X射线荧光光谱(XRF)法测定绿泥石中镁、铝、硅、磷、钾、钙、钛、铁元素含量

建立了X射线荧光光谱法测定绿泥石中镁、铝、硅、磷、钾、钙、钛、铁元素含量的快速分析方法。以标准物质及标准物质与基准试剂氧化镁、氧化钙人工混合配制标样的方法建立标准工作曲线,重点讨论了熔剂和脱模剂的选取。最佳熔样条件：采用8.0g四硼酸锂和偏硼酸锂混合熔剂(质量比67: 33)+0.8g样品并添加溴化锂作为脱模剂,熔样温度1100℃,熔样时间10min。该方法相对标准偏差(n=12)均小于5.57%,绿泥石样品测定结果与化学法一致,硅酸盐标准物质测定结果均满足不确定度要求。     

Speckle characteristics of simulated deep Fresnel region under shadowing effect

After the three-dimensional self-affine fractal random surface simulation, we use the optical scattering theory to calculate the deep Fresnel region speckle(DFRS) under consideration of the more strict shadowing effect. The evolution of DFRS with the scattering distance and the intensity probability distribution are studied. It is found that the morphology of the scatterer has an antisymmetric relationship with the intensity distribution of DFRS, and the effect of micro-lenses on the scattering surface causes the intensity probability distribution of DFRS to deviate from the Gaussian speckle in the high light intensity area.     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

An Image Compression Encryption Algorithm Based on Chaos and ZUC Stream Cipher

In order to improve the transmission efficiency and security of image encryption, we combined a ZUC stream cipher and chaotic compressed sensing to perform image encryption. The parallel compressed sensing method is adopted to ensure the encryption and decryption efficiency. The ZUC stream cipher is used to sample the one-dimensional chaotic map to reduce the correlation between elements and improve the randomness of the chaotic sequence. The compressed sensing measurement matrix is constructed by using the sampled chaotic sequence to improve the image restoration effect. In order to reduce the block effect after the parallel compressed sensing operation, we also propose a method of a random block of images. Simulation analysis shows that the algorithm demonstrated better encryption and compression performance.     

Simultaneous Determination of Six Immunosuppressants in Human Whole Blood by HPLC-MS/MS Using a Modified QuEChERS Method

A high-performance liquid chromatography-tandem mass spectrometry method was established for the simultaneous determination of mycophenolic acid, mycophenolate mofetil, tacrolimus, rapamycin, everolimus and pimecrolimus in human whole blood by optimizing the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) preparation method. Whole blood was extracted into ethyl acetate, salted out with anhydrous magnesium sulfate, and purified with ethylenediamine-N-propyl silane adsorbent. The supernatant was evaporated under nitrogen until dry and finally reconstituted in methanol. Chromatographic separation was performed on an Agilent Poroshell 120 EC-C18 column in methanol (mobile phase A)-water (optimized for 0.1% acetic acid and 10 mM ammonium acetate, mobile phase B) at a 0.3 mL·min⁻¹ flow rate. Electrospray ionization and positive ion multiple reaction monitoring were used for detection. The time for of analysis was 13 min. The calibration curves range of tacrolimus, rapamycin, everolimus and pimecrolimus were in the range of 1–100 ng·mL⁻¹, mycophenolate mofetil in the range of 0.1–10 ng·mL⁻¹ and mycophenolic acid at 10–1000 ng·mL⁻¹. All correlation coefficients were >0.993. The coefficients of variation (CV, %) for inter-day and intra-day precision were less than 10%, while the spiked recoveries were in the range of 92.1% to 116%. Our method was rapid, sensitive, specific, and reproducible for the simultaneous determination of six immunosuppressants in human whole blood. Importantly, our approach can be used to monitor drug concentrations in the blood to facilitate disease treatment.     

Steam-assisted crystallization of highly dispersed nanosized hierarchical zeolites from solid raw materials and their catalytic performance in lactide production

A solvent-free route based on solid raw materials affords higher product yield and lower waste production compared to the traditional hydrothermal synthesis. However, the as-made zeolites usually present blocky aggregation states, limiting their mass transfer and exposure of active sites in catalytic applications. Herein, highly dispersed nanosized hierarchical Beta zeolites with varied Si/Al ratios were prepared via steam-assisted crystallization from ball-milled solid raw materials. Thanks to the sufficient mixing of solid raw materials and favorable migration of solid mixture, nanosized Beta zeolites are obtained that are assembled from nanoparticles (∼15 nm) and possess abundant interconnected intraparticle mesopores. The strategy can also be extended to synthesize nanosized hierarchical ZSM-5 zeolites. The as-prepared Beta zeolite (Si/Al = 10) exhibits outstanding catalytic performance in conversion of lactic acid to lactide (as high as 77.5% in yield). This work provides avenues for simple and cost-efficient synthesis of highly dispersed nanosized hierarchical zeolites, promising their important catalytic applications.

A cost-effective synthesis strategy based on steam-assisted crystallization from ball-milled solid raw materials is developed to prepare a highly dispersed nanosized hierarchical Beta zeolite for conversion of lactic acid (LA) to lactide (LT).     

In Vitro and In Vivo Antibacterial Activity,Toxicity and Resistance Analysis of Pleuromutilin Derivative Z33 against Methicillin-Resistant Staphylococcus aureus

The novel pleuromutilin derivative, which showed excellent in vitro antibacterial activity against MRSA, 22-(2-(2-(4-((4-(4-nitrophenyl)piperazin-1-yl)methyl)-1H-1,2,3-triazol-1-yl)acetamido)phenyl)thioacety-l-yl-22-deoxypleuromutilin (Z33), was synthesized and characterized in our previous work. In this study, the preliminary pharmacodynamics and safety of Z33 were further evaluated. In in vitro antibacterial activity assays, Z33 was found to be a potent bactericidal antibiotic against MRSA that induced dose-dependent growth inhibition and long-term post-antibiotic effect (PAE). The drug-resistance test demonstrated that Z33 possessed a narrow mutant selection window and lower propensities to select resistance than that of tiamulin. Cytochrome P450 (CYP450) inhibition assay determined that the inhibitory effect of Z33 was similar to that of tiamulin against the activity of CYP3A4, and was lower than that of tiamulin on the activity of CYP2E1. Toxicity determination showed that both Z33 and tiamulin displayed low cytotoxicity of RAW264.7 cells. Furthermore, Z33 was found to be a high-security compound with a 50% lethal dose (LD₅₀) above 5000 mg/kg in the acute oral toxicity test in mice. In an in vivo antibacterial activity test, Z33 displayed better therapeutic effectiveness than tiamulin in the neutropenic mouse thigh infection model. In summary, Z33 was worthy of further development as a highly effective and safe antibiotic agent against MRSA infection.     

Cardiac-specific overexpression of Ndufs1 ameliorates cardiac dysfunction after myocardial infarction by alleviating mitochondrial dysfunction and apoptosis

Myocardial infarction (MI) is the leading cause of premature death among adults. Cardiomyocyte death and dysfunction of the remaining viable cardiomyocytes are the main pathological factors of heart failure after MI. Mitochondrial complexes are emerging as critical mediators for the regulation of cardiomyocyte function. However, the precise roles of mitochondrial complex subunits in heart failure after MI remain unclear. Here, we show that NADH:ubiquinone oxidoreductase core subunit S1 (Ndufs1) expression is decreased in the hearts of heart failure patients and mice with myocardial infarction. Furthermore, we found that cardiac-specific Ndufs1 overexpression alleviates cardiac dysfunction and myocardial fibrosis in the healing phase of MI. Our results demonstrated that Ndufs1 overexpression alleviates MI/hypoxia-induced ROS production and ROS-related apoptosis. Moreover, upregulation of Ndufs1 expression improved the reduced activity of complex I and impaired mitochondrial respiratory function caused by MI/hypoxia. Given that mitochondrial function and cardiomyocyte apoptosis are closely related to heart failure after MI, the results of this study suggest that targeting Ndufs1 may be a potential therapeutic strategy to improve cardiac function in patients with heart failure.Subject terms: Heart failure, Myocardial infarction, Myocardial infarction     

Green Tea (Camellia sinensis): A Review of Its Phytochemistry,Pharmacology, and Toxicology

Objectives Green tea (Camellia sinensis) is a kind of unfermented tea that retains the natural substance in fresh leaves to a great extent. It is regarded as the second most popular drink in the world besides water. In this paper, the phytochemistry, pharmacology, and toxicology of green tea are reviewed systematically and comprehensively. Key findings Green tea has been demonstrated to be good for human health. Nowadays, multiple pharmacologically active components have been isolated and identified from green tea, including tea polyphenols, alkaloids, amino acids, polysaccharides, and volatile components. Recent studies have demonstrated that green tea shows versatile pharmacological activities, such as antioxidant, anticancer, hypoglycemic, antibacterial, antiviral, and neuroprotective. Studies on the toxic effects of green tea extract and its main ingredients have also raised concerns including hepatotoxicity and DNA damage. Summary Green tea can be used to assist the treatment of diabetes, Alzheimer’s disease, oral cancer, and dermatitis. Consequently, green tea has shown promising practical prospects in health care and disease prevention.