Evidence map›Paper›PMID 26503553›Full record

ArticleScientific reports2015

Annual periodicity in planktonic bacterial and archaeal community composition of eutrophic Lake Taihu.

Junfeng Li, Junyi Zhang, Liyang Liu, Yucai Fan, Lianshuo Li, Yunfeng Yang, Zuhong Lu, Xuegong Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
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

29 citing papers in PubMed.

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  18. Four Major South Korea's Rivers Using Deep Learning Models.International journal of environmental research and public health · 2018
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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.

Junfeng LiMOE Key Lab of Bioinformatics; Bioinformatics Division/Center for Synthetic and Systems Biology, TNLIST and Department of Automation, Tsinghua University, Beijing, China.
Junyi ZhangState Key Lab for Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Liyang LiuMOE Key Lab of Bioinformatics; Bioinformatics Division/Center for Synthetic and Systems Biology, TNLIST and Department of Automation, Tsinghua University, Beijing, China.
Yucai FanState Key Lab for Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Lianshuo LiMOE Key Lab of Bioinformatics; Bioinformatics Division/Center for Synthetic and Systems Biology, TNLIST and Department of Automation, Tsinghua University, Beijing, China.
Yunfeng YangState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China.
Zuhong LuState Key Lab for Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Xuegong ZhangMOE Key Lab of Bioinformatics; Bioinformatics Division/Center for Synthetic and Systems Biology, TNLIST and Department of Automation, Tsinghua University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bacterioplankton plays a key role in nutrient cycling and is closely related to water eutrophication and algal bloom. We used high-throughput 16S rRNA gene sequencing to profile archaeal and bacterial community compositions in the surface water of Lake Taihu. It is one of the largest lakes in China and has suffered from recurring cyanobacterial bloom. A total of 81 water samples were collected from 9 different sites in 9 different months of 2012. We found that temporal variation of the microbial community was significantly greater than spatial variation (adonis, n = 9999, P < 1e-4). The composition of bacterial community in December was similar to that in January, and so was the archaeal community, suggesting potential annual periodicity. Unsupervised K-means clustering was used to identify the synchrony of abundance variations between different taxa. We found that the cluster consisting mostly of ACK-M1, C111 (members of acIV), Pelagibacteraceae (alfV-A) and Synechococcaceae showed relatively higher abundance in autumn. On the contrary, the cluster of Comamonadaceae and Methylophilaceae (members of lineage betI and betIV) had higher abundance in spring. The co-occurrence relationships between taxa were greatly altered during the cyanobacterial bloom according to our further network module analysis.

Indexed as

LakesArchaeaCluster AnalysisPlankton

Identifiers

PMID26503553
PMCPMC4621408

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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.