19 resultados para indexing


Relevância:

10.00% 10.00%

Publicador:

Resumo:

The crystal structures of several designed peptide hairpins have been determined in order to establish features of molecular conformations and modes of aggregation in the crystals. Hairpin formation has been induced using a centrally positioned (D)Pro-Xxx segment (Xxx = (L)Pro, Aib, Ac(6)c, Ala; Aib = alpha-aminoisobutyric acid; Ac(6)c = 1-aminocyclohexane-1-carboxylic acid). Structures of the peptides Boc-Leu-Phe-Val-(D)Pro-(L)Pro-Leu-Phe-Val-OMe (1), Boc-Leu-Tyr-Val-(D)Pro-(L)Pro-Leu-Phe-Val-OMe (2, polymorphic forms labeled as 2a and 2b), Boc-Leu-Val-Val-(D)Pro-(L)Pro-Leu-Val-Val-OMe (3), Boc-Leu-Phe-Val-(D)Pro-Aib-Leu-Phe-Val-OMe (4, polymorphic forms labeled as 4a and 4b), Boc-Leu-Phe-Val-(D)Pro-Ac(6)c-Leu-Phe-Val-OMe (5) and Boc-Leu-Phe-Val-(D)Pro-Ala-Leu-Phe-Val-OMe (6) are described. All the octapeptides adopt type II' beta-turn nucleated hairpins, stabilized by three or four cross-strand intramolecular hydrogen bonds. The angle of twist between the two antiparallel strands lies in the range of -9.8 degrees to -26.7 degrees. A detailed analysis of packing motifs in peptide hairpin crystals is presented, revealing three broad modes of association: parallel packing, antiparallel packing and orthogonal packing. An attempt to correlate aggregation modes in solution with observed packing motifs in crystals has been made by indexing of crystal faces in the case of three of the peptide hairpins. The observed modes of hairpin aggregation may be of relevance in modeling multiple modes of association, which may provide insights into the structure of insoluble polypeptide aggregates.

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Visual tracking is an important task in various computer vision applications including visual surveillance, human computer interaction, event detection, video indexing and retrieval. Recent state of the art sparse representation (SR) based trackers show better robustness than many of the other existing trackers. One of the issues with these SR trackers is low execution speed. The particle filter framework is one of the major aspects responsible for slow execution, and is common to most of the existing SR trackers. In this paper,(1) we propose a robust interest point based tracker in l(1) minimization framework that runs at real-time with performance comparable to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points is obtained via solving the proposed l(1) minimization problem. In order to prune the noisy matches, a robust matching criterion is proposed, where only the reliable candidate points that mutually match with target and candidate dictionary elements are considered for tracking. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance and accuracy of the proposed tracker is benchmarked with several complex video sequences. The tracker is found to be considerably fast as compared to the reported state of the art trackers. The proposed tracker is further evaluated for various local patch sizes, number of interest points and regularization parameters. The performance of the tracker for various challenges including illumination change, occlusion, and background clutter has been quantified with a benchmark dataset containing 50 videos. (C) 2014 Elsevier B.V. All rights reserved.

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Identifying cellular processes in terms of metabolic pathways is one of the avowed goals of metabolomics studies. Currently, this is done after relevant metabolites are identified to allow their mapping onto specific pathways. This task is daunting due to the complex nature of cellular processes and the difficulty in establishing the identity of individual metabolites. We propose here a new method: ChemSMP (Chemical Shifts to Metabolic Pathways), which facilitates rapid analysis by identifying the active metabolic pathways directly from chemical shifts obtained from a single two-dimensional (2D) C-13-H-1] correlation NMR spectrum without the need for identification and assignment of individual metabolites. ChemSMP uses a novel indexing and scoring system comprised of a ``uniqueness score'' and a ``coverage score''. Our method is demonstrated on metabolic pathways data from the Small Molecule Pathway Database (SMPDB) and chemical shifts from the Human Metabolome Database (HMDB). Benchmarks show that ChemSMP has a positive prediction rate of >90% in the presence of deduttered data and can sustain the same at 60-70% even in the presence of noise, such as deletions of peaks and chemical shift deviations. The method tested on NMR data acquired for a mixture of 20 amino acids shows a success rate of 93% in correct recovery of pathways. When used on data obtained from the cell lysate of an unexplored oncogenic cell line, it revealed active metabolic pathways responsible for regulating energy homeostasis of cancer cells. Our unique tool is thus expected to significantly enhance analysis of NMIR-based metabolomics data by reducing existing impediments.

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.