The Effect of Different Organic Acids and Their Combination on the Cell Barrier and Biofilm of Escherichia coli.

Organic acids are natural antimicrobial compounds commonly used in the food industry. In this study, acetic, lactic, butyric, citric, and malic acid at minimum inhibitory concentrations and their combinations at optimal inhibition concentrations were used to treat E. coli, and the effects on the cell barrier and biofilm of E. coli were evaluated. Acetic acid showed the highest membrane-damaging effect, while citric acid and malic acid could specifically damage the cell wall of E. coli, leading to alkaline phosphatase leakage. The RT-qPCR results showed that organic acids upregulated the membrane-protein-related genes of E. coli, and the combination of organic acids had a wider range of effects than single organic acid treatment. Moreover, organic acids inhibited the formation of E. coli biofilm and cellular activity within the biofilm. This study showed that the combination of organic acids plays a synergistic inhibitory role mainly through multiple destructive effects on the cell barrier and exhibited synergistic anti-biofilm effects. The three-three combination of acetic, lactic acid, and a third organic acid (butyric, citric, or malic) can play a better synergistic antibacterial effect than the two-pair combination of acetic and lactic acid. These findings have implications for the usage, development, and optimization of organic acid combinations.

Memory-augmented skip-connected autoencoder for unsupervised anomaly detection of rocket engines with multi-source fusion.

To ensure the safety and stability of the rocket, it is essential to implement accurate anomaly detection on key parts such as the liquid rocket engine (LRE). However, due to the indistinct features of signals under the interference of extreme conditions and the weak distinguishing ability to exist unsupervised methods, it is difficult to distinguish abnormal samples from normal samples, which leads to the failure of reliable anomaly detection. Aiming at this problem, this paper proposed an unsupervised learning algorithm named Memory-augmented skip-connected deep autoencoder (Mem-SkipAE) for anomaly detection of rocket engines with multi-source data fusion. Unlike traditional autoencoders, the input embedding for the decoder is not generated by an encoder but by a combination of memory items that record prototypical patterns of normal samples. Besides, each layer of the encoder and decoder has a skip connection to fully extract the multi-scale features of the normal sample in multi-dimensional space and suppress over-fitting caused by the memory-augmented network. Compared with existing methods and ablation control groups, experiments on four test sets prove the excellent generalization and satisfactory performances of the proposed Mem-SkipAE. Furthermore, the comparison of the single-source model and multi-source model verifies the effectiveness of multi-source fusion.