Random Computer Generation of 3D Molecular Structures: Theoretical and Statistical Analysis

Summary: A computer program has been developed to generate three‐dimensional molecular structures randomly from a given collection of elementary chemical functional groups: the so‐called fragment database. The gradual assembly of the various fragments present in the database is performed according to a “self‐generation algorithm” (SGA). The latter is based on the covalent binding, step by step, between the unoccupied electronic valencies associated with the fragments of the database, and those of the growing molecular structure. When the number of electronic valencies of the molecular structure is zero, the growth process for this particular molecule is completed. It is shown that SGA is able to reproduce the asymmetric mass distributions of some natural colloids, like humic substances. In this article, particular attention is given to the analysis of the relationship existing between the fragment composition of the database and that of the collection of molecules generated. Mathematical expressions are derived and discussed, to understand the relationship between the proportions of the different types of fragments and the final composition of the generated molecular ensembles. For that purpose, a “pathway” formalism is proposed to describe exhaustively the whole set of generated molecules by specifying the distribution function of all of the fragments therein integrated. A statistical analysis that satisfactorily reproduces the predictions of the pathway model is extensively discussed.

Example of a three‐dimensional structure obtained with the “self‐generation algorithm” (SGA).     

Statistical Spectral Analysis of liquid X-ray Diffraction Data

Abstract

The experimental procedure of determining the structure of a liquid by diffraction techniques is reformulated herein as a stochastic experiment subject to the data analysis formalism of statistical spectral analysis. Observed in such an experiment are averaged local microscopic fluctuations from the bulk density. The intensity function then represents a stochastic spectrum and it becomes necessary to statistically estimate a minimum bias, minimum variance covariance function which is the net radial distribution function. A low-pass tapered data window produces such an optimum estimate.     

Statistical Analysis of Multivariate Multiple Wavelength Liquid Chromatographic Response Data

Abstract

A standard mixture of uracil, phenol, benzaldehyde, and N,N-Diethyl-m-Toluamide was chromatographed under standard conditions at many wavelengths from 210 nm to 280 nm. the area percentage composition of the mixture varies depending on the wavelength chosen. Principal component analysis and factor analysis were done on the percent area composition data.     

一类用于多元光度分析的小波基主成分回归法

通过将变尺度小波分解滤噪与特征信息提取相结合,提出一类新的多元光度分析算法－小波基主成分回归（ＰＣＲＷ）方法。该法可有效地减小主成分向量残留噪声所引 误差显著提高多元校正准确性。将其用于分析氯霉素、醋酸对塞米松、尼泊金乙脂体系、得到满意的回收率,与ＰＣＲ法相比,分析结果的总平均相对误差从３．３８％降低到０．８３％。     

神经网络与多元统计在复杂化学信息模式分类中的集成应用

天然有机物料的化学组成十分复杂，其组分通常高达几百种，从而使其组成与性质间自关系，即构效关系，难以分辨、识别和确定．例如白酒、香料的构效关系就难以用化学机理描述．通常采用数据处理方法，将其归结为模式分类问题．复杂化学信息模式分类问题有模三维数高、样本容量小等特点，采用人工神经网络（ANN）具有一定的优势和不足．文献［1，2］讨论了从网络训练算法出发，改善ANN运行性能的技术．本文拟从另一角度探讨该问题．1对输入模式的分析当物料的组分数增多时，构效关系的复杂程度将剧增．为提高神经网络的表达和处理有力，必…     

Gd3+/Yb3+掺杂ZnO量子点作为双模式磁共振/CT成像探针

基于结合磁共振成像(MRI)的高空间分辨率和CT成像的深穿透能力设计思想,在乙醇溶液中水解醋酸锌、醋酸钆和醋酸镱,制备了油酸稳定的Gd3+/Yb3+掺杂ZnO量子点(ZnO∶Gd/Yb),并对其表面进行了氨基修饰。研究了ZnO∶Gd/Yb量子点的弛豫性能、X射线吸收性能、细胞毒性及体外MRI和CT成像。当Zn2+,Gd3+,Yb3+的摩尔比为1.0∶0.12∶0.20时,ZnO∶Gd/Yb量子点展现了最高的弛豫效率6.06 mmol/(L.s)对X-射线的吸收能力也显著高于临床CT造影剂碘比醇。体外MRI和CT成像实验表明,当Gd3+的浓度为1.5 mmol/L时,T1加权MRI信号明显增强,当Yb3+的浓度为5 g/L时,可呈现清晰的CT图像。细胞毒性实验表明,ZnO∶Gd/Yb量子点的浓度低于1.5 mmol/L(Gd3+)时,量子点的毒性相对较低。     

Comprehensive Evaluation of Amino Acids and Polyphenols in 69 Varieties of Green Cabbage (Brassica oleracea L. var. capitata L.) Based on Multivariate Statistical Analysis

The biological activities of the primary metabolites and secondary metabolites of 69 green cabbage varieties were tested. The LC-MS detection method was used to determine the content of 19 free amino acids (lysine, tryptophan, phenylalanine, methionine, threonine, isoleucine, leucine, valine, arginine, asparagine, glycine, proline, tyrosine, glutamine, alanine, aspartic acid, serine, and glutamate). The content of 10 polyphenols (chlorogenic acid, gallic acid, 4-coumaric acid, ferulic acid, gentisic acid, cymarin, erucic acid, benzoic acid, rutin, and kaempferol) was determined by the HPLC detection method. Considering the complexity of the data obtained, variance analysis, diversity analysis, correlation analysis, hierarchical cluster analysis (HCA), and principal component analysis (PCA) were used to process and correlate amino acid or polyphenol data, respectively. The results showed that there were significant differences between the different amino acids and polyphenols of the 69 cabbage varieties. The most abundant amino acids and polyphenols were Glu and rutin, respectively. Both amino acids and polyphenols had a high genetic diversity, and multiple groups of significant or extremely significant correlations. The 69 cabbage varieties were divided into two groups, according to 19 amino acid indexes, by PCA. Among them, seven varieties with high amino acid content all fell into the fourth quadrant. The HCA of amino acids also supports this view. Based on 10 polyphenols, the 69 cabbage varieties were divided into two groups by HCA. Based on 29 indexes of amino acids and polyphenols, 69 cabbage varieties were evaluated and ranked by PCA. Therefore, in this study, cabbage varieties were classified in accordance with the level of amino acids and polyphenols, which provided a theoretical basis for the genetic improvement of nutritional quality in cabbage.     

乌头与贝母配伍化学成分变化的UPLC/Q-TOFMS研究

利用UPLC/Q-TOFMS对中药十八反药对——乌头贝母配伍的合煎液与合并液进行检测,经过MassLynx 4.1分析其化学成分存在差异,发现合煎后次乌头碱水解受到部分抑制,而其他主要的双酯型二萜生物碱溶出较少且水解较彻底,乌头与川贝配伍合煎的毒性相对较小,因而可能通过合理炮制减毒后应用于临床治疗.     

用多变量统计分析方法辨识肺癌患者分类中的关键元素(英文)

用逐步判别、主成分分析和聚类方法研究了根据血清和毛发样品中元素含量对正常人和肺癌患者分类中的关键元素．用主成分分析的结果表明,在肺癌患者与正常人的分类中,血清中的Ｃａ,Ｃｒ,Ｃｕ,Ｐ和Ｚｎ是关键元素,而毛发中的Ａｌ,Ｂ,Ｃｒ,Ｐ和Ｓｒ是关键元素．对于正常人和癌症患者元素之间的欧氏距离不同     

Multivariate Statistical Assessment of Air Quality: A Case Study

The present paper deals with the application of several chemometrical methods (cluster and principal components analysis, source apportioning on absolute principal components scores) to an aerosol data collection from Unterloibach, Austria. It is shown that seven latent factors explaining almost 80% of the total variance are responsible for the data structure and are conditionally identified as secondary aerosol, mineral dust, oil burning, lead smelter, coal burning, salt and fertilizer emission sources. Furthermore, the contribution of each identified source to the formation of the particle total mass and chemical compounds total concentration is calculated. Thus, a reliable assessment of the air quality in the region is performed. The requirements of the sustainability concept for ecological indicators in this case is easily transformed into a multivariate statistical problem taking into account not separate indicators but the specific multivariate nature of aerosol pollution.     

Data Processing for 3D Mass Spectrometry Imaging

Data processing for three dimensional mass spectrometry (3D-MS) imaging was investigated, starting with a consideration of the challenges in its practical implementation using a series of sections of a tissue volume. The technical issues related to data reduction, 2D imaging data alignment, 3D visualization, and statistical data analysis were identified. Software solutions for these tasks were developed using functions in MATLAB. Peak detection and peak alignment were applied to reduce the data size, while retaining the mass accuracy. The main morphologic features of tissue sections were extracted using a classification method for data alignment. Data insertion was performed to construct a 3D data set with spectral information that can be used for generating 3D views and for data analysis. The imaging data previously obtained for a mouse brain using desorption electrospray ionization mass spectrometry (DESI-MS) imaging have been used to test and demonstrate the new methodology.     

自适应拉曼光谱成像数据去噪及其在植物细胞壁光谱分析中的应用

拉曼光谱成像数据存在基线漂移与宇宙射线干扰峰两类噪声信号,无法直接用于光谱分析研究,必须去除。现有单光谱去噪方法处理结果不稳定、可重复性差。针对这一问题,本研究提出了一种自适应拉曼光谱成像数据新型去噪法,采用优化的自适应迭代惩罚最小二乘法( Adaptive iteratively reweighted penalized least-squares,airPLS)和基于主成分分析( PCA)的干扰峰消除算法修正光谱基线漂移和宇宙射线干扰峰,具有输入参数少、光谱失真小、处理速度快、去噪结果稳定等优点。利用本方法去除了芒草( Miscanthus sinensis)细胞壁拉曼光谱成像数据(9010条光谱)中的噪声信号,并对去噪后数据进行PCA和聚类分析(CA),成功区分非植物光谱与植物光谱,分类结果优于未去噪数据。预期本方法可应用于其它光谱成像数据去噪,为光谱的解译和定量分析提供可靠的研究基础。     

分析测试中数据统计处理综合程序系统

近年来,分析测试数据已广泛应用计算机进行统计处理,文献报道的多是针对特定任务的单元程序,远不能适应分析测试数据统计处理多样性的要求。本文研究和开发了分析测试中常用数据统计处理的通用综合程序系统,操作方便,调用灵活,在单元程序的基础上,可以组合成各种综合程序,以满足不同分析测试数据统计处理的需要。     

Multivariate Analysis of MALDI Imaging Mass Spectrometry Data of Mixtures of Single Pollen Grains

Mixtures of pollen grains of three different species (Corylus avellana, Alnus cordata, and Pinus sylvestris) were investigated by matrix-assisted laser desorption/ionization time-of-flight imaging mass spectrometry (MALDI-TOF imaging MS). The amount of pollen grains was reduced stepwise from >?10 to single pollen grains. For sample pretreatment, we modified a previously applied approach, where any additional extraction steps were omitted. Our results show that characteristic pollen MALDI mass spectra can be obtained from a single pollen grain, which is the prerequisite for a reliable pollen classification in practical applications. MALDI imaging of laterally resolved pollen grains provides additional information by reducing the complexity of the MS spectra of mixtures, where frequently peak discrimination is observed. Combined with multivariate statistical analyses, such as principal component analysis (PCA), our approach offers the chance for a fast and reliable identification of individual pollen grains by mass spectrometry.

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Graphical Abstract ?

     

Quantitative Analysis and Differential Evaluation of Radix Bupleuri Cultivated in Different Regions Based on HPLC-MS and GC-MS Combined with Multivariate Statistical Analysis

The quality of Radix Bupleuri is greatly affected by its growing environment. In this study, Radix Bupleuri samples that were harvested from seven different regions across northwest China were examined by high-performance liquid chromatography (HPLC) and gas chromatography (GC) coupled with mass spectrometry (MS) to reveal significant differences in quality contributed by the cultivation region. An HPLC-MS method was firstly established and used in the multiple reaction monitoring mode for the quantitative analysis of five saikosaponins in Radix Bupleuri so as to evaluate the difference in the absolute content of saikosaponins attributable to the cultivation region. The effect on the components of Radix Bupleuri was further investigated based on the profiles of the representative saponins and volatile compounds, which were extracted from the Radix Bupleuri samples and analyzed by HPLC-MS and GC-MS. Multivariate statistical analysis was employed to differentiate the Radix Bupleuri samples cultivated in different regions and to discover the differential compositions. The developed quantitative method was validated to be accurate, stable, sensitive, and repeatable for the determination of five saikosaponins. Further statistical tests revealed that the collected Radix Bupleuri samples were distinctly different from each other in terms of both saponins and volatile compounds, based on the provinces where they were grown. In addition, twenty-eight saponins and fifty-eight volatile compounds were identified as the differentially accumulated compositions that contributed to the discrimination of the Radix Bupleuri samples. The Radix Bupleuri samples grown in Shouyang county showed the highest content of saikosaponins. All of the results indicated that the cultivation region significantly affected the accumulation and diversity of the main chemical components of Radix Bupleuri. The findings of this research provide insights into the effect of the cultivation region on the quality of Radix Bupleuri and the differentiation of Radix Bupleuri cultivated in different regions based on the use of HPLC-MS and GC-MS combined with multivariate statistical analysis.     

Quantification of Kerosene in Gasoline by Comprehensive Two-Dimensional Gas Chromatography and N-Way Multivariate Analysis

Abstract

In brazil, gasoline is usually adulterated by diesel oil, ethanol (in addition to the amount legally specified), petrochemical raffinates, and kerosene. This is an illegal action performed mainly in an attempt to raise profits. Therefore, methods for reliable identification of adulterated gasoline are very attractive. The aim of this work was to propose a method to quantify kerosene in gasoline through N-way multivariate analysis and a homemade Comprehensive Two-Dimensional Gas Chromatography with Flame Ionization Detection (GC × GC-FID). Models generated by Parallel Factor Analysis (PARAFAC), PARAFAC2, and Multi-way Partial Least Squares (N-PLS) allowed the quantification of kerosene in gasoline with Root Mean Square Error of Cross-Validation (RMSECV) values of 2.98%, 2.65%, and 2.08%, respectively.     

3D Reconstruction of Rough Surfaces by SEM Stereo Imaging

 The paper describes the current state of a multidisciplinary research project which aims to apply advanced stereovision methods to the 3D reconstruction of rough surfaces using scanning electron microscopy (SEM) imaging. SEM stereo imaging compares very favourably to atomic force microscopy (AFM) in a medium range corresponding to elevations less than 10 μm and scanned size less than 100 μm. But the interest of the stereoscopic method is that it enables to characterise wider fields with higher elevations.     

Comparative Analysis of Acanthopanacis Cortex and Periplocae Cortex Using an Electronic Nose and Gas Chromatography–Mass Spectrometry Coupled with Multivariate Statistical Analysis

Chinese Herbal Medicines (CHMs) can be identified by experts according to their odors. However, the identification of these medicines is subjective and requires long-term experience. The samples of Acanthopanacis Cortex and Periplocae Cortex used were dried cortexes, which are often confused in the market due to their similar appearance, but their chemical composition and odor are different. The clinical use of the two herbs is different, but the phenomenon of being confused with each other often occurs. Therefore, we used an electronic nose (E-nose) to explore the differences in odor information between the two species for fast and robust discrimination, in order to provide a scientific basis for avoiding confusion and misuse in the process of production, circulation and clinical use. In this study, the odor and volatile components of these two medicinal materials were detected by the E-nose and by gas chromatography–mass spectrometry (GC-MS), respectively. An E-nose combined with pattern analysis methods such as principal component analysis (PCA) and partial least squares (PLS) was used to discriminate the cortex samples. The E-nose was used to determine the odors of the samples and enable rapid differentiation of Acanthopanacis Cortex and Periplocae Cortex. GC-MS was utilized to reveal the differences between the volatile constituents of Acanthopanacis Cortex and Periplocae Cortex. In all, 82 components including 9 co-contained components were extracted by chromatographic peak integration and matching, and 24 constituents could be used as chemical markers to distinguish these two species. The E-nose detection technology is able to discriminate between Acanthopanacis Cortex and Periplocae Cortex, with GC-MS providing support to determine the material basis of the E-nose sensors’ response. The proposed method is rapid, simple, eco-friendly and can successfully differentiate these two medicinal materials by their odors. It can be applied to quality control links such as online detection, and also provide reference for the establishment of other rapid detection methods. The further development and utilization of this technology is conducive to the further supervision of the quality of CHMs and the healthy development of the industry.     

Quality Evaluation of Taxilli Herba from Different Hosts Based on Simultaneous Determination of Multiple Bioactive Constituents Combined with Multivariate Statistical Analysis

Taxilli Herba (TAXH) is an important traditional Chinese medicine with a long history, dating from the Eastern Han Dynasty to the present times. However, the active constituents in it that parasitize different hosts vary, affecting its clinical efficacy. Given the complexity of the host origins, evaluating the quality of TAXH is critical to ensure the safety and effectiveness of clinical medication. In the present study, a quantitative method based on ultra-fast liquid chromatography tandem triple quadrupole mass spectrometry (UFLC-QTRAP-MS/MS) was established, which simultaneously determined the content of 33 active constituents, including 12 flavonoids, 4 organic acids, 12 amino acids, and 5 nucleosides in 45 samples. Orthogonal partial least squares discriminant analysis (OPLS-DA) was employed to classify and distinguish between TAXH and its adulterants, Tolypanthi Herba (TOLH). A hierarchical clustering analysis (HCA) was conducted combined with a heatmap to visually observe the distribution regularity of 33 constituents in each sample. Furthermore, gray relational analysis (GRA) was applied to evaluate the quality of samples to get the optimal host. The results demonstrated that TAXH excelled TOLH in quality as a whole. The quality of TAXH parasitizing Morus alba was also better, while those that were parasitic on Cinnamomum camphora and Glyptostrobus pensilis had relatively poor quality. This study may provide comprehensive information that is necessary for quality control and supply a scientific basis for further exploring the quality formation mechanism of TAXH.