Lorentz Jäntschi

Exact Probabilities and Confidence Limits for Binomial Samples: Applied to the Difference between Two Proportions

An exact probabilities method is proposed for computing the confidence limits of medical binomial parameters obtained based on the 2×2 contingency table. The developed algorithm was described and assessed for the difference between two binomial proportions (a bidimensional parameter). The behavior of the proposed method was analyzed and compared to four previously defined methods: Wald and Wilson, with and without continuity corrections. The exact probabilities method proved to be monotonic in computing the confidence limits. The experimental errors of the exact probabilities method applied to the difference between two proportions has never exceeded the imposed significance level of 5%.

Design of Experiments: Useful Orthogonal Arrays for Number of Experiments from 4 to 16

A methodology for the design of an experiment is proposed in order to find asmany schemes as possible with the maximum number of factors with different levels for thesmallest number of experimental runs. An algorithm was developed and homemadesoftware was implemented. The abilities in generation of the largest groups of orthogonalarrays were analyzed for experimental runs of 4, 6, 8, 9, 10, 12, 14, 15, and 16. The resultsshow that the proposed method permits the construction of the largest groups of orthogonalarrays with the maximum number of factors.

A new mathematical model for the optimization of the mobile phase composition in HPTLC and the comparison with other models

Mobile phase optimization is highly important in planar chromatography. Solvent selection based on experience and chromatographic intuition can be very time consuming when applied to complex mixtures. In these cases, more systematic strategies are needed. This paper presents a new mathematical model for the optimization of the mobile phase composition used for the separation of a mixture of 1,4-benzodiazepines and a comparison of this mathematical approach with other models.

Comparison of Quantitative Structure-Activity Relationship Model Performances on Carboquinone Derivatives

Quantitative structure-activity relationship (qSAR) models are used to understand how the structure and activity of chemical compounds relate. In the present study, 37 carboquinone derivatives were evaluated and two different qSAR models were developed using members of the Molecular Descriptors Family (MDF) and the Molecular Descriptors Family on Vertices (MDFV). The usual parameters of regression models and the following estimators were defined and calculated in order to analyze the validity and to compare the models: Akaike?s information criteria (three parameters), Schwarz (or Bayesian) information criterion, Amemiya prediction criterion, Hannan-Quinn criterion, Kubinyi function, Steigers Z test, and Akaikes weights. The MDF and MDFV models proved to have the same estimation ability of the goodness-of-fit according to Steigers Z test. The MDFV model proved to be the best model for the considered carboquinone derivatives according to the defined information and prediction criteria, Kubinyi function, and Akaikes weights.

Predictivity approach for quantitative structure-property models. Application for blood-brain barrier permeation of diverse drug-like compounds.

The goal of the present research was to present a predictivity statistical approach applied on structure-based prediction models. The approach was applied to the domain of blood-brain barrier (BBB) permeation of diverse drug-like compounds. For this purpose, 15 statistical parameters and associated 95% confidence intervals computed on a 2 × 2 contingency table were defined as measures of predictivity for binary quantitative structure-property models. The predictivity approach was applied on a set of compounds comprised of 437 diverse molecules, 122 with measured BBB permeability and 315 classified as active or inactive. A training set of 81 compounds (~2/3 of 122 compounds assigned randomly) was used to identify the model and a test set of 41 compounds was used as the internal validation set. The molecular descriptor family on vertices cutting was the computation tool used to generate and calculate structural descriptors for all compounds. The identified model was assessed using the predictivity approach and compared to one model previously reported. The best-identified classification model proved to have an accuracy of 69% in the training set (95%CI [58.53–78.37]) and of 73% in the test set (95%CI [58.32–84.77]). The predictive accuracy obtained on the external set proved to be of 73% (95%CI [67.58–77.39]). The classification model proved to have better abilities in the classification of inactive compounds (specificity of ~74% [59.20–85.15]) compared to abilities in the classification of active compounds (sensitivity of ~64% [48.47–77.70]) in the training and external sets. The overall accuracy of the previously reported model seems not to be statistically significantly better compared to the identified model (~81% [71.45–87.80] in the training set, ~93% [78.12–98.17] in the test set and ~79% [70.19–86.58] in the external set). In conclusion, our predictivity approach allowed us to characterize the model obtained on the investigated set of compounds as well as compare it with a previously reported model. According to the obtained results, the reported model should be chosen if a correct classification of inactive compounds is desired and the previously reported model should be chosen if a correct classification of active compounds is most wanted.

ANTIOXIDANT CONTENT OF THREE DIFFERENT VARIETIES OF WINE GRAPES

ABSTRACT The antioxidant contents of wines, seeds and skin ethanolic extracts of ‘Cabernet Sauvignon’, ‘Merlot’ and ‘Pinot Noir’ cultivated grape varieties from Recas winery were used in order to verify the influence of the analyzed material (seeds and skins extracts, wine) and of the grape variety on the antioxidant content of samples and to estimate the relationships between different grape varieties based on their antioxidant content. The results showed that the antioxidant content of samples depends on the analyzed material and on the grape variety. The results also show that ‘Cabernet Sauvignon’ and ‘Merlot’ grapes varieties are very different in terms of antioxidant content.

A Structural Informatics Study on Collagen

The study integrates knowledge resulting from structure–activity relationships analysis of amino acids with respect to the characterization of α1 and α2 type I collagen chains. Specifically, 15 amino acids and 14 properties were investigated and their structure–activity relationship models were obtained. The models were integrated into a web application and were used to predict the properties of a set of six amino acids. The similarities in α1 and α2 type I collagen chains has been investigated starting from the observed and predicted properties of amino acids by using two‐step cluster analysis.

SOME APPLICATIONS OF STATISTICS IN ANALYTICAL CHEMISTRY

The review presents some considerations on applications of statistics in analytical chemistry such as the statistics of the point, data analysis by regression, correlation and self correlation, dispersional analysis and ANOVA model, validation of statistic hypothesis. Statistics have undergone an enormous impact from microelectronics, in the form of microcomputers and hand-held calculators. These have brought difficult statistical procedures within the reach of all practising scientists. The availability of the tremendous computing power naturally makes it all the

A Study of Genetic Algorithm Evolution on the Lipophilicity of Polychlorinated Biphenyls

The search for multivariate linear regression (MLR) in quantitative structure–property relationships (QSPR) is a hard problem, due to the dimension of the entire search space. A genetic algorithm (GA) was developed and assessed, to select proper descriptors for predicting the octan‐1‐ol/H2O partition coefficient of polychlorinated biphenyls. The GA was implemented as a Windows based FreePascal application with MySQL connectivity for fetching the data. An outcome study based on 30 runs was done keeping all parameters constant: sample size, 8; number of variables in the MLR, 2; adaptation‐imposed requirements; maximum number of generations, 1000; selection strategy, proportional; probability of mutation, 0.05; number of genes implied in mutation, 2; optimization parameter, r2; optimization score, minimum in sample; and optimization objective, maximum. The results revealed that the number of evolutions followed the Poisson distribution with the sample size as parameter. The average of the determination coefficient is higher than 98% of the determination coefficient obtained through complete search, and follows the Gaussian distribution. The correlation coefficients obtained by the best performing GA‐MLR models proved not to be statistically different from the correlation coefficient of the QSPR model obtained by complete search.

Thermal Energy Efficiency Analysis for Residential Buildings

The paper presents an interactive software application, for the calculus of the heat flux demand in residential houses, based on international trends, standards and specifications in the fields of thermal energy in buildings. These types of calculations are considerable useful in the context of the large and constant interest on the subjects of energy conservation, reduction of polluting emissions and use on large scale of renewable energies. In order to reach the objective of the research, the heat flux demand was parameterized to identify each influence on the thermal energy consumption and costs. The development of the mathematical model had the aim to allow the minimization of the heat losses into the environment and to choose the correct thermal power for the residential houses heating devices. By the use of PHP language, the mathematical model has been transposed into a client-server application. The interactive software system has been validated through a case study and the obtained results were consistent and relevant. Based on the results it was possible to extract key conclusions about the parameters that contribute to the heat losses.