Cortex fraxini is a widely used traditional Chinese medicine. Esculin is one of the main active ingredients of Cortex fraxini and has attracted more and more attention from scholars. The purpose of ...the review is to systematically review relevant studies on the pharmacological effects and pharmacokinetic characteristics of esculin to support its further application as therapeutic agents. Pharmacological studies have shown that the anti‐inflammatory and anti‐oxidative stress effects of esculin are outstanding. This indicates that esculin is promising to be used to treat a variety of diseases closely related to inflammation and oxidative damage. Esculin has anti‐diabetic effect, which is closely related to improving pancreas damage, promoting insulin release, and enhancing glucose homeostasis. In addition, esculin has anti‐cancer, antibiosis, anti‐virus, neuroprotection, anti‐thrombosis and treating eye diseases properties. Pharmacokinetic studies show that esculin can be quickly and evenly distributed in the body. However, the first pass effect of esculin is serious. In short, esculin is promising to treat many diseases, but further high quality studies are needed to firmly establish the clinical efficacy of esculin.
The
Astragalus
polysaccharide is an important bioactive component derived from the dry root of
Astragalus membranaceus
. This review aims to provide a comprehensive overview of the research progress ...on the immunomodulatory effect of
Astragalus
polysaccharide and provide valuable reference information. We review the immunomodulatory effect of
Astragalus
polysaccharide on central and peripheral immune organs, including bone marrow, thymus, lymph nodes, spleen, and mucosal tissues. Furthermore, the immunomodulatory effect of
Astragalus
polysaccharide on a variety of immune cells is summarized. Studies have shown that
Astragalus
polysaccharide can promote the activities of macrophages, natural killer cells, dendritic cells, T lymphocytes, B lymphocytes and microglia and induce the expression of a variety of cytokines and chemokines. The immunomodulatory effect of
Astragalus
polysaccharide makes it promising for the treatment of many diseases, including cancer, infection, type 1 diabetes, asthma, and autoimmune disease. Among them, the anticancer effect is the most prominent. In short,
Astragalus
polysaccharide is a valuable immunomodulatory medicine, but further high-quality studies are warranted to corroborate its clinical efficacy.
► Volatile–char interactions are a common phenomenon in gasification. ► Volatile–char interactions affect almost every aspect of low-rank-fuel gasification. ► This paper provides an overview of the ...progress in this area.
Volatile–char interactions are an important phenomenon in almost all existing gasification processes. The volatile–char interactions can very significantly affect almost every aspect of low-rank fuel gasification, including the volatilisation of alkali and alkaline earth metallic species that are inherent catalysts for gasification, the evolution of char structure, the dispersion of inherent catalysts and thus the reactivity of char. The volatile–char interactions can also influence the formation of pollutant-forming species such as NH3. This paper provides an overview of our recent work in this area. The essence of volatile–char interactions appears to be the interactions between radicals, especially H radicals, and the char during pyrolysis and gasification. The volatile–char interactions must be an important consideration in the development of new gasification technologies for low-rank fuels such as brown coal and biomass to minimise the adverse effects and maximise the positive effects of volatile–char interactions during the gasification of low-rank fuels.
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•Excitation-emission matrix fluorescence was proposed to trace sources of oilfield wastewater.•ATLD-PLS-DA and N-PLS-DA were used to predict the sources of oilfield wastewater.•Both ...methods showed good discrimination abilities for oilfield wastewater with 100% accuracy.•The proposed strategy can reduce analysis time and consumption of toxic organic solvents.•The proposed strategy could be used as a potential tool for tracing sources of oilfield wastewater.
In order to prevent the illegal discharge of oilfield wastewater, this work proposed excitation-emission matrix fluorescence (EEMF) spectroscopy coupled with two kinds of chemical pattern recognition methods for tracing the sources of oilfield wastewater. The first pattern recognition method was built from the relative concentrations extracted by alternating trilinear decomposition (ATLD) based on partial least squares-discriminant analysis (PLS-DA) algorithm, and the other one was modeled based on strictly multi-way partial least squares-discriminant analysis (N-PLS-DA). Both methods showed good discrimination abilities for oilfield wastewater samples from three different sources. The total recognition rates of the training and prediction sets are 100%, the values of sensitivity and selectivity are 1. This study showed that EEMF spectroscopy combined with chemical pattern recognition techniques could be used as a potential tool for tracing the sources of oilfield wastewater.
Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators ...removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients.
Direct ink writing technology is capable of using 2D MXene to construct 3D architectures for electrochemical energy storage (EES) devices that are normally difficult to achieve using conventional ...techniques. However, to meet specific rheological requirements for 3D printing, a large amount of MXene is needed in the ink, resulting in a severe self‐restacking structure after drying. Herein, a series of cellulose nanofibers (CNFs) with different morphologies and surface chemistries are applied to enhance the rheology of the MXene‐based inks with exceptional 3D printability. Various 3D architectures with superior shape fidelity and geometric accuracy are successfully printed using the optimized hybrid ink at a low solid content, generating self‐standing, hierarchically porous structures after being freeze‐dried, which improves surface area accessibility, ion transport efficiency, and ultimately, capacitive performance. A solid‐state interdigitated symmetrical supercapacitor is further 3D printed, which delivers an areal capacitance of 2.02 F cm−2 and an energy density of 101 μWh cm−2 at a power density of 0.299 mW cm−2, and maintains a capacitance retention rate of 85% after 5000 cycles. This work demonstrates the integration of 1D CNFs and 2D MXene in 3D printing technology to prepare customized, multiscale, and multidimensional architectures for the next generation of EES devices.
By rationally controlling the dimension and surface chemistry of cellulose nanofibers (CNFs), CNFs are successfully applied as rheology modifiers to formulate viscoelastic, 3D printable MXene‐based ink at a low solid concentration of 8 wt%. The freestanding, hierarchically porous MXene‐based electrode architectures can be achieved by 3D printing and freeze‐drying, which holds great potential in electrochemical energy storage devices.
It is a great challenge to develop UV nonlinear optical (NLO) material due to the demanding conditions of strong second harmonic generation (SHG) intensity and wide band gap. The first ultraviolet ...NLO selenite material, Y3F(SeO3)4, has been obtained by control of the fluorine content in a centrosymmetric CaYF(SeO3)2. The two new compounds represent similar 3D structures composed of 3D yttrium open frameworks strengthened by selenite groups. CaYF(SeO3)2 has a large birefringence (0.138@532 nm and 0.127@1064 nm) and a wide optical band gap (5.06 eV). The non‐centrosymmetric Y3F(SeO3)4 can exhibit strong SHG intensity (5.5×KDP@1064 nm), wide band gap (5.03 eV), short UV cut‐off edge (204 nm) and high thermal stability (690 °C). So, Y3F(SeO3)4 is a new UV NLO material with excellent comprehensive properties. Our work shows that it is an effective method to develop new UV NLO selenite material by fluorination control of the centrosymmetric compounds.
The non‐centrosymmetric hydrogen‐free selenite Y3F(SeO3)4 was synthesized from the centrosymmetric CaYF(SeO3)2 by controlling the fluorine content. Y3F(SeO3)4 features large SHG intensity, wide band gap, short UV cut‐off edge and high thermal stability.
We use a long time series of daily data for 682 firms over a period from January, 1990 to December, 2012. Each firm includes 5,772 daily observations. Our sample involves a total of 3,936,504 ...observations to investigate how U.S. stock returns respond differently to oil price shocks prior to, during, and after a financial crisis. We provide evidence that U.S. stock returns in turn respond positively to the changes in oil prices during and after such a crisis. We use firm-level data to find that positive and negative oil price shocks have asymmetric effects on stock returns during the crisis and after the crisis. Then, we examine whether the effect of an oil price shock on stock returns varies across oil-intensive industries. Within the crisis and post-crisis, our results indicate that stock returns in response to oil price shocks across industries are heterogeneous, and the stock returns of some energy-intensive manufacturing industries respond more positively to oil price shocks compared with less energy-intensive manufacturing industries. We use total assets, total revenue, and the number of employees as proxy variables to measure each firm’s size and then examine whether oil price shocks affect stock returns differently across firm sizes. We find that big firms are the most strongly and negatively influenced by an oil price shock prior to the crisis. On the other hand, our results indicate that an oil price shock in the post-financial crisis period is positively amplified in the case of medium-sized firms.
•We investigate how stock returns respond differently to oil price shocks prior to, during, and after a financial crisis.•We add the firm size to investigate the effect of oil price shocks on stock returns.•Stock returns respond positively to the changes in oil prices during and after such a crisis.•Stock returns of medium-sized firms are positively affected more by an oil price increase in the post-crisis.
Recent advances in a low-rank matrix completion have enabled the exact recovery of incomplete data drawn from a low-dimensional subspace of a high-dimensional observation space. However, in many ...applications, the data are drawn from multiple low-dimensional subspaces without knowing which point belongs to which subspace. In such cases, using a single low-dimensional subspace to complete the data may lead to erroneous results, because the complete data matrix need not be low rank. In this paper, we propose a structured sparse plus structured low-rank (S 3 LR) optimization framework for clustering and completing data drawn from a union of low-dimensional subspaces. The proposed S 3 LR framework exploits the fact that each point in a union of subspaces can be expressed as a sparse linear combination of all other points and that the matrix of the points within each subspace is low rank. This framework leads to a nonconvex optimization problem, which we solve efficiently by using a combination of a linearized alternating direction method of multipliers and spectral clustering. In addition, we discuss the conditions that guarantee the exact matrix completion in a union of subspaces. Experiments on synthetic data, motion segmentation data, and cancer gene data validate the effectiveness of the proposed approach.
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