Effective action and adiabatic expansions for the 1-D and 2-D hubbard models at half-filling

We analyze here the occurrence of antiferromagnetic (AFM) correlations in the half-filled Hubbard model in one and two space dimensions using a natural fermionic representation of the model and a newly proposed way of implementing the half-filling constraint. We find that our way of implementing the constraint is capable of enforcing it exactly already at the lowest levels of approximation. We discuss how to develop a systematic adiabatic expansion for the model and how Berry's phase contributions arise quite naturally from the adiabatic expansion. At low temperatures and in the continuum limit the model gets mapped onto an O(3) nonlinear sigma model (NLsigma). A topological, Wess-Zumino term is present in the effective action of the ID NLsigma as expected, while no topological terms are present in 2D. Some specific difficulties that arise in connection with the implementation of an adiabatic expansion scheme within a thermodynamic context are also discussed, and we hint at possible solutions.

On the importance of backbone loop and peptide flip in the Walker sequence in F-1-ATPase action

The Walker sequence, GXXXXGKT, present in all the six subunits of F-1-ATPase exists in a folded form, known as phosphate-binding loop (P-loop). Analysis of the Ramachandran angles showed only small RMS deviation between the nucleotide-bound and nucleotide-free forms. This indicated a good overlap of the backbone loops. The catalytic beta-subunits (chains D, E and F) showed significant changes in the Ramachandran angles and the side chain torsion angles, but not the structural alpha-subunits (chains A, B and C). Most striking among these are the changes associated with Val160 and Gly161 corresponding to a flip in the peptide unit between them when a nucleotide is bound (chains D or F compared to nucleotide-free chain E). The conformational analysis further revealed a hitherto unnoticed hydrogen bond between amide-N of the flipped Gly161 and terminal phosphate-O of the nucleotide. This assigns a role for this conserved amino acid, otherwise ignored, of making an unusual direct interaction between the peptide backbone of the enzyme protein and the incoming nucleotide substrate. Significance of this interaction is enhanced, as it is limited only to the catalytic subunits, and also likely to involve a mechanical rotation of bonds of the peptide unit. Hopefully this is part of the overall events that link the chemical hydrolysis of ATP with the mechanical rotation of this molecule, now famous as tiny molecular motor.

The biological and structural characterization of Mycobacterium tuberculosis UvrA provides novel insights into its mechanism of action

Mycobacterium tuberculosis is an extremely well adapted intracellular human pathogen that is exposed to multiple DNA damaging chemical assaults originating from the host defence mechanisms. As a consequence, this bacterium is thought to possess highly efficient DNA repair machineries, the nucleotide excision repair (NER) system amongst these. Although NER is of central importance to DNA repair in M. tuberculosis, our understanding of the processes in this species is limited. The conserved UvrABC endonuclease represents the multi-enzymatic core in bacterial NER, where the UvrA ATPase provides the DNA lesion-sensing function. The herein reported genetic analysis demonstrates that M. tuberculosis UvrA is important for the repair of nitrosative and oxidative DNA damage. Moreover, our biochemical and structural characterization of recombinant M. tuberculosis UvrA contributes new insights into its mechanism of action. In particular, the structural investigation reveals an unprecedented conformation of the UvrB-binding domain that we propose to be of functional relevance. Taken together, our data suggest UvrA as a potential target for the development of novel anti-tubercular agents and provide a biochemical framework for the identification of small-molecule inhibitors interfering with the NER activity in M. tuberculosis.

Location and conformation of pantothenate and its derivatives in Mycobacterium tuberculosis pantothenate kinase: insights into enzyme action

Previous studies of complexes of Mycobacterium tuberculosis PanK (MtPanK) with nucleotide diphosphates and non-hydrolysable analogues of nucleoside triphosphates in the presence or the absence of pantothenate established that the enzyme has dual specificity for ATP and GTP, revealed the unusual movement of ligands during enzyme action and provided information on the effect of pantothenate on the location and conformation of the nucleotides at the beginning and the end of enzyme action. The X-ray analyses of the binary complexes of MtPanK with pantothenate, pantothenol and N-nonylpantothenamide reported here demonstrate that in the absence of nucleotide these ligands occupy, with a somewhat open conformation, a location similar to that occupied by phosphopantothenate in the `end' complexes, which differs distinctly from the location of pantothenate in the closed conformation in the ternary `initiation' complexes. The conformation and the location of the nucleotide were also different in the initiation and end complexes. An invariant arginine appears to play a critical role in the movement of ligands that takes place during enzyme action. The work presented here completes the description of the locations and conformations of nucleoside diphosphates and triphosphates and pantothenate in different binary and ternary complexes, and suggests a structural rationale for the movement of ligands during enzyme action. The present investigation also suggests that N-alkylpantothenamides could be phosphorylated by the enzyme in the same manner as pantothenate.

Current‐limiting action of mixed‐phase BaTiO3 ceramic semiconductors

Stable and highly reproducible current‐limiting characteristics are observed for polycrystalline ceramics prepared by sintering mixtures of coarse‐grained, donor‐doped BaTiO3 (tetragonal) as the major phase and ultrafine, undoped cubic perovskite such as BaSnO3, BaZrO 3, SrTiO3, or BaTiO3 (cubic). The linear current‐voltage (I‐V) relation changes over to current limiting as the field strength increases, when thermal equilibrium is attained. The grain‐boundary layers with low donor and high Sn, Zr, or Sr have depleted charge carrier density as compared to that in the grain bulk. The voltage drop at the grain‐boundary layers diminishes the temperature gradient between the interior and surface regions.

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.

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.

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.

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.