The rational design and precise synthesis of multifunctional hybrid nanostructures with a tailored active core and a large, dendritic, modified mesoporous structured shell can promote catalysis, energy storage, and bi...The rational design and precise synthesis of multifunctional hybrid nanostructures with a tailored active core and a large, dendritic, modified mesoporous structured shell can promote catalysis, energy storage, and biological applications. Here, an oil-water biphase stratification coating strategy has been developed to prepare monodisperse magnetic dendritic mesoporous silica core-shell structured nano- spheres. These sophisticated Fe3O4@SiO2@dendritic-mSiO2 nanospheres feature large dendritic open pores （2.7 and 10.3 nm）. Significantly, the silica shells can be converted into dendritic mesoporous aluminosilicate frameworks with unchanged porosity, a Si/Al molar ratio of 14, and remarkably strong acidic sites, through a post-synthesis approach. In addition, the resultant magnetic dendritic mesoporous aluminosilicate nanospheres exhibit outstanding properties and promising application in phosphate removal from wastewater.展开更多
Gastric cancer is one of the most common malignancies worldwide; however, the molecular mechanism in tumorigenesis still needs exploration. BCL2L11 belongs to the BCL-2 family, and acts as a central regulator of the i...Gastric cancer is one of the most common malignancies worldwide; however, the molecular mechanism in tumorigenesis still needs exploration. BCL2L11 belongs to the BCL-2 family, and acts as a central regulator of the intrinsic apoptotic cascade and mediates cell apoptosis. Although miRNAs have been reported to be involved in each stage of cancer development, the role of miR-24 in GC has not been reported yet. In the present study, miR- 24 was found to be up-regulated while the expression of BCL2L11 was inhibited in tumor tissues of GC. Studies from both in vitro and in vivo shown that miR-24 regulates BCL2L11 expression by directly binding with 3＇UTR of mRNA, thus promoting cell growth, migration while inhibiting cell apoptosis. Therefore, miR-24 is a novel onco-miRNA that can be potential drug targets for future clinical use.展开更多
Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these resea...Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these research fields,flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes.Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time.Deep learning technology has recently shown significant potential in the same field,especially in terms of efficiency,helping to overcome the time-consuming associated with traditional methods.This study explores the potential of deep learning models in predicting flood velocity.More specifically,we use a Multi-Layer Perceptron(MLP)model,a specific type of Artificial Neural Networks(ANNs),to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions.Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training,optimization,and testing of the MLP model.Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time.Meanwhile,we discuss the limitations for the improvement in future work.展开更多
基金We acknowledge the financial support from State Key Laboratory of Pollution Control and Resource Reuse Foundation （No. PCRRF14017）, the National Natural Science Foundation of China （No. 21210004） and the China Postdoctoral Science Foundation （No. 2014M551455）. J. P. Y. appreciates the funding supported by the Commonwealth of Australia through the Automotive Australia 2020 Cooperative Research Centre （Auto CRC） and DP120101194. The authors would like to thank Dr. T. Silver for critical reading of this manuscript.
文摘The rational design and precise synthesis of multifunctional hybrid nanostructures with a tailored active core and a large, dendritic, modified mesoporous structured shell can promote catalysis, energy storage, and biological applications. Here, an oil-water biphase stratification coating strategy has been developed to prepare monodisperse magnetic dendritic mesoporous silica core-shell structured nano- spheres. These sophisticated Fe3O4@SiO2@dendritic-mSiO2 nanospheres feature large dendritic open pores （2.7 and 10.3 nm）. Significantly, the silica shells can be converted into dendritic mesoporous aluminosilicate frameworks with unchanged porosity, a Si/Al molar ratio of 14, and remarkably strong acidic sites, through a post-synthesis approach. In addition, the resultant magnetic dendritic mesoporous aluminosilicate nanospheres exhibit outstanding properties and promising application in phosphate removal from wastewater.
基金This work was supported by grants from the National research platform of clinical evaluation technology for new anticancer drugs （No. 2013ZX09303001 ）, the National Natural Science Foundation of China （Grant Nos. 81201946 and 81372394） and Tianjin City High School Science ＆ Technology Fund Planning Project （20130122）. The funders had no role in study design collection, analysis, and interpretation of data+1 种基金 in the writing of the report and in the decision to submit this article for publication.
文摘Gastric cancer is one of the most common malignancies worldwide; however, the molecular mechanism in tumorigenesis still needs exploration. BCL2L11 belongs to the BCL-2 family, and acts as a central regulator of the intrinsic apoptotic cascade and mediates cell apoptosis. Although miRNAs have been reported to be involved in each stage of cancer development, the role of miR-24 in GC has not been reported yet. In the present study, miR- 24 was found to be up-regulated while the expression of BCL2L11 was inhibited in tumor tissues of GC. Studies from both in vitro and in vivo shown that miR-24 regulates BCL2L11 expression by directly binding with 3＇UTR of mRNA, thus promoting cell growth, migration while inhibiting cell apoptosis. Therefore, miR-24 is a novel onco-miRNA that can be potential drug targets for future clinical use.
文摘Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these research fields,flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes.Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time.Deep learning technology has recently shown significant potential in the same field,especially in terms of efficiency,helping to overcome the time-consuming associated with traditional methods.This study explores the potential of deep learning models in predicting flood velocity.More specifically,we use a Multi-Layer Perceptron(MLP)model,a specific type of Artificial Neural Networks(ANNs),to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions.Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training,optimization,and testing of the MLP model.Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time.Meanwhile,we discuss the limitations for the improvement in future work.
