Storm surge assessment methodology based on numerical modelling

Coastal zones face severe weaknesses and high-risk situations due to coastal threats like erosion and storms and due to an increasing intensive occupation. Tropical storms events can contribute to the occurrence of these situations, by causing storm surges with high water levels and, consequently, episodes of waves overtopping and coastal flooding. This work aims to describe a methodology to estimate the storm surge occurrences in the Portuguese coastal zone, recurring to historical tropical storms data that occurred in the vicinity of Portugal and to numerical modeling of its characteristics. Delft3D software together with DelfDashboard tools were applied for the numerical modelling. An automatic generation procedure of storms was implemented based on the few available historical storms data characteristics. Obtained results allows to characterize storm surges along the Portuguese coast, identifying the most vulnerable areas and, consequently contributing for its proper planning and management.info:eu-repo/semantics/publishedVersio

Lipid composition and dynamics of cell membranes of Bacillus stearothermophilus adapted to amiodarone

Bacillus stearothermophilus, a useful model to evaluate membrane interactions of lipophilic drugs, adapts to the presence of amiodarone in the growth medium. Drug concentrations in the range of 1-2 [mu]M depress growth and 3 [mu]M completely suppresses growth. Adaptation to the presence of amiodarone is reflected in lipid composition changes either in the phospholipid classes or in the acyl chain moieties. Significant changes are observed at 2 [mu]M and expressed by a decrease of phosphatidylethanolamine (relative decrease of 23.3%) and phosphatidylglycerol (17.9%) and by the increase of phosphoglycolipid (162%). The changes in phospholipid acyl chains are expressed by a decrease of straight-chain saturated fatty acids (relative decrease of 12.2%) and anteiso-acids (22%) with a parallel increase of the iso-acids (9.8%). Consequently, the ratio straight-chain/branched iso-chain fatty acids decreases from 0.38 (control cultures) to 0.30 (cultures adapted to 2 [mu]M amiodarone). The physical consequences of the lipid composition changes induced by the drug were studied by fluorescence polarization of diphenylhexatriene and diphenylhexatriene-propionic acid, and by differential scanning calorimetry. The thermotropic profiles of polar lipid dispersions of amiodarone-adapted cells are more similar to control cultures (without amiodarone) than those resulting from a direct interaction of the drug with lipids, i.e., when amiodarone was added directly to liposome suspensions. It is suggested that lipid composition changes promoted by amiodarone occur as adaptations to drug tolerance, providing the membrane with physico-chemical properties compatible with membrane function, counteracting the effects of the drug.http://www.sciencedirect.com/science/article/B6VNN-419BF60-K/1/8f2d4fae7f9c131d26230cf4123da94

Hazard assessment of storm events for the portuguese northern coast

Coastal zones currently face severe weaknesses and are subject to high-risk situations. Tropical storm events can contribute to the occurrence of these high-risk situations by causing storm surges with high water levels and, consequently, episodes of wave-overtopping and coastal flooding. This work considers a series of storm scenarios and analyzes their impacts through numerical modelling. Firstly, historical storm tracks and intensities are characterized for the Portuguese northern coast in terms of probability of occurrence. Secondly, several storm events with a high potential of occurrence are generated using a specific tool of the DelftDasboard interface for Delft3D software. Hydrodynamic models are then used to generate an ensemble of simulations to assess the storms? effects on coastal water levels. Based on the statistical data of the numerical modeling results, a synthesis of the coastal storms? impacts at different locations within the study area is performed. Depending on the storm category, surge heights can reach 1.10 meters above tide levels under simulated conditions on the Portuguese northwestern coast.info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost