Tumor-initiating stem-like cells (TICs) are resistant to chemotherapy and associated with hepatocellular carcinoma (HCC) caused by HCV and/or alcohol-related chronic liver injury. Using HCV Tg mouse models and patients with HCC, we isolated CD133 + TICs and identified the pluripotency marker NANOG as a direct target of TLR4, which drives the tumor-initiating activity of TICs. These TLR4/NANOG-dependent TICs were defective in the TGF-β tumor suppressor pathway. Functional oncogene screening of a TIC cDNA library identified Yap1 and Igf2bp3 as NANOG-dependent genes that inactivate TGF-β signaling. Mechanistically, we determined that YAP1 mediates cytoplasmic retention of phosphorylated SMAD3 and suppresses SMAD3 phosphorylation/activation by the IGF2BP3/AKT/mTOR pathway. Silencing of both YAP1 and IGF2BP3 restored TGF-β signaling, inhibited pluripotency genes and tumorigenesis, and abrogated chemoresistance of TICs. Mice with defective TGF-β signaling (Spnb2 +/-mice) exhibited enhanced liver TLR4 expression and developed HCC in a TLR4-dependent manner. Taken together, these results suggest that the activated TLR4/NANOG oncogenic pathway is linked to suppression of cytostatic TGF-β signaling and could potentially serve as a therapeutic target for HCV-related HCC.

The standard of OSEK/VDX which used in the embedded operating system of car was analysized, and based on this, to select the open-source real-time operating system μC/OS-II as a operation system that will be loaded in the control chip. After that the kernel structure of the μC/OS-II was analysized, and modified the kernel of the system in accordance with OSEK/VDX standards, then translated the μC/OS-II system to the LPC2131 development board for the follow-up application development laid the foundation.

In traditional dynamic principal component analysis (DPCA) for fault detection, there are some drawbacks such as an excess of the number of principal components (PCs), low computational efficiency, etc. For dealing with the problem, this paper develops a hybrid dynamic principal component analysis (HDPCA) technique, this method can remove spacial and serial correlation by divide-and-conquer algorithm instead of parallel processing strategy, which can detect individual fault accurately and efficiently. The strip breaking fault in steel rolling process is used to demonstrate the improved performance of developed technique in comparison with traditional DPCA fault detection methods. It can be perceived that HDPCA algorithm has the better performance of fault detection and computational efficiency.

In view of the process of automatic flatness control and automatic gauge control that is a nonlinear system with multi-dimensions, multi-variables, strong coupling and time variation, a novel control method called self-tuning PID with diagonal recurrent neural network (DRNN-PID) based on Q learning is proposed. It is able to coordinate the coupling of flatness control and gauge control agents to get the satisfactory control requirements without decoupling directly and amend output control laws by DRNN-PID adaptively. Decomposition-coordination is utilized to establish a novel multi-agent system for coordination control including flatness agent, gauge agent and Q learning agent. Simulation result demonstrates the validity of our proposed method.