The coronavirus disease 2019 (COVID-19) pandemic has exposed humans to the highest physical and mental risks. Thus, it is becoming a priority to probe the mental health problems experienced during the pandemic in different populations. We performed a meta-analysis to clarify the prevalence of postpandemic mental health problems. Seventy-one published papers (n = 146,139) from China, the United States, Japan, India, and Turkey were eligible to be included in the data pool. These papers reported results for Chinese, Japanese, Italian, American, Turkish, Indian, Spanish, Greek, and Singaporean populations. The results demonstrated a total prevalence of anxiety symptoms of 32.60% (95% confidence interval (CI): 29.10–36.30) during the COVID-19 pandemic. For depression, a prevalence of 27.60% (95% CI: 24.00–31.60) was found. Further, insomnia was found to have a prevalence of 30.30% (95% CI: 24.60–36.60). Of the total study population, 16.70% (95% CI: 8.90–29.20) experienced post-traumatic stress disorder (PTSD) symptoms during the COVID-19 pandemic. Subgroup analysis revealed the highest prevalence of anxiety (63.90%) and depression (55.40%) in confirmed and suspected patients compared with other cohorts. Notably, the prevalence of each symptom in other countries was higher than that in China. Finally, the prevalence of each mental problem differed depending on the measurement tools used. In conclusion, this study revealed the prevalence of mental problems during the COVID-19 pandemic by using a fairly large-scale sample and further clarified that the heterogeneous results for these mental health problems may be due to the nonstandardized use of psychometric tools.

Procrastination, the voluntary and irrational delay of an intended course of action, has troubled individuals and society extensively. Various studies have been conducted to explain why people procrastinate and to explore the neural substrates of procrastination. First, research has identified many contributing factors to procrastination. Specifically, task aversiveness, future incentives, and time delay of these incentives have been confirmed as three prominent task characteristics that affect procrastination. On the other hand, self-control and impulsivity have been identified as two most predictive traits of procrastination. After identifying contributing factors, two important theories proposed to explain procrastination by integrating these factors are reviewed. Specifically, an emotion-regulation perspective regards procrastination as a form of self-regulation failure that reflects giving priority to short-term mood repair over achieving long-term goals. However, temporal motivation theory explains why people's motivation to act increases when time approaches a deadline with time discounting effect. To further specify the cognitive mechanism underlying procrastination, this study proposes a novel theoretical model which clarifies how the motivation to act and the motivation to avoid vary differently when delaying a task, explaining why people decide not to act now but are willing to act in the future. Of note, few recent studies have investigated neural correlates of procrastination. Specifically, it was revealed that individual differences in procrastination are correlated with structural abnormalities and altered spontaneous metabolism in the parahippocampal cortex and the prefrontal cortex, which might contribute to procrastination through episodic future thinking or memory and emotion regulation, respectively.

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Globally, about 17% individuals are suffering from the maladaptive procrastination until now, which impacts individual’s financial status, mental health, and even public policy. However, the comprehensive understanding of neuroanatomical understructure of procrastination still remains gap. 688 participants including 3 independent samples were recruited for this study. Brain morphological dynamics referred to the idiosyncrasies of both brain size and brain shape. Multilinear regression analysis was utilized to delineate brain morphological dynamics of procrastination in Sample 1. In the Sample 2, cross-validation was yielded. Finally, prediction models of machine learning were conducted in Sample 3. Procrastination had a significantly positive correlation with the gray matter volume (GMV) in the left insula, anterior cingulate gyrus (ACC), and parahippocampal gyrus (PHC) but was negatively correlated with GMV of dorsolateral prefrontal cortex (dlPFC) and gray matter density of ACC. Furthermore, procrastination was positively correlated to the cortical thickness and cortical complexity of bilateral orbital frontal cortex (OFC). In Sample 2, all the results were cross-validated highly. Predication analysis demonstrated that these brain morphological dynamic can predict procrastination with high accuracy. This study ascertained the brain morphological dynamics involving in self-control, emotion, and episodic prospection brain network for procrastination, which advanced promising aspects of the biomarkers for it.

The impact of the Drosophila experimental system on studies of modern biology cannot be understated. The ability to tag endogenously expressed proteins is essential to maximize the use of this model organism. Here, we describe a method for labeling endogenous proteins with self-complementing split fluorescent proteins (split FPs) in a cell-type–specific manner in Drosophila. A short fragment of an FP coding sequence is inserted into a specific genomic locus while the remainder of the FP is expressed using an available GAL4 driver line. In consequence, complementation fluorescence allows examination of protein localization in particular cells. Besides, when inserting tandem repeats of the short FP fragment at the same genomic locus, we can substantially enhance the fluorescence signal. The enhanced signal is of great value in live-cell imaging at the subcellular level. We can also accomplish a multicolor labeling system with orthogonal split FPs. However, other orthogonal split FPs do not function for in vivo imaging besides split GFP. Through protein engineering and in vivo functional studies, we report a red split FP that we can use for duplexed visualization of endogenous proteins in intricate Drosophila tissues. Using the two orthogonal split FP systems, we have simultaneously imaged proteins that reside in distinct subsynaptic compartments. Our approach allows us to study the proximity between and localization of multiple proteins endogenously expressed in essentially any cell type in Drosophila.

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Agarwood is broadly used in incense and medicine. Traditionally, agarwood formation is induced by wounding the trunks and branches of some species of Aquilaria spp., including A. sinensis. As recently evidenced, some fungi or their fermentation liquid may have the potential of inducing agarwood formation. The present study aimed to analyze the fungi isolated from an agarwood-producing A. sinensis tree and subsequently identify the fungi capable of promoting agarwood formation. We identified a total of 110 fungi isolates based on their morphological characteristics and rDNA ITS sequences. These isolates came from four different layers (namely the decomposing layer, agarwood layer, transition layer, and normal layer) near the agarwood formation site of the trunk. According to the experimental results, most of them belonged to Dothideomycetes (81.82%), while the others to Sordariomycetes (13.64%) or Eurotiomycetes (4.55%). Of note, 88 isolates were shown belonging to the species of Lasiodiplodia theobromae that are most frequently isolated from different layers. In addition, when the fermentation liquid of two isolates of L. theobromae (AF4 and AF12) and one isolate of Fusarium solani (AF21) was inoculated into the A. sinensis wood using the Agar-Wit technique, promoted agarwood formation was observed; however, the effect of AF21 did not keep stable in the later test, while AF4 and AF12 still functioned 1 year later. This study may lay a foundation for exploring the underlying mechanism of agarwood formation as well as fungi application in agarwood production.