Independent influence of behavioral stressors on platelet function.

<p>WBA, platelet count, and plasma epinephrine were increased by each of the three stressors, with a (partial) recovery during recovery. In contrast, while the platelet surface markers showed a gradual increase across the approximately 3-h test battery, there was no consistent increase and recovery caused by the three stressors. Mental, mental stress test; tilt, passive head up tilt table test; exercise, cycle exercise test; B, baseline; S, stress test; R, recovery; x-axes, minutes from first blood sample within test battery; left y-axes, absolute values; right y-axes, data expressed as a percentage of each individual’s mean values across the forced desynchrony protocol; error bars, SEM; P-values, significance for effect of time across full stress test battery (9 time points); *, significance for change between consecutive samples (from baseline to stress test and from stress test to recovery). Note platelet ATP release is not shown (see above text).</p

Protocol.

<p>Rasterplot, including two baseline days, twelve 20-h cycles, and discharge day of an example subject with a habitual bedtime of midnight. Blue bars, baseline and discharge wake episodes in normal room light (∼90 lux); solid back bars, scheduled sleep (0 lux); gray bars, wake episodes in dim light (∼1.8 lux); red bars, timing of the test batteries; dotted line illustrates the circadian core body temperature minimum throughout the protocol, with an average circadian period of 24.09h in these subjects. Each test battery consisted of a mental, tilt, and exercise stress (S) test, each preceded and followed by a baseline (B) and recovery (R) episode. The timing of the blood draws is indicated as red filled circles.</p

Endogenous circadian rhythms in platelet function from cosinor analyses.

<p>Endogenous circadian rhythms in platelet function from cosinor analyses.</p

The fractal patterns of multi-unit neural activity fluctuations <i>in vivo</i> and the non-fractal patterns of multi-unit activity fluctuations <i>in vitro</i>.

<p>(<b>A</b>) Results of two representative individual mice: one for <i>in vivo</i> recordings during LD and DD and one for the <i>in vitro</i> recording. Corresponding raw data are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048927#pone-0048927-g001" target="_blank">Figure 1A</a>. (<b>B</b>) The group averages of mice. (<b>C</b>) Results of two representative individual rats: one for <i>in vivo</i> recordings during LD and DD and one for the <i>in vitro</i> recording. Corresponding raw data are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048927#pone-0048927-g001" target="_blank">Figure 1B</a>. (<b>D</b>) The group averages of rats. Data were shown in log-log plots and were vertically shifted for a better visualization of differences between the <i>in vivo</i> and <i>in vitro</i> recordings. At time scales from ∼1 minute up to ∼10 hours, the function <i>in vivo</i> shows a power-law form (straight line in the log-log plot) with the scaling exponent α≈1.0, indicating strong fractal correlations in raw data. For the group averages, the data of each subject were normalized to account for individual differences in the standard deviation of multi-unit activity. The fractal pattern of the <i>in vivo</i> recordings is virtually identical during LD and DD and is consistent for both mice and rats. In contrast, the fluctuation function of multi-unit neural activity <i>in vitro</i> did not have a power-law form, indicating complete loss of the scale-invariant/fractal correlations. The non-fractal pattern of the <i>in vitro</i> activity is virtually identical for mice and rats, showing a local slope close to 0.5 at time scales of 1–6 minutes and >1.5 at time scales of 2–5 hours.</p

Single unit and subpopulation neural activity of <i>in vitro</i> SCN possess the same non-fractal fluctuation patterns as observed in <i>in vitro</i> multi-unit activity.

<p>(<b>A</b>) Examples of single unit, subpopulation, and multi-unit neural activity within the <i>in vitro</i> SCN of the same mouse. The recordings were selected to reflect the possibility that single-unit, subpopulation, and multi-unit data could show different circadian profiles as described before <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048927#pone.0048927-Schaap1" target="_blank">[18]</a>, e.g., the single-unit data peaked at Zeitgeber time 18 hours; the subpopulation data peaked at 7 and 18 hours, respectively; and the multi-unit activity peaked at 7 hours and 3 hours, respectively. (<b>B</b>) The fluctuation functions of the individual recordings (shown in panel A), and the group averages. Data were shown in log-log plots. The form of the function was almost identical for single-unit and subpopulation data as well as for the MUA recordings except for a vertical shift which indicates an expected difference in mean fluctuation amplitude. The subpopulation data were obtained from the analysis of action potentials with the target average firing rate of 10 Hz in the 30-minute windows centered at the peaks of MUA (see Methods). The form remained the same for different subpopulation data with different target average firing rate at MUA peak(s). The multi-unit results were vertically shifted for a better visualization of the similar non-power-law form as compared to single-unit and subpopulation results.</p

Multi-unit activity recordings of the SCNs from mice and rats.

<p>Representative recordings <i>in vivo</i> from (A) a mouse and (B) a rat during LD (Top panels) and DD (Middle panels), and representative recordings <i>in vitro</i> of a mouse and a rat (Bottom Panels). Gray bar indicates dark condition for <i>in vivo</i> recordings.</p

Circadian gene variants influence sleep and the sleep electroencephalogram in humans

<p>The sleep electroencephalogram (EEG) is highly heritable in humans and yet little is known about the genetic basis of inter-individual differences in sleep architecture. The aim of this study was to identify associations between candidate circadian gene variants and the polysomnogram, recorded under highly controlled laboratory conditions during a baseline, overnight, 8 h sleep opportunity. A candidate gene approach was employed to analyze single-nucleotide polymorphisms from five circadian-related genes in a two-phase analysis of 84 healthy young adults (28 F; 23.21 ± 2.97 years) of European ancestry. A common variant in <i>Period2</i> (<i>PER2</i>) was associated with 20 min less slow-wave sleep (SWS) in carriers of the minor allele than in noncarriers, representing a 22% reduction in SWS duration. Moreover, spectral analysis in a subset of participants (<i>n</i> = 37) showed the same <i>PER2</i> polymorphism was associated with reduced EEG power density in the low delta range (0.25–1.0 Hz) during non-REM sleep and lower slow-wave activity (0.75–4.5 Hz) in the early part of the sleep episode. These results indicate the involvement of <i>PER2</i> in the homeostatic process of sleep. Additionally, a rare variant in <i>Melatonin Receptor 1B</i> was associated with longer REM sleep latency, with minor allele carriers exhibiting an average of 65 min (87%) longer latency from sleep onset to REM sleep, compared to noncarriers. These findings suggest that circadian-related genes can modulate sleep architecture and the sleep EEG, including specific parameters previously implicated in the homeostatic regulation of sleep.</p