The human health effects of Florida Red Tide (FRT) blooms : an expanded analysis

Author Posting. © The Author(s), 2014. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Environment International 68 (2014): 144-153, doi:10.1016/j.envint.2014.03.016.Human respiratory and digestive illnesses can be caused by exposures to brevetoxins from blooms of the marine alga Karenia brevis, also known as Florida red tide (FRT). K. brevis requires macro-nutrients to grow; although the sources of these nutrients have not been resolved completely, they are thought to originate both naturally and anthropogenically. The latter sources comprise atmospheric depositions, industrial effluents, land runoffs, or submerged groundwater discharges. To date, there has been only limited research on the extent of human health risks and economic impacts due to FRT. We hypothesized that FRT blooms were associated with increases in the numbers of emergency room visits and hospital inpatient admissions for both respiratory and digestive illnesses. We sought to estimate these relationships and to calculate the costs of associated adverse health impacts. We developed environmental exposure-response models to test the effects of FRT blooms on human health, using data from diverse sources. We estimated the FRT bloom-associated illness costs, using extant data and parameters from the literature. When controlling for resident population, a proxy for tourism, and seasonal and annual effects, we found that increases in respiratory and digestive illnesses can be explained by FRT blooms. Specifically, FRT blooms were associated with human health and economic effects in older cohorts (≥ 55 years of age) in six southwest Florida counties. Annual costs of illness ranged from $60,000 to$700,000 annually, but these costs could exceed $1.0 million per year for severe, long-lasting FRT blooms, such as the one that occurred during 2005. Assuming that the average annual illness costs of FRT blooms persist into the future, using a discount rate of 3$2-24 million.This research was sponsored by the National Science Foundation under NSF/CNH Grant No. 1009106.L.E. Fleming acknowledges support from the European Regional Development Fund and the European Social Fund Convergence Programme for Cornwall and the Isles of Scilly

The 63^{63}Ni(n,γ\gamma) cross section measured with DANCE

The neutron capture cross section of the s-process branch nucleus $^{63}$Ni affects the abundances of other nuclei in its region, especially $^{63}$Cu and $^{64}$Zn. In order to determine the energy dependent neutron capture cross section in the astrophysical energy region, an experiment at the Los Alamos National Laboratory has been performed using the calorimetric 4$\pi$ BaF$_2$ array DANCE. The (n,$\gamma$) cross section of $^{63}$Ni has been determined relative to the well known $^{197}$Au standard with uncertainties below 15%. Various $^{63}$Ni resonances have been identified based on the Q-value. Furthermore, the s-process sensitivity of the new values was analyzed with the new network calculation tool NETZ.Comment: 11 pages, 13 page

Mining and Visualizing Research Networks using the Artefact-Actor-Network Approach

Reinhardt, W., Wilke, A., Moi, M., Drachsler, H., & Sloep, P. B. (2012). Mining and Visualizing Research Networks using the Artefact-Actor-Network Approach. In A. Abraham (Ed.), Computational Social Networks. Mining and Visualization (pp. 233-268). Springer. Also available at http://www.springer.com/computer/communication+networks/book/978-1-4471-4053-5Virtual communities are increasingly relying on technologies and tools of the so-called Web 2.0. In the context of scientific events and topical Research Networks, researchers use Social Media as one main communication channel. This raises the question, how to monitor and analyze such Research Networks. In this chapter we argue that Artefact-Actor-Networks (AANs) serve well for modeling, storing and mining the social interactions around digital learning resources originating from various learning services. In order to deepen the model of AANs and its application to Research Networks, a relevant theoretical background as well as clues for a prototypical reference implementation are provided. This is followed by the analysis of six Research Networks and a detailed inspection of the results. Moreover, selected networks are visualized. Research Networks of the same type show similar descriptive measures while different types are not directly comparable to each other. Further, our analysis shows that narrowness of a Research Network's subject area can be predicted using the connectedness of semantic similarity networks. Finally conclusions are drawn and implications for future research are discussed