Variable predictive model based classification algorithm for effective separation of protein structural classes

Variable predictive model based class discrimination (VPMCD) algorithm is proposed as an effective protein secondary structure classification tool. The algorithm mathematically represents the characteristics amino acid interactions specific to each protein structure and exploits them further to distinguish different structures. The new concept and the VPMCD classifier are established using well-studied datasets containing four protein classes as benchmark. The protein samples selected from SCOP and PDB databases with varying homology (25-100%) and non-uniform distribution of class samples provide challenging classification problem. The performance of the new method is compared with advanced classification algorithms like component coupled, SVM and neural networks. VPMCD provides superior performance for high homology datasets. 100% classification is achieved for self-consistency test and an improvement of 5% prediction accuracy is obtained during Jackknife test. The sensitivity of the new algorithm is investigated by varying model structures/types and sequence homology. Simpler to implement VPMCD algorithm is observed to be a robust classification technique and shows potential for effective extensions to other clinical diagnosis and data mining applications in biological systems.     

Efficient Route Selection Scheme in MANET Using Enhanced AODV Protocol

Mobile Ad Hoc network comprises of mobile nodes communicating over wireless medium. Due to ease of communication and flexibility, MANET has become an active area of research in wireless communication. However, issues such as limited battery capacity, dynamic topology and node mobility cause frequent link breakage due to which reroute discovery process is required to be initiated resulting in increase in latency and routing overheads. Many research works have been carried out for improvement of an existing routing scheme, however they do not scale-up well to provide stable and energy efficient route. This paper introduces a novel routing algorithm ENH-AODV (Enhanced AODV) that focuses on selection of an efficient route on the basis of quality of both links and nodes impending during route discovery process. In addition, each node maintains a list, consisting of details of nearby nodes with good energy level through hello mechanism. Simulation is carried out over network simulator and results prove that the proposed scheme is more effective and advantageous than AODV in terms of normalized routing load, end to end delay, packet delivery ratio and throughput.

     

Hypergeometrically Represented Responsive and Reliable Cloud Service Model for Personal and Private Clouds

Wireless Personal Communications - Last two decades have witnessed a tremendous evolution and growth of cloud computing paradigm and reliability estimation techniques. In spite of recent...     

Thiol-stabilized luminescent CdTe quantum dot as biological fluorescent probe for sensitive detection of methyl parathion by a fluoroimmunochromatographic technique

Quantum dots (QDs) are preferred as high-resolution biological fluorescent probes because of their inherent optical properties compared with organic dyes. This intrinsic property of QDs has been made use of for sensitive detection of methylparathion (MP) at picogramme levels. The specificity of the assay was attributed to highly specific immunological reactions. Competitive binding between free MP and CdTe QD bioconjugated MP (MP-BSA-CdTe) with immobilized anti-MP IgY antibodies was monitored in a flow-injection system. The fluorescence intensity of MP-BSA-CdTe bioconjugate eluted from the column was found to be directly proportional to the free MP concentration. Hence, it was possible to detect MP in a linear range of 0.1–1 ng mL⁻¹ with a regression coefficient R ² = 0.9905. In this investigation, IgY proved advantageous over IgG class immunoglobulins in terms of yield, stability, cost effectiveness, and enhancement of assay sensitivity. The photo-absorption spectrum of bioconjugated CdTe QD (λ _max = 310 nm) confirmed nano-biomolecular interactions. The results suggest the potential application of bioconjugation and nano-biomolecular interactions of QDs for biological labeling and target analyte detection with high sensitivity.     

Efficient Ad Hoc On Demand Distance Vector Routing Protocol Based on Route Stability in MANETs

International Journal of Wireless Information Networks - Nodes in Mobile Ad hoc Networks (MANETs) communicate wirelessly without any dedicated infrastructure and administrative control. In MANETs,...     

Variable interaction network based variable selection for multivariate calibration

Multivariate calibration problems often involve the identification of a meaningful subset of variables, from a vast number of variables for better prediction of output variables. A new graph theoretic method based on partial correlations (variable interaction network—VIN) is proposed. Many well studied representative calibration datasets spanning different application domains are selected for investigating the performance. Partial least squares (PLS) regression models combined with variable selection techniques are employed for benchmarking the performance. Subsets of variables with different number of variables are retained for the final analysis after VIN selection and progressive prediction accuracies are used for comparison. VIN-PLS results show significant improvement in prediction efficiencies and variable subset optimization. Improvement of up to 45% over existing methods with significantly fewer variables is achieved using the new method. Advantages of VIN based variable selection are highlighted.