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Cinnamomum camphora fruit extract was used to biosynthesise silver nanoparticles (AgNPs), and the optimised synthesis system was ascertained through solution colour change and ultraviolet–visible absorption spectra. It contained 20 ml of fruit extract, 4 mM Ag nitrate, and pH 7. AgNPs obtained based on such conditions were spherical and finely dispersed, with an average size of 20.3 nm. As‐synthesised AgNPs exhibited excellent antifungal effect against Fusarium oxysporum. At a dose of 400 μg/ml of AgNPs, the inhibition rate of colony growth reached 61.00% and an IC50 value of 154.39 μg/ml. In addition, the conidia germination was totally inhibited at 100 μg/ml of AgNPs. Results of this study provide a new approach for biological control of plant pathogenic fungi, and it makes that possible for developing a brand new fungistat. [ABSTRACT FROM AUTHOR]

Inferring porosity of subsurface from seismic data is of profound significance to many fields of Earth science and engineering applications, including but not limited to: hydrocarbon reservoir characterization, underground water flow modeling, geological CO2 storage, and geothermal energy exploitation. Traditional model‐driven approaches confront the problems of strong nonlinearity and geological heterogeneity, while machine learning is good at nonlinear mapping, providing higher efficiency and accuracy as well. We propose a Random Forest (RF) based method using multiple seismic attributes to predict the underground porosity distribution with uncertainty quantification. The standard deviation of base models' predictions is used to quantify the regression uncertainty of RF. The uncertainty can robustly indicate the prediction quality in numerous experiments, where low uncertainty corresponds to relatively precise prediction and high uncertainty gives a possibility of larger errors. Furthermore, we utilize the quantified uncertainty to improve the RF regression accuracy by correcting the originally predicted porosity according to the statistical relationship between the absolute error and the standard deviation. The application of the proposed method on seismic data shows its potential to characterize spatially varying reservoir parameters, and the quantified uncertainty profile offers insights into risk evaluation for hydrocarbon exploration and development. Plain Language Summary: Estimating underground porosity distributions from seismic data is of considerable interest in a broad range of Earth science, from hydrocarbon reservoir characterization to geological CO2 storage. The relationship between the related seismic attributes and porosity is highly nonlinear, especially in complex geological conditions, which impedes conventional approaches of porosity estimation. However, machine learning provides a more flexible and intelligent means to map directly from seismic data to porosity. In this study, we use a machine learning algorithm named Random Forest, which is an ensemble of many decision trees, to predict the underground porosity distribution from multiple seismic attributes. Meanwhile, the prediction uncertainty that measures the prediction reliability is quantified based on the diversity of predictions from different trees within a forest. We find that the uncertainty is approximately positively correlated with the prediction error. Thus, we further develop a strategy to correct the originally predicted porosity based on the quantified uncertainty for higher accuracy. Using the proposed method, a two‐dimensional profile of porosity distribution with the uncertainty profile is produced, which demonstrates its potential to delineate spatially varying reservoir properties from large‐scale seismic data. Key Points: A machine learning based method is developed to directly estimate porosity from multi‐seismic attributesThe standard deviation derived from the Random Forest regressor is capable of indicating the porosity prediction uncertaintyThe quantified uncertainty is further utilized to improve the performance of porosity prediction [ABSTRACT FROM AUTHOR]

Internal mixer is an important mixing equipment. After working for a long time, the end face of the internal mixer will be worn. The abrasion of the end face can increase the gap between the mixing chamber and the end face, and then material leakage occurs, which reduces the mixing effect and affects the performance of rubber compound. Therefore, it is very necessary to study the wear of the end face in the mixing process. As one‐dimensional nanomaterial, carbon nanotubes (CNTs) have a perfect hexagonal structure and many unusual mechanical, electrical and chemical properties. In recent years, with the deepening of the research of CNTs and nanomaterial's, their broad application prospects have also been continuously revealed. This paper analyzes the influence of rubber compounds with different CNTs content on the friction and wear of the end face from the perspective of formulation, and the correlation between corrosive wear and abrasive wear is studied. In the present study, it is found that as CNTs increase, the proportion of abrasive wear increases, the proportion of corrosive wear decreases, and the amount of the metal wear decreases. With the increase of CNTs, the hindering effect of CNTs on SiO2 molecules is strengthened, the degree of silanization reaction is reduced, the output of high‐temperature water vapor is reduced, and the corrosive wear of the metal is reduced. The spatial structure of CNTs is a cylinder with hexagonal plane, and it has extremely high toughness and softness, so it has slight wear on the metal. On the one hand, CNTs hinder the movement of SiO2 molecules. On the other hand, because CNTs have the wrapping effect on SiO2 molecules, some silica aggregates have no direct contact with the metal, CNTs become the medium for silica aggregates to contact with the metal. Therefore, with the increase of CNTs, the proportion of abrasive wear gradually increases, but the total amount of the metal wear gradually decreases. [ABSTRACT FROM AUTHOR]

The link between serum uric acid (SUA) and the risk of venous thromboembolism (VTE) is well established. Recent data suggested a causative role of UA in endothelial cells (ECs) dysfunction. However, the molecular mechanism of high UA on thrombogenesis is unknown. We investigate whether high UA induce phosphatidylserine (PS) externalization and microparticle (MP) shedding in cultured EC, and contribute to UA‐induced hypercoagulable state. In the present study, we demonstrate that UA induces PS exposure and EMP release of EC in a concentration‐ and time‐dependent manner, which enhances the procoagulant activity (PCA) of EC and inhibited over 90% by lactadherin in vitro. Furthermore, hyperuricemic rat model was used to evaluate the development of thrombi following by flow stasis in the inferior vena cava (IVC). Hyperuricemia group is more likely to form large and hard thrombi compared with control. Importantly, we found that TMEM16F expression is significantly upregulated in UA‐treated EC, which is crucial for UA‐induced PS exposure and MP formation. Additionally, UA increases the generation of reactive oxygen species (ROS), lipid peroxidation, and cytosolic Ca2+ concentration in EC, which might contribute to increased TMEM16F expression. Using confocal microscopy, we also observed disruption of the actin cytoskeleton, suggesting that depolymerization of actin filaments might be required for TMEM16F activation and followed by PS exposure and membrane blebbing in UA‐treated EC. Our results demonstrate a thrombotic role of EC in hyperuricemia through TMEM16F‐mediated PS exposure and MPs release. [ABSTRACT FROM AUTHOR]

Importance A high prevalence of retinal abnormalities have been reported in congenital heart disease (CHD), but quantitative analysis of retinal vasculature is scarce. Optical coherence tomography angiography (OCTA) is a noninvasive method to quantitatively assess the retinal microvasculature. Background: To investigate the retinal microvasculature changes in CHD patients by using OCTA. Design Cross‐sectional study. Participants: A total of 158 participants including 57 cyanotic CHD (CCHD) patients, 60 acyanotic CHD (ACHD) patients and 41 control subjects were included. Methods: All participants underwent a comprehensive ophthalmologic examination, including refraction measurement, intraocular pressure measurement and OCTA. Main Outcome Measures: Vessel density (VD) was measured within the radial peripapillary capillary (RPC), superficial capillary plexus (SCP) and deep capillary plexus (DCP) of the macula. Results: CCHD patients had significantly lower VD in the RPC, SCP and DCP (all P <.01) compared to control subjects, and significantly lower VD in the RPC and DCP (both P <.05) compared to ACHD patients. Besides, among the CHD group, VD in the RPC was positively correlated with oxygen saturation (whole image, ρ = 0.45; peripapillary, ρ = 0.48) and negatively correlated with haematocrit (whole image, ρ = 0.55; peripapillary, ρ = 0.55) (all P <.001). Conclusions and Relevance: Retinal VD might be a surrogate to reflect the effect of chronic systemic hypoxemia in CHD patients. OCTA could be a convenient and noninvasive tool to evaluate the retinal structure and function in CHD patients. [ABSTRACT FROM AUTHOR]

An azide terminated ethylene oxide‐tetrahydrofuran copolymer with urethane segments (ATUPET) as a novel binder pre‐polymer, has been prepared through ethylene oxide‐tetrahydrofuran random copolymer (PET) end‐capping modification via one‐pot method. The structure characterization of the modifier has been analyzed by FTIR, 1H NMR, 13C NMR and GPC. In comparison with PET, ATUPET has a slightly higher viscosity because it has additional hydrogen bonding interaction generated by the urethane in ATUPET. Triazole cross‐linked elastomers based on ATUPET with various functional molar ratios were prepared using tripropargylamine as a curing agent and cross‐linker. Mechanical properties indicate that the modulus E and tensile strength σb exhibit a parabolic dependence with the increase in R. At around the stoichiometric ratio, the modulus E and tensile strength σb reach a maximum and the elongation at break exhibit an acceptable value at the same time. Swelling tests demonstrate that the apparent cross‐linking densities (N0) have a maximum value at the stoichiometric ratio. Thermal analysis shows that the ATUPET prepolymer and its polytriazoles elastomers exhibit a satisfactory stability. The results demonstrated that ATUPET might be a promising polymeric binder for future propellant formulations especially in the field of isocyanate‐free curing technology. [ABSTRACT FROM AUTHOR]