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.

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 Temporal dynamics of stomatal conductance of plants under water deficit: can homeostasis be improved by more complex dynamics?

2004 ◽  
Vol 47 (3) ◽  
pp. 423-431 ◽  
Author(s):  
Gustavo Maia Souza ◽  
Ricardo Ferraz de Oliveira ◽  
Victor José Mendes Cardoso
Keyword(s):  
Stomatal Conductance ◽  
Sugar Beet ◽  
Water Deficit ◽  
Time Series Data ◽  
Temporal Dynamics ◽  
Complex Dynamics ◽  
Fractal Dimensions ◽  
Series Data ◽  
Complex Behaviour ◽  
Lz Complexity

In this study we hypothesized that chaotic or complex behavior of stomatal conductance could improve plant homeostasis after water deficit. Stomatal conductance of sunflower and sugar beet leaves was measured in plants grown either daily irrigation or under water deficit using an infrared gas analyzer. All measurements were performed under controlled environmental conditions. In order to measure a consistent time series, data were scored with time intervals of 20s during 6h. Lyapunov exponents, fractal dimensions, KS entropy and relative LZ complexity were calculated. Stomatal conductance in both irrigated and non-irrigated plants was chaotic-like. Plants under water deficit showed a trend to a more complex behaviour, mainly in sunflower that showed better homeostasis than in sugar beet. Some biological implications are discussed.

scholarly journals Uncovering the environmental drivers of short-term temporal dynamics in an epibenthic community from the Western English Channel

2019 ◽  
Vol 99 (7) ◽  
pp. 1467-1479
Author(s):  
Elizabeth Talbot ◽  
Jorn Bruggeman ◽  
Chris Hauton ◽  
Stephen Widdicombe
Keyword(s):  
Time Series Data ◽  
Temporal Dynamics ◽  
Anthropogenic Impacts ◽  
Benthic Communities ◽  
Food Limitation ◽  
Environmental Drivers ◽  
Sediment Surface ◽  
Series Data ◽  
English Channel ◽  
Larval Recruitment

AbstractBenthic communities, critical to the health and function of marine ecosystems, are under increasing pressure from anthropogenic impacts such as pollution, eutrophication and climate change. In order to refine predictions of likely future changes in benthic communities resulting from these impacts, we must first better constrain their responses to natural seasonality in environmental conditions. Epibenthic time series data (July 2008–May 2014) have been collected from Station L4, situated 7.25 nautical miles south of Plymouth in the Western English Channel. These data were analysed to establish patterns in community abundance, wet biomass and composition, and to link any observed patterns to environmental variables. A clear response to the input of organic material from phytoplankton blooms was detected, with sediment surface living deposit feeders showing an immediate increase in abundance, while predators and scavengers responded later, with an increase in biomass. We suggest that this response is a result of two factors. The low organic content of the L4 sediment results in food limitation of the community, and the mild winter/early spring bottom water temperatures allow the benthos to take immediate advantage of bloom sedimentation. An inter-annual change in community composition was also detected, as the community shifted from one dominated by the anomuran Anapagurus laevis to one dominated by the gastropod Turitella communis. This appeared to be related to a period of high larval recruitment for T. communis in 2013/2014, suggesting that changes in the recruitment success of one species can affect the structure of an entire community.

scholarly journals Assessing the Use of Sentinel-2 Time Series Data for Monitoring Cork Oak Decline in Portugal

2019 ◽  
Vol 11 (21) ◽  
pp. 2515 ◽  
Author(s):  
Ana Navarro ◽  
Joao Catalao ◽  
Joao Calvao
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Temporal Dynamics ◽  
Cork Oak ◽  
Series Data ◽  
Tree Level ◽  
Oak Decline ◽  
Sentinel 2

In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing to the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsened. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables monitoring of gradual processes. These processes can be monitored using spectral vegetation indices (VI) as their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

Bias in the Estimation of Functional Relationships from Time Series Data

1985 ◽  
Vol 42 (1) ◽  
pp. 147-149 ◽  
Author(s):  
Carl J. Walters
Keyword(s):  
Time Series ◽  
Measurement Errors ◽  
Time Series Data ◽  
Series Data ◽  
Parameter Estimates ◽  
Natural Experiments ◽  
Independent Variables ◽  
Functional Relationships ◽  
Stock Recruitment ◽  
The Relationship

Functional relationships, such as stock–recruitment curves, are generally estimated from time series data where natural "random" factors have generated both deviations from the relationship and also informative variation in the independent variables. Even in the absence of measurement errors, such natural experiments can lead to severely biased parameter estimates. For stock–recruitment models, the bias is misleading for management: the stock will appear too productive when it is low, and too unproductive when it is large. The likely magnitude of such biases can and should be determined for any particular case by Monte Carlo simulations.

scholarly journals Stochastic Claims Reserving Methods with State Space Representations: A Review

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 198
Author(s):  
Nataliya Chukhrova ◽  
Arne Johannssen
Keyword(s):  
Time Series ◽  
State Space ◽  
Life Insurance ◽  
Time Series Data ◽  
Temporal Dynamics ◽  
Insurance Companies ◽  
Series Data ◽  
Time Series Models ◽  
Life Insurer ◽  
High Flexibility

Often, the claims reserves exceed the available equity of non-life insurance companies and a change in the claims reserves by a small percentage has a large impact on the annual accounts. Therefore, it is of vital importance for any non-life insurer to handle claims reserving appropriately. Although claims data are time series data, the majority of the proposed (stochastic) claims reserving methods is not based on time series models. Among the time series models, state space models combined with Kalman filter learning algorithms have proven to be very advantageous as they provide high flexibility in modeling and an accurate detection of the temporal dynamics of a system. Against this backdrop, this paper aims to provide a comprehensive review of stochastic claims reserving methods that have been developed and analyzed in the context of state space representations. For this purpose, relevant articles are collected and categorized, and the contents are explained in detail and subjected to a conceptual comparison.

scholarly journals Time Stand Still: Effects of Temporal Window Selection on Eye Tracking Analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jonathan E. Peelle ◽  
Kristin J. Van Engen
Keyword(s):  
Eye Tracking ◽  
Degrees Of Freedom ◽  
Time Series Data ◽  
Time Window ◽  
Time Windows ◽  
Basis Set ◽  
Series Data ◽  
Parameter Estimates ◽  
Start Time ◽  
Window Selection

The number of possible approaches to conducting and analyzing a research study—often referred to as researcher degrees of freedom—has been increasingly under scrutiny as a challenge to the reproducibility of experimental results. Here we focus on the specific instance of time window selection for time series data. As an example, we use data from a visual world eye tracking paradigm in which participants heard a word and were instructed to click on one of four pictures corresponding to the target (e.g., “Click on the hat”). We examined statistical models for a range of start times following the beginning of the carrier phrase, and for each start time a range of window lengths, resulting in 8281 unique time windows. For each time window we ran the same logistic linear mixed effects model, including effects of time, age, noise, and word frequency on an orthogonalized polynomial basis set. Comparing results across these time ranges shows substantial changes in both parameter estimates and p values, even within intuitively “reasonable” boundaries. In some cases varying the window selection in the range of 100–200 ms caused parameter estimates to change from positive to negative. Rather than rush to provide specific recommendations for time window selection (which differs across studies), we advocate for transparency regarding time window selection and awareness of the effects this choice may have on results. Preregistration and multiverse model exploration are two complementary strategies to help mitigate bias introduced by any particular time window choice.

scholarly journals Application of OU processes to modelling temporal dynamics of the human microbiome, and calculating optimal sampling schemes

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Toby Kenney ◽  
Junqiu Gao ◽  
Hong Gu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Temporal Dynamics ◽  
Stable State ◽  
Mean Reversion ◽  
Series Data ◽  
Optimal Sampling ◽  
Healthy Individuals ◽  
Temporal Continuity ◽  
Better Than

Abstract Background The vast majority of microbiome research so far has focused on the structure of the microbiome at a single time-point. There have been several studies that measure the microbiome from a particular environment over time. A few models have been developed by extending time series models to accomodate specific features in microbiome data to address questions of stability and interactions of the microbime time series. Most research has observed the stability and mean reversion for some microbiomes. However, little has been done to study the mean reversion rates of these stable microbes and how sampling frequencies are related to such conclusions. In this paper, we begin to rectify this situation. We analyse two widely studied microbial time series data sets on four healthy individuals. We choose to study healthy individuals because we are interested in the baseline temporal dynamics of the microbiome. Results For this analysis, we focus on the temporal dynamics of individual genera, absorbing all interactions in a stochastic term. We use a simple stochastic differential equation model to assess the following three questions. (1) Does the microbiome exhibit temporal continuity? (2) Does the microbiome have a stable state? (3) To better understand the temporal dynamics, how frequently should data be sampled in future studies? We find that a simple Ornstein–Uhlenbeck model which incorporates both temporal continuity and reversion to a stable state fits the data for almost every genus better than a Brownian motion model that contains only temporal continuity. The Ornstein–Uhlenbeck model also fits the data better than modelling separate time points as independent. Under the Ornstein–Uhlenbeck model, we calculate the variance of the estimated mean reversion rate (the speed with which each genus returns to its stable state). Based on this calculation, we are able to determine the optimal sample schemes for studying temporal dynamics. Conclusions There is evidence of temporal continuity for most genera; there is clear evidence of a stable state; and the optimal sampling frequency for studying temporal dynamics is in the range of one sample every 0.8–3.2 days.

A quantum approach for time series data based on graph and Schrödinger equations methods

2018 ◽  
Vol 33 (35) ◽  
pp. 1850208 ◽  
Author(s):  
Pritpal Singh ◽  
Gaurav Dhiman ◽  
Amandeep Kaur
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Error Rates ◽  
Series Data ◽  
Quantum Model ◽  
Effective Solution ◽  
Proposed Model ◽  
Quantum Approach ◽  
Speed Up ◽  
Competing Models

The supremacy of quantum approach is able to solve the problems which are not practically feasible on classical machines. It suggests a significant speed up of the simulations and decreases the chance of error rates. This paper introduces a new quantum model for time series data which depends on the appropriate length of intervals. To provide effective solution of this problem, this study suggests a new graph-based quantum approach. This technique is useful in discretization and representation of logical relationships. Then, we divide these logical relations into various groups to obtain efficient results. The proposed model is verified and validated with various approaches. Experimental results signify that the proposed model is more precise than existing competing models.

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