Slow Signal Research Articles

Fast signaling by glucocorticoids shapes neural representations of behaviors

Glucocorticoids are stress hormones that play central roles in the immediate and slower adaptive responses of the brain and body to new behavioral experience. The exact mechanisms by which the rapid and slow processes underlying glucocorticoid mnemonic effects unfold are under intensive scrutiny. It is possible that glucocorticoids rapidly modify memory representations in the brain by interfering with synaptic functions between inhibitory and excitatory neurons in a timing and context dependent manner. In particular, activity-dependent trans-synaptic messengers appear to have all the necessary attributes to engage in the rapid signaling by glucocorticoids and regulate the brain and behaviors. Novel frameworks for the treatment of stress-related disorders could emerge from a better characterization of the dynamic interplay between the rapid and slow signaling components by glucocorticoids on large-scale brain networks. Here I present some of the exact factors that could help reach this objective.

Tunneling induced swapping of orbital angular momentum in a quantum dot molecule

In this paper, we have examined the effectiveness exchange of optical vorticity via three-wave mixing (TWM) technique in a four-level quantum dot (QD) molecule by means of the electron tunneling effect. Our analytical analysis demonstrates that the TWM procedure can result in the production of a new weak signal beam that may be absorbed or amplified within the QD molecule. We have taken into account the electron tunneling as well as the relative phase of the applied lights to assess the absorption and dispersion characteristics of the newly generated light. We have discovered that the slow light propagation and signal amplification can be achieved. Our results show that the exchange of the orbital angular momentum of light can transfer from coupling optical vortex light to the new generated light in high efficiency.

Resisting Action

Initially I stood at a threshold peering into a place of momentous ending. I shook my head. Gundungurra country needs to burn, I imagine its people say across time, just not like this. There is a requirement for care-full in/action when living with and on a post-fire terrain while still in trauma. It is a difficult balancing act for humans who like to fix things. Beyond the domestic clean-up of burnt buildings and infrastructure, my teachers await my attention. They are not of the human kind, rather they are the critters that perch in branches or skitter under rock. They are the soil movers, the crevice crouchers and the mark makers. They are the stirring plants and the underground tendrils of fungal hyphae. They send slow signals and resist tidy aesthetics. They challenge the perception of “dead” and question short-term human economies of usefulness. Ultimately, they remind me that home is made up of many intersecting homes weaving, twisting and turning in a constant process of becoming.

Earthquake Control: An Emerging Application for Robust Control. Theory and Experimental Tests

This article addresses the possibility of using robust control theory for preventing earthquakes through fluid injections in the Earth’s crust. The designed robust controllers drive aseismically a fault system to a new equilibrium point of lower energy by tracking a slow reference signal. The control design is based on a reduced-order nonlinear model able to reproduce earthquake-like instabilities. Uncertainties related to the frictional and mechanical properties of the underlying physical process and external perturbations are considered. Two types of controllers are derived. The first one is based on the sliding-mode theory and leads to local finite-time convergence of the tracking error and rejection of Lipschitz w.r.t. time perturbations. The second controller is based on linear quadratic regulator (LQR) control and presents global exponential stability of the tracking error and rejection of Lipschitz w.r.t. state perturbations. Both the controllers generate a continuous control signal, attenuating the chattering effect in the case of the sliding-mode algorithms. The developed controllers are tested extensively and compared on the basis of numerical simulations and experiments in the laboratory. The present work opens new perspectives for the application of the robust nonlinear control theory to complex geosystems, earthquakes, and sustainable energy production.

Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering.

Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.

Clinical Application of a New Ultrasound Technique: Microvascular Imaging of the Liver

Microvascular imaging is a new ultrasound technique aiming to image and quantify the microvasculature, comprising arterioles, venules, and maybe even capillaries. The technique uses an advanced clutter filter to eliminate artifacts caused by breathing and movement and retains the low-speed blood signals in microvessels. The great advantage of microvascular imaging in ultrasonography is that it allows a physician intuitively to detect very slow blood signals in microvessels, providing additional information about flow distribution in the target area without the use of contrast media. This technical advance in ultrasonography will improve diagnosis in various clinical situations. In this review, we focus on microvascular imaging in the liver.

Β-GPA administration activates slow oxidative muscle signaling pathways and protects soleus muscle against the increased fatigue under 7-days of rat hindlimb suspension

Unloading of slow-twitch muscles results in increased muscle fatigue and the mechanisms of this effect are poorly studied. We aimed to analyze the role of high-energy phosphates accumulation during the first week of rat hindlimb suspension plays in a fiber-type phenotype shift towards fast-type fatigable muscle fibers. Male Wistar rats were divided into 3 groups (n = 8): C - vivarium control; 7HS - 7-day hindlimb suspension; 7HB - 7-day hindlimb suspension with intraperitoneal injection of beta-guanidine propionic acid (β-GPA, 400 mg/kg b w). β‐GPA is a competitive inhibitor of creatine kinase and it reduces concentrations of ATP and phosphocreatine. In the 7HB group, β-GPA treatment protected a slow-type signaling network in an unloaded soleus muscle, including MOTS-C, AMPK, PGC1 α and micro-RNA-499. These signaling effects resulted in a preserved soleus muscle fatigue resistance, slow-type muscle fibers percentage and mitochondrial DNA copy number under muscle unloading.

Development of neutron detection systems at the NDPS of RAON

The Nuclear Data Production System (NDPS) at RAON has been built to produce nuclear data generated by the reactions induced by neutrons of tens of MeV. For the neutron Time-Of-Flight (TOF) measurement, neutron monitoring detectors based on a gas-filled Parallel Plate Avalanche Counter (PPAC) and a MICRO-MEsh-GASeous (MICROMEGAS) detector have been developed by the Rare Isotope Science Project (RISP) and Sungkyunkwan University (SKKU). These detectors have a neutron converter with a thin 232Th layer, which produces fission products due to fast neutrons. The PPAC achieved a 1 ns FWHM time resolution in a test with an 241Am α source and also showed good performance when tested with fast neutrons generated by a 45 MeV proton beam through the 9Be(p, n)9B reaction. Additionally, EJ-301, liquid scintillation detectors are assembled for the measurement of neutron flux with pulse shape discrimination capability. Slow charge signals as well as fast timing signals from the detectors will be processed for particle identification by a data acquisition (DAQ) system, located at a separate control room through 30 m long cables. Development of the detection system and the test results will be reported together with the on-site assembly status.

Wireless Human Motion Detection with a Highly Sensitive Wearable Pressure Sensing Technology

AbstractWireless flexible sensing devices using inductive‐capacitive (LC) resonator‐enabled transmission technologies are attractive in the areas of human motion detection, health inspection, and implantable medical devices. However, challenges remain in LC‐based wireless sensing devices, including low device sensitivity/resolution and slow signal readout speed based on sweeping excitation frequencies. Herein, an LC resonator‐based wireless pressure sensing technology (LC‐WPS) is proposed for flexible contact pressure measurements in a real‐time manner. It includes a fully flexible passive LC pressure sensor and a signal‐reading circuit for wireless communication with the sensor and real‐time data processing. The passive sensor detects external pressure by the changes in resonant frequency. Importantly, by optimizing the design of the LC resonator in the sensor, the LC‐WPS converts the pressure‐dependent resonant frequency into circuit output voltage to a great extent, achieving a high sensitivity (1.23 × 10−2 kPa−1) and resolution (54 Pa) within a pressure range of 0–15 kPa. In addition, the LC‐WPS utilizes a single operating frequency (i.e., 13.56 MHz) instead of sweeping the operating frequencies to identify the sensor resonant frequency, featuring fast and real‐time data analysis and processing. Real‐time monitoring of human motions, such as laryngeal movement, neck bending and wrist rotation, is demonstrated, verifying the applicability of the LC‐WPS in the fields of wearable medical care and sports biomechanics.

Abstract 1148: 1XX mutations slow CAR T cell signaling and increase in vivo persistence

Abstract Purpose: Develop CAR T cells that have increased persistence in vivo and kill low antigen expressing solid tumor cancer cells. Experimental: Three CARs were generated that were all targeted to the tumor with the same antibody fragment, huMNC2, that recognizes a cryptic epitope on the MUC1 cleavage product called MUC1* (muk 1 star). One construct comprises a 41BB co-stimulatory domain, while the second construct has a CD28 co-stimulatory domain. The first 2 CARs have a wild-type CD3z, whereas the 3rd bears Michel Sadelain’s “1XX” mutations in CD3z. These are 2 Tyrosine to Phenylalanine mutations in ITAM 2 and ITAM 3. The 1XX mutations were designed to slow signaling to eliminate the problem of CAR T cell exhaustion. Human T cells were separately transduced with the 3 CARs and tested in NSG mice bearing human breast tumors. The tumors were heterogeneous tumors comprising parent cells that fluoresce red and express medium to low amounts of the antigen, MUC1*, and cells that fluoresce green wherein the parent cells have been transduced to express more MUC1*. The percent of the tumor that expressed high levels of MUC1* was varied from 7.5% to 15% to 30%. 150 female NSG mice were implanted with 500,000 tumor cells. The animals received a single CAR T cell injection of one of the three CAR T cells at an Effector to Target ratio of 10:1, 5:1, or 1:1. The experiment was allowed to proceed for 96 days, with periodic IVIS measurements to track tumor growth with a focus on tumor recurrence. After sacrifice, residual tumors were excised, dissociated and analyzed to determine if tumor recurrence was being driven by the high antigen cells or the low antigen cells. CAR T cells were recovered from the blood and the spleen. They were enumerated and analyzed by flow cytometry for the presence of markers of exhaustion. Unpublished Results: Tumor recurrence occurred in mice treated with CAR T cells bearing wild-type CD3z, but essentially did not recur in the mice treated with CAR T cells bearing the 1XX mutations. Four hundredfold more CAR T cells were recovered from the mice treated with the CAR-1XX cells compared to CARs with wild-type CD3z, and the CAR-1XX cells appeared not to be exhausted even after 96 days. CAR-1XX T cells administered at a 1:1 E:T ratio, with only 7.5% of the tumor comprised of high antigen expressing cells, were effectively killed with no significant tumor recurrence. In contrast, the CARs with wild-type CD3z had no effect on tumor growth in this group. Surprisingly, analysis of excised tumors showed that tumor recurrence in mice treated with CAR wild-type CD3z T cells was driven by the low antigen density cells, whereas the CAR-1XX T cells killed high antigen and low antigen density cells at the same rate. Conclusions: 1XX mutations in CD3z greatly increase CAR T persistence in vivo and unexpectedly enable the killing of low antigen tumor cells. Citation Format: Cynthia C. Bamdad, Benoit J. Smagghe, Mark G. Carter, Danica M. Walkley, Jac-Leen S. Nash, Kevin R. Yi, Trevor J. Grant, Laura M. Reale, Michael J. Nash, Gregory L. Riley, Andrew K. Stewart. 1XX mutations slow CAR T cell signaling and increase in vivo persistence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1148.

The cholinergic basal forebrain provides a parallel channel for state-dependent sensory signaling to auditory cortex.

Cholinergic basal forebrain (CBF) signaling exhibits multiple timescales of activity with classic slow signals related to brain and behavioral states and fast, phasic signals reflecting behavioral events, including movement, reinforcement and sensory-evoked responses. However, it remains unknown whether sensory cholinergic signals target the sensory cortex and how they relate to local functional topography. Here we used simultaneous two-channel, two-photon imaging of CBF axons and auditory cortical neurons to reveal that CBF axons send a robust, nonhabituating and stimulus-specific sensory signal to the auditory cortex. Individual axon segments exhibited heterogeneous but stable tuning to auditory stimuli allowing stimulus identity to be decoded from population activity. However, CBF axons displayed no tonotopy and their frequency tuning was uncoupled from that of nearby cortical neurons. Chemogenetic suppression revealed the auditory thalamus as a major source of auditory information to the CBF. Finally, slow fluctuations in cholinergic activity modulated the fast, sensory-evoked signals in the same axons, suggesting that a multiplexed combination of fast and slow signals is projected from the CBF to the auditory cortex. Taken together, our work demonstrates a noncanonical function of the CBF as a parallel channel for state-dependent sensory signaling to the sensory cortex that provides repeated representations of a broad range of sound stimuli at all points on the tonotopic map.

An active dose-measuring device for X-rays generated by ultra-short, ultra-intense laser pulses

Ultra-short, ultra-intense laser pulses can create extreme physical conditions for a wide range of applications in atomic and molecular physics, materials chemistry, and inertial-confinement fusion. However, laser-matter interactions can be accompanied by significant X-ray emission that introduces radiation risks to the nearby environment and personnel. It is usually to monitor the radiation dose during in high-intensity laser-target interactions with optically stimulated luminescence and thermo-luminescence devices. However, these passive methods cannot measure the radiation dose in real time, while most active dosimeters cannot accurately measure pulsed radiation doses. Here, transient pulse X-ray radiation doses are converted by CdWO4 crystals into slow signals. Because the crystals have a 14-μs luminescence decay time, they can absorb sub-nanosecond X-ray pulses and release the energy at a 100-μs rate, thus reducing the linear-response pressure of subsequent devices. A pulse detector based on a CdWO4 crystal, a phototube, and a custom signal-processing circuit was developed. Experiments were performed at the 45-TW femtosecond laser facility of the Laser Fusion Research Center. The detector deviation was less than ±20% relative to that of an ionization-chamber detector. This initially verified its feasibility for real-time pulsed X-ray radiation detection.

Fast MRI reconstruction using StrainNet with dual-domain loss on spatial and frequency spaces

One of the main challenges to obtain a high throughput in the MRI process is a slow signal acquisition. This process could be improved using a parallel imaging technique, where fewer raw data with multiple radio frequency (RF) coils are acquired simultaneously to reconstruct a final MR image. Nowadays, all multi-coil MRI machines have a parallel imaging technique for the image reconstruction. However, the parallel imaging still cannot accelerate sufficiently to reduce the overall acquisition time. In another way, this paper proposes a solution relying on a deep convolution neural network (CNN) to generate high-quality reconstruction MR images with higher acceleration factors. The proposed method, called StrainNet, performs the reconstructions by encoding the under-sampled data (i.e., for the speeding up process) into high-level features. Then the important part of the network, called Strainer, is applied to discard irrelevance information, and decodes remaining features back to reconstruct MR images. The proposed network could be trained end-to-end with a newly presented loss function, Dual-Domain Loss (DDL), combining both spatial and frequency losses. The experimental results are based on the fastMRI dataset and show that StrainNet outperforms the competing methods for both 4- and 8-fold accelerations.

Behavioral encoding across timescales by region-specific dopamine dynamics

The dorsal (DS) and ventral striatum (VS) receive dopaminergic projections that control motor functions and reward-related behavior. It remains poorly understood how dopamine release dynamics across different temporal scales in these regions are coupled to behavioral outcomes. Here, we employ the dopamine sensor dLight1.3b together with multiregion fiber photometry and machine learning-based analysis to decode dopamine dynamics across the striatum during self-paced exploratory behavior in mice. Our data show a striking coordination of rapidly fluctuating signal in the DS, carrying information across dopamine levels, with a slower signal in the VS, consisting mainly of slow-paced transients. Importantly, these release dynamics correlated with discrete behavioral motifs, such as turns, running, and grooming on a subsecond-to-minute time scale. Disruption of dopamine dynamics with cocaine caused randomization of action selection sequencing and disturbance of DS-VS coordination. The data suggest that distinct dopamine dynamics of DS and VS jointly encode behavioral sequences during unconstrained activity with DS modulating the stringing together of actions and VS the signal to initiate and sustain the selected action.

Parallel processing in data analysis of the JUNO experiment

The JUNO experiment is being built mainly to determine the neutrino mass hierarchy by detecting neutrinos generated in the Yangjiang and Taishan nuclear plants in southern China. The detector will record 5.6 TB raw data every day for offline analysis, but each day it can only collect about 60 neutrino events scattered among huge background events. Selection of extremely sparse neutrino events brings a big challenge to offline data analysis. A typical neutrino physics event normally spans across a number of consecutive readout events, flagged by a fast positron signal followed by a slow neutron signal within a varying-size time window. To facilitate this analysis, a two-step data processing scheme has been proposed. In the first step (called data preparation), the event index data is produced and skimmed, which only contains information of minimum physics quantities of events as well as their addresses in the original reconstructed data file. In the second step (called time correlation analysis), event index data is further selected with stricter criteria. And then, for each selected event, the time correlation analysis is performed by reading all associated events within a pre-defined time window from the original data file according to the selected event’s address and timestamp.This contribution will start to introduce the design of the above data processing scheme and then focus on the multi-threaded implementation of time correlation analysis based on the Intel Threading Building Block (TBB) in the SNiPER framework. Afterwards, this contribution will describe the implementation of distributed analysis using MPI in which the time correlation analysis task is divided into sub-tasks running on multiple computing nodes. At last, this contribution will present the detailed performance measurements made on a multiple-node test bed. By using both skimming and indexing techniques, the total amount of data finally used for neutrino signal time correlation analysis is significantly reduced, and the processing time could be reduced by two orders of magnitude.

Decentralized Dynamic Event-Triggered Adaptive Fuzzy Control for Switched Nonlinear Systems With Unstable Inverse Dynamics

In this article, the problem of decentralized dynamic event-triggered (ET) adaptive fuzzy control for a class of strong interconnected switched uncertain nonlinear systems with unstable inverse dynamics and unknown control gains is studied. By utilizing the adaptive backstepping technique and new fuzzy logic systems, decentralized ET adaptive fuzzy controllers for subsystems and a new type of switching decentralized dynamic event-triggering mechanisms are constructed. Then, the asynchronous switching problem between candidate controllers and subsystems is addressed, and the upper bound of asynchronous time is not directly restricted. Meanwhile, by ensuring that the interexecution times are lower bounded by a positive constant, Zeno behavior is avoided. Furthermore, it is proved by the multiple Lyapunov functions method that all signals in the switched closed-loop system are bounded under a new class of slow switching signals. The validity of the derived result is illustrated using two examples which include a two inverted pendulum system.

Modulating the structural topologies from star-shape to cross-shape for Co–Dy heterometallic complexes with slow magnetic relaxation behavior

A star-like CoIII2DyIII2 complex (1) and a cross-shaped CoII/III–Dy complex (2) have been obtained, in which 1 exhibits double magnetic relaxation behavior, while 2 only shows slow magnetic relaxation signals.

Breaking with a dogma: persisting diffusion restrictions (pDWI) in follow-up after endovascular treatment for stroke

BackgroundPost-stroke diffusion weighted imaging (DWI) signal transformation of the infarct core, which results in high apparent diffusion coefficient (ADC) values and variable DWI signal intensity, is completed no later than...

Motion-robust, blood-suppressed, reduced-distortion diffusion MRI of the liver.

To evaluate feasibility and reproducibility of liver diffusion-weighted (DW) MRI using cardiac-motion-robust, blood-suppressed, reduced-distortion techniques. DW-MRI data were acquired at 3T in an anatomically accurate liver phantom including controlled pulsatile motion, in eight healthy volunteers and four patients with known or suspected liver metastases. Standard monopolar and motion-robust (M1-nulled, and M1-optimized) DW gradient waveforms were each acquired with single-shot echo-planar imaging (ssEPI) and multishot EPI (msEPI). In the motion phantom, apparent diffusion coefficient (ADC) was measured in the motion-affected volume. In healthy volunteers, ADC was measured in the left and right liver lobes separately to evaluate ADC reproducibility between the two lobes. Image distortions were quantified using the normalized cross-correlation coefficient, with an undistorted T2-weighted reference. In the motion phantom, ADC mean and SD in motion-affected volumes substantially increased with increasing motion for monopolar waveforms. ADC remained stable in the presence of increasing motion when using motion-robust waveforms. M1-optimized waveforms suppressed slow flow signal present with M1-nulled waveforms. In healthy volunteers, monopolar waveforms generated significantly different ADC measurements between left and right liver lobes ( , reproducibility coefficients (RPC)= mm /s for monopolar-msEPI), while M1-optimized waveforms showed more reproducible ADC values ( , mm /s for M1-optimized-msEPI). In phantom and healthy volunteer studies, motion-robust acquisitions with msEPI showed significantly reduced image distortion ( ) compared to ssEPI. Patient scans showed reduction of wormhole artifacts when combining M1-optimized waveforms with msEPI. Synergistic effects of combined M1-optimized diffusion waveforms and msEPI acquisitions enable reproducible liver DWI with motion robustness, blood signal suppression, and reduced distortion.

Nucleus accumbens dopamine tracks aversive stimulus duration and prediction but not value or prediction error.

There is active debate on the role of dopamine in processing aversive stimuli, where inferred roles range from no involvement at all, to signaling an aversive prediction error (APE). Here, we systematically investigate dopamine release in the nucleus accumbens core (NAC), which is closely linked to reward prediction errors, in rats exposed to white noise (WN, a versatile, underutilized, aversive stimulus) and its predictive cues. Both induced a negative dopamine ramp, followed by slow signal recovery upon stimulus cessation. In contrast to reward conditioning, this dopamine signal was unaffected by WN value, context valence, or probabilistic contingencies, and the WN dopamine response shifted only partially toward its predictive cue. However, unpredicted WN provoked slower post-stimulus signal recovery than predicted WN. Despite differing signal qualities, dopamine responses to simultaneous presentation of rewarding and aversive stimuli were additive. Together, our findings demonstrate that instead of an APE, NAC dopamine primarily tracks prediction and duration of aversive events.