Anthony J. Kearsley

National Institute of Standards and Technology

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Paul N. Patrone

National Institute of Standards and Technology

W.E. Wallace

National Institute of Standards and Technology

Arun S. Moorthy

Trent University

Gregory A. Cooksey

National Institute of Standards and Technology

Charles M. Guttman

National Institute of Standards and Technology

Prajakta Bedekar

National Institute of Standards and Technology

Ryan M. Evans

National Institute of Standards and Technology

Paul T. Boggs

Sandia National Laboratories California

James D. Benson

University of Saskatchewan

Daniel Anderson

George Mason University

Cooperative Institutions

National Institute of Standards and Technology

National Institute of Standards

Material Measurement Laboratory

University of Maryland, College Park

Information Technology Laboratory

National Institutes of Health

United States Food and Drug Administration

University of Massachusetts Chan Medical School

Johns Hopkins University

Chinese Academy of Sciences

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Measuring microfluidic flow rates: Monotonicity, convexity, and uncertainty

Applied Mathematics Letters (2020)

Paul N. Patrone Amy Q.H. Li Gregory A. Cooksey Anthony J. Kearsley

Convexity

10.1016/j.aml.2020.106694

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Citations (4)

Atmospheric analyses of southern peculiar A stars

Monthly Notices of the Royal Astronomical Society (1978)

Anthony J. Kearsley G. Wegner

Spectroscopic observations of five southern Ap stars (HD 136347, 166469, 183806, 187474 and 189832) are reported and new data are given on their spectra and radial velocities. A curve of growth analysis is carried out for four of the stars using their Hγ line profiles and spectral energy distributions in the vicinity of the Balmer jump to estimate Teff and log g from the atmospheric models of Carbon & Gingerich. The Ap nature of all these stars is confirmed.

Balmer series

Line (geometry)

Carbon star

10.1093/mnras/182.2.117

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Mass Spectral Similarity Mapping Applied to Fentanyl Analogs

Arun S. Moorthy Anthony J. Kearsley W. Gary Mallard W.E. Wallace

: This manuscript outlines a straight-forward procedure for generating a map of similarity between spectra of a set. When applied to a reference set of spectra for Type I fentanyl analogs (molecules differing from fentanyl by a single modification), the map illuminates clustering that is applicable to automated structure assignment of unidentified molecules. An open-source software implementation that generates mass spectral similarity mappings of unknowns against a library of Type I fentanyl analog spectra is available at http://github.com/asm3-nist/FentanylClassifier.

Similarity (geometry)

Spectral Clustering

NIST

10.26434/chemrxiv.11816841.v1

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Diffusion-limited Reactions in Nanoscale Electronics

arXiv (Cornell University) (2017)

Ryan M. Evans Arvind Balijepalli Anthony J. Kearsley

A partial differential equation (PDE) was developed to describe time-dependent ligand-receptor interactions for applications in biosensing using field effect transistors (FET). The model describes biochemical interactions at the sensor surface (or biochemical gate) located at the bottom of a solution-well, which result in a time-dependent change in the FET conductance. It was shown that one can exploit the disparate length scales of the solution-well and biochemical gate to reduce the coupled PDE model to a single nonlinear integrodifferential equation (IDE) that describes the concentration of reacting species. Although this equation has a convolution integral with a singular kernel, a numerical approximation was constructed by applying the method of lines. The need for specialized quadrature techniques was obviated and numerical evidence strongly suggests that this method achieves first-order accuracy. Results reveal a depletion region on the biochemical gate, which non-uniformly alters the surface potential of the semiconductor.

Convolution (computer science)

Kernel (algebra)

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Predicting Kovats Retention Indices Using Graph Neural Networks

arXiv (Cornell University) (2020)

Chen Qu Barry I. Schneider Anthony J. Kearsley Walid Keyrouz Thomas C. Allison

The \kovats retention index is a dimensionless quantity that characterizes the rate at which a compound is processed through a gas chromatography column. This quantity is independent of many experimental variables and, as such, is considered a near-universal descriptor of retention time on a chromatography column. The \kovats retention indices of a large number of molecules have been determined experimentally. The "NIST 20: GC Method\slash Retention Index Library" database has collected and, more importantly, curated retention indices of a subset of these compounds resulting in a highly valued reference database. The experimental data in the library form an ideal data set for training machine learning models for the prediction of retention indices of unknown compounds. In this article, we describe the training of a graph neural network model to predict the \kovats retention index for compounds in the NIST library and compare this approach with previous work \cite{2019Matyushin}. We predict the \kovats retention index with a mean unsigned error of 28 index units as compared to 44, the putative best result using a convolutional neural network \cite{2019Matyushin}. The NIST library also incorporates an estimation scheme based on a group contribution approach that achieves a mean unsigned error of 114 compared to the experimental data. Our method uses the same input data source as the group contribution approach, making its application straightforward and convenient to apply to existing libraries. Our results convincingly demonstrate the predictive powers of systematic, data-driven approaches leveraging deep learning methodologies applied to chemical data and for the data in the NIST 20 library outperform previous models.

Kovats retention index

NIST

10.48550/arxiv.2012.14798

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Advanced Numerical Methods for Polymer Mass Spectral Data Analysis | NIST

W.E. Wallace Charles M. Guttman Anthony J. Kearsley Javier Galbán Bernal

NIST

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Making the most of DNA melt curves: data collapse with affine transformations and consequences of experimental design

Biophysical Journal (2022)

Jacob M. Majikes Paul N. Patrone Robert F. DeJaco Anthony J. Kearsley Michael Zwolak

Melting curve analysis

10.1016/j.bpj.2021.11.2388

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An Approach to Parallelizing Isotonic Regression

Anthony J. Kearsley R. A. Tapia Michael W. Trosset

Isotonic regression is the problem of fitting data to order constraints. We demonstrate that the isotonic regression of a finite set of numbers Y can be obtained by decomposing Y into subsets, performing parallel isotonic regressions on each subset, then performing a trivial isotonic regression on the resulting combined set. Numerical experiments confirm the efficacy of this approach.

Isotonic regression

Isotonic saline

10.1007/978-3-642-99789-1_10

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Citations (6)

Prevalence estimation and optimal classification methods to account for time dependence in antibody levels

Journal of Theoretical Biology (2022)

Prajakta Bedekar Anthony J. Kearsley Paul N. Patrone

Serology testing can identify past infection by quantifying the immune response of an infected individual providing important public health guidance. Individual immune responses are time-dependent, which is reflected in antibody measurements. Moreover, the probability of obtaining a particular measurement from a random sample changes due to changing prevalence (i.e., seroprevalence, or fraction of individuals exhibiting an immune response) of the disease in the population. Taking into account these personal and population-level effects, we develop a mathematical model that suggests a natural adaptive scheme for estimating prevalence as a function of time. We then combine the estimated prevalence with optimal decision theory to develop a time-dependent probabilistic classification scheme that minimizes the error associated with classifying a value as positive (history of infection) or negative (no such history) on a given day since the start of the pandemic. We validate this analysis by using a combination of real-world and synthetic SARS-CoV-2 data and discuss the type of longitudinal studies needed to execute this scheme in real-world settings.

Seroprevalence

10.1016/j.jtbi.2022.111375

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Citations (10)

DYNAMIC MEASUREMENT OF NANOLITER PER MINUTE FLOW BY SCALED DOSAGE OF FLUORESCENT SOLUTIONS

Gregory A. Cooksey Paul N. Patrone James R. Hands Stephen Meek Anthony J. Kearsley

Fluorescent labelling

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Citations (3)