Domain Signal Research Articles

E3 ligase RNF2 inhibits porcine circovirus type 3 replication by targeting its capsid protein for ubiquitination-dependent degradation.

Porcine circovirus type 3 (PCV3) is closely associated with various diseases, such as the porcine dermatitis, nephropathy syndrome, and multisystemic clinicopathological diseases. PCV3-associated diseases are increasingly recognized as severe diseases in the global swine industry. Ring finger protein 2 (RNF2), an E3 ubiquitin ligase exclusively located in the nucleus, contributes to various biological processes. This ligase interacts with the PCV3 Cap. However, its role in PCV3 replication remains unclear. This study confirmed that the nuclear localization signal domain of the Cap and the RNF2 N-terminal RING domain facilitate the interaction between the Cap and RNF2. Furthermore, RNF2 promoted the binding of K48-linked polyubiquitination chains to lysine at positions 139 and 140 (K139 and K140) of the PCV3 Cap, thereby degrading the Cap. RNF2 knockdown and overexpression increased or decreased PCV3 replication, respectively. Moreover, the RING domain-deleted RNF2 mutant eliminated the RNF2-induced degradation of the PCV3 Cap and RNF2-mediated inhibition of viral replication. This indicates that both processes were associated with its E3 ligase activity. Our findings demonstrate that RNF2 can interact with and degrade the PCV3 Cap via its N-terminal RING domain in a ubiquitination-dependent manner, thereby inhibiting PCV3 replication.IMPORTANCEPorcine circovirus type 3 is a recently described pathogen that is prevalent worldwide, causing substantial economic losses to the swine industry. However, the mechanisms through which host proteins regulate its replication remain unclear. Here, we demonstrate that ring finger protein 2 inhibits porcine circovirus type 3 replication by interacting with and degrading the Cap of this pathogen in a ubiquitination-dependent manner, requiring its N-terminal RING domain. Ring finger protein 2-mediated degradation of the Cap relies on its E3 ligase activity and the simultaneous existence of K139 and K140 within the Cap. These findings reveal the mechanism by which this protein interacts with and degrades the Cap to inhibit porcine circovirus type 3 replication. This consequently provides novel insights into porcine circovirus type 3 pathogenesis and facilitates the development of preventative measures against this pathogen.

Cell autonomous functions of CD47 in regulating cellular plasticity and metabolic plasticity.

CD47 is a ubiquitously expressed cell surface receptor, which is widely known for preventing macrophage-mediated phagocytosis by interacting with signal regulatory protein α (SIRPα) on the surface of macrophages. In addition to its role in phagocytosis, emerging studies have reported numerous noncanonical functions of CD47 that include regulation of various cellular processes such as proliferation, migration, apoptosis, differentiation, stress responses, and metabolism. Despite lacking an extensive cytoplasmic signaling domain, CD47 binds to several cytoplasmic proteins, particularly upon engaging with its secreted matricellular ligand, thrombospondin 1. Indeed, the regulatory functions of CD47 are greatly influenced by its interacting partners. These interactions are often cell- and context-specific, adding a further level of complexity. This review addresses the downstream cell-intrinsic signaling pathways regulated by CD47 in various cell types and environments. Some of the key pathways modulated by this receptor include the PI3K/AKT, MAPK/ERK, and nitric oxide signaling pathways, as well as those implicated in glucose, lipid, and mitochondrial metabolism. These pathways play vital roles in maintaining tissue homeostasis, highlighting the importance of understanding the phagocytosis-independent functions of CD47. Given that CD47 expression is dysregulated in a variety of cancers, improving our understanding of the cell-intrinsic signals regulated by this molecule will help advance the development of CD47-targeted therapies.

Research on the algorithm for optimal selection of detection modes for rail crack detection

In the application of ultrasonic guided wave testing for rail crack detection, it is necessary to select a guided wave mode that is more sensitive to cracks as the detection mode. However, ultrasonic guided waves have multi-mode and dispersive characteristics. In order to extract mode information from complex signals, this paper proposes an optimal detection mode selection method based on the sensitivity of guided wave modes to cracks. This method is different from the traditional method of determining mode types by calculating the mode velocity through the arrival time of wave packets in the time domain signal. Based on the dispersion characteristics and mode features of guided wave modes, this paper establishes a crack sensitivity evaluation index. In a wide frequency band and among numerous modes, the guided wave modes suitable for detecting cracks in different regions of the full cross-section of rails are accurately selected. Experimental results show that the guided wave modes selected by the mode selection method proposed in this paper, based on the crack area energy and crack reflection intensity evaluation indexes, can accurately identify rail cracks, laying a foundation for the research on rail crack detection and localization methods.

V-DAFT: visual technique for texture image defect recognition with denoising autoencoder and fourier transform

Texture is the surface qualities and visual attributes of an object, determined by the arrangement, size, shape, density, and proportion of its fundamental components. In the manufacturing industry, products typically have uniform textures, allowing for automated visual inspections of the product surface to recognize defects. During this process, texture defect recognition techniques can be employed. In this paper, we propose a method that combines a convolutional autoencoder architecture with Fourier transform analysis. We employ a normal reconstructed template as defined in this study. Despite its simple structure and rapid training and inference capabilities, it offers recognition performance comparable to state-of-the-art methods. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals, which is essential for effective defect recognition as texture defects often exhibit characteristic changes in specific frequency ranges. The experiment evaluates the recognition performance using the AUC metric, with the proposed method showing a score of 93.7%. To compare with existing approaches, we present experimental results from previous research, an ablation study of the proposed method, and results based on the high-pass filter used in the Fourier mask.

Hu8F4-CAR T cells with mutated Fc spacer segment improve target specificity and mediate anti-leukemia activity in vivo

Background aimsHu8F4 is a T-cell receptor–like antibody with high affinity for the leukemia-associated antigen PR1/HLA-A2 epitope. Adapted into a chimeric antigen receptor (CAR) format, Hu8F4-CAR is composed of the Hu8F4 single-chain variable fragment, the human IgG1 CH2CH3 extracellular spacer domain, a human CD28 costimulatory domain and the human CD3ζ signaling domain. We have demonstrated high efficacy of Hu8F4-CAR-T cells against PR1/HLA-A2-expressing cell lines and leukemic blasts from patients with acute myeloid leukemia in vitro. Previous studies have shown that modification of the Fc domains of IgG4 CH2CH3 spacer regions can eliminate activation-induced cell death and off-target killing mediated by mouse Fc gamma receptor–expressing cells. MethodsWe generated Hu8F4-CAR(PQ) with mutated Fc receptor binding sites on the CH2 domain of Hu8F4-CAR to prevent unwanted interactions with Fc gamma receptor–expressing cells in vivo. ResultsThe primary human T cells transduced with Hu8F4-CAR(PQ) can specifically lyse HLA-A2+ PR1-expressing leukemia cell lines in vitro. Furthermore, both adult donor-derived and cord blood-derived Hu8F4-CAR(PQ)-T cells are active and can eliminate U937 leukemia cells in NSG mice. ConclusionsHerein, we demonstrate that modification of the IgG1-based spacer can eliminate Fc receptor binding–induced adverse effects and Hu8F4-CAR(PQ)-T cells can kill leukemia in vivo.

Evaluation of grouting effectiveness for Semi-Rigid pavement base layer cracks based on Time-Frequency domain signal characteristics of 3D GPR

This study aims to address the deficiency of rapid, comprehensive, and non-destructive methods for evaluating the effectiveness of grouting treatment on semi-rigid pavement base layer cracks. By integrating radar simulation with empirical measurements, we establish a comprehensive evaluation method for the grouting treatment effectiveness of pavement base layer cracks. Initially, we propose a GprMax simulation of crack models with varying grout-filled volumes, analyzing the time–frequency domain signal characteristics before and after grouting. Based on data sensitivity, thirteen time–frequency domain signal characteristics are selected as grading evaluation indicators. Subsequently, relying on an engineering case study, we conduct a graded evaluation of the grouting effect on base layer cracks, analyzing the correlation and weight of evaluation indicators to derive a comprehensive evaluation method. Field coring results confirm the accuracy of the comprehensive graded evaluation method.

Masked cross-domain self-supervised deep learning framework for photoacoustic computed tomography reconstruction

Accurate image reconstruction is crucial for photoacoustic (PA) computed tomography (PACT). Recently, deep learning has been used to reconstruct PA images with a supervised scheme, which requires high-quality images as ground truth labels. However, practical implementations encounter inevitable trade-offs between cost and performance due to the expensive nature of employing additional channels for accessing more measurements. Here, we propose a masked cross-domain self-supervised (CDSS) reconstruction strategy to overcome the lack of ground truth labels from limited PA measurements. We implement the self-supervised reconstruction in a model-based form. Simultaneously, we take advantage of self-supervision to enforce the consistency of measurements and images across three partitions of the measured PA data, achieved by randomly masking different channels. Our findings indicate that dynamically masking a substantial proportion of channels, such as 80%, yields meaningful self-supervisors in both the image and signal domains. Consequently, this approach reduces the multiplicity of pseudo solutions and enables efficient image reconstruction using fewer PA measurements, ultimately minimizing reconstruction error. Experimental results on in-vivo PACT dataset of mice demonstrate the potential of our self-supervised framework. Moreover, our method exhibits impressive performance, achieving a structural similarity index (SSIM) of 0.87 in an extreme sparse case utilizing only 13 channels, which outperforms the performance of the supervised scheme with 16 channels (0.77 SSIM). Adding to its advantages, our method can be deployed on different trainable models in an end-to-end manner, further enhancing its versatility and applicability.

Few-shot bearing fault diagnosis by semi-supervised meta-learning with graph convolutional neural network under variable working conditions

Aiming at the problems of low accuracy and weak generalization ability in fault diagnosis caused by complex working conditions and limited fault samples of bearings, a few-shot bearing fault diagnosis method by semi-supervised meta-learning with simplifying graph convolutional neural network (semi-meta-sgc) is proposed. Firstly, the time domain signal is converted into the frequency domain by fast Fourier transform (FFT). Secondly, considering each spectrum sample as a node, a k-nearest neighbor (KNN) graph is constructed according to the adjacencies between nodes, thus transforming the bearing fault classification into a graphical node classification. Then, relying on meta-learning, graph convolutional neural network, and semi-supervised learning, a high-accuracy node classifier is obtained by using a small number of training samples to achieve the classification of bearing faults under variable working conditions. Finally, the proposed method is verified by four cases and compared with other methods. The results show that this method has higher recognition accuracy and generalization performance.

Damage identification of honeycomb sandwich structures based on Lamb waves and 1D-CNN

A specific method combining Lamb waves and 1D-CNN is proposed in this paper for damage assessment of honeycomb sandwich structures, which can identify the location and degree of damage simultaneously. The applicability of the Lamb waves method is verified by simulating the interaction between the Lamb waves and different degrees of damage on the honeycomb panel. In the experiment, four identical piezoelectric wafers were placed on a honeycomb plate in a square shape, and a mass block was placed inside to simulate damage. One of the sensors was used as an actuator to generate the Lamb waves signal, and the remaining three sensors received the response signals after interacting with the damage. The collected signals were denoised by Kalman filter, and the time domain signals were transformed into the frequency domain signals by Fourier transform, so as to extract the feature more effectively. The amplitude frequency characteristics of Lamb wave signals were used as inputs to the 1D-CNN model, and the damage categories were used as outputs. 365 samples were used for model testing, 364 were correctly identified with a correct recognition rate of 99.7 %. The result shows that the proposed method is feasible for damage identification of honeycomb sandwich structures.

Research into Prediction Method for Pressure Pulsations in a Centrifugal Pump Based on Variational Mode Decomposition-Particle Swarm Optimization and Hybrid Deep Learning Models.

Centrifugal pump pressure pulsation contains various signals in different frequency domains, which interact and superimpose on each other, resulting in characteristics such as intermittency, non-stationarity, and complexity. Computational Fluid Dynamics (CFD) and traditional time series models are unable to handle nonlinear and non-smooth problems, resulting in low accuracy in the prediction of pressure fluctuations. Therefore, this study proposes a new method for predicting pressure fluctuations. The pressure pulsation signals at the inlet of the centrifugal pump are processed using Variational Mode Decomposition-Particle Swarm Optimization (VMD-PSO), and the signal is predicted by Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) model. The results indicate that the proposed prediction model combining VMD-PSO with four neural networks outperforms the single neural network prediction model in terms of prediction accuracy. Relatively high accuracy is achieved by the VMD-PSO-CNN-LSTM model for multiple forward prediction steps, particularly for a forward prediction step of 1 (Pre = 1), with a root mean square error of 0.03145 and an average absolute percentage error of 1.007%. This study provides a scientific basis for the intelligent operation of centrifugal pumps.

Development of GPC3-CAR-NK cells and optimization as a therapy for HCC.

Hepatocellular carcinoma (HCC) is a highly malignant tumor characterized by insidious onset and rapid progression, with limited treatment choices. One treatment modality, chimeric antigen receptor (CAR)-modified natural killer (NK) cell immunotherapy, has shown promise for various cancers. In this study, we developed two GPC3-specific CAR-NK-92 cell lines (GPC3-CAR-NK) and explored their antitumor efficacy for the treatment of HCC. Significant levels of cytokine production and in vitro cytotoxicity were produced following co-culture of GPC3+ HCC cells with the developed GPC3-CAR-NK cells. GC33-G2D-NK cells with NK cell-specific signaling domains showed better activation and killing abilities than GC33-CD28-NK cells containing T cell-specific signaling domains. Moreover, GC33-G2D-NK cells efficiently eliminated tumors in cell-derived xenograft and patient-derived xenograft mouse models. In an abdominal metastasis model, intraperitoneally delivered GC33-G2D-NK cells showed better antitumor ability than intravenously injected cells. Finally, the combination of microwave ablation with GC33-G2D-NK cell administration showed greater CAR-NK infiltration and tumor regression in ablated tumors than monotherapy alone. These findings indicate that administration of GPC3-CAR-NK cells may be a potential strategy for the treatment of HCC, and regional delivery or their combination with microwave ablation may optimize their efficacy against HCC and may have translational value.

EEG BASED SEQUENTIAL EPILEPTIC SEIZURE DETECTION USING CNN

The objective of the research work is to propose an electroencephalography based sequential approach for epileptic seizure detection method in a real time environment using chronological 2D convolutional neural network (CNN). Even though in link with CNN an electroencephalography (EEG) shows a substantial characters in observing the brain commotion of patients detecting epilepsy, it is pretended to investigate number of EEG illustration and its histories to perceive apprehensive epileptic activity. The proposed Model flows towards a BiLSTM (Bi-directional LSTM) to find multi-channel EEG signals and deliberates longitudinal temporal association, a feature in epileptic seizure discovery based on 2D convolutional layers. This research also been motivated to invoke and frame of CNN based raw electroencephalography indicators to advance the accuracy of finding epileptic seizure, as an alternative of regular feature abstraction to differentiate ictal, preictal, and interictal variations to find epileptic seizure detection. It was compared the routines of time and regularity domain signals in the detection of epileptic signals which is constructed and based on the health organization and its collaburation worldwide with scalp record which is transportable and potential in these parameters. Sorting the sequential approach and its consequences show that CNN has an approaching ability in the classification of EEG signals with a sequential verification and validation to recognition an accurate epileptic seizures by reaching 99.18% of global classification accuracy. Key words: Convolutional neural network (CNN), Bi-LSTM, Electroencephalography (EEG), Epileptic seizure.

IMMU-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 and healthy tissues do not. However, a roadblock to tumor clearance is the inability of B7-H3 CAR-T cells to persist, suggesting that improving persistence will benefit anti-tumor efficacy. The goal of this study was to compare head-to-head knocking out TET2 or 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 moderately 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. We believe this is due to the aggressive nature of G3MB and we are optimizing in vivo studies for better representation of CAR-T cell persistence.

IMMU-03. DELETION OF RASA2 IN CAR T CELLS OVERCOMES DYSFUNCTIONAL IMMUNE SYNAPSE FORMATION AGAINST DIPGS, ENHANCING IN VITRO AND IN VIVO ANTITUMOR RESPONSE

Abstract BACKGROUND Diffuse intrinsic pontine gliomas (DIPGs) are highly lethal pediatric brain tumors and a leading cause of cancer-related death in children. There is currently no cure for DIPG, highlighting an urgent need for novel therapeutics. Chimeric antigen receptor (CAR)-T-cell therapy offers great promise for DIPG treatment; however, several limitations need to be addressed. The goal of this study is to identify why CAR T cells are ineffective against DIPG and overcome this roadblock by improving the immune synapse (IS), or the interaction between tumor and CAR T cells. METHODS In this study, we targeted the tumor associated antigen B7-H3 by generating B7-H3 specific CAR T cells with CD28z signaling domain. We compared in vitro and in vivo antitumor response of CAR T cells against patient derived DIPGs and non-DIPG brain tumors. We contrasted the IS quality in DIPGs against other brain tumors by confocal microscopy. We further generated IS-enhanced CAR T cells by knocking out RASA2 and compared their performance against control KO in terms of IS-quality, killing kinetics, expansion, cytokine secretion, and in vivo tumor control. RESULTS Our data shows that CAR T cells have potent in vitro antitumor activity but fail in vivo against DIPGs which correlates with the formation of deficient IS, reflected by small synapses and poor calcium flux and cell polarity. RASA2 deletion in CAR T cells resulted in improved IS quality evidenced by bigger synapses, increased calcium flux, and CAR clustering upon antigen activation. In concordance, RASA2 KO CAR T cells elicited increased expansion, cytokine secretion, and in vivo antitumor activity against DIPGs compared to control KO. CONCLUSIONS This study shows that poor IS formation contributes to CAR T cell failure against DIPGs. Enhancing the IS formation in CAR T cells improves antitumor activity against DIPGs and should be considered for clinical translation.

EPEN-02. 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. We hypothesize that antigen density heterogeneity governs 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 second generation 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 (6.3e4 – 3.4e5 molecules/ cell). In contrast, surface density of HER2 was consistently and significantly lower (860 – 2.5e4 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 (up to over 100-billion-fold) and greatly exceeded that of B7-H3-CAR-T cells against the same EPN cells despite much lower antigen density. CONCLUSIONS Our studies reveal significant antigen density heterogeneity of B7-H3 and HER2 in EPN, which may lead to target-specific impacts on CAR T-cell persistence. Indeed, results suggest that lower antigen density of HER2 may be superior for sustained 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.

Proofreading mechanisms of the innate immune receptor RIG-I: distinguishing self and viral RNA.

The RIG-I-like receptors (RLRs), comprising retinoic acid-inducible gene I (RIG-I), melanoma differentiation-associated gene 5 (MDA5), and laboratory of genetics and physiology 2 (LGP2), are pattern recognition receptors belonging to the DExD/H-box RNA helicase family of proteins. RLRs detect viral RNAs in the cytoplasm and respond by initiating a robust antiviral response that up-regulates interferon and cytokine production. RIG-I and MDA5 complement each other by recognizing different RNA features, and LGP2 regulates their activation. RIG-I's multilayered RNA recognition and proofreading mechanisms ensure accurate viral RNA detection while averting harmful responses to host RNAs. RIG-I's C-terminal domain targets 5'-triphosphate double-stranded RNA (dsRNA) blunt ends, while an intrinsic gating mechanism prevents the helicase domains from non-specifically engaging with host RNAs. The ATPase and RNA translocation activity of RIG-I adds another layer of selectivity by minimizing the lifetime of RIG-I on non-specific RNAs, preventing off-target activation. The versatility of RIG-I's ATPase function also amplifies downstream signaling by enhancing the signaling domain (CARDs) exposure on 5'-triphosphate dsRNA and promoting oligomerization. In this review, we offer an in-depth understanding of the mechanisms RIG-I uses to facilitate viral RNA sensing and regulate downstream activation of the immune system.

Blepharophimosis with intellectual disability and Helsmoortel-Van Der Aa Syndrome share episignature and phenotype.

Blepharophimosis with intellectual disability (BIS) is a recently recognized disorder distinct from Nicolaides-Baraister syndrome that presents with distinct facial features of blepharophimosis, developmental delay, and intellectual disability. BIS is caused by pathogenic variants in SMARCA2, that encodes the catalytic subunit of the superfamily II helicase group of the BRG1 and BRM-associated factors (BAF) forming the BAF complex, a chromatin remodeling complex involved in transcriptional regulation. Individuals bearing variants within the bipartite nuclear localization (BNL) signal domain of ADNP present with the neurodevelopmental disorder known as Helsmoortel-Van Der Aa Syndrome (HVDAS). Distinct DNA methylation profiles referred to as episignatures have been reported in HVDAS and BAF complex disorders. Due to molecular interactions between ADNP and BAF complex, and an overlapping craniofacial phenotype with narrowing of the palpebral fissures in a subset of patients with HVDAS and BIS, we hypothesized the possibility of a common phenotype-specific episignature. A distinct episignature was shared by 15 individuals with BIS-causing SMARCA2 pathogenic variants and 12 individuals with class II HVDAS caused by truncating pathogenic ADNP variants. This represents first evidence of a sensitive phenotype-specific episignature biomarker shared across distinct genetic conditions that also exhibit unique gene-specific episignatures.

Enhancing secure transmission and key distribution for ultrahigh-order QAM via delta-sigma modulation and discrete memristive-enhanced chaos.

In this Letter, we propose a method for ultrahigh-order QAM secure transmission and key distribution based on delta-sigma modulation (DSM) and discrete memristive-enhanced chaos (DMEC). The disturbance vectors generated by the DMEC scramble the DSM signals in both frequency and time domains, resulting in highly secure DSM signals. Through the key modulation and power adjustment and then superimposing them on the encrypted signals, the method achieves simultaneous transmission of keys and signals without the need for additional spectral resources. This approach allows for secure communication with continuous key iteration and updates, offering an effective solution for implementing "one-time pad" encryption. In the experimental demonstration, we achieved a secure transmission and key distribution of a 16384QAM signal at a rate of 17.09 Gb/s over 25 km in an intensity-modulated direct detection (IMDD) system, based on DSM.

Comparative analysis of Bcl-2 family protein overexpression in CAR T cells alone and in combination with BH3 mimetics.

Approximately 50% of patients with hematologic malignancies relapse after chimeric antigen receptor (CAR) T cell treatment; mechanisms of failure include loss of CAR T persistence and tumor resistance to apoptosis. We hypothesized that both of these challenges could potentially be overcome by overexpressing one or more of the Bcl-2 family proteins in CAR T cells to reduce their susceptibility to apoptosis, both alone and in the presence of BH3 mimetics, which can be used to activate apoptotic machinery in malignant cells. We comprehensively investigated overexpression of different Bcl-2 family proteins in CAR T cells with different signaling domains as well as in different tumor types. We found that Bcl-xL and Bcl-2 overexpression in CAR T cells bearing a 4-1BB costimulatory domain resulted in increased expansion and antitumor activity, reduced exhaustion, and decreased apoptotic priming. In addition, CAR T cells expressing either Bcl-xL or a venetoclax-resistant Bcl-2 variant led to enhanced antitumor efficacy and survival in murine xenograft models of lymphoma and leukemia in the presence or absence of the BH3 mimetic venetoclax, a clinically approved BH3 mimetic. In this setting, Bcl-xL overexpression had stronger effects than overexpression of Bcl-2 or the Bcl-2(G101V) variant. These findings suggest that CAR T cells could be optimally engineered by overexpressing Bcl-xL to enhance their persistence while opening a therapeutic window for combination with BH3 mimetics to prime tumors for apoptosis.

Spectral changes in skin blood flow during pressure manipulations or sympathetic stimulation.

Skin blood flow is commonly determined by laser Doppler flowmetry (LDF). It has been suggested that pathophysiological conditions can be assessed by analysis of specific frequency domains of the LDF signals. We tested whether physiological stimuli that activate myogenic and neurogenic mechanisms would affect relevant portions of the laser Doppler spectrum. LDF sensors were placed on the right forearm of 14 healthy volunteers for myogenic (six females) and 13 for neurogenic challenge (five females). Myogenic responses were tested by positioning the arm ∼50° above/below heart level. Neurogenic responses were tested by immersing the left hand into an ice slurry with and without topical application of local anaesthetic. Short-time Fourier analyses were computed over the range of 0.06 to 0.15Hz for myogenic and 0.02 to 0.06Hz for neurogenic. No significant differences in spectral density were observed (P=0.40) in the myogenic range with arm above (7±54×10-4dB) and below heart (7±14×10-4dB). Neurogenic spectral density showed no significant increase from baseline to cold pressor test (0.0017±0.0013 and 0.0038±0.0039dB; P=0.087, effect size 0.47). After application of anaesthetic, neurogenic spectral density was unchanged between the baseline and cold pressor test (0.0014±0.0025 and 0.0006±0.0005dB; P=0.173). These results suggest that changes in the myogenic and neurogenic spectral density of LDF signals did not fully reflect the skin vascular function activated by pressure manipulation and sympathetic stimulation. Therefore, LDF myogenic and neurogenic spectral density data should be interpreted with caution.