Recurrence Quantitative Analysis of Wavelet-Based Surrogate Data for Nonlinearity Testing in Heart Rate Variability.

Exploring the presence of nonlinearity through surrogate data testing provides insights into the nature of physical and biological systems like those obtained from heart rate variability (HRV). Short-term HRV time series are of great clinical interest to study autonomic impairments manifested in chronic diseases such as the end stage renal disease (ESRD) and the response of patients to treatment with hemodialysis (HD). In contrast to Iterative Amplitude Adjusted Fourier Transform (IAAFT), the Pinned Wavelet Iterative Amplitude Adjusted Fourier Transform (PWIAAFT) surrogates preserve nonstationary behavior in time series, a common characteristic of HRV. We aimed to test synthetic data and HRV time series for the existence of nonlinearity. Recurrence Quantitative Analysis (RQA) indices were used as discriminative statistics in IAAFT and PWIAAFT surrogates of linear stationary and nonstationary processes. HRV time series of healthy subjects and 29 ESRD patients before and after HD were tested in this setting during an active standing test. Contrary to PWIAAFT, linear nonstationary time series may be erroneously regarded as nonlinear according to the IAAFT surrogates. Here, a lower proportion of HRV time series was classified as nonlinear with PWIAAFT, compared to IAAFT, confirming that the nonstationarity condition influences the testing of nonlinear behavior in HRV. A contribution of nonlinearity was found in the HRV data of healthy individuals. A lower proportion of nonlinear time series was also found in ESRD patients, but statistical significance was not found. Although this proportion tends to be lower in ESRD patients, as much as 60% of time series proved to be nonlinear in healthy subjects. Given the important contribution of nonlinearity in HRV data, a nonlinear point of view is required to achieve a broader understanding of cardiovascular physiology.

Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control.

In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent. More precisely, we study both the effects of changing the context in which an agent learns (e.g., a change in flow rates experienced by it), as well as the effects of reducing agent observability of the true environment state. Partial observability may cause distinct states (in which distinct actions are optimal) to be seen as the same by the traffic signal agents. This, in turn, may lead to sub-optimal performance. We show that the lack of suitable sensors to provide a representative observation of the real state seems to affect the performance more drastically than the changes to the underlying traffic patterns.

Phylogenetic eigenvectors and nonstationarity in the evolution of theropod dinosaur skulls.

Despite the long-standing interest in nonstationarity of both phenotypic evolution and diversification rates, only recently have methods been developed to study this property. Here, we propose a methodological expansion of the phylogenetic signal-representation (PSR) curve based on phylogenetic eigenvectors to test for nonstationarity. The PSR curve is built by plotting the coefficients of determination R(2) from phylogenetic eigenvector regression (PVR) models increasing the number of phylogenetic eigenvectors against the accumulated eigenvalues. The PSR curve is linear under a stationary model of trait evolution (i.e. the Brownian motion model). Here we describe the distribution of shifts in the models R(2) and used a randomization procedure to compare observed and simulated shifts along the PSR curve, which allowed detecting nonstationarity in trait evolution. As an applied example, we show that the main evolutionary pattern of variation in the theropod dinosaur skull was nonstationary, with a significant shift in evolutionary rates in derived oviraptorosaurs, an aberrant group of mostly toothless, crested, birdlike theropods. This result is also supported by a recently proposed Bayesian-based method (AUTEUR). A significant deviation between Ceratosaurus and Limusaurus terminal branches was also detected. We purport that our new approach is a valuable tool for evolutionary biologists, owing to its simplicity, flexibility and comprehensiveness.

Current and historical climate signatures to deconstructed tree species richness pattern in South America / Efeitos dos climas atual e histórico no padrão desconstruído de riqueza de espécies arbóreas na região Neotropical

The purpose of this study was to investigate the importance of present and historical climate as determinants of current species richness pattern of forestry trees in South America. The study predicted the distribution of 217 tree species using Maxent models, and calculated the potential species richness pattern, which was further deconstructed based on range sizes and modeled against current and historical climates predictors using Geographically Weighted Regressions (GWR) analyses. The current climate explains more of the wide-ranging species richness patterns than that of the narrow-ranging species, while the historical climate explained an equally small amount of variance for both narrow-and-wide ranging tree species richness patterns. The richness deconstruction based on range size revealed that the influences of current and historical climate hypotheses underlying patterns in South American tree species richness differ from those found in the Northern Hemisphere. Notably, the historical climate appears to be an important determinant of richness only in regions with marked climate changes and proved Pleistocenic refuges, while the current climate predicts the species richness across those Neotropical regions, with non-evident refuges in the Last Glacial Maximum. Thus, this study's analyses show that these climate hypotheses are complementary to explain the South American tree species richness.

O objetivo deste estudo foi testar qual dos climas, atual ou histórico, é o principal preditor do padrão atual de riqueza de espécies arbóreas de interesse comercial. Nós modelamos a distribuição de 217 espécies usando Maxent e usamos esses mapas preditivos para obter o padrão de riqueza de espécies. A riqueza foi desconstruída em relação ao tamanho da distribuição geográfica das espécies e modelada contra os climas atual e histórico utilizando Regressões Geograficamente Ponderadas. O clima atual explicou melhor o padrão de riqueza das espécies com ampla distribuição geográfica do que de espécies com distribuição restrita, enquanto o clima histórico explicou a mesma variância na riqueza dos dois grupos de espécies. Nossas análises com plantas sul americanas mostram diferentes relações da riqueza de espécies ampla e restritamente distribuídas com os climas atual e histórico, quando comparado aos resultados encontrados no hemisfério norte. O clima histórico se mostra como importante preditor da riqueza somente em regiões com mudanças climáticas acentuadas e onde ocorreram refúgios Pleistocênicos, enquanto o clima atual é o melhor da riqueza nas regiões Neotropicais sem evidências de refúgios durante o máximo da ultima glaciação. Dessa maneira, nossos resultados indicam que essas hipóteses são complementares para explicar a riqueza predita de espécies arbóreas da América do Sul.

Current and historical climate signatures to deconstructed tree species richness pattern in South America / Efeitos dos climas atual e histórico no padrão desconstruído de riqueza de espécies arbóreas na região Neotropical

The purpose of this study was to investigate the importance of present and historical climate as determinants of current species richness pattern of forestry trees in South America. The study predicted the distribution of 217 tree species using Maxent models, and calculated the potential species richness pattern, which was further deconstructed based on range sizes and modeled against current and historical climates predictors using Geographically Weighted Regressions (GWR) analyses. The current climate explains more of the wide-ranging species richness patterns than that of the narrow-ranging species, while the historical climate explained an equally small amount of variance for both narrow-and-wide ranging tree species richness patterns. The richness deconstruction based on range size revealed that the influences of current and historical climate hypotheses underlying patterns in South American tree species richness differ from those found in the Northern Hemisphere. Notably, the historical climate appears to be an important determinant of richness only in regions with marked climate changes and proved Pleistocenic refuges, while the current climate predicts the species richness across those Neotropical regions, with nonevident refuges in the Last Glacial Maximum. Thus, this study"s analyses show that these climate hypotheses are complementary to explain the South American tree species richness.(AU)

O objetivo deste estudo foi testar qual dos climas, atual ou histَrico, é o principal preditor do padrمo atual de riqueza de espécies arbَreas de interesse comercial. Nَs modelamos a distribuiçمo de 217 espécies usando Maxent e usamos esses mapas preditivos para obter o padrمo de riqueza de espécies. A riqueza foi desconstruيda em relaçمo ao tamanho da distribuiçمo geogrلfica das espécies e modelada contra os climas atual e histَrico utilizando Regressُes Geograficamente Ponderadas. O clima atual explicou melhor o padrمo de riqueza das espécies com ampla distribuiçمo geogrلfica do que de espécies com distribuiçمo restrita, enquanto o clima histَrico explicou a mesma variância na riqueza dos dois grupos de espécies. Nossas anلlises com plantas sul americanas mostram diferentes relaçُes da riqueza de espécies ampla e restritamente distribuيdas com os climas atual e histَrico, quando comparado aos resultados encontrados no hemisfério norte. O clima histَrico se mostra como importante preditor da riqueza somente em regiُes com mudanças climلticas acentuadas e onde ocorreram refْgios Pleistocênicos, enquanto o clima atual é o melhor da riqueza nas regiُes Neotropicais sem evidências de refْgios durante o mلximo da ultima glaciaçمo. Dessa maneira, nossos resultados indicam que essas hipَteses sمo complementares para explicar a riqueza predita de espécies arbَreas da América do Sul.(AU)

Diminished heart beat non-stationarities in congestive heart failure.

Studies on heart rate variability (HRV) have become popular and the possibility of diagnosis based on non-invasive techniques compels us to overcome the difficulties originated on the environmental changes that can affect the signal. We perform a non-parametric segmentation which consists of locating the points where the signal can be split into stationary segments. By finding stationary segments we are able to analyze the size of these segments and evaluate how the signal changes from one segment to another, looking at the statistical moments given in each patch, for example, mean and variance. We analyze HRV data for 15 patients with congestive heart failure (CHF; 11 males, 4 females, age 56±11 years), 18 elderly healthy subjects (EH; 11 males, 7 females, age 50±7 years), and 15 young healthy subjects (YH; 11 females, 4 males, age 31±6 years). Our results confirm higher variance for YH, and EH, while CHF displays diminished variance with p-values <0.01, when compared to the healthy groups, presenting higher HRV in healthy subjects. Moreover, it is possible to distinguish between YH and EH with p < 0.05 through the segmentation outcomes. We found high correlations between the results of segmentation and standard measures of HRV analysis and a connection to results of detrended fluctuation analysis (DFA). The segmentation applied to HRV studies detects aging and pathological conditions effects on the non-stationary behavior of the analyzed groups, promising to contribute in complexity analysis and providing risk stratification measures.