Background Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. Objective We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non–COVID-19 pneumonia and nonpneumonia diseases. Methods A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. Results Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). Conclusions FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.

Proteomic approaches using two-dimensional gel electrophoresis (2-DE) were adopted to identify proteins from rice leaf that are differentially expressed in response to the rice blast fungus, Magnaporthe grisea. Microscopic observation of inoculated leaf with M. grisea revealed that callose deposition and hypersensitive response was clearly visible in incompatible interactions but excessive invading hypha with branches were evident in compatible interactions. Proteins were extracted from leaves 24, 48, and 72 hours after rice blast fungus inoculation. Eight proteins resolved on the 2-DE gels were induced or increased in the inoculated leaf. Matrix-assisted laser desorption/ionization-time of flight analysis of these differentially displayed proteins showed them to be two receptor-like protein kinases (RLK), two beta-1.3-glucanases (Glu1, Glu2), thaumatin-like protein (TLP), peroxidase (POX 22.3), probenazole-inducible protein (PBZ1), and rice pathogenesis-related 10 (OsPR-10). Of these proteins, RLK, TLP, PBZ, and OsPR-10 proteins were induced more in the incompatible interactions than in compatible ones. A phytohormone, jasmonic acid also induced all eight proteins in leaves. To confirm whether the expression profile is equal to the 2-DE data, seven cDNA clones were used as probes in Northern hybridization experiments using total RNA from leaf tissues inoculated with incompatible and compatible rice blast fungal races. The genes encoding POX22.3, Glu1, Glu2, TLP, OsRLK, PBZ1, and OsPR-10 were activated in inoculated leaves, with TLP, OsRLK, PBZ1, and OsPR-10 being expressed earlier and more in incompatible than in compatible interactions. These results suggest that early and high induction of these genes may provide host plants with leading edges to defend themselves. The localization of two rice PR-10 proteins, PBZ1 and OsPR-10, was further examined by immunohistochemical analysis. PBZ1 accumulated highly in mesophyll cells under the attachment site of the appressorium. In contrast, OsPR-10 expression was mainly localized to vascular tissue.

Biological synthesis of plant secondary metabolites has attracted increasing attention due to their proven or assumed beneficial properties and health-promoting effects. Phenylpropanoids are the precursors to a range of important plant metabolites such as the secondary metabolites belonging to the flavonoid/stilbenoid class of compounds. In this study, engineered Escherichia coli containing artificial phenylpropanoid biosynthetic pathways utilizing tyrosine as the initial precursor were established for production of plant-specific metabolites such as ferulic acid, naringenin, and resveratrol. The construction of the artificial pathway utilized tyrosine ammonia lyase and 4-coumarate 3-hydroxylase from Saccharothrix espanaensis, cinnamate/4-coumarate:coenzyme A ligase from Streptomyces coelicolor, caffeic acid O-methyltransferase and chalcone synthase from Arabidopsis thaliana, and stilbene synthase from Arachis hypogaea.

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The phytohormones gibberellic acid (GA) and abscisic acid (ABA) play essential and often antagonistic roles in regulating plant growth, development, and stress responses. Using a proteomics-based approach, we examined the role of GA and ABA in the modulation of protein expression levels during seed germination. Rice seeds were treated with GA (200 microM), ABA (10 microM), ABA followed by GA, GA followed by ABA, and water as a control and then incubated for 3 days. The embryo was dissected from germinated seeds, and proteins were subjected to 2-DE. Approximately, 665 total protein spots were resolved in the 2-D gels. Among them, 16 proteins notably modulated by either GA or ABA were identified by MALDI-TOF MS. Northern analyses demonstrated that expression patterns of 13 of these 16 genes were consistent with those of the proteome analysis. Further examination of two proteins, rice isoflavone resuctase (OsIFR) and rice PR10 (OsPR10), using Western blot and immunolocalization, revealed that both are specifically expressed in the embryo but not in the endosperm and are dramatically downregulated by ABA.