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β-glucosidase is a key enzyme in the degradation of lignocellulosic biomass, which is responsible for the conversion of oligosaccharides from cellulose hydrolysates to glucose. However, its required high temperatures and the presence of inhibitors have limited its use in industry. In this study, a new β-glucosidase gene, named thbg2, was obtained from the metagenome Ruidian Hot Spring, Tengchong City, Yunnan Province, southwestern China. The gene was synthesized, cloned, heterologously expressed, and enzymatically characterized. Its optimum temperature and pH were 60 °C and pH 5.6, respectively. ThBg2 exhibited more than 60% relative activity in temperatures ranging from 40 °C to 70 °C and across a pH of 4.0–6.6. It maintained 100% relative activity after incubation at either 50 °C for 24 h or 60 °C for 12 h and more than 80% residual activity after incubation at pH 4.0–6.0 for 24 h. Moreover, it maintained more than 80% relative activity in the presence of heavy metal ions, ethanol, SDS etc. Furthermore, glucose yields from corn stalks increased by 20% after ThBg2 (0.05 mg/mL) was added to the commercial cellulase reaction system. Overall, this work identified a thermophilic and inhibitor-tolerant β-glucosidase with potential applications in commercial lignocellulose utilization and the bioenergy industry.

As reservoirs subject to sedimentation, the dam gradually loses its ability to store water. The identification of the sources of deposited sediments is an effective and efficient means of tackling sedimentation problems. A state-of-the-art Lagrangian stochastic particle tracking model with backward–forward tracking methods is applied to identify the probable source regions of deposited sediments. An influence function is introduced into the models to represent the influence of a particular upstream area on the sediment deposition area. One can then verify if a specific area might be a probable source by cross-checking the values of influence functions calculated backward and forward, respectively. In these models, the probable sources of the deposited sediments are considered to be in a grid instead of at a point for derivation of the values of influence functions. The sediment concentrations in upstream regions must be known a priori to determine the influence functions. In addition, the accuracy of the different types of diffusivity at the water surface is discussed in the study. According to the results of the case study of source identification, the regions with higher sediment concentrations computed by only backward simulations do not necessarily imply a higher likelihood of sources. It is also shown that from the ensemble results when the ensemble mean of the concentration is higher, the ensemble standard deviation of the concentration is also increased.