Detrended fluctuation analysis (DFA) is a scaling analysis method used to estimate long-range power-law correlation exponents in noisy signals. Many noisy signals in real systems display trends, so that the scaling results obtained from the DFA method become difficult to analyze. We systematically study the effects of three types of trends -linear, periodic, and power-law trends, and offer examples where these trends are likely to occur in real data. We compare the difference between the scaling results for artificially generated correlated noise and correlated noise with a trend, and study how trends lead to the appearance of crossovers in the scaling behavior. We find that crossovers result from the competition between the scaling of the noise and the "apparent" scaling of the trend. We study how the characteristics of these crossovers depend on (i) the slope of the linear trend; (ii) the amplitude and period of the periodic trend; (iii) the amplitude and power of the power-law trend and (iv) the length as well as the correlation properties of the noise. Surprisingly, we find that the crossovers in the scaling of noisy signals with trends also follow scaling laws -i.e. long-range power-law dependence of the position of the crossover on the parameters of the trends. We show that the DFA result of noise with a trend can be exactly determined by the superposition of the separate results of the DFA on the noise and on the trend, assuming that the noise and the trend are not correlated. If this superposition rule is not followed, this is an indication that the noise and the superimposed trend are not independent, so that removing the trend could lead to changes in the correlation properties of the noise. In addition, we show how to use DFA appropriately to minimize the effects of trends, and how to recognize if a crossover indicates indeed a transition from one type to a different type of underlying correlation, or the crossover is due to a trend without any transition in the dynamical properties of the noise.

Detrended fluctuation analysis (DFA) is a scaling analysis method used to quantify long-range power-law correlations in signals. Many physical and biological signals are "noisy", heterogeneous and exhibit different types of nonstationarities, which can affect the correlation properties of these signals. We systematically study the effects of three types of nonstationarities often encountered in real data. Specifically, we consider nonstationary sequences formed in three ways: (i) stitching together segments of data obtained from discontinuous experimental recordings, or removing some noisy and unreliable parts from continuous recordings and stitching together the remaining parts -a "cutting" procedure commonly used in preparing data prior to signal analysis; (ii) adding to a signal with known correlations a tunable concentration of random outliers or spikes with different amplitude, and (iii) generating a signal comprised of segments with different properties -e.g. different standard deviations or different correlation exponents. We compare the difference between the scaling results obtained for stationary correlated signals and correlated signals with these three types of nonstationarities. We find that introducing nonstationarities to stationary correlated signals leads to the appearance of crossovers in the scaling behavior and we study how the characteristics of these crossovers depend on: (a) the fraction and size of the parts cut out from the signal; (b) the concentration of spikes and their amplitudes; (c) the proportion between segments with different standard deviations or different correlations; and (d) the correlation properties of the stationary signal. We show how to develop strategies for pre-processing "raw" data prior to analysis, which will minimize the effects of nonstationarities on the scaling properties of the data and how to interpret the results of DFA for complex signals with different local characteristics.

Dysfunction in 24-h circadian rhythms is a common occurrence in aging adults, however, circadian rhythm disruptions (CRD) are more severe in people with age-related neurodegenerative diseases, including Alzheimer's disease and related dementias (ADRD) and Parkinson's disease (PD). Manifestations of CRD differ according to type and severity of neurodegenerative disease, and importantly, could occur before onset of typical clinical symptoms of neurodegeneration. Evidence from preliminary studies suggest that-in addition to being a symptom of neurodegeneration-CRD might also be a potential risk factor for developing ADRD and PD, although large, longitudinal studies are needed to confirm this. The mechanistic link between *

Shift work is a risk factor for hypertension, inflammation, and cardiovascular disease. This increased risk cannot be fully explained by classic risk factors. One of the key features of shift workers is that their behavioral and environmental cycles are typically misaligned relative to their endogenous circadian system. However, there is little information on the impact of acute circadian misalignment on cardiovascular disease risk in humans. Here we show-by using two 8-d laboratory protocols-that short-term circadian misalignment (12-h inverted behavioral and environmental cycles for three days) adversely affects cardiovascular risk factors in healthy adults. Circadian misalignment increased 24-h systolic blood pressure (SBP) and diastolic blood pressure (DBP) by 3.0 mmHg and 1.5 mmHg, respectively. These results were primarily explained by an increase in blood pressure during sleep opportunities (SBP, +5.6 mmHg; DBP, +1.9 mmHg) and, to a lesser extent, by raised blood pressure during wake periods (SBP, +1.6 mmHg; DBP, +1.4 mmHg). Circadian misalignment decreased wake cardiac vagal modulation by 8-15%, as determined by heart rate variability analysis, and decreased 24-h urinary epinephrine excretion rate by 7%, without a significant effect on 24-h urinary norepinephrine excretion rate. Circadian misalignment increased 24-h serum interleukin-6, C-reactive protein, resistin, and tumor necrosis factor-α levels by 3-29%. We demonstrate that circadian misalignment per se increases blood pressure and inflammatory markers. Our findings may help explain why shift work increases hypertension, inflammation, and cardiovascular disease risk.circadian misalignment | hypertension | inflammatory markers | night work | shift work