Alzheimer’s disease (AD) is a neurodegenerative disease characterized by amyloid plaques and neurofibrillary tangles which significantly affects people’s life quality. Recently, AD has been found to be closely related to autophagy. The aim of this study was to identify autophagy-related genes associated with the pathogenesis of AD from multiple types of microarray and sequencing datasets using bioinformatics methods and to investigate their role in the pathogenesis of AD in order to identify novel strategies to prevent and treat AD. Our results showed that the autophagy-related genes were significantly downregulated in AD and correlated with the pathological progression. Furthermore, enrichment analysis showed that these autophagy-related genes were regulated by the transcription factor myocyte enhancer factor 2A (MEF2A), which had been confirmed using si-MEF2A. Moreover, the single-cell sequencing data suggested that MEF2A was highly expressed in microglia. Methylation microarray analysis showed that the methylation level of the enhancer region of MEF2A in AD was significantly increased. In conclusion, our results suggest that AD related to the increased methylation level of MEF2A enhancer reduces the expression of MEF2A and downregulates the expression of autophagy-related genes which are closely associated with AD pathogenesis, thereby inhibiting autophagy.

Cognitive impairment is associated with aging; however, the underlying mechanism remains unclear. Our previous study found that polyphenol-rich blueberry–mulberry extract (BME) had an antioxidant capability and effectively alleviated cognitive impairment in a mouse model of Alzheimer’s disease. Thus, we hypothesized that BME would improve cognitive performance in naturally aging mice and assessed its effects on related signaling pathways. Eighteen-month-old C57BL/6J mice were gavaged with 300 mg/kg/d of BME for 6 weeks. Behavioral phenotypes, cytokine levels, tight junction protein levels, and the histopathology of the brain were assessed, and 16S ribosomal RNA sequencing and targeted metabolome analyses were used for gut microbiota and metabolite measurements. Our results showed that the cognitive performance of aged mice in the Morris water maze test was improved after BME treatment, neuronal loss was reduced, IL-6 and TNF-α levels in the brain and intestine were decreased, and the levels of intestinal tight junction proteins (ZO-1 and occludin) were increased. Further, 16S sequencing showed that BME significantly increased the relative abundance of Lactobacillus, Streptococcus, and Lactococcus and decreased the relative abundance of Blautia, Lachnoclostridium, and Roseburia in the gut. A targeted metabolomic analysis showed that BME significantly increased the levels of 21 metabolites, including α-linolenic acid, vanillic acid, and N-acetylserotonin. In conclusion, BME alters the gut microbiota and regulates gut metabolites in aged mice, which may contribute to the alleviation of cognitive impairment and to inflammation inhibition in both the brain and the gut. Our results provide a basis for future research on natural antioxidant intervention as a treatment strategy for aging-related cognitive impairment.

Abstract:Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in which the hybrid Euclidean distance is used as the similarity measurement between two sets of time series. In order to improve the efficiency, prediction performance, as well as the ability of real-time updating of the model, in this paper, the recombination samples of the model is derived by Deep Extreme Learning Machine (DELM). The experiments show that local prediction model gets accurate results in one-step and multi-step forecasting, and the model has good generalization performance through the test on the five data sets selected from Time Series Database Library (TSDL).

In the operation process of wet ball mill, there are often multi-modal and multi-condition problems. In this paper, a multi-view based domain adaptive extreme learning machine (MVDAELM) was used to measure the mill load. Firstly, the correlation relationship between the load parameters and the two views (vibration and acoustic signals of the ball mill) was obtained by Canonical Correlation Analysis (CCA) respectively. Secondly, a small number of labeled data from the target domain were introduced to construct a Domain Adaptation Extreme Learning Machine (DAELM) model under manifold constraints, which solve the mismatch problem caused by the change of working conditions in the multi-condition grinding process. Finally, based on the correlation coefficient obtained before, the two views domain adaptive load parameter soft sensor model was integrated to solve the uncertainty problem in single-modal data modeling. The experimental results show that the proposed method can effectively improve the learning accuracy of the soft sensor model under multi-modal conditions.