<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

Center for Informatics

Antimicrobial peptides (AMPs) are gaining popularity as better substitute to antibiotics. These peptides are shown to be active against several bacteria, fungi, viruses, protozoa and cancerous cells. Understanding the role of primary structure of AMPs in their specificity and activity is essential for their rational design as drugs. Collection of Anti-Microbial Peptides (CAMP) is a free online database that has been developed for advancement of the present understanding on antimicrobial peptides. It is manually curated and currently holds 3782 antimicrobial sequences. These sequences are divided into experimentally validated (patents and non-patents: 2766) and predicted (1016) datasets based on their reference literature. Information like source organism, activity (MIC values), reference literature, target and non-target organisms of AMPs are captured in the database. The experimentally validated dataset has been further used to develop prediction tools for AMPs based on the machine learning algorithms like Random Forests (RF), Support Vector Machines (SVM) and Discriminant Analysis (DA). The prediction models gave accuracies of 93.2% (RF), 91.5% (SVM) and 87.5% (DA) on the test datasets. The prediction and sequence analysis tools, including BLAST, are integrated in the database. CAMP will be a useful database for study of sequence-activity and-specificity relationships in AMPs. CAMP is freely available at . http://www.bicnirrh.res.in/antimicrobial. © The Author(s) 2009. Published by Oxford University Press.

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Nucleic Acids Research

CAMP: A useful resource for research on antimicrobial peptides

We study magnetism and hyperfine structure in Si16 H16 fullerenes endohedrally doped with a transition-metal atom M=Cr, Mn and Fe using density functional theory calculations. In all cases the endohedral fullerene maintains the same magnetic moment as in a free M atom and it is mainly localized on M with partial redistribution on the Si16 H16 cage. However, a comparison between the isotropic hyperfine parameter Aiso of M dopants with that of free M atoms reveals that the electronic wave function at the nucleus of Mn and Fe undergo a significant change both quantitatively and qualitatively as they become doped inside Si16 H16 cage, while for Cr it remains nearly the same. The endohedral doping of Mn and Fe atoms increases the value of the spin density at the nucleus of the M atom, $\rho$s (RM) and correspondingly, the value of Aiso. Analyzing the trend of the core spin-polarization induced by the singly occupied molecular orbitals (SOMOs), it is shown that the increase in the value of Aiso for Mn and Fe atoms comes directly from an increase in the contribution of SOMOs to $\rho$s (RM) and indirectly from hybridization of 3d orbitals of the dopants with the s - and p -like orbitals of the cage. The latter results in a decrease in the spin-polarization of the core 2s and 3s orbitals so that they less effectively contribute to $\rho$s (RM). These results are likely to have wider applicability and could offer a way to identify endohedral doping. {\textcopyright} 2009 The American Physical Society.

Physical Review B - Condensed Matter and Materials Physics

First-principles calculations of hyperfine structure in M -doped Si16 H16 fullerene cages (M=Cr, Mn and Fe)

APS March Meeting Abstracts, A26.

Clusters, Platelets and Nanowires of Mo-S and Their Assemblies

The geometric and electronic structures of NaN, CuN and AgN metal clusters are systematically studied based on the density functional theory over a wide range of cluster sizes 2N75. A remarkable similarity is observed between the optimized geometric structures of alkali and noble metal clusters over all of the calculated cluster sizes N. The most stable structures are the same for the three different metal clusters for approximately half the cluster sizes N considered in this study. Even if the most stable structures are different, the same types of structures are obtained when the metastable structures are also considered. For all of the three different metal clusters, the cluster shapes change in the order of linear, planar, opened and closed structures with increasing N. This structural-type transition leads to a deviation from the monotonic increase in the specific volume with N. A remarkable similarity is also observed for the N dependence of the cluster energy E (N) for the most stable geometric structures. The amplitude of this energy difference is larger in the two noble metal clusters than in the alkali metal cluster. This is attributed to the contribution of d electrons to the bonds. The magic number is explicitly defined with a new criterion in the framework of total energy calculations. In the case of NaN, a semiquantitative comparison between the experimental abundance spectra [Knight, Phys. Rev. Lett. 52, 2141 (1984)] and the total energy calculations is carried out. The changing aspect of the Kohn-Sham eigenvalues from N=2 to N=75 is presented for the three different metal clusters. The feature of the bulk density of states already appears at N=75 for all of three clusters. With increasing N, the highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) gap clearly exhibits an odd-even alternation and converges to 0. Although there is a similarity in the N dependence of the HOMO-LUMO gap between the three metal clusters, it is much stronger between the two noble metal clusters. The growth aspect of the d band below the Fermi level of the noble metal clusters with increasing N is presented. A good correspondence is observed in the d characteristic of the electronic states between the cluster composed of 75 atoms and the bulk metal. The similarities observed in the N dependence of the geometric structures and E(N)s originate from the similarity in that of the electronic structures. {\textcopyright} 2009 American Institute of Physics.

Journal of Chemical Physics

Comprehensive study of sodium, copper and silver clusters over a wide range of sizes 2≤N≤75

The objective of this study was to develop a unified correlation for the volumetric mass-transfer coefficient (kLa) and effective interfacial area (a) in bubble columns for various gas-liquid systems using support vector regression (SVR-) based modeling technique. From the data published in the open literature, 1600 data points from 27 open sources spanning the years 1965-2007 for kLa and 1330 data points from 28 open sources spanning the years 1968-2007 for a were collected. Generalized SVR-based models were developed for the relationship between kLa (and a) and each design and operating parameters such as column and sparger geometry, gas-liquid physical properties, operating temperature, pressure, superficial gas velocity, and so on. Further, these models for kLa and a are available online at http://www.esnips.com/web/UICT-NCL. The proposed generalized SVR-based correlations for kLa and a have prediction accuracies of 99.08% and 98.6% and average absolute relative errors (AAREs) of 7.12% and 5.01%, respectively. Also, the SVR-based correlation provided much improved predictions compared to those obtained using empirical correlations from the literature. © 2009 American Chemical Society.

Industrial and Engineering Chemistry Research

Development of unified correlations for volumetric mass-transfer coefficient and effective interfacial area in bubble column reactors for various gas-liquid systems using support vector regression

The objective of this study was to develop hybrid genetic algorithm-support vector regression (GA-SVR)- based correlations for overall gas hold-up (εG), volumetric mass-transfer coefficient (k1a), and effective interfacial area (a) in bubble column reactors for gas-liquid systems employing viscous Newtonian and non-Newtonian systems as the liquid phase. The hybrid GA-SVR is a novel technique based on the feature generation approach using genetic algorithm (GA). In the present study, GA has been used for nonlinear rescaling of attributes. These, exponentially scaled, are eventually subjected to SVR training. The technique is an extension of conventional SVR technique, showing relatively enhanced results. For this purpose an extensive literature search was done. From the published literature, 1629 data points for viscous Newtonian and 845 data points for viscous non-Newtonian systems for εG, 500 data points for viscous Newtonian and 556 data points for viscous non-Newtonian systems for kLa, and 208 data points for viscous non-Newtonian systems for a, respectively, were collected. These data sets were collected spanning the years 1965-2007. Correlations were developed after taking into account all the parameters affecting εG, kLa, and a such as column and sparger geometry, gas-liquid properties, operating temperature, pressure, and superficial gas and liquid velocities. The correlations thus developed gave prediction accuracies of 0.994 and 0.999 and average absolute relative errors (AARE) of 3.75 and 1.65% for viscous Newtonian and non-Newtonian systems for εG, prediction accuracies of 0.983 and 0.998 and AARE of 8.62 and 1.91% for viscous Newtonian and non-Newtonian systems for kLa, and prediction accuracy of 0.999 and AARE of 1% for viscous non-Newtonian systems for a, respectively. These correlations also showed much improved results when compared with all the existing correlations proposed in literature. To facilitate their usage, all the hybrid GA-SVR-based correlations have been uploaded on the web link http://www.esnips.com/web/UICT-NCL. © 2009 American Chemical Society.

Development of correlations for overall gas hold-up, volumetric mass transfer coefficient, and effective interfacial area in bubble column reactors using hybrid genetic algorithm-support vector regression technique: Viscous newtonian and non-newtonian liquids

Accurate classification of instances depends on identification and removal of redundant features. Classification of data having high dimensionality is usually performed in conjunction with an appropriate feature selection method. Feature selection enables identification of the most informative feature subset from the enormously vast search space that can accurately classify the given data. We propose an ant colony optimization (ACO)/random forest based hybrid filter-wrapper search technique, which traverses the search space and selects a feature subset with high classifying ability. We evaluate the performance of our algorithm on four widely studied CoEPrA (Comparative Evaluation of Prediction Algorithms, http://coepra.org) datasets. The performance of the software ants mediated hybrid filter/wrapper approach compares well with the available competition results. Thus, the proposed Ant Colony Optimization based technique can effectively find small feature subsets capable of classifying with a very good accuracy and can be employed for feature subset selection with a high level of confidence. © 2009 Bentham Science Publishers Ltd.

Combinatorial Chemistry and High Throughput Screening

Feature selection and classification employing hybrid ant colony optimization/random forest methodology

Interaction of hydrogen with aluminum phosphide clusters has been investigated using the density functional method of Becke's three-parameter hybrid functional with the nonlocal correlation of Lee, Yang and Parr. Berny structural optimization and frequency analyses are performed with the basis of 6-311 + G(d). Our results show large binding energies of a single hydrogen atom on small AlP clusters and large highest occupied and lowest unoccupied molecular-orbital gaps for (AlP)H and (AlP)5H making these species behave like magic clusters. Calculations on two hydrogen atoms on AlP clusters show large binding energies for (AlP)nH2 with n = 1, 3, 5 and 7. In general the binding energy of H and 2H are both found to decrease with an increase in the cluster size. And the calculations also suggest that hydrogen should be dissociated on (AlP)2 and (AlP)3. {\textcopyright} 2009 Elsevier B.V. All rights reserved.

Materials Chemistry and Physics

Magic behavior and bonding nature in hydrogenated aluminum phosphide clusters

A quantitative structure-property relationship (QSPR) approach was used to develop a predictive model for viscosities of pure organic liquids using a set of 403 compounds that belong to diverse classes of organic chemicals. A pool of 116 descriptors that encode topostructural, topochemical, electrotopological, geometrical, and quantum chemical properties of the organic compounds was used to develop QSPR models, based on the robust Random Forest (RF) regression algorithm. The performance of the algorithm, in terms of correlation coefficients and mean square errors, was determined to be good. The capability of the algorithm to build models and select the most-informative features simultaneously is very useful for several quantitative structure-activity/ property relationship tasks. The eight most-dominant features selected by the RF regression algorithm primarily contained predictors that encode characteristics of atoms and groups that form hydrogen bonds, as well as factors involving molecular shape and size. © 2009 American Chemical Society.

Quantitative structure-property relationship (QSPR) prediction of liquid viscosities of pure organic compounds employing random forest regression

We report the atomic structure of a multicomponent Cu45Zr45Ag10 bulk metallic glass investigated by state-of-the-art experimental and computational techniques. In comparison with a binary Cu50Zr50 metallic glass, Zr-rich interpenetrating clusters centered by paired and stringed Ag atoms and Cu-rich icosahedra are widely observed in the ternary Cu45Zr45Ag10 alloy. The atomic-scale heterogeneity caused by chemical short- and medium-range order is found to play a key role in stabilizing the liquid phase and in improving the glass forming ability of the multicomponent alloy. {\textcopyright} 2009 The American Physical Society.