Porphyrin–BODIPY-based hybrid model compounds for artificial photosynthetic reaction centers

In this review, we report the synthesis and photophysical studies of porphyrin–4,4-difluoro-4-bora-3a,4a-diaza-s-indacene (BODIPY) compounds linked either with different covalent bonds or with axial coordination to metalloporphyrin. BODIPY moiety significantly increases the light absorption capability of porphyrins by efficient BODIPY to porphyrin excitation energy transfer. The type of linkage between the two chromophores significantly affects the energy transfer efficiency. The most efficient energy transfer was proved for compounds linked via a cyanuric chloride bridge (∼99% quenching). Therefore, this type of bond seems to be more appropriate choice in constructing porphyrin–BODIPY assemblies for light harvesting applications. Moreover, the functionalization of the conjugates with fullerenes appears to be interesting electron transfer dynamics in the excited state.     

CSSP2: an improved method for predicting contact-dependent secondary structure propensity

The calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native β-strand propensities in the core sequences of amyloidogenic proteins. Here we describe a noble energy-based CSSP method implemented on dual artificial neural networks that rapidly and accurately estimate the potential for the non-native secondary structure formation in local regions of protein sequences. In this method, we attempted to quantify long-range interaction patterns in diverse secondary structures by potential energy calculations and decomposition on a pairwise per-residue basis. The calculated energy parameters and seven-residue sequence information were used as inputs for artificial neural networks (ANNs) to predict sequence potential for secondary structure conversion. The trained single ANN using the >(i, i ± 4) interaction energy parameter exhibited 74% accuracy in predicting the secondary structure of test sequences in their native energy state, while the dual ANN-based predictor using (i, i ± 4) and >(i, i ± 4) interaction energies showed 83% prediction accuracy. The present method provides a simple and accurate tool for predicting sequence potential for secondary structure conversions without using 3D structural information.     

Analyses of the temperature and pH effects on the complexation of magnesium and calcium in human blood plasma: an approach using artificial neural networks

The temperature and pH effects on the equilibrium of a blood plasma model have been studied on the basis of artificial neural networks. The proposed blood plasma was modeled considering two important metals, calcium and magnesium, and six ligands, namely, alanate, carbonate, citrate, glycinate, histidinate and succinate. A large data set has been used to simulate different concentrations of magnesium and calcium as a function of temperature and pH and these data were used for training the neural network. The proposed model allowed different types of analyses, such as the effects of pH on calcium and magnesium concentrations, the competition between calcium and magnesium for ligands and the effects of temperature on calcium and magnesium concentrations. The model developed was also used to predict how the variation of calcium concentration can affect magnesium concentrations. A comparison of neural network predictions against experimental data produced errors of about 3%. Moreover, in agreement with experimental measurements (Wang et al. in Arch. Pathol. 126:947–950, 2002; Heining et al. in Scand. J. Clin. Lab. Invest. 43:709–714, 1983), the artificial neural network predicted that calcium and magnesium concentrations decrease when pH increases. Similarly, the magnesium concentrations are less sensitive than calcium concentrations to pH changes. It is also found that both calcium and magnesium concentrations decrease when the temperature increases. Finally, the theoretical model also predicted that an increase of calcium concentrations will lead to an increase of magnesium concentration almost at the same rate. These results suggest that artificial neural networks can be efficiently applied as a complementary tool for studying metal ion complexation, with especial attention to the blood plasma analysis. Figure Artificial neural networks for predicting the behavior of calcium and magnesium as a function of pH and temperature in human blood plasma     

Determination of loratadine and pseudoephedrine sulfate in pharmaceuticals based on non-linear second-order spectrophotometric data generated by a pH-gradient flow injection technique and artificial neural networks

Loratadine (LOR) and pseudoephedrine sulfate (PES) were determined in pharmaceutical samples by using non-linear second-order data generated by a pH-gradient flow injection analysis (FIA) system with diode-array detection. Determination of both analytes was performed on the basis of differences between the acid–base and spectral features of each drug species. Non-linearities were detected by using both qualitative and quantitative tools. As a consequence of the non-linearity, a recently reported algorithm, artificial neural networks followed by residual bilinearization (ANN/RBL), was shown to furnish more satisfactory results. Recoveries of 99.7% (LOR) and 95.6% (PES) were obtained when analyzing a validation set containing unexpected components (the usual excipients found in pharmaceutical preparations). The average value obtained by implementation of the method on four replicates was compared with that obtained by a reference method based on HPLC (difference not significant; p > 0.05).     

Modeling the low-cycle fatigue behavior of visco-hyperelastic elastomeric materials using a new network alteration theory: Application to styrene-butadiene rubber

Although several theories were more or less recently proposed to describe the Mullins effect, i.e. the stress-softening after the first load, the nonlinear equilibrium and non-equilibrium material response as well as the continuous stress-softening during fatigue loading need to be included in the analysis to propose a reliable design of rubber structures. This contribution presents for the first time a network alteration theory, based on physical interpretations of the stress-softening phenomenon, to capture the time-dependent mechanical response of elastomeric materials under fatigue loading, and this until failure. A successful physically based visco-hyperelastic model is revisited by introducing an evolution law for the physical material parameters affected by the network alteration. The general form of the model can be basically represented by two parallel networks: a nonlinear equilibrium response and a time-dependent deviation from equilibrium, in which the network parameters become functions of the damage rate (defined as the ratio of the applied cycle over the applied cycle to failure). The mechanical behavior of styrene-butadiene rubber was experimentally investigated, and the main features of the constitutive response under fatigue loading are highlighted. The experimental results demonstrate that the evolution of the normalized maximum stress only depends on the damage rate endured by the material during the fatigue loading history. The average chain length and the average chain density are then taken as functions of the damage rate in the proposed network alteration theory. The new model is found to adequately capture the important features of the observed stress-strain curves under loading-unloading for a large spectrum of strain and damage levels. The model capabilities to predict variable amplitude tests are critically discussed by comparisons with experiments.     

A novel hybrid immune algorithm and its convergence based on the steepest descent algorithm

This paper proposes a novel hybrid immune algorithm (HIA) that can overcome the typical drawback of the artificial immune algorithm (AIA), which runs slowly and experiences slow convergence. The HIA combines the adaptive AIA based on the steepest descent algorithm. The HIA fully displays global search ability and the global convergence of the immune algorithm. At the same time, it inserts a quasi-descent operator to strengthen its local search ability. A good convergence of the HIA with the quasi-descent idea is shown as well. Numerical experiment results show that the HIA successfully improves running speed and convergence performance.     

Improved delay-dependent stability criterion for neural networks with time-varying delay

In this paper, the problem of delay-dependent asymptotic stability criterion for neural networks with time-varying delay has been considered. A new class of Lyapunov functional which contains a triple-integral term is constructed to derive some new delay-dependent stability criteria. The obtained criteria are less conservative because free-weighting matrices method and a convex optimization approach are considered. Finally, numerical examples are given to illustrate the effectiveness of the proposed method.     

The model reference control for the four-dimensional energy supply-demand system

The four-dimensional energy supply-demand system is a nonlinear complex system. The purpose of this paper is to introduce the model reference control method in the four-dimensional energy supply-demand system. By establishing the energy supply-demand reference system, controlling the imports in the four-dimensional energy supply-demand system to the ideal state.