Variations in solar and geomagnetic activity with periods near to 1.3 year

2009 ◽  
Vol 1 (2) ◽  
Author(s):  
Jaroslav Střeštïk
Keyword(s):  
Geomagnetic Activity ◽  
Time Series Data ◽  
Interplanetary Space ◽  
Short Interval ◽  
Biological Data ◽  
Series Data ◽  
Solar Cycles ◽  
Lower Amplitude ◽  
Periodic Signals ◽  
Odd Cycles

AbstractIt is known that solar wind velocity fluctuates regularly with a period of about 1.3 years. This periodicity (and other signals with periods near to 1.1 and 0.9 years) has also been observed in biological data. The variation is a temporary feature, mostly being observed in the early 1990s. Here, the occurrence of these periodic signals in solar and geomagnetic activity between 1932 and 2005 has been investigated. The signal with 1.3 year period is present in geomagnetic activity only in a short interval after 1990 and to a lesser extent around 1942. At other times the signal is very weak or not present at all. Other periods are much lower amplitude and appear only sporadically throughout the time investigated. A connection between these periods and solar cycles (e.g. different even or odd cycles) has not been proven. It is possible that there is a long-term periodicity in the occurrence of the 1.3 year period but the time series data available is insufficient to confirm this. There are no such periodicities in solar activity. In order to gain a greater understanding of these periodic signals, we should search for their origin in interplanetary space.

scholarly journals Estimation of the probability of epidemic fade-out from multiple outbreak data

2021 ◽  
Author(s):  
Punya Alahakoon ◽  
James M. McCaw ◽  
Peter G. Taylor
Keyword(s):  
Time Series Data ◽  
Stochastic Dynamics ◽  
Synthetic Data ◽  
Recurrent Infection ◽  
Biological Data ◽  
Series Data ◽  
Model Parameters ◽  
Sufficient Information ◽  
Epidemic Dynamics ◽  
Sirs Model

Deterministic epidemic models, such as the SIRS model or an SIR model with demography, that allow for replenishment of susceptibles typically display damped oscillatory behaviour. If the population is initially fully susceptible, once an epidemic takes off a distinct trough will exist between the first and second waves of infection where the number of infectious individuals falls to a low level. Epidemic dynamics are, however, influenced by stochastic effects, particularly when the number of infectives is low. At the beginning of an epidemic, stochastic die-out is possible and well characterised through use of a branching process approximation to the full non-linear stochastic dynamics. Conditional on an epidemic taking off, stochastic extinction is highly unlikely during the first epidemic wave, but the probability of extinction increases again as the wave declines. Extinction during this period, prior to a potential second wave of infection, is defined as "epidemic fade-out". We consider a set of observed epidemics, each distinct and having evolved independently, in which some display fade-out and some do not. While fade-out is necessarily a stochastic phenomenon, in general the probability of fade-out will depend on the model parameters associated with each epidemic. Accordingly, we ask whether time-series data for the epidemics contain sufficient information to identify the key driver(s) of different outcomes\textemdash fade-out or otherwise\textemdash across the sub-populations supporting each epidemic. We apply a Bayesian hierarchical modelling framework to synthetic data from an SIRS model of epidemic dynamics and demonstrate that we can 1) identify when the sub-population specific model parameters supporting each epidemic have significant variability and 2) estimate the probability of epidemic fade-out for each sub-population. We demonstrate that a hierarchical analysis can provide more accurate and precise estimates of the probability of fade-out than is possible if considering each epidemic in isolation. Our methods may be applied more generally, to both epidemiological and other biological data to identify where differences in outcome—fade-out or recurrent infection/waves —across are purely due to chance or driven by underlying changes in the parameters driving the dynamics.

Inference of gene regulatory networks using pseudo-time series data

2021 ◽  
Author(s):  
Yuelei Zhang ◽  
Xiao Chang ◽  
Xiaoping Liu
Keyword(s):  
Time Series ◽  
Gene Regulatory Networks ◽  
Biological Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Biological Data ◽  
Supplementary Information ◽  
Series Data ◽  
Gene Regulatory

Abstract Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific dataset. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. Results Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. Availabilityand implementation The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Magnetotelluric distortions directly observed with lightning data

2017 ◽  
Vol 210 (2) ◽  
pp. 1171-1175
Author(s):  
Lachlan Hennessy ◽  
James Macnae
Keyword(s):  
Electric Field ◽  
Electric Fields ◽  
Time Series Data ◽  
Statistical Processing ◽  
Series Data ◽  
Time Of Arrival ◽  
Source Information ◽  
Near Surface ◽  
Lower Amplitude ◽  
Galvanic Distortion

AbstractGalvanic distortions complicate magnetotelluric (MT) soundings. In this research, we use lightning network data to identify specific sferics in MT measurements and analyse these events on the basis of the lightning source location. Without source information, identification and removal of galvanic distortion is a fundamentally ill-posed problem, unless data are statistically decomposed into determinable and indeterminable parts. We use realistic assumptions of the earth-ionosphere waveguide propagation velocity to accurately predict the time of arrival, azimuth and amplitude for every significant sferic in our time-series data. For each sferic with large amplitude, we calculate the rotation of the electric field from the measured to the predicted arrival azimuth. This rotation of the electric field is a primary parameter of distortion. Our results demonstrate that a rudimentary model for near-surface galvanic distortion consistently fits observed electric field rotations. When local features rotate regional electric fields, then counter-rotating data to predicted arrival azimuths should correct the directional dependence of static shift. Although we used amplitude thresholds to simplify statistical processing, future developments should incorporate both signal-to-noise improvements and multisite decompositions. Lower amplitude signal may also be useful after the appropriate signal processing for noise reduction. We anticipate our approach will be useful for further work on MT distortion.

scholarly journals TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data

2020 ◽  
Author(s):  
Elan Ness-Cohn ◽  
Rosemary Braun
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Detection Method ◽  
Persistent Homology ◽  
Null Distribution ◽  
Algebraic Method ◽  
Optimal Choice ◽  
Biological Data ◽  
Series Data ◽  
Topological Features

AbstractThe circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics, coupled with the significant implications of the circadian clock for human health, has sparked an interest in circadian profiling studies to discover genes under circadian control. Here we present TimeCycle: a topology-based rhythm detection method designed to identify cycling transcripts. For a given time-series, the method reconstructs the state space using time-delay embedding, a data transformation technique from dynamical systems. In the embedded space, Takens’ theorem proves that the dynamics of a rhythmic signal will exhibit circular patterns. The degree of circularity of the embedding is calculated as a persistence score using persistent homology, an algebraic method for discerning the topological features of data. By comparing the persistence scores to a bootstrapped null distribution, cycling genes are identified. Results in both synthetic and biological data highlight TimeCycle’s ability to identify cycling genes across a range of sampling schemes, number of replicates, and missing data. Comparison to competing methods highlights their relative strengths, providing guidance as to the optimal choice of cycling detection method.

scholarly journals A jump distance based parameter inference scheme for particulate trajectories in biological settings

2017 ◽  
Author(s):  
Rebecca Menssen ◽  
Madhav Mani
Keyword(s):  
Time Series Data ◽  
Temporal Dynamics ◽  
Mean Square Displacement ◽  
Parameter Extraction ◽  
Biological Data ◽  
Series Data ◽  
Parameter Estimates ◽  
Selection Scheme ◽  
Jump Distance ◽  
Competing Models

ABSTRACTOne type of biological data that needs more quantitative analytical tools is particulate trajectories. This type of data appears in many different contexts and across scales in biology: from the trajectory of bacteria performing chemotaxis to the mobility of ms2 spots within nuclei. Presently, most analyses performed on data of this nature has been limited to mean square displacement (MSD) analyses. While simple, MSD analysis has several pitfalls, including difficulty in selecting between competing models, handling systems with multiple distinct sub-populations, and parameter extraction from limited time-series data. Here, we provide an alternative to MSD analysis using the jump distance distribution (JDD). The JDD resolves several issues: one can select between competing models of motion, have composite models that allow for multiple populations, and have improved error bounds on parameter estimates when data is limited. A major consequence is that you can perform analyses using a fraction of the data required to get similar results using MSD analyses, thereby giving access to a larger range of temporal dynamics when the underlying stochastic process is not stationary. In this paper, we construct and validate a derivation of the JDD for different transport models, explore the dependence on dimensionality of the process, and implement a parameter estimation and model selection scheme. We demonstrate the power of this scheme through an analysis of bacterial chemotaxis data, highlighting the interpretation of results and improvements upon MSD analysis. We expect that our proposed scheme provides quantitative insights into a broad spectrum of biological phenomena requiring analysis of particulate trajectories.

scholarly journals Frequency-independent biological signal identification (FIBSI): A free program that simplifies intensive analysis of non-stationary time series data

2020 ◽  
Author(s):  
Ryan M. Cassidy ◽  
Alexis G. Bavencoffe ◽  
Elia R. Lopez ◽  
Sai S. Cheruvu ◽  
Edgar T. Walters ◽  
...  
Keyword(s):  
Experimental Data ◽  
Time Series ◽  
Biological Activity ◽  
Background Noise ◽  
Time Series Data ◽  
Biological Data ◽  
Series Data ◽  
Signal Identification ◽  
The Real ◽  
Frequency Independent

AbstractExtracting biological signals from non-linear, dynamic and stochastic experimental data can be challenging, especially when the signal is non-stationary. Many currently available methods make assumptions about the data structure (e.g., signal is periodic, sufficient recording time) and modify the raw data in pre-processing using filters and/or transformations. With an agnostic approach to biological data analysis as a goal, we implemented a signal detection algorithm in Python that quantifies the dimensional properties of waveform deviations from baseline via a running fit function. We call the resulting free program frequency-independent biological signal identification (FIBSI). We demonstrate the utility of FIBSI on two disparate types of experimental data: in vitro whole-cell current-clamp electrophysiological recordings of rodent sensory neurons (i.e., nociceptors) and in vivo fluorescence image time-lapse movies capturing gastrointestinal motility in larval zebrafish. In rodent nociceptors, depolarizing fluctuations in membrane potential are irregular in shape and difficult to distinguish from noise. Using FIBSI, we determined that nociceptors from naïve mice generate larger, more frequent fluctuations compared to naïve rats, suggesting species-specific specializations in rodent nociceptors. In zebrafish, measuring gut motility is a useful tool for addressing developmental and disease-related mechanisms associated with gut function. However, available methods are laborious, technically complex, and/or not cost-effective. We developed and tested a novel assay that can characterize intestinal peristalsis using imaging time series datasets. We used FIBSI to identify muscle contractions in the fluorescence signals and compared their frequencies in unfed and fed larvae. Additionally, FIBSI allowed us to discriminate between peristalsis and oscillatory sphincter-like movements in functionally distinct gut segments (foregut, midgut, and cloaca). We conclude that FIBSI, which is freely available via GitHub, is widely useful for the unbiased analysis of non-stationary signals and extraction of biologically meaningful information from experimental time series data and can be employed for both descriptive and hypothesis-driven investigations.Author SummaryBiologists increasingly work with large, complex experimental datasets. Those datasets often encode biologically meaningful signals along with background noise that is recorded along with the biological data during experiments. Background noise masks the real signal but originates from other sources, for example from the equipment used to perform the measurements or environmental disturbances. When it comes to analyzing the data, distinguishing between the real biological signals and the background noise can be very challenging. Many existing programs designed to help scientists with this problem are either difficult to use, not freely available, or only appropriate to use on very specific types of datasets. The research presented here embodies our goal of helping others to analyze their data by employing a powerful but novice-friendly program that describes multiple features of biological activity in its raw form without abstract transformations. We show the program’s applicability using two different kinds of biological activity measured in our labs. It is our hope that this will help others to analyze complex datasets more easily, thoroughly, and rigorously.

scholarly journals Comparison of Geomagnetic Indices During Even and Odd Solar Cycles SC17 – SC24: Signatures of Gnevyshev Gap in Geomagnetic Activity

Solar Physics ◽  
2021 ◽  
Vol 296 (1) ◽  
Author(s):  
Jouni Takalo
Keyword(s):  
Time Series ◽  
Geomagnetic Activity ◽  
Sunspot Group ◽  
Solar Cycles ◽  
Geomagnetic Disturbances ◽  
Similar Length ◽  
Dst Index ◽  
Small Decline ◽  
Ap Index ◽  
Odd Cycles

AbstractWe show that the time series of sunspot group areas has a gap, the so-called Gnevyshev gap (GG), between ascending and descending phases of the cycle and especially so for the even-numbered cycles. For the odd cycles this gap is less obvious, and is only a small decline after the maximum of the cycle. We resample the cycles to have the same length of 3945 days (about 10.8 years), and show that the decline is between 1445 – 1567 days after the start of the cycle for the even cycles, and extending sometimes until 1725 days from the start of the cycle. For the odd cycles the gap is a little earlier, 1332 – 1445 days after the start of the cycles with no extension. We analyze geomagnetic disturbances for Solar Cycles 17 – 24 using the Dst-index, the related Dxt- and Dcx-indices, and the Ap-index. In all of these time series there is a decline at the time, or somewhat after, the GG in the solar indices, and it is at its deepest between 1567 – 1725 days for the even cycles and between 1445 – 1567 days for the odd cycles. The averages of these indices for even cycles in the interval 1445 – 1725 are 46%, 46%, 18%, and 29% smaller compared to surrounding intervals of similar length for Dst, Dxt, Dcx, and Ap, respectively. For odd cycles the averages of the Dst- and Dxt-indices between 1322 – 1567 days are 31% and 12% smaller than the surrounding intervals, but not smaller for the Dcx-index and only 4% smaller for the Ap-index. The declines are significant at the 99% level for both even and odd cycles of the Dst-index and for the Dxt-, Dcx- and Ap-indices for even cycles. For odd cycles of the Dxt-index the significance is 95%, but the decline is insignificant for odd cycles of the Dcx- and Ap-indices.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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