Neural mechanism of the pressor response to obstructive and nonobstructive apnea

Obstructive and nonobstructive apneas elicit substantial increases in muscle sympathetic nerve activity and arterial pressure. The time course of change in these variables suggests a causal relationship; however, mechanical influences, such as release of negative intrathoracic pressure and reinflation of the lungs, are potential contributors to the arterial pressure rise. To test the hypothesis that apnea-induced pressor responses are neurally mediated, we measured arterial pressure (photoelectric plethysmography), muscle sympathetic nerve activity (peroneal microneurography), arterial O2 saturation (pulse oximeter), and end-tidal CO2 tension (gas analyzer) during sustained Mueller maneuvers, intermittent Mueller maneuvers, and simple breath holds in six healthy humans before, during, and after ganglionic blockade with trimethaphan (3-4 mg/min, titrated to produce complete disappearance of sympathetic bursts from the neurogram). Ganglionic blockade abolished the pressor responses to sustained and intermittent Mueller maneuvers (-4 +/- 1 vs. +15 +/- 3 and 0 +/- 2 vs. +15 +/- 5 mmHg) and breath holds (0 +/- 3 vs. +11 +/- 3, all P < 0.05). We conclude that the acute pressor response to obstructive and nonobstructive voluntary apnea is sympathetically mediated.     

Emissions of volatile aldehydes from heated cooking oils

Emissions of volatile organic compounds, including aldehydes, formed during heating of cooking oils: coconut, safflower, canola, and extra virgin olive oils were studied at different temperatures: 180, 210, 240, and 240 °C after 6 h. Fumes were collected in Tedlar^® bags and later analysed by GC–MS. The emissions of volatiles were constant with time and increased with the oil temperature. When the temperature of the oil was above its smoke point, the emission of volatiles drastically increased, implying that oils with low smoke point, such as coconut, are not useful for deep-frying operations. Canola was the oil generating the lowest amount of potentially toxic volatile chemicals. Acrolein formation was found even at low temperatures, indicating that home cooking has to be considered as an indoor pollution problem.     

Micromachined thermal shear-stress sensor for underwater applications

This paper reports the development of micromachined thermal shear-stress sensors for underwater applications. The thermal shear-stress sensor is a polysilicon resistor sitting atop a vacuum-insulated nitride diaphragm. Special challenges for underwater measurements, such as the waterproof coating and minimization of pressure crosstalk, have been addressed. More rigid diaphragms than the aerial sensors are implemented to increase the operating range and reduce pressure crosstalk, with the cost of larger power consumption and lower sensitivity. Sensors with different diaphragm dimensions and resistor lengths have been fabricated and tested. Nearly zero pressure sensitivity has been achieved by either reducing the diaphragm width or adjusting the sensing element length. The effects of overheat ratio and operating mode on the sensor's pressure crosstalk have been discussed. Parylene C is chosen as the waterproof material for the underwater shear-stress sensors. The primary failure mode is identified as the corrosion of the soldering pads.     

A posteriori error estimation and adaptive mesh refinement for combined thermal-stress finite element analysis

Local and global error estimators and an associated h-based adaptive mesh refinement schemes are proposed for coupled thermal-stress problems. The error estimators are based on the “flux smoothing” technique of Zienkiewicz and Zhu with important modifications to improve convergence performance and computational efficiency. Adaptive mesh refinement is based on the concept of adaptive accuracy criteria, previously presented by the authors for stress-based problems and extended here for coupled thermal-stress problems. Three methods of mesh refinement are presented and numerical results indicate that the proposed method is the most efficient in terms of number of adaptive mesh refinements required for convergence in both the thermal and stress solutions. Also, the proposed method required a smaller number of active degrees of freedom to obtain an accurate solution.     

Comparative Pharmacognostic Evaluation and HPTLC Analysis of Petals of Spiny and Non-Spiny Safflower Cultivars

To establish the identity and quality of safflower petals used as herbal tea, four spiny and non-spiny cultivars (APRR3, TSF-1, NARI-NH-01, and NARI-06) were analyzed for various pharmacognostic characters, toxic metals (As, Pb, Hg, Cr, Cd by atomic absorption spectroscopy), pesticide residue (GC-ECD/PFPD), and flavonoid constituents, like quercetin, quercetin-3-O-rutinoside, and kaempferol, by high performance thin layer chromatography. Arsenic and lead were found to be absent in the decoction of all varieties. Although mercury and chromium were detected in standard acceptable levels, cadmium was exceeding the limit except in APRR3 (0.3 mg/kg). While none of the samples contained pesticide residue and saponins, presence of tannins (19–25%), bitterness principle (1257–2200 units/g), mucilaginous substances (0.93–2.83 mL/g), and volatile matter (4–16%) were observed and estimated in all varieties. Microscopic examination of NARI-06 and APRR3 petals explored similar features having slightly thick cylindrical style with dense spikes and corolla tube with five slightly thick ridges possessing ribbon like outgrowths. High performance thin layer chromatography analysis revealed APRR3 to possess higher amount of quercetin (116.6 ng/g), quercetin-3-O-rutinoside (189.7 ng/g), and kaempferol (185.07 ng/g). Thus, the spiny APRR3 safflower petals encompassing anti-oxidative flavonoids and complying heavy metals test, was identified as a safe variety for human consumption.     

Comparison of 2D and 3D density-weighted displacement speed statistics and implications for laser based measurements of flame displacement speed using direct numerical simulation data

In a recent study, a light sheet imaging approach has been proposed (Hartung et al., J. Appl. Phys. B 96 (2009) 843–862) which permits measurement of a quantity , which is the two-dimensional projection of the actual density-weighted displacement speed for turbulent premixed flames. Here the statistics of and are compared using a direct numerical simulation database of statistically planar turbulent premixed flames. It is found that the probability density functions (pdfs) of approximate the pdfs of satisfactorily for small values of root-mean-square turbulent velocity fluctuation u′, though the pdfs are wider than the pdfs. Although the agreement between the pdfs and the standard-deviations of and deteriorate with increasing u′, the mean values of correspond closely with the mean values of for all cases considered here. The pdfs of two-dimensional curvature and the two-dimensional tangential-diffusion component of density-weighted displacement speed are found to be narrower than their three-dimensional counterparts (i.e. κ_m and respectively). It has been found that the pdfs, mean and standard-deviation of and faithfully capture the pdfs, mean and standard-deviation of the corresponding three-dimensional counterparts, κ_m and respectively. The combination of wider pdfs in comparison to pdfs, and narrower pdfs in comparison to pdfs, leads to wider pdfs than the pdfs of combined reaction and normal-diffusion components of density-weighted displacement speed . This is reflected in the higher value of standard-deviation of , than that of its three-dimensional counterpart . However, the mean values of remain close to the mean values of . The loss of perfect correlation between two and three-dimensional quantities leads to important qualitative differences between the and , and between the and correlations. For unity Lewis number flames, the correlation remains strongly negative, whereas a weak correlation is observed between and . The study demonstrates the strengths and limitations of the predictive capabilities of the planar imaging techniques in the context of the measurement of density-weighted displacement speed, which are important for detailed model development or validation based on experimental data.     

Estimating modes of a complex dynamical network from impulse response data: Structural and graph‐theoretic characterizations

We examine the role played by a linear dynamical network's topology in inference of its eigenvalues from noisy impulse‐response data. Specifically, for a canonical linear‐time‐invariant network dynamics, we relate the Cramer–Rao bounds on eigenvalue estimator performance (from impulse‐response data) to structural properties of the transfer function and in turn, to the network's topological structure. We begin by reviewing and enhancing algebraic characterizations of such eigenvalue estimates, which are based on pole‐residue and pole‐zero representations of the network's dynamics. We use these results to characterize mode estimation in networks with slow‐coherency structures, finding that stimulus and observation in each strongly connected network subgraph is needed for high‐fidelity estimation. We also obtain spectral and graphical characterizations of estimator performance for other graph classes (e.g., trees) and for the general case. These characterizations are used to determine the role of measurement and actuation locations in estimation performance. Finally, application of our results in dynamical‐network security is illustrated through a simple example, and a concrete procedure for network mode estimation that draws on our structural results is introduced to conclude the article. Copyright © 2014 John Wiley & Sons, Ltd.     

River Stage Forecasting using Enhanced Partial Correlation Graph

Various time series forecasting methods have been successfully applied for the water-stage forecasting problem. Graphical time series models are a class of multivariate time series to model the spatio-temporal dependencies between the sensors. Constructing graph-based models involve data pre-processing and correlation analysis to capture the dynamics of different water flow scenarios, which is not scalable for a large network of sensors. This paper presents a novel approach to model spatio-temporal dependencies across river network stations using a partial correlation graph. We also provide a method to enrich this partial correlation graph by eliminating the spurious correlations. We demonstrate the utility of enriched partial correlation graphs in multivariate forecasting for various scenarios and state-of-the-art multivariate forecasting models. We observe that the forecasting techniques that use information from the enriched partial correlation graph outperform standard time series forecasting approaches for river network forecasting.