Prediction of Cavity Length Using an Interpretable Ensemble Learning Approach

The cavity length, which is a vital index in aeration and corrosion reduction engineering, is affected by many factors and is challenging to calculate. In this study, 10-fold cross-validation was performed to select the optimal input configuration. Additionally, the hyperparameters of three ensemble learning models—random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting tree (XGBOOST)—were fine-tuned by the Bayesian optimization (BO) algorithm to improve the prediction accuracy and compare the five empirical methods. The XGBOOST method was observed to present the highest prediction accuracy. Further interpretability analysis carried out using the Sobol method demonstrated its ability to reasonably capture the varying relative significance of different input features under different flow conditions. The Sobol sensitivity analysis also observed two patterns of extracting information from the input features in ML models: (1) the main effect of individual features in ensemble learning and (2) the interactive effect between each feature in SVR. From the results, the models obtaining individual information both predict the cavity length more accurately than that using interactive information. Subsequently, the XGBOOST captures more correct information from features, which leads to the varied Sobol index in accordance with outside phenomena; meanwhile, the predicted results fit the experimental points best

The Recognition of Teacher Behavior Based on Multimodal Information Fusion

Teaching reflection based on videos is the main method in teacher education and professional development. However, it takes a long time to analyse videos, and teachers are easy to fall into the state of information overload. With the development of “AI + education,” automatic recognition of teacher behavior to support teaching reflection has become an important research topic. In this paper, taking online open classroom teaching video as the data source, we collected and constructed a teacher behavior dataset. Using this dataset, we explored the behavior recognition methods based on RGB video and skeleton information, and the information fusion between them is carried out to improve the recognition accuracy. The experimental results show that the fusion of RGB information and skeleton information can improve the recognition accuracy, and the early-fusion effect is better than the late-fusion effect. This study helps to solve the problems of time-consumption and information overload in teaching reflection and then helps teachers to optimize the teaching strategies and improve the teaching efficiency

Combating increased antifungal drug resistance in Cryptococcus, what should we do in the future?

Few therapeutic drugs and increased drug resistance have aggravated the current treatment difficulties of Cryptococcus in recent years. To better understand the antifungal drug resistance mechanism and treatment strategy of cryptococcosis. In this review, by combining the fundamental features of Cryptococcus reproduction leading to changes in its genome, we review recent research into the mechanism of four current anti-cryptococcal agents, coupled with new therapeutic strategies and the application of advanced technologies WGS and CRISPR-Cas9 in this field, hoping to provide a broad idea for the future clinical therapy of cryptococcosis

Data_Sheet_1_RNase III coding genes modulate the cross-kingdom biofilm of Streptococcus mutans and Candida albicans.docx

Streptococcus mutans constantly coexists with Candida albicans in plaque biofilms of early childhood caries (ECC). The progression of ECC can be influenced by the interactions between S. mutans and C. albicans through exopolysaccharides (EPS). Our previous studies have shown that rnc, the gene encoding ribonuclease III (RNase III), is implicated in the cariogenicity of S. mutans by regulating EPS metabolism. The DCR1 gene in C. albicans encodes the sole functional RNase III and is capable of producing non-coding RNAs. However, whether rnc or DCR1 can regulate the structure or cariogenic virulence of the cross-kingdom biofilm of S. mutans and C. albicans is not yet well understood. By using gene disruption or overexpression assays, this study aims to investigate the roles of rnc and DCR1 in modulating the biological characteristics of dual-species biofilms of S. mutans and C. albicans and to reveal the molecular mechanism of regulation. The morphology, biomass, EPS content, and lactic acid production of the dual-species biofilm were assessed. Quantitative real-time polymerase chain reaction (qRT-PCR) and transcriptomic profiling were performed to unravel the alteration of C. albicans virulence. We found that both rnc and DCR1 could regulate the biological traits of cross-kingdom biofilms. The rnc gene prominently contributed to the formation of dual-species biofilms by positively modulating the extracellular polysaccharide synthesis, leading to increased biomass, biofilm roughness, and acid production. Changes in the microecological system probably impacted the virulence as well as polysaccharide or pyruvate metabolism pathways of C. albicans, which facilitated the assembly of a cariogenic cross-kingdom biofilm and the generation of an augmented acidic milieu. These results may provide an avenue for exploring new targets for the effective prevention and treatment of ECC.</p