The voluntary control of piloerection

Autonomic nervous systems in the human body are named for their operation outside of conscious control. One rare exception is voluntarily generated piloerection (VGP)—the conscious ability to induce goosebumps—whose physiological study, to our knowledge, is confined to three single-individual case studies. Very little is known about the physiological nature and emotional correlates of this ability. The current manuscript assesses physiological, emotional, and personality phenomena associated with VGP in a sample of thirty-two individuals. Physiological descriptions obtained from the sample are consistent with previous reports, including stereotypical patterns of sensation and action. Most participants also reported that their VGP accompanies psychological states associated with affective states (e.g., awe) and experience (e.g., listening to music), and higher than typical openness to new experiences. These preliminary findings suggest that this rare and unusual physiological ability interacts with emotional and personality factors, and thus merits further study

Polaritonic molecular clock for all-optical ultrafast imaging of wavepacket dynamics without probe pulses

Conventional approaches to probing ultrafast molecular dynamics rely on the use of synchronized laser pulses with a well-defined time delay. Typically, a pump pulse excites a molecular wavepacket. A subsequent probe pulse can then dissociate or ionize the molecule, and measurement of the molecular fragments provides information about where the wavepacket was for each time delay. Here, we propose to exploit the ultrafast nuclear-position-dependent emission obtained due to large light–matter coupling in plasmonic nanocavities to image wavepacket dynamics using only a single pump pulse. We show that the time-resolved emission from the cavity provides information about when the wavepacket passes a given region in nuclear configuration space. This approach can image both cavity-modified dynamics on polaritonic (hybrid light–matter) potentials in the strong light–matter coupling regime and bare-molecule dynamics in the intermediate coupling regime of large Purcell enhancements, and provides a route towards ultrafast molecular spectroscopy with plasmonic nanocavitiesThis work has been funded by the European Research Council grant ERC-2016-STG-714870 and the Spanish Ministry for Science, Innovation, and Universities—AEI grants RTI2018-099737-B-I00, PCI2018-093145 (through the QuantERA program of the European Commission), and CEX2018-000805-M (through the María de Maeztu program for Units of Excellence in R&D

Accuracy of advanced versus strictly conventional 12-lead ECG for detection and screening of coronary artery disease, left ventricular hypertrophy and left ventricular systolic dysfunction

<p>Abstract</p> <p>Background</p> <p>Resting conventional 12-lead ECG has low sensitivity for detection of coronary artery disease (CAD) and left ventricular hypertrophy (LVH) and low positive predictive value (PPV) for prediction of left ventricular systolic dysfunction (LVSD). We hypothesized that a ~5-min resting 12-lead <it>advanced </it>ECG test ("A-ECG") that combined results from both the advanced and conventional ECG could more accurately screen for these conditions than strictly conventional ECG.</p> <p>Methods</p> <p>Results from nearly every conventional and advanced resting ECG parameter known from the literature to have diagnostic or predictive value were first retrospectively evaluated in 418 healthy controls and 290 patients with imaging-proven CAD, LVH and/or LVSD. Each ECG parameter was examined for potential inclusion within multi-parameter A-ECG scores derived from multivariate regression models that were designed to optimally screen for disease in general or LVSD in particular. The performance of the best retrospectively-validated A-ECG scores was then compared against that of optimized pooled criteria from the strictly conventional ECG in a test set of 315 additional individuals.</p> <p>Results</p> <p>Compared to optimized pooled criteria from the strictly conventional ECG, a 7-parameter A-ECG score validated in the training set increased the sensitivity of resting ECG for identifying disease in the test set from 78% (72-84%) to 92% (88-96%) (P < 0.0001) while also increasing specificity from 85% (77-91%) to 94% (88-98%) (P < 0.05). In diseased patients, another 5-parameter A-ECG score increased the PPV of ECG for LVSD from 53% (41-65%) to 92% (78-98%) (P < 0.0001) without compromising related negative predictive value.</p> <p>Conclusion</p> <p>Resting 12-lead A-ECG scoring is more accurate than strictly conventional ECG in screening for CAD, LVH and LVSD.</p

Magic Curiosity Arousing Tricks (MagicCATs): a novel stimulus collection to induce epistemic emotions

There has been considerable interest in empirical research on epistemic emotions, i.e. emotions related to knowledge-generating qualities of cognitive tasks and activities such as curiosity, interest, and surprise. One big challenge when studying epistemic emotions is systematically inducting these emotions in restricted experimental settings. The current study created a novel stimulus set called Magic Curiosity Arousing Tricks (MagicCATs): a collection of 166 short magic trick video clips that aim to induce a variety of epistemic emotions. MagicCATs are available for research, and can be used in a variety of ways to examine epistemic emotions. Rating data also supports that the magic tricks elicit a variety of epistemic emotions with sufficient inter-stimulus variability, demonstrating good psychometric properties for their use in psychological experiments

Cardiovascular Risk Stratification in Decision Support Systems: A Probabilistic Approach. Application to pHealth

There is a growing demand for developing personalized and non-hospital based care systems to improve the management of cardiac care. The EPI- MEDICS project has designed a personal ECG monitor (PEM) capable of recording a simplified 4-electrode, professional quality 3-lead ECG, detecting arrhythmias and ischemia by means of committees of Artificial Neural Networks (ANN), and alerting the relevant health care professionals. Our objective is to improve the patient risk stratification and to reduce the number of false positive and false negative alarms by taking into account the demographic and clinical data featured by the user's electronic health record (EHR) stored in the PEM device. To design and assess such a new type of system, we adopted a decision making solution based on Bayesian networks (BN) that we trained to predict the risk of a cardiovascular event (infarction, stroke, or cardiovascular death) based on a set of demographic and clinical data (age, BMI, etc.) as provided by the INDANA database, from which we randomly extracted a training set of 15013 subjects and a testing set of 5004 subjects. The BN is then compared to an ANN committee (N=50) and a logistic regression (LR) model in terms of sensitivity, specificity, and area under the ROC curve (AUC). AUC = 0.80 for the BN, 0.75 for the ANN committee and 0.74 for the LR model. The Bayesian network approach achieved a high overall accuracy over both the neural network and logistic regression models on the testing set, and therefore can be useful in pHealth systems such as the PEM