RESUMO
Machine learning (ML) models have recently shown important advantages in predicting nanomaterial properties, which avoids many trial-and-error explorations. However, complex variables that control the formation of nanomaterials exhibiting the desired properties still need to be better understood owing to the low interpretability of ML models and the lack of detailed mechanism information on nanomaterial properties. In this study, we developed a methodology for accurately predicting multiple synthesis parameter-property relationships of nanomaterials to improve the interpretability of the nanomaterial property mechanism. As a proof-of-concept, we designed glutathione-gold nanoclusters (GSH-AuNCs) exhibiting an appropriate fluorescence quantum yield (QY). First, we conducted 189 experiments and synthesized different GSH-AuNCs by varying the thiol-to-metal molar ratio and reaction temperature and time in reasonable ranges. The fluorescence QY of GSH-AuNCs could be systematically and independently programmed using different experimental parameters. We used limited GSH-AuNC synthesis parameter data to train an extreme gradient boosting regressor model. Moreover, we improved the interpretability of the ML model by combining individual conditional expectation, double-variable partial dependence, and feature interaction network analyses. The interpretability analyses established the relationship between multiple synthesis parameters and fluorescence QYs of GSH-AuNCs. The results represent an essential step towards revealing the complex fluorescence mechanism of thiolated AuNCs. Finally, we constructed a synthesis phase diagram exceeding 6.0 × 104 prediction variables for accurately predicting the fluorescence QY of GSH-AuNCs. A multidimensional synthesis phase diagram was obtained for the fluorescence QY of GSH-AuNCs by searching the synthesis parameter space in the trained ML model. Our methodology is a general and powerful complementary strategy for application in material informatics.
RESUMO
In rhizospheres, chemical barrier-forming natural compounds play a key role in preventing pathogenic bacteria from infecting plant roots. Here, we sought to identify specific phenolic exudates in tobacco (Nicotiana tobaccum) plants infected by the soil-borne pathogen Ralstonia solanacearum that may exhibit antibacterial activity and promote plant resistance against pathogens. Among detected phenolic acids, only caffeic acid was significantly induced in infected plants by R. solanacearum relative to healthy plants, and the concentration of caffeic acid reached 1.95 µg/mL. In vivo, caffeic acid at 200 µg/mL was highly active against R. solanacearum and obviously damaged the membrane structure of the R. solanacearum cells, resulting in the thinning of the cell membrane and irregular cavities in cells. Moreover, caffeic acid significantly inhibited biofilm formation by repressing the expression of the lecM and epsE genes. In vitro, caffeic acid could effectively activate phenylalanine ammonia-lyase (PAL) and peroxidase (POD) and promote the accumulation of lignin and hydroxyproline. In pot and field experiments, exogenous applications of caffeic acid significantly reduced and delayed the incidence of tobacco bacterial wilt. Taken together, all these results suggest that caffeic acid played a crucial role in defending against R. solanacearum infection and was a potential and effective antibacterial agent for controlling bacterial wilt.
RESUMO
Pancreatic lymphoepithelial cyst is a rare pancreatic lesion of undetermined pathogenesis, which is a true pancreatic cyst. Castleman's disease is a rare lymphoproliferative disorder, and a mesenteric location is unusual. The simultaneous occurrence of the two diseases are rarer than metachronous ones and has not been reported to date. We present a case report of a patient with simultaneous occurrence of pancreatic lymphoepithelial cyst and duodenal mesenteric Castleman's disease.