基于显微共聚焦拉曼技术对三种食源性致病菌的快速鉴别研究

采用显微共聚焦拉曼技术,建立了对三种常见食源性致病菌快速鉴别的检测方法。使用XploRA PLUS共聚焦拉曼光谱仪,在激光功率为5 mW、积分时间为30s、积分次数为1次的条件下,对德尔卑沙门氏菌、副溶血性弧菌和金黄色葡萄球菌进行了拉曼光谱数据的采集。对拉曼光谱采用多项式平滑算法和荧光背底扣除后,采用主成分分析法(PCA)对预处理后的数据进行降维,提取出前三个主成分的累计方差贡献率达到了95.4％,样本明显的聚为了3类。同时结合Fisher判别分析法(FLD)构建分类模型,对三种样本进行交叉验证,分类准确率达到了100％。结果表明,采用显微共聚焦拉曼技术与PCA-FLD方法结合可实现对三种食源性致病菌的快速准确鉴别且模型检测精度高,方法具有一定的实用性及参考价值。     

Software tools for green and sustainable chemistry

In this review, we consider green chemistry metrics, related software tools, and the opportunities and challenges for their use in research laboratories. We provide an overview of state-of-the-art software designed both to aid researchers in planning and conducting chemical experiments and to assess sustainability of individual reactions and synthetic routes. The increasing digitalisation of research means that there is great opportunity for more extensive use of computational tools by synthetic chemists and for closer integration of green chemistry principles into the routine work of chemical laboratories. We discuss the scope for using software tools in the laboratory and assisting synthetic chemists in the adoption of green and sustainable chemistry approaches that are suitable for their specific purposes.     

互联网B/S架构交互式软件在核心概念教学中的应用*——以国际IB课程中“电解质”内容为例

基于核心概念教学的思路,国际IB课程互联网B/S架构交互式软件能够使学生围绕“电解质”核心概念,通过自行操作软件获得多个从微观到宏观、定性与定量相结合、可视化的实验证据,有利于学生深度理解概念与概念之间的逻辑和发展关系,促进学科知识系统框架的形成。     

CORSICA: correction of structured noise in fMRI by automatic identification of ICA components

When applied to functional magnetic resonance imaging (fMRI) data, spatial independent component analysis (sICA), a data-driven technique that addresses the blind source separation problem, seems able to extract components specifically related to physiological noise and brain movements. These components should be removed from the data to achieve structured noise reduction and improve any subsequent detection and analysis of signal fluctuations related to neural activity. We propose a new automatic method called CORSICA (CORrection of Structured noise using spatial Independent Component Analysis) to identify the components related to physiological noise, using prior information on the spatial localization of the main physiological fluctuations in fMRI data. As opposed to existing spectral priors, which may be subject to aliasing effects for long-TR data sets (typically acquired with TR >1 s), such spatial priors can be applied to fMRI data, regardless of the TR of the acquisitions. By comparing the proposed automatic selection to a manual selection performed visually by a human operator, we first show that CORSICA is able to identify the noise-related components for long-TR data with a high sensitivity and a specificity of 1. On short-TR data sets, we validate that the proposed method of noise reduction allows a substantial improvement of the signal-to-noise ratio evaluated at the cardiac and respiratory frequencies, even in the gray matter, while preserving the main fluctuations related to neural activity.     

半实物仿真中试验总控软件的设计与实现

试验总控软件是应用于半实物仿真中对参试系统进行管理和控制的一种软件,它可以对半实物仿真中的参试系统进行配置,实现对整个试验仿真的管理和监控。解析各参试系统的数据交互协议是试验总控软件对参试系统进行监控和管理的关键功能。通常试验总控软件解析功能代码完全按照各参试系统数据交互协议开发,这样一旦数据交互协议发生更改,解析功能代码需要根据新协议重新开发导致软件重复开发,影响软件开发效率和通用性。为了解决这个问题,文中提出一种试验总控软件设计框架并采用动态解析数据帧技术将解析代码与数据交互协议隔离,不仅提高软件通用性,同时缩短半实物仿真开发周期,减少人力财力开支。     

Experimental and four‐component relativistic DFT studies of tungsten carbonyl complexes

We present a theoretical and experimental study of the structure and nuclear magnetic resonance (NMR) parameters of the pentacarbonyltungsten complexes of η¹‐2‐(trimethylstannyl)‐4,5‐dimethylphosphinine, η²‐norbornene, and imidazolidine‐2‐thione. The three complexes have a pseudo‐octahedral molecular structure with the six ligands bonded to the tungsten atom. The η¹‐2‐(trimethylstannyl)‐4,5‐dimethylphosphinine‐pentacarbonyl tungsten complex was synthesized for the first time. For all compounds, we present four‐component relativistic calculations of the NMR parameters at the Dirac–Kohn–Sham density functional level of theory using hybrid functionals. These large‐scale relativistic calculations of NMR chemical shifts and spin–spin coupling constants were compared with available experimental data, either taken from the literature or measured in this work. The inclusion of solvent effects modeled using a conductor‐like screening model was found to improve agreement between the calculated and experimental NMR parameters, and our best estimates for the NMR parameters are generally in good agreement with available experimental results. The present work demonstrates that four‐component relativistic theory has reached a level of maturity that makes it a convenient and accurate tool for modeling and understanding chemical shifts and indirect spin–spin coupling constants of organometallic compounds containing heavy elements, for which conventional non‐relativistic theory breaks down. Copyright © 2015 John Wiley & Sons, Ltd.     

Overview of software options for processing,analysis and interpretation of mass spectrometric proteomic data

Recently, the interests in proteomics have been intensively increased, and the proteomic methods have been widely applied to many problems in cell biology. If the age of 1990s is considered to be a decade of genomics, we can claim that the following years of the new century is a decade of proteomics. The rapid evolution of proteomics has continued through these years, with a series of innovations in separation techniques and the core technologies of two‐dimensional gel electrophoresis and MS. Both technologies are fueled by automation and high throughput computation for profiling of proteins from biological systems. As Patterson ever mentioned, ‘data analysis is the Achilles heel of proteomics and our ability to generate data now outstrips our ability to analyze it’. The development of automatic and high throughput technologies for rapid identification of proteins is essential for large‐scale proteome projects and automatic protein identification and characterization is essential for high throughput proteomics. This review provides a snap shot of the tools and applications that are available for mass spectrometric high throughput biocomputation. The review starts with a brief introduction of proteomics and MS. Computational tools that can be employed at various stages of analysis are presented, including that for data processing, identification, quantification, and the understanding of the biological functions of individual proteins and their dynamic interactions. The challenges of computation software development and its future trends in MS‐based proteomics have also been speculated. Copyright © 2014 John Wiley & Sons, Ltd.     

Direct Enantioselective Three‐Component Synthesis of Optically Active Propargylamines in Water

An enantioselective three‐component reaction of aldehydes, amines, and alkynes in water by using a bis(imidazoline)–Cu^I catalysts having a hydrophobic substituent and sodium dodecyl sulfate as a surfactant was developed. The reaction was applied to a broad range of aldehydes and alkynes to give optically active propargylamines with excellent yields (up to 99 %) and enantiomeric excesses (up to 99 % ee).     

Analysis of plant genomic DNAs and the genetic relationship among plants by using surface-enhanced Raman spectroscopy

The study aimed to distinguish genomic DNAs from nine species of plants belonging to six families and analyze their genetic relationship by using surface-enhanced Raman scattering (SERS). The silver nano-colloid and excitation wavelength of 785 nm used in this study yielded excellent quality of the SERS spectra. Raman signals were remarkably enhanced. Although the spectra for the nine species of plants appeared very similar, there were significant differences according to the analysis of variance analysis. There were three strong characteristic peaks. The peak at 625 cm⁻¹ was due to the vibration overlap of C3′-endo/anti deoxyribose, cytosine, and guanine; the one at 715 cm⁻¹ was due to the scissoring vibrations of C2N1C6 of adenine; and that at 1011 cm⁻¹ was due to the stretching vibration of the CO bond of deoxyribose and vibrations of cytosine. The SERS data were smoothed and standardized and evaluated using second derivative analysis, principal component analysis, and hierarchical cluster analysis. A model was established using the data from hierarchical cluster analysis and principal components of the second derivative. The clustering result of this model was highly consistent with the traditional classification of plants; all plant species investigated were correctly clustered into classes according to the cluster distance coefficient among them; the accuracy of clustering was 100%. Chinese cabbage (Brassica pekinensis Rupr.) and green cabbage (Brassica chinensis L.) belonging to Cruciferae, maize (Zea mays L.) and bamboo (Sinocalamus affinis McClure) belonging to Gramineae, and magnolia (Magnolia delavayi Franch.) and champaca (Michelia alba DC.) belonging to Magnoliaceae were clustered into three separate classes, and fern (Nephrolepis auriculata L., Nephrolepidaceae), garlic (Allium sativum L., Amaryllidaceae), and ginkgo (Ginkgo biloba L., Ginkgoaceae) were each clustered into separate classes. These findings suggest that the SERS spectra of plant genomic DNAs can be used to classify species and analyze their genetic relationship. It is an effective and perfect supplement to traditional classification and can form the basis for genetic analysis.