Evidence map›Paper›PMID 30610394›Full record

ArticleAMB Express2019

Structure and predictive functional profiling of microbial communities in two biotrickling filters treated with continuous/discontinuous waste gases.

Rongfang Feng, Meiying Xu, Jianjun Li, Shaobin Huang, Gang Zhao, Xiang Tu, Guoping Sun, Jun Guo

Open access · goldAbstract read
In one paragraph

Article in AMB Express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 40% 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

2 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. 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

8 authors at 4 institutions in 1 country.

Rongfang FengSchool of Bioscience and Bioengineering, South China University of Technology, Guangzhou, 510006, People's Republic of China.
Meiying XuGuangdong Institute of Microbiology, Guangzhou, 510070, People's Republic of China.
Jianjun LiGuangdong Institute of Microbiology, Guangzhou, 510070, People's Republic of China. lijianjun1672@163.com.
Shaobin HuangSchool of Environment and Energy, South China University of Technology, Guangzhou, 510006, People's Republic of China.
Gang ZhaoGuangdong Institute of Microbiology, Guangzhou, 510070, People's Republic of China.
Xiang TuGuangdong Institute of Microbiology, Guangzhou, 510070, People's Republic of China.
Guoping SunGuangdong Institute of Microbiology, Guangzhou, 510070, People's Republic of China.
Jun GuoSchool of Bioscience and Bioengineering, South China University of Technology, Guangzhou, 510006, People's Republic of China.
Institute of Microbiology · CNKey Laboratory of Guangdong Province · CNSouth China University of Technology · CNSouthern Medical University · CN

Funding

National Natural Science Foundation of China U1701243Science and Technology Project of Guangdong Province, China 2014A020216022Science and Technology Project of Guangdong Province, China 2016A030310314Science and Technology Project of Guangdong Province, China 2016B070701017Science and Technology Project of Guangzhou 201504010014Science and Technology Project of Guangzhou 201704020204Science and Technology Project of Guangzhou 201707010377
6 · The paper itself

Abstract

Two biotrickling filters were operated in continuous (BTF1) and discontinuous (BTF2) modes at a constant empty bed residence time of 60 s for 60 days. From day 60, the operation mode of each BTF was oppositely switched. Higher removal efficiencies of five aromatic pollutants were recorded with BTF1 (> 77.2%). The switch in the operation mode did not alter the removal performance of BTF1. Comparatively, BTF2 was not successfully acclimated in the discontinuous operation mode. Once the mode had been switched to continuous mode, the removal efficiencies of BTF2 on all pollutants increased drastically and finally exceeded the values observed in BTF1, with the single exception of p-xylene. Principle coordinate analysis and analysis of similarities (ANOSIM) showed that the structure of the microbial communities differed considerably between both BTFs (R = 1.000, p < 0.01) as well as before and after the switch in BTF2 (R = 0.996, p < 0.01). The random forest model demonstrated that Mycobacterium, Burkholderia, and Comamonas were the three most important bacterial genera contributing to the differences in microbial communities between the two BTFs. Metagenomics inferred by PICUSt (phylogenetic investigation of communities by reconstruction of unobserved states) indicated that BTF2 had high degradation potential for aromatic pollutants, although those genes involved in biofilm formation were less active in BTF2 than those in BTF1.

Indexed as

Biotrickling filterDiscontinuous operationMicrobial communityPICRUStVolatile organic compounds (VOCs)

Identifiers

PMID30610394
PMCPMC6320331
OpenAlexW2918457291

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.