benign epithelium, reference-free, etc) using correlation, sensitivity/specificity, and cross-patient robustness. Results: For detecting tumor-containing spots without histology, CopyKAT achieved the most reliable automatic identification when run in a reference-free mode after deconvolution-based selection of epithelial enriched spots. For CNV calling, inferCNV achieved the best performance when using a ‘pure-benign’ reference—histologically benign epithelial spots without CNV changes. We developed an automated pipeline to define pure-benign references and applied it to multi-patient cohorts, revealing spatially coherent clones shared between primary and metastatic tumors. Conclusions: SpatialCNV enables genome-wide characterization of prostate cancer clonality and metastatic relevant CNV events. Our benchmark provides pratical guidance and workflows, and supports understanding of prostate cancer clonality and spatial distribution. Citation Format: Jintong Shi. Benchmarking SpatialCNV in Prostate Cancer: tools, reference strategies, and workflows across simulated and real spatial transcriptomics [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr A059.

Roadside areas have become obvious sinks of waste dumping and deposition of emissions, wear and tear of vehicular parts in urban industrial areas in India, and exposure to metal-polluted roadside dust (RSD) poses a serious threat to human health and the environment. Here, RSD of the Singrauli power belt in Central India was investigated to assess metal pollution and human health risks. Metals were significantly elevated in RSD compared to the background local surface sediments (LSS). Average enrichment ratio (ER:Sample/LSS) varied in the order: Pb(4.5) > Cr(3.6) > Zn(2.5) > Cu(2.3) > Co(2.0) > Mn(1.7) > Ni(1.2). Compared to the coarse size fraction (> 63µm), the finer fraction (< 63µm) constituted 18-27% of the total mass of RSD but accounted for > 75% of total metal contents and exhibited a high metal ER in the range of 1.3-2.2 for all metals. Spatial metal distribution was wide spread in the area, with certain hotspots in/around the close proximity to industrial operations and mining. Contamination factor, pollution load index, and geo-accumulation index suggested that the RSD were moderately to heavily polluted for Cr, Cu, Pb, and Zn. Multivariate statistical analysis suggested that Zn, Pb, Cr, and Cu were strongly linked to anthropogenic sources, while Ni and Co were ascribed to both anthropogenic and lithogenic sources. The hazard index of metals indicated a high degree of non-carcinogenic health risk of Cr and Pb to children. Carcinogenic risk was greater than the acceptable level, with ingestion as the major pathway. Chromium posed the highest carcinogenic risk. Contaminated RSD could be a potential source of metals in soil and water over time and changing environmental conditions.

Breast cancer is one of the most common malignant tumors among women worldwide, and its high heterogeneity poses significant challenges for early diagnosis and precise treatment. Despite the continuous development of molecular typing and targeted therapy, how to precisely distinguish the benign and malignant regions of tumors at the spatial level and analyze their molecular characteristics remains an urgent problem to be solved. This study is based on spatial transcriptomics technology on the 10x Genomics Visium platform. Systematic transcriptional analyses were conducted on human breast cancer tissue sections. Through differential expression analysis of DCIS/LCIS and IDC regions, a total of 1,143 significantly differentially expressed genes were identified, among which genes such as S100A6, STC2, and TFF3 were significantly upregulated in breast cancer progression, suggesting a key role in malignant transformation of tumors. Gene ontology enrichment analysis revealed that the differentially expressed genes were significantly aggregated in key functional modules such as metabolic pathways, MAPK signaling pathways, cell cycle and cancer-related pathways. in particular, the significant enrichment of the Rap1 signaling pathway and Pathways in cancer revealed the synergistic activation mechanism of multi-level biological processes such as cell adhesion, migration, and matrix degradation during the transformation of breast cancer from benign to malignant. In summary, this study revealed the molecular heterogeneity in the benign and malignant regions of breast cancer and the spatial distribution characteristics of key signaling pathways at the tissue level through spatial transcriptomics, providing new theoretical basis and spatial omics evidence for spatial molecular typing of breast cancer, targeted drug development, and optimization of individualized treatment strategies.

This study investigates the seasonal and spatial distribution of benzo[a]pyrene (BaP) in street dust across Raniganj, revealing significant variations linked to both seasonal shifts and land use types. BaP concentrations in street dust samples ranged from 82.2 ng.g-1 to 531.6 ng.g-1, with a mean value of 262.45±75.55 ng.g-1. The highest BaP levels were observed during winter, particularly in heavy traffic, coal mines, and industrial areas, suggesting contributions from industrial activities and vehicular emissions, coal chemical production, and gangue accumulation. An analysis by land use type indicated that BaP levels were highest in busy traffic areas, coal mine areas, and industrial areas, with traffic-congested sites showing the highest average concentration (328.29 ng.g-1). Seasonal analysis showed that winter BaP concentrations were the highest on average (336.28±93.43 ng.g-1), followed by monsoon and summer. These seasonal differences may be due to winter-specific factors, such as increased vehicular traffic, indoor heating, and atmospheric stability. In all five sampling locations, the hazard index (HI) values were moderate for both adults and children. Adults had an average overall cancer risk value of 2.89E-03, whereas children had an average of 2.61E-03, indicating that both age groups are at high risk. Samples collected from various land use types revealed a distinct difference in mean total BaP levels, as well as total cancer risk levels, with the following order observed: busy traffic area &gt; coal mine area &gt; industrial area &gt; commercial area &gt; residential area. The findings underscore the impact of anthropogenic activities and seasonal changes on BaP levels, emphasizing the need for targeted pollution management strategies in heavy-traffic and industrial regions, along with coal mining regions in Raniganj.

Experimental advancements in neuroscience have identified cellular engrams—ensembles of neurons whose activation is necessary and sufficient for memory retrieval. Synaptic plasticity, including long-term potentiation, is fundamental to memory encoding and recall, but the relationship between learning-induced dendritic spine potentiation and neuron-wide activation remains unclear. In this study, we employed a post-synaptic translation-dependent reporter consistent with potentiation (SA-PSDΔVenus) and a neuronal activation reporter (ESARE-dTurquoise) to determine their spatiotemporal correlation in the mouse hippocampal CA1 following contextual fear conditioning (CFC). SA-PSDΔVenus+ spines were enriched in ESARE-dTurquoise+ neurons, with distribution varying across CA1 layers at different phases of memory: SA-PSDΔVenus+ were more frequent in activated neurons in stratum oriens and stratum lacunosum moleculare after CFC (encoding), while recall-activated neurons showed a larger number of SA-PSDΔVenus+ in the stratum radiatum . These findings demonstrate that the relative weight and spatial distribution of potentiated synaptic inputs to hippocampal CA1 pyramidal neurons change between the encoding and retrieval phases of memory.

There is a clear international commitment to protect old-growth forests for their biodiversity and ecological functions. However, it remains unclear which particular solutions would best mitigate overall biodiversity loss given uncertainties about the future, and how to assess that. As an approach, we modelled and assessed the current and likely future habitat distribution of a conspicuous deadwood-inhabiting fungal species, Anthoporia albobrunnea, across Europe. We used spatial distribution modelling (MaxEnt) to predict the potential future distributions for alternative climatic and habitat shifts, and we used two distinct ('cautious' and 'optimistic') interpretations of those products by also considering current habitat protection. We found that, although the higher radiative forcing climate scenario (SSP5-8.5) reduced habitat more than the conservative SSP2-4.5, an even larger difference was caused by using or neglecting a precautionary approach while generalising from multiple scenarios. This difference would further increase when habitat-turnover estimates (primarily due to timber harvesting) are added. Our approach shows that simply incorporating the best technical knowledge is insufficient for improved forest conservation, as the outcomes of decision-making critically depend on how uncertainties are assessed during the process.

Investigating the characteristics and influencing mechanisms of urban vitality in mountainous cities can contribute to enhanced urban resilience, optimised resource allocation, and sustainable development. However, most existing studies have focused on static analyses at single spatial scales, making it difficult to fully reveal the evolutionary trends of urban vitality under complex topographic constraints or the spatiotemporal heterogeneity of its influencing factors. This study examines Guiyang, one of China’s fastest-growing cities, focusing on both its economic development and population growth. Based on social media data and geospatial big data from 2019 to 2024, the spatiotemporal permutation scan statistics (STPSS) model was employed to identify spatiotemporal areas of interest (ST-AOIs) and to analyse the spatial distribution and day-night dynamics of urban vitality across different phases. Furthermore, by incorporating transportation and topographic factors characteristic of mountainous cities, the multiscale geographically and temporally weighted regression (MGTWR) model was applied to reveal the driving mechanisms of urban vitality. The main findings are as follows: (1) Urban vitality exhibits a multi-center, clustered structure, gradually expanding from gentle to steeper slopes over time, with activity patterns shifting from an afternoon peak to an all-day distribution. (2) Significant differences in regional vitality resilience were observed: the core vitality areas exhibited stable ST-AOI spatial patterns, flexible temporal rhythms, and strong adaptability; the emerging vitality areas recovered quickly with low losses, while low-vitality areas showed slow recovery and insufficient resilience. (3) The density of commercial service facilities and the level of housing prices were continuously enhancing factors for vitality improvement, whereas the density of subway stations and the degree of functional mix played key roles in supporting resilience during the COVID-19 pandemic. (4) The synergistic effect between transportation systems and commercial facilities is crucial for forming high-vitality zones in mountainous cities. In contrast, reliance on a single factor tends to lead to vitality spillover. This study provides a crucial foundation for promoting sustainable urban development in Guiyang and other mountainous regions.

During the long-term operation of composite insulators in transmission lines, they are easily affected by harsh environments, resulting in hidden defects such as surface contamination, shed damage, and adhesive failure. A defect detection method based on microwave for composite insulators was proposed, and a corresponding numerical simulation model was established. A large-aperture horn antenna model with a wide frequency band and high gain was built, the accuracy of which was verified. In the simulation, shed crack defects were selected as representative probes to model typical defects in the sheds, sheath, and core rod of composite insulators. This study investigated defects with varying severity levels and spatial distributions while also exploring optimal placement configurations for detection antennas. An experimental platform was built for testing, and it was found that the experimental results showed a similar changing trend to the simulation results, which further verified the accuracy of the simulation model and the feasibility of simulating defects.

In situ measurements of ambient dose equivalent rates were conducted across the territory of Macedonia at five-year intervals in 2010, 2015, and 2020. Data were collected from 68 uniformly distributed locations in 2010 and from 72 locations in both 2015 and 2020, ensuring representative spatial coverage. The main objective of this study was to establish a baseline dataset of outdoor gamma dose rates, evaluate their potential temporal variations, and identify the dominant factors influencing their spatial variability. The results indicate a high degree of temporal stability over the investigated decade, with mean values of 113 nSv/h in 2010 and 110 nSv/h in both 2015 and 2020. Following descriptive statistical analysis, spatial distribution maps were created, revealing that the observed dose rate variability is primarily associated with the country’s diverse geology rather than anthropogenic sources. These findings confirm the reliability of direct in situ monitoring and provide a robust reference framework for assessing environmental and atmospheric contributions to external gamma radiation exposure in Macedonia.

ABSTRACT This study investigates the intensification of tropical cyclones (TCs) that encounter oceanic cyclonic eddies (CEs) across global ocean basins from 1993 to 2021. TCs of all intensity levels are likely to intensify when passing over CEs, with more than 33% of them having experienced intensification. These intensification events mainly occur in regions where sea surface temperatures (SSTs) exceed 26°C. This spatial distribution of TC intensity change aligns well with that of SST anomalies (SSTAs) and sea surface height anomalies (SSHAs) observed at the centres of encountered CEs, with the SSTAs and SSHAs gradually decreasing as latitude increases. Composite analyses reveal that the intensification of TCs associated with CEs can be mainly attributed to three favourable oceanic and atmospheric conditions. First, warm SST prevails within CEs. Second, high upper ocean heat content (UOHC) exists within CEs, which can be inferred from the thick upper ocean layer and the uniform upper ocean thermal structure. Third, the environmental vertical wind shear (VWS) surrounding TC centres is weak, while the mid‐troposphere relative humidity (RH) around TC centres is high. Additionally, the small scale of TCs is also conducive to their intensification when encountering CEs. Furthermore, weak TCs are more prone to intensification compared to strong TCs, even when the CEs they encounter exhibit similar central SSTA and SSHA values. Our work provides, for the first time, valuable insights into the relationship between TC intensification and CEs, thereby enhancing our understanding of the role CEs play in influencing TC intensity.

Helical static-mixer insert for pediatric and neonatal gas blending: RANS-CFD comparison of commercial and in-house monolithic designs.

Abstract Thermal interface materials serve as critical components for facilitating heat transfer between cells and the cooling system in electric vehicle battery packs. However, internal porosity introduced during manufacturing can substantially reduce their thermal conductivity. In this study, the internal pore structure of actual thermal interface material samples was characterised using X-ray computed tomography, and porosity was quantitatively determined through a regression-based thresholding method implemented in MATLAB. The resulting pore distributions were incorporated into finite volume-based thermal simulations in Ansys Fluent, enabling the calculation of effective thermal conductivity as a function of porosity ratio. The simulated effective thermal conductivity values closely matched those predicted by the effective medium theory, particularly at low porosity levels. In contrast, conjugate thermal-fluid analyses of electric vehicle battery packs revealed that local temperature increases of up to 1.3°C can occur depending on pore location and distribution. These findings indicate that effective medium theory-based average conductivity models are inadequate for capturing localized thermal hotspots. Consequently, thermal interface material application processes and design strategies should address not only the allowable porosity threshold but also the spatial distribution of pores to ensure robust thermal management in electric vehicle battery systems.

The High Mountain Asia (HMA) region has the highest concentrations of high-altitude lakes in the world and experienced more frequent glacial lake outburst floods (GLOFs) in recent decades. Continuous monitoring and mapping of glacial lakes at high spatiotemporal resolution are crucial for understanding this rapidly evolving and vast landscape as well as associated disasters. While existing automated methods show significant promise for mapping glacial lakes, their completeness and accuracy need further improvement to regularly produce glacial lake databases in highly dynamic regions like HMA. In this study, we present a fully automated method integrating open-source remote sensing datasets, including Landsat-8, Sentinel-1, Sentinel-2, and Copernicus Digital Elevation Model (DEM), to map glacial lakes and generate a comprehensive inventory of glacial lakes across the HMA region. Our 2022 inventory comprising 31,698 glacial lakes across HMA covers an area of approximately 2,240 km[Formula: see text]. Most lakes are situated between 4000-5400 m asl elevations, with the Eastern Himalaya exhibiting the greatest lake area coverage. We achieved robust accuracy exceeding 96% in the glacial lake size bin (20,000-100,000 m[Formula: see text]), demonstrating the effectiveness of our method in mapping and detecting small lakes while successfully delineating all larger glacial lakes (> 100,000 m[Formula: see text]). The method was applied to two observation periods (2016-17 and 2022-24), enabling analysis of changes in lake area and spatial distribution patterns. Across HMA subregions, Qilian Shan region showed the highest expansion rate of 22.5% between the observation periods, while the Pamir region showed the least changes (2.9%) in this period, contributing to a 5.5% net change in overall glacial lake area at the HMA scale. Our automated approach provides substantial improvements over previous methodologies in data integration, accuracy and completeness, which can be utilized for routine updating of glacial lakes in HMA and elsewhere.

Macroscopic substrate surface errors and microscopic groove parameters influence the optical performance of curved diffractive microstructures. However, existing profile measurement techniques face a trade-off between large-area coverage and high resolution, which limits the ability of conventional two-dimensional (2D) line-profile methods to capture the global grating morphology. To address existing limitations, this study proposes a three-dimensional (3D) profile characterization method for curved gratings across macro- and micro-scales. Seamless reconstruction of full-aperture 3D topography with submicron-scale features was achieved using laser scanning confocal microscopy-based stitching measurements. Preprocessing for feature extraction was then performed using frequency-domain separation and the iterative closest point algorithm. The 2D Gabor filter bank, traditionally used for image texture feature extraction, was extended to 3D space to precisely characterize the period distribution of the microstructures. When combined with local planar least-squares fitting, the method enables precise characterization of the 3D spatial distribution of the grating blaze angle. Experimental results demonstrate close agreement between 3D and 2D characterization, with deviations below 0.01µm in mean period and 0.05° in mean blaze angle, confirming the accuracy and reliability of the method. This study overcomes the limitations of conventional 2D line-profile analysis by enabling high-precision, cross-scale 3D global characterization of curved diffractive microstructures, supporting process optimization and quality control in advanced optical manufacturing.

Maturation of the gut microbiota coincides with neurodevelopmental processes such as myelination, essential for efficient neural signal transmission. While a role for the microbiome in regulating adult prefrontal cortex (PFC) myelination is known, its effects on early-life myelin formation, growth, and integrity remain unclear. Using a cross-species approach in germ-free (GF) mice and zebrafish, we examined how the microbiota influences early myelination and neural development. Multi-system, multi-level analyses showed that the microbiota impacts glial maturation and myelination across species. In GF mice, we observed sex- and age-dependent alterations in pathways linked to neuronal activity and myelination, with myelin-related transcriptomic changes correlating with functional shifts in neurotransmission- and metabolism-related metabolites over time. Myelin growth and integrity were also affected in a sex- and time-dependent manner. As microglia regulate neuronal activity and engulf myelin, we examined microbiota-microglia interactions and found altered expression of genes involved in microglia maturation and synaptic pruning in both species. In zebrafish larvae, the microbiota influenced the spatial distribution of microglia and oligodendrocytes within the brain and spinal cord. These findings reveal conserved microbiota-mediated modulation of neuronal activity, myelination, and glial maturation in early life, providing a foundation for future studies into these mechanisms.