126 resultados para 2-domain Arginine Kinase
Resumo:
The large protein L of negative-sense RNA viruses is a multifunctional protein involved in transcription and replication of genomic RNA. It also possesses enzymatic activities involved in capping and methylation of viral mRNAs. The pathway for mRNA capping followed by the L protein of the viruses in the Morbillivirus genus has not been established, although it has been speculated that these viruses may follow the unconventional capping pathway as has been shown for some viruses of Rhabdoviridae family. We had earlier shown that the large protein L of Rinderpest virus expressed as recombinant L-P complex in insect cells as well as the ribonucleoprotein complex from purified virus possesses RNA triphosphatase (RTPase) and guanylyltransferase activities, in addition to RNA dependent RNA polymerase activity. In the present work, we demonstrate that RTPase as well as nucleoside triphosphatase (NTPase) activities are exhibited by a subdomain of the L protein in the C terminal region (a.a. 1640 1840). The RTPase activity depends absolutely on a divalent cation, either magnesium or manganese. Both the RTPase and NTPase activities of the protein show dual metal specificity. Two mutant proteins having alanine mutations in the glutamic acid residues in motif-A of the RTPase domain did not show RTPase activity, while exhibiting reduced NTPase activity suggesting overlapping active sites for the two enzymatic functions. The RTPase and NTPase activities of the L subdomain resemble those of the Vaccinia capping enzyme D1 and the baculovirus LEF4 proteins. (C) 2015 Elsevier Inc. All rights reserved.
Resumo:
For a multilayered specimen, the back-scattered signal in frequency-domain optical-coherence tomography (FDOCT) is expressible as a sum of cosines, each corresponding to a change of refractive index in the specimen. Each of the cosines represent a peak in the reconstructed tomogram. We consider a truncated cosine series representation of the signal, with the constraint that the coefficients in the basis expansion be sparse. An l(2) (sum of squared errors) data error is considered with an l(1) (summation of absolute values) constraint on the coefficients. The optimization problem is solved using Weiszfeld's iteratively reweighted least squares (IRLS) algorithm. On real FDOCT data, improved results are obtained over the standard reconstruction technique with lower levels of background measurement noise and artifacts due to a strong l(1) penalty. The previous sparse tomogram reconstruction techniques in the literature proposed collecting sparse samples, necessitating a change in the data capturing process conventionally used in FDOCT. The IRLS-based method proposed in this paper does not suffer from this drawback.
Resumo:
This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.
Resumo:
An optimal control problem in a two-dimensional domain with a rapidly oscillating boundary is considered. The main features of this article are on two points, namely, we consider periodic controls in the thin periodic slabs of period epsilon > 0, a small parameter, and height O(1) in the oscillatory part, and the controls are characterized using unfolding operators. We then do a homogenization analysis of the optimal control problems as epsilon -> 0 with L-2 as well as Dirichlet (gradient-type) cost functionals.
Resumo:
A new 2,2-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS)-radical scavenging and antiproliferative agents of pyrrolo1,2-a]quinoline derivatives have been synthesized. An efficient method for the synthesis of 14 novel diversified pyrrolo1,2-a]quinoline derivatives has been described using 4-(1,3-dioxolan-2-yl)quinoline and different phenacyl bromides in acetone and followed by reacting with different acetylenes in dimethylformamide/K2CO3. The structure of the newly synthesized compounds was determined by infrared, H-1 NMR, C-13 NMR, mass spectrometry, and elemental analysis. The in vitro antioxidant activity revealed that among all the tested compounds 5n exhibited maximum scavenging activity with ABTS. Compound 5b has showed good antiproliferative activity as an inhibitor of epidermal growth factor receptor (EGFR) tyrosine kinase.
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.