Cross-view geo-localization between drone and satellite images is severely challenged by rapid weather variations, which induce appearance shifts, occlusions, and texture degradation. Inspired by human foveal attention, we propose the Fovea Attention Network (FANet), a robust dual-branch framework comprising: 1) the Weather-Adaptive Global Branch (WAGB) that explicitly injects weather cues (e.g., 'rain/snow') into the feature space via a style-modulation encoder, then captures large-scale structural consistency through a Learnable Region Reassembly (LRR) mechanism; and 2) the Local Semantic Attention Branch (LSAB) that leverages a pretrained segmentation model to generate high-confidence masks, distilling discriminative features from salient regions. An adaptive fusion strategy module fuses global context with fine-grained semantic cues. We further adopt multi-weather adaptive training, treating weather types as related tasks with shared parameters to reduce cross-weather confounding. Extensive experiments on University-1652, SUES-200, and CVUSA show that FANet achieves competitive Recall@1 across all conditions, attaining the highest overall mean with the lowest variance. Notably, it improves Recall@1 by 6.79% under severe low-illumination ('dark') conditions, demonstrating robustness and stability. Our code is available at https://github.com/Jahawn-Wen/FANet.
The safety risks associated with urban rail transit equipment are characterized by multi-source heterogeneity and dynamic evolution. Traditional expert-driven static management models often fail to meet the proactive prevention demands in complex scenarios, leading to critical issues such as ambiguous risk identification and insufficiently targeted prevention measures. This study proposes a novel risk assessment and inference method that integrates knowledge graphs with Bayesian networks. First, a safety risk knowledge graph is constructed based on historical accident case reports. Then, a mapping method is proposed to convert the knowledge graph into a Bayesian network. Subsequently, data-driven statistical approaches are employed to estimate the network parameters. Finally, a case study involving equipment failures in urban rail transit is conducted to validate the proposed method. Experimental results demonstrate that the proposed method effectively identifies key risk factors and accurately traces accident causes through backward inference. The method also significantly outperforms traditional approaches in terms of practical accuracy. The findings provide intelligent decision support for the risk management of urban rail transit equipment.
Drug-target affinity (DTA) prediction is a pivotal task in computational drug discovery, enabling the estimation of binding affinities between small molecules and their target proteins. This process is essential for reducing the costs, development time, and risks inherent in traditional drug development pipelines. Current DTA prediction models primarily rely on separate extraction and concatenation of drug and protein features. However, these models often fail to account for the complex semantic relationships within protein sequences, which limits their ability to accurately predict affinity. In response to these challenges, we propose MDM-DTA, a novel framework leveraging a Mixture of Experts (MoE) strategy to integrate diverse molecular and protein representations. For drug representation, MDM-DTA utilizes molecular graphs, which are processed via Message Passing Neural Networks (MPNNs), alongside molecular descriptors that are passed through a three-layer convolutional neural network (CNN). Protein features are extracted using a deep convolutional network enhanced with Squeeze-and-Excitation (SE) mechanisms to capture inter-channel dependencies. Furthermore, protein sequence semantics are encoded through pre-trained embeddings from a knowledge-guided Bidirectional Encoder Representations from Transformers (BERT) model and the Evolutionary Scale Modeling 2 (ESM2) model, enabling the model to capture contextual relationships within protein sequences. Extensive experiments on three benchmark datasets demonstrate that MDM-DTA consistently outperforms state-of-the-art models of similar complexity in terms of predictive accuracy. The incorporation of both structural and semantic features significantly enhances the model's ability to predict drug-target binding affinities, highlighting the importance of a multi-modal representation approach. The proposed MDM-DTA framework effectively integrates both molecular and semantic protein representations, providing superior performance in DTA prediction tasks. The results underscore the potential of MDM-DTA to improve the accuracy of computational drug discovery models, facilitating the identification of novel drug candidates and advancing the field of in silico drug development.
Employee knowledge hiding severely hinders organizational innovation, yet how a positive organizational care climate inhibits this behavior remains insufficiently understood. Most existing single-theory explanations fail to clearly articulate the complete psychological pathway linking environmental resources to individual behaviors. This study integrates Conservation of Resources Theory and Affective Events Theory to systematically construct and test a serial mediation model, illuminating how organizational care reduces knowledge hiding via organization-based self-esteem and employee loyalty. A two-wave questionnaire survey was conducted among employees from enterprises in Shanghai, Beijing, Hubei, Henan, and Guangdong, China, with a 6-month interval between data collections. The survey yielded 335 valid responses. Structural equation modeling was used to analyze the data and test the hypothesized model. The results show that organizational care is significantly and negatively related to knowledge hiding behavior. While organization-based self-esteem does not have a significant direct mediating effect, employee loyalty acts as a significant mediator. Furthermore, organization-based self-esteem and employee loyalty play a chain-mediated role in the relationship between organizational care and knowledge hiding. Specifically, organizational care enhances organization-based self-esteem, which in turn increases employee loyalty and ultimately reduces knowledge hiding behavior. This study demonstrates that organizational care effectively reduces knowledge hiding behavior by enhancing employees’ organization-based self-esteem and strengthening their loyalty to the organization. These findings enrich the theoretical frameworks of resource conservation and affective event theories and provide practical guidance for organizations to manage negative knowledge behaviors by fostering a supportive organizational environment.
Engineering organoids that faithfully replicate the intricate architecture and region-specific features of bodily organs and extraembryonic tissues remains a significant scientific challenge. Previously, we demonstrated that craniofacial skin organoids (cSkOs), containing epidermis, dermis, and hair, could be generated by co-developing epidermal progenitors with cranial mesenchyme. Building on this approach, we precisely adjusted cellular composition and signaling environments to generate ventral skin or-ganoids (vSkOs) with lateral plate mesoderm (LPM) progenitors, successfully recapitulating features of abdominal or groin skin. Modulating early BMP and FGF signaling redi-rected these vSkOs toward an extraembryonic fate, producing human amnion-like tis-sues, termed Amnioids. Like native human amnion, Amnioids rapidly expanded into large, avascular, hairless cysts, in sharp contrast to the primitive vasculature and abundant hair follicles of vSkOs. Single-cell RNA sequencing identified divergent molecular signatures and developmental trajectories, highlighting key roles for NOTCH, WNT, and YAP/Hippo signaling pathways. Functional studies further underscored mesenchymal-epithelial interactions and mechanical forces as critical regulators of epithelial expansion. Together, these models provide potent tools to investigate human development at the embryonic-extraembryonic interface, offering critical insights into congenital skin and amniotic disorders and opening new avenues for precision regenerative therapies.
Lactic acid (LA) production from food waste fermentation offers a promising route for organic waste valorization and aligns with a circular economy concept. However, the microbial metabolic interactions remain poorly understood. This study demonstrates that regulating indigenous microbiota with eggshell (EG) buffer can achieve efficient LA production (33.2 ± 2.6 g COD/L), a yield only 3.6 % lower than that obtained with anaerobic sludge inoculation. Metabolic pathway analysis indicated that EG addition not only stabilized fermentative pH but also redirected the carbon flux toward LA. High-throughput sequencing showed that homofermentative LA bacteria (Streptococcus and Enterococcus) accounted for 37.3 % of the microbial community under EG addition alone. Additionally, EG modulated genes in the Embden-Meyerhof-Parnas, the hexose monophosphate, and the hexokinase pathway. This study confirms that an EG-buffered fermentation system can activate indigenous microbiota for high-efficiency LA production, demonstrating that exogenous inoculation is non-essential and offering a cost-effective approach for sustainable LA synthesis.
The architectural optimization of multifunctional components within MoS2-based nanohybrids demonstrates potential for amplifying cooperative catalytic effects in biomimetic catalytic systems. Employing APTES@MoS2 composites as specialized sorbents, their inherent amine coordination ability and surface negative charge were exploited to investigate their efficacy in adsorbing transition metal ions such as Ni2+, Fe3+, and Co2+ ions. High-temperature carbonization treatment gradually converted the amorphous APTES polymer into N-doped carbon (denoted as CAPTES), while simultaneously enhancing crystallinity and enabling transition metal doping in MoS2 nanosheets (NSs) through thermal treatment, leading to enhanced catalytic performance. Dense decoration of palladium nanoparticles (Pd NPs) is incorporated to amplify the hydrophilicity and the catalytic efficiency of CAPTES@Fe-MoS2 nanocomposites. Benefiting from the hierarchical hollow configuration with extensively exposed edge sites, optimized charge transfer dynamics, and homogeneously distributed active centers, the engineered tubular heterostructures demonstrate exceptional catalytic performance in both 4-nitrophenol (4-NP) reduction and biomimetic enzymatic reactions. This template-directed synthesis strategy provides a scalable platform for manufacturing heterostructured MoS2-based materials with tunable synergistic functionalities, enabling advanced catalytic solutions for environmental and analytical applications.
Although nanozymes have been widely adopted in the field of colorimetric detection of heavy metal contaminants as well as disease markers, it still has development potential to develop a catalyst with high enzyme activity for application in tri-mode: UV-vis, smartphone and Raman. In this study, flower-like Ag/MgMn2O4 microspheres were fabricated via an integrated solvothermal and chemical reduction approach, demonstrating remarkable oxidase-like activity. Leveraging the exceptional oxidase-like activity of Ag/MgMn2O4, we developed a tri-mode (UV-vis-smartphone-Raman) sensing platform with multiplex analytical capability for GSH/Cu2+ detection. The system demonstrated broad linear responses (GSH: 0.5-300 μM; Cu2+: 0.1-300 μM) achieving ultrasensitive detection limits of 0.086 μM (GSH) and 0.062 μM (Cu2+). Furthermore, mechanistic investigations into the target detection revealed that the sulfhydryl groups in glutathione (GSH) readily coordinate with metal sites, thereby suppressing the catalytic activity of the catalyst. This phenomenon demonstrates promising potential for urinary analysis applications in clinical diagnostics. Conversely, the formation of Complex formed by Cu2+ and GSH effectively inhibits the coordination between GSH and metal atoms, which provides great helpful for quantitative detection of Cu2+ in environmental lake water. Therefore, the tri-mode sensor based on Ag/MgMn2O4 oxidase-like activity has a wide application prospect in biomedical fields and environmental detection fields.
Cross-view geo-localization (CVGL) presents significant challenges due to the drastic variations in perspective and scene layout between unmanned aerial vehicle (UAV) and satellite images. Existing methods have made certain advancements in extracting local features from images. However, they exhibit limitations in modeling the interactions among local features and fall short in aligning cross-view representations accurately. To address these issues, we propose a Multi-Scale Cascade and Feature Adaptive Alignment (MCFA) network, which consists of a Multi-Scale Cascade Module (MSCM) and a Feature Adaptive Alignment Module (FAAM). The MSCM captures the features of the target's adjacent regions and enhances the model's robustness by learning key region information through association and fusion. The FAAM, with its dynamically weighted feature alignment module, adaptively adjusts feature differences across different viewpoints, achieving feature alignment between drone and satellite images. Our method achieves state-of-the-art (SOTA) performance on two public datasets, University-1652 and SUES-200. In generalization experiments, our model outperforms existing SOTA methods, with an average improvement of 1.52% in R@1 and 2.09% in AP, demonstrating its effectiveness and strong generalization in cross-view geo-localization tasks.
The advancement of highly efficient, selective catalysts stands as a pivotal challenge in methanol steam reforming (MSR) for hydrogen production. In conventional Ni-based catalysts, metallic Ni0 acts as the active site, synergizing with abundant surface oxygen vacancies of the oxide support and generating the intermediate *OCH₂ tends to directly decompose into CO, which further reacts with *H generated from H₂O activation to form CH₄, resulting in poor product selectivity. Herein, we propose a novel Ni/In₂O₃ catalyst that significantly enhances the selectivity of CO₂, increasing it from less than 10% to 90%. Comprehensive characterizations including XRD, XPS, H₂-TPR, UV-vis and TPSR reveal that, unlike conventional Ni0-based catalysts, the Ni/In₂O₃ catalyst forms a very active and selective Ni₂In₃ alloy phase that enables highly dispersed Ni species while endowing the catalyst with excellent capability for H₂O activation. Combined with further theoretical calculations, the catalytic reaction mechanisms of the CH₃OH and CH₃OH-H₂O systems on the catalyst surface are elucidated in detail. Density functional theory (DFT) calculations demonstrate that the d-p orbital hybridization in the Ni₂In₃ alloy effectively alters the electronic structure of Ni species and tunes the adsorption strength of reaction intermediates and balances the surface coverage of CH₂O* and *OH species, facilitating the critical HO-CH₂O* coupling step for CO₂ generation during methanol steam reforming and thereby improving the CO₂ selectivity of the Ni/In₂O₃ catalyst. The study presented in this work indicates that the formation of Ni-In active alloy phase may effectively overcome the inherent product selectivity issue of conventional Ni-based catalysts by regulating the surface coverage of the critical intermediate and therefore reaction pathway during methanol steam reforming.
Cardiac fibrosis is a major pathological feature of cardiovascular diseases and a key driver of ventricular remodeling and heart failure, yet effective therapeutic strategies remain limited due to incomplete mechanistic understanding. Heme oxygenase-1 (HO-1), a key redox-regulating enzyme, has recently been implicated in cardiac injury. However, its role in cardiac fibrosis, particularly through the regulation of ferroptosis, remains poorly understood. This study systematically investigated the effects of HO-1-mediated ferroptosis on the progression of cardiac fibrosis. Myocardial infarction was induced by ligation of the left anterior descending coronary artery, and cardiac fibroblasts were stimulated with transforming growth factor-β1 (TGF-β1) to induce fibrosis. Ferroptosis was markedly activated, accompanied by increased ferrous iron (Fe2+) accumulation, enhanced reactive oxygen species (ROS) generation, and elevated levels of lipid peroxidation. Inhibition of ferroptosis significantly alleviated fibrotic responses and improved cardiac function. Mechanistically, ferroptosis suppression restored autophagic flux by correcting lysosomal abnormalities and normalizing LC3B and p62 accumulation. Furthermore, HO-1 was markedly upregulated in fibrotic conditions, promoting ferroptosis and myocardial fibrosis. Activation of HO-1 induced myocardial fibrosis by triggering ferroptosis-associated autophagic dysfunction, whereas HO-1 inhibition mitigated ferroptotic injury, restored autophagic homeostasis, and attenuated fibrosis. Blocking autophagy abolished the protective effects of HO-1 suppression, demonstrating that the antifibrotic role of HO-1-mediated ferroptosis is dependent on autophagic activity. These findings reveal a novel mechanism by which HO-1-mediated ferroptosis impairs autophagic homeostasis to drive cardiac fibrosis, highlighting HO-1 inhibition as a potential therapeutic strategy for attenuating cardiac fibrosis.
Conventional mandibular reconstruction frequently results in stress shielding, compromised osteotomy site healing, or bone resorption. This study introduces an Embedded Polygonal Bone Reconstruction Structure (EPBRS) that eliminates titanium plate dependency, enhanced through fillet-based topological optimization. A computational model of the Embedded Polygonal Bone Reconstruction Structure (EPBRS) was developed from CT scans. Four variants with fillet radii (0.2, 0.4, 0.6, and 0.8 mm) were designed. Physiological masticatory loading was simulated under identical conditions. The values of the von Mises stress and peak displacements were calculated for all configurations. With increasing fillet radius: The Maximum von Mises stress in the fibula graft decreased from 87.21 MPa to 37.59 MPa. The Maximum von Mises stress in the mandible decreased from 100.73 MPa to 23.9 MPa. The Maximum von Mises stress in titanium screws remained statistically invariant (170-185 MPa). Peak displacement (fibula graft, screws, mandible) decreased by approximately 0.1 mm between 0 mm and 0.2 mm fillet radii, remaining stable with further increases CONCLUSION: The embedded polygonal reconstruction demonstrates significant biomechanical safety and reliability. Fillet optimization substantially reduced the Maximum von Mises stress and improved deformation resistance.
Here, we reported a novel and efficient method for the synthesis of S-alkyl substituted sulfilimines by employing a base-promoted ring-opening reaction of sulfonium salts with sulfenamides. This reaction proceeds smoothly under transition metal-free conditions and under an air atmosphere, allowing facile access to the corresponding products in good to excellent yields through selective C-S bond cleavage and simultaneous formation of a new C-S bond. This strategy features a broad substrate scope and holds promise for applications in late-stage functionalization.
Magnetic resonance imaging (MRI) has become a core imaging modality for prostate cancer screening and diagnosis. Accurate and automatic segmentation of lesion regions is critical for subsequent staging assessment and treatment planning. To this end, this research proposes a two-stage segmentation framework for multimodal MRI. In the first stage, the prostate gland is segmented to extract the region of interest (ROI), thereby removing complex pelvic background structures. In the second stage, fine-grained prostate cancer lesion segmentation is performed within the ROI, enabling the model to focus on anatomically plausible lesion regions.A segmentation network, termed MSTM-Net, is developed based on this framework. The network adopts a Swin Transformer-based decoder architecture. At the input stage, T2-weighted images and apparent diffusion coefficient (ADC) maps are spatially aligned and concatenated along the channel dimension. During decoding, a Mamba module based on state-space modeling is introduced to jointly capture local structural information and long-range dependencies. Multi-head attention fusion and multi-scale feature fusion are further integrated into the skip connections to enhance the consistency between shallow spatial details and deep semantic representations. Experiments conducted on the cleaned PROSTATEx dataset demonstrate that the proposed method achieves a Dice score of 95.38% for prostate gland segmentation and 63.89% for lesion segmentation, outperforming the best comparative network by approximately 4% points, with an mIoU of 61.32%. Furthermore, cross-dataset validation on the PI-CAI dataset yields a Dice score of 63.14%, indicating good generalization ability and clinical feasibility for automated prostate cancer segmentation. The proposed MSTM-Net demonstrates effective performance for prostate cancer segmentation in multimodal MRI, achieving improved accuracy and feature representation compared with existing methods. The results indicate that the two-stage framework combined with multi-modal fusion and state-space modeling is a promising approach, although further validation on larger and more diverse datasets is required to enhance robustness and generalization.
Boron neutron capture therapy (BNCT) is a binary cancer treatment strategy that relies on the selective delivery of a 10B-containing drug followed by thermal neutron irradiation, generating localized high-LET particles that destroy tumor cells. L-BPA is the most recent and currently the only marketed boron carrier; however, its poor tumor retention and low solubility significantly limit therapeutic efficacy. Thus, developing improved L-BPA derivatives, as well as tools for efficient monitoring of L-BPA biodistribution, is critically important. Herein, we report a turn-on fluorescent probe, WF324, which undergoes rapid and selective fluorescence enhancement upon interaction with L-BPA. WF324 exhibits a pronounced fluorescence turn-on response at 578 nm (λex = 488 nm) along with a obvious Stokes shift of 90 nm, the probe displays a rapid response with a half-response time (t₁/₂) of 12 s and reaching a stable plateau within 150 s in aqueous solution and shows excellent selectivity with minimal interference from metal ions. The probe exhibited a good linear response toward L-BPA in the concentration range of 0-6 μM, with a detection limit as low as 80.48 nM. In real samples, the sensor achieved recoveries of 90.40-103.11% with RSDs of 0.78-3.96% for L-BPA detection in human urine. These properties make WF324 a promising tool for sensitive detection and real-time monitoring of L-BPA, offering valuable support for future BNCT research and development.
Food waste fermentation liquid, rich in volatile fatty acids (VFAs) and carbohydrates, serves as a sustainable electron donor for biological nitrogen removal. However, the compositional fluctuation of fermentation liquid often leads to unstable denitrification, and the mechanistic influence of mixed VFA-saccharide interactions on microbial ecology remains poorly understood. In this study, four carbon-source systems-three simulating typical mixed fermentation products (acetate + sucrose, propionate + sucrose, butyrate + sucrose) and one single-carbon control (acetate alone)-were systematically evaluated in sequencing batch reactors (SBRs). Results indicated that the butyrate-sucrose system (A3) exhibited superior performance, achieving a nitrate removal efficiency of 98.5%, which was 13.5% and 8.2% higher than that of the acetate-sucrose (A1) and propionate-sucrose (A2) systems, respectively. Furthermore, A3 maintained the lowest nitrite accumulation (<0.5 mg/L). Mechanistically, A3 facilitated the selective enrichment of functional genera Ferruginibacter and Terrimonas. PICRUSt2 functional predictions revealed that this specific combination significantly enhanced KEGG pathways related to membrane transport (ABC transporters) and energy metabolism, suggesting a synergistic effect that accelerates electron transfer and metabolic turnover. This study demonstrates that regulating acidogenic fermentation towards a butyrate-dominant composition is a promising strategy to maximize the utility of food waste as a carbon source, ensuring robust nitrogen removal in wastewater treatment.
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Somatic mutations accumulate throughout life and have been hypothesized to drive organismal decline. Yet whether these mutations are distributed randomly or whether cells shield their most critical components has remained unresolved. Here we analyze over a million somatic mutations across thirteen human tis-sues, finding that the aging genome exhibits organized vulnerability, captured by the existence of hypo-mutated genes and longevity-associated pathways that have significantly lower mutation burden. Highly connected network hubs are systematically protected from mutation, while peripheral, condition-specific genes accumulate disproportionate burdens. We show that this organized vulnerability arises from the interplay of two independent mechanisms: transcription-coupled repair, and selective filtering. Finally, we validate our findings under experimental mutagenesis, demonstrating intrinsic mechanisms of protection rather than tissue-specific confounders. These findings reframe the somatic mutation hypothesis: organismal decline may not reflect total mutational burden, but where those mutations fall within the cellular network.
A dual-emission metal-organic framework (UiO - 66 - TCPP) was prepared using 2 - aminoterephthalic acid (BDC - NH2) and tetrakis(4 - carboxyphenyl) porphyrin (H2TCPP) as the dual-ligand for ratiometric fluorescent, colorimetric, and visualization triple-mode detection of hypochlorous acid (HClO). Structural and spectroscopic characterization confirmed that the dual-ligand strategy integrated the nanosize of UiO - 66 and the red emission of H2TCPP, resulting in a stable monodisperse and dual-emission at 465 and 668 nm under single excitation at 390 nm for UiO - 66 - TCPP. Upon exposure to HClO, UiO - 66 - TCPP displayed the enhanced emission at 668 nm, while that at 465 nm increased a little for ratiometric sensing of HClO. Correspondingly, the color transition from yellow to pale pink achieved colorimetric detection of HClO. The detection limits were 0.15 μM for ratiometric sensing and 0.36 μM for colorimetric detection, with a linear range of 5-200 μM for both modes. A test strip was developed for visual assay, allowing on-site fluorescent detection of HClO by RGB analysis with a smartphone. The performance of UiO - 66 - TCPP was confirmed with the recovery testing for water and serum samples. The response mechanism was investigated as the oxidation of pyrrole nitrogen and amino groups, resulting in the multi-modality detection. Thus, we proposed the dual-ligand strategy to realize the nanosize and dual-emission, simultaneously, as a robust platform for sensitive and visible detection of HClO with great potential for environmental and biological monitoring.