ARTICLE | doi:10.20944/preprints202001.0182.v1
Subject: Chemistry, Medicinal Chemistry Keywords: dinoflagellate; Karenia mikimotoi; glycolipids; monogalactosyldiacylglycerol; monogalactosylmonoacylglycerol; polyunsaturated fatty acid methyl ester; Staphylococcus aureus; Escherichia coli; Candida albicans; anti-inflammatory activity
Online: 17 January 2020 (09:18:08 CET)
A New monogalactosyldiacylglycerol (MGDG), a known monogalactosylmonoacylglycerol (MGMG) and a known polyunsaturated fatty acid methyl ester (PUFAME) were isolated from the marine dinoflagellate Karenia mikimotoi. The planar structure of the glycolipids was elucidated using MS and NMR spectroscopic analyses and comparisons to the known glycolipid to confirm its structure. The isolation of PUFAME strongly supports the polyunsaturated fatty acid fragment of these glycolipids. The relative configuration of the sugar was deduced by comparisons of 3JHH values and proton chemical shifts with those of known glycolipids. All isolated compounds MGDG, MGMG and PUFAME (1-3) were evaluated for their antimicrobial and anti-inflammatory activity. All compounds modulated macrophage responses, with compound 3 exhibiting the greatest anti-inflammatory activity.
ARTICLE | doi:10.20944/preprints202212.0464.v1
Subject: Earth Sciences, Environmental Sciences Keywords: satellite monitoring; spectral shape algorithm; Karenia bloom evolution; harmful algal bloom
Online: 26 December 2022 (02:55:48 CET)
The environmental disaster in Kamchatka in the autumn of 2020 was caused by an extensive bloom of harmful microalgae of the genus Karenia. A spectral shape algorithm was used to detect algae. The algorithm calibration of in situ species composition data made it possible to identify areas where harmful algae dominated in biomass. Satellite images of chlorophyll-a concentra-tion, turbidity, specific fluorescence, and spectral shape parameter were computed. The images were used to recognize the stages of algal bloom: intensive growth, blooming, and change in the dominant algal species. Cases of an increase in the concentration of harmful substances in the coastal zone due to wind impact were analyzed. The following explanation of events has been offered. After the stage of intensive growth of microalgae, nutrient deficiency stimulated the production of metabolites that have a harmful effect on the environment. The change of the dominant alga species in the second half of September and the past storm contributed to a sharp increase in the concentration of metabolites and dead organic matter in the coastal zone, which caused an ecological disaster. The subsequent mass bloom of alga species of the same genus, and the regular wind impact leading to the concentration of harmful substances in the coastal zone, contributed to the development of this catastrophic phenomena.
ARTICLE | doi:10.20944/preprints201809.0038.v1
Subject: Earth Sciences, Environmental Sciences Keywords: Karenia brevis, harmful algal bloom (HAB), moderate resolution imaging Spectroradiometer (MODIS), prediction, chlorophyll, multivariate regression
Online: 3 September 2018 (13:52:41 CEST)
Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the reported historical events. The models were designed to specifically capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web-based GIS (http://www.esrs.wmich.edu/webmap/bloom/). Our findings include the following: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, SST, chlorophyll-a [OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable, dependent on readily available remote sensing data sets, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.