Optogenetic Control of Bacterial Cell-Cell Adhesion Dynamics: Unraveling the Influence on Biofilm Architecture and Functionality.

The transition of bacteria from an individualistic to a biofilm lifestyle profoundly alters their biology. During biofilm development, the bacterial cell-cell adhesions are a major determinant of initial microcolonies, which serve as kernels for the subsequent microscopic and mesoscopic structure of the biofilm, and determine the resulting functionality. In this study, the significance of bacterial cell-cell adhesion dynamics on bacterial aggregation and biofilm maturation is elucidated. Using photoswitchable adhesins between bacteria, modifying the dynamics of bacterial cell-cell adhesions with periodic dark-light cycles is systematic. Dynamic cell-cell adhesions with liquid-like behavior improve bacterial aggregation and produce more compact microcolonies than static adhesions with solid-like behavior in both experiments and individual-based simulations. Consequently, dynamic cell-cell adhesions give rise to earlier quorum sensing activation, better intermixing of different bacterial populations, improved biofilm maturation, changes in the growth of cocultures, and higher yields in fermentation. The here presented approach of tuning bacterial cell-cell adhesion dynamics opens the door for regulating the structure and function of biofilms and cocultures with potential biotechnological applications.

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution.

Conventional wind speed sensors face difficulties in measuring wind speeds at multiple points, and related research on predicting rotor effective wind speed (REWS) is lacking. The utilization of a lidar device allows accurate REWS prediction, enabling advanced control technologies for wind turbines. With the lidar measurements, a data-driven prediction framework based on empirical mode decomposition (EMD) and gated recurrent unit (GRU) is proposed to predict the REWS. Thereby, the time series of lidar measurements are separated by the EMD, and the intrinsic mode functions (IMF) are obtained. The IMF sequences are categorized into high-, medium-, and low-frequency and residual groups, pass through the delay processing, and are respectively used to train four GRU networks. On this basis, the outputs of the four GRU networks are lumped via weighting factors that are optimized by an equilibrium optimizer (EO), obtaining the predicted REWS. Taking advantages of the measurement information and mechanism modeling knowledge, three EMD-GRU prediction schemes with different input combinations are presented. Finally, the proposed prediction schemes are verified and compared by detailed simulations on the BLADED model with four-beam lidar. The experimental results indicate that compared to the mechanism model, the mean absolute error corresponding to the EMD-GRU model is reduced by 49.18%, 53.43%, 52.10%, 65.95%, 48.18%, and 60.33% under six datasets, respectively. The proposed method could provide accurate REWS prediction in advanced prediction control for wind turbines.

Blue Light Signaling Regulates Escherichia coli W1688 Biofilm Formation and l-Threonine Production.

Escherichia coli biofilm may form naturally on biotic and abiotic surfaces; this represents a promising approach for efficient biochemical production in industrial fermentation. Recently, industrial exploitation of the advantages of optogenetics, such as simple operation, high spatiotemporal control, and programmability, for regulation of biofilm formation has garnered considerable attention. In this study, we used the blue light signaling-induced optogenetic system Magnet in an E. coli biofilm-based immobilized fermentation system to produce l-threonine in sufficient quantity. Blue light signaling significantly affected the phenotype of E. coli W1688. A series of biofilm-related experiments confirmed the inhibitory effect of blue light signaling on E. coli W1688 biofilm. Subsequently, a strain lacking a blue light-sensing protein (YcgF) was constructed via genetic engineering, which substantially reduced the inhibitory effect of blue light signaling on biofilm. A high-efficiency biofilm-forming system, Magnet, was constructed, which enhanced bacterial aggregation and biofilm formation. Furthermore, l-threonine production was increased from 10.12 to 16.57 g/L during immobilized fermentation, and the fermentation period was shortened by 6 h. IMPORTANCE We confirmed the mechanism underlying the inhibitory effects of blue light signaling on E. coli biofilm formation and constructed a strain lacking a blue light-sensing protein; this mitigated the aforementioned effects of blue light signaling and ensured normal fermentation performance. Furthermore, this study elucidated that the blue light signaling-induced optogenetic system Magnet effectively regulates E. coli biofilm formation and contributes to l-threonine production. This study not only enriches the mechanism of blue light signaling to regulate E. coli biofilm formation but also provides a theoretical basis and feasibility reference for the application of optogenetics technology in biofilm-based immobilized fermentation systems.