Wavelet analysis of ecological time series

Wavelet analysis is a powerful tool that is already in use throughout science and engineering. The versatility and attractiveness of the wavelet approach lie in its decomposition properties, principally its time-scale localization. It is especially relevant to the analysis of non-stationary systems, i.e., systems with short-lived transient components, like those observed in ecological systems. Here, we review the basic properties of the wavelet approach for time-series analysis from an ecological perspective. Wavelet decomposition offers several advantages that are discussed in this paper and illustrated by appropriate synthetic and ecological examples. Wavelet analysis is notably free from the assumption of stationarity that makes most methods unsuitable for many ecological time series. Wavelet analysis also permits analysis of the relationships between two signals, and it is especially appropriate for following gradual change in forcing by exogenous variables.     

Assessing co-regulation of directly linked genes in biological networks using microarray time series analysis

Differential expression of genes detected with the analysis of high throughput genomic experiments is a commonly used intermediate step for the identification of signaling pathways involved in the response to different biological conditions. The impact analysis was the first approach for the analysis of signaling pathways involved in a certain biological process that was able to take into account not only the magnitude of the expression change of the genes but also the topology of signaling pathways including the type of each interactions between the genes. In the impact analysis, signaling pathways are represented as weighted directed graphs with genes as nodes and the interactions between genes as edges. Edges weights are represented by a β factor, the regulatory efficiency, which is assumed to be equal to 1 in inductive interactions between genes and equal to −1 in repressive interactions. This study presents a similarity analysis between gene expression time series aimed to find correspondences with the regulatory efficiency, i.e. the β factor as found in a widely used pathway database. Here, we focused on correlations among genes directly connected in signaling pathways, assuming that the expression variations of upstream genes impact immediately downstream genes in a short time interval and without significant influences by the interactions with other genes. Time series were processed using three different similarity metrics. The first metric is based on the bit string matching; the second one is a specific application of the Dynamic Time Warping to detect similarities even in presence of stretching and delays; the third one is a quantitative comparative analysis resulting by an evaluation of frequency domain representation of time series: the similarity metric is the correlation between dominant spectral components. These three approaches are tested on real data and pathways, and a comparison is performed using Information Retrieval benchmark tools, indicating the frequency approach as the best similarity metric among the three, for its ability to detect the correlation based on the correspondence of the most significant frequency components.     

Disorders of the Menstrual Cycle in Elite Female Ice Hockey Players and Figure Skaters

The purpose of this study was to assess and compare the incidence of delayed menarche and menstrual dysfunction among elite ice hockey players and figure skaters. Forty-three ice hockey players (23.5 ± 4.8 years, 68.2 ± 1.2 kg, 1.68 ± 0.01 m) and 39 figure skaters (17.5 ± 3.4 years, 53.7 ± 5.8 kg, 1.64 ± 0.05 m) completed a self-administered questionnaire on their menstrual status and history, training regimens and lifestyle. Age at menarche did not differ significantly between ice hockey players (13.3 ± 1.3 years) and figure skaters (13.7 ± 1.4 years). Menarche was unrelated to nationality, vigorous training premenarche or age at which the athlete began her sport, but was correlated with the age at menarche of the athletes’ mothers (r = 0.39, p < 0.05). Hormonal contraceptives were used by 35% of ice hockey players and 15% of the figure skaters. Amenorrhea and oligomenorrhea were experienced by 7.1% and 38.7% of postmenarcheal, ice hockey players and figure skaters respectively not using hormonal contraceptives. Menstrual dysfunction was associated with both age and age at menarche in the ice hockey players only. Training factors, and psychological pressure were perceived by the athletes to contribute to menstrual dysfunction. A greater training volume, younger age at commencing sport, lower body mass, greater subjective body image pressure and younger biological and gynaecological age were reported among the figure skaters, and were proposed to explain the higher incidence of menstrual dysfunction among the figure skaters compared with the ice hockey players. Figure skaters appear at increased risk of amenorrhea and oligomenorrhea compared with ice hockey players, which may be linked to training and physical characteristics of the sports.     

山东省典型地表覆被NDVI时间序列谐波分析

NDVI时间序列的谐波分析可描述不同地表覆被的季节变化.本文运用谐波分析法对山东省一年的MDOIS NDVI时间序列数据进行分析处理,提取了该地区几种典型地表覆被的谐波特征.结果表明:利用谐波分析生成的前几个谐波分量就可重建原始时间序列,且重建的时间序列剖面为平滑曲线,因此谐波分析不仅可减少数据量,并可去除噪声的影响;...     

Semiparametric models and inference for biomedical time series with extra-variation

Biomedical trials often give rise to data having the form of time series of a common process on separate individuals. One model which has been proposed to explain variations in such series across individuals is a random effects model based on sample periodograms. The use of spectral coefficients enables models for individual series to be constructed on the basis of standard asymptotic theory, whilst variations between individuals are handled by permitting a random effect perturbation of model coefficients. This paper extends such methodology in two ways: first, by enabling a nonparametric specification of underlying spectral behaviour; second, by addressing some of the tricky computational issues which are encountered when working with this class of random effect models. This leads to a model in which a population spectrum is specified nonparametrically through a dynamic system, and the processes measured on individuals within the population are assumed to have a spectrum which has a random effect perturbation from the population norm. Simulation studies show that standard MCMC algorithms give effective inferences for this model, and applications to biomedical data suggest that the model itself is capable of revealing scientifically important structure in temporal characteristics both within and between individual processes.     

River bacteria time series analysis: a field and laboratory study which demonstrates aquatic ecosystem health

Time series analysis of heterotrophic baeterial viable counts on agar plates incubated at 20° and 4°C from weekly river water samples for 1 year has revealed a periodicity of 6 to 7 weeks in the total numbers. A cycle of increase and decrease in the counts which could not be attributed to season, river water temperature, pH, and flow rate has been observed in the heterotrophic bacteria which were isolated from river water samples. Spectral analysis has demonstrated a hidden periodicity in population increase and decrease which could not be detected by examination of the raw count data. Fluctuations with periodicity could imply that this is the expected behaviour in a well-balanced ecosystem. Pseudomonas fluorescens, the predominant species isolated from a river water sample which was at 0°C at the time of sampling, utilized two different pathways at 5° and 30°C. In addition, 2-ketogluconate, a product of the utilization of glucose, was utilized by the same organism. Fluctuations in enzymic responses could contribute to the overall periodicity in bacterial numbers. The flexibility in bacterial metabolism would serve to maintain P. fluorescens in the predominant flora. A feature which sustains health in this ecosystem, P. fluorescens, is one of the most successful of all biodegraders in the aquatic system.     

起伏型时间序列分析方法在害虫测报上的应用

介绍了起伏型时间序列（analysisforwavetypetimeseries）害虫预测方法。对安徽省凤阳县稻纵卷叶螟四（2）代蛾主峰高峰日进行建模预测,对历史资料的拟合和1989、1990两年的试报,结果令人满意。这是一种新的时间序列分析法。     

Spectral estimation in unevenly sampled space of periodically expressed microarray time series data

Background  

Periodogram analysis of time-series is widespread in biology. A new challenge for analyzing the microarray time series data is to identify genes that are periodically expressed. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, and unevenly sampled time points. Most methods used in the literature operate on evenly sampled time series and are not suitable for unevenly sampled time series.     

Nonparametric mixed effects models for unequally sampled noisy curves

We propose a method of analyzing collections of related curves in which the individual curves are modeled as spline functions with random coefficients. The method is applicable when the individual curves are sampled at variable and irregularly spaced points. This produces a low-rank, low-frequency approximation to the covariance structure, which can be estimated naturally by the EM algorithm. Smooth curves for individual trajectories are constructed as best linear unbiased predictor (BLUP) estimates, combining data from that individual and the entire collection. This framework leads naturally to methods for examining the effects of covariates on the shapes of the curves. We use model selection techniques--Akaike information criterion (AIC), Bayesian information criterion (BIC), and cross-validation--to select the number of breakpoints for the spline approximation. We believe that the methodology we propose provides a simple, flexible, and computationally efficient means of functional data analysis.     

杉木生长的起伏型时间序列模拟研究

起伏型时间序列法是一种新的时间序列分析法,本文介绍了起伏型时间序列林木模拟与预测方法,以杉木人工林生长为研究对象,对杉木胸径生长进行建模模拟,模拟工达到98．3％,结果令人满意,且比逐步回归法,多维时间序列分析法模拟效果更优,说明起伏型时间序列分析方法可应用于林木生长模拟与预测,从而丰富了林木生长预测与预报方法。