1,326 research outputs found
A New Model of Electron Pitch Angle Distributions and Loss Timescales in the Earth's Radiation Belts
As the number of satellites on orbit grows it is increasingly important to understand their operating environment. Physics-based models can simulate the behavior of the Earth's radiation belts by solving a Fokker-Planck equation. Three-dimensional models use diffusion coefficients to represent the interactions between electromagnetic waves and the electrons. One-dimensional radial diffusion models neglect the effects of energy diffusion and represent the losses due to the waves with a loss timescale. Both approaches may use pitch angle distributions (PADs) to create boundary conditions, to map observations from low to high equatorial pitch angles and to calculate phase-space density from observations. We present a comprehensive set of consistent PADs and loss timescales for 2 ≤ L* ≤ 7, 100 keV ≤ E ≤ 5 MeV and all levels of geomagnetic activity determined by the Kp index. These are calculated from drift-averaged diffusion coefficients that represent all the VLF waves that typically interact with radiation belt electrons and show good agreement with data. The contribution of individual waves is demonstrated; magnetosonic waves have little effect on loss timescales when lightning-generated whistlers are present, and chorus waves contribute to loss even in low levels of geomagnetic activity. The PADs vary in shape depending on the dominant waves. When chorus is dominant the distributions have little activity dependence, unlike the corresponding loss timescales. Distributions peaked near 90° are formed by plasmaspheric hiss for L* ≤ 3 and E 3 and E > 1 MeV. When hiss dominates, increasing activity broadens the distribution but when EMIC waves dominate increasing activity narrows the distribution
Threshold criterion for wetting at the triple point
Grand canonical simulations are used to calculate adsorption isotherms of
various classical gases on alkali metal and Mg surfaces. Ab initio adsorption
potentials and Lennard-Jones gas-gas interactions are used. Depending on the
system, the resulting behavior can be nonwetting for all temperatures studied,
complete wetting, or (in the intermediate case) exhibit a wetting transition.
An unusual variety of wetting transitions at the triple point is found in the
case of a specific adsorption potential of intermediate strength. The general
threshold for wetting near the triple point is found to be close to that
predicted with a heuristic model of Cheng et al. This same conclusion was drawn
in a recent experimental and simulation study of Ar on CO_2 by Mistura et al.
These results imply that a dimensionless wetting parameter w is useful for
predicting whether wetting behavior is present at and above the triple
temperature. The nonwetting/wetting crossover value found here is w circa 3.3.Comment: 15 pages, 8 figure
Consistent Anisotropic Repulsions for Simple Molecules
We extract atom-atom potentials from the effective spherical potentials that
suc cessfully model Hugoniot experiments on molecular fluids, e.g., and
. In the case of the resulting potentials compare very well with the
atom-atom potentials used in studies of solid-state propertie s, while for
they are considerably softer at short distances. Ground state (T=0K) and
room temperatu re calculations performed with the new potential resolve
the previous discrepancy between experimental and theoretical results.Comment: RevTeX, 5 figure
Random forest for gene selection and microarray data classification
A random forest method has been selected to perform both gene selection and classification of the microarray data. In this
embedded method, the selection of smallest possible sets of genes with lowest error rates is the key factor in achieving highest
classification accuracy. Hence, improved gene selection method using random forest has been proposed to obtain the smallest
subset of genes as well as biggest subset of genes prior to classification. The option for biggest subset selection is done to assist
researchers who intend to use the informative genes for further research. Enhanced random forest gene selection has performed
better in terms of selecting the smallest subset as well as biggest subset of informative genes with lowest out of bag error rates
through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to
lower prediction error rates compared to existing method and other similar available methods
Glucocorticoids regulate AKR1D1 activity in human liver in vitro and in vivo
Steroid 5β-reductase (AKR1D1) is highly expressed in human liver where it inactivates endogenous glucocorticoids and catalyses an important step in bile acid synthesis. Endogenous and synthetic glucocorticoids are potent regulators of metabolic phenotype and play a crucial role in hepatic glucose metabolism. However, the potential of synthetic glucocorticoids to be metabolised by AKR1D1 as well as to regulate its expression and activity has not been investigated. The impact of glucocorticoids on AKR1D1 activity was assessed in human liver HepG2 and Huh7 cells; AKR1D1 expression was assessed by qPCR and Western blotting. Genetic manipulation of AKR1D1 expression was conducted in HepG2 and Huh7 cells and metabolic assessments were made using qPCR. Urinary steroid metabolite profiling in healthy volunteers was performed pre- and post-dexamethasone treatment, using gas chromatography-mass spectrometry. AKR1D1 metabolised endogenous cortisol, but cleared prednisolone and dexamethasone less efficiently. In vitro and in vivo, dexamethasone decreased AKR1D1 expression and activity, further limiting glucocorticoid clearance and augmenting action. Dexamethasone enhanced gluconeogenic and glycogen synthesis gene expression in liver cell models and these changes were mirrored by genetic knockdown of AKR1D1 expression. The effects of AKR1D1 knockdown were mediated through multiple nuclear hormone receptors, including the glucocorticoid, pregnane X and farnesoid X receptors. Glucocorticoids down-regulate AKR1D1 expression and activity and thereby reduce glucocorticoid clearance. In addition, AKR1D1 down-regulation alters the activation of multiple nuclear hormone receptors to drive changes in gluconeogenic and glycogen synthesis gene expression profiles, which may exacerbate the adverse impact of exogenous glucocorticoids
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