9 research outputs found
Model Augmented Deep Neural Networks for Medical Image Reconstruction Problems
Solving an ill-posed inverse problem is difficult because it doesn\u27t have a unique solution. In practice, for some important inverse problems, the conventional methods, e.g. ordinary least squares and iterative methods, cannot provide a good estimate. For example, for single image super-resolution and CT reconstruction, the results of these conventional methods cannot satisfy the requirements of these applications. While having more computational resources and high-quality data, researchers try to use machine-learning-based methods, especially deep learning to solve these ill-posed problems. In this dissertation, a model augmented recursive neural network is proposed as a general inverse problem method to solve these difficult problems. In the dissertation, experiments show the satisfactory performance of the proposed method for single image super-resolution, CT reconstruction, and metal artifact reduction
RNA-seq reveals role of cell-cycle regulating genes in the pathogenicity of a field very virulent infectious bursal disease virus
Infectious bursal disease virus (IBDV) infection causes highly contagious and immunosuppressive disease in poultry. The thymus, serving as the primary organ for T cell maturation and differentiation, plays an important role in the pathogenicity of IBDV in the infected chickens. However, there are no reports on the molecular pathogenesis of IBDV in the thymus currently. The aim of the study was to elucidate the molecular mechanisms underlying the pathogenicity of a field very virulent (vv) IBDV strain NN1172 in the thymus of SPF chickens using integrative transcriptomic and proteomic analyses. Our results showed that a total of 4,972 Differentially expressed genes (DEGs) in the thymus of NN1172-infected chickens by transcriptomic analysis, with 2,796 up-regulated and 2,176 down-regulated. Meanwhile, the proteomic analysis identified 726 differentially expressed proteins (DEPs) in the infected thymus, with 289 up-regulated and 437 down-regulated. Overall, a total of 359 genes exhibited differentially expression at both mRNA and protein levels, with 134 consistently up-regulated and 198 genes consistently down-regulated, as confirmed through a comparison of the RNA-seq and the proteomic datasets. The gene ontology (GO) analysis unveiled the involvement of both DEGs and DEPs in diverse categories encompassing cellular components, biological processes, and molecular functions in the pathological changes in IBDV-infected thymus. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the host mainly displayed severely disruption of cell survival/repair, proliferation and metabolism pathway, meanwhile, the infection triggers antiviral immune activation with a potential emphasis on the MDA5 pathway. Network inference analysis identified seven core hub genes, which include CDK1, TYMS, MCM5, KIF11, CCNB2, MAD2L1, and MCM4. These genes are all associated with cell-cycle regulating pathway and are likely key mediators in the pathogenesis induced by NN1172 infection in the thymus. This study discovered dominant pathways and genes which enhanced our understanding of the molecular mechanisms underlying IBDV pathogenesis in the thymus