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

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

Complex systems and the technology of variability analysis

Characteristic patterns of variation over time, namely rhythms, represent a defining feature of complex systems, one that is synonymous with life. Despite the intrinsic dynamic, interdependent and nonlinear relationships of their parts, complex biological systems exhibit robust systemic stability. Applied to critical care, it is the systemic properties of the host response to a physiological insult that manifest as health or illness and determine outcome in our patients. Variability analysis provides a novel technology with which to evaluate the overall properties of a complex system. This review highlights the means by which we scientifically measure variation, including analyses of overall variation (time domain analysis, frequency distribution, spectral power), frequency contribution (spectral analysis), scale invariant (fractal) behaviour (detrended fluctuation and power law analysis) and regularity (approximate and multiscale entropy). Each technique is presented with a definition, interpretation, clinical application, advantages, limitations and summary of its calculation. The ubiquitous association between altered variability and illness is highlighted, followed by an analysis of how variability analysis may significantly improve prognostication of severity of illness and guide therapeutic intervention in critically ill patients

Response of Forage Species Seeded for Mule Deer in Western Juniper Types of South-central Oregon

Mule deer and livestock forage supplies were increased by seeding 11 species of grasses, forbs, and shrubs within chained and nonchained western juniper thermal cover stands in south-central Oregon. Standard crested wheatgrass and Siberian wheatgrass were the only species that established in significant amounts. Wheatgrass densities were greater in chain-drill treatments than in drill-only treatments. Among all experimental units, differences in emergence and establishment (plants/m2) were greater than were differences in seeding rates (viable seeds/m2). Standard crested wheatgrass density exceeded that of Siberian wheatgrass over both treatments and six pretreatment vegetation subtypes. Emergence of seedlings and establishment of wheatgrass were all significantly related to subtype. The chain-drill treatment produced more spring forage than did the drill-only treatment. Neither treatment provided more winter forage.This material was digitized as part of a cooperative project between the Society for Range Management and the University of Arizona Libraries.The Journal of Range Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform August 202

Response of Selected Plant Species Seeded on Mule Deer Winter Range

A selection of 13 bunchgrasses, 4 legumes, and 2 shrubs were planted in 2 seasons in 5 plant communities within the sagebrush-bunchgrass and juniper zones of the Fort Rock mule herd winter range in south-central Oregon. Rate of establishment averaged 3.9% for all planted species, and it was generally dependent on seeding rate, season, and plant community. Standard crested wheatgrass, Siberian wheatgrass, smooth brome, hard fescue, and antelope bitterbrush established better when planted in the fall. Intermediate wheatgrass, streambank wheatgrass, Ladak alfalfa, and hairy vetch established better when planted in the spring. From 31 to 3% of the plants of standard crested wheatgrass, Siberian wheatgrass, pubescent wheatgrass, hard fescue, and antelope bitterbrush survived to the sixth growing season. Standard crested wheatgrass, Siberian wheatgrass, and pubescent wheatgrass survived best in the juniper/big sagebrush-antelope bitterbrush community, but antelope bitterbrush survived at a slightly higher rate in the juniper/antelope bitterbrush-big sagebrush community.This material was digitized as part of a cooperative project between the Society for Range Management and the University of Arizona Libraries.The Journal of Range Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform August 202

Novel Jumbo Biopsy Forceps for Surveillance of Inflammatory Bowel Disease: A Comparative Retrospective Assessment

Background and Study Aims. Most available jumbo cup forceps require a 3.7 mm biopsy channel, necessitating the use of standard-sized colonoscope. A newer jumbo forceps (Radial Jaw 4 Jumbo Biopsy Forceps [RJ4]) fits within a 3.2 mm biopsy channel, allowing use with a pediatric colonoscope. To assure the RJ4 did not alter biopsy adequacy, we compared the size and quality of specimens to a historical jumbo cup forceps (Radial Jaw 3 Max Capacity Biopsy Forceps, [RJ3 MC]). Patients and Methods. A retrospective comparative study of biopsies taken with either forceps. Biopsies were compared for diameter, depth, crush artifact, and acceptability for diagnosis. Results. 333 specimens were taken with RJ4 and 335 specimens with the RJ3 MC. Mean sample diameter was 4.45 mm and 4.55 mm for the RJ4 and RJ3 MC (=0.41). Mean depth of biopsies with the RJ4 was greater (<0.01). Conclusions. Biopsies from the RJ4 are similar in size and quality to biopsies from the RJ3 MC. The RJ4 has the advantage of fitting in a smaller biopsy channel

Identifying ecologically relevant scales of habitat selection: diel habitat selection in elk

Although organisms make resource selection decisions at multiple spatiotemporal scales, not all scales are ecologically relevant to any given organism. Ecological patterns and rhythms such as behavioral and climatic patterns may provide a consistent method for identifying ecologically relevant scales of habitat selection. Using elk (Cervus canadensis) as an example species, we sought to test the ability of behavioral patterns to empirically partition diel scales for modeling habitat selection. We used model selection to partition diel scales by shifts in dominant behavior and then used resource selection probability functions to model elk habitat selection hierarchically at diel scales within seasons. Model selection distinguished four diel temporal partitions following elk crepuscular behavioral patterns: dawn, midday, dusk, and night. Across seasons, model-averaged coefficients indicated that elk shifted from selecting grassland cover at dawn/dusk, to selecting for greater canopy and forest cover at midday, and then to areas with greater herbaceous biomass at night. Top models changed between diel intervals in spring and fall but stayed the same across diel intervals in winter and summer. In winter, elk selected for southern aspects during midday, for unburned areas at dawn/dusk, and for areas burned within 1–3 yr at dawn/dusk and night. In spring, elk selected for northern aspects and for areas burned within 1–3 yr at midday, for areas farther from roads at dawn/dusk and midday, and for areas farther from water at midday. In summer, elk changed diel preferences for fewer covariates: At dawn/dusk and midday, elk selected for areas farther from water and avoided forest cover, and at night, elk selected for areas burned within 1–3 yr. In fall, elk selected for areas burned the previous year at dawn/dusk and night, for higher elevations at midday, and for areas closer water at night. Using behavioral patterns to identify ecologically relevant scales can help identify overlooked habitat requirements such as diel changes in preference for fire history, forage availability, and cover. We show that the ecological relevancy of a given scale (e.g., a diel temporal scale) can change throughout a given extent (e.g., across seasons)