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Acrylamide has been shown to be neurotoxic. Brain-derived neurotrophic factor (BDNF) can alleviate acrylamide-induced synaptic injury; however, the underlying mechanism remains unclear. In this study, dibutyryl-cyclic adenosine monophosphate-induced mature human neuroblastoma (NB-1) cells were exposed with 0-100 μg/mL acrylamide for 24-72 hours. Acrylamide decreased cell viability and destroyed synapses. Exposure of co-cultured NB-1 cells and Schwann cells to 0-100 μg/mL acrylamide for 48 hours resulted in upregulated expression of synapsin I and BDNF, suggesting that Schwann cells can activate self-protection of neurons. Under co-culture conditions, activation of the downstream TrkB-MAPK-Erk1/2 pathway strengthened the protective effect. Exogenous BDNF can increase expression of TrkB, Erk1/2, and synapsin I, while exogenous BDNF or the TrkB inhibitor K252a could inhibit these changes. Taken together, Schwann cells may act through the BDNF-TrkB-MAPK-Erk1/2 signaling pathway, indicating that BDNF plays an important role in this process. Therefore, exogenous BDNF may be an effective treatment strategy for acrylamide-induced nerve injury. This study was approved by the Laboratory Animal Welfare and Ethics Committee of the National Institute of Occupational Health and Poison Control, a division of the Chinese Center for Disease Control and Prevention (approval No. EAWE-2017-008) on May 29, 2017.

In order to explore the role of apoptosis in alpha-amatoxin (α-AMA) induced liver injury and probable upstream activation signals, we established animal and cellular models, respectively, for this pathophysiological condition. To this end, we evaluated the survival rate and serum biochemical parameters in BALB/c mice exposed to α-AMA at different time periods, along with the levels of oxidative and antioxidant enzymes in the liver tissue of these mice and proteins involved in apoptosis-related pathways. Our results reveal that α-AMA-induced apoptosis occurs primarily through the mitochondrial apoptotic pathway and is associated with oxidative damage. Further, in order to verify the key nodes and important upstream activators in this apoptotic pathway, we estimated the levels of p53 protein and downstream mitochondrial apoptotic pathway-related proteins in L-02 cells, all of which were found to change significantly. We also found that the levels of total and mitochondrial reactive oxygen species (ROS) in L-02 cells increased with time. Collectively, our findings suggest that α-AMA affects many cellular processes, including the expression of p53 independent of transcription and the expression of Bax and Bcl-2, thereby activating the subsequent caspase cascade pathways. In addition, we identified ROS to be an upstream signaling molecule involved in the α-AMA-induced apoptosis of mouse liver cells and L-02 cells.

In this paper, a modified decentralized relay test is designed for multivariable processes, and then, a new method is proposed to identify the frequency response matrix at multiple points based on the test. In the modified decentralized relay test, only very first periods of oscillating inputs are implemented, so that the unsymmetrical two-sided pulse signals are produced automatically. The two-sided pulse waveform contains enough continuous spectrum over the important frequency range so that the frequency response matrix of interest can be obtained by using Fourier analysis without prior knowledge of the processes. Simulation examples are given to show both effectiveness and practicality of the identification method for a wide range of multivariable processes.

In control engineering, a process variable is normally regulated by a process manipulated variable. Due to physical limitations, any manipulated variable will have constraints on its amplitude. A practical problem frequently encountered in industry but less addressed by researchers is the output unreachability under input saturation: for a large disturbance or set-point change, the process variable may never reach the set-point even when the manipulated variable has been driven to saturation. The process variable can be brought back to the set-point only by activating auxiliary manipulated variables. However, timing of activation remains an open problem, A common practice adopted in industry is to wait for the process variable to reach a new steady state and then take action accordingly. This is time-consuming and may cause serious loss of productivity. In this paper, a new strategy based on disturbance estimation and output prediction is presented- It can detect unreachability early and activate the auxiliary variables timely so that the process variable will return to the set-point much faster. Simulation and real-time testing on a pilot mini-HVAC system show the effectiveness of the proposed strategy.

This paper presents a loop pairing method for determining the control configuration for multi-input–multi-output (MIMO) processes represented by Takagi–Sugeno (T–S) fuzzy models. The method is simple with straightforward calculation and it provides more accurate results compared with existing fuzzy pairing approaches, since both steady-state and dynamic information for the system is utilized. Each individual loop in the MIMO process is represented by a T–S fuzzy model based on the data and the models are then assembled to form the MIMO model. Simple formulae are derived to calculate the steady-state and dynamic information for the loops. In this way, interactions among the loops can be assessed and loop pairing can be determined according to the relative normalized gain array (RNGA) criterion. Two examples are provided to show that loop pairing decisions obtained from T–S fuzzy models are the same as those obtained from precise mathematical models. This demonstrates the effectiveness of the proposed interaction measure and the loop pairing method.

In this paper, a new technique for online multivariable process identification in the frequency domain is presented. Set-point changes in the control loops are performed so that the process frequency-response matrix is estimated with signal decomposition and frequency spectrum analysis method, and accurate transform-function matrix is identified as well. The required input and output data are obtained while the processes are still in normal closed-loop operation. Simulation example is given to show both effectiveness and practicality of the identification method for multivariable processes.