Suchergebnisse

Study objective: European Union legislation requires large industrial and civil development projects to undergo environmental impact assessment. The study objective was to identify environmental health risk estimates for these developments from the epidemiological literature and to develop, and apply these within, a mathematical health impact assessment model.

Public health draws from a range of academic disciplines, social, medical and statistical, and answers questions relevant to improving the health of populations. We have initiated a Europe-wide study, Strengthening Public Health Research in Europe, to assess the development and use of public health research in both public policy and local decision making. The contemporary challenge for public health research is to integrate the capabilities of different academic disciplines to address policies for health. We have considered the development of public health research in five fields: political epidemiology, community health, health services, economics,and evaluation evidence and synthesis. The organisation and funding of research in Europe should be able to support new research fields and issues, to contribute to policy development and public health practice. Public health draws from a range of academic disciplines, social, medical and statistical, and answers questions relevant to improving the health of populations. We have initiated a Europe-wide study, Strengthening Public Health Research in Europe, to assess the development and use of public health research in both public policy and local decision making. The contemporary challenge for public health research is to integrate the capabilities of different academic disciplines to address policies for health. We have considered the development of public health research in five fields: political epidemiology, community health, health services, economics,and evaluation evidence and synthesis. The organisation and funding of research in Europe should be able to support new research fields and issues, to contribute to policy development and public health practice.

The negative molecular ion C3N- has been detected at millimeter wavelengths in a low-pressure laboratory discharge, and then with frequencies derived from the laboratory data in the molecular envelope of IRC+10216. Spectroscopic constants derived from laboratory measurements of 12 transitions between 97 and 378 GHz allow the rotational spectrum to be calculated well into the submillimeter-wave band to 0.03 km s(-1) or better in equivalent radial velocity. Four transitions of C3N- were detected in IRC+10216 with the IRAM 30 m telescope at precisely the frequencies calculated from the laboratory measurements. The column density of C3N- is 0.5% that of C3N, or approximately 20 times greater than that of C4H- relative to C4H. The C3N- abundance in IRC+10216 is compared with a chemical model calculation by Petrie & Herbst. An upper limit in TMC-1 for C3N- relative to C3N (< 0.8%) and a limit for C4H- relative to C4H (< 0.004%) that is 5 times lower than that found in IRC+10216, were obtained from observations with the NRAO 100 m Green Bank Telescope (GBT). The fairly high concentration of ; NRF ; Korean government MEST 2012R1A1A1014646, 2012M4A2026720 ; Southeast Physics Network (SEP-Net) ; Science and Technology Facilities Council ST/F002858/1, ST/I000976/1 ; Swedish Research Council 2009-4088 ; U.S. NSF AST-0708176, AST-1009799 ; NASA NNX07AH09G, NNG04G177G, NNX11AE09G ; Chandra grant SAO TM8-9009X ; Biochemistry

Species distributions are influenced by processes occurring at multiple spatial scales. It is therefore insufficient to model species distribution at a single geographic scale, as this does not provide the necessary understanding of determining factors. Instead, multiple approaches are needed, each differing in spatial extent, grain, and research objective. Here, we present the first attempt to model continent-wide great ape density distribution. We used site-level estimates of African great ape abundance to (1) identify socioeconomic and environmental factors that drive densities at the continental scale, and (2) predict range-wide great ape density. We collated great ape abundance estimates from 156 sites and defined 134 pseudo-absence sites to represent additional absence locations. The latter were based on locations of unsuitable environmental conditions for great apes, and on existing literature. We compiled seven socioeconomic and environmental covariate layers and fitted a generalized linear model to investigate their influence on great ape abundance. We used an Akaike-weighted average of full and subset models to predict the range-wide density distribution of African great apes for the year 2015. Great ape densities were lowest where there were high Human Footprint and Gross Domestic Product values; the highest predicted densities were in Central Africa, and the lowest in West Africa. Only 10.7% of the total predicted population was found in the International Union for Conservation of Nature Category I and II protected areas. For 16 out of 20 countries, our estimated abundances were largely in line with those from previous studies. For four countries, Central African Republic, Democratic Republic of the Congo, Liberia, and South Sudan, the estimated populations were excessively high. We propose further improvements to the model to overcome survey and predictor data limitations, which would enable a temporally dynamic approach for monitoring great apes across their range based on key indicators.