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Autophagy is a highly conserved intracellular degradation pathway by which misfolded proteins or damaged organelles are delivered in a double-membrane vacuolar vesicle and finally degraded by lysosomes. The risk of colorectal cancer (CRC) is high, and there is growing evidence that autophagy plays a critical role in regulating the initiation and metastasis of CRC; however, whether autophagy promotes or suppresses tumor progression is still controversial. Many natural compounds have been reported to exert anticancer effects or enhance current clinical therapies by modulating autophagy. Here, we discuss recent advancements in the molecular mechanisms of autophagy in regulating CRC. We also highlight the research on natural compounds that are particularly promising autophagy modulators for CRC treatment with clinical evidence. Overall, this review illustrates the importance of autophagy in CRC and provides perspectives for these natural autophagy regulators as new therapeutic candidates for CRC drug development.

This study aims to develop an effective method of classification concerning time series signals for machine state prediction to advance predictive maintenance (PdM).   Conventional machine learning (ML) algorithms are widely adopted in PdM, however, most existing methods assume that the training (source) and testing (target) data follow the same distribution, and that labeled data are available in both source and target domains. For real-world PdM applications, the heterogeneity in machine original equipment manufacturers (OEMs), operating conditions, facility environment, and maintenance records collectively lead to heterogeneous distribution for data collected from different machines. This will significantly limit the performance of conventional ML algorithms in PdM. Moreover, labeling data is generally costly and time-consuming. Finally, industrial processes incorporate complex conditions, and unpredictable breakdown modes lead to extreme complexities for PdM. In this study, similarity-based multi-source transfer learning (SiMuS-TL) approach is proposed for real-time classification of time series signals. A new domain, called "mixed domain," is established to model the hidden similarities among the multiple sources and the target. The proposed SiMuS-TL model mainly includes three key steps: 1) learning group-based feature patterns, 2) developing group-based pre-trained models, and 3) weight transferring. The proposed SiMuS-TL model is validated by observing the state of the rotating machinery using a dataset collected on the Skill boss manufacturing system, publicly available standard bearing datasets, Case Western Reserve University (CWRU), and Paderborn University (PU) bearing datasets. The results of the performance comparison demonstrate that the proposed SiMuS-TL method outperformed conventional Support Vector Machine (SVM), Artificial Neural Network (ANN), and Transfer learning with neural networks (TLNN) without similarity-based transfer learning methods.

2016-11-02T18:49:00