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DOE OSTI2021

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d -1 (95% CI: 1.06 to 1.27 d -1 ), 0.777 d -1 (0.716 to 0.838 d -1 ), and 0.450 d -1 (0.378 to 0.522 d -1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Broadband Characterization and Circuit Model Development of Transmission-Scale Transformers

This report describes broadband measurements of transmission-scale transformers typical in the electric power grid. This work was performed as part of the EMP Resilient Grid LDRD project at Sandia National Laboratories to generate circuit models that can be used for high-altitude electromagnetic pulse (HEMP) coupling simulations and response predictions. The objective of the work was to obtain characterization data of substation yard equipment across a frequency range relevant to HEMP. Vector network analyzer measurements up to 100 MHz were performed on two power transformers at ABB-Hitachi and a single ITEC potential transformer. Custom cable breakouts were designed to interface with the transformer terminals and provide ground connections to the chassis at the base of the transformer bushings. The three-phase terminals of the power transformers were measured as a common mode impedance using a parallel resistive splitter, and the single-phase terminals of the potential transformer were measured directly. A vector fitting algorithm was used to empirically fit circuit models to the resulting two-port networks and input impedances of the measured objects. Simplified circuit representations of the input impedances were also generated to assess the degree of precision needed for high-altitude electromagnetic pulse response predictions, which were performed in Sandia's XYCE circuit simulator platform. HEMP coupling simulations using the transformer models showed significant reduction in the voltage peak and broadening in the pulse width seen at the power transformer compared to the traveling wave voltage. This indicated the importance of the load condition when defining the coupled insult in an electric power substation. Simplified circuit models showed a similar voltage at the transformer with a smoothed waveform. The presence of potential transformers in the simulation did not significantly change the simulated voltage at the power transformer. Single-port input impedance models were also developed to define load conditions when transfer characteristics were not necessary.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1980

Microwave radiometric observations of snowpacks

Models for the microwave emission from snowpacks were generated on the basis of radiometric observations made at 10.7 GHz, 37 HGz, and 94 GHz at a test site near Steamboat Springs, Colorado. In addition to conducting measurements on an approximately daily basis over a six week observation period, measurements were made over several diurnal cycles during which the change in snow wetness was tracked by the microwave radiometers. Also, the variation in emissivity with snow water equivalent was examined, as was the sensitivity to changes in snow surface geometry. The microwave emissivity was observed to (1) decrease exponentially with snow water equivalent and (2) increase with snow wetness. Thus, the emission behavior is the reverse of the backscattering behavior observed by the radar. By fitting the models to the measured data, the variation of the optical depth with snow wetness was estimated.

Ulaby, F. T.↗

DOE OSTI2018

An automatic performance model-based scheduling tool for coupled climate system models

The prediction of the climate system is highly depended on the efficient integration of observations and simulations of the Earth. This is regarded as a canonical example of the cyber-physical system. The climate system model, the simulation engine in this cyber-physical system, is one of most challenging applications in scientific computing. It utilizes the multi-physics simulation that couples multiple components, conducts decadal to millennium simulations, and has long been an important application on supercomputers. However, current climate system models suffer from the inefficient task scheduling methods resulting in an intolerable simulation time. Take the Community Earth System Model (CESM), the most widely used climate system model, as an example, one major reason that CESM suffers from bad performances is the huge overhead to rationally distribute processes among the coupled heterogeneous components. According to the report of NCAR, every percent improvement in CESM performance frees up to the equivalent of $250,000 in computing resources in their scientific experiments. To address such challenge, our paper firstly constructs a lightweight and accurate performance model for effectively capturing and predicting the heterogeneous time-to-solution performance of end-to-end CESM components with a given simulation configuration. Then, based on the performance model, we further propose an efficient scheduling strategy based on rectangular packing method to determine the best process layout among different components, and the process numbers assigned to each component. Our evaluations show that we can achieve 58% average run time reductions on CESM comparing to the widely used sequential process layout for a scale of 144 to 480 cores on typical CPU clusters. And we can save 4 million CPU hours when we conduct one standard scientific experiment (a 2870-year simulation), which equals to save $40,089 with a charge of $0.01 per CPU hour. Meanwhile, 26% extra performance improvements could also be gained in our methods comparing to the heuristic branch and bound algorithm with the guidance of the known curve-fitting performance model.

58 GEOSCIENCES↗

DOE OSTI2015

Materials prediction via classification learning

In the paradigm of materials informatics for accelerated materials discovery, the choice of feature set (i.e. attributes that capture aspects of structure, chemistry and/or bonding) is critical. Ideally, the feature sets should provide a simple physical basis for extracting major structural and chemical trends and furthermore, enable rapid predictions of new material chemistries. Orbital radii calculated from model pseudopotential fits to spectroscopic data are potential candidates to satisfy these conditions. Although these radii (and their linear combinations) have been utilized in the past, their functional forms are largely justified with heuristic arguments. Here we show that machine learning methods naturally uncover the functional forms that mimic most frequently used features in the literature, thereby providing a mathematical basis for feature set construction without a priori assumptions. We apply these principles to study two broad materials classes: (i) wide band gap AB compounds and (ii) rare earth-main group RM intermetallics. The AB compounds serve as a prototypical example to demonstrate our approach, whereas the RM intermetallics show how these concepts can be used to rapidly design new ductile materials. In conclusion, our predictive models indicate that ScCo, ScIr, and YCd should be ductile, whereas each was previously proposed to be brittle.

36 MATERIALS SCIENCE↗

NASA NTRS1987

A linearized Euler analysis of unsteady flows in turbomachinery

A method for calculating unsteady flows in cascades is presented. The model, which is based on the linearized unsteady Euler equations, accounts for blade loading shock motion, wake motion, and blade geometry. The mean flow through the cascade is determined by solving the full nonlinear Euler equations. Assuming the unsteadiness in the flow is small, then the Euler equations are linearized about the mean flow to obtain a set of linear variable coefficient equations which describe the small amplitude, harmonic motion of the flow. These equations are discretized on a computational grid via a finite volume operator and solved directly subject to an appropriate set of linearized boundary conditions. The steady flow, which is calculated prior to the unsteady flow, is found via a Newton iteration procedure. An important feature of the analysis is the use of shock fitting to model steady and unsteady shocks. Use of the Euler equations with the unsteady Rankine-Hugoniot shock jump conditions correctly models the generation of steady and unsteady entropy and vorticity at shocks. In particular, the low frequency shock displacement is correctly predicted. Results of this method are presented for a variety of test cases. Predicted unsteady transonic flows in channels are compared to full nonlinear Euler solutions obtained using time-accurate, time-marching methods. The agreement between the two methods is excellent for small to moderate levels of flow unsteadiness. The method is also used to predict unsteady flows in cascades due to blade motion (flutter problem) and incoming disturbances (gust response problem).

Hall, Kenneth C.↗

NASA NTRS1989

Calculation of unsteady flows in turbomachinery using the linearized Euler equations

Hall, Kenneth C.↗

NASA NTRS2005

The Abundance of Interstellar Fluorine

The primary objective of this program was to obtain FUSE observations of the interstellar absorption lines of F I at 951 and 954 Angstroms to derive the abundance of fluorine toward the star HD 164816. The nucleosynthetic source(s) of fluorine are still a matter of debate - the present day abundance of fluorine can potentially constrain models for pulsationally driven dredge-up in asymptotic giant branch stars. An accurate measure for the depletion behavior of fluorine will determine whether it may be detectable in QSO absorption line systems - an unambiguous detection of fluorine at suitably high redshifts would provide the best evidence to date for the neutrino process in massive stars. Furthermore, due to its extreme reactivity, measurement of the gas-phase interstellar fluorine abundance is important for models of grain chemistry. Despite the importance of measuring the interstellar fluorine abundance, at the time of our proposal only one previous detection has been made due to the low relative abundance of fluorine, the lack of lines outside the far-UV, and the blending of the available F I transitions with lines of Hz. The star HD 164816 is associated with the Lagoon nebula (M8), and at a distance of approximately 1.5 kpc probes both distant and local gas. Beginning April 8th, 2004 FUSE FP-Split observations of the star HD 164816 were obtained for this program. This data became available in the FUSE data archive May 21, 2004, and these observations were then downloaded and we began our analysis. Our analysis procedure has involved (1) fitting stellar models to the FUSE spectra, (2) using the multiple lines of Hz and N I at other wavelengths in the FUSE bandpass to derive column densities for the lines of H2 and N I which are blended with the F I features at 951 and 954 angstroms (3) the measurement of the column densities of F I and the species O I and C1 I which are important species for the dis-entangling of dust and nucleosynthetic effects. As discussed in our poster paper presented at the Winter 2005 American Astronomical Society meeting (Lauroesch et al. 2004, AAS, 205, 57.06), our analysis of the data taken for this star suggests that the observed fluorine gas-phase abundance (and hence inferred depletion onto dust grains) are consistent with previous measures toward delta Sco (Copernicus) and two recent measures toward stars in Cep OB2 obtained by others with FUSE. We are currently revising the draft a paper for submission to the Astrophysical Journal which presents the results of these observations and in addition our recently obtained FUSE observations of another star (HD 103779, FUSE GO program D073) which shows an apparent slight enhancement in the gas-phase fluorine abundance.

Lauroesch, James T.↗

DOE OSTI2022

Practical guide to understanding goodness-of-fit metrics used in chemical state modeling of x-ray photoelectron spectroscopy data by synthetic line shapes using nylon as an example

Chemical state analysis of a sample surface through fitting bell-shaped curves to x-ray photoelectron spectroscopic polymer data is reviewed using nylon to introduce and discuss aspects of data analysis. Different strategies for modeling chemistry in nylon spectra are presented and in so doing, a case is made to include in published science the design logic and implementation in terms of line shapes and optimization parameter constraints between components in a peak model. Imperfections in line shape relative to the true shape for photoemission lines, when compensated for using constraints to optimization parameters, are shown to provide chemical state information about a sample that justify, for peak models constructed with these limitations, metrics for goodness-of-fit different from those expected for pulse-counted data.

Materials Science↗

NASA NTRS1986

Application of a relativistic jet model to the study of BL Lacertae objects

The development of a relativistic jet model and its use in fitting coordinated observations of the BL Lacertae objects Mrk 180, Mrk 421, and Mrk 501 are described. This model is a modified version of one by Koenigl (1981). The present model fits show that the properties of the jets in all three objects are similar. The variation in the angle between the jet axis and the observer's line of sight accounts for the differing degrees of variability of the sources. For the case where the jet is seen close to the line of sight (Mrk 421), the Doppler enhanced nonthermal radiation dominates the optical flux. The variation of particle density and magnetic field strength along the jets is similar in all three sources, and suggests that the energy flux is dominated by the magnetic field in the inner regions and by the particle energy at large distances from the jet apex. This result is consistent with models of jets in which the Poynting flux drives particle acceleration at large jet radii.

Hutter, D. J.↗

DOE OSTI2019

Using the Diffusion Equation to Predict CST Resin Breakthroughs - 19051

The crystalline silicotitanate, CST, resin manufactured by UOP Honeywell is well-suited for single-use decontamination of caustic liquid wastes at Hanford and Savannah River Sites. There are many measurements of column loading and breakthrough for CST resin decontamination of Cs-137 in various Hanford and SRS waste simulants. Such measurements provide a very good basis for estimating CST resin demand and these more refined predictions make the CST column loading as efficient as possible given the very expensive CST resin. Although there are a variety of tools for predicting chromatographic resin column performance, [1-4], there is a particularly useful method that is suitable as a simple spreadsheet model [4]. This spreadsheet model is an analytical error function for the mass transfer zone (mtz) diffusion width in resin columns and is well-suited for resin breakthrough and column loading. This paper describes just such an error function spreadsheet model and how well the error function agrees with more traditional models and fits the observed small scale breakthrough curves for CST that can then better predict large scale column loading efficiency. The very high sorption of Cs onto CST tends to be slower than that of other lower sorption resins and as a result of the slow CST kinetics, there is also a marked asymmetry in many CST breakthrough curves. The CST breakthrough asymmetry is largely due to the distribution of CST particle sizes because smaller particles sorb much faster than larger particles. It is then the smaller particles that determine the leading mtz at a decontamination factor of 1,000 breakthrough and therefore the decontamination efficiency for a given loading. The larger CST particles correspondingly determine the tailing mtz width and therefore the overall loading efficiency and it is important to model both leading and tailing mtz widths to best optimize CST for a particular Cs-137 decontamination application. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2014

Constraining possible variations of the fine structure constant in strong gravitational fields with the Kα iron line

In extensions of general relativity and in theories aiming at unifying gravity with the forces of the Standard Model, the value of the ''fundamental constants'' is often determined by the vacuum expectation value of new fields, which may thus change in different backgrounds. Variations of fundamental constants with respect to the values measured today in laboratories on Earth are expected to be more evident on cosmological timescales and/or in strong gravitational fields. In this paper, I show that the analysis of the Kα iron line observed in the X-ray spectrum of black holes can potentially be used to probe the fine structure constant α in gravitational potentials relative to Earth of Δφ ≈ 0.1. At present, systematic effects not fully under control prevent to get robust and stringent bounds on possible variations of the value of α with this technique, but the fact that current data can be fitted with models based on standard physics already rules out variations of the fine structure constant larger than some percent.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Ensemble survival trees for identifying subpopulations in personalized medicine

Recently, personalized medicine has received great attention to improve safety and effectiveness in drug development. Personalized medicine aims to provide medical treatment that is tailored to the patient's characteristics such as genomic biomarkers, disease history, etc., so that the benefit of treatment can be optimized. Subpopulations identification is to divide patients into several different subgroups where each subgroup corresponds to an optimal treatment. For two subgroups, traditionally the multivariate Cox proportional hazards model is fitted and used to calculate the risk score when outcome is survival time endpoint. Median is commonly chosen as the cutoff value to separate patients. However, using median as the cutoff value is quite subjective and sometimes may be inappropriate in situations where data are imbalanced. Here, we propose a novel tree‐based method that adopts the algorithm of relative risk trees to identify subgroup patients. After growing a relative risk tree, we apply k‐means clustering to group the terminal nodes based on the averaged covariates. We adopt an ensemble Bagging method to improve the performance of a single tree since it is well known that the performance of a single tree is quite unstable. A simulation study is conducted to compare the performance between our proposed method and the multivariate Cox model. The applications of our proposed method to two public cancer data sets are also conducted for illustration.

Chen, Yu‐Chuan↗

DOE OSTI2019

Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model

A Bayesian model that uses the spatial dependence induced by the river network topology, and the leading principal components of regional tree ring chronologies for paleo-streamflow reconstruction is presented. In any river basin, a convergent, dendritic network of tributaries come together to form the main stem of a river. Consequently, it is natural to think of a spatial Markov process that recognizes this topological structure to develop a spatially consistent basin-scale streamflow reconstruction model that uses the information in streamflow and tree ring chronology data to inform the reconstructed flows, while maintaining the space-time correlation structure of flows that is critical for water resource assessments and management. Given historical data from multiple streamflow gauges along a river, their tributaries in a watershed, and regional tree ring chronologies, the model is fit and used to simultaneously reconstruct the full network of paleo-streamflow at all gauges in the basin progressing upstream to downstream along the river. Our application to 18 streamflow gauges in the Upper Missouri River Basin shows that the mean adjusted R 2 for the basin is approximately 0.5 with good overall cross-validated skill as measured by five different skill metrics. The spatial network structure produced a substantial reduction in the uncertainty associated with paleo-streamflow as one proceeds downstream in the network aggregating information from upstream gauges and tree ring chronologies. Uncertainty was reduced by more than 50% at six gauges, between 6% and 50% at one gauge, and by less than 5% at the remaining 11 gauges when compared with the traditional principal component regression reconstruction model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Measurement–model comparison of stabilized Criegee intermediate and highly oxygenated molecule production in the CLOUD chamber

Abstract. Atmospheric oxidation is an important phenomenon which produces large quantities of low-volatility compounds such as sulfuric acid and oxidized organic compounds. Such species may be involved in the nucleation of particles and enhance their subsequent growth to reach the size of cloud condensation nuclei (CCN). In this study, we investigate α -pinene, the most abundant monoterpene globally, and its oxidation products formed through ozonolysis in the Cosmic Leaving Outdoor Droplets (CLOUD) chamber at CERN(the European Organization for Nuclear Research). By scavenging hydroxyl radicals (OH) with hydrogen (H 2 ), we were able to investigate the formation of highly oxygenated molecules (HOMs) purely driven by ozonolysis and study the oxidation of sulfur dioxide (SO 2 ) driven by stabilized Criegee intermediates (sCIs). We measured the concentrations of HOM and sulfuric acid with a chemical ionization atmospheric-pressure interface time-of-flight (CI-APi-TOF) mass spectrometer and compared the measured concentrations with simulated concentrations calculated with a kinetic model.We found molar yields in the range of 3.5–6.5 % for HOM formation and 22–32 % for the formation of stabilized Criegee intermediates by fitting our model to the measured sulfuric acid concentrations. The simulated time evolution of the ozonolysis products was in good agreement with measured concentrations except that in some of the experiments sulfuric acid formation was faster than simulated. In those experiments the simulated and measured concentrations met when the concentration reached a plateau but the plateau was reached 20–50 min later in the simulations. The results shown here are consistent with the recently published yields for HOM formation from different laboratory experiments. Together with the sCI yields, these results help us to understand atmospheric oxidation processes better and make there action parameters more comprehensive for broader use.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

OBSERVED VARIABILITY OF THE SOLAR Mg II h SPECTRAL LINE

The Mg ii h and k doublet are two of the primary spectral lines observed by the Sun-pointing Interface Region Imaging Spectrograph (IRIS). These lines are tracers of the magnetic and thermal environment that spans from the photosphere to the upper chromosphere. We use a double-Gaussian model to fit the Mg ii h profile for a full-Sun mosaic data set taken on 2014 August 24. We use the ensemble of high-quality profile fits to conduct a statistical study on the variability of the line profile as it relates the magnetic structure, dynamics, and center-to-limb viewing angle. The average internetwork profile contains a deeply reversed core and is weakly asymmetric at h2. In the internetwork, we find a strong correlation between h3 wavelength and profile asymmetry as well as h1 width and h2 width. The average reversal depth of the h3 core is inversely related to the magnetic field. Plage and sunspots exhibit many profiles that do not contain a reversal. These profiles also occur infrequently in the internetwork. We see indications of magnetically aligned structures in plage and network in statistics associated with the line core, but these structures are not clear or extended in the internetwork. The center-to-limb variations are compared to predictions of semi-empirical model atmospheres. We measure a pronounced limb darkening in the line core that is not predicted by the model. The aim of this work is to provide a comprehensive measurement baseline and preliminary analysis on the observed structure and formation of the Mg ii profiles observed by IRIS.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Logistic Regression in Clinical Studies

• A logistic regression model is used when the outcome of interest is binary. The term “logistic” refers to the underlying “logit” (log odds) function that is used to model the binary outcome. • Odds ratios are produced from a logistic regression model and have a useful interpretation. • Tips, tricks and concepts used to fit logistic regression models are similar to those used in linear regression models. • Modeling building that is knowledge-based rather than automatic is preferred in most applications of logistic regression. • A logistic regression model that is overparameterized (ie, too many variables for too few events) can result in odds ratios that are implausibly large and confidence intervals that are wide and uninterpretable. These types of “overfitted” models should be avoided. • Logistic regression models can be fit using most standard statistical software.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2013

Solid inflation

We develop a cosmological model where primordial inflation is driven by a 'solid', defined as a system of three derivatively coupled scalar fields obeying certain symmetries and spontaneously breaking a certain subgroup of these. The symmetry breaking pattern differs drastically from that of standard inflationary models: time translations are unbroken. This prevents our model from fitting into the standard effective field theory description of adiabatic perturbations, with crucial consequences for the dynamics of cosmological perturbations. Most notably, non-gaussianities in the curvature perturbations are unusually large, with f{sub NL} ∼ 1/(εc{sub s}{sup 2}), and have a novel shape: peaked in the squeezed limit, with anisotropic dependence on how the limit is approached. Other unusual features include the absence of adiabatic fluctuation modes during inflation — which does not impair their presence and near scale-invariance after inflation — and a slightly blue tilt for the tensor modes.

79 ASTRONOMY AND ASTROPHYSICS↗