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To systematically elucidate how metal-binding antimicrobial peptide SIF4 exerts its antimicrobial activity by targeting DNA topoisomerase without destroying the cytoplasmic membrane, Escherichia coli was used as a model strain to investigate the binding mode of SIF4 with its genomic DNA and the impact of SIF4 on DNA topoisomerase I and II activities and intracellular nucleic acid biosynthesis. Results showed that SIF4 could bind to genomic DNA in a manner similar to ethidium bromide (EB) intercalation with strong inhibitory effect on topoisomerase I but weak effect on topoisomerase II, and catalyzed RNA transcription to exert antimicrobial activity by interfering with the unwinding of negative DNA supercoils and RNA polymerase binding. It was also found that the biosynthesis of intracellular DNA and RNA was inhibited to different degrees after 12 h treatment with SIF4, which exhibited a good dose-effect relationship. There was no significant difference in the amounts of intracellular DNA and RNA between the 1/2 minimum inhibitory concentration (MIC) group and the control group (P > 0.05), but there was a significant difference between the MIC and 2 MIC groups and the control group (P < 0.05). Our results may provide theoretical support for the application of SIF4 in the biocontrol of foodborne E. coli.

To explore how metal-binding antimicrobial peptide SIF4 kills foodborne Escherichia coli by targeting nucleic acid and protein in the cytoplasmic membrane without cytoplasmic membrane damage, the effect of SIF4 on intracellular nucleic acid biosynthesis was investigated, and fluorescence spectral analysis of the competition between SIF4 and ethidium bromide (EB) for binding to genomic DNA, ultraviolet (UV) spectral analysis of the interaction between SIF4 and genomic DNA, and the binding mode between SIF4 and genomic DNA were studied. Besides, the effect of SIF4 on intracellular protein biosynthesis was systematically evaluated. Results demonstrated that SIF4 could bind to E. coli genomic DNA through groove insertion and inhibit dose-dependently nucleic acid biosynthesis. Fluorescence spectral analysis showed that SIF4 could compete with EB for binding to genomic DNA through intercalation binding and electrostatic adsorption. UV spectroscopy showed that combination with SIF4 changed the molecular conformation of genomic DNA, but did not break its double strand structure. Circular dichroism (CD) spectroscopy showed that the base stacking force of genomic DNA was weakened, the double helix structure became loose, and the genomic DNA structure was changed from B to C configuration after combination with SIF4. In addition, SIF4 could significantly affect intracellular protein biosynthesis, and its inhibition effect was positively correlated with the treatment time and dose of SIF4. It is believed that SIF4 can enter the DNA groove through electrostatic adsorption or intercalation with genomic DNA, affecting DNA replication, RNA transcriptional biomass and protein translation to produce antimicrobial effect against Escherichia coli without cytoplasmic membrane damage. These results can provide support for the biocontrol of foodborne E. coli.

This study aimed to systematically elaborate on the inhibitory mechanism of metal-binding antimicrobial peptide SIF4 on the respiratory and energy metabolism of Escherichia coli. By analyzing changes in the metabolic activity, individual and synergistic respiratory inhibition rate, cytoplasmic membrane ion channel ATPase and intracellular ATP level of E. coli after being treated with SIF4, the effects of SIF4 on the cell metabolic activity, respiratory metabolic pathway, cytoplasmic membrane ion channel ATPase and intracellular ATP biosynthesis were studied. Results showed that the metabolic activity of E. coli decreased significantly with increasing dose of SIF4 (P < 0.05), and decreased by 70.41% in the 2 × minimal inhibitory concentration (MIC) group compared with the control group. SIF4 had good inhibitory effect on the respiration of Escherichia coli, with an inhibition rate of (19.387 ± 0.168)% and (25.222 ± 0.326)% at MIC and 2 × MIC, respectively. The synergistic respiratory inhibition rate of SIF4 combined with iodoacetic acid was the lowest ((19.982 ± 0.133)%), indicating that SIF4 could exhibit high antimicrobial activity mainly by inhibiting the glycolysis pathway of E. coli. The activities of cytoplasmic membrane ion channel Na+K+-ATPase and Ca2+Mg2+-ATPase decreased after treatment with SIF4, and this effect was positively correlated with SIF4 dose and treatment time, but weaker than that of the positive control Triton X-100. As SIF4 dose and treatment time increased, the intracellular ATP concentration decreased significantly, and after 12 h, the intracellular ATP concentration in the 2 × MIC group was significantly lower than that in the control group but higher than that in the positive control group (P < 0.05). All results confirmed that SIF4 could exhibit high antimicrobial activity against E. coli by interfering with respiratory metabolism, weakening cytoplasmic membrane ion channel ATPase activity and inhibiting the biosynthesis of intracellular ATP, which can provide theoretical support for the biocontrol of foodborne E. coli.

Bioactive wound dressings that are capable of regulating the local wound microenvironment have attracted a very large interest in the field of regenerative medicine. Macrophages have many critical roles in normal wound healing, and the dysfunction of macrophages significantly contributes to impaired or non-healing skin wounds. Regulation of macrophage polarization towards an M2 phenotype provides a feasible strategy to enhance chronic wound healing, mainly by promoting the transition of chronic inflammation to the proliferation phase of wound healing, upregulating the level of anti-inflammatory cytokines around the wound area, and stimulating wound angiogenesis and re-epithelialization. Based on this, modulation of macrophage functions by the rational design of bioactive scaffolds has emerged as a promising way to accelerate delayed wound healing. This review outlines current strategies to regulate the response of macrophages using bioactive materials, with an emphasis on extracellular matrix-based scaffolds and nanofibrous composites.

Urban public space is essential in improving population carrying capacity and economic efficiency. However, the characteristics of urban public space, such as complex structure, relatively close and large population mobility, make it prone to fire, stampedes and other safety accidents. This study aims to develop a systematic approach to identify the key factors that affect the safety risk of urban public spaces and assess the risk. Based on the literature review, 250 structured questionnaires were randomly distributed. Finally, 219 available questionnaires were collected. Based on the above data, a model of urban public space is built using SEM. The results show that construction equipment, road traffic, social governance, urban environment and behaviour significantly affect public space (from high to low). Specifically, regardless of the model or actual situation, we should pay attention to fire awareness and empirical prevention awareness. Based on previous studies, this study considers the influencing factors of urban public safety risks hierarchically and more practically and makes contributions to the field of urban safety. In addition, governments and developers can conduct valuable actual scenario analysis from this study.

In this paper, we consider the difference in the abstraction level of features extracted by different perceptual layers and use a weighted perceptual loss-based generative adversarial network to deblur the UAV images, which removes the blur and restores the texture details of the images well. The perceptual loss is used as an objective evaluation index for training process monitoring and model selection, which eliminates the need for extensive manual comparison of the deblurring effect and facilitates model selection. The UNet jump connection structure facilitates the transfer of features across layers in the network, reduces the learning difficulty of the generator, and improves the stability of adversarial training.

Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural machinery. To improve the accuracy of the field-road segmentation, this study proposed an XGBoost model based on dual feature extraction and recursive feature elimination called DR-XGBoost. DR-XGBoost takes only a small amount of agricultural machine trajectory features as input. Firstly, the model adopted the dual feature extraction method we designed to rapidly expand the number of features and then adequately extract local trajectory features by the time window and feature extraction operator. Secondly, the model applies the recursive feature elimination algorithm to eliminate redundant features from the perspective of the model segmentation effect and thus reduce the computational consumption of model training. Thirdly, it trains XGBoost to complete the trajectory segmentation. To evaluate the effectiveness of DR-XGBoost, we conducted a series of experiments on a real trajectory dataset of agricultural machines. The model achieves a 98.2% Macro-F1 score on the dataset, which is 10.9% higher than the previous state-of-art. The proposal of DR-XGBoost fills the knowledge gap of trajectory feature extraction for agricultural machinery and provides a reasonable and effective feature selection scheme for the field-road segmentation problem. [ABSTRACT FROM AUTHOR]