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.

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

Leonurine, an active alkaloid extracted from , 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.

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.

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.