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Overexpression of Zinc Finger Protein of Capsicum annuum(PIF1) in Tobacco Enhances Cold Tolerance
Eul Won Hwang,Soo Chul Park,Myung Ok Byun,Mie Young Choi,Hawk Bin Kwon 한국유전학회 2008 Genes & Genomics Vol.30 No.2
Plants encounter various stresses from their surrounding environment, such as low temperature, drought and high salinity. Thus the plant responds to abiotic stress such as cold and drought through a range of different strategies including physiological and biochemical processes that are usually mediated by up-regulation of the expression of stress regulated genes. Zinc finger proteins play a role in abiotic stress tolerance such as cold, dehydration and salt. As one way to approach to cold defense mechanism in plants, we previously identified a gene for zinc finger protein (PIF1) from hot pepper (Capsicum annuum) as cold inducible using cDNA microarray analysis coupled with Northern blot analysis. We previously showed that the PIF1 gene was induced strongly by cold stress, slightly by ABA. The PIF1 gene was engineered under control of CaMV 35S promoter for constitutive expression in transgenic tobacco plants by Ti-plasmid of Agrobacterium-mediated transformation. The resulting PIF1 transgenic tobacco plants showed significantly increased cold stress resistance. In addition, all PIF1 transgenic plants showed no visible phenotypic alteration compared to wild type plants. These results suggest the biological role of PIF1 in plant stress tolerances.
Kwon, Chiheon,Kang, Koung Mi,Byun, Min Soo,Yi, Dahyun,Song, Huijin,Lee, Ji Ye,Hwang, Inpyeong,Yoo, Roh-Eul,Yun, Tae Jin,Choi, Seung Hong,Kim, Ji-hoon,Sohn, Chul-Ho,Lee, Dong Young Korean Society of Magnetic Resonance in Medicine 2021 Investigative Magnetic Resonance Imaging Vol.25 No.3
Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant<sup>®</sup>, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion: Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.