Intelligent Generation of Cross Sections Using a Conditional Generative Adversarial Network and Application to Regional 3D Geological Modeling

The cross section is the basic data for building 3D geological models. It is inefficient to draw a large number of cross sections to build an accurate model. This paper reports the use of multi-source and heterogeneous geological data, such as geological maps, gravity and aeromagnetic data, by a conditional generative adversarial network (CGAN) and implements an intelligent generation method of cross sections to overcome the problem of inefficient modeling data based on CGAN. Intelligent generation of cross sections and 3D geological modeling are carried out in three different areas in Liaoning Province. The results show that: (a) the accuracy of the proposed method is higher than the GAN and Variational AutoEncoder (VAE) models, achieving 87%, 45% and 68%, respectively; (b) the 3D geological model constructed by the generated cross sections in our study is consistent with manual creation in terms of stratum continuity and thickness. This study suggests that the proposed method is significant for surmounting the difficulty in data processing involved in regional 3D geological modeling

The pharmacodynamic and differential gene expression analysis of PPAR α/δ agonist GFT505 in CDAHFD-induced NASH model.

Peroxisome proliferator-activated receptor α/δ (PPAR α/δ), regulating glucolipid metabolism and immune inflammation, has been identified as an effective therapeutic target in non-alcoholic steatohepatitis (NASH). Dual PPAR α/δ agonist, such as GFT505 (also known as elafibranor), demonstrated potential therapeutic effect for NASH in clinical trials. To profile the regulatory network of PPAR α/δ agonist in NASH, the choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) induced NASH model was used to test the pharmacodynamics and transcriptome regulation of GFT505 in this study. The results showed that GFT505 ameliorated hepatic steatosis, inflammation and fibrosis in CDAHFD mice model. RNA-sequencing yielded 3995 up-regulated and 3576 down-regulated genes with GFT505 treatment. And the most significant differentialy expressed genes involved in glucolipid metabolism (Pparα, Acox1, Cpt1b, Fabp4, Ehhadh, Fabp3), inflammation (Ccl6, Ccl9, Cxcl14) and fibrosis (Timp1, Lamc3, Timp2, Col3a1, Col1a2, Col1a1, Hapln4, Timp3, Pik3r5, Pdgfα, Pdgfβ, Tgfβ1, Tgfβ2) were confirmed by RT-qPCR. The down-regulated genes were enriched in cytokine-cytokine receptor interaction pathway and ECM-receptor interaction pathway, while the up-regulated genes were enriched in PPAR signaling pathway and fatty acid degradation pathway. This study provides clues and basis for further understanding on the mechanism of PPAR α/δ agonist on NASH