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Temporal and spatial transcriptomic and micro RNA dynamics of CAM photosynthesis in pineapple
Not provided.
Image Processing And Control Of A Programmable Spatial Light Modulator For Spatial Beam Shaping
Abstract not provided
On Space Charge and Spatial Distribution of Defects in Yttria-Stabilized Zirconia
Space charge in 8YSZ is examined theoretically. The Poisson-Boltzmann equation is solved by free energy minimization. The Helmholtz free energy of a crystal containing various defects is minimized subject to two constraints: The total dopant concentration is fixed and the maximum amount of surface charge is limited by site density. Large dopant concentration leads to defect concentrations which deviate from Boltzmann distribution. When free energy isminimized assuming all defects attain their equilibrium distributions, the corresponding electrochemical potentials are equilibrated through the crystal. These calculations correspond to long annealing times. Calculations are also conducted for a case in which only oxygen vacancies are allowed to equilibrate at lower temperature after a high temperature anneal. At lower temperatures, cations are frozen corresponding to high temperature anneal. In this case, only the electrochemical potential of oxygen vacancies equilibrates. In all cases Y segregates to grain boundaries/free surfaces lowering the corresponding oxygen vacancy concentration, consistent with reported effect of space charge on grain boundary conductivity. The results show a significant concentration of oxygen vacancies form defect complexes with Y dopant. Thus, free oxygen vacancy concentration is considerably lower than the usually assumed electroneutrality condition based on total dopant concentration.
Leveling Transient and Spatial Gradients in Planar Solid Oxide Cells Using Spatially Varied Microstructures: A Numerical Study
17th International Symposium on Solid Oxide Fuel Cells (SOFC-XVII), Virtual, July 18-23, 2021
Where are we going and where have we been? Examining the effects of maps on spatial learning in an indoor guided navigation task.
Abstract not provided.
Data for The Stem Cell-Type Transcriptome of Bioenergy Sorghum Reveals the Spatial Regulation of Secondary Cell Wall Networks
Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum’s 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about stem cell-type gene expression and regulation was available to enable engineering. To obtain this information, Laser Capture Microdissection (LCM) was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type specific gene regulatory networks (GRNs) revealed that unique TF families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with a stem developmental transcriptome dataset to identify the GRN that differentially activates the secondary cell wall (SCW) formation in stem xylem sclerenchyma and epidermal cells. The cell-type transcriptomic dataset provides a valuable source of information about the function of sorghum stem cell types and GRNs that will enable the engineering of bioenergy sorghum stems.
Software-Defined Silicon-Photonics-Based Metro Node for Spatial and Wavelength Superchannel Switching
Not Available
Prediction of Spatial Distributions of Equilibrium Product Species from High Explosive Blasts in Air.
Abstract not provided.
Scanning Lidar Spatial Calibration and Alignment Method for Wind Turbine Wake Characterization.
Abstract not provided.
Spatially-Resolved Surface Temperature Measurements of a Rocket Motor Nozzle using an Acousto-optic Modulator.
Abstract not provided.
Patterns and Sources of Spatial Heterogeneity in Soil Matrix From Contrasting Long Term Management Practices
Abstract not provided
Improving the Quality and Spatial Resolution of Aerially-Collected Data Using Spatially-Variant Deconvolution.
Abstract not provided.
Improving the Quality and Spatial Resolution of Aerially-Collected Radiation Data using Spatially-Variant Deconvolution.
Abstract not provided.
Spatial biomarker of disease and detection of spatial organization of cellular receptors
A signature of a condition of a live cell is established in an assay that allows distribution of the receptors on the cell surface in response to binding a ligand. The receptors can be optically detected and quantified to provide a value for the condition, Test drugs can be screened for therapeutic potential in the assay: a potentially efficacious drug is identified by an ability to modulate an established signature. The receptor distribution signature can be corroborated with an mRNA expression profile of several genes, indicating, for example, metastasis.
Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks
The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing the heterogeneity of terrestrial hydrological and biogeochemical processes in Earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a data set with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales ( s = 100, 200, and 500 m and 1, 2, 5, and 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions ( R 2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 m to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98 % of variability in the variance of SOC stocks. We found moderately accurate linear relationships between mean and higher-order moments of predicted SOC stocks ( R 2 ∼ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks could improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.
Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks
The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing heterogeneity of terrestrial hydrological and biogeochemical processes in earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a dataset with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales ( s = 100, 200, 500 m, 1, 2, 5, 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions ( R 2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98% of variability in the variance of SOC stocks. We found moderately-accurate linear relationships between mean and higher-order moments of predicted SOC stocks ( R 2 ~ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks can improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.