RESUMO
Existing flood modeling studies over coastal catchments involving different combinations of model chain setup imparting complex information fails to entail the needs of policy or decision-makers. Thus, a comprehensive framework that pertains to the requirements of practitioners and provides more perspicuous flood hazard information is required. In this paper, a novel approach translating complex flood hazard information in the form of decision priority maps derived using a rational combination of models (physical and statistical) is elucidated at the finest administrative scale. The proposed methodology is illustrated over a highly flood-prone deltaic region in Mahanadi River Basin, India, to characterize impacts of climate change for a 1:100 years return period flood event under future conditions (2026-2055). The modeled flood events are further analyzed to capture the transformation dynamics of flood hazard classes (FHCs) in near-future, for prioritizing areas with greater hazard potential. Interestingly, the results capture a high transformation characteristic from low to high FHCs in agriculture-dominated areas, which are significantly greater than the areas experiencing flood hazard reduction. The results show a significant increase of 12.5% and 27.35% in areas with high FHCs under RCP4.5 and RCP8.5 scenarios, respectively. Moreover, a notable climate change response is indicated under both climate change scenarios, with approximately 22% (RCP4.5) and 25% (RCP8.5) in villages showing a drastic increment in flood hazard magnitude. The results thus highlight the importance of identifying and prioritizing the areas for flood adaptation where a relative change in flood hazard potential is higher due to climate change. Therefore, we conclude that this study can provide an insight into the implication of new approaches for effective communication of flood information by bridging the gaps between scientific communities and decision-makers in appraisal for better flood adaptation measures.
RESUMO
Tricyclodecan-9-yl-xanthogenate (D609) is known for its antiviral and antitumor properties. D609 actions are widely attributed to inhibiting phosphatidylcholine (PC)-specific phospholipase C (PC-PLC). D609 also inhibits sphingomyelin synthase (SMS). PC-PLC and/or SMS inhibition will affect lipid second messengers 1,2-diacylglycerol (DAG) and/or ceramide. Evidence indicates either PC-PLC and/or SMS inhibition affected the cell cycle and arrested proliferation, and stimulated differentiation in various in vitro and in vivo studies. Xanthogenate compounds are also potent antioxidants and D609 reduced Aß-induced toxicity, attributed to its antioxidant properties. Zn²âº is necessary for PC-PLC enzymatic activity; inhibition by D609 might be attributed to its Zn²âº chelation. D609 has also been proposed to inhibit acidic sphingomyelinase or down-regulate hypoxia inducible factor-1α; however these are down-stream events related to PC-PLC inhibition. Characterization of the mammalian PC-PLC is limited to inhibition of enzymatic activity (frequently measured using Amplex red assay with bacterial PC-PLC as a standard). The mammalian PC-PLC has not been cloned; sequenced and structural information is unavailable. D609 showed promise in cancer studies, reduced atherosclerotic plaques (inhibition of PC-PLC) and cerebral infarction after stroke (PC-PLC or SMS). D609 actions as an antagonist to pro-inflammatory cytokines have been attributed to PC-PLC. The purpose of this review is to comprehensively evaluate the literature and summarize the findings and relevance to cell cycle and CNS pathologies.
