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A Simple Pit Assay Protocol to Visualize and Quantify Osteoclastic Resorption In Vitro

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A combinatorial framework to quantify peak/pit asymmetries in complex dynamics.

Uri Hasson^1,2, Jacopo Iacovacci^3,4, Ben Davis⁵

¹Center for Mind and Brain Sciences, University of Trento, Trento, Italy. uri.hasson@unitn.it.

Scientific Reports

|February 25, 2018

Summary

This summary is machine-generated.

This study introduces a new method to analyze time series fluctuations by comparing local minima and maxima. This approach effectively distinguishes complex dynamics across various fields, outperforming existing metrics.

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Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

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Published on: June 15, 2022

Area of Science:

Complex Systems Analysis
Data Science
Time Series Analysis

Background:

Time series data often exhibit complex dynamics that are challenging to quantify.
Existing methods may not fully capture the asymmetries present in local fluctuations.

Purpose of the Study:

To develop and validate a combinatorial framework for quantifying asymmetries between minima and maxima in time series.
To demonstrate the framework's ability to distinguish diverse complex dynamics and outperform current metrics.

Main Methods:

Development of a combinatorial framework to analyze local fluctuations.
Application to synthetic datasets and canonical dynamical models (stochastic, chaotic).
Validation through extensive numerical simulations and real-world data from neurobiology, finance, and climate science.

Main Results:

The framework rigorously quantifies asymmetries in time series fluctuations.
It successfully distinguishes between different complex dynamics, including stochastic and chaotic processes.
Performance was superior to state-of-the-art metrics in several benchmark cases.

Conclusions:

Asymmetries between local maxima and minima are highly informative of underlying complex dynamics.
A graph-theoretic approach enables the use of these features for statistical learning.
The methodology offers a powerful new tool for analyzing complex time series data across disciplines.