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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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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Consumption, supply and transport: self-organization without direct communication

Swimming bacteria of the species Bacillus subtilis require and consume oxygen. In static liquid cultures the cells' swimming behaviour leads them to accumulate up oxygen concentration gradients generated by consumption and supply. Since the density of bacterial cells exceeds that of the fluid in which they live, fluid regions where cells have accumulated are denser than depleted regions. These density variations cause convection. The fluid motion is dynamically maintained by the swimming of the cells toward regions of attraction: the air-fluid interface and the fluctuating advecting attractors, gradients of oxygen concentration that are embedded in the convecting fluid. Because of the fluid dynamical conservation laws, these complex physical and biological factors generate patterns ordered over distances > 10000 bacterial cell diameters. The convection enhances long-range transport and mixing of oxygen, cells and extracellular products by orders of magnitude. Thus, through the interplay of physical and biological factors, a population of undifferentiated selfish cells creates functional dynamic patterns. Populations of bacteria that have organised themselves into regularly patterned regions of vigorous convection and varying cell concentration interact with their environment as if they were one purposeful, coherent multicellular individual. The mathematical and experimental ingredients of these remarkable phenomena are presented here.

Non-NASA Center↗
NASA NTRS1995

Paths and patterns: the biology and physics of swimming bacterial populations

The velocity distribution of swimming micro-organisms depends on directional cues supplied by the environment. Directional swimming within a bounded space results in the accumulation of organisms near one or more surfaces. Gravity, gradients of chemical concentration and illumination affect the motile behaviour of individual swimmers. Concentrated populations of organisms scatter and absorb light or consume molecules, such as oxygen. When supply is one-sided, consumption creates gradients; the presence of the population alters the intensity and the symmetry of the environmental cues. Patterns of cues interact dynamically with patterns of the consumer population. In suspensions, spatial variations in the concentration of organisms are equivalent to variations of mean mass density of the fluid. When organisms accumulate in one region whilst moving away from another region, the force of gravity causes convection that translocates both organisms and dissolved substances. The geometry of the resulting concentration-convection patterns has features that are remarkably reproducible. Of interest for biology are (1) the long-range organisation achieved by organisms that do not communicate, and (2) that the entire system, consisting of fluid, cells, directional supply of consumables, boundaries and gravity, generates a dynamic that improves the organisms' habitat by enhancing transport and mixing. Velocity distributions of the bacterium Bacillus subtilis have been measured within the milieu of the spatially and temporally varying oxygen concentration which they themselves create. These distributions of swimming speed and direction are the fundamental ingredients required for a quantitative mathematical treatment of the patterns. The quantitative measurement of swimming behaviour also contributes to our understanding of aerotaxis of individual cells.

Non-NASA Center↗
NASA NTRS1998

Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Methods and Applications

Thermal Infrared (TIR) remote sensing data can provide important measurements of surface energy fluxes and temperatures, which are integral to understanding landscape processes and responses. One example of this is the successful application of TIR remote sensing data to estimate evapotranspiration and soil moisture, where results from a number of studies suggest that satellite-based measurements from TIR remote sensing data can lead to more accurate regional-scale estimates of daily evapotranspiration. With further refinement in analytical techniques and models, the use of TIR data from airborne and satellite sensors could be very useful for parameterizing surface moisture conditions and developing better simulations of landscape energy exchange over a variety of conditions and space and time scales. Thus, TIR remote sensing data can significantly contribute to the observation, measurement, and analysis of energy balance characteristics (i.e., the fluxes and redistribution of thermal energy within and across the land surface) as an implicit and important aspect of landscape dynamics and landscape functioning. The application of TIR remote sensing data in landscape ecological studies has been limited, however, for several fundamental reasons that relate primarily to the perceived difficulty in use and availability of these data by the landscape ecology community, and from the fragmentation of references on TIR remote sensing throughout the scientific literature. It is our purpose here to provide evidence from work that has employed TIR remote sensing for analysis of landscape characteristics to illustrate how these data can provide important data for the improved measurement of landscape energy response and energy flux relationships. We examine the direct or indirect use of TIR remote sensing data to analyze landscape biophysical characteristics, thereby offering some insight on how these data can be used more robustly to further the understanding and modeling of landscape ecological processes.

Quattrochi, Dale A.↗
NASA NTRS2015

Technical Report Series on Global Modeling and Data Assimilation: Soil Moisture Active Passive (SMAP) Project Calibration and Validation for the L4_C Beta-Release Data Product - Volume 42

During the post-launch Cal/Val Phase of SMAP there are two objectives for each science product team: 1) calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the L1 science requirements according to the Cal/Val timeline. This report provides analysis and assessment of the SMAP Level 4 Carbon (L4_C) product specifically for the beta release. The beta-release version of the SMAP L4_C algorithms utilizes a terrestrial carbon flux model informed by SMAP soil moisture inputs along with optical remote sensing (e.g. MODIS) vegetation indices and other ancillary biophysical data to estimate global daily NEE and component carbon fluxes, particularly vegetation gross primary production (GPP) and ecosystem respiration (Reco). Other L4_C product elements include surface (<10 cm depth) soil organic carbon (SOC) stocks and associated environmental constraints to these processes, including soil moisture and landscape FT controls on GPP and Reco (Kimball et al. 2012). The L4_C product encapsulates SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying freeze/thaw and soil moisture constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity in northern ecosystems.

Cal/Val↗
NASA NTRS2016

Soil Moisture Active Passive Mission L4_C Data Product Assessment (Version 2 Validated Release)

The SMAP satellite was successfully launched January 31st 2015, and began acquiring Earth observation data following in-orbit sensor calibration. Global data products derived from the SMAP L-band microwave measurements include Level 1 calibrated and geolocated radiometric brightness temperatures, Level 23 surface soil moisture and freezethaw geophysical retrievals mapped to a fixed Earth grid, and model enhanced Level 4 data products for surface to root zone soil moisture and terrestrial carbon (CO2) fluxes. The post-launch SMAP mission CalVal Phase had two primary objectives for each science product team: 1) calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the L1 science requirements. This report provides analysis and assessment of the SMAP Level 4 Carbon (L4_C) product pertaining to the validated release. The L4_C validated product release effectively replaces an earlier L4_C beta-product release (Kimball et al. 2015). The validated release described in this report incorporates a longer data record and benefits from algorithm and CalVal refinements acquired during the SMAP post-launch CalVal intensive period. The SMAP L4_C algorithms utilize a terrestrial carbon flux model informed by SMAP soil moisture inputs along with optical remote sensing (e.g. MODIS) vegetation indices and other ancillary biophysical data to estimate global daily net ecosystem CO2 exchange (NEE) and component carbon fluxes for vegetation gross primary production (GPP) and ecosystem respiration (Reco). Other L4_C product elements include surface (10 cm depth) soil organic carbon (SOC) stocks and associated environmental constraints to these processes, including soil moisture and landscape freeze/thaw (FT) controls on GPP and respiration (Kimball et al. 2012). The L4_C product encapsulates SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying FT and soil moisture constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity in northern ecosystems. There are no L1 science requirements for the L4_C product; however self-imposed requirements have been established focusing on NEE as the primary product field for validation, and on demonstrating L4_C accuracy and success in meeting product science requirements (Jackson et al. 2012). The other L4_C product fields also have strong utility for carbon science applications; however, analysis of these other fields is considered secondary relative to primary validation activities focusing on NEE. The L4_C targeted accuracy requirements are to meet or exceed a mean unbiased accuracy (ubRMSE) for NEE of 1.6 g C/sq m/d or 30 g C/sq m/yr, emphasizing northern (45N) boreal and arctic ecosystems; this is similar to the estimated accuracy level of in situ tower eddy covariance measurement-based observations (Baldocchi 2008).

Cal/Val↗
NASA NTRS1996

Osteoblast hydraulic conductivity is regulated by calcitonin and parathyroid hormone

It is our hypothesis that osteoblasts play a major role in regulating bone (re)modeling by regulating interstitial fluid (ISF) flow through individual bone compartments. We hypothesize that osteoblasts of the blood-bone membrane lining the bone surfaces are capable of regulating transosseous fluid flow. This regulatory function of the osteoblasts was tested in vitro by culturing a layer of rat calvarial osteoblasts on porous membranes. Such a layer of osteoblasts subjected to 7.3 mm Hg of hydrostatic pressure posed a significant resistance to fluid flow across the cell layer similar in magnitude to the resistance posed by endothelial monolayers in vitro. The hydraulic conductivity, the volumetric fluid flux per unit pressure drop, of the osteoblast layer was altered in response to certain hormones. Hydraulic conductivity decreased approximately 40% in response to 33 nM parathyroid hormone, while it exhibited biphasic behavior in response to calcitonin: increased 40% in response to 100 nM calcitonin and decreased 40% in response to 1000 nM calcitonin. Further, activation of adenylate cyclase by forskolin dramatically increased the hydraulic conductivity, while elevation of intracellular calcium, [Ca2+]i, by the calcium ionophore A23187 initially decreased the hydraulic conductivity at 5 minutes before increasing conductivity by 30 minutes. These results suggest that cyclic adenosine monophosphate (cAMP) and [Ca2+]i may mediate changes in the osteoblast hydraulic conductivity. The increase in hydraulic conductivity in response to 100 nM calcitonin and the decrease in response to PTH suggest that the stimulatory and inhibitory effects on bone formation of calcitonin and parathyroid hormone, respectively, may be due in part to alterations in bone fluid flow.

Non-NASA Center↗
NASA NTRS2013

Photosynthesis-dependent Isoprene Emission from Leaf to Planet in a Global Carbon-chemistry-climate Model

We describe the implementation of a biochemical model of isoprene emission that depends on the electron requirement for isoprene synthesis into the FarquharBallBerry leaf model of photosynthesis and stomatal conductance that is embedded within a global chemistry-climate simulation framework. The isoprene production is calculated as a function of electron transport-limited photosynthesis, intercellular and atmospheric carbon dioxide concentration, and canopy temperature. The vegetation biophysics module computes the photosynthetic uptake of carbon dioxide coupled with the transpiration of water vapor and the isoprene emission rate at the 30 min physical integration time step of the global chemistry-climate model. In the model, the rate of carbon assimilation provides the dominant control on isoprene emission variability over canopy temperature. A control simulation representative of the present-day climatic state that uses 8 plant functional types (PFTs), prescribed phenology and generic PFT-specific isoprene emission potentials (fraction of electrons available for isoprene synthesis) reproduces 50 of the variability across different ecosystems and seasons in a global database of 28 measured campaign-average fluxes. Compared to time-varying isoprene flux measurements at 9 select sites, the model authentically captures the observed variability in the 30 min average diurnal cycle (R2 6496) and simulates the flux magnitude to within a factor of 2. The control run yields a global isoprene source strength of 451 TgC yr1 that increases by 30 in the artificial absence of plant water stress and by 55 for potential natural vegetation.

photosynthesis↗
NASA NTRS1996

Permeation of protons, potassium ions, and small polar molecules through phospholipid bilayers as a function of membrane thickness

Two mechanisms have been proposed to account for solute permeation of lipid bilayers. Partitioning into the hydrophobic phase of the bilayer, followed by diffusion, is accepted by many for the permeation of water and other small neutral solutes, but transient pores have also been proposed to account for both water and ionic solute permeation. These two mechanisms make distinctively different predictions about the permeability coefficient as a function of bilayer thickness. Whereas the solubility-diffusion mechanism predicts only a modest variation related to bilayer thickness, the pore model predicts an exponential relationship. To test these models, we measured the permeability of phospholipid bilayers to protons, potassium ions, water, urea, and glycerol. Bilayers were prepared as liposomes, and thickness was varied systematically by using unsaturated lipids with chain lengths ranging from 14 to 24 carbon atoms. The permeability coefficient of water and neutral polar solutes displayed a modest dependence on bilayer thickness, with an approximately linear fivefold decrease as the carbon number varied from 14 to 24 atoms. In contrast, the permeability to protons and potassium ions decreased sharply by two orders of magnitude between 14 and 18 carbon atoms, and leveled off, when the chain length was further extended to 24 carbon atoms. The results for water and the neutral permeating solutes are best explained by the solubility-diffusion mechanism. The results for protons and potassium ions in shorter-chain lipids are consistent with the transient pore model, but better fit the theoretical line predicted by the solubility-diffusion model at longer chain lengths.

NASA Program Exobiology↗
NASA NTRS2002

Biological-Physical Coupling in the Gulf of Maine: Satellite and Model Studies of Phytoplankton Variability

The goals of this project were to acquire, process, QC, archive and analyze SeaWiFS chlorophyll fields over the Gulf of Maine and Scotia Shelf region. The focus of the analysis effort was to calculate and quantify seasonality and interannual. variability of SeaWiFS-measured phytoplankton biomass in the study area and compare these to physical forcing and hydrography. An additional focus within this effort was on regional differences within the heterogeneous biophysical regions of the Gulf of Maine / Scotia Shelf. Overall goals were approached through the combined use of SeaWiFS and AVHRR data and the development of a coupled biology-physical numerical model.

Thomas, Andrew C.↗
NASA NTRS1985

Cell wall elasticity: I. A critique of the bulk elastic modulus approach and an analysis using polymer elastic principles

The traditional bulk elastic modulus approach to plant cell pressure-volume relations is inconsistent with its definition. The relationship between the bulk modulus and Young's modulus that forms the basis of their usual application to cell pressure-volume properties is demonstrated to be physically meaningless. The bulk modulus describes stress/strain relations of solid, homogeneous bodies undergoing small deformations, whereas the plant cell is best described as a thin-shelled, fluid-filled structure with a polymer base. Because cell walls possess a polymer structure, an alternative method of mechanical analysis is presented using polymer elasticity principles. This initial study presents the groundwork of polymer mechanics as would be applied to cell walls and discusses how the matrix and microfibrillar network induce nonlinear stress/strain relationships in the cell wall in response to turgor pressure. In subsequent studies, these concepts will be expanded to include anisotropic expansion as regulated by the microfibrillar network.

Plants/chemistry/cytology/ultrastructure↗
NASA NTRS1994

Alpha-helical hydrophobic polypeptides form proton-selective channels in lipid bilayers

Proton translocation is important in membrane-mediated processes such as ATP-dependent proton pumps, ATP synthesis, bacteriorhodopsin, and cytochrome oxidase function. The fundamental mechanism, however, is poorly understood. To test the theoretical possibility that bundles of hydrophobic alpha-helices could provide a low energy pathway for ion translocation through the lipid bilayer, polyamino acids were incorporated into extruded liposomes and planar lipid membranes, and proton translocation was measured. Liposomes with incorporated long-chain poly-L-alanine or poly-L-leucine were found to have proton permeability coefficients 5 to 7 times greater than control liposomes, whereas short-chain polyamino acids had relatively little effect. Potassium permeability was not increased markedly by any of the polyamino acids tested. Analytical thin layer chromatography measurements of lipid content and a fluorescamine assay for amino acids showed that there were approximately 135 polyleucine or 65 polyalanine molecules associated with each liposome. Fourier transform infrared spectroscopy indicated that a major fraction of the long-chain hydrophobic peptides existed in an alpha-helical conformation. Single-channel recording in both 0.1 N HCl and 0.1 M KCl was also used to determine whether proton-conducting channels formed in planar lipid membranes (phosphatidylcholine/phosphatidylethanolamine, 1:1). Poly-L-leucine and poly-L-alanine in HCl caused a 10- to 30-fold increase in frequency of conductive events compared to that seen in KCl or by the other polyamino acids in either solution. This finding correlates well with the liposome observations in which these two polyamino acids caused the largest increase in membrane proton permeability but had little effect on potassium permeability. Poly-L-leucine was considerably more conductive than poly-L-alanine due primarily to larger event amplitudes and, to a lesser extent, a higher event frequency. Poly-L-leucine caused two populations of conductive events, one in the 0.1-0.5 pA range, and one in the 1.0-5.0 pA range, whereas nearly all events caused by poly-L-alanine were in the 0.1-0.5 pA range at an applied voltage of +60 mV. The channel-like activity appeared to switch between conductive and nonconductive states, with most open-times in the range of 50-200 ms. We conclude that hydrophobic polyamino acids produce proton-conducting defects in lipid bilayers that may be used to model functional proton channels in biological membranes.

NASA Discipline Exobiology↗
NASA NTRS1999

Physics Based Modeling and Rendering of Vegetation in the Thermal Infrared

We outline a procedure for rendering physically-based thermal infrared images of simple vegetation scenes. Our approach incorporates the biophysical processes that affect the temperature distribution of the elements within a scene. Computer graphics plays a key role in two respects. First, in computing the distribution of scene shaded and sunlit facets and, second, in the final image rendering once the temperatures of all the elements in the scene have been computed. We illustrate our approach for a simple corn scene where the three-dimensional geometry is constructed based on measured morphological attributes of the row crop. Statistical methods are used to construct a representation of the scene in agreement with the measured characteristics. Our results are quite good. The rendered images exhibit realistic behavior in directional properties as a function of view and sun angle. The root-mean-square error in measured versus predicted brightness temperatures for the scene was 2.1 deg C.

Smith, J. A.↗
NASA NTRS2001

Monte Carlo predictions of DNA fragment-size distributions for large sizes after HZE particle irradiation

DSBs (double-strand breaks) produced by densely ionizing space radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. DSB clustering at large scales, from >100 Mbp down to approximately 2 kbp, is modeled using a Monte-Carlo algorithm. A random-walk model of chromatin is combined with a track model, that predicts the radial distribution of energy from an ion, and the RLC (randomly-located-clusters) formalism, in software called DNAbreak. This model generalizes the random-breakage model, whose broken-stick fragment-size distribution is applicable to low-LET radiation. DSB induction due to track interaction with the DNA volume depends on the radiation quality parameter Q. This dose-independent parameter depends only weakly on LET. Multi-track, high-dose effects depend on the cluster intensity parameter lambda, proportional to fluence as defined by the RLC formalism. After lambda is determined by a numerical experiment, the model reduces to one adjustable parameter Q. The best numerical fits to the experimental data, determining Q, are obtained. The knowledge of lambda and Q allows us to give biophysically based extrapolations of high-dose DNA fragment-size data to low doses or to high LETs.

NASA Center JSC↗
NASA NTRS2006

Spatial Growth Modeling and High Resolution Remote Sensing Data Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world s population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include business as usual and smart growth scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared with USGS lkm land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the Georgia Environmental Protection Division to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rational decisions on urban growth and sustainability for the metropolitan area in the future.

Quattrochi, Dale A.↗
NASA NTRS2007

Reconstructed Historical Land Cover and Biophysical Parameters for Studies of Land-Atmosphere Interactions within the Eastern United States

The local environment where we live within the Earth's biosphere is often taken for granted. This environment can vary depending on whether the land cover is a forest, grassland, wetland, water body, bare soil, pastureland, agricultural field, village, residential suburb, or an urban complex with concrete, asphalt, and large buildings. In general, the type and characteristics of land cover influence surface temperatures, sunlight exposure and duration, relative humidity, wind speed and direction, soil moisture amount, plant life, birds, and other wildlife in our backyards. The physical and biological properties (biophysical characteristics) of land cover help to determine our surface environment because they directly affect surface radiation, heat, and soil moisture processes, and also feedback to regional weather and climate. Depending on the spatial scale and land use intensity, land cover changes can have profound impacts on our local and regional environment. Over the past 350 years, the eastern half of the United States, an area extending from the grassland prairies of the Great Plains to the Gulf and Atlantic coasts, has experienced extensive land cover and land use changes that began with land clearing in the 1600s, led to extensive deforestation and intensive land use practices by 1920, and then evolved to the present-day landscape. Determining the consequences of such land cover changes on regional and global climate is a major research issue. Such research requires detailed historical land cover data and modeling experiments simulating historical climates. Given the need to understand the effects of historical land cover changes in the eastern United States, some questions include: - What were the most important land cover transformations and how did they alter biophysical characteristics of the land cover at key points in time since the mid-1600s? - How have land cover and land use changes over the past 350 years affected the land surface environment including surface weather, hydrologic, and climatic variability? - How do the potential effects of regional human-induced land cover change on the environment compare to similar changes that are caused by the natural variations of the Earth's climate system? To help answer these questions, we reconstructed a fractional land cover and biophysical parameter dataset for the eastern United States at 1650, 1850, 1920, and 1992 time-slices. Each land cover fraction is associated with a biophysical parameter class, a suite of parameters defining the biophysical characteristics of that kind of land cover. This new dataset is designed for use in computer models of land-atmosphere interactions, to understand and quantify the effects of historical land cover changes on the water, energy, and carbon cycles

Steyaert, Louis T.↗
NASA NTRS2009

Development of a GCR Event-based Risk Model

A goal at NASA is to develop event-based systems biology models of space radiation risks that will replace the current dose-based empirical models. Complex and varied biochemical signaling processes transmit the initial DNA and oxidative damage from space radiation into cellular and tissue responses. Mis-repaired damage or aberrant signals can lead to genomic instability, persistent oxidative stress or inflammation, which are causative of cancer and CNS risks. Protective signaling through adaptive responses or cell repopulation is also possible. We are developing a computational simulation approach to galactic cosmic ray (GCR) effects that is based on biological events rather than average quantities such as dose, fluence, or dose equivalent. The goal of the GCR Event-based Risk Model (GERMcode) is to provide a simulation tool to describe and integrate physical and biological events into stochastic models of space radiation risks. We used the quantum multiple scattering model of heavy ion fragmentation (QMSFRG) and well known energy loss processes to develop a stochastic Monte-Carlo based model of GCR transport in spacecraft shielding and tissue. We validated the accuracy of the model by comparing to physical data from the NASA Space Radiation Laboratory (NSRL). Our simulation approach allows us to time-tag each GCR proton or heavy ion interaction in tissue including correlated secondary ions often of high multiplicity. Conventional space radiation risk assessment employs average quantities, and assumes linearity and additivity of responses over the complete range of GCR charge and energies. To investigate possible deviations from these assumptions, we studied several biological response pathway models of varying induction and relaxation times including the ATM, TGF -Smad, and WNT signaling pathways. We then considered small volumes of interacting cells and the time-dependent biophysical events that the GCR would produce within these tissue volumes to estimate how GCR event rates mapped to biological signaling induction and relaxation times. We considered several hypotheses related to signaling and cancer risk, and then performed simulations for conditions where aberrant or adaptive signaling would occur on long-duration space mission. Our results do not support the conventional assumptions of dose, linearity and additivity. A discussion on how event-based systems biology models, which focus on biological signaling as the mechanism to propagate damage or adaptation, can be further developed for cancer and CNS space radiation risk projections is given.

Cucinotta, Francis A.↗
NASA NTRS2026

LAI Assimilation Schedules to Constrain Uncertain Cultivars and Soils in CERES-Maize

In anticipation of large-domain crop model applications where precise local configuration and calibration is not possible, we describe benefits and potential drawbacks of employing a crop pest module to achieve leaf area index (LAI) assimilation into a high performing CERES-Maize crop model configuration at a field experiment site in Perry, Iowa. Simulation experiments explore the use of MODIS satellite-derived LAI to constrain and adjust LAI to counter imprecise cultivar and soil configurations often occurring in the absence of high-quality local information. Simulations using single-day, window, and continuous LAI replacement across 5 cultivars and 2 soil calibration approaches for nine corn rotation years from 2004 to 2020 led to different yield outcomes and reverberations throughout the field environment. Evaluating variance and mean bias, results indicate minimal interventions in early vegetative and grain-filling stages were more beneficial than use of continuous LAI adjustments, as they minimized disruptions to the internal resource balances governing plant stresses and grain production. LAI adjustment was particularly helpful in constraining growth related to uncertain thermal unit requirements and leaf tip appearance rates (P1 and PHINT cultivar parameters, respectively). Findings underscore the need to assimilate additional state variables to ensure internal biophysical coherence. This approach shows promise for applications spanning wider domains with prediction time pressure where detailed configuration, more complex assimilation methods, or recalibration of crop model parameters may not be practical.

Phenology↗
NASA NTRS1992

Spectral vegetation indexes and the remote sensing of biophysical parameters

Combinations of remotely sensed data from different spectral bands have been combined into spectral vegetation indexes (SVIs) and used to determine biophysical parameters. The characteristics of two-band SVIs made up of visible and near-infrared reflectances are examined. Two canopy reflectance models, a turbid media model and a geometrical model, are used to study the effects of different canopy structures on the measurement of leaf area index and the fraction of photosynthetically intercepted active radiation.

Huemmrich, Karl F.↗