Detrended fluctuation analysis as a statistical tool to monitor the climate

Detrended fluctuation analysis is used to investigate power law relationship between the monthly averages of the maximum daily temperatures for different locations in the western US. On the map created by the power law exponents, we can distinguish different geographical regions with different power law exponents. When the power law exponents obtained from the detrended fluctuation analysis are plotted versus the standard deviation of the temperature fluctuations, we observe different data points belonging to the different climates, hence indicating that by observing the long-time trends in the fluctuations of temperature we can distinguish between different climates.Comment: 8 pages, 4 figures, submitted to JSTA

Global climate models violate scaling of the observed atmospheric variability

We test the scaling performance of seven leading global climate models by using detrended fluctuation analysis. We analyse temperature records of six representative sites around the globe simulated by the models, for two different scenarios: (i) with greenhouse gas forcing only and (ii) with greenhouse gas plus aerosol forcing. We find that the simulated records for both scenarios fail to reproduce the universal scaling behavior of the observed records, and display wide performance differences. The deviations from the scaling behavior are more pronounced in the first scenario, where also the trends are clearly overestimated.Comment: Accepted for publishing in Physical Review Letter

Intentional and unintentional medication nonadherence in psoriasis: the role of patients’ medication beliefs and habit strength [abstract only]

The accurate diagnosis of psoriasis has remained a challenge, as no disease-specific biomarkers have yet been identified. Currently, the diagnosis of chronic inflammatory diseases relies mainly on the assessment of visible symptoms or the histological features of the biopsy. This approach is heavily reliant on the experience of the clinician and, therefore, may lead to misdiagnosis as there are numerous different chronic inflammatory skin diseases that may present similar clinical features. Hence, the need for diagnostic biomarkers is clear. Although different investigations have reported the discovery of potential psoriasis biomarkers, still no accurate and reliable biomarker is available. Rather than searching for a single valid biomarker, we propose that applying a multicomponent bio-marker-based approach would result in a higher degree of success and translation into clinical practice. An extensive review of published studies to identify the most relevant psoriasis-specific biomarker candidates was conducted. This led us to conclude that the expression levels of specific genes in the skin hold the most promise as discriminatory biomarkers, resulting in the selection of five genes, the expression levels of which have been demonstrated to be exclusive for psoriasis vulgaris. We first conducted a preliminary validation study applying support vector machine-based classification and principle component analysis on the skin-derived expression data of 12 patients with psoriasis vulgaris and 12 healthy controls, previously produced in our departments. We then confirmed that the expression levels of the five genes in psoriatic lesions indeed present a unique pattern. Encouraged by these results, we continued to develop a quantitative polymerase chain reaction panel to allow the accurate measurement of expression levels for the five genes to be used in the studies to follow. Although we have yet to confirm these results in the context of other chronic inflammatory skin diseases, the results of previously published studies regarding these five genes are promising. Therefore, we are in the process of collecting additional skin samples from patients with chronic inflammatory disease (including different papulosquamous disorders and atopic dermatitis) to validate the discriminatory power of our panel. These results may further be translated to viable clinical diagnostic tests in the near future. This work was supported by the ERA Chair for Translational Genomics and Personalized Medicine at the University of Tartu

Statistical Properties of Share Volume Traded in Financial Markets

We quantitatively investigate the ideas behind the often-expressed adage `it takes volume to move stock prices', and study the statistical properties of the number of shares traded $Q_{\Delta t}$ for a given stock in a fixed time interval $\Delta t$. We analyze transaction data for the largest 1000 stocks for the two-year period 1994-95, using a database that records every transaction for all securities in three major US stock markets. We find that the distribution $P(Q_{\Delta t})$ displays a power-law decay, and that the time correlations in $Q_{\Delta t}$ display long-range persistence. Further, we investigate the relation between $Q_{\Delta t}$ and the number of transactions $N_{\Delta t}$ in a time interval $\Delta t$, and find that the long-range correlations in $Q_{\Delta t}$ are largely due to those of $N_{\Delta t}$. Our results are consistent with the interpretation that the large equal-time correlation previously found between $Q_{\Delta t}$ and the absolute value of price change $| G_{\Delta t} |$ (related to volatility) are largely due to $N_{\Delta t}$.Comment: 4 pages, two-column format, four figure

Characteristics and outcomes of patients treated with apremilast in the real world: results from the APPRECIATE study

Background APPRECIATE is a multinational, observational, retrospective, cross‐sectional study in patients treated for psoriasis with apremilast, an oral phosphodiesterase 4 inhibitor. Objectives To describe the characteristics of patients with psoriasis treated with apremilast in the clinical setting, to evaluate real‐world outcomes of psoriasis treatment with apremilast and to better understand the perspectives of patients and physicians on treatment outcomes. Methods In six European countries, patients with chronic plaque psoriasis treated in clinical practice who could be contacted 6 (±1) months after apremilast initiation were enrolled. Patient characteristics, Dermatology Life Quality Index (DLQI) and Psoriasis Area and Severity Index (PASI) were obtained from medical records when available. Outcomes were evaluated using patient/physician questionnaires. Results In 480 patients at treatment initiation, mean [median; 95% confidence interval (CI)] PASI and DLQI scores were 12.5 (10.7; 11.6–13.4) and 13.4 (13.0; 11.4–14.2), respectively. At 6 (±1) months, 72.3% of patients (n = 347) continued apremilast treatment [discontinuations: lack of efficacy (13.5%), safety (11.7%), other (2.5%)]. In patients continuing treatment, 48.6% achieved a ≥75% reduction in PASI score; mean (95% CI) DLQI score was 5.7 (4.5–6.9), and mean (SD) Patient Benefit Index score was 2.8 (1.2). Physicians perceived clinical improvement in 75.6% of patients. Physicians’ perspective on overall success of apremilast in meeting expectations correlated with patients’ perception of treatment benefit (r = 0.691). Most commonly reported adverse events (>5% of patients) were diarrhoea, nausea and headache. Conclusions Patients in APPRECIATE reported high disease burden despite more moderate skin involvement than those who enrolled in clinical trials of apremilast. Findings from APPRECIATE demonstrate the real‐world value of apremilast for psoriasis treatment, as 7 of 10 patients continued therapy and showed notable improvement in disease severity and quality of life 6 (±1) months after apremilast initiation

Effect of Trends on Detrended Fluctuation Analysis

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.Comment: 20 pages, 16 figure

Characterization of Sleep Stages by Correlations of Heartbeat Increments

We study correlation properties of the magnitude and the sign of the increments in the time intervals between successive heartbeats during light sleep, deep sleep, and REM sleep using the detrended fluctuation analysis method. We find short-range anticorrelations in the sign time series, which are strong during deep sleep, weaker during light sleep and even weaker during REM sleep. In contrast, we find long-range positive correlations in the magnitude time series, which are strong during REM sleep and weaker during light sleep. We observe uncorrelated behavior for the magnitude during deep sleep. Since the magnitude series relates to the nonlinear properties of the original time series, while the signs series relates to the linear properties, our findings suggest that the nonlinear properties of the heartbeat dynamics are more pronounced during REM sleep. Thus, the sign and the magnitude series provide information which is useful in distinguishing between the sleep stages.Comment: 7 pages, 4 figures, revte

Effect of nonstationarities on detrended fluctuation analysis

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.Comment: 17 pages, 10 figures, corrected some typos, added one referenc

Molecular characterization of Miraflores peach variety and relatives using SSRs

The definitive version is published in: http://www.sciencedirect.com/science/journal/03044238Some traditional peach varieties, originated from the region of Aragón (Spain), were analysed by SSRs (Simple Sequence Repeats). The aim of this research was to characterize 19 clones related to ‘Miraflores’ variety, with unknown pedigrees, to assess their genetic diversity and to elucidate their possible relationships with 10 traditional peach varieties. Twenty SSR primer pairs with high levels of polymorphism, which have been previously developed for peach, were used in this study. A total of 46 alleles were obtained for all the microsatellites studied, ranging from one to six alleles per locus, with a mean value of 2.3 alleles per locus. Fourteen SSRs were polymorphic in the set of varieties studied and permitted to distinguish 16 different genotypes out of the 30 initially studied, although fourteen ‘Miraflores’ clones showed identical gel profiles. The genetic distance matrix was used to construct Neighbor joining cluster and to perform principal coordinate analysis which allowed the arrangement of all the genotypes according to their genetic relationships. The genetic relationships among these traditional peach varieties, and in particular among ‘Miraflores’ clones are discussed. The obtained results confirm that microsatellite markers are very useful for these purposes.We are thankful to T.N. Zhebentyayeva and G.L. Reighard for helpful comments on the manuscript. This research was funded by CICYT (Comisión Interministerial de Ciencia y Tecnología, AGL2002-04219 and AGL 2005-05533), INIA (Instituto Nacional de Investigación y Tecnología Agraria y Alimentación, RF03-014-C2), Bilateral Spain-France (HF03-273) and DGA (A28, A44) projects and co-funded by the European Regional Development Fund. M. Bouhadida was supported by a fellowship from the AECI (Agencia Española de Cooperación Internacional) of the Spanish Ministry of Foreign Affairs.Peer reviewe