Electroactive microorganisms (EAMs) use bidirectional extracellular electron transfer (EET) pathways to exchange electrons with environments, enabling a diverse array of bio-electrochemical systems (BES). [1] These BES include microbial fuel cells (MFC) for electricity production from biomass and organic wastes, [2] electrochemical microbial biosensors for biotoxicity detection, [3] microbial desalination cells for seawater desalination, [4] microbial electrolysis cells for H 2 production, [5] and microbial electrosynthesis (MES) and photoelectrochemical biohybrid systems for the production of chemicals and fuels from CO 2 . [6] The EET efficiency is a dominating factor for the practical applications of BES. [7] Shewanella oneidensis MR-1, as a model EAM with contact-based and electron shuttle-based EET pathways, has been widely used in the EET study. [8] EET is however associated with multiple energy and materials metabolisms and cellular processes, for example, intracellular redox conditions, redox mediators, anaerobic carbon metabolism, and biofilm formation, etc. [9] This complexity makes it urgent to develop powerful genetic manipulation tools to engineer S. oneidensis MR-1 for improving the EET efficiency.Many gene expression and regulation toolbox have been developed in S. oneidensis MR-1, including plasmid expression toolkit [10] and CRISPR (clustered regularly interspaced short palindromic repeat)-mediated genome editing and regulation approaches. [11] A CRISPR-mediated base editing system (pCBEso) was recently developed in S. oneidensis MR-1, [12] in which C to T conversion in a 6-nt editing window could be achieved by the fusion protein of nCas9 (D10A) and cytidine deaminase rAPOBEC1. Base editing could achieve gene deactivation via mutating the CAG, CAA, CGA, TGG codons into premature stop codons (TAA, TAG, and TGA). Compared with the CRISPR-based recombination system that requires multiple components (two plasmids and ssDNA repair template), [11a] the base editing technologies are easier to operate, which avoid introduction of DNA double-strand breaks into the genome. However, to deactivate genes by base editing effectively, the premature stop codon should be introduced as close as possible Shewanella oneidensis MR-1, as a model electroactive microorganism (EAM) for extracellular electron transfer (EET) study, plays a key role in advancing practical applications of bio-electrochemical systems (BES). Efficient genome-level manipulation tools are vital to promote EET efficiency; thus, a powerful and rapid base editing toolbox in S. oneidensis MR-1 is developed. Firstly a CRISPR/ dCas9-AID base editor that shows a relatively narrow editing window restricted to the "−20 to −16" range upstream of the protospacer adjacent motif (PAM) is constructed. Cas9 is also confined by its native PAM requirement, NGG. Then to expand the editable scope, the sgRNA and the Cas-protein to broaden the editing window to "−22 to −9" upstream of the PAM are engineered, and the PAM field to NNN is opened up. Consequently, th...
Accurate and stable wind speed prediction is crucial for the safe operation of large-scale wind power grid connections. Existing methods are typically limited to a certain fixed area when learning the information of the wind speed sequence, which cannot make full use of the spatiotemporal correlation of the wind speed sequence. To address this problem, in this paper we propose a new wind speed prediction method based on collaborative filtering against a virtual edge expansion graph structure in which virtual edges enrich the semantics that the graph can express. It is an effective extension of the dataset, connecting wind turbines of different wind farms through virtual edges to ensure that the spatial correlation of wind speed sequences can be effectively learned and utilized. The new collaborative filtering on the graph is reflected in the processing of the wind speed sequence. The wind speed is preprocessed from the perspective of pattern mining to effectively integrate various information, and the k-d tree is used to match the wind speed sequence to achieve the purpose of collaborative filtering. Finally, a model with long short-term memory (LSTM) as the main body is constructed for wind speed prediction. By taking the wind speed of the actual wind farm as the research object, we compare the new approach with four typical wind speed prediction methods. The mean square error is reduced by 16.40%, 11.78%, 9.57%, and 18.36%, respectively, which demonstrates the superiority of the proposed new method.
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