Likelihood Of Correlation Research Articles

Using CME Progenitors to Assess CME Geoeffectiveness

Abstract There have been a few previous studies claiming that the effects of geomagnetic storms strongly depend on the orientation of the magnetic cloud portion of coronal mass ejections (CMEs). Aparna & Martens, using halo-CME data from 2007 to 2017, showed that the magnetic field orientation of filaments at the location where CMEs originate on the Sun can be used to credibly predict the geoeffectiveness of the CMEs being studied. The purpose of this study is to extend their survey by analyzing the halo-CME data for 1996–2006. The correlation of filament axial direction on the solar surface and the corresponding Bz signatures at L1 are used to form a more extensive analysis for the results previously presented by Aparna & Martens. This study utilizes Solar and Heliospheric Observatory Extreme-ultraviolet Imaging Telescope 195 Å, Michelson Doppler Imager magnetogram images, and Kanzelhöhe Solar Observatory and Big Bear Solar Observatory Hα images for each particular time period, along with ACE data for interplanetary magnetic field signatures. Utilizing all these, we have found that the trend in Aparna & Martens’ study of a high likelihood of correlation between the axial field direction on the solar surface and Bz orientation persists for the data between 1996 and 2006, for which we find a match percentage of 65%.

Assessing multimodal speech-gesture coordination: correlation map analysis for signal comparison

Speech acoustics and body movements are systematically linked during speaking; however, there is no consensus regarding which acoustic and/or kinematic signals are relevant for this coordination nor what critical landmarks facilitate this linkage. To address this, we use correlation map analysis (CMA) of speech acoustics and body movement to assess the hypothesis that speech and gesture are coordinated at prosodically prominent speech regions. CMA allows for the analysis of correlation between any two continuous signals both instantaneously and at user-defined ranges of delay. In this study, we examine the relationship of the speech’s RMS acoustic amplitude signal with the dominant hand’s velocity signal at speech turns, phrase boundaries, acoustic amplitude peaks, and other (elsewhere) regions. We find that turns and phrase edges exhibit the greatest likelihood of positive correlation. Additionally, the likelihood of correlation is higher when the hand velocity signal is delayed with respect to the amplitude signal. These results suggest that speech and gesture may be strongly linked at speech turn landmarks for the purpose of signaling a floor exchange. Thus in addition to coordination for semantic and prosodic purposes, we propose that speech-gesture coordination can serve with prosody to signal speech turns in conversation. [Work supported by NIH.]

Probabilistic networks of blood metabolites in healthy subjects as indicators of latent cardiovascular risk.

The complex nature of the mechanisms behind cardiovascular diseases prevents the detection of latent early risk conditions. Network representations are ideally suited to investigate the complex interconnections between the individual components of a biological system that underlies complex diseases. Here, we investigate the patterns of correlations of an array of 29 metabolites identified and quantified in the plasma of 864 healthy blood donors and use a systems biology approach to define metabolite probabilistic networks specific for low and high latent cardiovascular risk. We adapted methods based on the likelihood of correlation and methods from information theory and combined them with resampling techniques. Our results show that plasma metabolite networks can be defined that associate with latent cardiovascular disease risk. The analysis of the networks supports our previous finding of a possible association between cardiovascular risk and impaired mitochondrial activity and highlights post-translational modifications (glycosilation and oxidation) of lipoproteins as a possible target-mechanism for early detection of latent cardiovascular risk.