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A renewable double plasma mirror for Petawatt-class lasers
The two-stage prediction method for traffic spillover dissipation at short-distance intersections based on Bi-LSTM
The association between physical activity and the prevalence of comorbidity: a cross-sectional survey
Identification of Protocatechuic acid as an anti-acne component in extracts of black rice bran
Nonlocality in quantum cloning states
High performance double perovskites of Cs2InAgBr6 and Cs2InAgCl6 structural electronic optical and thermoelectric properties for next generation photovoltaics
Associations of pain phenotypes and pain relief medications with stroke and its subtypes: a prospective cohort study
Bone morphogenetic protein-9 controls pulmonary vascular growth and remodeling
Pulmonary arterial hypertension (PAH) and hereditary hemorrhagic telangiectasia (HHT) are two distinct vascular diseases linked to impaired signaling through bone morphogenetic protein (BMP) receptor complexes in endothelial cells. Although BMP-9 plays a central role in activating this pathway by binding to ALK1 and BMPR-II, its precise function in the pulmonary microvasculature has remained unclear. In this study, we demonstrate a role for BMP-9 in regulating pulmonary vascular architecture and homeostasis. Our findings reveal that BMP-9 signaling intersects with VEGF pathways and contributes to the delicate balance between vascular growth and remodeling in the lungs. We also show that disruption of this pathway can shift vascular responses toward an HHT-like state, potentially altering disease susceptibility. These insights offer a unique perspective on how BMP-9 and ALK1 shape pulmonary vascular biology and suggest that targeting this axis could inform future strategies for treating complex vascular diseases such as PAH.
Remote Iron dynamics of NiFe (oxy)hydroxides toward robust active sites in water oxidation
The human ciliopathy protein RSG1 links the CPLANE complex to transition zone architecture
Un(der)explored links between plant diversity and particulate and mineral-associated organic matter in soil
Structural Basis for the Catalytic Mechanism of ATP‐Dependent Diazotase CmaA6
Abstract Although several diazotases have been recently reported, the details of the reaction mechanism are not yet understood. In this study, we investigated the mechanism of CmaA6, an ATP‐dependent diazotase, which catalyzes the diazotization of 3‐aminocoumaric acid using nitrous acid. X‐ray crystallography and cryogenic electron microscopy‐single particle analysis revealed CmaA6 structures in the substrate‐free and AMP‐binding states. Kinetic analysis suggested that CmaA6 catalyzes diazotization via a sequential reaction mechanism in which three substrates (nitrous acid, ATP, and 3‐aminocoumaric acid) are simultaneously bound in the reaction pocket. The nitrous acid and 3‐aminocoumaric acid binding sites were predicted based on the AMP‐binding state and confirmed by site‐directed mutagenesis. In addition, computational analysis revealed a tunnel for 3‐aminocoumaric acid to enter the reaction pocket, which was advantageous for the sequential reaction mechanism. This study provides important insights into the catalytic mechanism of diazotization in natural product biosynthesis.
Ring of capillary actuators as trap, tweezers and ratchet for floating particles
Machine learning evaluation model of pilot workload in a low-visibility environment
Genome-wide association study of the taste and hedonic ratings of the low-calorie sweetener acesulfame potassium
Survival analysis of PLWH on antiretroviral therapy in Henan Province from 2004 to 2024
Comprehensive datasets for RNA design, machine learning, and beyond
Abstract RNA molecules are essential in regulating biological processes such as gene expression, cellular differentiation, and development. Accurately predicting RNA secondary structures and designing sequences that fold into specific configurations remain significant challenges in computational biology, with far-reaching implications for medicine, synthetic biology, and biotechnology. While machine learning methodologies have been proposed to enhance prediction capabilities, they require high-quality training data. The lack of standardized benchmark datasets further hinders the development and evaluation of these tools. To address this, we created a comprehensive dataset of over 320 thousand instances from experimentally validated sources to establish a new community-wide benchmark for RNA design and modeling algorithms. Our dataset comprises numerous challenging structures for which state-of-the-art RNA inverse folders provide results of varying accuracy. We demonstrated the potential of the dataset by testing it with several popular open-source RNA design algorithms. Furthermore, we illustrated how our dataset can be used to train machine learning models that consider both RNA sequence and structure, potentially advancing RNA design and prediction capabilities.