Signal Dynamics Research Articles

Automatic detection of obstructive sleep apnea through nonlinear dynamics of single-lead ECG signals

Automatic detection of obstructive sleep apnea through nonlinear dynamics of single-lead ECG signals

Dynamic modelling of signalling pathways when ordinary differential equations are not feasible.

Mathematical modelling plays a crucial role in understanding inter- and intracellular signalling processes. Currently, ordinary differential equations (ODEs) are the predominant approach in systems biology for modelling such pathways. While ODE models offer mechanistic interpretability, they also suffer from limitations, including the need to consider all relevant compounds, resulting in large models difficult to handle numerically and requiring extensive data. In previous work, we introduced the retarded transient function (RTF) as an alternative method for modelling temporal responses of signalling pathways. Here, we extend the RTF approach to integrate concentration or dose-dependencies into the modelling of dynamics. With this advancement, RTF modelling now fully encompasses the application range of ODE models, which comprises predictions in both time and concentration domains. Moreover, characterizing dose-dependencies provides an intuitive way to investigate and characterize signalling differences between biological conditions or cell types based on their response to stimulating inputs. To demonstrate the applicability of our extended approach, we employ data from time- and dose-dependent inflammasome activation in bone marrow-derived macrophages treated with nigericin sodium salt. Our results show the effectiveness of the extended RTF approach as a generic framework for modelling dose-dependent kinetics in cellular signalling. The approach results in intuitively interpretable parameters that describe signal dynamics and enables predictive modelling of time- and dose-dependencies even if only individual cellular components are quantified. The presented approach is available within the MATLAB-based Data2Dynamics modelling toolbox at https://github.com/Data2Dynamics and https://zenodo.org/records/14008247 and as R code at https://github.com/kreutz-lab/RTF.

Collaborative control framework at isolated signalized intersections under the mixed connected automated vehicles environment

AbstractThis study proposes a collaborative control framework under the mixed traffic environment of connected and automated vehicles and connected human‐driven vehicles, which can simultaneously optimize the signal timing, lane settings, and vehicle trajectories at isolated intersections. Initially, considering the dynamics of traffic demand and incompatible signals, we analyze the vehicle delay of each lane. Based on the delay analysis, the spatiotemporal resource collaborative optimization model of lane setting and signal timing is established to minimize the average delay. Subsequently, in the buffer zone, a graph‐theoretic‐based sorting and platooning model provides a clear and concise representation of the transformation process from the initial state to the target state of vehicles, enabling the platoon formation. Additionally, trajectory optimization is integrated into the collaborative control framework by the optimal control model and car‐following model in the passing zone. Simulation experiments and sensitivity analyses demonstrate the effectiveness of the proposed framework in reducing average vehicle delay, improving fuel consumption, and coping with changing traffic demand at intersections.

Ultrafast Excitonic Transitions in Enantiopure and Racemic Squaraine Thin Films

ABSTRACTWhile chirality is a prevalent character of numerous biological and synthetic organic molecules, its selective absorption of circularly polarized light, known as circular dichroism (CD), is typically small due to intrinsically weak coupling between magnetic and electric dipoles. However, thin films of aggregated, enantiopure prolinol‐derived squaraine molecules (ProSQ‐C16) exhibit an unusually large excitonic CD signal, although the underlying mechanism is not yet known. In this study, we employ steady‐state and ultrafast transient absorption spectroscopy to investigate the nature and dynamics of excitons in aggregates of enantiopure and racemic ProSQ‐C16 thin films. Highly resembling transient responses of enantiopure thin films under excitations at different photon energies strongly indicate that a single type of aggregate dominates the linear optical response, that is, a strong red‐shifted (J‐like) and weak blue‐shifted (H‐like) absorption band. On the other hand, the transient properties of the racemic thin film deviate from this pattern and remain largely ambiguous. The short lifetime of excited states and coherent oscillations present in the dynamics of the transient absorption signal indicate that the early time dynamics are governed by a transition towards a dark intermediate state, which might arise from intermolecular charge transfer with potential contributions from the coupling of excitons to the vibrations. This non‐radiative relaxation pathway explains the unusually weak fluorescence of the predominately J‐like behaving aggregate. Our findings conclusively show that the chiral aggregate structure has a strong impact on the optical and dynamic response of the excitons and underline the significance of non‐Frenkel exciton states for the optical properties of anilino squaraine dyes.

Characterizing EEG signal dynamics in healthy, seizure-free, and seizure states using the chaos decision tree algorithm

Abstract Epilepsy is a multifaceted neurological condition marked by repetitive seizures that arise from irregular electrical activity in the brain. To understand this condition, a thorough examination of brain signals captured in different states is needed. In order to examine the dynamic behavior of brain signals in three different conditions: healthy, seizure-free, and seizure periods, this study uses the chaos decision tree algorithm. The findings show notable variations in these situations’ dynamics. Chaos is evident during seizure moments, showing extremely chaotic activity. The signals mostly exhibit stochastic behavior in the healthy condition, which is consistent with typical brain dynamics. It is noteworthy that an intermediate state exhibiting a blend of stochastic and chaotic signal dynamics is exhibited throughout the seizure-free time. Furthermore, the research shows that the frequency of chaotic signals rises with increasing proximity to the epileptogenic zone. These discoveries clarify the complex nature of epilepsy and offer insightful information about the dynamic properties of brain signals in various stages, aiding in improved understanding and potential diagnostic approaches.

Filtering algorithm for systems with transmission random failures and packet loss prediction compensation

Abstract The problem of least-squares linear filtering is explored for a specific type of networked systems, where the measurements are affected by random parameters and experience random delays and packet dropouts. To prevent network congestion, only one packet is transmitted per sampling interval. However, due to these phenomena, either a single packet, two packets, or no data at all may be received at each sampling time. In the latter case, the estimator compensates by utilizing a predicted value. An innovation-based method is used to derive a recursive filter that does not require the signal dynamics, but only some statistical properties (first-order and second-order moments) of the stochastic processes in the observation model.

Enhanced probabilistic damage localization for composite plate based on joint recurrence plots

Localizing damage is a key aspect of monitoring composite laminate structural health. To address the problem of generating the damage index (DI) of composite laminates using Lamb waves, a reconstruction approach for probabilistic damage inspection (RAPID) based on joint recurrence plots (JRP) was proposed. First, the dynamics of the Lamb wave signals are analyzed using phase space reconstruction. Second, the damage information in each sensing path is represented using JRP, and the DI based on the JRP is built with variational auto-encoder (VAE). Finally, the relative magnitude of the DI was employed to modify RAPID’s weight distribution function, allowing for composite plate damage localization detection. The findings of finite element modeling and experiment data reveal that the approach can achieve accurate localization imaging of the composite plate’s interior damage while also properly identifying the damage under 20 dB and 10 dB noise interference. The approach does not require an understanding of Lamb wave dispersion characteristics and involve the extraction of complicated wave packet signals, making it promising for structural health monitoring based on damage index.

Submillisecond Electric Field Sensing with an Individual Rare-Earth Doped Ferroelectric Nanocrystal.

Understanding the dynamics of electrical signals within neuronal assemblies is crucial to unraveling complex brain functions. Despite recent advances in employing optically active nanostructures in transmembrane potential sensing, there remains room for improvement in terms of response time and sensitivity. Here, we report the development of such a nanosensor capable of detecting electric fields with a submillisecond response time at the single-particle level. We achieve this by using ferroelectric nanocrystals doped with rare-earth ions that produce upconversion (UC). When such a nanocrystal experiences a variation of surrounding electric potential, its surface charge density changes, inducing electric polarization modifications that vary, via a converse piezoelectric effect, the crystal field around the ions. The latter variation is finally converted into UC spectral changes, enabling optical detection of the electric potential. To develop such a sensor, we synthesized erbium and ytterbium-doped barium titanate crystals of ≈160 nm in size. We observed distinct changes in the UC spectrum when individual nanocrystals were subjected to an external field via a conductive atomic force microscope tip, with a response time of 100 μs. Furthermore, our sensor exhibits a remarkable sensitivity of 4.8 kV/cm/, enabling time-resolved detection of a fast-changing electric field of amplitude comparable to that generated during a neuron action potential.

Temporal dynamics of scent mark composition in field‐experimental lizard populations

Abstract Animal signals are dynamic traits that can undergo considerable spatial and temporal changes and that are influenced by factors such as age, health condition and interactions with both the abiotic and biotic environment. However, much of our understanding of signal changes throughout an individual's lifetime stems from cross‐sectional, often laboratory‐based, studies focused on visual and auditory signals. Longitudinal field investigations of temporal variation in chemical signals, especially in vertebrates, remain rare despite chemical communication being the most ubiquitous form of information exchange in the natural world. To remedy this, we conducted a unique, replicated field experiment to study the temporal signal dynamics in free‐living lizard populations on natural islands. Specifically, we collected scent marks from individually marked lizards across five populations during the spring of two consecutive years and analysed the lipophilic chemical composition of these scent marks. Our findings demonstrate that the overall scent mark composition of individual lizards changed over time, shifting consistently in both direction and magnitude from year to year among individuals and across replicate populations. Similar patterns were observed for the chemical richness and diversity of scent marks. Temporal variation in the relative proportions of three potentially socially relevant signalling compounds in lizard scent marks revealed a more complex pattern: α‐tocopherol remained stable over time, oleic acid decreased and the change in octadecanoic acid proportion was body size‐dependent. Together, our results provide novel insights into how individual vertebrate chemical signals may fluctuate across space and time. We discuss the potential causes of the observed temporal variability and its consequences for chemical signal evolution. Read the free Plain Language Summary for this article on the Journal blog.

Adaptive fault-tolerant control of uncertain systems with unknown actuator failures and input delay

Unknown failures and time delays of actuators which may degrade system performance seem inevitable in practical systems. However, the available results to compensate for unknown failures of time delay actuators based on adaptive approaches are very limited. In this paper, we address such a problem by considering controlling a class of second-order systems with unknown actuator failures and input delays. Firstly, the controlled system is transformed into a triangular structure model. Meanwhile, the input time delay is transformed into dynamics of the output signal. Then, an adaptive controller is developed using backstepping. Not only can the uncertainties due to actuator failure be compensated, but the unknown input delay has also been effectively restrained under the proposed control scheme. The upper bound condition of strength scalar [Formula: see text] is proposed. It is a sufficient condition for system stability. When the strength scalar satisfies this condition, the design parameters exist and the system is stable under the controlling of the proposed controller. Finally, the effectiveness of the proposed control scheme is verified through simulation results, including numerical simulation and actual system simulation. Through the comparative simulation, it can be seen that the control scheme proposed in this paper has faster convergence speed.

A Stochastic Dynamic Operator Framework That Improves the Precision of Analysis and Prediction Relative to the Classical Spike-Triggered Average Method, Extending the Toolkit.

Here we test the stochastic dynamic operator (SDO) as a new framework for describing physiological signal dynamics relative to spiking or stimulus events. The SDO is a natural extension of existing spike-triggered average (STA) or stimulus-triggered average techniques currently used in neural analysis. It extends the classic STA to cover state-dependent and probabilistic responses where STA may fail. In simulated data, SDO methods were more sensitive and specific than the STA for identifying state-dependent relationships. We have tested SDO analysis for interactions between electrophysiological recordings of spinal interneurons, single motor units, and aggregate muscle electromyograms (EMG) of major muscles in the spinal frog hindlimb. When predicting target signal behavior relative to spiking events, the SDO framework outperformed or matched classical spike-triggered averaging methods. SDO analysis permits more complicated spike-signal relationships to be captured, analyzed, and interpreted visually and intuitively. SDO methods can be applied at different scales of interest where spike-triggered averaging methods are currently employed, and beyond, from single neurons to gross motor behaviors. SDOs may be readily generated and analyzed using the provided SDO Analysis Toolkit We anticipate this method will be broadly useful for describing dynamical signal behavior and uncovering state-dependent relationships of stochastic signals relative to discrete event times.

Evolutionary dynamics of polyadenylation signals and their recognition strategies in protists.

The poly(A) signal, together with auxiliary elements, directs cleavage of a pre-mRNA and thus determines the 3' end of the mature transcript. In many species, including humans, the poly(A) signal is an AAUAAA hexamer, but we recently found that the deeply branching eukaryote Giardia lamblia uses a distinct hexamer (AGURAA) and lacks any known auxiliary elements. Our discovery prompted us to explore the evolutionary dynamics of poly(A) signals and auxiliary elements in the eukaryotic kingdom. We use direct RNA sequencing to determine poly(A) signals for four protists within the Metamonada clade (which also contains G. lamblia) and two outgroup protists. These experiments reveal that the AAUAAA hexamer serves as the poly(A) signal in at least four different eukaryotic clades, indicating that it is likely the ancestral signal, whereas the unusual Giardia version is derived. We find that the use and relative strengths of auxiliary elements are also plastic; in fact, within Metamonada, species like G. lamblia make use of a previously unrecognized auxiliary element where nucleotides flanking the poly(A) signal itself specify genuine cleavage sites. Thus, despite the fundamental nature of pre-mRNA cleavage for the expression of all protein-coding genes, the motifs controlling this process are dynamic on evolutionary timescales, providing motivation for future biochemical and structural studies as well as new therapeutic angles to target eukaryotic pathogens.

Machine Learning Models for Probability Classification in Spectrographic EEG Seizures Dataset

The examination of brain signals, namely the Electroencephalogram (EEG) signals, is an approach to possibly detect seizures of the brain. Due to the nature of these signals, deep learning techniques have offered the opportunity to perform automatic or semi-automatic analysis which could support decision and therapeutical approaches. This paper focuses on the possibility of classifying EEG seizure using convolutional layers (namely EfficientNetV2 architectures, i.e., EfficientNetV2S and EfficientNetV2B2), Long Short-Term Memory (LSTM) units, and fine-tuned mechanisms of attention. We use these techniques to untangle the complexity of these signals and accurately predict seizures. The proposed system provided interesting results with an 86.45% accuracy under the Kullback-Leibler Divergence loss of 0.95. Moreover, these results showed that embedding LSTM layers deeply increases the quality of the results since these layers support the analysis of the spatial-temporal dynamics of the EEG signals. On the other hand, it is important to mention that hardware limitations could affect these results and therefore it is important, when setting this architectural system, to fine-tune the data set and balance the performance vs the computational cost of the process.

Particle‐based MR modeling with diffusion, microstructure, and enzymatic reactions

AbstractPurposeTo develop a novel particle‐based in silico MR model and demonstrate applications of this model to signal mechanisms which are affected by the spatial organization of particles, including metabolic reaction kinetics, microstructural effects on diffusion, and radiofrequency (RF) refocusing effects in gradient‐echo sequences.MethodsThe model was developed by integrating a forward solution of the Bloch equations with a Brownian dynamics simulator. Simulation configurations were then designed to model MR signal dynamics of interest, with a primary focus on hyperpolarized 13C MRI methods. Phantom scans and spectrophotometric assays were conducted to validate model results in vitro.ResultsThe model accurately reproduced the reaction kinetics of enzyme‐mediated conversion of pyruvate to lactate. When varying proportions of restrictive structure were added to the reaction volume, nonlinear changes in the reaction rate measured in vitro were replicated in silico. Modeling of RF refocusing effects characterized the degree of diffusion‐weighted contribution from preserved residual magnetization in nonspoiled gradient‐echo sequences.ConclusionsThese results show accurate reproduction of a range of MR signal mechanisms, establishing the model's capability to investigate the multifactorial signal dynamics such as those underlying hyperpolarized 13C MRI data.

Dynamics of BMP signaling and stable gene expression in the early Drosophila embryo.

In developing tissues, morphogen gradients are thought to initialize gene expression patterns. However, the relationship between the dynamics of morphogen-encoded signals and gene expression decisions is largely unknown. Here we examine the dynamics of the Bone Morphogenetic Protein (BMP) pathway in Drosophila blastoderm-stage embryos. In this tissue, the BMP pathway is highly dynamic: it begins as a broad and weak signal on the dorsal half of the embryo, then 20-30 min later refines into a narrow, intense peak centered on the dorsal midline. This dynamical progression of the BMP signal raises questions of how it stably activates target genes. Therefore, we performed live imaging of the BMP signal and found that dorsal-lateral cells experience only a short transient in BMP signaling, after which the signal is lost completely. Moreover, we measured the transcriptional response of the BMP target gene pannier in live embryos and found it to remain activated in dorsal-lateral cells, even after the BMP signal is lost. Our findings may suggest that the BMP pathway activates a memory, or 'ratchet' mechanism that may sustain gene expression.

Single-pulse ultrafast real-time simultaneous planar imaging of femtosecond laser-nanoparticle dynamics in flames

The creation of carbonaceous nanoparticles and their dynamics in hydrocarbon flames are still debated in environmental, combustion, and material sciences. In this study, we introduce single-pulse femtosecond laser sheet-compressed ultrafast photography (fsLS-CUP), an ultrafast imaging technique specifically designed to shed light on and capture ultrafast dynamics stemming from interactions between femtosecond lasers and nanoparticles in flames in a single-shot. fsLS-CUP enables the first-time real-time billion frames-per-second (Gfps) simultaneous two-dimensional (2D) imaging of laser-induced fluorescence (LIF) and laser-induced heating (LIH) that are originated from polycyclic aromatic hydrocarbons (PAHs) and soot particles, respectively. Furthermore, fsLS-CUP provides the real-time spatiotemporal map of femtosecond laser-soot interaction as elastic light scattering (ELS) at an astonishing 250 Gfps. In contrast to existing single-shot ultrafast imaging approaches, which are limited to millions of frames per second only and require multiple laser pulses, our method employs only a single pulse and captures the entire dynamics of laser-induced signals at hundreds of Gfps. Using a single pulse does not change the optical properties of nanoparticles for a following pulse, thus allowing reliable spatiotemporal mapping. Moreover, we found that particle inception and growth are derived from precursors. In essence, as an imaging modality, fsLS-CUP offers ultrafast 2D diagnostics, contributing to the fundamental understanding of nanoparticle’s inception and broader applications across different fields, such as material science and biomedical engineering.

A spatial threshold for astrocyte calcium surge.

There is a spatial threshold for the astrocyte intracellular calcium signal propagation that is determined by astrocyte intrinsic properties and controls gliotransmission.

Charge Transfer Mechanisms of Adaptive Multicomponent Solutions at Solid-Liquid Interfaces for Real-Time Coolant State Monitoring.

Charge-transfer mechanisms in adaptive multicomponent solutions at liquid-solid interfaces with triboelectric probes are crucial for understanding chemistry dynamics. However, liquid-solid charge transfer becomes unpredictable, due to the components or interactions in solutions, restricting its potential application for precise monitoring of liquid environments. This study utilizes triboelectric probes to investigate the charge transfer of chemicals, applying this approach to real-time coolant state monitoring. Analysis of electrical signal dynamics induced by ethylene glycol and its oxidation byproduct, oxalic acid, in ethylene glycol solutions reveals that hydrogen bond and ion adsorption diminishes the efficiency of electron transfer at the liquid-solid interface. These findings promote the engineering of the triboelectric probe that enhances coolant quality with remarkable sensitivity (detection limit: 0.0001%) and a broad freezing point operational range (0 to -49 °C). This work advances the precise control of the charge dynamics and demonstrates the potential of triboelectric probes for interdisciplinary applications.

Comparative analysis of parametric B-spline and Hermite cubic spline based methods for accurate ECG signal modeling

Analyzing Electrocardiogram (ECG) signals is imperative for diagnosing cardiovascular diseases. However, evaluating ECG analysis techniques faces challenges due to noise and artifacts in actual signals. Machine learning for automatic diagnosis encounters data acquisition hurdles due to medical data privacy constraints. Addressing these issues, ECG modeling assumes a crucial role in biomedical and parametric spline-based methods have garnered significant attention for their ability to accurately represent the complex temporal dynamics of ECG signals. This study conducts a comparative analysis of two parametric spline-based methods—B-spline and Hermite cubic spline—for ECG modeling, aiming to identify the most effective approach for accurate and reliable ECG representation. The Hermite cubic spline serves as one of the most effective interpolation methods, while B-spline is an approximation method. The comparative analysis includes both qualitative and quantitative evaluations. Qualitative assessment involves visually inspecting the generated spline-based models, comparing their resemblance to the original ECG signals, and employing power spectrum analysis. Quantitative analysis incorporates metrics such as root mean square error (RMSE), Percentage Root Mean Square Difference (PRD) and cross correlation, offering a more objective measure of the model's performance. Preliminary results indicate promising capabilities for both spline-based methods in representing ECG signals. However, the analysis unveils specific strengths and weaknesses for each method. The B-spline method offers greater flexibility and smoothness, while the cubic spline method demonstrates superior waveform capturing abilities with the preservation of control points, a critical aspect in the medical field. Presented research provides valuable insights for researchers and practitioners in selecting the most appropriate method for their specific ECG modeling requirements. Adjustments to control points and parameterization enable the generation of diverse ECG waveforms, enhancing the versatility of this modeling technique. This approach has the potential to extend its utility to other medical signals, presenting a promising avenue for advancing biomedical research.

Discrete protein condensation events govern calcium signal dynamics in T cells.

Ca 2+ fluctuations in T cells reflect stochastic protein condensation events triggered by single molecular antigen-TCR binding.