A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions

The study of complex systems deals with emergent behaviour that arises as a result of nonlinear spatio-temporal interactions between a large number of components both within the system, as well as between the system and its environment. There is a strong case to be made that neural systems as well as their emergent behaviour and disorders, can be studied within the framework of complexity science. In particular, the field of neuroimaging has begun to apply both theoretical and experimental procedures originating in complexity science – usually in parallel with traditional methodologies. Here, we demonstrate that the use of such traditional models may distort the outcomes of neuroimaging experiments – hence affecting their interpretability and raising questions about their reliability.Therefore, we argue in favor of adopting a complex systems-based methodology in the study of neuroimaging, alongside appropriate experimental paradigms, and with minimal influences from non-complex systems approaches. Our exposition includes a review of the fundamental mathematical concepts, combined with practical examples and a compilation of results from the literature.

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Characterization of Complex Systems by Aperiodic Driving Forces

10.21236/ada245832 ◽

1989 ◽

Author(s):

Daniel Bensen ◽

Michael Welge ◽

Alfred Huebler ◽

Norman Packard

Keyword(s):

Complex Systems ◽

Driving Forces

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Spatio-Temporal Characterization of Bio-Acoustic Scatterers in Complex Media

10.21236/ada575088 ◽

2012 ◽

Author(s):

Karim G. Sabra

Keyword(s):

Complex Media ◽

Spatio Temporal

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Spatio-temporal characterization of rainfall in Bangladesh: an innovative trend and discrete wavelet transformation approaches

Theoretical and Applied Climatology ◽

10.1007/s00704-020-03508-6 ◽

2021 ◽

Author(s):

Jayanta Das ◽

Tapash Mandal ◽

A. T. M. Sakiur Rahman ◽

Piu Saha

Keyword(s):

Wavelet Transformation ◽

Discrete Wavelet ◽

Discrete Wavelet Transformation ◽

Spatio Temporal

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Fluorescence Fluctuation Spectroscopy enables quantification of potassium channel subunit dynamics and stoichiometry

Scientific Reports ◽

10.1038/s41598-021-90002-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Giulia Tedeschi ◽

Lorenzo Scipioni ◽

Maria Papanikolaou ◽

Geoffrey W. Abbott ◽

Michelle A. Digman

Keyword(s):

Plasma Membrane ◽

Protein Diffusion ◽

Ion Diffusion ◽

Fluorescence Fluctuation Spectroscopy ◽

Biophysical Characterization ◽

Kv Channels ◽

Subunit Stoichiometry ◽

Spatio Temporal ◽

Voltage Sensing Domain

AbstractVoltage-gated potassium (Kv) channels are a family of membrane proteins that facilitate K+ ion diffusion across the plasma membrane, regulating both resting and action potentials. Kv channels comprise four pore-forming α subunits, each with a voltage sensing domain, and they are regulated by interaction with β subunits such as those belonging to the KCNE family. Here we conducted a comprehensive biophysical characterization of stoichiometry and protein diffusion across the plasma membrane of the epithelial KCNQ1-KCNE2 complex, combining total internal reflection fluorescence (TIRF) microscopy and a series of complementary Fluorescence Fluctuation Spectroscopy (FFS) techniques. Using this approach, we found that KCNQ1-KCNE2 has a predominant 4:4 stoichiometry, while non-bound KCNE2 subunits are mostly present as dimers in the plasma membrane. At the same time, we identified unique spatio-temporal diffusion modalities and nano-environment organization for each channel subunit. These findings improve our understanding of KCNQ1-KCNE2 channel function and suggest strategies for elucidating the subunit stoichiometry and forces directing localization and diffusion of ion channel complexes in general.

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SPATIO-TEMPORAL CHARACTERIZATION of litter at a touristic sandy beach in south Brazil

Environmental Pollution ◽

10.1016/j.envpol.2021.116927 ◽

2021 ◽

pp. 116927

Author(s):

Bruna de Ramos ◽

Melanie Vianna Alencar ◽

Fábio Lameiro Rodrigues ◽

Ana Luzia de Figueiredo Lacerda ◽

Maíra Carneiro Proietti

Keyword(s):

Sandy Beach ◽

Spatio Temporal ◽

South Brazil

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Video action detection by learning graph-based spatio-temporal interactions

Computer Vision and Image Understanding ◽

10.1016/j.cviu.2021.103187 ◽

2021 ◽

Vol 206 ◽

pp. 103187

Author(s):

Matteo Tomei ◽

Lorenzo Baraldi ◽

Simone Calderara ◽

Simone Bronzin ◽

Rita Cucchiara

Keyword(s):

Action Detection ◽

Spatio Temporal ◽

Temporal Interactions

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Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification

Remote Sensing ◽

10.3390/rs13132604 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2604

Author(s):

Patrick Osei Darko ◽

Margaret Kalacska ◽

J. Pablo Arroyo-Mora ◽

Matthew E. Fagan

Keyword(s):

Statistical Physics ◽

Near Infrared ◽

Information Gain ◽

Hyperspectral Imagery ◽

Full Range ◽

Surface Reflectance ◽

Mangrove Species ◽

New Approach ◽

Spatio Temporal ◽

Shortwave Infrared

Hyperspectral remote sensing across multiple spatio-temporal scales allows for mapping and monitoring mangrove habitats to support urgent conservation efforts. The use of hyperspectral imagery for assessing mangroves is less common than for terrestrial forest ecosystems. In this study, two well-known measures in statistical physics, Mean Information Gain (MIG) and Marginal Entropy (ME), have been adapted to high spatial resolution (2.5 m) full range (Visible-Shortwave-Infrared) airborne hyperspectral imagery. These two spectral complexity metrics describe the spatial heterogeneity and the aspatial heterogeneity of the reflectance. In this study, we compare MIG and ME with surface reflectance for mapping mangrove extent and species composition in the Sierpe mangroves in Costa Rica. The highest accuracy for separating mangroves from forest was achieved with visible-near infrared (VNIR) reflectance (98.8% overall accuracy), following by shortwave infrared (SWIR) MIG and ME (98%). Our results also show that MIG and ME can discriminate dominant mangrove species with higher accuracy than surface reflectance alone (e.g., MIG–VNIR = 93.6% vs. VNIR Reflectance = 89.7%).

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