A toxicity evaluation and predictive system based on neural networks and wavelets

A computational approach has been developed for performing efficient and reasonably accurate toxicity evaluation and prediction. The approach is based on computational neural networks linked to modern computational chemistry and wavelet methods. In this paper, we present details of this approach and results demonstrating its accuracy and flexibility for predicting diverse biological endpoints including metabolic processes, mode of action, and hepato- and neurotoxicity. The approach also can be used for automatic processing of microarray data to predict modes of action.     

QSAR as a random event: criteria of predictive potential for a chance model

The CORAL software (http://www.insilico.eu/coral) was suggested as a tool to build up quantitative structure–property/activity relationships (QSPRs/QSARs). This software is based on conception “a QSPR/QSAR model should be interpreted as a random event.” This is reflection of fact: different distributions into the training set (substances involved in modeling process) and the validation set (substances, which are not known at the moment of the modeling process) give models with significant dispersion in the statistical quality of the QSPR/QSAR. Results of experiments with the software and possible ways of further improvement of this software are discussed. The most attractive new ways to estimate predictive potential of the CORAL model seem to be the following ones: (i) index of ideality of correlation and (ii) correlation contradiction index. These can be also proposed as criteria of predictive potential for arbitrary QSPR/QSAR.

     

Measurements and predictive models for the N-methyl-2-pyrrolidone/water/methanol system

This work reports on the properties and structure of N-methyl-2-pyrrolidone in binary and ternary liquid mixtures with water and/or methanol. A comprehensive set of thermophysical properties have been measured at 298.15 K and 0.1 MPa over the whole composition range. On the basis of the derived excess and mixing properties, the fluid structure was analyzed by looking into the forces and geometry factors that effectively control the mixture behavior. Application of the inverted Kirkwood-Buff theory provides a reasonable link between the macroscopic properties and the microscopic features. A density functional theory computation was carried out to analyze the structure and energy features of the hydrogen bonded complexes formed between the components. Use of semiempirical models in the framework of molecular-based equations of state according to the PC-SAFT approach led to unsatisfactory predicted thermophysical properties. The conclusions arrived at give away the existence of strong heteroassociations and hydrogen bonding self-associations that determine the complex nature of the fluid structure.     

Predicting toxicity through computers: a changing world

The computational approaches used to predict toxicity are evolving rapidly, a process hastened on by the emergence of new ways of describing chemical information. Although this trend offers many opportunities, new regulations, such as the European Community's 'Registration, Evaluation, Authorisation and Restriction of Chemicals' (REACH), demand that models be ever more robust.     

Chromatographic methods for carcinogenic/mutagenic nitropolycyclic aromatic hydrocarbons

Nitropolycyclic aromatic hydrocarbons (NPAHs), which are known to be carcinogenic and/or mutagenic, are considered to be one of the air pollutants that cause lung cancer. In the last two decades, a number of sensitive and selective methods have been developed for the determination of NPAHs and related compounds in environmental and biological samples. This paper describes the state of the art of the methods and applications.     

Recent advances in microbial fuel cell-based toxicity biosensors: Strategies for enhanced toxicity response

Microbial fuel cells (MFCs) have been extensively studied as self-powered toxicity biosensors; however, their applications are limited by the relatively poor toxicity responses. The toxicity responses are known to be related to the factors such as the resistance of species to toxicants, the bioavailability of toxicants and the type of sensing elements. Accordingly, some strategies have already been proposed to enhance the toxicity responses in the past several years, including the external resistance tuning, quorum sensing effect, shear stress control, nutrient level control, electrode material choice, sensing element choice, and cell configuration design. This work introduces and discusses these strategies, and the suggestion for future work is also provided finally.     

Effects of solute-solvent interactions on electronic spectra: a predictive analysis

Ultraviolet-visible and fluorescence spectra have long been utilized for qualitative and quantitative purposes despite numerous complicating factors. In this study, anthracene, 1-naphthol, and 9-acetylanthracene are used as model systems to distinguish solventinduced effects via multiple regression techniques. The ability to predict changes in spectral peak locations and quantum efficiencies arising from solute—solvent interactions is critical to the development of a versatile computer-searchable library of electronic spectra.     

Activation barriers for DNA alkylation by carcinogenic methane diazonium ions

Methylation reactions of the DNA bases with the methane diazonium ion, which is the reactive intermediate formed from several carcinogenic methylating agents, were examined. The SN2 transition states of the methylation reactions at N7, N3, and O6 of guanine; N7, N3, and N1 of adenine; N3 and O2 of cytosine; and O2 and O4 of thymine were calculated using the B3LYP density functional method. Solvation effects were examined using the conductor-like polarizable continuum method and the combined discrete/SCRF method. The transition states for reactions at guanine N3, adenine N7, and adenine N1 are influenced by steric interactions between the methane diazonium ion and exocyclic amino groups. Both in the gas phase and in aqueous solution, the methylation reactions at N atoms have transition states that are looser, and generally occur earlier along the reaction pathways than reactions at O atoms. The forming bonds in the transition states in water are 0.03 to 0.13 A shorter than those observed in the gas phase, and the activation energies are 13 to 35 kcal/mol higher. The combined discrete/SCRF solvation energy calculations using base-water complexes with three water molecules yield base solvation energies that are larger than those obtained from the CPCM continuum method, especially for cytosine. Reactivities calculated using barriers obtained with the discrete/SCRF method are consistent with the experimentally observed high reactivity at N7 of guanine.     

Sensitivity analysis of a predictive model for the fire behaviour of a sandwich panel

A sensitivity analysis to assumptions and input variables is carried out for a predictive model previously developed [1] for the fire response of a glass-fibre/polyester panel and a glass-fibre/polyester-Vermiculux sandwich. It is an unsteady, one-dimensional model using the porous medium approximation and a constant gas pressure with two-step, finite rate kinetics for the thermal decomposition and combustion of the polymeric resin, moisture evaporation described by an Arrhenius rate law, heat and mass transfer by convection, heat conduction and radiation described by effective thermal conductivities, variation of the volumetric fractions of the polymeric resin and the moisture with the conversion degree, effective specific heats, external heat transfer resistances and surface ablation. The strongest impact on the model predictions is exerted by the imposed external heat flux with variations on the characteristic process times between 49 and 774%. An important role in sample heating/conversion is also played by surface ablation and/or external heat transfer resistance with variations up to 30-72% or, when ablation is disregarded, with temperatures along the core layer well below those of the degrading skin. These are also significantly affected by surface heat losses, with the assumption of adiabatic bottom surface leading to heterogeneous ignition of the lower skin, and evaporation of moisture with variations in the characteristic times up to 35%. The model for the effective thermal conductivity of the fibre-reinforced skin (the Parallel, the Maxwell-Eucken and the Effective Medium Theory models versus the Series model) is also important resulting in characteristic time variations up to 35%. The absence of local thermal equilibrium between the condensed and the gas/vapour phase and the kinetic details of the polymer reactions are comparatively less important (maximum diminution in the characteristic times of 16%). Moreover, although over-pressures, modelled by the Darcy law, become quite high especially during the moisture evaporation stage (up to ten times the atmospheric value), their effects on the thermal response of the structure are completely negligible when structural changes are not modelled. Finally, a sensitivity analysis is also carried out to input parameters.     

Systematic prediction error correction: a novel strategy for maintaining the predictive abilities of multivariate calibration models

The development of reliable multivariate calibration models for spectroscopic instruments in on-line/in-line monitoring of chemical and bio-chemical processes is generally difficult, time-consuming and costly. Therefore, it is preferable if calibration models can be used for an extended period, without the need to replace them. However, in many process applications, changes in the instrumental response (e.g. owing to a change of spectrometer) or variations in the measurement conditions (e.g. a change in temperature) can cause a multivariate calibration model to become invalid. In this contribution, a new method, systematic prediction error correction (SPEC), has been developed to maintain the predictive abilities of multivariate calibration models when e.g. the spectrometer or measurement conditions are altered. The performance of the method has been tested on two NIR data sets (one with changes in instrumental responses, the other with variations in experimental conditions) and the outcomes compared with those of some popular methods, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS). The results show that SPEC achieves satisfactory analyte predictions with significantly lower RMSEP values than global PLS and SBC for both data sets, even when only a few standardization samples are used. Furthermore, SPEC is simple to implement and requires less information than PDS, which offers advantages for applications with limited data.