Effects of mixed culture fermentation of Bacillus amyloliquefaciens and Trichoderma longibrachiatum on its constituent strains and the biocontrol of tomato Fusarium wilt.

AIMS: To study the effects of mixed culture fermentation (MCF) of Bacillus amyloliquefaciens and Trichoderma longibrachiatum on its constituent strains and the application values for agricultural production, with the intention of developing efficient and environmentally friendly biocontrol agents. METHODS AND RESULTS: In this study, an in vitro antifungal growth experiment showed that the inhibitory rate of the MCF broth on pathogenic fungi (Fusarium oxysporum f. sp. lycopersici, Botrytis cinerea, Trichothecium roseum and Colletotrichum gloeosporioides) was less than that of B. amyloliquefaciens culture fermentation (BCF). Moreover, the content and gene expression of lipopeptide antibiotics were also lower than that in the BCF group. However, the pot experiments based on irrigation with appropriately diluted fermentation broth showed that the biocontrol effect of MCF on tomato Fusarium wilt was significantly higher than that of TCF (T. longibrachiatum culture fermentation) and BCF, and was approximately 15.79% higher than that of the BTF group which made by mixing equivalent amounts of BCF and TCF. In MCF broth, two micro-organisms antagonized and coexisted, and the growth of T. longibrachiatum was inhibited. Using transcriptomic analysis, we speculated that MCF can upregulate the expression of genes related to carbon and nitrogen metabolism, oxidation-reduction activity, sporulation, environmental information response and chemotaxis, and biosynthesis of secondary metabolites of B. amyloliquefaciens, which might enhance the nutrient substances metabolism and competitiveness, survival ability, colonization and adaptability to the environment to increase its biocontrol potential. CONCLUSIONS: Mixed culture fermentation could promote the more reasonable and effective utilization of biocontrol micro-organisms though improving biocontrol effect, enhancing strains survival and competitiveness, increasing beneficial metabolites, combined with resistance induction or synergistic control. SIGNIFICANCE AND IMPACT OF THE STUDY: Using MCF agronomically utilizes biocontrol agents in an efficient way, which has a good potential for commercial implementation and could reduce production costs.

Long-term oral administration of burdock fructooligosaccharide alleviates DSS-induced colitis in mice by mediating anti-inflammatory effects and protection of intestinal barrier function.

BACKGROUND: Ulcerative colitis, a typical subtype of inflammatory bowel disease, can cause many serious complications. Burdock fructooligosaccharide (BFO), a linear inulin with a purity of 99.439% and a molecular weight of 2345 Da, demonstrates anti-inflammatory and immunomodulatory properties. METHODS: The Kunming mice were divided into two experimental models: a normal pretreatment model and a colitis experimental model. During the experimental treatment period, we assessed changes in weight and disease activity index (DAI), quantified the intestinal index, and determined myeloperoxidase (MPO) activity and reactive oxide species (ROS) levels in colitis mice. We also photographed colon morphology to investigate alterations in the integrity of the intestinal barrier function. Finally, we performed ELISA and qRT-PCR to evaluate the anti-inflammatory effect of BFO treatment on colitis mice. RESULT: The long-term oral administration of BFO alone exhibited protective effects by preventing disruption of the intestinal functional structure and increasing the colon index in mice. However, in a dextran sodium sulfate (DSS)-induced colitis mouse model, BFO administration facilitated quick recovery of body weight and effectively reduced the DAI, especially in the BFO-H group (500 mg/kg/day). BFO treatment maintained the integrity of the intestinal barrier by attenuating the crypt distortion and increasing the goblet cells count It restored the DSS-induced colon shortening and reduced the symptoms of colitis. These effects may be attributed to the appropriate concentrations of BFO effectively inhibiting MPO activity, clearing excessive ROS, and relieving spleen abnormalitie. BFO also attenuated the overexpression and excessive secretion of inflammatory cytokines (TNF-α, IL-1ß, IL-6, and MCP-1) induced by DSS, reduced intestinal inflammation, and consequently protected the intestinal barrier function. CONCLUSION: BFO effectively alleviated the symptoms of DSS-induced colitis by mediating anti-inflammatory effects and protecting the intestinal barrier integrity, thereby potentially facilitating the utilization of safer and more efficacious polysaccharides for managing chronic inflammatory diseases.

A feature selection and multi-model fusion-based approach of predicting air quality.

With the rapid development of China's industrialization, the air pollution is becoming more and more serious. It is vital for us to predict the air quality for determining the further prevention measures of avoiding the brought disasters. In this paper, we are going to propose an approach of predicting the air quality based on the multiple data features through fusing the multiple machine learning models. The approach takes the meteorological data and air quality data for the past six days as one batch of input (the whole data set is for 46 days) and employs a multi-model fusion to provide an improved 24-hour prediction of PM2.5 pollutant concentration all over Beijing. During the above process, two focal feature groups are composed. The first focal feature group contains the historical meteorological data, while the second group includes the statistical information, the date information and the polynomial variations. Besides the two groups, we complement one million more data items by employing the time sliding means. Among the supplementary data, we select the most critical 500 features with Light Gradient Boosting Machine (LightGBM) model and send the features as the input to Gradient Boosting Decision Tree (GBDT) and LightGBM models. Meanwhile, we screen the most critical 300 features with eXtreme Gradient Boosting (XGBoost) model and send them as the input to the three prediction models. Referring to each of the models, we respectively gain the optimal parameters through grid search methods and then fuse the models' contribution with the linear weighting. The experiments indicate that the proposed approach based on the weighting fusion is better than that provided by a single modeling scheme, and the loss value is 0.4158 under the SMAPE index.