Solvent effect on the fluorescence quenching of biologically active carboxamide by aniline and carbon tetrachloride in different solvents using S-V plots

The fluorescence quenching studies of carboxamide namely (E)-N-(3-Chlorophenyl)-2-(3,4,5-trimethoxybenzylideneamino)-4,5,6,7 tetrahydrobenzob]thiophene-3-carboxamide ENCTTTC] by aniline and carbon tetrachloride in six different solvents namely toluene, cyclohexane, n-hexane, n-heptane, n-decane and n-pentane have been carried out at room temperature with a view to understand the quenching mechanisms. The Stern-Volmer (S-V) plots have been found to be nonlinear with a positive deviation for all the solvents studied. In order to interpret these results we have invoked the ground state complex formation and sphere of action static quenching models. Using these models various quenching rate parameters have been determined. The magnitudes of these parameters suggest that sphere of action static quenching model agrees well with the experimental results. Hence the positive deviation is attributed to the static and dynamic quenching. Further, with the use of Finite Sink approximation model, it was possible to check these bimolecular reactions as diffusion-limited and to estimate independently distance parameter R' and mutual diffusion coefficient D. Finally an effort has been made to correlate the values of R' and D with the values of the encounter distance R and the mutual coefficient D determined using the Edward's empirical relation and Stokes Einstein relation. (C) 2011 Elsevier B.V. All rights reserved.

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.

3-MANIFOLD GROUPS, KÄHLER GROUPS AND COMPLEX SURFACES

Let G be a Kahler group admitting a short exact sequence 1 -> N -> G -> Q -> 1 where N is finitely generated. (i) Then Q cannot be non-nilpotent solvable. (ii) Suppose in addition that Q satisfies one of the following: (a) Q admits a discrete faithful non-elementary action on H-n for some n >= 2. (b) Q admits a discrete faithful non-elementary minimal action on a simplicial tree with more than two ends. (c) Q admits a (strong-stable) cut R such that the intersection of all conjugates of R is trivial. Then G is virtually a surface group. It follows that if Q is infinite, not virtually cyclic, and is the fundamental group of some closed 3-manifold, then Q contains as a finite index subgroup either a finite index subgroup of the three-dimensional Heisenberg group or the fundamental group of the Cartesian product of a closed oriented surface of positive genus and the circle. As a corollary, we obtain a new proof of a theorem of Dimca and Suciu in Which 3-manifold groups are Kahler groups? J. Eur. Math. Soc. 11 (2009) 521-528] by taking N to be the trivial group. If instead, G is the fundamental group of a compact complex surface, and N is finitely presented, then we show that Q must contain the fundamental group of a Seifert-fibered 3-manifold as a finite index subgroup, and G contains as a finite index subgroup the fundamental group of an elliptic fibration. We also give an example showing that the relation of quasi-isometry does not preserve Kahler groups. This gives a negative answer to a question of Gromov which asks whether Kahler groups can be characterized by their asymptotic geometry.

Positive Relationships between Association Strength and Phenotypic Similarity Characterize the Assembly of Mixed-Species Bird Flocks Worldwide

Competition theory predicts that local communities should consist of species that are more dissimilar than expected by chance. We find a strikingly different pattern in a multicontinent data set (55 presence-absence matrices from 24 locations) on the composition of mixed-species bird flocks, which are important sub-units of local bird communities the world over. By using null models and randomization tests followed by meta-analysis, we find the association strengths of species in flocks to be strongly related to similarity in body size and foraging behavior and higher for congeneric compared with noncongeneric species pairs. Given the local spatial scales of our individual analyses, differences in the habitat preferences of species are unlikely to have caused these association patterns; the patterns observed are most likely the outcome of species interactions. Extending group-living and social-information-use theory to a heterospecific context, we discuss potential behavioral mechanisms that lead to positive interactions among similar species in flocks, as well as ways in which competition costs are reduced. Our findings highlight the need to consider positive interactions along with competition when seeking to explain community assembly.

Fully complex-valued ELM classifiers for human action recognition

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

A soluble-lead redox flow battery with corrugated graphite sheet and reticulated vitreous carbon as positive and negative current collectors

A soluble-lead redox flow battery with corrugated-graphite sheet and reticulated-vitreous carbon as positive and negative current collectors is assembled and performance tested. In the cell, electrolyte comprising of 1 center dot 5 M lead (II) methanesulfonate and 0 center dot 9 M methanesulfonic acid with sodium salt of lignosulfonic acid as additive is circulated through the reaction chamber at a flow rate of 50 ml min (-aEuro parts per thousand 1). During the charge cycle, pure lead (Pb) and lead dioxide (PbO2) from the soluble lead (II) species are electrodeposited onto the surface of the negative and positive current collectors, respectively. Both the electrodeposited materials are characterized by XRD, XPS and SEM. Phase purity of synthesized lead (II) methanesulfonate is unequivocally established by single crystal X-ray diffraction followed by profile refinements using high resolution powder data. During the discharge cycle, electrodeposited Pb and PbO2 are dissolved back into the electrolyte. Since lead ions are produced during oxidation and reduction at the negative and positive plates, respectively there is no risk of crossover during discharge cycle, preventing the possibility of lowering the overall efficiency of the cell. As the cell employs a common electrolyte, the need of employing a membrane is averted. It has been possible to achieve a capacity value of 114 mAh g (-aEuro parts per thousand 1) at a load current-density of 20 mA cm (-aEuro parts per thousand 2) with the cell at a faradaic efficiency of 95%. The cell is tested for 200 cycles with little loss in its capacity and efficiency.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

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.

Polymer template-assisted microemulsion synthesis of large surface area, porous Li2MnO3 and its characterization as a positive electrode material of Li-ion cells

Lithium-rich manganese oxide (Li2MnO3) is prepared by reverse microemulsion method employing Pluronic acid (P123) as a soft template and studied as a positive electrode material. The as-prepared sample possesses good crystalline structure with a broadly distributed mesoporosity but low surface area. As expected, cyclic voltammetry and charge-discharge data indicate poor electrochemical activity. However, the sample gains surface area with narrowly distributed mesoporosity and also electrochemical activity after treating in 4 M H2SO4. A discharge capacity of about 160 mAh g(-1) is obtained. When the acid-treated sample is heated at 300 A degrees C, the resulting porous sample with a large surface area and dual porosity provides a discharge capacity of 240 mAh g(-1). The rate capability study suggests that the sample provides about 150 mAh g(-1) at a specific discharge current of 1.25 A g(-1). Although the cycling stability is poor, the high rate capability is attributed to porous nature of the material.

Antithyroid drugs and their analogues: synthesis, structure, and mechanism of action

Thyroid hormones are essential for the development and differentiation of all cells of the human body. They regulate protein, fat, and carbohydrate metabolism. In this Account, we discuss the synthesis, structure, and mechanism of action of thyroid hormones and their analogues. The prohormone thyroxine (14) is synthesized on thyroglobulin by thyroid peroxidase (TPO), a heme enzyme that uses iodide and hydrogen peroxide to perform iodination and phenolic coupling reactions. The monodeiodination of T4 to 3,3',5-triiodothyronine (13) by selenium-containing deiodinases (ID-1, ID-2) is a key step in the activation of thyroid hormones. The type 3 deiodinase (ID-3) catalyzes the deactivation of thyroid hormone in a process that removes iodine selectively from the tyrosyl ring of T4 to produce 3,3',5'-triiodothyronine (rT3). Several physiological and pathological stimuli influence thyroid hormone synthesis. The overproduction of thyroid hormones leads to hyperthyroidism, which is treated by antithyroid drugs that either inhibit the thyroid hormone biosynthesis and/or decrease the conversion of T4 to T3. Antithyroid drugs are thiourea-based compounds, which indude propylthiouracil (PTU), methimazole (MM I), and carbimazole (CBZ). The thyroid gland actively concentrates these heterocyclic compounds against a concentration gradient Recently, the selenium analogues of PTU, MMI, and CBZ attracted significant attention because the selenium moiety in these compounds has a higher nucleophilicity than that of the sulfur moiety. Researchers have developed new methods for the synthesis of the selenium compounds. Several experimental and theoretical investigations revealed that the selone (C=Se) in the selenium analogues is more polarized than the thione (C=S) in the sulfur compounds, and the selones exist predominantly in their zwitterionic forms. Although the thionamide-based antithyroid drugs have been used for almost 70 years, the mechanism of their action is not completely understood. Most investigations have revealed that MMI and PTU irreversibly inhibit TPO. PTU, MTU, and their selenium analogues also inhibit ID-1, most likely by reacting with the selenenyl iodide intermediate. The good ID-1 inhibitory activity of Pill and its analogues can be ascribed to the presence of the -N(H)-C(=O)- functionality that can form hydrogen bonds with nearby amino add residues in the selenenyl sulfide state. In addition to the TPO and ID-1 inhibition, the selenium analogues are very good antioxidants. In the presence of cellular reducing agents such as GSH, these compounds catalytically reduce hydrogen peroxide. They can also efficiently scavenge peroxynitrite, a potent biological oxidant and nitrating agent.

Positive temperature coefficient and structural relaxations in selectively localized MWNTs in PE/PEO blends

The dispersion state of multiwall carbon nanotubes (MWNTs) in melt mixed polyethylene/polyethylene oxide (PE/PEO) blends has been assessed by both surface and volume electrical conductivity measurements and the structural relaxations have been assessed by broadband dielectric spectroscopy. The selective localization of MWNTs in the blends was controlled by the flow characteristics of the components, which led to their localization in the energetically less favored phase (PE). The electrical conductivity and positive temperature co-efficient (PTC) measurements were carried out on hot pressed samples. The neat blends exhibited only a negative temperature coefficient (NTC) effect while the blends with MWNTs exhibited both a PTC and a NTC at the melting temperatures of PE and PEO respectively. These phenomenal changes were corroborated with the different crystalline morphology in the blends. It was deduced that during compression molding, the more viscous PEO phase spreads less in contrast to the less viscous PE phase. This has further resulted in a gradient in morphology as well as the distribution state of the MWNTs in the samples and was supported by scanning electron and scanning acoustic microscopy (SAM) studies and contact angle measurements. SAM from different depths of the samples revealed a gradient in the microstructure in the PE/PEO blends which is contingent upon the flow characteristics of the components. Interestingly, the surface and volume electrical conductivity was different due to the different dispersion state of the MWNTs at the surface and bulk. The observed surface and volume electrical conductivity measurements were corroborated with the evolved morphology during processing. The structural relaxations in both PE and PEO were discerned from broadband dielectric spectroscopy. The segmental dynamics below and above the melting temperature of PEO were significantly different in the presence of MWNTs.