Search Results (442)

Cardiovascular disease encompasses a wide group of conditions that affect the heart and blood vessels. Of these diseases, cardiomyopathies and arrhythmias specifically have been well-studied in their relationship to cardiac dyads, nanoscopic structures that connect electrical signals to muscle contraction. The proper development and positioning of dyads is essential in excitation–contraction (EC) coupling and, thus, beating of the heart. Three proteins, namely CMYA5, JPH2, and BIN1, are responsible for maintaining the dyadic cleft between the T-tubule and junctional sarcoplasmic reticulum (jSR). Various other dyadic proteins play integral roles in the primary function of the dyad—translating a propagating action potential (AP) into a myocardial contraction. Ca²⁺, a secondary messenger in this process, acts as an allosteric activator of the sarcomere, and its cytoplasmic concentration is regulated by the dyad. Loss-of-function mutations have been shown to result in cardiomyopathies and arrhythmias. Adeno-associated virus (AAV) gene therapy with dyad components can rescue dyadic dysfunction, which results in cardiomyopathies and arrhythmias. Overall, the dyad and its components serve as essential mediators of calcium homeostasis and excitation–contraction coupling in the mammalian heart and, when dysfunctional, result in significant cardiac dysfunction, arrhythmias, morbidity, and mortality. Full article

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short‑Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end‑to‑end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean‑energy technologies. Full article

Terahertz (THz) frequencies, spanning from 0.1 to 1 THz, are poised to play a pivotal role in the development of future 6G wireless communication systems. These systems aim to utilize photonic technologies to enable ultra-high data rates—on the order of terabits per second—while maintaining low latency and high efficiency. In this work, we present a novel photonic method for generating sub-THz vector signals within the THz band, employing a semiconductor optical amplifier (SOA) and phase modulator (PM) to create an optical frequency comb, combined with in-phase and quadrature (IQ) modulation techniques. We demonstrate, both through simulation and experimental setup, the generation and successful transmission of a 0.1 THz vector. The process involves driving the PM with a 12.5 GHz radio frequency signal to produce the optical comb; then, heterodyne beating in a uni-traveling carrier photodiode (UTC-PD) generates the 0.1 THz radio frequency signal. This signal is transmitted over distances of up to 30 km using single-mode fiber. The resulting 0.1 THz electrical vector signal, modulated with quadrature phase shift keying (QPSK), achieves a bit error ratio (BER) below the hard-decision forward error correction (HD-FEC) threshold of ${3.8 \times 10}^{-3}$. To the best of our knowledge, this is the first experimental demonstration of a 0.1 THz photonic vector THz wave based on an SOA and a simple PM-driven optical frequency comb. Full article

Climate change poses an increasing threat to livestock reproduction, with heat stress (HS) known to significantly impair ovarian function. This study aimed to elucidate the impact of HS on ovarian function and circRNA expression profiles in Hu sheep. Twelve ewes were randomly assigned to a control (Con, n = 6) or HS group (n = 6) and exposed to different temperatures for 68 days. Compared with the Con group, HS significantly increased the respiratory rate (108.33 ± 3.72 vs. 63.58 ± 2.42 breaths/min), pulse rate (121.17 ± 3.98 vs. 78.08 ± 3.31 beats/min), and rectal temperature (40.17 ± 0.14 °C vs. 39.02 ± 0.21 °C; p < 0.05). Concurrently, serum antioxidant levels were markedly decreased, including total antioxidant capacity (T-AOC), total superoxide dismutase (T-SOD), and glutathione peroxidase (GSH-Px) (p < 0.05). Histological analysis revealed a significant reduction in the numbers of primordial, primary, secondary, and mature follicles, alongside an increase in antral follicles (p < 0.05). TUNEL staining demonstrated enhanced granulosa cell apoptosis (p < 0.05), accompanied by the upregulation of pro-apoptotic genes Bax and Caspase-3 and downregulation of the anti-apoptotic gene Bcl-2, as confirmed by qPCR (p < 0.05). CircRNA sequencing identified 152 differentially expressed circRNAs (120 upregulated, 32 downregulated), and enrichment analyses indicated their involvement in apoptosis, mitophagy, and the FoxO signaling pathway. Collectively, these findings demonstrate that HS impairs ovarian physiology and antioxidant defense, induces follicular damage and cell apoptosis, and alters circRNA expression profiles, providing new insights into the molecular mechanisms underlying HS-induced reproductive dysfunction in Hu sheep. Full article

The electrocardiogram (ECG) is one of the vital physiological signals for human health. Lightweight neural network (NN) models integrated into a low-resource wearable device can benefit the user with a low-power, real-time edge computing system for continuous and daily monitoring. This work introduces a novel edge-computing wearable device for real-time beat-by-beat ECG arrhythmia classification. The proposed wearable integrates the light AI model into a 32-bit ARM^® Cortex^™-based custom printed circuit board (PCB). The work analyzes the performance of artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM) models for real-time wearable implementation. The wearable is capable of real-time QRS detection and feature extraction from raw ECG data. The QRS detection algorithm offers high reliability with a 99.5% F1 score and R-peak position error (RPE) of 6.3 ms for R-peak-to-R-peak intervals. The proposed method implements a combination of top time series, spectral, and signal-specific features for model development. Lightweight, pretrained models are deployed on the custom wearable and evaluated in real time using mock data from the MIT-BIH dataset. We propose an LSTM model that provides efficient performance over accuracy, inference latency, and memory consumption. The proposed model offers 98.1% accuracy, with 98.2% sensitivity and 99.5% specificity while testing in real time on the wearable. Real-time inferencing takes 20 ms, and the device consumes as low as 5.9 mA of power. The proposed method achieves efficient performance in real-time testing, which indicates the wearable can be effectively used for real-time continuous arrhythmia detection. Full article

Loop diuretics like furosemide are commonly used in heart failure (HF) treatment, but their effects on disease progression are still unclear. Furosemide treatment accelerates HF deterioration in a swine model, but the mechanism of acceleration is poorly understood. We hypothesized that furosemide activates inflammatory signaling in the failing left ventricular (LV) myocardium, leading to adverse remodeling of the extracellular matrix (ECM). A total of 14 Yorkshire pigs underwent permanent transvenous pacemaker implantation and were paced at 200 beats per minute; 9 non-instrumented pigs provided controls. Seven paced animals received normal saline, and seven received furosemide at a dose of 1 mg/kg intramuscularly. Weekly echocardiograms were performed. Furosemide-treated animals reached the HF endpoint a mean of 3.2 days sooner than saline-treated controls (mean 28.9 ± 3.8 SEM for furosemide and 32.1 ± 2.5 SEM for saline). The inflammatory signaling protein transforming growth factor-beta (TGF-β) and its downstream proteins were significantly (p ≤ 0.05) elevated in the LV after furosemide treatment. The regulatory factors in cell proliferation, mitogen-activated protein kinase signaling pathway proteins, and matrix metalloproteinases were elevated in the furosemide-treated animals (p ≤ 0.05). Our data showed that furosemide treatment increased ECM remodeling and myocardial fibrosis, reflecting increased TGF-β signaling factors, supporting prior results showing worsened HF. Full article

The primary measurement involves detecting tiny (picometer-level) pathlength fluctuations between satellites using heterodyne laser interferometry for space-based gravitational wave detection. The interference of two laser beams with a MHz-level frequency difference produces a MHz beat-note, in which the gravitational wave signal is encoded in the phase of the beat-note. The phasemeter then performs micro-radian accuracy phase measurement and communication information demodulation for this beat-note. To mitigate the impact of phase modulation, existing solutions mostly alleviate it by reducing the modulation depth and optimizing the structure of the pseudo-random noise (PRN) codes. Since the phase modulation is not effectively separated from the phase of the beat-note phase measurement, it has a potential impact on the phase extraction of the micro-radian accuracy of the beat-note. To solve this problem, this paper analyzes the influence mechanism of phase modulation on beat-note phase measurement and proposes a method to separate the modulated phase based on complex rotation. The beat-note is processed by complex conjugate rotation, which can effectively eliminate the PRN modulated phase. Simulation and analysis results demonstrate that this method can significantly enhance the purity of the measured phase in the beat-note while maintaining the ranging and communication functions. Targeting the application of the micro-radian phasemeter in space-based gravitational wave detection, this study presents the reconstruction and separation method of the ranging and communication phase in beat-note, which also provides a new direction for the final selection of modulation depth in the future. Full article

ECG monitoring is central to the early detection of cardiac abnormalities. We compared 28 manually selected signal-specific features with 159 automatically extracted signal-independent descriptors from the MIT BIH Arrhythmia Database. ANOVA reduced features to the 10 most informative attributes, which were evaluated alone and in combination with the signal-specific features using Random Forest, SVM, and deep neural networks (CNN, RNN, ANN, LSTM) under an interpatient 80/20 split. Merging the two feature groups delivered the best results: a 128-layer CNN achieved 100% accuracy. Power profiling revealed that deeper models improve accuracy at the cost of runtime, memory, and CPU load, underscoring the trade-off faced in edge deployments. The proposed hybrid feature strategy provides beat-by-beat classification with a reduction in the number of features, enabling real-time ECG screening on wearable and IoT devices. Full article

Stress significantly impacts our society, making strategies for its mitigation necessary. A possible approach may involve binaural beats (BBs), i.e., an auditory stimulation obtained by presenting pure tones with slightly different frequencies to the user’s ears, resulting in a third phantom beat $\left(f_{BB}\right)$. While studies in the literature investigate the effects of BBs at a constant stimulation frequency, with this pilot study, we present an innovative approach that adapts the beat frequency in real time within the theta range (4.0–8.0 Hz) to reduce acute mental stress. A stress index, obtained from the predictions of a random forest regressor, was considered to adjust the stimulation. The regressor considered features from an electrocardiogram (ECG) and the ECG-derived respiratory signal. Thirteen healthy subjects underwent a stressful protocol involving multiple mental arithmetic tasks during which constant (CBB) or adapted (ABB) stimulation occurred. Task performances like accuracy and reaction times were recorded. The results show that ABBs significantly lowered the average stress index ($p<0.05$) and heart rate ($p<0.05$) compared to CBBs. No statistically significant differences were detected in task performance. The results support the importance of adaptive and personalized approaches for mitigating stress. Future research is necessary to assess the goodness of our proposal, considering a larger sample, different stressors, and an objective and external assessment of stress (e.g., cortisol levels). Full article

In gravitational wave measurement and inter-satellite laser communication systems, the relative rotation and motion between the transmitter and receiver terminals introduces small angular deviations over a link distance of thousands of kilometers, which need to be measured with high accuracy and sensitivity. The heterodyne coherent angle measurement has a higher measurement accuracy and detection sensitivity compared with the traditional direct detection technique, which performs angle measurement through the phase of a beat frequency signal. In this paper, we propose a four-quadrant heterodyne coherent angle measurement technique with μrad accuracy and nW-level detection sensitivity. A mathematical model of a differential wavefront sensing (DWS) angle solution was formulated, and a Monte Carlo simulation system was built for performance testing. An experimental system was devised to assess the accuracy and sensitivity of the heterodyne coherent measurement method and to compare the performance with that of the direct detection method. The experimental results showed that for azimuth and pitch axes, the accuracy of the heterodyne coherent angle measurement was 2.54 μrad and 2.85 μrad under the same signal power of −16 dBm, which had a 5-fold improvement compared with direct detection. The sensitivity of the heterodyne coherent detection was −50 dBm at the 20 μrad accuracy threshold, which was a 1000-fold improvement compared with direct detection. This research is of great significance for the phase measurement and tracking system in the field of gravitational wave detection and has a guiding role in system design work in the field of inter-satellite laser communication. Full article

Cross-database electrocardiogram (ECG) classification remains a critical challenge due to variations in patient populations, recording conditions, and annotation granularity. Existing methodologies for ECG arrhythmia classification have primarily utilized datasets with either fine-grained or coarse-grained labels, but seldom both simultaneously. Fine-grained labels provide beat-level annotations, whereas coarse-grained labels offer only record-level labels. In this study, we propose an innovative cross-database learning framework that utilizes both fine-grained and coarse-grained labels in tandem, thereby enhancing classification performance across heterogeneous datasets. Specifically, our approach begins with the pretraining of a CNN-based beat classifier that takes ECG signals as the input and predicts beat types on a finely labeled dataset, namely the MIT-BIH Arrhythmia Database (MITDB). The pretrained model is then fine-tuned using weakly supervised learning on two coarsely labeled datasets: the SPH one, which contains four rhythm classes, and the PTB-XL one, which involves binary classification between the sinus rhythm (SR) and atrial fibrillation (AFIB). Once the beat classifier is adapted to a new dataset, it generates a two-dimensional beat-score-map (BSM) representation from the input ECG signal. This 2D BSM is subsequently utilized as the input for arrhythmia rhythm classification. The proposed method achieves F1 scores of 0.9301 on the SPH dataset and 0.9267 on the PTB-XL dataset, corresponding to the multi-class and binary rhythm classification tasks described above. These results demonstrate a robust cross-database classification of complex cardiac arrhythmia rhythms. Furthermore, t-SNE visualizations of the 2D BSM representations, after adaptation to the coarsely labeled SPH and PTB-XL datasets, validate how our method significantly enhances the ability to differentiate between various arrhythmia rhythm types, thus highlighting its effectiveness in cross-database ECG analysis. Full article

Frequency tuning nonlinearities in semiconductor lasers constitute a critical factor that degrades measurement precision and spectral resolution in frequency-modulated continuous-wave (FMCW) LiDAR systems. This study systematically investigates the influence of nonlinear beat signal phase distortions on spectral peak broadening and develops a phase-fitting-based pre-correction algorithm. To further enhance system performance, an electro-optic phase-locked loop architecture combined with non-uniform discrete Fourier transform signal processing is implemented, establishing a comprehensive solution for tuning nonlinearity suppression. Experimental validation demonstrates a sub-18 µm standard deviation in absolute distance measurements at a 19 m target range. This integrated approach represents a significant advancement in coherent frequency-sweep detection methodologies, offering considerable potential for high-precision photonic radar applications. Full article

Kagome lattices have attracted significant research interest due to their unique interplay of geometry, topology, and material properties. They provide deep insights into strongly correlated electron systems, novel quantum phases, and advanced material designs, making them fundamental in condensed matter physics and material engineering. This work presents an efficient method for terahertz (THz) wave generation across the entire THz spectrum, leveraging high-quality-factor Kagome-shaped silicon photonic crystal resonators. In the proposed simulation-based approach, an infrared (IR) single-frequency wave interacts with an induced resonance mode within the resonator, producing a THz beat frequency. This beat note is then converted into a standalone THz radiation (T-ray) wave using an amplitude demodulator. Simulations confirm the feasibility of our method, demonstrating that a conventional single-frequency wave can induce resonance and generate a stable beat frequency. The proposed technique is highly versatile, extending beyond THz generation to frequency conversion in electronics, optics, and acoustics, among other domains. Its high efficiency, compact design, and broad applicability offer a promising solution to challenges in THz technology. Furthermore, our findings establish a foundation for precise frequency manipulation, unlocking new possibilities in signal processing, sensing, detection, and communication systems. Full article

Radars are promising tools for contactless vital sign monitoring. As a screening device, radars could supplement polysomnography, the gold standard in sleep medicine. When the radar is placed lateral to the person, vital signs can be extracted simultaneously from multiple body parts. Here, we present a method to select every available breathing and heartbeat signal, instead of selecting only one optimal signal. Using multiple concurrent signals can enhance vital rate robustness and accuracy. We built an algorithm based on persistence diagrams, a modern tool for time series analysis from the field of topological data analysis. Multiple criteria were evaluated on the persistence diagrams to detect breathing and heartbeat signals. We tested the feasibility of the method on simultaneous overnight radar and polysomnography recordings from six healthy participants. Compared against single bin selection, multiple selection lead to improved accuracy for both breathing (mean absolute error: 0.29 vs. 0.20 breaths per minute) and heart rate (mean absolute error: 1.97 vs. 0.66 beats per minute). Additionally, fewer artifactual segments were selected. Furthermore, the distribution of chosen vital signs along the body aligned with basic physiological assumptions. In conclusion, contactless vital sign monitoring could benefit from the improved accuracy achieved by multiple selection. The distribution of vital signs along the body could provide additional information for sleep monitoring. Full article

Accurate delineation of ECG signals is critical for effective cardiovascular diagnosis and treatment. However, previous studies indicate that models developed for specific datasets and environments perform poorly when used with varying ECG signal morphology characteristics. This paper presents a novel approach to ECG signal delineation using a multi-layer filter (MLF) combined with a bidirectional long short-term memory (BiLSTM) model, namely iCOR. The proposed iCOR architecture enhances noise removal and feature extraction, resulting in improved classification of the P-QRS-T-wave morphology with a simpler model. Our method is evaluated on a combination of two standard ECG databases, the Lobachevsky University Electrocardiography Database (LUDB) and QT Database (QTDB). It can be observed that the classification performance for unseen sets of LUDB datasets yields above 90.4% and 98% accuracy, for record-based and beat-based approaches, respectively. Beat-based approaches outperformed the record-based approach in overall performance metric results. Similar results were shown in an unseen set of the QTDB, in which beat-based approaches performed with accuracy above 97%. These results highlight the robustness and efficacy of the iCOR model across diverse ECG signal datasets. The proposed approach offers a significant advancement in ECG signal analysis, paving the way for more reliable and precise cardiac health monitoring. Full article