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[1] The state of the atmosphere is governed by the classical laws of fluid motion and exhibits correlations in various spatial and temporal scales. These correlations are crucial to understand the short-and long-term trends in climate. Cirrus clouds are important ingredients of the atmospheric boundary layer. To improve future parameterization of cirrus clouds in climate models, it is important to understand the cloud properties and how they change within the cloud. We study correlations in the fluctuations of radar signals obtained at isodepths of winter and fall cirrus clouds. In particular, we focus on three quantities: (1) the backscattering cross-section, (2) the Doppler velocity, and (3) the Doppler spectral width. They correspond to the physical coefficients used in Navier Stokes equations to describe flows, i.e., bulk modulus, viscosity, and thermal conductivity. In all cases we find that power law time correlations exist with a crossover between regimes at about 3 to 5 min. We also find that different type of correlations, including 1/f behavior, characterize the top and the bottom layers and the bulk of the clouds. The underlying mechanisms for such correlations are suggested to originate in ice nucleation and crystal growth processes.INDEX TERMS: 3250 Mathematical Geophysics: Fractals and multifractals; 3360 Meteorology and Atmospheric Dynamics: Remote sensing; KEYWORDS: ice, correlations, cirrus clouds, scaling laws, fractals, radar backscattering cross section Citation: Ivanova, K., T. P. Ackerman, E. E. Clothiaux, P. Ch. Ivanov, H. E. Stanley, and M. Ausloos, Time correlations and 1/f behavior in backscattering radar reflectivity measurements from cirrus cloud ice fluctuations,

Skeletal muscles continuously coordinate to facilitate a wide range of movements. Muscle fiber composition and timing of activation account for distinct muscle functions and dynamics necessary to fine tune muscle coordination and generate movements. Here we address the fundamental question of how distinct muscle fiber types dynamically synchronize and integrate as a network across muscles with different functions. We uncover that physiological states are characterized by unique inter-muscular network of muscle fiber cross-frequency interactions with hierarchical organization of distinct sub-networks and modules, and a stratification profile of links strength specific for each state. We establish how this network reorganizes with transition from rest to exercise and fatigue—a complex process where network modules follow distinct phase-space trajectories reflecting their functional role in movements and adaptation to fatigue. This opens a new area of research, Network Physiology of Exercise, leading to novel network-based biomarkers of health, fitness and clinical conditions.

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We review recent attempts to understand the influence of sleep and wake states, sleep-stage transitions during sleep and the endogenous circadian rhythms on the neuroautonomic regulation of cardiac dynamics as represented by the scale-invariant organization of heartbeat fluctuations. We find that the probability distribution, the long-range temporal correlations as well as the nonlinear properties of the heartbeat fluctuations are significantly altered with transition from sleep to wake state, across sleep-stages and circadian phases. These sleep and circadian mediated changes in cardiac dynamics occur simultaneously over a broad range of time scales, suggesting a more complex then previously known interaction between the neural systems of sleep and circadian regulation with the neuroautonomic cardiac control, beyond rhythmic modulation at a characteristic time scale.

The physiological changes that occur in the main body systems and organs during physical exercise are well described in the literature. Despite the key role of brain in processing afferent and efferent information from organ systems to coordinate and optimize their functioning, little is known about how the brain works during exercise. The present study investigated tonic and transient oscillatory brain activity during a single bout of aerobic exercise. Twenty young males (19-32 years old) were recruited for two experimental sessions on separate days. Electroencephalographic (EEG) activity was recorded during a session of cycling at 80% (moderate-to-high intensity) of VO 2max (maximum aerobic capacity) while performing an oddball task where participants had to detect infrequent targets presented among frequent non-targets. This was compared to a (baseline) light intensity session (30% VO 2max ). The light intensity session was included to control for any potential effect of dual-tasking (i.e., pedaling and performing the oddball task). A warm-up and cool down periods were completed before and after exercise, respectively. A cluster-based nonparametric permutations test showed an increase in power across the entire frequency spectrum during the moderate-to-high intensity exercise, with respect to light intensity. Further, we found that the more salient target lead to lower increase in (stimulus-evoked) theta power in the 80% VO 2max with respect to the light intensity condition. On the contrary, higher decrease alpha and lower beta power was found for standard trials in the moderate-to-high exercise condition than in the light exercise condition. The present study unveils, for the first time, a complex brain activity pattern during acute exercise (at 80% of maximum aerobic capacity). These findings might help to elucidate the nature of changes that occur in the brain during physical exertion.

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