Suzaku observations of the low surface brightness cluster A76

Context: We present results of Suzaku observations of a nearby galaxy cluster A76 at z=0.0395. This cluster is characterized by extremely low X-ray surface brightness and is hereafter referred to as the LSB cluster. Aims: To understand the nature and thermodynamic evolution of the LSB cluster by studying the physical properties of the hot intracluster medium in A76. Methods: We conducted two-pointed Suzaku observations of A76 and examined the global gas properties of the cluster by XIS spectral analysis. We also performed deprojection analysis of annular spectra and derived radial profiles of gas temperature, density and entropy out to approximately 850 kpc (~ 0.6 r_200) and 560 kpc (~0.4 r_200) in A76 East and A76 West, respectively. Results: The measured global temperature and metal abundance are approximately 3.3 keV and 0.24 solar, respectively. From the deprojection analysis, the entropy profile is found to be flat with respect to radius. The entropy within the central region (r < 0.2r_200) is exceptionally high (~400 keV cm^2). This phenomenon is not readily explained by either gravitational heating or preheating. The X-ray morphology is clumped and irregular, and the electron density is extremely low (1e-4 -- 1e-3 cm^-3) for the observed high temperature, suggesting that A76 is in the early phase of cluster formation and the gas compression due to gravitational potential confinement is lagging behind the gas heating.Comment: 7 pages, 5 figures, A&A accepte

An analysis of the effect of a particular class of PFM on noise inputs

Statistical analysis of pulse frequency modulation systems with white noise inpu

Heart Rate Variability Monitoring Using a Wearable Armband

A wearable electrocardiogram (ECG) monitor is evaluated as heart rate variability (HRV) monitor. The device consists of an armband designed to be worn on the left upper arm which provides 3 ECG channels based on 3 pairs of dry (no hydrogel) electrodes. Armband-ECG and conventional-Holter-ECG signals were simultaneously recorded from 14 subjects during 5 minutes in supine position. Spacial principal component analysis was used to obtain a unique armband ECG signal in which the electromyogram contribution is attenuated. QRS complexes were automatically detected. Five traditional HRV parameters were derived: SDNN, RMSSD, pNN50, and powers within low frequency (LF, [0.04, 0.15] Hz) and high frequency (HF, [0.15, 0.4] Hz) bands. The Pearson''s correlation coefficient between the measurements from the armband device and the measures from the Holter device was computed. Results show very high correlations (1.0000, 0.9999, 0.9984, 1.0000, and 0.9999 for SDNN, RMSSD, pNN50, and powers at LF and HF, respectively), suggesting that the quality of armband-ECG signals is enough to estimate HRV parameters during stationary movement restricted conditions

How frequent are close supermassive binary black holes in powerful jet sources?

24 pages, 36 figures. © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)Supermassive black hole binariesmay be detectable by an upcoming suite of gravitationalwave experiments. Their binary nature can also be revealed by radio jets via a short-period precession driven by the orbital motion as well as the geodetic precession at typically longer periods. We have investigated Karl G. Jansky Very Large Array and Multi-Element Radio Linked Interferometer Network (MERLIN) radio maps of powerful jet sources for morphological evidence of geodetic precession. For perhaps the best-studied source, Cygnus A, we find strong evidence for geodetic precession. Projection effects can enhance precession features, for which we find indications in strongly projected sources. For a complete sample of 33 3CR radio sources, we find strong evidence for jet precession in 24 cases (73 per cent). The morphology of the radio maps suggests that the precession periods are of the order of 10 6- 10 7 yr. We consider different explanations for the morphological features and conclude that geodetic precession is the best explanation. The frequently observed gradual jet angle changes in samples of powerful blazars can be explained by orbital motion. Both observations can be explained simultaneously by postulating that a high fraction of powerful radio sources have subparsec supermassive black hole binaries.We consider complementary evidence and discuss if any jetted supermassive black hole with some indication of precession could be detected as individual gravitational wave source in the near future. This appears unlikely, with the possible exception of M87.Peer reviewedFinal Published versio

Electrocardiogram Derived Respiration for Tracking Changes in Tidal Volume from a Wearable Armband

A pilot study on tracking changes in tidal volume (TV) using ECG signals acquired by a wearable armband is presented. The wearable armband provides three ECG channels by using three pairs of dry electrodes, resulting in a device that is convenient for long-term daily monitoring. An additional ECG channel was derived by computing the first principal component of the three original channels (by means of principal component analysis). Armband and spirometer signals were simultaneously recorded from five healthy subjects who were instructed to breathe with varying TV. Three electrocardiogram derived respiration (EDR) methods based on QRS complex morphology were studied: the QRS slopes range (SR), the R-wave angle (), and the R-S amplitude (RS). The peak-to-peak amplitudes of these EDR signals were estimated as surrogates for TV, and their correlations with the reference TV (estimated from the spirometer signal) were computed. In addition, a multiple linear regression model was calculated for each subject, using the peak-to-peak amplitudes from the three EDR methods from the four ECG channels. Obtained correlations between TV and EDR peak-to-peak amplitude ranged from 0.0448 up to 0.8491. For every subject, a moderate correlation (>0.5) was obtained for at least one EDR method. Furthermore, the correlations obtained for the subject-specific multiple linear regression model ranged from 0.8234 up to 0.9154, and the goodness of fit was 0.73±0.07 (median ± standard deviation). These results suggest that the peak-to-peak amplitudes of the EDR methods are linearly related to the TV. opening the possibility of estimating TV directly from an armband ECG device.Clinical Relevance - This opens the door to possible continuous monitoring of TV from the armband by using EDR

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients\u27 AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices

Structural and electrical properties of bismuth magnesium tantalate pyrochlores.

The subsolidus cubic pyrochlore phases in the Bi2O3–MgO–Ta2O5 (BMT) system were prepared with the proposed formula, Bi3+(5/2)xMg2−xTa3−(3/2)xO14−x (0.12 ≤ x ≤ 0.22). Replacement of smaller cations, Mg2+ and Ta5+ by larger Bi3+ cations with considerable oxygen non-stoichiometry within structure was proposed. The synthesised samples were confirmed phase pure by X-ray powder diffraction and their refined lattice parameters were in the range of 10.5532(4)–10.5672(9) Å. The grain sizes of the samples determined by SEM analysis were in the range of 0.6–10.60 μm and their average relative densities were more than 80%. Five infrared-active modes were also observed in their FTIR spectra due to their metalsingle bondoxygen bonds. The BMT pyrochlores were highly electrical resistive with high dielectric constants, ɛ′ in the range of ∼70–85; dielectric losses, tan δ in the order of 10−3 at frequency 1 MHz and a negative temperature coefficient of permittivities, TCɛ′ of ∼−158 to −328 ppm/°C

Premature Atrial and Ventricular Contraction Detection using Photoplethysmographic Data from a Smartwatch

We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincare plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC