Amino Acids Sequences Analysis on Collagen

Staring from available information about amino acids properties and sequences on collagen type I chains, the aims of the study were to identify the principal property component and to analyze the similarities within and between collagens on five species. The principal component analysis applied on twentyfour amino acids properties revealed that the hydrophobic or hydrophilic character measured by Wealling et al. is more stable comparing with the other investigated properties. Similarity analysis identified similar and dissimilar within and between studied species from the viewpoint of amino acids sequences on collagen type I alpha 1 and 2 chains

National Trends on Agricultural Crops Production: Cluster Analysis

Staring from descriptive data on crop production and cultivated area at national level during on fifteen years, the aim of this study is to reveal the trends on crops cultivation. The cluster analysis reveals linkages between crops classes as well as between different crops, which can be partly assigned to crops rotation. Time series analysis reveals dramatically reducing of production of some crops, such as flax, hemp, and sugar beet, and increasing of production, such at sunflower, and increasing of productivity, such at potatoes and field vegetables

Antiallergic Activity of Substituted Benzamides: Characterization, Estimation and Prediction

Antiallergic activity of twenty-three substituted N 4-methoxyphenyl benzamides was model by the use of an original methodology. After sketching out the compounds structure and creating the file with the observed activities, strictly based on compounds structure, the molecular descriptors family was generated and descriptors entered into a multiple linear regression analysis. The multi-varied model with four descriptors proved to render higher ability in estimation (squared correlation coefficient, r2 = 0.9986) as well as in prediction (cross-validation leave-one-out score, r2cv-loo = 0.9956) of antiallergic activity of compounds, obtained significantly greater correlation coefficient compared with the previously reported model (p < 0.01). Characterization of antiallergic activity of substituted N 4-methoxyphenyl benzamides by integration of complex structure information provides a stable and efficient multi-varied model with four descriptors. According with the multi-varied model with four descriptors the antiallergic activity of substituted N 4-methoxyphenyl benzamides is like to be of geometry nature, depending by the number of directly bonded hydrogen’s, and the atomic relative mass, being in relation with the partial charge of compounds

Quantitative Structure-Activity Relationships: Linear Regression Modelling and Validation Strategies by Example

Quantitative structure-activity relationships are mathematical models constructed based on the hypothesis that structure of chemical compounds is related to their biological activity. A linear regression model is often used to estimate and predict the nature of the relationships between a measured activity and some measure or calculated descriptors. Linear regression helps to answer main three questions: does the biological activity depend on structure information; if so, the nature of the relationship is linear; and if yes, how good is the model in prediction of the biological activity of new compounds. This manuscript presents the steps on linear regression analysis moving from theoretical knowledge to an example conducted on sets of endocrine disrupting chemicals

Distribution Fitting 2. Pearson-Fisher, Kolmogorov-Smirnov, Anderson- Darling, Wilks-Shapiro, Cramer-von-Misses and Jarque-Bera Statistics

Abstract. The methods measuring the departure between observation and the model were reviewed. The following statistics were applied on two experimental data sets: ChiSquared, Kolmogorov-Smirnov, Anderson-Darling, Wilks-Shapiro, and Jarque-Bera. Both investigated sets proved not to be normal distributed. The Grubbs&apos; test identified one outlier and after its removal the normality of the set of 205 chemical active compounds was accepted. The second data set proved not to have any outliers. Kolmogorov-Smirnov statistic is less affected by the existence of outliers (positive variation expressed as percentage smaller than 2). The outliers bring to Kolmogorov-Smirnov statistic errors of type II and to the AndersonDarling statistic errors of type I