Monoclonal Antibodies against Hepatitis C Genotype 3a Virus Like Particle Inhibit Virus Entry in Cell Culture System

The envelope protein (E1-E2) of Hepatitis C virus (HCV) is a major component of the viral structure. The glycosylated envelope protein is considered to be important for initiation of infection by binding to cellular receptor(s) and also known as one of the major antigenic targets to host immune response. The present study was aimed at identifying mouse monoclonal antibodies which inhibit binding of virus like particles of HCV to target cells. The first step in this direction was to generate recombinant HCV-like particles (HCV-LPs) specific for genotypes 3a of HCV (prevalent in India) using the genes encoding core, E1 and E2 envelop proteins in a baculovirus expression system. The purified HCV-LPs were characterized by ELISA and electron microscopy and were used to generate monoclonal antibodies (mAbs) in mice. Two monoclonal antibodies (E8G9 and H1H10) specific for the E2 region of envelope protein of HCV genotype 3a, were found to reduce the virus binding to Huh7 cells. However, the mAbs generated against HCV genotype 1b (D2H3, G2C7, E1B11) were not so effective. More importantly, mAb E8G9 showed significant inhibition of the virus entry in HCV JFH1 cell culture system. Finally, the epitopic regions on E2 protein which bind to the mAbs have also been identified. Results suggest a new therapeutic strategy and provide the proof of concept that mAb against HCV-LP could be effective in preventing virus entry into liver cells to block HCV replication.

Multi-port network modeling for fractal shape microstrip antenna

In this paper we demonstrate the use of multi-port network modeling to analyze one such antenna with fractal shaped parts. Based on simulation and experimental studies, it has been demonstrated that model can accurately predict the input characteristics of antennas with Minkowski geometry replacing a side micro strip square ring.

Variance-Reduced Particle Filters for Structural System Identification Problems

A few variance reduction schemes are proposed within the broad framework of a particle filter as applied to the problem of structural system identification. Whereas the first scheme uses a directional descent step, possibly of the Newton or quasi-Newton type, within the prediction stage of the filter, the second relies on replacing the more conventional Monte Carlo simulation involving pseudorandom sequence with one using quasi-random sequences along with a Brownian bridge discretization while representing the process noise terms. As evidenced through the derivations and subsequent numerical work on the identification of a shear frame, the combined effect of the proposed approaches in yielding variance-reduced estimates of the model parameters appears to be quite noticeable. DOI: 10.1061/(ASCE)EM.1943-7889.0000480. (C) 2013 American Society of Civil Engineers.

Motion segmentation using curve fitting on Lagrangian particle trajectories

In this paper we present a segmentation algorithm to extract foreground object motion in a moving camera scenario without any preprocessing step such as tracking selected features, video alignment, or foreground segmentation. By viewing it as a curve fitting problem on advected particle trajectories, we use RANSAC to find the polynomial that best fits the camera motion and identify all trajectories that correspond to the camera motion. The remaining trajectories are those due to the foreground motion. By using the superposition principle, we subtract the motion due to camera from foreground trajectories and obtain the true object-induced trajectories. We show that our method performs on par with state-of-the-art technique, with an execution time speed-up of 10x-40x. We compare the results on real-world datasets such as UCF-ARG, UCF Sports and Liris-HARL. We further show that it can be used toper-form video alignment.

Dependence of electron recombination time and light to electricity conversion efficiency on shape of the nanocrystal light sensitizer

Tunability of electron recombination time and light to electricity conversion efficiency to superior values in semiconductor sensitized solar cells via optimized design of nanocrystal light sensitizer shape is discussed here.

Determination of favorable inter-particle interactions for formation of substitutionally ordered solid phases from a binary mixture of oppositely charged colloidal suspensions

The solid phase formed by a binary mixture of oppositely charged colloidal particles can be either substitutionally ordered or substitutionally disordered depending on the nature and strength of interactions among the particles. In this work, we use Monte Carlo molecular simulations along with the Gibbs-Duhem integration technique to map out the favorable inter-particle interactions for the formation of substitutionally ordered crystalline phases from a fluid phase. The inter-particle interactions are modeled using the hard core Yukawa potential but the method can be easily extended to other systems of interest. The study obtains a map of interactions depicting regions indicating the type of the crystalline aggregate that forms upon phase transition.

A peer-peer particle swarm optimizer

Particle Swarm Optimization is a parallel algorithm that spawns particles across a search space searching for an optimized solution. Though inherently parallel, they have distinct synchronizations points which stumbles attempts to create completely distributed versions of it. In this paper, we attempt to create a completely distributed peer-peer particle swarm optimization in a cluster of heterogeneous nodes. Since, the original algorithm requires explicit synchronization points we modified the algorithm in multiple ways to support a peer-peer system of nodes. We also modify certain aspect of the basic PSO algorithm and show how certain numerical problems can take advantage of the same thereby yielding fast convergence.

Feature weighting for clustering by particle swarm optimization

Clustering has been the most popular method for data exploration. Clustering is partitioning the data set into sub-partitions based on some measures say the distance measure, each partition has its own significant information. There are a number of algorithms explored for this purpose, one such algorithm is the Particle Swarm Optimization(PSO) which is a population based heuristic search technique derived from swarm intelligence. In this paper we present an improved version of the Particle Swarm Optimization where, each feature of the data set is given significance accordingly by adding some random weights, which also minimizes the distortions in the dataset if any. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The experimental results shows that our proposed methodology performs significantly better than the previously performed experiments.

Multiobjective discrete particle swarm optimization for multisensor image alignment

A new technique is proposed for multisensor image registration by matching the features using discrete particle swarm optimization (DPSO). The feature points are first extracted from the reference and sensed image using improved Harris corner detector available in the literature. From the extracted corner points, DPSO finds the three corresponding points in the sensed and reference images using multiobjective optimization of distance and angle conditions through objective switching technique. By this, the global best matched points are obtained which are used to evaluate the affine transformation for the sensed image. The performance of the image registration is evaluated and concluded that the proposed approach is efficient.

Effect of thermal and thermo-mechanical cycling on the microstructure of Ni-rich NiTi shape memory alloys

Microstructural changes of Ni-rich NiTi shape memory alloy during thermal and thermo-mechanical cycling have been investigated using Electron Back Scattered Diffraction. A strong dependence of the orientation of the prior austenite grain on the misorientation development has been observed during thermal cycling and thermo-mechanical cycling. This effect is more pronounced at the grain boundaries compared to grain interior. At a larger applied strain, the volume fraction of stabilized martensite phase increases with increase in the number of cycling. Deformation within the martensite leads to stabilization of martensitic phase even at temperatures slightly above the austenite finish temperature. Modulus variation with respect to temperature has been explained on the basis of martensitic transformation.

Temperature dependence of indentation recovery ratios in austenitic and martensitic shape memory alloys

Instrumented microindentation (IM) on two Ni-Ti shape memory alloys (SMAs), where one is austenitic and the other is martensitic at room temperature, were conducted from 40 to 150 degrees C. Results show that the depth and work recovery ratios, eta(d) and eta(w) respectively, are complementary to each other. While eta(d) decreases gradually with temperature for austenite, it drops markedly for the martensite in the martensite-to-austenite transformation regime. These results affirm the utility of IM for characterizing SMAs.

Simultaneous measurement of mechanical and electrical contact resistances during nanoindentation of NiTi shape memory alloys

The nanoindentation technique can be employed in shape memory alloys (SMAs) to discern the transformation temperatures as well as to characterize their mechanical behavior. In this paper, we use it with simultaneous measurements of the mechanical and the electrical contact resistances (ECR) at room temperature to probe two SMAs: austenite (RTA) and martensite (RTM). Two different types of indenter tips - Berkovich and spherical - are employed to examine the SMAs' indentation responses as a function of the representative strain, epsilon(R). In Berkovich indentation, because of the sharp nature of the tip, and in consequence the high levels of strain imposed, discerning the two SMAs on the basis of the indentation response alone is difficult. In the case of the spherical tip, epsilon(R) is systematically varied and its effect on the depth recovery ratio, eta(d), is examined. Results indicate that RTA has higher eta(d) than RTM, but the difference decreases with increasing epsilon(R) such that eta(d) values for both the alloys would be similar in the fully plastic regime. The experimental trends in eta(d) vs. epsilon(R) for both the alloys could be described well with a eta(d) proportional to (epsilon(R))(-1) type equation, which is developed on the basis of a phenomenological model. This fit, in turn, directs us to the maximum epsilon(R), below which plasticity underneath the indenter would not mask the differences in the two SMAs. It was demonstrated that the ECR measurements complement the mechanical measurements in demarcating the reverse transformation from martensite to austenite during unloading of RTA, wherein a marked increase in the voltage was noted. A correlation between recovery due to reverse transformation during unloading and increase in voltage (and hence the electrical resistance) was found. (C) 2013 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Combining indentation and diffusion couple techniques for combinatorial discovery of high temperature shape memory alloys

We demonstrate the possibility of accelerated identification of potential compositions for high-temperature shape memory alloys (SMAs) through a combinatorial material synthesis and analysis approach, wherein we employ the combination of diffusion couple and indentation techniques. The former was utilized to generate smooth and compositionally graded inter-diffusion zones (IDZs) in the Ni-Ti-Pd ternary alloy system of varying IDZ thickness, depending on the annealing time at high temperature. The IDZs thus produced were then impressed with an indenter with a spherical tip so as to inscribe a predetermined indentation strain. Subsequent annealing of the indented samples at various elevated temperatures, T-a, ranging between 150 and 550 degrees C allows for partial to full relaxation of the strain imposed due to the shape memory effect. If T-a is above the austenite finish temperature, A(f), the relaxation will be complete. By measuring the depth recovery, which serves as a proxy for the shape recovery characteristic of the SMA, a three-dimensional map in the recovery temperature composition space is constructed. A comparison of the published Af data for different compositions with the Ta data shows good agreement when the depth recovery is between 70% and 80%, indicating that the methodology proposed in this paper can be utilized for the identification of promising compositions. Advantages and further possibilities of this methodology are discussed.

Parametrisation-free determination of the shape parameters for the pion electromagnetic form factor

Recent data from high-statistics experiments that have measured the modulus of the pion electromagnetic form factor from threshold to relatively high energies are used as input in a suitable mathematical framework of analytic continuation to find stringent constraints on the shape parameters of the form factor at t = 0. The method uses also as input a precise description of the phase of the form factor in the elastic region based on Fermi-Watson theorem and the analysis of the pi pi scattering amplitude with dispersive Roy equations, and some information on the spacelike region coming from recent high precision experiments. Our analysis confirms the inconsistencies of several data on the modulus, especially from low energies, with analyticity and the input phase, noted in our earlier work. Using the data on the modulus from energies above 0.65 GeV, we obtain, with no specific parametrisation, the prediction < r(pi)(2)> is an element of (0.42, 0.44) fm(2) for the charge radius. The same formalism leads also to very narrow allowed ranges for the higher-order shape parameters at t = 0, with a strong correlation among them.