Lysophospholipids (LPLs) are metabolic intermediates in bacterial phospholipid turnover. Distinct from their diacyl counterparts, these inverted cone-shaped molecules share physical characteristics of detergents, enabling modification of local membrane properties such as curvature. The functions of LPLs as cellular growth factors or potent lipid mediators have been extensively demonstrated in eukaryotic cells but are still undefined in bacteria. In the envelope of Gram-negative bacteria, LPLs are derived from multiple endogenous and exogenous sources. Although several flippases that move non-glycerophospholipids across the bacterial inner membrane were characterized, lysophospholipid transporter LplT appears to be the first example of a bacterial protein capable of facilitating rapid retrograde translocation of lyso forms of glycerophospholipids across the cytoplasmic membrane in Gram-negative bacteria. LplT transports lyso forms of the three bacterial membrane phospholipids with comparable efficiency, but excludes other lysolipid species. Once a LPL is flipped by LplT to the cytoplasmic side of the inner membrane, its diacyl form is effectively regenerated by the action of a peripheral enzyme, acyl-ACP synthetase/LPL acyltransferase (Aas). LplT-Aas also mediates a novel cardiolipin remodeling by converting its two lyso derivatives, diacyl or deacylated cardiolipin, to a triacyl form. This coupled remodeling system provides a unique bacterial membrane phospholipid repair mechanism. Strict selectivity of LplT for lyso lipids allows this system to fulfill efficient lipid repair in an environment containing mostly diacyl phospholipids. A rocker-switch model engaged by a pair of symmetric ion-locks may facilitate alternating substrate access to drive LPL flipping into bacterial cells.

The high target specificity and multifunctionality of proteins has led to great interest in their clinical use. To this end, the development of delivery systems capable of preserving their bioactivity and improving bioavailability is pivotal to achieve high effectiveness and satisfactory therapeutic outcomes. Electrohydrodynamic (EHD) techniques, namely electrospinning and electrospraying, have been widely explored for protein encapsulation and delivery. In this work, monoaxial and coaxial electrospinning and electrospraying were used to encapsulate alkaline phosphatase (ALP) into poly(ethylene oxide) fibres and particles, respectively, and the effects of the processing techniques on the integrity and bioactivity of the enzyme were assessed. A full morphological and physicochemical characterisation of the blend and core-shell products was performed. ALP was successfully encapsulated within monolithic and core-shell electrospun fibres and electrosprayed particles, with drug loadings and encapsulation efficiencies of up to 21% and 99%, respectively. Monoaxial and coaxial electrospinning were equally effective in preserving ALP function, leading to no activity loss compared to fresh aqueous solutions of the enzyme. While the same result was observed for monoaxial electrospraying, coaxial electrospraying of ALP caused a 40% reduction in its bioactivity, which was attributed to the high voltage (22.5 kV) used during processing. This demonstrates that choosing between blend and coaxial EHD processing for protein encapsulation is not always straightforward, being highly dependent on the chosen therapeutic agent and the effects of the processing conditions on its bioactivity.

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Three-dimensional (3D) point cloud semantic segmentation is fundamental in complex scene perception. Currently, although various efficient 3D semantic segmentation networks have been proposed, the overall effect has a certain gap to 2D image segmentation. Recently, some transformer-based methods have opened a new stage in computer vision, which also has accelerated the effective development of methods in 3D point cloud segmentation. In this paper, we propose a novel semantic segmentation network named LLGF-Net that can aggregate features from both local and global levels of point clouds, effectively improving the ability to extract feature information from point clouds. Specifically, we adopt the multi-head attention mechanism in the original Transformer model to obtain the local features of point clouds and then use the position-distance information of point clouds in 3D space to obtain the global features. Finally, the local features and global features are fused and embedded into the encoder–decoder network to generate our method. Our extensive experimental results on the 3D point cloud dataset demonstrate the effectiveness and superiority of our method.

This paper reports a designed method of fault diagnosis, estimation, and fault-tolerant control aiming at solving the problems of time–delay variation of system parameters, actuator time-varying failure, and external disturbance under the flight mode of air-ground platform. Firstly, a robust fault observer is designed to accurately detect the fault of unmanned air-ground attitude system with time-varying parameter delay and reduce the false alarm rate through reasonable assumptions; secondly, considering the actual computing power of the system, the method of estimating the overall fault size of the system instead of estimating each sub fault separately is adopted to reduce the memory space and computation. Then, based on the fault diagnosis and estimation, the fault-tolerant control rate is designed, the integral term is reasonably introduced to eliminate the chattering problem in the fault-tolerant control, and the appropriate nonlinear function is selected as the ideal control input to optimize the transient performance of the system. Finally, the stability of the system is proved, and the effectiveness of the proposed method is verified by simulation.