聚苯乙烯非晶区结构的RDF研究

本文介绍了以X-射线衍射法测定非品区结构的径向分市函数RDF方法，并将其应用于聚苯乙烯（PS）非晶区的结构研究．     

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species

Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ineffective after the outburst of sequencing data through the advent of cost-effective and advanced sequencing techniques. To manage the pace of annotation with that of data generation, there is a shift to computational approaches which are based on homology, sequence and structure-based features, protein-protein interaction networks, phylogenetic profiles, and physicochemical properties, etc. A combination of these features has proven to be promising for protein function prediction in terms of improving prediction accuracy. In the present work, we have employed a combination of features based on sequence, physicochemical property, subsequence and annotation features with a total of 9890 features extracted and/or calculated for 171,212 reviewed prokaryotic proteins of 9 bacterial phyla from UniProtKB, to train a supervised deep learning ensemble model with the aim to categorize a bacterial hypothetical/unreviewed protein’s function into 1739 GO terms as functional classes. The proposed system being fully dedicated to bacterial organisms is a novel attempt amongst various existing machine learning based protein function prediction systems based on mixed organisms. Experimental results demonstrate the success of the proposed deep learning ensemble model based on deep neural network method with F1 measure of 0.7912 on the prepared Test dataset 1 of reviewed proteins.     

Bacterial protein structures reveal phylum dependent divergence

Protein sequence space is vast compared to protein fold space. This raises important questions about how structures adapt to evolutionary changes in protein sequences. A growing trend is to regard protein fold space as a continuum rather than a series of discrete structures. From this perspective, homologous protein structures within the same functional classification should reveal a constant rate of structural drift relative to sequence changes. The clusters of orthologous groups (COG) classification system was used to annotate homologous bacterial protein structures in the Protein Data Bank (PDB). The structures and sequences of proteins within each COG were compared against each other to establish their relatedness. As expected, the analysis demonstrates a sharp structural divergence between the bacterial phyla Firmicutes and Proteobacteria. Additionally, each COG had a distinct sequence/structure relationship, indicating that different evolutionary pressures affect the degree of structural divergence. However, our analysis also shows the relative drift rate between sequence identity and structure divergence remains constant.     

Electron kinetics of collision dominated r.f. bulk plasma in CO: The role of second-kind collisions

Electron energy distribution functions (EEDF) and related properties in the bulk region of the rf CO plasma at the reduced rf field frequency /p₀=×10⁷ sec^–1 torr^–1 have been calculated by solving the time-dependent spatially homogeneous Boltzmann equation in the presence of second-kind collisions and have been interpreted on a microphysical basis. The results show that second-kind collisions (vibrational and electronic) strongly affect the temporal evolution of EEDF, of the mean energy, and of the mean collision frequencies for vibrational and electronic excitation processes, as well as for ionization. In particular, second-kind collisions in the CO rf bulk plasma strongly decrease the modulation of the mean ionization frequency during its periodical alteration in the rf field. Furthermore, the effect of second-kind collisions on an approximate determination of the time-averaged EEDF in the rf bulk plasma using the so-called effective-field appriximation has been estimated.     

Artificial Neural Networks in water analysis: Theory and applications

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.     

The simultaneous elimination of arsenic and mercury ions via hollow fiber supported liquid membrane and their reaction mechanisms: Experimental and modeling based on DFT and generating function

This study investigates the simultaneous removal of low concentrations of arsenic and mercury ions from synthetic produced water via hollow fiber supported liquid membrane (HFSLM). Results show that HFSLM can remove both arsenic and mercury ions from synthetic produced water to less than 0.02 and 0.001 mg·L^-1, respectively. These final concentrations comply with the wastewater standard of Thailand. Percentages of extraction for arsenic and mercury ions proved to be about 100 %. Those of recovery for arsenic and mercury ions reached 70 % and 75 %, respectively. A quantum model based on density functional theory (DFT) is introduced to analyze the forming and breaking of supramolecular complex species in the processes of extraction and recovery, respectively. Furthermore, the concept of Generating Function is applied to construct a mathematical model for forecasting the potential of removing metal ions via HFSLM. The mathematical model conforms to the experimental data, having an average relative deviation of 5 %. The results of this study provide a better understanding of the transport mechanisms of arsenic and mercury ions.     

Limitations of the Clausius‐Mossotti function used in dielectrophoresis and electrical impedance studies of biomacromolecules

Dielectrophoresis (DEP) studies have progressed from the microscopic scale of cells and bacteria, through the mesoscale of virions to the molecular scale of DNA and proteins. The Clausius‐Mossotti function, based on macroscopic electrostatics, is invariably employed in the analyses of all these studies. The limitations of this practice are explored, with the conclusion that it should be abandoned for the DEP study of proteins and modified for native DNA. For macromolecular samples in general, a DEP theory that incorporates molecular‐scale interactions and the influence of permanent dipoles is more appropriate. Experimental ways to test these conclusions are proposed.     

嵌入Y型分子筛中钯簇合成与结构的研究

Pd clusters encaged in Y-zeolite (Pd0Y) have been prepared by means of exchanging zeolite HY with [Pd(NH3)4]2+ under microwave radiation. The product formed was deaminized by heating, washed sufficiently with de-ionized water and reduced with hydrogen. The crystal phase diffraction of Pd was not found in the XRD spectrogram of Pd0y. According to polycrystal X-ray diffraction data. Radial Electron Distribution Function (REDF) of Pd0Y was calculated to elucidate the structure of Pd cluster. The results show that the Pd clusters are of the Al type closely packed arrangement. The dimension of them is about 12 Å. They are encaged in the supercage of zeolite Y. Their occupancy on the supercage is as small as 0.06 so that the framework structure of zeolite Y is unchanged. Therefore, the high dispersing Pd cluster aggregating in supercage exhibit strong catalytic effect. The CO-conversion of Pd0Y with Pd 0.72% and 6.13% (in mass fraction) is 67 % and 100 % (in volume fraction) respectively. Evaluation conditions:
mixed gas containing 0.02% CO and air,
space velocity 3000 h-1,
reaction temperature 0 ℃.     

JEE—4x型真空镀膜仪功能扩展

报道了对ＪＥＥ－４ｘ型真空镀膜仪蒸发装置的改进以及建立蒸发材料回收装置的具体方法，并介绍了改进后该仪器在高分辨扫描电镜样品制备上的应用实例．实验证明，上述改进不但扩展了ＪＥＥ—４ｘ型真空镀膜仪的制样功能又节省了蒸发材料，集中了离子溅射法及常规高真空镀膜法的优点，完全满足ＳＥＭ样品的分析要求，该方法简单易操作．     

氯化1-乙基-3-甲基烷基咪唑分子结构及其氢键作用能的密度泛函研究

采用量子化学密度范函方法计算研究了氯化1-乙基-3-甲基咪唑盐（[EMIM]C1）正负离子间相互作用的平衡构型和Cl^-离子从不同方位与咪唑阳离子的结合能．计算发现Cl^-可在咪唑环平面上结合形成离子键，其结合能为．368．97kJ／mol，还可与环上的三个H原子形成氢键，其氢键键长和结合能分别为0．2000nm／-378．03kJ／mol、0．2339nm／-344，69kJ／mol和0．2301nm／-346，01kJ／mol．结合能包括氢键作用能和正负离子的静电作用能，后者为主要贡献．从理论上展示了该离子液体的氢键结构．