Activated DBP degradation and relevant signal transduction path via quorum sensing autoinducers in Streptomyces sp. SH5.

Microbe-mediated DBP (dibutyl phthalate) mineralization is acknowledged to be affected by diverse extracellular factors. However, little is known about the regulatory effects from quorum sensing (QS) signals. In this study, extracellularly applied QS signals A-like (hydroxymethyl dihydrofuran) was discovered to significantly enhance DBP degradation efficiency in Streptomyces sp. SH5. Monobutyl phthalate, protocatechuic acid and beta-ketoadipate were discovered as degradation intermediates by HPLC-TOF-MS/MS. Multi-omics analysis revealed the up-regulation of multiple hydrolases, transferases and decarboxylases that potentially contributed to A-like accelerated DBP degradation. Transcription of Orf2708, an orthologue of global transcriptional activator, was significantly induced by A-like. Orf2708 was demonstrated to interact specifically with the promoter of hydrolase orf2879 gene by EMSA, and the overexpression of orf2879 led to an enhanced DBP degradation in SH5. Taken together with the molecular docking studies showing the stability of ligand-receptor complex of A-like and its potential receptor Orf3712, a hierarchical regulatory cascade underlying the QS signal mediated DBP degradation was proposed as A-like/Orf3712 duplex formation, enhanced orf2708 expression and the downstream specific activation of hydrolase Orf2879. Our study presents the first evidence of GBLs-type promoted DBP degradation among bacteria, and the elucidated signal transduction path indicates a universal application potential of this activation strategy.

Cognitive Load Prediction from Multimodal Physiological Signals using Multiview Learning.

Predicting cognitive load is a crucial issue in the emerging field of human-computer interaction and holds significant practical value, particularly in flight scenarios. Although previous studies have realized efficient cognitive load classification, new research is still needed to adapt the current state-of-the-art multimodal fusion methods. Here, we proposed a feature selection framework based on multiview learning to address the challenges of information redundancy and reveal the common physiological mechanisms underlying cognitive load. Specifically, the multimodal signal features (EEG, EDA, ECG, EOG, & eye movements) at three cognitive load levels were estimated during multiattribute task battery (MATB) tasks performed by 22 healthy participants and fed into a feature selection-multiview classification with cohesion and diversity (FS-MCCD) framework. The optimized feature set was extracted from the original feature set by integrating the weight of each view and the feature weights to formulate the ranking criteria. The cognitive load prediction model, evaluated using real-time classification results, achieved an average accuracy of 81.08% and an average F1-score of 80.94% for three-class classification among 22 participants. Furthermore, the weights of the physiological signal features revealed the physiological mechanisms related to cognitive load. Specifically, heightened cognitive load was linked to amplified δ and θ power in the frontal lobe, reduced α power in the parietal lobe, and an increase in pupil diameter. Thus, the proposed multimodal feature fusion framework emphasizes the effectiveness and efficiency of using these features to predict cognitive load.