Rationale Psychological resilience is characterized as the ability to respond to extreme stress or trauma or adverse experience successfully. While the relation between public emergencies and psychological distress is well known, research on therelationship between psychological resilience and mental health is very limited during the outbreak of public health emergencies. Objective This research investigated the relationship between psychological resilience and mental health (depression, anxiety, somatization symptoms) among the general population in China. Method Psychological resilience, depression, anxiety, and somatization symptoms of 1770 Chinese citizens were investigated during the epidemic peak of coronavirus disease 2019 (COVID-19) (23rd February 2020 to 2nd March 2020). The analyses were done through the Connor-Davidson Resilience Scale (CD-RISC), the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7) scale, and the Patient Health Questionnaire-15 (PHQ-15) scale. Results The prevalence of depression, anxiety, somatization symptoms was found to be 47.1%, 31.9%, 45.9%, respectively, among all participants. From them, 18.2% showed moderate to severe symptoms of depression, 8.8% showed moderate to severe symptoms of anxiety, and 16.6% showed moderate to severe symptoms of somatization. Psychological resilience was negatively correlated with depression (standardized β = −0.490, P < 0.001), anxiety (standardized β = −0.443, P < 0.001), and somatization symptom scores (standardized β = −0.358, P < 0.001), while controlling for confounding factors. Analysis of the three-factor resilience structure showed that strength and tenacity were correlated with depression (standardized β = −0.256, P < 0.001; standardized β = −0.217, P < 0.001), anxiety (standardized β = −0.268, P < 0.001; standardized β = −0.147, P < 0.001), and somatization symptoms (standardized β = −0.236, P < 0.001; standardized β = −0.126, P < 0.01). Conclusions Our results suggest that there is a high prevalence of psychological distresses among the general population at the peak of the COVID-19 epidemic in China, which is negatively correlated with resilience. Psychological resilience represents an essential target for psychological intervention in a public health emergency.

This article presents a mechanistic study of silver nanodecahedra prepared by the method of photoassisted sodium citrate reduction under irradiation of blue light-emitting diodes (LEDs). This synthesis of silver nanodecahedra can be easily reproduced in the absence of silver seeds and can be completed in one-pot. The data suggest a combination of two processes including gradual growth from multiple-twinned particles and subsequent plasmonmediated crystal growth. More than 85% of as-prepared silver nanoparticles are decahedra with edge lengths of 43.5 ± 5.2 nm. In addition, the as-prepared silver colloids exhibit a spectroscopic enhancement in comparison with spherical silver nanoparticle colloids and silver nanoprism colloids in the measurement of surface enhanced Raman spectroscopy (SERS) spectra of Rhodamin 6G, and with the decahedral silver colloids synthesized in the presence of PVP for detecting SERS signal of Rhodamin 6G. Overall, this photoassisted citrate reduction process is simple, and highly reproducible. As-prepared silver nanodecahedra are more stable and provide optimal SERS signal than those synthesized using commonly used plasmon-mediated photochemical methods.

Trajectory planning is of vital importance to decision-making for autonomous vehicles. Currently, there are three popular classes of cost-based trajectory planning methods: sampling-based, graph-search-based, and optimization-based. However, each of them has its own shortcomings, for example, high computational expense for sampling-based methods, low resolution for graph-search-based methods, and lack of global awareness for optimization-based methods. It leads to one of the challenges for trajectory planning for autonomous vehicles, which is improving planning efficiency while guaranteeing model feasibility. Therefore, this paper proposes a hybrid planning framework composed of two modules, which preserves the strength of both graph-search-based methods and optimization-based methods, thus enabling faster and smoother spatio-temporal trajectory planning in constrained dynamic environment. The proposed method first constructs spatio-temporal driving space based on directed acyclic graph and efficiently searches a spatio-temporal trajectory using the improved A* algorithm. Then taking the search result as reference, locally convex feasible driving area is designed and model predictive control is applied to further optimize the trajectory with a comprehensive consideration of vehicle kinematics and moving obstacles. Results simulated in four different scenarios all demonstrated feasible trajectories without emergency stop or abrupt steering change, which is kinematic-smooth to follow. Moreover, the average planning time was 31 ms, which only took 59.05%, 18.87%, and 0.69%, respectively, of that consumed by other state-of-the-art trajectory planning methods, namely, maximum interaction defensive policy, sampling-based method with iterative optimizations, and Graph-search-based method with Dynamic Programming.

Major depressive disorder (MDD) seriously endangers adolescent mental and physical health. Extracellular vesicles (EVs) are mediators of cellular communication and are involved in many physiological brain processes. Although EV miRNAshave been implicated in adults with major psychiatric disorders, investigation into their effects in adolescent MDDremains scarce. In discovery set, we conducted a genome-wide miRNA sequencing of serum EVs from 9 untreated adolescents with MDD and 8 matched healthy controls (HCs), identifying 32 differentially expressed miRNAs (18 upregulated and 14 downregulated). In the validation set, 8 differentially expressed and highly enriched miRNAs were verified in independent samples using RT-PCR, with 4 (miR-450a-2-3p, miR-3691-5p, miR-556-3p, and miR-2115-3p) of the 8 miRNAs found to be significantly elevated in 34 untreated adolescents with MDD compared with 38 HCs and consistent with the sequencing results. After the Bonferroni correction, we found that three miRNAs (miR-450a-2-3p, miR-556-3p, and miR-2115-3p) were still significantly different. Among them, miR-450a-2-3p showed the most markeddifferential expression and was able to diagnose disease with 67.6% sensitivity and 84.2% specificity. Furthermore, miR-450a-2-3p partially mediated the associations between total childhood trauma, emotional abuse, and physical neglect and adolescent MDD. We also found that the combination of miR-450a-2-3p and emotional abuse could effectively diagnose MDD in adolescents with 82.4% sensitivity and 81.6% specificity. Our data demonstrate the association of serum EV miRNA dysregulation with MDD pathophysiology and, furthermore, show that miRNAs may mediate the relationship between early stress and MDD susceptibility. We also provide a valid integrated model for the diagnosis of adolescent MDD.

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