Binary progenitor systems for Type Ic supernovae.

Core-collapse supernovae are explosions of massive stars at the end of their evolution. They are responsible for metal production and for halting star formation, having a significant impact on galaxy evolution. The details of these processes depend on the nature of supernova progenitors, but it is unclear if Type Ic supernovae (without hydrogen or helium lines in their spectra) originate from core-collapses of very massive stars (>30 M⊙) or from less massive stars in binary systems. Here we show that Type II (with hydrogen lines) and Ic supernovae are located in environments with similar molecular gas densities, therefore their progenitors have comparable lifetimes and initial masses. This supports a binary interaction for most Type Ic supernova progenitors, which explains the lack of hydrogen and helium lines. This finding can be implemented in sub-grid prescriptions in numerical cosmological simulations to improve the feedback and chemical mixing.

Global needs for nitrogen fertilizer to improve wheat yield under climate change.

Increasing global food demand will require more food production1 without further exceeding the planetary boundaries2 while simultaneously adapting to climate change3. We used an ensemble of wheat simulation models with improved sink and source traits from the highest-yielding wheat genotypes4 to quantify potential yield gains and associated nitrogen requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The improved sink and source traits increased yield by 16% with current nitrogen fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential-a 52% increase in global average yield under a mid-century high warming climate scenario (RCP8.5), fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil nitrogen availability and nitrogen use efficiency, along with yield potential.

Extragalactic Science with the Orbiting Astronomical Satellite Investigating Stellar Systems (OASIS) Observatory.

The Orbiting Astronomical Satellite for Investigating Stellar Systems (OASIS), a proposed Astrophysics MIDEX-class mission concept, has an innovative 14-meter diameter inflatable primary mirror that will provide the sensitivity to study far-infrared continuum and line emission from galaxies at all redshifts with high spectral resolution heterodyne receivers. OASIS will have the sensitivity to follow the water trail from galaxies to the comets that create oceans. It will bring an understanding of the role of water in galaxy evolution and its part of the oxygen budget, by measuring water emission from local to intermediate redshift galaxies, observations that have not been possible from the ground. Observation of the ground-state HD line will accurately measure gas mass in a wide variety of astrophysical objects. Thanks to its exquisite spatial resolution and sensitivity, OASIS will, during its one-year baseline mission, detect water in galaxies with unprecedented statistical significance. This paper reviews the extragalactic science achievable and planned with OASIS.

Identification of strontium in the merger of two neutron stars.

Half of all of the elements in the Universe that are heavier than iron were created by rapid neutron capture. The theory underlying this astrophysical r-process was worked out six decades ago, and requires an enormous neutron flux to make the bulk of the elements1. Where this happens is still debated2. A key piece of evidence would be the discovery of freshly synthesized r-process elements in an astrophysical site. Existing models3-5 and circumstantial evidence6 point to neutron-star mergers as a probable r-process site; the optical/infrared transient known as a 'kilonova' that emerges in the days after a merger is a likely place to detect the spectral signatures of newly created neutron-capture elements7-9. The kilonova AT2017gfo-which was found following the discovery of the neutron-star merger GW170817 by gravitational-wave detectors10-was the first kilonova for which detailed spectra were recorded. When these spectra were first reported11,12, it was argued that they were broadly consistent with an outflow of radioactive heavy elements; however, there was no robust identification of any one element. Here we report the identification of the neutron-capture element strontium in a reanalysis of these spectra. The detection of a neutron-capture element associated with the collision of two extreme-density stars establishes the origin of r-process elements in neutron-star mergers, and shows that neutron stars are made of neutron-rich matter13.

Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions.

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

The uncertainty of crop yield projections is reduced by improved temperature response functions.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Transthoracic atrial defibrillation energy thresholds are correlated to uniformity of current density distributions.

Previous studies have shown that successful defibrillation depends on the uniformity of current density in the heart and the percentage of total current reaching the heart. This study uses an anatomically-realistic finite element computer model of the human torso for external atrial defibrillation to (1) examine the defibrillation energy thresholds and current density distributions for common clinical paddle placements and (2) investigate the effects of electrode shifts on these defibrillation parameters. The model predicts atrial defibrillation threshold (AD FT) energy by requiring a voltage gradient of 5 V/cm over at least 95% of atrial myocardium. This study finds that variation in electrode placement by only a few centimeters increases ADFTs by up to 46% with a corresponding change of 38% between the average current density in the left and right atria and 34% between the heterogeneity indices of atrial current density distributions. Additionally, the heterogeneity index, or degree of uniformity, is linearly correlated to the ADFT (R2=0.9). We suggest that uniformity of current density distribution, in addition to minimum current density, may be an important parameter to use for predicting successful defibrillation when testing new electrode placements.