Search

Background: The pregnant women with intrahepatic cholestasis were at high risk of fetal distress, preterm birth and unexpected stillbirth. Intrahepatic cholestasis of pregnancy (ICP) was mainly caused by disorder of bile acid metabolism, whereas the specific mechanism was obscure. Methods: We performed proteomics analysis of 10 ICP specimens and 10 placenta specimens from patients without ICP through data-independent acquisition (DIA) technique to disclose differentially expressed proteins. We executed metabolomic analysis of 30 ICP specimens and 30 placenta specimens from patients without ICP through UPLC-MS/MS to identify differentially expressed metabolites. Enrichment and correlation analysis was used to obtain the direct molecular insights of ICP development. The ICP rat models were constructed to validate pathological features. Results: The heatmap of proteomics analysis showed the top 30 up-regulated and 30 down-regulated proteins. The metabolomic analysis revealed 20 richer and 4 less abundant metabolites in ICP samples compared with placenta specimens from patients without ICP, and enrichment pathways by these metabolites included primary bile acid biosynthesis, cholesterol metabolism, bile secretion, nicotinate and nicotinamide metabolism, purine metabolism and metabolic pathways. Combined analysis of multiple omics results demonstrated that bile acids such as Glycohyocholic acid, Glycine deoxycholic acid, beta-Muricholic acid, Noncholic acid, cholic acid, Gamma-Mercholic Acid, alpha-Muricholic acid and Glycochenodeoxycholic Aicd were significantly associated with the expression of GLRX3, MYL1, MYH7, PGGT1B, ACTG1, SP3, LACTB2, C2CD5, APBB2, IPO9, MYH2, PPP3CC, PIN1, BLOC1S1, DNAJC7, RASAL2 and ATCN3 etc. The core protein ACAT2 was involved in lipid metabolic process and animal model showed that ACAT2 was up-regulated in placenta and liver of pregnant rats and fetal rats. The neonates had low birth weight and Safranin O-Fast green FCF staining of animal models showed that poor osteogenic and chondrogenic differentiation of fetal rats. Conclusion: Multiple metabolites-alpha-Muricholic acid, beta-Muricholic acid, Glycine deoxycholic acid and Glycochenodeoxycholic Acid etc. were perfect biomarkers to predict occurrence of ICP. Bile acids were significantly associated with varieties of protein expression and these proteins were differentially expressed in ICP samples. Our study provided several biomarkers for ICP detection and potential therapeutic targets for ICP development. [ABSTRACT FROM AUTHOR]

In scenarios such as nearshore and inland waterways, the ship spots in a marine radar are easily confused with reefs and shorelines, leading to difficulties in ship identification. In such settings, the conventional ARPA method based on fractal detection and filter tracking performs relatively poorly. To accurately identify radar targets in such scenarios, a novel algorithm, namely YOSMR, based on the deep convolutional network, is proposed. The YOSMR uses the MobileNetV3(Large) network to extract ship imaging data of diverse depths and acquire feature data of various ships. Meanwhile, taking into account the issue of feature suppression for small-scale targets in algorithms composed of deep convolutional networks, the feature fusion module known as PANet has been subject to a lightweight reconstruction leveraging depthwise separable convolutions to enhance the extraction of salient features for small-scale ships while reducing model parameters and computational complexity to mitigate overfitting problems. To enhance the scale invariance of convolutional features, the feature extraction backbone is followed by an SPP module, which employs a design of four max-pooling constructs to preserve the prominent ship features within the feature representations. In the prediction head, the Cluster-NMS method and α-DIoU function are used to optimize non-maximum suppression (NMS) and positioning loss of prediction boxes, improving the accuracy and convergence speed of the algorithm. The experiments showed that the recall, accuracy, and precision of YOSMR reached 0.9308, 0.9204, and 0.9215, respectively. The identification efficacy of this algorithm exceeds that of various YOLO algorithms and other lightweight algorithms. In addition, the parameter size and calculational consumption were controlled to only 12.4 M and 8.63 G, respectively, exhibiting an 80.18% and 86.9% decrease compared to the standard YOLO model. As a result, the YOSMR displays a substantial advantage in terms of convolutional computation. Hence, the algorithm achieves an accurate identification of ships with different trail features and various scenes in marine radar images, especially in different interference and extreme scenarios, showing good robustness and applicability. [ABSTRACT FROM AUTHOR]

Based on a constant volume combustion chamber, the initial ambient pressure effect on gasoline combustion performance and flame propagation of the active and passive pre-chamber ignition systems were studied. Compared with the passive pre-chamber, the active pre-chamber can significantly expand the lean combustion limit from 1.2 to 1.5, enhance the ignition and combustion performance, and speed up the combustion of the main combustion chamber. At slightly lean combustion conditions, the heat release performance has a positive correlation with the initial ambient pressure. As the premixed λ increases, the heat release performance and the initial ambient pressure begin to become negatively correlated. Ignition delay can be decreased by approximately 50% at an initial pressure of 0.75 MPa, premixed lambda of 1.2 with APC compared with PPC. The high ambient pressure (1.0 MPa) in the constant volume chamber greatly reduces the mixture entrainment ability of the jet flame at lean combustion conditions, thereby reducing the combustion speed and peak heat release rate. [ABSTRACT FROM AUTHOR]