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Deconvolution of isotope signals from bundles of multiple hairs

Overview of attention for article published in Oecologia, May 2014
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Article details
Title
Deconvolution of isotope signals from bundles of multiple hairs
Published in
Oecologia, May 2014
DOI 10.1007/s00442-014-2945-3
Pubmed ID
Authors
Abstract

Segmental analysis of hair has been used in diverse fields ranging from forensics to ecology to measure the concentration of substances such as drugs and isotopes. Multiple hairs are typically combined into a bundle for segmental analysis to obtain a high-resolution series of measurements. Individual hair strands cycle through multiple phases of growth and grow at different rates when in the growth phase. Variation in growth of hair strands in a bundle can cause misalignment of substance concentration between hairs, attenuating the primary body signal. We developed a mathematical model based on the known physiology of hair growth to describe the signal averaging caused by bundling multiple hairs for segmental analysis. The model was used to form an inverse method to estimate the primary body signal from measurements of a hair bundle. The inverse method was applied to a previously described stable oxygen isotope chronology from the hair of a murder victim and provides a refined interpretation of the data. Aspects of the reconstruction were confirmed when the victim was later identified.

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Mendeley demographics

Mendeley demographics

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.
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Geographical breakdown

Geographical breakdown
Country Count As %
United Kingdom 2 4%
Japan 1 2%
Unknown 48 94%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 9 18%
Student > Ph. D. Student 8 16%
Student > Master 8 16%
Student > Bachelor 6 12%
Other 2 4%
Other 7 14%
Unknown 11 22%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 13 25%
Environmental Science 9 18%
Earth and Planetary Sciences 8 16%
Biochemistry, Genetics and Molecular Biology 3 6%
Arts and Humanities 2 4%
Other 4 8%
Unknown 12 24%