Evidence map›Paper›PMID 31848793›Full record

ArticleJournal of industrial microbiology & biotechnology2020

Characterization of microbial communities in ethanol biorefineries.

Fernanda C Firmino, Davide Porcellato, Madison Cox, Garret Suen, Jeffery R Broadbent, James L Steele

Abstract read
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In one paragraph

Article in Journal of industrial microbiology & biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Dynamics of contaminant microbes in bioethanol production from sugarcane.Journal of industrial microbiology & biotechnology · 2026
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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

6 authors.

Fernanda C FirminoDepartment of Food Science, University of Wisconsin, Madison, WI, USA.ORCID http://orcid.org/0000-0001-7162-7802
Davide PorcellatoNorwegian University of Life Sciences, As, Akershus, Norway.
Madison CoxDepartment of Bacteriology, University of Wisconsin, Madison, WI, USA.
Garret SuenDepartment of Bacteriology, University of Wisconsin, Madison, WI, USA.
Jeffery R BroadbentLallemand Biofuels and Distilled Spirits, Lebanon, NH, USA.
James L SteeleDepartment of Food Science, University of Wisconsin, Madison, WI, USA. jsteele@lallemand.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bacterial contamination of corn-based ethanol biorefineries can reduce their efficiency and hence increase their carbon footprint. To enhance our understanding of these bacterial contaminants, we temporally sampled four biorefineries in the Midwestern USA that suffered from chronic contamination and characterized their microbiomes using both 16S rRNA sequencing and shotgun metagenomics. These microbiotas were determined to be relatively simple, with 13 operational taxonomic units (OTUs) accounting for 90% of the bacterial population. They were dominated by Firmicutes (89%), with Lactobacillus comprising 80% of the OTUs from this phylum. Shotgun metagenomics confirmed our 16S rRNA data and allowed us to characterize bacterial succession at the species level, with the results of this analysis being that Lb. helveticus was the dominant contaminant in this fermentation. Taken together, these results provide insights into the microbiome of ethanol biorefineries and identifies a species likely to be commonly responsible for chronic contamination of these facilities.

Indexed as

MicrobiotaBacillotaBioreactorsEthanolFermentationLactobacillusMetagenomicsRNA, Ribosomal, 16SEthanolRNA, Ribosomal, 16SContaminantsEthanolic fermentationLactic acid bacteriaMetagenomics

Identifiers

What OpenQuestion holds

Textmetadata
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.