Characterization of the Conus bullatus genome and its venom-duct transcriptome

<p>Abstract</p> <p>Background</p> <p>The venomous marine gastropods, cone snails (genus <it>Conus</it>), inject prey with a lethal cocktail of conopeptides, small cysteine-rich peptides, each with a high affinity for its molecular target, generally an ion channel, receptor or transporter. Over the last decade, conopeptides have proven indispensable reagents for the study of vertebrate neurotransmission. <it>Conus bullatus </it>belongs to a clade of <it>Conus </it>species called <it>Textilia</it>, whose pharmacology is still poorly characterized. Thus the genomics analyses presented here provide the first step toward a better understanding the enigmatic <it>Textilia </it>clade.</p> <p>Results</p> <p>We have carried out a sequencing survey of the <it>Conus bullatus </it>genome and venom-duct transcriptome. We find that conopeptides are highly expressed within the venom-duct, and describe an <it>in silico </it>pipeline for their discovery and characterization using RNA-seq data. We have also carried out low-coverage shotgun sequencing of the genome, and have used these data to determine its size, genome-wide base composition, simple repeat, and mobile element densities.</p> <p>Conclusions</p> <p>Our results provide the first global view of venom-duct transcription in any cone snail. A notable feature of <it>Conus bullatus </it>venoms is the breadth of A-superfamily peptides expressed in the venom duct, which are unprecedented in their structural diversity. We also find SNP rates within conopeptides are higher compared to the remainder of <it>C. bullatus </it>transcriptome, consistent with the hypothesis that conopeptides are under diversifying selection.</p

Digital Image Coding For Robust Multimedia Transmission

This paper presents an alternative to the above techniques for providing robust digital image transmission --- reconstruction of lost information from correctly received information. As such, reconstruction for robust transmission requires no special FEC or protocol features. Image data is highly correlated, and when the properties of the human visual system are considered, lost visual data can be reconstructed from received data such that the reconstructed data is reasonably correct (high PSNR) and visually pleasing. These two criteria are not the same --- it is possible to have a higher PSNR with a visually less pleasing result, and a lower PSNR with a better looking result. Providing reconstruction capability as well as compression places requirements on coding of the image data, where coding is defined to include both the actual source coding and the This document was created with FrameMaker 4.0.2 packetization of the compressed data. This paper describes coding of digital images that considers not only compression but also ease and quality of reconstruction to allow for robust transmission. Block-based transform coding techniques are considered so that direct extensions to JPEG-like and MPEG-like coding are possible. First, the trade-offs among decoder computation, transmission bandwidth, and visual quality are described, leading to two approaches to providing robust image transmission. Next, packetization requirements for reconstruction are considered. An approach to block-based reconstruction is described, and three specific solutions are given as examples, requiring different combinations of transmission bandwidth and decoder computation and all providing good visual quality. The reconstruction techniques presented are non-iterative and computationally efficie..

Supporting visual quality assessment with machine learning

Objective metrics for visual quality assessment often base their reliability on the explicit modeling of the highly non-linear behavior of human perception; as a result, they may be complex and computationally expensive. Conversely, machine learning (ML) paradigms allow to tackle the quality assessment task from a different perspective, as the eventual goal is to mimic quality perception instead of designing an explicit model the human visual system. Several studies already proved the ability of ML-based approaches to address visual quality assessment; nevertheless, these paradigms are highly prone to overfitting, and their overall reliability may be questionable. In fact, a prerequisite for successfully using ML in modeling perceptual mechanisms is a profound understanding of the advantages and limitations that characterize learning machines. This paper illustrates and exemplifies the good practices to be followed.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc