Combinations of waste seaweed liquid fertilizer and biochar on tomato (Solanum lycopersicum L.) seedling growth in an acid-affected soil of Jiaodong Peninsula, China

Physiological responses and transcriptome analysis of Hemerocallis citrina Baroni exposed to Thrips palmi feeding stress

Thrips are serious pests of Hemerocallis citrina Baroni (daylily), affecting crop yield and quality. To defend against pests, daylily has evolved a set of sophisticated defense mechanisms. In the present study, induction of systemic resistance in Hemerocallis citrina 'Datong Huanghua' by Thrips palmi feeding was investigated at both biochemical and molecular levels. The soluble sugar content of daylily leaves was significantly lower than that in control check (CK) at all time points of feeding by T. palmi, whereas the amino acid and free fatty acid contents started to be significantly lower than those in CK after 7 days. Secondary metabolites such as tannins, flavonoids, and total phenols, which are harmful to the growth and reproduction of T. palmi, were increased significantly. The activities of defense enzymes such as peroxidase (POD), phenylalanine ammonia lyase (PAL), and polyphenol oxidase (PPO) were significantly increased, and the degree of damage to plants was reduced. The significant increase in protease inhibitor (PI) activity may lead to disrupted digestion and slower growth in T. palmi. Using RNA sequencing, 1,894 differentially expressed genes (DEGs) were identified between control and treatment groups at five timepoints. DEGs were mainly enriched in secondary metabolite synthesis, jasmonic acid (JA), salicylic acid (SA), and other defense hormone signal transduction pathways, defense enzyme synthesis, MAPK signaling, cell wall thickening, carbohydrate metabolism, photosynthesis, and other insect resistance pathways. Subsequently, 698 DEGs were predicted to be transcription factors, including bHLH and WRKY members related to biotic stress. WGCNA identified 18 hub genes in four key modules (Purple, Midnight blue, Blue, and Red) including MYB-like DNA-binding domain (TRINITY_DN2391_c0_g1, TRINITY_DN3285_c0_g1), zinc-finger of the FCS-type, C2-C2 (TRINITY_DN21050_c0_g2), and NPR1 (TRINITY_DN13045_c0_g1, TRINITY_DN855_c0_g2). The results indicate that biosynthesis of secondary metabolites, phenylalanine metabolism, PIs, and defense hormones pathways are involved in the induced resistance to T. palmi in daylily.

Microbiome-based classification models for fresh produce safety and quality evaluation

Small sample sizes and loss of sequencing reads during the microbiome data preprocessing can limit the statistical power of differentiating fresh produce phenotypes and prevent the detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored a machine learning-based k-mer hash analysis strategy to identify DNA signatures predictive of produce safety (PS) and produce quality (PQ) and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step and ASV-based taxonomy strategy. Random forest-based classifiers for PS and PQ using 7-mer hash data sets had significantly higher classification accuracy than those using the ASV data sets. We also demonstrated that the proposed combination of integrating multiple data sets and leveraging a 7-mer hash strategy leads to better classification performance for PS and PQ compared to the ASV method but presents lower PS classification accuracy compared to the feature-selected ASV-based taxonomy strategy. Due to the current limitation of generating taxonomy using the 7-mer hash strategy, the ASV-based taxonomy strategy with remarkably less computing time and memory usage is more efficient for PS and PQ classification and applicable for important taxa identification. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing data sets for the application of machine learning in the area of microbial safety and quality of food.

IMPORTANCE

Identification of generalizable indicators for produce safety (PS) and produce quality (PQ) improves the detection of produce contamination and quality decline. However, effective sequencing read loss during microbiome data preprocessing and the limited sample size of individual studies restrain statistical power to identify important features contributing to differentiating PS and PQ phenotypes. We applied machine learning-based models using individual and integrated k-mer hash and amplicon sequence variant (ASV) data sets for PS and PQ classification and evaluated their classification performance and found that random forest (RF)-based models using integrated 7-mer hash data sets achieved significantly higher PS and PQ classification accuracy. Due to the limitation of taxonomic analysis for the 7-mer hash, we also developed RF-based models using feature-selected ASV-based taxonomic data sets, which performed better PS classification than those using the integrated 7-mer hash data set. The RF feature selection method identified 480 PS indicators and 263 PQ indicators with a positive contribution to the PS and PQ classification.

Waste milk humification product can be used as a slow release nano-fertilizer

The demand for milk has increased globally, accompanied by an increase in waste milk. Here, we provide an artificial humification technology to recycle waste milk into an agricultural nano-fertilizer. We use KOH-activated persulfate to convert waste milk into fulvic-like acid and humic-like acid. We mix the product with attapulgite to obtain a slow-release nano fulvic-like acid fertilizer. We apply this nano-fertilizer to chickweeds growing in pots, resulting in improved yield and root elongation. These results indicate that waste milk could be recycled for agricultural purposes, however, this nano-fertilizer needs to be tested further in field experiments.