Dataset Information

Draft Genome Sequence of Strain R_RK_3, an Iron-Depositing Isolate of the Genus Rhodomicrobium, Isolated from a Dewatering Well of an Opencast Mine.

ABSTRACT: Rhodomicrobium sp. strain R_RK_3 is an iron-depositing bacterium from which we report the draft genome. This strain was isolated from ochrous depositions of a mining well pump in Germany. The Illumina NextSeq technique was used to sequence the genome of the strain.

SUBMITTER: Braun B

PROVIDER: S-EPMC5571419 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Publications

Draft Genome Sequence of Strain R_RK_3, an Iron-Depositing Isolate of the Genus <i>Rhodomicrobium</i>, Isolated from a Dewatering Well of an Opencast Mine.

Braun Burga B Künzel Sven S Schröder Josephin J Szewzyk Ulrich U

Genome announcements 20170824 34

<i>Rhodomicrobium</i> sp. strain R_RK_3 is an iron-depositing bacterium from which we report the draft genome. This strain was isolated from ochrous depositions of a mining well pump in Germany. The Illumina NextSeq technique was used to sequence the genome of the strain. ...[more]

PMID: 28839033

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Project description:Heavy metals contaminations in mining areas aroused wide concerns globally. Efficient evaluation of its pollution status is a basis for further soil reclamation. Visible and near-infrared reflectance (Vis-NIR) spectroscopy has been diffusely used for retrieving heavy metals concentrations. However, the reliability and feasibility of calibrated models were still doubtful. The present study estimated zinc (Zn) concentrations via the random forest (RF) and partial least squares regression (PLSR) using ground in-situ Zn concentrations as well as soil spectral reflectance at an Opencast Coal Mine of Ordos, China in February 2020. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and the ratio of performance to deviation (RPD) were selected to assess the robustness of the methods in estimating Zn contents. Moreover, the characteristic bands were chosen by Pearson correlation analysis and Boruta Algorithm. Finally, the comparison between RF and PLSR combined with eight spectral reflectance transformation methods was conducted for four concentration groups to determine the optimal model. The results indicated that: (1) Zn contents represented a skewed distribution (coefficient of variation (CV) = 33%); (2) the spectral reflectance tended to decrease with the increase of Zn contents during 580-1850 nm based on Savitzky-Golay smoothing (SG); (3) the continuous wavelet transform (CWT) demonstrated higher effectiveness than other spectral reflectance transformation methods in enhancing spectral responses, the R2 between Zn contents and the soil spectral reflectance achieved the highest (R2 = 0.71) by using CWT; (4) the RF combined with CWT exhibited the best performance than other methods in the current study (R2 = 0.97, RPD = 3.39, RMSE = 1.05 mg kg-1, MAE = 0.79 mg kg-1). The current study supplied a scientific scheme and theoretical support for predicting heavy metals concentrations via the Vis-NIR spectral method in possible contaminated areas such as coal mines and metallic mineral deposit areas.

Dataset Information

Draft Genome Sequence of Strain R_RK_3, an Iron-Depositing Isolate of the Genus Rhodomicrobium, Isolated from a Dewatering Well of an Opencast Mine.

Publications

Draft Genome Sequence of Strain R_RK_3, an Iron-Depositing Isolate of the Genus <i>Rhodomicrobium</i>, Isolated from a Dewatering Well of an Opencast Mine.

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