Discrimination Of Signals Research Articles

Real-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devices

The real-time detection and analysis of seismic signals is crucial in geophysics research, especially when it comes to monitoring catastrophic events. We present an evolutionary deep learning method that yields a model named MCU-Quake. This model encodes the discrimination process as a single numerical value, offering interpretability with only 2693 parameters. Trained on raw seismic waveforms from Utah, USA, MCU-Quake demonstrates its generalization capability across a global natural earthquake dataset. Notably, the model effectively identifies typical explosions during the Russia-Ukraine war in Europe. The knowledge to discriminate between ambient noise, explosions and natural earthquakes can be represented by values of −5.01 (std: 1.14), 1.96 (std: 0.36), 1.01 (std: 0.49), respectively. The model can be deployed on Internet of Things (IoT) devices, including most microcontrollers, which are constrained by limited computational resources (kilo-bytes of memory) and energy consumption (micro-Watts). The results indicate the prospect of on-site missions of artificial intelligent sensors.

Upgrade of the CMS Electromagnetic Calorimeter for High-Luminosity LHC

The CMS Electromagnetic Calorimeter (ECAL) is a homogeneous PbWO4-based detector. The upcoming High-Luminosity upgrade of the CERN LHC will provide detectors with unprecedented instantaneous luminosity, entailing an increase of the average number of proton-proton collisions per bunch crossing up to a value of 200. To cope with such busy environment and with the trigger latency and rate, the ECAL readout and trigger electronics system will be replaced. The front-end electronics will deploy custom ASICs optimised for precise timing, improved signal discrimination, lossless data compression, and fast transmission. Off-detector FPGA processor boards will form trigger primitives and perform basic signal reconstruction. The upgraded system has been validated over several test beam campaigns. In terms of physics performance, it will match the outstanding energy resolution of the current ECAL detector, while significantly enhancing the time resolution of electrons and photons above 50 GeV.

Determination of size and particle number concentration of metallic nanoparticles using isotope dilution analysis combined with single particle ICP-MS to minimise matrix effects.

Single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) is a powerful tool for metallic nanoparticle (NP) characterisation in terms of concentration and, taking into account several assumptions, also size. However, this technique faces challenges, such as the intrinsic matrix effect, which significantly impact the results when analysing real complex samples. This issue is critical for the calculations of key SP-ICP-MS parameters ultimately altering the final outcomes. Novel analytical approaches with high metrological quality such as isotope dilution analysis (IDA) can overcome these limitations by improving signal discrimination in challenging SP-ICP-MS scenarios. This alternative has mainly been applied for NP size characterisation but remains underexplored in modern ICP-MS and SP set-ups. Thus, the implementation of a revised version of IDA-SP-ICP-MS, including recent advances in quadrupole ICP-MS and SP data processing, which enables reliable NP sizing and counting, would be of utmost interest. In this work, this combination using the species-unspecific IDA mode has been investigated as an alternative to tackle matrix effect caused by complex matrices with platinum NPs as a case study. The optimum ionic tracer concentration has been evaluated for different PtNP sizes, resulting in a range of 500 to 1000ng L-1 due to differences in the mean NP signal. A valuable in-house spreadsheet for the data treatment has also been developed. The successful applicability of the methodology for determining the size and particle number concentration of 30 and 50nm PtNPs has been demonstrated not only in environmental samples (synthetic and natural seawater), but also, for the first time, in biological matrices such as cell culture media and human urine.

Low noise InGaAs/InP single-photon detector with DC to 1 GHz tunable gate frequency

An InGaAs/InP single-photon detector (SPD) typically operates in gated mode, but the capacitive response of a single-photon avalanche diode introduces spike noise, obscuring the avalanche signal. Most avalanche signal discrimination schemes cannot completely eliminate spike noise, resulting in residual noise. Limited by residual noise, these schemes have large noise and a limited frequency tuning range. However, for applications like quantum key distribution and laser ranging, a low noise, frequency-tunable InGaAs/InP SPD is crucial for enhancing system performance. Here, we propose a (residual noise assisted) discrimination method that aligns the maximum amplitude of the avalanche signal with the peak of the residual noise. This method turns the residual noise from adversity into an advantage for signal discrimination. With this method, we achieve tunable gating frequency from DC to 1 GHz. Additionally, this method enables the discrimination of weak avalanche signals, allowing effective single-photon detection at low avalanche gain. Across the entire tuning range, at a 20% detection efficiency, the dark count rate is approximately 5.0×10−7 per gate, and the afterpulse probability is less than 1.0%, significantly lower than in previous experiments. The proposed SPD exhibits low noise and a wide tunable gating frequency range, providing a reliable foundation for various applications.

Bearing fault diagnosis based on sparsity structure pruning graph attention network

Abstract Graph neural networks have been widely used in the field of bearing fault diagnosis, which can deal with non-Euclidean space data and dig deep the relationship between signals. However, most graph neural networks do not distinguish the importance of nodes in information aggregation, and do not take edge noise and data redundancy into account when constructing the graph structure, which affects the diagnostic accuracy. To solve these problems, a fault diagnosis method of graph attention network based on sparsity structure pruning is proposed. Firstly, a sparsity coefficient is introduced to construct the graph structure, and pruning operations are carried out according to the coefficient and the weight of the edges to avoid invalid fusion of information. Then, a graph attention network model based on sparsity structure pruning is constructed, and features of different scales are aggregated into new node representations through multi-head attention mechanism. Finally, the fault diagnosis of bearing is carried out according to the extracted signal discrimination characteristics. To verify the effectiveness of the proposed method, experiments are performed on two different fault diagnosis datasets and compared with other graph neural network methods. The results show that the accuracy and stability of the proposed method are superior to other methods even under the condition of low signal to noise ratio (SNR).

Development of Functional Biointerface Using Mixed Zwitterionic Silatranes.

Strategies to design multifunctional interfaces for biosensors have been extensively investigated to acquire optimal sensitivity, specificity, and accuracy. However, heterogeneous ingredients in clinical samples inevitably generate background signals, exposing challenges in biosensor performance. Polymer coating has been recognized as a crucial method to functionalize biointerfaces by providing tailored properties that are essential for interacting with biological systems. Herein, we introduce for the first time two oligomeric silatranes, MPS-MPCn and MPS-PEGMACOOHm, which were copolymerized from mercaptopropylsilatrane (MPS) with either zwitterionic monomer 2-methacryloyloxyethyl phosphorylcholine (MPC) or carboxylated poly(ethylene glycol) methacrylate (PEGMACOOH) through thiol-ene polymerization. These oligomeric silatranes were prepared individually and in combinations in acidic and nonacid solvents for deposition on silicon wafers. Afterward, coating properties, including wettability, thickness, and elemental composition, were characterized by contact angle meter, ellipsometer, and X-ray photoelectron spectroscopy (XPS), respectively. Importantly, MPS-MPCn polymers were found to form thin films with high hydrophilicity and superior fouling repulsion to bacteria and protein, while mixed coating involving 70% MPS-PEGMACOOH2.5 and 30% MPS-MPC2.5 exhibited thinnest coating with best wettability among COOH-terminated coatings. Furthermore, the functional COOH group in the coated surfaces was exploited for postmodification with biological molecules via intermediated N-hydroxysuccinimide (NHS) ester group by amine coupling chemistry. Once again, the combination of 70% MPS-PEGMACOOH2.5 and 30% MPS-MPC2.5 provided an ultimate reduction in nonspecific adsorption (NSA) and established a finest signal discrimination through enzyme-linked immunosorbent assay. Consequently, these novel mixed oligomeric silatranes offer a promising approach for the construction of biosensor interfaces with dual functions in both nonspecific binding prevention and conjugation of biomolecules.

Artificial intelligence diagnosis and analysis in roto-dynamic machinery faults recognition using AI techniques

In the domain of industrial machinery, predictive maintenance plays a pivotal role in minimizing downtime and optimizing operations. This comprehensive research explores an innovative approach to predictive maintenance by harnessing the vibration sensor signals analysis. The primary goal is to autonomously predict the timing and root causes of faults in roto-dynamic machinery, revolutionizing maintenance practices. The methodology involves advanced artificial intelligence techniques, with a focus on deep learning, to analyse real-time vibration sensor data. Cutting-edge equipment with high-resolution data acquisition capabilities is employed, enhancing signal investigation and discrimination. Machine learning models like Convolutional Neural Networks (CNNs) [19] and Transfer Learning are integrated for accurate fault prediction. The research demonstrates the effectiveness of deep learning algorithms in predictive maintenance. These algorithms can autonomously identify fault timing and causes, reducing reliance on human expertise and enhancing accuracy. A practical case study involving a centrifugal pump within a Reverse Osmosis process validates the system’s capabilities[26]. The benefits of predictive maintenance using vibration sensor signals are substantial, including cost savings, reduced unplanned downtime, and improved operational safety. The study emphasizes the importance of proactive maintenance in optimizing industrial efficiency and ensuring the longevity of critical equipment. The device underwent rigorous testing at the Jubail pilot plant in WTIIRA premises, where it was installed in the pump of the nano unit for an extensive one-month trial period. During this testing phase, the device demonstrated its capabilities by promptly detecting an alarm signal related to the bearing of the pump. This early warning provided crucial insights into the machinery’s health and potential issues, identifying breakdowns and facilitating timely maintenance interventions. The successful trial at the Jubail pilot plant underscored the device’s practical applicability and its potential to revolutionize predictive maintenance practices in the industrial sector. This research showcases the transformative potential of artificial intelligence and deep learning in predictive maintenance. Proactive maintenance practices are highlighted as essential for operational efficiency and the preservation of vital industrial equipment.

Evaluation of new Li2CaSiO4:Eu/ scintillation plastic phoswich for combined alpha-beta and gamma-neutron detectors

A phoswich-type detector element based on a new light inorganic scintillation material, Li2CaSiO4:Eu2+, and a plastic scintillator, EJ-200, was evaluated for separation of signals of neutrons, β-, α-particles and γ-rays. It has been shown that the use of scintillators in a phoswich with a quite large difference in the scintillation kinetics, by two orders of magnitude, despite the effect of excitation of inorganic material luminescence by scintillation of plastic, provides reliable discrimination of signals from the layers of the phoswich detector. The developed approach to constructing a detecting element can find applications for discrimination of α- and β-particles, as well as neutrons and γ-rays in combined α/β and γ-neutron detectors for radiometry and dosimetry. The separation of signals and measurement of the response when recording interaction products in the reverse β-decay reaction are of particular interest.

Codoping Pr3+ and Er3+ into NaLaTi2O6 to realize dual-mode FIR temperature sensing properties

In this report, a Pr3+/Er3+ doped NaLaTi2O6 phosphor was prepared as self-reference optical thermometer via a typical solid-state sintering method. The phase component, crystal structure and luminescence properties were elaborated in detail. A broad IVCT band along with several narrow 4f–4f excitation bands were readily found when monitored at 608 nm in Pr3+ singly doped NaLaTi2O6 material. In addition, the material showed typical 4f–4f transitions with two dominant bands around 495 nm and 609 nm originating from 3P0 → 3H4 and 1D2 → 3H4, respectively, upon ICVT or Pr3+ unique 4f–4f excitation. In Pr3+, Er3+ co-doped samples, the up-conversion (UC) emission bands around 522 nm, 548 nm and 661 nm, originating from characteristic transitions 2H11/2 → 4I15/2, 4S3/2 → 4I15/2 and 4F9/2 → 4I15/2 of Er3+ ion was found, respectively, upon 980 nm radiation. Besides, the four main bands around 495 nm, 522 nm, 543 nm, 609 nm assigned to Pr3+ 3P0 → 3H4, Er3+ 2H11/2 → 4I15/2, Er3+ 4S3/2 → 4I15/2, Pr3+ 1D2 → 3H4, respectively, can be observed upon 379 nm co-excitation. By monitoring thermal responses emission intensities of versatile transitions under UC and down-shift (DS) excitation modes, good temperature sensitivity and signal discriminability based on FIR technique have been achieved in phosphor NaLaTi2O6:Pr3+, Er3+. Additionally, the maximal absolute temperature sensitivity Sa and relative temperature sensitivity Sr reach 0.0831 K−1 at 294 K and 1.15 % K−1 at 294 K under 980 nm excitation mode, and 0.01742 K−1 at 453 K and 1.826 % K−1 at 294 K under 379 nm excitation mode, respectively, indicating the prepared material in this work can be considered as a latent candidate for optical thermometry. More inspired, this work sets up a new pathway of codoping Pr3+ and Er3+ into suitable matrices to devise excellent FIR optical thermometry.

Plasma‐Generated Luminescent Coatings: Innovations in Thermal Sensitivity and Corrosion Resistance

AbstractThe strategic design of traditional coating materials has long been pivotal in broadening their range of applications. In this work, europium‐doped TiO2 coatings are grown in situ on the surface of titanium substrate using plasma electrolytic oxidation technology. The core reaction took no more than five minutes. Incorporating europium into the coating preserved the inherent corrosion resistance of PEO coatings while imparting anticipated thermal‐sensitive luminescence capabilities. The intrinsic emission of TiO2 and the characteristic emission of Eu3+ (5D0 → 7F2) are employed as the self‐reference for the LIR thermometry. The absolute and relative temperature sensitivity of the coating reached 0.0087 K−1 and 0.739% K−1, respectively. Notably, the coating exhibited a signal discriminability of up to 5100 cm−1 and a temperature uncertainty of only 0.18 K, which is comparable to some TiO2: Eu nanoparticles. The ingenious fusion of corrosion resistance and thermal‐sensitive luminescence of the coating not only makes it a classic protective structure but also facilitates its applicability to diverse scenarios, including optical thermometry in extreme environments.

19F NMR-Based Chiral Analysis of Organoboron Compounds via Chiral Recognition of Fluorine-Labeled Boronates with Cobalt Complexes.

This study aims to develop a method for the chiral analysis of organoboron compounds using nuclear magnetic resonance (NMR) spectroscopy. It addresses the longstanding challenge associated with these chiral organoboron compounds, which often require derivatization and pretreatment prior to chromatographic analysis. Our method utilizes tridentate ligands to facilitate effective ligand exchange and incorporates fluorine labels, allowing for the precise discrimination of 19F NMR signals. This is achieved in conjunction with a chiral cationic cobalt complex, serving as the chiral solvating agent. This approach provides reliable and rapid determination of enantiomeric excess in a wide range of organoboron compounds, featuring various functional groups, and establishes a universal tool for assessing the optical purity of these substances.

Discrimination of ULF signals from an underground seismogenic current

A numerical model has been elaborated to calculate ULF electromagnetic fields in the ground-atmosphere–ionosphere system created by an underground horizontal current source of a finite length. The modeling has enabled us to examine in detail characteristic features of ULF response to an underground large-scale emitter that may be used for a search of electromagnetic earthquake precursors. The most promising features that might discriminate signals from an underground source and from a magnetosphere-ionosphere source are (a) the apparent impedance of the electromagnetic field derived from simultaneous observations of horizontal magnetic and electric fields, and (b) the ratio of vertical and horizontal magnetic component amplitudes. Besides that, the amplitude-phase gradients of signals from an underground source differ significantly from those of the magnetospheric source. For the same magnitude of horizontal magnetic disturbance on surface, an underground current source produces in a borehole a much larger vertical electric field Ez than a magnetospheric source does. At the same time, some properties, such as the ratio between the vertical and horizontal electric components, are shown to be ineffective. However, all these differences with ionosphere-magnetosphere source reveal themselves only in a vicinity of lithospheric source (< 30 km for depth 20 km).Graphical

Integrating DIA Single-Cell Proteomics Data with the DiagnoMass Proteomic Hub for Biological Insights.

Single-cell proteomics has emerged as a powerful technology for unraveling the complexities of cellular heterogeneity, enabling insights into individual cell functions and pathologies. One of the primary challenges in single-cell proteomics is data generation, where low mass spectral signals often preclude the triggering of MS2 events. This challenge is addressed by Data Independent Acquisition (DIA), a data acquisition strategy that does not depend on peptide ion isotopic signatures to generate an MS2 event. In this study, we present data generated from the integration of DIA single-cell proteomics with a version of the DiagnoMass Proteomic Hub that was adapted to handle DIA data. DiagnoMass employs a hierarchical clustering methodology that enables the identification of tandem mass spectral clusters that are discriminative of biological conditions, thereby reducing the reliance on search engine biases for identifications. Nevertheless, a search engine (in this work, DIA-NN) can be integrated with DiagnoMass for spectral annotation. We used single-cell proteomic data from iPSC-derived neuroprogenitor cell cultures as a test study of this integrated approach. We were able to differentiate between control and Rett Syndrome patient cells to discern the proteomic variances potentially contributing to the disease's pathology. Our research confirms that the DiagnoMass-DIA synergy significantly enhances the identification of discriminative proteomic signatures, highlighting critical biological variations such as the presence of unique spectra that could be related to Rett Syndrome pathology.

A distributed PD detection method for high voltage cables based on high precision clock synchronization

Partial discharge (PD) is a crucial cause of high voltage (HV) cable failures. Detecting and locating PD efficiently and effectively is paramount for HV cable maintenance. However, the attenuation of the PD signal after long-distance propagation makes it challenging to identify. This paper presents a distributed PD detection method that significantly reduces the propagation distance of PD signals before the sensor captures them. The technique uses the time-of-arrival to locate PD, whose localization accuracy is directly linked to the clock synchronization (CS) accuracy. CS between different sensors is achieved through three times interactions of the CS signal. The linear frequency modulation (LFM) signal is selected as the CS signal, which can generate a significant gain after pulse compression. It ensures that the CS signal can be effectively recognized after the attenuation of long-distance propagation. The paper also introduces a signal discrimination method based on the short-time Fourier transform that eliminates the influence of the LFM signal on PD identification. The cableless CS method makes it easier to set up before the onsite test. A prototype was implemented and tested. The CS accuracy was measured within 4 ns in the laboratory. An onsite test was carried out to evaluate the performance of the distributed PD detection system. A defect was found at a joint location, which was also confirmed by an additional ultrasonic PD test.

Study on the impact of aging on the brain activity patterns in auditory speech comprehension dual-route regions

Objective: To elucidate the patterns of neural activity alterations associated with auditory speech comprehension across the lifespan and the impact of varying listening environments on these dynamics. Methods: Functional near-infrared spectroscopy (fNIRS) was employed to measure the concentration of oxygenated hemoglobin in the brains of 93 adults aged from 20 to 70 with normal hearing. These participants were recruited from Beijing Tongren Hospital, affiliated with Capital Medical University, between March 2021 and February 2023. Brain activity was recorded as subjects passively listened to sentences in both silent and noise conditions with varying signal-to-noise ratios (SNR). The alterations in brain activity were analyzed to delineate the age-related trends under different auditory conditions. Statistical analysis was performed using SPSS 22.0 software. Results: The bilateral primary auditory cortex, superior temporal gyrus, and Wernicke's area, critical for sound signal discrimination and perception, exhibited enhanced activity post-stimulus presentation. Broca's area, pivotal for speech production, demonstrated an initial decrease in activity followed by an increment after stimulus onset. The ventral middle temporal gyrus and dorsal postcentral gyrus showed augmented activity in later time windows. Furthermore, it was observed that in quiet conditions and at low noise levels (SNR=10 dB), auditory cortical activity diminished with age. With increasing noise levels (SNR=5 dB), compensatory brain regions (right ventral middle temporal gyrus and dorsal postcentral gyrus) showed enhanced activity with advancing age. As noise intensity further escalated (SNR=0, SNR=-5 dB), not only did auditory cortical activity decline, but also the activity in regions associated with semantic processing and motor functions reduced with age. Conclusion: During auditory speech comprehension, dual-pathway brain regions exhibit distinct activity patterns. With heightened noise exposure, an increasing number of brain regions are influenced by aging, manifesting as a general decline in activity in most dual-pathway regions, alongside a selective augmentation in some compensatory regions on the right hemisphere.

Self-Supervised Marine Noise Learning with Sparse Autoencoder Network for Generative Target Magnetic Anomaly Detection

As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. Recently, learning-based detectors have been widely studied for the discrimination of magnetic anomaly signal and achieve superior performance than traditional rule-based detectors. Nevertheless, learning-based detectors require abundant data for model parameter training, which are difficult to access in practical marine applications. In practice, target magnetic anomaly data are usually expensive to acquire, while rich marine magnetic noise data are readily available. Thus, there is an urgent need to develop effective models to learn discriminative features from the abundant marine magnetic noise data for newly appearing target anomaly detection. Motivated by this, in this paper we formulate MAD as a single-edge detection problem and develop a self-supervised marine noise learning approach for target anomaly classification. Specifically, a sparse autoencoder network is designed to model the marine noise and restore basis geomagnetic field from the collected noisy magnetic data. Subsequently, reconstruction error of the network is used as a statistical decision criterion to discriminate target magnetic anomaly from cluttered noise. Finally, we verify the effectiveness of the proposed approach on real sea trial data and compare it with seven state-of-the-art MAD methods on four numerical indexes. Experimental results indicate that it achieves a detection accuracy of 93.61% and has a running time of 21.06 s on the test dataset, showing superior MAD performance over its counterparts.

Numerical investigation of non-uniform arrays of directive antennas for radiation pattern analysis using spatial modulation schemes

This research presents a novel Non-Uniform Array (NUA) design for Spatial Modulation (SM) systems, aiming to improve Bit Error Rate (BER) performance. The NUA utilizes directional antennas with low-similarity radiation patterns, enabling the receiver to effectively distinguish between signals transmitted from different antenna elements. This enhanced signal discrimination capability directly contributes to improved system capacity and potential spectral efficiency gains for massive MIMO systems employing SM techniques. The proposed NUA was optimized for operation at 5.2 GHz, achieving a high degree of pattern distinguishability. Measured results confirm the effectiveness of the design, demonstrating good agreement with simulations. Moreover, an analysis based on the similarity of the radiation pattern was conducted

Разработка и реализация классификатора пост-образов на основе статистического метода различения сигналов

Разработка и реализация классификатора пост-образов на основе статистического метода различения сигналов

Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences

Understanding individuals’ distinct movement patterns is crucial for health, rehabilitation, and sports. Recently, we developed a machine learning-based framework to show that “gait signatures” describing the neuromechanical dynamics governing able-bodied and post-stroke gait kinematics remain individual-specific across speeds. However, we only evaluated gait signatures within a limited speed range and number of participants, using only sagittal plane (i.e., 2D) joint angles. Here we characterized changes in gait signatures across a wide range of speeds, from very slow (0.3 m/s) to exceptionally fast (above the walk-to-run transition speed) in 17 able-bodied young adults. We further assessed whether 3D kinematic and/or kinetic (ground reaction forces, joint moments, and powers) data would improve the discrimination of gait signatures. Our study showed that gait signatures remained individual-specific across walking speeds: Notably, 3D kinematic signatures achieved exceptional accuracy (99.8%, confidence interval (CI) 99.1–100%) in classifying individuals, surpassing both 2D kinematics and 3D kinetics. Moreover, participants exhibited consistent, predictable linear changes in their gait signatures across the entire speed range. These changes were associated with participants’ preferred walking speeds, balance ability, cadence, and step length. These findings support gait signatures as a tool to characterize individual differences in gait and predict speed-induced changes in gait dynamics.

Medial prefrontal cortex connectivity with the nucleus accumbens is related to HIV serostatus, perceptions of psychological stress, and monocyte expression of TNF-a

Post-menopausal persons living with HIV (PWH) report elevated levels of psychological stress and monocyte activation compared to persons living without HIV (PWOH). Resting state functional connectivity (rsFC) of mesolimbic brain regions underpinning stress and emotion regulation are susceptible to inflammatory insult. Although psychological stress is elevated, rsFC reduced, and CD16+ monocytes overexpressed in the brains of PWH, it is unclear whether the relationships amongst these variables differ compared to PWOH.An ethnically diverse sample of postmenopausal women, 24 PWH and 30 PWOH provided self-report mood surveys and provided peripheral blood specimens to quantify LPS-stimulated CD16+/− expression of TNF-α via flow cytometric analysis. An anatomical and resting state functional MRI scan were used to derive time-series metrics of connectivity between the medial prefrontal cortex (mPFC) and the nucleus accumbens (NAcc) as well as the amygdala.A positive association was observed between levels of perceived stress and CD16+/− TNF-α in both LPS-stimulated and unstimulated cells. PLWH showed lower connectivity between mPFC and NAcc. In turn, lower rsFC between these regions predicted greater psychological stress and proportion of CD16−, but not CD16+, cells expression of TNF-α.Neuroimmune effects of monocyte inflammation on the functional connectivity of mesolimbic regions critical for discrimination of uncertainty-safety and reward signals were observed in an ethnically diverse sample of postmenopausal women living with and without HIV. PWH showed lower mPFC-NAcc functional connectivity, which in turn was associated with greater perceived stress.