Chicoric Acid Ameliorated Beta-Amyloid Pathology and Enhanced Expression of Synaptic-Function-Related Markers via L1CAM in Alzheimer's Disease Models.

Alzheimer's disease (AD) is the most common progressive neurodegenerative disease. The accumulation of amyloid-beta (Aß) plaques is a distinctive pathological feature of AD patients. The aims of this study were to evaluate the therapeutic effect of chicoric acid (CA) on AD models and to explore its underlying mechanisms. APPswe/Ind SH-SY5Y cells and 5xFAD mice were treated with CA. Soluble Aß1-42 and Aß plaque levels were analyzed by ELISA and immunohistochemistry, respectively. Transcriptome sequencing was used to compare the changes in hippocampal gene expression profiles among the 5xFAD mouse groups. The specific gene expression levels were quantified by qRT-PCR and Western blot analysis. It was found that CA treatment reduced the Aß1-42 levels in the APPswe/Ind cells and 5xFAD mice. It also reduced the Aß plaque levels as well as the APP and BACE1 levels. Transcriptome analysis showed that CA affected the synaptic-plasticity-related genes in the 5xFAD mice. The levels of L1CAM, PSD-95 and synaptophysin were increased in the APPswe/Ind SH-SY5Y cells and 5xFAD mice treated with CA, which could be inhibited by administering siRNA-L1CAM to the CA-treated APPswe/Ind SH-SY5Y cells. In summary, CA reduced Aß levels and increased the expression levels of synaptic-function-related markers via L1CAM in AD models.

Aircraft Engine Fault Diagnosis Model Based on 1DCNN-BiLSTM with CBAM.

As the operational status of aircraft engines evolves, their fault modes also undergo changes. In response to the operational degradation trend of aircraft engines, this paper proposes an aircraft engine fault diagnosis model based on 1DCNN-BiLSTM with CBAM. The model can be directly applied to raw monitoring data without the need for additional algorithms to extract fault degradation features. It fully leverages the advantages of 1DCNN in extracting local features along the spatial dimension and incorporates CBAM, a channel and spatial attention mechanism. CBAM could assign higher weights to features relevant to fault categories and make the model pay more attention to them. Subsequently, it utilizes BiLSTM to handle nonlinear time feature sequences and bidirectional contextual feature information. Finally, experimental validation is conducted on the publicly available CMAPSS dataset from NASA, categorizing fault modes into three types: faultless, HPC fault (the single fault), and HPC&Fan fault (the mixed fault). Comparative analysis with other models reveals that the proposed model has a higher classification accuracy, which is of practical significance in improving the reliability of aircraft engine operations and for Remaining Useful Life (RUL) prediction.

Command filter-based adaptive fuzzy decentralized control for stochastic nonlinear large-scale systems with saturation constraints and unknown disturbance.

In this article, the problem of decentralized fuzzy adaptive control is addressed for a class of stochastic interconnected nonlinear large-scale systems including saturation and unknown disturbance. Fuzzy logic systems (FLSs) are used to estimate packaged nonlinear uncertainties. The command filter technique is presented to eliminate the "explosion of complexity" obstacle associated with the backstepping procedures and the corresponding error compensation mechanism is constructed to alleviate the effect of the errors generated by command filters. The influence of input saturation is compensated by introducing an auxiliary system. Meanwhile, an improved adaptive fuzzy decentralized controller is developed and it is able to minimize calculation time since there is no need for repeated differentiation for the virtual control laws. The presented control scheme not only assures the semi-global boundedness of all the signals in the closed-loop system, but also makes the output tracking errors reach a small neighborhood around the origin. Finally, both numerical and practical examples are provided to illustrate the efficiency and effectiveness of our theoretic result.

Command Filter-Based Adaptive Neural Control Design for Nonstrict-Feedback Nonlinear Systems With Multiple Actuator Constraints.

This article proposes an adaptive neural-network command-filtered tracking control scheme of nonlinear systems with multiple actuator constraints. An equivalent transformation method is introduced to address the impediment from actuator nonlinearity. By utilizing the command filter method, the explosion of complexity problem is addressed. With the help of neural-network approximation, an adaptive neural-network tracking backstepping control strategy via the command filter technique and the backstepping design algorithm is proposed. Based on this scheme, the boundedness of all variables is guaranteed and the output tracking error fluctuates near the origin within a small bounded area. Simulations testify the availability of the designed control strategy.

Hypercholesterolemia risk associated Abca6 does not regulate lipoprotein metabolism in mice or hamster.

Hypercholesterolemia has strong heritability and about 40-60% of hypercholesterolemia is caused by genetic risk factors. A number of monogenic genes have been identified so far for familial hypercholesterolemia (FH). However, in the general population, more than 90% of individuals with LDL cholesterol over 190 mg/dL do not carry known FH mutations. Large scale whole-exome sequencing has identified thousands of variants that are predicted to be loss-of-function (LoF) and each individual has a median of about twenty rare LoF variants and several hundreds more common LoF variants. However, majority of those variants have not been characterized and their functional consequence remains largely unknown. Rs77542162 is a common missense variant in ABCA6 and is strongly associated with hypercholesterolemia in different populations. ABCA6 is a cholesterol responsive gene and has been suggested to play a role in lipid metabolism. However, whether and how rs77542162 and ABCA6 regulate lipoprotein metabolism remain unknown. In current study, we systemically characterized the function of rs77542162 and ABCA6 in cultured cells and in vivo of rodents. We found that Abca6 is specifically expressed on the basolateral surface of hepatocytes in mouse liver. The rs77542162 variant disrupts ABCA6 protein stability and results in loss of functional protein. However, we found no evidence that Abca6 plays a role in lipoprotein metabolism in either normal mice or hypercholesterolemia mice or hamsters. Thus, our results suggest that Abca6 does not regulate lipoprotein metabolism in rodents and highlight the challenge and importance of functional characterization of disease-associated variants in animal models.