Engineering a lever into the kinesin neck

To probe for a lever arm action in the kinesin stepping mechanism, we engineered a rodlike extension piece into the tail of rat kinesin at various points close to the head-tail junction and measured its effects on the temperature dependence of velocity in microtubule gliding assays. The insert comprised two contiguous alpha-actinin triple-coil repeats and was predicted to fold into a stiff rodlike module about 11 nm long. The effects of this module were greater the closer it was placed to the head-tail junction. When inserted distal to the head-tail junction, at Asn401 in the dimeric K partial differential401GST, the insert had no effect. When inserted closer to the heads at Val376 into K partial differential376GST, the insert slowed progress below 22 degreesC but accelerated progress to approximately 125% of wild type above 22 degreesC. The most dramatic effect of the synthetic lever occurred when it was inserted very close to the head-neck junction, at Glu340 into the single-headed construct K partial differential340GST. This construct was immotile without the insert, but motile with it, at about 30% of the velocity of the dimeric control. The alpha-actinin module thus confers some gain-of-function when inserted close to the head-neck junction but not when placed distal to it. The data exclude the presence of a lever arm C-terminal to Val376 in the kinesin tail but suggest that a short-throw lever arm may be present, N-terminal to Val376 and contiguous with the head-neck junction at Ala339.     

Abrasion Resistance of Field-Assisted Ion-Exchanged Glass

The fracture strength of HF-etched samples was measured as a function of field-assisted K-Na ion exchange depth, as well as the severity of abrasion. As expected, the more severe the abrasion, the greater the depth required to make the strength insensitive to abrasion. An abrasion-independent weakening mechanism that increased with exchange depth was also found to be operative.     

GPU accelerated novel particle filtering method

In this paper, a graphics processor unit (GPU) accelerated particle filtering algorithm is presented with an introduction to a novel resampling technique. The aim remains in the mitigation of particle impoverishment as well as computational burden, problems which are commonly associated with classical (systematic) resampled particle filtering. The proposed algorithm employs a priori-space dependent distribution in addition to the likelihood, and hence is christened as dual distribution dependent (D3) resampling method. Simulation results exhibit lesser values for root mean square error (RMSE) in comparison to that for systematic resampling. D3 resampling is shown to improve particle diversity after each iteration, thereby affecting the overall quality of estimation. However, computational burden is significantly increased owing to few excessive computations within the newly formulated resampling framework. With a view to obtaining parallel speedup we introduce a CUDA version of the proposed method for necessary acceleration by GPU. The GPU programming model is detailed in the context of this paper. Implementation issues are discussed along with illustration of empirical computational efficiency, as obtained by executing the CUDA code on Quadro 2000 GPU. The GPU enabled code has a speedup of 3 and 4 over the sequential executions of systematic and D3 resampling methods respectively. Performance both in terms of RMSE and running time have been elaborated with respect to different selections for threads per block towards effective implementations. It is in this context that, we further introduce a cost to performance metric (CPM) for assessing the algorithmic efficiency of the estimator, involving both quality of estimation and running time as comparative factors, transformed into a unified parameter for assessment. CPM values for estimators obtained from all such different choices for threads per block have been determined and a final value for the chosen parameter is resolved for generation of a holistic effective estimator.     

Efficient mining of discriminative co-clusters from gene expression data

Discriminative models are used to analyze the differences between two classes and to identify class-specific patterns. Most of the existing discriminative models depend on using the entire feature space to compute the discriminative patterns for each class. Co-clustering has been proposed to capture the patterns that are correlated in a subset of features, but it cannot handle discriminative patterns in labeled datasets. In certain biological applications such as gene expression analysis, it is critical to consider the discriminative patterns that are correlated only in a subset of the feature space. The objective of this paper is twofold: first, it presents an algorithm to efficiently find arbitrarily positioned co-clusters from complex data. Second, it extends this co-clustering algorithm to discover discriminative co-clusters by incorporating the class information into the co-cluster search process. In addition, we also characterize the discriminative co-clusters and propose three novel measures that can be used to evaluate the performance of any discriminative subspace pattern-mining algorithm. We evaluated the proposed algorithms on several synthetic and real gene expression datasets, and our experimental results showed that the proposed algorithms outperformed several existing algorithms available in the literature.     

Mechanical and dynamic mechanical properties of jute fibers–Novolac–epoxy composite laminates

A study was done of jute composite using a polymer matrix of epoxidized Novolac resin (ENR), diglycidyl ether of bisphenol A (DGEBA)–based epoxy, and their blends with different weight percentages of the resins. It was found that on blending ENR with DGEBA, the storage modulii at room temperature are enhanced by about 100% or more in the case of 30 and 40% ENR‐containing matrices, whereas the enhancement in the case of 20 and 12% ENR‐containing matrices is only 50% that of the pure matrix. It was also observed that the tan δ peak heights of the composites containing 30 and 40% ENR are closer to that of 20% ENR‐containing composite. The probable explanation drawn on the basis of experimental findings of DMA and mechanical analysis is that by blending ENR with DGEBA epoxy it is possible to manufacture jute composites with increased stiffness without sacrificing their ductility. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 2800–2807, 2002     

Elevated temperature instrumented Charpy impact of a sintered silicon carbide

A commercially available, densely sintered alpha silicon carbide was tested in air from room temperature to 1700°C using a modified instrumented pendulum unit and standard Charpy size test specimens. The resistance heated silicon carbide specimens exhibited only elastic behavior, even at 1700°C. A compliance analysis of the test revealed a linear elastic decrease of the absorbed impact energy and also a decrease of the fracture stress of the silicon carbide with increasing temperature.     

Analysing the patterns of spatial contrast discontinuities in natural images for robust edge detection

Pattern Analysis and Applications - The pattern of spatial contrast discontinuities in natural images has been analysed in the present work, and based on it, a new adaptive model of the...     

One pot synthesis of nano Ag in calcium alginate beads and its catalytic application in p‐Nitrophenol reduction with kinetic parameter estimation and model fitting

In this work, one step process of synthesis of silver nanoparticles (Agnp) embedded in insitu formed calcium alginate (CA) beads is stated. CA, formed from the reaction between sodium alginate and calcium hydroxide, acts as reducing and stabilizing agent as well as support for nanoparticles. The reaction mechanism for the formation and stabilization of Agnp is proposed where the vicinal dihydroxy groups of alginate are assumed to act as the reducing agent for Ag⁺ to Ag°. Transmission electron microscopy (TEM), x‐ray diffraction (XRD), UV‐vis spectroscopy, field emission scanning electron microscopy (FESEM), and atomic absorption spectroscopy (AAS) were used to characterize the Agnp. The formation of spherical nanoparticles with average size range of 4‐5 nm was confirmed by TEM. Catalytic activity of this nano silver‐calcium alginate (Agnp‐CA) composite was evaluated in the reduction of p‐nitrophenol. Concentrations of sodium alginate, calcium hydroxide, and AgNO₃ are found to be the parameters that critically affect the synthesis of Agnp. The efficacy of the catalyst is expressed on the basis of suitable reaction parameters. Both pseudo‐homogeneous and heterogeneous kinetic models are proposed for the reaction to find the best model and the Eley‐Riedel model is found to fit well with the experimental data. The novelty of this work is that the tandem process of CA bead formation, Agnp formation, and Agnp entrapment in CA have been transformed into a single‐step process. Moreover, elaborations of each step of the ionic mechanisms of Agnp formation and p‐NP reduction with Agnp and the establishment of a heterogeneous kinetic model for the reaction are reported for the first time here.