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Response of amino acids, phenolic acids, organic acids, and mineral elements to fulvic acid in spinach (Spinacia oleracea L.) under nitrate stress
Abstract Fulvic acid (FA) acid has many physiological activities, but the specific metabolic responses and changes in mineral element contents of spinach by FA in response to nitrate stress are unknown. Herein, we used liquid chromatography-mass spectrometry (LC–MS) and wet digestion using H2SO4-H2O2 to analyze the metabolic response and changes in the mineral element content of spinach to nitrate stress (150 mM NO3 −) after FA (0.15%) foliar spray application. After 2 days of the stress treatment, FA was sprayed thrice (once every 7 days), sampled 4 days after the last spraying, and metabolites and mineral element contents were measured. FA treatment significantly increased organic acid contents (tartaric acid, malic acid, citric acid, and ascorbic acid) and amino acid contents (threonine, asparagine, valine, tyrosine, alanine, glutamate, serine, histidine, arginine, and glutamine) under nitrate stress. FA application also significantly improved mineral element contents (P, Na, Fe, and Zn) under nitrate stress. This study provides comprehensive insights into metabolite accumulation of metabolites and the improvement of nutritional quality in spinach through FA application under nitrate stress. Further research should focus on elucidating additional underlying molecular mechanisms of these metabolic responses for better utilization of this natural compound in agriculture.
Focal Point Association of Core-Crystalline Micelles with an Amphiphilic Corona Block
Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm
Unlocking the Potential of Palmierite Oxides: High Oxide Ion Conductivity via Induced Interstitial Defects
Investigating the stability of individual differences in face recognition behavior
Coupled Differential Electrochemical Mass Spectrometry and Surface-Enhanced Infrared Absorption Spectroscopic Studies Unravel the Mechanism of Nitric Oxide Electroreduction on Platinum
Graphene and its modifications for enhanced adhesion in dental restoratives: a molecular docking and dynamics study
Can Earth’s rotation generate power? Physicists divided over controversial claim
Internally welded steel bars enhance the bonding between steel pipes and concrete and prevent disengagement and debonding
Abstract In this work, the bonding properties of steel pipes and concrete enhanced by internally welded steel bars are investigated, and a method to prevent debonding is proposed. A series of experiments and ultrasonic tests revealed that the internal welding of steel bars effectively improved the bond strength of the interface between the steel pipe and the concrete. A Q420qD steel pipe and C60 self-compacting compensating concrete were used in the experiment, and the filling was uniformly ensured by pumping and vacuum-assisted perfusion. The experimental results show that the concrete-filled steel tube structure has good compactness and stability, but the local voiding phenomenon indicates that the material may have voids or defects. The performance of HPB300 grade steel bars under dynamic loading is also evaluated through cube compressive strength tests and rollout tests. The tests revealed that with an increasing number of internally welded steel rings, the push-out length decreases, indicating better bonding performance. In addition, the finite element simulation and the comparison of experimental data further reveal the bond-slip characteristics of the internally welded reinforcement components of different internal forms of concrete-filled steel tubes, which provides not only important data support for the optimal design and dynamic calculation of concrete-filled steel tube structures but also a reference for subsequent material repair and reinforcement work.
Cross-Electrophile Coupling to Form Sterically Hindered C(sp<sup>2</sup>)–C(sp<sup>3</sup>) Bonds: Ni and Co Afford Complementary Reactivity
Subset selection for domain adaptive pre-training of language model
Abstract Pre-trained language models have brought significant performance improvements in many natural language understanding tasks. Domain-adaptive language models, which are trained with a specific domain corpus, exhibit high performance in their target domains. However, pre-training these models with a large amount of domain-specific data requires a substantial computational budget and resources, necessitating the development of efficient pre-training methods. In this paper, we propose a novel subset selection method called AlignSet, which extracts an informative subset from a given domain dataset for efficient pre-training. Our goal is to extract an informative subset that enables faster learning of the language model compared to learning from the entire dataset. By experiments across multiple domains, we demonstrate that AlignSet generates better subsets than other methods.