Channel Signal Research Articles

Same actions, different effects: The conditionality of monetary policy instruments

This paper explores the signaling effect of central bank announcements clarifying the reaction function of policy interventions. We exploit the unique setting provided by ECB asset purchase programs. We find that the same action – purchases of identical assets – undertaken under different titles generates different responses. PSPP affects inflation swaps whereas PEPP impacts sovereign spreads, so that only the variables associated with the communicated rationale of each program react. We highlight the importance of clarifying the conditional path of policy instruments for the transmission of monetary policy. We also provide evidence of this signaling channel from other ECB and BoE announcements.

Tactile texture recognition of multi-modal bionic finger based on multi-modal CBAM-CNN interpretable method

To solve the problem of lacking surface information while using a single-modal sensor to scan fine textures, a multi-modal bionic finger tactile sensor is used to perceive the information contained in textures, and a multi-modal tactile texture recognition method is proposed to address the issue of poor interpretability for tactile texture recognition models. First, the experimental platform of multi-modal bionic finger tactile texture information acquisition system is built, which can simultaneously collect 14 channels of tactile electrode signals, one channel of pressure signals and one channel of temperature signals. Second, the multi-modal tactile signals can be fused at the data level after signal processing, and then the wavelet transform method is carried out on the basis of data fusion. Third, a multi-modal CBAM-CNN network (Convolutional Block Attention Module, Convolutional Neural Network) is established to extract the multi-modal tactile features after the wavelet transform and the multi-modal tactile features are fused at feature level. To verify the performance of multi-modal CBAM-CNN model, 27(3*9) kinds of millimeter-level fine textures of 3 datasets are tested and the recognition accuracy of each dataset is 98.47%, 97.63%, 99.16%. And it can also be seen that the 14-channel modal data contributes the most to the performance of multi-modal CBAM-CNN model for various textures. The results of ablation experiments prove that the identification accuracy of the fused multi-modal CBAM-CNN is higher than the single-modal CBAM-CNN no matter in time domain or time–frequency domain. Moreover, the accuracy of the weighted fusion method based on correlation function is higher than horizontal fusion method in 14 channels of tactile electrode signals modal. Finally, to interpret the classification decision of the multi-modal CBAM-CNN network in each modal intuitively, the improved multi-modal CAM (Class Activation Map) algorithm is adopted to visualize the interest regions of the samples in detail, and the activated areas in the map make the identification result of CBAM-CNN in each modal more transparent and interpretable.

A review on electroencephalogram based channel selection

The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.

Testosterone Enhances KV Currents and Airway Smooth Muscle Relaxation Induced by ATP and UTP through P2Y4 Receptors and Adenylyl Cyclase Pathway.

Numerous studies suggest the involvement of adenosine-5'-triphosphate (ATP) and similar nucleotides in the pathophysiology of asthma. Androgens, such as testosterone (TES), are proposed to alleviate asthma symptoms in young men. ATP and uridine-5'-triphosphate (UTP) relax the airway smooth muscle (ASM) via purinergic P2Y2 and P2Y4 receptors and K+ channel opening. We previously demonstrated that TES increased the expression of voltage-dependent K+ (KV) channels in ASM. This study investigates how TES may potentiate ASM relaxation induced by ATP and UTP. Tracheal tissues treated with or without TES (control group) from young male guinea pigs were used. In organ baths, tracheas exposed to TES (40 nM for 48 h) showed enhanced ATP- and UTP-evoked relaxation. Tetraethylammonium, a K+ channel blocker, annulled this effect. Patch-clamp experiments in tracheal myocytes showed that TES also increased ATP- and UTP-induced K+ currents, and this effect was abolished with flutamide (an androgen receptor antagonist). KV channels were involved in this phenomenon, which was demonstrated by inhibition with 4-aminopyridine. RB2 (an antagonist of almost all P2Y receptors except for P2Y2), as well as N-ethylmaleimide and SQ 22,536 (inhibitors of G proteins and adenylyl cyclase, respectively), attenuated the enhancement of the K+ currents induced by TES. Immunofluorescence and immunohistochemistry studies revealed that TES did not modify the expression of P2Y4 receptors or COX-1 and COX-2, while we have demonstrated that this androgen augmented the expression of KV1.2 and KV1.5 channels in ASM. Thus, TES leads to the upregulation of P2Y4 signaling and KV channels in guinea pig ASM, enhancing ATP and UTP relaxation responses, which likely limits the severity of bronchospasm in young males.

Encoding independent wavefronts in a single metasurface for high-order optical vortex recognition

The orbital angular momentum (OAM) of vortex beams has great potential in optical communications due to its communication confidentiality and low crosstalk. It is necessary to design a plausible OAM pattern recognition mechanism. Abandoning AI models that require large datasets, a single passive all-dielectric metasurface consisting of TiO2 nanopillars on a SiO2 substrate is used to recognize high-order optical vortexes. In this configuration, the proposed device is capable of simultaneously encoding the wavefront and the transmission paths in different incident OAM beams. Due to the presence of spin angular momentum (SAM), the vortex beam to be identified is spatially separated after passing through the metasurface. As a proof of concept, 14 signal channels are considered in the constructed metasurface, 12 of them can be encoded at will for the detection of any vortex beam with a predefined topological charge. These results make use of metasurfaces to enable OAM pattern recognition in an effective way, which may open avenues for the ultimate miniaturization of optical vortex communication and advanced OAM detection technologies.

The asymmetric effects of quantitative tightening and easing on financial markets

We study the asymmetric impact of US quantitative tightening (QT) and easing (QE) on financial markets using high-frequency large-scale asset purchase surprises around FOMC announcements. We document that QT surprises since 2017 had larger and more persistent effects on US Treasury yields than QE surprises. Using numerous empirical decompositions of bond yields, we show that this asymmetry arises from the differential effect of QT vs. QE surprises on expectations of future short-term rates (linked to the so-called signalling channel) at shorter maturities.

Leukocyte Ig-like receptor B4 (Lilrb4a) alleviates cardiac dysfunction and isoproterenol-induced arrhythmogenic remodeling associated with cardiac fibrosis and inflammation

BackgroundHeart failure is usually accompanied by activation of the sympathetic nerve, and excessive activation of the sympathetic nerve promotes cardiac remodeling and cardiac dysfunction. In the isoproterenol (ISO)–induced animal model, it is often accompanied by myocardial hypertrophy, fibrosis, and inflammation. Leukocyte immunoglobulin-like receptor B4a (Lilrb4a), an immunosuppressive regulatory receptor, plays a vital role in cardiovascular disease. However, the effect of Lilrb4a on ventricular arrhythmia in an ISO-induced mouse model remains unclear. ObjectiveThe purpose of this study was to explore the role and molecular mechanism of Lilrb4a in ISO-induced arrhythmogenic remodeling. MethodsLilrb4a knockout mice and Lilrb4a overexpression mice were infused with ISO (15 mg/kg per 24 hours, 4 weeks). Echocardiography and histology evaluations of myocardial hypertrophy and cardiac structural remodeling were conducted. Surface electrocardiography and electrophysiologic examination were used to evaluate cardiac electrical remodeling and susceptibility to ventricular arrhythmias. Quantitative reverse transcriptase–polymerase chain reaction analysis and Western blotting were used to detect the expression levels of ion channel proteins and signal pathway proteins. ResultsThe results discovered that ISO induced cardiac hypertrophy, fibrosis, and inflammation and led to electrical remodeling and the occurrence of ventricular arrhythmias. Lilrb4a alleviated cardiac structural and electrical remodeling and protected against the occurrence of ventricular arrhythmias in ISO-induced mice by gain-of-function or loss-of-function approaches. The mechanism is that Lilrb4a inhibited NF-κB signaling and MAPK signaling activation mediated by transforming growth factor kinase 1. ConclusionLilrb4a alleviates cardiac dysfunction and ISO-induced arrhythmogenic remodeling associated with cardiac fibrosis and inflammation through the regulation of NF-κB signaling and MAPK signaling activation.

Signalling with Private Monitoring

Abstract We study dynamic signalling when the sender does not see the signals that her actions generate. The sender then uses her past play to forecast what a receiver believes, in turn forcing the receiver to forecast the previous forecast, and so forth. We identify a class of linear-quadratic-Gaussian games where this endogenous higher-order uncertainty can be handled. The sender’s second-order belief is key: it is a private state that she controls, and it creates a new channel for information transmission. We examine the role of higher-order uncertainty and this new signalling channel in applications to macroeconomics, reputation, and trading: inflationary biases under discretion can be larger; career-concerned agents may benefit from not knowing their reputations; and informed trades can carry more price impact. We also introduce an existence method for boundary value problems that can be used in other dynamic games.

Machine Learning Algorithms for Performance Enhancement in DCO-OFDM-Based LiFi Systems

This study describes how machine learning methods can be used to enhance the performance of LiFi (light fidelity) systems that are based on DCO-OFDM (direct current bias optical orthogonal frequency division multiplexing). LiFi, a rapidly developing technology that uses light for wireless communication, has to contend with issues like channel impairments and signal interference. This work investigates several machine learning strategies to address these problems and improve the DCO-OFDM LiFi systems' overall performance. It is determined how well various machine learning techniques perform in terms of improving system parameters, decreasing latency, and raising throughput by analyzing real-world data and simulation studies. Various sorts of symmetrical recurrence division multiplexing (OFDM, for example, DC one-sided optical OFDM (DCO-OFDM), are utilized in light devotion (LiFi). A critical DC predisposition in DCO-OFDM brings about optical power failure, while a minor inclination increment cutting commotion. In this manner, it's essential to decide the appropriate DC predisposition level for DCO-OFDM. This examination finds the ideal DC-predisposition an incentive for DCO-OFDM based LiFi frameworks utilizing machine learning (ML) algorithms. To do this, a MATLAB device is utilized to create a dataset for DCO-OFDM. Thusly, Python writing computer programs is utilized to apply machine learning methods. ML is used to recognize the basic DCO-OFDM qualities that influence the ideal DC predisposition. Here, it is exhibited that the base, greatest, and standard deviation of the bipolar OFDM signal, as well as the size of the heavenly body, decide the ideal DC inclination.

A Novel DC Electroosmotic Micromixer Based on Helical Vortices

This work introduces a novel direct current electroosmosis (DCEO) micromixer designed for rapid and efficient fluid mixing. This micromixer demonstrates excellent capability, achieving approximately 98.5% mixing efficiency within a one-second timespan and 99.8% efficiency within two seconds, all within a simple channel of only 1000 µm in length. A distinctive feature of this micromixer is its ability to generate robust and stable helical vortices by applying a controlled DC electric field. Unlike complex, intricate microfluidic designs, this work proposes a simple yet effective approach to fluid mixing, making it a versatile tool suitable for various applications. In addition, through simple modifications to the driving signal configuration and channel geometry, the mixing efficiency can be further enhanced to 99.3% in one second.

Low mutual-coupling MIMO antenna using dielectric resonators

This paper presents a design for a low-coupling multiple-input multiple-output (MIMO) antenna with two antenna elements. There is a dielectric resonator centrally and four inverted-F slots around on each element. This dielectric resonator consists of a dielectric block with an I-shaped metal pattern and is encircled by a metal ring with air holes. The use of inverted-F slots and the dielectric resonator effectively reduces the mutual coupling between the two antenna elements. Simulation and measurement results indicate that the designed MIMO antenna achieves a high level of isolation, maintains a low envelope correlation coefficient, and possesses a relatively high overall active reflection coefficient efficiency within the bandwidth of 6.41 GHz–6.62 GHz. These characteristics make the MIMO antenna perform exceptionally well in enhancing communication system performance, particularly in terms of signal quality, channel capacity, and system efficiency.

Signal Processing and Channel Modelling for 5G Millimeter-Wave Communication Environment

Compared to frequency bands below 6 GHz, 5G millimeter waves offer several advantages, including a large bandwidth, minimal null delay, and flexible null port configuration. To comprehend the channel characteristics of 5G millimeter-wave technology, conducting channel measurements on it is essential. Hence, to ensure precise 5G millimeter-wave channel measurements and facilitate channel modelling, this study recommends utilizing a phased array antenna-based method for the channel measurement. The experimental outcomes demonstrated that the shadow fading term of the actual measurement data follows a normal distribution in both line-of-sight and non-line-of-sight scenarios. Additionally, the K-S test confirms that all Boolean variables are equal to 1 and all numerical variables are greater than α. The line-of-sight scenario produces a logarithmic mean delay extension of –7.4 and a standard deviation of 0.12, based on actual measured data. Meanwhile, the 3GPP shows a logarithmic mean of –7.4 and a standard deviation of 0.15. In the non-line-of-sight scenario, the logarithmic mean delay extension is –7.3 with a standard deviation of 0.17, while the 3GPP produces a logarithmic mean of –7.4 and a standard deviation of 0.19. The data presented closely adheres to the 3GPP model. It is evident that the channel measurement method, proposed within the study, effectively measures the parameters within the delay domain. Concerning the prolonged pitch angle and azimuth angle ranges, they measure 14°–31° and 14°–29° in the line-of-sight situation, and 21°–33° and 19°–37° in the corresponding non-line-of-sight situation. Additionally, the logarithmic mean and standard deviation for both the pitch angle and azimuth angle in the line-of-sight scenario are 1.32 and 0.09, respectively. The logarithmic mean and standard deviation of the azimuth angle of arrival are 1.35 and 0.08, respectively. The above results show that the method proposed in the study enables the measurement of 5G millimeter-wave channels and is important for millimeter-wave channel modelling.

Model-Assisted Muti-source Fusion Hypergraph Convolutional Neural Networks for intelligent few-shot fault diagnosis to Electro-Hydrostatic Actuator

Electro-Hydrostatic Actuator (EHA) is a critical electro-hydraulic actuator system widely used in aerospace equipment. To ensure its normal operation, the intelligent fault diagnosis of the EHA system has gained increasing attention. However, the EHA exhibits strong nonlinearity, high structural complexity, and difficulty obtaining fault samples. A Model-Assisted Multi-source Fusion Hypergraph Convolutional Neural Network (MAMF-HGCN) is proposed to address the few-shot intelligent fault diagnosis of EHA. Specifically, the fault data obtained from the hydraulic simulation model is used to establish the relationship among each channel signal. This assists in constructing a hypergraph structure for actual multi-source fault data under limited samples. Each node in the hypergraph employs message transmission to fuse signals from different channels. Subsequently, the hypergraph data are input into the constructed hypergraph convolutional neural network to perform fault classification. Finally, validating the EHA hydraulic system test rig from Nanjing University of Science and Technology illustrates the method’s effectiveness in diagnosing hydraulic system problems under limited fault samples.

A Weighted Adaptive Range-Averaging Technique to Improve the Precision Consistency of Polarimetric Variable Fields

Abstract The two main metrics for the performance evaluation of radar-variable estimators are the bias and standard deviation (SD) of estimates. Depending on the estimator properties, the bias may increase as the signal-to-noise ratio (SNR) decreases. The standard deviation, however, always rises as the SNR becomes smaller. For instance, if estimates are computed from 16 samples (typically used for WSR-88D surveillance scans) using a rectangular data window and the maximum unambiguous velocity is ∼9 m s−1, the standard deviation of reflectivity estimates increases 1.6 times as the SNR drops from 20 to 2 dB. But for estimates of differential reflectivity, differential phase, and copolar correlation coefficient, SDs increase ∼6.7, ∼6, and ∼54 times, respectively. Hence, this effect impacts the polarimetric variables substantially more than the spectral moments. Additionally, the polarimetric variable SD is also sensitive to the correlation between signals in horizontal and vertical channels leading to reduced data quality in the regions where the correlation coefficient is low. Such increases in the variability of estimates are observable in the fields of dual polarization variables as an increased spatial inhomogeneity (or noisiness) in the areas where radar echoes exhibit low-to-moderate SNRs and/or decreased correlation coefficient. These effects can obscure the visual identification of weather features as well as adversely impact algorithms. Herein, a novel method that applies variable smoothing in the range where the smoothing intensity depends on the SDs of estimates is presented. It applies little or no range averaging in the regions where data SDs are deemed adequate while using more aggressive smoothing in areas where data appear noisy. Significance Statement The noisiness in the fields of polarimetric variables is an issue that has plagued dual-polarization weather radars since their inception. This is because standard deviations of polarimetric variable estimates increase significantly more with the reduction in SNR than those of spectral moment estimates. A typical mitigation approach that indiscriminately averages a fixed number of estimates in the range may lead to unnecessary loss of range resolution in regions where data appearance is satisfactory. Further, such an approach can be inadequate in regions with a high variability of estimates leading to insufficient enhancement of weather feature visibility. In this study, a method that mitigates these issues is proposed.

Research progress in the regulation of autophagy and mitochondrial homeostasis by AMPK signaling channels

AMP-activated protein kinase (AMPK) is a widely distributed and evolutionarily conserved serine/threonine protein kinase present in eukaryotic cells. In regulating cellular energy metabolism, AMPK plays an extremely important role as an energy metabolic kinase. When the body is in a low energy state, AMPK is activated in response to changes in intracellular adenine nucleotide levels and is bound to adenosine monophosphate (AMP) or adenosine diphosphate (ADP). Activated AMPK regulates various metabolic processes, including lipid and glucose metabolism and cellular autophagy. AMPK directly promotes autophagy by phosphorylating autophagy-related proteins in the mammalian target of rapamycin complex 1 (mTORC1), serine/threonine protein kinase-dysregulated 51-like kinase 1 (ULK1) and type III phosphatidylinositol 3-kinase-vacuolar protein-sorting 34 (PIK3C3-VPS34) complexes. AMPK also indirectly promotes autophagy by regulating the expression of downstream autophagy-related genes of transcription factors such as forkhead box O3 (FOXO3), lysosomal function transcription factor EB (TFEB) and bromodomain protein 4 (BRD4). AMPK also regulates mitochondrial autophagy, induces the division of damaged mitochondria and promotes the transfer of the autophagic response to damaged mitochondria. Another function of AMPK is to regulate mitochondrial health by stimulating mitochondrial biogenesis and participating in various aspects of mitochondrial homeostasis regulation. This review discusses the specific regulation of mitochondrial biology and internal environmental homeostasis by AMPK signaling channels as central to the cellular response to energy stress and regulation of mitochondria, highlighting the key role of AMPK in regulating cellular autophagy and mitochondrial autophagy, as well as advances in research on the regulation of mitochondrial homeostasis.

Bridging the Mind and Gut: Uncovering the Intricacies of Neurotransmitters, Neuropeptides, and their Influence on Neuropsychiatric Disorders.

The gut-brain axis (GBA) is a bidirectional signaling channel that facilitates communication between the gastrointestinal tract and the brain. Recent research on the gut-brain axis demonstrates that this connection enables the brain to influence gut function, which in turn influences the brain and its cognitive functioning. It is well established that malfunctioning of this axis adversely affects both systems' ability to operate effectively. Dysfunctions in the GBA have been associated with disorders of gut motility and permeability, intestinal inflammation, indigestion, constipation, diarrhea, IBS, and IBD, as well as neuropsychiatric and neurodegenerative disorders like depression, anxiety, schizophrenia, autism, Alzheimer's, and Parkinson's disease. Multiple research initiatives have shown that the gut microbiota, in particular, plays a crucial role in the GBA by participating in the regulation of a number of key neurochemicals that are known to have significant effects on the mental and physical well-being of an individual. Several studies have investigated the relationship between neuropsychiatric disorders and imbalances or disturbances in the metabolism of neurochemicals, often leading to concomitant gastrointestinal issues and modifications in gut flora composition. The interaction between neurological diseases and gut microbiota has been a focal point within this research. The novel therapeutic interventions in neuropsychiatric conditions involving interventions such as probiotics, prebiotics, and dietary modifications are outlined in this review. The findings of multiple studies carried out on mice show that modulating and monitoring gut microbiota can help treat symptoms of such diseases, which raises the possibility of the use of probiotics, prebiotics, and even dietary changes as part of a new treatment strategy for neuropsychiatric disorders and their symptoms. The bidirectional communication between the gut and the brain through the gut-brain axis has revealed profound implications for both gastrointestinal and neurological health. Malfunctions in this axis have been connected to a range of disorders affecting gut function as well as cognitive and neuropsychiatric well-being. The emerging understanding of the role of gut microbiota in regulating key neurochemicals opens up possibilities for novel treatment approaches for conditions like depression, anxiety, and neurodegenerative diseases.

Development of GNSS measurement models with the use of base stations and radio modules

Global navigation satellite systems (GNSS) historically been known as one of the newest technologies since the 1970s. GNSS originally developed for military purposes in the USA (GPS – Global Position System). There are several satellite systems in the world. Satelites, International Research Base Stations, regional/national and local base stations form a permanent geodetic frame. Research on the size and shape of the Earth-planet, climate, sea, urban planning. In geodesy, a network of global positioning base stations makes it possible to asses the movements of continents, land plates at international level. GNSS is an important technology in navigation, logistics, economics, land surveying and other “geo” sectors. GNSS equipment/receivers and their manufacturers are applying new designs and electronics. Initially GNSS instruments used with single frequency signal reception, later expanding the number of GNSS signal channels to two frequencies. Such technological improvements nowadays improve the certainty, reliability and accuracy – the overall quality – of GNSS measurements. The GNSS base station enables the surveyors, other user’s GNSS receiver to determine coordinates with an accuracy of two centimeters in real time (RTK) and with an accuracy of five millimetres using the accumulated post-processing data. Various types of factors hamper GNSS measurements. The GNSS signal (radio wave) travels in airspace, in urban environments and is a physical parameter. Any obstacle – tree, building walls, and atmospheric effect – makes GNSS measurements less accurate. The GNSS signal must be strong and free from attenuation and suppression effects. This study develops GNSS models that show the comparison, certainty and reliability of GNSS measurements using different types of GNSS techniques. Evaluation of Latvian Global Positioning Reference Station Network – LatPos system measurements against a corresponding RTK solution method using Latvian Global Positioning Network geodetic point (G2 class).

The value of environmental information disclosure: evidence from China’s carbon-reduction policy

ABSTRACT The impact of environmental information disclosure on firm value is unclear in the literature. Using China’s announcement of its carbon policy as an exogenous shock, this study examines the impact of environmental information disclosure. We find that stock prices increased faster for firms disclosing carbon information through the signalling channel. This study also lends support for disclosing carbon information.

Exploration of Four-Channel Coherent Optical Chaotic Secure Communication with the Rate of 400 Gb/s Using Photonic Reservoir Computing Based on Quantum Dot Spin-VCSELs

In this work, we present a novel four-channel coherent optical chaotic secure communication (COCSC) system, incorporating four simultaneous photonic reservoir computers in tandem with four coherent demodulation units. We employ a quartet of photonic reservoirs that capture the chaotic dynamics of four polarization components (PCs) emitted by a driving QD spin-VCSEL. These reservoirs are realized utilizing four PCs of a corresponding reservoir QD spin-VCSEL. Through these four concurrent photonic reservoir structures, we facilitate high-quality wideband-chaos synchronization across four pairs of PCs. Leveraging wideband chaos synchronization, our COCSC system boasts a substantial 4 × 100 GHz capacity. High-quality synchronization is pivotal for the precise demasking or decoding of four distinct signal types, QPSK, 4QAM, 8QAM and 16QAM, which are concealed within disparate chaotic PCs. After initial demodulation via correlation techniques and subsequent refinement through a variety of digital signal processing methods, we successfully reconstruct four unique baseband signals that conform to the QPSK, 4QAM, 8QAM and 16QAM specifications. Careful examination of the eye diagrams, bit error rates, and temporal trajectories of the coherently demodulated baseband signals indicates that each set of baseband signals is flawlessly retrieved. This is underscored by the pronounced eye openings in the eye diagrams and a negligible bit error rate for each channel of baseband signals. Our results suggest that delay-based optical reservoir computing employing a QD spin-VCSEL is a potent approach for achieving multi-channel coherent optical secure communication with optimal performance and enhanced security.

Satellite glial GPR37L1 and its ligand maresin 1 regulate potassium channel signaling and pain homeostasis.

G protein-coupled receptor 37-like 1 (GPR37L1) is an orphan GPCR with largely unknown functions. Here, we report that Gpr37l1/GRP37L1 ranks among the most highly expressed GPCR transcripts in mouse and human dorsal root ganglia (DRGs) and is selectively expressed in satellite glial cells (SGCs). Peripheral neuropathy induced by streptozotoxin (STZ) and paclitaxel (PTX) led to reduced GPR37L1 expression on the plasma membrane in mouse and human DRGs. Transgenic mice with Gpr37l1 deficiency exhibited impaired resolution of neuropathic pain symptoms following PTX- and STZ-induced pain, whereas overexpression of Gpr37l1 in mouse DRGs reversed pain. GPR37L1 is coexpressed with potassium channels, including KCNJ10 (Kir4.1) in mouse SGCs and both KCNJ3 (Kir3.1) and KCNJ10 in human SGCs. GPR37L1 regulates the surface expression and function of the potassium channels. Notably, the proresolving lipid mediator maresin 1 (MaR1) serves as a ligand of GPR37L1 and enhances KCNJ10- or KCNJ3-mediated potassium influx in SGCs through GPR37L1. Chemotherapy suppressed KCNJ10 expression and function in SGCs, which MaR1 rescued through GPR37L1. Finally, genetic analysis revealed that the GPR37L1-E296K variant increased chronic pain risk by destabilizing the protein and impairing the protein's function. Thus, GPR37L1 in SGCs offers a therapeutic target for the protection of neuropathy and chronic pain.