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Backgroundβ-Galactosidases can be used to produce low-lactose milk and dairy products for lactose intolerant people. Although commercial β-galactosidases have outstanding lactose hydrolysis ability, their thermostability is low, and reaction products have strong inhibition to these enzymes. In addition, the β-galactosidases possessing simultaneously high thermostability and tolerance of galactose and glucose are still seldom reported until now. Therefore, identification of novel β-galactosidases with high thermostability and tolerance to reaction products from unculturable microorganisms accounting for over 99% of microorganisms in the environment via metagenomic strategy is still urgently in demand.ResultsIn the present study, a novel β-galactosidase (Gal308) consisting of 658 amino acids was identified from a metagenomic library from soil samples of Turpan Basin in China by functional screening. After being overexpressed in Escherichia coli and purified to homogeneity, the enzymatic properties of Gal308 with N-terminal fusion tag were investigated. The recombinant enzyme displayed a pH optimum of 6.8 and a temperature optimum of 78°C, and was considerably stable in the temperature range of 40°C - 70°C with almost unchangeable activity after incubation for 60 min. Furthermore, Gal308 displayed a very high tolerance of galactose and glucose, with the highest inhibition constant Ki,gal (238 mM) and Ki,glu (1725 mM) among β-galactosidases. In addition, Gal308 also exhibited high enzymatic activity for its synthetic substrate o-nitrophenyl-β-D-galactopyranoside (ONPG, 185 U/mg) and natural substrate lactose (47.6 U/mg).ConclusionThis study will enrich the source of β-galactosidases, and attract some attentions to β-galactosidases from extreme habitats and metagenomic library. Furthermore, the recombinant Gal308 fused with 156 amino acids exhibits many novel properties including high activity and thermostability at high temperatures, the pH optimum of 6.8, high enzyme activity for lactose, as well as high tolerance of galactose and glucose. These properties make it a good candidate in the production of low-lactose milk and dairy products after further study.

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Accurate and timely fault diagnosis for the diesel engine is crucial to guarantee it works safely and reliably, and reduces the maintenance costs. A novel diagnosis method based on variational mode decomposition (VMD) and kernel-based fuzzy c-means clustering (KFCM) is proposed in this paper. Firstly, the VMD algorithm is optimized to select the most suitable K value adaptively. Then KFCM is employed to classify the feature parameters of intrinsic mode functions (IMFs). Through the comparison of many different parameters, the singular value is selected finally because of the good classification effect. In this paper, the diesel engine fault simulation experiment was carried out to simulate various faults including valve clearance fault, fuel supply fault and common rail pressure fault. Each kind of machine fault varies in different degrees. To prove the effectiveness of VMD-KFCM, the proposed method is compared with empirical mode decomposition (EMD)-KFCM, ensemble empirical mode decomposition (EEMD)-KFCM, VMD-back propagation neural network (BPNN), and VMD-deep belief network (DBN). Results show that VMD-KFCM has advantages in accuracy, simplicity, and efficiency. Therefore, the method proposed in this paper can be used for diesel engine fault diagnosis, and has good application prospects.