Incorporation of β-Alanine in Cu(II) ATCUN Peptide Complexes Increases ROS Levels,DNA Cleavage and Antiproliferative Activity**

Redox-active Cu(II) complexes are able to form reactive oxygen species (ROS) in the presence of oxygen and reducing agents. Recently, Faller et al. reported that ROS generation by Cu(II) ATCUN complexes is not as high as assumed for decades. High complex stability results in silencing of the Cu(II)/Cu(I) redox cycle and therefore leads to low ROS generation. In this work, we demonstrate that an exchange of the α-amino acid Gly with the β-amino acid β-Ala at position 2 (Gly2→β-Ala2) of the ATCUN motif reinstates ROS production (^•OH and H₂O₂). Potentiometry, cyclic voltammetry, EPR spectroscopy and DFT simulations were utilized to explain the increased ROS generation of these β-Ala2-containing ATCUN complexes. We also observed enhanced oxidative cleavage activity towards plasmid DNA for β-Ala2 compared to the Gly2 complexes. Modifications with positively charged Lys residues increased the DNA affinity through electrostatic interactions as determined by UV/VIS, fluorescence, and CD spectroscopy, and consequently led to a further increase in nuclease activity. A similar trend was observed regarding the cytotoxic activity of the complexes against several human cancer cell lines where β-Ala2 peptide complexes had lower IC₅₀ values compared to Gly2. The higher cytotoxicity could be attributed to an increased cellular uptake as determined by ICP-MS measurements.     

A kinetic Monte Carlo simulation study of inositol 1,4,5-trisphosphate receptor (IP3R) calcium release channel

Most of the previously theoretical studies about the stochastic nature of the IP3R calcium release channel gating use the chemical master equation (CME) approach. Because of the limitations of this approach we have used a stochastic simulation algorithm (SSA) presented by Gillespie. A single subunit of De Young-Keizer (DYK) model was simulated using Gillespie algorithm. The model has been considered in its complete form with eight states. We investigate the conditions which affect the open state of the model. Calcium concentrations were the subject of fluctuation in the previous works while in this study the population of the states is the subject of stochastic fluctuations. We found out that decreasing open probability is a function of Ca(2+) concentration in fast time domain, while in slow time domain it is a function of IP3 concentration. Studying the population of each state shows a time dependent reaction pattern in fast and medium time domains (10(-4) and 10(-3)s). In this pattern the state of X(010) has a determinative role in selecting the open state path. Also, intensity and frequency of fluctuations and Ca(2+) inhibitions have been studied. The results indicate that Gillespie algorithm can be a better choice for studying such systems, without using any approximation or elimination while having acceptable accuracy. In comparison with the chemical master equation, Gillespie algorithm is also provides a wide area for studying biological systems from other points of view.     

Estimation of mutual information by the fuzzy histogram

Mutual Information (MI) is an important dependency measure between random variables, due to its tight connection with information theory. It has numerous applications, both in theory and practice. However, when employed in practice, it is often necessary to estimate the MI from available data. There are several methods to approximate the MI, but arguably one of the simplest and most widespread techniques is the histogram-based approach. This paper suggests the use of fuzzy partitioning for the histogram-based MI estimation. It uses a general form of fuzzy membership functions, which includes the class of crisp membership functions as a special case. It is accordingly shown that the average absolute error of the fuzzy-histogram method is less than that of the naïve histogram method. Moreover, the accuracy of our technique is comparable, and in some cases superior to the accuracy of the Kernel density estimation (KDE) method, which is one of the best MI estimation methods. Furthermore, the computational cost of our technique is significantly less than that of the KDE. The new estimation method is investigated from different aspects, such as average error, bias and variance. Moreover, we explore the usefulness of the fuzzy-histogram MI estimator in a real-world bioinformatics application. Our experiments show that, in contrast to the naïve histogram MI estimator, the fuzzy-histogram MI estimator is able to reveal all dependencies between the gene-expression data.     

Describing function based methods for predicting chaos in a class of fractional order differential equations

This paper deals with two different methods for predicting chaotic dynamics in fractional order differential equations. These methods, which have been previously proposed for detecting chaos in classical integer order systems, are based on using the describing function method. One of these methods is constructed based on Genesio–Tesi conjecture for existence of chaos, and another method is introduced based on Hirai conjecture about occurrence of chaos in a nonlinear system. These methods are restated to use in predicting chaos in a fractional order differential equation of the order between 2 and 3. Numerical simulation results are presented to show the ability of these methods to detect chaos in two fractional order differential equations with quadratic and cubic nonlinearities.     

Regular oscillations or chaos in a fractional order system with any effective dimension

This paper introduces a fractional order system which can generate regular oscillations or create chaos. It shows that this system is capable to create regular or nonregular oscillations under suitable conditions. These necessary conditions are achieved by violation of the no-chaos criteria. The effective dimension of the proposed system can be chosen any order less than three. Therefore, this system is a good example for limit cycle or chaos generation via fractional-order systems with low orders. Numerical simulations illustrate behavior of the proposed system in different situations.     

Solid-phase extraction based on multi-walled carbon nanotube (MWCNT) sorbents combined with bio-coacervation extraction for the determination of atrazine from water samples followed by HPLC analysis

In this study, combined technique of solid-phase extraction based on multi-walled carbon nanotubes with bio-coacervation extraction (SPE-MWCNT-BCAE) has been developed as a new sample preparation method for the determination of atrazine from water samples. The proposed method involves two steps: analyte enrichment on the solid sorbent and subsequently elution of the analyte by an appropriate solvent. Multi-walled carbon nanotubes (MWCNTs) were used as the sorbent. They have high specific surface area, nano-scale structure and high diffusion rate. The second step is based on the use of bioaggregates for analyte re-enrichment, which consists of biosurfactants and ionic liquid. This method follows the principles of green chemistry. Parameters affecting the extraction efficiency were optimized. Under optimum conditions, the enrichment factor was 176. The linear dynamic range (LDR) and limit of detection (LOD) were 2–100 µg L^?1 and 0.66 µg L^?1, respectively. The relative standard deviation (RSD) for six replicate measurements was 3.8%. The method was applied to the determination of ultratrace levels of atrazine in environmental water samples with satisfactory results.