Search Results (53,933)

Pompeii preserves rare traces of ballistic marks on the stone ashlars of its northern fortification system, traditionally attributed to the Sullan siege of 89 BC. Although mentioned in the literature, these features have never been systematically documented through metric analysis. This study presents the first georeferenced morphometric dataset of circular cavities along the northern walls using reality-based 3D surveying and reverse-modelling techniques. A multiscale workflow allows the preliminary scoring and identification of potential ballistic imprints, their spatial cataloging, the selection of representative case studies for high-resolution acquisition, and subsequent geometric analysis. By treating each cavity as a geometric constraint, the analysis estimates Maximum Compatible Sphere values for the theoretical impacting projectiles and enables preliminary comparison with known calibers of Roman artillery ammunition. Full article

The Late Ediacaran Dengying Formation in the Sichuan Basin is a key target for deep-gas exploration, yet the controls on facies differentiation in the Penglai area, on the northern slope of the Central Sichuan Paleo-Uplift, remain debated. This study integrates 3D seismic data, well logs, cores, and thin sections from 20 wells to characterize the microfacies, microfacies associations, and sedimentary architecture of the Dengying Formation. Nine microfacies types are recognized and grouped into three microfacies associations: tidal flat, mound–shoal complex, and inter-mound. The same nine microfacies types occur in both members, but the mound–shoal complex association in the third and fourth members is more grain-rich, more strongly overprinted by recrystallization, and makes up meter-scale shallowing-upward cycles. The lateral variations in microfacies types across the study area are minor; facies differentiation is instead expressed through variations in microfacies associations and their cumulative thicknesses. Facies distribution analysis and 3D seismic paleogeomorphology demonstrate that the Penglai area developed a broad, gently NW-dipping carbonate ramp without distinct slope breaks, in contrast with the previously proposed rimmed platform model of the Gaoshiti–Moxi area. Within this ramp, mound–shoal complexes record large-scale lateral migration driven by high-frequency relative sea-level fluctuations, but their along-strike continuity is constrained by NE-trending basement faults oriented sub-perpendicular to the NW-trending rift axis. A hierarchical three-level control on facies differentiation is proposed: regional paleogeomorphology provides the first-order NW-dipping ramp framework; syn-sedimentary basement faults impose a second-order segmentation; and high-frequency sea-level fluctuations drive the third-order stacking patterns. This hierarchical mechanism refines the sedimentary model of the Dengying Formation and provides a predictive basis for delineating high-quality reservoir targets along the northern slope of the Central Sichuan Paleo-Uplift. Full article

Growth traits, as core economic indicators in pig breeding, are closely associated with production costs, rearing duration, and final carcass quality and have thus consistently been a major focus of genetic improvement. This study aimed to identify candidate genes affecting Age to 120 kg live weight (AGE120), Backfat thickness at 120 kg (BF120), and Loin muscle depth at 120 kg (LMD120) in pigs. Ear tissue samples were collected from 3364 healthy adult pigs (including 558 boars and 2805 sows) from three breeds: Large White, Landrace, and Duroc. Genotyping was performed using an 80 K functional site array, and quality-controlled SNP (Single-Nucleotide Polymorphism) loci were subjected to genotype imputation, resulting in 15,447,611 loci obtained. Genome-wide association studies (GWASs) for Age to 120 kg live weight, Back fat thickness at 120 kg, and Loin muscle depth at 120 kg were conducted using a mixed linear model in Genome-wide Complex Trait Analysis (GCTA). Genes located within 500 kb upstream and downstream of significant GWAS loci were extracted using the biomaRt package in R. Furthermore, colocalization analysis was performed using expression Quantitative Trait Locus (eQTL) data of 34 tissues from the PigGTEx database to identify genes that share the same causal variant as the GWAS signals. Through integrated GWAS and eQTL colocalization analysis, in addition to five previously reported genes associated with pig growth traits (TAF11, ZC3HAV1L, ANKS1A, USP20, and TBC1D1), a set of novel, high-confidence candidate genes was identified: ZNF215, UBE2Z, HOXB7, SARDH, ADAMTSL2, ATP6V0A4, RPL10A, PGM2, and RELL1. These findings enrich our understanding of the genetic architecture underlying growth traits in pigs at heavy body weights and provide an important foundation for subsequent functional validation and molecular breeding applications. Full article

Maternal and paternal attachment may relate differently to adolescent depressive symptoms, but the potential contribution of oxytocin receptor (OXTR) gene single nucleotide polymorphisms (SNPs) to these associations remains unclear. This six-month longitudinal study included 746 seventh- and tenth-grade students (50.7% female) from Guangzhou, China. Parent-child attachment and saliva samples for genotyping were collected at baseline, and depressive symptoms were assessed at follow-up using the CES-D-10. Overall, 48.8% of adolescents screened positive for depressive symptoms. Compared with adolescents below the cutoff, those who screened positive reported lower parental trust/communication and higher alienation (all p < 0.01). In adjusted linear regression models, father-child trust/communication was negatively associated with subsequent depressive symptoms (β = −0.26, p < 0.01), whereas mother-child alienation was positively associated with subsequent depressive symptoms (β = 0.15, p = 0.02). Exploratory moderation analyses showed a nominal rs2254295 × mother-child alienation interaction before correction (β = −0.07, p = 0.046), but this effect did not survive FDR correction. Father-child trust/communication and mother-child alienation showed distinct associations with adolescent depressive symptoms. The possible moderating role of OXTR rs2254295 should be regarded as preliminary and requires replication in larger independent samples. Full article

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is an idiopathic, multisystem disorder marked by debilitating fatigue, post-exertional malaise, cognitive dysfunction and neuroinflammation. Its etiology remains unclear, yet emerging evidence implicates a complex interplay between immune dysregulation, metabolic impairment, mitochondrial bioenergetics, and environmental stressors such as viral infection or low-dose ionizing radiation (LDIR). To develop treatments for ME/CFS, it is essential to identify suitable targets for therapy. In this narrative review, we discuss recent findings on the overlap of ME/CFS with LDIR effects and examine potential mechanistic links that may arise from LDIR-induced bystander effects (RIBEs). We highlight potential candidate biomarkers that bridge these domains: mitochondrial respiratory dysfunction, altered ornithine transport via SLC25A15 (ORNT1), possible roles of CD38 in the context of immunity and NAD+ depletion, cyclin D1–dependent metabolic reprogramming and modulation of gene expression, and α-synuclein as a potential neuroinflammatory damage-associated molecular pattern (DAMP). While the involvement of these biomarkers in ME/CFS is yet to be confirmed experimentally, evidence from in vitro studies of irradiated cells, exosome profiling, and patient samples suggests that RIBEs can, in theory, produce prominent cellular ME/CFS phenotypes through associated mechanisms, including those exhibiting oxidative stress, impaired ATP production, and immune modulation. We propose a hypothetical, exploratory model wherein LDIR initiates or contributes to adaptive metabolic shifts (including CD38 upregulation and cyclin D1 stabilization) that, coupled with persistent bystander signaling, could potentially culminate in chronic fatigue and neurocognitive symptoms in some reported ME/CFS cases. Finally, we outline a research agenda encompassing the establishment of standardized diagnostic criteria, multi-omics profiling of patient cohorts, exosome analysis, functional mitochondrial assays, and targeted therapeutic trials focusing on possible anti-CD38 antibodies and NAD+ precursor therapy. By integrating recent findings in low-dose radiation biology with ME/CFS pathophysiology, this review aims to promote interdisciplinary investigations that may uncover mechanistic insights and novel biomarkers for diagnosis and treatment of ME/CFS. We further review steps in a proposed model taking us from low-dose radiation exposure to a number of possible targets. Full article

Referring remote sensing image segmentation (RRSIS) aims to segment a target instance from remote sensing imagery according to a natural-language expression. It provides a flexible way to retrieve and localize specific objects in remote sensing scenes, benefiting intelligent Earth observation applications. Although existing methods have achieved promising progress by strengthening vision–language alignment, most of them still represent the expression as a holistic language feature and rely on convolution-dominated decoding for mask prediction. Such a paradigm tends to entangle target category, inter-object relation, and spatial position cues, making it difficult to distinguish the intended instance from multiple same-class distractors in complex remote sensing scenes. To address this limitation, we propose SRGFormer, a graph reasoning framework for RRSIS. Specifically, a semantic role decomposition (SRD) module decomposes the referring expression into target, relation, and position semantics, providing explicit linguistic priors for instance-level localization. Guided by the decomposed relation semantics, a semantic-relational graph transformer (SRGT) performs relation-aware graph reasoning over fused multi-scale visual features, enabling long-range dependency modeling among spatially distributed candidate instances. Furthermore, a progressive mask refinement (PMR) module continuously injects the decomposed semantic priors into semantic modulation, query initialization, and iterative mask decoding, thereby alleviating semantic fading during mask generation. Extensive experiments demonstrate that SRGFormer achieves substantial improvements on RefSegRS, attaining 66.08% mIoU and 76.93% oIoU (surpassing the prior state of the art by 3.96% and 2.83%, respectively) along with a notable 15.95% gain in Pr@0.7. Experiments on the additional RRSIS-D benchmark further demonstrate the general applicability of our approach, where SRGFormer maintains competitive performance (65.87% mIoU and 24.61% Pr@0.9) against existing methods. These results demonstrate that the proposed framework improves target localization and fine-grained mask prediction in complex remote sensing scenes. Full article

Offline action prediction does not by itself guarantee reliable closed-loop flight for unmanned aerial vehicle (UAV) vision-language-action (VLA) models. We study a controlled AirSim Blocks waypoint task using 100 expert episodes and 3385 RGB-D, instruction, state, and action records. Checkpoints are selected only on val-seen data, after which val-unseen is evaluated once. A 132,840-parameter policy reaches $0.9278\pm 0.0019$ final-test action accuracy across three training seeds, yet raw VLA control fails in closed loop. We therefore embed its action proposals in progress-aware hybrid execution with explicit recovery and near-goal precision. The strongest checkpoint reaches 59/60 goals, but crossing three independently trained checkpoints with three target seeds yields a more conservative 147/180 successes (81.7%) with zero recorded collisions and marked checkpoint sensitivity. Substantial overrides and fallback-tagged steps further show that the reported closed-loop outcomes are properties of the hybrid system, not of the learned policy alone. These findings are restricted to the controlled AirSim Blocks benchmark and do not demonstrate real-UAV deployment, sim-to-real transfer, or field robustness. Full article

Addressing the critical challenge of signal degradation in biosensor cardiac monitoring, this paper introduces an efficient hybrid dilated–long short-term memory (LSTM) with residual learning (HDLR), which is a novel architecture engineered for a high-fidelity electrocardiogram (ECG) denoising signal. The proposed HDLR model synergistically integrates with dilated convolutions to expand the receptive field for multi-scale feature extraction. This is an LSTM-based backbone used to resolve long term temporal dependencies with residual learning paths to stabilize gradient flow and accelerate convergence. The proposed HDLR architecture integrates three core functional components with dilated convolutional layers utilized for local temporal feature extraction, where varying dilation rates expand the receptive field to capture both local waveform patterns and broader morphological structures without increasing computational complexity. Experimental results demonstrate a significant leap in performance, with the HDLR model achieving a mean squared error (MSE) of 0.002176, a signal-to-noise ratio (SNR) of 14.4420 dB, and a Matthews correlation coefficient (MCC) of 0.9822. Beyond quantitative metrics, the proposed HDLR architecture exhibits exceptional robustness in preserving cardiac morphology, specifically the P-wave and QRS complex of the ECG signal under stochastic noise conditions. These findings underscore the HDLR model’s potential as a backbone for next generation, real time diagnostic systems in intelligent healthcare. Full article

Glutamate excitotoxicity is a key contributor to the pathogenesis of glaucoma, a leading cause of irreversible blindness worldwide; however, the molecular events driving progressive retinal ganglion cell (RGC) loss and axonal degeneration remain incompletely understood, and effective neuroprotective therapies are lacking. Here, we evaluated the preventive potential of K9-C-peptide, a biopolymer-conjugated human C-peptide, in a mouse model of N-methyl-D-aspartate (NMDA)-induced retinal neurodegeneration and optic nerve axonal transport impairment, and examined potential mechanisms underlying its protective effects. In NMDA-induced excitotoxic mouse retinas, intracellular Ca²⁺ elevation mediated NMDA-induced oxidative stress, including both intracellular and mitochondrial reactive oxygen species (ROS) generation and lipid peroxidation. NMDA exposure induced activation of Müller glia and microglia and upregulation of inflammatory cytokines, ultimately leading to RGC death; these effects were attenuated by prolonged intraocular delivery of ROS scavengers. K9-C-peptide significantly reduced NMDA-induced retinal degeneration, including RGC loss and retinal thinning, and preserved optic nerve axonal transport function in both whole-mount retinas and optic nerve longitudinal sections. These protective effects were associated with suppression of NMDA-induced oxidative stress, mitochondrial dysfunction, and inflammation and reactive gliosis, without altering intracellular Ca²⁺ levels. Notably, sustained intraocular delivery of human C-peptide conferred robust neuroprotection for at least 3 weeks against NMDA-induced retinal degeneration and optic nerve axonal transport impairment. These findings suggest that K9-C-peptide acts as a long-acting neuroprotective agent that mitigates oxidative stress-driven retinal damage and axonal dysfunction, highlighting its translational potential as a C-peptide-based neuroprotective strategy for retinal glutamate excitotoxicity. Full article

Background/Objectives: Given the potential for achieving high concentrations at the target site while limiting systemic exposure, delivering drugs directly to the female reproductive tract (FRT) is emerging as a promising strategy for enhancing women’s reproductive health. However, quantitative data describing matrix-specific solubility, matrix-specific binding, and permeability across FRT tissues remain limited, constraining development of physiologically based pharmacokinetic (PBPK) models for intravaginal and intrauterine therapies. Methods: Our work was conducted to help fill this critical gap, evaluating four model drugs with diverse physicochemical and transporter profiles, dapivirine (DPV), levonorgestrel (LNG), MK-2048, and 4′-ethynyl-2-fluoro-2′-deoxyadenosine (EFdA; also known as islatravir or MK-8591) in in vitro and ex vivo human models. Plasma solubility, matrix-specific binding in plasma, cervicovaginal fluid, and FRT tissues, and bidirectional permeability across FRT tissues were quantified. Results: Our results demonstrated that the plasma solubility varied markedly across compounds, following lipophilicity trends, with DPV (34.14 ± 1.04 µg/mL) and EFdA (1808.02 ± 67.36 µg/mL) exhibiting the lowest and highest solubility, respectively. Hydrophobicity-dependent solubility enhancement by plasma proteins (~2× to >30× higher comparing to aqueous solubility in the literature) was observed for all four model drugs. Apparent binding in plasma, cervicovaginal fluid, and FRT tissues was highly correlated with the model compounds’ lipophilicity, with DPV having the most highly matrix-specific binding (97–99%) and EFdA having the least matrix-specific binding with the greatest variability (15–62%). Regional permeability differed significantly across FRT tissues: the human ectocervix, myometrium, endometrium, and fallopian tubes demonstrated distinct transport patterns consistent with epithelial architecture and the transporter-substrate status of the model compounds. Efflux transporter involvement was evident for MK-2048 and EFdA in Caco-2 models (efflux ratios 2.59 and 7.14, respectively), but was less pronounced in the 3D vaginal model and ex vivo tissues. Across all datasets, permeability and binding were strongly influenced by drug lipophilicity and ionization characteristics. Conclusions: Collectively, these findings demonstrate the interplay among solubility, matrix-specific binding, and tissue permeability in governing local drug distribution within the FRT. The experimentally derived parameters provide quantitative inputs for FRT PBPK model development, and are expected to inform design of safe and effective localized therapies for women’s reproductive health. Full article

Urban traffic noise poses a critical environmental health burden; however, its mitigation via urban greenery is constrained by the limitations of conventional two-dimensional (2D) metrics. To address this gap, the Landscape Forest Space Density (LFSD), as a novel volumetric index quantifying the ratio of acoustically effective biomass to total canopy volume via geometric crown approximation, is introduced in this study. Field investigations were conducted along a 30 m transect perpendicular to a six-lane expressway in Nanjing, China. A-weighted sound pressure levels were recorded at 18 nodes (3 forest types × 4 distances, plus controls) under controlled meteorological conditions, with seasonal replication during spring (full-leaf) and autumn (leaf-off). Results indicate that deciduous coniferous forests delivered superior noise attenuation, achieving a peak insertion loss of 27.16 dB at 20 m depth in spring—exceeding evergreen broadleaf configurations by over 10 dB. Noise reduction followed a logarithmic growth pattern relative to roadside greenbelt width, reaching saturation beyond 20 m. Regression analysis identified LFSD as the dominant predictor of insertion loss (R² = 0.86, N = 864), substantially outperforming traditional 2D metrics such as canopy coverage (R² = 0.15) and optical porosity (R² = 0.42). While a bivariate model incorporating both LFSD and porosity yielded an adjusted R² of 0.89–0.94, the marginal incremental gain confirms that volumetric biomass packing, rather than planar void fraction, governs acoustic attenuation. Notably, even during defoliation, high-LFSD conifers maintained robust sound scattering through their fractal branch architectures. On the whole, a quantitative design threshold of LFSD ≥ 0.4 is recommended to achieve >15 dB attenuation in spatially constrained urban environments. Full article

This study addresses the problems of vessel congestion and inefficient resource utilization caused by the independent scheduling of port berths and locks in river–sea intermodal transport. Moreover, this study explores the joint scheduling optimization problem for a single port and a single lock under the direct transshipment mode of seagoing and river vessels. First, considering the spatiotemporal coupling characteristics of the port–lock system, a mixed-integer linear programming model integrating seaside and riverside berth allocation, lock batch scheduling, and 2D layout constraints of the lock chambers is constructed. The objective is to minimize the time costs of seagoing vessels in a port, river vessels in a port, and river vessels passing through the locks. Second, a hierarchical iterative collaborative optimization algorithm is designed. The outer layer uses an iterative algorithm framework, while the inner layer utilizes a heuristic algorithm to address the lock scheduling problem. An adaptive large neighborhood search algorithm is used to address the berth scheduling problem. Finally, the model and algorithm are validated through numerical instances. Compared with the commercial solver CPLEX, the proposed algorithm HICOA achieves up to 81.45% lower costs in medium-to-large instances. Compared with independent scheduling, joint scheduling reduces the total vessel dwell time cost by up to 66.22%, demonstrating the significant value of port–lock collaboration. Furthermore, the arrival time window of river vessels and the ratio of the number of vessels to the number of berths have a significant influence on system performance. Full article

This study proposes an integrated finite element methodology for evaluating and redesigning three critical subsystems of an XCMG XC740K skid-steer loader: the excavation attachment, the arm–bucket charging system, and the ROPS/FOPS operator protection cab. The components were reconstructed by reverse engineering and 3D scanning, modeled in CAD, and simulated in ANSYS Workbench/Mechanical under load cases derived from hydraulic parameters, soil–tool interaction, and international safety standards. The novelty of the work lies in applying a single FEM-based workflow to three interacting subsystems of the same compact machine, rather than optimizing isolated components independently. The original configuration showed critical effort concentrations in the cab and charging system. Localized geometric reinforcements and the use of high-strength and wear-resistant steels improved stiffness and safety margins in the excavation bucket, loading bucket, and ROPS/FOPS cab. However, the arm–quick coupler region remained the controlling weak point of the loading assembly, indicating the need for further redesign. The proposed approach provides a transferable computational framework for identifying structural vulnerabilities and prioritizing redesign actions in compact earthmoving machinery. Because the study is numerical, future experimental validation is required before certification or field implementation. Full article

Salt stress severely restricts soybean seedling growth and yield formation, and the redundant indicators and low efficiency of conventional salt tolerance evaluation methods limit the large-scale screening and breeding of salt-tolerant soybean germplasms. In this study, we aimed to establish a simplified and efficient salt tolerance evaluation system for soybean seedlings under saline pond conditions. We determined 16 phenotypic traits of 100 soybean germplasm resources under soil salt stress (0.3% soil salt content, EC 5.0 dS/m), calculated the salt tolerance coefficient (STC) of each trait, and comprehensively analyzed phenotypic variation, correlation, germplasm classification, and core evaluation indices via principal component analysis (PCA), K-means clustering, random forest model, SHAP interpretation, 10-fold nested cross-validation, and correlation network analysis. Biomass-related traits exhibited abundant phenotypic variation, with coefficients of variation ranging from 42.6% to 48.4%. Five principal components explained 82.90% of the total phenotypic variation and divided the accessions into four salt tolerance categories. Total fresh weight (TFW), stem fresh weight (SFW), and leaf fresh weight (LFW) were identified as the core indices, together accounting for over 93% of the total feature importance in the random forest model, whereas the remaining 13 traits each contributed less than 1.2%. The simplified three-index model showed strong consistency with the full 16-trait model (Pearson r > 0.970, AUC = 0.970) and achieved a screening accuracy of 90.0% under 10-fold nested cross-validation. Under the experimental conditions examined, fresh biomass accumulation emerged as the dominant phenotypic characteristic associated with seedling salt tolerance. This simplified evaluation framework may facilitate rapid preliminary screening of salt-tolerant soybean germplasms at the seedling stage, pending further validation across diverse environments and genetic backgrounds. Full article

Depth-averaged 2D models have shown considerable success at predicting the presence of tidal sand ridges in the nearshore environment while only requiring minimal efforts of parameter tuning. 3D models, on the other hand, fail to predict the growth of coarse-grain sand waves unless unrealistic assumptions on eddy viscosity are applied. When a vertically varying eddy viscosity profile is adopted in the model, sand waves will invariably be the fastest growing mode unless suspended load dominates, which contradicts observations. Through both numerical and analytical approaches, this paper will show that the residual circulation in the vertical plane due to mass continuity and vertical shear is a possible underlying mechanism that accounts for the dominant growth of sand waves. The strength of this residual circulation is proportional to the shear of the tidal velocity. A consequence is that sand ridges are more likely to develop in certain models with a small slip parameter in the bottom boundary condition (hence, less shear in tidal velocity), and such a parameter choice often, though not necessarily, leads to stronger mixing due to model configurations. Hence, sand waves are favoured in coastal seas due to the strong shear of the tidal current. The above findings suggest that there could be some unresolved processes that cause a transition in the bedform-building mechanism, which eventually inhibits the growth of sand waves and promotes the growth of sand ridges. Full article