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Coal seam microorganisms have attracted much attention for their special functions that are closely related to secondary biogenic gas formation, but the influence of subsurface microbial invasion on coal microorganisms remains poorly understood. In this study, subsurface microorganisms from soils at different depths were used to infect coal samples, and the abundance and composition of the microbial community were analyzed by quantitative polymerase chain reaction (PCR) and high-throughput sequencing. The results showed that methane production from the coal samples treated with soil extractions did not increase significantly nor did the abundance and transcriptional activity of archaeal 16S rRNA and mcrA genes. In contrast, two soil extractions significantly improved the abundance and transcriptional activity of the coal bacteria. There was no significant difference in the effects of different soil extractions on bacterial abundance and transcriptional activity, but differences in the effects on α- and β-bacterial diversity between the coal samples treated by different soil extractions were found. Soil extraction from the shallow layer soil far away from the coal mine increased the bacterial α-diversity of the coal samples and changed the bacterial community composition. In addition, this soil extraction increased various bacterial groups in the coal sample and changed the predictive metagenome functional contents of the coal samples from the bacterial sequence data. In summary, this study provides basic microbial information for subsurface microbial invasion of coal seams and helps increase understanding of the source of microorganisms in in situ coal seams and the changes in the microbial community during subsurface microbial migration.

Automatic law article prediction aims to determine appropriate laws for a case by analyzing its corresponding fact description. This research constitutes a relatively new area which has emerged from recommended algorithm. Therefore, the task is still a challenge due to the highly imbalanced long-tail data distribution and lack of discrimination in the feature representation. To deal with these challenges, we proposed a codex enhanced multi-task framework, which consists of two modules. The first one is a codex learning module that estimates the broad codex attributes related to the case fact for alleviating the long-tail issue. The other one is a Bidirectional Text Convolutional Neural Network, which predicts the law articles by considering both local and global information of the facts. These two modules are learning simultaneously through a multi-task loss function. To evaluate the performance of the method proposed in this paper, we construct a new law article prediction data by collecting the judgment documents from the China Judgement online. Experimental results on the dataset demonstrate the effectiveness of our proposed method and can outperform other comparison methods.

This study aims to better understand the relationship between the response to acid mine drainage (AMD) stress of tolerant plants and changes in root-related bacterial communities. In this study, reed stems were planted in AMD-polluted and unpolluted soils, and high-throughput sequencing was conducted to analyze the bacterial community composition in the soil, rhizosphere, rhizoplane and endosphere. The results showed that the effect of AMD pollution on root-associated bacterial communities was greater than that of rhizo-compartments. Proteobacteria were dominant across the rhizo-compartments between treatments. The microbiomes of unpolluted treatments were enriched by Alphaproteobacteria and Betaproteobacteria and depleted in Gammaproteobacteria ranging from the rhizoplane into the endosphere. However, the opposite trend was observed in the AMD pollution treatment, namely, Gammaproteobacteria were enriched, and Alphaproteobacteria and Deltaproteobacteria were mostly depleted. In addition, endophytic microbiomes were dominated by Comamonadaceae and Rhodocyclaceae in the unpolluted treatment and by Enterobacteriaceae in the AMD-polluted soils. PICRUSt showed that functional categories associated with membrane transport, metabolism and cellular processes and signaling processes were overrepresented in the endosphere of the AMD-polluted treatment. In conclusion, our study reveals significant variation in bacterial communities colonizing rhizo-compartments in two soils, indicating that plants can recruit functional bacteria to the roots in response to AMD pollution.

Asymbiotic nitrogen fixation in the subsurface of mine soils is often neglected despite it playing an important role in the ecological restoration of mine soils. Here, we examined the effect of artificial root exudates on the community structure of asymbiotic diazotrophs in the subsurface of mine soils by simulating the oxygen conditions in the root zone of mine soils via application of three artificial root exudates under two oxygen states (oxygen deficiency with 10% oxygen and anaerobic) in microcosm experiments and 16S rRNA and nifH mRNA transcript analysis. The results showed that the three artificial root exudates significantly increased the nitrogen fixation ratio in mine soils under both the anaerobic and oxygen deficiency conditions, and the mine soils treated with the same artificial root exudates showed a higher nitrogen fixation ratio in the oxygen deficiency condition than in the anaerobic condition. The effects of different artificial root exudates on the bacterial and diazotrophic α-diversity indexes were significantly different. In addition, artificial root exudates can change the structures of bacterial and diazotrophic communities. Furthermore, artificial root exudates significantly increased the abundance of Clostridium, which are not the dominant bacteria responsible for asymbiotic nitrogen fixation in subsurface mine soils. In parallel, artificial root exudates mainly stimulated sulfate-reducers and uncultured bacteria for nitrogen fixation. This study is of great importance to strengthen our understanding of the nitrogen fixation dynamics in the ecological restoration of abandoned mining lands.