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DOE OSTI2021

Quantitative analysis of Raman spectral parameters for carbon fibers: practical considerations and connection to mechanical properties

Although the literature on the Raman spectra of carbon fibers is vast, no consistent, robust predictive relationship between mechanical properties of carbon fibers and spectral parameters exists. This shortcoming is due to the use of numerous fitting functions to evaluate Raman spectra of carbon fibers and the inconsistencies in establishing the best fitting models in a statistically robust fashion. To address this gap, we present a comprehensive work on the Raman spectra of carbon fibers that combines a vast library of experimental data with a robust numerical analysis and a statistical evaluation of a wide range of suggested fitting models. This manuscript begins with a brief review of the commonly applied fitting models. Then, the Raman spectra of 32 commercially available polyacrylonitrile-based carbon fibers collected at excitation wavelengths 532, 633, and 785 nm are presented and the best fit for all fibers is evaluated based on several statistical criteria in conjunction with numerical calculations and physical arguments. The results suggest that high-performance fibers must be fit with at least five peaks, whereas high-tensile modulus fibers are best fit with at least six distinct peaks. Finally, we employ simultaneous fitting of the Raman spectra of specific fibers and wavelengths and demonstrate that strong correlations exist between mechanical properties and the D1 peak position and shape across the range of evaluated mechanical properties. Finally, we suggest straightforward improvements in fitting analysis procedures that can be implemented to increase coherency in the understanding of the underlying carbon fiber microstructure intuited from Raman spectroscopy.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Deriving Stable Peak Models to Fit Complex XPS Data From Cu Contaminated Pt Electrocatalysts

X-ray Photoelectron Spectroscopy spectra peak models, designed to partition photoemission signals emanating from different elements or chemical states within an atom, are fitted to data limited to an energy interval over which inelastically scattered photoemission signal can be estimated. While the choice of background approximation and line shapes of components to the peak model requires careful consideration, the energy interval used to define the data to which the peak model is optimized has a significant impact on the final peak model. The relationship between the background intensity and data intensity at the start and end of the energy interval dictates the line shapes used in the peak model. In this work, we devise a method to peak fit a complex overlapping Cu 3p and Pt 4f XPS peak structure to perform the elemental quantification. We first use an Al 2s peak to illustrate how background curves approach data at the limits of the energy interval over which the background is defined, influencing the analysis of XPS spectra. Next, we demonstrate the nature of interactions between specific line shapes (Voigt and pseudo-Voigt profiles) suitable for photoemission peaks and a specific background curve (Shirley) and a peak model is presented that includes components to the peak model that accommodates background intensity during fitting of the peak model to data. The peak model allowed for quantification of the contributions of Pt 4f peaks emanating from the substrate that exhibits strong asymmetry in the presence of the inhomogeneously distributed Cu species, mostly of Lorentzian character.

XPS↗

DOE OSTI2021

Tensile Property and Lifetime Prediction for Low-Temperature Aged Uranium-Niobium Alloys

Thermal aging models and lifetime predictions for uranium-niobium (U-Nb) alloys were created using an approach similar to those previously employed. Lifetime estimates for generic U-6Nb components were thus updated; the reported value being 800 years. This update represents a small change in lifetime vs. that of the 2012 assessment (540 years). This lifetime estimate emerged from consideration of several model fits specific to the aging datasets and properties chosen. Aging was quantified using quasi-static tensile properties measured on specimens artificially aged for up to 10 years. The major change relative to the most recent 2012 LANL assessment was that a more comprehensive body of U-Nb literature data was mined, in addition to being augmented by the latest LANL and UK AWE data. The tensile data compilation was published separately (LANL report LA-14493, December 2016). Recognizing the chemical banding of industrially produced U-6Nb, models were developed for the mid-range (6 wt.%) and extrema (4 and 8 wt.%) compositions. Lifetime estimates were calculated for all three alloy classes (4, 6, 8 wt.% nominal) and two measures of total tensile elongation (TE) to failure, namely TE-ext. — extensometer method, and TE-NCD — normalized crosshead displacement method. The conservative assumption was made that whichever composition (4, 6, or 8 wt.% Nb) and property (TE-ext or TE-NCD) was the first to cross the ductility failure threshold would limit the lifetime of the entire component. Tensile strength properties did not figure into the lifetime predictions, but could be useful as age-sensitive diagnostics and were also modeled. Of these, only first yield strength is expected to show a change at 40°C aging vs. time = 0 over the ~100-year timespan of engineering interest. Second yield strength evolves more slowly, and ultimate tensile strength slower still. Among all the models, the apparent activation energies for aging were mostly in the narrow 29– 37 kcal/mol range, which is close to that for diffusion of Nb in gamma-uranium. This agreement may be coincidental. The data from recent long-term aging studies substantially improved the model fit quality and robustness of the lifetimes. Appendices document sensitivity studies of the model fits and lifetimes with respect to using more limited datasets. These results highlight the limitations of relying solely on data from scattered literature studies and smaller datasets more generally.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Effects of random forest modeling decisions on biogeochemical time series predictions

Abstract Random forests (RF) are an increasingly popular machine learning approach used to model biogeochemical processes in the Earth system. While RF models are robust to many assumptions that complicate deterministic models, there are several important parameterization decisions for appropriate use and optimal model fit. We explored the role that parameter decisions, including training/testing data splitting strategies, variable selection, and hyperparameters play on RF goodness‐of‐fit by constructing models using 1296 unique parameter combinations to predict concentrations of nitrate, a key nutrient for biogeochemical cycling in aquatic ecosystems. Models were built on long‐term, publicly available water quality and meteorology time series collected by the National Estuarine Research Reserve monitoring network for two contrasting ecosystems representing freshwater and brackish estuaries. We found that accounting for temporal dependence when splitting data into training and testing subsets was key for avoiding over‐estimation of model predictive power. In addition, variable selection, the ratio of training to testing data, and to a lesser degree, variables per split and number of trees, were significant parameters for optimizing RF goodness‐of‐fit. We also explored how model parameter decisions influenced interpretation of the relative importance of predictors to the model, and model predictor‐dependent variable relationships, with results suggesting that both data structure and model parameterization influence these factors. Because much of the current RF literature is written for the computational and statistical science communities, the primary goal of this study is to provide guidelines for aquatic scientists new to machine learning to apply RF techniques appropriately to aquatic biogeochemical datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Broadband distortion modeling in Lyman-α forest BAO fitting

Recently, the Lyman-α absorption observed in the spectra of high-redshift quasars has been used as a tracer of large-scale structure by means of the three-dimensional Lyman-α forest auto-correlation function at redshift z≃ 2.3, but the need to fit the quasar continuum in every absorption spectrum introduces a broadband distortion that is difficult to correct and causes a systematic error for measuring any broadband properties. Here, we describe a k-space model for this broadband distortion based on a multiplicative correction to the power spectrum of the transmitted flux fraction that suppresses power on scales corresponding to the typical length of a Lyman-α forest spectrum. In implementing the distortion model in fits for the baryon acoustic oscillation (BAO) peak position in the Lyman-α forest auto-correlation, we find that the fitting method recovers the input values of the linear bias parameter b F and the redshift-space distortion parameter β F for mock data sets with a systematic error of less than 0.5%. Applied to the auto-correlation measured for BOSS Data Release 11, our method improves on the previous treatment of broadband distortions in BAO fitting by providing a better fit to the data using fewer parameters and reducing the statistical errors on βF and the combination b F (1+β F ) by more than a factor of seven. The measured values at redshift z=2.3 are βF=1.39 +0.11 +0.24 +0.38 -0.10 -0.19 -0.28 and bF(1+βF)=-0.374 +0.007 +0.013 +0.020 -0.007 -0.014 -0.022 (1σ, 2σ and 3σ statistical errors). Our fitting software and the input files needed to reproduce our main results are publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

AT2017gfo: Bayesian inference and model selection of multicomponent kilonovae and constraints on the neutron star equation of state

The joint detection of the gravitational wave GW170817, of the short γ-ray burst GRB170817A and of the kilonova AT2017gfo, generated by the the binary neutron star (NS) merger observed on 2017 August 17, is a milestone in multimessenger astronomy and provides new constraints on the NS equation of state. We perform Bayesian inference and model selection on AT2017gfo using semi-analytical, multicomponents models that also account for non-spherical ejecta. Observational data favour anisotropic geometries to spherically symmetric profiles, with a log-Bayes’ factor of ~104, and favour multicomponent models against single-component ones. The best-fitting model is an anisotropic three-component composed of dynamical ejecta plus neutrino and viscous winds. Using the dynamical ejecta parameters inferred from the best-fitting model and numerical–relativity relations connecting the ejecta properties to the binary properties, we constrain the binary mass ratio to q < 1.54 and the reduced tidal parameter to $120\lt \tilde{\Lambda }\lt 1110$. Finally, we combine the predictions from AT2017gfo with those from GW170817, constraining the radius of a NS of 1.4 M ⊙ to 12.2 ± 0.5 km (1σ level). This prediction could be further strengthened by improving kilonova models with numerical-relativity information.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Simple analytical model for fitting QENS data from liquids

The advent of new generation of neutron spectrometers for quasielastic scattering measurements has demonstrated the need for multi-component (at least two-component) fitting of data collected from liquids. The narrowest component is associated with long-range translational diffusion, whereas the broader component originates from localized center-of-mass motions in the transient cage of the nearest neighbors. Intra-particle dynamics (e.g., of molecular side groups) may give rise to additional components besides the center-of-mass motions. We present a simple analytical model for two-component fits of quasielastic neutron scattering data collected from liquids. Here, the use of such models is optional (albeit helpful) in the traditional approach, where fits are performed sequentially and independently for the data collected at separate Q values, but imperative in the global fitting, where the data are fitted simultaneously at all Q values, which may be necessary to rationalize the data from increasingly complex systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference

This work introduces a method for learning low-dimensional models from data of high-dimensional black-box dynamical systems. The novelty is that the learned models are exactly the reduced models that are traditionally constructed with classical projection-based model reduction techniques. Thus, the proposed approach learns models that are guaranteed to have the well-studied properties of reduced models known from model reduction, without requiring full knowledge of the governing equations and without requiring the operators of the high-dimensional systems. The key ingredient is a new data sampling scheme to obtain re-projected trajectories of high-dimensional systems that correspond to Markovian dynamics in low-dimensional subspaces. The exact recovery of reduced models from these re-projected trajectories is guaranteed pre-asymptotically under certain conditions for finite amounts of data and for a large class of systems with polynomial nonlinear terms. Numerical results demonstrate that the low-dimensional models learned with the proposed approach match reduced models from traditional model reduction up to numerical errors in practice. In conclusion, the numerical results further indicate that low-dimensional models fitted to re-projected trajectories are predictive even in situations where models fitted to trajectories without re-projection are inaccurate and unstable.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods—3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration—are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-the-art automatic methods based on ASM.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Constraint-dependent CTOA determination for stable ductile crack growth

The crack tip opening angle (CTOA) has been used as a reliable fracture toughness to characterize stable crack growth for thin-wall structures in low-constraint conditions. Recently, it is found that CTOA can also be utilized as a robust fracture parameter to describe the arrest fracture toughness for gas transmission pipelines in ductile steels. For better understanding the concept of CTOA, this paper presents a brief review on the CTOA definitions and its test methods. And then, four CTOA models are developed for determining the CTOA toughness using conventional single edge notched bend (SENB) specimens, including the load–displacement linear fit model, the logarithmic load–displacement linear fit model, the stable tearing energy model, and the J-differentiation model. Here fracture test data from SENB specimens in A285 carbon steel are employed to evaluate the proposed CTOA models. The results show that these CTOA models can determine either identical or comparable critical CTOA values for stable ductile crack growth using the SENB specimens. A constraint-corrected CTOA resistance curve and a constraint-corrected critical CTOA equation are also obtained for A285 carbon steel.

42 ENGINEERING↗

DOE OSTI2014

Testing MONDian dark matter with galactic rotation curves

MONDian dark matter (MDM) is a new form of dark matter quantum that naturally accounts for Milgrom's scaling, usually associated with modified Newtonian dynamics (MOND), and theoretically behaves like cold dark matter (CDM) at cluster and cosmic scales. In this paper, we provide the first observational test of MDM by fitting rotation curves to a sample of 30 local spiral galaxies (z ≈ 0.003). For comparison, we also fit the galactic rotation curves using MOND and CDM. We find that all three models fit the data well. The rotation curves predicted by MDM and MOND are virtually indistinguishable over the range of observed radii (∼1 to 30 kpc). The best-fit MDM and CDM density profiles are compared. We also compare with MDM the dark matter density profiles arising from MOND if Milgrom's formula is interpreted as Newtonian gravity with an extra source term instead of as a modification of inertia. We find that discrepancies between MDM and MOND will occur near the center of a typical spiral galaxy. In these regions, instead of continuing to rise sharply, the MDM mass density turns over and drops as we approach the center of the galaxy. Our results show that MDM, which restricts the nature of the dark matter quantum by accounting for Milgrom's scaling, accurately reproduces observed rotation curves.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Improving Empirical Magnetic Field Models by Fitting to In Situ Data Using an Optimized Parameter Approach

A method for comparing and optimizing the accuracy of empirical magnetic field models using in situ magnetic field measurements is presented in this paper. The optimization method minimizes a cost function—τ—that explicitly includes both a magnitude and an angular term. A time span of 21 days, including periods of mild and intense geomagnetic activity, was used for this analysis. A comparison between five magnetic field models (T96, T01S, T02, TS04, and TS07) widely used by the community demonstrated that the T02 model was, on average, the most accurate when driven by the standard model input parameters. The optimization procedure, performed in all models except TS07, generally improved the results when compared to unoptimized versions of the models. Additionally, using more satellites in the optimization procedure produces more accurate results. This procedure reduces the number of large errors in the model, that is, it reduces the number of outliers in the error distribution. The TS04 model shows the most accurate results after the optimization in terms of both the magnitude and direction, when using at least six satellites in the fitting. It gave a smaller error than its unoptimized counterpart 57.3% of the time and outperformed the best unoptimized model (T02) 56.2% of the time. Its median percentage error in |B| was reduced from 4.54% to 3.84%. Finally, the difference among the models analyzed, when compared in terms of the median of the error distributions, is not very large. However, the unoptimized models can have very large errors, which are much reduced after the optimization.

58 GEOSCIENCES↗

DOE OSTI2018

An evaluation of equilibrium conditions and temperature-dependent speciation in a laser-produced air plasma

A laser-induced air breakdown plasma is a dynamical system and its fundamental parameters change significantly during its lifetime. The temporal evolution of the spectral features from an air plasma shows emission from bremsstrahlung followed by discrete electronic transitions from ions, atoms, and rotational and vibrations bands from molecules. The molecular band emission from air plasma typically appears at times ≳5 μs and persists for 100s of μs. Here, an evaluation is made on the temperature evolution and speciation in a laser-produced air plasma. The air plasmas are generated by focusing 1064 nm, 6 ns pulses from an Nd:YAG laser. High-resolution emission spectra of various molecules such as N 2 , N 2 + , CN, OH, NH, NO are acquired for fits to spectral models. Fitting of atomic and molecular emission features permits tracking of the air spark temperature evolution from 1 to 200 µs. Though the excitation and molecular temperatures show good overlap at times ≲10 μs, the molecular temperatures obtained from different species show a discontinuity at times ~ 30 μs and this is related to shock collapse and subsequent changes in hydrodynamics and chemistry of the plume. The fitting of multiple species in broadband spectra has permitted calculation of the relative concentrations of various molecules as a function of temperature that provides insight into the air spark speciation. Finally, the measured mole fractions from laser-induced air plasma are within a factor of 2 - 6 of the values estimated by speciation model under the assumption of thermal and chemical equilibrium across the temperature ranges studied and probable reasons for the observed deviation are described.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Efficient flexible characterization of quantum processors with nested error models

We present a simple and powerful technique for finding a good error model for a quantum processor. The technique iteratively tests a nested sequence of models against data obtained from the processor, and keeps track of the best-fit model and its wildcard error (a metric of the amount of unmodeled error) at each step. Each best-fit model, along with a quantification of its unmodeled error, constitutes a characterization of the processor. We explain how quantum processor models can be compared with experimental data and to each other. We demonstrate the technique by using it to characterize a simulated noisy two-qubit processor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Effect of Temperature and Hydrophilic Ratio on the Structure of Poly( N -vinylcaprolactam)- block -poly(dimethylsiloxane)- block -poly( N -vinylcaprolactam) Polymersomes

Nanosized polymeric vesicles (polymersomes) assembled from ABA triblock copolymers of poly(N-vinylcaprolactam)-poly(dimethylsiloxane)-poly(N-vinylcaprolactam) (PVCL–PDMS–PVCL) are a promising platform for biomedical applications, as the temperature-responsiveness of the PVCL blocks enables reversible vesicle shrinkage and permeability of the polymersome shell at elevated temperatures. Herein, we explore the effects of molecular weight, polymer block weight ratios, and temperature on the structure of these polymersomes via electron microscopy, dynamic light scattering, small angle neutron scattering (SANS), and all-atom molecular dynamics methods. We show that the shell structure and overall size of the polymersome can be tuned by varying the hydrophilic (PVCL) weight fraction of the polymer: at room temperature, polymers of smaller hydrophilic ratios form larger vesicles that have thinner shells, whereas polymers with higher PVCL content exhibit interchain aggregation of PVCL blocks within the polymersome shell above 50 °C. Model fitting and model-free analysis of the SANS data reveals that increasing the mass ratio of PVCL to the total copolymer weight from 0.3 to 0.56 reduces the temperature-induced change in vesicle diameter by a factor of 3, while simultaneously increasing the change in shell thickness by a factor of 1.5. Finally, by analysis of the shell structures and overall size of polymersomes with various PVCL weight ratios and those without temperature-dependent polymer components, we bring into focus the mechanism of temperature-triggered drug release reported in a previous study. This work provides new fundamental perspectives on temperature-responsive polymersomes and elucidates important structure–property relationships of their constituent polymers.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Multiwavelength analysis of annihilating dark matter as the origin of the gamma-ray emission from M31

Indirect detection of dark matter (DM) by multiwavelength astronomical observations provides a promising avenue for probing the particle nature of DM. In the case of DM consisting of weakly interacting massive particles (WIMPs), self-annihilation ultimately produces various observable products including e ± pairs and gamma rays. The gamma rays can be detected directly, while the e ± pairs can be detected by radio emission from synchrotron radiation or X rays and soft gamma rays from inverse Compton scattering. An intriguing region to search for astrophysical signs of DM is the Galactic center (GC) of the Milky Way, due in part to an observed excess of gamma rays that could be DM. A recent observation by the Fermi-LAT collaboration of a similar excess in the central region of the Andromeda galaxy (M31) leads us to explore the possibility of a DM-induced signal there as well. We use the RX-DMFIT tool to perform a multifrequency analysis of potential DM annihilation emissions in M31. We consider WIMP particle models consistent with the GC excess and calculate the expected emission across the electromagnetic spectrum in comparison with available observational data from M31. We find that the particle models that best fit the M31 excess favor lower masses than the GC excess. The best-fitting models are for a $b\bar{b}$ final state with M χ = 11 GeV and $\langle$σv$\rangle$ = 2.6 × 10 -26 cm 3 s -1 , as well as an evenly mixed $\bar{b}$b/τ + τ - final state with M χ = 5.8 GeV and $\langle$σv$\rangle$ = 2.03 × 10 -26 cm 3 s -1 . For conservative estimates of the diffusion and magnetic field models the expected radio emissions appear to be in tension with currently available data in the central region of M31, although this constraint has a fairly strong dependence on the values chosen for parameters describing the magnetic field strength and geometry.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Glass formulation and composition optimization with property models: A review

Abstract Glass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–property relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed.

Lu, Xiaonan↗

DOE OSTI2020

The effect of enrofloxacin on enteric Escherichia coli : Fitting a mathematical model to in vivo data

Antimicrobial drugs administered systemically may cause the emergence and dissemination of antimicrobial resistance among enteric bacteria. To develop logical, research-based recommendations for food animal veterinarians, we must understand how to maximize antimicrobial drug efficacy while minimizing risk of antimicrobial resistance. Our objective is to evaluate the effect of two approved dosing regimens of enrofloxacin (a single high dose or three low doses) on Escherichia coli in cattle. We look specifically at bacteria above and below the epidemiological cutoff (ECOFF), above which the bacteria are likely to have an acquired or mutational resistance to enrofloxacin. We developed a differential equation model for the antimicrobial drug concentrations in plasma and colon, and bacteria populations in the feces. The model was fit to animal data of drug concentrations in the plasma and colon obtained using ultrafiltration probes. Fecal E. coli counts and minimum inhibitory concentrations were measured for the week after receiving the antimicrobial drug. We predict that the antimicrobial susceptibility of the bacteria above the ECOFF pre-treatment strongly affects the composition of the bacteria following treatment. Faster removal of the antimicrobial drugs from the colon throughout the study leads to improved clearance of bacteria above the ECOFF in the low dose regimen. If we assume a fitness cost is associated with bacteria above the ECOFF, the increased fitness costs leads to reduction of bacteria above the ECOFF in the low dose study. These results suggest the initial E. coli susceptibility is a strong indicator of how steers respond to antimicrobial drug treatment.

59 BASIC BIOLOGICAL SCIENCES↗