Self-reactive IgE exacerbates interferon responses associated with autoimmunity.

Canonically, immunoglobulin E (IgE) mediates allergic immune responses by triggering mast cells and basophils to release histamine and type 2 helper cytokines. Here we found that in human systemic lupus erythematosus (SLE), IgE antibodies specific for double-stranded DNA (dsDNA) activated plasmacytoid dendritic cells (pDCs), a type of cell of the immune system linked to viral defense, which led to the secretion of substantial amounts of interferon-α (IFN-α). The concentration of dsDNA-specific IgE found in patient serum correlated with disease severity and greatly potentiated pDC function by triggering phagocytosis via the high-affinity FcɛRI receptor for IgE, followed by Toll-like receptor 9 (TLR9)-mediated sensing of DNA in phagosomes. Our findings expand the known pathogenic mechanisms of IgE-mediated inflammation beyond those found in allergy and demonstrate that IgE can trigger interferon responses capable of exacerbating self-destructive autoimmune responses.

Deep learning-based analysis of basins of attraction.

This research addresses the challenge of characterizing the complexity and unpredictability of basins within various dynamical systems. The main focus is on demonstrating the efficiency of convolutional neural networks (CNNs) in this field. Conventional methods become computationally demanding when analyzing multiple basins of attraction across different parameters of dynamical systems. Our research presents an innovative approach that employs CNN architectures for this purpose, showcasing their superior performance in comparison to conventional methods. We conduct a comparative analysis of various CNN models, highlighting the effectiveness of our proposed characterization method while acknowledging the validity of prior approaches. The findings not only showcase the potential of CNNs but also emphasize their significance in advancing the exploration of diverse behaviors within dynamical systems.

Planetary influences on the solar cycle: A nonlinear dynamics approach.

We explore the effect of some simple perturbations on three nonlinear models proposed to describe large-scale solar behavior via the solar dynamo theory: the Lorenz and Rikitake systems and a Van der Pol-Duffing oscillator. Planetary magnetic fields affecting the solar dynamo activity have been simulated by using harmonic perturbations. These perturbations introduce cycle intermittency and amplitude irregularities revealed by the frequency spectra of the nonlinear signals. Furthermore, we have found that the perturbative intensity acts as an order parameter in the correlations between the system and the external forcing. Our findings suggest a promising avenue to study the sunspot activity by using nonlinear dynamics methods.

Ultrasensitive vibrational resonance induced by small disturbances.

We have found two kinds of ultrasensitive vibrational resonance in coupled nonlinear systems. It is particularly worth pointing out that this ultrasensitive vibrational resonance is transient behavior caused by transient chaos. Considering a long-term response, the system will transform from transient chaos to a periodic response. The pattern of vibrational resonance will also transform from ultrasensitive vibrational resonance to conventional vibrational resonance. This article focuses on the transient ultrasensitive vibrational resonance phenomenon. It is induced by a small disturbance of the high-frequency excitation and the initial simulation conditions, respectively. The damping coefficient and the coupling strength are the key factors to induce the ultrasensitive vibrational resonance. By increasing these two parameters, the vibrational resonance pattern can be transformed from ultrasensitive vibrational resonance to conventional vibrational resonance. The reason for different vibrational resonance patterns to occur lies in the state of the system response. The response usually presents transient chaotic behavior when the ultrasensitive vibrational resonance appears and the plot of the response amplitude vs the controlled parameters shows a highly fractalized pattern. When the response is periodic or doubly periodic, it usually corresponds to the conventional vibrational resonance. The ultrasensitive vibrational resonance not only occurs at the excitation frequency, but it also occurs at some more nonlinear frequency components. The ultrasensitive vibrational resonance as transient behavior and the transformation of vibrational resonance patterns are new phenomena in coupled nonlinear systems.

Control of escapes in two-degree-of-freedom open Hamiltonian systems.

We investigate the possibility of avoiding the escape of chaotic scattering trajectories in two-degree-of-freedom Hamiltonian systems. We develop a continuous control technique based on the introduction of coupling forces between the chaotic trajectories and some periodic orbits of the system. The main results are shown through numerical simulations, which confirm that all trajectories starting near the stable manifold of the chaotic saddle can be controlled. We also show that it is possible to jump between different unstable periodic orbits until reaching a stable periodic orbit belonging to a Kolmogorov-Arnold-Moser island.

Noncanonical autophagy is required for type I interferon secretion in response to DNA-immune complexes.

Toll-like receptor-9 (TLR9) is largely responsible for discriminating self from pathogenic DNA. However, association of host DNA with autoantibodies activates TLR9, inducing the pathogenic secretion of type I interferons (IFNs) from plasmacytoid dendritic cells (pDCs). Here, we found that in response to DNA-containing immune complexes (DNA-IC), but not to soluble ligands, IFN-α production depended upon the convergence of the phagocytic and autophagic pathways, a process called microtubule-associated protein 1A/1B-light chain 3 (LC3)-associated phagocytosis (LAP). LAP was required for TLR9 trafficking into a specialized interferon signaling compartment by a mechanism that involved autophagy-related proteins, but not the conventional autophagic preinitiation complex, or adaptor protein-3 (AP-3). Our findings unveil a new role for nonconventional autophagy in inflammation and provide one mechanism by which anti-DNA autoantibodies, such as those found in several autoimmune disorders, bypass the controls that normally restrict the apportionment of pathogenic DNA and TLR9 to the interferon signaling compartment.

Introduction to Focus Issue: Recent advances in modeling complex systems: Theory and applications.

This is an introductory paper of the Focus Issue Recent advances in modeling complex systems: Theory and applications, where papers presenting new advances and insights into chaotic dynamics, fractional dynamics, complex oscillations, complex traffic dynamics, and complex networks, as well as their applications, are collected. All these different problems share common ideas and methods and provide new perspectives for further progress in the modeling of complex systems.

Stochastic resonance induced by an unknown linear frequency modulated signal in a strong noise background.

Stochastic resonance (SR) is widely used as a signal enhancement technique in recovering and enhancing periodic or aperiodic signals submerged in noise. However, system parameters and noise intensity tend to influence the SR performance. To achieve better resonance performance, several indices are often used to determine these parameters, including signal-to-noise, amplification factor, and cross-correlation coefficient. Nevertheless, for a linear frequency modulated (LFM) signal, such indices may no longer work and consequently make SR unable to recover the unknown LFM signal from raw signals. Thus, this limits the application of SR to some extent. To deal with this problem, we define here a new index to characterize the unknown LFM signal with the help of the fractional Fourier transform. Guided by this index, SR is thus able to recover the unknown LFM signal from the raw signal. In addition, a cloud model based genetic algorithm is used to achieve an adaptive SR in order to improve the effectiveness of signal processing.

Low-density granulocytes activate T cells and demonstrate a non-suppressive role in systemic lupus erythematosus.

OBJECTIVES: The presence of proinflammatory low-density granulocytes (LDG) has been demonstrated in autoimmune and infectious diseases. Recently, regulatory neutrophilic polymorphonuclear myeloid-derived suppressor cells (PMN-MDSC) were identified in systemic lupus erythematosus (SLE). Because LDG and PMN-MDSC share a similar phenotype with contrasting functional effects, we explored these cells in a cohort of patients with SLE. METHODS: LDG and normal-density granulocytes (NDG) were isolated from fresh blood of healthy donors (HD) and patients with SLE. Associations between LDG and clinical manifestations were analysed. Multicolor flow cytometry and confocal imaging were performed to immunophenotype the cells. The ability of LDG and NDG to suppress T cell function and induce cytokine production was quantified. RESULTS: LDG prevalence was elevated in SLE versus HD, associated with the interferon (IFN) 21-gene signature and disease activity. Also, the LDG-to-lymphocyte ratio associated better with SLE disease activity index than neutrophil-to-lymphocyte ratio. SLE LDG exhibited significantly heightened surface expression of various activation markers and also of lectin-like oxidised low-density lipoprotein receptor-1, previously described to be associated with PMN-MDSC. Supernatants from SLE LDG did not restrict HD CD4+ T cell proliferation in an arginase-dependent manner, suggesting LDG are not immunosuppressive. SLE LDG supernatants induced proinflammatory cytokine production (IFN gamma, tumour necrosis factor alpha and lymphotoxin alpha) from CD4+ T cells. CONCLUSIONS: Based on our results, SLE LDG display an activated phenotype, exert proinflammatory effects on T cells and do not exhibit MDSC function. These results support the concept that LDG represent a distinct proinflammatory subset in SLE with pathogenic potential, at least in part, through their ability to activate type 1 helper responses.

Low-dimensional paradigms for high-dimensional hetero-chaos.

The dynamics on a chaotic attractor can be quite heterogeneous, being much more unstable in some regions than others. Some regions of a chaotic attractor can be expanding in more dimensions than other regions. Imagine a situation where two such regions and each contains trajectories that stay in the region for all time-while typical trajectories wander throughout the attractor. Furthermore, if arbitrarily close to each point of the attractor there are points on periodic orbits that have different unstable dimensions, then we say such an attractor is "hetero-chaotic" (i.e., it has heterogeneous chaos). This is hard to picture but we believe that most physical systems possessing a high-dimensional attractor are of this type. We have created simplified models with that behavior to give insight into real high-dimensional phenomena.

The dose-dense principle in chemotherapy.

Chemotherapy is a cancer treatment modality that uses drugs to kill tumor cells. A typical chemotherapeutic protocol consists of several drugs delivered in cycles of three weeks. We present mathematical analyses demonstrating the existence of a maximum time between cycles of chemotherapy for a protocol to be effective. A mathematical equation is derived, which relates such a maximum time with the variables that govern the kinetics of the tumor and those characterizing the chemotherapeutic treatment. Our results suggest that there are compelling arguments supporting the use of dose-dense protocols. Finally, we discuss the limitations of these protocols and suggest an alternative.

Role of IgE in autoimmunity.

There is accumulating evidence to suggest that IgE plays a significant role in autoimmunity. The presence of circulating self-reactive IgE in patients with autoimmune disorders has been long known but, at the same time, largely understudied. However, studies have shown that the increased IgE concentration is not associated with higher prevalence for atopy and allergy in patients with autoimmune diseases, such as systemic lupus erythematosus. IgE-mediated mechanisms are conventionally known to facilitate degranulation of mast cells and basophils and promote TH2 immunity, mechanisms that are not only central to mounting an appropriate defense against parasitic worms, noxious substances, toxins, venoms, and environmental irritants but that also trigger exuberant allergic reactions in patients with allergies. More recently, IgE autoantibodies have been recognized to participate in the self-inflicted damaging immune responses that characterize autoimmunity. Such autoimmune responses include direct damage on tissue-containing autoantigens, activation and migration of basophils to lymph nodes, and, as observed most recently, induction of type 1 interferon responses from plasmacytoid dendritic cells. The importance of IgE as a central pathogenic mechanism in autoimmunity has now been clinically validated by the approval of omalizumab, an anti-IgE mAb, for patients with chronic spontaneous urticaria and for the clinical benefit of patients with bullous pemphigoid. In this review we summarize recent reports describing the prevalence of self-reactive IgE and discuss novel findings that incriminate IgE as central in the pathogenesis of inflammatory autoimmune disorders.

Effect of geometry on the classical entanglement in a chaotic optical fiber.

The effect of boundary deformation on the classical entanglement which appears in the classical electromagnetic field is considered. A chaotic billiard geometry is used to explore the influence of the mechanical modification of the optical fiber cross-sectional geometry on the production of classical entanglement within the electromagnetic fields. For the experimental realization of our idea, we propose an optical fiber with a cross section that belongs to the family of Robnik chaotic billiards. Our results show that a modification of the fiber geometry from a regular to a chaotic regime can enhance the transverse mode classical entanglement.

Avoiding healthy cells extinction in a cancer model.

We consider a dynamical model of cancer growth including three interacting cell populations of tumor cells, healthy host cells and immune effector cells. For certain parameter choice, the dynamical system displays chaotic motion and by decreasing the response of the immune system to the tumor cells, a boundary crisis leading to transient chaotic dynamics is observed. This means that the system behaves chaotically for a finite amount of time until the unavoidable extinction of the healthy and immune cell populations occurs. Our main goal here is to apply a control method to avoid extinction. For that purpose, we apply the partial control method, which aims to control transient chaotic dynamics in the presence of external disturbances. As a result, we have succeeded to avoid the uncontrolled growth of tumor cells and the extinction of healthy tissue. The possibility of using this method compared to the frequently used therapies is discussed.

A validated mathematical model of tumor growth including tumor-host interaction, cell-mediated immune response and chemotherapy.

We consider a dynamical model of cancer growth including three interacting cell populations of tumor cells, healthy host cells and immune effector cells. The tumor-immune and the tumor-host interactions are characterized to reproduce experimental results. A thorough dynamical analysis of the model is carried out, showing its capability to explain theoretical and empirical knowledge about tumor development. A chemotherapy treatment reproducing different experiments is also introduced. We believe that this simple model can serve as a foundation for the development of more complicated and specific cancer models.

Control of collective network chaos.

Under certain conditions, the collective behavior of a large globally-coupled heterogeneous network of coupled oscillators, as quantified by the macroscopic mean field or order parameter, can exhibit low-dimensional chaotic behavior. Recent advances describe how a small set of "reduced" ordinary differential equations can be derived that captures this mean field behavior. Here, we show that chaos control algorithms designed using the reduced equations can be successfully applied to imperfect realizations of the full network. To systematically study the effectiveness of this technique, we measure the quality of control as we relax conditions that are required for the strict accuracy of the reduced equations, and hence, the controller. Although the effects are network-dependent, we show that the method is effective for surprisingly small networks, for modest departures from global coupling, and even with mild inaccuracy in the estimate of network heterogeneity.

Hamming distance as a measure of spatial chaos in evolutionary games.

From a context of evolutionary dynamics, social games can be studied as complex systems that may converge to a Nash equilibrium. Nonetheless, they can behave in an unpredictable manner when looking at the spatial patterns formed by the agents' strategies. This is known in the literature as spatial chaos. In this paper we analyze the problem for a deterministic prisoner's dilemma and a public goods game and calculate the Hamming distance that separates two solutions that start at very similar initial conditions for both cases. The rapid growth of this distance indicates the high sensitivity to initial conditions, which is a well-known indicator of chaotic dynamics.

Energy-based stochastic resetting can avoid noise-enhanced stability.

The theory of stochastic resetting asserts that restarting a stochastic process can expedite its completion. In this paper, we study the escape process of a Brownian particle in an open Hamiltonian system that suffers noise-enhanced stability. This phenomenon implies that under specific noise amplitudes the escape process is delayed. Here, we propose a protocol for stochastic resetting that can avoid the noise-enhanced stability effect. In our approach, instead of resetting the trajectories at certain time intervals, a trajectory is reset when a predefined energy threshold is reached. The trajectories that delay the escape process are the ones that lower their energy due to the stochastic fluctuations. Our resetting approach leverages this fact and avoids long transients by resetting trajectories before they reach low-energy levels. Finally, we show that the chaotic dynamics (i.e., the sensitive dependence on initial conditions) catalyzes the effectiveness of the resetting strategy.

Relativistic chaotic scattering: Unveiling scaling laws for trapped trajectories.

In this paper we study different types of phase space structures which appear in the context of relativistic chaotic scattering. By using the relativistic version of the Hénon-Heiles Hamiltonian, we numerically study the topology of different kind of exit basins and compare it with the case of low velocities in which the Newtonian version of the system is valid. Specifically, we numerically study the escapes in the phase space, in the energy plane, and in the ß plane, which richly characterize the dynamics of the system. In all cases, fractal structures are present, and the escaping dynamics is characterized. In every case a scaling law is numerically obtained in which the percentage of the trapped trajectories as a function of the relativistic parameter ß and the energy is obtained. Our work could be useful in the context of charged particles which eventually can be trapped in the magnetosphere, where the analysis of these structures can be relevant.

New developments in classical chaotic scattering.

Classical chaotic scattering is a topic of fundamental interest in nonlinear physics due to the numerous existing applications in fields such as celestial mechanics, atomic and nuclear physics and fluid mechanics, among others. Many new advances in chaotic scattering have been achieved in the last few decades. This work provides a current overview of the field, where our attention has been mainly focused on the most important contributions related to the theoretical framework of chaotic scattering, the fractal dimension, the basins boundaries and new applications, among others. Numerical techniques and algorithms, as well as analytical tools used for its analysis, are also included. We also show some of the experimental setups that have been implemented to study diverse manifestations of chaotic scattering. Furthermore, new theoretical aspects such as the study of this phenomenon in time-dependent systems, different transitions and bifurcations to chaotic scattering and a classification of boundaries in different types according to symbolic dynamics are also shown. Finally, some recent progress on chaotic scattering in higher dimensions is also described.