Unveiling Unusual Features of Formation of Septal Partition and Constriction in Mycobacteria-an Ultrastructural Study

The ultrastructural functions of the electron-dense glycopeptidolipid-containing outermost layer (OL), the arabinogalactan-mycolic acid-containing electron-transparent layer (ETL), and the electron-dense peptidoglycan layer (PGL) of the mycobacterial cell wall in septal growth and constriction are not clear. Therefore, using transmission electron microscopy, we studied the participation of the three layers in septal growth and constriction in the fast-growing saprophytic species Mycobacterium smegmatis and the slow-growing pathogenic species Mycobacterium xenopi and Mycobacterium tuberculosis in order to document the processes in a comprehensive and comparative manner and to find out whether the processes are conserved across different mycobacterial species. A complete septal partition is formed first by the fresh synthesis of the septal PGL (S-PGL) and septal ETL (S-ETL) from the envelope PGL (E-PGL) in M. smegmatis and M. xenopi. The S-ETL is not continuous with the envelope ETL (E-ETL) due to the presence of the E-PGL between them. The E-PGL disappears, and the S-ETL becomes continuous with the E-ETL, when the OL begins to grow and invaginate into the S-ETL for constriction. However, in M. tuberculosis, the S-PGL and S-ETL grow from the E-PGL and E-ETL, respectively, without a separation between the E-ETL and S-ETL by the E-PGL, in contrast to the process in M. smegmatis and M. xenopi. Subsequent growth and invagination of the OL into the S-ETL of the septal partition initiates and completes septal constriction in M. tuberculosis. A model for the conserved sequential process of mycobacterial septation, in which the formation of a complete septal partition is followed by constriction, is presented. The probable physiological significance of the process is discussed. The ultrastructural features of septation and constriction in mycobacteria are unusually different from those in the well-studied organisms Escherichia coli and Bacillus subtilis.

Pillar height dependent formation of unprecedented Pd-8 molecular swing and Pd-6 molecular boat via multicomponent self-assembly

Three-component self-assembly of a cis-blocked 90 degrees Pd(II) acceptor with a mixture of a tetraimidazole and a linear dipyridyl donor self-discriminated into unusual Pd-8 molecular swing (1) and Pd-6 molecular boat (2), which are characterized by single-crystal X-ray diffraction analysis; their ability to bind C-60 in solution is established by fluorescence titration.

Partition function for hindered, one-and multi-dimensional rotors

Writing the hindered rotor (hr) partition function as the trace of (rho) over cap = e(-beta(H) over cap hr), we approximate it by the sum of contributions from a set of points in position space. The contribution of the density matrix from each point is approximated by performing a local harmonic expansion around it. The highlight of this method is that it can be easily extended to multidimensional systems. Local harmonic expansion leads to a breakdown of the method a low temperatures. In order to calculate the partition function at low temperatures, we suggest a matrix multiplication procedure. The results obtained using these methods closely agree with the exact partition function at all temperature ranges. Our method bypasses the evaluation of eigenvalues and eigenfunctions and evaluates the density matrix for internal rotation directly. We also suggest a procedure to account for the antisymmetry of the total wavefunction in the same. (C) 2012 Elsevier B.V. All rights reserved.

Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.

Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks

Lack of supervision in clustering algorithms often leads to clusters that are not useful or interesting to human reviewers. We investigate if supervision can be automatically transferred for clustering a target task, by providing a relevant supervised partitioning of a dataset from a different source task. The target clustering is made more meaningful for the human user by trading-off intrinsic clustering goodness on the target task for alignment with relevant supervised partitions in the source task, wherever possible. We propose a cross-guided clustering algorithm that builds on traditional k-means by aligning the target clusters with source partitions. The alignment process makes use of a cross-task similarity measure that discovers hidden relationships across tasks. When the source and target tasks correspond to different domains with potentially different vocabularies, we propose a projection approach using pivot vocabularies for the cross-domain similarity measure. Using multiple real-world and synthetic datasets, we show that our approach improves clustering accuracy significantly over traditional k-means and state-of-the-art semi-supervised clustering baselines, over a wide range of data characteristics and parameter settings.

Analysis of the temperature-dependent current-voltage characteristics and the barrier-height inhomogeneities of Au/GaN Schottky diodes

In this report, the currentvoltage (IV) characteristics of Au/GaN Schottky diodes have been carried out in the temperature range of 300510?K. The estimated values of the Schottky-barrier height (SBH) and the ideality factor of the diodes based on the thermionic emission (TE) mechanism were found to be temperature dependent. The barrier height was found to increase and the ideality factor to decrease with increasing temperature. The conventional Richardson plot of ln(Is/T2) versus 1/kT gives the SBH of 0.51?eV and Richardson constant value of 3.23?X?10-5?A?cm-2?K-2 which is much lower than the known value of 26.4?A?cm-2?K-2 for GaN. Such discrepancies of the SBH and Richardson constant value were attributed to the existence of barrier-height inhomogeneities at the Au/GaN interface. The modified Richardson plot of ln(Is/T2)q2 sigma 2/2k2T2 versus q/kT, by assuming a Gaussian distribution of barrier heights at the Au/GaN interface, provided the SBH of 1.47?eV and Richardson constant value of 38.8?A?cm-2?K-2. The temperature dependence of the barrier height is interpreted on the basis of existence of the Gaussian distribution of the barrier heights due to the barrier-height inhomogeneities at the Au/GaN interface.

On the height of fall of dropping mass in SASW measurements for asphaltic road pavements

A number of spectral analysis of surface waves (SASW) tests were performed on asphaltic road pavements by dropping a metallic 6.5 kg sphere, from a height (H) ranging from 1 to 3 m. Various combinations of source to first receiver distance (S) and receiver spacing (X) were employed. By increasing the height of the fall of the dropping mass, the maximum wavelength (lambda(max)), up to which the shear wave velocity profile can be predicted with the usage of the SASW measurements, was found to increase continuously. The height of fall of the dropping mass also seems to affect the admissible range of the wavelength for given combinations of X and S. Irrespective of different chosen combinations of S, X and H, a unique combined dispersion curve was generated in all the cases for a given pavement site as long as the threshold minimum value of the coherence function is greater than 0.90.

Relation between the work function and Young's modulus of RhSi and estimate of Schottky-barrier height at RhSi/Si interface: An ab-initio study

Density-functional calculations are performed to explore the relationship between the work function and Young's modulus of RhSi, and to estimate the p-Schottky-barrier height (SBH) at the Si/RhSi(010) interface. It is shown that the Young's modulus and the workfunction of RhSi satisfy the generic sextic relation, proposed recently for elemental metals. The calculated p-SBH at the Si/RhSi interface is found to differ only by 0.04 eV in opposite limits, viz., no-pinning and strong pinning. We find that the p-SBH is reduced as much as by 0.28 eV due to vacancies at the interface. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4761994]

Pattern clustering using cooperative game theory

In this paper, we approach the classical problem of clustering using solution concepts from cooperative game theory such as Nucleolus and Shapley value. We formulate the problem of clustering as a characteristic form game and develop a novel algorithm DRAC (Density-Restricted Agglomerative Clustering) for clustering. With extensive experimentation on standard data sets, we compare the performance of DRAC with that of well known algorithms. We show an interesting result that four prominent solution concepts, Nucleolus, Shapley value, Gately point and \tau-value coincide for the defined characteristic form game. This vindicates the choice of the characteristic function of the clustering game and also provides strong intuitive foundation for our approach.

Quasi-based hierarchical clustering for land cover mapping using satellite images

This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC). Themain objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach - NPSO with quasi distribution(Faure). These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.

Clustering using Levy Flight Cuckoo Search

In this paper, a comparative study is carried using three nature-inspired algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) on clustering problem. Cuckoo search is used with levy flight. The heavy-tail property of levy flight is exploited here. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results are tabulated and analysed using various techniques. Finally we conclude that under the given set of parameters, cuckoo search works efficiently for majority of the dataset and levy flight plays an important role.

Identification of coherent synchronous generators in a multi- machine power system using support vector clustering

This paper illustrates the application of a new technique, based on Support Vector Clustering (SVC) for the direct identification of coherent synchronous generators in a large interconnected Multi-Machine Power Systems. The clustering is based on coherency measures, obtained from the time domain responses of the generators following system disturbances. The proposed clustering algorithm could be integrated into a wide-area measurement system that enables fast identification of coherent clusters of generators for the construction of dynamic equivalent models. An application of the proposed method is demonstrated on a practical 15 generators 72-bus system, an equivalent of Indian Southern grid in an attempt to show the effectiveness of this clustering approach. The effects of short circuit fault locations on coherency are also investigated.

A Huber-loss-driven clustering technique and its application to robust cell detection in confocal microscopy images

We address the problem of detecting cells in biological images. The problem is important in many automated image analysis applications. We identify the problem as one of clustering and formulate it within the framework of robust estimation using loss functions. We show how suitable loss functions may be chosen based on a priori knowledge of the noise distribution. Specifically, in the context of biological images, since the measurement noise is not Gaussian, quadratic loss functions yield suboptimal results. We show that by incorporating the Huber loss function, cells can be detected robustly and accurately. To initialize the algorithm, we also propose a seed selection approach. Simulation results show that Huber loss exhibits better performance compared with some standard loss functions. We also provide experimental results on confocal images of yeast cells. The proposed technique exhibits good detection performance even when the signal-to-noise ratio is low.

Hybrid online non-negative matrix factorization for clustering of documents

When document corpus is very large, we often need to reduce the number of features. But it is not possible to apply conventional Non-negative Matrix Factorization(NMF) on billion by million matrix as the matrix may not fit in memory. Here we present novel Online NMF algorithm. Using Online NMF, we reduced original high-dimensional space to low-dimensional space. Then we cluster all the documents in reduced dimension using k-means algorithm. We experimentally show that by processing small subsets of documents we will be able to achieve good performance. The method proposed outperforms existing algorithms.

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.