Luffa acutangula agglutinin: Primary structure determination and identification of a tryptophan residue involved in its carbohydrate-binding activity using mass spectrometry

A lectin from phloem exudates of Luffa acutangula (ridge gourd) was purified on chitin affinity chromatography and characterized for its amino acid sequence and to study the role of tryptophan in its activity. The purified lectin was subjected to various proteolytic digestions, and the resulting peptides were analyzed by liquid chromatography coupled electrospray ionization ion trap mass spectrometer. The peptide precursor ions were fragmented by collision-induced dissociation or electron transfer dissociation experiments, and a manual interpretation of MS/MS was performed to deduce amino acid sequence. This gave rise to almost complete sequence coverage of the lectin which showed high-sequence similarity with deduced sequences of phloem lectins present in the database. Chemical modification of lysine, tyrosine, histidine, arginine, aspartic acid, and glutamic acid residues did not inhibit the hemagglutinating activity. However, the modification of tryptophan residues using N-bromosuccinimide showed the loss of hemagglutinating activity. Additionally, the mapping of tryptophan residues was performed to determine the extent and number of residues modified, which revealed that six residues per molecule were oxidized suggesting their accessibility. The retention of the lectin activity was seen when the modifications were performed in the presence of chitooligosaccharides due to protection of a tryptophan residue (W-102) in the protein. These studies taken together have led to the identification of a particular tryptophan residue (W-102) in the activity of the lectin. (c) 2015 IUBMB Life, 67(12):943-953, 2015

Ellipse Fitting Using the Finite Rate of Innovation Sampling Principle

Standard approaches for ellipse fitting are based on the minimization of algebraic or geometric distance between the given data and a template ellipse. When the data are noisy and come from a partial ellipse, the state-of-the-art methods tend to produce biased ellipses. We rely on the sampling structure of the underlying signal and show that the x- and y-coordinate functions of an ellipse are finite-rate-of-innovation (FRI) signals, and that their parameters are estimable from partial data. We consider both uniform and nonuniform sampling scenarios in the presence of noise and show that the data can be modeled as a sum of random amplitude-modulated complex exponentials. A low-pass filter is used to suppress noise and approximate the data as a sum of weighted complex exponentials. The annihilating filter used in FRI approaches is applied to estimate the sampling interval in the closed form. We perform experiments on simulated and real data, and assess both objective and subjective performances in comparison with the state-of-the-art ellipse fitting methods. The proposed method produces ellipses with lesser bias. Furthermore, the mean-squared error is lesser by about 2 to 10 dB. We show the applications of ellipse fitting in iris images starting from partial edge contours, and to free-hand ellipses drawn on a touch-screen tablet.

CROWD FLOW SEGMENTATION IN COMPRESSED DOMAIN USING CRF

Crowd flow segmentation is an important step in many video surveillance tasks. In this work, we propose an algorithm for segmenting flows in H.264 compressed videos in a completely unsupervised manner. Our algorithm works on motion vectors which can be obtained by partially decoding the compressed video without extracting any additional features. Our approach is based on modelling the motion vector field as a Conditional Random Field (CRF) and obtaining oriented motion segments by finding the optimal labelling which minimises the global energy of CRF. These oriented motion segments are recursively merged based on gradient across their boundaries to obtain the final flow segments. This work in compressed domain can be easily extended to pixel domain by substituting motion vectors with motion based features like optical flow. The proposed algorithm is experimentally evaluated on a standard crowd flow dataset and its superior performance in both accuracy and computational time are demonstrated through quantitative results.

SALIENT OBJECT DETECTION VIA OBJECTNESS MEASURE

Salient object detection has become an important task in many image processing applications. The existing approaches exploit background prior and contrast prior to attain state of the art results. In this paper, instead of using background cues, we estimate the foreground regions in an image using objectness proposals and utilize it to obtain smooth and accurate saliency maps. We propose a novel saliency measure called `foreground connectivity' which determines how tightly a pixel or a region is connected to the estimated foreground. We use the values assigned by this measure as foreground weights and integrate these in an optimization framework to obtain the final saliency maps. We extensively evaluate the proposed approach on two benchmark databases and demonstrate that the results obtained are better than the existing state of the art approaches.