Potato cultivar Atlantic is widely grown for potato chips in the world. However, this economically important potato cultivar exhibits very poor yields and traits under severe environmental stress. To develop an efficient plant transformation system that could be used to produce large scale transgenic potato plants with enhanced tolerance to environmental stress and therefore would be beneficial for potato processing industry, Agrobacterium-mediated transformation of internodal stem explants using both superoxide dismutase (SOD) and ascorbate peroxidase (APX) genes under the control of an oxidative stress-inducible SWPA2 promoter was performed. Comparing to leaf explants, stem internodal explants were less liable to damage during manipulation, more amenable to in vitro conditions. The addition of silver thiosulfate to the selection medium considerably promoted the shoot induction from explant-derived callus. Seven to nine shoots per stem explant were obtained. By combining the best treatments, this system yielded shoot induction frequency of 94.2% and transformation frequency of 80% of internodal stem explants. Stable integration of the transgenes was confirmed by PCR and Southern blot analyses. In conclusion, short duration (7～8 weeks), high efficiency and easy process make this system well suited for wider commercial applications of transgenic Atlantic potato plants.

In plants, oxidative stress is one of the major causes of damage as a result of various environmental stresses and it is primarily due to the excessive accumulation of reactive oxygen species. To develop transgenic potato plants with enhanced tolerance to environmental stress, transgenic potato plants (Solanum tuberosum L. cv. Atlantic) expressing the Cu/ZnSOD and APX genes in chloroplasts were generated under the control of the oxidative stress-inducible promoter. To investigate oxidative stress tolerance, transgenic plants were evaluated at the level of leaf discs and plantlets after methyl viologen (MV) and salt treatment. Leaf discs from transgenic potato plants showed 13% less membrane damage compared to non-transgenic (NT) plants suffering 10 μmol/L MV treatment of 48 h, and showed 1.6-fold higher chlorophyll contents than those of NT plants at 1.0mol/L NaCl treatment (31% vs. 19%). In addition, transgenic potato plants maintained higher rooting rates (75%) during 100mmol/L NaCl treatment than those (12%) from NT plants. Moreover, the tolerance to salt stress in transgenic plants was consistent to increased transcript levels and higher activities of SOD and APX compared to NT plants. These results suggest that expression of Cu/ZnSOD and APX in chloroplasts could be used in plants to enhance the tolerance to environmental stresses.

Background/Aims: Risk prediction models using a deep neural network (DNN) have not been reported to predict the risk of advanced colorectal neoplasia (ACRN). The aim of this study was to compare DNN models with simple clinical score models to predict the risk of ACRN in colorectal cancer screening. Methods: Databases of screening colonoscopy from Kangbuk Samsung Hospital (n=121,794) and Kyung Hee University Hospital at Gangdong (n=3,728) were used to develop DNN-based prediction models. Two DNN models, the Asian-Pacific Colorectal Screening (APCS) model and the Korean Colorectal Screening (KCS) model, were developed and compared with two simple score models using logistic regression methods to predict the risk of ACRN. The areas under the receiver operating characteristic curves (AUCs) of the models were compared in internal and external validation databases. Results: In the internal validation set, the AUCs of DNN model 1 and the APCS score model were 0.713 and 0.662 (p<0.001), respectively, and the AUCs of DNN model 2 and the KCS score model were 0.730 and 0.667 (p<0.001), respectively. However, in the external validation set, the prediction performances were not significantly different between the two DNN models and the corresponding APCS and KCS score models (both p>0.1). Conclusions Simple score models for the risk prediction of ACRN are as useful as DNN-based models when input variables are limited. However, further studies on this issue are warranted to predict the risk of ACRN in colorectal cancer screening because DNN-based models are currently under improvement.