Bicoherence analysis of new cardiovascular spectral components observed in heart-transplant patients: statistical approach for bicoherence thresholding

Following heart transplant (HT), the patient's heart functions under complete cardiac denervation. As a result, the variability in physiologic signals is extremely reduced. We have previously reported that in addition to the typical spectral components (of very low amplitude), part of the HT patients (above 50%) demonstrated unexpected additional peaks in their heart rate and blood pressure spectra. These peaks may be a result of the development of compensatory mechanism induced by loss of parasympathetic control, or of increased importance of nonlinear control interactions. It is important to quantify these strange, unexpected very-high frequency (VHF) peaks, to understand their origin and their contribution to cardiac control in transplant patients. In this paper, we chose to examine these VHF peaks by applying the bicoherence approach. The reduced signal to noise ratio, occurring in these patients, results, however, in an extremely noisy bicoherence. We, therefore, developed several statistical tools in order to distinguish between "true" bicoherence peaks (reflecting true phase coupling) and spurious peaks. The outcome of these methods was an efficient and sensitive bicoherence thresholding procedure, able to identify most of the spurious peaks. Applying these tools to the bicoherence of cardiovascular signals which display VHF peaks, revealed several significant bicoherence peaks. Interestingly, these peaks consisted of two different types. The first type of VHF peaks simply reflects nonlinear cardiac-respiratory coupling, imposed by nonsinusoidal breathing. The second type, however, is clearly not induced by the respiratory system. We believe that these type-2 VHF peaks reflect the evolution of a new, yet unexplained, compensatory mechanism.     

Electrospun Ca3Co4−xO9+δ nanofibers and nanoribbons: Microstructure and thermoelectric properties

Oxide-based ceramics offer promising thermoelectric (TE) materials for recycling high-temperature waste heat, generated extensively from industrial sources. To further improve the functional performance of TE materials, their power factor should be increased. This can be achieved by nanostructuring and texturing the oxide-based ceramics creating multiple interphases and nanopores, which simultaneously increase the electrical conductivity and the Seebeck coefficient. The aim of this work is to achieve this goal by compacting electrospun nanofibers of calcium cobaltite Ca₃Co_4−xO_9+δ, known to be a promising p-type TE material with good functional properties and thermal stability up to 1200 K in air. For this purpose, polycrystalline Ca₃Co_4−xO_9+δ nanofibers and nanoribbons were fabricated by sol–gel electrospinning and calcination at intermediate temperatures to obtain small primary particle sizes. Bulk ceramics were formed by sintering pressed compacts of calcined nanofibers during TE measurements. The bulk nanofiber sample pre-calcined at 973 K exhibited an improved Seebeck coefficient of 176.5 S cm⁻¹ and a power factor of 2.47 μW cm⁻¹ K⁻² similar to an electrospun nanofiber-derived ceramic compacted by spark plasma sintering.     

Mitigation of symmetry condition in positive realness for adaptive control

Feasibility of nonlinear and adaptive control methodologies in multivariable linear time-invariant systems with state-space realization {A,B,C} is apparently limited by the standard strictly positive realness conditions that imply that the product CB must be positive definite symmetric. This paper expands the applicability of the strictly positive realness conditions used for the proofs of stability of adaptive control or control with uncertainty by showing that the not necessarily symmetric CB is only required to have a diagonal Jordan form and positive eigenvalues. The paper also shows that under the new condition any minimum-phase systems can be made strictly positive real via constant output feedback. The paper illustrates the usefulness of these extended properties with an adaptive control example.     

Surface-sediment dynamics in a dust source from spaceborne multispectral thermal infrared data

This study utilized spaceborne multispectral thermal infrared (TIR) data to document spatial relationships of surface sediments over time in a modern depositional environment associated with dust emissions, Soda Lake playa, Mojave Desert, United States. The approach employed here involved time-series TIR data acquired from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and a linear spectral mixture analysis. An automated algorithm was applied to derive emissivity image endmembers. Evaluation of the chosen endmembers revealed that they can be categorized into five major spectra classes based on diagnostic absorption features. Each spectrum has been identified in relation to mineral abundance and soil arrangement that are common in playa settings: A, “clayey silt-rich crust”; B, “intermediate-salt crust”; C, “quartz-rich deposit”; D, “salt-rich rough crust”; E, “sulfate-rich crust”. Spectral classes A-B-C-D yielded the lowest RMS errors (0-0.025) over time in the iterative deconvolution algorithm between the measured and modeled spectra. The produced fractional abundance images show high areal concentrations for clayey silt-rich crust, salt-rich rough crust, and quartz-rich deposit, as the first surficial mapping of Soda Lake. Significant changes in the spatial relationships of the major surface sediments in Soda Lake were observed after a flooding event (2005) and a relatively dry period (2006). The approach utilized in this study can be advantageous for continuous monitoring of environments characterized by a small area and a complex surface, which may enable a better understanding of their responses to climate changes and potential for dust emissions.     

Experimental Analysis of the Wear,Life and Behavior of PTFE Coated Thrust Washer Bearings Under Non-Axisymmetric Loading

In this work the behavior and life of a PTFE coating on a flat thrust washer bearing is investigated. The thrust washer bearing is located between a helical gear and its carrier, and is subjected to non-axisymmetric loading and wear. The volume of worn material is approximated by measuring the difference in height between the worn and unworn surfaces. It was also found that the surface roughness of tested washers increases with the severity of wear, in most cases. After a finite number of cycles the effective coefficient of friction between the surfaces increases, suggesting that the coating is wearing off and losing effectiveness. The rate at which the coating wears off also varies with load and speed, hence, there is a region of operation that minimizes the wear and friction.     

Modelling of nitric acid gas adsorption by atmospheric dust particles

We suggest a two-dimensional model of adsorption of trace atmospheric gases by mineral dust particles with origin from desert soils. The model is based on the application of theory of turbulent diffusion in the atmospheric boundary layer (ABL) in conjunction with the model of gas adsorption by porous solid particles. The numerical model is formulated using parameterizations based on eolian (by wind) dust emission experiments. The eolian field experiments were performed at a dust source (loess soil in Northern Negev, Israel) using a portable boundary layer wind tunnel to determine the emitted particulate matter (PM) fluxes for different wind speeds and varying soil conditions. The numerical analysis is performed for the adsorption of gas-phase HNO₃ by dust PM. We determined numerically concentration distributions of the atmospheric dust PM, and trace gas using shear velocity and emitted dust flux from the soils employed in the experiments. Analysis was performed for the case of neutral and slightly stable stratification of ABL typical for dust storm events in the East Mediterranean. The numerical analysis showed that during dust events the slightly stable atmosphere is characterized by higher concentration of PM₁₀ in the surface layer than the neutrally stable atmosphere. It is found that in the case of neutral stratification as well as for stable atmospheric stratification the concentration of nitric acid HNO₃ strongly depends on concentration of atmospheric dust particles. The developed model enhances our capacity of quantification of atmospheric dust effects in climate models as well as health risk assessment.

Copyright © 2019 American Association for Aerosol Research     

1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) decreases glutamate uptake in cultured astrocytes

The deleterious effect of the parkinsonian neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) on dopaminergic neurons of the substantia nigra is well established. In addition, increased glutamatergic drive to basal ganglia output nuclei is considered a likely contributor to the pathogenesis of Parkinson's disease. One possibility for the increased excitatory tone may be related to an impairment in glutamate uptake. As astrocytes possess efficient transport mechanisms for both MPTP and glutamate, we have examined the effect of this agent on D-aspartate uptake into these cells. Treatment of cultures with 50 microM MPTP for 24 h decreased uptake by 39%. Kinetic analysis revealed that this effect was due to a 35% decrease in Vmax with no change in the Km. Treatment with deprenyl, a monoamine oxidase B inhibitor, produced a complete reversal of MPTP-induced uptake inhibition, but was ineffective following exposure of cells to the MPTP metabolite, 1-methyl-4-phenylpyridinium (MPP+). Removal of MPTP from cultures resulted in a complete restoration of glutamate uptake after 24 h. These results show that MPTP reversibly compromises glutamate uptake in cultured astrocytes, which is dependent on the conversion of MPTP to MPP+. Such findings suggest that the glutamate transporter in astrocytes plays an important role in MPTP-induced neurotoxicity and possibly in parkinsonism.     

On Learning Gene Regulatory Networks Under the Boolean Network Model

Boolean networks are a popular model class for capturing the interactions of genes and global dynamical behavior of genetic regulatory networks. Recently, a significant amount of attention has been focused on the inference or identification of the model structure from gene expression data. We consider the Consistency as well as Best-Fit Extension problems in the context of inferring the networks from data. The latter approach is especially useful in situations when gene expression measurements are noisy and may lead to inconsistent observations. We propose simple efficient algorithms that can be used to answer the Consistency Problem and find one or all consistent Boolean networks relative to the given examples. The same method is extended to learning gene regulatory networks under the Best-Fit Extension paradigm. We also introduce a simple and fast way of finding all Boolean networks having limited error size in the Best-Fit Extension Problem setting. We apply the inference methods to a real gene expression data set and present the results for a selected set of genes.