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Functional characterization of specialized immune cells in a cnidarian reveals an ancestral antiviral program
Abstract Examining early-branching animal phyla can help reconstruct the evolutionary origins of immune cells. Here, we characterize immune-related cell programs in embryos of the sea anemone Nematostella vectensis , a model of Cnidaria, which diverged ~600 million years ago from other animals. Using a transgenic Nematostella reporter line expressing mCherry under the RLRb antiviral promoter, we identify a morphologically and transcriptomically distinct cell population activated by the viral mimic poly(I:C). These cells upregulate immune effector and regulator genes and show increased phagocytic activity. Bulk RNA sequencing of RLRb expressing cells and single-cell transcriptomics reveal gene regulatory programs expressed in specialized immune cells under basal conditions and upon activation. Comparing Nematostella’s immune expression profile with that of stony corals treated with the immunostimulant 2′3′-Cyclic GMP-AMP demonstrate a conserved immune response across Hexacorallia. This study uncovers a cnidarian immune cell type involved in antiviral immunity, providing insights into the evolutionary history of innate immunity.
High glucose enhances Nε-(carboxymethyl)lysine (CML)-induced colorectal cancer progression via the ANO6/Smad2/3-E-cadherin signaling
Global blue carbon losses from salt marshes exceed restoration gains
A model-based image fusion framework using discrete band-limited shearlets
Abstract The limited dynamic range of digital imaging sensors often leads to under- or over-exposed images. While deep learning methods currently dominate multi-exposure image fusion (MEF), they suffer from data dependency and poor interpretability. This paper proposes a novel model-based MEF framework using the Discrete Band-Limited Shearlet Transform (DBLST), which provides superior directional representation compared to traditional wavelets. Our method decomposes source images using DBLST and fuses coefficients according to specifically designed rules for low-frequency and high-frequency components. Extensive experiments demonstrate that the proposed algorithm achieves superior performance to several representative transform-based methods (DWT, NSCT, NSST) in terms of detail preservation and information richness, positioning DBLST as a powerful tool for model-based fusion. The results confirm DBLST as an efficient and interpretable, training-free alternative to data-driven deep learning models for rendering high-dynamic-range images, particularly in scenarios where transparency and reproducibility are prioritized.
Digital defocus aberration interference for automated optical microscopy
Generating explainable hypotheses for drug repurposing with graph neural networks
Abstract Biomedical knowledge discovery increasingly relies on computational tools to uncover patterns in complex datasets, yet generating explainable, evidence-based hypotheses about biological interactions remains challenging. This study introduces XAIPath , an interpretable pipeline that leverages biomedical knowledge graphs and Graph Neural Networks (GNNs) to uncover and explain novel drug–disease relationships, aiding applications such as drug repurposing. The pipeline combines GNN-based predictions with a post-hoc interpretability layer that extracts simple paths connecting drug and disease nodes in a biomedical knowledge graph and compares them using MinHash-based similarity. Similar paths are grouped via K-means clustering to build interpretable clusters that represent alternative mechanistic hypotheses. The method was applied to the NeDRex knowledge graph for drug indication prediction, with performance evaluated using AUROC, AUPRC, precision, sensitivity, and specificity. XAIPath achieved strong predictive performance, with AUROC exceeding 95% and AUPRC over 90% across training, validation, and test sets, while precision, sensitivity, and specificity all surpassed 85%. Most high-scoring predictions were supported by existing literature, and the extracted path clusters closely aligned with DrugMechDB annotations, supporting the plausibility of the generated hypotheses. Overall, XAIPath offers a scalable and explainable approach for identifying drug–disease associations, facilitating hypothesis generation and biological validation, and highlighting the value of explainable AI in biomedical research and drug repurposing.
PE/PPE proteins contribute to Mycobacterium tuberculosis drug resistance
Abstract The mycobacterial outer membrane (OM) creates a formidable permeability barrier, and whether drugs traverse it by mechanisms other than passive diffusion remains unclear. The proline-glutamic acid (PE) and proline-proline-glutamic acid (PPE) proteins of pathogenic mycobacteria include several OM transporters. Because bacterial transporters are also major contributors to drug resistance, we tested the role of PE/PPE proteins in Mycobacterium tuberculosis ( Mtb ) drug susceptibility. We identified mutations in multiple pe/ppe genes that were strongly associated with drug resistance in a genetic association study. A mutation in ppe42 linked to clinical amikacin resistance also conferred higher amikacin resistance in vitro. Deletion of ppe51 led to in vitro resistance to multiple drugs and was accompanied by upregulation of inner membrane efflux pumps. Deletion of a pe/ppe pair that responded transcriptionally to drug exposure, pe25/ppe41 , led to increased resistance to isoniazid (INH) in strains across all major Mtb lineages and accelerated the emergence of INH resistance in vitro. These data show a role of several Mtb PE/PPE proteins in drug resistance consistent with the PE/PPE transporter paradigm and suggest a wider role of the PE/PPE family in Mtb drug susceptibility and clinical drug resistance.
Comparative metabolic profiling of pediatric cerebrospinal fluid reveals disease-specific biomarkers in HIV and TB meningitis
Trophic status strongly regulates nitrous oxide but not methane production in global freshwater lake sediments
Curcumin and ferulic acid activate PPARγ–PGC1α signaling and improve mitochondrial function in a 6-OHDA-induced Parkinson’s cellular model
Multiferroic phase transition between multiple types of collinear compensated magnets
Implementation status and barriers to multimodal management of low back pain among physical therapists: A cross-sectional study
Modeling early human heart development using an iPSC-based 3D bioprinted model of embryonic heart tube
A method for CRISPR/Cas9-induced genetic barcoding and lineage tracing in sheep
Engineering temperature- and radiation-resistant van der Waals oxide optoelectronics via heteroatom-intercalation
Oxymatrine regulates SPHKS/S1P signaling pathway in diabetic retinopathy: an observational clinical cohort study
MicroRNA-30c analog C2 decreases plasma cholesterol and atherosclerosis without causing liver injury in preclinical studies
Spatial early-warning assessment of ecological security in the Poyang Lake Basin based on PSR and Spatial Durbin Modeling
Abstract Constructing a basin-level ecological security early-warning (ESEW) system is critical for overcoming the fragmentation of administrative management and achieving holistic watershed governance. Focusing on the Poyang Lake Basin, this study develops a spatial ESEW assessment framework based on the Pressure-State-Response (PSR) model. By integrating 1 km grid-scale data with the Spatial Durbin Model (SDM), we investigate the spatiotemporal evolution of ecological security and quantify the cross-regional spillover effects of key drivers from 2000 to 2020. The results reveal a structural “deterioration-then-recovery” trajectory: the area of “Moderate Warning” expanded from 52.40% in 2000 to a peak of 64.53% in 2010 due to rapid industrialization, before dropping to 37.93% in 2020 as “Light Warning” areas became dominant (62.00%). Spatial analysis confirms significant clustering of ecological risks. Crucially, the SDM identifies a strong spatial spillover effect, indicating that ecological degradation is highly contagious across county borders. Specifically, industrial sulfur dioxide emissions and urbanization intensity (NUADI) in neighboring areas exert significant negative spillovers on local ecological security, whereas farmland-to-forest conversion generates positive externalities. Based on these findings, we propose a differentiated governance strategy, including “Source Control” for high-risk pollution clusters and market-oriented ecological compensation mechanisms to internalize the benefits of upstream protection, providing a scientific basis for cross-regional collaborative governance.