Effects of Autonomous Learning Software on Chinese Learners’ English Performance and Course Assessment

Based on the construction of an autonomous learning platform for college English learners, a one-year teaching reform experiment has been carried out and 204 subjects were involved. Data collection was conducted mainly through questionnaires and the subjects’ autonomous learning achievements, regular grades, final exam performance, and English listening achievements. The software SPSS17.0 was applied to analyze those data. The results reveal that the experimental class’ achievements on the self-learning platform are positively correlated with their achievements in the final examination. In addition, the correlation between the experimental class’ regular grades and final exam performance is more statistically significant than the control class; moreover, the experimental class performed significantly better than the control class in the English listening test. The vast majority of the students in the experimental class hold positive attitudes towards the software; however, there is still some room to improve it

Mean change-point model for aero-engine component faults

Recognizing aero-engine component faults is important in prognostics and health management research, particularly in engine monitoring systems and condition-based maintenance. The former primarily concentrates on recognizing engine working parameters and component performance, and neglects quantitative changes in component faults. Taking lubricating oil consumption as an example, quantitative changes in component faults are analyzed using a mean change-point model based on change-point theory. The change-point stage is presented through the minimum variance algorithm. The change point corresponding to the failure mode is tested using exhaust electrostatic data from a turbojet engine life span experiment to verify the validity and feasibility of the theory

Experimental investigation on electrostatic monitoring technology for civil turbofan engine

This study analyzes the necessity, development, and principles of aero-engine electrostatic monitoring technology. An electrostatic sensor with specific size is assembled in the exhaust nozzle of an RB-211 turbofan engine located near the low-pressure turbine outlet, a stress checking procedure for safety is conducted. Two test program cycles are included in the whole experimental process. Electrostatic signal processing flow is presented, and feature parameters used for analysis are root-mean-square (RMS), activity level (AL), negative event rate (NER), positive event rate (PER), kurtosis, impulse factor, and absolute mean value. Thrust is used to parameterize the working conditions of the turbofan engine. Moreover, data fitting is conducted to determine the relations between feature and performance parameters. Accordingly, lubrication oil leakage fault and fuel-rich combustion condition are detected in two test run cycles, which result in the appearance of abnormal signals. The AL, RMS, and absolute mean values exhibit similar trends with the change in thrust. A positive linear correlation is also observed between the AL and the thrust in the varying thrust test period. The method of blade-casing rubbing fault recognition is discussed. Experiment results show that the electrostatic sensor is very sensitive to large-sized charged particles in the exhaust emissions

Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms.Comment: 13pages, 9figure

Research progress of integrated stress response in pathogenesis of Alzheimer's disease

Integrated stress response (ISR) is a cellular adaptive response induced by stress, which is strictly regulated by multiple phosphokinases, phosphatases and other proteins to maintain protein homeostasis. Studies have shown that ISR is abnormally activated in Alzheimer's disease, and targeted regulation of different proteins in ISR pathway inhibits the abnormal activation of ISR, leading to restoration of protein homeostasis and alleviation of the neuropathological changes and memory impairment in Alzheimer's disease models. These lines of evidence suggest that ISR has the potential to be a therapeutic target in Alzheimer's disease treatment. This paper reviews the abnormal activation and regulation mechanism of ISR in Alzheimer's disease and discusses the application of ISR as therapeutic targets to Alzheimer's disease models

Exposure to High Aerial Ammonia Causes Hindgut Dysbiotic Microbiota and Alterations of Microbiota-Derived Metabolites in Growing Pigs

Ammonia, an atmospheric pollutant in the air, jeopardizes immune function, and perturbs metabolism, especially lipid metabolism, in human and animals. The roles of intestinal microbiota and its metabolites in maintaining or regulating immune function and metabolism are irreplaceable. Therefore, this study aimed to investigate how aerial ammonia exposure influences hindgut microbiota and its metabolites in a pig model. Twelve growing pigs were treated with or without aerial ammonia (35 mg/m3) for 25 days, and then microbial diversity and microbiota-derived metabolites were measured. The results demonstrated a decreasing trend in leptin (p = 0.0898) and reduced high-density lipoprotein cholesterol (HDL-C, p = 0.0006) in serum after ammonia exposure. Besides, an upward trend in hyocholic acid (HCA), lithocholic acid (LCA), hyodeoxycholic acid (HDCA) (p < 0.1); a downward trend in tauro-deoxycholic acid (TDCA, p < 0.1); and a reduced tauro-HDCA (THDCA, p < 0.05) level were found in the serum bile acid (BA) profiles after ammonia exposure. Ammonia exposure notably raised microbial alpha-diversity with higher Sobs, Shannon, or ACE index in the cecum or colon and the Chao index in the cecum (p < 0.05) and clearly exhibited a distinct microbial cluster in hindgut indicated by principal coordinate analysis (p < 0.01), indicating that ammonia exposure induced alterations of microbial community structure and composition in the hindgut. Further analysis displayed that ammonia exposure increased the number of potentially harmful bacteria, such as Negativibacillus, Alloprevotella, or Lachnospira, and decreased the number of beneficial bacteria, such as Akkermansia or Clostridium_sensu_stricto_1, in the hindgut (FDR < 0.05). Analysis of microbiota-derived metabolites in the hindgut showed that ammonia exposure increased acetate and decreased isobutyrate or isovalerate in the cecum or colon, respectively (p < 0.05). Unlike the alteration of serum BA profiles, cecal BA data showed that high ammonia exposure had a downward trend in cholic acid (CA), HCA, and LCA (p < 0.1); a downward trend in deoxycholic acid (DCA) and HDCA (p < 0.05); and an upward trend in glycol-chenodeoxycholic acid (GCDCA, p < 0.05). Mantel test and correlation analysis revealed associations between microbiota-derived metabolites and ammonia exposure-responsive cecal bacteria. Collectively, the findings illustrated that high ammonia exposure induced the dysbiotic microbiota in the hindgut, thereby affecting the production of microbiota-derived short-chain fatty acids and BAs, which play a pivotal role in the modulation of host systematic metabolism

Meta-analysis Followed by Replication Identifies Loci in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as Associated with Systemic Lupus Erythematosus in Asians

Systemic lupus erythematosus (SLE) is a prototype autoimmune disease with a strong genetic involvement and ethnic differences. Susceptibility genes identified so far only explain a small portion of the genetic heritability of SLE, suggesting that many more loci are yet to be uncovered for this disease. In this study, we performed a meta-analysis of genome-wide association studies on SLE in Chinese Han populations and followed up the findings by replication in four additional Asian cohorts with a total of 5,365 cases and 10,054 corresponding controls. We identified genetic variants in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as associated with the disease. These findings point to potential roles of cell-cycle regulation, autophagy, and DNA demethylation in SLE pathogenesis. For the region involving TET3 and that involving CDKN1B, multiple independent SNPs were identified, highlighting a phenomenon that might partially explain the missing heritability of complex diseases