Acoustic emission source positioning research of 3D braided composite material based on the wavelet network

The acoustic emission detection technology is used to position the acoustic emission source of 3D braided composite material. Through comprehensive utilization of the characteristic parameters of acoustic emission signals, the wavelet neural network (WNN) is used to conduct damage positioning and computation, and by combining the shuffled frog leaping algorithm (SFLA), it can improve the convergence performance. Through experiment comparison with traditional positioning computation method, after optimization with the frog leaping algorithm, the wavelet network acoustic emission source positioning method can effectively improve the precision of damage positioning

Upcycling of PET oligomers from chemical recycling processes to PHA by microbial co-cultivation

Polyethylene terephthalate (PET) is the most widely consumed polyester plastic and can be recycled by many chemical processes, of which glycolysis is most cost-effective and commercially viable. However, PET glycolysis produces oligomers due to incomplete depolymerization, which are undesirable by-products and require proper disposal. In this study, the PET oligomers from chemical recycling processes were completely bio-depolymerized into monomers and then used for the biosynthesis of biodegradable plastics polyhydroxyalkanoates (PHA) by cocultivation of two engineered microorganisms Escherichia coli BL21 (DE3)-LCCICCG and Pseudomonas putida KT2440-ΔRDt-ΔZP46C-M. E. coli BL21 (DE3)-LCCICCG was used to secrete the PET hydrolase LCCICCG into the medium to directly depolymerize PET oligomers. P. putida KT2440-ΔRDt-ΔZP46C-M that mastered the metabolism of aromatic compounds was engineered to accelerate the hydrolysis of intermediate products mono-2- (hydroxyethyl) terephthalate (MHET) by expressing IsMHETase, and biosynthesize PHA using ultimate products terephthalate and ethylene glycol depolymerized from the PET oligomers. The population ratios of the two microorganisms during the co-cultivation were characterized by fluorescent reporter system, and revealed the collaboration of the two microorganisms to bio-depolymerize and bioconversion of PET oligomers in a single process. This study provides a biological strategy for the upcycling of PET oligomers and promotes the plastic circular economy

Identification of genome integration sites for developing a CRISPR‐based gene expression toolkit in Yarrowia lipolytica

Summary With the rapid development of synthetic biology, the oleaginous yeast Yarrowia lipolytica has become an attractive microorganism for chemical production. To better optimize and reroute metabolic pathways, we have expanded the CRISPR‐based gene expression toolkit of Y. lipolytica. By sorting the integration sites associated with high expression, new neutral integration sites associated with high expression and high integration efficiency were identified. Diverse genetic components, including promoters and terminators, were also characterized to expand the expression range. We found that in addition to promoters, the newly characterized terminators exhibited large variations in gene expression. These genetic components and integration sites were then used to regulate genes involved in the lycopene biosynthesis pathway, and different levels of lycopene production were achieved. The CRISPR‐based gene expression toolkit developed in this study will facilitate the genetic engineering of Y. lipolytica

MOESM1 of Easy regulation of metabolic flux in Escherichia coli using an endogenous type I-E CRISPR-Cas system

Additional file 1. All strain’s name, plasmids, primers used in the study as well as figures mentioned in the main text are available in this file

y Nonprecious Nanoalloys Embedded in N-Enriched Mesoporous Carbons Derived from a Dual-MOF as Highly Active Catalyst towards Oxygen Reduction Reaction

In this work, we reported the synthesis of a novel electro-catalyst composed of Fe and Co bimetallic nanoalloys embedded in N-enriched carbon framework (FeCo@NC) for oxygen reduction reaction. The FeCo@NC was synthesized through pyrolyzing a dual metal-organic framework (MOF) under argon atmosphere. This FeCo@NC electrocatalyst showed remarkable performance towards ORR with a high half-wave potential of 0.827 V versus reversible hydrogen potential (RHE), which was comparable to the state-of-the-art commercial Pt/C catalyst. The enhanced activity could be ascribed to the large specific surface area, the abundant active sites and the synergistic effect of bimetallic alloy

Valorization of Polyethylene Terephthalate to Muconic Acid by Engineering <i>Pseudomonas Putida</i>

Plastic waste is rapidly accumulating in the environment and becoming a huge global challenge. Many studies have highlighted the role of microbial metabolic engineering for the valorization of polyethylene terephthalate (PET) waste. In this study, we proposed a new conceptual scheme for upcycling of PET. We constructed a multifunctional Pseudomonas putida KT2440 to simultaneously secrete PET hydrolase LCC, a leaf-branch compost cutinase, and synthesize muconic acid (MA) using the PET hydrolysate. The final product MA and extracellular LCC can be separated from the supernatant of the culture by ultrafiltration, and the latter was used for the next round of PET hydrolysis. A total of 0.50 g MA was produced from 1 g PET in each cycle of the whole biological processes, reaching 68% of the theoretical conversion. This new conceptual scheme for the valorization of PET waste should have advantages over existing PET upcycling schemes and provides new ideas for the utilization of other macromolecular resources that are difficult to decompose, such as lignin

Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation

Personalised recommendation is a difficult problem that has received a lot of attention to academia and industry. Because of the sparse user–item interaction, cold-start recommendation has been a particularly difficult problem. Some efforts have been made to solve the cold-start problem by using model-agnostic meta-learning on the level of the model and heterogeneous information networks on the level of data. Moreover, using the memory-augmented meta-optimisation method effectively prevents the meta-learning model from entering the local optimum. As a result, this paper proposed memory-augmented meta-learning on meta-path, a new meta-learning method that addresses the cold-start recommendation on the meta-path furthered. The meta-path builds at the data level to enrich the relevant semantic information of the data. To achieve fast adaptation, semantic-specific memory is utilised to conduct the model with semantic parameter initialisation, and the method is optimised by a meta-optimisation method. We put this method to the test using two widely used recommended data set and three cold-start scenarios. The experimental results demonstrate the efficiency of our proposed method