Who’s the Boss? Concepts of Social Power Across Development

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137499/1/cdev12643.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137499/2/cdev12643_am.pd

EU Peatlands: Current Carbon Stocks and Trace Gas Fluxes

Peatlands in Europe has formed a significant sink for atmospheric CO2 since the last glacial maximum. Currently they are estimated to hold ca. 42 Gt carbon in the form of peat and are therefore a considerable component in the European carbon budget. Due to the generally wet soil conditions in peatlands they are also significant emitters of the strong greenhouse gas (GHG) methane (CH4) and in some cases also of nitrous oxide (N2O). The EU funded CarboEurope-GHG Concerted Action attempts to develop a reliable and complete greenhouse gas budget for Europe and this report aims to provide a review and synthesis of the available information about GHG exchanges in European peatlands and their underlying processes. A best estimate for all the European countries shows that some are currently sinks for atmospheric CO2 while others are sources. In contrast, for CH4 and N2O, only the sources are relevant. Whilst some countries are CO2 sinks, all countries are net GHG emitters from peatlands. The results presented, however, carry large uncertainties, which cannot be adequately quantified yet. One outstanding uncertainty is the distribution of land use types, particular in Russia, the largest European peat nation. The synthesis of GHG exchange, nevertheless, indicates some interesting features. Russia hosts an estimated 41% of European peatlands and contributes most to all GHG exchanges (CO2: 25%, CH4: 52%, N2O: 26%, Total: 37%). Germany is the second-largest emitter (12% of European total) although it contains only 3.2% of European peatlands. The reason is the use of most of the peatland area for intensive cropland and grassland. The largest CO2 emitters are countries with large agricultural peatland areas (Russia, Germany, Belarus, Poland), the largest N2O emitters are those with large agricultural fen areas (Russia, Germany, Finland). In contrast, the largest CH4 emitters are concentrated in regions with large areas of intact mires, namely Russia and Scandinavia. High average emission densities above 3.5 t C-equiv. ha-1 are found in the Southeast Mediterranean, Germany and the Netherlands where agricultural use of peatlands is intense. Low average emission densities below 0.3 t C-equiv. ha-1 occur where mires and peatland forests dominate, e.g. Finland and the UK. This report concludes by pointing at key gaps in our knowledge about peatland carbon stocks and GHG exchanges which include insufficient basic information on areal distribution of peatlands, measurements of peat depth and also a lack of flux datasets providing full annual budgets of GHG exchanges

Profile and functional analysis of small RNAs derived from Aspergillus fumigatus infected with double-stranded RNA mycoviruses

Background: Mycoviruses are viruses that naturally infect and replicate in fungi. Aspergillus fumigatus, an opportunistic pathogen causing fungal lung diseases in humans and animals, was recently shown to harbour several different types of mycoviruses. A well-characterised defence against virus infection is RNA silencing. The A. fumigatus genome encodes essential components of the RNA silencing machinery, including Dicer, Argonaute and RNA-dependent RNA polymerase (RdRP) homologues. Active silencing of double-stranded (ds)RNA and the generation of small RNAs (sRNAs) has been shown for several mycoviruses and it is anticipated that a similar mechanism will be activated in A. fumigatus isolates infected with mycoviruses. Results: To investigate the existence and nature of A. fumigatus sRNAs, sRNA-seq libraries of virus-free and virus-infected isolates were created using Scriptminer adapters and compared. Three dsRNA viruses were investigated: Aspergillus fumigatus partitivirus-1 (AfuPV-1, PV), Aspergillus fumigatus chrysovirus (AfuCV, CV) and Aspergillus fumigatus tetramycovirus-1 (AfuTmV-1, NK) which were selected because they induce phenotypic changes such as coloration and sectoring. The dsRNAs of all three viruses, which included two conventionally encapsidated ones PV and CV and one unencapsidated example NK, were silenced and yielded characteristic vsiRNAs together with co-incidental silencing of host fungal genes which shared sequence homology with the viral genomes. Conclusions: Virus-derived sRNAs were detected and characterised in the presence of virus infection. Differentially expressed A. fumigatus microRNA-like (miRNA-like) sRNAs and small interfering RNAs (siRNAs) were detected and validated. Host sRNA loci which were differentially expressed as a result of virus infection were also identified. To our knowledge, this is the first study reporting the sRNA profiles of A. fumigatus isolates

Global Aerosol Health Impacts: Quantifying Uncertainties

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).Atmospheric fine particulate matter <2.5 μm (PM2.5) can cause cardiovasculatory and respiratory damages and mortalities. Assessing population exposure to and damages from PM2.5 is important for policy, but measurement networks are only available in a few regions. We assess variation resulting from using different sources of concentration information to constrain PM2.5 exposure worldwide, and compare the magnitude of this variation to uncertainties in epidemiological exposure-response functions and economic valuation of health impacts. We find that only 10% of global population is in areas constrained by ground-based data. We calculate and compare regionally-averaged population-weighted concentrations using two atmospheric models: the MIT/NCAR CAM3 aerosol-climate model, and the GEOS-Chem atmospheric chemistry model; and a satellite-derived PM2.5 product. We examine the contributions of different aerosol components to population-weighted PM2.5, and find large differences in exposure between U.S. and global populations. We use the MIT Emissions Prediction and Policy Analysis Health Effects model (EPPA-HE) to assess global health impacts and related economic costs, and conduct probabilistic uncertainty analysis of concentration-response functions. We use these combined approaches to project uncertainty ranges for health impacts and related economic costs from present-day PM2.5. We find large uncertainties in simulated PM2.5, especially globally; the magnitude of concentration variation among estimation methods is comparable to uncertainties in epidemiological functions and economic valuations. We identify major contributors to concentration variation, notably the parameterization of atmospheric dust. We estimate an annual global welfare cost of present-day (2000-2005) PM2.5 of $US 280 billion (range$120 – 510 billion), and related annual mortalities at 1.3 million per year (630,000 – 2.1 million).United States Environmental Protection Agency’s Science to Achieve Results (STAR) program (RD-83427901-0) and the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsor