Evidence map›Paper›PMID 35751757›Full record

ArticleArchives of microbiology2022

Microbial community composition and function prediction involved in the hydrolytic bioreactor of coking wastewater treatment process.

Baoshan Zhang, Jinsi Deng, Junting Xie, Haizhen Wu, Cong Wei, Zemin Li, Guanglei Qiu, Chaohai Wei, Shuang Zhu

Abstract read
PubMed Publisher
In one paragraph

Article in Archives of microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 60% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Baoshan ZhangGuangdong Province Key Laboratory for Biotechnology Drug Candidates, School of Biosciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou, China.
Jinsi DengGuangdong Province Key Laboratory for Biotechnology Drug Candidates, School of Biosciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou, China.
Junting XieGuangdong Province Key Laboratory for Biotechnology Drug Candidates, School of Biosciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou, China.
Haizhen WuSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.
Cong WeiSchool of Environment and Energy, South China University of Technology, Guangzhou, China.
Zemin LiSchool of Environment and Energy, South China University of Technology, Guangzhou, China.
Guanglei QiuSchool of Environment and Energy, South China University of Technology, Guangzhou, China.
Chaohai WeiSchool of Environment and Energy, South China University of Technology, Guangzhou, China. cehwei@scut.edu.cn.
Shuang ZhuGuangdong Province Key Laboratory for Biotechnology Drug Candidates, School of Biosciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou, China. 15683727@qq.com.ORCID http://orcid.org/0000-0002-2223-0961
South China University of Technology · CNGuangdong Pharmaceutical University · CN

Funding

the fundamental research funds for the central universities, china No. 2019ZD21the key program of the national natural science foundation of china No. U1901218the national natural science foundation of china No. 51778238the national natural science foundation of china No. 51808297the program for science and technology of guangdong province, china 2017A020216001the program for science and technology of guangdong province, china 2017A030313066the program for science and technology of guangdong province, china 2018A050506009the program for science and technology of guangdong province, china 2021A1515012256
6 · The paper itself

Abstract

The hydrolytic acidification process has a strong ability to conduct denitrogenation and increase the biological oxygen demand/chemical oxygen demand ratio in O/H/O coking wastewater treatment system. More than 80% of the total nitrogen (TN) was removed in the hydrolytic bioreactor, and the hydrolytic acidification process contributed to the provision of carbon sources for the subsequent nitrification process. The structure and diversity of microbial communities were elaborated using high-throughput MiSeq of the 16S rRNA genes. The results revealed that the operational taxonomic units (OTUs) belonged to phyla Bacteroidetes, Betaproteobacteria, and Alphaproteobacteria were the dominant taxa involved in the denitrogenation and degradation of refractory contaminants in the hydrolytic bioreactor, with relative abundances of 22.94 ± 3.72, 29.77 ± 2.47, and 18.23 ± 0.26%, respectively. The results of a redundancy analysis showed that the OTUs belonged to the genera Thiobacillus, Rhodoplanes, and Hylemonella in the hydrolytic bioreactor strongly positively correlated with the chemical oxygen demand, TN, and the removal of phenolics, respectively. The results of a microbial co-occurrence network analysis showed that the OTUs belonged to the phylum Bacteroidetes and the genus Rhodoplanes had a significant impact on the efficiency of removal of contaminants that contained nitrogen in the hydrolytic bioreactor. The potential function profiling results indicate the complementarity of nitrogen metabolism, methane metabolism, and sulfur metabolism sub-pathways that were considered to play a significant role in the process of denitrification. These results provide new insights into the further optimization of the performance of the hydrolytic bioreactor in coking wastewater treatment.

Indexed as

CokeMicrobiotaWater PurificationBioreactorsNitrogenRNA, Ribosomal, 16SWastewaterCokeNitrogenRNA, Ribosomal, 16SWastewater16S rDNABacterial communityCoking wastewaterDenitrogenationHydrolytic bioreactorNetwork

Identifiers

PMID35751757
OpenAlexW4283516240

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.