Signal Analysis Research Articles

Application of vibration signal analysis method in the fault diagnosis of mechanical gearboxes

Application of vibration signal analysis method in the fault diagnosis of mechanical gearboxes

REDT: a specialized transformer model for the respiratory phase and adventitious sound detection.

In contrast to respiratory sound classification, respiratory phase and adventitious sound event detection provides more detailed and accurate respiratory information, which is clinically important for respiratory disorders. However, current respiratory sound event detection models mainly use convolutional neural networks to generate frame-level predictions. 
A significant drawback of the frame-based model lies in its pursuit of optimal frame-level predictions rather than the best event-level ones. Moreover, it demands post-processing and is incapable of being trained in an entirely end-to-end fashion. Based on the above research status, this paper proposes an event-based Transformer method - Respiratory Events Detection Transformer (REDT) for multi-class respiratory sound event detection task to achieve efficient recognition and localization of the respiratory phase and adventitious sound events. Firstly, REDT approach employs the Transformer for time-frequency analysis of respiratory sound signals to extract essential features. Secondly, REDT converts these features into timestamp representations and achieves sound event detection by predicting the location and category of timestamps. Our method is validated on the public dataset HF_Lung_V1. The experimental results show that our F1 scores for inspiration, expiration, Continuous Adventitious Sound(CAS) and Discontinuous Adventitious Sound(DAS) are 90.5%, 77.3%, 78.9%, and 59.4%, respectively. These results demonstrate the method's significant performance in respiratory sound event detection.

Piezoelectric biosensor with dissipation monitoring enables the analysis of bacterial lytic agent activity

Antibiotic-resistant strains of Staphylococcus aureus pose a significant threat in healthcare, demanding urgent therapeutic solutions. Combining bacteriophages with conventional antibiotics, an innovative approach termed phage-antibiotic synergy, presents a promising treatment avenue. However, to enable new treatment strategies, there is a pressing need for methods to assess their efficacy reliably and rapidly. Here, we introduce a novel approach for real-time monitoring of pathogen lysis dynamics employing the piezoelectric quartz crystal microbalance (QCM) with dissipation (QCM-D) technique. The sensor, a QCM chip modified with the bacterium S. aureus RN4220 ΔtarM, was utilized to monitor the activity of the enzyme lysostaphin and the phage P68 as model lytic agents. Unlike conventional QCM solely measuring resonance frequency changes, our study demonstrates that dissipation monitoring enables differentiation of bacterial growth and lysis caused by cell-attached lytic agents. Compared to reference turbidimetry measurements, our results reveal distinct alterations in the growth curve of the bacteria adhered to the sensor, characterized by a delayed lag phase. Furthermore, the dissipation signal analysis facilitated the precise real-time monitoring of phage-mediated lysis. Finally, the QCM-D biosensor was employed to evaluate the synergistic effect of subinhibitory concentrations of the antibiotic amoxicillin with the bacteriophage P68, enabling monitoring of the lysis of P68-resistant wild-type strain S. aureus RN4220. Our findings suggest that this synergy also impedes the formation of bacterial aggregates, the precursors of biofilm formation. Overall, this method brings new insights into phage-antibiotic synergy, underpinning it as a promising strategy against antibiotic-resistant bacterial strains with broad implications for treatment and prevention.

Stereoelectroencephalographic exploration and surgical outcome in Lennox-Gastaut syndrome.

Lennox-Gastaut syndrome (LGS) is typically characterized by drug-resistant epilepsy and subsequent cognitive deterioration. Surgery is a rare but viable option for the control of seizures in a subset of patients with LGS. This study aimed to describe the organization of the epileptogenic zone network (EZN) in patients with LGS using stereoelectroencephalography (SEEG) and to report the outcome of post-SEEG treatment. A quantitative SEEG signal analysis was conducted in 14 consecutive patients with LGS, in whom a potentially localized EZN was suggested based on a comprehensive noninvasive evaluation. The EZN and the irritative zone network were identified using relevant biomarkers during ictal (epileptogenicity index and connectivity epileptogenicity index) and interictal (spikes and high-frequency oscillations) recordings. The applied post-SEEG treatments were assessed, including SEEG-guided radiofrequency thermocoagulation (RF-TC), surgery, and neurostimulation. The seizure onset patterns showed some specificity by seizure type, with 84% of tonic seizures involving low-voltage fast activity. The EZN of patients with LGS was often, but not always, complex and extensive, involving two or more lobes (79%) and both hemispheres (64%). The lateral neocortical structures, particularly the lateral premotor and dorsolateral prefrontal cortices, were identified as being most frequently involved in the EZN. Among the explored subcortical structures, only the pulvinar, central-lateral thalamic nucleus, and hypothalamic hamartoma belonged to the EZN. Twelve patients (86%) underwent SEEG-guided RF-TC, with 50% experiencing a >50% reduction in baseline seizure frequency. Four patients (29%) underwent curative surgery for significant involvement of a lesion in the EZN, and one case achieved an Engel class I outcome. This is the first quantitative SEEG study in patients with LGS to demonstrate the utility of SEEG in identifying patients who may benefit from surgery and to perform SEEG-guided RF-TC. Nevertheless, the indications for SEEG should be carefully assessed, as localized EZN is uncommon in LGS.

Assessment of the Conditions of Anchor Bolts Grouted with Resin and Cement Through Impact-Echo Testing and Advanced Spectrum Analysis

Anchor bolts, such as rock bolts and concrete anchors, are widely used in civil, geotechnical, and mining engineering for anchorage and ground support. They are used in retaining walls, dry docks, dams, mines, and prestressed concrete structures. Evaluating the grouting condition of anchor bolts is essential to ensure the safety of these applications. Spectrum techniques have been used to develop non-destructive methods for estimating the grouting quality of grouted anchor bolts. The spectrum methods include fast Fourier transform, time–frequency analysis, wavelet transform analysis, and empirical mode decomposition. In this study, we introduce the parameter-optimized variational mode decomposition (VMD) method for the spectrum analysis of impact echo signals of anchor bolts. This method overcomes the difficulty of conventional spectrum methods that cannot separate highly coupled natural modes for advanced analysis. The parameter-optimized VMD method enables the generation of a new evaluation index for quantifying bolt grouting conditions, which has the potential to significantly enhance the quality evaluation of anchor bolts compared with conventional analysis of natural frequencies. This study uses impact response to establish a new benchmark for the integrity diagnosis of anchor bolts, paving the way for more accurate and reliable safety assessments.

"Multimodal Sleep Signal Tensor Decomposition and Hidden Markov Modeling for Temazepam-Induced Anomalies Across Age Groups".

Recent advances in multimodal signal analysis enable the identification of subtle drug-induced anomalies in sleep that traditional methods often miss. We develop and introduce the Dynamic Representation of Multimodal Activity and Markov States (DREAMS) framework, which embeds explainable artificial intelligence (XAI) techniques to model hidden state transitions during sleep using tensorized EEG, EMG, and EOG signals from 22 subjects across three age groups (18-29, 30-49, and 50-66 years). By combining Tucker decomposition with probabilistic Hidden Markov Modeling, we quantified age-specific, temazepam-induced hidden states and significant differences in transition probabilities. Jensen-Shannon Divergence (JSD) was employed to assess variability in hidden state transitions, with older subjects (50-66 years) under temazepam displaying heightened transition variability and network instability as indicated by a 48.57% increase in JSD (from 0.35 to 0.52) and reductions in network density by 12.5% (from 0.48 to 0.42) and modularity by 21.88% (from 0.32 to 0.25). These changes reflect temazepam's disruptive impact on sleep architecture in older adults, aligning with known age-related declines in sleep stability and pharmacological sensitivity. In contrast, younger subjects exhibited lower divergence and retained relatively stable, cyclical transition patterns. Anomaly scores further quantified deviations in state transitions, with older subjects showing increased transition uncertainty and marked deviations in REM-like to NREM state transitions. This XAI-driven framework provides transparent, age-specific insights into temazepam's impact on sleep dynamics, going beyond traditional methods by identifying subtle, pharmacologically induced changes in sleep stage transitions that would otherwise be missed. DREAMS supports the development of personalized interventions based on sleep transition variability across age groups, offering a powerful tool to understand temazepam's age-dependent effects on sleep architecture.

Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment

This study focuses on the diagnostic analysis of cartilage damage in the knee joint based on acoustic signals generated by the joint. The research utilizes a combination of advanced signal processing techniques, specifically empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA), alongside convolutional neural networks (CNNs) for classification and detection tasks. Acoustic signals, often reflecting the mechanical behavior of the joint during movement, serve as a non-invasive diagnostic tool for assessing the cartilage condition. EEMD is applied to decompose the signals into intrinsic mode functions (IMFs), which are then analyzed using DFA to quantify the scaling properties and detect irregularities indicative of cartilage damage. The separation of individual frequency components allows for multi-scale analysis of the signals, with each of the functions resulting from the analysis reflecting local variations in the amplitude and frequency over time and allowing for effective removal of noise present in the signal. The CNN model is trained on features extracted from these signals to accurately classify different stages of cartilage degeneration. The proposed method demonstrates the potential for early detection of knee joint pathology, providing a valuable tool for preventive healthcare and reducing the need for invasive diagnostic procedures. The results suggest that the combination of EEMD-DFA for feature extraction and CNN for classification offers a promising approach for the non-invasive assessment of cartilage damage.

Ultrasensitive Detection of Circulating Plasma Cells Using Surface-Enhanced Raman Spectroscopy and Machine Learning for Multiple Myeloma Monitoring.

Multiple myeloma is a hematologic malignancy characterized by the proliferation of abnormal plasma cells in the bone marrow. Despite therapeutic advancements, there remains a critical need for reliable, noninvasive methods to monitor multiple myeloma. Circulating plasma cells (CPCs) in peripheral blood are robust and independent prognostic markers, but their detection is challenging due to their low abundance. Next-generation flow cytometry is commonly used for CPC detection but is not performed in routine clinical practice because it requires expensive instruments, is costly, and time-consuming. This study introduces a cost-effective, rapid surface-enhanced Raman spectroscopy (SERS) assay leveraging gold-deposited magnetic nanoparticles and plasmonic nanoparticles functionalized with anti-CD138 and anti-CD38 antibodies for detecting CPCs in peripheral blood samples. A portable optical device was used for signal recording, enhancing the potential for point-of-care applications. The developed assay is highly sensitive and specific, capable of detecting as few as one or two cells. The application of machine learning algorithms to SERS signal analysis yielded area under the curve values ranging from 0.90 to 0.95, demonstrating excellent performance in differentiating multiple myeloma patients from healthy donors. This SERS method provides a sensitive and accessible way for CPC detection, showing significant potential for multiple myeloma diagnosis, treatment monitoring, and prognosis prediction.

Investigating the Effect of Vibration Signal Length on Bearing Fault Classification Using Wavelet Scattering Transform

Bearing condition monitoring and prognosis are crucial tasks for ensuring the proper operation of rotating machinery and mechanical systems. Vibration signal analysis is one of the most effective techniques for bearing condition monitoring and prognosis. In this study, the wavelet scattering transform, derived from wavelet transforms and incorporating concepts from scattering transform and convolutional network architectures, was utilized to extract quantitative features from vibration signals. The number of wavelet scattering coefficients obtained from vibration signals of different lengths varied due to the use of predefined wavelet and scaling filters in the wavelet scattering network. Additionally, these wavelet scattering coefficients are associated with different scattering paths within the corresponding wavelet scattering networks. Eight different lengths of vibration signals, associated with fifteen classes of rolling element bearing faults and conditions, were investigated using wavelet scattering feature extraction. The classes of rolling element bearing faults and conditions included normal bearings as well as ball and inner race faults with various fault diameters ranging from 0.007 inches to 0.028 inches. For the specific dataset validated, the computational results indicated that excellent bearing fault classification was achievable using wavelet scattering feature vectors derived from vibration signals with lengths of at least 6000 samples.

Technological Transformations, Formation of GMS (Global Mental System) and GFS (Global Forecasting System) —“Right-Brain Technologies” Based on Biological Entities with Consciousness, Artificial Intelligence, Quantum Computing, and Blockchain: “Banchenko-Market” (Global Market of Lucid Dreams and Other Transcendental States of Consciousness)

This article is a logical continuation of our previous work [1]. In this article, we continue to explore the possibilities of the economy of states of consciousness. In this continuation, drawing on intuitive insights, we significantly develop the theme outlined in the previous article [1]. Modern research in the field of transmission and fixation of consciousness states opens up new possibilities for studying cognitive processes and their objectification. This article examines methodological approaches to transmitting consciousness states from one subject to another using advanced neurointerface technologies and quantum-resistant blockchain systems. Special attention is paid to the objectification of consciousness states through stable patterns of neural activity, captured using technologies such as electroencephalography (EEG), magnetoencefalografi (MEG), and functional magnetic resonance imaging (fMRI). Algorithms of artificial intelligence (AI) are applied for processing and transmitting data, enabling classification and analysis of neural signals. The article also discusses the theoretical aspects of the quantum nature of consciousness, implying the use of quantum processes in the brain to explain cognitive states. The connection of these processes with quantum principles such as superposition and entanglement provides an opportunity for the application of quantum technologies in transmitting data about the state of consciousness. Within the study, quantum-resistant cryptographic systems are proposed, such as Quantum Resistant Ledger (QRL), which ensure data transmission security and protection against hacking by quantum computers. In this article, we use the term neurointerface as the closest to the goals of our research, which involve seeking technological solutions for transferring states from one biologically conditioned subject to another. As the term brain-computer interface implies interaction between the brain and a computer, which is not the focus of our research, we do not use this term in this article. In conclusion, ethical and philosophical aspects of transmitting consciousness states are discussed, as well as the prospects for further research in the integration of technologies and cognitive processes.

Symmetry in Genetic Distance Metrics: Quantifying Variability in Neurological Disorders for Personalized Treatment of Alzheimer’s and Dementia

This paper aims to adapt and apply genetic distance metrics in biomedical signal processing to improve the classification and monitoring of neurological disorders, specifically Alzheimer’s disease and frontotemporal dementia. The primary objectives are: (1) to quantify the variability in EEG signal patterns among the distinct subtypes of neurodegenerative disorders and healthy individuals, and (2) to explore the potential of a novel genetic similarity metric in establishing correlations between brain signal dynamics and clinical progression. Using a dataset of resting-state EEG recordings (eyes closed) from 88 subjects (36 with Alzheimer’s disease, 23 with frontotemporal dementia, and 29 healthy individuals), a comparative analysis of brain activity patterns was conducted. Symmetry plays a critical role in the proposed genetic similarity metric, as it captures the balanced relationships between intra- and inter-group EEG signal patterns. Our findings demonstrate that this approach significantly improves disease subtype identification and highlights the potential of the genetic similarity metric to optimize the predictive models. Furthermore, this methodology supports the development of personalized therapeutic interventions tailored to individual patient profiles, making a novel contribution to the field of neurological signal analysis and advancing the application of EEG in personalized medicine.

P0103 Systems biology analysis of cytokine signalling in IBD highlights the importance of TYK2-dependent pathways

Abstract Background Ulcerative colitis (UC) and Crohn’s disease (CD), collectively inflammatory bowel disease (IBD), are characterized by chronic gastrointestinal tract inflammation. Recently, there has been interest in the use of selective oral tyrosine kinase 2 (TYK2) inhibitors for IBD treatment, which inhibit signalling downstream of multiple pro-inflammatory cytokines and may have an improved safety profile relative to non-specific Janus kinase (JAK) inhibition. Here, we use a transcriptomic approach to assess the involvement of TYK2-dependent signalling pathways in IBD, and how these are impacted by currently available treatments. Methods TYK2-dependent gene signatures were characterized using cytokine stimulation studies and co-expression analysis. Involvement of signature modules in IBD was assessed using disease model data generated from 4059 biopsy samples from 2993 unique patients in 15 UC and 38 CD clinical studies, including assessments of golimumab, infliximab, ustekinumab, and vedolizumab. Pathway differential expression analysis was performed using Gene Set Enrichment Analysis (GSEA) and single-sample GSEA methods.1,2 Results Seven TYK2-dependent gene signature modules were defined, including two for type I interferon (IFN), three for IL-23, and two for IL-12. Type I IFN signatures were strongly enriched in both UC and CD, while enrichment of IL-23 was stronger for UC than for CD, and enrichment of IL-12 was stronger for CD than for UC. In each case, gene signature module enrichment was associated with greater disease severity. Pathway enrichment analysis of data from individual clinical studies demonstrated that specific type I IFN and IL-12 modules were significantly reduced, but not completely ameliorated, following treatment with golimumab, infliximab, ustekinumab, or vedolizumab. Further assessment of response to therapy in these studies showed that enrichment of type I IFN, IL-23, and IL-12 signature modules was associated with non-response to golimumab, infliximab, and vedolizumab. Conclusion Enrichment of TYK2-dependent gene signatures, including type I IFN, IL-23, and IL-12, is associated with both UC and CD. These TYK2-dependent enrichments are not fully mitigated through currently available treatments, and may be associated with non-response to treatment. It is important that all three signalling pathways are considered in the development of future treatment options.

Coaxial Cable Distributed Strain Sensing: Methods, Applications and Challenges

Distributed strain sensing is a powerful tool for in situ structural health monitoring for a wide range of critical engineering infrastructures. Strain information from a single sensing device can be captured from multiple locations simultaneously, offering a reduction in hardware, wiring, installation costs, and signal analysis complexity. Fiber optic distributed strain sensors have been the widely adopted approach in this field, but their use is limited to lower strain applications due to the fragile nature of silica fiber. Coaxial cable sensors offer a robust structure that can be adapted into a distributed strain sensor. They can withstand greater strain events and offer greater resilience in harsh environments. This paper presents the developments in methodology for coaxial cable distributed strain sensors. It explores the two main approaches of coaxial cable distributed strain sensing such as time domain reflectometry and frequency domain reflectometry with applications. Furthermore, this paper highlights further areas of research challenges in this field, such as the deconvolution of strain and temperature effects from coaxial cable distributed strain sensor measurements, mitigating the effect of dielectric permittivity on the accuracy of strain measurements, addressing manufacturing challenges with the partial reflectors for a robust coaxial cable sensor, and the adoption of data-driven analysis techniques for interrogating the interferogram to eliminate concomitant measurement effects with respect to temperature, dielectric permittivity, and signal-to-noise ratio, amongst others

Harnessing Long-Range Temporal Correlations for Advanced Epilepsy Classification

In this study, we suggest that detrended fluctuation analysis may reveal alternations in long-range temporal correlations associated with epileptic status. We consider two groups of subjects: patients with confirmed focal epilepsy, and healthy controls. Both groups were exposed to intermittent photic stimulation. Analysis based on event-related spectral perturbations revealed that photic driving in epileptic patients is higher in the α band and lower in other bands. This result is related to altered excitability in nonphotosensitive epilepsy. To prove this, we tested two hypotheses. First, we assess long-range temporal correlations using detrended fluctuation analysis, with the objective of evaluating whether this metric differs between epileptic patients and healthy controls through the application of between-subject statistics. Second, we investigate whether detrended fluctuation analysis provides more valuable insights into the aforementioned differences in comparison to traditional spectral analysis of brain signals. More precisely, we test if a machine learning algorithm trained on detrended fluctuation analysis-based features outperforms one trained on the spectral-based features in classifying between epileptic patients and controls. Furthermore, we study whether the features differ between classifiers by implementing feature importance assessment. Our findings demonstrate that the classifier based on detrended fluctuation analysis exhibits higher efficiency, and its features are notably distinct from those of the classifier based on spectral analysis. We postulate that the long-range temporal correlations captured via detrended fluctuation analysis reveal novel aspects of the epileptic brain response to intermittent photic stimulation, and they are more pronounced than the features captured via spectral analysis. Published by the American Physical Society 2025

GRK5 is required for adipocyte differentiation through ERK activation.

Previous studies have identified G protein-coupled receptor (GPCR) kinase 5 (GRK5) as a genetic factor contributing to obesity pathogenesis, but the underlying mechanism remains unclear. We demonstrate here that Grk5 mRNA is more abundant in stromal vascular fractions of mouse white adipose tissue, the fraction that contains adipose progenitor cells, or committed preadipocytes, than in adipocyte fractions. Thus, we generated a GRK5 knockout (KO) 3T3-L1 preadipocyte to further investigate the mechanistic role of GRK5 in regulating adipocyte differentiation. During adipogenic stimulation, GRK5 KO preadipocytes had decreased lipid accumulation and delayed mature adipocyte development compared to wildtype cells coupled with suppressed adipogenic and lipogenic gene expression. Beside GPCR signaling, RNA sequencing and pathway analysis identified insulin-like growth factor 1 (IGF-1) signaling to be one of the top 5 significantly dysregulated pathways in GRK5 KO cells. GRK5 KO cells also displayed decreased insulin-stimulated ERK phosphorylation, a downstream target of insulin-stimulated IGF-1 receptor activation, suggesting that GRK5 acts through this critical pathway to impact 3T3-L1 adipocyte differentiation. To find a more translational approach, we identified a new small molecule GRK5 inhibitor that was able to reduce 3T3-L1 adipogenesis. These data suggest that GRK5 is required for adipocyte differentiation through IGF-1 receptor/ERK activation and may be a promising translational target for obesity.

A new tool to assess patient-ventilator synchrony in preterm infants receiving non-invasive ventilation: a randomized crossover pilot study.

Nasal synchronized intermittent positive pressure ventilation (nSIPPV) is an effective non-invasive ventilation technique, especially for preterm infants. Patient-ventilator synchrony is essential for providing effective respiratory support; however, no automated system is currently available for monitoring this parameter. A new tool for automatic assessment of patient-ventilator synchrony, the SyncNIV system, was developed and applied in this pilot study to evaluate differences between nSIPPV and non-synchronized nasal intermittent positive pressure ventilation (nIPPV) in preterm infants with respiratory distress. This study involved designing a custom algorithm for signal analysis. Data were collected through a polygraph that could simultaneously gather respiratory data from the patients and ventilator. Patient-ventilator synchrony was evaluated by applying the SyncNIV system in a randomized crossover study designed to compare nSIPPV and nIPPV. The primary outcome was the mean instant Synchrony Index (i-SI), defined as the portion of the inspiration effort sustained by ventilator inflation, expressed as a percentage. Fourteen infants with a median (IQR) gestational age of 28.6 (25.6-30.3), were enrolled. We analyzed 43,304 ventilator inflations and 50,221 patient breaths. The i-SI was 54.69% (44.49-60.09) in nSIPPV and 39.54% (33.40-48.75) in nIPPV, p<0.05. The SyncNIV system confirmed better i-SI during nSIPPV than during nIPPV, demonstrating its effectiveness in assessing the differences between these two modes of non-invasive ventilation in preterm infants. The SyncNIV system could be a useful tool for optimizing the ventilation parameters and improving the effectiveness and comfort of respiratory support systems.

EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism.

Attention-Deficit/Hyperactivity Disorder (ADHD) represents a widely prevalent and heterogeneous neurodevelopmental condition in pediatric populations, often exhibiting a substantial propensity to persist into adulthood. ADHD is a multifaceted disorder that resists straightforward diagnostic tests. Clinicians must invest substantial time and effort to secure an accurate diagnosis and implement effective treatment. ADHD diagnosis is primarily based on psychiatric tests, as there is currently no clinically utilized objective diagnostic tool. Nonetheless, several studies in have documented endeavors to create objective instruments designed to assist in the diagnostic process of ADHD, aiming to enhance diagnostic accuracy and reduce subjectivity. This research endeavor sought to establish an objective diagnostic modality for ADHD through the utilization of electroencephalography (EEG) signal analysis. With the use of innovative deep learning techniques, this research seeks to improve the diagnosis of ADHD using EEG data. To capture complex patterns in EEG data, this study proposes a double-augmented attention mechanism ResNet-based model. Using an autoencoder for feature extraction, the Reptile Search Algorithm for feature selection, and a modified ResNet architecture for model training comprise the technique. AUC, F1-score, accuracy, precision, recall, and other standard classifiers like Random Forest and AdaBoost were utilized to compare the model's performance. By a wide margin, the proposed ResNet model outperforms the traditional models with a 99.42% accuracy, 99.03% precision, 99.82% recall, and 99.42% F1-score. ROC AUC score of 0.99 for the model underscores its remarkable capability to differentiate between children with and without ADHD, thereby minimizing misclassification errors and improving diagnostic precision.

Estimating Wingbeat Frequency of Hummingbirds using a No-labeling Learning Computer Vision Approach.

Wingbeat frequency estimation is an important aspect for the study of avian flight, energetics, and behavioral patterns, among others. Hummingbirds, in particular, are ideal subjects to test a method for this estimation due to their fast wing motions and unique aerodynamics, which results from their ecological diversification, adaptation to high-altitude environments, and sexually selected displays. Traditionally, wingbeat frequency measurements have been done via "manual" image/sound processing. In this study, we present an automated method to detect, track, classify, and monitor hummingbirds in high-speed video footage, accurately estimating their wingbeat frequency using computer vision techniques and signal analysis. Our approach utilizes a zero-shot learning algorithm that eliminates the need for labeling during training. Results demonstrate that our method can produce automated wingbeat frequency estimations with minimal supervision, closely matching those performed by trained human observers. This comparison indicates that our method can, in some scenarios, achieve low or zero error compared to a human, making it a valuable tool for flight analysis. Automating video analysis can assist wingbeat frequency estimation by reducing processing time and, thus, lowering barriers to analyze biological data on fields such as aerodynamics, foraging behavior, and signaling.

Toll-like Receptors 1, 3 and 7 Activate Distinct Genetic Features of NF-κB Signaling and γ-Protocadherin Expression in Human Cardiac Fibroblasts.

Fibroblasts play a pivotal role in key processes within the heart, particularly in cardiac remodeling that follows both ischemic and non-ischemic injury. During remodeling, fibroblasts drive fibrosis and inflammation by reorganizing the extracellular matrix and modulating the immune response, including toll-like receptor (TLR) activation, to promote tissue stabilization. Building on findings from our prior research on heart tissue from patients with advanced coronary artery disease and aortic valve disease, this study sought to explore specific effects of TLR1, TLR3, and TLR7 activation on NF-κB signaling, proinflammatory cytokine production, and γ-protocadherin expression in cardiac fibroblasts. Human cardiac fibroblasts were exposed to agonists for TLR1, TLR3, or TLR7 for 24h, followed by an analysis of NF-κB signaling, cytokine production, and γ-protocadherin expression. The activation of these TLRs triggered distinct responses in the NF-κB signaling pathway, with TLR3 showing a stronger activation profile compared to TLR1 and TLR7, particularly in downregulating γ-protocadherin expression. These findings highlight a potential role for TLR3 in amplifying inflammatory responses and reducing γ-protocadherin levels in cardiac fibroblasts, correlating with the enhanced inflammation and lower γ-protocadherin expression observed in diseased myocardium from patients with coronary artery disease and aortic valve disease. Consequently, TLR3 represents a potential therapeutic target for modulating immune responses in cardiovascular diseases.

The Protective Effect of Nimodipine in Schwann Cells Is Related to the Upregulation of LMO4 and SERCA3 Accompanied by the Fine-Tuning of Intracellular Calcium Levels.

Nimodipine is the current gold standard in the treatment of subarachnoid hemorrhage, as it is the only known calcium channel blocker that has been proven to improve neurological outcomes. In addition, nimodipine exhibits neuroprotective properties in vitro under various stress conditions. Furthermore, clinical studies have demonstrated a neuroprotective effect of nimodipine after vestibular schwannoma surgery. However, the molecular mode of action of nimodipine pre-treatment has not been well investigated. In the present study, using real-time cell death assays, we demonstrated that nimodipine not only reduces cell death induced by osmotic and oxidative stress but also protects cells directly at the time of stress induction in Schwann cells. Nimodipine counteracts stress-induced calcium overload and the overexpression of the Cav1.2 calcium channel. In addition, we found nimodipine-dependent upregulation of sarcoplasmic/endoplasmic reticulum calcium ATPase 3 (SERCA3) and LIM domain only 4 (LMO4) protein. Analysis of anti-apoptotic cell signaling showed an inhibition of the pro-apoptotic protein glycogen synthase kinase 3 beta (GSK3β). Nimodipine-treated Schwann cells exhibited higher levels of phosphorylated GSK3β at serine residue 9 during osmotic and oxidative stress. In conclusion, nimodipine prevents cell death by protecting cells from calcium overload by fine-tuning intracellular calcium signaling and gene expression.