ArticleFrontiers in microbiology2020
Can We Use Functional Genetics to Predict the Fate of Nitrogen in Estuaries?
Article in Frontiers in microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
The trial behind it
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Who cites it
3 citing papers in PubMed, 23 citations in OpenAlex.
- Using metatranscriptomics to better understand the role of microbial nitrogen cycling in coastal sediment benthic flux denitrification efficiency.Environmental microbiology reports · 2023Article
- Genome-Resolved Metaproteomics Decodes the Microbial and Viral Contributions to Coupled Carbon and Nitrogen Cycling in River Sediments.mSystems · 2022Article
- Omics-based ecosurveillance for the assessment of ecosystem function, health, and resilience.Emerging topics in life sciences · 2022Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasing nitrogen (N) loads present a threat to estuaries, which are among the most heavily populated and perturbed parts of the world. N removal is largely mediated by the sediment microbial process of denitrification, in direct competition to dissimilatory nitrate reduction to ammonium (DNRA), which recycles nitrate to ammonium. Molecular proxies for N pathways are increasingly measured and analyzed, a major question in microbial ecology, however, is whether these proxies can add predictive power around the fate of N. We analyzed the diversity and community composition of sediment
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Registered trials
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.