Prevalence, distribution, and diversity of cryptic piroplasm infections in raccoons from selected areas of the United States and Canada
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54 ENVIRONMENTAL SCIENCES↗
SEARCH · Engineering Papers
Results for “distribution selection”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Online Adaptive Hyperthermia Treatment Planning During Locoregional Heating to Suppress Treatment-Limiting Hot Spots
Background: Adequate tumor temperatures during hyperthermia are essential for good clinical response, but excessive heating of normal tissue should be avoided. This makes locoregional heating using phased array systems technically challenging. Online application of hyperthermia treatment planning could help to improve the heating quality. The aim of this study was to evaluate the clinical benefit of online treatment planning during treatment of pelvic tumors heated with the AMC-8 locoregional hyperthermia system. Methods: For online adaptive hyperthermia treatment planning, a graphical user interface was developed. Electric fields were calculated in a preprocessing step using our in-house–developed finite-difference–based treatment planning system. This allows instant calculation of the temperature distribution for user-selected phase-amplitude settings during treatment and projection onto the patient's computed tomographic scan for online visualization. Online treatment planning was used for 14 treatment sessions in 8 patients to reduce the patients' reports of hot spots while maintaining the same level of tumor heating. The predicted decrease in hot spot temperature should be at least 0.5°C, and the tumor temperature should decrease less than 0.2°C. These predictions were compared with clinical data: patient feedback about the hot spot and temperature measurements in the tumor region. Results: In total, 17 hot spot reports occurred during the 14 sessions, and the alternative settings predicted the hot spot temperature to decrease by at least 0.5°C, which was confirmed by the disappearance of all 17 hot spot reports. At the same time, the average tumor temperature was predicted to change on average −0.01°C (range, −0.19°C to 0.34°C). The measured tumor temperature change was on average only −0.02°C (range, −0.26°C to 0.31°C). In only 2 cases the temperature decrease was slightly larger than 0.2°C, but at most it was 0.26°C. Conclusions: Online application of hyperthermia treatment planning is reliable and very useful to reduce hot spots without affecting tumor temperatures.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Statistical analysis of HAADF-STEM images to determine the surface coverage and distribution of immobilized molecular complexes
The surface immobilization of molecular catalysts is attractive because it combines the benefits of homogeneous and heterogeneous catalysis. However, determining the surface coverage and distribution of a molecular catalyst on a solid support is often challenging, inhibiting our ability to design improved catalytic systems. Here, in this work, we demonstrate that the combination of scanning transmission electron microscopy (STEM) and image analysis of the individual positions of heavy atoms in transition metal complexes via a convolutional neural network (CNN) allows statistically robust determination of the surface coverage and distribution of immobilized molecular catalysts. These observations provide information about how changes in the functionalization conditions, attachment group, and structure of the molecular catalyst affect the surface coverage and distribution, providing insight into the chemical mechanism of surface immobilization. The method could be generally valuable for correlating the surface coverage and distribution to the activity, selectivity, and stability of a catalytic system.
HAADF-STEM↗
Porous Silicon Gradient Refractive Index Micro-Optics
We see the emergence and growth of transformation optics over the past decade has revitalized interest in how a gradient refractive index (GRIN) can be used to control light propagation. Two-dimensional demonstrations with lithographically defined silicon (Si) have displayed the power of GRIN optics and also represent a promising opportunity for integrating compact optical elements within Si photonic integrated circuits. Here, we demonstrate the fabrication of three-dimensional Si-based GRIN micro-optics through the shape-defined formation of porous Si (PSi). Conventional microfabrication creates Si square microcolumns (SMCs) that can be electrochemically etched into PSi elements with nanoscale porosity along the shape-defined etching pathway, which imparts the geometry with structural birefringence. Free-space characterization of the transmitted intensity distribution through a homogeneously etched PSi SMC exhibits polarization splitting behavior resembling that of dielectric metasurfaces that require considerably more laborious fabrication. Coupled birefringence/GRIN effects are studied by way of PSi SMCs etched with a linear (increasing from edge to center) GRIN profile. The transmitted intensity distribution shows polarization-selective focusing behavior with one polarization focused to a diffraction-limited spot and the orthogonal polarization focused into two laterally displaced foci. Optical thickness-based analysis readily predicts the experimentally observed phenomena, which strongly match finite-element electromagnetic simulations.
36 MATERIALS SCIENCE↗
ReaxFF Study of the Oxidation of Softwood Lignin in View of Carbon Fiber Production
We investigate the oxidative, thermal conversion of softwood lignin by performing molecular dynamics simulations based on a reactive force field (ReaxFF). The lignin samples are constructed from coniferyl alcohol units, which are connected through linkages that are randomly selected from a natural distribution of linkages in softwood. The goal of this work is to simulate the oxidative stabilization step during carbon fiber production from lignin precursor. We find that at simulation conditions where stabilization reactions occur, the lignin fragments have already undergone extensive degradation. The 5-5 linkage shows the highest reactivity towards cyclization and dehydrogenation.
36 MATERIALS SCIENCE↗
On the cosmological evolution of long gamma-ray burst properties
ABSTRACT We examine the relationship between a number of long gamma-ray burst (lGRB) properties (isotropic emitted energy, luminosity, intrinsic duration, jet opening angle) and redshift. We find that even when accounting for conservative detector flux limits, there appears to be a significant correlation between isotropic equivalent energy and redshift, suggesting cosmological evolution of the lGRB progenitor. Analysing a sub-sample of lGRBs with jet opening angle estimates, we find the beaming-corrected lGRB emitted energy does not correlate with redshift, but jet opening angle does. Additionally, we find a statistically significant anticorrelation between the intrinsic prompt duration and redshift, even when accounting for potential selection effects. We also find that, for a given redshift, isotropic energy is positively correlated with intrinsic prompt duration. None of these GRB properties appear to be correlated with galactic offset. From our selection-effect-corrected redshift distribution, we estimate a co-moving rate density for lGRBs, and compare this to the global cosmic star formation rate (SFR). We find the lGRB rate mildly exceeds the global star formation rate between a redshift of 3 and 5, and declines rapidly at redshifts above this (although we cannot constrain the lGRB rate above a redshift of about 6 due to sample incompleteness). We find the lGRB rate diverges significantly from the SFR at lower redshifts. We discuss both the correlations and lGRB rate density in terms of various lGRB progenitor models and their apparent preference for low-metallicity environments.
79 ASTRONOMY AND ASTROPHYSICS↗
Driving Magnetization Dynamics in an On-Demand Magnonic Crystal via the Magnetoelastic Interactions
Using spatial light interference of ultrafast laser pulses, we generate a lateral modulation in the magnetization profile of an otherwise uniformly magnetized film, whose magnetic excitation spectrum is monitored via the coherent and resonant interaction with elastic waves. In this study, we find an unusual dependence of the magnetoelastic coupling as the externally applied magnetic field is angle- and field-tuned relative to the wave vector of the magnetization modulation, which can be explained by the emergence of spatially inhomogeneous spin-wave modes. In this regard, the spatial light interference methodology can be seen as a user-configurable, temporally windowed, on-demand magnonic crystal, potentially of arbitrary two-dimensional shape, which allows control and selectivity of the spatial distribution of spin waves. Finally, calculations of spin waves using a variety of methods, demonstrated here using the plane-wave method and micromagnetic simulation, can identify the spatial distribution and associated energy scales of each excitation, which opens the door to a number of excitation methodologies beyond our chosen elastic wave excitation.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Nutrient foraging by mycorrhizas: From species functional traits to ecosystem processes
1. Plant roots and the associated mycorrhizal fungal hyphae often selectively proliferate into patchily distributed soil nutrient hotspots, but interactions between these two components of a mycorrhizal root system are usually ignored or experimentally isolated in nutrient foraging studies. 2. From studies in which both roots and mycorrhizal hyphae had access to nutrient hotspots, we compiled data on root foraging precision (increase in roots in nutrient hotspots relative to outside hotspots) of plant species from different ecosystems, ranging from temperate grasslands to subtropical forests. We found that root for- aging precision across the wide range of plant species was strongly influenced by root morphology and mycorrhizal type. 3. The precision of root nutrient foraging, as a plant functional trait, may coordinate with other root traits that are related to the economics of nutrient acquisition. High foraging precision is expected to associate with the strategy of fast return on the investment in roots, such as low construction cost, high metabolic rate and rapid turnover. 4. Nutrient foraging by mycorrhizal fungi alone may be influenced by functional traits such as hyphal exploration distance, hyphal turnover, and hyphal uptake capacity and efficiency, but such data are limited to a small portion of mycorrhizal fungal species. 5. We propose a conceptual framework in which to simulate nitrogen and phosphorus acquisition from both nutrient hotspots and outside hotspots in mixed-species plant communities. Simulation outputs suggest that plant species with varying root morphology and mycorrhizal type can be adaptive to a range of nutrient heterogeneity. 6. Although there are still knowledge gaps related to nutrient foraging, as well as many unexplored plant and fungal species, we suggest that scaling nutrient foraging from individual plants to communities would advance understanding of plant species interactions and below-ground ecosystem function.
54 ENVIRONMENTAL SCIENCES↗
WE-E-BRE-05: Ensemble of Graphical Models for Predicting Radiation Pneumontis Risk
Purpose: We propose a prior knowledge-based approach to construct an interaction graph of biological and dosimetric radiation pneumontis (RP) covariates for the purpose of developing a RP risk classifier. Methods: We recruited 59 NSCLC patients who received curative radiotherapy with minimum 6 month follow-up. 16 RP events was observed (CTCAE grade ≥2). Blood serum was collected from every patient before (pre-RT) and during RT (mid-RT). From each sample the concentration of the following five candidate biomarkers were taken as covariates: alpha-2-macroglobulin (α2M), angiotensin converting enzyme (ACE), transforming growth factor β (TGF-β), interleukin-6 (IL-6), and osteopontin (OPN). Dose-volumetric parameters were also included as covariates. The number of biological and dosimetric covariates was reduced by a variable selection scheme implemented by L1-regularized logistic regression (LASSO). Posterior probability distribution of interaction graphs between the selected variables was estimated from the data under the literature-based prior knowledge to weight more heavily the graphs that contain the expected associations. A graph ensemble was formed by averaging the most probable graphs weighted by their posterior, creating a Bayesian Network (BN)-based RP risk classifier. Results: The LASSO selected the following 7 RP covariates: (1) pre-RT concentration level of α2M, (2) α2M level mid- RT/pre-RT, (3) pre-RT IL6 level, (4) IL6 level mid-RT/pre-RT, (5) ACE mid-RT/pre-RT, (6) PTV volume, and (7) mean lung dose (MLD). The ensemble BN model achieved the maximum sensitivity/specificity of 81%/84% and outperformed univariate dosimetric predictors as shown by larger AUC values (0.78∼0.81) compared with MLD (0.61), V20 (0.65) and V30 (0.70). The ensembles obtained by incorporating the prior knowledge improved classification performance for the ensemble size 5∼50. Conclusion: We demonstrated a probabilistic ensemble method to detect robust associations between RP covariates and its potential to improve RP prediction accuracy. Our Bayesian approach to incorporate prior knowledge can enhance efficiency in searching of such associations from data. The authors acknowledge partial support by: 1) CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290) and 2) The Terry Fox Foundation Strategic Training Initiative for Excellence in Radiation Research for the 21st Century (EIRR21)
60 APPLIED LIFE SCIENCES↗
WE-AB-207B-07: Dose Cloud: Generating “Big Data” for Radiation Therapy Treatment Plan Optimization Research
Purpose: To provide a tool to generate large sets of realistic virtual patient geometries and beamlet doses for treatment optimization research. This tool enables countless studies exploring the fundamental interplay between patient geometry, objective functions, weight selections, and achievable dose distributions for various algorithms and modalities. Methods: Generating realistic virtual patient geometries requires a small set of real patient data. We developed a normalized patient shape model (PSM) which captures organ and target contours in a correspondence-preserving manner. Using PSM-processed data, we perform principal component analysis (PCA) to extract major modes of variation from the population. These PCA modes can be shared without exposing patient information. The modes are re-combined with different weights to produce sets of realistic virtual patient contours. Because virtual patients lack imaging information, we developed a shape-based dose calculation (SBD) relying on the assumption that the region inside the body contour is water. SBD utilizes a 2D fluence-convolved scatter kernel, derived from Monte Carlo simulations, and can compute both full dose for a given set of fluence maps, or produce a dose matrix (dose per fluence pixel) for many modalities. Combining the shape model with SBD provides the data needed for treatment plan optimization research. Results: We used PSM to capture organ and target contours for 96 prostate cases, extracted the first 20 PCA modes, and generated 2048 virtual patient shapes by randomly sampling mode scores. Nearly half of the shapes were thrown out for failing anatomical checks, the remaining 1124 were used in computing dose matrices via SBD and a standard 7-beam protocol. As a proof of concept, and to generate data for later study, we performed fluence map optimization emphasizing PTV coverage. Conclusions: We successfully developed and tested a tool for creating customizable sets of virtual patients suitable for large-scale radiation therapy optimization research.
60 APPLIED LIFE SCIENCES↗
Covariance-Free Bifidelity Control Variates Importance Sampling for Rare Event Reliability Analysis
Multifidelity modeling has been steadily gaining attention as a tool to address the problem of exorbitant model evaluation costs that makes the estimation of failure probabilities a significant computational challenge for complex real-world problems, particularly when failure is a rare event. To implement multifidelity modeling, estimators that efficiently combine information from multiple models/sources are necessary. In past works, the variance reduction techniques of control variates (CV) and importance sampling (IS) have been leveraged for this task. In this paper, we present the CVIS framework—a creative take on a coupled CV and IS estimator for bifidelity reliability analysis. The framework addresses some of the practical challenges of the CV method by using an estimator for the control variate mean and sidestepping the need to estimate the covariance between the original estimator and the control variate through a clever choice for the tuning constant. Furthermore, the task of selecting an efficient IS distribution is also considered, with a view towards maximally leveraging the bifidelity structure and maintaining expressivity. Additionally, a diagnostic is provided that indicates both the efficiency of the algorithm as well as the relative predictive quality of the models utilized. Finally, the behavior and performance of the framework is explored through analytical and numerical examples.
Markov chain Monte Carlo↗
The low noise limit in gene expression
Protein noise measurements are increasingly used to elucidate biophysical parameters. Unfortunately noise analyses are often at odds with directly measured parameters. Here we show that these inconsistencies arise from two problematic analytical choices: (i) the assumption that protein translation rate is invariant for different proteins of different abundances, which has inadvertently led to (ii) the assumption that a large constitutive extrinsic noise sets the low noise limit in gene expression. While growing evidence suggests that transcriptional bursting may set the low noise limit, variability in translational bursting has been largely ignored. We show that genome-wide systematic variation in translational efficiency can-and in the case of E. coli does-control the low noise limit in gene expression. Therefore constitutive extrinsic noise is small and only plays a role in the absence of a systematic variation in translational efficiency. Lastly, these results show the existence of two distinct expression noise patterns: (1) a global noise floor uniformly imposed on all genes by expression bursting; and (2) high noise distributed to only a select group of genes.
59 BASIC BIOLOGICAL SCIENCES↗
Assured Resource Sharing in Ad-Hoc Collaboration
The project seeks an innovative framework to enable users to access and selectively share resources in distributed environments, enhancing the scalability of information sharing. We have investigated secure sharing & assurance approaches for ad-hoc collaboration, focused on Grids, Clouds, and ad-hoc network environments.
97 MATHEMATICS AND COMPUTING↗
Intra-Hour Dispatch and Automatic Generator Control Demonstration with Solar Forecasting - Final Report
In this project we address multiple resource integration challenges associated with increasing levels of solar penetration that arise from the variability and uncertainty in solar irradiance. We will model the SMUD service region as its own balancing region, and develop an integrated, real-time operational tool that takes solar-load forecast uncertainties into consideration and commits optimal energy resources and reserves for intra-hour and intra-day decisions. The primary objectives of this effort are to reduce power system operation cost by committing appropriate amount of energy resources and reserves, as well as to provide operators a prediction of the generation fleet’s behavior in real time for realistic PV penetration scenarios. The proposed methodology includes the following steps: clustering analysis on the expected solar variability per region for the SMUD system, Day-ahead (DA) and real-time (RT) load forecasts for the entire service areas, 1-year of intra-hour CPR forecasts for cluster centers, 1-year of smart re-forecasting CPR forecasts in real-time for determination of irreducible errors, and uncertainty quantification for integrated solar-load for both distributed and central stations (selected locations within service region) PV generation.
14 SOLAR ENERGY↗
National Ignition Facility (NIF) FY2015 Facility Use Plan
Major features of the FY2015 NIF Use Plan include: • Performing a record number of layered DT experiments with 28 planned compared with 15 in FY2014. Executing the first plutonium experiments on the NIF in support of the Science Campaigns. • Over 300 targets shots, a 57% increase compared to FY14. This is a stretch goal defined in the 120-Day Study document, and relies upon the success of many shot-rate improvement actions, as well as on the distribution of shot type selected by the users. While the Plan is consistent with this goal, the increased proportion of layered DT experiments described above reduces the margin against this goal. • Commissioning of initial ARC capability, which will support both SSP-HED and SSPICF programs. • Increase in days allocated to Discovery Science to a level that supports an ongoing program for academic use of NIF and an annual solicitation for new proposals. • Six Facility Maintenance and Reconfiguration (FM&R) periods totaling 30 days dedicated to major facility maintenance and modifications. • Utilization of the NIF Facility Advisory Schedule Committee (FASC) to provide stakeholder review and feedback on the NIF schedule. The Use Plan assumes a total FY2015 LLNL NIF Operations funding in MTE 10.7 of $229.465M and in MTE 10.3 of 47.0M. This Use Plan will be revised in the event of significant changes to the FY2015 funding or if NNSA provides FY2016 budget guidance significantly reduced compared to FY2015.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Economics of Crop Rotations With and Without Carinata for Sustainable Aviation Fuel Production in the SE United States
In 2019, the aviation sector in the United States emitted 255 million metric tons of carbon dioxide (CO 2 ) emissions, i.e., about five percent of the total domestic CO 2 emissions from the energy sector. The sustainable aviation fuel (SAF) derived from carinata (Brassica carinata) could reduce CO 2 emissions of the aviation sector in the United States. Therefore, it is important to estimate changes in farm economics with and without carinata for ascertaining its production feasibility. In this context, the current study first assesses a combination of 12 popular rotations of corn, cotton, peanut, and soybean with winter crops of winter wheat and carinata in South Georgia over 4 years. Then, the net present values (NPVs) of 292 feasible cropping systems over 4 years are calculated. Finally, this study develops a risk model for ascertaining the probability distributions of NPVs for selected cropping systems subject to uncertainties related to prices and yields of summer and winter crops. Carinata in the corn-corn-soybean rotation has the highest NPV ($\$$2,996/ha). The least risky rotation is cotton-cotton-peanut, with a 58.9% probability of a positive NPV. Carinata can decrease the risk level of crop rotations by 8.1%, only if a contract price of $\$$440.9/t is offered. Therefore, a risk averse, risk neutral, or risk acceptant farmer can potentially include carinata in the rotation. Overall, carinata would increase the profitability of farm operations and decrease risk in the SE United States, and therefore, a high likelihood exists, that farmers would adopt it for meeting the growing demand for SAF in the United States.
09 BIOMASS FUELS↗
Real-Time Alpine Measurement System Using Wireless Sensor Networks
Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributedWireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. In conclusion, combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.
47 OTHER INSTRUMENTATION↗
The Chandra cosmos legacy survey: clustering of X-ray-selected AGNs at 2.9 ≤ z ≤ 5.5 using photometric redshift probability distribution functions
We present the measurement of the projected and redshift-space two-point correlation function (2pcf) of the new catalog of Chandra COSMOS-Legacy active galactic nucleus (AGN) at 2.9 ≤ z ≤ 5.5 (〈L{sub bol}〉 ∼ 10{sup 46} erg s{sup −1}) using the generalized clustering estimator based on phot-z probability distribution functions in addition to any available spec-z. We model the projected 2pcf, estimated using π {sub max} = 200 h{sup −1} Mpc with the two-halo term and we derive a bias at z ∼ 3.4 equal to b = 6.6{sub −0.55}{sup +0.60}, which corresponds to a typical mass of the hosting halos of log M {sub h} = 12.83{sub −0.11}{sup +0.12} h{sup −1} M {sub ⊙}. A similar bias is derived using the redshift-space 2pcf, modeled including the typical phot-z error σ {sub z} = 0.052 of our sample at z ≥ 2.9. Once we integrate the projected 2pcf up to π {sub max} = 200 h{sup −1} Mpc, the bias of XMM and Chandra COSMOS at z = 2.8 used in Allevato et al. is consistent with our results at higher redshifts. The results suggest only a slight increase of the bias factor of COSMOS AGNs at z ≳ 3 with the typical hosting halo mass of moderate-luminosity AGNs almost constant with redshift and equal to log M {sub h} = 12.92{sub −0.18}{sup +0.13} at z = 2.8 and log M {sub h} = 12.83{sub −0.11}{sup +0.12} at z ∼ 3.4, respectively. The observed redshift evolution of the bias of COSMOS AGNs implies that moderate-luminosity AGNs still inhabit group-sized halos at z ≳ 3, but slightly less massive than observed in different independent studies using X-ray AGNs at z ≤ 2.
79 ASTRONOMY AND ASTROPHYSICS↗