scholarly journals Evidence for neural rhythms embedded within binocular rivalry

2019 ◽  
Vol 116 (30) ◽  
pp. 14811-14812 ◽  
Author(s):  
Oakyoon Cha ◽  
Randolph Blake
Keyword(s):  
Binocular Rivalry ◽  
Time Course ◽  
Time Series Data ◽  
Brain Activity ◽  
Series Data ◽  
Neural Oscillations ◽  
Large Dataset ◽  
Perceptual Judgments ◽  
Multistable Perception ◽  
Periodic Fluctuations

Evidence for perceptual periodicity emerges from studies showing periodic fluctuations in visual perception and decision making that are accompanied by neural oscillations in brain activity. We have uncovered signs of periodicity in the time course of binocular rivalry, a widely studied form of multistable perception. This was done by analyzing time series data contained in an unusually large dataset of rivalry state durations associated with states of exclusive monocular dominance and states of mixed perception during transitions between exclusive dominance. Identifiable within the varying durations of dynamic mixed perception are rhythmic clusters of durations whose incidence falls within the frequency band associated with oscillations in neural activity accompanying periodicity in perceptual judgments. Endogenous neural oscillations appear to be especially impactful when perception is unusually confounding.

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

scholarly journals How to predict relapse in leukaemia using time series data: A comparative in silico study

2020 ◽  
Author(s):  
Helene Hoffmann ◽  
Christoph Baldow ◽  
Thomas Zerjatke ◽  
Andrea Gottschalk ◽  
Sebastian Wagner ◽  
...  
Keyword(s):  
Data Quality ◽  
Computational Methods ◽  
In Silico ◽  
Prediction Accuracy ◽  
Time Course ◽  
Time Series Data ◽  
Specific Treatment ◽  
Series Data ◽  
Patient Specific ◽  
Clinical Markers

SummaryRisk stratification and treatment decisions for leukaemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improving the predictions for patient-specific treatment response.We analyzed the potential of different computational methods to accurately predict relapse for chronic and acute myeloid leukaemia, particularly focusing on the influence of data quality and quantity. Technically, we used clinical reference data to generate in-silico patients with varying levels of data quality. Based hereon, we compared the performance of mechanistic models, generalized linear models, and neural networks with respect to their accuracy for relapse prediction. We found that data quality has a higher impact on prediction accuracy than the specific choice of the method. We further show that adapted treatment and measurement schemes can considerably improve prediction accuracy. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukaemia patients.

P–368 Dynamic metabolomic profiling during early implantation period

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Y Zhang ◽  
Y W Zhao ◽  
C C Wang ◽  
T C Li
Keyword(s):  
Mass Spectrometry ◽  
Time Series ◽  
Embryo Transfer ◽  
Study Design ◽  
Empirical Bayes ◽  
Time Course ◽  
Time Series Data ◽  
Series Data ◽  
Metabolomic Profiling ◽  
Implantation Period

Abstract Study question To investigate the different metabolomic profiling in serum between pregnant and non-pregnant women during early implantation period. Summary answer Metabolomics of progesterone-related hormones enhances from ET day3 for pregnancy women compared with non-pregnancy women. What is known already Metabolomics is based on high-throughput analytical methods to identify and quantify metabolites. Compared to other omics study, metabolomics is the closest one to the phenotype, allowing the observation of dynamic changes in phenotype at specific timepoints. So far there is no published work about the metabolomics profile in human early implantation period. Study design, size, duration: Study design: comparative study. Size: 14 pregnancy women and 14 non-pregnancy women. duration: time-course. Participants/materials, setting, methods Participants: pregnancy women and unpregnancy women after embryo transfer (ET). Setting: university-based study. Methods: Peripheral blood were collected at ET day0, 3, 6 and 9. metabolomic profiling in serum by platforms of capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography–mass spectrometry (LC-MS). Main results and the role of chance There were no statistical difference of the age, BMI, basal FSH level, endometrium thickness on the day of embryo transfer, distribution of primary and secondary fertility, embryo transfer cycle as well as the infertile types between the two groups. After deleting those with over 50% missing data, we finally have 310 metabolites into statistical analysis. Among the 310 metabolite, lipid metabolites account the largest percentage, nearly half of all metabolites. The second biggest class of metabolites in our data was organic acids. Combined results in repeated measurement ANOVA (RM-ANOVA) and ANOVA-simultaneous component analysis (ASCA) as well as multivariate empirical Bayes time-series analysis (MEBA), we finally found that progesterone-related hormones were the most important metabolites for the whole time-series data. Those significant metabolites showed a significant down regulation from ET day0 to ET day3 and up regulation from ET day3 to ET day9. Limitations, reasons for caution we have limited sample size for this study and further validation is necessary for confirmation. Wider implications of the findings: The phenomenon of upregulation of progesterone-related hormones from day3 in pregnancy group might be related to the embryo-originated hcg. Because the embryo has entered into endometrium at day3 and produced cytokines, hcg and other interaction with endometrium. Trial registration number NA

Analysing and forecasting China containerized freight index with a hybrid decomposition–ensemble method based on EMD, grey wave and ARMA

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanhui Chen ◽  
Bin Liu ◽  
Tianzi Wang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Grey System Theory ◽  
Ensemble Method ◽  
Series Data ◽  
Forecasting Model ◽  
Content Type ◽  
Forecasting Method ◽  
Periodic Fluctuations ◽  
Wave Forecasting

PurposeThis paper applied grey wave forecasting in a decomposition–ensemble forecasting method for modelling the complex and non-linear features in time series data. This application aims to test the advantages of grey wave forecasting method in predicting time series with periodic fluctuations.Design/methodology/approachThe decomposition–ensemble method combines empirical mode decomposition (EMD), component reconstruction technology and grey wave forecasting. More specifically, EMD is used to decompose time series data into different intrinsic mode function (IMF) components in the first step. Permutation entropy and the average of each IMF are checked for component reconstruction. Then the grey wave forecasting model or ARMA is used to predict each IMF according to the characters of each IMF.FindingsIn the empirical analysis, the China container freight index (CCFI) is applied in checking prediction performance. Using two different time periods, the results show that the proposed method performs better than random walk and ARMA in multi-step-ahead prediction.Originality/valueThe decomposition–ensemble method based on EMD and grey wave forecasting model expands the application area of the grey system theory and graphic forecasting method. Grey wave forecasting performs better for data set with periodic fluctuations. Forecasting CCFI assists practitioners in the shipping industry in decision-making.

scholarly journals Inferring phage-bacteria infection networks from time series data

2016 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infects which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold-standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage-bacteria infection networks. This method uses time series data of fluctuating population densities to estimate the complete interaction network without having to test each phage-bacteria pair individually. We use in silico experiments to analyze the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network and mitigate against the possibility of evolutionary changes to infection during the time-course of measurement.

scholarly journals Investigating the Effect of Intrinsic Motivation on Alpha Desynchronization Using Sample Entropy

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 237 ◽  
Author(s):  
Tustanah Phukhachee ◽  
Suthathip Maneewongvatana ◽  
Thanate Angsuwatanakul ◽  
Keiji Iramina ◽  
Boonserm Kaewkamnerdpong
Keyword(s):  
Intrinsic Motivation ◽  
Time Series Data ◽  
Brain Activity ◽  
Sample Entropy ◽  
Entropy Method ◽  
Series Data ◽  
Eeg Data ◽  
Smart Education ◽  
Eeg Alpha ◽  
Spectral Perturbation

The effect of motivation and attention could play an important role in providing personalized learning services and improving learners toward smart education. These effects on brain activity could be quantified by EEG and open the path to analyze the efficiency of services during the learning process. Many studies reported the appearance of EEG alpha desynchronization during the attention period, resulting in better cognitive performance. Motivation was also found to be reflected in EEG. This study investigated the effect of intrinsic motivation on the alpha desynchronization pattern in terms of the complexity of time series data. The sample entropy method was used to quantify the complexity of event-related spectral perturbation (ERSP) of EEG data. We found that when participants can remember the stimulus, ERSP was significantly less complex than when they cannot. However, the effect of intrinsic motivation cannot be defined by using sample entropy directly. ERSP’s main effect showed that motivation affects the complexity of ERSP data; longer continuous alpha desynchronization patterns were found when participants were motivated. Therefore, we introduced an algorithm to identify the longest continuous alpha desynchronization pattern. The method allowed us to understand that intrinsic motivation has an effect on recognition at the frontal and left parietal area directly.

scholarly journals Analyzing the Time Course of Pupillometric Data

2019 ◽  
Vol 23 ◽  
pp. 233121651983248 ◽  
Author(s):  
Jacolien van Rij ◽  
Petra Hendriks ◽  
Hedderik van Rijn ◽  
R. Harald Baayen ◽  
Simon N. Wood
Keyword(s):  
Regression Analysis ◽  
Language Processing ◽  
Nonlinear Regression ◽  
Time Course ◽  
Time Series Data ◽  
Pupil Dilation ◽  
Series Data ◽  
Measured Signal ◽  
Full Time ◽  
Psycholinguistic Research

This article provides a tutorial for analyzing pupillometric data. Pupil dilation has become increasingly popular in psychological and psycholinguistic research as a measure to trace language processing. However, there is no general consensus about procedures to analyze the data, with most studies analyzing extracted features from the pupil dilation data instead of analyzing the pupil dilation trajectories directly. Recent studies have started to apply nonlinear regression and other methods to analyze the pupil dilation trajectories directly, utilizing all available information in the continuously measured signal. This article applies a nonlinear regression analysis, generalized additive mixed modeling, and illustrates how to analyze the full-time course of the pupil dilation signal. The regression analysis is particularly suited for analyzing pupil dilation in the fields of psychological and psycholinguistic research because generalized additive mixed models can include complex nonlinear interactions for investigating the effects of properties of stimuli (e.g., formant frequency) or participants (e.g., working memory score) on the pupil dilation signal. To account for the variation due to participants and items, nonlinear random effects can be included. However, one of the challenges for analyzing time series data is dealing with the autocorrelation in the residuals, which is rather extreme for the pupillary signal. On the basis of simulations, we explain potential causes of this extreme autocorrelation, and on the basis of the experimental data, we show how to reduce their adverse effects, allowing a much more coherent interpretation of pupillary data than possible with feature-based techniques.

scholarly journals Inferring phage–bacteria infection networks from time-series data

2016 ◽  
Vol 3 (11) ◽  
pp. 160654 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage–bacteria infection network establishes which virus types infect which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage–bacteria infection networks. This method uses time-series data of fluctuating population densities to estimate the complete interaction network without having to test each phage–bacteria pair individually. We use in silico experiments to analyse the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network. This approach also mitigates against the possibility of evolutionary changes to relevant phenotypes during the time course of measurement.

IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN TIME-COURSE MICROARRAY EXPERIMENT WITHOUT REPLICATE

2007 ◽  
Vol 05 (02a) ◽  
pp. 281-296 ◽  
Author(s):  
XU HAN ◽  
WING-KIN SUNG ◽  
LIN FENG
Keyword(s):  
Time Series ◽  
Differentially Expressed Genes ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Microarray Experiment ◽  
Differentially Expressed ◽  
Series Data ◽  
Gene Expressions ◽  
Partial Energy

Replication of time series in microarray experiments is costly. To analyze time series data with no replicate, many model-specific approaches have been proposed. However, they fail to identify the genes whose expression patterns do not fit the pre-defined models. Besides, modeling the temporal expression patterns is difficult when the dynamics of gene expression in the experiment is poorly understood. We propose a method called Partial Energy ratio for Microarray (PEM) for the analysis of time course microarray data. In the PEM method, we assume the gene expressions vary smoothly in the temporal domain. This assumption is comparatively weak and hence the method is general enough to identify genes expressed in unexpected patterns. To identify the differentially expressed genes, a new statistic is developed by comparing the energies of two convoluted profiles. We further improve the statistic for microarray analysis by introducing the concept of partial energy. The PEM statistic can be easily incorporated into the SAM framework for significance analysis. We evaluated the PEM method with an artificial dataset and two published time course cDNA microarray datasets on yeast. The experimental results show the robustness and the generality of the PEM method in identifying the genes of interest.

scholarly journals A Parameter-Free Model Comparison Test Using Differential Algebra

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Heather A. Harrington ◽  
Kenneth L. Ho ◽  
Nicolette Meshkat
Keyword(s):  
Time Course ◽  
Model Comparison ◽  
Time Series Data ◽  
Dynamic Properties ◽  
Differential Algebra ◽  
Series Data ◽  
Comparison Test ◽  
Competing Models ◽  
Model Equations ◽  
Parameter Values

We present a method for rejecting competing models from noisy time-course data that does not rely on parameter inference. First we characterize ordinary differential equation models in only measurable variables using differential-algebra elimination. This procedure gives input-output equations, which serve as invariants for time series data. We develop a model comparison test using linear algebra and statistics to reject incorrect models from their invariants. This algorithm exploits the dynamic properties that are encoded in the structure of the model equations without recourse to parameter values, and, in this sense, the approach is parameter-free. We demonstrate this method by discriminating between different models from mathematical biology.

Sign in / Sign up

Export Citation Format

Share Document