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This study proposes machine learning-based models to automatically evaluate the severity of myocardial infarction (MI) from physiological, clinical, and paraclinical features. Two types of machine learning models are investigated for the MI assessment: the classification models classify the presence of the infarct and the persistent microvascular obstruction (PMO), and the regression models quantify the Percentage of Infarcted Myocardium (PIM) of patients suspected of having an acute MI during their reception in the emergency department. The ground truth labels for these supervised models are derived from the corresponding Delayed Enhancement MRI (DE-MRI) exams and manual annotations of the myocardium and scar tissues. Experiments were conducted on 150 cases and evaluated with cross-validation. Results showed that for the MI (PMO inclusive) and the PMO (infarct exclusive), the best models obtained respectively a mean error of 0.056 and 0.012 for the quantification, and 88.67 and 77.33% for the classification accuracy of the state of the myocardium. The study of the features' importance also revealed that the troponin value had the strongest correlation to the severity of the MI among the 12 selected features. For the proposal's translational perspective, in cardiac emergencies, qualitative and quantitative analysis can be obtained prior to the achievement of MRI by relying only on conventional tests and patient features, thus, providing an objective reference for further treatment by physicians.

Organic geochemical analysis, organic petrology and fluid inclusion methods were employed to investigate a series of cuttings and cores from a single well for evaluation of the oil-generating potential of the Guangan structure in the Shichuan Basin. Information was obtained on the abundance and type of kerogens in the Mesozoic and Paleozoic strata in the region; the fluid inclusion study provided information about the maturity of organic matter in the samples. The results show that the Jurassic shales, as well as the Permian and Carboniferous carbonates, have good oil- and gas-generating potential. In addition, the Jurassic source rocks were shown to have reached the stage of oil generation, whereas the Permian and Carboniferous source rocks have reached the zone of gas generation. Evidence is given to show that the Carboniferous carbonates may be good petroleum reservoirs in this area.

154 core and outcrop samples of Proterozoic and Palaeozoic reservoir and non-reservoir strata were collected from Renqui, Subei, Guangxi, Sichuan, etc. Organic inclusions contained in the samples were studied by means of polarized light microscope, fluorescence microscope, micro-cooling-heating stage and compositional analysis. According to HC phase, colour, refractive index and fluorescent properties, organic inclusions can be classified as two braod types: HC inclusions and HC-containing inclusions, which can be further subdivided into eight subtypes. Also discussed in this paper are the types and peculiarities of organic inclusions, as well as the composition of gases, parameters for organic molecules and paleotemperature, which are considered to be useful indicators for oil and gas migration, evolution and prospective evaluation. Some typical examples are also given in this study.