Protein Folding Database (PFD 2.0): an online environment for the International Foldeomics Consortium

The Protein Folding Database (PFD) is a publicly accessible repository of thermodynamic and kinetic protein folding data. Here we describe the first major revision of this work, featuring extensive restructuring that conforms to standards set out by the recently formed International Foldeomics Consortium. The database now adopts standards for data acquisition, analysis and reporting proposed by the consortium, which will facilitate the comparison of folding rates, energies and structure across diverse sets of proteins. Data can now be easily deposited using a rich set of deposition tools. Enhanced search tools allow sophisticated searching and graphical data analysis affords simple data analysis online. PFD can be accessed freely at

High sensitivity of future global warming to land carbon cycle processes

Unknowns in future global warming are usually assumed to arise from uncertainties either in the amount of anthropogenic greenhouse gas emissions or in the sensitivity of the climate to changes in greenhouse gas concentrations. Characterizing the additional uncertainty in relating CO2 emissions to atmospheric concentrations has relied on either a small number of complex models with diversity in process representations, or simple models. To date, these models indicate that the relevant carbon cycle uncertainties are smaller than the uncertainties in physical climate feedbacks and emissions. Here, for a single emissions scenario, we use a full coupled climate–carbon cycle model and a systematic method to explore uncertainties in the land carbon cycle feedback. We find a plausible range of climate–carbon cycle feedbacks significantly larger than previously estimated. Indeed the range of CO2 concentrations arising from our single emissions scenario is greater than that previously estimated across the full range of IPCC SRES emissions scenarios with carbon cycle uncertainties ignored. The sensitivity of photosynthetic metabolism to temperature emerges as the most important uncertainty. This highlights an aspect of current land carbon modelling where there are open questions about the potential role of plant acclimation to increasing temperatures. There is an urgent need for better understanding of plant photosynthetic responses to high temperature, as these responses are shown here to be key contributors to the magnitude of future change

Mass spectrometry-based proteomic analysis of the matrix microenvironment in pluripotent stem cell culture

The cellular microenvironment comprises soluble factors, support cells, and components of the extracellular matrix (ECM) that combine to regulate cellular behavior. Pluripotent stem cells utilize interactions between support cells and soluble factors in the microenvironment to assist in the maintenance of self-renewal and the process of differentiation. However, the ECM also plays a significant role in shaping the behavior of human pluripotent stem cells, including embryonic stem cells (hESCs) and induced pluripotent stem cells. Moreover, it has recently been observed that deposited factors in a hESC-conditioned matrix have the potential to contribute to the reprogramming of metastatic melanoma cells. Therefore, the ECM component of the pluripotent stem cell microenvironment necessitates further analysis. In this study we first compared the self-renewal and differentiation properties of hESCs grown on Matrigel™pre-conditioned by hESCs to those on unconditioned Matrigel™. We determined that culture on conditioned Matrigel™ prevents differentiation when supportive growth factors are removed from the culture medium. To investigate and identify factors potentially responsible for this beneficial effect, we performed a defined SILAC MS-based proteomics screen of hESC-conditioned Matrigel™. From this proteomics screen, we identified over 80 extracellular proteins in matrix conditioned by hESCs and induced pluripotent stem cells. These included matrix-associated factors that participate in key stem cell pluripotency regulatory pathways, such as Nodal/Activin and canonical Wnt signaling. This work represents the first investigation of stem-cell-derived matrices from human pluripotent stem cells using a defined SILAC MS-based proteomics approach. © 2012 by The American Society for Biochemistry and Molecular Biology, Inc

South American fires and their impacts on ecosystems increase with continued emissions

Unprecedented fire events in recent years are leading to a demand for improved understanding of how climate change is already affecting fires, and how this could change in the future. Increased fire activity in South America is one of the most concerning of all the recent events, given the potential impacts on local ecosystems and the global climate from the loss of large carbon stores under future socio-environmental change. However, due to the complexity of interactions and feedbacks, and lack of complete representation of fire biogeochemistry in many climate models, there is currently low agreement on whether climate change will cause fires to become more or less frequent in the future, and what impact this will have on ecosystems. Here we use the latest climate simulations from the UK Earth System Model UKESM1 to understand feedbacks in fire, dynamic vegetation, and terrestrial carbon stores using the JULES land surface model, taking into account future scenarios of change in emissions and land use. Based on evaluation of model performance for the present day, we address the specific policy-relevant question: how much fire-induced carbon loss will there be over South America at different global warming levels in the future? We find that burned area and fire emissions are projected to increase in the future due to hotter and drier conditions, which leads to large reductions in carbon storage, especially when combined with increasing land-use conversion. The model simulates a 30% loss of carbon at 4°C under the highest emission scenario, which could be reduced to 7% if temperature rise is limited to 1.5°C. Our results provide a critical assessment of ecosystem resilience under future climate change, and could inform the way fire and land-use is managed in the future to reduce the most deleterious impacts of climate change

Ground state parameters, finite-size scaling, and low-temperature properties of the two-dimensional S=1/2 XY model

We present high-precision quantum Monte Carlo results for the S=1/2 XY model on a two-dimensional square lattice, in the ground state as well as at finite temperature. The energy, the spin stiffness, the magnetization, and the susceptibility are calculated and extrapolated to the thermodynamic limit. For the ground state, we test a variety of finite-size scaling predictions of effective Lagrangian theory and find good agreement and consistency between the finite-size corrections for different quantities. The low-temperature behavior of the susceptibility and the internal energy is also in good agreement with theoretical predictions.Comment: 6 pages, 8 figure

Precipitation and winter temperature predict long-term range-scale abundance changes in Western North American birds

Predicting biodiversity responses to climate change remains a difficult challenge, especially in climatically complex regions where precipitation is a limiting factor. Though statistical climatic envelope models are frequently used to project future scenarios for species distributions under climate change, these models are rarely tested using empirical data. We used long-term data on bird distributions and abundance covering five states in the western US and in the Canadian province of British Columbia to test the capacity of statistical models to predict temporal changes in bird populations over a 32-year period. Using boosted regression trees, we built presence-absence and abundance models that related the presence and abundance of 132 bird species to spatial variation in climatic conditions. Presence/absence models built using 1970–1974 data forecast the distributions of the majority of species in the later time period, 1998–2002 (mean AUC = 0.79 ± 0.01). Hindcast models performed equivalently (mean AUC = 0.82 ± 0.01). Correlations between observed and predicted abundances were also statistically significant for most species (forecast mean Spearman′s ρ = 0.34 ± 0.02, hindcast = 0.39 ± 0.02). The most stringent test is to test predicted changes in geographic patterns through time. Observed changes in abundance patterns were significantly positively correlated with those predicted for 59% of species (mean Spearman′s ρ = 0.28 ± 0.02, across all species). Three precipitation variables (for the wettest month, breeding season, and driest month) and minimum temperature of the coldest month were the most important predictors of bird distributions and abundances in this region, and hence of abundance changes through time. Our results suggest that models describing associations between climatic variables and abundance patterns can predict changes through time for some species, and that changes in precipitation and winter temperature appear to have already driven shifts in the geographic patterns of abundance of bird populations in western North America

Intelligence within BAOR and NATO's Northern Army Group

During the Cold War the UK's principal military role was its commitment to the North Atlantic Treaty Organisation (NATO) through the British Army of the Rhine (BAOR), together with wartime command of NATO's Northern Army Group. The possibility of a surprise attack by the numerically superior Warsaw Pact forces ensured that great importance was attached to intelligence, warning and rapid mobilisation. As yet we know very little about the intelligence dimension of BAOR and its interface with NATO allies. This article attempts to address these neglected issues, ending with the impact of the 1973 Yom Kippur War upon NATO thinking about warning and surprise in the mid-1970s. It concludes that the arrangements made by Whitehall for support to BAOR from national assets during crisis or transition to war were - at best - improbable. Accordingly, over the years, BAOR developed its own unique assets in the realm of both intelligence collection and special operations in order to prepare for the possible outbreak of conflict

Early warning signals of simulated Amazon rainforest dieback

Copyright © The Author(s) 2013. This article is published with open access at Springerlink.comWe test proposed generic tipping point early warning signals in a complex climate model (HadCM3) which simulates future dieback of the Amazon rainforest. The equation governing tree cover in the model suggests that zero and non-zero stable states of tree cover co-exist, and a transcritical bifurcation is approached as productivity declines. Forest dieback is a non-linear change in the non-zero tree cover state, as productivity declines, which should exhibit critical slowing down. We use an ensemble of versions of HadCM3 to test for the corresponding early warning signals. However, on approaching simulated Amazon dieback, expected early warning signals of critical slowing down are not seen in tree cover, vegetation carbon or net primary productivity. The lack of a convincing trend in autocorrelation appears to be a result of the system being forced rapidly and non-linearly. There is a robust rise in variance with time, but this can be explained by increases in inter-annual temperature and precipitation variability that force the forest. This failure of generic early warning indicators led us to seek more system-specific, observable indicators of changing forest stability in the model. The sensitivity of net ecosystem productivity to temperature anomalies (a negative correlation) generally increases as dieback approaches, which is attributable to a non-linear sensitivity of ecosystem respiration to temperature. As a result, the sensitivity of atmospheric CO2 anomalies to temperature anomalies (a positive correlation) increases as dieback approaches. This stability indicator has the benefit of being readily observable in the real world.NERCJoint DECC/Defra Met Office Hadley Centre Climate ProgrammeUniversity of Exete

Amelogenesis Imperfecta caused by N-Terminal Enamelin Point Mutations in Mice and Men is driven by Endoplasmic Reticulum Stress

‘Amelogenesis imperfecta’ (AI) describes a group of inherited diseases of dental enamel that have major clinical impact. Here, we identify the aetiology driving AI in mice carrying a p.S55I mutation in enamelin; one of the most commonly mutated proteins underlying AI in humans. Our data indicate that the mutation inhibits the ameloblast secretory pathway leading to ER stress and an activated unfolded protein response (UPR). Initially, with the support of the UPR acting in pro-survival mode, Enam(p.S55I) heterozygous mice secreted structurally normal enamel. However, enamel secreted thereafter was structurally abnormal; presumably due to the UPR modulating ameloblast behaviour and function in an attempt to relieve ER stress. Homozygous mutant mice failed to produce enamel. We also identified a novel heterozygous ENAM(p.L31R) mutation causing AI in humans. We hypothesize that ER stress is the aetiological factor in this case of human AI as it shared the characteristic phenotype described above for the Enam(p.S55I) mouse. We previously demonstrated that AI in mice carrying the Amelx(p.Y64H) mutation is a proteinopathy. The current data indicate that AI in Enam(p.S55I) mice is also a proteinopathy, and based on comparative phenotypic analysis, we suggest that human AI resulting from the ENAM(p.L31R) mutation is another proteinopathic disease. Identifying a common aetiology for AI resulting from mutations in two different genes opens the way for developing pharmaceutical interventions designed to relieve ER stress or modulate the UPR during enamel development to ameliorate the clinical phenotype

Proteomic Biomarkers for Acute Interstitial Lung Disease in Gefitinib-Treated Japanese Lung Cancer Patients

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10−25), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control