Decreased Neuroautonomic Complexity in Men during an Acute Major Depressive Episode: Analysis of Heart Rate Dynamics

Major depression affects multiple physiologic systems. Therefore, analysis of signals that reflect integrated function may be useful in probing dynamical changes in this syndrome. Increasing evidence supports the conceptual framework that complex variability is a marker of healthy, adaptive control mechanisms and that dynamical complexity decreases with aging and disease. We tested the hypothesis that heart rate (HR) dynamics in non-medicated, young to middle-aged males during an acute major depressive episode would exhibit lower complexity compared with healthy counterparts. We analyzed HR time series, a neuroautonomically regulated signal, during sleep, using the multiscale entropy method. Our results show that the complexity of the HR dynamics is significantly lower for depressed than for non-depressed subjects for the entire night (P<0.02) and combined sleep stages 1 and 2 (P<0.02). These findings raise the possibility of using the complexity of physiologic signals as the basis of novel dynamical biomarkers of depression

A new perspective on turbulent Galactic magnetic fields through comparison of linear polarisation decomposition techniques

We compare two rotationally invariant decomposition techniques on linear polarization data: the spin-2 spherical harmonic decomposition in two opposite parities, the E- and B-mode, and the multiscale analysis of the gradient of linear polarization, |∇ P|. We demonstrate that both decompositions have similar properties in the image domain and the spatial frequency domain. They can be used as complementary tools for turbulence analysis of interstellar magnetic fields in order to develop a better understanding of the origin of energy sources for the turbulence, the origin of peculiar magnetic field structures and their underlying physics. We also introduce a new quantity |∇EB| based on the E- and B-modes and we show that in the intermediate- and small-scale limit |∇EB| |∇ P|. Analysis of the 2.3 GHz S-band Polarization All Sky Survey shows many extended coherent filament-like features appearing as ‘double jumps’ in the |∇ P| map that are correlated with negative and positive filaments of B-type polarization. These local asymmetries between the two polarization types, E and B, of the non-thermal Galactic synchrotron emission have an influence on the E- and B-mode power spectra analyses. The wavelet-based formalism of the polarization gradient analysis allows us to locate the position of E- or B-mode features responsible for the local asymmetries between the two polarization types. In analysed subregions, the perturbations of the magnetic field are trigged by star clusters associated with H II regions, the Orion–Eridanus superbubble and the North Polar Spur at low Galactic latitude

Needlet estimation of cross-correlation between CMB lensing maps and LSS

In this paper we develop a novel needlet-based estimator to investigate the crosscorrelation between cosmic microwave background (CMB) lensing maps and large-scale structure (LSS) data. We compare this estimator with its harmonic counterpart and, in particular, we analyze the bias effects of different forms of masking. In order to address this bias, we also implement a MASTER-like technique in the needlet case. The resulting estimator turns out to have an extremely good signal-to-noise performance. Our analysis aims at expanding and optimizing the operating domains in CMB-LSS cross-correlation studies, similarly to CMB needlet data analysis. It is motivated especially by next generation experiments (such as Euclid) which will allow us to derive much tighter constraints on cosmological and astrophysical parameters through cross-correlation measurements between CMB and LSS

Dark energy survey year 1 results: the photometric data set for cosmology

We describe the creation, content, and validation of the Dark Energy Survey (DES) internal year-one cosmology data set, Y1A1 GOLD, in support of upcoming cosmological analyses. The Y1A1 GOLD data set is assembled from multiple epochs of DES imaging and consists of calibrated photometric zero-points, object catalogs, and ancillary data products—e.g., maps of survey depth and observing conditions, star–galaxy classification, and photometric redshift estimates—that are necessary for accurate cosmological analyses. The Y1A1 GOLD wide-area object catalog consists of ~137 million objects detected in co-added images covering ~1800deg^2 in the DES grizY filters. The 10σ limiting magnitude for galaxies is g=23.4, r=23.2, i=22.5, z=21.8, and Y=20.1. Photometric calibration of Y1A1 GOLD was performed by combining nightly zero-point solutions with stellar locus regression, and the absolute calibration accuracy is better than 2% over the survey area. DES Y1A1 GOLD is the largest photometric data set at the achieved depth to date, enabling precise measurements of cosmic acceleration at z < 1

Photoactivate release of silver nanoparticles in cell membranemodels for bacterial control.

International audienceWe present a forward-modeling simulation framework designed to model the data products from the Dark Energy Survey (DES). This forward-model process can be thought of as a transfer function—a mapping from cosmological/astronomical signals to the final data products used by the scientists. Using output from the cosmological simulations (the Blind Cosmology Challenge), we generate simulated images (the Ultra Fast Image Simulator) and catalogs representative of the DES data. In this work we demonstrate the framework by simulating the 244 deg2 coadd images and catalogs in five bands for the DES Science Verification data. The simulation output is compared with the corresponding data to show that major characteristics of the images and catalogs can be captured. We also point out several directions of future improvements. Two practical examples—star-galaxy classification and proximity effects on object detection—are then used to illustrate how one can use the simulations to address systematics issues in data analysis. With clear understanding of the simplifications in our model, we show that one can use the simulations side-by-side with data products to interpret the measurements. This forward modeling approach is generally applicable for other upcoming and future surveys. It provides a powerful tool for systematics studies that is sufficiently realistic and highly controllable

Inference from the small scales of cosmic shear with current and future Dark Energy Survey data

Cosmic shear is sensitive to fluctuations in the cosmological matter density field, including on small physical scales, where matter clustering is affected by baryonic physics in galaxies and galaxy clusters, such as star formation, supernovae feedback and AGN feedback. While muddying any cosmological information that is contained in small scale cosmic shear measurements, this does mean that cosmic shear has the potential to constrain baryonic physics and galaxy formation. We perform an analysis of the Dark Energy Survey (DES) Science Verification (SV) cosmic shear measurements, now extended to smaller scales, and using the Mead et al. 2015 halo model to account for baryonic feedback. While the SV data has limited statistical power, we demonstrate using a simulated likelihood analysis that the final DES data will have the statistical power to differentiate among baryonic feedback scenarios. We also explore some of the difficulties in interpreting the small scales in cosmic shear measurements, presenting estimates of the size of several other systematic effects that make inference from small scales difficult, including uncertainty in the modelling of intrinsic alignment on nonlinear scales, `lensing bias', and shape measurement selection effects. For the latter two, we make use of novel image simulations. While future cosmic shear datasets have the statistical power to constrain baryonic feedback scenarios, there are several systematic effects that require improved treatments, in order to make robust conclusions about baryonic feedback

Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics

Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis

Stellar mass as a galaxy cluster mass proxy: application to the Dark Energy Survey redMaPPer clusters

We introduce a galaxy cluster mass observable, μ⋆, based on the stellar masses of cluster members, and we present results for the Dark Energy Survey (DES) Year 1 (Y1) observations. Stellar masses are computed using a Bayesian model averaging method, and are validated for DES data using simulations and COSMOS data. We show that μ⋆ works as a promising mass proxy by comparing our predictions to X-ray measurements. We measure the X-ray temperature–μ_{⋆} relation for a total of 129 clusters matched between the wide-field DES Y1 redMaPPer catalogue and Chandra and XMM archival observations, spanning the redshift range 0.1 < z < 0.7. For a scaling relation that is linear in logarithmic space, we find a slope of α = 0.488 ± 0.043 and a scatter in the X-ray temperature at fixed μ_{*} of σ1nT_{x}|μ_{*} = 0.266_{-0.020}^{+0.019} for the joint sample. By using the halo mass scaling relations of the X-ray temperature from the Weighing the Giants program, we further derive the μ⋆-conditioned scatter in mass, finding σ1nM|μ_{*} = 0.26_{-0.10}^{+0.15}. These results are competitive with well-established cluster mass proxies used for cosmological analyses, showing that μ_{⋆} can be used as a reliable and physically motivated mass proxy to derive cosmological constraints