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As a core component of hydraulic systems, hydraulic pumps generate vibration signals that contain abundant key features reflecting the operational state of internal machinery. However, most existing fault diagnosis methods rely solely on single-channel vibration data, neglecting the correlations and complementarities among multi-channel signals, which results in unstable and less accurate diagnostic outcomes. To address this limitation, this study proposes an intelligent fault diagnosis approach for hydraulic pumps based on multi-source signal fusion and a dual attention mechanism. First, vibration, pressure, and acoustic signals are transformed into time-frequency feature images, and an RGB image fusion strategy is applied to map the time-frequency representations of different signals into the individual channels of a color image. Subsequently, a convolutional neural network incorporating enhanced channel and spatial attention mechanisms is constructed to extract features from the fused images and perform classification. Experimental results demonstrate that the proposed method significantly improves fault diagnosis performance and outperforms other deep learning-based approaches, offering a novel strategy for intelligent hydraulic pump diagnostics with promising engineering applications.

In the field of bearing fault diagnosis, the phenomenon of stochastic resonance (SR) has been proven to effectively utilize noise to enhance weak features of early faults. The classical bistable stochastic resonance (CBSR) model, as one of the most widely applied SR methods, faces limitations in feature enhancement due to the complexity of parameter tuning and the issue of output saturation. To address these issues, this paper proposes an improved piecewise unsaturated bistable stochastic resonance (PUBSR) method, which employs an asymmetric potential function to effectively mitigate the output saturation problem of CBSR. Additionally, the cuckoo search (CS) algorithm is used to optimize the potential function parameters, enhancing fault diagnosis performance. Finally, the proposed method is applied to both simulated signals and early bearing fault engineering data. The results demonstrate that compared to the CBSR method, the proposed approach more than doubles the spectral peak value when extracting characteristic frequencies, significantly improving the identifiability of fault features and diagnostic accuracy.

BackgroundLung adenocarcinoma (LUAD) has a very high morbidity and mortality rate, and its pathogenesis and treatment are still in the exploratory stage. Fatty acid metabolism plays a significant role in tumorigenesis, progression, and immune regulation. However, the gene expression of fatty acid metabolism in patients with LUAD and its relationship with prognosis remain unclear.MethodsWe collected 309 fatty acid metabolism-related genes, established a LUAD risk model based on The Cancer Genome Atlas (TCGA) using Least Absolute Shrinkage Selection Operator (LASSO) regression analysis, and divided LUAD patients into high-risk and low-risk groups, which were further validated using the Gene Expression Omnibus (GEO) database. The nomogram, principal component analysis (PCA), and receiver operating characteristic (ROC) curves showed that the model had the best predictive performance. The ROC curves and calibration plots confirmed that the nomogram had good predictive power. We further analyzed the differences in clinical characteristics, immune cell infiltration, immune-related functions, chemotherapy drug sensitivity, and immunotherapy efficacy between the high-risk and low-risk groups. We also analyzed the enrichment pathways and protein–protein interaction (PPI) networks of different genes in the high-risk and low-risk groups to screen for target genes and further explored the correlation between target genes and differences in survival prognosis, clinical characteristics, gene mutations, and immune cells.ResultsRisk score and staging are independent prognostic factors for patients with LUAD. The high-risk group had lower immune cell infiltration, was more sensitive to chemotherapeutic agents, and had a poorer survival prognosis. We also obtained three pivotal genes with poor survival prognosis in the high expression group, which were strongly associated with clinical symptoms and immune cells.ConclusionRisk score and staging are independent prognostic factors for patients with LUAD. The high-risk group had lower immune cell infiltration, was more sensitive to chemotherapeutic agents, and had a poorer survival prognosis. We also obtained three survival prognosis-associated target genes that are closely associated with clinical symptoms and immune cells and may be potential targets for immune-targeted therapy in LUAD.

Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of multiple pressure signals through the Multi-source Information Fusion (MSIF) method. The comprehensive expression of device status information is obtained. After that, this paper proposes a fault detection Algorithm GA-SVDD-neg, which uses Genetic Algorithm (GA) to optimize Support Vector Data Description with negative examples (SVDD-neg). Through joint optimization with the Mutual Information (MI) feature selection algorithm, the features that are most sensitive to the state deterioration of the adjustment hydraulic servomotor are selected. Experiments show that the MI algorithm has a better performance than other feature dimensionality reduction algorithms in the field of the abnormal detection of adjustment hydraulic servomotors, and the GA-SVDD-neg algorithm has a stronger robustness and generality than other anomaly detection algorithms. In addition, to make full use of the advantages of deep learning in automatic feature extraction and classification, this paper realizes the fault diagnosis of the adjustment hydraulic servomotor based on 1D Convolutional Neural Network (1DCNN). The experimental results show that this algorithm has the same superior performance as the traditional algorithm in feature extraction and can accurately diagnose the known faults of the adjustment hydraulic servomotor. This research is of great significance for the intelligent transformation of adjustment hydraulic servomotors and can also provide a reference for the fault warning and diagnosis of the Electro-Hydraulic (EH) system of the same type of steam turbine.

A high-pressure hydraulically adjusted servomotor is an electromechanical–hydraulic integrated system centered on a servo valve that plays a crucial role in ensuring the safe and stable operation of steam turbines. To address the issues of difficult fault diagnoses and the low maintenance efficiency of adjusted hydraulic servomotors, this study proposes a model for detecting abnormalities of hydraulically adjusted servomotors. This model uses a multi-scale one-dimensional residual neural network (M1D_ResNet) for feature extraction and a genetic algorithm (GA)-optimized support vector data description (SVDD). Firstly, the multi-scale features of the vibration signals of the hydraulically adjusted servomotor were extracted and fused using one-dimensional convolutional blocks with three different scales to construct a multi-scale one-dimensional residual neural network binary classification model capable of recognizing normal and abnormal states. Then, this model was used as a feature extractor to create a feature set of normal data. Finally, an abnormal detection model for the hydraulically adjusted servomotor was constructed by optimizing the support vector data domain based on this feature set using a genetic algorithm. The proposed method was experimentally validated on a hydraulically adjusted servomotor dataset. The results showed that, compared with the traditional single-scale one-dimensional residual neural network, the multi-scale feature vectors fused by the multi-scale one-dimensional convolutional neural network contained richer state-sensitive information, effectively improving the performance of detecting abnormalities in the hydraulically adjusted servomotor.

Thymomas and thymic carcinomas are malignant thymic epithelial tumors (TETs) with poor outcomes if non-resectable. However, the tumorigenesis, especially the metabolic mechanisms involved, is poorly studied. Untargeted metabolomics analysis was utilized to screen for differential metabolic profiles between thymic cancerous tissues and adjunct noncancerous tissues. Combined with transcriptomic data, we comprehensively evaluated the metabolic patterns of TETs. Metabolic scores were constructed to quantify the metabolic patterns of individual tumors. Subsequent investigation of distinct clinical outcomes and the immune landscape associated with the metabolic scores was conducted. Two distinct metabolic patterns and differential metabolic scores were identified between TETs, which were enriched in a variety of biological pathways and correlated with clinical outcomes. In particular, a high metabolic score was highly associated with poorer survival outcomes and immunosuppressive status. More importantly, the expression of two prognostic genes (ASNS and BLVRA) identified from differential metabolism-related genes was significantly associated with patient survival and may play a key role in the tumorigenesis of TETs. Our findings suggest that differential metabolic patterns in TETs are relevant to tumorigenesis and clinical outcome. Specific transcriptomic alterations in differential metabolism-related genes may serve as predictive biomarkers of survival outcomes and potential targets for the treatment of patients with TETs.

Cervical squamous cell carcinoma (CSCC) represents a significant global health concern among females. Identifying new biomarkers and therapeutic targets is pivotal for improving the prognosis of CSCC. This study investigates the prognostic relevance of CCZ1 in CSCC and elucidates its downstream pathways and targets using a combination of bioinformatics analysis and experimental validation. Transcriptomic analysis of 239 CSCC and 3 normal cervical samples from The Cancer Genome Atlas database reveals a marked upregulation of CCZ1 mRNA levels in CSCC, and elevated CCZ1 mRNA levels were associated with poor prognosis. Immunohistochemical analysis of clinical samples also confirmed these findings. Furthermore, functional assays, including Cell Counting Kit-8, colony formation, Transwell, and flow cytometry, elucidated the influence of CCZ1 on CSCC cell proliferation, migration, invasion, and cell cycle progression. Remarkably, CCZ1 knockdown suppressed CSCC progression both in vitro and in vivo. Mechanistically, CCZ1 knockdown downregulated MMP2 and MMP17 expression. Restoring MMP2 or MMP17 expression rescued phenotypic alterations induced by CCZ1 knockdown. Hence, CCZ1 promotes CSCC progression by upregulating MMP2 and MMP17 expression, emerging as a novel biomarker in CSCC and presenting potential as a therapeutic target in CSCC.

ABSTRACT Background Previous studies reported that patients with keloid, acne, or hypertrophic scar (HS) had lower serum vitamin D levels compared to healthy controls. Whereas, these works failed to verify their causal relationships. Hence, we performed a Mendelian randomization (MR) aimed to investigate the causal relationship between vitamin D and keloid, acne, or HS. Methods Utilizing summary datasets from genome‐wide association studies, we conducted a two‐sample MR analysis to explore the causal relationship between vitamin D and keloid, acne, or HS. The inverse‐variance weighted MR approach served as the primary analysis, with additional support from various sensitivity methods, including MR‐Egger, weighted median, simple mode, weighted mode, and MR‐PRESSO, to enhance the reliability of our findings. Results From our MR analyses, no causal relationships were found between vitamin D level and keloid (OR: 1.15, 95% CI: 0.853–1.16, p = 0.36), acne (OR: 0.94, 95% CI: 0.66–1.30, p = 0.65) or HS (OR: 1.20, 95% CI 0.79–1.82, p = 0.40) formation. After eliminating outliers through MR‐PRESSO, no significant associations were found between serum vitamin D level and keloid, acne, as well as HS. Conclusion Our findings, backed by rigorous IV selection and consistent outcomes across various MR approaches, suggest no causal link between serum 25(OH)D levels and keloid, acne, or HS. While vitamin D is involved in wound healing, its connection to these conditions may not be straightforward, emphasizing the need for further research.

Primary Sjögren’s syndrome (pSS) is a chronic autoimmune disease characterized by lymphocytic infiltration and tissue destruction of exocrine glands such as salivary glands. Although the formation of ectopic lymphoid tissue in exocrine glands and overproduction of autoantibodies by autoreactive B cells highlight the critical involvement of B cells in disease development, the precise roles of various B cell subsets in pSS pathogenesis remain partially understood. Current studies have identified several novel B cell subsets with multiple functions in pSS, among which autoreactive age-associated B cells, and plasma cells with augmented autoantibody production contribute to the disease progression. In addition, tissue-resident Fc Receptor-Like 4 (FcRL4) + B cell subset with enhanced pro-inflammatory cytokine production serves as a key driver in pSS patients with mucosa-associated lymphoid tissue (MALT)-lymphomas. Recently, regulatory B (Breg) cells with impaired immunosuppressive functions are found negatively correlated with T follicular helper (Tfh) cells in pSS patients. Further studies have revealed a pivotal role of Breg cells in constraining Tfh response in autoimmune pathogenesis. This review provides an overview of recent advances in the identification of pathogenic B cell subsets and Breg cells, as well as new development of B-cell targeted therapies in pSS patients.

Tree restoration can cool or warm the local climate through biophysical processes. However, the magnitude of these effects remains unconstrained at large scales, as most previous observational studies rely on land surface temperature (Ts) rather than the more policy-relevant air temperature (Ta). Using satellite observations, we show that Ta responds to tree cover change at only 15–30% of the magnitude observed in Ts. This difference is supported by independent evidence from site observations, and can be attributed to the reduced aerodynamic resistance and the resultant flatter near-surface temperature profiles in forests compared to non-forests. At mid- or high-latitudes, the maximum seasonal biophysical Ta warming or cooling only accounts for approximately 10% of the equivalent climate effect of carbon sequestration in terms of magnitude, whereas the biophysical Ts effect can reach 40%. These findings highlight the importance of selecting the appropriate temperature metric in different applications to avoid exaggerating or underestimating the biophysical impacts of forestation. The authors provide observational evidence that the biophysical cooling or warming effect of potential tree restoration is 4–5 times stronger for space-measured land surface temperature than for the more policy-relevant near-surface air temperature.