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This work aims to explore the impact of fulvic acid (FA) on denitrification within the purification process of sewage in the deep subsurface wastewater infiltration system (DSWIS). In the system, an organic glass column (height = 2.40 m; radius = 0.30 m) was filled with several layers of soil. Simulated domestic wastewater and extracted FA from landfill leachate were used in the experiments. It was found that before and after the addition of FA, COD, and NH4+-N were efficiently removed when a hydraulic load was 8 cm·d-1. Moreover, after FA addition, the removal efficiency of TN was enhanced from 67.74% to 78.01%. Organic matter transformation analysis indicated that in the under part, the shortage of carbon sources limited the denitrification prior to FA addition, resulting in a low TN removal efficiency. However, after adding FA, more FA-like substances were transferred into protein-like matters than before the addition of FA, which has helped produce more easily biodegradable organics for denitrification. So, the addition of FA could enhance the denitrification process in the system of DSWIS.

According to traditional Chinese medicine theory, tongue coatings reflect changes in the body. The goal of this study was to identify a metabolite or a set of metabolites capable of classifying characteristics of traditional Chinese medicine syndromes in erosive gastritis. In this study, we collected tongue coatings of patients with erosive gastritis with damp-heat syndrome (DHS), liver depression and qi stagnation syndrome (LDQSS), and healthy volunteers. Then, we analyzed the differences in metabolic characteristics between the two groups based on metabolomics. We identified 14 potential biomarkers related to the DHS group, and six metabolic pathways were enriched. The differential pathways included pyrimidine metabolism, pantothenate and CoA biosynthesis, citrate cycle (TCA cycle), pyruvate metabolism, glycolysis/gluconeogenesis, and purine metabolism. Similarly, in the LDQSS group, we identified 25 potential biomarkers and 18 metabolic pathways were enriched. The top five pathways were the TCA cycle, sphingolipid metabolism, fatty acid biosynthesis, pantothenate and CoA biosynthesis, and the pentose phosphate pathway. In conclusion, the DHS group and the LDQSS group have different characteristics.

Advanced microwave technologies constitute the foundation of a wide range of modern sciences, including microwave integrated circuits, quantum computing, microwave photonics, spintronics, etc. To facilitate the design of chip-based microwave devices, there is an increasing demand for state-of-the-art microscopic techniques that are capable of characterizing near-field microwave distribution and performance. In this work, we integrate Josephson junctions onto a nanosized quartz tip, forming a highly sensitive microwave mixer on-tip. This allows us to conduct spectroscopic imaging of near-field microwave distributions with high spatial resolution. By leveraging its microwave-sensitive characteristics, our Josephson microscopy achieves a broad detecting bandwidth of ≤200 GHz, as well as remarkable frequency and intensity resolutions. Near-field characterizations of microwave circuits are also conducted to demonstrate the capabilities of Josephson microscopy. Our work emphasizes the benefits of utilizing Josephson microscopy as a real-time, non-destructive technique to advance integrated microwave devices. [ABSTRACT FROM AUTHOR]

Leonurus japonicus Houtt is an important anti-skin pigmentation herb used in traditional Chinese medicine. However, the molecular basis for this activity is complex and not fully understood. In this study, water and ethanol extracts and polysaccharide extract from L. japonicus (LJPs) were analyzed by LC-MS/MS and HPLC-DAD separately. Cytotoxicity was analyzed by using CCK-8, antioxidant activity using flow cytometer, anti-MMPs, anti-tyrosinase and signalling pathway analysis using Western blotting to investigate their anti-melanogenesis function. The results showed that the water and ethanol extracts contained alkaloids, flavonoids, and phenolic acids. The LJPs mainly contain glucose, fucose, glucuronic acid, mannose, threonine and arginine, and structure characterization by FITR analyses indicated that LJPs have β- or α-D-glycosidic bonds and contain pyranose rings. The L. japonicus extracts displayed high cell viability at their maximum concentration. The water extract and polysaccharides significantly reduced lipopolysaccharide (LPS)-induced intracellular reactive oxygen species (ROS) content and exhibited a cytoprotective role. Also, these extracts displayed higher matrix metalloproteinase-2 (anti-MMP-2), anti-MMP-9 and anti-tyrosinase activities. Furthermore, the polysaccharides displayed significantly greater inhibitory effect on intracellular ROS and tyrosinase protein expression than α-arbutin and ursolic acid used for the clinical treatment of skin pigmentation. This study also investigated the polysaccharide inhibition of melanin synthesis by repressing the expression of melanocytic lineage-specific transcription factor (MITF) and melanogenic enzymes via modulation of the phosphoinositide 3-kinase (PI3K-Akt-mTOR) and β-catenin pathways. The overall results indicate that L. japonicus is a promising candidate for anti-pigmentation treatment. [ABSTRACT FROM AUTHOR]

Author(s): Lianxiang Jiang [sup.1], Fang Zhang [sup.1], Na Wei [sup.2], Jingjing Lv [sup.1], Wei Chen [sup.1], Zeping Dai [sup.1] Author Affiliations: (1) grid.452929.1, Department of Anaesthesia, The First Affiliated Hospital [...], Neuraxial anesthesia is a common practice in obstetrics. Evidence suggests that preprocedural ultrasound versus the conventional landmark location method accurately identifies a given intervertebral space and predicts the needle insertion depth required to reach the spinal canal. However, whether the preprocedural ultrasound examination improves the first-pass success (FPS) rate remains elusive. Major databases were systematically searched for all relevant studies published in English up to June 2019. Eighteen randomized controlled trials including 1844 patients were enrolled. The quality of eligible studies was assessed, and predefined outcomes were synthesized by meta-analysis. The primary results showed that preprocedural ultrasound increased the FPS rate in patients with predicted puncture difficulty but not in patients who were easily punctured. Preprocedural ultrasound reduced the number of redirections and punctures and decreased the incidence of vascular puncture and backache. There was no evidence of a reduction in failed punctures. We also noted that preprocedural ultrasound prolonged the identification time but not the procedure time. Thus, this systematic review provides evidence that preprocedural ultrasound does not improve the FPS rate of neuraxial anesthesia in patients who are easily palpated, although it increases the FPS rate in patients who are difficult to palpate.

ESG standards are increasingly becoming indispensable factors in corporate decision-making, with profound implications for the long-term sustainability of businesses. This study utilizes longitudinal data from 2010 to 2021 to investigate the influence of environmental, social, and governance (ESG) performance on excessive debt among publicly traded manufacturing companies in China. Employing panel regression alongside analysis of threshold, intermediary, and interaction effects, we meticulously dissect the mechanisms and influencing factors involved. Our findings reveal a significant adverse effect of ESG performance on excessive debt, characterized by heterogeneity across geographic locations, revenue growth rates, and ownership concentrations. Notably, company size and age exhibit a dual-threshold effect on excessive debt. Moreover, ESG performance demonstrates an intermediary effect, which is mitigated by proxy cost-to-asset turnover and debt financing cost COD2. Institutional attention and equity capital cost synergistically amplify the suppressive impact of ESG performance on excessive debt. Based on the research findings above, companies should carefully consider and adjust their ESG performance to reduce excessive debt risks, thereby enhancing their sustainable competitiveness. [ABSTRACT FROM AUTHOR]

Different turbulent entrainment‐mixing mechanisms between clouds and environment are essential to cloud‐related processes; however, accurate representation of entrainment‐mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment‐mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the "black box" scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment‐mixing. Specifically, number concentration after entrainment‐mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment‐mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment‐mixing is negatively correlated with liquid water content affected by entrainment‐mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment‐mixing in weather/climate models. Plain Language Summary: Entrainment‐mixing processes occurring between clouds and environmental air have significant effects on cloud‐climate feedback, precipitation, and radiative transfer. Accurately representing these processes has been challenging with previously proposed parameterizations. Machine learning (ML) excels at identifying complex nonlinear relationships and avoids the inherent limitations of conventional parameterizations. Thus, we explore the use of ML to acquire the number concentration and cloud droplet spectral dispersion affected by entrainment‐mixing processes. Simulation data from Explicit Mixing Parcel Model are employed to train, validate, and test the four MLs, including Light Gradient Boosting Machine (LGB), eXtreme Gradient Boosting, Random Forest and Support Vector Regression. After evaluation, the LGB is shown to obtain the most accurate microphysics among the four ML schemes and traditional parameterizations. Additionally, the interpreting method for peeking inside the "black box" reveals that a smaller number concentration and a larger spectral dispersion are indicative of the more inhomogeneous entrainment‐mixing. The relative correlations between predicted microphysics and various features align with our expectations and the underlying physical principles. Sensitivity tests further confirm that incorporating more features produces a more robust and efficient prediction. Overall, this study affirms the reliability and applicability of ML to develop/replace subgrid parameterizations in actual weather/climate models. Key Points: Machine learning schemes are trained to predict cloud microphysical properties affected by turbulent entrainment‐mixing processesThe proposed Light Gradient Boosting Machine provides more accurate microphysical properties compared with traditional parameterization schemesThe partial dependencies of microphysics on features prove a robust alignment with physical mechanisms through the interpreting method [ABSTRACT FROM AUTHOR]