Domain Signal Research Articles

EXTH-03. CAR T-CELLS TARGETED TO B7-H3 OR HER2 FUNCTION INDEPENDENTLY OF ANTIGEN DENSITY IN XENOGRAFT MODELS OF EPENDYMOMA

Abstract BACKGROUND Targeted treatment options are desperately needed for ependymomas (EPN). Chimeric antigen receptor (CAR) T-cells have immense potential to positively transform treatment outcomes; however, little is known regarding antitumor efficacy of CAR T-cells against EPNs. Specifically, antigen density has emerged as a major influencer of CAR T-cell potency. However, the specific high and low antigen density thresholds governing CAR T-cell efficacy for EPN are unknown. The goal of this project is to determine the role of antigen density in CAR T-cell potency against EPN. METHODS Using quantitative flow cytometry, we determined antigen density of B7-H3 and HER2 (the two CAR targets currently in pediatric EPN clinical trials), on EPN patient derived cell lines (1425, ST1, CPITT, ST2, EPI, L46SJ). We generated B7-H3 and HER2-CAR T-cells with CD28z signaling domain and compared long-term cytolytic activity, expansion, and cytokine production against EPNs. RESULTS We found that all EPN cell lines express remarkably high yet variable density of B7-H3 (62,700 – 335,440 molecules/ cell). In contrast, surface density of HER2 was consistently and significantly lower (860 – 24,488 molecules/ cell). In repeated-stimulation assays, B7-H3-CAR T-cells killed 7-10 times and expanded up to 325,360-fold when cultured against EPN. However, we observed no significant difference in total expansion or correlation with antigen density against EPNs. Surprisingly, expansion of HER2-CAR T-cells against EPNs was drastically higher and greatly exceeded that of B7-H3-CAR-T cells against the same EPN cells despite much lower antigen density. CONCLUSIONS Our work shows that both B7-H3 and HER2 are good targets against EPN, despite drastic differences in antigen density. Results suggest that lower antigen density of HER2 may be superior for CAR T-cell activity. Future work will validate whether HER-CAR T-cells have superior antitumor efficacy in vivo to further delineate role of antigen density thresholds in CAR T-cell potency.

EXTH-02. MODIFYING CAR T CELL EPIGENETIC PROGRAMS TO IMPROVE THEIR PERSISTENCE AGAINST GROUP 3 MEDULLOBLASTOMAS

Abstract BACKGROUND Safe, effective therapies are desperately needed to improve outcomes for patients with medulloblastoma (MB), a malignant pediatric brain cancer. Chimeric antigen receptor (CAR)-T cells are a promising therapeutic option as they target T-cell cytotoxicity exclusively to tumor associated antigens. B7 homolog 3 (B7-H3) is an ideal target for CAR-T cell therapy as all MB subtypes express B7-H3 but healthy tissues do not. However, a major roadblock to tumor clearance and control is the inability of CAR-T cells to persist, suggesting that improving persistence will improve therapeutic efficacy. The goal of this study was to compare head-to-head the impact of knocking out TET2 and DNMT3A negative epigenetic regulators of CAR-T cell persistence against group 3 MB (G3MB), the most malignant MB subtype. METHODS We generated B7-H3 CAR-T cells with CD28ζ signaling domain and performed CRISPR/Cas9 knockout (KO) of TET2, DNMT3A, and AAVS1 as a control. We compared resulting CAR-T cell persistence via repeat stimulation assays in vitro against G3MB cell lines with varying B7-H3 antigen density (D341low, HDMB03mid, and D425high). We further compared KO CAR-T cells in vivo against D341low, HDMB03mid, and D425high orthotopic xenografts. RESULTS Our data shows that DNMT3A KO, but not TET2 KO, consistently improves the persistence and expansion of CAR-T cells against G3MB in vitro. In vivo, KO of epigenetic regulators, improves CAR-T cell therapy against highly aggressive D341low, HDMB03mid, and D425high but is insufficient to elicit curative responses. CONCLUSIONS Our study demonstrates that genetic KO of negative epigenetic regulators of CAR-T cell persistence can improve anti-tumor function against G3MB. We conclude that for the B7-H3.CD28ζ CAR, DNMT3A KO is superior for improving persistence over TET2 KO, but that DNMT3A KO is insufficient in vivo.

Application of the AVMD-AIMCKD method to the enhancement of rolling bearing fault features

Abstract Rolling bearings are prone to failure due to harsh working environments, which can affect production efficiency. Given the background environmental noise interference, it is difficult to extract the fault characteristics of rolling bearings in the early stages of weak fault signals. Based on adaptive VMD combined with adaptive IMCKD (AVMD-AIMCKD), a rolling bearing fault diagnosis method is proposed in this paper. The vibration signal from the rolling element bearing is processed by adaptive variational modal decomposition (AVMD) to extract and select the time-frequency domain signal characteristics. The Adaptive Improved maximum correlated kurtosis deconvolution (AIMCKD) algorithm is used for noise reduction optimization to obtain the best extraction results. Because the traditional method requires input parameter values for VMD and IMCKD, it is not adaptive. The particle swarm optimization algorithm (PSO) is utilized in this study to optimize two variables: the penalty factor and decomposition mode number of variable mode decomposition (VMD). The filter length and shift order of the improved maximum correlated kurtosis deconvolution (IMCKD) are optimized to make it adaptive. The viability of the method is substantiated through simulations, and the efficacy and precision are validated by comparative studies. The experimental results show that the AVMD-AIMCKD method has high accuracy and robustness in rolling bearing fault diagnosis and provides an accurate reference for rolling bearing health monitoring in engineering practice. The AVMD-AIMCKD method effectively extracts the early fault features through adaptive optimization, overcomes the restrictions of traditional methods, and provides a more accurate and reliable tool for bearing fault signal diagnosis.

Real-time in-situ ultrasound monitoring of soft hydrogel 3D printing with subwavelength resolution

3D bioprinting has excellent potential in tissue engineering, regenerative medicine, and drug delivery systems due to the ability to fabricate intricate structures that are challenging to make with conventional manufacturing methods. However, the complexity of parametric combinations and lack of product quality control have restricted soft hydrogel bioprinting from practical applications. Here we show an in-situ ultrasound monitoring system that reveals the alginate-gelatin hydrogel’s additive manufacturing process. We use this technique to understand the parameters that influenced transient printing behaviors and material properties in approximately real-time. This unique monitoring process can facilitate the detection of minor errors/flaws during the printing. By analyzing the ultrasonic reflected signals in both time and frequency domains, transient printing information can be obtained from 3D printed soft hydrogels during the processes with a depth subwavelength resolution approaching 0.78λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}. This in-situ technique monitors the printing behaviors regarding the constructed film, interlayer bonding, transient effective elastic constant, layer-wise surface roughness (elastic or plastic), nozzle indentation/scratching, and gravitational spreading. The simulation-verified experimental methods monitored fully infilled printing and gridded pattern printing conditions. Furthermore, the proposed ultrasound system also experimentally monitored the post-crosslinking process of alginate-gelatin hydrogel in CaCl2 solution. The results can optimize crosslinking time by balancing the hydrogel’s stiffness enhancement and geometrical distortion.

A Transfer Learning Model for Rolling Bearing Fault Diagnosis Based on Texture Loss Strategy and Nuclear Norm Regularization

Abstract In recent years, transfer learning (TL) approaches have seen extensive application in diagnosing bearing faults due to their exceptional performance. However, mechanical noise, equipment aging, and wear lead to notable disparities and differences in the multi-level feature distributions across the source and target domain signals. The issue is addressed by proposing a transfer learning model based on a texture loss strategy and nuclear norm regularization method. First, a Feature-Enhanced Network (MSRnet) is designed, which significantly improves the ability to capture local details and long-range dependencies by combining a multi-scale feature extraction module with a dilated residual module. Next, a texture loss strategy is proposed to align multi-scale features across domains by minimizing the Gram matrix of signal features. Finally, a nuclear norm regularization method is proposed to perform low-rank approximation on the signal matrix, facilitating the extraction of more robust feature data and mitigating the risk of overfitting. The experimental results demonstrate that the proposed method achieved an average accuracy of 98.58% on the University of Ottawa bearing fault dataset and 98.11% on the Jiangnan University (JNU) bearing dataset, surpassing eight other algorithms in bearing fault diagnosis.

Multi-channel Fused Vision Transformer Network for Bearing Fault Diagnosis under Different Working Conditions

Abstract Many of the current fault diagnosis methods rely on time-domain signals. While the richest information are contained in these signals, their complexity poses challenges to network learning and limits the ability to fully characterize them. To address these issues, a novel Multi-channel Fused Vision Transformer Network (MFVTN) is proposed in this paper. Firstly, the Overlapping Patch Embedding (OPE) module is introduced to overlap the time-domain map with edge information, preserving the global continuous features of the time-domain map and adding positional encoding for sorting. This integration helps the Vision Transformer (ViT) merge detailed features and construct the global mapping. Secondly, multiple dimensional time domain signal features are extracted and fused in parallel, enabling multi-domain fault diagnosis of bearings. In order to enhance the network ability to extract domain-invariant features, an adversarial training strategy combined with Wasserstein distance is utilized. The results demonstrate that the diagnostic accuracy of the proposed MFVTN can reach 98.2%.

Evaluation of the Use of the Angular Domain and Order Domain in a Bearing Fault Detection Framework using Deep Learning

Bearing failures are very common in the industrial environment, requiring effective fault detection methods, which can be categorized into physics-based, knowledge-based and data-driven types. Data-driven methods are efficient in differentiating healthy conditions from faulty conditions by characterizing machine signals, involving stages of data acquisition, feature extraction, and condition determination. Traditionally, feature extraction and condition determination were manual, but advances in artificial intelligence and machine learning, especially deep learning, have automated this process. Although deep learning automatically learns the best features from the input data, the signal domain can influence the model's performance. Time and frequency domain representations are widely used in fault detection methodologies using vibration signals, while angular and order domains are more common in variable operating conditions, but direct use of these domains with deep learning is still rare in the literature. Considering this, this study evaluates a bearing fault detection methodology using vibration signals in different domains (time, frequency, angular, and order) under various rotational conditions. Three distinct approaches were tested to assess the effectiveness of these representations. The results indicated that the frequency domain representation had the best overall performance and the study concluded that the angular and order domains do not offer significant advantages compared to the frequency domain. Nonetheless, it is recommended to conduct a more in-depth analysis with more diverse datasets, especially those containing early-stage bearing fault signals.

915 nm pumping kilowatt fiber oscillator with high optical-to-optical efficiency

We demonstrate an all-fiber oscillator with high optical-to-optical efficiency using laser diodes working at 915nm as the pump sources to reduce the demand for thermal management. When the output power of the bidirectional pumped oscillator is 1.16kW, the optical-to-optical efficiency is as high as 75.4%. In this working state, the output characteristics of the oscillator are observed. The Raman suppression ratio is 41.23dB and the beam quality factor Mx2 = 1.14, My2 = 1.29. Analyzing the time trajectory of the highest power output, there are no significant characteristic peaks in the time domain signal and the frequency domain signal, which indicates that the oscillator has good stability.Improving the output characteristics of the 915nm pumping lasers has a positive significance for the application of non-strict ambient temperature control.

Targeted therapy of multiple myeloma by IL21-NKG2D CAR-T cells.

NKG2D chimeric antigen receptor (CAR)-modified T cells (NKG2D CAR-T cells) have been reported to be preclinically efficient in several tumors, but little is known whether NKG2D CAR-T cells co-expressing IL21 (IL21-NKG2D CAR-T cells) display greater antitumor activity in multiple myeloma (MM). In this study, the lentivirus has been produced for expression of the IL21 sequence linked to the extracellular NKG2D sequence with the signal peptide linked through the CD8α hinge-transmembrane domain to the 4-1BB molecule fused with the CD3-ζ chain signaling domain, and the engineered IL21-NKG2D CAR-T cells and NKG2D CAR-T cells were constructed. The CAR expression on CAR-T cells was assessed by flow cytometry, and the killing effects of CAR-T cells on MM were assessed by the cytotoxicity assay and ELISA assay. Moreover, xenograft models were also established to evaluate the ability of IL21-NKG2D-CAR-T cells to eliminate MM in vivo. Our results indicated that NKG2D CAR-T cells had dramatic cytotoxicity on MM cells in vitro, and co-expression of IL-21 significantly increased the cytotoxicity of NKG2D CAR-T cells on MM cells. Remarkably, we found that dexamethasone enhanced the cytotoxicity of IL21-NKG2D CAR-T cells on MM cells. Furthermore, IL21-NKG2D CAR-T cells also displayed significant anti-myeloma activity in vivo. In conclusion, IL21-NKG2D CAR-T cells had dramatic cytotoxicity on MM cells in vitro and in vivo, and a system to apply IL21-NKG2D CAR-T cells and low dosage of dexamethasone for the future study of the targeted therapy for MM has been established.

End-to-End Intelligent Fault Diagnosis of Transmission Bearings in Electric Vehicles Based on CNN

Environmental noise and transmission components can cause significant interference in vibration signals, rendering the extraction of bearing fault features challenging in service scenarios. Traditional fault diagnosis methods rely heavily on professional domain knowledge, prior models, and signal preprocessing methods. The accuracy of fault diagnosis depends on the quality of the fault-sensitive features extracted by vibration signal preprocessing methods. An improved convolutional neural network (CNN) end-to-end intelligent fault diagnosis model based on raw vibration data (RVDCNN) is proposed. The time-domain vibration signal of the transmission bearing is converted into a continuous two-dimensional numerical matrix, and a two-dimensional CNN model is constructed through network structure optimization. The original time-domain vibration signal numerical matrix of the bearing is trained and tested to extract and learn abstract fault features of different fault types, and then the fault classification of the bearing is achieved. To verify the generalizability of the RVDCNN intelligent fault diagnosis model, it is applied to the recognition of rolling bearings in the two-speed mechanical automatic transmission of electric vehicles, achieving recognition accuracy of 99.11% for seven types of bearings.

Optimization of Interference Suppression Algorithm for Polarization Sensitive Conformal Array in Complex Electromagnetic Environment

With the development of modern electronic technology, the electromagnetic environment is becoming increasingly complex, which poses a severe challenge to the normal work of radar, communication and other systems. Polarization sensitive array can more effectively distinguish and suppress interference signals by using polarization information of electromagnetic waves, and improve the signal-to-noise ratio (SNR) and anti-interference ability of the system. Conformal array can be closely attached to the carrier surface, reducing space occupation and improving signal receiving efficiency and accuracy. However, at present, polarization-sensitive conformal arrays still face technical problems in interference suppression, especially in complex electromagnetic environment, how to optimize interference suppression algorithms to improve system performance has become the research focus. In this paper, an adaptive interference suppression algorithm combining spatial-polarization domain processing is proposed. The algorithm digitally processes the received signal, extracts spatial and polarization domain information, constructs the joint feature vector of the signal in spatial and polarization domain, and designs a joint filter in spatial and polarization domain. The minimum mean square error (MMSE) criterion and the least mean square (LMS) algorithm are used to update the weight vector of the filter in real time, enabling the filter to automatically adapt to changes in the electromagnetic environment and achieve optimal interference suppression. Simulation experiments show that the algorithm exhibits good stability and adaptability under different electromagnetic environments, effectively suppressing interference and extracting the desired signal. Compared with traditional interference suppression algorithms based solely on spatial domain, the joint spatial-polarization domain adaptive interference suppression algorithm significantly improves the interference suppression ratio (ISR) and signal-to-interference-plus-noise ratio (SINR) performance indicators, verifying that the introduction of polarization domain information can enhance the effect of interference suppression.

Fatigue Damage Monitoring of Composite Structures Based on Lamb Wave Propagation and Multi-Feature Fusion

To address the challenges associated with fatigue damage monitoring in load-bearing composite structures, we developed a method that utilizes Lamb wave propagation and partial least squares regression (PLSR) for effective monitoring. Initially, we extracted diverse characteristics from both the time and frequency domains of the Lamb wave signal to capture the essence of the damage. Subsequently, we constructed a PLSR model, leveraging Lamb wave multi-feature fusion, specifically tailored for in-service fatigue damage monitoring. The efficacy of our proposed approach in quantitatively monitoring fatigue damage was thoroughly validated through rigorous standard fatigue tests. In practical applications, our model effectively mitigated the impact of multicollinearity among feature variables on model accuracy. Furthermore, the PLSR model demonstrated superior accuracy compared to the PCR model, given an equal number of principal components. To strike a harmonious balance between efficiency and precision, we optimized the size of the feature variable. The results show that the optimized PLSR model achieved an R-squared value exceeding 97% in predicting the in-service damage area. This underscores the robustness and reliability of our method in accurately monitoring fatigue damage in load-bearing composite structures.

A new frequency domain denoising method for coal mine transient electromagnetic measuring data

Abstract Due to limitation of space in mine tunnel, only a 2m × 2m rectangle coil can be employed to collect the electromagnetic signals for the mine transient electromagnetic analysis. Hence, a small emission magnetic moment and weak detection signal strength will increase the electromagnetic analysis difficulty. Moreover, the transient electromagnetic response signal in mines exhibits exponential decay over time, with late-stage amplitudes being notably small, which makes these signals susceptible to noise interference. As a result, it is necessary to study effective noise removal methods to enhance the signal-to-noise ratio of the late-stage electromagnetic data. Addressing this challenge task, this study proposes a new method for frequency domain denoising of transient electromagnetic data. This new method utilizes the Fourier Transform to convert the original transient electromagnetic data from the time domain to the frequency domain, and then, the noise is identified and removed through the frequency characters. Lastly, the processed frequency domain information is transformed to the time domain by the Fourier Inverse Transform to restore the time domain signals. The effect of this frequency domain denoising method is demonstrated through numerical simulations and underground coal mine field data to show significant noise reduction performance on the abnormality detection in the underground coal mine.

7222 Structural and Mechanistic Studies of the Bioactive Anti-Müllerian Hormone Procomplex

Abstract Disclosure: J.A. Howard: None. L. Hok: None. N.J. Sanford: None. R.L. Cate: None. K. Hart: Employee; Self; Regeneron Pharmaceuticals. E. Leach: None. D. Pepin: None. P.K. Donahoe: None. T.B. Thompson: Advisory Board Member; Self; Oviva Therapeutics, Keros Therapeutics. The Anti-Mullerian hormone (AMH), a divergent member of the TGF-beta signaling superfamily, plays crucial roles in reproductive development and maintenance. Owing to its key role as a negative feedback regulator in folliculogenesis, AMH has garnered significant interest for therapeutic applications in contraception and oncofertility. The bioactive form of AMH is a noncovalent complex, comprising the TGFβ-like signaling domain and a dimeric prodomain whose structure remains largely unexplored. The prodomain component is necessary for the dimerization and secretion of the signaling domain, shuttling the ligand to autocrine, paracrine, and endocrine targets. In contrast, the structure and biochemistry of this domain remain poorly characterized, and its role in enhancing AMH signaling remains unclear. This gap in understanding significantly limits the enhancement and wider application of AMH-based protein therapeutics. Molecular modeling unveiled an unexpected AMH prodomain structure: an N-terminal dimerizing domain (NTD) linked to a novel C-terminal binding domain (CTD). This atypical design was affirmed by both small-angle X-ray scattering and negative-stain EM. The CTD introduces a unique TGFβ superfamily fold consisting of a four-helix bundle and an unstructured binding “belt”. The more prodomain-like NTD does not interact with the signaling ligand, nor does it consistently interact with the CTD. Both domains exhibit high levels of interspecies conservation as well as disease-causing mutations, such as those found in PMDS, PCOS, and POI. Using single-particle Cryo-EM we have elucidated the molecular mechanisms of the interaction between the AMH prodomain and signaling domain bound by mAb 6E11. Our 3.2Å cryo-EM structure confirmed the predicted architecture and binding function of the CTD and demonstrated a new dimension of conformational plasticity within the signaling ligand. We observed movements up to 5Å at the fingertips and 10Å in the outer helices of the CTD. More unexpectedly, the conformation of the AMH signaling ligand in the procomplex form is far more open and extended than has been observed in any other structure of a TGF-beta superfamily ligand, including the AMH ligand bound to its receptor AMHR2. We see that transitioning from a prodomain-bound to a receptor-bound state involves significant conformational remodeling of the signaling domain. This reorganization supports a conformational shift mechanism for AMH signaling in which bivalent binding of AMHR2 at the cell surface induces a >15Å and 38° closing of the ligand fingertips, leading to prodomain displacement and subsequent recruitment of type I receptors. Our findings correct earlier structural misconceptions and build a comprehensive model for interpreting existing functional data. This structure provides a foundation for understanding AMH dysregulation and developing advanced AMH therapeutics. Presentation: 6/1/2024

Sound quality characteristics of reciprocating hermetic refrigeration compressors

In recent years, sound quality (SQ) has become an increasingly important factor in the home appliance industry, enhancing the customer experience and comfort. Manufacturers that prioritize SQ in their designs can gain a competitive advantage in the market and increase customer satisfaction. The compressor is one of the main sources of noise in a refrigerator. While manufacturers typically provide refrigerator sound quality and noise labels, research that specifically evaluates the compressor sound quality metrics at a component level is not common. Therefore, this study aims to isolate the compressor from the refrigerator to investigate SQ metrics for hermetic refrigeration compressors. To achieve this, compressors with different capacities are operated and recorded at different working speeds for different thermal conditions. Objective parameters are calculated and tested using time domain signals and analyzed with spectral analysis methods. In addition to the objective parameters, subjective results are obtained through jury tests consisting of 75 people of different genders and age ranges. By considering objective parameters of sound quality as input and sound quality evaluation values as output, a regression model is established. It is found that Loudness, Sharpness, and Degree of Modulation play an important role in compressor sound quality evaluations consider.

One RING to rule them all: Mind bomb-1 ccRING3 controls Notch signaling

In this issue of Structure, Cao etal.1 use X-ray crystallography, biochemical, and genetic studies to define the key role of the Mind bomb-1 ccRING3 domain in triggering Notch signaling, and they demonstrate that ccRING3-mediated dimerization is a key step in ligand activation.

Identifying and Evaluating Biological Markers of Postherpetic Neuralgia: A Comprehensive Review.

Postherpetic neuralgia (PHN) manifests as persistent chronic pain that emerges after a herpes zoster outbreak and greatly diminishes quality of life. Unfortunately, its treatment efficacy has remained elusive, with many therapeutic efforts yielding less than satisfactory results. The research to discern risk factors predicting the onset, trajectory, and prognosis of PHN has been extensive. However, these risk factors often present as nonspecific and diverse, indicating the need for more reliable, measurable, and objective detection methods. The exploration of potential biological markers, including hematological indices, pathological insights, and supportive tests, is increasing. This review highlights potential biomarkers that are instrumental for the diagnosis, management, and prognosis of PHN while also delving deeper into its genesis. Drawing from prior research, aspects such as immune responsiveness, neuronal injury, genetic makeup, cellular metabolism, and pain signal modulation have emerged as prospective biomarkers. The immune spectrum spans various cell subtypes, with an emphasis on T cells, interferons, interleukins, and other related cytokines. Studies on nerve injury are directed toward pain-related proteins and the density and health of epidermal nerve fibers. On the genetic and metabolic fronts, the focus lies in the detection of predisposition genes, atypical protein manifestations, and energy-processing dynamics, with a keen interest in vitamin metabolism. Tools such as functional magnetic resonance imaging, electromyography, and infrared imaging have come to the forefront in the pain signaling domain. This review compiles the evidence, potential clinical implications, and challenges associated with these promising biomarkers, paving the way for innovative strategies for predicting, diagnosing, and addressing PHN.

FGFR4-specific CAR-T cells with inducible caspase-9 suicide gene as an approach to treat rhabdomyosarcoma.

Metastatic rhabdomyosarcoma is associated with poor survival and unsatisfactory treatment outcomes. Therefore, new immunotherapeutic methods are urgently required. Fibroblast growth factor receptor 4 (FGFR4), a new therapeutic target for rhabdomyosarcoma, plays a crucial role in its onset and development. This study aimed to generate FGFR4 single-chain variable fragment-based chimeric antigen receptor (CAR) T cells without causing evident toxicity and incorporating an inducible caspase-9 (iCasp9) suicide gene system to enhance their safety. FGFR4 antigen expression was evaluated in normal murine tissues, normal human tissues, and specimens from patients with rhabdomyosarcoma. Combined with a 4-1BB co-stimulatory domain, a CD3ζ signaling domain, and an iCasp9 suicide gene, CAR-T cells with an FGFR4-specific single-chain variable fragment were developed. The specific cytotoxic effects, T-cell proliferation, cytokine secretion, apoptosis induction by chemical dimerization (AP20187), and toxicity of FGFR4 CAR-T cells were investigated in vitro and in vivo. FGFR4 CAR-T cells generated a variety of immune-promoting cytokines, including tumor necrosis factor α, interleukin 2, and interferon γ, and displayed effective cytotoxic activity against FGFR4-overexpressing rhabdomyosarcoma cells in vitro. FGFR4 CAR-T cells were relatively effective against FGFR4-overexpressing rhabdomyosarcoma, with tumor regression and poor survival in a subcutaneous xenograft model. The iCasp9 gene was incorporated into FGFR4 CAR-T cells and it was demonstrated that effective and reliable suicide gene activity depends on the administration of AP20187. By making use of the cross-reaction of FGFR4 CAR-T cells with murine FGFR4 in a syngeneic tumor model, this study found that FGFR4 CAR-T cells could regulate the growth of tumors without evident toxicity. Our study demonstrates that FGFR4 is a prospective target for CAR-T cell therapy in rhabdomyosarcoma without serious on-target off-tumor toxicity. FGFR4 CAR-T cells with the iCasp9 suicide gene system as a safety switch to limit toxicity may broaden the clinical applications of cellular therapy.

Defect classification and quantification method based on AC magnetic flux leakage time domain signal characteristics

Pipelines play a crucial role in industries such as petroleum and nuclear power, where non-destructive testing is essential. Magnetic flux leakage testing methods are widely used for pipeline inspection due to their ability to detect both internal and external defects. However, accurately classifying and evaluating the size of such defects poses challenges due to the complex coupling relationship between ferromagnetic materials and defect magnetic fields. This paper proposes a method for defect classification and quantification based on the time domain characteristics of AC magnetic flux leakage signals. Firstly, the paper explores the shielding effects caused by transient high magnetic permeability within the material on signals from internal defects. It analyzes the differences in signals between internal and external defects. Then, based on the nonlinear attributes of defect signals, the paper proposes a defect classification method based on derivatives analysis of the windowed time-domain signal. Moreover, the study finds that rising time and zero-crossing time can be used to assess the depth of internal and external defects separately, which can decouple the width and depth. Finally, experimental validation confirms the effectiveness of defects classification and quantification. This paper provides a feasible method for evaluating the defect of ferromagnetic materials.

Design and Implementation of Image Acquisition and Signal Transmission Functions based on FPGA

Abstract In recent years, video network monitoring systems have evolved rapidly, from the first generation of digital-analogue systems to the fully IP-based systems. Based on the FPGA core, this design focuses on image acquisition and information transmission. The FPGA expansion board contains several peripheral modules, including the OV7670 camera captures images, DRAM stores data, and FIFO handles cross-clock domain signal transmission. While the reference clock is 40ns, in this system, the clock period is 20ns, making two clock cycles equal to one reference clock cycle. Then, two counters are used, one is the reference clock and another is the line synchronization (cnt1) to count 800 clock cycles per line. The end of a line is indicated by end_cnt0, and FIFO data is stored in SDRAM. Configuring the VGA display driver allows image data to be transmitted and displayed on the VGA screen. The system features a simple structure, high resource utilization, and strong reliability.