The Antioxidant and Hypolipidemic Effects of Mesona Chinensis Benth Extracts

In this study, the antioxidant and hypolipidemic effects of Mesona Chinensis Benth (MCB) extracts were evaluated. Seven fractions (F0, F10, F20, F30, F40, F50 and MTF) were obtained from the MCB ethanol extracts. Compared to the commercial antioxidants (vitamin C), MTF and F30 exhibited higher antioxidant activities in the antiradical activity test and the FRAP assay. The half-inhibition concentration (IC50) for MTF and F30 were 5.323 µg/mL and 5.278 µg/mL, respectively. MTF at 200 µg/mL significantly decreased the accumulation of TG in oleic acid (OA)-induced HepG2 cells and reversed the inhibitory effect of Compound C on AMPK (MTF and F30 significantly increased the glucose utilization of insulin-induced HepG2 cells). In addition, the components of MTF were identified by HPLC-MS, which were caffeic acid, quercetin 3-O-galactoside, isoquercetin, astragalin, rosmarinic acid, aromadendrin-3-O-rutinoside, rosmarinic acid-3-O-glucoside and kaempferol-7-O-glucoside. Through statistical correlations by Simca P software, it was found that the main antioxidant and hypolipidemic components of MCB might be caffeic acid, kaempferol-7-O-glucoside, rosmarinic acid-3-O-glucoside and aromadendrin-3-O-rutinoside, which may play important roles in the AMPK pathway. MTF and F30 in MCB could be potential health products for the treatment of hyperlipidemia.     

Chemical Constituents from the Flowers of Carthamus tinctorius L. and Their Lung Protective Activity

A new flavonoid, saffloflavanside (1), a new sesquiterpene, safflomegastigside (2), and a new amide, saffloamide (3), together with twenty-two known compounds (4–25), were isolated from the flowers of Carthamus tinctorius L. Their structures were determined based on interpretation of their spectroscopic data and comparison with those reported in the literature. The protective effects against lipopolysaccharide (LPS)-stimulated damage on human normal lung epithelial (BEAS-2B) cells of the compounds were evaluated using MTT assay and cellular immunofluorescence assay. The results showed that compounds 2–3, 8–11, and 15–19 exhibited protective effects against LPS-induced damage to BEAS-2B cells. Moreover, compounds 2–3, 8–11, and 15–19 can significantly downregulate the level of nuclear translocation of NF-κB p-p65. In summary, this study revealed chemical constituents with lung protective activity from C. tinctorius, which may be developed as a drug for the treatment of lung injury.     

Molecular Weight Distribution Control for Polymerization Processes Based on the Moment-Generating Function

The molecular weight distribution is an important factor that affects the properties of polymers. A control algorithm based on the moment-generating function was proposed to regulate the molecular weight distribution for polymerization processes in this work. The B-spline model was used to approximate the molecular weight distribution, and the weight state space equation of the system was identified by the subspace state space system identification method based on the paired date of B-spline weights and control inputs. Then, a new performance criterion mainly consisting of the moment-generating function was constructed to obtain the optimal control input. The effectiveness of the proposed control method was tested in a styrene polymerization process. The molecular weight distribution of the styrene polymers can be approximated by the B-spline model effectively, and it can also be regulated towards the desired one under the proposed control method.     

PCBs的超(亚)临界水催化氧化及还原裂解

简要分析了多氯联苯(PCBs)的来源及其对环境构成的危害,介绍了PCBs在超（亚）临界水中的反应及其处理效果。分别从超临界水氧化、超临界水裂解及亚临界水还原三个方面阐明了超临界反应过程中PCBs降解的反应路径和降解效率,解释了共溶剂（甲醇、苯）、碱催化剂（Na₂CO₃、NaOH）、氧化剂（NaNO₃、NaNO₂）等对PCBs脱氯和分解的增效作用机理。发现在超临界水氧化与超临界水裂解条件下CH3OH对PCBs降解反应的促进机制有所不同,碱催化剂通过中和反应过程中产生的HCl生成NaCl沉淀导致体系中Cl的含量降低,从而促进脱氯反应的进行。对反应器防腐、处理的经济性方面略作讨论,在总结上述研究工作的基础上提出了PCBs的超临界反应处理技术未来发展的若干研究方向。     

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.     

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.     

A MXene-derived redox homeostasis regulator perturbs the Nrf2 antioxidant program for reinforced sonodynamic therapy

Ultrasound (US)-mediated sonodynamic therapy (SDT) has emerged as a spatiotemporally controllable therapeutic modality in combating cancer because of its high tissue-penetration depth and minimal invasiveness. However, the elevated nuclear factor erythroid 2-related factor 2 (Nrf2) antioxidant program in cancer cells can serve as a chief reactive oxygen species (ROS) detoxification system to alleviate oxidative injury and promote tumorigenesis, and thus greatly antagonize the therapeutic efficacy of ROS-mediated anticancer therapies. Herein, we report that vanadium carbide MXene-derived carbon dots (PMQDs) can act as high-efficacy sonosensitizers to efficiently generate ROS upon US irradiation and simultaneously hinder the Nrf2 antioxidant program for enhanced sonodynamic therapy of cancer. These PMQDs show superior US-triggered ROS generating ability because of their efficient migration/separation of electron–hole pairs and narrow bandgap. Importantly, these PMQDs can serve as efficient redox homeostasis regulators to perturb the Nrf2 antioxidant mechanism and thus reduce its effects on ROS neutralization for enhanced SDT efficacy. Overall, the present study will not only provide a new paradigm to augment SDT by perturbing the Nrf2 antioxidant program, but also give valuable insights into developing high-efficacy MXene-derived nanoagents for cancer therapy.

A redox homeostasis regulator has been developed as a high-efficacy sonosensitizer to efficiently generate reactive oxygen species upon ultrasound irradiation and simultaneously hinder the Nrf2 antioxidant program for enhanced sonodynamic therapy.     

晶格畸变检测仪研究碳化硅晶片中位错缺陷分布

利用晶格畸变检测仪研究了SiC晶片位错分布情况,通过对熔融KOH腐蚀后的SiC晶片进行全片或局部扫描,从而得到完整SiC晶片或局部区域的位错分布.与LEXT OLS40003D激光共聚焦显微镜扫描腐蚀图进行比较,晶格畸变检测仪扫描腐蚀图可以将晶片上位错腐蚀坑信息完全呈现出来,且根据腐蚀坑呈现的颜色及尺寸大小,可以分辨出...     

3英寸6H-SiC单晶的生长

SiC是宽带隙半导体材料的典型代表,具有优良的热学、力学、化学和电学性质,不但可以用作基于GaN的蓝色发光二极管的衬底材料,同时又是制作高温、高频、大功率电子器件的最佳材料之一,因此高质量、大直径SiC单晶的生长一直是材料研究领域的热点课题。目前美国的Cree公司在SiC单晶生长领域研发方面起步早、投入大,SiC单晶的直径达到4英寸,处于领先地位。我国在“十五”期间投入了一定的人力、物力进行了SiC单晶生长的研究,在生长2英寸SiC单晶的工作中取得了一定的成绩[1],但更大直径的SiC单晶生长技术进展缓慢,至今未见国内报道。而对…     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.