The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi'an, China.

Selecting an appropriate intensity of epidemic prevention and control measures is of vital significance to promoting the two-way dynamic coordination of epidemic prevention and control and economic development. In order to balance epidemic control and economic development and suggest scientific and reasonable traffic control measures, this paper proposes a SEIQR model considering population migration and the propagation characteristics of the exposed and the asymptomatic, based on the data of COVID-19 cases, Baidu Migration, and the tourist economy. Further, the factor traffic control intensity is included in the model. After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. With Xi'an City as an example. The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. With the continuous rise of traffic control intensity in different simulation scenarios, the cumulative number of cases decreases by a significant amplitude. While balancing the requirements for epidemic prevention and control and for tourist economy development, the model works out the control intensity to be 0.68, under which some traffic control measures are suggested. The model presented in this paper can be used to analyze the impacts of different traffic control intensities on epidemic transmission. The research results in this paper reveal the traffic control measures balancing the requirements for epidemic prevention and control and for economic development.

Rashba-splitting-induced topological flat band detected by anomalous resistance oscillations beyond the quantum limit in ZrTe5.

Topological flat bands - where the kinetic energy of electrons is quenched - provide a platform for investigating the topological properties of correlated systems. Here, we report the observation of a topological flat band formed by polar-distortion-assisted Rashba splitting in the three-dimensional Dirac material ZrTe5. The polar distortion and resulting Rashba splitting on the band are directly detected by torque magnetometry and the anomalous Hall effect, respectively. The local symmetry breaking further flattens the band, on which we observe resistance oscillations beyond the quantum limit. These oscillations follow the temperature dependence of the Lifshitz-Kosevich formula but are evenly distributed in B instead of 1/B at high magnetic fields. Furthermore, the cyclotron mass gets anomalously enhanced about 102 times at fields ~ 20 T. Our results provide an intrinsic platform without invoking moiré or order-stacking engineering, which opens the door for studying topologically correlated phenomena beyond two dimensions.

N-acyl homoserine lactones lactonase est816 suppresses biofilm formation and periodontitis in rats mediated by Aggregatibacter actinomycetemcomitans.

Aims: The current study aimed to explore the adjuvant therapeutic effect of N-acyl homoserine lactones (AHLs)-lactonase est816 on Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) biological behaviors and periodontitis progression. Methods: The inhibitory properties of est816 were detected by live/dead bacterial staining, scanning electron microscope (SEM), crystal-violet staining and reverse-transcription quantitative PCR (RT-qPCR). Biocompatibility of est816 on human gingival fibroblasts (HGFs) and human gingival epithelial cells (HGEs) was evaluated by CCK8 and ELISA. The ligature-induced periodontitis model was established in rats. Micro computed tomography and immunohistochemical and histological staining served to evaluate the effect of est816 on the prevention of periodontitis in vivo. Results: est816 significantly attenuated biofilm formation, reduced the mRNA expression of cytolethal distending toxin, leukotoxin and poly-N-acetyl glucosamine (PNAG) and downregulated expressions of interleukin-6 and tumor necrosis factor-α with low cell toxicity. In vivo investigations revealed est816 decreased alveolar bone resorption, suppressed matrix metalloproteinase-9 expression and increased osteoprotegerin expression. Conclusion: est816 inhibited A. actinomycetemcomitans biofilm formation and virulence release, resulting in anti-inflammation and soothing of periodontitis in rats, indicating that est816 could be investigated in further research on periodontal diseases.

Machine learning-Based model for prediction of Narcolepsy Type 1 in Patients with Obstructive Sleep Apnea with Excessive Daytime Sleepiness.

Background: Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked. Machine learning (ML) models can enable the early detection of these conditions, which has never been applied for diagnosis of NT1. Objective: The study aimed to develop ML prediction models to help non-sleep specialist clinicians identify high probability of comorbid NT1 in patients with OSA early. Methods: Totally, clinical features of 246 patients with OSA in three sleep centers were collected and analyzed for the development of nine ML models. LASSO regression was used for feature selection. Various metrics such as the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA) were employed to evaluate and compare the performance of these ML models. Model interpretability was demonstrated by Shapley Additive explanations (SHAP). Results: Based on the analysis of AUC, DCA, and calibration curves, the Gradient Boosting Machine (GBM) model demonstrated superior performance compared to other machine learning (ML) models. The top five features used in the GBM model, ranked by feature importance, were age of onset, total limb movements index, sleep latency, non-REM (Rapid Eye Movement) sleep stage 2 and severity of OSA. Conclusion: The study yielded a simple and feasible screening ML-based model for the early identification of NT1 in patients with OSA, which warrants further verification in more extensive clinical practices.