Klebicin E, a pore-forming bacteriocin of Klebsiella pneumoniae, exploits the porin OmpC and the Ton system for translocation.

Bacteriocins, which have narrow-spectrum activity and limited adverse effects, are promising alternatives to antibiotics. In this study, we identified klebicin E (KlebE), a small bacteriocin derived from Klebsiella pneumoniae. KlebE exhibited strong efficacy against multidrug-resistant K. pneumoniae isolates and conferred a significant growth advantage to the producing strain during intraspecies competition. A giant unilamellar vesicle leakage assay demonstrated the unique membrane permeabilization effect of KlebE, suggesting that it is a pore-forming toxin. In addition to a C-terminal toxic domain, KlebE also has a disordered N-terminal domain and a globular central domain. Pulldown assays and soft agar overlay experiments revealed the essential role of the outer membrane porin OmpC and the Ton system in KlebE recognition and cytotoxicity. Strong binding between KlebE and both OmpC and TonB was observed. The TonB-box, a crucial component of the toxin-TonB interaction, was identified as the 7-amino acid sequence (E3ETLTVV9) located in the N-terminal region. Further studies showed that a region near the bottom of the central domain of KlebE plays a primary role in recognizing OmpC, with eight residues surrounding this region identified as essential for KlebE toxicity. Finally, based on the discrepancies in OmpC sequences between the KlebE-resistant and sensitive strains, it was found that the 91st residue of OmpC, an aspartic acid residue, is a key determinant of KlebE toxicity. The identification and characterization of this toxin will facilitate the development of bacteriocin-based therapies targeting multidrug-resistant K. pneumoniae infections.

Pancreatic cancer pathology image segmentation with channel and spatial long-range dependencies.

Based on deep learning, pancreatic cancer pathology image segmentation technology effectively assists pathologists in achieving improved treatment outcomes. However, compared to traditional image segmentation tasks, the large size of tissues in pathology images requires a larger receptive field. While methods based on dilated convolutions or attention mechanisms can enhance the receptive field, they cannot capture long-range feature dependencies. Directly applying self-attention mechanisms to capture long-range dependencies results in intolerable computational complexity. To address these challenges, we introduce a channel and spatial self-attention (CS) Module designed for efficiently capturing both channel and spatial long-range feature dependencies in pancreatic cancer pathological images. Specifically, the channel and spatial self-attention module consists of an adaptive channel self-attention module and a window-shift spatial self-attention module. The adaptive channel self-attention module adaptively pools features to a fixed size to capture long-range feature dependencies. While the window-shift spatial self-attention module captures spatial long-range dependencies in a window-based manner. Additionally, we propose a re-weighted cross-entropy loss to mitigate the impact of long-tail distribution on performance. Our proposed method surpasses state-of-the-art on both our Pancreatic Cancer Pathology Image (PCPI) dataset and the GlaS challenge dataset. The mDice and mIoU have achieved 73.93% and 59.42% in our PCPI dataset.

Enhancing the Output Performance of a Triboelectric Nanogenerator Based on Modified Polyimide and Sandwich-Structured Nanocomposite Film.

Recently, scientists have been facing major obstacles in terms of improving the performances of dielectric materials for triboelectric nanogenerators. The triboelectric nanogenerator (TENG) is one of the first green energy technologies that can convert random mechanical kinetic energy into electricity. The surface charge density of TENGs is a critical factor speeding up their commercialization, so it is important to explore unique methods to increase the surface charge density. The key to obtaining a high-performance TENG is the preparation of dielectric materials with good mechanical properties, thermal stability and output performance. To solve the problem of the low output performance of PI-based triboelectric nanogenerators, we modified PI films by introducing nanomaterials and designed a new type of sandwich-shaped nanocomposite film. Herein, we used polyimide (PI) with ideal mechanical properties, excellent heat resistance and flexibility as the dielectric material, prepared an A-B-A sandwich structure with PI in the outer layer and modified a copper calcium titanate/polyimide (CCTO/PI) storage layer in the middle to improve the output of a TENG electrode. The doping amount of the CCTO was tailored. The results showed that at 8 wt% CCTO content, the electrical output performance was the highest, and the open-circuit voltage of CCTO/PI was 42 V. In the TENG, the open-circuit voltage, short-circuit current and transfer charge of the prepared sandwich-structured film were increased by 607%, 629% and 672% compared to the TENG with the PI thin film, respectively. This study presents a novel strategy of optimizing dielectric materials for triboelectric nano-generators and has great potential for the future development of high output-performance TENGs.

Quantitative Evaluation of Thermal Ageing State of Cross-Linked Polyethylene Insulation Based on Polarization and Depolarization Current.

The widespread use of cross-linked polyethylene (XLPE) as insulation in cables may be attributed to its outstanding mechanical and dielectric properties. In order to quantitatively evaluate the insulation status of XLPE after thermal ageing, an accelerated thermal ageing experimental platform is established. Polarization and depolarization current (PDC) as well as elongation at break of XLPE insulation under different ageing durations are measured. XLPE insulation state is determined by the elongation at break retention rate (ER%). Based on the extended Debye model, the paper proposed the stable relaxation charge quantity and the dissipation factor at 0.1 Hz to evaluate the insulation state of XLPE. The results show that the ER% of XLPE insulation decreases with the growth of ageing degree. The polarization and depolarization current of XLPE insulation will increase obviously with thermal ageing. Conductivity and trap level density will also increase. The number of branches of the extended Debye model increases, and new polarization types appear. Stable relaxation charge quantity and dissipation factor at 0.1 Hz proposed in this paper have a good fitting relationship with ER% of XLPE insulation, which can evaluate the thermal ageing state of XLPE insulation effectively.