Microwave Devices Employing Magnetic Waves

Contains reports on three research projects.Joint Services Electronics Program (Contract DAAB07-76-C-1400)National Science Foundation (Grant ENG76-18359

Integrated active and passive control design methodology for the LaRC CSI evolutionary model

A general design methodology to integrate active control with passive damping was demonstrated on the NASA LaRC CSI Evolutionary Model (CEM), a ground testbed for future large, flexible spacecraft. Vibration suppression controllers designed for Line-of Sight (LOS) minimization were successfully implemented on the CEM. A frequency-shaped H2 methodology was developed, allowing the designer to specify the roll-off of the MIMO compensator. A closed loop bandwidth of 4 Hz, including the six rigid body modes and the first three dominant elastic modes of the CEM was achieved. Good agreement was demonstrated between experimental data and analytical predictions for the closed loop frequency response and random tests. Using the Modal Strain Energy (MSE) method, a passive damping treatment consisting of 60 viscoelastically damped struts was designed, fabricated and implemented on the CEM. Damping levels for the targeted modes were more than an order of magnitude larger than for the undamped structure. Using measured loss and stiffness data for the individual damped struts, analytical predictions of the damping levels were very close to the experimental values in the (1-10) Hz frequency range where the open loop model matched the experimental data. An integrated active/passive controller was successfully implemented on the CEM and was evaluated against an active-only controller. A two-fold increase in the effective control bandwidth and further reductions of 30 percent to 50 percent in the LOS RMS outputs were achieved compared to an active-only controller. Superior performance was also obtained compared to a High-Authority/Low-Authority (HAC/LAC) controller

Dynamics of light propagation in spatiotemporal dielectric structures

Propagation, transmission and reflection properties of linearly polarized plane waves and arbitrarily short electromagnetic pulses in one-dimensional dispersionless dielectric media possessing an arbitrary space-time dependence of the refractive index are studied by using a two-component, highly symmetric version of Maxwell's equations. The use of any slow varying amplitude approximation is avoided. Transfer matrices of sharp nonstationary interfaces are calculated explicitly, together with the amplitudes of all secondary waves produced in the scattering. Time-varying multilayer structures and spatiotemporal lenses in various configurations are investigated analytically and numerically in a unified approach. Several new effects are reported, such as pulse compression, broadening and spectral manipulation of pulses by a spatiotemporal lens, and the closure of the forbidden frequency gaps with the subsequent opening of wavenumber bandgaps in a generalized Bragg reflector

Microwave Devices Employing Magnetic Waves

Contains reports on six research projects.Joint Services Electronics Program (Contract DAAG29-78-C-0020)National Science Foundation (Grant ENG76-18359

Steady-state brain glucose transport kinetics re-evaluated with a four-state conformational model

Glucose supply from blood to brain occurs through facilitative transporter proteins. A near linear relation between brain and plasma glucose has been experimentally determined and described by a reversible model of enzyme kinetics. A conformational four-state exchange model accounting for trans-acceleration and asymmetry of the carrier was included in a recently developed multi-compartmental model of glucose transport. Based on this model, we demonstrate that brain glucose (Gbrain) as function of plasma glucose (Gplasma) can be described by a single analytical equation namely comprising three kinetic compartments: blood, endothelial cells and brain. Transport was described by four parameters: apparent half saturation constant Kt, apparent maximum rate constant Tmax, glucose consumption rate CMRglc, and the iso-inhibition constant Kii that suggests Gbrain as inhibitor of the isomerisation of the unloaded carrier. Previous published data, where Gbrain was quantified as a function of plasma glucose by either biochemical methods or NMR spectroscopy, were used to determine the aforementioned kinetic parameters. Glucose transport was characterized by Kt ranging from 1.5 to 3.5 mM, Tmax/CMRglc from 4.6 to 5.6, and Kii from 51 to 149 mM. It was noteworthy that Kt was on the order of a few mM, as previously determined from the reversible model. The conformational four-state exchange model of glucose transport into the brain includes both efflux and transport inhibition by Gbrain, predicting that Gbrain eventually approaches a maximum concentration. However, since Kii largely exceeds Gplasma, iso-inhibition is unlikely to be of substantial importance for plasma glucose below 25 mM. As a consequence, the reversible model can account for most experimental observations under euglycaemia and moderate cases of hypo- and hyperglycaemia

The role of copeptin as a diagnostic and prognostic biomarker for risk stratification in the emergency department

The hypothalamic-pituitary-adrenal axis is activated in response to stress. One of the activated hypothalamic hormones is arginine vasopressin, a hormone involved in hemodynamics and osmoregulation. Copeptin, the C-terminal part of the arginine vasopressin precursor peptide, is a sensitive and stable surrogate marker for arginine vasopressin release. Measurement of copeptin levels has been shown to be useful in a variety of clinical scenarios, particularly as a prognostic marker in patients with acute diseases such as lower respiratory tract infection, heart disease and stroke. The measurement of copeptin levels may provide crucial information for risk stratification in a variety of clinical situations. As such, the emergency department appears to be the ideal setting for its potential use. This review summarizes the recent progress towards determining the prognostic and diagnostic value of copeptin in the emergency department

Methods for adjusting population structure and familial relatedness in association test for collective effect of multiple rare variants on quantitative traits

Because of the low frequency of rare genetic variants in observed data, the statistical power of detecting their associations with target traits is usually low. The collapsing test of collective effect of multiple rare variants is an important and useful strategy to increase the power; in addition, family data may be enriched with causal rare variants and therefore provide extra power. However, when family data are used, both population structure and familial relatedness need to be adjusted for the possible inflation of false positives. Using a unified mixed linear model and family data, we compared six methods to detect the association between multiple rare variants and quantitative traits. Through the analysis of 200 replications of the quantitative trait Q2 from the Genetic Analysis Workshop 17 data set simulated for 697 subjects from 8 extended families, and based on quantile-quantile plots under the null and receiver operating characteristic curves, we compared the false-positive rate and power of these methods. We observed that adjusting for pedigree-based kinship gives the best control for false-positive rate, whereas adjusting for marker-based identity by state slightly outperforms in terms of power. An adjustment based on a principal components analysis slightly improves the false-positive rate and power. Taking into account type-1 error, power, and computational efficiency, we find that adjusting for pedigree-based kinship seems to be a good choice for the collective test of association between multiple rare variants and quantitative traits using family data

Rare variant collapsing in conjunction with mean log p-value and gradient boosting approaches applied to Genetic Analysis Workshop 17 data

In addition to methods that can identify common variants associated with susceptibility to common diseases, there has been increasing interest in approaches that can identify rare genetic variants. We use the simulated data provided to the participants of Genetic Analysis Workshop 17 (GAW17) to identify both rare and common single-nucleotide polymorphisms and pathways associated with disease status. We apply a rare variant collapsing approach and the usual association tests for common variants to identify candidates for further analysis using pathway-based and tree-based ensemble approaches. We use the mean log p-value approach to identify a top set of pathways and compare it to those used in simulation of GAW17 dataset. We conclude that the mean log p-value approach is able to identify those pathways in the top list and also related pathways. We also use the stochastic gradient boosting approach for the selected subset of single-nucleotide polymorphisms. When compared the result of this tree-based method with the list of single-nucleotide polymorphisms used in dataset simulation, in addition to correct SNPs we observe number of false positives