Background: Over 200 published studies of more than 30 plant species have reported a role for miRNAs in regulating responses to abiotic stresses. However, data from these individual reports has not been collected into a single database. The lack of a curated database of stress-related miRNAs limits research in this field, and thus a cohesive database system should necessarily be constructed for data deposit and further application. Description: PASmiR, a literature-curated and web-accessible database, was developed to provide detailed, searchable descriptions of miRNA molecular regulation in different plant abiotic stresses. PASmiR currently includes data from ~200 published studies, representing 1038 regulatory relationships between 682 miRNAs and 35 abiotic stresses in 33 plant species. PASmiR’s interface allows users to retrieve miRNA-stress regulatory entries by keyword search using plant species, abiotic stress, and miRNA identifier. Each entry upon keyword query contains detailed regulation information for a specific miRNA, including species name, miRNA identifier, stress name, miRNA expression pattern, detection method for miRNA expression, a reference literature, and target gene(s) of the miRNA extracted from the corresponding reference or miRBase. Users can also contribute novel regulatory entries by using a web-based submission page. The PASmiR database is freely accessible from the two URLs of http://hi.ustc.edu.cn:8080/PASmiR, and http://pcsb.ahau.edu.cn:8080/PASmiR. Conclusion: The PASmiR database provides a solid platform for collection, standardization, and searching of miRNA-abiotic stress regulation data in plants. As such this database will be a comprehensive repository for miRNA regulatory mechanisms involved in plant response to abiotic stresses for the plant stress physiology community.

Precise cancer classification is a central challenge in clinical cancer research such as diagnosis, prognosis and metastasis prediction. Most existing cancer classification methods based on gene or metabolite biomarkers were limited to single genomics or metabolomics and lacked integration and utilization of multiple ‘omics’ data. The accuracy and robustness of these methods when applied to independent cohorts of patients must be improved. In this study, we propose a directed random walk-based method to evaluate the topological importance of each gene in a reconstructed gene–metabolite graph by integrating information from matched gene expression profiles and metabolomic profiles. The joint use of gene and metabolite information contributes to accurate evaluation of the topological importance of genes and reproducible pathway activities. We constructed classifiers using reproducible pathway activities for precise cancer classification and risk metabolic pathway identification. We applied the proposed method to the classification of prostate cancer. Within-dataset experiments and cross-dataset experiments on three independent datasets demonstrated that the proposed method achieved a more accurate and robust overall performance compared to several existing classification methods. The resulting risk pathways and topologically important differential genes and metabolites provide biologically informative models for prostate cancer prognosis and therapeutic strategies development.

Membrane transporters play crucial roles in the fundamental cellular processes of living organisms. Computational techniques are very necessary to annotate the transporter functions. In this study, a multi-class K nearest neighbor classifier based on the increment of diversity (KNN-ID) was developed to discriminate the membrane transporter types when the increment of diversity (ID) was introduced as one of the novel similarity distances. Comparisons with multiple recently published methods showed that the proposed KNN-ID method outperformed the other methods, obtaining more than 20% improvement for overall accuracy. The overall prediction accuracy reached was 83.1%, when the K was selected as 2. The prediction sensitivity achieved 76.7%, 89.1%, 80.1% for channels/pores, electrochemical potential-driven transporters, primary active transporters, respectively. Discrimination and comparison between any two different classes of transporters further demonstrated that the proposed method is a potential classifier and will play a complementary role for facilitating the functional assignment of transporters.

Composting is a potential alternative for cephalosporin C fermentation dregs (CCFDs) compared with incineration process or landfill because of its advantage of recovering nutrients. In this research, CCFDs and activated sludge (AS) were co-composted to analyze the feasibility of recycling the nutrients in CCFDs. A pilot-scale aerobic composting system with an auto-control system was used in this research, and the maturity and security of the compost product were evaluated. The temperature of the composting mixtures was maintained above 55°C for more than 3 days during the composting, indicating that co-composting of CCFDs and AS could reach the compost maturity standard, and the seeds germination index (GI) increased from 17.61% to 68.93% by the end of the composting process (28 days). However, the degradation rate of cephalosporin C (CPC) was only 6.58% during the composting process. Monitoring the quality of antibiotic resistance genes (ARGs) in the composts showed that the log copy of blaTEM in the composts increased from 2.15 in the initial phase to 6.37 after 28 days. Long-term investigation of CPC degradation and ARGs variation was conducted for the composts; CPC could still be detected after the maturity phases. A removal efficiency of 49.10% could be achieved in 110 days, while the log copy of ARGs increased to 7.93. Although a higher GI value (>80.00%) was observed, the risk of recycling the CCFDs compost product into land is still high.