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Humic-reducing microorganisms (HRMs) can utilize humic substance as terminal electron mediator promoting the bioremediation of contaminate, which is ubiquitous in composts. However, the impacts of HRMs on antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) in composts and different HRMs community composition following the types of biowastes effected the spread of ARGs have not been investigated. Herein, the dynamics and mobility of ARGs and HRMs during protein-, lignocellulose- and lignin-rich composting were investigated. Result show that ARGs change significantly at the thermophilic phase, and the relative abundance of most ARGs increase during composting. Seven groups of HRMs communities are classified as primary host HRMs of ARGs, and most host HRMs groups from protein-rich composts. Conclusively, regulating methods for inhibiting ARGs spread for different composts are proposed. HRMs show a higher ARGs dissemination capacity in protein-rich composts than lignocellulose- and lignin-rich composts, but the spread of ARGs can be inhibited by regulate physicochemical parameters in protein-rich composts. In contrary, most HRMs have inhibitory effects on ARGs spread in lignocellulose- and lignin-rich composts, and those HRMs can be used as a new agent that inhibits the spread of ARGs. Our results can help in understanding the potential risk spread of ARGs by inoculating functional bacteria derived from different biowastes composts for environmental remediation, given their expected importance to developing a classification-oriented approach for composting different biowastes.

Measuring similarity of objects in information network is a primitive problem and has attracted many studies for widely applications, such as recommendation and information retrieval. With the advent of large-scale heterogeneous information network that consist of multi-type relationships, it is important to research similarity measure in such networks. However, most existing similarity measures are defined for homogeneous network and cannot be directly applied to HINs since different semantic meanings behind edges should be considered. This paper proposes GSimRank that is the extended form of the famous SimRank to compute similarity on HINs. Rather than summing all meeting paths for two nodes in SimRank, GSimRank selects linked nodes of the same semantic category as the next step in the pairwise random walk, which ensure the two meeting paths share the same semantic. Further, in order to weight the semantic edges, we propose a domain-independent edge weight evaluation method based on entropy theory. Finally, we proof that GSimRank is still based on the expected meeting distance model and provide experiments on two real world datasets showing the performance of GSimRank.

Supplementary methods and materials. (DOCX 28 kb)

According to the law that modification coefficient changes with the position of the pitch-lines of gears, varying-coefficient-shift-modification gear is designed to eliminate backlash in gear transmission effectively. The tooth profile curve of varying-coefficient-shift-modification gears is obtained by generating rack, based on the theory of rack cutting method. The modification amount changing with the position of the pitch-lines of gears is obtained separately, and then added to standard rack, to obtain the model of generating rack. Finally,the model of generating rack and the pure-rolling model are joined together by using the established equation of no clearance mesh, to get the mathematical model of tooth profile in the coordinate system of the gear. For varying-coefficient-shift-modification gear in the transmission of eccentric involutes gears, the kinematics analysis of the model is conducted through the joint of MATLAB, Pro/E and ADAMS, to verify the effectiveness of the modeling method and the correctness of the mathematical model.

A SVDD (Support Vector Data Description) based MCUSUM (Multivariate Cumulative Sum) chart is proposed and referred as S-MCUSUM chart, which has an advantage of distribution free. Numerical experiments on the performance of the S-MCUSUM chart is compared to the COT (Cumulative of T) chart. The results show that the COT chart is somewhat better than the S-MCUSUM chart for multivariate normally distributed data. However, the S-MCUSUM chart is much better than the COT chart for banana-shaped distributed data which is a typical non-normal distribution.