The effect of Internet addiction on mind wandering: Resilience and academic burnout as mediators among Chinese adolescents

Attention, particularly mind wandering, has garnered research interest because of the development of technologies that empower a person to focus on several tasks simultaneously. This study investigated the mediating roles of resilience and academic burnout in the relationship between the Internet addiction and mind wandering of Chinese adolescents. Participants were 2,335 adolescents who anonymously completed questionnaires on Internet addiction, resiliency, academic burnout, and mind wandering. We used descriptive statistics and structural equation modeling for data analysis. The results showed that Internet addiction was positively and directly associated with mind wandering, and that there was also an indirect effect via resilience, academic burnout, and a sequential mediating effect of resilience and academic burnout. These results highlight the importance of resiliency as a construct in explaining mind wandering and have implications as regards the necessary steps to prevent mind wandering in adolescents.

Close relatives of MERS-CoV in bats use ACE2 as their functional receptors.

Middle East respiratory syndrome coronavirus (MERS-CoV) and several bat coronaviruses use dipeptidyl peptidase-4 (DPP4) as an entry receptor1-4. However, the receptor for NeoCoV-the closest known MERS-CoV relative found in bats-remains unclear5. Here, using a pseudotype virus entry assay, we found that NeoCoV and its close relative, PDF-2180, can efficiently bind to and use specific bat angiotensin-converting enzyme 2 (ACE2) orthologues and, less favourably, human ACE2 as entry receptors through their receptor-binding domains (RBDs) on the spike (S) proteins. Cryo-electron microscopy analysis revealed an RBD-ACE2 binding interface involving protein-glycan interactions, distinct from those of other known ACE2-using coronaviruses. We identified residues 337-342 of human ACE2 as a molecular determinant restricting NeoCoV entry, whereas a NeoCoV S pseudotyped virus containing a T510F RBD mutation efficiently entered cells expressing human ACE2. Although polyclonal SARS-CoV-2 antibodies or MERS-CoV RBD-specific nanobodies did not cross-neutralize NeoCoV or PDF-2180, an ACE2-specific antibody and two broadly neutralizing betacoronavirus antibodies efficiently inhibited these two pseudotyped viruses. We describe MERS-CoV-related viruses that use ACE2 as an entry receptor, underscoring a promiscuity of receptor use and a potential zoonotic threat.

Stacking Ensemble and ECA-EfficientNetV2 Convolutional Neural Networks on Classification of Multiple Chest Diseases Including COVID-19.

RATIONALE AND OBJECTIVES: Early detection and treatment of COVID-19 patients is crucial. Convolutional neural networks have been proven to accurately extract features in medical images, which accelerates time required for testing and increases the effectiveness of COVID-19 diagnosis. This study proposes two classification models for multiple chest diseases including COVID-19. MATERIALS AND METHODS: The first is Stacking-ensemble model, which stacks six pretrained models including EfficientNetV2-B0, EfficientNetV2-B1, EfficientNetV2-B2, EfficientNetV2-B3, EfficientNetV2-S and EfficientNetV2-M. The second model is self-designed model ECA-EfficientNetV2 based on ECA-Net and EfficientNetV2. Ten-fold cross validation was performed for each model on chest X-ray and CT images. One more dataset, COVID-CT dataset, was tested to verify the performance of the proposed Stacking-ensemble and ECA-EfficientNetV2 models. RESULTS: The best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.21%, Precision of 99.23%, Recall of 99.25%, F1-score of 99.20%, and (area under the curve) AUC of 99.51% on chest X-ray dataset; the best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.81%, Precision of 99.80%, Recall of 99.80%, F1-score of 99.81%, and AUC of 99.87% on chest CT dataset. The differences for five metrics between Stacking-ensemble and ECA-EfficientNetV2 models are not significant. CONCLUSION: Ensemble model achieves better performance than single pretrained models. Compared to the SOTA, Stacking-ensemble and ECA-EfficientNetV2 models proposed in this study demonstrate promising performance on classification of multiple chest diseases including COVID-19.

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images.

BACKGROUND AND OBJECTIVES: The traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether it is COVID-19 disease, which is easy to cause identification mistakes. In this study, the technology of convolutional neural network is expected to be able to efficiently and accurately identify the COVID-19 disease. METHODS: This study uses and fine-tunes seven convolutional neural networks including InceptionV3, ResNet50V2, Xception, DenseNet121, MobileNetV2, EfficientNet-B0, and EfficientNetV2 on COVID-19 detection. In addition, we proposes a lightweight convolutional neural network, LightEfficientNetV2, on small number of chest X-ray and CT images. Five-fold cross-validation was used to evaluate the performance of each model. To confirm the performance of the proposed model, LightEfficientNetV2 was carried out on three different datasets (NIH Chest X-rays, SARS-CoV-2 and COVID-CT). RESULTS: On chest X-ray image dataset, the highest accuracy 96.50% was from InceptionV3 before fine-tuning; and the highest accuracy 97.73% was from EfficientNetV2 after fine-tuning. The accuracy of the LightEfficientNetV2 model proposed in this study is 98.33% on chest X-ray image. On CT images, the best transfer learning model before fine-tuning is MobileNetV2, with an accuracy of 94.46%; the best transfer learning model after fine-tuning is Xception, with an accuracy of 96.78%. The accuracy of the LightEfficientNetV2 model proposed in this study is 97.48% on CT image. CONCLUSIONS: Compared with the SOTA, LightEfficientNetV2 proposed in this study demonstrates promising performance on chest X-ray images, CT images and three different datasets.

[Psychological and behavioral problems in children and adolescents during the coronavirus disease 2019 epidemic: a Scoping review]. / æ–°åž‹å† çŠ¶ç— æ¯’è‚ºç‚Žæµè¡ŒæœŸé—´å„¿ç«¥å’Œé’å°‘å¹´çš„å¿ƒç†è¡Œä¸ºé—®é¢˜ï¼šScoping综述.

OBJECTIVES: To investigate the psychological and behavioral problems and related influencing factors in children and adolescents during the coronavirus disease 2019 (COVID-19) epidemic. METHODS: China National Knowledge Infrastructure, Wanfang Data, PubMed, and Web of Science were searched using the method of subject search for articles published up to March 31, 2022, and related data were extracted for Scoping review. RESULTS: A total of 3 951 articles were retrieved, and 35 articles from 12 countries were finally included. Most of the articles were from the journals related to pediatrics, psychiatry, psychology, and epidemiology, and cross-sectional survey was the most commonly used research method. Psychological and behavioral problems in children and adolescents mainly included depression/anxiety/stress, sleep disorder, internet behavior problems, traumatic stress disorder, and self-injury/suicide. Influencing factors were analyzed from the three aspects of socio-demographic characteristics, changes in living habits, and ways of coping with COVID-19. CONCLUSIONS: During the COVID-19 epidemic, the psychological and behavioral problems of children and adolescents in China and overseas are severe. In the future, further investigation and research can be carried out based on relevant influencing factors to improve the psychological and behavioral problems.

Multidrug-resistant enteroaggregative Escherichia coli (EAEC) enters dormant state during heat treatment: A potential hazard in municipal sludge.

Reuse of sewage sludge is a general trend and land application is an essential way to reuse sludge. The outbreak of coronavirus disease has raised concerns about human pathogens and their serious threat to public health. The risk of pathogenic bacterial contamination from land application of municipal sludge has not been well assessed. The purpose of this study was to investigate the presence of pathogenic bacteria in municipal sewage sludge and to examine the survival potential of certain multidrug-resistant enteroaggregative Escherichia coli (EAEC) strain isolated from sewage sludge during heat treatment. The sewage sludge produced in the two wastewater treatment plants contained pathogenic bacteria such as pathogenic E. coli, Shigella flexneri, and Citrobacter freundii. The environmental strain of EAEC isolated from the sludge was resistant to eight types of antibiotics. It could also enter the dormant state after 4.5 h of treatment at 55 °C and regrow at 37 °C, while maintaining its antibiotic resistance. Our results indicate that the dormancy of EAEC might be why it is heat-resistant and could not be killed completely during the sludge heat treatment process. Owing to the regrowth of the dormant pathogenic bacteria, it is risky to apply the sludge to land even if the sludge is heat-treated, and there is also a risk of spreading antibiotic resistance.