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Hotspot pocket-based discovery of urea transporter selective inhibitors
Abstract Urea transporter (UT) inhibitors are a promising class of diuretics, as selective inhibitors targeting UT-A subtypes have demonstrated considerable therapeutic potential. Herein, we employ a two-round progressive hotspot pocket-based virtual screening approach combined with biological validation to identify M353-0039 as a highly potent and selective inhibitor of UT-A2. We conduct cryo-electron microscopy to solve the structures of UT-A2 bound with the two inhibitors, M353-0039 and E822-1968, at the resolution of 2.7 Å and 2.9 Å respectively, and elucidate the structural mechanism underlying the superior efficacy and selectivity of M353-0039. Compared with the inhibitor HQA2 and E822-1968, M353-0039 occupies a deeper binding pocket and forms more interactions with UT-A2, thus leading to greater inhibitory potency. We demonstrate that the selectivity of M353-0039 is driven by the nonconserved residues C285 and G322 within the “T-T” subpocket of UT-A2. Finally, we validate the selective effects of M353-0039 in inhibiting UT-A2 function both in mouse models and hepatic cell. These findings not only identify a selective inhibitor as a tool that can be applied to elucidate the unique physiological roles of UT-A2 but also provide an available method for efficiently developing UT-A-selective inhibitors with potent activity as the next-generation diuretics.
Transcriptome analysis and effects of low salinity stress on ovarian development in Litopenaeus vannamei
Generative approaches to kinetic parameter inference in metabolic networks via latent space exploration
Abstract Dynamic (kinetic) models track time-varying metabolite concentrations, fluxes, and enzyme levels, quantifying responses to genetic and environmental perturbations. Yet building these models at scale is hindered by scarce enzyme kinetic parameters. Generative neural networks can rapidly parameterize near-genome-scale kinetic models, but their representations are hard to interpret and often require new training to move across species or physiological states. Here we introduce a latent-space exploration framework that repurposes a trained generative network to produce models with targeted dynamics in new regimes without additional training. We show in Escherichia coli that latent inputs tune aerobic response speed, identify rate-limiting enzymes, and retarget the generative network to anaerobic dynamics. We extend our approach to Saccharomyces cerevisiae , demonstrating robust control of metabolic dynamics across training stages and diverse latent inputs. Latent variables thus become practical control knobs for kinetic model behavior, accelerating cell-factory design and enabling personalized metabolic modeling.
Enhanced YOLOv8 for efficient road damage detection with spatial-channel reconstruction and multi-scale attention
Recurrent DNA break clusters drive replication-stress-induced copy number variants and genome diversification
Abstract Copy number variants (CNVs) are strongly implicated in neurological and psychiatric disorders and brain cancer, yet the process by which replication stress generates CNVs—and why some recur while others remain rare—remains poorly understood. Here, we show that recurrent DNA-break clusters (RDCs) act as common initiating lesions that drive both recurrent and non-recurrent CNVs. In murine neural progenitor cells subjected to chemically induced replication stress, bulk whole-genome sequencing identifies recurrent CNVs enriched at late-replicating RDCs within actively transcribed genes. Single-cell genome sequencing further uncovers frequent, non-recurrent CNVs associated with RDCs that arise during the transition from early to late DNA replication. These CNVs represent stable, heritable structural variants with breakpoints consistently enriched at RDCs. CRISPR/Cas9-mediated transcriptional suppression abolishes both RDC formation and CNV generation, establishing RDC-associated breaks as a shared upstream source. Mechanistically, CNV formation depends on DNA repair context: CNVs are Pol θ-dependent in NHEJ-deficient cells but arise independently of Pol θ in NHEJ-proficient cells. Together, these findings define RDCs as central drivers of replication-stress-induced genome diversification.
A degrader of HER2 and EGFR abolishes p95HER2 and shows robust antitumor efficacy in HER2-positive breast cancer
Author Correction: Large emissions of CO2 and CH4 due to active-layer warming in Arctic tundra
Retraction Note: Accurate and real-time brain tumour detection and classification using optimized YOLOv5 architecture
Exploratory analyses of clinical trials - what can they tell us and what are the potential pitfalls?
Rheology and gelation kinetics of sulfonated polyacrylamide/Cr(III) acetate gels for water shut-off: effects of salinity, temperature, and shear
Predicting and achieving self-recoverable mechanoluminescence based on contact electrification
Demand-oriented regionalization with local data
Abstract Climate adaptation planning often relies on census-based or neighborhood boundaries, yet such fixed units seldom match the diverse and evolving problems that interventions seek to resolve. This underscores the necessity of designing demand-oriented regionalization that is problem-specific and responsive to local priorities. Traditional regionalization methods, however, struggle to balance socioeconomic and environmental variables while maintaining spatial coherence to meet practical and physical constraints. To address these shortcomings, we present RepSC-SOM , a Rep resentative-initialized, S patially C onstrained S elf- O rganizing M ap that extends traditional SOM with representative-based initialization, adaptive geographic filtering, and region-growing refinement. The method maximizes within-region similarity and between-region dissimilarity while maintaining spatial coherence. Notably, the framework is designed to enhance transparency and interpretability by standardizing how planning regions are defined, reducing reliance on subjective or historically bounded spatial units. Applied to flood-induced water contamination (E. coli concentrations) in Jacksonville, FL, RepSC-SOM-generated regions provided a more accurate characterization of the problem than standard units of analysis. Specifically, these regions achieved a higher average pairwise difference (223 per 100mL) compared to census tracts (154 per 100mL, $$p=0.002,r=0.088$$ ), traffic analysis zones (118 per 100mL, $$p<0.001, r=0.154$$ ), and neighborhoods (145 per 100mL, $$p=0.001,r=0.101$$ ), indicating accurate detection and more precise delineation of contamination hot spots. These results suggest a strong potential for applying RepSC-SOM in real planning contexts to guide targeted interventions, prioritize resource allocation, and support coordinated climate adaptation strategies.
Elucidating the Facet-Dependent Oxygen Reduction Reaction Performance of Cu Nanosheet over N-O co-Doped Graphene
Spatiotemporal trends and the change detection of the yearly eco-environmental quality in the Yellow River Basin, China
Ozone mitigates extended growing season and enhanced vegetation greenness driven by environmental change
Abstract Rising temperature and elevated CO 2 concentrations lead to extended growing seasons and enhanced vegetation greenness in terrestrial ecosystems, especially across the Northern Hemisphere. However, whether and to what extent surface ozone, an anthropogenic environmental factor, affects vegetation phenology and greenness remains unexplored at a large scale. Integrating ground-based ozone observations with multiple satellite observations, we demonstrate that surface ozone significantly shortened the growing season by delaying start of season and advancing end of season. Additionally, ozone reduced growing-season vegetation greenness, as reflected in decreased annual accumulated Enhanced Vegetation Index and maximum Enhanced Vegetation Index. These impacts show pronounced spatial heterogeneity, varying in magnitude across the United States, Europe, and China over the past decade, highlighting ozone’s diverse impact on vegetation across regions. Our study predicts that continuously increasing surface ozone concentration will mitigate warming-driven lengthening the growing season by 2 to 4 days, reduce maximum Enhanced Vegetation Index by 0.4% to 8.3%, and reduce annual accumulated Enhanced Vegetation Index by 1.0% to 5.6% in 2050 under Shared Socioeconomic Pathway 5-8.5 scenario. Our findings highlight the imperative need for strategic surface ozone regulation to optimize vegetation health and maximize the capacity for carbon sequestration.
Improving performance for multi-category anthropogenic debris detection in river environments by using a size-tailored annotation approach and data augmentation
Multi-omic mapping of Drosophila protein secretomes reveals tissue-specific origins and inter-organ trafficking
Abstract Secreted proteins regulate many aspects of animal biology and are attractive targets for biomarkers and therapeutics. However, comprehensively identifying the “secretome”, along with their tissues of origin, remains extremely challenging. To address this, we employed multiple ‘omics methods to define a tissue-secretome map of 535 blood plasma proteins derived from specific cell-types and organs in Drosophila melanogaster . This map was enabled by methodological improvements including a collection of transgenic flies to label endogenous secreted proteins in 10 major tissue types, large-scale blood isolation, whole animal snRNA-seq, and 40 CRISPR knock-in strains. Using this map, we identify features of circulating proteins: most originate from specific tissues including unusual sources (e.g. glia), many are uncharacterized, and some are shed ectodomains of transmembrane proteins. In addition, in vivo experiments revealed circulating proteins with tissue-specific expression, as well as proteins that are deposited in a different tissue from where they are synthesized, suggesting potential inter-organ functions. Our secretome map will serve as a resource to investigate blood protein function, discover candidate tissue-tissue communication signals, and mine for homologues of human biomarkers.