Effect of lipopolysaccharide on alteration of phospholipids and their fatty acid composition in spleen and thymus by in vitro metabolic labeling

Lipopolysaccharide (LPS) is an endotoxin, a potent stimulator of immune response and induction of LPS leads to acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). ARDS is a life-threatening disease worldwide with a high mortality rate. The immunological effect of LPS with spleen and thymus is well documented; however the impact on membrane phospholipid during endotoxemia has not yet been studied. Hence we aimed to investigate the influence of LPS on spleen and thymus phospholipid and fatty acid composition by 32P]orthophosphate labeling in rats. The in vitro labeling was carried out with phosphate-free medium (saline). Time course, LPS concentration-dependent, pre- and post-labeling with LPS and fatty acid analysis of phospholipid were performed. Labeling studies showed that 50 mu g LPS specifically altered the major phospholipids, phosphatidylcholine and phosphatidylglycerol in spleen and phosphatidylcholine in thymus. Fatty acid analysis showed a marked alteration of unsaturated fatty acids/saturated fatty acids in spleen and thymus leading to immune impairment via the fatty acid remodeling pathway. Our present in vitro lipid metabolic labeling study could open up new vistas for exploring LPS-induced immune impairment in spleen and thymus, as well as the underlying mechanism.

Effect of crystallite size and clustering in influencing the stability of phases of a very large tetragonality ferroelectric system 0.6BiFeO(3)-0.4PbTiO(3)

The role of crystallite size and clustering in influencing the stability of the structures of a large tetragonality ferroelectric system 0.6BiFeO(3)-0.4PbTiO(3) was investigated. The system exhibits cubic phase for a crystallite size similar to 25 nm, three times larger than the critical size reported for one of its end member PbTiO3. With increased degree of clustering for the same average crystallite size, partial stabilization of the ferroelectric tetragonal phase takes place. The results suggest that clustering helps in reducing the depolarization energy without the need for increasing the crystallite size of free particles.

Feature weighting for clustering by particle swarm optimization

Clustering has been the most popular method for data exploration. Clustering is partitioning the data set into sub-partitions based on some measures say the distance measure, each partition has its own significant information. There are a number of algorithms explored for this purpose, one such algorithm is the Particle Swarm Optimization(PSO) which is a population based heuristic search technique derived from swarm intelligence. In this paper we present an improved version of the Particle Swarm Optimization where, each feature of the data set is given significance accordingly by adding some random weights, which also minimizes the distortions in the dataset if any. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The experimental results shows that our proposed methodology performs significantly better than the previously performed experiments.

Robust formulations for clustering-based large-scale classification

Chebyshev-inequality-based convex relaxations of Chance-Constrained Programs (CCPs) are shown to be useful for learning classifiers on massive datasets. In particular, an algorithm that integrates efficient clustering procedures and CCP approaches for computing classifiers on large datasets is proposed. The key idea is to identify high density regions or clusters from individual class conditional densities and then use a CCP formulation to learn a classifier on the clusters. The CCP formulation ensures that most of the data points in a cluster are correctly classified by employing a Chebyshev-inequality-based convex relaxation. This relaxation is heavily dependent on the second-order statistics. However, this formulation and in general such relaxations that depend on the second-order moments are susceptible to moment estimation errors. One of the contributions of the paper is to propose several formulations that are robust to such errors. In particular a generic way of making such formulations robust to moment estimation errors is illustrated using two novel confidence sets. An important contribution is to show that when either of the confidence sets is employed, for the special case of a spherical normal distribution of clusters, the robust variant of the formulation can be posed as a second-order cone program. Empirical results show that the robust formulations achieve accuracies comparable to that with true moments, even when moment estimates are erroneous. Results also illustrate the benefits of employing the proposed methodology for robust classification of large-scale datasets.

Growth and Characterization of Vertically Aligned ZnO Hierarchical Nanostructures

Vertically aligned zinc oxide (ZnO) hierarchical nanostructures were developed by homo-epitaxial growth method using nickel as catalyst, and their physical properties were investigated and reported. ZnO nanorods grown by vapor-liquid-solid method are single crystalline and grown along the < 001 > direction, whereas the second order nano-branches are grown along the < 110 > direction. The homo-epitaxial relation between nano-branches (ZnOb) and ZnO cores (ZnOc) is found to be (110)ZnOb//(110)ZnOc and (002)ZnOb//(002)ZnOc. The simple and hierarchical nanostructures exhibited ultra-violet emission peak at 380 nm as near band edge emission of ZnO and have very weak defects related peak at 492 nm. (C) 2013 The Electrochemical Society. All rights reserved.

Compositional hierarchical monitoring automaton construction for LTL

In this paper we give a compositional (or inductive) construction of monitoring automata for LTL formulas. Our construction is similar in spirit to the compositional construction of Kesten and Pnueli [5]. We introduce the notion of hierarchical Büchi automata and phrase our constructions in the framework of these automata. We give detailed constructions for all the principal LTL operators including past operators, along with proofs of correctness of the constructions.

Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

Learning from positive and unlabelled examples using maximum margin clustering

Learning from Positive and Unlabelled examples (LPU) has emerged as an important problem in data mining and information retrieval applications. Existing techniques are not ideally suited for real world scenarios where the datasets are linearly inseparable, as they either build linear classifiers or the non-linear classifiers fail to achieve the desired performance. In this work, we propose to extend maximum margin clustering ideas and present an iterative procedure to design a non-linear classifier for LPU. In particular, we build a least squares support vector classifier, suitable for handling this problem due to symmetry of its loss function. Further, we present techniques for appropriately initializing the labels of unlabelled examples and for enforcing the ratio of positive to negative examples while obtaining these labels. Experiments on real-world datasets demonstrate that the non-linear classifier designed using the proposed approach gives significantly better generalization performance than the existing relevant approaches for LPU.

Modified particle swarm optimization for pattern clustering

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning and data mining. Clustering is grouping of a data set or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait according to some defined distance measure. In this paper we present the genetically improved version of particle swarm optimization algorithm which is a population based heuristic search technique derived from the analysis of the particle swarm intelligence and the concepts of genetic algorithms (GA). The algorithm combines the concepts of PSO such as velocity and position update rules together with the concepts of GA such as selection, crossover and mutation. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The performance of our method is better than k-means and PSO algorithm.

Simultaneous feature selection and clustering using particle swarm optimization

Data clustering groups data so that data which are similar to each other are in the same group and data which are dissimilar to each other are in different groups. Since generally clustering is a subjective activity, it is possible to get different clusterings of the same data depending on the need. This paper attempts to find the best clustering of the data by first carrying out feature selection and using only the selected features, for clustering. A PSO (Particle Swarm Optimization)has been used for clustering but feature selection has also been carried out simultaneously. The performance of the above proposed algorithm is evaluated on some benchmark data sets. The experimental results shows the proposed methodology outperforms the previous approaches such as basic PSO and Kmeans for the clustering problem.

Support vector clustering-based direct coherency identification of generators in a multi-machine power system

This study investigates the application of support vector clustering (SVC) for the direct identification of coherent synchronous generators in large interconnected multi-machine power systems. The clustering is based on coherency measure, which indicates the degree of coherency between any pair of generators. The proposed SVC algorithm processes the coherency measure matrix that is formulated using the generator rotor measurements to cluster the coherent generators. The proposed approach is demonstrated on IEEE 10 generator 39-bus system and an equivalent 35 generators, 246-bus system of practical Indian southern grid. The effect of number of data samples and fault locations are also examined for determining the accuracy of the proposed approach. An extended comparison with other clustering techniques is also included, to show the effectiveness of the proposed approach in grouping the data into coherent groups of generators. This effectiveness of the coherent clusters obtained with the proposed approach is compared in terms of a set of clustering validity indicators and in terms of statistical assessment that is based on the coherency degree of a generator pair.

H.264 COMPRESSED VIDEO CLASSIFICATION USING HISTOGRAM OF ORIENTED MOTION VECTORS (HOMV)

In this paper, we have proposed a simple and effective approach to classify H.264 compressed videos, by capturing orientation information from the motion vectors. Our major contribution involves computing Histogram of Oriented Motion Vectors (HOMV) for overlapping hierarchical Space-Time cubes. The Space-Time cubes selected are partially overlapped. HOMV is found to be very effective to define the motion characteristics of these cubes. We then use Bag of Features (B OF) approach to define the video as histogram of HOMV keywords, obtained using k-means clustering. The video feature, thus computed, is found to be very effective in classifying videos. We demonstrate our results with experiments on two large publicly available video database.

Novel spherical hierarchical structures of GdOOH and Eu:GdOOH: rapid microwave-assisted synthesis through self-assembly, thermal conversion to oxides, and optical studies

We report a novel, rapid, and low-temperature method for the synthesis of undoped and Eu-doped GdOOH spherical hierarchical structures, without using any structure-directing agents, through the microwave irradiation route. The as-prepared product consists of nearly monodisperse microspheres measuring about 1.3 mu m in diameter. Electron microscopy reveals that each microsphere is an assembly of two-dimensional nanoflakes (about 30 nm thin) which, in turn, result from the assembly of crystallites measuring about 9 nm in diameter. Thus, a three-level hierarchy can be seen in the formation of the GdOOH microspheres: from nanoparticles to 2D nanoflakes to 3D spherical structures. When doped with Eu3+ ions, the GdOOH microspheres show a strong red emission, making them promising candidates as phosphors. Finally, thermal conversion at modest temperatures leads to the formation of corresponding oxide structures with enhanced luminescence, while retaining the spherical morphology of their oxyhydroxide precursor.

Green ionothermal synthesis of hierarchical nanostructures of SnS2 and their Li-ion storage properties

Flower-like hierarchical architectures of layered SnS2 have been synthesized ionothermally for the first time, using a water soluble EMIM]BF4 ionic liquid (IL) as the solvent medium. At lower reaction temperatures, the hierarchical structures are formed of few-layered polycrystalline 2D nanosheet-petals composed of randomly oriented nanoparticles of SnS2. The supramolecular networks of the IL serve as templates on which the nanoparticles of SnS2 are glued together by combined effects of hydrogen bonding, electrostatic, hydrophobic and imidazolium stacking interactions of the IL, giving rise to polycrystalline 2D nanosheet-petals. At higher reaction temperatures, single crystalline plate-like nanosheets with well-defined crystallographic facets are obtained due to rapid inter-particle diffusion across the IL. Efficient surface charge screening by the IL favors the aggregation of individual nanosheets to form hierarchical flower-like architectures of SnS2. The mechanistic aspects of the ionothermal bottom-up hierarchical assembly of SnS2 nanosheets are discussed in detail. Li-ion storage properties of the pristine SnS2 samples are examined and the electrochemical performance of the sample synthesized at higher temperatures is found to be comparable to that reported for pristine SnS2 samples in the literature.

Regional flood frequency analysis using kernel- based fuzzy clustering approach

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. Key Points Kernel-based regionalization approach is presented for flood frequency analysis Kernel procedure to estimate flood quantiles at ungauged sites is developed A set of fuzzy regions is delineated in Ohio, USA