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A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks

Overview of attention for article published in Discover Nano, November 2016
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Title
A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks
Published in
Discover Nano, November 2016
DOI 10.1186/s11671-016-1724-y
Pubmed ID
Authors

Finbarr Murphy, Barry Sheehan, Martin Mullins, Hans Bouwmeester, Hans J. P. Marvin, Yamine Bouzembrak, Anna L. Costa, Rasel Das, Vicki Stone, Syed A. M. Tofail

Abstract

While control banding has been identified as a suitable framework for the evaluation and the determination of potential human health risks associated with exposure to nanomaterials (NMs), the approach currently lacks any implementation that enjoys widespread support. Large inconsistencies in characterisation data, toxicological measurements and exposure scenarios make it difficult to map and compare the risk associated with NMs based on physicochemical data, concentration and exposure route. Here we demonstrate the use of Bayesian networks as a reliable tool for NM risk estimation. This tool is tractable, accessible and scalable. Most importantly, it captures a broad span of data types, from complete, high quality data sets through to data sets with missing data and/or values with a relatively high spread of probability distribution. The tool is able to learn iteratively in order to further refine forecasts as the quality of data available improves. We demonstrate how this risk measurement approach works on NMs with varying degrees of risk potential, namely, carbon nanotubes, silver and titanium dioxide. The results afford even non-experts an accurate picture of the occupational risk probabilities associated with these NMs and, in doing so, demonstrated how NM risk can be evaluated into a tractable, quantitative risk comparator.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
United States 1 2%
Unknown 49 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 27%
Student > Ph. D. Student 7 14%
Student > Master 6 12%
Other 4 8%
Student > Bachelor 3 6%
Other 6 12%
Unknown 11 22%
Readers by discipline Count As %
Engineering 13 25%
Environmental Science 5 10%
Agricultural and Biological Sciences 4 8%
Materials Science 3 6%
Philosophy 1 2%
Other 7 14%
Unknown 18 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 November 2016.
All research outputs
#17,285,036
of 25,373,627 outputs
Outputs from Discover Nano
#538
of 1,146 outputs
Outputs of similar age
#202,268
of 311,950 outputs
Outputs of similar age from Discover Nano
#14
of 18 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,146 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
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We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.