Publications

Moderate exercise has well-founded benefits in cardiovascular health. However, increasing, yet controversial, evidence suggests that extremely trained athletes may not be protected from cardiovascular events as much as moderately trained individuals. In our rodent model, intensive but not moderate training promoted aorta and carotid stiffening and elastic lamina ruptures, tunica media thickening of intramyocardial arteries, and an imbalance between vasoconstrictor and relaxation agents. An up-regulation of angiotensin-converter enzyme, miR-212, miR-132, and miR-146b might account for this deleterious remodeling. Most changes remained after a 4-week detraining. In conclusion, our results suggest that intensive training blunts the benefits of moderate exercise. (C) 2022 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.

JTD Keywords: atherosclerosis, cacs, coronary artery calcium score, cad, coronary artery disease, coronary artery disease, cv, cardiovascular, endurance exercise, extreme sport, mmp9, matrix metalloproteinase 9, no, nitric oxide, phe, phenylephrine, vsmc, vascular smooth muscle cell, Age, Atherosclerosis, Cacs, coronary artery calcium score, Cad, coronary artery disease, Coronary artery disease, Coronary atherosclerosis, Cv, cardiovascular, Disease, Endurance exercise, Extreme sport, Metalloproteinases, Micrornas, Mmp9, matrix metalloproteinase 9, No, nitric oxide, Phe, phenylephrine, Physical-activity, Prevalence, Rats, Relevance, Risk, Vascular stiffening, Vsmc, vascular smooth muscle cell

Patients with spinal cord injury (SCI) have an increased risk of sleep-disordered breathing (SDB), which can lead to serious comorbidities and impact patients’ recovery and quality of life. However, sleep tests are rarely performed on SCI patients, given their multiple health needs and the cost and complexity of diagnostic equipment. The objective of this study was to use a novel smartphone system as a simple non-invasive tool to monitor SDB in SCI patients. We recorded pulse oximetry, acoustic, and accelerometer data using a smartphone during overnight tests in 19 SCI patients and 19 able-bodied controls. Then, we analyzed these signals with automatic algorithms to detect desaturation, apnea, and hypopnea events and monitor sleep position. The apnea–hypopnea index (AHI) was significantly higher in SCI patients than controls (25 ± 15 vs. 9 ± 7, p < 0.001). We found that 63% of SCI patients had moderate-to-severe SDB (AHI ? 15) in contrast to 21% of control subjects. Most SCI patients slept predominantly in supine position, but an increased occurrence of events in supine position was only observed for eight patients. This study highlights the problem of SDB in SCI and provides simple cost-effective sleep monitoring tools to facilitate the detection, understanding, and management of SDB in SCI patients.

JTD Keywords: apnea syndrome, biomedical signal processing, individuals, mhealth, monitoring, nasal resistance, people, position, prevalence, questionnaire, sample, sleep apnea, sleep position, sleep-disordered breathing, smartphone, time, Apnea-hypopnea indices, Biomedical signal processing, Biomedical signals processing, Cost effectiveness, Diagnosis, Mhealth, Monitoring, Noninvasive medical procedures, Oximeters, Oxygen-saturation, Patient rehabilitation, Simple++, Sleep apnea, Sleep position, Sleep research, Sleep-disordered breathing, Smart phones, Smartphone, Smartphones, Spinal cord injury, Spinal cord injury patients

The mechanisms of how the brain orchestrates multi-limb joint action have yet to be elucidated and few computational sensorimotor (SM) learning approaches have dealt with the problem of acquiring bimanual affordances. We propose a series of bidirectional (forward/inverse) SM maps and its associated learning processes that generalize from uni- to bimanual interaction (and affordances) naturally, reinforcing the motor equivalence property. The SM maps range from a SM nature to a solely sensory one: full body control, delta SM control (through small action changes), delta sensory co-variation (how body-related perceptual cues covariate with object-related ones). We make several contributions on how these SM maps are learned: (1) Context and Behavior-Based Babbling: generalizing goal babbling to the interleaving of absolute and local goals including guidance of reflexive behaviors; (2) Event-Based Learning: learning steps are driven by visual, haptic events; and (3) Affordance Gradients: the vectorial field gradients in which an object can be manipulated. Our modeling of bimanual affordances is in line with current robotic research in forward visuomotor mappings and visual servoing, enforces the motor equivalence property, and is also consistent with neurophysiological findings like the multiplicative encoding scheme.

JTD Keywords: Affordances, Bimanual affordances, Goal babbling, Interlimb coordination, Motor equivalence, Sensorimotor learning

Estimating the affective state of a user during a computer task traditionally relies on either subjective reports or analysis of physiological signals, facial expressions, and other measures. These methods have known limitations, can be intrusive and may require specialized equipment. An alternative would be employing a ubiquitous device of everyday use such as a standard keyboard. Here we investigate if we can infer the emotional state of a user by analyzing their typing patterns. To test this hypothesis, we asked 400 participants to caption a set of emotionally charged images taken from a standard database with known ratings of arousal and valence. We computed different keystroke pattern dynamics, including keystroke duration (dwell time) and latency (flight time). By computing the mean value of all of these features for each image, we found a statistically significant negative correlation between dwell times and valence, and between flight times and arousal. These results highlight the potential of using keystroke dynamics to estimate the affective state of a user in a non-obtrusive way and without the need for specialized devices.

JTD Keywords: Feature extraction, Correlation, Keyboards, Task analysis, Statistical analysis, Affective computing, Standards, Keystroke, Keyboard, Typing, Arousal, Valence, Affect