Though essential oils exhibit antibacterial activity against food pathogens, their underlying mechanism is understudied. We extracted ginger essential oil (GEO) using supercritical CO2 and steam distillation. A chemical composition comparison by GC-MS showed that the main components of the extracted GEOs were zingiberene and α-curcumene. Their antibacterial activity and associated mechanism against Staphylococcus aureus and Escherichia coli were investigated. The diameter of inhibition zone (DIZ) of GEO against S. aureus was 17.1 mm, with a minimum inhibition concentration (MIC) of 1.0 mg/mL, and minimum bactericide concentration (MBC) of 2.0 mg/mL. For E. coli, the DIZ was 12.3 mm with MIC and MBC values of 2.0 mg/mL and 4.0 mg/mL, respectively. The SDS-PAGE analysis revealed that some of the electrophoretic bacterial cell proteins bands disappeared with the increase in GEO concentration. Consequently, the nucleic acids content of bacterial suspension was raised significantly and the metabolic activity of bacteria was markedly decreased. GEO could thus inhibit the expression of some genes linked to bacterial energy metabolism, tricarboxylic acid cycle, cell membrane-related proteins, and DNA metabolism. Our findings speculate the bactericidal effects of GEO primarily through disruption of the bacterial cell membrane indicating its suitability in food perseveration.

No abstract

Bacteriocins are ribosomally synthesized peptides with antimicrobial activity produced by numerous bacteria. A novel bacteriocin-producing strain, Lactobacillus plantarum JLA-9, isolated from Suan-Tsai, a traditional Chinese fermented cabbage, was screened and identified by its physiobiochemical characteristics and 16S rDNA sequence analysis. A new bacteriocin, designated plantaricin JLA-9, was purified using butanol extraction, gel filtration, and reverse-phase high-performance liquid chromatography. The molecular mass of plantaricin JLA-9 was shown to be 1044 Da by MALDI-TOF-MS analyses. The amino acid sequence of plantaricin JLA-9 was predicted to be FWQKMSFA by MALDI-TOF-MS/MS, which was confirmed by Edman degradation. This bacteriocin exhibited broad-spectrum antibacterial activity against Gram-positive and Gram-negative bacteria, especially Bacillus spp., high thermal stability (20 min, 121 °C), and narrow pH stability (pH 2.0-7.0). It was sensitive to α-chymotrypsin, pepsin, alkaline protease, and papain. The mode of action of this bacteriocin responsible for outgrowth inhibition of Bacillus cereus spores was studied. Plantaricin JLA-9 had no detectable effects on germination initiation over 1 h on monitoring the hydration, heat resistance, and 2,6-pyridinedicarboxylic acid (DPA) release of spores. Rather, germination initiation is a prerequisite for the action of plantaricin JLA-9. Plantaricin JLA-9 inhibited growth by preventing the establishment of oxidative metabolism and disrupting membrane integrity in germinating spores within 2 h. The results suggest that plantaricin JLA-9 has potential applications in the control of Bacillus spp. in the food industry.

No abstract

Antimicrobial peptides (AMPs) have gained increasing attention, as they can overcome recurring microbial invasions. However, their poor antimicrobial activity and potential cytotoxicity remain impediments to their clinical applications as novel therapeutic agents. To enhance the antimicrobial activity and cell selectivity of AMPs, a series of amphiphilic peptides based on leucocin A were designed by substituting noncharged hydrophilic residues with arginine and leucine. Of the engineered peptides, peptide 7 (WRL3) (WLRAFRRLVRRLARGLRR-NH2) exhibited the highest cell selectivity toward bacterial cells over erythrocytes and macrophages. Fluorescent measurements and microscopic observations demonstrated that 7 increased cell membrane permeability and disrupted membrane envelope integrity, and eventually led to whole cell lysis. Additionally, flow cytometry analysis and subcellular localization studies revealed that 7 showed potent cytotoxicity against human hepatoma cells HepG2. In summary, the data indicate that these engineered peptides, in particular 7, have enormous promise for antibacterial and/or antitumor therapeutics.

No abstract

Deep learning has been applied successfully to many biomedical image segmentation tasks. However, due to the diversity and complexity of biomedical image data, manual annotation for training common deep learning models is very timeconsuming and labor-intensive, especially because normally only biomedical experts can annotate image data well. Human experts are often involved in a long and iterative process of annotation, as in active learning type annotation schemes. In this paper, we propose representative annotation (RA), a new deep learning framework for reducing annotation effort in biomedical image segmentation. RA uses unsupervised networks for feature extraction and selects representative image patches for annotation in the latent space of learned feature descriptors, which implicitly characterizes the underlying data while minimizing redundancy. A fully convolutional network (FCN) is then trained using the annotated selected image patches for image segmentation. Our RA scheme offers three compelling advantages: (1) It leverages the ability of deep neural networks to learn better representations of image data; (2) it performs one-shot selection for manual annotation and frees annotators from the iterative process of common active learning based annotation schemes; (3) it can be deployed to 3D images with simple extensions. We evaluate our RA approach using three datasets (two 2D and one 3D) and show our framework yields competitive segmentation results comparing with state-of-the-art methods.

No abstract