不同常压流化床煤气化方案的模型预测 Ⅰ. 模型建立及验证

为评价不同气化方案对常压流化床气化的影响，从化学动力学角度并结合化学平衡建立了流化床气化模型，该模型考虑了煤热解和气化所经历的各反应过程。模型预测结果与文献报道的试验数据吻合较好，气化组分的平方误差和在10%左右，表明该模型可以用来预测各种气化方案对常压流化床气化的气化过程、生成煤气组分和气化效率等方面的影响。     

Establishing structure-property correlations and classification of base oils using statistical techniques and artificial neural networks

The present paper describes various classification techniques like cluster analysis, principal component (PC)/factor analysis to classify different types of base stocks. The API classification of base oils (Group I-III) has been compared to a more detailed NMR derived chemical compositional and molecular structural parameters based classification in order to point out the similarities of the base oils in the same group and the differences between the oils placed in different groups. The detailed compositional parameters have been generated using and nuclear magnetic resonance (NMR) spectroscopic methods. Further, oxidation stability, measured in terms of rotating bomb oxidation test (RBOT) life, of non-conventional base stocks and their blends with conventional base stocks, has been quantitatively correlated with their NMR and elemental (sulphur and nitrogen) data with the help of multiple linear regression (MLR) and artificial neural networks (ANN) techniques. The MLR based model developed using NMR and elemental data showed a high correlation between the ‘measured’ and ‘estimated’ RBOT values for both training (R=0.859) and validation (R=0.880) data sets. The ANN based model, developed using fewer number of input variables (only NMR data) also showed high correlation between the ‘measured’ and ‘estimated’ RBOT values for training (R=0.881), validation (R=0.860) and test (R=0.955) data sets.     

螺旋结构与旋光性——2.脂族链状化合物的螺旋结构和旋光性

利用弯曲键理论,详细讨论了旋光性的脂肪族链状化合物分子内存在的螺旋结构,同样是右螺是右旋的,左螺旋是左旋的。从螺旋方向可以推断旋光方向,知道旋光方向也可以推断构型。     

Structure-property modelling of complex formation of strontium with organic ligands in water

The method of substructural molecular fragments based on representation of the molecular graph by ensembles of fragments and involving calculations of those contributions to the given property is applied to the modelling of stability constants of the complexes of strontium(II) with organic ligands in water. Reliability of predictions of developed structure-property models was examined using three different test sets of structurally diverse ligands. The obtained models have been used for generation and screening of combinatorial library of virtual ligands. Some hypothetical efficient Sr(II) binders were suggested.     

What to Learn from a Comparative Genomic Sequence Analysis of <Emphasis Type="Italic">L</Emphasis>-Carnitine Dehydrogenase

Summary. In contrast to eukaryotic cells certain eubacterial strains have acquired the ability to utilize L-carnitine (R-(–)-3-hydroxy-4-(trimethylamino)butyrate) as sole source of energy, carbon and nitrogen. The first step of the L-carnitine degradation to glycine betaine is catalysed by L-carnitine dehydrogenase (L-CDH, EC 1.1.1.108) and results in the formation of the dehydrocarnitine. During the oxidation of L-carnitine a simultaneous conversion of the cofactor NAD⁺ to NADH takes place. This catabolic reaction has always been of keen interest, because it can be exploited for spectroscopic L-carnitine determination in biological fluids – a quantification method, which is developed in our lab – as well as L-carnitine production.Based on a cloned L-CDH sequence an expedition through the currently available prokaryotic genomic sequence space began to mine relevant information about bacterial L-carnitine metabolism hidden in the enormous amount of data stored in public sequence databases. Thus by means of homology-based and context-based protein function prediction is revealed that L-CDH exists in certain eubacterial genomes either as a protein of approximately 35 kDa or as a homologous fusion protein of approximately 54 kDa with an additional putative domain, which is predicted to possess a thioesterase activity. These two variants of the enzyme are found on one hand in the genome sequence of bacterial species, which were previously reported to decompose L-carnitine, and on the other hand in gram-positive bacteria, which were not known to express L-CDH. Furthermore we could not only discover that L-CDH is located in a conserved genetic entity, which genes are very likely involved in this L-carnitine catabolic pathway, but also pinpoint the exact genomic sequence position of several other enzymes, which play an essential role in the bacterial metabolism of L-carnitine precursors.     

疏水分配常数用于反相液相色谱保留值的预测

在反相液相色谱保留值基本方程log k_′=a+_cC_B的基础上,描述了采用疏水分配常数及氢键作用能来预测a、c参数的方法,并系统讨论了疏水分配常数对参数a、c的影响,借此对反相液相色谱宽浓度范围内的保留值进行了预测。     

Partial least-squares regression and fuzzy clustering — A joint approach

Chemical and physical analyses of malt, the main ingredient of beer, have been used to predict the concentration of certain volatile compounds in the finished beer.The prediction was done by means of the partial least squares regression (PLS2) in SIMCA. The total data set as well as individual malt clusters were submitted to PLS analysis. Best prediction was obtained by separating the total object matrix in classes according to similarity found by fuzzy pattern recognition (FCV). FCV was also used to separate the beer variables in classes and to select the subset of variables to be predicted.A joint approach of fuzzy pattern recognition to identify groups of samples and SIMCA-PLS2 to predict several dependent variables is suggested as a powerful tool in process-analytical chemistry.     

predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance

Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site.     

Protein fold recognition

Summary An important, yet seemingly unattainable, goal in structural molecular biology is to be able to predict the native three-dimensional structure of a protein entirely from its amino acid sequence. Prediction methods based on rigorous energy calculations have not yet been successful, and best results have been obtained from homology modelling and statistical secondary structure prediction. Homology modelling is limited to cases where significant sequence similarity is shared between a protein of known structure and the unknown. Secondary structure prediction methods are not only unreliable, but also do not offer any obvious route to the full tertiary structure. Recently, methods have been developed whereby entire protein folds are recognized from sequence, even where little or no sequence similarity is shared between the proteins under consideration. In this paper we review the current methods, including our own, and in particular offer a historical background to their development. In addition, we also discuss the future of these methods and outline the developments under investigation in our laboratory.     

A new method for predicting conduction anesthesia

A new method for predicting conduction anesthesia has been suggested. The method is based on calculation of theP matrix probabilities of interatomic contacts for each molecule of the compounds considered. TheP matrix enables one to evaluate the main tendencies of atoms and atomic groups to interact in biochemical sorption on the nerve fiber surface. The minimum effective concentrations calculated for 25 compounds are in good agreement with the experimental data. The correlation coefficient between the experimental and calculated values is 0.98 when the standard deviation is 0.1 mmol L⁻¹. Translated fromIzvestiya Akademii Nauk. Seriya Khimicheskaya, No. 10, pp. 1781–1784, October, 1997.