Signaling Components Research Articles

Multi-dimensional hybrid bilinear CNN-LSTM models for epileptic seizure detection and prediction using EEG signals.

Automatic detection and prediction of epilepsy are crucial for improving patient care and quality of life. However, existing methods typically focus on single-dimensional information and often confuse the periodic and aperiodic components in electrophysiological signals. We propose a novel deep learning framework that integrates temporal, spatial, and frequency information of EEG signals, in which periodic and aperiodic components are separated in the frequency domain. Specifically, we calculated the periodic and aperiodic components in single channel and the synchronization index of each component between channels. A self-attention mechanism is employed to filter single-channel features by selectively focusing on the most distinguishing features. Then, a hybrid bilinear deep learning network is utilized to capture the spatiotemporal features by combining a convolutional neural network (CNN) and a long short-term memory (LSTM) network. Finally, a bilinear pooling layer is employed to extract second-order features based on interactions between these spatiotemporal features. The model achieves exceptional performance，with a detection accuracy of 98.84% on the CHB-MIT dataset, and a prediction accuracy of 98.44% on CHB-MIT and 97.65% on the Kaggle dataset, both with an false positive rate (FPR) of 0.02. This work paves the way for developing real-time, wearable epilepsy prediction devices to improve patient care.

Interplay between Netrin-1 and Norrin controls arteriovenous zonation of blood–retina barrier integrity

The integrity of the blood-retina barrier (BRB) is crucial for phototransduction and vision, by tightly restricting transport of molecules between the blood and surrounding neuronal cells. Breakdown of the BRB leads to the development of retinal diseases. Here, we show that Netrin-1/Unc5b and Norrin/Lrp5 signaling establish a zonated endothelial cell gene expression program that controls BRB integrity. Using single-cell RNA sequencing (scRNA-seq) of postnatal BRB-competent mouse retina endothelial cells (ECs), we identify >100 BRB genes encoding Wnt signaling components, tight junction proteins, and ion and nutrient transporters. We find that BRB gene expression is zonated across arteries, capillaries, and veins and regulated by opposing gradients of the Netrin-1 receptor Unc5b and Lrp5-β-catenin signaling between retinal arterioles and venules. Mice deficient for Ntn1 or Unc5b display more BRB leakage at the arterial end of the vasculature, while Lrp5 loss of function causes predominantly venular BRB leakage. ScRNA-seq of Ntn1 and Unc5b mutant ECs reveals down-regulated β-catenin signaling and BRB gene expression that is rescued by Ctnnb1 overactivation, along with BRB integrity. Mechanistically, we demonstrate that Netrin-1 and Norrin additively enhance β-catenin transcriptional activity and Lrp5 phosphorylation via the Discs large homologue 1 (Dlg1) scaffolding protein, and endothelial Lrp5-Unc5b function converges in protection of capillary BRB integrity. These findings explain how arteriovenous zonation is established and maintained in the BRB and reveal that BRB gene expression is regulated at the level of endothelial subtypes.

A Power Analysis Method for Self-Interference Signal Components in Full-Duplex Transceivers Under Constant/Nonconstant Modulus Signal Stimulation

The existence of multiple self-interference (SI) signal components, particularly the nonlinear ones, seriously constrains the performance of self-interference cancellation (SIC) methods. To decrease the complexity of SIC methods in full-duplex devices, this article proposes a power analysis method for SI signal components in a full-duplex transceiver. The proposed method comprises a separate analysis algorithm and a system-level power model. Initially, the algorithm is conducted to obtain the spectrum of the linear and nonlinear components in the power amplifier (PA) output signal. Once the linear-to-nonlinear power ratio (LNPR) has been obtained, a system-level power model is constructed by taking both the transmitter noise and analog-to-digital converter (ADC) quantization noise into account. The proposed power model allows for the allocation of SIC method performance in multiple domains during the design of full-duplex transceivers at the top level, thereby reducing the overall system complexity. The simulation results demonstrate that in a full-duplex transceiver with only antenna isolation, the power of the SI signal component is susceptible to alterations due to the operating waveform and transmission power. Finally, the accuracy of the power analysis method is verified through measurement and Simulink.

THE ATTENUATION CHARACTERISTICS OF DIFFERENT FREQUENCY COMPONENTS OF ACOUSTIC EMISSION SIGNAL DURING PROPAGATION IN ELM AND PINE WOOD

In order to gain a deeper understanding of the attenuation characteristics of different frequency components of acoustic emission signal when propagating in wood, this research conduct pencil lead fracture experiments on the surface of elm and pine specimen. Original AE signals acquired by different sensors are decomposed using 5-level wavelet transform, the attenuation characteristics of different frequency components are studied, and the acoustic emission source is located according to the energy of different frequency components. The results indicate that the propagation distance is the main factor affecting the attenuation of AE signals. The longer the propagation distance, the greater the degree of attenuation. The attenuation characteristics of high-frequency components of acoustic emission signals deviate from the ideal attenuation model after the propagation distance greater than 10 cm. The higher the frequency components of acoustic emission signals, the faster they attenuatewhen propagation in elm and pine specimen.

A Systematic Review on the Role of SnRK2 Gene in <i>Arabidopsis thaliana</i> Growth Stages under Abiotic Stresses

This systematic review examines the role of SnRK2 (Sucrose non-fermenting 1-Related protein Kinase 2) genes in Arabidopsis thaliana growth and responses to abiotic stresses. SnRK2 protein kinases are key components of abscisic acid (ABA) signaling and osmotic stress responses in plants. The review synthesizes findings from numerous studies on how different SnRK2 genes regulate Arabidopsis growth, development, and stress tolerance at various life stages. Key topics covered include SnRK2 functions under environmental stresses like drought, salinity, cold, and nutrient deficiency; SnRK2 roles in seed germination and early seedling growth; and applications of SnRK2 genes in developing transgenic Arabidopsis with enhanced stress tolerance. The review highlights the complex regulatory networks involving SnRK2 kinases and their interactions with other signaling components like PP2C phosphatases and AREB/ABF transcription factors. Overall, this comprehensive analysis provides insights into the multifaceted roles of SnRK2 genes in modulating plant growth and stress adaptation, with potential applications for improving crop resilience. Further research directions are suggested to elucidate remaining questions about SnRK2 functions and regulatory mechanisms in plants.

Cross-Correlations Between Signal’s Components

Abstract Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with other elements, and the environment. It is subsequently composed of many components, only some of which take part in the couplings. In this paper we present a framework to detect the component correlation pattern. Firstly, the interested trajectories are decomposed into components by using decomposing methods such as the Fourier Expansion and the Wavelet Transformation. Secondly, the cross-correlations between the components are calculated, resulting into a component cross-correlation matrix (network). Finally, the dominant structure in the network is identified to characterize the coupling pattern in the system. Several deterministic dynamical models turn out to be characterized with rich structures such as the clustering of the components. The pattern of correlation between Respiratory(RESP) and ECG signals is composed of five sub-clusters that are mainly formed by the components in ECG signal. Interestingly, only 7 components from RESP (scattered in four sub-clusters) take part in the realization of coupling between the two signals.

A Noise Reduction Method for Signal Reconstruction and Error Compensation of a Maglev Gyroscope Under Persistent External Interference

To eliminate the noise interference caused by continuous external environmental disturbances on the rotor signals of a maglev gyroscope, this study proposes a noise reduction method that integrates an adaptive particle swarm optimization variational modal decomposition algorithm with a strategy for error compensation of the trend term in reconstructed signals, significantly improving the azimuth measurement accuracy of the gyroscope torque sensor. The optimal parameters for the variational modal decomposition algorithm were determined using the adaptive particle swarm optimization algorithm, allowing for the accurate decomposition of noisy rotor signals. Additionally, using multi-scale permutation entropy as a criterion for discriminant, the signal components were filtered and summed to obtain the denoised reconstructed signal. Furthermore, an empirical mode decomposition algorithm was employed to extract the trend term of the reconstructed signal, which was then used to compensate for the errors in the reconstructed signal, achieving significant noise reduction. On-site experiments were conducted on the high-precision GNSS baseline of the Xianyang Yuan Tunnel in the second phase of the project to divert water from the Han River to the Wei River, where this method was applied to process and analyze multiple sets of rotor signals. The experimental results show that this method effectively suppresses continuous external environmental interference, reducing the average standard deviation of the compensated signals by 46.10% and the average measurement error of the north azimuth by 45.63%. Its noise reduction performance surpasses that of the other four algorithms.

A Fault Diagnosis Method for Turnout Switch Machines Based on Sound Signals

The turnout switch machine, a vital outdoor component of railway signaling, controls train steering amidst complex operations and high frequencies. Its malfunction significantly disrupts train operations, potentially causing derailments. This paper proposes a sound-based fault diagnosis method, termed ERS (a method combining EMD, ReliefF, and SVM), for effective monitoring and detection of turnout switch machines. The method employs Eigenmode Decomposition (EMD) to smooth the sound signal, reduce noise, and extract key statistical parameters of both the time and frequency domains. To address redundant information in high-dimensional features, the ReliefF algorithm is utilized for feature selection, dimension reduction, and fault classification based on weighted parameters. Subsequently, the selected feature parameters are used to train the Support Vector Machine (SVM). A comparison with results obtained without ReliefF feature selection demonstrates the necessity of this step. The results show that the fault diagnosis accuracy reaches 98% in the positioning work mode and 95.67% in the reversing work mode, verifying the method’s effectiveness and feasibility.

Comparison of analysis methods for separating and recognizing multicomponent radar signals

This paper proposes a model for separating and recognizing a mixture of radar signals using combined multiresolution methods and convolution neural networks. The model involves three main steps: separating the signal into individual components using the Multiresolution analysis (MRA) methods: Empirical mode decomposition (EMD), Variational mode decomposition (VMD), and Maximal overlap discrete wavelet packet transform (MODWPT); transforming these components into the time-frequency domain using Wigner-Ville distribution (WVD) and storing them as images; and then feeding these images into the SqueezeNet for recognition. These multiresolution methods are then compared based on three criteria: The number of successful separations, the SNR ratio of the input signal, and the correlation between the separated signal components and the original signal components. Additionally, we evaluate the performance of the SqueezeNet with real-time signals.

Mitophagosomes induced during EV-D68 infection promote viral nonlytic release.

Enterovirus-D68 (EV-D68) is a plus-strand RNA virus that primarily causes infant respiratory infections. In rare pediatric cases, infection with EV-D68 has been associated with acute flaccid myelitis, a polio-like paralytic disease. We have previously demonstrated that EV-D68 induces nonselective autophagy for its benefit. Here, we demonstrate that EV-D68 induces mitophagy, the specific autophagic degradation of mitochondria. EV-D68 infection induces mitophagosome formation and several hallmarks of mitophagy, including mitochondrial fragmentation, mitochondrial membrane potential loss, and Parkin translocation to the mitochondria were observed in EV-D68 infected cells. The 3C protease of EV-D68 cleaves the mitochondrial fusion protein, mitofusin-2, near the C-terminal HR2 domain to induce mitochondrial fragmentation, and these fragmented mitochondria colocalized with double-stranded RNA (dsRNA), which labels viral RNA replication sites after peak viral RNA replication. Depleting components of mitophagy signaling specifically reduced EV-D68 release without impacting viral intracellular titers. Our results suggest that whereas the machinery of macroautophagy supports various stages of enterovirus replication, including viral genomic RNA replication and capsid maturation, mitophagy is the specific form of autophagy that regulates the nonlytic release of enteroviruses from cells.

Examination of Sex-Related Differences in Fatigability and Frequency Components of Mechanomyographic Signals During Sustained Exercise

Background: Surface mechanomyographic (sMMG) signals have been used to examine sex-specific differences in the mechanical behavior of muscle during fatiguing exercise. However, studies often utilize simple amplitude- and frequency-based analyses, which only reveal the static components of the sMMG signal. Methods: Thus, a wavelet-based analysis was used to examine changes in the spectral intensity of the non-dominant limb’s vastus lateralis during a fatiguing, maximal, unilateral isometric leg extension in recreationally active men (n = 11) and women (n = 10). Relative changes in spectral intensities and instantaneous mean frequency (IMF) were examined using linear mixed-effect models. Time-to-task failure was compared using an independent sample t-test. Results: The neuromuscular responses demonstrated parallel decreases in IMF (p < 0.001). Further, there were parallel, nonlinear, decreases in spectral intensity across wavelets (p < 0.001) and there were no sex differences in time-to-task failure (p = 0.15). Conclusions: These data showed no sex-specific differences in exercise fatigability or muscle mechanics during fatiguing exercise of the leg extensors. However, when collapsed across sex, wavelet-specific changes in spectral intensity over time reveal novel insights into the interplay between low- and high-frequency components during fatigue.

The calcium sensor AtCML8 contributes to Arabidopsis plant cell growth by modulating the brassinosteroid signaling pathway.

Calcium signaling plays an essential role in integrating plant responses to diverse stimuli and regulating growth and development. While some signaling components and their roles are well-established, such as the ubiquitous calmodulin (CaM) sensor, plants possess a broader repertoire of calcium sensors. Notably, CaM-like proteins (CMLs) represent a poorly characterized class for which interacting partners and biological functions remain largely elusive. Our work investigates the role of Arabidopsis thaliana CML8 that exhibits a unique expression profile in seedlings. A reverse genetic approach revealed a function of CML8 in regulating root growth and hypocotyl elongation. RNA-seq analyses highlighted CML8 association with the regulation of numerous genes involved in growth and brassinosteroid (BR) signaling. Using co-immunoprecipitation experiments, we demonstrated that CML8 interacts with the BR receptor, BRI1, in planta in a ligand-dependent manner. This finding suggests the existence of a novel regulatory step in the BR pathway, involving calcium signaling.

An engineered α1β1 integrin-mediated FcγRI signaling component to control enhanced CAR macrophage activation and phagocytosis

Treatment of solid tumors remains difficult, and therefore there has been increased focus on chimeric antigen receptor macrophages (CAR-M) to challenge solid tumors. However, CAR domain design of of adoptive cell therapy, which leads to differences in antitumor activity and triggered antitumor potential, remains poorly understood for macrophages. We developed an α1β1 integrin-mediated Fc-gamma receptor I (FcγRI) signaling component for CAR-M specific activation and its antitumor potential. We evaluated CAR-M effects with α1β1 integrin-mediated FcγRI signaling (ACT CAR-M) on the activation and antitumor phagocytic response of macrophages in vitro. Subcutaneous tumor model in BALB/c mice and carcinomatosis model in immunodeficient mice were used to test the antitumor effect of ACT CAR-M compared with CD3ζ CAR-M. The α1β1 integrin-mediated FcγRI signaling engagement of CAR-M was associated with enhanced macrophage activation and specific phagocytosis in primary human macrophages, and significantly improved tumor control and survival in multiple cancer models when compared to CD3ζ CAR-M. RNA-sequencing suggested that α1β1 integrin-mediated FcγRI engagement increased antitumor immunity by enhancing pro-inflammatory M1 phenotype-associated pathways, such as Toll-like receptor signaling, tumor necrosis factor signaling, and IL-17 signaling. α1β1 integrin-mediated FcγRI signaling engagement markedly enhanced antitumor effects of CAR-M immunotherapy, which is proposed as an advanced engineering CAR domain material to expand the clinical application of CAR-M.

Gibberellin and cytokinin signaling antagonistically control female-germline cell specification in Arabidopsis.

How do growth hormones interact to specify female-germline cell types in flowering plants and control production of the first female-germline cell? Here, we find that gibberellin (GA) biosynthesis and signaling are restricted in ovule primordia, with overexpression of receptors and biosynthetic enzymes resulting in multiple and enlarged megaspore mother cells (MMCs) in Arabidopsis. GA signaling machinery interacts with and promotes the degradation of cytokinin (CK) type-B Arabidopsis response regulators (ARR1/10/12), which also directly interact with DELLA proteins. CK biosynthesis and signaling components are expressed in both MMCs and sporophytic cells, with signaling negatively controlled by GA in ovule primordia, and perturbations leading to the induction of multiple, enlarged MMC-like cells. The vacuolar sorting protein SHRUBBY (SHBY) interacts with GA and CK signaling components to block GA-induced degradation. CK signaling restricts multiple sub-epidermal cells in distal ovule primordia from acquiring MMC identity. By balancing degradation activity, GA and CK signaling antagonistically control female-germline cell specification.

Analysis of muscle fatigue using entropybased measures from the recurrence pattern of surface electromyography signals

Abstract Neuromuscular fatigue can be monitored using complexity domain analysis of surface electromyography (sEMG) signals. This study provides a technique to detect fatigue induced change in signal complexity using the entropic measurements of its recurrence pattern. For this purpose, fifteen participants have been recruited to perform isometric fatiguing exercise and signals are acquired from the biceps brachii muscle of the dominant hands. Thresholded recurrence plot is constructed from the non-fatigue and fatigue segments of the signal. Bidimensional dispersion entropy (DispEn2D) is calculated to quantify the textural change in the resulting recurrence plot. The results show that the present approach can distinguish between non-fatigue and fatigue states. Decrease in the DispEn2D values in the fatigue state shows there is an increase in periodic component of sEMG signal during fatiguing contraction.

Effect of COL11A1 on oral squamous cell carcinoma

BackgroundOral squamous cell carcinoma (OSCC) is the most common malignant tumor of the oral cavity, which is mainly a series of atypical hyperplasia of oral epithelial cells, and the overall prognosis remains poor. MethodsGSE37991 and GSE38517 were downloaded from gene expression omnibus (GEO) to identify differentially expressed genes (DEGs). Functional enrichment analysis of DEGs was performed, weighted gene co-expression network analysis (WGCNA) was used. Protein-protein interaction (PPI) network was constructed. Core gene expression was visualized using a heatmap. Comparative toxicogenomics database (CTD) and miRNA analyses identified related diseases and regulatory miRNAs. Western blot (WB) was conducted to examine expression of COL11A1 and TGF-SMAD signaling components in OSCC samples. Results5163 DEGs were identified. DEGs were enriched in metabolic processes and signaling pathways, including TGF-β/SMAD and PI3K-Akt. WGCNA identified 11 core modules. PPI network analysis revealed five core genes: COL11A1, AURKA, MELK, CCNA2, and BUB1. Heatmap analysis showed that COL11A1 is highly expressed in OSCC. CTD analysis indicated that COL11A1 is associated with OSCC. miRNA prediction identified potential regulatory factors. Western blot analysis demonstrated that COL11A1 is overexpressed in OSCC and is associated with TGF-SMAD signaling, inflammation, and cell cycle progression. ConclusionCOL11A1 is highly expressed in OSCC and may serve as a target gene interacting with the TGF-SMAD signaling pathway.

Classification of cyclic alternating patterns of sleep using EEG signals

Cyclic alternating patterns (CAP) occur in electroencephalogram (EEG) signals during non-rapid eye movement sleep. The analysis of CAP can offer insights into various sleep disorders. The first step is the identification of phases A and B for the CAP cycles. In this work, we develop an easy-to-implement accurate system to differentiate between CAP A and CAP B. Small segments of the EEG signal are processed using Gaussian filters to obtain sub-band components. Features are extracted using some statistical characteristics of these signal components. Minimum redundancy maximum relevance test is employed to identify the more significant features. Three different machine learning classifiers are considered and their performance is compared. The results are analyzed for both the balanced and unbalanced datasets. The k-nearest neighbour (kNN) classifier achieves 79.14% accuracy and F-1 score of 79.24% for the balanced dataset. The proposed method outperforms the existing methods for CAP classification. It is easy-to-implement and can be considered as a candidate for real-time deployment.

Reducing electrocardiographic interference in the multichannel electromyogram to help muscle fatigue assessment in low-intensity contractions.

Surface electromyography (EMG) can be used in the rehabilitation of musculoskeletal disorders, for evaluating the coordination of muscles that stabilize a joint, or as an objective tool in assessing muscle fatigue. This latter may be achieved by evaluating the low-frequency content of the EMG. However, the electrocardiogram (ECG) interference is also recorded when EMG is acquired close to the heart. It may mask or even modify the information of interest in the EMG. Different signal processing techniques have been proposed to eliminate ECG artifacts from the EMG signals, the high-pass filter being the most used one. Nevertheless, this classic filtering approach would also attenuate EMG activities below 30 Hz, which contain information from low-intensity muscle contractions. This work addresses the automatic ECG attenuation when multichannel EMG is collected on the left pectoralis major muscle during a muscle fatigue test. The proposed ECG artifact mitigation extends a successful automatic template subtraction (TS) approach, which does not require an extra reference channel and is now applied to independent EMG source signal components extracted using a blind source separation technique (ICA, Independent Component Analysis). The automatic detection of ECG components was performed through two alternative measures: entropy and Kullback-Leibler divergence. While the ECG interference seems to hamper the detection of muscle fatigue in the low-contraction regime, the association ICA+TS preserved better the EMG low-frequency content, and the entropy-based automatic detection was found to be more suitable, avoiding possible errors that might arise from manual detection procedures.

A comparison of the Information Content of Orthogonally Polarized Components of Lidar Echo Signal for Evaluating Hydrooptical Characteristics of the Near-Surface Layer

A series of lidar measurements were conducted at stations with a homogeneous vertical distribution of hydrooptical char acteristics in the near-surface layer using a two-channel shipborne polarization lidar PLD-1. Lidar sounding was accompanied by synchronous contact measurements of a number of hydrooptical characteristics. A large dataset of measurement data was obtained in waters where hydrooptical characteristics varied widely. As a result of the statistical processing of these data, regression relationships were obtained linking the seawater beam attenuation coefficient c, absorption coefficient a, and diffuse attenuation coefficient Kd to the lidar attenuation coefficients of the co- and cross-polarized components. In most cases, a linear relationship between hydrooptical characteristics and the lidar attenuation coefficients of the polarized components is observed. These rela tionships are characterized by high values of the coefficient of determination — from 0.8 to 0.95. An exception is the relationship between the seawater beam attenuation coefficient c and the lidar attenuation coefficient of the cross-polarized component, where a second-degree polynomial is used to describe this relationship (coefficient of determination is 0.88). Data on the hydrooptical characteristics obtained using the cross-polarized component of the lidar echo signal mostly duplicate the data of the co-polarized component. However, the use of a two-channel optical receiving system increases the reliability and accuracy of the obtained data and provides the possibility of controlling the homogeneity of the underwater section of the sounding path.

Enhancement of cyclic spectral coherence map by statistical testing approach—application to bearing faults diagnosis in electric motors

Abstract Efficiency of fault detection in rolling element bearings is heavily influenced by the quality of data. In controlled environments, such as test rigs designed for bearing diagnostics, data quality is relatively good. Similarly, diagnosing bearings that support shafts in industrial machinery is relatively straightforward. However, diagnosing bearings in electric motors presents greater complexity due to the influence of additional cyclic components on vibration signals. These extra components, originating from mechanical or electrical sources, complicate frequency-based analysis.
This paper proposes a novel approach for diagnosing bearings in electric motors, utilizing statistical analysis within the bi-frequency domain through a cyclostationary framework. The method involves applying a statistical testing procedure to individual pixels on the Cyclic Spectral Coherence (CSC) map. The statistical significance of these pixels is assessed based on quantiles of CSC maps obtained from a dataset representing a healthy bearing. This process results in an enhanced or cleaned CSC map, facilitating the identification of fault-related components. Consequently, this approach enables the detection of defects in electric motor bearings, even when additional signal components unrelated to the defect, but characteristic of a healthy bearing, are present.