Subthreshold Signal Research Articles

Search for Gravitational-lensing Signatures in the Full Third Observing Run of the LIGO–Virgo Network

Gravitational lensing by massive objects along the line of sight to the source causes distortions to gravitational wave (GW) signals; such distortions may reveal information about fundamental physics, cosmology, and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO-Virgo network. We search for repeated signals from strong lensing by (1) performing targeted searches for subthreshold signals, (2) calculating the degree of overlap among the intrinsic parameters and sky location of pairs of signals, (3) comparing the similarities of the spectrograms among pairs of signals, and (4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by (1) frequency-independent phase shifts in strongly lensed images, and (2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the nondetection of GW lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects.

Spiking neural networks in the Alexiewicz topology: A new perspective on analysis and error bounds

In order to ease the analysis of error propagation in neuromorphic computing and to get a better understanding of spiking neural networks (SNN), we address the problem of mathematical analysis of SNNs as endomorphisms that map spike trains to spike trains. A central question is the adequate structure for a space of spike trains and its implication for the design of error measurements of SNNs including time delay, threshold deviations, and the design of the reinitialization mode of the leaky-integrate-and-fire (LIF) neuron model. First, we identify the underlying topology by analyzing the closure of all sub-threshold signals of a LIF model. For zero leakage this approach yields the Alexiewicz topology, which we adopt to LIF neurons with arbitrary positive leakage. As a result, LIF can be understood as spike train quantization in the corresponding norm. This way we obtain various error bounds and inequalities such as a quasi-isometry relation between incoming and outgoing spike trains. Another result is a Lipschitz-style global upper bound for the error propagation and a related resonance-type phenomenon.

Detecting stochastic multiresonance in neural networks via statistical complexity measure

This paper employs statistical complexity measure (SCM) to investigate the occurrence of stochastic multiresonance (SMR) induced by noise and time delay in small-world neural networks coupled with FitzHugh-Nagumo (FHN) neurons. Our findings reveal that SCM exhibits four local maxima at four optimal noise levels, providing evidence for the occurrence of quadruple stochastic resonances. When time delay τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au$$\\end{document} is taken into account in the information transmission, under moderate noise levels, SCM shows several local maxima when τ=nTe\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au = nT_{e}$$\\end{document} with n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n$$\\end{document} being a positive integer and Te\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T_{e}$$\\end{document} being the period of subthreshold signal. This indicates the appearance of delay-induced SMR at the multiples of the period of subthreshold signal. Intriguingly, at low noise levels, a strong coherence between time delay and neuronal firing dynamics emerges at τ=nTe-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au = nT_{e} - 2$$\\end{document}, as confirmed by a series of SCM maxima at these time delays. Furthermore, the study demonstrates that by adjusting the degrees and sizes of small-world networks, as well as the coupling strength, it is possible to optimize the strength of delay-induced SMR, thus maximizing the detection capability of subthreshold signal. The research results may provide us with an effective approach for understanding the role of time delay in signal detection and information transmission.

Signal encoding performance of astrocyte-dressed Morris Lecar neurons

There are more support cells called glia in the central nervous system than neurons. Although previous studies have shown that one of these glial cells, astrocytes, can communicate with neurons bidirectionally, giving a silent neuron the ability to fire or changing the neuron’s firing frequency, understanding the role of astrocytes in neuroscience is still in its infancy. With this motivation, in this study, the weak signal encoding performance of different types of astrocyte-dressed Morris Lecar neurons in the presence of noise naturally found in biological systems is systematically investigated. The results showed that astrocyte-dressed Morris Lecar neurons had a subthreshold signal frequency suitable for optimal encoding and that Morris Lecar neurons resonated at this frequency. In addition, the rate of inositol trisphosphate production in the astrocyte has been shown to affect signal detection at different noise intensities and coupling strengths. Finally, simulations performed by including the internal noise of the astrocytes originating from calcium ion channels in the model have shown how the signal transmission changes depending on the calcium channel number, noise intensity, and coupling strength. In this context, the presented study also contains important information in terms of examining stochastic astrocyte dynamics that affect the weak signal detection performance of different types of astrocyte-dressed Morris Lecar neurons.

Detecting the subthreshold signal in a neural network via statistical complexity measure

This paper proposes an information theory approach for detecting the subthreshold signal in a small-world network composed of Fitz Hugh-Nagumo (FHN) neurons. Statistical complexity measure (SCM) and normalized Shannon-entropy (NSE) have been defined based on the specific and nonconsecutive firing time intervals series, and employed to quantify the stochastic multiresonance (SMR) phenomena in this small-world neural network. The results show that there are several maxima of SCM and several minima of NSE at various optimal noise levels, which is regarded as the signature of the occurrence of SMR. This also implies that the subthreshold signal can be accurately detected across multiple levels of noise. More intriguingly, we have discovered that the degree of this neural network can induce the generation of multiple resonance-like behaviors. In addition, our findings demonstrate that the SCM outperforms the traditional signal-to-noise ratio (SNR) in accurately identifying firing dynamical regularity under certain noisy conditions. Furthermore, the SCM can capture subtle resonance-like behavior induced by the degree, which the SNR is unable to detect. Thus, the SCM could have potential power on detecting weak signals in neural networks.

Is phase locking crucial to improve hearing thresholds in tinnitus patients?

Temporal processing of auditory data plays a crucial role in our proposed model of tinnitus development through stochastic resonance (SR). The model assumes a physiological mechanism optimizing auditory information transmission (as quantified by autocorrelation [AC] analysis) into the brain by adding the optimal amount of neuronal noise to otherwise subthreshold signals. We hypothesize that this takes place at the second synapse of the auditory pathway in the dorsal cochlear nucleus (DCN). We propose that after hearing loss, this neuronal noise is increased in the affected frequency band to improve hearing thresholds at the cost of upward propagation of this added noise, which finally may be perceived as tinnitus. We already showed the improvement of hearing thresholds in a large population of patients. Until now, we did not investigate the differences in hearing thresholds based on the biological constraints of early auditory temporal processing (phase locking) that is only possible up to frequencies of 5kHz. In this report, we grouped our patient database (N= 47,986) according to tinnitus pitch (TP) of below (TP<5kHz ) or above (TP>5kHz ) the 5kHz limit or having no tinnitus (NT) and compared their mean audiograms. We found that TP<5kHz patients showed significantly better hearing thresholds than all other patient groups independent of age. No improvement was seen for TP>5kHz patients who even showed worse thresholds than NT patients for high frequencies. These results are further evidence for our SR model of tinnitus development and the existence of AC analysis at the level of the DCN.

Comparative Study of the Myocardium of Patients from Four COVID-19 Waves.

Few studies have compared COVID-19 patients from different waves. This study aims to conduct a clinical and morphological analysis of patients who died from COVID-19 during four waves. The study involved 276 patients who died from COVID-19 during four waves, including 77 patients in the first wave, 119 patients in the second wave, and 78 patients in the third wave. We performed a histological examination of myocardium samples from autopsies and additionally analyzed the samples by PCR. We conducted immunohistochemistry of the myocardium for 21 samples using antibodies against CD3, CD45, CD8, CD68, CD34, Ang1, VWF, VEGF, HLA-DR, MHC1, C1q, enteroviral VP1, and SARS-CoV-2 spike protein. We also did immunofluorescent staining of three myocardial specimens using VP1/SARS-CoV-2 antibody cocktails. Further, we ran RT-ddPCR analysis for 14 RNA samples extracted from paraffin-embedded myocardium. Electron microscopic studies of the myocardium were also performed for two samples from the fourth wave. Among the 276 cases, active myocarditis was diagnosed in 5% (15/276). Of these cases, 86% of samples expressed VP1, and individual cells contained SARS-CoV-2 spike protein in 22%. Immunofluorescence confirmed the co-localization of VP1 and SARS-CoV-2 spike proteins. ddPCR did not confidently detect SARS-CoV-2 RNA in the myocardium in any myocarditis cases. However, the myocardium sample from wave IV detected a sub-threshold signal of SARS-CoV-2 by qPCR, but myocarditis in this patient was not confirmed. Electron microscopy showed several single particles similar to SARS-CoV-2 virions on the surface of the endothelium of myocardial vessels. A comparison of the cardiovascular complication incidence between three waves revealed that the incidence of hemorrhage (48 vs. 24 vs. 17%), myocardial necrosis (18 vs. 11 vs. 4%), blood clots in the intramural arteries (12 vs. 7 vs. 0%), and myocarditis (19 vs. 1 vs. 6%) decreased over time, and CD8-T-killers appeared. Immunohistochemistry confirmed the presence of endotheliitis in all 21 studied cases. This study compared myocardial damage in patients who died during three COVID-19 waves and showed a decrease in the incidence of endotheliitis complications (thrombosis, hemorrhage, necrosis) and myocarditis over time. However, the connection between myocarditis and SARS-CoV-2 infection remains unproven.

Multiple Subthreshold GPCR Signals Combined by the G-Proteins Gαq and Gαs Activate the Caenorhabditis elegans Egg-Laying Muscles.

Individual neurons or muscle cells express many G-protein-coupled receptors (GPCRs) for neurotransmitters and neuropeptides, yet it remains unclear how cells integrate multiple GPCR signals that all must activate the same few G-proteins. We analyzed this issue in the Caenorhabditis elegans egg-laying system, where multiple GPCRs on muscle cells promote contraction and egg laying. We genetically manipulated individual GPCRs and G-proteins specifically in these muscle cells within intact animals and then measured egg laying and muscle calcium activity. Two serotonin GPCRs on the muscle cells, Gαq-coupled SER-1 and Gαs-coupled SER-7, together promote egg laying in response to serotonin. We found that signals produced by either SER-1/Gαq or SER-7/Gαs alone have little effect, but these two subthreshold signals combine to activate egg laying. We then transgenically expressed natural or designer GPCRs in the muscle cells and found that their subthreshold signals can also combine to induce muscle activity. However, artificially inducing strong signaling through just one of these GPCRs can be sufficient to induce egg laying. Knocking down Gαq and Gαs in the egg-laying muscle cells induced egg-laying defects that were stronger than those of a SER-1/SER-7 double knockout, indicating that additional endogenous GPCRs also activate the muscle cells. These results show that in the egg-laying muscles multiple GPCRs for serotonin and other signals each produce weak effects that individually do not result in strong behavioral outcomes. However, they combine to produce sufficient levels of Gαq and Gαs signaling to promote muscle activity and egg laying.SIGNIFICANCE STATEMENT How can neurons and other cells gather multiple independent pieces of information from the soup of chemical signals in their environment and compute an appropriate response? Most cells express >20 GPCRs that each receive one signal and transmit that information through three main types of G-proteins. We analyzed how this machinery generates responses by studying the egg-laying system of C. elegans, where serotonin and multiple other signals act through GPCRs on the egg-laying muscles to promote muscle activity and egg laying. We found that individual GPCRs within an intact animal each generate effects too weak to activate egg laying. However, combined signaling from multiple GPCR types reaches a threshold capable of activating the muscle cells.

The Gravitational Wave Universe Toolbox

Context. In the current multi-messenger astronomy era, it is important that information about joint gravitational wave (GW) and electromagnetic (EM) observations through short gamma-ray bursts (sGRBs) remains easily accessible to each member of the GW-EM community. The possibility for non-experts to execute quick computations of joint GW-sGRB detections should be facilitated. Aims. For this study, we constructed a model for sGRBs and added this to the framework of the previously built Gravitational Wave Universe Toolbox (GWToolbox or Toolbox). We provide expected joint GW-sGRB detection rates for different combinations of GW detectors and high-energy (HE) instruments. Methods. We employed and adapted a generic GRB model to create a computationally low-cost top-hat jet model suitable for the GWToolbox. With the Toolbox, we simulated a population of binary neutron stars (BNSs) observed by a user-specified GW detector such as LIGO, Virgo, the Einstein Telescope (ET), or the Cosmic Explorer (CE). Based on the characteristics of each binary, our model predicts the properties of a resulting sGRB, as well as its detectability for HE detectors such as Fermi/GBM, Swift/BAT, or GECAM. Results. We report predicted joint detection rates for combinations of GW detectors (LIGO and ET) with HE instruments (Fermi/GBM, Swift/BAT, and GECAM). Our findings stress the significance of the impact that ET will have on multi-messenger astronomy. While the LIGO sensitivity is currently the limiting factor regarding the number of joint detections, ET will observe BNSs at such a rate that the vast majority of detected sGRBs will have a GW counterpart observed by ET. These conclusions hold for CE as well. Additionally, since LIGO can only detect BNSs up to a redshift of ~0.1 where few sGRBs exist, a search for sub-threshold GW signals at higher redshifts using sGRB information from HE detectors has the potential to be very successful and significantly increase the number of joint detections. Equivalently, during the ET era, GW data can assist in finding sub-threshold sGRBs, potentially increasing, for example, the number of joint ET-Fermi/GBM observations by ~270%. Lastly, we find that our top-hat jet model underestimates the number of joint detections that include an off-axis sGRB. We corrected for this by introducing a second, wider and weaker jet component. We predict that the majority of joint detections during the LIGO/Virgo era will include an off-axis sGRB, making GRB170817A not as unlikely as one would think based on the simplest top-hat jet model. In the ET era, most joint detections will contain an on-axis sGRB.

Investigation of chaotic resonance in Type-I and Type-II Morris-Lecar neurons

The signal encoding capacity of the nervous system is significantly related to the spiking regime of neurons, and the firing behaviors of neurons are vibrant due to their complex structure. Chaotic fluctuations emerge at both micro and macro levels in the neuronal medium. It is thought that chaotic oscillations are the beneficial component, rather than disruptive, for cognitive functions of the nervous system. It is found that external chaotic activity at a suitable level enhances the weak signal encoding performance of neurons, particularly when the weak signal frequency is chosen close to that of the chaotic fluctuations-induced sub-threshold oscillations frequencies. This manipulation of the signal encoding performance of neurons by chaotic fluctuations is explained by the “chaotic resonance” phenomenon. In the current work, we systematically examine the impacts of the extrinsic chaotic stimulus from the Lorenz system on the subthreshold periodic signal encoding performance of Type-I and Type-II Morris-Lecar neurons. Our results show that weak signal encoding performance of both Type-I and Type-II Morris-Lecar neurons exhibits resonance behavior and its resonance mainly depends on external chaotic current intensity. Also, we reveal that suitable chaotic current level for the best encoding in Morris-Lecar neurons of the sub-threshold signal changes with signal frequency. In addition, we indicate that there is a frequency range in which Morris-Lecar neurons are sensitive to signals. We also demonstrate the existence of a chaotic current intensity range at these frequencies, enabling neurons to improve weak signal encoding performance. Finally, it is observed that the frequency ranges in which Type-I and Type-II neurons are sensitive to weak signals are quite different. In this regard, our research provides important insights into the possible roles of chaotic resonance in the weak signal encoding of different types of neurons.

Sinoatrial node cells and tissue operate via stochastic resonance.

Sinoatrial node (SAN), the primary heart pacemaker, is a highly heterogeneous, complex cellular network. Recent imaging data indicate that synchronized action potentials (APs) that exit the SAN emerge from heterogeneous signals, including subthreshold, irregular signals. We tested the hypothesis that SAN cells and tissue operate via stochastic resonance, i.e. biological noise within and among SAN cells plays an important role in generation of cardiac impulses. We performed patch-clamp experiments in current-clamp mode in isolated single rabbit SAN cells. When uniformly random current noise of a few pA amplitude with a zero average was applied via the patch pipette, cells that had been firing APs rarely and irregularly began firing more frequently and rhythmically. Further, similar noise application to non-firing cells (dormant cells or artificially hyperpolarized cells) initiated AP firing. These results were supported by simulations of Maltsev-Lakatta numerical model of SAN cells. Further, simulations using a numerical model of heterogeneous SAN cellular network, in which the rate of Ca pumping and ICaL conductance varied, revealed that random current noise applied within cells also improved the tissue function. In scenarios at the edge of criticality, in which the cell network could not operate (99.84% of non-firing cells), almost all cells (97.12%) fired APs when noise was applied to cells within the network. Although artificial noise was applied in our experiments and simulations, similar noise is likely to be generated under physiological conditions by stochastic openings of ion channels (RyRs and L-type Ca-channels) within cells and by subthreshold signals from neighboring cells in SAN tissue. Our results support the idea that the SAN operates via stochastic resonance, thus shifting the paradigm of SAN operation from dictatorship and democracy theories, to a complex neuronal-type modus operandi, i.e. the SAN operates as “the heart's brain”.

Enhancement of Vestibular Motion Discrimination by Small Stochastic Whole-body Perturbations in Young Healthy Humans

Noisy galvanic vestibular stimulation has been shown to improve vestibular perception in healthy subjects. Here, we sought to obtain similar results using more natural stimuli consisting of small-amplitude motion perturbations of the whole body. Thirty participants were asked to report the perceived direction of antero-posterior sinusoidal motion on a MOOG platform. We compared the baseline perceptual thresholds with those obtained by applying small, stochastic perturbations at different power levels along the antero-posterior axis, symmetrically distributed around a zero-mean. At the population level, we found that the thresholds for all but the highest level of noise were significantly lower than the baseline threshold. At the individual level, the threshold was lower with at least one noise level than the threshold without noise in 87% of participants. Thus, small, stochastic oscillations of the whole body can increase the probability of recognizing the direction of motion from low, normally subthreshold vestibular signals, possibly due to stochastic resonance mechanisms. We suggest that, just as the external noise of the present experiments, also the spontaneous random oscillations of the head and body associated with standing posture are beneficial by enhancing vestibular thresholds with a mechanism similar to stochastic resonance.

Resonance effects in neuronal-astrocyte model with ion channel blockage

Blockage of the specific ion channel is significant for neuronal dynamics. Astrocytes, the active partners of neurons, play the role of regulators of many neural activity processes. The effects of ion channel blockage and astrocyte responses to subthreshold periodic signals of different frequencies were investigated in a stochastic extended Hodgkin-Huxley model. We demonstrated that the neuronal response to weak signals is weakened by sodium channel blockage, whereas it is improved by modulating the degree of potassium channel blockage. There is a certain value of potassium channel blocking intensity, corresponding to the optimal response output, that is, the system has reached the resonance state. When taking the astrocyte into account, the system's response to weak stimuli can be enhanced a little. The more significant role of the astrocyte is that they have different responses to weak signals at different frequencies. The greater the neuron-astrocyte coupling strength factor, the better the response of the neuron to high-frequency weak signals. Therefore, we noted the phenomenon of “frequency selection” of the system's response to weak signals: at low frequencies, potassium channel blockage improves the response, whereas the astrocyte can modulate the neuronal response to external stimuli in the higher frequency range.

The paradigm shift: Heartbeat initiation without "the pacemaker cell".

The current dogma about the heartbeat origin is based on "the pacemaker cell," a specialized cell residing in the sinoatrial node (SAN) that exhibits spontaneous diastolic depolarization triggering rhythmic action potentials (APs). Recent high-resolution imaging, however, demonstrated that Ca signals and APs in the SAN are heterogeneous, with many cells generating APs of different rates and rhythms or even remaining non-firing (dormant cells), i.e., generating only subthreshold signals. Here we numerically tested a hypothesis that a community of dormant cells can generate normal automaticity, i.e., "the pacemaker cell" is not required to initiate rhythmic cardiac impulses. Our model includes 1) non-excitable cells generating oscillatory local Ca releases and 2) an excitable cell lacking automaticity. While each cell in isolation was not "the pacemaker cell", the cell system generated rhythmic APs: The subthreshold signals of non-excitable cells were transformed into respective membrane potential oscillations via electrogenic Na/Ca exchange and further transferred and integrated (computed) by the excitable cells to reach its AP threshold, generating rhythmic pacemaking. Cardiac impulse is an emergent property of the SAN cellular network and can be initiated by cells lacking intrinsic automaticity. Cell heterogeneity, weak coupling, subthreshold signals, and their summation are critical properties of the new pacemaker mechanism, i.e., cardiac pacemaker can operate via a signaling process basically similar to that of "temporal summation" happening in a neuron with input from multiple presynaptic cells. The new mechanism, however, does not refute the classical pacemaker cell-based mechanism: both mechanisms can co-exist and interact within SAN tissue.

Mitigating Threshold Effects in Human Control by Stochastic Resonance With Fractional Colored Noise

In industrial applications, mechanical and physiological thresholds may limit the capability of human manipulating machine via control devices, such as joysticks and steering wheels. These thresholds can result in loss of information in the control signals that are kept below the threshold of detection of the device or the human operator. One approach to mitigate these effects is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stochastic resonance</i> , i.e., by injecting additive noise into a signal to raise its energy content over the threshold of detection. Though this noise partially corrupts the signal, it can increase the detectability of the signal by the control device. This article provides, for the first time, research towards using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stochastic resonance</i> to improve human performance in control tasks. In particular, it shows that using adaptive colored noise can improve the detectability of the steering control signals recorded from human participants. The approach converts a signal processing task to an optimization problem, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">particle swarm optimization</i> is employed to obtain the optimal color (or spectral exponent) of the injected additive noise, generated through an intelligent technique with fractional order filters. The results have shown that the proposed method improves the detectability of subthreshold steering control signals. This method can be widely applicable to other industrial domains, such as energy harvesting and enhancing sensory perception.

Influence of temperature and noise on the propagation of subthreshold signal in feedforward neural network

Temperature is an important environmental factor that all creatures depend on. Suitable temperature is essential for maintaining the normal physiological functions of nervous system. Subthreshold signal is generated by weak activities in the nervous system that is difficult to be detected. Based on an improved Hodgkin-Huxley (HH) neuron model considering temperature and noise, the ten-layers pure excitatory feedforward neural network and the ten-layers excitatory-inhibitory (EI) neural network are constructed to study the propagation of subthreshold excitatory postsynaptic current signal. It's found that increasing temperature can restrain the signal propagation, and raise the noises intensity threshold where the failed signal propagation can transform into succeed signal propagation. Under the large noise, the signal propagation in network of different temperatures exhibits different anti-noise capabilities. Moreover, temperature and noise can modulate the spontaneous activity of the EI neural network to transform between the synchronous regular (SR) state and the asynchronous irregular (AI) state. The EI network's spontaneous activity will completely cover subthreshold signal, and block the signal propagation under large noise. The jumping phenomenon in the value of fidelity, which measures the quality of signal propagation, appears in both pure excitatory network and EI network. This paper provides potential value for understanding the regulation of both temperature and noise in information propagation in neural network.

Wireless Communication Between Paired Leadless Pacemakers for Dual-Chamber Synchrony.

Leadless pacemakers (LPs) can mitigate conventional pacemaker complications related to the transvenous leads and subcutaneous pocket surrounding the pulse generator. Although single-chamber leadless pacing has been established, multichamber pacing requires wireless bidirectional communication across multiple LPs to maintain synchrony. This preclinical study demonstrates the chronic performance of implant-to-implant (i2i) communication that achieves synchronous, dual-chamber pacing with 2 LPs. The i2i communication modality employs subthreshold electrical signals conducted between implanted LPs through the blood and myocardial tissue on a beat-by-beat basis. Right atrial and right ventricular LPs were implanted in 9 ovine subjects. The i2i transmission performance was evaluated 13 weeks after implant. Right atrial and right ventricular LPs were implanted successfully and without complication in 9 ovine subjects. A total of 8715±457 right atrial-to-right ventricular and right ventricular-to-right atrial transmissions were sent per hour, with a success rate of 99.2±0.9%. Of periods with i2i communication failure when DDD pacing was not possible, 97.3±1.8% were resolved within 6 s. For the first time, synchronized, dual-chamber pacing has been demonstrated in a chronic preclinical feasibility study by 2 leadless pacemakers using beat-to-beat, wireless communication, achieving a success rate of 99.2%.

Optimal signal amplification in globally coupled bistable oscillators: The role of weight between positive and negative couplings

Recently, it was revealed that uniformly positive and negative couplings perform complementary roles in the signal amplification of coupled bistable oscillators. However, the coupling weights between oscillators are usually nonuniform due to adaptivity in many nonlinear systems. For this reason, we introduce a weight ratio of positive and negative couplings and investigate how it affects the response of globally coupled bistable oscillators to a subthreshold signal. We show that the weight ratio can significantly optimize the system response to the subthreshold signal compared to the case of uniform couplings. Finally, we propose a reduced model and compare its analytical approximations with the numerical results.

Search for Gravitational Waves Associated with Gamma-Ray Bursts Detected by Fermi and Swift during the LIGO–Virgo Run O3b

We search for gravitational-wave signals associated with gamma-ray bursts (GRBs) detected by the Fermi and Swift satellites during the second half of the third observing run of Advanced LIGO and Advanced Virgo (2019 November 1 15:00 UTC–2020 March 27 17:00 UTC). We conduct two independent searches: a generic gravitational-wave transients search to analyze 86 GRBs and an analysis to target binary mergers with at least one neutron star as short GRB progenitors for 17 events. We find no significant evidence for gravitational-wave signals associated with any of these GRBs. A weighted binomial test of the combined results finds no evidence for subthreshold gravitational-wave signals associated with this GRB ensemble either. We use several source types and signal morphologies during the searches, resulting in lower bounds on the estimated distance to each GRB. Finally, we constrain the population of low-luminosity short GRBs using results from the first to the third observing runs of Advanced LIGO and Advanced Virgo. The resulting population is in accordance with the local binary neutron star merger rate.

Boundary-Preserved Deep Denoising of Stochastic Resonance Enhanced Multiphoton Images.

Objective: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress the perturbative noise in high-contrast images; however, for low photon budget multiphoton images, a high detector gain will not only boost the signals but also bring significant background noise. In such a stochastic resonance imaging regime, subthreshold signals may be detectable with the help of noise, meaning that a denoising filter capable of removing noise without sacrificing important cellular features, such as cell boundaries, is desirable. Method: We propose a convolutional neural network-based denoising autoencoder method — a fully convolutional deep denoising autoencoder (DDAE) — to improve the quality of three-photon fluorescence (3PF) and third-harmonic generation (THG) microscopy images. Results: The average of 200 acquired images of a given location served as the low-noise answer for the DDAE training. Compared with other conventional denoising methods, our DDAE model shows a better signal-to-noise ratio (28.86 and 21.66 for 3PF and THG, respectively), structural similarity (0.89 and 0.70 for 3PF and THG, respectively), and preservation of the nuclear or cellular boundaries (F1-score of 0.662 and 0.736 for 3PF and THG, respectively). It shows that DDAE is a better trade-off approach between structural similarity and preserving signal regions. Conclusions: The results of this study validate the effectiveness of the DDAE system in boundary-preserved image denoising. Clinical Impact: The proposed deep denoising system can enhance the quality of microscopic images and effectively support clinical evaluation and assessment.