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Negative Air Ions (NAIs), essential for environmental and human health, facilitate air purification and offer antimicrobial benefits. Our study achieves hourly estimations of NAIs using a machine learning framework, developed from a multi‐layer selection pipeline of over 200 variables, to identify the key determinants critical for adapting to high‐resolution NAIs dynamics. Addressing site sparsity and NAIs volatility, we introduced a hybrid stacking incorporating pseudo sites generated from Kriging Spatiotemporal Augmentation (KSTA) to mitigate spatial overfitting. Our approach, validated in Zhejiang, China, demonstrates exceptional accuracy, achieving R2 values of 0.90 (sample‐based), 0.85 (temporal‐based), and 0.79 (site‐based). This work not only sheds light on NAIs behavior in relation to diurnal shifts, land use, and environmental events, but also integrates a health grading system, enhancing public health strategies through precise air quality assessment. Plain Language Summary: Our study advances the environmental science by developing a machine‐learning model capable of estimation the hourly concentrations of negative air ions (NAIs) across Zhejiang Province, China. NAIs, vital for air purification and human health, are significantly related to environmental conditions. Taking advantage of a novel approach that reduces errors commonly seen in similar models, we provide a highly accurate, hour‐by‐hour analysis of air quality. Our research goes beyond mere prediction; it investigates how various factors like vegetation, soil type, and weather events influence NAI levels. The insights gained offer a comprehensive view of how NAI distribution changes over time and across different landscapes, informing environmental policy and public health strategies with precise air quality assessments. This study contributes significantly to the field by introducing an essential tool for continuous environmental monitoring, thereby facilitating more effective decision‐making in environmental and public health sectors. Key Points: A hybrid stacking machine learning framework shows superior accuracy (R2 = 0.90), combing a filter pipeline dealing with more than 200 variablesKriging Spatiotemporal Augmentation (KSTA) as a pseudo‐label strategy to resolve spatial overfitting due to sparse sitesAn hourly estimate of NAIs reveals NAIs dynamics correlations with environmental factors and land use, supporting public health policy [ABSTRACT FROM AUTHOR]

BACKGROUND: The discovery of agricultural fungicide candidates from natural products is one of the key strategies for developing environment friendly agricultural fungicides with high efficiency, high selectivity and unique modes‐of‐action. Based on previous work, a series of novel α‐methylene‐γ‐butyrolactone (MBL) derivatives containing benzothiophene moiety were designed and synthesized. RESULTS: The majority of the proposed compounds displayed moderate to considerable antifungal efficacy against the tested pathogenic fungi and oomycetes, some exhibiting broad spectrum antifungal activity. Notably, compounds 2 (3‐F‐Ph) and 7 (4‐Cl‐Ph) showed excellent antifungal activity against Rhizoctonia with half maximal effective concentration (EC50) values of 0.94 and 0.99 mg L−1, respectively, comparable to the commercial fungicide tebuconazole (EC50 = 0.96 mg L−1), and also displayed significant inhibitory effects against V alsa mali with EC50 values of 2.26 and 1.67 mg L−1, respectively – better than famoxadone and carabrone. The in vivo protective and curative effects against R. solani of compound 2 were 57.2% and 53.7% at 100 mg L−1, respectively, which were equivalent to tebuconazole (51.6% and 52.4%). Further investigations found that compound 2 altered the ultrastructure of R. solani cell, significantly increased the relative conductivity of the cells, and reduced the activity of complex III in a dose‐dependent manner. Molecular docking results showed that compound 2 matched well with the Qo pocket. CONCLUSION: The results revealed that MBL derivatives containing benzothiophene moiety are promising antifungal candidates and provide a new backbone structure for further optimization of novel fungicides. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Telomerase is unique among the reverse transcriptases in containing a noncoding RNA (known as telomerase RNA (TER)) that includes a short template that is used for the processive synthesis of G-rich telomeric DNA repeats at the 3' ends of most eukaryotic chromosomes.sup.1. Telomerase maintains genomic integrity, and its activity or dysregulation are critical determinants of human longevity, stem cell renewal and cancer progression.sup.2,3. Previous cryo-electron microscopy structures have established the general architecture, protein components and stoichiometries of Tetrahymena and human telomerase, but our understandings of the details of DNA-protein and RNA-protein interactions and of the mechanisms and recruitment involved remain limited.sup.4-6. Here we report cryo-electron microscopy structures of active Tetrahymena telomerase with telomeric DNA at different steps of nucleotide addition. Interactions between telomerase reverse transcriptase (TERT), TER and DNA reveal the structural basis of the determination of the 5' and 3' template boundaries, handling of the template-DNA duplex and separation of the product strand during nucleotide addition. The structure and binding interface between TERT and telomerase protein p50 (a homologue of human TPP1.sup.7,8) define conserved interactions that are required for telomerase activation and recruitment to telomeres. Telomerase La-related protein p65 remodels several regions of TER, bridging the 5' and 3' ends and the conserved pseudoknot to facilitate assembly of the TERT-TER catalytic core. Cryo-electron microscopy structures of Tetrahymena telomerase with telomeric DNA at several steps of nucleotide addition provide insights into the structural basis of telomere repeat synthesis., Author(s): Yao He [sup.1] [sup.2] , Yaqiang Wang [sup.1] , Baocheng Liu [sup.1] , Christina Helmling [sup.1] , Lukas Susac [sup.1] , Ryan Cheng [sup.1] , Z. Hong Zhou [sup.2] [...]