A full-length coding domain sequence of a gene analogous to granule-bound starch synthase (GBSS; ADP-glucose-starch glucosyltransferase, EC 2.4.1.21) was cloned and defined as OsGBSSII based on a Nitrogen (N)-starvation-induced cDNA library constructed using the rapid subtraction hybridization method. The deduced amino acid sequence of OsGBSSII was 62-85% identical to those of GBSS proteins from other plant species. The exon/intron organization of OsGBSSII was similar to that of OsGBSSI. OsGBSSII was mainly expressed in leaves and its protein was exclusively bound to starch granules in rice leaves, which suggests that the amylose in rice leaves is synthesized by OsGBSSII. N-starvation-induced expression of OsGBSSII could be repressed by supplying nitrate, ammonia or amino acid (glutamic acid or glutamine), glucosamine (an inhibitor of hexokinase) or dark conditions. These results indicate that N-starvation induction was dependent on the photosynthetic product and hexokinase in rice leaves. Sugars induced the accumulation of OsGBSSII transcripts in excised leaves through glycolysis-dependent pathways. OsGBSSII gene expression is regulated by the circadian rhythm in rice leaves.

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Significant advances in video compression systems have been made in the past several decades to satisfy the near-exponential growth of Internet-scale video traffic. From the application perspective, we have identified three major functional blocks, including preprocessing, coding, and postprocessing, which have been continuously investigated to maximize the end-user quality of experience (QoE) under a limited bit rate budget. Recently, artificial intelligence (AI)-powered techniques have shown great potential to further increase the efficiency of the aforementioned functional blocks, both individually and jointly. In this article, we review recent technical advances in video compression systems extensively, with an emphasis on deep neural network (DNN)based approaches, and then present three comprehensive case studies. On preprocessing, we show a switchable texturebased video coding example that leverages DNN-based scene understanding to extract semantic areas for the improvement Manuscript

Genetic skeletal disorders (GSD) involving the skeletal system arises through disturbances in the complex processes of skeletal development, growth and homeostasis and remain a diagnostic challenge because of their clinical heterogeneity and genetic variety. Over the past decades, tremendous effort platforms have been made to explore the complex heterogeneity, and massive new genes and mutations have been identified in different GSD, but the information supplied by literature is still limited and it is hard to meet the further needs of scientists and clinicians. In this study, combined with Nosology and Classification of genetic skeletal disorders, we developed the first comprehensive and annotated genetic skeletal disorders database, named ‘SkeletonGenetics’, which contains information about all GSD-related knowledge including 8225 mutations in 357 genes, with detailed information associated with 481 clinical diseases (2260 clinical phenotype) classified in 42 groups defined by molecular, biochemical and/or radiographic criteria from 1698 publications. Further annotations were performed to each entry including Gene Ontology, pathways analysis, protein–protein interaction, mutation annotations, disease–disease clustering and gene–disease networking. Furthermore, using concise search methods, intuitive graphical displays, convenient browsing functions and constantly updatable features, ‘SkeletonGenetics’ could serve as a central and integrative database for unveiling the genetic and pathways pre-dispositions of GSD.Database URL: http://101.200.211.232/skeletongenetics/