From Isotropic to Anisotropic Side Chain Representations: Comparison of Three Models for Residue Contact Estimation

The criterion to determine residue contact is a fundamental problem in deriving knowledge-based mean-force potential energy calculations for protein structures. A frequently used criterion is to require the side chain center-to-center distance or the -to- atom distance to be within a pre-determined cutoff distance. However, the spatially anisotropic nature of the side chain determines that it is challenging to identify the contact pairs. This study compares three side chain contact models: the Atom Distance criteria (ADC) model, the Isotropic Sphere Side chain (ISS) model and the Anisotropic Ellipsoid Side chain (AES) model using 424 high resolution protein structures in the Protein Data Bank. The results indicate that the ADC model is the most accurate and ISS is the worst. The AES model eliminates about 95% of the incorrectly counted contact-pairs in the ISS model. Algorithm analysis shows that AES model is the most computational intensive while ADC model has moderate computational cost. We derived a dataset of the mis-estimated contact pairs by AES model. The most misjudged pairs are Arg-Glu, Arg-Asp and Arg-Tyr. Such a dataset can be useful for developing the improved AES model by incorporating the pair-specific information for the cutoff distance

Approximation with rates by Perturbed Kantorovich–Choquet neural network operators

This chapter deals with the determination of the rate of convergence to the unit of Perturbed Kantorovich–Choquet univariate and multivariate normalized neural network operators of one hidden layer. These are given through the univariate and multivariate moduli of continuity of the involved univariate or multivariate function or its high order derivatives and that appears in the right-hand side of the associated univariate and multivariate Jackson type inequalities. The activation function is very general, especially it can derive from any univariate or multivariate sigmoid or bell-shaped function. The right hand sides of our convergence inequalities do not depend on the activation function. It follows (Anastassiou, Quantitative Approximation by Perturbed Kantorovich–Choquet Neural Network Operators (2018) [1])

Separation of Radioactive Elements from Ethiopian Kenticha Pegmatite Ore by Hydrometallurgical Methods

The leaching and extraction behavior of uranium and thorium from a high-grade Ethiopian pegmatite ore in a mixture of hydrofluoric acid and sulfuric acid has been investigated. The effects of variables such as the temperature, particle size, acid concentration, and leaching time were studied. The leaching efficiency of uranium increased with increasing temperature to 150°C, at which 96% removal of uranium was achieved. Particles in the size range of − 100 + 75 μm resulted in the highest leaching of uranium, while formation of a colloidal suspension was observed when using a fine particle size fraction (− 75 μm). The dissolution of uranium increased with increasing leaching time. No significant systematic dependence of the leachability of thorium on the above variables was observed. Optimum extraction of uranium and thorium using D2EHPA was obtained when using aqueous/organic phase volume ratio of 1:1, solvent concentration of 0.3 M, and contact time of 20 min