Visualising and assessing the probable error from downscaling ecological time series data

Collecting natural data at regular, fine scales is an onerous and often costly procedure. However, there is a basic need for fine scale data when applying inductive methods such as neural networks or genetic algorithms for the development of ecological models. This paper will address the issues involved in interpolating data for use in machine learning methods by considering how to determine if a downscaling of the data is valid. The approach is based on a multi-scale estimate of errors. The resulting function has similar properties to a time series variogram; however, the comparison at different scales is based on the variance introduced by rescaling from the original sequence. This approach has a number of properties, including the ability to detect frequencies in the data below the current sampling rate, an estimate of the probable average error introduced when a sampled variable is downscaled and a method for visualising the sequences of a time series that are most susceptible to error due to sampling. The described approach is ideal for supporting the ongoing sampling of ecological data and as a tool for assessing the impact of using interpolated data for building inductive models of ecological response.     

Symbolic dynamics for identifying similarity between rhythms of ecological time series

Explaining the associations between animal populations or between population and environmental signals is an important challenge. The time series that quantify animal populations are often complex, nonlinear, noisy and non‐stationary. These characteristics may make it inappropriate to use traditional techniques when analysing these time series and their mutual dependencies. Here I propose to use symbolic dynamics and techniques from Information Theory to evaluate the degree of dynamic cohesion between time series fluctuations. The main idea is to check whether two (or more) signals tend to oscillate simultaneously, rising and falling together with the same rhythm. Based on synthetic and real time series, I demonstrate that this method is robust to the presence of noise and to the short length of the analysed time series and gives relevant information about the weak relationships between different series. Furthermore, this method appears as simple as classical cross‐correlation and outperforms it in the analysed examples.     

Fractal dynamics in circadian cardiac time series of corticotropin-releasing factor receptor subtype-2 deficient mice

Non-linear fractal analysis of circadian 24 hr heartbeat interval time series was performed in corticotropin releasing factor receptor-subtype 2 (CRFR2) deficient mice. We hypothesized that, as a result of its central as well as its peripheral expression, CRFR2 would mediate or interfere with the circadian rhythmicity. The dynamical properties of cardiac interbeat intervals were expected to be different between CRFR2 (+/+) and CRFR2 (–/–) mice when studied over an extended circadian 24 hr cycle. The dynamics of neurocardiac control were found to remain remarkably stable throughout the circadian cycle. In disagreement with the initial hypothesis, the dynamical properties underlying the cardiac control process were common to both CRFR2 (+/+) and CRFR2 (–/–) mice suggesting that control of heart rate does not rely on the elaborate interaction of the CRFR2-sensor and its intrinsic feedback arrangement. Lack of expression of CRFR2 would not compromise cardiac control and its dynamical output or is subserved by other, unknown mechanisms. Functional integrity of CRFR2 would not constitute an indispensable requirement of physiologic cardiac control. The circadian rhythm of heart rate is generated centrally and is independent of expression of CRFR2. While `normal' strain C57BL/6N mice exhibit a circadian dark/light cycle of heart rate, absence of circadian fluctuations in transgenic CRFR2-mice (both +/+ and –/–) and `normal' strain C57BL/6J mice points at the importance of other deficiencies that may be related to a common genetic background. Mutant mice that share a common 129SvJ- or C57BL/6J-derived genetic background may not present an optimal model for physiological studies of cardiovascular control.     

Periodicity and time trends in the prevalence of total births and conceptions with congenital malformations among Jews and Muslims in Israel, 1999‐2006: A time series study of 823,966 births

BACKGROUND Congenital malformations (CMs) are a leading cause of infant disability. Geophysical patterns such as 2‐year, yearly, half‐year, 3‐month, and lunar cycles regulate much of the temporal biology of all life on Earth and may affect birth and birth outcomes in humans. Therefore, the aim of this study was to evaluate and compare trends and periodicity in total births and CM conceptions in two Israeli populations. METHODS Poisson nonlinear models (polynomial) were applied to study and compare trends and geophysical periodicity cycles of weekly births and weekly prevalence rate of CM (CMPR), in a time‐series design of conception date within and between Jews and Muslims. The population included all live births and stillbirths (n = 823,966) and CM (three anatomic systems, eight CM groups [n = 2193]) in Israel during 2000 to 2006. Data were obtained from the Ministry of Health. RESULTS We describe the trend and periodicity cycles for total birth conceptions. Of eight groups of CM, periodicity cycles were statistically significant in four CM groups for either Jews or Muslims. Lunar month and biennial periodicity cycles not previously investigated in the literature were found to be statistically significant. Biennial cycle was significant in total births (Jews and Muslims) and syndactyly (Muslims), whereas lunar month cycle was significant in total births (Muslims) and atresia of small intestine (Jews). CONCLUSION We encourage others to use the method we describe as an important tool to investigate the effects of different geophysical cycles on human health and pregnancy outcomes, especially CM, and to compare between populations. Birth Defects Research (Part A) 2012. © 2012 Wiley Periodicals, Inc.     

Nonstationarity and duration of the R-R-segment in the diagnostics of functional states of operators

The effect of the nonstationarity of R-R interval series on the diagnostics of functional states of operators has been analyzed. The functional states were diagnosed by means of a factor model of heart rate variability. The heart rate was recorded in the supine position, before the performance of an important task, and after its completion. A high resistance of the diagnostics of functional states to nonstationarity was found for all periods. Indices of heart rate variability resistant to nonstationarity were defined. Also, the effect of R-R segment duration on functional states diagnostics was explored. The results obtained allow one to conclude that the diagnostics of functional states based on the three-factor model of heart rate variability can be used on short segments within a range of 256 divided 32 R-R intervals. The indices of the factor model of heart rate variability must be normalized for corresponding R-R segment duration before diagnostics. In addition, the effect of the duration of R-R segment on the indices of heart rate variability was analyzed for different functional states. The indices resistant to the duration of R-R segments and conditions necessary for heart rate recording were defined.     

植被归一化指数(NDVI)及气候因子相关起伏型时间序列变化分析

利用中国大陆1km分辨率的月NDVI数据和相应的400个国家标准气象站点的月降水、月均温数据,依据中国气象局提出的气候分区,探讨了不同气候区NDVI与气候因子之间的时、空变化格局.结果表明,各月份降水与NDVI相关性均显著相关;起伏型时间序列模型能很好地拟合相关性的时间序列的动态变化,NDVI与降水的相关性拟合结果好于与温度相关性的拟合结果,全国范围内,与降水相关性拟合的R₂为0.91,与温度相关性拟合的R₂为0.86.     

Unsupervised clustering of fMRI and MRI time series

Unsupervised clustering represents a powerful technique for self-organized segmentation of biomedical image time series data describing groups of pixels exhibiting similar properties of local signal dynamics. The theoretical background is presented in the beginning, followed by several medical applications demonstrating the flexibility and conceptual power of these techniques. These applications range from functional MRI data analysis to dynamic contrast-enhanced perfusion MRI and breast MRI. For fMRI, these methods can be employed to identify and separate time courses of interest, along with their associated spatial patterns. When applied to dynamic perfusion MRI, they identify groups of voxels associated with time courses that are clinically informative and straightforward to interpret. In breast MRI, a segmentation of the lesion is achieved and in addition a subclassification is obtained within the lesion with regard to regions characterized by different MRI signal time courses. In the present paper, we conclude that unsupervised clustering techniques provide a robust method for blind analysis of time series image data in the important and current field of functional and dynamic MRI.     

Non-stationary time series and the robustness of circadian rhythms

Circadian rhythms are regular oscillations in the value of behavioral and physiological variables of organisms that recur on a daily basis. The purpose of this study was to evaluate the extent of non-stationarity of circadian rhythms over several days, to determine how damaging is the violation of the assumption of stationarity in the analysis of circadian rhythms, and to formalize the concept of "rhythm robustness" as an index of oscillatory ("weak") stationarity. Simulated (computer-generated) and experimental data sets (rhythms of body temperature and running-wheel activity in several rodent species) were analysed. Tests of stationarity based on the variance of the daily means and the variance of the daily variances revealed that most experimental data sets are not stationary. Analysis of linear trends indicated that significant trends are rare in experimental data sets. Although the non-stationarity of the experimental data sets reduced the spectral energy of the Enright periodogram used to assess rhythmicity, detection of circadian rhythmicity was not prevented in any of the rhythmic data sets. The results of the various analyses allow the inference that, after high-frequency noise is filtered out, the value of the periodogram's Q(P) statistic reflects the extent of stationarity of the time series. Thus, the "robustness" of a circadian rhythm (i.e. the magnitude of the empirical Q(P) value as compared to the Q(P) value associated with a perfectly rhythmic time series) can serve as an index of the stationarity of the rhythm.     

On the alignment of multiple time series fragments

We consider a local least-squares criterion for aligning multipletime series fragments differing by locations and show the consistencyof the time-lag estimator and the asymptotic normality of thelocation estimator. We apply the criterion to the problem ofaligning 50 glacial varve fragments and construct a 3000-yearsurrogate for global temperature.     

播栽期对水稻产量和产量构成因素及生育期的影响

研究了不同播栽期对水稻产量和产量构成因素及生育期的影响。结果表明 ,随播栽期推迟 ,水稻产量有所降低。每穗成粒数减少是推迟播栽期引起水稻减产的主要原因 ,其次是千粒重的下降和成穗数的降低。针对目前的生产实际 ,提出了一些应对播栽期推迟的技术措施。     

Effects of weather and climate on the dynamics of animal population time series

Weather is one of the most basic factors impacting animal populations, but the typical strength of such impacts on population dynamics is unknown. We incorporate weather and climate index data into analysis of 492 time series of mammals, birds and insects from the global population dynamics database. A conundrum is that a multitude of weather data may a priori be considered potentially important and hence present a risk of statistical over-fitting. We find that model selection or averaging alone could spuriously indicate that weather provides strong improvements to short-term population prediction accuracy. However, a block randomization test reveals that most improvements result from over-fitting. Weather and climate variables do, in general, improve predictions, but improvements were barely detectable despite the large number of datasets considered. Climate indices such as North Atlantic Oscillation are not better predictors of population change than local weather variables. Insect time series are typically less predictable than bird or mammal time series, although all taxonomic classes display low predictability. Our results are in line with the view that population dynamics is often too complex to allow resolving mechanisms from time series, but we argue that time series analysis can still be useful for estimating net environmental effects.     

Variability of force and interresponse time under random interval reinforcement schedules

Response variability was examined under five random interval schedules with minimum mean interreinforcement intervals of 15, 30, 60, 120, and 240 sec. Distributions of response force-time integrals showed no systematic variability changes with increases in reinforcement intermittency. Corresponding distributions of interresponse times revealed increases in variability accompanying increases in reinforcement intermittency. The relationship of variability to these two measures, and the potential effect of behavior outside the experimentally measured response class, on recorded variability within the class, are considered.