From Motion to Emotion : Accelerometer Data Predict Subjective Experience of Music

Music is often discussed to be emotional because it reflects expressive movements in audible form. Thus, a valid approach to measure musical emotion could be to assess movement stimulated by music. In two experiments we evaluated the discriminative power of mobile-device generated acceleration data produced by free movement during music listening for the prediction of ratings on the Geneva Emotion Music Scales (GEMS-9). The quality of prediction for different dimensions of GEMS varied between experiments for tenderness (R12(first experiment) = 0.50, R22(second experiment) = 0.39), nostalgia (R12 = 0.42, R22 = 0.30), wonder (R12 = 0.25, R22 = 0.34), sadness (R12 = 0.24, R22 = 0.35), peacefulness (R12 = 0.20, R22 = 0.35) and joy (R12 = 0.19, R22 = 0.33) and transcendence (R12 = 0.14, R22 = 0.00). For others like power (R12 = 0.42, R22 = 0.49) and tension (R12 = 0.28, R22 = 0.27) results could be almost reproduced. Furthermore, we extracted two principle components from GEMS ratings, one representing arousal and the other one valence of the experienced feeling. Both qualities, arousal and valence, could be predicted by acceleration data, indicating, that they provide information on the quantity and quality of experience. On the one hand, these findings show how music-evoked movement patterns relate to music-evoked feelings. On the other hand, they contribute to integrate findings from the field of embodied music cognition into music recommender systems

Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling

<p>Abstract</p> <p>Background</p> <p>With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.</p> <p>Results</p> <p>Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.</p> <p>Conclusion</p> <p>SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.</p

Circadian and cyclic environmental determinants of blood pressure patterning and implications for therapeutic interventions

Blood pressure (BP) exhibits significant 24 h variation; in most normotensive and uncomplicated hypertensive persons, BP declines during the first half of nighttime sleep by 10–20% from its daytime mean level, starts rising in the second half of sleep, further increases with commencement of diurnal activity, and peaks in the afternoon or early evening. Environmental 24 h cycles of temperature and noise; behavior-driven nyctohemeral patterning of food, liquid, and stimulant consumption, posture, mental and emotional stress, and physical activity; plus innate circadian rhythms in wake/sleep, autonomic nervous, hypothalamic-pituitary-adrenal, renal hemodynamic, opioid, renin-angiotensin-aldosterone, endothelial, and vasoactive peptide systems constitute the key determinants of the BP day/night variation. The current perspective is the environmental and behavioral cycles are far more influential than the innate circadian ones in determining the BP nyctohemeral profile. Yet, the facts that the: (i) BP 24h pattern of secondary hypertension, e.g., diabetes and other endocrine disorders, renal disease, heart failure, is different -- BP fails to decline as expected during nighttime sleep typically due to pathological alteration of autonomic nervous system and other influential circadian rhythms, and (ii) scheduling of conventional long-acting medications at bedtime, rather than in the morning, results in much better hypertension control and vascular risk reduction, presumably because highest drug concentration coincides closely with the peak of most key circadian determinants of the BP 24h profile, indicates the endogenous rhythmic influences are of much greater importance than previously appreciate