The results were consistent with those in GES-1 and SGC-7901 cell lines. Meanwhile, we found that hsa-miR-328-3p can bind to the 3'-UTR of the potential target gene STAT3. Furthermore, propofol significantly inhibited the proliferation of gastric cancer cell line SGC-7901, where hsa-miR-328-3p was up-regulated and the expression of STAT3 and downstream proliferation-related target genes were down-regulated. However, the growth inhibition of propofol on SGC-7901 cell was significantly reversed after the inhibition of hsa-miR-328-3p. To sum up, propofol suppressed the STAT3 pathway via up-regulating hsa-miR-328-3p to inhibit gastric cancer proliferation.To sum up, propofol suppressed the STAT3 pathway via up-regulating hsa-miR-328-3p to inhibit gastric cancer proliferation.Essential proteins are assumed to be an indispensable element in sustaining normal physiological function and crucial to drug design and disease diagnosis. The discovery of essential proteins is of great importance in revealing the molecular mechanisms and biological processes. Owing to the tedious biological experiment, many numerical methods have been developed to discover key proteins by mining the features of the high throughput data. Appropriate integration of differential biological information based on protein-protein interaction (PPI) network has been proven useful in predicting essential proteins. The main intention of this research is to provide a comprehensive study and a review on identifying essential proteins by integrating multi-source data and provide guidance for researchers. Detailed analysis and comparison of current essential protein prediction algorithms have been carried out and tested on benchmark PPI networks. In addition, based on the previous method TEGS (short for the network Topology, gene Expression, Gene ontology, and Subcellular localization), we improve the performance of predicting essential proteins by incorporating known protein complex information, the gene expression profile, Gene Ontology (GO) terms information, subcellular localization information, and protein's orthology data into the PPI network, named CEGSO. The simulation results show that CEGSO achieves more accurate and robust results than other compared methods under different test datasets with various evaluation measurements.Accumulating evidence witnesses the negative influence of air pollution on human health, but the relationship between air pollution and premature babies has been inconsistent. In this study, the association between weekly average concentration of air pollutants and preterm birth (PTB) was conducted in Xuzhou, a heavy industry city, in China. We constructed a distributed lag non-linear model (DLNM), an ecological study, to access the associations between ambient air pollutants and PTB in this study. Totally, 5408 premature babies were included, and the weekly average levels of PM2.5, PM10, SO2, NO2, O3, and CO were 61.24, 110.21, 22.55, 40.55, 104.45, and 1.04 mg/m3, respectively. We found that PM2.5, PM10, SO2, and NO2 significantly increased the risk of PTB, and the susceptibility windows of these contaminants were the second trimester and third trimester (from 12 to 29 weeks). Every 10 μg/m3 increase of PM2.5, PM10, SO2, and NO2, the greatest relative risk (RR) values and 95% confidence interval (CI) on PTB were 1.0075 [95% CI, 1.0019-1.0131], 1.0053 [95% CI, 1.0014-1.0092], 1.0203 [95% CI, 1.0030-1.0379], and 1.0170 [95% CI, 1.0052-1.0289] in lag 16th, 18th, 19th, and 20th gestational weeks, respectively. No significant influence of O3 and CO were found on preterm birth. Subgroup analysis showed that the risk of premature delivery was higher for younger pregnant women and in warm season. This finding shows that prenatal exposure to ambient air pollutants is associated with preterm birth, and there existed an exposure window period.A novel periodical mixed-integer mathematical model in the field of fresh agri-food product's distribution (tomato product for instance) is developed in this paper. Green topics and two significant features of fresh agricultural products, namely freshness and ripeness, are added to the inventory routing problem (IRP) with simultaneous pickup and delivery for perishable products. The objective function tends to optimize the system total interest. In this model, traditional costs such as transportation and holding costs are considered besides up-to-date points such as expired products and customer's dissatisfaction costs. To compute the customer's dissatisfaction costs, the level of deviation from ideal quality should be measured to specify the biological postharvest behavior of fresh products. To determine the fair pricing, the patterns of quality decay have been applied. Considering the environmental effects and recycling requirements of expired crops, the reverse logistics notion has been applied to collect and reuse the wasted products. The level of greenhouse gas (GHG) emission has been controlled to reduce the harmful impressions of this gas and protect the environment. In the optimization procedure, the system total costs are developed by assuming fuzzy quality levels and fuzzy holding costs. learn more Also, a bipolar approach has been applied for fuzzy programming. Finally, a numerical example besides sensitivity analysis and managerial insights is presented. Results show that remarking fuzzy parameters lead to lower profit and different routing and transmission. Also, applying less pollutant vehicles and increasing plant's delivery levels can be noted to reach a green environment and higher level of profit.In light of the rapidly growing industrialization in BRICS and G7 regions, thorough energy, financials, and environmental analyses are essential for sustainable financial development in these countries. In this context, this work analyzes the relationship between energy, financial, and environmental sustainability and the regions' social performance. Data from 2000 to 2017 is analyzed through a data envelopment analysis (DEA) like a composite index. Results show China and Brazil's better performance in the region, with a sustainability score of 0.96, India was the third, followed by South Africa and Russia. Japan, the UK, and the USA were the most energy-efficient countries for five consecutive years. A 0.18%, 0.27%, 0.22%, 0.09%, 0.31%, and 0.32% reduction in carbon emission is observed with a 1% increase in R&D costs by Canada, France, Germany, Italy, Japan, and the USA, respectively. This work contributes to the existing literature regarding an eco-friendly sustainable policy design for the G7 countries based on multiple indicators.