光催化研究与发展的文献计量分析

对ＣＡ１９６１－１９９６年期间卷双期号期刊中收录的与光催化有关的各种文献进行了详细的统计,结果表明,光催化研究正处于快速的发展期,以消除环境污染为目的应用研究是该快速发展期中的主题,日本,美国,俄罗斯和中国发表文献量居世界前４位。     

Fluorescence observations on complex formation between linear and hyperbranched polyelectrolytes in dilute aqueous solutions

The complexation between poly(ethylene imine) (PEI) and poly(2-acrylamido-2-methylpropanesulfonic acid) (PAMPS) or AMPS copolymers was investigated with the relative excimer emission intensity I_E/I_M of a cationic probe 1-pyrenemethylamine hydrochloride (PyMeA · HCl), fluorescence nonradiative energy transfer (NRET) I_Py/I_Np of naphthalene to pyrene labels, the fluorescence anisotropy r and I_E/I_M of pyrene labels. PEI was a hyperbranched weak polycation in acid solution, which formed complex with anionic polyelectrolytes due to the electrostatic attraction. The I_E/I_M of PyMeA · HCl probe decreased to zero, the intra-, intermolecular NRET I_Py/I_Np and I_E/I_M of pyrene labels on the AMPS copolymers reached their maxima when χ was increased from 0 to 2.4, which was defined as the mole ratio of the amino group in PEI to the AMPS group in the polyanion. These facts indicated the formation of nonstoichiometric complex of the oppositely charged polyelectrolytes when χ = 2.4 at the concentration much lower than their overlap concentrations. The intermolecular aggregate appeared as indicated by an increase in the intermolecular I_Py/I_Np and r with χ up to 2.4 due to neutralizing and hydrophobizing the polyelectrolytes and the bridging effect of the PEI chain bound on different polyanion chains. At high pH, PEI became a neutral polymer and did not bind with the AMPS anion to form the complex as illustrated by the constant value of r for the pyrene labels attached to the AMPS polyanion as that without addition of PEI. The amino group in PEI quenched pyrene and naphthalene emission, resulting a decrease in both I_Np and I_Py.     

高频介质系统介电性能与相组成的定量关系分析

采用普通电子陶瓷工艺制备两种高频介质系统ZnO-B2O3-SiO2三元系和BaO-PbO-Nd2O3-Bi2O3-TiO2五元系,通过XRD分析确定各系统的主晶相, 运用衍射峰强度计算法准确测定出各组分的体积百分含量,并代入Lichnetecker对数混合定则,计算出系统的介电性能,获得了分析介质系统介电性能与物相含量之间定量关系的新方法.     

HACCP体系的原理

论述了美国从１９９７年１２月１８日零时起执行的并被国际认同的ＨＡＣＣＰ体系的原理，对我国水产品加工企业出口产品与编制产品的ＨＡＣＣＰ计划有重要指导作用。     

多信道接入微谐振环波长选择开关特性

对一种新型结构微谐振环波长选择开关的特性进行了研究.分析了器件的工作状态和相应的开关操作,利用新型光强传递函数公式,对器件不同工作状态的光谱响应和不同开关操作的开关响应进行了数值模拟,讨论了耦合系数和损耗对光谱响应和开关响应的影响.结果表明,该器件可以实现三个信道同时接入、两个信道同时接入、单信道接入以及无信道接入等四种方式的信号波长选择性接入.多信道接入方式的开态串扰性能变差,关态的串扰性能不受影响.实现器件上述接入方式之间转换的开关操作可以分成三类,开关操作达到最佳关态串扰时的小微环折射率变化值都在6.0×10－3左右,而实现完全开关时的小微环折射率变化值小于8.0×10－4,表明易于通过热-光效应实现开关操作,并且温度调制的控制容差较大.损耗对器件开关特性的影响结果表明可根据损耗的实际数值,优化信道关闭时的微环折射率取值.     

Semi-Supervised Cross-Subject Emotion Recognition Based on Stacked Denoising Autoencoder Architecture Using a Fusion of Multi-Modal Physiological Signals

In recent decades, emotion recognition has received considerable attention. As more enthusiasm has shifted to the physiological pattern, a wide range of elaborate physiological emotion data features come up and are combined with various classifying models to detect one’s emotional states. To circumvent the labor of artificially designing features, we propose to acquire affective and robust representations automatically through the Stacked Denoising Autoencoder (SDA) architecture with unsupervised pre-training, followed by supervised fine-tuning. In this paper, we compare the performances of different features and models through three binary classification tasks based on the Valence-Arousal-Dominance (VAD) affection model. Decision fusion and feature fusion of electroencephalogram (EEG) and peripheral signals are performed on hand-engineered features; data-level fusion is performed on deep-learning methods. It turns out that the fusion data perform better than the two modalities. To take advantage of deep-learning algorithms, we augment the original data and feed it directly into our training model. We use two deep architectures and another generative stacked semi-supervised architecture as references for comparison to test the method’s practical effects. The results reveal that our scheme slightly outperforms the other three deep feature extractors and surpasses the state-of-the-art of hand-engineered features.     

Ceria supported Ru~0-Ru~(δ+) clusters as efficient catalyst for arenes hydrogenation

Selective hydrogenation of aromatic amines,especially chemicals such as aniline and bis(4-aminocyclohexyl)methane for non-yellowing polyurethane,is of particular interests due to the extensive applications.To conquer the existing difficulties,in selective hydrogenation,,the Ru~0-Ru~(δ+)/CeO_2 catalyst with solid frustrated Lewis pairs was developed for aromatic amines hydrogenation with excellent activity and selectivity under relative milder conditions.The morphology,electronic and chemical properties,especially the Ru~O-Ru~(δ+) clusters and reducible ceria were characterized by X-ray diffraction(XRD),transmission electron microscopy(TEM),sca nning electronic microscopy(SEM),X-ray photoelectron sp ectroscopy(XPS),CO_2 tempe rature programmed deso rption(CO_2-TPD),H_2 tempe rature programmed reduction(H_2-TPR),H_2 diffuse reflectance Fourier transform infrared spectroscopy(H_2-DRIFT),Raman,etc.The 2% Ru/CeO_2 catalyst exhibited good conversion of 95% and selectivity greater than 99% toward cyclohexylamine.The volcano curve describing the activity and Ru state was found.Owning to the "acidic site isolation" by surrounding alkaline sites,condensation between the neighboring amine molecules could be effectively suppressed.The catalyst also showed good stability and applicability for other aromatic amines and heteroarenes containing different functional groups.     

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.     

A Novel Strategy for Regulating mRNA’s Degradation via Interfering the AUF1’s Binding to mRNA

The study on the mechanism and kinetics of mRNA degradation provides a new vision for chemical intervention on protein expression. The AU enrichment element (ARE) in mRNA 3′-UTR can be recognized and bound by the ARE binding protein (AU-rich Element factor (AUF1) to recruit RNase for degradation. In the present study, we proposed a novel strategy for expression regulation that interferes with the AUF1-RNA binding. A small-molecule compound, JNJ-7706621, was found to bind AUF1 protein and inhibit mRNA degradation by screening the commercial compound library. We discovered that JNJ-7706621 could inhibit the expression of AUF1 targeted gene IL8, an essential pro-inflammatory factor, by interfering with the mRNA homeostatic state. These studies provide innovative drug design strategies to regulate mRNA homeostasis.     

A Comprehensive Review of Rosmarinic Acid: From Phytochemistry to Pharmacology and Its New Insight

Polyphenolic acids are the widely occurring natural products in almost each herbal plant, among which rosmarinic acid (RA, C₁₈H₁₆O₈) is well-known, and is present in over 160 species belonging to many families, especially the Lamiaceae. Aside from this herbal ingredient, dozens of its natural derivatives have also been isolated and characterized from many natural plants. In recent years, with the increasing focus on the natural products as alternative treatments, a large number of pharmacological studies have been carried out to demonstrate the various biological activities of RA such as anti-inflammation, anti-oxidation, anti-diabetes, anti-virus, anti-tumor, neuroprotection, hepatoprotection, etc. In addition, investigations concerning its biosynthesis, extraction, analysis, clinical applications, and pharmacokinetics have also been performed. Although many achievements have been made in various research aspects, there still exist some problems or issues to be answered, especially its toxicity and bioavailability. Thus, we hope that in the case of natural products, the present review can not only provide a comprehensive understanding on RA covering its miscellaneous research fields, but also highlight some of the present issues and future perspectives worth investigating later, in order to help us utilize this polyphenolic acid more efficiently, widely, and safely.