Airborne particulate matter(PM),especially PM2.5,can be easily adsorbed by human respiratory system.Their roles in carrying pathogens for spreading epidemic diseases has attracted great concern.Herein,we developed a n...Airborne particulate matter(PM),especially PM2.5,can be easily adsorbed by human respiratory system.Their roles in carrying pathogens for spreading epidemic diseases has attracted great concern.Herein,we developed a novel gelatin filter-based and culture-independent method for investigation of the microbial diversity in PM samples during a haze episode in Tianjin,China.This method involves particle capture by gelatin filters,filter dissolution for DNA extraction,and high-throughput sequencing for analysis of the microbial diversity.A total of 584 operational taxonomic units(OTUs)of bacteria and 370 OTUs of fungi at the genus level were identified during hazy days.The results showed that both bacterial and fungal diversities could be evaluated by this method.This study provides a convenient strategy for investigation of microbial biodiversity in haze,facilitating accurate evaluation of airborne epidemic diseases.展开更多
BiOBr nanosheets are important photocatalytic nanomaterials. However, their biological effects remain to be explored. In this study, we investigated the antifungal effect of BiOBr nanosheets on Candida albicans. Strik...BiOBr nanosheets are important photocatalytic nanomaterials. However, their biological effects remain to be explored. In this study, we investigated the antifungal effect of BiOBr nanosheets on Candida albicans. Strikingly,the nanosheets strongly inhibited the growth of C. albicans [IC50=（96±4.7） mg/L],hyphal development and biofilm formation. Compareed to the antifungal effect of the cationic surfactant cetyltrimethylammonium bromide, the inhibitory effect of the nanosheets on fungal pathogen was attributed to cetyltrimethylammonium bromide adsorbed by the nanosheets. Thermal gravity analysis and cetyltrimethylammonium bromide release experiment indicated that only 0.42% cetyltrimethylammonium bromide on BiOBr nanosheets was released. Taken together, this study uncovers the contribution of surfactant released from the nanosheets to their antifungal activity.展开更多
Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties.Modelling the complex relationships between these factors and the resulting colour is challengi...Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties.Modelling the complex relationships between these factors and the resulting colour is challenging.In this case,a physics-informed approach for multi-output regression to model CIELAB colour values from dyeing ingredient and process inputs is proposed.Leveraging attention mechanisms and multi-task learning,the model outperforms baseline methods at predicting multiple colour outputs jointly.Specifically,the Transformer model’s attention mechanism captures the complex interactions between dyeing ingredients and process parameters,while the multi-task learning framework exploits the intrinsic correlations among the L^(*),a^(*),and b^(*)dimensions of the CIELAB colour space.In addition,the incorporation of physical knowledge through a physics-informed loss function integrates the CMC colour difference formula.This loss function,along with the attention mechanisms,enables the model to learn the nuanced relationships between the dyeing process variables and thefinal colour output,thereby improving the overall prediction accuracy.This reduces trial-and-error costs and resource waste,contributing to environmental sustainability by minimizing water and energy consumption and chemical emissions.展开更多
基金supported by Project of Science and Technology Development in Wuqing District,Tianjin (No.WQKJ201614)Tianjin 131 innovative talent training project,Postdoctoral Science Foundation
文摘Airborne particulate matter(PM),especially PM2.5,can be easily adsorbed by human respiratory system.Their roles in carrying pathogens for spreading epidemic diseases has attracted great concern.Herein,we developed a novel gelatin filter-based and culture-independent method for investigation of the microbial diversity in PM samples during a haze episode in Tianjin,China.This method involves particle capture by gelatin filters,filter dissolution for DNA extraction,and high-throughput sequencing for analysis of the microbial diversity.A total of 584 operational taxonomic units(OTUs)of bacteria and 370 OTUs of fungi at the genus level were identified during hazy days.The results showed that both bacterial and fungal diversities could be evaluated by this method.This study provides a convenient strategy for investigation of microbial biodiversity in haze,facilitating accurate evaluation of airborne epidemic diseases.
基金supported by Tianjin 131 Innovative Talent Training ProjectPostdoctoral Science Foundation
文摘BiOBr nanosheets are important photocatalytic nanomaterials. However, their biological effects remain to be explored. In this study, we investigated the antifungal effect of BiOBr nanosheets on Candida albicans. Strikingly,the nanosheets strongly inhibited the growth of C. albicans [IC50=（96±4.7） mg/L],hyphal development and biofilm formation. Compareed to the antifungal effect of the cationic surfactant cetyltrimethylammonium bromide, the inhibitory effect of the nanosheets on fungal pathogen was attributed to cetyltrimethylammonium bromide adsorbed by the nanosheets. Thermal gravity analysis and cetyltrimethylammonium bromide release experiment indicated that only 0.42% cetyltrimethylammonium bromide on BiOBr nanosheets was released. Taken together, this study uncovers the contribution of surfactant released from the nanosheets to their antifungal activity.
文摘Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties.Modelling the complex relationships between these factors and the resulting colour is challenging.In this case,a physics-informed approach for multi-output regression to model CIELAB colour values from dyeing ingredient and process inputs is proposed.Leveraging attention mechanisms and multi-task learning,the model outperforms baseline methods at predicting multiple colour outputs jointly.Specifically,the Transformer model’s attention mechanism captures the complex interactions between dyeing ingredients and process parameters,while the multi-task learning framework exploits the intrinsic correlations among the L^(*),a^(*),and b^(*)dimensions of the CIELAB colour space.In addition,the incorporation of physical knowledge through a physics-informed loss function integrates the CMC colour difference formula.This loss function,along with the attention mechanisms,enables the model to learn the nuanced relationships between the dyeing process variables and thefinal colour output,thereby improving the overall prediction accuracy.This reduces trial-and-error costs and resource waste,contributing to environmental sustainability by minimizing water and energy consumption and chemical emissions.
