scholarly journals Identification of Early Warning Signals at the Critical Transition Point of Colorectal Cancer Based on Dynamic Network Analysis

2020 ◽  
Vol 8 ◽  
Author(s):  
Lei Liu ◽  
Zhuo Shao ◽  
Jiaxuan Lv ◽  
Fei Xu ◽  
Sibo Ren ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Early Warning ◽  
Transition Point ◽  
Dynamic Network ◽  
Critical Transition ◽  
Dynamic Network Analysis ◽  
Warning Signals ◽  
Early Warning Signals

scholarly journals Variance and Rate-of-Change as Early Warning Signals for a Critical Transition in an Aquatic Ecosystem State: A Test Case From Tasmania, Australia

2018 ◽  
Vol 123 (2) ◽  
pp. 495-508 ◽  
Author(s):  
Kristen K. Beck ◽  
Michael-Shawn Fletcher ◽  
Patricia S. Gadd ◽  
Henk Heijnis ◽  
Krystyna M. Saunders ◽  
...  
Keyword(s):  
Early Warning ◽  
Aquatic Ecosystem ◽  
Rate Of Change ◽  
Test Case ◽  
Critical Transition ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Ecosystem State

scholarly journals Detecting early-warning signals of influenza outbreak based on dynamic network marker

2018 ◽  
Vol 23 (1) ◽  
pp. 395-404 ◽  
Author(s):  
Pei Chen ◽  
Ely Chen ◽  
Luonan Chen ◽  
Xianghong Jasmine Zhou ◽  
Rui Liu
Keyword(s):  
Early Warning ◽  
Dynamic Network ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Influenza Outbreak

Flickering gives early warning signals of a critical transition to a eutrophic lake state

Nature ◽  
2012 ◽  
Vol 492 (7429) ◽  
pp. 419-422 ◽  
Author(s):  
Rong Wang ◽  
John A. Dearing ◽  
Peter G. Langdon ◽  
Enlou Zhang ◽  
Xiangdong Yang ◽  
...  
Keyword(s):  
Early Warning ◽  
Eutrophic Lake ◽  
Critical Transition ◽  
Warning Signals ◽  
Early Warning Signals

Seizures Start without Common Signatures of Critical Transition

2016 ◽  
Vol 26 (08) ◽  
pp. 1650053 ◽  
Author(s):  
Piotr Milanowski ◽  
Piotr Suffczynski
Keyword(s):  
Ocean Circulation ◽  
Early Warning ◽  
Human Subjects ◽  
Epileptic Seizures ◽  
Critical Transition ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Human Epilepsy ◽  
Critical Transitions ◽  
Eeg Recordings

Complex dynamical systems may exhibit sudden autonomous changes from one state to another. Such changes that occur rapidly in comparison to the regular dynamics have been termed critical transitions. Examples of such phenomena can be found in many complex systems: changes in climate and ocean circulation, changes in wildlife populations, financial crashes, as well as in medical conditions like asthma attacks and depression. It has been recognized that critical transitions, even if they arise in completely different contexts and situations, share several common attributes and also generic early-warning signals that indicate that a critical transition is approaching. In the present study, we review briefly the general characteristics that have been observed in systems prior to critical transitions and apply these general indicators to nearly 300 epileptic seizures collected from human subjects using invasive EEG. Only in about 8% of the patients was evidence of critical transitions found. In the remaining majority of cases no early warning signals that behaved consistently prior to seizures were observed. These results do not rule out the possibility of critical transitions to seizure but point to limited relevance of their early warning signals in the context of human epilepsy observed using intracranial EEG recordings.

scholarly journals Early warning signals and the prosecutor's fallacy

2012 ◽  
Vol 279 (1748) ◽  
pp. 4734-4739 ◽  
Author(s):  
Carl Boettiger ◽  
Alan Hastings
Keyword(s):  
Early Warning ◽  
Spatial Scales ◽  
Experimental Studies ◽  
Statistical Error ◽  
Warning Signal ◽  
Experimental Manipulation ◽  
Critical Transition ◽  
Natural Experiments ◽  
Warning Signals ◽  
Early Warning Signals

Early warning signals have been proposed to forecast the possibility of a critical transition, such as the eutrophication of a lake, the collapse of a coral reef or the end of a glacial period. Because such transitions often unfold on temporal and spatial scales that can be difficult to approach by experimental manipulation, research has often relied on historical observations as a source of natural experiments. Here, we examine a critical difference between selecting systems for study based on the fact that we have observed a critical transition and those systems for which we wish to forecast the approach of a transition. This difference arises by conditionally selecting systems known to experience a transition of some sort and failing to account for the bias this introduces—a statistical error often known as the prosecutor's fallacy. By analysing simulated systems that have experienced transitions purely by chance, we reveal an elevated rate of false-positives in common warning signal statistics. We further demonstrate a model-based approach that is less subject to this bias than those more commonly used summary statistics. We note that experimental studies with replicates avoid this pitfall entirely.

scholarly journals Genome-wide dynamic network analysis reveals a critical transition state of flower development in Arabidopsis

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Fuping Zhang ◽  
Xiaoping Liu ◽  
Aidi Zhang ◽  
Zhonglin Jiang ◽  
Luonan Chen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Transition State ◽  
Flower Development ◽  
Dynamic Network ◽  
Critical Transition ◽  
Dynamic Network Analysis ◽  
Genome Wide

Early-Warning Signals for a Critical Transition in the Coupled Amazon Rainforest-South American Monsoon System

2021 ◽  
Author(s):  
Nils Bochow
Keyword(s):  
Early Warning ◽  
Amazon Rainforest ◽  
South American ◽  
Critical Transition ◽  
The South ◽  
Warning Signals ◽  
Early Warning Signals ◽  
South American Monsoon ◽  
Monsoon System ◽  
South American Monsoon System

<p>The Amazon rainforest is widely recognised as a potential tipping element in the Earth's climate system. While several studies suggest a sudden dieback of the rainforest ecosystem after partial deforestation [e.g., 1, 2], there is still a lack of understanding where to search for early-warning signals that might precede such a dieback. In this work we employ a non-linear model of the moisture transport across the Amazon Basin to propose several statistical and physical early warning signals for a critical transition in the coupled dynamics of the Amazon rainforest and the atmospheric circulation of the South American monsoon. </p><p>Widespread deforestation and its effects on evapotranspiration and radiation have been shown to potentially trigger a collapse of the positive feedback related to latent heat release over the rainforest [3], resulting in substantially reduced rainfall amounts. The model includes a nonlinear contribution representing the acceleration of low-level moisture flow caused by condensational latent heating.  </p><p>Guided by our modelling results, we associate characteristic changes in the hydrological cycle as well as statistical indicators in observed data with deforestation-induced circulation changes that are consistent with the identified early-warning signals. Our findings indicate that in response to deforestation, the coupled atmosphere-vegetation system is destabilising and that further deforestation could trigger a transition of the Amazon rainforest to a savanna state. </p><p>[1] Nobre, C. A., & Borma, L. D. S. (2009). “Tipping points” for the Amazon forest. Current Opinion in Environmental Sustainability. https://doi.org/10.1016/j.cosust.2009.07.003</p><p>[2] Hirota, M., Holmgren, M., Van Nes, E. H., & Scheffer, M. (2011). Global resilience of tropical forest and savanna to critical transitions. Science, 334(6053), 232–235. https://doi.org/10.1126/science.1210657</p><p>[3] Boers, N., Marwan, N., Barbosa, H. M. J., & Kurths, J. (2017). A deforestation-induced tipping point for the South American monsoon system. Scientific Reports, 7. https://doi.org/10.1038/srep41489</p>

scholarly journals Critical transitions in the hydrological system: Early-warning signals and network analysis

2021 ◽  
Author(s):  
Xueli Yang ◽  
Zhi-Hua Wang ◽  
Chenghao Wang
Keyword(s):  
Network Analysis ◽  
Early Warning ◽  
Clustering Coefficient ◽  
Hydrological Processes ◽  
Modular Organization ◽  
Precipitation Pattern ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Hydrological System

Abstract. In this study, we identified the critical transitions of hydrological processes including precipitation and potential evapotranspiration by analysing their early-warning signals and system-based network structures. The statistical early-warning signals are manifest in increasing trends of autocorrelation and variance in the hydrology system ranging from regional to global scales, prior to climate shifts in the 1970s and 1990s in agreement with observations. We further extended the conventional statistics-based measures of early-warning signals to system-based network analysis in urban areas across the contiguous United States. The topology of urban precipitation network features hub-periphery (clustering) and modular organization, with strong intra-regional connectivity and inter-regional gateways (teleconnection). We found that several network parameters (mean correlation coefficient, density, and clustering coefficient) gradually increased prior to the critical transition in the 1990s, signifying the enhanced synchronization among urban precipitation pattern. These topological parameters not only can serve as novel system-based early-warning signals to critical transitions in hydrological processes, but also shed new lights on structure-dynamic interactions in the complex hydrological system.

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