OBJECTIVE While most studies on Central Sleep Apnea (CSA) have focused on breathing and metabolic disorders, the neuronal dysfunction that causes CSA remains largely unknown. Here, we investigate the underlying neuronal mechanism of CSA by studying the sleep-wake dynamics as derived from hypnograms. METHODS We analyze sleep data of seven groups of subjects healthy adults (n=48), adults with obstructive sleep apnea (OSA) (n=48), adults with CSA (n=25), healthy children (n=40), children with OSA (n=18), children with CSA (n=73) and CSA children treated with CPAP (n=10). We calculate sleep-wake parameters based on the probability distributions of wake-bout durations and sleep-bout durations. We compare these parameters with results obtained from a neuronal model that simulates the interplay between sleep- and wake-promoting neurons. RESULTS We find that sleep arousals of CSA patients show a characteristic time scale (i.e., exponential distribution) in contrast to the scale-invariant (i.e., power-law) distribution that has been reported for arousals in healthy sleep. Furthermore, we show that this change in arousal statistics is caused by triggering more arousals of similar durations, which through our model can be related to a higher excitability threshold in sleep-promoting neurons in CSA patients. CONCLUSIONS We propose a neuronal mechanism to shed light on CSA pathophysiology and a method to discriminate between CSA and OSA. We show that higher neuronal excitability thresholds can lead to complex reorganization of sleep-wake dynamics.  https://www.selleckchem.com/products/AdipoRon.html  SIGNIFICANCE The derived sleep parameters enable a more specific evaluation of CSA severity and can be used for CSA diagnosis and monitor CSA treatment.OBJECTIVE We attempt to reconstruct brachial arterial pressure (BAP) waves from finger arterial pressure waves measured using the vascular unloading technique without arm-cuff calibration. A novel method called two-level optimization (TOP) strategy is proposed as follows. METHODS We first derive a simplified transfer function (TF) based on a tube-load model with only two parameters to be estimated, a coefficient B and a time delay Δt. Then, at level one, two minimization problems are formulated to estimate the optimal coefficient Bopt and time delay ∆topt. Then, we can derive an optimal TF hopt(t). However, this derivation requires true (or reference) BAP waves. Therefore, at level two, we apply multiple linear regression (MLR) to further model the relationship between the derived optimal parameters and subjects' physiologic parameters. Hence, eventually, one can estimate coefficient BMLR and time delay ∆tMLR from subject's physiologic parameters to derive the MLR-based TF hMLR(t) for the BAP reconstruction. RESULTS Twenty-one volunteers were recruited for the data collection. The mean ± standard deviation of the root mean square errors between the reference BAP waves and the BAP waves reconstructed by hopt(t), hMLR(t), and a generalized transfer function (GTF) were 3.46 ± 1.42 mmHg, 3.61 ± 2.28 mmHg, and 6.80 ± 3.73 mmHg (significantly larger with p less then 0.01), respectively. CONCLUSIONS The proposed method can a semi-individualized TF which reconstructs significantly better BAP waves compared to GTF. SIGNIFICANCE The proposed TOP strategy can potentially be useful in more general reconstruction of proximal BP waves.OBJECTIVE Fibromyalgia syndrome (FMS) and chronic fatigue syndrome (CFS) are complicated medical disorders, with little known etiologies. The purpose of this research is to characterize FMS and CFS by studying the variations in cortisol secretion patterns, timings, amplitudes, the number of underlying pulses, as well as infusion and clearance rates of cortisol. METHODS Using a physiological state-space model with plausible constraints, we estimate the hormonal secretory events and the physiological system parameters (i.e., infusion and clearance rates). RESULTS Our results show that the clearance rate of cortisol is lower in FMS patients as compared to their matched healthy individuals based on a simplified cortisol secretion model. Moreover, the number, magnitude, and energy of hormonal secretory events are lower in FMS patients. During early morning hours, the magnitude and energy of the hormonal secretory events are higher in CFS patients. CONCLUSION Due to lower cortisol clearance rate, there is a higher accumulation of cortisol in FMS patients as compared to their matched healthy subjects. As the FMS patient accumulates higher cortisol residues, internal inhibitory feedback regulates the hormonal secretory events. Therefore, the FMS patients show a lower number, magnitude, and energy of hormonal secretory events. Though CFS patients have the same number of secretory events, they secrete lower quantities during early morning hours. When we compare the results for CFS patients against FMS patients, we observe different cortisol alteration patterns. SIGNIFICANCE Characterizing CFS and FMS based on the cortisol alteration will help us to develop novel methods for treating these disorders.In this article, we address three different topics in scientific visualization. The first part introduces optimization strategies that determine the visibility of line and surface geometry, such that a balance between occlusion avoidance and preservation of context is found. The second part proposes new methods for the visualization of time-dependent fluid flows, including the accurate depiction of Lagrangian scalar fields, as well as a new category of vortex identification methods. The third part introduces finite-sized particles as new application area for flow visualization, covering geometry-based methods, particle separation, topology, vortex corelines, and the determination of the origin of finite-sized particles.We share our experiences teaching university students about clustering algorithms using EduClust, an online visualization we developed. EduClust supports professors in preparing teaching material and students in visually and interactively exploring cluster steps and the effects of changing clustering parameters. We used EduClust for two years in our computer science lectures on clustering algorithms and share our experience integrating the online application in a data science curriculum. We also point to opportunities for future development.