Dataset Information

Prediction of Hydrophobic Reagent for Flotation Process Using Molecular Modeling.

ABSTRACT: The interaction or nonbonded energies of base organic ions and water molecules during the flotation process of minerals have important meanings for organizing hydrophobic and stable collectors. Furthermore, the interaction, cross-term, and valence energies of optimized structures are important for understanding the properties and structures of selective collectors. The simulation of pure scheelite mineral (PSM) surfaces with four different negative ions, using an adsorption locator module is demonstrated. The interaction energies for base organic ions and water molecules were resolved and detected by shaping the best hydrophobic interaction and the most stable suspension over the PSM surface (112) and (101). The adsorption locator results for base organic ions and water molecules on PSM surfaces (112) and (101) using buffer width 0.5 Å and temperature range from 318.15 to 283.15 K confirmed the results obtain from Forcite calculations. The results have demonstrated that the possibilities of using consistent valence force field implemented by Forcite and adsorption locator modules in the selection of flotation reagents are cost saving. Furthermore, hydrophobicity of the main negative ions in soaps were solved by the simulation methods and results are in a good agreement with the experimental methods that proved that mustard soap is more selective on the mineral surfaces than sunflower soap when used as a collector. Increasing the molecular weight of negative ions increases the interaction energy between base collector ions and PSM surfaces (112) and (101) significantly.

SUBMITTER: Abdalla MAM

PROVIDER: S-EPMC6644759 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Prediction of Hydrophobic Reagent for Flotation Process Using Molecular Modeling.

Abdalla Mohamed A M MAM Peng Huiqing H Wu Di D Abusin Lenna L Mbah Tawum J TJ

ACS omega 20180618 6

The interaction or nonbonded energies of base organic ions and water molecules during the flotation process of minerals have important meanings for organizing hydrophobic and stable collectors. Furthermore, the interaction, cross-term, and valence energies of optimized structures are important for understanding the properties and structures of selective collectors. The simulation of pure scheelite mineral (PSM) surfaces with four different negative ions, using an adsorption locator module is dem ...[more]

PMID: 31458827

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Project description:MotivationSomatic mutations result from processes related to DNA replication or environmental/lifestyle exposures. Knowing the activity of mutational processes in a tumor can inform personalized therapies, early detection, and understanding of tumorigenesis. Computational methods have revealed 30 validated signatures of mutational processes active in human cancers, where each signature is a pattern of single base substitutions. However, half of these signatures have no known etiology, and some similar signatures have distinct etiologies, making patterns of mutation signature activity hard to interpret. Existing mutation signature detection methods do not consider tumor-level clinical/demographic (e.g. smoking history) or molecular features (e.g. inactivations to DNA damage repair genes).ResultsTo begin to address these challenges, we present the Tumor Covariate Signature Model (TCSM), the first method to directly model the effect of observed tumor-level covariates on mutation signatures. To this end, our model uses methods from Bayesian topic modeling to change the prior distribution on signature exposure conditioned on a tumor's observed covariates. We also introduce methods for imputing covariates in held-out data and for evaluating the statistical significance of signature-covariate associations. On simulated and real data, we find that TCSM outperforms both non-negative matrix factorization and topic modeling-based approaches, particularly in recovering the ground truth exposure to similar signatures. We then use TCSM to discover five mutation signatures in breast cancer and predict homologous recombination repair deficiency in held-out tumors. We also discover four signatures in a combined melanoma and lung cancer cohort-using cancer type as a covariate-and provide statistical evidence to support earlier claims that three lung cancers from The Cancer Genome Atlas are misdiagnosed metastatic melanomas.Availability and implementationTCSM is implemented in Python 3 and available at https://github.com/lrgr/tcsm, along with a data workflow for reproducing the experiments in the paper.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Prediction of Hydrophobic Reagent for Flotation Process Using Molecular Modeling.

Publications

Prediction of Hydrophobic Reagent for Flotation Process Using Molecular Modeling.

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