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At least 37 records · Page 2

Root system architecture and environmental flux analysis in mature crops using 3D root mesocosms

Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new “3D Root Mesocosms” and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.

59 BASIC BIOLOGICAL SCIENCES↗

Does the Blazar Gamma-ray Spectrum Harden with Increasing Flux? - Analysis of Nine Years of EGRET Data

The Energetic Gamma Ray Experiment Telescope (EGRET) on the Compton Gamma Ray Observatory (CGRO) discovered gamma-ray emission from more than 67 blazars during its nine-year lifetime. We conducted an exhaustive search of the EGRET archives and selected all the blazars that were observed multiple times and were bright enough to enable a spectral analysis using standard powerlaw models. The sample consists of 18 flat-spectrum radio quasars (FSRQs), 6 low-frequency-peaked BL Lacs (LBLs) and 2 high-frequency-peaked BL Lacs (HBLs). We do not detect any clear pattern in'the variation of spectral index with flux. Some of the blazars do not show any statistical evidence for spectral variability. The spectrum hardens with increasing flux in a few cases. There is also evidence for a flux-hardness anticorrelation at lo\v fluxes in five blazars. The well observed blazars (3C 279,3C 273, PKS 0528-i-134, PKS 1622-297, PKS 0208- 512) do not show any overall trend in the long-term spectral dependence on flux, but the sample shows a mixture of hard and soft states. We observed spectral hysteresis at weekly timescales in all the three FSRQs for which data from flares lasting for 3 approx. 4 weeks were available. All three sources show a counterclockwise rotation despite the widely different flux profiles. Hysteresis in the spectral index vs. flux space has never been observed in FSRQs in gamma-rays at weekly timescales. itre analyze the observed spectral behavior in the context of various inverse-Compton mechanisms believed to be responsible for emission in the EGRET energy range. Our analysis uses the EGRET skymaps that were regenerated to include the changes in performance during the mission.

Nandikotkur, Giridhar↗

Joint CO 2 Mole Fraction and Flux Analysis Confirms Missing Processes in CASA Terrestrial Carbon Uptake Over North America

Terrestrial biosphere models (TBMs) play a key role in the detection and attribution of carbon cycle processes at local to global scales and in projections of the coupled carbon-climate system. TBM evaluation commonly involves direct comparison to eddy-covariance flux measurements. This study uses atmospheric CO 2 mole fraction ([CO 2 ]) measured in situ from aircraft and tower, in addition to flux-measurements from summer 2016 to evaluate the CASA TBM. WRF-Chem is used to simulate [CO 2 ] using biogenic CO 2 fluxes from a CASA parameter-based ensemble and CarbonTracker version 2017 (CT2017) in addition to transport and CO 2 boundary condition ensembles. The resulting “super ensemble” of modeled [CO 2 ] demonstrates that the biosphere introduces the majority of uncertainty to the simulations. Both aircraft and tower [CO 2 ] data show that the CASA ensemble net ecosystem exchange (NEE) of CO 2 is biased high (NEE too positive) and identify the maximum light use efficiency E max a key parameter that drives the spread of the CASA ensemble in summer 2016. These findings are verified with flux-measurements. The direct comparison of the CASA flux ensemble with flux-measurements confirms missing sink processes in CASA. Separating the daytime and nighttime flux, we discover that the underestimated net uptake results from missing sink processes that result in overestimation of respiration. NEE biases are smaller in the CT2017 posterior biogenic fluxes, which assimilates observed [CO 2 ]. Flux tower analyses, however, reveal an unrealistic overestimation of nighttime respiration in CT2017 due to the limitation of inversion strategy.

54 ENVIRONMENTAL SCIENCES↗

Computational Heat Flux Analysis for HEATheR Electric Aircraft Wing Ice Protection Systems

Increased electrification of aircraft systems, a growing trend in the aircraft industry, affects subsystems that have historically been pneumatically driven by the engine. Optimized design of propulsion and power systems must account for the requirements of these subsystems. One such subsystem, the wing ice protection system, is the focus of this paper. A preliminary computational icing analysis has been conducted using the LEWICE and ANSYS FENSAP-ICE software to characterize the requirements of a thermal ice protection system for three More Electric Aircraft concepts. This paper summarizes the methodology, simulation execution, and icing conditions used for the analysis as well as discusses the results, observed trends, and impacts to system design. This initial analysis indicates that the computed heat requirements for the wing ice protection systems are not within the capabilities of known traditional systems. A discussion of potential methods for reducing the heat flow requirement by the ice protection system is included. Potential refinements to the analysis to improve the accuracy are also discussed.

Nic Heersema↗

Flux Histograms for 2017 Analysis

Flux for 2017 Analysis histograms for FHC and RHC:- The fluxes are integrated over the fiducial volume limited by -176 < X <177, -172 < Y < 179 and 25 < Z < 1150 cm. The fluxes are PPFX corrected, and both, the hadron production and focusing systematics are included (see docs 23441 and 17608 for more details).- These fluxes have been made with the official FHC (RHC) ND MC CAFAna files using the function DeriveFlux from CAFAna/XSec/Flux.*.- The root files have two types of histograms: flux_N and fractional_uncertainty_N, where N is the neutrino type. The entries are normalized by 10^6 POT, m2 and the bin width.- The text files contain tables of the flux normalized by 10^6 POT and m2.- The fractional uncertainties are made by the hadron production and beam focusing uncertainties added in quadrature. This corresponds to the sigma fraction respect to the central value and it meant to be used to make an uncertainty band (the covariance matrix is not included yet).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Data for "Quantifying the Propagation of Parametric Uncertainty on Flux Balance Analysis"

In the repository are example scripts that perform uncertainty injection and propagation to flux balance analysis with outputs for a small sample size (for demonstration purpose only). For proper analysis, user should download the scripts and run for a large sample size (e.g., 10,000 samples). If you use the scripts, please cite the following Metabolic Engineering article: “Quantifying the propagation of parametric uncertainty on flux balance analysis” (https://doi.org/10.1016/j.ymben.2021.10.012) There are two subdirectories: /uncFBA/uncBiom: injection of normally distributed noise to biomass precursor coeffcients and ATP maintenance (growth-associated ATP maintenance (GAM) and non-growth associated ATP maintenance (NGAM)) /uncFBA/uncRHS: departure from steady-state by adding noise drawn from normal distribution to the RHS terms of mass balance constraints

Metabolomics↗

A software package for plasma facing component analysis and design: the Heat flux Engineering Analysis Toolkit (HEAT)

The engineering limits of plasma facing components (PFCs) constrain the allowable operational space of tokamaks. Poorly managed heat fluxes that push the PFCs beyond their limits not only degrade core plasma performance via elevated impurities, but can also result in PFC failure due to thermal stresses or melting. Simple axisymmetric assumptions fail to capture the complex interaction between 3D PFC geometry and 2D or 3D plasmas. This results in fusion systems that must either operate with increased risk or reduce PFC loads, potentially through lower core plasma performance, to maintain a nominal safety factor. High precision 3D heat flux predictions are necessary to accurately ascertain the state of a PFC given the evolution of the magnetic equilibrium. A new code, the Heat flux Engineering Analysis Toolkit (HEAT), has been developed to provide high precision 3D predictions and analysis for PFCs. HEAT couples many otherwise disparate computational tools together into a single open source python package. Magnetic equilibrium, engineering CAD, finite volume solvers, scrape off layer plasma physics, visualization, high performace computing, and more, are connected in a single web-based user interface. Linux users may use HEAT without any software prerequisites via an appImage. This manuscript introduces HEAT, discusses the software architecture, presents first HEAT results, and outlines physics modules in development.

divertor physics↗

Analysis of field measurements of carbon dioxide and water vapor fluxes

Analysis of the field measurements of carbon dioxide and water vapor fluxes is discussed. These data were examined in conjunction with reflectance obtained from helicopter mounted Modular Multiband Radiometer. These measurements are representative of the canopy scale (10 to 100 m)(exp 2) and provide a good basis for investigating the hypotheses/relationship potentially useful in remote sensing applications. All the micrometeorological data collected during FIFE-89 were processed and fluxes of CO2, water vapor, and sensible heat were calculated. Soil CO2 fluxes were also estimated. Employing these soil CO2 flux values, in conjunction with micrometeorological measurements, canopy photosynthesis is being estimated. A biochemical model of leaf photosynthesis was adapted to the prairie vegetation. The modeled leaf photosynthesis rates were scaled up to the canopy level. This model and a multiplicative stomatal conductance model are also used to calculate canopy conductance.

Verma, Shashi B.↗

Periods of High Intensity Solar Proton Flux

Analysis is presented for times during a space mission that specified solar proton flux levels are exceeded. This includes both total time and continuous time periods during missions. Results for the solar maximum and solar minimum phases of the solar cycle are presented and compared for a broad range of proton energies and shielding levels. This type of approach is more amenable to reliability analysis for spacecraft systems and instrumentation than standard statistical models.

Xapsos, Michael A.↗

Interannual Variability in Soil Trace Gas (CO2, N2O, NO) Fluxes and Analysis of Controllers

Interannual variability in flux rates of biogenic trace gases must be quantified in order to understand the differences between short-term trends and actual long-term change in biosphere-atmosphere interactions. We simulated interannual patterns (1983-1988) of global trace gas fluxes from soils using the NASA Ames model version of CASA (Carnegie-Ames-Stanford Approach) in a transient simulation mode. This ecosystem model has been recalibrated for simulations driven by satellite vegetation index data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) over the mid-1980s. The predicted interannual pattern of soil heterotropic CO2 emissions indicates that relatively large increases in global carbon flux from soils occurred about three years following the strong El Nino Southern Oscillation (ENSO) event of 1983. Results for the years 1986 and 1987 showed an annual increment of +1 Pg (1015 g) C-CO2 emitted from soils, which tended to dampen the estimated global increase in net ecosystem production with about a two year lag period relative to plant carbon fixation. Zonal discrimination of model results implies that 80-90 percent of the yearly positive increments in soil CO2 emission during 1986-87 were attributable to soil organic matter decomposition in the low-latitudes (between 30 N and 30 S). Soils of the northern middle-latitude zone (between 30 N and 60 N) accounted for the residual of these annual increments. Total annual emissions of nitrogen trace gases (N2O and NO) from soils were estimated to vary from 2-4 percent over the time period modeled, a level of variability which is consistent with predicted interannual fluctuations in global soil CO2 fluxes. Interannual variability of precipitation in tropical and subtropical zones (30 N to 20 S appeared to drive the dynamic inverse relationship between higher annual emissions of NO versus emissions of N2O. Global mean emission rates from natural (heterotrophic) soil sources over the period modeled (1983-1988) were estimated at 57.1 Pg C-CO2yr-1, 9.8Tg (1012 g) N-NO yr-1, and 9.7 Tg N-N2O yr-1. Chemical fertilizer contributions to global soil N gas fluxes were estimated at between 1.3 to 7.3 Tg N-NO yr-1, and 1.2 to 4.0 Tg N-N2O yr-1.

Potter, C.↗

Coupling flux balance analysis with reactive transport modeling through machine learning for rapid and stable simulation of microbial metabolic switching

Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial growth and metabolism. Despite promising demonstrations in the past, computational inefficiency has been pointed out as a critical issue to overcome because it requires repeated application of linear programming (LP) to obtain flux balance analysis (FBA) solutions in every time step and spatial grid. To address this challenge, we propose a new simulation method where we train and validate artificial neural networks (ANNs) using randomly sampled FBA solutions and incorporate the resulting surrogate FBA model (represented as algebraic equations) into RTMs as source/sink terms. We demonstrate the efficiency of our method via a case study of Shewanella oneidensis MR-1. During aerobic growth on lactate, S. oneidensis produces metabolic byproducts (such as pyruvate and acetate), which are subsequently consumed as alternative carbon sources when the preferred nutrients are depleted. To effectively simulate these complex dynamics, we used a cybernetic approach that models metabolic switches as the outcome of dynamic competition among multiple growth options. In both zero-dimensional batch and one-dimensional column configurations, the ANN-based surrogate models achieved substantial reduction of computational time by several orders of magnitude compared to the original LP-based FBA models. Moreover, the ANN models produced robust solutions without any special measures to prevent numerical instability. These developments significantly promote our ability to utilize genome-scale networks in complex, multi-physics, and multi-dimensional ecosystem modeling.

59 BASIC BIOLOGICAL SCIENCES↗