Rethink left-behind experience: new categories and its relationship with aggression

Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places. College students with left-behind experience showed higher aggression levels. To further explore the relationship between left-behind experience and aggression, the current study categorized left-behind experience using latent class analysis and explored its relationship with aggression. One thousand twenty-eight Chinese college students with left-behind experience were recruited, and their aggression levels were assessed. The results showed that there were four categories of left-behind experience: “starting from preschool, frequent contact” (35.5%), “less than 10 years in duration, limited contact” (27.0%), “starting from preschool, over 10 years in duration, limited contact” (10.9%), and “starting from school age, frequent contact” (26.6%). Overall, college students who reported frequent contact with their parents during the left-behind period showed lower levels of aggression than others did. Females were less aggressive than males in the “starting from preschool, frequent contact” left-behind situation, while males were less aggressive than females in the “starting from school age, frequent contact” situation. These findings indicate that frequent contact with leaving parents contributes to decreasing aggression of college students with left-behind experience. Meanwhile, gender is an important factor in this relationship

Sex-related responses in rhizosphere processes of dioecious Populus cathayana exposed to drought and low phosphorus stress

Extensive research has shown that dioecious plants exhibit sexual dimorphism under extreme environments. However, sex-specific differences in responses to drought, phosphorus (P) shortage or their combination are less known. In our study, impacts of drought, P shortage and their combination on the performance of Populus cathayana males and females were investigated. Drought and P deficiency caused a greater negative impact on female growth than on male growth. P application ameliorated the more negative effect of drought on the shoot dry matter accumulation and P concentration in male leaves, while smaller effects were observed in females. The concentration of citrate in the rhizosphere of males was higher under drought combined with P application than under adequate water availability, and the increase was greater in males than in females. Males also showed a higher abundance of main soil microbial groups, including bacteria, actinomycetes, arbuscular mycorrhizal fungi (AMF), and Gram+ and Gram- bacteria in the rhizosphere, resulting in a more resistant microhabitat. In contrast, the abundance of bacteria and AMF was less in the rhizosphere of females exposed to stress conditions, while saprophytic fungi increased significantly. P enhanced drought resistance more in stress-resistant males but less in females under relatively severe drought stress. Increased drought resistance by P in males might be associated with greater plasticity in rhizosphere processes when compared with females.Peer reviewe

GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels

Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as deployed GNNs face significant performance uncertainty when inferring on unseen and unlabeled test graphs, due to mismatched training-test graph distributions. In this paper, we study a new problem, GNN model evaluation, that aims to assess the performance of a specific GNN model trained on labeled and observed graphs, by precisely estimating its performance (e.g., node classification accuracy) on unseen graphs without labels. Concretely, we propose a two-stage GNN model evaluation framework, including (1) DiscGraph set construction and (2) GNNEvaluator training and inference. The DiscGraph set captures wide-range and diverse graph data distribution discrepancies through a discrepancy measurement function, which exploits the outputs of GNNs related to latent node embeddings and node class predictions. Under the effective training supervision from the DiscGraph set, GNNEvaluator learns to precisely estimate node classification accuracy of the to-be-evaluated GNN model and makes an accurate inference for evaluating GNN model performance. Extensive experiments on real-world unseen and unlabeled test graphs demonstrate the effectiveness of our proposed method for GNN model evaluation.Comment: Accepted by NeurIPS 202

Alkaline arginine promotes the gentamicin-mediated killing of drug-resistant Salmonella by increasing NADH concentration and proton motive force

IntroductionAntimicrobial resistance, especially the development of multidrug-resistant strains, is an urgent public health threat. Antibiotic adjuvants have been shown to improve the treatment of resistant bacterial infections.MethodsWe verified that exogenous L-arginine promoted the killing effect of gentamicin against Salmonella in vitro and in vivo, and measured intracellular ATP, NADH, and PMF of bacteria. Gene expression was determined using real-time quantitative PCR.ResultsThis study found that alkaline arginine significantly increased gentamicin, tobramycin, kanamycin, and apramycin-mediated killing of drug-resistant Salmonella, including multidrug-resistant strains. Mechanistic studies showed that exogenous arginine was shown to increase the proton motive force, increasing the uptake of gentamicin and ultimately inducing bacterial cell death. Furthermore, in mouse infection model, arginine effectively improved gentamicin activity against Salmonella typhimurium.DiscussionThese findings confirm that arginine is a highly effective and harmless aminoglycoside adjuvant and provide important evidence for its use in combination with antimicrobial agents to treat drug-resistant bacterial infections

Improved SnO2 Electron Transport Layers Solution-Deposited at Near Room Temperature for Rigid or Flexible Perovskite Solar Cells with High Efficiencies

Electron transport layer (ETL) is a functional layer of great significance for boosting the power conversion efficiency (PCE) of perovskite solar cells (PSCs). To date, it is still a challenge to simultaneously reduce the surface defects and improve the crystallinity in ETLs during their low-temperature processing. Here, a novel strategy for the mediation of in situ regrowth of SnO2 nanocrystal ETLs is reported: introduction of controlled trace amounts of surface absorbed water on the fluorinated tin oxide (FTO) or indium-tin oxide (ITO) surfaces of the substrates using ultraviolet ozone (UVO) pretreatment. The optimum amount of adsorbed water plays a key role in balancing the hydrolysis-condensation reactions during the structural evolution of SnO2 thin films. This new approach results in a full-coverage SnO2 ETL with a desirable morphology and crystallinity for superior optical and electrical properties, as compared to the control SnO2 ETL without the UVO pretreatment. Finally, the rigid and flexible PSC devices based on the new SnO2 ETLs yield high PCEs of up to 20.5% and 17.5%, respectively