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In this paper, a novel non-illuminated Graphical Electromagnetic Computing Method (NI-GRECO) based on GPU acceleration has been proposed. It has the advantages of high accuracy and efficiency compared to the traditional GRECO method, and does not suffer from the limitation in the illumination model anymore. In this NI-GRECO we also proposed a pixel subdivision technique, which can improve the display accuracy of the target model and then increase computing accuracy. In case of the data output, the method of data extraction by using the Frame Buffer Object (FBO) can also improve computing accuracy. In this way, the data output is no longer subject to color restriction. The extraction accuracy of the normal vector increased from 1/256 to 1/1000000. Additionally, the novel method has a significant speedup, which is up to 36 times than the original GRECO method for a sphere RCS simulation thanks to the parallel GPU processing. [ABSTRACT FROM AUTHOR]

Metabolomics facilitates investigation of the mechanisms of disease and screening for biomarkers. Here, a gas chromatography-mass spectrometry (GC-MS) and ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based metabolomics approach was employed to identify plasma biomarkers of acute liver failure (ALF) in pigs. Blood was collected from pigs at 12 h intervals during ALF. Hepatic injury was quantified by determining liver function and histopathology. Based on a multivariate data matrix and pattern recognition, two upregulated metabolites, namely, amino acids and conjugated bile acids, and two downregulated metabolites, lysophosphatidylcholines (LPCs) and phosphatidylcholines (PCs), were identified. All of these metabolites showed a strong relationship with the extent of liver injury. Amino acids were biomarkers of the severity of liver impairment, conjugated bile acids were predictive of early stage liver damage, and LPCs and PCs were related to the prognosis of liver injury. In conclusion, our results demonstrated the occurrence of marked metabolic disturbances during ALF and that integrated metabolomics analysis facilitates identification of biomarkers of disease. [ABSTRACT FROM AUTHOR]