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Artificial intelligence (AI) has experienced exponential growth in recent years, profoundly transforming various aspects of life, particularly in education. As a team of educational technology innovators, we have developed a series of generative AI features within our social learning management system, CourseNetworking, to boost student engagement in online discussions and enhance instructor efficiency. This article introduces three key features: automated hashtagging, automated post reflection, and AI-driven quiz question generation. We examine the goals and technical implementation of each feature, present solutions to challenges encountered during development and deployment, and share the best practices we have accumulated. Additionally, we outline our plans for future advancements. This paper seeks to foster understanding and collaboration among developers, educators, institutional leaders, and researchers, with the aim of using AI technology to improve educational products and services, ultimately elevating learning experiences and outcomes. [ABSTRACT FROM AUTHOR]

Emerging and recurrent infectious diseases caused by coronaviruses remain a significant public health concern. Here, we present a targeted approach to elicit antibodies capable of neutralizing SARS-CoV-2 variants and other SARS-related coronaviruses. By introducing amino acid mutations at mutation-prone sites, we engineered glycosylation modifications to the Receptor Binding Domain (RBD) of SARS-CoV-2, thereby exposing more conserved, yet less accessible epitopes. We developed both messenger RNA (mRNA) and recombination subunit vaccines using these engineered-RBDs (M1, M2) and the wild-type RBD as immunogens. The engineered-RBD vaccines elicited robust neutralizing responses against various SARS-CoV-2 variants as well as SARS-CoV and WIV1-CoV, and conferred protection in mice challenged with the XBB.1.16 strain. Furthermore, We highlighted that glycan masking is a decisive factor in antibody binding changes and RBD-conserved antibody response. Additionally, the glycan-engineered RBD mRNA vaccines stimulated stronger cell-mediated immune responses. Our glycan modification strategy significantly enhances broad-spectrum neutralizing efficacy and cellular immunity, providing valuable insights for the development of vaccines against a wide range of SARS-related coronaviruses. Author summary: Since late 2020, numerous SARS-CoV-2 variants have emerged across different regions, with the Omicron variant and its subvariants (e.g., BA.5, XBB, JN.1, etc.) posing significant challenges to ongoing efforts in controlling the pandemic. This highlights the urgent need for broadly protective vaccines that target both SARS-CoV-2 variants and other SARS-related coronaviruses that could cause future outbreaks. In this study, we developed a glycan modification strategy based on N-glycosylation by introducing amino acid mutations in the RBD. Using these engineered RBDs (M1, M2) and wild-type RBD as immunogens, we created thermostable mRNA vaccines and recombinant subunit vaccines. Our results demonstrate that these engineered-RBD vaccines elicit potent neutralizing responses against notonly various SARS-CoV-2 variants, but also other coronaviruses like SARS-CoV and WIV1-CoV, surpassing the immune response triggered by the wild-type RBD. Furthermore, glycan-engineered RBD mRNA vaccines stimulated stronger cell-mediated immune responses and provided effective protection against XBB strain challenge in mice. We identified key glycosylation modifications at residues K417, L452, R457, and Q493 that play a critical role in inducing broad-spectrum neutralizing antibodies. In particular, the glycosylation of residues R457 and Q493 was especially effective in enhancing this immune response. These findings offer valuable insights for the design of broad-spectrum vaccines against SARS-related coronaviruses. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study was to examine the psychometric properties of the Chinese version of the Academic Self-Efficacy Scale (ASES-C) and confirm its measurement invariance across gender identities. Methods: In this study, 502 university students (29.68% male, 70.32% female) with a mean age of 19.93 years (SD = 1.64) voluntarily participated. The Academic Self-Efficacy Scale (ASE) was utilized as a unidimensional measure of students' learning efficacy. The English version of ASES was translated into Chinese using a forward-backward translation procedure. Confirmatory factor analysis (CFA) and invariance testing were conducted with the single-factor model of ASES. Composite reliability (CR) and internal consistency were calculated based on Cronbach's alpha. Results: Upon re-specification of the model, CFA results for the hypothesized single-factor model with eight items indicated an acceptable fit (CFI = 0.959, TLI = 0.943, SRMR = 0.036, RMSEA = 0.065). Cronbach's alpha and CR values were 0.785 and 0.880, respectively. Multi-group CFA results demonstrated measurement equivalence for the Chinese version of ASES across gender identities. The findings supported the measurement invariance of ASES-C for both male and female participants. Conclusion: The ASES-C, consisting of one factor and eight items, is a reliable instrument for assessing Chinese university students' self-efficacy in learning. Furthermore, it is suitable for making meaningful comparisons across gender identities. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can introduce substantial errors. We utilize the C-loss function when dealing with zero values in the raw data. The primary advantage of the C-loss function is that it imposes a smaller punishment for larger errors, which results in more robust factorization when handling outliers. Various datasets of different sizes and zero rates are used to evaluate the performance of scRNMF against other state-of-the-art methods. Our method demonstrates its power and stability as a tool for imputation of scRNA-seq data. Author summary: It is still difficult to analyze scRNA-seq data because a significant portion of expressed genes have zeros. Gene expression levels can be restored through the imputation of scRNA-seq data, facilitating downstream analysis. To overcome this challenge, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. Through the use of several simulated and real datasets, we perform an comprehensively evaluation of scRNMF against existing methods. scRNMF can enhance various aspects of downstream analysis, including gene expression data recovery, cell clustering analysis, gene differential expression analysis, and cellular trajectory reconstruction. The results of our study demonstrate that scRNMF is a powerful tool that can improve the accuracy of single-cell data analysis. [ABSTRACT FROM AUTHOR]

Researchers in a variety of biomedical fields have utilized frequency domain properties of heart rate variability (HRV), or the elapsed time between consecutive heart beats. HRV is measured from the electrocardiograph signal through the interbeat interval series. Popular approaches for estimating power spectra from these interval data apply common spectral analysis methods that are designed for the analysis of evenly sampled time series. The application of these methods to the interbeat interval series, which is indexed over an uneven time grid, requires a bias-inducing transformation. The goal of this article is to explore the use of penalized sum of squares for nonparametric estimation of the spectrum of HRV directly from the interbeat intervals. A novel cross-validation procedure is introduced for smoothing parameter selection. Empirical properties of the proposed estimation procedure are explored and compared with popular methods in a simulation study. The proposed method is used in an analysis of data from an insomnia study, which seeks to illuminate the association between the power spectrum of HRV during different periods of sleep with response to behavioral therapy.

In this study, the effects of microwave treatment on protease activity, dough properties and protein quality in sprouted wheat were investigated. Microwave treatment led to a significant (p < 0.05) reduction in protease activity in sprouted wheat. Proteases with a pH optimum of 4.4 (cysteine proteinases) were more susceptible to microwave heating, which contributed mostly to protease inactivation. Significant improvements (p < 0.05) in the dough properties and gluten quality of sprouted wheat were observed, which are probably attributable to the synergistic effectiveness of protease inactivation and heat-induced gluten cross-linking. After microwave treatment, the decrease in the solubility and extractability of protein in sprouted wheat indicated protein polymerization, which was induced by intermolecular disulfide bond cross-linking. The changes in gliadin were less pronounced due to the relatively low temperature of the microwave treatment. The cross-linking in sprouted wheat that occurred after microwave treatment seemed to mainly involve glutenin, especially B/C low-molecular-weight glutenin subunits (B/C-LMW-GSs) in the range of 30–50 kD. [ABSTRACT FROM AUTHOR]