Accelerated biodegradation of polyethylene terephthalate by Thermobifida fusca cutinase mediated by Stenotrophomonas pavanii.

Polyethylene terephthalate (PET) is a general plastic that produces a significant amount of waste due to its non-biodagradable properties. We obtained four bacteria (Stenotrophomonas pavanii JWG-G1, Comamonas thiooxydans CG-1, Comamonas koreensis CG-2 and Fulvimonas soli GM-1) that utilize PET as a sole carbon source through a novel stepwise screening and verification strategy. PET films pretreated with S. pavanii JWG-G1 exhibited weight loss of 91.4% following subsequent degradation by Thermobifida fusca cutinase (TfC). S. pavanii JWG-G1 was able to colonize the PET surface and maintain high cell viability (over 50%) in biofilm, accelerating PET degradation. Compared with PET films with no pretreatment, pretreatment with S. pavanii JWG-G1 caused the PET surface to be significantly rougher with greater hydrophilicity (contact angle of 86.3 ± 2° vs. 96.6 ± 2°), providing better opportunities for TfC to contact and act on PET. Our study indicates that S. pavanii JWG-G1 could be used as a novel pretreatment for efficiently accelerating PET biodegradation by TfC.

Leonurine Regulates Treg/Th17 Balance to Attenuate Rheumatoid Arthritis Through Inhibition of TAZ Expression.

Leonurine, an active alkaloid extracted from Herba leonuri, is reported to have potent anti-inflammatory activity against rheumatoid arthritis (RA). However, the molecular mechanism of action of leonurine in RA remains poorly understood. In this study, we detected 3,425 mRNAs differentially expressed between CD4+ T cells of RA patients and those of healthy individuals using microarray raw data mining. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that transcriptional coactivator with PDZ-binding motif (TAZ) regulates a variety of biological processes including T-helper (Th)-17 cell development, and was thus selected for functional verification. In a naïve CD4+ T cell differentiation assay, we found that TAZ overexpression was associated with impaired balance between T regulatory (Treg) and Th17 cells in vitro. TAZ overexpression increased the levels of the pro-inflammatory cytokines interleukin (IL)-17, IL-1ß, and tumor necrosis factor (TNF)-α and decreased that of the anti-inflammatory cytokine IL-10. Leonurine treatment had a direct recovery effect on the impaired balance and reduced the expression of TAZ and led to normalization of IL-17, IL-1ß, and TNF-α and IL-10. Furthermore, IL-6 was found to promote the expression of TAZ and receptor activator of nuclear factor kappa-B ligand (RANKL), and RANK. Leonurine significantly inhibited TAZ-mediated expression of RANKL, and RANK and IL-6 in synovial fibroblasts. We conclude that the therapeutic effect of leonurine was through suppression of TAZ led to restoration of Treg/Th17 balance and suppression of synovial fibroblast action.

Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC.

Optimal load allocation of multiple fuel boilers.

This paper presents a new methodology for optimally allocating a set of multiple industrial boilers that each simultaneously consumes multiple fuel types. Unlike recent similar approaches in the utility industry that use soft computing techniques, this approach is based on a second-order gradient search method that is easy to implement without any specialized optimization software. The algorithm converges rapidly and the application yields significant savings benefits, up to 3% of the overall operating cost of industrial boiler systems in the examples given and potentially higher in other cases, depending on the plant circumstances. Given today's energy prices, this can yield significant savings benefits to manufacturers that raise steam for plant operations.