Bioinformatic Approach to Investigate Larvae Gut Microbiota Cellulosimicrobium protaetiae via Whole-Genome Analysis.

The insect larvae Protaetia brevitarsis seulensis have recently been researched as a nutritious food source and concentrated on their environmental impacts. Therefore, their gut microbiota has been studied to elucidate their effects and roles on the environment. Of the abundance of bacterial genus identified based on the 16S rRNA genes from isolates of the gut of insect larva Protaetia brevitarsis seulensis, six of the prominent genus were identified as Bacillus (40.2%), Cellulosimicrobium (33.5%), Microbacterium (2.8%), Streptomyces (3%), Krasilnikoviella (17.5%), and Isoptericola (3%) and their similarity of 16S rRNA blast changed from 99 to 100%. Cellulosimicrobium protaetiae BI34T showed strong denitrification and cellulose degradation activity. The newly complete genome sequence of BI34T and the genomes of five species was published in the genus Cellulosimicrobium with emphasis on the denitrification and secondary metabolite genes. In order to elucidate the relationship between the strain BI34T and the host insect larva, the whole-genome sequence was analyzed and compared with the genomes of five strains in the same genus, Cellulosimicrobium, loaded from GenBank. Our results revealed the composition of the gut microbiota of the insect larvae and analyzed the genomic data for the new strain to predict its characteristics and to understand the nitrogen metabolism pathway.

Extending and Matching a High Dynamic Range Image from a Single Image.

Extending the dynamic range can present much richer contrasts and physical information from the traditional low dynamic range (LDR) images. To tackle this, we propose a method to generate a high dynamic range image from a single LDR image. In addition, a technique for the matching between the histogram of a high dynamic range (HDR) image and the original image is introduced. To evaluate the results, we utilize the dynamic range for independent image quality assessment. It recognizes the difference in subtle brightness, which is a significant role in the assessment of novel lighting, rendering, and imaging algorithms. The results show that the picture quality is improved, and the contrast is adjusted. The performance comparison with other methods is carried out using the predicted visibility (HDR-VDP-2). Compared to the results obtained from other techniques, our extended HDR images can present a wider dynamic range with a large difference between light and dark areas.