Tiantian Li

Electroencephalogram has been widely utilized in emotion recognition. Psychologists have found that emotions can be divided into conscious emotion and unconscious emotion. In this article, we explore to classify subliminal emotions with EEG signals elicited by subliminal face stimulation, that is to select appropriate features to classify subliminal emotions. First, multi-scale sample entropy, wavelet packet energy, and wavelet packet entropy of EEG signals are extracted. Then, these features are fed into the decision tree and improved random forest, respectively. The classification accuracy with Ei and WpEn is higher than MSpEn, which shows that Ei and WpEn can be used as effective features to classify subliminal emotions. We compared the classification results of different features combined with the decision tree algorithm and the improved random forest algorithm. The experimental results indicate that the improved random forest algorithm attains the best classification accuracy for subliminal emotions. Finally, subliminal emotions and physiological proof of subliminal affective priming effect are discussed.

Targeted protein degradation (TPD) has emerged as a highly promising approach for eliminating disease‐associated proteins in the field of drug discovery. Among the most advanced TPD technologies, PROteolysis TArgeting Chimera (PROTAC), functions by bringing a protein of interest (POI) into proximity with an E3 ubiquitin ligase, leading to ubiquitin (Ub)‐dependent proteasomal degradation. However, the designs of most PROTACs are based on the utilization of a limited number of available E3 ligases, which significantly restricts their potential. Recent studies have shown that phytoplasmas, a group of bacterial plant pathogens, have developed several E3‐ and ubiquitin‐independent proteasomal degradation (UbInPD) mechanisms for breaking down host targets. This suggests an alternative approach for substrate recruitment and TPD. Here, we present existing evidence that supports the feasibility of UbInPD in eukaryotic cells and propose candidate proteins that can serve as docking sites for the development of E3‐independent PROTACs.

This study conceptualized digital competence in line with self-determined theory and investigated how it alongside help-seeking and learning agency collectively preserved university students’ psychological well-being by assisting them to manage cognitive load and academic burnout, as well as increasing their engagement in online learning during the coronavirus disease 2019 pandemic. Moreover, students’ socioeconomic status and demographic variables were examined. Partial least square modeling and cluster analysis were performed on the survey data collected from 695 students. The findings show that mental load and mental effort were positively related to academic burnout, which was significantly negatively associated with student engagement in online learning. Digital competence did not directly affect academic burnout, but indirectly via its counteracting effect on cognitive load. However, help-seeking and agency were not found to be significantly negatively related to cognitive load. Among the three SDT constructs, digital competence demonstrated the greatest positive influence on student engagement. In addition, female students from humanities and social sciences disciplines and lower-income families seemed to demonstrate the weakest digital competence, lowest learning agency, and least help-seeking behaviors. Consequently, they were more vulnerable to high cognitive load and academic burnout, leading to the lowest learning engagement. This study contributes to the ongoing arguments related to the psychological impact of the COVID-19 pandemic and informs the development of efficient interventions that preserve university students’ psychological well-being in online learning.