Isolation and Identification of Bacterial Pathogens of Proteus vulgaris and Chryseobacterium meningosepticum from Peodiscus sinensis and Observation on the Histopathology

Two dominant strains of bacteria Lb18-01 and Lb18-02 were isolated and purified from scabies,liver and intestine of diseased Peodiscus sinensis.By artificial infection test,the two strains were verified as pathogenic strains with similar characters to that of natural infectious cases.The strains Lb18-01 and Lb18-02 showed strong pathogenicity to healthy P.sinensis in the artificial infection experiment,so they were the pathogenic strain of the disease.According to the morphology,physicochemical characteristics,16 S rDNA sequence analysis and phylogenetic tree clustering,the pathogenic strains Lb18-01 and Lb18-02 were identified as Proteus vulgaris and Chryseobacterium meningosepticum which were tolerant to 20 drugs such as penicillin,tetracycline and ampicillin.Histopathological observation on diseased P.sinensis showed the pathological symptoms of sepsis such as hemorrhage and congestion of liver,spleen and intestine,and glomerular disintegration.

Water quality prediction of copper-molybdenum miningbeneficiation wastewater based on the PSO-SVR model

The mining-beneficiation wastewater treatment is highly complex and nonlinear.Various factors like influent quality,flow rate,pH and chemical dose,tend to restrict the effluent effectiveness of miningbeneficiation wastewater treatment.Chemical oxygen demand(COD)is a crucial indicator to measure the quality of mining-beneficiation wastewater.Predicting COD concentration accurately of miningbeneficiation wastewater after treatment is essential for achieving stable and compliant discharge.This reduces environmental risk and significantly improves the discharge quality of wastewater.This paper presents a novel AI algorithm PSO-SVR,to predict water quality.Hyperparameter optimization of our proposed model PSO-SVR,uses particle swarm optimization to improve support vector regression for COD prediction.The generalization capacity tested on out-of-distribution(OOD)data for our PSOSVR model is strong,with the following performance metrics of root means square error(RMSE)is 1.51,mean absolute error(MAE)is 1.26,and the coefficient of determination(R2)is 0.85.We compare the performance of PSO-SVR model with back propagation neural network(BPNN)and radial basis function neural network(RBFNN)and shows it edges over in terms of the performance metrics of RMSE,MAE and R2,and is the best model for COD prediction of mining-beneficiation wastewater.This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures.Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment.In addition,PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.