Sympathetic and parasympathetic modulation of pupillary unrest

Pupillary unrest is an established indicator of drowsiness or sleepiness. How sympathetic and parasympathetic activity contribute to pupillary unrest is not entirely unclear. In this study, we investigated 83 young healthy volunteers to assess the relationship of pupillary unrest to other markers of the autonomic nervous system. Sample entropy (SE) and the established pupillary unrest index (PUI) were calculated to characterize pupil size variability. Autonomic indices were derived from heart rate, blood pressure, respiration, and skin conductance. Additionally, we assessed individual levels of calmness, vigilance, and mood. In an independent sample of 26 healthy participants, we stimulated the cardiovagal system by a deep breathing test. PUI was related to parasympathetic cardiac indices and sleepiness. A linear combination of vagal heart rate variability [root mean square of heart beat interval differences (RMSSD)] and skin conductance fluctuations (SCFs) was suited best to explain interindividual variance of PUI. Complexity of pupil diameter (PD) variations correlated to indices of sympathetic skin conductance. Furthermore, we found that spontaneous fluctuations of skin conductance are accompanied by increases of pupil size. In an independent sample, we were able to corroborate the relation of PUI with RMSSD and skin conductance. A slow breathing test enhanced RMSSD and PUI proportionally to each other, while complexity of PD dynamics decreased. Our data suggest that the slow PD oscillations ( f < 0.15 Hz) quantified by PUI are related to the parasympathetic modulation. Sympathetic arousal as detected by SCFs is associated to transient pupil size increases that increase non-linear pupillary dynamics

Baroreflex Coupling Assessed by Cross-Compression Entropy

Estimating interactions between physiological systems is an important challenge in modern biomedical research. Here, we explore a new concept for quantifying information common in two time series by cross-compressibility. Cross-compression entropy (CCE) exploits the ZIP data compression algorithm extended to bivariate data analysis. First, time series are transformed into symbol vectors. Symbols of the target time series are coded by the symbols of the source series. Uncoupled and linearly coupled surrogates were derived from cardiovascular recordings of 36 healthy controls obtained during rest to demonstrate suitability of this method for assessing physiological coupling. CCE at rest was compared to that of isometric handgrip exercise. Finally, spontaneous baroreflex interaction assessed by CCEBRS was compared between 21 patients suffering from acute schizophrenia and 21 matched controls. The CCEBRS of original time series was significantly higher than in uncoupled surrogates in 89% of the subjects and higher than in linearly coupled surrogates in 47% of the subjects. Handgrip exercise led to sympathetic activation and vagal inhibition accompanied by reduced baroreflex sensitivity. CCEBRS decreased from 0.553 ± 0.030 at rest to 0.514 ± 0.035 during exercise (p &lt; 0.001). In acute schizophrenia, heart rate, and blood pressure were elevated. Heart rate variability indicated a change of sympathovagal balance. The CCEBRS of patients with schizophrenia was reduced compared to healthy controls (0.546 ± 0.042 vs. 0.507 ± 0.046, p &lt; 0.01) and revealed a decrease of blood pressure influence on heart rate in patients with schizophrenia. Our results indicate that CCE is suitable for the investigation of linear and non-linear coupling in cardiovascular time series. CCE can quantify causal interactions in short, noisy and non-stationary physiological time series

Using machine learning to estimate the calendar age based on autonomic cardiovascular function

IntroductionAging is accompanied by physiological changes in cardiovascular regulation that can be evaluated using a variety of metrics. In this study, we employ machine learning on autonomic cardiovascular indices in order to estimate participants’ age.MethodsWe analyzed a database including resting state electrocardiogram and continuous blood pressure recordings of healthy volunteers. A total of 884 data sets met the inclusion criteria. Data of 72 other participants with an BMI indicating obesity (&gt;30 kg/m²) were withheld as an evaluation sample. For all participants, 29 different cardiovascular indices were calculated including heart rate variability, blood pressure variability, baroreflex function, pulse wave dynamics, and QT interval characteristics. Based on cardiovascular indices, sex and device, four different approaches were applied in order to estimate the calendar age of healthy subjects, i.e., relevance vector regression (RVR), Gaussian process regression (GPR), support vector regression (SVR), and linear regression (LR). To estimate age in the obese group, we drew normal-weight controls from the large sample to build a training set and a validation set that had an age distribution similar to the obesity test sample.ResultsIn a five-fold cross validation scheme, we found the GPR model to be suited best to estimate calendar age, with a correlation of r=0.81 and a mean absolute error of MAE=5.6 years. In men, the error (MAE=5.4 years) seemed to be lower than that in women (MAE=6.0 years). In comparison to normal-weight subjects, GPR and SVR significantly overestimated the age of obese participants compared with controls. The highest age gap indicated advanced cardiovascular aging by 5.7 years in obese participants.DiscussionIn conclusion, machine learning can be used to estimate age on cardiovascular function in a healthy population when considering previous models of biological aging. The estimated age might serve as a comprehensive and readily interpretable marker of cardiovascular function. Whether it is a useful risk predictor should be investigated in future studies

Quasi-spherical ice in convective clouds

Homogeneous freezing of supercooled droplets occurs in convective systems in low and midlatitudes. This droplet-freezing process leads to the formation of a large amount of small ice particles, so-called frozen droplets, that are transported to the upper parts of anvil outflows, where they can influence the cloud radiative properties. However, the detailed microphysics and, thus, the scattering properties of these small ice particles are highly uncertain. Here, the link between the microphysical and optical properties of frozen droplets is investigated in cloud chamber experiments, where the frozen droplets were formed, grown, and sublimated under controlled conditions. It was found that frozen droplets developed a high degree of small-scale complexity after their initial formation and subsequent growth. During sublimation, the small-scale complexity disappeared, releasing a smooth and near-spherical ice particle. Angular light scattering and depolarization measurements confirmed that these sublimating frozen droplets scattered light similar to spherical particles: that is, they had angular light-scattering properties similar to water droplets. The knowledge gained from this laboratory study was applied to two case studies of aircraft measurements in midlatitude and tropical convective systems. The in situ aircraft measurements confirmed that the microphysics of frozen droplets is dependent on the humidity conditions they are exposed to (growth or sublimation). The existence of optically spherical frozen droplets can be important for the radiative properties of detraining convective outflows.Peer reviewe

Addressing climate change with behavioral science:A global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.</p

Addressing climate change with behavioral science: a global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors

Addressing climate change with behavioral science:A global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.</p

Novel genetic loci underlying human intracranial volume identified through genome-wide association

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five novel loci for intracranial volume and confirmed two known signals. Four of the loci are also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρgenetic=0.748), which indicated a similar genetic background and allowed for the identification of four additional loci through meta-analysis (Ncombined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, Parkinson’s disease, and enriched near genes involved in growth pathways including PI3K–AKT signaling. These findings identify biological underpinnings of intracranial volume and provide genetic support for theories on brain reserve and brain overgrowth