Characteristics of hand tremor time series

Tremor is classified into physiological, essential, and parkinsonian tremor by means of clinical criteria. The aim of our work was to extract quantitative features from the measurements of the acceleration of human postural hand tremor. Different mathematical methods were adopted and modified in order to separate these three types of tremor. Best discrimination between physiological and pathological tremors has been achieved by methods distinguishing nonlinear from linear behavior. On the other hand, methods separating different forms of nonlinear behavior have been found to be superior in discriminating parkinsonian and essential tremor. By these methods physiological and pathological tremors can be separated with an error rate below 20% and essential and parkinsonian tremor with an error rate below 10%. This may help to classify tremor time series by objective mathematical criteria and may increase the understanding of the pathophysiological differences underlying these kinds of tremor.     

Analyzing time series gene expression data

MOTIVATION: Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. However, when analyzing these experiments researchers face many new computational challenges. Algorithms that are specifically designed for time series experiments are required so that we can take advantage of their unique features (such as the ability to infer causality from the temporal response pattern) and address the unique problems they raise (e.g. handling the different non-uniform sampling rates). RESULTS: We present a comprehensive review of the current research in time series expression data analysis. We divide the computational challenges into four analysis levels: experimental design, data analysis, pattern recognition and networks. For each of these levels, we discuss computational and biological problems at that level and point out some of the methods that have been proposed to deal with these issues. Many open problems in all these levels are discussed. This review is intended to serve as both, a point of reference for experimental biologists looking for practical solutions for analyzing their data, and a starting point for computer scientists interested in working on the computational problems related to time series expression analysis.     

Features of parkinsonian and essential tremor of the human hand

Using spectral, wavelet, multifractal, and recurrence analyses we examined the features of involuntary shaking (tremor) that occur during the performance of a given motor task. The task was to maintain the efforts of fingers under isometric conditions by a healthy subject, a patient with primary bilateral parkinsonism, and a patient with essential tremor syndrome. The physiological tremor was characterized by the lowest amplitude, a broad power spectrum, the lowest energy of the wavelet spectrum, the highest degree of multifractality, the lowest degree of determinism, and the highest entropy of the recurrence time density. In the case of the essential tremor we observed a significant enhancement of the wavelet spectrum energy and a decrease of the oscillation complexity. This was evident via the occurrence of clear peaks in the power spectra, a decrease in the degree of multifractality, the emergence of a quasi-periodic structure in the recurrence diagrams, an increase in determinism and a decrease of the entropy of recurrence time density. All these trends were increased for the parkinsonian tremor data. These characteristics enable us to quantitatively estimate the degree of deviation of motor function from the healthy case.     

Metagenomics meets time series analysis: unraveling microbial community dynamics

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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.     

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.     

Boolean dynamics of genetic regulatory networks inferred from microarray time series data

MOTIVATION: Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this article we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction. RESULTS: We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our method first clusters and discretizes the gene expression data using k-means and support vector regression. We then enumerate Boolean activation-inhibition networks to match the discretized data. Finally, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Parkinsonian tremor and simplification in network dynamics

We explore the behavior of richly connected inhibitory neural networks under parameter changes that correspond to weakening of synaptic efficacies between network units, and show that transitions from irregular to periodic dynamics are common in such systems. The weakening of these connections leads to a reduction in the number of units that effectively drive the dynamics and thus to simpler behavior. We hypothesize that the multiple interconnecting loops of the brain’s motor circuitry, which involve many inhibitory connections, exhibit such transitions. Normal physiological tremor is irregular while other forms of tremor show more regular oscillations. Tremor in Parkinson’s disease, for example, stems from weakened synaptic efficacies of dopaminergic neurons in the nigro-striatal pathway, as in our general model. The multiplicity of structures involved in the production of symptoms in Parkinson’s disease and the reversibility of symptoms by pharmacological and surgical manipulation of connection parameters suggest that such a neural network model is appropriate. Furthermore, fixed points that can occur in the network models are suggestive of akinesia in Parkinson’s disease. This model is consistent with the view that normal physiological systems can be regulated by robust and richly connected feedback networks with complex dynamics, and that loss of complexity in the feedback structure due to disease leads to more orderly behavior.     

Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models

The nature and role of re-infection and partial immunity are likely to be important determinants of the transmission dynamics of human respiratory syncytial virus (hRSV). We propose a single model structure that captures four possible host responses to infection and subsequent reinfection: partial susceptibility, altered infection duration, reduced infectiousness and temporary immunity (which might be partial). The magnitude of these responses is determined by four homotopy parameters, and by setting some of these parameters to extreme values we generate a set of eight nested, deterministic transmission models. In order to investigate hRSV transmission dynamics, we applied these models to incidence data from eight international locations. Seasonality is included as cyclic variation in transmission. Parameters associated with the natural history of the infection were assumed to be independent of geographic location, while others, such as those associated with seasonality, were assumed location specific. Models incorporating either of the two extreme assumptions for immunity (none or solid and lifelong) were unable to reproduce the observed dynamics. Model fits with either waning or partial immunity to disease or both were visually comparable. The best fitting structure was a lifelong partial immunity to both disease and infection. Observed patterns were reproduced by stochastic simulations using the parameter values estimated from the deterministic models.     

Frequency and displacement amplitude relations for normal hand tremor.

Spectral analysis of hand tremor records obtained from normal subjects during continuous extension of the hand for 15-45 min revealed that the root-mean-square (rms) displacement amplitude of the tremor increased from control levels of about 30 mum to levels on the order of 100-1,000 times control. Associated with this increase in the displacement was a systematic decrease in the hand tremor frequency from control values of 8-9 Hz to values of 4-6 Hz. Spectral analysis of demodulated extensor EMG records indicated a consistent relation between EMG modulation amplitude at the tremor frequency and the tremor displacement amplitude for tremor records with rms displacement above about 100 mum. No consistent relation was found between these two variables for tremor records with displacements below 100 mum. Consideration of both mechanical and neural reflex effects indicated that a viscoelastic-mass mechanism primarily determined the small-amplitude (less than 100 mum) tremors, while the large displacement tremors may have involved both mechanical and neural feed back factors.     

Shape selection in Landsat time series: a tool for monitoring forest dynamics

We present a new methodology for fitting nonparametric shape‐restricted regression splines to time series of Landsat imagery for the purpose of modeling, mapping, and monitoring annual forest disturbance dynamics over nearly three decades. For each pixel and spectral band or index of choice in temporal Landsat data, our method delivers a smoothed rendition of the trajectory constrained to behave in an ecologically sensible manner, reflecting one of seven possible ‘shapes’. It also provides parameters summarizing the patterns of each change including year of onset, duration, magnitude, and pre‐ and postchange rates of growth or recovery. Through a case study featuring fire, harvest, and bark beetle outbreak, we illustrate how resultant fitted values and parameters can be fed into empirical models to map disturbance causal agent and tree canopy cover changes coincident with disturbance events through time. We provide our code in the r package ShapeSelectForest on the Comprehensive R Archival Network and describe our computational approaches for running the method over large geographic areas. We also discuss how this methodology is currently being used for forest disturbance and attribute mapping across the conterminous United States.     

枣粮间作系统节肢动物群落组成与时序动态

为揭示枣粮间作系统中枣园和农田两个亚系统节肢动物群落的关系,将枣粮间作系统区分为枣园亚系统和农田亚系统进行节肢动物生态调查并分析时序动态.调查共发现节肢动物14 936头,分属3纲、14目、96科,其中枣园亚系统个体5 992头,分属3纲、14目、82科,农田亚系统个体8 971头,分属3纲、14目、80科,且枣园、农田两个亚系统节肢动物群落的结构存在一定差异.对系统进行时间序列的最优分割可将其划分为4个不同的时期,即麦田亚系统衰落期、系统物种增长期、系统发展稳定期、系统动荡衰落期.系统主要天敌物种在4个阶段中表现出相互迁移的特性,且两个亚系统寄生性天敌物种对另一系统的害虫存在着制约作用.枣粮间作系统中枣园和农田两个亚系统节肢动物群落间的物种迁移和相互作用对提高系统的稳定性和控害能力有积极意义.     

A time series and geographical analysis of population dynamics of the red-backed vole in Hokkaido,Japan

Summary A time series and geographical analysis of the long term (25-years and 16-years) census data of the red-backed vole, Clethrionomys rufocanus bedfordiae, populations in Hokkaido, Japan was carried out. Eighty seven populations from all over Hokkaido were grouped into the following 4 types according to their flucturation patterns: type A: low density-constant type; type B: medium density-gradual increase type; type C: medium density-wavy change type; type D: high density-large amplitude type. The border between type B and type C was not clear. Although the distribution of the fluctuation types did not show any geographical cline, the high density type (type D) occurred in limited areas where the winter was severe and spring came later and almost all the populations in the Oshima peninsula were the low density type (type A). The most common type of population in Hokkaido, type C, greatly fluctuated from year to year with the maximum density level/the minimum one being over 10. The periodicity of the peak years was not statistically significant, although peaks often occurred at 3 or 4 year intervals. The population grew from spring to autumn in almost all years so that the pattern of the seasonal change was qualitatively constant. The population density levels in spring were not significantly different between peak years and others, while those in autumn were greatly different between them, so that the population growth rates from spring to autumn were variable from year to year. This differential pattern was also found between high and low density areas.     

Measuring the directionality of coupling: phase versus state space dynamics and application to EEG time series

Measuring the directionality of coupling between dynamical systems is one of the challenging problems in nonlinear time series analysis. We investigate the relative merit of two approaches to assess directionality, one based on phase dynamics modeling and one based on state space topography. We analyze unidirectionally coupled model systems to investigate the ability of the two approaches to detect driver-responder relationships and discuss certain problems and pitfalls. In addition we apply both approaches to the intracranial electroencephalogram (EEG) recorded from one epilepsy patient during the seizure-free interval to demonstrate the general suitability of directionality measures to reflect the pathological interaction of the epileptic focus with other brain areas.