Evidence map›Paper›PMID 27904880›Full record

ArticlemSphere

Microbial Community Patterns Associated with Automated Teller Machine Keypads in New York City.

Holly M Bik, Julia M Maritz, Albert Luong, Hakdong Shin, Maria Gloria Dominguez-Bello, Jane M Carlton

Abstract read
In one paragraph

Article in mSphere. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

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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.

Holly M BikCenter for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA.ORCID 0000-0002-4356-3837
Julia M MaritzCenter for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA.
Albert LuongHuman Microbiome Program, New York University School of Medicine, New York, New York, USA.
Hakdong ShinHuman Microbiome Program, New York University School of Medicine, New York, New York, USA.ORCID 0000-0001-7615-9809
Maria Gloria Dominguez-BelloHuman Microbiome Program, New York University School of Medicine, New York, New York, USA.
Jane M CarltonCenter for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In densely populated urban environments, the distribution of microbes and the drivers of microbial community assemblages are not well understood. In sprawling metropolitan habitats, the "urban microbiome" may represent a mix of human-associated and environmental taxa. Here we carried out a baseline study of automated teller machine (ATM) keypads in New York City (NYC). Our goal was to describe the biodiversity and biogeography of both prokaryotic and eukaryotic microbes in an urban setting while assessing the potential source of microbial assemblages on ATM keypads. Microbial swab samples were collected from three boroughs (Manhattan, Queens, and Brooklyn) during June and July 2014, followed by generation of Illumina MiSeq datasets for bacterial (16S rRNA) and eukaryotic (18S rRNA) marker genes. Downstream analysis was carried out in the QIIME pipeline, in conjunction with neighborhood metadata (ethnicity, population, age groups) from the NYC Open Data portal. Neither the 16S nor 18S rRNA datasets showed any clustering patterns related to geography or neighborhood demographics. Bacterial assemblages on ATM keypads were dominated by taxonomic groups known to be associated with human skin communities (

Indexed as

16S rRNA18S rRNAautomated teller machineenvironmental sequencingNew York Cityurban microbiome

Identifiers

PMID27904880
PMCPMC5112336

What OpenQuestion holds

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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.