Search Results (6,812)

Background: Type 2 diabetes mellitus (T2DM) is a metabolic disorder, and urinary exosomal microRNAs (miRNAs) were utilized as potential disease prediction or diagnostic biomarkers in numerous studies. This study investigated the differential expression of urinary exosomal miRNAs between non-diabetes mellitus (NDM) individuals and those with T2DM. Aim: To elucidate the association between urinary exosomal miRNAs and T2DM. Methods: We recruited patients diagnosed with T2DM and NDM individuals in West China Hospital, Sichuan University, from November 2023 to February 2024. Subsequently, we performed sequencing of urinary exosomal microRNAs in both groups. The obtained sequencing results were further validated using RT-qPCR in both the training set and the validation set. Additionally, we conducted logistic regression analysis and Spearman correlation analysis on miRNAs with significant differential expression, as well as analysis of their biological functions. Results: A total of 118 urine samples were collected, 59 from individuals diagnosed with T2DM and 59 from NDM. There were differentially expressed miR-183-5p (p = 0.034) and miR-125a-5p (p = 0.008) between the two groups. Furthermore, multivariate regression analysis demonstrated that higher miR-125a-5p levels were negatively associated with the risk of T2DM (p = 0.044; OR: 0.046; 95% CI: 0.002, 0.922). Bioinformatics analysis indicated that the target genes of miR-183-5p were predominantly involved in insulin signaling and glucose transport processes, while those target genes of miR-125a-5p primarily mediated autophagy. Conclusions: miR-183-5p and miR-125a-5p might be involved in the pathogenesis of T2DM, while higher urinary exosomal miR-125a-5p was negatively associated with the risk of T2DM. Full article

The ARF gene family plays a vital role in regulating multiple aspects of plant growth and development. However, detailed research on the role of the ARF family in regulating flower development in petunia and other plants remains limited. This study investigates the distinct roles of PhARF5 and PhARF19a in Petunia hybrida flower development. Phylogenetic analysis identified 29 PhARFs, which were grouped into four clades. VIGS-mediated silencing of PhARF5 and PhARF19a led to notable phenotypic changes, highlighting their non-redundant functions. PhARF5 silencing resulted in reduced petal number and limb abnormalities, while PhARF19a silencing disrupted corolla tube formation and orientation. Both genes showed high expression in the roots, leaves, and corollas, with nuclear localization. The transcriptomic analysis revealed significant overlaps in DEGs between PhARF5 and PhARF19a silencing, indicating shared pathways in hormone metabolism, signal transduction, and stress responses. Phytohormone analysis confirmed their broad impact on phytohormone biosynthesis, suggesting involvement in complex feedback mechanisms. Silencing PhARF5 and PhARF19a led to differential transcription of numerous genes related to hormone signaling pathways beyond auxin signaling, indicating their direct or indirect crosstalk with other phytohormones. However, significant differences in the regulation of these signaling pathways were observed between PhARF5 and PhARF19a. These findings reveal the roles of ARF genes in regulating petunia flower development, as well as the phylogenetic distribution of the PhARFs involved in this process. This study provides a valuable reference for molecular breeding aimed at improving floral traits in the petunia genus and related species. Full article

The accessory olfactory bulb (AOB) processes chemical signals crucial for species-specific socio-sexual behaviors. There is limited information about the AOB of wild rodents, and this study aims to characterize the neurochemical organization of the AOB in the fossorial water vole (Arvicola scherman), a subterranean Cricetidae rodent. We employed histological, immunohistochemical, and lectin-histochemical techniques. The AOB of these voles exhibits a distinct laminar organization, with prominent mitral cells and a dense population of periglomerular cells. Lectin histochemistry and G-protein immunohistochemistry confirmed the existence of an antero-posterior zonation. Immunohistochemical analysis demonstrated significant expression of PGP 9.5, suggesting its involvement in maintaining neuronal activity within the AOB. In contrast, the absence of SMI-32 labelling in the AOB, compared to its strong expression in the main olfactory bulb, highlights functional distinctions between these two olfactory subsystems. Calcium-binding proteins allowed the characterization of atypical sub-bulbar nuclei topographically related to the AOB. All these features suggest that the AOB of Arvicola scherman is adapted for enhanced processing of chemosensory signals, which may play a pivotal role in its subterranean lifestyle. Our results provide a foundation for future studies exploring the implications of these adaptations, including potential improvements in the management of these vole populations. Full article

Accurate prediction of the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and efficiency of industrial systems. This study introduces a novel methodology integrating advanced machine learning and optimization techniques to address this challenge. (1) A transformer-attention model was developed to process segmented vibration signals, effectively capturing complex patterns. The model showed better performance than traditional approaches, with an RMSE of 0.989. (2) A Deep Neural Network (DNN) was designed to predict the extended RUL of bearings after laser shock peening (LSP) remanufacturing. The fruit fly optimization (FFO) algorithm was employed to optimize the remanufacturing parameters; a 29.33% improvement was achieved in fitness compared to the baseline. (3) The DNN model predictions were validated against Finite Element Analysis (FEA) simulations, with a low relative error of 2.5% to 5.8%; the model showed good accuracy in capturing the effects of optimized LSP parameters on bearing life extension. Full article

The Nao-zhou stock large yellow croaker (Larimichthys crocea) is a unique economic seawater fish species in China and exhibits significant dimorphism in both male and female phenotypes. Cultivating all-female seedlings can significantly improve breeding efficiency. To accelerate the cultivation process of all female seedlings of this species, it is necessary to deeply understand the regulatory mechanisms of sexual differentiation and gonadal development. This study used Illumina high-throughput sequencing to sequence the transcriptome of the testes and ovaries of Nao-zhou stock large yellow croaker to identify genes and molecular functions related to sex determination. A total of 10,536 differentially expressed genes were identified between males and females, including 5682 upregulated and 4854 downregulated genes. Functional annotation screened out 70 important candidate genes related to sex, including 34 genes highly expressed in the testis (including dmrt1, foxm1, and amh) and 36 genes highly expressed in the ovary (including gdf9, hsd3b1, and sox19b). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis found that differentially expressed genes were significantly enriched in nine signaling pathways related to sex determination and gonadal development, including steroid hormone biosynthesis, MAPK signaling pathway, and the TGF-beta signaling pathway. By screening sex-related differentially expressed genes and mapping protein–protein interaction networks, hub genes such as dmrt1, amh, and cyp19a1a were found to be highly connected. The expression levels of 15 sex-related genes, including amh, dmrt1, dmrt2a, foxl1, and zp3b, were determined by qRT–PCR and RNA sequencing. This study screened for differentially expressed genes related to sex determination and differentiation of Nao-zhou stock large yellow croaker and revealed the signaling pathways involved in gonad development of male and female individuals. The results provide important data for future research on sex determination and differentiation mechanisms, thereby providing a scientific basis for the cultivation of all-female seedlings. Full article

In capacitance-coupled voltage-sensing technology, the degree of coupling capacitance is affected by the sensing area, relative position deviation, and other factors, and thus the measurement coefficient is often difficult to determine accurately and presents greater implementation difficulties in actual deployment. This paper proposes a dynamic reconfiguration based on the measurement circuit topology of the voltage sensor adaptive calibration method in order to measure voltage sensor gain in the process of automatic measuring. Firstly, the basic principle of voltage measurement is introduced, and the self-calibration method is proposed, considering the influence of the sensing area and the relative position error on the change in the coupling capacitance. On this basis, the influence of calibration accuracy on sensor structure parameters is analyzed using network sensitivity analysis, and the parameter selection principle is given, according to which the selection criterion of parameter optimization is formulated to complete the sensor design. By analyzing the coupling effect of the three-phase measurement, the installation method of the sensing structure is proposed. An experimental platform is built to test the accuracy of the voltage measurement of the sensor under laboratory conditions. The experimental results show that the maximum relative error of the voltage measurement amplitude is 2.24%. In order to verify the feasibility of the sensor technology designed, the measurement models that integrate communication, acquisition, and processing are installed on both ends of the circuit breaker wire, and the voltage waveform generated during the circuit breaker closing process is recorded in real time. The experimental results show that the sensor technology can accurately record the voltage waveform of the signal to be measured, and the feasibility of its application in switchgear equipment signal measurement is preliminarily verified by the results. Full article

Supernumerary teats (SNTs) or nipples often emerge around the mammary line. This study performed a genome-wide selective sweep analysis (GWS) at the copy number variant (CNV) level using two selected signal calculation methods (V_ST and F_ST) to identify candidate genes associated with SNTs in goats. A total of 12,310 CNVs were identified from 37 animals and 123 CNVs, with the top 1% V_ST values including 84 candidate genes (CDGs). Of these CDGs, minichromosome maintenance complex component 3, ectodysplasin A receptor associated via death domain, and cullin 5 demonstrated functions closely related to mammary gland development. In addition, 123 CNVs with the top 1% F_ST values were annotated to 97 CDGs. 5-Hydroxytryptamine receptor 2A, CCAAT/enhancer-binding protein alpha, and the polymeric immunoglobulin receptor affect colostrum secretion through multiple signaling pathways. Two genes, namely, RNA-binding motif protein 46 and β-1,3-galactosyltransferase 5, showed a close relation to mammary gland development. Six CNVs were identified and annotated to five genes by intersecting the top 1% of candidate CNVs with both parameters. These genes include LOC102185621, LOC102190481, and UDP-glucose pyrophosphorylase 2, which potentially affect the occurrence of BC through multiple biological processes, such as cell detoxification, glycogen synthesis, and phospholipid metabolism. In conclusion, we discovered numerous genes related to mammary development and breast cancer (BC) through a GWS, which suggests the mechanism of SNTs in goats and a certain association between mammary cancer and SNTs. Full article

To investigate the soil-specific metabolites of Codonopsis pilosula under different stubble management practices, this study analyzed differentially abundant metabolites in the rhizosphere soils of rotational (DS) and continuous (LS) cropping systems via liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based metabolomic approaches. The results revealed that 66 metabolites, including amino acids and their derivatives, nucleic acids, alcohols, organic acids, amines, fatty acids, purines, and sugars, were significantly different (p < 0.05) between the DS and LS groups. Under continuous cropping, the levels of amines, fatty acids, organic acids, and sugars in the rhizosphere soil were significantly greater (p < 0.05) than those under rotational cropping, whereas the levels of amino acids and their derivatives, nucleic acids, and purines and pyrimidines were significantly lower (p < 0.05). KEGG pathway enrichment analysis revealed that these differentially abundant metabolites were enriched in metabolic pathways such as amino acid metabolism (e.g., alanine, aspartate, and glutamate metabolism), carbon metabolism, the cAMP signaling pathway, ABC transporter proteins, phenylalanine metabolism, and the biosynthesis of plant secondary metabolites. These metabolic pathways were involved in osmoregulation, energy supply, and resilience in plants. In conclusion, inter-root soil metabolites in rotational and continuous cropping of Codonopsis pilosula were able to influence soil physicochemical properties and microbial populations by participating in various biological processes. Full article

Cocoa drying is the post-harvest thermal process used to condition the beans to a moisture content between 6.5 and 7% for storage and further processing. Convective drying is an energy-intensive process where time and temperature are considered critical factors for the degradation of bioactive compounds in edible products. In the present study, the energy parameters, vibrational spectroscopy, and changes in bioactive compounds of cocoa beans were studied during thin-layer hot air drying at 50 °C, 60 °C, and 70 °C. Moisture loss, specific energy consumption (SEC), energy efficiency, total phenolics (TPs), total flavonoids (TFs), and antioxidant activity (DPPH) were determined. Fourier transform infrared (FT-IR) spectroscopy with attenuated total reflectance (ATR) was used to characterize the samples, and a multivariate analysis was applied to find interactions among the components. The obtained SEC was 18,947.30–24,469.51 kJ/kg, and the energy efficiency was 9.73–12.31%. When the temperature was 70 °C, the best values for SEC and energy efficiency were obtained. The results also showed that the convective drying generated changes in the TP levels for the three temperatures, mainly after 300 min, with maximum levels between 360 and 600 min, at 70 °C; however, it does not have a clear relationship with the TFs and the antioxidant activity. The FT-IR and the multivariate analysis revealed changes in several signals in the 1800 to 400 cm⁻¹ range, confirming the variation in the associated signal with phenolic compounds. Full article

In lightweight design, the usage of different optimised materials is widespread. The interfaces between two different materials are prone to damage and, therefore, the Structural Health Monitoring (SHM) of these areas is of interest. A new method for the damage evaluation of joints is developed and validated. The released mechanical energy (RME) during static loading of a metal–composite lap shear joint is considered as a damage assessment parameter and is set into relation to the detected Acoustic Emission (AE) energy. Eleven specimens with identical geometry but different surface treatments are used to form a statistical database for the method, i.e. to calculate the energy ratio and the fluctuation range, and the twelfth specimen is used for the validation of the method. The energy ratio varies significantly, but, considering the fluctuation analysis, the RME with a known range can be predicted on the basis of the AE signal. The whole process is repeated twelve times to validate the methodology. This method can be applied to different geometries and load cases without sophisticated modelling of the damage behaviour. However, load–displacement curves of the pristine joint need to be known, and the monitored joints need to be damage-tolerant and must show similar damage behaviour. Full article

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. Full article

To tackle the issue of poor accuracy in single-snapshot data processing for Direction of Arrival (DOA) estimation in passive radar systems, this paper introduces a method for judiciously leveraging multi-snapshot data. This approach effectively enhances the accuracy of DOA estimation and spatial angle resolution in passive radar systems. Additionally, in response to the non-convex nature of the mixed norm, we propose a hyperbolic tangent model as a replacement, transforming the problem into a directly solvable convex optimization problem. The rationality of this substitution is thoroughly demonstrated. Lastly, through a comparative analysis with existing discrete grid DOA estimation methods, we illustrate the superiority of the proposed approach, particularly under conditions of medium signal-to-noise ratio, varying numbers of snapshots, and close target angles. This method is less affected by the number of array elements, and is more usable in practices verified in real-world scenarios. Full article

Cremastrae Pseudobulbus Pleiones Pseudobulbus (CPPP) is derived from the dried pseudobulb of the orchid family plants Cremastra appendiculata (D.Don) Makino, Pleione bulbocodioides (Franch.) Rolfe, or Pleione yunnanensis Rolfe, and has the properties of clearing heat, detoxification, resolving phlegm, and dispersing nodules. It is frequently used for the treatment of various malignant tumors in clinical practice, especially lung cancer. CPPP is divided into two commercial specifications in the market, Maocigu (MCG) and Bingqiuzi (BQZ). However, owing to a lack of appropriate research strategies, the active ingredients and molecular mechanisms involved have not yet been clarified. This study intended to discover the combination of effective anti-lung-cancer ingredients in CPPP and explore their potential mechanisms of action. In this study, UHPLC-MS fingerprints of MCG and BQZ were established separately. Inhibitory effects on the proliferative viability and migratory ability of A459 and H1299 cells were evaluated as pharmacodynamic indicators. GRA and BCA were used to determine spectrum–effect relationships. Next, the identification and analysis of components of drug-containing serum were performed using UHPLC-Q-Exactive Orbitrap MS. Then, the results of the two analyses were combined to jointly screen out the anti-lung-cancer candidate active monomers of CPPP, and their in vitro activities were verified. Afterward, all effective ingredient combinations of MCG (MCGC) and BQZ (BQZC) were prepared according to their contents in the original medicinal materials. Their anti-lung-cancer activities in vitro and in vivo were compared and verified. Finally, we used the human lung cancer cell line A549 and the Lewis tumor xenograft model to investigate how BQZC would influence autophagy and apoptosis processes and the mechanisms involved. Overall, 11 predominant anti-lung-cancer active ingredients from CPPP were screened. Next, MCGC and BQZC were prepared according to their contents in the original medicinal materials, respectively, and their anti-tumor effects were equivalent to those of the original materials in vitro and in vivo. We found that BQZC could inhibit lung cancer cell growth and induce protective autophagy and apoptosis in lung cancer cells by activating the AMPK–mTOR–ULK1/BMF signaling pathway. These results provide important evidence for the clinical application and deep development of CPPP against tumors. Full article

Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive SAR data, there is an urgent need for the global perception and detection of interference. The existing RFI detection method usually only uses a single type of data for detection, ignoring the information association between the data at all levels of the real SAR product, resulting in some computational redundancy. Meanwhile, current deep learning-based algorithms are often unable to locate the range of RFI coverage in the azimuth direction. Therefore, a novel RFI processing framework from quick-looks to single-look complex (SLC) data and then to raw echo is proposed. We take the data of Sentinel-1 terrain observation with progressive scan (TOPS) mode as an example. By combining the statistics-assisted network with the sliding-window algorithm and the error-tolerant training strategy, it is possible to accurately detect and locate RFI in the quick looks of an SLC product. Then, through the analysis of the TOPSAR imaging principle, the position of the RFI in the SLC image is preliminarily confirmed. The possible distribution of the RFI in the corresponding raw echo is further inferred, which is one of the first attempts to use spaceborne SAR data to elucidate the RFI location mapping relationship between image data and raw echo. Compared with directly detecting all of the SLC data, the time for the proposed framework to determine the RFI distribution in the SLC data can be shortened by 53.526%. All the research in this paper is conducted on Sentinel-1 real data, which verify the feasibility and effectiveness of the proposed framework for radio frequency signals monitoring in advanced spaceborne SAR systems. Full article

This study presents an ultrasonic non-destructive method with convolutional neural networks (CNN) used for the detection of interface defects in adhesively bonded dissimilar structures. Adhesive bonding, as the weakest part of such structures, is prone to defects, making their detection challenging due to various factors, including surface curvature, which causes amplitude variations. Conventional non-destructive methods and processing algorithms may be insufficient to enhance detectability, as some influential factors cannot be fully eliminated. Even after aligning signals reflected from the sample surface and interface, in some cases, due to non-parallel interfaces, persistent amplitude variations remain, significantly affecting defect detectability. To address this problem, a proposed method that integrates ultrasonic NDT and CNN, and which is able to recognize complex patterns and non-linear relationships, is developed in this work. Traditional ultrasonic pulse-echo testing was performed on adhesive structures to collect experimental data and generate C-scan images, covering the time gate from the first interface reflection to the time point where the reflections were attenuated. Two classes of datasets, representing defective and defect-free areas, were fed into the neural network. One subset of the dataset was used for model training, while another subset was used for model validation. Additionally, data collected from a different sample during an independent experiment were used to evaluate the generalization and performance of the neural network. The results demonstrated that the integration of a CNN enabled high prediction accuracy and automation of the analysis process, enhancing efficiency and reliability in detecting interface defects. Full article