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RationaleNitrogen isotopic compositions (δ15N) of source and trophic amino acids (AAs) are crucial tracers of N sources and trophic enrichments in diverse fields, including archeology, astrobiochemistry, ecology, oceanography, and paleo‐sciences. The current analytical technique using gas chromatography‐combustion‐isotope ratio mass spectrometry (GC/C/IRMS) requires derivatization, which is not compatible with some key AAs. Another approach using high‐performance liquid chromatography‐elemental analyzer‐IRMS (HPLC/EA/IRMS) may experience coelution issues with other compounds in certain types of samples, and the highly sensitive nano‐EA/IRMS instrumentations are not widely available. MethodsWe present a method for high‐precision δ15N measurements of AAs (δ15N‐AA) optimized for canonical source AA‐phenylalanine (Phe) and trophic AA‐glutamic acid (Glu). This offline approach entails purification and separation via high‐pressure ion‐exchange chromatography (IC) with automated fraction collection, the sequential chemical conversion of AA to nitrite and then to nitrous oxide (N2O), and the final determination of δ15N of the produced N2O via purge‐and‐trap continuous‐flow isotope ratio mass spectrometry (PT/CF/IRMS). ResultsThe cross‐plots of δ15N of Glu and Phe standards (four different natural‐abundance levels) generated by this method and their accepted values have a linear regression slope of 1 and small intercepts demonstrating high accuracy. The precisions were 0.36‰–0.67‰ for Phe standards and 0.27‰–0.35‰ for Glu standards. Our method and the GC/C/IRMS approach produced equivalent δ15N values for two lab standards (McCarthy Lab AA mixture and cyanobacteria) within error. We further tested our method on a wide range of natural sample matrices and obtained reasonable results. ConclusionsOur method provides a reliable alternative to the current methods for δ15N‐AA measurement as IC or HPLC‐based techniques that can collect underivatized AAs are widely available. Our chemical approach that converts AA to N2O can be easily implemented in laboratories currently analyzing δ15N of N2O using PT/CF/IRMS. This method will help promote the use of δ15N‐AA in important studies of N cycling and trophic ecology in a wide range of research areas.
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Probabilistic methods for approximate archetypal analysis
Abstract Archetypal analysis (AA) is an unsupervised learning method for exploratory data analysis. One major challenge that limits the applicability of AA in practice is the inherent computational complexity of the existing algorithms. In this paper, we provide a novel approximation approach to partially address this issue. Utilizing probabilistic ideas from high-dimensional geometry, we introduce two preprocessing techniques to reduce the dimension and representation cardinality of the data, respectively. We prove that provided data are approximately embedded in a low-dimensional linear subspace and the convex hull of the corresponding representations is well approximated by a polytope with a few vertices, our method can effectively reduce the scaling of AA. Moreover, the solution of the reduced problem is near-optimal in terms of prediction errors. Our approach can be combined with other acceleration techniques to further mitigate the intrinsic complexity of AA. We demonstrate the usefulness of our results by applying our method to summarize several moderately large-scale datasets.
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- PAR ID:
- 10367051
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Information and Inference: A Journal of the IMA
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2049-8772
- Page Range / eLocation ID:
- p. 466-493
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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