The recently published Intergovernmental Panel on Climate Change (IPCC) projections to 2100 give likely ranges of global temperature increase in four scenarios for population, economic growth and carbon use1. However these projections are not based on a fully statistical approach. Here we use a country-specific version of Kaya’s identity to develop a statistically-based probabilistic forecast of CO2 emissions and temperature change to 2100. Using data for 1960-2010, including the UN’s probabilistic population projections for all countries2–4, we develop a joint Bayesian hierarchical model for GDP per capita and carbon intensity. We find that the 90% interval for cumulative CO2 emissions includes the IPCC’s two middle scenarios but not the extreme ones. The likely range of global temperature increase is 2.0–4.9°C, with median 3.2°C and a 5% (1%) chance that it will be less than 2°C (1.5°C). Population growth is not a major contributing factor. Our model is not a “business as usual” scenario, but rather is based on data which already show the effect of emission mitigation policies. Achieving the goal of less than 1.5°C warming will require carbon intensity to decline much faster than in the recent past.

Disease, external stimuli (such as drugs and toxins), and mutations cause changes in the rate of protein synthesis, post-translational modification, inter-compartmental transport, and degradation of proteins in living systems. Recognizing and identifying the small number of proteins involved is complicated by the complexity of biological extracts and the fact that post-translational alterations of proteins can occur at many sites in multiple ways. It is shown here that a variety of new tools and methods based on internal standard technology are now being developed to code globally all peptides in control and experimental samples for quantification. The great advantage of these stable isotope-labeling strategies is that mass spectrometers can rapidly target those proteins that have changed in concentration for further analysis. When coupled to stable isotope quantification, targeting can be further focused through chromatographic selection of peptide classes on the basis of specific structural features. Targeting structural features is particularly useful when they are unique to types of regulation or disease. Differential displays of targeted peptides show that stimulus-specific markers are relatively easy to identify and will probably be diagnostically valuable tools.

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Introduced in the late 1980s as a reducing reagent, Tris (2-carboxyethyl) phosphine (TCEP) has now become one of the most widely used protein reductants. To date, only a few studies on its side reactions have been published. We report the observation of a side reaction that cleaves protein backbones under mild conditions by fracturing the cysteine residues, thus generating heterogeneous peptides containing different moieties from the fractured cysteine. The peptide products were analyzed by high performance liquid chromatography and tandem mass spectrometry (LC/MS/MS). Peptides with a primary amine and a carboxylic acid as termini were observed, and others were found to contain amidated or formamidated carboxy termini, or formylated or glyoxylic amino termini. Formamidation of the carboxy terminus and the formation of glyoxylic amino terminus were unexpected reactions since both involve breaking of carbon-carbon bonds in cysteine.

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