A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM

Protein function is related to its chemical reaction to the surrounding environment including other proteins. On the other hand, this depends on the spatial shape and tertiary structure of protein and folding of its constituent components in space. The correct identification of protein domain fold solely using extracted information from protein sequence is a complicated and controversial task in the current computational biology. In this article a combined classifier based on the information content of extracted features from the primary structure of protein has been introduced to face this challenging problem. In the first stage of our proposed two-tier architecture, there are several classifiers each of which is trained with a different sequence based feature vector. Apart from the application of the predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, and different dimensions of pseudo-amino acid composition vectors in similar studies, the position specific scoring matrix (PSSM) has also been used to improve the correct classification rate (CCR) in this study. Using K-fold cross validation on training dataset related to 27 famous folds of SCOP, the 28 dimensional probability output vector from each evidence theoretic K-NN classifier is used to determine the information content or expertness of corresponding feature for discrimination in each fold class. In the second stage, the outputs of classifiers for test dataset are fused using Sugeno fuzzy integral operator to make better decision for target fold class. The expertness factor of each classifier in each fold class has been used to calculate the fuzzy integral operator weights. Results make it possible to provide deeper interpretation about the effectiveness of each feature for discrimination in target classes for query proteins.     

Effect of substitution of Ce on superconducting properties of Bi1.7Pb0.3Sr2Ca2−x Ce x Cu3O10+δ system

We have investigated the superconducting properties of the Bi_1.7 Pb_0.3Sr₂Ca_2−xCe _x Cu₃O_10+δ system with x=0.00, 0.02, 0.04, 0.08 and 0.1 by X-ray diffraction and magnetic susceptibility. The substitution of Ce for Ca has been found to drastically change the superconducting properties of the system. X-ray diffraction studies on these compounds indicate decrease in the c-parameter with increased substitution of Ce at Ca site and volume fraction of high T _c (2 : 2 : 2 : 3) phase decreases and low T _c phase increases. The magnetic susceptibility of this compound shows that the diamagnetic on set superconducting transition temperature (onset) varies from 109 K to 51 K for x=0.00, 0.02, 0.04, 0.08 and 0.1. These results suggest the possible existence of Ce in a tetravalent state rather than a trivalent state in this system; that is, Ca²⁺ → Ce⁴⁺ replacement changes the hole carrier concentration. Hole filling is the cause of lowering T _c of the system.     

Self-assembled growth of nanostructural Ge islands on bromine-passivated Si(111) surfaces at room temperature

We have deposited relatively thick (∼60 nm) Ge layers on Br-passivated Si(111) substrates by thermal evaporation under high vacuum conditions at room temperature. Ge has grown in a layer-plus-island mode although it is different from the Stranski-Krastanov growth mode observed in epitaxial growth. Both the islands and the layer are nanocrystalline. This appears to be a consequence of reduction of surface free energy of the Si(111) substrate by Br-passivation. The size distribution of the Ge nanoislands has been determined. The Br-Si(111) substrates were prepared by a liquid treatment, which may not produce exactly reproducible surfaces. Nevertheless, some basic features of the nanostructural island growth are reasonably reproducible, while there are variations in the details of the island size distribution.     

Swarm contours: A fast self‐organization approach for snake initialization

A major obstacle in real‐time performance of a visual tracking system is its initialization phase. Inspired by social behavior in fish and ant groups, a fast self‐organization approach to active‐contour initialization is proposed. Contours are emerged during two separate phases of aggregation and self‐assembly. Aggregation is achieved by a superposition of simpler behaviors, hunting, avoidance, and opportunism. Self‐assembly, which constitutes the explicit contour formation, occurs by mating behavior when the swarm becomes quite stable. Experiments indicate that the proposed method outperforms exhaustive image search for finding contours in high resolutions. © 2006 Wiley Periodicals, Inc. Complexity 12: 41–52, 2006