Real Time Static and Dynamic Sign Language Recognition using Deep Learning

Sign language recognition systems are used for enabling communication between deaf-mute people and normal user. Spatial localization of the hands could be a challenging task when hands-only occupies 10% of the entire image. This is overcome by designing a real-time efficient system that is capable of performing the task of extraction, recognition, and classification within a single network with the use of a deep convolution network. The recognition is performed for static image dataset with a simple and complex background, dynamic video dataset. Static image dataset is trained and tested using a 2D deep-convolution neural network whereas dynamic video dataset is trained and tested using a 3D deep-convolution neural network. Spatial augmentation is done to increase the number of images of static dataset and key-frame extraction to extract the key-frames from the videos for dynamic dataset. To improve the system performance and accuracy Batch-Normalization layer is added to the convolution network. The accuracy is nearly 99% for dataset with a simple background, 92% for dataset with complex background, and 84% for the video dataset. By obtaining a good accuracy, the system is proved to be real-time efficient in recognizing and interpreting the sign language gestures

abd-A Regulation by the iab-8 Noncoding RNA

The homeotic genes in Drosophila melanogaster are aligned on the chromosome in the order of the body segments that they affect. The genes affecting the more posterior segments repress the more anterior genes. This posterior dominance rule must be qualified in the case of abdominal-A (abd-A) repression by Abdominal-B (Abd-B). Animals lacking Abd-B show ectopic expression of abd-A in the epidermis of the eighth abdominal segment, but not in the central nervous system. Repression in these neuronal cells is accomplished by a 92 kb noncoding RNA. This “iab-8 RNA” produces a micro RNA to repress abd-A, but also has a second, redundant repression mechanism that acts only “in cis.” Transcriptional interference with the abd-A promoter is the most likely mechanism

Identification and characterization of starvation induced msdgc-1 promoter involved in the c-di-GMP turnover

C-di-GMP Bis-(3'-5')-cyclic-dimeric-guanosine monophosphate], a second messenger is involved in intracellular communication in the bacterial species. As a result several multi-cellular behaviors in both Gram-positive and Gram-negative bacteria are directly linked to the intracellular level of c-di-GMP. The cellular concentration of c-di-GMP is maintained by two opposing activities, diguanylate cyclase (DGC) and phosphodiesterase (PDE-A). In Mycobacterium smegmatis, a single bifunctional protein MSDGC-1 is responsible for the cellular concentration of c-di-GMP. A better understanding of the regulation of c-di-GMP at the genetic level is necessary to control the function of above two activities. In this work, we have characterized the promoter element present in msdgc-1 along with the + 1 transcription start site and identified the sigma factors that regulate the transcription of msdgc-1. Interestingly, msdgc-1 utilizes SigA during the initial phase of growth, whereas near the stationary phase SigB containing RNA polymerase takes over the expression of msdgc-1. We report here that the promoter activity of msdgc-1 increases during starvation or depletion of carbon source like glucose or glycerol. When msdgc-1 is deleted, the numbers of viable cells are similar to 10 times higher in the stationary phase in comparison to that of the wild type. We propose here that msdgc-1 is involved in the regulation of cell population density. (C) 2013 Elsevier B.V. All rights reserved