RESUMO
Nitrate is one of the essential variables in the ocean that is a primary control of the upper ocean pelagic ecosystem. Its three-dimensional (3D) structure is vital for understanding the dynamic and ecosystem. Although several gridded nitrate products exist, the possibility of reconstructing the 3D structure of nitrate from surface data has never been exploited. In this study, we employed two advanced artificial intelligence (AI) networks, U-net and Earthformer, to reconstruct nitrate concentration in the Indian Ocean from surface data. Simulation from an ecosystem model was utilized as the labeling data to train and test the AI networks, with wind vectors, wind stress, sea surface temperature, sea surface chlorophyll-a, solar radiation, and precipitation as the input. We compared the performance of two networks and different pre-processing methods. With the input features decomposed into climatology and anomaly components, the Earthformer achieved optimal reconstruction results with a lower normalized mean square error (NRMSE = 0.1591), spatially and temporally, outperforming U-net (NRMSE = 0.2007) and the climatology prediction (NRMSE = 0.2089). Furthermore, Earthformer was more capable of identifying interannual nitrate anomalies. With a network interpretation technique, we quantified the spatio-temporal importance of every input feature in the best case (Earthformer with decomposed inputs). The influence of different input features on nitrate concentration in the adjacent Java Sea exhibited seasonal variation, stronger than the interannual one. The feature importance highlighted the role of dynamic factors, particularly the wind, matching our understanding of the dynamic controls of the ecosystem. Our reconstruction and network interpretation technique can be extended to other ecosystem variables, providing new possibilities in studies of marine environment and ecology from an AI perspective.
RESUMO
Diets rich in taurine can increase the production of taurine-conjugated bile acids, which are known to exert antihypertensive effects. Despite their benefits to the heart, kidney and arteries, their role in the central nervous system during the antihypertensive process remains unclear. Since hypothalamic paraventricular nucleus (PVN) plays a key role in blood pressure regulation, we aimed to investigate the function of bile acids in the PVN. The concentration of bile acids in the PVN of spontaneously hypertensive rats (SHRs) and normotensive Wistar-Kyoto rats (WKY) fed with normal chow was measured using LC-MS/MS, which identified taurocholic acid (TCA) as the most down-regulated bile acid. To fully understand the mechanism of TCA's functions in the PVN, bi-lateral PVN micro-infusion of TCA was carried out. TCA treatment in the PVN led to a significant reduction in the blood pressure of SHRs, with decreased plasma levels of norepinephrine and improved morphology of cardiomyocytes. It also decreased the number of c-fos+ neurons, reduced the inflammatory response, and suppressed oxidative stress in the PVN of the SHRs. Most importantly, the TGR5 receptors in neurons and microglia were activated. PVN infusion of SBI-115, a TGR5 specific antagonist, was able to counteract with TCA in the blood pressure regulation of SHRs. In conclusion, TCA supplementation in the PVN of SHRs can activate TGR5 in neurons and microglia, reduce the inflammatory response and oxidative stress, suppress activated neurons, and attenuate hypertension.