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.
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.
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.
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.
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.
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.
Electronic skin (e-skin) faces challenges in achieving long-term signal stability and wearability due to the poor breathability, sweat accumulation, and limited sensitivity. This paper reports a multifunctional nanofibrous e-skin (PTZ-PPPB-PPT) fabricated via layer-by-layer electrospinning, integrating a hydrophobic layer (PVDF-TrFE/ZnO), a piezoelectric enhancement layer (PAN/PVP/PDA@BTO), and a thermochromic layer (PAN/PVP/TCM). Benefited from the asymmetric wettability and hierarchical fiber structure, the device enables unidirectional sweat transport (contact angle reduces from 132.8° to 0° within 5.72 s) while blocking reverse osmosis (hydrostatic resistance of 40 mmH₂O). When the piezoelectric sensor operates under excessive sweating conditions, the unidirectional sweat transport maintains skin surface dryness, thereby ensuring stable piezoelectric output during movement. Notably, the E-skin achieves a high output voltage (40 V at 30 N with a sensitivity of 0.825 V/N), exhibits rapid response/recovery (100/80 ms). It also demonstrates reversible thermochromism (25-40 °C) for real-time temperature visualization. Additionally, the device ensures superior comfort during prolonged wear by maintaining exceptional air permeability (8.05 mm/s) and outstanding mechanical flexibility (187.75 % elongation at break). This multifunctional integrated E-skin synergizes sweat management with temperature visualization, holding promising potential for applications in wearable healthcare, human-computer interaction, and dynamic environmental monitoring.
Flexible actuators can perform complex motions such as rotation and bending in response to external stimuli. They have significant application in intelligent sensing, robotics, and bionic systems. Fiber-structured actuators, with their unique twistable and weavable properties, have emerged as a key area of research in bionic muscle. However, current technologies still struggle to achieve all of the following key performance metrics simultaneously: high load capacity, high toughness, and a rapid response to multiple stimuli. Drawing inspiration from the coaxial structure of biological muscles, this study has successfully developed a coaxial fiber actuator based on carboxymethyl cellulose (CMC) and two-dimensional transition metal dichalcogenide (MXene) by Wet spinning. Through interfacial interactions, the two materials form a stable, synergistic coaxial structure that enables dual-response drive functionality in response to light and humidity. Performance testing shows that, when stimulated by near-IR light, the actuator achieves a rotational speed of 643 rpm while remaining stable for 236 cycles. When stimulated by humidity, the maximum driving speed is 632.1 r/min, with a recovery speed of 1013.4 r/min. Building on these exceptional properties, the study has developed several practical applications, including an intelligent lifting device, a rotating fan and a revolving door with controllable speed.
An isolated ulna referable to Docodon, from the Upper Jurassic Morrison Formation of Wyoming, resembles the ulna of Borealestes in the presence of a small facet for the radial condyle of the humerus that is confined to the middle of the lateral side of the facet for the ulnar condyle of the humerus and a prominent tubercle on the latter. These taxa and Haldanodon are characterized by the presence of a deep, slit-like lateral fossa for one of the ligaments of the elbow joint. These features are currently only known for Docodonta. However, the ulna of Docodon differs from those of Borealestes and Haldanodon in the lack of an anterior curvature at its proximal end. It shows the most plesiomorphic morphology currently known for docodontans, suggesting that this taxon lacks specialized fossorial or aquatic adaptations.
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Multimodal sarcasm detection involves identifying sarcasm across multiple modalities, with the key challenge being modeling incongruity within and between modalities. Current methods often focus on inter-modal incongruity while underexploring intra-modal semantic information. To address this, we propose the Granularity-Based Inter and Intra-Modal Fusion Network (GIIFN). We leverage pre-trained visual and language models to extract semantic features from images and text, and introduce a learnable granularity grouping module to adaptively partition features into multiple semantic granularities. Furthermore, we design a bidirectional cross-attention mechanism to fuse intra-modal and inter-modal features at each granularity level. Experiments demonstrate that our approach achieves state-of-the-art performance.
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.
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Nucleosome assembly protein 1-like 1 (NAP1L1), a canonical member of the NAP1 family, orchestrates chromatin architecture and nucleosome dynamics to regulate cell cycle progression, development and proliferation. NAP1L1 is mainly expressed in actively growing cells, and its dysregulation is closely associated with the occurrence of a variety of diseases, including neurodevelopmental disorders, cardiovascular diseases and cancers. Recent studies have revealed that NAP1L1 can influence disease progression by modulating multiple classic signaling pathways such as cGAS-STING, PI3K/AKT and WNT pathways, highlighting its complex involvement in cellular signaling networks. In this review, we systematically summarized the discovery, structural features, and multifaceted biological functions of NAP1L1, with a particular focus on its pathogenic roles in cancer and cardiovascular diseases. We evaluated its potential as a druggable target by integrating computational biology approaches with structural and pharmacological evidence, identifying conserved ligandable pockets and predicting plausible interactions with known bioactive compounds. These findings position NAP1L1 as a potential druggable target with promising prospects at the intersection of chromatin plasticity and signal transduction, and provide comprehensive insights into the therapeutic potential of targeting NAP1L1, providing information and advancement for future clinical strategies.
The protein-protein interaction (PPI) between metadherin (MTDH) and Staphylococcal nuclease domain-containing protein 1 (SND1) is a pivotal oncogenic driver in various cancers, yet the atomic-level details of their binding mechanism remain elusive, hindering targeted drug discovery. This study employs integrated computational approaches, including molecular dynamics (MD) simulations, binding free energy calculations, and residue interaction network analysis, to identify hotspot residues at the MTDH-SND1 interface and elucidate the binding mechanism. The results demonstrate that the MTDH-SND1 complex exhibits strong binding affinity, primarily driven by electrostatic and hydrophobic interactions. Structural stability analysis confirmed the complex's integrity during simulations, while dynamic cross-correlation and mutual information analyses revealed a key interaction region (R1) with correlated motions, which was further proved by contact probability analysis. Hydrogen bond analysis identified a stable network involving residues Arg239, Arg243, and Hie263, which were confirmed as hotspot residues by the alanine scanning mutagenesis method. Furthermore, the binding and interaction mechanisms between SND1 and 12 activity-known small molecule inhibitors were investigated and compared with that in the MTDH-SND1 complex. Energy decomposition highlighted that the conserved triad-Arg239, Arg243, and Hie263-is crucial across all systems. This work provides unprecedented atomic-level insights into the MTDH-SND1 interaction and offers a robust structural foundation for the rational design of high-affinity inhibitors against this oncogenic PPI.
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.
The widespread adoption of donor-acceptor polymer photocatalysts remains constrained by two fundamental limitations: inefficient charge carrier separation and the practical drawbacks of powdered catalysts. To overcome these challenges, we implement a synergistic local polarization and mass-transfer engineering strategy. Through rational molecular design, we developed a thiazole-bridged d-A polymer (TATZ) with strong π-conjugation and an enhanced built-in electric field. This molecular architecture not only promotes exceptionally efficient electron-hole separation but also directs the oxygen reduction pathway almost exclusively toward superoxide radical (•O2⁻) formation, responsible for 96.7% of the observed photocatalytic activity. The optimized material achieves a tenfold improvement in photo-oxidation kinetics, enabling complete degradation of ciprofloxacin within 25 min. To translate these molecular advantages into practical implementation, we constructed a macroscopic, scalable sponge-based reactor (20 × 20 cm) through controlled deformation of a TATZ-incorporated hydrogel. This integrated system demonstrates excellent contaminant removal efficiency under continuous-flow operation using natural sunlight, confirming its potential for real-world application. Our work establishes a comprehensive design framework for advancing photocatalytic technology across multiple scales-from molecular motifs to functional macroscopic systems.
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.
Aqueous sodium-ion batteries (ASIBs) have surfaced as viable solutions for grid-scale applications characterized by exceptional safety, cost efficiency and eco-friendliness. However, the high freezing point severely restricts low-temperature viability. To overcome this issue, the dimethylacetamide (DMAC) is employed as a co-solvent for the inorganic and cheap 2 m NaCl (m: mol kg-1) electrolyte, achieving a freezing point below -45 °C with remarkable ionic conductivity (2.93 mS cm-1 under -30 °C). Theoretical calculations and experimental measurements reveal that carbonyl group in DMAC engages hydroxyl group from H2O molecules to primarily form 1H₂O-DMAC conformation, disrupting the intrinsic hydrogen bonds interaction of H2O molecules, effectively lowering freezing point of hybrid system. Using optimized electrolyte, the assembled Na2CoFe(CN)6//activated carbon (AC) batteries deliver 70.7 mAh g-1 at 1C (1 C = 150 mA g-1)，with 95 % capacity retention over 10,000 cycles at 10C at -30 °C. Notably, ASIBs successfully power light emitting diodes (1.8 V) at -40 °C. The electrolyte engineering strategy not only significantly enhances the performance of aqueous batteries in cold environments but also underscores their substantial potential for energy storage.