Serum paraoxonase activity and lipid hydroperoxide levels in adult football players after three days football tournament

Background: It has been suggested that physical activity is an important factor in the prevention and treatment of cardiovascular diseases. Low serum paraoxononase–1 (PON1) activity is with an associated risk of atherosclerotic disease.Objectives: In this study, we aimed to investigate serum PON1 activity and lipid hydroperoxide (LOOH) levels in adult football players after three days football tournament.Methods: Twenty-three adult male football players and 23 sedentary male subjects after three days football tournament were enrolled. Serum paraoxonase, arylesterase activities and LOOH levels were determined.Results: Serum paraoxonase and arylesterase activities were signiûcantly higher in football players than sedentary subjects (all, p<0.05), while LOOH levels were significantly lower (p< 0.05). Serum LOOH levels were inversely correlated with paraoxonase and arylesterase activities (r=-0.552, p<0.001; r=-0.812, p<0.001; respectively) in adult football players.Conclusion: Our data show, for the first time, that physical activity is associated with increased PON1 activity and decreased oxidative stress after three days football tournament. In addition, physical activity for a healthy life is important in increasing serum PON1 activity, and this may play a role in the prevention of atherosclerosis.Key words: Football players, physical activity, PON1 activity, lipid hydroperoxid

Complex signal recovery from two fractional Fourier transform intensities: order and noise dependence

Cataloged from PDF version of article.The problem of recovering a complex signal from the magnitudes of two of its fractional Fourier transforms is addressed. This corresponds to phase retrieval from the transverse intensity profiles of an optical field at two arbitrary locations along the optical axis. The convergence of the iterative algorithm, the effects of noise or measurement errors, and their dependence on the fractional transform order are investigated. It is observed that in general, better results are obtained when the fractional transform order is close to unity and poorer results are obtained when the order is close to zero. It follows that to the extent that conditions allow, the fractional order between the two measurement planes should be chosen as close to unity (or other odd integer) as possible for best results. (C) 2004 Elsevier B.V. All rights reserved

Complex signal recovery from multiple fractional Fourier-transform intensities

Cataloged from PDF version of article.The problem of recovering a complex signal from the magnitudes of any number of its fractional Fourier transforms at any set of fractional orders is addressed. This problem corresponds to the problem of phase retrieval from the transverse intensity profiles of an optical field at arbitrary locations in an optical system involving arbitrary concatenations of lenses and sections of free space. The dependence of the results on the number of orders, their spread, and the noise is investigated. Generally, increasing the number of orders improves the results, but with diminishing return beyond a certain point. Selecting the measurement planes such that their fractional orders are well separated or spread as much as possible also leads to better results. (c) 2005 Optical Society of Americ

Vascular variations of the kidney, retrospective analysis of computed tomography images of ninety-one laparoscopic donor nephrectomies, and comparison of computed tomography images with perioperative findings

Background: In this retrospective study, we aimed to determine the variations of kidney arteries and veins in kidney donor patients who underwent preoperative, computed tomography angiography (CTA).Materials and methods: We analysed kidney CTA findings of 91 donor nephrectomy patients operated from July 2016 through December 2017. Demographics, vascular diameters, abnormalities, numbers, branching variations, routing variations of arteries, and veins were assessed according to CTA images. We also compared the radiological findings with perioperative findings. Two radiologists evaluated CTA images, and the same surgical team performed all donor nephrectomies by laparoscopic approach.Results: Ninety-one of the 96 patients involved to study. Forty-six (50.5%) patients were female. Thirty-five (38.4%) of 91 cases had accessory arteries. Seven (7.6%) right, 1 (1.1%) left and 8 (8.7%) bilateral double hilar artery was observed on CTA. No statistically significant difference was observed in the evaluation of the side of accessory/polar arteries (p > 0.05), and in the evaluation of the distribution of arterial/venous variations according to perioperative findings (p > 0.05). However, in the evaluation of CTA images, we found that the diameter of the kidney artery and vein differed according to gender and side.Conclusions: The knowledge of the vascular variations of the kidney is essential for surgeons performing kidney transplantation. It is also essential for urologist and vascular surgeons. Incompatible with the literature, the right kidney has more vascular variations and, one kidney artery is found in the majority of Turkish kidneydonor patients

Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization

Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies.Sponsored by: IEEE Geoscience and Remote Sensing SocietyRitrýnt tímaritPeer reviewedPre prin

DRBM-ClustNet: A Deep Restricted Boltzmann-Kohonen Architecture for Data Clustering

A Bayesian Deep Restricted Boltzmann-Kohonen architecture for data clustering termed as DRBM-ClustNet is proposed. This core-clustering engine consists of a Deep Restricted Boltzmann Machine (DRBM) for processing unlabeled data by creating new features that are uncorrelated and have large variance with each other. Next, the number of clusters are predicted using the Bayesian Information Criterion (BIC), followed by a Kohonen Network-based clustering layer. The processing of unlabeled data is done in three stages for efficient clustering of the non-linearly separable datasets. In the first stage, DRBM performs non-linear feature extraction by capturing the highly complex data representation by projecting the feature vectors of $d$ dimensions into $n$ dimensions. Most clustering algorithms require the number of clusters to be decided a priori, hence here to automate the number of clusters in the second stage we use BIC. In the third stage, the number of clusters derived from BIC forms the input for the Kohonen network, which performs clustering of the feature-extracted data obtained from the DRBM. This method overcomes the general disadvantages of clustering algorithms like the prior specification of the number of clusters, convergence to local optima and poor clustering accuracy on non-linear datasets. In this research we use two synthetic datasets, fifteen benchmark datasets from the UCI Machine Learning repository, and four image datasets to analyze the DRBM-ClustNet. The proposed framework is evaluated based on clustering accuracy and ranked against other state-of-the-art clustering methods. The obtained results demonstrate that the DRBM-ClustNet outperforms state-of-the-art clustering algorithms.Comment: 14 pages, 7 figure

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

Cell-phone traces reveal infection-associated behavioral change

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadEpidemic preparedness depends on our ability to predict the trajectory of an epidemic and the human behavior that drives spread in the event of an outbreak. Changes to behavior during an outbreak limit the reliability of syndromic surveillance using large-scale data sources, such as online social media or search behavior, which could otherwise supplement healthcare-based outbreak-prediction methods. Here, we measure behavior change reflected in mobile-phone call-detail records (CDRs), a source of passively collected real-time behavioral information, using an anonymously linked dataset of cell-phone users and their date of influenza-like illness diagnosis during the 2009 H1N1v pandemic. We demonstrate that mobile-phone use during illness differs measurably from routine behavior: Diagnosed individuals exhibit less movement than normal (1.1 to 1.4 fewer unique tower locations; [Formula: see text]), on average, in the 2 to 4 d around diagnosis and place fewer calls (2.3 to 3.3 fewer calls; [Formula: see text]) while spending longer on the phone (41- to 66-s average increase; [Formula: see text]) than usual on the day following diagnosis. The results suggest that anonymously linked CDRs and health data may be sufficiently granular to augment epidemic surveillance efforts and that infectious disease-modeling efforts lacking explicit behavior-change mechanisms need to be revisited. Keywords: call detail records; disease; influenza; outbreak; surveillance.Alan Turing Institute Engineering and Physical Sciences Research Council EP/N510129/1 UK Research & Innovation (UKRI) Medical Research Council UK (MRC) European Commission National Institute for Health Research (NIHR) Health Protection Research Unit in Evaluation of Interventions at the University of Brist

Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification from VHR Imagery

© 1980-2012 IEEE. Imbalanced training sets are known to produce suboptimal maps for supervised classification. Therefore, one challenge in mapping land cover is acquiring training data that will allow classification with high overall accuracy (OA) in which each class is also mapped onto similar user's accuracy. To solve this problem, we integrated local adaptive region and box-and-whisker plot (BP) techniques into an iterative algorithm to expand the size of the training sample for selected classes in this article. The major steps of the proposed algorithm are as follows. First, a very small initial training sample (ITS) for each class set is labeled manually. Second, potential new training samples are found within an adaptive region by conducting local spectral variation analysis. Lastly, three new training samples are acquired to capture information regarding intraclass variation; these samples lie in the lower, median, and upper quartiles of BP. After adding these new training samples to the ITS, classification is retrained and the process is continued iteratively until termination. The proposed approach was applied to three very high-resolution (VHR) remote-sensing images and compared with a set of cognate methods. The comparison demonstrated that the proposed approach produced the best result in terms of OA and exhibited superiority in balancing user's accuracy. For example, the proposed approach was typically 2%-10% more accurate than the compared methods in terms of OA and it generally yielded the most balanced classification