Combining Bayes Classification and Point Group Symmetry under Boolean Framework for Enhanced Protein Quaternary Structure Inference

Our ability to infer the protein quaternary structure automatically from atom and lattice information is inadequate, especially for weak complexes, and heteromeric quaternary structures. Several approaches exist, but they have limited performance. Here, we present a new scheme to infer protein quaternary structure from lattice and protein information, with all-around coverage for strong, weak and very weak affinity homomeric and heteromeric complexes. The scheme combines naive Bayes classifier and point group symmetry under Boolean framework to detect quaternary structures in crystal lattice. It consistently produces >= 90% coverage across diverse benchmarking data sets, including a notably superior 95% coverage for recognition heteromeric complexes, compared with 53% on the same data set by current state-of-the-art method. The detailed study of a limited number of prediction-failed cases offers interesting insights into the intriguing nature of protein contacts in lattice. The findings have implications for accurate inference of quaternary states of proteins, especially weak affinity complexes.

Generalization of Abrams' law

Considering cement based composites as chemically bonded ceramics (CBC) the consequent strength development with age is essentially a constant volume solidification process, such that the hydrated gel particles fill the space resulting in the compatible gel space ratios. Analysis has been done of the extensively used graphical method of mix design (British method of mix design) i.e., the relation between the compressive strength and the free water - cement ratio. By considering the strength (S) at w/c 0.5 (S-0.5) as the reference state to reflect the synergetic effects between constituents of concrete a generalized relationship obtained is of the form {S/S-0.5} = a + b {1/(w/c)}.

Differences in the glass-forming ability of rapidly solidified and mechanically alloyed Ti-Ni-Cu alloys

Melt spinning of Ti50Ni50 ? xCux (x = 10, 25, 40) alloys showed that the glass-forming ability is good for Cu-rich compositions and poor for Ni-rich compositions. The results of mechanical alloying experiments in the same system showed a reverse trend as far as the glass-forming ability is concerned. These contradictory results are explained in the light of thermodynamic and kinetic considerations. Crystallization results of the melt spun alloys are also presented.

Thermomechanical generalization of partial contact in pin joints

Mechanical fasteners introduce structural weakness, still they are an essential constituent of most structures as they permit interchangeability of parts and flexible construction programs; Variable temperature operations of Aerospace and Nuclear structures make it imperative to investigate the thermoelastic behaviour of joints. This paper explores analytically similar mechanical and thermal parameters to generalise the thermomechanical behaviour of a pin joint in an isotropic Sheet for a class of configurations. This generalization enables virtually direct application of existing information regarding joints under pure mechanical loading to joints subjected to combined thermomechanical loading, thus reducing the efforts of both the analyst and the designer by an order of magnitude. Copyright (C) 1996 Published by Elsevier Science Ltd.

Japanese encephalitis virus infection of mouse cell lines: ability to prime mice for generation of virus specific cytotoxic T lymphocytes and differences in CTL recognisable viral determinants

Ten different mouse cell lines were examined for Japanese encephalitis virus (JEV) infection in vitro and then tested for their ability to generate virus specific cytotoxic T lymphocytes (CTL). Among all cell lines examined, Neuro La (a neuroblastoma) was readily infected with JEV as examined by immunofluorescence and viral replication. Among other cells, P388D1, RAW 264.7 (Macrophage origin), Sp2/0 (B-cell Hybridoma), YAC-1 (T-cell lymphoma), and L929 (Fibroblast) were semipermissive to JEV infection. The cytopathic effects caused by progressive JEV infection varied from cell line to cell line. In the case of YAC-1 cells long-term viral antigen expression was observed without significant alterations in cell viability. Intermediate degrees of cytopathicity are seen in RAW 264.7 and L929 cells while infection of PS, Neuro 2a, P388D1 and Sp2/0 caused major viability losses. All infected cell lines were able to prime adult BALB/c (H-2(d)) mice for the generation of secondary JEV specific CTL. In contrast to YAC-1, the permissive neuroblastoma cell line Neuro 2a (H-2K(k)D(d)) was found to be least efficient in its ability to stimulate anti-viral CTL generation. Cold target competition studies demonstrated that both Neuro 2a and YAC-1 (H-2K(k)D(d)) cells expressed similar viral determinants that are recognised by CTL, suggesting that the reason for the lower ability of Neuro 2a to stimulate anti-viral CTL was not due to lack of viral CTL determinants. These findings demonstrate that a variety of mouse cell lines can be infected with Japanese encephalitis virus, and that these infected cells could be utilised to generate virus specific CTL in BALB/c mice.

Studies in crystal engineering: Steering ability of fluorine in styrylcoumarins

In continuation of our studies on crystal engineering using fluorine as a steering group, the photobehaviour of di and tri fluoro 4-styrylcoumarins has been examined. It is found that out of the five derivatives, four crystallize into P-packing mode producing syn-HH photodimer upon irradiation whereas the parent hydrocarbon produces an anti K-T dimer. The packing features of the photolabile crystals of 4-(4-fluorostyryl)-6-fluorocoumarin (1), 4-(2,6-difluorostyryl) 6-fluorocoumarin (2) and the photodimer (3a) of 4-(2,6-fluorostyryl)-7-fluorocoumarin (3) have been determined by single crystal X-ray diffraction studies. The stereochemistry of the photodimer of 4-(2-fluorostyryl)-6-fluorocoumarin (4) is deduced based on preliminary X-ray crystallographic data. However, 4-(2,6-difluorostyryl) coumarin (5) is photoinert. The remarkable steering ability of fluorine is established with the molecular packing in the crystal lattice leading to the formation of syn H-H dimer in the above four examples. (C) 1999 Elsevier Science Ltd. All rights reserved.

A fast quasi-Newton method for semi-supervised SVM

Due to its wide applicability, semi-supervised learning is an attractive method for using unlabeled data in classification. In this work, we present a semi-supervised support vector classifier that is designed using quasi-Newton method for nonsmooth convex functions. The proposed algorithm is suitable in dealing with very large number of examples and features. Numerical experiments on various benchmark datasets showed that the proposed algorithm is fast and gives improved generalization performance over the existing methods. Further, a non-linear semi-supervised SVM has been proposed based on a multiple label switching scheme. This non-linear semi-supervised SVM is found to converge faster and it is found to improve generalization performance on several benchmark datasets. (C) 2010 Elsevier Ltd. All rights reserved.

Rough Core Vector Clustering

Support Vector Clustering has gained reasonable attention from the researchers in exploratory data analysis due to firm theoretical foundation in statistical learning theory. Hard Partitioning of the data set achieved by support vector clustering may not be acceptable in real world scenarios. Rough Support Vector Clustering is an extension of Support Vector Clustering to attain a soft partitioning of the data set. But the Quadratic Programming Problem involved in Rough Support Vector Clustering makes it computationally expensive to handle large datasets. In this paper, we propose Rough Core Vector Clustering algorithm which is a computationally efficient realization of Rough Support Vector Clustering. Here Rough Support Vector Clustering problem is formulated using an approximate Minimum Enclosing Ball problem and is solved using an approximate Minimum Enclosing Ball finding algorithm. Experiments done with several Large Multi class datasets such as Forest cover type, and other Multi class datasets taken from LIBSVM page shows that the proposed strategy is efficient, finds meaningful soft cluster abstractions which provide a superior generalization performance than the SVM classifier.

Relation between Local Structure and Glass Forming Ability of Liquid Alloys

An attempt has been made to describe the glass forming ability (GFA) of liquid alloys, using the concepts of the short range order (SRO) and middle range order (MRO) characterizing the liquid structure.A new approach to obtain good GFA of liquid alloys is based on the following four main factors: (1) formation of new SRO and competitive correlation with two or more kinds of SROs for crystallization, (2) stabilization of dense random packing by interaction between different types of SRO, (3) formation of stable cluster (SC) or middle range order (MRO) by harmonious coupling of SROs, and (4) difference between SRO characterizing the liquid structure and the near-neighbor environment in the corresponding equilibrium crystalline phases. The atomic volume mismatch estimated from the cube of the atomic radius was found to be a close relation with the minimum solute concentration for glass formation. This empirical guideline enables us to provide the optimum solute concentration for good GFA in some ternary alloys. Model structures, denoted by Bernal type and the Chemical Order type, were again tested in the novel description for the glass structure as a function of solute concentration. We illustrated the related energetics of the completion between crystal embryo and different types of SRO. Recent systematic measurements also provide that thermal diffusivity of alloys in the liquid state may be a good indicator of their GFA.

Hybrid Bayesian Classifier for Improved Classification Accuracy

The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique-Hybrid Bayesian Classifier (HBC)-where the class prior probabilities are determined by unmixing a supplemental low spatial-high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial-low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments-first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS-III MS data through Bayesian classifier. In the second experiment, abundances obtained by unmixing Landsat Enhanced Thematic Mapper Plus are used as priors, and posterior probabilities are determined from the ground data to classify IKONOS MS images through Bayesian classifier. The results indicated that HBC systematically exploited the information from two image sources, improving the overall accuracy of LISS-III MS classification by 6% and IKONOS MS classification by 9%. Inclusion of prior probabilities increased the average producer's and user's accuracies by 5.5% and 6.5% in case of LISS-III MS with six classes and 12.5% and 5.4% in IKONOS MS for five classes considered.

A meta-analysis of the traits affecting dispersal ability in butterflies: can wingspan be used as a proxy?

1. Dispersal ability of a species is a key ecological characteristic, affecting a range of processes from adaptation, community dynamics and genetic structure, to distribution and range size. It is determined by both intrinsic species traits and extrinsic landscape-related properties. 2. Using butterflies as a model system, the following questions were addressed: (i) given similar extrinsic factors, which intrinsic species trait(s) explain dispersal ability? (ii) can one of these traits be used as a proxy for dispersal ability? (iii) the effect of interactions between the traits, and phylogenetic relatedness, on dispersal ability. 3. Four data sets, using different measures of dispersal, were compiled from published literature. The first data set uses mean dispersal distances from capture-mark-recapture studies, and the other three use mobility indices. Data for six traits that can potentially affect dispersal ability were collected: wingspan, larval host plant specificity, adult habitat specificity, mate location strategy, voltinism and flight period duration. Each data set was subjected to both unifactorial, and multifactorial, phylogenetically controlled analyses. 4. Among the factors considered, wingspan was the most important determinant of dispersal ability, although the predictive powers of regression models were low. Voltinism and flight period duration also affect dispersal ability, especially in case of temperate species. Interactions between the factors did not affect dispersal ability, and phylogenetic relatedness was significant in one data set. 5. While using wingspan as the only proxy for dispersal ability maybe problematic, it is usually the only easily accessible species-specific trait for a large number of species. It can thus be a satisfactory proxy when carefully interpreted, especially for analyses involving many species from all across the world.

CNT loading into cationic cholesterol suspensions show improved DNA binding and serum stability and ability to internalize into cancer cells

Methods which disperse single-walled carbon nanotubes (SWNTs) in water as `debundled', while maintaining their unique physical properties are highly useful. We present here a family of cationic cholesterol compounds (Chol(+)) {Cholest-5en-3 beta-oxyethyl pyridinium bromide (Chol-PB+), Cholest-5en-3 beta-oxyethyl N-methyl pyrrolidinium bromide (Chol-MPB+), Cholest-5en-3 beta-oxyethyl N-methyl morpholinium bromide (Chol-MMB+) and Cholest-5en-3 beta-oxyethyl diazabicyclo octanium bromide (Chol-DOB+)}. Each of these could be easily dispersed in water. The resulting cationic cholesterol (Chol(+)) suspensions solubilized single-walled carbon nanotubes (SWCNTs) by the non-specific physical adsorption of Chol(+) to form stable, transparent, dark aqueous suspensions at room temperature. Electron microscopy reveals the existence of highly segregated CNTs in these samples. Zeta potential measurements showed an increase in potential of cationic cholesterol aggregates on addition of CNTs. The CNT-Chol(+) suspensions were capable of forming stable complexes with genes (DNA) efficiently. The release of double-helical DNA from such CNT-Chol(+) complexes could be induced upon the addition of anionic micellar solution of SDS. Furthermore, the CNT-based DNA complexes containing cationic cholesterol aggregates showed higher stability in fetal bovine serum media at physiological conditions. Confocal studies confirm that CNT-Chol(+) formulations adhere to HeLa cell surfaces and get internalized more efficiently than the cationic cholesterol suspensions alone (devoid of any CNTs). These cationic cholesterol-CNT suspensions therefore appear to be a promising system for further use in biological applications.

Generalization of the collision cone approach for motion safety in 3-D environments

Avoidance of collision between moving objects in a 3-D environment is fundamental to the problem of planning safe trajectories in dynamic environments. This problem appears in several diverse fields including robotics, air vehicles, underwater vehicles and computer animation. Most of the existing literature on collision prediction assumes objects to be modelled as spheres. While the conservative spherical bounding box is valid in many cases, in many other cases, where objects operate in close proximity, a less conservative approach, that allows objects to be modelled using analytic surfaces that closely mimic the shape of the object, is more desirable. In this paper, a collision cone approach (previously developed only for objects moving on a plane) is used to determine collision between objects, moving in 3-D space, whose shapes can be modelled by general quadric surfaces. Exact collision conditions for such quadric surfaces are obtained and used to derive dynamic inversion based avoidance strategies.

Human action recognition using a fast learning fully complex-valued classifier

In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, ``fast learning fully complex-valued neural (FLFCN) classifier'' is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier. (c) 2012 Elsevier B.V. All rights reserved.