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Kiwifruit has a high decay rate, in part because quality changes during storage cannot be easily monitored in real time. In order to better monitor the shelf life of kiwifruit and understand the quality changing process during storage, internal quality indexes such as hardness, respiratory intensity and TSS(Total Soluble Solid) were considered into the prediction models. The prediction models were constructed based on BPNN (Back Propagation Neural Network), Random Forest (RF) and XGBoost (eXtreme Gradient Boosting) respectively. And transfer learning algorithm was used to construct the quality prediction models with BPNN, RF, and XGBoost algorithms as the base learner. In the experiments, sample data were augmented by adding Gaussian noise, which effectively prevented the model from over-fitting. The experimental results showed that the prediction accuracy of each index based on transfer learning was better than that of individual BPNN, RF and XGBoost. Moreover, the average prediction accuracy of the models based on transfer learning was 96.2%, and that of respiratory intensity was as high as 99.4%. Therefore transfer learning can be used to effectively analyze and predict changes of kiwifruit quality indexes during storage. [ABSTRACT FROM AUTHOR]

Rescuing the function of mutant p53 protein is an attractive cancer therapeutic strategy. Small molecule CP-31398 was shown to restore mutant p53 tumor suppressor functions in cancer cells. Here, we determined the effects of CP-31398 on the growth of p53-mutated colorectal cancer (CRC) cells in vitro and in vivo. CRC cells which carry p53 mutation in codon 273 were treated with CP-31398 and the control, and the effects of CP-31398 on cell cycle, cell apoptosis, and proliferation were determined. The expression of p53-responsive downstream genes was evaluated by quantitative reverse transcriptase PCR (RT-PCR) and Western blot. CP-31398 was administrated into xenograft tumors created by the inoculation of HT-29 cells, and then the effect of CP-31398 on the growth of xenograft tumors was examined. CP-31398 induced p53 downstream target molecules in cultured HT-29 cells, which resulted in the inhibition of CRC cell growth assessed by the determination of cell cycle, apoptosis, and cell proliferation. In xenograft tumors, CP-31398 modulated the expression of Bax, Bcl-2, caspase 3, cyclin D, and Mdm2 and then blocked the growth of xenograft tumors. CP-31398 would be developed as a therapeutic candidate for p53-mutated CRC due to the restoration of mutant p53 tumor suppressor functions.