JPEG2000-Based Semantic Image Compression using CNN

Some of the computer vision applications such as understanding, recognition as well as image processing are some areas where AI techniques like convolutional neural network (CNN) have attained great success. AI techniques are not very frequently used in applications like image compression which are a part of low-level vision applications. Intensifying the visual quality of the lossy video/image compression has been a huge obstacle for a very long time. Image processing tasks and image recognition can be addressed with the application of deep learning CNNs as a result of the availability of large training datasets and the recent advances in computing power. This paper consists of a CNN-based novel compression framework comprising of Compact CNN (ComCNN) and Reconstruction CNN (RecCNN) where they are trained concurrently and ideally consolidated into a compression framework, along with MS-ROI (Multi Structure-Region of Interest) mapping which highlights the semiotically notable portions of the image. The framework attains a mean PSNR value of 32.9dB, achieving a gain of 3.52dB and attains mean SSIM value of 0.9262, achieving a gain of 0.0723dB over the other methods when compared using the 6 main test images. Experimental results in the proposed study validate that the architecture substantially surpasses image compression frameworks, that utilized deblocking or denoising post- processing techniques, classified utilizing Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measures (SSIM) with a mean PSNR, SSIM and Compression Ratio of 38.45, 0.9602 and 1.75x respectively for the 50 test images, thus obtaining state-of-art performance for Quality Factor (QF)=5

Identification of Novel Single Nucleotide Polymorphisms in Inflammatory Genes as Risk Factors Associated with Trachomatous Trichiasis

infection, the primary cause of trachoma. Despite control programs that include mass antibiotic treatment, reinfection and recurrence of trachoma are common after treatment cessation. Furthermore, a subset of infected individuals develop inflammation and are at greater risk for developing the severe sequela of trachoma known as trachomatous trichiasis (TT). While there are a number of environmental and behavioral risk factors for trachoma, genetic factors that influence inflammation and TT risk remain ill defined. = 0.001] with the combination of TNFA (-308A), LTA (252A), VCAM1 (-1594C), SCYA 11 (23T) minor allele, and the combination of TNFA (-308A), IL9 (113M), IL1B (5′UTR-T), and VCAM1 (-1594C). However, TT risk increased 13.5 times [odds ratio = 13.5 (95% confidence interval 3.3–22), p = 0.001] with the combination of TNFA (-308G), VDR (intron G), IL4R (50V), and ICAM1 (56M) minor allele.Evaluating genetic risk factors for trachoma will advance our understanding of disease pathogenesis, and should be considered in the context of designing global control programs

MutTILL: Development of induced mutant resources for genome-wide mutations screening by using next generation sequencing technologies

Mutagenesis based TILLING (MutTILL) is a reverse genetic approach that can be applied in plant research and breeding programs to broaden the genetic variability and for the identification of novel mutant alleles in specific genes of interest with improved varieties. MutTILL method is non-transgenic approach, further removing the barriers in marketing new varieties. It has been widely adapted to different species across genera to study the gene function. Traditionally, TILLING work flow comprises of mutagenesis, followed by scanning of amplicon region of interest and cleavage by mismatch specific endonucleases at the site of mismatch, and detection either by using gel electrophoresis or by high resolution melt analysis. However, with the advent of improving sequencing technologies and the decrease in cost, TILLING can be made less laborious and faster using next-generation sequencing-based approaches. Nevertheless, sequencing the whole genome of thousands of mutagenized individuals remains expensive. But the NGS-based technologies can be applied to TILLING, either in the form of whole exome sequencing or by using target re-sequencing. We at AgriGenome Labs are working on development of NGS-based TILLING platforms in various crop species like Chilli, Cotton, Rice, and Tomato, among others with the plan to further expand to other important crops. Currently, a large mutagenized population of the above crops is at different stages of development. The developed mutant populations will help in the identification of novel mutations for various biotic and abiotic stresses. We are aiming to provide complete and customized solutions to research institutes, companies and individuals for developing and providing genetic variations for their crops of interest with improved trait quality

Genotyping of Tomato Cultivars and Hybrids using ddRAD

Tomato (Solanum lycopersicum) is a major crop plant and a model system for fruit development. Solanum is one of the largest angiosperm genera and includes annual and perennial plants from diverse habitats. ddRAD-seq is one of the most cost-effective methods in next generation sequencing (NGS) for generating robust genotyping data which permits high throughput simultaneous discovery and genotyping of sequence polymorphism either with or without an existing reference genome. Advantage of ddRAD technique was investigated by performing data analysis of sequence obtained through low pass whole genome sequencing and ddRAD protocol. Here we present a high-quality reduced represented genome sequence of domesticated tomato with the aim of understanding genetic variations in cultivated tomato; single nucleotide polymorphism (SNP) markers covering the whole genome of eight cultivars and four F1 hybrids were developed through Genotyping-By-Sequencing. We have sequenced twelve tomato varieties using Illumina HiSeq 4000, next generation sequencing platform. The raw data was subjected to preprocessing and aligned with reference tomato genome downloaded from ensembl release 36. The SNPs/INDELs were identified for each of the tomato varieties. A total of 30746 SNPs and 913 INDELs were identified. We investigated for homozygous polymorphic markers between PKM-1 and Arka Abha and found 745 markers which can be used as markers for fingerprinting.The homozygous polymorphic markers will be utilized for genetic mapping and trait association in a mapping population