Continuous Signal Research Articles

Development of a non-invasive heart rate measurement method for sea turtles with dense keratinous scutes through effective electrode placement

Measuring the heart rate of sea turtles is important for understanding their physiological adaptations to the environment. Non-invasive methods to measure the electrocardiogram (ECG) of sea turtles have been developed by attaching electrodes to their carapace. However, this method has only been applicable to sea turtles with sparse keratin on their shell surfaces, such as loggerhead turtles, and it is difficult to detect heartbeats in sea turtles with dense keratinous scutes, including green sea turtles. Here, we explored the electrode placements on the plastron that can be applied to ECG measurement in green turtles. ECG signals were checked using a handheld ECG monitor at three sets of electrode placement on the plastron. When ECG signals could be detected, they were measured in the water tanks for several days to confirm the clarity of the ECG signals. Of the 29 green turtles, when the negative electrode was placed near the neck area of the plastron, clear ECG signals were obtained in nine individuals (39.1%), whereas ECG signals were not detected at any placements in four individuals (17.4%). Furthermore, in the water tank experiments, continuous ECG signals were successfully recorded by attaching a negative electrode near the neck: almost noiseless clear ECG signals even during moving in seven out of ten individuals and slightly weak and noisy signals in other individuals. The measured heart rate of ten individuals during resting was 8.6 ± 2.9 (means ± s.d.) beats min−1 and that during moving was 12.2 ± 4.7 beats min−1, similar to those reported in a previous study involving the insertion of electrodes inside the body. Therefore, for measuring the ECG of green turtles, the negative electrode should be placed closer to the neck, and the positive and earth electrodes should be placed to the lower left of the plastron. Although the selection of suitable individuals for measurements is required, this heart rate measurement method will contribute to a better understanding of the physiological status of sea turtles with dense keratinous scutes, including green turtles.

Tissue-resident NK cells do their own glandscaping

In this issue of JEM, Sparano et al. (https://doi.org/10.1084/jem.20240930) present compelling evidence that salivary gland trNK cells originate from cNK cells and are developmentally distinct from ILC1 cells. Mechanistically, they demonstrate that continuous autocrine TGF-β signaling drives salivary gland tissue residency and works in synergy with IL-15 to enhance Hobit-dependent cytotoxicity.

Profiling paracrine interactions between hypoxic and normoxic skeletal muscle tissue in a microphysiological system fabricated from 3D printed components.

Disrupted blood flow in conditions such as peripheral artery disease and critical limb ischemia leads to variations in oxygen supply within skeletal muscle tissue, creating regions of poorly perfused, hypoxic skeletal muscle surrounded by regions of adequately perfused, normoxic muscle tissue. These oxygen gradients may have significant implications for muscle injury or disease, as mediated by the exchange of paracrine factors between differentially oxygenated tissue. However, creating and maintaining heterogeneous oxygen landscapes within a controlled experimental setup to ensure continuous paracrine signaling is a technological challenge. Here, we engineer oxygen-controlled microphysiological systems to investigate paracrine interactions between differentially oxygenated engineered muscle tissue. We fabricated microphysiological systems with dual oxygen landscapes that also had engineered control over paracrine interactions between hypoxic and normoxic skeletal muscle tissues, which were differentiated from C2C12 myoblasts cultured on micromolded gelatin hydrogels. The microphysiological systems interfaced with a new 3D-printed oxygen control well plate insert, which we designed to distribute flow to multiple microphysiological systems and minimize evaporation for longer timepoints. With our system, we demonstrated that amphiregulin, a myokine associated with skeletal muscle injury, exhibits unique upregulation in both gene expression and secretion after 24 hours due to paracrine interactions between hypoxic and normoxic skeletal muscle tissue. Our platform can be extended to investigate other impacts of paracrine interactions between hypoxic and normoxic skeletal muscle and can more broadly be used to elucidate many forms of oxygen-dependent crosstalk in other organ systems.

Microseism Amplitude and Wave Power in the Mediterranean Sea (1996–2023)

AbstractIn this work, we integrate seismic data recorded by nine coastal Mediterranean seismic stations and wave hindcast data for 1 January 1996 through 15 October 2023. We examine the relationships between the ocean wave‐generated microseism signal (the most continuous and ubiquitous seismic signal on Earth) in terms of temporally varying spectral content, root mean square amplitude, and microseism power spectral density, with the main features of principal ocean wave attributes, specifically significant wave heights, wave period and wave power. To explore relationships between microseism and sea state, we performed a correlation analysis between seismic root square mean amplitude and significant wave height time series for the entire Mediterranean Sea for 1996–2023, including retrieving long‐term trends for microseism energy and independently estimated wave power and calculating the Spearman correlation coefficient between the two trend time series. Despite the small number of stations available the analysis allows for a useful exploratory study on the microseism and its relationship with Mediterranean Sea state and wave power spanning 27 years. Given the recent increase in the number of regional seismic stations, the growth of data sharing, and the intensification of global warming and climate extreme events, the results and methods explored here can be further implemented and developed in coming years for coastal monitoring purposes in complement with other data sources.

A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph

Pediatric diabetes I is an endemic and an especially difficult disease; indeed, at this point, there does not exist a cure, but only careful management that relies on anticipating hypoglycemia. The changing physiology of children producing unique blood glucose signatures, coupled with inconsistent activities, e.g., playing, eating, napping, makes “forecasting” elusive. While work has been done for adult diabetes I, this does not successfully translate for children. In the work presented here, we adopt a reinforcement approach by leveraging the de Bruijn graph that has had success in detecting patterns in sequences of symbols–most notably, genomics and proteomics. We translate a continuous signal of blood glucose levels into an alphabet that then can be used to build a de Bruijn, with some extensions, to determine blood glucose states. The graph allows us to “tune” its efficacy by computationally ignoring edges that provide either no information or are not related to entering a hypoglycemic episode. We can then use paths in the graph to anticipate hypoglycemia in advance of about 30 minutes sufficient for a clinical setting and additionally find actionable rules that accurate and effective. All the code developed for this study can be found at: https://github.com/KurbanIntelligenceLab/dBG-Hypoglycemia-Forecast.

Autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modeling

This paper proposes a novel autoencoder-based neural network for compressing and reconstructing underwater acoustic signals collected by Directional Frequency Analysis and Recording sonobuoys. To improve both signal compression rates and reconstruction performance, we integrate Residual Vector Quantization and a Compensation Module into the decoding process to effectively compensate for quantization errors. Additionally, an unstructured pruning technique is applied to the encoder to minimize computational load and parameters, addressing the battery limitations of sonobuoys. Experimental results demonstrate that the proposed method reduces the data transmission size by approximately 31.25% compared to the conventional autoencoder-based method. Moreover, the spectral mean square errors are reduced by 60.58% for continuous wave signals and 55.25% for linear frequency modulation signals under realistic air channel simulations.

Data analysis protocol for early autonomic dysfunction characterization after severe traumatic brain injury.

Traumatic brain injury (TBI) disrupts normal brain tissue and functions, leading to high mortality and disability. Severe TBI (sTBI) causes prolonged cognitive, functional, and multi-organ dysfunction. Dysfunction of the autonomic nervous system (ANS) after sTBI can induce abnormalities in multiple organ systems, contributing to cardiovascular dysregulation and increased mortality. Currently, detailed characterization of early autonomic dysfunction in the acute phase after sTBI is lacking. This study aims to use physiological waveform data collected from patients with sTBI to characterize early autonomic dysfunction and its association with clinical outcomes to prevent multi-organ dysfunction and improving patient outcomes. This data analysis protocol describes our pre-planned protocol using cardiac waveforms to evaluate early autonomic dysfunction and to inform multi-dimensional characterization of the autonomic nervous system (ANS) after sTBI. We will collect continuous cardiac waveform data from patients managed in an intensive care unit within a clinical trial. We will first assess the signal quality of the electrocardiogram (ECG) using a combination of the structural image similarity metric and signal quality index. Then, we will detect premature ventricular contractions (PVC) on good-quality ECG beats using a deep-learning model. For arterial blood pressure (ABP) data, we will employ a singular value decomposition (SVD)-based approach to assess the signal quality. Finally, we will compute multiple indices of ANS functions through heart rate turbulence (HRT) analysis, time/frequency-domain analysis of heart rate variability (HRV) and pulse rate variability, and quantification of baroreflex sensitivity (BRS) from high-quality continuous ECG and ABP signals. The early autonomic dysfunction will be characterized by comparing the values of calculated indices with their normal ranges. This study will provide a detailed characterization of acute changes in ANS function after sTBI through quantified indices from cardiac waveform data, thereby enhancing our understanding of the development and course of eAD post-sTBI.

The human auditory cortex concurrently tracks syllabic and phonemic timescales via acoustic spectral flux.

Dynamical theories of speech processing propose that the auditory cortex parses acoustic information in parallel at the syllabic and phonemic timescales. We developed a paradigm to independently manipulate both linguistic timescales, and acquired intracranial recordings from 11 patients who are epileptic listening to French sentences. Our results indicate that (i) syllabic and phonemic timescales are both reflected in the acoustic spectral flux; (ii) during comprehension, the auditory cortex tracks the syllabic timescale in the theta range, while neural activity in the alpha-beta range phase locks to the phonemic timescale; (iii) these neural dynamics occur simultaneously and share a joint spatial location; (iv) the spectral flux embeds two timescales-in the theta and low-beta ranges-across 17 natural languages. These findings help us understand how the human brain extracts acoustic information from the continuous speech signal at multiple timescales simultaneously, a prerequisite for subsequent linguistic processing.

Возмущения ионосферного радиоканала во время магнитных бурь в ноябре–декабре 2023 г.

This paper presents the results of analysis of oblique ionospheric sounding data obtained with continuous chirp signal on the subauroral paths Magadan—Irkutsk and Norilsk—Irkutsk. It specifies the interplanetary sources of magnetic storms in November–December 2023. It was established that signals propagating outside the great-circle arc and additional diffuse reflections can be found in oblique sounding ionograms in intense magnetospheric convection field. Their appearance can be related to refraction of radio waves on the polar wall of the main ionospheric trough and scattering by small-scale inhomogeneities. Connection has been revealed between variations in the maximum observed frequencies of HF radio wave propagation modes with the spatial position of the main ionospheric trough and the equatorial boundary of diffuse electron precipitation zone.

Disturbances of ionospheric radio channel during magnetic storms in November–December 2023

This paper presents the results of analysis of oblique ionospheric sounding data obtained with continuous chirp signal on the subauroral paths Magadan—Irkutsk and Norilsk—Irkutsk. It specifies the interplanetary sources of magnetic storms in November–December 2023. It was established that signals propagating outside the great-circle arc and additional diffuse reflections can be found in oblique sounding ionograms in intense magnetospheric convection field. Their appearance can be related to refraction of radio waves on the polar wall of the main ionospheric trough and scattering by small-scale inhomogeneities. Connection has been revealed between variations in the maximum observed frequencies of HF radio wave propagation modes with the spatial position of the main ionospheric trough and the equatorial boundary of diffuse electron precipitation zone.

DEEP FOCUS EARTHQUAKE on NOVEMBER 30, 2020 in the TATAR STRAIT, Мw=6.4 (SAKHALIN ISLAND)

. On November 30, 2020, at 22h54m UTC, an earthquake with a magnitude Mw=6.4 occurred in the Tatar Strait at a depth of 600 km. The earthquake was felt in the vast territory of the Russian Far East and on the islands of the Japanese ridge. The seismic event was recorded by seismic stations of international agencies and regional seismological centers. The article presents the results of a detailed study of the earthquake waveforms at the Yuzhno-Sakhalinsk station, analyzes spectral characteristics of P- and S-waves using methods aimed at stud ying continuous seismic signals. For the first time in the practice of the Sakhalin Branch GS RAS, an analysis of the macroseismic manifestation of the earthquake on November 30, 2020 was carried out together with intensities calculated from instrumental data.

Predictive Modeling of Heart Rate from Respiratory Signals at Rest in Young Healthy Humans.

Biological signals such as respiration (RSP) and heart rate (HR) are oscillatory and physiologically coupled, maintaining homeostasis through regulatory mechanisms. This report models the dynamic relationship between RSP and HR in 45 healthy volunteers at rest. Cross-correlation between RSP and HR was computed, along with regression analysis to predict HR from RSP and its first-order time derivative in continuous signals. A simulation model tested the possibility of replicating the RSP-HR relationship. Cross-correlation results showed a time lag in the sub-second range of these signals (849.21 ms ± SD 344.84). The possible modulation of HR by RSP was mediated by the RSP amplitude and its first-order time derivative (in 45 of 45 cases). A simulation of this process allowed us to replicate the physiological relationship between RSP and HR. These results provide support for understanding the dynamic interactions in cardiorespiratory coupling at rest, showing a short time lag between RSP and HR and a modulation of the HR signal by the first-order time derivative of the RSP. This dynamic would optionally be incorporated into dynamic models of resting cardiopulmonary coupling and suggests a mechanism for optimizing respiration in the alveolar system by promoting synchrony between the gases and hemoglobin in the alveolar pulmonary system.

Detection and Characterization of Low Probability of Intercept Triangular Modulated Frequency Modulated Continuous Wave Radar Signals in Low SNR Environments using the Scalogram and the Reassigned Scalogram

Digital intercept receivers are currently moving away from Fourier-based analysis and towards classical time-frequency analysis techniques for the purpose of analyzing low probability of intercept radar signals. This paper presents the novel approach of characterizing low probability of intercept frequency modulated continuous wave radar signals through utilization and direct comparison of the Scalogram versus the Reassigned Scalogram. Triangular modulated frequency modulated continuous wave signals were analyzed. The following metrics were used for evaluation: percent error of: carrier frequency, modulation bandwidth, modulation period, and chirp rate. Also used were: percent detection, lowest signal-to-noise ratio for signal detection, and time-frequency localization (x and y direction). Experimental results demonstrate that overall, the Reassigned Scalogram produced more accurate characterization metrics than the Scalogram.

The Deformation of a Liquid Metal Droplet Under Continuous Acceleration in a Variable Cross-Section Groove.

This paper constructs a numerical simulation model for the deformation of droplets in a variable cross-section groove of a liquid droplet MEMS switch under different directions, amplitudes, frequencies, and waveforms of acceleration. The numerical simulation utilizes the level set method to monitor the deformation surface boundary of the metal droplets. The simulation outcomes manifest that when the negative impact acceleration on the X-axis is 12.9 m/s2, the negative impact acceleration on the Y-axis is 90 m/s2, the negative impact acceleration on the Z-axis is 34.5 m/s2, and the metal droplet interfaces with the metal electrode. The droplet deformation under the effect of a sine wave acceleration signal in the X and Y directions is lower than that under impact acceleration, while in the Z direction, the deformation is higher than that under impact acceleration. The deformation of metal droplets under square wave acceleration is more pronounced than that under sinusoidal wave acceleration. The deformation escalates with the augmentation in square wave amplitude and dwindles with the reduction in square wave acceleration frequency. Furthermore, there exists a phase difference between the deformation curve of the metal droplet and the continuous acceleration signal curve, and the phase difference is dependent of the material properties of the metal droplet. This work elucidates the deformation of the liquid-metal droplets under continuous acceleration and furnishes the foundation for the continuous operation design of MEMS droplet switches.

Impact of Infrared Indocyanine Green Fluorescence Imaging-guided Laparoscopic Hepatectomy on Securing the Resection Margin for Colorectal Liver Metastasis.

Laparoscopic hepatectomy for colorectal liver metastases (CRLM) is performed worldwide. However, owing to a lack of palpatory information and difficulties associated with accurate intraoperative ultrasonographic diagnosis, the tumor may be exposed at the hepatic transection margin. This study aimed to investigate the pathological significance of near-infrared (NIR) fluorescence imaging with indocyanine green (ICG)-guided laparoscopic hepatectomy and determine its usefulness in securing the resection margin for CRLMs. Fifty-nine patients who underwent laparoscopic hepatectomy for CRLM using NIR fluorescence imaging between February 2017 and June 2021 at Sapporo Medical University Hospital were included. Generally, all patients received intravenous ICG (2.5mg/body) as a fluorescence agent 1 to 2 days before surgery. During the surgical procedure, real-time NIR fluorescence imaging was repeatedly performed to assess the surgical margins. Of the 94 tumors in 59 patients, laparoscopic NIR fluorescence imaging identified 56 tumors (59.6%) on the liver surface. Pathological analysis indicated clear margins in 96.6% (57/59) of patients. Examination of paraffin-embedded sections, which were successful in only 20 of 94 cases (21.3%), revealed that there were no tumor cells positive for NIR fluorescence, and the median distance of the continuous fluorescent signal from the tumor margin was 1.074mm. We demonstrated a high R0 rate using NIR fluorescence-guided hepatectomy. This technique has the potential to improve intraoperative tumor identification and tumor margin assurance and reduce the rate of positive resection margins in patients with CRLMs.

Pulsed Orthogonal Time Frequency Space: A Fast Acquisition and High-Precision Measurement Signal for Low Earth Orbit Position, Navigation, and Timing

The recent rapid development of low Earth orbit (LEO) constellation-based navigation techniques has enhanced the ability of position, navigation, and timing (PNT) services in deep attenuation and interference environments. However, existing navigation modulations face the challenges of high acquisition complexity and do not improve measurement precision at the same signal strength. We propose a pulsed orthogonal time frequency space (Pulse-OTFS) signal, which naturally converts continuous signals into pulses through a special delay-Doppler domain pseudorandom noise (PRN) code sequence arrangement. The performance evaluation indicates that the proposed signal reduces at least 89.4% of the acquisition complexity. The delay measurement accuracy is about 8 dB better than that of the traditional binary phase shift keying (BPSK) signals with the same bandwidth. It also provides superior compatibility and anti-multipath performance. The advantages of fast acquisition and high-precision measurement are verified by processing the real signal in the developed software receiver. As Pulse-OTFS occupies only one time slot of a signal period, it can be easily integrated with OTFS-modulated communication signals and used as a navigation signal from broadband LEO satellites as an effective complement to the global navigation satellite system (GNSS).

Characterisation of Electrochemical Systems from Periodic Chronoamperometry Signals Using Computational Tools

Electrochemical systems are complex, involving various chemical and physical processes (e.g. electron transfer reactions, diffusive and convective mass transfer, charge transfer processes). In most cases, electrochemical systems are summarised using simple signals (e.g. amperometric or potentiometric signals), which do not, however, adequately capture their complexity. These mostly unidimensional signals are usually insufficient to facilitate the full understanding of the electrochemical system and its underlying, simultaneously occurring processes.Established electroanalytical methods are commonly utilised to characterise specific (electro-) chemical and physical processes of electrochemical systems (e.g. potentiometry, amperometry/voltammetry, electrochemical impedance spectroscopy, etc.). Despite the usefulness of these traditional electroanalysis techniques, they are often restricted to the analysis of a very specific set of system characteristics and parameters. Moreover, the experimental nature of these techniques, usually requiring a specific protocol, sets limits to their employment for on-line measurements of industrial electrochemical systems (e.g. reactors, fuel cells etc.).To avoid these limitations, a new alternative approach to understanding and characterising electrochemical systems is proposed. Computational techniques have the potential to extract various system parameters from periodic chronoamperometry signals of electrochemical systems. Such techniques include methods that are commonly used in chemometric investigations in addition to other modern signal processing tools.Early findings from this ongoing study include the separation of Faradaic and non-Faradaic current contributions from a variety of simulated chronoamperometric signals. Moreover, qualitative sensitivity studies appear promising for the subsequent quantitative recovery of multiple parameters from stagnant and hydrodynamic electrochemical systems (e.g. capacitance, resistance).As a consequence, this novel computer-aided approach can pave the way for real-time analyses of electrochemical applications. Furthermore, this strategy will likely reduce the need for specific electroanalytic experiments to determine certain parameters. Instead, complex electrochemical systems and their underlying chemical and physical processes have the potential to be characterised from simple, continuous chronoamperometric signals by means of computational techniques.

Triboelectric-Inertial Sensing Glove Enhanced by Charge-Retained Strategy for Human-Machine Interaction.

As technology advances, human-machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capturing intricate hand movements, are emerging as vital HMI tools. Moreover, triboelectric-based sensors, with their self-powered, cost-effective, and material various characteristics, can offer promising solutions for enhancing existing glove systems. However, a key limitation of these sensors is that charge leakage in the measurement circuit results in capturing only transient signals, rather than continuous changes. To address this issue, a charge-retained circuit that effectively prevents triboelectric signal attenuation is developed, enabling accurate measurement of continuous finger movements. This innovation forms the foundation of a highly integrated smart glove system, enhancing HMI functionality by combining continuous triboelectric signals with inertial sensor data. The system showcases a diverse range of applications, including complex robotic control, virtual reality interaction, smart home lighting adjustments, and intuitive interface operations. Furthermore, by leveraging artificial intelligence (AI) techniques, the system achieves accurate recognition of complex sign language with an impressive 99.38% accuracy. This work presents a charge-retained approach for continuous sensing with triboelectric-based sensors, offering valuable insights for developing future multifunctional HMI and sign language recognition systems. The proposed smart glove system, with its dual-mode sensing and AI integration, holds great potential for revolutionizing various domains and enhancing user experiences.

Electrochemical Aptasensor for Progesterone Detection

Electrochemical biosensors have revolutionized point-of-care diagnostics through commercial accessibility while maintaining detection specificity and sensitivity. Medical biosensors are designed to detect biomarkers in bodily fluids (blood, urine, saliva, sweat) and commonly use enzymes, antibodies, or nucleic acids to bind biomarker targets. An emerging method of biomarker detection uses aptamers which are short, single-stranded DNA or RNA molecules that bind to specific target molecules with high affinity and selectivity. Aptamer sensors are an affordable alternative to antibody-based sensors, have a wider target scope compared to antibody- or enzyme-based assays, and inherently have high structure flexibility and design for redox robe modification. In particular, electrochemical aptamer biosensors (E-AB) represent a promising device architecture wherein an aptamer sequence with a redox probe is tethered to an alkane thiol self-assembled monolayer on a gold electrode (Au-SAM). Upon substrate binding, the conformational changes in the aptamer then enable fast electron transfer to the redox probe and allow rapid and continuous signal transduction of analyte concentrations. E-ABs have been used to target various biomarkers (cocaine, thrombin, tobramycin, vancomycin, etc.) and operate well in vivo, making them an attractive technology for point-of-care diagnostics.In this work, an E-AB is designed for progesterone monitoring. Progesterone is a key biomarker for monitoring reproductive health, as irregular levels are predictive of unsuccessful pregnancy, abnormal uterine bleeding, early embryonic mortality, or ectopic pregnancy. However, there are no continuous, non-invasive methods for progesterone detection. Current methods are limited to laboratory blood samples, ovulation prediction kits, and cycle tracking, but these methods fall short of achieving continuous, accurate, reliable, non-invasive, accessible, and comprehensive detection. Here, we develop an E-AB with a specific affinity for progesterone using the established Au-SAM architecture, a methylene blue redox reporter, and an aptamer sequence with a high affinity for progesterone. These experiments show proof of concept that an immobilized aptamer with methylene blue can detect progesterone electrochemically and is a promising platform for continuous monitoring of progesterone in vivo.

Probe set selection for targeted spatial transcriptomics

Targeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing the spatial signals present in a tissue. This requires selecting the most informative, yet minimal, set of genes to profile (gene set selection) for which it is possible to build probes (probe design). However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or new states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both gene set specificity for cell type identification and within-cell type expression variation to resolve spatially distinct populations while considering prior knowledge as well as probe design and expression constraints. We evaluated Spapros and show that it outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a single-cell resolution in situ hybridization on tissues (SCRINSHOT) experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types.