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Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework.

Coronary artery disease (CAD) in dyslipidemic and diabetic subjects remains the leading cause of death in the Western society. Current therapeutic strategies to prevent cardiovascular diseases are primarily based on the use of statins, which inhibit key enzyme in the cholesterol synthesis, HMG-CoA reductase. Another prominent risk factor for developing premature atherosclerosis is the low levels of high-density lipoprotein cholesterol (HDL-C). Despite documented benefits of statins a good proportion of individuals still remain at a higher risk of developing CAD. Therefore, focus has shifted on HDL-raising therapeutics to further improve the CV outcome. While Niacin and fenofibrate have not shown clinical benefits in two separate trials, results from CETP inhibitors, Torcetrapib and Dalcetrapib, in phase 3 clinical studies have been disappointing despite substantial increase in HDL-C. One of the challenges encountered with measuring plasma HDL as a biomarker for cardiovascular risk is the fact that the antiinflammatory HDL becomes pro-inflammatory after it undergoes oxidative modification in diabetics. Pro-inflammatory states have also been shown to reduce HDL functionality. Thus, the lack of translatability of animal results to humans has further added challenges in the HDL therapeutics area. These findings necessitated rethinking about HDL therapeutics leading to transition in HDL therapy concept that seems to focus more around HDL functionality rather than HDL level. This PhD dissertation focuses on factors that influence HDL functionality and analyzes a correlation between HDL functionality and aortic lipid deposition. Several agents that raise HDL levels have been utilized to dissect HDL functionality. In addition, animal model of diabetes and atherosclerosis was employed to establish a correlation between HDL functionality, hyperglycemia and inflammation. Through a series of studies, it is shown that HDL function correlates with beneficial effects, and diabetes and inflammation dampens HDL functionality leading to increased accumulation of aortic lesion formation. Agents that attenuate hyperlipidemia, glycemic index and proinflammatory state improved HDL functionality leading to reductions in atherosclerotic burden.

A number of clinical findings suggested HDL-raising as a plausible approach to treat residual risk of CVD. However, lack of CVD risk reduction by elevated HDL cholesterol (HDL-C) through cholesterol ester transfer protein (CETP) inhibition and enhanced risk reduction in apolipoprotein A-I Milano (apoAI-M) individuals with low HDL-C shifted the focus from HDL-C level to HDL function. In the present study, we investigated correlations between HDL-C, HDL function, fecal cholesterol excretion, and ex vivo plasma cholesterol efflux capacity (CEC) in animal models using two HDL modulators, LXR and PPAR-α agonists. In C57Bl mice, LXR agonist, T1317, raised HDL-C by 30%, while PPAR-α agonist, fenofibrate, reduced HDL-C by 30%, but fecal cholesterol showed twofold increase in both cases. CEC showed a 30–40% increase. Combination of LXR and PPAR-α agonists showed no changes in HDL-C, but, interestingly, fecal cholesterol increased by 4.5-fold, and CEC by 40%, suggesting existence of additional pathway for fecal cholesterol excretion. Regression analysis showed a lack of correlation between HDL-C and fecal cholesterol and CEC, while fecal cholesterol showed significant correlation with CEC, a measure of HDL function. ABCA1 and G1, the two important players in RCT showed greater induction with LXR agonist than PPAR-α agonist. HDL-C increased by 40 and 80% in LXR and PPAR-α treated apoA-I transgenic mice, respectively, with 80% increase in fecal cholesterol. A fivefold increase in fecal cholesterol with no correlation with either plasma HDL-C or CEC following co-treatment with LXR and PPAR-α agonists suggested existence of an HDL-independent pathway for body cholesterol elimination. In hyperlipidemic diabetic ob/ob mice also combination of LXR and PPAR-α agonists showed marked increases in fecal cholesterol content (10–20-fold), while HDL-C rise was only 40%, further suggesting HDL-independent elimination of body cholesterol in mice treated with combination of LXR and PPAR-α agonists. Atherosclerosis attenuation by LXR and PPAR-α agonists in LDLr-deficient mice was associated with increased fecal cholesterol, but not HDL-C. However, fecal cholesterol counts showed inverse correlation with aortic cholesteryl ester content. These data suggest: (a) lack of correlation between HDL-C and fecal or aortic cholesterol content; (b) HDL function (CEC) correlated with fecal cholesterol content; (c) association of reduced aortic lipids in LDLr−/− mice with increased fecal cholesterol, but not with HDL-C, and (d) existence of an HDL-independent pathway for fecal cholesterol excretion following co-treatment with LXR and PPAR-α agonists.