Physical exercise improves cardiac autonomic modulation in hypertensive patients independently of angiotensin-converting enzyme inhibitor treatment

We investigated the influence of angiotensin-converting enzyme inhibitor (ACEi) treatment and physical exercise on arterial pressure (AP) and heart rate variability (HRV) in volunteer patients with hypertension. A total of 54 sedentary volunteers were divided into three groups: normotensive (NT Group), hypertensive (HT Group) and HT volunteers treated with ACEi (ACEi Group). All volunteers underwent an aerobic physical-training protocol for 15 weeks. HRV was investigated using a spectral analysis of a time series of R-R interval (RRi) that was obtained in a supine position and during a tilt test. Physical training promoted a significant reduction in the mean arterial pressure of the HT group (113 +/- 3 vs. 106 +/- 1 mm Hg) and the ACEi group (104 +/- 2 vs. 98 +/- 2 mm Hg). Spectral analysis of RRi in the supine position before physical training demonstrated that the NT and ACEi groups had similar values at low frequency (LF; 0.04-0.15 Hz) and high frequency (HF; 0.15-0.5 Hz) oscillations. The HT group had an increase in LF oscillations in absolute and normalized units and a decrease in HF oscillations in normalized units compared with the other groups. The HT group had the lowest responses to the tilt test during LF oscillations in normalized units. Physical training improved the autonomic modulation of the heart rate in the supine position only in the HT group. Physical training promoted a similar increase in autonomic modulation responses in the tilt test in all groups. Our findings show that aerobic physical training improves cardiac autonomic modulation in HT volunteers independently of ACEi treatment. Hypertension Research (2012) 35, 82-87; doi:10.1038/hr.2011.162; published online 29 September 2011

Millennial-scale climate cycles in Permian-Carboniferous rhythmites: Permanent feature throughout geologic time?

Two late Paleozoic glacial rhythmite successions from the Itarare Group (Parana Basin, Brazil) were examined for paleoclimate variations. Paleomagnetic (characteristic remanent magnetization, ChRM) and magnetic susceptibility (K(z)) measurements taken from the rhythmites are interpreted as paleoclimatic proxies. Ratios of low-frequency components in the K(z) variations suggest Milankovitch periodicities; this leads to recognition of other, millennial-scale variations reminiscent of abrupt climate changes during late Quaternary time, and are suggestive of Bond cycles and the 2.4 k.y. solar cycle. We infer from these patterns that millennial-scale climate change is not restricted to the Quaternary Period, and that millennial forcing mechanisms may have been prevalent throughout geologic time.

Uric acid biorhythm, a feature of long-term variation in a clinical laboratory database

Background: The biorhythm of serum uric acid was evaluated in a large sample of a clinical laboratory database by spectral analysis and the influence of the gender and age on uric acid variability. Methods: Serum uric acid values were extracted from a large database of a clinical laboratory from May 2000 to August 2006. Outlier values were excluded from the analysis and the remaining data (n = 73,925) were grouped by gender and age ranges. Rhythm components were obtained by the Lomb Scargle method and Cosinor analysis. Results: Serum uric acid was higher in men than in women older than 13 years (p<0.05). Compared with 0-12 year group, uric acid increased in men but not in women older than 13 years (p<0.05). Circannual (12 months) and transyear (17 months) rhythm components were detected, but they were significant only in adult individuals (>26 years, p<0.05). Cosinor analysis showed that midline estimating statistic of rhythm (MESOR) values were higher in men (range: 353-368 mu mol/L) than in women (range: 240-278 mu mol/L; p<0.05), independent of the age and rhythm component. The extent of predictable change within a cycle, approximated by the double amplitude, represented up to 20% of the corresponding MESOR. Conclusions: Serum uric acid biorhythm is dependent on gender and age and it may have relevant influence on preanalytical variability of clinical laboratory results.

New Improved Version of J 1 ...J 4 Interferometry Method and its Application to Nanometric Vibration Measurements

Piezoelectric ceramics, such as PZT, can generate subnanometric displacements, bu t in order to generate multi- micrometric displacements, they should be either driven by high electric voltages (hundreds of volts ), or operate at a mechanical resonant frequency (in narrow band), or have large dimensions (tens of centimeters). A piezoelectric flextensional actuator (PFA) is a device with small dimensions that can be driven by reduced voltages and can operate in the nano- and micro scales. Interferometric techniques are very adequate for the characterization of these devices, because there is no mechanical contact in the measurement process, and it has high sensitivity, bandwidth and dynamic range. A low cost open-loop homodyne Michelson interferometer is utilized in this work to experimentally detect the nanovi brations of PFAs, based on the spectral analysis of the interfero metric signal. By employing the well known J 1 ...J 4 phase demodulation method, a new and improved version is proposed, which presents the following characteristics: is direct, self-consistent, is immune to fading, and does not present phase ambiguity problems. The proposed method has resolution that is similar to the modified J 1 ...J 4 method (0.18 rad); however, differently from the former, its dynamic range is 20% larger, does not demand Bessel functions algebraic sign correction algorithms and there are no singularities when the static phase shift between the interferometer arms is equal to an integer multiple of  /2 rad. Electronic noise and random phase drifts due to ambient perturbations are taken into account in the analysis of the method. The PFA nanopositioner characterization was based on the analysis of linearity betw een the applied voltage and the resulting displacement, on the displacement frequency response and determination of main resonance frequencies.

MATHEMATICAL ANALYSIS OF MAXIMUM POWER GENERATED BY PHOTOVOLTAIC SYSTEMS AND FITTING CURVES FOR STANDARD TEST CONDITIONS

The rural electrification is characterized by geographical dispersion of the population, low consumption, high investment by consumers and high cost. Moreover, solar radiation constitutes an inexhaustible source of energy and in its conversion into electricity photovoltaic panels are used. In this study, equations were adjusted to field conditions presented by the manufacturer for current and power of small photovoltaic systems. The mathematical analysis was performed on the photovoltaic rural system I- 100 from ISOFOTON, with power 300 Wp, located at the Experimental Farm Lageado of FCA/UNESP. For the development of such equations, the circuitry of photovoltaic cells has been studied to apply iterative numerical methods for the determination of electrical parameters and possible errors in the appropriate equations in the literature to reality. Therefore, a simulation of a photovoltaic panel was proposed through mathematical equations that were adjusted according to the data of local radiation. The results have presented equations that provide real answers to the user and may assist in the design of these systems, once calculated that the maximum power limit ensures a supply of energy generated. This real sizing helps establishing the possible applications of solar energy to the rural producer and informing the real possibilities of generating electricity from the sun.

A novel method for power quality multiple disturbance decomposition based on Independent Component Analysis

In this paper, a novel method for power quality signal decomposition is proposed based on Independent Component Analysis (ICA). This method aims to decompose the power system signal (voltage or current) into components that can provide more specific information about the different disturbances which are occurring simultaneously during a multiple disturbance situation. The ICA is originally a multichannel technique. However, the method proposes its use to blindly separate out disturbances existing in a single measured signal (single channel). Therefore, a preprocessing step for the ICA is proposed using a filter bank. The proposed method was applied to synthetic data, simulated data, as well as actual power system signals, showing a very good performance. A comparison with the decomposition provided by the Discrete Wavelet Transform shows that the proposed method presented better decoupling for the analyzed data. (C) 2012 Elsevier Ltd. All rights reserved.

Discrimination of Basal Cell Carcinoma and Melanoma from Normal Skin Biopsies in Vitro Through Raman Spectroscopy and Principal Component Analysis

Objective: Raman spectroscopy has been employed to discriminate between malignant (basal cell carcinoma [BCC] and melanoma [MEL]) and normal (N) skin tissues in vitro, aimed at developing a method for cancer diagnosis. Background data: Raman spectroscopy is an analytical tool that could be used to diagnose skin cancer rapidly and noninvasively. Methods: Skin biopsy fragments of similar to 2 mm(2) from excisional surgeries were scanned through a Raman spectrometer (830 nm excitation wavelength, 50 to 200 mW of power, and 20 sec exposure time) coupled to a fiber optic Raman probe. Principal component analysis (PCA) and Euclidean distance were employed to develop a discrimination model to classify samples according to histopathology. In this model, we used a set of 145 spectra from N (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues. Results: We demonstrated that principal components (PCs) 1 to 4 accounted for 95.4% of all spectral variation. These PCs have been spectrally correlated to the biochemicals present in tissues, such as proteins, lipids, and melanin. The scores of PC2 and PC3 revealed statistically significant differences among N, BCC, and MEL (ANOVA, p < 0.05) and were used in the discrimination model. A total of 28 out of 30 spectra were correctly diagnosed as N, 93 out of 96 as BCC, and 13 out of 19 as MEL, with an overall accuracy of 92.4%. Conclusions: This discrimination model based on PCA and Euclidean distance could differentiate N from malignant (BCC and MEL) with high sensitivity and specificity.