Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans.

Atherosclerosis (AS), a leading cause of cardio-cerebrovascular disease worldwide, is driven by the accumulation of lipid contents and chronic inflammation. Traditional strategies primarily focus on lipid reduction to control AS progression, leaving residual inflammatory risks for major adverse cardiovascular events (MACEs). While anti-inflammatory therapies targeting innate immunity have reduced MACEs, many patients continue to face significant risks. Another key component in AS progression is adaptive immunity, but its potential role in preventing AS remains unclear. To investigate this, we conducted a retrospective cohort study on tumor patients with AS plaques. We found that anti-programmed cell death protein 1 (PD-1) monoclonal antibody (mAb) significantly reduces AS plaque size. With multi-omics single-cell analyses, we comprehensively characterized AS plaque-specific PD-1+ T cells, which are activated and pro-inflammatory. We demonstrated that anti-PD-1 mAb, when captured by myeloid-expressed Fc gamma receptors (FcγRs), interacts with PD-1 expressed on T cells. This interaction turns the anti-PD-1 mAb into a substitute PD-1 ligand, suppressing T-cell functions in the PD-1 ligands-deficient context of AS plaques. Further, we conducted a prospective cohort study on tumor patients treated with anti-PD-1 mAb with or without Fc-binding capability. Our analysis shows that anti-PD-1 mAb with Fc-binding capability effectively reduces AS plaque size, while anti-PD-1 mAb without Fc-binding capability does not. Our work suggests that T cell-targeting immunotherapy can be an effective strategy to resolve AS in humans.

Resolution-increased fiber-optic strain sensor with a large dynamic range driven by white light.

The white light interferometer is advantageous for wavelength division multiplexing (WDM), but the excessive noise floor limits its application in practicality. In this Letter, we propose a fiber-optic sensor driven by a broadband light source, which uses a fiber-optic Fabry-Perot cavity and a reference interferometer to enhance strain resolution. In the experiment, the strain resolution of a 5.86 m resonant sensor is 18.5 fɛ/H z at 1.5 kHz, while the maximum detectable signal is over 230 rad at 1 kHz. With low cost, this method provides a new, to the best of our knowledge, solution for WDM sensing arrays with a large dynamic range.

Intensity Equalization of Bidirectional Fiber Laser Based on a Non-Reciprocal Optical Attenuator.

The application of a bidirectional laser requires the laser intensity in both directions to be balanced. However, the CW and CCW light intensities in current bidirectional erbium-doped fiber laser experiments differ due to the gain competition effect. There is no report on equalizing the intensity in the CW and CCW directions. This paper proposes a bidirectional non-reciprocal optical attenuator using the Faraday optical rotation effect. Continuous attenuation adjustment is realized by changing the angle between the polarizer's transmission axis and the linear polarized light. In this study, we analyzed the influence of different parameters on the device's performance, built a non-reciprocal attenuator, and tested the bidirectional attenuation curve, which was consistent with the simulation results. The device was integrated into a bidirectional fiber laser, and the light intensity in both directions was balanced through non-reciprocal adjustment. Combined with closed-loop control, the average intensity difference fluctuation between the two directions was controlled at 0.28% relative to the average power, realizing stable long-term bidirectional fiber laser intensity equalization.

Frequency-Modulated Signal Measurement Using Closed-Loop Methodology.

Frequency-modulated (FM) signals are widely used in sensing, measurement, and signal detection due to their strong anti-interference and easy transmission characteristics. Although the high-precision measurement methods for static signals are quite complete, the high-precision measurement methods for dynamic FM signals still need to be studied, and the measurement accuracy in the high-sampling system still has room for improvement. Traditionally, the equal-precision measurement method is widely applied in most scenarios. However, its accuracy is limited by the quantization error of ±1 word and the sampling gate time, making it difficult to improve the frequency measurement accuracy while ensuring a high sampling rate at the same time. In this paper, a high-precision feedback frequency measurement system with the capability to eliminate the quantization error of ±1 word is proposed. The proposed system consists of two stages, the rough measurement stage based on the equal-precision measurement method and the precise measurement stage based on the negative feedback tracking architecture using the phase-frequency detector (PFD) and direct digital synthesizer (DDS). The effectiveness and feasibility of the system are verified by both simulation and experiment. At the sampling rate of 2 kHz, the frequency measurement accuracy is improved by more than 30 dB.

Study on the Method of Charge Accumulation Suppression of Electrostatic Suspended Accelerometer.

Electrostatic suspended accelerometers (ESAs) are widely used in high accuracy acceleration measurement. However, there exist accumulated charges on the isolated mass which damage the accuracy and the stability of ESAs. In this paper, we propose to apply actuation voltage with a combined waveform to suppress the acceleration noise due to deposited charge. A model of the electrostatic force on the mass is established and the deviation voltage is found to be the dominant source of charge noise. Based on the analysis of disturbance electrostatic force under DC and AC signals, actuation combined with DC and AC voltage is designed and the disturbance force due to charge can be suppressed through adjustment towards the duty cycle of different compositions. Simulations and experiments are carried out and the results indicate that the disturbance due to charge can be suppressed up to 40%, which validates the efficiency of the scheme.

A Source Localization Method Using Complex Variational Mode Decomposition.

Source localization with a passive sensors array is a common topic in various areas. Among the popular source localization algorithms, the compressive sensing (CS)-based method has recently drawn considerable interest because it is a high-resolution method, robust with coherent sources and few snapshots, and applicable for mixed near-field and far-field source localization. However, the CS-based methods rely on the dense grid to ensure the required estimation precision, which is time-consuming and impractical. This paper applies the complex variational mode decomposition (CVMD) to source localization. Specifically, the signal model of the source localization problem is similar to the time-domain frequency-modulated signal model. Motivated by this, we extend CVMD, initially designed for nonstationary time-domain signal analysis, to array signal processing. The decomposition results of the array measurements can correspond to the potential sources at different locations. Then, the sources' direction and range can be estimated by model fitting with the decomposed subsignals. The simulation results show that the proposed CVMD-based method can locate the pure far-field, pure near-field, mixed far-field, and near-field sources. Notably, it can yield high-resolution localization for the coherent sources with one single snapshot with low computing time.

A New Self-Calibration and Compensation Method for Installation Errors of Uniaxial Rotation Module Inertial Navigation System.

Calibration and compensation techniques are essential to improve the accuracy of the strap-down inertial navigation system. Especially for the new uniaxial rotation module inertial navigation system (URMINS), replacing faulty uniaxial rotation modules introduces installation errors between modules and reduces navigation accuracy. Therefore, it is necessary to calibrate these systems effectively and compensate for the installation error between modules. This paper proposes a new self-calibration and compensation method for installation errors without additional information and equipment. Using the attitude, velocity, and position differences between the two sets of navigation information output from URMINS as measurements, a Kalman filter is constructed and the installation error is estimated. After URMINS is compensated for the installation error, the average of the demodulated redundant information is taken to calculate the carrier's navigation information. The simulation results show that the proposed method can effectively assess the installation error between modules with an estimation accuracy better than 5". Experimental results for static navigation show that the accuracy of heading angle and positioning can be improved by 73.12% and 81.19% after the URMINS has compensated for the estimated installation errors. Simulation and experimental results further validate the effectiveness of the proposed self-calibration and compensation method.

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming.

Utilizing the difference in phase and power spectrum between signals and noise, the estimation of direction of arrival (DOA) can be transferred to a spatial sample classification problem. The power ratio, namely signal-to-noise ratio (SNR), is highly required in most high-resolution beamforming methods so that high resolution and robustness are incompatible in a noisy background. Therefore, this paper proposes a Subspaces Deconvolution Vector (SDV) beamforming method to improve the robustness of a high-resolution DOA estimation. In a noisy environment, to handle the difficulty in separating signals from noise, we intend to initial beamforming value presets by incoherent eigenvalue in the frequency domain. The high resolution in the frequency domain guarantees the stability of the beamforming. By combining the robustness of conventional beamforming, the proposed method makes use of the subspace deconvolution vector to build a high-resolution beamforming process. The SDV method is aimed to obtain unitary frequency matrixes more stably and improve the accuracy of signal subspaces. The results of simulations and experiments show that when the input SNR is less than -27 dB, signals of decomposition differ unremarkably in the subspace while the SDV method can still obtain clear angles. In a marine background, this method works well in separating the noise and recruiting the characteristics of the signal into the DOA for subsequent processing.

Cold Starting Temperature Drift Modeling and Compensation of Micro-Accelerometer Based on High-Order Fourier Transform.

The traditional temperature modeling method is based on the full heating of the accelerometer to achieve thermal balance, which is not suitable for the cold start-up phase of the micro-accelerometer. For decreasing the complex temperature drift of the cold start-up phase, a new temperature compensation method based on a high-order Fourier transform combined model is proposed. The system structure and repeatability test of the micro digital quartz flexible accelerometer are provided at first. Additionally, we analyzed where the complex temperature drift of the cold start-up phase comes from based on the system structure and repeatability test. Secondly, a high-order temperature compensation model combined with K-means clustering and the symbiotic organisms search (SOS) algorithm is established with repeatability test data as training data. To verify the proposed temperature compensation model, a test platform was built to transmit the measured values before and after compensation with the proposed Fourier-related model and the other time-related model, which is also a model aiming at temperature compensation in the cold start-up phase. The experimental results indicate that the proposed method achieves better compensation accuracy compared with the traditional temperature compensation methods and the time-related compensation model. Furthermore, the compensation for the cold start-up phase has no effect on the original accuracy over the whole temperature range. The stability of the accelerometer can be significantly improved to about 30 µg in the start-up phase of different temperatures after compensation.

A Modified Complex Variational Mode Decomposition Method for Analyzing Nonstationary Signals with the Low-Frequency Trend.

Complex variational mode decomposition (CVMD) has been proposed to extend the original variational mode decomposition (VMD) algorithm to analyze complex-valued data. Conventionally, CVMD divides complex-valued data into positive and negative frequency components using bandpass filters, which leads to difficulties in decomposing signals with the low-frequency trend. Moreover, both decomposition number parameters of positive and negative frequency components are required as prior knowledge in CVMD, which is difficult to satisfy in practice. This paper proposes a modified complex variational mode decomposition (MCVMD) method. First, the complex-valued data are upsampled through zero padding in the frequency domain. Second, the negative frequency component of upsampled data are shifted to be positive. Properties of analytical signals are used to get the real-valued data for standard variational mode decomposition and the complex-valued decomposition results after frequency shifting back. Compared with the conventional method, the MCVMD method gives a better decomposition of the low-frequency signal and requires less prior knowledge about the decomposition number. The equivalent filter bank structure is illustrated to analyze the behavior of MCVMD, and the MCVMD bi-directional Hilbert spectrum is provided to give the time-frequency representation. The effectiveness of the proposed algorithm is verified by both synthetic and real-world complex-valued signals.

The Minimum AC Signal Model of Bipolar Transistor in Amplification Region for Weak Signal Detection.

This paper presents a minimum signal model via the AC small-signal model and the uncertainty principle, which reveals the minimum AC signal that can be amplified by a bipolar transistor. The Ebers-Moll model (EM3) can describe the small signal amplification process, but it is difficult to define the minimum amplifiable signal of the bipolar transistor. In this study, the correspondence relationship between the non-equilibrium carrier and the electric injection is proved, and the relationship between the life of the non-equilibrium carrier and the measurable signal is proposed by the uncertainty principle. Next, the limit of perceived minimum voltage is also derived in this paper. Then, combining with EM3 model, the minimum AC signal model of bipolar transistor is presented to calculate the minimum voltage signal of bipolar transistor that can be amplified. Finally, a number of the simulation and experiment results show that when the minimum signal in the model is used as input, the carrier concentration of the bipolar transistor does not change and the base electrode cannot perceive the signal, which verifies the validity of the minimum AC signal model.

Cold starting temperature time-related compensation model of inertial sensors based on particle swarm optimization algorithm.

With the miniaturization of inertial instruments, sensors mounted inside are vulnerable to interference. In a complex thermal transmission environment, temperature drift is the main factor restricting the precision of high-performance inertial sensors. To solve this problem, a new method for compensating the time-related cold starting temperature drift of the inertial sensors is introduced in this paper. Based on the perspective that temperature drift can be regarded as the response curve of the sensor system to temperature and temperature gradient, temperature compensation models of first-order, second-order, and higher-order are proposed. Meanwhile, the particle swarm optimization algorithm is used to solve the model parameters. Under various practical circumstances, the method can be used to flexibly compensate the temperature drift and reduce the standard deviation of the output signal by about four times. Compared to other models or algorithms, the simulation and experimental results indicate that the proposed model is superior in adaptability, stability, and reliability.

GdClean: removal of Gadolinium contamination in mass cytometry data.

MOTIVATION: Mass cytometry (Cytometry by Time-Of-Flight, CyTOF) is a single-cell technology that is able to quantify multiplex biomarker expressions and is commonly used in basic life science and translational research. However, the widely used Gadolinium (Gd)-based contrast agents (GBCAs) in magnetic resonance imaging (MRI) scanning in clinical practice can lead to signal contamination on the Gd channels in the CyTOF analysis. This Gd contamination greatly affects the characterization of the real signal from Gd-isotope-conjugated antibodies, severely impairing the CyTOF data quality and ruining downstream single-cell data interpretation. RESULTS: We first in-depth characterized the signals of Gd isotopes from a control sample that was not stained with Gd-labeled antibodies but was contaminated by Gd isotopes from GBCAs, and revealed the collinear intensity relationship across Gd contamination signals. We also found that the intensity ratios of detected Gd contamination signals to the reference Gd signal were highly correlated with the natural abundance ratios of corresponding Gd isotopes. We then developed a computational method named by GdClean to remove the Gd contamination signal at the single-cell level in the CyTOF data. We further demonstrated that the GdClean effectively cleaned up the Gd contamination signal while preserving the real Gd-labeled antibodies signal in Gd channels. All of these shed lights on the promising applications of the GdClean method in preprocessing CyTOF datasets for revealing the true single-cell information. AVAILABILITY AND IMPLEMENTATION: The R package GdClean is available on GitHub at https://github.com/JunweiLiu0208/GdClean. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Applications of Single-Cell Omics in Tumor Immunology.

The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem's complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques.

High-Sensitivity Cuboid Interferometric Fiber-Optic Hydrophone Based on Planar Rectangular Film Sensing.

Interferometric fiber-optic hydrophones are an important means in the field of underwater acoustic detection. The design of the hydrophone sensor head is the key technology related to its detection sensitivity. In this paper, a high-sensitivity cuboid interferometric fiber-optic hydrophone based on planar rectangular film sensing is proposed, and the sensitivity of the sensor is compared with that of the widely used air-backed mandrel hydrophone under the same conditions. The acoustic characteristic models of the two types of sensors were established by theoretical calculation and simulation analysis to obtain the theoretical pressure sensitivity. Some experiments were performed to examine the theory and design. According to the experiment results, the mean phase sensitivity of the mandrel type was -112.85 dB re 1 rad/µPa in the operating frequency range of 10-300 Hz, and that of the cuboid type was -84.50 dB re 1 rad/µPa. The latter was 28.35 dB higher than the former was. These results are useful for improving hydrophone sensitivity.

The Dead Time Characterization Method of Quartz Flexure Accelerometers Using Monotonicity Number.

Dead time estimation is important in the design process of quartz flexure accelerometers. However, to the authors' knowledge, the dead time existing in quartz flexure accelerometers is not well investigated in conventional identification studies. In this paper, the dead time, together with the open-loop transfer function of quartz flexure accelerometers, is identified from step excitation experiments using two steps. Firstly, a monotonicity number was proposed to estimate the dead time. Analysis showed that the monotonicity number was robust enough to measurement noise and sensitive to step excitation. Secondly, parameters of the open-loop transfer function were identified using the least mean squares algorithm. A simulation example was applied to demonstrate the validity of the proposed method. The verified method was used to test a quartz flexure accelerometer. The experimental result shows that the dead time was 500 µs.

Fusion of High-Dynamic and Low-Drift Sensors Using Kalman Filters.

In practice, a high-dynamic vibration sensor is often plagued by the problem of drift, which is caused by thermal effects. Conversely, low-drift sensors typically have a limited sample rate range. This paper presents a system combining different types of sensors to address general drift problems that occur in measurements for high-dynamic vibration signals. In this paper, the hardware structure and algorithms for fusing high-dynamic and low-drift sensors are described. The algorithms include a drift state estimation and a Kalman filter based on a linear prediction model. Key issues such as the dimension of the drift state vector, the order of the linear prediction model, are analyzed in the design of algorithm. The performance of the algorithm is illustrated by a simulation example and experiments. The simulation and experimental results show that the drift can be removed while the high-dynamic measuring ability is retained. A high-dynamic vibration measuring system with the frequency range starting from 0 Hz is achieved. Meanwhile, measurement noise was improved 9.3 dB through using the linear-prediction-based Kalman filter.

Attitude heading reference system using MEMS inertial sensors with dual-axis rotation.

This paper proposes a low cost and small size attitude and heading reference system based on MEMS inertial sensors. A dual-axis rotation structure with a proper rotary scheme according to the design principles is applied in the system to compensate for the attitude and heading drift caused by the large gyroscope biases. An optimization algorithm is applied to compensate for the installation angle error between the body frame and the rotation table's frame. Simulations and experiments are carried out to evaluate the performance of the AHRS. The results show that the proper rotation could significantly reduce the attitude and heading drifts. Moreover, the new AHRS is not affected by magnetic interference. After the rotation, the attitude and heading are almost just oscillating in a range. The attitude error is about 3° and the heading error is less than 3° which are at least 5 times better than the non-rotation condition.

Respiratory monitoring by a field ionization sensor based on Trichel pulses.

In this paper, a novel method for respiratory monitoring is presented. The method is based on Trichel pulses (TPs) using a simple field ionization sensor which consists of a needle electrode and a plate electrode. Experiments have been conducted to demonstrate that different respiratory patterns, including normal, ultra-fast, deep breaths, and apnea could be easily monitored in real time by detecting the changes in the TP frequency. The vital capacity could also be assessed by calculating the variation of TP frequency. It is found that the operation principle of the proposed sensor is based on the effects of breath airflow and the atomized water in exhaled air on the TP frequency by changing the ionization process and the dynamics of charged particles in the short gap. The influences of applied voltage and ambient parameters have also been investigated.