In this paper we describe CB513 a non-redundant dataset, suitable for development of algorithms for prediction of secondary protein structure. A program was made in Borland Delphi for transforming data from our dataset to make it suitable for learning of neural network for prediction of secondary protein structure implemented in MATLAB Neural-Network Toolbox. Learning (training and testing) of neural network is researched with different sizes of windows, different number of neurons in the hidden layer and different number of training epochs, while using dataset CB513.

In this work, microcalorimetric technique was used to analyze Pb(II) toxic action on the metabolic activities of Candida humicola and Bacillus subtilis. The experimental results revealed that Pb(II) had a stimulating effect on C. humicola and B. subtilis growth at a relatively low concentration (10.0 g·mL−1); while, C. humicola and B. subtilis were inhibited completely when the concentrations were up to 320.0 and 160.0 g·mL−1, respectively, and the relationships between growth rate constant (k) and doses of Pb(II) were approximately linear for the two microbes at certain concerntrations. At the same time, their cell dry weight and turbidity (OD600) during growth were also obtained. Their thermogenic curves of the growth coincided well with their turbidity curves, elucidating that the microcalorimetric method agreed with the routine microbiology methods. All of these corroborate the validity and sensitivity of the microcalorimetric technique to investigate the toxic effect of Pb(II) on soil microorganisms.

We present a novel approach for computing the surface roughness-limited thermal conductivity of silicon nanowires with diameter D<100 nm. A frequency-dependent phonon scattering rate is computed from perturbation theory and related to a description of the surface through the root-mean-square roughness height Delta and autocovariance length L. Using a full phonon dispersion relation, we find a quadratic dependence of thermal conductivity on diameter and roughness as (D/Delta)(2). Computed results show excellent agreement with experimental data for a wide diameter and temperature range (25-350 K), and successfully predict the extraordinarily low thermal conductivity of 2 W m(-1) K-1 at room temperature in rough-etched 50 nm silicon nanowires.