To better understand DNA methylation in Alzheimer’s disease (AD) from both mechanistic and biomarker perspectives, we performed an epigenome-wide meta-analysis of blood DNA methylation in two large independent blood-based studies in AD, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR, CDH6 genes, and intergenic regions, that are significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four brain methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model includes age, sex, immune cell type proportions, and methylation risk score based on prioritized CpGs in cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 − 0.770, P-value = 2.78 × 10−5). Our study offers new insights into epigenetics in AD and provides a valuable resource for future AD biomarker discovery.
Deep eutectic solvents (DESs) is a newly developed green solvent with low cost, easy preparation and regeneration. Because of its excellent solubility and swelling effect in lignocellulose, it has received widespread attention and recognition. In this study, choline-based deep eutectic solvents (DESs)—choline chloride-urea (CC-U), choline chloride-ethylene glycol (CC-EG), choline chloride-glycerol (CC-G), choline chloride-lactic acid (CC-LA), and choline chloride-oxalic acid (CC-OA)—were used to extract and separate bagasse. The effects of hydrogen bond donors on lignin separation and the fiber and lignin structure were investigated. All five DESs could dissolve lignin from bagasse; acidic DESs exhibited higher solubility than basic DESs. CC-OA effectively separated lignin and hemicellulose. CC-LA showed weaker lignin separation ability than CC-OA. CC-G, CC-EG, and CC-U were more inclined to selectively separate lignin than hemicellulose. The crystalline cellulose II structure was retained after DES pretreatment. Acidic DESs effectively improved the crystallinity of bagasse fiber; the crystallinities for CC-OA and CC-LA pretreatment were 62.26% and 61.65%, respectively. The lignin dissolved in DES was mainly syringyl lignin. The lignin dissolved in CC-U, CC-LA, and CC-OA contained a small amount of guaiacyl lignin.
Servo systems driven by a permanent magnet linear synchronous motor (PMLSM) are often affected by uncertain disturbances, such as magnetic resistance, friction, and external disturbances, which increase tracking errors and reduce motion accuracy. Traditional control methods may have difficulty to achieve satisfactory control performance in terms of tracking accuracy and disturbance rejection. In this paper, we propose an internal model control PID method based on a model linear extended state observer, which is termed as IMC-PID-MLESO method. With this method, the nominal model parameters of the PMLSM servo system are obtained via system identification. The model linear extended state observer (MLESO), with known parameter information, is designed to improve the estimation accuracy for the system states and total unknown uncertainties. As the uncertainties are compensated in the feedback control law, the PMLSM servo system model is transformed into a known nominal model. Based on the nominal model, the IMC-PID feedback controller is designed to ensure satisfactory performance on disturbance rejection and tracking error reduction. Simulation and experimental results show that the IMC-PID-MLESO method can effectively improve the position tracking accuracy of the PMLSM servo system and rapidly suppress the system disturbance.INDEX TERMS Disturbance suppression, internal model control, permanent magnet linear synchronous motor, tracking accuracy, uncertainties.
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