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Colour-magnitude diagrams reveal a population of blue (hot) sub-luminous objects with respect to the main sequence. These hot sub-luminous stars are the result of evolutionary processes that require stars to expel their obscuring, hydrogen-rich envelopes to reveal the hot helium core. As such, these objects offer a direct window into the hearts of stars that are otherwise inaccessible to direct observation. We showcase MeerLICHT's capabilities of detecting faint hot subdwarfs and identifying the dominant frequency in the photometric variability of these compact hot stars, in comparison to their $Gaia$ DR3 data. We hunt for oscillations, which will be an essential ingredient for accurately probing stellar interiors in future asteroseismology. Comparative MeerLICHT and $Gaia$ colour-magnitude diagrams are presented as a way to select hot subdwarfs from our sample. A dedicated frequency determination technique is developed and applied to the selected candidates to determine their dominant variability using time-series data from MeerLICHT and $Gaia$ DR3. We explore the power of both datasets in determining the dominant frequency. Using the $g-i$ colour, MeerLICHT offers a colour-magnitude diagram that is comparable in quality to that of $Gaia$ DR3. The MeerLICHT colour-colour diagrams allow for the study of different stellar populations. The frequency analysis of MeerLICHT and $Gaia$ DR3 data demonstrates the superiority of our MeerLICHT multi-colour photometry in estimating the dominant frequency compared to the sparse $Gaia$ DR3 data. MeerLICHT's multi-band photometry leads to the discovery of high-frequency faint subdwarfs. Our MeerLICHT results are a proof-of-concept of the capacity of the BlackGEM instrument currently in the commissioning stage at ESO's La Silla Observatory in Chile., Accepted from A&A journal. Publication is still in process

Paroxysmal atrial fibrillation (PAF) is the initial stage of atrial fibrillation, one of the most common arrhythmia types. PAF worsens with time and affects the patient's life quality negatively. In this study, we aimed to diagnose PAF early, so patients can start taking precautions before this disease gets worse. We used the atrial fibrillation prediction database, an open data from Physionet and constructed our approach using convolutional neural networks. Heart rate variability (HRV) features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations (fast Fourier transform, Lomb-Scargle, and Welch periodogram), time-frequency-domain measures using wavelet transform, and nonlinear Poincare plot measures. We also normalized these features using min-max normalization and z-score normalization methods. In addition, we also applied alternatively the heart rate normalization (HRN), which gave promising results in a few HRV-based research, before calculating these features. Thus, HRV data, HRN data, and HRV features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural networks classifier. We tuned the classifiers using 90% of samples and tested the classifiers' performances using 10% of data. The proposed approach resulted in 95.92% accuracy, 100% precision, 91.84% recall, and 95.74% f1-score in HRV with z-score feature normalization. When the heart rate normalization was also applied, the proposed approach reached 100% accuracy, 100% precision, 100% recall, and 100% f1-score in HRV with z-score feature normalization. The proposed method with heart rate normalization and z-score normalization methods resulted in better classification performance than similar studies in the literature. In addition, although deep learning models offer no use of separate feature extraction processes, this study reveals that using HRV-specific feature extraction techniques may improve the performance of deep learning algorithms in HRV-based studies. Comparing the existing studies, we concluded that our approach provides a much better tool to diagnose PAF patients. WOS:000709712800008 2-s2.0-85117118593

Air pollution exhibits hyper-local variation, especially near emissions sources. In addition to people's time-activity patterns, this variation is the most critical element determining exposure. Pollution exposure is time-activity- and path-dependent, with specific behaviours such as mode of commuting and time spent near a roadway or in a park playing a decisive role. Compared to conventional air pollution monitoring stations, nodes containing low-cost air pollution sensors can be deployed with very high density. In this study, a network of 18 nodes using low-cost air pollution sensors was deployed in Newcastle-under-Lyme, Staffordshire, UK, in June 2020. Each node measured a range of species including nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5 and PM10); this study focuses on NO2 and PM2.5 over a 1-year period from 1 August 2020 to 1 October 2021. A simple and effective temperature, scale, and offset correction was able to overcome data quality issues associated with temperature bias in the NO2 readings. In its recent update, the World Health Organization (WHO) dramatically reduced annual exposure limit values from 40 to 10 µg m−3 for NO2 and from 10 to 5 µg m−3 for PM2.5. We found that the average annual mean NO2 concentration for the network was 17.5 µg m−3 and 8.1 µg m−3 for PM2.5. While in exceedance of the WHO guideline levels, these average concentrations do not exceed legally binding UK/EU standards. The network average NO2 concentration was 12.5 µg m−3 higher than values reported by a nearby regional air quality monitoring station, showing the critical importance of monitoring close to sources before pollution is diluted. We demonstrate how data from a low-cost air pollution sensor network can reveal insights into patterns of air pollution and help determine whether sources are local or non-local. With spectral analysis, we investigate the variation of the pollution levels and identify typical periodicities. Both NO2 and PM2.5 have contributions from high-frequency sources; however, the low-frequency sources are significantly different. Using spectral analysis, we determine that at least 54.3±4.3 % of NO2 is from local sources, whereas, in contrast, only 37.9±3.5 % of PM2.5 is local.

The aim was to assess the association between fetal growth restriction (FGR) and fetal heart rate variability (FHRV) in relation to fetal movements. A prospective observational cohort study was performed. Non-invasive fetal electrocardiography (NI-FECG) allowed beat-to-beat assessments with

Purpose: The spatio-molecular distribution of choline and its metabolites in tumors is highly heterogeneous. Due to regulation of choline metabolism by hypoxic transcriptional signaling and other survival factors, we envisage that detection of such heterogeneity in patient tumors could provide the basis for advanced localized therapy. However, non-invasive methods to assess this phenomenon in patients are limited. We investigated such heterogeneity in Non-Small Cell Lung Cancer (NSCLC) with [18F]fluoromethyl-(1,2-2H4) choline ([18F]D4-FCH) and positron emission tomography/computed tomography (PET/CT). Experimental design: [18F]D4-FCH (300.5±72.9MBq [147.60-363.6MBq]) was administered intravenously to 17 newly diagnosed NSCLC patients. PET/CT scans were acquired concurrently with radioactive blood sampling to permit mathematical modelling of blood-tissue transcellular rate constants. Comparisons were made with biopsy-derived choline kinase-α (CHKα) expression and diagnostic [18F]fluorodeoxyglucose ([18F]FDG) scans. Results: Oxidation of [18F]D4-FCH to [18F]D4-fluorobetaine was suppressed (48.58±0.31% parent at 60 min) likely due to the deuterium isotope effect embodied within the design of the radiotracer. Early (5 min) and late (60 min) images showed specific uptake of tracer in all 51 lesions (tumors, lymph nodes and metastases) from 17 patients analyzed. [18F]D4-FCH-derived uptake (SUV60max) in index primary lesions (n=17) ranged between 2.87-10.13; lower than that of [18F]FDG PET [6.89-22.64]. Mathematical modelling demonstrated net irreversible uptake of [18F]D4-FCH at steady-state, and parametric mapping of the entire tumor showed large intratumorally heterogeneity in radiotracer retention, which is likely to have influenced correlations with biopsy-derived CHKα expression. Conclusions: [18F]D4-FCH is detectable in NSCLC with large intratumorally heterogeneity, which could be exploited in the future for targeting localized therapy.

This article presents a modified algorithm and a MATLAB-based program for computing the Moho topography, which uses the combination of the spatial domain technique and the FFT-based algorithm. The advantages of the algorithm are that it performs rapid computations with high precision, and the computation procedure does not need to use a low pass filter during iteration. The applicability of the proposed technique is demonstrated on three 3D synthetic and real gravity data from the Arabian Shield, Saudi Arabia. For the synthetic data cases, the obtained results are compatible with the assumed structures. Application on real data indicates that the result estimated by the proposed technique compares well with seismic data.

Background Worldwide, hypertensive disorders of pregnancy (HDP), fetal growth restriction (FGR) and preterm birth remain the leading causes of maternal and fetal pregnancy-related mortality and (long-term) morbidity. Fetal cardiac deformation changes can be the first sign of placental dysfunction, which is associated with HDP, FGR and preterm birth. In addition, preterm birth is likely associated with changes in electrical activity across the uterine muscle. Therefore, fetal cardiac function and uterine activity can be used for the early detection of these complications in pregnancy. Fetal cardiac function and uterine activity can be assessed by two-dimensional speckle-tracking echocardiography (2D-STE), non-invasive fetal electrocardiography (NI-fECG), and electrohysterography (EHG). This study aims to generate reference values for 2D-STE, NI-fECG and EHG parameters during the second trimester of pregnancy and to investigate the diagnostic potential of these parameters in the early detection of HDP, FGR and preterm birth. Methods In this longitudinal prospective cohort study, eligible women will be recruited from a tertiary care hospital and a primary midwifery practice. In total, 594 initially healthy pregnant women with an uncomplicated singleton pregnancy will be included. Recordings of NI-fECG and EHG will be made weekly from 22 until 28 weeks of gestation and 2D-STE measurements will be performed 4-weekly at 16, 20, 24 and 28 weeks gestational age. Retrospectively, pregnancies complicated with pregnancy-related diseases will be excluded from the cohort. Reference values for 2D-STE, NI-fECG and EHG parameters will be assessed in uncomplicated pregnancies. After, 2D-STE, NI-fCG and EHG parameters measured during gestation in complicated pregnancies will be compared with these reference values. Discussion This will be the a large prospective study investigating new technologies that could potentially have a high impact on antepartum fetal monitoring. Trial registration Registered on 26 March 2020 in the Dutch Trial Register (NL8769) via https://www.trialregister.nl/trials and registered on 21 October 2020 to the Central Committee on Research Involving Human Subjects (NL73607.015.20) via https://www.toetsingonline.nl/to/ccmo_search.nsf/Searchform?OpenForm.

Context. In asteroseismology the pulsation mode frequencies of a star are the fundamental data that are compared to theoretical predictions to determine a star's interior physics. Recent significant advances in the numerical, theoretical and statistical asteroseismic methods applied to main sequence stars with convective cores have renewed the interest in investigating the propagation of observational uncertainties within a forward asteroseismic modelling framework. Aims. We aim to quantify the impact of various choices made throughout the observational aspects of extracting pulsation mode frequencies in main sequence stars with gravity modes. Methods. We use a well-studied benchmark slowly pulsating B star, KIC 7760680, to investigate the sensitivity of forward asteroseismic modelling to various sources of observational uncertainty that affect the precision of the input pulsation mode frequencies. Results. We quantify the impact of the propagation of the observational uncertainties involved in forward asteroseismic modelling. We find that one of the largest sources of uncertainty in our benchmark star is in the manual building of period spacing patterns, such that the inclusion of a potentially ambiguous pulsation mode frequency may yield differences in model parameters of up to 10% for mass and age depending on the radial order of the mode. Conclusions. We conclude that future asteroseismic studies of main sequence stars with a convective core should quantify and include observational uncertainties introduced by the light curve extraction, iterative pre-whitening and the building of period spacing patterns, as these propagate into the final modelling results., 21 pages, proposed for acceptance for publication in A&A

BACKGROUND AND OBJECTIVE: The development, control and optimisation of new x-ray breast imaging modalities could benefit from a quantitative assessment of the resulting image textures. The aim of this work was to develop a software tool for routine radiomics applications in breast imaging, which will also be available upon request. METHODS: The tool (developed in MATLAB) allows image reading, selection of Regions of Interest (ROI), analysis and comparison. Requirements towards the tool also included convenient handling of common medical and simulated images, building and providing a library of commonly applied algorithms and a friendly graphical user interface. Initial set of features and analyses have been selected after a literature search. Being open, the tool can be extended, if necessary. RESULTS: The tool allows semi-automatic extracting of ROIs, calculating and processing a total of 23 different metrics or features in 2D images and/or in 3D image volumes. Computations of the features were verified against computations with other software packages performed with test images. Two case studies illustrate the applicability of the tool - (i) features on a series of 2D 'left' and 'right' CC mammograms acquired on a Siemens Inspiration system were computed and compared, and (ii) evaluation of the suitability of newly proposed and developed breast phantoms for x-ray-based imaging based on reference values from clinical mammography images. Obtained results could steer the further development of the physical breast phantoms. CONCLUSIONS: A new image analysis toolbox was realized and can now be used in a multitude of radiomics applications, on both clinical and test images. ispartof: PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS vol:89 pages:114-128 ispartof: location:Italy status: published

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder, which results in daytime symptoms, a reduced quality of life as well as long-term negative health consequences. OSA diagnosis and severity rating is typically based on the apnea-hypopnea index (AHI) retrieved from overnight poly(somno)graphy. However, polysomnography is costly, obtrusive and not suitable for long-term recordings. Here, we present a method for unobtrusive estimation of the AHI using ECG-based features to detect OSA-related events. Moreover, adding ECG-based sleep/wake scoring yields a fully automatic method for AHI-estimation. Importantly, our algorithm was developed and validated on a combination of clinical datasets, including datasets selectively including OSA-pathology but also a heterogeneous, “real-world” clinical sleep disordered population (262 participants in the validation set). The algorithm provides a good representation of the current gold standard AHI (0.72 correlation, estimation error of 0.56 ± 14.74 events/h), and can also be employed as a screening tool for a large range of OSA severities (ROC AUC ≥ 0.86, Cohen’s kappa ≥ 0.53 and precision ≥70%). The method compares favourably to other OSA monitoring strategies, showing the feasibility of cardiovascular-based surrogates for sleep monitoring to evolve into clinically usable tools.

EEG spectral-power density was analyzed in a group of nine highly hypnotizable subjects via ten frontal, central, parietal, and occipital electrodes under four conditions: 1) wake state, 2) neutral hypnosis, 3) hypnotic suggestion for altering perception of tones, and 4) post-hypnosis. Results indicate no theta-power changes between conditions, challenging previous findings that increased theta power is a marker of hypnosis. A decrease in gamma power under hypnotic suggestion and an almost significant decrease under neutral hypnosis were observed, compared to post-hypnosis. Anteroposterior power distribution remained stable over all conditions. The results are discussed and compared to earlier studies, which report heterogenous findings. CC BY 4.0© 2021 The Author(s)This research did not receive any specific grant from funding agenciesin the public, commercial, or not-for-profit sectors.

Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precision and robustness to noise. To validate the proposed algorithm, we performed an extensive comparison against the classical Kalman filter, in both realistic surrogate networks and real electroencephalography (EEG) data. In both simulations and real data, we show that the STOK filter estimates time-frequency patterns of directed connectivity with significantly superior performance. The advantages of the STOK filter were even clearer in real EEG data, where the algorithm recovered latent structures of dynamic connectivity from epicranial EEG recordings in rats and human visual evoked potentials, in excellent agreement with known physiology. These results establish the STOK filter as a powerful tool for modeling dynamic network structures in biological systems, with the potential to yield new insights into the rapid evolution of network states from which brain functions emerge., Author summary During normal behavior, brains transition between functional network states several times per second. This allows humans to quickly read a sentence, and a frog to catch a fly. Understanding these fast network dynamics is fundamental to understanding how brains work, but up to now it has proven very difficult to model fast brain dynamics for various methodological reasons. To overcome these difficulties, we designed a new Kalman filter (STOK) by innovating on previous solutions from control theory and state-space modelling. We show that STOK accurately models fast network changes in simulations and real neural data, making it an essential new tool for modelling fast brain networks in the time and frequency domain.

Mayer waves may synchronize overlapping propriobulbar interneuronal microcircuits constituting the respiratory rhythm and pattern generator, sympathetic oscillators, and cardiac vagal preganglionic neurons. Initially described by Sir Sigmund Mayer in the year 1876 in the arterial pressure waveform of anesthetized rabbits, authors have since extensively observed these oscillations in recordings of hemodynamic variables, including arterial pressure waveform, peripheral resistance, and blood flow. Authors would later reveal the presence of these oscillations in sympathetic neural efferent discharge and brainstem and spinal zones corresponding with sympathetic oscillators. Mayer wave central tendency proves highly consistent within, though the specific frequency band varies extensively across, species. Striking resemblance of the Mayer wave central tendency to the species-specific baroreflex resonant frequency has led the majority of investigators to comfortably presume, and generate computational models premised upon, a baroreflex origin of these oscillations. Empirical interrogation of this conjecture has generated variable results and derivative interpretations. Sinoaortic denervation and effector sympathectomy variably reduces or abolishes spectral power contained within the Mayer wave frequency band. Refractorines of Mayer wave generation to barodeafferentation lends credence to the hypothesis these waves are chiefly generated by brainstem propriobulbar and spinal cord propriospinal interneuronal microcircuit oscillators and likely modulated by the baroreflex. The presence of these waves in unitary discharge of medullary lateral tegmental field and rostral ventrolateral medullary neurons (contemporaneously exhibiting fast sympathetic rhythms [2–6 and 10 Hz bands]) in spectral variability in vagotomized pentobarbital-anesthetized and unanesthetized midcollicular (i.e., intercollicular) decerebrate cats supports genesis of Mayer waves by supraspinal sympathetic microcircuit oscillators. Persistence of these waves following high cervical transection in vagotomized unanesthetized midcollicular decerebrate cats would seem to suggest spinal sympathetic microcircuit oscillators generate these waves. The widespread presence of Mayer waves in brainstem sympathetic-related and non-sympathetic-related cells would seem to betray a general tendency of neurons to oscillate at this frequency. We have thus presented an extensive and, hopefully cohesive, discourse evaluating, and evolving the interpretive consideration of, evidence seeking to illumine our understanding of origins of, and insight into mechanisms contributing to, the genesis of Mayer waves. We have predicated our arguments and conjectures in the substance and matter of empirical data, though we have occasionally waxed philosophical beyond these traditional confines in suggesting interpretations exceeding these limits. We believe our synthesis and interpretation of the relevant literature will fruitfully inspire future studies from the perspective of a more intimate appreciation and conceptualization of network mechanisms generating oscillatory variability in neuronal and neural outputs. Our evaluation of Mayer waves informs a novel set of disciplines we term quantum neurophysics extendable to describing subatomic reality. Beyond informing our appreciation of mechanisms generating sympathetic oscillations, Mayer waves may constitute an intrinsic property of neurons extant throughout the cerebrum, brainstem, and spinal cord or reflect an emergent property of interactions between arteriogenic and neuronal oscillations.

Introduction Baroreflex sensitivity (BRS) is essential to ensure rapid adjustment to variations in blood pressure (BP). Spontaneous baroreflex function can be assessed using continuous recordings of blood pressure. The goal of this study was to compare four methods for BRS quantification [the sequence, Bernardi's (BER), frequency and transfer function methods] to identify the most consistent method across an extreme range of conditions: rest and exercise, in normoxia, hypoxia, hypocapnia, and hypercapnia. Methods Using intra-radial artery BP in young healthy participants, BRS was calculated and compared using the four methods in normoxia, acute and chronic hypoxia (terrestrial altitude of 5,260 m) in hypocapnia (hyperventilation), hypercapnia (rebreathing) and during ramp exercise to exhaustion. Results The sequence and BER methods for BRS estimation showed good agreement during the resting and exercise protocols, whilst the ultra- and very-low frequency bands of the frequency and transfer function methods were more discrepant. Removing respiratory frequency from the blood pressure traces affected primarily the sequence and BER methods and occasionally the frequency and transfer function methods. Discussion/Conclusion The sequence and BER methods contained more respiratory related information than the frequency and transfer function methods, indicating that the former two methods predominantly rely on respiratory effects of BRS. BER method is recommended because it is the easiest to compute and even though it tends to overestimate BRS compared to the sequence method, it is consistent with the other methods, whilst its interquartile range is the smallest.

This study is motivated by the need to develop stochastic parameterisations for representing the effects of mesoscale oceanic eddies in non-eddy-resolving and eddy-permitting ocean circulation models. A necessary logical step on the way to such parameterisations is the understanding of flow responses to spatially stationary and localised, time-dependent ‘plunger’ forcings intended to represent transient eddy flux divergences. Specifically, this study develops an understanding of the plunger-induced convergence of potential vorticity (PV) fluxes using the linearised single-layer shallow-water model. Time-periodic solutions are obtained and the ‘footprint’, defined as the time-mean, quasi-linear PV flux convergence, quantifies the cumulative PV redistribution induced by the plunger. Using the footprint, the equivalent eddy flux (EEF) is defined such that it succinctly quantifies the extent of the PV redistribution, and its dependencies on the forcing latitude and the background flow are examined in detail. For a uniform background flow the EEF is positive for all forcing latitudes, corresponding to net-poleward PV flux convergence, as expected by current theory of $\unicode[STIX]{x1D6FD}$-plane Rossby waves. The EEF also has a robust dependence on the direction and magnitude of a uniform background flow, which is a useful quality for the EEF to provide a basis for a parameterisation of eddy PV fluxes. We also examine the PV redistribution due to forcing on top of a Gaussian jet background flow and find that forcing proximity to the jet core is the primary factor in determining whether the jet is sharpened or broadened.

The Nyquist–Shannon criterion indicates the sample rate necessary to identify information with particular frequency content from a dynamical system. However, in experimental applications such as the interrogation of a flow field using particle image velocimetry (PIV), it may be impracticable or expensive to obtain data at the desired temporal resolution. To address this problem, we propose a new approach to identify temporal information from undersampled data, using ideas from modal decomposition algorithms such as dynamic mode decomposition (DMD) and optimal mode decomposition (OMD). The novel method takes a vector-valued signal, such as an ensemble of PIV snapshots, sampled at random time instances (but at sub-Nyquist rate) and projects onto a low-order subspace. Subsequently, dynamical characteristics, such as frequencies and growth rates, are approximated by iteratively approximating the flow evolution by a low-order model and solving a certain convex optimisation problem. The methodology is demonstrated on three dynamical systems, a synthetic sinusoid, the cylinder wake at Reynolds number $Re=60$ and turbulent flow past the axisymmetric bullet-shaped body. In all cases the algorithm correctly identifies the characteristic frequencies and oscillatory structures present in the flow.

OBJECTIVE: Tracking brain states with electrophysiological measurements often relies on short-term averages of extracted features and this may not adequately capture the variability of brain dynamics. The objective is to assess the hypotheses that this can be overcome by tracking distributions of linear models using anesthesia data, and that anesthetic brain state tracking performance of linear models is comparable to that of a high performing depth of anesthesia monitoring feature.METHODS: Individuals' brain states are classified by comparing the distribution of linear (Auto-Regressive Moving Average - ARMA) model parameters estimated from electroencephalographic (EEG) data obtained with a sliding window to distributions of linear model parameters for each brain state. The method is applied to frontal EEG data from 15 subjects undergoing propofol anesthesia and classified by the observers assessment of alertness/sedation (OAA/S) scale. Classification of the OAA/S score was performed using distributions of either ARMA parameters or the benchmark feature, Higuchi Fractal Dimension.RESULTS: The highest average testing sensitivity of 59% (chance sensitivity 17%) was found for ARMA (2; 1) models and Higuchi Fractal Dimension achieved 52%, however, no statistical difference was observed. For the same ARMA case, there was no statistical difference if medians are used instead of distributions (sensitivity: 56%).CONCLUSION: The model-based distribution approach is not necessarily more effective than a median/short-term average approach, however, it performs well compared to a distribution approach based on a high performing anesthesia monitoring measure.SIGNIFICANCE: These techniques hold potential for anesthesia monitoring and may be generally applicable to tracking brain states.

Oceanic multiple jets are seen to possess spatio-temporal variability imposed by varying bottom topography resulting in jets that can drift and merge. The dynamics of multiple jets over a topographic zonal slope is studied in a two-layer quasi-geostrophic model. The jets tilt from the zonal direction and drift meridionally. In addition to the tilted jets, other large-scale spatial patterns are observed, which are extracted using the principal component analysis. The variances of these patterns are strongly influenced by the values of eddy viscosity and bottom friction parameters. The contribution of the tilted jets to the full flow field decreases with decreasing friction and viscosity parameters, and purely zonal large-scale modes, propagating in the meridional direction, populate the flow field. Linear stability analysis and two-dimensional kinetic-energy spectrum analysis suggest that the zonal modes gain energy from ambient eddies as well as from the tilted jets through nonlinear interactions. However, viscous dissipation and bottom friction tend to suppress the nonlinear interactions, which results in the inhibition of the upscale energy transfer from eddies to the zonal modes. These simulations suggest that, in the presence of topography, alternating jet patterns may be sustained through interactions among various large-scale modes. This is different from the classical zonal jet formation arguments, in which direct eddy forcing maintains the jets.

0000-0003-3237-4392, WOS: 000474703500070, In this paper, we proposed an approach for locomotive maintenance systems by observing engine lube oil. The mechanical particles in lube oil give information about locomotive engine system condition. The engine lubricant is monthly monitored by a spectral analyzer (SA) to detect engine system failure and routine maintenance time. However, this old fashioned technique has many disadvantages such as non-real time measuring, high cost and time consumption. A novel approach is proposed to eliminate these disadvantages. The new method determines the lubricant sample conditions with respect to electrical characteristics by using artificial neural network (ANN). The study focuses on a relationship between mechanical particles (in ppm) and dielectric characteristics of the lube oil samples. Therefore, ANN method is applied to observe linear relation between observed and predicted dielectric constant and loss factor values of the engine oil samples. The electrical characteristics of the samples are observed at four frequency points (2.40 GHz, 5.80 GHz, 7.40 GHz and 9.60 GHz). ANN studies are realized by using data at these frequency points. The regression (R) coefficients are obtained as 0.7239, 0.7951, 0.8513 and 0.7463 for dielectric constant and 0.7627, 0.7196, 0.8015 and 0.7334 for dielectric loss, respectively. Moreover, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are calculated and examined. The obtained results are very sufficient and this approach can be applied to a sensor device having low cost and real time working mechanism in the future. (C) 2019 Elsevier Ltd. All rights reserved., Scientific Project Unit of Cukurova University [FDK-2018-10488], The authors would like to acknowledge the Scientific Project Unit of Cukurova University (FDK-2018-10488).

This study explores the relationship between coherent eddies and zonally elongated striations. The investigation involves an analysis of two baroclinic quasigeostrophic models of a zonal and double-gyre flow and a set of altimetry sea level anomaly data in the North Pacific. Striations are defined by either spatiotemporal filtering or empirical orthogonal functions (EOFs), with both approaches leading to consistent results. Coherent eddies, identified here by the modified Okubo–Weiss parameter, tend to propagate along well-defined paths, thus forming “eddy trains” that coincide with striations. The striations and eddy trains tend to drift away from the intergyre boundary at the same speed in both the model and observations. The EOF analysis further confirms that these striations in model simulations and altimetry are not an artifact of temporal averaging of random, spatially uncorrelated vortices. This study suggests instead that eddies organize into eddy trains, which manifest themselves as striations in low-pass filtered data and EOF modes.

Multiple zonal jets observed in many parts of the global ocean are often embedded in large-scale eastward and westward vertically sheared background flows. Properties of the jets and ambient eddies, as well as their dynamic interactions, are found to be different between eastward and westward shears. However, the impact of these differences on overall eddy dynamics remains poorly understood and is the main subject of this study. The roles of eddy relative vorticity and buoyancy fluxes in the maintenance of oceanic zonal jets are studied in a two-layer quasigeostrophic model. Both eastward and westward uniform, zonal vertically sheared cases are considered in the study. It is shown that, despite the differences in eddy structure and local characteristics, the fundamental dynamics are essentially the same in both cases: the relative-vorticity fluxes force the jets in the entire fluid column, and the eddy-buoyancy fluxes transfer momentum from the top to the bottom layer, where it is balanced by bottom friction. It is also observed that the jets gain more energy via Reynolds stress work in the layer having a positive gradient in the background potential vorticity, and this is qualitatively explained by a simple reasoning based on Rossby wave group velocity.

This paper introduces a new modeling and inference framework for multivariate and anisotropic point processes. Building on recent innovations in multivariate spatial statistics, we propose a new family of multivariate anisotropic random fields, and from them a family of anisotropic point processes. We give conditions that make the proposed models valid. We also propose a Palm likelihood-based inference method for this type of point process, circumventing issues of likelihood tractability. Finally we illustrate the utility of the proposed modeling framework by analyzing spatial ecological observations of plants and trees in the Barro Colorado Island data.

Introduction:Individual differences in heart rate variability (HRV) can be partly attributed to genetic factors that may be more pronounced during stress. Using data from the Oman Family Study (OFS), we aimed to estimate and quantify the relative contribution of genes and environment to the variance of HRV at rest and during stress; calculate the overlap in genetic and environmental influences on HRV at rest and under stress using bivariate analyses of HRV parameters and heart rate (HR).Methods:Time and frequency domain HRV variables and average HR were measured from beat-to-beat HR obtained from electrocardiogram recordings at rest and during two stress tests [mental: Word Conflict Test (WCT) and physical: Cold Pressor Test (CPT)] in the OFS - a multigenerational pedigree consisting of five large Arab families with a total of 1326 participants. SOLAR software was used to perform quantitative genetic modelling.Results:Heritability estimates for HRV and HR ranged from 0.11 to 0.31 for rest, 0.09-0.43 for WCT, and 0.07-0.36 for CPT. A large part of the genetic influences during rest and stress conditions were shared with genetic correlations ranging between 0.52 and 0.86 for rest-WCT and 0.60-0.92 for rest-CPT. Nonetheless, genetic rest-stress correlations for most traits were significantly smaller than 1 indicating some stress-specific genetic effects.Conclusion:Genetic factors significantly influence HRV and HR at rest and under stress. Most of the genetic factors that influence HRV at rest also influence HRV during stress tests, although some unique genetic variance emerges during these challenging conditions.

The dynamics of multiple alternating oceanic jets has been studied in the presence of a simple bottom topography with constant slope in the zonal direction. A baroclinic quasi-geostrophic model forced with a horizontally uniform and vertically sheared background flow generates mesoscale eddies and jets that are tilted from the zonal direction and drift with constant speed. The governing dynamical equations are rewritten in a tilted frame of reference moving with the jets, and the cross-jet time-mean profiles of the linear and nonlinear stress terms are analysed. Here, the linear stress terms are present because of the zonally asymmetric topography. It is demonstrated that the linear dynamics controls the drift mechanism. Also, it is found that the drifting jets are directly forced by the imposed vertical shear, whereas the eddies oppose the jets, although this is limited to continuously forced dissipative systems. This role of the eddies is opposite to the one in the classical baroclinic model of stationary, zonally symmetric multiple jets. This is expected to be more generic in the ocean, which is zonally asymmetric nearly everywhere.

We present a timing analysis of multiple XMM-Newton and NuSTAR observations of the ultra-luminous pulsar NGC 7793 P13 spread over its 65d variability period. We use the measured pulse periods to determine the orbital ephemeris, confirm a long orbital period with P_orb = 63.9 (+0.5,-0.6) d, and find an eccentricity of e, 10 pages, 6 figures, accepted for publication in A&A

Background: Preferred models for the pharmacokinetic analysis of [C-11] flumazenil human studies have been previously established. However, direct translation of these models and settings to animal studies might be sub-optimal. Therefore, this study evaluates pharmacokinetic models for the quantification of [11C] flumazenil binding in the rat brain.Dynamic (60 min) [11C] flumazenil brain PET scans were performed in two groups of male Wistar rats (tracer dose (TD), n = 10 and pre-saturated (PS), n = 2). Time-activity curves from five regions were analyzed, including the pons (pseudo-reference region). Distribution volume (V-T) was calculated using one-and two-tissue compartment models (1TCM and 2TCM) and spectral analysis (SA). Binding potential (BPND) was determined from full and simplified reference tissue models with one or two compartments for the reference tissue (FRTM, SRTM, and SRTM-2C). Model preference was determined by Akaike information criterion (AIC), while parameter agreement was assessed by linear regression, repeated measurements ANOVA and Bland-Altman plots.Results: 1TCM and 2TCM fits of regions with high specific binding showed similar AIC, a preference for the 1TCM, and good VT agreement (0.1% difference). In contrast, the 2TCM was markedly preferred and necessary for fitting low specific-binding regions, where a worse V-T agreement (17.6% difference) and significant VT differences between the models (p Conclusions: Although the 1TCM was sufficient for the quantification of high specific-binding regions, the 2TCM was found to be the most adequate for the quantification of [C-11] flumazenil in the rat brain based on (1) higher fit quality, (2) lower AIC values, and (3) ability to provide reliable fits for all regions. Reference models resulted in negatively biased BPND and were affected by specific binding in the pons of the rat.

BackgroundAutonomic nervous system dysfunction is common after acute stroke and is associated with elevated risk of cardiac arrhythmia and mortality. Heart rate variability and baroreceptor sensitivity have been investigated as parameters of autonomic nervous system dysfunction for the prediction of stroke outcome.SummaryWe performed a systematic literature review on heart rate variability and baroreceptor sensitivity as parameters for autonomic nervous function in acute stroke. Twenty-two studies were included. Associations between heart rate variability or baroreceptor sensitivity and stroke severity, early and late complications, dependency and mortality were reported. However, interpretability of most studies and extrapolation to general stroke population are limited due to many confounding factors such as varying methodology, small sample sizes, survival selection, and exclusion of patients with frequently occurring comorbidities in stroke. Key issues, such as the effect of thrombolytic therapy on autonomic function, autonomic nervous system dysfunction in the hyperacute phase of stroke, and correlation with the risk of recurrent stroke have not been investigated. Also, nonlinear techniques have remained largely unexplored in this domain, in spite of their advantage to provide more solid evaluation in the occurrence of arrhythmia.Key messagesCardiac autonomic dysfunction, represented by reduced heart rate variability or impaired baroreceptor sensitivity, is associated with stroke severity, early and late complications, dependency, and mortality. Large-scale prospective studies applying internationally accepted standards of measures for analysis of heart rate variability and baroreceptor sensitivity are needed in patients with acute stroke.

L-[1-11C]leucine PET can be used to measure in vivo protein synthesis in the brain. However, the relationship between regional protein synthesis and on-going neural dynamics is unclear. We use a graph theoretical approach to examine the relationship between cerebral protein synthesis (rCPS) and both static and dynamical measures of functional connectivity (measured using resting state functional MRI, R-fMRI). Our graph theoretical analysis demonstrates a significant positive relationship between protein turnover and static measures of functional connectivity. We compared these results to simple measures of metabolism in the cortex using [18F]FDG PET). Whilst some relationships between [18F]FDG binding and graph theoretical measures was present, there remained a significant relationship between protein turnover and graph theoretical measures, which were more robustly explained by L-[1-11C]Leucine than [18F]FDG PET. This relationship was stronger in dynamics at a faster temporal resolution relative to dynamics measured over a longer epoch. Using a Dynamic connectivity approach, we also demonstrate that broad-band dynamic measures of Functional Connectivity (FC), are inversely correlated with protein turnover, suggesting greater stability of FC in highly interconnected hub regions is supported by protein synthesis. Overall, we demonstrate that cerebral protein synthesis has a strong relationship independent of tissue metabolism to neural dynamics at the macroscopic scale., Highlights • Spontaneous, neural dynamics are fundamental for information processing & function • We combine L-[1-11C]leucine PET with Static and Dynamic measures of network topology • ‘Hub’ brain regions are linked to increased protein synthesis, independent of tissue metabolism • Stability of network hubs is inversely correlated with protein turnover • Strong, stable hubs are supported by protein synthesis

Challenges and pitfalls for developing age models for long lacustrine sedimentary records are discussed and a comparison is made between radiocarbon dating, visual curve matching, and frequency analysis in the depth domain in combination with cyclostratigraphy. A core section of the high resolution 284-ka long temperature record developed from Lake Fuquene in the Northern Andes is used to explore four different age models (a-d). (a) A model based on 46 AMS C-14 dates of bulk sediment is hampered by low concentrations of organic carbon. (b) A model based on the comparison of the radiocarbon dated pollen record to the well-established record from Cariaco Basin using curve matching and visual tie points. For the upper 26 m of the core this approach yields an age interval of 28-59.5 ka. (c) Another age model is based on curve matching and the Intcal09 radiocarbon calibration curve, yielding an age range of 22.5-80.4 ka for the same core interval. (d) Finally, a model is developed based on spectral analysis in the depth domain of the temperature-related altitudinal migrations of the upper forest line. This method identifies periodicities without a pre-conceived idea of age. The main frequency of 9.07 m appears to reflect the 41-kyr orbital signal of obliquity, which is tuned to the filtered 41-kyr temperature signal from the well-constrained LRO4 marine benthic delta O-18 stack record (Lisiecki and Raymo, 2005). Using this last age-modelling approach, the upper 26 m of core Fq-9C yields a temporal interval of 27-133 ka. Problems arising from radiocarbon dating carbon poor sediments from a large lake are addressed and the visual curve matching approach is compared to the analysis of cyclic changes in sediment records in developing an age model. We conclude that the frequency analysis and cyclostratigraphy model is the most reliable one of the four approaches. These results show that cyclostratigraphy may provide a useful method for developing an age model for long terrestrial records including multiple orbital cycles. (C) 2014 Elsevier B.V. Al! rights reserved.

Misperception of Sleep Onset Latency, often found in Primary Insomnia, has been cited to be influenced by hyperarousal, reflected in EEG- and ECG-related indices. The aim of this retrospective study was to examine the association between Central Nervous System (i.e. EEG) and Autonomic Nervous System activity in the Sleep Onset Period and the first NREM sleep cycle in Primary Insomnia (n = 17) and healthy controls (n = 11). Furthermore, the study examined the influence of elevated EEG and Autonomic Nervous System activity on Stage2 sleep-protective mechanisms (K-complexes and sleep spindles). Confirming previous findings, the Primary Insomnia-group overestimated Sleep Onset Latency and this overestimation was correlated with elevated EEG activity. A higher amount of beta EEG activity during the Sleep Onset Period was correlated with the appearance of K-complexes immediately followed by a sleep spindle in the Primary Insomnia-group. This can be interpreted as an extra attempt to protect sleep continuity or as a failure of the sleep-protective role of the K-complex by fast EEG frequencies following within one second. The strong association found between K-alpha (K-complex within one second followed by 8-12 Hz EEG activity) in Stage2 sleep and a lower parasympathetic Autonomic Nervous System dominance (less high frequency HR) in Slow-wave sleep, further assumes a state of hyperarousal continuing through sleep in Primary Insomnia. (C) 2013 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Introduction - The fruits of Vaccinium vitis-idaea L. are a valuable source of biologically active flavonoid derivatives. For studies focused on the purification of its quercetin glycosides (QGs) and related glycosides from plants and for the purpose of biological studies, the availability of numeric datasets from computer-assisted 1H iterative full spin analysis (HiFSA), that is, 1H-NMR fingerprinting, can replace and assist the repetitive and tedious two-dimensional NMR identification protocol required for both known and new compounds, respectively. Objective - To fully interpret the complex 1H-NMR fingerprints of eight QGs obtained from the berries of V. vitis-idaea and provide complete and unambiguous signal assignments. Methods - Vaccinium vitis-idaea QGs were purified in a single run by long-bed gel permeation chromatography and identified by comparison with commercially available compounds using LC–MS combining ion-trap and time-of-flight detection and one- or two-dimensional NMR. The HiFSA analysis yielded full sets of 1H chemical shifts and proton–proton coupling constants, allowing for field-independent spectral simulation. Results - Signal assignments were achieved for the reference standards and the QGs that dominated in purified fractions. However, even mixtures of two to three QGs could be fitted using the HiFSA approach. In the case of the overlapped sugar resonances, the initial fitting of the 1H spectra of reference compounds, together with values extracted from the two-dimensional NMR data and literature data, assisted in the process. Conclusion - The HiFSA method revealed for the first time the presence of Q-3-O-ß-glucopyranoside and Q-3-O-ß-glucuronopyranoside in the berries of V. vitis-idaea, and unambiguously confirmed the structures of Q-3-O-[4¿-(3-hydroxy-3-methylglutaroyl)]--rhamnopyranoside, Q-3-O--rhamnopyranoside, Q-3-O-ß-galactopyranoside, Q-3-O--arabinofuranoside, Q-3-O-ß-xylopyranoside and Q-3-O--arabinopyranoside.

In their article, Monfredi et al1 addressed the relationship between mean heart rate (HR) and its variability (HRV). In their carefully conducted experiments and with clear reasoning, they demonstrated a universal exponential decay-like relationship between HRV and HR and concluded from this that HRV cannot be used in any simple way to assess autonomic nerve activity to the heart. Like Stauss,2 we tend to agree with this conclusion as it points out a highly relevant and important issue in the field of cardiovascular medicine. However, we note that the complex biophysical model presented is merely an example of a restriction-of-range phenomena, which has been acknowledged in psychophysiological research3,4. These insights were not picked-up and included in the guiding HRV Task Force paper5 and likely, therefore, largely missed. Thirty years ago, Akselrod et al6 elegantly explained that the nature of the interbeat interval (IBI) time series can explain the relationship between HRV and HR. In …

Background: Social stressors are associated with an increased risk of psychosis. Stress sensitisation is thought to be an underlying mechanismand may be reflected in an altered autonomic stress response. Using an experimental Virtual Reality design, the autonomic stress response to social stressorswas examined in participantswith different liability to psychosis.Method: Fifty-five patients with recent onset psychotic disorder, 20 patients at ultra-high risk for psychosis, 42 siblings of patientswith psychosis and 53 controls were exposed to social stressors (crowdedness, ethnicminority status and hostility) in a Virtual Reality environment. Heart rate variability parameters and skin conductance levels were measured at baseline and during Virtual Reality experiments.Results: High psychosis liability groups had significantly increased heart rate and decreased heart rate variability compared to low liability groups both at baseline and during Virtual Reality experiments. Both low frequency (LF) and high frequency (HF) power were reduced, while the LF/HF ratiowas similar between groups. The number of virtual social stressors significantly affected heart rate, HF, LF/HF and skin conductance level. There was no interaction between psychosis liability and amount of virtual social stress.Conclusion: High liability to psychosis is associated with decreased parasympathetic activity in virtual social environments, which reflects generally high levels of arousal, rather than increased autonomic reactivity to social stressors. (C) 2016 Elsevier B.V. All rights reserved.

When measuring operator states the predictive power of cardiovascular and respiratory measures in relation to mental workload has been questioned. One of the main questions is to what extent do cardiovascular measures actually reflect mental workload. This question arises because good measures of mental workload should be sensitive to changes in mental effort alone and not to other influences or at least the changes associated with mental workload should be easy to isolate. In the case of cardiovascular measures, the physiological change brought on by the baroreflex is a compensatory control effect that can potentially overshadow changes in physiology due to mental effort and therefore reduce the usefulness of cardiovascular measures. However, this does not need to be the case. Despite the effects caused by the baroreflex differences in heart rate, heart rate variability and other cardiovascular measures associated with task related effort can still be found using short-term response patterns. The short-segment analysis approach described in this paper is based on a time-frequency method in which the spectral power of the cardiovascular measures in specified spectral bands is computed from small time segments, i.e. 30 s. To demonstrate the effectiveness of this technique two studies which made use of a simulation of an ambulance dispatcher's task are described, both with easy and difficult task conditions. A short-lasting increase in task demand was found to be reflected in short-lasting increases in heart rate and blood pressure in combination with corresponding decreases in heart rate variability and blood pressure variability. These effects were larger in easy task conditions than in hard conditions, likely due to a higher overall effort-level during the hard task conditions. However, the developed measures are still very sensitive to mental effort and if this brief segmentation approach is used cardiovascular measures show promise as good candidates for reflecting mental effort during the assessment of operator state. (C) 2012 Elsevier B.V. All rights reserved.

The autonomic nervous system (ANS) reacts to nociceptive stimulation during sleep, but whether this reaction is contingent to cortical arousal, and whether one of the autonomic arms (sympathetic/parasympathetic) predominates over the other remains unknown. We assessed ANS reactivity to nociceptive stimulation during all sleep stages through heart rate variability, and correlated the results with the presence of cortical arousal measured in concomitant 32-channel EEG. Fourteen healthy volunteers underwent whole-night polysomnography during which nociceptive laser stimuli were applied over the hand. RR intervals (RR) and spectral analysis by wavelet transform were performed to assess parasympathetic (HF(WV)) and sympathetic (LF(WV) and LF(WV)/HF(WV) ratio) reactivity. During all sleep stages, RR significantly decreased in reaction to nociceptive stimulations, reaching a level similar to that of wakefulness, at the 3rd beat post-stimulus and returning to baseline after seven beats. This RR decrease was associated with an increase in sympathetic LF(WV) and LF(WV)/HF(WV) ratio without any parasympathetic HF(WV) change. Albeit RR decrease existed even in the absence of arousals, it was significantly higher when an arousal followed the noxious stimulus. These results suggest that the sympathetic-dependent cardiac activation induced by nociceptive stimuli is modulated by a sleep dependent phenomenon related to cortical activation and not by sleep itself, since it reaches a same intensity whatever the state of vigilance.

Symmetric differential expressions @? of order n=2k with real valued coefficients give rise to self adjoint operators in the space of weighted square integrable functions. Characterization theorems exist in the literature that describe such self-adjoint operators. All such characterizations begin by constructing the maximal domain of definition of the expression @?. The Glazman-Krein-Naimark theorem constructs the maximal domain in terms of eigenfunctions corresponding to a nonreal parameter @l. Representations in terms of certain functions related to a real parameter @l can also be found in the literature. In this paper we construct the maximal domain from two complementary self-adjoint realizations of @?. One operator is assumed to be known and the other one is computed explicitly. From these two operators we explicitly give all other self-adjoint operators associated with @?. A special class of operators associated with @? is what we call Type I operators. They arise in connection with a certain bilinear form that results from the weak formulation of the expression @?. Depending on the deficiency index of @? and the properties of the bilinear form we can have two complementary self-adjoint operators (two Type I operators) and, as it turns out, one of them is the celebrated Friedrich Extension. The other operator appears to be new. As in the general case, using these two operators we give an explicit characterization of all other operators of the same Type I.

Hypotension or low blood pressure (BP) is a common problem in preterm neonates and has been associated with adverse short and long-term neurological outcomes. Deciding when and whether to treat hypotension relies on an understanding of the relationship between BP and brain functioning. This study aims to investigate the interaction (coupling) between BP and continuous multichannel unedited EEG recordings in preterm infants less than 32 weeks of gestational age. The EEG was represented by spectral power in four frequency sub-bands: 0.3 +/- 3 Hz, 3 +/- 8 Hz, 8 +/- 15 Hz and 15 +/- 30 Hz. BP was represented as mean arterial pressure (MAP). The level of coupling between the two physiological systems was estimated using linear and nonlinear methods such as correlation, coherence and mutual information. Causality of interaction was measured using transfer entropy. The illness severity was represented by the clinical risk index for babies (CRIB II score) and contrasted to the computed level of interaction. It is shown here that correlation and coherence, which are linear measures of the coupling between EEG and MAP, do not correlate with CRIB values, whereas adjusted mutual information, a nonlinear measure, is associated with CRIB scores (r = -0.57, p = 0.003). Mutual information is independent of the absolute values of MAP and EEG powers and quantifies the level of coupling between the short-term dynamics in both signals. The analysis indicated that the dominant causality is from changes in EEG producing changes in MAP. Transfer entropy (EEG to MAP) is associated with the CRIB score (0.3 +/- 3 Hz: r = 0.428, p = 0.033, 3 +/- 8 Hz: r = 0.44, p = 0.028, 8 +/- 15 Hz: r = 0.416, p = 0.038) and indicates that a higher level of directed coupling from brain activity to blood pressure is associated with increased illness in preterm infants. This is the first study to present the nonlinear measure of interaction between brain activity and blood pressure and to demonstrate its relation to the initial illness severity in the preterm infant. The obtained results allow us to hypothesise that the normal wellbeing of a preterm neonate can be characterised by a nonlinear coupling between brain activity and MAP, whereas the presence of weak coupling with distinctive directionality of information flow is associated with an increased mortality rate in preterms.

Autonomic nervous system (ANS) dysfunction is a potential mechanism connecting psychosocial stress to functional somatic disorders (FSD), such as chronic fatigue syndrome, fibromyalgia and irritable bowel syndrome. We present the first meta-analysis and systematic review of methodological study quality on the association between cardiac ANS dysfunction, measured as parasympathetic nervous system (PNS) activity using heart rate variability (HRV), and FSD. Literature search revealed 23 available studies including data on 533 FSD patients. Meta-analysis on a subgroup of 14 studies with suitable outcome measures indicated lower PNS activity in FSD patients compared to controls (weighted standardized mean difference (SMD)=-0.32, 95% CI -0.63 to -0.01, p=0.04). The reliability of this summary estimate was, however, significantly limited by unexplained heterogeneity in the effect sizes and potential publication bias (weighted SMD after correction for funnel plot asymmetry=0.01, 95% CI -0.34 to 0.36, p=0.95). The systematic review of overall methodological quality of HRV studies in FSD demonstrates that there is substantial room for improvement, especially in selection of healthy control subjects, blinding of researchers performing HRV measurements, report of adequate HRV outcomes, and assessment of and adjustment for potential confounders. Methodological study quality was, however, not a significant predictor of study findings. We conclude that current available evidence is not adequate to firmly reject or accept a role of ANS dysfunction in FSD. Quality criteria and recommendations to improve future research on HRV in FSD are provided.

Objective: To compare the output from a novel high power, wide separation laser Doppler flow probe (DP1-V2-HP, 4 mm, with IRLD20) with that of a standard flow probe (DP1-V2, 0.5 mm, with DRT4) (Moor UK) and to explore its potential for use in the noninvasive measurement of blood flow in deeper tissues in humans.Methods: Monte Carlo modeling was used to predict depths of light scattering in skin with each probe, geometry. Experimentally, forearm blood flow was measured at rest and during local warming of the skin surface and post occlusion reactive hyperaemia (PORH). Laser Doppler blood flux (LDF) and the power spectral density of its component frequency intervals, were compared.Results: Monte Carlo modeling indicated that while the majority of wide probe LD signal derives from deeper tissue, a significant portion is from superficial (dermal) tissue (and vice versa for standard probe). Perturbation of local blood flow differentially increased LDF and spectral power as measured by the two probes, with the standard skin probe showing a significantly greater response to local skin warming (pConclusions: These differences support our hypothesis that the wide probe is recording predominantly blood flux within the vasculature of sub-dermal tissue. This is in agreement with Monte Carlo simulation. (C) 2009 Elsevier Inc. All rights reserved.

In this study, we look for the mid-term variations in the daily average data of solar radius measurements made at the Solar Astrolabe Station of TUBITAK National Observatory (TUG) during solar cycle 23 for a time interval from 2000 February 26 to 2006 November 15. Due to the weather conditions and seasonal effect dependent on the latitude, the data series has the temporal gaps. For spectral analysis of the data series, thus, we use the Date Compensated Discrete Fourier Transform (DCDFT) and the CLEANest algorithm, which are powerful methods for irregularly spaced data. The CLEANest spectra of the solar radius data exhibit several significant mid-term periodicities at 393.2, 338.9, 206.5, 195.2, 172.3 and 125.4 days which are consistent with periods detected in several solar time series by several authors during different solar cycles. The knowledge relating to the origin of solar radius variations is not yet present. To see whether these variations will repeat in next cycles and to understand how the amplitudes of such variations change with different phases of the solar cycles, we need more systematic efforts and the long-term homogeneous data. Since most of the periodicities detected in the present study are frequently seen in solar activity indicators, it is thought that the physical mechanisms driving the periodicities of solar activity may also be effective in solar radius variations. (C) 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

This study examines effects of momentary changes in mental effort on cardiovascular and eye activity measures. A total of 19 male pilots performed an instrument flight task. Task load was manipulated by having the pilots perform flight manoeuvres of varying complexity. Multilevel analyses demonstrated clear effects of momentary changes in mental effort on both the cardiovascular and the eye activity measures. An increase in task load resulted in an increase of heart rate and a decrease in heart rate variability, mean dwell time and fixation duration. Heart rate differentiated between resting period and task execution. Heart rate variability from short data segments provided more insight in intermediate levels of mental effort. The eye activity measures were sensitive to intermediate levels of mental effort as well. Attitude changes resulted in an increase of mean dwell time and mean fixation duration. Task analysis is required to use eye measures as valid indices of mental effort. Having indications of the effects of changing mental demands during daily work of operators is of great importance nowadays. This paper presents an approach to estimate such effects on the basis of heart rate and eye activity measures. In particular, the use of averaged short-term heart rate variability measures is a relatively new aspect.

We investigated whether women show larger heart rate variability (HRV) than men after controlling for a large number of health-related covariates, using two indices of HRV, namely respiratory sinus arrhythmia (RSA) and approximate entropy (ApEn). In a twin design, the heritability of both indices was examined. The covariation between RSA and ApEn, a measure of heart rate dynamics derived from nonlinear dynamical systems theory, was decomposed into genetic and environmental components. Subjects were 196 male and 210 female middle-aged twins. Females showed larger HRV than men before (ApEn: p < .001; RSA: p = .052) and after adjustment for covariates (ApEn: p < .001; RSA: p = .015). This sex difference was confirmed by significant intrapair differences in the opposite-sex twin pairs for both ApEn (p < .001) and RSA (p = .03). In addition to sex, only heart period and age (both p < .001) were found to be independent predictors of ApEn, whereas RSA was also influenced by respiration rate and smoking (both p < .001). Age explained 16% and 6% of the variance in RSA and ApEn, respectively. Oral contraceptive use and menopausal status had no effect on HRV. Genetic model fitting yielded moderate heritability estimates for RSA (30%) and ApEn (40%) for both males and females. The correlation between RSA and ApEn (r = .60) could be attributed to genetic factors (48%), environmental factors (36%) and age (16%). The present study found support for a gender difference in HRV with women having greater HRV than men even after controlling for a large number of potential confounders. Indices of heart rate dynamics derived from nonlinear dynamical systems theory are moderately heritable and may be more sensitive than traditional indices of HRV to reveal subtle sex differences with important implications for health and disease.

The LISA Pathfinder mission will demonstrate the technology of drag-free test masses for use as inertial references in future space-based gravitational wave detectors. To accomplish this, the Pathfinder spacecraft will perform drag-free flight about a test mass while measuring the acceleration of this primary test mass relative to a second reference test mass. Because the reference test mass is contained within the same spacecraft, it is necessary to apply forces on it to maintain its position and attitude relative to the spacecraft. These forces are a potential source of acceleration noise in the LISA Pathfinder system that are not present in the full LISA configuration. While LISA Pathfinder has been designed to meet it's primary mission requirements in the presence of this noise, recent estimates suggest that the on-orbit performance may be limited by this `suspension noise'. The drift-mode or free-flight experiments provide an opportunity to mitigate this noise source and further characterize the underlying disturbances that are of interest to the designers of LISA-like instruments. This article provides a high-level overview of these experiments and the methods under development to analyze the resulting data., 13 pages, 5 figures. Accepted to Journal Of Physics, Conference Series. Presented at 10th International LISA Symposium, May 2014, Gainesville, FL, USA

Background Autonomic nervous system dysfunction is common after acute stroke and is associated with elevated risk of cardiac arrhythmia and mortality. Heart rate variability and baroreceptor sensitivity have been investigated as parameters of autonomic nervous system dysfunction for the prediction of stroke outcome. Summary We performed a systematic literature review on heart rate variability and baroreceptor sensitivity as parameters for autonomic nervous function in acute stroke. Twenty-two studies were included. Associations between heart rate variability or baroreceptor sensitivity and stroke severity, early and late complications, dependency and mortality were reported. However, interpretability of most studies and extrapolation to general stroke population are limited due to many confounding factors such as varying methodology, small sample sizes, survival selection, and exclusion of patients with frequently occurring comorbidities in stroke. Key issues, such as the effect of thrombolytic therapy on autonomic function, autonomic nervous system dysfunction in the hyperacute phase of stroke, and correlation with the risk of recurrent stroke have not been investigated. Also, nonlinear techniques have remained largely unexplored in this domain, in spite of their advantage to provide more solid evaluation in the occurrence of arrhythmia. Key messages Cardiac autonomic dysfunction, represented by reduced heart rate variability or impaired baroreceptor sensitivity, is associated with stroke severity, early and late complications, dependency, and mortality. Large-scale prospective studies applying internationally accepted standards of measures for analysis of heart rate variability and baroreceptor sensitivity are needed in patients with acute stroke.

Background. Neuroticism is widely used as an explanatory concept in etiological research of psychopathology. In order to clarify what neuroticism actually represents, we investigated the genetic association between neuroticism and cardiovascular measures.Method. In 125 female twin pairs (18–30 years), electrocardiogram and continuous finger blood pressure were assessed during two rest and two mental stress conditions. Mean values for baroreflex sensitivity (BRS), heart rate variability (HRV) and inter-beat interval (IBI) were calculated for each condition. Neuroticism was assessed by multiple questionnaires. Multivariate genetic model-fitting analyses were used to investigate the genetic correlation between latent neuroticism and the cardiovascular autonomic nervous system (ANS) measures.Results. Neuroticism was negatively correlated to BRS and HRV. Neuroticism was not correlated to IBI. For BRS, this phenotypical relation was entirely determined by shared genetic influences. For HRV, the genetic contribution to the phenotypical correlation was not significant, but the proportions of explained covariance showed a trend of more genetic than environmental influences on the phenotypical relationship.Conclusions. High neuroticism is associated with a deregulated ANS. Pleiotropic genetic effects may be partly responsible for this effect.

Objective: Baroreflex sensitivity (BRS) is a predictor of cardiovascular mortality and an indicator of sympathetic and parasympathetic autonomic regulation. Although the BRS is influenced by genetic factors, the evidence is limited, and it is unknown whether contributions of genes and environment to individual variation in BRS differ during rest and mental stress conditions.Design and methods: In 250 female twins, electrocardiogram and continuous finger blood pressure (BP) were assessed during two rest and two mental stress conditions. BRS was calculated as the mean modulus between inter-beat-interval and systolic BP. Genetic model fitting was used to investigate the relative contribution of genetic and environmental influences to individual differences in the BRS measures.Results: Familial resemblance for all conditions was found which was clearly mainly due to genetic contributions. A trend was found for higher genetic influences in the mental stress conditions (42 and 45%) compared to rest conditions (14 and 22%), and higher shared environmental effects in rest conditions (14 and 16%) compared to mental stress conditions (0.5 and 1%). Although their magnitude differed, the same genetic and shared environmental factors affected individual differences in BRS in all four conditions.Conclusion: Genetic influences explained up to 45% of the individual variation in BRS. This considerable proportion of genetic variance would make BRS an useful candidate trait for future association and linkage studies in the search for genes influencing autonomic regulation and cardiovascular disease risk.