Long-term flood-hazard modeling for coastal areas using InSAR measurements and a hydrodynamic model: The case study of Lingang New City, Shanghai

In this paper, we study long-term coastal flood risk of Lingang New City, Shanghai, considering 100- and 1000-year coastal flood return periods, local seal-level rise projections, and long-term ground subsidence projections. TanDEM-X satellite data acquired in 2012 were used to generate a high-resolution topography map, and multi-sensor InSAR displacement time-series were used to obtain ground deformation rates between 2007 and 2017. Both data sets were then used to project ground deformation rates for the 2030s and 2050s. A 2-D flood inundation model (FloodMap-Inertial) was employed to predict coastal flood inundation for both scenarios. The results suggest that the sea-level rise, along with land subsidence, could result in minor but non-linear impacts on coastal inundation over time. The flood risk will primarily be determined by future exposure and vulnerability of population and property in the floodplain. Although the flood risk estimates show some uncertainties, particularly for long-term predictions, the methodology presented here could be applied to other coastal areas where sea level rise and land subsidence are evolving in the context of climate change and urbanization

Data from the infection assay and feeding rate assay in the first experiment.

The transmission model (equ. S1 and S2) and foraging model (equ. S4) were simultaneously fit to these data from the infection and feeding rate assays, respectively. For more details on how these data were used to estimate components of transmission potential, see 'Statistical methods for estimating parameters' in Appendix S1

Data from the infection assay in the second experiment.

The transmission model (equ. S1 and S2) was fit to these data to estimate infection risk. For more details on how these data were used to estimate components of transmission potential, see 'Statistical methods for estimating parameters' in Appendix S1

Pharmacokinetics, Metabolism, and in Vivo Efficacy of the Antimalarial Natural Product Bromophycolide A

A suite of pharmacokinetic and pharmacological studies show that bromophycolide A (<b>1</b>), an inhibitor of drug-sensitive and drug-resistant <i>Plasmodium falciparum</i>, displays a typical small molecule profile with low toxicity and good bioavailability. Despite susceptibility to liver metabolism and a short in vivo half-life, <b>1</b> significantly decreased parasitemia in a malaria mouse model. Combining these data with prior structure–activity relationship analyses, we demonstrate the potential for future development of <b>1</b> and its bioactive ester analogues

Biomarkers of Whale Shark Health: A Metabolomic Approach

<div><p>In a search for biomarkers of health in whale sharks and as exploration of metabolomics as a modern tool for understanding animal physiology, the metabolite composition of serum in six whale sharks (<em>Rhincodon typus</em>) from an aquarium collection was explored using ¹H nuclear magnetic resonance (NMR) spectroscopy and direct analysis in real time (DART) mass spectrometry (MS). Principal components analysis (PCA) of spectral data showed that individual animals could be resolved based on the metabolite composition of their serum and that two unhealthy individuals could be discriminated from the remaining healthy animals. The major difference between healthy and unhealthy individuals was the concentration of homarine, here reported for the first time in an elasmobranch, which was present at substantially lower concentrations in unhealthy whale sharks, suggesting that this metabolite may be a useful biomarker of health status in this species. The function(s) of homarine in sharks remain uncertain but it likely plays a significant role as an osmolyte. The presence of trimethylamine oxide (TMAO), another well-known protective osmolyte of elasmobranchs, at 0.1–0.3 mol L⁻¹ was also confirmed using both NMR and MS. Twenty-three additional potential biomarkers were identified based on significant differences in the frequency of their occurrence between samples from healthy and unhealthy animals, as detected by DART MS. Overall, NMR and MS provided complementary data that showed that metabolomics is a useful approach for biomarker prospecting in poorly studied species like elasmobranchs.</p> </div

Loading plot for PC1 as a means to identify NMR spectroscopic features corresponding to relevant metabolites within the serum of whale sharks.

<p>Loading plot for PC1 as a means to identify NMR spectroscopic features corresponding to relevant metabolites within the serum of whale sharks.</p

PCA of ¹H NMR spectra of extracted whale shark serum (42 samples, showing PC1 scores plotted against time of sampling for the unhealthy animals.

<p>: unhealthy individual 1 : unhealthy individual 2 : average for healthy individual 3 (n = 2) : average for healthy individual 4 (n = 3) : average for healthy individual 5 (n = 5).</p

Differences in concentration of homarine (A) and trimethylamine-oxide (TMAO) (B) in serum samples from two unhealthy (animals 1–2) and three healthy (3–5) whale sharks.

<p>Differences in concentration of homarine (A) and trimethylamine-oxide (TMAO) (B) in serum samples from two unhealthy (animals 1–2) and three healthy (3–5) whale sharks.</p

PCA scores plots from analysis of (a) NMR and (b) MS metabolomics datasets of only those whale shark serum samples that were analysed by both methods.

<p>: unhealthy individual 1 : unhealthy individual 2 : healthy individual 3 (n = 2) : healthy individual 4 (n = 3) : healthy individual 5 (n = 5).</p