Dataset Information

Continuous degradation of phenanthrene in cloud point system by reuse of Sphingomonas polyaromaticivorans cells.

ABSTRACT: Extractive biodegradation of phenanthrene by Sphingomonas polyaromaticivorans was previously carried out in cloud point system (CPS). In this study, we explored the possibility of further increasing the efficiency of the culture by repeatedly reusing cells and the system for biodegradation. Three different recycling strategies were adopted. In reuse of cells plus CPS, cells were reused for 3 times while maintaining high degradation rates (>?90%). Thereafter, the accumulation of metabolites in the dilute phase resulted in a decrease in cell viability. This inhibition was avoided in recycling the cells plus coacervate phase by replacing the dilute phase with fresh Medium. However, due to the slow adaptation of the cells to the new degradation environment and the reduction in the volume of the coacervate phase, the cells were only reused twice and their activity decreased. However, the same long degradation cycle (5 days) as the reuse of cells plus coacervate phase reduced the overall degradation efficiency of phenanthrene. Finally, a combined strategy of 3 times of cells plus CPS reuse and individual cells reuse once was employed and run for two cycles. 3 rounds of reuse of cells plus CPS improved cells utilization and phenanthrene degradation efficiency. Then, the subsequent round of reuse of cells alone relieved the effect of increasing metabolites on cell viability. This study provides a potential application for reusing cells to continuously degrade phenanthrene in soil and water in CPS.

SUBMITTER: Pan T

PROVIDER: S-EPMC6339633 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Continuous degradation of phenanthrene in cloud point system by reuse of Sphingomonas polyaromaticivorans cells.

Pan Tao T Wang Rennv R Xiao Kun K Ye Wei W Dong Wei W Xu Meiying M

AMB Express 20190119 1

Extractive biodegradation of phenanthrene by Sphingomonas polyaromaticivorans was previously carried out in cloud point system (CPS). In this study, we explored the possibility of further increasing the efficiency of the culture by repeatedly reusing cells and the system for biodegradation. Three different recycling strategies were adopted. In reuse of cells plus CPS, cells were reused for 3 times while maintaining high degradation rates (> 90%). Thereafter, the accumulation of metabolites in th ...[more]

PMID: 30661204

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Project description:Before pushbroom hyperspectral imaging (HSI) data can be applied in remote sensing applications, it must typically be preprocessed through radiometric correction, atmospheric compensation, geometric correction and spatial resampling procedures. After these preprocessing procedures, HSI data are conventionally given as georeferenced raster images. The raster data model compromises the spatial-spectral integrity of HSI data, leading to suboptimal results in various applications. Inamdar et al. (2021) developed a point cloud data format, the Directly-Georeferenced Hyperspectral Point Cloud (DHPC), that preserves the spatial-spectral integrity of HSI data more effectively than rasters. The DHPC is generated through a data fusion workflow that uses conventional preprocessing protocols with a modification to the digital surface model used in the geometric correction. Even with the additional elevation information, the DHPC is still stored with file sizes up to 13 times smaller than conventional rasters, making it ideal for data distribution. Our article aims to describe the DHPC data fusion workflow from Inamdar et al. (2021), providing all the required tools for its integration in pre-existing processing workflows. This includes a MATLAB script that can be readily applied to carry out the modification that must be made to the digital surface model used in the geometric correction. The MATLAB script first derives the point spread function of the HSI data and then convolves it with the digital surface model input in the geometric correction. By breaking down the MATLAB script and describing its functions, data providers can readily develop their own implementation if necessary. The derived point spread function is also useful for characterizing HSI data, quantifying the contribution of materials to the spectrum from any given pixel as a function of distance from the pixel center. Overall, our work makes the implementation of the DHPC data fusion workflow transparent and approachable for end users and data providers.•Our article describes the Directly-Georeferenced Hyperspectral Point Cloud (DHPC) data fusion workflow, which can be readily implemented with existing processing protocols by modifying the input digital surface model used in the geometric correction.•We provide a MATLAB function that performs the modification to the digital surface model required for the DHPC workflow. This MATLAB script derives the point spread function of the hyperspectral imager and convolves it with the digital surface model so that the elevation data are more spatially consistent with the hyperspectral imaging data as collected.•We highlight the increased effectiveness of the DHPC over conventional raster end products in terms of spatial-spectral data integrity, data storage requirements, hyperspectral imaging application results and site exploration via virtual and augmented reality.

Dataset Information

Continuous degradation of phenanthrene in cloud point system by reuse of Sphingomonas polyaromaticivorans cells.

Publications

Continuous degradation of phenanthrene in cloud point system by reuse of Sphingomonas polyaromaticivorans cells.

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