Multisectoral Climate Impact Hotspots in a Warming World

The impacts of global climate change on different aspects of humanity's diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 degC above the 1980-2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 degC. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty

Long-term treatment of acromegaly with pegvisomant, a growth hormone receptor antagonist

"Background Pegvisomant is a new growth hormone receptor antagonist that improves symptoms and normalises insulin-like growth factor-1 (IGF-1) in a high proportion of patients with acromegaly treated for up to 12 weeks. We assessed the effects of pegvisomant in 160 patients with acromegaly treated for an average of 425 days. Methods Treatment efficacy was assessed by measuring changes in tumour volume by magnetic resonance imaging, and serum growth hormone and IGF-1 concentrations in 152 patients who received pegvisomant by daily subcutaneous injection for up to 18 months. The safety analysis included 160 patients some of whom received weekly injections and are excluded from the efficacy analysis. Findings Mean serum IGF-1 concentrations fell by at least 50%: 467 μg/L (SE 24), 526 μg/L (29), and 523 μg/L (40) in patients treated for 6, 12 and 18 months, respectively (p less than 0·001), whereas growth hormone increased by 12·5 μg/L (2·1), 12·5 μg/L (3·0), and 14·2 μg/L (5·7) (p less than 0·001). Of the patients treated for 12 months or more, 87 of 90 (97%) achieved a normal serum IGF-1 concentration. In patients withdrawn from pegvisomant (n=45), serum growth hormone concentrations were 8·0 μg/L (2·5) at baseline, rose to 15·2 μg/L (2·4) on drug, and fell back within 30 days of withdrawal to 8·3 μg/L (2·7). Antibodies to growth hormone were detected in 27 (16·9%) of patients, but no tachyphylaxis was seen. Serum insulin and glucose concentrations were significantly decreased (p less than 0·05). Two patients experienced progressive growth of their pituitary tumours, and two other patients had increased alanine and asparate aminotransferase concentrations requiring withdrawal from treatment. Mean pituitary tumour volume in 131 patients followed for a mean of 11·46 months (0·70) decreased by 0·033 cm3(0·057; p=0·353). Interpretation Pegvisomant is an effective medical treatment for acromegaly.

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Dynamic restructuring drives catalytic activity on nanoporous gold-silver alloy catalysts.

Bimetallic, nanostructured materials hold promise for improving catalyst activity and selectivity, yet little is known about the dynamic compositional and structural changes that these systems undergo during pretreatment that leads to efficient catalyst function. Here we use ozone-activated silver-gold alloys in the form of nanoporous gold as a case study to demonstrate the dynamic behaviour of bimetallic systems during activation to produce a functioning catalyst. We show that it is these dynamic changes that give rise to the observed catalytic activity. Advanced in situ electron microscopy and X-ray photoelectron spectroscopy are used to demonstrate that major restructuring and compositional changes occur along the path to catalytic function for selective alcohol oxidation. Transient kinetic measurements correlate the restructuring to three types of oxygen on the surface. The direct influence of changes in surface silver concentration and restructuring at the nanoscale on oxidation activity is demonstrated. Our results demonstrate that characterization of these dynamic changes is necessary to unlock the full potential of bimetallic catalytic materials

Dynamic restructuring drives catalytic activity on nanoporous gold-silver alloy catalysts.

Bimetallic, nanostructured materials hold promise for improving catalyst activity and selectivity, yet little is known about the dynamic compositional and structural changes that these systems undergo during pretreatment that leads to efficient catalyst function. Here we use ozone-activated silver-gold alloys in the form of nanoporous gold as a case study to demonstrate the dynamic behaviour of bimetallic systems during activation to produce a functioning catalyst. We show that it is these dynamic changes that give rise to the observed catalytic activity. Advanced in situ electron microscopy and X-ray photoelectron spectroscopy are used to demonstrate that major restructuring and compositional changes occur along the path to catalytic function for selective alcohol oxidation. Transient kinetic measurements correlate the restructuring to three types of oxygen on the surface. The direct influence of changes in surface silver concentration and restructuring at the nanoscale on oxidation activity is demonstrated. Our results demonstrate that characterization of these dynamic changes is necessary to unlock the full potential of bimetallic catalytic materials

Hubble space telescope STIS spectroscopy of long-period dwarf novae in quiescence

We present the results of a synthetic spectral analysis of HST STIS spectra of five long-period dwarf novae obtained during their quiescence to determine the properties of their white dwarfs, which are little known for systems above the CV period gap. The five systems, TU Men, BD Pav, SS Aur, TT Crt, and V442 Cen, were observed as part of an HST Snapshot project. The spectra are described and fitted with combinations of white dwarf photospheres and accretion disks. We provide evidence that the white dwarfs in all five systems are at least partially exposed. We discuss the evolutionary implications of our model-fitting results and compare these dwarf novae to previously analyzed FUV spectra of other dwarf novae above the period gap. The dispersion in CV WD temperatures above the period gap is substantially greater than one finds below the period gap, where there is a surprisingly narrow dispersion in temperatures around 15,000 K. There appears to be a larger spread of surface temperatures in dwarf novae above the period than is seen below the gap

FLUXNET and modelling the global carbon cycle

Measurements of the net CO2 flux between terrestrial ecosystems and the atmosphere using the eddy covariance technique have the potential to underpin our interpretation of regional CO2 source–sink patterns, CO2 flux responses to forcings, and predictions of the future terrestrial C balance. Information contained in FLUXNET eddy covariance data has multiple uses for the development and application of global carbon models, including evaluation/validation, calibration, process parameterization, and data assimilation. This paper reviews examples of these uses, compares global estimates of the dynamics of the global carbon cycle, and suggests ways of improving the utility of such data for global carbon modelling. Net ecosystem exchange of CO2 (NEE) predicted by different terrestrial biosphere models compares favourably with FLUXNET observations at diurnal and seasonal timescales. However, complete model validation, particularly over the full annual cycle, requires information on the balance between assimilation and decomposition processes, information not readily available for most FLUXNET sites. Site history, when known, can greatly help constrain the model-data comparison. Flux measurements made over four vegetation types were used to calibrate the land-surface scheme of the Goddard Institute for Space Studies global climate model, significantly improving simulated climate and demonstrating the utility of diurnal FLUXNET data for climate modelling. Land-surface temperatures in many regions cool due to higher canopy conductances and latent heat fluxes, and the spatial distribution of CO2 uptake provides a significant additional constraint on the realism of simulated surface fluxes. FLUXNET data are used to calibrate a global production efficiency model (PEM). This model is forced by satellite-measured absorbed radiation and suggests that global net primary production (NPP) increased 6.2% over 1982–1999. Good agreement is found between global trends in NPP estimated by the PEM and a dynamic global vegetation model (DGVM), and between the DGVM and estimates of global NEE derived from a global inversion of atmospheric CO2 measurements. Combining the PEM, DGVM, and inversion results suggests that CO2 fertilization is playing a major role in current increases in NPP, with lesser impacts from increasing N deposition and growing season length. Both the PEM and the inversion identify the Amazon basin as a key region for the current net terrestrial CO2 uptake (i.e. 33% of global NEE), as well as its interannual variability. The inversion\u27s global NEE estimate of −1.2 Pg [C] yr−1 for 1982–1995 is compatible with the PEM- and DGVM-predicted trends in NPP. There is, thus, a convergence in understanding derived from process-based models, remote-sensing-based observations, and inversion of atmospheric data. Future advances in field measurement techniques, including eddy covariance (particularly concerning the problem of night-time fluxes in dense canopies and of advection or flow distortion over complex terrain), will result in improved constraints on land-atmosphere CO2 fluxes and the rigorous attribution of mechanisms to the current terrestrial net CO2 uptake and its spatial and temporal heterogeneity. Global ecosystem models play a fundamental role in linking information derived from FLUXNET measurements to atmospheric CO2 variability. A number of recommendations concerning FLUXNET data are made, including a request for more comprehensive site data (particularly historical information), more measurements in undisturbed ecosystems, and the systematic provision of error estimates. The greatest value of current FLUXNET data for global carbon cycle modelling is in evaluating process representations, rather than in providing an unbiased estimate of net CO2 exchange

for Skewed Returns

This paper studies portfolio holdings and as- set prices in an economy in which people’s natu-ral tendency to be optimistic about the payout from their investments is tempered by the ex post costs of basing their portfolio decisions on incorrect beliefs. We show that this model can generate the following three stylized facts. First, households ’ portfolios are not optimally diversified according to various theoretical-base