l-pGlu-(2-propyl)-l-His-l-ProNH2 attenuates 4-aminopyridine-induced epileptiform activity and sodium current: a possible action of new thyrotropin-releasing hormone analog for its anticonvulsant potential

L-PGlu-(2-proPyl)-L-His-L-ProNH(2) (NP-647) is a CNS active thyrotropin-releasing hormone (TRH) analog with potential application in various CNS disorders including seizures. In the present study, mechanism of action for protective effect of NP-647 was explored by studying role of NP-647 on epileptiform activity and sodium channels by using patch-clamp methods. Epileptiform activity was induced in subicular pyramidal neurons of hippocampal slice of rat by perfusing 4-aminopyridine (4-AP) containing Mg(+2)-free normal artificial cerebrospinal fluid (nACSF). Increase in mean firing frequency was observed after perfusion of 4-AP and zero Mg(+2) (2.10+/-0.47 Hz) as compared with nACSF (0.12+/-0.08 Hz). A significant decrease in mean firing frequency (0.61+/-0.22 Hz), mean frequency of epileptiform events (0.03+/-0.02 Hz vs. 0.22+/-0.05 Hz of 4-AP+0 Mg), and average number of action potentials in paroxysmal depolarization shift-burst (2.54+/-1.21 Hz vs. 8.16+/-0.88 Hz of 4-AP +0 Mg) was observed. A significant reduction in peak dV/dt (246+/-19 mV ms(-1) vs. 297 18 mV ms-1 of 4-AP+0 Mg) and increase (1.332+/-0.018 ms vs. 1.292+/-0.019 ms of 4-AP+0 Mg) in time required to reach maximum depolarization were observed indicating role of sodium channels. Concentration-dependent depression of sodium current was observed after exposure to dorsal root ganglion neurons to NP-647. NP-647 at different concentrations (1, 3, and 10 mu M) depressed sodium current (15+/-0.5%, 50+/-2.6%, and 75+/-0.7%, respectively). However, NP-647 did not show change in the peak sodium current in CNa18 cells. Results of present study demonstrated potential of NP-647 in the inhibition of epileptiform activity by inhibiting sodium channels indirectly. (C) 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

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.