Transient phenomena in ecology Hastings, Alan; Abbott, Karen C; Cuddington, Kim ...
Science (American Association for the Advancement of Science),
09/2018, Volume:
361, Issue:
6406
Journal Article
Peer reviewed
Open access
The importance of transient dynamics in ecological systems and in the models that describe them has become increasingly recognized. However, previous work has typically treated each instance of these ...dynamics separately. We review both empirical examples and model systems, and outline a classification of transient dynamics based on ideas and concepts from dynamical systems theory. This classification provides ways to understand the likelihood of transients for particular systems, and to guide investigations to determine the timing of sudden switches in dynamics and other characteristics of transients. Implications for both management and underlying ecological theories emerge.
There has been tremendous development in linear controllability of complex networks. Real-world systems are fundamentally nonlinear. Is linear controllability relevant to nonlinear dynamical ...networks? We identify a common trait underlying both types of control: the nodal "importance". For nonlinear and linear control, the importance is determined, respectively, by physical/biological considerations and the probability for a node to be in the minimum driver set. We study empirical mutualistic networks and a gene regulatory network, for which the nonlinear nodal importance can be quantified by the ability of individual nodes to restore the system from the aftermath of a tipping-point transition. We find that the nodal importance ranking for nonlinear and linear control exhibits opposite trends: for the former large-degree nodes are more important but for the latter, the importance scale is tilted towards the small-degree nodes, suggesting strongly the irrelevance of linear controllability to these systems. The recent claim of successful application of linear controllability to Caenorhabditis elegans connectome is examined and discussed.
The current development status of mitogen-activated protein kinase kinase (MEK) inhibitors, including the preclinical data and clinical study progress, has been summarized in this review. Different ...MEK inhibitors, possessing specific physicochemical properties and bioactivity characteristics, may provide different options for patients seeking treatment for cancer. Moreover, the combination of the MEK inhibitors with other therapies-such as chemotherapy, targeted therapy, and immunotherapy-may be a promising approach for clinical use.
Rhodium‐catalyzed directed carbene insertions into aromatic CH bonds of S‐aryl sulfoximines lead to intermediates, which upon dehydration provide 1,2‐benzothiazines in excellent yields. The ...domino‐type process is regioselective and shows a high functional‐group tolerance. It is scalable, and the only by‐products are dinitrogen and water. Three illustrative transformations underscore the synthetic value of the products.
Domino effect: Rhodium‐catalyzed annulation reactions provide 1,2‐benzothiazines in excellent yields starting from S‐aryl sulfoximines and diazo compounds. The catalysis shows a high functional‐group tolerance and the process demonstrates excellent regioselectivity.
Studies of human mobility in the past decade revealed a number of general scaling laws. However, to reproduce the scaling behaviors quantitatively at both the individual and population levels ...simultaneously remains to be an outstanding problem. Moreover, recent evidence suggests that spatial scales have a significant effect on human mobility, raising the need for formulating a universal model suited for human mobility at different levels and spatial scales. Here we develop a general model by combining memory effect and population-induced competition to enable accurate prediction of human mobility based on population distribution only. A variety of individual and collective mobility patterns such as scaling behaviors and trajectory motifs are accurately predicted for different countries and cities of diverse spatial scales. Our model establishes a universal underlying mechanism capable of explaining a variety of human mobility behaviors, and has significant applications for understanding many dynamical processes associated with human mobility.
Multiple resonance (MR) type thermally activated delayed fluorescence (TADF) material is currently a research hotspot in organic light‐emitting diodes (OLEDs) due to their high color purity and high ...exciton utilization. However, there are only a handful of MR‐TADF emitters with emissions beyond the blue‐to‐green region. The very limited emission colors for MR‐TADF emitters are mainly caused by the fact that so far molecular modifications of MR‐TADF do not offer much change in the emission colors. Here, we report a new approach to modifying a prototypical MR core of DABNA by fusing carbazoles to the MR framework. The carbazole‐fused molecule (TCZ‐F‐DABNA) basically maintains the MR‐dominated features of DABNA while red‐shifting the emission. Its OLED achieves an external quantum efficiency of 39.2 % with a peak at 588 nm, which is a record‐high efficiency for OLEDs with peaks beyond 560 nm. This work provides a new approach for significantly tunning emission colors of MR‐TADF emitters.
A new approach is reported by fusing carbazoles to a MR framework DABNA to significantly redshift emission while maintaining the MR‐dominated features. The carbazole‐fused molecule TCZ‐F‐DABNA can deliver a high PLQY of 99 %. Its OLEDs reached a record‐high EQE of 39.2 %.
Increasing evidence suggests that abnormally hyperphosphorylated tau plays a vital role in the pathogenesis of Alzheimer's disease (AD). Mitochondrial dysfunction also has a recognized role in the ...pathophysiology of AD. In recent years, mitochondrial dysfunction has been strongly associated with tau pathology in AD. Overexpression of hyperphosphorylated and aggregated tau appears to damage the axonal transport, leading to abnormal mitochondrial distribution. In addition, pathological tau impairs mitochondrial dynamics by regulating mitochondrial fission/fusion proteins, and further causes mitochondrial dysfunction and neuronal damage. Moreover, mitochondrial dysfunction is also involved in promoting tau pathology in AD. In this article, we evaluate the relationship between phosphorylated tau and mitochondrial dysfunction in AD.
The problem of reconstructing nonlinear and complex dynamical systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social ...sciences, as well as engineering and economics. The classic approach to phase-space reconstruction through the methodology of delay-coordinate embedding has been practiced for more than three decades, but the paradigm is effective mostly for low-dimensional dynamical systems. Often, the methodology yields only a topological correspondence of the original system. There are situations in various fields of science and engineering where the systems of interest are complex and high dimensional with many interacting components. A complex system typically exhibits a rich variety of collective dynamics, and it is of great interest to be able to detect, classify, understand, predict, and control the dynamics using data that are becoming increasingly accessible due to the advances of modern information technology. To accomplish these goals, especially prediction and control, an accurate reconstruction of the original system is required.
Nonlinear and complex systems identification aims at inferring, from data, the mathematical equations that govern the dynamical evolution and the complex interaction patterns, or topology, among the various components of the system. With successful reconstruction of the system equations and the connecting topology, it may be possible to address challenging and significant problems such as identification of causal relations among the interacting components and detection of hidden nodes. The “inverse” problem thus presents a grand challenge, requiring new paradigms beyond the traditional delay-coordinate embedding methodology.
The past fifteen years have witnessed rapid development of contemporary complex graph theory with broad applications in interdisciplinary science and engineering. The combination of graph, information, and nonlinear dynamical systems theories with tools from statistical physics, optimization, engineering control, applied mathematics, and scientific computing enables the development of a number of paradigms to address the problem of nonlinear and complex systems reconstruction. In this Review, we describe the recent advances in this forefront and rapidly evolving field, with a focus on compressive sensing based methods. In particular, compressive sensing is a paradigm developed in recent years in applied mathematics, electrical engineering, and nonlinear physics to reconstruct sparse signals using only limited data. It has broad applications ranging from image compression/reconstruction to the analysis of large-scale sensor networks, and it has become a powerful technique to obtain high-fidelity signals for applications where sufficient observations are not available. We will describe in detail how compressive sensing can be exploited to address a diverse array of problems in data based reconstruction of nonlinear and complex networked systems. The problems include identification of chaotic systems and prediction of catastrophic bifurcations, forecasting future attractors of time-varying nonlinear systems, reconstruction of complex networks with oscillatory and evolutionary game dynamics, detection of hidden nodes, identification of chaotic elements in neuronal networks, reconstruction of complex geospatial networks and nodal positioning, and reconstruction of complex spreading networks with binary data.. A number of alternative methods, such as those based on system response to external driving, synchronization, and noise-induced dynamical correlation, will also be discussed. Due to the high relevance of network reconstruction to biological sciences, a special section is devoted to a brief survey of the current methods to infer biological networks. Finally, a number of open problems including control and controllability of complex nonlinear dynamical networks are discussed.
The methods outlined in this Review are principled on various concepts in complexity science and engineering such as phase transitions, bifurcations, stabilities, and robustness. The methodologies have the potential to significantly improve our ability to understand a variety of complex dynamical systems ranging from gene regulatory systems to social networks toward the ultimate goal of controlling such systems.
Epidemic spreading processes in the real world depend on human behaviors and, consequently, are typically non-Markovian in that the key events underlying the spreading dynamics cannot be described as ...a Poisson random process and the corresponding event time is not exponentially distributed. In contrast to Markovian type of spreading dynamics for which mathematical theories have been well developed, we lack a comprehensive framework to analyze and fully understand non-Markovian spreading processes. Here we develop a mean-field theory to address this challenge, and demonstrate that the theory enables accurate prediction of both the transient phase and the steady states of non-Markovian susceptible-infected-susceptible spreading dynamics on synthetic and empirical networks. We further find that the existence of equivalence between non-Markovian and Markovian spreading depends on a specific edge activation mechanism. In particular, when temporal correlations are absent on active edges, the equivalence can be expected; otherwise, an exact equivalence no longer holds.
Computerized adaptive testing (CAT) is a mode of testing which enables more efficient and accurate recovery of one or more latent traits. Traditionally, CAT is built upon Item Response Theory (IRT) ...models that assume unidimensionality. However, the problem of how to build CAT upon latent class models (LCM) has not been investigated until recently, when Tatsuoka (J. R. Stat. Soc., Ser. C, Appl. Stat. 51:337–350,
2002
) and Tatsuoka and Ferguson (J. R. Stat., Ser. B 65:143–157,
2003
) established a general theorem on the asymptotically optimal sequential selection of experiments to classify finite, partially ordered sets. Xu, Chang, and Douglas (Paper presented at the annual meeting of National Council on Measurement in Education, Montreal, Canada,
2003
) then tested two heuristics in a simulation study based on Tatsuoka’s theoretical work in the context of computerized adaptive testing. One of the heuristics was developed based on Kullback–Leibler information, and the other based on Shannon entropy. In this paper, we showcase the application of the optimal sequential selection methodology in item selection of CAT that is built upon cognitive diagnostic models. Two new heuristics are proposed, and are compared against the randomized item selection method and the two heuristics investigated in Xu et al. (Paper presented at the annual meeting of National Council on Measurement in Education, Montreal, Canada,
2003
). Finally, we show the connection between the Kullback–Leibler-information-based approaches and the Shannon-entropy-based approach, as well as the connection between algorithms built upon LCM and those built upon IRT models.
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