DCiPatho: deep cross-fusion networks for genome scale identification of pathogens.

Pathogen detection from biological and environmental samples is important for global disease control. Despite advances in pathogen detection using deep learning, current algorithms have limitations in processing long genomic sequences. Through the deep cross-fusion of cross, residual and deep neural networks, we developed DCiPatho for accurate pathogen detection based on the integrated frequency features of 3-to-7 k-mers. Compared with the existing state-of-the-art algorithms, DCiPatho can be used to accurately identify distinct pathogenic bacteria infecting humans, animals and plants. We evaluated DCiPatho on both learned and unlearned pathogen species using both genomics and metagenomics datasets. DCiPatho is an effective tool for the genomic-scale identification of pathogens by integrating the frequency of k-mers into deep cross-fusion networks. The source code is publicly available at https://github.com/LorMeBioAI/DCiPatho.

MBPD: A multiple bacterial pathogen detection pipeline for One Health practices.

Bacterial pathogens are one of the major threats to biosafety and environmental health, and advanced assessment is a prerequisite to combating bacterial pathogens. Currently, 16S rRNA gene sequencing is efficient in the open-view detection of bacterial pathogens. However, the taxonomic resolution and applicability of this method are limited by the domain-specific pathogen database, taxonomic profiling method, and sequencing target of 16S variable regions. Here, we present a pipeline of multiple bacterial pathogen detection (MBPD) to identify the animal, plant, and zoonotic pathogens. MBPD is based on a large, curated database of the full-length 16S genes of 1986 reported bacterial pathogen species covering 72,685 sequences. In silico comparison allowed MBPD to provide the appropriate similarity threshold for both full-length and variable-region sequencing platforms, while the subregion of V3-V4 (mean: 88.37%, accuracy rate compared to V1-V9) outperformed other variable regions in pathogen identification compared to full-length sequencing. Benchmarking on real data sets suggested the superiority of MBPD in a broader range of pathogen detections compared with other methods, including 16SPIP and MIP. Beyond detecting the known causal agent of animal, human, and plant diseases, MBPD is capable of identifying cocontaminating pathogens from biological and environmental samples. Overall, we provide a MBPD pipeline for agricultural, veterinary, medical, and environmental monitoring to achieve One Health.

Application of magnetic immobilized enzyme of nano dialdehyde starch in deacidification of rice bran oil.

Glutaraldehyde is usually used as a cross-linking agent in the immobilization of enzymes, but this will have a negative effect on the enzyme. Dialdehyde starch can effectively replace glutaraldehyde as a cross-linking agent, but the large particle size of dialdehyde starch affects the performance of immobilized enzyme. In this study, dialdehyde starch nanoparticles (DSNP) were combined with modified Fe3O4 to obtain magnetic carrier (MDSNP), and Candida Antarctica lipase B (CALB) was crosslinked to the carrier to obtain magnetic immobilized enzyme (MDSNPCALB). The characterization results show that the functional groups of each material have obvious characteristic absorption peaks, strong diffraction peaks and typical crystal structures, high magnetism, no coercivity, relatively good dispersion and nano particle size. MDSNPCALB was added to degummed rice bran oil (RBO) and ethanolamine was used as an acyl receptor for acylation and deacidification. After repeated use for 10 times, MDSNPCALB remained highly active, indicating that MDSNPCALB can be effectively used for the deacidification of RBO.

Effect of high-intensity ultrasonic treatment on the emulsion of hemp seed oil stabilized with hemp seed protein.

In this study, hemp seed oil (HSO) emulsions stabilized with hemp seed protein (HPI) were prepared and treated with high intensity ultrasonic (HIU). The effects of different treatment powers (0, 150, 300, 450, 600 W) on the properties, microstructure and stability of emulsions were investigated. HIU-treated emulsions showed improved emulsifying activity index and emulsifying stability index, reduced particle size, and increased absolute values of ζ-potential, with the extreme points of these indices occurring at a treatment power of 450 W. Here, the emulsion showed the best dispersion and the smallest particle size in fluorescence microscopy observation, with the highest adsorbed protein content (30.12%), and the highest tetrahydrocannabinol (THC) retention rate (87.64%). The best thermal and oxidative stability of the emulsions were obtained under HIU treatment with a power of 450 W. The D43 and the peroxide values (POV) values after 30 d storage were the smallest at 985.74 ± 64.89 nm and 4.6 µmol/L, respectively. Therefore, 450 W was optimal HIU power to effectively improve the properties of HPI-stabilized HSO emulsion and promote the application of HSO and its derivatives in food processing production.