Selective adsorption of a high-performance depressant onto dolomite causing effective flotation separation of magnesite from dolomite.

This research focused on the adsorption features and depression mechanism of 1-hydroxyethylene-1,1-diphosphonic acid (HEDP) used as a novel dolomite depressant on dolomite and magnesite surfaces, to extend the application of HEDP for the selective flotation of magnesite from dolomite. The depression impacts of HEDP on the flotation behaviors of the two minerals were investigated through micro-flotation tests. The flotation results indicated that, when sodium oleate (NaOl) was used as the collector, HEDP displayed an outstanding depression effect on the dolomite flotation, whereas it had only a slight influence on the magnesite flotation. Dolomite and magnesite could be efficiently separated at approximately pH 10 with a reagent scheme of 200 mg/L HEDP and 120 mg/L NaOl. The selective depression mechanism of HEDP for dolomite was revealed using contact angle, X-ray photoelectron spectroscopy (XPS), zeta potential, and infrared spectrum (IR) analyses. The results from the contact angle tests indicated that HEDP selectively reduced the surface hydrophobicity of dolomite in the NaOl system. Besides, zeta-potential measurements and IR analyses revealed that the addition of HEDP prior to NaOl had no significant impact on the adsorption of NaOl onto magnesite; however, this addition strongly prevented NaOl from being adsorbed onto dolomite, resulting in a significant difference in the flotation performances of the two minerals. Furthermore, crystal chemistry calculations and XPS analyses confirmed that the strong adsorption of HEDP on the dolomite surface could be attributed to the interaction between the HEDP electron-rich groups and the calcium species exposed to dolomite. Thus, HEDP could be used as a high-performance depressant for the dolomite flotation to realize the decalcification of the magnesite flotation.

A New Network-Based Strategy for Predicting the Potential miRNA-mRNA Interactions in Tumorigenesis.

MicroRNA (miRNA) plays an important role in the degradation and inhibition of mRNAs and is a kind of essential drug targets for cancer therapy. To facilitate the clinical cancer research, we proposed a network-based strategy to identify the cancer-related miRNAs and to predict their targeted genes based on the gene expression profiles. The strategy was validated by using the data sets of acute myeloid leukemia (AML), breast invasive carcinoma (BRCA), and kidney renal clear cell carcinoma (KIRC). The results showed that in the top 20 miRNAs ranked by their degrees, 90.0% (18/20), 70.0% (14/20), and 70.0% (14/20) miRNAs were found to be associated with the cancers for AML, BRCA, and KIRC, respectively. The KEGG pathways and GO terms enriched with the genes that were predicted as the targets of the cancer-related miRNAs were significantly associated with the biological processes of cancers. In addition, several genes, which were predicted to be regulated by more than three miRNAs, were identified to be the potential drug targets annotated by using the human protein atlas database. Our results demonstrated that the proposed strategy can be helpful for predicting the miRNA-mRNA interactions in tumorigenesis and identifying the cancer-related miRNAs as the potential drug targets.

Comparative analysis of oncogenes identified by microarray and RNA-sequencing as biomarkers for clinical prognosis.

AIMS: Although RNA-sequencing has been widely used to identify the differentially expressed genes (DEGs) as biomarkers to guide the therapeutic treatment, it is necessary to investigate the concordance of DEGs identified by microarray and RNA-sequencing for the clinical prognosis. MATERIAL & METHODS: By using The Cancer Genome Atlas data sets, we thoroughly investigated the concordance of DEGs identified from microarray and RNA-sequencing data and their molecular functions. RESULTS: The DEGs identified by both technologies averaged ~98.6% overlap. The cancer-related gene sets were significantly enriched with the DEGs and consistent between two technologies. CONCLUSIONS: The highly consistency of DEGs in their regulation directionality and molecular functions indicated the good reproducibility between microarray and RNA-sequencing in identifying potential oncogenes for clinical prognosis.

Genome-wide association study combined with biological context can reveal more disease-related SNPs altering microRNA target seed sites.

BACKGROUND: Emerging studies demonstrate that single nucleotide polymorphisms (SNPs) resided in the microRNA recognition element seed sites (MRESSs) in 3'UTR of mRNAs are putative biomarkers for human diseases and cancers. However, exhaustively experimental validation for the causality of MRESS SNPs is impractical. Therefore bioinformatics have been introduced to predict causal MRESS SNPs. Genome-wide association study (GWAS) provides a way to detect susceptibility of millions of SNPs simultaneously by taking linkage disequilibrium (LD) into account, but the multiple-testing corrections implemented to suppress false positive rate always sacrificed the sensitivity. In our study, we proposed a method to identify candidate causal MRESS SNPs from 12 GWAS datasets without performing multiple-testing corrections. Alternatively, we used biological context to ensure credibility of the selected SNPs. RESULTS: In 11 out of the 12 GWAS datasets, MRESS SNPs were over-represented in SNPs with p-value ≤ 0.05 (odds ratio (OR) ranged from 1.1 to 2.4). Moreover, host genes of susceptible MRESS SNPs in each of the 11 GWAS dataset shared biological context with reported causal genes. There were 286 MRESS SNPs identified by our method, while only 13 SNPs were identified by multiple-testing corrections with a given threshold of 1 × 10-5, which is a common cutoff used in GWAS. 27 out of the 286 candidate SNPs have been reported to be deleterious while only 2 out of 13 multiple-testing corrected SNPs were documented in PubMed. MicroRNA-mRNA interactions affected by the 286 candidate SNPs were likely to present negatively correlated expression. These SNPs introduced greater alternation of binding free energy than other MRESS SNPs, especially when grouping by haplotypes (4210 vs. 4105 cal/mol by mean, 9781 vs. 8521 cal/mol by mean, respectively). CONCLUSIONS: MRESS SNPs are promising disease biomarkers in multiple GWAS datasets. The method of integrating GWAS p-value and biological context is stable and effective for selecting candidate causal MRESS SNPs, it reduces the loss of sensitivity compared to multiple-testing corrections. The 286 candidate causal MRESS SNPs provide researchers a credible source to initialize their design of experimental validations in the future.