Toward the Integrated Marine Debris Observing System

Plastics and other artificial materials pose new risks to the health of the ocean. Anthropogenic debris travels across large distances and is ubiquitous in the water and on shorelines, yet, observations of its sources, composition, pathways, and distributions in the ocean are very sparse and inaccurate. Total amounts of plastics and other man-made debris in the ocean and on the shore, temporal trends in these amounts under exponentially increasing production, as well as degradation processes, vertical fluxes, and time scales are largely unknown. Present ocean circulation models are not able to accurately simulate drift of debris because of its complex hydrodynamics. In this paper we discuss the structure of the future integrated marine debris observing system (IMDOS)thatisrequiredtoprovidelong-termmonitoringofthestateofthisanthropogenic pollution and support operational activities to mitigate impacts on the ecosystem and on the safety of maritime activity. The proposed observing system integrates remote sensing and in situ observations. Also, models are used to optimize the design of the system and, in turn, they will be gradually improved using the products of the system. Remote sensing technologies will provide spatially coherent coverage and consistent surveying time series at local to global scale. Optical sensors, including high-resolution imaging, multi- and hyperspectral, fluorescence, and Raman technologies, as well as SAR will be used to measure different types of debris. They will be implemented in a variety of platforms, from hand-held tools to ship-, buoy-, aircraft-, and satellite-based sensors. A network of in situ observations, including reports from volunteers, citizen scientists and ships of opportunity, will be developed to provide data for calibration/validation of remote sensors and to monitor the spread of plastic pollution and other marine debris. IMDOS will interact with other observing systems monitoring physical, chemical, and biological processes in the ocean and on shorelines as well as the state of the ecosystem, maritime activities and safety, drift of sea ice, etc. The synthesized data will support innovative multi-disciplinary research and serve a diverse community of users

Describing Treatment Effects to Patients: How They Are Expressed Makes a Difference

OBJECTIVE: To examine the impact of different presentations of equivalent information (framing) on treatment decisions faced by patients. DESIGN: A systematic review of the published literature was conducted. English language publications allocating participants to different frames were retrieved using electronic and bibliographic searches. Two reviewers examined each article for inclusion, and assessed methodological quality. Study characteristics were tabulated and where possible, relative risks (RR; 95% confidence intervals) were calculated to estimate intervention effects. MEASUREMENTS AND MAIN RESULTS: Thirty-seven articles, yielding 40 experimental studies, were included. Studies examined treatment (N = 24), immunization (N = 5), or health behavior scenarios (N = 11). Overall, active treatments were preferred when outcomes were described in terms of relative rather than absolute risk reductions or number needed to treat. Surgery was preferred to other treatments when treatment efficacy was presented in a positive frame (survival) rather than a negative frame (mortality) (relative risk [RR] = 1.51, 95% confidence interval [CI], 1.39 to 1.64). Framing effects were less obvious for immunization and health behavior scenarios. Those with little interest in the behavior at baseline were influenced by framing, particularly when information was presented as gains. In studies judged to be of good methodological quality and/or examining actual decisions, the framing effect, although still evident, was less convincing compared to the results of all included studies. CONCLUSIONS: Framing effects varied with the type of scenario, responder characteristics, scenario manipulations, and study quality. When describing treatment effects to patients, expressing the information in more than one way may present a balanced view to patients and enable them to make informed decisions

Computational prediction of eukaryotic protein-coding genes

The human genome sequence is the book of our life. Buried in this large volume are our genes, which are scattered as small DNA fragments throughout the genome and comprise a small percentage of the total text. Finding these indistinct 'needles' in a vast genomic 'haystack' can be extremely challenging. In response to this challenge, computational prediction approaches have proliferated in recent years that predict the location and structure of genes. Here, I discuss these approaches and explain why they have become essential for the analyses of newly sequenced genomes