Shear induced crystallization in different polymorphic forms of PVDF induced by surface functionalized MWNTs in PVDF/PMMA blends

Shear induced crystallization in PVDF/PMMA blends, especially at higher fractions of PMMA, can be quite interesting in understanding the structure-property correlation and processing of these blends. In a recent submission (Phys. Chem. Chem. Phys., 2014, 16, 2693-2704), we clearly demonstrated, using dielectric spectroscopy, that the origin of segmental relaxations concerning the crystalline segments of PVDF in PVDF/PMMA blends in the presence of MWNTs (multiwalled nanotubes) was strongly contingent on the size of the crystallite. We now understand that the fraction of PMMA in the blends governs the origin of polymorphism in PVDF. This motivated us to systematically study the effect of shear on the crystallization behavior of PVDF especially in blends with different polymorphic forms of PVDF. Two model blends were selected; one with a mixture of alpha and beta crystals and the other predominantly rich in alpha crystals. Initially, physical ageing, at different oscillation frequencies (1 rad s(-1) and 0.1 rad s(-1)), was monitored by melt rheology and subsequently, the effect of steady shear was probed in situ without changing the history of the samples. Intriguingly, the rate of crystallization was observed to be significantly higher for higher oscillation frequencies, which essentially suggest that shear has induced crystallization in the blends. More interestingly, the effect of steady shear was more pronounced in the blends rich in alpha crystals (bigger crystallites as observed from SAXS) and at lower oscillation frequencies.

Haloarchaeal gas vesicle nanoparticles displaying Salmonella SopB antigen reduce bacterial burden when administered with live attenuated bacteria

Innovative vaccines against typhoid and other Salmonella diseases that are safe, effective, and inexpensive are urgently needed. In order to address this need, buoyant, self-adjuvating gas vesicle nanoparticles (GVNPs) from the halophilic archaeon Halobacterium sp. NRC-1 were bioengineered to display the highly conserved Salmonella enterica antigen SopB, a secreted inosine phosphate effector protein injected by pathogenic bacteria during infection into the host cell. Two highly conserved sopB gene segments near the 3'-coding region, named sopB4 and B5, were each fused to the gvpC gene, and resulting GVNPs were purified by centrifugally accelerated flotation. Display of SopB4 and B5 antigenic epitopes on GVNPs was established by Western blotting analysis using antisera raised against short synthetic peptides of SopB. Immunostimulatory activities of the SopB4 and B5 nanoparticles were tested by intraperitoneal administration of recombinant GVNPs to BALB/c mice which had been immunized with S. enterica serovar Typhimurium 14028 Delta pmrG-HM-D (DV-STM-07), a live attenuated vaccine strain. Proinflammatory cytokines IFN-gamma, IL-2, and IL-9 were significantly induced in mice boosted with SopB5-GVNPs, consistent with a robust Th1 response. After challenge with virulent S. enterica serovar Typhimurium 14028, bacterial burden was found to be diminished in spleen of mice boosted with SopB4-GVNPs and absent or significantly diminished in liver, mesenteric lymph node, and spleen of mice boosted with SopB5-GVNPs, indicating that the C-terminal portions of SopB displayed on GVNPs elicit a protective response to Salmonella infection in mice. SopB antigen-GVNPs were found to be stable at elevated temperatures for extended periods without refrigeration in Halobacterium cells. The results all together show that bioengineered GVNPs are likely to represent a valuable platform for the development of improved vaccines against Salmonella diseases. (C) 2014 Elsevier Ltd. All rights reserved.

Role of Cu2+ ions substitution in magnetic and conductivity behavior of nano-CoFe2O4

Cobalt copper ferrite nanopowders with composition Co1-xCu5Fe2O4 (0.0 <= x <= 0.5) was synthesized by solution combustion method. The powder X-ray diffraction studies reveal the formation of single ferrite phase with particle size of similar to 11-35 nm. Due to increase in electron density with in a material, X-ray density increase with increase of Cu2+ ions concentration. As Cu2+ ions concentration increases, saturation magnetization decreases from 38.5 to 26.7 emu g(-1). Further, the squareness ratio was found to be similar to 0.31-0.46 which was well below the typical value 1, which indicates the existence of single domain isolated ferrimagnetic samples. The dielectric and electrical modulus was studied over a frequency range of 1 Hz to 1 MHz at room temperature using the complex impedance spectroscopy technique. Impedance plots showed only one semi-circle which corresponds to the contributions of grain boundaries. The lower values of dielectric loss at higher frequency region may be quite useful for high frequency applications such as microwave devices. (C) 2014 Elsevier B.V. All rights reserved.

Give-and-take based peer-to-peer content distribution networks

Content Distribution Networks (CDNs) are widely used to distribute data to large number of users. Traditionally, content is being replicated among a number of surrogate servers, leading to high operational costs. In this context, Peer-to-Peer (P2P) CDNs have emerged as a viable alternative. An issue of concern in P2P networks is that of free riders, i.e., selfish peers who download files and leave without uploading anything in return. Free riding must be discouraged. In this paper, we propose a criterion, the Give-and-Take (G&T) criterion, that disallows free riders. Incorporating the G&T criterion in our model, we study a problem that arises naturally when a new peer enters the system: viz., the problem of downloading a `universe' of segments, scattered among other peers, at low cost. We analyse this hard problem, and characterize the optimal download cost under the G&T criterion. We propose an optimal algorithm, and provide a sub-optimal algorithm that is nearly optimal, but runs much more quickly; this provides an attractive balance between running time and performance. Finally, we compare the performance of our algorithms with that of a few existing P2P downloading strategies in use. We also study the computation time for prescribing the strategy for initial segment and peer selection for the newly arrived peer for various existing and proposed algorithms, and quantify cost-computation time trade-offs.

De novo inference of protein function from coarse-grained dynamics

Inference of molecular function of proteins is the fundamental task in the quest for understanding cellular processes. The task is getting increasingly difficult with thousands of new proteins discovered each day. The difficulty arises primarily due to lack of high-throughput experimental technique for assessing protein molecular function, a lacunae that computational approaches are trying hard to fill. The latter too faces a major bottleneck in absence of clear evidence based on evolutionary information. Here we propose a de novo approach to annotate protein molecular function through structural dynamics match for a pair of segments from two dissimilar proteins, which may share even <10% sequence identity. To screen these matches, corresponding 1 mu s coarse-grained (CG) molecular dynamics trajectories were used to compute normalized root-mean-square-fluctuation graphs and select mobile segments, which were, thereafter, matched for all pairs using unweighted three-dimensional autocorrelation vectors. Our in-house custom-built forcefield (FF), extensively validated against dynamics information obtained from experimental nuclear magnetic resonance data, was specifically used to generate the CG dynamics trajectories. The test for correspondence of dynamics-signature of protein segments and function revealed 87% true positive rate and 93.5% true negative rate, on a dataset of 60 experimentally validated proteins, including moonlighting proteins and those with novel functional motifs. A random test against 315 unique fold/function proteins for a negative test gave >99% true recall. A blind prediction on a novel protein appears consistent with additional evidences retrieved therein. This is the first proof-of-principle of generalized use of structural dynamics for inferring protein molecular function leveraging our custom-made CG FF, useful to all. (C) 2014 Wiley Periodicals, Inc.

Curvature-constrained trajectory generation for waypoint following for miniature air vehicle

This paper addresses trajectory generation problem of a fixed-wing miniature air vehicle, constrained by bounded turn rate, to follow a given sequence of waypoints. An extremal path, named as g-trajectory, that transitions between two consecutive waypoint segments (obtained by joining two waypoints in sequence) in a time-optimal fashion is obtained. This algorithm is also used to track the maximum portion of waypoint segments with the desired shortest distance between the trajectory and the associated waypoint. Subsequently, the proposed trajectory is compared with the existing transition trajectory in the literature to show better performance in several aspects. Another optimal path, named as loop trajectory, is developed for the purpose of tracking the waypoints as well as the entire waypoint segments. This paper also proposes algorithms to generate trajectories in the presence of steady wind to meet the same objective as that of no-wind case. Due to low computational burden and simplicity in the design procedure, these trajectory generation approaches are implementable in real time for miniature air vehicles.

A constant factor approximation algorithm for boxicity of circular arc graphs

The boxicity (resp. cubicity) of a graph G(V, E) is the minimum integer k such that G can be represented as the intersection graph of axis parallel boxes (resp. cubes) in R-k. Equivalently, it is the minimum number of interval graphs (resp. unit interval graphs) on the vertex set V, such that the intersection of their edge sets is E. The problem of computing boxicity (resp. cubicity) is known to be inapproximable, even for restricted graph classes like bipartite, co-bipartite and split graphs, within an O(n(1-epsilon))-factor for any epsilon > 0 in polynomial time, unless NP = ZPP. For any well known graph class of unbounded boxicity, there is no known approximation algorithm that gives n(1-epsilon)-factor approximation algorithm for computing boxicity in polynomial time, for any epsilon > 0. In this paper, we consider the problem of approximating the boxicity (cubicity) of circular arc graphs intersection graphs of arcs of a circle. Circular arc graphs are known to have unbounded boxicity, which could be as large as Omega(n). We give a (2 + 1/k) -factor (resp. (2 + log n]/k)-factor) polynomial time approximation algorithm for computing the boxicity (resp. cubicity) of any circular arc graph, where k >= 1 is the value of the optimum solution. For normal circular arc (NCA) graphs, with an NCA model given, this can be improved to an additive two approximation algorithm. The time complexity of the algorithms to approximately compute the boxicity (resp. cubicity) is O(mn + n(2)) in both these cases, and in O(mn + kn(2)) = O(n(3)) time we also get their corresponding box (resp. cube) representations, where n is the number of vertices of the graph and m is its number of edges. Our additive two approximation algorithm directly works for any proper circular arc graph, since their NCA models can be computed in polynomial time. (C) 2014 Elsevier B.V. All rights reserved.

Multiscale Symmetry Detection in Scalar Fields by Clustering Contours

The complexity in visualizing volumetric data often limits the scope of direct exploration of scalar fields. Isocontour extraction is a popular method for exploring scalar fields because of its simplicity in presenting features in the data. In this paper, we present a novel representation of contours with the aim of studying the similarity relationship between the contours. The representation maps contours to points in a high-dimensional transformation-invariant descriptor space. We leverage the power of this representation to design a clustering based algorithm for detecting symmetric regions in a scalar field. Symmetry detection is a challenging problem because it demands both segmentation of the data and identification of transformation invariant segments. While the former task can be addressed using topological analysis of scalar fields, the latter requires geometry based solutions. Our approach combines the two by utilizing the contour tree for segmenting the data and the descriptor space for determining transformation invariance. We discuss two applications, query driven exploration and asymmetry visualization, that demonstrate the effectiveness of the approach.

MAXIMUM A-POSTERIORI ESTIMATION OF MISSING SAMPLES WITH CONTINUITY CONSTRAINT IN ELECTROMAGNETIC ARTICULOGRAPHY DATA

Electromagnetic Articulography (EMA) technique is used to record the kinematics of different articulators while one speaks. EMA data often contains missing segments due to sensor failure. In this work, we propose a maximum a-posteriori (MAP) estimation with continuity constraint to recover the missing samples in the articulatory trajectories recorded using EMA. In this approach, we combine the benefits of statistical MAP estimation as well as the temporal continuity of the articulatory trajectories. Experiments on articulatory corpus using different missing segment durations show that the proposed continuity constraint results in a 30% reduction in average root mean squared error in estimation over statistical estimation of missing segments without any continuity constraint.